Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. i McClanahan Tim biodiversity marine global the in center corals of resistance thermal Higher Muthiga nmle Gete l 02 clnhn21;Hge ta.21a.Hwvr h eitne(=expo- understood warm-SST resistance being experiencing the slowly after However, only and is 2019a). variability 2019), of al. al. scales et spatial et Hughes large Sully protein 2017; envi- at 2018; changing (SST) corals McClanahan by scleractinian al. temperature 2012; adapt of sea-surface et or sure/sensitivity) al. variable Safaie acclimatize et in needs 2016; symbionts corals (Guest environmental of that pressing anomalies al,. switching most shown et Earth’s have 2014), (Boulette the studies ronments al. among Localized et is (Palumbi change 2017). expressions climate to al. adapt et to (Hughes corals of capacity The acclimation/adaptation. future in Consequently, differences Introduction geographic sites. strong Triangle for non-Coral the account but than to significant resistance need statistically higher will were having stress factors of Triangle thermal influences Most Coral the of genera. for - predictions historical coral models geography as of possible was number 128 estimated factor and by evaluated was cover, strongest estimated geographic was was variation coral Exposure sensitivity, sensitivity resistance variation, Site geographic Site and SST (CE). observations. flow for event. historical exposure water bleaching geography, and account global-bleaching thermal coordinated light, using of SST, 2016 poorly sites of ratio index 226 the and multivariate for the a during exposure and on (CTA) estimated thermal heat Based summer was on excess cumulative stress. resistance based thermal coral largely to of are resistance variability reef and coral sensitivity biological of in future the for Predictions Abstract 2020 5, May 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Shedrawi George Mangubhai h tt nvriyo azbrSho fNtrladSca Sciences Social and Natural Island of Rhode School of Zanzibar Wildlife University of and University Parks State of The Department Australia Western of Government Foundation Conservation Nature Madagascar Society Conservation Mombasa Wildlife Kenya Society Conservation Wildlife University Macquire University Macquarie Society Conservation Wildlife UNMaldives IUCN Park Marine Coelacanth Tanga Park Marine Island Mafia Campus Bentley University Curtin India Society Conservation Wildlife 4 uinLeblond Julien , 1 l Ussi Ali , 11 1 oehMaina Joseph , uisPagu Julius , 8 ieleGuillaume Mireille , 5 oa Arthur Rohan , 12 aur Ndagala January , 2 ml Darling Emily , 6 8 tc Jupiter Stacy , utnHumphries Austin , 1 1 tpai D’agata Stephanie , 13 n are Grimsditch Gabriel and , 1 hu Wilson Shaun , 9 ada Patankar Vardhan , 3 Nyawira , 7 Sangeeta , 14 10 , Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. h otl enSTciaooy(..MMciaooyo toge l 04.W hncluae the calculated then We 2004). 5-km of al. maximum the et the from Strong web- to of series referenced NOAA climatology anomalies time the Hooidonk MMM SST SST van from (i.e. positive daily available climatology as 2010; SST extracted defined 2015, mean we hotspots, and al. monthly monthly site, the 1985 et calculate each between (Eakin to For the used reefs products measurements were or assess temperature coral 3.1 2019). months, Daily to version of Carilli (https://coralreefwatch.noaa.gov/satellite/bleaching5km/index.php). metric or Watch state site used weeks & Reef future commonly ex- heating Donner Coral most the of degree 2013; the NOAA and cumulative metric is bleaching weighted of and al. a concept coral baseline, et of summer into the of index a flow on (cu- probability environmental above water based exposure the multivariate temperatures and is a temperature excess winds on model of excess CTA amount based of temperature, The one variable integrates and single that posure. model) a (CE) CTA on exposure the based climate weeks, one heating were models, degree mulative possibilities exposure two these considered of We number weighted, a be Therefore, to either needs sensitivity. as Exposure often calculated and and be exposure below. can units, described between resistance and different sensitivity ratio Moreover, evaluated having and or