Microbial ecoenzyme stoichiometry, nutrient limitation, and organic matter decomposition in wetlands of the conterminous United States

湿地 生物地球化学循环 生态区 环境科学 生物地球化学 生态系统 溶解有机碳 环境化学 有机质 土壤碳 营养物 分解 营养循环 土壤有机质 生态学 化学 土壤科学 生物 土壤水分
作者
Brian H. Hill,Colleen M. Elonen,Alan T. Herlihy,Terri M. Jicha,Gregg Serenbetz
出处
期刊:Wetlands Ecology and Management [Springer Science+Business Media]
卷期号:26 (3): 425-439 被引量:34
标识
DOI:10.1007/s11273-017-9584-5
摘要

Microbial respiration (Rm) and ecoenzyme activities (EEA) related to microbial carbon, nitrogen, and phosphorus acquisition were measured in 792 freshwater and estuarine wetlands (representing a cumulative area of 217,480 km2) across the continental United States as part of the US EPA's 2011 National Wetland Condition Assessment. EEA stoichiometry was used to construct models for and assess nutrient limitation, carbon use efficiency (CUE), and organic matter decomposition (- k). The wetlands were classified into ten groups based on aggregated ecoregion and wetland type. The wetlands were also assigned to least, intermediate, and most disturbed classes, based on the extent of human influences. Ecoenzyme activity related to C, N and P acquisition, Rm, CUE, and (- k differed among ecoregion-wetland types and, with the exception of C acquisition and (- k, among disturbance classes. Rm and EEA were positively correlated with soil C, N and P content (r = 0.15-0.64) and stoichiometry (r = 0.15-0.48), and negatively correlated with an index of carbon quality (r = - 0.22 to - 0.39). EEA stoichiometry revealed that wetlands were more often P- than N-limited, and that P-limitation increases with increasing disturbance. Our enzyme-based approach for modeling C, N, and P acquisition, and organic matter decomposition, all rooted in stoichiometric theory, provides a mechanism for modeling resource limitations of microbial metabolism and biogeochemical cycling in wetlands. Given the ease of collecting and analyzing soil EEA and their response to wetland disturbance gradients, enzyme stoichiometry models are a cost-effective tool for monitoring ecosystem responses to resource availability and the environmental drivers of microbial metabolism, including those related to global climate changes.

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