Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making
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 managemen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.986206/full |
| _version_ | 1811312707970793472 |
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| author | Sarah L. C. Giering Phil F. Culverhouse David G. Johns Abigail McQuatters-Gollop Sophie G. Pitois |
| author_facet | Sarah L. C. Giering Phil F. Culverhouse David G. Johns Abigail McQuatters-Gollop Sophie G. Pitois |
| author_sort | Sarah L. C. Giering |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T10:41:24Z |
| format | Article |
| id | doaj.art-6cbe5fce802d470387e4bfe8dcded44d |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-13T10:41:24Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-6cbe5fce802d470387e4bfe8dcded44d2022-12-22T02:49:55ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-11-01910.3389/fmars.2022.986206986206Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-makingSarah L. C. Giering0Phil F. Culverhouse1David G. Johns2Abigail McQuatters-Gollop3Sophie G. Pitois4Ocean BioGeoscience, National Oceanography Centre, Southampton, United KingdomPlankton Analytics Ltd., Plymouth, United KingdomMarine Biological Association, Plymouth, United KingdomSchool of Biological and Marine Sciences, University of Plymouth, Plymouth, United KingdomCentre for Environment Fisheries and Aquatic Sciences (Cefas), Lowestoft, United KingdomZooplankton 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.https://www.frontiersin.org/articles/10.3389/fmars.2022.986206/fullin situ imagingartificial intelligence/machine learningtaxonomydigital samplesecosystem assessmentlong-term monitoring |
| spellingShingle | Sarah L. C. Giering Phil F. Culverhouse David G. Johns Abigail McQuatters-Gollop Sophie G. Pitois Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making Frontiers in Marine Science in situ imaging artificial intelligence/machine learning taxonomy digital samples ecosystem assessment long-term monitoring |
| title | Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making |
| title_full | Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making |
| title_fullStr | Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making |
| title_full_unstemmed | Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making |
| title_short | Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making |
| title_sort | are plankton nets a thing of the past an assessment of in situ imaging of zooplankton for large scale ecosystem assessment and policy decision making |
| topic | in situ imaging artificial intelligence/machine learning taxonomy digital samples ecosystem assessment long-term monitoring |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.986206/full |
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