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Volume 97, Issue 9
Article

Linking individual‐level functional traits to tree growth in a subtropical forest

Xiaojuan Liu

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of Sciences, Beijing 100093, China

Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich CH‐8057, Switzerland

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Nathan G. Swenson

Department of Biology, University of Maryland, College Park, Maryland, 20742 USA

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Dunmei Lin

Key Laboratory of the Three Gorges Reservoir Region's Eco‐Environment, Ministry of Education, Chongqing University, Chongqing 400044, China

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Xiangcheng Mi

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of Sciences, Beijing 100093, China

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María Natalia Umaña

Department of Biology, University of Maryland, College Park, Maryland, 20742 USA

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Bernhard Schmid

Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich CH‐8057, Switzerland

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Keping Ma

Corresponding Author

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of Sciences, Beijing 100093, China

E‐mail:

kpma@ibcas.ac.cn

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First published: 29 April 2016
Citations: 22
Corresponding Editor: M. Uriarte.

Abstract

Forging strong links between traits and performance is essential for understanding and predicting community assembly and dynamics. Functional trait analyses of trees that have correlated single‐trait values with measures of performance such as growth and mortality have generally found weak relationships. A reason for these weak relationships is the failure to use individual‐level trait data while simultaneously putting that data into the context of the abiotic setting, neighborhood composition, and the remaining axes constituting the overall phenotype. Here, utilizing detailed growth and trait data for 59 species of trees in a subtropical forest, we demonstrate that the individual‐level functional trait values are strongly related to individual growth rates, and that the strength of these relationships critically depends on the context of that individual. We argue that our understanding of trait–performance relationships can be greatly improved with individual‐level data so long as that data is put into the proper context.