Applying a mechanistic model to predict interacting effects of chemical exposure and food availability on fish populations

Mintram, K. S., Maynard, S. K., Brown, A. R., Boyd, R., Johnston, A. S. A., Sibly, R. M. ORCID: https://orcid.org/0000-0001-6828-3543, Thorbek, P. and Tyler, C. R. (2020) Applying a mechanistic model to predict interacting effects of chemical exposure and food availability on fish populations. Aquatic Toxicology, 224. 105483. ISSN 0166-445X doi: 10.1016/j.aquatox.2020.105483

Abstract/Summary

The potential environmental impacts of chemical exposures on wildlife are of growing concern. Freshwater ecosystems are vulnerable to chemical effects and wildlife populations, including fish, can be exposed to concentrations known to cause adverse effects at the individual level. Wild fish populations are also often subjected to numerous other stressors simultaneously which in temperate climates often include sustained periods of food limitation. The potential interactive effects of chemical exposures and food limitation on fish populations are however difficult to establish in the field. Mechanistic modelling approaches can be employed to help predict how the physiological effects of chemicals and food limitation on individuals may translate to population-level effects. Here an energy budget-individual-based model was developed and the control (no chemical) model was validated for the three-spined stickleback. Findings from two endocrine active chemical (EAC) case studies, (ethinyloestradiol and trenbolone) were then used to investigate how effects on individual fecundity translated into predicted population-level effects for environmentally relevant exposures. The cumulative effects of chemical exposure and food limitation were included in these analyses. Results show that effects of each EAC on the population were dependent on energy availability, and effects on population abundance were exacerbated by food limitation. Findings suggest that chemical effects and density dependent food competition interact to determine population responses to chemical exposures. Our study illustrates how mechanistic modelling approaches might usefully be applied to account for specific chemical effects, energy budgets and density-dependent competition, to provide a more integrated evaluation of population outcomes in chemical risk assessments.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/91050
Item Type	Article
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Ecology and Evolutionary Biology
Publisher	Elsevier
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