Data from : "#Vers2022: 50-year resurvey data of French earthworm assemblages obtained after resampling Bouché's historical sites"

1 Data Paper published in Ecology, an Ecological Society of America journal Metadata S1 #Vers2022: 50-year resurvey data of French earthworm assemblages obtained after resampling Bouché’s historical sites Sylvain Gérard, Thibaud Decaëns, Daniel F. Marchán, Marie Beauchesne, Laurent Berlioz, Yvan Capowiez, Julia Clause, Luis Decaëns, Raphaël Dellavedova, Clément-Blaise Duhaut, César GarnierFière, Arnaud Goulpeau, Juliette Goussopoulos, Maeva Iannelli, Claire Marsden, Aurélien Navarro, Solène Orrière, Camille Revertégat, Apollon Vannier, Cyril Versavel, Mickaël Hedde 2 Introduction Soils are recognized for the pivotal role they play in the functioning of terrestrial ecosystems (Wall et al. 2012, Bardgett and van der Putten 2014), as well as for being some of the most important habitats in terms of global biodiversity (Decaëns et al. 2006, Anthony et al. 2023). However, amidst the overarching challenge of global change, the risks posed to soil organisms remain a significant "black box" due to a lack of comprehensive knowledge (Guerra et al. 2020, Phillips et al. 2022). Earthworms are considered a major soil taxon (Blouin et al. 2013), being called ‘ecosystem engineers’. Extensive studies have delved into the impacts of global change (agricultural practices, pollution, climate change) on earthworms (Lévêque et al. 2015, Singh et al. 2019, de Lima e Silva and Pelosi 2023, Mamy et al. 2023, Chatelain et al. 2024). Yet, the majority focus on the plot level, operate within short time scales, and predominantly measure impacts on alpha diversity. There is a general consensus in the public that soil biodiversity is declining and especially earthworms. Some renowned scientists like Hubert Reeves raised public awareness on a general and worldwide decline of earthworm population (France 2 channel, 2018) but without scientific literature backing this information. Despite a great number of publications showing that local soil management can lead to an extreme decrease in the earthworm population (Chan 2001, Rothwell et al. 2011, Blakemore 2018), most of them focus only on local and agricultural plots. The advancement in data collection and harmonization has facilitated the assessment of soil biodiversity patterns and distributions globally (Phillips et al. 2021), yet, these efforts often fall short of addressing the temporal dynamic aspects of earthworm diversity and abundance, due to a lack of time series and long term studies, and predominantly focusing on agricultural environments, particularly in temperate regions. In the 1960s, Marcel Bouché sampled over 1300 earthworm assemblages in mainland France, mostly in semi-natural habitats (Bouché 1972), providing an excellent opportunity to understand how earthworm assemblages and diversity have changed over the long term, on a large scale, and in nonagricultural areas. The project #Vers2022 was launched in 2019 and involved resampling as many localities as possible from Bouché's study in 1972, i.e. more than 50 years later. 3 This Data Paper sheds light on the outcomes of the #Vers2022 project, presenting a wealth of data that goes beyond the usual scope of previous research. It consists of three csv files: (1) Bouché (1972) comprehensive dataset on earthworm assemblage (sensu Fauth et al., 1996) composition and habitat characterization, (2) the concordance between the taxonomic nomenclature used by Marcel Bouché in 1972, the currently used nomenclature, and the latest developments, and (3) the data obtained when resampling 418 (about one-third) of Bouché's sampling sites 50 years later (from 2019 to 2023), including the body mass of each identified individual. This informational document serves as a compendium, making the data from Bouché's 1972 study available in English, and in a computerized format. The assemblage data was already published by Mathieu and Davies (2014), but we kept all original names given by Bouché, without merging any taxon, added absence data, a thorough land use description, original soil measurements. A key to help understanding the taxonomy used in 1972 in relation to the currently accepted taxonomy accompanies it. Furthermore, the data obtained from the resampling not only provides insights into current species occurrences and co-occurrences, diversity, biomass, and a checklist of French earthworm species at a national scale, but it also offers a unique window into the long-term and large-scale changes of this important soil taxon. Finally, the inclusion of individual mass data allows for a more nuanced understanding of interand intra-species response to environmental drivers. With these comprehensive datasets at two time periods (1961-70 and 2019-23), we aim not only to improve our understanding of the drivers of earthworm assemblage dynamics during the Anthropocene, but also to catalyze future research efforts by providing a solid basis for comparative analysis and further exploration. 4 Class I. Data Set Descriptors A. Data set identity #Vers2022: 50-year resurvey data of French earthworm assemblages obtained after resampling Bouché’s historical sites. B. Data set identification code VERS2022_assemblage.csv VERS2022_stations.csv VERS2022_taxonomy.csv C. Data set description 1. Originators Sylvain Gérard, [email protected]; [email protected] Thibaud Decaëns, [email protected] Daniel F. Marchán, [email protected] Mickaël Hedde, [email protected] 2. Abstract Earthworms are key organisms in terrestrial ecosystems. They are found globally and provide significant ecological functions and ecosystem services, so their conservation should be a priority. Yet little is known about the large-scale impacts of global change on earthworm diversity, species distribution and assemblage structure. More importantly, there is no comprehensive data on changes over long periods of time. In the 1960s, Marcel Bouché conducted a study by sampling earthworm assemblages in 1399 locations in mainland France, including the island of Corsica. Through the #Vers2022 project, we achieved the resampling of 418 of these historical sites, creating the first dataset 5 to assess the long-term changes in earthworm assemblages at a large scale. This dataset includes Bouché (1972) assemblage data, with original taxon’s names, absence data, and site descriptions, published in 1972 in French, which were still not freely accessible in a standardized, computerized format. It consists of 36,079 individual earthworm records from 1399 sites, documenting 127 species and subspecies between 1961 and 1970. Additionally, it provides the results of the #Vers2022 resurvey, which includes 11,137 individual records (22,344 including unidentified) from 418 resampled sites, documenting 122 species and subspecies between 2019 and 2023. We followed the framework of quasi-permanent plots, ensuring that the environment was as similar as possible between the historical sampling and its current resampling. These sites were sampled using a standardized, reproducible, and quantitative protocol, unlike the historical sampling, which was qualitative. The dataset also includes information on the individual body mass of each specimen, the total earthworm biomass of each assemblage, and soil analysis data. Furthermore, this work presents the updated taxonomy of each taxon provided by Bouché, along with an assessment of each species name in view of current taxonomy, as well as proposed names for future studies utilizing this data. The #Vers2022 dataset represents a significant improvement in our understanding of French earthworm diversity and can be used to assess changes in diversity and assemblages over a span of more than 50 years, including patterns in individual body mass. The data are released under a CC-BY-NC-SA license. D. Key words 1961-2023, earthworm, annelida, France, individual body mass, resurvey, semi-natural, taxonomy, temperate, time series, historical, quasi-permanent plot 6 Class II. Research origin descriptors A. Overall project description 1. Identity Same as Class I.A. 2. Originators Same as Class I.C.1. 3. Period of study From 1961 to 2023, with one part from 1961 to 1970 and one part from 2019 to 2023. 