13 results on '"Möller, Klas Ove"'
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2. Machine learning techniques to characterize functional traits of plankton from image data
- Author
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Orenstein, Eric C, Ayata, Sakina‐Dorothée, Maps, Frédéric, Becker, Érica C, Benedetti, Fabio, Biard, Tristan, Garidel‐Thoron, Thibault, Ellen, Jeffrey S, Ferrario, Filippo, Giering, Sarah LC, Guy‐Haim, Tamar, Hoebeke, Laura, Iversen, Morten Hvitfeldt, Kiørboe, Thomas, Lalonde, Jean‐François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark D, Pradalier, Cédric, Romagnan, Jean‐Baptiste, Schröder, Simon‐Martin, Sonnet, Virginie, Sosik, Heidi M, Stemmann, Lars S, Stock, Michiel, Terbiyik‐Kurt, Tuba, Valcárcel‐Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M, and Irisson, Jean‐Olivier
- Subjects
Biological Sciences ,Ecology ,Earth Sciences ,Environmental Sciences ,Marine Biology & Hydrobiology ,Biological sciences ,Earth sciences ,Environmental sciences - Abstract
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.
- Published
- 2022
3. Storm events alter marine snow fluxes in stratified marine environments
- Author
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Rühl, Saskia and Möller, Klas Ove
- Published
- 2024
- Full Text
- View/download PDF
4. Publisher Correction: Making marine image data FAIR
- Author
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Schoening, Timm, Durden, Jennifer M., Faber, Claas, Felden, Janine, Heger, Karl, Hoving, Henk-Jan T., Kiko, Rainer, Köser, Kevin, Krämmer, Christopher, Kwasnitschka, Tom, Möller, Klas Ove, Nakath, David, Naß, Andrea, Nattkemper, Tim W., Purser, Autun, and Zurowietz, Martin
- Published
- 2022
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5. Making marine image data FAIR
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Schoening, Timm, Durden, Jennifer M., Faber, Claas, Felden, Janine, Heger, Karl, Hoving, Henk-Jan T., Kiko, Rainer, Köser, Kevin, Krämmer, Christopher, Kwasnitschka, Tom, Möller, Klas Ove, Nakath, David, Naß, Andrea, Nattkemper, Tim W., Purser, Autun, and Zurowietz, Martin
- Published
- 2022
- Full Text
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6. Impact of aggregate‐colonizing copepods on the biological carbon pump in a high‐latitude fjord.
- Author
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Svensen, Camilla, Iversen, Morten, Norrbin, Fredrika, Möller, Klas Ove, Wiedmann, Ingrid, Skarðhamar, Jofrid, Barth‐Jensen, Coralie, Kwasniewski, Slawomir, Ormanczyk, Mateusz, Dąbrowska, Anna Maria, and Koski, Marja
- Subjects
ANIMAL droppings ,BIOGEOCHEMICAL cycles ,COPEPODA ,FJORDS ,KRILL - Abstract
Zooplankton consumption of sinking aggregates affects the quality and quantity of organic carbon exported to the deep ocean. Increasing laboratory evidence shows that small particle‐associated copepods impact the flux attenuation by feeding on sinking particles, but this has not been quantified in situ. We investigated the impact of an abundant particle‐colonizing copepod, Microsetella norvegica, on the attenuation of the vertical carbon flux in a sub‐Arctic fjord. This study combines field measurements of vertical carbon flux, abundance, and size‐distribution of marine snow and degradation rates of fecal pellets and algal aggregates. Female M. norvegica altered their feeding behavior when exposed to aggregates, and ingestion rates were 0.20 μg C ind.−1 d−1 on marine snow and 0.11 μg C ind.−1 d−1 on intact krill fecal pellets, corresponding to 48% and 26% of the females' body carbon mass. Due to high sea surface abundance of up to ~ 50 ind. L−1, the population of M. norvegica had the potential to account for almost all the carbon removal in the upper 50 m of the water column, depending on the type of the aggregate. Our observations highlight the potential importance of abundant small‐sized copepods for biogeochemical cycles through their impact on export flux and its attenuation in the twilight zone. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. Machine learning in marine ecology: an overview of techniques and applications
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Rubbens, Peter, primary, Brodie, Stephanie, additional, Cordier, Tristan, additional, Destro Barcellos, Diogo, additional, Devos, Paul, additional, Fernandes-Salvador, Jose A, additional, Fincham, Jennifer I, additional, Gomes, Alessandra, additional, Handegard, Nils Olav, additional, Howell, Kerry, additional, Jamet, Cédric, additional, Kartveit, Kyrre Heldal, additional, Moustahfid, Hassan, additional, Parcerisas, Clea, additional, Politikos, Dimitris, additional, Sauzède, Raphaëlle, additional, Sokolova, Maria, additional, Uusitalo, Laura, additional, Van den Bulcke, Laure, additional, van Helmond, Aloysius T M, additional, Watson, Jordan T, additional, Welch, Heather, additional, Beltran-Perez, Oscar, additional, Chaffron, Samuel, additional, Greenberg, David S, additional, Kühn, Bernhard, additional, Kiko, Rainer, additional, Lo, Madiop, additional, Lopes, Rubens M, additional, Möller, Klas Ove, additional, Michaels, William, additional, Pala, Ahmet, additional, Romagnan, Jean-Baptiste, additional, Schuchert, Pia, additional, Seydi, Vahid, additional, Villasante, Sebastian, additional, Malde, Ketil, additional, and Irisson, Jean-Olivier, additional
