These data are associated with a manuscript by Mark Westoby, Samuel C. Andrew, Rachael V. Gallagher, and Julian Schrader, titled "Species gain and loss per degree Celsius". The manuscript (in review) and associated R code will explain the methods.All included files can be used to run the Rmarkdown file "Species_turnover_Aus_231103.Rmd" for data prep and The R script "Species_turnover_plots_figshare.R" to reproduce main figures.Summary of data files:Species_climate_limits_ALA_220929.rds - cleaned herbarium occurrence records for Australia.full_bioclim_220929.rds - our download of Worldclim/bioclim data for Australia.climate_layers.csv - metadata for climate layers used for processing and transformation.Grid_cells_tab_220929.csv - list of Australian equal areas grid cells for climate data extraction.Climate_data_bioclim_220929.rds - Climate data for 10x10 km equal area grid cells for Australia.Grid_cell_numbers_230901.csv - summary of grid cells per temperature bin for east coast transect with different mean annual precipitation cut-offs.SDM_press_base_raster_10km.tif - raster mask of Australia for plotting spatial data.The plant herbarium occurrence data where sourced from the Atlas of Living Australia (ALA) and are described in Andrew et al., (2021. Journal of Vegetation Science, 32(2), e13018. https://doi.org/10.1111/jvs.13018). These data were downloaded in December 2019 and a fresh download of ALA occurrence data is probably recommended for future studies.

README File Manifest.txt lists and describes all the files.

File Name:

CCCB_Fitness.csv contains the results from our greenhouse reciprocal transplant experiment. The number of variables has been reduced to just what we used for our study.

CCCB_Fitness_metadata.csv contains explanations for each variable name in CCCB_Fitness.csv.

Greenhouse_GLM_Fig5_6.R is the R code used in creating the zero-inflated and conditional generalized linear mixed models, as well as the graphs for Figure 5, Figure 6 and Supplemental Figure XX.

Bioclim_Figure2b_WilcoxonTests.R is the R script used to create box plots of the values associated with each of the BioClim variables with >10% percent contributions to our species distribution models.

c.c_occurence_data_cleaned.csv is the occurrence data for Clarkia concinna used in conjunction with BioClim data.

c.b _occurence_data_cleaned.csv is the occurrence data for Clarkia breweri used in conjunction with the BioClim data.

SDM_PctContribution_Grap...

Bold font indicates variables considered in initial model run;†superscript indicates the two variables included in final model.

Habitat stability is important for maintaining biodiversity by preventing species extinction, but this stability is being challenged by climate change. The tropical alpine ecosystem is currently one of the ecosystems most threatened by global warming, and the flora close to the permanent snow line is at high risk of extinction. The tropical alpine ecosystem, found in South and Central America, Malesia and Papuasia, Africa, and Hawaii, is of relatively young evolutionary age, and it has been exposed to changing climates since its origin, particularly during the Pleistocene. Estimating habitat loss and gain between the Last Glacial Maximum (LGM) and the present allows us to relate current biodiversity to past changes in climate and habitat stability. In order to do so, 1) we developed a unifying climate-based delimitation of tropical alpine regions across continents, and 2) we used this delimitation to assess the degree of habitat stability, i.e. the overlap of suitable areas between the ..., The dataset consists of a set of script developed for the corresponding publication using CHELSA v.1.2 (https://chelsa-climate.org/downloads/) to delimit tropical alpine regions based on bioclimatic variables. Using the scripts and setting the limits of the respective bioclimatic variables, will result in the GIS shapefiles with the delimitied region. Here, we provide the corresponding GIS shapefiles and figures for the above mentioned publication, that were the outcome of running the R scripts. The shapefiles and figures are based on the mean temperature of the coldest and warmest quarter (bioclim 10 and bioclim 11) of -3 to +10/+18°C respectively, plus a restriction to the tropics based on bioclim 3, the ratio of diurnal variation to annual variation in temperatures, ranging from 50 to 300 °C/10., , # ClimateAnalyzer

ClimateAnalyzer is a set of script written in R to delimit areas based on bioclimatic variables.

