Accessing BCO-DMO data
Brought to you by BCO-DMO
|
|
BCO-DMO ERDDAP
Accessing BCO-DMO data |
log in
Brought to you by BCO-DMO |
| Dataset Title: | The density (mg CaCO3/cm^3) of the skeleton of Clathromorphum nereostratum, when assessed as function of increasing seawater temperature and pCO2 concentration 
|
| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_755809) |
| Information: | Summary
| License
| ISO 19115
| Metadata
| Background
| Files
| Make a graph
|
Attributes {
s {
treatment_temp {
Float32 _FillValue NaN;
Float32 actual_range 6.5, 8.5;
String bcodmo_name "treatment";
String description "target temperature";
String long_name "Treatment Temp";
String units "degrees Celsius";
}
treatment_pCO2 {
Int16 _FillValue 32767;
Int16 actual_range 330, 850;
String bcodmo_name "treatment";
String description "target pCO2 concentration";
String long_name "Treatment P CO2";
String units "microatmospheres (uatm)";
}
tank {
Byte _FillValue 127;
Byte actual_range 1, 3;
String bcodmo_name "replicate";
String description "replicate tank";
String long_name "Tank";
String units "unitless";
}
tank_temp_ave {
Float32 _FillValue NaN;
Float32 actual_range 6.33, 8.91;
String bcodmo_name "temperature";
String description "average tank temperature during study";
String long_name "Tank Temp Ave";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
String units "degrees Celsius";
}
tank_pCO2_ave {
Float32 _FillValue NaN;
Float32 actual_range 323.1, 1110.49;
String bcodmo_name "pCO2";
String description "average tank pCO2 concentration during study";
String long_name "Tank P CO2 Ave";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PCO2C101/";
String units "microatmospheres (uatm)";
}
sample_ID {
Byte _FillValue 127;
Byte actual_range 1, 32;
String bcodmo_name "sample";
String description "unique identifier for each algal sample";
String long_name "Sample ID";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
String units "unitless";
}
replicate {
Byte _FillValue 127;
Byte actual_range 1, 3;
String bcodmo_name "replicate";
String description "replicate coralline alga";
String long_name "Replicate";
String units "unitless";
}
ROI {
Byte _FillValue 127;
Byte actual_range 1, 3;
String bcodmo_name "sample";
String description "region of interest scanned within each alga";
String long_name "ROI";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
String units "unitless";
}
mg_CaCO3_cm3 {
Float32 _FillValue NaN;
Float32 actual_range 1218.6, 1791.3;
String bcodmo_name "density";
String description "skeletal (CaCO3) density within the ROI";
String long_name "Mg Ca CO3 Cm3";
String units "milligrams/cm^3";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
String acquisition_description
"To evaluate whether rates of calcification within C. nereostratum have changed
or will change with ocean warming and acidification, we cultured C.
nereostratum under experimental conditions mimicking past, present, and
predicted future levels of ocean temperature and pCO2 in the region, then
followed this four-month incubation period with measurements of skeletal
density using micro-computed tomography (microCT). Small C. nereostratum
colonies (~4-5 cm diameter) were live collected from Adak in 2015 and
immediately transported to the Northeastern University Marine Science Center
in Nahant, Massachusetts. There, all specimens were acclimated to laboratory
conditions at 8.5 degrees C for two weeks, after which individual C.
nereostratum colonies were attached to the underside of plastic Petri dishes
using cyanoacrylate glue and then allowed to acclimate for an additional two
weeks before being moved to experimental aquaria. Conditions were then
incrementally modified to achieve target temperature and pCO2 levels (see
below) over a one-week period. After reaching target conditions, each 42-L
aquarium was dosed with 213 mL of calcein fluorescent dye (Western Chemicals
Inc.), which was recirculated in the aquaria for three days and then flushed
from the system. Coralline algae incorporate the dye into their skeleton, thus
creating a distinct line that can be viewed via fluorescent microscopy to
demark the region of new growth within each individual.
We employed four pCO2 conditions and three temperatures that, while
factorially crossed, spanned pre-industrial, present-day, and projected year
2100 conditions (assuming an IPCC \\\"business as usual\\\" carbon emissions
scenario; Pachauri and Meyer 2014). More extreme temperature (12.5 degrees C)
and pCO2 (2800 micro-atm) conditions were also employed in the broader
experiment but were not included in our study because they are not predicted
to occur until year 2500, or later. For each treatment, we set values to
average summertime conditions, the time when ~75% of C. nereostratum growth
occurs (Adey et al. 2013).
