Better environmental data may reverse conclusions about niche- and dispersal-based processes in community assembly.

Variation partitioning of species composition into components explained by environmental and spatial variables is often used to identify a signature of niche- and dispersal-based processes in community assembly. Such interpretation, however, strongly depends on the quality of the environmental data available. In recent studies conducted in forest dynamics plots, the environment was represented only by readily available topographical variables. Using data from a subtropical broad-leaved dynamics plot in Taiwan, we focus on the question of how would the conclusion about importance of niche- and dispersal-based processes change if soil variables are also included in the analysis. To gain further insight, we introduced multiscale decomposition of a pure spatial component [c] in variation partitioning. Our results indicate that, if only topography is included, dispersal-based processes prevail, while including soil variables reverses this conclusion in favor of niche-based processes. Multiscale decomposition of [c] shows that if only topography was included, broad-scaled spatial variation prevails in [c], indicating that other as yet unmeasured environmental variables can be important. However, after also including soil variables this pattern disappears, increasing importance of meso- and fine-scaled spatial patterns indicative of dispersal processes.

Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data.

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as "MCF" or "non-MCF". This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest's location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF.