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| Dataset Title: | [Particles and Zooplankton AA-CSIA] - Particles and Zooplankton Amino Acid Compound Specific Isotope Analyses (AA-CSIA) and zooplankton biomass at Station ALOHA and the Equatorial Pacific from R/V Kilo Moana cruises KM1407, KM1418, & KM1515 from 2014-2015 (Evaluating the relative importance of suspended and sinking particles to the meso and bathypelagic food web in the central North Pacific) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_806502) |
| Information: | Summary
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| ISO 19115
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Attributes {
s {
Cruise {
String bcodmo_name "cruise_id";
String description "Cruise identifier";
String long_name "Cruise";
String units "unitless";
}
Date_initial {
String bcodmo_name "date";
String description "Sampling initial date (UTC); format: yyyy-mm-dd";
String long_name "Date Initial";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
String source_name "Date_initial";
String time_precision "1970-01-01";
String units "unitless";
}
Date_final {
String bcodmo_name "date";
String description "Sampling final date (UTC); format: yyyy-mm-dd";
String long_name "Date Final";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
String time_precision "1970-01-01";
String units "unitless";
}
Site {
String bcodmo_name "site";
String description "Site identifier";
String long_name "Site";
String units "unitless";
}
latitude {
String _CoordinateAxisType "Lat";
Float64 _FillValue NaN;
Float64 actual_range 5.0, 22.0;
String axis "Y";
String bcodmo_name "latitude";
Float64 colorBarMaximum 90.0;
Float64 colorBarMinimum -90.0;
String description "Latitude";
String ioos_category "Location";
String long_name "Latitude";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
String standard_name "latitude";
String units "degrees_north";
}
longitude {
String _CoordinateAxisType "Lon";
Float64 _FillValue NaN;
Float64 actual_range -158.0, -155.0;
String axis "X";
String bcodmo_name "longitude";
Float64 colorBarMaximum 180.0;
Float64 colorBarMinimum -180.0;
String description "Longitude";
String ioos_category "Location";
String long_name "Longitude";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
String standard_name "longitude";
String units "degrees_east";
}
Season {
String bcodmo_name "season";
String description "Season: Winter = February; Summer = August and September";
String long_name "Season";
String units "unitless";
}
DayNight {
String bcodmo_name "time_of_day";
String description "DayNight: Day = 9:00 – 15:00; Night = 20:00 – 3:00";
String long_name "Day Night";
String units "unitless";
}
Type {
String bcodmo_name "sample_descrip";
String description "Type of sample";
String long_name "Type";
String units "unitless";
}
SizeFraction_min {
Float32 _FillValue NaN;
Float32 actual_range 0.7, 5000.0;
String bcodmo_name "samp_fraction";
Float64 colorBarMaximum 1.0;
Float64 colorBarMinimum 0.0;
String description "Minimum size fraction of a sample";
String long_name "Size Fraction Min";
String units "micrometers (um)";
}
SizeFraction_max {
Int16 _FillValue 32767;
Int16 actual_range 53, 5000;
String bcodmo_name "samp_fraction";
Float64 colorBarMaximum 1.0;
Float64 colorBarMinimum 0.0;
String description "Maximum size fraction of a sample";
String long_name "Size Fraction Max";
String units "micrometers (um)";
}
depth {
String _CoordinateAxisType "Height";
String _CoordinateZisPositive "down";
Float64 _FillValue NaN;
Float64 actual_range 7.0, 1250.0;
String axis "Z";
String bcodmo_name "depth";
Float64 colorBarMaximum 8000.0;
Float64 colorBarMinimum -8000.0;
String colorBarPalette "TopographyDepth";
String description "Sampling depth";
String ioos_category "Location";
String long_name "Depth";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
String positive "down";
String standard_name "depth";
String units "m";
}
DepthInterval_max {
Int16 _FillValue 32767;
Int16 actual_range 50, 1500;
String bcodmo_name "depth";
Float64 colorBarMaximum 8000.0;
Float64 colorBarMinimum -8000.0;
String colorBarPalette "TopographyDepth";
String description "Maximum depth of MOCNESS net";
String long_name "Depth";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
String standard_name "depth";
String units "meters (m)";
}
DepthInterval_min {
Int16 _FillValue 32767;
Int16 actual_range 0, 1000;
String bcodmo_name "depth";
Float64 colorBarMaximum 8000.0;
Float64 colorBarMinimum -8000.0;
String colorBarPalette "TopographyDepth";
String description "Minimum depth of MOCNESS net";
String long_name "Depth";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
String standard_name "depth";
String units "meters (m)";
}
d15NSrcAA {
Float32 _FillValue NaN;
Float32 actual_range -3.1, 12.4;
String bcodmo_name "d15N_bio";
String description "Average nitrogen isotopic composition of source amino acids: phenylalanine, serine, glycine and lysine of a sample";
String long_name "D15 NSrc AA";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/IRBO/";
String units "‰, vs AIR";
}
Propagation_error {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 2.7;
String bcodmo_name "d15N_bio";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Propagation error of the replicate measurement of nitrogen isotopic composition of source amino acids of a sample";
String long_name "Propagation Error";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/IRBO/";
String units "‰, vs AIR";
}
POC {
Float32 _FillValue NaN;
Float32 actual_range 0.21, 22.04;
String bcodmo_name "POC";
String description "Particulate Organic Carbon";
String long_name "Particulate Organic Carbon";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CORGCAP1/";
String units "micrograms per liter (ug L-1)";
}
PC {
Float32 _FillValue NaN;
Float32 actual_range 0.3, 25.19;
String bcodmo_name "C";
String description "Total Particulate Carbon";
String long_name "PC";
String units "ug L-1";
}
Biomass {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 4.31;
String bcodmo_name "biomass";
String description "Zooplankton biomass";
String long_name "Biomass";
String units "mg DW m-3";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
String acquisition_description
"Particles were collected using in situ McLane pumps equipped with mini-MULVFS
(Bishop et al. 2012) 2-tiered filter holders. Particle collection captured
sequentially large (>53 \\u00b5m) particulates on acid-cleaned Nitex mesh
filters and small particles (<53 \\u00b5m) on pre-combusted GFF or QMA filters
at discrete depths for amino acid compound-specific isotope analysis (AA CSIA)
and for particulate carbon and nitrogen. This method is designed to exclude
motile metazoans but include all other ambient, non-swimming particulate
matter (see Bishop et al. 2012). Immediately after collection, large particles
were rinsed off of the Nitex screens and onto pre-combusted 25-mm QMA filters
using 0.2 \\u00b5m filtered seawater. All filters were frozen at -20\\u00b0C or
-80\\u00b0C as soon as possible after collection.
Zooplankton were collected using a 1-m\\u00b2 MOCNESS net during the day
(~09:00-15:00) and at night (~20:00-03:00). Night and day tows were repeated
to obtain replicate samples for biomass and isotopic analyses. Upon
collection, each sample was size-fractionated using 0.2, 0.5, 1.0, 2.0 and 5.0
mm mesh sieves, filtered onto pre-weighed 47 mm filters of 0.2 mm Nitex mesh
and stored frozen at -80\\u00baC.
For nitrogen isotope composition of the amino acids, particles and zooplankton
samples were freeze dried and analyzed following the methods Hannides et al.
(2013).\\u00a0\\u03b4\\u00b9\\u2075N values of source amino acids (
\\u03b4\\u00b9\\u2075NSrc\\u208bAA ) for both particles and zooplankton were
calculated as the average \\u03b4\\u00b9\\u2075N of: serine, phenylalanine,
lysine and glycine. Freeze-dried zooplankton filters for all size fractions
were weighed to calculate zooplankton biomass (mg in dry weight/m\\u00b3) at
each depth during the day and at nighttime.";
String awards_0_award_nid "537122";
String awards_0_award_number "OCE-1333734";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1333734";
String awards_0_funder_name "NSF Division of Ocean Sciences";
String awards_0_funding_acronym "NSF OCE";
String awards_0_funding_source_nid "355";
String awards_0_program_manager "David L. Garrison";
String awards_0_program_manager_nid "50534";
String awards_1_award_nid "560590";
String awards_1_award_number "OCE-1433846";
String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1433846";
String awards_1_funder_name "NSF Division of Ocean Sciences";
String awards_1_funding_acronym "NSF OCE";
String awards_1_funding_source_nid "355";
String awards_1_program_manager "Donald L. Rice";
String awards_1_program_manager_nid "51467";
String cdm_data_type "Other";
String comment
"Particles and Zooplankton AA-CSIA
PI: Brian N. Popp (University of Hawaii)
Co-PI: Jeffrey C. Drazen (Univeristy of Hawaii)
Version date: 19-March-2020";
String Conventions "COARDS, CF-1.6, ACDD-1.3";
String creator_email "info@bco-dmo.org";
String creator_name "BCO-DMO";
String creator_type "institution";
String creator_url "https://www.bco-dmo.org/";
String data_source "extract_data_as_tsv version 2.3 19 Dec 2019";
String date_created "2020-03-19T15:29:51Z";
String date_modified "2020-03-31T21:14:41Z";
String defaultDataQuery "&time<now";
String doi "10.26008/1912/bco-dmo.806502.1";
Float64 Easternmost_Easting -155.0;
Float64 geospatial_lat_max 22.0;
Float64 geospatial_lat_min 5.0;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max -155.0;
Float64 geospatial_lon_min -158.0;
String geospatial_lon_units "degrees_east";
Float64 geospatial_vertical_max 1250.0;
Float64 geospatial_vertical_min 7.0;
String geospatial_vertical_positive "down";
String geospatial_vertical_units "m";
String history
"2024-11-23T17:11:34Z (local files)
2024-11-23T17:11:34Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_806502.html";
String infoUrl "https://www.bco-dmo.org/dataset/806502";
String institution "BCO-DMO";
String instruments_0_acronym "IR Mass Spec";
String instruments_0_dataset_instrument_description "Nitrogen isotope composition of the amino acids determined using an isotope ratio mass spectrometer (Thermo Scientific Delta V Plus or Thermo Scientific MAT 253 IRMS) interfaced with a Thermo Finnigan GC-C III.";
String instruments_0_dataset_instrument_nid "806549";
String instruments_0_description "The Isotope-ratio Mass Spectrometer is a particular type of mass spectrometer used to measure the relative abundance of isotopes in a given sample (e.g. VG Prism II Isotope Ratio Mass-Spectrometer).";
String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB16/";
String instruments_0_instrument_name "Isotope-ratio Mass Spectrometer";
String instruments_0_instrument_nid "469";
String instruments_0_supplied_name "isotope ratio mass spectrometer";
String instruments_1_acronym "MULVFS";
String instruments_1_dataset_instrument_description "Particles were collected using in situ McLane pumps equipped with mini-MULVFS (Bishop et al. 2012) 2-tiered filter holders.";
String instruments_1_dataset_instrument_nid "806547";
String instruments_1_description "The Multiple Unit Large Volume Filtration System (MULVFS), consists of multiple (commonly 12) specialized particulate matter pumps, mounted in a frame and tethered to the ship by a cable (Bishop et al., 1985; Bishop and Wood, 2008). The MULVFS filters particulates from large volumes of seawater, although the exact protocols followed will vary for each project.";
String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/32/";
String instruments_1_instrument_name "Multiple Unit Large Volume Filtration System";
String instruments_1_instrument_nid "509";
String instruments_1_supplied_name "MULVFS";
String instruments_2_acronym "MOCNESS";
String instruments_2_dataset_instrument_description "Multiple opening-closing net and environmental sensing system (MOCNESS; Wiebe et al. 1976) net with 1 m2 opening using 0.2 mm mesh plankton nets.";
String instruments_2_dataset_instrument_nid "806548";
String instruments_2_description "The Multiple Opening/Closing Net and Environmental Sensing System or MOCNESS is a family of net systems based on the Tucker Trawl principle. There are currently 8 different sizes of MOCNESS in existence which are designed for capture of different size ranges of zooplankton and micro-nekton Each system is designated according to the size of the net mouth opening and in two cases, the number of nets it carries. The original MOCNESS (Wiebe et al, 1976) was a redesigned and improved version of a system described by Frost and McCrone (1974).(from MOCNESS manual) This designation is used when the specific type of MOCNESS (number and size of nets) was not specified by the contributing investigator.";
String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/NETT0097/";
String instruments_2_instrument_name "MOCNESS";
String instruments_2_instrument_nid "511";
String instruments_2_supplied_name "1-m2 MOCNESS";
String instruments_3_acronym "McLane Pump";
String instruments_3_dataset_instrument_description "Particles were collected using in situ McLane pumps equipped with mini-MULVFS (Bishop et al. 2012) 2-tiered filter holders.";
String instruments_3_dataset_instrument_nid "806546";
