Can Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient?

Abstract

High‐resolution spectroscopy can be used to measure leaf chemical and structural traits. Such leaf traits are often highly correlated to other traits, such as photosynthesis, through the leaf economics spectrum. We measured VNIR (visible‐near infrared) leaf reflectance (400–1,075 nm) of sunlit and shaded leaves in ~150 dominant species across ten, 1 ha plots along a 3,300 m elevation gradient in Peru (on 4,284 individual leaves). We used partial least squares (PLS) regression to compare leaf reflectance to chemical traits, such as nitrogen and phosphorus, structural traits, including leaf mass per area (LMA), branch wood density and leaf venation, and “higher‐level” traits such as leaf photosynthetic capacity, leaf water repellency, and woody growth rates. Empirical models using leaf reflectance predicted leaf N and LMA (r2 > 30% and %RMSE < 30%), weakly predicted leaf venation, photosynthesis, and branch density (r2 between 10 and 35% and %RMSE between 10% and 65%), and did not predict leaf water repellency or woody growth rates (r2<5%). Prediction of higher‐level traits such as photosynthesis and branch density is likely due to these traits correlations with LMA, a trait readily predicted with leaf spectroscopy.

Imaging spectroscopy reveals the effects of topography and logging on the leaf chemistry of tropical forest canopy trees

“…The best predictions were achieved for foliar [P] with lower predictive performance for N:P ratio, foliar [N] and SLA. Surprisingly, SLA exhibited the worst predictive performance despite being generally well predicted in other studies (Asner, Martin, et al, ; Doughty et al, ) and was not found to be meaningfully predicted by LOTO cross‐validation. These R 2 values were markedly lower than those ranging from 0.54 to 0.71 reported previously (Asner, Martin, et al, ).…”

“…Instead, our predictions likely reflect patterns detectable through spectral differences at the plot scale, which has more broadly been shown to be successful (Asner, Martin, Ford, Metcalfe, & Liddell, 2009;Asner et al, 2011;1975). The low variance in community-weighted SLA among the plots (Supporting Information S2) perhaps explains why SLA was predicted with low precision compared with other studies that have found that SLA is well predicted Asner et al, 2011;Doughty et al, 2017). Better predictive performance has been achieved by predicting leaf traits from spectra measured under laboratory conditions (Doughty et al, 2017;Nunes et al, 2017).…”

Greater stem growth, woody allocation, and aboveground biomass in Paleotropical forests than in Neotropical forests

“…, Doughty et al. ). In particular, more information is needed for Africa to investigate the variation and central tendencies of forest C dynamics in its extensive tropical forests.…”

Changes in leaf functional traits of rainforest canopy trees associated with an El Niño event in Borneo

“…To our knowledge, our study is the first to analyse the morphological (LMA) and a range of chemical traits along with spectral reflectance of leaves of emergent canopies and tropical tree species during and after El Niño. Many previous studies have shown that leaf reflectance can predict the morphological and chemical leaf traits in the tropics that are commonly used for determining functional diversity (Asner et al 2012, Asner et al 2014, Doughty et al 2017 and also capture variation that is not traditionally measured (Schweiger et al 2018). We evaluated the variation of 17 leaf traits using spectral reflectance and were able to validate these measurements against field data.…”