# StoichLife: A Global Dataset of Plant and Animal Elemental Content

Angélica L González, Julian Merder, Karl Andraczek, Ulrich Brose, Michal Filipiak, W. Stanley Harpole, Helmut Hillebrand, Michelle C Jackson, Malte Jochum, Shawn J Leroux, et al.

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Angélica L González, Julian Merder, Karl Andraczek, Ulrich Brose, Michal Filipiak, et al. StoichLife: A Global Dataset of Plant and Animal Elemental Content. Scientific Data, 2025, 12 (1), pp.569. 10.1038/s41597-025-04852-w. hal-05178126

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# StoichLife: A Global Dataset of Plant and Animal Elemental Content

Angélica L. González et al.#

The elemental content of life is a key trait shaping ecology and evolution, yet organismal stoichiometry has largely been studied on a case-by-case basis. This limitation has hindered our ability to identify broad patterns and mechanisms across taxa and ecosystems. To address this, we present StoichLife, a global dataset of 28,049 records from 5,876 species spanning terrestrial, freshwater, and marine realms. Compiled from published and unpublished sources, StoichLife documents elemental content and stoichiometric ratios (%C, %N, %P, C:N, C:P, and N:P) for individual plants and animals. The dataset is standardized and, where available, includes information on taxonomy, habitat, body mass (for animals), geography, and environmental conditions such as temperature, solar radiation, and nutrient availability. By providing an unprecedented breadth of organismal stoichiometry, StoichLife enables the exploration of global patterns, ecological and evolutionary drivers, and context-dependent variations. This resource advances our understanding of the chemical makeup of life and its responses to environmental change, supporting progress in ecological stoichiometry and related fields.

## Background & Summary

Owing to their shared evolutionary history, all living organisms possess a common biochemistry of 28 naturally occurring chemical elements¹,². These elements play essential roles in core biological processes, including the storage of genetic information, metabolic regulation, mechanical support, and protective mechanisms³⁻⁵. Despite this universal elemental composition, the relative proportions of these elements —referred to as stoichiometry—vary both within and among species³,⁶. Such stoichiometric variation underlies key functional traits related to resource uptake, assimilation, storage, and release⁷⁻⁹, reflecting the evolution of diverse life-history strategies shaped by organismal morphology and function. Furthermore, stoichiometric variation may reflect ecological adaptations to the environmental conditions in which different organisms have evolved³,⁴,¹⁰,¹¹.

The framework of ecological stoichiometry³ has driven extensive research into how organisms acquire, store, and transfer nutrients essential for growth and reproduction across diverse environments. Despite significant advancements, progress has been hindered by the limited availability and synthesis of elemental content data for plants and animals across broad spatial and taxonomic scales, as well as insufficient integration between ecological and evolutionary mechanisms (but see⁶,¹²). Therefore, a comprehensive understanding of spatiotemporal patterns and underlying mechanisms governing stoichiometric diversity is crucial. Beyond explaining observed patterns in organismal elemental content, such insights would enhance our ability to predict how organisms will respond to ongoing and future environmental changes, particularly under intensifying global change drivers.

To address this critical gap, we introduce StoichLife — the first global dataset for biogeographical and macroevolutionary patterns in organismal stoichiometry, developed within the sBIOMAPS working group at iDiv in Germany¹³. StoichLife provides openly accessible data without restrictions, though we kindly ask users to acknowledge this paper when using the database. The database aims to advance research in ecological stoichiometry, functional biogeography, macroecology, and macroevolution. Additionally, StoichLife remains open to integrating new data and future updates.

<footnote># A full list of authors and their affiliations appears at the end of the paper.</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Workflow and structure of the StoichLife dataset. The database includes 227 plant and animal elemental content datasets across terrestrial, freshwater, and marine realms. StoichLife comprises 38 variables, including environmental descriptors related to temperature, solar radiation, and environmental N and P availability. These were compiled from the literature using bioinformatic approaches to allow for reproducibility, consistency, and efficiency. Each data type was checked and validated using the R programming language (see Methods - Data processing section).</figcaption></figure>

