Spatial planning dataset and QGIS project for Marxan-based risk-informed conservation prioritisation in the Massaciuccoli Lake basin wetland (Tuscany, Italy)

This geographical collection presents the QGIS project and associated geospatial datasets used for Marxan-based spatial conservation prioritisation within the ecosystem risk domain of the Massaciuccoli Lake basin wetland, Tuscany, Italy. The dataset supports the identification of priority areas for conservation and restoration within zones affected by the risk of forest and grassland habitat degradation and associated biodiversity loss. The spatial planning analysis integrates planning units, anthropogenic cost variables, conservation features, predictive ecosystem risk hotspots, Marxan input files, Marxan outputs, percentile-based planning scenarios, selection frequency maps, best solutions, and final priority classes. The QGIS project is structured in the WGS84 EPSG:4326 coordinate reference system and is organised into the following main groups of layers: Basin limit – including the basin boundary, Ramsar areas, and regional parks and reserves used as cartographic and spatial reference layers. Spatial planning analysis – including the planning units within the ecosystem risk domain, the dataset of planning units with features, costs, and risk values, Marxan scenarios, best solutions, selection frequency outputs, cumulative best solution maps, aggregated priority classes, and percentile-based planning scenarios. Anthropogenic cost – including the spatial representation of the anthropogenic cost layer used to guide Marxan prioritisation and minimise conflicts with human pressure and unsuitable land-use conditions. Risk – Predictive analysis – including the predictive ecosystem risk hotspots obtained through the Multi K-means on Variational Autoencoder approach, representing areas associated with forest and grassland habitat degradation risk and related biodiversity loss. Original variables – including the environmental, ecological, and anthropogenic variables used as input conservation features and cost components for the spatial planning workflow. The Marxan prioritisation was implemented using an ensemble scenario with Boundary Length Modifier equal to 2250. The final cumulative best solution classifies 7,909 planning units into six relative priority classes, from Class 0, representing the lowest relative priority, to Class 5, representing the highest relative priority. Percentile-based scenarios identify increasing conservation selectivity thresholds, corresponding to the 75th, 80th, 85th, 90th, and 95th percentiles, respectively representing 25%, 20%, 15%, 10%, and 5% of the top-quality conservation features. The dataset is intended to support reproducibility, spatial interpretation, and reuse of the Marxan-based prioritisation workflow for wetland conservation planning, restoration targeting, and ecosystem risk-informed environmental management.