Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago

15 Pages·2016·1.23 MB·English

remote sensing
Article
Satellite SST-Based Coral Disease Outbreak
Predictions for the Hawaiian Archipelago
JamieM.Caldwell1,*,ScottF.Heron2,3,4,C.MarkEakin2andMeganJ.Donahue1
1 Hawai’iInstituteofMarineBiology,SchoolofOceanandEarthScienceandTechnology,
UniversityofHawai’i,Ka¯ne‘ohe,HI96744,USA;[email protected]
2 CoralReefWatch,U.S.NationalOceanicandAtmosphericAdministration,CollegePark,MD20740,USA;
[email protected](S.F.H.);[email protected](C.M.E.)
3 MarineGeophysicalLaboratory,PhysicsDepartment,CollegeofScience,TechnologyandEngineering,
JamesCookUniversity,Townsville,QLD4811,Australia
4 GlobalScienceandTechnology,Inc.,Greenbelt,MD20770,USA
* Correspondence:[email protected];Tel.:[phone]
AcademicEditors:StuartPhinn,ChrisRoelfsema,XiaofengLi,RaphaelM.KudelaandPrasadS.Thenkabail
Received:15September2015;Accepted:20January2016;Published:26January2016
Abstract: Predicting wildlife disease risk is essential for effective monitoring and management,
especiallyforgeographicallyexpansiveecosystemssuchascoralreefsintheHawaiianarchipelago.
Warmingoceantemperaturehasincreasedcoraldiseaseoutbreakscontributingtodeclinesincoral
coverworldwide. Inthisstudyweinvestigatedseasonaleffectsofthermalstressontheprevalence
ofthethreemostwidespreadcoraldiseasesinHawai’i: Montiporawhitesyndrome,Poritesgrowth
anomaliesandPoritestissuelosssyndrome. Topredictoutbreaklikelihoodwecompareddisease
prevalencefromsurveysconductedbetween2004and2015from18HawaiianIslandsandatollswith
biotic(e.g.,coraldensity)andabiotic(satellite-derivedseasurfacetemperaturemetrics)variables
using boosted regression trees. To date, the only coral disease forecast models available were
developedforAcroporawhitesyndromeontheGreatBarrierReef(GBR).Giventhecomplexitiesof
diseaseetiology,differencesinhostdemographyandenvironmentalconditionsacrossreefregions,
itisimportanttorefineandadaptsuchmodelsfordifferentdiseasesandgeographicregionsofinterest.
SimilartotheAcroporawhitesyndromemodels,anomalouslywarmconditionswereimportantfor
predictingMontiporawhitesyndrome,possiblyduetoarelationshipbetweenthermalstressanda
compromisedhostimmunesystem. However,coraldensityandwinterconditionswerethemost
importantpredictorsofallthreecoraldiseasesinthisstudy,enablingdevelopmentofaforecasting
systemthatcanpredictregionsofelevateddiseaseriskuptosixmonthsbeforeanexpectedoutbreak.
Ourresearchindicatessatellite-derivedsystemsforforecastingdiseaseoutbreakscanbeappropriately
adaptedfromtheGBRtoolsandappliedforavarietyofdiseasesinanewregion. Thesemodels
canbeusedtoenhancemanagementcapacitytoprepareforandrespondtoemergingcoraldiseases
throughoutHawai’iandcanbemodifiedforotherdiseasesandregionsaroundtheworld.
Keywords: diseaseoutbreaks;corals;SSTmetrics;coldsnaps;hotsnaps;wintercondition;MPSA;
boostedregressiontrees;Hawaiianarchipelago;models
1. Introduction
Climate change and associated ocean warming have been linked to increasing frequency
and severity of infectious diseases in several economically and ecologically important marine
organisms [1,2]. While diseases are an integral component of normally functioning ecosystems,
outbreakscanalterpopulationstructure,leadtolarge-scaledie-offsandevenhostextinctions[3,4].
