Contributors | Affiliation | Role |
---|---|---|
Johnson, Zackary I. | Duke University | Principal Investigator, Contact |
Hunt, Dana | Duke University | Co-Principal Investigator |
Ake, Hannah | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Environmental and physical data associated with ocean acidification microbe adaptation.
For related research, go to: http://oceanography.ml.duke.edu/johnson/research/pico/
DIC: Water was sampled using a 5 L niskin bottle centered at 1 m with a bottle length of 0.7 m. DIC was measured on mercuric chloride poisoned samples by acidification and subsequent quantification of released CO2 using a CO2 detector (Li-Cor 7000). DIC samples were collected following recommended procedures {Dickson et al., 2007} and measurements were calibrated against Certified Reference Materials provided by Dr. A. G. Dickson at Scripps Institution of Oceanography (SIO), University of California, San Diego (UCSD).
pH: Water was sampled using a 5 L niskin bottle centered at 1 m with a bottle length of 0.7 m. pH was measured spectrophotometrically {Clayton and Byrne, 1993} in triplicate at standard temperature (25°C) immediately following collection. pH samples were collected following recommended procedures {Dickson et al., 2007}.
Secchi Depth: Secchi depth was measured in duplicate using a 20 cm disk with four alternating white and black quadrants (Cole Parmer #EW-05492-00) by lowering the disk until no longer visible and recording the depth.
Salinity: Water was sampled using a 5 L niskin bottle centered at 1 m with a bottle length of 0.7 m. Salinity was measured using a calibrated handheld digital refractometer (Atago PAL-06S), using a refractometer (Vista A366ATC), or using a Guideline Portasal 8410A all according to manufacturer’s instructions and calibrated against known reference materials. In situ salinity at the same depth was measured using a YSI Pro30.
Turbidity: Turbidity was measured in duplicate on discrete samples using a calibrated handheld turbidimeter (Orion AQ4500).
Dissolved Oxygen: Oxygen was measured optically in situ and atmospheric pressure measured near the sea surface using a calibrated probe (YSI ProODO) using manufactures recommendations.
Chlorophyll: Water was sampled using a 5 L niskin bottle centered at 1 m with a bottle length of 0.7 m. Methods described in Johnson et al. 2010: Chlorophyll concentrations were measured by filtering 25 mL of seawater sample onto a 0.22 µm pore size polycarbonate filter using gentle vacuum (<100 mm Hg) and extracting in 100% MeOH at -20°C in the dark for >24 h following (Holm-Hansen and Riemann, 1978). Fluorescence was measured using a Turner Designs 10-AU fluorometer following (Welschmeyer, 1994) that was calibrated against a standard chlorophyll solution (Ritchie, 2008).
Bacteria: Bacterioplankton (i.e. ‘bacteria’) were enumerated using a FACSCalibur flow cytometer (Becton Dickinson) and populations characterized as previously described (Johnson et al., 2010). Briefly, cells were excited with a 488 nm laser (15 mW Ar) and inelastic forward (<15°) scatter, inelastic side (90°) scatter (SSC), green (530 ± 30 nm) fluorescence, orange fluorescence (585 ± 42 nm), and red fluorescence (> 670 nm) emissions were measured. Bacterioplankton were quantified by staining the samples with the nucleic acid stain SYBR Green –I (Molecular Probes Inc.) (Marie et al., 1997).
Nutrients: Water was filtered through a 0.22 µm Sterivex cartridge filter, Millipore #SVGPL10RC using a peristaltic pump input line at 1 m for later nutrient analysis (NO3, NO2, PO4, SiOH4) and water was placed into duplicate HCl-cleaned HDPE bottles (VWR#414004-110) and stored at -80°C until later analysis using an Astoria-Pacific A2 autoanalyzer following the manufacturer’s recommended protocols running each replicate sample in duplicate.
Certified reference materials were used to verify protocols (Inorganic Ventures: QCP-NT, QCP-NUT-1, CGSI1-1). The detection limit was NO2 = 0.05 µM, NO3 = 0.1 µM, PO4 = 0.05 µM, SiOH4 = 0.2 µM). Values measured below these limits are reported as zero.
Temperature: Water was sampled using a 5 L niskin bottle centered at 1 m with a bottle length of 0.7 m. Temperature was measured in duplicate using NIST traceable thermocouples (VWR#23609-232). In situ water temperature at the same depth was measured using a YSI Pro30.
Quality Scores (Q) as follows: 1=excellent (no known issues), 2=suspect, 3=poor (known reason to suspect data)
Nutrients: Samples that had a mean concentration (mean of replicated samples) below the nominal detection limit are reported as zero.
Bacteria: Cells counts were normalized to volume sampled to determined cells per mL.
