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Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation

  • Healthy Reefs Initiative
  • Consultant / Independent researcher
  • Corals and Climate

Abstract and Figures

Increasing heat stress due to global climate change is causing coral reef decline, and the Caribbean has been one of the most vulnerable regions. Here, we assessed three decades (1985–2017) of heat stress exposure in the wider Caribbean at ecoregional and local scales using remote sensing. We found a high spatial and temporal variability of heat stress, emphasizing an observed increase in heat exposure over time in most ecoregions, especially from 2003 identified as a temporal change point in heat stress. A spatiotemporal analysis classified the Caribbean into eight heat-stress regions offering a new regionalization scheme based on historical heat exposure patterns. The temporal analysis confirmed the years 1998, 2005, 2010–2011, 2015 and 2017 as severe and widespread Caribbean heat-stress events and recognized a change point in 2002–2004, after which heat exposure has been frequent in most subsequent years. Major heat-stress events may be associated with El Niño Southern Oscillation (ENSO), but we highlight the relevance of the long-term increase in heat exposure in most ecoregions and in all ENSO phases. This work produced a new baseline and regionalization of heat stress in the basin that will enhance conservation and planning efforts underway.
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SCIENTIFIC REPORTS | (2019) 9:11013 |
Three decades of heat stress
exposure in Caribbean coral reefs:
a new regional delineation to
enhance conservation
Aarón Israel Muñiz-Castillo1, Andrea Rivera-Sosa1, Iliana Chollett2, C. Mark Eakin
Luisa Andrade-Gómez
4, Melanie McField5 & Jesús Ernesto Arias-González
Increasing heat stress due to global climate change is causing coral reef decline, and the Caribbean has
been one of the most vulnerable regions. Here, we assessed three decades (1985–2017) of heat stress
exposure in the wider Caribbean at ecoregional and local scales using remote sensing. We found a high
spatial and temporal variability of heat stress, emphasizing an observed increase in heat exposure
over time in most ecoregions, especially from 2003 identied as a temporal change point in heat
stress. A spatiotemporal analysis classied the Caribbean into eight heat-stress regions oering a new
regionalization scheme based on historical heat exposure patterns. The temporal analysis conrmed
the years 1998, 2005, 2010–2011, 2015 and 2017 as severe and widespread Caribbean heat-stress
events and recognized a change point in 2002–2004, after which heat exposure has been frequent in
most subsequent years. Major heat-stress events may be associated with El Niño Southern Oscillation
(ENSO), but we highlight the relevance of the long-term increase in heat exposure in most ecoregions
and in all ENSO phases. This work produced a new baseline and regionalization of heat stress in the
basin that will enhance conservation and planning eorts underway.
Reefs worldwide are being exposed to heat stress at greater frequency and intensity15. Heat stress disrupts the
symbiotic relationship between coral and the microscopic algae that inhabit the coral. is loss of symbionts in
the coral host is termed “bleaching” and impedes the coral’s ability to obtain energy via photosynthesis. It may
also lead to coral death unless temperatures improve and the densities of its symbiotic algae are restored6,7. Severe
heat stress acts as the main precursor to large-scale bleaching, many disease outbreaks, and consequent mortal-
ity3,4,611. Bleaching increases the vulnerability of corals to other anthropogenic stressors and can have devastating
impacts on reef biodiversity and ecosystem services6,7,12. ese ecological consequences are of signicant global
concern, as many nations depend on coral reefs ecosystem services, such as coastal protection, sheries and
tourism for their livelihood and survival13. Also, future projections predict that under the scenario that reects a
continuation of current emissions (RCP 8.5 used by the Intergovernmental Panel on Climate Change) coral reefs
are likely to be exposed to severe heat stress every year by mid-21st century2,14.
Heat stress is a fundamental stressor that must be characterized and prioritized to best identify potentially
resilient reefs for conservation. Along with other indicators (e.g. depth, connectivity, ocean currents), heat stress
can oer a portfolio of optimal reefs for conservation and restoration2,1519. One common approach is to iden-
tify sites with a history of minimal past heat stress to seek possible refugia from climate change2,1517. e other
approach includes seeking if past heat stress may have increased the tolerance of corals and therefore inuenced
coral adaptation2026. Historical patterns of heat stress are also useful in placing projections of future climate
change in context2,14,27,28. Consequently, identifying regional variations in historical heat stress is crucial in
1Laboratorio de Ecología de Ecosistemas de Arrecifes Coralinos, Departamento de Recursos del Mar, Centro de
Investigación y de Estudios Avanzados del I.P.N. Mérida, 97310, Yucatán, Mexico. 2Smithsonian Marine Station,
Smithsonian Institution, Fort Pierce, Florida, 34949, USA. 3Coral Reef Watch, National Oceanic and Atmospheric
Administration, College Park, Maryland, 20740, USA. 4Unidad de Recursos Naturales, Centro de Investigación
Cientíca de Yucatán, A.C., Mérida, 97200, Yucatán, Mexico. 5Healthy Reefs for Healthy People, Smithsonian Marine
Station, Fort Pierce, Florida, 34949, USA. Correspondence and requests for materials should be addressed to A.I.M.-C.
(email: or J.E.A.-G. (email:
Received: 25 January 2019
Accepted: 10 July 2019
Published: xx xx xxxx
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determining which areas have been exposed to the greatest and the least risk of coral bleaching in the past and a
minimum of what is likely in the future.
On a large spatiotemporal scale, one of the major drivers of heat stress causing bleaching is El Niño-Southern
Oscillation (ENSO)1,7,21. ENSO is a complex phenomenon and is one of the most forceful drivers of climate pat-
terns worldwide29,30. ENSO is linked to the Caribbean via a tropical atmospheric bridge, although the Caribbean
is also inuenced by the thermal inertia of Atlantic variability3133. ENSO events building atop global heat stress
has corresponded with global bleaching events (1997–1998, 2010, 2014–2017)1,35,7,34 and El Niño has been linked
to heat stress, bleaching and other impacts in the Caribbean1,7,9,11,24,3537. But ENSO has not always been the
driver of heat stress, as tropical forcing probably played a minor role in the 2005 Caribbean bleaching event38,39.
Additionally, heat stress is not solely related to the warm-phase, El Niño, since warm thermal anomalies are pres-
ent somewhere in both positive and negative ENSO phases. As a result, La Niña leads to coral bleaching in some
locations, and warming global ocean temperatures have caused La Niña years now to be warmer than they were
during El Niño events three decades ago1,3.
e Caribbean has historically been one of the areas most exposed to heat stress and is characterized by high
spatial variation in its thermal patterns2,17,40. ese heat stress patterns subsequently resulted in the observed
magnitude3,7,10,4143 and the spatial footprint of coral bleaching across the Caribbean10. Long-term assessments
of heat stress in the basin can oer an understanding of past disturbance patterns related to the current state and
variation of coral cover and species composition3,6,7,12. ose heat stress patterns can be useful in identifying
potential “thermal refugia” (regions that escaped heat stress)15,16,19,44 or regions with frequent past heat stress
where surviving corals may have developed adaptation2024,26. is information also helps to better understand
the potential impact of projections of future heat stress2,14,27,28. erefore, assessing historical variability becomes
critical to understand heat stress exposure, especially when constant and severe bleaching risk is predicted for
Caribbean reefs by 20502,14.
Here we apply a newly available SST dataset from 1985 to 20175 and provide a spatiotemporal contextualiza-
tion of the wider Caribbean heat stress. is study aimed to:
(a) Characterize the geographical extent and variability of heat stress in the Caribbean ecoregions45 during the
last three decades,
(b) classify the wider Caribbean into new heat-stress regions based on historical heat stress,
(c) assess the temporal variability of heat stress in the Caribbean ecoregions45 and its relation to past ENSO
events based on the Oceanic Niño Index-ONI.
Spatiotemporal variability in overall heat stress. e ecoregions45 within the wider Caribbean exhib-
ited a high spatial variability of heat stress exposure (maximum Degree Heating Weeks, DHW) from 1985 to 2017
(Fig.1a,b). Heat stress within 20 km of coral reefs around the wider Caribbean ranged from 0.0 to 25.6 °C-weeks
across the entire time series. 83% of Caribbean reef area was exposed to “bleaching risk” (4 °C-weeks) at some
time between 1985 and 2017 (Fig.1c,d), and 42% of the area was exposed to “mortality risk” (8 °C-weeks) at
least once (Fig.1e,f). roughout the paper, we refer to these two thresholds because they are dened as the levels
of heat stress likely to cause coral bleaching and mortality2,10,46.
e ecoregions with the highest heat stress were the Southern Caribbean (SC), Eastern Caribbean (EC),
Southwestern Caribbean (SWC), Southern Gulf of Mexico (SGoM) and Western Caribbean (WC; Fig.1;
TablesS1–S8). These five ecoregions experienced significantly higher heat stress than the rest of the wider
Caribbean according to a heteroscedastic one-way ANOVA and post hoc tests for most indicators (TablesS5–S8).
ese ecoregions experienced exposure to elevated DHW values and bleaching and mortality risk events (Fig.1a–f;
TablesS1–S3). All these regions except for the EC showed an increase that ranged from 0.10 to 0.35 °C-weeks
per year, obtained from the trend analysis of annual maximum DHWs (Fig.1g,h; TableS4). e SC was the most
exposed to bleaching and mortality risk because most of the area within that ecoregion experienced more than
eight bleaching risk events and all the area showed at least one mortality risk event (Fig.1c–f). e SWC was
another ecoregion subjected to high heat stress, where most of the area experienced more than three bleaching
risk events and was exposed to at least one mortality risk event (Fig.1c–f). In contrast, the ecoregions least
exposed to heat stress were the Bahamian (BHM), Floridian (FL) and Greater Antilles (GA; Fig.1; TablesS1–S8).
ese ecoregions exhibited the greatest percentage of their areas without bleaching and mortality risk (Fig.1c–f).
