Secondary forest areas are increasing worldwide and understanding how these forests interact with climate change including frequent and extreme events becomes increasingly important. This study aims to investigate the effects of the strong 2015/2016 El Niño-induced drought on species-specific leaf phenology, dieback and tree mortality in a secondary dry dipterocarp forest (DDF) in western Thailand. During the 2015/2016 El Niño event, rainfall and soil water content were lower than 25 mm and 5% during 5–6 consecutive months. The dry season was 3–4 months longer during the El Niño than during non-El Niño events. We found that this prolonged drought induced the earlier shedding and a delay in leaf emergence of the DDF. The deciduousness period was also longer during the El Niño event (5 months instead of 2–3 months during non-El Niño event). We found that the DDF species showed different phenological responses and sensitivities to the El Niño-induced drought. The leaf phenology of stem succulent species Lannea coromandelica (Houtt.) Merr. and a complete deciduous species with low wood density. Sindora siamensis Teijsm. ex Miq. was only slightly affected by the El Niño-induced drought. Conversely, a semi-deciduous species such as Dipterocarpus obtusifolius Teijsm. ex Miq. showed a higher degree of deciduousness during the El Niño compared to non-El Niño events. Our results also highlight that dieback and mortality during El Niño were increased by 45 and 50%, respectively, compared to non-El Niño events, pointing at the importance of such events to shape DDF ecosystems.

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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 Long Short-Term Memory neural network (LSTM) was used to create model to predict leaf phenology in dry dipterocarp forest. The SOM-based clustering was able to classify 92.24% of the individual trees. The result of mapping the clustering data with lag time analysis revealed that each cluster has a different lag time depending on the timing and amount of rainfall. Incorporating the time lags improved the performance of the litterfall prediction model, reducing the average root mean square percent error (RMSPE) from 14.35% to 12.06%. This study should help researchers understand how each species responds to climate change. The litterfall prediction model will be useful for managing dry dipterocarp forest especially with regards to forest fires.

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