4. Objectives The purpose of #Vers2022 is to understand current French earthworm diversity, along with changes in diversity and assemblages over a span of more than 50 years, and to know patterns in individual body mass of earthworm species. Overall, it should help improve our understanding of the drivers of earthworm assemblage dynamics during the Anthropocene. 5. Abstract Same as Class I.C.2. 6. Sources of funding Most of the funding was provided by INRAE (National Institute for Research in Agriculture, Environment and Food, Paris, France), funded by the 'AgroEcoSystems' Scientific Department. Samplings in Normandy were funded by the INPN (National Inventory of Natural Heritage, Paris, France). Sylvain Gérard's PhD thesis was funded by a thesis grant from the Ecole Normale Supérieure 7 (Paris, France). Daniel F. Marchán's contribution was funded by the Make Our Planet Great Again programme (Campus France, Paris), via a postdoctoral grant. Arnaud Goulpeau and Maeva Iannelli contributions were made as part of their PhD, funded by the University of Montpellier (Montpellier, France) and the Fondation de France (Paris, France), respectively. Raphaël Dellavedova's contribution was made as part of the GlobNet project (French Research Agency, Paris, France; project leader: Dr. Wilfried Thuiller, CNRS, Grenoble). Marie Beauchesne, Laurent Berlioz, César Garnier-Fière, Camille Revertégat and Apollon Vannier internships were funded by Eco&Sols research unit (INRAE, Montpellier, France). Aurélien Navarro internship was funded by INRAE’s metaprogramme “Biosefair” (Promoting biodiversity and strengthening ecosystem service networks). B. Specific subproject description 1. Site description a. Site type All sites are terrestrial and most of them are semi-natural ecosystems. The dataset is mostly composed of grasslands (EUNIS: E), shrublands (EUNIS: F), woodlands (EUNIS: G), riparian lands (EUNIS: C) and other habitats, with a majority of grasslands for both 1961-1970 and 2019-2023 data (More information is provided in II.B.1.c. 8 Figure 1. Level 1 EUNIS habitat type repartition of #Vers2022, ranked by total number of sites. 9 b. Geography All sites are located in mainland France (Europe, Paleartic), including Corsica Island (Figure 2). Both surveys took place in all French administrative region (Figure 3). 16 EUNIS level 1 code EUNIS level 1 description EUNIS level 2 code EUNIS level 2 description EUNIS level 3 code EUNIS level 3 description EUNIS most precise code Corine land cover classification Corine land cover description Number of sites in 1961-1970 data Number of sites in 2019-2023 data G1A Mesoand eutrophic Quercus, Carpinus, Fraxinus, Acer, Tilia, Ulmus and related woodland G1A 311 Broad-leaved forest 98 32 G1A Decaying trunk in Mesoand eutrophic Quercus, Carpinus, Fraxinus, Acer, Tilia, Ulmus and related woodland G1A 311 Decaying trunk in Broadleaved forest 6 0 G1D Fruit and nut tree orchards G1D 222 Fruit trees and berry plantations 32 4 G2 Broadleaved evergreen woodland G21 Mediterranean evergreen Quercus woodland G21 311 Broad-leaved forest 15 7 G21 Decaying trunk in Mediterranean evergreen Quercus woodland G21 311 Decaying trunk in Broadleaved forest 1 0 G24 Olea europaea - Ceratonia siliqua woodland G24 323 Sclerophyllous vegetation 1 0 G26 Ilex aquifolium woods G26 311 Broad-leaved forest 1 1 G29 Evergreen orchards and groves G29 223 Olive groves 5 1 G3 Coniferous woodland NA NA G3 312 Coniferous forest 12 5 G31 Abies and Picea woodland G31 312 Coniferous forest 6 3 G32 Alpine Larix - Pinus cembra woodland G32 312 Coniferous forest 4 0 G39 Coniferous woodland dominated by Cupressaceae or Taxaceae G39 312 Coniferous forest 2 0 G3F Highly artificial coniferous plantations G3F 324 or 312 Transitional woodland/shrub or Coniferous forest 3 1 17 EUNIS level 1 code EUNIS level 1 description EUNIS level 2 code EUNIS level 2 description EUNIS level 3 code EUNIS level 3 description EUNIS most precise code Corine land cover classification Corine land cover description Number of sites in 1961-1970 data Number of sites in 2019-2023 data G4 Mixed deciduous and coniferous woodland NA NA G4 313 Mixed forest 24 6 G46 Mixed Abies - Picea - Fagus woodland G46 313 Mixed forest 7 2 G4C Mixed Pinus sylvestris - thermophilous Quercus woodland G4C 311 or 313 Broad-leaved forest or Mixed forest 9 4 G5 Lines of trees, small anthropogenic woodlands, recently felled woodland, earlystage woodland and coppice G57 Coppice and earlystage plantations G57 311 or 324 Broad-leaved forest or Transitional woodland/shrub 4 2 H Inland unvegetated or sparsely vegetated habitats H1 Terrestrial underground caves, cave systems, passages and waterbodies NA NA H1 NA NA 3 0 H2 Screes H26 Calcareous and ultrabasic screes of warm exposures H26 333 Sparsely vegetated areas 1 1 H3 Inland cliffs, rock pavements and outcrops H35 Almost bare rock pavements, including limestone pavements H35 332 Bare rock 1 0 H5 Miscellaneous inland habitats with very sparse or no vegetation H56 Trampled areas H56 311 Broad-leaved forest 1 1 I Regularly or recently cultivated agricultural, horticultural and domestic habitats I1 Arable land and market gardens I11 Intensive unmixed crops I11 21 Arable land 25 0 I12 Mixed crops of market gardens and horticulture I12 21 Arable land 2 0 I15 Bare tilled, fallow or recently abandoned arable land I15 141 or 211 Green urban areas (141) or Non-irrigated arable land (211) 11 4 I2 Cultivated areas of gardens and parks NA NA I2 141 Green urban areas 9 0 J Constructed, industrial and J4 Transport networks and other constructed hard-surfaced areas J42 Road networks J42 122 Road and rail networks and associated land 1 0 18 EUNIS level 1 code EUNIS level 1 description EUNIS level 2 code EUNIS level 2 description EUNIS level 3 code EUNIS level 3 description EUNIS most precise code Corine land cover classification Corine land cover description Number of sites in 1961-1970 data Number of sites in 2019-2023 data other artificial habitats J6 Waste deposits NA NA J6 321 Agricultural and horticultural waste in wet Natural grassland 1 0 J64 Agricultural and horticultural waste J64 231 or 321 or 321 or 324 or 331 or 411 or NA Agricultural and horticultural waste in Pastures or Natural grassland (wet or no) or Transitional woodland/shrub or Beaches, dunes, sands or Inland marshes or Other/Not precised 16 0 NA Other or not precised NA NA NA NA NA NA NA 12 0 19 d. Geology, landform The sites are located all around France and represent the diversity of French landscapes. The sites are located on various geological terrains with sedimentary, metamorphic, or igneous (plutonic and volcanic) bedrock, with a majority of soils with sedimentary substrate (Figure 4A). The elevation ranges from 10 to 1600 m above sea level for the 2019-2023 data and from 10 to 2700 m above sea level for the 1961-1970 data (Figure 4B). Soils physicochemical characteristics (min and max) for the 2019-2023 data are provided in Table 4 in Class IV.B. 20 Figure 4. Distribution of the geological nature of the substrates (A) and of the altitude (B) of #Vers2022 sites, ranked by total number of sites in panel A. Altitude are given as ‘meter above sea level’. 21 e. Watersheds, hydrology NA f. Site history For each site, we know the land cover in the 1960s and in the 2020s, but not in between. Thanks to satellite maps of 1950 to 1965 from the National Institute of Geographic information (1950-1965 satellite map, https://www.geoportail.gouv.fr/), we resampled places with identical land cover in the 1960s and the 2020s. g. Climate Sites are located in 8 different Köppen-Geiger classification climate types (Beck et al. 2023) for the 1961-1970 data , and 6 for the 2019-2023 data, with a majority of sites in a temperate oceanic climate (Figure 5A). We also separated sites between climates defined by Joly et al. (2010), who described 8 types of climate only accountable for France. Corsica was not included in their classification so we added a ninth type called (Type Corsica), which sould be close to Type 8 climate (Beck et al. 2023). All 9 types of climate were represented in the 1961-1970 data and the 2019-2023 data, with a majority of sites in Type 3 (degraded oceanic of Central and Northern plains climate) (Figure 5B). Sites are located in 8 different ecoregions (Dinerstein et al. 2017) for the 1961-1970 data and the 2019-2023 data, with a majority of sites in Western European broadleaf forests, European Atlantic mixed forests and Northeast Spain and Southern France Mediterranean forests (Figure 5C). 