## Methods

**Data compilation.** We developed the StoichLife template structure, which contains data pertaining to elemental content and their ratios (%C, %N, %P, C:N, C:P, and N:P) alongside body size measurements, and information regarding sampling locality, country, and taxonomic affiliation (i.e., phylum, class, order, family, species or morphospecies). This template was distributed among prospective data contributors actively engaged in sampling and analyzing plant and animal elemental content across diverse regions worldwide (between 2014 and 2022). As part of this effort, we engaged 24 researchers who contributed 50 datasets on animals (both invertebrates and vertebrates), including 15 previously published datasets<sup>14-28</sup>, one dataset from two sources<sup>29,30</sup>, and 34 unpublished datasets (details of collection outlined in Supplementary Text 1; refs. <sup>31-55</sup>). Additionally, we incorporated data from six large databases used in prior studies, comprising one zooplankton dataset<sup>56</sup>, 20 aquatic animal datasets<sup>57</sup>, five datasets for both animals and plants<sup>10</sup>, three datasets for coral reef macroalgae (Pangaea database<sup>58</sup>), 80 datasets for green leaves<sup>59</sup>, and 35 datasets for plants<sup>60</sup>. Datasets were included based on the following criteria: (i) elemental analyses were conducted on individual organisms under natural conditions, excluding those subjected to experimental manipulations such as nutrient enrichment; (ii) for animals, analyses were performed on whole-body (bulk) tissue, while for plants, stoichiometry was primarily assessed through leaf or shoot elemental composition; and (iii) georeferenced coordinates of sampling sites were available to facilitate spatial analyses.

**Data search.** To complement data contributors, we conducted a systematic literature review to identify ecological stoichiometry studies published before 2021. Using Clarivate Analytics' Web of Science Core database, we employed a set of search terms: "nutrient content" OR "nutrient composition" OR "elemental content" OR "elemental composition" OR "chemical composition" OR "nitrogen content" OR "nitrogen composition" OR "phosphorus content" OR "phosphorus composition" OR "percent nitrogen" OR "percent phosphorus" OR "N:P" OR "nitrogen-to-phosphorus" OR "ecological stoichiometry".

This rigorous search yielded a total of 2,620 papers, which were further filtered to exclude: (i) microbial data, which typically represent analyses of entire microbial communities rather than individual cells; (ii) studies involving laboratory or field experiments; and (iii) literature reviews and opinion papers. Following this
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Distribution of sampling locations in the StoichLife dataset. The histogram describes the number of records within bin sizes of 4° × 6° (latitude and longitude, respectively).</figcaption></figure>

refinement, 110 eligible papers remained. After thoroughly examining these papers, we narrowed the selection down to 33 papers that met our criteria<sup>8,41,61–91</sup>. In cases where archived datasets lacked essential details (e.g., species-mean resolution), we contacted authors to request raw data. Additionally, we cross-referenced all identified datasets within Andrieux *et al.*<sup>92</sup>, the most comprehensive synthesis study on animal stoichiometry to date. However, no additional datasets meeting our criteria were found through this process.

StoichLife consolidates 227 datasets, comprising an unprecedented 28,049 individual records from both published and unpublished sources<sup>13</sup>. At the time of data compilation (2022/09/19): 14% of datasets (n = 31) and 26% of records (n = 7,361) were unpublished. StoichLife contains data on plant and animal elemental content (%C, %N, and %P; Fig. 1) from terrestrial, freshwater, and marine realms. Spanning a broad geographical extent from 68°S (Antarctica) to 81°N (Arctic) and from 160°W (Hawaii, USA) to 177°E (New Zealand), StoichLife encompasses a diverse array of taxonomic groups, including plants and animals, across realms (Fig. 1). Despite its inherent limitations, the StoichLife dataset is the most comprehensive compilation of animal and plant data from terrestrial, freshwater, and marine realms across the globe to date (Figs. 1 and 2).

## Data Processing

**Dataset checking, cleaning, and formatting.** Three distinct data types were processed: quantitative, taxonomic, and spatial. These data underwent rigorous validation and quality assurance procedures using the R software<sup>93</sup>. The quantitative data include elements such as individual %C and element ratios (e.g., C:N) and individual body mass measurements (dry mass). Elemental content values were verified to represent the percentage of each element in dry body mass, while elemental ratios were checked to ensure they accurately reflected both mass and molar ratios.