Host-pathogeninteractionsoftenshiftinresponsetoprolongedorsevereenvironmentaldisturbance,
RemoteSens.2016,8,93;doi:10.3390/rs8020093 www.mdpi.com/journal/remotesensing
RemoteSens.2016,8,93 2of15
suchashightemperaturesorrainfall,whichaltersinteractionsinfavorofeitherthehostorthepathogen
(e.g.,promotingpathogengrowthwhilereducinghostimmuneefficacy)[5]. Temperaturechanges
alonecanaffecthostandpathogenbehavior,shiftpathogenrangesandincreasehostsusceptibilityand
pathogenvirulence[2,5]. Forexample,a+1˝Cincreaseinwatertemperatureincreasestheabundance
ofpathogenicbacteriaVibrioharveyiinthewatercolumnandthesusceptibilityofitsabalonehost
Haliotis tuerculata [6]. As periods of anomalously high ocean temperatures occur more frequently,
marinediseaseoutbreaksareexpectedtobecomemorecommonandmoresevere.
Linksbetweenwarmingoceantemperatures,increasedtemperaturevariabilityandcoraldisease
outbreaks threaten coral reef ecosystems across the globe. With increasing disease outbreaks and
coralbleaching,manycoralsworldwidearethreatenedbyextinction[7],presentingconcernsforfood
security,livelihoodsandshorelineprotectionforcoastalcommunities[8]. Diseaseprevalencepatterns
varyacrossoceanbasinsandarestronglycorrelatedwithregionallywarmtemperatureanomalies[9].
Previous studies investigating temperature-disease relationships have found that increased ocean
temperaturespromotelesionandpathogengrowthrates[10,11],increasetransmissionandvirulence
ofpathogens,andreducecoralresistancetopathogensforavarietyofcoraldiseasessuchasBlack
BandDisease,YellowBandDiseaseandWhitePlague[1,2,12,13]. Whilediseaseoutbreakshavebeen
mostsevereacrosstheCaribbean,coraldiseaseoutbreakshavebecomeincreasinglycommoninthe
Indo-Pacific during the last decade [14–16]. Mild winters and anomalously warm summers have
coincidedwithmanyoftheseoutbreakevents[7,17–19]suggestinganassociationbetweenbroadscale
oceanictemperatureregimesanddiseaseonset.
Four previous modeling efforts have advanced our ability to predict coral disease outbreaks
usingremotelysensedseasurfacetemperature(SST)anomalies. Brunoetal. usedtheweeklySST
anomaliesmetric(WSSTA;thenumberofweeksintheprecedingyearatorabove+1˝Cabovethe
weeklymean),coralcoverandlong-termAcroporawhitesyndromeobservationstomodeloutbreak
eventsontheGreatBarrierReef(GBR)[20]. Theyfoundthatsiteswith>50%coralcoverandmore
thanfiveanomalouslywarmweekswereassociatedwithhigherdiseaseabundance. Maynardetal.
alsoassessedtherelationshipamongwhitesyndromeabundance,coralcoverandwarm-seasonhigh
thermalstressusingtheMeanPositiveSummerAnomaly(MPSA)metricinaregressionmodelthat
predictedoutbreaklikelihoodwith>90%accuracyonreefswith>26%coralcover[18]. Thisstudy
revealedthatastemperaturestressincreased,outbreakscouldoccuratlowerthresholddensities.
Heronetal. incorporatedthemagnitude(notjustoccurrence)ofsummeranomaliesandalso
includedanomalouswintertemperaturemetrics(WinterConditionandColdSnap)duringtheyear
priortoadiseaseeventtopredictanoutbreak[17]. Theirstudyconfirmedtheimportanceofhighcoral
cover(threshold>30%)andanomalouslywarmsummertemperaturesforincreasedriskofAcropora
whitesyndrome,butalsodemonstratedthatmildwintertemperaturesmaybeequallyasimportantas
thetwootherfactorsinsusceptibilitytodiseaseoutbreaks. TheNationalOceanicandAtmospheric
Administration(NOAA)nowregularlyproducesapredictivetoolforcoraldiseaseoutbreakriskon
theGBRbasedonthesefindings[21].
RandallandvanWoesikdevelopedamodelforwhite-banddiseaseonAcroporaintheCaribbean
by comparing recent outbreaks with historical and contemporary SST metrics [22]. Their study
revealedthatthemostusefulpredictorsofwhite-banddiseasediffereddependingonthehostspecies,
wheredepth,30-yearrateoftemperaturechangeandminimumtemperaturesweremostimportant
forAcroporapalmatawhilemaximum,minimumandmonthlyrateoftemperaturechangewasmost
importantforAcroporacervicornis. Thesefourresearcheffortssetthefoundationtodeveloppredictive
modelsforadditionaltemperature-dependentcoraldiseasesinotherregionsacrosstheglobe.
