Search for: All records

Abstract: This .zip archive contains Quality Controlled float data for biogeochemical profiling floats deployed by the Southern Ocean Carbon and Climate Observation and Modeling (SOCCOM) and Global Ocean Biogeochemistry Array (GO-BGC) projects. Additional University of Washington/MBARI floats deployed outside of these programs have also been included in the archive. Data for all floats within this archive were processed by the float data management team at the Monterey Bay Aquarium Research Institute (MBARI). A comprehensive listing of floats processed by MBARI (including internal floatID, WMOID, float platform type, and program) is included within each archive. Note that historical snapshots, frozen prior to December 2022, can be found under the SOCCOM collection (https://doi.org/10.6075/J0TX3C9X). FORMAT: The ascii files contained herein were formatted to be compatible with Ocean Data View (ODV). Character encoding is UTF-8. ODV is freely available at https://odv.awi.de/. In addition, a Matlab function has been provided in each .zip archive for parsing the .txt files into data structures within Matlab (see get_FloatViz_data.m). Please note that the data files within this .zip archive represent a snapshot of all SOCCOM and GO-BGC float data processed at MBARI as of the date listed in the file name. Therefore, be aware that processing updates (** AND THUS CHANGES TO THE DATA **) may have occurred since the time the snapshot was created. For the most up-to-date files (processed every 4 hours), visit https://ftp.mbari.org/pub/SOCCOM/FloatVizData/ or https://www.mbari.org/science/upper-ocean-systems/chemical-sensor-group/floatviz/ ALTERNATE FORMATS: NetCDF and Matlab Matlab and NetCDF formatted files are provided for each ODV text file. The Matlab format is loaded as structure, FloatViz, with the ODV parameter names as the structure's fieldnames. The NetCDF format is similar to ARGO Float NetCDF format in its structure. The parameter names and flagging, however, match the ODV text parameter names. In addition to the quality control flag string entries, a numeric array of quality control flags is provided in the NetCDF files for programming convenience. Additionally, note that an explanation of Quality Flag Identifiers is included in each alternate format file. QUALITY CONTROL DOCUMENTATION: Delayed-mode quality control (DMQC) of biogeochemical float data within this archive is performed routinely by data managers at MBARI following Maurer et al (2021), https://doi.org/10.3389/fmars.2021.683207. QC notification emails are currently being sent out periodically to inform users of key updates to processing, QC and/or sensor calibrations for specific floats. All QC emails as of the date of this snapshot are included in each downloadable zip file. PARAMETERS: For information pertaining to float identification, sensor arrays, data parameters, and quality control please refer to descriptions within the file headers. Each snapshot within this collection includes estimated total alkalinity and derived carbon parameters for DIC and pCO2 using one of three algorithms (LIAR or CANYONB or ESPER). Estimated total alkalinity and derived carbon parameters from the ESPER algorithm are new additions to the standard snapshot collection as of December 2025. It is anticipated that ESPER will replace LIAR estimated total alkalinity and derived carbon parameters in future snapshot collection releases. Floats without a pH sensor will not have these additional parameters within their respective data files. See file headers for details. Additionally, files located at the urls listed above will include carbon parameters derived using observed pH and total alkalinity estimated by the LIAR method. All carbonate system variables calculated with CO2SYS for Matlab (Sharp et al., 2020; see also Lewis and Wallace 1998) used the following conditions: pH was reported on the total scale. K1 and K2 dissociation constants were from Lueker et al., 2000, doi:10.1016/S0304-4203(00)00022-0. The KSO4 dissociation constant was from Dickson, 1990, doi: 10.1016/0021-9614(90)90074-Z. The KF dissociation constant was from Perez and Fraga 1987, doi: 10.1016/0304-4203(87)90036-3. The borate to salinity ratio was from Lee et al., 2010, doi:10.1016/j.gca.2009.12.027. Silicate and Phosphate were not measured by the float, but estimates based on Redfieldian ratios improved the carbonate system estimates. If a nitrate value was considered to be of good quality silicate = nitrate*2.5 and phosphate = nitrate/16, otherwise the best estimate for both was considered to be 0. When pCO2 was estimated from TALK_LIAR (or TALK_ESPER) and pHinsitu, a bias was first added to pH (25C,0dbar) following Williams et al., 2017, doi: https:doi.org/10.1002/2016GB005541 , section 3.4, equation 3. This correction is not necessary for DIC and DIC is computed with the reported pH and the TALK_LIAR (or TALK_ESPER) value. RESOLUTION: This archive