Chlorophyll: >0.22 um referred to as “total” or simply “chlorophyll”
BCO-DMO Data Processing Notes:
- replaced NaN with nd
- added ISO DateTime column
- separated date and time into two columns
File |
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PICO2017.csv (Comma Separated Values (.csv), 54.17 KB) MD5:19c9b2560b506ab16b05f6dfa726c969 Primary data file for dataset ID 700974 |
Parameter | Description | Units |
date | Date of sampling; YYYY/MM/DD | unitless |
time | Time of sampling; HH:MM | unitless |
depth | Depth ID | unitless |
bacteriaA | Concentration of bacteria; Sample A; Includes Archaea and Prochlorococcus | cells per milliliter |
bacteriaB | Concentration of bacteria; Sample B; Includes Archaea and Prochlorococcus | cells per milliliter |
BarometricPressureA | Atomospheric (barometric) pressure; Sample A | hectopascals |
BarometricPressureB | Atomospheric (barometric) pressure; Sample B | hectopascals |
ChlExtractA | Extracted chlorophyll concentrations (greater than 0.22 um); Sample A | milligrams of chlorophyll a per meter cubed |
ChlExtractB | Extracted chlorophyll concentrations (greater than 0.22 um); Sample B | milligrams of chlorophyll a per meter cubed |
DICA | Dissolved inorganic carbon; Sample A | uM |
DICB | Dissolved inorganic carbon; Sample B | uM |
DICC | Dissolved inorganic carbon; Sample C | uM |
NH4A | Inorganic nutrient concentration; Sample A | uM |
NH4B | Inorganic nutrient concentration; Sample B | uM |
NH4C | Inorganic nutrient concentration; Sample C | uM |
NO2A | Inorganic nutrient concentration; Sample A | uM |
NO2B | Inorganic nutrient concentration; Sample B | uM |
NO2C | Inorganic nutrient concentration; Sample C | uM |
NO3A | Inorganic nutrient concentration; Sample A | uM |
NO3B | Inorganic nutrient concentration; Sample B | uM |
NO3C | Inorganic nutrient concentration; Sample C | uM |
OxygenA | Oxygen concentration; Sample A | uM |
OxygenB | Oxygen concentration; Sample B | uM |
OxygenSaturationA | Percent of theoretical saturation value for a given temperature salinity and pressure; Sample A | milligrams of oxygen per liter |
OxygenSaturationB | Percent of theoretical saturation value for a given temperature salinity and pressure; Sample B | milligrams of oxygen per liter |
pHT25A | pH measurement; Sample A | pH |
pHT25B | pH measurement; Sample B | pH |
pHT25C | pH measurement; Sample C | pH |
PICONumber | Sample number | unitless |
PO4A | Inorganic nutrient concentration; Sample A | uM |
PO4B | Inorganic nutrient concentration; Sample B | uM |
PO4C | Inorganic nutrient concentration; Sample C | uM |
QbacteriaA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QbacteriaB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QBarometricPressureA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QBarometricPressureB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QChlExtractA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QChlExtractB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QDepth | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QDICA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QDICB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QDICC | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNH4A | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNH4B | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNH4C | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNO2A | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNO2B | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNO2C | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNO3A | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNO3B | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QNO3C | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QOxygenA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QOxygenB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QOxygenSaturationA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QOxygenSaturationB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QpHT25A | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QpHT25B | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QpHT25C | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QPO4A | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QPO4B | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QPO4C | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSalinityAtagoA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSalinityAtagoB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSalinityPortasalA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSalinityPortasalB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSalinityPro30A | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSalinityPro30B | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSalinitySpyGlassA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSalinitySpyGlassB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSecchiDepthA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSecchiDepthB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSiOH4A | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSiOH4B | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QSiOH4C | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QTempBottleA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QTempBottleB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QTempPro30ProbeA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QTempPro30ProbeB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QTurbidityA | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
QTurbidityB | Quality score: 1=excellent (no known issues); 2=suspect; 3=poor (known reason to suspect data) | unitless |
SalinityAtagoA | Salinity measurement by instrument; Sample A | PSU |
SalinityAtagoB | Salinity measurement by instrument; Sample B | PSU |
SalinityPortasalA | Salinity measurement by instrument; Sample A | PSU |
SalinityPortasalB | Salinity measurement by instrument; Sample B | PSU |
