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.

Plastic litter is an environmental problem of great concern. Despite the magnitude of the plastic pollution in our water bodies, only limited scientific understanding is available about the risk to the environment, particularly for microplastics. The apparent magnitude of the problem calls for quickly developing sound scientific guidance on the ecological risks of microplastics. The authors suggest that future research into microplastics risks should be guided by lessons learned from the more advanced and better understood areas of (eco) toxicology of engineered nanoparticles and mixture toxicity. Relevant examples of advances in these two fields are provided to help accelerate the scientific learning curve within the relatively unexplored area of microplastics risk assessment. Finally, the authors advocate an expansion of the "vector effect" hypothesis with regard to microplastics risk to help focus research of microplastics environmental risk at different levels of biological and environmental organization.

In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps.

No abstract

No abstract