Optimization of thermal stress thresholds on regional coral bleaching monitoring by satellite measurements of sea surface temperature

Abstract

Coral bleaching events have become more frequent in recent years due to the impact of widespread marine heatwaves. The Coral Reef Watch (CRW) program, part of the National Oceanic and Atmospheric Administration (NOAA), assesses bleaching risk by considering measures of daily coral heat stress (Hotspot, HS) and accumulated heat stress (Degree Heating Week, DHW). However, there is a mismatch between coral bleaching alerts through satellite monitoring and records of coral bleaching in the South China Sea (SCS) and its surrounding seas in the historical database. Through comparison with field records of bleaching events in the SCS, this study examined the optimization of the DHW under a fixed or variable HS threshold, evaluating the accuracy of coral bleaching monitoring through a range of evaluation indices, including the Peirce Skill Score (PSS) and the Area Under the Curve (AUC). Our results show that when the DHW index was calculated based on the current operational HS threshold (1°C), reducing the DHW threshold from 4°C to 1.86°C-weeks significantly improved PSS from 0.17 to 0.66, and AUC from 0.58 to 0.83. Further, by optimizing both HS and DHW, evaluation statistics were further improved, with the PSS increasing to 0.71 and the AUC increasing to 0.85. While both methods could significantly optimize the operational bleaching alert level for the SCS, the results suggest that optimization of both the HS and DHW thresholds is better than optimizing DHW alone. As marine heatwaves become more frequent, accurately predicting when and where coral bleaching is likely to occur will be critical to improving the estimation of regional coral stress due to climate change and for understanding coral reefs’ response to recurrent bleaching events.

Full text

Optimization of thermal stress
thresholds on regional coral
bleaching monitoring by satellite
measurements of sea
surface temperature
Bailu Liu
1,2,3
, Shawna A. Foo
2
and Lei Guan
1,3
*
1
College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of
China/Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and
Technology Center, Qingdao, China,
2
School of Life and Environmental Sciences, the University of
Sydney, Sydney, NSW, Australia,
3
Sanya Oceanographic Institution, Ocean University of China/SANYA
Oceanographic Laboratory, Sanya, China
Coral bleaching events have become more frequent in recent years due to the
impact of widespread marine heatwaves. The Coral Reef Watch (CRW) program,
part of the National Oceanic and Atmospheric Administration (NOAA), assesses
bleaching risk by considering measures of daily coral heat stress (Hotspot, HS)
and accumulated heat stress (Degree Heating Week, DHW). However, there is a
mismatch between coral bleaching alerts through satellite monitoring and
records of coral bleaching in the South China Sea (SCS) and its surrounding
seas in the historical database. Through comparison with field records of
bleaching events in the SCS, this study examined the optimization of the DHW
under a fixed or variable HS threshold, evaluating the accuracy of coral bleaching
monitoring through a range of evaluation indices, including the Peirce Skill Score
(PSS) and the Area Under the Curve (AUC). Our results show that when the DHW
index was calculated based on the current operational HS threshold (1°C),
reducing the DHW threshold from 4°C to 1.86°C-weeks significantly improved
PSS from 0.17 to 0.66, and AUC from 0.58 to 0.83. Further, by optimizing both HS
and DHW, evaluation statistics were further improved, with the PSS increasing to
0.71 and the AUC increasing to 0.85. While both methods could significantly
optimize the operational bleaching alert level for the SCS, the results suggest that
optimization of both the HS and DHW thresholds is better than optimizing DHW
alone. As marine heatwaves become more frequent, accurately predicting when
and where coral bleaching is likely to occur will be critical to improving the
estimation of regional coral stress due to climate change and for understanding
coral reefs’response to recurrent bleaching events.
KEYWORDS
coral bleaching monitoring, South China Sea, sea surface temperature, degree heating
week, thermal stress
Frontiers in Marine Science frontiersin.org01
OPEN ACCESS
EDITED BY
Eslam O. Osman,
King Abdullah University of Science and
Technology, Saudi Arabia
REVIEWED BY
Walter Rich,
King Abdullah University of Science and
Technology, Saudi Arabia
Thomas Goreau,
Global Coral Reef Alliance, United States
*CORRESPONDENCE
Lei Guan
leiguan@ouc.edu.cn
RECEIVED 25 May 2024
ACCEPTED 11 November 2024
PUBLISHED 28 November 2024
CITATION
Liu B, Foo SA and Guan L (2024) Optimization
of thermal stress thresholds on regional coral
bleaching monitoring by satellite
measurements of sea surface temperature.
