DOI: 10.71615/bexis.32032
title: Data and code for the manuscript 'Schreiner et al. (2026). Slow and context-dependent: Responses to grassland extensification across a comprehensive set of ecosystem attributes.'

description: This supporting information accompanies the manuscript Schreiner et al. (2026), “Slow and context-dependent: Responses to grassland extensification across a comprehensive set of ecosystem attributes.” In this manuscript, we assess the potential of extensification as a grassland restoration strategy using a large-scale field experiment conducted across 45 grassland sites in three German regions. Sampling was conducted in 2023, after three years of experimental treatments. We evaluate above- and belowground responses to land-use reduction relative to predefined restoration targets and demonstrate that extensification induces rapid changes in many plant-related indicators, whereas belowground properties exhibit delayed responses. Restoration outcomes were strongly context-dependent, with greater progress observed in less productive regions, highlighting the importance of site-specific planning and long-term monitoring in grassland restoration.&lt;br&gt;&lt;br&gt;Data and code are provided in a structured folder system that enables full reproducibility of all statistical analyses, figures, and tables presented in the manuscript.&lt;br&gt;&lt;br&gt;The folder system has the following structure:&lt;br&gt;&lt;br&gt;\analysis\data\Data.csv = the dataset containing all variables.&lt;br&gt;&lt;br&gt;\analysis\data\Metadata.xlsx = the corresponding metadata.&lt;br&gt;&lt;br&gt;\analysis\scripts\Effect_of_extensification_on_restoration_goals.Rmd = a R Markdown file used to conduct the analyses and generate outputs (stored in \analysis\output).&lt;br&gt;&lt;br&gt;\analysis\scripts\Effect_of_extensification_on_restoration_goals.html = the corresponding HTML document. Figures and tables generated by the script are embedded in the HTML document.&lt;br&gt;&lt;br&gt;\analysis\output\results\Effect_table.xlsx = a table summarising extensification effects for all variables (generated by the script).&lt;br&gt;&lt;br&gt;\analysis\output\results\Model_table.xlsx = a table with parameter for all fitted models (generated by the script).&lt;br&gt;&lt;br&gt;\analysis\output = a folder containing several subfolders in which residual diagnostic plots (generated by the script) for all fitted models are stored.
description: Study design: The reduced land-use intensity experiment (REX) was established in autumn 2019 within the Biodiversity Exploratories framework. It includes 45 grassland sites across three German regions (Schorfheide-Chorin, Hainich-Dün, and Schwäbische Alb), covering a broad range of environmental conditions typical of Central Europe. In 2020, two 50 × 50 m plots were established per site: control plots maintained under existing high-intensity management and reduced plots subjected to extensification. Extensification consisted of one late-summer mowing per year, with fertilisation and grazing discontinued, thereby simulating a realistic reduction in land-use intensity commonly targeted by conservation and agri-environmental policies. See the Bexis Dataset 31457 (https://www.bexis.uni-jena.de/ddm/data/Showdata/31457) or the manuscript ‘Schreiner et al. (2026). The potential of land-use extensification as grassland restoration tool.’ for more details on study design. | Sampling design: Below, we provide a brief overview on sampling, laboratory analyses and data processing for details see the manuscript ‘Schreiner et al. (2026). The potential of land-use extensification as grassland restoration tool.’ &lt;br&gt;&lt;br&gt;The following data were generated within this study: soil physicochemical properties (including pH, water content, bulk density, nutrient pools and organic carbon), cover of bare soil, measurements of light availability and heterogeneity, sward height, community means of fine-root traits, soil bacterial and fungal community composition, and relative abundance of fungal guilds, microbial biomass carbon and nitrogen, extracellular enzyme activities, aboveground plant biomass and productivity, plant biomass carbon and nitrogen and stable isotope ratios (δ¹⁵N).&lt;br&gt;&lt;br&gt;The following data were obtained from other projects (available in separate BExIS-datasets, as referenced below) and integrated into this dataset: vegetation survey data (Hinderling & Keller 2023; Hinderling & Keller 2024), plant trait data from the TRY database (Neyret & Manning 2023), land-use intensity index (LUI), soil texture (Schöning et al., 2021) and topographic wetness indices (Boesing 2023). | Sample analysis: Laboratory analyses&lt;br&gt;&lt;br&gt;Soil samples were processed to determine pH, gravimetric soil water content, bulk density, and plant-available nitrogen, phosphorus, and soil organic carbon. Microbial biomass carbon and nitrogen were quantified using chloroform fumigation–extraction.