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through filtered water etc.) was suitable for keeping fragments alive and that heat stress was the cause of bleaching and mortality responses (i.e., rather than effects due to the tank setup). Coral heat tolerance throughout the experimental exposure, expressed as the average BSI, ranged from 0.47 to 0.91 among colonies. The critical level of accumulated heat stress (DHW — degree heating weeks) at onset of bleaching and mortality ( first fixed DHW value at which BSI ≤ 0.75) was highly variable across colonies, with an average critical DHW of 6.7 °C-weeks (±1.1 °C-weeks SD) but ranging from 4.3 to 9.2 °C-weeks in the least and most tolerant individuals. Notably, this range corresponds to almost an entire categorical shift of the NOAA Coral Reef Watch bleaching alert system (e.g., Alert Level 1 to 2, moving from mass bleaching expected to mass mortality expected). Similar Symbiodiniaceae communities . Symbiodiniaceae com- munity composition was consistent among colonies, whereby all individual colonies were dominated by C40 Cladocopium spp. (Fig. 1d) and 96% of colonies contained a single ITS2 type pro file. For 70% of colonies, the dominant symbiont strain was C40-C3- C115-C40h. Only 2 colonies had mixed symbiont communities, with 29% relative abundance of C15h-C15hf-C15hg in colony A75 and 31% D1/D4-D4c-D1c-D1h-D2 in colony A96. There was no signi ficant effect of symbiont ITS2 type on bleaching and mortality responses (Fig. 1d) with strongly overlapping con- fidence intervals across all ITS2 type pro file groups (Fig. S1) (GLMM Tukey test, P > 0.05 for all pairwise comparisons, Table S2). Although the colony with Durusdinium spp. symbionts appeared to bleach and die faster than other colonies, with N = 1 for this ITS2 pro file type there was insuf ficient statistical power to detect whether this particular BSI trajectory differed from the rest of the population. This was also shown from principal compo- nent analysis based on ITS2 type pro files and grouping colony heat tolerance into broad equal-sized categories (average BSI: high ≥ 0.8 > medium ≥ 0.7 > low; Fig. S2), with strong overlap among each category. 3D colony growth . Coral colony growth was highly variable in terms of both live surface area growth (216 ± 722 cm 2 yr−1, average ± SD) and volumetric growth (404 ± 647 cm 3 yr−1, average ± SD). There were a range of relatively fast-growing colonies, stable-sized colonies, and others that either lost live tissue or volume through partial mortality or breakage (example colonies of each growth type shown in Fig. 1e, and all colony 3D models shown in Fig. S4). Once growth metrics were corrected by initial colony size, there was no signi ficant trend between live surface area growth and colony size (linear regression, P > 0.05; Fig. S6). However, there was a signi ficant negative trend between total volumetric growth and colony size, whereby larger corals had lower rates of volumetric change (linear regression, P = 0.042; Fig. S6). Notably, colony partial mortality had no effect on fecundity or symbiont community traits (ANOVA and Tukey tests or pairwise Bonferroni-corrected Wilcoxon tests, all P values

0.05; Fig. S7), a weak negative effect on heat tolerance (average BSI; ANOVA, P = 0.023; Fig. S7), and a stronger negative effect on growth which was expected as tissue loss reduces size (pairwise Bonferroni-corrected Wilcoxon tests for live surface area growth, P = 0.006; ANOVA and Tukey tests for volumetric growth, P = 0.005; Fig. S7). Fecundity. Egg density averaged 4.9 eggs per polyp (±1.4 eggs per polyp SD) across all coral colonies, with polyps containing between 1 and 11 eggs. The geometric mean diameter of eggs was in line with that shown for Acropora digitifera39, although slightly larger (Fig. 1f, red dashed line). Average egg volume across all colonies was 0.11 ± 0.03 mm 3 SD, ranging from 0.06 to 0.2 mm 3, similar to that shown in previous work 34. The estimates of total colony egg production (274,590 ± 236,624 eggs colony −1, average ± SD) and total colony egg volume (32 ± 27 cm 3 colony−1, average ± SD) were highly variable among colonies, likely due to variability in colony size. Lack of trade-offs with heat tolerance and positive trait cor- relations. There was no evidence for trade-offs between heat tolerance (average BSI per colony) and either growth (Fig. 2a), fecundity (Fig. 2b), or Symbiodiniaceae community composi- tion (Fig. S1, Fig. S2). The lack of a relationship with Symbio- diniaceae relates to the high similarity of the symbiont community among colonies. Contrary to expectations, we found positive associations between heat tolerance and growth metrics (live surface area growth and volumetric growth). Despite having a weak association due to high levels of uncertainty (i.e., 95% credible intervals of the slope posterior distributions intersected zero, Fig. 2a), the probability that these slopes were positive, and that growth and heat tolerance act in concert was high; 90% probability for live surface area growth and 94% probability for volumetric growth (Fig. S8). Although the slope values seem small (i.e., ×10 −5)t h i si sd u et oad i s p a r i t yi nt h e order of magnitude between growth values (×10 3) and average BSI values (×10 −1). Accordingly, these differences in growth correspond to weak but potentially important shifts in average BMI. For instance, moving from the 10th to 90th percentile of volumetric colony growth corresponds to a shift in heat toler- ance from the 40th to 60th percentile of the population (Fig. 2a), or an increased bleaching heat stress tolerance of 0.7–0.9 °C-weeks (Fig. S9). These r esults were markedly similar even when colonies that had experienced shrinkage (reduction in surface area or volume) were excluded from the analysis (Fig. S10). In comparison, any effect of fecundity metrics (total colony egg production and total colony egg volume) on heat tolerance were unmeasurable given our sampling design (Fig. 2b), with close to 50:50 odds of the relationship being negative or positive (trade-off or co-bene fit )( F i g .S 8 ) .T h i s trend remained the same even when the most fecund colony, a potential outlier, was removed from the analysis. Throughout the course of the 5-week heatwave emulation experiment (Fig. 3), the relationships between coral colony growth (measured from 3D models on the reef) and instantaneous bleaching and survival responses (measured as BSI) were variable. The onset of b leaching responses occurred after ~3 weeks of elevated temperatures (Fig. 1b) at a DHW value of ~4 –6° C - w e e k s ( F i g .3a, Fig. S9). However, bleaching onset was delayed in fragments sampled from positive-growth colonies compared to negative-growth colonies by approxi- mately 1 °C-week (Fig. 3a, Fig. S9). Accordingly, during this period of differential bleaching o nset (at ~5 °C-weeks), the slope of the BSI-growth relationship (concept shown in Fig. 3b) was strongly positive showing a pron ounced peak (e.g., 95% credible interval of the slope not intersecting zero, Fig. 3c). Faster growing colonies needed higher levels of heat stress to trigger the onset of bleaching responses. The BSI-growth slope progressed throughout the experiment with a double peak pattern. Moving on to 7 °C-weeks, as bleaching responses aligned across all colonies of different growth rates, the BSI-growth slope decreased from the first bleaching- associated peak toward zero (Fig. 3c). As the heat stress exposure reached 9 °C-weeks, the BSI-growth slope again increased to a second peak, this time re flecting the delayed onset of mortality responses in positive-growth colonies compared to negative- growth colonies. Toward the end of the exposure (10.7 °C-weeks), ARTICLE COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-023-04758-6 4 COMMUNICATIONS BIOLOGY | (2023) 6:400 | https://doi.org/10.1038/s42003-023-04758-6 | www.nature.com/commsbio the BSI-growth slope moved back toward zero, re flecting critical bleaching and mortality responses across all colonies regardless of colony growth rates. Notably, beyond 7 °C-weeks, the uncertainty around the BSI- growth slope widened (Fig. 3c), intersecting with zero. Yet despite this, the probability of a positive BSI-growth slope (i.e., no trade- off) remained at 72 –91% throughout the later stages of the heat stress exposure as bleaching and mortality responses progressed to critical levels. The strength of this temporal analysis (Fig. 3) is that it is based on raw BSI values (not summary statistics like average BSI). While the analysis of average BSI (Fig. 2), suggests a weak co- benefit between heat tolerance and growth but with high uncertainty, the temporal analysis (Fig. 3c) shows the nuance of this co-bene fit. It is at the onset of bleaching and at the onset of mortality that there is a maximum co-bene fit (greatest slope value), and at the onset of bleaching that there is the highest level of con fidence of this co-bene fit (95% credible intervals not overlapping zero). Throughout the heat stress exposure, the progression of instantaneous BSI-growth relationships for volumetric growth (Fig. S11) showed a similar pattern to that of live surface area growth (Fig. 3). Slopes were consistently positive with credible intervals deviating from zero at bleaching onset (Fig. S12). However, the progression of instantaneous BSI-fecundity rela- tionships (total colony egg production, and total colony egg volume) was static, centred at zero throughout the heat stress exposure (Fig. S11). These results show that growth has a positive association with heat tolerance, but that fecundity has no association with heat tolerance (Fig. S12). Discussion In coral reef ecology, theory and evidence suggest that high coral heat tolerance is associated with a growth trade-off, especially for corals hosting certain symbiotic dino flagellates (e.g., Durusdinium trenchii) 16,19,21,23. Here, we investigated heat tolerance-associated trait trade-offs in a shallow outer reef crest coral population primarily hosting C40-dominated Symbiodiniaceae communities. We found no evidence for trade-offs between coral heat tolerance and either growth or fecundity. Contrary to expectations, we found weak positive associations between heat tolerance and colony growth. Indeed, during a marine heatwave emulation experiment, fragments taken from faster growing coral colonies on the reef were able to withstand higher levels of experimental heat stress before the onset of bleaching and mortality. Previous work has identi fied consider- able trade-offs