Contributors | Affiliation | Role |
---|---|---|
Carlson, Craig A. | University of California-Santa Barbara (UCSB-MSI) | Principal Investigator |
Copley, Nancy | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
This dataset includes analyses from Niskin bottle samples collected on R/V Atlantis cruises AT32, AT34, AT38 and AT39-6 as part of the NASA NAAMES campaign (2015-2018). Reported are survey biogeochemical including dissolved organic carbon, dissolved organic nitrogen, total dissolved amino acids.
Samples were collected on RV/Atlantis cruises in the North Atlantic between November 2015 and April 2018. Bacterial abundance was determined by flow cytometry on AT32 and by direct microscope counts for the rest of the cruises (AT34, AT38, AT39-6). Amino acid analysis was conducted for cruises AT34, AT38, and AT39-06.
Dissolved organic carbon DOC) and total dissolved nitrogen (TDN): See Supplemental Files.
Carlson C, Hansell D, Nelson N, Siegel D, Smethie W, Khatiwala S et al. (2010). Dissolved organic carbon export and subsequent remineralization in the mesopelagic and bathypelagic realms of the North Atlantic basin. Deep Sea Res Part II: Topical Stud Oceanogr 57: 1433–1445.
Bacterial production by Leucine Incorporation: See Supplemental Files.
Smith, D.C. and F. Azam (1992). A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Marine Microbial Food Webs 6:107-114.
Bacterioplankton abundance by flow cytometry: See Supplemental Files.
Nelson, C.E., A.L. Alldredge, E.A. McCliment, L.A. Amaral-Zettler, and C.A. Carlson. 2011. Depleted dissolved organic carbon and distinct Bacterial communities in the water column of a rapid-flushing coral reef ecosystem. The ISME Journal 5: 1374–1387. doi:10.1038/ismej.2011.12
Bacterioplankton abundance by DAPI DNA binding stain and epifluorescence microscopy: See Supplemental Files.
BATS Methods Manual. Chapter 17. Determination of Bacterial Abundance.Updated by K.Orcutt 4/1997, pp. 111-114.version 4.
Amino acid concentration using HPLC: See Supplemental Files (includes reference list.)
BCO-DMO Processing:
- added conventional header with dataset name, PI name, version date
- replaced blank cells and those with -999 with 'nd' (no data)
File |
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biogeochem_all.csv (Comma Separated Values (.csv), 977.59 KB) MD5:4444e94b5e8f660a491ee44f6ad0b2d4 Primary data file for dataset ID 659131 |
File |
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Bacterial abundance using DAPI DNA binding stain and Epifluorescence microscopy filename: Bacterial_Abundance_using_DAPI_DNA_binding_stain_and_EFM.pdf (Portable Document Format (.pdf), 367.91 KB) MD5:bcc7f60bea95a080fe056e1bb095536e C. Carlson [version date: 2017-08-29] |
Determination of Amino Acid Concentrations using HPLC filename: Method-total_dissolved_amino_acids_Carlson_2020-08-19.pdf (Portable Document Format (.pdf), 435.78 KB) MD5:fd43f2485298a9bde730c64b516f56cc This procedure describes the measurement of total dissolved amino acids (TDAA) and its 18 constituents using high performance liquid chromatography (HPLC). Craig Carlson [2018-10-30] |
Flow Cytometry Protocol for Determination of Bacterial Abundance filename: Flow_Cytometry_Protocol_for_Determination_of_Bacterial_Abundance.pdf (Portable Document Format (.pdf), 369.15 KB) MD5:d44db238e643d3f4828a732ee3447215 Flow Cytometry Protocol for Determination of Bacterial Abundance
Craig Carlson [version date: 2017-08-29] |
Protocol: Bacterial Production Rates via 3H-Leucine incorporation filename: Microcentrifuge_Method_for_Bacterial_Production_Rates_via_3H-Leucine_incorp.pdf (Portable Document Format (.pdf), 445.74 KB) MD5:08a05d9a15af3ba56cabb7836296f99f Microcentrifuge Method Protocol for
