Scorodophloeus zenkeri Harms  in Central Africa. Additionally, we characterize genetic diversity, selfing rate and fine-scale spatial genetic structure within populations to estimate the gene dispersal capacity of the species.Comparative phylogeographic studies on rainforest species that are widespread in Central Africa often reveal genetic discontinuities within and between biogeographic regions, indicating  barriers to gene flow, possibly due to repeated and/or long-lasting population fragmentation during glacial periods according to the forest refuge hypothesis. The impact of forest fragmentation seems to be modulated by the ecological amplitude and dispersal capacities of each species, resulting in different demographic histories. Moreover, while multiple studies investigated the western part of Central Africa (Lower Guinea), few have sufficiently sampled the heart of the Congo Basin (Congolia). In this study, we look for genetic discontinuities between populations of the widespread tropical tree g) for this outcrossing species with ballistic seed dispersal, which range between 100 and 250 m in areas where S. zenkeri occurs in high densities, and are in the low range of σg values compared to other tropical trees. Gene dispersal distances are larger in low density populations, probably due to extensive pollen dispersal capacity.Clear intraspecific genetic discontinuities occur throughout the species’ distribution range, with two genetic clusters in Congolia and four in Lower Guinea, and highest differentiation occurring between these bioregions. Genetic diversity is higher in Lower Guinea than Congolia. A spatial genetic structure characteristic of isolation by distance occurs within the genetic clusters. This allowed us to estimate gene dispersal distances (σS. zenkeri populations seems to have occurred not only in Lower Guinea but also in the Congo Basin, though not necessarily according to previously postulated forest refuge areas. The lower genetic diversity in Congolia compared to Lower Guinea parallels the known gradient of species diversity, possibly reflecting a stronger impact of past climate changes on the forest cover in Congolia. Despite its bisexual flowers, S. zenkeri appears to be mostly outcrossing. The limited dispersal observed in this species implies that genetic discontinuities resulting from past forest fragmentation can persist for a long time before being erased by gene flow.Fragmentation of The online version contains supplementary material available at 10.1186/s12862-021-01781-1. The rainforest in West and Central Africa, known as the Guineo–Congolian rainforest, is renowned for its high species richness and endemism rate, harbouring more than 10,000 vascular plant species of which more than 30% are endemic . Based oAlthough the distribution and diversity of biogeographic regions is often studied at the species level or higher taxonomical level, additional insights can be gained by investigating differences in intraspecific genetic diversity within those bioregions . Indeed, diversity patterns at the level of communities (species richness) and populations (genetic diversity) are partially driven by similar processes , generaScorodophloeus zenkeri Harms . Scorodophloeus zenkeri is a medium-sized to large tree (up to 40 m in height) and is a typical element of mixed evergreen forests with well-drained soils [Scorodophloeus zenkeri is typical for dense mature evergreen forests and since it does not tolerate waterlogging, the species is absent in the northern part of Congo. This causes a gap in its distribution area, meaning that populations from Lower Guinea and Congolia are currently only connected in the south-western part of DR Congo. Scorodophloeus zenkeri is a shade-tolerant tree that can be dominant in mature terra firme rainforests (which are for instance found in inland Gabon and around Kisangani in DR Congo), and it often has an aggregated distribution [In the current study, we aim to assess the impact of past forest fragmentation events in Central Africa, with a special focus on Congolia, by investigating putative genetic discontinuities in the widespread tropical African tree species ed soils . As a reribution , 20. Theribution , 15.S. zenkeri, we evaluate the impact of past forest fragmentation in Central African lowland rainforests. Furthermore, genetic diversity indices are calculated to identify regions with a high intraspecific diversity (indicative of ancient forest refugia), and genetic divergence is estimated. In addition, we study the fine-scale spatial genetic structure in populations throughout the species’ distribution range, as well as seed dispersal distances in order to assess if the dispersal capacity of this species is limited, as suggested by the fruit morphology.By applying genetic clustering methods on nuclear microsatellite data from S. zenkeri?Within the current study, the following questions are addressed: (1) Are the populations from Lower Guinea and Congolia genetically differentiated ? (2) If so, do we observe genetic discontinuities between populations within Congolia, indicating (ancient) population fragmentation in forest refugia? (3) How efficient is gene dispersal in S. zenkeri populations in Lower Guinea are genetically well differentiated from those in Congolia, with both biogeographic regions harbouring several distinct genetic subclusters , different clustering analyses and an ordination of genotypic data revealed that the P(D) substantially increased up to K = 3, followed by a small increase up to K = 6, and a small decrease to K = 10. The run with the highest Ln P(D) was observed at K = 6, as well as the highest average Ln P(D) among the 10 iterations  among iterations was low across all K-values.The Bayesian clustering algorithm implemented in STRUCTURE  inferredK = 2)    increased with K, first substantially up to K = 3, then slightly up to K = 5  was observed at K = 5, as well as the highest average Ln P(D) among the 10 iterations. At K = 5, the same clusters obtained with the complete SSR dataset were retrieved, except that the two Congolian clusters under K = 6 were grouped into a single cluster. To further investigate this discrepancy, we ran an additional analysis in STRUCTURE restricted to samples from Congolia . This additional clustering run indicated K = 2 as most plausible solution , the avsnapclust function of the adegenet [K = 7 when the complete SSR dataset was used  mostly occurring in contact zones between genetic clusters  and the observed heterozygosity (Ho) were highest in cluster ‘Congo west’, while the allelic richness (AR) was highest in cluster ‘LG southwest’ , followed by ‘Congo east’ . Estimated inbreeding (Fi) was significantly different from 0 in all clusters except for ‘Congo west’, which is most probably due to the presence of null alleles  and potentially biparental inbreeding. Scorodophloeus zenkeri has bisexual flowers but it appears to be an outcrossing species, since the estimated selfing rate was non-significantly different from zero for all genetic clusters  to 0.30 (‘LG northwest’ vs ‘Congo west’) and is generally lower (FST < 0.2) between clusters from the same bioregion , except for cluster ‘LG northwest’ which is well differentiated from all the other ones, and ‘Congo west’ which is not much differentiated from ‘LG east’ (FST = 0.16) . Allele size permutation tests showed no significant difference between FST and RST for any pair of clusters, indicating that differentiation between clusters results more from genetic drift than the accumulation of stepwise mutations [µ generations ago, where µ is the mutation rate of microsatellites [µ ranges between 10–4 and 10–3, and that the generation time of S. zenkeri is between 100 and 200 years [Pairwise genetic differentiation as measured by utations , which stellites . Assumin FST = 0. (Table RFij) first decayed slowly from 0.18 to 0.16 between individuals separated by less than 1 km to 50 km, then decayed quickly at larger distances, reaching negative values beyond c. 800 km  using a method implemented in SPAGeDi [De) and σg under an isolation by distance theoretical model [When assessing the spatial genetic structure in the complete SSR dataset, the kinship coefficient , respectively. Similar gene dispersal distance estimates  were obtained when considering the regression between σg and 100σg. Cluster ‘LG northwest’ was characterized by the lowest density of adult trees (D = 0.14 ha−1) and gene dispersal distance estimates were much higher, reaching 1861 to 2334 m , and between 394 and 1136 m in cluster ‘LG southwest’ (D = 1.12 ha−1), illustrating the negative correlation between gene dispersal distance and population density. No estimates were obtained for ‘LG east’ since the iterative method in SPAGeDi did not converge. This could be because the genetic structure in this cluster does not reflect isolation by distance or because the distribution of genotyped samples was not optimal for such inference. In contrast to σg estimates, neighbourhood size estimates were quite stable, ranging between 125 and 134 in the Yangambi and Yoko reserves, between 105 and 152 in the cluster ‘LG northwest’, between 48 and 76 in ‘LG west’, and between 90 and 182 in ‘LG southwest’.The kinship-distance decay measured in the more densely sampled areas of the Yangambi and Yoko reserves, within cluster ‘Congo east’, led to Scorodophloeus zenkeri shows clear intraspecific genetic discontinuities throughout its Central African distribution range. Our clustering analyses revealed that the populations in Lower Guinea are genetically well differentiated from those in Congolia, with each bioregion harbouring several distinct genetic clusters. While Congolia is underrepresented in previous phylogeographic studies, our study is among the first to indicate that forest fragmentation also occurred in this bioregion. The division of the Lower Guinean and Congolian bioregions [S. zenkeri. Additionally, the overall premise that Lower Guinea has a higher diversity and rate of endemism than Congolia in terms of species