• Energy Research
• US close
• FR close
• GB close
• Netherlands Research Portal close

CASCADE is a global dataset for 139 extant coccolithophore taxonomic units. CASCADE includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-specific global spatiotemporal distributions (Lat/Lon/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. CASCADE covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 taxonomic-specific abundance observations. Within CASCADE, we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies. Full details of the data set are provided in the associated Scientific Data manuscript. The repository contains five main folders: 1) "Classification", which contains YAML files with synonyms, family-level classifications, and life cycle phase associations and definitions; 2) "Concatenated literature", which contains the merged datasets of size, PIC and POC and which were corrected for taxonomic unit synonyms; 3) "Resampled cellular datasets", which contains the resampled datasets of size, PIC and POC in long format as well as a summary table; 4) "Gridded data sets", which contains gridded datasets of abundance, PIC and POC; 5) "Species lists", which contains spreadsheets of the "common" (>20 obs) and "rare" (<20 obs) species and their number of observations. The CASCADE data set can be easily reproduced using the scripts and data provided in the associated github repository: https://github.com/nanophyto/CASCADE/ (zenodo.12797197) Correspondence to: Joost de Vries, joost.devries@bristol.ac.uk v.0.1.2 has some fixes: 1. The wrongly specified S. neapolitana was removed from synonyms.yml (this species is now S. nana)2. Longitudes were corrected for Guerreiro et al., 20233. A double entry for Dimizia et al., 2015 was fixed4. Units in Sal et al., 2013 were correct to cells/L (previously cells/ml)5. Data from Sal et al., 2013 was re-done, as some species were missing6. Duplicate entries from Baumann et al., 2000 were dropped

Wheat Biomass for Kherson and Poltava regions in Ukraine The dataset contains Dry Above Ground Biomass (DAM) estimates over the Kherson and Poltava regions in Ukraine for years 2020,2021 and 2022. - Processing:The processing is done using the AgriCarbon-EOv1.5 processing chain, using the TREX processing centre at CNES France.The input remote sensing data are L2A Sentinel-2 surface reflectances provided by the MAJA processing chain based on the Copernicus Sentinel-2 L1C data.The Landcover maps are provided using ML Deep learning based on the Copernicus L2A data.The daily weather data is extracted from ERA5Land products (C3S). -Geophysical variable:Dry Above ground biomass of winter wheat in g/m2. - Extents: * DAM estimates over the Copernicus Sentinel-2 tile 36TWT cover the Kherson region.* DAM estimates over the Copernicus Sentinel-2 tile 36UVA cover the Poltava region. - Spatial resolution:10m resolution estimlates over wheat plots identified in the landcover map. - Temporal coverage:Estimates are provided at the end of the wheat cycle for cycles:* The year 2020 correspond to cycle: 2019-2020* The year 2021 corresponds to cycle : 2020-2021* The year 2022 corresponds to cycle : 2021-2022 - Projection: EPSG:32636 - File content: Each Raster file has 2 bands containing respectively: * band1: mean value of DAM in g/m2. * band2: standard deviation of DAM in g/m2. - List of maps:* Dry_aboveground_biomass_2020_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2020_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2021_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2021_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2022_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2022_T36UVA_Poltava_Ukraine.tif

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed rice.

This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5). The dataset is composed of four files in GeoTIFF format: calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage. projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage. Spatial resolution: 0.0083 degree (ca. 1 km) Coordinate reference system: Geographic Coordinate System - Datum WGS84

These data record the results of processing otter trawl catches (OTSB14; Merrett & Marshall, 1980) from the National Oceanography Centre (NOC, UK) long-term study of the Porcupine Abyssal Plain (PAP), including the PAP-Sustained Observatory time-series. The data concern catches recovered during the RRS Challenger cruise 135 in 1997. Billett, D.S.M. et al. (1998). RRS Challenger Cruise 135, 15 Oct-30 Oct 1997. BENGAL: High resolution temporal and spatial study of the BENthic biology and Geochemistry of a north-eastern Atlantic abyssal Locality. Southampton Oceanography Centre Cruise Report, No. 19, 49pp.| https://www.bodc.ac.uk/resources/inventories/cruise_inventory/reports/ch135_97.pdf

