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  • 11
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Luo, Yawei; Doney, Scott C; Anderson, L A; Benavides, Mar; Berman-Frank, I; Bode, Antonio; Bonnet, S; Boström, Kjärstin H; Böttjer, D; Capone, D G; Carpenter, E J; Chen, Yaw-Lin; Church, Matthew J; Dore, John E; Falcón, Luisa I; Fernández, A; Foster, R A; Furuya, Ken; Gomez, Fernando; Gundersen, Kjell; Hynes, Annette M; Karl, David Michael; Kitajima, Satoshi; Langlois, Rebecca; LaRoche, Julie; Letelier, Ricardo M; Marañón, Emilio; McGillicuddy Jr, Dennis J; Moisander, Pia H; Moore, C Mark; Mouriño-Carballido, Beatriz; Mulholland, Margaret R; Needoba, Joseph A; Orcutt, Karen M; Poulton, Alex J; Rahav, Eyal; Raimbault, Patrick; Rees, Andrew; Riemann, Lasse; Shiozaki, Takuhei; Subramaniam, Ajit; Tyrrell, Toby; Turk-Kubo, Kendra A; Varela, Manuel; Villareal, Tracy A; Webb, Eric A; White, Angelicque E; Wu, Jingfeng; Zehr, Jonathan P (2012): Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth System Science Data, 4, 47-73, https://doi.org/10.5194/essd-4-47-2012
    Publication Date: 2023-12-09
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs abundance and biomass, computed from a collection of source data sets.
    Keywords: 33KB20020923; 33RR20030714; A2/19921126; A2/1992-11-27; AMT8/1999-05-05; AMT8/1999-05-06; AMT8/1999-05-07; AMT8/1999-05-08; AMT8/1999-05-10; AMT8/1999-05-12; AMT8/1999-05-13; AMT8/1999-05-18; AMT8/1999-05-20; AMT8/1999-05-21; AMT8/1999-05-23; AMT8/1999-05-25; AMT8/1999-05-26; AMT8/1999-05-28; AMT8/1999-05-29; AT19641122; AT19641123; AT19641202; AT19641203; AT19641208; Atlantic; Barbados; Barbados_1974-07-09_1; Barbados_1974-07-16_1; Barbados_1974-07-23_1; Barbados_1974-07-28_1; Barbados_1974-08-07_1; Barbados_1974-08-11_1; Barbados_1974-08-21_1; Barbados_1974-08-27_1; Barbados_1974-09-03_1; Barbados_1974-09-10_1; Barbados_1974-09-17_1; Barbados_1974-09-24_1; Barbados_1974-10-03_1; Barbados_1974-10-08_1; Barbados_1974-10-15_1; Barbados_1974-10-22_1; Barbados_1974-10-29_1; Barbados_1974-11-05_1; Barbados_1974-11-12_1; Barbados_1974-11-19_1; Barbados_1974-11-29_1; Barbados_1974-12-03_1; Barbados_1974-12-10_1; Barbados_1974-12-17_1; Barbados_1974-12-23_1; Barbados_1974-12-30_1; Barbados_1975-01-07_1; Barbados_1975-01-14_1; Barbados_1975-01-21_1; Barbados_1975-01-31_1; Barbados_1975-02-04_1; Barbados_1975-02-11_1; Barbados_1975-02-15_1; Barbados_1975-03-05_1; Barbados_1975-03-18_1; Barbados_1975-04-01_1; Barbados_1975-04-18_1; Barbados_1975-04-29_1; Barbados_1975-05-13_1; Barbados_1975-05-21_1; Barbados_1975-05-27_1; Barbados_1975-06-10_1; Barbados_1975-06-24_1; Barbados_1975-07-08_1; Barbados_1975-08-05_1; Barbados_1975-08-25_1; Barbados_1975-10-15_1; Barbados_1975-11-17_1; Barbados_1975-12-10_1; Barbados_1976-01-02_1; Barbados_1976-01-19_1; Barbados_1976-02-10_1; Barbados_1976-03-12_1; Barbados_1976-04-15_1; Barbados_1976-05-14_1; BATS1995-05-15; BATS1996-10-10; Bermuda, Atlantic Ocean; Bottle, Niskin; CAIBEX-I; CAIBEX-I_2; CAIBEX-I_3; CAIBEX-I_5; CAIBEX-I_6; CAIBEX-II; CAIBEX-II_01; CAIBEX-II_02; CAIBEX-II_03; CAIBEX-II_04; CAIBEX-II_05; CAIBEX-II_06; CAIBEX-II_07; CAIBEX-II_08; CAIBOX; CAIBOX_01; CAIBOX_02; CAIBOX_03; CAIBOX_04; CAIBOX_05; CAIBOX_06; CAIBOX_07; CAIBOX_08; CAIBOX_09; CAIBOX_10; CAIBOX_11; CAIBOX_12; CAIBOX_13; CAIBOX_14; CAIBOX_15; CAIBOX_16; CAIBOX_17; Calculated; Calothrix, associated species; Calothrix, carbon per cell; Calothrix abundance, cells; China Sea; Chlorophyll total, areal concentration; CTD, Seabird; CTD/Rosette; CTD-R; CTD-RO; Date/Time