Engaging Citizen Scientists in Biodiversity Monitoring: Insights from the WildLIVE! Project

Abstract

The growing public interest in biodiversity monitoring has led to a significant increase in initiatives that unite citizen scientists, researchers, and machine learning technologies. In this context, we introduce WildLIVE!, a dynamic biomonitoring and citizen science project. In WildLIVE!, participants analyze a vast array of images from a long-term camera trapping project in Bolivia to investigate the impacts of shifting environmental factors on wildlife. From 2020 to 2023, more than 850 participants registered for WildLIVE!, contributing nearly 9,000 hours of voluntary work. We explore the motivators and sentiments of participant engagement and discuss the key strategies that have contributed to the project’s initial success. The findings from a questionnaire highlight that the primary motivational factors for our participants are understanding and knowledge, as well as engagement and commitment. However, expressions of positive and negative sentiments can be found regarding involvement. Participants appeared to be driven primarily by a desire for intellectual growth and emotional fulfillment. Factors crucial to the success of this digital citizen science project include media exposure, creating emotional connections through virtual and in-person communication with participants, and visibility on public citizen science portals. Moreover, the project’s labeled dataset serves as a valuable resource for machine learning, aiding the development of a new platform that is compliant with the FAIR principles. WildLIVE! not only contributes to outcomes in science, society, and nature conservation, but also demonstrates the potential of creating a collaborative bridge between the general public, scientific research, biodiversity conservation, and advanced technological applications.

Keywords

• artificial intelligence
• camera trapping
• covid-19 pandemic
• digital ecology
• fair digital objects
• participant motivations