Dataset: Laboratory Odontella Aurita Disaggregation

Name: Data from laboratory disaggregation experiments with Odontella aurita using a disaggregation roller tank that exposed aggregates to time-varying laminar shear in 2020
Published: 2023-12-15
Keywords: Odontella Aurita, marine snow, Disaggregation, oceans

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Temporal Extent: 2021-09-17 - 2021-09-21

Project:

Collaborative Research: The importance of particle disaggregation on biogeochemical flux predictions (Disaggregation)

Principal Investigator:

Matthew Rau (Pennsylvania State University, PSU)

Student:

Yixuan Song (Pennsylvania State University, PSU)

BCO-DMO Data Manager:

Amber D. York (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2023-12-15

Restricted:

Validated:

Current State:

Data not available

Data from laboratory disaggregation experiments with Odontella aurita using a disaggregation roller tank that exposed aggregates to time-varying laminar shear in 2020

Abstract:

These data include particle aggregate size, morphology, and fragmentation statistics obtained from laboratory experiments using a disaggregation roller tank that exposed aggregates to time-varying laminar shear. Laboratory experiments were conducted at Penn State University in 2021. Particle aggregates were formed by rolling sterile cultures of Odontella Aurita at a slow constant rate. Fragmentation was induced by oscillating the roller tank motion (following published methods) to create time-varying laminar shear with values relevant to turbulent shear in the open ocean. Aggregate sizes and morphologies were tracked in time using a bright side-illumination, a high-speed camera, and a particle tracking algorithm. Aggregates were tracked until they either left the camera's field of view or fragmented into two pieces. These data include capture of 79 fragmentation events. The fragmented aggregates ranged from 1 mm to 5 mm in major axis length, though deformed substantially prior to fragmentation due to shear exposure.

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Description

See the Related Datasets section for access to the results of other disaggregation experiments conducted as part of this study.

These data were published in Song et al. (2023).
* This results publication includes additional supplementary material related to this dataset:
https://www.frontiersin.org/articles/10.3389/fmars.2023.1224518/full#supplementary-material

Methods & Sampling

Samples were incubated using f/2 medium and laboratory-made artificial seawater with a salinity of 30 ppt. Culturing was done with a light intensity of 30 micromol per square meter per second on a 12:12 hour light/dark cycle. All culturing was conducted at a room temperature of 20.9 deg-C. Strains were maintained in their exponential growth phases by splitting at a regular rate prior to experimentation.

Odontella aurita experiments were conducted by filling oscillating roller tanks with ~1L of artificial seawater and strain with a cell concentration of ~46,000 cells/ml. Cells were aggregated into large aggregates through differential sedimentation by rolling them for 3 hrs at a rotation rate of 4 rpm prior to creating laminar shear. To create laminar shear and induce disaggregation, the flow motion inside the rotating/oscillating tank was a superposition of a solid body rotation and a harmonic oscillatory flow. We provided an analytical solution to this viscous oscillatory flow in the attached publication. Given proper boundary conditions, this roller tank created calibrated laminar shear that is similar in magnitude to that in the ocean.

With high-speed imaging techniques, we captured the fragmentation of laboratory-made diatom aggregates exposed to the calibrated oscillatory flow. We have also developed a unique image processing method that enabled continuous tracking of particle positions, sizes, and morphologies, as well as the determination of individual aggregate breakup events. The image processing consisted of background removal, particle identification, particle matching, and breakup detection. The analytical solution of fluid flow allowed us to couple the hydrodynamic conditions to the recorded time history.

Instrument summary:

The experimental facility consisted of a transparent acrylic cylindrical tank supported by four identical casters. The tank had an inner diameter of 100 mm and a wall thickness of 5 mm, with a total length of 300 mm. The tank was long enough to have a two-dimensional flow along the central tank cross-section. We controlled the rotation of this tank using an electric servo-motor (Dynamixel MX-28T, Trossen Robotics), programmed with Python. A timing belt drive system that connected the motor to one end of the tank precisely synchronized the rotation with a gear ratio of one. The motor had a resolution of 0.012 rad/s in rotation speed and 0.088º in operating angle. The optical transparency of the tank permitted illumination through the cylinder wall and the imaging of suspended particles through one end of the tank. An LED light panel (StudioPRO S-1200D, Fovitec) shone through a slit aperture (10 mm wide) to illuminate suspended particles along the central tank cross-section. We mounted a high-speed camera (FASTCAM Mini AX200, Photron) with its viewing axis perpendicular to the resulting light sheet. It captured particle motion through a macro lens with a 105 mm focal length (Sigma 105mm f/2.8 EX DG).

Problems/Issues

The data included here consist of disaggregation events collected of aggregate made with a single, sterile strain of diatoms. Marine aggregates typically involve more than one type of phytoplankton along with other organic detritus and possibly inorganic minerals, all of which can affect aggregate strength. The facility used also uses oscillating laminar shear to induce disagregation. While laminar shear is excellent for repeatability and for determining local shear rates around each aggregate, the use of laminar flow prevents us from studying fragmentation under the statistical variability of turbulence. Experimentally, lighting nonuniformities and particle shadows can introduce uncertainty in the particle identification and matching process used with this method. Practically, this limits the particle concentration we can study as too many aggregates will obscure much of the illuminated central tank cross-section. Additionally, discontinuity caused by particle shadows limited the number of frames we could track particles so we could not obtain the entire history of shear and morphology from the very beginning of the tank oscillation.

Data Processing Description

Data processing was conducted using custom-made codes to extract aggregate population statistics and individual characteristics from the high-speed videos acquired during experimentation. Data processing consisted of image background removal, particle identification, particle matching across frames, and breakup detection. The analytical solution of the fluid flow allowed us to couple the hydrodynamic conditions to the recorded time history. See the Supplemental Files section for data processing steps, which were written in Matlab R2018.

More Information about this dataset

Funding Sources

Funding Source	Award Number
NSF Division of Ocean Sciences	OCE-1948283

Deployments

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Instruments

high-speed camera

Supplied Name: FASTCAM Mini AX200, Photron

Supplied Description:

Instrument Type

Generic Name: high-speed camera

Generic Description:

A high-speed imaging camera is capable of recording rapid phenomena with high-frame rates. After recording, the images stored on the medium can be played back in slow motion. The functionality in a high-speed imaging device results from the frame rate, or the number of individual stills recorded in the period of one second (fps). Common video cameras will typically record about 24 to 40 fps, yet even low-end high-speed cameras will record 1,000 fps.

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Dataset: Laboratory Odontella Aurita Disaggregation