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Collaborative Research: A general approach to partitioning contributions from multiple drivers affecting individuals, populations, and communities

协作研究：划分影响个人、人口和社区的多个驱动因素贡献的通用方法

基本信息

批准号：
1933612
负责人：
Robin Snyder
金额：
$ 14.87万
依托单位：
Case Western Reserve University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933612&HistoricalAwards=false
关键词：
Collaborative Research general approach partitioning

项目摘要

The patterns in nature that ecologists strive to understand are usually the result of many interacting processes. Why are there about 1100 bird species in the US, rather than 110 or 11,000? To answer a question like that, it is not enough to list all the contributing factors; we need to know which ones are more and less important. Like a cook who knows which ingredients are essential for a recipe, ecologists ask, for example, what 'ingredients' are crucial for preserving biodiversity in an ecosystem altered by human activities, and which are less critical. The goal of this project are first, to give ecologists better tools for identifying the factors most important in creating observed patterns, based on a general statistical method called "Functional Analysis of Variance" (fANOVA). Second, the new tools will be used to extend ecological theories explaining how competing species can coexist, to identify which vital rates (e.g., survival rates at different ages) contribute most to fluctuations in population abundance, and to identify which vital rates, at which ages or life-stages, contribute most to the large within-population variation in lifetime outcomes (such total number of offspring) that cannot be explained by observable traits. The researchers will also develop new computing methods and statistical theory to broaden the applicability of fANOVA in ecology and conduct workshops to teach others to use the new tools. fANOVA is a general variance decomposition method for nonlinear input-output relationships, decomposing output variance into direct contributions from variation in each input, contributions from interactions of all orders (pairwise, triplets, etc.), and unexplained residual variation. Many ecological questions involve processes operating over large spatial and temporal scales, so experimental manipulations are infeasible and inference must come from dynamic models fitted to empirical data. This project will explore how fANOVA can play the role in these situations that conventional ANOVA does in simple experimental designs, answering questions about the relative importance of different processes using models fitted to empirical data. fANOVA is not 'plug and play': the general recipe is often computationally intractable and hard to interpret in high-dimensional situations. Each new application needs to overcome these challenges. Specific objectives include: (1) Develop an exact and complete version of Life Table Response Experiment analysis, and using a meta-analysis of hundreds of published models to contrast fANOVA with current approaches; (2) Extend recently developed fANOVA-based methods of quantifying coexistence mechanisms to include systems with explicit spatial structure and clumped species distributions; (3) Determine how the magnitude of within- population random variation in lifetime reproductive success is related to life history and functional traits through a meta-analysis of published models; (4) Develop general systematic tools to determine when or where in the life cycle luck (random differences in outcomes such as survival and fecundity) matters the most for lifetime outcomes; (5) Develop statistical theory to determine how models should be constructed for optimal estimation and inference about luck.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生态学家努力理解的自然界模式通常是许多相互作用过程的结果。为什么美国有大约 1100 种鸟类，而不是 110 或 11,000 种？要回答这样的问题，仅仅列出所有影响因素是不够的。我们需要知道哪些更重要，哪些不那么重要。就像厨师知道食谱中哪些成分是必不可少的一样，生态学家会问，哪些“成分”对于保护因人类活动而改变的生态系统中的生物多样性至关重要，哪些成分不太重要。该项目的目标首先是，基于称为“方差函数分析”(fANOVA) 的通用统计方法，为生态学家提供更好的工具来识别创建观察模式中最重要的因素。其次，新工具将用于扩展解释竞争物种如何共存的生态理论，确定哪些生命率（例如，不同年龄的存活率）对种群丰度波动的影响最大，并确定哪些生命率、在哪个年龄或生命阶段对生命结果（如后代总数）的群体内巨大变化影响最大，而这种变化无法用可观察到的特征来解释。研究人员还将开发新的计算方法和统计理论，以扩大 fANOVA 在生态学中的适用性，并举办研讨会来教其他人使用新工具。 fANOVA 是一种用于非线性输入-输出关系的通用方差分解方法，将输出方差分解为每个输入变化的直接贡献、所有阶次（成对、三元组等）相互作用的贡献以及无法解释的残差变化。许多生态问题涉及在大空间和时间尺度上运行的过程，因此实验操作是不可行的，推理必须来自适合经验数据的动态模型。该项目将探讨 fANOVA 如何在这些情况下发挥传统方差分析在简单实验设计中的作用，并使用适合经验数据的模型回答有关不同过程的相对重要性的问题。 fANOVA 不是“即插即用”：一般方法通常在计算上难以处理，并且在高维情况下难以解释。每个新的应用程序都需要克服这些挑战。具体目标包括：（1）开发精确且完整的生命表响应实验分析版本，并使用数百个已发布模型的荟萃分析来将 fANOVA 与当前方法进行对比； (2) 扩展最近开发的基于 fANOVA 的量化共存机制的方法，以包括具有明确空间结构和聚集物种分布的系统； (3) 通过对已发表模型的荟萃分析，确定终生繁殖成功率的群体内随机变异的大小如何与生活史和功能特征相关； (4) 开发通用的系统工具来确定生命周期中运气（生存和繁殖力等结果的随机差异）对一生结果最重要的时间或地点； (5) 发展统计理论，以确定如何构建模型以对运气进行最佳估计和推断。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The influence of life‐history strategy on ecosystem sensitivity to resource fluctuations

生命历史策略对生态系统对资源波动敏感性的影响

DOI：
10.1111/1365-2745.13779
发表时间：
2021
期刊：
Journal of Ecology
影响因子：
5.5
作者：
Felton, Andrew J.;Snyder, Robin E.;Shriver, Robert K.;Suding, Katharine N.;Adler, Peter B.
通讯作者：
Adler, Peter B.

Snared in an Evil Time: How Age-Dependent Environmental and Demographic Variability Contribute to Variance in Lifetime Outcomes

陷入邪恶的时代：年龄相关的环境和人口变化如何导致一生结果的差异