ArticlePDF Available

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.
Content may be subject to copyright.
1
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
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
3,
Luisa Andrade-Gómez
4, Melanie McField5 & Jesús Ernesto Arias-González
1
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: aaron.muniz@cinvestav.mx) or J.E.A.-G. (email: earias@cinvestav.mx)
Received: 25 January 2019
Accepted: 10 July 2019
Published: xx xx xxxx
OPEN
Content courtesy of Springer Nature, terms of use apply. Rights reserved
2
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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.
Results
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).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
3
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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 (https://www.qgis.org/en/site/)73.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
4
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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 (https://www.qgis.org/en/site/)73.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
5
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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 (https://www.qgis.org/en/site/)73.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
6
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
7
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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;
Fig.S11).
Discussion
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
8
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
9
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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 (http://origin.cpc.ncep.noaa.gov/products/analysis_
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
10
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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
reefs.
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.
Methods
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-
alreefwatch.noaa.gov/product/5). e Maximum Monthly Mean (MMM) was also obtained from the Coral Reef
Watch Program version 3.1 dataset at 5 km (https://coralreefwatch.noaa.gov/satellite/bleaching5km/index.php),
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.
=
−>
≤.
HS
SST MMM SST MMM
SST MMM
,
0,
(1)
dailydaily
daily,
=≥°
=
=
DHWHSifHS
1
7(, 1C)
(2)
i
j
ii
1
84
Oceanic Niño index data. e El Niño Southern Oscillation cycles and variation were determined using
the NOAA´s Oceanic Niño Index (ONI) version ve (http://origin.cpc.ncep.noaa.gov/products/analysis_mon-
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
Content courtesy of Springer Nature, terms of use apply. Rights reserved
11
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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
(http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostu/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
Content courtesy of Springer Nature, terms of use apply. Rights reserved
12
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
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://
coralreefwatch.noaa.gov/satellite/coraltemp.php. e ONI time series data are available from NOAA: http://or-
igin.cpc.ncep.noaa.gov/products/analysismonitoring/ensostu/ONIv5.php. e main data used for the gures
and analyses were submitted to the NOAA National Centers for Environmental Information (NCEI).
References
1. Lough, J. M., Anderson, . D. & Hughes, T. P. Increasing thermal stress for tropical coral reefs: 1871–2017. Sci. Rep. 8, 6079 (2018).
2. Heron, S. F., Maynard, J. A., van Hooidon, . & Eain, C. M. Warming Trends and Bleaching Stress of the World’s Coral eefs
1985–2012. Sci. Rep. 6, 38402 (2016).
3. Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science (80-.). 359, 80–83 (2018).
4. Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).
5. Sirving, W. J. et al. e relentless march of mass coral bleaching: a global perspective of changing heat stress. Coral Reefs, https://
doi.org/10.1007/s00338-019-01799-4 (2019).
6. Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidication. Science 318, 1737–42 (2007).
7. Baer, A. C., Glynn, P. W. & iegl, B. Climate change and coral reef bleaching: An ecological assessment of long-term impacts,
recovery trends and future outloo. Estuar. Coast. Shelf Sci. 80, 435–471 (2008).
8. andall, C. J. & van Woesi, . Contemporary white-band disease in Caribbean corals driven by climate change. Nat. Clim. Chang.
5, 1–5 (2015).
9. andall, C. J. & Van Woesi, . Some coral diseases trac climate oscillations in the Caribbean. Sci. Rep. 7, 1–8 (2017).
10. Eain, C. M. et al. Caribbean corals in crisis: ecord thermal stress, bleaching, and mortality in 2005. PLoS One 5 (2010).
11. Precht, W. F., Gintert, B. E., obbart, M. L., Fura, . & Van Woesi, . Unprecedented Disease-elated Coral Mortality in
Southeastern Florida. Sci. Rep, https://doi.org/10.1038/srep31374 (2016).
12. Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
13. Pendleton, L. et al. Coral eefs and People in a High-CO2 World: Where Can Science Mae a Dierence to People? PLoS One 11,
e0164699 (2016).
14. van Hooidon, ., Maynard, J. A., Liu, Y. & Lee, S. . Downscaled projections of Caribbean coral bleaching that can inform
conservation planning. Glob. Chang. Biol. 21, 3389–3401 (2015).
15. Beyer, H. L. et al. is-sensitive planning for conserving coral reefs under rapid climate change. Conserv. Lett. e12587, https://doi.
org/10.1111/conl.12587 (2018).
