DYNAMICALLY GENERATED VARIABILITY IN PLANT-PATHOGEN SYSTEMS WITH BIOLOGICAL-CONTROL

Using a combination of replicated microcosm experiments, simple nonlin ear modelling and model fitting we show that unexpected levels of vari ability can be detected and described in the dynamics of plant disease . Temporal development of damping-off disease of radish seedlings caus ed by an economically important plant pathogen, Rhizoctonia solani, is quantified, with and without the addition of an antagonistic fungus, Trichoderma viride. The biological control agent reduces the average a mount of disease but also greatly enhances the variability among repli cates. The results are shown to be consistent with predictions from a nonlinear model that exhibits dynamically generated variability in whi ch small differences in the initiation of infection associated with th e antagonist are later amplified as the pathogen spreads from plant to plant. The effect of dynamically generated variability is mediated by the interruption of transient disease progress curves for separate re plicates by an exponential decrease in susceptibility of the host over time. The decay term essentially 'freezes' the dynamics of the transi ent behaviour so that the solutions for different replicates settle on asymptotes that depend on initial conditions and parameter values. Th e effect is further magnified by nonlinear terms in the infection forc e in the models. A generalization of the Lyapunov exponent is introduc ed to quantify the amplification. The observed behaviour has profound consequences for the design and interpretation of ecological experimen ts, and can also account for the notorious failure of many biological control strategies through the creation of 'hot spots', created by the amplification of plant to plant infection, where the control by the a ntagonist is locally unsuccessful.