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Contact Us

Gulf Ecology Division

Sandy Raimondo
(850) 934-2424
raimondo.sandy@epa.gov

Education
  • Ph.D., West Virginia University; Entomology, 2003
  • M.S., Marshall University, Huntington, West Virginia; Biology, 1999
  • B.S., Pennsylvania State University; Biology, 1996
Current Position
  • Research Biologist
Previous Positions
  • 2009: Biologist (60d detail assignment), Environmental Fate and Effects Division, Office of Pesticide Programs, U.S. Environmental Protection Agency
  • 2003 - 2005: Postdoctoral Research Associate, Gulf Ecology Division, Gulf Breeze, FL
Areas of Specialization
  • Organism and population-level models to predict ecological effects of environmental stressors
  • Estuarine fish population dynamics
  • Ecological Risk Assessment
Current Research
  • Spatial Fish Population Modeling for Ecological Risk Assessment (ERA) - The objective of this project is to develop and evaluate the uncertainty of a spatially-explicit population model for ERA. The model system uses the sheepshead minnow and oil exposure in Barataria Bay, LA in a case study of population-level ecological risk assessment. The project aims to reduce model uncertainty through laboratory and field data collection. Field studies include habitat suitability and contaminant exposure and laboratory studies focus on density dependence and toxicant effects. Uncertainty analyses of model layers and parameters will inform how spatial models may be simplified for improved utility in data-limited risk assessments. This research will help develop guidelines on model complexity, realism, utility, and value-added compared to traditional ERA approaches.
  • Web-based Interspecies Correlation Estimation (Web-ICE) - This project develops and evaluates predictive and distribution-based models of organism-level toxicity that may be used in screening level assessments. Models to predict acute toxicity from surrogate species are available on the GED-developed application, Web-ICE, which also contains modules to predict toxicity to endangered species taxa, and generate Species Sensitivity Distributions.

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