Here, we use a machine learning approach to quantify the importance of environmental factors and apply it to generate spatial predictions of the species diversity of all trees (dbh ≥ 10 cm) and for very large trees (dbh ≥ 70 cm) using data from 243 forest plots (108,450 trees and 2832 species) distributed across different forest types and biogeographic regions of the Brazilian Amazon.
The aim of the study was to investigate the effects of environmental conditions on the intra- and interspecific variability of WD for tree assemblages in forests of the northern Brazilian Amazon.
The harvesting of açaí berries (palm fruits from the genus Euterpe ) in Amazonia has increased over the last 20 years due to a high local and global market demand and triggered by their widely acclaimed health benefits as a ‘superfood’