Information on the population dynamics of a reservoir species have been increasingly adopted to understand and eventually predict the dispersal patterns of infectious diseases throughout an area. Although potentially relevant, to date there are no studies which have investigated the genetic structure of the red fox population in relation to infectious disease dynamics. Therefore, we genetically and spatially characterised the red fox population in the area stretching between the Eastern and Dinaric Alps, which has been affected by both distemper and rabies at different time intervals. Red foxes collected from north-eastern Italy, Austria, Slovenia and Croatia between 2006–2012, were studied using a set of 21 microsatellite markers. We confirmed a weak genetic differentiation within the fox population using Bayesian clustering analyses, and we were able to differentiate the fox population into geographically segregated groups. Our finding might be due to the presence of geographical barriers that have likely influenced the distribution of the fox population, limiting in turn gene flow and spread of infectious diseases. Focusing on the Italian red fox population, we observed interesting variations in the prevalence of both diseases among distinct fox clusters, with the previously identified Italy 1 and Italy 2 rabies as well as distemper viruses preferentially affecting different sub-groups identified in the study. Knowledge of the regional-scale population structure can improve understanding of the epidemiology and spread of diseases. Our study paves the way for an integrated approach for disease control coupling pathogen, host and environmental data to inform targeted control programs in the future. Vulpes vulpes  is a globally widespread and non-migratory species [V. v. crucigera  and V. v. ichnusae (in Sardinia) [The red fox,  species , which sardinia) . The redardinia) . Fox popardinia) , with a ardinia) , with inNyctereutes procyonoides), also sporadically present in north-eastern Italy, might act as rabies transmitter in eastern and central Europe [The red fox is currently considered the principal reservoir of rabies virus  in mainland Europe, although the raccoon dog , a pathogen of great relevance to wildlife conservation. CDV infects a wide variety of mammalian species , 14 but versus the Iberian Peninsula; or northern Europe versus eastern Europe versus single European countries) [In the last decade, genetic data have been widely used to investigate the structure, gene flow and mixing among individuals of a given animal species –25, whicuntries) –34–35. Suntries) –38. Clusuntries) –40.In this study, we investigated the genetic structure of the red fox population in a wide geographical area stretching between the Eastern and Dinaric Alps. We also evaluated the association between the diseases (canine distemper or rabies) in north-eastern Italy and different fox subgroups and we discussed the most likely reasons for our findings. To our knowledge, this is the first study, which has used microsatellite analyses to investigate the red fox population genetic structure in relation to a combination of two different diseases, rabies and distemper, thus representing an innovative approach to improve the surveillance and control of infectious diseases on a regional scale.2.The study area includes the northern regions of Italy and the territories of Austria, Slovenia and Croatia, covering an area of 200,596 kmV. v. crucigera) were collected for the diagnosis of rabies and CDV and a phylogenetic characterisation of CDV circulating in north-eastern Italy has already been documented [Croatian Veterinary Institute in Zagreb, Croatia. The main goal of the purposive sampling was to focus on particular characteristics of interest of the population. A non-random selection of these samples allowed the selection of a uniform sub-sample across the years and the affected regions. The final sample size (n = 627) accounted for the laboratory capacity and for the condition of samples at collection. We then selected 379 Italian fox samples from the original panel of available specimens collected between 2006 and 2011, 98 Austrian fox samples collected between 2011–2012, 86 Slovenian fox samples collected between 2010–2011 and 64 Croatian fox samples collected in 2012  as an internal size standard. The molecular size of microsatellite alleles was evaluated by using GeneMapper4.0 (Applied Biosystems). Genotyping was checked by re-amplification and analysis of 10% of the samples for each multiplex PCR. The error rates between replicates for all loci were low (1–2%). To minimize scoring errors two operators independently read and edited the program output.Due to the genetic similarity between dogs and foxes, a set of canine microsatellite markers was adapted for the s genome –46. Totas genome  and Sacks genome : AHT-121s genome ; *RF-CPHs genome ; AHT-137s genome ; *RF-CXXs genome ; *RF-INUs genome ; RF-CXX4s genome . Forwards genome , polymers genome , Moore es genome  and MullWe tested the reliability of 21 microsatellite loci. We evaluated the presence of null alleles and of genotyping errors due to large allele drop-out or stuttering with Micro-checker, adopting a 95% confidence interval for the Monte Carlo simulations . We alsoTo improve our understanding of the spatial and genetic structure of the fox population we performed a spatial autocorrelation analysis and we assessed the presence of isolation by distance through the Mantel test, considering the genetic and spatial distances at individual level. Both tests were implemented in GenAlex version 6.5 . We alsoThe population structure was investigated using a Bayesian clustering analysis implemented in Structure 2.3.3 , which aIn order to measure the genetic differences between the identified clusters, we calculated the pairwise Fst values (probability based on 999 permutations) and performed an Analysis of Molecular Variance . This analysis tested the null hypothesis that the clusters are part of a single randomly mating genetic population. We performed these analyses for the individuals with cluster membership Q ≥ 0.7 . Using GIn order to visualize the genetic clusters (as per Structure results) and to explore the presence of landscape features  which may have influenced the spatial characterization and movements of the individuals belonging to different clusters, we generated and assessed integrated maps showing genetic clusters as well as rivers and highways using ArcGIS 9.2 .The number of different alleles and of effective alleles, the observed heterozygosity (Ho), the expected heterozygosity (He) and the F index ((He-Ho)/He) for different groups and clusters were obtained by using GenAlEx 6.5.This study was performed on samples collected and submitted to the national reference laboratories for rabies within the framework of the national rabies surveillance plans. The investigation did not involve endangered species, neither the intentional killing of animals. All samples originated from wild red foxes found dead or legally hunted. In Europe, such procedures do not require any specific ethical approval as hunting plans are organised by national authorities in the framework of disease control programs, according to the EU guidelines and to the national vaccination control plans approved by the EU . The samThe 21 selected microsatellites had an average of 12.6 alleles per locus . The observed (Ho) and expected (He) averaged heterozygosity per locus were relatively high  and the mean F index was 0.05 (range: 0.01–0.11). Considering the whole population, six loci significantly departed from HWE ; in the presence of multiple clusters, loci that show a deviation could be the most informative, as they reflect the very population structure.The number (K) of possible clusters was estimated in Structure assuming no prior knowledge about structuring in populations of individuals. The maximum likelihood estimate for the number of clusters was K = 2 . The assThe result for K = 3, although less probable according to Structure results, was also biologically meaningful. In this case, the three groups of individuals, namely, A, B and C , were alThe pairwise Fst value ((Ht-Hs)/Ht, Ht = total expected heterozygosity, Hs = average within population heterozygosity) showed a very low but statistically significant (p<0.001) differentiation among group A and group B (Fst = 0.009). The results of AMOVA showed that the highest percentage of variation was found within the study area (94%), while differentiation among clusters was very low but significant . Geneland analysis supported the hypothesis of a weak population structure. The uncorrelated spatial model identified 2 clusters (K = 2) , the spaWe then focused on the Italian subset as the three disease statuses  were all present for individuals collected from this area only. No animal showed co-infection of distemper and rabies.The Structure analysis estimated that the Italian fox population was most likely divided into 4 clusters , S2 Fig.The mean number of different alleles, effective alleles, observed heterozygosity, expected heterozygosity and F index for clusters 1, 2, 3 and 4 are shown in Table E in Considering the subgrouping of the Italian samples in clusters 1, 2, 3 and 4 towards the two main groups A and B we found that 89.2% of cluster 1 was included in group A, 100% of clusters 2 and 96% of cluster 3 were included in group B while cluster 4 belonged to group A (30%) and B (70%) as per the reported percentage values.We then stratified by disease status  we overall expected a weak genetic differentiation of the red fox population. Furthermore, due to the absence of co-infections of the two pathogens observed in northern Italy , we hypoFindings from our analysis of the complete dataset (n = 627) were consistent with observations of previous works, which revealed the presence of a single fox population characterized by a faint structure –34, 72. Based on the results obtained, we were able to identify two genetic groups, that we arbitrarily named as A and B. The group further east (group A) extends mainly across Slovenia, Croatia, eastern Austria and the far eastern Italian territories located on the border with Slovenia. Group B includes few Austrian foxes but mainly consists of Italian samples and covers the remaining territories of the study area  . Such a 2, is characterised by eight clusters comprising two main regions and much admixture with Fst values ranging from 0.006 to 0.059 [Based on the fox behaviour, a highly vagile species  that canto 0.059 . Of noteto 0.059 , Poland to 0.059 , Croatiato 0.059  and Portto 0.059 . Our resto 0.059 . A recento 0.059 . Differeto 0.059 –78.The two groups identified in the area stretching between the Eastern and Dinaric Alps are geographically separated by natural and artificial barriers. Group B seems to be confined in Italy by the Western and Eastern Rhaetian Alps of the state of Tyrol in Austria and the Carnic Alps and the Karawanks of the Carinthian lands. In the Friuli Venezia Giulia region, the Tagliamento river and the fenced highway running along the river (A23) (eastern barrier) seem to separate group B from group A. In support to our results, particular geographic features and barriers, such as narrow land passes (70 km wide) combined with mountain ranges higher than 1000 m, have been previously held responsible for limiting fox migration and inhibiting gene flow, consequently leading to a pronounced genetic structure in Croatia . The occCapreolus capreolus) in northern Italy [Rupicapra) [Focusing on the Italian sub-set, the four genetic groups clusters 1 to 4) identified were distributed in three zones of the target area, namely eastern (cluster 1), corresponding to group A, and central/western zones (clusters 2–4), corresponding to group B. Of note, two clusters were located in the central zone occupying the northern and southern parts of the study area, between the eastern (Tagliamento river and highway A23) and western barriers (Adige river and highway A22). As for findings obtained from the entire dataset, although the genetic differentiation of the four clusters is low based on the pairwise Fst values obtained, such a spatial characterization seemed to be relatively stable, with each identified cluster consistently present in the same zones across the whole study period (2006–2011). Assignment probabilities for each cluster showed evidence of admixture and revealed a limited gene flow between the eastern and central areas and between the central and western zones, although a high gene flow characterized the whole population , Fig 5.  to 4 idern Italy  and champicapra) . Our respicapra) . As for picapra) , our popIn relation to the distribution and spread of rabies and distemper in foxes, our findings might help explain and possibly forecast the two-pathogen disease dynamics in this area. Stratifying the individual classification according to disease status (Vulpes lagopus) in Alaska mirrors the distribution of the three known rabies virus variants [Findings from our study are in line with previous evidence in the north-eastern United States demonstrating the dampening effect of rivers on rabies spread , 81. Intvariants .0 1.26), rabies has a shorter generation time so that its spread is much faster than that of distemper [Procyon lotor), such an association is difficult to demonstrate in practice [The distribution and spread of rabies and distemper proved to be quite different and varied according to the areas of the territory object of our study. For instance, rabies did not expand across the Adige River while distemper reached the most western boundaries. Although the transmission potential of the two diseases in the north-eastern Italian setting have been estimated as equal  in the study area. Considering structure result K = 3 for the complete dataset, individuals assigned to groups A, B and C are identified with violet, light green and orange dots, respectively. Samples with an assignment probability greater than or equal to 0.7 (Q ≥ 0.7) are shown with dots; admixed individuals are shown with grey squares.(TIF)Click here for additional data file.S2 FigStructure analysis performed on the Italian subset estimated K = 4 as the most likely number of clusters.(TIF)Click here for additional data file.S3 FigCorrelogram of the whole dataset with distance classes of 10 km. Correlation coefficient (r) is shown in the vertical axis. Error bars bound the 95% confidence interval determined by bootstrap resampling (999 iterations). Upper (U) and lower (L) confidence limits bound the 95% confidence interval for the null hypothesis of no spatial autocorrelation (r = 0) as determined by 999 permutations.(TIF)Click here for additional data file.S4 FigGeographic Distance in km (GGD) on the x-axes and Linear Genetic Distance (LinGD) on the y-axes.(TIF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S1 DatasetTable showing in sequence year of collection, country of origin, specific location, disease status, sample ID, list of alleles for each microsatellite locus.(XLSX)Click here for additional data file.


