Dataset: AT50-04 Larval Sample List
Deployment: AT50-04_Alvin_Dives

View Data: For data, See Dataset Metadata Page: https://www.bco-dmo.org/dataset/944748

Principal Investigator:

Shawn M. Arellano (Western Washington University, WWU)

David B. Eggleston (North Carolina State University, NCSU)

Craig M. Young (University of Oregon, OIMB)

Co-Principal Investigator:

Ruoying He (North Carolina State University, NCSU)

Student:

Caitlin Q. Plowman (University of Oregon, OIMB)

BCO-DMO Data Manager:

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

Project:

Collaborative Research: dispersal depth and the transport of deep-sea, methane-seep larvae around a biogeographic barrier (SALT)

Version:

Coordinated Deployments:

AT50-04

Expand/Collapse All

Description

See "Related Datasets" section for other logs and sample lists from this cruise. Data from this and other cruises in this project are listed under the SALT project page https://www.bco-dmo.org/project/820030.

SALT = Seep Animal Larval Transport.

Methods & Sampling

Dataset acquisition description

Individual larvae were sorted out of MOCNESS samples, HOV Alvin slurp samples, and from the water in the bottom of the bioboxes on the Alvin basket. Samples were sorted by teams of students using dissecting scopes and each larva was assigned a morphotype ID. Individual larvae were photographed under a compound scope and preserved in 100% ethanol for DNA barcoding by the Arellano Lab at Western Washington University.

Data Processing Description

Dataset Processing Description

Changed date to yyyy-mm-dd

Changed coordinates to decimal degrees

More information about this dataset deployment

Funding

Award Number	Funding Source
OCE-1851286	NSF Division of Ocean Sciences
OCE-1851383	NSF Division of Ocean Sciences
OCE-1851421	NSF Division of Ocean Sciences

Instruments

HOV Alvin

Supplied Name:

Supplied Description:

Instrument Type

Generic Name: HOV Alvin

Generic Description:

Human Occupied Vehicle (HOV) Alvin is part of the National Deep Submergence Facility (NDSF). Alvin enables in-situ data collection and observation by two scientists to depths reaching 6,500 meters, during dives lasting up to ten hours.

Commissioned in 1964 as one of the world’s first deep-ocean submersibles, Alvin has remained state-of-the-art as a result of numerous overhauls and upgrades made over its lifetime. The most recent upgrades, begun in 2011 and completed in 2021, saw the installation of a new, larger personnel sphere with a more ergonomic interior; improved visibility and overlapping fields of view; longer bottoms times; new lighting and high-definition imaging systems; improved sensors, data acquisition and download speed. It also doubled the science basket payload, and improved the command-and-control system allowing greater speed, range and maneuverability.

With seven reversible thrusters, it can hover in the water, maneuver over rugged topography, or rest on the sea floor. It can collect data throughout the water column, produce a variety of maps and perform photographic surveys. Alvin also has two robotic arms that can manipulate instruments, obtain samples, and its basket can be reconfigured daily based on the needs of the upcoming dive.

Alvin's depth rating of 6,500m gives researchers in-person access to 99% of the ocean floor. Alvin is a proven and reliable platform capable of diving for up to 30 days in a row before requiring a single scheduled maintenance day. Recent collaborations with autonomous vehicles such as Sentry have proven extremely beneficial, allowing PIs to visit promising sites to collect samples and data in person within hours of their being discovered, and UNOLs driven technological advances have improved the ability for scientific outreach and collaboration via telepresence

Alvin is named for Allyn Vine, a WHOI engineer and geophysicist who helped pioneer deep submergence research and technology.

(from https://www.whoi.edu/what-we-do/explore/underwater-vehicles/hov-alvin/, accessed 2022-09-09)

MOCNESS

Supplied Name:

Supplied Description:

Instrument Type

Generic Name: MOCNESS

Community Identifier: http://vocab.nerc.ac.uk/collection/L22/current/NETT0097/

Generic Description:

The Multiple Opening/Closing Net and Environmental Sensing System or MOCNESS is a family of net systems based on the Tucker Trawl principle. There are currently 8 different sizes of MOCNESS in existence which are designed for capture of different size ranges of zooplankton and micro-nekton Each system is designated according to the size of the net mouth opening and in two cases, the number of nets it carries. The original MOCNESS (Wiebe et al, 1976) was a redesigned and improved version of a system described by Frost and McCrone (1974).(from MOCNESS manual) This designation is used when the specific type of MOCNESS (number and size of nets) was not specified by the contributing investigator.

Parameters

None available yet

Database

Contribute Data

Dataset: AT50-04 Larval Sample List
Deployment: AT50-04_Alvin_Dives

Dataset acquisition description

Dataset Processing Description

Database

Contribute Data

Dataset: AT50-04 Larval Sample ListDeployment: AT50-04_Alvin_Dives

Dataset acquisition description

Dataset Processing Description

Dataset: AT50-04 Larval Sample List
Deployment: AT50-04_Alvin_Dives