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.

使用贝叶斯模型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对环境变化做出即时反应，而是对过去的条件具有“记忆”。这种记忆改变了生态系统对气候压力反应的程度和时间尺度。因此，对控制生态系统记忆的因素缺乏了解可能会导致气候模式和生物地球化学动态的预测不准确。微生物群落控制着土壤中的碳和氮（地球化学）循环。与以前的假设相反，最近的证据表明，微生物群落本身具有记忆，可能有助于生态系统的反应。然而，微生物的记忆还没有被明确量化，或评估作为一个可能的机制，有助于生态系统的记忆。该项目的目的是：开发一个模型框架，以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;并确定与它们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）。这项研究结合了创新的统计工具（分层贝叶斯模型）和新的生物学概念（微生物记忆），弥合了生态学和数学之间的概念和合作差距。该项目将提高博士后研究员？的统计技能，拓宽了未来工作的范围，并为研究员提供了分析大型生态数据集的新工具。该项目的成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，并有助于向生命科学家介绍贝叶斯方法的生态讲习班。