Climate‐based approach for modeling the distribution of montane forest vegetation in Taiwan. Issue 2 (9th March 2020)

• Record Type: Journal Article
• Authors: Lin, Huan‐Yu; Li, Ching‐Feng; Chen, Tze‐Ying; Hsieh, Chang‐Fu; Wang, Guangyu; Wang, Tongli; Hu, Jer‐Ming
• Editors: Ohlemuller, Ralf
• Is Part Of: Applied vegetation science. Volume 23:Issue 2(2020)
• Page Start: 239
• Page End: 253
• Publication Date: 2020-03-09
• Subjects: climate -- eastern Asia -- ecological niche modeling -- montane forest -- random forest -- subtropical forest -- Taiwan -- vegetation mapping; Plant ecology -- Periodicals; Plant communities -- Periodicals; Plant populations -- Periodicals; Nature -- Effect of human beings on -- Periodicals; 581.705
• DOI: 10.1111/avsc.12485
• Languages: English
• ISSNs: 1402-2001

Abstract

Abstract: Aims: Climate shapes forest types on our planet and also drives the differentiation of zonal vegetation at regional scale. A climate‐based ecological model may provide an effective alternative to the traditional approach for assessing limitations, thresholds, and the potential distribution of forests. The main objective of this study is to develop such a model, with a machine‐learning approach based on scale‐free climate variable estimates and classified vegetation plots, to generate a fine‐scale predicted vegetation map of Taiwan, a subtropical mountainous island. Location: Taiwan. Methods: A total of 3, 824 plots from 13 climate‐related forest types and 57 climatic variable estimates for each plot were used to build an individual ecological niche model for each forest type with random forest (RF). A predicted vegetation map was developed through the assemblage of RF predictions for each forest type at the spatial resolution of 100 m. The accuracy of the ensemble RF model was evaluated by comparing the predicted forest type with its original classification by plot. Results: The climate environment of regions higher than 100 m above sea level in Taiwan was classified into potential habitats of 13 forest types by using model predictions. The predicted vegetation map displays a distinct altitudinal zonation from subalpine to montane cloud forests, followed by the latitudinal differentiation of subtropical mountain forests in the north and tropical montane forests in the south, with an average mismatch rate of 6.59%. An elevational profile and 3D visualization demonstrate the excellence of the model in estimating a fine, precise, and topographically corresponding potential distribution of forests. Conclusions: The machine‐learning approach is effective for handling a large number of variables and to provide accurate predictions. This study provides a statistical procedure integrating two sources of training data: (a) the locations of field sampling plots; and (b) their corresponding climate variable estimates, to predict the potential distribution of climate‐related forests. Abstract : Climate is one of the most important factors regulating the distribution of forests. This paper performs a machine‐learning approach to generate a predicted vegetation map based on fine‐scaled climatic parameters. A total of 13 forest types were distinctly mapped by this modeling framework on a subtropical mountainous island, Taiwan.