Dataset: Synthesis of doliolid imagery and oceanographic data from six ecosystems collected from multiple research cruises conducted between 2010 and 2019

Final no updates expectedDOI: 10.26008/1912/bco-dmo.885637.1Version 1 (2023-02-07)Dataset Type:Other Field ResultsDataset Type:model results

Principal Investigator: Adam T. Greer (Skidaway Institute of Oceanography)

Co-Principal Investigator: Marc E. Frischer (Skidaway Institute of Oceanography)

BCO-DMO Data Manager: Shannon Rauch (Woods Hole Oceanographic Institution)


Project: The significance of doliolid-microbial interactions: Do doliolids fundamentally alter the trophic structure and productivity of sub-tropical continental shelf food webs? (DolMICROBE)


Abstract

Doliolids are common gelatinous grazers in marine ecosystems around the world. Aggregations or blooms of these organisms occur frequently, but they are difficult to measure or predict and ecological studies typically target a single region or site that does not encompass the range of possible habitats favoring doliolid proliferation. To address these limitations, we combined in situ imaging data from six coastal ecosystems, including the Oregon shelf, northern California, southern California Big...

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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:

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.

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.

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

4. GulfofMexicoDoliolidImages-ManuallyClassified – This folder contains actual images identified to 3 different life stages of doliolids from the northern Gulf of Mexico.

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.

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).

7. R-Scripts – This folder contains the R-scripts used to conduct all analyses synthesizing these datasets

8. DolTransectLocations.csv – This file 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.

The Supplemental File named "File_Descriptions.pdf" contains additional details on each file within each folder.


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Results

Greer, A. T., Schmid, M. S., Duffy, P. I., Robinson, K. L., Genung, M. A., Luo, J. Y., Panaïotis, T., Briseño‐Avena, C., Frischer, M. E., Sponaugle, S., & Cowen, R. K. (2022). In situ imaging across ecosystems to resolve the fine‐scale oceanographic drivers of a globally significant planktonic grazer. Limnology and Oceanography. Portico. https://doi.org/10.1002/lno.12259
Methods

Bez, N. (2000). On the use of Lloyd’s index of patchiness. Fisheries Oceanography, 9(4), 372–376. doi:10.1046/j.1365-2419.2000.00148.x
Methods

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
Software

R Core Team (2019). R: A language and environment for statistical computing. R v3.6.1. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/