A Risk Assessment Example for Soil Invertebrates Using Spatially Explicit Agent-Based Models

Current risk assessment methods for measuring the toxicity of plant protection products (PPPs) on soil invertebrates use
standardized laboratory conditions to determine acute effects on mortality and sublethal effects on reproduction. If an
unacceptable risk is identified at the lower tier, population-level effects are assessed using semifield and field trials at a
higher tier because modeling methods for extrapolating available lower-tier information to population effects have not yet
been implemented. Field trials are expensive, time consuming, and cannot be applied to variable landscape scenarios.
Mechanistic modeling of the toxicological effects of PPPs on individuals and their responses combined with simulation of
population-level response shows great potential in fulfilling such a need, aiding ecologically informed extrapolation. Here,
we introduce and demonstrate the potential of 2 population models for ubiquitous soil invertebrates (collembolans and
earthworms) as refinement options in current risk assessment. Both are spatially explicit agent-based models (ABMs),
incorporating individual and landscape variability. The models were used to provide refined risk assessments for different
application scenarios of a hypothetical pesticide applied to potato crops (full-field spray onto the soil surface [termed
“overall”], in-furrow, and soil-incorporated pesticide applications). In the refined risk assessment, the population models
suggest that soil invertebrate populations would likely recover within 1 year after pesticide application, regardless of
application method. The population modeling for both soil organisms also illustrated that a lower predicted average
environmental concentration in soil (PECsoil) could potentially lead to greater effects at the population level, depending on
the spatial heterogeneity of the pesticide and the behavior of the soil organisms. Population-level effects of spatial-temporal
variations in exposure were elucidated in the refined risk assessment, using ABMs and population-level endpoints while
yielding outputs that directly address the protection goals. We recommend choosing model outputs that are closely related
to specific protection goals, using available toxicity data and accepted fate models to the extent possible in parameterizing
models to minimize additional data needs and testing, evaluating, and documenting models following recent guidance.