DOI: 10.5061/dryad.qv9s4mwf3
title: Global maps of current (1979-2013) and future (2061-2080) habitat suitability probability for 1,485 European endemic plant species

description: 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. Location: Global 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.
description: <i>Modelling the
 potential alien ranges of plant species under current climatic and
 land-use conditions</i> Species
 selection 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 &gt; 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 <i>~ </i>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. Environmental
 variables 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). <i>Modelling the potential
 alien ranges of plant species under future climatic
 conditions</i> 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. <i>References</i> <b> </b>Cayuela, L., Stein, A., & Oksanen, J. (2017). <i>Taxonstand: taxonomic standardization of plant species names v.2.1</i>. 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). <i>rgbif: interface to the Global 'Biodiversity' Information Facility API v.1.3.0</i>. 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). <i>Harmonized Global Land Use for Years 1500 – 2100, V1. Data set</i>. 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. <i>Applied Vegetation Science</i>, 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. <i>PLoS ONE</i>, 9, e105992. Hobohm, C. (Ed.) (2014). <i>Endemism in Vascular Plants</i>. [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. <i>Scientific Data</i>, 4, 170122. Mauri, A., Strona, G., & San-Miguel-Ayanz, J. (2017). EU-Forest, a high-resolution tree occurrence dataset for Europe. <i>Scientific Data</i>, 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. <i>Science of the Total Environment</i>, 534, 43–51.
description: 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).