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High coral heat tolerance at local-scale thermal refugia [1]

['Liam Lachs', 'School Of Natural', 'Environmental Sciences', 'Newcastle University', 'Newcastle Upon Tyne', 'United Kingdom', 'Department Of Geography', 'Institute Of Resources', 'Environment', 'Sustainability']

Date: 2024-08

Marine heatwaves and mass bleaching have devastated coral populations globally, yet bleaching severity often varies among reefs. To what extent a reef’s past exposure to heat stress influences coral bleaching and mortality remains uncertain. Here we identify persistent local-scale hotspots and thermal refugia among the reefs of Palau, Micronesia, based on 36 years of satellite-derived cumulative heat stress (degree heating weeks–DHW, units: °C-weeks). One possibility is that hotspots may harbour more heat tolerant corals due to acclimatisation, directional selection, and/or loss of tolerant genotypes. Historic patterns of assemblage-wide mass bleaching and marine heatwaves align with this hypothesis, with DHW-bleaching responses of hotspots occurring at 1.7°C-weeks greater heat stress than thermal refugia. This trend was consistent yet weaker for Acropora and corymbose Acropora, with severe bleaching risk reduced by 4–10% at hotspots. However, we find a contrasting pattern for Acropora digitifera exposed to a simulated marine heatwave. Fragments of 174 colonies were collected from replicate hotspot and thermal refugium outer reefs with comparable wave exposure and depth. Higher heat tolerance at thermal refugia (+0.7°C-weeks) and a correlation with tissue biomass suggests that factors other than DHW may overwhelm any spatially varying effects of past DHW exposure. Further, we found considerable A. digitifera heat tolerance variability across sites; compared to the least-tolerant 10% of colonies, the most-tolerant 10% could withstand additional heat stresses of 5.2 and 4.1°C-weeks for thermal refugia and hotspots, respectively. Our study demonstrates that hotspot reefs do not necessarily harbour more heat tolerant corals than nearby thermal refugia, and that mass bleaching patterns do not necessarily predict species responses. This nuance has important implications for designing climate-smart initiatives; for instance, in the search for heat tolerant corals, our results suggest that investing effort into identifying the most tolerant colonies within individual reefs may be warranted.

Funding: This work was supported by a UKRI Mitacs Globalink grant to L.L., J.R.G, and S.D. (NE/T014547/1), an International Coral Reef Society Ruth Gates Fellowship to L.L., an IDEAWILD fieldwork equipment grant to L.L. (LACHPALA1219), the Natural Environment Research Council’s ONE Planet Doctoral Training Partnership Studentship (NE/S007512/1) to L.L., a Royal Geographical Society Ralph Brown Expedition Award (RBEA 04.23) to L.L. and J.R.G., and a European Research Council Horizon 2020 project CORALASSIST (725848) to J.R.G. We also thank Dr. Harmony Martell, Dr. Pedro Gonzalez-Espinosa, and Xinru Li for their thoughts on this work, PICRC staff for supporting our research, Arius Merep for help with aquarium tank construction, and the boat operators Geory Mereb, and Nelson Masang.

Data Availability: All original data and code (R version 4.0.2, GNU Bash version 5.0.16(1) and CDO version 1.9.9rc1) have been deposited at 10.25405/data.ncl.22731194 (available before publication at https://figshare.com/s/faac387cb999778055cc to be completed). All datasets analysed are publicly available as of the date of publication. Any additional information required to reanalyse the data reported in this paper is available from the lead contact upon request.

Here, we combine geographic-scale concepts of local adaptation of different populations, with a more detailed exploration of within-population variability of coral heat tolerance, given new research on the role of microgeographic adaptation. These smaller within-population local spatial scales are highly important for management actions (e.g., patrolling marine protected areas, or collecting corals for restoration) as logistics become far more challenging when managing reefs over larger distances (i.e., multiple populations). Our study takes a local-scale seascape perspective, drawing on long-term historic spatial datasets available from this region on mass coral bleaching and marine heatwave intensity, and a species-specific experimental manipulation. Because the mass bleaching dataset is given at the assemblage level [ 36 ], possible adaptive or acclimatory responses to past heat stress could be masked by numerous environmental variables (e.g., temperature variability, irradiance) and biotic factors (e.g., coral community composition, Symbiodiniaceae community). For this reason, we also performed a reanalysis of Acropora-specific bleaching response for the only marine heatwave from which data were available (2010), and conducted a 5-week simulated marine heatwave experiment under controlled conditions to test the heat tolerance of a common coral species collected from both hotspots and thermal refugia. If we find that the broad geographic-scale patterns (i.e., higher heat tolerance at warmer reefs [ 4 ]) are also seen at the within-population scale, then it simplifies the provision of advice to ecosystem managers, that hotspot reefs might harbour corals with predictably higher levels of heat tolerance than thermal refugia.

Across seascapes, some coral reefs are routinely exposed to higher-than-average levels of heat stress while other reefs are consistently exposed to less-intense levels of heat stress, herein referred to as hotspots and thermal refugia, respectively. These distinct thermal stress regimes have been identified across broad geographic regions (>1000 km, e.g., the Great Barrier Reef [ 24 ], Red Sea [ 25 ], and the Caribbean [ 26 ]). As for many other marine and terrestrial species, the thermal limit of corals largely tracks broad latitudinal climatological gradients [ 4 , 12 , 27 ]. When exposed to marine heatwaves, corals at hotspots may be expected to fare better than those at thermal refugia, through means of acclimatisation [ 28 , 29 ], adaptation [ 30 , 31 ], and/or loss of heat-sensitive coral genotypes [ 32 ]. However, the opposite trend has been noted for the Red Sea, where corals in the cooler north have a higher heat tolerance than those in the south, possibly because they are theorised to have experienced a historic selective filter (upon recolonising the Red Sea after the last ice age), and now have a thermal threshold above the ambient water temperature [ 25 ]. From a perspective of managing species populations, it is desirable if heterogeneity in recent (i.e., in the satellite record) exposure of reefs to marine heatwave stress leads to predictable differences in coral population responses across seascapes, as this could allow managers to prioritise population interventions (e.g., protection, outplanting, translocations) in locations where they are likely to be most effective. This principle also holds true from the wider coral reef management perspective, where target coral assemblages comprise numerous species with different bleaching susceptibilities [ 33 ], and the ability to predict locations of tolerant assemblages could support effective management decision-making (e.g., which reefs to protect). However, there is growing recognition that the heat stress responses of individual conspecific corals are highly variable even within single populations [ 34 ], which could influence the feasibility of spatial seascape management plans if, for instance, species-specific heat tolerance variability were to be greater within than between reefs. Because coral reef management typically takes place over local within-population scales (i.e., 10s to 100s km for corals given their dispersal capacity [ 35 ]), there is a growing need to study smaller environmental gradients. Specifically in this paper we test whether the large-scale among-population expectation–that hotspots should harbour higher levels of heat tolerance than thermal refugia–holds true over smaller, more manageable distances within coral populations, or whether this effect is overwhelmed by drivers other than past heat stress exposure.

Reef-building corals epitomise this problem because of their acute sensitivity to marine heatwave stress, which disrupts their symbiosis with phototrophic microalgae and can lead to mass bleaching and mortality [ 11 – 13 ]. The ability of corals to tolerate heat stress is influenced by numerous other thermal drivers (e.g., temperature variability [ 14 ], heating rate [ 15 ], and peak temperature [ 16 ]), non-thermal environmental variables (e.g., light stress [ 17 ], turbidity as a mediator of irradiance exposure [ 18 ], water flow [ 19 ], and water quality [ 20 ]), and biological factors (e.g., energy reserves [ 21 ] and Symbiodiniaceae taxa [ 22 ]). However, it is the accumulation of heat stress over the warm season–a variable that is mapped at daily resolution in near real-time by satellites [ 23 ]–that is the ultimate trigger for mass coral bleaching. Therefore, leveraging spatial variations in heat stress exposure to identify reefs with high heat tolerant corals would be a useful tool for coral reef management.