of event; Depth, bottom/max; Depth, top/min; DEPTH, water; Diazotrophs, total biomass as carbon; East China Sea; ECS1993-11-15_1; ECS1993-11-15_2; ECS1993-11-15_3; ECS1993-11-15_4; ECS1993-11-15_5; ECS1993-11-15_6; ECS1994-03-15_1; ECS1994-03-15_2; ECS1994-03-15_3; ECS1994-03-15_4; ECS1994-03-15_5; ECS1994-05-05_1; ECS1994-05-05_2; ECS1994-07-05_1; ECS1994-07-05_2; ECS1994-07-05_3; ECS1994-07-05_4; ECS1995-03-28_1; ECS1995-03-28_2; ECS1995-04-17_1; ECS1995-04-17_2; ECS1995-04-17_3; ECS1995-04-17_4; ECS1995-04-17_5; ECS1995-10-01_1; ECS1995-10-01_10; ECS1995-10-01_11; ECS1995-10-01_12; ECS1995-10-01_13; ECS1995-10-01_2; ECS1995-10-01_3; ECS1995-10-01_4; ECS1995-10-01_5; ECS1995-10-01_6; ECS1995-10-01_7; ECS1995-10-01_8; ECS1995-10-01_9; ECS1996-01-04; ECS1996-04-26_1; ECS1996-04-26_2; ECS1996-04-26_3; ECS1996-04-26_4; ECS1996-04-26_5; ECS1996-04-26_6; ECS1996-04-26_7; ECS1996-04-26_8; ECS1996-04-26_9; Event label; GOFLO; Go-Flo bottles; Gomez2004-10-26; Gomez2004-10-30; Gomez2004-11-03; Gomez2004-11-07; Gomez2004-11-11; Gomez2004-11-15; Gomez2004-11-19; Gomez2004-11-23; Gomez2004-11-27; Gomez2004-12-01; Gomez2004-12-05; Gomez2004-12-09; HakuhoMaru2002-12-07; HakuhoMaru2002-12-09; HakuhoMaru2002-12-11; HakuhoMaru2002-12-13; HakuhoMaru2002-12-15; HakuhoMaru2002-12-17; HakuhoMaru2002-12-18; Heterocyst, biomass; Indian Ocean; Iron; Latitude of event; Longitude of event; MAREMIP; MARine Ecosystem Model Intercomparison Project; Measured at sea surface; Meville2002-06-24; Meville2002-06-26; Meville2002-06-28; Meville2002-06-30; Meville2002-07-02; Meville2002-07-03; Meville2002-07-04; Meville2002-07-05; Meville2002-07-06; Meville2002-07-07; Meville2002-07-08; Meville2002-07-11; Meville2002-07-12; Mirai2003-01-15; Mirai2003-01-17; Mirai2003-01-18; Mirai2003-01-20; Mirai2003-01-21; Mirai2003-01-23; Mirai2003-01-24; Mirai2003-01-26; Mirai2003-01-28; MP-6; MP-6_01; MP-6_02; MP-6_03; MP-6_04; MP-6_05; MP-6_06; MP-6_07; MP-6_08; MP-6_09; MP-6_10; MP-6_11; MP-6_12; MP-6_13; MP-6_14; MP-6_15; MP-6_16; MP-6_17; MP-6_19; MP-6_20; MP-6_21; MP-6_22; MP-6_23; MP-9; MP-9_01; MP-9_02; MP-9_03; MP-9_04; MP-9_05; MP-9_06; MP-9_08; MP-9_09; MP-9_10; MP-9_11; MP-9_12; MP-9_13; MP-9_14; MP-9_15; MP-9_16; MP-9_17; MP-9_18; MP-9_19; MP-9_20; MP-9_21; MP-9_22; MP-9_23; MP-9_24; MP-9_25; MP-9_27; MULT; Multiple investigations; MW19950822_21; NA1975-05-25; NA19750526; NA19750527; NA19750528; NA1975-05-29; NA19750530; NA19750531; NA1975-06-01; NA1975-06-02; NA1975-06-03; NA1975-06-04; NA1975-06-05; NA1975-06-06; NewHorizon2003-08-22; NewHorizon2003-08-25; NewHorizon2003-08-26; NewHorizon2003-08-27; NewHorizon2003-08-28; NewHorizon2003-08-30; NewHorizon2003-08-31; NewHorizon2003-09-01; NewHorizon2003-09-03; NewHorizon2003-09-04; NewHorizon2003-09-05; NewHorizon2003-09-07; NewHorizon2003-09-08; NewHorizon2003-09-09; NewHorizon2003-09-11; NewHorizon2003-09-12; NewHorizon2003-09-13; NewHorizon2003-09-14; NIS; Nitrate; North Atlantic; Northeast Atlantic; North Pacific; North Pacific Ocean; Northwest Pacific; NPO1969-08-28; NPO1969-09-01; NPO1969-09-05; NPO1969-09-09; NPO1969-09-11; NPO1969-09-14; NPO1969-09-17; NPO1969-09-19; NPO1969-09-23; NPO1969-09-27; NPO1969-10-01; NPO1969-10-05; NPO1969-10-10; NWP2002-10-21_1; NWP2002-10-21_2; NWP2002-10-21_3; NWP2002-10-21_4; NWP2002-10-21_5; NWP2004-02-11; NWP2004-02-22; NWP2004-05-05; NWP2004-06-26; NWP2004-06-30; NWP2004-07-04; NWP2004-08-07; NWP2004-11-06; NWP2005-03-31; NWP2005-04-22; NWP2005-04-23; NWP2005-04-24; NWP2005-04-25_1; NWP2005-04-25_2; NWP2005-04-26; NWP2005-04-27; NWP2005-04-28; NWP2005-04-29; NWP2005-04-30_1; NWP2005-04-30_2; NWP2005-05-01; NWP2005-08-10; NWP2005-08-15; NWP2005-11-10; NWP2005-12-26; NWP2006-07-03; NWP2006-10-21; NWP2006-12-20; NWP2006-12-25; NWP2007-01-15; OR-I/414_1; OR-I/414_2; OR-I/448; OR-II/034; OR-II/111_1; OR-II/111_2; OR-II/149_1; OR-II/149_2; Phosphate; Richelia, associated