This README file was generated on 2024-03-04 by Adriana Parra. ## GENERAL INFORMATION 1\. Title of Dataset: **Climate-limited vegetation change in the conterminous United States of America** 2\. Author Information A. First Author Contact Information Name: Adriana Parra Institution: University of Nevada, Reno Address: Reno, NV USA Email: adrianaparra@unr.edu B. Co-author Contact Information Name: Jonathan Greenberg Institution: University of Nevada, Reno Address: Reno, NV USA Email: jgreenberg@unr.edu 3\. Coverage period of the dataset: 1986-2018 4\. Geographic location of dataset: Conterminous United States 5\. Description: This dataset contains the input and the resulting rasters for the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal. The dataset includes a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor. Additionally, the dataset includes a legend file for the identified climatic limiting factor rasters. ## SHARING/ACCESS INFORMATION 1\. Links to publications that cite or use the data: **Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204)** 2\. Links to other publicly accessible locations of the data: None 3\. Links/relationships to ancillary data sets: None 4\. Was data derived from another source? Yes A. If yes, list source(s): "Vegetative Lifeform Cover from Landsat SR for CONUS" product publicly available in the ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) TerraClimate data catalog publicly available at the website https://www.climatologylab.org/terraclimate.html 5\. Recommended citation for this dataset: Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204) ## DATA & FILE OVERVIEW This dataset contains 16 geotiff files, and one csv file. There are 4 geotiff files per each of the lifeform classes evaluated in this study: herbaceous, tree, shrub, and non-vegetation. The files corresponding to each lifeform class are indicated by the first two letters in the file name, HC indicates herbaceous cover, TC indicates tree cover, SC indicates shrub cover, and NC indicates non-vegetation cover. 1\. File List: a) Observed change: Trends of vegetation change between 1986 and 2018. b) Potential predict: Predicted rates of vegetation change form the climate limiting factor analysis. c) Potential observed difference: Difference between the potential and the observed vegetation rates of change. d) Limiting variable: Climate variable identified as the limiting factor for each pixel the conterminous United States. e) Legend of the Limiting variable raster All the geotiff files are stored as Float 32 type, and in CONUS Albers Equal Area coordinate system (EPSG:5070) The csv file included in the dataset is the legend for the limiting variable geotiff files. This file includes the name of the climate variable corresponding to each number in the limiting variable files, as well as information on the variable type and the corresponding time lag. 2\. Relationship between files, if important: None 3\. Additional related data collected that was not included in the current data package: None 4\. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA Input data We use the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS” product (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover. The information for the climate factors was derived from the TerraClimate data catalog (https://www.climatologylab.org/terraclimate.html). We downloaded data from this catalog for the period 1971 to 2018 for the following variables: minimum temperature (TMIN), precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), and climatic water deficit (DEF). Preprocessing of vegetation fractional cover data We resampled and aligned the maps of fractional cover using pixel averaging to the extent and resolution of the TerraClimate dataset (~ 4 km). Then, we calculated rates of lifeform cover change per pixel using the Theil-Sen slope analysis (Sen, 1968; Theil, 1992). Preprocessing of climate variables data To process the climate data, we defined a year time step as the months from July of one year to July of the next. Following this definition, we constructed annual maps of each climate variable for the years 1971 to 2018. The annual maps of each climate variable were further summarized per pixel, into mean and slope (calculated as the Theil-Sen slope) across one, two, three, four, five, ten-, and 15-year lags. Estimation of climate potential We constructed a final multilayer dataset of response and predictor variables for the CONUS including the resulting maps of fractional cover rate of change (four response variables), the mean and slope maps for the climate variables for all the time-lags (70 predictor variables), and the initial percent cover for each lifeform in the year 1986 (four predictor variables). We evaluated for each pixel in the CONUS which of the predictor variables produced the minimum potential rate of change in fractional cover for each lifeform class. To do that, we first calculated the 100% quantile hull of the distribution of each predictor variable against each response variable. To calculate the 100% quantile of the predictor variables’ distribution we divided the total range of each predictor variable into equal-sized bins. The size and number of bins were set specifically per variable due to differences in their data distribution. For each of the bins, we calculated the maximum value of the vegetation rate of change, which resulted in a lookup table with the lower and upper boundaries of each bin, and the associated maximum rate of change. We constructed a total of 296 lookup tables, one per lifeform class and predictor variable combination. The resulting lookup tables were used to construct spatially explicit maps of maximum vegetation rate of change from each of the predictor variable input rasters, and the final climate potential maps were constructed by stacking all the resulting maps per lifeform class and selecting for each pixel the minimum predicted rate of change and the predictor variable that produced that rate. Identifying climate-limited areas We defined climate-limited areas as the parts of the CONUS with little or no differences between the estimated climate potential and the observed rates of change in fractional cover. To identify these areas, we subtracted the raster of observed rates of change from the raster of climate potential for each lifeform class. In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal, we evaluated the effects of climate conditions on vegetation composition and distribution in the conterminous United States (CONUS). To disentangle the direct effects of climate change from different non-climate factors, we applied "Liebig's law of the minimum" in a geospatial context, and determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change. We then compared these potential rates against observed change rates for the period 1986 to 2018 to identify areas of the CONUS where vegetation change is likely being limited by climatic conditions. This dataset contains the input and the resulting rasters for the study which include a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor.

