Martin Jung 

The extent and intactness of natural ecosystems is a key factor enabling species populations to thrive. However, the distribution of ecosystems is changing owing to both climatic and anthropogenic factors. Recently negotiated European policy directives, such as the Nature Restoration Law, argue for the restoration of natural ecosystems. Yet to determine what is to be restored the range of possible outcomes should be first explored, also with regards to future climatic conditions. Here the concept of potential natural vegetation (PNV) is applied and mapped in a data-driven manner at European extent, exploring where PNV transitions are most likely to happen under contemporary and future conditions. Specifically, I predict the distribution of current and future potential coverage of six natural vegetation types at 1 km² grain using Bayesian machine learning approaches. I find that most current land cover and land use could develop to no single, but multiple PNV states, although options for some types, such as areas suitable for wetlands might become rarer under future climatic conditions. Furthermore, the challenge of transitioning to PNV was found to be particularly high for current intensively cultivated landscapes. Overall data-driven PNV mapping holds considerable promise for assessing land potentials and supporting restoration assessments. Future work should expand the thematic grain of vegetation maps and consider feedback with biotic factors.