species; Richelia, carbon per cell; Richelia abundance, cells; Roger A. Revelle; RV Kilo Moana; Salinity; Sample comment; Sample method; Sargasso Sea; SargassoSea_1973-09-17; SargassoSea_1973-09-19; SargassoSea_1973-09-20; SargassoSea_1973-09-21; SargassoSea_1973-09-28; SargassoSea_1973-09-29; SargassoSea_1973-10-01; SargassoSea_1973-10-02; SargassoSea_1973-10-03; SargassoSea_1974-02-06; SargassoSea_1974-02-08; SargassoSea_1974-02-11; SargassoSea_1974-02-12; SargassoSea_1974-02-13; SargassoSea_1974-02-14; SargassoSea_1974-02-16; SargassoSea_1974-02-17; SargassoSea_1974-02-18; SargassoSea_1974-02-19; SargassoSea_1974-02-20; SargassoSea_1974-02-21; SargassoSea_1974-02-22; SargassoSea_1974-02-26; SargassoSea_1974-02-27; SargassoSea_1974-03-01; SargassoSea_1974-03-02; SargassoSea_1974-03-03; SargassoSea_1974-03-04; SargassoSea_1974-03-05; SargassoSea_1974-08-08; SargassoSea_1974-08-09; SargassoSea_1974-08-10_1; SargassoSea_1974-08-10_2; SargassoSea_1974-08-11; SargassoSea_1974-08-12; SargassoSea_1974-08-13; SargassoSea_1974-08-14; SargassoSea_1974-08-15; SargassoSea_1974-08-16; SargassoSea_1974-08-17; SargassoSea_1974-08-18; SargassoSea_1974-08-19; SargassoSea_1974-08-20; SargassoSea_1974-08-21; Sarmiento de Gamboa; SCS2000-07-04; SCS2000-07-08; SCS2000-07-12; SCS2000-10-05; SCS2000-10-06; SCS2000-10-07; SCS2000-10-08; SCS2000-10-09; SCS2000-10-10; SCS2000-10-11; SCS2000-10-12; SCS2001-03-21; SCS2001-03-22; SCS2001-03-23; SCS2001-03-24; SCS2001-03-25; SCS2001-03-26; SCS2001-03-27; SCS2001-03-28; SCS2001-03-29; SCS2001-03-30; SCS2001-06-28; SCS2001-06-30; SCS2001-07-02; SCS2001-07-04; SCS2001-07-06; SCS2001-10-23; SCS2001-10-25; SCS2001-10-27; SCS2001-10-29; SCS2001-10-31; SCS2002-03-04; SCS2002-03-05; SCS2002-03-06; SCS2002-03-07; SCS2002-03-08; SCS2002-03-09; SCS2002-03-10;
    Type: Dataset
    Format: text/tab-separated-values, 8546 data points
    Location Call Number Limitation Availability
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  • 12
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Luo, Yawei; Doney, Scott C; Anderson, L A; Benavides, Mar; Berman-Frank, I; Bode, Antonio; Bonnet, S; Boström, Kjärstin H; Böttjer, D; Capone, D G; Carpenter, E J; Chen, Yaw-Lin; Church, Matthew J; Dore, John E; Falcón, Luisa I; Fernández, A; Foster, R A; Furuya, Ken; Gomez, Fernando; Gundersen, Kjell; Hynes, Annette M; Karl, David Michael; Kitajima, Satoshi; Langlois, Rebecca; LaRoche, Julie; Letelier, Ricardo M; Marañón, Emilio; McGillicuddy Jr, Dennis J; Moisander, Pia H; Moore, C Mark; Mouriño-Carballido, Beatriz; Mulholland, Margaret R; Needoba, Joseph A; Orcutt, Karen M; Poulton, Alex J; Rahav, Eyal; Raimbault, Patrick; Rees, Andrew; Riemann, Lasse; Shiozaki, Takuhei; Subramaniam, Ajit; Tyrrell, Toby; Turk-Kubo, Kendra A; Varela, Manuel; Villareal, Tracy A; Webb, Eric A; White, Angelicque E; Wu, Jingfeng; Zehr, Jonathan P (2012): Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth System Science Data, 4, 47-73, https://doi.org/10.5194/essd-4-47-2012
    Publication Date: 2023-12-18
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs nitrogen fixation rates, computed from a collection of source data sets.
