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Bernard Amiaud

Researcher at University of Lorraine

Publications -  55
Citations -  2399

Bernard Amiaud is an academic researcher from University of Lorraine. The author has contributed to research in topics: Species richness & Vegetation. The author has an hindex of 21, co-authored 54 publications receiving 1581 citations. Previous affiliations of Bernard Amiaud include University of Rennes & Nancy-Université.

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Journal ArticleDOI

TRY plant trait database : Enhanced coverage and open access

Jens Kattge, +754 more
TL;DR: The extent of the trait data compiled in TRY is evaluated and emerging patterns of data coverage and representativeness are analyzed to conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements.
Journal ArticleDOI

Mapping local and global variability in plant trait distributions

Ethan E. Butler, +54 more
TL;DR: Using the largest global plant trait database and state of the art Bayesian modeling, fine-grained global maps of plant trait distributions that can be applied to Earth system models are created and reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.
Journal ArticleDOI

Factors and processes affecting plant biodiversity in permanent grasslands. A review

TL;DR: In this article, a conceptual model based on three ecological challenges (dispersal, establishment, and persistence) is proposed to study plant diversity in permanent grasslands. But, the model is limited to a single species.
Book ChapterDOI

Biodiversity: Function and Assessment in Agricultural Areas: A Review

TL;DR: The relevance of assessment tools is required in order to understand and evaluate the impact of farm practices on the different compartments of biodiversity at the patch scale to the landscape scale and their suitability and limits are discussed.
Journal ArticleDOI

Towards a thesaurus of plant characteristics: an ecological contribution

TL;DR: Harmonizing definitions of concepts, as proposed by TOP, forms the basis for better integration of data across heterogeneous data sets and terminologies, thereby increasing the potential for data reuse and enhanced scientific synthesis.