Data from: Decomposing changes in phylogenetic and functional diversity over space and time

1. The α, β, γ diversity decomposition methodology is commonly used to investigate changes in diversity over space or time but rarely conjointly. However, with the ever-increasing availability of large-scale biodiversity monitoring data, there is a need for a sound methodology capable of simultaneously accounting for spatial and temporal changes in diversity. 2. Using the properties of Chao's index, we adapted Rao's framework of diversity decomposition between orthogonal dimensions to a multiplicative α, β, γ decomposition of functional or phylogenetic diversity over space and time, thereby combining their respective properties. We also developed guidelines for interpreting both temporal and spatial β-diversities and their interaction. 3. We characterised the range of β-diversity estimates and their relationship to the nested decomposition of diversity. Using simulations, we empirically demonstrated that temporal and spatial β-diversities are independent from each other and from α and γ-diversities when the study design is balanced, but not otherwise. Furthermore, we showed that the interaction term between the temporal and the spatial β-diversities lacked such properties. 4. We illustrated our methodology with a case study of the spatio-temporal dynamics of functional diversity in bird assemblages in four regions of France. Based on these data, our method makes it possible to discriminate between regions experiencing different diversity changes in time. Our methodology may therefore be valuable for comparing diversity changes over space and time using large-scale datasets of repeated surveys.

1. α、β、γ多样性分解方法（α, β, γ diversity decomposition methodology）通常被用于探究空间或时间维度上的多样性变化，但极少同时兼顾二者。然而，随着大规模生物多样性监测数据的可用性不断提升，亟需一套可靠的方法学，能够同时考量多样性在空间和时间上的变化。2. 借助Chao指数（Chao's index）的特性，我们将Rao的正交维度多样性分解框架（Rao's framework of diversity decomposition between orthogonal dimensions）适配为可覆盖空间与时间的功能多样性（functional diversity）或系统发育多样性（phylogenetic diversity）的乘性α、β、γ分解模型，从而整合了两类方法的各自优势。此外，我们还构建了针对时间、空间β多样性（β-diversities）及其交互效应的解读指南。3. 我们刻画了β多样性估计值的分布区间，及其与多样性嵌套分解的关联。通过模拟实验，我们实证证明：当研究设计平衡时，时间与空间β多样性彼此独立，且与α、γ多样性相互独立；反之则不成立。进一步研究发现，时间与空间β多样性的交互项并不具备此类独立特性。4. 我们以法国四个区域的鸟类群落（bird assemblages）功能多样性时空动态（spatio-temporal dynamics of functional diversity）为案例研究，演示了所提方法的应用效果。基于该数据集，我们的方法能够区分不同区域在时间维度上的多样性变化模式。因此，本方法可用于依托大规模重复调查数据集，开展空间与时间维度上的多样性变化对比研究。