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Grid DAP Data | Sub- set | Table DAP Data | Make A Graph | W M S | Source Data Files | Acces- sible | Title | Sum- mary | FGDC, ISO, Metadata | Back- ground Info | RSS | E | Institution | Dataset ID |
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data | graph | files | public | [HHQ Flow Cytometry] - Flow cytometry measurements from HHQ experiments conducted during the MesoHux mesocosm experiment, May 2017, Bergen, Norway (Collaborative Research: Building a framework for the role of bacterial-derived chemical signals in mediating phytoplankton population dynamics) | I M | background | BCO-DMO | bcodmo_dataset_753431 |
Row Type | Variable Name | Attribute Name | Data Type | Value |
---|---|---|---|---|
attribute | NC_GLOBAL | access_formats | String | .htmlTable,.csv,.json,.mat,.nc,.tsv |
attribute | NC_GLOBAL | acquisition_description | String | Triplicate 5 mL samples were preserved for flow cytometry with 0.5% glutaraldehyde (final concentration), incubated at 4\u00b0C for 10 min and frozen (-80\u00b0C) until analysis (within 2-3 weeks; Kemp et al. 1993). To calculate phytoplankton group abundances, 200 \u00b5l aliquots of fixed sample were added to a 96-well plate and run on a Guava flow cytometer (Millipore). Filtered seawater (0.45 \u00b5m) was run as a blank\u00a0and instrument- specific beads were used to calibrate the cytometer. Samples were analyzed at low flow rate (0.24 \u00b5l s-1) for 3 min. Three major phytoplankton groups were distinguishable based on plots of forward scatter vs. orange (phycoerythrin-containing, Synechococcus spp.) or red (pico- and nanoeukaryotes) fluorescence signals (Worden and Binder 2003).\u00a0 Samples for enumerating bacteria were stained prior to running on the Guava in 0.5% v/v SybrGreen I DNA stain for 1 hour at room temperature in the dark. Mesocosm treatment for all HHQ experiments was as follows: Redfield: N:P added in a 16:1 ratio during the first 3 days of the experiment, no shading HHQ treatments here are as follows: High HHQ - 100 ng mL-1 (410 uM) added to triplicate 5L bottles. DMSO control - equivalent (v:v) DMSO added to triplicate 5L bottles. \u00a0All bottles were incubated for 24h in a flow-through tank, that was shaded to mimic in situ conditions. Chlorophyll samples were taken at T0 and T24 for all experiments. Data were processed in Excel with statistics run in Excel, R, or Matlab. |
attribute | NC_GLOBAL | awards_0_award_nid | String | 709952 |
attribute | NC_GLOBAL | awards_0_award_number | String | OCE-1657898 |
attribute | NC_GLOBAL | awards_0_data_url | String | http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1657898 |
attribute | NC_GLOBAL | awards_0_funder_name | String | NSF Division of Ocean Sciences |
attribute | NC_GLOBAL | awards_0_funding_acronym | String | NSF OCE |
attribute | NC_GLOBAL | awards_0_funding_source_nid | String | 355 |
attribute | NC_GLOBAL | awards_0_program_manager | String | David L. Garrison |
attribute | NC_GLOBAL | awards_0_program_manager_nid | String | 50534 |
attribute | NC_GLOBAL | cdm_data_type | String | Other |
attribute | NC_GLOBAL | comment | String | HHQ Flow Cytometry from MesoHux mesocosm experiment, May 2017, Bergen, Norway PI: E. Harvey (SkIO) version: 2019-01-23 |
attribute | NC_GLOBAL | Conventions | String | COARDS, CF-1.6, ACDD-1.3 |
attribute | NC_GLOBAL | creator_email | String | info at bco-dmo.org |
attribute | NC_GLOBAL | creator_name | String | BCO-DMO |
attribute | NC_GLOBAL | creator_type | String | institution |
attribute | NC_GLOBAL | creator_url | String | https://www.bco-dmo.org/ |
attribute | NC_GLOBAL | data_source | String | extract_data_as_tsv version 2.3 19 Dec 2019 |
attribute | NC_GLOBAL | date_created | String | 2019-01-23T17:23:32Z |
attribute | NC_GLOBAL | date_modified | String | 2019-03-14T19:39:56Z |
attribute | NC_GLOBAL | defaultDataQuery | String | &time<now |
attribute | NC_GLOBAL | doi | String | 10.1575/1912/bco-dmo.753431.1 |
attribute | NC_GLOBAL | infoUrl | String | https://www.bco-dmo.org/dataset/753431 |
attribute | NC_GLOBAL | institution | String | BCO-DMO |
attribute | NC_GLOBAL | instruments_0_acronym | String | Niskin bottle |
attribute | NC_GLOBAL | instruments_0_dataset_instrument_description | String | Used to collect water samples. |
attribute | NC_GLOBAL | instruments_0_dataset_instrument_nid | String | 753438 |
attribute | NC_GLOBAL | instruments_0_description | String | 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. |
attribute | NC_GLOBAL | instruments_0_instrument_external_identifier | String | https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/ |