Exposure comparisons. variables, difference appropriate it. multiple the calculate for or to normalized ways single or and biolog- by variables standardized, and potential measured exposure of environmental be number between balance a can the are estimate 2001). There to sensitivity. al. used ical metric et light-absorbing system-level McClanahan in a decline 2001; is the Resistance al. for et proxy (Fitt a mortality methods color, coral and lost we to Materials that here lead corals but can of forms, to which percentage various measurements densities, take the field symbiont can as developed algal Sensitivity have it sensitivity. coral biologists document SSTs of ecological field and of observations therefore reef define sum field and coral with incremental 2018). bleaching Additionally, weighted associated cumulative coral a al. 2008). strongly is estimate the et which and al. variables (CE), - Muller-Kager et flow exposure 2011; stress (Maina water climate from 2008; bleaching of temperature and derived metric light, al. of multivariate heat, et a models metric combining (2) Maina two single index and 2010; (CTA), a considered SSTs that al. summer (1) variables we et local ocean above subsequent (Eakin exposure, models: essential corals and two reef of to number used sensitivity coral stress We a estimate coral evaluate for to To variable proxies perturbations used provided by are of that 2019). change, attenuated measurements extent climate satellite potentially with and AVHRR al. NOAA increases also duration, et perturbations but degree, light (Sully and threatened the evaluated resistance heat We is as to resilience exposure exposure 1a). coral define reef ecological (Fig. As of We 2016 sensitivity levels. resulting in background the exposure. gradient beyond to and stress surveys geographic that heat bleaching large to extreme coral large to a communities of exposure across across effort components: al. stress two coordinated untested et through heat globally currently McManus resistance remain to a 2013; are resistance with patterns observations al. that coral These SST et evaluate models Freeman satellite combine 2013; impact 2019). we al. climate Here, et future al. (Couce calibrating exposure et thermal for (Edmunds Sully projected 2019). critical composition and 2014; current be taxonomic on will and al. based but history stress, et evolutionary scales stress Palumbi thermal and thermal biogeographic to geographical 2008; to in adaptation exposure Gates variation diversity, or by & influenced genotypic conditions bleach be environmental increasing al. associated may should current the et reefs and resistance some Under (Hughes and Ultimately, whereby stress historical resistance, 2012). thermal 1a). inform on of al. to (Fig. based potential indicator et its expected obvious (McClanahan for and than considered disturbance early less often resilience a an less reef survive is is coral Bleaching and bleaching of component resist coral 2018). key change, to a climate reef and of stress, a impacts to likely of exposed and ability when variable change the geographically system is or of stress measure heat a to is corals Resistance of histories. resistance environmental that differential show the we by Here, driven 2019). al. et (Sully 2 Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. fbece oaswr vlae o hi rdciesrnt ae nACvle frltosiswith relationships of values AIC percentage on as based sensitivity strength ecological predictive and their observations) for temperature evaluated were (satellite corals exposure bleached the to of susceptibility of estimates two higher timing The a bleaching. p The with to 0.92, susceptible assemblage = less a summed. coral (r is provides a that and correlated susceptibility assemblage indicate observations highly coral Bleaching values were a recent taxon. indicate higher or values each where lower historical for by and site, observations bleaching multiplied either each intensity was on for bleaching taxa number based each 2016 single taxa of the abundance that used relative estimate we for to the the Here, intensity used weight where bleaching metric to bleaching, related categories mean to a these community the is uses the susceptibility seven Bleaching and of into