Citation:

Etterson, M. AND R. Bennett. Quantifying the effects of pesticide exposure on annual reproductive success of birds. Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, 9(4):590-599, (2013).

Impact/Purpose:

This manuscript describes a series of simulations designed to explore the use of the MCnest model, developed by the authors, for use in avian chemical risk assessments for reproductive effects. Simulations explore the effects of timing of pesticide use and the type of reproductive effect on the resulting estimates of reproductive success. Results are compared to risk quotients for the same scenarios. The manuscript will primarily be of interest to the risk assessment community and to ornithologists interested in contaminants.

Description:

The Markov chain nest productivity model (MCnest) was developed for quantifying the effects of specific pesticide-use scenarios on the annual reproductive success of simulated populations of birds. Each nesting attempt is divided into a series of discrete phases (e.g., egg laying, incubation, nestling rearing), and results from avian toxicity tests are used to represent the types of effects possible in the field during each breeding phase. The expected exposure dose each day throughout the breeding season can be compared to the toxicity thresholds assigned to each breeding phase to determine whether the nest attempt is at risk. The primary output of the model is an estimate of the number of successful nest attempts per female per year. In this paper we present a series of MCnest simulations to demonstrate the extent to which the magnitude of change in annual reproductive success can be affected by considering life history attributes and the timing of pesticide applications relative to a species’ typical breeding phenology. For a given pesticide-use scenario, MCnest can identify which species are at greatest risk. By allowing multiple species to be run under a single scenario, it can also help to identify the life-history traits that contribute to a species’ vulnerability to a given pesticide-use scenario. Finally, it can also determine which application dates have the greatest impact and demonstrate the extent to which pesticide characteristics (e.g., residue half-life, mode of action) affect productivity.

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