The taxonomic data encompass classifications ranging from species and morphospecies to higher taxonomic ranks, including families, orders, classes, phylum, and kingdoms. Data validation involved both automated and manual inspection to correct spelling errors, complete missing taxonomic information where feasible, address ambiguously identified morphospecies (e.g., "Geophilidae," "Psychodidae sp.1"), and ensure the accuracy of currently accepted names. In cases where two distinct morphospecies with identical names appeared in different datasets (e.g., "Psychodidae sp.1"), we assigned unique identifiers to distinguish them (e.g., "Psychodidae sp.1_A" and "Psychodidae sp.1_B" in the two datasets). Taxonomic affiliations were validated using the Global Biodiversity Information Facility (GBIF), Integrated Taxonomic Information System (ITIS), and Catalogue of Life (COL) databases via the *taxadb*-package (version 0.1.5<sup>94</sup>) in R. Additionally, plant taxonomy was verified using the Plants of the World online website (https://powo.science.kew.org/; Fig. 1).

To standardize taxonomic names across datasets, we applied a set of harmonization criteria. First, when missing taxonomic information was identified and multiple synonyms were available, we retained the accepted name based on established taxonomic databases (GBIF, ITIS, and COL). Second, taxonomic synonyms found
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Number of observations within taxonomic classes. Classes are sorted by decreasing number of observations. Numbers beside bars indicate the number of species or morphospecies within each class. Number of observations is given in the thousands. Classes with <10 observations are not shown (n = 13). Species or morphospecies for which the class was not identified are not shown (n = 600).</figcaption></figure>

between original publications and taxonomic databases were standardized to their accepted names. Third, in cases where taxonomic information in original sources aligned with taxonomic information from GBIF, ITIS, or COL, we prioritized GBIF as the primary reference unless substantial discrepancies were found. Finally, when inconsistencies remained unresolved after validation (e.g., differing taxonomic information between the original publication and taxonomic sources), we deferred to the taxonomic information provided by the original
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Distribution of elemental content for the 16 phyla included in the database. Figures on top of each boxplot indicate the number of observations. Species or morphospecies for which the phylum was not identified are not shown (n = 7).</figcaption></figure>

publications or data contributors. The StoichLife dataset preserves both the initial taxonomic classifications pro-
vided by contributors and the revised taxonomy to maintain transparency and facilitate future updates.

The spatial data include latitude and longitude coordinates of sampling locations. These coordinates under-
went extensive validation through visual inspection, where they were plotted onto a global map to identify and
correct any spatial errors. Common discrepancies, such as marine data mistakenly recorded as inland or vice
versa, were rectified using geographical details provided in the original publications (Fig. 1). These steps ensured
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Distribution of elemental ratios for the 16 phyla included in the database. Figures on top of each boxplot indicate the number of observations. Species or morphospecies for which the phylum was not identified are not shown (n = 7). For the sake of visual clarity, values above 100, 3000, and 400 were removed for C:N (n = 8), C:P (n = 17), and N:P (n = 5), respectively.</figcaption></figure>

that spatial data were accurate and georeferenced correctly, enhancing their utility for ecological and biogeographical analyses.

StoichLife comprises 38 variables (columns) and 28,049 individual records (rows; Supplementary Table 1). Each record corresponds to at least one measurement of one elemental content or ratio taken at the level (i.e., %C, %N, %P, C:N, C:P, and N:P) of an individual organism. Records in the StoichLife dataset are distributed
<figure><img src="image_8.png" /><figcaption>**Fig. 6** Distribution of elemental content across realms and trophic groups. For the sake of visual clarity, values above 100, 3000, and 400 were removed for C:N (n = 8), C:P (n = 17), and N:P (n = 5), respectively.</figcaption></figure>

across 1,120 locations, each associated with both latitude and longitude coordinates (n = 23,290 records; Fig. 2; Supplementary Table 1).

In addition, 3,616 records contain only latitudinal information, while 1,143 records lack spatial information entirely. The dataset spans terrestrial (n = 16,832), freshwater (n = 8,935), and marine (n = 2,282) realms. Most records originate from the northern hemisphere (n = 19,268) compared to the southern hemisphere (n = 7,638; Fig. 2). Geographically, the dataset exhibits extensive coverage in Europe, the Americas, East Asia, and Eastern Australia, with notable gaps observed in Africa (excluding South Africa), the Middle East, Central and Southeast Asia, Western Australia, and Russia (Fig. 2). In the marine realm, Oceania (Western Pacific) and the Eastern Indian Ocean are underrepresented.

