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Can avian reproductive outcomes estimated with MCnest be made more robust using stochastic parameterizations?
Citation:
Carbone, J. AND M. Etterson. Can avian reproductive outcomes estimated with MCnest be made more robust using stochastic parameterizations? SETAC North America, Minneapolis, MN, November 12 - 16, 2017.
Impact/Purpose:
This research will help USEPA and stakeholders, including the regulated community, to better understand the predictions made using the MCnest model. The work will also help us understand which parameters in MCnest are disproportionately important for MCnest risk predictions.
Description:
The Markov chain nest productivity model, or MCnest, is a set of algorithms for integrating the results of avian toxicity tests with reproductive life-history data to project the relative magnitude of chemical effects on avian reproduction. The mathematical foundation of MCnest is in the analysis of Markov chains, which provides a flexible template for modeling the variation in avian breeding cycles among species. MCnest quantitatively estimates the relative change in the number of successfully fledged broods per female per year of avian species exposed to a specific pesticide application scenario. The relative change in the number of successful broods is estimated by comparing model results based on a defined pesticide application scenario with a no-pesticide scenario. MCnest requires data from avian toxicity tests, avian species life-history profiles and a pesticide-use scenario that defines the timing and temporal pattern of exposures. In addition, MCnest uses algorithms from the USEPA Office of Pesticide Programs Terrestrial Residue EXposure model or T-REX to translate the application rate (expressed as pounds AI/acre) into doses (expressed as mg/kg body wt./day) for both adult and juveniles taking into account the species typical diet. Under ideal circumstances, McNest estimates would be reflected in similar real world occurrences in terms of species reproductive outcomes following pesticide exposures. Validation of model estimated outcomes versus empirical data can however be difficult, particularly in light of the use of deterministic input of sensitive parameters. The goal of the current analysis is to employ a stochastic approach to model parameterization in terms of toxicity endpoints, life history variability, exposure timing and magnitude in order to present a robust profile of avian reproductive outcomes as a function of variability in selected sensitive input parameters.