The scripts have been developed to delimit tropical alpine areas based on bioclimatic variables from CHELSA (). The work has been presented in Kandziora et al (under review) "The ghost of past climate acting on present-day plant diversity: lessons from a climate-based delimitation of the tropical alpine ecosystem".

Description of the data and file structure

The uploaded GIS shapefiles and figures are based on a delimitation based on the mean temperature of the coldest and warmest quarter (bioclim 10 and bioclim 11) of -3 to +10 °C, plus a restriction to the tropics based on bioclim 3, the ratio of diurnal variation to annual variation in temperatures, ranging from 50 to 300 °C/10.

The delimitation was done for current climatic conditions as well as two reconstructions of the climate during the last glacial maximum, based on MPI-M_MPI-ESM-P (abbreviated to MPI) and...

Thermal requirement data of lizard populations of the genus Sceloporus was obtained from the literature. We collected data on preferred body temperature (Tpref), body temperature in the field (Tb), critical minimum temperature (CTmin), and critical maximum temperature (CTmax) of Sceloporus lizards. Additionally, we reported air and substrate temperature at the location of capture, if reported in the papers, and the relation of body temperature with these environmental temperatures. Whenever possible, we reported coordinates and elevation of the study sites, with available data on environmental temperatures (i.e., bioclim data, data from nearby meteorological stations, and the Köppen-Geiger climate classification). When reported, thermal efficiency indexes are given (i.e. thermoregulation accuracy, thermoquality of the habitat, and thermoregulation efficiency). , Thermal requirement data of lizard populations of the genus Sceloporus. Thermal requirement data was obtained from published work, undergraduate or graduate thesis, and unpublished data of the authors. Full references are given within the file. Geographic coordinates, when not given in the original research paper, were geo-referenced with Google Maps. Data on body size (maximum snout vent length) were obtained from Roll et al. 2017 and Meiri 2018. Data on environmental temperatures were obtained with the packages Bioclim (Hijmans and Van Etten 2012, Fick and Hijmans 2017) and GSODR (Sparks et al. 2017) in R. The Köppen-Geiger classification at the site of the populations was obtained with the kgc package (Bryant et al. 2017) in R (R Core Team 2021)., Data is presented in an excel file with several sheets. Sheet 1. Definitions: Definitions of all terms and abbreviations used in the file. Sheet 2. References: Reference list of references cited in the file. Sheet 3. Complete: The complete dataset with all thermal requirement data obtained. References are given. Also, Bioclim data, data from local meteorology stations (GSODR), and Köppen-Geiger classification are given in raw format. Sheet 4. Per species: Data from the complete dataset averaged per species, with standard deviation and sample size are given. Note that for Köppen-Geiger classification several types of climate classification may be given for a single species. Sheet 5. TEI complete: Thermal efficiency indexes of Sceloporus populations obtained from literature. References are given. Also, Bioclim data, data from local meteorology stations (GSODR), and Köppen-Geiger classification are given in raw format. Sheet 6. TEI species: Thermal efficiency indexes averaged per species,..., TITLE: Sceloporus thermal requirements

GENERAL INFORMATION:

Data from: Flexibility in thermal requirements: a comparative analyses of the wide-spread lizard genus Sceloporus

This data set presents data on thermal ecology of populations of the lizard genus Sceloporus with data on geographic location and climate data of the study site.

Methods: Data was obtained from published work, undergraduate or graduate thesis, and unpublished data of the authors. We performed an extensive literature search in Google Scholar with the search words: Sceloporus and body temperature, thermal limits, or thermal indices (in English and the Spanish translation). Full references are given within the file.

Data: Thermal ecology data of Sceloporus populations: preferred body temperature, body temperature in the field and critical thermal limits.

Body size: Given as snout vent length, when mentioned within the reference. Also, data on maximum snout vent length are included, these were obtained from Roll ...

