Population managers are frequently faced with the challenge of selecting the most effective management strategy from a set of available strategies. In the case of classical weed biological control, this requires predicting a priori which of a group of candidate biocontrol agent species has the greatest probability of suppressing the target plant. Previous demographic studies of weedy plant populations have sought to guide the development of management plans by identifying vulnerable transitions in the plant’s life cycle and matching them with management techniques or biocontrol agents that target those transitions. One frequent, but false, assumption of these analyses is that demographic rates are fixed within individual populations. As a result of this, optimal target transitions for management are also assumed to remain constant over time. We explored the consequences of relaxing this assumption in population models of the invasive weed Alliaria petiolata (garlic mustard) parameterized using three generations of demographic data from twelve natural populations in Michigan and Illinois. Linear deterministic matrix models were used to analyze how spatial and temporal variation in population elasticity structures affects the rankings of target demographic transitions. A megamatrix model was then used to summarize temporal variation in growth within each population and generate preliminary estimates of successfully controlling A. petiolata with either single or multiple biocontrol agent species.

Results/Conclusions

Our results demonstrate that A. petiolata exhibits strikingly different growth rates (λ) over time and space, with some populations struggling to survive and others rapidly expanding. Estimates of λ ranged from 0.48 to 5.88 across all sites and years. Within sites λ was temporally variable, ranging from 0.80 to 5.88 within one site. Site megamatrix growth rates (λ_M) ranged from 0.83 to 3.54 (mean = 1.90). Elasticity analyses indicated the importance of the seed bank to A. petiolata’s success and persistence during bad years. Elasticity rankings varied with λ, indicating that the transitions with the largest impacts on population growth differ for growing and declining populations and within populations during good and bad years. Thus, the optimal management strategy for A. petiolata represents a moving target. Using available data on candidate biocontrol agent performance, our models suggest that the root-crown feeding weevil, Ceutorhynchus scrobicolis, alone should be able to control 33% of the garlic mustard populations in our study area. The addition of a leaf and stem feeding agent like C. alliariae may result in control of up to 50% of populations.