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- 2023
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8. Machine learning in marine ecology: an overview of techniques and applications
- Author
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Rubbens, Peter, Brodie, Stephanie, Cordier, Tristan, Destro Barcellos, Diogo, Devos, Paul, Fernandes-Salvador, Jose A, Fincham, Jennifer I, Gomes, Alessandra, Handegard, Nils Olav, Howell, Kerry, Jamet, Cédric, Kartveit, Kyrre Heldal, Moustahfid, Hassan, Parcerisas, Clea, Politikos, Dimitris, Sauzède, Raphaëlle, Sokolova, Maria, Uusitalo, Laura, Van den Bulcke, Laure, van Helmond, Aloysius T M, Watson, Jordan T, Welch, Heather, Beltran-Perez, Oscar, Chaffron, Samuel, Greenberg, David S, Kühn, Bernhard, Kiko, Rainer, Lo, Madiop, Lopes, Rubens M, Möller, Klas Ove, Michaels, William, Pala, Ahmet, Romagnan, Jean-Baptiste, Schuchert, Pia, Seydi, Vahid, Villasante, Sebastian, Malde, Ketil, Irisson, Jean-Olivier, Whidden, Christopher, Rubbens, Peter, Brodie, Stephanie, Cordier, Tristan, Destro Barcellos, Diogo, Devos, Paul, Fernandes-Salvador, Jose A, Fincham, Jennifer I, Gomes, Alessandra, Handegard, Nils Olav, Howell, Kerry, Jamet, Cédric, Kartveit, Kyrre Heldal, Moustahfid, Hassan, Parcerisas, Clea, Politikos, Dimitris, Sauzède, Raphaëlle, Sokolova, Maria, Uusitalo, Laura, Van den Bulcke, Laure, van Helmond, Aloysius T M, Watson, Jordan T, Welch, Heather, Beltran-Perez, Oscar, Chaffron, Samuel, Greenberg, David S, Kühn, Bernhard, Kiko, Rainer, Lo, Madiop, Lopes, Rubens M, Möller, Klas Ove, Michaels, William, Pala, Ahmet, Romagnan, Jean-Baptiste, Schuchert, Pia, Seydi, Vahid, Villasante, Sebastian, Malde, Ketil, Irisson, Jean-Olivier, and Whidden, Christopher
- Abstract
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.
- Published
- 2023
- Full Text
- View/download PDF
9. Machine learning techniques to characterize functional traits of plankton from image data
- Author
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Orenstein, Eric C., Ayata, Sakina‐dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, De Garidel‐thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy‐haim, Tamar, Hoebeke, Laura, Iversen, Morten Hvitfeldt, Kiørboe, Thomas, Lalonde, Jean‐françois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark D., Pradalier, Cédric, Romagnan, Jean-baptiste, Schröder, Simon‐martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars S., Stock, Michiel, Terbiyik‐kurt, Tuba, Valcárcel‐pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., Irisson, Jean‐olivier, Orenstein, Eric C., Ayata, Sakina‐dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, De Garidel‐thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy‐haim, Tamar, Hoebeke, Laura, Iversen, Morten Hvitfeldt, Kiørboe, Thomas, Lalonde, Jean‐françois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark D., Pradalier, Cédric, Romagnan, Jean-baptiste, Schröder, Simon‐martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars S., Stock, Michiel, Terbiyik‐kurt, Tuba, Valcárcel‐pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., and Irisson, Jean‐olivier
- Abstract
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.
- Published
- 2022
- Full Text
- View/download PDF
10. Machine learning techniques to characterise functional traits of plankton image data
- Author
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Orenstein, Eric, Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica, Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey, Ferrario, Filippo, Giering, Sarah, Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten, Kiorboe, Thomas, Lalonde, Jean-Francois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark, Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon-Martin, Sonnet, Virginie, Sosik, Heidi, Stemmann, Lars, Stock, Michiel, Terbiyik-Kurt, Tuba, Valcárcel-Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya, Irisson, Jean-Olivier, Orenstein, Eric, Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica, Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey, Ferrario, Filippo, Giering, Sarah, Guy-Haim, Tamar, Hoebeke, Laura, Iversen, Morten, Kiorboe, Thomas, Lalonde, Jean-Francois, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, Mark, Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon-Martin, Sonnet, Virginie, Sosik, Heidi, Stemmann, Lars, Stock, Michiel, Terbiyik-Kurt, Tuba, Valcárcel-Pérez, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya, and Irisson, Jean-Olivier
- Abstract
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.
