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

Abstract

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 >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 Biome 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 Biome 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 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.

eLife digest

The internet has allowed people to share their experiences through images, videos or audio recordings. This has led to the creation of online communities around a variety of topics, including biodiversity. In 2019, a smartphone app, called Biome, was created to fuel biodiversity engagement by making wildlife surveying an easy and fun activity via gamification and assisted species identification through image recognition and ecological analyses.

These types of observations are essential for understanding biological communities and species habitats, and they can indicate where and when species occur. Across Japan, Biome has gathered over 6.5 million observations of different species. For biologists, this type of data is extremely useful because it is continuous and enables advanced statistical estimations of species distributions. The fact that the approach is enjoyable to the user also means more people are willing to participate, lowering the barriers to collecting data about biodiversity loss.

However, questions remain regarding whether community-sourced data is robust enough for scientific purposes. To address this, Atsumi et al. investigated the quality of occurrence data collected in Biome. The researchers found that community identification of birds, reptiles, mammals and amphibians all exceeded 95% in accuracy. However, the accuracy fell for harder-to-judge seed plants, molluscs and fish species, ranging below 90%.

Atsumi et al. also compared how estimated distributions of each species changed when only scientific data was used, versus when it was combined with community data. To perform this analysis, the scientists recognized variations in observation efforts across different locations and individuals and adjusted for these biases in their estimations. They found that adding community-sourced data significantly improved the accuracy of species distribution estimations, including endangered species.

Atsumi et al. demonstrate that Biome data is useful when deciding which areas to designate as protected in terms of biodiversity. Additionally, these data can provide guidance for stakeholder-informed ecosystem service assessments. The element of rapid and reliable data collection can contribute to growing positive attitudes towards nature and biodiversity, The platform's community-driven nature also indicates an increase in biodiversity awareness and may link to crafting informative socio-environmental policy commitments.