Published March 21, 2025 | Version v1
Data paper Open

TraitCH: a multi-taxa functional trait dataset for Switzerland and Europe

Authors/Creators

  • 1. ROR icon Swiss Federal Institute of Aquatic Science and Technology
  • 2. ROR icon Swiss Federal Institute for Forest, Snow and Landscape Research
  • 3. Research Center for Advanced Science and Technology (RCAST), The University of Tokyo
  • 4. ROR icon University of Bayreuth
  • 5. ROR icon University of Reading
  • 6. ROR icon Technical University of Munich
  • 7. Eidgenössische Forschungsanstalt für Wald Schnee
  • 8. Centre Alpien de Phytogéographie, Fondation du Jardin botanique Flore-Alpe
  • 9. ROR icon University of Geneva
  • 10. ROR icon University of Zurich
  • 11. Eawag: Swiss Federal Institute of Aquatic Science and Technology
  • 12. ETH Zurich

Description

Description: TraitCH is a comprehensive dataset of functional traits spanning over 71,000 species across 17 major taxonomic groups. Compiled from 43 published and unpublished sources, TraitCH provides a robust representation of total species richness and composition for Switzerland and Europe.

Main Types of Variables Contained: For each species, we compiled their taxonomic hierarchy (Genus, Family, Order, Class, Phylum), existing synonymy, geographic origin (Swiss and European), conservation status (Swiss, European and IUCN Red Lists), micro- and macro-habitat types, global range size (km2) and available ecological trait values. TraitCH consists of 17 trait tables (one per major taxonomic group), each available in two formats: (1) original and (2) completed versions with missing trait values imputed using a tree-based modelling method.

Spatial Location and Grain: TraitCH was integrated with a comprehensive checklist of European species (~210,000), encompassing authoritative Swiss and European checklists as primary reference sources, with the exception of Fungi and Lichen, for which only Swiss checklists were available.

Time Period and Grain: This dataset incorporates trait information published between 2004 and 2025.

Major Taxa and Level Measurement: 71,874 species across 17 taxonomic groups: Apocrita (2,278), Arachnida (3,728), Coleoptera (8,565), Ephemeroptera/Plecoptera/Trichoptera (1,349), Lepidoptera (3,757), Odonata (234), Orthoptera (1,283), Bryobiotina (2,285), Fungi (12,469), Lichen (2,435), Mollusca (7,493), Pisces (838), Amphibia (151), Aves (1,356), Mammalia (522), Reptilia (298), and Tracheophyta (22,833).

Software Format: Space-delimited text file. Please read the readme file for dataset details and description. For imputed trait data, "S_MEAN*" (model averaged) version is to be employed.

Files

x_TraitCH_1.0.zip

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Additional details

Related works

Continues
Dataset: 10.5281/zenodo.13253601 (DOI)

Funding

ETH Zurich
ETH-Board Joint Initiatives scheme SPEED2ZERO
Swiss National Science Foundation
SNSF 310030_197410
Swiss National Science Foundation
Postdoc.Mobility P500PB_225432
Swiss National Science Foundation
Postdoc.Mobility P500PN_217754

Software

Repository URL
https://github.com/8Ginette8/TraitCH
Programming language
R
Development Status
Active

