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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Thyrring, Jakob; Wegeberg, Susse; Blicher, Martin E.; Krause-Jensen, Dorte; +6 Authors

    The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: de Vries, Joost; Poulton, Alex J.; Young, Jeremy R.; Monteiro, Fanny M.; +5 Authors

    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

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Metsaranta, Juha; Mamet, Steven; Maillet, Jay; Barr, Alan;

    These datasets are associated with the following paper: Metsaranta, J.M., Mamet, S.D., Maillett, J., Barr, A.G. (2021). Comparison of tree-ring and eddy covariance derived annual ecosystem production estimates for jack pine and trembling aspen forests in Saskatchewan, Canada. Agricultural and Forest Meteorology. There are two files: (1) CBMOutput.zip. This contains the hybrid biometric modelled ecosystem C stock and flux estimates. (2) StandReconstructionData.zip. This contains the field measurement data and the tree level biomass and wood volume data for the Stand Reconstruction plots used to develop the hybrid biometric modelled estimates. The data are formatted as .csv files, and an associated Microsoft Excel spreadsheet explains the data columns and provides information on the associated units of measure.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Stewart, Frances; Micheletti, Tatiane; McIntire, Eliot; Chubaty, Alex;

    Most research on boreal populations of Woodland caribou (Rangifer tarandus caribou) has been conducted in areas of high anthropogenic disturbance. However, a large portion of the species’ range overlaps relatively pristine areas primarily disturbed by natural disturbances, such as wildfire. Climate-driven habitat change is a key concern for the conservation of boreal-dependent species, where management decisions have yet to consider knowledge from multiple ecological domains integrated into a cohesive and spatially explicit forecast of species-specific habitat and demography. We used a novel ecological forecasting framework to provide climate-sensitive projections of habitat and demography for five boreal caribou monitoring areas within the Northwest Territories (NWT), Canada, over 90 years. Importantly, we quantify uncertainty around forecasted mean values. Our results suggest habitat suitability may increase in central and southwest regions of the NWT’s Taiga Plains ecozone but decrease in southern and northwestern regions driven by conversion of coniferous to deciduous forests. We do not project boreal caribou population growth rates to change despite forecasted changes to habitat suitability. Our results emphasize the importance of efforts to protect and restore northern boreal caribou habitat despite climate uncertainty while highlighting expected spatial variations that are important considerations for local people who rely on them. An ability to reproduce previous work, and critical thought when incorporating sources of uncertainty, will be important to refine forecasts, derive management decisions, and improve conservation efficacy for northern species at risk. Please see the README document ("README.md") and the accompanying published article: Stewart, Micheletti et al. 2023. Climatepinformed forecasts reveal dramatic local habitat shifts and population uncertainty for nothern boreal caribou. Ecological Applications.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    Borealis
    Dataset . 2023
    Data sources: Datacite
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      Borealis
      Dataset . 2023
      Data sources: Datacite
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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  • Authors: Reinsch, S.; Koller, E.; Sowerby, A.; De Dato, G.; +17 Authors

    The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

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    Authors: Cooper, Sam;

    This spreadsheet contains the results for the article, "Meeting the costs of decarbonising industry – the potential effects on prices and competitiveness (a case study of the UK)". These include projected impacts for industrial process decarbonisation (costs, fuel use, residual emissions), for key years (2030, 2040, 2050), distributed in the following ways: - Directly allocated to industrial sector in which they occur - Shared between sectors in proportion to the share of GVA of each supply chain - Embodied in final products - Embodied in final products, aggregated to consumption patterns The source of the projections and the method to perform the distribution are described in detail in the associated article. Further relevant documentation may be found in the following resources. Cooper, S. J.G., Allen, S. R., Gailani, A., Norman, J. B., Owen, A., Barrett, J., and Taylor, P., 2024. Meeting the costs of decarbonising industry – The potential effects on prices and competitiveness (a case study of the UK). Energy Policy, 184, 113904. Available from: https://doi.org/10.1016/j.enpol.2023.113904. For details of the methods used, please see the associated journal article.

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    University of Bath Research Data Archive
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      University of Bath Research Data Archive
      Dataset . 2024
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      Data sources: Datacite
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    Dataset including raw Photoluminescence (PL) spectral data, UV-vis Absorbance (ABS) spectral data, Photoluminescence quantum yield (PLQY) data and calculations, and the average Suprapartice (SP) size data.

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    University of Strathclyde KnowledgeBase Datasets
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      University of Strathclyde KnowledgeBase Datasets
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Aguirre Gutierrez, Jesus; Malhi, Yadvinder;

    Maps created and resulting data from analysis in changes in community weighted mean of traits. The raw trait data and forest census data used are available from their sources in www.gem.tropicalforests.ox.ac.uk and ForestPlots.net.

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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hansen, Teis; Keaney, Monica; Bulkeley, Harriet A.; Cooper, Mark; +10 Authors

    This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.

