Tissue turnover and stable isotope clocks to quantify resource shifts in anadromous rainbow trout
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Stable isotopes can illuminate resource usage by organisms, but effective interpretation is predicated on laboratory validation. Here we develop stable isotope clocks to track resource shifts in anadromous rainbow trout (Oncorhynchus mykiss). We used a diet-switch experiment and model fitting to quantify N stable isotope (δ15N) turnover rates and discrimination factors for seven tissues: plasma, liver, fin, mucus, red blood cells, muscle, and scales. Among tissues, diet-tissue δ15N discrimination factors ranged from 1.3 to 3.4 ‰. Model-supported tissue turnover half-lives ranged from 9.0 (fin) to 27.7 (scale) days. We evaluated six tissue turnover models using Akaike’s information criterion corrected for small sample sizes. The use of equilibrium tissue values was supported in all tissues and two-compartment models were supported in plasma, liver, and mucus. Using parameter estimates and their uncertainty we developed stable isotope clocks to estimate the time since resource shifts. Longer turnover tissues provided accurate estimates of time since resource switch for durations approximately twice their half-life. Faster turnover tissues provided even higher precision estimates, but only within their half-life post-switch. Averaging estimates of time since resource shift from multiple tissues provided the highest precision estimates of time since resource shift for the longest duration (up to 64 days). This study therefore provides insight into physiological processes that underpin stable isotope patterns, explicitly tests alternative models, and quantifies key parameters that are the foundation of field-based stable isotope analysis.
KeywordsOncorhynchus mykiss Migration Mixing model Nitrogen Discrimination factor
We are grateful for support from Susan Sogard, without which this project would not have been possible. We thank Sora Kim and Paul Koch for discussions on stable isotope analysis, as well as Stephan Munch, Mark Novak, Peter Raimondi, and Andrew Shelton for discussions regarding data analysis. We thank Mark Carr, Craig Layman, Carlos Martínez del Rio, Joseph Merz and an anonymous reviewer for their helpful comments on the manuscript. We thank Erick Sturm for facilities support, and Dyke Andreasen, Nicolas Retford, Cristina Cois, and Ashley Lila Pearson for assistance in the laboratory. W. N. H. was partially funded by a CALFED SeaGrant Fellowship (R/SF-11) during this research.
- Burnham KP, Anderson DR (2002) Model selection and multi-model inference: a practical information-theoretic approach, 2nd edn. Springer, New YorkGoogle Scholar
- Hayes SA, Bond MH, Hanson CV, Jones AW, Ammann AJ, Harding JA, Collins AL, Perez J, Macfarlane RB (2011) Down, up, down and “smolting” twice? Seasonal movement patterns by juvenile steelhead (Oncorhynchus mykiss) in a coastal watershed with a bar closing estuary. Can J Fish Aquat Sci 68:1341–1350CrossRefGoogle Scholar
- Koch PL, Fogel ML, Tuross N (1994) Tracing the diets of fossil animals using stable isotopes. In: Lajtha K, Michener RH (eds) Stable isotopes in ecology and environmental science. Blackwell, Boston, pp 63–92Google Scholar
- Martínez del Rio C, Wolf BO (2005) Mass-balance models for animal isotopic ecology. In: Starck MJ, Wang T (eds) Physiological and ecological adaptations to feeding in vertebrates. Science Publishers, EnfieldGoogle Scholar
- Moyle PB (2002) Inland fishes of California. University of California Press, BerkeleyGoogle Scholar
- Newsome SD, Clementz MT, Koch PL (2010) Using stable isotope biogeochemistry to study marine mammal ecology. Mar Mammal Sci 26:509–572Google Scholar
- Ott RL (1993) An introduction to statistical methods and data analysis, 4th edn. Duxbury, BelmontGoogle Scholar
- R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar