Majekova, MariaPaal, TaaviPlowman, Nichola S.Bryndova, MichalaKasari, LiisNorberg, AnnaWeiss, MatthiasBishop, Tom RhysLuke, Sarah H.Sam, KaterinaLe Bagousse-Pinguet, YoannLeps, JanGotzenberger, LarsDe Bello, Francesco2016-05-172016-05-172016-02-16Májeková, M., Paal, T., Plowman, N.S., Bryndová, M., Kasari, L., Norberg, A., et al. (2016) Evaluating Functional Diversity: Missing Trait Data and the Importance of Species Abundance Structure and Data Transformation. PLoS ONE 11(2): e0149270. DOI: 10.1371/journal.pone.0149270.1932-620310.1371/journal.pone.0149270http://hdl.handle.net/2263/52645APPENDIX S1. Study sites and sampling methods. Detailed description of the sampling and trait collection in the three communities.APPENDIX S2. Results of the linear mixed effect models. Tables A1 –A5 presenting results of all linear mixed effects models.DATASET S1. Data used for the analysis. Abundance and trait data for our plant, ant, and bird communities.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.en© 2016 Májeková et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.Functional diversity (FD)OrganismsData distributionTrait dataArticle