Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow

This dataset contains a set of future climate rasters derived from the CHELSA v.2.1 bioclim dataset at a spatial resolution of 1 × 1km (Karger et al., 2021; Brun et al., 2022). Climate rasters are available for two future periods (2041-2070 and 2071-2100), and for each period, projections are provid...

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Detalles Bibliográficos
Autores: Delva, Soria, Rossetto, Federica, Adriaens, Tim, Strubbe, Diederik
Tipo de recurso: conjunto de datos
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:dnet:digitalcsic_::b356d545b346692a4c521e7bc31329e8
Acceso en línea:http://hdl.handle.net/10261/429668
Access Level:acceso abierto
Palabra clave:Species distribution modelling
wiSDM v.2
Invasive alien species
Climate predictors
Land cover predicators
Sampling effort grids
Climate change
Range shifts
climate change
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oai_identifier_str oai:dnet:digitalcsic_::b356d545b346692a4c521e7bc31329e8
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow
title Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow
spellingShingle Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow
Delva, Soria
Species distribution modelling
wiSDM v.2
Invasive alien species
Climate predictors
Land cover predicators
Sampling effort grids
Climate change
Range shifts
climate change
title_short Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow
title_full Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow
title_fullStr Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow
title_full_unstemmed Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow
title_sort Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow
dc.creator.none.fl_str_mv Delva, Soria
Rossetto, Federica
Adriaens, Tim
Strubbe, Diederik
author Delva, Soria
author_facet Delva, Soria
Rossetto, Federica
Adriaens, Tim
Strubbe, Diederik
author_role author
author2 Rossetto, Federica
Adriaens, Tim
Strubbe, Diederik
author2_role author
author
author
dc.contributor.none.fl_str_mv European Commission
Rossetto, Federica [0000-0001-7479-063X]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Species distribution modelling
wiSDM v.2
Invasive alien species
Climate predictors
Land cover predicators
Sampling effort grids
Climate change
Range shifts
climate change
topic Species distribution modelling
wiSDM v.2
Invasive alien species
Climate predictors
Land cover predicators
Sampling effort grids
Climate change
Range shifts
climate change
description This dataset contains a set of future climate rasters derived from the CHELSA v.2.1 bioclim dataset at a spatial resolution of 1 × 1km (Karger et al., 2021; Brun et al., 2022). Climate rasters are available for two future periods (2041-2070 and 2071-2100), and for each period, projections are provided for three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). Each layer represents the mean values across five different global circulation models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL), which were standardized relative to the corresponding present-day climate layer by subtracting the present-day mean and dividing by the present-day standard deviation.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
http://purl.org/coar/resource_type/c_ddb1
format dataset
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/429668
url http://hdl.handle.net/10261/429668
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/HE/101181413
info:eu-repo/grantAgreement/EC/HE/101180559
info:eu-repo/grantAgreement/EC/HE/101059592
https://doi.org/10.5281/zenodo.17724735
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv image/tiff
dc.publisher.none.fl_str_mv Zenodo
publisher.none.fl_str_mv Zenodo
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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spelling Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflowDelva, SoriaRossetto, FedericaAdriaens, TimStrubbe, DiederikSpecies distribution modellingwiSDM v.2Invasive alien speciesClimate predictorsLand cover predicatorsSampling effort gridsClimate changeRange shiftsclimate changeThis dataset contains a set of future climate rasters derived from the CHELSA v.2.1 bioclim dataset at a spatial resolution of 1 × 1km (Karger et al., 2021; Brun et al., 2022). Climate rasters are available for two future periods (2041-2070 and 2071-2100), and for each period, projections are provided for three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). Each layer represents the mean values across five different global circulation models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL), which were standardized relative to the corresponding present-day climate layer by subtracting the present-day mean and dividing by the present-day standard deviation.In addition to future climate layers, this dataset contains a set of land cover predictors. Land cover information was extracted from the Corine Land Cover 2018 raster (version U2018_CLC2018_V2020_20u1), a 100m resolution dataset in which each cell represents a specific land-cover category. These cells were aggregated into 1 × 1 km grid cells to quantify the percentage cover of broader land-use classes (see Table 1 for an overview). Grid cells containing more than 5% NA values or more than 50% sea or ocean cells were assigned NA, while cells with a value of zero indicate the absence of a given land-cover class.To mitigate the effects of uneven sampling effort in species distribution modelling, we provide taxonomic sampling effort grids that quantify large-scale spatial variation in sampling intensity. These grids can be used to guide the selection of background points or pseudo-absences, for example by reducing or excluding areas with low sampling effort (Phillips et al., 2009; Elith et al., 2010; Barbet-Massin et al., 2012). To generate the sampling effort grids, we downloaded all available GBIF occurrences for each taxonomic group from 1974 to 2024. Records were filtered to retain only those with valid geographic coordinates and no reported geospatial issues. Sampling grids were then constructed by using CHELSA climate grids as the basis for a global 1° spatial reference raster in WGS84. We mapped each occurrence record to a 1° raster cell and counted the total number of records within each cell. The resulting values represent taxon-specific sampling effort, with higher values indicating more intensely surveyed regions. To improve interpretability and reduce the influence of highly surveyed regions, values are provided in a log-transformed format using log(1 + N), where N is the number of occurrences in that cell.These grids are used as input data for the wiSDM v.2 species distribution modelling workflow of invasive alien species designed for the Tracking Invasive Alien Species (TrIAS) project.El dataset se puede consultar y descargar en el siguiente enlace https://doi.org/10.5281/zenodo.17724735European Commission GuardIAS - GuardIAS - Guarding European Waters from IAS 101181413; European Commission OneSTOP - OneBiosecurity Systems and Technology for People, Places and Pathways 101180559; European Commission B3 - Biodiversity Building Blocks for policy 101059592Peer reviewedZenodoEuropean CommissionRossetto, Federica [0000-0001-7479-063X]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262025info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1image/tiffhttp://hdl.handle.net/10261/429668reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/HE/101181413info:eu-repo/grantAgreement/EC/HE/101180559info:eu-repo/grantAgreement/EC/HE/101059592https://doi.org/10.5281/zenodo.17724735Síinfo:eu-repo/semantics/openAccessoai:dnet:digitalcsic_::b356d545b346692a4c521e7bc31329e82026-05-22T06:33:51Z
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