The distribution and survival of trees during the last glacial maximum (LGM) has been of interest to paleoecologists, biogeographers, and geneticists. Ecological niche models that associate species occurrence and abundance with climatic variables are widely used to gain ecological and evolutionary insights and to predict species distributions over space and time. The present study deals with the glacial history of walnut to address questions related to past distributions through genetic analysis and ecological modeling of the present, LGM and Last Interglacial (LIG) periods. A maximum entropy method was used to project the current walnut distribution model on to the LGM (21–18 kyr BP) and LIG (130–116 kyr BP) climatic conditions. Model tuning identified the walnut data set filtered at 10 km spatial resolution as the best for modeling the current distribution and to hindcast past (LGM and LIG) distributions of walnut. The current distribution model predicted southern Caucasus, parts of West and Central Asia extending into South Asia encompassing northern Afghanistan, Pakistan, northwestern Himalayan region, and southwestern Tibet, as the favorable climatic niche matching the modern distribution of walnut. The hindcast of distributions suggested the occurrence of walnut during LGM was somewhat limited to southern latitudes from southern Caucasus, Central and South Asian regions extending into southwestern Tibet, northeastern India, Himalayan region of Sikkim and Bhutan, and southeastern China. Both CCSM and MIROC projections overlapped, except that MIROC projected a significant presence of walnut in the Balkan Peninsula during the LGM. In contrast, genetic analysis of the current walnut distribution suggested a much narrower area in northern Pakistan and the surrounding areas of Afghanistan, northwestern India, and southern Tajikistan as a plausible hotspot of diversity where walnut may have survived glaciations. Overall, the findings suggest that walnut perhaps survived the last glaciations in several refugia across a wide geographic area between 30° and 45° North latitude. However, humans probably played a significant role in the recent history and modern distribution of walnut. Paleobotanical studies suggest Quaternary climatic fluctuations beginning in Plio-Pleistocene transition profoundly impacted biodiversity and altered the floristic composition throughout the Holarctic –4. FurthJuglans regia L.; henceforth referred to as walnut) belongs to the section Juglans within the genus Juglans of the family Juglandaceae and is considered to be a Neogene relict from the Tertiary forests of Eurasia [Juglans is riddled with widespread extinctions, range reduction, fragmentation, and bottlenecks during the Late Tertiary climatic deterioration and Quaternary glaciations. Palynological data indicate that walnut populations were extirpated from Eastern Europe to southwestern Turkey at the end of the LGM [J. shanwangensis from Linju, Shanwang, Shandong provinces that resembles the modern walnut, carbonized shells found in the ruins of Cishan, Hebei, and pollen dating back to 4000–5000 BCE. But most observers believe that walnut was introduced from the Persian Empire and southern Tibet by traders along the ancient silk routes during the Han Dynasty (206 BCE-220 CE) [English walnut  .J. sigillata, exists. However, Zohary et al. [Although the origin of walnut is obscure, it is believed to have multiple centers of origin in the Carpathian Mountains, Transcaucasia, northeastern Turkey, northern Iran, the western Tien Shan Mountains, eastern Himalayas, and the Tibetan Plateau, where a primitive endemic walnut, y et al.  proposedy et al.  considery et al. , 39. Dody et al.  describeChloroplast genomic diversity has been extensively used to analyze the historical phylobiogeography of plants at interspecific and intergeneric levels, but limited organelle DNA polymorphisms make it unsuitable to study infraspecific genetic diversity, population structure, and differentiation. Alternatively, genomic DNA polymorphisms offer excellent opportunities to study spacio-temporal genetic diversity, population structure, and differentiation resulting from the dynamic interaction of evolutionary forces at infraspecific levels. This study focuses on: (1) examining the genetic structure and differentiation of modern walnut to identify the plausible hotspots of diversity, and (2) ecological niche modeling (ENM) to elucidate present and project past distributions during the last glacial maximum , and the Last Interglacial . We address the following questions: (1) where did walnut survive during the LGM and LIG? (2) does the modern genetic structure and differentiation patterns provide evidence for the potential location(s) of Pleistocene refugia; and (3) does ecological niche modeling identify location(s) of refugia congruent with genetic evidence?The study used 643 genotypes comprising 317 diverse accessions representing the modern range of distribution of walnut maintained at the National Clonal Germplasm Repository, USDA-ARS, Davis, California . Five maμL. Nineteen microsatellite loci, WGA001, WGA004, WGA009, WGA069, WGA089, WGA106, WGA118, WGA178, WGA202, WGA223, WGA225, WGA237, WGA318, WGA321, WGA331, WGA332, WGA338, WGA349, and WGA384 [μL reaction mixture containing 10 mM Tris–HCl, pH 8.3, and 50 mM KCl , 2 mM MgCl2, 0.9 pmol of each primer, 0.2 mM of each dNTP, 0.6 U of Taq polymerase , and approximately 25 ng of template DNA. The PCR conditions were as follows: 1 cycle of 94°C for 5 min, 30 cycles of 94°C for 30 sec, 55°C for 30 sec, and 72°C for 40 sec, and a final elongation of 72°C for 7 min. Amplified products were resolved by capillary electrophoresis using an ABI 3130xl Genetic Analyzer with Data Collection software, version 3.0 . The data was further analyzed using Genotyper, Version 2.5 (Applied Biosystems) and data assembled as bi-allelic genotypes  using the Neighbor-Joining (NJ) algorithm as implemented in the MEGA 6.0 software  using a adegenet . The accK) based on multilocus genotype data, so as to minimize deviations from Hardy-Weinberg and linkage equilibrium. The program uses a Markov Chain Monte Carlo (MCMC) procedure to estimate P(X|K), the posterior probability that the data (X) fit the hypothesis of K clusters. The analysis assigns individuals into each of the K clusters based on the membership coefficient  which sums to unity over the number of clusters (K) assumed. STRUCTURE was set to ignore population information, and to use an admixture model with correlated allele frequencies, as it is considered the best option for subtle population structure [α) was allowed to be inferred from the data. α is close to zero when most individuals are from one population or another, while it is greater than one when most individuals are admixed [λ) was set to one as suggested in the STRUCTURE manual. From a pilot study, we found that burn-in and MCMC simulation lengths of 100,000 replicate runs were optimum to achieve accurate parameter estimates. We let the number of clusters (K) vary between 2 and 18 with 20 replicate runs to quantify the variation of the likelihood for each K. The K value that provides the maximum likelihood (Ln P(D) in STRUCTURE) across runs is generally inferred as the most probable number of clusters. However, the interpretation of K should be treated with care as it merely provides an ad hoc approximation [ad hoc statistic ΔK to choose the optimum number of clusters (K) based on the second order rate of change in the log probability of data between successive K values as proposed by Evanno et al. [The genotypic data were subjected to a Bayesian model-based CA using the software package STRUCTURE 2.3.1  to detertructure . The deg admixed . The allximation  and someo et al. .A), observed (Ho) and expected (He) levels of heterozygosity, and fixation index (F) for different loci. Allelic richness (Ar) and private allelic richness (PAr) for each population were estimated using the rarefaction method [hp-rare 1.1 [Ar and PAr were geographically projected using an inverse distance weighted (IDW) interpolation tool implemented in the ArcMap 10.1 . Gene diversity analysis was performed on the allele frequency data from the five geographic groups by following the method suggested by Nei [HT) was partitioned into gene diversity due to variation within groups (HG), and the component due to variation between groups (DGT). Differentiation between groups was calculated as GGT = DGT/HT, where GGT can vary between 0 (when HG = HT) and 1 (when HG = 0), i.e. groups fixed for different alleles.The multilocus genotype data were pooled into five geographic groups matching the results of the CA and subjected to analysis of total and within-group genetic diversity measures such as mean number of alleles per locus  as implemented in the software package ARLEQUIN version 3.6 . The tothttp://www.ars-grin.gov/npgs/index.html), the Global Biodiversity Information Facility , field collections, and published literature  lower than those excluding the 10% of training localities with the lowest predicted suitability. Under either threshold rule, pixels with values equal to or higher than the threshold are considered suitable. Omission rates greater than the theoretical expectation for a given threshold typically indicate model overfitting. The AIC corrected for small sample size (AICc) reflects both model goodness-of-fit and complexity, where the best model has the lowest value (i.e. ΔAICc = 0).The unfiltered data with 237 data points and two filtered data with 136 and 112 occurrence points, were subjected to model tuning using an R package ENMeval  to identlidation , 68; (2)on [TEST ); (ii) A AUCTEST ; (iii) mEST [MTP ); (iv) 1[MTP [10 ); and (vP [10 [c ). AUCTESc equal to zero is considered the best model [We applied the “block” method to partition both occurrence and background data, which splits data along the latitude and longitude lines, and allocates equally into four bins for cross validation. It is the best method for studies involving model transfer across space and time . We builst model . We compThe walnut germplasm collection examined exhibited considerable polymorphism with observed number of alleles ranging from 8 for WGA089, WGA237, and WGA384 to 20 for WGA 202 with an overall mean of 12 alleles/locus . The obsMultivariate genetic structure revealed by the CA identified five major groups closely matching with the geographic affiliations of different walnut accessions . EasternThe PCA based on mutlilocus genotype data unraveled genetic relationships within and among different geographic groups similar to CA. The two-dimensional projection of accessions defined by the first two principal axes accounting for 13.66% and 9.82% of the total variation, respectively, revealed genetic differentiation within and among groups . The firX|K) attained a maximum value at K = 5  averaged over loci among groups was 0.128 (P < 0.01).Estimates of within-group diversity parameters indicated that the total number of alleles across 19 loci ranged from 191 with a mean of 10.1 alleles/locus for the SW Asian group to 100 with a mean of 5.26 alleles/locus for the Caucasus. The allelic richness adjusted to the minimum sample size of 49 genes ranged from 7.19 for the SW Asian group to 4.52 for the Central Asian group with an average of 5.29 alleles/locus and the private allelic richness followed the same trend . There wGGT) accounted for genetic differentiation among groups and there was considerable variation among loci ranging from 8.3% for WGA331 to 22.2% for WGA384, and on average 88% of the total variation was found within group variation   and the Central Asian group (0.103).Geographic differentiation among the walnut groups was estimated using Wright’s fixation index (FST) . The Cauc-selected models suggested that the data set filtered at 10 km logged in the lowest values for AUCDIFF (0.014), ORMTP (0.029) and OR10 (0.103) among the three models followed by the unfiltered