However, even these ecoregions had high heat stress in some locations. e Florida Keys, Cuba, and areas of the
BHM showed high heat stress exposure and an increase of 0.1–0.2 °C-weeks per year (Fig.1a,c,e,g).
e most prominent heat-stress events in the wider Caribbean occurred during the years 1998, 2005, 2010,
2011, 2015 and 2017 (Fig.2). We found a high spatial variation in heat stress during the dierent major heat-stress
events (Fig.2c). e temporal patterns showed a constant exposure to heat stress from 2003 onwards, since this
year ~10% of the wider Caribbean has been exposed to bleaching risk annually (Fig.2d,e).
e most widespread event occurred in 2005 when 42% of the wider Caribbean suered its highest heat stress
(Fig.2a–c). e year 2010 was the second most widespread heat-stress event when 15% of the area reached its
maximum DHW (Fig.2a–c). e heat stress in 2010 was more intense than any other year, exposing the area of
the SC to values close to 25 °C-weeks, the highest DHW magnitude in the time series (Fig.2c,d). During these
two events, more than 50% of the wider Caribbean was exposed to bleaching risk and about 20% was exposed to
mortality risk (Fig.2e). e next warmest event for the entire basin occurred during 2015–2017, a variable but
long-lasting period, in which 25% of the area experienced its maximum heat stress (Fig.2a,b). In each of these
years, more than 20% of the wider Caribbean was exposed to bleaching risk and more than 5% of the area was
exposed to mortality risk (Fig.2e). Two other major heat-stress events were 1998 and 2011, in which 6–7% of the
wider Caribbean suered its maximum DHW (Fig.2a,b) and about 30% of the area was exposed to bleaching risk
in each of these years (Fig.2e).
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Heat-stress regions. e spatiotemporal variation of heat stress (cluster analysis using K-means and eight
optimal regions obtained using elbow criteria; Figs3a and S3) yielded eight spatially distinct heat-stress regions
(HSR) characterized by dierent time patterns of exposure levels (Figs3a–c and S4). e HSRs were consistent
with the heat stress patterns (Fig.3b), but did not follow the ecoregional delineation for the wider Caribbean - two
to three heat-stress regions were included within most ecoregions45 (Fig.3a). HSRs 1–5 were the most exposed
to elevated DHW, with a greater risk of bleaching and mortality, as well as a greater tendency to increase than the
other HSRs (Fig.3a,b; TablesS9–S16).
HSRs 1–3 were the most exposed to heat stress (Fig.3a; TablesS9–S12). ese HSRs were exposed to high
DHW values in several years, including 1995, 1998, and constant exposure since 2003, especially in 2003–2006,
2008, 2010–2011, and the last heat-stress event of 2014–2017 (Fig.3c). HSR 1, located along the Venezuelan coast
Figure 1. Spatial variability of heat stress exposure indicators in the wider Caribbean region from 1985–2017.
(a) Map showing heat stress values per pixel. (b) Histogram of the distribution of heat stress for ecoregions
and the wider Caribbean in the entire time series. (c) Map and (d) histogram of bleaching risk events
(4 °C-weeks). (e) Map and (f) histogram of mortality risk events (8 °C-weeks). (g) Map showing trend
of annual maximum DHW obtained by a Generalized Least Squares model (GLS), considering a temporal
autocorrelation; the grey pixels show non-signicant trend coecients (p-value > 0.05). (h) Histogram of the
annual trend of maximum DHW. Histograms for the ecoregions are ordered by statistical signicance supported
by a pairwise post hoc comparison of the heteroscedastic one-way ANOVA test (TablesS5–S8). e total
number of pixels (25,591) for the complete region represents an area of about 127,405 km2. e corresponding
numbers of pixels included in each ecoregion are shown in parenthesis. Maps were created using QGIS version
3.2.0 (
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had the highest increase in annual maximum DHW and the most elevated frequency of bleaching and mortality
risk events (Fig.3a,b, TablesS9–S12). HSR 2 (Honduran and Nicaraguan Miskito Cays) and HSR 3 (lower Lesser
Antilles and western Venezuela) also exhibited high heat stress exposure (Fig.3a,b, TablesS9–S12).
Other HSRs considerably exposed to heat stress were HSR 4 (the Florida Keys, Bahamas, and southwestern
Cuba) and HSR 5 located in the southern Gulf of Mexico and the Gulf of Honduras (Fig.3a,b). ese areas
experienced high heat stress exposure and a considerable increase in the annual maximum DHW (Fig.3a,b;
TablesS9–S12), in these HSRs their maximum exposure to heat stress occurred during 2014–2017 (Fig.3c).
In contrast, HSRs 6–8 were least exposed to heat stress in the wider Caribbean (Fig.3a,b; TablesS9–S16). HSR
6 (upper Lesser Antilles) was the most exposed of HSRs 6–8, characterized by high heat stress exposure in 2005
and 2010 (Fig.3c) and suered the highest heat stress in the wider Caribbean during 2005. HSR 6 suered many
bleaching and mortality risk events, but the annual maximum DHW increased slowly (Fig.3b). HSR 7 (contain-
ing part of the Mesoamerican Reef, southern Cuba, Jamaica, Costa Rica and Panama) had low exposure to heat
stress, but a considerable increase in annual maximum DHW (Fig.3a,b; TablesS9–S16). Surprisingly, HSR 8
included the largest part of the wider Caribbeans reef area (41.7%). HSR 8 was the area least exposed to heat stress
and was located mainly at northern latitudes (Fig.3a,b; TablesS9–S16).
Temporal cycles of heat stress and relationship to ENSO phases. Time series analyses (median of
the regional DHW values on a given day) conrmed that the strongest heat-stress events were observed during
1998–1999, 2004–2005, 2010–2011 and 2014–2017 (Fig.4a–h). e heteroscedastic one-way ANOVA test for
Figure 2. Spatiotemporal summary of heat-stress events in the wider Caribbean basin during 1985–2017. (a)
Percent of pixels with maximum DHW value in each year. (b) Year with maximum DHW value for the eight
ecoregions. (c) Major heat-stress events. (d) Temporal distribution of annual maxima (interquartile range and
median are represented with white box, outliers are represented with black points) and; (e) percentage of area
with bleaching risk (4 °C-weeks) and mortality risk (8 °C-weeks). Maps of annual maximum DHW for
the whole time series (1985–2017) can be found in the Supplementary FigsS1 and S2. Maps were created using
QGIS version 3.2.0 (
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the time series showed that the SC, SGoM, WC, SWC and EC ecoregions had the greatest heat stress over the last
three decades (Fig.4a–h; TablesS17 and S18). ese areas were above the median of the wider Caribbean DHW
values in most years and during the strongest events, with values greater than 5 °C-weeks during the strongest
heat-stress events (Fig.4a–c; Fig.S5). e BHM and FL ecoregions showed median values higher than 5 °C-weeks
in 1997–1998, 2005, 2010 and 2014–2015 (Figs4f,g and S5). In the GA, the years 2005 and 2010 were the highest
heat-stress events, in which median regional values of ~3.5 °C-weeks were observed (Figs4h and S5).
Most ecoregions and the wider Caribbean have experienced constant heat stress since 2003. Change point
analysis identied the period of 2002–2004 as the temporal point when the time series changed signicantly
(Figs4a–h and S6; TableS19). is change point was dierent in the EC and FL, where it occurred between 1997
and 1998, and no signicant change point was observed in the GA (Fig.4e,g,h; TableS19). Moreover, the wavelet
analysis also showed that since 2003, the annual cycles of DHWs presented signicant periodicities in most sub-
sequent years (Fig.S7). e wavelet identied the frequencies and timing in which the major anomalies occurred,
Figure 3. Heat-stress regions and their maximum annual DHW during 1985–2017. (a) Reef locations within
heat-stress regions 1–8 (clusters) outlined by ecoregions. (b) Total annual maximum Degree Heating Weeks
(DHW), bleaching and mortality risk events and trends of annual maximum DHW. (c) Heat-stress regions 1–8
showing distribution of annual maxima, interquartile range and median are represented with white box, outliers
are represented with black points. e pink shadow represents the limit of mortality risk (8 °C-weeks). Map
was created using QGIS version 3.2.0 (
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considered as signicant periods and time ranges in which the variation was higher than expected47. ese anal-
yses strengthened the results previously presented and recognized 1998, 2003–2006, 2008–2011 and 2014–2017
periods as the highest heat-stress events (Figs4a–h and S7).
