## 2 Materials and Methods
### Statistical Analysis  
Prior to conducting any statistical analyses, the primary data underwent a process of normalization and transformation to achieve optimal data distribution and fulfill the assumptions necessary for subsequent analyses. Initially, we employed the z-score normalization technique to standardize the measured traits and indices on a common scale. This approach was particularly valuable given the inherent variations in characteristics observed across different species and functional forms. Furthermore, although our measurements were conducted during the spring season when temperature and humidity levels on the island remained relatively consistent, we assessed whether there were any differences in microclimate conditions during infrared image collection across the four study sites. The results revealed no statistically significant differences among the study sites in terms of ambient temperature [F (3, 95.23) = 2.41, \(p\) = 0.07] and relative humidity [F (3, 95.90) = 1.36, \(p\) = 0.25)], and thus we concluded that treating the sites separately was unwarranted, and we pooled the data of all the trees for subsequent analyses. The assessment of temperature distribution on tree trunks holds significance as an indicator of their health state [68; 70; 71; 72]. Recognizing the influence of water supply conditions and individual-specific anatomical characteristics on the surface temperature fluctuations of  
Figure 2: Sample infrared images depicting the tree trunks of the five tree species and the main methodological procedure for extracting their temperatures using the ArcGIS Analysis toolbox: (**a**) refers to the calibrated images after being imported in the TESTO IRSoft® (v. 4.3) software with the temperature palette applied; (**b**) refers to the trees’ trunk after polygon creation using shapefile format. These images showcase various types of defects and/or wood-decay fungi present in the trunks, including cavities in _R. pseudoaaccia_ and _O. europea_, wounds in _M. alba_ and _P. brutia_, and the presence of rotten bark in _M. azedarach_. tree trunks [89; 90], it becomes crucial to estimate this distribution using the extracted trunk thermal indices (T\({}_{\text{IQR}}\), T\({}_{\text{TPR}}\)). In light of this, we focused on examining thermal pattern variations among the five tree species and among trees classified into the three categories, which were based on the presence or absence of defects. To investigate this, we employed Welch ANOVA, followed by the Games-Howell test for multiple comparisons. Furthermore, we explored within-species thermal pattern disparities by conducting [PERSON]'s t-test, comparing the trunk thermal indices in terms of the level of abnormalities' occurrence. We used these statistical tests as they are appropriate when there are unequal variances or unequal sample sizes among the groups being compared, making them suitable for our analysis. In accordance with the examined thermal patterns, we further investigated differences in the LAI among trees categorized into the three distinct groups based on the presence or absence of defects. For investigating the relationship between trees' phenotypic traits and trunk thermal indices we used correlation statistics. Furthermore, in order to examine the effect of trees' phenotypic traits and trunk thermal indices on LAI we used multiple linear regression analysis, with a backward elimination procedure; LAI was chosen as the dependent variable, representing tree productivity, while variables of the trees' traits served as the independent ones. However, to account for potential heteroscedasticity and unequal variances in the data, we opted for a weighted least square regression approach instead of traditional linear regression. We performed this analysis separately for deciduous and evergreen species and for the entire dataset of recorded trees.