Background

Vegetation distribution maps are of great significance for nature protection and management. In diverse tropical forests, accurate spatial mapping of vegetation types is challenging; the high species diversity and abundance of rare species challenge classification concepts, while remote sensing signals may not vary systematically with species composition, complicating the technical capability for delineating vegetation types in the landscape.

Methods

We used a combination of field-based compositional data and their relations to environmental variables to predict the distribution of forest types in the Wuzhishan National Natural Reserve (WNNR), Hainan Island, China, using multivariate regression trees (MRT). The MRT was based on arboreal vegetation composition in 132 plots of 20 ​m ​× ​20 ​m with a regular spacing of 1 ​km. Apart from the MRT, non-metric multidimensional scaling (NMDS) was used to evaluate vegetation-environment relationships.

Results

The MRT model worked best when using 14 key environmental variables including topography, climate, latitude and soil, although the difference with the simpler model including only topographical variables was small. The full model classified the 132 plots into 3 vegetation types, 6 formation groups, 20 formations and 65 associations at different hierarchical syntaxonomic levels. This model was the basis for forest vegetation maps for the WNNR. MRT and NMDS showed that elevation was the main driving force for the distribution of vegetation types and formation groups. Climate, latitude, and soil (especially available P), together with topographic variables, all influenced the distribution of formations and associations.

Conclusions

While elevation determines forest-type distributions, lower-level syntaxonomic forest classes respond to the topographic diversity typical for mountains. Apart from providing the first detailed forest vegetation map for any part of WNNR, we show how, in spite of limitations, MRT with existing environmental data can be a useful method for mapping diverse and remote tropical forests.