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    • Towards predicting coral fate with a molecular biotechnology+machine-learning approach (NCEI Accession 0254274)

    Metadata Updated: November 1, 2023

    Given the widespread decline of coral reefs across the globe on account of climate change-induced rises in seawater temperature, a series of temperature-focused models have been generated to predict when and where bleaching events may occur (e.g., NOAA’s Coral Reef Watch). Although such algorithms are adept at forecasting the onset of periods of severe bleaching in many parts of the world, they suffer from poor predictive capacity in areas featuring high numbers of corals that have either adapted or acclimatized to life in marginalized environments, such as stress-hardened corals of the Florida Keys. In these areas, it may instead be superior to use physiological data from the corals themselves to make predictions about coral bleaching susceptibility. To that end, both field and laboratory analyses were undertaken with the massive Caribbean reef-builder Orbicella faveolata whereby, after elucidating the cellular pathways underlying both bleaching and high-temperature tolerance in diverse genotypes ex situ, the protein profiles of tagged field colonies were tracked across seasons. Neural networks trained with proteomic data from the laboratory specimens were then tested using proteomic data from bleaching-susceptible and bleaching-resistant field colonies, and the resulting artificial intelligence (AI) was capable of predicting with a high degree of confidence whether a coral colony would bleach. This ‘Omics+AI approach could be of potential use in delineating O. faveolata climate resilience elsewhere in the Florida Keys, and perhaps beyond. This dataset includes raw files from the mass spectrometer, as well as "distilled" mass spectrometry data that can be analyzed and interpreted by those with access to a personal computer and the Microsoft Office suite.

    Access & Use Information

    License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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    Dates

    Metadata Date 2023-10-06T18:04:26Z
    Metadata Created Date March 17, 2023
    Metadata Updated Date November 1, 2023
    Reference Date(s) June 29, 2022 (publication)
    Frequency Of Update asNeeded

    Metadata Source

    Harvested from NOAA/NESDIS/ncei/accessions

    Other Data Resources

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    Additional Metadata

    Resource Type Dataset
    Metadata Date 2023-10-06T18:04:26Z
    Metadata Created Date March 17, 2023
    Metadata Updated Date November 1, 2023
    Reference Date(s) June 29, 2022 (publication)
    Responsible Party (Point of Contact)
    Contact Email
    Guid gov.noaa.nodc:0254274
    Access Constraints Cite as: Mayfield, Anderson B. (2022). Towards predicting coral fate with a molecular biotechnology+machine-learning approach (NCEI Accession 0254274). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0254274. Accessed [date]., Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose.
    Bbox East Long -80.50205
    Bbox North Lat 24.95375
    Bbox South Lat 24.89742
    Bbox West Long -80.61573
    Coupled Resource
    Frequency Of Update asNeeded
    Graphic Preview Description Preview graphic
    Graphic Preview File https://www.ncei.noaa.gov/access/metadata/landing-page/bin/gfx?id=gov.noaa.nodc:0254274
    Graphic Preview Type PNG
    Harvest Object Id c00791b6-bab8-4c02-b520-cdb55c8a6ddf
    Harvest Source Id c084a438-6f6b-470d-93e0-16aeddb9f513
    Harvest Source Title NOAA/NESDIS/ncei/accessions
    Licence accessLevel: Public
    Lineage
    Metadata Language eng
    Metadata Type geospatial
    Old Spatial {"type": "Polygon", "coordinates": [[[-80.61573, 24.89742], [-80.50205, 24.89742], [-80.50205, 24.95375], [-80.61573, 24.95375], [-80.61573, 24.89742]]]}
    Progress completed
    Spatial Data Service Type
    Spatial Reference System
    Spatial Harvester True
    Temporal Extent Begin 2019-07-10
    Temporal Extent End 2019-12-04

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