data set with 0.029, 0.042, and 0.118, and filtered at 25 km with 0.040, 0.065 and 0.185, respectively, suggesting filtering somewhat improved model efficiency. Visual examination of models generated from hindcasting LGM and LIG distribution of walnut using the three data sets , and northeastern Danube region , Spain, the Atlas Mountains of North Africa, western China (Xinjiang Province), parts of northern China bordering Mongolia , and southeastern China in the Fujian and Guangxi provinces, showed relatively low rate of occurrence of walnut. Some scattered areas of northern and southern Turkey, southeastern Adriatic Sea coastal region including northwestern Greece, western Albania and northwestern Spain bordering Portugal, showed relative high rate of occurrence of walnut. Overall, the current climatic model roughly predicted the current natural distributional range of walnut .The LGM-CCSM projection predicted the areas of relatively high rate of occurrence of walnut shifted to lower latitudes than projected in the current climatic model. Distribution was fragmented and interspersed with areas of marginal occurrence. Eastern Pakistan extending in the north to Tajikistan and parts of northeastern Afghanistan, southeastern Turkmenistan, western Iran, southern Turkey bordering the Mediterranean Sea, the Hyrcanian and Colchic regions of the southern Caucasus including the Talysh and Alburz mountain ranges of Azerbaijan and Iran, Armenia and border areas of the Black Sea, showed relatively high rates of occurrence of walnut. However the entire Turkey, southern Balkans and eastern coastal regions of Adriatic Sea and in Central Asia, southern Turkmenistan, Uzbekistan down to Tajikistan, northeastern Afghanistan, and western Himalayan state of Kashmir extending up to southwestern Tibet exhibited moderate to low rates of occurrence. The LGM-MIROC model projected a similar distribution as LGM-CCSM, but regions of high relative occurrence concentrated in north eastern Pakistan, Tajikistan, northeastern Afghanistan, southern Turkmenistan on eastern coast of Caspian Sea, southwestern Balkans (southern Bulgaria), northwestern Turkey, north western coastal regions of the Adriatic Sea. Both CCSM and MIROC models projected a moderate rate of occurrence of walnut in Xinjiang province of western China, low occurrence in central China and relatively high occurrence rate in the southeastern China (Fujian and Jiangxi provinces and neighboring areas), and somewhat fragmented distributions in northeastern India. There were regions of low occurrence in central China, but overall there was a reduction in the occurrence of walnut in East Asia during LGM, as compared to the present day distribution. The LIG projection indicated relatively high rates of occurrence in a narrow region comprising southern Iran and northwestern Pakistan extending into southern Afghanistan, tapering off eastward along the southern Himalayan foothills extending into Nepal, Sikkim, Bhutan into Arunachal and north Assam. There was also a region of high occurrence in the northeastern and central regions of Xinjiang province. There was a narrow region of relatively high occurrence in Shaanxi and Shanxi provinces. Surprisingly the southeastern region of high occurrence in China seen during LGM was not obvious during the LIG. Eastern Kazakhstan bordering the northern Caspian Sea showed a moderate rate of occurrence, while southeastern Turkey along Mediterranean coast, eastern coastal regions of Greece, Albania, and Montenegro showed high rates of occurrence. There was an incidence of extended low occurrence throughout Western Europe including Spain, Portugal, France, and Belgium extending into Germany and Parts of the United Kingdom, except for a small northeastern region of Spain above Portugal showed high occurrence. Morocco along the Atlas Mountains showed moderate occurrence of walnut during the LIG.The distribution and survival of trees during the LGM has been of interest to paleoecologists, biogeographers, and geneticists. Paleodistribution modeling in conjunction with population genetic analyses can predict the past distributions and aid in locating Pleistocene refugia of plant species. Ecological niche models (ENMs) that associate species occurrence and abundance with climatic variables are extensively used to gain ecological and evolutionary insights, and to predict species distributions across landscapes over space and time. The present study deals with the glacial history of walnut to address questions related to past distributions during the LGM and LIG periods. The results include population genetic analysis of a germplasm collection representing the modern range of walnut, and ecological modeling of present distribution and the LGM and LIG projections, to predict past climatic niches and locate the Pleistocene refugia.J. accuminata, similar to the present day walnut, comes from Europe and the Caucasus dating back to the Miocene and Pliocene [Juglans within the genus Juglans [Walnut is considered a Tertiary relict, native to a broad geographic area extending from the Near East through Central Asia to the Himalayas and Western China , 73. ZevPliocene , 74, 77.Pliocene , 73 appr Juglans .Fagus, Juglans, Pterocarya, and Tsuga south of 57°-58°N [J. regia, Carpinus betulus, Tilia cordata, T. tomentosa, Quercus spp. Corylus avellana and Alunus spp. [in situ refugia [Juglans, Castanea, Platanus, Olea and Fagus is thought to be anthropogenic intervention [It is widely accepted that during the LGM, most nemoral tree species were restricted to refugia in the Iberian, Italian, and Balkan peninsulas –81. Howe57°-58°N , 83, as nus spp. , 85. Fos refugia . The LGMrvention , 87–89 dJuglans endemic to Eurasia probably had ample opportunity to diversify and much of the ancestral taxa must have gone extinct during glaciations. Incomplete palaeobotanical records from Eurasia and perhaps the difficulty in recognizing intrasectional diversity in pollen and other microfossil flora have obscured the ancestral taxonomic diversity of the section Juglans. However, palynological evidence suggests that walnut survived in Central Europe in small cryptic refugia during the LIG [During pre-glacial periods the section  the LIG , 84–86 a the LIG  during t the LIG , 91–93. The climatic deterioration during the late Tertiary followed by the Quaternary glacial and intergalcial fluctuations played a major role in shaping the present-day genetic diversity, population structure, and differentiation patterns of plant species , 15, 94.Humans apparently played a role in shaping the modern genetic structure, but the signature of biogeographic events should permit speculation on the mode and tempo of the evolution of walnut –101. OurA significant amount of genetic diversity was detected in the walnut germplasm collection, and the loci assayed differed considerably for the number of alleles per locus, observed and expected levels of heterozygosity, and fixation index . GeneralCentral Asian walnuts show the lowest level of allelic richness and low heterozygosity as expected in recently colonized populations. Moderate allelic richness and heterozygosity of Eastern European walnut observed here is unexpected and probably due to historic migration of germplasm, recent introductions from other walnut growing regions, and directional selection during domestication that occurred in this region compared to other walnut regions. Surprisingly, the East Asian walnut exhibited moderate allelic richness and private allelic richness compared to the Transcaucasian and Central Asian walnuts, probably due to historic introductions of diverse germplasm from Persia, Tibet, and India , and posThe CA and PCA results suggest close association of the SW Asian, Caucasus, and Eastern European walnut groups, while Central Asian and East Asian walnut are somewhat separate groups. Presence of one or more moderate to high frequency alleles common across loci and among groups and low differentiation of SW Asia walnut from other groups indicate that walnut probably expanded from SW Asia into other regions following glaciations. At the same time, the presence of several moderate frequency alleles across loci restricted to one or more groups suggest either local genetic differentiation after recolonization or separate expansion events from different refugia. However, high genetic diversity and close genetic affinity of SW Asian walnut strongly support a single refugial source located in the mountainous regions of SW Asia during the LGM that further expanded and spread northward into Central Asia and westward into Europe and other regions, which was further facilitated by human migration along ancient trade routes. Furthermore, human mediated dispersal and local domestication events since Greek and Roman times perhaps significantly influenced the current distribution of genetic diversity in walnut. Historic migration of walnut along the ancient trade routes from Persia, Tibet, and the Himalayan regions of India into China during the Han dynasty founded an important secondary center of diversity for walnut .The two likely scenarios for recolonization of trees: (1) rapid colonization from southern refugia mediated by long-distance dispersal , which iThe presence of the Plio-Pleistocene cryptic refugia in regions other than SW Asia is not ruled out as they present moderate levels of genetic variation and differentiation within each group. In the Caucasus, the Colchis and Talysh regions served as species-rich refugia for many members of the Arcto-Tertiary flora where perhaps walnut survived during the glaciations , 103. ThJ. regia and J. fallax) and willow-poplar-birch forests at altitudes of 1000 to 1400 m and are considered to be relict formations of the Iranian and Turanian floras with eastern Mediterranean species occurring within distinct areas [FST, which suggests that the East Asian group exhibits closer affinity to the SW Asian group than the other four regional groups, perhaps suggesting historical migration of walnut from this region through early trade along the ancient silk route connecting these two regions. Nonetheless, during the last glacial maximum, at least two independent refugia were maintained across northeastern China for J. mandshurica, a species representing the section Cardiocaryon within the genus Juglans [Central Asian Mountain ranges such as the Pamir, Kopet Dagh, and Tien Shan are important centers of biological diversity and believed to be a center of origin and diversity of walnut –110. Thect areas . The walct areas . Our anact areas . This is Juglans .Pterocarya and walnut survived in this region throughout the Pleistocene [Juglans pollen found in two peat bogs in Kashmir between 17,000 and 10,000 cal yr BP onward supports the results of population genetic analysis of modern walnut distribution.Ecological niche modeling of current climatic and species occurrence data predicted southern Caucasus, parts of West and Central Asia extending into South Asia encompassing northern Afghanistan, Pakistan, northwestern Himalayan region, and southwestern Tibet as the favorable climatic niche matching the modern distribution of walnut. Hindcasting explicitly correlates climatic factors with species distributions and complements the genetic analysis in locating Pleistocene refugia. Our LGM hindcasts using data from the CCSM and MIROC models suggested disjunct distributions of walnut populations restricted to Transcaucasia, Central and South Asian regions extending into southwestern Tibet, northeastern India, Himalayan region of Sikkim and Bhutan, and southeastern China. CCSM and MIROC projections overlapped, but MIROC projected a significant presence of walnut in the Balkan Peninsula during the LGM . In contistocene . Hyrcaniistocene –120 confistocene . The LIGThe paleoclimatic predictions show that the distribution of walnut was affected by Quaternary climatic fluctuations with population contractions and fragmentation. The LGM-CCSM and LGM-MIROC models suggested broad areas of the Caucasus, SW Asia including northeastern Afghanistan, Northern Pakistan, northeastern India and Central Asian Republic of Tajikistan as favorable climatic regions where walnut probably survived in multiple refugia. The LIG prediction suggested that walnut perhaps had expanded distribution in southern Europe from the Iberian Peninsula through southern France, Italian Peninsula, Adriatic Coastal regions, Greece, the Caucasus, southern Black Sea regions of Turkey up on to southern Russia, western Kazakhstan, East Asia and scattered distribution in SW Asia. Palynological evidence confirms occurrence of walnut in many of these areas during the Quaternary period. However, a cautionary note on paleoreconstructions is that they only predict the potential climatic niche suitable for species and may not confirm actual existence of refugia.Population genetic analysis of walnut representing the modern distributional range suggested a general area of northern Pakistan and surrounding areas including northeastern Afghanistan, southern Tajikistan and northwestern India as the possible hotspot of diversity where walnut probably survived the last ice age. The genetic analysis also indicated that walnut probably spread into neighboring Central Asia, the Caucasus, West Asia and eventually Eastern Europe during the Roman period as confirmed by fossil pollen evidence. Overall, the findings suggest that walnut possibly survived the last glaciations in several refugia across a wide geographic area between 30 and 45 degrees north latitude. However, humans have played a significant role in the recent history and modern distribution of walnut.