For the climatic characterization of the sites, we first calculated the values for the 19 BIOCLIM variables using the ‘biovars’ method of the R package dismo 0.9-1.

This collection provides 121 sets of 19 bioclimatic variables (see Booth et al. 2014) describing the historical (1986–2015) and projected future (CMIP6) climates of Bhutan with a spatial resolution of 250 m. The future 19 bioclimatic variables include four shared socio-economic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) (O'Neill et al., 2016; Riahi et al., 2017) and three periods (2021–2050, 2051–2080, and 2071–2100) using 10 global climate models (GCMs). These data can be used for many applications in environmental and agricultural science. Lineage: Each of the 19 bioclimatic variables (see Booth et al., 2014) were generated in R using the dismo package (Hijmans et al., 2017). CMIP6 GCM outputs were acquired from the Copernicus Climate Change Service (C3S) (https://cds.climate.copernicus.eu/). The CMIP6 GCM outputs are downscaled against historical data, developed with the national weather station network (Stewart et al. 2017, Stewart et al. 2021), using the delta change method applied to anomalies interpolated using bivariate thin plate splines (i.e., a function of easting and northing). Further details regarding this collection are provided in the attached README document.

Coordinate reference system: EPSG:5266 - DRUKREF 03 / Bhutan National Grid.

References:

Booth, T. H., Nix, H. A., Busby, J. R., & Hutchinson, M. F. (2014). bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions, 20(1), 1-9. doi:10.1111/ddi.12144

Dorji S, Stewart S, Shabbir A, Bajwa A, Aziz A, & Adkins S. (2025). Comparative Analysis of Mechanistic and Correlative Models for Global and Bhutan-Specific Suitability of Parthenium Weed and Vulnerability of Agriculture in Bhutan. Plants, 14(1). doi:10.3390/plants14010083

Hijmans, R. J., Phillips, S., Leathwick, J. R., & Elith, J. (2017). dismo: Species Distribution Modeling. R package version 1.1-4. Retrieved from https://CRAN.R-project.org/package=dismo

O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., . . . Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9(9), 3461-3482. doi:10.5194/gmd-9-3461-2016

Riahi K, van Vuuren DP, Kriegler E, Edmonds J, O’Neill BC, Fujimori S, . . . Tavoni M (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42:153-168.

Stewart, S. B., Choden, K., Fedrigo, M., Roxburgh, S. H., Keenan, R. J., & Nitschke, C. R. (2017). The role of topography and the north Indian monsoon on mean monthly climate interpolation within the Himalayan Kingdom of Bhutan. International Journal of Climatology, 37(S1), 897-909. doi:10.1002/joc.5045

Stewart, S. B., Fedrigo, M., Kasel, S., Roxburgh, S. H., Choden, K., Tenzin, K., . . . Nitschke, C. R. (2021). Interpolated climate variables for the Himalayan Kindom of Bhutan [Raster]. Retrieved from: https://doi.org/10.25919/m8yh-gt42

Species redistribution and invasion are becoming increasingly common due to climate change and anthropogenic impacts. Understanding the resultant shifts in host-parasite associations is important for anticipating disruptions to host communities, disease cycles, and conservation efforts. In this paper, we bring together the enemy release and vacated niche hypotheses to relate parasite acquisition and retention, two distinct yet intertwined processes that play out during host invasion. Using the Global Mammal Parasite Database, we test for net enemy release based on differences in parasite species richness, and we develop a novel taxonomic null modeling approach to demonstrate that parasites fill vacated niches. We find evidence of net enemy release, and our taxonomic null models indicate replacement of lost parasites by taxonomically similar acquired ones, over and above what might be expected by chance. Our work suggests that both enemy release and vacated niche hypotheses provide valua..., , , # Code and data: Evidence for the vacated niche hypothesis in parasites of invasive mammals

Annakate M. Schatz and Andrew W. Park

https://doi.org/10.5061/dryad.37pvmcvsp

All data preparation, analysis, and modeling were done in R v3.5.1-4.2.1.