All treatments (4 pCO2 concentrations x 3 temperatures, fully factorial) were
housed on individual shelves and consisted of three 42-liter acrylic aquaria
and one 65-liter sump (n = 3 tanks/treatment). The aquaria were connected to a
sump via a common overflow and return line but were each independently and
continuously replenished with new seawater\\u2014thereby establishing them as
true experimental replicates. The sump contained a filter box with a nylon
mesh particle filter and activated carbon, a protein skimmer (Eshopps PSK-75),
and a return pump, all of which was connected to a water chiller (Coralife
1/4HP). Filtered natural seawater was added via Darhor manual flow controllers
at a rate of 50 mL/min/tank, resulting in full replacement of treatment water
every ~21 hours - sufficiently fast to prevent material depletion of the
dissolved constituents of the seawater yet slow enough to allow the mixed
gases being sparged into the experimental treatments to approach equilibrium
with the seawater. Mixed gases were sparged into each tank with 91 cm long
flexible bubblers at the rate of ~1 L/min via Darhor needle-valve gas flow
controllers. Two 12,000K LED light arrays (Ecoxotic Panorama, Pro 24V) were
mounted above each tank and set to an irradiance that mirrored average summer
daylight irradiance at 10 m depth in the Aleutian Islands (~258 micro-E m-2
s-1; 12 hr light:12 hr dark cycle).
Over the course of the four-month experiment, we measured pH (Accumet AB15 pH
meter with Accufet solid state probe), salinity (YSI3200 meter with K=10
conductivity electrode and temperature probe), and temperature (NIST traceable
red spirit glass thermometer) in each tank every Monday, Wednesday, and
Friday. The pCO2 of the gas mixtures was measured with a Qubit S151 infrared
CO2 analyzer and calibrated with certified mixed CO2 from Airgas Incorporated.
Every 10 days, we characterized the full carbonate system chemistry of the
experimental treatments from measured total alkalinity, dissolved inorganic
carbon, temperature, and salinity. For this, seawater samples were obtained in
250 mL borosilicate ground-glass-stoppered bottles and immediately poisoned
with 100 micro-L of saturated HgCl2 solution to halt biological activity
(Dickson et al. 2007). Total alkalinity was measured via closed-cell
potentiometric Gran titration and dissolved inorganic carbon was measured with
a UIC 5400 Coulometer on a VINDTA 3C (Marianda Incorporated) using Dickson
certified seawater reference material. Seawater pCO2, pH, carbonate ion
concentration ([CO32-]) bicarbonate ion concentration ([HCO3-]), aqueous CO2,
and calcite saturation state were calculated with the program CO2SYS (Lewis
and Wallace 1998), using Roy and colleague's (1993) values for the K1 and K2
carbonic acid constants, the Mucci (1983) value for the stoichiometric calcite
solubility product, the seawater pH scale, and an atmospheric pressure of
1.015 atm.
At the beginning of the experiment we measured the buoyant weight of each
specimen. We then scrubbed each specimen with a toothbrush and reweighed it
every month and at the end of the experiment. With each weighing, we also
photographed the specimen with a ruler and Reef Watch coral bleaching card in
the field of view. We then measured the 2-d surface area of the photographed
specimens (Image J, NIH). At the end of the experiment, all coralline algae
were sectioned with a diamond lapidary saw (Inland Craft SwapTop 6.5\\u201d
Diamond Trim Saw) and either frozen for genetic analysis or sectioned into 6
mm slices, rinsed in a series of two 90% Ethanol baths, and allowed to air dry
for further examination of growth and skeletal density.
We measured the density of the calcified skeleton deposited by C. nereostratum
during the four-month laboratory experiment via micro-computed tomography
(microCT); see Chan et al. (2017) for methods development and analytical
setup. In brief, samples were scanned in a GE Locus RS-9 (General Electric
Health Care, London, Ontario) x-ray microCT at an energy of 90kVp and tube
current of 450 micro-A. Two frames, each 4500 ms in duration, were averaged at
900 projection angles over a 360-degree rotation of the gantry to produce data
that was processed into a 3D image with 20 micron isotropic voxel spacing.
