ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2020
Comparison of Bayesian and numerical optimization-based diet estimation on herbivorous zooplankton.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 18 citations in OpenAlex.
- Assessing the influence of blubber stratification on the estimation of diet in phocid seals using the MixSIAR model.Scientific reports · 2025Article
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- MesopTroph, a database of trophic parameters to study interactions in mesopelagic food webs.Scientific data · 2022Article
- Poor nutritional quality of primary producers and zooplankton driven by eutrophication is mitigated at upper trophic levels.Ecology and evolution · 2022Article
- Complementary use of stable isotopes and fatty acids for quantitative diet estimation of sympatric predators, the Antarctic pack-ice seals.Oecologia · 2021Article
- Evaluating the performance of the Bayesian mixing tool MixSIAR with fatty acid data for quantitative estimation of diet.Scientific reports · 2020Article
- The critical importance of experimentation in biomarker-based trophic ecology.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
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3 authors at 1 institution in 1 country.
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Abstract
Consumer diet estimation with biotracer-based mixing models provides valuable information about trophic interactions and the dynamics of complex ecosystems. Here, we assessed the performance of four Bayesian and three numerical optimization-based diet estimation methods for estimating the diet composition of herbivorous zooplankton using consumer fatty acid (FA) profiles and resource library consisting of the results of homogeneous diet feeding experiments. The method performance was evaluated in terms of absolute errors, central probability interval checks, the success in identifying the primary resource in the diet, and the ability to detect the absence of resources in the diet. Despite occasional large inconsistencies, all the methods were able to identify the primary resource most of the time. The numerical optimization method QFASA using χ
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