An example of population-level risk assessments for small mammals using individual-based population models

Area of Science:

• Ecological risk assessment
• Population modeling
• Environmental toxicology

Background:

• Ecological risk assessments (ERAs) often use deterministic models, which may not capture complex population dynamics.
• Individual-based models (IBMs), also known as agent-based models, offer a more mechanistic approach to simulate population responses.
• Pesticides pose potential risks to non-target organisms like small mammals, necessitating robust assessment methods.

Purpose of the Study:

• To demonstrate the application of three spatially explicit, individual-based models (IBMs) for higher-tier ecological risk assessment of a hypothetical fungicide (FungicideX) on small mammal populations.
• To evaluate the long-term population effects of FungicideX under different exposure scenarios (cereals, orchards, seed treatment) and landscape complexities.
• To refine risk assessment by comparing IBM predictions with initial deterministic assessments indicating high risk.

Main Methods:

• Utilized three distinct individual-based, spatially explicit population models (IBMs) simulating common vole, field vole, and wood mouse.
• Applied a hypothetical fungicide (FungicideX) with a defined toxicological profile across varied model landscapes (size, structural complexity).
• Simulated different application scenarios: cereal spraying, orchard spraying, and cereal seed treatment.

Main Results:

• Across all three IBM scenarios, small mammal populations exhibited low sensitivity to FungicideX, except at 10x higher application rates.
• Populations generally demonstrated rapid recovery from simulated local impacts.
• Concerns regarding recovery arose only when patch extinctions occurred, with recolonization determining the recovery period.

Conclusions:

• Individual-based models (IBMs) provide significant advantages for higher-tier ecological risk assessments of small mammals, offering transparent links to protection goals and realistic scenarios.
• Key input considerations for IBMs include exposure levels, simulation duration, number of replicates, and reported endpoints.
• Further research is needed to standardize landscape attributes (size, structure, crop rotation) for regulatory risk assessment scenarios.