Abstract
SummaryWeeds tend to aggregate in patches within fields, and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at various scales, the strength of the relations between soil properties and weed density would also be expected to be scale‐dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We developed a general method that uses novel within‐field nested sampling and residual maximum‐likelihood (reml) estimation to explore scale‐dependent relations between weeds and soil properties. We validated the method using a case study of Alopecurus myosuroides in winter wheat. Using reml, we partitioned the variance and covariance into scale‐specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales, we optimised the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.
Highlights
Many weed species have patchy distributions in arable fields that can be strongly affected by their environments, in particular the soil (Radosevich et al, 2007).The spatial variation in soil results from numerous processes operating at several spatial scales, so the variation in some soil properties can be patchy though not necessarily on the same scales as the weeds
We address these issues by applying sampling methodologies designed in the field of soil science to optimise sampling effort to the study of weed patches and how they may relate to environmental properties at multiple spatial scales
We assumed no prior knowledge of the spatial scale(s) on which the weed varied in fields and so we explored its distribution in one particular field by sampling with a nested design followed by a hierarchical statistical analysis to partition the variance and covariances with soil properties according to spatial scale
Summary
Many weed species have patchy distributions in arable fields that can be strongly affected by their environments, in particular the soil (Radosevich et al, 2007).The spatial variation in soil results from numerous processes operating at several spatial scales, so the variation in some soil properties can be patchy though not necessarily on the same scales as the weeds. Sampling at fine scales would make sampling the whole of a large field very expensive and, almost certainly, unnecessarily so if the aim is to understand the general position of patches within the field rather than small changes in the location of patches These difficulties associated with the design of discrete sampling protocols for studying weed patches, as either a tool for understanding weed ecology or mapping weeds to guide patch spraying, have been thoroughly reviewed by Rew and Cousens (2001). An understanding of the edaphic drivers of weed patch dynamics and the scales at which they operate is both of theoretical interest to weed ecologists and could allow these ‘weed vulnerable zones’ to be identified based on maps of soil properties We address these issues by applying sampling methodologies designed in the field of soil science to optimise sampling effort to the study of weed patches and how they may relate to environmental properties at multiple spatial scales
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