Table 4.
Potential outliers associated with environmental variables.
| Markers | BAYESCAN log10PO |
DFDIST | Samβada, LFMM, and rstanarm | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Aspect | BIO1 | BIO7 | BIO12 | NDVI | PET | RainD | Slope | |||
| P1_1409 | 0.0041 | b | ||||||||
| P1_1715 | 0.6723 | *,** | B | *,** | a,* | |||||
| P4_1326 | 0.0075 | * | *,** | |||||||
| P5_1061 | 0.0001 | *,** | * | *,** | ||||||
| P5_2456 | 1.1684 | b,*,** | a | a | * | a,*,** | ||||
| P7_2874 | 0.0067 | |||||||||
| P9_1014 | 1.4171 | *,** | a,*,** | * | ||||||
| P9_1688 | 0.0095 | |||||||||
| P11_1715 | 0.6865 | *,** | B | *,** | a,* | |||||
| P12_2853 | 0.0098 | B | ||||||||
| P12_3406 | 0.0080 | |||||||||
| P13_1547 | 0.0017 | B | * | |||||||
| P15_1446 | 1.5961 | a | a,B | a | a | |||||
| P15_1918 | 0.8566 | * | *,** | |||||||
| P18_1421 | 0.0089 | *,** | ||||||||
Fifteen potential outliers were identified by FST genome scan methods (BAYESCAN and DFDIST) and 12 of them were found to be strongly associated with environmental variables using regression approach (Samβada, LFMM, and rstanarm).
a and b represent significant correlation of AFLP markers with individual environmental variables identified, respectively, by Samβada and LFMM. B represents a |Z| ≥ 1.5 in LFMM analysis.
*,** significance based on 95% and 99% posterior credible intervals for the potential outliers found to have strongly correlated with environmental variables using the stan_glm function of R package rstanarm.
Aspect (0–360°) and slope (0–90°).
BIO1, annual mean temperature; BIO7, annual temperature range; BIO12, annual precipitation; RainD, number of rainfall days per year. NDVI, normalized difference vegetation index; PET,The annual total potential evapotranspiration.