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Dataset Title:  Potential nitrification rates and ammonia oxidizer gene abundances collected
on R/V Endeavor (SQO-Delta) in the San Francisco Bay Delta during September and
October 2007
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_654371)
Range: longitude = -121.850914 to -121.59703°E, latitude = 38.017616 to 38.167118°N
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  date {
    String bcodmo_name "date";
    String description "date when sample was taken; mm/dd/yyyy";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String source_name "date";
    String time_precision "1970-01-01";
    String units "unitless";
  }
  station {
    String bcodmo_name "station";
    String description "station where sample was taken";
    String long_name "Station";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 38.017617, 38.167117;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -121.850917, -121.597033;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
  AOA_amoA {
    Float64 _FillValue NaN;
    Float64 actual_range 72772.35, 641670.69;
    String bcodmo_name "abundance";
    String description "abundance of archaeal amoA genes in surface sediments";
    String long_name "AOA Amo A";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "genes g -1 of wet sediment";
  }
  AOB_amoA {
    Float64 _FillValue NaN;
    Float64 actual_range 10865.6, 1026692.51;
    String bcodmo_name "abundance";
    String description "abundance of bacterial amoA genes in surface sediments";
    String long_name "AOB Amo A";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "genes g -1 of wet sediment";
  }
  log_AOAtoAOB {
    Float32 _FillValue NaN;
    Float32 actual_range -1.149, 1.771;
    String bcodmo_name "abundance";
    String description "AOA gene abundances divided by AOB gene abundances, log10 transformed";
    String long_name "Log AOAto AOB";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "dimensionless";
  }
  NO2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.14, 0.77;
    String bcodmo_name "NO2";
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum 0.0;
    String description "nitrite concentrations in bottom water";
    String long_name "Mole Concentration Of Nitrite In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/NTRIAAZX/";
    String units "micromoles";
  }
  NO3 {
    Float32 _FillValue NaN;
    Float32 actual_range 1.79, 22.57;
    String bcodmo_name "NO3";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "nitrate concentrations in bottom water";
    String long_name "Mole Concentration Of Nitrate In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/NTRAIGGS/";
    String units "micromoles";
  }
  NH4 {
    Float32 _FillValue NaN;
    Float32 actual_range 1.08, 27.12;
    String bcodmo_name "Ammonium";
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum 0.0;
    String description "ammonium concentrations in bottom water";
    String long_name "Mole Concentration Of Ammonium In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AMONAAZX/";
    String units "micromoles";
  }
  nitrification {
    Float32 _FillValue NaN;
    Float32 actual_range 8.29, 146.0;
    String bcodmo_name "unknown";
    String description "potential nitrification rates in surface sediments";
    String long_name "Nitrification";
    String units "nmol NOx g-1 h-1";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Surface sediment was retrieved using a modified Van Veen grab. Duplicate cores
were taken from each grab sample using sterile, cut-off 5 mL syringes and
immediately placed on dry ice prior to storage at \\u201380 degrees celsius.
Bottom water nutrient samples were collected in triplicate using a hand-held
Niskin bottle, immediately filtered (0.2 um pore size), and frozen on dry ice
prior to storage at \\u201320 degrees celsius. Nutrient (NH4+, NO2-, and NO3-)
concentrations were measured using a QuikChem 8000 Flow Injection Analyzer
(Lachat Instruments).\\u00a0
 
Sediment samples for potential nitrification rate measurements were collected
in triplicate into the barrels of cut-off 60 mL syringes, which were sealed
with parafilm and transported to the laboratory on ice. Potential rates were
measured using amended sediment slurries. Slurries included 5 g of sediment
(top 1 cm) homogenized in 100 mL of filtered bottom water augmented with NH4+
and phosphate to final additional concentrations of 500 and 100 uM,
respectively. Amended slurries were shaken (200 rpm) in the dark for 24 hours
at room temperature (about 22 degrees celsius). Aliquots for the determination
of NO3- plus NO2- (NOX) were collected at evenly spaced intervals through the
incubation period and stored at \\u201320 degrees celsius. Prior to analysis,
aliquots were thawed and passed through Whatman No. 42 filter paper, and the
filtrate was analyzed for the accumulation of NOx over time, using a SmartChem
200 Discrete Analyzer (Unity Scientific). Rates were determined by linear
regression of NOx concentrations over time.
