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Record W4210667089 · doi:10.1002/lob.10479

The Marine Biodiversity Observation Network Plankton Workshops: Plankton Ecosystem Function, Biodiversity, and Forecasting—Research Requirements and Applications

2022· article· en· W4210667089 on OpenAlexaboutno aff
Maria Grigoratou, Enrique Montes, Anthony J. Richardson, Jason D. Everett, Esteban Acevedo‐Trejos, Clarissa R. Anderson, Bingzhang Chen, Tamar Guy‐Haim, Jana Hinners, Christian Lindemann, Tatiane Martins Garcia, Klas Ove Möller, Fanny Monteiro, Aimee Neeley, Todd O’Brien, Artur Palacz, Alex J. Poulton, A. E. Friederike Prowe, Áurea E. Rodríguez‐Santiago, Cécile S. Rousseaux, Jeffrey A. Runge, Juan Francisco Saad, Ioulia Santi, Rowena Stern, Alice Soccodato, Selina Våge, Meike Vogt, Soultana Zervoudaki, Frank Müller‐Karger

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersOffice of Naval ResearchNational Science Foundation of Sri LankaNatural Environment Research CouncilNuclear Safety and Security CommissionNational Aeronautics and Space AdministrationNorges ForskningsrådAustralian Research CouncilNational Oceanic and Atmospheric AdministrationSight Research UKNational Science Foundation
KeywordsPlanktonBiodiversityEcosystemMarine ecosystemEcologyBiologyEnvironmental resource managementEnvironmental science