SalinityPro30A | Salinity measurement by instrument; Sample A | PSU |
SalinityPro30B | Salinity measurement by instrument; Sample B | PSU |
SalinitySpyGlassA | Salinity measurement by instrument; Sample A | PSU |
SalinitySpyGlassB | Salinity measurement by instrument; Sample B | PSU |
SecchiDepthA | Secchi depth; Sample A | meters |
SecchiDepthB | Secchi depth; Sample B | meters |
SiOH4A | Inorganic nutrient concentration; Sample A | uM |
SiOH4B | Inorganic nutrient concentration; Sample B | uM |
SiOH4C | Inorganic nutrient concentration; Sample C | uM |
TempBottleA | Temperature; Sample A | Celsius |
TempBottleB | Temperature; Sample B | Celsius |
TempPro30ProbeA | Temperature from Pro 30 Probe; Sample A | Celsius |
TempPro30ProbeB | Temperature from Pro 30 Probe; Sample B | Celsius |
TurbidityA | Turbidity; Sample A | Nephelometric turbidity units (NTU) |
TurbidityB | Turbidity; Sample B | Nephelometric turbidity units (NTU) |
ISO_DateTime_UTC | ISO_Date format | unitless |
Dataset-specific Instrument Name | HDPE bottle |
Generic Instrument Name | Bottle |
Dataset-specific Description | Used in nutrient analysis |
Generic Instrument Description | A container, typically made of glass or plastic and with a narrow neck, used for storing drinks or other liquids. |
Dataset-specific Instrument Name | Li-Cor 7000 |
Generic Instrument Name | CO2 Analyzer |
Dataset-specific Description | Used to sample DIC |
Generic Instrument Description | Measures atmospheric carbon dioxide (CO2) concentration. |
Dataset-specific Instrument Name | NIST traceable thermocouples |
Generic Instrument Name | digital thermometer |
Dataset-specific Description | Used to measure temperature |
Generic Instrument Description | An instrument that measures temperature digitally. |
Dataset-specific Instrument Name | 10-AU Turner Designs Fluorometer |
Generic Instrument Name | Fluorometer |
Dataset-specific Description | Used to measure fluorescence |
Generic Instrument Description | A fluorometer or fluorimeter is a device used to measure parameters of fluorescence: its intensity and wavelength distribution of emission spectrum after excitation by a certain spectrum of light. The instrument is designed to measure the amount of stimulated electromagnetic radiation produced by pulses of electromagnetic radiation emitted into a water sample or in situ. |
Dataset-specific Instrument Name | Niskin |
Generic Instrument Name | Niskin bottle |
Dataset-specific Description | Used to collect water samples |
Generic Instrument Description | A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24, or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc. |
Dataset-specific Instrument Name | Astoria-Pacific A2 autoanalyzer |
Generic Instrument Name | Nutrient Autoanalyzer |
Dataset-specific Description | Used in nutrient analysis |
Generic Instrument Description | Nutrient Autoanalyzer is a generic term used when specific type, make and model were not specified. In general, a Nutrient Autoanalyzer is an automated flow-thru system for doing nutrient analysis (nitrate, ammonium, orthophosphate, and silicate) on seawater samples. |
Dataset-specific Instrument Name | YSI ProODO |
Generic Instrument Name | Oxygen Sensor |
Dataset-specific Description | Used to measure dissolved oxygen |
Generic Instrument Description | An electronic device that measures the proportion of oxygen (O2) in the gas or liquid being analyzed |
Dataset-specific Instrument Name | Peristaltic pump |
Generic Instrument Name | Pump |
Dataset-specific Description | Used in nutrient analysis |
Generic Instrument Description | A pump is a device that moves fluids (liquids or gases), or sometimes slurries, by mechanical action. Pumps can be classified into three major groups according to the method they use to move the fluid: direct lift, displacement, and gravity pumps |
Dataset-specific Instrument Name | Atago PAL-06S Digital Refractometer |
Generic Instrument Name | Refractometer |
Dataset-specific Description | Used to sample salinity |
Generic Instrument Description | A refractometer is a laboratory or field device for the measurement of an index of refraction (refractometry). The index of refraction is calculated from Snell's law and can be calculated from the composition of the material using the Gladstone-Dale relation.
In optics the refractive index (or index of refraction) n of a substance (optical medium) is a dimensionless number that describes how light, or any other radiation, propagates through that medium. |
Dataset-specific Instrument Name | Spectrometer |
Generic Instrument Name | Spectrometer |
Dataset-specific Description | Used to sample pH |
Generic Instrument Description | A spectrometer is an optical instrument used to measure properties of light over a specific portion of the electromagnetic spectrum. |
Dataset-specific Instrument Name | Orion AQ4500 |
Generic Instrument Name | Turbidity Meter |
Dataset-specific Description | Used to analyze turbidity |
Generic Instrument Description | A turbidity meter measures the clarity of a water sample. A beam of light is shown through a water sample. The turbidity, or its converse clarity, is read on a numerical scale. Turbidity determined by this technique is referred to as the nephelometric method from the root meaning "cloudiness". This word is used to form the name of the unit of turbidity, the NTU (Nephelometric Turbidity Unit). The meter reading cannot be used to compare the turbidity of different water samples unless the instrument is calibrated. Description from: http://www.gvsu.edu/wri/education/instructor-s-manual-turbidity-10.htm
(One example is the Orion AQ4500 Turbidimeter) |
Website | |
Platform | Duke University Marine Lab |
Start Date | 2010-06-28 |
End Date | 2012-06-26 |
Description | The PICO time series is sampled weekly (or more frequently) to capture physical, chemical and biological variability in the coastal ocean. This time series enables the investigator to collaborate with a number of researchers and will serve as a long-term research focus.