NOTE: Before running code, please move the data from Dryad into a "data" subfolder in the same parent folder as the code from Zenodo ("Schatz_Park_code"), as this is the structure via which the code files will try to load data.

CODE

0.data-prep.R: filter and clean host-parasite data on acquistion and retention for analyses

• Data loaded: cluster-data-previous-work.Rda
• Data created: data-prep.Rda

0a.ecoregion-bioclim.R: extract bioclimatic data for all relevant ecoregions associated with focal hosts

• Data loaded: range-data-previous-work.Rda, wc2.1_10m_bio (folder)
  • NOTE: The WorldClim data in the folder "wc2.1_10m_bio" cannot be uploaded to Dryad due to differences in license...

_ The datasets are associated with this paper (OA): https://doi.org/10.1016/j.soilbio.2022.108637 If you use this data for your research, please feel free to contact me: colinedeveautour@gmail.com The team that worked on this immense project gives you access to this data. As such, remember to cite the paper and this dataset whenever this data is reused. _ The data contains 3 files: 1) The metadata and soil data: containing the a) description of the samples, b) physicochemical properties of the soil, c) climate conditions extracted from BioClim and d) Soil potential nitrification and denitrification, e) functional gene abundance. 2) An RDS file containing the 16S community matrix and information relating to the community. 3) An RDS file containing the ITS community matrix and information relating to the community. Raw DNA sequencing data are available on the NCBI database under the accession number BioProject PRJNA788893. The metadata and soil data: “MINEsoilData.csv” A .csv table containing a) description of the samples, b) physicochemical properties of the soil, c) climate conditions extracted from BioClim and d) Soil potential nitrification and denitrification, e) functional gene abundance. All information relative to the farmer practices and exact location were removed to respect their privacy. The methods to obtain the values of this dataset are in the associated paper. Description of each column: SampleID: sample identification number, which is the sample number used in all the datasets. Site: location where the samples were collected in Ireland. Date: date of sampling soilMoist: soil moisture measured on site with Delta T WET-2 sensor, measures correspond to volumetric soil moisture content (θ, in %). soilCond: soil conductivity measured on site with Delta T WET-2 sensor (mS/m). soilTemp: soil temperature (°C) Altitude: meters above the sea at sampling location. pH: soil pH P_morgans: phosphorus (mg/L soil) K_Morgans: potassium (mg/L soil) Mg_Morgans: magnesium (mg/L soil) Al_Mehlich: aluminium (mg/L soil) Ca_Mehlich: calcium (mg/L soil) Fe_Mehlich: iron (mg/L soil) S: sulfur (mg/L soil) CEC: Cation-exchange capacity (meq/100g) LOI: Loss on ignition (%) NH4_mgL: ammonium (mg/L) NO3_mgL: nitrate (mg/L) sandPerc: soil texture, percentage of sand siltPerc: soil texture, percentage of silt clayPerc: soil texture, percentage of clay TNperc: total nitrogen (percentage) TICperc: total inorganic carbon (percentage) TOCperc: total organic carbon (percentage) TCperc: total carbon (percentage) CuEDTA: copper (mg/L soil) ZnEDTA: zinc (mg/L soil) MnEDTA: manganese (mg/L soil) bulkDens: soil bulk density (g/cm3) Nitrification: soil potential nitrification (mg N / soil kg . day) Denitrification: soil potential denitrification (mg N2O-N / soil kg . day) Ratio_N2O: potential N2O/(N2O + N2) ratio X16s.Bact: Abundance of bacteria based on qPCR (gene copy number / ng DNA) ITS: abundance of fungi (ITS) based on qPCR (gene copy number / ng DNA) X16S.Archae: abundance of archaea (16S) based on qPCR (gene copy number / ng DNA) AOA: abundance of ammonia-oxidizing archaea abundance based on qPCR (gene copy number / ng DNA) AOB: abundance of ammonia-oxidizing bacteria based on qPCR (gene copy number / ng DNA) COMAMMOX: complete ammonia-oxidizing bacterial abundance based on qPCR (gene copy number / ng DNA) NirK: nirK genes’ abundance based on qPCR (gene copy number / ng DNA) NosZI: nosZI genes’ abundance based on qPCR (gene copy number / ng DNA) NosZII: nosZII genes’ abundance based on qPCR (gene copy number / ng DNA) NirS: nirS genes’ abundance based on qPCR (gene copy number / ng DNA) AvgTemp: mean annual temperature (°C) based on BIOClim Prec: mean annual precipitation (mm) based on BIOClim 16S community data: “16Scommunitydata.rds” This data is compiled in a RDS file containing an ASV table (matrix with sample ID and ASV abundance), the taxonomy table (identity for each ASV) and the object containing the sequences of each ASV. This dataset can be accessed on R. Load the ‘phyloseq’ library and use the function “readRDS()”. ITS community data: “ITScommunitydata.rds” This data is compiled in a RDS file containing an ASV table (matrix with sample ID and ASV abundance), the taxonomy table (identity for each ASV) and the object containing the sequences of each ASV. This dataset can be accessed on R. Load the ‘phyloseq’ library and use the function “readRDS()”.

