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.