Measuring the effectiveness of management interventions at regional scales by integrating ecological monitoring and modelling. Issue 10 (23rd November 2017) - Freckleton, Robert P; Hicks, Helen L; Comont, David; Crook, Laura; Hull, Richard; Neve, Paul; Childs, Dylan Z
Abstract: BACKGROUND: Because of site‐specific effects and outcomes, it is often difficult to know whether a management strategy for the control of pests has worked or not. Population dynamics of pests are typically spatially and temporally variable. Moreover, interventions at the scale of individual fields or farms are essentially unreplicated experiments; a decrease in a target population following management cannot safely be interpreted as success because, for example, it might simply be a poor year for that species. Here, we argue that if large‐scale data are available, population models can be used to measure outcomes against the prevailing mean and variance. We apply this approach to the problem of rotational management of the weed Alopecurus myosuroides . RESULTS: We derived density‐structured population models for a set of fields that were not subject to rotational management (continuous winter wheat) and another group that were (rotated into spring barley to control A. myosuroides ). We used these models to construct means and variances of the outcomes of management for given starting conditions, and to conduct transient growth analysis. We show that, overall, this management strategy is successful in reducing densities of weeds, albeit with considerable variance. However, we also show that one variant (rotation to spring barley along with variable sowing) shows little evidence for additional control. CONCLUSION: Our results suggest that rotational strategies can be effective in the control of this weed, but also that strategies require careful evaluation against a background of spatiotemporal variation. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Abstract : Using large‐scale data on the dynamics of weed population, local management can be benchmarked relative to the expected variance in outcomes. We apply this to the use of rotational management of resistant weeds.