bleached 2007). white sensitivity colonies al. bleached bleached fully the placed et 100% to metric of (McClanahan to intensity bleaching pale percentage bleaching normal of were or weighted from responses that weighted ranging a The colonies bleaching sampled. and coral of corals unweighted of categories all percentage an of percentage the both the calculated as using method ways, unweighted two The in corals. estimated leading taxa was variables coral the Sensitivity of colonies, more observed of of one analyses. analyses number missing some Data the were in summarized sites coral sizes Three the hard sample estimated site. lower and also each slightly 5% We for to management. nearest abundance and the relative type, and to habitat (McClanahan richness, depth, quadrat bleaching site’s of each measure the in weighted recorded a cover also and We colonies 2007). coral al. bleaching of et percent average the estimate to of Within series bleaching. a for categories: bleached; colonies following them in the coral scored colonies using hard and severity coral bleaching taxa identified to individual we colonies quadrat, for identified each bleaching between subsequently countries and of where 12 quadrats severity methodology m photographed in observer and (˜1.5 roving sites quadrats frequency a 226 replicated used the the haphazardly We SSTs evaluates sampled 2016. summer We September observer post-peak and 2001). an March the al. between after et months undertaken summer (McClanahan were during during All greatest bleaching event. coral was anomaly of bleaching surveys thermal when coordinated global Ni˜no on and based El were 2016 sensitivity ecological the coral estimate to work more Field were means CTA the ( Sensitivity found models ends sites exposure high 1). 226 the two Fig. towards the the (Supplementary frequently to for skew Comparing more relationship values distributed strong were 2008). 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Data may be preliminary. etlfre vrteps w eae,adatiue ftecrltx n omnte.W specifically We communities. and taxa coral the of attributes environ- recent and hypotheses location, decades, the biogeographical two tested by past and influenced were the sensitivity, as over by site geographically divided forces (Supple- and each mental exposure study locally as this for differed of resistance coverage values resistance estimated geographic that then the second We across 1). spread and Fig. good first show mentary metrics The regions. sensitivity and two R. exposure the Both between in differences package for vegan tested Community and multivariate the by extracted evaluated using to were non- communities pooled Most therefore Analysis Coral were (n=199). regions. and data two Correspondence Temperature Triangle tests) the variables. Coral in all KSL, major distributions of the Kolmogorov-Smirnov-Lilliefors, their comparisons by (using outside visualize for bleaching undertaken normality and were of and (n=27) tests Wilcoxon tests communities, within parametric pass variables coral to comparable environments, failed all thermal data with resistance, sites (Burnham in for models models difference taxon set best top for not the the tested but of We significant results selecting the subjectively all 1998). computed of present Anderson were changes We and the sub-models against R. reduces CE) possible in models vs All package (CTA possible MuMIn structure. models values the error AICc exposure fit from log-link delta two and where function Gaussian the framework dredge of a inference each the with multi-model from using variables approach, a calculated predictor (GLM) used ratio 7 Model we resistance the Thereafter, Linear the the with found. 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Data may be preliminary. a h ecnaeo lahdcrl,tewihe n negtdbecigrsossadtepercentage the and differ responses consistently bleaching did unweighted What and 4). weighted Fig. the 2, corals, (Table bleached community’s geographies of the between percentage and differ composition the differences taxonomic not was to relative did attributable the bleaching cover, be coral to cannot that susceptibility found sites was We non-Triangle communities. kurtosis and coral Triangle when the Coral in negative. increased between was skewness resistance and warm-water in declined Differences but skewness sites increased, Triangle kurtosis non- as Triangle and, in Coral 2.5 high. kurtosis variable distinctions. 