One pair of 1m2 MOCNESS (Multiple Open Closing Nets with Environmental Sensor System) tows were performed during each cruise- one during the day, and one at night (MOCNESS, Wiebe et al, 1985). Nets with 150um mesh were used to better capture the smaller midwater zooplankton community in the region. Eight nets were fired in sequence along the upcast to capture spatially discrete zooplankton samples between 600m and the surface. While nets one, two, and three consistently targeted depths of 600-500m, 500m-400m, and 400-300m, depths for nets four through eight varied based on hydrographic features including the thermocline, deep chlorophyll maximum, and oxygen minimum zone (Maas et al, 2014, Steinberg et al, 2008). Once onboard, samples were split in two using a Motoda splitter (Motoda, 1959) with half preserved with sodium tetraborate buffered 4% formalin in seawater to be scanned with a ZooSCAN (Gorsky et al, 2010) and half placed in 95% undenatured ethanol for metabarcoding.
A representative subsample of the formalin-preserved zooplankton community from each net were imaged using a ZooSCAN ver. 4 at either 4,800 dpi or 2,400 dpi (following the methods in: Gorsky et al., 2010, Vandromme et al., 2012 as detailed in Maas et al. 2021). The change in resolution partway through the project was a result of recommendations from Hydroptic and loss of software support for 4800dpi imaging. In order to better represent all size classes in the images, the original sample was divided into three size categories. All individuals larger than 2 cm were selected by eye and scanned separately from all the others (fraction "d1"). The remainder of the sample was sieved through a 1-mm mesh sieve, and both size fractions were individually scanned ("d2" >1000um, "d3" 153-1000um). From these smaller size fractions, at least 1500 particles were scanned after subsampling using a Motoda splitter (Motoda, 1959), requiring generation of two separate scans for both size classes. This resulted in a total of five images per net.
ZooSCAN Image names:
Image names include: cruise#_mocnessID_net#_sizefraction_ and _a|b if a replicate and end in _raw_1.tif
Multiple images of the same size fraction were sometimes taken to obtain a sufficient number of particles. These replicates are named a or b. If there is no replicate they don’t have a letter in the image name. An a and b scan were always done for size classes d2 and d3. This was important because the split size is for the sum of a+b (e.g. if a is ¼ and b is ¼, the acq_sub_part will be 0.5).
Example of image names:
ae2112_m22_n4_d3_a_raw_1.tif [a replicate]
ae2112_m22_n4_d3_b_raw_1.tif [b replicate]
ae2204_m27_n5_d1_raw_1.tif [no replicate]
This dataset contains the "object_id" (the particle identifier) which is constructed the same way as the image name except it as an additional _# at the end. This additional number in the object_id is added by the ZooProcess software (Hydroptic, 2016).
e.g.
object_id: ae1614_m3_n1_d2_a_1_100
image_name: ae1614_m3_n1_d2_a_1.tif
File and particle names:
Names for both files and particles follow the pattern "CruiseID_MocnessID_NetNumber_ScanFraction", where files are followed by ".meas" and particles are followed by "_1_XXX", with "1" being automatically added by the software to indicate no duplicates of that scan and "XXX" being the unique particle number within that scan.
Parameter (column name) nomenclature and data origin:
* (see the "Parameters" section which contains all column information for the ecotaxa output table)
Parameters (column names) beginning with "object" include basic identifying metadata input by the user as well as all particle measurement data generated by ZooProcess. Any parameters beginning with "object_annotation" parameters are added by Ecotaxa. Parameters that begin with "sample" are sampling metadata input by the user during the scanning process. "Process" parameters describe the software and assumptions or corrections input during the data processing. "Acq" describes the portion of the sample scanned (input by user) and provides some summary data about the scanned image.
Interpreting acq_sub_part
For this project, the acq sub part is the fraction of the individual scan only. "A" and "B" scans of the same fraction can be statistically combined for analysis (e.g. d2_a and d2_b from the same net can be combined by adding the sub parts to create just a "d2" group).
Instruments:
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). The MOCNESS used in this experiment is a 1m2 (mouth size) rigged with nine 150um mesh nets. One is flown open on the downcast to balance the net (Net 0- contents preserved but not analyzed), and the other eight (Net 1-8) are triggered on the upcast at desired depths. This particular MOCNESS was originally manufactured by Biological Environmental Sensor Systems (BESS), but was refit with new electronics from SIO/STS in 2017 (Net Interface Unit, Net Angle Sensor) to allow it to interface with Seabird instruments (SBE9Plus CTD, SBE3S Temperature, SBE4C Conductivity, SBE11 Deck Box).
The ZooSCAN (CNRS patent) system makes use of scanner technology with custom lighting and a watertight scanning chamber into which liquid zooplankton samples can be placed. The scanner recovers a high-resolution, digital image and the sample can be recovered without damage. These digital images can then be investigated by computer processing. While the resolution of the digitized zooplankton images is lower than the image obtained using a binocular microscope, this technique has proven to be more than adequate for large sample sets. Identification of species is done by automatic comparison of the image (vignette) of each individual animal in the scanned image with a library data set which may be built by the investigator for each individual survey or imported from a previous survey. The latest machine learning algorithm allows high recognition levels even if we recommend complementary manual sorting to achieve a high number of taxonomic groups. Scans for this dataset performed with a ZooSCAN (Hydroptic, HYDROPTIC_V4) running with Vuescan (version 9.5.24) and ZooProcess (version 8.22, ImageJ macro suite).
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