Abstract

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Plankton is a massive and phylogenetically diverse group of thousands of prokaryotes, protists (unicellular eukaryotic organisms), and metazoans (multicellular eukaryotic organisms; Fig. 1). Plankton functional diversity is at the core of various ecological processes, including productivity, carbon cycling and sequestration, nutrient cycling (Falkowski 2012), interspecies interactions, and food web dynamics and structure (D'Alelio et al. 2016). Through these functions, plankton play a critical role in the health of the coastal and open ocean and provide essential ecosystem services. Yet, at present, our understanding of plankton dynamics is insufficient to project how climate change and other human-driven impacts affect the functional diversity of plankton. That limits our ability to predict how critical ecosystem services will change in the future and develop strategies to adapt to these changes. The Marine Biodiversity Observation Network (MBON; https://geobon.org/bons/thematic-bon/mbon/, last accessed date: 22 Dec 2021), with the support of the Modelling Different Components of Marine Plankton Biodiversity team (MODIV; https://modiv.w.uib.no/, last accessed date: 22 Dec 2021), organized four virtual workshops (first in November 2020, second and third in October 2021, and fourth in November 2021) titled: “Plankton ecosystem functions, biodiversity, and forecasting—research requirements and applications” (https://eqmh.github.io/MBON-Plankton/index.html, last accessed date: 22 Dec 2021). The first workshop held in November 2020 was an initiative of the US-MBON and MODIV teams to bring together members of the ocean sciences community involved in plankton observing and modeling to meet, build rapport, and exchange expertise. The following workshops were organized to accommodate time differences: one for South, Central, and North America in October 2021; one for Africa, Europe, the Middle East, and India in October 2021; and one for East Asia and Oceania in November 2021. Each workshop was held for two consecutive days, and participation was limited to 20–25 participants per workshop to enable interactive discussions. In total, 80 participants from 26 countries attended at least one of the workshops (Fig. 2). A detailed list of participants can be found in the workshops' website (https://eqmh.github.io/MBON-Plankton/participants.html, last accessed date: 22 Dec 2021). The United States of America and Australia were the countries with the most participants, followed by Canada, Germany, China, the United Kingdom, and Argentina. The objectives of the workshops were to: (1) identify requirements with respect to the definition of essential ocean variables (EOVs) and associated measurements, as well as compile the data needed to address critical knowledge gaps related to the role of plankton biodiversity functions to provide ecosystem services; (2) discuss ways to better link empirical observations to theoretical concepts of plankton biodiversity and ecosystem dynamics; and (3) suggest methods to better communicate the value of plankton to peers and non-scientific audiences. Participants discussed possible actions to overcome these challenges and limitations. There was agreement that observations in natural ocean habitats are fundamental to the formulation and testing of new hypotheses and conceptual models. More active communication between data providers and users will benefit the data collection, interpretation and analysis, and usability. For example, data that allow quantifying relationships among species traits (e.g., size, stoichiometry) will help inform understanding of species responses to environmental conditions. Although participants agreed that there is a lack of standardization of methods, data formatting, and distribution methods, in addition to a paucity of information on data quality and uncertainty, they also recognized the challenges of establishing a global protocol for field practices considering the regional environmental differences. For example, a net with a smaller mesh size is needed in tropical regions because plankton body size generally declines with warming (Campbell et al. 2021), in contrast a net with a larger mesh size which is towed faster is needed if the target is euphausiids rather than copepods. As a solution, participants suggested the use, further expansion, and explanation of existing protocols as best practices, and the set of minimum data and measurements required for data synthesis be laid out. Additionally, the creation of a community-driven unit conversion policy will help to normalize datasets and allow for a better understanding of variability and uncertainty. These approaches can help develop and validate models and minimize the uncertainty in data-driven meta-analysis studies. It was also agreed that, as a community, we need to build capacity at three levels: taxonomic ability, data science, and data management. We need to highlight the fundamental role of taxonomists in providing high-quality data, and of data scientists for data-driven meta-analysis and synthesis. Databases should follow Findability, Accessibility, Interoperability, and Reuse principles, to credit and recognize the original data providers. Sufficient funds for the curation and archiving of project data should be included in future funding proposals at the national and international levels. Participants identified the ideal scenario for data users is interlinked data repositories, standardized conversion tables, interoperable data collection protocols, and documented uncertainty levels. Each is a rather challenging task. As a first step, participants recommend an inventory of plankton databases with a summary of their holdings such as the geographic area covered, time covered, types of plankton data included, and information on data format and access (e.g., GOOS Biology and Ecosystems Panel, https://www.goosocean.org/index.php?option=com_oe&task=viewGroupRecord&groupID=339, last accessed date: 28 Jan 2022). The value of plankton is mainly invisible and difficult to quantify, mostly because the public is aware of the ultimate ecosystem service delivered, but not the underlying ecosystem functions of organisms that deliver that service (e.g., the connection of plankton to fish and fisheries). That is, plankton are valuable through their provisioning of ecological and biogeochemical services that enhance the cultural and economic value of the marine environment. They are responsible for approximately half of the Earth's oxygen production and photosynthetic carbon fixation (Field et al. 1998), and play the fundamental “bio” role in biogeochemical cycling of carbon, nitrogen, oxygen, and many other elements. They are linked through the food web to higher trophic levels and generate economic and recreational benefits for humans. Plankton are involved in feedback processes that affect the evolution and survival of all marine species (Falkowski 2012), and promote marine biodiversity such as fish, benthic organisms, marine and even terrestrial birds, and mammals. Many participants highlighted that although public awareness of the value of the services provided by plankton are often unrecognized, plankton is usually known for negative reasons, such as blooms of pathogenic bacteria, harmful algae, or jellyfish. The value of plankton to policy makers and the public can be highlighted by quantifying in economic terms and illustrating the link between plankton biodiversity and things we value (e.g., iconic species, recreational activities, water quality, carbon storage, nature conservation). Citizen science, education, science-art projects, and outreach activities are important ways to raise this awareness. There are many plankton-related outreach activities for all ages, but as outreach is commonly the least-funded component of scientific projects, it is imperative that we find ways to advance them. Models, virtual reality, and artificial intelligence could also be further developed as heuristic educational tools. One way to improve awareness is to integrate social scientists and those involved with science advice and policy into the development of research grants, approach mass media communication, and also consider opportunities presented by the Decade of Ocean Science for Sustainable Development (2021–2030) as proclaimed by the United Nations (https://en.unesco.org/ocean-decade, last accessed date: 22 Dec 2021). We would like to thank all participants for the fruitful discussions during the workshops. Special thanks to the keynote speakers for their introductory talks. This work was supported by National Science Foundation (OCE-1851866) to MG, Australian Research Council Discovery Project DP190102293 to AJR and JDE, EuroMarine and The Research Council of Norway through the FILAMO project to the MODIV team, NASA grant 80NSSC18K0318 to EM, NASA grants NNX14AP62A and 80NSSC20K0017; NOAA IOOS/ONR grant NA19NOS0120199; and NSF grant 1728913 Ocean Observation Research Coordination Network/RCN to FMK. None declared. All authors acted as chairs and note-takers during the workshops. MG, FKM, EM, AJR, JDE, EAT, CA, BC, CL, AP, FP, JR, SV, MV, and SZ organized the workshops. EM designed the website. MG, FKM, AJR, JDE, CL, and JR wrote the manuscript with the contribution of all authors. All authors gave feedback on the manuscript before submission.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.226
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2022
Admission routes1
Has abstractyes

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