Contingency in ecosystem but not plant community response to multiple global change factors

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Community and ecosystem responses to global environmental change are contingent on the magnitude of change and interacting global change factors. To reveal whether responses are also contingent on the magnitude of each interacting factor, multifactor, multilevel experiments are required, but are rarely conducted. We exposed model grassland ecosystems to six levels of atmospheric CO2 and six levels of nitrogen enrichment, applying the latter both chronically (simulating deposition) and acutely (simulating fertilization). The 66 treatments were maintained for 6 months under controlled growing conditions, with biomass harvested every 28 d and sorted to species. Aboveground plant productivity responses to CO2 were contingent on nitrogen amount, and the responses to nitrogen amount were dependent on whether applications were chronic or acute. Specifically, productivity responses to increasing CO2 concentrations were accentuated with higher nitrogen enrichments, and productivity was greater when higher nitrogen enrichments were applied acutely. Plant community composition was influenced only by nitrogen enrichment, where the co-dominant grass species with the greatest leaf trait plasticity increasingly dominated with higher nitrogen amounts. Community processes are considered to be unpredictable, but our data suggest that the prediction of the impacts of simultaneous global changes is more complex for ecosystem processes, given that their responses are contingent on the levels of interacting factors.





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Bradford, MA, SA Wood, FT Maestre, JF REYNOLDS and RJ Warren (2012). Contingency in ecosystem but not plant community response to multiple global change factors. New Phytologist, 196(2). pp. 462–471. 10.1111/j.1469-8137.2012.04271.x Retrieved from

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James F. Reynolds

Professor Emeritus

Integrated assessment of complex human-environmental systems; Land degradation and desertification in global drylands; Conceptual frameworks and models to advance the science of dryland development

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