Detect and exploit hidden structure in fatty acid signature data

Abstract

Citation: Bromaghin, J. F., S. M. Budge, and G. W. Thiemann. 2017. Detect and exploit hidden structure in fatty acid signature data. Ecosphere 8(7):e01896. 10. 1002/ecs2.1896 Abstract. Estimates of predator diet composition are essential to our understanding of their ecology.Although several methods of estimating diet are practiced, methods based on biomarkers have become increasingly common. Quantitative fatty acid signature analysis (QFASA) is a popular method that continues to be refined and extended. Quantitative fatty acid signature analysis is based on differences in the signatures of prey types, often species, which are recognized and designated by investigators. Similarly, predator signatures may be structured by known factors such as sex or age class, and the season or region of sample collection. The recognized structure in signature data inherently influences QFASA results in important and typically beneficial ways. However, predator and prey signatures may contain additional, hidden structure that investigators either choose not to incorporate into an analysis or of which they are unaware, being caused by unknown ecological mechanisms. Hidden structure also influences QFASA results, most often negatively. We developed a new method to explore signature data for hidden structure, called divisive magnetic clustering (DIMAC). Our DIMAC approach is based on the same distance measure used in diet estimation, closely linking methods of data exploration and parameter estimation, and it does not require data transformation or distributional assumptions, as do many multivariate ordination methods in common use. We investigated the potential benefits of the DIMAC method to detect and subsequently exploit hidden structure in signature data using two prey signature libraries with quite different characteristics. We found that the existence of hidden structure in prey signatures can increase the confusion between prey types and thereby reduce the accuracy and precision of QFASA diet estimates. Conversely, the detection and exploitation of hidden structure represent a potential opportunity to improve predator diet estimates and may lead to new insights into the ecology of either predator or prey. The DIMAC algorithm is implemented in the R diet estimation package qfasar.

Measuring repeatability of compositional diet estimates: An example using quantitative fatty acid signature analysis

“…Following an exploratory analysis to determine whether the FA signatures of the selected prey contained any hidden structure (see Bromaghin, Budge, & Thiemann, 2017) some prey species within the set were subdivided into smaller clusters prior to estimating seal diets (Table 3). American plaice ( Hippoglossoides platessoides ) were separated into two clusters based on size (small, ≤25 cm, and large, >25 cm).…”

Review of Estimating Trophic Relationships by Quantitative Fatty Acid Signature Analysis

“…In the application of the QFASA model, the statistical characteristics of predator dietary estimation is usually estimated through computer simulations [42], e.g., commercial software, Fortran programs [18], R package [13,19,30,32,44,45], a combination program of R and Fortran [6,41], and Matlab with its optimization toolbox [14,15,17,36]. In recent years, Bromaghin summarized a new R package named QFASAR, calculating the goodness-of-fit diagnosis, which may enhance the performance of the prey signature database [19,44].…”

State of art and best practices for fatty acid analysis in aquatic sciences

“…FA have accordingly been used to study energy flow in food webs in freshwater, estuarine, coastal, and deepsea environments in all latitudes and to examine functional responses to stressors (Kelly and Scheibling, 2012). More recently, the FATM concept has culminated in quantitative FA-based diet modelling (Iverson et al, 2004;Galloway et al, 2014;Bromaghin et al, 2017), and recent methodological developments allow for accurate estimation of prey contribution to consumer diet (Litmanen et al, 2020). However, diet modelling is based on the fact that aquatic consumers generally lack the ability to synthesize long-chain polyunsaturated FA (LC-PUFA, !C20) and have limited capacity for the bioconversion of short-chain PUFA into long-chain PUFA (Castell et al, 1972;Langdon and Waldock, 1981;Sargent et al, 1999;Taipale et al, 2011).…”