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

Abstract

Kunming-Montreal Global Biodiversity Framework increased the demand for biodiversity distribution data. To gather species observation from the public, we introduced a mobile application called 'Biome' in Japan. By employing species identification algorithms and gamification elements, Biome has gathered >5M observations since its launch in 2019. However, cloud-sourced data often exhibit spatial and taxonomic biases. Species distribution models (SDMs) enable infer species distribution while accommodating such bias. We investigated Biome data's quality and how incorporating Biome data influences the performance of SDMs. Species identification accuracy of Biome data exceeds 95% for birds, reptiles, mammals, and amphibians, but seed plants, molluscs, and fishes scored below 90%. The distributions of 132 terrestrial plants and animals across Japan were modeled, and their accuracy was improved by incorporating Biome data into traditional survey data. For endangered species, traditional survey data required >2,000 records to build accurate models (Boyce index >0.9), though only ca.300 records were required when Biome was blended. The unique data distributions may explain this improvement: Biome data covers urban-natural gradients uniformly, while traditional data is biased towards natural areas. Combining multiple data sources offers insights into species distributions across Japan, aiding protected area designation and ecosystem service assessment.