Contributors | Affiliation | Role |
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Popp, Brian N. | University of Hawaiʻi at Mānoa (SOEST) | Principal Investigator |
Close, Hilary G. | University of Miami Rosenstiel School of Marine and Atmospheric Science (UM-RSMAS) | Co-Principal Investigator |
Rauch, Shannon | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Zooplankton were collected using a multiple opening-closing net and environmental sensing system (MOCNESS) on R/V Roger Revelle cruise RR1813 during August-September 2018 (EXPORTS cruise). Onboard, zooplankton were wet-sieved into different size fractions using filtered seawater and 0.2, 0.5, 1.0, 2.0, and 5.0 mm mesh sieves and frozen at -20ºC. Zooplankton were dried and each fraction ground using a mortar and pestle. Samples were weighed and amino acids were isolated and purified by hydrolysis in 6N hydrochloric acid followed by cation exchange chromatography. The purified amino acids were then treated with isopropanol and trifluoroacetic acid yielding trifluoroacetic amino acid esters. The resulting solution was further purified by liquid-liquid extraction into chloroform before being stored in a dichloromethane-trifluoroacetic acid solution, and then finally transferred into ethyl acetate just prior to mass spectrometric isotopic analysis. CSIA-AA was carried out on a gas chromatograph coupled to an isotope ratio mass spectrometer (GC-IRMS), with all samples measured in triplicate when amino acid concentrations were sufficient.
BCO-DMO Processing:
- converted dates to YYYY-MM-DD;
- added the ISO8601 date-time column;
- renamed fields to comply with BCO-DMO naming conventions.
File |
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zoop_CSIA-AA.csv (Comma Separated Values (.csv), 22.63 KB) MD5:fdd12d891ef2d18c47c9df06192da8ac Primary data file for dataset ID 868193 |
Parameter | Description | Units |
Cruise | Cruise designation | unitless |
R2R_Event | R2R catalog identifier for this cruise | unitless |
Latitude | Latitude in decimal degrees North | decimal degrees North |
Longitude | Longitude in decimal degrees West | decimal degrees West |
ISO_DateTime_UTC | Date and time (UTC) of sampling in ISO8601 format: YYYY-MM-DDThh:mm:ssZ | unitless |
Date | Sampling date (UTC); format: YYYY-MM-DD | unitless |
Time | Sampling time (UTC)/deployment of net; format: hh:mm:ss | unitless |
MOCNESS_Tow | Shipboard MOCNESS tow designation | unitless |
DayNight | Indicates if sampling was conducted during day or night | unitless |
MOCNESS_Net | Net number out of 10 nets | unitless |
DepthInterval_max | maximum depth of sample | meters (m) |
DepthInterval_min | minimum depth of sample | meters (m) |
SizeFraction_min | minimum wet sieved size fraction | micrometers (um) |
SizeFraction_max | maximum wet sieved size fraction | micrometers (um) |
Taxa | which taxa were analyzed | text |
d15N_Ala | the nitrogen isotopic composition of alanine | permil relative to AIR (‰) |
d15N_Gly | the nitrogen isotopic composition of glycine | permil relative to AIR (‰) |
d15N_Thr | the nitrogen isotopic composition of threonine | permil relative to AIR (‰) |
d15N_Ser | the nitrogen isotopic composition of serine | permil relative to AIR (‰) |
d15N_Val | the nitrogen isotopic composition of valine | permil relative to AIR (‰) |
d15N_Leu | the nitrogen isotopic composition of leucine | permil relative to AIR (‰) |
d15N_Iso | the nitrogen isotopic composition of isoleucine | permil relative to AIR (‰) |
d15N_Pro | the nitrogen isotopic composition of proline | permil relative to AIR (‰) |
d15N_Asx | the nitrogen isotopic composition of aspartic acid | permil relative to AIR (‰) |
d15N_Met | the nitrogen isotopic composition of aspartic acid | permil relative to AIR (‰) |
d15N_Glx | the nitrogen isotopic composition of glutamic acid | permil relative to AIR (‰) |
d15N_Phe | the nitrogen isotopic composition of phenylalanine | permil relative to AIR (‰) |
d15N_Tyr | the nitrogen isotopic composition of tyrosine | permil relative to AIR (‰) |
d15N_Lys | the nitrogen isotopic composition of lysine | permil relative to AIR (‰) |
