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Sci Rep
2020 May 08;101:7735. doi: 10.1038/s41598-020-64411-8.
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Predicting coral-reef futures from El Niño and Pacific Decadal Oscillation events.
Houk P
,
Yalon A
,
Maxin S
,
Starsinic C
,
McInnis A
,
Gouezo M
,
Golbuu Y
,
van Woesik R
.
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El Niño Southern Oscillation (ENSO) events modulate oceanographic processes that control temperature and productivity in tropical waters, yet potential interactions with low frequency climate variability, such as the Pacific Decadal Oscillation (PDO), are poorly understood. We show that ENSO and PDO together predicted (i) maximum sea-surface temperatures (SST), which were associated with coral bleaching and declines in coral cover, and (ii) maximum chlorophyll-a concentrations, which were associated with high densities of coral-predatory Acanthaster starfish, across the tropical north Pacific Ocean since 1980. Asynchrony between the positive PDO and negative ENSO (i.e., La Niña) was associated with peaks in annual SST. By contrast, synchrony between the positive PDO and positive ENSO (i.e., El Niño) was associated with peaks in chlorophyll-a. Both conditions led to ecological disturbances and significant loss of coral cover, however, spatial models revealed where impacts to reefs were expected under varying climate scenarios. The 2015/17 ENSO event was coupled with a positive PDO and resulted in high SST and Acanthaster abundances in eastern Micronesia, while positive coral growth occurred in western Micronesia. Our novel approach for forecasting coral growth into the future may be applicable to other oceanic regions with differing oceanographic modulators.
Figure 1. Map of the western Pacific Ocean with a geographical box surrounding the tropical North Pacific study region of Micronesia. Numbers indicate islands where long-term biological data were collected: (1) Palau, (2) Yap, (3) Chuuk, (4) Pohnpei, and (5) Kosrae. Map was created by author PH using ArcGIS software 10.2.2 and freely available base maps associated with the online account.
Figure 2. Oceanographic and biological data across the Micronesia study region. Degree heating weeks (a) provide an indication of heat stress accumulated in each sub-region since 2013, and highlight spatial differences associated with the 2015–2017 ENSO. Biological data show the dynamics of coral cover on each island (b) and the densities of Acanthaster cf. solaris observed at each site (c). Small black circles indicate coral cover at each site, whereas large black circles indicate island means. Color area fills provide a breakdown of the total coral cover into three major families, Acroporidae, Merulinidae, and Poritidae, and all other corals grouped together. Small black circles for Acanthaster densities represent numbers per site, or per 1000 m2. Photographs were taken by Simon Lorenz who provided written permission for use.
Figure 3. Best-fit regression models showing the predicted (dashed) and observed (solid) SST (a) and chlorophyll-a (b) concentrations associated with the geographical study region. Correlation plots for ENSO and PDO model terms are provided to highlight criteria associated with SST (c) and chlorophyll-a (d) maximum. Data points enclosed by the dotted circles in the correlation plots (c,d) correspond to the large black circles in the SST and chlorophyll-a trajectories (a,b).
Figure 4. Smoothed spatial regressions highlighting the variation in R2 fit values (a) and model estimates for each term (b-ENSO, c-PDO, and d-ENSO × PDO). Blue colors indicate negative changes in SST with indices, whereas yellow and red colors indicate positive changes in SST with indices. Note the same scale is used for R2 values and model estimates. Maps were created by author PH using the R computing platform and packages raster, ncdf4, and rgdal38–41.
Figure 5. Smoothed spatial regressions highlighting the variation in R2 fit values (a) and model estimates for each term (b-PDO-winter, c-ENSO-winter). Blue colors indicate negative changes in chlorophyll-a with indices, while yellow and red colors indicate positive changes in chlorophyll-a with indices. Note the different scales used for R2 values and model estimates. Maps were created by author PH using the R computing platform and packages raster, ncdf4, and rgdal38–41.
Bruno,
Climate Change, Coral Loss, and the Curious Case of the Parrotfish Paradigm: Why Don't Marine Protected Areas Improve Reef Resilience?
2019, Pubmed
Bruno,
Climate Change, Coral Loss, and the Curious Case of the Parrotfish Paradigm: Why Don't Marine Protected Areas Improve Reef Resilience?
2019,
Pubmed
Chesher,
Destruction of Pacific Corals by the Sea Star Acanthaster planci.
1969,
Pubmed
,
Echinobase
Chikamoto,
Multi-year predictability of climate, drought, and wildfire in southwestern North America.
2017,
Pubmed
Darling,
Evaluating life-history strategies of reef corals from species traits.
2012,
Pubmed
Darling,
Social-environmental drivers inform strategic management of coral reefs in the Anthropocene.
2019,
Pubmed
De'ath,
The 27-year decline of coral cover on the Great Barrier Reef and its causes.
2012,
Pubmed
,
Echinobase
Houk,
Coral reef disturbance and recovery dynamics differ across gradients of localized stressors in the Mariana Islands.
2014,
Pubmed
,
Echinobase
Houk,
The Micronesia Challenge: Assessing the Relative Contribution of Stressors on Coral Reefs to Facilitate Science-to-Management Feedback.
2015,
Pubmed
Hughes,
Spatial and temporal patterns of mass bleaching of corals in the Anthropocene.
2018,
Pubmed
Hughes,
Global warming and recurrent mass bleaching of corals.
2017,
Pubmed
Lough,
Increasing thermal stress for tropical coral reefs: 1871-2017.
2018,
Pubmed
Mann,
Absence of internal multidecadal and interdecadal oscillations in climate model simulations.
2020,
Pubmed
Mcleod,
The future of resilience-based management in coral reef ecosystems.
2019,
Pubmed
Perry,
Regional-scale dominance of non-framework building corals on Caribbean reefs affects carbonate production and future reef growth.
2015,
Pubmed
Razak,
Growth responses of branching versus massive corals to ocean warming on the Great Barrier Reef, Australia.
2020,
Pubmed
Sully,
A global analysis of coral bleaching over the past two decades.
2019,
Pubmed
Thompson,
Corals escape bleaching in regions that recently and historically experienced frequent thermal stress.
2009,
Pubmed
Wang,
Combined effects of the Pacific Decadal Oscillation and El Niño-Southern Oscillation on global land dry-wet changes.
2014,
Pubmed
Woesik,
Predicting coral dynamics through climate change.
2018,
Pubmed
Woesik,
Climate-change refugia in the sheltered bays of Palau: analogs of future reefs.
2012,
Pubmed
Wooldridge,
Environmental triggers for primary outbreaks of crown-of-thorns starfish on the Great Barrier Reef, Australia.
2015,
Pubmed
,
Echinobase
Zhang,
Opposite polarities of ENSO drive distinct patterns of coral bleaching potentials in the southeast Indian Ocean.
2017,
Pubmed
van Hooidonk,
Local-scale projections of coral reef futures and implications of the Paris Agreement.
2016,
Pubmed
