Global maps of current (1979-2013) and future (2061-2080) habitat suitability probability for 1,485 European endemic plant species
Cite this dataset
Pouteau, Robin et al. (2021). Global maps of current (1979-2013) and future (2061-2080) habitat suitability probability for 1,485 European endemic plant species [Dataset]. Dryad. https://doi.org/10.5061/dryad.qv9s4mwf3
Aims: The rapid increase in the number of species that have naturalized beyond their native range is among the most apparent features of the Anthropocene. How alien species will respond to other processes of future global changes is an emerging concern and remains largely misunderstood. We therefore ask whether naturalized species will respond to climate and land-use change differently than those species not yet naturalized anywhere in the world.
Methods: We investigated future changes in the potential alien range of vascular plant species endemic to Europe that are either naturalized (n = 272) or not yet naturalized (1,213) outside of Europe. Potential ranges were estimated based on projections of species distribution models using 20 future climate-change scenarios. We mapped current and future global centres of naturalization risk. We also analyzed expected changes in latitudinal, elevational and areal extent of species’ potential alien ranges.
Results: We showed a large potential for more worldwide naturalizations of European plants currently and in the future. The centres of naturalization risk for naturalized and non-naturalized plants largely overlapped, and their location did not change much under projected future climates. Nevertheless, naturalized plants had their potential range shifting poleward over larger distances, whereas the non-naturalized ones had their potential elevational ranges shifting further upslope under the most severe climate change scenarios. As a result, climate and land-use changes are predicted to shrink the potential alien range of European plants, but less so for already naturalized than for non-naturalized species.
Main conclusions: While currently non-naturalized plants originate frequently from mountain ranges or boreal and Mediterranean biomes in Europe, the naturalized ones usually occur at low elevations, close to human centres of activities. As the latter are expected to increase worldwide, this could explain why the potential alien range of already naturalized plants will shrink less.
Modelling the potential alien ranges of plant species under current climatic and land-use conditions
We focused exclusively on vascular plant species endemic to Europe. Here, ‘Europe’ is used in a geographical sense and defined as bordered by the Arctic Ocean to the north, the Atlantic Ocean to the west (the Macaronesian archipelagos were excluded), the Ural Mountains and the Caspian Sea to the east, and the Lesser Caucasus and the Mediterranean Sea to the south (Mediterranean islands included, Anatolia excluded).
The most recent version of the database ‘Endemic vascular plants in Europe’ (EvaplantE; Hobohm, 2014), containing > 6,200 endemic plant taxa, was used here as a baseline for species selection. Scientific names were standardized based on The Plant List (http://www.theplantlist.org/). This taxonomic standardization was done with the R package ‘Taxonstand’ (Cayuela et al., 2017). Infraspecific taxa were excluded from the list, resulting in 4,985 species.
Compilation of occurrence records
To comprehensively compile the distribution of our studied set of endemic species in their native continent, we combined occurrence data in Europe from five sources. The first source was the ‘Global Biodiversity Information Facility’ (GBIF), one of the largest and most widely used biodiversity databases (https://www.gbif.org/). Currently, GBIF provides access to more than 600,000 distributional records for European endemic plant species. Records of European endemic plants deemed erroneous were discarded. All occurrences from GBIF were downloaded using the R package ‘rgbif’ (Chamberlain et al., 2019). The second source was the ‘EU-Forest’ dataset, providing information on European tree species distribution, including more than half a million occurrences at a 1 km (~ 50 arcsec at 50° latitude) resolution (Mauri et al., 2017). The third source we used was the ‘European Vegetation Archive’ (EVA), which assembles observations from more than one million vegetation plots across Europe (Chytrý et al., 2016). The fourth source was the digital version of the Atlas Florae Europaeae offering gridded maps. The fifth source was the ‘Plant Functional Diversity of Grasslands’ network (DIVGRASS), combining data on plant diversity across ~ 70,000 vegetation plots in French permanent grasslands (Violle et al., 2015).
When several occurrences from these different sources were duplicated on the same 0.42° × 0.42° grid cell, only one record was kept to avoid pseudoreplication. After removing duplicate records, species with fewer than 10 occurrences were not further considered since the resulting SDM might be insufficiently accurate. The final dataset comprised 104,313 occurrences for 1,485 European endemic species.
We selected six environmental predictors related to climate, soil physico-chemical properties and land use, commonly considered to shape the spatial distribution of plants (Gurevitch et al., 2006). Annual mean temperature (°C), annual sum of precipitation (mm) and precipitation seasonality representing the period 1979-2013 were extracted from the CHELSA climate model at a 30 arcsec resolution (Karger et al., 2017). Organic carbon content (g per kg) and soil pH in the first 15 cm of topsoil were extracted at a 1 km resolution from the global gridded soil information database SoilGrids (Hengl et al., 2014). The proportion of primary land-cover (land with natural vegetation that has not been subject to human activity since 1500) averaged over the period 1979-2013 in each 0.5° resolution grid cell (variable ‘gothr’) based on the Harmonized Global Land Use dataset was also used (Chini et al., 2014). Environmental variables were aggregated at a spatial resolution of 0.42° × 0.42° to approach the cell size of the occurrence records with the coarsest resolution (i.e. the Atlas Florae Europaeae).