SD_d15N_Ala | the standard deviation of the nitrogen isotopic composition of alanine | permil relative to AIR (‰) |
SD_d15N_Gly | the standard deviation of the nitrogen isotopic composition of glycine | permil relative to AIR (‰) |
SD_d15N_Thr | the standard deviation of the nitrogen isotopic composition of threonine | permil relative to AIR (‰) |
SD_d15N_Ser | the standard deviation of the nitrogen isotopic composition of serine | permil relative to AIR (‰) |
SD_d15N_Val | the standard deviation of the nitrogen isotopic composition of valine | permil relative to AIR (‰) |
SD_d15N_Leu | the standard deviation of the nitrogen isotopic composition of leucine | permil relative to AIR (‰) |
SD_d15N_Ile | the standard deviation of the nitrogen isotopic composition of isoleucine | permil relative to AIR (‰) |
SD_d15N_Pro | the standard deviation of the nitrogen isotopic composition of proline | permil relative to AIR (‰) |
SD_d15N_Asx | the standard deviation of the nitrogen isotopic composition of aspartic acid | permil relative to AIR (‰) |
SD_d15N_Met | the standard deviation of the nitrogen isotopic composition of aspartic acid | permil relative to AIR (‰) |
SD_d15N_Glx | the standard deviation of the nitrogen isotopic composition of glutamic acid | permil relative to AIR (‰) |
SD_d15N_Phe | the standard deviation of the nitrogen isotopic composition of phenylalanine | permil relative to AIR (‰) |
SD_d15N_Tyr | the standard deviation of the nitrogen isotopic composition of tyrosine | permil relative to AIR (‰) |
SD_d15N_Lys | the standard deviation of the nitrogen isotopic composition of lysine | permil relative to AIR (‰) |
d13C_Ala | the carbon isotopic composition of alanine | permil relative to VPDB (‰) |
d13C_Gly | the carbon isotopic composition of glycine | permil relative to VPDB (‰) |
d13C_Thr | the carbon isotopic composition of threonine | permil relative to VPDB (‰) |
d13C_Ser | the carbon isotopic composition of serine | permil relative to VPDB (‰) |
d13C_Val | the carbon isotopic composition of valine | permil relative to VPDB (‰) |
d13C_Leu | the carbon isotopic composition of leucine | permil relative to VPDB (‰) |
d13C_Iso | the carbon isotopic composition of isoleucine | permil relative to VPDB (‰) |
d13C_Pro | the carbon isotopic composition of proline | permil relative to VPDB (‰) |
d13C_Asx | the carbon isotopic composition of aspartic acid | permil relative to VPDB (‰) |
d13C_Met | the carbon isotopic composition of aspartic acid | permil relative to VPDB (‰) |
d13C_Glx | the carbon isotopic composition of glutamic acid | permil relative to VPDB (‰) |
d13C_Phe | the carbon isotopic composition of phenylalanine | permil relative to VPDB (‰) |
d13C_Tyr | the carbon isotopic composition of tyrosine | permil relative to VPDB (‰) |
d13C_Lys | the carbon isotopic composition of lysine | permil relative to VPDB (‰) |
SD_d13C_Ala | the standard deviation of the carbon isotopic composition of alanine | permil relative to VPDB (‰) |
SD_d13C_Gly | the standard deviation of the carbon isotopic composition of glycine | permil relative to VPDB (‰) |
SD_d13C_Thr | the standard deviation of the carbon isotopic composition of threonine | permil relative to VPDB (‰) |
SD_d13C_Ser | the standard deviation of the carbon isotopic composition of serine | permil relative to VPDB (‰) |
SD_d13C_Val | the standard deviation of the carbon isotopic composition of valine | permil relative to VPDB (‰) |
SD_d13C_Leu | the standard deviation of the carbon isotopic composition of leucine | permil relative to VPDB (‰) |
SD_d13C_Ile | the standard deviation of the carbon isotopic composition of isoleucine | permil relative to VPDB (‰) |
SD_d13C_Pro | the standard deviation of the carbon isotopic composition of proline | permil relative to VPDB (‰) |
SD_d13C_Asx | the standard deviation of the carbon isotopic composition of aspartic acid | permil relative to VPDB (‰) |
SD_d13C_Met | the standard deviation of the carbon isotopic composition of aspartic acid | permil relative to VPDB (‰) |
SD_d13C_Glx | the standard deviation of the carbon isotopic composition of glutamic acid | permil relative to VPDB (‰) |