between heat tolerance and growth in terms of calcification rates caused by the presence of different symbionts 16, flagging this as a potential barrier to successful coral adaptation under climate change 19. Our results show that heat tolerance and whole colony growth (in terms of surface area and volume) can be positively associated, offering a more optimistic outlook for coral populations. This builds on the recent finding of co-tolerance of individual corals to multiple stressors (e.g., thermal stress, bac- terial infection)15. Particularly we found a double-peak pattern in BSI-growth regression slopes throughout a 5-week heat stress Colony growth Colony fecundity β0 = 0.75 [0.72, 0.77] β1 = 2.6e−05 [−7e−06, 6e−05] 0.5 0.6 0.7 0.8 0.9 −2000 −1000 0 1000 Live surface area growth (cm2 yr) β0 = 0.74 [0.71, 0.77] β1 = 2.4e−05 [−1.2e−05, 6.1e−05] 0.5 0.6 0.7 0.8 0.9 −2000 −1000 0 1000 2000 Volumetric growth (cm3 yr) β0 = 0.75 [0.71, 0.79] β1 = −0.00012 [−0.01, 0.01] 0369 Eggs per colony ( × 105) β0 = 0.74 [0.69, 0.79] β1 = 0.00018 [−0.00093, 0.0013] 0 25 50 75 100 125 Total egg volume per colony (cm3) a c e b d f Colony heat tolerance (average BSI) Colony heat tolerance (average BSI) Fig. 2 No apparent trait trade-offs associated with overall coral colony heat tolerance. Associations between colony heat tolerance (average BSI through time) and either corrected growth metrics ( c – live surface area growth, and e – volumetric growth) or colony fecundity ( d – eggs per colony, and f – total egg volume per colony). Example 3D models used to measure growth are shown for a single colony ( a), and microscope images of eggs which were counted and measured to determine fecundity are also shown ( b). Each plot shows the median and 95% credible interval of intercept ( β0) and slope ( β1) parameters of linear regressions, where a negative slope would re flect a trade-off, however weak positive associations are present for growth ( c, e). COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-023-04758-6 ARTICLE COMMUNICATIONS BIOLOGY | (2023) 6:400 | https://doi.org/10.1038/s42003-023-04758-6 | www.nature.com/commsbio 5 exposure (Fig. 3c). First the onset of bleaching was delayed in fragments of faster growing coral colonies (Fig. 3c, first peak), and then the onset of mortality was delayed (Fig. 3c, second peak). Our findings suggest that selecting corals for heat tolerance either through natural selection (i.e., selective mortality of heat sensitive individuals) or assisted evolution (i.e., propagating heat tolerant individuals) may not compromise growth or fecundity. A key consideration here is separating the distribution of traits within a contemporary population from what happens to the future population as temperatures continue to rise under climate change. In general, we found that more heat tolerant individuals also tended to have higher colony growth rates. This implies that post-bleaching coral populations may not necessarily have lower overall growth in terms of changes in colony size. However, the existence of thermal optima — as demonstrated for coral calcification40 and photosynthesis 41— still imply that long-lived corals may experience declines in their growth as temperatures rise, even if they are the more heat tolerant members of the earlier population. As we did not measure growth post-bleaching, there is a need for future research to understand the plasticity of trait associations after stress. As such, further work is also needed to understand whether selection for heat tolerance can also select for other bene ficial traits. Our study focussed on corals from similar depths on a single reef to limit the in fluence of the environment on organism physiology. Further research is needed to under- stand if positive associations between heat tolerance and growth are also present in other coral species and over broader spatial scales. Under climate change, coral heat tolerance will likely be one of the most important fitness-related traits in determining popula- tion persistence or collapse 42,43. However, heat tolerance can come at a cost to other traits, like growth. This premise is typi- cally based on Symbiodiniaceae-derived coral heat tolerance, where certain dominant symbiont taxa (e.g., Durusdinium tren- chii) confer higher tolerance at the expense of photosynthetic energetics and ultimately growth as calci fication44. Many coral populations, particularly in the Indo-Paci fic, host Symbiodinia- ceae communities that are either dominated by a single taxon of symbiotic algae or by a single community type with similar relative abundance of different symbiont taxa 28,45. Determining whether heat tolerance trade-offs persist for corals hosting broadly similar symbiont communities can improve our under- standing of coral population functioning and the potential of adaptation to climate change. Trade-off theory suggests that corals have contrasting strate- gies, either being resistant to high temperatures or showing +0.08 0.00 Regression slope of modelBayesian credible intervals 95% 75% 50%Colony GrowthBSIx Heat tolerance trait trade-off (-) slope Degree heating weeks throughout exposure (°C-weeks) 048 Concept -0.04 Population average BSI at 21 evenly- spaced levels of DHW (x) throughout the exposure 0.00.51.0 048 