Only specimens raised in the experimental temperature and pCO2 treatments
employed in the feeding assay (6 treatments, n = 3 specimens/treatment) were
studied. For each specimen, three cuboid regions of interest (ROI) were then
selected, focusing on the region of new growth as indicated by the calcein
mark. ROI size was similar for all measurements (1445-1575 voxels); however,
dimensions were adjusted depending on the amount of accretion incurred and to
avoid overlap with the epithallus or tissues deposited prior to the
experiment. Grayscale thresholding to eliminate non-calcified tissue was
unnecessary, given that conceptacles were not present in the newly deposited
tissue and intracellular pore spaces (6 microns) are smaller than the microCT
voxel size (20 microns) and were therefore not resolved. However, an analysis
employing thresholding (Chan et al. 2017) produced virtually identical
results. We quantified the skeletal density within each ROI by calculating the
fractional mineral content of the ROI (i.e., fractional composition of each
voxel that is CaCO3), converting each value to units of pure crystal calcite
(physical density: 2.71 g/cm^3), then averaging over all voxels in the ROI.";
String awards_0_award_nid "526658";
String awards_0_award_number "PLR-1316141";
String awards_0_data_url "http://nsf.gov/awardsearch/showAward?AWD_ID=1316141";
String awards_0_funder_name "NSF Arctic Sciences";
String awards_0_funding_acronym "NSF ARC";
String awards_0_funding_source_nid "390";
String awards_0_program_manager "Henrietta N Edmonds";
String awards_0_program_manager_nid "51517";
String cdm_data_type "Other";
String comment
"Skeletal density of Clathromorphum nereostratum
PI's: Steneck (Umaine), J. Estes (UCSC), D. Rasher (BLOS)
version: 2019-02-013";
String Conventions "COARDS, CF-1.6, ACDD-1.3";
String creator_email "info@bco-dmo.org";
String creator_name "BCO-DMO";
String creator_type "institution";
String creator_url "https://www.bco-dmo.org/";
String data_source "extract_data_as_tsv version 2.3 19 Dec 2019";
String date_created "2019-02-14T13:53:41Z";
String date_modified "2019-02-25T20:51:25Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.755809.1";
String history
"2024-04-20T10:18:21Z (local files)
2024-04-20T10:18:21Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_755809.html";
String infoUrl "https://www.bco-dmo.org/dataset/755809";
String institution "BCO-DMO";
String instruments_0_acronym "CO2 coulometer";
String instruments_0_dataset_instrument_description "To measure dissolved inorganic carbon";
String instruments_0_dataset_instrument_nid "755821";
String instruments_0_description "A CO2 coulometer semi-automatically controls the sample handling and extraction of CO2 from seawater samples. Samples are acidified and the CO2 gas is bubbled into a titration cell where CO2 is converted to hydroxyethylcarbonic acid which is then automatically titrated with a coulometrically-generated base to a colorimetric endpoint.";
String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB12";
String instruments_0_instrument_name "CO2 Coulometer";
String instruments_0_instrument_nid "507";
String instruments_0_supplied_name "UIC 5400 Coulometer on a VINDTA 3C";
String instruments_1_acronym "Salinometer";
String instruments_1_dataset_instrument_description "To measure salinity and temperature of tanks";
String instruments_1_dataset_instrument_nid "755818";
String instruments_1_description "A salinometer is a device designed to measure the salinity, or dissolved salt content, of a solution.";
String instruments_1_instrument_name "Salinometer";
String instruments_1_instrument_nid "677";
String instruments_1_supplied_name "YSI3200 meter with K=10 conductivity electrode and temperature probe";
String instruments_2_acronym "Benchtop pH Meter";
String instruments_2_dataset_instrument_description "To measure pH of tanks";
String instruments_2_dataset_instrument_nid "755817";
String instruments_2_description
"An instrument consisting of an electronic voltmeter and pH-responsive electrode that gives a direct conversion of voltage differences to differences of pH at the measurement temperature. (McGraw-Hill Dictionary of Scientific and Technical Terms)
This instrument does not map to the NERC instrument vocabulary term for 'pH Sensor' which measures values in the water column. Benchtop models are typically employed for stationary lab applications.";
String instruments_2_instrument_name "Benchtop pH Meter";
String instruments_2_instrument_nid "681";
String instruments_2_supplied_name "Accumet AB15 pH meter with Accufet solid state probe";
String instruments_3_acronym "inorganic carbon and alkalinity analyser";
