DOI: 10.5061/dryad.kwh70rzdc
title: Environmental and calcification data for widespread scope for coral adaptation under combined ocean warming and acidification

description: Reef-building coral populations are at serious risk of collapse due to the
 combined effects of ocean warming and acidification. However, many corals
 show potential to adapt to changing ocean conditions. Here, we examine the
 broad sense heritability (H2) of coral calcification rates across an
 ecologically and phylogenetically diverse sampling of eight of the primary
 reef-building corals across the Indo-Pacific. We show that all eight
 species exhibit relatively high heritability of calcification rates under
 combined warming and acidification (0.23-0.56). Further, tolerance to each
 factor is positively correlated, and the two factors do not interact in
 most species, contrary to the idea of tradeoffs between temperature and pH
 sensitivity. All eight species can co-evolve tolerance to elevated
 temperature and reduced pH. Using these values together with historical
 data, we estimate potential increases in thermal tolerance of 1.0-1.7 °C
 over the next 50 years, depending on species. None of these species are
 likely capable of keeping up with a high global change scenario, and
 climate change mitigation is essential if reefs are to persist. Such
 estimates are critical for our understanding of how corals may respond to
 global change, accurately parameterizing modeled responses, and predicting
 rapid evolution.
description: <strong>Coral collection</strong>: Corals were
 collected using a hammer and chisel at 2±1 m depth from a total of six
 locations around O‘ahu, Hawai‘i (Fig. 1). Each species was collected from
 3-5 of the sites, depending on their local abundances and sizes, for a
 total of 22 colonies of *Montipora flabellata* and 30 colonies (genets) of
 the remaining seven species. The eight species examined in this study were
 *Montipora capitata*, *Montipora flabellata*, *Montipora patula*, *Porites
 compressa*, *Porites evermanni*, *Porites lobata*, *Pocillopora acuta*,
 and *Pocillopora meandrina*. After collection, corals
 were returned to the Hawai‘i Institute of Marine Biology (HIMB) on Moku o
 Lo‘e (Coconut Island), fragmented into 4-12 replicate nubbins (3-5 cm
 coral fragments, referred to as ‘ramets’) using a diamond-coated band saw,
 individually attached to a labeled ceramic tile using cyanoacrylate gel,
 and allowed to recover for 2.5 months in a common garden under present-day
 average temperature for O‘ahu and ambient pH conditions, with both
 temperature and pH following the seasonal cycle (Fig. S1).
 <strong>Approach</strong>: There was a need to
 include both within genet variation and among genet variation in our
 estimates. Given logistical constraints about how many coral ramets could
 possibly be accommodated, we attempted to balance these conflicting needs
 by including 1) three replicate ramets from four genets for each coral
 species within each treatment, and 2) one ramet per genet per treatment
 for the remaining genets (N = 22 total genets for *Montipora flabellata*;
 N= 30 for the other seven species, four of which were replicated within
 treatments per species). All coral ramets were randomly divided among
 mesocosms with no more than 1 ramet per genet in each mesocosm, resulting
 in 3-4 ramets per species within each mesocosm tank (Fig. 1).
 This experiment was part of a larger mesocosm project, other
 components of which have been described elsewhere [1–7]. The experimental
 system received constant flow-through of unfiltered sea water from the
 adjacent reef, and was initially set up with reef sand, rubble, algae,
 invertebrates, and fish to provide a reef-like habitat (see Supplementary
 Information for additional details regarding the mesocosm design).
 Temperature and pH of the incoming seawater were adjusted according to
 treatment in a series of header tanks, using aquarium heaters and CO2 gas
 injection, prior to flowing into the 70 L mesocosms at a rate of about 1.2
 L min-1, for a residence time of about 1 hr. Additional water circulation
 (4900 L hr-1) was generated by seawater pumps within each mesocosm to
 provide water flow speeds (10-15 cm s-1) similar to those *in situ*. Both
 temperature and pH were allowed to vary according to natural daily and
 seasonal cycles while maintaining appropriate offsets according to
 treatment: control treatment (present-day temperature and pH), ocean
 warming treatment (+2 °C and present-day pH), ocean acidification
 treatment (present-day temperature and -0.2 pH units), or combined future
 ocean treatment (+2 °C and -0.2 pH units) with 10 replicate mesocosms per
 treatment in a 40 mesocosm system (see Supplementary Information; Fig.
 S1). The corals were then randomly assigned to mesocosm with either 1 or 3
 replicate nubbins (ramets) per colony (genet) in each treatment, and no
 more than 1 ramet per genet in each mesocosm. After 2.5
 months of acclimation in a common garden, temperature and pH were slowly
 adjusted starting on 1 February 2016 until target values were reached on
 20 February 2016. The corals were then allowed 5.5 months to acclimate to
 treatment conditions before calcification rates were evaluated, thereby
 excluding short-term history as a factor in their responses. Corals
 experienced heat stress during the final 9 weeks of the study, during
 which the calcification assay was conducted (Fig. S1). Calcification rates
 were assessed via the buoyant weighing technique [8], with initial weights
 taken 3-15 August 2016 (shortly after the onset of thermal stress in the
 heated treatments), final weights taken 26 September – 8 October 2016
 (shortly after the seasonal peak in temperatures), and calcification rates
 were normalized to initial weight. In total, the study was conducted over
 nearly 1 year with about 8 months of exposure under experimental treatment
 conditions, and the calcification assay was conducted over the last 8
 weeks of the experiment. <strong>Coral
 genotyping</strong> Multilocus genotyping of
 coral hosts was performed following published methods [9,10]. Briefly,
 total genomic DNA was isolated using the E.Z.N.A. Tissue DNA Kit (Omega
 Bio-tek, Inc., Norcross, GA, USA) following the manufacturer protocol.
 Amplicons were generated via PCR using microsatellite primers [11], but
 with short unique barcodes [12] added to each primer to identify each
 position in a 96 well plate. Amplicons were pooled equimolarly, and a
 dual-index system of adaptors was used to identify individuals on each
 plate and libraries were sequenced on an Illumina MiSeq platform (v3 2x300
 PE) at the Hawaiʻi Institute of Marine Biology. We used a custom
 bioinformatic genotyping workflow pipeline [9] to call alleles, which were
 then converted to GenoDive v. 2.0b27 [13] file format for analyses.
 Individual genotypes were created using two different methods. First, we
 used sequence length (equivalent to peak calling in a microsatellite
 fragment analysis *sensu *[14]), such that all sequences of the same
 length, regardless of underlying sequence variation, would be scored as
 the same allele (sequence length). Second, we identified alleles by their
 sequence (ID) so that only two exactly identical alleles had the same ID,
 whereas alleles with the same length but differing in nucleotide
 composition would have different allelic IDs. Similar to previous findings
 [9], both approaches gave the same result. Using the 'assign
 clones' feature of GenoDive [15], we tested whether coral colonies
 sampled in the field had a unique multilocus genotype. To be conservative,
 we allowed for up to 2 scoring errors among individuals and checked
 potential clones against the location of collection. 
 <strong>Historical temperature
 tolerances</strong> Over the last 50 years, the
 mean seawater temperature around O‘ahu has warmed at a rate of 1.9 °C
 century-1 and acidified at a rate of 0.13 pH units century-1 [16,17] (but
 note that pH data first become available in 1992) (Fig. S2). The rate of
 acidification will accelerate in the future as a consequence of reduced
 seawater buffer capacity [18]. In 1970, survivorship of corals exposed to
 temperatures near their upper thermal limits (31.0 °C) were assessed for
 two of the species included in this study (*Montipora capitata* and
 *Pocillopora acuta)* [19]. In 2017 this study was repeated (31.4 °C) [16]
 (data shown in the original publications). We examined changes in their
 temperature tolerances by fitting logistic regressions to survivorship
 using the R package “MASS” in the following way (given differences in data
 overlap among datasets). For *Montipora capitata* we assessed the LD20
 (Degree Heating Weeks, DHW, which result in 20% mortality at these upper
 thermal limits) and for *Pocillopora acuta* we assessed the LD50 (DHW
 which result in 50% mortality at these upper thermal limits) in each of
 1970 and 2017, again using “MASS” to define these values. These fits
 resulted in the following estimates: LD20 for *Montipora capitata *of
 1.30±0.39 and 20.17±2.46 DHW in 1970 and 2017, respectively; LD50 for
 *Pocillopora acuta* of 1.47±0.56 and 18.68±1.24 DHW in 1970 and 2017,
 respectively. We then estimated the change in these DHW tolerances for
 each species as proxies for their responses to selection (R) over the
 period 1970-2017, as described below.
 <strong>Statistics</strong>: To examine treatment
 effects on calcification rates, for each species individually, an ANOVA
 was fit with temperature, pH, and collection site as fixed factors, and
 coral colony (genet), header tank, and mesocosm as nested factors. Due to
 the smaller sample size for *Montipora flabellata*, there were an
 insufficient number of degrees of freedom to fit the full model. Instead,
 we first fit a model with all factors included except coral genet.
 Mesocosm effects were not significant, so this factor was dropped and a
 second model was then fit which included genet. Model fits were assessed
 via diagnostic plots of the residuals and in all cases the data adequately
 satisfied ANOVA assumptions. A Tukey HSD was used as a *post hoc* to
 examine significant treatment effects. Broad sense
 heritability (H2) was estimated using a Bayesian modelling approach,
 similar to that used in previous work [20,21]. The models were fit with
 the R package “MCMCglmm” [22] with temperature and pH as fixed effects and
 coral genet, collection site, header tank, and mesocosm as random effects.
 Models were run for 100,000 iterations, storing the Markov chain every 50
 iterations, and with the first 15,000 iterations used as a burn-in period.
 Heritability was estimated as the proportion of the phenotypic variance
 which was explained by genotype [20,21].           To
 test for possible tradeoffs between temperature and pH tolerance, and the
 hypothesis that these tolerances are related to a general stress-tolerance
 strategy, the association between temperature and pH response was examined
 for each species using Pearson’s correlation. Temperature tolerance was
 calculated as the change in mean calcification rate between the control
 and the ocean warming treatment, whereas pH tolerance was the difference
 between the control and the ocean acidification treatment. Further, we
 considered if there were interactive effects between temperature and pH on
 calcification rates identified by the ANOVAs, as another indicator of
 tradeoffs in these tolerances. For *Montipora capitata*
 and *Pocillopora acuta* we estimated their responses to selection (R)
 based on changes in their LD20 and LD50 values, respectively, from 1970 to
 2017. The error associated with these estimates was determined from Monte
 Carlo simulations using the R package “propagate”, and derived from
 100,000 simulations. Along with our heritability estimates, as described
 above, as well as the classical (univariate) breeder’s equation, R = H2S,
 we estimate the selection coefficients for these species under the
 selective pressure that they have experienced over the last 50 years,
 where H2 is the broad sense heritability (including additive, dominance
 and epistatic variance) which represents the proportion of the selection
 differential (S) that can be realized as the response (R) to selection
 [20,23–25], and with the error associated with S propagated in the same
 way. Again, we assume that broad sense heritability (H2) provides an upper
 bound for narrow sense heritability (h2), though the two values are likely
 similar if these traits are influenced by many genetic loci. Selection
 differentials for the remaining 6 species are unknown, so to be
 conservative we assumed that they experience selection similar to that for
 *Montipora capitata* (the lower of the two selection coefficients) and
 estimated their responses to selection (R) based on these assumed values
 and their measured heritabilities, with uncertainties propagated as
 above. Analyses were performed in R v.4.0.3.
 [26]. Effects of unbalanced sampling design: We
 considered how an unbalanced sampling design might affect our estimates.
 In particular, our study was slightly unbalanced because most of the coral
 genets contributed one ramet per treatment whereas four genets per species
 contributed three ramets per treatment. In addition, a small percentage of
 the ramets died during the acclimation phase (18 nubbins, or 1.5% of the
 total, and affecting 12 of the 232 genets) resulting in representation of
 87-100% of the genets across all four treatments, depending on
 species. For the ANOVAs, this slight unbalance has very
 little effect. ANOVA is highly robust to modest discrepancies in sample
 size and missing observations, such as occur in this study. With the
 heritability estimates, modeling work [24,25] suggests that our sampling
 design results in a &lt;3% additional uncertainty in H2 for
 *Pocillopora acuta* and* Pocillopora meandrina*, and far less among the
 other species. This small uncertainty yields only a trivial effect on our
 estimates of selection coefficients (S) or responses to selection
 (R). <strong>Supplementary
 Methods:</strong> This study was conducted at the
 University of Hawai‘i at Mānoa, Hawai‘i Institute of Marine Biology (HIMB)
 on Moku o Lo‘e (Coconut Island), immediately adjacent to the island of
 O‘ahu in the Hawaiian Archipelago. After collection, coral colonies were
 fragmented into either 4 or 12 replicate nubbins, attached to ceramic
 tiles with cyanoacrylate gel, and allowed to recover under present-day
 average temperature and ambient sea water chemistry in a 40 tank mesocosm
 system for at least 2.5 months. The mesocosms received
 constant flow-through of unfiltered seawater drawn from the adjacent coral
 reef (2-3 m depth, depending on tide). Relative to the offshore source
 water, seawater temperature and chemistry are naturally modified by
 reef-associated physical and biogeochemical processes as the water flows
 through Kāne‘ohe bay. To restore water temperature and chemistry of the
 incoming seawater close to that of the original source water, all incoming
 seawater was directed into a mixing tank where temperature was adjusted
 using a commercial heat pump on a temperature controller and chemistry was
 adjusted with small additions of 1.0 N NaOH via a peristaltic pump to
 achieve average present-day offshore temperature and chemistry conditions
 in Hawaiʻi (temperature 23.5-27.5 °C, pH 7.97-8.07, annually). These small
 adjustments resulted in modifications to the temperature, pH, and total
 alkalinity in the seawater input which, when combined with the same
 physical and biogeochemical processes in the mesocosms, allowed us to
 achieve conditions very similar to those observed on the reef [27–29].
 This incoming water was then split off into a series of header tanks where
 it was heated or acidified according to treatment, with 2 replicate header
 tanks per treatment. Temperature was adjusted using commercial aquarium
 heaters on temperature controllers and seawater was dosed with CO₂ gas
 using high precision needle valves connected to venturi valves on aquarium
 pumps to deliver a precisely controlled quantity of CO₂ gas that was
 completely dissolved into the seawater in the header tanks before flowing
 into the mesocosms. The heated treatments were set to remain 2 °C above
 the control (i.e., present-day average) temperature, whereas the acidified
 treatments were maintained at 0.2 pH units below the control treatment,
 thereby replicating conditions expected at the end of the century. All
 mesocosms experienced natural daily and seasonal fluctuations in light,
 seawater temperature, and carbonate chemistry with appropriate offsets
 according to treatment (Fig. 3).  Two approaches were
 used to characterize the water temperature and chemistry in the mesocosms.
 First, water samples were taken from each mesocosm at 1200 hr local time
 once per week for total alkalinity and spectrophotometric pH, whereas
 salinity and temperature were measured with a YSI multimeter. All these
 procedures followed standard protocols [30]. The precision of these
 measurements were: pH ±0.002 units, salinity ±0.01 psu, temperature ±0.01
 °C, total alkalinity ±7 µmol kg-1. The accuracies of these measurements
 are estimated as: pH ±0.005 units, salinity ±0.3 psu, temperature ±0.1 °C,
 total alkalinity ±7 µmol kg-1. The accuracy and precision of total
 alkalinity titrations were assessed using Certified Reference Materials
 (CRMs) obtained from Andrew Dickson (Scripps Institution of Oceanography).
 Second, the temperature and chemistry measurements as described above,
 were assessed every 4 hr over the diel cycle once per quarter. For the
 remaining two months per quarter, bottle samples were taken for pH and
 total alkalinity only at 1200 and 0000 hr and a pH meter was used to
 assess pH at the other diel sampling points (1600, 2000, 0400, and 0800
 hr). The pH meter was empirically calibrated to the 1200 and 0000 hr pH
 bottle samples at the time of collection, yielding an uncertainty of ±0.02
 units for these sample points. The monthly diel samples were used to
 assess the hourly temperature and chemistry variation in the mesocosms as
 well as the daily mean values on those sampling dates. The weekly water
 samples, along with the empirically derived relationships between the
 offset of the mesocosm sea water to the incoming sea water measured at
 1200 hr and the daily mean values (characterized during the diel
 sampling), were used to estimate the daily mean parameters for these
 remaining dates. These estimates yielded the following uncertainties in
 the calculated daily means: salinity ±0.12 psu, pH ±0.03 units,
 temperature ±0.17 °C, and total alkalinity ±16 µmol kg-1. The remaining
 carbonate chemistry parameters were calculated with CO2SYS [31]. This
 sampling protocol yielded data resolution similar to or greater than that
 used in previous studies [8,32,33], but over a longer timeframe and at a
 much higher level of replication.  To reach initial
 target treatment temperature and pH values at the beginning of the
 experiment, adjustments were made in 0.5 °C and 0.03-0.05 pH unit
 increments every 10 days (starting on 1 February 2016 from baseline values
 that were intermediate between the temperature and pH treatment levels)
 until target values were reached (20 February 2016). The slow ramp to
 target temperature and pH during the winter minimized the chances of
 shocking the communities and was slower than many natural warming events.