The revised taxonomy within StoichLife comprises 5,876 species (65.4%) and morphospecies (34.6%), spanning 837 families, 208 orders, 50 classes, 16 phyla, and 2 kingdoms. Animalia is the most extensively documented kingdom (n = 19,664 entries), followed by Plantae (n = 8,385 entries; Fig. 3; Supplementary Table 1). The percentage of taxa identified at the species level is higher in plants (92.4% of all plant taxa) than in animals (27.7% of all animal taxa). Two major classes, Insecta (Animalia) and Magnoliopsida (Plantae), account for approximately 44% of all entries in StoichLife, with 7,205 individual records for Insecta and 5,130 for Magnoliopsida (Fig. 3). Among these, Magnoliopsida has the highest number of species or morphospecies, (n = 2,420), followed by Insecta (n = 1,366) (Fig. 3).

However, 30 out of 51 classes contain 50 or fewer individual records. The dataset includes 10,322 individual animal records (1,570 species or morphospecies) and 6,510 individual plant records (3,048 species or morphospecies) from the terrestrial realm. In freshwater habitats, there are 7,882 individual animal records (751 species or morphospecies) and 1,053 individual plant records (206 species or morphospecies). Meanwhile, marine data contain 1,460 individual animal records (133 species or morphospecies) and 822 individual plant records (172 species or morphospecies; primarily algae). Given that certain species inhabit several environments due to their life cycles, habitat classifications were preserved as indicated in the original sources. For example, terrestrial insects with aquatic larval stages were classified according to their primary feeding environment, distinguishing between aquatic larvae and terrestrial adults.

The number of individual records varies by element and stoichiometric ratio, with %N (n = 25,652) being the most common, followed by %C (n = 18,558), C:N (n = 18,373), %P (n = 14,457), N:P (n = 12,244), and C:P (n = 7,092). Only 7,091 individual records (25.3% of all entries) contain values for all three elements (i.e., %C, %N, and %P), with the majority belonging to animals (n = 5,635; 20.1% of all records) rather than plants (n = 1,456; 5.2% of all records). Overall, StoichLife provides a wide range of elemental values and their ratios (Figs. 4 and 5; Supplementary Table 1): %C [0.6–78.1], %N [0.06–19.5], %P [0.004–8.9], C:N molar [2.07–223.3], C:P molar [7.4–9154.7], and N:P molar [0.2–789.4].

These values exhibit substantial variations across taxonomic and trophic groups (i.e., phyla; Figs. 4 and 5) and realms (i.e., freshwater, marine, and terrestrial; Fig. 6).
<figure><img src="image_9.png" /><figcaption>**Fig. 7** Distribution of dry body mass across animal phyla. Masses are displayed as body dry masses (g) plotted on a log10 axis. The boxplots within the violin plot show the median, upper, and lower quartile per phylum.</figcaption></figure>

Body mass data, measured as whole-organismal dry mass in g, were recorded for 9,942 individual animals. The two most represented taxa were Arthropoda (n = 6,552) and Chordata (n = 2,868; Fig. 7), with smaller contributions from Mollusca (n = 149), Annelida (n = 132), Nematoda (n = 62), Platyhelminthes (n = 58), Chaetognatha (n = 48), Cnidaria (n = 35), Acanthocephala (n = 27), and Ctenophora (n = 11). Terrestrial animals accounted for 3,885 individual records, while 5,347 originated from freshwater and 710 from marine realms. Body dry mass values ranged from <0.001 g (copepod nauplii) to over 800 g (Salmo salar Linnaeus, 1758; Supplementary Table 1), demonstrating substantial variation across taxa (Fig. 7).

To facilitate investigations into environmental drivers of organism elemental content, StoichLife integrates information on environmental factors such as air temperature, solar radiation, as well as environmental nitrogen and phosphorus availability. Mean annual air temperature at 10 m above ground or sea surface (T10M; °C; 0.5° × 0.5° resolution) and solar radiation data (ALLSKY_SFC_SW_DWN: All Sky Insolation Incident on a Horizontal Surface; W/m²; 1° × 1° resolution) from each sampling site were extracted from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources project (NASA POWER; https://power.larc.nasa.gov). Both temperature and solar data have temporal coverage from 1981 to 2022/09.