Corvus kubaryi), is a critically endangered species , endemic to the islands of Guam and Rota in the Mariana Archipelago. It is locally extinct on Guam, and numbers have declined dramatically on Rota to a historical low of less than 55 breeding pairs throughout the island in 2013. Because of its extirpation on Guam and population decline on Rota, it is of critical importance to assess the genetic variation among individuals to assist ongoing recovery efforts. We conducted a population genomics analysis comparing the Guam and Rota populations and studied the genetic structure of the Rota population. We used blood samples from five birds from Guam and 78 birds from Rota. We identified 145,552 candidate single nucleotide variants (SNVs) from a genome sequence of an individual from Rota and selected a subset of these to develop an oligonucleotide in-solution capture assay. The Guam and Rota populations were genetically differentiated from each other. Crow populations sampled broadly across their range on Rota showed significant genetic structuring – a surprising result given the small size of this island and the good flight capabilities of the species. Knowledge of its genetic structure will help improve management strategies to help with its recovery.The Mariana Crow, or Åga ( The geographic isolation of oceanic islands makes them useful for studying basic ecological and evolutionary processes, such as population dynamics, adaptive radiation, speciation, and community assembly ,2. IslanBoiga irregularis) to Guam after World War II [Rallus owstoni; [Pteropus mariannus; [Todiramphus cinnamominus; [Aerodramus bartschi; [Corvus kubaryi).Within the Mariana Archipelago, forest bird populations have been declining from several causes, notably the introduction of the Australasian brown tree snake , the Mariannus; ), the Mimominus; ), the Maartschi; ), and th2) and Rota  is currently unknown, low genetic variability and potentially high levels of inbreeding may be contributing factors ,29,30. TIn our study, we sequenced and assembled the genome of a single Mariana Crow from Rota and subsequently identified 145,552 putative single-nucleotide variants (SNVs), including 101,040 high-quality candidate polymorphisms. We selected a subset of 19,991 of these markers to use for the synthesis of an in-solution oligo capture bait set that could be used to assess variation in Mariana Crows. Here, we use these resources: (a) to compare genetic variability and assess differentiation between crows on Guam and Rota; (b) to chart the genetic structure of crow populations across different regions of Rota; (c) to assess possible patterns of dispersal from genetic structure data; and (d) to estimate preliminary levels of kinship among individual birds on Rota.We collected blood samples from wild Mariana Crows on Rota throughout their range from recent conservation research projects (2009–2015), performed by the University of Washington and funded by the Commonwealth of the Northern Mariana Islands Department of Land and Natural Resources (CNMI DLNR). Samples were obtained by venipuncture from nestlings, fledglings and subadults (n = 51), and adults (n = 13). In addition, we included 13 samples  from the 1990s, which had previously been analyzed by Tarr & Fleischer ,31. An aCorvus, we sequenced the complete genome of an individual male Mariana Crow from Rota  on an Illumina HiSeq . We trimmed the reads using Trimmomatic 0.33 ILLUMINACLIP:TruSeq3-PE.fa:2:30:10, LEADING: 3, TRAILING: 3, SLIDINGWINDOW:4:28, MINLEN:36). We used FastQC 0.11.2 to check read qualities and ensure that adapter sequences were removed [https://github.com/campanam/genome_coverage), we estimated genome coverage as 33.4x [https://github.com/pbfrandsen/fasta_metadata_parser). We estimated genome completeness using BUSCO v3 [Corvus cornix transcripts (from Genbank assembly ASM73873v2) to the genome with BLAT and used the blat2hints Augustus utility to create a hints file for training Augustus. We then ran Augustus with RepeatMasker and BLAT hints  as the model species, and then using the BUSCO-generated crow-specific Augustus retraining gene models. A consensus GFF was generated with EVidenceModeler [As part of a separate ongoing comparative genomics study of the genus  removed . Trimmedas 33.4x . Genome BUSCO v3  with thets (v3.3 ) twice, The sequenced individual’s trimmed reads were re-mapped against the assembled genome using BWA-MEM 0.7.12 . We remo® 2.0 dsDNA HS assays  and library size-ranges and qualities were inspected using 2100 Bioanalyzer  high-sensitivity DNA chips. Libraries were pooled at the same concentration in groups of four to six individuals and hybridized to the MYBaits capture kit (1.25 µL baits per capture) for ~24 h according to the MYBaits protocol v. 3.0. Finally, pooled libraries were 2 × 150 bp paired-end sequenced on a HiSeq 2500  at the Tufts University Core Facility (TUCF) genomics services  and using a MiSeq  at the Center for Conservation Genomics . Supplementary 2 × 123 bp paired-end Illumina HiSeq sequencing was performed at RTL Genomics .We extracted DNA from the blood samples using Qiagen DNeasy Blood & Tissue kits . DNA samples were sheared to ~500 bp using a Q800R sonicator  and converted into double-indexed Illumina libraries using KAPA Library Preparation kits – Illumina . Library concentrations were quantified using QubitSamples were demultiplexed using the BaseSpace  pipeline or deML (option --m 1) . LibrarySequence variants within the baited regions were identified using BCFtools 1.9 mpileup  and BCFtools call (options -m -v). We removed non-SNVs and SNVs below quality 20 using BCFtools filter to obtain a dataset of 37,272 SNVs (some baited regions contained multiple SNV sites). Using VCFtools 0.1.15 , we furtWe performed population structure analyses on two datasets: (a) the dataset including all 83 individuals from both Guam and Rota (“complete dataset”); and (b) a restricted dataset including only the Rota birds (“Rota-only dataset”) of known provenance (n = 72), to help resolve population substructure on Rota. Using SNPRelate 1.14.0  in R 3.5K values between 1 and 10, with three replicate runs per K value. We identified optimal K values using the ΔK [ΔFST (both using no optimization and optimization by Q-matrix correlation) statistics, as well as Q-matrix correlations (both row-and-column and average maximum correlation methods) in CorrSieve 1.6-6 [K = 1 to 10, four replicates per K) to infer fine-scale population structure on this island. Since fine-scale population structure can be more difficult to resolve using Bayesian approaches, we increased the run lengths to 100,000 burn-in and 200,000 data collection steps for this analysis. As an alternative methodology to infer population structure, using Adegenet 2.1.1 [K-means clustering, retaining all components for each analysis. To avoid overfitting, we determined the number of retained principal components using the optimized a-score and cross-validation as implemented in Adegenet [K-means clustering at K = 3 (complete dataset) or K = 2 (Rota-only dataset), and two discriminant functions. We also assayed population structure in the two datasets using ADMIXTURE 1.3.0 for K = 1 to 10 under default settings except that the random number seed was set to the system clock and we used ten-fold cross-validation to identify the best K values [We inferred population structure in the complete dataset using the admixture model in Structure 2.3.4  under deg the ΔK  and ΔFSTve 1.6-6 . We repeet 2.1.1 , we perfAdegenet . Cross-vK values .https://github.com/campanam/kinshipUtils). We calculated the 95% confidence intervals for the kinship estimates using 100 bootstrap replicates. The kinship analysis included 5872 SNVs with a minor allele frequency greater than 0.05. We identified all individuals roughly on second order of kinship (r > 0.1) to be compared against individuals of known relatedness. Inbreeding coefficients (FIS) were calculated using VCFtools 0.1.15 [We calculated mean kinship coefficients between individuals using maximum likelihood estimation in SNPRelate 1.14.0  and custs 0.1.15 .Corvus, further assessments of genome assembly quality, and functional genomics) will be presented in upcoming publications .The assembled genome was 1.07 Gb long, similar to previously assembled passeriform genomes. The assembly N50 was 79,541 with a longest contig of 677,288 bp and a mean contig length of 19,865 bp . The genWe generated analyzable sequencing data for 83 individual Mariana Crows . We sequenced between 130,968 and 10,400,122 read pairs per analyzed individual  . Raw datThe first axis (encompassing 7.17% of the variation) of the PCA including the 83 crow samples differentiated the Rota and Guam populations from each other . There wK = 3 populations by all methods in CorrSieve  corresponded to the five individuals from Guam, and the other two (orange and purple) only occurred on Rota. For the DAPC, the BIC analysis of K-means clustering also determined an optimal K of 3. The final DAPC retained 4 principal components (comprising 22.0% of the variation) and two discriminant functions. The DAPC cluster assignments largely agreed with those of the Structure analysis but showed less evidence of mixed ancestry within individuals  error suggested that K = 6 (CV = 0.53560) was a slightly better solution than K = 3 (CV = 0.54818) for the ADMIXTURE analysis. The K = 6 model suggests some additional population substructure within Rota  and one discriminant