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

Comprehensive biodiversity data is crucial for ecosystem protection. The mobile app, launched in Japan, efficiently gathers species observations from the public using species identification algorithms and gamification elements. The app has amassed >6 million observations since 2019. Nonetheless,...

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Bibliographic Details
Published in:eLife 2024-06, Vol.13
Main Authors: Atsumi, Keisuke, Nishida, Yuusuke, Ushio, Masayuki, Nishi, Hirotaka, Genroku, Takanori, Fujiki, Shogoro
Format: Article
Language:English
Subjects:
Accuracy
Animal models
Animals
Artificial intelligence
Biodiversity
birds
Climate change
Conservation of Natural Resources - methods
Ecology
Ecosystem
Ecosystems
Endangered species
Financial disclosure
Gamification
Geographical distribution
insects
Japan
Mobile Applications
Phenology
Plants
Protected areas
Protected species
seed plants
Smartphone
Smartphones
Surveys
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Summary:Comprehensive biodiversity data is crucial for ecosystem protection. The mobile app, launched in Japan, efficiently gathers species observations from the public using species identification algorithms and gamification elements. The app has amassed >6 million 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 data and its impact on SDM performance. Species identification accuracy exceeds 95% for birds, reptiles, mammals, and amphibians, but seed plants, molluscs, and fishes scored below 90%. Our SDMs for 132 terrestrial plants and animals across Japan revealed that incorporating data into traditional survey data improved accuracy. For endangered species, traditional survey data required >2000 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 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.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.93694