Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring

• Published in: Patterns (New York, N.Y.) Vol. 1; no. 7; p. 100109
• Main Authors: McClure, Eva C., Sievers, Michael, Brown, Christopher J., Buelow, Christina A., Ditria, Ellen M., Hayes, Matthew A., Pearson, Ryan M., Tulloch, Vivitskaia J.D., Unsworth, Richard K.F., Connolly, Rod M.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 09.10.2020; Elsevier
• Subjects: automation; big data; biological conservation; data processing; deep learning; machine learning; CS; deep learning; automation; DSML 5: Mainstream: Data science output is well understood and (nearly) universally adopted; biological conservation; data processing; AI; machine learning; big data
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2020.100109

The development and uptake of citizen science and artificial intelligence (AI) techniques for ecological monitoring is increasing rapidly. Citizen science and AI allow scientists to create and process larger volumes of data than possible with conventional methods. However, managers of large ecological monitoring projects have little guidance on whether citizen science, AI, or both, best suit their resource capacity and objectives. To highlight the benefits of integrating the two techniques and guide future implementation by managers, we explore the opportunities, challenges, and complementarities of using citizen science and AI for ecological monitoring. We identify project attributes to consider when implementing these techniques and suggest that financial resources, engagement, participant training, technical expertise, and subject charisma and identification are important project considerations. Ultimately, we highlight that integration can supercharge outcomes for ecological monitoring, enhancing cost-efficiency, accuracy, and multi-sector engagement. Citizen science and artificial intelligence (AI) are often used in isolation for ecological monitoring, but their integration likely has emergent benefits for management and scientific inquiry. We explore the complementarity of citizen science and AI for ecological monitoring, highlighting key opportunities and challenges. We show that strategic integration of citizen science and AI can improve outcomes for conservation activities. For example, coupling the public engagement benefits of citizen science with the advanced analytical capabilities of AI can increase multi-stakeholder accord on issues of public and scientific interest. Furthermore, both techniques speed up data collection and processing compared with conventional scientific techniques, suggesting that their integration can fast-track monitoring and conservation actions. We present key project attributes that will assist project managers in prioritizing the resources needed to implement citizen science, AI, or preferably both. The development and uptake of citizen science and artificial intelligence (AI) techniques for ecological monitoring are increasing rapidly. Citizen science and AI allow scientists to create and process larger volumes of data than possible with conventional methods. However, managers of large ecological monitoring projects have little guidance on whether citizen science, AI, or both, best suit their resource capacity and objectives. To highlight the benefits of integrating the two techniques and guide future implementation by managers, we explore the opportunities, challenges, and complementarities of using citizen science and AI for ecological monitoring. We identify project attributes to consider when implementing these techniques, and suggest that financial resources, engagement, participant training, technical expertise, and subject charisma and identification are important project considerations. Ultimately, we highlight that integration can supercharge outcomes for ecological monitoring, enhancing cost-efficiency, accuracy, and multi-sector engagement.