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Use of Site-Specific Data for Modeling Selenium Bioaccumulation by Terrestrial Animals.

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A new bioaccumulation model predicts selenium (Se) in wildlife at Kesterson Reservoir. This model uses soil Se data to estimate Se concentrations in terrestrial biota, aiding environmental risk assessments.

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Area of Science:

  • Environmental Science
  • Ecotoxicology
  • Biogeochemistry

Background:

  • Selenium (Se) contamination poses risks to wildlife.
  • Bioaccumulation models are crucial for assessing environmental contamination.
  • Kesterson Reservoir has a history of selenium contamination.

Purpose of the Study:

  • To develop and refine a bioaccumulation model for predicting selenium concentrations in terrestrial biota.
  • To assess ongoing wildlife risks at Kesterson Reservoir using the developed model.
  • To provide a transferable model for other selenium-contaminated sites.

Main Methods:

  • Utilized extensive monitoring data (1989-2006) from Kesterson Reservoir.
  • Modeled selenium concentrations in soil (water-extractable and total) to predict biota concentrations.
  • Validated and calibrated the model with sequential data updates and field sampling.
  • Predicted selenium levels in small mammals, bird blood, and bird eggs.

Main Results:

  • The bioaccumulation model accurately predicts central tendency of selenium concentrations in biota.
  • Predicted upper limits of biotic exposure were generally conservative (similar or higher than measured).
  • Model performance was validated across multiple years of monitoring data.
  • Variability in predictions was attributed to individual animal diets and feeding ranges.

Conclusions:

  • The refined bioaccumulation model effectively predicts selenium levels in terrestrial wildlife.
  • The model's transfer factors and regression equations are adaptable to other contaminated sites.
  • The model aids in assessing and managing selenium risks to wildlife in contaminated ecosystems.
  • Continued refinement enhances the model's accuracy and applicability for environmental risk assessment.