&lt;br&gt;&lt;br&gt;Extracellular enzyme activities involved in carbon, nitrogen and phosphorus cycling were measured using fluorescent labelled (MUF) substrates. Enzyme kinetic parameters (Vmax and Km) were derived and normalised to microbial biomass.&lt;br&gt;&lt;br&gt;Soil bacterial and fungal communities were characterised using amplicon sequencing. Operational taxonomic units were assigned and used to derive taxonomic diversity metrics and to classify fungal OTUs into functional guilds.&lt;br&gt;&lt;br&gt;Carbon and nitrogen concentrations and δ¹⁵N values were measured for above- and belowground plant biomass. Above- and belowground C:N ratios were calculated as indicators of biomass quality and nutrient cycling. &lt;br&gt;&lt;br&gt;Root samples were washed using a multi-step cleaning procedure, scanned with an image analysis system, and analysed to derive fine-root traits. Plot-level mean values were obtained for root diameter, specific root length, root tissue density, and root dry matter content.&lt;br&gt;&lt;br&gt;Data processing&lt;br&gt;&lt;br&gt;Community-level taxonomic diversity metrics and ordination-based composition metrics were calculated for vascular plants, bacteria, and fungi. Functional composition was quantified as relative cover (plants) or relative abundance (fungi) of predefined functional groups. Vegetation surveys stem from Hinderling & Keller (2023) and Hinderling & Keller (2024).&lt;br&gt;&lt;br&gt;We calculated community-weighted means of plant species-specific indicator values (Ellenberg et al., 2001; Briemle et al., 2002) based on vegetation data from Hinderling & Keller (2024). Functional structure of plant communities was characterised using community-weighted distribution moments (mean, variance, skewness, and kurtosis). Aboveground trait data stems from the TRY database (cleaned and aggregated by Neyret & Manning 2023) and the BIOLFLOR database (Klotz et al., 2002). Root trait diversity metrics (variance, skewness, and kurtosis) was calculated based on data from the GRooT database (Guerrero-Ramírez et al., 2021). &lt;br&gt;&lt;br&gt;Classification of fungal OTUs into functional guilds (pathogenic, saprotrophic, symbiotic, arbuscular mycorrhizal fungi) was based on assignments from the FUNGuild database (Nguyen et al., 2016). | Data analysis: We calculated treatment effect size (mean ∆) to survey overall effect of the extensification treatment for each parameter. In order to identify context-dependent extensification effects we analyzed variation in extensification response between geographical regions and along a gradient of land-use intensity  (LUI) using a linear model approach. | Data references: Data from BExIS:&lt;br&gt;&lt;br&gt;Boesing, A.L. (2023) Topography of 300 forest and grassland EPs: BEXIS Id: 14686. Available from: https://www.bexis.uni-jena.de/ddm/data/Showdata/31371.&lt;br&gt;&lt;br&gt;Hinderling, J. & Keller, S. (2023) Vegetation summary in 2023 without species identities for the new grassland experiment REX and LUX on 45 Grassland EPs. Available from: https://www.bexis.uni-jena.de/ddm/data/Showdata/31572?version=3.&lt;br&gt;&lt;br&gt;Hinderling, J. & Keller, S. (2024) Vegetation records 2020-2023 for new grassland experiment REX and LUX. Available from: https://www.bexis.uni-jena.de/ddm/data/Showdata/31571?version=5.&lt;br&gt;&lt;br&gt;Neyret, M. & Manning, P. (2023) Trait data for all plant species, cleaned and aggregated from TRY: BEXIS Id: 27610. Available from: https://www.bexis.uni-jena.de/ddm/data/Showdata/27610?version=5.&lt;br&gt;&lt;br&gt;Schöning, I., Solly, E., Klötzing, T., Trumbore, S. & Schrumpf, M. (2021) MinSoil 2011 - Soil Texture: BEXIS Id: 31371. Available from: https://www.bexis.uni-jena.de/ddm/data/Showdata/14686?version=10.&lt;br&gt;&lt;br&gt;Data from other resources:&lt;br&gt;&lt;br&gt;Briemle, G., Nietsche, S. & Nitsche, L. (2002) Nutzungswertzahlen für Gefäßpflanzen des Grünlandes. Schriftenreihe für Vegetationskunde, (H. 38), 203–225.&lt;br&gt;&lt;br&gt;Ellenberg, H., Weber, H., Düll, R., Wirth, V. & Werner, W. (2001) Zeigerwerte von Pflanzen in Mitteleuropa: = Indicator values of plants in Central Europe, 3rd edition. Goltze: Göttingen.&lt;br&gt;&lt;br&gt;Guerrero‐Ramírez, N.R., Mommer, L., Freschet, G.T., Iversen, C.M., McCormack, M.L. & Kattge, J. et al. (2021) Global root traits (GRooT) database. Global Ecology and Biogeography, 30(1), 25–37. Available from: https://doi.org/10.1111/geb.13179.&lt;br&gt;&lt;br&gt;Klotz, S., Kühn, I., Durka, W. & Briemle, G. (2002) BIOLFLOR: Eine Datenbank mit biologisch-ökologischen Merkmalen zur Flora von Deutschland. Bundesamt für Naturschutz Bonn.&lt;br&gt;&lt;br&gt;Nguyen, N.H., Song, Z., Bates, S.T., Branco, S., Tedersoo, L. & Menke, J. et al. (2016) FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology, 20, 241–248. Available from: https://doi.org/10.1016/j.funeco.2015.06.006.