Integrating field- and remote sensing data to perceive species heterogeneity across a climate gradient

Tropical forests exhibit significant diversity and heterogeneity in species distribution. Some tree species spread abundantly, impacting the functional aspects of communities. Understanding how these facets respond to climate change is crucial. Field data from four protected areas (PAs) were combined with high-resolution Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets to extract large-scale plot data of abundant species and their functional traits. A supervised component generalized linear regression (SCGLR) model was used to correlate climate components with the distribution of abundant species across PAs. The recorded rainfall gradient influenced the proportion of PA-specific species in the observed species assemblages. Community weighted means (CWMs) of biochemical traits showed better correlation values (0.85-0.87) between observed and predicted values compared to biophysical traits (0.52-0.79). The model-based projection revealed distinct distribution responses of each abundant species to the climate gradient. Functional diversity and functional traits maps highlighted the interplay between species heterogeneity and climate. The appearance dynamics of abundant species in dark diversity across PAs demonstrated their assortment strategy in response to the climate gradient. These observations can significantly aid in the ecological management of PAs exposed to climate dynamics.