Models are useful tools for understanding and improving biological control of arthropod pests by means of natural enemies. Thus, models can be applied to simulate various scenarios in order to identify optimal control strategies. Although simulations can never replace real experiments, they can often serve as guidelines for choosing relevant field experiments and thereby save a lot of laborious and costly field work.
Whereas the processes underlying population dynamics (e.g. dispersal, functional response, mutual interference) can be studied under laboratory conditions, large-scaled experiments in the field or in greenhouses are unsuited for this purpose. Instead such experiments may provide information about the patterns (e.g. spatial distributions of prey and predators) generated by the underlying processes. A major purpose of modeling is to link the patterns to the processes that generate these patterns.
Petri-dish and single plant experiments have clearly demonstrated the capacity of predacious mite Phytoseiulus persimilis to feed effectively on the two-spotted spider mite Tetranychus urticae. This quickly leads to reductions in the abundance of prey, followed by a decline in predator abundance and eventual extinction. However, when larger systems, consisting of many hundred plants, are infested with the two mite species, extinction of one or both species seems less likely at the system level, although it may still occur at the individual plant level. The qualitative difference between small and large systems with respect to persistence and extinction risks is attributed to the fact that mites move among plants, but to prove that dispersal per se plays a role for the overall dynamics is hard to demonstrate experimentally. To circumvent this problem, I developed a stochastic simulation model of a greenhouse system that explicitly incorporates within and between plant dynamics. The model is used for analyzing a series of experiments with biological control of spider mites in multi-plant systems. In these experiments, the number of plants as well as their connectivity and the numbers of introduced mites were varied in order to examine whether these factors affect e.g. the predator-prey ratio or the time to extinction of one or both species.
In my presentation I will also demonstrate an interactive version of the model (called DynaMite). It allows the user to interfere in the system during a simulation so as to mimic the options a grower has in order to prevent losses and to maximize his profit. Such options include spraying with acaricides, releasing predators, and replanting in substitute of damaged plants. By choosing different control strategies, the user may gradually improve his skills according to the principle of learning by experience. The model can be freely downloaded from http://www1.bio.ku.dk/ansatte/beskrivelse/?id=43077

• Gösta Nachman(Department of Biology, Section of Ecology and Evolution, University of Copenhagen, Denmark)