EAGER: Effects of environmental variability on population dynamics in the Long-Term Ecological Research network

EAGER：长期生态研究网络中环境变化对种群动态的影响

• 批准号: 1543651
• 负责人: Thomas Miller
• 金额: $ 25.77万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1543651&HistoricalAwards=false
• 关键词: EAGER; Effects; environmental; variability; population

A common perception is that environmental fluctuations - drought one year, floods the next - have negative effects on populations and could drive them to extinction. This proposal will use existing long-term population data collected at Long Term Ecological Research sites to test this perception rigorously. Results could overturn this long-standing assumption and provide fundamental new insights to the field of population ecology. Data synthesized by this project will be available through a global population dynamics database that is used worldwide. A clearer understanding of how environmental variability affects population dynamics will improve conservation and management efforts. Evidence for positive effects of environmental variability will alter strategies to conserve threatened species or to manage invasive species. The project will support a postdoctoral researcher and a data analyst, and will provide training opportunities at the interface of modeling, statistical analyses, and data synthesis for graduate and undergraduate students. This project will attempt the first large-scale synthesis of demographic data to assess the effects of environmental variability on population dynamics. Twenty-five Long Term Ecological Research (LTER) projects have been collecting population-level data for decades, yet these data are rarely used to address key questions in population ecology. The investigator will collect all LTER time series of abundances or vital rates by manually inspecting each of the 6,000 data sets available from the LTER data portal, by combing data archives, and by contacting LTER researchers directly. The data will be analyzed to test the magnitude and direction of skewness in the distribution of log(population growth rate) as an indicator of non-linear responses to environmental variation. When spatially-replicated time series are available, they will be used to separate process error from sampling error and analyzed with hierarchical models. Once skewness in distributions of population growth rates is quantified, the investigator will attempt to identify key environmental drivers. This is the riskiest part of the project; when possible, these will allow the researcher to quantify demographic response functions and predict the effects of variability on population dynamics with greater confidence. Evidence that effects of environmental variability are commonly neutral or positive will fundamentally alter understanding of population dynamics in stochastic environments.

人们普遍认为，环境波动——一年干旱，下一年洪水——会对人口产生负面影响，并可能导致它们灭绝。该提案将使用在长期生态研究站点收集的现有长期人口数据来严格测试这种看法。结果可能会推翻这一长期存在的假设，并为种群生态学领域提供根本性的新见解。该项目综合的数据将通过全球使用的全球人口动态数据库提供。更清楚地了解环境变化如何影响种群动态将改善保护和管理工作。环境变化的积极影响的证据将改变保护受威胁物种或管理入侵物种的策略。该项目将支持一名博士后研究员和一名数据分析师，并将为研究生和本科生提供建模、统计分析和数据合成界面的培训机会。该项目将尝试首次大规模综合人口数据，以评估环境变化对人口动态的影响。数十年来，二十五个长期生态研究 (LTER) 项目一直在收集种群水平的数据，但这些数据很少用于解决种群生态学中的关键问题。研究人员将通过手动检查 LTER 数据门户提供的 6,000 个数据集、梳理数据档案以及直接联系 LTER 研究人员，收集所有 LTER 丰度或生命率时间序列。将分析数据以测试对数（人口增长率）分布中偏度的大小和方向，作为对环境变化的非线性响应的指标。当空间复制时间序列可用时，它们将用于将过程误差与采样误差分开，并使用分层模型进行分析。一旦人口增长率分布的偏度被量化，调查人员将尝试确定关键的环境驱动因素。这是该项目中风险最大的部分；如果可能的话，这些将使研究人员能够量化人口反应函数并更有信心地预测变异性对人口动态的影响。环境变化的影响通常是中性或积极的证据将从根本上改变对随机环境中种群动态的理解。