Scaling up from an individual to a population-level assessment for risks of pesticides to threatened and endangered birds
The U.S. Environmental Protection Agency (EPA), in collaboration with the National Marine Fisheries Service and the U.S. Fish and Wildlife Service (collectively referred to as "the Services") is currently developing a methodology to assess the risks of pesticides to federally listed threatened and endangered species (listed species), based on recommendations from the National Academy of Sciences (NAS) report, Assessing Risks to Endangered and Threatened Species from Pesticides (National Research Council, 2013). This report provided advice on a range of subjects related to risk assessment and the consultation process including best available data, consideration of sub-lethal, indirect and cumulative effects, assessing the effects of chemical mixtures and inert ingredients, the role and use of models, uncertainty, and the use of geospatial information and datasets. In this poster, we present an integrated modeling approach that scales across ecological processes to capture mechanistic effects due to simulated pesticide exposure to listed birds.
Our linked set of models uses toxicity inputs from standard toxicity tests generally available as part of the pesticide registration process. We illustrate this refined methodology with a specific case study on the endangered Kirtland’s Warbler (Setophaga kirtlandii), and demonstrate a systematic approach to interpret standard toxicity data in the context of survival and fecundity at the population level. Through the use of toxicity translation tools, i.e., the Terrestrial Investigation Model (TIM) and MCnest (the Markov Chain Nest Productivity Model), we parameterize a stochastic, stage-structured population model to explore probabilistically how impacts to individuals scale to affect population response and recovery. Tackling these issues from a regulatory perspective is an essential step in determining whether the species is in jeopardy and for identifying mitigation strategies and best use practices based on protection goals.