Diet estimation and comparison of fatty acid-based diet modelling methods

Trophic interactions have been a popular research subject among ecologists for decades because understanding the structure of food webs is essential in understanding consumer-resource interactions and complex ecosystems. Consumer diet estimation with biological tracer-based mixing models is an important and recently rapidly developing tool for deciphering aquatic food webs. Bayesian frameworks have been introduced to estimate diets accurately with stable isotope proportions. The low quantity of different stable isotope tracers, however, limits greatly the diet estimation accuracy of complex consumer diets. Therefore, fatty acids have been used as biological tracers to multiply the quantity of tracers in the Bayesian mixing models. Aquatic consumer diet estimation has also been conducted with a numerical optimization method. Daphnia is an important herbivorous zooplankton genus and thus a popular model species to research aquatic food webs. In this thesis I identified the most reliable fatty acid-based diet estimation method by comparing Bayesian methods MixSIR and SIAR conducted with FASTAR, and a numerical method QFASA conducted with QFASAR in R statistical software. These methods were compared with systematic and extensive simulations using an extended version of a previously published Daphnia resource library. MixSIR was the most reliable method. However, the structure of resource library significantly affected the diet estimations, and an upgraded error structure fixing modelling method MixSIAR was published after the testing phase of this thesis. Therefore, I recommend precise construction of the resource library, and the usage of MixSIAR instead of any of the modelling models tested in this thesis.