SD_d13C_Phe | the standard deviation of the carbon isotopic composition of phenylalanine | permil relative to VPDB (‰) |
SD_d13C_Tyr | the standard deviation of the carbon isotopic composition of tyrosine | permil relative to VPDB (‰) |
SD_d13C_Lys | the standard deviation of the carbon isotopic composition of lysine | permil relative to VPDB (‰) |
Dataset-specific Instrument Name | gas chromatograph |
Generic Instrument Name | Gas Chromatograph |
Dataset-specific Description | CSIA-AA was carried out on a gas chromatograph coupled to an isotope ratio mass spectrometer (GC-IRMS). |
Generic Instrument Description | Instrument separating gases, volatile substances, or substances dissolved in a volatile solvent by transporting an inert gas through a column packed with a sorbent to a detector for assay. (from SeaDataNet, BODC) |
Dataset-specific Instrument Name | Thermo Scientific Delta V Plus |
Generic Instrument Name | Isotope-ratio Mass Spectrometer |
Dataset-specific Description | Thermo Scientific Delta V Plus isotope ratio mass spectrometer with CC-C III combustion interface and Trace gas chromatograph. |
Generic Instrument 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). |
Dataset-specific Instrument Name | Thermo Scientific MAT 253 |
Generic Instrument Name | Isotope-ratio Mass Spectrometer |
Dataset-specific Description | Thermo Scientific MAT 253 isotope ratio mass spectrometer with GC Isolink combustion interface and Trace GC Ultra gas chromatograph. |
Generic Instrument 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). |
Dataset-specific Instrument Name | Multiple opening-closing net and environmental sensing system (MOCNESS) |
Generic Instrument Name | MOCNESS |
Dataset-specific Description | Multiple opening-closing net and environmental sensing system (MOCNESS) net with 1 m2 opening using 0.2 mm mesh plankton nets (see Wiebe, P. H., K. H. Burt, S. H. Boyd and A. W. Morton (1976) A multiple opening/closing net and environmental sensing system for sampling zooplankton. Journal of Marine Research 34:313-326.) |
Generic Instrument 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. |
Website | |
Platform | R/V Roger Revelle |
Report | |
Start Date | 2018-08-10 |
End Date | 2018-09-12 |
Description | Additional cruise information is available from the Rolling Deck to Repository (R2R): https://www.rvdata.us/search/cruise/RR1813 |
NSF Award Abstract:
The biological pump is largely responsible for the vertical transport of organic carbon from the surface to the ocean interior. However, only a small fraction of organic material produced in surface waters is sequestered in the deep ocean. The rest is consumed, or respired, by bacteria and larger organisms. The overarching goal of the proposed work is to characterize the relative influences of bacteria versus larger organisms on the degradation of organic material with depth. Guided by recent results from the subtropical Pacific, the investigators will use measurements of stable isotopes of nitrogen in different amino acids (compound-specific isotopic analysis of amino acids, known as AA-CSIA), along with measurements of the abundances of different forms of amino acids, and other parameters derived from these analyses to identify how the partitioning and flux of large and small particles are affected by different degradation processes. By improving the interpretive power of the AA-CSIA technique the investigators propose to determine: 1) the relative importance of microbial and zooplankton consumption on the efficiency of the biological carbon pump in the subarctic northeast Pacific, and 2) how much microbially-altered small particles fuel the metabolisms of mid-water zooplankton. This work capitalizes on an existing, comprehensive field program (NASA EXPORTS) specifically focused on building a predictive framework relating surface ocean properties to the vertical flux of organic carbon. The tremendous amount of data to be collected on all aspects of the biological pump as part of the EXPORTS program will aid the development and interpretation of the investigators' amino acid isotopic tool. Results will be broadly communicated via production and distribution of several episodes of Voice of the Sea, a local television program that will air in Hawaii and across many Pacific islands. Episodes also will be posted online and publicized through social media to the south Florida community. This project will support a Ph.D. student and an undergraduate student at University of Miami, which serves a 25% Hispanic population, and an M.S. student and an undergraduate student at University of Hawaii, which is a designated minority-serving institution.
