What drives cultural ecosystem services in mountain protected areas? An AI-assisted answer using social media data

Abstract

Understanding how environmental and social factors shape the distribution of Cultural Ecosystem Services (CES) is essential for balancing biodiversity conservation and human well-being in protected landscapes. In this study, we integrate crowdsourced social media data, deep learning image classification (AI), and ecological niche models (ENMs) to map and compare the supply and demand of CES in mountain protected areas (PAs) across different biogeographical regions in southern Europe. Using geotagged photographs classified into eight CES types, we assessed model performance, identified key environmental and social predictors, explored CES bundles, and evaluated the influence of protection categories on CES suitability. Our models performed well across most CES types, confirming that social media data, when combined with AI and ENMs, provide a reliable and scalable approach for mapping human-nature interactions. The analysis revealed that while no universal set of predictors explains CES supply, some variables, particularly nighttime lights, distance to landmarks, and accessibility, consistently influenced multiple CES types. In contrast, nature-related CES such as Fauna & Flora and Nature & Landscape were mainly shaped by ecological and scenic features. We found that CES are generally clustered into two dominant bundles: one associated with cultural and heritage values and another linked to nature-based experiences. PAs showed higher overall CES suitability than unprotected zones, with Biosphere Reserves exhibiting particularly balanced outcomes between conservation and human use. This study shows how combining social media data, AI, and ecological modelling can reveal spatial drivers of CES, supporting sustainable management and fair distribution of nature’s cultural benefits in mountain PAs.