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Tablehttp://npgsweb.ars-grin.gov/gringlobal/search.aspx) and Global Biodiversity Information Facility .Walnut occurrence data with geographic coordinates sourced from the Genetic Resources Information Network Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Tablec = 0.Rows in bold shows the model setting with ΔAIC(XLSX)Click here for additional data file.S1 FigModel tuning results for three different walnut data sets: (A) unfiltered with 237 occurrence points and two filtered data sets rarified at (B) 10 and (C) 25 km geographic resolutions with 137 and 112 occurrence points, respectively. Evaluation metrics generated from MaxEnt models with six different settings for feature Class: Linear (L); Linear and Quadratic (LQ); Hinge (H), Linear, Quadratic, and Hinge (LQH); Linear, Quadratic, Hinge, and Product (LQHP); and Linear, Quadratic, Hinge, Product, and Threshold (LQHPT) and regularization multipliers ranging from 1 to 5 with increments of 0.5.(TIFF)Click here for additional data file.S2 Figc-selected model prediction of occurrences of walnut for current, last glacial maximum , and last interglacial  climatic conditions for unfiltered data set with 237 occurrence points and filtered at 25 km spatial resolutions with 112 occurrence points, respectively (refer to AIC(TIF)Click here for additional data file.


Pelophylax nigromaculatus (PN) and Fejervarya limnocharis (FL). These two species differ in several key life history traits; PN has a larger body size and larger clutch size, and reaches sexual maturity later than FL. Microsatellite DNA data were collected and analyzed using conventional , AMOVA) and recently developed  landscape genetic methods. As predicted, a higher level of population structure in FL (FST′ = 0.401) than in PN (FST′ = 0.354) was detected, in addition to FL displaying strong IBD patterns (r = .861) unlike PN (r = .073). A general north–south break in FL populations was detected, consistent with the IBD pattern, while PN exhibited clustering of northern‐ and southern‐most populations, suggestive of altered dispersal patterns. Species‐specific resistant landscape features were also identified, with roads and land cover the main cause of resistance to FL, and elevation the main influence on PN. These different species–landscape interactions can be explained mostly by their life history traits, revealing that closely related and ecologically similar species have different responses to the same landscape features. Comparative landscape genetic studies are important in detecting such differences and refining generalizations about amphibians in monitoring environmental changes.Amphibians are often considered excellent environmental indicator species. Natural and man‐made landscape features are known to form effective genetic barriers to amphibian populations; however, amphibians with different characteristics may have different species–landscape interaction patterns. We conducted a comparative landscape genetic analysis of two closely related syntopic frog species from central China,  Mountain ranges are often perceived as major genetic barriers to amphibian species, and Zhan et al.  and the wood frog (Lithobates sylvatica). Contrasting levels of population structure and different response patterns were detected between the two species, despite experiencing the same landscape features. The observed differences were attributed to key differences in movement ability and life history between the two species. Although the number of studies employing this approach is limited, its importance and necessity are gaining wide recognition  and Fejervarya limnocharis  are two ranid frogs  and a larger clutch size  than F. limnocharis . F. limnocharis reaches sexual maturity in 1 year, while P. nigromaculatus takes 3 years to reach sexual maturity. These life history traits have important impacts on a species’ demography and dispersal potentials, and they are related to the factors that affect gene flow and may present unique manners of interaction with the landscape. An understanding of these differences between syntopic, closely related species allows us to make a priori expectations. Compared to F. limnocharis, P. nigromaculatus has a larger body size and therefore likely higher agility, allowing us to predict that this species has low population differentiation and that the landscape would generate low surface resistance. Furthermore, the longer generation time and larger clutch sizes of P. nigromaculatus suggest a large effective population size (NE), which would slow changes driven by genetic drift, and hence reduce population substructure.P. nigromaculatus  and recently developed  landscape genetic analyses.In this study, we examine the population genetic structure of s Figure a and F. s Figure b and lan22.1Our study area is located in central China, and its landscape structure includes a major river (the Yangtze River) and several of its tributary rivers  as well mountain ranges that separate them Figure a. AgricuP. nigromaculatus, n = 371 and F. limnocharis, n = 432) were obtained from June 6–13, 2008. We aimed at ~50 samples from each site for each species for microsatellite DNA analysis, and for most samples, the two species were collected side by side. One toe from each adult frog was clipped, and the tissue samples were preserved in 95% ethanol and later stored in a −80°C freezer. Detailed sampling site and sample size information is provided in Table Both species are abundant in our study area, and all samples  were examined using primers developed in this study and previous publications , 25 mmol/L MgCl2, 0.2 mmol/L of each dNTP, 0.75U of TaKaRa Taq™ DNA Polymerase, and 10 μmol/L of each primer with the forward primer labeled with tetrachloro‐6‐carboxy‐fluorescein (TET). Polymerase chain reaction conditions include an initial denaturation step at 95°C for 5 min, then 30 cycles at 95°C for 30 s, primer‐specific annealing temperatures for 30 s (Appendix ® laser scanner (Hitachi). Alleles were scored relative to a TAMRA™ size standard marker  using IMAGE ANALYSIS 3.0 software program .Total genomic DNA was isolated using a standard phenol–chloroform protocol , observed heterozygosity (HO), and expected heterozygosity (HE), were estimated for each site. Each population was also examined for deviations from Hardy–Weinberg equilibrium using exact test with 1,000,000 Markov chain length and 100,000 dememorization steps. Tests for linkage disequilibrium were conducted between all pairs of loci. All calculations were performed in arlequin 3.1  was also computed in order to compare between the two species  change over K, Delta K, as well as individual assignment probabilities.Individual assignment tests were performed for each species to determine the number of naturally occurring genetic clusters within the samples. These tests were conducted using 2.5arlequin with 10,000 permutations and were evaluated at four hierarchical levels: among groups, among sites within groups, among individuals within sites, and within individuals.We conducted locus‐by‐locus AMOVA in order to assess the impacts of landscape features that were hypothesized to contribute to population differentiation. The analysis was performed using a priori hypotheses. (1) Mountain ranges form significant barriers and populations from the same side of a mountain range would have similar genetic makeup. (2) The Yangtze River is a major barrier to population connectivity. To test the first hypothesis, sampling sites separated by the presence of a mountain range were grouped together, and the eight sites were separated into five groups: , (3), (4), (5), and . The grouping was determined by the location of each site on a digital elevation map produced by the Consultative Group for International Agricultural Research‐Shuttle Radar Topography Mission (CGIAR‐SRTM). A second analysis with the same grouping but excluding sites 1 and 8 was also conducted. This provides a more stringent evaluation by eliminating the possible compounding effect of the Yangtze River as well as the potential effect of the plain as a site of mixing for all populations  values  and the Universal Transverse Mercator (UTM) projection (Zone 49N). Mantel test was used to determine correlation between the two matrices and was carried out with 10,000 permutations using arlequin.For each species, an IBD analysis was conducted to determine whether geographic distance contributes to the observed population subdivision. The pairwise Rousset,  were plo2.7circuitscape 4.0 . Elevation data were obtained through Consultative Group for International Agricultural Research‐Shuttle Radar Topography Mission (CGIAR‐SRTM). All other landscape features were available through the Digital Chart of the World.To prepare a geospatial map, all layers were projected using UTM Zone 49N and at a cell size resolution of 800 × 800 m. The elevation raster was reclassified into six classes, and each included a 500‐m elevational interval. Road vector data were converted into a raster and were reclassified into two classes (road and trail). The original land cover raster classes were reclassified into seven classes to summarize related land cover types. Water areas and river vector data were converted into raster data and were then mosaicked together with the reclassified land cover layer into a new raster dataset. This modification was to avoid redundancy among similar land cover types and yet include all the information. The resulting mosaic was then classified into nine land cover classes, which joined the pixels matching the Yangtze River and gave inundated land and other minor streams and tributaries their own respective classes Table .circuitscape, and correlated with pairwise FST/(1 − FST) using partial Mantel test while controlling for geographic distance. All partial Mantel tests were performed in SPSS statistics 23. The landscape resistance parameterization with the highest correlation coefficient (r) was considered optimal, which were subsequently used as basis for additive landscape model (with multiple landscape layers) parameterization.The relative contribution to landscape resistance of each layer  was first evaluated separately. Resistance values of each class of each layer were first assigned based on expert