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...
| Autores: | , , , |
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| 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 |
| Sumario: | 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. |
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