ScientistsandmanagerscurrentlyusetheMaynardetal.andHeronetal.Acroporawhitesyndrome
modelstoplanresearchandconservationeffortsinAustralia[23]. Remotesensingofenvironmental
conditionsassociatedwithdiseaseoutbreaksisparticularlyusefulforreefmanagementagencieswhere
timeandfinancialresourcesarelimited,allowingthemtobettermonitorbroadgeographicregions
withlowbackgroundlevelsofdiseaseandpatchydistributionofoutbreaks. Giventhecomplexitiesof
RemoteSens.2016,8,93 3of15
diseaseetiology,differencesinhostdemographyandenvironmentalconditionsacrossreefregions,
suchmodelsmayneedtoberefinedandadaptedfordifferentdiseasesandgeographicregions.
MetricsfromHeronetal. wereadaptedtoproduceacomplementary,experimentalpredictive
tool for the Hawaiian archipelago [17]. The modifications included determining appropriate SST
climatologies, adjusting metric thresholds to represent the new location, and refining the release
scheduleforseasonalriskoutlooksandassessments. However,thesemetricsandthresholdshad,until
now,neverbeenquantitativelyevaluatedusingdiseaseobservations.
Here,weevaluatetheapplicabilityofSST-basedmetricsdescribinganomalouslywarmandcold
conditionsforforecastingthreecommoncoraldiseasesinHawai’i: Montiporawhitesyndrome,Porites
growthanomaliesandPoritestissuelosssyndrome. Thesediseaseshavecausedsignificantmorbidity,
reducedfecundity,andoftencolonymortalityintheregion’sdominantreef-buildingcorals. Montipora
whitesyndromeandPoritestissuelosssyndromearecharacterizedbygradualtorapidtissuemortality
asthediseaseprogressesacrossthecolony,sometimeskillingcoloniesinlessthantwoweeks[24–26];
Poritesgrowthanomaliesarechronic,protuberantmassesofthecoralskeletonthatdonotcauserapid
mortality,butdohaveanumberofdeleteriouseffectsoncoralincludingreducedgrowth,fecundity,
fitnessandoverallsurvival[27].
Montipora (and Acropora) white syndrome, Porites growth anomalies and Porites tissue loss
syndromehavebeenobservedtoberelatedtohightemperature[26,28],whichsuggeststhatthese
threediseasesaregoodcandidatestotestthermalstressmetricsforforecastingfutureoutbreaks. The
pathogenicbacteriathatcausewhitesyndromesinAcroporaandMontipora(Vibriospp.) havebeen
observed to alter their disease presentation, decrease time until disease onset and/or shorten the
incubation period in higher water temperatures [29,30]. The pathogens that cause Porites growth
anomaliesandtissuelosssyndromearecurrentlyunknown,butpreviousstudieshaveshownthat
siteswithlowerthermalstresswereassociatedwithhigherdiseaseprevalence[26]. Todetermine
outbreak risk in this study, we used boosted regression trees, an ensemble statistical modeling
approachthatallowedustoidentifyoptimalpredictorsandtheirrelativeimportancewhileminimizing
predictivedeviance.
2. ExperimentalSection
2.1. FieldSurveysofCoralDiseases
Weused789coralhealthsurveysfromtheHawai’iCoralDiseasedatabase(HICORDIS)conducted
intheHawaiianarchipelagobetween2004and2015toexploreassociationsamongSST,bioticfactors
anddiseaseprevalence. Surveyswereconductedbytendifferentresearchgroupson18islandsand
atolls across the ~2800 km Hawaiian archipelago (Figure 1) representing large variations in coral
communitycomposition,densitiesanddiseaseprevalenceacrossninedegreesoflatitude(Table1).
Coralhealthobservationswerecollectedusingoneofthreemethods: belttransects,line-intercept
andestimatedprevalence,acrossa1–24.5mdepthrange. Forthebelttransectmethod,diversrecorded
healthinformationonallcoralcolonieswithinaspecifiedlengthandwidth(averagelength=20m,
range=8–50m;averagewidth=1m,range=1–6m). Intheline-interceptmethod,diversrecorded
coralhealthinformationforallcolonieslyingdirectlybeneath25moftransecttape. Intheestimated
prevalencemethod,diverscountedthenumberofcoloniesinasubsetofabelttransect(average=
10ˆ2m2,range=10ˆ2m2 to10ˆ6m2),andcountedalldiseasedcolonieswithinthelargerbelt
transectarea(average=25ˆ2m2,range=25ˆ2m2to25ˆ6m2).