diversity [S. zenkeri in terms of allelic diversity , based on past rainfall distribution patterns, suggested the occurrence of multiple forest refugia fragments in Congolia [S. zenkeri in Congolia [In Congolia, two distinct genetic clusters were inferred for Congolia . In contCongolia . The twoCongolia . HoweverCongolia , 15, 35.Congolia  and MaleCongolia . Also, tCongolia , 36, genS. zenkeri populations in Lower Guinea and Congolia. This occurrence of open-forest formations with an abundance of light demanding tree species has been linked to past human activities and drier climatic periods during the Holocene [FST = 0.16), and that the present-day distribution break solely originates from the Holocene vegetation changes. However, since the divergence between genetic clusters within Lower Guinea was linked to forest retractions during the Pleistocene [The open-forest formations and swamp forests that dominate northeastern Republic of the Congo  seem to Holocene . It is pistocene , the maiS. zenkeri, in agreement with its gregarious distribution and a fruit morphology that suggests ballistic dispersal [Sp statistic  lies in the range of Sp values found in other tropical tree species, but is lower than the average of 0.017 [S. zenkeri appears fairly high (c. 18 adult trees per ha), gene dispersal distances (σg) due to seed and pollen dispersal appears to be limited, with estimates of σg ranging from 105 to 241 m. This is in the low range of estimates reported for tropical trees, where σg typically ranges from a few hundred meters [S. zenkeri occurs at high densities. While limited seed dispersal was expected, our results suggest that pollen dispersal must be quite limited as well, at least in those high-density areas such as the Yangambi and Yoko forests. In areas where S. zenkeri occurs at lower density, such as in northwestern and southwestern Lower Guinea, gene dispersal distances appear to be much higher, with estimates up to c. 2300 m. This inverse correlation between population density and gene dispersal distance is expected since pollen dispersal is more extensive under low population density simply because potential mating pairs are more distant from each other [S. zenkeri. Southwestern Gabon is characterized by a highly heterogeneous landscape with a lot of elevation variability and savannah-forest mosaics, and S. zenkeri is uncommon here [Gene dispersal estimates revealed limited dispersal capacities of ispersal , 20. Ourof 0.017 . Howeverd meters –42 to a d meters , 44. Hench other , 40. Indch other , 15, whiS. zenkeri in Congolia and Lower Guinea, the historical genetic structure has not been erased (yet). This can be explained by the long generation time of tropical trees such as S. zenkeri, meaning that millions of years might be needed to homogenize populations through gene flow [S. zenkeri is a shade-tolerant tree, indicative of mature forests, and incapable of colonizing open habitats. This, in combination with its limited dispersal capacities, can explain its slow spatial dynamics and the preservation of the ancient genetic structure induced by biogeographic events.Despite the current continuous distribution of ene flow , 12, 15.Scorodophloeus zenkeri throughout Central Africa, caused by ancient population fragmentation, presumably during glacial periods. The populations in Lower Guinea are genetically differentiated from those in Congolia, with each bioregion harbouring distinct genetic clusters. While Congolia is underrepresented in previous phylogeographic studies, our study suggests that forest fragmentation also occurred in this bioregion, though not necessarily according to previously postulated refuge hypotheses. The lower genetic diversity in Congolia compared to Lower Guinea possibly reflects a stronger impact of past climate changes on the forest cover. The fine-scale genetic structure detected in S. zenkeri populations confirms that seed and pollen dispersal are very limited, at least in the dense populations of eastern Congo, further limiting gene flow among genetic clusters, even though S. zenkeri is mostly outcrossing. In low density populations, gene dispersal distances are larger, since pollen dispersal is forced to be more extensive. Future phylogeographic studies will increasingly apply whole-genome datasets, therewith enabling accurate dating of population divergence, and providing a more detailed image of the evolutionary history of the Central African rainforest.The present study reveals intraspecific genetic discontinuities within the tropical tree Scorodophloeus zenkeri trees on field missions in Cameroon, Gabon, Congo and DR Congo. Additionally, leaf material was collected from 91 dried herbarium specimens present in the herbarium of Meise Botanic Garden (BR). Total DNA was extracted from the 265 individuals using a modified cetyltrimethylammonium bromide (CTAB) protocol [Silica-dried leaf samples or cambium slashes were collected from 174 protocol  that incS. zenkeri using two multiplex reactions as described by Piñeiro et al. [n = 465) generated in this study includes 174 samples from the Congo Basin, making it the first phylogeographic study on S. zenkeri to completely cover the known species’ distribution area  was run using the same parameters as described by Piñeiro et al. [K = 1 and K = 10, and 10 iterations for each K. Both the burn-in period and the number of MCMC replicates were set at 100,000. Second, the same settings were used for running a reduced SSR dataset (n = 368), which was obtained by subsampling a maximum of three individuals per square area with a side of 0.01° (approximately 1 km2), since some areas were more densely sampled . This was done to test whether such an overrepresentation of geographic areas was causing a biased clustering [K, the log-likelihood of the data Ln P(D) was plotted against K [K was assessed.Genetic clusters were first inferred using the Bayesian clustering algorithm implemented in the STRUCTURE software 2.3.4 . Two runo et al. : admixtuustering . For botgainst K  and the snapclust function of the adegenet [To ensure a robust clustering outcome, genetic clusters were also inferred using the adegenet  package adegenet , which aadegenet [ade4 [The genetic clusters inferred with both methods were mapped using QGIS 3.4.5 to assess their geographic distribution. Genetic diversity in the complete SSR dataset was summarized with a principal component analysis (PCA) and visualized as a scatterplot with R  packagesadegenet  and ade4et [ade4 .q < 0.8) from the inferred genetic clusters, the following multilocus diversity parameters were calculated with SPAGeDi 1.5d [He), observed heterozygosity (Ho), rarefied allelic richness (AR), effective number of alleles (NAe), and inbreeding coefficient (FIS). The same genetic diversity parameters were calculated for Lower Guinea and Congolia, after subsampling to avoid bias due to overrepresented areas. Furthermore, FST [RST [RST is based on allele size, it can be used to estimate the contribution of stepwise mutations to genetic differentiation [RST values were compared to the FST values obtained from 10,000 permutations of allele sizes among alleles, to test for a phylogeographic signal at the SSRs.After removing admixed individuals  by calculating the pairwise kinship coefficient (Fij) between individuals from the respective clusters, and plotting Fij against the geographic distance between compared individuals. The decay of the kinship-distance curves was quantified by the Sp statistic, based on the regression slope of Fij on the logarithm of geographic distances [Fij to their frequency distributions obtained after 10,000 random permutations of all individuals (or those assigned to the respective clusters) with respect to their spatial positions [Patterns of spatial genetic structure were analysed in the complete dataset and within the largest inferred genetic clusters (minimum istances . Signifiositions .Fij decays approximately linearly with ln(distance) at a rate proportional to (Fn-1)/(4π.De. σg2), where σg2 is half the mean squared parent–offspring distance, De the effective density of adults, and Fn the mean Fij between neighbours, here considered as individuals separated by less than 100 m. This theoretical approximation is good for distances ranging from σg to approximately 0.56σg/(2µ)1/2, where µ is the mutation rate [g and lower than 20σg or 100σg, while σg was estimated using an iterative procedure implemented in SPAGeDi 1.5d [De must be given as a fixed parameter. Using 100σg as upper limit for the regression allowed us to increase precision by considering more Fij pairs, but at the cost of some upward bias if µ > 10–5. We assumed that De ranged between one tenth and one half of the density of adult trees [S. zenkeri density per hectare of 23 trees with a diameter at breast height (dbh) larger than 10 cm, and 14 trees with a dbh larger than 30 cm in the Yoko reserve , and of 35 trees with a dbh larger than 10 cm in the Yangambi reserve . Assuming that S. zenkeri reproduces regularly from a dbh of 30 cm, and has similar dbh structures at both localities, we considered a mean density of 18 adults per ha. Thus, we tested the estimation procedure of σg assuming that De equals 9, 4.5 or 1.8 individuals per ha . Large-scale forest inventories in Lower Guinea ([S. zenkeri densities (dbh > 30 cm): 0.14 trees/ha for ‘LG northwest’ (688 ha inventoried), 4.51 trees/ha for ‘LG east’ (4402 ha inventoried), 1.35 trees/ha for ‘LG west’ (4764 ha inventoried), and 1.12 trees/ha for ‘LG southwest’ (6366 ha inventoried).We estimated gene dispersal distances from the kinship-distance decay in the more densely sampled clusters , using an indirect method based on isolation-by-distance models , 39. Thiion rate . TherefoeDi 1.5d , where D Guinea (, Réjou-MFinally, selfing rate was estimated in each genetic cluster. In order to avoid bias due to null alleles, selfing was estimated from identity disequilibrium, with standard errors estimated by jackknifing over loci , 54. AllAdditional file 1. Additional figures and tables.


