Fujiki, S. | bioRxiv Science

bioRxiv2024

Harnessing Community Science and Open Research-based Data to Track Distributions of Invasive Species in Japan

At the forefront of invasive alien species (IAS) control, information gaps about the latest IAS distribution can hinder the required actions of local governments. In Japan, many prefectural governments still lack a list of invasive species despite the request stipulated in the Invasive Alien Species Management Action Plan enacted in 2015. Here, we examined to what extent open research-based data deposited by museums and herbaria (ORD) and community science data deposited by volunteers (CSD) can fill the gaps. We focused on 145 plant and 38 insect invasive species, and updated their distribution maps using ORD and CSD. We found complementarity as well as common limitations between ORD and CSD. While taxonomic biases were weaker in ORD, CSD had better prefectural coverage. In addition, some important taxa have rarely been captured by CSD or ORD. Mixed strategies of facilitating community science, supporting local museums, and taxon-specific monitoring by experts are necessary.

ecology↗

bioRxiv2023

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating citizen 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.

ecology↗