Foreachsurvey,wecalculateddiseaseprevalenceasthenumberofinfectedcoloniesdividedby
thetotalnumberofhostcolonies(i.e.,MontiporaorPorites)observed. Fortheestimatedprevalence
method,weextrapolatedthetotalnumberofhostcoloniesobservedfortheentiretransectsurveyed
fromtheabundancescountedinthesubsetregion. Weexcludedsurveysthathad<20totalcoralcolony
observationsfromthedatabecausetheydisproportionatelyresultedinexceptionallyhighprevalence
values. Wecalculateddensitiesasthetotalnumberofcoloniesdividedbytheareasurveyed.
RemoteSens.2016,8,93 4of15
Remote Sens. 2016, 8, 93 4 of 15
175°0'0"W 170°0'0"W 165°0'0"W 160°0'0"W 155°0'0"W
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outbreakoccurred.
Table 1. Host distribution and environmental predictor variables used in boosted regression trees.
Table1.Hostdistributionandenvironmentalpredictorvariablesusedinboostedregressiontrees.
Variable Type Description and Unit Min Max
Total Vcoarriaalb alebundance TByipoetic NuDmebsecrri opfti coonloanndieUs/nsiutrvey M2i0n 26M3a3x
TotTaoltcaolr acloarbaul nddeannsciety BBioitoictic NuNmubmerboefr coofl ocnoileosn/iseusr/vmey2 02.015 5226.433
TotPaolcroitreasl ddeennssitiyty BBioitoictic NNuummbbeerro offc ocloolnoinesi/esm/m2 2 00..1052 95.429.4
MPoonritteispodrean sditeynsity BBioitoictic NNuummbbeerro offc ocloolnoinesi/esm/m2 2 00..0022 89..44 9
Depth Abiotic Meters below sea surface <1 24.5
Montiporadensity Biotic Numberofcolonies/m2 0.02 8.4
Accumulation of positive and
WintDeerp Cthondition AAbbioitoictic Metersbelowseasurface −1<01.162 212.446.55
negative thermal anomalies; °C-weeks
Accumulationofpositiveandnegative
WinterCondition Abiotic Magnitude and duration of cold ´10.162 21.465
Cold Snap Abiotic thermalanomalies;˝C-weeks −5.0255 0
stress; °C-weeks
Magnitudeanddurationofcoldstress;
ColdSnap Abiotic Mean number of degree heating days ´5.0255 0
MPSA Abiotic ˝C-weeks 0 0.78077
in summer; °C
Meannumberofdegreeheatingdays
MPSA Abiotic Magnitude and duration of heat 0 0.78077
Hot Snap Abiotic insummer;˝C 0 11.02
stress; °C-weeks
Magnitudeanddurationofheatstress;
HotSnap Abiotic 0 11.02
˝C-weeks
2.2. Defining Disease Outbreaks
2.2. DefiWnein cgaDlciusleaatseedO duitsberaesaek sprevalence thresholds by statistically isolating outliers as “outbreaks”
(i.e., above average levels of disease prevalence), according to the Heron et al. [17] method. Briefly,
Wecalculateddiseaseprevalencethresholdsbystatisticallyisolatingoutliersas“outbreaks”(i.e.,
for each disease, we first isolated the maximum disease prevalence value for each year from 2004 to
above average levelsof disease prevalence), according to the Heron et al. [17] method. Briefly, for
2015 and calculated the mean and standard deviation (SD) of the maximum prevalence across years.
eacAhndyi saenansuea,lw vealfiuresst tihsoalta etxecdeethdeedm tahxei mmueman dpilsuesa soenep rSeDv awleenrcee revpallauceedfo wreitahc hthyee naerxftr ohmigh2e0s0t4 vtaolu2e0 15
andcalculatedthemeanandstandarddeviation(SD)ofthemaximumprevalenceacrossyears. Any
annualvaluesthatexceededthemeanplusoneSDwerereplacedwiththenexthighestvaluefrom
RemoteSens.2016,8,93 5of15
thatyearandthemeanandSDrecalculated. Thiswasiterativelyrepeateduntilnovalueswereabove
thethreshold. Anyvaluesabovethefinalthresholdwereconsideredoutbreaks.
