Citizen science and machine learning: Interdisciplinary approach to non-invasively monitoring a northern marine ecosystem

• Published in: Frontiers in Marine Science
• Main Authors: Ashleigh M. Westphal, C-Jae C. Breiter, Sarah Falconer, Najmeh Saffar, Ahmed B. Ashraf, Alysa G. McCall, Kieran McIver, Stephen D. Petersen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: citizen science; machine learning; Cnidara; Ctenophora; conservation; deep learning; Science; Q; General. Including nature conservation; geographical distribution; QH1-199.5; Beluga; Beluga*; Delphinapterus leucas
• Online Access: https://doi.org/10.3389/fmars.2022.961095; https://doaj.org/article/0e54a081a28a46c8a6795421bace44aa

Successful conservation efforts often require novel tactics to achieve the desired goals of protecting species and habitats. One such tactic is to develop an interdisciplinary, collaborative approach to ensure that conservation initiatives are science-based, scalable, and goal-oriented. This approach may be particularly beneficial to wildlife monitoring, as there is often a mismatch between where monitoring is required and where resources are available. We can bridge that gap by bringing together diverse partners, technologies, and global resources to expand monitoring efforts and use tools where they are needed most. Here, we describe a successful interdisciplinary, collaborative approach to long-term monitoring of beluga whales (Delphinapterus leucas) and their marine ecosystem. Our approach includes extracting images from video data collected through partnerships with other organizations who live-stream educational nature content worldwide. This video has resulted in an average of 96,000 underwater images annually. However, due to the frame extraction process, many images show only water. We have therefore incorporated an automated data filtering step using machine learning models to identify frames that include beluga, which filtered out an annual average of 67.9% of frames labelled as “empty” (no beluga) with a classification accuracy of 97%. The final image datasets were then classified by citizen scientists on the Beluga Bits project on Zooniverse (https://www.zooniverse.org). Since 2016, more than 20,000 registered users have provided nearly 5 million classifications on our Zooniverse workflows. Classified images are then used in various researcher-led projects. The benefits of this approach have been multifold. The combination of machine learning tools followed by citizen science participation has increased our analysis capabilities and the utilization of hundreds of hours of video collected each year. Our successes to date include the photo-documentation of a previously tagged beluga and of the common ...