Model setting
We used Maxent v 3.4.1 (Phillips, Dudík and Schapire) to model the species distributions. We used Maxent’s current default output format Cloglog since it gives a better result over logistic when bias correction is used (Phillips, 2017; Phillips et al. 2017). We used four sets of FCs resulting from the use of individual FCs independently or in combination with other FCs, namely (i) linear (L), (ii) linear-quadratic (LQ), (iii) hinge (H) and (iv) linear-quadratic-hinge (LQH) after Galante et al. (2018) to build models given our species had only a small number of occurrence records (Table S1). Higher FCs were found to produce less complex (i.e., lesser number of parameters) and less overfitting (i.e., lower omission rates) models when occurrences were small (Radosavljevic and Anderson 2014). Specifically, the hinge feature was better for species with small occurrence values (Galante et al. 2018).
RMs with values less than default produce models which are overfit to occurrence data and are not well generalized, while larger RMs would produce spread out and less localized models (Phillips, 2017). Though Radosavljevic and Anderson (2014) also observed a slight peak in the model discriminatory ability around the default RM, they found substantial reduction in overfitting when RMs of two to four times that of the default were used. However, they also found both the model quality and the overall discriminatory power declined when RMs were above 4 (Radosavljevic and Anderson 2014). Hence, we used conservatively 11 different RM values, namely .25, .5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5 and 5.
We used Maxent’s default replication method of cross-validation since it is a better replicate option with small occurrence data. It randomly splits occurrence data into folds and uses all the folds in turn to build and evaluate models (Phillips, 2017). We set the number of iterations for each FC-RM combination equal to the number of occurrence (n) for each species thus making it equivalent to n-1 jackknife folds, which is a good approach for species with small occurrence data (Warren and Seifert 2011, Shcheglovitova and Anderson 2013, Radosavljevic and Anderson 2014, Galante et al. 2018). Crossing 11 RM values and four FC sets we built 44 sets of models for each species. Maxent generates (n+1) models including one composite (average) model for each set of RM-FC combination.