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Leaf dry matter content is better at predicting above‐ground net primary production than specific leaf area
Author(s) -
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,
HerreroJáuregui Cristina,
Hodgson John Gavin
Publication year - 2017
Publication title -
functional ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.272
H-Index - 154
eISSN - 1365-2435
pISSN - 0269-8463
DOI - 10.1111/1365-2435.12832
Subject(s) - biology , specific leaf area , trait , primary production , dry matter , intraspecific competition , ecosystem , abundance (ecology) , temperate climate , leaf area index , ecology , agronomy , botany , photosynthesis , computer science , programming language
Summary 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 ( R 2 = 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.