Dalbergia cochinchinensis and Dalbergia oliveri using microsatellite data from populations across Indochina and relates it to landscape characteristics and life‐history traits. We found that the major water bodies in the region, Mekong and Tonle Sap, represented barriers to gene flow and that higher levels of genetic diversity were found in populations in the center of the distribution area, particularly in Cambodia. We suggest that this pattern is ancient, reflecting the demographic history of the species and possible location of refugia during earlier time periods with limited forest cover, which was supported by signs of old genetic bottlenecks. The D. oliveri populations had generally high levels of genetic diversity (mean He = 0.73), but also strong genetic differentiation among populations , while D. cochinchinensis had a moderate level of genetic diversity (mean He = 0.55), and an even stronger level of differentiation . These differences in genetic structure can be accounted for by a higher level of gene flow in D. oliveri due to a higher dispersal capacity, but also by the broader distribution area for D. oliveri, and the pioneer characteristics of D. cochinchinensis. This study represents the first detailed analysis of landscape genetics for tree species in Indochina, and the found patterns might be common for other species with similar ecology.Indochina is a biodiversity hot spot and harbors a high number of endemic species, most of which are poorly studied. This study explores the genetic structure and reproductive system of the threatened endemic timber species  The area harbors a high overall biodiversity and very high levels of endemism for both plant and animal species, but is also experiencing high rates of primary habitat loss and currently has one of the world′s highest levels of deforestation  across the Indochinese landscape. Dalbergia cochinchinensis Pierre and Dalbergia oliveri Gamble ex Prain  explore and describe the landscape genetic diversity patterns of the two species across the study area, 2) relate the patterns to current and ancient geographical characteristics of the Indochina landscape, 3) evaluate whether the recent deforestation and fragmentation levels have impacted the genetic diversity and inbreeding level of the two species, and 4) compare the genetic structure between the two species and relate it to differences in life‐history traits.For these purposes, we apply highly variable microsatellite markers specifically developed for this study to samples of 22.1Dalbergia cochinchinensis and Dalbergia oliveri are medium‐large trees reaching up to 35 m in height and a diameter at breast height (DBH) up to 90 cm. Both species produce attractive red colored wood, which is durable and resistant to termites  and one population of D. oliveri (RBL). A total of 711 seeds from 17 mother trees from the NOY population, 382 seeds from 14 mother trees from the SPEU population, and 381 seeds from 18 mother trees from the RBL population were sown without any prior treatment in an app. 50/50 mix of vermiculite and standard potting soil and kept in a glasshouse at min. 25°C and watered daily. Leaves from seedlings were collected and stored at −20°C until DNA extraction.We sampled 677 individuals from 26 populations of 14 Table . Eight o14 Table , Fig. S12.2® Pro DNA isolation kit .DNA from leaf or cambium samples was extracted using a CTAB protocol , using enriched genomic libraries and pyrosequencing as described by Malausa et al.  and eight  polymorphic markers with high amplification rates were selected for D. cochinchinensis and D. oliveri, respectively, for use in this study , and PCRs were performed using the QIAGEN Multiplex PCR kit  according to the manufacturer's instructions, except that total reaction volume was reduced to 10 μl. The PCRs were conducted on a Bio‐RAD C1000 Thermal Cycler  or a GeneAmp 2700 Thermal Cycler  with the following cycling conditions: 95°C for 15 min, followed by 30 cycles of 95°C for 30 s, 55°C for 90 sec, and 72°C for 90 s, and a final step at 72°C for 10 min.Microsatellite markers for a et al. . For eacFragment sizes were determined on an ABI 3130XL Genetic Analyzer  and analyzed using the microsatellite plugin in Geneious 6.0.5 to 7.1.7 .2.32.3.1D. oliveri dataset, before any analyses.The marker OLI_1 showed poor amplification in some populations, and the total percentage of missing data for this locus across all samples was 18%. This locus was therefore removed from the R=G−1/N−1, where G is the number of unique genotypes and N is the number of samples, was calculated according to Dorken and Eckert .Duplicate genotypes were removed before any subsequent analysis (to 523 genotypes for We first tested the datasets for deviations from Hardy–Weinberg (HW) proportions using a modified Markov chain algorithm as described in Guo and Thompson , as wellFST , number of private alleles (Np), observed heterozygosity (Ho), and expected heterozygosity (He). The number of observed alleles corrected for sample size differences, Na (rare), was calculated in HP‐rare 1.1 . Arlequin was used to compute FIS values for each population and locus, and values were tested for deviation from zero with 1000 permutations, using α = 0.05, and sequential Bonferroni procedure was conducted to calculate table‐wide levels of significance : mean number of alleles  if the population has experienced a recent bottleneck and negative (heterozygote deficiency) if the population has experienced a recent expansion. We also calculated the Garza–Williamson index Mm , Jost′s among populations, a Mantel test  as impleK for each species. We used the default settings . The likelihood for each K from 1–10 was estimated with 10 iterations per K, and a burn‐in of 20.000 MCMC and a simulation run of 200 000 MCMC for each iteration. In order to calculate the average likelihood value of each K over the 10 iterations and estimate ΔK , 2. north/central Cambodia , 3. southwest Cambodia , 4. northeast Cambodia/ Central Highlands Vietnam , 5. southeast Cambodia/south Vietnam , and 6. southern part of Cat Tien National Park in Vietnam .To analyze the partitioning of genetic variation at different hierarchical levels , we performed an AMOVA in Arlequin and tested the significance with 1000 permutations. For K to vary between 1 and 12. The run with the highest posterior probability was chosen and treated with a burn‐in of 200.To include geographical location of populations as parameter, we conducted an additional genetic clustering analysis in Geneland , the average single‐locus outcrossing rate (ts), and the difference between these (at population level only) (tm − ts), and rp, the correlation of paternity. If tm is larger than ts, it is an indication of biparental inbreeding. We used the Newton–Raphson method and estimated standard errors of the calculated parameters with resampling of individuals within families based on 1000 bootstrap iterations.Genotypes of germinated seedlings and their mother trees were used to estimate outcrossing rates for the two populations of Ritland, , which u33.1D. cochinchinensis and 616 samples of D. oliveri, but only found 523 and 465 unique genotypes, respectively.We genotyped 677 individual samples of −3 to 5.7E−10 for D. cochinchinensis and 1.6E−4 to 7.7E−10 for D. oliveri). Levels of clonality differed greatly among populations, with several populations having clonal richness estimates of 1 or close to 1  and the ANG D. cochinchinensis population as low as 0.16. For both species, especially populations from southwest Cambodia had very low clonal richness  (Table The Probability of Identity (PI) showed that identical genotypes were likely to represent clones of the same individual .Comparison of D. cochinchinensis (mean He = 0.55) and high levels for D. oliveri populations (mean He = 0.73) (Table D. cochinchinensis with levels of diversity differing greatly among regions. The highest levels of diversity for this species was observed around the Dangrek mountains in the northern border area between Cambodia and Thailand (populations PJ to NOY with He around 0.70), followed by the Cardamom and Elephant mountains area (populations RO to SPEU with He from 0.61–0.66). The Vietnamese and the three northeastern Thai populations had intermediate diversity levels (He = 0.47–0.50), and the lowest values were found for the Laotian populations (He = 0.34–0.43).Overall levels of expected heterozygosity indicated medium levels of genetic diversity for 3) Table . In bothD. oliveri in general had the highest He values, almost all around 0.8, which was also found for the two northernmost Vietnamese populations, CMR and YOD. The Laotian populations had slightly lower He from 0.68–0.79, while the Cambodian SEI and southern Vietnamese populations had considerably lower levels of diversity (He = 0.58–0.67). For both species, allelic richness measured both by raw number of alleles and adjusted by sample size generally showed the same regional patterns.The Cambodian populations of FIS averaged at 0.004 for D. cochinchinensis and at −0.017 for D. oliveri, with no population values differing significantly from zero. For D. cochinchinensis, the positive value in population PK could be due to the effect of null alleles present in this population, as is also the case for the PW, TAL, and NIA populations , well below the critical values of 0.68  and three D. oliveri populations  showed signs of recent bottlenecks; although none were significant after adjusting with table‐wide sequential Bonferroni.The 3.2D. oliveri  and a very high level among populations of D. cochinchinensis . The alternative estimators of genetic differentiation, G′′ST and Jost′ D, which are not affected by level of heterozygosity  than for D. oliveri   and the populations in southwest Cambodia (RO to DNT)  and a south group  with very limited admixture between them  from the rest, but with a high level of admixture on both population and individual level  and the most SE Vietnamese populations (DAL to SAM) were almost exclusively assigned to a single cluster, while the remaining populations were more admixed. However, the SEI, CYS, and LUS populations can be recognized as a group from the same patterns of admixture, as can the SW Cambodian populations (RO to DNT) and the eastern Cambodian and central Vietnamese populations . In the center of the sampling area, the Cambodian populations TBM and PL shared some patterns, while the SMK population appeared quite distinct , SEI and CYS, as well as of the SMK population. However, it differed in the separation of TKK from the other Laotian populations, and the clustering of the Vietnamese YOD + CYS. The unresolved patterns