Climatic stressors are causing profound impacts on organisms and ecological systems globally [ 1 , 2 ]. This is especially true for sessile organisms which cannot move to escape their environment. To persist under climate change, many species will need to adapt, and this will require sufficient standing genetic variability of climate resilience traits within populations [ 3 ]. Over large geographic scales (> several degrees of latitude, and between populations), gradients in climatic exposure are known to shape organism tolerance to climatic stressors [ 4 – 6 ]. Higher exposure to stress has been shown to drive higher tolerance through local adaptation (e.g., drought tolerance in European trees [ 5 ], thermal tolerance in corals [ 4 ], and upper/lower thermal limits for both terrestrial and marine ectotherms [ 6 ]). However, this pattern has also been shown to break down over the smaller spatial scales at which organisms and populations operate (< several degrees of latitude, or within populations) due to the influence of different abiotic selective forces (e.g., upper thermal limits and cold tolerance not explained by mean temperature [ 7 , 8 ]) and abiotic factors (e.g., genetic correlations between different thermal tolerance traits [ 9 ]). Furthermore, there is an emerging appreciation that microgeographic adaptation–adaptive divergence that occurs within a single population even amid high gene flow–can play an important role in ecological and evolutionary dynamics in nature [ 10 ]. To effectively manage populations under climate change, there is a crucial need for more information collected at local spatial scales (i.e., within populations, which could vary in terms of absolute distance depending on species range and dispersal capacity [ 10 ]). Despite this, the extent to which organism tolerance is influenced by local scale gradients in climatic exposure remains poorly understood.

2. Methods

Here, we evaluated local-scale thermal stress regimes among the reefs of a small remote archipelago using a 36-year satellite record of sea surface temperatures (SST). The Republic of Palau serves as an ideal case study as the spatial scale of its reef system is an order of magnitude smaller (<150 km long, within one coral population–local) than reef systems previously linked to gradients of thermal exposure and coral heat tolerance (1000s km [24–26], between multiple coral populations–not local). To test whether persistent hotspots or thermal refugia are present in Palau, spatial patterns in thermal history and marine heatwaves were assessed based on accumulated warm season heat stress. Firstly, the spatial variability of bleaching risk across the entire coral assemblage was tested using a Bayesian statistical modelling approach linking heat stress data with historic bleaching survey observations from across Palau. Then we restricted our spatial comparison to a single species to avoid any possible confounding effects of species compositional changes on coral responses to heat stress. We achieved this by conducting a long-term (sensu [37]) 5-week marine heatwave experiment with assays of bleaching and mortality every 1–3 days.

2.1. Historic heat stress data Heat stress on Palauan coral reefs was calculated from CoralTemp version 3.1, a daily 0.05° x 0.05° latitude-longitude (~5 km) resolution satellite-based Sea Surface Temperature (SST) dataset (1985 to 2020) available from the National Oceanic and Atmospheric Administration’s Coral Reef Watch (NOAA CRW) [38]. Although coral bleaching mortality can be influenced by light intensity, cold spells, nutrient enrichment, and sedimentation, among other factors, chronic levels of accumulated heat stress is the primary driver of mass coral bleaching and mortality [11]. NOAA CRW uses the CoralTemp satellite dataset to measure accumulated heat stress in terms of Degree Heating Weeks (DHW, units: °C-weeks), a metric combining both the intensity and duration of marine heatwaves. Coral reefs exposed to higher levels of DHW have a greater risk of mass bleaching and mortality [39]. As such, DHW is used by NOAA CRW to provide real-time bleaching risk forecasts, whereby DHW of 4–8°C-weeks corresponds to the expectation of significant bleaching, and DHW > 8°C-weeks corresponds to the expectation of significant bleaching and mortality. Here we use the same standard NOAA DHW dataset as presented in Lachs et al. (2023a). Briefly, we follow the NOAA CRW methodology [23], DHW on a given day (i) is computed as the sum of the last 12 weeks (84 days) of daily temperature anomalies (HotSpots, a standard parameter used by NOAA, not the thermal regime) relative to a standard coral stress threshold baseline (MMM, maximum of monthly means) which is held constant through time, and only HotSpots > 1°C are accumulated and then divided by 7 to make the standard DHW a weekly (rather than daily) metric.

