Treating coral bleaching as weather: a framework to validate and optimize prediction skill
Few coral reefs remain unscathed by mass bleaching over the past several decades, and much of the coral reef science conducted today relates in some way to the causes, consequences, or recovery pathways of bleaching events. Most studies portray a simple cause and effect relationship between anomalou...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2020-07-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/9449.pdf |
| _version_ | 1797422158774796288 |
|---|---|
| author | Thomas M. DeCarlo |
| author_facet | Thomas M. DeCarlo |
| author_sort | Thomas M. DeCarlo |
| collection | DOAJ |
| description | Few coral reefs remain unscathed by mass bleaching over the past several decades, and much of the coral reef science conducted today relates in some way to the causes, consequences, or recovery pathways of bleaching events. Most studies portray a simple cause and effect relationship between anomalously high summer temperatures and bleaching, which is understandable given that bleaching rarely occurs outside these unusually warm times. However, the statistical skill with which temperature captures bleaching is hampered by many “false alarms”, times when temperatures reached nominal bleaching levels, but bleaching did not occur. While these false alarms are often not included in global bleaching assessments, they offer valuable opportunities to improve predictive skill, and therefore understanding, of coral bleaching events. Here, I show how a statistical framework adopted from weather forecasting can optimize bleaching predictions and validate which environmental factors play a role in bleaching susceptibility. Removing the 1 °C above the maximum monthly mean cutoff in the typical degree heating weeks (DHW) definition, adjusting the DHW window from 12 to 9 weeks, using regional-specific DHW thresholds, and including an El Niño threshold already improves the model skill by 45%. Most importantly, this framework enables hypothesis testing of other factors or metrics that may improve our ability to forecast coral bleaching events. |
| first_indexed | 2024-03-09T07:29:10Z |
| format | Article |
| id | doaj.art-54614f301bd14103a8902efd931280be |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:29:10Z |
| publishDate | 2020-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-54614f301bd14103a8902efd931280be2023-12-03T06:47:12ZengPeerJ Inc.PeerJ2167-83592020-07-018e944910.7717/peerj.9449Treating coral bleaching as weather: a framework to validate and optimize prediction skillThomas M. DeCarloFew coral reefs remain unscathed by mass bleaching over the past several decades, and much of the coral reef science conducted today relates in some way to the causes, consequences, or recovery pathways of bleaching events. Most studies portray a simple cause and effect relationship between anomalously high summer temperatures and bleaching, which is understandable given that bleaching rarely occurs outside these unusually warm times. However, the statistical skill with which temperature captures bleaching is hampered by many “false alarms”, times when temperatures reached nominal bleaching levels, but bleaching did not occur. While these false alarms are often not included in global bleaching assessments, they offer valuable opportunities to improve predictive skill, and therefore understanding, of coral bleaching events. Here, I show how a statistical framework adopted from weather forecasting can optimize bleaching predictions and validate which environmental factors play a role in bleaching susceptibility. Removing the 1 °C above the maximum monthly mean cutoff in the typical degree heating weeks (DHW) definition, adjusting the DHW window from 12 to 9 weeks, using regional-specific DHW thresholds, and including an El Niño threshold already improves the model skill by 45%. Most importantly, this framework enables hypothesis testing of other factors or metrics that may improve our ability to forecast coral bleaching events.https://peerj.com/articles/9449.pdfCoralCoral bleachingSea surface temperature |
| spellingShingle | Thomas M. DeCarlo Treating coral bleaching as weather: a framework to validate and optimize prediction skill PeerJ Coral Coral bleaching Sea surface temperature |
| title | Treating coral bleaching as weather: a framework to validate and optimize prediction skill |
| title_full | Treating coral bleaching as weather: a framework to validate and optimize prediction skill |
| title_fullStr | Treating coral bleaching as weather: a framework to validate and optimize prediction skill |
| title_full_unstemmed | Treating coral bleaching as weather: a framework to validate and optimize prediction skill |
| title_short | Treating coral bleaching as weather: a framework to validate and optimize prediction skill |
| title_sort | treating coral bleaching as weather a framework to validate and optimize prediction skill |
| topic | Coral Coral bleaching Sea surface temperature |
| url | https://peerj.com/articles/9449.pdf |
| work_keys_str_mv | AT thomasmdecarlo treatingcoralbleachingasweatheraframeworktovalidateandoptimizepredictionskill |