Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing.

There is an increasing interest in identifying theories, empirical data sets, and remote-sensing metrics that can quantify tropical forest alpha diversity at a landscape scale. Quantifying patterns of tree species richness in the field is time consuming, especially in regions with over 100 tree species/ha. We examine species richness in a 50-ha plot in Barro Colorado Island in Panama and test if biophysical measurements of canopy reflectance from high-resolution satellite imagery and detailed vertical forest structure and topography from light detection and ranging (lidar) are associated with species richness across four tree size classes (>1, 1-10, >10, and >20 cm dbh) and three spatial scales (1, 0.25, and 0.04 ha). We use the 2010 tree inventory, including 204,757 individuals belonging to 301 species of freestanding woody plants or 166 ± 1.5 species/ha (mean ± SE), to compare with remote-sensing data. All remote-sensing metrics became less correlated with species richness as spatial resolution decreased from 1.0 ha to 0.04 ha and tree size increased from 1 cm to 20 cm dbh. When all stems with dbh > 1 cm in 1-ha plots were compared to remote-sensing metrics, standard deviation in canopy reflectance explained 13% of the variance in species richness. The standard deviations of canopy height and the topographic wetness index (TWI) derived from lidar were the best metrics to explain the spatial variance in species richness (15% and 24%, respectively). Using multiple regression models, we made predictions of species richness across Barro Colorado Island (BCI) at the 1-ha spatial scale for different tree size classes. We predicted variation in tree species richness among all plants (adjusted r² = 0.35) and trees with dbh > 10 cm (adjusted r² = 0.25). However, the best model results were for understory trees and shrubs (dbh 1-10 cm) (adjusted r² = 0.52) that comprise the majority of species richness in tropical forests. Our results indicate that high-resolution remote sensing can predict a large percentage of variance in species richness and potentially provide a framework to map and predict alpha diversity among trees in diverse tropical forests.

Relating belowground microbial composition to the taxonomic, phylogenetic, and functional trait distributions of trees in a tropical forest.

The complexities of the relationships between plant and soil microbial communities remain unresolved. We determined the associations between plant aboveground and belowground (root) distributions and the communities of soil fungi and bacteria found across a diverse tropical forest plot. Soil microbial community composition was correlated with the taxonomic and phylogenetic structure of the aboveground plant assemblages even after controlling for differences in soil characteristics, but these relationships were stronger for fungi than for bacteria. In contrast to expectations, the species composition of roots in our soil core samples was a poor predictor of microbial community composition perhaps due to the patchy, ephemeral, and highly overlapping nature of fine root distributions. Our ability to predict soil microbial composition was not improved by incorporating information on plant functional traits suggesting that the most commonly measured plant traits are not particularly useful for predicting the plot-level variability in belowground microbial communities.