Front. Mar. Sci. 11:1438087.
doi: 10.3389/fmars.2024.1438087
COPYRIGHT
© 2024 Liu, Foo and Guan. This is an open-
access article distributed under the terms o f
the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
are credited and that the original publication
in this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Original Research
PUBLISHED 28 November 2024
DOI 10.3389/fmars.2024.1438087

1 Introduction
In recent years, extreme weather induced by climate change has
caused many irreversible effects, including increases in coral
bleaching and mortality (IPCC, 2022). With high biodiversity and
primary productivity, coral reef ecosystems are among the most
important in the ocean (Moberg and Folke, 1999). Corals are highly
sensitive to environmental factors such as temperature, salinity,
light, and water turbidity, which can cause the breakdown of the
symbiotic relationship between coral polyps and zooxanthellae,
resulting in coral bleaching (Hoegh-Guldberg, 1999). Continuous,
extremely high temperatures are considered to be the primary cause
of large-scale coral bleaching events (Douglas, 2003). In recent
decades, frequent and intense marine heatwave events worldwide
have led to multiple coral bleaching events and large-scale coral
mortality (Hughes et al., 2017,2018;Eakin et al., 2019;Skirving
et al., 2019;Virgen-Urcelay and Donner, 2023). Live coral cover in
the South China Sea (SCS) has significantly declined since the
1960s, and live coral cover in some areas of the SCS has been
reduced to less than 10% in 2020 (Huang, 2021).
Long-term research has shown that thermal stress indicators based
on sea surface temperature (SST) data can effectively alert large-scale
coral bleaching by monitoring the thermal stress state in which corals
are exposed (Liu et al., 2003;Heron et al., 2014). Historical field
research found that severe mass coral bleaching can be predicted when
the water temperature exceeds the average of the warmest historical
month by more than 1°C (Goreau, 1990;Williams and Bunkley-
Williams, 1990;Hayes and Goreau, 1991;Goreau, 1991;Goreau et al.,
1993). Corals face a risk of bleaching when the thermal anomaly
exceeds 1-2°C (Glynn and D'croz, 1990). Based on these studies,
Goreau and Hayes (1994) proposed the concept of Hotspot (HS),
suggesting that during the warm season, coral bleaching can be
monitored by positive anomalous SST (Hotspot, in °C). In 1995,
Gleeson et al. proposed the Degree Heating Weeks (DHW)
indicator, which represents the cumulative bleaching heat pressure
corals face by accumulating heat exceeding a certain temperature over
several weeks, i.e. an accumulation of the magnitude of HS values
(DHW,°C-weeks).In2000,theNational Oceanic and Atmospheric
Administration’s (NOAA) Coral Reef Watch (CRW) program began
using HS and DHW as key indicators to measure and monitor coral
bleaching thermal stress (Liu et al., 2005). HS and DHW products are
the short-term thermal anomaly in 1 day and the heat stress
accumulation over 12 weeks, respectively (Liu et al., 2003). Based on
regional coral biological experiments (Glynn and D'croz, 1990;
Berkelmans and Willis, 1999)andfield observational reports, CRW
defined the combination of ecologically significant coral bleaching alert
thresholds as an HS threshold of 1°C and DHW threshold of 4°C-
weeks; when both HS and DHW thresholds are reached, the minimum
bleaching alert level is reached and ecologically significant bleaching is
expected (Liu et al., 2003). The magnitude of DHW is independent of
how quickly heat accumulates, so a 4°C HS lasting 1 week versus a 1°C
HS lasting 4 weeks both have a DHW of 4°C-weeks.
An evaluation by Donner (2011) showed that the false negative
rate, i.e. coral bleaching occurring but not predicted, of global coral
bleaching prediction under CRW thresholds was 60%. Similarly, for
the SCS and surrounding waters, CRW thresholds for coral
bleaching alerts may be too conservative. For example, coral
bleaching was observed in both Spratly and Paracel Islands in the
SCS during field surveys, while the DHW did not reach 4°C-weeks
(Li et al., 2011;Zuo et al., 2015). Therefore, although the global coral
bleaching heat stress monitoring product suite developed by CRW
has been instrumental in monitoring and managing coral bleaching,
many bleaching events continue to go undetected.
To increase bleaching detection accuracy, several studies have
attempted to modify the thermal stress threshold in the coral
bleaching alert model. DeCarlo (2020) found that removing the 1°C
HS filter threshold used in the calculation of DHW, while reducing the
DHW cumulative time window to 9 weeks, improved the accuracy of
bleaching detection. In addition to modifying the DHW calculation
threshold and cumulative window, Kumagai et al. (2018) found that
the optimal filtering threshold in the best generalized linear model
based on DHW was 0.68°C and the bleaching warning threshold was
2.07°C-weeks in the Ryukyu Islands.
Further, some methods directly optimize the DHW judgment
threshold. van Hooidonk and Huber (2009) evaluated the DHW
thresholds for different regions based on the Peirce Skill Score (PSS)
and found that the optimal DHW thresholds varied by region. Qin
et al. (2023) found that the optimized threshold of DHW for coral
bleaching in the SCS region was 3.32°C-weeks, and for severe coral
bleaching events, the optimized threshold of DHW was 4.52°C-
weeks. Finally, Whitaker et al. (2024) revised the global bleaching
threshold to a 0.4°C HS filter threshold with an 11-week cumulative
window and 3°C-weeks DHW. Almost all past global and regional
studies show that lower thermal stress thresholds improve the
accuracy of coral bleaching alerts.
