| Contributors | Affiliation | Role |
|---|---|---|
| Sutherland, Kelly Rakow | University of Oregon (OIMB) | Principal Investigator |
| Copley, Nancy | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | Guest, BCO-DMO Data Manager |
Related Dataset:
Tank and field turbulence
Current velocity measurements were taken from the collection site at the surface using a Vectrino Acoustic Doppler Velocimeter (ADV; Nortek, Oslo, Norway), at depth between 0.5 and 5 m using in situ PIV (Katija and Dabiri, 2008). The ADV measurements were collected at a sampling rate of 100 Hz over 1-5 min intervals.
The TKE dissipation rate at the surface was calculated from the ADV-produced velocities in the horizontal, x, direction, u, following (Sanford, 1997) Epsilon(ADV)=(urms)3/ZK where Z is the total water column depth (14 m) and κ is von Karman’s constant (0.4).
BCO-DMO Processing:
- added conventional header with dataset name, PI name, version date
- renamed parameters to BCO-DMO and BODC standards
- replaced blanks with 'nd', no data
- replaced '# to #' with '#-#'
- split date and time into separate columns
- added 'sampling rate' column
- reduced digits to right of decimal from 13 to 2
| File |
|---|
ADV_field.csv (Comma Separated Values (.csv), 3.77 KB) MD5:5acd113e299312489ab4711cb164a3e3 Primary data file for dataset ID 649913 |
| Parameter | Description | Units |
| year | year | unitless |
| filename | file name | unitless |
| date | date of measurements in field; formatted as yyyy-mm-dd | unitless |
| time_start | time of first measurement; formatted as HH:MM | unitless |
| time_end | time of last measurement; formatted as HH:MM | unitless |
| sample_rate | sampling rate | Hz |
| totl_measure | total number of measurements | unitless |
| time_tot_sec | total time in seconds | seconds |
| time_tot_min | total time in minutes | minutes |
| time_increment_min | time increments | minutes |
| meas_interval | observation interval range | measurements |
| flow | Flow speed estimated as speed = sqrt(rmsU^2 + rmsV^2 + rmsW^2); (Finelli et al. 2009 p 463 used (sqrt (u2 + v2 + w2 ) | cm/sec |
| mean_u | mean horizontal velocity 1 | cm/sec |
| mean_v | mean horizontal velocity 2 | cm/sec |
| mean_w | mean vertical velocity | cm/sec |
| u_rms | root mean square of u | cm/sec |
| v_rms | root mean square of v | cm/sec |
| w_rms | root mean square of w | cm/sec |
| current_avg | average current | cm/sec |
| TKE | turbulent kinetic energy: 0.5(ave_u_prime_sq+% ....) ; where u_prime is instantaneous deviations from the mean velocity (e.g. Finelli et al 2009) and the instantaneous values are averaged over time. | m^2/s^2 |
| dissip_rate | dissipation rate of the surface field: E = rmsV^3/depth*0.4 (Sanford 1997 p. 273; 0.4 is von Karman's constant); used V because it is usually the largest rms component. | m^2/s^3 |
| L_k_cm | kinematic viscosity at 11.5 deg C and 35 ppt is 1.3 x 10-6 m2 s-1 | cm |
| Taylor_microscale_cm | turbulence length scale is a length scale used to characterize a turbulent fluid flow: λ= sqrt(15vurms2/ε) | cm |
| Re | Reynolds number | unitless |
| Dataset-specific Instrument Name | |
| Generic Instrument Name | Acoustic Doppler Velocimeter |
| Dataset-specific Description | ADV; Nortek, Oslo, Norway |
| Generic Instrument Description | ADV is the acronym for acoustic doppler velocimeter. The ADV is a remote-sensing, three-dimensional velocity sensor. Its operation is based on the Doppler shift effect. The sensor can be deployed either as a moored instrument or attached to a still structure near the seabed.
Reference:
G. Voulgaris and J. H. Trowbridge, 1998. Evaluation of the Acoustic Doppler Velocimeter (ADV) for Turbulence Measurements. J. Atmos. Oceanic Technol., 15, 272–289. doi: http://dx.doi.org/10.1175/1520-0426(1998)0152.0.CO;2 |
| Website | |
| Platform | Friday_Harbor |
| Start Date | 2012-06-01 |
| End Date | 2016-06-30 |
Bloom-forming jellyfish are increasing in number, frequency and magnitude, in part due to anthropogenic impacts, underscoring a need for enhanced understanding of trophic exchanges in jellyfish-dominated ecosystems. Interactions between jellyfish and their prey are driven by morphology, behavior, and unique fluid signatures that result in species-specific prey selection patterns. Fluid signatures generated by predators entrain prey, and motile prey organisms have evolved to sense and respond to these stereotyped fluid signatures. The shape and coherence of these unique fluid signatures are strongly mediated by turbulence, which is ubiquitous in the ocean. Yet, the effects of turbulence are almost always neglected in feeding studies. This three-year project will investigate the influence of turbulence on predator-prey interactions using a suite of cnidarian hydromedusae with unique morphologies, fluid signatures and prey selection patterns collected in the region of Friday Harbor Laboratory, WA.
This project seeks to establish a detailed, mechanistic understanding of the effects of turbulence on organism-scale predator-prey interactions using gelatinous zooplankton predators with contrasting predation modes. The PI will investigate prey selection under varying levels of turbulence by studying swimming behavior, wake structure, and predator-prey interactions in a laboratory turbulence generator designed for fragile plankton. The PI will also make in situ measurements of turbulence and observations of organism behavior using a Self-contained Underwater Velocimetry Apparatus (SCUVA). This is a fully submersible instrument for flow visualization, and its use will provide a cross-calibration of field and laboratory rates and behaviors. The influence of turbulence on trophic position among the different species of hydromedusae will be quantified through field studies of prey selection patterns. The proposed comparative approach using species with distinct predation modes will provide insights applicable to other planktonic predators that can be similarly grouped.
| Funding Source | Award |
|---|---|
| NSF Division of Ocean Sciences (NSF OCE) |