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.