Seton Bachle | USGS Science

USGS2025

Drought intensity and duration interact to magnify losses in primary productivity

As droughts become longer and more intense, impacts on terrestrial primary productivity are expected to increase progressively. Yet, some ecosystems appear to acclimate to multiyear drought, with constant or diminishing reductions in productivity as drought duration increases. We quantified the combined effects of drought duration and intensity on aboveground productivity in 74 grasslands and shrublands distributed globally. Ecosystem acclimation with multiyear drought was observed overall, except when droughts were extreme (i.e., ≤1-in-100-year likelihood of occurrence). Productivity losses after four consecutive years of extreme drought increased by ~2.5-fold compared with those of the first year. These results portend a foundational shift in ecosystem behavior if drought duration and intensity increase, from maintenance of reduced functioning over time to progressive and profound losses of productivity when droughts are extreme.

Science↗

USGS2024

Extreme drought impacts have been underestimated in grasslands and shrublands globally

Drought has well-documented societal and economic consequences. Climate change is expected to intensify drought to even more extreme levels, but because such droughts have been historically rare, their impact on ecosystem functioning is not well known. We experimentally imposed the most frequent type of intensified drought—one that is ~1 y in duration—at 100 grassland and shrubland sites distributed across six continents. We found that loss of aboveground plant growth, a key measure of ecosystem function, was 60% greater when short-term drought was extreme (≤1-in-100-y historical occurrence). This drought-induced loss in function greatly exceeds previously reported losses for grasslands and shrublands, suggesting that the global impacts of projected increases in drought severity have been substantially underestimated.

Proceedings of the National Academy of Sciences↗

USGS2021

Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) 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 NEON’s readily available AOP derived data products – Leaf Area Index, 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 (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had a validation R2 of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 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 heterogenous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time, yet 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.

Ecology↗