## 3 Results  
The type of habitat played a stronger role than its structure in both years (Figure 3). Its relative importance increased in 2011 compared to 2001. The BRT model for 2001 had an explained deviance of 20%, a cross-validation correlation of 0.39 and standard error of 0.12. Instead, the model for 2011 had an explained deviance of 26%, a cross-validation correlation of 0.46 and standard error of 0.09.  
Moreover, in both years, spruce forests (paA, paF, paM, paX) showed a positive influence on carecaille presence, while fir (aa) and beech (fs) forests were avoided. No important changes occurred between 2001 and 2011 (Figure 3). Habitat structure types (i.e., development stages) did not show a clear pattern considering both years. In 2001 there was an indicative positive relationship of the presence of carecaille with mature (fma) and over-mature (fri) stands, while pole dense canopies (pe) had a negative impact.  
The BRT models with quantitative environmental and habitat variables had an explained deviance of 29%, a cross-validation correlation of 0.36 and standard error of 0.14 in 2001, while in 2011 they were 26%, 0.42 and 0.17, respectively.  
A remarkable outcome is the much higher influence of altitude (Alt) in 2011 than in 2001 (Figure 4). In 2001, in addition to altitude, the following variables had a relatively important influence on the carecaille summer presence: cover of spruce in the tree layer (CSp) and the mean height of spruce trees (HSP), cover of berries (CVv, CVm) and dwarf shrubs (CSu). Instead in 2011, apart from altitude (Alt), only slope (Slo) and cover of spruce (CSp) in the tree layer had a relative influence higher than 3%.  
Figure 3: Relative influence of habitat canopy (VEG) and structure (STR) and relationship of each habitat category with carecaille summer occurrence in 2001 and 2011 in a generalized boosted regression model obtained by integrating out all the other categories. A logistic regression was used, and y-axes report the fitted values on log-odds scale. The red line corresponds to a probability of 0.5 (odds (p) = 1, log odds (p) = 0). Explanatory variables, with their codes, are reported in Table 1.  
Overall, only a limited number of explanatory variables had a relative influence \(\geq\) 10% (Figure 5). Among these, for both years, altitude was a fundamental environmental factor. More specifically, in 2001, capercaillie was most commonly found at altitudes from 1570 to 1670 m a.s.l. with a decreasing trend at altitudes higher than 1670-1700 m a.s.l. On the contrary, the species had clearly moved to higher altitudes in 2011, ranging from 1620 to 1750 m a.s.l., with this factor being extremely important compared to other explanatory variables for the species (i.e., 65.3% of relative influence). Furthermore, there was no negative trend in presence of the species for higher altitudes.  
Factors related to spruce were important for the presence of capercaillie. In 2001, capercaillie preferred forests where this tree species proportion exceeded 50% and had a mean height over 17-18 m. In 2011, it is relatively evident that the species avoid slopes lower than 20%, without having preference for steep slopes.  
Figure 4: Relative influence of quantitative variables in a BRT model for capercaillie summer occurrence in 2001 and 2011. Signs indicate whether the relationship was negative (\(-\)) or positive (\(+\)) monotonic or non-monotonic (\(+\) \(-\)). Explanatory variables, with their codes, are reported in Table 2.  
Figure 5: Relationship of the most influential variables (relative influence \(\geq\) 10%) with capercaillie summer occurrence in 2001 and 2011, obtained by integrating out all other variables in a generalized boosted regression model. A logistic regression was used, and y-axes report the fitted values on log-odds scale (explanatory variables, with their codes, are reported in Table 2).