Season and dominant species effects on plant trait‐ecosystem function relationships in intensively grazed grassland

2018 
Grazed pasture managers are increasingly being asked to enhance productivity while simultaneously reducing environmental impacts. Using plant traits to design plant communities that optimise ecosystem functions (e.g. productivity, nitrogen retention) may help achieve this. However, trait–function relationships in intensively grazed systems are largely untested. We used a forage diversity experiment, intensively grazed by cows (i.e. 10–12 times per year), to test whether community leaf and root traits were consistent predictors of ecosystem functioning across seasons and dominant species identities. Diversity treatments consisted of adding further species to either a Lolium perenneTrifolium repens or a Festuca arundinacea–T. repens base mixture. Plant traits were better predictors of functioning in systems dominated by L. perenne than by F. arundinacea. Above-ground productivity, root biomass and soil nitrate concentrations were related to traits in all seasons, but the ability of traits to predict carbon cycling measures, and to a lesser extent, net N mineralisation rates, varied strongly across seasons. Leaf traits were better predictors of functioning than root traits. Despite limited trait breadth, leaf functional trait diversity was correlated with most ecosystem functions in at least one season, but effects were sometimes negative. Trait–function relationships were not always in the expected direction. Synthesis and applications. Our results indicate that manipulating plant community traits has potential to improve some ecosystem functions for some seasons in intensively grazed systems. However, the variable nature of the trait–function relationships found suggests that a deeper understanding of why and when traits relate to ecosystem functioning is required before managers can be confident that using a trait-based approach will consistently improve outcomes.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    45
    References
    5
    Citations
    NaN
    KQI
    []