 
DNA was extracted from approximately 0.5 g of surface sediments by extruding
and cutting the top 0.5 cm from frozen cores with a sterile scalpel and
immediately proceeding with the FastDNA SPIN Kit for Soil (MP Biomedicals),
including a FastPrep bead beating step of 30 s at speed 5.5. AOA and AOB amoA
genes were quantified using gene-specific SYBR qPCR assays on a StepOnePlus
Real-Time PCR System (Life Technologies). AOA amoA reactions contained iTaq
SYBR Green Supermix with ROX (Bio-Rad Laboratories), 0.4 uM primers Arch-amoAF
/Arch-amoAR (Francis et al., 2005) and 1 uL template DNA. AOA qPCR program
details were identical to previously published protocols (Mosier and Francis,
2008) but with a 10 s detection step at 78.5 degrees celsius. AOB amoA qPCR
reactions used primers amoA1F/amoA2R (Rotthauwe et al., 1997), and were set up
following Mosier and Francis (2008) but with a 10 s detection step at 83
degress celsius. Each plate included a standard curve (5 to 10^6
copies/reaction) made by serial dilution of linearized plasmids extracted from
previously sequenced clones, and negative controls that substituted sterile
water for DNA. The diversity of ammonia oxidizing communities was determined
by cloning and sequencing of PCR-amplified amoA genes using primers Arch-amoAF
/Arch-amoAR (Francis et al., 2005) and amoA1F*/amoA2R (Rotthauwe et al., 1997;
Stephen et al., 1999) for AOA and AOB, respectively. Reaction conditions and
PCR programs followed previously published protocols (Mosier and Francis,
2008). Triplicate reactions were qualitatively checked by gel electrophoresis,
pooled, and purified using the MinElute PCR Purification Kit or MinElute Gel
Extraction Kit (Qiagen), following the manufacturer\\u2019s instructions.
Purified products were cloned using the pGEM-T Vector System II (Promega), and
sequenced by Elim Biopharmaceuticals on a 3730xl capillary sequencer (Life
Technologies). Sequences were imported into Geneious (version 6.1.6 created by
Biomatters, available from
[http://www.geneious.com](\\\\\"http://www.geneious.com\\\\\")) and manually cleaned
prior to operational taxonomic unit (OTU) grouping (greater than or equal to
95% sequence similarity) using mothur (Schloss et al., 2009). Rarefaction
curves and diversity/richness estimators (Chao1 and Shannon indices) were
calculated using mothur. OTUs were aligned with reference sequences using the
MUSCLE alignment package within Geneious, using a gap open score of \\u2013750.
Alignments were manually checked and used to build neighbor-joining bootstrap
trees (Jukes-Cantor distance model, 1000 neighbor joining bootstrap
replicates) within Geneious. The amoA sequences generated in this study have
been deposited into GenBank with accession numbers KM000240 to KM000508 (AOB)
and KM000509 to KM000784 (AOA).
 
Two-tailed Spearman rank correlation coefficients (\\u03c1) were calculated
using R (R Core Team, 2014) to determine correlations between variables, using
the suggested critical value of 0.786 for 5% significance with a sample size
of 7 (Zar, 1972). Principal component and non-metric multidimensional scaling
analyses were performed using the vegan package in R (Oksanen, 2013).