16. Chollett, I., Enríquez, S. & Mumby, P. J. edening ermal egimes to Design eserves for Coral eefs in the Face of Climate
Change. PLoS One 9, e110634 (2014).
17. Selig, E. ., Casey, . S. & Bruno, J. F. New insights into global patterns of ocean temperature anomalies: Implications for coral reef
health and management. Glob. Ecol. Biogeogr. 19, 397–411 (2010).
18. Bayratarov, E., Pizarro, V., Eidens, C., Wile, T. & Wild, C. Bleaching susceptibility and recovery of Colombian Caribbean corals in
response to water current exposure and seasonal upwelling. PLoS One 8, 1–11 (2013).
19. Chollett, I. & Mumby, P. J. eefs of last resort: Locating and assessing thermal refugia in the wider Caribbean. Biol. Conserv. 167,
179–186 (2013).
20. Safaie, A. et al. High frequency temperature variability reduces the ris of coral bleaching. Nat. Commun. 9 (2018).
21. Coles, S. L. & Brown, B. E. Coral bleaching - Capacity for acclimatization and adaptation. Advances in Marine Biology 46, 183–223
(2003).
22. Guest, J. . et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS
One 7, 1–8 (2012).
23. owan, . Coral bleaching: ermal adaptation in reef coral symbionts. Nature 430, 742–742 (2004).
24. Scheufen, T., rämer, W. E., Iglesias-Prieto, . & Enríquez, S. Seasonal variation modulates coral sensibility to heat-stress and
explains annual changes in coral productivity. Sci. Rep. 7, 1–15 (2017).
25. Hughes, T. P. et al. Ecological memory modies the cumulative impact of recurrent climate extremes. Nat. Clim. Chang. 9, 40–43
(2019).
26. Maynard, J. A., Anthony, . . N., Marshall, P. A. & Masiri, I. Major bleaching events can lead to increased thermal tolerance in
corals. Mar. Biol. 155, 173–182 (2008).
27. Wol, N. H., Mumby, P. J., Devlin, M. & Anthony, . . N. Vulnerability of the Great Barrier eef to climate change and local
pressures. Glob. Chang. Biol. 24, 1978–1991 (2018).
28. Langlais, C. E. et al. Coral bleaching pathways under the control of regional temperature variability. Nat. Clim. Chang. 7, 839–844
(2017).
29. Timmermann, A. et al. El Niño–Southern Oscillation complexity. Nature 559, 535–545 (2018).
30. Chiamoto, Y. et al. Silful multi-year predictions of tropical trans-basin climate variability. Nat. Commun. 6, 1–7 (2015).
31. lein, S. A., Soden, B. J. & Lau, N.-C. emote Sea Surface Temperature Variations during ENSO: Evidence for a Tropical
Atmospheric Bridge. J. Clim. 12, 917–932 (1999).
32. Giannini, A., Chiang, J. C. H., Cane, M. A., ushnir, Y. & Seager, . e ENSO teleconnection to the Tropical Atlantic Ocean:
Contributions of the remote and local SSTs to rainfall variability in the Tropical Americas. J. Clim. 14, 4530–4544 (2001).
33. Eneld, D. B. & Mayer, Da Tropical Atlantic sea surface temperature variability and its relation to El Niño-Southern Oscillation. J.
Geophys. Res. Ocean. 102, 929–945 (1997).
34. Donner, S. D., icbeil, G. J. M. & Heron, S. F. A new, high-resolution global mass coral bleaching database. PLoS One 12, 1–17
(2017).
35. Spillman, C. M., Alves, O. & Hudson, D. A. Seasonal Prediction of ermal Stress Accumulation for Coral Bleaching in the Tropical
Oceans. Mon. Weather Rev. 139, 317–331 (2011).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
13
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
36. Lizcano-Sandoval, L. D., Marulanda-Gómez, Á., López-Victoria, M. & odriguez-amirez, A. Climate Change and Atlantic
Multidecadal Oscillation as Drivers of ecent Declines in Coral Growth ates in the Southwestern Caribbean. Front. Mar. Sci. 6,
1–10 (2019).
37. Carilli, J. E., Norris, . D., Blac, B., Walsh, S. M. & McField, M. D. Century-scale records of coral growth rates indicate that local
stressors reduce coral thermal tolerance threshold. Glob. Chang. Biol. 16, 1247–1257 (2010).
38. Donner, S. D., nutson, T. . & Oppenheimer, M. Model-based assessment of the role of human-induced climate change in the 2005
Caribbean coral bleaching event. Proc. Natl. Acad. Sci. 104, 5483–5488 (2007).
