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https://doi.org/10.5194/essd-2025-754
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2025-754
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Feb 2026
| 03 Feb 2026
Status: this preprint is currently under review for the journal ESSD.
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 Compiled from 43 published and unpublished sources, TraitCH provides a robust representation of total species richness and composition for Switzerland and Europe.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).
How to cite. Chauvier-Mendes, Y., Adde, A., Bergamini, A., Rambold, G., Stofer, S., Graf, N., Gross, A., Blaser, S., Roberts, S. P. M., Potts, S. G., Casanelles-Abella, J., Moretti, M., Nobis, M. P., Theurillat, J.-P., Hofmann, H., Hartwig, A.-M., Claude, F., Saucy, G., and Altermatt, F.: TraitCH: a multi-taxa functional trait dataset for Switzerland and Europe, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-754, in review, 2026.
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Status: open (until 25 Apr 2026)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- RC1: 'Comment on essd-2025-754', Stef Bokhorst, 06 Mar 2026 reply
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RC2: 'Comment on essd-2025-754', Anonymous Referee #2, 10 Apr 2026
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April 10, 2026
This manuscript describes the compilation of the TraitCH dataset which combines the traits datasets and databases for species primarily found in Switzerland and extending to other species in Europe. The manuscript documents how datasets were subset to fit the study area, how taxonomy was clarified, how trait records were curated, and how missing values were imputed. Evaluations on data coverage taxonomically, regionally, and between traits were then reported.
I wish to highlight a few points for clarification on the motivation and introduction of the work, on the analysis and presentation of the data, and on the quality of the data.
Motivation and introduction
- I suggest that the authors explicitly declare why this dataset was created. Was this dataset created to support a regional ecology project perhaps? Regarding the statement in line 25 that taxa-specific trait resources concentrate on biological groups and “less on specific biogeographic regions limiting their applicability in regional biodiversity assessments and conservation planning”, I think that trait databases being agnostic to biogeographic regions is actually their strength. I do not agree that the regional specificity of trait data resources can be used as a metric for how it can be applied. I would argue that the application of traits in biodiversity assessments and conservation planning should actually motivate compilations towards a global coverage, rather than region-specific as what the statement in line 25 seems to imply. Measuring traits and compiling trait data are expensive and time-consuming work and are often driven by taxa-specific community-driven efforts and the focus on specific taxa is needed to ensure the quality of the trait datasets. Presenting this as a limitation, for instances, when raw trait databases did not fit what is needed for another independent study, is not ideal recognizing the work it took have trait datasets to extract data from.
- I suggest that the authors use caution when narrating the comparison of the taxonomic groups and their respective trait compilation efforts. For example, line 59: “biased toward “charismatic” or well-studied taxa” and line 70: “neglected taxa such as…”. The fact that this study had trait databases to draw from means that the taxonomic group was not “neglected”. The spider traits is an excellent example of a group with have rich trait data. I think that the amount of trait data and size of the trait databases could not be simply generalized to these taxa being “charismatic” or “well-studied”. Yes, there are a lot of plant and fish trait data and these domains have popular databases, but there are many historical factors that could influence that. Note that the reference in line 61 (Troudet et al. 2017) pertains to bias in species occurrence data in GBIF, it is not about traits.
Analysis and presentation of the data
- I suggest that the authors clarify in the text and figures what the basis of the “trait completeness” and “trait coverage” were calculated from.
- Moreover, I strongly encourage the authors to rethink how the number of traits per taxonomic group is quantified, and consequently how the evaluation of data completeness is calculated. It seems like traits are counted according to the number of columns of the data table. Looking at the Amphibian trait table as an example. The number of traits reported in table 1 is 59. However, in the traits_metadata file, there are 34 columns of trait names. Additionally, the Diet_* composition trait is disaggregated to 7 rows/columns but this is a binary matrix of a single trait. Thus, for amphibians (and if habitat and environmental conditions characteristics are to be considered as traits), there are 28 traits to be counted, not 59.
- What are considered as unique traits in this manuscript influences how data completeness is calculated. Such that a single trait name with multiple categories disaggregated as a binary matrix with multiple columns (e.g., diet composition) would have disproportionate influence in the accounting of data completeness compared to individual traits with a single column. This may bias the perception towards making the dataset seem more complete.