The conservative thresholds in current satellite monitoring of
coral bleaching were developed primarily to detect severe, global-
scale bleaching events (Liu et al., 2014). While widespread bleaching
events in recent years have significantly impacted coral ecosystems
(Goreau et al., 2005;Goreau and Hayes, 2024), field temperature
records indicate that local features, such as upwelling, can lower
water temperatures and thus reduce coral bleaching (Riegl et al.,
2019). This underscores the need to reassess thermal stress
thresholds to account for local thermal histories and better reflect
regional coral bleaching patterns. Further, in the development of HS
and DHW thresholds, biological experiments supporting their
thresholds were separately considered, without considering their
combination, where the role of HS and its impact on the calculation
of DHW has not been thoroughly evaluated in previous studies. In
the CRW bleaching watch system, the bleaching alert levels are
triggered only when both HS and DHW values surpass their
respective thresholds. In this process, the HS threshold is used to
calculate the DHW value, which means only heating above the HS
threshold will be accumulated in the DHW. Thus, determining an
accurate HS threshold is critical for improving coral
bleaching alerts.
Therefore, based on historical coral bleaching survey records in
the SCS and its surrounding seas, this study aimed to find the most
accurate coral bleaching thermal stress alert thresholds by
optimizing DHW thresholds independently with a fixed 1°C HS
threshold (i.e. the current CRW threshold), and synergistically
optimizing both HS and DHW thresholds. Improved monitoring
Liu et al. 10.3389/fmars.2024.1438087
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of coral bleaching thermal stress will better support coral reef
practitioners in their management and response to coral
bleaching events. Further, as there is evidence that corals can
develop resilience to increased temperature through exposure to
recurrent heatwaves (Brown and Barott, 2022), accurately
identifying coral bleaching thresholds that result in bleaching
is critical.
2 Materials and methods
2.1 Study region and coral bleaching
event data
This study focused on the SCS and its surrounding seas (105°E
to 125°E, 0 to 25°N). The study area is located in the western Pacific
Ocean, adjacent to the western boundary of the Coral Triangle, and
covers more than 3 million square kilometers (Morton and
Blackmore, 2001). With almost 600 known coral species, the SCS
and its surrounding seas are an important part of global coral reef
ecosystems (Huang et al., 2015).
To ensure that the thermal stress indicators accurately reflect
the thermal stress corals experienced in historical studies, the
quality of the historical coral bleaching event database is critical.
The primary source of coral bleaching event data for this study is
version 2.0 of the Historical Coral Bleaching Event Database
compiled by Virgen-Urcelay and Donner (2023), which covers
global coral bleaching events from 1963 to 2017. Data sources
include scientific literature and conference archives, the Reefbase
database, and bleaching event information provided by scientists
and related organizations, in addition to NOAA CRW internal
database records. The database was geo-corrected and spatially
interpolated for the geographic coordinates of each coral
bleaching event, using a 0.05°×0.05° grid layer to define reef
locations. The 0.05° spatial resolution matches the spatial
resolution of the SST data used in this study, ensuring that the
corresponding thermal stress index can be calculated at each event
coordinate location. In addition, 28 coral bleaching records from
other sources were added to the database for the study areas by
referencing pieces of Chinese literature (Yu et al., 2006;Tang et al.,
2010;Chen et al., 2012;Li et al., 2012;Zuo et al., 2015;Huang, 2021;
Liu et al., 2021;Meng et al., 2022). This resulted in a total of 3513
events between the time range of 1981-2020, the spatial range of 0°-
25°N, 105°-125°E, including 2342 non-bleaching events and 1176
bleaching events of differing severity. The geographical location of
the study area and the distribution of coral bleaching records are
shown in Figure 1.
All historical event records in the database have been through a
strict 4-step quality control to ensure the accuracy of subsequent
analysis results. (1) Event quality control: For the study of thermal
stress monitoring of coral bleaching, the first step is to ensure that
all bleaching events are dominated by thermal stress, thus all
bleaching events caused by non-thermal stress marked in the
database (Virgen-Urcelay and Donner, 2023) were excluded. In
addition, when calculating the required DHW, it is necessary to
know the specific location of the event, the survey month, and the
degree of bleaching, so this study excluded events with grades less
than “AA”(Virgen-Urcelay and Donner, 2023) to ensure that all
events can obtain the corresponding survey month information.
This step excluded 175 records. (2) Exclusion of mild bleaching
events: Coral bleaching rates of 10% or less are generally considered
to not be ecologically significant (Lachs et al., 2021). Further, mild
bleaching events are likely to be associated with observational errors
(Virgen-Urcelay and Donner, 2023). Therefore, in this study, 583
records of minor bleaching events (1%< coral bleaching rate ≤10%)
were excluded from the database to reduce the impact of potential
observational errors. (3) Water depth control: The influence of
thermal stress on coral communities is tightly linked to water depth,
and the change in water depth will affect the influence of thermal
stress and light radiation on coral bleaching (Oliver et al., 2009;
Perez-Rosales et al., 2021). In order to mitigate the error caused by
depth on coral bleaching, only records of coral bleaching events in
shallow water areas are analyzed in this study. With reference to the
depth threshold adopted by Williamson et al. (2022) in monitoring
FIGURE 1
Study area and location of all coral bleaching records, with blue dots
representing records of non-bleaching and red dots representing
records of bleaching events.
