Fatty acid (FA) compositions provide insights about storage and feeding modes of marine organisms, characterizing trophic relationships in the marine food web. Such compositional data, which are normalized to sum to 1, have values—and thus derived statistics as well—that depend on the particular mix of components that constitute the composition. In FA studies, if the set of FAs under investigation is different in two separate studies, all the summary statistics and relationships between the FAs that are common to the two studies are artificially changed due to the normalization, and thus incomparable. Ratios of FAs, however, are invariant to the particular choice of FAs under consideration—they are said to be subcompositionally coherent. Here, we document the collaboration between a biochemist (M.G.) and a statistician (M.J.G.) to determine a suitable small set of FA ratios that effectively replaces the original data set for the purposes of univariate and multivariate analysis. This strategy is applied to two FA data sets, on copepods and amphipods, respectively, and is widely applicable in other contexts. The selection of ratios is performed in such a way as to satisfy substantive requirements in the context of the respective data set, namely to explain phenomena of interest relevant to the particular species, as well as the statistical requirement to explain as much variance in the FA data set as possible. Benefits of this new approach are (1) univariate statistics that can be validly compared between different studies, and (2) a simplified multivariate analysis of the reduced set of ratios, giving practically the same results as the analysis of the full FA data set.

中文翻译：

---

脂肪酸比例的选择和分析：海洋中上层生物中脂肪酸营养标记的单变量和多变量分析的新方法

脂肪酸（FA）组合物提供了有关海洋生物的存储和进食方式的见解，表征了海洋食物网中的营养关系。归一化为总和为1的此类成分数据，其值（以及由此得出的统计量）也取决于构成成分的特定成分混合。在FA研究中，如果两个独立研究中所研究的FA集合不同，则由于归一化，这两个研究所共有的所有摘要统计信息和FA之间的关系都会人为更改，因此无法进行比较。但是，FA的比例对于所考虑的FA的特定选择是不变的-据说它们在子组合上是连贯的。在这里，我们记录了生物化学家（MG）和统计学家（MJG ）确定合适的小型FA比率集，以有效替代原始数据集以进行单变量和多变量分析。该策略分别应用于co足类和两栖动物的两个FA数据集，并且在其他情况下也可广泛应用。比率的选择要满足相应数据集的实质性要求，即解释与特定物种有关的感兴趣现象，以及统计要求以解释FA中的最大差异。数据集越好。这种新方法的好处是（1）可以在不同研究之间进行有效比较的单变量统计信息，以及（2）对简化比例集的简化多元分析，得出的结果几乎与完整FA数据集的分析结果相同。