Description: Leaf and invertebrate biomass in streams Project: This dataset was collected as part of the following SAFE research project: A preliminary study of the allochthonous inputs into tropical streams across a land use gradient in Sabah, Malaysia XML metadata: GEMINI compliant metadata for this dataset is available here Data worksheets: There are 2 data worksheets in this dataset: Insects (Worksheet Insects) Dimensions: 23 rows by 11 columns Description: Insect capture rates Fields: Location: SAFE project riparian site (Field type: Location) Stream: SAFE project stream (Field type: ID) Repeat: sample number for that stream (Field type: ID) Total Mass of Insects (g): the total dried mass of insects collected for each of the repeats (Field type: Numeric) Total Insects: the total number of insects collected in each repeat (Field type: Abundance) Hymenoptera: the total number of hymenoptera in each repeat (Field type: Abundance) Diptera: the total number of diptera in each repeat (Field type: Abundance) Coleoptera: the total number of coleoptera in each repeat (Field type: Abundance) Other.Insect: the grouped total of Hemiptera, Thysanoptera, Orthoptera, Blattodea, Trichoptera, Mantodea, Ephemeroptera, Dermaptera for each repeat (Field type: Abundance) Other: the grouped total of Arachnida, Entognatha, Diplopoda, Chilopoda for each repeat (Field type: Abundance) Hydrology (Worksheet Hydrology) Dimensions: 60 rows by 17 columns Description: River characteristics and litter quantities Fields: Location: SAFE project riparian site (Field type: Location) Stream Code: The stream from which the sample was taken (LFE, 15m, 30m, VJR or OP) (Field type: ID) Transect No.: The point of each sample within the 100m transect at each stream (Field type: ID) Channel Width: The bank full width of the channel at this point (Field type: Numeric) Wetted Width: The width of the runnin water at this point (Field type: Numeric) SAFE Habitat Quality Right: the SAFE Habitat quality on the right of the channel when looking upstream (Field type: Ordered Categorical) SAFE Habitat Quality Centre: the SAFE Habitat quality in the centre of the channel when looking upstream (Field type: Ordered Categorical) SAFE Habitat Quality Left: the SAFE Habitat quality on the left of the channel when looking upstream (Field type: Ordered Categorical) Flow Rate Right (s): the time taken for a tennis ball to travel 10m in the water on the right of the channel when looking upstream (Field type: Numeric) Flow Rate Centre (s): the time taken for a tennis ball to travel 10m in the water in the centre of the channel when looking upstream (Field type: Numeric) Flow Rate Left (s): the time taken for a tennis ball to travel 10m in the water on the left of the channel when looking upstream (Field type: Numeric) Average Flow Rate (s): an average of flow rate centre, flow rate left and flow rate right (Field type: Numeric) Leaf Litter Retention (g): the dried mass of leaf litter retained across the wetted width of the stream at each point (Field type: Numeric) Average Substrate Size: the average size of the substrate across the channel width of the stream at each point (Field type: Numeric) Leaf Litter Trap Position: the position where the leaf litter trap was placed relative to the stream when looking upstream (left, right or centre) (Field type: Categorical) Leaf Litter Mass: the dried mass of leaf litter collected in the leaf litter trap at each point (Field type: Numeric) Date range: 2017-02-06 to 2017-07-06 Latitudinal extent: 4.6314 to 4.7273 Longitudinal extent: 117.4556 to 117.6233 Taxonomic coverage: All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets. Animalia - Arthropoda - - Insecta - - - Coleoptera - - - Diptera - - - Hymenoptera - - [Other.Insect]

