Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data
Curation statements for this article:-
Curated by eLife
eLife assessment
This important study uses citizen science-generated diversity records and quantitative methodologies to improve species distribution estimates. This combination of fields, technologies, and methodologies is solid and improves species distribution maps formerly based solely on limited data gathered by scientists in traditional ways/surveys. This paper will be of interest to researchers interested in citizen science and new sources of big data in biodiversity, and to biogeographers exploring the distributions of species on the planet.
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- Evaluated articles (eLife)
- Ecology (eLife)
Abstract
Ecosystem services, which derive in part from biological diversity, are a fundamental support for human society. However, human activities are causing harm to biodiversity, ultimately endangering these critical ecosystem services. Halting nature loss and mitigating these impacts necessitates comprehensive biodiversity distribution data, a requirement for implementing the Kunming-Montreal Global Biodiversity Framework. To efficiently collect species observations from the public, we launched the ‘ Biome ’ mobile application in Japan. By employing species identification algorithms and gamification elements, the app has gathered >6M observations since its launch in 2019. However, community-sourced data often exhibit spatial and taxonomic biases. Species distribution models (SDMs) enable inferring species distribution while accommodating such bias. We investigated Biome data’s quality and how incorporating the data influences the performance of SDMs. Species identification accuracy 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 modelled, and their accuracy was improved by incorporating our 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 the two data sources were blended. The unique data distributions may explain this improvement: Biome data covers urban-natural gradients uniformly, while traditional data is biassed towards natural areas. Combining multiple data sources offers insights into species distributions across Japan, aiding protected area designation and ecosystem service assessment. Providing a platform to accumulate community-sourced distribution data and improving data processing protocol will contribute to not only conserving natural ecosystems but also detecting species distribution changes and testing ecological theories.
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eLife assessment
This important study uses citizen science-generated diversity records and quantitative methodologies to improve species distribution estimates. This combination of fields, technologies, and methodologies is solid and improves species distribution maps formerly based solely on limited data gathered by scientists in traditional ways/surveys. This paper will be of interest to researchers interested in citizen science and new sources of big data in biodiversity, and to biogeographers exploring the distributions of species on the planet.
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Reviewer #1 (Public Review):
Summary:
The study presented by Atsumi et al. is about using smartphone-driven, community-sourced data to enhance biodiversity monitoring. The idea is to leverage the widespread use of smartphones to gather data from the community quickly, contributing to a more comprehensive understanding of biodiversity. The authors discuss the importance of ecosystem services linked to biodiversity and the threats posed by human activities. It emphasizes the need for comprehensive biodiversity data to implement the Kunming-Montreal Global Biodiversity Framework. The 'Biome' mobile app, launched in Japan, uses species identification algorithms and gamification to gather over 6 million observations since 2019. While community-sourced data may have biases, incorporating it into Species Distribution Models (SDMs) improves …Reviewer #1 (Public Review):
Summary:
The study presented by Atsumi et al. is about using smartphone-driven, community-sourced data to enhance biodiversity monitoring. The idea is to leverage the widespread use of smartphones to gather data from the community quickly, contributing to a more comprehensive understanding of biodiversity. The authors discuss the importance of ecosystem services linked to biodiversity and the threats posed by human activities. It emphasizes the need for comprehensive biodiversity data to implement the Kunming-Montreal Global Biodiversity Framework. The 'Biome' mobile app, launched in Japan, uses species identification algorithms and gamification to gather over 6 million observations since 2019. While community-sourced data may have biases, incorporating it into Species Distribution Models (SDMs) improves accuracy, especially for endangered species. The app covers urban-natural gradients uniformly, enhancing traditional survey data biased towards natural areas. Combining these sources provides valuable insights into species distributions for conservation, protected area designation, and ecosystem service assessment.Strengths:
The use of a smartphone app ('Biome') for community-driven species occurrence data collection represents an innovative and inclusive approach to biodiversity monitoring, leveraging the widespread use of smartphones. The app has successfully accumulated a large volume of species occurrence data since its launch in 2019, showcasing its effectiveness in rapidly gathering information from diverse locations. Despite challenges with certain taxa, the study highlights high species identification accuracy, especially for birds, reptiles, mammals, and amphibians, making the 'Biome' app a reliable tool for species observation. The integration of community-sourced data into Species Distribution Models (SDMs) improves the accuracy of predicting species distributions. This has implications for conservation planning, including the designation of protected areas and assessment of ecosystem services. The rapid accumulation of data and advancements in machine learning methods open up opportunities for conducting time-series analyses, contributing to the understanding of ecosystem stability and interaction strength over time. The study emphasizes the collaborative nature of the platform, fostering collaboration among diverse stakeholders, including local communities, private companies, and government agencies. This inclusive approach is essential for effective biodiversity assessment and decision-making. The platform's engagement with various stakeholders, including local communities, supports biodiversity assessment, management planning, and informed decision-making. Additionally, the app's role in fostering nature-positive awareness in society is highlighted as a significant contribution to creating a sustainable society.
Weaknesses:
While the studies make significant contributions to biodiversity monitoring, they also have some weaknesses. Firstly, relying on smartphone-driven, community-sourced data may introduce spatial and taxonomic biases. The 'Biome' app, for example, showed lower accuracy for certain taxa like seed plants, molluscs, and fishes, potentially impacting the reliability of the gathered data. Furthermore, the effectiveness of Species Distribution Models (SDMs) relies on the assumption that biases in community-sourced data can be adequately accounted for. The unique distribution patterns of the 'Biome' data, covering urban-natural gradients uniformly, might not fully represent the diversity of certain ecosystems, potentially leading to inaccuracies in the models. Moreover, the divergence in data distribution patterns along environmental gradients between 'Biome' data and traditional survey data raises concerns. The app data shows a more uniform distribution across natural-urban gradients, while traditional data is biased towards natural areas. This discrepancy may impact the representation of certain ecosystems and influence the accuracy of Species Distribution Models (SDMs). While the integration of 'Biome' data into SDMs improves accuracy, the study notes that controlling the sampling efforts is crucial. Spatially-biased sampling efforts in community-sourced data need careful consideration, and efforts to control biases are essential for reliable predictions.
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