Little fish, big world: Finding opportunities beyond the research plan
Citation:
Raimondo, Sandy. Little fish, big world: Finding opportunities beyond the research plan. SE SETAC annual meeting, Pensacola, FL, September 13, 2019.
Impact/Purpose:
This presentation will be the keynote address at the annual meeting of the Southeastern Chapter of the Society of Environmental Toxicology and Chemistry. The talk will highlight an EPA research project that advanced the science of population modeling for ecological risk assessment through exploration of model complexity and uncertainty using a case study of the Deepwater Horizon oil spill impacts on small estuarine fish. The presentation will demonstrate how unforeseen opportunities that arise throughout a research project can enhance products and their impacts throughout the science community.
Description:
As Ecological risk assessments (ERA) move beyond organism-based determinations towards probabilistic population-level assessments, model complexity must be evaluated against the goals of the assessment, the information available to parameterize components with minimal dependence on assumptions, and critical knowledge-based uncertainties. We explored model complexity and value of information in a case study of the sheepshead minnow (Cyprinodon variegatus) exposed to contaminated oil in Barataria Bay, Louisiana following the Deepwater Horizon oil spill. Model components were developed and evaluated through field (habitat suitability, fish distribution, oil distribution), laboratory (contaminant concentration-response, density dependence), and modeling (temperature dependent-vital rates, avoidance behavior) studies. Components were synthesized into a population models of varying complexity to simulated fish dynamics under various scenarios of oil exposure. Results of the study were used to develop a framework for population models to be consistent with the objectives of the assessment. The framework demonstrates that both ERAs and models contain trade-offs of generality, realism, and precision that can be used to ensure models meet the objectives of the assessment. The work highlights how unforeseen shifts in research can be harnessed to improve the impact of a research project.