2.2. Identifying persistent hotspots and thermal refugia Whether or not hotspots and thermal refugia were present in Palau was determined using annual maps (n = 36, 1985–2020) of maximum DHW (the maximum DHW in the year reflects peak bleaching heat stress, herein the maximum annual DHW is referred to as DHW for simplicity), combined with a thermal consistency biplot analysis (Fig 1), which computes the typical heat stress received by a given reef and compares this to the consistency of the reef’s heat stress performance relative to other reefs. For each year independently, maps of DHW (S1A Fig) were transformed to anomalies (based on the Palau mean DHW for that year) and percentile ranks (S1B Fig) to capture relative spatial differences in heat stress performance for all grid cells that intersect coral reefs in Palau [40]. Then for each reef, we calculated the mean DHW anomaly through time to describe the reef’s heat stress exposure relative to other reefs, and the standard deviation of DHW percentile ranks through time to measure the inter-annual consistency with which the reef performs relative to other reefs (different than sub-daily thermal variability). As some reefs are always more or less heat stressed in comparison to others, the most- and least-heat stressed reefs in Palau could be determined using the mean DHW anomaly based on the upper and lower terciles. However, to be considered hotspots or thermal refugia, reefs must have such thermal conditions consistently through time. Therefore, the reefs were also categorised based on DHW percentile SD (standard deviation), with the most-consistent thermal regimes occurring in the lower tercile (DHW percentile SD cutoff of 23.4), and the least-consistent thermal regimes occurring in the upper tercile. Using the thermal consistency biplot analysis, which plots typical heat stress against inconsistency, the tercile groups resulted in 9 regions. The lower left and lower right terciles of the biplot (Fig 1B and 1C) thus indicate the most-persistent thermal refugia and hotspots in the study region. This definition of thermal refugia does not necessarily equate to long-term buffering against any future heat stress, as shown by Dixon et al. (2022), but rather that heat stress will be lower than other regions of Palau when heatwaves do occur. The historic DHW trends for these regions were further inspected, confirming that bleaching-level heat stress conditions occurred far more often in the hotspots, while non-heat stress years were far more frequent in the thermal refugia. Notably, the emergent patterns in the thermal consistency biplot and bleaching-level heat stress hold true if, rather than terciles, quartiles (S2 Fig) or quintiles are used. For simplicity, we focus the study on the use of terciles, which does not alter the main conclusions of the study. We also report on the median daily SST of Palauan reefs and the variability of daily SSTs computed as the 99% range (0.5th percentile SST to 99.5th percentile SST). PPT PowerPoint slide

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TIFF original image Download: 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/). https://doi.org/10.1371/journal.pclm.0000453.g001

2.3. Assemblage-wide coral bleaching data The historic coral bleaching survey data used in this study is the same subset of the publicly available global dataset [36,42] presented in Lachs et al. (2023a) (1998–2017, n = 239). Notably, bleaching observations (primarily reported as percentage of corals bleached) are given as severity scores, ranging from 0 to 3, namely: no bleaching (0%), mild bleaching (1–10%), moderate bleaching (11–50%), and severe bleaching (>50%). We filtered out any survey records from the southern Palauan islands which are over 500 km from the main island of Palau (n = 2, Helen Reef and Tobi Island in 1998) and any records with suspected incorrect coordinates, where the location did not intersect with reef area (n = 3 in 1998).