They demonstrate that speciation has occurred despite periods of contact. By showing that genetic differentiation along the genome is too variable to be accounted for by a simple allopatric model, they confirm that gene flow between the diverging species must have happened, with timescales for contact on the order of 100 000  years. Further, they use a novel approach to estimate the minimum time for complete gene flow to be cut off if divergence were to happen entirely in allopatry, through comparisons of subspecies, and show that this is too long to be a feasible means of creating species diversity.A basic conundrum in speciation is that isolation is required for differentiation, but dispersal is required to produce populations in different places, which can then diverge . These cThe paper combines cutting-edge genomic analysis with an impressive dataset to draw strong conclusions about times needed to create enough divergence that populations will not collapse back into one upon coming into contact. It also leads to increased understanding of mechanisms producing the extraordinary marine diversity in the region. Finally, the results set the stage for asking whether contact between divergent forms, such as in hybrid zones, could actually accelerate speciation rather than retard it, such as by promoting chromosomal inversions  or drivi


Streptococcus salivarius, Lactobacillus helveticus, Lactobacillus delbrueckii, Enterobacter xiangfangensis, and Acinetobacter baumannii were the primary bacterial species in the fermented dairy product samples. Principal coordinates analysis showed that koumiss and shubat stood out from the other samples. Moreover, permutational ANOVA tests revealed that the types of fermented dairy products and geographical origin significantly affected microbial diversity. However, different processing techniques did not affect microbial diversity. In addition, results of hierarchical clustering and canonical analysis of the principal coordinates were consistent. In conclusion, geographical origin and types of fermented dairy products determined the bacterial diversity in spontaneously fermented dairy product samples.Spontaneously fermented dairy products have a long history, and present diverse microorganisms and unique flavors. To provide insight into the bacterial diversity, 80 different types of spontaneously fermented dairy product samples’ sequence data that were downloaded from MG-RAST and NCBI and 8 koumiss and 4 shubat were sequenced by the PacBio SMRT sequencing platform. All samples including butter, sour cream, cottage cheese, yogurt, koumiss, shubat, and cheese, were collected from various regions in Russia, Kazakhstan, Mongolia and Inner Mongolia (China). The results revealed that Firmicutes and Proteobacteria were the most dominant phyla (>99%), and 11 species were identified with a relative abundance exceeding 1%. Furthermore,  Spontaneously fermented milk products have been produced and consumed for thousands of years. About 5000 BC, in the Mesopotamian plains, nomadic communities stored milk in stone pots or bags made from animal skins, leading to the production of the first spontaneously fermented milk. Subsequently, fermented milk was introduced into East Asia, including Russia, China, and Mongolia, by Tartars and Huns via the “Silk Road” . FermentTraditionally, the production of spontaneously fermented dairy products relies on co-fermentation with various lactic acid bacteria (LAB) and yeast, resulting in various unique flavors and nutrients ,6. It isWith the continuous development of bioinformatics and biotechnology, the utilization of high-throughput DNA sequencing has become more prevalent due to its cost-efficiency. Several studies have analyzed the microbiota of fermented dairy products using second-generation sequencing techniques. Zhong et al. (2016) examined the bacterial compositions of 85 spontaneously fermented milk samples from China, Mongolia, and Russia using a second-generation sequencing technique. The results indicated that the geographic origin and sample type influence the microbial diversity of these products . HoweverIn summary, the spontaneously fermented dairy products exhibit an abundance of microbial resources, and may contain a substantial amount of lactic acid bacteria and probiotics. However, previous studies were either limited to a certain type or region of spontaneously fermented dairy products, or could not fully clarify the microbial diversity. In this study, eight types of koumiss and four types of shubat were collected from Mongolia and Inner Mongolia in China. The full-length 16S rRNA gene data of the flora of 80 spontaneously fermented dairy products sequenced via the PacBio SMRT sequencing platform, and collected from Mongolia, Russia, Kazakhstan, and Chinese Inner Mongolia, were downloaded. In total, 92 spontaneously fermented dairy products collected from 4 different countries , Table 1This study reanalyzed data from 80 previously collected spontaneously fermented dairy product samples using the PacBio SMRT sequencing platform. Here, 14 jiaoke (a kind of fermented butter produced in Inner Mongolia Xilin Gol) , 9 cheese , 7 cottage cheese, 3 butter, and 5 sour cream , 14 koumiss , and 16 koumiss, 9 cottage cheese, and 3 yogurt . We collected 8 koumiss and 4 shubat (a kind of fermented camel milk) from Mongolian Hangay and Central Gobi province, and Inner Mongolia Xilin Gol sequenced by the same sequencing platform .TM, Omega Bio-Tek, Norcross, GA, USA). The quality of DNA was checked by agarose gel electrophoresis on a 0.8% gel and spectrophotometry . The full-length region of 16S rRNA genes was amplified by the forward primers 27F (5′-GAGAGTTTGATCCTGGCTCAG-3′) and the reverse primers 1541R (5′-AAGGAGGTGATCCAGCCGCA-3′) with the PCRBIO Taq DNA polymerase . The primers contained 16-base barcodes to distinguish between samples. The PCR programs were as follows: 95 °C for 5 min, then 30 cycles at 95 °C for 30 s, 58 °C for 45 s, and 72 °C for 1 min, with a final extension of 72 °C for 7 min . The quality of the PCR products were checked by the Agilent DNA 1000 Kit and an Agilent 2100 Bioanalyzer . PCR products were electrophoresed in a 1.2% agarose gel and were used for constructing DNA libraries with the Pacific Biosciences Template Prep Kit 2.0 (Pacific Biosciences). Sequencing was performed using P6-C4 chemistry on a PacBio RS II platform  in accordance with the manufacturer’s instructions. Quality control for PCR and sequence preprocessing was performed as described previously [Koumiss and shubat were obtained from Mongolia and Inner Mongolia. The samples were collected using a sterile spoon, stored in 50 mL sterile centrifuge tubes, and sent to our laboratory packed on ice. Total DNA was extracted by the Omega DNA isolation kit  A minimum of up to five full passes (ii) a minimum predicted accuracy of 90 (iii) a minimum of a 1400 read length of inserts and (iv) a maximum of a 1800 read length of inserts in the SMRT Portal version 2.3  of the protocol RS_ReadsOfinsert.1.The QIIME  (versionhttp://www.r-project.org/ accessed on 10 August 2021). Differences between the microbial population groups were evaluated using the Kruskal–Wallis test. The permutational ANOVA (PERMANOVA) test was performed to detect the effect of geographical origin, types of fermented dairy products, and production methods on the bacterial diversity in the spontaneously fermented dairy products.Statistical analyses were performed by using R packages  levels and lower Firmicutes (p = 0.001) levels.To analyze the bacterial composition of each sample, the sequences of 92 spontaneously fermented dairy product samples were analyzed using the PacBio SMRT sequencing platform and were classified to the phylum, genus, and species levels. At the phylum level, 22 phyla were identified from the samples. The relative abundance of Firmicutes and Proteobacteria exceeded 99% A. The coLactobacillus, Lactococcus, Streptococcus, Enterobacter, Acetobacter, Pseudomonas, and Acinetobacter, accounting for 90.18% , E. xiangfangensis (p < 0.001), and L. kefiranofaciens (p < 0.001) were predominant in yogurt, cottage cheese, and shubat, respectively. The abundance of L. lactis exceeded 40% in jiaoke, sour cream, and butter. The content of L. helveticus in koumiss surpassed the levels of other bacterial species, while Lac. lactis, L. helveticus, and L. delbrueckii dominated in cheese.Since the PacBio SMRT sequencing platform can cover the full read length of the 16S rRNA gene , the relative abundance at the species level was resolved. The relative abundances of 11 species exceeded 1%, accounting for 81.37% of the total microbiota population, and included ongensis C. It disongensis D. ComparS. salivarius, L. helveticus, L. delbrueckii, E. xiangfangensis, and Acinetobacter baumannii. Therefore, it was inferred that these five species represented the primary bacteria in the spontaneously fermented dairy products. A network map was constructed based on the OTUs shared between the pairs of spontaneously fermented dairy products , was abundant in the cottage cheese sample, clearly distinguishing cottage cheese from other samples. These results indicated that bacterial composition was possibly associated with different types of spontaneously fermented dairy products.The bacterial structures of the spontaneously fermented dairy product products were analyzed regarding different types of fermented dairy product and different geographical origins using PCoA. First, PCoA was performed based on weighted and unweighted UniFrac distances A,B. SomeE. xiangfangensis in the cottage cheese samples collected from Mongolia  and weighted UniFrac distances (p < 0.001 in Mongolia and Inner Mongolia) of these samples. Next, the cottage cheese and koumiss samples were used to assess the impact of different geographical origins on bacterial diversity. Significant differences in bacterial diversity were observed between the 16 types of cottage cheese (p < 0.001 in unweighted and weighted UniFrac distance) collected from Mongolia (9) and Russia (7). Similar results were found in 40 koumisses (p < 0.001 in unweighted and weighted UniFrac distance) collected from Mongolia (12) and Inner Mongolia (28). Moreover, this study also evaluated the impact of different processing methods on the bacterial diversity in three kinds of butter, five sour creams, and seven types of cottage cheese, which were processed using cow’s milk and collected in Russia. No significant differences were found among these samples (p = 0.0076 in unweighted UniFrac distance and p = 0.27 in weighted UniFrac distance). Additionally, the UniFrac distance was used to perform hierarchical clustering in the unweighted pair group method with arithmetic mean (UPGMA) to observe the evolutionary distance among samples according to the distance of the branches and clusters  based on 85% of the principal components calculated using PCoA analysis was performed to compare the microbiota of different geographical areas A–D and tCorynebacterium, Halomonas, Pseudomonas, Pseudoalteromonas, and Vibrio, have exhibited poor growth in the absence of a fungal partner [Lac. lactis and relatively low levels of L. helveticus, L. delbrueckii, E. xiangfangensis, and L. kefiranofaciens , squeezing, splitting, and seasoning. During complex manufacturing processes, thousands of bacteria and fungi are inoculated into cheese and cottage cheese and they are especially prevalent during the final seasoning procedure. As shown in  partner . Moreoveofaciens C.S. salivarius, L. helveticus, L. delbrueckii, E. xiangfangensis, and A. baumannii were identified in all the specimens, accounting for 39.72%. Of these five species, A. baumannii is a potential pathogen that causes ventilator-associated pneumonia (VAP), urinary tract infections, and bacteremia in intensive care units (ICUs) [Acinetobacter was also detected in fermented dairy products. Gao et al. (2013) and Parker et al. (2018) detected Acinetobacter in Tibetan kefir grains and lait caillé [Acinetobacter in infant formula using culture-dependent and culture-independent methods [S. salivarius and E. xiangfangensis are Gram-positive LAB [S. salivarius was detected in cheese, jiaoke, sour cream, shubat, and cottage cheese samples, with a relative abundance exceeding 1%. Besides being typical commensal bacteria in the mouths of infants and adults, S. salivarius, S. vestibularis, and S. thermophiles are genetically similar [S. salivarius and E. xiangfangensis were predominant in the cottage cheese samples collected from Mongolia. However, the same type of fermented dairy product collected from Russia displayed a significantly low percentage (74.10% vs. 2.25%) [Regarding bacterial composition, 698 bacterial species were identified in the samples. Although these species were unevenly distributed in various fermented dairy products samples, s (ICUs) . Howevert caillé ,26. Moreo et al. 13 and Pa methods . S. salitive LAB . In this similar ,30,31, a similar . S. sali. 2.25%) . On an i. 2.25%) . AdditioLactobacillus is the predominant genus in naturally fermented dairy products [Lactobacillus, Streptococcus, and Lactococcus were the predominant genera in 85 naturally fermented milk products [Lactobacillus was the highest relatively abundant genus in all samples. L. helveticus,L. kefiranofaciens, and L. delbrueckii were widely present as the primary bacterial species in the fermented dairy product samples. Besides Lactobacillus, Lactococcus, and Streptococcus, species such as Lac. lactis, Lac. raffinolactis, S. salivarius, and S. parauberis were also highly abundant in fermented dairy products. Previous studies have shown that different types of fermented dairy products from different locations exhibited substantial bacterial diversity. Gsudu et al. (2016) and Yahya et al. (2017) observed that L. helveticus, L. kefiranofaciens, and S. parauberis were the dominant species in koumiss and shubat [L. delbrueckii is typically used to produce yogurt [Lac. lactis represented more than 40% in the above two sample types, and it was speculated that its lipid metabolism capacity makes it adaptable to high-fat environments [products ,35. Usinproducts . In thisd shubat ,37. L. de yogurt  and accoe yogurt . Lac. laronments ,41. In aronments ,43.This study focused on the factors that determined the bacterial diversity of naturally fermented dairy products. PCoA, PERMANOVA, hierarchical clustering, and CAP analyses revealed significant variations in the naturally fermented dairy products’ microbiota structures across sampling sites and fermented dairy products. These results were consistent with previous studies, which found that geographical origin could influence the bacterial structure of naturally fermented dairy products ,36,37. HIn conclusion, this work provides insight into the bacterial structure of 92 spontaneously fermented dairy product samples, including jiaoke, cheese, koumiss, cottage cheese, yogurt, butter, sour cream, and shubat, collected from Russia, Kazakhstan, Mongolia, and Inner Mongolia. These samples were sequenced using the PacBio SMRT sequencing technique. Furthermore, this study characterizes the bacterial configuration, indicating that geographical origins and type of fermented dairy product are both essential in shaping the bacterial diversity of naturally fermented dairy products.