String instruments_3_dataset_instrument_nid "755822";
String instruments_3_description "The Versatile INstrument for the Determination of Total inorganic carbon and titration Alkalinity (VINDTA) 3C is a laboratory alkalinity titration system combined with an extraction unit for coulometric titration, which simultaneously determines the alkalinity and dissolved inorganic carbon content of a sample. The sample transport is performed with peristaltic pumps and acid is added to the sample using a membrane pump. No pressurizing system is required and only one gas supply (nitrogen or dry and CO2-free air) is necessary. The system uses a Metrohm Titrino 719S, an ORION-Ross pH electrode and a Metrohm reference electrode. The burette, the pipette and the analysis cell have a water jacket around them. Precision is typically +/- 1 umol/kg for TA and/or DIC in open ocean water.";
String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0481/";
String instruments_3_instrument_name "MARIANDA VINDTA 3C total inorganic carbon and titration alkalinity analyser";
String instruments_3_instrument_nid "686";
String instruments_4_acronym "MFC";
String instruments_4_dataset_instrument_nid "755849";
String instruments_4_description "Mass Flow Controller (MFC) - A device used to measure and control the flow of fluids and gases";
String instruments_4_instrument_name "Mass Flow Controller";
String instruments_4_instrument_nid "712";
String instruments_4_supplied_name "Darhor manual flow controllers";
String instruments_5_acronym "CO2 Analyzer";
String instruments_5_dataset_instrument_description "To measure pCO2 in tanks";
String instruments_5_dataset_instrument_nid "755820";
String instruments_5_description "Measures atmospheric carbon dioxide (CO2) concentration.";
String instruments_5_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/382/";
String instruments_5_instrument_name "CO2 Analyzer";
String instruments_5_instrument_nid "491476";
String instruments_5_supplied_name "Qubit S151 infrared CO2 analyzer";
String instruments_6_acronym "Aquarium chiller";
String instruments_6_dataset_instrument_description "To maintain temperature in tanks";
String instruments_6_dataset_instrument_nid "755845";
String instruments_6_description "Immersible or in-line liquid cooling device, usually with temperature control.";
String instruments_6_instrument_name "Aquarium chiller";
String instruments_6_instrument_nid "522982";
String instruments_6_supplied_name "Coralife 1/4HP";
String instruments_7_acronym "CT Scanner";
String instruments_7_dataset_instrument_description "To produce 3D imagery of specimens";
String instruments_7_dataset_instrument_nid "755846";
String instruments_7_description "A CT scan makes use of computer-processed combinations of many X-ray measurements taken from different angles to produce cross-sectional (tomographic) images (virtual \"slices\") of specific areas of a scanned object.";
String instruments_7_instrument_name "Computerized Tomography (CT) Scanner";
String instruments_7_instrument_nid "707113";
String instruments_7_supplied_name "GE Locus RS-9 (General Electric Health Care, London, Ontario) x-ray microCT";
String instruments_8_dataset_instrument_description "To measure temperature in the tanks";
String instruments_8_dataset_instrument_nid "755819";
String instruments_8_instrument_name "Thermometer";
String instruments_8_instrument_nid "725867";
String instruments_8_supplied_name "NIST traceable red spirit glass thermometer";
String keywords "ave, bco, bco-dmo, biological, carbon, carbon dioxide, carbonate, chemical, cm3, co2, co3, data, dataset, dioxide, dmo, erddap, management, mg_CaCO3_cm3, oceanography, office, preliminary, replicate, roi, sample, sample_ID, tank, tank_pCO2_ave, tank_temp_ave, temperature, treatment, treatment_pCO2, treatment_temp";
String license "https://www.bco-dmo.org/dataset/755809/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/755809";
String param_mapping "{'755809': {}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/755809/parameters";
String people_0_affiliation "University of Maine";
String people_0_affiliation_acronym "U Maine DMC";
String people_0_person_name "Robert S. Steneck";
String people_0_person_nid "526659";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "University of California-Santa Cruz";
String people_1_affiliation_acronym "UC Santa Cruz";
String people_1_person_name "James Estes";
String people_1_person_nid "51389";
String people_1_role "Co-Principal Investigator";
String people_1_role_type "originator";
String people_2_affiliation "Bigelow Laboratory for Ocean Sciences";
String people_2_person_name "Douglas B. Rasher";
String people_2_person_nid "480721";