To identify whether heat-stress events recognized in the wider Caribbean and across ecoregions may be
related to ENSO, we performed a cross-wavelet analysis to identify the signicant common periodicities between
the heat-stress events and the ONI48,49. 1998–2000 was the rst heat-stress period sharing common periodicities
with the ONI (Figs5b and S8). A strong El Niño occurred in 1997–1998 followed by a long-lasting La Niña event
in 1999–2000 (Figs4i and 5a). In 2005, ENSO had low inuence on heat stress as there was only a weak El Niño
followed by a brief, weak La Niña (Figs4i and 5a). e period from 2010 to 2012 showed the highest values
(darkest red) in the cross-wavelet, caused by the combination of high DHW and ONI variation (Fig.5b). e
2010–2011 period was classied as an El Niño event, followed by La Niña and a long neutral phase during 2012–
2013 (Figs4i and 5a). 2014–2017 also showed strong common periodicities with ENSO, when most ecoregions
were inuenced by the forceful 2015–2016 El Niño event (Figs5 and S8). e inuence observed in this last event
was remarkable even in the high latitude ecoregions such as the FL and BHM, where the common periodicities
with ONI were noted starting in 2014, perhaps due to the incipient El Niño in late 2014 (Figs5a,b and S8). Our
ecoregional results for the entire time series showed similar behavior with no dierences from the cross-wavelet
analysis. is consistency in the temporal heat stress patterns may have been related to the main events in the
wider Caribbean (1998, 2005, 2010, 2015 and 2017), which also correspond to the ecoregional level cross-wavelet
results (Figs5a,b and S8).
e cross-correlation analysis revealed a signicant positive correlation between El Niño (positive phase of
ENSO) and heat stress, this relationship presented the highest values in temporal lags of 6 to 12 months (p < 0.05,
Figure 4. Temporal patterns of Degree Heating Weeks (DHW) for ecoregions in the wider Caribbean and the
ONI during years 1985 to 2017. For each ecoregion (ah) the vertical dotted black line shows the change point
analysis obtained via a Pettit test (TableS19), the horizontal dotted red line shows the limit of mortality risk
(8 °C-weeks), the black curve shows the median of the wider Caribbean DHW values on a given day and the
pink curve shows the median of the ecoregional DHW values on a given day. For ONI (i), red bars indicate El
Niño phases, blue bars indicate La Niña phases, and grey bars show neutral phases.
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Figs5c and S9). e 6–12 month time lag, with a signicant positive correlation, may be related to the delay that
occurs between the mature phase of El Niño in November to February and the heat stress peak that occurs in
August to December (Fig.S10). e cross-correlation analysis highlighted a signicant eight-month lag in the
wider Caribbean and in most ecoregions (Figs5c and S9). However, FL did not exhibit a signicant correlation
and SC showed a signicant negative and positive correlation at dierent time lags (Fig.S9).
e Generalized Linear Model (GLM) of annual variation of heat stress (annual hottest monthly average gen-
erated from the median of the regional DHW values on a given day) showed a signicant temporal increase in all
ecoregions (Table1; Figs6 and S11). e GLM obtained for the wider Caribbean and the ecoregions presented
a suitable t, with a lower Akaike Information Criterion (AICc) value than the Generalized Least Square (GLS)
models (TableS20), adding that the models obtained did not present temporal autocorrelation in the residuals.
During El Niño years, ecoregions generally experienced higher heat stress than the other ENSO phases (Figs6
and S11). e additive eect of the ENSO phases was signicant at the wider Caribbean level, and for the EC,
BHM, and GA ecoregions (Table1; Figs6 and S11). However, heat stress increased in all phases of ENSO, espe-
cially aer 2003, this long-term trend exceeded ENSO inuence in most ecoregions, nding that the additive
eect of ENSO phases was not signicant in four of the ve ecoregions most exposed to heat stress (Table1;
Heat stress in the ecoregions was highly variable, with both spatial and temporal heterogeneity, but following a
general latitudinal gradient, as expected, across the wider Caribbean2,17,40. Generally, the ecoregions in the north-
ern Caribbean were the least exposed to heat stress and those in the south were the most exposed (Fig.1b). e
regions with the highest heat magnitude typically had an increase in heat stress through time and a high fre-
quency of heat-stress events. Time series analyses showed that the most relevant heat-stress events (1998, 2005,
2010–2011, 2014–2017) coincided with the most extreme bleaching episodes reported globally3,7,34 and in the
Caribbean3,7,10,4143. e 2005 and 2010 events had the highest heat stress and can be considered the two periods
of greatest coral reef crisis in the Caribbean to date3,7,10,34,42,43.
The spatial variability of temporal heat stress exposure (annual maximum DHW) was used to develop
heat-stress regions (HSRs), a new scheme based on heat stress history that is more explanatory for heat stress pat-
terns than traditional ecoregions45. HSRs dene Caribbean areas that share a common history of exposure to heat
stress, providing a useful tool for spatial conservation and management15. In this sense, we recommend the use of
these HSRs at dierent scales (e.g. the wider Caribbean, within ecoregions or at country level). is new classica-
tion system can help identify regions exposed to recurring extreme heat stress such as HSR 1 and 2 (o Venezuela
and Miskito Cays, considered “historical hotspots”) where corals could potentially either suer repeated mor-
tality or develop adaptations that may increase resistance to bleaching2026. Likewise, acclimatization studies are
needed in “emerging heat-stress regions”, regions that have experienced their greatest stress to date during the
Figure 5. Wider Caribbean DHW temporal patterns and ENSO relationship. (a) 1.5 years smoothed mean of
DHW (black line) and ONI (red line) time series. (b) Cross-wavelet showing the common power (color bar)
and phases (arrows). Phases arrow direction represents decreases of ONI and increases in DHW (le); increase
of ONI and increase in DHW (right). Black solid lines show the signicance of cross-wavelet power at 95%
condence. e ‘cone of inuence’ is represented by the white shadow; only results inside the cone of inuence
was ben considered and interpreted (outside the cone = high uncertainty). (c) cross-correlation between DHW-
ONI at dierent time lags. Red bars represent signicant positive correlation and blue bars represent signicant
negative correlation at 95% condence.
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SCIENTIFIC REPORTS | (2019) 9:11013 |
latest mass-bleaching event (2014–2017)1,3,50. ese “emerging heat-stress regions” include some areas such as the
Mesoamerican Reef, southern Cuba and Florida Keys (HSR 4 and 5; Figs2 and 3). Within this heat-stress classi-
cation, the large HSR 8 region stands out for its low past heat stress exposure (a potential heat-stress refugium),
with reefs that have experienced few or no exposures to severe mortality risk events since suering moderate heat
stress and considerable bleaching in 200510. Studies suggest that while recent heat stress may inuence suscepti-
bility to bleaching25,26, this inuence decreases as the time since previous heat stress increases4.
Local-scale variability in oceanographic conditions such as depth, upwelling, currents, and water circulation
also inuences heat stress patterns at the local scale1820,44,51. Regions such as northern Quintana Roo (HSR 7)
have lower heat stress due to the inuence of colder waters, high wave exposure and upwelling19,52. However,
upwelling has not provided refuge to the Caribbean’s most exposed region (HSR 1 in the Southern Caribbean19),
which experienced frequent and intense heat stress since 1990 (Figs3 and 4). Upwelling must be synchronous
with heat-stress events to reduce severe warming, making the timing of these events critical and adding complex-
ity to local-scale analyses of heat stress patterns and bleaching risk18,51. is complexity highlights the urgent need
for systematic coordinated Caribbean-wide bleaching monitoring programs that can provide a better understand-
ing of coral community responses to heat stress and environmental conditions.
Climate change projections of SST and heat stress that apply statistical downscaling analyses base their down-
scaling on historical data14,27. Given the spatiotemporal variability in heat stress found in this study, downscaling
eorts should try to include long time series in their analyses and only use spatial patterns that are stationary
through time. Additionally, given the stochastic nature and importance of episodic bleaching events, these pro-
jections should be updated frequently to capture new events. For example, some ecoregions strongly aected in
past years, such as the Eastern Caribbean with maximum heat stress in 2005, have experienced lower heat stress
in recent years, resulting in a low annual increase in heat stress. In contrast, “emerging heat-stress regions”, such
as the Southern Mesoamerican Reef and the Florida Keys were most exposed during 2014–2017, leading to a
signicantly increasing heat stress trend. However, events like these include a signicant stochastic component.
ese results suggest that the constant change in heat stress forms a problematic basis for long-term designation
of ‘resilient reefs’ or conservation areas more likely to survive the impacts of climate change. us, we recommend
caution in the use of heat stress patterns and thermal regimes for the prioritization of coral reef conservation
based on historical data16,17,44, particularly for those analyses that consider short term time series or only include
Ecoregion (explained deviance) Terms (df, dfr) Residual
Deviance F p-value
Wider Caribbean (0.527)
Null 8.6301
Years (1, 30) 6.0229 16.1568 0.00040
ENSO (2, 28) 4.082 6.0141 0.00672
Southern Caribbean (0.345)
Null 25.326
Years (1, 30) 18.834 10.909 0.00262
ENSO (2, 28) 16.583 1.8912 0.16966
Southern Gulf of Mexico (0.505)
Null 15.5578
Years (1, 30) 9.2556 19.2895 0.00015
ENSO (2, 28) 7.7055 2.3723 0.11175
Western Caribbean (0.562)
Null 13.4729
Years (1, 30) 7.1254 27.7206 0.00001
ENSO (2, 28) 5.8964 2.6837 0.08584
Southwestern Caribbean (0.448)
Null 18.0882
Years (1, 30) 11.2214 18.8147 0.00017
ENSO (2, 28) 9.9758 1.7064 0.19986
Eastern Caribbean (0.497)
Null 21.33
Years (1, 30) 14.829 13.9972 0.00084
ENSO (2, 28) 10.726 4.4176 0.02150
Bahamian (0.526)
Null 10.062
Years (1, 30) 8.5275 8.5552 0.00676
ENSO (2, 28) 4.7682 10.4791 0.00040
Floridian (0.236)
Null 11.9031
Years (1, 30) 9.8 5.819 0.02265
ENSO (2, 28) 9.0903 0.9818 0.38716
Greater Antilles (0.372)
Null 6.4482
Years (1, 30) 5.3653 6.3445 0.01776
ENSO (2, 28) 4.0447 3.8688 0.03285
Table 1. Analysis of the deviance obtained for Generalized Linear Model (GLM) with tests of the signicance
of the additive terms of years and phases of ENSO, with their respective degrees of freedom (df) and degrees
of freedom of residuals (dfr). e statistics for F tests and the p-value obtained for the Caribbean and the
ecoregions are presented. Values of p in bold are those considered statistically signicant.