This R Markdown document, titled "Species-Energy Relationships in Environmental Space," provides a comprehensive guide to reproducing the main statistical analyses presented in the manuscript "Consistent environmental energy pathways shape global species diversity". The document includes step-by-step instructions using R, covering data structure exploration, environmental space plotting, and statistical analysis. All processed data used in this analysis is provided in the "Input_Data//" directory.Data Sources:The study examines global data on terrestrial vertebrates in relation to energy-related factors within climate space rather than traditional geographical space. The biological and climatic data are publicly accessible from:CHELSA (climate data): https://chelsa-climate.org/bioclim/CGIAR (climate data): https://cgiarcsi.communityIUCN (range maps for amphibians and mammals): https://iucn.orgBirdLife (bird range maps): http://datazone.birdlife.orgSquamate range maps: https://doi.org/10.1038/s41559-017-0332-2Data Files:The files in the "Input_Data//" folder contain the necessary datasets for the analysis, organized into one-dimensional and two-dimensional environmental spaces. These files follow a naming convention that includes the species group, environmental variables, resolution, and whether the data is used for Structural Equation Modeling (SEM).One-Dimensional Climate Spaces: Files such as amphibian_Temp_Space_20_bins.csv represent temperature environmental spaces where temperature is divided into specified bins.Two-Dimensional Climate Spaces: Files like amphibian_temp_precip_20x20_SEM.csv represent climate spaces with both temperature and precipitation divided into bins for SEM.Data Structure:The document includes sections detailing the structure of the data, both for one-dimensional and two-dimensional environmental spaces. Examples are provided to explain how each file is organized and how the data can be used for analysis.Plotting Environmental Space:The document demonstrates how to visualize patterns within environmental spaces using the ggplot2 package, with functions provided to plot both one-dimensional and two-dimensional spaces.Statistical Analysis:The analysis focuses on the relationship between species richness and environmental factors. It includes:Data transformation functions for use in testing the metabolic theory of ecology.Regression analysis functions to model the relationship between species richness and inverse temperature.Structural Equation Modeling (SEM) to explore direct and indirect effects of environmental variables on species richness.Tools to check for autocorrelation, variance inflation factors, and relative importance of predictors.Group-Specific Analysis:The document details the analysis process for different groups of terrestrial vertebrates, including amphibians, reptiles, mammals, and birds. Each group's data is processed and analyzed at multiple resolutions to explore species-energy relationships across environmental gradients.

