Table 3.
Results from multiple regression on distance matrices (MLM) and generalized dissimilarity modelling (GDM) analyses demonstrating the proportion of genomic variation explained by geographical distance, environmental distance (bioclimatic variables) and two resistance surfaces
| Models | Linear (MRM) | Non-linear (GDM) | |||
|---|---|---|---|---|---|
| R 2 | Significant variable (coefficient) | Deviance | Percentage of variance explained | Important variable (importance) | |
| 1 (Full model: GEO + ENV + ALT + ANT) | 0.119 | GEO (0.176) | 72.73 | 28.45 | PC1 (21.62)* ALT (82.67)* |
| 2 (GEO) | 0.042 | GEO (0.204) | 98.05 | 3.53 | GEO |
| 3 (ENV) | 0.059 | NA | 96.96 | 4.61 | PC1 (70.42) PC3 (29.91) |
| 4 (ALT + ANT) | 0.156 | NA | |||
| 5 (GEO + ENV) | 0.046 | GEO (0.206) | 92.89 | 8.61 | PC1 (48.30) GEO (53.21)* |
| 6 (GEO + ALT) | 0.098 | GEO (0.169) ALT (−0.269) | 80.33 | 20.97 | ALT (33.27)* ANT (2.24) |
| 7 (GEO + ANT) | 0.109 | GEO (0.175) ANT (−0.309) | |||
| 8 (ENV + ALT) | 0.076 | ALT (−0.305) | 72.79 | 28.38 | PC1 (21.62) ALT (87.67)* |
| 9 (ENV + ANT) | 0.087 | ANT (−0.343) | |||
*Variable significance at P < 0.05.
GEO: geographic distance; ENV: three PC axes of the bioclimatic variables – PC1 (Minimum temperature of the coldest month), PC2 (Maximum temperature of the warmest month), PC3 (Mean diurnal range); ALT (altitude), ANT (human global footprint).