in the area corresponding to Thailand, southern Laos, and Central Vietnam are probably due to the large geographical gap in sampling between the Laotian populations and the rest Figure .D. cochinchinensis and D. oliveri, the Geneland maps present suture zones running north–south roughly on the location of the Mekong River, thus proposing this as an important barrier to gene flow in both species. Suture zones are also found at the approximate location of the Tonle Sap Lake and River, isolating the southwestern Cambodian populations (RO‐DNT) from the rest, although most pronounced for D. oliveri , and the offspring from these trees were therefore pooled, resulting in 16 families for the NOY population and 11 families from the SPEU population.The mother genotypes revealed that some of the D. cochinchinensis was 22% for the NOY population and 36% for the SPEU population and 37% for the D. oliveri RBL population, with highly variable individual germination rates for both species.Average germination success for D. oliveri seed genotypes showed a realized estimated outcrossing rate of 95% indicating almost exclusive outcrossing, some levels of selfing was found in both D. cochinchinensis populations with outcrossing rates of 82% and 71%, respectively  across the distribution range from India to Laos, with decreasing diversity along a west–east gradient, and interpreted the pattern as an indication that teak has its center of origin in India and then spread eastwards. The patterns for D. oliveri and D. cochinchinensis may also be ancient, reflecting the area of origin of the two species or perhaps reflect common refugia during earlier time periods with reduced forest cover, from where the species spread to more peripheral regions. In both cases, repeated founder effects during initial colonization or recolonization from refugia would be responsible for the reduced diversity toward the edges of the distribution area.Common for both species was an apparent reduced genetic diversity in more peripheral populations compared to the most diverse populations in Cambodia, although most pronounced for n et al.  also fouMm for both species could indicate that they experienced reduced population sizes due to forest fragmentation during the earlier time periods, that is, the Pleistocene. During the Pleistocene, glacial periods caused dry and cool conditions in mainland SE Asia, which according to several studies resulted in substantial (rain) forest contraction and fragmentation, accompanied by extension of grassland and savannah habitats  and southern  refugia. The populations in the northern cluster showed slightly higher Mm values than the southern populations, which could indicate a less severe bottleneck, maybe due to larger and more continuous forest refugia supported by the vast highland areas of northern Laos and Vietnam. However, the existing north/south pattern is also likely a result of the high degree of IBD in D. cochinchinensis and the deficiency of sampling in the area between the clusters. In general, there was a tendency that the highest levels of diversity were found in the mountainous regions and surrounding lowland areas of the Cardamom mountains (Cambodia), the Dangrek mountains (Thai/Cambodian border) and the Central Annamites/Highlands (Vietnam). It could indicate that these areas have had a continuous presence of suitable forest habitats for Dalbergia, persisting through earlier time periods such as glacial cycles. As the preferred habitat for D. cochinchinensis is deciduous and semi‐deciduous forest, and semi‐deciduous to evergreen forests for D. oliveri, it could explain why there is less overlap with refugia found for rainforest‐associated species. According to Cannon et al. . There were no signs of inbreeding (high FIS) in the adult generation from these two populations perhaps suggesting strong selection against selfed progeny due to inbreeding depression in the natural populations. It is, however, also possible that the observed significant levels of selfing represented a very recent change in the species’ mating system as an immediate response to severely reduced population size. Outcrossing rates can differ both spatially and temporary . Pioneer characteristics include ability of rapid colonization of new habitats only with a few individuals, and higher frequency of such founder events may explain the lower diversity in populations of D. cochinchinensis compared to D. oliveri and would also result in higher differentiation levels among populations.cies So, , with hiD. cochinchinensis could also be a factor responsible for different differentiation and diversity levels between the two species. However, as there were no signs of inbreeding in the adult populations of D. cochinchinensis (seeabove), we do not consider it likely that selfing caused the observed differences among the two species.As selfing species generally have lower levels of genetic diversity and higher levels of differentiation than outcrossing species . This could also explain the higher diversity levels in D. oliveri (Hamrick & Godt, Finally, sis CTSP , thus suDalbergia species, both species exhibit common characteristics in population structure, which can be explained by current and ancient landscape patterns in Indochina. The present study provides an initial assessment of intraspecific genetic diversity patterns in Indochina, and it is likely that the landscape effects of, for example, the Mekong River and Tonle Sap Lake found for the Dalbergia species will also be reflected in other Indochinese species. Further studies with other focal species will reveal more about current and ancient landscape effects on the distribution and diversity of species in this highly diverse but also highly threatened region.Despite the apparent differences in life‐history traits and levels of genetic diversity and geographical differentiation between the two None declared.IH, ST, SC, HTT, SB, IT, EDK, and LRN involved in study concept and design. IH, SC, and HTT involved in fieldwork and sample acquisition. IH, SC and HTT involved in laboratory work. IH, EDK, and LRN involved in analysis and interpretation of data. IH and LRN involved in drafting of manuscript. IH, ST, SC, HTT, SB, IT, EDK, and LRN involved in critical revision.KR558727‐KR558743. Microsatellite genotypes can be found at Dryad Digital Repository: doi.org/10.5061/dryad.2dg7c.Primer and microsatellite sequences are deposited in GenBank accession number  Click here for additional data file.


Casearia sylvestris in the Atlantic Forest of Brazil. We sampled leaves from approximately 24 adult individuals in each of the study sites: two restoration plantations (27 and 62 years old) and two forest remnants. We prepared and sequenced a genotyping-by-sequencing library, SNP markers were identified de novo using Stacks pipeline, and genetic parameters and structure analyses were then estimated for populations. The sequencing step was successful for 80 sampled individuals. Neutral genetic diversity was similar among restored and natural populations , which were not genetically structured by population subdivision. In spite of this absence of genetic structure by population we found genetic structure within populations but even so there is not spatial genetic structure in any population studied. Less than 1% of the neutral alleles were exclusive to a population. In general, contrary to our expectations, restoration plantations were then effective for conserving tree genetic diversity in human-modified tropical landscapes. Furthermore, we demonstrate that genotyping-by-sequencing can be a useful tool in restoration genetics.The primary focus of tropical forest restoration has been the recovery of forest structure and tree taxonomic diversity, with limited attention given to genetic conservation. Populations reintroduced through restoration plantings may have low genetic diversity and be genetically structured due to founder effects and genetic drift, which limit the potential of restoration to recover ecologically resilient plant communities. Here, we studied the genetic diversity, genetic structure and differentiation using single nucleotide polymorphisms (SNP) markers between restored and natural populations of the native tree  Increasing environmental degradation has led to the promotion of ecological restoration worldwide, particularly for forest ecosystems –3. ForesGenetic diversity is defined as the variety of alleles and genotypes in a population, which drives morphological, physiological and behavioral differences in individuals and populations . It is iForest restoration projects, for example, began long before the recognition of the importance of genetic diversity for their long-term sustainability . The priForest restoration plantings are often implemented using seedlings from few mother trees, and the level of genetic diversity and kinship among planted seedlings has received little attention ,23. PopuThe advent of short-read sequencing, which can assess large-scale sets of single nucleotide polymorphisms (SNPs), has eased the study of population genetics in a genomic scale using populations of non-model species , such asC. sylvestris in restoration plantations and natural remnants of the Brazilian Atlantic Forest to verify if there are differences in genetic diversity level between these type of forest fragments.In this context, we assessed the genetic diversity and genetic structure of populations of the native tree Casearia sylvestris Sw.  is a pioneer tree species highly abundant in secondary forests [C. sylvestris is usually pollinated by various species of flies, whereas seed dispersal occurs by small birds over short distances [ forests –35, dist forests . The mat forests ,38. Simiistances .C. sylvestris individuals, particularly near the borders and in the understory. The CO population was established with nursery-grown seedlings produced with seeds harvested in an old landscaping planting in the “Luiz de Queiroz” College of Agriculture of the University of São Paulo in Piracicaba–SP, whereas the IR population was established with nursery-grown seedlings bought from a forest nursery in Campinas–SP and another in São Paulo—SP. Leaves from approximately 24 adult individuals, separated by approximately 12 m, were collected from each of the four populations. Using GPS equipment, a minimum distance of 12 m was established between individuals, with a margin of error of a maximum of 4 m  and regenerating individuals  may have been sampled.We performed the study in the Brazilian Atlantic Forest, a global hot spot for biodiversity conservation that is estimated to contain from 1% to 8% of the world's terrestrial