Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Parameters
• Instruments
• Deployments
• Project Information
• Program Information
• Funding

Subsets of these data were published in the papers listed in the "Related Resources" section.

Related dataset:
* ScanFish Optical Plankton Counter (OPC) data: https://www.bco-dmo.org/dataset/746081

Methodology:
Plankton samples were collected from discrete depths during CTD casts using a high-capacity, diaphragm pump with an intake hose mounted to the CTD Rosette. Subsamples of sample contents were manually counted, identified, and measured in the laboratory

Sampling and analytical procedures:
For each sample, the pump was run for a timed period of nominally 5 min. The flow rate of the pump was usually measured immediately prior to sampling, by recording the time required to fill a barrel of known volume. Samples were preserved and stored in 5% buffered formalin. The laboratory protocol was to separate samples into two size fractions by passing them through large (200 or 500 μm) then small (64 μm) mesh sieves; split, dilute, and sub-sample each size fraction as needed; count and identify all plankters (minimum n = 50) in each subsample; and estimate the length and width of exactly 100 plankters (the first 50 from the large and small fraction, respectively) to the nearest 50 μm using a dissecting microscope. Three replicate small size fractions per sample were processed starting in 2006.

Data column "vol" was calculated as follows:
vol = length(unique(replicate)*aliquot/beaker/2^splits*50/s_per_50l*d_min*60*1e-3

Data column "flag" contains the following codes:
flag 1: length & width were not measured, but estimated from similar samples
flag 2: shallow & deep samples not labeled, but deduced from sample contents
flag 3: depth estimate less accurate than usual (± ~3 m)
flag 4: time estimate less accurate than usual (± ~15 min)
flag 5: pump flow rate estimated by nominal flow rate

Data processing:
Nominal sampling time and depth were corrected by analysis of CTD upcast data. CTD pressure sensor measurements at depth were adjusted to compensate for non-zero readings on deck of the research vessel over the course of each cruise. Bottom depth was estimated from filtered CTD altimeter and pressure data. Longitude and latitude were taken from the vessel’s MIDAS system or a separate GPS unit at the time the CTD was turned on. Inconsistent taxonomic identifications (e.g., misspelled or misleading colloquial categories) were corrected by manually creating and automatically applying a lookup table. All data processing was performed using the R language and environment for statistical computing.

BCO-DMO Data Manager Processing Notes:
* added a conventional header with dataset name, PI name, version date
* modified parameter names to conform with BCO-DMO naming conventions
* added ISO Timestamp column
* rounded decimal places of columns. Number of decimal places provided by data contributor.

Data version 1: 2018-09-12 replaced by data version 2: 2018-10-22. Dataset was modified to use unique ctd profile identifiers and remove redundant time/date columns (y,m,d,h,m,s).

NGOMEX - Living Organisms of the Northern Gulf of Mexico
A synthesis of data collected in the Northern Gulf of Mexico from 2003-2004, 2006-2008 and 2010
Data include:
- CTD Profiles
- Rosette Samples
- MIDAS underway metereological
- Towed SCANFISH
- Net Trawls
- Zooplankton counts

High-resolution mapping of the major ecosystem components of the NGOMEX by year

References:
Kimmel, D. G., W. C. Boicourt, J. J. Pierson, M. R. Roman, X. Zhang. 2010. The vertical distribution and diel variability of mesozooplankton biomass, abundance and size in response to hypoxia in the northern Gulf of Mexico USA. Journal of Plankton Research 32(8): 1185-1202. doi:10.1093/plankt/fbp136

Pierson, J. J., M. R. Roman, D. G. Kimmel, W. C. Boicourt, & X. Zhang. 2009. Quantifying changes in the vertical distribution of mesozooplankton in response to hypoxic bottom waters. Journal of Experimental Marine Biology and Ecology 381: S74-S79. doi.org/10.1016/j.jembe.2009.07.013

Kimmel, D. G., W. C. Boicourt, J. J. Pierson, M. R. Roman, & X. Zhang. 2009. A comparison of the mesozooplankton response to hypoxia in Chesapeake Bay and the northern Gulf of Mexico using the biomass size spectrum. Journal of Experimental Marine Biology and Ecology 381: S65-S73. doi.org/10.1016/j.jembe.2009.07.012

Zhang, H., S. A. Ludsin, D. M. Mason, A. T. Adamack, S. B. Brandt, X. Zhang, D. G. Kimmel, M. R. Roman, & W. C. Boicourt. 2009. Hypoxia-driven changes in the behavior and spatial distribution of pelagic fish and mesozooplankton in the northern Gulf of Mexico. Journal of Experimental Marine Biology and Ecology. 381: S80-91. http://dx.doi.org/10.1016/j.jembe.2009.07.014

GOM - Broader Impacts
The need to understand the impact of this largest oil spill to date on ecosystems and biochemical cycling is self evident. The consequences of the disaster and accompanying clean up measures (e.g. the distribution of dispersants) need to be evaluated to guide further mediating measures and to develop and improve responses to similar disasters in the future. Would it be advantageous if such oil aggregates sink, or should it rather remain suspended? Possibly measures can be developed to enhance sinking or suspension (e.g. addition of ballast minerals) once we understand their current formation and fate. Understanding the particle dynamics following the input of large amounts of oil and dispersants into the water is a prerequisite to develop response strategies for now and in the future.