    Keywords: 33KB20020923; 33RR20030714; Alis; ALOHA2000-07-26; ALOHA2000-11-30; ALOHA2001-03-21; ALOHA2001-06-14; ALOHA2004-11-28; ALOHA2005-02-02; ALOHA2005-03-05; ALOHA2005-06-15; ALOHA2005-07-16; ALOHA2005-08-14; ALOHA2005-09-09; ALOHA2005-10-08; ALOHA2005-11-16; ALOHA2005-12-13; ALOHA2006-01-25; ALOHA2006-02-15; ALOHA2006-03-10; ALOHA2006-04-01; ALOHA2006-05-26; ALOHA2006-06-13; ALOHA2006-07-12; ALOHA2006-08-08; ALOHA2006-09-15; ALOHA2006-10-19; ALOHA2006-11-08; ALOHA2006-12-09; ALOHA2007-02-06; ALOHA2007-03-20; ALOHA2007-05-03; ALOHA2007-06-09; ALOHA2007-07-07; ALOHA2007-08-02; ALOHA2007-09-02; ALOHA2007-12-20; ALOHA2008-01-28; ALOHA2008-02-23; ALOHA2008-05-27; ALOHA2008-06-25; ALOHA2008-07-26; ALOHA2008-08-17; ALOHA2008-10-11; ALOHA2008-12-01; ALOHA2009-01-21; ALOHA2009-02-18; ALOHA2009-04-29; ALOHA2009-05-28; ALOHA2009-07-04; ALOHA2009-07-25; ALOHA2009-09-26; ALOHA2009-11-05; ALOHA2010-04-08; ALOHA2010-05-20; ALOHA2010-06-10; ALOHA2010-07-10; ALOHA2010-08-09; ALOHA2010-09-05; ALOHA2010-10-05; AMT17/01; AMT17/02; AMT17/03; AMT17/04; AMT17/05; AMT17/06; AMT17/07; AMT17/08; AMT17/09; AMT17/10; Arabian Sea; AT19641122; AT19641123; AT19641202; AT19641203; Atalante20080627; Atalante20080628; Atalante20080704; Atalante20080705; Atalante20080709/1; Atalante20080710; Atalante20080712; Atalante20080713; Atalante20080714; Atlantic; BIOSOPE_EGY; BIOSOPE_GYR; BIOSOPE_HLNC; BIOSOPE_MAR; BIOSOPE_UPW; BIOSOPE04-10-28; BIOSOPE04-10-30; BIOSOPE04-11-03; BIOSOPE04-11-04; BIOSOPE04-11-06; BIOSOPE04-11-07; BIOSOPE04-11-08; BIOSOPE04-11-10; BIOSOPE04-11-12; BIOSOPE04-11-20; BIOSOPE04-11-21; BIOSOPE04-11-23; BIOSOPE04-11-24; BIOSOPE04-11-28; BIOSOPE04-12-01; BIOSOPE04-12-02; BIOSOPE04-12-03; BIOSOPE04-12-04; BIOSOPE04-12-05; Bottle, Niskin; CAIBEX-I; CAIBEX-I_1; CAIBEX-I_2; CAIBEX-I_3; CAIBEX-I_4; CAIBEX-I_5; CAIBEX-I_6; CAIBEX-I_7; CAIBEX-II; CAIBEX-II_01; CAIBEX-II_02; CAIBEX-II_03; CAIBEX-II_04; CAIBEX-II_05; CAIBEX-II_06; CAIBEX-II_07; CAIBEX-II_08; CAIBOX; CAIBOX_01; CAIBOX_02; CAIBOX_03; CAIBOX_04; CAIBOX_05; CAIBOX_06; CAIBOX_07; CAIBOX_08; CAIBOX_09; CAIBOX_10; CAIBOX_11; CAIBOX_12; CAIBOX_13; CAIBOX_14; CAIBOX_15; CAIBOX_16; CAIBOX_17; Calculated; Cape Verde; CATO-I/9; Chlorophyll total, areal concentration; CLIMAX_VII/1973-08-18; CLIMAX_VII/1973-08-27; CLIMAX_VII/1973-08-29; CLIMAX_VII/1973-08-31; CLIMAX_VII/1973-09-02; CLIMAX_VII/1973-09-04; CLIMAX_VII/1973-09-07; CLIMAX_VII/1973-09-09; Cook25_7; CTD/Rosette; CTD-RO; D325_Stn-A-01; D325_Stn-C-01; D325_Stn-D-07; D325_Stn-E-01; D325_Stn-F-07; Date/Time of event; Depth, bottom/max; Depth, top/min; DEPTH, water; Diapalis-3; Diapalis-3_1; Diapalis-3_2; Diapalis-3_3; Diapalis-3_4; Diapalis-4; Diapalis-4_1; Diapalis-4_2; Diapalis-4_3; Diapalis-4_4; Diapalis-5; Diapalis-5_1; Diapalis-5_3; Diapalis-5_4; Diapalis-5_5; Diapalis-6; Diapalis-6_1; Diapalis-6_2; Diapalis-6_3; Diapalis-6_4; Diapalis-6_5; Diapalis-6_6; Diapalis-7; Diapalis-7_1; Diapalis-7_2; Diapalis-7_3; Diapalis-7_4; Diapalis-7_6; Diapalis-7_7; Diapalis-9; Diapalis-9_1; Diapalis-9_2; Diapalis-9_3; Diapalis-9_4; Diapalis-9_5; DIAPAZON_Diapalis-3; DIAPAZON_Diapalis-4; DIAPAZON_Diapalis-5; DIAPAZON_Diapalis-6; DIAPAZON_Diapalis-7; DIAPAZON_Diapalis-9; DYFAMED2003-03-26; DYFAMED2003-03-30; DYFAMED2004-01-25; DYFAMED2004-02-24; DYFAMED2004-04-25; DYFAMED2004-05-27; DYFAMED2004-07-01; DYFAMED2004-07-31; DYFAMED2004-08-31; DYFAMED2004-09-18; DYFAMED2004-10-14; Equatorial Pacific; Event label; GoA_StnA2010-03-18; GOFLO; Go-Flo bottles; Gulf of Aqaba; Gundersen_1; Gundersen_2; Hawaiian Islands, North Central Pacific; Hesperides_03a; Hesperides_05a; Hesperides_06a; Hesperides_07a; Hesperides_08a; Hesperides_12a; Hesperides_13a; Hesperides_14a; Hesperides_17a; Hesperides_18a; Hesperides_19a; Hesperides_20a; Hesperides_21a; Hesperides_23a; Hesperides_24a; Hesperides_25a; Hesperides_26a; Hesperides_27a; Hesperides_28a; Hesperides_29a; Hesperides_30a; Hesperides_31a; Hesperides_32a; Hesperides_33a; Hesperides_34a; Hesperides_36a; Hesperides_37a; Hesperides_38a; Hesperides_39a; Hesperides_40a; Hesperides_41a; Hesperides_42a; Heterocyst, nitrogen fixation rate; Iron; KiloMoana20060609/1; KiloMoana20060609/2; KiloMoana20060821; KiloMoana20060826; KiloMoana20060922; KiloMoana20060923; KiloMoana20060925; KiloMoana20060927; KiloMoana20060930; KiloMoana20061009; Latitude of event; LB2008-09-12; LB2008-09-16; Levantine Basin; Ligurian Sea, Mediterranean; Longitude of event; MAREMIP; MARine Ecosystem Model Intercomparison Project; Measured at sea surface; Mediterranean Sea; Mooring (long