String instruments_3_description "McLane pumps sample large volumes of seawater at depth. They are attached to a wire and lowered to different depths in the ocean. As the water is pumped through the filter, particles suspended in the ocean are collected on the filters. The pumps are then retrieved and the contents of the filters are analyzed in a lab.";
String instruments_3_instrument_name "McLane Pump";
String instruments_3_instrument_nid "627";
String instruments_3_supplied_name "McLane pumps";
String keywords "bco, bco-dmo, biological, biomass, carbon, chemical, cruise, d15, d15NSrcAA, data, dataset, date, Date_final, day, DayNight, depth, DepthInterval_max, DepthInterval_min, dmo, erddap, error, final, fraction, initial, latitude, longitude, management, max, min, night, nsrc, oceanography, office, organic, particulate, POC, preliminary, propagation, Propagation_error, season, site, size, SizeFraction_max, SizeFraction_min, time, type";
String license "https://www.bco-dmo.org/dataset/806502/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/806502";
Float64 Northernmost_Northing 22.0;
String param_mapping "{'806502': {'Latitude': 'flag - latitude', 'Depth': 'flag - depth', 'Longitude': 'flag - longitude'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/806502/parameters";
String people_0_affiliation "University of Hawaii at Manoa";
String people_0_affiliation_acronym "SOEST";
String people_0_person_name "Brian N. Popp";
String people_0_person_nid "51093";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "University of Hawaii at Manoa";
String people_1_affiliation_acronym "SOEST";
String people_1_person_name "Jeffrey C. Drazen";
String people_1_person_nid "491313";
String people_1_role "Co-Principal Investigator";
String people_1_role_type "originator";
String people_2_affiliation "Woods Hole Oceanographic Institution";
String people_2_affiliation_acronym "WHOI BCO-DMO";
String people_2_person_name "Shannon Rauch";
String people_2_person_nid "51498";
String people_2_role "BCO-DMO Data Manager";
String people_2_role_type "related";
String project "SuspendSinkPart,Hg_Biogeochemistry";
String projects_0_acronym "SuspendSinkPart";
String projects_0_description
"Description from NSF award abstract:
The ocean's midwaters are the largest living space on the planet. The mesopelagic food web plays key roles in the biological carbon pump and the production of food for commercially harvested species, but its functioning is understudied because it is remote and technologically challenging to sample. Recent estimates indicate respiratory demand outstrips measured sinking particle supply by up to 2-3 orders of magnitude suggesting that some food inputs to the mesopelagic food web have been underestimated or missed. Suspended particles frequently are not sampled effectively and may be an overlooked food source. Because identifying the principal inputs of organic matter to the deep-sea food web is critical to understanding its function, the investigators propose to evaluate the relative importance of suspended and sinking particles to the meso- and bathypelagic food web in the central North Pacific. They will characterize the isotopic compositions of specific groups of mesopelagic and bathypelagic zooplankton and micronekton, and identify the extent to which they consume suspended or sinking particles using mass balance approaches. The investigators recently have recognized differences in delta 15N and delta 13C values of amino acids (AA) of sinking and suspended particles; these patterns diverge with depth, providing a means to distinguish between food web pathways. The research will define the source-specific isotopic values of suspended and sinking particles at several depths from the surface to the bathypelagic and test proposed microbial mechanisms driving these depth patterns. At corresponding depths, MOCNESS trawls will sample diverse metazoa: zooplankton size fractions, plus targeted resident, migrating and likely suspension-feeding taxa of zooplankton and micronekton. Preliminary data suggest that suspended particles are a secondary food source, containing less labile organic matter than sinking particles that exhibit a seasonal cycle in flux in the central North Pacific. This study will determine if suspended particles become more important to zooplankton and micronekton during a time of year when sinking particle flux is low (Jan/Feb) in comparison to when it is high (Aug), allowing an evaluation of how temporal change in surface ocean productivity affects the functioning of mesopelagic food webs.