Global nitrogen (N) availability at each sampling site, represented by inorganic N deposition (kg N/km²/year¹; resolution of 2° × 2.5°); was retrieved from a published database⁹⁵ covering the period from 1984 to 2016). Soil phosphorus (P) data (P labile; g P/m²; resolution 0.5° × 0.5° resolution) were extracted from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC; https://daac.ornl.gov). While these soil P data lack specific temporal coverage, they are nominally representative of pre-industrial conditions ca. 1850. Additionally, marine total P data (sea surface P measurement; micromoles P/kg; resolution 1° × 1° resolution were sourced from the National Center for Atmospheric Research World Ocean Atlas (NCAR WOA; https://climatedataguide.ucar.edu; WOA13), covering the period from 1955 to 2012. To ensure consistency across datasets, we computed the average values for each of these four environmental factors
across the respective temporal windows. This approach was chosen for several reasons: (1) our primary objective was to examine spatial rather than temporal variations in organismal elemental content; (2) samples were collected at different times by various researchers, and specific sampling dates were often unavailable; and (3) the environmental data were measured or modeled over different temporal timeframes, making direct temporal alignment impractical.

## Data Record

The dataset is provided as an Excel file at Dryad¹³, which includes key information such as elemental content in % of dry mass, spatial coordinates, the biological level of organization (e.g., organ- or individual-level measurement, mean per species or population), taxonomic information (e.g., from species or morphospecies to kingdom), realm identity, the ontogenetic stage when possible (e.g., larvae, juvenile, adult, seed, sprout). Additionally, the dataset incorporates any other relevant information provided by contributors. A metadata file 'StoichLife_metadata.xlsx' accompanies the dataset, detailing column headings, measurement units, and descriptions of numerical variables. This file also includes a comprehensive list of data sources used in compiling the database, ensuring full transparency and reproducibility.

## Technical Validation

Each dataset underwent thorough inspection using R (version 4.0.0 2020-04-24⁹³) to ensure data integrity and consistency. We generated histograms and estimated value ranges for each element and elemental ratio to detect extreme values, potential outliers, or measurement errors. In most cases, extreme values were retained unless they were deemed implausible based on clear indications of measurement errors. Only four values were excluded due to implausibility: one C value exceeding 80%, one N value below 0.01%, and two P values exceeding 20%. Importantly, original datasets with missing information were not excluded from the StoichLife dataset. Some datasets contain missing data for specific variables, such as sampling location, specifically longitude, body mass, and trophic group (Supplementary Table 1). These gaps are retained to preserve data availability and allow future users to apply imputation or additional validation as needed.

## Code availability

All analyses and figures were produced using R. The R code is hosted in Zenodo⁹⁶, and the StoichLife database is in Dryad¹³.

Received: 11 October 2024; Accepted: 18 March 2025;

Published online: 03 April 2025

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## Acknowledgements

This paper is a joint effort of the working group sBiomaps kindly supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118, 202548816). Members of sBiomaps include ALG, OD, JM, KA, UB, MF, MJa, Mjo, WSH, HH, SJL, MPN, REO, GLWP, RP, AR, JS, ES, MS, and EZ. This research was partially supported by a grant to ALG from the National Science Foundation (NSF: DEB-1754326). MF was supported by the Jagiellonian University, Faculty of Biology, Institute of Environmental Sciences (N18/DBS/000022). HH was supported by the German Research Foundation (DFG HI848/26-2), SJL by the Natural Sciences and Engineering Research Council of Canada, Discovery Grant (RGPIN-2020-04132), JSs by the Research Foundation Flanders (FWO), EZ by the
FAPERJ JCNE (E-26/201.286/2021, E-26/203.213/2017), NE by the DFG FOR 5000, VFF was partially supported by the CNPq-Brazil productivity grant (310119/2018-9), JH by the Federal Aid in Sport Fish Restoration Program (F-69-P, Fish Management in Ohio) administered jointly by the U.S. Fish and Wildlife Service and the ODNR-DOW (project FADX09), PK by the Royal Society, Newton Advanced Fellowship (NAF\R2\180791), CL by the Australian Research Council awards (DP0986179 and DP160103669), AMP by the German Research Foundation (DFG, German Research Foundation) – (DFG 493345801 and 202548816, GQR was supported by the FAPESP grant (2019/08474-8) and by the CNPq-Brazil productivity grant (310119/2018-9), AT was supported by the Russian Science Foundation (22-14-00363).

