Fig 1.
Characterisation of thermal regimes across Palau showing conceptual diagrams (a-b) and empirical results (c-e) based on the typical heat stress a reef receives and its consistency in performing this way relative to other reefs. (a) The typical heat stress that reefs receive relative to one another, or degree heating week (DHW) anomalies, were calculated from annual maps of peak DHW heat stress. The consistency of a reefs performance relative to other reefs was calculated by first converting annual DHW maps to percentiles, and then computing the standard deviation (SD) of DHW percentiles through time for each reef. (b) A thermal consistency biplot is used to compare these two metrics: typical heat stress (x-axis) and inconsistency (y-axis). Equal-sized tercile subsets of each variable demarcate distinct thermal regimes, where persistent thermal refugia and persistent hotspots are in the lower left and right corners, respectively. (c) Maps of these two metrics for all Palauan reefs. (d) Each reefs position in the thermal consistency biplot, with an overall hump-shaped pattern across all reefs. (e) Persistent thermal refugia are located among the northern reefs, while persistent hotspots dominate in the southwest. The Palau shoreline shapefile was from the NOAA National Centre for Coastal Ocean Science [41] (https://products.coastalscience.noaa.gov/collections/benthic/e102palau/).
Fig 2.
Assemblage-wide bleaching risk across Palau based on historic bleaching survey data and satellite-derived heat stress.
(a) The predicted effect of DHW on severe coral bleaching is shown as the mean and 95% credible intervals with notable overprediction and underprediction for some bleaching survey records (points shown with vertical jitter). (b) Overlay of historic bleaching survey records on top of thermal regime classifications. Spatial correlated uncertainty in the bleaching predictions shown in (a) is calculated across a high-resolution Delaunay triangulation mesh of the study area comprising 5,710 nodes (c). (d) The Gaussian Markov spatial random field reports the spatial correlated uncertainty (residual bleaching susceptibility) as u values (see linear combination equation). Compared to what would have been predicted based on DHW alone (i.e., without a spatial random effect), areas of higher bleaching susceptibility are shown in red (underpredictions) and areas of higher bleaching resistance are shown in blue (overpredictions). Notably, the blue-white-red colour scheme here (d) is distinct from the hotspots/refugia colour scheme in Fig 1E. (e) Thermal refugia have higher mass bleaching susceptibility for a given heat stress dosage (u values) than hotspots (Wilcoxon sum rank test) and a higher diversity of responses. The Palau shoreline shapefile was from the NOAA National Centre for Coastal Ocean Science [41] (https://products.coastalscience.noaa.gov/collections/benthic/e102palau/).
Fig 3.
Differences in Acropora digitifera heat tolerance between hotspots (red) and thermal refugia (blue).
(a) Bleaching and mortality index (BMI) trajectories are shown for each individual coral (faint lines) throughout a 5-week marine heatwave experiment relative to degree heating weeks (DHW), with a significant difference between thermal regime intercepts (mean ± 95% confidence intervals, n = 174 colonies) (GLMM, P < 0.001). (b) Distribution of individual heat tolerances (DHW50) are shown for each thermal regime with a significantly higher mean heat tolerance (dashed lines) at thermal refugia (LM, P = 0.009).
Fig 4.
Within-population variability in heat tolerance for Acropora digitifera corals at hotspots (a) and those at thermal refugia (b), expressed as differences between the least-tolerant decile (paler shading) and most-tolerant decile (darker shading) of the population (n = 9 colonies for each subsample in each thermal regime) in terms of DHW tolerance (ΔDHW, mean and upper and lower 95% confidence limits) evaluated at BMI = 0.5 (grey dashed line).
Fig 5.
Phenotypic drivers of heat tolerance between thermal regimes.
(a,b) Differences in trait values between thermal refugia (blue) and hotspots (red), and influence of traits on heat tolerance (c,d). Each test was performed using separate linear mixed effect models (LMMs), accounting for site-level differences by fitting a random intercept for each site. LMMs with hypothesised interactions were reduced using stepwise backward selection. (a) Coral colonies were significantly larger at thermal refugia (P < 0.001). (c) There was a positive association between colony size and heat tolerance associated with a moderate level of uncertainty (P = 0.096), and with no interaction between thermal regimes (P > 0.05). (b) No differences in mean tissue biomass were detected between thermal regimes (P = 0.84). (d) Tissue biomass was positively associated with colony heat tolerance at thermal refugia (refugia slope, P = 0.015), but this effect was not present at hotspots (interaction, P = 0.037) where the trend did not differ significantly from the null expectation of a slope equal to zero (P > 0.05). P value significance is given by black symbols for P < 0.1 & > 0.05 (.), P < 0.05 (*), P < 0.01 (**), P < 0.001 (***).