High levels of methylmercury (MeHg) have been reported in Arctic marine biota, posing health risks to wildlife and human beings. Although MeHg concentrations of some Arctic species have been monitored for decades, the key environmental and ecological factors driving temporal trends of MeHg are largely unclear. We develop an ecosystem-based MeHg bioaccumulation model for the Beaufort Sea shelf (BSS) using the Ecotracer module of Ecopath with Ecosim, and apply the model to explore how MeHg toxicokinetics and food web trophodynamics affect bioaccumulation in the BSS food web. We show that a food web model with complex trophodynamics and relatively simple MeHg model parametrization can capture the observed biomagnification pattern of the BSS. While both benthic and pelagic production are important for transferring MeHg to fish and marine mammals, simulations suggest that benthic organisms are primarily responsible for driving the high trophic magnification factor in the BSS. We illustrate ways of combining empirical observations and modelling experiments to generate hypotheses about factors affecting food web bioaccumulation, including the MeHg elimination rate, trophodynamics, and species migration behavior. The results indicate that population dynamics rather than MeHg elimination may determine population-wide concentrations for fish and lower trophic level organisms, and cause large differences in concentrations between species at similar trophic levels. Finally, we apply the model to explore key drivers of mercury bioaccumulation in various species in the Beaufort Sea beluga food web over the past four decades through scenarios of historical environmental change informed by both Western science and Inuvialuit Knowledge. We find This research presents a new tool and lays the groundwork for future research to assess the pathways of global environmental changes in MeHg bioaccumulation in Arctic ecosystems in the past and the future

Investigating the dynamics of methylmercury bioaccumulation in the Beaufort Sea shelf food web: A modeling perspective

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