Stylidium hispidum, an endemic southwestern Australian triggerplant. As a result we were able to estimate the seed transfer distance within which genetic divergence is low and population fitness less likely to be impacted by maladaptation, and identify environmental variables that may be relevant for future restoration of this species.Ecological restoration is often conducted with limited consideration of genetic diversity and the environmental factors that drive variation within species. We studied genetic diversity and environmental variation among 16 populations of  Stylidium hispidum (Stylidiaceae) along a north–south transect of the species' range with the goal to estimate the distance at which significant genetic differences occur among source and recipient populations in restoration. In addition, we tested AFLP markers for signatures of selection, and examined the relationship of neutral and putatively selected markers with climate variables. Estimates of population genetic structure revealed significant levels of differentiation (ΦPT = 0.23) and suggested a global provenance distance of 45 km for pairwise comparisons of 16 populations. Of the 134 markers, 13 exhibited evidence of diversifying selection (ΦPT = 0.52). Using data for precipitation and thermal gradients, we compared genetic, geographic and environmental distance for subsets of neutral and selected markers. Strong isolation by distance was detected in all cases, but positive correlations with climate variables were present only for markers with signatures of selection. We address findings in light of defining local provenance in ecological restoration.Efforts to re-establish native plant species should consider intraspecific variation if we are to restore genetic diversity and evolutionary potential. Data describing spatial genetic structure and the scale of adaptive differentiation are needed for restoration seed sourcing. Genetically defined provenance zones provide species-specific guidelines for the distance within which seed transfer likely maintains levels of genetic diversity and conserves locally adapted traits. While a growing number of studies incorporate genetic marker data in delineation of local provenance, they often fail to distinguish the impacts of neutral and non-neutral variation. We analysed population genetic structure for 134 amplified fragment length polymorphism (AFLP) markers in  Ecological restoration is often conducted with limited consideration of genetic diversity or the environmental factors that influence intraspecific variation . EffortsData for intraspecific variation in adaptive traits are difficult to obtain for most native, non-commercial species, especially at the scale of large restoration programmes .While there is considerable evidence for the association of genetic diversity and fitness , the corStylidium hispidum, endemic to southwestern Australia  for early fitness of the plant species ustralia . At the S. hispidum, including 4 populations represented in the original study of hybrid fitness  forest. Records indicate that the region has experienced a 17 % decline in precipitation between 1975 and 2011, and the increasing severity of drought reflects higher temperatures as well as rainfall deficiencies  is endemic to the SWAFR and can be found in the jarrah forest understorey along the Darling Scarp east of the Swan Coastal Plain . We conducted regression analysis to test the relationship between population size and estimates of genetic diversity. We also recorded the number of private markers, as well as locally common markers found in <50 % of populations. Differentiation among and within populations  was estimated using an analysis of molecular variance (AMOVA) implemented in GenAlEx and based on 9999 permutations. We examined the relative genetic dissimilarity among sites using non-metric multidimensional scaling (MDS) ordination in PRIMER 6.1.13 software  and unbiased estimates of expected heterozygosity  the minimum distance representing significant genetic divergence for S. hispidum and (ii) the global provenance distance inferred from the intercept of the global R value with the line of best fit . The sampled area spanned much of the known S. hispidum range, which occurs primarily in the high rainfall zone, along a north–south precipitation gradient from ∼700–1200 mm.To characterize environmental differences, BIOCLIM variables were obtained for each of the 16 sites by extrapolating climate data to the GPS coordinates for each population using DIVA-GIS software . The BIOr| > 0.8 and subsequently conducted principal component analysis (PCA) in JMP 9.0 software. Factor loadings resulting from Varimax rotation were examined to determine the variables with the greatest contribution to the variance in the data (10(x + 1) transformed to improve normality and reduce heteroscedasticity. Dissimilarity matrices of Euclidean distances were calculated among normalized climate variables using PRIMER software. A matrix of geographic distances among sites was generated from GPS coordinates with the SoDA package in R software and also log10 transformed  that was not detected for He. Locally common markers were identified for each of the 16 populations, but private markers were not present. The AMOVA partitioned 23 % of the total genetic variation among sites and 77 % of the total genetic variation within sites  and Bungendore Nature Reserve (BG) , the relatively isolated collection at BG and southern sites. However, ANOSIM did not detect significant differences among six of the southern populations. Instead, populations at Serpentine National Park (SERP1 and SERP2) were significantly differentiated from nearby populations in the northern Dwellingup forest , and both of these clusters differed from the population (YS) furthest south. ANOSIM, therefore, supported six genetically distinct population clusters similar to the second tier of Bayesian cluster analyses, although population clusters reflected some differences between the two methods.Results of ordination analyses reflected the outcomes of Bayesian clustering methods, and indicated a distribution of populations consistent with their geographic distance Fig. . ANOSIM R statistic of 0.687 with a significance level of P < 0.0001. Of the 120 pairwise combinations, 76 sites were significantly differentiated from one another. Investigation of the geographic separation among sites that had significant pairwise comparisons indicated that the shortest distance between any two sites that were significantly genetically differentiated was 12.7 km (WD and YS in the southern region). All remaining significant pairwise comparisons occurred between sites 22.6 km or more apart, suggesting a minimum patch size of ∼13–23 km  but similar levels of polymorphism (81.7 %) relative to the full dataset .BayeScan identified 13 outliers that exceeded the 1 % threshold for selection , and we noted one pair of S. hispidum populations that represented significant genetic differentiation at a very short distance (YS and YRLP located ∼0.8 km apart). However, all but two significant comparisons  once again occurred among sites separated by 23 km or greater, the global provenance distance was ∼45 km and pairs of sites >73 km apart were significantly different in all cases. In effect, the subset of genetic data for outliers replicated results for the full AFLP dataset, but pointed to the potential for sites to differ in adaptive traits at shorter distances than those identified by use of principally neutral marker datasets.We ran the ANOSIM analysis a second time using the genetic distance matrix representing the 13 markers with signatures of selection among all individuals for the 16 sites. In this case, 71 of the 120 comparisons were significantly differentiated based on the global The PCA of climate variables described three factors that explained 96.1 % of the variation in the data. We selected four variables that contributed significantly to factor loadings and had low levels of intercorrelation . These vPT values . At the same time, outliers continued to exhibit a strong correlation with geographic distance when partial Mantel tests removed the effects of climate . The effects of spatial autocorrelation remained strongly significant for outliers in all cases.Mantel tests indicated a strong correlation in all cases between geographic distance and genetic distance represented by matrices of linearized Φte Table . We subsS. hispidum along a north–south transect of the species' range. This pattern is consistent with prior results characterizing genetic structure among 4 of the 16 sampled populations, including evidence of strong differentiation between northern and southern sites  between any two adjacent populations along the sampled transect, and may indicate a limit for pollen dispersal. The largest span between any two adjacent populations occurred between John Forrest National Park and the northernmost sites , and this distance was also reflected in results of MDS and Bayesian cluster analysis. Evidence of hierarchical structure suggests that populations are nested within northern and southern regions, and defined by limits of gene flow as well as biotic and abiotic factors that drive adaptation  crosses  and industry partners Alcoa World Alumina of Australia and BHP Billiton Worsley Alumina Pty Ltd.This study was supported by an K.M.H., S.L.K., H.L. and E.J.V. conceived and designed the study. K.M.H. and S.L.K. conducted field collections. K.M.H. conducted laboratory and data analyses and wrote the manuscript with S.L.K., H.L. and E.J.V.None declared.


