Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
  • Problem Description
• Data Files
• Supplemental Files
• Related Datasets
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

The primary file of this dataset is a tabular file of data containing an overview of each in situ video included in this analysis, the nearest CTD cast to the recording location of that vide, and selected environemntal data from the nearest CTD cast.

Videos were recorded only under low-flow (< 1 cm/s) conditions, using infrared lighting to avoid modifying zooplankton behavior. The video camera recorded swimming in the X (left, right) and Z (up, down) directions, observing true vertical motion and projected horizontal motion. Videos were recorded at an average frame rate of 20 frames per second. The frame rate varied slightly during videos due to data throughput limitations of the onboard, single-board computer. However, each video frame was precisely timestamped, and the exact difference in time between frames was used when converting speeds from pixels per frames to mm per second. Each frame was 650 x 876 pixels, and each pixel was 0.088 mm2, corresponding to a 57.2 x 77.1 mm field of view.

Zooplankton and other particles appeared in videos as grayscale images against a dark background. Regions of Interest (ROIs) from each frame were identified using the Python package OpenCV (version 4.7.0) (Bradski 2000). OpenCV-fitted contours and rotated bounding boxes were used to calculate width, height, and area metrics for each ROI. The nominal “length” of each ROI was defined as the longer of its width and height measurements. Position, defined as the optical centroid of the zooplankter outline, and size metrics were saved for each ROI that were potentially zooplankton (areas larger than 30 pixels) within each frame.

Zooplankton positions were assembled into swimming trajectories using the Matlab software Tracker3D (Chan and Grünbaum 2010). Small particles in videos were used to reconstruct a background flow field with Particle Image Velocimetry (PIV), using the Python package OpenPIV (version 0.23.9) (Liberzon et al. 2021).

Each zooplankton ROI was assigned a taxonomic identification using a Machine Learning (ML) image classification algorithm. The algorithm was generated using a pre-trained convolutional neural network 50 layers deep (ResNet-50) with PyTorch, an open-source, Python-based machine learning framework.

- Changed column name of video column to "vid_id" to match the "vid_id" columns of the supplemental files
- Changed date time format in nearest_ctd_cast from %m/%d/%y %H:%M to %Y-%m-%d %H:%M
- A human readable datetime column was created from the unix_datetimestamp value present in the filename, this column is named "datetime_utc"
- All video data has been compressed and uploaded to this data package in a zip folder called "fps_20.zip" the contents of this zip are represented in the primary tabular data file associated with this dataset

NSF Award Abstract:
Low oxygen (hypoxia) and low pH are known to have profound physiological effects on zooplankton, the microscopic animals of the sea. It is likely that many individual zooplankton change vertical mirgration behaviors to reduce or avoid these stresses. However, avoidance responses and their consequences for zooplankton distributions, and for interactions of zooplankton with their predators and prey, are poorly understood. This study will provide information on small-scale behavioral responses of zooplankton to oxygen and pH using video systems deployed in the field in a seasonally hypoxic estuary. The results will deepen our understanding of how zooplankton respond to low oxygen and pH conditions in ways that could profoundly affect marine ecosystems and fisheries through changes in their populations and distributions. This project will train graduate students and will engage K-12 students and teachers in under-served coastal communities by developing ocean technology-based citizen-scientist activities and curricular materials in plankton ecology, ocean change, construction and use of biological sensors, and quantitative analysis of environmental data.

Individual directional motility is a primary mechanism underlying spatio-temporal patterns in zooplankton population distributions. Motility is used by most zooplankton species to select among water column positions that differ in biotic and abiotic variables such as prey, predators, light, oxygen concentration, and pH. Species-specific movement responses to de-oxygenation and acidification are likely mechanisms through which short-term, localized impacts of these stressful conditions on individual zooplankton will be magnified or suppressed as they propagate up to population, community, and ecosystem-level dynamics. This study will quantify responses by key zooplankton species to oxygen and pH using in situ video systems to measure changes in individual behavior in hypoxic, low- pH versus well-oxygenated, high-pH regions of a seasonally hypoxic estuary. Distributions and movements of zooplankton will be quantified using three approaches: 1) an imaging system deployed in situ on a profiling mooring over two summers in a hypoxic region, 2) imagers deployed on Lagrangian drifters to sample simultaneously throughout the water column, and 3) vertically-stratified pumps and net tows to verify species identification and video-based abundance estimates. These field observations will be combined with laboratory analysis of zooplankton movements in oxygen and pH gradients, and with spatially-explicit models to predict how behavioral mechanisms lead to large-scale impacts of environmental stresses.

The following deployments were conducted in 2017 and 2018:
CB1077: https://www.bco-dmo.org/deployment/735746
CB1072: https://www.bco-dmo.org/deployment/735748
Zoocam_ORCA_Twanoh_2017: https://www.bco-dmo.org/deployment/735762
RC0008: https://www.bco-dmo.org/deployment/775288
Mooring ORCA_Hoodsport; NANOOS-APL4: https://www.bco-dmo.org/deployment/775291