This dataset contains files produced for and generated from the CCAFS Household Baseline Study carried out in sites in Latin America (Trifinio in Honduras/Guatemala, and Cauca in Colombia) and in South-East Asia (a site in each of Cambodia, Laos and Vietnam) in the latter months of 2014 and the early months of 2015. There are six sites in all (two sites from Trifinio). Before downloading any of the files, particularly the data files, please download and read the CCAFS ReadMe file which is prefixed by the code 0000. To gain access to the GPS coordinates from the restricted files please download and complete the Non-disclosure agreement from the file "0002 Non-Disclosure Agreement 2013-01-20.pdf" and send this to Wiebke Foerch at w.foerch@cgiar.org The study was based on earlier baseline studies carried out in sites in West and East Africa and in South Asia. Data and other files from these earlier studies are available in a separate dataset in this Dataverse archive. (CCAFS Household Baseline Survey 2010-2012). If you are intending to use data from both studies together we suggest you read the file "0001 Questionnaire Differences & Recoding Details 2015-10-29.pdf" which explains differences between the two studies.

# Nitrogen-fixing plants increase soil nitrogen and neighboring plant biomass, but decrease community diversity: A meta-analysis reveals the mediating role of soil texture [https://doi.org/10.5061/dryad.4qrfj6qk1](https://doi.org/10.5061/dryad.4qrfj6qk1) ## Description of the data and file structure This data file was constructed by gathering and extracting data from published scientific papers identified using a rigorous selection process (see manuscript for details on the selection process). The papers included in this data are identified within the primary dataset here, but also in the supplementary file of the manuscript. This dataset includes original manuscript information, data extractor, geographical and ecological data, and notation of any treatments or differences in groups, along with the means and error terms for each data point extracted. This is the raw data used for this paper. The 'yi' and 'vi' terms in the dataset are the individual log response ratio (lnRR) and variation, respectively. These are the terms used in all analyses presented in the final manuscript. There are moderators included in this dataset to account for and test for heterogeneity within the response of interest. However, given the nature of this type of analysis, there are quite a few missing data points from the various moderators; these are noted with an "N/A" in the dataset. The data file is titled "Ortiz-Wolf-2024-JoE.xslx" - this data file contains two spreadsheets: 'metadata' and 'dataset'. The 'metadata' spreadsheet describes each attribute (including abbreviations and units) in 'dataset'. The 'dataset' spreadsheet contains the independent effect sizes (Log Response Ratio) for each data point and the available moderator data there were used in our meta-analyses and used to generate figures presented in our manuscript and supplemental file. ## Sharing/Access Information NA Several recent regional studies have cast doubt on the widespread assumption that nitrogen-fixing plants (N-fixers) act as facilitators of neighboring plant communities. We conducted a meta-analysis to synthesize the effects of N-fixers on plant communities and to understand how ecological context moderates these effects. We analyzed studies that assessed paired effects of N-fixers and non-fixers on soil N, neighboring-plant (non-fixer) biomass, and plant community diversity; ecological moderators included climate, soil texture, and N-fixer growth form and invasive status. N-fixers led to higher soil N and neighboring plant biomass, but lower community diversity compared to non-fixers. The effect of N-fixers on neighboring plant biomass was strongly mediated by soil texture; N-fixer invasive status and growth form were also significant mediators of the facilitative effects of N-fixers. N-fixer effects lie on a continuum between facilitation and suppression that is moderated by intrinsic and extrinsic processes, and this analysis provides insight into how these factors moderate the effects of N-fixers. Overall, N-fixers facilitate neighbor biomass but suppress diversity, though high variation in these effects can be explained in part by ecological context.