BeeFunc, a comprehensive trait database for French bees

Given pollinator's reported decline, it is of utmost importance to better understand the vulnerability of wild bees to human pressures. One way to achieve this goal is to explore how their traits are associated with exposure to anthropogenic perturbations. To date, there is no database synthesizing traits of bees at the species level in France, limiting the functional interpretation of inventories. We present BeeFunc 1.0, the first database on traits for the entire fauna of French wild bees. Based on extensive literature research and expert knowledge, the database is structured according to the French taxonomic register (TAXREF) and its associated knowledge base. The base gathers 26,176 trait information from 483 sources, describing 932 species for 20 features related to morphology, ecology, biogeography, and conservation. BeeFunc is intended to be collaborative and regularly updated. Bees can finally be better considered from a functional perspective. We expect this database to be widely used by researchers, conservationists, naturalists, and stakeholders, stimulating future research on wild bees.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-07-26
DOI
https://doi.org/10.1038/s41597-025-05626-0
Citations
6
Primary Topic
Plant and animal studies
Type
article
Field-Weighted Citation Impact
18.16

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

BeeFunc, a comprehensive trait database for French bees

Olivier Gargominy, Bertrand Schatz, Benoît Geslin, Sandrine Tercerie et al.
6 citations
Scientific Data
Plant and animal studies
18.16
article

BeeFunc, a comprehensive trait database for French bees

Olivier Gargominy, Bertrand Schatz, Benoît Geslin, Sandrine Tercerie, David Genoud, Lucas Aubouin, Bérénice Givord-Coupeau
article en
6 citations

Abstract

Given pollinator's reported decline, it is of utmost importance to better understand the vulnerability of wild bees to human pressures. One way to achieve this goal is to explore how their traits are associated with exposure to anthropogenic perturbations. To date, there is no database synthesizing traits of bees at the species level in France, limiting the functional interpretation of inventories. We present BeeFunc 1.0, the first database on traits for the entire fauna of French wild bees. Based on extensive literature research and expert knowledge, the database is structured according to the French taxonomic register (TAXREF) and its associated knowledge base. The base gathers 26,176 trait information from 483 sources, describing 932 species for 20 features related to morphology, ecology, biogeography, and conservation. BeeFunc is intended to be collaborative and regularly updated. Bees can finally be better considered from a functional perspective. We expect this database to be widely used by researchers, conservationists, naturalists, and stakeholders, stimulating future research on wild bees.

Scientific DataVol. 12(1)
Centre National de la Recherche Scientifique (FR), École Pratique des Hautes Études (FR), Université de Montpellier (FR), Artelia (France) (FR), Université Rennes 2 (FR), Centre d'Écologie Fonctionnelle et Évolutive (FR), Muséum national d'Histoire naturelle (FR), Universidad de Guantánamo (CU), Institut de Recherche pour le Développement (FR), Université de Rennes (FR)
Institut de Recherche pour le Développement, Office Français de la Biodiversité, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Zero hunger
Openalex Percentile: Top 1%
Plant and animal studies
18.16
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.