Functional trait-mediated limiting similarity shapes weed biomass acquisition, fecundity, and reproductive phenology

According to the theories of limiting similarity and life history trade-offs, (i) the probability of species coexistence decreases as interspecific similarity increases, and (ii) stronger competition intensity may accelerate reproductive phenology. Based on these theories, we predict that (i) weed biomass and fecundity will decrease and (ii) reproductive phenology will increase when competing with functionally similar crops. To test these predictions, we evaluated biomass acquisition, fecundity, time of anthesis, and seed shattering of four eudicot and two monocot weed species growing in two crops [Glycine max (L.) Merr. and Zea mays L.]. We then used a trait-based approach to assess limiting similarity and its feasibility as a predictor of crop-weed competitive outcomes. Results revealed greater functional similarity between G. max and four eudicot weeds than between Z. mays and these species. In accordance with our first prediction, weed biomass and fecundity of eudicot species were, on average, 25 and 11% lower in G. max than in Z. mays, respectively, suggesting higher competitive interactions. In contrast, the functional similarity between Z. mays and the two monocot weeds was higher than that between G. max and these two monocot weeds. Accordingly, biomass and fecundity of these species were, on average, 35 and 13% lower, respectively, in Z. mays than in G. max. In accordance with our second prediction, greater crop-weed functional similarity was positively correlated with seed shattering across all weed species. Our results demonstrate that crop-weed functional similarity may be a useful indicator for predicting weed biomass, fecundity, and seed shattering under competitive filtering (e.g., crops and cover crops), which are fundamental components of ecologically based weed management. Therefore, a trait-based approach in agroecosystems could provide mechanistic insights into crop-weed interactions, inform management priorities for problematic weed species, and help increase biodiversity and ecosystem services, supporting the design of resilient cropping systems.