Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring

Overview

Journal Patterns (N Y)

Date 2020 Nov 18

PMID 33205139

Citations 6

Authors

Eva C McClure

Michael Sievers

Christopher J Brown

Christina A Buelow

Ellen M Ditria

Matthew A Hayes

Ryan M Pearson

Vivitskaia J D Tulloch

Richard K F Unsworth

Rod M Connolly

Affiliations

Soon will be listed here.

Abstract

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.

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