Species' spatiotemporal distribution platform based on citizen science through desirable circulation between the real and digital worlds

Abstract

Anthropogenic disturbances and climate change have caused multi-dimensional changes in biodiversity and, conse- quently, in ecosystem services. In this changing world, citizen science and monitoring are essential ways to observe species’ spatiotemporal distributions and enhance, simultaneously, public awareness of biodiversity. Recent technological innovations have provided several digital platforms for citizen science using personal digital devices, but these platforms and the data-induced improvement in citizen observation programs have not been discussed. This paper compared three main platforms in Japan (iNaturalist, "Ikimono-log", and Biome) and discussed possible solutions to general challenges that citizen science has faced through data analyses. The three platforms differ in the applicable data type, usability, policy for data-opening, etc. Monitoring project organisers should understand the characteristics of each platform and select a suitable one. The main challenges for citizen science were data quality and quantity, such as species identification, spatiotemporal data bias and the shortage of specific data (e.g. the northern/southern limits of distributions and phenology data). There were also problems with project management, such as limited communication with participants and the continuity of each project. The accuracy of species identification has increased by using artificial intelligence and photographs of observed species with location information. Solutions for other challenges, such as the spatiotemporal data bias and shortage of specific data, should be developed by using and analysing stacked observation data. Visualisation of observed data for each species, model predictions filling the spatiotemporal gaps in observations and con- tribution reports for each observed dataset should be useful for solving these data collection challenges. Sharing real-world ob- servation data broadens analyses in the digital world, and the results should be shown on participants’ devices as a way to improve their observations and further their enjoyment in making observations, which would improve the model analyses. Creating an observation system targeting this desirable circulation between the real and digital worlds should motivate citizen science further, enhancing the connectivity of a nature-friendly society.