Model prediction diagnostics for three hurdle regression models predicting the occurrence (binomial) and abundance (count) of steelhead redds in the John Day River basin, Oregon.

1For the occurrence component, percent correctly classified (PCC; cutoff = 0.5) and area under the curve (AUC) statistics with standard deviations are presented.2For the abundance component, the results of a “0.632+” bootstrap evaluation of Pearson’s r, Spearman’s ρ, average error (AVEerror), and root mean square error (RMSE) of observed versus predicted redd counts are shown.