Colony GrowthBSIx Heat tolerance co-benefit (+) slope +0.04 (+) growth (-) growth healthy bleaching onset earlier mortality onset earlier healthy bleaching onset later mortality onset later Slopes from 21 regression models in the form: BSIx ~ LiveSA growth Bleaching and mortality responsesa cb Example of one regression model at one DHW level (x) On reef Fig. 3 Progression of bleaching and mortality throughout heat stress exposure as a function of donor colony growth rate. The association between corrected annual colony growth in terms of live surface area (LiveSA from 3D models on the reef) and acute bleaching survival responses (BSI) of colony fragments in heat stress tanks. a Progression of bleaching and mortality responses (colour bar) is shown at 21 evenly spaced degree heating week (DHW) levels (x) on the x-axis. Fragments from positive-growth colonies take longer to bleach and die than those from negative-growth colonies. b Conceptual diagram linking the slope of one BSI-growth regression model (for all colonies with available data) at a single DHW level (x) to positive trait correla tions (positive slope) or trade-offs (negative slope). A slope of zero is shown by the dashed red line. c Progression of the relationship between colony growth and fragment BSI at 21 DHW levels (x, bold ticks on top), shown as the median slope (blue line) and the 50%, 75%, and 95% Bayesian credible intervals (grey shading) from posterior distributions of 21 regression models. ARTICLE COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-023-04758-6 6 COMMUNICATIONS BIOLOGY | (2023) 6:400 | https://doi.org/10.1038/s42003-023-04758-6 | www.nature.com/commsbio enhanced calcification rates, with concurrent disadvantages being reported for both strategies 16,17. Our results show that heat tol- erance can be positively associated with whole colony growth, where some individuals would expectedly have higher fitness due to excelling in multiple traits simultaneously. However, our results cannot be compared directly to calci fication-based studies since we have measured growth as changes in colony size (to capture net positive and negative changes) which may bear dif- ferent implications for coral populations. Still, organisms must partition resources among costly physiological processes, sug- gesting that one should find trade-offs between colony growth and heat tolerance. However, total resource budgets can be highly variable among individuals, especially in wild populations, due to processes such as the ef ficiency of resource acquisition from the environment12. As such, even though trade-offs must occur at some level of biological organisation, they can be masked at ecological scales due to high variability of resource acquisition among individuals. In line with trade-off theory, such energetic variability among individuals can result in apparent positive associations among resource intensive traits 12,14. It is likely that the physiological processes underpinning high growth rates are also linked to high resilience to heat stress, as suggested by the concept of co-tolerance 13. None of the colonies with high live surface area growth rates underwent partial mortality. Those with colony shrinkage due to partial mortality were likely to have been affected by other stressors, such as disease, competition, physical damage, or predation, and as such were also associated with lower levels of heat tolerance. Yet even when colonies that had experienced shrinkage (reduction in surface area or volume) were removed from the analysis a weak positive association between heat tolerance and colony growth remained, suggesting the trend observed in this study was not an artefact of colonies undergoing shrinkage (e.g., through processes including predation, tissue necrosis, or breakage). The physiological cost of tissue repair, fighting infection or regrowth after breakage could deplete energy reserves46,47 rendering corals more susceptible to bleaching and mortality under acute heat stress. It may be possible that while we find weak positive associations between heat tolerance and whole colony growth, trade-offs with other traits such as calci fication could still exist

16. Such a trade-off could compromise individual fitness of more heat tolerant corals particularly during storm surges when there is a higher risk of colony breakage. Tolerance to extreme temperature stress is a vital trait for corals in the weeks or months during marine heatwaves. How- ever, heatwaves currently do not happen every year and generally occur only in the warmest months. As such, heat tolerance is unlikely to directly bene fit corals during cooler months or years, without considering associations between heat tolerance and other traits. Comparatively, other traits like growth or fecundity are of importance throughout every year in sexually mature adult corals (i.e., over 3 years old for Acropora spp.48). Colonies grow year-round and typically spawn during one season per year 49, whilst developing eggs for the rest of the annual gametogenic cycle. Together, these results suggest that corals exist along an energetic continuum, where positive trait correlations may be derived from underlying physiological drivers like immunity 47, feeding ef ficiency50, or energy storage 