species  and morem of 4 m . In the DNA was extracted from leaf tissue samples of individuals from each of the four populations following the method described by Doyle and Doyle (1987) . To incrprocess_radtags program from the Stacks package v. 1.44 [process_radtags) was applied across each sample to retain only high-quality sequencing reads. Reads with any non-called base were discarded . Barcodes were recovered when they had a maximum of one mismatch . Because a reference genome is not available for C. sylvestris, the loci were built de novo. Sequences from each sample were aligned using a minimum depth of 3 and allowing up to 2 mismatches with the ustacks program . Subsequently, a catalog of loci was built from subsets of samples, allowing 2 mismatches  between loci from distinct samples. All samples were matched against the catalog, using sstacks, to determine the set of loci in each. We then implemented a correction step with rxstacks, discarding loci when the mean log likelihood of the locus in the metapopulation was less than -10; confounded loci were also removed. Using the populations program, a filter was applied across loci and individuals to retain loci with a maximum of 20% missing data  of 0.05, as implemented in the Lositan software [Sequences of each sample were demultiplexed and cleaned using the ing data . To sepasoftware . SubsequIS). We tested for significant differences in heterozygosities and FIS using confidence intervals calculated based on 1000 bootstrap resamples. Estimates of population genetic parameters were calculated using the PopGenKit [hierfstat [VennDiagram package [R software [To compare the genetic diversity in forest restoration populations and natural remnants, we used the following population parameters: number of different alleles, number of private alleles, observed heterozygosity, expected heterozygosity and the inbreeding coefficient  [Sp= - βlog/ 1—F1; where βlog is the regression slope of the kinship coefficient on the log of geographic distance, and F1 is the mean of the kinship coefficient between individuals estimated for the first distance class. For each distance class, the 95% confidence interval was obtained based on 1000 permutations. For all analyses of SGS within the populations, we used the SPAGeDi program developed by Hardy and Vekemans (2002) [R software [The spatial genetic structure (SGS) analysis was performed based on the kinship coefficient proposed by Loiselle et al. (1995) . This kiy (2004) , which is (2002) , whereassoftware .C. sylvestris individuals distributed across the four populations. For these results, the mean sequencing depth per individual ranged from 1.25 to 43.40, whereas mean depth per locus ranged from 4.70 to 83.22 (SD = 8.58). After all filtering steps it was possible to retain 80 individuals for the subsequent analyses. Using the FST approach in pairwise population comparisons to detect outlier loci, we identified 662 neutral candidate loci and 199 outlier candidate loci.Considering the high number of reads obtained , the sequencing was successful. After cleaning, demultiplexing and filtering of missing data, 861 high-quality biallelic SNPs were identified, which were used to characterize the genetic diversity of We found no significant differences in observed and expected heterozygosities and inbreeding coefficient between the natural remnants and forest restoration populations .Of the 1324 different alleles from neutral loci detected in the four populations, all populations shared approximately 79%, 11% corresponded to alleles shared by at least three populations, 9% were in at least two populations, and less than 1% were exclusive to a population . These rST estimates  was formed by samples from all populations , reinforWe found no significant positive spatial autocorrelation at any distance interval . MoreoveThe value of research involving population genetics in a genomic scale using non-model species without previous knowledge of the genome size and complexity has been previously demonstrated in conservation genomics. With the advent of inexpensive sequencing and the advancement of techniques that reduce genome complexity, such as GBS, the study of genetic diversity and structure in natural populations can be based on hundreds of SNPs ,57. HereC. sylvestris populations in natural remnants and forest restorations. The similar values between forest restorations and natural remnants are promising results for Brazilian Atlantic Forest conservation, because they indicate the effectiveness of forest restoration in maintaining the genetic diversity of the species, and consequently, the potential to self-perpetuate in the restored communities. Heterozygosity and allelic richness are good indicators of individual fitness in a population [C. sylvestris varieties in different ecosystems of Brazil, a similar pattern of genetic diversity was found in almost all populations. Thus, as measured by genetic diversity, the forest restoration plantations studied here were as conserved as forest remnants.We found no differences in the estimates of genetic diversity for pulation ,58 and rpulation . In prevpulation  on populCasearia sylvestris is a pioneer species that is very abundant in disturbed fragments of the region and is highly dispersed by bats and birds, which may promote an intense gene flow between restoration plantations and remnants at the landscape level. Otálora et al. (2011) [IS values. Despite the mixed mating system of C. sylvestris, the differences found for these estimates might be related to the overwhelming level of forest fragmentation, as found in other studies conducted in fragmented landscapes, such as those of Vranckx et al. (2012) [. (2011) , studyin. (2012)  and Dick. (2012) .IS. Andrianoelina et al. (2006) [IS in fragmented populations can be explained by high rates of self-fertilization caused by the isolation of adult individuals after population fragmentation.For the TT natural remnant, which is a highly fragmented forest, we expect that the reduction of population size in this type of forest and the isolation of the trees in the fragment contributed to the increase in F. (2006) , studyinST and the pairwise FST estimates among populations also suggested that the genetic diversity in natural remnants and forest restoration plantations was not structured through population subdivision. The DAPC results are consistent with the FST estimates and reinforce the potential gene flow among the studied forests and with other fragments of the region. Martins et al. (2016) [Copaifera langsdorffii, and these authors suggest that small forest fragments preserve the genetic diversity that existed before fragmentation. Therefore, forest fragments are crucial for in situ species conservation and the maintenance of long-term genetic diversity in human-modified tropical landscapes. Moreover, by establishing restoration plantations with high genetic diversity, the risk of extinctions of tropical trees threatened by reproductive isolation in fragmented landscapes can be reduced.The results for the general F. (2016)  conducteC. sylvestris populations in Brazilian Atlantic Forest fragments [C. sylvestris, which is self-compatible, may be experiencing gene flow within and among populations in such a way that genetic diversity is uniformly distributed in space.To explain the absence of spatial genetic structure, we suggest that seed and pollen dispersal in these populations might play a role . This reragments . PopulatSato et al. (2006) , studyinTherefore, we suggest that efforts are required to not only conserve large natural remnants but also the small forest fragments that are most of the remaining remnants in the Brazilian Atlantic Forest . These rS1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S1 Dataset(XLSX)Click here for additional data file.S2 Dataset(VCF)Click here for additional data file.


Venturia inaequalis isolates, collected from different apple cultivars of eighteen different provinces in Iran, was investigated using 22 polymorphic microsatellite markers. Analysis of molecular variation, Bayesian clustering and Nei's genetic distance analyses based on 88 microsatellite alleles indicated substantial levels of gene flow among the collection sites. Ninety three percent of the variation was observed among the individuals within the populations and only 7% variation was observed among the populations. Structure analysis grouped the isolates into two populations. Maximum number of pathogen genotypes (44) was observed in the North of Iran that grows various different apple cultivars. Investigation on the variation of the pathogen on different cultivars in the North of Iran suggested a significant differentiation of the pathogen populations between wild apple and commercial cultivars. During sampling, varying ranges of scab infection were observed on various apple cultivars in forests, monoculture and mix orchards. Wild type apple  along the Caspian Sea Coast had the most infection in comparison with the Iranian endemic and commercial cultivars. Based on the genetic analysis and host tracking scenario of the pathogen, it was presumed that Iran could potentially be the center of origin of V. inaequalis, which requires further detailed studies with isolates collected from different parts of central Asia and world for confirmation.The population genetic structure of 278  Venturia inaequalis (Cooke) G. Winter is one of the most important diseases of the apple growing regions worldwide [Scab caused by orldwide , especiaorldwide . It is corldwide  causing orldwide . Apple sorldwide . It was orldwide .Venturia inaequalis is a heterothallic Ascomycetes fungus that overwinters as pseudothecia in the leaf litter, whereas in regions with moderate winter it survives as conidia in dormant buds [ant buds . The lifVenturia inaequalis is known to overcome host resistance genes [Rvi6 (Vf), have been overcome by the pathogen in several regions [Malus hosts carrying single resistance genes for avirulence genes of V. inaequalis, provided the first hand information on the 17 gene-for-gene relationships. Based on the information of identifying the complex races of V. inaequalis, a long-term method of plant breeding could provide with resistant cultivars can be provided with the resistance sources carrying pyramided resistances [ce genes . The abice genes . Develop regions . Bus et V. inaequalis were studied in Czech Republic [V. inaequalis collected from around the world showed that genetic diversity within the populations was more than that among the populations [Genetic variation and population structure of Republic , Spain [Republic , Sweden Republic , Brazil