attribute | NC_GLOBAL | instruments_0_instrument_name | String | Niskin bottle |
attribute | NC_GLOBAL | instruments_0_instrument_nid | String | 413 |
attribute | NC_GLOBAL | instruments_0_supplied_name | String | 5 L Niskin |
attribute | NC_GLOBAL | instruments_1_acronym | String | Flow Cytometer |
attribute | NC_GLOBAL | instruments_1_dataset_instrument_description | String | Used for cell counts |
attribute | NC_GLOBAL | instruments_1_dataset_instrument_nid | String | 753444 |
attribute | NC_GLOBAL | instruments_1_description | String | Flow cytometers (FC or FCM) are automated instruments that quantitate properties of single cells, one cell at a time. They can measure cell size, cell granularity, the amounts of cell components such as total DNA, newly synthesized DNA, gene expression as the amount messenger RNA for a particular gene, amounts of specific surface receptors, amounts of intracellular proteins, or transient signalling events in living cells. (from: http://www.bio.umass.edu/micro/immunology/facs542/facswhat.htm) |
attribute | NC_GLOBAL | instruments_1_instrument_external_identifier | String | https://vocab.nerc.ac.uk/collection/L05/current/LAB37/ |
attribute | NC_GLOBAL | instruments_1_instrument_name | String | Flow Cytometer |
attribute | NC_GLOBAL | instruments_1_instrument_nid | String | 660 |
attribute | NC_GLOBAL | instruments_1_supplied_name | String | Millipore Guava inCyte BG HT flow cytometer |
attribute | NC_GLOBAL | keywords | String | 15um, bacteria, bco, bco-dmo, biological, chemical, data, dataset, date, dmo, erddap, experiment, Experiment_num, management, nanoeukaryotes, num, oceanography, office, phytoplankton, picoeukaryotes, preliminary, replication, sample, synechococcus, time, time2, total, Total_Phytoplankton_lt_15um |
attribute | NC_GLOBAL | license | String | https://www.bco-dmo.org/dataset/753431/license |
attribute | NC_GLOBAL | metadata_source | String | https://www.bco-dmo.org/api/dataset/753431 |
attribute | NC_GLOBAL | param_mapping | String | {'753431': {}} |
attribute | NC_GLOBAL | parameter_source | String | https://www.bco-dmo.org/mapserver/dataset/753431/parameters |
attribute | NC_GLOBAL | people_0_affiliation | String | Skidaway Institute of Oceanography |
attribute | NC_GLOBAL | people_0_affiliation_acronym | String | SkIO |
attribute | NC_GLOBAL | people_0_person_name | String | Elizabeth Harvey |
attribute | NC_GLOBAL | people_0_person_nid | String | 645518 |
attribute | NC_GLOBAL | people_0_role | String | Principal Investigator |
attribute | NC_GLOBAL | people_0_role_type | String | originator |
attribute | NC_GLOBAL | people_1_affiliation | String | University of Rhode Island |
attribute | NC_GLOBAL | people_1_affiliation_acronym | String | URI |
attribute | NC_GLOBAL | people_1_person_name | String | Dr David Rowley |
attribute | NC_GLOBAL | people_1_person_nid | String | 709954 |
attribute | NC_GLOBAL | people_1_role | String | Co-Principal Investigator |
attribute | NC_GLOBAL | people_1_role_type | String | originator |
attribute | NC_GLOBAL | people_2_affiliation | String | Haverford College |
attribute | NC_GLOBAL | people_2_affiliation_acronym | String | Haveford |
attribute | NC_GLOBAL | people_2_person_name | String | Kristen E. Whalen |
attribute | NC_GLOBAL | people_2_person_nid | String | 709960 |
attribute | NC_GLOBAL | people_2_role | String | Co-Principal Investigator |
attribute | NC_GLOBAL | people_2_role_type | String | originator |
attribute | NC_GLOBAL | people_3_affiliation | String | Woods Hole Oceanographic Institution |
attribute | NC_GLOBAL | people_3_affiliation_acronym | String | WHOI BCO-DMO |
attribute | NC_GLOBAL | people_3_person_name | String | Nancy Copley |
attribute | NC_GLOBAL | people_3_person_nid | String | 50396 |
attribute | NC_GLOBAL | people_3_role | String | BCO-DMO Data Manager |
attribute | NC_GLOBAL | people_3_role_type | String | related |
attribute | NC_GLOBAL | project | String | HHQSignals |
attribute | NC_GLOBAL | projects_0_acronym | String | HHQSignals |
attribute | NC_GLOBAL | projects_0_description | String | NSF Abstract: Bacteria and phytoplankton play a central role in the modification and flow of materials and nutrients through the marine environment. While it has been established that interactions between these two domains are complex, the mechanisms that underpin these interactions remain largely unknown. There is increasing recognition, however, that dissolved chemical cues govern these microbial interactions. This project focuses on establishing a mechanistic framework for how bacterially derived signaling molecules influence interactions between phytoplankton and bacteria. The quorum-sensing (QS) molecule, 2-heptyl-4-quinolone (HHQ) will be