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Data may be preliminary. ta.21)hsteptnilt emdfidb te akrudSTdsrbto atr n o utmean just not and factors distribution SST Sully between background 2018; other relationships al. by complex et modified (Claar be the to association in potential SSTs-bleaching clearly heavy- the mean has been by most the 2019) repeatedly negatively Thus, differ al. have skewness. et and and and SSTs mean resistance kurtosis, background by and variance, Indian Consequently, mean influenced the sensitivity 2018). of positively influence al. scale to be et basin shown to (Ateweberhan the On distributions found processes. SST was acclimation distribution SST richness tailed these coral right taxonomic of influence Both Skewed coral to positive corals. Additionally, 2018). expected all Ocean, corals. of be al. cover stress-resistant would lower et variables with of (Zinke associated shape cover was lower abundance distributions, with heavy-tailed coral or associated were predicting kurtosis, skewness example, and for for kurtosis variables were, SST Australian, large- top distributions heavy-tailed western a the – in in composition Further, shapes among community 2010). distribution and frequently McClanahan cover and & coral variation (Ateweberhan of SST mortality study coral by scale coral in influenced higher change was with example, For event associated studies. bleaching distributions other 1998 in patterns predictions. the and similar over weaken studies, produced cover can have ground-truthing that distributions most observations SST in anomalous troubles Differences mismatch some that scale and a problem is errors there a in that observations, results appreciated bleaching be can should and It km communication). exposure expected 5 mid- personal than at between S. and lower Wilson, even retained Tringle 2006; produced measurements al. Coral-non-Coral that Satellite Ningaloo localized et eddies 2016). from (Woo the at arose offshore al. to Ningaloo by sites et in exception stirring (Xu outlier found and bleaching an resistance Statistical transport, expected being and geostrophic than 2016). by productivity, higher currents onshore the upwelling, al. insight temperatures, ocean that some et suggest clarity, radiation, We provide Gove patterns. water in may latitude 2005; variation in analysis al. variability localized that our et likely some localized in associated it (McClanahan provide creates but are may resistance Triangle investigation which that and Coral more kurtosis, downwelling require the high and skewness of to negative up- neutral the nature and of island skewness causes the The negative were resistance. having skewness increased in particularly with differs and belt. Triangle average kurtosis equatorial to Coral of this attributable The parameters along was variation sites shape bleaching distinguish that in to found variation likely we Some more 20 but 2019). equator - variation al. the (15 that SST et to latitudes indicates (Sully background close mid-tropical which equator in resistance equator, the reported in near the bleaching Variation Africa variability. CTA higher to East explain overall closer from the cannot distributed gradient explain alone east-west unequally may radiation the but thermal along higher to resistance is coral exposures resistance anomalies of Triangle Moreover, thermal drivers Coral warm Fiji. longitudinally inter-annual primary by and were to experience fluctuated variation environment Pacific ENSO long greater adaptive that western suggest the have the with findings of in ENSO Our part 2003). corals the be al. et most harbor of (Cobb and such corals strengths and millennium processes, for The oceanographic environment Pacific by (ENSO). the temperature influenced across Oscillation to been unique Ni˜no has related Southern Triangle El a clearly Coral as have not the Historically, is to exposure. that to appears resistance regionalism Triangle of levels Coral component resistance strong The different having a composition. taxa have species similar the to coral differential to among in appears their due communities differences resistance and likely in Thus, most exposure similarities differences were 4). to enough large-scale differences (Fig were coals the observed There of Yet, equatorial that level. 