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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #345679

Research Project: Management Technologies for Conservation of Western Rangelands

Location: Range Management Research

Title: Regional grassland productivity responses to precipitation during multiyear above- and below-average rainfall periods

Author
item Petrie, Matt - New Mexico State University
item Peters, Debra - Deb
item Yao, Jin
item Blair, J - Kansas State University
item Burruss, N. Dylan - New Mexico State University
item Collins, Scott - University Of New Mexico
item Derner, Justin
item Gherardi, L - Arizona State University
item Hendrickson, John
item Sala, Osvaldo - Arizona State University
item Starks, Patrick - Pat
item Steiner, Jean

Submitted to: Global Change Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/7/2017
Publication Date: 5/1/2018
Citation: Petrie, M., Peters, D.C., Yao, J., Blair, J.M., Burruss, N., Collins, S., Derner, J.D., Gherardi, L.A., Hendrickson, J.R., Sala, O., Starks, P.J., Steiner, J.L. 2018. Regional grassland productivity responses to precipitation during multiyear above- and below-average rainfall periods. Global Change Biology. 24:1935-1951. https://doi.org/10.1111/gcb.14024.
DOI: https://doi.org/10.1111/gcb.14024

Interpretive Summary: We leveraged a unique set of long-term data to better understand relationships between precipitation (PPT) and grassland productivity (ANPP) at eight sites in the Central Plains of the United States, especially during sustained, multi-year periods of above- and below-average precipitation. We found differences in PPT-ANPP relationships in above- and below-average years compared to long-term site averages, and variation in ANPP not explained by PPT totals that likely is attributed to legacy effects. These legacy effects may have a greater influence on ANPP than within-year PPT in many years. Our results emphasize the importance of legacy effects on productivity for sequences of above- versus below-average precipitation years, and illustrate the utility of long-term data to examine these patterns.

Technical Abstract: There is considerable uncertainty in the magnitude and direction of changes in precipitation associated with climate change, and ecosystem responses are also uncertain. Multiyear periods of above- and below-average rainfall may foretell consequences of changes in rainfall regime. We compiled long-term aboveground net primary productivity (ANPP) and precipitation (PPT) data for eight North American grasslands, and quantified relationships between ANPP and PPT at each site, and in 1-3 year periods of above- and below-average rainfall for mesic, semiarid cool, and semiarid warm grassland types. Our objective was to improve understanding of ANPP dynamics associated with changing climatic conditions by contrasting PPT-ANPP relationships in above- and below-average PPT years to those that occurred during sequences of multiple above- and below-average years. We found differences in PPT-ANPP relationships in above- and below-average years compared to long-term site averages, and variation in ANPP not explained by PPT totals that likely are attributed to legacy effects. The correlation between ANPP and current- and prior-year conditions changed from year to year throughout multiyear periods, with some legacy effects declining, and new responses emerging. Thus, ANPP in a given year was influenced by sequences of conditions that varied across grassland types and climates. Most importantly, the influence of prior-year ANPP often increased with the length of multiyear periods, whereas the influence of the amount of current-year PPT declined. Although the mechanisms by which a directional change in the frequency of above- and below-average years imposes a persistent change in grassland ANPP require further investigation, our results emphasize the importance of legacy effects on productivity for sequences of above- vs. below-average years, and illustrate the utility of long-term data to examine these patterns.