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Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

The version 1.0 contains the supporting data for the work (still under submission) "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)". The following files are here available (all file are georeferenced in EPSG: 3003): - AVG_Rainfall_1990-2019.tif -> Raster map of the mean annual precipitation for the northern Tuscany, Italy. It encompasses the portion of the Tuscany region northern of the cities of Livorno - Florence. The interpolation was validated via a leave one out cross-validation procedure. - D3-1_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1921 to 1950 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy). - D3-2_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1951 to 1980 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy). - DeltaSHP_Points_AVG_Annual_Rainfall.zip -> Shape file of the raingauges locations with the mean annual precipitation values of the period 1990 to 2019. - RaingaugesSHP_Points_AVG_Annual_Rainfall_1990-2019.zip -> Shape file of the raingauges locations with the following information: differences in the mean annual precipitation values between the two 3-decades periods 1951 to 1980 and 1990 to 2019 (named D3-2); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1951 to 1980 and 1990 to 2019; difference in the mean annual precipitation values between the two 3-decades periods 1921 to 1950 and 1990 to 2019 (named D3-1); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1921 to 1950 and 1990 to 2019.

The aggregation of data concerned 103 Italian cities and for each city 45 indicators were considered

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

Anthropogenic structures (e.g. weirs and dams) fragment river networks and restrict the movement of migratory fish. Poor understanding of behavioural response to hydrodynamic cues at structures currently limits the development of effective barrier mitigation measures. This study aimed to assess the effect of flow constriction and associated flow patterns on eel behaviour during downstream migration. In a field experiment, we tracked the movements of 40 tagged adult European eels (Anguilla anguilla) through the forebay of a redundant hydropower intake under two manipulated hydrodynamic treatments. Interrogation of fish trajectories in relation to measured and modelled water velocities provided new insights into behaviour, fundamental for developing passage technologies for this endangered species. Eels rarely followed direct routes through the site. Initially, fish aligned with streamlines near the channel banks and approached the intake semi-passively. A switch to more energetically costly avoidance behaviours occurred on encountering constricted flow, prior to physical contact with structures. Under high water velocity gradients, fish then tended to escape rapidly back upstream, whereas exploratory ‘search’ behaviour was common when acceleration was low. This study highlights the importance of hydrodynamics in informing eel behaviour. This offers potential to develop behavioural guidance, improve fish passage solutions and enhance traditional physical screening. Fish_detections_UL_CHFish positions derived from acoustic telemetry contained within excel file with 5 columns. 'Record' denotes tag detection numbered consecutively in sequence; 'tag_number' denotes the fish identification number; ‘PosX’ denotes fish x coordinate in UTM; ‘PosY’ denotes fish y coordinate in UTM, ‘Treatment’ denotes experimental treatment

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

This database includes a large collection of quality-controlled and homogenized historical snow records measured in the 1951-2001 period in the Central and Southern Apennine Mountains (Italy). Such data have been manually digitized from the Hydrological Yearbooks of the Italian National Hydrological and Mareographic Service (hereafter, NHMS), the institution that managed the hydro-meteorological data collection in Italy from 1917 to 2002. More specifically, the rescued dataset includes the monthly observations of three different variables: · The snow cover duration (SCD), which is defined as total number of days in a given month with snow depth on the ground >=1 cm. This variable is available for 110 stations between 288 and 1430 m above the sea level (ASL). · The number of days with snowfall (NDS), which is total number of days in a given month on which the accumulated snowfall (i.e. the amount of fresh snow with respect to the previous observations) is at least 1 cm. This variable is available for 114 stations between 288 and 1430 m ASL. · The height of new snow (HN), which is defined as the monthly amount of fresh snow (expressed in cm). The monthly value is intended as the sum of daily HN data observed in a determined month. This variable is available for 120 stations between 288 and 1750 m ASL. Note that for HN variable, the data availability is restricted to the period 1971-2001. The considered dataset has been subjected to an accurate quality control consisting of several statistical tests: the gross error test, which flags the data that are above or below acceptable physical limits, the consistency test, which involves an inter-variable check, and the tolerance test, which is focused on the outlier detection. In addition, the homogeneity of the rescued time series has been checked using Climatol method (Guijarro, 2018). The latter is based on the Standard Normal Homogeneity Test (Alexandersson, 1986) for the identification of the breaks and on a linear regression approach for the adjustments (Easterling and Peterson, 1995). Climatol has been also employed for the filling of missing values. The database is structured into three different folders (one for each variable). In a determined folder, the user finds two files, one containing the main information regarding the available stations (code, station name, latitude and longitude (in decimal degrees) and altitude ASL (in m)), the other one the monthly time series for the considered variable. Note that the original data sources of this database, the Hydrological Yearbooks of the NHMS, are freely accessible in printed version (i.e. as scanned images in portable document format) through the Italian Institute for Environmental Protection and Research (ISPRA) website (http://www.bio.isprambiente.it/annalipdf). Additional information about the data rescue processing can be found in the preprint “Historical snowfall measurements in the Central and Southern Apennine Mountains: climatology, variability and trend”, open for discussion in The Cryosphere journal (https://doi.org/10.5194/egusphere-2024-1056). References Alexandersson, H.: A homogeneity test applied to precipitation data, J. Climatol., 6, 661–675, 1986. Easterling, D. R. and Peterson, T.C.: A new method for detecting and adjusting for undocumented discontinuities in climatological time series, International Journal Climatol.,15, 369–377, https://doi.org/10.1002/joc.3370150403, 1995. Guijarro, J. A.: Homogenization of climatic series with Climatol, Climatol manual, https://www.climatol.eu/homog_climatolen.pdf (last access: 15 February 2024), 2018.

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. 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. 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. 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.