Model evaluation
We found variation in performance of the four algorithms used, with some models having TSS < 0.7 for several species. RF was utilised for all 110 species, while MARS, MAXENT, and ANN were limited to 93, 90, and 89 species respectively. For 17 species (Cynopterus sphinx ,Hipposideros fulvus , Lyroderma lyra , Pipistrellus ceylonicus , P. coromandra , P. tenuis , Pteropus medius , Rhinolophus lepidus , Rhinopoma hardwickii ,R. microphyllum , Rousettus leschenaultii ,Scotophilus heathii , S. kuhlii , Scotozous dormeri ,Sphaerias blanfordi , Taphozous longimanus , and T. nudiventris ) RF was the only algorithm represented in the final ensemble. There was a significant negative correlation between the number of occurrence points and the number of models used in the final ensemble (Pearson’s r = -0.791; p < 0.001). RF was the only model represented in the final ensemble for most species with more than 120 occurrences; Taphozous nudiventris and Sphaerias blanfordi were the two exceptions to this, with 97 and 17 occurrences respectively. RF models also had the highest mean AUC and TSS scores across all species (AUC: 0.998 ± 0.001 [0.991 - 1.000]; TSS: 0.980 ± 0.014 [0.940 - 1.000]), followed by MARS (AUC: 0.970 ± 0.016 [0.920 - 0.997]; TSS: 0.897 ± 0.055 [0.761 - 0.995]), MAXENT (AUC: 0.958 ± 0.019 [0.904 - 0.994]; TSS: 0.859 ± 0.052 [0.754 - 0.978]), and ANN (AUC: 0.955 ± 0.024 [0.887 - 1.000]; TSS: 0.869 ± 0.059 [0.750 - 0.999]).