Discussion
Mean SSTs, rates of temperature rise, and CTAs are the key metrics used
to model current and future impacts and refuge from climate change
(Hoegh-Guldberg 1990; van Hooidonk et al., 2013, 2016; Beyer et al.
2018). Use of these and related thermal metrics produce dire predictions
for corals, especially in warm equatorial regions such as the Coral
Triangle (Couce et al. 2013; McManus et al. 2019). Yet, the large-scale
differences observed here were largely attributable to the variable
responses of coals to exposure and their differential spatial
acclimation/adaptation at the taxa level. There were enough similarities
in communities among the studied sites that observed differences were
most likely due to similar taxa having different resistance levels (Fig
4). Thus, resistance appears to have a strong component of regionalism
that is not clearly related to differences in coral species composition.
The Coral Triangle appears to have a unique temperature environment and
harbor corals with greater resistance to exposure. Historically, the
Coral Triangle has been influenced by oceanographic processes, such as
El Niño Southern Oscillation (ENSO). The strengths of the ENSO have long
fluctuated longitudinally across the Pacific for millennium and most be
part of the adaptive environment experience by Coral Triangle corals
(Cobb et al. 2003). Our findings suggest that ENSO variation and
inter-annual warm thermal anomalies in the western Pacific were primary
drivers of coral resistance along the east-west gradient from East
Africa to Fiji. Moreover, resistance is higher but unequally distributed
closer to the equator, which indicates that exposures to thermal
radiation alone cannot explain variability. Variation in resistance
close to the equator may explain the overall higher bleaching reported
in mid-tropical latitudes (15 - 20o) despite equal or
higher CTA near the equator (Sully et al. 2019). Some variation in
bleaching variation was attributable to average background SST variation
but we found that shape parameters of kurtosis and particularly skewness
were more likely to distinguish sites along this equatorial belt.
The Coral Triangle differs in having negative skewness and neutral to
high kurtosis, which are associated with increased resistance. The
causes of the negative skewness require more investigation but it likely
that the island nature of the Coral Triangle creates localized
variability in water clarity, ocean currents and up- and downwelling
that may provide some localized variation in radiation, temperatures,
productivity, and resistance (McClanahan et al. 2005; Gove et al. 2016).
Statistical outlier sites at Ningaloo retained in our analysis may
provide some insight by being an exception to the Coral-non-Coral
Tringle and mid-latitude patterns. We suggest that the higher than
expected resistance found in Ningaloo arose from localized upwelling,
onshore geostrophic transport, and stirring by offshore eddies that
produced lower than expected bleaching (Xu et al. 2016). Satellite
measurements even at 5 km2 may not capture these local
processes well (Woo et al. 2006; Wilson, S. personal communication). It
should be appreciated that there is a scale mismatch between exposure
and bleaching observations, a problem that troubles most ground-truthing
studies, and can results in errors and some anomalous observations that
can weaken predictions.
Differences in SST distributions have produced similar patterns in other
studies. For example, change in coral cover over the 1998 bleaching
event was influenced by SST variation and distribution shapes –
heavy-tailed distributions associated with higher coral mortality
(Ateweberhan & McClanahan 2010). Further, in a large-scale study of
coral cover and community composition in western Australian, SST
kurtosis and skewness were frequently among the top variables for
predicting coral abundance (Zinke et al. 2018). Skewed right SST
distributions were, for example, associated with lower cover of
stress-resistant corals. Additionally, positive kurtosis, or
heavy-tailed distributions, was associated with lower cover of all
corals. Both of these distribution shape variables would be expected to
influence coral acclimation processes. On the basin scale of the Indian
Ocean, coral taxonomic richness was found to be positively influenced by
mean and negatively by heavy-tailed SST distributions (Ateweberhan et
al. 2018). Consequently, background SSTs have repeatedly been shown to
influence sensitivity and resistance and differ most clearly in the
complex relationships between mean variance, kurtosis, and skewness.
Thus, the mean SSTs-bleaching association (Claar et al. 2018; Sully et
al. 2019) has the potential to be modified by other background SST
distribution factors and not just mean variance (Langlais et al. 2017;
Safaie et al. 2018). For example, we found that the potentially negative
effects of positive kurtosis, which is expected to reduce predictable
SST variation and acclimation in corals, may not be detrimental when
temperatures profiles lack positive skewness. Thus, their interaction in
space and time may prove useful in exposing some of the complexities of
stress and coral responses.
Coral community variables had statistically significant influence on
resistance, particularly when mean SST was eliminated from the
predictive variables. Coral cover-resistance relationships were complex
due to the change in direction of this variable for the two exposure
models. Number of genera was, however, consistently positively related
to resistance and particularly in the absence of mean SSTs.
Consequently, mean SST may influence coral richness as found in other
geographic studies but it is not clear which of these variables is
driving the response to exposures. Given that mean SST and geography
could be proxies rather than physical or ecological driver of
resistance, number of taxa could be a modest driver of resistance given
the expected diversity-portfolio responses (Cardinale et al. 2012;
Schindler et al. 2015). Distinguishing cause and effect between these
variables and associations with resistance is a priority area for future
research.
Measuring future resistance in corals will depend on the effectiveness
of exposure and sensitivity to reflect stresses and responses that will
vary over time. Changes and increasing ocean heat forced by climate
change means that exposure and sensitivity could change and potentially
decouple in the future. Moreover, there is the question of how good
bleaching is at measuring sensitivity to thermal stress (Buddemeier et
al. 2004). Bleaching is potentially one of a number of possible adaptive
responses to heat or climate warming stress. Mortality without
bleaching, for example, is an infrequently examined response that could
influence resistance estimates (McClanahan 2004). Differential rates and
clearly identified causes of mortality and recovery among taxa creates
challenges for large-scale evaluations of climate impacts (McClanahan et
al. 2001; Darling et al. 2019). Estimating mortality requires
inter-annual monitoring to evaluate changes that could be poorly tied to
heat stress alone (Darling et al. 2013; Donner & Carilli 2019). While
we acknowledge this weakness, bleaching is currently the most commonly
used and quantifiable way to measure sensitivity to heat stress (Donner
et al. 2017; Sully et al. 2019).
Difference between the two exposure models contributes to understanding
the geography of environmental stress in the tropics. The propagation of
east-west inter-annual SST variability driven by the ENSO is a critical
exposure force. Longitudinal propagation of exposure can explain the
uneven distribution of CTAs, differences between the two exposure
models, and the separation of the Coral from non-Triangle sites. ENSO is
likely to be the dominant force over historical time, controlling reef
development in the eastern Pacific and recently increasing in strength
in the western Pacific (Peñaflor et al. 2009; Toth et al. 2015). The
increasing strength of the ENSO has also been associated with increases
in the penetration of warm waters into the eastern Indian Ocean (Zinke
et al. 2015). Additionally, the Indian Ocean Dipole has been increasing
in strength since the 1920s and adding to the ENSO heat stress (Nakamura
et al. 2009; McClanahan 2017). Consequently, the more recent origins of
these forces in the Indian Ocean, may explain the higher sensitivity and
lower resistance of corals observed here.
The higher resistance of corals in the western Pacific or Indo-Pacific
biodiversity center indicates geographic variability that reflects
shallow-water biodiversity patterns (Veron et al. 2011; Parravicini et
al. 2013). The Coral Triangle has a spatially variable SST environment
(Peñaflor et al. 2009; McLeod et al. 2010) but we found a combination of
high anomalies, neutral to cold-SST skew, and neutral kurtosis. We
suggest that these heat patterns provided some resistance to episodic
strong thermal disturbances. High biodiversity in the Coral Triangle has
arisen from a number of interacting forces that are likely to include
environmental as well as geologic complexity, isolation, and changing
sea level forces (Barber & Meyer 2015).
Our findings support the contention that historical forces may also be
associated with a higher capacity to tolerate episodic large-scale
global heat stress, as observed during this pan-tropical thermal stress
event of 2014-2016. Many models that predict the future of coral reefs
treat CTAs, bleaching, and mortality as interchangeable. Yet, we show
here that sensitivity is highly variable and contextual. Therefore,
better predictions for the future state of coral reefs should use
resistance metrics rather than just the initial and projected exposure
metrics. Greater resistance to thermal stress in the Coral Triangle may
delay and attenuate the observed increases in warm-water stress
responses (Hughes et al. 2018). Thus, our findings indicate a limited
window of opportunity to better manage the impacts and produce less
severe outcomes in the Coral Triangle. Nevertheless, reducing
heat-retaining gas emissions, developing sustainable fisheries, and
improving watershed and pollution management remain priorities for coral
persistence.