opinion  per locus ranged from 2 to 21, and average expected heterozygosity by site (HE) ranged from 0.455 to 0.769. The two variables were the highest at site #8. For F. limnocharis, all microsatellite DNA loci were polymorphic except one. The number of alleles for this species ranged from 1 to 21 alleles per locus and the average HE by site ranged from 0.255 to 0.447, much lower than these of P. nigromaculatus. Similarly, the highest NA and HE occurred at site #8. For both species, most populations for most loci were in Hardy–Weinberg equilibrium. Similarly, most pairs of loci were in linkage equilibrium, and no pair was in disequilibrium for a large number of population pairs. Detailed indices  are presented in Appendices For 3.2FST and standardized global FST (FST′) were greater for F. limnocharis  than P. nigromaculatus . Similarly, F. limnocharis had higher pairwise FST estimates (range: 0.090–0.558) than P. nigromaculatus (range: 0.037–0.288). Individual pairwise FST estimates are present in Table Global F. limnocharis ; however, when site 8 was excluded, a strong correlation was observed  . On the other hand, P. nigromaculatus revealed a very different pattern, and no significant correlation was detected both when all sites were included  and when site 8 was excluded . In addition, all eight significant models included elevation . Also, the top nine best models all included one or both of roads and land cover . Indeed, it is not surprising that the Yangtze River imposes a significant barrier effect on both species as such water bodies are well documented to impede amphibian movement  than F. limnocharis . A large body size provides high mobility for a species, allows individuals to travel further distances, and is often positively correlated with range size and negatively correlated with regional differentiation  than F. limnocharis . Large clutch size often promotes juvenile dispersal, which plays a key role in amphibian migration . Both its large body size and thus longer age to sexual maturity suggest that P. nigromaculatus has a longer lifespan and increased fitness  as well as the two southern‐most populations (sites 6 and 7) into the same genetic cluster . This possible direction of movement is consistent with our resistance model where the foothill region imposes low landscape resistance to P. nigromaculatus (Figure Site 8 represents an interesting case. It is located on a plain in the east as opposed to the mountainous region of the other sites Figure a. In ther Figure . This sus Figure b,c.4.2Our study adds to the limited number of landscape genetic studies that underline interspecific variations among species, an approach suggested to generate more useful information for conservation management (Keller, Holderegger, van Strien, & Bolliger, Amphibians will continue to serve as a chief focal animal group in conducting such landscape genetic studies. Throughout the development of landscape genetics, amphibians are at the forefront for detecting landscape barriers due to their commonly known characteristics of low vagility and high philopatry (Crosby et al., 4.3It is important to continue comparative studies that emphasize among‐species variations in order to refine previously established generalizations. The supposed applications and solutions that landscape genetic studies offer to conservation managers often end up in the literature and only a few find actual implementation (Keller et al., 5Our comparative analysis of two syntopic amphibian species revealed that closely related and ecologically similar species may have quite different responses to the same landscape features. Although amphibians in general are sensitive to landscape heterogeneity and are good environmental indicator species, significant differences in species–landscape interaction patterns between species should be expected, which has significant implications in predicting organism response to environmental changes or monitoring environmental changes with organism responses. This study also highlights the importance of using multiple species in conducting landscape genetic studies as well as choosing the right species in land‐use management as inherent attributes of organisms influence their response to any particular landscape.None declared.Fejervarya limnocharis, performed data analyses, and drafted the manuscript. Catie Ivy collected the genetic data for Pelophylax nigromaculatus. Jinzhong Fu conceptualized and supervised the project and finalized the manuscript. All authors read and approved the manuscript.Vhon Garcia collected the genetic data for All original microsatellite DNA data are provided in Appendices  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Atypical porcine pestivirus (APPV) was recently reported to be associated with neurologic disorders in newborn piglets. Investigations of 1,460 serum samples of apparently healthy pigs from different parts of Europe and Asia demonstrate a geographically wide distribution of genetically highly variable APPV and high APPV genome and antibody detection rates. Flaviviridae. The recently discovered atypical porcine pestivirus (APPV) is capable of inducing neurologic disorder in its host, like other pathogens of this family . Several recently published reports demonstrate that APPV is a prominent cause of virus-induced congenital tremor in pigs  3 encoding region and confirmed specificity of amplification by gel electrophoresis . Genome We used individual samples with high genome loads to generate amplicons in a seminested PCR and subsequently performed Sanger sequencing . We generated 20 different APPV NS3 sequences from apparently healthy pigs of all countries (sequences deposited into GenBank under accession nos. MF279213–32). Genetic differences reflect geographic origin to a low degree ; genetic,In addition to the NS3 fragments, we determined APPV complete coding sequences (CDS) from 1 sample from a healthy pig from China (deposited into GenBank under accession no. MF167292) and 2 samples (accession nos. MF167290 and MF167291) obtained from pigs during outbreaks of congenital tremors in Germany ,.rns antibody ELISA, as described (>60% of the animals showed intermediate to high reactivity in the antibody ELISA (We applied an indirect APPV Edy ELISA , which idy ELISA . We founOur findings indicate that the recently discovered APPV is abundant on several continents. APPV must be regarded as a pig pathogen of likely worldwide relevance.Additional information from genetic analysis of atypical porcine pestivirus in animals from Europe and Asia


A unique collection of common oat landraces, cultivated in the temperate climate of central Europe until the end of the twentieth century, is preserved in the Polish gene bank. It consists of 91 accessions that have never been used in breeding programs. In order to optimise the use of this genetic resource, we aimed to: (1) determine genetic and agro-morphological diversity, (2) identify internal genetic variation of the tested accessions, (3) form a core collection and (4) recognise the accessions useful for breeding programs or re-release for cultivation. The collection was screened using ISSR markers (1520 loci) and eight agro-morphological traits. Uniquely, we performed molecular studies based on 24 individuals of every accession instead of bulk samples. Therefore, assessment of the degree of diversity within each population and the identification of overlapping gene pools were possible. The observed internal diversity (Nei unbiased coefficient) was in the range of 0.17–0.31. Based on combined genetic and agro-morphological data, we established the core collection composed of 21 landraces. Due to valuable compositions of important traits, some accessions were also identified as useful for breeding programs. The population structure and principal coordinate analysis revealed two major clusters. Based on the previous results, the accessions classified within the smaller one were identified as obsolete varieties instead of landraces. Our results show that the oat landraces are, in general, resistant to local races of diseases, well adapted to local conditions and, in some cases, yielding at the level of modern varieties. Therefore,  Bipolaris maydis in the 1970’s in the United States [The domestication of plants can be considered as one of the greatest achievements of mankind. It is associated with the long-term and targeted selection which greatly reduced the gene pool size of the domesticated species. It is estimated that the crop species retained only about 20% of the original variability of their wild ancestors –3. Moreod States . The lacd States . Landracd States . IntensiDespite the development of the next-generation sequencing, the DNA fingerprinting is still the preferred way of plant genetic resources (PGR) genetic characterization –13. MoleThe overall aims of this work were (i) to determine ISSR based genetic diversity of the oat landraces stored in the Polish gene bank, (ii) to evaluate variation of agro-morphological traits, (iii) to identify internal genetic variation of each tested accession, (iv) to develop a core set representing the maximum diversity of Polish oat landraces and (v) to recognise accessions potentially useful for breeding programs or re-release for cultivation.(1), PL 50079 (2) and PL 50381 (5) [The study involved 91 indigenous accessions of common oat  preserve0381 (5) . Each of2 plots. The characters were scored at an appropriate growth stage of the oat plants following the International Convention for the Protection of New Varieties of Plants [Agro-morphological data were obtained from the EGISET i.e. the database of NCPGR . Evaluatf Plants . We anal−1 for PCR amplification.The DNA of 2,184 individual plantlets was extracted from tissue of young leaves using the Genomic Mini AX Plant kit (A&A Biotechnology). The DNA concentration was determined spectrophotometrically (NanoDrop Sectrophotometer ND-1000) and the DNA samples were diluted to 25 ng μlThe previously described set of eight ISSR primers  was applk is the number of phenotypic classes for a character and pi is the proportion of the total number of entries in the ith class [The agro-morphological data were standardised and a proximity matrix by the Gower coefficient  was deveth class . For thiIn addition, we calculated the Pearson correlation coefficients between all agro-morphological traits (p < 0.05).Initially, the GeneMapper software (Applied Biosystem) was used to analyse the capillary electrophoresis raw data. A binary matrix of the ISSR results was developed in the MS Excel 2010. Only clear and apparently unambiguous peaks were scored. The informativeness of a marker was determined by the Polymorphic Information Content (PIC) .The pair-wise genetic dissimilarities between individuals were calculated by the Dice algorithm , whereasThe population structure was also estimated using a Bayesian assignment test as implemented in the program STRUCTURE v. 2.3.4 . An admiBased on the passport data, a geographic distance matrix was calculated using the Geographic Distance Matrix Generator . A matriThe Mantel test  with 10,On the basis of the agro-morphological and molecular marker data, a core set was extracted from the whole tested accessions using the advanced M strategy implemented through a modified heuristic algorithm using the PowerCore . The homMe is the mean of the entire collection, Mc is the mean of the core collection and m is the number of traits.The mean Difference (MD %) was estimated using the equationRe is the range of the entire collection, Rc is the range of the core collection and m is the number of traits.The coincidence rate (CR %) was estimated as:Ve is the variance of the entire collection, Vc is the variance of the core collection and m is the number of traits.The variance Difference (VD %) was estimated as:CVe is the coefficient of variation of the entire collection, CVc is the coefficient of variation of the core collection and m is the