Data from: Individual-level trait diversity concepts and indices to comprehensively describe community change in multidimensional trait space

1. Global environmental change can influence ecosystem processes directly or through changes in the trait composition of natural communities. Traits are individual-level features of organisms, and theory predicts that diversity in traits should relate to ecosystem processes. Validated indices that account for both intra- and interspecific trait variation in multidimensional trait space are lacking. In this article we highlight how an individual-level perspective requires new concepts for trait diversity (TD) and we validate a set of measures suitable to study trait richness, evenness and divergence at the individual scale. 2. First, we tested a selection of multivariate indices for trait richness, evenness and divergence from the literature (FRic, FEve, FDis and the Rao coefficient) using simulated and real individual-level data. We compared the observed changes in the tested indices with those predicted from their expected/required behaviour (that is, increase or decrease under specific manipulation of community trait structure) and found unsatisfactory results only for FRic and FEve, whereas FDis and the Rao coefficient showed the expected changes. 3. Therefore, we propose two novel concepts and related indices for individual-level trait richness (TOP = Trait Onion Peeling) and evenness (TED = Trait Even Distribution). TOP represents the sum of all successive convex hull areas touching all individuals (points) within a multidimensional trait distribution. TED is a measure of how evenly distributed are individuals within the multidimensional trait space. It is calculated comparing the probability distributions of pairwise distances between individuals and between points of a perfectly even reference distribution. We tested TOP and TED on the same simulated and real data as above, and results indicated appropriate behaviour for TOP (trait richness) and TED (trait evenness). 4. By validating TD indices in an individual-level context, the present study contributes to the expansion of functional ecology towards individual-level dynamics. Future comprehensive investigations of individual trait differences in natural communities may improve our understanding of the pathways by which environmental changes affect ecosystem functioning through biodiversity change.

1. 全球环境变化可直接或通过改变自然群落的性状组成，对生态系统过程产生影响。性状（trait）是生物体的个体水平特征，理论预测性状多样性（trait diversity, TD）应与生态系统过程相关。目前尚缺乏能在多维性状空间中同时考虑种内和种间性状变异的已验证指数。本文阐述了个体水平视角下性状多样性需全新的概念框架，并验证了一套适用于个体尺度下性状丰富度、均匀度和分异度研究的量化方法。2. 首先，我们利用模拟及真实个体水平数据，对文献中已有的一批用于表征性状丰富度、均匀度和分异度的多元指数（FRic、FEve、FDis及Rao系数）开展了测试。我们将测试指数的观测变化与理论预期行为（即群落性状结构经特定操控后的增减趋势）进行对比，结果仅FRic与FEve的表现未达预期，而FDis及Rao系数则呈现出符合预期的变化趋势。3. 据此，我们提出了两个适用于个体水平性状丰富度的新概念及对应量化指数：TOP（Trait Onion Peeling，性状洋葱剥皮法）与性状均匀度指数TED（Trait Even Distribution，性状均匀分布法）。TOP指的是多维性状分布中，覆盖所有个体（数据点）的连续凸包面积之和。TED用于衡量多维性状空间内个体的分布均匀程度，其计算方式为对比个体间两两距离的概率分布与完全均匀分布参考点的两两距离概率分布。我们利用前述相同的模拟及真实数据对TOP与TED进行了测试，结果显示二者分别在表征性状丰富度与均匀度时表现良好。4. 本研究通过在个体水平框架下验证性状多样性指数，推动功能生态学向个体水平动态研究方向拓展。未来针对自然群落中个体性状差异的系统性研究，将有助于深化我们对环境变化通过生物多样性变化影响生态系统功能的作用路径的认知。