http://lod.bco-dmo.org/id/dataset/885637
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
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2022-12-20
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Synthesis of doliolid imagery and oceanographic data from six ecosystems collected from multiple research cruises conducted between 2010 and 2019
2023-02-07
publication
2023-02-07
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2023-05-02
publication
https://doi.org/10.26008/1912/bco-dmo.885637.1
Adam T. Greer
Skidaway Institute of Oceanography
principalInvestigator
Marc E. Frischer
Skidaway Institute of Oceanography
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Greer, A. T., Frischer, M. E. (2023) Synthesis of doliolid imagery and oceanographic data from six ecosystems collected from multiple research cruises conducted between 2010 and 2019. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2023-02-07 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.885637.1 [access date]
Doliolid Distribution Synthesis Dataset Description: <p>This dataset is a synthesis of doliolid imagery and oceanographic data from six ecosystems collected from multiple research cruises conducted between 2010 and 2019. The data are organized into the following folders/files:</p>
<p>1.DoliolidAbundances-All – This folder contains the calculated concentrations and average oceanographic variables in each bin from both automated (using computer vision) and manually verified data.</p>
<p>2. DoliolidEnvironment-Automated – This folder contains the raw data from the different ecosystems generated with computer vision algorithms where each row is an individual doliolid and all of the oceanographic parameters associated with it.</p>
<p>3. DoliolidEnvironment-Manual – This folder contains the manually verified doliolid identifications in the Gulf of Mexico and Southern California. Southern California had individual "casts" analyzed, so the times for these chunks of data are also contained in this folder</p>
<p>4. GulfofMexicoDoliolidImages-ManuallyClassified – This folder contains actual images identified to 3 different life stages of doliolids from the northern Gulf of Mexico.</p>
<p>5. GulfPhysicalOceanographicData – This folder contains oceanographic sensor data from the towed imaging system, as well as linearly interpolated data used to make plots of the doliolid distributions in the paper.</p>
<p>6. LiteratureReview-PSEM – This folder contains a compilation of data from two open-access databases used to make some calculations in the published manuscript. The folder also contains the data frame used to run the piecewise structural equation models (PSEM).</p>
<p>7. R-Scripts – This folder contains the R-scripts used to conduct all analyses synthesizing these datasets</p>
<p>8. DolTransectLocations.csv&nbsp;– This file&nbsp;contains the Start and Stop latitudes and longitudes for all of the transects analyzed in the manuscript. This was used to generate the map in the manuscript. BCO-DMO converted this file from Microsoft Excel format to .csv.</p>
<p>The Supplemental File named "File_Descriptions.pdf" contains additional details on each file within each folder.</p> Methods and Sampling: <p>All methods below are excerpted from Greer et al. (2022). Please refer to the publication for a more complete description of the data collection and analysis methods.</p>
<p><strong>Imaging system and field sampling:</strong><br />
All high-resolution images were collected with a towed In Situ Ichthyoplankton Imaging System (ISIIS, Cowen and Guigand 2008). The ISIIS uses shadowgraph lighting and a line-scan camera (Teledyne DALSA) to image water with a 13 cm field of view over a depth of field of 50 centimeters. The system is towed at a speed of approximately 2.5 meters per second, using motor-controlled wings to undulate between about 1 meter from the surface and a maximum depth of about 120 meters (or 2–4 m from the benthos) in a "tow-yo" pattern. The camera scans approximately 35,000 pixel lines per second, producing a continuous strip of imaged ocean water that is parsed by acquisition software into 2048 px by 2048 px images (~17 Hz). The instrument is equipped with various oceanographic sensors, including conductivity, temperature, depth (SBE 49, Seabird Electronics), chlorophyll-a fluorescence (ECO FL-RT), and dissolved oxygen (SBE 43) to measure oceanographic conditions associated with each image. Transects were conducted in six different ecosystems (the Oregon shelf, northern California, southern California Bight, northern Gulf of Mexico, Straits of Florida, and Mediterranean Sea) and used similar towing methods (refer to Figure 1 of Greer et al., 2022)</p>
<p>To compare data among sampling sites, abundances and associated oceanographic data were summed or averaged over a consistent volume sampled. For organism counts, both stage-specific and total doliolids were summed across 7-second bins, which corresponded to approximately 1 cubic meter of imaged water. The oceanographic variables were also averaged over this volume. We were interested in doliolids and their different life stages: nurses, phorozooids, and gonozooids, all of which are relatively easy to distinguish.</p>
<p>To evaluate the spatial changes in the accuracy of the automatically generated abundances for these three life stages, we leveraged a human-annotated dataset from the northern Gulf of Mexico. This matched the life stages classified in the automated datasets for a more robust comparison. These manual identifications were binned using the same procedure as the automated data.</p>
<p><strong>Mean carbon biomass:</strong><br />
We computed the mean carbon biomass of doliolids within the shallowest 100 m for all sites. Doliolid carbon biomass in cubic meters was then calculated using the average concentration of individuals in the upper 100 meters in each sampling region multiplied by the average individual weight.</p>
<p><strong>Statistical analyses and modeling</strong><br />
Data binning, summary statistics, and visualizations were performed in R (R Core Team 2019, v.3.6.1), with extensive use of the packages ggplot2, reshape2, and plyr (Wickham 2016). Patchiness or degree of aggregation was quantified using the Lloyd index of patchiness (Bez 2000).</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2023133 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2023133
completed
Adam T. Greer
Skidaway Institute of Oceanography
912-598-2334
10 Ocean Science Circle
Savannah
GA
31411
USA
atgreer@uga.edu
pointOfContact
Marc E. Frischer
Skidaway Institute of Oceanography
(912) 598-2308
10 Ocean Science Circle
Savannah
GA
31411
USA
marc.frischer@skio.usg.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
In situ Ichthyoplankton Imaging System (ISIIS)
theme
None, User defined
In Situ Ichtyoplankton Imaging System
instrument
BCO-DMO Standard Instruments
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
The significance of doliolid-microbial interactions: Do doliolids fundamentally alter the trophic structure and productivity of sub-tropical continental shelf food webs?
