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Dragonfly larvae as biosentinels of Hg bioaccumulation in Northeastern and Adirondack lakes: relationships to abiotic factors.

Sarah J NelsonCelia Y ChenJeffrey S Kahl
Published in: Ecotoxicology (London, England) (2019)
Mercury (Hg) is a toxic pollutant, widespread in northeastern US ecosystems. Resource managers' efforts to develop fish consumption advisories for humans and to focus conservation efforts for fish-eating wildlife are hampered by spatial variability. Dragonfly larvae can serve as biosentinels for Hg given that they are widespread in freshwaters, long-lived, exhibit site fidelity, and bioaccumulate relatively high mercury concentrations, mostly as methylmercury (88% ± 11% MeHg in this study). We sampled lake water and dragonfly larvae in 74 northeastern US lakes that are part of the US EPA Long-Term Monitoring Network, including 45 lakes in New York, 43 of which are in the Adirondacks. Aqueous dissolved organic carbon (DOC) and total Hg (THg) were strongly related to MeHg in lake water. Dragonfly larvae total mercury ranged from 0.016-0.918 μg/g, dw across the study area; Adirondack lakes had the minimum and maximum concentrations. Aqueous MeHg and dragonfly THg were similar between the Adirondack and Northeast regions, but a majority of lakes within the highest quartile of dragonfly THg were in the Adirondacks. Using landscape, lake chemistry, and lake morphometry data, we evaluated relationships with MeHg in lake water and THg in dragonfly larvae. Lakewater DOC and lake volume were strong predictors for MeHg in water. Dragonfly THg Bioaccumulation Factors (BAFs, calculated as [dragonfly THg]:[aqueous MeHg]) increased as lake volume increased, suggesting that lake size influences Hg bioaccumulation or biomagnification. BAFs declined with increasing DOC, supporting a potential limiting effect for MeHg bioavailability with higher DOC.
Keyphrases
  • water quality
  • aedes aegypti
  • heavy metals
  • fluorescent probe
  • human health
  • climate change
  • health risk assessment
  • aqueous solution
  • zika virus
  • deep learning
  • big data