time); MOORY; MP-6; MP-6_01; MP-6_02; MP-6_03; MP-6_04; MP-6_05; MP-6_06; MP-6_07; MP-6_08; MP-6_09; MP-6_10; MP-6_11; MP-6_12; MP-6_13; MP-6_14; MP-6_15; MP-6_16; MP-6_18; MP-6_19; MP-6_20; MP-6_21; MP-6_22; MP-6_23; MP-9; MP-9_01; MP-9_02; MP-9_03; MP-9_04; MP-9_05; MP-9_06; MP-9_07; MP-9_09; MP-9_10; MP-9_11; MP-9_12; MP-9_13; MP-9_14; MP-9_15; MP-9_16; MP-9_17; MP-9_18; MP-9_19; MP-9_20; MP-9_21; MP-9_22; MP-9_23; MP-9_24; MP-9_25; MP-9_26; MP-9_27; MR07-01/02; MR07-01/03; MR07-01/04; MR07-01/05; MR07-01/06; MR07-01/07; MR07-01/08; MR07-01/09; MR07-01/10; MR07-01/11; Mulholland_2006-07-01; Mulholland_2006-07-02; Mulholland_2006-07-03; Mulholland_2006-07-04; Mulholland_2006-07-05; Mulholland_2006-07-06; Mulholland_2006-07-07; Mulholland_2006-07-08; Mulholland_2006-07-09; Mulholland_2006-07-10; Mulholland_2006-07-11; Mulholland_2006-07-12; Mulholland_2006-07-13; Mulholland_2006-07-14; Mulholland_2006-10-25; Mulholland_2006-10-26; Mulholland_2006-10-27; Mulholland_2006-10-28; Mulholland_2006-10-29; Mulholland_2006-10-30; Mulholland_2006-10-31; Mulholland_2006-11-01; Mulholland_2006-11-02; Mulholland_2006-11-03; Mulholland_2006-11-04; Mulholland_2006-11-05; Mulholland_2006-11-06; Mulholland_2006-11-07; Mulholland_2006-11-08; Mulholland_2006-11-09; Mulholland_2008-05-03_1; Mulholland_2008-05-04_1; Mulholland_2008-05-05_1; Mulholland_2008-05-05_2; Mulholland_2008-05-06_1; Mulholland_2008-05-07_1; Mulholland_2008-05-10_1; Mulholland_2008-05-11_1; Mulholland_2008-05-12_1; Mulholland_2008-05-13_1; Mulholland_2008-05-14_1; Mulholland_2008-05-15_1; Mulholland_2008-05-16_1; Mulholland_2008-05-17_1; Mulholland_2008-05-18_1; Mulholland_2008-05-19_1; Mulholland_2008-05-20_1; Mulholland_2008-05-21_1; Mulholland_2008-05-22_1; Mulholland_2008-05-24_1; Mulholland_2009-08-17_1; Mulholland_2009-08-18_1; Mulholland_2009-08-18_2; Mulholland_2009-08-19_1; Mulholland_2009-08-19_2; Mulholland_2009-08-20_1; Mulholland_2009-08-20_3; Mulholland_2009-08-21_1; Mulholland_2009-08-21_3; Mulholland_2009-08-22_1; Mulholland_2009-08-22_3; Mulholland_2009-08-23; Mulholland_2009-08-24_1; Mulholland_2009-08-24_3; Mulholland_2009-08-25_3; Mulholland_2009-08-26_3; Mulholland_2009-08-27_2; Mulholland_2009-08-27_3; Mulholland_2009-11-04_2; Mulholland_2009-11-05_1; Mulholland_2009-11-08_1; Mulholland_2009-11-09_3; Mulholland_2009-11-10_3; Mulholland_2009-11-11_1; Mulholland_2009-11-18_1; Mulholland_2009-11-18_3; NA19750526; NA19750527; NA19750528; NA19750530; NA19750531; NIS; Nitrate; Nitrogen fixation rate, integrated per day; Nitrogen fixation rate, whole seawater; North Atlantic; Northeast Atlantic; North Pacific; Pacific; Phosphate; PUMP; Rahav_2009-07-13_1; Rahav_2009-07-14_1; Rahav_2009-07-16_1; Rahav_2009-12-07_1; Rees2004-03-05/01; Rees2004-04-05; Rees2004-05-16; Rees2004-05-19/01; Rees2004-05-21/01; Rees2004-07-05/01; Rees2004-09-05/01; Roger A. Revelle; RV Kilo Moana; Salinity; Sample comment; Sample method; Sargasso Sea; SargassoSea_1973-09-17; SargassoSea_1973-09-19; SargassoSea_1973-09-20; SargassoSea_1973-09-21; SargassoSea_1973-09-28; SargassoSea_1973-09-29; SargassoSea_1973-10-01; SargassoSea_1973-10-02; SargassoSea_1973-10-03; SargassoSea_1974-02-06; SargassoSea_1974-02-08; SargassoSea_1974-02-11; SargassoSea_1974-02-13; SargassoSea_1974-02-14; SargassoSea_1974-02-16; SargassoSea_1974-02-17; SargassoSea_1974-02-18; SargassoSea_1974-02-19; SargassoSea_1974-02-20; SargassoSea_1974-02-21; SargassoSea_1974-02-26;
    Type: Dataset
    Format: text/tab-separated-values, 5926 data points
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  • 13
    Publication Date: 2024-03-30
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present collection presents the original data sets used to compile Global distributions of diazotrophs abundance, biomass and nitrogen fixation rates
    Keywords: MAREDAT_Diazotrophs_Collection; MAREMIP; MARine Ecosystem Model Intercomparison Project
    Type: Dataset
    Format: application/zip, 94 datasets
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  • 14
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    Unknown
    PANGAEA