2019). responses warm sites taxa al. variable studied in the et the especially McManus at to corals, 2013; 2018). acclimation/adaptation attributable for al. al. spatial largely et et predictions were (Couce Beyer dire Triangle here 2016; produce Coral observed 2013, metrics the al., thermal as impacts et such future related Hooidonk and regions van and current model 1990; these to (Hoegh-Guldberg of used change metrics Use key climate the from are Coral CTAs refuge and the and rise, outside temperature of bleaching rates SSTs, higher Mean showed metrics All CTAs. 2016. and Discussion in SSTs bleached historical were mean lower that despite tax 6 2 a o atr hs oa rcse well processes local these capture not may o ept qa rhigher or equal despite ) Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. itosfrteftr tt fcrlressol s eitnemtisrte hnjs h nta and initial the just than rather attenu- and metrics delay may resistance Triangle use Coral as the should in mortality stress reefs pre- and thermal better coral bleaching, to Therefore, resistance CTAs, of contextual. Greater treat and metrics. state capacity reefs variable exposure event future highly higher coral projected stress is the a of thermal sensitivity for future with pan-tropical that this the dictions here associated during show predict be we observed that Yet, also as interchangeable. models may stress, Many heat forces 2014-2016. global historical of 2015). large-scale that Meyer as episodic contention environmental & tolerate include (Barber the to to forces support likely that level in are findings sea suggest biodiversity that changing Our We forces High and interacting kurtosis. disturbances. isolation, of complexity, neutral thermal number geologic we strong a and as from but episodic skew, well arisen 2010) to cold-SST has al. resistance Triangle to et Coral some neutral the McLeod provided The anomalies, 2009; patterns 2013). al. high heat (Pe˜naflor al. et of these environment et combination SST Parravicini geographic variable 2011; a indicates spatially al. found center a et biodiversity (Veron has Indo-Pacific patterns Triangle or biodiversity Pacific Coral shallow-water western 2017). the reflects McClanahan sensitivity in in that higher 2009; corals variability increasing the of al. explain resistance been et may higher (Nakamura Ocean, here. has the The Indian observed stress into Dipole the corals heat waters in of Ocean ENSO warm forces resistance Indian these the of lower of and the to penetration origins Additionally, recent adding the more and in 2015). the Consequently, 1920s increases increasing al. The the with et 2015). since associated al. (Zinke strength been and et Ocean Toth Pacific also eastern 2009; Indian has the al. (Pe˜nafloreastern in ENSO et Pacific development the western reef a ENSO controlling of the sites. time, is in non-Triangle strength historical CTAs, from strength ENSO over Coral in of force the the increasing distribution dominant of by recently the uneven separation driven be the the to variability and explain likely models, SST can is exposure inter-annual exposure two environmental of east-west the of between propagation of geography differences Longitudinal propagation the force. understanding The exposure to tropics. critical contributes the sensitivity models measure in 2019). exposure to stress al. acknowledge two way we et the quantifiable While Sully between 2019). and 2017; Carilli Difference used al. & et commonly Donner that (Donner 2013; most changes stress al. the al. evaluate et heat currently et (Darling to to is alone monitoring (McClanahan bleaching stress inter-annual and impacts weakness, heat requires mortality to climate this mortality tied of of al. Estimating poorly evaluations causes be 2019). et large-scale could identified al. warming (Buddemeier influence for et climate clearly could challenges stress or Darling and that heat 2001; creates thermal rates response to taxa examined to Differential responses among infrequently 