1. Pest management strategies should be informed by research on a broad suite of biotic and abiotic interactions. We used a life table response experiment (LTRE) to assess the reliability of ragwort Jacobaea vulgaris management recommendations based on interactions of (i) time of disturbance to initiate experimental units, (ii) herbivory from two biological control organisms, the cinnabar moth Tyria jacobaeae and ragwort flea beetle Longitarsus jacobaeae and (iii) interspecific competition by perennial grasses.
2. Our LTRE combines a factorial experiment with a linear, deterministic matrix model for ragwort populations representing transitions among three stages: 1st year juveniles, ≥2nd year juveniles and adults. Elasticity analysis identified potentially vulnerable ragwort transitions, and a contributions analysis confirmed which treatments influenced these transitions. Ultimate treatment effects were quantified as the reduction in population growth rates and time to local extinction.
3. Elasticity analyses found the ragwort’s biennial pathway, juvenile to adult transition and fertility transition were most influential and most amenable to manipulation across all community configurations. The flea beetle and perennial grass competition had negative effects on survival and fertility, whereas the cinnabar moth only reduced fertility and induced the perennial pathway.
4. All combinations of insects or increased plant competition reduced the growth rate of ragwort. Full interspecific competition and the flea beetle resulted in a significantly greater and faster decline in the ragwort populations than the cinnabar moth. Moreover, this pattern was consistent between two times of initial disturbance.
5.Synthesis and applications. Maximizing plant competition provides the fastest way to control ragwort. If this option is unavailable, for example, grazed or disturbed land, the ragwort flea beetle provides excellent management to lower ragwort densities without the potential nontarget effects of the cinnabar moth. Factorial experiments and matrix models help to evaluate interacting factors that influence invasive species’ vulnerabilities, inform how to intervene in a weed life cycle to reduce weed abundance and confirm recommendations that are robust to community variation.