Large-scale disturbances, biological control and the dynamics of gorse populations

1. Simulation and analytical models were developed for gorse Ulex europaeus. The simulation model incorporated spatially local density-dependent competition, disturbance, asymmetric competition between seedlings and established plants, a seed bank, local seed dispersal, an age structured established plant population, and temporal variation in the probability of disturbance. The analytical models were simple approximations of the simulation. 2. The models extended our previously published model for Scotch broom Cytisus scoparius to include large-scale disturbances and possible management options, such as the use of fire, herbicides and oversewing with perennial grasses. Fire was assumed to influence established plant mortality, seed survival in the seed bank, and the probability of germination. 3. We reviewed published data on the demography of gorse in New Zealand, the current management techniques, and the ongoing biological control programme. 4. Over a wide range of biologically reasonable parameter values, the analytical models accurately predicted the outcome of the simulations. The analytical models worked well, providing gorse occupied a high proportion of the available sites and large-scale disturbances did not occur too frequently. 5. The potential impact of seed-feeding biological control agents on gorse abundance was assessed, using the models, for several environmental and management scenarios. In particular, we explored how large-scale disturbance, such as fire and herbicide application, influences the outcome of biological control. 6. The success of a biological control programme was found to depend critically on the frequency and intensity of disturbance, whether disturbed sites became suitable for recruitment, and the effects of disturbance on germination and seed mortality. 7. The models highlight the need to manage recruitment opportunities carefully in order to maximize the effect of biological control agents. The models also indicate that details of plant population biology can have a profound effect on the success of any management strategy.