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The model based on the local R-squared of the predictors (Fig. 7) showed terminal nodes with a wide range of habitat suitability values, specially for nodes 3, 5 and 6, and a general low reliability (high difference between actual and predicted suitability according to the recursive partition model). Node 3 highlighted localities in which both slope and temperature of the warmest month where important defining low habitat suitability values, and correspond to areas in which both variables show extreme values, like Pyrenees (ID=28, steep terrain and cold climate) or Central Anatolia (ID=5, flat terrain and hot climate). According to this model, the highest habitat suitability (node 7) is reached when the local R-squared of slope is lower than 0.38, and no any other predictor seem to be important. We will discuss this counterintuitive result in the Discussion section.  Finally, the model based on the local coefficients of the predictors showed that higher habitat suitability values happen when the coefficient of the slope is between 0.07 and -0.006 (zero indicates optimum slope) and the coefficient of the average temperature of the coldest month is lower than 0.004 (approaching zero and indicating optimum value for the predictor). A very high coefficient of the slope (>0.07) indicated a lack of rugosity in the terrain, and therefore low suitability, as in the Po Valley (ID=32), or the Pannonian Plain (ID=8).