function. The optimal ADMIXTURE solution was found at K = 5 (CV = 0.51973). These results are consistent with those of the complete dataset with the Guam population removed.The analysis of the Rota-only dataset did not detect further statistically reliable substructure. Both p = 0.0001, DAPC: p = 0.0002). The dendrogram clustered the individuals from Guam and Rota into two separate clades . To gain a better understanding of the Rota crows’ known genetic relationships, we plotted each sample on a map of Rota, and grouped them by proximity based on their geographic location . The twoe clades ; howeverK = 5 was the best solution for the complete dataset  was significantly higher  for individuals from Guam  than for those on Rota . However, the Rota birds exhibited substantial variation in their inbreeding coefficients, with some birds being highly inbred  and others being very outbred (13 with FIS < −0.1).The inbreeding coefficient , an island endemic from New Caledonia in the South Pacific, also maintains small home ranges [Despite the observed genetic variation among Rota individuals, a future genetic bottleneck remains possible, given the few (55) numbers of breeding pairs remaining in the wild . Furectares; ) or comme ranges . Smallere ranges .Pyrrhocorax pyrrhocorax) in the Iberian Peninsula. Morinha and collaborators [Our isolation-by-distance analysis suggests that most of the pattern of the genetic differentiation is due to the distance between populations from the northern and southern portions of the island. There is only one documented dispersal between the north and south sides of Rota: a female dispersed 12,687 m while all other dispersals were less than 5000 m . While cborators  found thborators , associaborators  and notaborators .Corvus hawaiiensis - 43% [Several factors continue to impact the Mariana Crow on Rota, such as habitat loss due to land use conflicts, disease and feral cat predation ,17,61. Tis - 43% ) – anothis - 43% . While tOur genomic analyses imply that even though this species is critically endangered and has low genetic variability the extant Rota population does not appear to be highly inbred. Nonetheless, it is still important to assess the potential role of inbreeding in the low nesting success of Mariana Crows on Rota, especially since inbreeding coefficients show substantial variation among individuals. Some wild pairs have not produced viable young from their eggs, and some eggs harvested from wild pairs have proved to be inviable. This crow population not only exhibits high first-year mortality , but alsSimilar to the Mariana crow, the ‘Alalā or Hawaiian Crow, experienced a population crash in the last century, eventually becoming extinct in the wild in 2002 . FortunaIn a recent effort to increase the population, nine Mariana Crows were collected as eggs or chicks, early in the breeding season, allowing wild pairs to re-nest during the same season. Eggs and nestlings were collected from multiple sub-populations and reared in captivity for two years to protect them during the period of high mortality in wild birds. These individuals were released in September 2018 as reproductively mature adults on the north end of the island  where population declines have left large areas of vacant crow habitat. Eight of these birds were collected from the wild as eggs or nestlings, and one was taken into rehabilitation as a fledgling. Five of these birds came from southern areas of the island, so this effort will potentially increase genetic diversity within the Lalayak subpopulation.The Mariana Crow is a prime example of a species that is critically endangered because of invasive species, diseases, human persecution and habitat degradation. It was extirpated from Guam in 2011 and the Rota population has declined dramatically over the past 31 years. Genetic analyses are an essential prerequisite for implementing effective management strategies for severely bottlenecked bird populations. Using high-quality SNV data, we found that, surprisingly, the Rota population does not yet exhibit high levels of inbreeding, suggesting that it is not too late to preserve extant genetic diversity. In wild populations, high first-year mortality, low egg viability, and infertility of some adults are major concerns. Nevertheless, despite the threats the species faces, with adequate management, it is still possible to prevent its extinction. The information we have presented on the genetic structure of the Rota subpopulations will enable managers to evaluate future release locations on the basis of genetic structuring.


Phaseolus vulgaris L.) is the most important food legume for direct human consumption around the world, as it represents a valuable source of components with nutritional and health benefits.Common bean (Phaseolus vulgaris L.) germplasm from Portugal to Ukraine, along a western-to-eastern line of southern European countries, including Poland. This was based on the P. vulgaris genetic structure, and was designed to better describe its distribution and domestication pathways in Europe. Using the multi-crop passport descriptors that include geographic origin and different phaseolin types (corresponding to the Mesoamerican and Andean gene pools), 782 accessions were obtained from nine gene banks and 12 geographic origins. We selected 33 genome/ gene-related/ gene-pool-related nuclear simple sequence repeat markers that covered the genetic diversity across the P. vulgaris genome. The overall polymorphic information content was 0.800. Without specifying geographic origin, global structure cluster analysis generated 10 genetic clusters. Among the PvSHP1 markers, the most informative for gene pool assignment of the European P. vulgaris germplasm was PvSHP1-B. Results of AMOVA show that 89% of the molecular variability is shared within the 782 accessions, with 4% molecular variability among the different geographic origins along this western-to-eastern line of southern Europe (including Poland).We conducted a study to define and explain the genetic relatedness and diversification level of common bean  to the more eastern areas of southern Europe. This progression defines three geographically separated subgroups, as the northern , southern , and central  areas of eastern Europe.This study shows that the diversification line of the European  Phaseolus vulgaris L.) is the most important food legume throughout the world for direct use . P. vuloperties .Phaseolus have been collected ever more intensively in different parts of Europe, and these are stored in national gene banks. At present, the Phaseolus database in the web-based European Search Catalogue for Plant Genetic Resources (EURISCO) contains over 50,000 records, which includes more than 40,000 characterisation and evaluation records, and 585 photographs , the diversification line of the European  Pizarro , 49. The Pizarro , 50, 51. Pizarro  reportedOn the basis of the genetic structure, the most genetically admixture accessions were from Hungary, where only three genetic clusters were seen. For the PCoA for the Romania accessions, the lower proportion of molecular variability seen via the covariance distance matrix (62.8%) was explained by the first three axes, which reflected the minor significance of the distribution of these accessions, compared to other geographic origins.PvSHP1 markers, five genetic clusters were seen, which revealed one subgroup for each gene pool  and a common subgroup that comprised accessions from mixed origins (possible putative hybrids). This finding can be briefly confirmed by the genetic relatedness of the accessions with known phaseolin type, where five groups were also defined by the phylogram obtained. The typical Andean phaseolin type T was distinguished from the Mesoamerican type S with few exceptions . When specifying the population of geographic origin, the PvSHP1 markers distinguished among the whole collection between the Mesoamerican and Andean gene pools. Moreover, this showed that the Andean accessions are more prevalent than the Mesoamerican ones within some specific geographic origins. The global distributions of the accessions using no prior information revealed three main groups that again represented the Mesoamerican, Andean and mixed groups for the whole western-to-eastern European P. vulgaris germplasm. The combination of the PvSHP1 markers was therefore the only efficient analysis to show the detailed gene-pool affiliations of these European accessions (with PvSHP-1B as the most distinguishing locus), especially for the central-eastern part of Europe. In contrast, the PvSHP1-C locus was not informative for the Bulgaria, Albania and Portugal accessions, and similarly, the PvSHP1-A locus was not informative for the Spain accessions. As seen from these data, which PvSHP1 is applied for which geographic origin within the European P. vulgaris germplasm is important. Hence, a few well-chosen SSR markers are better for discerning gene-pool affiliations than many SSRs. The geographic origin and informativity/ usefulness of the PvSHP1 markers applied in the present study reflect the data shown in Fig. PvSHP1 indel-spanning markers are useful for germplasm identification, and particularly to trace the distributions of the domesticated Mesoamerican and Andean gene pools. These three markers were the molecular markers that highlighted the polymorphism among the different common bean genotypes due to the presence/ absence of indels in the PvSHP1 gene sequence. The data from