39. Trenberth, . E. & Shea, D. J. Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett. 33, 1–4 (2006).
40. Chollett, I., Müller-arger, F. E., Heron, S. F., Sirving, W. & Mumby, P. J. Seasonal and spatial heterogeneity of recent sea surface
temperature trends in the Caribbean Sea and southeast Gulf of Mexico. Mar. Pollut. Bull. 64, 956–965 (2012).
41. McWilliams, J. P., Côté, I. M., Gill, Ja, Sutherland, W. J. & Watinson, A. . Accelerating impacts of temperature-induced coral
bleaching in the Caribbean. Ecology 86, 2055–2060 (2005).
42. Alemu I, J. B. & Clement, Y. Mass coral bleaching in 2010 in the Southern Caribbean. PLoS One 9, (2014).
43. Oxenford, Ha et al. Quantitative observations of a major coral bleaching event in Barbados, Southeastern Caribbean. Clim. Change
87, 435–449 (2008).
44. iegl, B. & Piller, W. E. Possible refugia for reefs in times of environmental stress. Int. J. Earth Sci. 92, 520–531 (2003).
45. Spalding, M. D. et al. Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas. Bioscience 57, 573 (2007).
46. Liu, G. et al. eef-scale thermal stress monitoring of coral ecosystems: New 5-m global products from NOAA coral reef watch.
Remote Sens. 6, 11579–11606 (2014).
47. Grinsted, A., Moore, J. & Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series.
Nonlinear Process. Geophys. 11, 561–566 (2004).
48. Torrence Christopher, P. & Compo Gilbert, A. Practical Guide to Wavelet Analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).
49. Liu, Y., San Liang, X. & Weisberg, . H. ectication of the Bias in the Wavelet Power Spectrum. J. Atmos. Ocean. Technol. 24,
2093–2102 (2007).
50. Eain, C. et al. Ding, Dong, e Witch is Dead (?)– ree Years of Global Coral Bleaching 2014–2017. Reef Encount. 32, 33–38
(2017).
51. Chollett, I., Mumby, P. J. & Cortés, J. Upwelling areas do not guarantee refuge for coral reefs in a warming Ocean. Mar. Ecol. Prog.
Ser. 416, 47–56 (2010).
52. Varela, ., Costoya, X., Enriquez, C., Santos, F. & Gómez-Gesteira, M. Dierences in coastal and oceanic SST trends north of
Yucatan Peninsula. J. Mar. Syst. 182, 46–55 (2018).
53. Mceld, M. et al. 2018 Report Card for the Mesoamerican Reef, at, http://www.healthyreefs.org (2018).
54. Lough, J. M. & Cantin, N. E. Perspectives on massive coral growth rates in a changing ocean. Biol. Bull, https://doi.org/10.1086/
BBLv226n3p187 (2014).
55. Atlantic and Gulf apid eef Assessment (AGA) Program. Stony coral tissue loss disease. at http://www.agrra.org/coral-disease-
outbrea/
56. Meyer, J. L. et al. Microbial community shis associated with the ongoing stony coral tissue loss disease outbrea on the Florida eef
Tract. bioRxiv 626408, https://doi.org/10.1101/626408, (2019).
57. Lowe, . J. & Falter, J. L. Oceanic forcing of coral reefs. Ann. Rev. Mar. Sci. 7, 43–66 (2015).
58. UNEP-WCMC & Centre, W. F. Global Distribution of Coral eefs. World Wide Web Electron. Publ. http//data.unep-wcmc.org/
datasets 1–4 (2010).
59.  Core Team. : A language and environment for statistical computing. (2017).
60. Hijmans, . J. aster: Geographic Data Analysis and Modeling. R package version 2.6–7 (2017).
61. Pebesma, E. J. & Bivand, . S. Classes and methods for spatial data in .  News 5. 2 (2005).
62. Bivand, . S., Pebesma, E. J. & Gomez-ubio, V. Applied spatial data analysis with ; Second edition. (2013).
63. Weatherhead, E. C. et al. Factors aecting the detection of trends: Statistical considerations and applications to environmental data.
J. Geophys. Res. Atmos. 103, 17149–17161 (1998).
64. Pinheiro, J., Bates, D., Deboy, S. S. D. & . C. T. nlme: Linear and Nonlinear Mixed Eects Models.  pacage version 3.1-131,
https://CAN.-project.org/pacage=nlme. R Packag. version 3.1-131, https//CAN.-project.org/pacage=nlme, https://doi.
org/10.1016/j.tibs.2011.05.003 (2017).
