DOI: 10.5281/zenodo.17724734
title: Future climate rasters, land cover predictors and sampling effort grids for invasive species distribution modelling in Europe using the wiSDM v.2 workflow

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.

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. Next to these Corine land cover predictors, three freshwater-related predictors were derived from the EU-Hydro dataset (Copernicus):



log_total_water_length.tif: Captures the total length of linear water features (e.g., rivers, canals, and drainage ditches) within each grid cell. This quantifies narrow but widespread water elements that polygon mapping often misses, particularly in agricultural landscapes or riparian networks. The output is provided as a log-transformed raster layer.

proportion_total_water_polygon_cover.tif: Describes how much of each grid cell’s surface is covered by broader water bodies such as lakes, ponds, reservoirs or wide river sections. Values range from 0 to 1, where 0.25 indicates that 25% of the pixel is covered by water. 

log_distance_to_water.tif: Represents the distance to the nearest pixel that contains water. Areas that directly touch a riverbank or lake shoreline have values near zero, while more distant, upland areas have larger values. It expresses the physical access to water across the landscape and is presented in a log-transformed format.


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.

Table 1. Overview of Corine land cover predictors and the classes they comprise.




Land use predictor
Corine label


Agriculture
Non-irrigated arable land; Permanently irrigated land; Rice fields; Vineyards; Fruit trees and berry plantations; Olive groves; Pastures; Annual crops associated with permanent crops; Complex cultivation patterns; Land principally occupied by agriculture, with significant areas of natural vegetation; Agro-forestry areas


Artificial
Continuous urban fabric; Discontinuous urban fabric; Industrial or commercial units; Road and rail networks and associated land; Port areas; Airports; Mineral extraction sites; Dump sites; Construction sites; Green urban areas; Sport and leisure facilities


Coastal_wetland
Salt marshes; Salines; Intertidal flats


Coniferous_forest
Coniferous forest


Deciduous_forest
Broad-leaved forest


Inland_wetland
Inland marshes; peat bogs


Mixed_forest
Mixed forest


Shrub_and_herbaceous
Natural grasslands; Moors and heathland; Sclerophyllous vegetation; Transitional woodland-shrub




 

1. Future climate layers

Layers are named as follows: scaled_layer_CHELSA_abbreviation_period_scenario.tif, with period being either 2041-2070 or 2071-2100, and scenario being one of the following: ssp126, ssp370, and ssp585.

Variable (abbreviation).



Mean annual air temperature (meantemp_1)

Temperature seasonality (temp_seasonality_4)

Mean daily maximum air temperature of the warmest month (maxTmpWarmestMon_5)

Mean daily minimum air temperature of the coldest month (minTmpColdestMon_6)

Annual range of air temperature (temp_annRange_7)

Annual precipitation amount (annPrecip_12)

Precipitation amount of the wettest month (precipWettestMon_13)

Precipitation amount of the driest month (precipDriestMon_14)

Precipitation seasonality (precip_Seasonality_15)


2. Land cover predictors



Agriculture.tif

Artificial.tif

Coastal_wetland.tif

Coniferous_forest.tif

Deciduous_forest.tif

Inland_wetland.tif

Mixed_forest.tif

Shrub_and_herbaceous.tif

log_distance_to_water.tif

log_total_water_length.tif

proportion_total_water_polygon_cover.tif


3. Taxonomic sampling effort grids



log_amphibians_1degree_layer.tif

log_birds_1degree_layer.tif

log_fish_1degree_layer.tif

log_hydrozoa_1degree_layer.tif

log_insects_1degree_layer.tif

log_malacostraca_1degree_layer.tif

log_mammals_1degree_layer.tif

log_mollusca_1degree_layer.tif

log_plants_1degree_layer.tif

log_reptiles_1degree_layer.tif