22 23 Figure 5. Climate and ecoregion repartition of #Vers2022 sites, ranked by total number of sites. A: Repartition of Köppen-Geiger classification. x-axis is represented with common logarithmic scale (log10). BSk = Cold semi-arid climate, Cfa = Humid subtropical climate, Cfb = Temperate oceanic climate, Csa = Hot-summer Mediterranean climate, Csb = Warm-summer Mediterranean climate, Dfb = Warm-summer humid continental climate, Dfc = Subarctic climate, ET = Tundra climate. B: Repartition of Joly et al. 2010 climate type. Type 1 = Mountain climates, Type 2 = Semi-continental and mountain edges climate, Type 3 = Degraded oceanic of Central and Northern plains climate, Type 4 = Altered oceanic climate, Type 5 = Clear oceanic climate, Type 6 = Altered Mediterranean climate, Type 7 = Southwestern basin climate, Type 8 = Clear Mediterranean climate, Type Corsica = Corsican climate. C: Repartition of ecoregions. 24 2. Experimental or sampling design a. Design characteristics Marcel Bouché's work in 1972 aimed to systematically study the earthworm fauna in France, providing comprehensive insights into the distribution and ecology of each species. The primary objectives of his research included: (1) achieving comprehensive and regular coverage of the French territory, ensuring that each site was no more than 30 km apart, (2) sampling typical biotopes in the covered regions, (3) focusing on specific microhabitats like decaying logs and composts, (4) sampling at various altitudes, (5) maintaining a well-balanced selection of habitats while excluding crops, and (6) conducting sampling at different time periods. However, as illustrated in Figure 1, the distribution of habitats was not perfectly balanced. Additionally, Figure 3 indicates that southern and eastern France were also over-represented in the sampling as known hotspots of earthworm diversity, involving some geographical biases in the study design. It's crucial to consider these imbalances when interpreting the results and generalizing findings from Bouché's work. In his study, Marcel Bouché employed qualitative sampling techniques at each site, involving the exploration of 3 to 4 one-meter-squared blocks of soil at a depth of 25 to 30 centimeters. Exceptions were made for instance in very rocky soils in the south of France and heavily root-infested soils in woodlands. For particularly large earthworm species belonging to the genus Scherotheca, he occasionally utilized a 1:20 formaldehyde dilution extraction method, although specific details regarding the sites where this technique was applied were not provided. The collection process involved gathering both adult and juvenile specimens to accurately represent the earthworm assemblage present at each site. Marcel Bouché deliberately avoided oversampling rare species. This meticulous sampling approach aimed to capture the diversity and distribution of earthworm species across different microhabitats and geographic locations. The objective of #Vers2022 was to revisit and resample as many localities as possible among those initially studied by Marcel Bouché. Originally conceived as a citizen science project in late 2019, its progression was impeded by the COVID-19 pandemic, leading to a modification of the sampling design. 25 The authors of this paper subsequently carried out the bulk of the sampling efforts, although some participation from citizens persisted. In alignment with Bouché's goals, we aimed for a comprehensive coverage across France and the inclusion of a diverse array of habitats. Despite these adjustments, the resampling initiative sought to adhere to the spirit of Bouché's original study by providing valuable insights into the contemporary state of earthworm fauna across various regions in France. To ensure standardization and facilitate reproducibility, we opted for a quantitative sampling approach. At each site, we dug out six replicates of soil blocks of 25 x 25 x 25 cm, spaced approximately 10 meters apart, with their locations chosen randomly on site. All earthworm individuals were collected by meticulously hand sorting the soil of each block in the field. In cases where our quantitative method yielded a significant amount of unidentifiable juveniles, we complemented our survey with qualitative sampling at the same site, i.e. digging large surfaces of soil randomly until finding the targeted adults. This was particularly useful in places housing large earthworm species, e.g. within the genera Scherotheca and Boucheona, which are challenging to capture on small volumes of soil. It is noteworthy that, unlike Bouché, we refrained from using formaldehyde extraction for those large species, due to the documented dangerous effects of this product on both human and environmental health. Some sites were exclusively sampled qualitatively, aligning with Bouché's protocol, albeit without formaldehyde extraction. Despite the lack of standardization in these qualitative samples, we deemed them valuable for inclusion in this Data Paper. Qualitative samples are indicated in the dataset (see Table 4, variable identity “type”). We assert that these qualitative assemblage data, although not standardized, remain relevant and comparable to Bouché's sampling, given the use of a similar qualitative method. b. Permanent plots Bouché (1972) originally presented geographical coordinates in his site descriptions, with latitudes expressed in grades (4 digits) and longitudes in grades, using the Paris meridian as the origin meridian (3 digits). For the purpose of compatibility and modern mapping standards, we transformed these historical coordinates to the WGS 84 (World Geodetic System 1984) spatial reference system. The transformation process involved converting the original coordinates in grades to the corresponding 32 33 Figure 9. Period of sampling of #Vers2022. A: Yearly distribution of sampling of the 1961-1970 data, B: Yearly repartition of the 2019-2023 data, C: Monthly repartition of the 1961-1970 data (grey) and the 2019-2023 data (orange). 34 3. Research methods a. Field/laboratory Bouché (1972) initially identified specimens directly in the field, based on external morphology. Then, specimens were killed in a 1:25 formaldehyde solution and were identified again in the laboratory, where both external morphology and internal anatomy characteristics were considered. Some juvenile specimens were also bred for subsequent identification purposes. After specimen were preserved in a 1:25 formaldehyde solution, measurements, including individual mass, length, diameter, and details on the position, number, and shape of organs, were meticulously recorded. These measurements were conducted at the species level, providing a comprehensive characterization and description for each species, but are not available at the specimen level. In addition to collecting earthworm specimens, soils were collected on the 0-30 cm layer (depth of ~25/30 cm according to Bouché), on a surface of 1m², but the maximum depth could be less according to the nature of the site. Marcel Bouché conducted thorough soil analyses as part of his research: - pH was measured with a pH meter on a mixture of soil and an equivalent volume of water. - Carbonate content was measured with a Bernard calcimeter, with subsequent calculations carried out on an IBM 1130 computer, assuming CO2 behaved as an ideal gas. - Organic carbon content was determined using the Anne method (Anne 1945). - Nitrogen content was determined using the Kjeldahl method (Kjeldahl 1883). Moreover, he provided a description of soil texture, categorizing it into different classes based on empirical observations. This multifaceted approach allowed Bouché to integrate soil characteristics into his ecological investigations, providing a comprehensive understanding of the habitats associated with the earthworm assemblages he studied. In the #Vers2022 campaign collected