RT Journal Article
T1 Leaf dry matter content is better at predicting abovegroundnet primary production than specific leaf area
A1 Smart, Simon Mark
A1 Glanville, Helen Catherine
A1 Blanes, María del Carmen
A1 Mercado, Lina María
A1 Emmett, Bridget Anne
A1 Jones, David Leonard
A1 Cosby, Bernard Jackson
A1 Marrs, Robert Hunter
A1 Butler, Adam
A1 Marshall, Miles Ramsvik
A1 Reinsch, Sabine
A1 Herrero-Jáuregui, Cristina
A1 Hodgson, John Gavin
AB 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.
PB Wiley
SN 0269-8463, ESSN: 1365-2435
YR 2017
FD 2017-06
LK https://hdl.handle.net/20.500.14352/17916
UL https://hdl.handle.net/20.500.14352/17916
LA eng
NO UK Natural Environment Research Council Macronutrients Program
DS Docta Complutense
RD 5 abr 2025