DOI: 10.6084/m9.figshare.31429526.v1
title: <b>Measured and modelled rates of natural forest expansion in the European Alps (1800–2020)</b>

description: General DescriptionThis repository contains spatial datasets supporting the manuscript <i>Land abandonment and global warming drove two centuries of natural forest expansion in the Alps, </i>submitted to Nature Communications. The data provide both empirically measured and modelled estimates of natural forest expansion (NFE) across the European Alps over the past ~200 years in the form of rate of change (RoC; % forest change year<sup>-1</sup>), together with predictor variables used for statistical modelling.All spatial data are provided in <b>ETRS89-extended / LAEA Europe </b>(EPSG:3035) at <b>1-km resolution </b>unless otherwise specified.Repository ContentData include:<b>Measured annual rate of change </b>(RoC) of the compiled local cases as GeoTiff and CSV;Wall-to-wall <b>spatial predictions of RoC </b>with associated uncertainty levels (standard deviation of ensemble models and area of applicability) as GeoTiff;<b>Reconstructed tree cover density </b>(TCD) for 1800, 1870, 1950, 1980, and 2018 as GeoTiff.<b><i>1) ForestChange_predictors.csv </i></b>Spreadsheet containing the forest RoC and predictors for the 1-km cells:<i>ID_cell = </i>the ID of the 1-km cell<i>RefPeriod </i>= reference period (e.g., 1800-2020)<i>FORCHANGE_Absol</i> = absolute value of forest RoC (% year<sup>-1</sup>)<i>x, y </i>= the x and y coordinates of the cell centroid in EPSG:3035SOCIO, CLIM, VEG, SOIL, TOPO predictors<i>Notes</i>: RoC represents normalized annual change in percent forest cover per 1-km grid cell. Elevation range restricted to 1000–3000 m a.s.l. Outliers (&lt;1st and &gt;99th percentiles) removed prior to modelling. This dataset was used for GLM inference and machine learning ensemble calibration.<b><i>2) Measured_RoC</i></b> Folder with gridded measured RoC layers for compiled case studies (GeoTiffs). These rasters correspond to the harmonized 1-km INSPIRE grid and represent observed data used to calibrate models.<b><i>3) spatPrediction</i></b> Folder with gridded predictions (GeoTiffs) of <b>annual forest cover change (RoC) </b>across the entire European Alps at 1-km resolution. Cell values represent predicted <b>weighted average of annual rate of forest cover change</b> (% year⁻¹) derived from a two-stage <b>ensemble </b>machine learning framework (Random Forest, BRT, Lasso, GAM, BART). Together with the weighted average, standard deviation [<b>StDev</b>, representing tandard deviation across ensemble learners represents pixel-wise standard deviation across base learners, used as an indicator of model uncertainty] and area of applicability [<b>AoA</b>, Binary mask identifying areas where environmental conditions are within the multivariate domain represented by the calibration dataset; predictions outside the AoA should be interpreted with caution] are provided. A <b><i>README.md</i></b> file is provided to support replicability. <b><i>4) TCD</i></b>Folder with gridded reconstructed <b>tree cover density</b> (TCD) (% forest cover) at 1-km resolution for the years <b>1800</b>, <b>1870</b>, <b>1950</b>, and <b>1980 </b>+ Copernicus TCD for <b>2018 </b>(available at https://doi.org/10.2909/4dc35722-09ce-427f-9a1b-775a8640da27). Historical TCD layers were reconstructed by <b>backward propagation </b>of <b>predicted RoC </b>values from the <b>2018 </b>Copernicus Tree Cover Density product. These layers represent estimated forest proportion (%) per grid cell and provide spatially explicit forest cover reconstructions across two centuries.Methodological SummaryForest cover maps derived from <b>historical cadastral maps</b>, <b>aerial photography</b>, and <b>satellite imagery </b>were harmonized to a binary forest / non-forest classification and aggregated to a standardized 1-km INSPIRE grid. Annual <b>rate of change </b>(RoC) was calculated as the difference between final and initial forest cover of a certain period divided by the temporal extent of that period (number of years). <b>Spatial predictions </b>were generated using a two-stage ensemble machine learning framework implemented in the mlr3 ecosystem, including spatial autocorrelation as explicit covariate and spatial block cross-validation for model evaluation. Full methodological details are provided in the associated publication.Data Interpretation NotesRoC values represent normalized annual rates and do not imply linear trajectories within each period.Forest definition harmonized across source datasets; slight differences in historical forest definitions may persist.Predictions should be interpreted within the Area of Applicability (AoA).Model uncertainty increases toward environmental margins and sparsely sampled regions.Code AvailabilityR scripts for harmonization, RoC calculation, statistical modelling, and ensemble predictions are available from the corresponding author upon reasonable request.