Determination of Bacterial Production Rates via 3H-Leucine incorporation. Craig Carlson [version date: 2017-08-29] |
Protocols for Dissolved Organic Carbon and Total Dissolved Nitrogen Analysis filename: DOC_TDN_method_Carlson.pdf (Portable Document Format (.pdf), 258.09 KB) MD5:46973edce747f7f77099db51ac36acfa Version date: 2017-10-10. UCSB - CRAIG CARLSON. v |
Parameter | Description | Units |
Cruise | Cruise | unitless |
Station | Station | unitless |
Type | Sample Type (ODV format) | unitless |
Time_Stamp | Date/Time (ODV format); formatted as ISO_DateTime yyyy-mm-ddThh:mm or yyyy-mm-ddT | unitless |
Latitude | Latitude; north is positive | decimal degrees |
Longitude | Longitude; east is positive | decimal degrees |
Bottom_Z | Bottom Depth | meters |
CruiseCN | Cast number according to CTD/Bottle log sheets (cruise sequential cast number) | unitless |
SCN | Cast numbers restart at each station i.e. for Station x; Biology Cast 1 = SxC1; Biology Cast 2 = SxC2; Deep Cast 1 =SxC3; Deep Cast 2 = SxC4 (station sequential cast number) | unitless |
CampCN | Cast numbers carried through the entire NAAMES campaign; never resetting for an individual cruise nor station (campaign sequential cast number) | unitless |
Niskin | Niskin Number | unitless |
Cast_Type | Biology; Deep; Shallow (does not fit Biology or Deep cast scheme); Microlayer | unitless |
Target_Z | Target Depth according to CTD/Bottle log sheets | meters |
Pressure | Pressure from Digiquartz sensor | decibars (db) |
Density00 | Density; 1st sensor | kilograms/meter^3 (kg/m3) |
Density11 | Density; 2nd sensor | kilograms/meter^3 (kg/m3) |
Sal11 | Salinity; 2nd Sensor | Practical Salinity Units (psu) |
SoundVel | Sound velocity; 1st sensor | meters/second (m/s) |
SoundVel1 | Sound velocity; 2nd sensor | meters/second (m/s) |
Oxygen_uM | Oxygen; 1st sensor | micromol/liter (umol/l) |
Oxygen_V | Oxygen; 1st sensor; voltage | volts (V) |
Temperature | Temperature; 1st sensor | degrees C |
Temperature1 | Temperature; 2nd sensor | degrees C |
Conductivity | Conductivity; 1st sensor | Siemans/meter (S/m) |
Conductivity1 | Conductivity; 2nd sensor | Siemans/meter (S/m) |
BeamT | Beam Transmission; 1st Sensor | percent |
BeamAt | Bean Attenuation; 1st sensor | per meter |
Fluorescence | Fluorescence; 1st sensor | milligrams/meter^3 (mg/m3) |
Turbidity | Turbidity; 1st sensor | Nephelometric Turbidity Units (NTU) |
TOC | Total Organic Carbon; Method: High temperature combustion/oxidation (HTCO) (Carlson et al; 2010) | micromol carbon/liter (umol C/L) |
TOC_QF | Total Organic Carbon Quality Flag: 1 sample taken; 2 acceptable measurement; 3 Questionable measurement; 4 Bad measurement; 5 not reported; 9 no sample drawn | unitless |
TOC_sd | Total Organic Carbon Standard Deviation | micromol carbon/liter (umol C/L) |
DOC | Dissolved Organic Carbon; Method: High temperature combustion/oxidation (HTCO). Glass fiber filtrate type GF/F (Whatman) (Carlson et al; 2010) | micromol carbon/liter (umol C/L) |
DOC_QF | Dissolved Organic Carbon Quality Flag: WOCE Quality Flags (QF): 1 sample taken; 2 acceptable measurement; 3 Questionable measurement; 4 Bad measurement; 5 not reported; 9 no sample drawn | unitless |
DOC_sd | Dissolved Organic Carbon Standard Deviation | micromol carbon/liter (umol C/L) |
TDN | Total Dissolved Nitrogen | micromol nitrogen/liter (umol N/L) |
TDN_QF | Total Dissolved Nitrogen Quality Flag: WOCE Quality Flags (QF): 1 sample taken; 2 acceptable measurement; 3 Questionable measurement; 4 Bad measurement; 5 not reported; 9 no sample drawn | unitless |
TDN_sd | Total Dissolved Nitrogen Standard Deviation | micromol nitrogen/liter (umol N/L) |
BactProd | Bacterial Production by 3H Leu uptake (Smith & Azam; 1992) | picomol Leucine/liter/hour (pmol Leu /L/h) |