2004). sensitivity adaptive recovery an (McClanahan possible measuring is there of example, estimates at Moreover, number for resistance is future. bleaching, a without the of bleaching Mortality one in good stress. change potentially decouple climate how reflect is potentially by Bleaching to forced and of sensitivity heat 2004). change question ocean and could increasing the exposure sensitivity and of is and Changes effectiveness time. exposure over the that vary on research. will means depend effect that future will responses for and expected and corals area cause the stresses priority in given Distinguishing a resistance resistance 2015). is future is of al. resistance variables Measuring driver with et these modest associations Schindler a of and 2012; be which variables physical al. could these than clear rather between et taxa not proxies of (Cardinale be is number could responses it geography resistance, diversity-portfolio and but of may SST studies driver SST mean that mean ecological geographic Consequently, Given or other exposures. SSTs. to mean in response consistently of found however, the absence was, driving as the the genera richness to of in Number coral particularly due models. and influence complex exposure resistance two were to the relationships related for SST cover-resistance variable positively mean this Coral of when variables. direction responses. particularly predictive in coral resistance, change the and on and from influence stress space eliminated significant may of in was statistically complexities corals, interaction had the in variables their of acclimation community Thus, some Coral and skewness. exposing variation positive in effects SS 7 Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. .Bdeee,RW,Bkr .. atn ..&Jcb,JR 20) h dpiehptei of (1998). hypothesis D.R. adaptive Anderson, The & K.P. approach (2004). Information-theoretic Burnham, J.R. Jacobs, 8. & D.G. Fautin, A.C., 427-444. Baker, In: R.W., bleaching. Buddemeier, corals. L.M. reef-building Peplow, 7. in H.M., switching symbiont Putnam, for P.L., Journal evidence Harrison, ISME provides A.G., The biosphere rare Carroll, Symbiodinium S.J., the Dalton, Exploring N.M., change. Boulotte, climate rapid E.S. 6. under Darling, J.E., reefs Cinner, coral C.A., conserving Chen, for M., Beger, planning E.V., Kennedy, H.L., Beyer, 5. stress (2015). and Kingdom. Ocean. C.P. United Indian energy Meyer, Press, the Thermal University & in P.H. richness (2018). species Barber, C. coral 4. Sheppard, of distribution & regional J. of Biogeography drivers Maina, of main T.R., the as McClanahan, properties M., Ateweberhan, 3. Ocean. Indian western the 964-970. va- in 60, temperature mortality sea-surface , coral historical change-induced between climate Relationship and (2010). D. T.R. riability Reef. Ogawa, McClanahan, Barrier & A., Great M. Ateweberhan, Grech, the 2. P.J., on Mumby, protection J.C., bleaching Ortiz, coral S.F., disables change Heron, T.D.A., C. Ainsworth, Komeno, 1. J.-L. Summers, N. R. Pardede, Keith, S. S. Nand, Y. REFERENCES Halford, Muhando, A. C. Montocchio, Baird, the Yadav. E. A. thank John Muttaqin, S. greatly collection: the E. and We by Muhidin, data Philanthropies. dan supported Cargill with A. was A. assistance Miternique, collection Margaret for data and Foundation people Indonesia received MacArthur York. following ATH T. New (No.10114). Catherine of Foundation and Rese- VILLUM Corporation D. National the Carnegie Spanish funding by the the received funded from from RA was Islands. funding Project SAK Intramural Andaman an (CSIC-201330E062). in the and Council in collection Philanthropies collection arch Nilekani data Z. data Rohini and supported with the Programme, Foundation Faculty from VJP DST-INSPIRE MacArthur assisted support the Chandrasekhar T. from S. support Catherine & received Tiffany and VJP Tyabji The was D. reefs. (SUZA). National Zanzibar Ocean John Zanzibar French Indian in of Western the the University some collection and State from the Foundation Data by support Co. ANR-15-CE03-0003). coordinated Solomon received & (No. project Maldives the MMMG NORHED project Fiji. by in USAID. in STORISK supported collection Foundation and partly the Waitt Data IUCN under the Foundation. by by Agency MacArthur