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 simultaneou...

Full description

Bibliographic Details
Main Authors: Chalmandrier, Loïc, Münkemüller, Tamara, Devictor, Vincent, Lavergne, Sébastien, Thuiller, Wilfried
Format: Dataset
Language:unknown
Published: Data Archiving and Networked Services (DANS) 2015
Subjects:
Online Access:https://doi.org/10.5061/dryad.93n5r
Description
Summary: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. DataThe datafile contains five files: (1) The site by species matrices of the four studied regions. Row labels indicate the identity of the site and the year of sampling separated by "_". (2) the functional tree of 217 bird species from France in Newick format, see supplementary material for details about its construction.