Table 3.
Regression relationships for node properties of a fisher (Martes pennanti) genetic network in ON, Canada
| Model | Constant | Parameter estimate | r2 | P |
|---|---|---|---|---|
| Degree | ||||
| Prop. immigrants | 6.4 (0.94) | −5.0 (4.3) | 0.11 | 0.033 |
| Snow depth | 9.1 (3.4) | −0.09 (0.08) | 0.15 | 0.021 |
| Coniferous forest | – | −8.0 (13) | – | – |
| Eigenvector centrality | ||||
| Prop. immigrants | 0.20 (0.037) | −0.27 (0.17) | 0.24 | 0.004 |
| Snow depth | 0.31 (0.14) | −0.004 (0.0003) | 0.16 | 0.010 |
| Coniferous forest | – | −0.23 (0.53) | – | – |
Linear regression models relate degree and eigenvector centrality to the proportion of genetically identified immigrants as well as snow depth and coniferous forest cover. P-values were generated by randomly permuting the dependent variable and are the proportion randomly generated parameter estimates that were more extreme than those generated from the data.