NSF Postdoctoral Fellowship in Biology FY 2012

利用贝叶斯模型量化微生物记忆及其对生态系统时间滞后的影响生态系统不会对环境变化做出即时反应，而是拥有对过去条件的“记忆”。这种记忆改变了生态系统对气候压力反应的幅度和时间尺度。因此，缺乏对控制生态系统记忆的因素的理解可能会导致对气候模式和生物地球化学动力学的不准确预测。微生物群落控制着土壤中的碳和氮（生物地球化学）循环。与先前的假设相反，最近的证据表明，微生物群落本身拥有一种记忆，可以促进生态系统的反应。然而，微生物记忆还没有被明确量化，也没有被评估为促进生态系统记忆的可能机制。该项目的目的是：开发一个模型框架来检查微生物记忆的大小和时间尺度；确定该微生物机制在预测土壤气体通量滞后中的重要性；并确定微生物特征（生理或群落组成），最好地联系到更大规模的功能，他们调解。这项研究结合了创新的统计工具（层次贝叶斯模型）和新的生物学概念（微生物记忆），弥合了生态学和数学之间的概念和协作差距。该项目会提高博士后水平吗？拓宽未来工作的范围，并为同事们提供分析大型生态数据集的新工具。该项目的结果将通过同行评审出版物和大学介导的公共研讨会广泛传播，并为向生命科学家介绍贝叶斯方法的生态研讨会做出贡献。

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.