used as a model compound for these investigations. Previously published work suggests that exposure to very low levels of HHQ results in phytoplankton mortality. Gaining a mechanistic understanding of these ecologically important interactions will help to inform mathematical models for the accurate prediction of the cycling of material through the marine microbial loop. This work initiates a new, hybrid workshop-internship undergraduate research program in chemical ecology, with a focus Bacteria and phytoplankton play a central role in the modification and flow of materials and nutrients through the marine environment. While it has been established that interactions between these two domains are complex, the mechanisms that underpin these interactions remain largely unknown. There is increasing recognition, however, that dissolved chemical cues govern these microbial interactions. This project focuses on establishing a mechanistic framework for how bacterially derived signaling molecules influence interactions between phytoplankton and bacteria. The quorum-sensing (QS) molecule, 2-heptyl-4-quinolone (HHQ) will be used as a model compound for these investigations. Previously published work suggests that exposure to very low levels of HHQ results in phytoplankton mortality. Gaining a mechanistic understanding of these ecologically important interactions will help to inform mathematical models for the accurate prediction of the cycling of material through the marine microbial loop. This work initiates a new, hybrid workshop-internship undergraduate research program in chemical ecology, with a focus into bacteria-phytoplankton interactions. Undergraduate students participate in an intense summer learning experience where research and field-based exercises are supplemented with short-lecture based modules. Students return to their home institutions and work closely with the PIs to conduct interdisciplinary research relating to the aims and scope of the summer research. This research also provides training and career development to two graduate students and a postdoctoral scientist. Interactions between phytoplankton and bacteria play a central role in mediating biogeochemical cycling and microbial trophic structure in the ocean. The intricate relationships between these two domains of life are mediated via excreted molecules that facilitate communication and determine competitive outcomes. Despite their predicted importance, identifying these released compounds has remained a challenge. The PIs recently identified a bacterial QS molecule, HHQ, produced by globally distributed marine gamma-proteobacteria, which induces phytoplankton mortality. The PIs therefore hypothesize that bacteria QS signals are critical drivers of phytoplankton population dynamics and, ultimately, biogeochemical fluxes. This project investigates the timing and magnitude of HHQ production, and the physiological and transcriptomic responses of susceptible phytoplankton species to HHQ exposure, and quantifies the influence of HHQ on natural algal and bacterial assemblages. The work connects laboratory and field-based experiments to understand the governance of chemical signaling on marine microbial interactions, and has the potential to yield broadly applicable insights into how microbial interactions influence biogeochemical fluxes in the marine environment. |
attribute | NC_GLOBAL | projects_0_end_date | String | 2020-03 |
attribute | NC_GLOBAL | projects_0_geolocation | String | Bergen, Norway |
attribute | NC_GLOBAL | projects_0_name | String | Collaborative Research: Building a framework for the role of bacterial-derived chemical signals in mediating phytoplankton population dynamics |
attribute | NC_GLOBAL | projects_0_project_nid | String | 709948 |
attribute | NC_GLOBAL | projects_0_start_date | String | 2017-04 |
attribute | NC_GLOBAL | publisher_name | String | Biological and Chemical Oceanographic Data Management Office (BCO-DMO) |
attribute | NC_GLOBAL | publisher_type | String | institution |
attribute | NC_GLOBAL | sourceUrl | String | (local files) |
attribute | NC_GLOBAL | standard_name_vocabulary | String | CF Standard Name Table v55 |
attribute | NC_GLOBAL | summary | String | This dataset includes flow cytometry measurements from HHQ experiments conducted during the MesoHux mesocosm experiment, May 2017, Bergen, Norway. Microbial mesocosms were spiked with 2-heptyl-4-quinolone (HHQ). |
attribute | NC_GLOBAL | title | String | [HHQ Flow Cytometry] - Flow cytometry measurements from HHQ experiments conducted during the MesoHux mesocosm experiment, May 2017, Bergen, Norway (Collaborative Research: Building a framework for the role of bacterial-derived chemical signals in mediating phytoplankton population dynamics) |