BactProd_QF | Bacterial Production Quality Flag:WOCE Quality Flags (QF): 1 sample taken; 2 acceptable measurement; 3 Questionable measurement; 4 Bad measurement; 5 not reported; 9 no sample drawn | unitless |
BactProd_sd | Bacterial Production Standard Deviation | picomol Leucine/liter/hour (pmol Leu /L/h) |
BactAbund | Bacterial abundance by epifluorescent microscopy and flow cytometry (Porter & Feig,1980; Halewood et al, 1980) | 10^8 cells/liter (E8 cells/L) |
BactAbund_QF | Bacterial Abundance; Quality Flag: 1 sample taken; 2 acceptable measurement; 3 Questionable measurement; 4 Bad measurement; 5 not reported; 9 no sample drawn | unitless |
BactAbund_sd | Bacterial Standard Deviation | 10^8 cells/liter (E8 cells/L) |
TDAA | Total Dissolved Amino Acids by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
TDAA_QF | Total Dissolved Amino Acids Quality Flag: 1 sample taken; 2 acceptable measurement; 3 Questionable measurement; 4 Bad measurement; 5 not reported; 9 no sample drawn | unitless |
TDAA_sd | Total Dissolved Amino Acids Standard Deviation | nanomolar (nM) |
Asp | Aspartic Acid concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Glu | Glutamic Acid concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
His | Histadine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Ser | Serine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Arg | Arginine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Thr | Threonine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Gly | Glycine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Tau | Taurine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Bala | Beta-alanine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Tyr | Tyrosine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Ala | Alanine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
GABA | Gamma-aminobutyric acid concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Met | Methionine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Val | Valine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Phe | Phyenylalanine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Ile | Isoleucine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Leu | Leucine concentration by High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
Lys | Lysine by concentration High performance liquid chromatography (HPLC) (Liu et al; 2020) | nanomolar (nM) |
file_name | All cruise data tables were combined into one table. This is the original file name. | unitless |
Dataset-specific Instrument Name | |
Generic Instrument Name | Centrifuge |
Dataset-specific Description | For measurement of bacterial production. |
Generic Instrument Description | A machine with a rapidly rotating container that applies centrifugal force to its contents, typically to separate fluids of different densities (e.g., cream from milk) or liquids from solids. |
Dataset-specific Instrument Name | BD LSR II equipped with a BD High Throughput Sampler (HTS) - Biosciences, San Jose, CA, USA |
Generic Instrument Name | Flow Cytometer |
Dataset-specific Description | Used to measure bacterial abundance.
Flow cytometer equipped with a high throughput sampler, coherent sapphire 488nm laser and a default suite of six detectors (side-scatter and forward-scatter photodiodes and green, orange, red and far-red photomultipliers). |
Generic Instrument Description | Flow cytometers (FC or FCM) are automated instruments that quantitate properties of single cells, one cell at a time. They can measure cell size, cell granularity, the amounts of cell components such as total DNA, newly synthesized DNA, gene expression as the amount messenger RNA for a particular gene, amounts of specific surface receptors, amounts of intracellular proteins, or transient signalling events in living cells.