Republic , India [Republic , and PenRepublic  and MinnRepublic  within tRepublic . Populatulations . Based oPopulation genetic structure of a pathogen reflects its history and evolutionary potential . SimilarV. inaequalis, Iran can be considered as an important region in the distribution of V. inaequalis along the Silk Road. Apple is one of the superior crops in Iran due to its nutritional and export value. According to the Food and Agriculture Organization , Iran is ranked as the seventh largest country in the world for apple production. Apple scab is endemic to Iran because of the suitable environmental conditions in apple orchards for V. inaequalis (cold and wet weather in spring and early summer); it is one of the serious threats for agricultural economy in Iran. However, comprehensive information on the genetic structure of apple scab disease in Iran is lacking. The present study was undertaken with an objective of investigating the variation of this pathogen on different apple cultivars in different regions of Iran, and reports on the population genetic structure of V. inaequalis in Iran.Apple is the most common and culturally important fruit crop worldwide. The center of origin of apple is considered to be the mountain ranges of Central Asia along the Silk Roads stretching from Asia to Europe . Based oMalus orientalis), Iranian endemic cultivars  and commercial cultivars . Sampling was done from the trees where there was at least one spot on a leaf.Five hundred and forty infected samples were collected from 18 provinces of Iran  during years 2013 and 2014 . The ownThe fungus from the infected samples was isolated using single spore method by streaking out spores on plates containing 2% water agar (WA) and culturing of single germinated conidium on potato dextrose agar (PDA). Pure fungal cultures were obtained by transferring single germinated spore on PDA. Infected leaves were also collected in 2009 and maintained at the arboretum of the University of Tehran, Karaj. Fungal spores were isolated from these leaves for further studies.Fungal isolates were cultured on cellophane discs placed on PDA for 10 to 14 days at 16–18°C under continuous dark. Mycelia were collected and after freeze-dried, subjected to DNA extraction using Iraizol DNA extraction buffer . DNA extraction of a few isolates was conducted directly from infected leaf showing symptoms according to Iraizol DNA extraction protocol. DNA was quantified and quality-checked using a ND-1000 spectrophotometer  and was diluted to a working concentration of 25 ng/μl and stored at -20°C until further use.2, 0.16 mM dNTP, 0.2 μl of Taq DNA polymerase , 2 μM of each primer and 2 μl DNA template. PCR amplification was carried out in thermal Cycler  using the following conditions: an initial denaturation at 94°C for 5 min, followed by 30 cycles of denaturation step at 94°C for 40 s, 40 s of annealing at 55°C, 40 s of extension at 72°C, and a final extension of 10 min at 72°C. The amplified PCR products were resolved in 3.5% Agarose SFR  gel at 100 V for 4 h and visualized using a Kodak Gel Logic 200 documentation system . Allele sizes were determined using the Molecular Image Analyzer software .Twenty eight published SSR primer pairs , 23 wereThe North of Iran  has more number of different apple cultivars in comparison with other regions of Iran. During sampling, wild apple was found only in the North. Hence, 51 isolates of different apple cultivars from North population were selected to investigate their genetic diversity using polymorphic microsatellite markers.ST and Nm, respectively, using GenALex. The genetic diversity within a population (Hs) and total heterozygosity (Ht) for every locus, and expected heterozygosity (He) and observed heterozygosity (Ho) for every population calculated with PopGene and Arlequin.The PCR-generated bands were scored as ‘1’ (for presence) and ‘0’ (absence) in a binary matrix for further analysis. To check whether the loci are neutral or targeted by natural selection, Ewans-Waterson test was simulated with PopGene ver. 1.32 . Nei's gA hierarchical analysis of molecular variance (AMOVA) was performed using the GenALex and Arlequin using default parameters to identify the distribution of population substructure at different geographic scales. The average number of alleles per locus was estimated using Arlequin. An unweighted pair group method with arithmetic mean (UPGMA) tree was produced with Nei's  Genetic Assignment of individuals to a specified number of clusters (K) and population ancestry was done using Structure ver. 2.3.4 software , which ird statistics [Associations of alleles among different loci were examined in each isolate using the index of association atistics , 33, whiatistics . The nulDuring sampling, a range of differential scab infections was observed on different cultivars in forests, monoculture and mixed orchards . All of Of the 540 scab-infected samples, 280 isolates from different regions were selected for investigation of their population genetic structure. Genomic DNA of two isolates could not be amplified with the SSR primers tested, so these two isolates were not included in further genetic analysis. The remaining 278 isolates were divided into five populations based on their geographical origin, such as Northwest, West, Central, North and Northeast . Of the Genotype of each isolate was defined as the combination of alleles for the 22 SSR loci tested. Among the 278 isolates analyzed, 33 to 44 genotypes in different populations were observed based on a total of 88 different alleles. The number of effective alleles in every population was 1.27 to 1.3  and the V. inaequalis within the populations , random mating was evident among individuals and Hardy-Weinberg equilibrium was apparent in all five populations. The observed heterozygosity within individuals for all populations was comparable but significantly lower than the expected heterozygosity.Gene diversity was comparable among the populations, ranging from 0.17 to 0.19 . Shannonulations . Based oA hierarchical AMOVA revealed the distribution of population substructure at different geographic scales. While most of the variation (93%) was explained among the individuals within a population, a significant proportion of the variation (7%) was also attributable to differences among populations from different regions .ST values showed an average value of differentiation between different populations and that the five populations had different proportion of migration and gene flow between each other. The total value of FST was 0.07  with the theta value of 0.075 . The FST values were consistently highest between North and West populations with a minimum gene flow (Nm = 4.88). The gene flow was highest between West and Central populations (Nm = 26.09).All pairwise population differences were statistically significant (P = 0.001) . The FSTV. inaequalis collected from different places of Iran were divided into two populations (K = 2)  . Isolate (K = 2) . Moreove (K = 2) .ST revealed a significant differentiation between wild and commercial apple cultivars. Also, based on FST and Nm, Iranian endemic cultivars had apparently more gene flow with wild apple than commercial cultivars. Overall, Iranian endemic cultivars did not show significant differentiation with wild apple and commercial cultivars , while there was low variation among the populations (7%)  . Similarinnesota  and in dinnesota . High reariation , 36.M. orientalis) was found only in the forests of North of Iran with a severe infection with apple scab. Samples were also collected from different Iranian endemic apple cultivars in the North, but the commercial apple cultivars were rare in the North, or were uninfected with scab in the mixed infected orchards. So, the variation in fungal genotypes in the North in comparison with other populations can be because of the diversity of the apple cultivars and suitable weather condition of cold and wet early spring. Northwest Iran had the second highest number of genotypes (42). In Northwest of Iran, apple is cultivated as an important and valuable crop. The cold weather of this area is conducive for V. inaequalis. Orchards in West Azerbaijan were severely infected with scab, especially on Red delicious. Golden delicious was infected less than other cultivars in all regions.Maximum number of pathogen genotypes (44) was observed in the North of Iran  where thST values between different populations showed highest genetic differentiation between North and West populations . This could be because of the long physical distance and geographical barriers byt Alborz and Zagros Mountains that restrict pathogen migration between the two regions. Maximum migration and gene flow was observed between West and Central populations that resulted in less differentiation of the isolates between these two regions  in Iran, which increases the probability of the presence of different pathogen genotypes.The present research investigated population genetic structure of otential . In addiotential . The hosotential . StructuV. inaequalis had been in Iran before the domestication of apple, and thus, the pathogen was able to adapt and overcome the resistance genes in wild apples during its long pathogenic life [During collections of isolates from different domesticated and undomesticated apples from different regions of Iran, different infection range was observed on cultivars in forests, monoculture and mix orchards . Interesnic life .V. inaequalis evolution in Iran and also in the world, that this pathogen existed in Iran for a long time before apple cultivation and that Central Asia, especially Iran, is the probable center of origin of V. inaequalis in the world. However, further extensive studies including identification of resistance genes in different apple cultivars, analysis of the pathogen populations based on resistance genes, and comparison of other isolates from Central Asia and around the world would validate the present presumption on the origin of the pathogen.Genetic analysis of the isolates collected from different cultivars in the North of Iran with 18 polymorphic SSR loci showed significant differentiation between wild apple and commercial cultivars populations, which suggested a low level of gene flow between these two populations. Iranian endemic cultivars had more gene flow with wild apple than commercial cultivars. But, Iranian endemic cultivars did not show significant differentiation from commercial cultivars. The present results on apple scab pathogen based host tracking are in agreement with the results of Gharghani et al.  that shoS1 Table(DOCX)Click here for additional data file.


