Measures and Models of Phylogenetic Structure in Community Composition and Dynamics

了解生态群落的功能是生态科学的核心问题。 理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。 决定群落组成的两种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，只有那些能够忍受环境的物种。 例如，pH值低的湖泊只含有能够耐受酸性条件的鱼类。 物种之间的相互作用影响群落的组成，因为一些物种将其他物种排除在群落之外。 例如，一种食肉鱼类的存在可能会排除另一种特别容易被捕食的鱼类。 拟议中的研究将利用统计方法调查群落的组成，这些方法将物种之间进化（=系统发育）相关性的信息纳入其中。 使用系统发育信息的原因是生态学家通常不知道是什么环境因素或与其他物种的相互作用驱动了群落组成。 由于密切相关的物种有许多相同的特征，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。 这项工作的更广泛影响包括在数学和生物学之间的界面上培训本科生和研究生，以开发和应用新的统计技术，并开发方便用户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.