the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
TraitCH: a multi-taxa functional trait dataset for Switzerland and Europe
Abstract. Functional traits of species are becoming increasingly used in ecological research, providing key insights into organisms-environment interactions, ecosystem functions, and responses to environmental changes. In recent years, substantial initiatives have generated major open-access datasets of species' functional traits. However, these resources typically concentrate on a handful of well-studied biological groups—such as plants, birds, and fishes—and less on specific biogeographic regions limiting their applicability in regional biodiversity assessments and conservation planning. Here, we present TraitCH, a comprehensive dataset of functional traits spanning over 71,874 species (≥ 1 functional trait) across 17 major 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). Compiled from 43 published and unpublished sources, TraitCH provides a robust representation of total species richness and composition for Switzerland and Europe. For each species, we compiled their taxonomic hierarchy, existing synonymy, geographic origin, conservation status, micro- and macro-habitat types, global range size 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. TraitCH was also embedded within a comprehensive checklist of European species from the same groups (~210,000 taxa), encompassing authoritative Swiss and European checklists, with the exception of Fungi and Lichen, for which only Swiss checklists were available. TraitCH is available on Zenodo: https://doi.org/10.5281/zenodo.15063844 (Chauvier et al., 2025).
- Preprint
(1962 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 25 Apr 2026)
- RC1: 'Comment on essd-2025-754', Stef Bokhorst, 06 Mar 2026 reply
Model code and software
TraitCH Yohann Chauvier-Mendes https://github.com/8Ginette8/TraitCH
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 407 | 214 | 26 | 647 | 37 | 31 |
- HTML: 407
- PDF: 214
- XML: 26
- Total: 647
- BibTeX: 37
- EndNote: 31
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This data paper compiled trait data across different taxonomic groups for Switzerland. Such trait databases can be very useful for modelling work and as such are valuable. It is less clear how this work addressed the main arguments raised at the end of the introduction (lines 60-70). Species names and synonyms are dealt with but it is unclear whether raw trait data was checked, standardized and retrieved from the hard to obtain grey literature.
In addition, I wonder how for instance, taxa morphology is comparable across these taxonomic groups? Is there any point in comparing the morphology of a lichen with that of a fish? The added value of this regional dataset would lie with comparable trait values, but these are currently limited to distribution patterns.
It would be helpful if the ‘significant advancement’ (line 243) of TraitCH is explained in greater detail and how it would outperform in comparison to for instance TRY? Or in other words, what questions can TraitCH address that are not possible with TRY?
Line 45 the ‘while increasingly collected in a standardized manner’ doesn’t link logically to the preceding part of the sentence.
Line 51 please explain abbreviation “TRY” and any others in the ms.
Line 69 unclear if and how this study addressed the issues mentioned above. Did this work address standardizing trait definitions (line 64)? Did this work trawl through the difficult to reach and grey literature (lines 64-65)? Based on the information provided for fungi this work simply used data provided by Zanne et al (a general paper on fungi traits) and Gross et al (a records database) – how does this resolve issues of standardization and grey literature data?
Lines 119-120 I think it could be very valuable if a table is included to explain which variables are included for each trait category. “morphology, life-history, ecological behaviour, environmental niche and habitat of each species” is useful information but can be interpreted in various ways and is not always directly comparable between taxa.
What is the ‘Noun’ project?
Lines 145-150: Was trait aggregation manually checked? Species names/synonyms can be misspelled or otherwise mistakenly labelled and simply averaging trait values can results in values that are incorrect for both species. Did you check for trait value units between studies?