- Regarding trait imputation. I recommend that the authors specify if the imputation was based on the compiled Switzerland/European trait databases or from the global trait datasets. If the imputation was based on the regional dataset only, why not use the all the trait records for a taxonomic group? The tree-based imputation might be more accurate if all the trait data for a group were used in the statistics. Even when data is sparse, non-region specific and global trait datasets are important to “capture evolutionary relationships and shared adaptive history” as described I line 188.
- I encourage the authors to discuss how the names of traits and trait categories were harmonized and report which traits in the dataset are similarly named across taxonomic groups and which are not. It would be useful to evaluate this comparison in the discussion.
Quality of the data
- I suggest that the authors justify why environmental or spatial traits such as “Habitat”, “Ecological Indicator Value”, “Proportion of species range map covered…”, average species range temperature/precipitation/etc are considered traits in this work. The definitions for functional traits in the introduction does not necessarily encompass environmental characteristics associated with species occurrence as organismal traits.
- I suggest that the motivation for using static trait records per species be explained in the methods section. The discussion section (lines 254-259) states that intraspecific variability is relevant and the trait datasets used in this study could have contained the information to capture intraspecific variability already but these were aggregated in the data processing for TraitCH, effectively removing the intraspecific variability that is then being recommended back into future research in line 259.
- I recommend that the authors evaluate and report in the Discussion section how this dataset adheres to FAIR data principles.
- Specifically regarding interoperability, I recommend that the authors report which controlled vocabularies and trait definitions were used in harmonizing the dataset.
- Specifically regarding reusability, I suggest that the authors modify how data provenance is preserved. Looking at the files in the folder outputs/raw_traits/, the references or sources of the information are not visible. Broadly listing the data sources in Table 1 is not sufficient. For this dataset to be FAIR, the data tables should contain the data source information and if there were aggregated trait records, list which sources were aggregated.
Citation: https://doi.org/10.5194/essd-2025-754-RC2
Model code and software
TraitCH Yohann Chauvier-Mendes https://github.com/8Ginette8/TraitCH
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Yohann Chauvier-Mendes
CORRESPONDING AUTHOR
Swiss Federal Institute of Aquatic Science and Technology (EAWAG); 8600 Dübendorf, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
Antoine Adde
Swiss Federal Institute of Aquatic Science and Technology (EAWAG); 8600 Dübendorf, Switzerland
Ariel Bergamini
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Gerhard Rambold
Department of Mycology, University of Bayreuth, Bayreuth 95440, Germany
Silvia Stofer
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Ninetta Graf
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Andrin Gross
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Stefan Blaser
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Stuart P. M. Roberts
Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, RG6 6UR Reading, UK
Simon G. Potts
Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, RG6 6UR Reading, UK
Joan Casanelles-Abella
Urban Productive Ecosystems, TUM School of Life Sciences, Hans Carl-von-Carlowitz-Platz 2, Feising 85354, Germany
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Marco Moretti
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Michael P. Nobis
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL); 8903 Birmensdorf, Switzerland
Jean-Paul Theurillat
Centre Alpien de Phytogéographie, Fondation du Jardin botanique Flore-Alpe, 1938 Champex-Lac, Switzerland
Department of Botany and Plant Biology, University of Geneva, 1292 Chambésy, Switzerland
Heike Hofmann
Swissbryophytes, Institut für Systematische und Evolutionäre Botanik, Universität Zürich, Zollikerstrasse, 107, 8008 Zürich, Switzerland
Ann-Michelle Hartwig
Swissbryophytes, Institut für Systematische und Evolutionäre Botanik, Universität Zürich, Zollikerstrasse, 107, 8008 Zürich, Switzerland
Francois Claude
info fauna, Avenue Bellevaux 51, 2000 Neuchâtel, Switzerland
Gauvain Saucy
Dos la Velle 72, 2718 Lajoux, Switzerland
Florian Altermatt
CORRESPONDING AUTHOR
Swiss Federal Institute of Aquatic Science and Technology (EAWAG); 8600 Dübendorf, Switzerland
Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8006 Zürich, Switzerland
Short summary
TraitCH is a large, free database describing how 71,874 species live — their morphologies, habitats, ranges and conservation status — across 17 species groups (from plants and fungi to birds and beetles). Pulled from 43 published/unpublished sources and for Switzerland and Europe, it fills important data gaps and makes it easy for scientists, conservationists and nature lovers to explore and protect biodiversity.
TraitCH is a large, free database describing how 71,874 species live — their morphologies,...
Altmetrics
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?