Leaf phenological type, functional traits, and hyperspectral reflectance to predict volatile isoprenoid emissions in central Amazon Forest trees

Volatile isoprenoids take part in a wide range of forest-atmosphere processes that scale from plant cell regulation to atmospheric particle formation. Major drivers of plant leaf emissions are light and temperature - i.e., seasonality - and leaf age, suggesting leaf phenological type (i.e., evergreen or brevideciduous) may exert control over emission rates. The Amazon Forest is the greatest and most diverse source of volatile isoprenoid emissions, but the lack of leaf-level studies and the logistical challenges of measuring in such remote and highly bio-diverse sites bring high levels of uncertainty to modeled estimates. Studies indicate that hyperspectral leaf reflectance is an effective tool for estimating leaf morphological, physiological, and chemical traits, being perhaps a promising tool for remotely assessing volatile isoprenoid emissions from vegetation. Considering this, our research aimed at evaluating i) whether leaf phenological type and functional traits are determinants of the presence and magnitude of isoprene emissions and of mono- and sesquiterpene storage, and ii) whether leaf-level hyperspectral reflectance can be used to predict the presence of isoprene emissions and mono- and sesquiterpene storage in central Amazon forest trees. We found that isoprene-emitting evergreen trees were less likely to store monoterpenes and had tougher and less photosynthetically active leaves, while higher isoprene emission rates in brevideciduous trees associated with higher storage of sesquiterpene and phenolic compounds, suggesting that isoprene emissions possibly mediate a mechanical-chemical defense trade-off in evergreen and brevideciduous trees in this forest. Furthermore, we saw that dry leaf hyperspectral reflectance data and fresh leaf reflectance at selected wavelengths (616, 694, and 1155 nm) predicted the presence of isoprene emissions with accuracies of 0.67 and 0.72, respectively. Meanwhile, the presence of terpene storage was well predicted from fresh leaf reflectance data for monoterpene storage (accuracy = 0.65) and sesquiterpene storage (accuracy = 0.67). These results indicate the possibility of using spectral readings from herbarium specimens to assist in the development of more efficient sampling designs targeted at potential isoprene emitters, as well as of using fresh leaf reflectance data to calibrate multi-sensor equipment to remotely detect potential isoprene emitters or orientate sampling efforts in the field toward potential terpene-storing trees. The use of spectral tools for detecting potential volatile isoprenoid emitters and a more functional trait-based, mechanistic representation of emissions can combine to reduce modeling emission uncertainties and contribute to understanding the roles of volatile isoprenoids within forest-atmosphere interactions, atmospheric chemistry, and the carbon cycle.