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Data from: Extending the concept of diversity partitioning to characterize phenotypic complexity

Citation

Marion, Zachary H.; Fordyce, James A.; Fitzpatrick, Benjamin M. (2015), Data from: Extending the concept of diversity partitioning to characterize phenotypic complexity, Dryad, Dataset, https://doi.org/10.5061/dryad.7vh21

Abstract

Most components of an organism’s phenotype can be viewed as the expression of multiple traits. Many of these traits operate as complexes, where multiple subsidiary parts function and evolve together. As trait complexity increases, so does the challenge of describing complexity in intuitive, biologically meaningful ways. Traditional multivariate analyses ignore the phenomenon of individual complexity and provide relatively abstract representations of variation among individuals. We suggest adopting well-known diversity indices from community ecology to describe phenotypic complexity as the diversity of distinct subsidiary components of a trait. Using a hierarchical framework, we illustrate how total trait diversity can be partitioned into within-individual complexity (α diversity) and between-individual components (β diversity). This approach complements traditional multivariate analyses. The key innovations are (i) addition of individual complexity within the same framework as between-individual variation and (ii) a group-wise partitioning approach that complements traditional level-wise partitioning of diversity. The complexity-as-diversity approach has potential application in many fields, including physiological ecology, ecological and community genomics, and transcriptomics. We demonstrate the utility of this complexity-as-diversity approach with examples from chemical and microbial ecology. The examples illustrate biologically significant differences in complexity and diversity that standard analyses would not reveal.

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Location

United States
Southeastern United States
Western United States
Michigan