DOI: 10.5061/dryad.j0zpc86j1
title: Plant invasion in Mediterranean Europe: current hotspots and future scenarios

description: These are the raw data that can be used to reproduce results of the paper:
 "Plant invasion in Mediterranean Europe: current invasion hotspots
 and future scenarios".  The Mediterranean Basin has
 historically been subject to alien plant invasions that threaten its
 unique biodiversity. This seasonally dry and densely populated region is
 undergoing severe climatic and socioeconomic changes, and it is unclear
 whether these changes will worsen or mitigate plant invasions. Predictions
 are often biased, as species may not be in equilibrium in the invaded
 environment, depending on their invasion stage and ecological
 characteristics. To address future predictions uncertainty, we identified
 invasion hotspots across multiple biased modelling scenarios and
 ecological characteristics of successful invaders. We selected 92 alien
 plant species widespread in Mediterranean Europe and compiled data on
 their distribution in the Mediterranean and worldwide. We combined these
 data with environmental and propagule pressure variables to model global
 and regional species niches and map their current and future habitat
 suitability. We identified invasion hotspots, examined their potential
 future shifts, and compared the results of different modelling strategies.
 Finally, we generalised our findings by using linear models to determine
 the traits and biogeographic features of invaders most likely to benefit
 from global change. Currently, invasion hotspots are found near ports and
 coastlines throughout Mediterranean Europe. However, many species occupy
 only a small portion of the environmental conditions to which they are
 preadapted, suggesting that their invasion is still an ongoing process.
 Future conditions will lead to declines in many currently widespread
 aliens, which will tend to move to higher elevations and latitudes. Our
 trait models indicate that future climates will generally favour species
 with conservative ecological strategies that can cope with reduced water
 availability, such as those with short stature and low specific leaf area.
 Taken together, our results suggest that in future environments, these
 conservative aliens will move farther from the introduction areas and
 upslope, threatening mountain ecosystems that have been spared from
 invasions so far. With these data (environmental variables, species
 presences and background points, and distance to ports cities and to the
 coast) and using the R software following the ODMAP protocol attached to
 the original paper all results meet the criteria of reproducible science.
description: Datasets from the EVA and GBIF were processed following the
 Material and Methods section of the paper, to derive the attached files of
 regional and global presences and background points. 
 The environmental variables used were processed as explained in
 the paper. Files of distances to the cities, ports and
 coast were elaborated from the raw data downloadable as reported in the
 data availability statement.
description: The data is in .csv format and can be read by any text editor
 file. We recommend their usage in R. To reproduce analyses please use
 Biomod 2 R package. 
description: Data files included: 1\) all.pres_global.csv: is a classic plot (on rows)
 x species (in column) dataset of presences for all alien species recorded
 at the global scale. These contain the Global Biodiversity Information
 Facility (GBIF) and European Vegetation Archive (EVA) presences of alien
 species recorded globally in the global buffer. Columns correspond to: *
 source: can be either GBIF or EVA, depending on the original dataset from
 which data are sourced * Longitude\Latitude: two columns to georeference
 plots, coordinates in geographic WGS 84 * cells: a unique identifier
 shared among all the datasets to identify the raster cell to which all
 other columns refer * 93 columns of species names: these columns display 1
 if the corresponding alien species have been found in the corresponding
 cell, and 0 if at least one other alien species has been found in the same
 cell. In this case, 0 does not correspond to absences but should be
 interpreted as a table filler * 7 columns for the environmental variables:
 these represent the environmental variables extracted for the relevant
 cells in which at least one alien species was observed. Variable names
 match the original ones, refer to ly.names.def.csv for a more intuitive
 description 2\) all.pres_regional.csv: is a classic plot (on rows) x
 species (in column) dataset of presences for all alien species recorded at
 the local/regional scale, i.e., in Mediterranean Europe. These contain the
 Global Biodiversity Information Facility (GBIF) and European Vegetation
 Archive (EVA) presence of alien species in the regional buffer. Columns
 correspond to: * source: can be either GBIF or EVA, depending on the
 original dataset from which data are sourced * Longitude\Latitude: two
 columns to georeference plots, coordinates in geographic WGS 84 * cells: a
 unique identifier shared among all the datasets to identify the raster
 cell to which all other columns refer * 93 columns of species names: these
 columns display 1 if the corresponding alien species have been found in
 the corresponding cell, and 0 if at least one other alien species has been
 found in the same cell. In this case, 0 does not correspond to absences
 but should be interpreted as a table filler * 7 columns for the
 environmental variables: these represent the environmental variables
 extracted for the relevant cells in which at least one alien species was
 observed. Variable names match the original ones, refer to
 ly.names.def.csv for a more intuitive description 3\) ly.names.def.csv: is
 a character vector file (just one row) to define intuitive names of the
 environmental variables. 4\) Global_BKG.csv: represents all background
 points used to fit the global model. These were used to extract three sets
 of background points, after weithging by the regional sampling intensity.
 Columns correspond to: * Longitude\Latitude: two columns to georeference
 plots, coordinates in geographic WGS 84 * EVA_Nr._plots: the number of
 sampled EVA plots in the corresponding cell * cells: a unique identifier
 shared among all the datasets to identify the raster cell to which all
 other columns refer * GBIF_Nr._plots: the number of sampled GBIF plots in
 the corresponding cell. The two cell's number (i.e., EVA_Nr._plots
 and GBIF_Nr._plots) were summed and used to weigh absences (only once for
 all species) that were then used to randomly extract the three background
 point samples in the global background * 7 columns for the environmental
 variables: these represent the environmental variables extracted for the
 relevant cells in which at least one alien species was observed. Variable
 names match the original ones, refer to ly.names.def.csv for a more
 intuitive description 5\) Local_BKG.csv: represents all background points
 used to fit the local/regional model. These were used to extract three
 sets of background points, after weithging by the regional sampling
 intensity. Columns correspond to: * Longitude\Latitude: two columns to
 georeference plots, coordinates in geographic WGS 84 * EVA_Nr._plots: the
 number of sampled EVA plots in the corresponding cell * cells: a unique
 identifier shared among all the datasets to identify the raster cell to
 which all other columns refer * GBIF_Nr._plots: the number of sampled GBIF
 plots in the corresponding cell . The two cells (i.e., EVA_Nr._plots and
 GBIF_Nr._plots) were summed and used to weight absences (only once for all
 species) that were then used to randomly extract the three background
 point samples in the local/regional background * 7 columns for the
 environmental variables: these represent the environmental variables
 extracted for the relevant cells in which at least one alien species was
 observed. Variable names match the original ones, refer to
 ly.names.def.csv for a more intuitive description 6\) myexpl.var30... : by
 their extended name, represent the environmental variables used to project
 the model in the current and future environmental conditions of
 Mediterranean Europe. This is a raster stack and each layer name is
 defined by the file ly.names.def.csv, which order is matched. 7\)
 Distance_to_cities: is a raster file that can be uploaded in R using the
 raster (function), and represents a cellwise distance to the major cities.
 8\) Distance_to_coast: is a raster file that can be uploaded in R using
 the raster (function), and represents a cellwise distance to the European
 coastline. 9\) Distance_to_ports: is a raster file that can be uploaded in
 R using the raster (function), and represents a cellwise distance to the
 major European ports.