contains both low resolution and high resolution data. The format is defined by the folder name: SOCCOM_GO-BGC_LoResQC_METHOD_ddmmmyyyy or SOCCOM_GO-BGC_HiResQC_METHOD_ddmmmyyyy (where METHOD = LIAR or CANYONB or ESPER). Note that for APEX floats, the low resolution files only report data at depths where biogeochemical sensors sample, while the high resolution files merge this low resolution data with higher resolution pressure, temperature and salinity data (sampled every two meters in the upper 1000 meters). Be aware that, due to the merging of the two separate sampling schemes by interleaving the LowRes samples into the HiRes sample structure, HiRes files could potentially contain separate sets of samples with duplicate pressure values. For Navis floats all biogeochemical sensors except nitrate are sampled every 2 meters in the upper 1000 m. Additionally, all Navis float data is contained in both the LoRes and HiRes archives (per user request). DISCLAIMER: These data are provided as-is. We do our best to provide high-quality, complete data but make no guarantees as to the presence of errors within the data themselves or the algorithms used in the generation of derived parameters. It is the user's responsibility to ensure that the data meets the user's needs. However, please report any observed discrepancies in the data to the contact listed below and we will do our best to fix them. CONTACT: Please report any discrepancies, problems or concerns to the following and include "FLOATVIZ SNAPSHOT PROCESSING" in the subject line of the email. Tanya Maurer tmaurer@mbari.org Nicola Guisewhite nicolag@mbari.org SOCCOM/GO-BGC Data Management MBARI 7700 Sandholdt Road Moss Landing, CA 95039 

Abstract: This .zip archive contains Quality Controlled float data for biogeochemical profiling floats deployed by the Southern Ocean Carbon and Climate Observation and Modeling (SOCCOM) and Global Ocean Biogeochemistry Array (GO-BGC) projects. Additional University of Washington/MBARI floats deployed outside of these programs have also been included in the archive. Data for all floats within this archive were processed by the float data management team at the Monterey Bay Aquarium Research Institute (MBARI). A comprehensive listing of floats processed by MBARI (including internal floatID, WMOID, float platform type, and program) is included within each archive. Note that historical snapshots, frozen prior to December 2022, can be found under the SOCCOM collection (https://doi.org/10.6075/J0TX3C9X). FORMAT: The ascii files contained herein were formatted to be compatible with Ocean Data View (ODV). Character encoding is UTF-8. ODV is freely available at https://odv.awi.de/. In addition, a Matlab function has been provided in each .zip archive for parsing the .txt files into data structures within Matlab (see get_FloatViz_data.m). Please note that the data files within this .zip archive represent a snapshot of all SOCCOM and GO-BGC float data processed at MBARI as of the date listed in the file name. Therefore, be aware that processing updates (** AND THUS CHANGES TO THE DATA **) may have occurred since the time the snapshot was created. For the most up-to-date files (processed every 4 hours), visit https://ftp.mbari.org/pub/SOCCOM/FloatVizData/ or https://www.mbari.org/science/upper-ocean-systems/chemical-sensor-group/floatviz/ ALTERNATE FORMATS: NetCDF and Matlab Matlab and NetCDF formatted files are provided for each ODV text file. The Matlab format is loaded as structure, FloatViz, with the ODV parameter names as the structure's fieldnames. The NetCDF format is similar to ARGO Float NetCDF format in its structure. The parameter names and flagging, however, match the ODV text parameter names. In addition to the quality control flag string entries, a numeric array of quality control flags is provided in the NetCDF files for programming convenience. Additionally, note that an explanation of Quality Flag Identifiers is included in each alternate format file. QUALITY CONTROL DOCUMENTATION: Delayed-mode quality control (DMQC) of biogeochemical float data within this archive is performed routinely by data managers at MBARI following Maurer et al (2021), https://doi.org/10.3389/fmars.2021.683207. QC notification emails are currently being sent out periodically to inform users of key updates to processing, QC and/or sensor calibrations for specific floats. All QC emails as of the date of this snapshot are included in each downloadable zip file. PARAMETERS: For information pertaining to float identification, sensor arrays, data parameters, and quality control please refer to descriptions within the file headers. Each snapshot within this collection includes estimated total alkalinity and derived carbon parameters for DIC and pCO2 using one of three algorithms (LIAR or CANYONB or ESPER). Estimated total alkalinity and derived carbon parameters from the ESPER algorithm are new additions to the standard snapshot collection as of December 2025. It is anticipated that ESPER will replace LIAR estimated total alkalinity and derived carbon parameters in future snapshot collection releases. Floats without a pH sensor will not have these additional parameters within their respective data files. See file headers for details. Additionally, files located at the urls listed above will include carbon parameters derived using observed pH and total alkalinity estimated by the LIAR method. All carbonate system variables calculated with CO2SYS for Matlab (Sharp et al., 2020; see also Lewis and Wallace 1998) used the following conditions: pH was reported on the total scale. K1 and K2 dissociation constants were from Lueker et al., 2000, doi:10.1016/S0304-4203(00)00022-0. The KSO4 dissociation constant was from Dickson, 1990, doi: 10.1016/0021-9614(90)90074-Z. The KF dissociation constant was from Perez and Fraga 1987, doi: 10.1016/0304-4203(87)90036-3. The borate to salinity ratio was from Lee et al., 2010, doi:10.1016/j.gca.2009.12.027. Silicate and Phosphate were not measured by the float, but estimates based on Redfieldian ratios improved the carbonate system estimates. If a nitrate value was considered to be of good quality silicate = nitrate*2.5 and phosphate = nitrate/16, otherwise the best estimate for both was considered to be 0. When pCO2 was estimated from TALK_LIAR (or TALK_ESPER) and pHinsitu, a bias was first added to pH (25C,0dbar) following Williams et al., 2017, doi: https:doi.org/10.1002/2016GB005541 , section 3.4, equation 3. This correction is not necessary for DIC and DIC is computed with the reported pH and the TALK_LIAR (or TALK_ESPER) value. RESOLUTION: This archive contains both low resolution and high resolution data. The format is defined by the folder name: SOCCOM_GO-BGC_LoResQC_METHOD_ddmmmyyyy or SOCCOM_GO-BGC_HiResQC_METHOD_ddmmmyyyy (where METHOD = LIAR or CANYONB or ESPER). Note that for APEX floats, the low resolution files only report data at depths where biogeochemical sensors sample, while the high resolution files merge this low resolution data with higher resolution pressure, temperature and salinity data (sampled every two meters in the upper 1000 meters). Be aware that, due to the merging of the two separate sampling schemes by interleaving the LowRes samples into the HiRes sample structure, HiRes files could potentially contain separate sets of samples with duplicate pressure values. For Navis floats all biogeochemical sensors except nitrate are sampled every 2 meters in the upper 1000 m. Additionally, all Navis float data is contained in both the LoRes and HiRes archives (per user request). DISCLAIMER: These data are provided as-is. We do our best to provide high-quality, complete data but make no guarantees as to the presence of errors within the data themselves or the algorithms used in the generation of derived parameters. It is the user's responsibility to ensure that the data meets the user's needs. However, please report any observed discrepancies in the data to the contact listed below and we will do our best to fix them. CONTACT: Please report any discrepancies, problems or concerns to the following and include "FLOATVIZ SNAPSHOT PROCESSING" in the subject line of the email. Tanya Maurer tmaurer@mbari.org Nicola Guisewhite nicolag@mbari.org SOCCOM/GO-BGC Data Management MBARI 7700 Sandholdt Road Moss Landing, CA 95039 

Marine phytoplankton play a central role in global biogeochemical cycling, carbon export, and the overall functioning of marine ecosystems. While chlorophyll a (Chl a ) is widely used as a proxy for phytoplankton biomass, identifying the proportion of Chl a attributable to different phytoplankton groups remains a major challenge in oceanography, especially for the picophytoplankton groups that often represent the majority of phytoplankton biomass in the open ocean. We describe a method for measuring picophytoplankton per-cell Chl a in field samples using fluorescence-activated cell sorting followed by solvent-based Chl a extraction and fluorescence quantification. Applying this method to surface samples from the Gulf of Mexico, we determined per-cell Chl a to be 0.24 ± 0.07, 0.6 ± 0.33, and 26.36 ± 20.9 fg Chl a cell -1 for Prochlorococcus , Synechococcus , and PPE, respectively (mean ± SD). Measurements of per-cell Chl a using this method are precise to within 1.7, 2.1, and 3.1% for Prochlorococcus , Synechococcus , and PPE, respectively. We demonstrate that this approach can be used to obtain estimates of group-specific Chl a for Prochlorococcus , Synechococcus , and picophytoeukaryotes, the latter two of which cannot be captured by existing methods. We also demonstrate that measurements of per-cell Chl a made using this method in field samples are sufficiently precise to capture relationships between per-cell Chl a and cytometer red fluorescence, providing a bridge between biomass estimates from cell counts and bulk measurements of total Chl a . 

