Pest control at a regional scale: identifying key criteria using a spatially explicit, agent-based model.
Abstract
Invasive mammals are a major threat to biodiversity. Understanding how their distributions and abundance could be affected by different temporal and spatial control strategies is fundamental for planning effective management programs. We developed a spatially explicit, agent-based model to test the impacts of different spatiotemporal management strategies on a pest population. As a case study, we used the common brushtail possum (Trichosurus vulpecula) population in the Cape-to-City treatment area in Hawke's Bay, New Zealand. We found striking differences in the effectiveness of different spatial control strategies - a well designed spatial control strategy could be up to twice as cost effective as poorly designed ones. The optimal spatial control strategy may depend on the total control effort. At low control effort, habitat-targeted control was more effective; at high control effort, homogeneously distributed control was more effective. Immigration rather than in situ breeding is likely to initiate the population recovery in treated areas after an initial knockdown. Therefore, increasing the size of treatment areas and maximizing the use of natural barriers to immigration could prolong treatment persistence. Synthesis and applications. We have demonstrated how a spatially explicit, agent-based model can be used to identify key criteria for a control strategy of open pest populations. The integration of available information on pest habitat use, population dynamics and actual levels of control allowed us to assess the deployment of control sites and assisted the choice of spatial control strategies. Important roles for this type of model are to help predict hotspots of mammalian pest activities, to suggest the most effective control strategy and to identify important parameters and data for improving predictions. Ultimately, further integration of spatial and temporal analyses is critical for updating and optimizing management strategies.