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

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 (alpha diversity) and between-individual components (beta 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.生物体表型的绝大多数组成部分均可视为多种性状的表达产物。其中诸多性状以复合体形式存在，其内部的多个附属组分协同行使功能并共同演化。随着性状复杂度不断提升，以直观且符合生物学意义的方式描述复杂度的难度也随之增大。传统多变量分析往往忽略个体复杂度这一现象，仅能对个体间的变异提供相对抽象的表征。我们建议采用群落生态学中成熟的多样性指数，将表型复杂度定义为某一性状内不同附属组分的多样性。借助层级化分析框架，我们阐明了如何将总性状多样性划分为个体内部复杂度（α多样性（alpha diversity））与个体间组分（β多样性（beta diversity））。该方法可作为传统多变量分析的补充手段。本研究的核心创新之处在于两点：其一，将个体复杂度纳入与个体间变异同源的统一分析框架；其二，提出分组式拆分方法，作为传统多样性层级拆分手段的补充。这种“以多样性表征复杂度”的方法在诸多领域均具备应用潜力，涵盖生理生态学、生态与群落基因组学以及转录组学等方向。我们通过化学生态学与微生物生态学的相关实例，验证了该“以多样性表征复杂度”方法的实用性。这些实例展现出标准分析方法无法捕捉到的、具备生物学意义的复杂度与多样性差异。