Predicting sport fish mercury contamination in heavily managed reservoirs: Implications for human and ecological health

Abstract

Mercury (Hg) is a concerning contaminant due to its widespread distribution and tendency to accumulate to harmful concentrations in biota. We used a machine learning approach called random forest (RF) to test for different predictors of Hg concentrations in three species of Colorado reservoir sport fish. The RF approach indicated that the best predictors of 864 mm northern pike (Esox lucius) Hg concentrations were covariates related to salmonid stocking in each study system, while system-specific metrics related to productivity and forage base were the best predictors of Hg concentrations of 381 mm smallmouth bass (Micropterus dolomieu), and walleye (Sander vitreus). Protecting human and ecological health from Hg contamination requires an understanding of fish Hg concentrations and variability across the landscape and through time. The RF approach could be applied to identify potential areas/systems of concern, and predict whether sport fish Hg concentrations may change as a result of a variety of factors to help prioritize, focus, and streamline monitoring efforts to effectively and efficiently inform human and ecological health.