## Author contributions

A.L.G. and O.D. wrote the first draft of the manuscript with inputs from the sBiomaps working group https://www.idiv.de/en/sbiomaps.html. O.D. and A.L.G. generated all analyses and graphs. A.L.G. and O.D. compiled the StoichLife database and obtained the iDiv funding that supported sBiomaps. A.L.G., O.D., K.A., U.B., M.F., H.H., W.S.H., M.Ja, M.Jo, M.P.N., R.E.O., G.L.W.P., R.P., M.S., A.R., E.S., J.S., S.J.L. and E.Z. cleaned the database and compiled environmental data. A.L.G., O.D., J.M., K.A., U.B., M.F., H.H., W.S.H., M.Ja, M.Jo, M.P.N., R.E.O., G.L.W.P., R.P., M.S., A.R., E.S., J.S., S.J.L. and E.Z. participated in one or more of the three sBiomaps workshops at iDiv where the sBiomaps initiative was organized, and the evaluation of the data and first drafts were discussed. All other authors contributed data. All authors contributed to writing and editing the manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41597-025-04852-w.

**Correspondence** and requests for materials should be addressed to A.L.G. or O.D.

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© The Author(s) 2025

Angélica L. González<fnref n="1" />,²,⁴,⁶✉, Julian Merder ID³, Karl Andraczek⁴,⁵, Ulrich Brose ID⁴,⁶, Michał Filipiak ID⁷, W. Stanley Harpole⁴,⁸,⁹, Helmut Hillebrand¹⁰,¹¹,¹², Michelle C. Jackson¹³, Malte Jochum ID⁴,¹⁴,¹⁵, Shawn J. Leroux¹⁶, Mark P. Nessel ID²,¹⁷, Renske E. Onstein ID⁴,¹⁸, Rachel Paseka ID¹⁹, George L. W. Perry²⁰, Amanda Rugenski²¹, Judith Sitters²²,²³, Erik Sperfeld²⁴,²⁵, Maren Striebel¹⁰, Eugenia Zandona²⁶, Jean-Christophe Aymes²⁷, Alice Blanckaert²⁸, Sarah L. Bluhm²⁹, Hideyuki Doi ID³⁰, Nico Eisenhauer ID⁴,¹⁵, Vinicius F. Farjalla³¹, James Hood³²,³³, Pavel Kratina ID³⁴, Jacques Labonne²⁷, Catherine E. Lovelock ID²⁸, Eric K. Moody³⁵, Attila Mozsár³⁶, Liam Nash³⁴, Melanie M. Pollierer²⁹, Anton Potapov⁴,²⁹,³⁷, Gustavo Q. Romero³⁸, Jean-Marc Roussel³⁹, Stefan Scheu ID²⁹,⁴⁰, Nicole Scheunemann²⁹,⁴¹, Julia Seeber ID⁴²,⁴³, Michael Steinwandter ID⁴², Winda Ika Susanti²⁹, Alexei Tiunov ID⁴⁴,⁴⁵ & Olivier Dézerald³⁹,⁴⁶✉