Ovis aries) has been an economically and culturally important farm animal species since its domestication around the world. A wide array of sheep breeds with abundant phenotypic diversity exists including domestication and selection as well as the indigenous breeds may harbor specific features as a result of adaptation to their environment. The objective of this study was to investigate the population structure of indigenous sheep in a large geographic location of the Chinese mainland. Six microsatellites were genotyped for 611 individuals from 14 populations. The mean number of alleles (±SD) ranged from 7.00 ± 3.69 in Gangba sheep to 10.50 ± 4.23 in Tibetan sheep. The observed heterozygote frequency (±SD) within a population ranged from 0.58 ± 0.03 in Gangba sheep to 0.71 ± 0.03 in Zazakh sheep and Minxian black fur sheep. In addition, there was a low pairwise difference among the Minxian black fur sheep, Mongolian sheep, Gansu alpine merino, and Lanzhou fat‐tailed sheep. Bayesian analysis with the program STRUCTURE showed support for 3 clusters, revealing a vague genetic clustering pattern with geographic location. The results of the current study inferred high genetic diversity within these native sheep in the Chinese mainland.The domestic sheep ( Ovis aries) has been an economically and culturally important farm animal species worldwide, since domestication. However, commercial lines and industrialized livestock production systems have spread over all continents resulting in decreasing of large indigenous sheep breeds in comparison with some commercial breeds. Many studies have assessed the diversity of native local sheep in India . The mixtures were heated at 94°C for 5 min and then immediately chilled on ice for 2 min. Genotyping was performed on a Genetic Analyzer 3130 xl .We genotyped 611 individuals from 14 breeds from different geographic locations in the Chinese mainland Table  that werHE), observed (HO) heterozygosity, mean number of alleles (NA), and polymorphism information content (PIC) were estimated from the allele frequencies using FSTAT 2.9.3.2  was plotted against ΔK = m| L′(K)|/s|L(K)|, and the optimal number of clusters was identified by the largest change in the log‐likelihood (L(K)) values between the estimated number of clusters  to 0.7634 (MCM527) among breeds (Table HE within a breed ranged from 0.61 ± 0.06 in LZD to 0.73 ± 0.07 in TS. The HO ranged from 0.58 ± 0.03 in GB to 0.71 ± 0.03 in HZK and MXB (Table The mean observed and expected heterozygote frequencies within loci across the breed was 0.6382 (0.3859 to 0.7647) and 0.6859 (0.5275 to 0.8013), respectively  within a breed range from 0.00 was MXB to 0.17 was ZT. It was below 0.1 in ten breeds and above this value in 4 breeds . There were two breeds (ZT and TS) carried the P‐value of inbreeding coefficients are significantly different from zero.The range of the inbreeding coefficient (MAF209 in TS was 20.31% (see Table S1).In total, 18 private alleles were distributed across 14 breeds and 6 loci. The frequency of several private alleles within certain breeds was particularly high. For example, the frequency of a private allele (135 bp) at the locus XY) and consistency to that in corrected average pairwise difference (πXY−(πX + πY)/2)  Table .K ≤ 14. At the lowest K‐value (K = 2), the MXB, MGH, GSH, and LZD breeds split from the others to form their own cluster. At K = 3 to K = 14, the TS separated and formed an independent cluster base on the clustering diagrams of K2, the optimal K‐value was thus 3 , HO (ranging from 0.65 to 0.69), and NA (ranging from 5.22 ± 1.67 to 8.92 ± 3.20) in Mongolian sheep  in TS also explains the deficiency of heterozygotes in this population that deviate from HWD.Deviations from HWE are expected if individual populations are substructured into flocks within populations that are isolated from each other or if inbreeding has occurred in the population , was generally lower than that observed between other breeds, thus indicating moderate‐to‐high genetic similarity in this subpopulation (Group 2). For the other subpopulation (Group 1), the high genetic differences indicated a more complex genetic background and different artificial selection direction during their domestication.The pairwise difference, FST value analysis described above  and Group 2 breeds were separated into their own clusters. In addition, the background of Group 1 was increasingly complex with increasing K‐value, similar to the result of the pairwise FST value, which indicates that gene flow exists in exchange or during multi‐complex ancient domestication. Gene flow between breeds can also be assessed by the abundance of a private allele (Slatkin and Barton Ovis vignei) and Agarl (Ovis ammon). In addition, the ancestor of Tibetan sheep was demisted from Ovis vignei which living in Qinghai–Tibetan Plateau. However, Mongolian group sheep were derived from argali in central Asian mountains region  were bred in Gansu Province, and one (MGH) was from Mongolian. This result suggests that the Gansu breeds and Mongolian sheep are indistinguishable, though they were separate for many hundreds of years at domestication sites and have different phenotypes. There may have been some gene flow between them in the past or shared ancestors. For a similar case, the Group 1 breeds, which represents an independent cluster, had a breed that was sampled over a large geographic region in the Chinese mainland and were not only separated into independent clusters but also carried a common large‐complex genetic background, which indicated the general exchange of genetic material. The strong gene flow among regions induced by human migration, commercial trade, and the extensive transport of sheep was identified by the variability of mtDNA (Zhao et al. The optimal In short, six microsatellites were genotyped for 611 individuals from 14 breeds to investigate the breed structure of indigenous sheep in China. The results of the current study infer affluent genetic diversity within breeds and strong gene flow exchange between native sheep in the Chinese mainland.None declared.Table S1. Allelic frequency of six microsatellite in each population.Click here for additional data file.


Bombus species and four more stable species with the use of microsatellite loci. Hereto we genotyped a unique collection of museum specimens. Specimens were collected between 1918 and 1926, in 6 provinces of the Netherlands which allowed us to make interspecific comparisons of genetic diversity. For the stable species B. pascuorum, we also selected populations from two additional time periods: 1949–1955 and 1975–1990. The genetic diversity and population structure in B. pascuorum remained constant over the three time periods. However, populations of declining bumblebee species showed a significantly lower genetic diversity than co-occurring stable species before their major declines. This historical difference indicates that the repeatedly observed reduced genetic diversity in recent populations of declining bumblebee species is not caused solely by the decline itself. The historically low genetic diversity in the declined species may be due to the fact that these species were already rare, making them more vulnerable to the major drivers of bumblebee decline.Worldwide most pollinators, e.g. bumblebees, are undergoing global declines. Loss of genetic diversity can play an essential role in these observed declines. In this paper, we investigated the level of genetic diversity of seven declining  All over the world different pollinator species are undergoing major declines (e.g. ). GeneraBased on contemporary specimens, several studies have shown that populations of declining bumblebee species have lower levels of genetic diversity compared to stable species ,15–18. HBombus pascuorum, B. hortorum, B. pratorum and B. lapidarius, and 7 declining species: B. muscorum, B. veteranus, B. ruderarius, B. sylvarum, B. humilis, B. ruderatus and B. subterraneus [In this paper, we compared the genetic diversity of declining and more stable bumblebee species before their major recent decline (between 1950–1980) –8. We userraneus . Sampleserraneus ,6–8. Furerraneus ,23–29 toMuseum specimens of 11 bumblebee species were selected from the Hymenoptera collection of the Naturalis Biodiversity Center in Leiden taking into consideration their distribution in the Netherlands .T-test, t = -6.465, d.f. = 5, P < 0.001). The group of declining and widespread species was not significantly different in range from the group of widespread but stable species (23.2% (2.8%); T-test, t = 1.937, d.f. = 4, P = 0.125).We divided the selected species in groups based on their presence and status on the red list of the Netherlands  S1 Tabl. BumblebB. pascuorum (N = 38); B. hortorum (N = 30), B. pratorum (N = 10) and B. lapidarius (N = 19), 3 declining but widespread species: B. muscorum (N = 20), B. veteranus (N = 8) and B. ruderarius (N = 28), and 4 declining but restricted species: B. sylvarum (N = 16), B. humilis (N = 20), B. ruderatus (N = 17) and B. subterraneus (N = 7). Populations were collected in the period 1918–1926 and in 5 Dutch