Community leaf dry matter content predicts plant production in simple and diverse grassland. Issue 5 (12th May 2022)

• Record Type: Journal Article
• Authors: Polley, H. Wayne; Collins, Harold P.; Fay, Philip A.
• Is Part Of: Ecosphere. Volume 13:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-12
• Subjects: aboveground net primary production -- leaf dry matter content -- leaf economics spectrum -- plant N concentration -- plant species mixture -- remote sensing -- switchgrass; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.4076
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Plant growth correlates with values of collinear (covarying) traits from the leaf economics spectrum. Environmental variation and differences in community composition may alter contributions of these traits to plant production and thereby limit the consistency of trait‐based growth predictions among years and plant communities. We tested effects of interannual variation in precipitation and differences in grassland community composition (planted monoculture of switchgrass, Panicum virgatum, and mixture of perennial herbaceous species) on the utility of two traits from the leaf economics spectrum (leaf dry matter content [LDMC] and plant [N]) to predict aboveground net primary production (ANP) during spring of 6 years. Spatial and temporal variation in spring production correlated with community‐scale (species abundance‐weighted) values of both traits, but community LDMC explained 66% of the variance in production and accounted for ≥89% of the variance explained by the two traits combined. The ANP response to trait variation and the variance in ANP explained by trait values differed with precipitation and between communities. Greater precipitation increased the production response to trait variation by increasing slopes of ANP–trait regression relationships and increased the variance in ANP explained by trait values. Communities differed in response to precipitation variation and in the role of annual variation in the [N]–LDMC relationship in explaining variance in ANP. Results indicate that mean trends in grassland production can be predicted using community‐scale values of LDMC. Trait‐based predictions of grassland production could be improved, however, by accommodating precipitation and community effects on production–trait relationships.