 The design included 40 mesocosms (0.5×0.5×0.3 m, 70 L). The mesocosms were
 randomly assigned to treatment. Seawater inflow rate was adjusted to 1.2 L
 min-1 for a residence time of 1 hr in all mesocosms. Additional water
 circulation was generated within each mesocosm by submerged seawater pumps
 with one Maxi-Jet Pro propeller pump (4900 L hr-1) in each 70 L mesocosm.
 Flow speeds in the vicinity of the benthic communities (10-15 cm s-1) were
 estimated from visual inspection of particle tracks and verified with an
 independent product review of the pumps [34]. Changes
 in seawater temperature and chemistry near O‘ahu were estimated using
 three datasets. First, temperature was derived from a dataset collected
 off Koko Head on the south-east shore of O‘ahu from 1970-1992. Second,
 temperature was provided by a global climate product (IGOSS) for the area
 around O‘ahu (1992-2020) and adjusted by -0.19 °C to correspond to the
 Koko Head record immediately offshore of O‘ahu, as described
 previously[35]. The pH record was derived from values for the upper 30 m
 of the water column as measured at Station ALOHA, north of O‘ahu
 [36]. The 40 mesocosms were initially stocked with a 2
 cm layer of carbonate reef sand and gravel as well as pieces of reef
 rubble (3 replicate 10-20 cm pieces randomly divided among mesocosms)
 collected from the adjacent reef, thereby including natural infaunal and
 surface-attached communities. In addition, a juvenile (3-8 cm) Convict
 surgeonfish (*Acanthurus triostegus*) and Threadfin butterflyfish
 (*Chaetodon auriga*), 5 herbivorous reef snails (*Trochus* sp.), and the
 eight regionally most common reef-building coral species were added to the
 mesocosms. Over the course of the experiment, additional *Trochus* snails
 recruited into the mesocosms, ranging from about 10-40 snails per mesocosm
 at the end. Corals were attached to ceramic plugs with cyanoacrylate gel,
 to allow them to be moved or manipulated during the experiment. The corals
 and rubble were placed on a plastic grate 6 cm above the bottom soft
 sediments, to simulate their attachment to hard substrate in nature. The
 surgeonfish is a generalist grazer on benthic algae whereas the
 butterflyfish is a generalist grazer on non-coral invertebrates. Together,
 the fish provided the essential ecological functions of herbivory and
 predation in the mesocosms, and at fish biomass values similar to those
 reported for Hawaiian reefs[37]. Fish were supplementally fed 3 g wet
 weight of frozen mysis or brine shrimp daily (which they consumed within
 about 5 min), thereby provisioning the fish and wider reef communities
 with allochthonous (i.e., non-local, imported) zooplankton at a rate
 similar to that measured in nature [38]. The eight
 coral species included in the mesocosms (*Montipora capitata, Montipora
 flabellata, Montipora patula, Pocillopora acuta, Pocillopora meandrina,
 Porites compressa, Porites evermanni,* and *Porites lobata*) are the
 dominant species across the Hawaiian archipelago and collectively comprise
 &gt;95% of the coral cover on Hawaiian reefs [39,40]. Historically,
 four of these species were once thought to be Hawaiian endemics
 (<em>Montipora flabellata</em>, <em>Montipora
 patula,</em> <em>Porites compressa</em>, and
 <em>Porites evermanni</em>), though more recent analyses have
 shown that all eight species (or their genetically indistinguishable kin)
 are in fact widespread across the Indo-Pacific [41–43]. These eight
 species represent both major lineages of reef-building corals (Complexa
 and Robusta) [44], and all  four of the major life history strategies
 exhibited by corals[45] including competitive (<em>Montipora
 capitata</em>, <em>Pocillopora meandrina</em>, and
 <em>Porites compressa</em>), generalist (<em>Montipora
 flabellata</em> and <em>Montipora patula</em>, inferred
 from the ecologically similar <em>Montipora
 monasteriata</em>), stress-tolerant (<em>Porites
 lobata</em> and Porites <em>evermanni</em>, with the
 strategy for <em>Porites evermanni </em>inferred from the
 ecologically similar <em>Porites lutea</em>), and weedy
 strategies (<em>Pocillopora acuta</em>, inferred from its
 ecologically similar sister species *Pocillopora damicornis*). These
 species also differ in trophic strategies [46] and microbial composition
 [47]. In prior thermal stress events these species have shown differing
 resistance to bleaching ranging from low (<em>Montipora flabellata,
 Pocillopora acuta, </em>and <em>Pocillopora
 meandrina)</em>, to moderate (<em>Montipora capitata,
 Montipora patula, Porites compressa,</em> and <em>Porites
 lobata</em>), to high (<em>Porites evermanni</em>) [35].