provinces: North-Holland, South-Holland, Gelderland, Overijssel and Limburg  and 1975–1990 (N = 49) and from one additional province: Drenthe. These extra 8 populations allowed us to investigate the genetic diversity of B. pascuorum over space and time. We suspected this to remain fairly stable as this species was and still is abundantly present in the Netherland. Deviations from this suspected result could indicate artefacts associated with the genotyping of museum specimens.For B. terrestris, and BL02 [B. lucorum. Microsatellites were then amplified by PCR and visualized with capillary electrophoreses as described in [Bumblebee DNA was extracted from one middle leg of each selected museum specimen with a Chelex method  as described in . Workersand BL02  as derivribed in . GenotypNot all genotyped individuals of a population were included in the analysis due to several validation steps. First, specimens which could not be scored in a reliable manner for a minimum of 5 microsatellite loci, were excluded. Second, we used the program Colony 2.0  employinB. terrestris and B. lucorum, we needed to validate if they could be used in a reliable manner in the different Bombus spp. We tested for genotypic linkage disequilibrium with FSTAT 2.9.3 [As the microsatellites used here were developed from AT 2.9.3  and for AT 2.9.3 . When exAT 2.9.3  was usedAR) and Nei’s unbiased expected heterozygosity  in SPSS (version 21.0.0.0) to examine if the genetic diversity differed significantly between the widespread stable versus the restricted and widespread declining species and an ANOVA with Repeated Measures Factors to examine the genetic diversity between populations of B. pascuorum.We estimated genetic diversity in each population using the allelic richness . The laty  for each population of the different Bombus spp. in the time period 1918–1926 based on more stringent exclusion policies for missing data. We started this analysis from a maximum of 50% missing values (or 5 loci) within one specimen towards a more stringent exclusion step of only 10% (or one locus) missing data.We conducted a sensitivity analysis of the calculated mean heterozygosity  to estimate the genetic diversity for the different Furthermore, we compare the genetic diversity between groups of species. However, the interpretation of the observed inter-specific differences cannot be made easily due to: (i) mutation rates which may vary at different microsatellites loci and (ii) differences in polymorphism of the microsatellite loci. To remedy these effects, we used the same microsatellite loci for each species and bumblebee specimens with similar distribution in The Netherlands. In addition, we compared a group of 7 declining species with a group of 4 stable bumblebee species instead of single species. Furthermore, each group consisted of bumblebee species of multiple subgenera. In this way we minimize inconsistencies and perform a valid comparison between groups of species –19.FST) between the B. pascuorum populations within years and within populations between years were calculated using 1000 permutations in FSTAT 2.9.3 [Genetic differentiation values  null allele frequencies for those loci.Bombus species, we estimated the genetic diversity of all B. pascuorum populations over the three time periods. As this species was and still is abundantly present in the Netherlands, we expected the genetic diversity to be fairly stable in space and time. If we detect a low genetic diversity in the past for B. pascuorum, this could suggest artefacts associated with the genotyping of museum specimens, such as the presence of null-alleles. Here, the genetic diversity of the B. pascuorum populations remained stable as there were no significant differences in genetic diversity over the different locations  and the three time periods  . So, B. pascuorum populations showed no or only marginal genetic differentiation.Comparison of the n (FST) . CorrectBombus species had a lower genetic diversity than stable species before their recent decline (AR) and expected heterozygosity (HE) of the declining species: 3.218 (SE 0.158) and 0.466 (SE 0.030), were significantly lower than that of the stable bumblebee species with 4.883 (SE 0.164) and 0.692 (SE 0.022)   had a comparable mean HE as some of the stable species  and the restricted/declining group was also significantly lower than that of the stable species for AR  and showed the same trend for HE . This analysis showed also no major impact of the non-amplifications on our dataset .Our results showed that historical populations of declining bumblebee species had a significantly lower genetic diversity than found within the historical populations of co-distributed more stable species . In studth century could be a lower abundance of these species in this time period. Indeed, small bumblebee populations can have a reduced genetic diversity as a result of higher genetic drift [B. veteranus  ranked with second lowest allelic richness (2.942) was described as “fairly common” in Belgium [Our results showed that declining bumblebee species had lower levels of genetic diversity than stable species. One possible explanation for the lower genetic diversity of the declining species in the early 20ic drift ,13. Howeic drift –45. For  Belgium –45. Howe Belgium . So, we Bombus species may help to detect populations at risk of decline.By comparing the levels of genetic variability among populations before a genetic bottleneck with those found in current populations, one will be able to determine if the observed lower genetic variation is a consequence of recent population declines, or an ancestral state –21. IndeHE lower than 0.550 and a AR lower than 3.5) have subsequently suffered strong declines in their distribution. Hence, as bumblebee populations with high genetic diversity may be less likely to decline or to go locally extinct, improving the genetic diversity of the populations of restricted bumblebee species is a valuable strategy.Our results have strong implications for conservation strategies. Determination of the genetic diversity of bumblebees can reveal which species are more vulnerable to local extinction in the longer term. Indeed, our study shows that all bumblebee species with a low genetic diversity and thus predicted to be vulnerable to decline, suffered more severe declines than the other species. However, it should be remarked that knowing the genetic diversity will not always identify which population is threatened. Indeed, two declining species showed similar levels of expected heterozygosity but had stronger declines than stable species with similar levels of heterozygosity . Thus, cBeing able to directly measure the genetic diversity from historical bumblebee populations allows for a better estimation of historical effective population sizes, levels of gene flow, and the relatedness between populations –21. As tIn conclusion, our results demonstrate that species with a lower genetic diversity are the ones that are currently endangered. However, species with a high genetic diversity could still be at risk for extinction. Indeed, the more stable species also underwent distribution declines but not as severe as the declining group. So, to preserve bumblebee diversity one must tackle also the current drivers of bumblebee decline, to ensure that these low and even high genetic diversity species will not go extinct. It is therefore recommended that conservation strategies create more suitable habitat for sustaining bumblebee populations.S1 TableBombus spp. following Peeters and Reemer [In this table we presented, the distribution before and after 1970, trend of decline and red list status of the different (PDF)Click here for additional data file.S2 TableFST (with ENA correction) for the different populations of B. pascuorum under the diagonal and the harmonic mean of Dest across loci above the diagonal, a) between locations within a time period, and b) within a location between time periods. With indication of the significance level, ** = P < 0.001 and * = P < 0.005.Pairwise (PDF)Click here for additional data file.S3 TableHE) for each population of the different Bombus spp. in the time period 1918–1926, based on more stringent exclusion policies for missing data. From a maximum of 5 microsatellite loci with missing values within one specimen towards only one locus with missing data. With n = the total number of workers in each exclusion step and * = too low number of specimens.After removal of identified sisters, we conducted a sensitivity analysis of the calculated mean expected heterozygosity ((PDF)Click here for additional data file.S4 TableB. subterraneus, B. ruderatus and B. lapidarius) and populations with non-amplifications and based on the same 8 microsatellite loci in each species.Recalculations of the genetic diversity were performed after removal of three species ((PDF)Click here for additional data file.S5 TableThe data was obtained from our study and from the available data on recent populations found in the literature. With time periods: ‘historical’ = 1895–1930; and ‘recent ‘ = 1975–2010’.(PDF)Click here for additional data file.