Pinus pinaster Aiton), a conifer widely distributed in southwestern Europe and northwestern Africa, grows under contrasted environmental conditions promoting local adaptation. Genetic variation at adaptive phenotypes, including height, spring phenology, and susceptibility to two fungal pathogens (Diplodia sapinea and Armillaria ostoyae) and an insect pest (Thaumetopoea pityocampa), was assessed in a range‐wide clonal common garden of maritime pine. Broad‐sense heritability was significant for height (0.219), spring phenology (0.165–0.310), and pathogen susceptibility , but not for pine processionary moth incidence in the common garden. The correlations of trait variation among populations revealed contrasting trends for pathogen susceptibility to D. sapinea and A. ostoyae with respect to height. Taller trees showed longer necrosis length caused by D. sapinea while shorter trees were more affected by A. ostoyae. Moreover, maritime pine populations from areas with high summer temperatures and frequent droughts were less susceptible to D. sapinea but more susceptible to A. ostoyae. Finally, an association study using 4227 genome‐wide SNPs revealed several loci significantly associated with each trait (range of 3–26), including a possibly disease‐induced translation initiation factor, eIF‐5, associated with needle discoloration caused by D. sapinea. This study provides important insights to develop genetic conservation and breeding strategies integrating species responses to biotic stressors.Forest ecosystems are increasingly challenged by extreme events, for example, drought, storms, pest attacks, and fungal pathogen outbreaks, causing severe ecological and economic losses. Understanding the genetic basis of adaptive traits in tree species is of key importance to preserve forest ecosystems, as genetic variation in a trait  determines its potential for human‐mediated or evolutionary change. Maritime pine ( Warmer Forest trees are long‐lived, sessile organisms. They are characterized by outcrossing mating systems, high standing genetic variation, large effective population sizes, and the production of vast numbers of seeds and seedlings exposed to strong selection  among populations than neutral genetic markers (FST) , a long‐lived conifer from southwestern Europe and northern Africa with a wide ecological amplitude. We assessed height, spring phenology , and susceptibility to pests/pathogens in a clonal common garden, which allowed us to explore variation in disease response and genetic correlations with other traits in range‐wide populations of maritime pine. Considering disease and growth traits together is relevant from an evolutionary and ecological perspective and can have important implications in terms of management, especially in breeding programs. We selected three important disease agents: two fungal pathogens, Diplodia sapinea (Botryosphaeriaceae) and Armillaria ostoyae , as well as the pine processionary moth, Thaumetopoea pityocampa (Thaumetopoeidae), a main defoliator of pine forests.Our study focused on maritime pine (https://www.fcba.fr/ressources/memento‐2020/). Despite the ecological and economic importance of maritime pine natural forests and plantations, only a few genetic association studies have been developed in this species. Lepoittevin et al. (korrigan (a pine gene orthologous to an Arabidopsis degrading enzyme cellulase) also significantly associated with growth traits . Finally, Bartholomé et al. ; (3) analyze the pattern of trait correlation within and among populations, in particular to identify putative correlations that could be useful/detrimental for conservation and breeding programs; and (4) identify loci associated with disease‐related, height, and spring phenology traits using genotype–phenotype association. Altogether, our approach, combining the evaluation of a clonal common garden and a genotyping array, produced relevant insights on the evolution, genetic basis, and architecture of adaptive traits in maritime pine, an ecologically and economically important forest tree species.The specific objectives of our study are to (1) estimate phenotypic variability and heritability within and among range‐wide populations of maritime pine for height, spring phenology, and susceptibility to pests/pathogens; (2) test for adaptive divergence across the maritime pine range for these traits  was planted in 2011 in Bordeaux , southwestern France  were measured in all individuals from 5–8 blocks, depending on the trait  in the tree crowns was assessed in March 2018.Height, spring phenology , and incidence of processionary moth , and maintained on malt agar medium. The identity of this strain as D. sapinea was confirmed by sequencing the ITS region, amplified using the primers ITS1‐F and ITS4  in ten populations, representing all differentiated gene pools known in maritime pine  in 2010 , malted water, and hazelnut wood chips in a 180‐mL plastic jar  were measured. In the jar, we also visually evaluated the level of humidity of the medium  and A. ostoyae growth. A total of six control branches, one genotype randomly chosen from each of the six gene pools, were prepared in the exact same manner, but with plugs of sterile malt agar as opposed to those colonized by A. ostoyae.For the inoculation with For this experiment, we randomly sampled ten (except for Tamrabta with eight) maritime pine genotypes from six populations, one for each of the six differentiated gene pools represented in the CLONAPIN common garden. We selected fully elongated current‐year shoots (bud burst stage 4 and 5), with a maximum diameter of 15 mm and a minimum length of 10 cm, as proxies. A total of 174 branches from 58 genotypes  were measured, cut, and taken to the laboratory to be inoculated, on October 3–4, 2018.2.4Summary climate data for the years 1950–2000 were retrieved for 32 variables from Worldclim  and desiccated using silica gel. Genomic DNA was extracted using the Invisorb® DNA Plant HTS 96 Kit/C kit . An Illumina Infinium SNP array developed by Plomion et al. (GenomeStudio v. 2.0), we kept 5176 polymorphic SNPs, including 4227 SNPs with a minor allele frequency (MAF) above 0.1.Needles were collected from one replicate per genotype  is the random effect of pop j nested within gene pool g, gjkgp(pop(genotype)) is the random effect of genotype k nested within population j and gene pool g, and ε is the overall residual effect. In model 2, cov represents the covariates implemented when modeling the presence of pine processionary moth nests  and necrosis caused by A. ostoyae . Using these models, the effects at the gene pool level had extremely wide confidence intervals, probably because some of them were represented by only a few highly contrasted populations. Thus, we also ran a set of models without the gene pool effect, as follows:To estimate the genetic variance components of the analyzed traits, we ran two different sets of mixed‐effect models. The first one included three hierarchical levels, to account for the strong population genetic structure in maritime pine: gene pool, population nested within gene pool, and genotype (clone) nested within population and gene pool, as follows:This set of models produced more accurate BLUP estimates, avoiding also confounded effects between gene pool and population, and was thus preferred for some of the subsequent analyses (see below).MCMCglmm  was used for fixed effects with the exception of the model for needle discoloration caused by D. sapinea where a gelman prior for V was set, as suggested by Gelman et al.  was consistently below 1.01  methods implemented in the R package n et al.  for ordin et al. . Parametn et al.  for modeH2) as:σgenotype2 is the variance among genotypes within populations and gene pools and σe2 the residual variance. In addition, we computed the variance ratios associated with the population (Hp2) and gene pool (Hgp2) effects as:σpop2 is the variance among populations within gene pools, and σgp2 is the variance among gene pools. When appropriate, we included an extra term in the denominator to account for implicit logit and probit link function variance  was calculated as in Spitze  did not overlap.Variance components from the first set of models (including gene pool effect) were then used to compute broad‐sense heritability  of the genotype (clone) effect , intermediate for height (H2: 0.219 [0.186–0.261]) and lowest for necrosis length caused by A. ostoyae , with trees from northern Africa and southern Spain showing shorter necrosis length than trees from Atlantic populations . Necrosis length caused by A. ostoyae was most pronounced in southern populations, especially in Morocco and southern Spain, while less pathogen growth was observed in northern populations, for example, in St. Jean de Monts from the French Atlantic gene pool . The tallest trees were found in populations from Atlantic France and Atlantic Iberian Peninsula gene pools and for one of the Corsican populations (Pinia), whereas the shortest ones originated from southeastern Spain and Morocco  indicating strong population differentiation  which is significantly lower than the QST estimates obtained for height and necrosis length caused by D. sapinea . Once the population effect was considered, a significant negative trait covariation was found between necrosis length caused by each of the two fungal pathogens , and positive for necrosis length caused by D. sapinea .All genetic correlations  involving height and spring phenology were significant while disease susceptibility traits from excised branches did not show significant genetic correlations except for a correlation between necrosis length and needle discoloration caused by ea Table . The strD. sapinea  while the variance of, for example, needle discoloration caused by D. sapinea explained by maximum temperatures in July was lower but also negative  but only a marginally non‐significant positive correlation with maximum temperatures in July .Moreover, using mixed linear models that account for gene pool as random factor, we observed a negative relationship for maximum temperatures in the summer months with height, duration of bud burst in 2017, and necrosis length and needle discoloration caused by ea Table . The str0.556–0.81, p‐valu3.3D. sapinea and another one to duration of bud burst in 2015 , as well as the variance ratios associated with the population and gene pool effects. Most of these adaptive traits showed significant broad‐sense heritability. Variation of necrosis length caused by pathogens among geographical provenances, as well as duration of bud burst in 2017, followed a latitudinal gradient, possibly corresponding to a climatic gradient, but in opposite directions for the two pathogens. A genetic association approach revealed several loci significantly associated with height, spring phenology, and traits related to pathogen infection in maritime pine. This information will be useful for current efforts to implement breeding strategies based on genome prediction  in this species. The presence of pine processionary moth nests evaluated in the common garden was not heritable but future studies should consider the level of infestation and/or assess damage more precisely.In the current context of climate change, understanding the genetic basis of adaptive traits in tree species is key for an informed forest management and the development of new breeding applications. In this study, we assessed variation in maritime pine for height and spring phenology traits, as well as for incidence of pine processionary moth nests and response to two pathogenic fungi, 4.1QST) is expected to be more informative. Neutral genetic