Untangling the phylogenetic and spatial components of trait variation in ecological assemblages

解开生态组合中性状变异的系统发育和空间组成部分

• 批准号: NE/V011782/1
• 负责人: Robert Freckleton
• 金额: $ 47.92万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV011782%2F1
• 关键词: Untangling; phylogenetic; spatial; components; trait

Traits are the set of evolved characteristics that permit a species to exploit its niche and live in a given environment. Explaining how and why traits vary among species is a basic problem in ecology and evolutionary biology. Fundamental to achieving this is the task of disentangling how evolutionary, environmental and ecological factors drive trait variation. Trait evolution is driven by the interplay of biotic and abiotic factors, whilst constrained by previous history, and plays out at all scales. In ecological terms, the composition and structure of communities is determined by the ecological and functional diversity of species present in a community, which is dependent on the traits of those species. Thus, evolutionary and ecological scales of organisation within a community are linked through species traits. To understand the variation in traits of species within a community, accounting for species' distributions and the degree to which species' ranges overlap is important. Within the communities in a geographic region, the similarities and differences between species' traits will be shaped by factors such as the extent to which species' ranges overlap, as well as the similarity of niches and habitat use within areas of range overlap compared with outside. Given this observation, the first main objective of this project is to develop comparative models that account for species' co-distributions in driving trait variation.When exploring the ecological drivers of trait variation, an obvious consideration is the extent to which species co-occur ecologically. For example, two species may have similar habitat requirements, with the consequence that they tend to occur together in the same communities. In this case, it might be expected that when species' ranges overlap they occur in the same communities. Consequently, species that occur in the same communities may be expected to share common suites of adaptations, and traits would co-vary positively. Alternatively, as a consequence of ecological interactions (e.g. competition), negative effects on trait covariance would also be possible: if species that co-occur only possess divergent traits, then covariance will be reduced. The second main objective of this project is to develop comparative models that link phylogeny with both large-scale distributions and local-scale patterns of occurrence within their ranges.We will use the methods we develop to test two hypotheses:Hypothesis 1: species with overlapping geographic distributions show greater similarity than those with non-overlapping ones. Hypothesis 2: species that co-occur within the same ecological communities show greater divergence, for given levels of range overlap, than those that do not.The strategy for developing methods will be as follows. First, we will create a model that simulates data according to well-defined processes. Second, a statistical framework will be developed that allows the model to be fitted to data and tested against the simulation outputs. Finally, we will apply our models to real datasets, testing our two main hypotheses using each dataset.To apply the methods, we require datasets that contain the following: (i) traits of species co-occurring in a series of ecological communities; (ii) a phylogeny, resolved as well as possible; (iii) maps of species' geo-graphic ranges; and, in addition, to apply the methods developed in Objective 2: (iv) ecological measures of species' occurrences. We will apply the models to two existing datasets that include these elements--one on Brazilian Atlantic forest trees, the other on Colombian birds--to generate new insights into the factors that drive community composition, in each case testing the two major hypotheses of the project.

性状是一组进化的特征，允许一个物种利用其生态位并生活在给定的环境中。解释物种间特征如何以及为何变化是生态学和进化生物学的基本问题。实现这一目标的基础是解开进化、环境和生态因素如何驱动性状变异的任务。性状进化是由生物和非生物因素的相互作用驱动的，同时受到先前历史的限制，并在所有尺度上发挥作用。从生态学的角度来看，群落的组成和结构取决于群落中物种的生态和功能多样性，而这种多样性又取决于这些物种的特性。因此，一个群落内组织的进化和生态尺度是通过物种特征联系在一起的。为了了解群落内物种特征的变化，解释物种的分布和物种分布区重叠的程度是重要的。在一个地理区域内的群落中，物种特征之间的相似性和差异性将受到诸如物种分布范围重叠的程度以及分布范围重叠区域内与外部相比的生态位和栖息地利用的相似性等因素的影响。鉴于这一观察结果，本项目的第一个主要目标是开发解释物种共同分布驱动性状变异的比较模型。在探索性状变异的生态驱动因素时，一个明显的考虑因素是物种在生态上共同出现的程度。例如，两个物种可能有相似的生境要求，结果它们往往出现在同一社区。在这种情况下，可以预期，当物种的分布范围重叠时，它们出现在相同的群落中。因此，出现在同一社区的物种可能会共享共同的适应套件，并且性状会积极地共同变化。或者，作为生态相互作用（例如竞争）的结果，对性状协方差的负面影响也是可能的：如果共存的物种只具有不同的性状，那么协方差将减少。本项目的第二个主要目标是开发比较模型，将重复发生与大规模分布和其范围内的局部发生模式联系起来。我们将使用我们开发的方法来测试两个假设：假设1：具有重叠地理分布的物种比那些没有重叠的物种表现出更大的相似性。假设二：对于给定的范围重叠水平，在同一生态群落内共存的物种比那些不共存的物种表现出更大的差异。首先，我们将创建一个模型，根据定义良好的流程模拟数据。第二，将制定一个统计框架，使该模型能够与数据拟合，并对照模拟输出进行测试。最后，我们将把我们的模型应用到真实的数据集上，使用每个数据集测试我们的两个主要假设。为了应用这些方法，我们需要包含以下内容的数据集：（i）在一系列生态群落中共存的物种的特征;（ii）尽可能解决的物种发生;（iii）物种地理范围的地图;此外，应用目标2中开发的方法：（iv）物种出现的生态量度。我们将把这些模型应用于两个现有的数据集，其中包括这些元素-一个关于巴西大西洋森林树木，另一个关于哥伦比亚鸟类-以产生对驱动社区组成的因素的新见解，在每种情况下测试该项目的两个主要假设。