## 3 Results
### Modelling the Incidence of Defects, Cavities, and Fungal Infestation  
Among the 407 trees that exhibited defects, 78.62% were found to have cavities, while 40.15% showed signs of fungal infestation. To explore the relationship between thermal patterns, phenotypic traits, and the presence or absence of defects, cavities, and fungal infestation, we utilized three binary logistic regression (BLR) models. Regarding trees that presented defects, the BLR model identified a thermal index (T\({}_{\rm IQR}\)), a vitality index (LAI\({}_{\rm cav}\)), and an architectural trait (CA) as the most significant among the nine variables entered initially in the model (\(\chi^{2}\) (3, N = 543) = 190.50, \(p<0.05\); Table 4a). These variables collectively explained 74.6% of the total variance in the data, as indicated by the Nagelkerke R\({}^{2}\). The goodness of fit of the model was supported by the Hosmer-Lemeshow test, which demonstrated non-significance, and thus it can be accepted (Hosmer-Lemeshow = 14.02; \(p>0.05\)). Furthermore, the area under the receiver operating characteristic (ROC) curve (AUC = 0.96, S.E. = 0.01, 95% CI 0.93-0.98, \(p<0.05\)) indicated that the model accurately classified trees with and without defects in 91.4% of cases. The predicted classification accuracy was 92.1% for trees without defects and 91.1% for trees with defects. Focusing on cavities, the model (\(\chi^{2}\) (4, N = 543) = 357.49, \(p<0.05\); Table 4b) displayed an overall classification accuracy of 88%, with an AUC of 0.93 (S.E. = 0.01, 95% CI 0.90-0.95, \(p<0.05\)). The Nagelkerke R\({}^{2}\) value of 0.65 and the non-significant Hosmer-Lemeshow test (Hosmer-Lemeshow = 40.37, \(p>0.05\)) indicated a satisfactory goodness of fit. The model correctly classified 90.6% of trees with cavities and 84.3% of those without, demonstrating its predictive ability. Lastly, when examining the fungal infestation, the BLR model classified 88.8% of trees correctly: 92.2% of the infected and 86.5% of the non-infected (\(\chi^{2}\) (3, N = 543) = 422.66; \(p<0.05\); Nagelkerke R\({}^{2}\) = 0.73; Hosmer-Lemeshow = 19.54, \(p>0.05\); AUC = 0.95, S.E. = 0.009, 95% CI 0.93-0.97, \(p<0.05\); Table 4c). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Trees Classification** & **Response Variable** & **Predictor Variables** & **B** & **SE B** & \(\beta\) & **t** & \(p\)**-Value** & **R\({}^{2}\) adj.** & **F** \\ \hline \multirow{4}{*}{All five species} & \multirow{4}{*}{LAI\({}_{\rm mean}\)} & (constant) & \(-0.13\) & \(0.04\) & & \(-3.26\) & \(0.001\) & \multirow{4}{*}{0.45} & \multirow{4}{*}{128.24} \\ \cline{3-4}  & & T\({}_{\rm IQR}\) & \(-0.5\) & \(0.03\) & \(-0.52\) & \(-14.16\) & \(0.001\) & \\ \cline{3-4}  & & LAI\({}_{\rm range}\) & \(-0.23\) & \(0.07\) & \(-0.12\) & \(-3.37\) & \(0.001\) & \\ \cline{3-4}  & & SL & \(-0.23\) & \(0.03\) & \(-0.23\) & \(-6.65\) & \(0.001\) & \\ \hline \multirow{4}{*}{Deciduous} & \multirow{4}{*}{LAI\({}_{\rm mean}\)} & (constant) & \(0.22\) & \(0.06\) & & \(3.57\) & \(0.001\) & \multirow{4}{*}{0.54} & \multirow{4}{*}{115.44} \\ \cline{3-4}  & & T\({}_{\rm IQR}\) & \(-0.25\) & \(0.09\) & \(-0.15\) & \(-2.65\) & \(0.008\) & \\ \cline{3-4}  & & LAI\({}_{\rm range}\) & \(-0.41\) & \(0.07\) & \(-0.21\) & \(-5.36\) & \(0.001\) & \\ \cline{3-4}  & & SL & \(-0.44\) & \(0.04\) & \(-0.55\) & \(-9.66\) & \(0.001\) & \\ \hline \multirow{2}{*}{Evergreen} & \multirow{2}{*}{LAI\({}_{\rm mean}\)} & (constant) & \(-0.55\) & \(0.04\) & & \(-13.21\) & \(0.001\) & \multirow{2}{*}{0.35} & \multirow{2}{*}{97.15} \\ \cline{3-4}  & & T\({}_{\rm IQR}\) & \(-0.29\) & \(0.03\) & \(-0.59\) & \(-9.85\) & \(0.001\) & \\ \hline \end{tabular}
\end{table} 
Table 3: Final models obtained from weighted least squares regression analyses for estimating LAI. All regression models were statistically significant (\(p<0.05\)).