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Keywords:

  • Biomonitoring;
  • Mechanistic models;
  • Ecotoxicogenomics;
  • Ecological relevance;
  • Risk assessment

Abstract

Substantial efforts have been devoted to developing and applying biomarkers for use in ecotoxicology. These efforts have resulted partly from a desire for early warning indicators that respond before measurable effects on individuals and populations occur and partly as an aid to identifying the causes of observed population- and community-level effects. Whereas older biomarkers focused on measures of organism physiology or biochemistry, advances in molecular biology are extending the biomarker philosophy to the level of the genes (i.e., ecotoxicogenomics). However, the extent to which biomarkers are able to provide unambiguous and ecologically relevant indicators of exposure to or effects of toxicants remains highly controversial. In the present paper, we briefly discuss the application of biomarkers in ecotoxicology and ecological risk assessment, and we provide examples of how they have been applied. We conclude that although biomarkers can be helpful for gaining insight regarding the mechanisms causing observed effects of chemicals on whole-organism performance and may, in some cases, provide useful indicators of exposure, individual biomarker responses should not be expected to provide useful predictions of relevant ecological effects—and probably not even predictions of whole-organism effects. Suites of biomarkers are only likely to provide increased predictability if they can be used in a comprehensive mechanistic model that integrates them into a measure of fitness. Until this can be achieved, biomarkers may be useful for hypothesis generation in carefully controlled experiments. However, because the aims of environmental monitoring and ecological risk assessment are to detect and/or predict adverse chemical impacts on populations, communities, and ecosystems, we should be focusing our efforts on improving methods to do this directly. This will involve developing and testing models of appropriate complexity that can describe real-world systems at multiple scales.