(from: http://www.bio.umass.edu/micro/immunology/facs542/facswhat.htm) |
Dataset-specific Instrument Name | Olympus BX51 Epiflourescence Microscope |
Generic Instrument Name | Fluorescence Microscope |
Dataset-specific Description | For bacterial abundance estimates |
Generic Instrument Description | Instruments that generate enlarged images of samples using the phenomena of fluorescence and phosphorescence instead of, or in addition to, reflection and absorption of visible light. Includes conventional and inverted instruments. |
Dataset-specific Instrument Name | |
Generic Instrument Name | GO-FLO Bottle |
Dataset-specific Description | For water sample collection |
Generic Instrument Description | GO-FLO bottle cast used to collect water samples for pigment, nutrient, plankton, etc. The GO-FLO sampling bottle is specially designed to avoid sample contamination at the surface, internal spring contamination, loss of sample on deck (internal seals), and exchange of water from different depths. |
Dataset-specific Instrument Name | Dionex ICS 5000+ |
Generic Instrument Name | High-Performance Liquid Chromatograph |
Dataset-specific Description | Used for amino acid concentration measurements. |
Generic Instrument Description | A High-performance liquid chromatograph (HPLC) is a type of liquid chromatography used to separate compounds that are dissolved in solution. HPLC instruments consist of a reservoir of the mobile phase, a pump, an injector, a separation column, and a detector. Compounds are separated by high pressure pumping of the sample mixture onto a column packed with microspheres coated with the stationary phase. The different components in the mixture pass through the column at different rates due to differences in their partitioning behavior between the mobile liquid phase and the stationary phase. |
Dataset-specific Instrument Name | Hidex 300 Liquid Scintillation Analyzer |
Generic Instrument Name | Liquid Scintillation Counter |
Dataset-specific Description | For microcentrifuge method protocol for determination of bacterial production rates via 3H-Leucine incorporationEnergy window settings:
Channel A: 0-19 KeV
Channel B: 2-19 KeV |
Generic Instrument Description | Liquid scintillation counting is an analytical technique which is defined by the incorporation of the radiolabeled analyte into uniform distribution with a liquid chemical medium capable of converting the kinetic energy of nuclear emissions into light energy. Although the liquid scintillation counter is a sophisticated laboratory counting system used the quantify the activity of particulate emitting (ß and a) radioactive samples, it can also detect the auger electrons emitted from 51Cr and 125I samples. |
Dataset-specific Instrument Name | |
Generic Instrument Name | Niskin bottle |
Dataset-specific Description | For water sample collection |
Generic Instrument Description | A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24, or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc. |
Dataset-specific Instrument Name | |
Generic Instrument Name | Shimadzu TOC-V Analyzer |
Dataset-specific Description | Used to measure dissolved organic carbon (DOC) and total dissolved nitrogen (TDN).
Shimadzu TOC-V analyzers (Shimadzu Scientific Instruments, Columbia, MD, USA) were slightly modified from the manufacturer’s model system. The condensation coil was removed and the head space of an internal water trap was reduced to minimize system dead space. The combustion tube contained 0.5 cm Pt pillows placed on top of Pt alumina beads to improve peak shape and to reduce alteration of the combustion matrix throughout the analytical run. CO2-free carrier gas was delivered to the TOC-V systems via commercial ultra high purity gas cylinders or a Whatmans gas generator. A magnesium perchlorate trap was added to the existing water and halide traps to ensure removal of water vapor from the gas line prior to entering a nondispersive infrared detector. The resulting peak area was integrated with Shimadzu chromatographic software. |
Generic Instrument Description | A Shimadzu TOC-V Analyzer measures DOC by high temperature combustion method. |
Website | |
Platform | R/V Atlantis |
Start Date | 2015-11-06 |
End Date | 2015-12-01 |
Description | North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) cruise |
Website | |
Platform | R/V Atlantis |
Start Date | 2016-05-11 |
End Date | 2016-06-05 |
Description | Part of the 'North Atlantic Aerosols and Marine Ecosystems Study' (NAAMES) project |
Website | |
Platform | R/V Atlantis |
Start Date | 2017-08-30 |
End Date | 2017-09-22 |
Description | North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) cruise |
Website | |
Platform | R/V Atlantis |
Start Date | 2018-03-20 |
End Date | 2018-04-13 |
Description | Cruise for project "Project: North American Aerosols and Marine Ecosystems Study (NAAMES)". |
Tracking the temporal and spatial variability of dissolved organic matter, its diagenetic state and bioavailability during various bloom states in the North Atlantic
Craig Carlson
ID: 1537943
The North Atlantic phytoplankton bloom is among the most conspicuous biological events annually recorded. This bloom represents a hot spot of biological activity during which a significant amount of dissolved organic matter is produced through bloom-associated food web processes. While recent work has shed some light on the spatial distribution of dissolved organic matter during the North Atlantic bloom, temporal resolution of dissolved organic matter variability in the context of the North Atlantic bloom is lacking. This project aims to understand the temporal and spatial dynamics of dissolved organic matter, its compositional variability, as well as the mechanisms that control its accumulation, persistence and export in the North Atlantic. This project will leverage a large, recently funded, NASA field-program called the North Atlantic Aerosols and Marine Ecosystem Study (NAAMES) designed to evaluate the fundamental controls of the north Atlantic phytoplankton bloom initiation, its magnitude and interannual variability. Results from this research will provide a mechanistic understanding of carbon cycling in the context of the North Atlantic phytoplankton bloom. The research will be carried out at the University of California ? Santa Barbara, a Hispanic-serving institution, and will involve educational opportunities for students from elementary through graduate school.