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SCIENTIFIC REPORTS | (2019) 9:11013 |
certain events (e.g. 1998, 2005 or 2010). We encourage a precautionary approach to selecting portfolios of conser-
vation areas, which includes reefs exposed to variable characteristics, such as those with high-frequency (daily or
weekly) variation in heat stress or temperature20, those with more constant heat stress exposure (potentially now
acclimated)2125, and those that have (to date) experienced constant low heat stress2,15,17, as these statistics could
change with the next major heat-stress event.
e heat stress increased in the Caribbean since 2002–2004, in agreement with previous work1,2,19. is was
a change point aer which heat stress has been higher than in previous decades. is temporal pattern is slightly
apparent in the largest available global coral bleaching database34, in which it is possible to observe that from 2003
to 2010 about 50% of the reefs sampled per year in the Caribbean had moderate (11–50%) to severe (>50%)
bleaching (Supplementary Fig.S12). However, consistent reporting of coral bleaching episodes throughout the
ecoregions is limited, making it dicult to validate the ecological impacts of the spatiotemporal patterns of heat
stress3,34. Also, the high past exposure in some areas may have contributed to acclimatization processes or histori-
cal environmental ltering that may have eliminated the most susceptible individuals2126, contributing to the lack
of relationship between current heat stress patterns and the local bleaching response. In this sense, we highlight
the importance of large ecoregional monitoring programs, such as the Healthy Reefs Initiative, which coordi-
nates regular reef monitoring and emergency response monitoring for beaching events, including the 2015–
2017 event53, with a publication focused on these data in preparation. Emerging heat stress has also occurred in
regions with insucient biological monitoring eorts; therefore, biodiversity loss related to bleaching and coral
diseases may have gone unreported in these areas (e.g., Miskito Cays in the HSR 2)16. is lack of information
is of particular concern given that major disease outbreaks have occurred during or aer heat-stress events in
the Caribbean8,9,11, highlighting the importance of monitoring aected areas during and aer heat-stress events.
Our results suggest that three out of four major heat-stress events in the Caribbean (1998, 2010–2011 and
2014–2017) have been inuenced by El Niño1,50. During these three Caribbean heat-stress events, bleaching, dis-
eases and a decrease in coral growth rates have all been associated with El Niño3,9,11,36,37. is relationship between
El Niño and heat stress showed a lag of 6–12 months, which partially corresponded with previously reported lag
times of 3–6 months for SST3133. is lag could be associated with the delay in the climatological forcing of the
mature phase of ENSO (December to February, during the austral summer)29 until the appearance of heat stress
in the Caribbean during the boreal summer3133 (Supplementary Fig.S10). Moreover, at the wider Caribbean level
and in the WC ecoregion, a signicant correlation was observed in a time lag of about two years, which may be
due to the eect of long-lasting events such as the 2014–2017. In this period an incomplete formation of a strong
El Niño in 2014–2015 was reported, followed by the 2015–2016 strong and long-lasting El Niño, which was linked
to a warm event that lasted until 201750.
Although some major Caribbean heat-stress events have been associated with El Niño, the long-term trend in
rising temperatures has caused heat stress during all ENSO phases - a pattern that has been recognized on reefs
globally3. Our results showed that this long-term trend is even more important in the most exposed ecoregions,
with four of the ve most exposed ecoregions showing no signicant additive eect of ENSO, while their overall
increase in heat stress was signicant (Table1; Fig.S11). Since the 1998 El Niño all subsequent El Niño events,
with the exception of the 2015–2016 El Niño, have been of lower intensity. However, even these weak or moderate
El Niño events can be associated with high exposure to heat stress as has been observed on coral reefs globally1,3.
For example, the most widespread heat-stress event in the Caribbean occurred in 2005, which was a relatively
weak El Niño event. e change in the heat stress regime since 2003 and the long-term trend observed could be
linked to other low-frequency patterns such as the recent Atlantic Multidecadal Oscillation (AMO) warm sig-
nal2,36,38,39 and anthropogenic climate change13,30,36,38. Both the AMO and climate change have been recognized as
important drivers in recent heat stress in the Caribbean, causing negative impacts on coral growth36 and climate
change has been strongly associated with slowing coral growth elsewhere54. is pattern of exposure to regular
Figure 6. Eect of time and ENSO phases in annual maximum of the monthly averages of wider Caribbean
DHW. (a) Conditional plot of time eect in annual heat stress, the color of points represents the dominant
ENSO phases in each year: neutral (black), La Niña (blue) and El Niño (red). (b) Cross-sectional plots
illustrating the t of the wider Caribbean annual heat stress with an additive interaction between time and
ENSO phases. (c) Box plots showing distribution of annual heat stress during ENSO phases. e ENSO
phase category was identied from the ONI time series (
monitoring/ensostu/ONI_v5.php), classifying El Niño years as those with anomalies above 0.05 °C, La Niña
years as those below 0.05 °C and neutral years as those in the range of 0.05 to 0.05 °C.
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SCIENTIFIC REPORTS | (2019) 9:11013 |
and increasing heat stress not only poses a risk of coral bleaching and associated mortality but the potential nega-
tive eects of heat stress extend to reduce the overall functionality and ecosystem services provided by Caribbean
This work produced a new contextualization of heat stress in the basin that will enhance conservation
and planning eorts currently underway. Given humanity’s critical dependence on marine resources in the
Caribbean13, the need to better understand and plan for future bleaching and disease events is paramount. We
highlight the relevance of multi-scale and retrospective analyses of heat stress in the contextualization of the
vulnerability of corals to bleaching in the wider Caribbean. It should be noted that the high spatial and temporal
variation found in heat stress exposure may aect the geographic patterns of potential adaptation or sensitivity
of corals to heat stress in the wider Caribbean. We also emphasize the potential impact of the last heat-stress
event (2014–2017) on some Caribbean ecoregions, particularly in the “emergent heat-stress regions”. Although
additional research is needed to identify the cause of low-frequency patterns on Caribbean heat stress, our results
provide evidence of a signicant change point in increasing heat stress since 2003. is chronic long term heat
stress in combination with acute heat-stress events may ultimately have an even greater impact on the condition
of Caribbean corals, by increasing their vulnerability to other stressors such as the devastating Stony Coral Tissue
Loss disease now aecting the wider Caribbean11,55,56.
Reef locations. Heat stress on coral reefs was characterized by analyzing the pixels located within 20 km of
reef locations within the wider Caribbean (32.7°N–8.4°N, 59.2°–97.0°W). By including contiguous areas, there is
a limitation within the analysis on the ecoregional and wider Caribbean scales, as zones with the absence of coral
reefs may be included. However, this 20-km buer was considered the best scale because it could identify oceanic
processes related to heat stress at the reef (100 m to 10 km) and regional scales (>10 km)57. is buer also allows
a better comparison with previous work, carried out applying a spatial resolution in a range from 4.5 km to 50
km1,2,4,10,19,42,43, recognized as the resolution range at which is possible to identify massive bleaching events46. Reef
locations were obtained from the Global Distribution of Coral Reefs58. is is the most comprehensive, published,
global dataset of warm-water coral reefs compiled from multiple sources.
Historical heat stress data. e spatiotemporal variation in daily Sea Surface Temperature (SST) from
1985 to 2017 was obtained from the NOAAs Coral Reef Watch Program “CoralTemp” dataset, the latest and most
complete global satellite-derived dataset at a resolution of 5 km (0.05°) available for 1985 to present5 (https://cor- e Maximum Monthly Mean (MMM) was also obtained from the Coral Reef
Watch Program version 3.1 dataset at 5 km (,
the MMM is a value of SST that represents the warmest monthly climatological mean from 1985 to 2012 for each
location46. We then calculated the coral bleaching HotSpot (HS) and Degree Heating Weeks (DHW) metrics. HS
represent daily positive anomalies above the MMM (Equation1)46. DHW quantify heat stress by summing HS
above 1 °C over 84-days (12 weeks), divided by 7 to express values per week (Equation2)46, and calculated daily.
