Developing hierarchical density‐structured models to study the national‐scale dynamics of an arable weed

• DOI: 10.1002/ecm.1449
• Publication Date: 2021-02-10T13:39:35Z
• Authors: Robert M. Goodsell; Dylan Z. Childs; Matthew Spencer; Shaun Coutts; Remi Vergnon; Tom Swinfield; Simon A. Queenbor...; Robert P. Freckleton
• Keywords: Population model; Arable land; Density dependence

Abstract Population dynamics can be highly variable in the face of environmental heterogeneity, and understanding this variation is central study ecology. Robust management decisions require that we understand how populations respond to at a range scales, under broad suite conditions. models are potentially valuable tools addressing challenge. However, without adequate data, fail produce useful results. Populations arable weeds particularly problematic respect, as they widespread their extremely variable. Owing inherent cost collecting most studies plant population derived from localized experiments small conditions, limiting extent which variance measured. Density‐structured provide route rapid, large‐scale analysis dynamics, expand scale ecological directly tied data. Here extend previous density‐structured include management, account for inter‐population variation. We develop, parameterize, test hierarchical common agricultural weed, black‐grass ( Alopecurus myosuroides ). model species response crop using survey data gathered over 4 yr 364 fields across network 45 UK farms. show substantial improvement nonhierarchical counterparts. Using these models, demonstrate several alternative rotations effective reducing weed densities. Rotations with high wheat prevalence exhibit severe infestations, diverse generally have lower key outcome many cases effect rotation compared variability arising spatiotemporal heterogeneity. This result highlights need monitor large spatial temporal scales order drivers dynamics. Our framework collection modeling provides means achieve this.