Environmental variables were z-transformed to standardize across different
scales and units by subtracting the population mean from each measurement and
dividing by the standard deviation. OTU count data were Hellinger-transformed
to standardize to relative abundances (Legendre and Legendre, 2012). Other
than unweighted UniFrac distances, which were calculated using the online
UniFrac portal (Lozupone et al., 2006), distance/dissimilarity indices were
calculated using the vegan package in R. All principle component analyses are
presented using scaling 1; therefore, the distance between sites on the biplot
represents their Euclidean distance, and the right-angle projection of a site
onto a descriptor vector shows the approximate position of that site on the
vector (Legendre and Legendre, 2012).\\u00a0";
    String awards_0_award_nid "546277";
    String awards_0_award_number "OCE-0847266";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0847266";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Delta Nitrification 
  Christopher A. Francis, PI 
  Version 17 August 2016";
    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 "2016-08-18T19:59:18Z";
    String date_modified "2019-05-22T14:36:45Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.654371.1";
    Float64 Easternmost_Easting -121.597033;
    Float64 geospatial_lat_max 38.167117;
    Float64 geospatial_lat_min 38.017617;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -121.597033;
    Float64 geospatial_lon_min -121.850917;
    String geospatial_lon_units "degrees_east";
    String history 
"2020-09-28T18:47:40Z (local files)
2020-09-28T18:47:40Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_654371.das";
    String infoUrl "https://www.bco-dmo.org/dataset/654371";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_description "Hand-held Niskin bottle";
    String instruments_0_dataset_instrument_nid "654377";
    String instruments_0_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.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/";
    String instruments_0_instrument_name "Niskin bottle";
    String instruments_0_instrument_nid "413";
    String instruments_0_supplied_name "Niskin bottle";
    String instruments_1_acronym "FIA";
    String instruments_1_dataset_instrument_description "Concentrations measured via QuikChem 8000 Flow Injection Analyzer";
    String instruments_1_dataset_instrument_nid "654378";
    String instruments_1_description "An instrument that performs flow injection analysis. Flow injection analysis (FIA) is an approach to chemical analysis that is accomplished by injecting a plug of sample into a flowing carrier stream. FIA is an automated method in which a sample is injected into a continuous flow of a carrier solution that mixes with other continuously flowing solutions before reaching a detector. Precision is dramatically increased when FIA is used instead of manual injections and as a result very specific FIA systems have been developed for a wide array of analytical techniques.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB36/";
    String instruments_1_instrument_name "Flow Injection Analyzer";
    String instruments_1_instrument_nid "657";
    String instruments_1_supplied_name "QuikChem 8000";
    String instruments_2_acronym "Thermal Cycler";
    String instruments_2_dataset_instrument_description "Genes quantified using gene-specific SYBR qPCR assays";
    String instruments_2_dataset_instrument_nid "654380";
    String instruments_2_description 
"General term for a laboratory apparatus commonly used for performing polymerase chain reaction (PCR). The device has a thermal block with holes where tubes with the PCR reaction mixtures can be inserted. The cycler then raises and lowers the temperature of the block in discrete, pre-programmed steps.

(adapted from http://serc.carleton.edu/microbelife/research_methods/genomics/pcr.html)";
    String instruments_2_instrument_name "PCR Thermal Cycler";
    String instruments_2_instrument_nid "471582";
    String instruments_2_supplied_name "StepOnePlus Real-Time PCR System";
    String instruments_3_acronym "Discrete Analyzer";
    String instruments_3_dataset_instrument_description "Filtrate analyzed from the accumulation of NOx over time using this discrete analyzer.";
    String instruments_3_dataset_instrument_nid "654379";
    String instruments_3_description "Discrete analyzers utilize discrete reaction wells to mix and develop the colorimetric reaction, allowing for a wide variety of assays to be performed from one sample. These instruments are ideal for drinking water, wastewater, soil testing, environmental and university or research applications where multiple assays and high throughput are required.";
    String instruments_3_instrument_name "Discrete Analyzer";
    String instruments_3_instrument_nid "654337";
    String instruments_3_supplied_name "SmartChem 200 Discrete Analyzer";
