Functional diversity (FD) is an important component of biodiversity that quantifies the difference
in functional traits between organisms. However, FD studies are often limited by the
availability of trait data and FD indices are sensitive to data gaps. The distribution of species
abundance and trait data, and its transformation, may further affect the accuracy of indices
when data is incomplete. Using an existing approach, we simulated the effects of missing
trait data by gradually removing data from a plant, an ant and a bird community dataset
(12, 59, and 8 plots containing 62, 297 and 238 species respectively). We ranked plots by
FD values calculated from full datasets and then from our increasingly incomplete datasets
and compared the ranking between the original and virtually reduced datasets to assess the
accuracy of FD indices when used on datasets with increasingly missing data. Finally, we
tested the accuracy of FD indices with and without data transformation, and the effect of
missing trait data per plot or per the whole pool of species. FD indices became less accurate
as the amount of missing data increased, with the loss of accuracy depending on the index.
But, where transformation improved the normality of the trait data, FD values from incomplete
datasets were more accurate than before transformation. The distribution of data and its transformation are therefore as important as data completeness and can even mitigate
the effect of missing data. Since the effect of missing trait values pool-wise or plot-wise
depends on the data distribution, the method should be decided case by case. Data distribution
and data transformation should be given more careful consideration when designing,
analysing and interpreting FD studies, especially where trait data are missing. To this end,
we provide the R package “traitor” to facilitate assessments of missing trait data.
S1 Appendix. Study sites and sampling methods. Detailed description of the sampling and
trait collection in the three communities.
S2 Appendix. Results of the linear mixed effect models. Tables A1 –A5 presenting results of
all linear mixed effects models.
S1 Dataset. Data used for the analysis. Abundance and trait data for our plant, ant, and bird