Integrating Open-Source Data and Advanced Machine Learning for Forest Conservation Prioritisation in Germany

Biodiversity is declining worldwide, driven by anthropogenic pressures such as land sealing, air pollution, agricultural practices, forest clearing, and climate-change impacts. These stressors interact in complex ways, altering habitats, disrupting ecosystem functions, and challenging the long-term survival of many species. In response, policy frameworks such as the European Union’s Biodiversity Strategy for 2030 aim to protect 30% of terrestrial and aquatic ecosystems by the end of the decade. Meeting these goals requires transparent and scalable approaches for identifying ecologically valuable areas. However, protected-area designation across Europe remains inconsistent, with national frameworks differing and often lacking transferability. At the same time, the growth of geospatial datasets, citizen science observations, and environmental time-series data offers new opportunities for data-driven conservation planning. Advances in machine learning lead to new tools to analyse heterogeneous datasets and support decision-making in complex ecological contexts.In this study, we use open-source environmental data together with citizen-science species observations collected between 2016 and 2024. These time-series data allow us to assess how environmental conditions, land use, and climate variability influence species occurrences over time. Using these inputs, we apply species distribution modelling to estimate habitat suitability and to map how suitable environments shift in response to external pressures. To support conservation planning, we apply the CAPTAIN reinforcement learning framework. Developed by Silvestro et al., this tool (2022) enables the optimisation of different conservation targets under ecological, spatial, and socio-economic constraints. It is based on the interaction between an agent and its environment, where the agent learns to take decisions that maximise a defined reward. The approach evaluates trade-offs, such as balancing biodiversity outcomes with economic considerations, and produces spatially explicit prioritisation maps that identify forest areas of consistently high ecological value, even under changing environmental conditions. The resulting framework is designed to be transferable and scalable. It supports nature-conservation planning in Germany and can be adapted to other regions, ecosystems, or policy objectives. By combining open data, citizen-science observations, and a machine learning-based optimisation, the approach contributes to national biodiversity strategies and aligns with broader international efforts such as the EU Biodiversity Strategy for 2030.