Journal
ECOLOGY AND EVOLUTION
Volume 11, Issue 1, Pages 123-132Publisher
WILEY
DOI: 10.1002/ece3.6974
Keywords
biodiversity; functional diversity; functional evenness; nearest‐ neighbor distance
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The metric of functional evenness FEve has several critical conceptual and practical drawbacks, making it not suitable for studying functional variability. Three alternative metrics are recommended, and researchers are encouraged to choose appropriate metrics based on the community properties they want to measure.
The metric of functional evenness FEve is an example of how approaches to conceptualizing and measuring functional variability may go astray. This index has several critical conceptual and practical drawbacks: Different values of the FEve index for the same community can be obtained if the species have unequal species abundances; this result is highly likely if most of the traits are categorical. Very minor differences in even one pairwise distance can result in very different values of FEve. FEve uses only a fraction of the information contained in the matrix of species distances. Counterintuitively, this can cause very similar FEve scores for communities with substantially different patterns of species dispersal in trait space. FEve is a valid metric only if all species have exactly the same abundances. However, the meaning of FEve in such an instance is unclear as the purpose of the metric is to measure the variability of abundances in trait space. We recommend not using the FEve metric in studies of functional variability. Given the wide usage of FEve index over the last decade, the validity of the conclusions based on those estimates is in question. Instead, we suggest three alternative metrics that combine variability in species distances in trait space with abundance in various ways. More broadly, we recommend that researchers think about which community properties (e.g., trait distances of a focus species to the nearest neighbor or all other species, variability of pairwise interactions between species) they want to measure and pick from among the appropriate metrics.
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