Chinese pine seed orchards are in a period of transition from first-generation to advanced-generations. How to effectively select populations for second-generation seed orchards and significantly increase genetic gain through rational deployment have become major issues. In this study, we examined open- and control-pollinated progeny of the first-generation Chinese pine seed orchards in Zhengning  and Xixian  to address issues related to phenotypic selection for high volume growth, genetic diversity analysis and genetic distance-based phylogenetic analysis of the selections by simple sequence repeats (SSRs), and phylogenetic relationship-based field deployment for advanced-generation orchards. In total, 40, 28, 20, and 13 superior individuals were selected from the large-scale no-pedigree open-pollinated progeny of Zhengning (ZN-NP), open-pollinated families of Zhengning (ZN-OP), open-pollinated families of Xixian (XX-OP), and control-pollinated families of Xixian, with mean volume dominance ratios of 0.83, 0.15, 0.25, and 0.20, respectively. Phylogenetic relationship analysis of the ZN-NP and XX-OP populations showed that the 40 superior individuals in the ZN-NP selected population belonged to 23 families and could be further divided into five phylogenetic groups, and that families in the same group were closely related. Similarly, 20 families in the XX-OP population were related to varying degrees. Based on these results, we found that second-generation Chinese pine seed orchards in Zhengning and Xixian should adopt a grouped, unbalanced, complete, fixed block design and an unbalanced, incomplete, fixed block design, respectively. This study will provide practical references for applying molecular markers to establishing advanced-generation seed orchards. Selection and deployment of improved materials are important in establishing an advanced-generation seed orchard. To increase genetic gain, advanced-generation seed orchards usually contain only a few elite clones. To decrease the risks of inbreeding depression , 2, propMolecular markers developed in the 1980s have brought phylogenetic analysis to a new level . Microsa2SCR) design [Field deployment is one of the most important activities in seed orchard establishment. The most important criteria of field deployment is to maximize genetic gain of target traits while maintaining an acceptable level of genetic diversity . Advance) design , have be) design , 25, 26,) design .Pinus tabuliformis) is a major afforestation tree species that is naturally distributed in the 3,000,000 km2 of mountainous areas between 32°–43°N and 102°–122°E in northern China [Chinese pine , which was calculated as:V is an individual tree's volume or a family's mean volume; and Rd values in the base population; the ZN-OP, XX-OP and XX-CP selected populations were composed of high-ranked individuals  within the high-ranked families  in the corresponding base populations. We conducted analyses of variance (ANOVAs) and multiple comparisons using R 2.15.3 software to describe differences in Rd values among the selected populations.The first-generation Chinese pine seed orchards in Zhengning (Gansu Province) and Xixian (Shanxi Province) contained about 300 clones each. We regarded no-pedigree open-pollinated progeny of clones in the Zhengning seed orchard (ZN-NP), open-pollinated families of clones in the Zhengning seed orchard (ZN-OP), open-pollinated families of clones in the Xixian seed orchard (XX-OP), and control-pollinated families of clones in the Xixian seed orchard (XX-CP) as the base population, with ages of 21, 21, 22, and 22 years, respectively. We measured individual's heights and diameters at 1.3 m, and calculated individual's volume as:We used superior individuals in the ZN-NP and XX-OP selected populations as samples for the phylogenetic analysis. We performed DNA extraction, PCR amplification, and polyacrylamide gel electrophoresis using the methods described above, and analyzed the genotyping results using PowerMarker 3.25 software. We also calculated frequency and frequency-based distance (Nei 1983), and reconstructed genetic distance-based phylogenetic trees using the unweighted pair-group method with the arithmetic average method. Phylogenetic trees were viewed using Fig.Tree 1.4.2 software.Ho), expected heterozygosity (He), and polymorphism information content (PIC).We analyzed the genetic diversity of superior individuals in the ZN-NP and XX-OP selection populations using PowerMarker 3.25 software. The parameters analyzed included observed heterozygosity , the number of genotypes (No. genotypes), and the number of alleles  of the 11 polymorphic SSR loci in the ZN-NP selected population had ranges of 0.3250–0.6875, 4.0000–10.0000, and 3.0000–11.0000, with means of 0.4568, 6.0909, and 5.5455, respectively. Expected heterozygosity (He) and observed heterozygosity (Ho) values varied from 0.4609 to 0.7931 and from 0.1000 to 1.000, respectively, with means of 0.6690 and 0.5614. Polymorphism information content (PIC) changed from 0.3977 to 0.7671, with a mean of 0.6202. The fixation index (F) had a range of −0.2676–0.8532, with a mean of 0.1732.MAF, numbers of genotypes, and numbers of alleles of the 11 polymorphic SSR loci in the selected XX-OP population had ranges of 0.2750−0.9500, 2.0000−13.0000, and 2.0000−10.0000, with means of 0.5846, 7.1818, and 6.0000, respectively. He and Ho varied from 0.0950 to 0.8438 and from 0.1000 to 0.8500, respectively, with means of 0.5703 and 0.5955. The PIC changed from 0.0905 to 0.8266, with a mean of 0.5324. F ranged between −0.2925 and 0.3956, with a mean of −0.0184.Rd values were selected in the corresponding families. Designs were prepared based on the criteria of maintaining maximum genetic distance and genetic diversity, increasing the frequency of the best selections, and fixed deployment.We conducted secondary selection among the elementarily selected individuals in the ZN-NP and XX-OP selected populations before preparing the deployment designs. Families with ≥ 2 individuals underwent within-family selection and only individuals with the highest p) according to their phylogenetic relationships  design for the second-generation Chinese pine seed orchards in Zhengning . Five Gd were needed when all 23 families were included. The families with the highest Rd values in each Gp . All blocks were the same in the design (fixed design). Within each block, 19 , worst families (families with the lowest Rd of volume in each Gp), and moderate families (the remaining families) were used. Consequently, the families were deployed unequally. Four blocks are shown in Seventeen individuals were removed by secondary selection, and 23 families with only one individual each were included in the final ZN-NP selected population. The 23 families were divided into six phylogenetic groups , and families within the same Gp were closely related, whereas families in different Gp were distantly related. For example, the family composed of individuals 28 and 29 and the family composed of individuals 15, 16, and 18 were in the same Gp. Similarly, individuals 13, 17, 22, 27, and 38 were in the same Gp  according to their relative phylogenetic relationships. Each Gd contained six families, with one best individual each, derived from a different Gp. Families in the same Gd were distantly related. In each block, the proportions of best, moderate, and worst families were 52.78%, 30.56%, and 16.67%, respectively. All 23 families were included in a block, and the blocks were the same across the entire design (Rd value from 1.00 in the balanced design to 1.76, thus maximizing genetic gain at the same level of genetic diversity.The second-generation Chinese pine seed orchards in Zhengning adopted a GUCFB design . The 23 e design . The groRd value from 0.24 in the balanced design to 0.34, which increased genetic gain at the same level of genetic diversity.Twenty open-pollinated families  and 10 c


The last several years have seen an increased interest in the use of remote sensing to identify the location of penguin colonies in Antarctica, and the estimation of the abundance of breeding pairs contained therein. High-resolution (sub-meter) commercial satellite imagery  is capable of colony detection and abundance estimation for both large and small colonies, and has already been used in a continental-scale survey of Adélie penguins. Medium-resolution Landsat imagery has been used successfully to detect the presence of breeding penguins, but has not been used previously for abundance estimation nor evaluated in terms of its minimum colony size detection threshold. We report on the first comprehensive analysis of the performance of these two methods for both detection and abundance estimation, identify the sensor-specific failure modes that can lead to both false positives and false negatives, and compare the abundance estimates of each method over multiple spatial scales. We find that errors of omission using Landsat imagery are low for colonies larger than ∼10,000 breeding pairs. Both high-resolution and Landsat imagery can be used to obtain unbiased estimates of abundance, and while Landsat-derived abundance estimates have high variance for individual breeding colonies relative to estimates derived from high-resolution imagery, this difference declines as the spatial domain of interest is increased. At the continental scale, abundance estimates using the two methods are roughly equivalent. Our comparison of these two methods represents a bridge between the more developed high-resolution imagery, which can be expensive to obtain, and the medium-resolution Landsat-7 record, which is freely available; this comparison of methodologies represents an essential step towards integration of these disparate sources of data for regional assessments of Adélie population abundance and distribution. Though the idea of using satellite imagery to map penguin colonies is over thirty-years old This paper presents the results of an automated retrieval of Adélie penguin colonies along the entire continent of Antarctica from medium-resolution Landsat-7 imagery (30 m pixel size), and its comparison with data on presence and abundance based on manual interpretation of VHR satellite imagery (∼0.60 m pixel size). Landsat-7 launched on April 15, 1999 and has collected imagery over Antarctica continuously since that time. Unfortunately, the ETM+ Scan Line Corrector failure in 2003 occurred just as some of the first very-high resolution images became available for the Antarctic, precluding direct contemporaneous comparison of abundance estimates from these two types of sensors. We leverage the recently published global Adélie survey Pygoscelis spp. penguin) separate from the Antarctic Peninsula in our analysis. Guano stains on the Antarctic Peninsula are ambiguous with respect to species in Landsat imagery and the three Pygoscelis spp often nest in colonies mixed at Landsat's larger spatial resolution. Additionally, as described in the Discussion, these two regions have different ‘failure modes’ with respect to detection and imagery interpretation.In our analysis and discussion, we explicitly distinguish between the probability of detection and the estimation of abundance conditional on the detection of a colony. The detection of a colony based on the spectral characteristics of its guano stain is a binary classification subject to errors of omission  and commission . Abundance estimation is a separate challenge in which success is judged by the