Journal
GLOBAL CHANGE BIOLOGY
Volume 22, Issue 5, Pages 1957-1964Publisher
WILEY
DOI: 10.1111/gcb.13219
Keywords
climate change; historical contingency; microbes; precipitation; soil enzymes; soil moisture
Funding
- Texas Ecolab program
- National Science Foundation [DGE-1110007]
- Direct For Biological Sciences
- Division Of Environmental Biology [1546740] Funding Source: National Science Foundation
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Soil moisture constrains the activity of decomposer soil microorganisms, and in turn the rate at which soil carbon returns to the atmosphere. While increases in soil moisture are generally associated with increased microbial activity, historical climate may constrain current microbial responses to moisture. However, it is not known if variation in the shape and magnitude of microbial functional responses to soil moisture can be predicted from historical climate at regional scales. To address this problem, we measured soil enzyme activity at 12 sites across a broad climate gradient spanning 442-887mm mean annual precipitation. Measurements were made eight times over 21months to maximize sampling during different moisture conditions. We then fit saturating functions of enzyme activity to soil moisture and extracted half saturation and maximum activity parameter values from model fits. We found that 50% of the variation in maximum activity parameters across sites could be predicted by 30-year mean annual precipitation, an indicator of historical climate, and that the effect is independent of variation in temperature, soil texture, or soil carbon concentration. Based on this finding, we suggest that variation in the shape and magnitude of soil microbial response to soil moisture due to historical climate may be remarkably predictable at regional scales, and this approach may extend to other systems. If historical contingencies on microbial activities prove to be persistent in the face of environmental change, this approach also provides a framework for incorporating historical climate effects into biogeochemical models simulating future global change scenarios.
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