Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

Sampling was conducted onboard R/V Kilo Moana at Station ALOHA (22.75°, ‑158.0°) on February 19-28, 2014 (KM1407) and August 29-September 11, 2014 (KM1418). Five to six in situ pumps (WTS-LV standard, McLane Research Laboratories, Inc., East Falmouth, MA) were deployed with sequential 142 mm-diameter filters mounted on tiered mini-MULVFS filter holders (Bishop et al. 2012) to capture submicron (0.2-0.7 µm; sterile-packed polyethersulfone), small (0.7-53 or 1-53 µm; pre-combusted glass or quartz fiber filters), and large (>53 µm; acid- and methanol-rinsed nylon mesh) particles. Pumps were deployed at depths from 25 m to 1205 m, pumping between ~400-2000 L per deployment at rates of 3-8 L min^-1. Filters used in this study were stored in combusted foil and frozen at ‑80°C until further processing on land. Large particles captured on Nitex mesh were thawed briefly and resuspended in 0.2 μm-filtered seawater; resuspended particles were gently vacuum-filtered onto a 47-mm glass fiber filter (GF/F; 0.7 μm pore size), re-frozen, and lyophilized. Dried filters were inspected under a dissecting microscope to remove whole swimmers and loose Nitex fibers, both of which were rare, and to make qualitative observations such as presence of Trichodesmium and carbonate skeletal fragments. All filter samples were subsampled, lyophilized overnight, and quantitatively split by weight for bulk analysis, compound-specific isotope analysis of amino acids (CSIA-AA), and amino acid enantiomer analysis.

Bulk N concentrations and δ¹⁵N values were determined using a Costech Elemental Analyzer interfaced to a Thermo Delta Plus XP isotope ratio mass spectrometer (IRMS) using standard methods in the Popp Laboratory (University of Hawaii).

Amino acid preparation generally followed the methods of Hannides et al. (2009) and references therein. Filter solids were removed by filtering sample hydrolysate through combusted glass wool and 0.2 μm polyethersulfone disc filters. Filter pieces were rinsed 3-5 times with 0.01 N HCl during this process, and the sample was fully extracted by squeezing the filter pieces in a 5 or 10 mL glass syringe plugged with glass wool. Amino acids were purified using cation exchange resin (50W-X8, 100-200 mesh, 1 mL bed volume), eluting in 2 N ammonium hydroxide, followed by reprotonation (0.2 N HCl, 110°C, 5 minutes) and derivatization to trifluoroacetyl/isopropyl esters in a two-step procedure (esterification of carboxyl groups, 4:1 isopropanol/acetyl chloride, 110°C, 1 hour; trifluoroacetylation of amino groups, 3:1 dichloromethane/trifluoroacetic anhydride, 100°C, 15 minutes). Excess salts were removed by liquid-liquid extraction (chloroform/phosphate buffer), and the trifluoroacetylation step was repeated. Samples were stored in airtight vials at ‑20°C in between procedural steps. Immediately before analysis sample aliquots were dried under ultra-purity N₂ gas and redissolved in 6-50 μL of ethyl acetate, depending on concentrations of AAs estimated from bulk N analysis. All glassware was pre-cleaned in 1.2 N HCl and combusted at 500°C overnight before use.

AA δ¹⁵N analysis was conducted using a Thermo Trace gas chromatograph (GC) via a split/splitless inlet operated in splitless mode and equipped with a BPX5 column (SGE, Inc.). Individual amino acids were converted to N₂ gas via a two-stage Thermo GC-C-III combustion (980°C) and reduction (650°C) interface. N₂ sample gas was purified over a liquid nitrogen cold loop and directed into a Delta-V Plus IRMS.

δ¹⁵N values (‰ vs. AIR) were calculated by the Thermo Isodat 3.0 software, using reference gas peaks introduced into each run from an externally calibrated N₂ gas cylinder and/or using co-injected reference peaks of isotopically known norleucine and aminoadipic acid standards derivatized identically to samples. Instrument accuracy was determined by injecting a mixture of amino acids of known δ¹⁵N values every fourth injection and derivatized identically to samples. Individual AA δ¹⁵N values were calculated as the average across 2-3 analytical replicates, and 1σ uncertainty was calculated as the standard deviation of these analytical replicates. For some small particle samples, the reported δ¹⁵N values represent the average and standard deviation across sampling replicates (same depth and near/identical size fraction). These are indicated by data with values reported for the parameter ISO_DateTime_UTC_rep. In some cases, results were averaged for a 0.7-53 um size fraction and a 1-53 um size fraction. In these cases, the value for parameter Size_Fraction_Min is reported as 0.85.

AA concentrations were determined based on IRMS peak area response of individual AA standards and the amount of sample injected. Estimated seawater concentrations of nitrogen deriving from amino acids in particles (nmol N L^-1) were determined as the sum of concentrations of all measured AAs.

δ¹⁵N values (‰ vs. AIR) were calculated by the Thermo Isodat 3.0 software, using reference gas peaks introduced into each run from an externally calibrated N₂ gas cylinder and/or using co-injected reference peaks of isotopically known norleucine and aminoadipic acid standards derivatized identically to samples. Instrument accuracy was determined by injecting a mixture of amino acids of known δ¹⁵N values every fourth injection and derivatized identically to samples. Individual AA δ¹⁵N values were calculated as the average across 2-3 analytical replicates, and 1σ uncertainty was calculated as the standard deviation of these analytical replicates. For some small particle samples, the reported δ¹⁵N values represent the average and standard deviation across sampling replicates (same depth and near/identical size fraction). These are indicated by data with values reported for the parameter ISO_DateTime_UTC_rep. In some cases, results were averaged for a 0.7-53 um size fraction and a 1-53 um size fraction. In these cases, the value for parameter Size_Fraction_Min is reported as 0.85.

AA concentrations were determined based on IRMS peak area response of individual AA standards and the amount of sample injected. Estimated seawater concentrations of nitrogen deriving from amino acids in particles (nmol N L^-1) were determined as the sum of concentrations of all measured AAs.

- Imported "Close_ALOHA_particle_AA_bulk_d15N_initial_submission.xlsx" into the BCO-DMO system
- Replaced dashes with underscores in the parameter names
- Exported file as "970402_v1_aloha_particle_aa_bulk_d15n.csv"

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.