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.

README

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.

Plant invasion in Mediterranean Europe: current hotspots and future scenarios

Data files

Abstract

README

Methods

Usage notes