A data-driven framework for forest conservation prioritisation: From citizen science data to protected area designation
Abstract
With the ongoing biodiversity decline around the world, the establishment of protected areas provides a key strategy for conservation. Yet, the designation of protected areas is complex and requires the integration of ecological, spatial, and socio-economic factors. Here, we present a data-driven framework that leverages open-source and citizen science data, species distribution modelling, and artificial intelligence (AI)-based optimisation to support strategic conservation planning. Using the algorithms ”Conservation Area Prioritization Through Artificial INtelligence” (CAPTAIN) and prioritizR, we test four policy scenarios to identify high-value forest habitats using forest indicator bird species in North Rhine-Westphalia (NRW), Germany. The framework captures spatio-temporal dynamics of species distributions between 2016 and 2022 and highlights opportunities and limitations of optimisation approaches for protected area planning. Our results show that priority forest areas for conservation are located in the uplands of south-eastern NRW. Considering opportunity costs for nature conservation outside existing protected areas in addition to ecological aspects leads to similarly high conservation outcomes in our study area, while lowering overall costs substantially. Finally, the results suggest that the CAPTAIN algorithm is able to leverage temporal dynamics in annual species distribution maps to identify priority forest areas for conservation. Our study demonstrates the potential of open-data-driven and AI-powered approaches to improve transparency, scalability, and ecological relevance in biodiversity conservation. Future research may apply this framework to larger study areas and a larger number of species, supporting future nature conservation planning at the national or international levels.