2.4. Spatial variability in assemblage-wide coral bleaching To quantify the spatial variability of DHW-bleaching responses across the coral assemblage (i.e., all hard coral taxa) in Palau, we combined CoralTemp with historic coral bleaching survey observations. The effect of DHW on bleaching was fitted using a spatial beta GLM via Integrated Nested Laplace Approximation (INLA) in R-INLA. While this model was also used as part of a separate project focussing on temporal variability [43], the present study provides extended analysis of the spatial component of the model and additional sensitivity analyses. The Bayesian statistical approach we apply provides an intuitive spatially explicit estimation of model uncertainty [44,45]. Essentially, this is a map of spatially correlated uncertainty that shows where bleaching is under- or over-predicted for a given heat stress dosage (DHW). To fit the beta distribution, bleaching severity scores were converted to proportions (divide by 3) and transformed to remove extremes of 0 and 1, which in practice is , where y is the bleaching severity score scaled from 0 to 1, and n is the sample size [45,46]. This approach preserves differences among bleaching categories that would be lost using other transformations (e.g., binary transformation for a binomial GLM [47]). The full model specifications are given in [43], but briefly, spatial variation in the uncertainty of bleaching-DHW responses was estimated across a high-resolution Delaunay triangulation mesh of the study area (5,710 nodes). The model estimates the probability of severe coral bleaching (P(SB i ), i.e., the occurrence or not of severe bleaching–bleaching in >50% of colonies) as a function of heat stress (DHW i ) whilst accounting for additional underlying spatial correlation among bleaching observations (random effect: u i ) and spatially uncorrelated error (ε i ) where β 0 is the intercept, β 1 is the slope for DHW, u i represents the smoothed spatial random effect. Notably, the probability of severe bleaching pertains to the occurrence, or not, of bleaching in > 50% of colonies [36,42] and so cannot resolve spatial patterns in the magnitude of severe bleaching (i.e., where 50–100% of colonies are bleached). Whether DHW-based predictions of bleaching severity (u values) were significantly different between hotspots and thermal refugia was tested using a Wilcoxon sum rank test on the subset of nodes that intersected with the area of these thermal regimes identified through the satellite data analysis described above. We also ran five sensitivity analyses to address assumptions of our modelling approach. (1) Potential outliers: we reran the beta R-INLA model but without the four most northern survey records (all from 1998) which showed moderate (n = 1) or severe bleaching (n = 3) and could have biased our results (S3 Fig). (2–3) Balanced design and mesh resolution: as the three main survey years had unbalanced sampling effort (1998: n = 29, 2010: n = 80, and 2016: n = 63), we randomly subsampled the more-surveyed years (2010 and 2016) 100 times, resulting in 100 balanced datasets, and reran a beta R-INLA model for each with a lower mesh resolution where maximum triangle edge length was set to 8 km instead of 2 km (S4 Fig). (4) Transformation to fit beta distribution: we reran the iterative test above using gaussian R-INLA models with raw severity scores (0,1,2,3) as the response variable (S5 Fig). (5) Equidistance between ordinal bleaching severity score: we tested for spatial effects in DHW-bleaching based on raw ordinal data using a cumulative link model (bleaching_severity ~ DHW + Latitude), where latitude is modelled as a proxy of gradient between hotspots in the southwest and thermal refugia in the north (S6 Fig). Together these analyses provide support the findings of the model in our main result.