accuracy (bias and variance) of estimates of population size. We use the detection probability model along with a probability model for colony size to estimate the total abundance of penguins missing given the results of a Landsat survey. This methodology allows us to correct future Landsat surveys for detection failures and paves the way for achieving regular unbiased estimates of the global Adélie population. We considered continental Antarctica  To estimate the lower detection threshold for Landsat-7 imagery, (2) to demonstrate that the number of pixels classified as penguin guano in Landsat-7 scales with abundance, and (3) to estimate variance in abundance estimates at varying scales of spatial aggregation. Our survey includes both continental Antarctica, where Landsat imagery has been used for detection of Adélie penguin colonies previously https://espa.cr.usgs.gov). This CDR applies a uniform processing to the entire dataset, and includes a host of products including top of atmosphere (TOA) reflectance, surface reflectance, brightness temperature, as well as masks for clouds, cloud shadows, adjacent clouds, land and water. The Landsat CDR TOA product was used for the automated retrieval of Adélie penguin colonies following the methods described in Retrieval of Adélie penguin colony location and spatial extent over the Antarctic Peninsula was conducted using Landsat-7 Enhanced Thematic Mapper-Plus ETM+ data. The Landsat data used in this study were obtained as a Climate Data Record (CDR), which was generated using the methods developed by We used the Adélie penguin survey by A penguin colony is detected using both methods.No penguin colony is detected by either approach. This situation encompasses the vast majority of the Antarctic coastline.A penguin colony is identified in An area is flagged as containing a colony of breeding penguins by the automated detection algorithm but was not reported in The conversion between sensor pixels flagged as guano and the abundance of penguins breeding within that area is determined by an ‘apparent density’ that is not necessarily equal to the actual density of breeding pairs as measured on the ground For the first component of total abundance, involving the estimation of abundance conditional on detection, we considered all breeding populations found by the Landsat-7 automated retrieval process whose population had been estimated previously by ith colony and e N is the number of breeding pairs, and M represents the binary probability that a colony is missing (M = 0) from a Landsat survey. The probability that a colony is missing conditional on its size   =  1-detection probability) can be estimated with a logistic regression model fit using the abundances of each colony as reported in P(N) is a long-tailed distribution best fit by a log-normal distribution. In theory, we could estimate the best-fit parameters for the colony size probability distribution P(N) using the partially-censored Landsat survey detections by inverting Eq. 2 for P(N); however, the low rates of detection for very small colonies make it difficult to establish the shape of the colony size distribution from the Landsat detections alone. We therefore fit a log-normal distribution to the abundance data in P(N) in Eq. 2 to estimate the total abundance of Adélie penguins missing from the Landsat survey due to non-detection.The total abundance of Adélie penguins not detected in a Landsat survey is a convolution of the probability distribution of colony abundance and the probability of detecting a colony given  its size. The expected size of a colony that is not detected by Landsat imagery can be calculated as the following conditional expectationThe sum in Eq. 2 can be calculated using Markov Chain Monte Carlo integration, and the resulting expectation can be multiplied by expected number of missing colonies to estimate the total missing abundance. We modeled the total number of undetected Adélie colonies in the surveyed area as a sample from a Negative Binomial distributionk = 5 colonies, we would draw 5 colonies at random from the total set of colonies in our study  and use Eq. 1  was modelled using the logistic transformationThe detection performance of the automated Landsat retrieval improved with increasing Adélie penguin colony population size . The procolonies .While nearly all of the smallest colonies go undetected using the automated interpretation of Landsat imagery, small Adélie penguin colonies (1 to 3000 pairs) contribute only ∼2% to the total Adélie population There were three Adélie colonies with populations greater than 4000 pairs that were missed in the Landsat retrievals: on Haswell Island (4011 pairs), Coulman South (14786 pairs), and Penguin Point (19377). As described in 2 and 0.52 nests/m2, respectively. As expected from a Poisson regression, the predictive envelope grows with colony size (The number of Landsat-7 pixels retrieved as penguin guano is significantly correlated to the abundance of breeding penguins as reported in ony size .P(N) (th percentile CI: 70364–188070), which is higher but generally consistent with the empirical value of 75582 breeding pairs found in the VHR survey We used the distribution of colony abundances reported in P(N) (6)and foPygoscelis papua) or chinstrap penguins (P. antarcticus), which can appear similar in satellite imagery The Landsat survey found eight colonies that were not detected in the VHR survey reported by Pygoscelis species and thus cannot be identified to species, three of which are large penguin colonies in the Danger Islands. The Danger Islands, a collection of islands off the tip of Joinville Island at the end of the Antarctic Peninsula, are poorly surveyed due to frequent heavy ice cover and steep terrain that preclude easy access, but are known to contain extremely large populations of Adélie and gentoo penguins th percentile CI: 123666–228268), 23649 (95th percentile CI: 17361–32163), and 7419 (95th percentile CI: 5384–9931) breeding pairs of penguins, respectively. Based on prior surveys of the area, these are likely to be overwhelmingly composed of Adélie penguins, making Brash Island potentially one of the largest Adélie penguin colonies in the world.th percentile CI: 2710–5064) breeding pairs of penguins (of unknown species) breeding at this location.Jorge Island was identified by An earlier study While abundance estimates of individual colonies are variable using Landsat retrievals, our model for estimating abundance from the number of pixels identified as guano appears unbiased and thus neither consistently overpredicts or underpredicts abundance. Consequently, abundance estimates using Landsat retrievals improve at larger scales of aggregation. We find that abundance estimates using Landsat retrievals are statistically indistinguishable from those obtained using very-high resolution methods for aggregations containing >c. 40 colonies.As described below, the retrievals of Adélie colony extent from medium-resolution imagery correlate well with comparable datasets, and exhibit relatively low errors of omission and commission. As has been demonstrated also for high-resolution imagery The higher threshold for colony detection on the Antarctic Peninsula likely stems from several factors. These include a larger ratio of exposed surface material compared to snow cover, a greater variability of rock and soil types compared to the rest of the continent, and increased surface weathering. The AP also experiences significantly more rainfall than the continental portions of Antarctica. Weathering may wash away guano making it more difficult to detect smaller colonies, and it may contribute to estimation errors as the guano stain grows or shrinks within a given breeding season. At the same time, the topography of the Antarctic Peninsula is highly complex, with steep terrain that may obscure smaller colonies. Finally, higher cloud cover results in fewer usable scenes each year.Pygoscelis spp. penguins are not the only source of errors of commission. Flying bird species, such as the blue-eyed shag , also produce guano that can be incorrectly identified as penguin guano and can cause errors of commission in both Landsat and VHR imagery.One of the major challenges of using satellite imagery to map Adélie penguin colonies on the Antarctic Peninsula is the existence of two other, closely related, species of penguins (gentoo and chinstrap) whose guano displays similar spectral signatures to that of the Adélie. Moreover, these three species frequently nest in mixed-species colonies, and while differentiation of species has been shown possible in high-resolution imagery Our experience with both VHR and Landsat imagery interpretation has shown that the causes of false positives (errors of commission) are both region and sensor specific. In the Antarctic Peninsula region, errors of commission can stem from breeding blue-eyed shags, as well as from large areas of snow algae, which is often red or pink in color, and iron-tinted scree on steep sloping ridges (Lynch Unpublished data). As well, areas used by penguins for molting can contain significant accumulations of guano and confuse mapping of breeding territories. Landsat imagery retrievals may contain areas used by flying birds either alone or with breeding Adélie penguins. We have also found abiotically-driven errors of commission in Landsat retrievals due to large wet alluvial fans, and others apparently due to sediment runoff or chlorophyll in the water just offshore.Increased weathering, complex terrain, and the existence of congeneric species make it difficult to estimate omission and commission error rates for the Antarctic Peninsula. While a Landsat retrieval of a gentoo colony is an error of commission in the context of an Adélie survey, it still has biological significance, particularly if it identifies a colony that was previously undiscovered. The use of prior information regarding the distribution of penguins and the Antarctic's flying bird species may allow us to use adaptive classification methods that minimize both errors of commission and omission by essentially ‘raising the bar’ for the detection of previously unknown Adélie penguin colonies and simultaneously ‘lowering the bar’ for the re-detection of known Adélie penguin colonies. In the same vein, prior estimates  of the fraction of penguins that are of each species  at mixed colonies may allow us to estimate Adélie abundance to produce less biased estimates at the regional of continental scale.While Landsat imagery lacks the sensitivity to detect small colonies, and is therefore inappropriate for studying processes such as colonization and range expansion, we have demonstrated that it can be used to obtain estimates of Adélie abundance over regional scales, and that bias in regional estimates can be corrected for non-detection. Corrections for non-detection will be made more accurate by better statistical models for colony size, but the existing precision of regional and global Adélie abundance is minimally affected by non-detection of the smallest colonies.Figure S1Penguin Point satellite imagery and photograph.(DOC)Click here for additional data file.Table S1Raw data used for the detection and abundance estimation modeling.(XLSX)Click here for additional data file.Table S2Details of the Landsat-7 imagery classification.(DOCX)Click here for additional data file.Table S3Locations and estimated abundance at previously unreported colonies.(DOC)Click here for additional data file.


