Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.

ABSTRACT: Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change differ. The objective of this research is to analyze the impact of climate variability on the leaf phenology in Thailand's tropical forests. Machine learning approaches were applied to model how leaf phenology in dry dipterocarp forest in Thailand responds to climate variability and El Niño. First, we used a Self-Organizing Map (SOM) to cluster mature leaf phenology at the species level. Then, leaf phenology patterns in each group along with litterfall phenology and climate data were analyzed according to their response time. After that, a Lon

SUBMITTER: Lamjiak T 

PROVIDER: S-EPMC8389403 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature