Trait-based classification and manipulation of plant functional groups for biodiversity–ecosystem function experiments
Article first published online: 15 MAR 2013
© 2013 International Association for Vegetation Science
Journal of Vegetation Science
Volume 25, Issue 1, pages 248–261, January 2014
How to Cite
Fry, E. L., Power, S. A., Manning, P. (2014), Trait-based classification and manipulation of plant functional groups for biodiversity–ecosystem function experiments. Journal of Vegetation Science, 25: 248–261. doi: 10.1111/jvs.12068
- Issue published online: 16 DEC 2013
- Article first published online: 15 MAR 2013
- Manuscript Accepted: 9 FEB 2013
- Manuscript Received: 12 JAN 2012
- Big Lottery Fund's Open Air Laboratories Project
- NERC PopNET
- Ecosystem function;
- Functional effects traits;
- Plant functional groups;
- Temperate grasslands
Biodiversity–ecosystem function (BDEF) experiments commonly group species into arbitrary a priori functional groups, e.g. the grass/forb/legume (GFL) classification. As a result, the causes of functional group diversity effects are often poorly understood. This paper presents a new process that uses functional trait data to create customized plant functional groups that can be tailored to address specific questions. This method is illustrated throughout with an example taken from a temperate mesotrophic grassland in southern England.
Silwood Park, Berkshire, UK.
The method described applies divisive hierarchical cluster analysis to plant functional trait data (from either field or greenhouse conditions) in order to cluster species into a user-specified number of groups. In our example, this was done using unweighted traits with clear links to C and N cycling. To ensure between-group variance had been maximized, we used a linear discriminant analysis. ANOVA should also be used to compare the mean trait values of groups, in order to make specific hypotheses regarding the effect that each group has upon ecosystem functioning. We compared the resulting groups with the GFL classification to see which was more likely to deliver functionally distinct groups.
The resulting groups had discrete functional characteristics, so simple hypotheses could be formulated. These groups also appeared to show stronger trait value differences than the GFL classification. Results from the experiment demonstrate that hypothesized removal effects on function were supported, thus validating our approach.
The method described is applicable to a wide range of communities and is able to recognize functionally distinct groups of species. General use of this approach could result in a more mechanistic understanding of biodiversity–ecosystem function relationships as it can establish experimentally validated links between functional effects traits and ecosystem functioning.