Predicted habitat
The EXP approach selected the maximum number of optimal models with comparatively smaller predicted habitat area among all the approaches; this was expected as the expert approach tried to avoid over prediction while selecting the optimal models (Figure 4a-b). In comparison, AUCDIFF approaches predicted a comparatively larger suitable habitat area for a greater number of both fish and odonate species over the ORTEST approaches (Figure 4a-b). Most often suitable habitats were over predicted by AUCDIFFapproaches and also for some species by ORTESTapproaches when compared to the area predicted by the EXP approach (Figure 4a-b). For instance, AUCDIFF_PER and AUCDIFF_BAL predicted habitat area above 38,000 km2 for four of the odonate species and for one fish species by AUCDIFF_BAL (Figure 4a-b).
The choice of threshold used to derive binary suitable habitat maps from the optimal models chosen may explain some of the variation in the area of habitat predicted. Though we did not assess for the effect of thresholds used on the habitat areas predicted we present the binary maps derived using percentile and balance thresholds for four species with varying occurrence records used for the model building as an example (Figure 5). In general, we observed the restrictive ‘percentile threshold’ seemed to restrict the predicted suitable habitat around occurrence data used for model building, while the less restrictive ‘balance threshold’ seemed to overpredict the habitat for most of the optimal models selected by sequential approaches (Figure 5).
However, there were statistically significant correlations between the areas of the optimal models chosen by the EXP approach, with optimal models chosen by all four sequential approaches (Table S4). Comparatively, the strongest correlation was with ORTEST_PER and ORTEST_BAL for fish and with ORTEST_PER and AUCDIFF_PER for odonates, though correlation strength differed with very high correlations for the former and moderate for the latter (Table S4).