String people_2_role "Co-Principal Investigator";
String people_2_role_type "originator";
String people_3_affiliation "Woods Hole Oceanographic Institution";
String people_3_affiliation_acronym "WHOI BCO-DMO";
String people_3_person_name "Nancy Copley";
String people_3_person_nid "50396";
String people_3_role "BCO-DMO Data Manager";
String people_3_role_type "related";
String project "OA Kelp Forest Function";
String projects_0_acronym "OA Kelp Forest Function";
String projects_0_description
"Extracted from the NSF award abstract:
Marine calcifying organisms are most at risk to rapid ocean acidification (OA) in cold-water ecosystems. The investigators propose to determine if a globally unique and widespread calcareous alga in Alaska's Aleutian archipelago, Clathromorphum nereostratum, is threatened with extinction due to the combined effects of OA and food web alterations. C. nereostratum is a slow growing coralline alga that can live to at least 2000 years. It accretes massive 'bioherms' that dominate the regions' rocky substrate both under kelp forests and deforested sea urchin barrens. It develops growth bands (similar to tree rings) in its calcareous skeleton, which effectively record its annual calcification rate over centuries. Pilot data suggest the skeletal density of C. nereostratum began to decline precipitously in the 1990's in some parts of the Aleutian archipelago. The investigators now propose to use high-resolution microscopy and microCT imaging to examine how the growth and skeletal density of C. nereostratum has changed in the past 300 years (i.e., since the industrial revolution) across the western Aleutians. They will compare their records of algal skeletal densities and their variation through time with reconstructions of past climate to infer causes of change. In addition, the investigators will examine whether the alga's defense against grazing by sea urchins is compromised by ongoing ocean acidification. The investigators will survey the extent of C. nereostratum bioerosion occurring at 10 sites spanning the western Aleutians, both inside and outside of kelp forests. At each site they will compare these patterns to observed and monitored ecosystem trophic structure and recent C. nereostratum calcification rates. Field observations will be combined with laboratory experiments to determine if it is a decline in the alga's skeletal density (due to recent OA and warming), an increase in grazing intensity (due to recent trophic-level dysfunction), or their interactive effects that are likely responsible for bioerosion patterns inside vs. outside of forests. By sampling C. nereostratum inside and outside of forests, they will determine if kelp forests locally increase pH via photosynthesis, and thus buffer the effects of OA on coralline calcification. The combination of field observations with laboratory controlled experiments, manipulating CO2 and temperature, will help elucidate drivers of calcification and project how these species interactions will likely change in the near future. The project will provide the first in situ example of how ongoing ocean acidification is affecting the physiology of long-lived, carbonate producing organisms in the subarctic North Pacific. It will also be one of the first studies to document whether OA, ocean warming, and food web changes to ecological processes are interacting in complex ways to reshape the outcome of species interactions in nature.";
String projects_0_end_date "2016-08";
String projects_0_name "Ocean Acidification: Century Scale Impacts to Ecosystem Structure and Function of Aleutian Kelp Forests";
String projects_0_project_nid "526660";
String projects_0_start_date "2013-09";
String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
String publisher_type "institution";
String sourceUrl "(local files)";
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "Skeletal density (mg CaCO3/cm^3) of Clathromorphum nereostratum, evaluated as a function of seawater temperature and pCO2 level that it was cultured in for 4 months in mesocosm. Density measurements were made using micro-computed tomography.";
String title "The density (mg CaCO3/cm^3) of the skeleton of Clathromorphum nereostratum, when assessed as function of increasing seawater temperature and pCO2 concentration";
String version "1";
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
Data Access Protocol (DAP) and its
selection constraints.
The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.
Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names,
followed by a collection of
constraints (e.g., variable<value),
each preceded by '&' (which is interpreted as "AND").
For details, see the tabledap Documentation.