Beetles exhibit an extraordinary diversity of brilliant and colourful appearances and optical effects invisible to humans. Their underlying mechanisms have received some attention, but we know little about the ecological variables driving their evolution. Here we investigated environmental correlates of reflectivity and circular polarization in a group of optically diverse beetles (Scarabaeidae–Rutelinae). We quantified the optical properties of 261 specimens representing 46 species using spectrophotometry and calibrated photographs. Then, we examined associations between these properties and environmental variables such as temperature, humidity, and vegetation cover, controlling for body size and phylogenetic relatedness. Our results showed larger beetles have higher visible reflectivity in drier environments. Unexpectedly, near-infrared (NIR) reflectivity was not correlated with ecological variables. However, we found a correlation between humidity and polarization (chiral nanostructu..., This data set was collected from 261 specimens of Australian scarabs representing 53 morphs of 46 species and 9 genera of the subfamily Rutelinae and one species of the subfamily Melolonthinae. Reflectivity and transmissivity were collected with spectroscopy methods. Absorptivity was calculated based on these two variables Circular polarization was studied using calibrated photographs of the beetle specimens in three filters, visible light, left-handed polarized visible light, and right-handed polarized visible light. The length of the beetles was calculated from the calibrated photographs and used as an indication of size. Climate data was extracted from ALA and BioClim. The original code is attached. Phylogenetic data was reconstructed from published COI data and additional sequences available in NCBI Processing: The linearization and equalization of the photographs were done with a custom-made protocol in MatLab. Most of the processing of the data was done in R Statistical Softwa..., , # Data and original code for: Explaining the diversity of optical effects in Christmas beetles: climate, history, and mechanisms

https://doi.org/10.5061/dryad.rv15dv4f7

This data set contains all the original files used in our manuscript. Despite the increasing number of studies on natural photonic structures, we seldom know the biological relevance or ecological drivers of these structures. In this study, we used a macroecological approach to look for potential ecological drivers of unique optical effects in 261 specimens representing 46 species of Australian scarabs, known as Christmas beetles (Scarabaeidae: Rutelinae).

This set contains the raw values of reflectance and transmittance measured with spectrometry techniques. As well as the correspondent reflectivity, transmissivity, and absorptivity (integrated across the wavelength range of the solar irradiance). In addition, it contains the raw RGB values extracted from calibrated photogra...

Environmental Niche Model (ENM) outputs for 49 commercial fish species under climate change until the decade of 2060 around northwestern Europe. A model ensemble of 5 ENMs was used (MaxEnt, Generalised Linear Models, Support Vector Machine, Random Forest and BIOCLIM ), and projections were made under three different emission scenarios: A1B, RCP4.5 and RCP 8.5. The data shows model agreement (normalised to 1) for presence/absence decadal projections from 2020 to 2060. Additionally we provide data on model performance, with the Area Under the Curve (AUC) scores of the Receiver Operator Characteristic (ROC) curve for each of the 5 ENMs trained for each combination of fish species and emission scenario. Only ENMs with an AUC score of at least 0.7 were considered.

The data is based on the output of an ensemble of 5 Environmental Niche Modeling techniques: Maximum Entropy (MaxEnt), Generalised Linear Model (GLM), Support Vector Machine (SVM), Random Forest and BIOCLIM. Each model was trained to capture the environmental requirements of individual fish species, using the 20 year-average in the period 1997-2016 of the environmental variables: near-bed sea temperature, sea surface temperature, near-bed salinity, sea surface salinity, the difference in near-bed and surface salinity and temperature (an approximation of stratification), and depth.

Once the models were trained, they were used to study future habitat suitability for the 49 commercially-important fish species in 20-year averages centered in 2020, 2030, 2040, 2050 and 2060. To produce maps of the suitable habitat, the model projections were converted to binary presence/absence data using the thresholds that optimized the True Skill Statistics (TSS) of each model. The data shows the model agreement, normalised to 1 ("1" denotes all ENMs agree an area is suitable, "0" denotes all ENMs agree an area is not suitable). The number of ENMs considered for each combination of fish species and emission scenario depends on model performance. A ENM model was considered if its Area Under the Curve (AUC) score of the Receiver Operator Characteristic (ROC) curve was at least 0.7. Model performance is listed in the file "Model_performance_AUC.xlsx".

