New statistical tools for quantitative fatty acid signature analysis and the development of an accompanying R package

统计模型，产生可靠的估计捕食者的饮食是至关重要的生物学家研究捕食者/猎物在海洋生态系统中的关系。定量脂肪酸特征分析（QFASA）是一种相对较新的饮食估计方法，其中通过使用统计方法匹配捕食者和猎物中的脂肪酸来确定饮食中每种猎物的估计比例。虽然QFASA验证研究表明，该方法比以前的捕食者（如海豹，海鸟和海狮）的饮食估计方法具有一些明显的优势，但它们也揭示了该技术的局限性，以及QFASA需要发展以克服现在更多理解的一些问题。在这项工作中，我们将调查新的统计技术的改造QFASA。从统计学的角度来看，处理QFASA数据是具有挑战性的，因为脂肪酸特征和饮食估计都是组成的，并且通常包含零，排除了使用标准的多变量分析程序和通常的对数比方法来管理组成数据。此外，样本掠食者的数量往往比感兴趣的变量的数量少得多。

Statistical models that yield reliable estimates of predators’ diets are critical to biologists studying predator/prey relationships in marine ecosystems. Quantitative fatty acid signature analysis (or QFASA) is a relatively recent diet estimation method in which the estimated proportion of each species of prey in the diet is determined by matching fatty acids in the predator and prey using statistical methods. While QFASA validation studies have demonstrated that the method offers some clear advantages over previous diet estimation methods for predators such as seals, seabirds and sea lions, they have also revealed limitations with the technique and a need for QFASA to evolve to overcome some of the issues that are now more understood. In this proposed work, we will investigate novel statistical techniques for the revamping of QFASA. Dealing with QFASA data is challenging from a statistical point of view because both the fatty acid signatures and diet estimates are compositional and often contain zeros, precluding the use of standard multivariate analysis procedures and the usual log ratio methods for managing compositional data. Furthermore the number of sampled predators is often much less than the number of variables of interest.