Abstract While algal phago-mixotrophs play a major role in aquatic microbial food webs, their diversity remains poorly understood. Recent studies have indicated several species of prasinophytes, early diverging green algae, to be able to consume bacteria for nutrition. To further explore the occurrence of phago-mixotrophy in green algae, we conducted feeding experiments with live fluorescently labeled bacteria stained with CellTracker Green CMFDA, heat-killed bacteria stained with 5-(4,6-dichlorotriazin-2-yl) aminofluorescein (DTAF), and magnetic beads. Feeding was detected via microscopy and/or flow cytometry in five strains of prasinophytes when provided with live bacteria: Pterosperma cristatum NIES626, Pyramimonas parkeae CCMP726, Pyramimonas parkeae NIES254, Nephroselmis pyriformis RCC618, and Dolichomastix tenuilepis CCMP3274. No feeding was detected when heat-killed bacteria or magnetic beads were provided, suggesting a strong preference for live prey in the strains tested. In parallel to experimental assays, green algal bacterivory was investigated using a gene-based prediction model. The predictions agreed with the experimental results and suggested bacterivory potential in additional green algae. Our observations underline the likelihood of widespread occurrence of phago-mixotrophy among green algae, while additionally highlighting potential biases introduced when using prey proxy to evaluate bacterial ingestion by algal cells. 

Abstract. Oligotrophic regions play a central role in global biogeochemical cycles, with microbial communities in these areas representing an important term in global carbon budgets. While the general structure of microbial communities has been well documented in the global ocean, some remote regions such as the western tropical South Pacific (WTSP) remain fundamentally unexplored. Moreover, the biotic and abiotic factors constraining microbial abundances and distribution remain not well resolved. In this study, we quantified the spatial (vertical and horizontal) distribution of major microbial plankton groups along a transect through the WTSP during the austral summer of 2015, capturing important autotrophic and heterotrophic assemblages including cytometrically determined abundances of non-pigmented protists (also called flagellates). Using environmental parameters (e.g., nutrients and light availability) as well as statistical analyses, we estimated the role of bottom–up and top–down controls in constraining the structure of the WTSP microbial communities in biogeochemically distinct regions. At the most general level, we found a typical tropical structure, characterized by a shallow mixed layer, a clear deep chlorophyll maximum at all sampling sites, and a deep nitracline. Prochlorococcus was especially abundant along the transect, accounting for 68±10.6% of depth-integrated phytoplankton biomass. Despite their relatively low abundances, picophytoeukaryotes (PPE) accounted for up to 26±11.6% of depth-integrated phytoplankton biomass, while Synechococcus accounted for only 6±6.9%. Our results show that the microbial community structure of the WTSP is typical of highly stratified regions, and underline the significant contribution to total biomass by PPE populations. Strong relationships between N₂ fixation rates and plankton abundances demonstrate the central role of N₂ fixation in regulating ecosystem processes in the WTSP, while comparative analyses of abundance data suggest microbial community structure to be increasingly regulated by bottom–up processes under nutrient limitation, possibly in response to shifts in abundances of high nucleic acid bacteria (HNA).