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
dc.contributor.author | Giering, SLC | |
dc.contributor.author | Culverhouse, PF | |
dc.contributor.author | Johns, DG | |
dc.contributor.author | McQuatters-Gollop, A | |
dc.contributor.author | Pitois, SG | |
dc.date.accessioned | 2023-01-16T15:46:34Z | |
dc.date.available | 2023-01-16T15:46:34Z | |
dc.date.issued | 2022-11-16 | |
dc.identifier.issn | 2296-7745 | |
dc.identifier.issn | 2296-7745 | |
dc.identifier.other | ARTN 986206 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/20176 | |
dc.description.abstract |
<jats:p>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 <jats:italic>in situ</jats:italic> 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.</jats:p> | |
dc.format.extent | 986206- | |
dc.language.iso | en | |
dc.publisher | Frontiers Media SA | |
dc.subject | in situ imaging | |
dc.subject | artificial intelligence | |
dc.subject | machine learning | |
dc.subject | taxonomy | |
dc.subject | digital samples | |
dc.subject | ecosystem assessment | |
dc.subject | long-term monitoring | |
dc.subject | zooplankton | |
dc.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 | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000892040300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.volume | 9 | |
plymouth.publication-status | Published online | |
plymouth.journal | Frontiers in Marine Science | |
dc.identifier.doi | 10.3389/fmars.2022.986206 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Biological and Marine Sciences | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA07 Earth Systems and Environmental Sciences | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
plymouth.organisational-group | /Plymouth/Users by role/Researchers in ResearchFish submission | |
dcterms.dateAccepted | 2022-09-26 | |
dc.rights.embargodate | 2023-1-17 | |
dc.identifier.eissn | 2296-7745 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.3389/fmars.2022.986206 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review | |
plymouth.funder | Plankton science for supporting the implementation of marine ecosystem-based management and conservation::NERC |