The proposed work introduces a new geochemical framework to distinguish microbial versus zooplankton alteration of marine organic matter. Piloted on samples from the subtropical Pacific, this approach interrogates unamended sinking material directly, using amino acid compound-specific isotopic analysis (AA-CSIA) to determine the progressive, cumulative impact of microbial and zooplankton degradative pathways. The proposed work (1) will extend this interpretive framework to explicitly define end-member signatures such as fecal pellets and will apply this refined method to a study site in the subarctic northeast Pacific to (2) determine the vertical progression of degradative mechanisms in an oceanographic location with contrasting productivity and vertical length scales of flux attenuation and (3) determine whether microbially- degraded biomass is important for fueling midwater metazoans under contrasting carbon flux conditions. The proposed work will be conducted in collaboration with the NASA EXPORTS program at the Ocean Station Papa time-series site. Teaming with this program presents a unique opportunity to refine AA-CSIA interpretation in parallel with intensive data collection defining productivity, particle size distribution and flux, and numerous biological parameters. In comparing subtropical and subarctic Pacific locations, the proposed work will test how differences in productivity and plankton community structure influence vertical patterns of consumption and alteration of phytodetritus by microbes and zooplankton, from surface to mesopelagic depths.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
EXport Processes in the Ocean from Remote Sensing (EXPORTS) is a large-scale NASA-led field campaign that will provide critical information for quantifying the export and fate of upper ocean net primary production (NPP) using satellite observations and state of the art ocean technologies.
Ocean ecosystems play a critical role in the Earth’s carbon cycle and the quantification of their impacts for both present conditions and for predictions into the future remains one of the greatest challenges in oceanography. The goal of the EXport Processes in the Ocean from Remote Sensing (EXPORTS) Science Plan is to develop a predictive understanding of the export and fate of global ocean net primary production (NPP) and its implications for present and future climates. The achievement of this goal requires a quantification of the mechanisms that control the export of carbon from the euphotic zone as well as its fate in the underlying "twilight zone" where some fraction of exported carbon will be sequestered in the ocean’s interior on time scales of months to millennia. In particular, EXPORTS will advance satellite diagnostic and numerical prognostic models by comparing relationships among the ecological, biogeochemical and physical oceanographic processes that control carbon cycling across a range of ecosystem and carbon cycling states. EXPORTS will achieve this through a combination of ship and robotic field sampling, satellite remote sensing and numerical modeling. Through a coordinated, process-oriented approach, EXPORTS will foster new insights on ocean carbon cycling that maximizes its societal relevance through the achievement of U.S. and International research agency goals and will be a key step towards our understanding of the Earth as an integrated system.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) | |
NSF Division of Ocean Sciences (NSF OCE) |