    In:  Supplement to: Luo, Yawei; Doney, Scott C; Anderson, L A; Benavides, Mar; Berman-Frank, I; Bode, Antonio; Bonnet, S; Boström, Kjärstin H; Böttjer, D; Capone, D G; Carpenter, E J; Chen, Yaw-Lin; Church, Matthew J; Dore, John E; Falcón, Luisa I; Fernández, A; Foster, R A; Furuya, Ken; Gomez, Fernando; Gundersen, Kjell; Hynes, Annette M; Karl, David Michael; Kitajima, Satoshi; Langlois, Rebecca; LaRoche, Julie; Letelier, Ricardo M; Marañón, Emilio; McGillicuddy Jr, Dennis J; Moisander, Pia H; Moore, C Mark; Mouriño-Carballido, Beatriz; Mulholland, Margaret R; Needoba, Joseph A; Orcutt, Karen M; Poulton, Alex J; Rahav, Eyal; Raimbault, Patrick; Rees, Andrew; Riemann, Lasse; Shiozaki, Takuhei; Subramaniam, Ajit; Tyrrell, Toby; Turk-Kubo, Kendra A; Varela, Manuel; Villareal, Tracy A; Webb, Eric A; White, Angelicque E; Wu, Jingfeng; Zehr, Jonathan P (2012): Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth System Science Data, 4, 47-73, https://doi.org/10.5194/essd-4-47-2012
    Publication Date: 2024-03-30
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs Gamma-A nifH genes abundance, computed from a collection of source data sets.
    Keywords: 06MT60_5; 06MT60_5/158; 06MT60_5/159; 06MT60_5/161; 06MT60_5/173; 06MT60_5/180; 06MT60_5/181; 06MT60_5/184; 06MT60_5/187; 06MT60_5/188; 06MT60_5/189; 06MT60_5/190; 06MT60_5/192; 06MT60_5/199; ALOHA2002-12-13; ALOHA2002-12-14; ALOHA2005-07-16; ALOHA2005-07-26_01; ALOHA2005-07-26_02; ALOHA2005-07-26_03; ALOHA2005-07-26_04; ALOHA2005-07-26_05; ALOHA2005-07-26_06; ALOHA2005-07-26_07; ALOHA2005-07-26_08; ALOHA2005-08-13; Arabian Sea; Bottle, Niskin; Calculated; Calothrix, abundance expressed in number of nifH gene copies; Calothrix, associated species; Calothrix, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; CD132; CD132 _AMBITION; CD132_AMBITION/1; CD132_AMBITION/2; CD132_AMBITION/3; CD132_AMBITION/4; CD132/1; CD132/2; CD132/3; CD132/4; Charles Darwin; China Sea; Chlorophyll a; CTD, Seabird; CTD/Rosette; CTD-R; CTD-RO; Date/Time of event; Depth, bottom/max; Depth, top/min; DEPTH, water; Diazotrophs, total biomass as carbon; Eastern equatorial Atlantic; EEA2007-06-14_Stn8; EEA2007-06-15_Stn9; Event label; Foster2008-07-12; Foster2008-07-14; Foster2008-07-15; Foster2008-07-18; Hawaiian Islands, North Central Pacific; Heterocyst, biomass; In situ pump; Iron; ISP; Latitude of event; Longitude of event; M55_30a; M55_36a; M55_38a; M55_44a; M55_45; M55_48a; M55/1; M60/5; M60/5_158; M60/5_159; M60/5_161; M60/5_163a; M60/5_173; M60/5_180; M60/5_181; M60/5_184; M60/5_187; M60/5_188; M60/5_189; M60/5_190; M60/5_192; M60/5_199; MAREMIP; MARine Ecosystem Model Intercomparison Project; Measured at sea surface; Meteor (1986); NIS; Nitrate; North Atlantic sub-tropical gyre; North Pacific; Phosphate; Proteobacteria, abundance expressed in number of nifH gene copies; Richelia, abundance expressed in number of nifH gene copies; Richelia, associated species; Richelia, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; Salinity; Sample comment; Sample method; SCS2009-08-10; SCS2009-08-15; SCS2009-08-20; SCS2009-08-25; SO187/2; SO187/2_33-1; SO187/2_44-1; SO187/2_45-1-1a; SO187/2_45-4; SO187/2_46-1; SO187/2_48-1; SO187/2_53-1a; SO187/2_54-2; Sonne; South China Sea; South Pacific Ocean; SPO2003-03-17; SPO2003-03-18; SPO2003-03-19; SPO2003-03-20; SPO2003-03-21; SPO2003-03-22-1; SPO2003-03-22-2; SPO2003-03-24; SPO2003-03-25; SPO2003-03-28; SPO2003-03-29; SPO2003-03-30; SPO2003-03-31; SPO2003-04-02; SPO2003-04-03; SPO2003-04-05; SPO2003-04-06; SPO2003-04-07; SPO2003-04-08; SPO2003-04-09; SPO2003-04-10; SPO2003-04-12; SPO2003-04-13; SW2006-06-22; SW2006-06-23; SW2006-06-27; SW2006-06-28; SW2006-06-29; SW2006-06-30; SW2006-07-01; SW2006-07-03; SW2006-07-04; SW2006-07-06; SW2006-07-07; SW2006-07-12; SW2006-07-13; SW2006-07-14_1; SW2006-07-14_2; SW2006-07-15; SW2006-07-17; SW2006-07-19; SW2006-07-20; SW2006-07-21; Temperature, water; Trichodesmium, abundance expressed in number of nifH gene copies; Trichodesmium, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; Trichodesmium, biomass as carbon; Tropical Atlantic; Unicellular cyanobacteria, biomass; Unicellular cyanobacteria-A, abundance expressed in number of nifH gene copies; Unicellular cyanobacteria-B, abundance expressed in number of nifH gene copies; Unicellular cyanobacteria-B, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; Unicellular cyanobacteria-C, abundance expressed in number of nifH gene copies; Unicellular cyanobacteria-C, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; Uniform resource locator/link to source data file; VIETNAM; Water sample; WesternFlyer2005-10-25; Western tropical north Atlantic; WS; WTNA2003-04-24_01; WTNA2003-04-26; WTNA2003-05-01; WTNA2003-05-04; WTNA2003-05-11; WTNA2003-05-12; WTNA2003-05-13; WTNA2003-05-14; WTNA2003-05-18; WTNA2003-05-20
    Type: Dataset
    Format: text/tab-separated-values, 2032 data points
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  • 15