51. Energetic variability could then result in higher levels of fitness and better perfor- mance across suites of different traits. Weak positive associations with heat tolerance occurred with growth but not fecundity, suggesting that the drivers of energy allocation to fecundity may act independently of heat tolerance. A trade-off between coral heat tolerance and key ecological traits like growth would have considerable negative implications for natural evolution under climate change 19. This would also apply to restoration efforts involving assisted evolution that aim to boost population resistance to heat stress by propagating more heat tolerant coral individuals via selective breeding or assisted gene flow. If coral heat tolerance was associated with lower growth or fecundity for instance, then out-planting large numbers of corals with these traits would have potentially damaging effects on natural population fitness. However, we found no evidence for such trade-offs between heat tolerance and either colony growth or fecundity, for a coral population associated with the same Symbiodiniaceae community. Although further work will be needed to understand whether these trends persist across larger spatial scales and for other species, our results suggest that selecting corals for heat tolerance through either natural selection or assisted evolution is unlikely to come at a cost to growth or fecundity. Under climate change, coral heat tolerance will be increasingly important to the persistence of coral populations. Contrary to expectations, selection for heat tolerance may not necessarily compromise other important parts of the coral life- history. Materials and methods Model system . To minimise the in fluence of environmental and interspeci fic drivers, we measured variation in traits on a single coral population at Mascherchur reef, Palau, Micronesia (7° 17 ’ 29.3” N, 134° 31 ’ 8.0” E), a semi- sheltered outer reef crest. The shallow-water Indo-Paci fic reef builder, Acropora digitifera, was chosen as a model species due to its high local abundance, broad geographic distribution, and corymbose growth form that allows sub-sampling of branches (fragments) to provide intra-organism statistical replication without sacrificing the colony. Seventy coral colonies were tagged at 2 –3 m depth and surveyed in situ repeatedly for different traits between 2017 and 2019. Large adult colonies of similar diameter (24 ± 8 cm, average ± SD) were chosen to limit the size-related variability in total resource budgets among individuals which could obscure trade-off relationships

Marine heatwave emulation experiment . Colony heat tolerance was determined in August 2018 at the Palau International Coral Reef Center by subjecting replicate fragments of each colony to a long-term 5-week marine heatwave emulation experiment (all tank experiment details are given in Table S1). In comparison to short heat-shock experiments that typically last 1 –2 days, this experimental tem- perature profile was designed to match more closely the duration of natural marine heatwaves8,38, with the assumption that the phenotypic bleaching and mortality responses would be more ecologically relevant. After fragments were collected and given a 7 –10-day acclimatisation period under ambient thermal conditions in all tanks, temperature was increased gradually (~0.8 °C week −1, Table S1) over the time course of the experiment (35 days), reaching a final bleaching-level tem- perature of approximately 33 °C, or 3.5 °C above the local climatological baseline (MMM – maximum of monthly means, Fig. 1a, Table S1, see ref. 8 for more detailed tank setup description). The use of flow-through tank systems allowed an element of natural diel temperature variability 52 in all tanks (4 heat stress and 2 procedural control tanks), while aquarium lights provided light conditions during the acclimatisation period and the heat stress exposure at a daily average intensity of 400 μmol m −2 s−1, corresponding to the average light intensity measured in Mascherchur at midday 8. HOBO loggers, with 0.14 °C resolution and 0.45 °C accuracy, were calibrated against a RBR TR-1050 using the average offset for temperatures between 27 and 35 °C in increments of 0.5 °C. Calibrated HOBO loggers were placed in each tank and recorded temperatures at 10-min intervals. To relate coral bleaching and mortality responses to accumulated heat stress, not instantaneous temperature, we calculated heat stress for each tank using the Degree Heating Weeks (DHW) metric. DHW was developed by the National Oceanic and Atmospheric Administration ’s Coral Reef Watch to provide a real-time coral bleaching alert system based on satellite-derived sea surface temperatures. DHW is a daily measure of both the intensity and duration of heat stress, calculated by accumulating temperature anomalies > 1 °C relative to a climatological baseline (MMM) over a 12-week (84-day) rolling window 53. To allow for comparisons between the DHW within our experiment and the NOAA bleaching forecasts, we adjusted the MMM from the 5 km grid cell encompassing the collection site based on the relationship between daily time series of 5 km sea surface temperature (CoralTemp v3.1) and daily averaged in situ temperature (recorded from additional HOBO loggers