Details:

Journal Title:
Frontiers in Marine Science
Personal Author:
Giering, S.L.C. ; Culverhouse, P.F. ; Johns, D.G. ; McQuatters-Gollop, A. ; Pitois, S.G.
Giering, S.L.C. ; Culverhouse, P.F. ; Johns, D.G. ; McQuatters-Gollop, A. ; Pitois, S.G. Less -
NOAA Program & Office:
CINAR (Cooperative Institute for the North Atlantic Region)
Description:
Zooplankton are fundamental to aquatic ecosystem services such as carbon and nutrient cycling. Therefore, a robust evidence base of how zooplankton respond to changes in anthropogenic pressures, such as climate change and nutrient loading, is key to implementing effective policy-making and management measures. Currently, the data on which to base this evidence, such as long time-series and large-scale datasets of zooplankton distribution and community composition, are too sparse owing to practical limitations in traditional collection and analysis methods. The advance of in situ imaging technologies that can be deployed at large scales on autonomous platforms, coupled with artificial intelligence and machine learning (AI/ML) for image analysis, promises a solution. However, whether imaging could reasonably replace physical samples, and whether AI/ML can achieve a taxonomic resolution that scientists trust, is currently unclear. We here develop a roadmap for imaging and AI/ML for future zooplankton monitoring and research based on community consensus. To do so, we determined current perceptions of the zooplankton community with a focus on their experience and trust in the new technologies. Our survey revealed a clear consensus that traditional net sampling and taxonomy must be retained, yet imaging will play an important part in the future of zooplankton monitoring and research. A period of overlapping use of imaging and physical sampling systems is needed before imaging can reasonably replace physical sampling for widespread time-series zooplankton monitoring. In addition, comprehensive improvements in AI/ML and close collaboration between zooplankton researchers and AI developers are needed for AI-based taxonomy to be trusted and fully adopted. Encouragingly, the adoption of cutting-edge technologies for zooplankton research may provide a solution to maintaining the critical taxonomic and ecological knowledge needed for future zooplankton monitoring and robust evidence-based policy decision-making.
Keywords:
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Artificial Intelligence Machine Learning Zooplankton Digital Samples Ecosystem Assessment In Situ Imaging Long-term Monitoring Taxonomy
Source:
Front. Mar. Sci. 9:986206
DOI:
https://doi.org/10.3389/fmars.2022.986206
Format:
PDF
Document Type:
Journal Article
License:
https://creativecommons.org/licenses/by/4.0/
Rights Information:
CC BY
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:3a41e8626912f65405c55b69b4c1f1e537b3e22049a015f0a6b5ec4e17713b40
Download URL:
https://repository.library.noaa.gov/view/noaa/51836/noaa_51836_DS1.pdf
File Type:

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