Abstract

Scientists are currently faced with the challenge of assessing the effects of anthropogenic stressors on aquatic ecosystems. Cellular stress response (CSR) biomarkers are ubiquitous and phylogenetically conserved among metazoans and have been successfully applied in environmental monitoring but they can also vary according to natural biotic and abiotic factors. The reported variability may thus limit the wide application of biomarkers in monitoring, imposing the need to identify variability levels in the field. Our aim was to carry out a comprehensive in situ assessment of the CSR (heat shock protein 70 kDa, ubiquitin, antioxidant enzymes) and oxidative damage (lipid peroxidation) in wild populations across marine taxa by collecting fish, crustaceans, mollusks and cnidarians during two different seasons (spring and summer) and two habitat types (coast and estuary). CSR end-point patterns were different between taxa with mollusks having higher biomarker levels, followed by the cnidarians, while fish and crustaceans showed lower biomarker levels. The PCA showed clear clusters related to mobility/sessile traits with sessile organisms showing greater levels (>2-fold) of CSR proteins and oxidative damage. Mean intraspecific variability in the CSR measured by the coefficient of variation (% CV) (including data from all seasons and sites) was elevated (35–94%). Overall, there was a seasonal differentiation in biomarker patterns across taxonomic groups, especially evident in fish and cnidarians. A differentiation in biomarker patterns between habitat types was also observed and associated with phenotypic plasticity or local adaptation. Overall, specimens collected in the estuary had lower biomarker levels when compared to specimens collected in the coast. This work highlights the importance of assessing baseline biomarker levels across taxa, seasons and habitats prior to applying biomarker analyses in environmental monitoring. Selecting bioindicator species, defining sampling strategies, and identifying confounding factors are crucial preliminary steps that ensure the success of biomarkers as powerful tools in biomonitoring.

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