GLORIA

GEOMAR Library Ocean Research Information Access

feed icon rss

Ihre E-Mail wurde erfolgreich gesendet. Bitte prüfen Sie Ihren Maileingang.

Leider ist ein Fehler beim E-Mail-Versand aufgetreten. Bitte versuchen Sie es erneut.

Vorgang fortführen?

Exportieren
Filter
Publikationsart
Schlagwörter
Erscheinungszeitraum
  • 1
    Publikationsdatum: 2022-01-31
    Beschreibung: In this paper we review on the technologies available to make globally quantitative observations of particles, in general, and plankton, in particular, in the world oceans, and for sizes varying from sub-micron to centimeters. Some of these technologies have been available for years while others have only recently emerged. Use of these technologies is critical to improve understanding of the processes that control abundances, distributions and composition of plankton, provide data necessary to constrain and improve ecosystem and biogeochemical models, and forecast changes in marine ecosystems in light of climate change. In this paper we begin by providing the motivation for plankton observations, quantification and diversity qualification on a global scale. We then expand on the state-of-the-art, detailing a variety of relevant and (mostly) mature technologies and measurements, including bulk measurements of plankton, pigment composition, uses of genomic, optical, acoustical methods and analysis using particles counters, flow cytometers and quantitative imaging devices. We follow by highlighting the requirements necessary for a plankton observing system, the approach to achieve it and associated challenges. We conclude with ranked action-item recommendations for the next ten years to move towards our vision of a holistic ocean-wide plankton observing system. Particularly, we suggest to begin with a demonstration project on a GO-SHIP line and/or a long-term observation site and expand from there ensuring that issues associated with methods, observation tools, data analysis, quality assessment and curation are addressed early in the implementation. Global coordination is key for the success of this vision and will bring new insights on processes associated with nutrient regeneration, ocean production, fisheries, and carbon sequestration.
    Materialart: Article , PeerReviewed
    Format: text
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 2
    Publikationsdatum: 2024-02-14
    Beschreibung: Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of & SIM;1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.
    Materialart: Article , PeerReviewed
    Format: text
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 3
    Publikationsdatum: 2022-10-18
    Beschreibung: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Orenstein, E., Ayata, S., Maps, F., Becker, É., Benedetti, F., Biard, T., Garidel‐Thoron, T., Ellen, J., Ferrario, F., Giering, S., Guy‐Haim, T., Hoebeke, L., Iversen, M., Kiørboe, T., Lalonde, J., Lana, A., Laviale, M., Lombard, F., Lorimer, T., Martini, S., Meyer, A., Möller, K.O., Niehoff, B., Ohman, M.D., Pradalier, C., Romagnan, J.-B., Schröder, S.-M., Sonnet, V., Sosik, H.M., Stemmann, L.S., Stock, M., Terbiyik-Kurt, T., Valcárcel-Pérez, N., Vilgrain, L., Wacquet, G., Waite, A.M., & Irisson, J. Machine learning techniques to characterize functional traits of plankton from image data. Limnology and Oceanography, 67(8), (2022): 1647-1669, https://doi.org/10.1002/lno.12101.
    Beschreibung: Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
    Beschreibung: SDA acknowledges funding from CNRS for her sabbatical in 2018–2020. Additional support was provided by the Institut des Sciences du Calcul et des Données (ISCD) of Sorbonne Université (SU) through the support of the sponsored junior team FORMAL (From ObseRving to Modeling oceAn Life), especially through the post-doctoral contract of EO. JOI acknowledges funding from the Belmont Forum, grant ANR-18-BELM-0003-01. French co-authors also wish to thank public taxpayers who fund their salaries. This work is a contribution to the scientific program of Québec Océan and the Takuvik Joint International Laboratory (UMI3376; CNRS - Université Laval). FM was supported by an NSERC Discovery Grant (RGPIN-2014-05433). MS is supported by the Research Foundation - Flanders (FWO17/PDO/067). FB received support from ETH Zürich. MDO is supported by the Gordon and Betty Moore Foundation and the U.S. National Science Foundation. ECB is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under the grant agreement no. 88882.438735/2019-01. TB is supported by the French National Research Agency (ANR-19-CE01-0006). NVP is supported by the Spanish State Research Agency, Ministry of Science and Innovation (PTA2016-12822-I). FL is supported by the Institut Universitaire de France (IUF). HMS was supported by the Simons Foundation (561126) and the U.S. National Science Foundation (CCF-1539256, OCE-1655686). Emily Peacock is gratefully acknowledged for expert annotation of IFCB images. LS was supported by the Chair VISION from CNRS/Sorbonne Université.
    Repository-Name: Woods Hole Open Access Server
    Materialart: Article
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 4
    Publikationsdatum: 2022-08-15
    Beschreibung: Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
    Repository-Name: EPIC Alfred Wegener Institut
    Materialart: Article , NonPeerReviewed
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 5
    Publikationsdatum: 2022-05-26
    Beschreibung: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Lombard, F., Boss, E., Waite, A. M., Vogt, M., Uitz, J., Stemmann, L., Sosik, H. M., Schulz, J., Romagnan, J., Picheral, M., Pearlman, J., Ohman, M. D., Niehoff, B., Moeller, K. M., Miloslavich, P., Lara-Lpez, A., Kudela, R., Lopes, R. M., Kiko, R., Karp-Boss, L., Jaffe, J. S., Iversen, M. H., Frisson, J., Fennel, K., Hauss, H., Guidi, L., Gorsky, G., Giering, S. L. C., Gaube, P., Gallager, S., Dubelaar, G., Cowen, R. K., Carlotti, F., Briseno-Avena, C., Berline, L., Benoit-Bird, K., Bax, N., Batten, S., Ayata, S. D., Artigas, L. F., & Appeltans, W. Globally consistent quantitative observations of planktonic ecosystems. Frontiers in Marine Science, 6, (2019):196, doi:10.3389/fmars.2019.00196.