- Published
- 2022
11. Supplemental Information: Machine learning techniques to characterize functional traits of plankton from image data
- Author
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Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., Irisson, Jean-Olivier, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., and Irisson, Jean-Olivier
- Published
- 2022
12. Machine learning techniques to characterize functional traits of plankton from image data
- Author
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Centre National de la Recherche Scientifique (France), Belmont Forum, Université Laval, Natural Sciences and Engineering Research Council of Canada, Research Foundation - Flanders, ETH Zurich, Gordon and Betty Moore Foundation, National Science Foundation (US), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), Agence Nationale de la Recherche (France), Ministerio de Economía y Competitividad (España), Institut Universitaire de France, Simons Foundation, Sorbonne Université, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., Irisson, Jean-Olivier, Centre National de la Recherche Scientifique (France), Belmont Forum, Université Laval, Natural Sciences and Engineering Research Council of Canada, Research Foundation - Flanders, ETH Zurich, Gordon and Betty Moore Foundation, National Science Foundation (US), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), Agence Nationale de la Recherche (France), Ministerio de Economía y Competitividad (España), Institut Universitaire de France, Simons Foundation, Sorbonne Université, Orenstein, Eric C., Ayata, Sakina-Dorothée, Maps, Frédéric, Becker, Érica C., Benedetti, Fabio, Biard, Tristan, de Garidel-Thoron, Thibault, Ellen, Jeffrey S., Ferrario, Filippo, Giering, Sarah L. C., Guy-Haim, Tamar, Hoebeke, Laura, Hvitfeldt, Morten, Kiørboe, Thomas, Lalonde, Jean François, Lana, Arancha, Laviale, Martin, Lombard, Fabien, Lorimer, Tom, Martini, Séverine, Meyer, Albin, Möller, Klas Ove, Niehoff, Barbara, Ohman, M. D., Pradalier, Cédric, Romagnan, Jean-Baptiste, Schröder, Simon Martin, Sonnet, Virginie, Sosik, Heidi M., Stemmann, Lars, Stock, Michiel, Terbiyik Kurt, Tuba, Valcárcel, Nerea, Vilgrain, Laure, Wacquet, Guillaume, Waite, Anya M., and Irisson, Jean-Olivier
- Abstract
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.
- Published
- 2022
13. Best practices and recommendations for plankton imaging data management: Ensuring effective data flow towards European data infrastructures. Version 1
- Author
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Martin-Cabrera, Patricia, Perez Perez, Ruben, Irisson, Jean-Olivier, Lombard, Fabien, Möller, Klas Ove, Rühl, Saskia, Creach, Veronique, Lindh, Markus, Stemmann, Lars, Schepers, Lennert, and Martin-Cabrera, Patricia
- Subjects
OBIS ,Data processing ,EMODnet Biology ,GBIF ,Data quality management ,EurOBIS ,Phytoplankton ,Darwin Core ,OBIS-ENV-DATA ,Zooplankton ,Plankton imaging instruments - Abstract
The best practices and recommendations for plankton imaging data management enable users to report a detailed taxonomic characterisation of plankton observations as well as quantitative information that is useful for ecological studies. This format allows biodiversity data portals to extend their scope beyond species occurrence data. Furthermore, proposing the use of more Darwin Core fields in this format, users now have the possibility to publish manually validated datasets, but also datasets produced by fully automated plankton identification workflows. The proposed data and file formats are simple and both human- and machine-readable to automatise workflows. This format will allow data generators to submit enriched plankton imaging datasets to the international biodiversity data portals, (Eur)OBIS and EMODnet Biology. We encourage plankton imaging data generators to implement these workflows into their pipelines, to share their data with the international data portals easily, enriching these databases with this valuable data. European Union; JERICO-S3 project, WP6- D6.4, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 871153. Published Also published as JERICO Deliverable JERICO-S3 D6.4-WP6 -Best practices & recommendations for plankton imaging data management, JERICO-S3-WP6-D6.4-07.04.2022-V1.0 Refereed Current 14.a Phytoplankton biomass and diversity Zooplankton biomass and diversity Pilot or Demonstrated Novel (no adoption outside originators) Species distributions Species abundances Species morphology Taxonomic/phylogenetic diversity Community abundance Plankton In-situ plankton imaging instruments Benchtop plankton imaging instruments Reports with methodological relevance
- Published
- 2022
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