differentiation, that is, FST, was moderate  and significantly lower than QST estimates obtained for height and necrosis length caused by D. sapinea, which suggested that divergent selection is promoting local adaptation in these traits . This is crucial information, for example, for the Landes maritime pine breeding program, which is based on artificial selection for height growth and straightness, and that may have selected at the same time for higher susceptibility to D. sapinea, a pathogen that is expected to increase its presence in maritime pine productive forests in the near future (see below).Height is a crucial, frequently studied trait in forest trees, as increased height growth is a main target of breeding programs  for bud burst varied from 0.191 to 0.275, which is comparable to a mean of 0.249 for bud flush averaged over several forest tree species  showed low‐to‐moderate broad‐sense heritability, depending on the year 2015 or 2017). Differentiation  was much lower than, for example, narrow‐sense heritability of lesion length caused by Fusarium circinatum  in our study, which was also significantly higher than neutral‐marker FST. This result pointed to adaptive divergence and substantial variability in susceptibility to D. sapinea across the geographical range of maritime pine. This population variability could be harnessed in maritime pine breeding programs to increase resistance to the disease , which is likely to affect the host response  indicated similar environmental clines driving differentiation at these traits. Especially, maximum temperatures during summer months showed significant correlations with genetic variability of susceptibility to pathogens across maritime pine populations. Trees from populations with high maximum summer temperatures were less susceptible to D. sapinea. This result can be interpreted in different ways: (1) If we assume that D. sapinea is native in Europe and northern Africa, the pathogen pressure can be expected to be stronger in southern regions, with a climate more favorable to D. sapinea pathogenic outbreaks, triggered by stress in the host plant, especially by droughts  for mortality due to A. ostoyae in an infected progeny trial of maritime pine seedlings than the values of broad‐sense heritability of necrosis length found in our study. These contrasting results call for further research to improve our understanding of the different components that affect resistance against A. ostoyae and which might vary among host populations. Armillaria ostoyae is a root pathogen and a critical point during natural infection that could be key for resistance mechanisms is the penetration into the root  under the additive genetic model . Nevertheless, the locus (BX679001‐1418), which was significantly associated with needle discoloration caused by D. sapinea, possibly codes for a translation initiation factor, eIF‐5, that has previously been reported to be involved in pathogen‐induced cell death and development of disease symptoms in A. thaliana , and for the invasive pathogen F. circinatum , and incidence of processionary moth nests in a range‐wide clonal common garden of maritime pine. We revealed strong genetic divergence at several adaptive traits, especially height and necrosis length caused by We declare no conflict of interest.Supplementary MaterialClick here for additional data file.Table S6Click here for additional data file.


Dioscorea rotundata Poir.) breeding population. For this, pedigree‐based best linear unbiased prediction (P‐BLUP) was used. The results revealed significant nonadditive genetic variances and medium to high (.45–.79) broad‐sense heritability estimates for the traits studied. The pattern of associations among the genetic values of the traits suggests that selection based on a multiple‐trait selection index has potential for identifying superior breeding lines. Parental breeding values predicted using progeny performance identified 13 clones with high genetic potential for simultaneous improvement of the measured traits in the yam breeding program. Subsets of progeny were identified for intermating or further variety testing based on additive genetic and total genetic values. Selection of the top 5% progenies based on the multi‐trait index revealed positive genetic gains for fresh tuber yield (t ha−1), tuber yield (kg plant−1), and average tuber weight (kg). However, genetic gain was negative for tuber dry matter content and Yam mosaic virus resistance in comparison with standard varieties. Our results show the relevance of P‐BLUP for the selection of superior parental clones and progenies with higher breeding values for interbreeding and higher genotypic value for variety development in yam.Better understanding of the genetic control of traits in breeding populations is crucial for the selection of superior varieties and parents. This study aimed to assess genetic parameters and breeding values for six essential traits in a white Guinea yam ( LRTlog‐likelihood ratio testP‐BLUPpedigree estimated best linear unbiased predictionREMLrestricted maximum likelihoodYMVYam mosaic virus.1Dioscorea spp.) is a clonally propagated monocotyledonous vine crop belonging to the genus Dioscorea in family Dioscoreaceae. This genus is composed of >600 species, of which 11 are economically important and widely cultivated for their starchy underground tubers or aerial bulbils   shows high importance for production, being the most planted and produced among cultivated species  since 1970 to improve crop quality and increase its value for the livelihoods of the present and future population. Their work comprises the development of resilient and productive yam varieties that respond well to the dynamics of current and future production challenges while meeting producer and consumer demand, which is a priority for ongoing research . The first trait was sett weight based on the weight of seed tubers or setts planted per unit area. The second trait was Yam mosaic virus (YMV) severity score based on a visual assessment on the relative area of plant tissue affected by yam mosaic virus disease. This trait was recorded as a proportion of plant surface affected using a five ordinal scale of 1–5 . The YMV scores were recorded on individual plants in a plot at 15‐d intervals in the trial at Ibadan and 30‐d intervals on trial at Abuja from 2 to 6 mo after planting. The timescale for YMV severity score data was used to calculate the progression curve for quantification of resistance. Other traits included the number of plants harvested per plot (count); the number of tubers per plant (count); tuber yield per plant (kg plant−1); average tuber weight (kg tuber−1); fresh tuber yield per hectare (t ha−1); and tuber dry matter content (%). Tuber yield per plant was determined as the weight of all fresh tubers divided by the number of plants harvested in a net plot. Average tuber weight was computed as the weight of all fresh tubers divided by the number of tubers harvested in a net plot. Fresh tuber yield per hectare was determined as the weight of all the fresh tubers harvested per plot multiplied by 10, and the result divided by the area of effective plot in square meters. The weight of all fresh tubers harvest from a net plot of size 3 m2 was measured in kilograms. To estimate dry matter content, 200 g of fresh tuber was chopped into small pieces and oven dried at 70 °C for 24–48 h until a constant weight was achieved. Dry matter content was determined asEight plant traits (six main traits and two as a covariate) were assessed using the procedure described in yam trait ontology  cumulatively accounted for a substantial fraction of the total genetic variation. Nj is a fixed trial effect. bj is a trial‐specific design‐based field blocking structure effect, gi is the main effect of clone (genotype) within trial, and εijis a random error within each trial. The direct sum structure for the residual error term was fitted. Sett weight (weight of seed tubers used for planting) and the number of plants at harvest were added to the model as covariates for tuber yield characteristics whenever significant using a Wald test. The plot bj∼N, clone main gi∼N, and random error εij∼N effects where I is an identity matrix. σb2, σg2, and σε2 are between‐plot, clone main, and within‐plot error variances, respectively.Data were subjected to different analyses to assess population structure, to estimate the relative genetic variability, as well as to predict the genetic values of parents and their progenies in the breeding population. The discriminant analysis of principal components (DAPC) was used on the pedigree relationship matrix to investigate the genetic structure using adegenet package , two genetic models with additive (A) and additive plus dominance (A+D) covariance structures from the pedigree matrix for the clone main effects within trial (gi) were tested. The total genetic variance was then partitioned to the additive, dominance, and residual genetic effects following the approach implemented in various studies  was decomposed into additive genetic a∼N and residual genetic g^∼N within‐trial effects, such that gi=a+d+g^. For the A+D model, the clone main effect within trial (gi) was partitioned into additive, dominance d∼N, and residual genetic effects, such that gi=a+d+g^. σa2, σd2, and σg^2 are additive, dominance, and residual genetic variances, respectively. The differences among the additive, dominance, and residual genetic effects are that the additive and dominance effects have a covariance structure proportional to additive and dominance genetic relationships derived from the pedigree, respectively, whereas the residual genetic effect corresponds to the independent clone effect  that assumes no genetic relatedness among clones (σb2) effect, clone main effect (σg2) that associated with additive effect (σa2), dominance effect (σd2), and residual genetic effect (σg^2) and within‐plot error (σe2) variances that varied according to the model. Other parameters such as coefficient of determination of plot effect (Cb2), coefficient of genotypic  variation (CVg), coefficient of phenotypic variation (CVp), residual coefficient of variation (CVe), and coefficient of relative variation (CVg/CVe) were calculated for the best fit model. Correlations among the genetic effect of the traits were assessed using GGally package in R  using packages asremlPlus and ASReml‐R 4 Brien, . The totH2) and narrow‐sense (h2) heritability estimates for each trait were calculated as h2=σa2/(σg2+σb2+σe2) and H2=σg2/(σg2+σb2+σe2), respectively. Heritability was categorized as low (0–30%), moderate (31–60%), and high (>60%) according to  were computed as the sum of main effects for a, d, and g^, whereas breeding values (BVi) were computed as the main effect for a (additive genetic) for an individual i from the best fit model, following the approach implemented in Ovenden et al.  for inbreeding.Genotypic  values , tuber yield per plant (kg plant−1), average tuber weight (kg), and tuber dry matter (%), 0.2 for tuber number per plant, and −0.14 for YMV . Aiming to generate future populations combining higher trait values, a subset of clones was selected with a 5% selection intensity based on the ranking obtained with the model described. Likewise, the additive selection index of the breeding values of the parental clones based on progeny performance was calculated and used as a backward selection to identify superior progenitor for further use in the breeding program.In ranking and selection of superior individuals for genetic improvement and gain for multiple traits simultaneously, the following additive selection index for an estimated breeding or genotypic values was used  or time (ΔGt) and a percentage of the population mean (ΔGm) were estimated to assess the expected genetic gain for traits considered for selection of superior clones using the procedure elaborated by Cobb et al. , σa is the additive genetic standard deviation for the trait, and time is cycle length, which is 4 yr.The genetic gain per cycle , moderate (10–20%) and high (>20%), as described by Johnson, Robinson, and Comstock  cultivars. ΔGc was used to quantify the genetic gain for the selection performed using the breeding values of the best 5% clones with a multiple‐trait index.The Comstock . The gen3Discriminant analysis of principal component (DAPC) on the pedigree‐based relationship matrix revealed population structure among the yam clones Figure . The fir3.1Variance component estimates for the six yam traits from three alternative models are presented in Supplemental Table S2. Adding either the additive or dominance variance–covariance structures into the baseline model was significant to describe the variances for all yam traits Table . However−1) to 4.45 for YMV severity (Table Cb2) was low (<0.11) for all the traits. The error coefficient variation (CVe) was relatively low, ranging from ∼8% for YMV severity to 29% for tuber yield per plant. The coefficients of genetic and phenotypic variations (CVg and CVp) were moderate to high for all measured traits. Yam mosaic virus severity score and tuber dry matter content had medium (between 10 and 20%) values of the genotypic and phenotypic coefficient of the estimated variances. In comparison, these values were higher (between 28 and 54%) for other traits.The proportion of total genetic variance due to additive genetic component ranged from 10 (YMV severity score) to 34% (average tuber weight) with the A model Figure . Inclusity Table . The coe−1) and YMV .Both narrow‐ and broad‐sense heritability estimates varied among the traits and for the different models Figure . For YMV−1], tuber yield kg per plant, and average tuber weight) were strong and positive (P < .001). On the other hand, the tuber number had strong and negative estimated genetic correlations with average tuber weight, fresh tuber yield per plant, and unit area. The correlations of genetic values of tuber dry matter were negative with all measured traits except positive but weak with tuber number per plant. Yam mosaic virus severity score had weak associations with all measured traits for the genetic effects.The genetic correlations among the measured traits varied in degree and direction Figure . The est3.2The ranking of parental clones and their clonal progenies for their net genetic merits on a multi‐trait selection index was obtained Figure . The mulWe also assessed the genetic merits of the clonal progenies based on an index that simultaneously considered the respective breeding and genotypic values of the six measured traits Figure . The proTable −1), tuber yield per plant (kg), and average tuber weight (kg tuber−1). However, such gain was negative over the outstanding standard variety (improved) for tubers per plant and all types of benchmark varieties for tuber dry matter and YMV severity score. The response to multi‐trait index selection for tuber dry matter and YMV severity score was not in the desired direction. The mean tuber dry matter content of the selected individual in comparison with the premier standard variety was lowest. The mean YMV severity score for the top 5% selected individuals was higher than that of the outstanding standard cultivar in the trial. However, three clonal progenies in the selection list outperformed the standard varieties in response to YMV infestation (data not shown). For tuber dry matter, none of the individuals in the selection list exceeded the leading standard cultivar.The top 5% progenies based on multiple selection index showed a positive genetic gain over both local and improved standard varieties for fresh tuber yield  with the inclusion of additional genetic covariance structure into the additive model  broad‐sense heritabilities of the focus traits in this study suggested the expectation for genetic improvement with clonal selection as highly positive. However, the estimates of expected genetic gain as a percentage of population means are low for all the measured traits except average tuber weight and tuber number per plant Figure . The expThe pattern of interrelation among the genetic effect for the traits justifies considering the selection index to improve the source population for multiple characteristics simultaneously Figure . BreedinApplying a multi‐trait selection index on the clonal progenies led to the identification of a subset of superior clones with high breeding values. A higher gain in selection for multiple‐trait breeding values compared with the single‐trait, especially when traits are negatively correlated , tuber yield (kg plant−1), and average tuber weight (kg tuber−1) could indicate progress achieved for these traits in the breeding program. For tuber dry matter content and YMV resistance, not much progress has been attained in the white yam breeding program. Hence, the conventional selection method will be inadequate to enhance genetic gain for these two traits. Indeed, new trials or studies are required to accurately estimate the realized genetic gain for the various traits in the yam breeding program.The selection for multi‐trait breeding values identified 71 clonal progenies with great potential for the simultaneous improvement for the measured traits. Crosses involving these selected clones from the genetically different groups Figure . Based oIpomoea batatas (L.) Lam.] for dry root yield, as well as recessively inherited traits such as resistance to sweet potato virus disease and sugar content (Grüneberg et al., Other suggestions to enhance genetic gain for such traits in clonally propagated crops include heterosis exploiting breeding schemes, or population hybrid breeding (Grüneberg et al., Overall, this study has provided valuable insights into the genetic control and genetic gain, as well as a glimpse of the status of the six essential traits in the white Guinea yam breeding program. Parental and progeny clones identified through backward and forward selection, respectively, with a high and positive index could be used as potential trait progenitors to generate progenies with good agronomic and end‐product quality in white Guinea yam. Hybridization of genetically different clones identified from the top 5% of superior progenies is expected to increase the frequency of favorable alleles while maximizing genetic variability. The superior clones having higher genotypic values identified through the multi‐trait selection index should be tested further for possible commercial deployment.Asrat Asfaw: Conceptualization; Formal analysis; Supervision; Writing‐original draft; Writing‐review & editing. Bunmi Olasanmi, and Ayodeji Abe: Supervision; Writing‐review & editing. Patrick Adebola, and David De Koeyer: Project administration; Writing‐review & editing. Kwabena Darkwa, Dotun Samuel Aderonmu & Paterne Agre: data collection; trial management; Writing‐review & editing. Robert Asiedu: Funding acquisition; Writing‐review & editingThe authors declare that there is no conflict of interest.Supplemental Table S1. Details of the field experiments.Click here for additional data file.


Background: Angiography-derived index of microcirculatory resistance (angio-IMR) is an emerging pressure-wire-free index to assess coronary microvascular function, but its diagnostic and prognostic value remains to be elucidated.Methods and Results: The study population consisted of three independent cohorts. The internal diagnostic cohort enrolled 53 patients with available hyperemic microcirculatory resistance (HMR) calculated from myocardial blood flow and pressure. The external diagnostic cohort included 35 ischemia and no obstructive coronary artery disease (INOCA) patients and 45 controls. The prognostic cohort included 138 coronary artery disease (CAD) patients who received PCI. Angio-IMR was calculated after the estimation of angiography-derived fractional flow reserve (angio-FFR) using the equation of angio-IMR = estimated hyperemic Pa × angio-FFR × [vessel length/(K × Vdiastole)]. The primary outcome was a composite of cardiac death or readmission due to heart failure at 28 months after index procedure. Angio-IMR demonstrated a moderate correlation with HMR  and its diagnostic accuracy, sensitivity, specificity, and area under the curve to diagnose INOCA were 79.8, 83.1, 78.0, and 0.84, respectively, with a best cut-off of 25.1. Among prognostic cohort, patients with angio-IMR ≥25.1 showed a significantly higher risk of cardiac death or readmission due to heart failure than those with an angio-IMR <25.1 . Angio-IMR ≥25.1 was an independent predictor for cardiac death or readmission due to heart failure .Conclusions: Angio-IMR showed a moderate correlation with HMR and high accuracy to predict microcirculatory dysfunction. Angio-IMR measured after PCI predicts the risk of cardiac death or readmission due to heart failure in patients with CAD.Clinical Trial Registration: Diagnostic and Prognostic Value of Angiography-derived IMR (CHART-MiCro), NCT04825028. Percutaneous coronary intervention (PCI) is one of the important treatments for coronary artery disease (CAD) and aims to increase myocardial blood flow (MBF). However, it has been reported that 20–60% of patients still experience recurrent angina after PCI , which wWith the technical development, angiographic derivation of fractional flow reserve (FFR) or IMR (angio-IMR), which does not require pressure wire, hyperemic agents, or theromdilution method, is proposed recently as a potential alternative for pressure wire–derived FFR or IMR , 5.In this regard, our study aimed to evaluate the diagnostic performance of angio-IMR for microcirculatory dysfunction and its prognostic implication after PCI in stable CAD.The study population was composed of three independent cohorts . PatientThe institutional review board or ethics committee at each participating center approved the current study protocol, which was in accordance with the Declaration of Helsinki. In addition, written informed consent was obtained from all participants.CZT-SPECT perfusion imaging was performed using a dedicated cardiac scanner . The single-day rest/stress imaging protocol was applied as described before .−1·min−1 for 5 min to induce pharmacological stress, followed by dynamic image acquisition. Rest and stress dynamic images were reconstructed into 32 time frames  for dynamic perfusion analysis. Using the previously established Renkin–Crone equation for 99mTc-sestamibi (MIBI), the MBF can be extrapolated from time–activity curves by inputting uptake rate K1 . For stress imaging, adenosine triphosphate (ATP) disodium was intravenously administrated at a rate of 140 μg·kg rate K1 . The gloIn the external diagnostic cohort, visual assessment for the stress and rest perfusion images was performed using the 17-segment model of the left ventricle and a five-point scale  . The sumTwo experienced nuclear physicians who were blinded to the clinical data and angiography or wire-derived physiologic indices analyzed the images using Corridor 4DM software  and QPS software .Coronary angiography was performed with standard techniques. All angiograms from the internal and the external diagnostic cohorts were recorded at 15 frames per second and analyzed at core laboratory  in a blinded fashion. Anatomical parameters including minimal lumen diameter, reference vessel size, lesion length, and percent diameter stenosis were analyzed, on the basis of quantitative coronary angiography (QCA) using the QAngio XA software package .−1·min−1) administration. After measurements, the pressure wire was pulled back to the guide catheter to identify possible pressure drift. FFR was calculated as the mean distal coronary pressure (Pd) divided by the mean aortic pressure (Pa) during maximal hyperemia. FFR ≤0.8 was considered positive for ischemia.Five thousand international units of intravenous heparin and intracoronary nitroglycerin were administrated before invasive FFR measurements. A 0.014-in. coronary pressure wire  equipped with pressure sensor was advanced to the distal segment of a target vessel after equalization. Maximal hyperemia was induced through intravenous ATP (140 μg·kgp = 0.375), demonstrating no myocardial oxygen demand change over the gap between two examinations.HMR was calculated as the ratio of hyperemic Pd to hyperemic MBF  11). Th. Th11). hyp) was assumed by mean arterial pressure (MAP) during the index procedure–MAP × 0.2 when MAP ≥95 mmHg or MAP-MAP × 0.15 when MAP <95 mmHg  . In briehyp is the mean pressure (unit: mmHg) at the distal position at the maximal hyperemia, which is computed by the FlashAngio software as the product of Pahyp and angio-FFR, Vdiastole is the mean flow velocity (unit: mm/s) at the distal position at diastole, and K is a constant (K = 2.1) obtained from a previous literature  in a blinded fashion for clinical data, CZT-SPECT, and wire-derived physiologic indices.Demographic data and cardiovascular risk factors were retrospectively collected. Outpatient visits or telephone contacts were performed every 2 months. The median follow-up duration of the prognostic cohort was 28 months (Q1–Q3 10.3–36.0).The primary outcome of the prognostic cohort was major adverse cardiac events (MACE) including cardiac death and readmission due to heart failure. All clinical outcomes were defined according to the Academic Research Consortium report . The secCategorical variables are presented as numbers and relative frequencies (percentages); continuous variables are presented either as mean ± SD or median with interquartile range (IQR) according to their distributions, which were checked by using the Kolmogorov–Smirnov and Levene tests. In the internal diagnostic cohort, correlation coefficients were calculated to assess the relationship between angio-FFR and FFR or between angio-IMR and HMR .In the external diagnostic cohort, the diagnostic performances  of angio-IMR to predict INOCA were assessed. The area under curve (AUC) in the receiver-operator characteristic (ROC) curve was calculated for angio-IMR, and the optimal cutoff value of angio-IMR to predict INOCA was calculated to maximize the product of sensitivity and specificity using ROC curves. Intra-individual variability was assessed by two repeated measurements of angio-IMR and angio-FFR with time interval.In the prognostic cohort, the cumulative incidence of clinical events was presented as Kaplan–Meier estimate and compared using a log-rank test. Multivariable Cox proportional hazard regression was used to calculate the adjusted hazard ratio (HR) and 95% confidence interval (CI) to compare the risk of clinical events according to angio-IMR. The adjusted covariables were age, sex, left ventricular ejection fraction (LVEF), and post-PCI angio-FFR values. The assumption of proportionality was assessed graphically by log-minus-log plot, and the Cox proportional hazard models for all clinical outcomes satisfied the proportional hazards assumption. The best cut-off value of angio-IMR to predict the risk of cardiac death or readmission due to heart failure was evaluated by the maximally selected log-rank statistics method. Independent predictors for cardiac death or readmission due to heart failure were evaluated by the multivariable Cox proportional hazard regression model, and the discriminant function of predictive model was evaluated with Harrell's c-statistics with 95% CI. The additive prognostic implications of angio-IMR into the model with clinical risk factors were evaluated by assessing improvement in the discriminant and reclassification ability of the models with angio-IMR compared with a reference model with clinical risk factors , using the category-free net reclassification index (NRI) and integrated discrimination improvement (IDI).p-values <0.05 were considered statistically significant. All statistical analyses were performed using R version 4.0.2  and SPSS 25.0 for Windows .All probability values were two-sided, and In the external diagnostic cohort , the medp < 0.001) and angio-IMR with HMR  and two repeated measures of angio-FFR  and angio-IMR  were significantly correlated and nearly the same without significant differences  (As shown in ctively) .p < 0.001) in INOCA patients, as well as that in normal controls . ROC analysis demonstrated that angio-IMR had a cut-off value of 25.1 to predict patients with microcirculatory dysfunction represented by normal coronary angiogram but abnormal CZT-SPECT perfusion imaging and showed an AUC of 0.839 (95% CI 0.781–0.898). Sensitivity, specificity, positive predictive value, negative predictive value, and the diagnostic accuracy of angio-IMR were 83.1, 78.0, 67.5, 89.3, and 79.8%, respectively   .p = 0.010)   . As for = 0.010) .The best cut-off value of angio-IMR to predict the risk of cardiac death or readmission due to heart failure was 27.3 . When weThe current study evaluated the diagnostic and prognostic implications of angio-IMR in CAD patients . Our maiSince the first PCI performed in 1977, it has been aimed to restore blood flow by relieving obstruction to the epicardial vessels. However, post-PCI angina and/or ischemia may recur or persist in a significant patient subset , with reThe rationale for identifying microvascular dysfunction is to provide a definitive diagnosis; then, a possible treatment may be anticipated . PositroIMR is a quantitative method for specifically assessing the microvascular function of the interrogated vessel . The emeWith the technical development, angiographic derivation of IMR without pressure wire, hyperemic agents, or thermodilution method is available as a potential alternative for pressure wire–derived IMR. In the internal diagnostic cohort of our study, our analysis demonstrated that angio-IMR had a significant correlation with HMR . Furthermore, in the external diagnostic cohort, angio-IMR demonstrated a good diagnostic accuracy for microvascular disease with an AUC of 0.839 and a diagnostic accuracy of 79.8%. With no additional angiogram imaging acquisition or need for a hyperemic agent, angio-IMR may represent a promising measure as an alternative to wire-derived IMR and potentially increase the adoption of the physiological assessment of microvascular diseases in the cardiac catheterization lab.Microvascular disease has been confirmed to be associated with a higher risk of cardiovascular events in patients without obstructive epicardial stenosis , 25. In Our study provided a simple and convenient quantitative index to assess patients' microcirculatory status at the time of PCI. Further research is needed to assess whether angio-IMR-guided strategies might improve prognosis in patients with microcirculation dysfunction compared with standard care.There are some limitations that should be considered. First, because of the comprehensive study protocol, the number of patients included in our study was limited; our findings need to be verified in other cohorts with a larger sample size. Second, as a retrospective study, our included patients did not receive IMR assessment, which is a “gold standard” for microvascular dysfunction; third, though we observed a moderate correlation between angio-IMR and HMR, no definite normal range for HMR was reported yet. As a complementary, we investigated the diagnostic performance of angio-IMR in INOCA patients and angio-IMR showed a high accuracy for predicting microcirculatory dysfunction. Fourth, ROC analysis determined the best cut-off value for angio-IMR as 25.1, which is very close to that for pressure wire–measured IMR. Thus, we used this cut-off value to define post-PCI microcirculatory dysfunction. However, the underlying mechanism between INOCA and post-PCI patients may be different. We further used the maximally selected log-rank statistics method to derive the best cut-off value of angio-IMR to predict clinical outcomes as 27.3; when we used this value as a cut-off, our primary results remained unchanged.In conclusion, this study demonstrated that angiography-derived IMR had a moderate correlation with HMR derived by CZT-SPECT and pressure-wire measurement and a good diagnostic accuracy to predict microcirculatory dysfunction. An elevated angio-IMR measured at the time of PCI predicts a higher risk of cardiac death or heart failure admission at 28 months.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Zhongshan Hospital Fudan University and Shanghai Tenth People's Hospital. The patients/participants provided their written informed consent to participate in this study.ND, WC, and LL drafted and revised the manuscript. WZ, GY, BX, YX, CL, KY, and DH collected the data and follow up the patients. SD and HY analyzed the data and did the statistical analysis. JG designed the study. All authors contributed to the article and approved the submitted version.This study was partly supported by National Key R&D Program of China 2016YFC1301203 and 2016YFC1301200. ND was funded by project 81600279 from the National Natural Science Foundation of China and project 2019M650075 from China Postdoctoral Science Foundation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Lepus europaeus Pallas) are able to hybridize with mountain hares (L. timidus Linnaeus) and produce fertile offspring, which results in cross-species gene flow. However, not much is known about the functional significance of this genetic introgression. Using targeted sequencing of candidate loci combined with mtDNA genotyping, we found the ancestral genetic diversity in the Finnish brown hare to be small, likely due to founder effect and range expansion, while gene flow from mountain hares constitutes an important source of functional genetic variability. Some of this variability, such as the alleles of the mountain hare thermogenin , might have adaptive advantage for brown hares, whereas immunity-related MHC alleles are reciprocally exchanged and maintained via balancing selection. Our study offers a rare example where an expanding species can increase its allelic variability through hybridization with a congeneric native species, offering a route to shortcut evolutionary adaptation to the local environmental conditions.Brown hares ( The leading edge of the species range expansion is typically populated by few individuals, which are also living at the boundary of their specific habitat preferences. The genetic landscape of these boundary populations is influenced by founder effect as well as low population densities resulting in increased genetic drift and further loss of genetic diversity2. This low variability, could in principle, reduce the ability of the population to adapt to the local environment, slowing down the range expansion1.Understanding of the ecological and evolutionary processes underlying species’ dispersal and range limits is a fundamental theme in evolutionary biology3. This is also the case with brown hares , native to Central Asia and mainland Europe, that are currently expanding their range northwards5 and have reached approximately the 65.3˚N in the Nordic countries6. The species arrived in Finland naturally from the south-east as