Sus scrofa) has been described as polygynous, although evidence of multiple paternity in litters has been found. Using 14 microsatellite markers, we evaluated the contribution of males and females to genetic variation in the next generation in independent wild boar populations from the Iberian Peninsula and Hungary. Genetic contributions of males and females were obtained by distinguishing the paternal and maternal genetic component inherited by the progeny. We found that the paternally inherited genetic component of progeny was more diverse than the maternally inherited component. Simulations showed that this finding might be due to a sampling bias. However, after controlling for the bias by fitting both the genetic diversity in the adult population and the number of reproductive individuals in the models, paternally inherited genotypes remained more diverse than those inherited maternally. Our results suggest new insights into how promiscuous mating systems can help maintain genetic variation.The maintenance of genetic diversity across generations depends on both the number of reproducing males and females. Variance in reproductive success, multiple paternity and litter size can all affect the relative contributions of male and female parents to genetic variation of progeny. The mating system of the wild boar ( The maintenance of genetic diversity in populations is strongly influenced by mating systems, which ultimately determine how variation is transmitted across generations Mating systems have been traditionally classified as monogamous, polygynous, polyandrous and promiscuous or polygynandrous eN) of populations, which can be defined as the size of an ideal population, with constant finite size and random union of gametes, and where the amount of change in allele frequencies due to genetic drift is the same as the actual population under study eNThe amount of genetic diversity depends on the effective size , eN is reduced, because of a high variance in male reproductive success, especially in highly polygynous species eN may be partially offset if more than one male sires the offspring within a litter . Traditionally, it has been accepted that MP reduces the variance in male mating success, increases eN and helps maintain genetic diversity MP on genetic diversity and eN. For instance, it has been proposed that MP might decrease eN and genetic diversity because MP might actually increase the variance in male reproductive success when compared to serial monogamy  MP on eN is likely to depend on population parameters such as the distribution of within-litter paternity The production of multiple offspring by a female in a single brood might also play an important role in determining When assessing sex-specific contributions to genetic diversity, the use of a limited number of samples instead of an entire population can cause an important sampling bias. For instance, in polygynous systems it is expected that females contribute more genetic diversity than males because of the low number of reproductive males and the high variance of male reproductive success Sus scrofa) has been traditionally described as polygynous based on the spatial distribution of reproductive individuals and on the degree of sexual dimorphism The mating system of the wild boar  and 502 foetuses . In Azagala we collected 35 females, approximately 18% of females in the area. In the Iberian area of Santa Amalia there were around 100 females ; 13 females were sampled giving a sampling intensity of ∼13%.All 703 samples used in this study were genotyped for a set of 14 microsatellite markers designed for parentage analyses in wild boar  K), ten independent runs from K = 1 to K = 8 were carried out with 500,000 iterations, following a burn-in period of 100,000 iterations. As an additional assessment, we conducted Structure analyses  for the Iberian Peninsula and Hungary populations separately, to look for minor genetic clustering within the major geographical areas. We regarded genetic clusters as populations if all the hunting locations of the clusters belonged to the same geographic neighbourhood.We did not have previous in-depth knowledge of the number and distribution of populations within the study areas. Therefore, STRUCTURE 2.0 ip is the frequency of the i allele in the genotype set. This index takes into account the number of different alleles and the frequency of each allele.Genetic diversity was measured using Shannon’s index For each population and marker four sex-specific genetic diversity values were obtained: genetic diversity in the random sample of adult males , genetic diversity in mothers , diversity in the paternally transmitted genetic component of progeny  and diversity in the maternally transmitted genetic component of progeny . Paternal and maternal genotypes of the progeny  were constructed by comparing each foetus’ genotype with its mother’s genotype Method 1. Ambiguities were removed and parental genotype diversity was quantified using only those alleles where there was no doubt about whether they were paternally or maternally inherited.Method 2. Alleles were assigned to the paternal genotype at random but with a probability proportional to the relative frequency of the ambiguous alleles in the population the foetus belongs to. In this case, the probability of assigning the most common allele to the paternal genotype increased.Method 3. Alleles were assigned to the maternal genotype at random but with a probability proportional to the relative frequency of the ambiguous alleles in the population. In this case, the most common allele was more likely to be assigned to the maternal genotype.In Method 1, only a single genetic diversity value for paternal and maternal genotypes in each population and marker was obtained. For Methods 2 and 3, a 1,000 replication process was conducted for the entire data set, which reproduced a mean genetic diversity for paternal and maternal genotypes in each population at each marker.MP rate), mean reproductive success for fathers and mothers, and variance of father and mother reproductive success. The number of mothers, as well as the mean and variance of mother reproductive success were quantified using the samples of pregnant females and the distribution of the observed litter sizes. The remaining mating system parameters were inferred using the COLONY 2.0 software We quantified the following mating system characteristics of the population samples we studied: number of fathers and mothers, mean number of mates per mother . The comparison between paternal and maternal contributions to genetic diversity after controlling for covariates such as the number of parents is the main focus of this work, so we additionally inferred the number of fathers by using an alternative paternity inference software: MOL_COANC versus maternal genotypes) as fixed factor, and marker and population as random factors . Finally, genetic diversity contributed by males and females was compared after controlling both for the sex-specific genetic diversity in adults and the number of fathers and mothers. In this case, a LMM was performed with sex-specific genetic diversity in parental genotypes as the dependent variable, sex-specific genetic diversity in adults , number of parents in the population  and sex as fixed factors, and marker as a random effect  in a Bayesian framework, using Markov chain Monte Carlo (MCMC) techniques. Firstly, genetic diversity estimates in adult males and mothers were compared by conducting a LMM with sex-specific adult genetic diversity as the dependent variable, sex as fixed factor, and marker and population as random effects (N = 112∶14 markers * 4 populations * 2 sexes). Secondly, we compared the amount of genetic diversity contributed by males and females to the following generation by performing a LMM with sex-specific genetic diversity in parental genotypes (haploid genetic diversity) as the dependent variable, sex , the MP rate, the mean litter size (LS) and the variance in female reproductive success .Because estimates of genetic diversity transmitted to the following generation by males and females might be influenced by sampling biases (see above), we simulated a promiscuous population and assessed the effect of sample size and sampling intensity on the relative contribution of males and females to genetic diversity. By simulating different scenarios it was possible to interpret our empirical results in the context of knowledge of whether incomplete sampling caused bias in downstream analyses. In the simulations, we assessed the number of inferred reproductive males and the relative contribution of males and females to genetic diversity when the number of sampled females was varied. The following parameters were explored in the simulations: the variance in male reproductive success (LS) was 4.564 and variance in litter size  was 3.367. We incorporated 110 males to this simulated population. For each individual in the population, genotypes were randomly assigned for a locus with 6 alleles . Females were allowed to randomly mate with males. To simulate MP, females mated with two males (a and b). Within litters, both males were assumed to have equal probability of paternity. When the litter size was odd, the number of offspring sired by male a was higher. To simulate polygyny, males were allowed to mate with more than one female. Variable male reproductive success was simulated by assuming 80% of males have a probability p of mating, 10% of males have a probability 2p of mating and 10% of males have a probability 3p of mating. After 1,000 replicates of this