Dataset: ZooSCAN Images Along Physical Gradients from OAPS
Deployment: OC473

Location: 

Northwestern Atlantic, 0 to 1000m depth, 35N, 52W to 50N, 42W along CLIVAR/WOCE line A20
Northeastern Pacific, 0 to 1000m depth, 50N, 150W to 35N, 135W along CLIVAR/WOCE line P17N

Methodology: 

To obtain samples, a 1m2 Multiple Opening/Closing Net and Environmental Sensing System  (MOCNESS; Wiebe et al., 1985) equipped with nine 150 m nets was deployed during the mid-day and mid-night on cruises carried out as described in dataset "MOC aqui log sheets" (https://www.bco-dmo.org/dataset/3546). Briefly, sampling was at consistent intervals including 1000-800, 800-600, 600-400, 400-200, 200-100, 100-50, 50-25, 25-0m at day/night stations from 35 to 50 N in the northwest Atlantic in 2011, and from 35 and 50N along CLIVAR line P17N  in 2012.

Upon retrieval, the catch from each of the eight discrete nets were divided into splits. One-half of a sample was preserved in 95% ethanol, one quarter was preserved in 5% buffered formalin, and one quarter was used for live viewing and picking, and then preserved in 70% ethanol.

A representative subsample of the formalin-preserved zooplankton community from each net were imaged using a ZooSCAN ver. 4 at 4,800 dpi (following the methods in: Gorsky et al., 2010, Vandromme et al., 2012, as detailed in Maas et al. 2021). 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 ("d1"). The remainder of the sample was sieved through a 1-mm mesh sieve, and both size fractions ("d2" >1000um but excluding d1; "d3" between 150-1000um) were individually scanned. 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.

Image names

Image names include:
cruise#_mocnessID_net#_sizefraction_ and _a|b if a replicate and end in _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:

ae1830_m13_n4_d3_a_1.tif  [a replicate]
ae1830_m13_n4_d3_b_1.tif  [a replicate]
ae1830_m13_n5_d1_1.tif      [no replicate]

Related Datasets may contain the "object_id" (the particle/organism id) 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

Instruments summaries:

MOCNESS

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). These nets allow for the discrete sampling of zooplankton at targeted depths while simultaneously capturing data on physical parameters. The MOCNESS used in all sampling for both cruises was a 1m² (mouth size) rigged as below. 

From OC473 Cruise Report (Lawson, et al. (2011) available from BCO DMO deployment page https://www.bco-dmo.org/deployment/58720):

"The MOCNESS was equipped with eight 150-um mesh nets (nets 1-8; borrowed from URI) and one 333-um mesh net (net 0). The underwater unit used was #169. In addition to the standard temperature and conductivity probes the system also had a beta-type strobe-light unit for reducing avoidance of the nets by some zooplankton and possibly small fish.  The strobe system has two units each with 12 LED sets (LUXEON Rebel LED) with peak output between 490-520 nm. Seven of the 24 LED sets were no longer working at the start of the sampling. The LEDs are powered by the MOCNESS battery and their pulse width, amplitude, flash rate period, and on/off are controlled by the MOCNESS software. For this cruise the pulse width was 2 ms, the relative amplitude was 99%, and the flash interval was 100 ms."

From NH1208 Cruise Report (Lawson, et al. (2012) available from BCO DMO deployment page https://www.bco-dmo.org/deployment/58830): 

"The MOCNESS was equipped with eight 150-um mesh nets (nets 1-8; borrowed from URI) and one 333-um mesh net (net 0). The system was equipped with the standard SeaBird  temperature and conductivity probes (units #535 and #120 respectively). The underwater unit used was #169. In addition to the standard temperature and conductivity probes the system also had a beta-type strobe-light unit for reducing avoidance of the nets by some zooplankton and possibly small fish.  The strobe system has two units each with 12 LED sets (LUXEON Rebel LED) with peak output between 490-520 nm. Seven of the 24 LED sets were no longer working at the start of the sampling. The LEDs are powered by the MOCNESS battery and their pulse width, amplitude, flash rate period, and on/off are controlled by the MOCNESS software. For this cruise the pulse width was 2 ms, the relative amplitude was 99%, and the flash interval was 100 ms. The strobe unit was only used for the first four tows, after which problems with blowing the underwater unit 5A fuse (a symptom typical of strobe unit problems from the past) led us to disconnect it and not use it for the remainder of the tows."

ZOOSCAN

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). Images were taken at 2400dpi with a large frame.