NSF Postdoctoral Fellowship in Biology FY 2012

2012 财年 NSF 生物学博士后奖学金

• 批准号: 1202882
• 负责人: Sarah Evans
• 金额: $ 12.3万
• 依托单位: Evans Sarah E
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1202882&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; FY

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.

使用贝叶斯建模量化微生物记忆及其对生态系统时滞的影响生态系统不会立即响应环境变化，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的反应程度和时间尺度。因此，缺乏对控制生态系统记忆的因素的了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。微生物群落控制土壤中的碳和氮（生物地球化学）循环。与之前的假设相反，最近的证据表明微生物群落本身拥有可能有助于生态系统反应的记忆。然而，微生物记忆尚未被明确量化，或被评估为有助于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；确定这种微生物机制在预测土壤气体通量滞后方面的重要性；并确定与其介导的更大规模功能最相关的微生物特征（生理学或群落组成）。这项研究将创新的统计工具（分层贝叶斯模型）和新的生物学概念（微生物记忆）结合起来，弥合了生态学和数学之间的概念和协作差距。该项目将提高博士后的统计技能，扩大未来的工作范围，并为博士后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的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。