2.3. SST-BasedMetrics
TohindcastSSTanomaliesoverthesametimeperiodasourdiseaseobservationsandtodevelop
atoolrelevantforforecastingdiseaseriskintothefuture,weconcatenatedthreeSSTdatasets. Weused
anupdatedrelease(v5.2)oftheretrospectivePathfinderSSTdataset[31]usedinotherstudies[17]that
spannedtheperiod1985–2012.Dailydatawerecompositedtoweeklytemporalresolution,maintaining
thenative1/24˝(~4km)resolutionandgapfilledfollowingHeronetal.[17]. Theseconddatasetwas
theNOAA/NESDISBlended5km(precisely0.05˝)datasetavailableinnearreal-timesinceMarch
2013withdailytemporalresolution[32]. Thiswascompositedasaweeklyaverageforconsistencyof
temporalresolutionwiththe4kmdataset. Asthe5kmdatasetisproducedinnearreal-time,itisthe
leadingoptiononwhichtobuildanyfutureforecastingofdiseaserisk. Tobridgethegapbetween
thesedatasetsandtofacilitatethebiasadjustmentbetweenthem(ensuringconsistencyofthetime
series),theblended11kmSSTpredecessortothe5kmproductwasused. Thisproductspannedthe
periodFebruary2009–October2013andwasalsocompositedtoweeklyresolution. Thethreedatasets
wereconcatenatedbylinearlyregressingpairedweeklyvaluesinthetemporaloverlapandadjusting
therelativebiasofthe4kmand11kmvaluestoemulatethe5kmdata. Thisresultedinaninternally
consistentSSTtimeseriesatweeklyresolutionforeachsurveylocationandfortheperiodJanuary
1985–June2015. Thiscoveredtheperiodofobservations(2004–2015)andalloweddevelopmentof
long-termclimatologiesusedinthecalculationofSSTmetrics.
Toexamineheatstressprecedingeachdiseasesurvey,weusedtwoSSTmetricsthathavebeen
successfully used to hind/forecast Acropora white syndrome on the Great Barrier Reef (Table 1).
MeanPositiveSummerAnomaly(MPSA)istheaverageofSSTanomaliesabovethecorresponding
monthly mean climatology plus one standard deviation (SD), calculated across the three summer
monthsandisexpressedas˝C[18]. HotSnap[17]isanotherindicatorofunusuallywarmconditions
experiencedprimarilyduringthesummerperiodaccountingforboththemagnitudeanddurationof
heatstress. TheHotSnapmetricusesasinglesummertimebaselinetoidentifystressfultemperature,
oneSDabovethesummermeanSST,andintegratedthemagnitudeoftemperatureanomaliesabove
thisbaselinethroughtime(unitsof˝C-weeks). HotSnapsalsoincludepositiveanomaliesoutside
of the climatological warmest months to include warming prior/subsequent to the three-month
summerseason.
We also examined two winter metrics that were designed to determine if unusually cold or
warmtemperaturesaffectpathogenloadingand/orhostsusceptibility,ultimatelyincreasingriskof
diseaseinthesubsequentwarmermonthsasthermalstressaccumulates[17]. ColdSnap[17]combine
themagnitudeanddurationofwintertimecoldstressbyintegratingtemperaturesthatareoneSD
belowthemeanwinterSST(climatologicalmeanacrossthreecoldestmonthsoftheyear). TheCold
Snapmetricconsistsofonlynegativevaluesandiscalculatedacrosstheninemonthsprecedingthe
summer. TheWinterCondition[17]complementstheColdSnapmetrictoprovideanoverallmeasure
ofwinterpre-conditioningthroughouttheyear.TheWinterConditionconsistsofpositiveandnegative
anomaliesaccumulatedaboutthemeanwintertemperaturethroughthedefinedthree-monthwinter
periodandalsoincorporatesadditionalnon-wintercoolperiodswhentemperaturesarebelowone
winterSDabovethewintermean.