Recent work examining the spatial distribution of dissolved organic matter in the North Atlantic coupled to measurements of water mass ventilation rates has estimated that a significant amount of carbon is vertically exported out of the surface ocean to deep waters as dissolved organic matter. However, an overarching gap in dissolved organic matter knowledge is the lack of valuable temporal resolution necessary to investigate the mechanisms that control dissolved organic matter production, accumulation, or its change in quality and bioavailability as a result of changing bloom phases and phytoplankton cycles. This research will examine the temporal and spatial variability of dissolved organic matter dynamics along a repeated meridional transect during four distinct phases associated with the North Atlantic spring phytoplankton bloom including 1) pre-bloom, mixing phase, 2) nutrient-replete, increasing biomass phase, 3) nutrient-stressed decreasing biomass phase, and 4) post bloom stratified phase. This will be accomplished by coupling continuous water column and surface layer ecosystem properties from autonomous in situ sensors, and satellite observations with four 26-day coordinated ship and airborne field campaigns.
The North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) is an interdisciplinary investigation resolving key processes controlling marine ecosystems and aerosols that are essential to our understanding of Earth system function and future change. NAAMES is funded by the NASA Earth Venture Suborbital Program and is the first EV-S mission focused on studying the coupled ocean ecosystem and atmosphere.
Plankton ecosystems of the global ocean profoundly affect climate and life on Earth. NASA's ocean color satellite record tells us that these invaluable ecosystems are highly responsive to climate variability, with changes in ocean production impacting food production, uptake of atmospheric carbon dioxide, and emission of climate-regulating aerosols. Intergovernmental Panel on Climate Change (IPCC) simulations suggest that surface ocean temperatures will warm by +1.3 to +2.8 degrees C globally over the 21st century, with major consequences on physical properties of the surface ocean where plankton populations thrive. The pressing question is, how will these changes alter plankton production, species composition, and aerosol emissions? Today, even the sign of these potential changes remains unresolved. Our ability to predict Earth System consequences of a warming ocean and develop realistic mitigation and adaption strategies depends on resolving conflicting hypotheses regarding the factors controlling plankton ecosystems and biogenic aerosol emissions.
NAAMES consists of four, combined ship and aircraft field campaigns that are each aligned to a specific event in the annual plankton lifecycle. Ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. These diverse data are extended over broader spatial scales through parallel airborne remote sensing measurements and in situ aerosol sampling that target ocean properties as well as the aerosols and clouds above. The airborne data crucially link local-scale processes and properties to the much larger scale continuous satellite record. Integrating the NAAMES observations with state-of-the-art climate and ecosystems models enables the creation of a process-based foundation for resolving plankton dynamics in other ocean regions, accurately interpreting historical satellite records, and improving predictions of future change and their societal impacts.
Funding Source | Award |
---|---|
NSF Division of Ocean Sciences (NSF OCE) |