Citation:

Raimondo, Sandy AND J. Awkerman. Spatially explicit risk assessment of an estuarine fish in Barataria Bay, Louisiana, following the Deepwater Horizon Oil spill: evaluating tradeoffs in model complexity and parsimony. SETAC North American 36th Annual Meeting, Salt Lake City, UT, November 01 - 05, 2015.

Impact/Purpose:

Evaluate model complexity for population models used in ecological risk assessment

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 spatially explicit population model that simulated fish dynamics and projected population growth from 2009 through 2014 under various scenarios of oil exposure. To evaluate the differences in levels of model complexity, simulations varied from temporally and spatially explicit, including seasonal variation and location-specific oiling, to a simple, deterministic matrix model based on chronic laboratory studies. Model results were compared to a risk quotient derived for the study area. The results of this study indicate that for lower tiered assessments, simple matrix models provide a more quantitative magnitude of risk over a chronic risk quote that is based on the same information. Seasonal fluctuations in demographic vital rates are important to include in all scenarios, but are the most important drivers of population dynamics in spatially implicit or non-spatial scenarios. For spatially explicit scenarios, the spatial and temporal variability of contaminant distribution contribute significantly to model outcome and uncertainty. While accurate contaminant distribution data may not be readily available for this layer in most situations, we demonstrate how using a range of approaches for estimating this layer can provide a range of “best” and “worse” case scenarios from which risk may be evaluated. Results of the study provide recommendations for applying population models for small fish in different tiers of ERA.

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