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  • 1
    Publication Date: 2022-05-25
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y., Brodie, S., Ortiz, I., Tommasi, D., Widlansky, M. J., Barrie, D., Capotondi, A., Cheng, W., Di Lorenzo, E., Edwards, C., Fiechter, J., Fratantoni, P., Hazen, E. L., Hermann, A. J., Kumar, A., Miller, A. J., Pirhalla, D., Buil, M. P., Ray, S., Sheridan, S. C., Subramanian, A., Thompson, P., Thorne, L., Annamalai, H., Aydin, K., Bograd, S. J., Griffis, R. B., Kearney, K., Kim, H., Mariotti, A., Merrifield, M., & Rykaczewski, R. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography, 183, (2020): 102307, doi:10.1016/j.pocean.2020.102307.
    Description: Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1–24 month) prediction of properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.
    Description: This study was supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program through grants NA17OAR4310108, NA17OAR4310112, NA17OAR4310111, NA17OAR4310110, NA17OAR4310109, NA17OAR4310104, NA17OAR4310106, and NA17OAR4310113. This paper is a product of the NOAA/MAPP Marine Prediction Task Force.
    Keywords: Prediction ; Predictability ; Forecast ; Ecological forecast ; Mechanism ; Seasonal ; Interannual ; Large marine ecosystem
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Capotondi, A., Jacox, M., Bowler, C., Kavanaugh, M., Lehodey, P., Barrie, D., Brodie, S., Chaffron, S., Cheng, W., Dias, D. F., Eveillard, D., Guidi, L., Iudicone, D., Lovenduski, N. S., Nye, J. A., Ortiz, I., Pirhalla, D., Buil, M. P., Saba, V., Sheridan, S., Siedlecki, S., Subramanian, A., de Vargas, C., Di Lorenzo, E., Doney, S. C., Hermann, A. J., Joyce, T., Merrifield, M., Miller, A. J., Not, F., & Pesant, S. Observational needs supporting marine ecosystems modeling and forecasting: from the global ocean to regional and coastal systems. Frontiers in Marine Science, 6, (2019): 623, doi:10.3389/fmars.2019.00623.
    Description: Many coastal areas host rich marine ecosystems and are also centers of economic activities, including fishing, shipping and recreation. Due to the socioeconomic and ecological importance of these areas, predicting relevant indicators of the ecosystem state on sub-seasonal to interannual timescales is gaining increasing attention. Depending on the application, forecasts may be sought for variables and indicators spanning physics (e.g., sea level, temperature, currents), chemistry (e.g., nutrients, oxygen, pH), and biology (from viruses to top predators). Many components of the marine ecosystem are known to be influenced by leading modes of climate variability, which provide a physical basis for predictability. However, prediction capabilities remain limited by the lack of a clear understanding of the physical and biological processes involved, as well as by insufficient observations for forecast initialization and verification. The situation is further complicated by the influence of climate change on ocean conditions along coastal areas, including sea level rise, increased stratification, and shoaling of oxygen minimum zones. Observations are thus vital to all aspects of marine forecasting: statistical and/or dynamical model development, forecast initialization, and forecast validation, each of which has different observational requirements, which may be also specific to the study region. Here, we use examples from United States (U.S.) coastal applications to identify and describe the key requirements for an observational network that is needed to facilitate improved process understanding, as well as for sustaining operational ecosystem forecasting. We also describe new holistic observational approaches, e.g., approaches based on acoustics, inspired by Tara Oceans or by landscape ecology, which have the potential to support and expand ecosystem modeling and forecasting activities by bridging global and local observations.
    Description: This study was supported by the NOAA’s Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) Program through grants NA17OAR4310106, NA17OAR4310104, NA17OAR4310108, NA17OAR4310109, NA17OAR4310110, NA17OAR4310111, NA17OAR4310112, and NA17OAR4310113. This manuscript is a product of the NOAA/MAPP Marine Prediction Task Force. The Tara Oceans consortium acknowledges support from the CNRS Research Federation FR2022 Global Ocean Systems Ecology and Evolution, and OCEANOMICS (grant agreement ‘Investissement d’Avenir’ ANR-11-BTBR-0008). This is article number 95 of the Tara Oceans consortium. MK and SD acknowledge support from NASA grant NNX14AP62A “National Marine Sanctuaries as Sentinel Sites for a Demonstration Marine Biodiversity Observation Network (MBON)” funded under the National Ocean Partnership Program (NOPP RFP NOAA-NOS-IOOS-2014-2003803 in partnership between NOAA, BOEM, and NASA), and the NOAA Integrated Ocean Observing System (IOOS) Program Office. WC, IO, and AH acknowledge partial support from the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement NA15OAR4320063, Contribution No. 2019-1029. This study received support from the European H2020 International Cooperation project MESOPP (Mesopelagic Southern Ocean Prey and Predators), grant agreement no. 692173.