65. Mair, P. & Wilcox, . . obust statistical methods in  using the WS2 pacage. J. Stat. Sow, https://doi.org/10.18637/jss.v000.i00
(2017).
66. Leutner, B., Horning, N., Schwalb-Willmann, J. & Hijmans, . J. Stoolbox: Tools for emote Sensing Data Analysis.  pacage
version 0.2.3 (2018).
67. Lê, S., Josse, J. & Husson, F. FactoMine: An  Pacage for Multivariate Analysis. J. Stat. Sow. 25 (2008).
68. Pettitt, A. N. A Non-Parametric Approach to the Change-Point Problem. Appl. Stat. 28, 126 (1979).
69. Gouhier, T. C., Grinsted, A. & Simo, V. Pacage ‘biwavelet. Conduct Analyses, Bivariate Wavelet (V.0.20.17). 1–39 (2018).
70. Trapletti, A. & Horni, . tseries: Time Series Analysis and Computational Finance. R Packag. version 0.10-42 at, https://cran.
biodis.org/web/pacages/tseries/tseries.pdf (2017).
71. Gergis, J. L. & Fowler, A. M. Classication of synchronous oceanic and atmospheric El Niño-Southern Oscillation (ENSO) events
for palaeoclimate reconstruction. Int. J. Climatol. 25, 1541–1565 (2005).
72. Burnham, . P. & Anderson, D. . Model selection and multimodel inference: a practical information-theoretic approach, Second
Edition. Book, https://doi.org/10.1016/j.ecolmodel.2003.11.004 (2002).
73. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation at, https://www.qgis.org/en/
site/ (2018).
Acknowledgements
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
14
SCIENTIFIC REPORTS | (2019) 9:11013 | https://doi.org/10.1038/s41598-019-47307-0
www.nature.com/scientificreports
www.nature.com/scientificreports/
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-47307-0.
Competing Interests: e authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-
ative Commons license, and indicate if changes were made. e images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons license and your intended use is not per-
mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© e Author(s) 2019
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
... For Cuban coastal waters, Planos et al. (2012) concluded that, around Cuba, SST increased by 1.0°C between 1966 and 2000, and this increase was highest on the occidental coasts of the country. Glenn et al. (2015) found a regional increase in SST of 0.015°C per year (greater than our results) for the period 1982-2012 in the Caribbean Region, but Muñiz-Castillo et al. (2019) reported that in the Caribbean there were differences in SST variability between regions (including our study site) and concluded that local-scale variability in oceanographic conditions such as depth, upwelling, currents and water circulation also influences heat stress patterns at the local scale. There were SST variations in the JRNP (up to 5.0°C) from one month (or group of months) to another. ...
... All climatic and oceanographic variables (SST, rain and storm frequency anomalies) resulting from El Niño and La Niña events could have influenced the behaviour of coral communities in the JRNP. The OM percentage was higher in 2001, which could have resulted from the severe 1998 El Niño event (Table 3, 1952-53 1951-52 1957-58 1982-83 1954-55 1955-56 1973-74 1953-54 1963-64 1965-66 1997-98 1964-65 1970-71 1975-76 1958-59 1968-69 1972-73 2015-16 1971-72 1995-96 1988-89 1969-70 1986-87 1987-88 1974-75 2011-12 1998-99 1976-77 1994-95 1991-92 1983-84 1999 (Table 3), although it was followed by a La Niña event in 1999-2000 (Table 3, Fig. 9) (Muñiz-Castillo et al. 2019). Not only El Niño events could be the cause of high OM in the JRNP in 2001; a period of 73 years without hurricanes (from the Hurricane of Santa Cruz del Sur in 1932 to Hurricane Dennis in 2005), could be another cause. ...
... (Table 3, Fig. 9). During the years 2010 and 2011, severe heat-related stress affected the Caribbean area (Muñiz-Castillo et al. 2019). However, in the JRNP, moderate bleaching values were reported in 2010 and 2011, with affecting between 11% and 30% of the colonies (Alcolado 2011(Alcolado , 2012. ...