Species distribution modelling
The potential distribution of 1,485 European endemic plant species was predicted by estimating environmental similarity to the sites of occurrence in Europe. To increase robustness of the predictions, we used six methods to generate species distribution models (SDMs): generalized additive models; generalized linear models; generalized boosting trees; maximum entropy; multivariate adaptive regression splines; and random forests. We evaluated the predictive performance of each SDM using a repeated split sampling approach in which SDMs were calibrated over 75% of the data and evaluated over the remaining 25%. This procedure was repeated 10 times. The evaluation was performed by measuring the area under the receiver operating characteristic (ROC) curve (AUC) and the true skill statistic (TSS). Continuous model predictions were transformed into binary ones by selecting the threshold maximizing TSS to ensure the most accurate predictions since it is based on both sensitivity and specificity.
Results of the different SDM methods were aggregated into a single consensus projection (i.e. map) to reduce uncertainties associated with each technique. To ensure the quality of the ensemble SDMs, we only kept the projections for which the accuracy estimated by AUC and TSS were higher than 0.8 and 0.6, respectively, and assembled the selected SDMs using a committee-average approach with a weight proportional to their TSS evaluation. The entire species distribution modelling process was performed within the ‘biomod2’ R platform (Thuiller et al., 2009).
Modelling the potential alien ranges of plant species under future climatic conditions
To model the potential spread of the European endemic flora outside of Europe in the future (period 2061-2080), we used projections for the four representative concentration pathways (RCPs) of both climate and land cover data for the years 2061-2080. Due to substantial climatic differences predicted by different general circulation models (GCMs), which result in concomitant differences in species range projections, simulations of future climate variables were based on five different GCMs: CCSM4, CESM1-CAM5, CSIRO-mk3-6-0, IPSL-CM5A-LR and MIROC5.
Cayuela, L., Stein, A., & Oksanen, J. (2017). Taxonstand: taxonomic standardization of plant species names v.2.1. R Foundation for Statistical Computing. Available at https://cran.r-project.org/web/packages/Taxonstand/index.html.
Chamberlain, S., Barve, V., Desmet, P., Geffert, L., Mcglinn, D., Oldoni, D., & Ram, K. (2019). rgbif: interface to the Global 'Biodiversity' Information Facility API v.1.3.0. R Foundation for Statistical Computing. Available at https://cran.r-project.org/web/packages/rgbif/index.html.
Chini, L.P., Hurtt, G.C., & Frolking, S. (2014). Harmonized Global Land Use for Years 1500 – 2100, V1. Data set. Oak Ridge National Laboratory Distributed Active Archive Center, USA. Available at http://daac.ornl.gov
Chytrý, M., Hennekens, S. M., Jiménez-Alfaro, B., Knollová, I., Dengler, J., Jansen, F., … Yamalov, S. (2016). European Vegetation Archive (EVA): an integrated database of European vegetation plots. Applied Vegetation Science, 19, 173–180.
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B. M., Ribeiro, E., … Gonzalez, M. R. (2014). SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE, 9, e105992.
Hobohm, C. (Ed.) (2014). Endemism in Vascular Plants. [Plant and Vegetation 9]. Dordrecht, The Netherlands: Springer.
Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., … Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific Data, 4, 170122.
Mauri, A., Strona, G., & San-Miguel-Ayanz, J. (2017). EU-Forest, a high-resolution tree occurrence dataset for Europe. Scientific Data, 4, 160123.
Violle, C., Choler, P., Borgy, B., Garnier, E., Amiaud, B., Debarros, G., … Viovy, N. (2015). Vegetation ecology meets ecosystem science: permanent grasslands as a functional biogeography case study. Science of the Total Environment, 534, 43–51.
This dataset includes raster files (.gri format) representing global maps of habitat suitability probability for the 1,485 most widespread European endemic plant species under current conditions (folder '1979-2013') and 20 future scenarios (folder '2061-2080'). Future scenarios have been generated for four representative concentration pathways (RCP) of both climate and land cover data (folder '26' for RCP 2.6, '45' for RCP 4.5, '60' for RCP 6.0, '85' for RCP 8.5) and five different general circulation models (CCSM4, CESM1-CAM5, CSIRO-mk3-6-0, IPSL-CM5A-LR and MIROC5). The spatial resolution of these maps is 0.4166667° × 0.4166667°. The geographic coordinate system is World Geodetic System 1984 (EPSG: 4326).
National Natural Science Foundation of China, Award: 31901176
Taizhou University, Award: 2018YQ001
Basque Government, Award: IT936‐16
Czech Science Foundation, Award: 19-28491X
Austrian Science Foundation FWF, Award: I2086-B16
Volkswagen Foundation, Award: A118199
Deutsche Forschungsgemeinschaft, Award: DFG–FZT 118, 202548816
Czech Science Foundation, Award: 19-28807X
Czech Academy of Sciences, Award: 67985939
The Velux Foundations, Award: 16549
Basque Government, Award: IT936‐16
Austrian Science Foundation FWF, Award: I2086-B16