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

Abstract

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

Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data

“…In our analyses, the most effective option for reducing weed population variables is cropping, which is consistent with several previous quantitative studies of black‐grass control (Goodsell et al., 2021; Lutman et al., 2013). Whilst cultivation and herbicide applications have noticeable but smaller effects on weed density compared to cropping, rotations are often collinear with particular management strategies (Figures S5 and S6).…”

“…For clarity and ease of reading, we drop the t and j subscripts in equations from here on, however, all probabilities are time and field‐dependent. We calculated probabilities of observing a given density state, where gives the probability of observing state k at time t , conditional on explanatory variables at quadrat i within field j :We then modelled field‐level weed population dynamics using density‐structured models (Freckleton et al., 2018; Goodsell et al., 2021). where n is a vector, of K density states.…”

“…We then modelled field-level weed population dynamics using densitystructured models (Freckleton et al, 2018;Goodsell et al, 2021).…”

“…However, a major challenge is collecting sufficient data to quantify the full range of variation in space and time. Density-structured methods, which record abundance as discrete density-states, allow rapid measurement of abundance, whilst still permitting insight into site-level population dynamics (Freckleton et al, 2011(Freckleton et al, , 2018Goodsell et al, 2021;Mieszkowska et al, 2013;Queenborough et al, 2011;Taylor & Hastings, 2004). These methods facilitate the study of population dynamics across a range of environments and phenotypes present in widely distributed populations.…”

“…Despite the need for integrated management strategies, the majority of studies typically focus on testing the impacts of single management interventions under controlled environmental conditions (Buhler, 1999;Chauvel et al, 2001;Harker & O'Donovan, 2013;Melander et al, 2005;Metcalfe et al, 2017Metcalfe et al, , 2018. Several reviews have emphasised variability in responses to interventions between studies and locations (Freckleton et al, 2008;Freckleton & Stephens, 2009;Lutman et al, 2013), and recent work has demonstrated the importance of environmental variability on plant population dynamics, and the need to understand how management outcomes vary under these conditions (Freckleton et al, 2018;Goodsell et al, 2021;Hicks et al, 2021). Furthermore, many weed populations have a high prevalence of evolved herbicide resistance (Evans et al, 2015;Hicks et al, 2018;Moss et al, 2011;Powles & Yu, 2010).…”