    Publication Date: 2022-05-25
    Description: Author Posting. © The Oceanography Society, 2014. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 27, no. 2 (2014): 18-23, doi:10.5670/oceanog.2014.56.
    Description: Continental shelves and the waters overlying them support numerous industries as diverse as tourism and recreation, energy extraction, fisheries, transportation, and applications of marine bio-molecules (e.g., agribusiness, food processing, pharmaceuticals). Although these shelf ecosystems exhibit impacts of climate change and increased human use of resources (Halpern et al., 2012; IPCC, 2013, 2014; Melillo et al., 2014), there are currently no standardized metrics for assessing changes in ecological function in the coastal ocean. Here, we argue that it is possible to monitor vital signs of ecosystem function by focusing on the lowest levels of the ocean food web. Establishment of biodiversity, biomass, and primary productivity baselines and continuous evaluation of changes in biological resources in these economically and ecologically valuable regions requires an internationally coordinated monitoring effort that fully integrates natural, social, and economic sciences to jointly identify problems and design solutions. Such an ocean observing network is needed to protect the livelihoods of coastal communities in the context of the goals of the Future Earth program (Mooney et al., 2013) and of the Intergovernmental Platform on Biodiversity and Ecosystem Services (http://www.ipbes.net). The tools needed to initiate these assessments are available today.
    Description: AEW and RML have been supported by C-MORE (NSF) and the Gordon and Betty Moore and Alfred P. Sloan Foundations. FMK and EM have been supported by NASA, NOAA, NSF, and EPA. FPC was supported by the David and Lucile Packard Foundation and NASA. HMS was supported by NASA and the Gordon and Betty Moore Foundation. EMJ received support from NOAA. MB received support from the NSF. MTK and SCD acknowledge support from C-MORE (NSF). MWL was supported by NSF and NASA. WMB was supported by NASA and NSF.
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
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  • 16
    Publication Date: 2022-05-25
    Description: Author Posting. © American Geophysical Union, 2015. 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 29 (2015): 1145–1164, doi:10.1002/2015GB005141.
    Description: Time-series observations are critical to understand the structure, function, and dynamics of marine ecosystems. The Hawaii Ocean Time-series program has maintained near-monthly sampling at Station ALOHA (22°45′N, 158°00′W) in the oligotrophic North Pacific Subtropical Gyre (NPSG) since 1988 and has identified ecosystem variability over seasonal to interannual timescales. To further extend the temporal resolution of these near-monthly time-series observations, an extensive field campaign was conducted during July–September 2012 at Station ALOHA with near-daily sampling of upper water-column biogeochemistry, phytoplankton abundance, and activity. The resulting data set provided biogeochemical measurements at high temporal resolution and documents two important events at Station ALOHA: (1) a prolonged period of low productivity when net community production in the mixed layer shifted to a net heterotrophic state and (2) detection of a distinct sea-surface salinity minimum feature which was prominent in the upper water column (0–50 m) for a period of approximately 30 days. The shipboard observations during July–September 2012 were supplemented with in situ measurements provided by Seagliders, profiling floats, and remote satellite observations that together revealed the extent of the low productivity and the sea-surface salinity minimum feature in the NPSG.
    Description: NOAA Climate Observation Division; National Science Foundation (NSF) Center for Microbial Oceanography: Research and Education (C-MORE) Grant Numbers: EF0424599, OCE-1153656, OCE-1260164; Gordon and Betty Moore Foundation Marine Microbiology Investigator
    Description: 2016-02-13
    Keywords: Primary productivity ; Microbial ecology ; Station ALOHA ; Temporal variability ; Biogeochemistry
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
    Format: application/msword
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  • 17
    Publication Date: 2022-05-25
    Description: Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Progress in Oceanography 120 (2014): 291-304, doi:10.1016/j.pocean.2013.10.013.