 They also include the major reef-building coral families worldwide
 (Acroporidae, Pocilloporidae, and Poritidae) yielding broad ecological and
 spatial relevance to this study. Corals were collected at 2±1 m depth from
 a total of six locations around O‘ahu [28] between 17 August 2015 and 13
 November 2015, and conspecifics were separated by at least 5 m to minimize
 the chances of accidentally sampling clones or biasing the sampling
 towards particular microenvironments [28]. All coral parent colonies
 sampled for this study were genotyped to ensure that they were distinct
 genets and results were not biased by inclusion of clonally derived
 colonies. After collection, the corals were allowed 12 weeks to recover
 and acclimate to the mesocosm system under the same temperature and pH
 conditions, thereby standardizing their short-term histories prior to
 beginning the experiment on 1 February 2016. Sunlight
 was attenuated with 30% shade cloth to provide irradiance similar to that
 at mean collection depth (2 m) with a maximum instantaneous irradiance of
 about 1730 µmol m-2 s-1 in the mesocosms and 2470 µmol m-2 s-1 in the air.
 A handheld quantum meter was used to take periodic spot checks to ensure
 that the desired level of shading was achieved. Light spectrum was not
 adjusted due to the trivial differences between 0.3 m (mesocosm depth) and
 2 m (mean collection depth). The nominal coral bleaching threshold (27.98
 °C) was estimated as the mean monthly maximum temperature for the Main
 Hawaiian Islands (26.98 °C) [48] +1 °C.
 <strong>References</strong> 1.    
     Bahr KD, Tran T, Jury CP, Toonen RJ. 2020 Abundance, size, and
 survival of recruits of the reef coral Pocillopora acuta under ocean
 warming and acidification. *PLOS ONE* **15**, e0228168.
 (doi:10.1371/journal.pone.0228168) 2.         McLachlan
 RH, Price JT, Muñoz-Garcia A, Weisleder NL, Levas SJ, Jury CP, Toonen RJ,
 Grottoli AG. 2022 Physiological acclimatization in Hawaiian corals
 following a 22-month shift in baseline seawater temperature and pH. *Sci.
 Rep.* **12**, 3712. (doi:10.1038/s41598-022-06896-z) 3.
         Henley EM, Bouwmeester J, Jury CP, Toonen RJ, Quinn M, Lager CVA,
 Hagedorn M. 2022 Growth and survival among Hawaiian corals outplanted from
 tanks to an ocean nursery are driven by individual genotype and species
 differences rather than preconditioning to thermal stress. *PeerJ* **10**,
 e13112. (doi:10.7717/peerj.13112) 4.         Timmers
 MA, Vicente J, Webb M, Jury CP, Toonen RJ. In press. Sponging up
 diversity: Evaluating metabarcoding performance for a taxonomically
 challenging phylum within a complex cryptobenthic community. *Environ.
 DNA* **n/a**. (doi:10.1002/edn3.163) 5.         Vicente
 J, Timmers MA, Webb MK, Bahr KD, Jury CP, Toonen RJ. 2022 Ecological
 succession of the sponge cryptofauna in Hawaiian reefs add new insights to
 detritus production by pioneering species. *Sci. Rep.* **12**, 15093.
 (doi:10.1038/s41598-022-18856-8) 6.         Vicente J,
 Webb MK, Paulay G, Rakchai W, Timmers MA, Jury CP, Bahr K, Toonen RJ. 2022
 Unveiling hidden sponge biodiversity within the Hawaiian reef cryptofauna.
 *Coral Reefs* **41**, 727–742. (doi:10.1007/s00338-021-02109-7)
 7.         Timmers MA, Jury CP, Vicente J, Bahr KD, Webb MK,
 Toonen RJ. 2021 Biodiversity of coral reef cryptobiota shuffles but does
 not decline under the combined stressors of ocean warming and
 acidification. *Proc. Natl. Acad. Sci.* **118**, e2103275118.
 (doi:10.1073/pnas.2103275118) 8.         Jokiel PL,
 Rodgers KS, Kuffner IB, Andersson AJ, Cox EF, Mackenzie FT. 2008 Ocean
 acidification and calcifying reef organisms: a mesocosm investigation.
 *Coral Reefs* **27**, 473–483. (doi:10.1007/s00338-008-0380-9)
 9.         Cros A, Toonen RJ, Davies SW, Karl SA. 2016 Population
 genetic structure between Yap and Palau for the coral Acropora hyacinthus.
 *PeerJ* **4**, e2330. (doi:10.7717/peerj.2330) 10.    
   Cros A, Toonen RJ, Donahue MJ, Karl SA. 2017 Connecting Palau’s marine
 protected areas: a population genetic approach to conservation. *Coral
 Reefs* **36**, 735–748. (doi:10.1007/s00338-017-1565-x)
 11.       Concepcion GT, Polato NR, Baums IB, Toonen RJ. 2010
 Development of microsatellite markers from four Hawaiian corals: Acropora
 cytherea, Fungia scutaria, Montipora capitata and Porites lobata.
 *Conserv. Genet. Resour.* **2**, 11–15.
 (doi:10.1007/s12686-009-9118-4) 12.       Faircloth BC,
 Glenn TC. 2012 Not All Sequence Tags Are Created Equal: Designing and
 Validating Sequence Identification Tags Robust to Indels. *PLOS ONE*
 **7**, e42543. (doi:10.1371/journal.pone.0042543) 13.  
     Meirmans P. In press. GenoDive version 2.b14. 14.  
     Selkoe KA, Toonen RJ. 2006 Microsatellites for ecologists: a practical
 guide to using and evaluating microsatellite markers. *Ecol. Lett.* **9**,
 615–629. (doi:10.1111/j.1461-0248.2006.00889.x) 15.    