and supported Research T. Foundation, was Research Catherine Wallace collection and the Coun- data D. Su- by Research John Engineering the supported and the was and Sciences Foundation Islands and was Natural MacArthur Canada, the ESD T. from of project. Fellowship Catherine NE-K010484-1 cil Postdoctoral (SPACES) and Banting Services a D. Ecosystem by John Coastal supported the from from Alleviation Poverty support stainable received NAM and TRM Coral persistence. the coral in for ACKNOWLEDGEMENTS outcomes priorities severe remain improving less and a management produce fisheries, indicate pollution sustainable and findings and developing impacts our emissions, watershed Thus, the gas 2018). manage heat-retaining al. reducing better et Nevertheless, to Triangle. (Hughes opportunity responses of stress window warm-water limited in increases observed the ate oa elhadDisease and Health Coral 5 1355-1366. 45, , 0 2693. 10, , n den pigr e York. 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Schindler, Barshis, coral 46. of G., risk Pawlak, the reduces T.R., variability McClanahan, temperature munications frequency N.J., High Silbiger, Correction: A., Author Safaie, 45. < .1 ae n24stsbcuesm ie akdcrlcvrestimates. cover coral lacked sites some because sites 204 on Based 0.01. iest n Distributions and Diversity aueCiaeChange Climate Nature ,2244. 9, , 5 259-268. 75, , aieadFehae Research Freshwater and Marine oaso h ol,Vl1–3 Vol World, the of Corals oa ef:A csse ntransition in ecosystem An reefs: Coral ,58-511. - 508 3, , e :Bs oesst( set models Best 1: Set 4 0 620. - 605 24, , 3 257-263. 13, , aueCommunications Nature aecaorpyadPaleoclimatology and Paleoceanography 11 7 291-301. 57, , utainIsiueo aieSine Townsville, Science, Marine of Institute Australian . < AICc) 2 LSOne PLoS tal. et 0 1-5. 10, , pigr odeh odeh,pp. Dordrecht, Dordrecht Springer, . 21) oa eodo otes In- southeast of record Coral (2015). ak n to oe outputs model of fit and Ranks . 1 e0145822. 11, , tal. et BioScience aueCommunications Nature cetfi Reports Scientific 21) rdet of Gradients (2018). 0 1113-1131. 30, , 7 573-583. 57, , tal. et tal. et tal. et aueCom- Nature < ultnof Bulletin .5 *= ** 0.05, (2016). (2018). (2007). tal. et 6, , flgi Icdlawih aiu VIF Maximum weight delta AICc logLik df R 2 Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. kwes-.8(.9 .3(.7 -7.3 6.3 8.3 2 Axis Community (0.27) 0.03 (0.49) -0.70 1 Axis Community (7.40) 18.80 taxa 5.7 coral of Number (0.19) 7.5 -0.48 % (0.31) cover, 0.05 coral Hard (4.30) 43.10 variables (0.26) community 1.03 Coral DHM Cumulative (0.22) 0.83 Skewness Kurtosis temperature (0.27) sea-surface 1.42 Mean variables (0.23) 1.38 Temperature CE Resistance, CTA Resistance, variables Resistance multipleVariable on based = CTA model exposure 2015. climate to = 1985 CE from weights Susceptibility DHM. months intensity first of heating Bleaching sum the degree taxa. variables. on period. = are the environmental based coral 2016 DHM model 2 of intensity. the of anomaly and bleaching Metrics during thermal evaluation (-) of cumulative 1 response multivariate % categories bleaching significance. axis and coral, 7 a mean Community of by Hard 27) a bleaching of tests = by responses. Wilcoxon Axes abundance (n (+) bleaching taxa the Kurtosis triangle Correspondence their weights by coral Community of compared (+) the second measures SST 199) within (+) two and taxa. (n= sites genera and of dominant triangle for communities No Skewness skewness) and coral coral + community, (-) and the genera latitude coral kurtosis outside of Absolute (mean, No temperature, sites + metrics of Longitude (-) temperature comparisons + Skewness SST term Kurtosis Regional + + Skewness (+) % + 2. Longitude coral, genera Table Hard CE) of + No exposure, latitude + (Climate Absolute Longitude Resistance + + b) Intercept % ˜ Models coral, (CE) Hard Resistance + latitude Absolute + Intercept ˜ (CE) Resistance 95 93 92 Models 90 87 models variable 78 Single 2: Set 69 models Rank variable Single 2: Set 3 1 Rank 01 03)00 07)-. NS NS 0.01 -0.4 NS -1.0 (0.74) 2.5 0.02 (0.76) 0.02 0.2 (7.16) 18.18 (0.39) -0.17 (0.78) (21.64) -0.11 43.75 (5.78) 21.26 6.3 (21.59) 43.65 variables community Coral (1.16) 27.57 (0.44) 28.93 variables Temperature