Background and aims: Predicting the response of plant communities to variation in resources and disturbance is still a challenge, because findings depend on how ecological gradients are characterized and how grassland functional composition is assessed. Focusing on leaf dry matter content (LDMC), the efficacy of different methods for evaluating the best response of plant communities to either environmental or disturbance change is examined.
Methods: Data were collected on 69 grasslands located at four sites in the Pyrenees and Massif Central. N-Ellenberg indices and plant nutrient content (Ni) were compared to assess fertility, and either LDMC (meas) measured or calculated from a trait database for which traits were measured under the same environmental conditions (db). Management regime (MR) was characterized in terms of categories (grazing, cutting) and plant height.
Key results: LDMCdb was positively correlated to LDMCmeas, but depended significantly on site temperature. N-Ellenberg and Ni were significantly correlated, and there was a significant effect of MR and temperature. LDMC responded to fertility, MR and temperature. Replacing MR by plant height in an REML analysis reduced the uncertainty of the LDMC prediction. LDMC was correlated to plant height at community level, whereas the correlation was weak at species level. Differences in LDMC between plant communities under any of the management regimes were significantly correlated to the standing herbage mass.
Conclusion: The N-Ellenberg index is a better indicator of fertility than Ni which is short-term and environment-dependent. LDMC taken from a database allows plant trait variation due to species abundance (excluding variation due to trait plasticity in response to management) to be captured. So the former is better suited for assessing agricultural services that mainly depend on plant phenology and tissue composition. LDMC responded to defoliation regime in addition to fertility because plant height is roughly correlated with LDMC at plant community level.