2.5. Acropora mass bleaching and simulated marine heatwave experiment Mass bleaching data captures trends at the coral assemblage level, missing genus- and species-level heat tolerance responses, especially for Acropora, a thermally sensitive yet keystone taxon of reef-building corals [48]. To address this, we performed an Acropora-specific spatial analysis of bleaching for the 2010 marine heatwave, and a simulated marine heatwave tank experiment on a common corymbose Acropora species, A. digitifera. Widespread data on Acropora mass bleaching (including corymbose Acropora) was only available from 2010 [49], and thus spatial trends do not reflect the residual long-term patterns discernible from the 20-year assemblage-level bleaching data used above. Acropora bleaching data were reported as the percentage of colonies bleached. Therefore, in order to keep the comparison with assemblage-level data robust, we converted percentage bleaching into bleaching severity scores following Virgen-Urcelay and Donner (2023) (see above). We then tested for spatial patterns in heat stress responses using the same Bayesian spatial beta GLM approach as mentioned for the assemblage-level data. To test the influence of transforming raw percentages to severity scores, we also ran a second version of the model based on the raw percentage of colonies bleached. As such, this second model predicts the probability of 100% coral bleaching, while the former predicts the probability of severe bleaching (50% of colonies bleached). Finally, we also ran the model on a subset of the data for corymbose Acropora, specifically. For the aquarium-based controlled manipulation, we quantified how thermal regimes influence heat tolerance by conducting a long-term 5-week simulated marine heatwave experiment, inducing bleaching and mortality in 1020 replicate fragments from 174 colonies of Acropora digitifera, a widely distributed reef-building coral in Palauan reefs (see S1 Table for sample size by site). Coral colonies were sampled haphazardly at 1–5 m depth but had to be large enough to provide at least 10 replicated nubbins (approximately 5 cm in length)–six for heat tolerance, three for tissue biomass (see below), and one for DNA extraction (not presented here). Fragments from 90 colonies were collected from six replicate northwest-facing reef crests located in hotspot grid cells (3 sites) and refugia grid cells (3 sites, with one chosen to be slightly more protected, but still with a northwest-facing aspect for logistical reasons), with at least 4 km between sites. Sites showed a very similar consistency in their thermal regimes with hotspots showing marginally higher DHW percentile SDs (22.8 across-site average) than thermal refugia (22.4 across-site average) (site-specific values shown in S1 Table). In comparison to short heat-shock experiments that typically last 1–2 days, the experimental temperature profile used here (S1 Table) was designed to match the intensity and duration of a natural marine heatwave more closely [34,37], with the assumption that the phenotypic bleaching and mortality responses would be ecologically relevant to the heat stresses that wild coral populations face during bleaching events. All collections were conducted with relevant National Marine Research Permits (Ministry of Natural Resources, Environment, and Tourism: RE-22-11) and individual State Permits (Ngarchelong State, Kayangel State, and Koror State). All work was in accordance with the ethical standards of and given ethical approval by Newcastle University. After collection between 3rd and 5th April 2022, fragments were acclimatised for 7–10 days in tanks maintained at ambient sea temperature (29.79 ± 1.18 SD °C). Relative to the site-specific adjusted (see below) climatological stress accumulation thresholds (MMM adj + 1°C, among-site mean of 30.1°C), the temperature in stress tanks was increased on 13th April gradually over two weeks (+ 0.4°C on day 1 and + 0.5°C on days 3, 5, 7, and 13) to reach a final bleaching-level temperature of approximately + 2.5°C. This temperature was maintained for the remainder of the experiment (S1 Fig, S1 Table). In stress tanks, night-time cooling was not allowed as this would be below the set temperature, however, the use of flow-through tank systems allowed an element of natural diel temperature variability due to short midday thermal peaks [50] (S7 Fig). Tanks were illuminated using aquarium lights at a constant light intensity matching the midday values from a Palauan outer reef (400 μmol photons m−2 s−1, S1 Table) using a 12 h:12 h diurnal cycle. Following our experimental protocol to determine A. digitifera heat tolerance in Palau [34], no ramping of light intensity to mimic sunrise and sunset was used, as this could confound comparison to that work. Calibrated HOBO Pendant loggers were placed in each tank and recorded temperatures at 10-minute intervals. To relate coral bleaching and mortality responses to accumulated heat stress, not instantaneous temperature, we calculated heat stress in the experiment using the DHW metric. To compare our experimental DHWs for each site with the NOAA coral bleaching forecasts (satellite-derived DHW), we used local in situ temperature data (night-time averages) to adjust the satellite-based CoralTemp baselines (MMM–maximum of monthly means climatology–from the 0.05° grid cell encompassing a collection site), following Humanes et al. (2022). This involved adjusting the MMM based on the linear regression between daily 0.05° sea surface temperature (CoralTemp v3.1) and daily averaged in situ temperatures (recorded from additional U22 HOBO Pro V2 loggers deployed at each collection site). This produced six adjusted local climatological baselines (MMM adj −adjusted MMM), one for each collection site.

