Table 4 Model performance for the logistic regression (occurrence) model and the negative binomial (count) model. Pseudo R2 values indicate proportional increase in deviance explained over the null. Fit statistics indicate a significant lack of fit if P-values <0.05. Similarly, Moran’s I indicates potential residual autocorrelation when P < 0.05. Diagnostic plots for the count models are given in Additional file 2. Models were for field data collected in 2016 on the Tripod Complex fires in the Okanogan-Wenatchee National Forest, Washington, USA. Models that were not run are indicated by dashes (--)
Logistic regression | Negative binomial | |||||||
|---|---|---|---|---|---|---|---|---|
Pseudo R2 | Hosmer-Lemeshow statistic | AUC | Moran's I | Pseudo R2 | Pearson's statistic | Spearman's rank | Moran's I | |
All species | -- | -- | -- | -- | 0.24 | χ2 = 59.05, P = 0.11 | ρ = 0.49 | I = −0.01, P = 0.91 |
Western larch | 0.54 | χ2 = 5.50, P = 0.70 | 0.94 | I = 0.02, P = 0.49 | 0.79 | χ2 = 28.12, P = 0.99 | ρ = 0.57 | I = −0.05, P = 0.43 |
Lodgepole pine | -- | -- | -- | -- | 0.30 | χ2 = 42.44, P = 0.69 | ρ = 0.57 | I = 0.03, P = 0.42 |
Engelmann spruce | 0.34 | χ2 = 5.55, P = 0.70 | 0.87 | I = 0.13, P = 0.01 | 0.50 | χ2 = 58.74, P = 0.12 | ρ = 0.50 | I = 0.07, P = 0.10 |
Ponderosa pine | 0.40 | χ2 = 9.17, P = 0.33 | 0.89 | I = 0.09, P = 0.05 | 0.58 | χ2 = 35.60, P = 0.89 | ρ = 0.73 | I = 0.08, P = 0.10 |
Douglas-fir | 0.27 | χ2 = 8.97, P = 0.35 | 0.86 | I = 0.10, P = 0.05 | 0.46 | χ2 = 51.26, P = 0.35 | ρ = 0.53 | I = 0.11, P = 0.03 |