Article
Full-text available
This study was conducted in the Jardines de la Reina National Park, Cuba. The health of the communities of corals and crustose coralline algae was studied in the years 2001, 2012 and 2017. The probable effect of hurricanes and sea surface temperature on these communities was also assessed. The area was only affected by three hurricanes and a tropical storm from 2000 to 2017. Sea surface temperature showed an increasing trend (by 0.03°C). The highest percentage of old mortality was recorded in 2001 (74% on the fore reef and 53% on reef crests) and the lowest of recent mortality in 2012 (0.03% on the fore reef and 0.17% on reef crests). Coral cover increased on the fore reef by between 3% and 2% in 2017 in comparison with 2001 and 2012. On the reef crests, the highest cover percentage was in 2001 (14.8%). Unlike local stressors, it was determined that hurricanes and sea surface temperature have likely negatively affected the coral reefs, particularly on reef crests. Both habitats have shown resistance and/or recovery capacity from the impacts suffered after 2001, which suggests some level of resilience.
... The role of sea surface temperature (SST) over marine life has been well documented. Some studies have evaluated the impacts of this important oceanographic factor over seagrasses (DÍAZ-ALMELA et al., 2007;JORDÀ et al., 2012;HALL et al., 2016, CARLSON et al., 2018, corals (BAKER et al., 2008;LOUGH et al., 2018;HUGHES et al., 2018;MUÑIZ-CASTILLO et al. 2019) and fish assemblages and fisheries (CONAND et al., 2007;RIJNSDORP et al., 2010;CHEUNG et al. 2013;JONES et al., 2015;MAHARAJ et al., 2018). ...
... Coral reefs are one of the most impacted marine ecosystems in the Caribbean region. According to Muñiz-Castillo et al. (2019) Caribbean reefs have been exposed to heat stress during the last 30 years, with major events in 1998, 2005, 2010-2011, 2015 and 2017 and a change point in 2002-2004. The Cuban Archipelago, located at the center of the Caribbean Region, is exposed to the same threats than the rest of the Caribbean. ...
Article
Full-text available
The Cuban Archipelago -particularly its coastal zones- is exposed to Global Warming. The rise of air and sea surface temperature value is a good indicator of its consequences to biodiversity. Air temperature was measured using an Automatic Meteorological Station at the Jardines de la Reina National Park between 2016 and 2017. Sea surface temperature was measured using temperature data loggers twice in the years 2000 (January and September) and 2016 (from January to June) in coral, mangrove and seagrass habitats. Air and sea surface temperature satellite data of the same studied sites were analyzed in the period 2003-2017 using in situ measurements. Results showed that all temperature values were similar to those reported in similar studies in Cuba and the Caribbean, and that extreme values were within the tolerance ranges previously reported for the studied habitats. Air temperature did not have a significant trend; however, sea surface temperature had a significant increase (0.01 °C) during the studied period. These results show the great influence of the Caribbean Sea over the marine waters and climate of this area. A combined monitoring system was proposed using in situ data logger measurements and satellite data temperature analysis to watch this important marine reserve of the Caribbean Region
... Within the Caribbean Sea, the Mesoamerican Barrier Reef System (MBRS) extends for ~ 1000 km from Mexico to Guatemala, Belize and Honduras and is the largest coral reef system in the Northern Hemisphere (Chollett et al. 2017). This system provides subsistence for many people within these countries (Gress et al. 2019) but has been heavily impacted by multiple stressors, including overfishing, coral disease outbreaks, hurricanes, coral bleaching, sedimentation, and land-based pollution (Mumby et al. 2007;Smith et al. 2008;Pandolfi 2010;Perry et al. 2013;Jackson et al. 2014;Muñiz-Castillo et al. 2019;Cáceres et al. 2020;França et al. 2020). Over the last 30 years, the average estimated coral cover in this region declined from approximately 50% to 10% (Gardner et al. 2003;Cramer et al. 2020). ...
... It is likely that anthropogenic impacts on shallow reef environments take many years to become apparent, suggesting that early protection and long-term mitigation efforts are needed for a higher probability of successful outcomes. Many of the anthropogenic factors that have been shown to negatively affect coral reefs (e.g., eutrophication, sedimentation, ocean warming, sea level rise, intensity and frequency of storms; e.g., Mumby et al. 2007;Smith et al. 2008;Pandolfi 2010;Perry et al. 2013;Jackson et al. 2014;Muñiz-Castillo et al. 2019;Cáceres et al. 2020;França et al. 2020) cannot be easily mitigated and are outside the reach of MPA management strategies. Until MPAs are staffed and funded at levels that permit effective enforcement and monitoring and governmental agencies work collectively to address the larger issues that threaten reef health, nearshore reefs are likely to face continued gradual declines and phase shifts. ...