2.6. Heat tolerance Since corals can recover from bleaching, a pragmatic definition of heat tolerance is a coral’s ability to survive levels of heat stress that would be sufficient to cause bleaching mortality in nature [51–53]. Notably, we have previously shown that the visual bleaching mortality responses of A. digitifera in the same experimental system used in this study correlate strongly to image-based measurements of bleaching (based on the values of red, green and blue channels), the density of zooxanthellae cells, and pigment concentrations (supplementary materials of Humanes et al. (2022)). We did not measure the photochemical efficiency of symbiotic microalgae (Fv/Fm), because this metric has uncertain relationships to coral mortality (the primary variable of interest in our study) and cannot be used as an indicator to differentiate bleached from recently dead corals. The six replicate fragments of each colony to be assayed for heat tolerance were dispersed in random locations among the ten heat stress tanks (which received 5 of the fragments per colony) and one of the two procedural control tanks (which received the remaining colony fragment and was used to control for possible colony handling effects during collection, see below). With more stress tanks (n = 10) than replicate stressed nubbins (n = 5), the fragments were dispersed in such a way that each tank had an equal representation of the six different sites, and a maximum of one fragment from any given colony. If a fragment died in a procedural control tank, held at non-stressful ambient temperature conditions, it was an indication of handling effects for that colony, so all remaining fragments from the colony were removed from the experiment and the colony was not assigned a heat tolerance score (only one colony out of 174 tested had a procedural control fragment that died). The health status of each fragment was scored visually at intervals of between 1 and 3 days. We used the bleaching and mortality index (BMI) [34] adapted from McClanahan et al. (2004) [54] to categorise coral bleaching and mortality responses, such that c 1 to c 5 are the proportion of replicate fragments (per colony) recorded as healthy (c 1 ), half bleached (c 2 ), bleached (c 3 ), partial mortality (c 4 ), or dead (c 5 ), and N is the total number of categories (here N = 5). For example, a colony whose replicate fragments are either all healthy or all dead at a certain time point, will have a BMI value of 0 or 1, respectively. The BMI of a particular colony is representative of only a single timepoint and will change throughout the heat stress exposure. Therefore, we calculated DHW 50 , a colony-specific metric of heat tolerance analogous to the effective dosage (EC 50 ) metric used in toxicology [55] to determine the dosage at which 50% of the fragments for each colony experiences an effect. DHW 50 for each colony was calculated by fitting a logistic dose response curve for all colonies, allowing a different intercept and slope per colony, and then computing the DHW value at which the fitted curve reaches 50% of the full bleaching and mortality response (i.e., a BMI value of 0.5) using the ‘qlogis’ function in R. This builds on coral heat stress experiments using the inverse average BMI through time as a measure of heat tolerance (1 –mean BMI). To allow for comparisons with previous work [34,56], we also computed the regression between the compliment of mean BMI (a unitless measure of heat tolerance) and DHW 50 (S8 Fig, R2 = 0.94). Intermediate BMI values (i.e., not zero or one) can reflect different combinations of health status across a colony’s replicate fragments. For instance, a colony having a BMI of 0.5 could be achieved if all replicate fragments are bleached, if half are healthy and half are dead, or other combinations of health status scores. Given this limitation of BMI metric, we also tested for differences in the effect of DHW (continuous fixed effect) on heat tolerance (health status scores, ordinal response) between hotspots and thermal refugia (2-level categorical fixed effect) using a logistic mixed effect cumulative link model, accounting for site and colony ID with random intercepts, in the form: Status ~ DHW + Region + (1|Colony_ID) + (1|Site). The logit cumulative link model (clmm) was set with the ‘flexible’ threshold parametrisation which does not impose any a priori restrictions on the ‘distance’ between ordinal scores (S9 Fig).

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