number of traits.The variable rate of the coefficient of variation (VR %) was estimated as:Dc is the number of classes occurring in the core subset, De is the number of classes occurring in the entire set and m is the number of traits [The coverage of all agro-morphological traits in the entire collection was estimated in the core as:f traits .The agro-morphological data of the Polish common oat landrace collection, assessed in the field trials, was obtained from the EGISET  databaseThe similarity measured by the Gower coefficient was in the range of 0.52–0.98. The cluster analysis of 84 accessions identified three distinct clusters composed of 30, 26 and 28 accessions . Within (16), PL 50593 (23), PL 50613 (24), PL 51440 (45) and PL 51604 (55), located outside of the group in the first and second quadrant, were highly resistant both to crown rust and powdery mildew. Three distinct accessions from the third and fourth quadrant PL 50079 (2), PL 552344 (84) and PL 51615 (64) had high thousand seed weight values.In the principal components analysis (PCA), the first three principal components  explained 63.56% of the variation. The first axis, explaining 32.3% of the variation, was linked to the variables yield, thousand seed weight  and heading date (r = -0.7). The second axis (17.1% of variation) was moderately correlated with lodging, crown rust resistance and days to maturity (r>|0.5|) . The thiSome pairs of the agro-morphological traits were correlated in the tested genotypes . The tho(86) to 0.31 PL 51439 (44) ((1), PL 50079 (2), PL 50338 (3), PL 50345 (4), PL 50381 (5), PL 50503 (9), PL 50613 (24) and PL 50622 (26). Three accessions classified earlier as the obsolete varieties i.e. PL 50076 (1), PL 50079 (2) and PL 50381 (5) were located in this group. It allows to suppose, that remaining five accessions might be also erroneously classified as a landrace instead of being obsolete varieties. The second group was located in the second and third quadrant of the coordinate system and consisted of the remaining 83 landraces. However, six landraces of this group showed some distinctiveness i.e. PL 51632 (69), PL 51440 (45), PL 51634 (70), PL 52431 (88), PL 51439 (44) and PL 50521 (12). The detailed plot clearly shows that the gene pools of individual accessions in both groups overlap  . AMOVA d439 (44)  composed overlap . AMOVA pThe STRUCTURE software was run for k = 1–15, based on the distribution of 1,520 different ISSR loci among 2,184 individuals of 91 oat accessions The results of the Bayesian STRUCTURE analysis identifying genetic groupings are illustrated in the The agro-morphological characterisation was available only for 84 landraces; therefore, the correlation between these traits and ISSR marker system was analysed with the accessions supplied with both sets of data only. The same procedure was used for geographic data. No significant linear relationships were detected between any of the tested pairs of proximity matrices .A combination of the ISSR and agro-morphological data was analysed using the generalized procrustes analysis (GPA). The consensus proportion for this data was 0.665, but the quantile (78.00) was below the confidence interval (95%).(1), PL 50503 (9), PL 50673 (28), PL 51632 (69) and PL 51439 (44), and it should be noted that one of them had short straw.The manual analysis of the molecular and the agro-morphological data allowed identifying five accessions with a high potential for breeding. The criteria were applied as follows: a high resistance to the pathogens combined with a high yielding and genetic distinctiveness. The selected accessions were: PL 50076 The PowerCore selected 21 out of 91 oat landraces, thus reducing the number of accessions by 76.9% for the entire set . The meaThe variance difference (VD%) indicates the difference in the coefficient of variation values existing in the core collection and in the entire collection. The VD% value (30.09%) showed that the variance of the core collection is slightly different from the variance for the entire collection.The variable rate of the coefficient of variance (VR%), enabling a comparison between the coefficient of variation values existing in the core subset and the entire collection, determines also the level of representation of the starting collection in the core subset. The core had a VR% values of 121.33% over the entire set, indicating a good representation.The coincidence rate of range (CR%) indicates whether the distribution ranges of each variable in the core set are well represented when compared to the entire set. The resulting CR% value (99.05%) indicated homogenous distribution ranges of agro-morphological traits. The value of calculated coverage coefficient for the resulting core was 100%. This indicates full coverage of the agro-morphological diversity present in the starting collection.The genetic diversity and the population structure of Polish oat germplasm has been analysed in the past using molecular markers ,28,42,43Analysis of the genetic diversity parameters showed a higher level of variability among the indigenous oat landraces than previously reported for 67 oat landraces . NotablyThe determination of the internal diversity level of each indigenous landrace was one of the goals set for this study. This type of data is currently not available in the literature because the internal variation was investigated only a few times, although landraces are regarded as heterogeneous . Apart fCurrently, we cannot determine precisely enough whether the variation level is specific to the accession or it is a derivative of the size of the collected seeds sample as well the way in which it represented the initial diversity. Without any prior information about the initial variability, we are not also able to prove that the reproduction and regeneration standards of the gene bank were sufficient to maintain the collected diversity. However, we consider that the obtained results can be a valuable reference point for future studies. A DNA biobank established during this study, in the future, will allow researchers a direct comparison of variation level even if they will use different analytical methods.The goal of our study was also to describe the variability patterns of important agro-morphological traits in the landrace collection in order to improve breeding selection of cross parents. The studies gave also the opportunity to distinguish some germplasm sources for breaking difficult correlations among the characters. Based on data obtained from the gene bank database EGISET , the divThe yield is an extremely complex characteristic, and its components are strongly influenced by a high number of environmental factors. Moreover, its inheritance is neither simple nor favourable for breeding practice. The importance of genetic dominance and non-additive interactions of epistatic genes for yield heritability is significantly greater than the effect of additive genes ,51. In m(69), PL 52195 (76) and PL 50530 (16) may be useful in the improvement of harvest index and resistance to lodging, in spite of the widespread opinion that landraces considered as cross components are generally too tall.The plant height is also a very complex character determined by interaction of numerous exo- and endogenous factors. Earlier studies have confirmed the polygenic nature of this trait . No typiFusarium avenaceum, F. culmorum oraz Bipolaris sorokiniana) and development of adventitious roots. Genetic studies of lodging are difficult and rarely performed because of their complexity [(67), PL 52268 (80) and PL 51443 (48).The analysed accessions were characterised by diverse resistance to lodging; moreover, this characteristic demonstrated variability between years during the field trial. This is related to the complexity of the feature that, similar to plant height, is affected by a high number of endogenous and exogenous factors. Lodging is significantly influenced by environmental conditions such as presence and strength of wind, humidity of the soil surface, fertilisation and the sowing rate. These factors interact with the genetically based characteristics such as plant height, size and properties of tissue mechanical of the lower internodes, straw flexibility, resistance to culm base diseases (mplexity . The stu(91), PL 51632 (69) and PL 521955 (76) have shown interesting combination of earliness and yield.The length of the vegetation period was among the analysed traits. It was measured as the heading date and days to maturity, both influenced by the seasonal changes of precipitation, temperature and the connected date of sowing. The differences in the heading date were larger than in the days to maturity, which is in agreement with similar studies . The lonPuccinia coronata Cda. f.sp. avenae Erikss., is one of the most dangerous and the most widely disseminated oats fungal diseases, with the yield losses as high as 40% [Puccinia coronata has the ability to adapt quickly to resistant lines or varieties of oat; therefore, constant search for a new source of resistance is necessary to effectively combat this disease. The investigated set of accessions was characterised by the sufficient resistance to crown rust; however, it was moderately infected. Resistance of heterogeneous landrace populations may be similar to that described in the oat multiline cultivars, in which interaction of polymorphic genes from different individuals caused significantly higher level of resistance than expected basing on the expression in genetically homogenous stocks [Crown rust, caused by h as 40% . The toth as 40% ,60. Puccs stocks . NeverthBlumeria graminis (DC.) E.O. Speer f.sp. avenae Em. Marchal is a serious foliar disease of oats. It is widespread in the north-western Europe and in the south-eastern parts of the US [A. sativa [Powdery mildew caused by f the US ,63 and rf the US . The graf the US ,66. Base. sativa . SimilarThe thousand seed weight (or thousand kernels weight—TKW) is a relatively stable factor for oat variety characterisation; however, it may be significantly decreased by biotic or abiotic stress at the end of the vegetation period. However, this trait is considered by breeders as much less important compared to percentage of husk or kernel size uniformity, which were not included in this study.The NCPGR preserves about 80 000 accessions of different species. All of them are available as an active collection. Regeneration of such amount of accessions is difficult and expensive, therefore it is reasonable to create core collections reflecting the variability of the preserved genus or species for common use as an active collection. The core collection of common oat landraces is the first one in NCPGR. It represents the genetic and morphological diversity of the entire landrace collection. Besides the gene bank benefits, the smaller set of well described accessions may act as incentive for breeders to incorporate obsolete germplasm into the breeding programs.in situ. This, however, requires both scientific expertise and traditional knowledge of farmers who should grow and manage oat landraces in places where the population originated.Oat landraces have a considerable potential for use in improving disease and abiotic stress tolerance. Additionally, compared to the use of crop wild relatives, there are no obstacles to transfer the valuable characteristics from landraces to new varieties. However, it needs to be remembered about the high level of internal variability that may complicate the process of breeding. The oat landraces provide a wide spectrum of the starting material for breeding programs, as they make it possible to choose the traits which are best suited to local conditions. They are adapted to local and show resistance to local diseases, with an increased ability to compete with indigenous weeds. Despite the large height diversification of the oat plants observed in this study, they tended to be high, which constitutes an indispensable characteristic for weed suppression. Therefore, it has to be concluded that oat landraces should also be grown and preserved in situ conservation, which may be satisfactory for both farmers and scientists.Our results show the high genetic diversity of Polish common oat landraces. However, the broadness of the gene pool turned out to be lower than previously expected, but was still significantly higher than that of contemporary varieties. Compilation of molecular and agro-morphological data allowed to select a set of landraces which may be a valuable “new “recommended source of diversity for breeding programs. Moreover, based on the presented results, we can conclude that many of the landraces which are preserved in the Polish gene bank may be directed to the