Project information: http://oceanography.ml.duke.edu/johnson/research/pico/ |
From the project website:
Carbon dioxide is rising at ~3% per year in the atmosphere and oceans leading to increases in dissolved inorganic carbon and a reduction in pH. This trend is expected to continue for the foreseeable future and ocean pH is predicted to decrease substantially making the ocean more acidic, potentially affecting the marine ecosystem. However, coastal estuaries are highly dynamic systems that often experience dramatic changes in environmental variables over short periods of times. In this study, the investigators are measuring key variables of the marine carbon system along with other potential forcing variables and characteristics of the ecosystem that may be affected by these pH changes. The goal of this project is to determine the time-scales and magnitude of natural variability that will be superimposed on any long term trends in ocean chemistry.
Other PICO-related projects in BCO-DMO:
Ocean Acidification: microbes as sentinels of adaptive responses to multiple stressors: contrasting estuarine and open ocean environments
Extracted from the NSF award abstract:
This collaborative project by Duke University and Georgia Institute of Technology researchers will combine oceanographic and advanced molecular techniques to characterize the adaptive responses of microbial communities to multiple stressors associated with OA. In particular, microbial communities from estuarine and coastal ecosystems as well as open ocean waters will be incubated under conditions of increased acidity or temperature or both, and their activities will be measured and quantified.
Preliminary data from time-series observations of a coastal temperate estuary shows that pH, temperature and other stressors vary over multiple space and time scales, and this variability is relatively higher than that observed in open ocean waters. Based on this evidence, the guiding hypothesis of this work is that microbes in coastal ecosystems are better adapted to ocean acidification as well as multiple stressors compared to similar microbes from the open ocean. To quantify the adaptive genetic, physiological and biogeochemical responses of microbes to OA, the team's specific goals are to: (1) characterize complex natural microbial community responses to multiple stressors using factorial mesocosm manipulations, (2) assemble a detailed view of genomic and physiological (including transcriptional) adaptations to OA at the single species level using cultured model marine microbes (e.g. Prochlorococcus, Synechococcus, Vibrio) identified as responsive to stressors in whole community mesocosm experiments, and (3) assess the power of model microbial strains and mesocosm experiments to predict microbial community responses to natural OA variability in a temporally dynamic, temperate estuary and along a trophic/pH gradient from the Neuse-Pamlico Sound to the Sargasso Sea. By comparing an estuarine ecosystem to its open ocean counterpart, this study will assess the sensitivity of microbial structure and function in response to ocean acidification.
This project is associated with Pivers Island Coastal Observatory.
NSF Climate Research Investment (CRI) activities that were initiated in 2010 are now included under Science, Engineering and Education for Sustainability NSF-Wide Investment (SEES). SEES is a portfolio of activities that highlights NSF's unique role in helping society address the challenge(s) of achieving sustainability. Detailed information about the SEES program is available from NSF (https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504707).
In recognition of the need for basic research concerning the nature, extent and impact of ocean acidification on oceanic environments in the past, present and future, the goal of the SEES: OA program is to understand (a) the chemistry and physical chemistry of ocean acidification; (b) how ocean acidification interacts with processes at the organismal level; and (c) how the earth system history informs our understanding of the effects of ocean acidification on the present day and future ocean.
Solicitations issued under this program:
NSF 10-530, FY 2010-FY2011
NSF 12-500, FY 2012
NSF 12-600, FY 2013
NSF 13-586, FY 2014
NSF 13-586 was the final solicitation that will be released for this program.
PI Meetings:
1st U.S. Ocean Acidification PI Meeting(March 22-24, 2011, Woods Hole, MA)
2nd U.S. Ocean Acidification PI Meeting(Sept. 18-20, 2013, Washington, DC)
3rd U.S. Ocean Acidification PI Meeting (June 9-11, 2015, Woods Hole, MA – Tentative)
NSF media releases for the Ocean Acidification Program:
Press Release 10-186 NSF Awards Grants to Study Effects of Ocean Acidification
Discovery Blue Mussels "Hang On" Along Rocky Shores: For How Long?
Press Release 13-102 World Oceans Month Brings Mixed News for Oysters
Funding Source | Award |
---|---|
NSF Division of Ocean Sciences (NSF OCE) |