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Towards a function-first framework to make soil microbial ecology predictive

Hicks, L. C., Frey, B., Kjoller, R., Lukac, M. ORCID:, Moora, M., Weedon, J. T. and Rousk, J. (2022) Towards a function-first framework to make soil microbial ecology predictive. Ecology, 103 (2). e03594. ISSN 0012-9658

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To link to this item DOI: 10.1002/ecy.3594


Soil microbial communities perform vital ecosystem functions, such as the decomposition of organic matter to provide plant nutrition. However, despite the functional importance of soil microorganisms, attribution of ecosystem function to particular constituents of the microbial community has been impeded by a lack of information linking microbial function to community composition and structure. Here, we propose a function-first framework to predict how microbial communities influence ecosystem functions. We first view the microbial community associated with a specific function as a whole, and describe the dependence of microbial functions on environmental factors (e.g. the intrinsic temperature dependence of bacterial growth rates). This step defines the aggregate functional response curve of the community. Second, the contribution of the whole community to ecosystem function can be predicted, by combining the functional response curve with current environmental conditions. Functional response curves can then be linked with taxonomic data in order to identify sets of “biomarker” taxa that signal how microbial communities regulate ecosystem functions. Ultimately, such indicator taxa may be used as a diagnostic tool, enabling predictions of ecosystem function from community composition. In this paper, we provide three examples to illustrate the proposed framework, whereby the dependence of bacterial growth on environmental factors, including temperature, pH and salinity, is defined as the functional response curve used to interlink soil bacterial community structure and function. Applying this framework will make it possible to predict ecosystem functions directly from microbial community composition.

Item Type:Article
Divisions:Life Sciences > School of Agriculture, Policy and Development > Department of Sustainable Land Management > Centre for Agri-environmental Research (CAER)
ID Code:101859


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