Analyses were conducted in R version 3.4.159 using the “raster”60 and “sp”61,62 libraries.
7(, 1C)
Oceanic Niño index data. e El Niño Southern Oscillation cycles and variation were determined using
the NOAA´s Oceanic Niño Index (ONI) version ve (
itoring/ensostu/ONI_v5.php). is time series dataset provides the monthly average anomalies from 1950 to
date. ese monthly values were based on a 3-month running anomaly, calculated centered on a reference of
30-year base periods updated every 5 years (e.g. for 2000–2005 the reference is the 1985–2015 base period). All
ONI values calculated aer 2005 used the period 1985–2015 as a baseline. e spatial reference zone was situated
in the Tropical Pacic Ocean (5°N–5°S, 120°–170°W; Niño 3.4 region).
Data analyses. Spatiotemporal variation of heat stress. e annual maximum DHW was the main indicator
used to evaluate the exposure to heat stress and represents the maximum heat stress occurred in the year2,10,15,46.
We calculated the heat stress value observed for each pixel and year for the entire time series (FigsS1 and S2).
e ve main metrics calculated for each pixel were: a) the maximum DHW value per pixel for the entire time
series, b) the frequency of annual maximum DHW values 4 °C-weeks (a predictor of coral “bleaching risk”) per
pixel, c) the frequency of annual maximum DHW values 8 °C-weeks (a predictor of bleaching-induced mor-
tality or “mortality risk”) per pixel2,10,15,46, d) the year in which the maximum DHW occurred, and e) the trend
of the annual maximum DHW (dened below) per pixel. Analyses were conducted in R version 3.4.159 using the
“raster”60 and “sp”61,62 libraries.
e trend of annual maximum DHW was calculated with a Generalized Least Squares model (GLS), intro-
ducing to the regression a structure of temporal autocorrelation (AR1, which represents the covariance of order
1 considering the temporal similarity between the nearest years)63. Because we calculated the trend from annual
values, the GLS model did not consider seasonality. Once the slope of the regression was obtained, the signi-
cance of the slope was calculated at a 95% condence, considering as null hypothesis that the tendency was equal
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SCIENTIFIC REPORTS | (2019) 9:11013 |
to zero. In all pixels in which the slope was not signicant, the value of zero was set to represent a null slope. e
analyses were performed from the functions available in the “nlme” library64 of program R59.
To determine the dierences in the maximum DHW, the frequency of bleaching risk and mortality risk,
and the trend of annual maximum DHW among the ecoregions and the Caribbean, a heteroscedastic one-way
ANOVA for trimmed means test (0.10) and the corresponding pairwise post hoc comparison were performed.
is analysis included a comparison among the mean DHWs for each ecoregion and only considered the data
found from the 10th to the 90th percentile. ese tests were performed from the functions available in the
“WRS2” library65 of program R59.
Heat-stress regions. e regionalization of heat stress was performed by a clustering analysis with the K-means
algorithm through the unsupervised classication function present in the “RStoolbox” library66. e maximum
annual DHWs during the years 1985–2017 were used as input to the clustering procedure. To identify the opti-
mal number of groups, we used the graphic elbow criterion. is evaluation illustrated a curve of the remaining
variation from the addition of each given number of groups, revealing a relationship of the variance among added
groups and the total variance. In this way, we chose the least number of groups that explained the greatest spati-
otemporal variation. In order to visualize the arrangement of each of the pixels and their corresponding groups
resulting from the K-means algorithm, they were plotted on a two-dimensional plot of the rst two components
obtained from a Principal Component Analysis using the function present in the “FactoMineR” library67.
To test the dierence in the total annual maximum DHW for each year and the other exposure indicators
among the heat-stress regions (HSR), we performed a heteroscedastic one-way ANOVA for trimmed means
test (0.10), along with the corresponding pairwise post hoc comparison. ese analyses included a comparison
among the mean of each HSR and only considered the data found from the 10th to the 90th percentiles. ese
tests were performed from the functions available in the “WRS2” library65 of program R59.
Temporal cycles of heat stress and relationship to ENSO phases. Spatiotemporal daily data were summarized to
describe the temporal patterns at an ecoregional scale by calculating the median of the regional DHW values on
a given day. We tested the dierence of the regional median values among the dierent ecoregions, for this, we
considered all the values present in the days found within the months from September to November (recognized
as the season with greatest regional DHW values) in all the time series. e test was performed using a hetero-
scedastic one-way ANOVA for trimmed means and the corresponding pairwise post hoc comparison. We only
considered the data found from the 10th to the 90th percentiles. ese tests were performed from the functions
available in the “WRS2” library65 of program R59.
To identify patterns in the frequency of months or years, and for subsequent comparisons, the time series of
the median regional DHW values on a given day was averaged over each month. Using this monthly average as
a lower frequency indicator, Pettit’s non-parametric test68 was applied to identify whether there was a signicant
change point in the time series at the monthly scale in each of the ecoregions and in the wider Caribbean. is
test is a non-parametric comparison of the rank values of the sequence similar to the Mann-Whitney test and
identies a time point at which there is a signicant change in the variation and magnitude of monthly values.
ese analyses were performed using a p-value = 0.05.
e monthly frequency of heat-stress events and the relationship between heat stress and the ONI (both at
ecoregional and wider Caribbean scales), were characterized by wavelet and cross-wavelet analyses. e frequen-
cies and time in which the main anomalies occurred were identied by a wavelet analysis4749. e cross-wavelets
analysis identied the common periodicities in the heat stress and ONI time series and assessed if they are
in phase (i.e., both time series increase in synchrony) or anti-phase (i.e., time series increases while the other
decreases)47. e frequencies and times considered as signicant were selected based on a Chi-Square test for
both techniques. For the statistical signicance in the case of wavelets, the null hypothesis was that the time
series was stationary at a given frequency over time, although in the cross-wavelet, the null hypothesis states that
time series had no variation in common and do not have signicant shared periodicities. For both analyses, we
rst applied a low-pass lter using the monthly mean to the daily DHW time series to match the temporal reso-
lution of the ONI to the monthly time series. To comply with the statistical assumption of normality needed for
this analysis47, we transformed the DHW data using a logarithmic transformation, this transformation allowed
us to improve the distribution of the data by decreasing the dierences in the values observed. Wavelet analy-
ses were conducted with the “biwavelet” library69, using the Morlet mother wavelet function and bias-corrected
cross-wavelet power with a 95% condence level4749.
In addition, we calculated the cross-correlation function between the DHW and ONI time series to identify
the existing correlation considering dierent time lag periods between the time series. For this analysis, we con-
sider a maximum lag of 38 months, to provide at least 10 cycles in the entire time series (33 years). e statistical
signicance of the cross-correlation was calculated considering a 95% condence level. is analysis was per-
formed by the “tseries” library70.
To identify a temporal trend and determine if the ENSO phases had a signicant eect on the annual hottest
monthly average DHW values, generated from the median of DHW values across each region on a given day,
we compared a Generalized Linear Model (GLM) with a GLS model considering temporal autocorrelation. e
annual hottest monthly average DHW was considered as the dependent variable, considering as explanatory var-
iables the years and the category corresponding to the ENSO phase (neutral, La Niña and El Niño) introduced in
the model as additive terms. e dominant ENSO phase category by year was identied from the ONI time series
(/ONI_v5.php). El Niño years were con-
sidered those with anomalies above 0.05 °C, La Niña years as those below 0.05 °C and Neutral years as those in
the range of 0.05 to 0.05 °C, these values had to be present in a range equal to or greater than ve months to be
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SCIENTIFIC REPORTS | (2019) 9:11013 |
designated as the dominant phase in each year71. In the GLS model, the temporal autocorrelation structure AR1
was introduced, which represents the covariance of order 1 (temporal similarity between the nearest years). In the
GLM model, the Gamma error family was chosen with a logarithm link function that adequately characterizes
continuous variables and is similar to the exponential curve. Once the models were made, graphical evaluations
of the residuals and the partial autocorrelation function were conducted, as well as a comparison between the
values of the Akaike Information Criterion of second order (for relatively small samples)72 for the two models
obtained by ecoregion and at the wider Caribbean level. e GLS model was made from the “nmle” library64,
while the other analyses were made from dierent functions available in the R program59.
Data Availability
Daily SST and the MMM data are available from NOAA CRW program CoralTemp Dataset version 3.1: https:// e ONI time series data are available from NOAA: http://or-/ONIv5.php. e main data used for the gures
and analyses were submitted to the NOAA National Centers for Environmental Information (NCEI).
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is paper is part of the fulllment requirements of the Ph.D. degree of AIMC in the postgraduate program of
Recursos Marinos of the Centro de Investigaciones y Estudios Avanzados (CINVESTAV) Unidad Mérida. is
program is acknowledged for providing four years of a CONACYT fellowship with grant number 340074 and
666908, to support the Ph.D. degree of AIMC and ARS respectively. We also thank CINVESTAV-IPN for the
funds to support researchers given to the corresponding authors and for the Mixed Funds for doctoral students
CINVESTAV-FOMIX 2018, granted to AIMC and ARS. e scientic results and conclusions, as well as any
views or opinions expressed herein, are those of the author and do not necessarily reect the views of NOAA or
the Department of Commerce.