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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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    ZENODO
    Dataset . 2018
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2018
    License: CC BY NC ND
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2018
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2018
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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    Authors: Pérez-Navarro, María Ángeles;

    This repository contains a series of .csv files developed for the study titled "Plant canopies promote climatic disequilibrium in Mediterranean recruit communities", authored by: Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcántara JM and Verdú M. The author of these files is Perez-Navarro MA. These files are used to characterize species niches, estimate climatic disequilibrium for recruit communities growing under plant canopies and open spaces, and conduct statistical analyses. Variables description of each table is compiled in the METADATA.txt file. Please visit Github readme () to correctly place these files in the folder tree and check for the corresponding scripts where they are required. Please notice that although alternative approaches were calibrated to estimate species niche (accordingly producing multiple niche, distances and disequilibrium dataframes), only niche centroid calibrated discarding 95 percentile of lowest niche density was used for paper results and figures. Also, in case of univariate analyses only bio01, bio06 and bio12 were used in analyses, though species niche and further niche and community estimations were obtained for all 19 variables. This is version 2 (v2) and include extra intermediate .csv required to run all the R scripts included in the abovementioned Github repository. NAs or empty cells present in the .csv files of this repository means no data and do not contribute to the analyses. Visit METADATA.txt file for variables description. These data are under CC0 license. It is possible to share, copy and redistribute the material in any medium or format, and adapt, remix, transform, and build upon the material for any purpose. Studies using R scripts or any data files from these study should cite the abovementioned paper (Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcantara JM, Verdu M. (2024). Plant canopies promote climatic disequilibrium in Mediterranean recruit communities). Please contact m.angeles582@gmail.com in case of having doubts or problems with the existing files and scripts. Current rates of climate change are exceeding the capacity of many plant species to track climate, thus leading communities to be in disequilibrium with climatic conditions. Plant canopies can contribute to this disequilibrium by buffering macro-climatic conditions and sheltering poorly adapted species to the oncoming climate, particularly in their recruitment stages. Here we analyze differences in climatic disequilibrium between understory and open ground woody plant recruits in 28 localities, covering more than 100,000 m2, across an elevation range embedding temperature and aridity gradients in the southern Iberian Peninsula. This study demonstrates higher climatic disequilibrium under canopies compared with open ground, supporting that plant canopies would affect future community climatic lags by allowing the recruitment of less arid-adapted species in warm and dry conditions, but also it endorse that canopies could favor warm-adapted species in extremely cold environments as mountain tops, thus pre-adapting communities living in these habitats to climate change.

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    ZENODO
    Dataset . 2024
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Thyrring, Jakob; Wegeberg, Susse; Blicher, Martin E.; Krause-Jensen, Dorte; +6 Authors

    The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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    Authors: de Vries, Joost; Poulton, Alex J.; Young, Jeremy R.; Monteiro, Fanny M.; +5 Authors

    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

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
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    Authors: Metsaranta, Juha; Mamet, Steven; Maillet, Jay; Barr, Alan;

    These datasets are associated with the following paper: Metsaranta, J.M., Mamet, S.D., Maillett, J., Barr, A.G. (2021). Comparison of tree-ring and eddy covariance derived annual ecosystem production estimates for jack pine and trembling aspen forests in Saskatchewan, Canada. Agricultural and Forest Meteorology. There are two files: (1) CBMOutput.zip. This contains the hybrid biometric modelled ecosystem C stock and flux estimates. (2) StandReconstructionData.zip. This contains the field measurement data and the tree level biomass and wood volume data for the Stand Reconstruction plots used to develop the hybrid biometric modelled estimates. The data are formatted as .csv files, and an associated Microsoft Excel spreadsheet explains the data columns and provides information on the associated units of measure.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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    Authors: Stewart, Frances; Micheletti, Tatiane; McIntire, Eliot; Chubaty, Alex;

    Most research on boreal populations of Woodland caribou (Rangifer tarandus caribou) has been conducted in areas of high anthropogenic disturbance. However, a large portion of the species’ range overlaps relatively pristine areas primarily disturbed by natural disturbances, such as wildfire. Climate-driven habitat change is a key concern for the conservation of boreal-dependent species, where management decisions have yet to consider knowledge from multiple ecological domains integrated into a cohesive and spatially explicit forecast of species-specific habitat and demography. We used a novel ecological forecasting framework to provide climate-sensitive projections of habitat and demography for five boreal caribou monitoring areas within the Northwest Territories (NWT), Canada, over 90 years. Importantly, we quantify uncertainty around forecasted mean values. Our results suggest habitat suitability may increase in central and southwest regions of the NWT’s Taiga Plains ecozone but decrease in southern and northwestern regions driven by conversion of coniferous to deciduous forests. We do not project boreal caribou population growth rates to change despite forecasted changes to habitat suitability. Our results emphasize the importance of efforts to protect and restore northern boreal caribou habitat despite climate uncertainty while highlighting expected spatial variations that are important considerations for local people who rely on them. An ability to reproduce previous work, and critical thought when incorporating sources of uncertainty, will be important to refine forecasts, derive management decisions, and improve conservation efficacy for northern species at risk. Please see the README document ("README.md") and the accompanying published article: Stewart, Micheletti et al. 2023. Climatepinformed forecasts reveal dramatic local habitat shifts and population uncertainty for nothern boreal caribou. Ecological Applications.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    Borealis
    Dataset . 2023
    Data sources: Datacite
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      Borealis
      Dataset . 2023
      Data sources: Datacite
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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  • Authors: Reinsch, S.; Koller, E.; Sowerby, A.; De Dato, G.; +17 Authors

