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Table 2 Results of outlier detection among the 83 AFLP markers in larvae of the large pine weevil using the frequentist method Dfdist and the Bayesian inference method BayeScan.

From: Genome scan to assess the respective role of host-plant and environmental constraints on the adaptation of a widespread insect

Method of detection   Frequentist Bayesian inference
Dataset Locus p-value F ST Posterior probability A F ST
Geography (Structure 11) 52 3 0.000 0.231 1 2.010 0.221
  68 0.000 0.338 1 1.790 0.191
  38 0.000 0.276 1 1.810 0.194
  10 0.000 0.248 0.758 1.000 0.105
  63 0.004 0.180 0.999 1.520 0.156
  13 0.099 0.061 0.974 1.320 0.136
  47 0.045 0.080 0.931 1.190 0.122
Geography + host-plant 38 0.000 0.259 1 2.090 0.208
(Structure 21) 52 0.000 0.256 1 2.180 0.220
  63 0.000 0.225 1 1.950 0.186
  68 0.000 0.231 0.999 1.650 0.151
  10 0.000 0.181 0.743 0.893 0.082
  30 0.016 0.088 0.908 1.110 0.098
  33 0.018 0.095 0.876 1.030 0.091
  27 0.102 0.066 0.882 1.040 0.092
  13 0.069 0.054 0.981 1.300 0.116
  47 0.043 0.065 0.865 1.030 0.092
Local host-plant differentiation       
Regions       
Finland2 (Structure 31)       
Limousin (Structure 41) 27 0.000 0.217 0.915 1.460 0.222
Ardeche2 (Structure 51)       
  1. 1As in Figure 1.
  2. 2No outliers were found with the significance level used.
  3. 3Bold type indicates markers that are detected by both methods with a type-I error (α) = 0.0006 for Dfdist, and with a posterior probability > 0.79 for BayeScan (see text for explanation about these values).