Dataset: Antarctic krill schooling: Speed and nearest neighbor distances

Name: Speed and nearest neighbor distances in each trial of Antarctic krill in lab experiments at Palmer Station, Antarctica in November 2022.
Published: 2024-03-26
Keywords: antarctic krill, schooling, kreisel tank, flow characterization, southern ocean ecology, collective behavior, stereophotogrammetry, oceans

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Palmer Station, Antarctica

Project:

Collaborative Research: Individual Based Approaches to Understanding Krill Distributions and Aggregations (Krill Aggregation)

Principal Investigator:

David Murphy (University of South Florida, USF)

Student:

Kuvvat Garayev (University of South Florida, USF)

Contact:

Kuvvat Garayev (University of South Florida, USF)

BCO-DMO Data Manager:

Karen Soenen (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2024-03-26

Restricted:

Validated:

Current State:

Data not available

Speed and nearest neighbor distances in each trial of Antarctic krill in lab experiments at Palmer Station, Antarctica in November 2022.

Abstract:

Laboratory experiments were conducted on schools of Antarctic krill in the novel annular flume at Palmer Station, Antarctica, in November 2022. Using overhead camera along with stereophotogrammetry system the swimming trajectories of krill were recorded while altering flow and light levels in the tank. The purpose of the study is to understand how Antarctic krill schooling structure changes under environmental cues such as flow and light, with the hope that distribution of these important species can be predicted through knowledge of the environment in the wild. Southern Ocean ecologists, biologists, and oceanographers in general could benefit from this work. Kuvvat Garayev and David Murphy from University of South Florida were responsible for the collection and interpretation of data.

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Methods & Sampling

We deployed on the R/V Laurence M. Gould to Palmer Station, Antarctica in October - December 2022. On the way, we collected krill using an Isaac-Kidd Midwater Trawl net at the following locations: 63°06.5′ S, 61°46.6′ W (on October 5th, 2022) and 64°37.9′ S, 62°14.5′ W (on October 6th, 2022). Krill were kept in two tanks with a continuous seawater supply on the boat until being transferred to holding tanks at the Station. Additional krill were collected from Wilhelmina Bay (64°42.0′ S, 62°16.6′ W) on November 12th by the R/V Nathaniel B. Palmer, transported to Palmer Station, and added to the same holding tanks filled with filtered seawater (100 µm).

Data Processing Description

For speed and nearest neighbor distance (NND) calculations, DLTdv8 app in MATLAB was used (Hedrick, T. L. (2008)).

“speed and nnd” is for calculating speed and nearest neighbor distances of krill in the field of view of 3 high-magnification cameras (i.e., cam2, cam5, and cam4). “speed and nnd” contains 4 folders: “trial 4”, “trial 31”, “trial 43”, and “trial 46”.

Each folder contains spreadsheet named 1_data.csv which has several bursts consisting of 3 consecutive frames (rows) separated by NaN rows. Columns, except Column 1 which is NaN, sequentially correspond to X, Y, and Z coordinates of krill in meters. For example, rows 2, 3, and 4 correspond to 3 consecutive time points that make up one burst. Columns 2 to 4 correspond to X, Y, and Z coordinates of one krill, and 5 to 7 correspond to X, Y, and Z coordinates of another krill, etc.

More Information about this dataset

Funding Sources

Funding Source	Award Number
NSF Office of Polar Programs (formerly NSF PLR)	OPP-1840941

Deployments

None available yet

Instruments

Machine Vision Camera System

Supplied Name:

Supplied Description:

Four 4 cameras (5 MP AVT Mako) were time synchronized using StreamPix software and recorded to a system at 20 Hz frame rate (Norpix, Canada). Overhead camera (Camera 1) is equipped with a 8 mm focal length lens (M0824-MPW2, Computar) whereas stereo cameras (Cameras 2-4) are equipped with 25 mm focal length lenses (M3520-MPW2, Computar).

Instrument Type

Generic Name: Machine Vision Camera System

Generic Description:

A machine vision camera captures images or videos of a scene, which are then processed and analyzed using machine vision algorithms.

Parameters

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Dataset: Antarctic krill schooling: Speed and nearest neighbor distances