earthworms were cleaned in water and readily killed in 70% to 99% denatured ethanol instead of formaldehyde. They were then stored in 99% denatured ethanol to allow for future potential DNA barcoding analysis. Earthworms were then identified in the laboratory 35 (Class II.B.3.c for more information on identification). Molecular analysis was employed for individuals suspected to belong to potentially new species to science. Each identifiable individual was individually weighed with a precision of 0.001 gram after it was fixed in ethanol. In the case of juveniles, weighing was performed collectively at the replicate level. Before weighing, surplus ethanol was removed using absorbent paper, and individuals were not voided. In the 2019-2023 data, some individuals were unidentified, but were still recorded in the assemblage data and weighted, so biomass and total individual per site can be correctly measured. These individuals were labelled as “Undetermined” (corresponding to t_160 as “original_id_taxon”) in the "VERS2022_taxonomy.csv" and "VERS2022_assemblages.csv" files. However, Bouché did not describe any unidentified individuals. On certain sites (described in Table 3), no earthworms were found, whether for the 1961-1970 data or the 2019-2023 data. We recorded these absence data in the "VERS2022_assemblages.csv" file, improving the published version of the dataset of Bouché (1972) by Mathieu and Davies (2014). In addition to earthworm collection, soil samples were gathered from each replicate (0-25 cm layer), or for each digging in case of a qualitative-only sampling,for subsequent soil analysis. Soil samples were air dried at 20 °C in a drying cupboard. Subsequent analyses of the soil samples were performed by AUREA Agrosciences © in Ardon. Soils were first dried at 38°C, ground and sieved at 2 mm (NF ISO 11464 standard). Then, the following soil parameters were measured: - Granulometry by measuring the fraction (percentage) of five types of particles based on their size: coarse sand (200-2000 µm), fine sand (50-200 µm), coarse silt (20-50 µm), fine silt (2-20 µm) and clay (≤ 2 µm) with the pipette sedimentation method (NF X31-107 standard). - Organic matter with the sulfochromic oxidation and visible spectroscopy method (NF ISO 14235 standard). - Total nitrogen with a dry combustion method (Dumas method) (NF ISO 13878 standard). - Organic carbon with sulfochromic oxidation and visible spectroscopy method (NF ISO 14235 standard). 36 - Carbonate content (total CaCO3) with the Bernard calcimeter (HCl reaction) method (NF ISO 10693 standard). - Cation-exchange capacity (CEC) with the ammonium acetate extraction method (Metson method) (NF X 31-130 standard). - pH H2O and pH KCl with the 1:5 extraction (in water or KCl) method (NF ISO 10390 standard). b. Instrumentation NA c. Taxonomy and systematics Bouché (1972) voucher specimens were destroyed (personal information), except for the holotypes and paratypes of the species he described which are preserved in the Paris National Museum of Natural History (MNHN, Paris, France). Voucher specimens for the 2019-2023 data are located at the Eco&Sols lab in Montpellier, France, with holotypes and some paratypes of newly described species (Marchán et al. 2023a, 2023d, 2023c, 2024, Gérard et al. 2023) located in the Paris National Museum of Natural History (MNHN, Paris, France). Marcel Bouché significantly contributed to the taxonomy of French earthworms through his 1972 monograph (Bouché 1972). He later (Qiu and Bouché 1998b) revised some identifications he made in Bouché (1972), in view of the latest taxonomic system at that time (Qiu and Bouché 1998a). However, this taxonomic system is not relevant in view of today’s accepted taxonomy. The taxonomic system we adopted in the #Vers2022 project is rooted in the work of Misirlioğlu et al. (2023) and the associated dataset by Brown et al. (2023), the most current and accepted list of world taxa. To ensure the taxonomy applied in the #Vers2022 campaign remains up to date, we also incorporated information from more recent publications. The details and justifications for these decisions can be found in the "VERS2022_taxonomy.csv" file. During the sampling of the #Vers2022 campaign, we found taxa that were not in the Bouché (1972) checklist. They correspond to three cases: (i) species already described in 1972 but not known in France, 37 (ii) taxonomic revisions made later (Qiu and Bouché 1998a, 1998b) or (iii) species described more recently. For the identification of species not known in France, we used Csuzdi and Pavlíček (2002) for Murchieona minuscula, and had the help of Central European colleagues for Octodrilus hemiandrus, Octodrilus transpadanoides, Octodriloides sp. and Aporrectodea s.l. sineporis. For species described more recently in France, we compiled the published diagnoses (Zicsi and Csuzdi 1999, Marchán et al. 2018, 2023a, 2023d, 2023c, Gérard et al. 2023). The translations between taxonomic systems are presented in the "VERS2022_taxonomy.csv" file. Ensuring accurate matching between ancient and current taxonomic systems is a critical aspect of resurveying and comparing historical data. In the provided "VERS2022_taxonomy.csv" file, a match table has been included for every taxon described in Bouché (1972) and in the #Vers2022 campaign. This table facilitates the identification of corresponding names in various taxonomic databases, including Bouché (1972), Qiu and Bouché (1998a), the DriloBASE (http://taxo.drilobase.org), Misirlioğlu et al. (2023) and the associated dataset by Brown et al. (2023), as well as the taxonomy presented in the present work, which is the most up-to-date. The rationale for selecting these taxonomic systems in the correlation table is as follows: 1. Bouché (1972): The taxon names in the seminal work of Marcel Bouché. 2. Qiu and Bouché (1998a): Taxonomic revision of specimens found in Bouché (1972). 3. DriloBASE: Online resource included for its easy accessibility (but is not up-to-date). 4. Misirlioğlu et al. (2023) and associated database Brown et al. (2023): Dataset chosen as the up-todate published list of world accepted taxa. 5. Present work: includes the most up-to-date taxonomy of species in France due to several recent taxonomic revisions and species descriptions that were not included in Brown et al. (2023) and Misirlioğlu et al. (2023). 38 The primary objective of #Vers2022 is to compare historical and present data. However, comparing past and present diversity including species described after the past data would be misleading. Thus, we also proposed a taxonomic system to use in comparison analyses. The basis for this proposal is the taxonomic system of this work, with some arrangements. Notably, today’s taxa valid to science described before 1998 were kept with their actualized name. Taxa described after 1998 were considered as “undetermined”, and for cases where taxa were split, a recommendation is made to use the genus level or designate them as a "complex" of species (e.g., Boucheona sp., Lumbricus “terrestris” complex, Scherotheca “corsicana” complex, Scherotheca “savignyi” complex). The details and justifications for these decisions can be found in the "VERS2022_taxonomy.csv" file. Finally, we also added all the other accepted taxa known from France, but not found in the 1961-1970 data and the 2019-2023 in the same file. We do not propose any taxonomic system for comparison purposes for these taxa. Table 2 gathers every taxon known from France and whether they were found in #Vers2022 (the 1961-1970 and/or the 2019-2023 data) or not, with the publications in which their presence in France is confirmed. If a taxon is present in the 1961-1970 and/or the 2019-2023 data, no other publication is given. Two species are new to France: Aporrectodea s.l. sineproris and Murchieona minuscula. French fauna counts 8 families, 43 genera, 198 species and 213 taxa when considering subspecies. The #Vers2022 dataset, encompassing both the the 1961-1970 data and the 2019-2023 data, contains 63% of families, 83% of genera, 72% of species and 72% of the subspecies known from France (Figure 10), with the Lumbricidae being the predominant and the most distributed family (Figure 11). The