Climate variability supersedes grazing to determine the anatomy and physiology of a dominant grassland species - Seton Bachle; Jesse B. Nippert (Oecologia, 2022)
Grassland ecosystems are historically shaped by climate, fire, and grazing which are essential ecological drivers. These grassland drivers influence morphology and productivity of grasses via physiological processes, resulting in unique water and carbon-use strategies among species and populations. Leaf-level physiological responses in plants are constrained by the underlying anatomy, previously shown to reflect patterns of carbon assimilation and water-use in leaf tissues. However, the magnitude to which anatomy and physiology are impacted by grassland drivers remains unstudied. To address this knowledge gap, we sampled from three locations along a latitudinal gradient in the mesic grassland region of the central Great Plains, USA during the 2018 (drier) and 2019 (wetter) growing seasons. We measured annual biomass and forage quality at the plot level, while collecting physiological and anatomical traits at the leaf-level in cattle grazed and ungrazed locations at each site. Effects of ambient drought conditions superseded local grazing treatments and reduced carbon assimilation and total productivity in A. gerardii . Leaf-level anatomical traits, particularly those associated with water-use, varied within and across locations and between years. Specifically, xylem area increased when water was more available (2019), while xylem resistance to cavitation was observed to increase in the drier growing season (2018). Our results highlight the importance of multi-year studies in natural systems and how trait plasticity can serve as vital tool and offer insight to understanding future grassland responses from climate change as climate played a stronger role than grazing in shaping leaf physiology and anatomy.

Poor relationships between NEON Airborne Observation Platform data and field‐based vegetation traits at a mesic grassland - Stephanie Pau; Jesse B. Nippert; Ryan Slapikas; Daniel Griffith; Seton Bachle; Brent R. Helliker; Rory C. O’Connor; William J. Riley; Christopher J. Still; Marissa Zaricor (Ecology, 2022)
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 of more than 0.25. For all vegetation traits, validation 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.