Generalized Linear Models (GLM’s)
For A. microtis , the global
model including all predictor variables was the most adequate model to
explain the variation in the occurrence of this species (Appendix-Table
3). When evaluating the isolated effect of each variable, we observed
that only Nat_open_areas did not affect the distribution of A.
microtis (Table 1). The variables, Forest, Water, and
Anthr_open_areas had a positive effect on the occurrence of the
species (Table 1) (Figure 5A). We observed an increase in the
probability of A. microtis by 74.4% for areas with higher
percentages of these variables, especially Forest and Water. The
Urban_areas variable negatively affected the occurrence of A.
microtis (Figure 5A). We estimated that this variable reduces the
occurrence of the species by about 50%.
Table 1. Predictive variables selected for Atelocynus
microtis by the ANOVA, using the most ajusted model: A.
microtis~Forest + Anthr_open_areas + Urban_areas +
Nat_open_areas + Water.