Towards the Integration of Spread and Economic Impacts of Biological Invasions in a Landscape of Learning and Imitating Agents

Carrasco, L.R., Cook, D.C., Mumford, J.D., MacLeod, A., Knight, J.D., Baker, R.H.A. (2012) Towards the integration of spread and economic impacts of biological invasions in a landscape of learning and imitating agents. Ecological Economics. 76, 95-103. doi:10.1016/j.ecolecon.2012.02.009.

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Invasions of harmful non-indigenous species (NIS) and their control by land managers are complex and opposing processes that generate economic costs in a dynamic and spatial fashion. Here we develop an agent-based model integrated with a spatial stochastic simulation NIS spread model where the agents (farmers) are endowed with learning and imitation capabilities. The model is applied to the case of the western corn rootworm invasion in England. We dynamically link the welfare loss of the producers to the spatial distribution of the invasion; allowing us to study the effects of control policies and farmers’ behaviour on welfare losses and the invasion process. The results show a trade-off between compliance costs and yield losses costs for different levels of control intensity, however, a laissez faire policy against the invasion would be preferred for England. When the farmers can learn from experiences and imitate each other, we find that control measures might fail completely if there is global knowledge of the burdens of compliance (e.g. through the media) and the farmers can foresee the future consequences of new actions. The effectiveness of the control program is respectively not affected or only partially affected if the farmers need to experience compliance to learn its consequences or communicate only locally. Attempts to take the pulse of negative opinions of land managers over NIS control programs and their media coverage might be a powerful predictor of the odds of failure of the programs.


biosecurity, dispersal, diabrotica virgifera virgifera, metapopulation, invasive alien species, pest risk analysis