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

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 anal...

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Autores principales: Lamjiak, Taninnuch, Kaewthongrach, Rungnapa, Sirinaovakul, Booncharoen, Hanpattanakit, Phongthep, Chithaisong, Amnat, Polvichai, Jumpol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8389403/
https://www.ncbi.nlm.nih.gov/pubmed/34437578
http://dx.doi.org/10.1371/journal.pone.0255962
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author Lamjiak, Taninnuch
Kaewthongrach, Rungnapa
Sirinaovakul, Booncharoen
Hanpattanakit, Phongthep
Chithaisong, Amnat
Polvichai, Jumpol
author_facet Lamjiak, Taninnuch
Kaewthongrach, Rungnapa
Sirinaovakul, Booncharoen
Hanpattanakit, Phongthep
Chithaisong, Amnat
Polvichai, Jumpol
author_sort Lamjiak, Taninnuch
collection PubMed
description 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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spelling pubmed-83894032021-08-27 Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms Lamjiak, Taninnuch Kaewthongrach, Rungnapa Sirinaovakul, Booncharoen Hanpattanakit, Phongthep Chithaisong, Amnat Polvichai, Jumpol PLoS One Research Article 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. Public Library of Science 2021-08-26 /pmc/articles/PMC8389403/ /pubmed/34437578 http://dx.doi.org/10.1371/journal.pone.0255962 Text en © 2021 Lamjiak et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lamjiak, Taninnuch
Kaewthongrach, Rungnapa
Sirinaovakul, Booncharoen
Hanpattanakit, Phongthep
Chithaisong, Amnat
Polvichai, Jumpol
Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms
title Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms
title_full Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms
title_fullStr Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms
title_full_unstemmed Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms
title_short Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms
title_sort characterizing and forecasting the responses of tropical forest leaf phenology to el nino by machine learning algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8389403/
https://www.ncbi.nlm.nih.gov/pubmed/34437578
http://dx.doi.org/10.1371/journal.pone.0255962
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