Liu et al. 10.3389/fmars.2024.1438087
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environmental stress on coral reefs, this study excluded 58 records
of events with unknown water depth or water depth >20 m. (4)
Merging or removing spatiotemporal coincident events: Since the
spatial resolution of the SST data source is 0.05°, some coral reef
areas that are close in space and surveyed at the same time are likely
to have both coral bleaching and non-bleaching records. There may
also be multiple records of bleaching at different depths in the same
location. To address conflicting or repeated events, groups of events
that occurred simultaneously and had a spatial difference of less
than 0.05° were consolidated into a single bleaching event record if
the proportion of bleaching events exceeded 80%. If the proportion
of both bleaching and non-bleaching events did not exceed 80%, all
events in that group were excluded. Through this step, this study
can effectively reduce contradicting events and false replication, in
turn reducing the spatial deviation of the event distribution. This
step removed 437 non-bleaching records and 84 bleaching records.
After quality control, the database had a total of 2182 events,
including 1877 non-bleaching events and 305 bleaching events.
2.2 SST dataset and climatology
This study used version 3.1 of the CoralTemp daily global 5 km
SST product, which provides data from 1985 onwards. The data
sources of CoralTemp include the 5 km daily global satellite SST
product produced by NESDIS (National Environmental Satellite,
Data and Information Service) (Maturi et al., 2017)andthe
Operational SST and Sea Ice Analysis (OSTIA) product produced
by the Met Office (Roberts-Jones et al., 2012). CoralTemp overlaps
and merges different data sources to ensure its internal
data consistency.
Since reef-building corals typically grow in waters near their
thermal limit (Glynn and D'croz, 1990), the thermal stress of coral
bleaching is usually calculated based on the local mean sea
temperature during their hottest period, the maximum monthly
mean (MMM). In this study, the MMM of the study area was
calculated using CoralTemp from 1985 to 2019 by first calculating
the local long-term monthly mean SST for 12 months, and then
selecting the maximum among the 12 long-term monthly mean
SST. Although the period of climatological calculation is extended
(1985-2019) compared to the climatological period of NOAA CRW
(1985-2012), this study still follows the climatological protocol
version3ofNOAACRW,byshiftingthemidpointofthe
climatology to 1985-1990 plus 1993 (Heron et al., 2014,2015;Liu
et al., 2017) to provide consistency between different versions of the
coral bleaching thermal stress monitoring model. The following
formula is used to recentralize the climatological baseline:
T85−19 =T85−93 −slope (t85−19 −t85−93) (1)
where Trepresents the climatological temperature, slope is the
long-term change rate of the monthly mean SST, and tis the time
center. The subscript refers to the year, 85-19 represents 1985 to
2019, and 85-93 represents 1985 to 1990 plus 1993 (CRW’s
temperature baseline), which are the two time periods before and
after the recentralization.
2.3 Thermal stress indicators
HS can reflect the positive outlier of the daily SST relative to the
MMM climatological basis, in °C. HS is calculated as follows, where
the subscript, daily, represents each day (Liu et al., 2014):
HS =SSTdaily −MMM,SSTdaily > MMM
0,SSTdaily ≤MMM
((2)
Although HS can reflect the abnormal short-term SST increase
to corals, studies have found that in addition to the influence of
short-term thermal stress, long-term heat accumulation stress is
also an important factor leading to coral bleaching, and cumulative
thermal stress can more effectively reflect the process of large-scale
coral bleaching (Berkelmans and Willis, 1999). As shown in
formula (3), the DHW index is the cumulative of HS in the last
12 weeks (84 days), and the unit is °C-weeks. It takes into account
both the magnitude of the thermal anomaly and the exposure time,
and represents the amount of cumulative thermal stress to corals by
a single number (Liu et al., 2014):
DHW =1
7o
84
i=1
(HSi, if HSi≥HS threshold°C) (3)
2.4 Calculation of HS and DHW for
historical events
Coral bleaching events commonly exhibit varying degrees of
temporal lag. Therefore, the direct calculation of the DHW of the
coral bleaching survey month may not accurately reflect the actual
thermal stress experienced during the coral bleaching period. This
study will refer to the local climatological hottest month for each
coral bleaching event to capture the accurate month in which
bleaching occurred. If the bleaching event was recorded before
the hottest month, the monthly maximum DHW of the survey
month will be calculated. Conversely, if the bleaching event was
recorded in the hottest month or the month after the hottest month,
the monthly maximum DHW of the hottest month will be
calculated to represent the level of thermal stress experienced
during the coral bleaching event. For non-bleaching events, the
historical record means that at the time of the survey, the thermal
stress level did not cause bleaching conditions or that the bleached
coral had recovered. Therefore, this study refers to the survey time
recorded in the database, using the maximum DHW for the survey
month to represent the thermal stress level that did not cause coral
bleaching. Meanwhile, the HS for each event is the maximum value
for the period in which the maximum DHW was obtained.
2.5 Performance evaluation
In this study, we aim to optimize new HS and DHW thresholds
for the SCS and surrounding seas by investigating the classification
ability of HS and DHW indicators against historical coral bleaching
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events for two different scenarios. Table 1 shows the definition of
coral bleaching alert models under the two scenarios examined.
