Functional trait metrics are sensitive to the completeness of the species' trait data?
- Functional diversity (FD) is an important concept for studies of both ecosystem processes and community assembly, so it is important to understand the behaviour of common metrics used to express it.
- Data from an existing study of the relationship between FD and environmental drivers were used to simulate the impact of a progressive failure to measure the traits of all the species present under three scenarios: intraspecific variation between sites ignored (i), assessed (ii) or (iii) ignored but with metrics calculated at the sampling unit rather than the site level.
- All the FD metrics were highly sensitive to failing to measure the traits of all the species present. Functional dispersion, functional richness and Rao's entropy all generally declined with a reduced proportion of species or cover assessed for traits, whilst functional divergence and evenness increased for some sites and decreased for others. Functional richness was the most sensitive (mean absolute deviation at 70% of species assessed had a range of 11·2–28·2% across scenarios), followed by functional evenness (range 6·4–38·5%), functional divergence (5·2–8·3%), Rao's entropy (1·4–7·0%) and functional dispersion (0·7–3·5%).
- It is clear that failing to measure the traits of all species at a site can have a serious impact on the value of any functional trait metric computed and on any conclusions drawn from such data. Future studies of FD need to concentrate on the potential impact of the sampling regime of both traits and species and the scale at which the computations are made on the behaviour of metrics and subsequent robustness of the results.