Procapra przewalskii) is one of the most endangered ungulates in the world, with fewer than 2,000 individuals surviving in nine habitat fragments on the Qinghai-Tibet Plateau and isolated by human settlements and infrastructure. In particular, the Qinghai-Tibet railway, which crosses the largest part of the gazelle’s distribution, remains a major concern because of its potential to intensify landscape genetic differentiation. Here, using mtDNA sequencing and microsatellite genotyping to analyze 275 Przewalski’s gazelle samples collected throughout the range, we observed low level of genetic diversity (mtDNA π = 0.0033) and strong phylogeographic structure. Overall, the nine patches of gazelles can be further clustered into five populations, with a strong division between the eastern vs. western side of Qinghai Lake. Our study provides the first evidence of the genetic divergence between the Haergai North and Haergai South gazelle populations, corresponding to the recent construction of a wired enclosure along the Qinghai-Tibet railway less than ten years ago, an equivalent of five generations. Well-designed wildlife corridors across the railway along with long-term monitoring of the anthropogenic effects are therefore recommended to alleviate further habitat fragmentation and loss of genetic diversity in Przewalski’s gazelle.The Przewalski’s gazelle ( Procapra przewalskii Büchner, 1891) is arguably one of the most endangered large mammals in the world. Historically, P. przewalskii was widespread across the semiarid grassland steppe in northeastern China but has suffered a severe population decline in the past century owing to illegal hunting, livestock competition, and habitat loss and fragmentation on an increasingly human-dominated landscape2. The first range-wide survey of the Przewalski’s gazelle, which was conducted in 1986 and repeated in the 1990s, estimated the number of free-ranging animals to be an alarmingly low 3004, and the species was denoted Critically Endangered on the IUCN Red List of Threatened Species in 1996. Three new populations that were discovered in 2003 doubled the population size to 6024 and the species has been downgraded to Endangered since 2008. The species now occurs as nine isolated populations around the Qinghai Lake region in northeastern Qinghai Province5, numbering at 1300–1600 individuals in total, according to the latest studies7.Endemic to the Qinghai-Tibet Plateau of China, the Przewalski’s gazelle  by the lakeside; Wayu , which is approximately 50 km southwest7; and a distant population in Tianjun , which is over 100 km northwest of Qinghai Lake. Six populations are located on the east side of Qinghai Lake, namely the Haergai North , Haergai South  and Talexuanguo  populations in Gangcha County and the Shadao , Hudong-Ketu  and Yuanzhe  populations in Haiyan County . The construction of the eastern section, which stretches 815 km from Xinning to Golmud and crosses through the terrain of Przewalski’s gazelles in Qinghai, began in 1974 and opened to traffic in 1984. The 1,142 km-long western section between Golmud and Lhasa, however, was not opened until 2006. In October of 2006, a 1.8-m high wired enclosure was built alongside the railway, thus making it impassable for ungulates except by bridges and culverts9. This area is also an important hub of the Qinghai-Tibet railway, which runs across the county and divides the gazelles’ range into two subregions, namely the Haergai North (N) and Haergai South (S) regions. There are eight culverts underneath the railway constructed to allow for pedestrian or vehicle traffic, or for water overflow along the 10-km Haergai North-South boundary, in hopes that they might mitigate the negative effects of the railway on wildlife. However, no signs of gazelles crossing these passages have been found in a survey from 2007–2009, thus casting doubt over the effectiveness of these artificial constructions as wildlife corridors10. On the basis of these observations that the railway remains an unbridgeable barrier for ungulates, gazelles from Haergai North and Haergai South have been considered as two isolated populations10. To the northeast of the railway, the Talexuanguo population is separated from Haergai North by highway G315  and Tibetan gazelle (Procapra picticaudata), as well as detours in the migration of Tibetan antelopes to circumvent obstacles18. Similarly, human settlements, roads and railways have separated Przewalski’s gazelles into small, isolated populations that are likely to be more vulnerable to environmental stochasticity19. However, the probable genetic consequences of the Qinghai-Tibet railway have not been thoroughly evaluated. Yang et al.11 have indicated that the most significant factor leading to landscape genetic differentiation among Przewalski’s gazelles is human settlements, such as villages and farms, although a moderate effect of the railway has also been reported.The effects of roads and railways on increasing habitat fragmentation have long been a focus of research in wildlife conservation20. In subsequent years, microsatellite markers were used to identify individuals from fecal samples and to evaluate genetic diversity generally22. Yang et al.23 collected samples from most known populations in 2011 and genotyped 13 microsatellite loci to analyze genetic structure from a landscape perspective; yet, information from two critical populations in Wayu and Talexuanguo was missing. Here, we collected specimens from across the geographic range of this species  and one indel (Table 24) and Mongolian gazelle (0.058525). The gene diversity (H) of this species was 0.840, a value comparable with that of the European roe deer (Capreolus capreolus), another ungulate species that had been significantly affected by human activities . All seven individuals from Bird Island (B) shared one mtDNA haplotype, H1, which was the most prevalent haplotype and was shared by 62 individuals from six populations . Similarly, diminishing mtDNA diversity was also observed in the second smallest population, Yuanzhe , consisting of two haplotypes that differed by only 1 bp  of 0.512 and a mean allele range (MAR) of 10.10. In accordance with the mtDNA diversity indices, the microsatellite diversity of the Bird Island population was the lowest among all populations, whereas the Tianjun, Wayu, Haergai North and Haergai South gazelle populations possessed relatively large heterozygosities and allele richness  and 90 tissues (out of 92 that were tried). The combined unbiased PID value of the ten microsatellite loci was 6.05e-8, thus indicating sufficient power of the panel of markers for individual identification. Combining microsatellite and mtDNA information, we identified 184 individuals, and duplicate samples were removed from the downstream analysis. The sex ratio was evaluated for a subset of 94 individuals, and 41 females and 53 males were recognized. No significant allele dropout or null allele was detected in the final dataset, and after Bonferroni correction, there was no significant linkage disequilibrium between loci. A total of 74 alleles were found with an overall observed heterozygosity  was used as an indicator of the outcome of different demographic variables on population divergence. A strong correlation was detected between the maintenance of migration and inter-population genetic connectivity. If gene flow could be maintained through migration between the two populations, the level of population differentiation would remain relatively low after 20 generations of separation  and would remain approximately the same even after another 30 generations . However, if gene flow were blocked, the average FST would increase to 0.0347 after only five generations (Model 2) and would further increase to 0.1119 after 35 generations (Model 4). Model 2 recapitulated the scenario of Haergai North and South Przewalski’s gazelles at present, in which the two populations have been delineated by the Qinghai-Tibet railway since the 1970s (20 generations ago); however, migration across the railway remained possible until the late 2000s (5 generations ago) when a 1.8 m high enclosure was wired alongside the railway, thus creating a barrier to the passage of wild ungulates. This simulation results suggest the significance of migration, even at low levels  and GENELAND (PopG4) in this study , where a population used to live but was not found during a survey in 200810. Some researchers thus suspected that the Wayu population might be the same group of gazelles that once occupied Qieji and that the population dispersed westward in approximately 200710. One study including samples from the Qieji population in 2011 identified this population as a distinct group in a microsatellite-based population structure analysis23, a result similar to the unique status found in the Wayu population in our study (PopS3 in STRUCTURE and PopG3 in GENELAND). However, because microsatellite genotyping data cannot be combined without calibration, we were not able to evaluate whether the Wayu and Qieji populations were indeed the same population or two subpopulations derived from a large historical population that already became differentiated from other populations. The Wayu population has a large census size (ranking fourth out of nine populations7) and possessed high genetic diversity relative to the other populations in terms of both mtDNA and microsatellite markers. However, the conservation outlook of the population is not optimistic, because the construction of a national expressway, the G6 from Beijing to Lhasa, has progressed to this region and opened to traffic since 2011. The Wayu section of the G6 cut through the gazelle’s habitat and divided it into two small isolated patches  and Wayu (PopS3) populations in the west and the Haergai South/Shadao/Hudong-Ketu (PopS5), Haergai North/Talexuanguo (PopS2), and Yuanzhe (PopS4) populations in the east. Our study provides the first evidence of the recent genetic divergence between the Haergai North and Haergai South gazelle populations, corresponding to the construction of the enclosure along the Qinghai-Tibet railway less than ten years ago, an equivalent of five generations. Such a visible genetic impact of habitat fragmentation on a migratory ungulate species like the Przewalski’s gazelle is alarming and highlights the need for wildlife-friendly passages along artificial infrastructure. Well-designed wildlife corridors across the railway and other major highways in this area are critical to alleviating habitat fragmentation and facilitating gene flow. Long-term monitoring of the effects of anthropogenic habitat fragmentation across the range of Przewalski’s gazelles and conservation measures are recommended to avoid further population differentiation and the loss of genetic diversity.During 2010–2012, 156 fecal and 119 tissue samples were collected from all nine Przewalski’s gazelle distribution areas , or a BioTeke Stool DNA rapid extraction kit . Tissue samples were extracted with a standard phenol-chloroform method. The DNA concentrations and quality were examined with a Nanodrop 2000 