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How weed communities assemble represents one the key issues of weed science. For a decade, functional approaches have been applied to investigate the processes that govern weed community assembly. In most previous studies, trait values have been generally averaged over multiple populations and habitats. Consequently, conspecifics display similar trait values while neglecting the considerable influence of intraspecific variability to detect changes in functional diversity in response to environmental drivers. However, this influence has been shown to be critical, especially, at local scales. Here, we studied changes in weed functional diversity at the field scale in four crop sequences. We focused on intra- and interspecific variability of four key functional traits involved in response to resource acquisition processes, the latter being modified by climate, management and competition. The relative influence of intra- and interspecific variability among the crop sequence types was highlighted using a diversity partitioning approach. It provides evidence for substantial amount of intraspecific variability in the weed community and underlines its essential role in response to fine-scale environmental drivers. In addition, we investigated the response of the three most abundant species to competition with the crop, the growing season and the crop sequence type. We highlighted that these species showed a wide range of combinations of trait values, suggesting the co-existence of several successful strategies. Based on these results, we emphasise that neglecting intraspecific variability can lead to substantial underestimations of the functional weed response to management and crop-weed competition at the field scale. [ABSTRACT FROM AUTHOR]

B orgy B, G aba S, P etit S & R eboud X (2012) Non-random distribution of weed species abundance in arable fields. Weed Research 52, 383-389. Summary Many decisions for pest control in agriculture are based on densities, rather than species presence only. Little is known about the distribution of pest abundance and even less when several species present in a field may interact. With the aim of developing an ecologically based approach to weed management, we compared observed co-occurrence patterns with a null model, to test whether the abundances of weed species were randomly assembled. The data set covered the pattern of co-occurrence of abundances of weeds within 200-m2 plots located in 1143 arable fields scattered across France. Our results pinpointed a highly significant 'similar attraction-dissimilar repulsion' rule, as species reaching similar abundance levels co-occurred significantly more often than expected under the hypothesis of a random distribution of abundance. A similar analysis applied to the 25% plots that had the highest weed densities indicated that the 'similar attraction-dissimilar repulsion' rule held true for low-abundance classes, but not in situations where at least one species was observed at densities above 20 individuals m−2. Our analysis extends the analysis of community beyond the sole species richness index by accounting for their abundance. Overall, weed species interfered or responded similarly to an external biotic or abiotic factor, with resulting adjustment of densities at the scale of the agricultural field. [ABSTRACT FROM AUTHOR]

An accurate weed management in a context of sustainable agriculture relies on the knowledge about spatial weed distribution within fields. To improve the representation of patchy spatial distributions of weeds, several sampling strategies are used and lead to various weed measurements (abundance, count, patch boundaries). Here, we propose a hierarchical Bayesian model which includes such multi-type data and which allows the interpolation of weed spatial distributions (using a MCMC algorithm). The weed pattern is modeled with a log Gaussian Cox process and the various weed measurements are modeled with different observation processes. The application of the method to simulated data shows the advantage of combining several types of data (instead of using only one type of data). The method is also applied to infer the weed spatial distribution for real data. [Copyright &y& Elsevier]