5 research outputs found

    Multifaceted functional diversity for multifaceted crop yield: Towards ecological assembly rules for varietal mixtures

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    International audience1.Ecological theories suggest that higher plant genetic diversity can increase productivity in natural ecosystems. So far, varietal mixtures, that is, the cultivation of different genotypes within a field, have shown contrasting results, notably for grain yield where both positive and negative mixing effects have been reported. Such discrepancy between ecological theories and agronomical applications calls for a better understanding of plant–plant interactions in crops.2.Using durum wheat Triticum turgidum ssp. durum as a model species, we investigated the effect of functional trait composition on productivity and grain quality of varietal mixtures by growing 179 highly diverse genotypes in pure stands and 197 two‐way mixtures in field conditions. We quantified the agronomic performance of the mixtures relative to their components grown in pure stands on two variables related to productivity, vegetative biomass yield and grain yield, and one variable related to grain quality, grain protein content. We then analysed the relationship between the relative performance of the mixtures and their functional composition that we characterized with trait means and trait differences on 19 above‐ and below‐ground traits. 3.We found that biomass and grain yield increased by 4% overall in mixtures relative to single varieties, but that mixing effects were non‐significant for grain protein content. The combined effects of trait means and trait differences explained 12%, 17% and 22% of the variability of relative grain yield, biomass yield and grain protein content, respectively, with different traits affecting productivity and grain quality. Clustering varieties into functional groups allowed us to identify the most beneficial associations for multifaceted agronomic performance.4.Synthesis and applications. Functional traits explained a significant part of the relative agronomic performance of mixtures compared to monocultures (12%–22%, depending on the yield component). They can thus serve as a basis to identify groups of varieties whose combinations are expected to generate positive mixing effects, especially for productivity, and without compromising grain quality. Selection could then target convergence between groups for some traits and divergence between groups for other traits using empirically derived relationships between functional traits and agronomic performance as a guideline

    Networks beyond pairwise interactions: Structure and dynamics

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    The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, a great variety of complex systems has been successfully described as networks whose interacting pairs of nodes are connected by links. Yet, in face-to-face human communication, chemical reactions and ecological systems, interactions can occur in groups of three or more nodes and cannot be simply described just in terms of simple dyads. Until recently, little attention has been devoted to the higher-order architecture of real complex systems. However, a mounting body of evidence is showing that taking the higher-order structure of these systems into account can greatly enhance our modeling capacities and help us to understand and predict their emerging dynamical behaviors. Here, we present a complete overview of the emerging field of networks beyond pairwise interactions. We first discuss the methods to represent higher-order interactions and give a unified presentation of the different frameworks used to describe higher-order systems, highlighting the links between the existing concepts and representations. We review the measures designed to characterize the structure of these systems and the models proposed in the literature to generate synthetic structures, such as random and growing simplicial complexes, bipartite graphs and hypergraphs. We introduce and discuss the rapidly growing research on higher-order dynamical systems and on dynamical topology. We focus on novel emergent phenomena characterizing landmark dynamical processes, such as diffusion, spreading, synchronization and games, when extended beyond pairwise interactions. We elucidate the relations between higher-order topology and dynamical properties, and conclude with a summary of empirical applications, providing an outlook on current modeling and conceptual frontiers.Comment: Accepted for publication in Physics Reports. 109 pages, 47 figure
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