spectrophotometer, and the samples were diluted to a working concentration for the downstream PCRs.Each fecal sample was extracted at least twice to ensure the accuracy of the microsatellite genotyping by using a modified phenol-chloroform methodCytB, 12 S rRNA, and the control region of P. przewalskii, were used in this study. These sets were either modified from those published for other Bovidae species31 or designed from a conspecific mtDNA sequence (GenBank accession number GU386355) using Primer332  or AmpliTaq Gold DNA polymerase , 1 µM forward and reverse primers, and 10–25 ng of genomic DNA. For the amplification of fecal samples, 10 µg of BSA  was added to the system to reduce the effect of potential inhibitors. The PCR amplification was performed in a GeneAmp PCR System 9700 thermal cycler  under the following conditions: denaturation at 94 °C for 3 min for Takara Taq or 10 min for TaqGold; a touchdown process of 94 °C for 30 s, 60–50 °C for 30 s with a decrease of two degrees every two cycles for each annealing temperature, and 72 °C for 45 s; followed by 35 cycles of 94 °C for 30 s, 50 °C for 30 s, and 72 °C for 45 s; and a final extension step of 72 °C for 10 min. The sequences were inspected in Sequencher 5.1, and the concatenated mtDNA haplotypes was aligned for downstream analysis. Measures of population genetic variation, such as the mean number of pairwise differences between populations, nucleotide diversity, and gene diversity, were estimated in Arlequin 3.533.Six primer sets, amplifying partial regions of mitochondrial 32 Table . PCR was34 was amplified under the same PCR conditions, and the sex of an individual was determined on the basis of agarose gel electrophoresis, in which two bands were present for males, one band each for the X (262 bp) and Y chromosomes (217 bp), or only one band was present for females.A sex-differentiation marker AMELX/YP. przewalskii (Table 23 and six that were redesigned on the basis of homologous sequences from related species (Gazella granti and Capra hircus)36. HEX with the M13 sequence was linked to the forward primers as a florescent label. PCR was performed at a volume of 10 µL, and each contained 1X PCR Buffer, 2 mM MgCl2, 1 mM dNTPs, 0.4 unit of Takara Taq DNA polymerase , 1 µM primer mix with the forward primer linked to a M13 sequence and fluorescently labeled with HEX, ~25 ng genomic DNA and 10 µg of BSA  for fecal samples. The PCR program began with a denaturation step at 94 °C for 3 min; a touchdown process with 94 °C for 15 s, 60–50 °C for 30 s with a decrease of two degrees every two cycles for each annealing temperature, and 72 °C for 45 s; 35 cycles of 94 °C for 15 s, 50 °C for 30 s and 72 °C for 45 s; and a final extension at 72 °C for 30 min. Microsatellite allele patterns were size fractionated in an ABI 3730xl DNA analyzer  and analyzed using GeneMapper 4.1 and Allelogram 2.2.Ten microsatellite markers were used for ii Table , includi37 and Genepop 4.2.138. To quantify the power of genetic individualization in the panel of microsatellite markers, we computed in Gimlet 1.3.339 the Probability of Identity (PID) value of these ten loci, which is defined as the overall probability that two individuals drawn at random from a given population share identical genotypes at all typed loci. The measures of microsatellite genetic variations in terms of the average number of alleles per locus, average allele size range per locus, average observed heterozygosity and expected heterozygosity were calculated using Arlequin 3.5 and FSTAT 2.9.3.240.To ensure genotyping reliability from the potentially degraded fecal DNA samples, a series of multi-tube measures were applied in which DNA extraction was performed at least twice for each sample, and each DNA extract was amplified at least twice for each marker. Thus, a total of four PCRs were conducted for each sample at each locus. We considered an animal to be heterozygous at a locus on the basis of consistent results from at least two duplicates and a homozygote from at least three replicates. Genotypes from different samples were considered to represent the same individual when mtDNA haplotypes were the same and all alleles at all microsatellite loci were identical or when one allele differed by one repeat unit at one locus only. The estimates of genetic variation and tests of genotyping errors and deviation from Hardy-Weinberg equilibrium or linkage equilibrium were carried out using Micro-Checker 2.2.3P. przewalskii mtDNA haplotypes were reconstructed with four approaches, and the Mongolian gazelle (P. gutturosa accession: JN632689.1) was used as outgroup. Jmodeltest 0.1.141 was applied to select the best substitution model fit for the dataset. A Maximum likelihood (ML) analysis with HKY  + I (with proportion of invariable sites) model, a maximum parsimony (MP) analysis with heuristic search and random addition of taxa, and a neighbor-joining (NJ) tree constructed from Kimura two-parameter distances followed by a branch-swapping procedure were performed in PAUP 4.042, with the reliability of each node in the topology assessed by 100 bootstrap replicates. A Bayesian approach was performed with MrBayes 3.2.143, and the analysis consisted of two parallel MCMC (Markov chain Monte Carlo) runs for 500,000 generations sampled every 500 generations. The migration rate between each population pairs was assessed by BayesAss 3.0.344, which estimates recent migration rates using MCMC. In addition, a statistical parsimony network of mtDNA haplotypes was constructed in TCS 1.2145 to infer phylogeographical relationships.Phylogenetic relationships among ST with mtDNA haplotypes (pairwise difference) and microsatellite genotypes using AMOVA, as implemented in Arlequin 3.5. Statistical significance was tested with 10,000 permutations.Assessments of pairwise population genetic differentiation and measures of different scenarios of geographic division in Przewalski’s gazelle populations were evaluated by F47 and the GENELAND package in R 2.15.149. In STRUCTURE, models of all combinations of the ancestry models and the allele frequency models were tested, and an admixture ancestry model with uncorrelated frequency was selected, with a burn-in period of 100,000 and 1,000,000 MCMC repetitions after burn-in. The number of population clusters (K) was set from two to nine, and two parallel runs were performed for each value of K. The simulated likelihood of these runs was summarized with STRUCTURE HARVESTER 0.6.9350 (http://taylor0.biology.ucla.edu/structureHarvester/#) to infer the best-fit number of population clusters  was calculated to evaluate the level of divergence under the specific set of demographic parameters. Four scenarios were set with two different lengths of divergence, each with or without migration between populations. Model 1 and Model 2 represented the Haergai North and Haergai South Przewalski’s gazelle populations at present, which have been separated for 20 generations, or 40 years, corresponding to the construction of Qinghai-Tibet railway in the 1970s. Model 3 and Model 4 projected 30 more generations into the future, or a total isolation length of 50 generations. Because the railway may have become an apparent barrier to ungulate migration only after the railway was upgraded with a fenced enclosure in the 2000s, we set the reduction or elimination of migration 15 generations, or 30 years, after the initial separation, corresponding to 5 and 35 generations of isolation in Model 2 and Model 4 respectively. For Model 1 and Model 3, a constant level of migration between populations was allowed (Table −1/2 52. We performed 1,000 permutations for each model and calculated the pairwise FST between the simulated populations in Arlequin 3.5.To evaluate the potential effect of the Qinghai-Tibet railway as a barrier to gene flow among Przewalski’s gazelle populations, we generated microsatellite genotype datasets for two isolated populations in fastsimcoal 2.5.2.21The GenBank accession numbers of the mtDNA sequences discussed in the paper are KP856162-KP856177.Supplementary Information


WFT), Frankliniella occidentalis, invaded China in the last 13 years and has rapidly become one of the most serious pests in the country. To assess whether this invasion involved a single event or multiple events, we examined patterns of genetic diversity and population structure of WFT across 12 Chinese populations and a native US population based on mitochondrial DNA and/or 18 microsatellite loci. The average allelic richness and haplotype diversity in Chinese populations were significantly lower than in a population from its native range. The distribution of mitochondrial haplotypes suggested multiple independent invasions of WFT into China, including two invasions into the Beijing region. Based on microsatellite data, two distinct clusters were identified, with both of them splitting further into two clusters; in the Beijing region, the microsatellite data also provided evidence for two introductions. Both the absence of isolation by distance and the fact that distant populations were similar genetically suggest patterns of WFT movement linked to human activities. Our study therefore suggests multiple introductions of WFT into China and human‐assisted spread.Historical invasion scenarios based on observational records are usually incomplete and biased, but these can be supplemented by population genetic data. The western flower thrips ( The first report of WFT establishment was in 2003 on glasshouse pepper in a suburban area of Beijing in northern China . Eleven populations were genotyped for both nuclear and mitochondrial markers, while the other two populations from Beijing (BJDX and BJCP) were genotyped for mitochondrial variation . For nuclear markers, 18 microsatellite loci designed by Cao, Li, et al.  were valcox1 gene sequenced by the primer pairs FO‐AF (5′ TTTCGTCTAACCATAAAGATATCGG 3′) and FO‐AR (5′ TAAACTTCTGGGTGCCCAAAAAATCA 3′) modified from previously published primers . Amplified products were purified and sequenced directly from both strands using an ABI 3730xl DNA Analyzer by Tsingke Biotechnology Co. Ltd .Mitochondrial variation was characterized based on a 609‐bp DNA segment from the 5′ end of mitochondrial 2.2AT) and the unbiased expected heterozygosity  and heterozygosity (HES) among samples with different sample sizes using a rarefaction method in GENCLONE. Allelic richness (AR) and allelic richness of private alleles (PAR) were calculated with a rarefaction approach in HP‐RARE version 1.1  for each locus at each site, linkage disequilibrium between each pair of loci at each site and heterozygosity excess and deficit were tested by GENEPOP version 4.2.1 Rousset, . GENCLONFST) using the excluding null alleles correction method with 10,000 bootstraps  for genetic differentiation , haplotype diversity (h), and nucleotide diversity (π) were analyzed in DnaSP version 5.10 . Sequences of the 2.3Phylogenetic relationships among the populations were inferred with POPTREE2  were run with 200,000 Markov chain Monte Carlo (MCMC) iterations after a burn‐in of 100,000 iterations. The outputs of STRUCTURE