Diporodrilidae, Hormogastridae and Megascolecidae are restricted to southern France. Acanthodrilidae can be found in southern and northern France, but are mostly distributed in southern France (Figure 11). Bouché also identified specimens as Haplotaxis gordioides (Hartmann, 1821), belonging to the family Haplotaxidae. This family is currently not considered belonging to the earthworm clade (i.e. Crassiclitellata). Hence, we did not include it in Figure 10 and Figure 11, but we left it in the database. A species accumulation curve is presented in Figure 12. It was built using the taxonomic system we proposed in Class II.B.3.c for comparison purposes. The curves for the 2019-2023 data and the 1961- 39 1970 data subselection are similar, and do not reach any plateau, meaning that a more extensive sampling should increase the number of taxa found. It is consistent with Figure 10 and with recent works focused on new species description (Marchán et al. 2020, 2023a, 2023d, 2023c, Szederjesi et al. 2021a, 2021b, Gérard et al. 2023). However, curve for the 1961-1970 data is closer to a plateau. The subselection of Bouché (1972) in the #Vers2022 project is maybe a bit biased toward more diverse localities. The proportion of sites in which each taxon occurred for the 2019-2023 data, the 1961-1970 data subselection and the 1961-1970 data, as well as habitats in which the taxon is most likely to be found for the 2019-2023 data, the 1961-1970 data subselection and the 1961-1970 data is presented in Figure 13. 40 Table 2. Up-to-date list of earthworm taxa to the subspecies level, with their up-to-date name. We did not put Haplotaxis gordioides (Hartmann, 1821) in this table as it is not considered a earthworm. Species new to France are highlighted in bold. Species written with "nov. sp." are species identified as new for science but not yet formally described. Taxon revised name Justification of presence in France Species/subspecies Ailoscolex lacteospumosus Bouché, 1969 This work: 1961-1970 data Allolobophora s.s. burgondiae Bouché, 1972 This work: 1961-1970 and 2019-2023 data Allolobophora s.s. chlorotica chlorotica (Savigny, 1826) This work: 1961-1970 and 2019-2023 data Allolobophora s.s. chlorotica postepheba Bouché, 1972 This work: 1961-1970 and 2019-2023 data Allolobophora s.s. chlorotica waldensis Ribaucourt, 1896 This work: 1961-1970 data Allolobophora s.s. delitescens Gérard, Marchán & Decaëns, 2023 This work: 2019-2023 data Allolobophora s.s. icterica (Savigny, 1826) This work: 1961-1970 and 2019-2023 data Amynthas corticis Kinberg, 1867 Rota's personnal data, according to DriloBASE. Amynthas diffringens (Baird, 1869) This work: 1961-1970 and 2019-2023 data Aporrectodea s. l. sineporis (Omodeo, 1952) This work: 2019-2023 data Aporrectodea s.l. handlirschi (Rosa, 1897) Cited in Bouché (1972): Tétry (1938) and Wilcke (1955) Aporrectodea s.l. pseudoantipai (Qiu & Bouché, 1998) Qiu and Bouché (1998b) Aporrectodea s.l. rosea (Savigny, 1826) This work: 1961-1970 and 2019-2023 data Aporrectodea s.l. tiginosa (Bouché, 1972) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. arverna (Bouché, 1969) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. balisa (Bouché, 1972) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. caliginosa caliginosa (Savigny, 1826) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. giardi (Ribaucourt, 1901) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. gogna (Bouché, 1972) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. longa (Ude 1885) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. nocturna (Evans, 1946) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. ripicola (Bouché, 1972) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. rubicunda acidicola (Bouché, 1972) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. rubicunda rubicunda (Vedovini, 1969) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. trapezoides (Dugès, 1828) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. velox (Bouché, 1967) This work: 1961-1970 and 2019-2023 data Aporrectodea s.s. voconca (Bouché, 1972) This work: 1961-1970 and 2019-2023 data Avelona ligra (Bouché, 1969) This work: 1961-1970 and 2019-2023 data Avelona yvesi.Gérard & Marchán, 2025 This work: 2019-2023 data Avelona zicsii (Bouché, 1972) This work: 1961-1970 and 2019-2023 data Bimastos eiseni (Levinsen, 1884) This work: 1961-1970 and 2019-2023 data Bimastos parvus (Eisen, 1874) This work: 2019-2023 data Bimastos rubidus (Savigny, 1826) This work: 1961-1970 and 2019-2023 data Boucheona corbierensis Marchán & Novo, 2023 This work: 2019-2023 data Boucheona gallica (Rota, 1994) Rota (1994) 41 Taxon revised name Justification of presence in France Boucheona rosae Marchán, Díaz Cosín & Novo, 2018 This work: 2019-2023 data Boucheona tenebrae Marchán & Novo, 2023 This work: 1961-1970 data Cataladrilus catalaunensis Bouché, 1972 This work: 1961-1970 data Cataladrilus porquerollensis Marchán & Decäens, 2020 Marchán et al. (2020) Cataladrilus sardonicus (Cognetti, 1904) cited in Bouché (1972) (no more details) Coventina putricola orionense (Zicsi, 1977) Zicsi and Csuzdi (1999) Coventina putricola putricola (Bouché, 1972) This work: 1961-1970 data Coventina putricola tebra (Bouché, 1972) This work: 1961-1970 data Criodrilus lacuum Hoffmeister, 1845 Graff (1962) Dendrobaena alpina zeugochaeta Bouché, 1972 This work: 1961-1970 data Dendrobaena attemsi (Michaelsen, 1901) This work: 1961-1970 and 2019-2023 data Dendrobaena byblica byblica (Rosa, 1893) This work: 1961-1970 and 2019-2023 data Dendrobaena cognetti Rosa, 1884 This work: 1961-1970 data Dendrobaena hortensis (Michaelsen, 1890) cited in Bouché (1972): Tétry (1938) Dendrobaena metallorum (Tétry, 1936) cited in Bouché (1972): Tétry (1936) Dendrobaena monspessulana Qiu & Bouché, 1998 Qiu and Bouché (1998b) Dendrobaena octaedra (Savigny, 1826) This work: 1961-1970 and 2019-2023 data Dendrobaena pantaleonis (Chinaglia, 1913) This work: 1961-1970 data Dendrobaena platyura (Fitzinger, 1833) This work: 1961-1970 data Dendrobaena pygmaea (Savigny, 1826) cited in Bouché (1972): Savigny and Cuvier (1826) Dendrobaena veneta veneta (Rosa, 1886) cited in Bouché (1972): Tétry (1939a) Diporodrilus jorgei Marchán & Decaëns, 2024 Marchán et al. (2024) Diporodrilus omodeoi Bouché, 1970 This work: 1961-1970 and 2019-2023 data Diporodrilus pilosus Bouché, 1970 This work: 1961-1970 and 2019-2023 data Diporodrilus rotundus Marchán & Decaëns, 2024 Marchán et al. (2024) Eisenia andrei Bouché, 1972 This work: 1961-1970 data Eisenia fetida (Savigny, 1826) This work: 1961-1970 and 2019-2023 data Eisenia lucens (Waga, 1857) cited in Bouché (1972): Pop (1948). Also cited in Zicsi and Csuzdi (1999): Eiseniella neapolitana (Örley, 1885) Qiu and Bouché (1998b) Eiseniella tetraedra (Savigny, 1826) This work: 1961-1970 and 2019-2023 data Ethnodrilus aveli Bouché, 1972 This work: 1961-1970 and 2019-2023 data Ethnodrilus gatesi Bouché, 1972 This work: 1961-1970 and 2019-2023 data Ethnodrilus lydiae Bouché, 1972 This work: 1961-1970 and 2019-2023 data Ethnodrilus zajonci Bouché, 1972 This work: 1961-1970 data Eukerria saltensis (Beddard, 1895) Rota (2013) Eumenescolex heideti Qiu & Bouché, 1998 Qiu and Bouché (1998b) Eumenescolex pereli (Bouché, 1972) This work: 1961-1970 and 2019-2023 data Eumenescolex zoltani Szederjesi, Pavlicék & Csuzdi, 2021 Szederjesi et al. (2021a) Flabellodrilus bartolii bartolii (Bouché, 1970) This work: 1961-1970 and 2019-2023 data Flabellodrilus bartolii meougensis (Bouché, 1970) This work: 1961-1970 data Flabellodrilus luberonensis Gérard, Marchán & Decaëns, 2023 This work: 2019-2023 data Flabellodrilus nov. sp. Gérard, Marchán & Decaëns, 2023 This work: 2019-2023 data Gatesona chaetophora argentatensis (Bouché, 1972) This work: 1961-1970 data Gatesona chaetophora chaetophora (Bouché, 1972) This work: 1961-1970 and 2019-2023 data 48 49 Figure 11. Occurrences of earthworm families in France. (1) to (5) maps show occurrences of families found in the 1961-1970 data and the 2019-2023 data: (1) Acanthodrilidae, (2) Diporodrilidae, (3) Hormogastridae, (4) Lumbricidae, (5) Megascolecidae. Each dot is a site where at least one individual of a given family was found: grey for the 1961-1970 data only, orange for the 2019-2023 data only or both the 2019-2023 and the 1961-1970 data. (6) shows the number of individuals found for each family, ranked for total number of individuals (grey: 1961-1970 data, orange: 2019-2023 data). x-axis is represented with a common logarithmic scale (log10). 