Table of Contents

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

The latitude and longitude coordinates of this dataset represent the collection sites where zooplankton were collected for experiments that later took place in an onshore laboratory at the University of Washington. The time bounds of this dataset represent the dates experiments were conducted (from the Experiment_Date column), not the dates of zooplankton collection (Collection_Date column).

Sample Collection

Zooplankton samples were collected using either a 60 cm diameter, 200 μm mesh ring net with a non-filtering cod- end or a 60 cm diameter, 335 μm mesh bongo net with non-filtering codends, lifted vertically from 10 m off the seafloor. Samples were stored in a cooler and air-bubbled for <24 h until actively swimming adult female C. pacificus were manually sorted under a microscope into 1 liter jars filled with 200 μm filtered seawater. The copepods were kept at 14°C and fed a pre-made mixture of five marine microalgae (Isochrysis, Pavlova, Tetraselmis, Thalassiosira weissflogii and T. pseudonana) daily, for no more than 2 wk until they were used in a single laboratory experiment.

Experiment Details 

Water treatments of 2 different salinities (29 and 31) were made using Instant Ocean (~36 and ~39 g l−1) and verified with a YSI Pro 2030 salinity probe. Replicate 2-layer water columns with stable haloclines were created by pumping light (low salinity) water into the bottom of the tanks using a peristaltic pump until the water level was 500 mm from the bottom. Then, heavy (high salinity) water was pumped slowly to avoid mix- ing into the bottom, displacing the light water up- wards until the water columns were 780 mm deep with haloclines located 280 mm above the bottom.

To create the different oxygen conditions, bottom water was split into 2 buckets of equal volume to use in treatment and control tanks. Treatment bottom water was then bubbled with pure N2 gas until dissolved oxygen (DO) was <0.2 mg l−1, as measured by a Pre- Sens oxygen dipping probe. Surface water and control bottom water were not bubbled and were left at ambi- ent oxygen concentrations (~10 mg l−1). At the end of each experiment, oxygen was measured at the surface, upper halocline, lower halocline, and bottom of each tank by lowering the oxygen probe slowly through the water column so as not to disrupt the halocline.

C. pacificus behaviors and vertical distributions in response to either hypoxic or acidic bottom waters were observed in an array of 4 replicate 1 × 0.1 × 0.1 m acrylic tanks, installed in an environmental chamber set to 14°C.  Stressful water layers (or non-stressful controls) were placed at the bottoms of salinity-stratified tanks, modeled after conditions experienced in the field. To start each experiment, 20 animals were gently introduced to the top of each of the 4 tanks. Swimming behavior was then observed for 90 min using 5-megapixel IR USB cameras. Experiments were run during the day in the dark, with the tanks backlit with IR LED strips behind and around the base of each tank. Two front-facing cameras (‘bottom camera’, ‘surface camera’) recorded swimming in the X (left, right) and Z (up, down) directions, observing true vertical motion and projected horizontal motion. An upwards-facing ‘base camera’ was added to each tank in 2020 to improve tracking and behavioral analysis of copepods near the bottom. Base cameras recorded the bottom 2 cm of each tank in the X and Y (front, back) directions and therefore observed true horizontal motion but not vertical motion.

Video output details

Videos were processed with the software Fosica (Wallingford Imaging) to distinguish moving copepods from stationary background and noise and to extract copepod pixel coordinates. Pixel coordinates were converted into physical space units and then assembled into individual swimming paths using the Matlab software package Tracker3D (Chan & Grünbaum 2010), neglecting parallax in the camera field of view. A smoothing spline was applied to remove features changing faster than 6 Hz, which were dominated by frame rate noise. X and Z (or, for the base cameras, X and Y) pixel coordinates for each object and the total projected speed and velocities were calculated at every frame for each swimming path. Swimming paths included only animals actively moving in the tank, excluding motionless (moribund) animals at the bottom of the tank. Copepod swimming paths were used to calculate the mean height from the bottom of the tank, mean number of copepod localizations per frame, and mean swimming speeds.

Our video system could not distinguish between individuals that were dead and those that were lying immobilized on the bottom for extended periods (a behavior leading, at least in hypoxia, to a high likelihood of eventual mortality). Therefore, for 2020 experiments, we developed a video-based metric using the base cameras to classify copepods at the bottom of tanks that were ‘moribund.’ Remaining motionless on the bottom of the tank is an uncommon behavior for C. pacificus, and we conservatively estimated that copepods motionless for a 2 minute threshold were in a ‘moribund’ or stressed state. To quantify moribundity, we calculated the mean brightness values for each pixel from frames in each of the last 1 minute sections (89th and 90th minutes). Because the videos were recorded in a dark field with IR back-lighting, copepods appeared as bright spots in the videos. The brightness of pixels representing a copepod in these 1 min means was a direct function of the number of frames in which it remained stationary. We then used a brightness threshold to classify copepods as moribund if pixel brightness indicated they had not moved during the last 2 minute of video observations.

More details on the methodology can be found here: Wyeth, A.C., Grünbaum D., Keister J.E. (2022). Effects of hypoxia and acidification on Calanus pacificus: behavioral changes in response to stressful environments. Marine Ecology Progress Series, 697: 15-29. https://doi.org/10.3354/meps14142.

Videos were processed with the software Fosica (Wallingford Imaging) to distinguish moving copepods from stationary background and noise and to extract copepod pixel coordinates. Pixel coordinates were converted into physical space units and then assembled into individual swimming paths using the Matlab software package Tracker3D (Chan & Grünbaum 2010).

- Originally, this data and related Zooplankton Acidification Lab Result Data (see related datasets section of this metadata page) were contained in the same .csv file, these data from separate but related experiments were parsed out and served separately through BCO-DMO in order to describe and represent data and experiments more accurately
- Column names across the Supplemental data tables and primary data table were made the same across all files
- Blank spaces in column names replaced with underscores ("_")
- Date columns within the data file (Experiment_Date, Collection_Date, and Sort_Date) were converted from %m%d%y format to %Y-%M-%D format
- Replaced NA missing data values in the dataset with blank values ("")
- Column name weight.avg changed to mean_height
- Column name camera changed to camera_ID
- Special characters in column names changed to underscores ("_")
- Latitude and longitude values rounded to 6 degrees of precision

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