Collaborative Research: Integral Projection Models for Populations in Varying Environments: Construction and Analysis

合作研究：不同环境中人群的整体投影模型：构建和分析

• 批准号: 1353078
• 负责人: Peter Adler
• 金额: $ 29万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1353078&HistoricalAwards=false
• 关键词: Collaborative; Research; Integral; Projection; Models

All environments are variable and uncertain: some years are hotter, some wetter; predators wax and wane in abundance. How organisms buffer themselves against this variability, and exploit it, are major challenges for ecology. This project will combine new statistical and mathematical theory with long-term data sets on semi-arid plant communities to address two challenges in understanding how variable environments affect populations. The first goal will be to identify which environmental variables most strongly affect plant demographic rates such as survival, growth, and fecundity. This work will draw upon long-term studies on natural communities that provide observations on thousands of individual plants over multiple decades, along with numerous environmental variables that are measured at high spatial and temporal resolution. With so many potential explanatory variables, standard statistical methods for variable selection are unstable and unreliable. This project will combine data mining methods from machine learning techniques with traditional statistical theory to identify important environmental drivers, and build more reliable demographic models. Ecological questions that will be addressed include whether demographic rates respond primarily to resource availability (e.g., soil moisture) versus non-resource variables such as temperature; how environment and competition interact; and how strongly past conditions affect current performance. The empirical data sets will be used to answer these questions and look for generalities across multiple communities. A second goal is to understand the individual mechanisms that underlie effects of environmental variation. For example, if a plant population's growth is most affected by rainfall, is it because many plants die immediately, or because of long-term effects such as decreased life expectancy and smaller size throughout life? To address such questions, statistical methods from discrete-state random process theory will be extended to continuous states (e.g., individual plant size) and varying environments, and applied to the fitted plant demographic models. An expected transformative outcome from this project will be new methods for demographic modeling and analysis, and much of its broader significance will be the applications of those tools in ecology, conservation biology and invasive species management. Statistical products from this work will be disseminated freely as R code modules that users can adapt to their own study systems. The project will also support the research training and mentoring of a doctoral student in statistics, and a postdoctoral student in quantitative ecology.

所有的环境都是可变的和不确定的：有些年份更热，有些年份更潮湿;捕食者的数量也在不断增加和减少。生物体如何缓冲这种变异性，并利用它，是生态学的主要挑战。该项目将联合收割机与半干旱植物群落的长期数据集相结合，以解决在了解可变环境如何影响种群方面的两个挑战。第一个目标将是确定哪些环境变量最强烈地影响植物的生存率，生长和繁殖力。这项工作将借鉴对自然群落的长期研究，这些研究提供了数十年来对数千株植物的观测，沿着以高空间和时间分辨率测量的许多环境变量。有这么多潜在的解释变量，标准的变量选择统计方法是不稳定和不可靠的。该项目将联合收割机数据挖掘方法从机器学习技术与传统的统计理论，以确定重要的环境驱动因素，并建立更可靠的人口模型。将讨论的生态问题包括人口增长率是否主要取决于资源的可用性（例如，土壤湿度）与温度等非资源变量之间的关系;环境与竞争如何相互作用;过去的条件对当前绩效的影响有多大。经验数据集将用于回答这些问题，并寻找跨多个社区的共性。第二个目标是了解环境变化影响的个体机制。例如，如果一个植物种群的生长受降雨的影响最大，是因为许多植物立即死亡，还是因为长期的影响，如寿命缩短和整个生命周期的尺寸变小？为了解决这些问题，离散状态随机过程理论的统计方法将扩展到连续状态（例如，个体植物大小）和变化的环境，并应用于拟合的植物人口模型。该项目的预期变革性成果将是人口建模和分析的新方法，其更广泛的意义将是这些工具在生态学，保护生物学和入侵物种管理中的应用。 这项工作的统计产品将作为R代码模块免费分发，用户可以根据自己的学习系统进行调整。该项目还将支持一名统计学博士生和一名数量生态学博士后的研究培训和指导。