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  • 1
    Publication Date: 2022-05-25
    Description: Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 27 (2013): 526–540, doi:10.1002/gbc.20050.
    Description: Satellite measurements allow global assessments of phytoplankton concentrations and, from observed temporal changes in biomass, direct access to net biomass accumulation rates (r). For the subarctic Atlantic basin, analysis of annual cycles in r reveals that initiation of the annual blooming phase does not occur in spring after stratification surpasses a critical threshold but rather occurs in early winter when growth conditions for phytoplankton are deteriorating. This finding has been confirmed with in situ profiling float data. The objective of the current study was to test whether satellite-based annual cycles in r are reproduced by the Biogeochemical Element Cycling–Community Climate System Model and, if so, to use the additional ecosystem properties resolved by the model to better understand factors controlling phytoplankton blooms. We find that the model gives a similar early onset time for the blooming phase, that this initiation is largely due to the physical disruption of phytoplankton-grazer interactions during mixed layer deepening, and that parallel increases in phytoplankton-specific division and loss rates during spring maintain the subtle disruption in food web equilibrium that ultimately yields the spring bloom climax. The link between winter mixing and bloom dynamics is illustrated by contrasting annual plankton cycles between regions with deeper and shallower mixing. We show that maximum water column inventories of phytoplankton vary in proportion to maximum winter mixing depth, implying that future reductions in winter mixing may dampen plankton cycles in the subarctic Atlantic. We propose that ecosystem disturbance-recovery sequences are a unifying property of global ocean plankton blooms.
    Description: This work was supported by the National Aeronautics and Space Administration, Ocean Biology and Biogeochemistry Program (grants NNX10AT70G, NNX09AK30G, NNX08AK70G, NNX07AL80G, and NNX08AP36A) and the Center for Microbial Oceanography Research and Education (C-MORE; grant EF-0424599), a National Science Foundation-supported Science and Technology Center.
    Keywords: Phytoplankton ; Bloom ; Atlantic ; Satellite ; Model ; Ecosystems
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-05-25
    Description: Author Posting. © American Geophysical Union, [year]. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 27 (2013): 1294–1296, doi:10.1002/2013GB004720.
    Description: 2014-06-12
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 3
    Publication Date: 2022-05-25
    Description: © The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.
    Description: The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration 〈2%, relative calibration of 0.2%, polarization sensitivity 〈1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.
    Description: National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC00006
    Keywords: Aquatic ; Coastal zone ; Ecology ; Essentail biodiversity variables ; H4 imaging ; Hyperspectral ; Remote sensing ; Vegetation ; Wetland
    Repository Name: Woods Hole Open Access Server
    Type: Article
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