Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data

Comprehensive biodiversity data is crucial for ecosystem protection. The ‘ Biome ’ mobile app, launched in Japan, efficiently gathers species observations from the public using species identification algorithms and gamification elements. The app has amassed >6M observations since 2019. Nonetheless, community-sourced data may exhibit spatial and taxonomic biases. Species distribution models (SDMs) estimate species distribution while accommodating such bias. Here, we investigated the quality of Biome data and its impact on SDM performance. Species identification accuracy exceeds 95% for birds, reptiles, mammals, and amphibians, but seed plants, mollusks, and fishes scored below 90%. Our SDMs for 132 terrestrial plants and animals across Japan revealed that incorporating Biome data into traditional survey data improved accuracy. For endangered species, traditional survey data required >2,000 records for accurate models (Boyce index ≥ 0.9), while blending the two data sources reduced this to around 300. The uniform coverage of urban-natural gradients by Biome data, compared to traditional data biased towards natural areas, may explain this improvement. Combining multiple data sources better estimates species distributions, aiding in protected area designation and ecosystem service assessment. Establishing a platform for accumulating community-sourced distribution data will contribute to conserving and monitoring natural ecosystems. ### Competing Interest Statement KA and YN are employed by Biome Inc., of which the CEO is SF and the CTO is TG. SF and TG are inventors of the species-identification-AI-algorithm JPN patents 6590417 and US patents 11048969. MU and HN declare that they have no competing interests. All authors will not financially benefit directly from the publication of this paper.