Discovering and exploiting clusters of similar fatty acid signatures.

What is it about?

Understanding predator diets provides essential insights into their ecology at scales ranging from individual animals to entire populations, and diet estimation is an active area of ecological research. Quantitative fatty acid signature analysis (QFASA) has become a popular method of estimating diets, especially for marine species such as seals and polar bears. The method is based on differences in the signatures of prey types defined by investigators, which are often simply species. In this paper, we develop a new method to discover whether it would be beneficial to divide prey type signatures into a number of distinct clusters prior to estimating diet composition. The results of a simulation study illustrate that when such distinct clusters exist within prey type data, partitioning the data into clusters can make the prey types more identifiable and substantially improve diet estimates. Exploring prey signature data for such cluster sub-structure is therefore an important component of preparing for diet estimation.

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Why is it important?

This paper provides an important new data-exploration tool for investigators to increase the accuracy of their diet estimates. This is the fifth in a series of papers in which we investigate the performance of QFASA diet estimators and provide recommendations for changes to traditional methods that improve the estimation of predator diet composition.