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

• 批准号: 1933561
• 负责人: Peter Adler
• 金额: $ 11.49万
• 依托单位: Utah State 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=1933561&HistoricalAwards=false
• 关键词: Collaborative; Research; general; approach; partitioning

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模式通常是许多相互作用过程的结果。为什么在美国有大约1100种鸟类，而不是110或11,000种？要回答这样的问题，这还不足以列出所有贡献因素。我们需要知道哪些越来越重要。就像知道哪种成分对于食谱至关重要的厨师一样，生态学家问，例如，哪些“成分”对于在人类活动改变的生态系统中保存生物多样性至关重要，而哪些成分不太关键。该项目的目的是首先，为生态学家提供更好的工具，以基于一种称为“方差的功能分析”（BATOVA）的一般统计方法来确定创建观察到模式最重要的因素。其次，新工具将用于扩展生态理论，以解释竞争物种如何共存，确定哪种重要率（例如，不同年龄段的生存率）最大程度地促进了人口丰度的波动，并确定了哪些重要率在哪些年龄或生命阶段对大型的生命内部的生命范围造成的途径造成了众多的范围（以下范围都无法察觉到库存数量（这些范围）（这些范围都无法居住）（这些都无法算法）（远不止于此）。研究人员还将开发新的计算方法和统计理论，以扩大甘诺瓦在生态学中的适用性，并进行讲习班以教他人使用新工具。 BATOVA是一种通用方差分解方法，用于非线性输入输出关系，将输出方差分解为每个输入变化的直接贡献，来自所有订单的相互作用（成对，三胞胎等）的贡献以及无法解释的残留变化。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，并且推断必须来自安装在经验数据中的动态模型。该项目将探讨范诺瓦如何在传统方差分析在简单的实验设计中所做的这些情况下扮演角色，从而回答有关使用拟合经验数据模型的不同过程相对重要性的问题。 BATOVA不是“插头”：一般食谱通常在计算上是棘手的，在高维情况下很难解释。每个新应用都需要克服这些挑战。具体目标包括：（1）开发出生命表响应实验分析的精确而完整的版本，并使用数百个已发表模型的荟萃分析与当前的方法对比了todorme; （2）扩展了最近开发的基于FAROVA的量化共存机制的方法，以包括具有显式空间结构和块状物种分布的系统； （3）确定终生生殖成功中种群内随机变化的幅度如何通过公开模型的荟萃分析与生活历史和功能性状有关； （4）开发一般的系统工具来确定生命周期中何时或何处运气（随机的结果和生存和繁殖力差异）对终生结果最重要； （5）开发统计理论，以确定应如何构建模型以进行最佳的估计和对运气的推断。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。

项目成果

A critical comparison of integral projection and matrix projection models for demographic analysis: Comment

用于人口分析的积分投影和矩阵投影模型的关键比较：评论

• DOI: 10.1002/ecy.3605
• 发表时间: 2022
• 期刊: Ecology
• 影响因子: 4.8
• 作者: Ellner, Stephen P.;Adler, Peter B.;Childs, Dylan Z.;Hooker, Giles;Miller, Tom E.;Rees, Mark
• 通讯作者: Rees, Mark