https://www.skio.uga.edu/dol-microbe/
The significance of doliolid-microbial interactions: Do doliolids fundamentally alter the trophic structure and productivity of sub-tropical continental shelf food webs?
<p><em>NSF Award Abstract:</em><br />
Gelatinous zooplankton play crucial but poorly understood roles in food webs and element cycling in the world's oceans. This is especially true for smaller mucus-feeding gelatinous animals including the pelagic tunicates (salps, pyrosomes, larvaceans, and doliolids). Doliolids form massive blooms in particle and nutrient-rich waters of the world’s continental shelves, often exceeding 1,000 zooids m-3 and extending 100’s of km. These blooms are likely to alter the structure and function of marine food webs. When crustacean zooplankton (copepods) are the primary consumers of phytoplankton they nourish larger species such as fish, birds, and marine mammals. The investigators hypothesize that doliolid blooms, in contrast, enhance microbial loop processes leading to a lower efficiency of trophic transfer to larger consumers. In this project, the investigators examine the role that doliolids play in linking and modifying microbial loop and classical food web processes in subtropical continental shelf systems. In addition to the project’s focus on a central theme in biological oceanography, the results of this project are of broader interest. Specifically, gelatinous zooplankton remain a poorly resolved component of ecosystem models, and a better understanding of how these communities interact with microbial processes and are influenced by chemical and physical conditions will improve predictions of population- and ecosystem-level responses to the myriad of environmental stressors. The project supports a targeted effort to increase the representation of African Americans in the Ocean Sciences, provides experiential research opportunities to K-12 educators, supports 2 PhD students and several undergraduate students, and involves an investigator at the beginning of his academic career.</p>
<p>The long-term goal of this project is to understand the ecological function of doliolids on subtropical continental shelves and their influence on microbial processes. In association with monthly oceanographic field expeditions on the mid-continental shelf at 31°N and two longer longitudinal expeditions complemented with experimental studies, the project investigates the relationship between doliolid abundance and life stage composition, their spatial relationships to marine snow aggregates and other zooplankton, water column microbial activity, bacterial production, and net system productivity. Utilizing a constellation of modern and classical approaches in microbial and zooplankton ecology, including deployment of an in situ zooplankton imaging system in the South Atlantic Bight, microbial metagenomics and transcriptomics, and stable isotope-based tracer experiments, these studies will allow the testing of three fundamental hypotheses. First, independent of total shelf productivity, the presence and abundance of doliolids is correlated with heterotrophy compared to periods when doliolids are not abundant. Second, doliolids predictably shape continental shelf microbial communities, activity, and function, and third, doliolids are detrital feeders benefiting nutritionally from the consumption of microbial enriched aggregated particles and fecal material - the pool to which they also contribute.</p>
<p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p>
DolMICROBE
largerWorkCitation
project
eng; USA
oceans
-125.1078
7.793519
24.9423
44.6521
2010-01-01
2019-12-31
Mid continental shelf of the South Atlantic Bight; Marsden Grid 117; Navy Op Area NA06
0
BCO-DMO catalogue of parameters from Synthesis of doliolid imagery and oceanographic data from six ecosystems collected from multiple research cruises conducted between 2010 and 2019
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
512
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https://doi.org/10.26008/1912/bco-dmo.885637.1
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onLine
dataset
<p>All methods below are excerpted from Greer et al. (2022). Please refer to the publication for a more complete description of the data collection and analysis methods.</p>
<p><strong>Imaging system and field sampling:</strong><br />
All high-resolution images were collected with a towed In Situ Ichthyoplankton Imaging System (ISIIS, Cowen and Guigand 2008). The ISIIS uses shadowgraph lighting and a line-scan camera (Teledyne DALSA) to image water with a 13 cm field of view over a depth of field of 50 centimeters. The system is towed at a speed of approximately 2.5 meters per second, using motor-controlled wings to undulate between about 1 meter from the surface and a maximum depth of about 120 meters (or 2–4 m from the benthos) in a "tow-yo" pattern. The camera scans approximately 35,000 pixel lines per second, producing a continuous strip of imaged ocean water that is parsed by acquisition software into 2048 px by 2048 px images (~17 Hz). The instrument is equipped with various oceanographic sensors, including conductivity, temperature, depth (SBE 49, Seabird Electronics), chlorophyll-a fluorescence (ECO FL-RT), and dissolved oxygen (SBE 43) to measure oceanographic conditions associated with each image. Transects were conducted in six different ecosystems (the Oregon shelf, northern California, southern California Bight, northern Gulf of Mexico, Straits of Florida, and Mediterranean Sea) and used similar towing methods (refer to Figure 1 of Greer et al., 2022)</p>
