The Potential for the Use of Agent-Based Models in Ecotoxicology

Abstract

This chapter introduces ABMs, their construction, and the pros and cons of their use. Although relatively new, agent-based models (ABMs) have great potential for use in ecotoxicological research -their primary advantage being the realistic simulations that can be constructed and particularly their explicit handling of space and time in simulations. Examples are provided of their use in ecotoxicology primarily exemplified by different implementations of the ALMaSS system. These examples presented demonstrate how multiple stressors, landscape structure, details regarding toxicology, animal behavior, and socioeconomic effects can and should be taken into account when constructing simulations for risk assessment. Like ecological systems, in ABMs the behavior at the system level is not simply the mean of the component responses, but the sum of the often nonlinear interactions between components in the system; hence this modeling approach opens the door to implementing and testing much more realistic and holistic ecotoxicological models than are currently used.

A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent‐Based Population Models for Ecological Risk Assessment

“…Unstructured models ignore population structure whereas structured models do not, and ABMs focus on the agency of individuals, in particular their adaptive behavior. Well‐known examples of these model types are provided by Liu et al (2005) for unstructured models, Caswell (2001) for structured models, and Grimm and Railsback (2005) and Topping et al (2009) for ABMs. The key aspects that distinguish the 3 model types are the kind of state variables they use to characterize a population and consequently, how demography, that is, survival and reproduction, is represented.…”

Integrating Spatial Behavioral Ecology in Agent-Based Models for Species Conservation

“…This modelling approach allows one to simultaneously filter combinations of parameter values and model structures in order to achieve the aims of testing the behavior of the agents in the model and of reducing parameter uncertainty. The greater the number of real-world patterns that can be simulated concurrently, the greater the confidence in the model, and typically the smaller the possible parameter space (Topping et al 2009). By extension, the POM approach can additionally allow for rigorous statistical approaches.…”

Population models in pesticide risk assessment: Lessons for assessing population‐level effects, recovery, and alternative exposure scenarios from modeling a small mammal

“…These scenarios can include exploring different landscapes or timing of exposure [10]. For example, in a landscape that offers many resources to a species potentially affected by a pesticide, effects of the pesticide at the population level may be much weaker and recovery may be faster than in a landscape offering only suboptimal habitats.…”