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A spatially explicit model for estimating risks of pesticide exposure on bird populations
Citation:
Etterson, M., N. Schumaker, K. Garber, S. Lennartz, A. Kanarek, AND J. Connolly. A spatially explicit model for estimating risks of pesticide exposure on bird populations. SETAC North America, Minneapolis, MN, November 12 - 16, 2017.
Impact/Purpose:
Although the utility of population models for chemical risk assessment has be recognized for decades, USEPA has yet to develop and adopt a methodology for population level risk assessment for birds. We provide an integrated modeling workflow that will allow USEPA risk assessors to conduct spatially explicit population level risk assessment for pesticides in agroecosystems. This work builds on previous USEPA work integrating TIM and MCnest by including HexSim to give a spatially explicit component. The model will likely be of interest to the regulated community and to the community of scientists conducting population level risk assessment for birds.
Description:
Product Description (FY17 Key Product): Current ecological risk assessment for pesticides under FIFRA relies on risk quotients (RQs), which suffer from significant methodological shortcomings. For example, RQs do not integrate adverse effects arising from multiple demographic processes, such as when exposure to a pesticide reduces both survival and fecundity. RQs also cannot quantify risk, which limits their utility in the development of mitigation strategies. Although these shortcomings have been recognized since at least the late 1990s, when USEPA formed the Ecological Committee for FIFRA Risk Assessment Methods (ECOFRAM), the development of more robust tools for probabilistic risk assessments has been slow. To this end, EPA researchers have developed a suite of three tools (TIM, MCnest, and HexSim) that, if deployed together, can be used to conduct scientifically defensible probabilistic population-level risk assessments for birds residing in heterogeneous landscapes. Pesticides are used widely in US agriculture and may cause effects to non-target organisms, including birds. Some pesticide classes (e.g., acetylcholinesterase inhibitors) are known or suspected to cause direct mortality to birds, while others (e.g., synthetic pyrethroids, neonicotinoids) may cause sublethal effects, such as impacts to reproduction. Recently, the United States Environmental Protection Agency has worked with other federal agencies, including the US Fish and Wildlife Service and National Marine Fisheries Service, to revise and strengthen methods for conducting pesticide risk assessments under section 7 of the U.S. Endangered Species Act (ESA). We developed an integrated modeling approach for spatially explicit population level risk assessment for birds in agroecosystems that could be used to assess risks of pesticides to avian species, including those listed as threatened or endangered under ESA. The integrated modeling approach includes the following three existing USEPA models: Terrestrial Investigation Model (TIM), Markov Chain Nest Productivity Model (MCnest), and HexSim model. The integrated model is parameterized using data currently required by the Federal Insecticide Rodenticide Act for registration of pesticides, together with species life history data available in the scientific literature. We demonstrate the model by simulating potential effects on the federally threatened California Gnatcatcher (Polioptila californica) in the US portion of the species range using malathion (an organophosphate), and ë-cyhalothrin (a pyrethroid), applied to wheat under varying spatially explicit usage consistent with the labeling for the two insecticides. The results of the modeling show declines in gnatcatcher abundance and changes in the distribution of the species following applications of each pesticide, although extinction of the species is not predicted (within 100 years). The integrated TIM/MCnest/HexSim model will allow risk assessors to evaluate spatial and temporal dynamics that are essential to understanding population persistence in complex spatial landscapes with multiple stressors.