Scenario one uses the current, fixed HS threshold of 1°C when
calculating DHW, therefore only considering the optimization of
DHW when alerting coral bleaching stress. Scenario two considers
the optimization of both HS and DHW to alert bleaching stress.
Each scenario includes four levels of thermal stress: ‘No stress’,
‘Watch’,‘Bleaching warning’and ‘Bleaching alert’(Table 1). Each
event would be classified as a bleaching event when the DHW
(scenario 1) or HS + DHW (scenario 2) reached the “Bleaching
alert”level. Conversely, the event is classified as non-bleaching if it
did not reach the “Bleaching alert”level.
To test the coral bleaching monitoring capabilities of different
threshold combinations, a performance evaluation of a binary
classification model is required for each event. For each coral
bleaching event, there were four possible coral bleaching alert
outcomes: the database records may reach the bleaching alert
level and successfully detect bleaching (true positive, TP), or may
not be successfully detected and missed (false negative, FN). On the
other hand, the unbleached event recorded in the database may not
reach the bleaching alert level and successfully detect no bleaching
(true negative, TN), or the unbleached event may reach the
bleaching alert level and result in a false positive (FP). All records
were classified as above and assessed according to the two scenarios
outlined in Table 1. The HS and DHW are used to assess whether
the events reached the bleaching alert level in two scenarios.
To comprehensively optimize thresholds, this study adopted
multiple evaluation indices that are widely used in the assessment of
coral bleaching threshold models (van Hooidonk and Huber, 2009;
Kumagai et al., 2018;Lachs et al., 2021).
Recall, also known as the true positive rate (TPR), is based on all
bleaching events and judges the probability of correct detection of
all bleaching events:
Recall =TP=(TP +FN) (4)
Precision represents the percentage of actual bleaching events
out of all events detected as bleaching:
Precision =TP=(TP +FP) (5)
Ideally, higher values for each indicator imply better
performance, but the reality is that the relationship between the
two indicators is usually positive and negative, so to
comprehensively consider the bleaching detection capability (TP)
of the model, the f1 score (f1_score) is introduced for evaluation:
f1_score =(2Precision Recall)=(Precision +Recall) (6)
The proportion of false detection events in all non-bleaching
events is the false positive rate (FPR):
FPR =FP=(FP +TN) (7)
The PSS (Peirce, 1884) metric is defined as the difference
between the TPR and the FPR values. It provides a
comprehensive assessment of bleaching monitoring.
PSS =TPR −FPR (8)
Since the proportion of non-bleaching events in the database of
this study is significantly higher than that of bleaching events, it is
easy to be affected by non-bleaching events when evaluating the
models, resulting in the bias of the evaluation results. In order to
evaluate the quality of the models more objectively, this study
considered the area under the curve (AUC) (Hanley and McNeil,
1982) as the standard for the final evaluation of the quality of the
threshold models. The receiver operating characteristic (ROC)
curve is a curve with FPR as the horizontal coordinate and TPR
as the vertical coordinate. The benefit of this evaluation curve is that
it can objectively evaluate the quality of the model regardless of the
unbalanced distribution of samples. AUC refers to the area under
the ROC curve. An AUC of 0.5 indicates almost random
classification, while an AUC closer to 1 indicates a better model
effect (Hanley and McNeil, 1982). Therefore, we used AUC as the
final indicator for model evaluation in this paper.
AsthemaximumAUCisusedheretodeterminetheoptimal
threshold, when assessing the DHW with a fixed HS threshold of 1°C
(scenario one), the DHW thresholds can assume the values of 0 to 8°C-
weeks where we consider an interval of 0.01°C-weeks. The best DHW
threshold corresponds to the maximum AUC. For the assessment of
DHW under a variable HS threshold (scenario two), the HS thresholds
are tested from 0 to 1.5°C, considering an interval of 0.01°C, and the
DHW thresholds are tested from 0 to8°C-weeks,withanintervalof
0.01°C-weeks.Wechosethisrangebecausebeyondthattheevaluation
metricsnolongerchange.Thisprocessresultsinathresholdmatrixof
151×801, where each point represents a combination of thresholds. For
each coral survey record in the database, the corresponding monthly
TABLE 1 Models of coral bleaching thermal stress alerts examined in
this study.
Scenarios Thermal
Stress Levels
Definition Relevant
studies
DHW (Degree
Heating
Weeks)
No stress HS ≤0van Hooidonk
and Huber
(2009);
Kayanne
(2017);
Qin
et al. (2023)
Watch 0< HS< 1
Bleaching warning 0< DHW<
DHW
threshold
Bleaching alert DHW
threshold
≤DHW
HS (Hotspot)
+ DHW
No stress HS ≤0Liu et al.
(2014);
Whitaker
et al. (2024)
Watch 0< HS<
HS threshold
Bleaching warning HS ≥HS
threshold and
0< DHW<
DHW
threshold
Bleaching alert HS ≥HS
threshold and
DHW ≥
DHW
threshold
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maximum DHW under 151 HS thresholds was calculated separately.
The optimized HS and DHW were determined using the intercept
between the highest AUC and the HS and DHW metrics.