attribute | NC_GLOBAL | version | String | 1 |
attribute | NC_GLOBAL | xml_source | String | osprey2erddap.update_xml() v1.3 |
variable | Date | String | ||
attribute | Date | bcodmo_name | String | date |
attribute | Date | description | String | sampling date formatted as Mon dd yyyy |
attribute | Date | long_name | String | Date |
attribute | Date | nerc_identifier | String | https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/ |
attribute | Date | units | String | unitless |
variable | Sample | String | ||
attribute | Sample | bcodmo_name | String | sample |
attribute | Sample | description | String | sample identifier |
attribute | Sample | long_name | String | Sample |
attribute | Sample | nerc_identifier | String | https://vocab.nerc.ac.uk/collection/P02/current/ACYC/ |
attribute | Sample | units | String | unitless |
variable | Experiment_num | byte | ||
attribute | Experiment_num | _FillValue | byte | 127 |
attribute | Experiment_num | actual_range | byte | 1, 8 |
attribute | Experiment_num | bcodmo_name | String | exp_id |
attribute | Experiment_num | description | String | experiment number |
attribute | Experiment_num | long_name | String | Experiment Num |
attribute | Experiment_num | units | String | unitless |
variable | time2 | byte | ||
attribute | time2 | _FillValue | byte | 127 |
attribute | time2 | actual_range | byte | 0, 24 |
attribute | time2 | bcodmo_name | String | incubation time |
attribute | time2 | description | String | time since start of experiment |
attribute | time2 | long_name | String | Time |
attribute | time2 | nerc_identifier | String | https://vocab.nerc.ac.uk/collection/P01/current/AZDRZZ01/ |
attribute | time2 | units | String | hours |
variable | Replication | byte | ||
attribute | Replication | _FillValue | byte | 127 |
attribute | Replication | actual_range | byte | 1, 3 |
attribute | Replication | bcodmo_name | String | replicate |
attribute | Replication | description | String | replicate number |
attribute | Replication | long_name | String | Replication |
attribute | Replication | units | String | unitless |
variable | Bacteria | int | ||
attribute | Bacteria | _FillValue | int | 2147483647 |
attribute | Bacteria | actual_range | int | 517817, 2152006 |
attribute | Bacteria | bcodmo_name | String | cell_concentration |
attribute | Bacteria | description | String | number of bacterial cells |
attribute | Bacteria | long_name | String | Bacteria |
attribute | Bacteria | units | String | cells/milliliter |
variable | Synechococcus | short | ||
attribute | Synechococcus | _FillValue | short | 32767 |
attribute | Synechococcus | actual_range | short | 420, 25704 |
attribute | Synechococcus | bcodmo_name | String | cell_concentration |
attribute | Synechococcus | description | String | number of Synechococcus cells |
attribute | Synechococcus | long_name | String | Synechococcus |
attribute | Synechococcus | units | String | cells/milliliter |
variable | Picoeukaryotes | int | ||
attribute | Picoeukaryotes | _FillValue | int | 2147483647 |
attribute | Picoeukaryotes | actual_range | int | 551, 40121 |
attribute | Picoeukaryotes | bcodmo_name | String | cell_concentration |
attribute | Picoeukaryotes | description | String | number of Picoeukaryotes cells |
attribute | Picoeukaryotes | long_name | String | Picoeukaryotes |
attribute | Picoeukaryotes | units | String | cells/milliliter |
variable | Nanoeukaryotes | short | ||
attribute | Nanoeukaryotes | _FillValue | short | 32767 |
attribute | Nanoeukaryotes | actual_range | short | 374, 32076 |
attribute | Nanoeukaryotes | bcodmo_name | String | cell_concentration |
attribute | Nanoeukaryotes | description | String | number of Nanoeukaryotes cells |
attribute | Nanoeukaryotes | long_name | String | Nanoeukaryotes |
attribute | Nanoeukaryotes | units | String | cells/milliliter |
variable | Total_Phytoplankton_lt_15um | int | ||
attribute | Total_Phytoplankton_lt_15um | _FillValue | int | 2147483647 |
attribute | Total_Phytoplankton_lt_15um | actual_range | int | 2592, 73204 |
attribute | Total_Phytoplankton_lt_15um | bcodmo_name | String | cell_concentration |
attribute | Total_Phytoplankton_lt_15um | description | String | total number of phytoplankton cells less than 15 microns in diameter |
attribute | Total_Phytoplankton_lt_15um | long_name | String | Total Phytoplankton Lt 15um |
attribute | Total_Phytoplankton_lt_15um | units | String | cells/milliliter |
The information in the table above is also available in other file formats (.csv, .htmlTable, .itx, .json, .jsonlCSV1, .jsonlCSV, .jsonlKVP, .mat, .nc, .nccsv, .tsv, .xhtml) via a RESTful web service.