Measures and Models of Phylogenetic Structure in Community Composition and Dynamics

了解生态群落的功能是生态学的核心问题。了解群落如何运作的第一步是解释为什么它们包含它们所包含的物种。决定群落组成的两个主要力量是环境和物种之间的相互作用。环境条件往往限制了群落的组成，只有那些能忍受环境的物种。例如，pH值较低的湖泊只含有能耐受酸性条件的鱼类。物种之间的相互作用会影响群落的组成，因为一些物种将其他物种排除在群落之外。例如，一种掠食性鱼类的存在可能会排除另一种特别容易被捕食的鱼类。拟议的研究将使用统计方法来调查群落的组成，该方法结合了物种之间进化（=系统发育）亲缘关系的信息。使用系统发育信息的原因是生态学家通常不知道什么环境因素或与其他物种的相互作用驱动群落组成。由于亲缘关系密切的物种具有许多相同的特征，这些特征决定了它们对环境的耐受性和对与其他物种相互作用的脆弱性，系统发育信息可以用来确定哪些环境因素和物种之间的相互作用在构建群落中最重要。这项工作的更广泛的影响包括在数学和生物学的界面上培训本科生和研究生，开发和应用新的统计技术，开发用户友好的统计工具和合作者网络，以调查生态系统的组成和功能。

Understanding how ecological communities function is a central problem in the science of ecology. A first step to understanding how communities function is explaining why they contain the species that they do. The two principle forces determining the composition of communities are the environment and the interactions among species. Environmental conditions often limit the composition of communities to only those species that can tolerate the environment. For example, lakes with low pH contain only fish species that can tolerate acidic conditions. Interactions among species affect community composition, because some species exclude other species from communities. For example, the presence of a predatory fish might exclude another fish species that is particularly vulnerable to predation. The proposed research will investigate the composition of communities using statistical methods that incorporate information about the evolutionary (= phylogenetic) relatedness among species. The reason for using phylogenetic information is that ecologists often do not know what environmental factors or what interactions with other species drive community composition. Because closely related species share many of the same characteristics that determine their environmental tolerances and vulnerability to interactions with other species, phylogenetic information can be used to identify which environmental factors and which interactions among species are most important in structuring communities. The broader impacts of this work include training undergraduate and graduate students at the interface between mathematics and biology to develop and apply new statistical techniques, and to develop user-friendly statistical tools and a network of collaborators to investigate the composition and function of ecosystems.