    The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

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    Authors: Cooper, Sam;

    This spreadsheet contains the results for the article, "Meeting the costs of decarbonising industry – the potential effects on prices and competitiveness (a case study of the UK)". These include projected impacts for industrial process decarbonisation (costs, fuel use, residual emissions), for key years (2030, 2040, 2050), distributed in the following ways: - Directly allocated to industrial sector in which they occur - Shared between sectors in proportion to the share of GVA of each supply chain - Embodied in final products - Embodied in final products, aggregated to consumption patterns The source of the projections and the method to perform the distribution are described in detail in the associated article. Further relevant documentation may be found in the following resources. Cooper, S. J.G., Allen, S. R., Gailani, A., Norman, J. B., Owen, A., Barrett, J., and Taylor, P., 2024. Meeting the costs of decarbonising industry – The potential effects on prices and competitiveness (a case study of the UK). Energy Policy, 184, 113904. Available from: https://doi.org/10.1016/j.enpol.2023.113904. For details of the methods used, please see the associated journal article.

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    University of Bath Research Data Archive
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      University of Bath Research Data Archive
      Dataset . 2024
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    Dataset including raw Photoluminescence (PL) spectral data, UV-vis Absorbance (ABS) spectral data, Photoluminescence quantum yield (PLQY) data and calculations, and the average Suprapartice (SP) size data.

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    University of Strathclyde KnowledgeBase Datasets
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      University of Strathclyde KnowledgeBase Datasets
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Aguirre Gutierrez, Jesus; Malhi, Yadvinder;

    Maps created and resulting data from analysis in changes in community weighted mean of traits. The raw trait data and forest census data used are available from their sources in www.gem.tropicalforests.ox.ac.uk and ForestPlots.net.

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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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    Authors: Hansen, Teis; Keaney, Monica; Bulkeley, Harriet A.; Cooper, Mark; +10 Authors

    This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.

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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2018
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2018
    License: CC BY NC ND
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2018
      Data sources: ZENODO
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      ZENODO
      Dataset . 2018
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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    Authors: Pérez-Navarro, María Ángeles;

    This repository contains a series of .csv files developed for the study titled "Plant canopies promote climatic disequilibrium in Mediterranean recruit communities", authored by: Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcántara JM and Verdú M. The author of these files is Perez-Navarro MA. These files are used to characterize species niches, estimate climatic disequilibrium for recruit communities growing under plant canopies and open spaces, and conduct statistical analyses. Variables description of each table is compiled in the METADATA.txt file. Please visit Github readme () to correctly place these files in the folder tree and check for the corresponding scripts where they are required. Please notice that although alternative approaches were calibrated to estimate species niche (accordingly producing multiple niche, distances and disequilibrium dataframes), only niche centroid calibrated discarding 95 percentile of lowest niche density was used for paper results and figures. Also, in case of univariate analyses only bio01, bio06 and bio12 were used in analyses, though species niche and further niche and community estimations were obtained for all 19 variables. This is version 2 (v2) and include extra intermediate .csv required to run all the R scripts included in the abovementioned Github repository. NAs or empty cells present in the .csv files of this repository means no data and do not contribute to the analyses. Visit METADATA.txt file for variables description. These data are under CC0 license. It is possible to share, copy and redistribute the material in any medium or format, and adapt, remix, transform, and build upon the material for any purpose. Studies using R scripts or any data files from these study should cite the abovementioned paper (Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcantara JM, Verdu M. (2024). Plant canopies promote climatic disequilibrium in Mediterranean recruit communities). Please contact m.angeles582@gmail.com in case of having doubts or problems with the existing files and scripts. Current rates of climate change are exceeding the capacity of many plant species to track climate, thus leading communities to be in disequilibrium with climatic conditions. Plant canopies can contribute to this disequilibrium by buffering macro-climatic conditions and sheltering poorly adapted species to the oncoming climate, particularly in their recruitment stages. Here we analyze differences in climatic disequilibrium between understory and open ground woody plant recruits in 28 localities, covering more than 100,000 m2, across an elevation range embedding temperature and aridity gradients in the southern Iberian Peninsula. This study demonstrates higher climatic disequilibrium under canopies compared with open ground, supporting that plant canopies would affect future community climatic lags by allowing the recruitment of less arid-adapted species in warm and dry conditions, but also it endorse that canopies could favor warm-adapted species in extremely cold environments as mountain tops, thus pre-adapting communities living in these habitats to climate change.

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    ZENODO
    Dataset . 2024
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
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