Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area

Smart, Simon Mark; Glanville, Helen Catherine; Blanes, Maria del Carmen; Mercado, Lina Maria; Emmett, Bridget Anne; Jones, David Leonard; Cosby, Bernard Jackson; Marrs, Robert Hunter; Butler, Adam; Marshall, Miles Ramsvik; Reinsch, Sabine; Herrero-Jáuregui, Cristina; Hodgson, John Gavin. 2017 Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area. Functional Ecology, 31 (6). 1336-1344.

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1. Reliable modelling of above-ground Net Primary Production (aNPP) at fine resolution is a significant challenge. A promising avenue for improving process models is to include response and effect trait relationships. However, uncertainties remain over which leaf traits are correlated most strongly with aNPP. 2. We compared abundance-weighted values of two of the most widely used traits from the Leaf Economics Spectrum (Specific Leaf Area and Leaf Dry Matter Content) with measured aNPP across a temperate ecosystem gradient. 3. We found that Leaf Dry Matter Content (LDMC) as opposed to Specific Leaf Area (SLA) was the superior predictor of aNPP (R2=0.55). 4. Directly measured in situ trait values for the dominant species improved estimation of aNPP significantly. Introducing intra-specific trait variation by including the effect of replicated trait values from published databases did not improve the estimation of aNPP. 5. Our results support the prospect of greater scientific understanding for less cost because LDMC is much easier to measure than SLA.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Emmett
ISSN: 0269-8463
Additional Keywords: Bayesian modelling, ecosystem, global change, measurement error, ecosystem function, intra-specific variation
NORA Subject Terms: Ecology and Environment
Date made live: 20 Jan 2017 14:43 +0 (UTC)

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