A species-level trait dataset of bats in Europe and beyond.
Jérémy S P FroidevauxNia ToshkovaLuc BarbaroAna Benítez-LópezChristian KerbiriouIsabelle Le ViolMichela PacificiLuca SantiniClare StawskiDanilo RussoJasja DekkerAntton AlberdiFrancisco AmorimLeonardo AncillottoKévin BarréYves BasLisette Cantú-SalazarDina K N DechmannTiphaine DevauxKatrine EldegardSasan FereidouniJoanna FurmankiewiczDaniela HamidovićDavina L HillCarlos F IbáñezJean-François JulienJavier JustePeter KaňuchCarmi KorineAlexis LaforgeGaëlle LegrasCamille LerouxGrzegorz LesińskiLéa MaritonJulie MarmetVanessa A MataClare M MifsudVictoria NistreanuRoberto Novella-FernandezHugo RebeloNiamh RocheCharlotte RoemerIreneusz RuczyńskiRune SøråsMarcel UhrinAdriana VellaChristian C VoigtOrly RazgourPublished in: Scientific data (2023)
Knowledge of species' functional traits is essential for understanding biodiversity patterns, predicting the impacts of global environmental changes, and assessing the efficiency of conservation measures. Bats are major components of mammalian diversity and occupy a variety of ecological niches and geographic distributions. However, an extensive compilation of their functional traits and ecological attributes is still missing. Here we present EuroBaTrait 1.0, the most comprehensive and up-to-date trait dataset covering 47 European bat species. The dataset includes data on 118 traits including genetic composition, physiology, morphology, acoustic signature, climatic associations, foraging habitat, roost type, diet, spatial behaviour, life history, pathogens, phenology, and distribution. We compiled the bat trait data obtained from three main sources: (i) a systematic literature and dataset search, (ii) unpublished data from European bat experts, and (iii) observations from large-scale monitoring programs. EuroBaTrait is designed to provide an important data source for comparative and trait-based analyses at the species or community level. The dataset also exposes knowledge gaps in species, geographic and trait coverage, highlighting priorities for future data collection.
Keyphrases
- genome wide
- electronic health record
- big data
- healthcare
- dna methylation
- climate change
- systematic review
- genetic diversity
- human health
- weight loss
- public health
- physical activity
- risk assessment
- mental health
- data analysis
- machine learning
- multidrug resistant
- gram negative
- drinking water
- gene expression
- deep learning