Article
Full-text available
Coral reefs ecosystems are facing an unprecedented decline due to the action of natural and anthropogenic stressors. The Caribbean Sea is considered to be one of the most impacted areas, as the average estimated scleractinian coral cover in this region decreased from approximately 50% to 10% over the last 30 years. In this study, a ten-year biodiversity survey was used to examine changes in abundance and percentage cover of benthic invertebrates on permanent transects located at four shallow coral reefs around Roatán, Honduras. This study represents the first long-term investigation of the coral ecosystem of Roatán and reports a decrease in scleractinian coral cover from 37.45 [± 5.37]% to 28.95 [± 3.62]% and a concomitant increase in macroalgal (7.02 [± 3.59]% to 13.94 [± 2.69]%) and turf (5.11[ ± 0.84]% to 7.23 [± 1.00]%) cover although no significant differences in the abundance of scleractinian corals, soft corals, or sponges were observed on the transects. While the four reef sites supported more variable benthic communities at the onset of the study, an overall homogenization of the benthic community composition occurred during the study period. Although our study sites were limited to a small region of Roatán’s southern coral reef system, these observations add to results from other Caribbean locations and provide insights into how Mesoamerican coral reefs have changed over the last decade.
... We included both declared and proposed MPAs in the analyses.2.1.3 | Historical thermal conditionsCorals are affected by both chronic, long-term warming and by acute, punctuated heat events(Chollett et al., 2012;Muñiz-Castillo et al., 2019). Both types of stress are likely to influence coral reef ecosystems in different ways, i.e. whilst chronic warming reduces coral calcification and growth, acute warming causes bleaching and mortality(Bozec & Mumby, 2015;Lindsey et al., 2013). ...
... Both types of stress are likely to influence coral reef ecosystems in different ways, i.e. whilst chronic warming reduces coral calcification and growth, acute warming causes bleaching and mortality(Bozec & Mumby, 2015;Lindsey et al., 2013). The exposure to this threat varies spatially both at global and regional scales(Heron et al., 2016;Muñiz-Castillo et al., 2019).Historical daily Sea Surface Temperature (SST) data at 0.01 degree spatial resolution for 1985-2019 were produced from two different observational SST datasets: the European Space Agency Climate Change Initiative SST Analysis at 5 km(1985( -2006Merchant et al., 2019), and the Multi-scale Ultra-high Resolution SST Analysis at 1 km spatial resolution(2006( , Chin et al., 2017. Details of the merging procedure can be found inDixon et al. (2022).For each site, we calculated a metric of chronic thermal stress (trend in temperature) and a metric of acute thermal stress (sum of Degree Heating Weeks, DHW, above 4°C weeks for the entire period). ...
Article
Full-text available
Climate change has become the greatest threat to the world's ecosystems. Locating and managing areas that contribute to the survival of key species under climate change is critical for the persistence of ecosystems in the future. Here, we identify ‘Climate Priority’ sites as coral reefs exposed to relatively low levels of climate stress that will be more likely to persist in the future. We present the first analysis of uncertainty in climate change scenarios and models, along with multiple objectives, in a marine spatial planning exercise and offer a comprehensive approach to incorporating uncertainty and trade‐offs in any ecosystem. We first described each site using environmental characteristics that are associated with a higher chance of persistence (larval connectivity, hurricane influence, and acute and chronic temperature conditions in the past and the future). Future temperature increases were assessed using downscaled data under four different climate scenarios (SSP1 2.6, SSP2 4.5, SSP3 7.0 and SSP5 8.5) and 57 model runs. We then prioritized sites for intervention (conservation, improved management or restoration) using robust decision‐making approaches that select sites that will have a benign climate under most climate scenarios and models. The modelling work is novel because it solves two important issues. (1) It considers trade‐offs between multiple planning objectives explicitly through Pareto analyses and (2) It makes use of all the uncertainty around future climate change. Priority intervention sites identified by the model were verified and refined through local stakeholder engagement including assessments of local threats, ecological conditions and government priorities. The workflow is presented for the Insular Caribbean and Florida, and at the national level for Cuba, Jamaica, Dominican Republic and Haiti. Our approach allows managers to consider uncertainty and multiple objectives for climate‐smart spatial management in coral reefs or any ecosystem across the globe. We identify priority reefs that will be more likely to survive climate change impacts in the future. Our approach is novel because it solves two important issues when selecting sites for conservation. It allows considering multiple desirable properties that an ideal site should have and choosing a site that is best for all, not only best for some. Also, it makes use of all the uncertainty around future climate change and chooses sites that will have a benign climate under all climate scenarios and models.