the three PvSHP1 markers successfully indicated the gene-pool origins of the European accessions  and an internationally available diversity panel [P. vulgaris accessions. For these (with 58 SSRs), the mean PIC was only 0.29. In the present study, for the Spain, Slovenia and Italy accessions, the levels of genetic diversity were high due to the highest uHe and He values seen. The highest genetic potential for association mapping studies was calculated for the Hungary, Serbia and Slovenia accessions considering the alleles that were specific for the accessions from each of these geographic origins . On the basis of chloroplast markers and two unlinked nuclear loci (PvSHP1), Angioi et al. [PvSHP1 markers, where five genetic clusters were defined for this western-to-eastern European germplasm. As a comparison, Sinkovič et al. [P. vulgaris and P. coccineus germplasm from a gene bank in Slovenia  consisted of three groups.Nowadays, technology allows thousands of SNP markers to be genotyped through many genomes –54. Recety panel . Even thty panel , who devč et al.  reportedP. vulgaris accessions, and have sufficient power to distinguish between and within the Mesoamerican and Andean germplasms. Moreover, three indel-spanning markers of the PvSHP1 gene are shown here to be efficient and informative for determination of the gene-pool affiliations of these European accessions. We have also shown that these European gene banks have successfully maintained, and are sharing, this highly diverse European P. vulgaris germplasm for scientific and applicative purposes.The data from the present study show that this collection of 782 accessions that originated along a western-to-eastern line of countries through southern Europe represents a valuable source of genetic variability. These have known backgrounds, which is generally useful for further genetic studies and for formation of a core collection that comprises the most promising accessions from these different geographic origins. Furthermore, we have shown that the 33 genome and gene-specific SSR markers used are highly applicable for diversification studies of European P. vulgaris. Such gene-based SNP markers from both of these previous studies might be particularly useful for analysis of the European P. vulgaris germplasm, where the Andean genotypes are dominant.We have shown here that both the Mesoamerican and Andean accessions came to Europe following a western-to-eastern line through southern Europe. During this spread, they adapted to the European agro-climatic environment, which is reflected in the increased diversification levels that include mixed subgroups of Mesoamerican and Andean accessions and alleles that are specific for each geographic origin. However, any conclusions relating to adaptation need to be drawn up with care given the study questions and the analyses that were carried out, whereby the only clues in this regard should come from new analyses. As a perspective of the present study, the use of Approximate Bayesian Computation modelling can be recommended to check the estimates from this study. Another perspective would be the use of newer approaches. Cortes et al.  identifiPhaseolus database, with the identification of > 800 accessions that covered diverse environments from different parts of the European continent, following from Spain to Ukraine, along a western-to-eastern line of southern European countries, with Poland included in the sampling. The available data related to basic multi-crop passport descriptors and the seed characteristics were acquired from different national gene-bank curators, including geographic origin, biological status, ancestral data, phenotypic seed characteristics, phaseolin type (corresponding to Mesoamerican/ Andean origins), and seed material.We used the web-based EURISCO for the The plant material was available in limited amounts, with 9 to 32 seeds received for each accession, depending on the gene-bank availability. Out of these > 800 accessions identified, we succeeded in acquiring 782 accessions based on certain gene-bank availability and the viability of the requested accession. Each ‘accession’ represented a population or genotype, which included cultivars, commercial varieties and landraces. The original sources of the plant material were the following gene banks: AIS (Slovenian and Albanian accessions), IBGR-UNI-BL (Bosnia and Herzegovina), CPD (Hungary), CSIC (Spain), DAB , IHAR (Poland), IPK (Germany), and IRP-NS (Serbia). All of the accessions included in the study are listed in Additional file P. coccineus accession was included as an outgroup accession . Accessions with available data on phaseolin type were used as internal standards or anchors, to assure the correct gene-pool assignment. These included the Mesoamerican S, M and B phaseolin types, and the Andean T, C and H phaseolin types. The main reason why we used phaseolin-based controls rather than well-known gene pool controls, such as Andean genotypes Calima (G4494) and Chaucha Chuga (G19833), as well as the Mesoamerican genotypes ICA Pijao (G5773) and Dorado (DOR364), is that there was the need to include and analyse accessions that were native to western and eastern European countries . Before sowing, six seeds from each accession were chemically disinfected with 5% sodium hypochlorite. At the phase of the first true leaves, 60 mg to 100 mg fresh and healthy plant tissue was sampled. The extraction procedure for DNA was performed as described by Pipan et al. , 30 and P. vulgaris germplasm, a set of 33 genome-specific markers were used, as developed by nine research groups [To define the diversification and genetic structure of the h groups , 37–44. h groups . Six marh groups , 41, 43,2 , 0.1 μL 10 μM forward primer , 0.25 μL 10 μM reverse primer , 0.183 μL 10 μM 5′-fluorescently labelled universal primer , and 0.5 μL 5 U Taq DNA polymerase . The forward primer of each SSR had an added 18-bp tail sequence of 5′-TGTAAAACGACGGCCAGT-3′ (M13(− 21)), as described by Schuelke [The PCR reactions were performed in a final volume of 11.5 μL that contained 8.4 ng genomic DNA and the following reagents, with initial concentrations: 1 μL of 10× PCR buffer , 0.2 μL of mix from each 10 mM dNTP , 0.5 μL 50 mM MgClSchuelke .The PCR analyses were performed using a thermal cycler  under the following ‘touch-down’ conditions, which was dependent on each primer pair: 94 °C for 4 min; 15 cycles at 94 °C for 1 min; decreased temperature from 60 (62) °C to 49.5 (51.5) °C at 0.7 °C per cycle for 30 s; 72 °C for 1 min; followed by 23 cycles at 94 °C for 30 s; 53 °C for 30 s; 72 °C for 1 min; and final extension for 5 min at 72 °C. Fragment analysis was performed on a genetic analyser , and allele lengths were determined by comparison with an internal size standard  using the GeneMapper 4.0 software (Applied Biosystems).No); expected heterozygosity (He); and polymorphic information content (PIC). The following further parameters were calculated for each of the 12 geographic groups of accessions using the GenAlEx 6.1 software [Ne) and private (Np) alleles; Shannon’s information index (I); fixation index (F); alleles with frequency > 5%; number of common alleles with proportions of ≤25% and ≤ 50%; unbiased expected heterozygosity (uHe); and locus-specific deviations from the Hardy–Weinberg equilibrium (HWE). The same software was used for principal coordinate analysis (PCoA). Analysis of molecular variance (AMOVA) was performed to determine the genetic variation within and among the groups of accessions  using the Arlequin software [HWE, and others) did not show any disturbances that might be influenced by significant levels of null alleles. The estimation of gene flow for each geographic origin was carried out by calculation of the effective numbers of migrants using the private allele method of Slatkin [P. coccineus, which was not originally defined as an outgroup as it was treated the same as the P. vulgaris accessions, while outgroups give polarity to unrooted trees and allow reconstruction of the ancestral character states and areas [Q value). Twenty independent runs for each K (from 1 to 15) were performed for the admixture model, with a burning period of 10,000 followed by 100,000 Markov chain Monte Carlo repeats. The real K value was selected based on the increase in the likelihood ratios between runs, using the Evanno delta K statistic [Ar) for accessions within each geographic origin were obtained using the FSTAT 2.9.3.2 software [The following parameters were calculated using the Identity 1.0  and Micrsoftware : number software . Due to  Slatkin  and the  Slatkin , which r Slatkin . The Pop Slatkin  was used Slatkin  from the Slatkin . The dennd areas . Statgrand areas  was usedtatistic , and imptatistic . An accetatistic , and thesoftware . SpecifiAdditional file 1: Figure S1. Structure plot of the 782 accessions.Additional file 2: Figure S2. Phylogenetic relationships for the complete set of accessions, based on Nei’s standard genetic distances and the UPGMA clustering method.Additional file 3: Figure S3. Genetic structure of the accessions, specifying their population of origin.Additional file 4: Table S1. Accessions used in the present study.Additional file 5: Table S2. SSR markers applied in the present study.Additional file 6: Table S3. Analysis of molecular variance considering the gene-bank origins of the accessions.Additional file 7: Table S4. Loci without statistically significant deviations from HWE (p > 0.05).