   Meirmans PG, Tienderen PHV. 2004 genotype and genodive: two programs for
 the analysis of genetic diversity of asexual organisms. *Mol. Ecol. Notes*
 **4**, 792–794. (doi:10.1111/j.1471-8286.2004.00770.x)
 16.       Coles SL, Bahr KD, Rodgers KS, May SL, McGowan AE,
 Tsang A, Bumgarner J, Han JH. 2018 Evidence of acclimatization or
 adaptation in Hawaiian corals to higher ocean temperatures. *PeerJ* **6**,
 e5347. (doi:10.7717/peerj.5347) 17.       Dore JE,
 Lukas R, Sadler DW, Church MJ, Karl DM. 2009 Physical and biogeochemical
 modulation of ocean acidification in the central North Pacific. *Proc.
 Natl. Acad. Sci.* **106**, 12235–12240.
 (doi:10.1073/pnas.0906044106) 18.       Jury CP, Thomas
 FIM, Atkinson MJ, Toonen RJ. 2013 Buffer Capacity, Ecosystem Feedbacks,
 and Seawater Chemistry under Global Change. *Water* **5**, 1303–1325.
 (doi:10.3390/w5031303) 19.       Jokiel PL, Coles SL.
 1977 Effects of temperature on the mortality and growth of Hawaiian reef
 corals. *Mar. Biol.* **43**, 201–208. (doi:10.1007/BF00402312)
 20.       Wright RM, Mera H, Kenkel CD, Nayfa M, Bay LK, Matz MV.
 2019 Positive genetic associations among fitness traits support
 evolvability of a reef-building coral under multiple stressors. *Glob.
 Change Biol.* **25**, 3294–3304. (doi:10.1111/gcb.14764)
 21.       Muller EM, Dungan AM, Million WC, Eaton KR, Petrik C,
 Bartels E, Hall ER, Kenkel CD. 2021 Heritable variation and lack of
 tradeoffs suggest adaptive capacity in Acropora cervicornis despite
 negative synergism under climate change scenarios. *Proc. R. Soc. B Biol.
 Sci.* **288**, 20210923. (doi:10.1098/rspb.2021.0923)
 22.       Hadfield JD. 2010 MCMC Methods for Multi-Response
 Generalized Linear Mixed Models: The MCMCglmm R Package. *J. Stat. Softw.*
 **33**, 1–22. (doi:10.18637/jss.v033.i02) 23.      
 Falconer DS, Mackay TFC. 1996 *Introduction to quantitative genetics*.
 Fourth edition. Benjamin-Cummings. 24.       Schmidt P,
 Hartung J, Rath J, Piepho H-P. 2019 Estimating Broad-Sense Heritability
 with Unbalanced Data from Agricultural Cultivar Trials. *Crop Sci.*
 **59**, 525–536. (doi:10.2135/cropsci2018.06.0376) 25.
       Piepho H-P, Möhring J. 2007 Computing Heritability and Selection
 Response From Unbalanced Plant Breeding Trials. *Genetics* **177**,
 1881–1888. (doi:10.1534/genetics.107.074229) 26.      
 R Core Team. 2020 R: A language and environment for statistical
 computing. 27.       Shamberger KEF, Feely RA, Sabine
 CL, Atkinson MJ, DeCarlo EH, Mackenzie FT, Drupp PS, Butterfield DA. 2011
 Calcification and organic production on a Hawaiian coral reef. *Mar.
 Chem.* **127**, 64–75. (doi:10.1016/j.marchem.2011.08.003)
 28.       Jury CP, Delano MN, Toonen RJ. 2019 High heritability
 of coral calcification rates and evolutionary potential under ocean
 acidification. *Sci. Rep.* **9**, 20419.
 (doi:10.1038/s41598-019-56313-1) 29.       Guadayol Ò,
 Silbiger NJ, Donahue MJ, Thomas FIM. 2014 Patterns in Temporal Variability
 of Temperature, Oxygen and pH along an Environmental Gradient in a Coral
 Reef. *PLOS ONE* **9**, e85213.