Author Contributions
A.I.M.C. conceived the study with input from all authors. A.I.M.C. conducted all the data analysis, gures, and
supplementary materials. A.I.M.C. and A.R.S. wrote the manuscript. I.C., C.M.E., L.A.G., M.M., and J.E.A.G
assisted in writing the manuscript. All authors reviewed, edited and approved the manuscript.
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... These results might suggest that these outstanding coral sites are still behaving as remote reefs in the Wider Caribbean, despite the stressors affecting the whole area. Besides, the percentage of bleaching and diseases is relatively low in Alacranes Reef compared to other sites located in the Wider Caribbean (Hernández-Landa and Aguilar-Perera, 2019; Muñiz-Castillo et al., 2019;Muñiz-Castillo and Arias-González, 2021;Randazzo-Eisemann et al., 2022) and no signs of the lethal Stony Coral Tissue Loss Disease (SCTLD) have been found during the monitoring (Garza-Pérez and Randazzo-Eisemann, 2023). ...
... Furthermore, the Southern Gulf of Mexico -where Alacranes Reef is located-is one of the regions of the Wider Caribbean with the highest heat stress over the last three decades, with maximum exposure occurring during (Muñiz-Castillo et al., 2019. Moreover, heatwave frequency has increased worldwide by over 34% during the last decade (Oliver et al., 2018), a trend expected to continue with increasing anthropogenic activity (Masson-Delmotte et al., 2021). ...
... The color of the ellipses represents the different site categories: D is depauperate sites in red (<10%), RC is regular cover in yellow (10-20%), and HC is high cover in green (>20%). if observed bleaching in Alacranes Reef has been low in past studies (Hernández-Landa and Aguilar-Perera, 2019;Muñiz-Castillo et al., 2019;Muñiz-Castillo and Arias-González, 2021), thermal adaptation processes might be slower than the intensity of thermal stress if greenhouse gas emissions are not significantly reduced (Coles and Riegl, 2013;Dee et al., 2019;Dziedzic et al., 2019;Silbiger et al., 2019;Lawman et al., 2022;Manzello et al., 2015;Parker et al., 2020;Webb et al., 2023). ...
Coral reefs worldwide are being exposed to an interconnected myriad of anthropogenic stressors degrading their structure and functions. Therefore, global coral cover and coral reef ecosystem services have declined by half since the 1950s. Yet, remote reefs represent a time capsule to study ecological processes on minimally disturbed reefs, and these findings can steer management decisions and actions to enhance resilience. Combining in situ and satellite-derived data from the remote Alacranes Reef system located in the Gulf of Mexico, we evaluated benthic cover data and coral structural complexity together with physical drivers and direct coral stressors to better understand the current reef conditions of this remote system. Our main results show that 57% of the reef sites have >10% hard coral cover and are dominated by structurally complex species. Moreover, sites with >20% coral cover were associated with significantly shallower depths, smaller wave significant height, fewer bleaching impacts, as well as fewer macroalgae and cyanobacteria cover. Despite cumulative stressors impacting the Alacranes reef system, coral species assemblages and conditions are still driven by natural physical gradients, and the system hosts potential refuges for structurally complex species in the wider Caribbean. However, not all these refuges are fully protected, and strategic local management is key to maintaining and enhancing coral resilience in the context of increasing global stressors.
... The Mexican Caribbean (MC) coral reef ecosystem follows the global trend of degradation resulting from increased sea surface temperature, intensification of hurricane and tropical storm occurrence and widespread incidence of diseases Guimarais et al., 2021;Hernández-Arana et al., 2014;Muñiz-Castillo et al., 2019;Ruiz-Zárate et al., 2003). Simultaneously, this coral reef ecosystem is embedded in an international tourist destination region that drives massive coastal zone land use changes, tourist visitors and an increasing demand for human use water resources that have contributed to water quality degradation (Banaszak, 2021). ...
... Thus, the coral reef decline in the southern Mexican Caribbean at the scale of decades is more likely attributed to global and regional stressors and disturbances, as noted by Steneck and Lang (2003). For example, the history of sea surface warming created larger temperature anomalies and consequent bleaching (Hernández-Arana et al., 2014;Muñiz-Castillo et al., 2019;Ruiz-Zárate et al., 2003), as well as the history of disease outbreaks affecting large, massive hard corals like A. palmata, Orbicella complex, Pseudodiploria genus, Siderastrea siderea, Montastraea cavernosa, Colpophyllia genus, Dendrogyra cylindrus . Also, diverse hurricanes have directly affected the southern Mexican Caribbean (Hurricanes Dean, 2007;Karl, 2010;Ernest, 2012;) (Sánchez-Sánchez et al., 2015). ...
Temporal free access: * The Mexican Caribbean coral reef ecosystem has endured the effects of global and regional stressors and, recently, the massive arrivals of the free-living, floating brown algae Sargassum spp. This study aimed to evaluate spatiotemporal changes in the stony coral community structure in the southern Mexican Caribbean by a temporal comparison of live coral cover and colony density using a data set collected in 2008–2009 and a recent survey in 2021 within a Protected Natural Area. A multivariate analysis approach was used to reveal spatiotemporal changes in coral cover and colony densities. Coral cover ranged from 6.9 to 8.9% in 2008–2009 to 6.5% in 2021, the lowest values recorded for the area. Coral colony density ranged from 0.68 to 0.78 colonies m−1 in 2008–2009 to 0.68 colonies m−1 in 2021. The present results appear to represent subtle changes during the last decade.
... -20171970s, e.g. (Krawczyk et al., 2020Muñiz-Castillo et al., 2019;Lough et al., 2018;Le Nohaïc et al., 2017). As global 300 warming progresses and oceans become significantly warmer, the incidence of mass bleaching is decoupling from El Niño warm phase (Veron et al., 2009), with warmer conditions compared to three decades ago (McGowan and Theobald, 2023;Muñiz-Castillo et al., 2019). ...
... (Krawczyk et al., 2020Muñiz-Castillo et al., 2019;Lough et al., 2018;Le Nohaïc et al., 2017). As global 300 warming progresses and oceans become significantly warmer, the incidence of mass bleaching is decoupling from El Niño warm phase (Veron et al., 2009), with warmer conditions compared to three decades ago (McGowan and Theobald, 2023;Muñiz-Castillo et al., 2019). The global recurrence of bleaching has reduced to an average of 6 years , sooner than expected from climate models and satellite-based sea temperatures. ...
Full-text available
Climate tipping elements are large-scale subsystems of the Earth that may transgress critical thresholds (tipping points) under ongoing global warming, with substantial impacts on biosphere and human societies. Frequently studied examples of such tipping elements include the Greenland Ice Sheet, the Atlantic Meridional Overturning Circulation, permafrost, monsoon systems, and the Amazon rainforest. While recent scientific efforts have improved our knowledge about individual tipping elements, the interactions between them are less well understood. Also, the potential of individual tipping events to induce additional tipping elsewhere, or stabilize other tipping elements is largely unknown. Here, we map out the current state of the literature on the interactions between climate tipping elements and review the influences between them. To do so, we gathered evidence from model simulations, observations and conceptual understanding, as well as archetypal examples of paleoclimate reconstructions where multi-component or spatially propagating transitions were potentially at play. Lastly, we identify crucial knowledge gaps in tipping element interactions and outline how future research could address those gaps.
... Here, we evaluated local-scale thermal stress regimes among the reefs of a small remote archipelago using a 36-year satellite record of sea surface temperatures (SST). The Republic of Palau serves as an ideal case study as it represents more manageable-relevant spatial scales an order of magnitude smaller (<150 km long) than regions previously linked to gradients of thermal exposure and coral heat tolerance (1000s km 4,9,10 ). At a first assessment, Palau's coral reefs appear to have broadly similar historic satellite-based thermal environments, with warm season SST climatologies differing by < 0.1 °C. ...
... However, this may only be applicable in the context of remote coral reef systems with limited habitatspecific environmental gradients, and populations with high genetic diversity (e.g., many remote Pacific reefs). The relative feasibility of selective breeding and assisted geneflow may be different for larger reef systems that span considerable thermal gradients (see 4,9,10 ), or for populations that have already suffered substantial declines with low remaining genetic diversity (e.g., Acropora palmata in the Caribbean 49 ). ...
Full-text available
Marine heatwaves and mass bleaching have led to global declines in coral reefs. Corals can adapt, yet, to what extent local variations in thermal stress regimes influence heat tolerance and adaptive potential remains uncertain. Here we identify persistent local-scale thermal refugia and hotspots among the reefs of a remote Pacific archipelago, based on 36 years of satellite-sensed temperatures. Theory suggests that hotspots should promote coral heat tolerance through acclimatisation and directional selection. While historic patterns of mass bleaching and marine heatwaves align with this expectation, we find a contrasting pattern for a single species, Acropora digitifera , exposed to a marine heatwave experiment. Higher heat tolerance at thermal refugia (+0.7 °C-weeks) and correlations with other traits suggest that non-thermal selective pressures may also influence heat tolerance. We also uncover widespread heat tolerance variability, indicating climate adaptation potential. Compared to the least-tolerant 10% of the A. digitifera population , the most-tolerant 10% could withstand an additional heat stress of 5.2 and 4.1 °C-weeks for thermal refugia and hotspots, respectively. Despite expectations, local-scale thermal refugia can harbour higher heat tolerance, and mass bleaching patterns do not necessarily predict species responses. This has important implications for designing climate-smart initiatives to tackle global-scale adaptive management problems.
