Table 2.
(A) Bayesian linear regression models showing the relationships between the predictor eco-physiological resilience (explanatory variables) and the degradation determinants (response variables) along with the posterior probabilities of the significance of linear model parameters. Estimates of the linear model parameters are obtained by posterior means.
| Equation no. | Degradation determinants (Response variables) | Eco-physiological resilience (Predictors) in addition to intercept | Posterior probabilities of inclusion of each of all predictors | R2 | Posterior probabilities of the model | Regression equation |
|---|---|---|---|---|---|---|
| (A) | ||||||
| 1 | Forest coverage | Intercept, WST, GB, SS, FAA | 1.000, 0.470, 0.723, 0.976, 0.957 | 0.884 | 0.035 | Forest coverage = 48.89 − 0.039*WST + 0.908*GB − 3.402*SS − 2.298*FAA |
| 2 | Ammonia-nitrogen | Intercept, PT, PT/ST, WST/LT, INO, FAA | 1.000, 0.972, 0.927, 0.857, 0.946, 0.959 | 0.844 | 0.241 | Ammonia-nitrogen = 3.171 − 0.021*PT + 0.009* PT/ST − 2.488* WST/LT − 0.029*INO − 0.144* FAA |
| 3 | Organic carbon | Intercept, LT, PT/ST, ST/LT, PINI, INO, FAA | 1.000,0.709,0.954, 0.663, 0.898, | 0.695 | 0.031 | Organic carbon = 0.799 − 0.001* LT + 0.000* PT/ST + 0. 143* ST/LT − 0.007*INO − 0.013* FAA |
| 4 | Tidal water conductivity | Intercept, WST/LT, PRO, INO, FAA | 1.000,0.908, 0.992, 0.647, 0.823 | 0.645 | 0.078 | Tidal water conductivity = 39.63 + 16.48* WST/LT + 2.521* PRO + 0.092*INO − 0.649*FAA |
| 5 | Soil conductivity | Intercept, LT, WST, PRO, SS, FAA, MAN | 1.000, 0.457, 0.599, 0.999, 0.562, 0.655, 0.991 | 0.647 | 0.029 | Soil conductivity = 13.59 − 0.007* LT + 0.016*WST + 2.132*PRO + 0.277*SS + 0.274*FAA + 0.0006*MAN |
| 6 | Plant available phosphorus | Intercept, PT, PT/ST, WST/LT, INO, FAA | 1.000, 0.538, 0.997, 0.496, 0.793, 0.888 | 0.764 | 0.037 | Plant available phosphorus = 5.842–0.016*PT + 0.029*PT/ST − 0.771*WST/LT − 0.042*INO − 0.232*FAA |
| 7 | Phenol oxidase activity | Intercept, PT, PT/LT, SS | 1.000, 0.783, 0.814, 0.758 | 0.734 | 0.035 | Phenol oxidase activity = 0.628 − 0.005*PT + 1.18*PT/LT − 0.035*SS |
| 8 | Sulfide-sulfur | Intercept, WST, ST, PT/ST, GB, PRO, FAA | 1.000, 0.824, 0.726, 0.476, 0.775, 0.901, 0.999 | 0.808 | 0.040 | Sulfide-sulfur = 3.423 + 0.024*WST – 0.023*ST – 0.006*PT/ST − 0.085*GB + 0.46*PRO + 0.375*FAA |
| (B) | ||||||
| Equation no. | Degradation determinants (Response variables) | Gene variables (Predictors) in addition to intercept | Posterior probabilities of inclusion of each of all predictors | R2 | Posterior probabilities of the model | Regression equation |
| 9 | Forest coverage | Intercept, F16BP, F26BP, SUS | 1.000, 0.999, 0.894, 0.853 | 0.927 | 0.536 | Forest coverage = 48.886 − 3.467*F16BP − 1.417*F26BP − 2.023*SUS |
| 10 | Ammonia-nitrogen | Intercept, P5CS, F16BP | 1.000, 0. 453, 0.993 | 0.822 | 0.241 | Ammonia-nitrogen = 3.171 − 0.038*P5CS − 0.269*F16BP |
| 11 | Organic carbon | Intercept, F16BP, FBA | 1.000, 0.994, 0.491 | 0.638 | 0.234 | Organic carbon = 0.799–0.035*F16BP – 0.009*FBA |
| 12 | Tidal water conductivity | Intercept, BADH | 1.000, 0.994 | 0.679 | 0.584 | Tidal water conductivity = 39.633 + 0.949*BADH |
| 13 | Soil conductivity | Intercept, BADH, FBA | 1.000, 0.999, 0.592 | 0.590 | 0.253 | Soil conductivity = 13.585 + 0.730*BADH − 0.279*FBA |
| 14 | Plant available phosphorus | Intercept, F16BP, FBA | 1.000, 0.999, 0.682 | 0.772 | 0.289 | Plant available phosphorus = 5.842 − 0.510*F16BP − 0.148*FBA |
| 15 | Phenol oxidase activity | Intercept, F16BP, F26BP | 1.000, 0.976, 0.519 | 0.746 | 0.212 | Phenol oxidase activity = 0.628 − 0.050*F16BP − 0.014*F26BP |
| 16 | Sulfide-sulfur | Intercept, MIPS, F26BP | 1.000, 0.980, 0.803 | 0.780 | 0.470 | Sulfide-sulfur = 3.423 + 0.221*MIPS + 0.138*F26BP |
(B) Bayesian linear regression models showing the relationships between the predictor gene expression variables (explanatory variables) and the degradation determinants (response variables) along with the posterior probabilities of the significance of linear model parameters. Estimates of the linear model parameters are obtained by posterior means.