Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring.

Autor:	McClure EC; Australian Rivers Institute - Coast and Estuaries, School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia., Sievers M; Australian Rivers Institute - Coast and Estuaries, School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia., Brown CJ; Australian Rivers Institute - Coast and Estuaries, School of Environment and Science, Griffith University, Nathan, QLD 4111, Australia., Buelow CA; Australian Rivers Institute - Coast and Estuaries, School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia., Ditria EM; Australian Rivers Institute - Coast and Estuaries, School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia., Hayes MA; Australian Rivers Institute - Coast and Estuaries, School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia., Pearson RM; Australian Rivers Institute - Coast and Estuaries, School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia., Tulloch VJD; Department of Forest and Conservation Science, University of British Columbia, Vancouver, BC, Canada., Unsworth RKF; Seagrass Ecosystem Research Group, College of Science, Swansea University, Swansea SA2 8PP, UK., Connolly RM; Australian Rivers Institute - Coast and Estuaries, School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia.
Jazyk:	angličtina
Zdroj:	Patterns (New York, N.Y.) [Patterns (N Y)] 2020 Oct 09; Vol. 1 (7), pp. 100109. Date of Electronic Publication: 2020 Oct 09 (Print Publication: 2020).
DOI:	10.1016/j.patter.2020.100109
Abstrakt:	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. (© 2020 The Authors.)
Databáze:	MEDLINE
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