How to measure oxidative stress in an ecological context: methodological and statistical issues

Authors

  • Peeter Hõrak,

    1. Institute of Ecology and Earth Sciences, Tartu University, Vanemuise 46, 51014 Tartu, Estonia
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  • Alan Cohen

    Corresponding author
    1. Groupe de recherche PRIMUS, Centre Hospitalier Universitaire de Sherbrooke, 3001 12 e Ave Nord, Sherbrooke, Quebec J1H 5N4, Canada
      Correspondence author. E-mail: alan.cohen@usherbrooke.ca
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Correspondence author. E-mail: alan.cohen@usherbrooke.ca

Summary

1. Reactive oxygen and nitrogen species can damage biomolecules if these lack sufficient antioxidant protection. Maintaining and up-regulating antioxidant defenses and repair of the damaged molecules require resources that could potentially be allocated to other functions, including life-history and signal traits.

2. Identifying the physiological mechanisms causing and counteracting oxidative damage may help to understand evolution of oxidative balance systems from molecular to macroevolutionary levels. This review addresses methodological and statistical problems of measuring and interpreting biomarkers of oxidative stress or damage.

3. A major methodological problem is distinguishing between controlled and uncontrolled processes that can lead either to shifts in dynamic balance of redox potential or cause pathological damage. An ultimate solution to this problem requires establishing links between biomarkers of antioxidant defenses and oxidative damage and components of fitness.

4. Biomarkers of redox balance must correspond to strict technical criteria, most importantly to validated measurement technology. Validation criteria include intrinsic qualities such as specificity, sensitivity, assessment of measurement precision, and knowledge of confounding and modifying factors.

5. The complexity of oxidative balance systems requires that assay choice be informed by statistical analyses incorporating context at biochemical, ecological and evolutionary levels. We review proper application of statistical methods, such as principal components analysis and structural equation modelling, that should help to account for these contexts and isolate the variation of interest across multiple biomarkers simultaneously.

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