Prunus dulcis, has been propagated by seeds. Almond grafting remained of little importance until recently. In Lebanon, both sexual and clonal reproductions are used for almond propagation. We used 15 microsatellite markers to investigate the effect of introducing graft-propagated cultivars and associated practices, on the structure of the genetic diversity among and within the two main Lebanese cultivars.Under cultivation, many outcrossing fruit tree species have switched from sexual reproduction to vegetative propagation. Traditional production systems have persisted, where cultivar propagation is based on a mixed reproductive system. For millenia, almond, As expected, the sexually propagated cultivar Khachabi exhibited more genotypic and genetic diversity than the vegetatively propagated cultivar Halwani. It also exhibited lower differentiation among populations. The distribution of clones showed that propagation modes were not exclusive: farmers have introduced clonal propagation in the seed-propagated cultivar while they have maintained a diversity of genotypes within populations that were mostly graft-propagated. These practices are also important to avoid mate limitations that hamper fruit production in a self-incompatible species.‘Khachabi’ is structured into two gene pools separated by the Lebanese mountains. As to ‘Halwani’, two different gene pools were introduced. The most ancient one shares the same geographic range as ‘Khachabi’; longtime coexistence and sexual reproduction have resulted in admixture with ‘Khachabi’. In contrast, the more recent introduction of the second gene pool in the Bekaa region followed an evolution towards more extensive clonal propagation of ‘Halwani’ limiting hybridizations. Furthermore, some pairs of geographically distant ‘Halwani’ orchards, exhibited low genetic distances, suggesting that a network of exchanges between farmers was effective on a large scale and/or that farmers brought clonal plant material from a common source.Almond diversification in Lebanon is clearly related to the evolution of propagation practices adapted to self-incompatible cultivars. The comparison between both cultivars demonstrated the genetic effects of the introduction of a new cultivar and the associated grafting propagation practices. Our study provided information to develop a strategy for in situ conservation of cultivars and to limit gene flow from introduced material to ancient orchards.The online version of this article (10.1186/s12870-018-1372-8) contains supplementary material, which is available to authorized users. Population genetic structure of a crop plant species is strongly determined by their mating system and mode of dissemination. Allogamous seed reproduction maintains the crop evolutive potential because it produces genetically variable progenies able to cope with and adapt to changing environments –3. CharaUnder cultivation, many outcrossing fruit tree species have switched from sexual reproduction to vegetative propagation , and theThe relationship between sexual recombination and genetic diversity works in both directions. While the suppression of sexual recombination reduces genotypic diversity, mate limitations conversely affect the potential for sexual recombination in monoclonal or polyclonal populations of obligate out-crossers. When the target of cultivation is a fruit, production can be ensured if the reproductive constraints are alleviated, for example by parthenocarpy, a suppression of the self-incompatibility system, or a shift to hermaphroditism in dioecious species , 11. NevPrunus dulcis (Miller) D.A. Webb  [P. dulcis and other Prunus species still grow in the wild [The eastern Mediterranean Basin is documented to be one of the major centers of origin for agriculture, with the first domestication events of cereals and fruit trees occurring in the Levant about 12,000 years B.P. [Batsch.) , is consthe wild . For milthe wild ; thus, cthe wild . Almond the wild . Thus, wthe wild , 17. HowPrunus fruit germplasm [Molecular tools such as nuclear microsatellite SSR (simple sequence repeat) analysis have been successfully used to assess genetic diversity as well as to reveal synonymous and erroneous labeling in cultivated ermplasm –22 and iermplasm , 24 and ermplasm . To our ermplasm ; its autermplasm  and Wangermplasm . In thesP. dulcis and its close wild relatives, P. orientalis (Mill.) Koehne and P. spartioides (Spach) C.K. Schneid., are still found in Lebanon [P. dulcis is present in different agro-climatic areas and is part of the Lebanese landscape. Old almond trees are still found over a wide range of elevations (0 to 1600 m). So far, studies on the diversity of almond in Lebanon have only concerned morphological traits [P. orientalis, present in Lebanon, Syria and Turkey [P. dulcis, since it is an important reservoir of almond genetic diversity [Spontaneous populations of  Lebanon –30. Cultl traits  and the l traits . These sd Turkey . Moreoveiversity .Almond has not only been cultivated under widely divergent ecological conditions, but also the history of almond cultivation in the country is long-standing. Two main horticultural forms are recognized by farmers, based on fruit shell hardness. The most common and oldest cultivar is known as ‘Khachabi’, referring to its fruit shell ‘as hard as wood’; for centuries, it has been cultivated and reproduced by seeds, maintaining a high level of morphological variability. Since the Ottoman period (16th to early twentieth century), many cultivars have been introduced to the country from different origins. Among them, the cultivar now called ‘Halwani’, was probably introduced to Lebanon during the last century; it has been widely accepted and mostly propagated by grafts, mainly in mono-varietal populations .Prunus species, with the aim of: (1) characterizing the pattern of genotypic and genetic structure of each cultivar, ‘Khachabi’ and ‘Halwani’, and (2) assessing the influence of the propagation mode on these patterns.Questions arise regarding the genetic diversity of ‘Khachabi’ (common hard shell cultivar) and ‘Halwani’ (introduced soft shell cultivar), as well as their relationships and internal structure, taking into account the influence of their respective propagation modes. Nowadays, sexual reproduction is still important in almond populations and this importance depends largely on whether farmers incorporate sexual progeny into their stocks of grafting material. This practice is common in many traditional farming systems, and has probably been continued from the origin of domestication up to our time. For the present study, we sampled a large range of cultivated individuals from different agro-climatic areas in Lebanon, and analyzed them with a set of 15 SSRs selected for their reproducibility and polymorphism in almond and other Field surveys were undertaken in 2009–2010 throughout Lebanon with the aim of collecting samples representative of the most common almond cultivars, ‘Khachabi’ and ‘Halwani’. We followed farmer’s determinations in the assignation of sampled trees to a given cultivar; nomenclature was usually based on shell hardness. Farmers usually propagated hard shell almonds from seed and soft ones by grafting. According to farmers, out of 331 ‘Khachabi’ individuals, 301 were propagated by seeds, and the 30 individuals from the youngest population were propagated by grafting. Out of the 285 ‘Halwani’ individuals, 255 were propagated by grafting and the 30 individuals from the oldest population were propagated either by grafting or seeds. In all, 14 ‘Khachabi’ and 11 ‘Halwani’ populations were sampled, covering the four major agro-climatic zones  abiding by the supplier’s instructions. Fifteen nuclear SSR markers previously selected for studying the genetic diversity of CPDCT045 –38. We aAllele size was read independently by two investigators using GEMEMAPPER 4.0 . Genotyping errors were evaluated by checking the reproducibility of migrations using samples replicated on the different plates; in all, 95.1% of tested individuals gave the same allele size .Redundant genotypes were searched both within cultivars and among populations of both ‘Khachabi’ and ‘Halwani’ to identify multi-locus genotypes (MLGs) using GENCLONE 2.0 software . Individpi and pj are the frequency of the ith and jth alleles; implemented in CERVUS 3.0.7 software [The discriminant power of the 15 markers used to differentiate MLGs present in the sample was explored by plotting the number of loci versus the maximum number of MLGs detected for all datasets and for each cultivar; values were calculated using a re-sampling procedure (3000 permutations) implemented in GENCLONE. The relevance of marker information was described by the number of alleles (Na) implemented in the software GENALEX 6.5.1  and the software .Genotypic richness was calculated following the corrected formula , R = (G-http://genepop.curtin.edu.au/genepop_op2.html). Exact P-values were estimated using the Markov Chain algorithm with 10,000 dememorization steps, 1000 batches and 10,000 iterations.Genotypic linkage disequilibrium (LD) between loci was calculated on the G dataset to avoid its overestimation; the coefficients of correlation (r) between loci were calculated within the G dataset, each cultivar and the distinct population subsets. Testing for genotypic linkage disequilibrium (LD) using the log likelihood ratio statistic test (G-test) was performed with the software GENEPOP 4.2 , 45, avahttp://www.biology.ualberta.ca/jbrzusto/cluster.php).We conserved a single copy of each MLG (G dataset) to identify the numbers of different alleles between each pair of compared MLGs in each cultivar. The histograms of pairwise comparisons were constructed using simple matching of pairs of MLGs using Unweighted Arithmetic Average, implemented in the CLUSTERING CALCULATOR program developed by J. Brzustowski (http://genepop.curtin.edu.au/genepop_op8.html). Deviations from Hardy-Weinberg equilibrium (HWE) were tested using both alternative hypotheses of a deficit or of an excess of heterozygotes for each locus and across loci, at population and at cultivar levels using Fisher method implemented in GENEPOP (http://genepop.curtin.edu.au/genepop_op1.html). The Hardy-Weinberg Equilibrium tests were calculated based on both N and G datasets.The existence and frequency of null alleles were tested (on the N dataset) using the Expectation Maximization (EM) algorithm  implemen2 N / (2 N-1)*He  implemented in GENALEX, the observed heterozygosity Ho and the Wright’s fixation index (Fis = 1-Ho/He) [Rarefied allelic richness and private allelic richness were computed on the N dataset using the rarefaction method implemented in ADZE 1.0 , with a 1-Ho/He) , impleme1-Ho/He) . DiffereThe genetic differentiation among collected populations was calculated for the N dataset. Null alleles may lead to overestimate population genetic differentiations as measured by Fst . To calchttp://mbb.univ-montp2.fr/MBB/subsection/onlineTools.php?section=2). STRUCTURE was run on the whole data, using both the G and the N datasets, to infer population structure in K genetic clusters and assign individuals to those clusters. Structure was run also on partitioned subsets, for each cultivar, in order to investigate lower levels of structure. Individuals were assigned by probability to a cluster, or jointly to two or more clusters if their genotypes indicated that they were admixed. Individuals for which more than 85% of alleles were assigned to a cluster were considered to belong to a single cluster. To infer posterior probabilities of K, we ran a series of independent runs