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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 (--)

From: Topography and post-fire climatic conditions shape spatio-temporal patterns of conifer establishment and growth

  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