Predictive Modeling of Mercury Bioaccumulation in Fish: Advances, Limitations, and Climate Interactions

We systematically evaluate five predictive modeling frameworks for methylmercury (MeHg) bioaccumulation in fish across freshwater and marine ecosystems, synthesizing developments from 1997 to 2025 and identifying cross-cutting limitations. The frameworks assessed span biokinetic mass balance models (assimilation efficiency 0.6–0.95), trophic magnification models (trophic magnification factor, TMF, 3.5–8.9 across tropical to polar systems), national-scale statistical models (R 2 = 0.40–0.63), mixed-effects hierarchical approaches, and machine learning methods (R 2 up to 0.85). Trophic position emerges as the dominant predictor across all frameworks, with MeHg biomagnifying 2–5-fold per trophic level and TMF increasing with latitude. A critical limitation across biokinetic models is the systematic overestimation of MeHg elimination rates by 2.4–5.5-fold relative to field observations. Climate change is projected to intensify bioaccumulation risk through enhanced methylation, food web restructuring, and elevated metabolic demand, with Arctic systems facing heightened vulnerability as permafrost thaw releases legacy mercury stores. We identify three priority research needs─field-validated elimination kinetics, hybrid mechanistic–machine learning frameworks, and expanded monitoring in Africa, Asia, and the Southern Hemisphere─essential for evaluating Minamata Convention effectiveness and strengthening fish consumption advisories worldwide.