Paeonia ostii, a member of tree peony, is an emerging oil crop with important medical and oil uses and widely cultivated in China. Dissolving the genetic diversity and domestication history of this species is important for further genetic improvements and deployments. We firstly selected 29 simple sequence repeats (SSRs) via transcriptome mining, segregation analyses and polymorphism characterizations; then, 901 individuals from the range-wide samples were genotyped using well-characterized SSR markers. We observed moderate genetic diversity among individuals, and Shaanxi Province was identified as the center of genetic diversity for our cultivated plants. Five well-separated gene pools were detected by STRUCTURE analyses, and the results suggested that multiple independent domestication origins occurred in Shaanxi Province and Tongling City (Anhui Province). Taken together, the genetic evidence and the historical records suggest multiple long-distance introductions after the plant was domesticated in Shandong, Henan and Hunan provinces. The present study provides the first genetic evaluation of the domestication history of P. ostii, and our results provide an important reference for further genetic improvements and deployments of this important crop. With the increasing demand for fats and oils, researchers have targeted plant sources to explore their uses and functional properties3 with the goal of developing improved varieties of edible oils that can withstand future challenges, such as climate change, sustainability and food security4. Paeonia ostii is a traditional medicinal plant that represents a newly discovered and underutilized woody oilseed plant with a considerable agronomic potential for edible oil production. Originally, this species was grown for the root bark, which is used as an antispasmodic throughout Asia5. Recent studies have indicated that this species presents a competitive agronomic performance as compared to established oil crops for its high seed yield and unsaturated fatty acids content especially high α-linolenic acid content in the seed oil7. Grain yields of over 13.34 kg/ha have been achieved with common cultivars of P. ostii, and a considerable variation in oil content in seed has been recorded in this species, with values ranging from 20–35%9. α-Linolenic acid, which is a necessary element that cannot be produced within the human body and must be acquired through diet, is commonly found at unusually high levels (above 40%)12 in the seed oil of P. ostii, which indicates that this plant is a valuable crop for linolenic acid production13. Moreover, seed oil of P. ostii is a versatile oil as its use is not limited to food, but it is also widely used in non-food applications, such as in oleochemicals, cosmetics, and pharmaceuticals15. Therefore, P. ostii is an important economic plant with simultaneous medicinal and oil applications. The rediscovery of the value of this oil crop and its rapid deployment in recent years represent unprecedented opportunities for farmers and have attracted considerable interest among plant breeders17.Edible oils represent an important food source for the rapidly growing global populationP. ostii belongs to the section Moutan of the genus Paeonia and the family Paeoniaceae18, and this diploid tree peony species, with 2n = 2x = 10 chromosomes, is indigenous to China19. This plant is cross-pollinated by insects and propagated by seeds20. Compared with other tree peonies in this section, P. ostii is clearly characterized with white or pale rose petals without the base blotch, and the leaves contain no more than fifteen leaflets and present red or purple filaments, a disc and a stigma21. Literature on its cultivation has been recorded since the Qin to Han dynasties , and its root bark was originally used as a medicine. However, the plant was not used for gardening22, although it may represent one of the parental species for breeding the popular ornamental tree peony24. Currently, P. ostii is being cultivated at high concentrations in Bozhou and Tongling city in Anhui Province and the southwest region of Hunan Province, and these areas supply thousands of kilograms of root bark materials for the medical industry annually. In addition, this species is sparsely cultivated across southern and northern China, mainly in Shanxi, Shandong and Shaanxi provinces. Shaanxi Province is often reported as the region with the largest species diversity of Paeonia25, and in this region, the largest number of wild populations of different woody and herb peonies have been reported26. In addition, historical literature records Shaanxi as the original cultivation location of P. ostii28. However, the cultivation history of P. ostii remains largely unclear. Studies have indicated that P. ostii cultivation originated in the Fenghuang Mountain area of Tongling city in Anhui Province30. However, multiple independent cultivations may have also occurred in Henan Province, which has represented a famous location for the cultivation and breeding of the ornamental peony for more than 1,000 years31. P. ostii is an important rootstock resource for grafting the ornamental tree peony and one of the parents for the ornamental tree peony30. The current specimens cited in the original taxonomic description were derived from plants collected in three Chinese provinces: Henan, Hunan and Shaanxi28. Recent searches for the species as a wild plant have been largely unsuccessful33. The plant may have been common in the wild at one time but has suffered from over-collection of its roots for use as a Chinese medicine32. When phenotypic variation was qualified across the popular cultivars34, little information was obtained on the divergence and relationship among cultivars. However, genetic evaluations may help to clarify the genetic diversity and relationship among the populations and provide insights on the domestication history and future breeding.P. ostii has been cultivated as a new crop for edible oil production, which represents an unprecedented opportunity for the peony oil industry; however, the genetic background of this species is poorly understood. Thus, proper initiation of breeding populations by the selection of optimal genotypes from the available germplasm is lacking and a proper resource management strategy across the entire country has not been developed. These questions present the main challenges for the deployment of P. ostii. Hence, comprehensive information on the genetic diversity and population structure of P. ostii is urgently required.In recent years, 36. To date, more than 500 SSR markers have been developed for the Moutan section46. Segregation analyses that incorporate genetic mapping may provide a good strategy for selecting a set of optimal SSR markers. These markers can then be used to determine the allele inheritance and independent assortment of P. ostii47 and for conducting a population genetic study.Population genetics analyses rely primarily on microsatellite markers, which are also powerful tools for marker-assisted selection (MAS) in plant breeding. Co-dominant inheritance, a high level of transferability, close associations with genes of known function and a low cost of development are some of the advantages that led to an increased focus on simple sequence repeat (SSR) markersP. ostii are still unknown, a complex pattern of population divergence is expected because of the long domestication history and outcrossing property of these plants. P. ostii presents an opportunity to investigate the genetic effects of migration, hybridization, adaptive divergence and anthropogenic influence on the genetic diversification of outcrossing woody plants. Clarifying the genetic variation could provide valuable information for the proper management of P. ostii genetic resources as well as for the deployment of these resources and directive breeding. We first screened a set of SSR markers that are suitable for population genetic evaluations based on segregation analyses, amplifications and polymorphic properties. Then, we performed a population genetic evaluation of a large collection of samples collected over a wide range in China. The goal of our research was to understand the genetic diversity among and within the population and the genetic structure among populations, infer the domestication history, and provide information for genetic assessments, regional genetic improvements and management of this emerging oil crop.Although the population structure and genetic diversity of Paeonia suffruticosa ‘LuoYang Hong’)48, which is a related species related to P. ostii, was mined for SSR markers. In total, 2,989 SSR loci were detected, and 788 SSR loci were selected and tested. Of these loci, 373 yielded fragments of expected sizes43. In addition, we developed a F1 mapping population for segregation analyses and genetic map construction. This mapping population included 195 progenies derived from a cross between P. ostii ‘FenDanBai’  and P. × suffruticosa ‘HongQiao’ . We then tested the 373 validated SSR loci in our mapping population and determined that 74 SSRs were polymorphic loci (Table 2) segregation test was performed and the genetic map was constructed using Joinmap 4.149. In total, 68 SSR loci were sorted into 5 segregation types  and successfully mapped to 5 linkage groups. Among these 68 SSR loci, 48 present Mendelian inheritance  of the 40 SSR loci. Furthermore, Ewen-Watterson tests performed using Arlequin v3.5.2.251 targeted 4 loci  that deviated significantly from a neutral equilibrium model. Additionally, an analysis using Genecap v1.452 determined that a pair of samples had an identical genotype; therefore, one member of this pair was removed. Finally, 29 markers (19 in Mendelian inheritance) considered unlinked and neutral with all 901 samples were remained for further analyses.Of the 68 mapped SSR loci, 40 showed polymorphisms among 902 sampled individuals of generally cultivated populations of tii Fig. . Using MN) ranged from 2 to 8, with an average of 4.897 alleles per locus. The mean polymorphism level of the loci (PIC) was 0.318, with a range from 0.108 (at the locus PS183) to 0.613 (PS112) (Table HO) ranged from 0.080 (at the locus PS074) to 0.801 (PS101), with an average of 0.343, and the expected heterozygosity (HE) ranged from 0.081 (at the locus PS183) to 0.625 (PS112), with an average of 0.321 (Table PA) were found at 20 loci distributed in the 14 populations (Table H) and allelic richness (AR) were 0.330 and 2.194, with H ranging from 0.286 for SXSLA to 0.415 for SXFX (two populations from Shaanxi Province) and AR ranging from 1.610 for XXFH (an isolated population in the south) to 2.510 for SXSL (a population from Shaanxi Province). We observed that the genetic diversity and allelic richness were higher in populations from Shaanxi Province (Table FIS) at the locus and population levels were −0.053 and −0.045 (Table F) values indicated an excess of heterozygotes in P. ostii, which is consistent with the self-incompatibility system of this species.A total of 142 alleles were found, and the number of alleles per locus (2) Table . The obs21 Table . At the ns Table . The ave45 Table . The negFST estimated for the 29 loci was 0.106 and ranged from 