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Title
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Mean annual precipitation predicts primary production resistance and resilience to extreme drought
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Author
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Abstract
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| Extreme drought is increasing in frequency and intensity in many regions globally, with uncertain consequences for the resistance and resilience of ecosystem functions, including primary production. Primary production resistance, the capacity to withstand change during extreme drought, and resilience, the degree to which production recovers, vary among and within ecosystem types, obscuring generalized patterns of ecological stability. Theory and many observations suggest forest production is more resistant but less resilient than grassland production to extreme drought; however, studies of production sensitivity to precipitation variability indicate that the processes controlling resistance and resilience may be influenced more by mean annual precipitation (MAP) than ecosystem type. Here, we conducted a global meta-analysis to investigate primary production resistance and resilience to extreme drought in 64 forests and grasslands across a broad MAP gradient. We found resistance to extreme drought was predicted by MAP; however, grasslands (positive) and forests (negative) exhibited opposing resilience relationships with MAP. Our findings indicate that common plant physiological mechanisms may determine grassland and forest resistance to extreme drought, whereas differences among plant residents in turnover time, plant architecture, and drought adaptive strategies likely underlie divergent resilience patterns. The low resistance and resilience of dry grasslands suggests that these ecosystems are the most vulnerable to extreme drought - a vulnerability that is expected to compound as extreme drought frequency increases in the future. (C) 2018 Elsevier B.V. All rights reserved. |
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Language
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English
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Source (journal)
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The science of the total environment. - Amsterdam, 1972, currens
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Publication
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Amsterdam
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2018
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ISSN
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0048-9697
[print]
1879-1026
[online]
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DOI
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10.1016/J.SCITOTENV.2018.04.290
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Volume/pages
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636
(2018)
, p. 360-366
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ISI
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000436599000034
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Pubmed ID
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29709853
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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