A probabilistic risk assessment for the Kirtland's warbler potentially exposed to chlorpyrifos and malathion during the breeding season and migration

Abstract

Two organophosphate pesticides, chlorpyrifos and malathion, are currently undergoing reregistration in the United States and were recently used by the US Environmental Protection Agency (USEPA) as case studies to develop a national procedure for evaluating risks to endangered species. One of the endangered bird species considered by the USEPA was the Kirtland's warbler (Setophaga kirtlandii). The Kirtland's warbler is an endangered migratory species that nests exclusively in young jack pine stands in Michigan and Wisconsin, and winters in the Bahamas. We developed probabilistic models to assess the risks of chlorpyrifos and malathion to Kirtland's warblers during the breeding season and the spring and fall migrations. The breeding area model simulates acute and chronic exposure and risk to each of 10 000 birds over a 60-d period following initial pesticide application. The model is highly species specific with regard to the foraging behavior of Kirtland's warblers during the breeding season. We simulated the maximum application rate and number of applications allowed on the labels for representative use patterns that could be found within 3 km of the breeding areas of Kirtland's warbler. The migration model simulates 10 000 birds during the course of their 12- to 23-d migration between their breeding area and the Bahamas. The model takes advantage of more than a century of observations of when, where, and for how long Kirtland's warblers forage in different habitats during the course of their migration. The data indicate that warblers only infrequently stop over in habitats that could be treated with chlorpyrifos and malathion. The breeding area and migration models resulted in predictions of very low acute and chronic risk for both pesticides to Kirtland's warblers. These results were expected, given that field observations indicate that the Kirtland's warbler has dramatically increased in abundance in recent decades. Integr Environ Assess Manag 2018;14:252-269. © 2017 SETAC.

Risk assessment for Birds and Mammals

“…In addition, several case studies on mechanistic effect models have been published for wild species of birds (e.g. Topping and Odderskær, 2004; Etterson and Bennett, 2013; Millot et al., 2015; Etterson et al., 2017; Topping and Luttik, 2017; Crocker and Lawrence, 2018; Moore et al., 2018) and mammals (e.g. Wang et al., 2001; Wang and Grimm, 2010; Dalkvist et al., 2013; Liu et al., 2013; Liu et al., 2014; Schmitt et al., 2016; Topping et al., 2016; Kleinmann and Wang, 2017; Wang et al., 2022) demonstrating the potential use of these models in higher tier risk assessments for pesticides.…”

Probabilistic co‐occurrence assessment for suites of listed species

“…Second, it is important to understand that spatial co‐occurrence analyses like those presented here do not quantify actual exposure and subsequent potential effects on individuals or populations. Listed species may occur in the same areas as a particular pesticide is applied, but may avoid exposure if their life history means they are inactive when applications are made or if they otherwise do not come in contact with residues (Moore et al, 2018). Effects of pesticide exposure will similarly vary according to application practices, physical properties of the formulation relative to drift and volatility, weather, environmental fate of residues, and other factors.…”

“…To resolve this backlog and ensure that co‐occurrence calculations are accurate, government agencies and other stakeholders should work to improve the efficiency of the consultation process. Screening tools can be applied to remove a subset of the species‐by‐product consultations, for example, those cases in which there is spatial co‐occurrence between a listed species and a use pattern, but species biology and agronomic practices dictate that there is little risk of actual exposure (Moore et al, 2018). For those cases not removed in such a screening process, spatial co‐occurrence analysis could be standardized and automated, allowing faster processing.…”

A critical review of effect modeling for ecological risk assessment of plant protection products

“…Population endpoints related to the maintenance of non-target species inhabiting the landscape constitute the outputs of the model in two thirds of the studies. The other ones predict contamination levels in non-target species (e.g., in hare Kleinmann and Wang 2017; Mayer et al 2020) or the exceeding of toxicity thresholds at the individual level (e.g., in a warbler, Moore et al 2018, or an owl, Engelman et al 2012) as a function of habitat occupancy, spatial or dietary behaviors, or landscape structure. Two thirds of the 24 landscape studies consider a spatially explicit représentation of the transfer and fate of PPP, 85% the spatialization of species life cycle (in particular for the use of trophic resources or habitats).…”

“…Another major point relating to life cycle characteristics in PPP ecological models is the phenology and timing of exposure in relation to cultural practices that influence the risk of population exposure, the capacities of démographie compensation, or the recovery after short-term exposure. These temporal aspects, which hâve been extensively studied in pest management and biocontrol (Stark et al, 2004;Tonnang et al, 2017;Tang et al, 2019), are now being emphasized as determining factors in the vulnerability of non-target species, and in the relative severity of impacts of PPP treatment practices: reproductive phenology in bird species (Etterson and Bennett, 2013;Etterson et al, 2017;Moore et al, 2018;Crocker and Lawrence, 2018), annual devel opment cycle in pollinators (Thompson et al, 2005), in aquatic invertebrates (Galic et al, 2012;Sorensen et al, 2020) or in plants exposed to herbicides (Schmitt et al, 2013). The other overarching element considered is the spatial dimension in the processes of exposure or in population dynamics response (Topping and Odderskaer, 2004;Dalkvist et al, 2009;Forbes et al, 2016;Accolla et al, 2021).…”