<footnote n="1">¹Department of Biology, Rutgers University, Camden, 08103, USA. ²Center for Computational and Integrative Biology, Rutgers University, Camden, 08103, USA. ³Department of Global Ecology, Carnegie Institution for Science, Stanford, 94305, USA. ⁴German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103, Leipzig, Germany. ⁵Department of Life Sciences, Systematic Botany and Functional Biodiversity, University Leipzig, 04103, Leipzig, Germany. ⁶Institute of Biodiversity, Friedrich-Schiller-University Jena, Jena, 07743, Germany.</footnote>
⁷Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, 30-387, Poland. ⁸Helmholtz Center for Environmental Research – UFZ, Department of Physiological Diversity, Leipzig, 04318, Germany. ⁹Martin Luther University Halle-Wittenberg, Halle (Saale), 06108, Germany. ¹⁰Institute for Chemistry and Biology of the Marine Environment (ICBM), School of Mathematics and Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, 26129, Germany. ¹¹Helmholtz-Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Oldenburg, 26129, Germany. ¹²Alfred Wegener Institute, Helmholtz-Centre for Polar and Marine Research (AWI), Bremerhaven, 27570, Germany. ¹³Department of Biology, University of Oxford, Oxford, OX1 3SZ, UK. ¹⁴Department of Global Change Ecology, Biocenter, University of Würzburg, Würzburg, 97074, Germany. ¹⁵Institute of Biology, Leipzig University, Leipzig, 04103, Germany. ¹⁶Department of Biology, Memorial University of Newfoundland, St. John's, NL A1B 3X9, Canada. ¹⁷Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, Oklahoma, 74078, USA. ¹⁸Naturalis Biodiversity Center, Leiden, 2333CR, The Netherlands. ¹⁹ASRC Federal / NASA Headquarters, Washington, DC, 20546, USA. ²⁰School of Environment, University of Auckland, Auckland, 1142, New Zealand. ²¹Odum School of Ecology, University of Georgia, Athens, GA, 30602, USA. ²²Department of Biology, Vrije Universiteit Brussel, Brussels, 1050, Belgium. ²³B-WARE Research Centre, Nijmegen, 6525 ED, The Netherlands. ²⁴German Environment Agency, Section IV 2.5, Berlin, 12307, Germany. ²⁵University of Greifswald, Zoological Institute and Museum, Greifswald, 17489, Germany. ²⁶Department of Ecology, Rio de Janeiro State University, Rio de Janeiro, 20550-013, Brazil. ²⁷UMR INRAE-UPPA, ECOBIOP, Université de Pau et Des Pays De l'Adour, Saint-Pée-sur-Nivelle, Pau, 64310, France. ²⁸School of Environment, The University of Queensland, St Lucia, QLD, 4072, Australia. ²⁹Department of Animal Ecology, J.-F. Blumenbach Institute for Zoology and Anthropology, University of Göttingen, Göttingen, 37073, Germany. ³⁰Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan. ³¹Department of Ecology, Federal University of Rio de Janeiro, Rio de Janeiro, 21941-590, Brazil. ³²Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Behavior, The Ohio State University, Columbus, OH, 43212, USA. ³³Translational Data Analytics Institute, The Ohio State University, Columbus, OH, 43212, USA. ³⁴Centre for Biodiversity and Sustainability, School of Biological and Behavioural Sciences, Queen Mary University of London, London, E1 4NS, UK. ³⁵Department of Biology, Middlebury College, Middlebury, VT, 05753, USA. ³⁶HUN-REN Balaton Limnological Research Institute, Tihany, 8237, Hungary. ³⁷Soil Zoology Division, Senckenberg Museum of Natural History Gorlitz, Görlitz, Germany. ³⁸Laboratory of Multitrophic Interactions and Biodiversity, Department of Animal Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Sao Paulo, 13083-865, Brazil. ³⁹DECOD, L'Institut Agro, IFREMER, INRAE, Rennes, 35000, France. ⁴⁰Centre of Biodiversity and Sustainable Land Use, University of Göttingen, Göttingen, 37077, Germany. ⁴¹Department of Soil Zoology, Section Mesofauna, Senckenberg Museum for Natural History Goerlitz, Goerlitz, 02826, Germany. ⁴²Institute for Alpine Environment, Eurac Research, Bozen/Bolzano, 39100, Italy. ⁴³Department of Ecology, Universität Innsbruck, Innsbruck, 6020, Austria. ⁴⁴A.N. Severtsov Institute of Ecology and Evolution of the Russian Academy of Sciences, Moscow, 119071, Russia. ⁴⁵Southern Branch of the Joint Russian-Vietnamese Tropical Research and Technological Center, Ho Chi Minh City, 700000, Vietnam. ⁴⁶These authors contributed equally: Angélica L. González, Olivier Dézerald. ✉e-mail: angelica.gonzalez@rutgers.edu; olivier.dezerald@inrae.fr