TY - JOUR
T1 - Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area
AU - Smart, Simon Mark
AU - Glanville, Helen Catherine
AU - Blanes, Maria del Carmen
AU - Mercado, Lina Maria
AU - Emmett, Bridget Anne
AU - Jones, David Leonard
AU - Cosby, Bernard Jackson
AU - Marrs, Robert Hunter
AU - Butler, Adam
AU - Marshall, Miles Ramsvik
AU - Reinsch, Sabine
AU - Herrero-Jáuregui, Cristina
AU - Hodgson, John Gavin
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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. 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. We found that leaf dry matter content (LDMC) as opposed to specific leaf area (SLA) was the superior predictor of aNPP (R2 = 0·55). 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. Our results support the prospect of greater scientific understanding for less cost because LDMC is much easier to measure than SLA. A lay summary is available for this article.
AB - 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. 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. We found that leaf dry matter content (LDMC) as opposed to specific leaf area (SLA) was the superior predictor of aNPP (R2 = 0·55). 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. Our results support the prospect of greater scientific understanding for less cost because LDMC is much easier to measure than SLA. A lay summary is available for this article.
KW - Bayesian modelling
KW - ecosystem function
KW - global change
KW - intraspecific variation
KW - measurement error
UR - http://www.scopus.com/inward/record.url?scp=85013945890&partnerID=8YFLogxK
U2 - 10.1111/1365-2435.12832
DO - 10.1111/1365-2435.12832
M3 - Article
AN - SCOPUS:85013945890
SN - 0269-8463
VL - 31
SP - 1336
EP - 1344
JO - Functional Ecology
JF - Functional Ecology
IS - 6
ER -