Fig 1.
Genetic diversity of 91 walnut populations in Eurasia.
Inverse Distance Weighted (IDW) interpolation of the allelic richness values (Rs) (a) and unbiased heterozygosity UHE (b) calculated for 91 walnut populations (black dots) in Eurasia using 14 SSR markers (abbreviations CN = China, UZ = Uzbekistan, KG = Kyrgyzstan, TJ = Tajikistan, PK = Pakistan, IR = Iran, GE = Georgia, TR = Turkey, MD = Moldova, RO = Romania, HU = Hungary, SK = Slovakia, GR = Greece, IT = Italy, FR = France, ES = Spain).
Fig 2.
Spatial genetic structure of 91 walnut populations in Eurasia.
Population structure inference for 91 walnut populations by Bayesian assignment using STRUCTURE for K = 4. Synthetic map of Inverse Distance Weighted (IDW) interpolations of the estimated mean population membership values (Qi) (a) and bar plot showing assignment probabilities of individuals to K clusters (b). Abbreviations: CN = China, UZ = Uzbekistan, KG = Kyrgyzstan, TJ = Tajikistan, PK = Pakistan, IR = Iran, GE = Georgia, TR = Turkey, MD = Moldova, RO = Romania, HU = Hungary, SK = Slovakia, GR = Greece, IT = Italy, FR = France, ES = Spain.
Fig 3.
Spatial genetic sub-structure of the inferred clusters 1, 2 and 4 of walnut populations.
Synthetic maps of Inverse Distance Weighted (IDW) interpolations of the estimated mean population membership values (Qi) and bar plot for (a) K’, the most probable number of sub-clusters, based on microsatellite analysis of 217 walnut samples of cluster 1, (b) for K”, the most probable number of sub-clusters, based on microsatellite analysis of 280 walnut samples of cluster 2 and (c) for K”‘ the most probable number of sub-clusters, based on microsatellite analysis of 929 walnut samples of cluster 4.
Fig 4.
Neighbor Joining cluster analysis of 91 walnut populations based on unbiased Nei’s genetic distance.
Neighbor Joining-based circular tree for 91 walnut populations from the species’ Eurasian range based on unbiased Nei’s genetic distance [43]. The assignment of walnut populations to four clusters and eight sub-clusters inferred by STRUCTURE is shown.
Table 1.
Most likely demographic scenario for European walnut by the DIYABC approach.
Posterior probability (P) and 95% confidence interval of P (in brackets) computed using a direct (P1) and logistic regression (P2) approach are provided for each scenario tested by the DIYABC approach. The most likely scenario for each stage is reported in grey. Confidence in scenarios was evaluated using type I error (False negative) and type II error (False positive) rates for logistic regression.
Table 2.
Parameters estimates of the most likely scenarios of walnut expansion.
Parameters estimates of the most likely scenarios (scenarios 6b) inferred by the Approximate Bayesian DIYABC Computation [45] in the stage 2. Estimation of parameters is based on 1% of the closest data sets and subsequent logit transformation.
Fig 5.
Human-mediated dispersal routes of walnut during the Late Holocene as inferred by DIYABC analysis.
Human-mediated dispersal routes of walnut during the Late Holocene as inferred by approximate Bayesian computation [45]. Arrows represent the relationships between population pools used in DIYABC analysis (Pool 1, Pool 2, Pool 3, Pool 4) as inferred from stage 2, scenario 6b. Hypothetical glacial refugia located in the Balkans (ancestral pool NG1) are reported in dark grey. Abbreviations: TR = Turkey, MO = Moldova, RO = Romania, HU = Hungary, SK = Slovakia, GR = Greece, IT = Italy, FR = France, ES = Spain.