variables Resistance (SD) mean triangle Coral e b etmdlwtotSST without model Best 1b: Set variables community Prob Coral variables Temperature Z variables Resistance (SD) mean triangle Non-coral 12 variables community Coral variables Temperature variables Resistance variables community Coral < < < < variables Temperature < < variables Resistance .0001 .0001 .0001 .0001 .0001 .0001 > | Z Long- | 3.16.3251 . .40.02 0.13 0.14 0.17 0.20 0.04 0.25 0.0001 0.35 0.0001 0.0 0.0001 0.0 245.15 0.0001 0.71 221.02 0.0 0.0001 67.53 0.0 218.36 43.40 -30.71 0.0001 0.0 211.80 0.72 0.0 40.74 -18.64 3 204.12 191.81 34.18 -17.31 Significance 3 0.0 2.75 26.50 -14.03 weight 3 161.23 14.19 -10.19 3 0.07 -16.39 delta -4.04 2.70 3 11.26 AICc 3 4.9 0.79 logLik 3 VIF -172.6 Maximum df 0.00 weight 94.7 -177.6 delta 8 97.2 AICc 8 logLik df R R 2 2 Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. Figures bleaching Coral % Porites bleaching % Coral branching, Porites % Pocillopora, bleaching % Coral Montipora, % Acropora, % intensity, bleaching Coral Bleaching % colonies, Bleached bleaching Coral % susceptibility, bleaching Community Variable massive, 63 2.5 38 3.4 380.0002 0.0003 0.05 0.005 0.008 0.0003 -3.8 0.0007 -3.6 -1.9 (34.44) 53.84 -2.8 -2.7 NS (38.41) 54.12 (36.29) 71.62 -3.6 (27.65) (40.17) 26.36 59.98 (34.36) 60.86 -3.4 (29.48) 19.38 (35.88) (17.13) 59.32 28.19 (24.31) 32.85 -1.4 (30.03) (28.60) 43.33 59.71 (13.13) 15.20 (2.43) 27.98 (25.11) 39.78 (2.69) 27.34 (SD) mean triangle Coral en(D Prob Z (SD) mean triangle Non-coral 13 > | Z | Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. uuaiedge etn ots(H)bten18 n 06etmtdfo aelt data. satellite from estimated as 2016 exposure evaluated and climate stress, 1982 multivariate heat between a chronic (DHM) represents using months shading (c) calculated heating background resistance and resistance; degree showing show model cumulative reefs dots exposure coral Coloured (CTA) Indo-Pacific model. environmental anomaly 226 to (CE) thermal exposure of cumulative from Map estimated a (b) bleaching, sensitivity. using resist ecological to by reefs coral divided Ni˜no anomaly of stress El thermal capacity 2016 the the measuring to for resistance metric reef coral Evaluating 1. Figure 14 a eitnei a is Resistance (a) . Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. nl.MM=ma otl umrmxmmtmeaue n T uuaietemlanomalies thermal cumulative Tri- = Coral months. CTA the heating and model, outside degree temperatures and exposure cumulative in (CTA) maximum the model anomaly summer temperatures (CE) on thermal monthly monthly exposure cumulative based mean on climate the multivariate = based the of Triangle MMM of function Coral function angle. a the a as as outside resistance Resistance sites (c) (b) 199 and and 2015. in to sites resistance. 1985 27 from coral the and of exposure, distributions ture distributions, Temperature 2. Figure 15 h a est ftempera- of density (a) The Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. 16 Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. w erc feooia estvt,ad()tetorssac oescluae rmec xouemodel. exposure each from calculated models resistance two the (c) and sensitivity, ecological of metrics two 1 Figure Supplementary are Triangle non-Coral and Coral the between different. comparison statistically not and and axes 2 two Table the in of presented Summary region. Triangle 4. Figure on based models resistance influencing factors the test to used sites. variables study metrics. skewness 226 temperature and sea-surface kurtosis, three SST, the mean between Scatterplots 3. Figure omnt orsodneaayi CA ftecrlcmuiisi n u fteCoral the of out and in communities coral the of (CCA) analysis correspondence Community est lt hwn h itiuino a h w xouemdl,()the (b) models, exposure two the (a) of distribution the showing plots Density . 17 eainhp between Relationships Posted on Authorea 19 Feb 2020 — CC BY 4.0 — https://doi.org/10.22541/au.158212994.40773562 — This a preprint and has not been peer reviewed. Data may be preliminary. ausfo h 2 ie nlddi hssuyfo 2cutisars h Indo-Pacific. the across countries show 12 plots from Density study model. this resistance in each included in sites metric 226 sensitivity the ecological the from values as used was bleaching Percent 18