Beyond the reaction progress variable: the meaning and significance of isotopic incorporation data

• Published in: Oecologia 2008-07, Vol.156 (4), p.765-772
• Main Authors: MARTINEZ DEL RIO, Carlos, ANDERSON-SPRECHER, Richard
• Format: Article
• Language: English
• Subjects: Animal tissues; Animal, plant and microbial ecology; Animals; Biological and medical sciences; Biomedical and Life Sciences; Diet; Drug design; Ecological modeling; Ecologists; Ecology; Estimation methods; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Hydrology/Water Resources; Isotopes; Isotopes - pharmacokinetics; Isotopic incorporation; Life Sciences; Linear regression; Methods and techniques (sampling, tagging, trapping, modelling...); Models, Biological; Parametric models; Pharmacokinetics; Physiological Ecology - Original Paper; Physiological Ecology - Original Papers; Plant Sciences; Reaction progress variable; Stable isotopes; Time Factors
• ISSN: 0029-8549; 1432-1939
• DOI: 10.1007/s00442-008-1040-z

Ecologists conduct isotopic incorporation experiments to determine the residence time of various stable isotopes in animal tissues. These experiments permit determining the time window through which isotopic ecologists perceive the course of diet changes, and therefore the scale of the inferences that we can make from isotopic data. Until recently, the results of these experiments were analyzed using first-order, one-compartment models. Cerling et al. (Oecologia 151:175-189, 2007) proposed an approach they named the reaction progress variable to: (1) determine how many compartments are needed to describe a pattern of istopic incorporation, and (2) to estimate the size and rate constant of each pool. We elaborate on the approach described by Cerling et al. (Oecologia 151:175-189, 2007) by providing a way to estimate average retention times for an isotope in a tissue (and its associate error) for multi-compartment models. We also qualify the interpretation of the parameters in multi-compartment models by showing that many possible mechanisms yield models with the same functional form. Multi-compartment models are phenomenological, rather than mechanistic descriptions, of incorporation data. Finally, we propose the use of information theoretic criteria to assess the number of compartments that must be included in models of isotopic incorporation.