with different user-defined values of K ranging from 1 to 10. The analysis was run ten times for 107 iterations of MCMC after a burn-in period of 106 based on pre-defined population genetics admixture and no linkage models. To identify the number of K clusters explaining the observed genetic structure we used the statistic parameters defined by Evanno et al. [Two different approaches were used to infer the population structure of the whole dataset. The first was a Bayesian clustering approach assuming Hardy-Weinberg and linkage equilibrium, as implemented in the STRUCTURE 2.3.4 software . This ano et al.  based ono et al. . The CLUo et al.  output so et al.  to graphThe population genetics model in STRUCTURE, relies on restrictive explicit assumptions (populations at Hardy-Weinberg equilibrium and linkage equilibrium) which are likely to be violated for cultivated species, especially those exposed to clonal propagation. The presence of substantial null allele frequencies may also affect the estimation of the population differentiation . Hence, DAPC is a multivariate method that relies on data transformation using a principal component analysis as a prior step to the DA. This prior transformation ensures that variables subjected to the DA are uncorrelated. The identification of genetic groups was achieved using K-means , a clustThe 15 SSR loci revealed a total number of 375 alleles, ranging from six alleles at the BPPCT036 locus to 41 at the CPDCT045 locus  was identified for ‘Khachabi’ r = 0.22 (and 95% of pairwise comparisons were in LD) while for ‘Halwani’ r = 0.43 (and 74% of pairwise comparisons were in LD) . Populations from ‘Khachabi’ exhibited higher genotypic diversity levels than populations from ‘Halwani’, except for the ‘Khachabi’ KS_Ta population, due to the presence of three farmer-selected clones (R = 0.59)  and no redundant MLGs were identified in the HN_Bt population (R = 1.00).The mean genotypic diversity value for cultivar Khachabi was significantly higher than the value reported for cultivar Halwani (R 9) Table .This indWe computed the distribution of the number of allelic differences between individuals (N dataset) for each cultivar; individuals were plotted after suppression of the redundant MLGs. In ‘Khachabi’, 48,798 pairwise comparisons ranged from 11 to 45 allelic differences with a peak at 30 differences, while only 28 pairwise comparisons were differing by 1 to 10 alleles Fig. . IndividForty-two redundant MLGs were defined as clones; three were observed in the clonally propagated ‘Khachabi’ population KS_Ta (data not shown) while 36 were observed in 10 of the 11 ‘Halwani’ populations. Among these, 24 were private (associated to one population) and 12 clones were found across different populations. Some clones were shared among geographically distant populations of ‘Halwani’.To better describe the genetic diversity existing within each cultivar and population, we decided to conduct analyses using the N dataset. ‘Khachabi’ exhibited a larger number of alleles than ‘Halwani’ with 361 and 221 alleles, respectively  for ‘Khachabi’ (Ar = 8.28 ± 0.50 and Pa = 5.48 ± 0.45), as compared to ‘Halwani’ (Ar = 4.80 ± 0.36 and Pa = 2.01 ± 0.31). The mean genetic diversity was significantly higher for ‘Khachabi’ (He = 0.86 ± 0.03) than for ‘Halwani’ (HE = 0.66 ± 0.04) . Cultivar Khachabi also displayed a significant deficit in heterozygosity (Fis = 0.20) while cultivar Halwani had no deficit in heterozygosity (Fis = − 0.03). The comparison of the fixation index (Fis) (N dataset) exhibited high differences between ‘Khachabi’ and ‘Halwani’ . The confidence intervals of Fis values are presented in . The number of rarefied private alleles (Pa) ranged from 0.12 to 0.56. All ‘Khachabi’ populations exhibited a high deficit in heterozygosity (P ≤ 0.05)  and the highest number in the HM_Je population (Ar = 6.10 ± 0.40) (Table P = 0.006) but not in ‘Halwani’   varied from 0.03 to 0.12 among ‘Khachabi’ populations, and from 0.01 to 0.31 among ‘Halwani’ populations; the highest Fst observed among populations from both cultivars was 0.33 (between HB_Ze and KB_Bi) ; 92% of ‘Khachabi’ individuals were assigned to cluster 1 (in red) and 81% of ‘Halwani’ individuals were assigned to cluster 2 (in green) Fig. . AdmixedThe STRUCTURE analysis of ‘Khachabi’ assigned its 314 MLGs into two clusters  Fig. . PopulatThe STRUCTURE analysis of the populations named as ‘Halwani’ assigned the 195 MLGs of this cultivar into three clusters  Fig. . As expeThe likelihood-based analysis of population structure using DAPC split the 509 MLGs from the 25 populations into four distinct groups Fig.  and b. TThe results from both Bayesian and non-model based analyses were compared at a threshold of 85% of assignment, for the global dataset  [P. sibirica L., another self-incompatible species, where He ranged from 0.66 to 0.80 [R = 0.59), where the farmer had selected, and graft-propagated, three trees selected for their fruits. Even so, this unusual situation did not affect the level of allelic richness, as the selected individuals had retained a level of heterozygosity as high as for the seed-propagated ‘Khachabi’ populations.The level of genetic diversity observed within ‘Khachabi’ (He = 0.86) was as high as what was observed in national germplasm collections of different Mediterranean countries , 20, 22. = 0.85) . The com to 0.80 . In our Prunus species, as in the examples of three genetic groups of commercial peach cultivars collected from North America and Europe (He from 0.36 to 0.43) [Prunus species. For example, similar traditional management has long been known in cultivated apricot, where much of the variability has been conserved using a mix between seed- and graft-propagation  [‘Halwani’ exhibited a lower genetic diversity than ‘Khachabi’; which is due to the introduction of a limited number of individuals followed by clonal propagation. However, genetic diversity of ‘Halwani’ populations is still relatively high (He from 0.42 to 0.59), compared to cultivated self-pollinated to 0.43)  and two to 0.43) . We relapricots) .‘Khachabi’ exhibited a high heterozygosity deficit at both cultivar and population levels. These deficits can be explained in part by the presence of null alleles in seven loci, whose frequencies were significantly different from zero. Null alleles lead to underestimate the observed heterozygosity within populations and overestimate the fixation index . It is lPrunus avium in three French sites [The heterozygote excesses observed in ‘Halwani’ populations were in accordance with expectations; as repeated clonal propagation has preserved the initial heterozygosity of founder individuals. Similar results were observed for the perennial outcrossing crop cassava in Brazil , for impch sites . Over geThe exploration of genetic structure by using both Bayesian and non-model methods has provided complementary information, by identifying different levels of genetic structure within the overall dataset Fig. . The firClustering results were in good accordance with cultivar denominations; the two major gene pools (STRUCTURE clusters C1 and C2) correspond to the studied cultivars, and the assignation of individual trees to one of them mostly reflects their denomination by farmers. Nevertheless, history of orchard establishment and rejuvenation has produced some discordance between farmers’ denominations and the genetic identity of some trees: 30 ‘Halwani’-named trees, from three different orchards, are assigned to the ‘Khachabi’ (C1) gene pool. This could be due either to the presence of ‘Khachabi’ trees in the ‘Halwani’ planting material coming from the nursery or to a failure of ‘Halwani’ grafting onto ‘Khachabi’ old trees when rejuvenating the orchard.Dominant clonal propagation of cultivar Halwani has led to limited gene flow with ‘Khachabi’. Only four ‘Khachabi’ and six ‘Halwani’ individuals were not assigned to any groups by DAPC, and the detection of admixture suggests that these 10 genotypes were produced by hybridization. This low level of admixture was the result of a combination of both sexual and clonal propagation practices: sowing spontaneous hybrids between both cultivars, followed by clonal propagation of selected hybrid trees; this ensured bi-directional gene flow between cultivars. This effect of the propagation mode on the genetic differentiation was also documented for cultivated apricot: even though apricot in Tunisia has experienced a single introduction event from the Irano-Caucasian area, it has been subject to local diversification as a result of different propagation practices that have led to the existence of two different gene pools, one propagated by seeds and the other one propagated by grafting .Our results demonstrate also that ‘Khachabi’ is structured into two relatively close gene pools (Fst = 0.033) separated geographically by the chain of Lebanese mountains; they correspond mainly to populations of the central plateau of the Bekaa region and its back mountains, for the first gene pool, and populations of the maritime range of coast and adjacent mountains, for the second gene pool. Admixed individuals were observed between these two gene pools. Farmers mainly established their ‘Khachabi’ orchards with seeds from nearby orchards or mountains (presumably ancient areas of cultivation). However, the presence of individuals from both gene pools, as well as admixed individuals, in the same orchards, attests for events of long distance seed migration among different geographic regions Fig. . RecurreAs to cultivar Halwani, our results indicate that two different gene pools Fst = 0.152) were introduced. This double origin was supported by the high values of Fst and Dc observed between some ‘Halwani’ populations. The first gene pool (C2.3) is mainly present in old orchards  was only represented by three individual trees. As the subsequent phase of diffusion of ‘Halwani’ was based on locally collected materials, admixed materials could be part of the process. Thus, in the HB_Bi orchard, even though the farmer was sure to have ‘Halwani’ trees , our genetic analysis identified trees with admixed MLGs resulting from hybridization between both cultivars. The presence of clones with admixed MLGs in the same geographic region further supports a scenario of propagation from nurseries. Another likely source of error in the diffusion of ‘Halwani’ was grafting failure, followed by ‘Kachabi’ rootstock suckering. This may be suspected for the HB_Na orchard; the farmer had bought his plants from a local nursery as ‘Halwani’, but the genetic analysis assigned many of them to ‘Khachabi’.Later on, the grafting technique was better adopted. The success of grafting increased, explaining the marked predominance of clones in the C 2.2 gene pool that was more recently introduced to the Bekaa region (HB_Ze and HB_Fe orchards). The reduction of on-farm sexual propagation and the corollary appropriation of almond grafting practices led to a reduction of the genetic diversity of ‘Halwani’. Furthermore, farmers do not propagate all clones at the same rate, and in the long term, some clones will be lost.In addition to the effect of farmers’ practices, networks of exchange have played an important role in structuring genetic diversity. Indeed, gene flow appeared to result from a network of exchanges of grafting material when establishing new orchards. The ‘Halwani’ grafts may have been taken from nurseries and/or orchards. This history of introduction and the exchange network of both ‘Halwani’ gene pools deserves to be explored more in depth in order to better understand the impact of human practices on the dynamics of this cultivar and its interactions with more ancient local germplasm.Our comparative study of the two main Lebanese almond cultivars and their interactions provided us with the main elements of a model of evolution of self-incompatible perennial fruit crops under domestication and traditional cultivation. Furthermore, the comparison allowed us to describe the genetic effects of the concomitant introduction of a new cultivar and the corresponding propagation techniques. Grafting techniques are recent in cultivated almond evolution, and the first phases of its domestication process only involved allogamous sexual propagation; the late adoption of vegetative propagation limits the interest of the parallels often established with the domestication of crops derived from wild relatives that propagate vegetatively, such as cactus pear, banana and pineapple.As long as allogamous sexual propagation is exclusive, genotypes cannot be fixed, and the genetic structure of cultivated populations remain similar to that of the fruit crop wild relatives, showing high genetic diversity within populations, limited differentiation among populations, and isolation by distance and topography. Thus, the genetic parameters reflecting the genetic organization of cultivar Khachabi populations in Lebanon compare excellently with those of close wild relatives.The introduction of clonal propagation through grafting techniques brings the advantages of clonal propagation, particularly the fixation of elite genotypes, as well as other genetic effects, and first the reduction of genotypic diversity and a marked differentiation among populations. These effects are not as rapid and dramatic as could be expected, because of the long generation time that maintains old genetic patterns, and because farmers adapt their practices progressively, introducing vegetative reproduction in sexually propagated cultivars, as well as sexual recombination in the new clonal cultivars, thus generating new genetic diversity. As the evolution of farmers’ practices gives more and more prevalence to vegetative reproduction, newly introduced populations of the clonal cultivar evolve differently, with a stronger loss of genetic diversity and less interactions with ancient cultivars. The observed evolution of the cultivar Halwani follows clearly such a pattern.The almond self-incompatibility system plays a particular role in these developments, imposing outcrossing in traditional, sexually propagated, cultivars, favoring recombination among ancient cultivars and new introductions, but also imposing a threshold of minimal genetic diversity to avoid mate limitation and the attendant yield reductions. Beyond this threshold, the system would evolve towards the exclusive use of vegetative propagation, as in western horticulture, implying the adoption of the complete technical package of modern horticulture , while resulting in a dramatic erosion of genetic diversity.The population approach has allowed us to identify the effect of practices of farmers and nurserymen on the dynamics of genetic diversity within and among cultivars. Our results have shown different patterns of genetic diversity and population genetic structure for almond cultivars cultivated in Lebanon. Our study does not only contrast the effects of seed- and graft- propagation on genetic and genotypic diversity; it also shows that these propagation modes are not exclusive: farmers introduce clonal propagation in the seed-propagated cultivar while they maintain a diversity of genotypes within populations that are mostly graft-propagated. In such a mixed system, genetic diversity is not negatively affected and orchard productivity is not limited by the availability of compatible pollinators.Prunus species.Our study also detected long-distance exchanges of planting materials. Elucidating the social networks driving them will lead to a better understanding of the spatial and temporal dynamics of the genetic structure of the Lebanese almond and of the diversification processes at work. It will provide more in-depth information to develop a strategy for in situ conservation of cultivars and to reduce gene flow from introduced material to ancient orchards, as well as to natural populations of various Additional file 1: Figure S1. The geographic locations of the collected populations. A total of 14 ‘Khachabi’ and 11 ‘Halwani’ populations were sampled, covering the four major agro-climatic zones. (TIF 925 kb)Additional file 2: Table S1. Statistics associated with microsatellite loci. Na, total number of alleles; PIC, polymorphic information content; Ho, observed heterozygosity; He, unbiased expected heterozygosity; Fis, inbreeding index calculated based on a matrix of N individuals or G genotypes; f (Null), estimation of significant frequency of null alleles. (a) Cipriani et al. 1999; (b) Testolin et al. 2000; (c) Sosinski et al. 2000; (d) Dirlewanger et al. 2002; (f) Aranzana et al. 2002. * HW significance with Bonferroni correction (P < 0.01). Fis (N) values were calculated with all trees found including repeated clone genotype. Fis (G) values were calculated with only one individual per multi locus genotype (MLG). (XLS 28 kb)Additional file 3: Table S2. Genotypic linkage disequilibrium for pairwise comparisons between loci conducted over different data sets without redundant MLGs. – data not available; The coefficient of correlation is indicated for P > 0.05. (XLS 45 kb)Additional file 4: Figure S2. Mean number of alleles per locus as a function of sample size. N, number of MLGs. Diamonds for global dataset, squares for ‘Khachabi’, triangles for ‘Halwani’. (TIF 36 kb)Additional file 5: Table S3. Confidence intervals for Fis values at 95%. Fis calculated for sample size over 15 individuals (N) or genotypes (G). (XLS 25 kb)Additional file 6: Figure S3. Plot of genetic distance (Dc) and geographic distance for A) 14 ‘Khachabi’ populations; B) 11 ‘Halwani’ populations. Significance at α = 0.05. (TIF 23 kb)


Lynx lynx dinniki, has one of the southernmost distributions in the Eurasian lynx range, covering Anatolian Turkey, the Caucasus and Iran. Little is known about the biology and the genetic status of this subspecies. To collect baseline genetic, ecological and behavioural data and benefit future conservation of L. l. dinniki, we monitored 11 lynx territories (396 km2) in northwestern Anatolia. We assessed genetic diversity of this population by non-invasively collecting 171 faecal samples and trapped and sampled 12 lynx individuals using box traps. We observed high allelic variation at 11 nuclear microsatellite markers, and found no signs of inbreeding despite the potential isolation of this population. We obtained similar numbers of distinct genotypes from the two sampling sources. Our results indicated that first order female relatives occupy neighbouring territories  and that territorial male lynx were highly unrelated to each other and to female territorial lynx, suggesting long distance male dispersal. Particular male and female resident territorial lynx and their offspring (kittens and subadults) were more likely to be trapped than resident floaters or dispersing (unrelated) lynx. Conversely, we obtained more data for unrelated lynx and higher numbers of territorials using non-invasive sampling (faeces). When invasive and non-invasive samples were analysed separately, the spatial organisation of lynx  affected measures of genetic diversity in such a way that estimates of genetic diversity were reduced if only invasive samples were considered. It appears that, at small spatial scales, invasive sampling using box traps may underestimate the genetic diversity in carnivores with permanent ranges and philopatry such as the Eurasian lynx. As non-invasive sampling can also provide additional data on diet and spatial organisation, we advocate the use of such samples for conservation genetic studies of vulnerable, endangered or data deficient territorial species.The Caucasian lynx,  Conservation of wildlife species often requires highly demanding practices such as habitat preservation and restoration, animal protection, animal relocation, captive breeding and reintroductions . For datLynx lynx has the widest geographic distribution. Populations occur in a wide variety of habitats, ranging in the Palearctic region from Scandinavia and central Europe to far eastern Russia, and can also be found south of the 20th degree of latitude . Wheread Tibet; , nucleard Tibet; , 13, 14.Lynx lynx in Asia, L. l. dinniki and L. l. isabellinus, are still little known in terms of their ecological requirements, spatial and genetic population structure and their genetic diversity. The Caucasian lynx L. l. dinniki (Satunin 1915) has one of the southernmost distributions of Eurasian lynx [Lynx pardinus), have a smaller body size [Lynx is otherwise only observed in bobcats . Highwa Satunin 15 has onPreviously, two phylogeographic studies included Caucasian lynx among sampled subspecies of the Eurasian lynx , 20. BotThree large Caucasian lynx populations occur in Turkey . The norNon-invasive sampling enables researchers to collect samples in the field without disturbing the animals or putting them at health risk, and potentially represents a means to obtain genetic material from many individuals. For example, it may be feasible to collect faecal samples from a large area at a reasonable cost and effort. Genotyping success of non-invasively collected samples depends on several factors such as duration of exposure of the faeces to sun and humidity, as they affect the speed of DNA degradation, presence of PCR inhibitors , the amoThe collection of invasive samples can also be challenging. Its success depends on the population density of the study species and the trapping methodology used. The study design needs to take animal welfare into account and may be costly in terms of time and the resources required , 25. MorAcinonyx jubatus) where both territorial animals and floaters were identified as constituting the population of residents [Some authors  have stresidents .To our knowledge, no study has followed up on this idea and actually compared how sampling source (invasive vs. non-invasive) might influence the assessment of genetic diversity measures in philopatric animal populations where adults occupy permanent ranges and thus are not randomly distributed. Since stationary trapping systems such as box traps are more likely to capture territorial residents as these are habituated to the presence of traps, we hypothesise that non-invasively collected faecal samples are more likely to provide evidence of other classes of residents such as floaters as well as dispersing or nomadic lynx, none of which are likely to habituate to traps, and thus increase the measurement of genetic diversity. These animals are part of the same population–therefore measures which include these lynx would more accurately reflect the genetic diversity of the entire population.L. l. dinniki population using nuclear microsatellite markers with the help of non-invasively and invasively sampled material. We examined the genetic variability of this potentially isolated Eurasian lynx population and evaluated it in the context of similar data for Eurasian lynx populations from central and Eastern Europe. In addition, we tested the predictions from our hypothesis and compared measures of genetic diversity obtained from different sample sources (invasive vs. non-invasive) to provide insights into the effect of different sampling methods on estimates of genetic diversity in a territorial carnivore at a small spatial scale.In