Multi‐Factor Coral Disease Risk: A new product for early warning and management

Author:

Caldwell Jamie M.12ORCID,Liu Gang3,Geiger Erick34,Heron Scott F.5,Eakin C. Mark6,De La Cour Jacqueline34,Greene Austin17,Raymundo Laurie8,Dryden Jen9,Schlaff Audrey9,Stella Jessica S.9,Kindinger Tye L.10ORCID,Couch Courtney S.1011,Fenner Douglas12,Hoot Whitney13,Manzello Derek3,Donahue Megan J.1ORCID

Affiliation:

1. Hawaiʻi Institute of Marine Biology Kaneohe Hawaii USA

2. High Meadows Environmental Institute, Princeton University Princeton New Jersey USA

3. NOAA/NESDIS/STAR Coral Reef Watch College Park Maryland USA

4. Global Science & Technology, Inc. Greenbelt Maryland USA

5. Physical Sciences and Marine Geophysics Laboratory, College of Science and Engineering James Cook University Townsville Queensland Australia

6. Corals and Climate Silver Spring Maryland USA

7. Woods Hole Oceanographic Institution Woods Hole Massachusetts USA

8. University of Guam Marine Laboratory Mangilao Guam USA

9. Great Barrier Reef Marine Park Authority Townsville Queensland Australia

10. Pacific Islands Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration Honolulu Hawaii USA

11. Cooperative Institute for Marine and Atmospheric Research, University of Hawaiʻi at Mānoa Honolulu Hawaii USA

12. Lynker Technologies, LLC, Contractor, NOAA Fisheries Service, Pacific Islands Regional Office Honolulu Hawaii USA

13. Guam Coral Reef Initiative, Government of Guam Hagatña Guam USA

Abstract

AbstractEcological 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.

Funder

Coral Reef Conservation Program

Publisher

Wiley

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