Publication Date:
2015-02-18
Description:
There is an increasing interest in identifying theories, empirical datasets, 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 per 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 topography from light detection and ranging (lidar) are associated with species richness across four tree size classes (〉 1, 1-10, 〉 10, 〉 20 cm dbh) and three spatial scales (1, 0.25, 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 per ha (mean ± SE), to compare with remote sensing data. All remote sensing metrics become 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 〉 1 cm in 1 ha plots were compared to remote sensing metrics, standard deviation in canopy reflectance can explain 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 make predictions of species richness across Barro Colorado Island (BCI) at the 1-ha spatial scale for different tree size classes. We predict variation in tree species richness amongst all plants (adjusted r2 = 0.35) and trees 〉 10 cm dbh (adjusted r2 = 0.25). However, the best model results were for understory trees and shrubs (1-10 cm dbh) (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 proportion of variance in species richness and potentially provide a framework to map and predict alpha diversity amongst trees in diverse tropical forests. # doi:10.1890/14-1593.1
Print ISSN:
1051-0761
Electronic ISSN:
1939-5582
Topics:
Biology
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