DOI: 10.5061/dryad.3tx95x6r2
title: StoichLife: A global dataset of plant and animal elemental content

description: 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 published and unpublished sources, StoichLife documents elemental content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.
description: # StoichLife: A global dataset of plant and animal elemental content
 [https://doi.org/10.5061/dryad.3tx95x6r2](https://doi.org/10.5061/dryad.3tx95x6r2) ## Description of the data and file structure *Data compilation* We developed the StoichLife template structure, which contains data about 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 disseminated among prospective data contributors actively sampling and analyzing plant and animal elemental content across diverse regions worldwide (between 2014 and 2022). In our endeavor, we engaged 24 researchers who contributed datasets on animals (both invertebrates and vertebrates), resulting in 50 datasets, among which some were already published (15 datasets and one dataset from two sources). Additionally, we incorporated datasets from six large databases used in prior studies, including one dataset for zooplankton, 20 datasets for aquatic animals, five datasets for animals and plants 10, three datasets for coral reef macroalgae (Pangaea database), 80 datasets for green leaves, and 35 datasets for plants. The primary criteria for data inclusion were that: (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 plant stoichiometry was commonly described by leaf or shoot elemental composition; and (iii) georeferenced coordinates of sampling sites were available to facilitate spatial analyses. *Data Search* In parallel with acquiring data from new contributors and existing large databases, 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 2,620 unique papers meeting our initial criteria. Given our focus on obtaining individual-level data on the elemental content of plants and animals under natural conditions, we excluded (i) microbial data, as these typically involve analyses of entire microbial communities rather than individual cells, and (ii) studies involving laboratory or field experiments. Moreover, we omitted literature reviews and opinion papers, refining our initial search to a subset of 110 eligible papers. After thoroughly examining these papers, we narrowed the selection down to 33 papers that met our criteria 8,35–66. In cases where downloaded, archived datasets lacked essential data at the desired resolution (such as datasets reporting species means), we proactively contacted the respective authors to request access to the raw data. Finally, we cross-referenced all identified datasets, the most comprehensive synthesis study on animal stoichiometry. However, additional datasets were kept from meeting our criteria through this process. These unpublished data represent a valuable resource that can now be harnessed to address stoichiometric questions using StoichLife. In compiling plant and animal stoichiometry data, we also gathered information on the spatial coordinates of the sampling locations. If this information was not explicitly provided in the publications, we contacted the corresponding authors to acquire the necessary spatial coordinates of the sampling sites. Despite its inherent limitations, the StoichLife database is the most comprehensive compilation of animal and plant data from terrestrial, freshwater, and marine realms across the globe to date. NAs represent not available or non-existing data to us. ### Files and variables #### File: StoichLife\_dataset\_2025\_02\_14.xlsx This file contains three sheets: 1. **Metadata Descriptions** – Provides detailed explanations of the dataset's variables and structure. 2. **References** – Lists all sources linked to the data, including published studies and unpublished datasets provided by authors. An additional file in the paper outlines the basic methods used for unpublished data. 3. **Data** – Contains the primary dataset. The **Metadata sheet** in the data file contains the description of all the variables along with their corresponding units. ## Code/software R (version 4.0.0 2020-04-24). This submission includes a « **datapaper_sbiomap*SciDat.R****«, which is the code used and »StoichLife*data_2025_02_14.xlsx », which contains the dataset associated.