50 Figure 12. Species accumulation curve for #Vers2022. 1961-1970 data in grey, 1961-1970 data subselection in blue, 2019-2023 data in orange. To draw these curves, we used the taxonomic system we proposed in Class II.B.3.c for comparison purposes. 51 52 53 54 55 Figure 13. Species occurrence and habitat preference in the 2019-2023 data, the 1961-1970 data subselection and the 1961-1970 data. Left: proportion of sites in which each taxon occurred in the 2019-2023 data (upper, orange), the 1961-1970 data subselection (middle, blue) and the 1961-1970 data (bottom, grey). Right: heat map of habitats in which each taxon is most likely to be found in the 2019-2023 data (upper), the 1961-1970 data subselection (middle) and the 1961-1970 data (bottom). The heat map represents the percentage of habitats in which the taxon would be found at an equivalent number of sites per habitat. 56 d. Permit history All participants asked the permission of every site owner before sampling, for all private and public sites. e. Legal/organizational requirements N/A 4. Project personnel Principal investigators: Sylvain Gérard, Mickaël Hedde, Thibaud Decaëns, Daniel F. Marchán Associated investigators: Yvan Capowiez, Julia Clause, Clément-Blaise Duhaut, Arnaud Goulpeau, Juliette Goussopoulos, Claire Marsden Technicians: Raphaël Dellavedova, Solène Orrière, Cyril Versavel Interns: Marie Beauchesne, Laurent Berlioz, Luis Decaëns, César Garnier-Fière, Maeva Iannelli, Aurélien Navarro, Camille Revertégat, Apollon Vannier Database managers: Sylvain Gérard, Mickaël Hedde Some citizens also helped with the sampling. We would like to thank them warmly here. 57 Class III. Data set status and accessibility A. Status 1. Latest update of data set February 22, 2024 2. Latest archive date March 7, 2025 (Zenodo release) 3. Metadata archive date April 22, 2025 4. Data verification For each taxon, we checked the localities and body mass. If a taxon was found in an unusual location, i.e. outside of the concave polygon constructed with all occurrences from Bouché (1972), we reidentified the specimen. Similarly, if a specimen had a body mass that deviated from the range of its species described in Bouché (1972), we re-identified and re-weighed it. We also performed a comprehensive check to identify any abnormal values in each dataset: we looked at outliers, and checked if they were outliers because of a unit mistake, incorrect measurement or true outliers (e.g., a very low soil pH from a Picea forest). B. Accessibility 1. Storage location and medium: In addition to this data paper, data are accessible in Gérard et al. (2025) at this link: https://doi.org/10.5281/zenodo.14990041. 64 Variable identity Variable definition Units of measurement Data type Data format Data anomalies juveniles of earthworm were weighted together (same for unidentified piece of earthworm), so that one observation is several indivuals and abundance can be superior to 1. However, some identified indivuals were sometimes weighted in group (such as some individuals in pieces, juveniles or subadults), or some adults were not weighted, so abundance can be superior to 1. 0078, 0084, 0087, 0104, 0126, 0153, 0171, 0180, 0277, 0435, 0467, 0517, 0567, 0596, 0597, 0648, 0686, 0697, 0764, 0794, 0799, 0800, 0835, 0838, 0902, 1074, 1078, 1291 No individuals in following 2019-2023 data sites (abundance = 0 for these sites): ECORDRE_0259, 0487, 0492, 0658, 1043, 1144, 1157, 1215, 1232 In ECORDRE_0133, 0343, 0344, 0345, for 1961-1970 data, abundance = 99 for most or all species, which is maybe a mistake by Bouché. We decided to leave them like that but these abundances must be taken with caution. 65 Variable identity Variable definition Units of measurement Data type Data format Data anomalies body_mass Body mass of the observation. Dry mass (i.e., after ethanol fixation), gut not voided. gram numeric Body mass of an observation. For identified ones, mass is given for each individual. Undetermined specimens are not weighted individually but are weighted together for each station, replicate, development stage and state of conservation. However, some identified individuals were sometimes weighted in group (such as some individuals in pieces, juveniles or subadults), or some adults were not weighted. 0.003 to 46.795 NA 3 decimals up to 5 digits No body mass value for the 1961-1970 data 66 Table 4. Variable information for “VERS2022_stations.csv”. This file contains all the information of the stations sampled, with metadata on localisation, habitat, geography, soil analysis. NA: not available; EUNIS: European Nature Information System. Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length id_station Name of the site (i.e. quasipermanent plot) sampled. Character Text All station names start with “ECORDRE_”. Then the number of the station is given, from 0001 to 1526 following Bouché (1972) codes. no missing value 12 characters original_id_station Name of the site in Bouché (1972) Character Text All station names starts with “P. ” and is followed with the same number of the id_station column. From P. 1 to P. 1526 no missing value 4 to 7 characters source Name of the study in which the sample was made Character Text 2 factors Bouche1972: sample conducted in Bouché (1972) (1961-1970 data) Vers2022: sample conducted in the #Vers2022 campaign (2019-2023 data) no missing value 8 or 10 characters sampler Type of sampler Character Text 5 factors authors: sample conducted by authors of this datapaper volunteer: sample conducted by a volunteer as part of the participative science program no missing value 7 or 9 characters 67 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length company: sample conducted by the company AUREA Agrosciences © bouche: sample conducted by Marcel Bouché donation_to_bouche: sample conducted by someone who gave it to Bouché type Type of sampling (i.e. qualitative or quantitative) Character Text 2 factors qualitative: qualitative sampling, as described in Class II.B.2.a. Every sampling from 19611970 is qualitative, but not every sampling from 2019-2023. quantitative: quantitative sampling, as described in Class II.B.2.a. Only samplings from 2019-2023 are quantitative (but some are qualitative). no missing value 11 to 12 characters latitude_grade_bouche Latitude in grades Grades Numeric Latitude is given in grades. 4598 to 5665 NA 0 decimal 4 digits Only for the 1961-1970 data. No data in ECORDRE_1510 site longitude_grade_bouche Longitude in grades Grades Numeric Longitude is given in grades, with Paris meridian as the origin meridian. -768 to 800 NA 0 decimal 1 to 3 digits Only for the 1961-1970 data. No data in ECORDRE_1510 site latitude_wgs84 Latitude in the WGS 84 (World Geodetic System Decimal degree Numeric Latitude is given in Decimal Degree following the World Geodetic System 1984 41.382 to 50.985 NA 3 decimals 5 digits No data in ECORDRE_1510 site 68 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length 1984) spatial reference system (WGS84). Positive for northern Earth hemisphere, negative for southern Earth hemisphere, with 0 for the equator. For the 1961-1970 data, latitude has been recalculated from the latitudes in grades given in Bouché (1972). For the 2019-2023 data, latitude has been measured on Google maps ©, based on satellite maps. longitude_wgs84 Longitude in the WGS 84 (World Geodetic System 1984) spatial reference system Decimal degree Numeric Longitude is given in Decimal Degree following the World Geodetic System 1984 (WGS84). Positive in the east of Prime (Greenwich) meridian, negative in the west of Prime (Greenwich) meridian, 0 for Prime (Greenwich) meridian. For the 1961-1970 data, longitude was recalculated from the latitudes in grades given in Bouché (1972). For the 2019-2023 data, longitude was measured on Google maps ©, based on aerial photography. -4.576 to 9.536 NA 3 decimals 4 digits No data in ECORDRE_1510 site day Day in which the sampling was made Day number Numeric Day is given from 1 to 31, following the dd/mm/yyyy date format. 