... Atmosphere-ocean heat transfer or advection of warm water has been associated as the origin of the MHW; however, still there is limited understanding of its physical drivers Schlegel et al., 2017). Due to the importance of temperature on the metabolism and physiological processes of organisms, extreme biological implications have been associated with MHW, such as extensive mortality of benthic and pelagic organisms (Evans et al., 2020;Garrabou et al., 2009), change in biodiversity patterns (Wernberg et al., 2013), change in species distribution (Burrows et al., 2011), coral reef decline (Muñiz-Castillo et al., 2019), and alteration of ecosystem goods and services (Smale et al., 2019). ...
Article
Since the historic marine temperature has been changing and consequently the ecological processes, this work studies the presence and magnitude of anomalous events at the sea surface temperature (SST) from a climate change perspective in a Non-eastern upwelling system. Concepts and metrics for Marine Heatwaves (MHW) and Marine Cold–Spell (MCS) were applied over the marine region of the Yucatan continental shelf and Yucatan Channel. To calculate the MHW/MCS and climatology between 1982 to 2019, a remote historic dataset of SST was used. Temporal differences of MHW/MCS events were calculated to assess the increase of MHW reported at a global scale in the last 10–20 years, splitting the resulting metrics of the study period. Furthermore, a frequency-domain cross-correlation analysis between SST and Chlorophyll-a was conducted to analyze a temporal relationship among those anomalies and the Red tide that struck the coastal region, in the year 2011. Results indicate that MHW/MCS have varied spatially and temporarily over the marine region of the Yucatan shelf, and have become more frequent and longer in the last twenty-years, tending to keep increasing in some sub-regions. MCS events are of equal or greater significance than MHW, mainly in the inner–shelf sub-region, where more than 110 days of MCS were recorded in 2011. The extreme biological event reported that year could be explained by MCS. Variability of the Yucatan current and upwelling, as well as large-scale synoptic atmospheric events are discussed as possible drivers for MHW/MCS.
... Caribbean coral populations have been declining for decades due to a combination of local and global stressors such as increasing sea surface temperatures, nutrient pollution, diseases, and coastal development (Hughes 1994;Hughes et al. 2010;de Bakker et al. 2017). Consequently, coral restoration efforts have become more widespread in recent years, acknowledging that coral reefs are unlikely to recover without human intervention (Morrison et al. 2019;Muñiz-Castillo et al. 2019). However, these efforts still produce outcomes with varying degrees of success, limited by high costs and a relatively low impact on an ecosystem level and at the spatial scales needed (Bayraktarov et al. 2016(Bayraktarov et al. , 2019Boström-Einarsson et al. 2020;Duarte et al. 2020). ...
Preprint
Full-text available
Crustose Coralline Algae (CCA) is a well-known settlement inducer for stony corals and, ultimately, recruitment, a vital component for reef growth and resilience. However, potential impacts of diseased CCA on larval settlement are not fully understood, especially on particular coral species. As oceans continue to warm, coral larvae need to be able to respond to settlement cues in elevated temperatures, yet the combined effects of thermal stress and CCA health status on larval behavior is not well known for most coral species. Here we assessed the effect of elevated temperatures and disease on the ability of the CCA Hydrolithon boergesenii to induce settlement of Diploria labyrinthiformis larvae. D. labyrinthiformis planulae were exposed to 4 substrate combinations (healthy CCA, diseased CCA, bare substratum, and bare tissue culture plate) and three temperatures (27.5°C, 29°C, and 31°C). Overall, settlement started earlier and was 1.5-3x higher at 31°C, regardless of CCA health status, but at this temperature, larval mortality increased two-fold in diseased CCA. Settlement differences between healthy and diseased H. boergesenii were only observed at 29°C, with healthy CCA facilitating twice as much settlement and having 3x lower mortality than diseased. Our findings suggest that, even though larvae can settle in both healthy and diseased CCA, temperature plays an important role in whether larvae will settle or perish. This study highlights the importance of healthy CCA to maintain and increase settlement and the ability of larvae to adapt to a warming ocean, contributing to the knowledge of D. labyrinthiformis larval ecology, valuable for larval rearing for restoration purposes.
Preprint
Full-text available
Losing coral diversity is one of the most important consequences of coral reefs' ongoing degradation. Alternate: As the planet enters its sixth global extinction event, the loss of biodiversity due to coral reef degradation becomes of paramount importance. However, the loss of coral species diversity and its relationship to multiple global and local stressors remains largely untested on different temporal or spatial scales. This study evaluates the change in coral species diversity and its relationship to different stressors and habitat characteristics, using ecological data from 73 sites in the Mesoamerican Reef (MAR) and a variety of potential explanatory variables derived from remote sensing. We found a loss of coral diversity in the period analyzed, from 2010 to 2018. In addition to a decrease in diversity, there was also a considerable change in coral assemblages. The coral reefs that presented a greater loss of species were those with higher initial diversity and those with a higher number of annual bleaching risk events. Surprisingly, coral reefs exposed to hurricanes and turbidity with intermediate magnitude did not experience the same loss in diversity; some reefs even experience an increased diversity in this timeframe. The rate of increase in macroalgal cover was related to the decrease in coral diversity. Our results highlight the need to protect reefs with high diversity and constantly exposed to high heat stress events. These reefs should be considered sites of relevance in future conservation plans in the current context of global environmental change.
Thesis
Full-text available
In the geographical area of the Gulf of Honduras -GoH- shared by Belize, Guatemala and Honduras, ecosystems, cultures and diverse economic activities converge. In this coastal and marine space, there is evidence of increasing impacts and threats, which must be addressed from a perspective of joint work. Even with common problems, there are no shared strategic initiatives for Integrated Coastal Zone Management -ICZM- in the sub-region at present. In addition, the three countries have different levels of formulation and implementation of their public policies for the better governance of their coasts and the sea. This research was based on an interdisciplinary analysis of national problems and policies related to ICZM in the transnational area of the GoH. With a broader spatial approach, we first analyzed the process of ICZM insertion in Central America, and then focused on the analysis of the GoH. An integrated and synthetic characterization of the corresponding scope of study was carried out with the coastal-marine spaces of each country, with emphasis on the physical-natural, socioeconomic-cultural, and legal-administrative subsystems. Then, to understand coastalmarine problems, the DAPSI(W)R(M) method was applied, where the Response section was complemented using the Decalogue of integrated coastal management. An analysis of the common initiatives of the three countries with implications for the ICZM of the GoH was subsequently carried out. All of the above made it possible to propose some elements of public policy that could contribute to the improvement of ecological, social and cultural conditions in this strategic space. Emphasis is placed on the proper alignment of national public policy responses and on the design and implementation of supranational instruments aimed at the sustainability of the GoH.
Article
Full-text available
The world’s oceans are warming at an unprecedented rate, causing dramatic changes to coastal marine systems, especially coral reefs. We used three complementary ocean temperature databases (HadISST, Pathfinder, and OISST) to quantify change in thermal characteristics of Caribbean coral reefs over the last 150 years (1871–2020). These sea surface temperature (SST) databases included in situ and satellite-derived measurements at multiple spatial resolutions. We also compiled a Caribbean coral reef database identifying 5,326 unique reefs across the region. We found that Caribbean reefs have been warming for at least a century. Regionally reef warming began in 1915, and for four of the eight Caribbean ecoregions we assessed, significant warming was detected for the latter half of the nineteenth century. Following the global mid-twentieth century stasis, warming resumed on Caribbean reefs in the early 1980s in some ecoregions and in the 1990s for others. On average, Caribbean reefs warmed by 0.18°C per decade during this period, ranging from 0.17°C per decade on Bahamian reefs (since 1988) to 0.26°C per decade on reefs within the Southern and Eastern Caribbean ecoregions (since 1981 and 1984, respectively). If this linear rate of warming continues, these already threatened ecosystems would warm by an additional ~1.5°C on average by 2100. We also found that marine heatwave (MHW) events are increasing in both frequency and duration across the Caribbean. Caribbean coral reefs now experience on average 5 MHW events annually, compared to 1 per year in the early 1980s, with recent events lasting on average 14 days. These changes in the thermal environment, in addition to other stressors including fishing and pollution, have caused a dramatic shift in the composition and functioning of Caribbean coral reef ecosystems.
Article
In the geographical area of the Gulf of Honduras (GoH) shared by Belize, Guatemala and Honduras, ecosystems, cultures and diverse economic activities converge. In this space, there is evidence of growing impacts and threats, which must be addressed from a joint working perspective. This research was based on an interdisciplinary analysis of the problems and public policies related to Integrated Coastal Zone Management in the GoH. The DAPSI(W)R(M) method was applied to understand the coastal-marine problems, where the Response section was complemented using the Decalogue of Integrated Coastal Zone Management. This was followed by an analysis of the common initiatives of the three countries. Emphasis is placed on the proper harmonisation of national public policy responses and the design and implementation of supranational instruments aimed at the sustainability of the GoH. It is a supranational space that currently has no common public policy initiative on Integrated Coastal Zone Management.
Preprint
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
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.