2.6 Comparing performance under
different threshold combinations
The third global mass coral bleaching event influenced by two
consecutive El Niño events in 2014-2016, led to the death of many
corals with far-reaching impacts on the marine environment (Eakin
et al., 2019). Records of bleaching events in the global historical coral
bleaching database confirmed that corals also experienced extensive
coral bleaching during 2014-2016. To demonstrate the improvement
that optimization of thresholds has on coral bleaching alert results, the
differences between CRW thresholds and optimized thresholds were
visualized for the SCS and surrounding seas during this global event.
3 Results
3.1 DHW with a fixed 1°C HS threshold to
monitor coral bleaching
Currently, the HS operational threshold for DHW calculation is
1°C (Liu et al., 2003). If the optimization of HS is not considered, i.e.,
the HS threshold of 1°C is used to calculate DHW, the evaluation of
the bleaching monitoring performance of DHW is shown in Figure 2.
As shown in Figure 2, FPR and Recall decrease with increasing DHW.
A lower DHW threshold increases the Recall value, improving the
classification of bleaching events. However, this also leads to a higher
rate of misclassification for non-bleaching events.
Regarding the overall evaluation metrics, PSS and f1_score initially
increase, then sharply decrease, followed by a gradual rise as the DHW
threshold changes. This trend is primarily driven by the early rapid
declineinthefalsepositiverate(FPR),whichgraduallyimprovesthe
accuracy of the classification model. In the light blue region with AUC
> 0.8, maximum AUC, f1_score, and PSS at DHW = 1.86°C-weeks.
However, the recall value drops sharply when the DHW exceeds about
2°C-weeks, which means that further increasing the DHW threshold
will significantly reduce the correct classification of bleaching events. In
the plateau following the initial rise and subsequent sharp decline, the
classification of non-bleaching events continues to improve, eventually
reaching an FPR of 0, indicating that all non-bleaching events were
correctly classified. At this stage, however, the number of unclassified
bleachingeventsismorethan60%withtheincreaseinPSSand
f1_score more greatly influenced by the large number of non-bleaching
events. Since the AUC is not affected by the unbalanced distribution of
thesourcedata,theAUCremainsatalowlevel.
3.2 Monitoring of coral bleaching through
optimized HS and DHW
This study adjusted the current operational HS threshold and
comprehensively assessed the combined impact of both optimized
HS and DHW on monitoring coral bleaching thermal stress. The
changes in different HS and DHW combinations are shown in
Figure 3. Due to the interaction of HS and DHW, the evaluation
metrics do not change continuously. Recall is higher at lower HS
and DHW thresholds. Additionally, for non-bleaching events, the
overall level of FPR remains low, with the maximum value at the
origin being only 43.2%. The thermal stress of the non-bleaching
events recorded in the survey is mostly low or zero. The best
evaluation values for PSS and f1_score are concentrated in the
middle of their respective value ranges, and the threshold changes of
HS and DHW have a synergistic effect on the final evaluation results
of the model.
Figure 4A showsthatmodelswithhighclassification ability and
large AUC are mainly concentrated in an irregular interval where the
HS threshold is less than 1.2°C and the DHW threshold is less than 6°
C-weeks. As shown in Figure 4B, when AUC is further restricted to >
0.8, a better combination of thresholds appears in the middle region.
Deviating from this range will degrade the model’s performance.
Finally, Figure 4C shows that the maximum AUC value of 0.853
occurs at HS=0.47°C and DHW=2.65°C-weeks. The optimal
threshold of HS is significantly lower than 0.99°C, while the optimal
threshold of DHW is slightly higher than 2°C-weeks, reflecting the
interaction between the two thermal stress evaluation indices.
3.3 Evaluating and comparing performance
under different threshold combinations
By assessing two different threshold optimization methods, the
optimal thresholds obtained in the SCS and surrounding seas vary.
FIGURE 2
Evaluation indices of coral bleaching thermal stress monitoring
performance for different DHW (Degree Heating Weeks) thresholds
when the HS (Hotspot) threshold = 1°C, The blue region is the
region with AUC (the area under the curve) > 0.8 and the dotted line
is the maximum value of the f1_score (the comprehensive bleaching
detection capability) versus PSS (Peirce Skill Score) in this region.
Liu et al. 10.3389/fmars.2024.1438087
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Table 2 provides a comparison of each evaluation metric alongside
the current operational threshold. All evaluation results of AUC
significantly improved after optimizing the classification threshold
based on real historical events, as compared to the current
operational threshold (Liu et al., 2014).
The best DHW threshold calculated with an HS threshold of 1°
C is 1.86°C-weeks for coral bleaching monitoring, which is
significantly lower than the current operational DHW threshold
of 4°C-weeks. The evaluation results based on historical events
show that using a lower DHW threshold can significantly increase
the recall of regional bleaching event monitoring from 17.7%
to 81.0%.
Threshold optimization considering both HS and DHW
resulted in the highest performance. Table 2 shows the synergistic
FIGURE 3
Performance evaluation of HS (Hotspot) + DHW (Degree Heating Weeks) coral bleaching thermal stress monitoring across all indices: (A) Recall (the
probability of correct detection of all bleaching events), (B) PSS (Peirce Skill Score), (C) f1_score (the comprehensive bleaching detection capability),
(D) Precision (actual bleaching events out of all events detected as bleaching), (E) FPR (the false positive rate), (F) AUC (the area under the curve).