at the collection site; 8), producing the local climatological baseline (MMMadj – adjusted MMM). This builds upon the eDHW method which suggest using the satellite-based MMM to compute experimental DHWs 54. However, our previous work on Mascherchur reef has found that the eDHWs underestimate true DHWs due to a mismatch between the satellite data and in situ reef conditions 8. Notably the NOAA CRW bleaching risk forecast considers DHW of 4 and 8 °C- weeks as Alert Level 1 (signi ficant bleaching expected) and Alert Level 2 (signi ficant bleaching and mortality expected), respectively 53. The final accumulated heat stress exposure reached in this experiment was 10.7 °C-weeks. COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-023-04758-6 ARTICLE COMMUNICATIONS BIOLOGY | (2023) 6:400 | https://doi.org/10.1038/s42003-023-04758-6 | www.nature.com/commsbio 7 Heat tolerance . Fragments (6 per colony) were dispersed in random locations among the four heat stress tanks (4 fragments colony −1) and two procedural control tanks (2 fragments colony −1). In total, fragments from 66 of 70 colonies were exposed to the assay, as four colonies were not found during collection. If fragments from a colony died in the procedural control tank which was under 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 (2 colonies). The health status of each fragment was scored visually into five categories based on stark whiteness and tissue state (see below) at intervals of between 1 and 3 days (total of 16 timepoints over 35 days). Notably the bleaching scores were highly correlated to pigment concentration and symbiont density 8. We used a bleaching survival index (BSI), the inverse of the commonly used bleaching and mortality index (BMI), to categorise coral bleaching and survivorship responses 55. This was done in order to have a positive correlation between the bleaching survival index and heat tolerance. For BSI, c 1 to c5 are the proportion of replicate fragments (per colony) recorded as healthy (c 1), half bleached (c 2), bleached (c 3), partial mortality (c4), 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, will have a BSI value of 1 or 0, respectively. BSI ¼ 1 /C0 BMI ð1Þ BMI ¼ 0c1 þ 1c2 þ 2c3 þ 3c4 þ 4c5 N /C0 1 ð2Þ Here we de fine the onset of the bleaching and mortality response to occur when BSI declines below 0.75. This BSI score was chosen as it represents a colony with an average health status of partially bleached across all replicate fragments, a health status indicating that bleaching and mortality responses have started and will progress further 8. To remove potential biases relating to lagged DHW pro files among tanks, we followed the method outlined in full detail in ref. 8 which aligns DHW pro files among tanks and interpolates health status scores at fixed DHW values with fixed intervals, providing unbiased BSI values among colonies. As the BSI of a particular colony is an instantaneous measure and will change throughout the heat stress exposure, the colony ’s overall heat tolerance was calculated as the average BSI through time. Symbiont identi fication. The composition of the symbiont community was identified from one tissue scraping (<1 cm) per colony sampled in March 2018 (September 2018 for two colonies not found in March), and is assumed to be stable since no major disturbances occurred during this time period 29. Due to some colonies not being relocated during speci fic surveys, DNA samples were available for 51 colonies. Polymerase chain reaction (PCR) was used to amplify DNA extracted from coral tissue and then sent for ITS2 sequencing. The hyper conserved ITS2 region was chosen to facilitate integration with the SymPortal database which assigns symbiont ITS2 Type pro files or distinct intragenomic variants (DIVs) that represent different symbiont taxa 56. A full description of the DNA extraction, sequencing protocol and Symportal analysis are provided in the supplementary materials (S1). Size and growth metrics . Previous studies investigating growth-heat tolerance trade-offs in corals have measured growth as calci fication rates based on Calcium incorporation 40 or buoyant weight techniques 16, which are both able to detect changes in total CaCO 3 growth including skeletal density and secondary skeletal infilling. These methods are invasive and require manipulation of the coral colony, which can potentially in fluence coral fitness27. Moreover, as colonial organisms, it is possible for corals to experience shrinkage and still survive (i.e., partial mortality or reduction in size) and shift between net positive or negative growth over multiple occasions. This phenomenon may be undetected using some growth measurement techniques (e.g., buoyant weight). Therefore, at the colony level we deemed it more appropriate to use photogrammetry, a non-invasive method of measuring growth that can capture any changes in colony size with high accuracy and precision 57,58. Coral colony size was measured from 3D models at successive timepoints throughout the study (November 2017, May 2018, and February 2019). Overall, 114 3D reconstructions were built for 45 coral colonies ( n = 24 for all time points, and n = 21 for two time points). Following Ferrari et al. 57, Metashape Professional (v 1.5, Agisoft) was used to