mating system, the resulting variance in male reproductive success was 15.547±2.983 (mean ± SD). For the simulated population one female was randomly sampled and the number of males she mated with was recorded, along with, the genetic diversity of the maternally and paternally inherited genotypes. We also quantified the difference between genetic diversity of maternally and paternally-inherited genotypes. Then, the same values were recorded after randomly sampling 2 to 110 females. Thus, we recorded the mean number of mates  and the genetic diversity of the maternally and paternally inherited genotypes for sampling regimes that ranged from 1 female to the entire subpopulation of females. We repeated each scenario 1,000 times.We used information in our wild boar populations to simulate a realistic initial population in which we modelled the sampling bias. In this initial population we used 110 females in which the litter size distribution was the same as that in the studied wild boar females. Mean litter size  were modified and the process was repeated. For each parameter two additional scenarios were created; one below and one above the initial condition. For malesV we used the same parameters as the initial population but with the following malesV values: all males have equal mating success ; 80% of males have a probability p of mating, 10% of males have a probability 2p of mating and 10% of males have a probability 5p of mating . For MP, two additional scenarios were simulated: females reproduced with one (single paternity) or with three males . Within litters, all males were modelled to have an equal number of paternities. When the litter size was not a multiple of 3, the number of offspring sired was a≥b≥c. For LS the following scenarios were explored: for all females we decreased the litter size by 2 offspring ; for all females we increased the litter size by 2 offspring (yielding LS = 6.564). Finally, for femalesV the two scenarios were: for females with more than 4 offspring in the initial population we decreased the litter size by 1 offspring and for females with less than 5 offspring we increased the litter size by 1 offspring ; for females with more than 4 offspring in the initial population we increased the litter size by 1 offspring and for females with less than 5 offspring we decreased the litter size by 1 offspring . Therefore, 8 additional scenarios were simulated. The impact of incomplete sampling on estimates of paternal and maternal contributions to offspring diversity was examined using the same procedure as for the initial population.Following the initial simulations, parameters of interest  was 1.781. In all populations the coefficient of variation (CV = standard deviation/mean) of reproductive success was higher in fathers than in mothers .The mating system of each wild boar population is summarised in pMCMC = 0.910).Genetic diversity was similar in adult males and pregnant females in all populations  in reproductive success was higher in fathers than in mothers in all four populations. The highest difference between father and mother variance in reproductive success was found in Hungary, where the litter size was also the highest of the study populations. Because of the higher variance of reproductive success in fathers than in mothers, a lower contribution of fathers to the genetic diversity of the following generation might be expected The wild boar populations we studied displayed a promiscuous mating system in which The amount of genetic diversity was not different between the sampled adult males and mothers. However, we found that paternally-transmitted genotypes in foetuses were more diverse than maternally-transmitted genotypes, by two of the three methods for dealing with ambiguously inherited alleles. This result was consistent in all sampled populations . The othThe simulation analyses showed that paternal genotypes can be estimated to be more diverse than maternal genotypes in this system due to a sampling bias. In our wild boar populations we presumably sampled a small proportion of the population (10–20%). When sampling intensity is low, the number of sampled females is typically lower than the estimated number of mates these females mated with. As a consequence, the estimated genetic diversity of paternally-transmitted genotypes is greater than that of maternally-transmitted genotypes. Therefore, the relatively high genetic diversity of paternally-transmitted genotypes could be due to a sampling bias with little biological relevance.The possible influence of a low sampling intensity on the comparison between maternal and paternal contributions to genetic diversity necessitated further analyses. When we compared the relationship between the number of sampled females and the genetic diversity transmitted by these females  are genetically more diverse than the overall population of reproductive-aged males , i.e. sexual selection favoured males with the greatest genetic diversity. In this case, the inferred paternally-transmitted genotypes would not be a random sample of all male genotypes in the population. Precopulatory and postcopulatory processes might favour males with the highest genetic diversity. On one hand, male-male mate competition is a precopulatory feature of the wild boar mating system. Males have developed aggressive behaviours, weapons and defences to compete for mating opportunities Macaca mulatta) populations, heterozygous males at MHC genes present higher reproductive success Mandrillus sphinx) populations, heterozygosity at microsatellite markers is associated with increased reproductive success The relationship between heterozygosity and reproductive success has been detected in different mammal groups such as primates Finally, male-biased dispersal in wild boar populations The difference in genetic diversity of paternal and maternal genotypes is suggestive of sexual selection driving a high diversity in paternally-transmitted genotypes. However, we cannot conclude that males transmitted more overall genetic diversity than females because it is likely that a relatively low fraction of each entire population was sampled. The simulations showed that when a high proportion of females are sampled, the amount of genetic diversity transmitted by females exceeds that transmitted by males. However, the simulations are ‘null models’ in the sense that any process that would increase the amount of genetic diversity contributed by males was not incorporated. Sex-specific contributions to diversity in wild boar populations will depend on the balance of the processes tending to increase and decrease genetic diversity MP was the most important factor in determining whether incomplete sampling caused a sex-bias in estimates of sex-specific contributions to genetic diversity. When single-sire paternity was simulated, the bias caused by incomplete sampling lost importance  the increase in MP did not have important consequences for estimates of the relative amounts of genetic diversity transmitted by males and females. It has been proposed that low MP rates have important consequences on transmission of genetic diversity only when litter sizes are large MP = 1.781; simulations: MP = 1–3) might not be large enough to detect any effect of MP rate on estimates of sex-specific contributions to genetic diversity.The simulation results suggest that the existence of MP necessary for its existence MP, polygyny-related processes such as the most genetically diverse males having greatest fitness In polygynandrous and promiscuous systems multiple mating carries costs, and several non-exclusive hypotheses have been proposed to explain the evolutionary benefits of In summary, our data suggest that the promiscuous mating system of wild boar can cause male contributions to genetic diversity to exceed those of females, and that the mating system is a mechanism by which the maintenance of genetic diversity is promoted. However, care must be taken in this type of study to ensure that spurious sex-specific differences do not arise due to sampling biases.S1 FigRelationship between the inferred number of reproductive individuals and the genetic diversity in paternal genotype.(DOC)Click here for additional data file.S1 TableSample sizes for each hunting site.(DOC)Click here for additional data file.S2 TableSample sizes and heterozygosity for each genetically different population.(DOC)Click here for additional data file.S3 TableGenetic information of microsatellite markers.(DOC)Click here for additional data file.S1 FileThe study area and the places in which hunting events were conducted. This file also contains Figures A and B. Figure A, Iberian Peninsula. Figure B, Hungary.(DOC)Click here for additional data file.S2 FileStructure results after splitting Iberian Peninsula and Hungary. This file also contains Figure S2. Figure S2a, Log-likelihood values for Iberian Peninsula (ten independent runs). Figure S2b, Membership coefficient of probability for Iberian Peninsula (K = 3). Figure S2c, Log-likelihood values for Hungary (ten independent runs).(DOC)Click here for additional data file.S3 FileRelationship between genetic diversity in parental genotypes and covariates. This file also contains Figure S3. Figure S3a, Covariate: Number of reproductive individuals. Figure S3b, Covariate: Genetic diversity in adults.(DOC)Click here for additional data file.S4 FileResults after using MOL_COANC for paternity analyses. This file also contains Tables A–C. Table A, Results for Method 1. Table B, Results for Method 2. Table C, Results for Method 3.(DOC)Click here for additional data file.


