Abstract
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.
Highlights
Functional trait-based approaches are increasingly used in ecology for understanding the environmental and evolutionary processes underlying biological diversity [1,2]
functional evenness (FEve) was more sensitive to missing trait information for the pool-wise scenario than for the plot-wise scenario
In the plot-wise scenario Rao’s quadratic entropy index (RaoQ) was less sensitive to missing trait information, and community weighted mean of traits (CWM), FEve, and functional richness (FRic) were more similar in both sampling scenarios (Fig 3B)
Summary
Functional trait-based approaches are increasingly used in ecology for understanding the environmental and evolutionary processes underlying biological diversity [1,2]. While traditional measures of biodiversity encompass the richness and abundance of organisms in an ecosystem, trait-based studies can provide additional information on their functions. A functional approach can allow generalizations beyond taxa and biogeographical regions, and can reveal both how species coexist together and how they might affect multiple ecosystem processes [4,5]. The more species for which trait data are available, the more FD indices will reflect the real community values [7]. It is common to be missing trait data for rare species, and they are generally the first to be omitted in incomplete datasets [7]. The omission of the rare species first is mainly because the most abundant species are expected to have the most functional influence on ecosystem functioning (see 'mass ratio hypothesis'[8] and [9]) and are sampled with higher priority
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
