Multi-Factor Coral Disease Risk : A new product for early warning and management
© 2024 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America..
Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near-real-time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi-Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict the risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the US National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of 6 months at a resolution of ~5 km based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012 to 2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid- to high-disease levels. Most of the prediction uncertainty arises from model uncertainty, so prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co-developed with end-user groups to improve use and understanding of ecological forecasts. The models provide near-real-time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real-world complexities and, in doing so, better supports near-term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Ecological applications : a publication of the Ecological Society of America - (2024) vom: 24. März, Seite e2961 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Caldwell, Jamie M [VerfasserIn] |
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| Links: |
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| Themen: |
Coral reefs |
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| Anmerkungen: |
Date Revised 24.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1002/eap.2961 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM37012510X |
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| 520 | |a © 2024 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America. | ||
| 520 | |a Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near-real-time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi-Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict the risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the US National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of 6 months at a resolution of ~5 km based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012 to 2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid- to high-disease levels. Most of the prediction uncertainty arises from model uncertainty, so prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co-developed with end-user groups to improve use and understanding of ecological forecasts. The models provide near-real-time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real-world complexities and, in doing so, better supports near-term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a disease | |
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| 650 | 4 | |a machine learning | |
| 650 | 4 | |a quantile regression forests | |
| 700 | 1 | |a Liu, Gang |e verfasserin |4 aut | |
| 700 | 1 | |a Geiger, Erick |e verfasserin |4 aut | |
| 700 | 1 | |a Heron, Scott F |e verfasserin |4 aut | |
| 700 | 1 | |a Eakin, C Mark |e verfasserin |4 aut | |
| 700 | 1 | |a De La Cour, Jacqueline |e verfasserin |4 aut | |
| 700 | 1 | |a Greene, Austin |e verfasserin |4 aut | |
| 700 | 1 | |a Raymundo, Laurie |e verfasserin |4 aut | |
| 700 | 1 | |a Dryden, Jen |e verfasserin |4 aut | |
| 700 | 1 | |a Schlaff, Audrey |e verfasserin |4 aut | |
| 700 | 1 | |a Stella, Jessica S |e verfasserin |4 aut | |
| 700 | 1 | |a Kindinger, Tye L |e verfasserin |4 aut | |
| 700 | 1 | |a Couch, Courtney S |e verfasserin |4 aut | |
| 700 | 1 | |a Fenner, Douglas |e verfasserin |4 aut | |
| 700 | 1 | |a Hoot, Whitney |e verfasserin |4 aut | |
| 700 | 1 | |a Manzello, Derek |e verfasserin |4 aut | |
| 700 | 1 | |a Donahue, Megan J |e verfasserin |4 aut | |
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