*3.1. Model Performance and the Importance of Environmental Variables*

Model performance evaluation aims to estimate the accuracy of machine learningbased prediction models and ensures confidence in the results obtained. The performance of this model obtained an area under the curve (AUC) value of 0.866 (Figure 3a), which is considered good (0.8 < AUC < 0.9). The response curves (Figure 3b–n) reflect the dependence of predicted suitability, both on the selected variable and on dependencies induced by correlations between the selected variable and other variables. Overall, 83% of the potential distribution of *Cedrela* was found to be driven mainly by four environmental variables, i.e., bio19 (precipitation of coldest quarter), soc (organic carbon), dem (elevation above mean sea level), and cec (cation exchange capacity) (Table 3). On the other hand, silt (slime content), bdod (bulk density of the fine earth fraction), and nitrogen were the three environmental variables that contributed the least. Figure 3o shows the results of jackknife test of variable importance. The environmental variable that reported the highest

gain when used in isolation was bio19, which therefore appeared to have the most useful information by itself. The environmental variable that decreased the gain the most on its omission was dem, which therefore appeared to have the most information that was not present in the other variables. Likewise, the Jackknife test (Figure 3o) identified that the variables bio 19 (coldest quarter precipitation), bio 12 (annual precipitation), soil pH, and elevation (DEM) contributed highly to the biogeographic distribution model of the *Cedrela* species.

**Figure 3.** Model performance based on the area under the curve (AUC) (**a**), mean response curves of the 100 replicated MaxEnt runs (red) and standard deviation (blue), showing the relationships between environmental variables and the probability of the presence of the *Cedrela* (**b**–**n**), and Jackknife test of environmental variables importance to MaxEnt model of the *Cedrela* (**o**).


**Table 3.** Relative contributions (%) of environmental variables to the MaxEnt model of the genus *Cedrela* in Peru.
