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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 FROM AUTHOR]

Montane cloud forest is one of the most endangered ecosystems. However, there are few comprehensive studies on the distribution of subtropical montane cloud forest (SMCF). Chamaecyparis forest is one type of SMCF in Taiwan, distributed across the whole island. This study describes eleven types of this forest in Taiwan based on the Braun-Blanquet approach. Plots were selected from the National Vegetation Database of Taiwan. Two alliances were defined, both of which belong to the order Fagetalia hayatae. Topography and altitude explain the contrasting habitat requirements of these two alliances, whereas seasonality of moisture, soil properties and altitude explain differences in floristic composition at the association level. The alliance of Chamaecyparidion formosanae on slopes and ridges includes coniferous or mixed coniferous and evergreen broad-leaved forests; it is found at higher altitudes and is more influenced by the summer monsoon than the other alliance. Five associations are defined within this alliance. The alliance of Pasanio kawakamii - Machilion japonicae growing on slopes and in valleys contains evergreen broad-leaved forests or forests with a mixture of coniferous and evergreen broad-leaved species. Six associations can be determined under the alliance of Pasanio kawakamii- Machilion japonicae. Classification of each syntaxon was formalized using Cocktail Determination Key. [ABSTRACT FROM AUTHOR]

Aim We identify the main forest vegetation types in Taiwan, provide their formal definitions and describe their species composition, habitat affinities and distribution. Location Taiwan. Methods A data set of 9822 vegetation plots with environmental characteristics recorded in the field or derived from digital maps in GIS was compiled from historical literature and an extensive field survey. Using expert knowledge, 6574 of these plots were used to build a classification into broad vegetation types. The units of the resulting classification were formally defined using a Cocktail determination key, which can be used for the automatic assignment of new vegetation plots to these vegetation types. Results Twelve vegetation types of zonal forests and nine types of azonal forests were distinguished. Zonal types in the subtropical region, from high mountains to foothills, are Juniperus subalpine coniferous woodland, Abies- Tsuga upper-montane coniferous forest, Chamaecyparis montane mixed cloud forest, Fagus montane deciduous broad-leaved cloud forest, Quercus montane evergreen broad-leaved cloud forest, Machilus- Castanopsis sub-montane evergreen broad-leaved forest, Phoebe- Machilus sub-montane evergreen broad-leaved forest and Ficus- Machilus semi-evergreen foothill forest. Zonal types in the tropical region, from high mountains to foothills, are Pasania- Elaeocarpus montane evergreen broad-leaved cloud forest, Drypetes- Helicia sub-montane evergreen broad-leaved forest, Dysoxylum- Machilus foothill evergreen broad-leaved forest and Aglaia- Ficus foothill evergreen broad-leaved forest. Azonal types are Illicium- Cyclobalanopsis tropical winter monsoon forest, Pyrenaria- Machilus subtropical winter monsoon forest, Diospyros- Champereia tropical rock-outcrop forest, Zelkova- Quercus subtropical rock-outcrop forest, Pinus successional woodland, Alnus successional woodland, Trema- Mallotus successional woodland, Scaevola- Hibiscus seashore woodland and Kandelia mangrove. Conclusions The diversity of forest vegetation in Taiwan is strongly structured by the temperature and moisture gradient. Along the temperature gradient, five altitudinal zones can be recognized. Azonal forest types develop at sites affected by the winter monsoon, on steep slopes, rocky soils, in seashore saline habitats and in places disturbed by fire, landslides and human activities. Zonal vegetation contains a higher ratio of endemic and Pacific species and occurs in wetter habitats, whereas azonal vegetation contains co-existing species from different regions and usually occurs in drier habitats. [ABSTRACT FROM AUTHOR]