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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).