Dataset: AUV_MontereyBay_Makai_CTD

Name: Autonomous Underwater Vehicle Monterey Bay Time Series - CTD from AUV Makai on 2016-02-03
Published: 2023-08-15
Keywords: Monterey Bay, AUV, CTD, fluorometer, Makai AUV, Environmental Sample Processor (ESP), oceans

Cite This Dataset

DOI:10.26008/1912/bco-dmo.644012.1

Spatial Extent: N:36.8338 E:-121.828 S:36.7837 W:-121.888

Temporal Extent: 2016-02-03

Project:

Center for Microbial Oceanography: Research and Education (C-MORE)

Principal Investigator:

Dr Chris Scholin (Monterey Bay Aquarium Research Institute, MBARI)

Contact:

Dr John P. Ryan (Monterey Bay Aquarium Research Institute, MBARI)

Data Manager:

Jasmine Nahorniak (Oregon State University, OSU-CEOAS)

BCO-DMO Data Manager:

Stephen R. Gegg (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Lynne M. Merchant (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2023-08-15

Restricted:

Validated:

Yes

Current State:

Final no updates expected

Autonomous Underwater Vehicle Monterey Bay Time Series - CTD from AUV Makai on 2016-02-03

Abstract:

Autonomous Underwater Vehicle (AUV) Monterey Bay Time Series from Feb 2016. This data set includes CTD and fluorometer data from the Makai AUV, as context for ecogenomic sampling using an onboard Environmental Sample Processor (ESP).

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Archival Copy

Version Date	Archive	Persistent Identifier	Date Assigned
2023-08-15	Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)	10.26008/1912/bco-dmo.644012.1	2023-10-03

Methods & Sampling

MBARI AUVs

Data Processing Description

Original sensor data were bin averaged to 2-second resolution.

BCO-DMO Processing

The date and time columns in the submitted file were combined to create an ISO datetime column named ISO_DateTime_UTC and of format YYYY-MM-DDThh:mm:ssZ

Data Files

File
AUV Monteray Bay CTD filename: 644012_v1_auv_monteray_bay_ctd.csv (Comma Separated Values (.csv), 2.80 MB) MD5:5885af1113684d9f2a739c4ae15de9a6 Primary data file for dataset ID 644012, version 1

More Information about this dataset

Funding Sources

Funding Source	Award Number
NSF Division of Biological Infrastructure	DBI-0424599
David and Lucile Packard Foundation	unknown AUV_MontereyBay Packard

Deployments

Deployment	Synonyms	Start Date	Platform	Investigator
AUV_Makai_Monterey_Bay_Time_Series	LRAUV Makai	2015-07-22	AUV Makai	Dr Chris A Scholin (Chief Scientist) Dr John P. Ryan (Contact)	data info

Instruments

Autonomous Underwater Vehicle

Supplied Name: Makai AUV

Supplied Description:

Instrument Type

Generic Name: Autonomous Underwater Vehicle

Acronym: AUV

Generic Description:

An Autonomous Underwater Vehicle (AUV) is a free-roving platform operating in the water column with propulsion but no human operator on board (e.g. Autosub, Gavia).

Environmental Sample Processor

Supplied Description:

Instrument Type

Generic Name: Environmental Sample Processor

Acronym: ESP

Generic Description:

The MBARI Environmental Sample Processor—the ESP—provides on-site (in situ) collection and analysis of water samples from the subsurface ocean. The instrument is an electromechanical/fluidic system designed to collect discrete water samples, concentrate microorganisms or particles, and automate application of molecular probes and qPCR which identify microorganisms and their gene products. The ESP also archives samples so that further analyses may be done after the instrument is recovered.

Environmental Sample Processor

See references below for methodology used on the ESP:
Greenfield, D.I., R. Marin III, S. Jensen, E. Massion, B. Roman, J. Feldman, C. Scholin (2006). Application of the Environmental Sample Processor (ESP) methodology for quantifying Pseudo-nitzschia australis using ribosomal RNA-targeted probes in sandwich and fluorescent in situ hybridization. Limnology and Oceanography: Methods 4: 426-435.

Greenfield, D., R. Marin III, G.J. Doucette, C. Mikulski, S. Jensen, B. Roman, N. Alvarado, and C.A. Scholin (2008). Field applications of the second-generation Environmental Sample Processor (ESP) for remote detection of harmful algae: 2006-2007. Limnology and Oceanography: Methods 6: 667-679.