FIGURE 4
Achieving the highest AUC (the area under the curve) by optimizing HS (Hotspot) and DHW (Degree Heating Weeks) by gradually narrowing the AUC
range: (A) 0.50≤AUC ≤0.90, (B) 0.80≤AUC ≤0.86, (C) 0.85≤AUC ≤0.855. The arrow indicates the point where the maximum AUC is located,
highest AUC=0.853.
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threshold optimization of both HS and DHW indices. The new
combination of regional optimization thresholds is HS = 0.47°C and
DHW = 2.65°C-weeks. After optimizing the HS and DHW, the
recall rate increased from 17.7% (considering NOAA's thresholds)
to 81.3%, and the AUC increased from 0.58 to 0.85. Furthermore,
optimizing both DHW and HS thresholds reduces the FPR for coral
bleaching detection to 10.7%, compared to 14.7% when optimizing
DHW alone.
3.4 Optimization thresholds during a
period of widespread bleaching
In the NOAA CRW results shown in Figures 5A-C, accurate
detection of bleaching events (true positive bleaching, green dots)
occurred only in Japan, the Paracel Islands, and one site in northern
Malaysia, while all other sites were underestimated (false negative
bleaching, blue dots). After optimizing the thresholds, as shown in
Figures 5D-F, all bleaching events were accurately detected apart
from two sites in northern Malaysia in 2014, where bleaching events
were still underestimated.
4 Discussion
4.1 Optimizing DHW vs. optimizing both
HS and DHW
The results demonstrate that the current global threshold
setting in the SCS and surrounding seas can be optimized in its
application. Previous studies showed that the addition of DHW,
which reflects long-term heat accumulation based on HS, better
balances model performance and improves the accuracy of
monitoring coral bleaching events (Gleeson and Strong, 1995). By
adding the optimized DHW, our model was greatly improved in
monitoring and predicting bleaching events in the SCS region. The
best DHW threshold calculated with an HS threshold of 1°C is 1.86°
C-weeks. This result is similar to the 2.07°C-weeks threshold
calculated for Japanese coral reefs (Kumagai et al., 2018). Table 2
demonstrates that the combined optimization of HS and DHW
yields superior results compared to optimizing DHW alone. This is
because when the HS threshold is fixed at 1°C, any heat below 1°C is
excluded from the DHW cumulative calculations. Prolonged
exposure to low heat stress can also cause coral bleaching
(Berkelmans, 2002). By lowering the HS threshold, we gain
valuable insights into potential bleaching risks that would
otherwise remain undetected, and what could be considered low
stress scenarios are still able to induce bleaching.
4.2 Importance of increasing the accuracy
of coral bleaching detection
By comparing data from coral bleaching survey databases, this
study determined the optimal regional thermal stress threshold for
bleaching through different optimization methods. This approach
to evaluating optimal thresholds can be broadly applied to other
regions. It is important to understand the temperature conditions
that lead to bleaching. Accurate bleaching alerts are extremely
important for coral reef managers, providing them with
appropriate time to respond, especially as local conditions can
magnify coral mortality after heatwaves (Donovan et al., 2021).
Further, accurate records of conditions that cause bleaching can
help us to understand the impacts of exposure to repeated marine
heat waves, and whether corals become more resilient after
experiencing multiple thermal stressors. The results indicate that
the current operational coral bleaching thermal stress monitoring
threshold misses a large number of coral bleaching events in the
SCS and surrounding seas.
After synergistic optimization of both HS and DHW, every
evaluation index improved. Reducing HS and DHW thresholds
could significantly correct the current underestimation of coral
bleaching monitoring (Recall from 17.7% to 81.3%, AUC from 0.58
to 0.85), while keeping the false non-bleaching detection rate FPR of
coral bleaching events at a low level (10.7%). Regional collaborative
optimization of HS and DHW leads to a better classification model
than optimizing them separately. This is further supported by
examining bleaching alerts during a period of widespread coral
bleaching (2014-2016) in the SCS and its surrounding seas.
4.3 Optimization of thresholds during a
period of widespread bleaching
The definitions of the threshold models for optimal
optimization and NOAA CRW are shown in Table 3. The new
TABLE 2 Comparison of evaluation metrics across the different scenarios tested.
Scenarios Thresholds Recall
(%)
Precision
(%) FPR (%) f1_score PSS AUC
Current
operational threshold
HS = 1.00°C,
DHW = 4.00°C-weeks*
(Liu et al., 2014)
17.7 79.4 0.7 0.29 0.17 0.58
Optimizing DHW DHW (with a fixed 1 °C HS threshold) = 1.86°
C-weeks 81.0 47.0 14.7 0.60 0.66 0.83
Optimizing DHW + HS HS = 0.47°C,
DHW = 2.65°C-weeks 81.3 55.2 10.7 0.66 0.71 0.85
* Thresholds for NOAA CRW operations.
