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Leaf dry matter content is better at predicting aboveground net primary production than specific leaf area

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Smart, Simon Mark and Glanville, Helen Catherine and Blanes, María del Carmen and Mercado, Lina María and Emmett, Bridget Anne and Jones, David Leonard and Cosby, Bernard Jackson and Marrs, Robert Hunter and Butler, Adam and Marshall, Miles Ramsvik and Reinsch, Sabine and Herrero-Jáuregui, Cristina and Hodgson, John Gavin (2017) Leaf dry matter content is better at predicting aboveground net primary production than specific leaf area. Functional Ecology, 31 (6). pp. 1336-1344. ISSN 0269-8463, ESSN: 1365-2435

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Official URL: http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12832/full



Abstract

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 intraspecific 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:Article
Uncontrolled Keywords:Bayesian modelling; Ecosystem function; Global change; Intraspecific variation; Measurement error
Subjects:Medical sciences > Biology > Ecology
ID Code:43819
Deposited On:07 Jul 2017 10:22
Last Modified:07 Jul 2017 10:22

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