NSF Postdoctoral Fellowship in Biology FY 2012

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Quantifying microbial memory and its influence on ecosystem time lags using Bayesian modelingEcosystems do not respond instantaneously to environmental change, but rather possess a "memory" of past conditions. This memory alters the magnitude and timescale of an ecosystem's response to climate stress. Therefore, a lack of understanding of what controls ecosystem memory could contribute to inaccurate predictions of climate patterns and biogeochemical dynamics. Microbial communities control carbon and nitrogen (biogeochemical) cycling in the soil. Contrary to previous assumptions, recent evidence shows that microbial communities themselves possess a memory that could contribute to ecosystem responses. However, microbial memory has not been explicitly quantified, or assessed as a possible mechanism contributing to ecosystem memory. The aim of this project is: to develop a model framework to examine the magnitude and timescale of microbial memory; to determine the importance of this microbial mechanism in predicting lags in soil gas flux; and to identify the microbial traits (physiology or community composition) that are best linked to the larger scale functions they mediate. This research couples innovative statistical tools (hierarchical Bayesian modeling) and new biological concepts (microbial memory), bridging conceptual and collaborative gaps between ecology and mathematics. The project will improve the postdoctoral fellow?s statistical skills, broaden the scope of future work, and give the fellow novel tools to analyze large ecological datasets. Results from this project will be widely disseminated through peer-reviewed publications and university-mediated public seminars, and contribute to ecological workshops that introduce life scientists to Bayesian methods.