... Coral reefs are in decline worldwide due to the effects of multiple global and local stressors [1]. In the Caribbean, warming events, disease outbreaks, hurricanes, overfishing, and water pollution due to inadequate coastal development have severely impacted coral reefs [2][3][4][5][6]. As a result of these stressors, coral cover has declined and been replaced by a high abundance of macroalgae in many Caribbean reefs [6][7][8][9][10]. ...
Full-text available
In the past decades, one of the most widely reported phenomena on Caribbean reefs is the general fall in coral cover and rise in macroalgae. Reefs with low coral cover and high macroalgal abundances are often presumed to provide poorer ecosystem functions and services. In this study, we assessed the condition of coral reefs on the Caribbean Coast of Costa Rica and determined how eight key ecosystem metrics varied in response to different coral and fleshy macroalgae covers. Most reefs surveyed had high fleshy macroalgae and low live coral covers, with an average (±SD) of 31 ± 28% and 14 ± 13% per site. The value of many of the ecosystem metrics estimated for coral reefs of the region appears to be lower than what has been reported for other areas in the Caribbean. We found that the rugosity, urchin density, fish richness, total fish biomass, large fish density, and the potential fishery value of the reef were higher in sites with low fleshy macroalgae covers (<10%). Our results concur with the prevailing paradigm that an increase in macroalgae abundance could reduce the ecosystem services provided by coral reefs.
... An increase in thermal anomalies could increase the deterioration in condition and abundance of reef-building corals such as Acropora or Orbicella species. Heat stress may cause coral bleaching, disease outbreaks, and subsequent coral mortality, also increasing corals vulnerability to other natural or anthropogenic stressors (Hughes et al., 2017;Hughes et al., 2018a;Hughes et al., 2018b;Muñiz Castillo et al., 2019). Heat stress has been considered a driver with a negative effect on the living coral cover of Australian reefs, with R 2 values of 0.39 (Ceccarelli et al., 2019). ...
Full-text available
Background The long-time study of coral reefs with low human impacts can provide information on the effects of regional pressures like climate change, and is an opportunity to document how these pressures are reflected in coral communities. An example of minimal local anthropogenic impacts are the Guanahacabibes coral reefs, located in the westernmost region of Cuba. The objectives of this study were: to evaluate the temporal variability of six benthic biological indicators of coral reefs, and to explore the possible relationship between predictive abiotic variables and biological response variables. Methods Four coral reef sites were sampled between 2008 and 2017, to analyze biological indicators (living coral cover, fleshy algae index, coral species richness, coral species abundance, coral trait groups species abundance, Functional Reef Index). Seven abiotic variables (wave exposure, sea surface temperature, degree heating week, chlorophyll-a concentration, particulate organic carbon, photosynthetically available radiation, and the diffuse attenuation coefficient) were compiled between 2007 and 2016, from remote sensing datasets, to analyze their relationship with the biological indicators. Permanova statistical analysis was used to evaluate trends in biological variables between sites and years, and Routine Analysis Based on Linear Distances (DISTLM) was used to explore some dependencies between biotic and abiotic variables. Results We found significant variability in the temporal analysis, with a decrease in living coral cover, a decline in the predominance of the branching and massive framework reef-building species, a decline in Orbicella species abundance, and an increase in the fleshy algae index. Some abiotic variables (average of degree heating weeks, standard deviation of the diffuse attenuation coefficient, average of the sea surface temperature, among others) significantly explained the variability of biological indicators; however, determination coefficients were low. Conclusions Certain decrease in the functionality of the coral reef was appreciated, taking into account the predominance of secondary and nom-massive framework reef-building species in the last years. A weak association between abiotic and biological variables was found in the temporal analysis. The current scenario of the condition of the coral reefs seems to be regulated by the global effects of climate change, weakly associated effects, and in longer terms.
... Ocean warming and the intensification of extreme weather and circulation events (such as the El Niňo and La Niňa Southern Oscillations) can cause acute coral bleaching events. A three-decadal analysis of climate change associated heat stress on Caribbean coral reefs, found high variability throughout the region (Muñiz-Castillo et al., 2019). The paper shows that the "Eastern Caribbean" region, within which the VI sits, received the highest levels of heat stress in 2005 and 2010, with generally less heat stress in more recent years (up to 2017). ...
Technical Report
Full-text available
The UK Overseas Territory of the Virgin Islands (VI) has a total Exclusive Economic Zone (EEZ) of 81,000 km2 but only 151 km2 is on land. The large marine component of the EEZ presents challenges in protecting biodiversity, achieving sustainable fisheries, and wider marine management. Collection and review of evidence for the marine area has, in the past, been fragmented. For example, it is generally acknowledged that many islanders fish for subsistence, sport or commerce, however the extent of illegal, unreported, and unregulated (IUU) fishing is unknown. Where recent surveys have been undertaken and survey data is available, the relevant government departments do not currently have the capacity to collate the available data and fully interrogate, analyse, and interpret these data. This project was commissioned to collate, review and consolidate the evidence base for the marine area. The report is designed to support the Government of the VI to progress with its marine environmental objectives and commitments, in order to move towards integrated fisheries and marine management for the VI marine area, balancing multiple uses and stakeholder interests. This report provides a comprehensive review of the state of marine fisheries in the VI, as well as providing extensive evidence relating to natural fisheries resources, associated human activities, and social and economic dimensions of the fisheries sector. This is followed by an analysis of key evidence gaps around each of these themes.
... Coral reefs are in decline worldwide due to the effects of multiple global and local stressors [1]. In the Caribbean, warming events, disease outbreaks, hurricanes, overfishing, and water pollution due to inadequate coastal development have severely impacted coral reefs [2][3][4][5][6]. Because of these stressors, coral cover declined and was replaced by a high abundance of macroalgae in many Caribbean reefs [6][7][8][9]. ...
Full-text available
In the past decades, one of the most widely reported phenomena on Caribbean reefs is the general fall in coral cover and rise in macroalgae. Reefs with low coral cover and high macroalgal abun-dances are often presumed to provide poorer ecosystem functions and services. In this study, we assessed the condition of coral reefs on the Caribbean Coast of Costa Rica and determined how eight key ecosystem metrics varied in response to differential coral and fleshy macroalgae covers. Most reefs surveyed had high fleshy macroalgae and low live coral covers, with an average (± SD) of 31 ± 28% and 14 ± 13% per site. We found that the rugosity, urchin density, fish richness, total fish biomass, large fish density, and the potential fishery value of the reef were higher in sites with low fleshy macroalgae covers (>10%). The value of many of the ecosystem metrics estimated for coral reefs of the region appears to be lower than what’s been reported for other areas in the Caribbean, which may be related to the high macroalgae covers found on the reefs. Our results concur with the prevailing paradigm that an increase in macroalgae abundance could reduce the ecosystem services provided by coral reefs.
... It is a priority region for conservation in the world as well as a critical fishing ground for thousands of small-scale fishers (Chollett et al., 2017;Heyman & Granados-Dieseldorff, 2012;Olson & Dinerstein, 2002). The MAR encompasses some of the warmest marine physicochemical provinces of the Caribbean and is currently in a state of increasing heat stress (Chollett et al., 2017;Muñiz-Castillo et al., 2019). To circumvent the lack of local life-history studies, data from other areas of lane snapper distribution have been incorporated into the fishery assessment models (Babcock et al., 2018;Sierra Castillo et al., 2020;Sierra Castillo & Fujiwara, 2021). ...
Research on life history variations in widely distributed fish species is needed to understand global warming impacts on populations and to improve fisheries management advice. The lane snapper Lutjanus synagris (Linnaeus, 1758) is commercially important to fisheries in the Western Central Atlantic, where spread information on its life history traits is available. We studied growth, age, reproduction and mortality of lane snapper in the Guatemalan Caribbean, the warmest part of its distribution range, and collated the new information with published data in a latitudinal analysis extending between 18°S and 30°N. Longevity was estimated at 11 years, and von Bertalanffy growth parameters were: asymptotic length (Linf) 45.6 and 42.2 cm for females and males respectively, the growth coefficient (K) was 0.1 year-1 , and the theoretical age at zero length (t0) was -4.4 year. Lane snapper grew slowest in April, prior to the rainy season, and at the onset of the reproductive season, which lasted from May to October. Fifty percent of female and male lane snappers matured at 23 and 17 cm, corresponding to 3.5 and 2.4 years of age respectively. A regional multivariate analysis found seawater temperature to be an important driver of life history variation. Lane snapper lifespan was shorter at the warm edge of its distribution range, and maximum size and peak reproductive investment were negatively related to sea surface temperature. The trade-offs in lane snapper life history traits and phenology likely enhance its fitness to differing environments. Interpolation from the present regional estimates to less studied regions of the Caribbean is useful for preliminary understanding of reaction norms and harvest potentials. This article is protected by copyright. All rights reserved.
