Species-specific trophic discrimination factors can reduce the uncertainty of stable isotope analyses
Veselý, Lukáš; Balzani, Paride; Haubrock, Phillip J.; Buřič, Miloš; Glon, Mael; Ercoli, Fabio; Ruokonen, Timo J.; Kainz, Martin J.; Hämäläinen, Heikki; Kouba, Antonín (2024)
Veselý, Lukáš
Balzani, Paride
Haubrock, Phillip J.
Buřič, Miloš
Glon, Mael
Ercoli, Fabio
Ruokonen, Timo J.
Kainz, Martin J.
Hämäläinen, Heikki
Kouba, Antonín
Julkaisusarja
Hydrobiologia
Springer Nature
2024
Veselý, L., Balzani, P., Haubrock, P.J. et al. Species-specific trophic discrimination factors can reduce the uncertainty of stable isotope analyses. Hydrobiologia (2024). https://doi.org/10.1007/s10750-024-05513-6
Julkaisun pysyvä osoite on
http://urn.fi/URN:NBN:fi-fe2024041919416
http://urn.fi/URN:NBN:fi-fe2024041919416
Tiivistelmä
Stable isotope analysis has been broadly used to study food webs, but often relies on inaccurate assumptions of trophic isotopic discriminations, which could lead to misinterpretation of obtained results. While many taxa exhibit similar trophic discrimination factors (TDFs), crayfish, exhibit omnivorous feeding strategies, yet TDFs are missing. In this study, we determined TDFs and tissue biokinetic parameters of the marbled crayfish Procambarus virginalis as a model species. Moreover, we compared commonly used TDFs and those determined from this study and applied them across species and ecosystems as a first attempt to compare the effect of species-specific TDFs in Bayesian trophic mixing models. Our results revealed differences between the TDFs of different tissues and biokinetic parameters of crayfish. Our result also revealed TDFs differences between crayfish relying mostly on plants versus those relying on an animal diet. We found differences of TDF suitability among species and ecosystems, highlighting the need for specific TDFs for different crayfish species. This study improves our understanding and the need for species-calibrated TDFs for robust statistical analysis of stable isotope data. Our approach is widely applicable across taxa and ecosystems to reduce the bias introduced by using unspecific TDFs in Bayesian mixing models.
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