Do Spatially Explicit Wildlife Exposure Models Improve the Estimation of Risk for Small Mammals? Case Study: Application of Spatially Explicit Exposure Model to Small Mammal Exposures to Lead in Heterogeneous Landscapes.

Understanding risks to terrestrial wildlife species from exposure to chemicals in the environment requires knowledge of how species make habitat decisions and how subsequent exposure events occur. Heterogeneity of chemical distribution and of habitat quality can influence exposure. Previous studies in birds have shown that individually based, spatially explicit models can be useful in predicting exposure and risk; however, studies investigating these influences in small mammals with limited ranges have been lacking. Here we test a spatially explicit, individually based exposure model (Spatially Explicit Exposure Model [SEEM]) in which model predictions based on life history traits, habitat preferences, and varying soil Pb concentrations are used and compared to those with field-collected blood or tissue Pb concentrations in small (e.g., Peromyscus, Blarina spp.) and medium-sized mammalian species (e.g., Lepus spp.) at 3 Pb-contaminated sites. These species were chosen because they were expected to be present in suitable habitat, and Pb was modeled when adequate tissue-based toxicity thresholds were available. Oral exposure estimates from SEEM were compared with a traditional deterministic model and with field-collected tissue Pb concentrations using ecological hazard quotients (EHQs) to normalize between oral and real-time tissue Pb concentrations. Ecological hazard quotients at the 90% population effect level (for SEEM) and at the 95% upper confidence level (assuming a single Pb concentration with no consideration of habitat quality in the deterministic model) were compared with maximum EHQs developed from blood or tissue Pb concentrations. Deterministic estimates and SEEM were similar for small mammal species, yet slightly overpredicted risk compared to field tissue or blood Pb data. Estimates for hares (medium-sized mammals) using SEEM provided more accurate predictions compared with field tissue data. These data suggest that spatially explicit models may be sensitive to grain size, given that small mammals experience the environment in limited spatial contexts, a scale at which habitat may not change significantly. Integr Environ Assess Manag 2021;17:259-272. Published 2020. This article is a US Government work and is in the public domain in the USA.

Spatially explicit ecological exposure models: a rationale for and path toward their increased acceptance and use.

Spatially explicit wildlife exposure models have been developed to integrate chemical concentrations dispersed in space and time, heterogeneous habitats of varying qualities, and foraging behaviors of wildlife to give more realistic wildlife exposure estimates for ecological risk assessments. These models not only improve the realism of wildlife exposure estimates, but also increase the efficiency of remedial planning. However, despite being widely available, these models are rarely used in baseline (definitive) ecological risk assessments. A lack of precedent for their use, misperceptions about models in general and spatial models in particular, non-specific or no enabling regulations, poor communication, and uncertainties regarding inputs are all impediments to greater use of such models. An expert workshop was convened as part of an Environmental Security Technology Certification Program Project to evaluate current applications for spatially explicit models and consider ways such models could bring increased realism to ecological exposure assessments. Specific actions (e.g., greater accessibility and innovation in model design, increased communication with and training opportunities for decision makers and regulators, explicit consideration during assessment planning and problem formulation) were discussed as mechanisms to increase the use of these valuable and innovative modeling tools. The intent of this workshop synopsis is to highlight for the ecological risk assessment community both the value and availability of a wide range of spatial models and to recommend specific actions that may help to increase their acceptance and use by ecological risk assessment practitioners.