were submitted to STRUCTURE HARVESTER WEB version 0.6.94  method  of replicated runs for each K were combined using CLUMPP version 1.1.2  and a discriminant analysis of principal components (DAPC) were used to investigate genetic structure across the populations. Population structure was first characterized with the Bayesian model‐based clustering method implemented in STRUCTURE version 2.3.4  with the null allele model selected to infer the number of clusters with 1,000,000 MCMC iterations—of which every 1,000th was retained. The replicates with the highest mean logarithm of posterior probability were used to compute posterior probabilities of population membership for each pixel of the spatial domain with a burn‐in of 500.We used GENELAND to estimate the number of clusters and their spatial patterns. The analysis was conducted over 10 replicates for each To identify the number of different genetic clusters in the microsatellite data, DAPC was performed using adegenet version 2.0.1 Jombart,  in the R2.5FST/(1 − FST)) with geographical distance using the package ade4 in R, with 10,000 permutations. Pairwise FST values between populations based on microsatellites were calculated with GENEPOP version 4.2.1  was tested in samples from China by correlating pairwise genetic differentiation , varying from 3.11 in JSYZ to 6.09 in LNCY. The standardized total number of alleles (AS) ranged from 73.39 in BJFS to 171.26 in YNHH. The standardized expected heterozygosity over all loci (HES) was between 0.466 (JSYZ) and 0.780  was significantly lower than in USA . Private allelic richness was also low for the populations from China when compared to USCA , indicating that null alleles did not generate bias in the estimation of FST. Pairwise FST values ranged from 0.014 (XJWL vs. LNCY) to 0.310 (BJFS vs. JSYZ) with an average FST of 0.134  revealed that all pairwise FST estimates were significant.The average frequencies of null alleles estimated per locus (and per population) ranged from 0.0046 in S36 (0.0265 in GZGY) to 0.0852 in S61 (0.0520 in LNCY). The 34 Table . The ave3.2KJ576869–KJ576886). Eight haplotypes were observed in the USCA population, while the number of haplotypes in invasive populations ranged from 2 to 6 (Table h = 0.778) was higher than in Chinese populations (ranging from 0.325 to 0.687). Haplotype 1 was common in populations from China  and Calg et al. , but it g et al. . In our Five populations  had private haplotypes. In USCA, private haplotypes were present in 31.58% of individuals (6/19). Thirteen novel haplotypes  were identified in our samples compared with previous studies .3.3In an unrooted NJ tree constructed using the microsatellite loci Figure , populat3.4K = 2 , within the large subgroup , or within Beijing populations . The Mantel tests based on mitochondrial haplotypes detected a significant association within the Beijing area , but not across all Chinese populations  or in populations outside the Beijing area . These results were not affected by exclusion of individuals with Hap3 and Hap16 that constituted a different mitochondrial lineage.The Mantel tests based on microsatellite data sets did not reveal a significant association between genetic and geographical distances across China  and improving assignment of individuals to populations  as well as more populations from the Beijing area where establishment of WFT was first reported, in contrast to a more limited set of locations considered in two previous studies   and WFTL/CM (Hap3 and Hap16) based on mitochondrial DNA sequence variation, but we were unable to link mitochondrial DNA variation with nuclear marker variation Figure  in contrThe common haplotypes found in this study were similar to those found in previous studies  and supports the notion that populations in China have multiple origins. The USCA population clustered with six populations from China, and the southern Beijing populations formed another distinct cluster in both STRUCTURE analysis and DAPC. It seems possible that USCA acted as a putative source population of these six populations.Based on historical reports, the WFT outbreak started in Beijing in 2003) and in Yunnan Province (in 2004), with WFT becoming common in both regions by 2005  were clustered together  and revealed evidence of high gene flow. These results may indicate migration of WFT mainly through human activities that span large distances. Yunnan Province is a major production area for fresh cut flowers and vegetables, and the products are transported to other regions of China and other countries Wu, , perhaps4.5Although we were unable to pinpoint the origins of Chinese populations accurately because only one sample was available from its native range, diversity levels in USCA provide a reference point for comparison. Average allelic richness and mitochondrial haplotype diversity of WFT populations in China were lower than in USCA and this may be a consequence of founder events during invasion. The average allele richness of WFT in Chinese populations was reduced to about half (60% in the case of LNCY) that found in USCA, while the average number of mitochondrial haplotypes was also reduced to 45.8% (up to 75% in GZGY and BJMT). In total, 14 mitochondrial haplotypes have been found in China (12 haplotypes in this study), accounting for 24.6% of the haplotypes found so far in WFT (Brunner & Frey, Hyphantria cunea (Cao, Wei, et al., Dendroctonus valens (Cai, Cheng, Xu, Duan, & Kirkendall, Obolodiplosis robiniae (Shang, Yao, Huai, & Zhao, Nevertheless, genetic diversity in WFT based on microsatellite markers and mitochondrial DNA was higher than in other invasive species introduced to China from America, such as the fall webworm 4.6Understanding the sources and likely dispersal pathways of invasive species is crucial for accurate risk assessment and for successful management (Stepien, Brown, Neilson, & Tumeo, 5In conclusion, we found that introduced WFT populations in China were genetically differentiated in contrast to findings from previous work. Both the mitochondrial DNA and microsatellite markers point to multiple introductions of WFT. Populations in the Beijing region arose from at least two independent introduction events, while populations in other areas likely resulted from secondary expansions out of Yunnan Province.The authors declare no conflict of interest.https://doi.org/10.5061/dryad.d3165.Data available from the Dryad Digital Repository:  Click here for additional data file.


Neophasia menapia), a typically univoltine species found throughout western North America. At two locations in the Coast Range of California there are two periods of adult emergence per year, one in early summer (July) and one in late summer/autumn (September/October). Differences in flight time are accompanied by differences in wing shape and pigmentation. Here we use a combination of population genomics and morphological analyses to assess the extent to which temporal isolation is able to limit gene flow between sympatric early and late flights. Not only did we detect both genetic and morphological differences between early and late flights at the two sites, we also found that the patterns of differentiation between the two flights were different at each location, suggesting an independent origin for the two sympatric flights. Additionally, we found no evidence that these sympatric flights originated via colonization from any of the other sampled localities. We discuss several potential hypotheses about the origin of these temporally isolated sympatric flights.Temporal isolation remains an understudied, and potentially under-appreciated, mechanism of reproductive isolation. Phenological differences have been discovered in populations of the pine white butterfly ( The study of the origin and maintenance of reproductive isolation remains a central focus in evolutionary biology and provides key insights into the process of speciation. Variation in phenology, the seasonal timing of life history events, can act as a reproductively isolating mechanism. Our knowledge of the evolutionary consequences of this isolation, specifically its role in diversification, is relatively incomplete –4. PhenoWhile temporal differentiation can facilitate divergence and speciation, regulation of activity and phenology typically results in synchronization of behavior within populations or species. Many factors may contribute to synchronization. For phenological synchronization in insects, one such strategy is diapause, a quiescent state in which annual periods of unfavorable climate are bypassed . Shifts Neophasia menapia, the pine white butterfly, which occurs throughout western North America [Here we investigate a possible case of temporal isolation in  America , 18. The America –24. In CN. menapia at these sites comprise a single population that might have undergone a shift in life history to become bivoltine (two generations per year), or exhibits plastic variation for diapause emergence. If this is the case there would be no reproductive isolation as flight status would not be heritable. 2). How different are wing pigmentation and wing shape between the two sympatric flights at each of the sites, and compared to other nearby univoltine N. menapia populations? In addition, we explore what hypotheses can be proposed to explain the origin(s) of the sympatric populations. The sympatric populations can have arisen in situ, or one or both of the early and late flights could have originated elsewhere and colonized the Coast Range. A combination of high resolution, multi-locus genomic data and morphometric analyses was used to address these questions.We use a combination of population genomics and morphological analyses to examine the extent to which these sympatric early and late flights in the Coast Range are differentiated and isolated and to test hypotheses on the possible origin of these sympatric flights. We address two specific questions: 1). Do sympatric early and late flights exhibit population genomic differentiation consistent with the hypothesis of temporal isolation? If no genetic differentiation is detected, this would be consistent with the alternative hypothesis that Neophasia (Pierinae) includes only two species worldwide, both occurring in North America. The common name of Pine White butterflies refers to their use of host plants from the Pinaceae [Neophasia menapia occurs throughout western North America while the second species, Neophasia terlootii occurs in southwestern USA and northwestern Mexico [The genus Pinaceae , Neophasn Mexico .N. menapia are white with strong black markings around the leading edge of the forewing that curve around to form a cell-end bar [The wings of -end bar , 25. The-end bar . Many fe-end bar .N. menapia are univoltine, meaning they have one generation and one flight period per year [N. menapia are known to fly from late July until early September, and are most common in August [N. menapia have not been systematically examined. It has been suggested that elevation may affect the time of flight, with earlier flights (July) occurring at low elevations and later flights (September) occurring at high elevations [Throughout their range per year , 27–32. n August –22. Thisevations , 30.Pinus pondersoa) is ubiquitous. While there are no mark-recapture studies of Neophasia to allow direct estimates of dispersal, our personal observation is that they are weak fliers with “strays” very rarely being encountered. Movements appear to be primarily between nectar sources and lekking sites, which are tall trees .Females lay eggs in rows along pine needles in groups of up to 40, they overwinter (diapause) as eggs, and larvae begin feeding in spring , 30. In N. menapia is not known to exhibit wing pattern polyphenism , nor is there any evidence of multiple generations. Unfortunately, females fail to oviposit in laboratory settings  which prevents manipulative experimental approaches to investigating the mechanisms of phenotypic differentiation. Therefore, we have approached the study of differentiation from a geographical, comparative perspective.To the best of our knowledge, Neophasia at these locations. Neither of the two species of Neophasia are listed as endangered or protected. We collected 173 N. menapia at five sites in California, and one site in Oregon  and Parchman et al. (2012) , 34. DNAπ, the expected herterozygosity, and Watterson’s θ for each population. To obtain the estimates we used expectation-maximization (EM) algorithm with 20 iterations for each populations [We obtained 36 million sequence reads which were processed using a series of quality control steps to identify variable sites, following the methods of Gompert et al. (2012) . In overulations .Data were trimmed to only include Single Nucleotide Polymorphisms (SNPs) with a minimum of 15 reads per population sample, producing 20,737 SNPs. We used the allele frequency model presented in Gompert and Buerkle (2011)  to estimN. terlootii and N. menapia, and a second PCA using only N. menapia populations. For the analysis using the program STRUCTURE, we sampled one sequence read for each SNP locus for each individual in proportion to the frequency of reads at that locus for each individual. Thus individuals were assigned either a 1 or a 2 depending on which sequence read was sampled for that individual and −9 (missing data) for the alternative allele for each locus . Our infile is similar to that used for dominant markers where heterozygosity at a locus cannot be verified. Individuals with more than 98 percent missing data were removed . For the STRUCTURE analysis 19,152 SNPs were included. The admixture model was used to estimate admixture proportions of each of K groups. Again, two analyses were conducted, one that included both nominal species and one that included just N. menapia populations. The model was run for K = 1–12 and K = 1–10 respectively, with 10 runs per K. Monte Carlo Markov Chain (MCMC) procedures were used to obtain estimates, with 100,000 steps and a burn in of 50,000 steps. To estimate the appropriate K (number of groups) the log of the marginal likelihood [ST statistics among all populations from allele frequency estimators [ST estimates were summarized using a non-metric multidimensional scaling (NMDS) conducted in R using the package MASS.Genetic structure at the individual level was summarized using a principal component analysis (PCA) and the admixture model in STRUCTURE 2.3.4 , 42. Thekelihood  was plotkelihood . At the timators . GST estN. menapia were photographed using a digital camera (Sony Cyber-shot HX9V) on a white background with a scale (mm ruler) (Table A in To quantify variation in wing pigment patterns (melanization) and wing shape, forewings of male All measurements for wing melanization were taken using IMAGEJ software . The area priori and canonical variables are calculated that maximize the amount of among group variance relative to within groups. This allows for visualization of the variation among groups. For both the PCA and the CVA, 95% confidence ellipses around the mean, using population as a classifier, were plotted. For CV1 and CV2 a transformation grid plot showing wing shape changes was plotted in MorphoJ [We identified 12 landmarks, located either at convergence points between wing veins or the intersection of a vein and the edge of the wing Fig A in . X Y, co MorphoJ .ST, 19,152 SNPs for STRUCTURE analysis) obtained from assembly of 36 million Illumina sequence reads. A principal component analysis (PCA) was conducted on all eight N. menapia sample groups and the one group of N. terlootii  clustering together, separate from Sierra Nevada sites . A second PCA was conducted to explore patterns of differentiation among the N. menapia samples  was used to visualize the relationships between N. menapia sampling groups using pairwise GST values and showed patterns of relatedness . A post hoc test, Tukey’s HSD test, was carried out to identify which pairwise comparisons were significantly different  were plotted for each sampling group . Goat MoA PCA was carried out on the 12 landmarks to identify the main axes of variation in wing shape. When PC1 (24.45% variance explained) and PC2 (15.48% variance explained) are plotted there appears to be little discernible clustering by sampling group . 95% cona priori and the analysis maximizes among-group differences relative to within-group differences. In a plot of CV1 (47.65% variance explained) and CV2 (16.07% variance explained), Goat Mountain early flight sample clusters towards the far end of CV1 away from the late flight at Goat Mountain, the same pattern can be seen for Mendocino Pass early and late flight groups  to a bivoltine (two generations per year) life cycle, or that emergence date is a non-heritable plastic trait. If populations had become bivoltine or emergence date was plastic then we would not expect to identify any significant genetic differentiation as early flight individuals would represent the parental populations of late flight individuals (and vice versa).To return to our initial research questions: we first wanted to explore the population genomics of sympatric early and late flights and identify if there were levels of genetic structure present that would be consistent with the hypothesis of temporal isolation. Our results provide support for the hypothesis of temporal isolation between sympatric early and late flights at both locations. At Goat Mountain, populations show higher levels of genetic differentiation relative to Mendocino Pass, as can be seen in the PCA of individual genotypes and the NMDS of pairwise GST’s . At GoatST’s . At MendN. menapia populations in wing pigmentation (melanization) and wing shape. These morphological traits were chosen based on field observations and represent a preliminary assessment of potentially adaptive differences between early and late flights. Significant differences in both wing melanization and wing shape were found between sympatric early and late flights at both Goat Mountain and Mendocino Pass  Sierra Nevada sites or that sympatric flights have arisen from within the Coast Range. We have found no support for the first hypothesis, colonization from an allopatric Sierra Nevada population. In terms of genetic differentiation the NMDS plot, PCA, and STRUCTURE assignment probability plots  [N. menapia in the Trinity Alps in northwestern California. There, butterflies at lower elevations (900m) fly earlier (June–July) and higher elevation (1500m) butterflies appear later (September–October), but without the phenotypic differentiation observed at Goat Mountain and Mendocino Pass. Similar phenological isolation has also been noted for other species of butterflies. For example, Shapiro and Forister (2005) [Hesperia colorado complex, with the later-flying population at one sympatric site being associated with serpentine soils. However, the causes of phenological isolation in that case, as with N. menapia, remain mysterious. Furthermore, we are not presently able to identify if sympatric early and late populations of N. menapia arose in situ or if there has been a colonization event from another Coast Range population that was not included in our sampling.Our final question aimed to explore hypotheses about the possible origins of sympatric early and late flights. The genetic differentiation seen among ots Figs  & 3 demo. (1979)  noted phr (2005)  describein situ, while at the other site colonization from an allopatric population with a later flight time may have occurred. Alternatively it may be that the two sites are different because isolation has arisen in sympatry at different times; Goat mountain populations may have been isolated from one another for longer than those at Mendocino Pass. Morphological measurements, wing melanization and wing shape  = N. terlootii from Arizonia, N. menapia samples from; DP (red) = Donner Pass, LA (orange) = Lang, WO (light red) = Woodfords, GE (light blue) = Goat Mountain early flight, GL (dark blue) = Goat Mountain late flight, ME (light green) = Mendocino Pass early flight, ML (dark green) = Mendocino Pass late flight, OR (purple) = Oregon. Fig C, Structure Plot for All Populations. A: STRUCTURE assignment plot for K = 2, includes all populations samples (N. terlootii and N. menapia); dark blue = AZ (N. terlootii), medium blue = all N. menapia populations. B: STRUCTURE assignment plot for K = 3, includes all populations samples (N. terlootii and N. menapia), dark blue = AZ, light blue = Sierra Nevada N. menapia, medium blue = Coast Range N. menpia. AZ = N. terlootii, DP = Donner Pass, GE = Goat Mountain early flight, GL = Goat Mountain late flight, LA = Lang, ME = Mendocino Pass early flight, ML = Mendocino Pass late flight, OR = Oregon, WO = Woodfords. Fig D, N. menapia STRUCTURE RunsDelta K for K 2 through K 10 for . Fig E, N. menapiaGenetic Diversity Estimates for  Bars show estimates of heterozygosity (π), square shows the estimate of Watterson’s θ. Fig F, Transformation Grid for Landmarks. Transformation grid for landmarks from CV1 (top) and CV2 (lower). Fig G, Neophasia menapiaBoxplots of melanization level for populations of . Unique letters indicate significant differences in melanization  DP = Donner Pass, GE = Goat Mountain early flight, GL = goat Mountain late flight, LA = Lang, ME = Mendocino Pass early flight, ML = Mendocino Pass late flight, OR = Oregon, WO = Woodfords. Table A, Neophasia menapia for wing melanization and wing shapeSample sizes for . Table B, Tukey’s HSD test for wing melanization. Significant differences are highlighted in bold. Table C, Pairwise Procrustes distances among populations for shape (upper triangle), and melanization level (lower triangle). *P≤0.05, **P≤0.01, **P≤0.001. Table D, One-way ANOVA of wing shape PC1 by sampling location. Table E, One-way ANOVA of wing shape PC2 by sampling location. Table F, One-way ANOVA of wing shape PC3 by sampling location. Table G, Tukey’s HSD test for PC1 of wing shape. Significant differences highlighted in bold. Table H, Tukey’s HSD test for PC2 of wing shape. Significant differences highlighted in bold. Table I, Tukey’s HSD test for PC3 of wing shape. Significant differences highlighted in bold.(PDF)Click here for additional data file.