Tropical deforestation and recolonization by exotic and native trees: spatial patterns of tropical forest biomass, functional groups, and species counts and links to stand age, geoclimate, and sustainability goals.
We mapped native, endemic, and introduced (i.e., exotic) tree species counts, relative basal areas of functional groups, species basal areas, and forest biomass from forest inventory data, satellite imagery, and environmental data for Puerto Rico and the Virgin Islands. Imagery included time series of Landsat composites and Moderate Resolution Imaging Spectroradiometer (MODIS)-based phenology. Environmental data included climate, land-cover, geology, topography, and road distances. Large-scale deforestation and subsequent forest regrowth are clear in the resulting maps decades after large-scale transition back to forest. Stand age, climate, geology, topography, road/urban locations, and protection are clearly influential. Unprotected forests on more accessible or arable lands are younger and have more introduced species and deciduous and nitrogen-fixing basal areas, fewer endemic species, and less biomass. Exotic species are widespread - except in the oldest, most remote forests on the least arable lands, where shade-tolerant exotics may persist. Although the maps have large uncertainty, their patterns of biomass, tree species diversity, and functional traits suggest that for a given geoclimate, forest age is a core proxy for forest biomass, species counts, nitrogen-fixing status, and leaf longevity. Geoclimate indicates hard-leaved species commonness. Until global wall-to-wall remote sensing data from specialized sensors are available, maps from multispectral image time series and other predictor data should help with running ecosystem models and as sustainable development indicators. Forest attribute models trained with a tree species ordination and mapped with nearest neighbor substitution (Phenological Gradient Nearest Neighbor method, PGNN) yielded larger correlation coefficients for observed vs. mapped tree species basal areas than Cubist regression tree models trained separately on each species. In contrast, Cubist regression tree models of forest structural and functional attributes yielded larger such correlation coefficients than the ordination-trained PGNN models.