Data and code from: The functional trait distinctiveness of plant species is scale dependent

Beyond the local abundance of species, their functional trait distinctiveness is now recognized as a key driver of community dynamics and ecosystem functioning. Yet, since the functional distinctiveness of a species is always relative to a given species pool, a species distinct at the regional scale might not necessarily be distinct at the local or community scale, and reciprocally. To assess the importance of scale (i.e the definition of a species pool) when quantifying the functional distinctiveness of species, and how it might distort the ecological conclusions derived from it, we quantified trait distinctiveness of 1,350 plant species at regional, local, and community scales over ca. 88 000 grassland plots in France. We measured differences in functional distinctiveness of species between regional, local and community scales and tested the influence of environmental predictors (climate and nitrogen input) and contexts (environmental distinctiveness, frequency, and heterogeneity) on ..., Please see the README document and the accompanying published article: Gaüzère et al., 2022. The functional trait distinctiveness of plant species is scale dependent. Ecography.
For details on the SIFLORE data, please refer to :  
Just, A. et al. 2015. SIFlore, a dataset of geographical distribution of vascular plants covering five centuries of knowledge in France: Results of a collaborative project coordinated by the Federation of the National Botanical Conservatories. - PhytoKeys: 47–60.
Data can be found at http://siflore.fcbn.fr 
For details on the DIVGRASS data, please refer to :
Violle, C. et al. 2015. Vegetation ecology meets ecosystem science: Permanent grasslands as a functional biogeography case study. - Sci. Total Environ. 534: 43–51.
Unfortunately, divgrass data are not openly accessible but can be accessed upon request at [e-mail]
All the data needed to reproduce the results are provided in this repository. , All the analyses were run in R, using several packages listed in each scripts.
See the README document for more details.Â