 (doi:10.1371/journal.pone.0085213) 30.       Dickson
 AG, Sabine CL, Christian JR. 2007 Guide to Best Practices for Ocean CO2
 Measurements. 31.       In press. OCADS - Program
 Developed for CO2 System Calculations. See
 [https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/CO2SYS/co2rprt.html](https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/CO2SYS/co2rprt.html) (accessed on 22 March 2021). 32.       Comeau S, Cornwall CE, Pupier CA, DeCarlo TM, Alessi C, Trehern R, McCulloch MT. 2019 Flow-driven micro-scale pH variability affects the physiology of corals and coralline algae under ocean acidification. *Sci. Rep.* **9**, 12829. (doi:10.1038/s41598-019-49044-w) 33.       Dove SG, Brown KT, Van Den Heuvel A, Chai A, Hoegh-Guldberg O. 2020 Ocean warming and acidification uncouple calcification from calcifier biomass which accelerates coral reef decline. *Commun. Earth Environ.* **1**, 1–9. (doi:10.1038/s43247-020-00054-x) 34.       Riddle D. In press. Product Review: Maxi-Jet Pro Series Pumps. *Reefs.com*. See [https://reefs.com/magazine/product-review-maxi-jet-pro-series-pumps/](https://reefs.com/magazine/product-review-maxi-jet-pro-series-pumps/) (accessed on 22 March 2021). 35.       Jokiel PL, Brown EK. 2004 Global warming, regional trends and inshore environmental conditions influence coral bleaching in Hawaii. *Glob. Change Biol.* **10**, 1627–1641. (doi:[https://doi.org/10.1111/j.1365-2486.2004.00836.x](https://doi.org/10.1111/j.1365-2486.2004.00836.x)) 36.       Karl DM, Church MJ. 2019 Station ALOHA: A Gathering Place for Discovery, Education, and Scientific Collaboration. *Limnol. Oceanogr. Bull.* **28**, 10–12. (doi:10.1002/lob.10285) 37.       Gorospe KD, Donahue MJ, Heenan A, Gove JM, Williams ID, Brainard RE. 2018 Local Biomass Baselines and the Recovery Potential for Hawaiian Coral Reef Fish Communities. *Front. Mar. Sci.* **5**. (doi:10.3389/fmars.2018.00162) 38.       Hamner WM, Jones MS, Carleton JH, Hauri IR, Williams DMcB. 1988 Zooplankton, Planktivorous Fish, and Water Currents on a Windward Reef Face: Great Barrier Reef, Australia. *Bull. Mar. Sci.* **42**, 459–479. 39.       Rodgers KS, Jokiel PL, Brown EK, Hau S, Sparks R. 2015 Over a Decade of Change in Spatial and Temporal Dynamics of Hawaiian Coral Reef Communities1. *Pac. Sci.* **69**, 1–13. (doi:10.2984/69.1.1) 40.       Franklin EC, Jokiel PL, Donahue MJ. 2013 Predictive modeling of coral distribution and abundance in the Hawaiian Islands. *Mar. Ecol. Prog. Ser.* **481**, 121–132. (doi:10.3354/meps10252) 41.       Forsman ZH, Barshis DJ, Hunter CL, Toonen RJ. 2009 Shape-shifting corals: Molecular markers show morphology is evolutionarily plastic in Porites. *BMC Evol. Biol.* **9**, 45. (doi:10.1186/1471-2148-9-45) 42.       Boulay JN, Hellberg ME, Cortés J, Baums IB. 2014 Unrecognized coral species diversity masks differences in functional ecology. *Proc. R. Soc. B Biol. Sci.* **281**, 20131580. (doi:10.1098/rspb.2013.1580) 43.       Veron JEN, Stafford-Smith MG, Turak E, DeVantier LM. 2016 *Corals of the World,*. 44.       Stolarski J, Kitahara MV, Miller DJ, Cairns SD, Mazur M, Meibom A. 2011 The ancient evolutionary origins of Scleractinia revealed by azooxanthellate corals. *BMC Evol. Biol.* **11**, 316. (doi:10.1186/1471-2148-11-316) 45.       Darling ES, Alvarez‐Filip L, Oliver TA, McClanahan TR, Côté IM. 2012 Evaluating life-history strategies of reef corals from species traits. *Ecol. Lett.* **15**, 1378–1386. (doi:[https://doi.org/10.1111/j.1461-0248.2012.01861.x](https://doi.org/10.1111/j.1461-0248.2012.01861.x)) 46.       Price JT, McLachlan RH, Jury CP, Toonen RJ, Grottoli AG. 2021 Isotopic approaches to estimating the contribution of heterotrophic sources to Hawaiian corals. *Limnol. Oceanogr.* **66**, 2393–2407. (doi:10.1002/lno.11760) 47.       Price JT, McLachlan RH, Jury CP, Toonen RJ, Wilkins MJ, Grottoli AG. 2021 Effect of species, provenance, and coral physiology on the composition of Hawaiian coral-associated microbial communities. *Coral Reefs* **40**, 1537–1548. (doi:10.1007/s00338-021-02164-0) 48.       NOAA Coral Reef Watch. 2018 NOAA Coral Reef Watch Version 3.1 Daily Global 5-km Satellite Coral Bleaching Degree Heating Week Product.
description: # Widespread scope for coral adaptation under combined ocean warming and
 acidification
 [https://doi.org/10.5061/dryad.kwh70rzdc](https://doi.org/10.5061/dryad.kwh70rzdc) ## Description of the data and file structure Included are seawater temperature data collected near O‘ahu, Hawai‘i, as well as pH data collected at Station ALOHA, north of O‘ahu. Also we include temperature and pH data from the experiment and calcification data for the corals. We have also provided the R code for all data visualization and analyses. ### Files and variables #### File: Heritability\_2.0\_code.R **Description:** R code for visualization and analyses. #### File: Koko\_Head\_and\_IGOSS\_temp\_combined.xlsx **Description:** Seawater temperature measured near O'ahu since 1970. ##### Variables * *Date.combined*: Date of the combined Koko Head and IGOSS temperature datasets. * *Day.since.1970*: Number of days since January 1, 1970. * *Temp.combined*: Temperature data for combined Koko Head and IGOSS datasets (°C). #### File: Coral\_calif..xlsx **Description:** Coral calcification data from the experiment. ##### Variables * *Tank*: Numeric identifier for the mesocosm (numbered 1-40). * *Header*: Numeric identifier for the header tank (numbered 1-8). * *Temp*: Temperature treatment according to target values (low vs. hi). * *CO2*: Carbon dioxide treatment according to target values (low vs. hi). * *Treatment*: Overall treatment (Control, Acid, Heat, or Heat.acid, for the