0.022 at locus PS260 to 0.529 at locus PS187. The pairwise multi-locus FST estimated for these loci ranged from 0.001 to 0.208, which indicated moderate population differentiation among the populations  reached a clear mode at K = 8 before decreasing, and the highest delta K was detected when K = 8 with the second highest values were detected at K = 2, K = 4 and K = 7 . Four major well-separated genetic clusters  were identified at K = 4–8, and one minor well-separated genetic cluster  was observed at K = 6–8. Models assuming different K values helped to detect admixed groups . Group (AHTL) of populations from Tongling in Anhui Province almost entirely made up cluster I; similarly, populations  from Shandong Province were always assigned to cluster II; three populations  from Hunan, Shaanxi and Henan Province were assigned to cluster III; three populations  from Shaanxi and Shandong Province were assigned to cluster IV. These four major and one minor well-separated genetic clusters revealed the five gene pools in the domestication of the species, indicating probably five times of independent domestication events. Two populations  from Bozhou City in Anhui Province showed a closer affiliation with cluster I at any K value, which may be attributed to recent migration from Tongling to Bozhou City in Anhui Province. When K = 4, the group (AHBZ) of populations from Bozhou City in Anhui Province, Linfen City in Shanxi Province  and Liaocheng City in Shandong Province  appeared to be admixed groups of clusters I and III, highlighting the important role of clusters I and III in recent breeding activities through hybridization. At K = 8, populations from Shaanxi Province were assigned to four different ancestral gene pools and populations from Shandong Province were assigned to two ancestral gene pools.STRUCTURE analyses indicated that the ln A principal coordinate analysis (PCA) partitioned 67.40% and 1.80% of the variance in the experimental data to the first two axes Fig. . CollectHNSY, HNLY and SXSN populations shared the same proportion of genetic ancestry in the STRUCTURE analyses; therefore, these samples were pooled into a single group (HN). Similarly, samples from the SDHZB, SDHZC, SDHZD and SXMXA populations were pooled together to form the SD2 group, samples from the SXTG and SXGQ populations were pooled together to form the SHX1 group, and samples from the SXSLA, SXSLB and SDHZA populations were pooled together to form the SHX3 group. Samples collected from Tongling and Bozhou City in Anhui Province were pooled into the AHTL and AHBZ groups, respectively. Similarly, samples from Shandong and Shaanxi Provinces were pooled into the SD1 and SHX2 groups, respectively. Because a significant structure was not observed for certain samples, samples from SXLFA, SXLFB, XXFH, HBBK and BJJF were pooled into a single group (MIX). In total, nine groups were analyzed: AHTL, AHBZ, SD1, SD2, SHX1, SHX2, MIX, HN and SHX3  showed the highest degree of genetic differentiation from the other groups (average FST = 0.130). Groups SHX1 and HN  showed moderate genetic differentiation compared with the other groups (average FST = 0.109–0.105). Group AHTL  and SD2 showed low genetic differentiation compared with group AHBZ, SD1, SHX2 and MIX, and this finding was consistent with the STRUCTURE analyses. The AMOVA analyses of the nine cluster groups revealed that 5.83% of the genetic variation occurred among regions, whereas 2.48% of the genetic variation occurred among populations within regions , clusters were grouped in a pattern similar to that of the STRUCTURE analyses and pairwise FST analyses. Four admixed groups  that grouped close together displayed a high degree of gene flow, and two genetically pure groups (AHTL and SD2) exhibited a relatively high gene flow with these four admixed groups. Three genetically distinct groups  appeared relatively isolated from the other groups.The directional relative migration networks as estimated by divMigrateups Fig. . For allP. ostii, and we selected unlinked and neutral markers and performed range-wide genotyping and genetic evaluation for this commercially important woody plant with a long history of cultivation.To establish breeding and management strategies for emerging crops, a thorough understanding of the population genetics of the species of interest has become essential for producing long-term sustainable goals. This work represents the first systematic population genetic study of P. ostii presents a wide distribution range, reproduces via insect pollination and outcrossing and has a self-incompatible mating system, and these characteristics allow this species to develop a higher genetic diversity. Consistent with other cross-pollinating plants55, most of the total genetic variation (>90%) in P. ostii occurs among individuals within a population, whereas a small proportion of the genetic variation occurs between populations. Compared with previous studies of wild peony species in the Moutan section, the genetic diversity indices obtained in the present study are lower than those in Paeonia jishanensis56  and Paeonia rockii57 . This could be attributed to long-term domestication of P. ostii involving selecting and modifying its wild progenitor to meet human needs58. The elite P. ostii cultivars that growers use today are the result of intensive selection applied to the founding stock and its descendants, which is presumed to have led to additional losses in genetic diversity. The reduction in genetic diversity does not bode well for future genetic gains in productivity and could result in broad susceptibility to newly emerging diseases or insect pests, thereby threatening long-term food and feed security60. Future germplasm collections for the creation of new cultivars should account for the vast reservoir of genetic diversity to maintain the genetic base of P. ostii.P. ostii samples. Interestingly, we found that samples in Shaanxi Province were assigned to four ancestral gene pools. In addition, the genetic diversity analyses demonstrated that both the diversity and allelic richness are generally higher in populations from Shaanxi Province. Based on these results, we proposed that Shaanxi Province is the main genetic diversity center of the cultivated materials of the species. The other important group of populations from Tongling (AHTL) in Anhui Province contained a well-separated gene pool and displayed extensive genetic exchanges with admixed groups as revealed by the divMigrate analyses. Therefore, the resources from these two provinces could represent priorities for future germplasm conservation and exploration.STRUCTURE analyses identified five main ancestral gene pools across the wide range of P. ostii for medicinal use originally occurred in Shaanxi, Henan, Shandong and Anhui (Tongling) provinces61. The hypothesis that the domestication origin of P. ostii occurred in Shaanxi Province has generally been accepted, although other hypotheses of its domestic origin are still popular61. However, the domestic origin hypotheses have not been tested using genetic data. The present study presented for the first time a genetic test for P. ostii based on an analysis of multi-locus data. Five genetically well-separated gene pools were identified in geographically isolated regions of Shaanxi Province, Tongling City in Anhui Province, and in Shandong, Henan and Hunan provinces. The allopatric distribution of distinct gene pools of cultivated P. ostii provided support for the hypothesis of independent domestication origins of this species. Consistent with the cultivated ornamental tree peony62, our study provided another case of multiple independent domestication for the cultivated tree peonies of the Moutan section.Cultivation of P. ostii. Interestingly, four distinct gene pools were found in Shaanxi Province, with two of these pools also found in Shandong. The richest diversity of both the wild populations and species of tree peony was observed in Shaanxi26, and this province also presents the longest recorded history of cultivation of tree peonies65. Reports have suggested that the tree peony was introduced from Shaanxi to Shandong Province several different times from ancient to modern times65. Given these facts, it is easy to predict that at least four times domestication origins of cultivated P. ostii ever happened in Shaanxi Province. The reconstruction of four well-separated gene pools identified by the STRUCTURE analyses clearly suggested that protection and utilization should be prioritized for the cultivars of different domestication origins that are isolated from each other in areas such as SXSN , SXMXA , SXSLA , SXSLB , SXGQ , and SXTG . Because most of these areas are located in the vicinity of the Qinling mountains, which presents the largest specific diversity of the Moutan section, we suspect that the Qinling Mountains in Shaanxi represents the likely origin of domesticated P. ostii. As the wild plants of P. ostii have been completely eradicated33 and all populations sampled from Shaanxi were cultivated, further analysis of P. ostii, including models of the domestication history and phylogenetic reconstructions based on the low-copy nuclear genes relative to that of the chloroplast and rDNA sequences, will be needed to validate this hypothesis.In the present study, the STRUCTURE results of five well-separated gene pools indicated that five independent domestication origins may have occurred over the long-term cultivation of P. ostii resources that are extensively used as rootstock for ornamental cultivated tree peony grafting. Our present study suggested that at least two rounds of independent long-distance P. ostii introductions likely occurred from Shaanxi to Shandong. We observed that one well-separated gene pool was identified in three distinct populations of SXSN , HNLY  and HNSY , reflected probably unique domestication in Shaanxi Province and following long-distance introduction to Henan and Hunan provinces. It is not difficult to see the importance of Shaanxi Province in domestication and deployment of P. ostii.As the world’s largest producer of ornamental tree peony, Heze City in Shandong Province cultivates P. ostii is Tongling City in Anhui Province, where the long-term cultivation of this species has been recorded27. We found a group (AHTL) of populations from Tongling that almost solely consisted of a unique well-separated gene pool (cluster I). This finding indicates at least one independent domestication origin in Tongling City, which is distinct from the origins in Shaanxi Province. It is interesting that all cultivated samples from Tongling City were assigned to one unique gene pool without introduction were found from Shaanxi Province.Another important region in the domestication of P. ostii. Bozhou City, which is located close to Tongling City, has earned a reputation as the “Capital of Traditional Herbs” and has become the central region for the cultivation of P. ostii66. The production of P. ostii in Bozhou accounts for more than 70% of the annual total production66. In our present study, both the STRUCTURE and divMigrate analyses revealed that cultivars in Bozhou City and admixed cultivars popular in other regions are similar in genetic makeup and present the closest relationships with cultivars from Tongling City in Anhui Province, Shaanxi and Shandong provinces. This finding demonstrates the important role of domestication in Tongling City in Anhui Province.Our study suggested that Tongling City has likely played an important role in the domestication and breeding of modern cultivars of Alongside with those from well-separated gene pools, admixed individuals were identified extensively across most of the sampled regions . We hypothesized that these individuals were likely derived from recent hybridization breeding processes, but we failed to find records that corroborated this hypothesis. Genomic data may help elucidate the origin of the admixed populations.P. ostii; (2) Shaanxi