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

Eileen Helmer,Thomas Brandeis,Humfredo Marcano-Vega,Bonnie Ruefenacht,Kirk Sherrill,Barry Wilson,Heather Erickson,Thomas Ruzycki,Michael Lefsky

doi:10.3390/rs10111724

Abstract

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.

Highlights

Tropical forests are important for many reasons, but humanity is constantly changing them
An important overall result of the mapping was that spatial patterns in the output maps of forest structure, functional groups, species counts, and of some individual species indicated a consistent network of drivers of these patterns
Disturbance history variables other than stand age include those related to forest fragmentation (Figure 3), like past and present patch isolation and spatial contagion, and variables like past land-use or disturbance type or intensity

Summary

Introduction

Tropical forests are important for many reasons, but humanity is constantly changing them. Predicting forest attributes measured in plot data with models that use a set of mapped variables, like climate and satellite imagery as explanatory variables, allows mapping of the likely characteristics of forest across a landscape. For the machine learning regression approach, we used the software Cubist It is a data-mining tool that builds rule-based models with linear regression models at the terminal node of each ruleset. It is proprietary, but it enhances published work [49] that improves decision-tree classification models to handle continuous variables. Studies use Cubist or other regression tree models to model forest attributes from lidar metrics [53,58]

Methods

Results

Discussion

Conclusion