Optimal application strategies for entomopathogenic nematodes: Integrating theoretical and empirical approaches

1. The nematode Steinernema feltiae has been developed commercially as a biocontrol agent and is successful in controlling sciarid flies in mushroom houses. We used a simple model developed in parallel with a series of field trials, to optimize the application strategy of the nematode. The first field trial provided life-history parameter estimates for both the sciarid flies and the nematodes and showed there to be substantial levels of nematode recycling, leading to high levels of control. Crucially, the field trial showed that by splitting the application into two, and applying half the nematodes at day 0 and half at day 7, the overall number of nematodes (dose) could be substantially reduced from currently recommended levels without sacrificing control success. 3. The model confirmed that there should be a benefit in splitting the dose, if the second application is timed to coincide with the peak in numbers of highly susceptible sciarid larvae. Importantly, the model provided insight into the dynamics of the sciarid larvae, even though the original data set only recorded adult fly numbers. 4. Using a split dose, optimally timed, the model suggested that total doses could be reduced by up to 75% and still achieve control comparable to that found with currently recommended dosages. The timing of the second application of nematodes was crucial in determining the level of control. 5. The model predictions were validated against a second independent field trial with considerably lower fly densities. Even under these different conditions, the predictions were accurate, indicating the robustness of the modelling approach. 6. To date models have rarely provided genuine practical advice to applied agriculturists and biocontrol practitioners. This study shows how a simple model developed in parallel with replicated field trials leads to a better understanding of the biological processes underlying successful control, resulting in improvements in recommended application strategies.