Marin III, R., and C. Scholin (2010). Sandwich Hybridization. In: Microscopic and molecular methods for quantitative phytoplankton analysis (Chapter 12), edited by B. Karlson, C. Cusack, and E. Bresnan, E.. IOC Manuals and Guides, no. 55. (IOC/2010/MG/55) Paris, UNESCO. 110 pp.

Ottesen, E.A., R. Marin III, C.M. Preston, C.R. Young, J.P. Ryan, C.A. Scholin, and E.F. DeLong (2011). Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton. The ISME Journal, 5: 1881-1895, doi: 10.1038/ismej.2011.70.

Ottesen, E.A., C.R. Young, J.M. Eppley, J.P. Ryan, F.P. Chavez, C.A. Scholin, and E.F. DeLong (2013). Pattern and synchrony of gene expression among sympatric marine microbial populations. Proceedings of the National Academy of Sciences, 110: E488-E497, doi: 10.1073/pnas.1222099110.

Ottesen, E.A., C.M. Young, S.M. Gifford, J.M. Eppley, R. Marin III, S.C. Schuster, C.A. Scholin, and E.F. DeLong (2014). Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science, 345: 207-212, 10.1126/science.1252476.

Preston, C.M., A. Harris, J.P. Ryan, B. Roman, R. Marin III, S. Jensen, C. Everlove, J. Birch, J.M. Dzenitis, D. Pargett, M. Adachi, K. Turk, J.P. Zehr, and C.A. Scholin (2011). Underwater application of quantitative PCR on an ocean mooring. PLoS ONE, 6:e22522, doi: 10.1371/journal.pone.0022522.

Robidart, J.C., C.M. Preston, R.W. Paerl, K.A. Turk, A.C. Mosier, C.A. Francis, C.A. Scholin, and J.P. Zehr (2011). Seasonal Synechococcus and Thaumarchaeal population dynamics examined with high resolution with remote in situ instrumentation. The ISME Journal, 6: 513-523, doi: 10.1038/ismej.2011.127.

Robidart, J., M.J. Church, J.P. Ryan, F. Ascani, S.T. Wilson, D. Bombar, R. Marin III, K.J. Richards, D.M. Karl, C.A. Scholin, and J.P Zehr (2014). Ecogenomic sensor reveals controls on N2-fixing microorganisms in the North Pacific Ocean. The ISME Journal, 8: 1175-1185, 10.1038/ismej.2013.244.

Saito, M.A., V.V. Bulygin, D.M. Moran, C. Taylor, and C. Scholin (2011). Examination of microbial proteome preservation techniques applicable to autonomous environmental sample collection. Frontiers in Aquatic Microbiology, 2: doi: 10.3389/fmicb.2011.00215.

Scholin, C.. (2010). What are “ecogenomic sensors?” A review and thoughts for the future. Ocean Science 6: 51-60.

Ussler III, W., C.M. Preston, P. Tavormina, D. Pargett, S. Jensen, B. Roman, R. Marin III, S.R. Shah, P.R. Girguis, J.M. Birch, V.J. Orphan, and C. Scholin (2013). Autonomous application of quantitative PCR in the deep sea: In situ surveys of aerobic methanotrophs using the deep-sea Environmental Sample Processor. Environmental Science and Technology, 47: 9339–9346, doi: 10.1021/es4023199.

Varaljay, V.A, J. Robidart, C.M. Preston, S.M. Gifford, B. Durham, A.S. Burns, J.P. Ryan, R. Marin III, R.P. Kiene, J.P Zehr, C.A. Scholin, M. Moran. 2015. Single-taxon field measurements of bacterial gene regulation controlling DMSP fate. ISME Journal, doi:10.1038/ismej.2015.23|

Parameters

Date and time formats are described in python strftime() syntax.

Supplied Name	Supplied description	Supplied Units	Standard Name
ISO_DateTime_UTC	Date and time (UTC) formatted to ISO 8601 standard Format: yyyy-MM-ddTHH:mm:ssZ	unitless	ISO_DateTime_UTC
lat	latitude (north is positive)	decimal degrees	lat
lon	longitude (east is positive)	decimal degrees	lon
depth	depth sample	meters	depth
temp	Temperature from CTD	degrees Celsius	temp
sal	Salinity from CTD	dimensionless	sal
chl_a_fluor	chlorophyll-a fluorometric method	micrograms/liter	chl_a_fluor

Status message

Database

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Dataset: AUV_MontereyBay_Makai_CTD