    Keywords: Marine ecosystems ; Modeling and forecasting ; Seascapes ; Genetics ; Acoustics
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 3
    Publication Date: 2022-05-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in van Putten, I., Kelly, R., Cavanagh, R. D., Murphy, E. J., Breckwoldt, A., Brodie, S., Cvitanovic, C., Dickey-Collas, M., Maddison, L., Melbourne-Thomas, J., Arrizabalaga, H., Azetsu-Scott, K., Beckley, L. E., Bellerby, R., Constable, A. J., Cowie, G., Evans, K., Glaser, M., Hall, J., Hobday, A. J., Johnston, N. M., Llopiz, J. K., Mueter, F., Muller-Karger, F. E., Weng, K. C., Wolf-Gladrow, D., Xavier, J. C. A decade of incorporating social sciences in the Integrated Marine Biosphere Research Project (IMBeR): much done, much to do? Frontiers in Marine Science, 8, (2021): 662350, https://doi.org/10.3389/fmars.2021.662350.
    Description: Successful management and mitigation of marine challenges depends on cooperation and knowledge sharing which often occurs across culturally diverse geographic regions. Global ocean science collaboration is therefore essential for developing global solutions. Building effective global research networks that can enable collaboration also need to ensure inter- and transdisciplinary research approaches to tackle complex marine socio-ecological challenges. To understand the contribution of interdisciplinary global research networks to solving these complex challenges, we use the Integrated Marine Biosphere Research (IMBeR) project as a case study. We investigated the diversity and characteristics of 1,827 scientists from 11 global regions who were attendees at different IMBeR global science engagement opportunities since 2009. We also determined the role of social science engagement in natural science based regional programmes (using key informants) and identified the potential for enhanced collaboration in the future. Event attendees were predominantly from western Europe, North America, and East Asia. But overall, in the global network, there was growing participation by females, students and early career researchers, and social scientists, thus assisting in moving toward interdisciplinarity in IMBeR research. The mainly natural science oriented regional programmes showed mixed success in engaging and collaborating with social scientists. This was mostly attributed to the largely natural science (i.e., biological, physical) goals and agendas of the programmes, and the lack of institutional support and push to initiate connections with social science. Recognising that social science research may not be relevant to all the aims and activities of all regional programmes, all researchers however, recognised the (potential) benefits of interdisciplinarity, which included broadening scientists’ understanding and perspectives, developing connections and interlinkages, and making science more useful. Pathways to achieve progress in regional programmes fell into four groups: specific funding, events to come together, within-programme-reflections, and social science champions. Future research programmes should have a strategic plan to be truly interdisciplinary, engaging natural and social sciences, as well as aiding early career professionals to actively engage in such programmes.
    Description: This publication resulted in part from support from the U.S. National Science Foundation (Grant OCE-1840868) to the Scientific Committee on Oceanic Research (SCOR).
    Keywords: marine science ; research networks ; disciplines ; global ; regional programmes
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 4
    Publication Date: 2023-09-25
    Description: Intersessional Science Board Meeting 2023 - Note from the new SB Chair. Future SSC's 9th Intersessional Meeting ~Highlights. PICES 2023 ~ See you in Seattle. The 5th International Conference on the Effects of Climate Change on the World's Ocean ECCWO5 (Together we will work; W1 Workshop Report; W2 Workshop Report; S2 Session Report; S3 Session Report; S10 Session Report; S18 Session Report; S19 Session Report; ECOP Update; Presentation Awards; EuroFish). PICES-MAFF Ciguartera Project - Summary of Activities. Exploring National ECOP hubs in PICES member countries. The 44th Pacific Ecology and Evolution Conference (PEEC). Regional Reports (The Bering Sea: Current Status and Recent Trends; Western North Pacific - Current Status and Updates: Sea surface temperatures for the 2022/2023 cold season; The Northeast Pacific: Update on marine heatwave status and trends). The Continuous Plankton Recorder as a platform for sensor development. ICES Annual Science Conference, 2022: Theme Session J. The Development of the SUPREME Network. Remembering Vera Alexander. PICES by the Numbers: #ECCWO5 Calculated Carbon Emissions. PICES Events Calendar. Your PICES Science Images. Open call for PICES Press submissions|About PICES Press
    Description: Published
    Description: Non Refereed
    Repository Name: AquaDocs
    Type: Book/Monograph/Conference Proceedings
    Format: 76
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  • 5
    Publication Date: 2022-01-31
    Description: Advances in ocean observing technologies and modeling provide the capacity to revolutionize the management of living marine resources. While traditional fisheries management approaches like single-species stock assessments are still common, a global effort is underway to adopt ecosystem-based fisheries management (EBFM) approaches. These approaches consider changes in the physical environment and interactions between ecosystem elements, including human uses, holistically. For example, integrated ecosystem assessments aim to synthesize a suite of observations (physical, biological, socioeconomic) and modeling platforms [ocean circulation models, ecological models, short-term forecasts, management strategy evaluations (MSEs)] to assess the current status and recent and future trends of ecosystem components. This information provides guidance for better management strategies. A common thread in EBFM approaches is the need for high-quality observations of ocean conditions, at scales that resolve critical physical-biological processes and are timely for management needs. Here we explore options for a future observing system that meets the needs of EBFM by (i) identifying observing needs for different user groups, (ii) reviewing relevant datasets and existing technologies, (iii) showcasing regional case studies, and (iv) recommending observational approaches required to implement EBFM. We recommend linking ocean observing within the context of Global Ocean Observing System (GOOS) and other regional ocean observing efforts with fisheries observations, new forecasting methods, and capacity development, in a comprehensive ocean observing framework.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 6
    Publication Date: 2024-02-14
    Description: 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.
    Type: Article , PeerReviewed
    Format: text
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