Trait-based classification and manipulation of plant functional groups for biodiversity-ecosystem function experiments

• Published in: Journal of vegetation science Vol. 25; no. 1; pp. 248 - 261
• Main Authors: Fry, Ellen L., Power, Sally A., Manning, Pete
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01-01-2014; Blackwell
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Biodiversity; Biological and medical sciences; Cluster analysis; Ecological function; Ecosystem function; Ecosystems; Functional diversity; Functional effects traits; Functional groups; Fundamental and applied biological sciences. Psychology; Linear discriminant analysis; Plant biodiversity; Plant ecology; Plant functional groups; Plants; Synecology; Temperate grasslands; Vegetation; British Isles, England; British Isles, England, Berkshire; Grassland; Functional trait; Ecosystem function; Plant ecology; Biodiversity; Temperate zone; Functionality; Manipulation; Classification; Temperate grasslands; Experimentation; Plant functional groups; Ecosystem functioning; Plant sciences; Functional effects traits; Functional group
• ISSN: 1100-9233; 1654-1103
• DOI: 10.1111/jvs.12068

Aim: 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. Location: SilwoodPark, Berkshire, UK. Methods: 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. Results: 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. Conclusions: 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.