<p>To compare data among sampling sites, abundances and associated oceanographic data were summed or averaged over a consistent volume sampled. For organism counts, both stage-specific and total doliolids were summed across 7-second bins, which corresponded to approximately 1 cubic meter of imaged water. The oceanographic variables were also averaged over this volume. We were interested in doliolids and their different life stages: nurses, phorozooids, and gonozooids, all of which are relatively easy to distinguish.</p>
<p>To evaluate the spatial changes in the accuracy of the automatically generated abundances for these three life stages, we leveraged a human-annotated dataset from the northern Gulf of Mexico. This matched the life stages classified in the automated datasets for a more robust comparison. These manual identifications were binned using the same procedure as the automated data.</p>
<p><strong>Mean carbon biomass:</strong><br />
We computed the mean carbon biomass of doliolids within the shallowest 100 m for all sites. Doliolid carbon biomass in cubic meters was then calculated using the average concentration of individuals in the upper 100 meters in each sampling region multiplied by the average individual weight.</p>
<p><strong>Statistical analyses and modeling</strong><br />
Data binning, summary statistics, and visualizations were performed in R (R Core Team 2019, v.3.6.1), with extensive use of the packages ggplot2, reshape2, and plyr (Wickham 2016). Patchiness or degree of aggregation was quantified using the Lloyd index of patchiness (Bez 2000).</p>
Specified by the Principal Investigator(s)
<p><strong>Data Processing:</strong><br />
Doliolids were enumerated manually in the images, or they were identified automatically using different versions of a convolutional neural network. All data were analyzed and processed in R (v.3.6.1) using a variety of packages detailed in the accompanying code.</p>
<p><strong>BCO-DMO Processing:</strong><br />
- BCO-DMO converted "DolTransectLocations.xlsx" to .csv format and changed the stop longitude values for San Diego to be negative.</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
In situ Ichthyoplankton Imaging System (ISIIS)
In situ Ichthyoplankton Imaging System (ISIIS)
PI Supplied Instrument Name: In situ Ichthyoplankton Imaging System (ISIIS) PI Supplied Instrument Description:In situ Ichthyoplankton Imaging System (ISIIS), which includes a camera system with a 13 centimeter (cm) field of view and 50 cm depth of field. The camera scans approximately 35,000 pixel (px) lines per second, producing a continuous strip of imaged ocean water that is parsed by acquisition software into 2048 px by 2048 px images (~17 Hz). The instrument is equipped with various oceanographic sensors, including conductivity, temperature, depth (SBE 49, Seabird Electronics), chlorophyll-a fluorescence (ECO FL-RT), and dissolved oxygen (SBE 43) to measure oceanographic conditions associated with each image. Instrument Name: In Situ Ichtyoplankton Imaging System Instrument Short Name:ISIIS Instrument Description: The In Situ Ichthyoplankton Imaging System (ISIIS) is an underwater imaging system aimed at capturing in situ, real time images of marine zooplankton of relatively low abundance such as fish larvae and fragile gelatinous organisms. The first prototype, delivered in 2007, was attached to a relatively simple vehicle towed by an oceanographic vessel at a speed of five knots. The vehicle, and associated imaging system and sensors, was moved up and down through the water column by paying cable in and out via an oceanographic winch. Subsequently, a new vehicle has been designed with the capacity of self undulation using motor actuated dive fins.
The ISIIS system utilizes a high-resolution line-scanning camera with a Light Emitting Diode (LED) light source, modified by plano-convex optics, to create a collimated light field to back-light a parcel of water.
ISIIS was developed in collaboration between the University of Miami's Rosenstiel School of Atmospheric and Marine Science (RSMAS) and the subsea engineering company, Bellamare, LLC, located in San Diego CA. See complete description from RSMAS.
Reference:
Cowen RK and Guigand CM. 2008. In situ Ichthyoplankton Imaging System (ISIIS): system design and preliminary results. Limnol. Oceanogr. Methods. 6:126-132. doi:10.4319/lom.2008.6.126