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As many as 22 of the 45 coral species on the Florida Reef Tract are currently affected by stony coral tissue loss disease (SCTLD). The ongoing disease outbreak was first observed in 2014 in Southeast Florida near Miami and as of early 2019 has been documented from the northernmost reaches of the reef tract in Martin County down to Key West. We examined the microbiota associated with disease lesions and apparently healthy tissue on diseased colonies of Montastraea cavernosa , Orbicella faveolata , Diploria labyrinthiformis , and Dichocoenia stokesii . Analysis of differentially abundant taxa between disease lesions and apparently healthy tissue identified five unique amplicon sequence variants enriched in the diseased tissue in three of the coral species, namely an unclassified genus of Flavobacteriales and sequences identified as Fusibacter (Clostridiales), Planktotalea (Rhodobacterales), Algicola (Alteromonadales), and Vibrio (Vibrionales). In addition, several groups of likely opportunistic or saprophytic colonizers such as Epsilonbacteraeota, Patescibacteria, Clostridiales, Bacteroidetes, and Rhodobacterales were also enriched in SCTLD disease lesions. This work represents the first microbiological characterization of SCTLD, as an initial step toward identifying the potential pathogen(s) responsible for SCTLD.
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The global coral bleaching event of 2014-2017 resulted from the latest in a series of heat stress events that have increased in intensity. We assessed global-and basin-scale variations in sea surface temperature-based heat stress products for 1985-2017 to provide the context for how heat stress during 2014-2017 compared with the past 3 decades. Previously, undefined ''Heat Stress Year'' periods (used to describe interannual variation in heat stress) were identified for the Northern and Southern Hemispheres, in which heat stress peaks during or shortly after the boreal and austral summers, respectively. The proportion of reef pixels experiencing bleaching-level heat stress increased through the record, accelerating during the last decade. This increase in accumulated heat stress at a bleaching level is a result of longer stress events rather than an increase in the peak stress intensity. Thresholds of heat stress extent for the three tropical ocean basins were established to designate ''global'' events, and a Global Bleaching Index was defined that relates heat stress extent to that observed in 1998. Notably, during the 2014-2017 global bleaching event, more than three times as many reefs were exposed to bleaching-level heat stress as in the 1998 global bleaching.
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Historical records of growth rates of the key Caribbean reef framework-building coral Orbicella faveolata can be fundamental not only to understand how these organisms respond to environmental changes but also to infer future responses of reef ecosystems in a changing world. While coral growth rates have been widely documented throughout the Caribbean, the drivers of coral growth variability remain poorly understood. Here we provide a record spanning 53 years (1963–2015) of the coral growth parameters for five O. faveolata core samples collected at Serrana Atoll, inside the Seaflower Biosphere Reserve, Colombian Caribbean. Coral cores were extracted from reefs isolated from direct anthropogenic impacts, and growth estimations were derived using computerized tomography. Master records of coral growth parameters were evaluated to identify long-term trends and to relate growth responses with sea surface temperature (SST), the Atlantic Multi-decadal Oscillation (AMO), North Atlantic Oscillation (NAO) and Southern Oscillation indexes, aragonite saturation state (Ωarag), and degree heating months (DHM). Mean density, linear extension and calcification rates were 1.08 g cm-3, 0.96 cm yr-1 and 1.02 g cm-2 yr-1, respectively. We found significant negative relationships between density and mean SST, maximum SST, AMO, and DHM. Moreover, density showed significant positive correlations with NAO and Ωarag. Extension rate did not show significant correlations with any environmental variable; however, there were significant negative correlations between calcification and maximum SST, AMO, and DHM. Trends of coral growth indicated a significant reduction in density and calcification over time, which were best explained by changes in Ωarag. Inter-annual declines in calcification and density up to 25% (relative to historical mean) were associated to the impacts of previously recorded mass bleaching events (1998, 2005 and 2010). Our study provides further evidence that AMO and Ωarag are important drivers affecting coral growth rates in the Southwestern Caribbean. Therefore, we suggest upcoming variations of AMO and future trajectories of Ωarag in the Anthropocene could have a substantial influence on future disturbances, ecological process and responses of the Caribbean reefs.
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Climate change is radically altering the frequency, intensity and spatial scale of severe weather events, such as heatwaves, droughts, floods and fires¹. As the time interval shrinks between recurrent shocks2–5, the responses of ecosystems to each new disturbance are increasingly likely to be contingent on the history of other recent extreme events. Ecological memory—defined as the ability of the past to influence the present trajectory of ecosystems6,7—is also critically important for understanding how species assemblages are responding to rapid changes in disturbance regimes due to anthropogenic climate change2,3,6–8. Here, we show the emergence of ecological memory during unprecedented back-to-back mass bleaching of corals along the 2,300 km length of the Great Barrier Reef in 2016, and again in 2017, whereby the impacts of the second severe heatwave, and its geographic footprint, were contingent on the first. Our results underscore the need to understand the strengthening interactions among sequences of climate-driven events, and highlight the accelerating and cumulative impacts of novel disturbance regimes on vulnerable ecosystems. © 2018, The Author(s), under exclusive licence to Springer Nature Limited.
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Coral reef ecosystems are seriously threatened by changing conditions in the ocean. Although many factors are implicated, climate change has emerged as a dominant and rapidly growing threat. Developing a long‐term strategic plan for the conservation of coral reefs is urgently needed yet is complicated by significant uncertainty associated with climate change impacts on coral reef ecosystems. We use Modern Portfolio Theory to identify coral reef locations globally that, in the absence of other impacts, are likely to have a heightened chance of surviving projected climate changes relative to other reefs. Long‐term planning that is robust to uncertainty in future conditions provides an objective and transparent framework for guiding conservation action and strategic investment. These locations constitute important opportunities for novel conservation investments to secure less vulnerable yet well‐connected coral reefs that may, in turn, help to repopulate degraded areas in the event that the climate has stabilized.
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The original version of the Article was missing an acknowledgement of a funding source. The authors acknowledge that A. Safaie and K.Davis were supported by National Science Foundation Award No. 1436254 and G. Pawlak was supported by Award No. 1436522. This omission has now been corrected in the PDF and HTML versions of the Article.
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Coral bleaching is the detrimental expulsion of algal symbionts from their cnidarian hosts, and predominantly occurs when corals are exposed to thermal stress. The incidence and severity of bleaching is often spatially heterogeneous within reef-scales (<1 km), and is therefore not predictable using conventional remote sensing products. Here, we systematically assess the relationship between in situ measurements of 20 environmental variables, along with seven remotely sensed SST thermal stress metrics, and 81 observed bleaching events at coral reef locations spanning five major reef regions globally. We find that high-frequency temperature variability (i.e., daily temperature range) was the most influential factor in predicting bleaching prevalence and had a mitigating effect, such that a 1 °C increase in daily temperature range would reduce the odds of more severe bleaching by a factor of 33. Our findings suggest that reefs with greater high-frequency temperature variability may represent particularly important opportunities to conserve coral ecosystems against the major threat posed by warming ocean temperatures.
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Global warming is rapidly emerging as a universal threat to ecological integrity and function, highlighting the urgent need for a better understanding of the impact of heat exposure on the resilience of ecosystems and the people who depend on them 1 . Here we show that in the aftermath of the record-breaking marine heatwave on the Great Barrier Reef in 2016 2 , corals began to die immediately on reefs where the accumulated heat exposure exceeded a critical threshold of degree heating weeks, which was 3-4 °C-weeks. After eight months, an exposure of 6 °C-weeks or more drove an unprecedented, regional-scale shift in the composition of coral assemblages, reflecting markedly divergent responses to heat stress by different taxa. Fast-growing staghorn and tabular corals suffered a catastrophic die-off, transforming the three-dimensionality and ecological functioning of 29% of the 3,863 reefs comprising the world's largest coral reef system. Our study bridges the gap between the theory and practice of assessing the risk of ecosystem collapse, under the emerging framework for the International Union for Conservation of Nature (IUCN) Red List of Ecosystems 3 , by rigorously defining both the initial and collapsed states, identifying the major driver of change, and establishing quantitative collapse thresholds. The increasing prevalence of post-bleaching mass mortality of corals represents a radical shift in the disturbance regimes of tropical reefs, both adding to and far exceeding the influence of recurrent cyclones and other local pulse events, presenting a fundamental challenge to the long-term future of these iconic ecosystems.
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Tropical corals live close to their upper thermal limit making them vulnerable to unusually warm summer sea temperatures. The resulting thermal stress can lead to breakdown of the coral-algal symbiosis, essential for the functioning of reefs, and cause coral bleaching. Mass coral bleaching is a modern phenomenon associated with increases in reef temperatures due to recent global warming. Widespread bleaching has typically occurred during El Niño events. We examine the historical level of stress for 100 coral reef locations with robust bleaching histories. The level of thermal stress (based on a degree heating month index, DHMI) at these locations during the 2015-2016 El Niño was unprecedented over the period 1871-2017 and exceeded that of the strong 1997-1998 El Niño. The DHMI was also 5 times the level of thermal stress associated with the 'pre-industrial', 1877-1878, El Niño. Coral reefs have, therefore, already shown their vulnerability to the modest (~0.92 °C) global warming that has occurred to date. Estimates of future levels of thermal stress suggest that even the optimistic 1.5 °C Paris Agreement target is insufficient to prevent more frequent mass bleaching events for the world's reefs. Effectively, reefs of the future will not be the same as those of the past.
El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño–Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.