Province has the largest genetic diversity; (3) multiple independent domestication origins occurred in Shaanxi Province and Tongling City in Anhui Province, and this species was introduced to Shandong, Henan and Hunan provinces. Our results provide comprehensive information that can be used in genetic assessments, regional genetic improvements and management of P. ostii and insights for the development of breeding strategies for this emerging oil crop.This study was the first comprehensive work to select well-characterized SSR markers for population genetics studies on peony species. Our study settled long-running debates by confirming that (1) moderate genetic diversity and differentiation in 67, and maintained at room temperature for DNA extraction.A total of 902 individuals from 36 populations that cover the vast majority of the distribution area in China were collected Fig. . The geoP. suffruticosa ‘Luo Yang Hong’48, and 4,373 potential SSRs were identified from 3,787 unigene sequences. In total, 2,989 SSR-containing sequences were appropriate for primer design, and 788 primer pairs were further selected for the primer synthesis. Of these, 373 primer pairs generated PCR amplification products of the expected sizes43. Those 373 SSR loci were then screened for polymorphism in a genetic mapping population. The mapping population was composed of 197 individuals, including the two parents and 195 F1 progeny. The parents were P. ostii ‘FenDanBai’  and P. × suffruticosa ‘HongQiao’ . The F1 mapping population plants were grown in the Beijing Guose Peony Garden, Beijing, China. The validity and polymorphism of 373 SSR loci were analyzed by using 6 progeny individuals randomly selected from F1 progeny, and 74 SSR loci were found to be polymorphic. We kept these 74 SSR loci for genotyping of 195 mapping individuals. All SSR genotypes we generated were combined with SNP genotypes68 from the same mapping population, and then used for chisquare test (χ2) of segregation and genetic map construction with Joinmap 4.1. Fifty-three SSR loci showed no deviation from Mendelian inheritance ratios (P < 0.05). Then, the optimal arrangement order was determined via regression analyses with the single-linkage clustering algorithm at logarithm of odds (LOD) ≥ 4.0 and recombination rates (r) ≤ 0.3. Based on the above results, the LOD threshold was relaxed to 3.0 and markers that showed segregation distortion with higher LOD values in the linkage analyses were also mapped to the linkage group without changing the order of marker linearity. The genetic distance between adjacent markers was then estimated. The graph of the genetic map with 4 SNP markers  and 68 SSR markers was visualized using MapChart v2.369.A total of 59,275 unigenes were obtained from the transcriptome assembly of  P. ostii individuals, 68 SSR loci on the genetic linkage map  were selected.To further examine the genetic diversity and structural analyses of the 902map Fig.  were use2O, 5 μL 1 × Taq PCR Master Mix , and 0.5 μL (10 pmol) each reverse and forward primer with fluorescent labeled 6-FAM, HEX, TAMRA or ROX . PCR amplification was performed with the following amplification protocol: pre-denaturation at 95 °C for 4 min, followed by 35 cycles of denaturation at 94 °C for 35 s, annealing at 47–60 °C for 35 s . The purity and concentration of the extracted genomic DNA was measured using a NanoDrop 2000 spectrophotometer at 260 nm  and adjusted to 50 ng/μL. PCR amplification was performed in a total volume of 10 μL containing 1 μL (50 ng) genomic DNA, 3 μL ddHWe further checked the suitability of each marker for the population genetic analyses. Micro-Checker was used to identify possible genotyping errors, including stuttering, large allele drop-outs and null alleles. We determined whether selected polymorphic microsatellite markers deviated significantly from a neutral equilibrium model using Ewen-Watterson tests in Arlequin.N), number of different alleles (Na), number of effective alleles (Ne), Shannon’s information index (I), observed heterozygosity (HO), expected heterozygosity (HE), percentage of polymorphic loci (PPL), and F-statistics  were calculated using GeneAlEx v6.50171. The polymorphism information content (PIC) was calculated with PowerMarker v3.2572. Hardy–Weinberg equilibrium (HWE) was calculated with the assistance of Genepop (http://genepop.curtin.edu.au/). Samples with identical genotypes were detected by GeneCap.The number of alleles per locus , ten independent runs were performed with a burn-in period of 200,000 followed by 200,000 Markov Chain Monte Carlo (MCMC) replications. The most likely K value was determined using Structure Harvester74 based on both the log likelihood and the maximum ΔK. Because clustering algorithms may incorporate stochastic simulations as part of the inference, independent analyses of the same data may result in several distinct outcomes; therefore, we used the computer program CLUMPP v1.1.275 to analyze the results from replicate analyses for optimal alignments of replicate clusters. The output from CLUMPP was graphically displayed by the cluster visualization program DISTRUCT76.The model-based (Bayesian) cluster software STRUCTURE v2.3.4FST distance matrix was performed using the “adegenet” package77 in R78.A PCA based on the pairwise P. ostii samples, sampled populations were pooled according to their geographic origin and genetic composition, and the global multi-locus FST for the 29 loci was calculated with GenAlEx. The pairwise multi-locus FST and significance of differences between FST values was assessed in exact tests conducted with Arlequin, and the gene flow (Nm) based on the FST values was calculated as Nm = 0.25 (1 − FST)/FST. A genetic distance matrix of pairwise FST values was also used for AMOVAs79 in Arlequin. Significance levels were determined using 1000 permutations.To identify the genetic differences in the 901 Supplement Table


Biscutella laevigata. The two gene pools identified, experiencing limited gene flow along a 1‐km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine‐scale topography. Using a large panel of DEM‐derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high‐resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes.Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine‐scale models to evaluate environmental heterogeneity may help detecting adaptation to micro‐habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant  PCR products amplified with FAM, VIC, NED fluorescent dye on the EcoRI primers were pooled with GeneScan 500 LIZ Size ladder and separated the 3730xl DNA analyzer capillary sequencer (Applied Biosystems). Resulting electropherograms were scored between 75 and 500 bp with GENEMAPPER v. 4.0 (Applied Biosystems) using AFLP default peak detection parameters. The scoring was checked manually, and AFLP loci were recorded as present (1) or absent (0) in binary matrices.All individuals were genotyped with amplified fragment length polymorphisms (AFLPs) following Parisod and Christin . DespiteAfter an initial assessment of polymorphism and reproducibility of 38 AFLP primer combinations, the six bests  were retained for genotyping. Individuals were randomly distributed among plates and the whole procedure was replicated on 15% of the samples to evaluate the error rate sensu Bonin, Taberlet, Miaud, and Pompanon .2.3An issue regularly encountered when studying patterns of genetic variation and local adaptation in plant populations is recent polyploidy   with an error rate estimated at 2.93% based on the replication of 15% samples. Clustering approaches evaluated the genetic structure based on individual genotypes, with the Calinsky criterion of the K‐means method highlighting two main genetic clusters within the study area . A majority of the individuals were unambiguously assigned to each of these genetically homogeneous clusters, whereas 105 admixed genotypes presented coefficients of membership between 0.2 and 0.8  showed a clear segregation Figure a. Genoty3.2All DEM‐derived variables, except TON, were found to be significantly different between populations Table . For ins3.3Few GLMMs between genetic loci and environmental variables turned out to be significant with α = 0.05 after Bonferroni's correction (significance level: 3.3E‐06). Only five genetic markers were significantly associated with Alt, TON, Nor, Ti12, and WEX (Table 4B. laevigata. We indeed evidenced a marked spatial genetic structure along a 1.2‐km ridge on which two coherent gene pools were identified, separated by an unsuitable rocky area (Figure The fine‐scale environmental heterogeneity of mountainous regions makes such landscapes ideal for the study of patterns of local adaptation. In this research, we report evidence of local differentiation and signatures of selection in an alpine plant population of a Figure . Among ta Figure .Those results do not unambiguously support local adaptation as the unoccupied rocky area may act as a strong barrier limiting gene flow. Consequently, it is not unlikely that genetic drift acted on both populations independently and that the influence of the clear habitat demarcation was not the major driver of the reported genetic differentiation. Accordingly, we found only a limited number of significant associations, despite the substantial number of environmental variables tested and the high number of individuals sampled. In contrast to predictions of such neutral model ruled by demography, the reported pattern also matched with expectations under weak selection, with the spatial distribution of significant associations along environmental gradients only weakly reflecting the population structure, and thus making them plausible signatures of selection. The observation that none of the identified genetic markers is correlated with the population membership coefficient is further consistent with adaptive processes having shaped the distribution of slightly more than 2% of the loci being surveyed.Bearing limitations of the approach in mind, our observations demonstrate that VHR DEMs can be suitably used to model fine‐scale environmental heterogeneity. Among investigated topographic variables, solar radiation, terrain ruggedness, and wind exposure appear to substantially differ between the two populations. Noticeably, they reflect climatic variability at microsite that is only identified by fine‐scale topographic models, demonstrating their usefulness for landscape genomics studies requiring such resolution (Leempoel et al., Multiscale models used here enabled precise analyses, thanks to ecologically relevant topographic proxies (Leempoel et al., Our framework illustrates that ecologically relevant DEM‐derived proxies are relatively easy to acquire and provide unique information on micro‐habitats for landscape genomics studies. However, we also highlight their sensitivity to changes in spatial resolution and argue that the interpretation of results obtained from DEMs at a single resolution should be cautiously considered. By no means, a single resolution, even the finest, may be sufficient to identify signatures of selection in highly heterogeneous landscapes. Accordingly, recommending an appropriate scale would likely be misleading and we rather suggest that future studies be based on high‐resolution models to explore multiscale derived variables, as we did in this study. While we focused on a single species, we expect these recommendations to be valid for a broad range of taxa and habitats. On the other hand, coarse resolution climatic variables interpolated over homogeneous landscapes may be sufficient for specific situations (Fick & Hijmans, Genetic and Environmental data are available on Zenodo.KL conducted fieldwork, performed analysis, and wrote the manuscript. CP designed and supervised the project and contributed to the writing. CG conducted fieldwork, performed the genetic analysis, and contributed to the discussion. SJ supervised the project and contributed to the writing.