Recent research has called for additional study of the ocean's deep midwaters. This study will provide new insights into the functioning of the meso- and bathypelagic food web and its coupling with surface ocean processes in the central North Pacific. The recently-demonstrated ecological tool of amino acid-specific isotopic analysis will provide a novel and comprehensive approach with which to address our hypotheses, and the project will develop the first AA isotopic dataset spanning particles to fish. Results will help identify the ecological underpinnings of increasing delta 15N values with depth in zooplankton -- apparently a common pattern. Zooplankton consumption of suspended particles also could constitute a mechanistic link between the microbial loop and higher trophic levels. The processes controlling the enormous attenuation of particle flux by mesopelagic consumers -- and thereby the strength of carbon sequestration to the deep ocean -- are not understood. Seasonal sampling will help us relate mesopelagic food web processes to changes in surface ocean productivity, furthering our understanding of future climate change impacts on deep-sea food webs and carbon flux. With regard to fisheries, many oceanic top predators such as tuna and swordfish feed on mesopelagic micronekton. A clearer understanding of the structure of mesopelagic food webs will help inform ecosystem models which are used to understand variation in fisheries production.";
String projects_0_end_date "2016-12";
String projects_0_geolocation "Subtropical waters north of Hawaii; Station Aloha (22° 45'N, 158° 00'W)";
String projects_0_name "Evaluating the relative importance of suspended and sinking particles to the meso and bathypelagic food web in the central North Pacific";
String projects_0_project_nid "537123";
String projects_0_start_date "2014-01";
String projects_1_acronym "Hg_Biogeochemistry";
String projects_1_description
"NSF award abstract:
Mercury is a pervasive trace element that exists in several states in the marine environment, including monomethylmercury (MMHg), a neurotoxin that bioaccumulates in marine organisms and poses a human health threat. Understanding the fate of mercury in the ocean and resulting impacts on ocean food webs requires understanding the mechanisms controlling the depths at which mercury chemical transformations occur. Preliminary mercury analyses on nine species of marine fish from the North Pacific Ocean indicated that intermediate waters are an important entry point for MMHg into open ocean food webs. To elucidate the process controlling this, researchers will examine mercury dynamics in regions with differing vertical dissolved oxygen profiles, which should influence depths of mercury transformation. Results of the study will aid in a better understanding of the pathways by which mercury enters the marine food chain and can ultimately impact humans. This project will provide training for graduate and undergraduate students, and spread awareness on oceanic mercury through public outreach and informal science programs.
Mercury isotopic variations can provide insight into a wide variety of environmental processes. Isotopic compositions of mercury display mass-dependent fractionation (MDF) during most biotic and abiotic chemical reactions and mass-independent fractionation (MIF) during photochemical radical pair reactions. The unusual combination of MDF and MIF can provide information on reaction pathways and the biogeochemical history of mercury. Results from preliminary research provide strong evidence that net MMHg formation occurred below the surface mixed layer in the pycnocline and suggested that MMHg in low oxygen intermediate waters is an important entry point for mercury into open ocean food webs. These findings highlight the critical need to understand how MMHg levels in marine biota will respond to changes in atmospheric mercury emissions, deposition of inorganic mercury to the surface ocean, and hypothesized future expansion of oxygen minimum zones. Using field collections across ecosystems with contrasting biogeochemistry and mercury isotope fractionation experiments researchers will fill key knowledge gaps in mercury biogeochemistry. Results of the proposed research will enable scientists to assess the biogeochemical controls on where in the water column mercury methylation and demethylation likely occur.
Related background publication with supplemental data section:
Joel D. Blum, Brian N. Popp, Jeffrey C. Drazen, C. Anela Choy & Marcus W. Johnson. 2013. Methylmercury production below the mixed layer in the North Pacific Ocean. Nature Geoscience 6, 879–884. doi:10.1038/ngeo1918";
String projects_1_end_date "2017-07";
String projects_1_geolocation "Pacific Subtropical Gyre, Station ALOHA 22.75N 158W; equatorial Pacific (10N 155W, 5N 155W)";
String projects_1_name "Collaborative Research: Isotopic insights to mercury in marine food webs and how it varies with ocean biogeochemistry";
String projects_1_project_nid "560580";
String projects_1_start_date "2014-08";
String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
String publisher_type "institution";
String sourceUrl "(local files)";
Float64 Southernmost_Northing 5.0;
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "Particles and Zooplankton Amino Acid Compound Specific Isotope Analyses (AA-CSIA) and zooplankton biomass at Station ALOHA and the Equatorial Pacific from R/V Kilo Moana cruises KM1407, KM1418, & KM1515 from 2014-2015.";
String title "[Particles and Zooplankton AA-CSIA] - Particles and Zooplankton Amino Acid Compound Specific Isotope Analyses (AA-CSIA) and zooplankton biomass at Station ALOHA and the Equatorial Pacific from R/V Kilo Moana cruises KM1407, KM1418, & KM1515 from 2014-2015 (Evaluating the relative importance of suspended and sinking particles to the meso and bathypelagic food web in the central North Pacific)";
String version "1";
Float64 Westernmost_Easting -158.0;
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
Data Access Protocol (DAP) and its
selection constraints.
The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.
Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names,
followed by a collection of
constraints (e.g., variable<value),
each preceded by '&' (which is interpreted as "AND").
For details, see the tabledap Documentation.