Optical Methods

LiZA: Phil Culverhouse, Plymouth University

VPR II: Andrew King, CALCOFI LTER

Optical systems have emerged over the past two decades as powerful tools for enumerating zooplankton. They form one third of the triad of in situ tools along with nets/pumps and high-frequency acoustics. In the laboratory, optical systems are also providing a means of archiving and processing zooplankton samples collected with nets or pumps.

In situ systems have the advantage of collecting images of zooplankton in their natural orientations and in association with predators, prey, or other particles from which behavior may be inferred. Fragile gelatinous taxa, which would normally be damaged or destroyed by nets/pumps can be enumerated using imaging systems. Organisms can be imaged on scales of centimeters to kilometers at resolutions that enable identification to family, genus, or in some cases - species. Examples of in situ systems include: the video plankton recorder (VPR), underwater video profiler (UVP), in situ ichthyoplankton imaging system (ISIIS), shadowed image particle profiler and environmental recorder (SIPPER), zooplankton visualization system (ZOOVIS), lightframe on-sight key species investigation (LOKI) system, FlowCytoBot, and others. Laboratory systems for processing plankton samples include the ZooScan, FlowCAM, flatbed scanners utilizing ZOOIMAGE software and ZooCam. A hybrid system called the Line-scanning Zooplankton Analyzer (LiZA) allows analysis of flow-through samples collected at sea. Optical imaging is a rapidly-developing field whose advances benefit from the availability of low-cost, high-resolution digital cameras and compact, computing systems.

Hardware is just a part of the imaging equation. With acquisition rates exceeding 10 Hz in some systems, it is quite possible to collect terabytes of data on a single cruise. Without software to enable rapid processing of images, optical systems would suffer from the same problem as net sampling … the accumulation of large amounts of unprocessed samples. Advances in computing power have allowed the development of segmentation algorithms, which isolate objects of interest from the background, combined with machine learning systems that utilize classification algorithms to train a computer to identify unknown objects based on training sets provided by a human. Many of these tasks are now performed in real-time, while at sea. This opens up a new dimension in sampling. No longer do we simply collect samples and then wait for months or years to see what they contain. Novel information can be provided to scientists at sea who can then modify their sampling design to capitalize on the new information. When used effectively, machines can perform repetitive tasks with ease, leaving humans to focus on correcting misclassifications and interpret patterns.

Links to optical system resources:

Multiple-instrument Review:

- Wiebe and Benfield. 2003. From the Hensen net toward 4 dimensional biological oceanography. Progress in Oceanography, 56: 7 - 136.

FlowCAM:

- Le Bourg, B. et al. 2015. FlowCAM as a tool for studying small (80-1000 µm) metazooplankton communities. J. Plankton Research, doi: 10.1093/plankt/fbv025

- more info: http://www.fluidimaging.com/products/flowcam-vs

FlowCytoBot:

- Olson, R.J. and H.M Sosik. 2007. A submersible imaging-in-flow instrument to analyze nano- and microplankton: Imaging FlowCytobot. Limnology and Oceanography: Methods, 5:195-203.

- more info: http://www.mclanelabs.com/master_page/product-type/samplers/imaging-flowcytobot

ISIIS:

- Cowen, R.K. and C.M. Guigand. 2008: In situ ichthyoplankton imaging system (ISIIS): system design and preliminary results. Limnology and Oceanography: Methods, 6: 126-132.

- more info: http://yyy.rsmas.miami.edu/groups/larval-fish/isiis%20website/isiispage1.htm