Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial Intelligence
Numerous physical, chemical, and biological factors influence coral resilience in situ, yet current models aimed at forecasting coral health in response to climate change and other stressors tend to focus on temperature and coral abundance alone. To develop more robust predictions of reef coral resi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12955 |
| _version_ | 1797461597524852736 |
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| author | Anderson B. Mayfield Alexandra C. Dempsey Chii-Shiarng Chen Chiahsin Lin |
| author_facet | Anderson B. Mayfield Alexandra C. Dempsey Chii-Shiarng Chen Chiahsin Lin |
| author_sort | Anderson B. Mayfield |
| collection | DOAJ |
| description | Numerous physical, chemical, and biological factors influence coral resilience in situ, yet current models aimed at forecasting coral health in response to climate change and other stressors tend to focus on temperature and coral abundance alone. To develop more robust predictions of reef coral resilience to environmental change, we trained an artificial intelligence (AI) with seawater quality, benthic survey, and molecular biomarker data from the model coral <i>Pocillopora acuta</i> obtained during a research expedition to the Solomon Islands. This machine-learning (ML) approach resulted in neural network models with the capacity to robustly predict (R<sup>2</sup> = ~0.85) a benchmark for coral stress susceptibility, the “coral health index,” from significantly cheaper, easier-to-measure environmental and ecological features alone. A GUI derived from an ML desirability analysis was established to expedite the search for other climate-resilient pocilloporids within this Coral Triangle nation, and the AI specifically predicts that resilient pocilloporids are likely to be found on deeper fringing fore reefs in the eastern, more sparsely populated region of this under-studied nation. Although small in geographic expanse, we nevertheless hope to promote this first attempt at building AI-driven predictive models of coral health that accommodate not only temperature and coral abundance, but also physiological data from the corals themselves. |
| first_indexed | 2024-03-09T17:21:41Z |
| format | Article |
| id | doaj.art-fc60fd0526ac46d599e95674b450ea13 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:21:41Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fc60fd0526ac46d599e95674b450ea132023-11-24T13:07:38ZengMDPI AGApplied Sciences2076-34172022-12-0112241295510.3390/app122412955Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial IntelligenceAnderson B. Mayfield0Alexandra C. Dempsey1Chii-Shiarng Chen2Chiahsin Lin3Coral Reef Diagnostics, Miami, FL 33129, USAKhaled bin Sultan Living Oceans Foundation, Annapolis, MD 21403, USANational Museum of Marine Biology and Aquarium, Checheng, Pingtung 944, TaiwanNational Museum of Marine Biology and Aquarium, Checheng, Pingtung 944, TaiwanNumerous physical, chemical, and biological factors influence coral resilience in situ, yet current models aimed at forecasting coral health in response to climate change and other stressors tend to focus on temperature and coral abundance alone. To develop more robust predictions of reef coral resilience to environmental change, we trained an artificial intelligence (AI) with seawater quality, benthic survey, and molecular biomarker data from the model coral <i>Pocillopora acuta</i> obtained during a research expedition to the Solomon Islands. This machine-learning (ML) approach resulted in neural network models with the capacity to robustly predict (R<sup>2</sup> = ~0.85) a benchmark for coral stress susceptibility, the “coral health index,” from significantly cheaper, easier-to-measure environmental and ecological features alone. A GUI derived from an ML desirability analysis was established to expedite the search for other climate-resilient pocilloporids within this Coral Triangle nation, and the AI specifically predicts that resilient pocilloporids are likely to be found on deeper fringing fore reefs in the eastern, more sparsely populated region of this under-studied nation. Although small in geographic expanse, we nevertheless hope to promote this first attempt at building AI-driven predictive models of coral health that accommodate not only temperature and coral abundance, but also physiological data from the corals themselves.https://www.mdpi.com/2076-3417/12/24/12955artificial intelligencebioprospectingcoral reefsglobal climate changemachine learningpredictive modeling |
| spellingShingle | Anderson B. Mayfield Alexandra C. Dempsey Chii-Shiarng Chen Chiahsin Lin Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial Intelligence Applied Sciences artificial intelligence bioprospecting coral reefs global climate change machine learning predictive modeling |
| title | Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial Intelligence |
| title_full | Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial Intelligence |
| title_fullStr | Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial Intelligence |
| title_full_unstemmed | Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial Intelligence |
| title_short | Expediting the Search for Climate-Resilient Reef Corals in the Coral Triangle with Artificial Intelligence |
| title_sort | expediting the search for climate resilient reef corals in the coral triangle with artificial intelligence |
| topic | artificial intelligence bioprospecting coral reefs global climate change machine learning predictive modeling |
| url | https://www.mdpi.com/2076-3417/12/24/12955 |
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