Evaluating Functional Diversity: Missing Trait Data and the Importance of Species Abundance Structure and Data Transformation.
Evaluating Functional Diversity: Missing Trait Data and the Importance of Species Abundance Structure and Data Transformation.
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DOI：
10.1371/journal.pone.0149270
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
de Bello F
中科院分区：
文献类型：
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作者：
Májeková M;Paal T;Plowman NS;Bryndová M;Kasari L;Norberg A;Weiss M;Bishop TR;Luke SH;Sam K;Le Bagousse-Pinguet Y;Lepš J;Götzenberger L;de Bello F
功能多样性（Functional diversity， FD）是生物多样性的重要组成部分，用于量化生物间功能性状的差异。然而，FD研究往往受到特征数据可用性的限制，FD指数对数据缺口很敏感。在数据不完整的情况下，物种丰度和性状数据的分布及其转换会进一步影响指标的准确性。利用现有的方法，我们通过逐步从植物、蚂蚁和鸟类群落数据集中（分别包含62、297和238个物种的12、59和8个样地）中删除数据来模拟缺失性状数据的影响。我们根据从完整数据集计算的FD值对图进行排序，然后从我们越来越不完整的数据集计算FD值，并比较原始数据集和几乎减少的数据集之间的排名，以评估FD指数在数据缺失越来越多的数据集上使用时的准确性。最后，对FD指数进行了数据转换和不进行数据转换的准确性检验，并对每样地或整个物种池缺失性状数据的影响进行了检验。随着缺失数据量的增加，FD指数变得不那么准确，准确性的损失取决于指数。但是，当转换改善了性状数据的正态性时，来自不完整数据集的FD值比转换前更准确。因此，数据的分布及其转换与数据完整性一样重要，甚至可以减轻丢失数据的影响。由于缺失性状值的影响取决于数据分布，因此该方法应根据具体情况确定。在设计、分析和解释FD研究时，特别是在缺少性状数据的情况下，应该更加仔细地考虑数据分布和数据转换。为此，我们提供了R包“叛徒”，以方便评估缺失的性状数据。
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.