Compositional Data Methodology in the Context of Quantitative Fatty Acid Signature Analysis

定量脂肪酸特征分析背景下的成分数据方法

• 批准号: RGPIN-2022-03217
• 负责人: Stewart, Connie
• 金额: $ 1.31万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759223
• 关键词: Compositional; Data; Methodology; Context; Quantitative

In the marine ecosystem, estimating predator diets can be especially challenging since feeding cannot typically be directly observed. In this context, and using nonparametric statistical techniques, quantitative fatty acid signature analysis (QFASA) was devised as an indirect method of estimating predator diets and has since successfully been applied to a variety of seabird species, marine mammals and fish. Recently, my research group developed maximum unified fatty acid signature analysis (MUFASA) which, like QFASA, also utilizes the relationship between predator and prey fatty acids but in the maximum likelihood estimation (MLE) framework. While these diet estimation methods have the advantage of being non-invasive, they rely on several assumptions that are difficult to verify in practice, but which can significantly impact results. Additionally, both the fatty acid signatures and diet estimates represent compositional data, commonly defined as multivariate data with components restricted to sum to a constant. These constraints, as well as other features of the data, such as the relatively large number of components and the propensity of the data to involve zeros, impose statistical challenges. In collaboration with biologists studying marine mammal populations, the long-term objective of my research program is to address current limitations inherent in FASA methods and provide improvements for more accurate diet estimation through novel statistical methodology appropriate for compositional data. This research will also simultaneously advance the area of compositional data analysis with techniques that can be extended to a variety of applications. One short-term objective of this research is the development of new variable selection techniques for compositional data. This aspect of my research is motivated by the important, but largely outstanding problem of how to choose the best subset of prey types and/or fatty acids to include in the models. Another goal is the incorporation of robustness into FASA methods to handle deviations from model assumptions. The newly developed likelihood structure will play a pivotal role in addressing these objectives. Being a new procedure, MUFASA has not yet been extensively studied and improvements, such as extensions to more flexible parametric densities, will also be considered. This research will contribute to our understanding of the advantages and limitations of FASA techniques for marine predator diet estimation, address some of the computational challenges associated with these methods, and potentially yield more accurate estimates of diet. The new methodology will be made accessible to relevant users through the existing R packages QFASA and Compositional. My proposed research will provide opportunities for interdisciplinary collaborations between statistics and biology, as well as training driven by real-life data and applications for 5 undergraduate and 4 graduate students.

在海洋生态系统中，估计捕食者的饮食尤其具有挑战性，因为通常无法直接观察进食情况。在此背景下，使用非参数统计技术，定量脂肪酸特征分析（QFASA）被设计为估计捕食者饮食的间接方法，并已成功应用于各种海鸟物种、海洋哺乳动物和鱼类。最近，我的研究小组开发了最大统一脂肪酸特征分析（MUFASA），与 QFASA 一样，它也利用捕食者和猎物脂肪酸之间的关系，但采用最大似然估计（MLE）框架。虽然这些饮食估计方法具有非侵入性的优点，但它们依赖于一些在实践中难以验证的假设，但可能会显着影响结果。此外，脂肪酸特征和饮食估计值都代表成分数据，通常定义为多变量数据，其组成部分仅限于总和为常数。这些限制以及数据的其他特征（例如相对较多的组成部分和数据涉及零的倾向）带来了统计挑战。 我的研究计划的长期目标是与研究海洋哺乳动物种群的生物学家合作，解决目前 FASA 方法固有的局限性，并通过适合成分数据的新颖统计方法来改进更准确的饮食估计。这项研究还将同时推进成分数据分析领域的发展，其技术可扩展到各种应用。这项研究的一个短期目标是开发用于成分数据的新变量选择技术。我的研究的这一方面是由一个重要但很大程度上突出的问题推动的，即如何选择模型中包含的最佳猎物类型和/或脂肪酸子集。另一个目标是将稳健性纳入 FASA 方法，以处理模型假设的偏差。新开发的可能性结构将在实现这些目标方面发挥关键作用。作为一种新程序，MUFASA 尚未得到广泛研究，还将考虑改进，例如扩展到更灵活的参数密度。这项研究将有助于我们了解 FASA 技术用于海洋捕食者饮食估计的优点和局限性，解决与这些方法相关的一些计算挑战，并有可能产生更准确的饮食估计。新方法将通过现有的 R 软件包 QFASA 和 Compositional 向相关用户开放。 我提出的研究将为统计学和生物学之间的跨学科合作提供机会，并为 5 名本科生和 4 名研究生提供由现实生活数据和应用驱动的培训。