1 to 31 NA 1 to 2 digits No data in following 19611970 sites: ECORDRE_0343, 0344, 69 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length 0345, 0901, 0902, 0903, 0904, 0906, 1151, 1388, 1389, 1390, 1391, 1425, 1426, 1510, 1526 No data in following 20192023 sites: ECORDRE_0082, 0271, 0670, 0687, 0688 month Month in which the sampling was made Month number Numeric Month is given from 1 to 12, following the dd/mm/yyyy date format. 1 to 12 NA 1 to 2 digits No data in following 19611970 sites: ECORDRE_0343, 0344, 0345, 0901, 0902, 0903, 0904, 0906, 1510, 1526 No data in following 20192023 sites: ECORDRE_0082, 0271, 0670, 0687, 0688 year Year in which the sampling was made Year number Numeric Year is given from 1961 to 2023, following the dd/mm/yyyy date format. 1961 to 1970 and 2019 to 2023 no missing value 4 digits city Name of the city in which the sampling took place, extracted from the map of French administrative cities Character Text 1103 levels (not listed here) NA 2 to 31 characters No data in following 19611970 sites: ECORDRE_1510 administrative_department Name of the French administrative department in which the sampling took place, Character Text 92 levels: Ain Aisne Allier no missing value 3 to 23 characters 70 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length extracted from the map of French administrative departments Alpes-de-Haute-Provence Alpes-Maritimes Ardèche Ardennes Ariège Aube Aude Aveyron Bas-Rhin Bouches-du-Rhône Calvados Cantal Charente Charente-Maritime Cher Corrèze Corse-du-Sud Côte-d'Or Côtes-d'Armor Creuse Deux-Sèvres Dordogne Doubs Drôme Essonne Eure Eure-et-Loir Finistère Gard Gers Gironde Haute-Corse Haute-Garonne Haute-Loire Haute-Marne Hautes-Alpes Haute-Saône 71 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length Haute-Savoie Hautes-Pyrénées Haute-Vienne Haut-Rhin Hérault Ille-et-Vilaine Indre Indre-et-Loire Isère Jura Landes Loire Loire-Atlantique Loiret Loir-et-Cher Lot Lot-et-Garonne Lozère Maine-et-Loire Manche Marne Mayenne Meurthe-et-Moselle Meuse Morbihan Moselle Nièvre Nord Oise Orne Pas-de-Calais Puy-de-Dôme Pyrénées-Atlantiques Pyrénées-Orientales Rhône Saône-et-Loire Sarthe 72 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length Savoie Seine-et-Marne Seine-Maritime Somme Tarn Tarn-et-Garonne Territoire de Belfort Val-d'Oise Var Vaucluse Vendée Vienne Vosges Yonne Yvelines administrative_region Name of the French administrative region in which the sampling took place, extracted from the map of French administrative regions Character Text 14 levels: Auvergne-Rhône-Alpes Bourgogne-Franche-Comté Bretagne Centre-Val de Loire Corse Grand Est Hauts-de-France Île-de-France Normandie Nouvelle-Aquitaine Occitanie Pays de la Loire Provence-Alpes-Côte d'Azur no missing value 5 to 26 characters altitude Altitude of the sampling place meter numeric Altitude is given following Bouché (1972) description, for both the 1961-1970 and the 2019-2023 data. 10 to 2770 NA 0 decimals 2 to 4 digits No data in following 19611970 sites: ECORDRE_1510, 1525, 1526 eunis Habitat following EUNIS 2018 Character Text 72 levels, all described in Table 1 NA 1 to 3 characters No data in following 19611970 sites: 73 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length classification. EUNIS codes are interpreted from Bouché (1972) description. A25: Coastal saltmarshes and saline reedbeds B14: Coastal stable dune grassland (grey dunes) C3: Inland surface waters C35: Periodically inundated shores with pioneer and ephemeral vegetation D2: Valley mires, poor fens and transition mires D51: Reedbeds normally without free-standing water E1: Dry grasslands E2: Mesic grasslands E21: Permanent mesotrophic pastures and aftermath-grazed meadows E23: Mountain hay meadows E26: Agriculturallyimproved, re-seeded and heavily fertilised grassland, including sports fields and grass lawns E27: Unmanaged mesic grassland E3: Seasonally wet and wet grasslands E32: Mediterranean short humid grassland E4: Alpine and subalpine grasslands E41: Vegetated snow-patch E5: Woodland fringes and clearings and tall forb stands E53: Pteridium aquilinum fields E71: Atlantic parkland ECORDRE_0010, 0017, 0018, 0062, 0827, 1202, 1262, 1389, 1390, 1425, 1426, 1526 80 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length codes are interpreted from Bouché (1972) description. areas 4: Wetlands 1202, 1262, 1389, 1390, 1391, 1425, 1426, 1505, 1511, 1526 corine_land_cover_2 Second level of habitat following Corine Land Cover classification. Corine Land Cover codes are interpreted from Bouché (1972) description. Character Text 10 levels: 12: Industrial, comercial and transport units 14: Artificial, non-agricultural vegetated areas 21: Arable land 22: Permanent crops 23: Pastures 31: Forest 32: Shrub and/or herbaceous vegetation associations 33: Open spaces with little or no vegetation 41: Inland wetlands 42: Coastal wetlands NA 2 characters No data in following 19611970 sites: ECORDRE_0010, 0013, 0017, 0018, 0054, 0062, 0112, 0346, 0348, 0349, 0350; 0827, 1202, 1262, 1389, 1390, 1391, 1425, 1426, 1505, 1511, 1526 corine_land_cover_particularity Habitat particularity not distinguishable with Corine Land Cover classification. Habitat particularity not distinguishable with Corine Land Cover classification are interpreted from Bouché (1972) description. Character Text 4 levels: wet grassland agricultural and horticultural waste agricultural and horticultural waste in wet grassland decaying trunk All the other sites that do not have any particularity are filled with NA. NA 13 to 53 characters climate_koppen Climate type following KöppenGeiger Character Text 8 levels: BSk: Cold semi-arid climate no missing value 2 or 3 characters 81 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length classification, extracted from 1990-2020 map of Beck et al. (2023) Cfa: Humid subtropical climate Cfb: Temperate oceanic climate Csa: Hot-summer Mediterranean climate Csb: Warm-summer Mediterranean climate Dfb: Warm-summer humid continental climate Dfc: Subarctic climate ET: Tundra climate climate_joly Climate type following Joly et al. (2010) classification, extracted from Joly et al. (2010) Character Text 9 levels: Type 1: Mountain climates Type 2: Semi-continental and mountain edges climate Type 3: Degraded oceanic of Central and Northern plains climate Type 4: Altered oceanic climate Type 5: Clear oceanic climate Type 6: Altered Mediterranean climate Type 7: Southwestern basin climate Type 8: Clear Mediterranean climate Type Corsica: Corsican climate NA 6 or 12 characters No data in following 19611970 sites: ECORDRE_1510 ecoregion Ecoregion name following Dinerstein et al. (2017) classification, Character Text 9 levels: Alps conifer and mixed forests Cantabrian mixed forests no missing value 24 to 57 characters 82 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length extracted from Dinerstein et al. (2017) Corsican montane broadleaf and mixed forests European Atlantic mixed forests Italian sclerophyllous and semi-deciduous forests Northeast Spain and Southern France Mediterranean forests Pyrenees conifer and mixed forests Tyrrhenian-Adriatic sclerophyllous and mixed forests Western European broadleaf forests geology Substrate type of the sampling place Character Text 5 levels: metamorphic: metamorphic substrate plutonic: plutonic substrate sedimentary: sedimentary substrate volcanic: volcanic substrate volcano-plutonic: volcanoplutonic substrate no missing value 8 to 16 characters coarse_sand_2019to2023 Fraction of coarse sand for the 20192023 data (0-25 cm) Percentage of dry fine soil Numeric Coarse sand corresponds to particles from 200 to 2000 µm. Measured with the pipette 0.19 to 79.40 NA 2 decimals 3 to 4 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 83 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length sedimentation method (NF X31-107 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 fine_sand_2019to2023 Fraction of fine sand for the 20192023 data (0-25 cm) Percentage of dry fine soil Numeric Fine sand corresponds to particles from 50 to 200 µm. Measured with the pipette sedimentation method (NF X31-107 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 