Field-Testing a Proteomics-Derived Machine-Learning Model for Predicting Coral Bleaching Susceptibility

Mayfield, Anderson B.; Lin, Chiahsin

doi:10.3390/app13031718

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Field-Testing a Proteomics-Derived Machine-Learning Model for Predicting Coral Bleaching Susceptibility

2023
By Mayfield, Anderson B. ; Lin, Chiahsin
Source: Applied Sciences, 13(3), 1718

[PDF-2.85 MB]

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Details:

Journal Title:

Applied Sciences
Personal Author:

Mayfield, Anderson B. ; Lin, Chiahsin

Mayfield, Anderson B. ; Lin, Chiahsin Less -
NOAA Program & Office:

CoRIS (Coral Reef Information System)
Description:

Given the widespread decline of coral reefs, temperature-focused models have been generated to predict when and where bleaching events may occur (e.g., Coral Reef Watch). Although such algorithms are adept at forecasting the onset of bleaching in many areas, they suffer from poor predictive capacity in regions featuring corals that have adapted or acclimatized to life in marginal environments, such as reefs of the Florida Keys (USA). In these locales, it may instead be preferred to use physiological data from the corals themselves to make predictions about stress tolerance. Herein proteomic data from both laboratory and field samples were used to train neural networks and other machine-learning models to predict coral bleaching susceptibility in situ, and the models’ accuracies were field-tested with massive corals (Orbicella faveolata) sampled across a 2019 bleaching event. The resulting artificial intelligence was capable of accurately predicting whether or not a coral would bleach in response to high temperatures based on its protein signatures alone, meaning that this approach could consequently be of potential use in delineating O. faveolata climate resilience.
Keywords:

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Computer Science Applications Fluid Flow And Transfer Processes General Engineering General Materials Science Instrumentation Process Chemistry And Technology
Source:

Applied Sciences, 13(3), 1718
DOI:

https://doi.org/10.3390/app13031718
ISSN:

2076-3417
Format:

PDF
Publisher:

MDPI AG
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Library
Main Document Checksum:

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urn:sha256:24de99b61a42632f28eb631e150932858759c9098a3e43c955c0ae7e364919fd
Download URL:

https://repository.library.noaa.gov/view/noaa/54018/noaa_54018_DS1.pdf
File Type:

	Machine readable version (XML)	xml
	Dataset from NCEI (Accession: 0243645)	htm

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