Evaluating functional diversity: Missing trait data and the importance of species abundance structure and data transformation

0456840 - BC 2017 RIV US eng J - Článek v odborném periodiku
Májeková, M. - Paal, T. - Plowman, Nichola S. - Bryndová, Michala - Kasari, L. - Norberg, A. - Weiss, Matthias - Bishop, T. R. - Luke, S. H. - Sam, Kateřina - Le Bagousse-Pinguet, Y. - Lepš, Jan - Götzenberger, Lars - de Bello, Francesco
Evaluating functional diversity: Missing trait data and the importance of species abundance structure and data transformation.
PLoS ONE. Roč. 11, č. 2 (2016), č. článku e0149270. ISSN 1932-6203. E-ISSN 1932-6203
Grant CEP: GA ČR GB14-36098G; GA ČR(CZ) GP14-32024P; GA ČR GAP505/12/1296
Grant ostatní: GA JU(CZ) 156/2013/P
Institucionální podpora: RVO:60077344 ; RVO:67985939
Klíčová slova: data incompleteness * functional diversity * species abundance
Kód oboru RIV: EH - Ekologie - společenstva; EH - Ekologie - společenstva (BU-J)
Impakt faktor: 2.806, rok: 2016
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149270

Functional diversity is a very important component of biodiversity that quantifies the difference in functional traits between organisms. However, functional diversity studies are often limited by the availability of trait data and functional diversity indices are very sensitive to missing data. The distribution of species abundance and trait data, and its transformation, may thus affect the accuracy of indices when data is incomplete. The transformation of the data used to calculate functional diversity indices was very often neglected by authors. Here we show how important the completeness and transformation of the data are. 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. We worked with datasets originating from completely surveyed 12, 59, and 8 plots and containing plant 62, and 297 and bird 238 species respectively. We ranked plots by functional diversity values calculated from full datasets and then from our increasingly incomplete datasets. We compared the ranking between the original and virtually reduced datasets to assess the accuracy of functional diversity indices when used on datasets with increasingly missing data. Finally, we tested the accuracy of functional diversity indices with and without data transformation, and the effect of missing trait data per plot or per the whole pool of species. Functional diversity 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, functional diversity 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.
Trvalý link: http://hdl.handle.net/11104/0257323