Model input data: Climate change projection data from Met Office and Plymouth Marine Laboratory (PML) and species presence and abundance records from online databases (Ocean Biodiversity Information System (OBIS), Global Biodiversity Information System (GBIF) and Marine Scotland (Moriarty, M., Greenstreet, S.P.R. and Rasmussen, J. (2017) Derivation of Groundfish Survey Monitoring and Assessment Data Product for the Northeast Atlantic Area. Scottish Marine and Freshwater Science, 8, 240pp. DOI: 10.7489/1984-1). Models were trained using the R and Maxent softwares.

Study region: northeast Atlantic shelf. Latitudes between -17° and 9.25°; longitudes between 44° and 65°.

These datasets were produced for the study in the article "Climate change projections of commercial fish distribution and suitable habitat around northwestern Europe", by Bryony L. Townhill, Elena Couce, Jonathan Tinker, Susan Kay, John K. Pinnegar, in Fish and Fisheries (2023; http://doi.org/10.1111/faf.12773).

This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication The effect of crop diversification and season on microbial carbon use efficiency across a European pedoclimatic gradient Julia Schroeder1*, Alexander König2, Christopher Poeplau1, Tobias Bölscher3, Katharina H.E. Meurer4, Monika Toleikienė5, Marjoleine Hanegraaf6, Annelein Meisner6, Josef Hakl7, Katharina M. Keiblinger 2, Abad Chabbi8, Marjetka Suhadolc9, Anton Govednik9, Erich Inselsbacher2, Heike Knicker10, Laura Gismero Rodríguez10, and Anke M. Herrmann4 1 Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany2 University of Natural Resources and Applied Life Sciences Vienna, Department of Forest and Soil Sciences, Institute of Soil Research, Vienna, Austria3 Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, Palaiseau, France4 Department of Soil & Environment, Swedish University of Agricultural Sciences - SLU, Uppsala, Sweden5 Lithuanian Research Centre for Agriculture and Forestry, Akademija, Lithuania6 Wageningen University & Research, Wageningen Plant Research, Wageningen, Netherlands7 Czech University of Life Sciences Prague, Czech Republic8 INRAE Centre de Recherche Nouvelle-Aquitaine-Poitiers, Unité de Recherche Pluridisciplinaire Prairies & Plantes Fourragères, Lusignan, France9 University of Ljubljana, Biotechnical Faculty, Ljubljana, Slovenia10 Instituto de la Grasa (IG-CSIC), Sevilla, Spain* Corresponding author: julia.schroeder@thuenen.de DOI: The study aimed to investigate the effect of crop diversification measures (cover crops, ley farming, vegetation stripes) on microbial carbon use efficiency (CUE) and its potential link to SOC accrual in agricultural soils across Europe. The central hypothesis was that the crop diversification treatment results in more efficient microbial use of C, thus enhancing the potential of soils to store C. The effect of treatment was expected to vary between seasons.Topsoil was sampled from eight experimental crop diversification sites across a pan-European pedoclimatic gradient (Sweden, Netherlands, Lithuania, Czech Republic, Austria, France, Slovenia, and Spain). At five sites, a second sampling was conducted to test the effect of season on CUE (Sweden, Netherlands, France, Slovenia, and Spain). CUE was assessed by the 18O-labelling method. To account for the different experimental layout between sites, a meta-analysis approach was used for statistical analysis. To test for a general pattern of the seasonal variation in CUE across the pedoclimatic gradient, weather data (representing 3-months weather prior sampling) was used to extract seasonal predictors. For further details, please see the peer-reviewed publication. The R code was developed under R v4.4.0. The repository includes the following files: data: EJP-EL_COORD_sites.csv - Provides coordinates, reference system, land use, diversification treatment and year of establishment for the experimental sites (n=8). EJP-EL_DATA_sites.csv - Provides texture, TOC