Advancing exposure assessment approaches to improve wildlife risk assessment

Abstract

The exposure assessment component of a Wildlife Ecological Risk Assessment aims to estimate the magnitude, frequency, and duration of exposure to a chemical or environmental contaminant, along with characteristics of the exposed population. This can be challenging in wildlife as there is often high uncertainty and error caused by broad‐based, interspecific extrapolation and assumptions often because of a lack of data. Both the US Environmental Protection Agency (USEPA) and European Food Safety Authority (EFSA) have broadly directed exposure assessments to include estimates of the quantity (dose or concentration), frequency, and duration of exposure to a contaminant of interest while considering “all relevant factors.” This ambiguity in the inclusion or exclusion of specific factors (e.g., individual and species‐specific biology, diet, or proportion time in treated or contaminated area) can significantly influence the overall risk characterization. In this review, we identify four discrete categories of complexity that should be considered in an exposure assessment—chemical, environmental, organismal, and ecological. These may require more data, but a degree of inclusion at all stages of the risk assessment is critical to moving beyond screening‐level methods that have a high degree of uncertainty and suffer from conservatism and a lack of realism. We demonstrate that there are many existing and emerging scientific tools and cross‐cutting solutions for tackling exposure complexity. To foster greater application of these methods in wildlife exposure assessments, we present a new framework for risk assessors to construct an “exposure matrix.” Using three case studies, we illustrate how the matrix can better inform, integrate, and more transparently communicate the important elements of complexity and realism in exposure assessments for wildlife. Modernizing wildlife exposure assessments is long overdue and will require improved collaboration, data sharing, application of standardized exposure scenarios, better communication of assumptions and uncertainty, and postregulatory tracking. Integr Environ Assess Manag 2023;00:1–25. © 2023 SETAC

Using emerging science to inform risk characterizations for wildlife within current regulatory frameworks

“…United States-Emerging science has assisted in refining and more accurately characterizing toxicity (Bean et al, 2023;Rattner et al, 2023) exposure (Sample et al, 2022) and ultimately risk, particularly for assessing contaminated sites. Stable isotope analysis and bioelution testing for wildlife have been used to more accurately define exposures and sources for toxicants at contaminated sites (Morrissey et al, 2023;Post, 2002;Ruby et al, 1993). Spatially explicit exposure modeling using individually based models has been used successfully in this context (USFWS, 2013b).…”

Key challenges and developments in wildlife ecological risk assessment: Problem formulation

“…Development-Improvements in spatially explicit/habitatfocused models and ecological understanding. Wildlife experience the environment in ways consistent with lifehistory attributes that are guided by habitat requirements (Morrissey et al, 2023). These can vary seasonally, where species can become gregarious only during months of the year (e.g., red-winged blackbirds, white-tailed deer).…”

“…As mentioned, many species vary in their seasonal, spatial, and habitat-specific food preferences. Much has been learned recently regarding considering the whole exposure or exposome approach (Morrissey et al, 2023) where life histories are tied to multiple exposure events to include various substances (i.e., mixtures). Information is continually developed that better informs species-specific parameters important in calculating and estimating exposure concentrations.…”

“…Although spatially explicit models that account for complex behaviors and spatial movements were published more than a dozen years ago, they have not received widespread acceptance. Spatially explicit models seek to incorporate life histories of target species and consider both direct and indirect effects (e.g., migration and recruitment patterns, habitat use changes initiated from seasonal niche patterns, and heterogeneous distribution of stressors including chemicals) to better inform wildlife ERA (Hope et al, 2011; Johnson et al, 2009; Pistocchi, 2008; Wickwire et al, 2011; also see Morrissey et al, 2023). Evaluating opportunities to incorporate scientific advancements into wildlife ERA should be an ongoing effort, and the present effort represents a necessary attempt to advance the process with a lens focused on the PF stage of wildlife ERA.…”

“…In PF, it is sufficient to be aware that valuable data can be assembled using these emerging methods and approaches. Additional discussion of some of these methods appears in companion papers (e.g., Bean et al, 2023;Rattner et al, 2023;Morrissey et al, 2023).…”