Abstract

Microorganisms drive biogeochemical processes, but linking these processes to real changes in microbial communities under field conditions is not trivial. Here, we present a model-based approach to estimate independent contributions of microbial community shifts to ecosystem properties. The approach was tested empirically, using denitrification potential as our model process, in a spatial survey of arable land encompassing a range of edaphic conditions and two agricultural production systems. Soil nitrate was the most important single predictor of denitrification potential (the change in Akaike's information criterion, corrected for sample size, AIC(c) = 20.29); however, the inclusion of biotic variables (particularly the evenness and size of denitrifier communities [AIC(c) = 12.02], and the abundance of one denitrifier genotype [AIC(c) = 18.04]) had a substantial effect on model precision, comparable to the inclusion of abiotic variables (biotic R-2 = 0.28, abiotic R-2 = 0.50, biotic + abiotic R-2 = 0.76). This approach provides a valuable tool for explicitly linking microbial communities to ecosystem functioning. By making this link, we have demonstrated that including aspects of microbial community structure and diversity in biogeochemical models can improve predictions of nutrient cycling in ecosystems and enhance our understanding of ecosystem functionality.

Keywords

denitrification; ecosystem services; functional diversity; model selection; multimodel inference; nitrogen cycling

Published in

Ecology
2015, volume: 96, number: 7, pages: 1985-1993
Publisher: ECOLOGICAL SOC AMER

Authors' information