construct 3D surface meshes using structure from motion photogrammetry, which had >93% photo alignment on average and an average (±SD) resolution (distance between mesh vertices) of 1.1 (0.4) mm and average (±SD) scaling error of 0.36 (0.38) mm. Geomagic Control (v 2015, 3D Systems) was used to align successive coral colony models to give a common base, and compute three size metrics: live surface area (LiveSA, excluding dead regions without live tissue), total surface area (SA) and total volume (V). The initial maximum diameter (D) was recorded in situ in 2017 for each coral colony using a measurement tape (i.e., independently of the 3D models). Further details of photogrammetry methods can be found in the supplementary materials (Supplementary Text 2, Fig. S3, Table S3). Annual growth rates, in terms of live surface area and volume, were estimated as the change in size relative to the time interval between successive photogrammetry surveys. For those colonies with two size comparisons (i.e., 2017–2018 and 2018 –2019), the average of both was taken. Due to some colonies not being relocated during speci fic surveys or model building issues, photogrammetry model comparisons were available for 45 colonies. However, variation in initial colony size can introduce bias to raw areal or volumetric growth measurements (i.e., for identical linear extension rates, large colonies appear to grow faster than small colonies in terms of surface area and volume). Therefore, growth metrics must be corrected for colony size. One option is to calculate percentage growth, by dividing the growth rate by the corresponding initial colony size. However, this results in growth overestimation for small colonies that may easily double in size from one year to the next (i.e., growth rate of 100%). Another option, which we adopted here, is to multiply the growth rate by a dimensionless adjustment factor (AF) that represents the difference between the population mean colony size and the size of an individual colony. AFs for each colony (i) were calculated as the ratio between the average initial size across all colonies relative to the initial size of colony i, such that Areal AFi ¼ /C22D Di ð3Þ Volumetric AFi ¼ /C22A Ai ð4Þ To avoid further bias, the colony size metrics used for the AF calculation were derived from D independently of the 3D models and were one dimension lower than their corresponding growth metric (e.g., correct the SA growth rate based on the diameter). For three colonies that lacked empirical measurements of D but had 3D photogrammetry models available, D was predicted from the initial SA of the photogrammetry model, such that log ðDÞ¼/C0 0:738 þ 0:537 ´ logðinitial SAÞ (F 40/1 = 239.1, R2 = 0.86, P < 0.001) (Fig. S5). The initial area (A) for use in the volumetric AF was given by the product of D times initial height (assumed to be D/2). Size-dependencies of each metric were tested using linear regressions. Notably, partial mortality was recorded from field surveys categorically, such that a colony without partial mortality must have been healthy continuously from the first until last 3D photogrammetry survey. Fecundity. Fecundity was measured as egg density (number of eggs per polyp) and egg volume (average egg volume per polyp). Two fragments from each tagged coral colony in the reef were removed prior to spawning in March 2018 or 2019, dec- alcified using 10% hydrochloric acid and stored in ethanol. Due to some colonies not being relocated during speci fic surveys or sample preservation issues, polyp fecundity measurements were available for 47 colonies (25 in 2018 and 22 in 2019). Ten polyps from each fragment were dissected using a dissection scope and each egg was photographed alongside a scale using an attached digital camera. All image analysis was performed using the semi-automated SizeExtractR work flow 59 to annotate polyp images and compute egg density and volume from the 20 polyps per colony. Egg volume (EV) was estimated from the geometric mean diameter (GMD) 59. We then estimated total colony egg production (TEP) and total colony egg volume (TEV) by combining dissection-derived fecundity data (EC – egg counts per polyp – and EV, respectively) with per colony estimates of polyp density per unit area (PD) and 3D model-derived estimates of total live surface area (LiveSA). EV ¼ 4 3 ´ π ´ GMD 2 /C0/C13 ð5Þ TEP ¼ EC ´ PD ´ LiveSA ð6Þ TEV ¼ EV ´ TEP ð7Þ Statistics and reproducibility . Traits hypothesised to in fluence coral heat toler- ance (average BSI) were colony growth (in terms of both LiveSA and V), colony fecundity (total egg production and total egg volume), and symbiont community. Trade-off analyses were conducted by regressing predictor variables against colony heat tolerance using Bayesian general linear models. In these regressions a negative slope represents a trait trade-off while a positive slope shows positive correlations among traits. Bayesian models were used to facilitate a more intuitive interpreta- tion of model uncertainty 60,61 and fit using integrated nested LaPlace approx- imation in the statistical package R-INLA 61,62. Uncertainty around a model parameter (e.g., the regression slope) is commonly described from using the lower and upper 95% credible intervals, which bound 95% of the area under