control, ocean acidification, ocean warming, and combined future ocean treatments, respectively). * *Spp*: Coral species (M. cap, M. flab, M. pat, P. com, P. ever, P. lob, P. dam, and P. mea for *Montipora capitata*, *Montipora flabellata*, *Montipora patula*, *Porites compressa*, *Porites evermanni*, *Porites lobata*, *Pocillopora acuta*, and  *Pocillopora meandrina*, respectively). * *Site*: Coral collection location (HIMB, Samp, Wai, Magi, Kahe, or Hale, for Moku o Lo‘e, Sampan Channel, Waimānalo, Magic Island, Kahe Point, and Haleiwa, respectively). * *Colony.number*: Arbitrary numeric used to identify coral genet when combined with site and species. * *Nubbin.number*: Arbitrary numeric used to identify coral ramet when combined with site, species, and colony number. * *Colony.ID*: Unique identifier for each coral ramet according to site, species, and genet. * *Growth.0.3*: Coral calcification data (mg g-1 d-1). #### File: Mesocosm\_env\_data.xlsx **Description:** Environmental and carbonate chemistry data from the mesocosms. ##### Variables * *Date*: Sampling date. * *Day.past.1.1.16*: Day past January 1, 2016. * *Treatment*: Treatment (AT ACO2, AT HCO2, HT ACO2, or HT HCO2 for the control, ocean acidification, ocean warming, and combined future ocean treatments, respectively). * *Salinity*: Estimated daily mean salinity according to treatment (psu). * *Temp (in situ)*: Estimated daily mean temperature (°C) according to treatment. * *TA (FTA)*: Estimated daily mean total alkalinity (µeq kg-1) according to treatment. * *pH out*: Estimated daily mean *in situ* pH according to treatment (reported on the Total hydrogen ion scale). * *fCO2 out (uatm)*: Estimated *in situ* daily mean fugacity of CO2 according to treatment. * pCO2 out (uatm): Estimated *in situ* daily mean partial pressure of CO2 according to treatment. * *HCO3 out (umol/kgSW)*: Estimated *in situ* daily mean bicarbonate ion concentration according to treatment. * *CO3 out (umol/kgSW)*: Estimated *in situ* daily mean carbonate concentration according to treatment. * *CO2 out (umol/kgSW)*: Estimated *in situ* daily mean dissolved CO2 concentration according to treatment. * *B Alk out (umol/kgSW)*: Estimated *in situ* daily mean borate alkalinity according to treatment. * *OH out (umol/kgSW)*: Estimated *in situ* daily mean hydroxide ion concentration according to treatment. * *P Alk out (umol/kgSW)*: Estimated *in situ* daily mean phosphate alkalinity according to treatment. * *Si Alk out (umol/kgSW)*: Estimated *in situ* daily mean silicate alkalinity according to treatment. * *Revelle out*: Estimated *in situ* daily mean Revelle factor according to treatment. * *WCa out*:  Estimated *in situ* daily mean calcite saturation state according to treatment. * *WAr out*: Estimated *in situ* daily mean aragonite saturation state according to treatment. * *xCO2 out (dry at 1 atm) (ppm)*: Estimated *in situ* daily mean partial pressure of CO2 in dry air according to treatment. * *Salinity.SD*: Standard deviation of estimated daily mean salinity according to treatment (psu). * *Temp (in situ).SD*: Standard deviation of estimated daily mean temperature (°C), according to treatment. * *TA (FTA).SD*: Standard deviation of estimated daily mean total alkalinity (µeq kg-1) according to treatment. * *pH out.SD*: Standard deviation of *in situ* estimated daily mean pH according to treatment (reported on the Total hydrogen ion scale). * *fCO2 out (uatm).SD*: Standard deviation of *in situ* estimated daily mean fugacity of CO2 according to treatment. * *pCO2 out (uatm).SD*: Standard deviation of *in situ* estimated daily mean partial pressure of CO2 according to treatment. * *HCO3 out (umol/kgSW).SD*: Standard deviation of *in situ* estimated daily mean bicarbonate ion concentration according to treatment. * *CO3 out (umol/kgSW).SD*: Standard deviation of *in situ* estimated daily mean carbonate ion concentration according to treatment. * *CO2 out (umol/kgSW).SD*: Standard deviation of *in situ* estimated daily mean dissolved CO2 concentration according to treatment. * *B Alk out (umol/kgSW).SD*: Standard deviation of *in situ* estimated daily mean borate alkalinity according to treatment. * *OH out (umol/kgSW).SD*: Standard deviation of *in situ* estimated daily mean hydroxide ion concentration according to treatment. * *P Alk out (umol/kgSW).SD*: Standard deviation of *in situ* estimated daily mean phosphate alkalinity according to treatment. * *Si Alk out (umol/kgSW).SD*: Standard deviation of *in situ* estimated daily mean silicate alkalinity according to treatment. * *Revelle out.SD*: Standard deviation of *in situ* estimated daily mean Revelle factor according to treatment. * *WCa out.SD*: Standard deviation of *in situ* estimated daily mean calcite saturation state according to treatment. * *WAr out.SD*: Standard deviation of *in situ* estimated daily mean aragonite saturation state according to treatment. * *xCO2 out (dry at 1 atm) (ppm).SD*: Standard deviation of *in situ* estimated daily partial pressure of CO2 in dry air according to treatment. #### File: Station\_ALOHA\_pH.xlsx **Description:** Sea water pH data from Station ALOHA. ##### Variables * *Column 1*: Sampling date. * *Column 2*: Arbitrary numeric associated with sampling date. * *Column 3*: Measured in situ pH (reported on the Total hydrogen ion scale). ## Code/software R is required to run Heritability_2.0_code, which is annotated throughout. The remaining datasets can be viewed in Microsoft Excel. ## Access information Other publicly accessible locations of the data: * Data are available through Dryad Data was derived from the following open-access sources: * Hawaii Ocean Time-Series * NOAA * IGOSS