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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: 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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Forest-Forward: Identifing future suitable areas for forestry in Indonesia, Spain, and Sweden.

Authors: Morris, Edward P.; Carrete Vega, Greta; Lajarin Sanchez, Blas; Paz Jimenez, Jorge; Peña Cerezo, Nieves; Teixeira, Luisa;

Forest-Forward: Identifing future suitable areas for forestry in Indonesia, Spain, and Sweden.

Abstract

This dataset includes the original data and modelling results used to create the interactive platform Forest-forward. These include; climatic variables (historical reanalysis ERA-5, and the future CMIP5 projections) derived from the Copernicus Climate Data Store that have been converted to 19 standard bioclimatic variables; species occurrence data for a limited number of tree species derived from GBIF; and, the results of ensemble Species Distribution Modelling (SDM) using historical and future bioclimatic variables as predictors, created using the R package biomod2 that have been assigned to a hexagonal grid for display. Code used for the full processing pipeline is available at GitHub. Data files are organised by region; Indonesia (IDN), mainland Spain (ESP), and Sweden (SWE). Bioclimatic variables are GeoTIFF files grouped per region in zipped directories representing historical reanalysis (1980-2019) and future predictions (further divided into 10 y time-intervals between 2020 and 2090). SDM results (and resampled bioclimatic variables) are supplied as CSV files with WKT geometries ("<iso3>_zonal_spp_uuid.csv", and "<iso3>_zonal_bv_uuid.csv", respectively). Species occurrence point data is supplied as a single CSV file with WKT geometries ("all_spp_occurrence.csv"); the field 'iso3' can be used to filter by region. Summaries of the total proportion of area of a country with suitable habitat for each species, and the area weighted mean of each bioclimatic variable per country are supplied as CSV files ("<iso3>_spp_summary_stats.csv", and "<iso3>_bv_summary_stats.csv", respectively).

This work was funded by the ECWMF contract C3S_428b_TECNALIA, see https://ted.europa.eu/udl?uri=TED:NOTICE:472276-2019:TEXT:EN:HTML

Keywords

climate change, Species Distribution Modelling, species distribution model, Copernicus Climate Data Store, forestry, climate, biomod2

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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