a posterior distribution density curve. To calculate the probability of no trade-off between heat tolerance and other traits (i.e., a flat line or even a positive association with a slope greater than zero), we measured the proportion of the posterior distribution which exceeded zero. The effect of multivariate symbiont community type on sigmoidal BSI-DHW responses was tested using generalised linear models with a binomial response distribution and post-hoc Tukey tests. In addition, confounding effects of partial mortality on all traits were tested using ANOVA paired to post-hoc Tukey tests or non-parametric equivalents. ARTICLE COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-023-04758-6 8 COMMUNICATIONS BIOLOGY | (2023) 6:400 | https://doi.org/10.1038/s42003-023-04758-6 | www.nature.com/commsbio During a long-term heat stress exposure, the bleaching and mortality response of sampled fragments may vary through the time course of the experiment. Therefore, we were interested to test not only the relationship between overall heat tolerance (BSI averaged across the whole heat stress exposure) and other traits, but also the relationship between the instantaneous BSI based on replicate fragments (response variable) and the known colony-level traits (predictor variable, e.g., fecundity or colony growth). To link bleaching responses to their ultimate driver — heat stress (not time) — we evaluated BSI-trait relationships at speci fic DHW levels instead of at survey time points. For each DHW level, we fit the linear regression between the instantaneous BSI (response variable) of each colony against their corresponding colony-level trait of interest (predictor variable). This allowed us to evaluate how the trait relationships change throughout the duration of the heat stress exposure, shown as changes in BSI-trait regression slopes. All LMs were fit using Bayesian inference in R-INLA 61, and uncertainty in regression slope estimations were quanti fied using Bayesian credible intervals (95%, 75%, 50%). These analyses can be reproduced using the open access code and data generated in this project. Inclusion and ethics statement . The research presented here adhered to the ethical and inclusivity standards consistent with the corresponding author ’s institutional and internal review board policies. Reporting summary . Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability All original data and R code produced in this study are publicly available on Figshare at https://doi.org/10.25405/data.ncl.20411589. ITS2 sequences have been archived publicly at NCBI under BioProject 864615 ( http://www.ncbi.nlm.nih.gov/bioproject/864615) and processed symbiont community composition can be explored publicly at https:// symportal.org. Any additional information required to reanalyse the data reported in this paper is available from the lead contact upon request. Received: 12 October 2022; Accepted: 24 March 2023; References

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61. Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B Stat. Methodol. 71, 319 –392 (2009). Acknowledgements We thank the numerous staff at the Palau International Coral Reef Centre (PICRC) who supported this research, Dr. Jamie Craggs for contributions to the experimental aquaria system, and Faith Paysinger for work on fecundity measurement. This research was funded by the Natural Environment Research Council ’s ONE Planet Doctoral Training Partnership (NE/S007512/1) to L.L. and the European Research Council Horizon 2020 project CORALASSIST (725848) awarded to J.R.G. The Reef Restoration and Adaptation Program is funded by the partnership between the Australian Government ’s Reef Trust and the Great Barrier Reef Foundation, which funded the time of R.F. on this project. Author contributions The first draft of the manuscript, 3D model comparisons, and statistical analyses were conducted by L.L., with supervision from J.R.G., P.J.M., J.C.B., and H.K.E. The marine heatwave emulation experiment was conducted by A.H., H.M., J.B., A.J.E., E.S., and J.R.G. 3D photogrammetry surveys were conducted by R.F., W.F.F., D.R.P., B.S., and J.R.G., and photogrammetry models were constructed by D.R.P., R.F., W.F.F., and B.S. Symbiont DNA extractions were conducted by J.C.B. and A.H. Y.G. supported fieldwork. L.L., A.H., D.R.P., J.C.B., P.J.M., R.F., W.F.F., E.B., H.K.E., A.J.E., Y.G., H.M.M., B.S., E.S., and J.R.G. contributed to the development of ideas and writing the final manuscript. Competing interests The authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s42003-023-04758-6. Correspondence and requests for materials should be addressed to Liam Lachs. Peer review information Communications Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Luke R. Grinham. Peer reviewer reports are available. Reprints and permission information is available at http://www.nature.com/reprints Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional af filiations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article ’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/. © The Author(s) 2023 ARTICLE COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-023-04758-6 10 COMMUNICATIONS BIOLOGY | (2023) 6:400 | https://doi.org/10.1038/s42003-023-04758-6 | www.nature.com/commsbio