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regional threshold combinations calculated here (HS = 0.47°C,
DHW = 2.65°C-weeks) are similar to the latest optimized
bleaching thresholds (HS = 0.4°C, DHW = 3°C-weeks) presented
by Whitaker et al. (2024) for the globe. Considering these
thresholds, the FPR for thermal stress coral bleaching reduces
from 14.7% to 10.7%. This reduction in false bleaching alerts
remarkably improves coral bleaching monitoring. When both
threshold models were applied simultaneously during a period of
widespread coral bleaching in the SCS and its surrounding seas
from 2014 to 2016, we find that the CRW thermal thresholds
underestimate most of the study region. Thus, the optimization of
the thermal thresholds improves the accuracy of coral bleaching
alerts widely.
Our results show that corals in the SCS and its surrounding seas
may experience moderate or severe bleaching at thermal thresholds
below the current CRW thresholds. The lowering of heat stress
thresholds may facilitate the monitoring of coral bleaching that
occurs during weak marine heatwaves (Lachs et al., 2021). On a
global scale, extensive regional coral bleaching has caused many
negative impacts on coral reefs (Goreau and Hayes, 2024).
Considering the optimization thresholds shown in Table 3 will
help to provide a more accurate reference for historical analysis.
FIGURE 5
Annual maximum bleaching alert levels and bleaching records detecting results for 2014-2016, with (A–C) NOAA CRW (the National Oceanic and
Atmospheric Administration, Coral Reef Watch program) thresholds (HS [Hotspot]= 1.00°C, DHW [Degree Heating Weeks]= 4.00°C -weeks), and (D–F)
optimal thresholds (HS = 0.47°C, DHW = 2.65°C-weeks), respectively. Blue dots represent coral bleaching records that were not correctly detected and
green dots represent records that were correctly detected.
TABLE 3 Comparison of regionally optimized SCS (the South China Sea)
and NOAA CRW (the National Oceanic and Atmospheric Administration,
Coral Reef Watch program) for coral bleaching thermal stress alerts.
Scenarios Thermal
Stress Levels Definition
Optimization of both HS
and DHW
No stress HS ≤0
Watch 0< HS< 0.47
Bleaching warning
HS ≥0.47
and 0<
DHW< 2.65
Bleaching alert
HS ≥0.47
and DHW
≥2.65
NOAA CRW
(Liu et al., 2014)
No stress HS ≤0
Watch 0< HS< 1
Bleaching warning HS ≥1 and
0< DHW< 4
Bleaching alert HS ≥1 and
DHW ≥4
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4.4 Limitations of remotely sensed SST
The optimal detection accuracy for coral bleaching events,
indicated by a Recall of 81.3% (Table 2, under threshold
combination HS = 0.47°C, DHW = 2.65°C-weeks), reveals that
approximately 18.7% of bleaching events remain undetected. To
improve future monitoring, it will be crucial to integrate additional
environmental stress parameters to consider a multi-parameter
model for coral bleaching. Additionally, limitations in remotely
sensed SST data, as highlighted by Leichter et al. (2006), may
obscure important temperature variations in certain coral reefs.
For example, coral atolls often exhibit significant temperature
differences between the inner reef and deeper waters due to depth
and thermal exchange (Colin and Johnston, 2020). Utilizing higher-
resolution SST data alongside in situ temperature measurements
would more effectively capture these dynamics, enabling a clearer
assessment of whether remotely sensed data accurately represents
the study area.
5 Conclusions
Through repeated exposure to heat waves, corals and their
symbiotic algae may increase their heat tolerance through plasticity
or through association with more heat-tolerant symbionts (Lachs
et al., 2023). Thus, to accurately monitor coral bleaching, it is
imperative to continually update and refine the alert threshold
based on real-world events. This will more accurately account for
cumulative heat stress events on coral reef communities, allowing
an understanding of whether there have been increases or decreases
in coral reef resilience. Furthermore, accurately linking thermal
stress with coral bleaching will be critical in identifying climate
refugia (McWhorter et al., 2022). Future research should
incorporate additional remotely sensed environmental factors to
enhance the accuracy of regional coral bleaching monitoring.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Author contributions
BL: Conceptualization, Formal analysis, Methodology, Writing –
original draft. SF: Methodology, Writing –review & editing. LG:
Conceptualization, Funding acquisition, Writing –review & editing.
Funding
The author(s) declare that financial support was received for the
research, authorship, and/or publication of this article. The research
was supported by the Hainan Province Science and Technology
Special Fund (ZDYF2024GXJS260 & SOLZSKY2024006), the Hainan
Provincial Natural Science Foundation of China (122CXTD519), the
Fundamental Research Funds for the Central Universities
(202261010), the China Scholarship Council (202306330058), the
Australian Research Council (grant number DE220100555), a
Westpac Research Fellowship from the Westpac Scholars Trust and
a Horizon Fellowship from the University of Sydney.
Acknowledgments
The authors would like to acknowledge the NOAA Coral Reef
Watch (https://coralreefwatch.noaa.gov) for providing the version
3.1 daily global 5 km satellite SST product CoralTemp, and Virgen-
Urcelay et al. for providing the high-resolution global mass coral
bleaching dataset Version 2.0.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
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