    Beschreibung: In this paper we review the technologies available to make globally quantitative observations of particles in general—and plankton in particular—in the world oceans, and for sizes varying from sub-microns to centimeters. Some of these technologies have been available for years while others have only recently emerged. Use of these technologies is critical to improve understanding of the processes that control abundances, distributions and composition of plankton, provide data necessary to constrain and improve ecosystem and biogeochemical models, and forecast changes in marine ecosystems in light of climate change. In this paper we begin by providing the motivation for plankton observations, quantification and diversity qualification on a global scale. We then expand on the state-of-the-art, detailing a variety of relevant and (mostly) mature technologies and measurements, including bulk measurements of plankton, pigment composition, uses of genomic, optical and acoustical methods as well as analysis using particle counters, flow cytometers and quantitative imaging devices. We follow by highlighting the requirements necessary for a plankton observing system, the approach to achieve it and associated challenges. We conclude with ranked action-item recommendations for the next 10 years to move toward our vision of a holistic ocean-wide plankton observing system. Particularly, we suggest to begin with a demonstration project on a GO-SHIP line and/or a long-term observation site and expand from there, ensuring that issues associated with methods, observation tools, data analysis, quality assessment and curation are addressed early in the implementation. Global coordination is key for the success of this vision and will bring new insights on processes associated with nutrient regeneration, ocean production, fisheries and carbon sequestration.
    Beschreibung: Much of this manuscript flows from discussions of the authors with the members of SCOR working groups 150 (TOMCAT) and 154 (P-OBS) as well as discussions with the greater community in various GOOS workshops. We also thank Mike Sieracki, Cabell Davis, Daniele Iudicone, Eric Karsenti, Sebastien Colin, Colomban de Vargas, Ulf Riebesell, Fabrice Not, David Checkley, George Jackson, Cédric Guigand, Ed Urban, Frank Muller-Karger, Sanae Chiba and Daniel Dunn, who contributed to the initial abstracts to OceanObs'19. FL is supported by the Institut Universitaire de France. EB is supported by the NASA biology and biogeochemistry program. RKi and HH were supported by the German Science Foundation through the Collaborative Research Center 754 ‘Climate-Biogeochemistry Interactions in the Tropical Ocean’. SDA acknowledges the CNRS for her sabbatical year as visiting researcher at ISYEB on the use of genomics and next generation sequencing for plankton studies. HS acknowledges support from the Simons Foundation, the U.S. National Science Foundation, and the U.S. National Oceanic and Atmospheric Administration through the Cooperative Institute for the North Atlantic Region. FL and EB contribution was also inspired by their years of work within the Tara Expeditions initiative.
    Schlagwort(e): plankton ; imaging ; OceanObs ; autonomous platforms ; global observing ; EOVs ; ECVs
    Repository-Name: Woods Hole Open Access Server
    Materialart: Article
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 6
    Publikationsdatum: 2023-11-09
    Schlagwort(e): Biovolume calculated from equivalent spherical diameter (ESD); Copepoda; Copepoda, biovolume; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; MON; Monitoring; Plankton, biovolume; Plankton abundance; Point_B; Sample code/label; Uniform resource locator/link to graphic; Uniform resource locator/link to thumbnail; Villefranche sur Mer, France; ZOOSCAN
    Materialart: Dataset
    Format: text/tab-separated-values, 234 data points
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 7
    Publikationsdatum: 2023-11-09
    Schlagwort(e): Biovolume calculated from equivalent spherical diameter (ESD); Copepoda; Copepoda, biovolume; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; MON; Monitoring; Plankton, biovolume; Plankton abundance; Point_B; Sample code/label; Uniform resource locator/link to graphic; Uniform resource locator/link to thumbnail; Villefranche sur Mer, France; ZOOSCAN
    Materialart: Dataset
    Format: text/tab-separated-values, 243 data points
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 8
    Publikationsdatum: 2023-11-09
    Schlagwort(e): Biovolume calculated from equivalent spherical diameter (ESD); Copepoda; Copepoda, biovolume; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; MON; Monitoring; Plankton, biovolume; Plankton abundance; Point_B; Sample code/label; Uniform resource locator/link to graphic; Uniform resource locator/link to thumbnail; Villefranche sur Mer, France; ZOOSCAN
    Materialart: Dataset
    Format: text/tab-separated-values, 234 data points
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 9
    Publikationsdatum: 2023-11-09
    Schlagwort(e): Biovolume calculated from equivalent spherical diameter (ESD); Copepoda; Copepoda, biovolume; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; MON; Monitoring; Plankton, biovolume; Plankton abundance; Point_B; Sample code/label; Uniform resource locator/link to graphic; Uniform resource locator/link to thumbnail; Villefranche sur Mer, France; ZOOSCAN
    Materialart: Dataset
    Format: text/tab-separated-values, 234 data points
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 10
    Publikationsdatum: 2023-11-09
    Schlagwort(e): Biovolume calculated from equivalent spherical diameter (ESD); Copepoda; Copepoda, biovolume; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; MON; Monitoring; Plankton, biovolume; Plankton abundance; Point_B; Sample code/label; Uniform resource locator/link to graphic; Uniform resource locator/link to thumbnail; Villefranche sur Mer, France; ZOOSCAN
    Materialart: Dataset
    Format: text/tab-separated-values, 162 data points
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
Schließen ⊗
Diese Webseite nutzt Cookies und das Analyse-Tool Matomo. Weitere Informationen finden Sie hier...