Integration of leaf spectral reflectance variability facilitates identification of plant leaves at different taxonomic levels

Plant identification is crucial to the conservation and management of natural areas. The shortwave spectral reflectance of leaves is a promising tool for rapidly identifying species at different taxonomic ranks. However, the spectral reflectance of leaves changes in response to biotic and abiotic conditions. This investigation asked how this variability affects the accuracy of methods used to predict plant taxonomies and what factors most influence the spectral signature of leaves. To answer these questions, we measured the reflectance of leaves of 62 woody species from the living collection at the NYBG twice in two pairwise samplings. We found that PLS-DA accuracy improved when we used a larger sample of natural variance in the classification model. Finally, to evaluate whether there was an influence of the species’ relatedness or the growing environment on structural and biochemical traits predicted from the leaf reflectance, we ran a phylogenetic signal analysis and a series of mixed effects model analyses that showed no phylogenetic but an environmental influence. We found that the increase in temperature and relative humidity variability explained the increment of predicted carotene and the decrease of Nitrogen content for the first pairwise analysis. For the second pairwise analysis, we found that the reduction of relative humidity variability explained leaf water and Nitrogen content decrease, and relative humidity decrease combined with day length decrease explained a decline in LMA.