    String keywords "ammonia, ammonium, amo, aoa, AOA_amoA, aoato, aob, AOB_amoA, bco, bco-dmo, biological, chemical, chemistry, concentration, data, dataset, date, dmo, earth, Earth Science > Oceans > Ocean Chemistry > Ammonia, Earth Science > Oceans > Ocean Chemistry > Nitrate, erddap, latitude, log, log_AOAtoAOB, longitude, management, mole, mole_concentration_of_ammonium_in_sea_water, mole_concentration_of_nitrate_in_sea_water, mole_concentration_of_nitrite_in_sea_water, n02, nh4, nitrate, nitrification, nitrite, NO2, no3, ocean, oceanography, oceans, office, preliminary, science, sea, seawater, station, time, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/654371/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/654371";
    Float64 Northernmost_Northing 38.167117;
    String param_mapping "{'654371': {'lat': 'master - latitude', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/654371/parameters";
    String people_0_affiliation "Stanford University";
    String people_0_person_name "Dr Christopher Francis";
    String people_0_person_nid "51553";
    String people_0_role "Lead Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Hannah Ake";
    String people_1_person_nid "650173";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "N-Cycling Microbial Communities";
    String projects_0_acronym "N-Cycling Microbial Communities";
    String projects_0_description 
"Description from the NSF award abstract:
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Although nitrogen (N) acts as a limiting nutrient in many marine ecosystems, from estuaries to the open ocean, N in excess can be extremely detrimental. Eutrophication is of particular concern in estuaries, with over half of the estuaries in the United States experiencing its effects. Harmful levels of N in estuaries can be diminished through tightly coupled processes in the microbial nitrogen cycle, including nitrification (chemoautotrophic oxidation of ammonia to nitrite and nitrate) and denitrification (the dissimilatory reduction of nitrate to N2 gas). In fact, coupled nitrification-denitrification can remove up to 50% of external dissolved inorganic nitrogen inputs to estuaries, thereby reducing the risk of eutrophication. Despite the biogeochemical importance of both nitrification and denitrification in estuarine systems, surprisingly little is known regarding the underlying microbial communities responsible for these processes, or how they are influenced by key physical/chemical factors.
The investigators will work in San Francisco Bay - the largest estuary on the west coast of the United States - using molecular, biogeochemical and cultivation approaches to explore how the distribution, diversity, abundance, and activities of key N-cycling communities are influenced by environmental gradients over temporal and spatial scales. Denitrifying communities will be studied using functional genes (nirK and nirS) encoding the key denitrification enzyme nitrite reductase, while genes encoding ammonia monooxygenase subunit A (amoA) will be used to study both ammonia-oxidizing bacteria (AOB) and the recently-discovered ammonia-oxidizing archaea (AOA)- members of one of the most ubiquitous and abundant prokaryotic groups on the planet, the mesophilic Crenarchaeota. Analyzing sediments from sites spanning a range of physical and chemical conditions in the Bay, seasonally over the course of several years, will represent an unprecedented opportunity to examine spatial, physical/chemical, and temporal effects on both denitrifier and ammonia-oxidizer communities in this large, urban estuary. Concurrently, an intensive cultivation effort will also be undertaken, in order to compile a novel culture collection of estuarine denitrifiers and ammonia-oxidizers, for which virtually nothing is currently known. Taken together, these complimentary approaches will help reveal how complex physical/chemical gradients influence the diversity and functioning of key estuarine N-cycling communities over time and space.";
    String projects_0_end_date "2015-05";
    String projects_0_geolocation "San Francisco Bay";
    String projects_0_name "Spatial and Temporal Dynamics of Nitrogen-Cycling Microbial Communities Across Physicochemical Gradients in the San Francisco Bay Estuary";
    String projects_0_project_nid "546278";
    String projects_0_start_date "2009-06";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 38.017617;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Potential nitrification rates and ammonia oxidizer gene abundances collected on R/V Endeavor (SQO-Delta) in the San Francisco Bay Delta during September and October 2007";
    String title "Potential nitrification rates and ammonia oxidizer gene abundances collected on R/V Endeavor (SQO-Delta) in the San Francisco Bay Delta during September and October 2007";
    String version "1";
    Float64 Westernmost_Easting -121.850917;
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

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.


 
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