ALMaSS, an agent-based model for animals in temperate European landscapes

Abstract

The animal, landscape and man simulation system (ALMaSS) was designed as a predictive tool for answering policy questions regarding the effect of changing landscape structure or management on key animal species in the Danish landscape. By combining agent-based models of animals with a comprehensive and dynamic landscape simulation, it aims to improve predictive ability. The landscape model comprises detailed mapping, weather, farm management, and vegetation growth. Each vegetated area has its own growth model and in the case of farmed areas, management is modelled in detail. Animal models are agent-based, designed using the state/transition concept, and are rule-based. Each animal may interact with others and directly with its local environment. Field vole (Microtus agrestis) is used as an example of the extent to which dynamic landscapes can influence the population dynamics. Simulations of crop diversity and rotation demonstrate significant effects of spatial and temporal heterogeneity on population sizes, population fluctuations and landscape permeability. These two factors interact and thus different responses to temporal factors occur at different levels of spatial heterogeneity. Spatial and temporal heterogeneity in both the model and the real world are often related to changes in land-use and management. Consequently, the impact of landscape changes on any population can be enormous and heavily spatially influenced. Therefore, the use of dynamic landscapes is seen as an important addition to the modeller’s toolkit.

Introduction

The animal, landscape and man simulation system (ALMaSS) was designed as a predictive tool for answering policy questions regarding the effect of changing landscape structure or management on key species in the Danish landscape. The goal of correctly predicting local interactions between animals and their environments in detail is perhaps impossibly remote. Paradoxically, unlike other disciplines, e.g. economics, the idea of prediction using best available knowledge, albeit imprecisely, is often an anathema to biologists. In contrast, in applied sciences answers are often required and hence the creation of ALMaSS. ALMaSS is an ecological simulation system, and is an attempt to make use of the best available knowledge to develop predictions under the understanding that the reliability of predictions may be hard to quantify. ALMaSS was designed to be comprehensive enough to tackle some of the questions typically posed by government agencies and pressure groups to applied research institutes. These questions often consist of wanting to evaluate the effect of changing land-use or management. However, these questions will usually implicitly incorporate questions of spatial and temporal dynamics, which given the complex structures of most European landscapes, are difficult to handle. A crucial element of this approach is the idea that local conditions in space and time exert a major influence on the success of individual animals, and that this has a population-dynamic impact. This justification supports the use of individual-based models (IBMs; Huston et al., 1988), and thus, ALMaSS is an individual-based modelling system. However, the extent to which the individuals in ALMaSS react to their environment and remember past events (if often only physiologically), make the term IBM a little imprecise. Rather we prefer the term agent-based model (ABM). The term agent comes from computing science and has been defined variously, however, the definitions of Huhns and Singh (1998) “agents are active, persistent (software) components that perceive, reason, act, and communicate”, and Franklin and Graesser (1997) “An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda so as to effect what it senses in the future”, embody the basic properties. It is in this way that animals in ALMaSS act, via sensing information from their local surroundings and making behavioural decisions. They can communicate (e.g. via breeding, or territory disputes), reason (e.g. habitat selection, movement), and act to pursue their own agenda of survival and reproduction. Thus, ALMaSS is a system based on the behavioural ecology of the animals in question. The use of agents or multi-agent systems (Ferber, 1999, Bousquet et al., 2001) requires the development not only of the agents themselves, but also of their environment. It is typical of the IBM literature that the emphasis is placed on the animals being modelled (e.g. Railsback, 2001), with the environment into which the models are placed receiving scant attention. But, if we follow the IBM paradigm and accept that changes in environmental conditions over space and time exert an impact on our results, then it is important to model these changes accurately. If we can combine both behaviour and environment at sensible scales, we will approach what Lima and Zollner (1996) term “a behavioural ecology of ecological landscapes”, and perhaps improve our predictive ability.

This paper describes ALMaSS emphasising its landscape representation and presents some example simulations of field vole dynamics investigating the effect of altering landscape structure and management.