bioRxivEcologyPreprint
Trophic position derived from amino-acid nitrogen isotopes reflect physiological status of both predator and prey over four decades
Abstract
Nitrogen isotope analyses of amino-acids ({delta}15N-AA) are increasingly used to decipher food webs. Interpretation of {delta}15N-AA in consumers relies on the assumption that physiological status has a negligible influence on the trophic enrichment factor (TEF). Recent experiments have shown that this is not always the case and there is a need to validate derived trophic position (TP) estimates using ecological data. We analyzed {delta}15N-AA in cod and herring (1980-2019) from the Baltic Sea, a species-poor system where dramatic reduction in condition status of cod has occurred. We expected that TEFcod-herring in trophic AAs would increase during periods of poor cod growth, resulting in inflated TP estimates. We found that TEF and TP estimates were negatively linked to individual condition status, prey fat content and the hypoxic state of the ecosystem. Statistically adjusting for these variables resulted in lower cod TP, highlighting the importance of including ecological knowledge when interpreting TP. Scientific Significance StatementNitrogen stable isotope analyses in amino acids are increasingly used in ecology to understand how environmental change impacts food-webs. Specifically, it is used to more accurately calculate trophic position (TP) of consumers. Controlled experiments have shown that physiological status can alter amino acid isotope composition and TP interpretation, but field studies are lacking. We use 40 years of archived material to demonstrate that TP estimates in Baltic Sea cod and its prey herring are directly related to physiological status. This has important implications for interpreting the real trophic ecology of consumers under environmental stress. By simultaneously measuring condition status in both predator and prey it is possible to adjust for them as confounding variables and decipher actual consumer TP.
- organism: multiple species
- assay: proteomics
- research focus: host-pathogen & immune response
- approach: statistical model
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