early as the nineteenth century8, but has experienced an impressive 300 km range expansion to north in only the last 30 years, quite likely facilitated by climate change9. As brown hares frequently hybridize with the resident mountain hare  an arctic/subarctic species that has occurred in Fennoscandia for approximately 10,000 years10, we hypothesized that hybridization may have substantial evolutionary implications for the two species. In the present study, we compare levels of genetic diversity among the Finnish mountain hares and brown hares with Central European brown hares. Specimens from both Finnish hare species represent a hybrid population, with varying degrees of introgression, whose intensity roughly correlates with the invasion front of the brown hare’s northward expansion 15 and mountain hares18 with beneficial alleles related to winter pelage color. Due to the shorter snow-covered season, camouflage mismatch with white winter pelage on bare ground has been shown to carry fitness costs19, which cannot be compensated through adaptive behavioral plasticity20. Interestingly, it seems that natural selection can be too weak to adaptively shift the phenology of color molt in mountain hares21, underscoring the importance of obtaining adapted color morph alleles.Range expansions are often associated with introduced invasive species but there are also numerous examples of naturally occurring range shifts, especially under current global climate changeUCP1). UCP1 is a mitochondrial inner membrane channel protein, which channels protons, bypassing ATP-synthase and converting the energy stored in the mitochondrial membrane potential to heat instead of ATP22. UCP1-mediated heat production is especially important for small and adolescent mammals23. In fact, positive selection on certain UCP1 variants has also been suggested to confer climatic adaptation in humans24. As an arctic/boreal species, mountain hare specific UCP1 alleles are expected to be cold adapted in contrast to those of the brown hare, which originate from a much milder climatic region. Other evolutionarily significant genes are those of the major histocompatibility complex (MHC) class II DQA and DQB loci, which are critical for adaptive immunity. As MHC class II receptors are required for the pathogen recognition, a broader range of genetic variants in a population should offer protection against a broader range of pathogens and parasites25. As the brown hares at the species’ expansion front are likely to show little ancestral genetic variation due to the founder effect, the mountain hare/brown hare hybrids could benefit from the rare allele advantage at the MHC loci28, where the rare alleles have been obtained from the mountain hare. An additional benefit for analyzing the MHC loci is that they typically comprise many alleles in all populations, making them practical in estimating overall genetic diversity of a population31.While the aforementioned studies have focused on how a local artic-alpine species might adapt to milder winter conditions due to climate changes, our interest was to detect potential adaptive gene flow to the expanding species. We chose four nuclear gene loci with known or expected evolutionary importance to serve as a proxy for adaptive introgression and contrasted these against the background introgression of mitochondrial DNA, assumably neutral nuclear gene as well as a panel of neutral SNPs for a subset of the samples. Our first candidate was thermogenin , a gene involved in innate immunity, whose allelic variants have been suggested to explain differences in pathogen resistance between various wild mammal populations34, as well as the succinate dehydrogenase subunit A locus (SDHa). SDHa encodes for a subunit of mitochondrial oxidative phosphorylation (OXPHOS) complex II (CII), which is the only OXPHOS complex encoded entirely by nuclear genes and therefore avoids any nuclear-mitochondrial incompatibility issues35. These in turn might cause mitochondrial DNA (mtDNA) driven selection on nuclear encoded mitochondrial genes. As a functionally conserved housekeeping gene, we expect the SDHa locus to be evolutionarily neutral and serve as a proxy for the introgression of nuclear genes in general.In addition, we analyzed Toll like receptor 2  and 0.65 ± 0.45 for the Finnish hybrid zone brown hares (n = 173). Interestingly, GD was lowest (0.53 ± 0.28) for the allopatric Austrian brown hares (n = 48) used as a reference to detect brown hare specific variation. Much of the difference in genetic variation among Finnish hares can be attributed to the MHC class II genes, as DQA and DQB loci were dominated by one common allele each in brown hares , while mountain hares showed more even genotype representation and higher number of alleles compared to brown hares  were higher in mountain hares than brown hares for both MHC class II loci whereas the other nuclear loci showed higher Hz levels in brown hares  as computed from the polymorphism data of all lociares n = 47 and 0.res Fig. . In addiares n = 47 and 0.0.53 ± 0.8 for theares n = 47 and 0.0.53 ± 0.8 for theFor the loci with more than three alleles across the entire dataset, we performed phylogenetic analyses to correlate the clustering of related alleles with their frequencies in the two species and four population groups Figs.  and 3. WUCP1, TLR2 and SDHa loci showed clear ancestral allele frequency differences  between mountain hares and brown hares . Evolutionarily however, this allele is well embedded in the brown hare clade but has presumably been lost or was unsampled in Austrian brown hares.37.In contrast, the MHC loci showed high levels of variation as well as considerable allele frequency differences between species and population groups Fig. A,B. HybrDQA (Leeu-DQA*001 and Leeu-DQA*010) and from DQB (Leeu-DQB*001 and Leeu-DQB*007) were shared across allopatric and sympatric populations. There were however, nine additional DQA alleles and 13 DQB alleles that were shared only between hybrids and parental species. We take this as evidence that, despite detecting the expected signal of TSP, there is additional strong introgression of MHC alleles in both directions from the parental lineages. While many of the DQA alleles were previously known39, all of the DQB variants were novel discoveries in mountain hares and only three had been seen previously in brown hares  and moderate balancing selection evident for UCP1 . No selection was detected for TLR2, SDHa. Fitting with the role of UCP1 in cold tolerance, mountain hare specific alleles are more common in hybrids in the higher latitudes , while mountain hares showed more even genotypic representation and higher number of alleles  and TLR2, required for innate immunity51. SDHa was expected to perform as a neutral marker, showing reduced evidence of adaptive introgression and mitochondrial DNA was included to track the maternal lineage hybridization of the individuals.Our prime candidates to detect potentially meaningful adaptive introgression in hares included the UCP1 and strong balancing selection at both MHC loci . This is also evident in the AMOVA analysis, which showed that the majority of the variation in neutral alleles was between the species, while the loci potentially under selection showed significant within-individual variation  clades, unlike UCP1. The timing of the nodes on the tree also gave rather young approximations for the ages of the clades when compared to the speciation estimate. This is likely because the applicability of fast evolving sequences as molecular clocks saturates at deeper divergences57. However, the allopatric Austrian brown hares had four private DQA alleles and seven DQB allelles, while sharing only four and three alleles respectively with the Finnish hares .The Finnish hare samples were obtained from hunters and the Finnish Food Authority, as well as from a GPS/GSM telemetry study9 genotype data for 6833 SNP loci for 36 specimens of the Finnish hares  packages.All specimens have been initially identified using morphological features. The species differ considerably in their habitat preferences and identification during most of the hunting season and are easily distinguished due to the white winter pelage of mountain hares. DNA from ear muscle biopsies was isolated using Chelex® 100 (Bio-Rad) method43. Briefly, the PCR product of the mitochondrial Cytb locus was digested with DdeI, which cuts mountain hare sequence twice and brown hare sequence four times. The distinct sized restriction fragments are then unequivocally identifiable on agarose gel.Mitochondrial DNA was genotyped for all samples Table  using a DQA (218 bp), and DQB (210 bp), which have been previously reported to show both spatial as well as temporal positive selection in hares48; a 372 bp fragment of the Toll like receptor 2 corresponding to the genes for Toll-interleukin-1 receptor domain protein (TLR2), involved in innate immunity; a 358 bp fragment of the uncoupling protein 1 (UCP1); and finally a 135 bp fragment of the neutral locus, succinate dehydrogenase complex subunit A (SDHa). As an additional control for neutrality, we had previous genotype data for 6833 neutral SNPs from 36 specimens9  were validated via Sanger sequencing in both directions, using the same primers as for their amplification. Genotype data for each sample is provided in Table The five candidate loci analyzed in this study are: a fragment of exon 2 including the antigen binding motif of the major histocompatibility complex (MHC) class II loci s9 Table . BrieflyDQA, DQB and UCP1. TLR2 and SDHa were excluded as they had only three alleles each with clear-cut separation between the species  is the measure of the number of alleles and their representation in the gene pool of the sampled population. Hardy–Weinberg equilibrium was tested using a Markov chain exact test with Forecasted chain length of 10,000 and 1000 dememorization steps.These analyses were conducted only on the Finnish material, representing reproductively connected hare populations. First pass data analysis, including the obtaining of standard diversity indices9. The first method was the F-statistics test based on genetic structure40, also provided in ARLEQUIN version 3.5.277, with a hierarchical island model applying 50,000 simulations, 100 demes per group for 10 groups. The method was chosen as it allows the population sample to be assigned to different a priori groups , allowing for different migrations rates between demes within groups and between groups. The latter is important because of the highly asymmetric nature of the gene flow between the two hare species. The simulation generates a joint distribution of Fst versus heterozygosity 83. We performed an Analysis of Molecular Variance (AMOVA) , the persistence of allelic lineages across species events, should be distinguishable from introgression if the shared alleles also occur in pure parental lines of both species. This is not trivial, as it is difficult to identify isolated populations of Finnish mountain hares or brown hares to select as “pure” parental lines. Nevertheless, we conservatively identified 22 individuals that could be considered pure mountain hares: most northerly samples with mountain hare mtDNA and no brown hare characteristic alleles at No animal was killed for research purposes only and the hunting followed the regional hunting seasons and legislation (Metsästyslaki [Hunting law] 1993/615/5§). Hunted specimens did not require an ethical assessment prior to being conducted. Apart from hunted animals, some DNA samples (location Joensuu, Botania and Linnunlahti, Table Supplementary Information 1.Supplementary Information 2.