0.20 to 65.32 NA 2 decimals 3 to 4 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 coarse_silt_2019to2023 Fraction of coarse silt for the 20192023 data (0-25 cm) Percentage of dry fine soil Numeric Coarse silt corresponds to particles from 20 to 50 µm. Measured with the pipette sedimentation method (NF X31-107 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 0.30 to 53.26 NA 2 decimals 3 to 4 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 fine_silt_2019to2023 Fraction of fine silt for the 2019-2023 data (0-25 cm) Percentage of dry fine soil Numeric Fine silt corresponds to particles from 2 to 20 µm. Measured with the pipette 0.60 to 37.50 NA 2 decimals 3 to 4 digits Only for the 2019-2023 data. Missing data in following sites: 84 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length sedimentation method (NF X31-107 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 clay_2019to2023 Fraction of clay for the 2019-2023 data (0-25 cm) Percentage of dry fine soil Numeric Clay corresponds to particles ≤ 2 µm. Measured with the pipette sedimentation method (NF X31-107 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 1.10 to 54.60 NA 2 decimals 3 to 4 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 organic_matter_2019to2023 Percentage of organic matter for the 2019-2023 data (0-25 cm) Percentage of dry fine soil Numeric Measured with sulfochromic oxidation and visible spectroscopy method (NF ISO 14235 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 0.90 to 12.17 NA 2 decimals 3 to 4 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 nitrogen_2019to2023 Percentage of total nitrogen for the 2019-2023 data (025 cm) Percentage of dry fine soil Numeric Measured with a dry combustion method (Dumas method) (NF ISO 13878 standard). 0.041 to 2.090 NA 3 decimals 4 digits Only for the 2019-2023 data. Missing data in following sites: 85 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 carbonates_2019to2023 Percentage of total carbonates (CaCO3 ) for the 2019-2023 data (0-25 cm) Percentage of dry fine soil Numeric Measured with Bernard calcimetry (HCl reaction) method (NF ISO 10693 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 0.0 to 79.30 NA 2 decimals 2 to 4 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 cec_2019to2023 Cation-exchange capacity (CEC) for the 2019-2023 data (0-25 cm) mEq per 100 grams of dry fine soil Numeric Measured with ammonium acetate extraction method (Metson method) (NF X 31130 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 2.09 to 46.65 NA 2 decimals 3 to 4 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 c_organic_2019to2023 Percentage of organic carbon for the 2019-2023 data (0-25 cm) Percentage of dry fine soil Numeric Measured with sulfochromic oxidation and visible spectroscopy method (NF ISO 14235 standard). 0.52 to 7.08 NA 2 decimals 3 digits Only for the 2019-2023 data. Missing data in following sites: 86 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 ph_water_2019to2023 pH measured in a water suspension for the 2019-2023 data (0-25 cm) No unity Numeric Measured with 1:5 extraction (in water) method (NF ISO 10390 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 2.09 to 8.67 NA 2 decimals 3 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 ph_kcl_2019to2023 pH measured in a KCl suspension for the 2019-2023 data (0-25 cm) No unity Numeric Measured with 1:5 extraction (in KCl) method (NF ISO 10390 standard). Soil extracted in the 0-25 cm layer, composite of all replicates (or composite of all digging if qualitative sampling). 2.09 to 8.19 NA 2 decimals 3 digits Only for the 2019-2023 data. Missing data in following sites: ECORDRE_0041, 0067, 0082, 0114, 0115, 0116, 0117, 0118, 0119, 0123, 0271, 0412, 0432, 0441, 0612, 0658, 0679, 1055, 1166, 1290, 1292, 1293, 1297, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327 soil_texture_1961to1970 Texture of soil for the 1961-1970 data (0-30 cm) Character Text 27 levels: clay clay on rock NA 4 to 19 characters Only for the 1961-1970 data. Missing data in following sites: 87 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length clay sandy clay sandy on rock clay sandy stony clay silt clay silt on rock clay silt stony clay stony sandy sandy clay sandy clay on rock sandy clay stony sandy on rock sandy silty sandy silty on rock sandy silty stony sandy stony silty silty clay silty clay on rock silty clay stony silty on rock silty sandy silty sandy on rock silty sandy stony silty stony Soil texture based on empirical observation. Soil extracted in the 0-30 cm layer. ECORDRE_0005, 0009, 0010, 0013, 0017, 0018, 0020, 0033, 0046, 0054, 0062, 0064, 0068, 0069, 0071, 0089, 0092, 0112, 0113, 0151, 0181, 0234, 0288, 0346, 0348, 0349, 0350, 0417, 0448, 0452, 0453, 0529, 0645, 0807, 0827, 0853, 0901, 0912, 0976, 0992, 0997, 1013, 1042, 1054, 1070, 1099, 1143, 1148, 1149, 1154, 1162, 1185, 1190, 1202, 1248, 1252, 1300, 1303, 1310, 1322, 1338, 1351, 1389, 1390, 1391, 1392, 1396, 1426, 1437, 1505, 1510, 1511, 1525, 1526 nitrogen_1961to1970 Nitrogen content for the 1961-1970 data (0-30 cm) 10*part per million of dry soil = 10*ppm of dry soil Numeric Measured with Kjeldahl method (Kjeldahl 1883). Soil extracted in the 0-30 cm layer. 56 to 2770 NA 0 decimal 2 to 4 digits Only for the 1961-1970 data. Missing data in following sites: ECORDRE_0001, 0004, 88 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length 0005, 0007, 0009, 0010, 0011, 0013, 0017, 0018, 0019, 0020, 0023, 0025, 0040, 0041, 0042, 0043, 0045, 0046, 0051, 0052, 0054, 0062, 0063, 0064, 0065, 0066, 0109, 0112, 0113, 0343, 0344, 0345, 0348, 0350, 0889, 0901, 0902, 0904, 0906, 1151, 1192, 1202, 1230, 1245, 1310, 1343, 1351, 1391, 1392, 1426 , 1505, 1510, 1511, 1521, 1522, 1523, 1525, 1526 carbonates_1961to1970 Per mille of total carbonates (CaCO3 ) for the 1961-1970 data (0-30 cm) Per mille of dry soil Numeric Measured with a Bernard calcimeter, with subsequent calculations carried out on an IBM 1130 computer, assuming CO2 behaved as an ideal gas. Soil extracted in the 0-30 cm layer. 0 to 762 NA 0 decimal 1 to 3 digits Only for the 1961-1970 data. Missing data in following sites: ECORDRE_0001, 0004, 0005, 0006, 0007, 0009, 0010, 0011, 0013, 0017, 0018, 0019, 0020, 0023, 0025, 0040, 0041, 0042, 0043, 0045, 0046, 0051, 0052, 0054, 0062, 0063, 0064, 0065, 0066, 0083, 0096, 0097, 0099, 0100, 0103, 0104, 0105, 0109, 0112, 0113, 0114, 01 15, 0117, 0118, 0119, 0120, 0122, 0123, 0125, 0126, 0127, 0128, 0129, 0162, 0164, 0212, 0214, 0228, 0230, 0235, 0237, 0238, 0241, 0247, 0255, 0256, 0257, 0258, 0260, 0262, 0271, 0273, 0274, 0278, 0288, 0289, 0308, 0328, 0338, 0343, 0344, 0345, 0348, 0879, 0880, 0881, 0882, 0883, 0884, 0885, 0887, 0888, 0889, 0890, 0891, 0892, 0893, 89 Variable identity Variable definition Units of measurement Data type Data format Data anomalies Storage type List and definition of variable code Range for numeric value Missing value code Number of decimal Fixed, variable length 0894, 0895, 0898, 0899, 0900, 0901, 0902, 0904, 0906, 1151, 1192, 1202, 1230, 1248, 1310, 1343, 1351, 1391, 1392, 1426, 1505, 1510, 1511, 1521, 1522, 1523, 1525, 1526 carbon_1961to1970 Organic carbon content for the 1961-1970 data (030 cm) 10*part per million of dry soil = 10*ppm of dry soil Numeric Measured with Anne method (Anne 1945). Soil extracted in the 0-30 cm layer. 570 to 90000 NA 0 decimal 3 to 5 digits Only for the 1961-1970 data. Missing data in following sites: ECORDRE_0001, 0004, 0005, 0007, 0009, 0010, 0011, 0013, 0017, 0018, 0019, 0020, 0023, 0025, 0040, 0041, 0042, 0043, 0045, 0046, 0051, 0052, 0054, 0062, 0063, 0064, 0065, 0066, 0109, 0112, 0113, 0343, 0344, 0345, 0348, 0350, 0889, 0901, 0902, 0904, 0906, 1151, 1192, 1202, 1230, 1245, 1310, 1343, 1351, 1391, 1392, 1426, 1505, 1510, 1511, 1521, 1522, 1523, 1525, 1526 ph_water_1961to1970 pH measured in a water suspension for the 1961-1970 data (0-30 cm) No unity Numeric Measure with a pH meter once equilibrium was achieved after fresh soil subsamples were mixed with an equivalent volume of water. Soil extracted in the 0-30 cm layer. 3.3 to 8.5 NA 1 decimal 2 digits Only for the 1961-1970 data. 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