TY - JOUR

T1 - Early subtropical forest growth is driven by community mean trait values and functional diversity rather than the abiotic environment

AU - Kröber, Wenzel

AU - Li, Ying

AU - Härdtle, Werner

AU - Ma, Keping

AU - Schmid, Bernhard

AU - Schmidt, Karsten

AU - Scholten, Thomas

AU - Seidler, Gunnar

AU - von Oheimb, Goddert

AU - Welk, Erik

AU - Wirth, Christian

AU - Bruehlheide, Helge

N1 - Publisher Copyright: © 2015 Published by John Wiley & Sons Ltd.

PY - 2015/9/1

Y1 - 2015/9/1

N2 - While functional diversity (FD) has been shown to be positively related to a number of ecosystem functions including biomass production, it may have a much less pronounced effect than that of environmental factors or species-specific properties. Leaf and wood traits can be considered particularly relevant to tree growth, as they reflect a trade-off between resources invested into growth and persistence. Our study focussed on the degree to which early forest growth was driven by FD, the environment (11 variables characterizing abiotic habitat conditions), and community-weighted mean (CWM) values of species traits in the context of a large-scale tree diversity experiment (BEF-China). Growth rates of trees with respect to crown diameter were aggregated across 231 plots (hosting between one and 23 tree species) and related to environmental variables, FD, and CWM, the latter two of which were based on 41 plant functional traits. The effects of each of the three predictor groups were analyzed separately by mixed model optimization and jointly by variance partitioning. Numerous single traits predicted plot-level tree growth, both in the models based on CWMs and FD, but none of the environmental variables was able to predict tree growth. In the best models, environment and FD explained only 4 and 31% of variation in crown growth rates, respectively, while CWM trait values explained 42%. In total, the best models accounted for 51% of crown growth. The marginal role of the selected environmental variables was unexpected, given the high topographic heterogeneity and large size of the experiment, as was the significant impact of FD, demonstrating that positive diversity effects already occur during the early stages in tree plantations. The manuscript addresses an approach to the framework suggested by Díaz et al. (2007, PNAS) to disentangle the effect of environment, species identity and functional diversity in tree communities. We present a dataset with 231 plots varying in ecological characteristics, species and functional diversity. We used a set of 41 plant functional traits for 23 tree species. Our most striking result is that the ecological environment only explained 4% of plot mean values in crown increment, whereas community weighted mean values and functional diversities of trait combinations explained 42 and 31%, respectively, adding up to 51% explained variation in combination. We can conclude that functional diversity even 3 years after planting has a significant impact on productivity.

AB - While functional diversity (FD) has been shown to be positively related to a number of ecosystem functions including biomass production, it may have a much less pronounced effect than that of environmental factors or species-specific properties. Leaf and wood traits can be considered particularly relevant to tree growth, as they reflect a trade-off between resources invested into growth and persistence. Our study focussed on the degree to which early forest growth was driven by FD, the environment (11 variables characterizing abiotic habitat conditions), and community-weighted mean (CWM) values of species traits in the context of a large-scale tree diversity experiment (BEF-China). Growth rates of trees with respect to crown diameter were aggregated across 231 plots (hosting between one and 23 tree species) and related to environmental variables, FD, and CWM, the latter two of which were based on 41 plant functional traits. The effects of each of the three predictor groups were analyzed separately by mixed model optimization and jointly by variance partitioning. Numerous single traits predicted plot-level tree growth, both in the models based on CWMs and FD, but none of the environmental variables was able to predict tree growth. In the best models, environment and FD explained only 4 and 31% of variation in crown growth rates, respectively, while CWM trait values explained 42%. In total, the best models accounted for 51% of crown growth. The marginal role of the selected environmental variables was unexpected, given the high topographic heterogeneity and large size of the experiment, as was the significant impact of FD, demonstrating that positive diversity effects already occur during the early stages in tree plantations. The manuscript addresses an approach to the framework suggested by Díaz et al. (2007, PNAS) to disentangle the effect of environment, species identity and functional diversity in tree communities. We present a dataset with 231 plots varying in ecological characteristics, species and functional diversity. We used a set of 41 plant functional traits for 23 tree species. Our most striking result is that the ecological environment only explained 4% of plot mean values in crown increment, whereas community weighted mean values and functional diversities of trait combinations explained 42 and 31%, respectively, adding up to 51% explained variation in combination. We can conclude that functional diversity even 3 years after planting has a significant impact on productivity.

KW - Ecosystems Research

KW - BEF-China

KW - Community-weighted mean traits

KW - ecosystem functioning

KW - plant functional traits

KW - stomatal density

KW - trees

KW - BEF-China

KW - Community-weighted mean traits

KW - ecosystem fuctioning

KW - plant functional traits

KW - stomatal density

KW - trees

UR - http://www.scopus.com/inward/record.url?scp=84941189314&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/289d7212-3a66-3380-b9ac-1d9759f90373/

U2 - 10.1002/ece3.1604

DO - 10.1002/ece3.1604

M3 - Journal articles

C2 - 26380685

VL - 5

SP - 3541

EP - 3556

JO - Ecology and Evolution

JF - Ecology and Evolution

SN - 2045-7758

IS - 17

ER -