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Advancements toward probabilistic risk assessments for endangered species
Citation:
Raimondo, Sandy, S. Sinnathamby, J. Minucci, L. Oliver, Y. Yuan, E. Waits, AND Tom Purucker. Advancements toward probabilistic risk assessments for endangered species. CSS Webinar Series, Gulf Breeze, Florida, March 24, 2020.
Impact/Purpose:
The protection of listed species through the Ecological Risk Assessment (ERA) process is encumbered by the number and diversity of species that need protection and the limited data available to inform assessments. Research advancements in this area will be discussed with a focus on probabilistic modeling for Estimated Environmental Concentration (EEC) in isolated wetlands, such as vernal pools. While this work is aimed at improving assessments for listed species, approaches can also be used in chemical assessments at the national level or in those with broader focus.
Description:
The protection of listed species through the Ecological Risk Assessment (ERA) process is encumbered by the number and diversity of species that need protection and the limited data available to inform assessments. Vernal pools are ephemeral wetlands that provide critical habitat to many listed species and are used a case study for development of approaches to improve listed species assessments. Research advancements in this area will be discussed with a focus on probabilistic modeling for Estimated Environmental Concentration (EEC) in isolated wetlands, such as vernal pools. The Pesticide Water Calculator (PWC), a model used for the regulation of pesticides in the US, was used to predict surface water and sediment pore water pesticide concentrations in vernal pool habitats. The PWC model was implemented with deterministic and probabilistic approaches and parameterized for three agricultural vernal pool watersheds located in the San Joaquin River basin in the Central Valley of California. Exposure concentrations for chlorpyrifos, diazinon and malathion were simulated. The deterministic approach used default values and professional judgment to calculate point values of estimated concentrations. In the probabilistic approach, Monte Carlo (MC) simulations were conducted across the full input parameter space with a sensitivity analysis that quantified the parameter contribution to model prediction uncertainty. Ongoing work is building off these results to integrate probabilistic effects modeling to provide likelihood of risk for listed species in the examples presented. While this work is aimed at improving assessments for listed species, approaches can also be used in chemical assessments at the national level or in those with broader focus.