Results
We found that resistance varied unevenly across our study sites with lowest resistance in the northern and western Indian Ocean (Fig. 1b,c). The highest resistance was observed in the equatorial Coral Triangle and intermediate resistance south of the equator and some sites, such as Ningaloo reef. For resistance estimated by CTA, there were two best models that contained 5 and 6 of the 7 variables. The first top model excluded coral cover and kurtosis while the second top model excluded only kurtosis (Table 1). The 9th ranked model, where mean SST was excluded,. included the three variables of longitude, number of genera, and skewness. The multivariate exposure model (CE) had only one top model and only kurtosis was excluded. Excluding mean SST produced the 3rd ranked model, which included all variables including kurtosis. The CTA model had somewhat higher strengths than the resistance models estimated from the CE (CTA R2=0.82 versus CE R2 = 0.72). Both models displayed separation in responses between Coral Triangle and non-Coral Triangle sites but differences were more evident in the CTA model. The CTA method estimated 62% and CE 38% higher resistance in Coral Triangle than non-Triangle sites (Table 2, Fig. 2). Consequently, the higher fits to the CTA and CE model was likely due to the greater separation and influences of CTAs on resistance in Coral Triangle versus non-Triangle sites. While most variables were included in top models, there were marked differences in the strengths of the variables and their order among different model combinations. Longitude, number of genera, mean SST, and kurtosis were positively while SST skewness and latitude negatively associated with resistance. Hard coral was, however, weak and complex in that the direction of the association with resistance differed for the exposure models, positive for CTA and negative for CE.
Geography played a significant role in affecting resistance in nearly all models, but it also interacted with SST and number of coral taxa patterns. Comparisons of the Coral Triangle and non-Coral Triangle sites illuminated some associations in the two distinct patterns of SST, coral communities, and resistance (Table 2, Fig. 3). Pooling and evaluating the SST time series indicated considerable differences between regions in exposure. The higher CTA in the Coral Triangle was one of the most pronounced difference with 2.5 times more 1985-2015 CTAs than non-Triangle sites. Moreover, Coral Triangle sites had warmer SSTs, more neutral kurtosis, and negative or cold-water skewness compared to non-Triangle sites (Fig. 3). In fact, distinct differences in the skewness-kurtosis associations was one of the main geographic distinctions. Coral Triangle sites had only neutral to high SST kurtosis and, as kurtosis increased, skewness declined and was negative. Kurtosis was highly variable in non- Triangle sites but warm-water skewness increased when kurtosis was high.
Differences in resistance between Coral Triangle and non-Triangle sites cannot be attributable to differences in the coral communities. We found that coral cover, the relative taxonomic composition and the community’s susceptibility to bleaching did not differ between geographies (Table 2, Fig. 4). What did consistently differ was the percentage of bleached corals, the weighted and unweighted bleaching responses and the percentage of the dominant taxa that were bleached in 2016. All metrics showed higher bleaching outside the Coral Triangle despite lower mean historical SSTs and CTAs.