Unravelling key factors of forest condition: Multidimensional assessment in Mediterranean pine ecosystems

Assessing forest health is crucial for managing and conserving forest ecosystems. Tree condition can be measured through a wide range of methods related to structural and physiological aspects and including both field and remote sensing data. Understanding how these indicators of forest decline interact and respond to environmental factors remain a challenge. We present an empirical work with a multidimensional approach to estimate forest condition in reforestation stands of Pinus sylvestris L using Structural Equation Modeling (SEM). First, forest condition was assessed as a latent variable that integrates defoliation, pigment, LiDAR and high spatial resolution multispectral remote sensing measurements. Then, we evaluated the direct and indirect effects of biotic factors at the population scale (intraspecific competition), and abiotic factors (climatic and topographic conditions) as drivers of change to forest condition. Most important forest condition variables were photosynthetic activity and vegetation growth, with significant relationships between defoliation and chlorophyll-a. Normalized Difference Vegetation Index (NDVI) showed a strong positive association, effectively integrating both structural and physiological information. Structural variables (LiDAR metrics), such as tree asymmetry, also contributed significantly to describing forest condition. Intraspecific competition (with proxies of maximum tree height and canopy cover) showed a more substantial impact than abiotic factors on forest condition. Climatic variables were individually influential, although as a group (i.e. SEM construct) did not show significant values to forest condition through its interaction with biotic factors. In conclusion, the integrated assessment of data in SEM models provides an effective approach to identify determinants of functioning in forest ecosystems and to gain insight into the complex ecological interactions underlying forest decline processes.