It is generally accepted that domestication of pigs took place in multiple locations across Eurasia; the breeds that originated in Europe and Asia have been well studied. However, the genetic structure of pig breeds from Russia, Belorussia, Kazakhstan and Ukraine, which represent large geographical areas and diverse climatic zones in Eurasia, remains largely unknown.This study provides the first genomic survey of 170 pigs representing 13 breeds from Russia, Belorussia, Kazakhstan and Ukraine; 288 pigs from six Chinese and seven European breeds were also included for comparison. Our findings show that the 13 novel breeds tested derived mainly from European pigs through the complex admixture of Large White, Landrace, Duroc, Hampshire and other breeds, and that they display no geographic structure based on genetic distance. We also found a considerable Asian contribution to the miniature Siberian pigs (Minisib breed) from Russia. Apart from the Minisib, Urzhum, Ukrainian Spotted Steppe and Ukrainian White Steppe breeds, which may have undergone intensive inbreeding, the breeds included in this study showed relatively high genetic diversity and low levels of homozygosity compared to the Chinese indigenous pig breeds.This study provides the first genomic overview of the population structure and genetic diversity of 13 representative pig breeds from Russia, Belorussia, Kazakhstan and Ukraine; this information will be useful for the preservation and management of these breeds.The online version of this article (doi:10.1186/s12711-016-0196-y) contains supplementary material, which is available to authorized users. Sus scrofa) is an important farm animal, as well as a relevant biomedical model for humans. The pig was independently domesticated in China and Europe approximately 9000 years ago [The pig , one breed from Belorussia , one breed from Kazakhstan , and six breeds from Ukraine   values were calculated using the ADZE v1.0 software [o) and runs of homozygosity (ROH) were computed with the PLINK v1.9 software [2) between pairwise SNPs was calculated by the commands –r2 and –ld-window-r2 in PLINK v1.9 [2. Effective population sizes (Ne) were computed using the equation of Herrero-Medrano et al. [T is effective population size at T generation ago, T is calculated by T = 1/2c [2 between SNPs separated by large and small genetic distances reflect recent and ancient Ne, respectively [Allelic richness . The admixture analysis suggests that the Minisib breed contains ancestries from Large White, Landrace and Luchuan or Tibetan pigs from China Fig. a, K = 6.We ran the admixture analysis from K = 2 to 26 and found that K = 19 was the optimal value, with the smallest cross-validation errors  and observed heterozygosity (Ho) were calculated to provide measures of the genetic diversity of the 13 populations  for the past 20 years was estimated for each population using the linkage disequilibrium and recombination rate data. The breeds with the largest Ne included the Breitov 148.7), Belorussian pork swine (145.8), and Red White Belted (142.8) breeds, whereas the Murom (86.2), Minisib (99.5), and Ukrainian pork swine (115.4) breeds had the smallest Ne  and neighbor-joining tree analyses, many of the 13 pig breeds investigated in this study have retained unique identities and are differentiated from mainstream commercial breeds such as the Large White and Landrace breeds. This can be attributed to several factors, including differences in the origin of the populations, a long period of genetic isolation, and differences in climate and locally available feed between West Russia, Belorussia, Ukraine and west Europe.We observed widespread Asian ancestries in all 13 pig breeds. Introgression of Asian haplotypes into European breeds, such as the Large White and Landrace breeds, have been reported in several previous studies , 31. TheA previous comparison of genomic diversity parameters based on whole-genome sequence (WGS) and 60K SNP data showed that while 60K SNP and WGS data provided similar results for European breeds, 60K SNP data underestimated the genetic diversity of the Asian populations due to ascertainment bias . This stThis is the first genetic survey of pig breeds from Russia, Belorussia, Kazakhstan and Ukraine using 60K SNP genotype data. We confirmed that these breeds were mainly of European origin, since they formed a separate group from Chinese breeds but were close to commercial breeds, including the Large White, Landrace, Duroc and Hampshire breeds. The Minisib and Urzhum breeds have been subject to severe inbreeding and consequently have limited genetic diversity. In contrast, most of the other breeds have a greater level of genetic diversity and a lower level of ROH because of their crossbreeding histories. This study provides the first genomic survey of the population structure and evolutionary history of pig breeds in Russia, Belorussia, Kazakhstan and Ukraine, which will contribute to conservation and breeding programs of these breeds.


Saccharomyces cerevisiae is not ideal because its evolution has been affected by domestication. Instead, ecologists and evolutionary biologists are focusing on close relatives of S. cerevisiae, the seven species in the genus Saccharomyces. The best-studied Saccharomyces yeast, after S. cerevisiae, is S. paradoxus, an oak tree resident throughout the northern hemisphere. In addition, several more members of the genus Saccharomyces have recently been discovered. Some Saccharomyces species are only found in nature, while others include both wild and domesticated strains. Comparisons between domesticated and wild yeasts have pinpointed hybridization, introgression and high phenotypic diversity as signatures of domestication. But studies of wild Saccharomyces natural history, biogeography and ecology are only beginning. Much remains to be understood about wild yeasts' ecological interactions and life cycles in nature. We encourage researchers to continue to investigate Saccharomyces yeasts in nature, both to place S. cerevisiae biology into its ecological context and to develop the genus Saccharomyces as a model clade for ecology and evolution. © 2014 The Authors. Yeast published by John Wiley & Sons Ltd.Yeast researchers need model systems for ecology and evolution, but the model yeast  Saccharomyces cerevisiae is arguably the most intensely studied eukaryotic organism besides human beings. Its genetic tractability has made it a valuable model organism for genetics, genomics, cell biology and biochemistry  when it is available. We recommend additional recent Saccharomyces reviews that focus on comparisons with S. cerevisiae . In revenge for the Franco-Prussian war, Pasteur did not permit his methods to be translated into German, instead using them to promote the competing French brewing industry   . Taxonom species . ThroughS. paradoxus was the first Saccharomyces yeast to be acknowledged as a non-domesticated species. It was first isolated from sap exudate of a tree in Russia  was discovered on hardwood bark in China  as a hybrid of S. cerevisiae and another yeast, but had difficulty identifying the second parent . Lager is now the most popular style of beer in the world  in the northern hemisphere, and southern beech trees (Nothofagus spp.) in the southern hemisphere , S. paradoxus. Population genetic and genomic data suggest that European and Far Eastern S. paradoxus go through a sexual cycle once every 1000 asexual generations, and that for each sexual cycle, 94% of matings are intra-tetrad, 5% are autodiploidization and 1% are inter-tetrad  into S. uvarum, and S. eubayanus into S. cerevisiae/S. uvarum and S. cerevisiae/S. kudriavzevii/S. uvarum hybrids . The introgression appears to be fixed in the America A/Europe S. paradoxus population but not present in other S. paradoxus populations  and may have influenced the biogeography of others  more highly diverged than Tibetan, Argentinian and Wisconsin strains from a broad geographic area? Is S. eubayanus speciating in East Asia, and are speciation mechanisms the same in Asian S. eubayanus and North American S. paradoxus?Like B Figuresb, and 3 S. kudriavzevii populations from Europe and Japan  is about 1%, except for a single Japanese isolate which is much more diverged : researchers sequenced the genomes of 35 S. paradoxus strains from four populations and compared them with 36 S. cerevisiae genomes from five populations (Domestication tends to increase phenotypic diversity, e.g. morphological diversity in dogs . S. cerexus Table (Liti etS. cerevisiae, genetic structure is weak and tends to track human usage, and most lineages are mosaics (S. cerevisiae isolates from primeval Chinese forests show strong geographic structure (S. uvarum in the same way. S. uvarum has only been isolated from natural substrates in the southern hemisphere, while northern hemisphere samples include domesticated and natural isolates. There are three S. uvarum populations: Australasia, South America B and South America A/Holarctic (FiguresS. cerevisiae, S. kudriavzevii and S. eubayanus, while isolates from the southern hemisphere do not (S. uvarum from the South America A population recently colonized the northern hemisphere and were dispersed during the course of domestication.Domestication increases dispersal and reduces geographic structure. In domesticated c Figuresb, and 3 Saccharomyces show that population bottlenecks, high phenotypic diversity, low DNA sequence divergence, hybridization and introgression are all associated with domestication. Conclusions about genomic evolution drawn from S. cerevisiae must take its history of domestication into account. Close study of the entire Saccharomyces clade will allow us to identify general evolutionary mechanisms, as opposed to those that are the result of domestication.Domestication has dramatic consequences for evolution. Comparisons between domesticated and wild Saccharomyces will enable us to better understand the natural history of S. cerevisiae and how selective pressures have shaped its evolution. But the basic biology of Saccharomyces is still poorly understood. Are they active or dormant on bark or soil? How do they interact with other microbes? When, where and how often do they have sex? How do they disperse? These answers will not only improve the utility of S. cerevisiae as a model organism for fundamental biology, they will also allow the development of the genus Saccharomyces as a model ecological and evolutionary system.Studying wild