and N content, and bulk density data for the experimental sites as provided by site managers (if applicable on plot basis)(n=106). EJP-EL_CUE_for_R.csv - Provides assessed per sample observations (n=220)WHC: water holding capacity, pH: soil pH measured in 1:5 w/w soil-water solution, TC_perc: total C (%), TIC_perc: total inorganic C (%), TOC_perc: total organic C (%), TN_perc: total N (%), total_DNA_gsoil: total amount of DNA extracted (ug DNA g-1 soil), Cmic_ugC_gsoil: microbial biomass C by CFE-method (ug microbial biomass C g-1 soil), fDNA: unitless conversion factor, Cgrowth_ngC_g_h: microbial growth rate (ng C g-1 soil h-1), Crespiration_ngC_g_h: microbial respiration rate (ng C g-1 soil h-1), CUE: unitless carbon use efficiency, mass_specific_growth_1_perd: mass specific growth rate (1 d-1), turnover_time_d: turnover rate (d), DW/WW_during_incub: ratio of dry weight to wet weight (indicator of water content) during the time of incubation (g g-1); WC_perc_related_to_DW_at_sampling: water content during the time of soil sampling expressed as percentage water refered to dry weight of soil (%) weather_data_b4_sampling_FS_nasa_power_mod_incl_radiation.csv - Extracted daily weather data for the respective site coordinates from the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) Project funded through the NASA Earth Science/Applied Science Program, i.e. NASA POWER project, for a 3-months period prior to each sampling event: mean daily air temperature at 2 m (T2M), the bias corrected average of total precipitation at the Earth’s surface (PRECTOTCORR), and the total photosynthetically active radiation incident at the Earth’s surface (ALLSKY_SFC_PAR_TOT). Table_S1.xlsx - Provides meta-data of sites including sampling (treatments, depth, number of samples, time), water content at sampling, management (last main crop, cover at sampling, phenological state at sampling, fertilisation dates, type of fertiliser, fertilisation rate, ploughing date, ploughing depth). Rproj: R_project_Schroeder_crop_div_and_season.Rproj - Rproject (load project to work on provided scripts and data) R scripts: 01_EJP-EL_CUE_load_data.R - Loads data from different provided csv and merges them into one data.frame, specifying assignment of categories (e.g. control vs one diversified treatment) and selections for individual analyses (e.g. seasonality (all treatments, 5 sites), EU.gradient (control vs. one diversified treatment, 8 sites)). 02_extract_climate_data_sites.R - Extracts climate data from BIOCLIM database (MAT, MAP, Growing season lenght, Köppen Geiger) for site coordinates. 03_meta-analysis_crop_diversification_EU_gradient.R - Conducts meta-analysis on EU.gradient selection (control vs diversified treatment, 8 sites) with crop diversification measures as subgroups. Plots results of the meta-analysis. Exports overall test statistics. 04_ANOVA_seasonality_effect.R - Plots CUE per site and sampling on seasonality selection. Conducts site-wise ANOVA to test for effects of sampling and treatment on CUE, and whether the effect of treatment varies with sampling (i.e. interaction). 05_define_season_by_weather_data_before_sampling.R - Extracts weather data from indicated NASA database for the 3-months period prior to sampling. Calculates seasonality predictors (see M&M of publication to this study for detailed background). Creates table with calculated predictors. 06_Driver_Analysis_seasonality.R - Checks for autocorrelation of seasonal predictors. Conducts linear mixed-effects model for driver analysis. Creates figure presenting results of the driver analysis. 07_PCA_pedo-climatic_gradient_EU.R - Conducts and plots a principal component analysis to visualise the spread of pedo-climatic properties across sites. 08_LMEM_France.R - Conducts a separate linear mixed-effects model statistics to test wehther ley farming is more similar to cropland or grassland (microbial CUE, Cmic, Cgrowth and SOC) at the Lusignan (France) site.