Quantifying visitor preferences for biodiversity: A community science approach to identify ecotourism assets

Abstract

Ecotourism plays a vital role in conserving ecosystems and revitalising rural economies. Sustainable management requires strategies that effectively balance conservation needs with tourism promotion, necessitating a clear understanding of which landscapes and species attract visitors. However, identifying these "charismatic" assets has traditionally relied on costly and spatially limited surveys. This study leverages crowdsourced biodiversity data from digital platforms (iNaturalist and Biome) to identify nature-based recreation sites and visitor-preferred species in Inabe City, a rural Japanese municipality with high recreational visitation. We analysed 12,764 observations within the city and 238,625 reference observations from across Japan. By applying the Out-of-Area Activity Index (OAAI) to biological recording data, we distinguished visitors from residents with high accuracy. Spatial analyses using hurdle models revealed distinct preferences: while both groups frequented recreational facilities, residents concentrated on lowland forests, whereas visitors were significantly drawn to wetlands and mountain habitats. To quantify specific biological interests, we developed the Visitor Preference Index (VPI). This index highlighted that visitors disproportionately recorded aquatic insects (e.g., dragonflies) and endangered understory plants compared to their observations elsewhere, identifying these taxa as key ecological assets. These findings underscore the critical importance of managing highland forests and wetlands--habitats often vulnerable to degradation--to support both biodiversity and tourism. This study illustrates how crowdsourced biodiversity data can contribute to understanding human-nature interactions and offer a scalable tool for regional planning that integrates ecological conservation with sustainable tourism development.