Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network’s (NEON’s) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high‐resolution trait mapping. We tested the accuracy of readily available data products of NEON’s AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground‐measured LAI (r = 0.32) and AOP Total Biomass and ground‐measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380–2,500 nm), as opposed to derived products, could predict vegetation traits using partial least‐squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation R2of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14–64%. Our results suggest that currently available AOP‐derived data products should not be used without extensive ground‐based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field‐based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in‐situ field measurements across a diversity of sites.
Grasses are cosmopolitan, existing in many biome and climate types from xeric to tropical. Traits that control physiological responses to drought vary strongly among grass lineages, suggesting that tolerance strategies may differ with evolutionary history. Here, we withheld water from 12 species representing six tribes of grasses to compare how species respond to drought in different grass lineages. We measured physiological, morphological, and anatomical traits. Dominant lineages from tropical savannas, like Andropogoneae, tolerated drought due to above and belowground morphological traits (specific leaf area and root length, SLA and SRL), while temperate grasses in this study utilized conservative leaf physiology (gas exchange) and anatomy traits. Increased intrinsic water-use efficiency coincided with a larger number of stomata, resulting in greater water loss (with inherently greater carbon gain) and increased drought sensitivity. Inherent leaf and root economic strategies impacting drought response were observed in all species, resulting in either high SLA or SRL, but not both. Our results indicate that grasses subjected to severe drought were influenced by anatomical traits (e.g., number of stomata and xylem area) and similar within lineages. In addition, grasses recovered at least 50% of physiological functioning across all lineages and 92% within Andropogoneae species, illustrating how drought can influence functional responses across diverse grass lineages.
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