Analysis of biokinetic parameters reveals patterns in mercury accumulation across aquatic species

Mercury (Hg) is a potent neurotoxicant and poses a risk to human health through the ingestion of Hg-contaminated fish. Mercury, especially in its organic form methylmercury (MeHg), biomagnifies up food chains such that even small aqueous concentrations of Hg can result in significant concentrations of total Hg in fish. Understanding the ecological and human health risks associated with Hg and MeHg exposure requires an understanding of the factors that affect its bioaccumulation in aquatic species. We compiled estimates of three biokinetic parameters: uptake rate (ku), assimilation efficiency (AE), and efflux rate (ke). These parameters describe contaminant uptake from aqueous (ku) and dietary (AE) exposure and the rate of excretion (ke). We found parameter values for 38 and 34 different species of fish and aquatic invertebrates, respectively, and collected 502 parameter values in total. We used a machine learning technique to establish the relationships between experimental and physiological variables and these parameter values. We found differences in which variables were associated with biokinetic parameter values for fish and aquatic invertebrates. The form of Hg was the most impactful variable, influencing values of all parameters except ku for invertebrates, for which aqueous exposure time was the only significant predicator variable. The parameter ke were the only values significantly influenced by more than one variable, with water type (freshwater, brackish, or marine), organism weight, and form of Hg significantly impacting parameter values for fish and/or invertebrates. To our knowledge, this study represents the most extensive review of biokinetic parameters of Hg and MeHg accumulation in aquatic organisms. Environmental parameters found to significantly impact Hg and MeHg bioaccumulation in past studies were not identified as important in our analyses across aquatic ecosystems and species. Our dataset and analysis reveal novel patterns that may help us better understand and manage Hg bioaccumulation. [Display omitted] •Estimates of Hg and MeHg bioaccumulation crucial in aquatic species to manage risk•Created database of Hg and MeHg biokinetic parameters in aquatic organisms•Machine learning used to identify significant predictors of parameter values•Form of Hg alone was the most important predictor for most parameters.•Findings may change understanding of Hg bioaccumulation across aquatic species.