References

  • 1. Gossner, M. M. et al. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Sci Data 2, 150013 (2015).
  • 2. Hagge, J. et al. What does a threatened saproxylic beetle look like? Modelling extinction risk using a new morphological trait database. Journal of Animal Ecology 90, 1934–1947 (2021).
  • 3. Waller, J. T., Willink, B., Tschol, M. & Svensson, E. I. The odonate phenotypic database, a new open data resource for comparative studies of an old insect order. Sci Data 6, 316 (2019).
  • 4. Middleton-Welling, J. et al. A new comprehensive trait database of European and Maghreb butterflies, Papilionoidea. Sci Data 7, 351 (2020).
  • 4. Shirey, V. et al. LepTraits 1.0 A globally comprehensive dataset of butterfly traits. Sci Data 9, 382 (2022).
  • 6. Klaiber, J. et al. Fauna Indicativa. WSL Berichte 54. 198 S. vol. 54 (Birmensdorf, 2017).
  • 7. Logghe, G. et al. An in-depth dataset of northwestern European arthropod life histories and ecological traits. Biodivers Data J 13, (2025).
  • 8. Mammola, S. et al. A trait database and updated checklist for European subterranean spiders. Sci Data 9, 236 (2022).
  • 9. Casanelles-Abella, J., Fontana, S., Meier, E. S., Moretti, M. & Fournier, B. Spatial mismatch between wild bee diversity hotspots and protected areas. Conservation Biology 37, e14082 (2023).
  • 10. Roberts, S. Pollinator loss module of the EU- FP6 ALARM project, version 01. (2017).
  • 11. Pekár, S. et al. The World Spider Trait database: a centralized global open repository for curated data on spider traits. Database 2021, (2021).
  • 12. van Zuijlen, K. et al. Bryophytes of Europe Traits (BET) data set: A fundamental tool for ecological studies. Journal of Vegetation Science 34, (2023).
  • 13. Hofmann, H. et al. Voilà: die neue Website der Moosflora der Schweiz ist da! Meylania 47, 42–43 (2011).
  • 14. Frimpong, E. A. & Angermeier, P. L. Fish Traits: A Database of Ecological and Life‐history Traits of Freshwater Fishes of the United States. Fisheries (Bethesda) 34, 487–495 (2009).
  • 15. Brosse, S. et al. FISHMORPH: A global database on morphological traits of freshwater fishes. Global Ecology and Biogeography 30, 2330–2336 (2021).
  • 16-17. Schmidt-Kloiber, A. & Hering, D. www.freshwaterecology.info – An online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol Indic 53, 271–282 (2015).
  • 18. Gross, A., Blaser, S. & Senn-Irlet, B. SwissFungi - Records Database for the Fungi of Switzerland. (2020).
  • 19. Zanne, A. E. et al. Fungal functional ecology: bringing a trait‐based approach to plant‐associated fungi. Biological Reviews 95, 409–433 (2020).
  • 20. Stofer, S. et al. SwissLichens - Webatlas Der Flechten Der Schweiz (Version 3 & Current Date in the Form of 14.12.2023). (WSL, Birmensdorf, 2019).
  • 21. Stofer, S. et al. LIAS light – A Database for Rapid Identification of Lichens – Subset Switzerland p. pte. Zenodo (2024) doi:10.5281/zenodo.13253601.
  • 22. Rambold, G. et al. Geographic heat maps of lichen traits derived by combining LIAS light description and GBIF occurrence data, provided on a new platform. Biodivers Conserv 25, 2743–2751 (2016).
  • 23. Saucy, G., Rüetschi, J., Richling, I., Heer, N. & Claude, F. Fauna Indicativa - Tabelle Der Mollusken. (info fauna, 2024).
  • 24. Tachet, H., Bournaud, M., Richoux, P. & Usseglio-Polatera, P. Invertébrés d'eau Douce - Systématique, Biologie, Écologie. Accessed through www.freshwaterecology.info - the taxa and autecology database for freshwater organisms, version 8.0 (accessed on 02.10.2024) (CNRS Editions, Paris, 2010).
  • 25. Wilman, H. et al. EltonTraits 1.0: Species‐level foraging attributes of the world's birds and mammals. Ecology 95, 2027–2027 (2014).
  • 26. Cox, D. T. C., Gardner, A. S. & Gaston, K. J. Diel niche variation in mammals associated with expanded trait space. Nat Commun 12, 1753 (2021).
  • 27. Soria, C. D., Pacifici, M., Di Marco, M., Stephen, S. M. & Rondinini, C. COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. Ecology 102, (2021).
  • 28. Genoud, M., Isler, K. & Martin, R. D. Comparative analyses of basal rate of metabolism in mammals: data selection does matter. Biological Reviews 93, 404–438 (2018).
  • 29. Oskyrko, O., Mi, C., Meiri, S. & Du, W. ReptTraits: a comprehensive dataset of ecological traits in reptiles. Sci Data 11, 243 (2024).
  • 30. Meiri, S. Traits of lizards of the world: Variation around a successful evolutionary design. Global Ecology and Biogeography 27, 1168–1172 (2018).
  • 31. Huang, N., Sun, X., Song, Y., Yuan, Z. & Zhou, W. Amphibian traits database: A global database on morphological traits of amphibians. Global Ecology and Biogeography 32, 633–641 (2023).
  • 32. Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. & Costa, G. C. AmphiBIO, a global database for amphibian ecological traits. Sci Data 4, 170123 (2017).
  • 33. Tobias, J. A. et al. AVONET: morphological, ecological and geographical data for all birds. Ecol Lett 25, 581–597 (2022).
  • 34. Jetz, W., Sekercioglu, C. H. & Böhning-Gaese, K. The Worldwide Variation in Avian Clutch Size across Species and Space. PLoS Biol 6, e303 (2008).
  • 35. Landolt, E. et al. Flora Indicativa: Okologische Zeigerwerte Und Biologische Kennzeichen Zur Flora Der Schweiz Und Der Alpen. (Haupt., Bern, 2010).
  • 36. Díaz, S. et al. The global spectrum of plant form and function: enhanced species-level trait dataset. Sci Data 9, 755 (2022).
  • 37. Lososová, Z. et al. Seed dispersal distance classes and dispersal modes for the European flora. Global Ecology and Biogeography 32, 1485–1494 (2023).
  • 38. Chauvier, Y. et al. Influence of climate, soil, and land cover on plant species distribution in the European Alps. Ecol Monogr 91, e01433 (2021).
  • 39. Chauvier, Y. et al. Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity. Ecography 2022, e05973 (2022).
  • 40. Chauvier-Mendes, Y. et al. Transnational conservation to anticipate future plant shifts in Europe. Nat Ecol Evol 8, 454–466 (2024).
  • 41. Moura, M. R. et al. A phylogeny-informed characterisation of global tetrapod traits addresses data gaps and biases. PLoS Biol 22, e3002658 (2024).
  • 42. Aeschimann, D., Lauber, K., Moser, D. M. & Theurillat, J. P. Flora Alpina: Ein Atlas Sämtlicher 4500 Gefässpflanzen Der Alpen. (Haupt, Bern, 2004).
  • 43. Dengler, J. et al. Ecological Indicator Values for Europe (EIVE) 1.0. Vegetation Classification and Survey 4, 7–29 (2023).