    Description: Comparative analyses of oceanic ecosystems require an objective framework to define coherent study regions and scale the patterns and processes observed within them. We applied the hierarchical patch mosaic paradigm of landscape ecology to the study of the seasonal variability of the North Pacific to facilitate comparative analysis between pelagic ecosystems and provide spatiotemporal context for Eulerian time-series studies. Using 13-year climatologies of sea surface temperature (SST), photosynthetically active radiation (PAR), and chlorophyll a (chl-a), we classified seascapes in environmental space that were monthly-resolved, dynamic and nested in space and time. To test the assumption that seascapes represent coherent regions with unique biogeochemical function and to determine the hierarchical scale that best characterized variance in biogeochemical parameters, independent data sets were analyzed across seascapes using analysis of variance (ANOVA), nested-ANOVA and multiple linear regression (MLR) analyses. We also compared the classification efficiency (as defined by the ANOVA F-statistic) of resultant dynamic seascapes to a commonly-used static classification system. Variance of nutrients and net primary productivity (NPP) were well characterized in the first two levels of hierarchy of eight seascapes nested within three superseascapes (R2 = 0.5-0.7). Dynamic boundaries at this level resulted in a nearly 2-fold increase in classification efficiency over static boundaries. MLR analyses revealed differential forcing on pCO2 across seascapes and hierarchical levels and a 33 % reduction in mean model error with increased partitioning (from 18.5 μatm to 12.0 μatm pCO2). Importantly, the empirical influence of seasonality was minor across seascapes at all hierarchical levels, suggesting that seascape partitioning minimizes the effect of non-hydrographic variables. As part of the emerging field of pelagic seascape ecology, this effort provides an improved means of monitoring and comparing oceanographic biophysical dynamics and an objective, quantitative basis by which to scale data from local experiments and observations to regional and global biogeochemical cycles.
    Description: This project was partially funded by a NASA ESS fellowship NNX07A032H (MTK), an AAAS/ NPS scholarship (MTK), and funds from the NSF Science and Technology Center for Microbial Oceanography: Research and Education (C-MORE, RML and AW).
    Keywords: North Pacific ; Seascapes ; Seasonal variations ; Pelagic environment ; Biogeochemistry ; Models
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
    Format: application/pdf
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  • 18
    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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  • 19
    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 Benway, H. M., Lorenzoni, L., White, A. E., Fiedler, B., Levine, N. M., Nicholson, D. P., DeGrandpre, M. D., Sosik, H. M., Church, M. J., O'Brien, T. D., Leinen, M., Weller, R. A., Karl, D. M., Henson, S. A., & Letelier, R. M. Ocean time series observations of changing marine ecosystems: An era of integration, synthesis, and societal applications. Frontiers in Marine Science, 6, (2019): 393, doi:10.3389/fmars.2019.00393.
    Description: Sustained ocean time series are critical for characterizing marine ecosystem shifts in a time of accelerating, and at times unpredictable, changes. They represent the only means to distinguish between natural and anthropogenic forcings, and are the best tools to explore causal links and implications for human communities that depend on ocean resources. Since the inception of sustained ocean observations, ocean time series have withstood many challenges, most prominently availability of uninterrupted funding and retention of trained personnel. This OceanObs’19 review article provides an overarching vision for sustained ocean time series observations for the next decade, focusing on the growing challenges of maintaining sustained ocean time series, including ship-based and autonomous coastal and open-ocean platforms, as well as remote sensing. In addition to increased diversification of funding sources to include the private sector, NGOs, and other groups, more effective engagement of stakeholders and other end-users will be critical to ensure the sustainability of ocean time series programs. Building a cohesive international time series network will require dedicated capacity to coordinate across observing programs and leverage existing infrastructure and platforms of opportunity. This review article outlines near-term observing priorities and technology needs; explores potential mechanisms to broaden ocean time series data applications and end-user communities; and describes current tools and future requirements for managing increasingly complex multi-platform data streams and developing synthesis products that support science and society. The actionable recommendations outlined herein ultimately form the basis for a robust, sustainable, fit-for-purpose time series network that will foster a predictive understanding of changing ocean systems for the benefit of society.
    Description: This work was led by HB in the Ocean Carbon and Biogeochemistry (OCB) Project Office, which is supported by the NSF OCE (1558412) and the NASA (NNX17AB17G).
    Keywords: Ocean time series ; Marine ecosystems ; Climate ; End-users ; Synthesis ; Sustained observations
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 20
    Publication Date: 2022-05-26
    Description: © International Council for the Exploration of the Sea, 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in ICES Journal of Marine Science 73 (2016): 1839-1850, doi: 10.1093/icesjms/fsw086.
    Description: For terrestrial and marine benthic ecologists, landscape ecology provides a framework to address issues of complexity, patchiness, and scale—providing theory and context for ecosystem based management in a changing climate. Marine pelagic ecosystems are likewise changing in response to warming, changing chemistry, and resource exploitation. However, unlike spatial landscapes that migrate slowly with time, pelagic seascapes are embedded in a turbulent, advective ocean. Adaptations from landscape ecology to marine pelagic ecosystem management must consider the nature and scale of biophysical interactions associated with organisms ranging from microbes to whales, a hierarchical organization shaped by physical processes, and our limited capacity to observe and monitor these phenomena across global oceans. High frequency, multiscale, and synoptic characterization of the 4-D variability of seascapes are now available through improved classification methods, a maturing array of satellite remote sensing products, advances in autonomous sampling of multiple levels of biological complexity, and emergence of observational networks. Merging of oceanographic and ecological paradigms will be necessary to observe, manage, and conserve species embedded in a dynamic seascape mosaic, where the boundaries, extent, and location of features change with time.
    Description: This work was supported by 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), the NOAA Integrated Ocean Observing System (IOOS) Program Office, and the LenFest Ocean Program.
    Keywords: Biodiversity ; Conservation ; Landscape ; Ocean observations ; Pelagic ; Phytoplankton ; Seascape
    Repository Name: Woods Hole Open Access Server
    Type: Article
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