Plant Production Responses to Precipitation Differ Along an Elevation Gradient and Are Enhanced Under Extremes

Munson, S.M., Bunting, E.L., Bradford, J.B. et al. Ecosystems (2018).
Year Published: 

"The sensitivity of plant production to precipitation underlies the functioning of ecosystems. Studies that relate long-term mean annual precipitation and production across multiple sites (spatial relationship) or examine interannual linkages within a site (temporal relationship) can reveal biophysical controls over ecosystem function but have limited ability to infer responses to extreme changes in precipitation that may become more common under climate change. To overcome limitations of using a single approach, we integrated satellite- and ground-based estimates of production with a standardized, multi-site precipitation manipulation experiment across a grassland elevation gradient in the southwestern USA. The responsiveness of production to changes in precipitation followed the order: temporal (0.06–0.13 g m−2 mm−1) < spatial (0.21 g m−2 mm−1) < experimental relationship (0.25–0.42 g m−2 mm−1), suggesting that spatial and temporal relationships determined with satellite- and ground-based estimates cannot be extrapolated to determine the effect of extreme events. A strong production response to differences in mean annual precipitation across sites reinforces a regional control of water availability. Interannual sensitivity to precipitation was strongest at the low elevation grasslands, and the high elevation mixed conifer meadow had a large reduction in production in a drought year. Extreme experimental drought strongly reduced production in low elevation grasslands, but water addition had mixed effects. High elevation meadows were insensitive to both extreme drought and water addition. Our results highlight the importance of accounting for extreme climate regimes and site-level factors when scaling climate change effects up to regional and global scales."

drought, climate change, plant production, precipitation, ecosystem health, remote sensing