this study we conducted the first assessment of the spatial organisation  and genetic variability of the northwest Anatolian 2 in the Nallıhan Mountains . Above this belt, temperate coniferous forest reaches up to 1500 m and is composed of black pine (Pinus nigra) and junipers (Juniperus excelsa and J. oxycedrus) with an understory of oak-dominated scrub  with frequent forest openings. The mean annual temperature is 9.6° Celsius and the mean annual total precipitation is 543 mm [Cervus elaphus) and wild boar (Sus scrofa) are the common large herbivores, and brown hare (Lepus europaeus) is the main lynx prey species here [Ursus arctos) and grey wolf (Canis lupus) are sympatric with lynx, and at lower elevations golden jackal (Canis aureus), red fox (Vulpes vulpes) and jungle cat (Felis chaus) occur, which rarely occur in lynx and wolf habitat [All samples were collected in an area of 396 kms 543 mm . The hum habitat .2 in total) that covered the territories of individually recognised male (n = 5) and female (n = 6) lynx that had previously been repeatedly identified over several years by camera trapping  were cotrapping . By repetrapping .Capture of lynx and field work were performed in collaboration with the Wildlife Department of the Turkish Ministry of Agriculture and Forestry (WDT) under protocol and permit number 30057506-030-1867 issued by the department. Five cage traps constructed by the WDT  were used for capturing lynx. Captured lynx individuals were anaesthetised and sampled by the authorised wildlife veterinarian of the WDT following national ethical legislation. No specific permit was required for anaesthesia and lynx treatment as it was conducted by the WDT. Traps were placed on lynx trails at nine trapping stations in the territories of four male and six female lynx . We moniFelis catus) [Lynx canadensis) [DNA was extracted from all sample types using a commercially available forensic DNA extraction kit  following the manufacturer’s instructions. As no other felid species was present in the lynx habitat (1000 m to 1500 m asl) and golden jackals and red fox were distributed at lower elevations close to human settlements due to interference competition by wolves and lynx , we did s catus) , 39 and adensis) : FCA006,As genotypes determined from non-invasive samples may be incomplete or suffer from errors , we applied a maximum likelihood approach  to ensurAllelematch version 2.5 [IDunb) and probability of identity given siblings (PIDsib) using the software GIMLET version 1.3.3 [Given the large number of samples taken in the field and the size of the area surveyed, multiple sampling of some individuals was expected. Applying the option ‘alleleMismatch = 2’ of the software sion 2.5 , we compon 1.3.3 .FIS) and expected (HE) and observed heterozygosities (HO). Allelic richness (AR) and Kosman and Leonard’s measure of genetic dissimilarity versus geographic distance [PopGenReport version 2.1 [AR was estimated using rarefaction of eight genotypes per population . Kosman and Leonard’s measure of genetic dissimilarity was applied to visualize the spatial organisation and pairwise relatedness among territorial lynx  in the study area. This required a spatial coordinate to represent the individuals included in the analysis. The procedure to allocate the appropriate spatial coordinate is explained below. Additionally, we conducted a spatial autocorrelation analysis using GenAlEx version 6.502 [xy [Demerelate version 0.9–3 [The probability for the presence of null alleles at the fourteen microsatellite loci was estimated using the software MICROCHECKER version 2.2.3 . Potentidistance  were detsion 2.1 . AR was on 6.502 , 50. To .502 [xy ) values on 0.9–3 .We also used our microsatellite data and reanalysed them in combination with the data of Bull et al.  using thHE) as measures for genetic variability, estimated using the function ‘subsample.gen’ of the R package Resamplediversity version 1.0 [HE. We then used Tukey’s test to examine whether HE values differed significantly between sample types. The test was conducted for the range of genotypes from 2 to 12, the latter being the maximum number of genotypes among invasive samples. Bonferroni’s inequality method [In order to evaluate whether the two sampling methods (“invasive” vs. “non-invasive”) affected estimates of population genetic diversity, we needed more samples with complete genotypes. Therefore, we removed three microsatellite loci that had generated missing data for numerous samples. The eight loci for which additional samples had a complete genotype were FCA008, FCA082, FCA097, FCA105, FCA229, FCA1023, LCA109 and LCA110. For this aforementioned comparison, we considered the mean number of alleles , we used the home range centroid estimated from GPS data as the spatial coordinate. For the remaining territorial residents (n = 5), we used the spatial coordinates of the centroid of the minimum convex polygon established from locations where faecal samples had been collected with this genotype and the locations of camera traps where pictures of that particular individual had been taken.NA) ranging from three to seven (FIS) and deviated significantly from Hardy-Weinberg equilibrium  at distance classes up to 7 km (P ≤ 0.05) and a significant negative correlation (r ≤ -0.11) at distance classes of 13 km and 16 km (P ≤ 0.05)  . In ordePIDunb = 1.02 × 10−9 and PIDsib = 3.12 × 10−4. The mean AR was 3.96, mean HE and HO were 0.65 and 0.69, respectively, and mean FIS was -0.055.Amplification success of the 11 loci across all samples was 66%. Among the 171 ‘non-invasive’ samples, 27 (15.8%) were successfully genotyped twice at nine to eleven microsatellite loci . Among the ‘invasive’ samples, all 9 tissue samples, the mouth swab and one out of two plucked hair samples generated data for the same number of loci (91.7%). Among the 27 non-invasive and 11 invasive samples (n = 38), we detected 18 unique lynx genotypes , 10 from non-invasive and 11 from invasive samples; three genotypes were detected using both sources. The cumulative estimates of probability of identity were: genotypes = 18, nloci = 10) had the second highest AR and HO values after the lynx population from Russia and the second lowest FIS value after the lynx population from Slovakia , an analysis of pairwise relatedness (Mxy) revealed 14 full-sibling/parent-offspring pairs (Mxy threshold = 0.59), 37 half-sibling pairs (Mxy threshold = 0.43), and 102 pairs of unrelated individuals for lynx in northwest Anatolia.When one locus was removed from the dataset and measures of genetic diversity were re-estimated from the 10 microsatellite loci matching the ones previously employed by Bull et al. , the meaSlovakia . Among tian lynx . Two autian lynx . Using tOver 52 survey days we collected 171 faecal samples (mean: 3.3 samples/ day) with the help of a scat detection dog . In 961 PIDunb = 3.79 ×10−7 and PIDsib = 2.67 × 10−3.A reduction of the number of loci to eight microsatellites see  increaseHE) differed between the two sampling methods (HE < 0.0015) or if all samples were combined . The diversity observed among genotypes from non-invasively collected samples did not significantly differ from the diversity measured among all genotypes .When we applied the subsampling analysis, the curves depicting the accumulation of mean number of alleles , and consider the consequences of using invasive sampling versus non-invasive sampling for measures of genetic diversity of this territorial felid.In this study, we report the first population genetic diversity measures for Caucasian lynx Lynx pardinus [L. pardinus [L.l. balcanicus [Panthera pardus orientalis [Panthera pardus melas [Genotyping is a valuable tool for assessing population genetic status and viability of endangered or data deficient animal populations , 57. Plapardinus ). Most ppardinus ; L.l. balcanicus ; Pantherientalis ; Pantherus melas ). Small L. l. dinniki population in northwestern Turkey, and because we were interested in generating baseline information on population genetics, we used both approaches. The ‘invasive’ sampling strategy was applied to ensure reliability of genotyping results, the ‘non-invasive’ sampling strategy was applied because it would increase the number of samples available for the study. The method was even improved by employing a scat detection dog. Having samples from both sampling approaches also provided an opportunity for a comparison of the results obtained in both approaches. Future surveys of the northwestern population and other populations of Caucasian lynx in Turkey, Caucasus and Iran could then potentially rely on data from ‘non-invasive’ sampling.Two sampling strategies are common: ‘invasive’ and ‘non-invasive’. As our study is the first one on the As described in other studies, genotyping success in faecal samples can be a relatively low in relation to sampling effort and this can vary among species , 63. In The spatial genetic analysis of the territorial members of northwestern Anatolian lynx population (combined sampling sources) revealed a unimodal genetic dissimilarity pattern, with a peak at 17 km line in . PairwisWe observed substantial differences between measures of genetic diversity of a single lynx population derived from two sampling approaches , for whiIf we had only used invasive samples for genetic monitoring, as was done for many preliminary conservation projects for endangered species , 68, 69,HE = 0.65, HO = 0.69) and lack of inbreeding (FIS = -0.05), the northwestern Anatolian lynx population currently does not appear to require any management to bolster its genetic diversity. In order to conserve its current genetic diversity, we highly recommend identification and maintenance of primary lynx habitats and corridors in northwest Anatolia. As there is no other study on Anatolian lynx we could compare our findings with, our data provide a ‘genetic baseline’ of a seemingly healthy lynx population, available to future studies to measure anthropogenic and other impacts on this population . Similar work is also needed for the other two Turkish populations of L. l. dinniki in order to determine whether the three big populations in Anatolia  than the north-western Anatolian population. We observed a low mean relatedness in the northwestern Anatolian population, similar to that observed for autochthonous populations sampled over much larger geographic areas  in northwestern Anatolia displayed high genetic diversity. Assessment of other Caucasian lynx populations in Anatolia and elsewhere is required to evaluate the conservation status of this subspecies. Our results show that sampling approach, territoriality and female philopatry can influence measures of genetic diversity, which may be relevant to conservation management decisions. ‘Non-invasive’ faecal sampling reduces the impact of female philopatry and territoriality on diversity measures and provides information on other important aspects of the biology and ecology of the species, which in turn can help to inform conservation management decisions.Caucasian lynx Click here for additional data file.S2 TableCamera trapping (CT), Genotyping (G), GPS telemetry (T). Bold values indicate total amount of genotypes obtained using 11 and 8 microsatellite loci.(XLSX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 FigFather of this male kitten was also captured in the same trap at another occasion.(PNG)Click here for additional data file.S1 VideoOne of the kittens in the video is Female 5, which was captured and collared next trapping season (14 months later) in the same trap.(AVI)Click here for additional data file.