Figure legends
Figure 1. Schematic diagram of selecting optimal models. Steps with red arrows are unique to AUCDIFF approaches while all other steps are common to all the four sequential approaches. Green boxes with “Single model selected” are the optimal models for either ORTEST or AUCDIFF approaches. Step 2a is followed then one will derive optimal models for AUCDIFFafter Step 2, otherwise optimal models derived will be for ORTEST approaches. While purple box with “Single model selected” is the optimal model for only AUCDIFFapproaches. Step 5 is the ultimate step wherein models with lower feature class is chosen as the optimal model when multiple models have same numbers of parameters and average absolute value.
Figure 2. Regularization multiplier and feature class (RM-FC) combinations for (a) fish and (b) odonate of the optimal models chosen by five model selection approaches.
Figure 3. Summary of different features of the optimal models selected through five optimal model selection approaches for the fish and odonate species of Bhutan. 10 percentile training presence test omission (1) and balance training omission, predicted area and threshold value test omission (2) for the (a) fish and (b) odonate; AUC difference for the (c) fish and (d) odonate; average number of parameters for the (e) fish and (f) odonate; test AUC for the (g) fish and (h) odonate species of Bhutan. Expert approach based on ecological plausibility of binary suitable/unsuitable model (EXP), sequential approaches using 10 percentile training presence test omission and test AUC (ORTEST_PER), 10 percentile training presence test omission, AUC difference and test AUC (AUCDIFF_PER), balance training omission, predicted area and threshold value test omission and test AUC (ORTEST_BAL) and balance training omission, predicted area and threshold value test omission, AUC difference and test AUC (AUCDIFF_BAL).
Figure 4. Area of the predicted habitats of the optimal models selected through the five model selection approaches for the (a) fish and (b) odonate species of Bhutan.
Figure 5. Examples of binary suitable habitat maps derived using 10 percentile training presence Cloglog threshold (Left panel) and balance training omission, predicted area and threshold value Cloglog threshold (Right panel) among the optimal models chosen by the Expert and the four sequential optimal model selection approaches. (a) Cyprinion semiplotum (5 occurrence; EXP, ORTEST_PER, ORTEST_BAL, AUCDIFF_PER and AUCDIFF_BAL), (b) Neolissochilus hexagonolepis(12 occurrence; EXP, ORTEST_PER); (c)Neolissochilus hexagonolepis (12 occurrence; ORTEST_BAL, AUCDIFF_PER and AUCDIFF_BAL); (d) Aristocypha quadrimaculata (8 occurrence; EXP, ORTEST_PER, ORTEST_BAL, AUCDIFF_PER and AUCDIFF_BAL); (e) Diplacodes trivialis (21 occurrence; EXP); (f) Diplacodes trivialis (21 occurrence; ORTEST_PER); (g) Diplacodes trivialis (21 occurrence; ORTEST_BAL, AUCDIFF_PERand AUCDIFF_BAL).