Benfield Mark C.
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ArticleGlobal observing needs in the deep ocean(Frontiers Media, 2019-03-29) Levin, Lisa A. ; Bett, Brian J. ; Gates, Andrew R. ; Heimbach, Patrick ; Howe, Bruce M. ; Janssen, Felix ; McCurdy, Andrea ; Ruhl, Henry A. ; Snelgrove, Paul V. R. ; Stocks, Karen ; Bailey, David ; Baumann-Pickering, Simone ; Beaverson, Chris ; Benfield, Mark C. ; Booth, David J. ; Carreiro-Silva, Marina ; Colaço, Ana ; Eblé, Marie C. ; Fowler, Ashley M. ; Gjerde, Kristina M. ; Jones, Daniel O. B. ; Katsumata, Katsuro ; Kelley, Deborah S. ; Le Bris, Nadine ; Leonardi, Alan P. ; Lejzerowicz, Franck ; Macreadie, Peter I. ; McLean, Dianne ; Meitz, Fred ; Morato, Telmo ; Netburn, Amanda ; Pawlowski, Jan ; Smith, Craig R. ; Sun, Song ; Uchida, Hiroshi ; Vardaro, Michael F. ; Venkatesan, Ramasamy ; Weller, Robert A.The deep ocean below 200 m water depth is the least observed, but largest habitat on our planet by volume and area. Over 150 years of exploration has revealed that this dynamic system provides critical climate regulation, houses a wealth of energy, mineral, and biological resources, and represents a vast repository of biological diversity. A long history of deep-ocean exploration and observation led to the initial concept for the Deep-Ocean Observing Strategy (DOOS), under the auspices of the Global Ocean Observing System (GOOS). Here we discuss the scientific need for globally integrated deep-ocean observing, its status, and the key scientific questions and societal mandates driving observing requirements over the next decade. We consider the Essential Ocean Variables (EOVs) needed to address deep-ocean challenges within the physical, biogeochemical, and biological/ecosystem sciences according to the Framework for Ocean Observing (FOO), and map these onto scientific questions. Opportunities for new and expanded synergies among deep-ocean stakeholders are discussed, including academic-industry partnerships with the oil and gas, mining, cable and fishing industries, the ocean exploration and mapping community, and biodiversity conservation initiatives. Future deep-ocean observing will benefit from the greater integration across traditional disciplines and sectors, achieved through demonstration projects and facilitated reuse and repurposing of existing deep-sea data efforts. We highlight examples of existing and emerging deep-sea methods and technologies, noting key challenges associated with data volume, preservation, standardization, and accessibility. Emerging technologies relevant to deep-ocean sustainability and the blue economy include novel genomics approaches, imaging technologies, and ultra-deep hydrographic measurements. Capacity building will be necessary to integrate capabilities into programs and projects at a global scale. Progress can be facilitated by Open Science and Findable, Accessible, Interoperable, Reusable (FAIR) data principles and converge on agreed to data standards, practices, vocabularies, and registries. We envision expansion of the deep-ocean observing community to embrace the participation of academia, industry, NGOs, national governments, international governmental organizations, and the public at large in order to unlock critical knowledge contained in the deep ocean over coming decades, and to realize the mutual benefits of thoughtful deep-ocean observing for all elements of a sustainable ocean.
ArticleDetermining dominant scatterers of sound in mixed zooplankton populations(Acoustical Society of America, 2007-12) Lavery, Andone C. ; Wiebe, Peter H. ; Stanton, Timothy K. ; Lawson, Gareth L. ; Benfield, Mark C. ; Copley, Nancy J.High-frequency acoustic scattering techniques have been used to investigate dominant scatterers in mixed zooplankton populations. Volume backscattering was measured in the Gulf of Maine at 43, 120, 200, and 420 kHz. Zooplankton composition and size were determined using net and video sampling techniques, and water properties were determined using conductivity, temperature, and depth sensors. Dominant scatterers have been identified using recently developed scattering models for zooplankton and microstructure. Microstructure generally did not contribute to the scattering. At certain locations, gas-bearing zooplankton, that account for a small fraction of the total abundance and biomass, dominated the scattering at all frequencies. At these locations, acoustically inferred size agreed well with size determined from the net samples. Significant differences between the acoustic, net, and video estimates of abundance for these zooplankton are most likely due to limitations of the net and video techniques. No other type of biological scatterer ever dominated the scattering at all frequencies. Copepods, fluid-like zooplankton that account for most of the abundance and biomass, dominated at select locations only at the highest frequencies. At these locations, acoustically inferred abundance agreed well with net and video estimates. A general approach for the difficult problem of interpreting high-frequency acoustic scattering in mixed zooplankton populations is described.
ArticleRAPID : research on automated plankton identification(Oceanography Society, 2007-06) Benfield, Mark C. ; Grosjean, Philippe ; Culverhouse, Phil F. ; Irigoien, Xabier ; Sieracki, Michael E. ; Lopez-Urrutia, Angel ; Dam, Hans G. ; Hu, Qiao ; Davis, Cabell S. ; Hansen, Allen ; Pilskaln, Cynthia H. ; Riseman, Edward M. ; Schultz, Howard ; Utgoff, Paul E. ; Gorsky, GabrielWhen Victor Hensen deployed the first true plankton1 net in 1887, he and his colleagues were attempting to answer three fundamental questions: What planktonic organisms are present in the ocean? How many of each type are present? How does the plankton’s composition change over time? Although answering these questions has remained a central goal of oceanographers, the sophisticated tools available to enumerate planktonic organisms today offer capabilities that Hensen probably could never have imagined.
ArticleEditorial: Technological advances for measuring planktonic components of the pelagic ecosystem: an integrated approach to data collection and analysis(Frontiers Media, 2023-01-26) Pitois, Sophie G. ; Fileman, Elaine S. ; Benfield, Mark C. ; Wiebe, Peter H. ; Lombard, FabienThe traditional collection of plankton samples, often using nets followed by visual sorting and taxonomic analysis is a labour intensive, time-consuming, and ultimately expensive process. The increasing demand for pelagic data combined with ever reducing budgets for monitoring and the general problem of the taxonomic impediment have driven the development of new tools and techniques for the sampling and analysis of this key ecosystem component.