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Nature-based recreation is a key ecosystem service that contributes to positive physical and mental welfare but, at the same time, nature-based recreational activities can increase human pressure and impacts on natural areas and biodiversity. Understanding people’s preference for visiting natural settings is challenging due to data and methodological limitations. Social media data can be used to map nature-based recreation. However, variation in popularity of platforms and limitations to data accessibility are highlighting the importance of exploring and using different data sources. We analyzed complementary crowdsourced data using an automated content analysis refined by manual identification to assess nature-based recreation ecosystem services across the Maltese archipelago. A content analysis of images uploaded to Flickr between 2015 and 2021 was performed using the Google Vision machine learning algorithm to identify nature-based interactions and nature visitation patterns were modeled based on landscape characteristics, environmental variables and socio-economic parameters. Flickr data were compared and complemented with publicly available geolocated data from the iNaturalist platform. Significant difference was found between the spatial distribution of Flickr and iNaturalist data. Generalized linear models identified coastal areas, protected areas, natural habitats and accessibility via the road network as significant predictors of nature-based recreational visits. Localities with a higher percentage of people receiving old age and unemployment benefits were also positively correlated with users’ preference for nature-based recreation. Finally, we discussed how the low resource methodology developed here to identify people’s nature-based recreational preferences can be used to assess which natural areas should be prioritized for ecological restoration efforts.

Characterizing nature-based recreation preferences in a Mediterranean small island environment using crowdsourced data