© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hudson, A. R., Peters, D. P. C., Blair, J. M., Childers, D. L., Doran, P. T., Geil, K., Gooseff, M., Gross, K. L., Haddad, N. M., Pastore, M. A., Rudgers, J. A., Sala, O., Seabloom, E. W., & Shaver, G. Cross-site comparisons of dryland ecosystem response to climate change in the US long-term ecological research network. Bioscience, 72(9), (2022): 889–907, https://doi.org/10.1093/biosci/biab134.
Long-term observations and experiments in diverse drylands reveal how ecosystems and services are responding to climate change. To develop generalities about climate change impacts at dryland sites, we compared broadscale patterns in climate and synthesized primary production responses among the eight terrestrial, nonforested sites of the United States Long-Term Ecological Research (US LTER) Network located in temperate (Southwest and Midwest) and polar (Arctic and Antarctic) regions. All sites experienced warming in recent decades, whereas drought varied regionally with multidecadal phases. Multiple years of wet or dry conditions had larger effects than single years on primary production. Droughts, floods, and wildfires altered resource availability and restructured plant communities, with greater impacts on primary production than warming alone. During severe regional droughts, air pollution from wildfire and dust events peaked. Studies at US LTER drylands over more than 40 years demonstrate reciprocal links and feedbacks among dryland ecosystems, climate-driven disturbance events, and climate change.
Funding was provided by the USDA-ARS SCINet Big Data Project (grant no. 0500–00093–001–00-D), and the National Science Foundation US LTER Program to New Mexico State University for the Jornada Basin (grant no. DEB 20–25166), Kansas State University for the Konza Prairie (grant no. DEB 2025849), the Kellogg Biological Station (grant no. DEB 1832042), Cedar Creek Ecosystem Science Reserve (grants no. DEB-1234162 and no. DEB-1831944), ARC (grant no. DEB-1637459), MCM (grant no. OPP-1637708), CAP (grant no. DEB-1832016), and SEV (grant no. DEB-1655499). Support was also provided by the Minnesota Supercomputer Institute and the University of Minnesota, Michigan State University AgBioResearch.
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