Whilethetemperatureprofileintheoceanvarieswithdepth,temperatureanomaliesfromthe
surfaceprovideaneffectiveestimateofanomaliestoatleastafewtensofmetersaswellasatthe
surface[17]. Aseachofthesemetricswasderivedusingtemperatureanomalies,theyareindicativeof
conditionsexperiencedatthedepthofcorals. However,depthmaycontributesignificantinformation
tothemodels,soitisindependentlyincludedasamodelparameterandasaninteractivetermwith
otherSSTmetrics. WecalculatedallSST-basedmetricsforeachpixelcontainingadiseasesurveyusing
theconcatenatedSSTtimeseries.
RemoteSens.2016,8,93 6of15
2.4. DeterminingOutbreakRisk
To assess the relationship among SST metrics, biotic factors (e.g., host density) and disease
prevalence,weusedboostedregressiontrees.Boostedregressiontrees(BRTs)fitresponsevariables(i.e.,
diseaseprevalence)totheirpredictorsthroughrecursivebinarysplitsinanadditivefashioninwhich
simpleregressiontreesarecombinedusingaboostingalgorithm(analgorithmtoreducedevianceat
eachiteration)toimprovepredictiveperformance[33]. BRTsaremodelsbuiltinastage-wisefashion
usingmachine-learningtechniquestominimizepredictiveerrorateachstageofmodelbuildingand
havesuperiorpredictiveperformancecomparedtoothermodelingtechniques[33–35].
ThreeparametersneedtobedefinedtooptimizemodelperformanceinBRTs: treecomplexity,
learningrate,andbagfraction. Treecomplexity(tc)controlsthenumberofnodesinatreebasedon
whetherinteractionsarefitted. Learningrate(lr)reducesthecontributionofeachtreeasitisadded
tothemodel. Together,treecomplexityandlearningratedeterminethenumberoftreesrequiredfor
optimalpredictivepower. Numberoftrees(nt)isthenumberofconsecutivetreesusedtobuildaBRT.
Theoptimalntindicatesthepointjustbeforethemodelbeginstooverfitthedata. Usinganntbeyond
theoptimalntmayincreasepredictivedeviance. Stochasticityaddsaccuracyandreducesoverfitting
andiscontrolledinBRTsthroughabagfraction,whichusesabootstrappedsubsetofdatatofiteach
newtree. Abagfractionof0.5meansthatateachiteration,individualtreesarefittedwith50%ofthe
datathataredrawnatrandom,withoutreplacement,fromthedataset.
Todeterminetheoptimalvaluesoflearningrate,treecomplexityandbagfraction,weexamined
thecross-validationdevianceoverlrvaluesof0.01,0.001,0.005,tcvaluesof1–5andbagfractionvalues
of 0.1, 0.5, 0.75. Cross validation is a technique for evaluating the model using withheld portions
of the data and the cross-validation deviance measures how much the predicted values based on
thenon-withhelddatadifferfromtheobservationsfromthewithhelddata[33]. Weranallpossible
combinationsusinggbm.stepinthegbmpackageinRstatisticalprogramversion3.3.1[36]with10-fold
cross-validationineachmodelrunandselectedthethree-parametercombinationthatproducedthe
lowest cross-validation deviance to produce the optimal BRT. We further optimized the final BRT
modelbyremovingredundant,non-informativepredictorvariablesselectedthroughgbm.simplify,a
methodanalogoustobackwardsselectioninregression.
For each disease we developed hierarchical BRT models to account for zero-inflated data.
The majority of surveys (50%–91% across the three diseases) had no diseased colonies (i.e., a
prevalenceofzero). Weaccountedforthezero-inflateddatadistributionusinganapproachsimilar
to Cappelleetal.[34], where we combined two BRT models: a presence-absence model assuming
a Bernoulli response distribution and a prevalence-if-present model using a Gaussian response
distribution on square-root transformed prevalence data. The final prevalence prediction was
calculatedastheprobabilityofdiseasepresencegivenbythepresence-absencemodel(0or1)multiplied
bythepredictedprevalencegivenbytheprevalence-if-presentmodel.
Totestpredictiveaccuracy, wecreatedallmodelsusingtheparametersdescribedaboveona
trainingdatasetconsistingofarandomlyselected75%subsetofsurveysandusedthewithheld25%of
surveys(testdata)forindependentvalidationofthemodels. ToevaluatetheperformanceofeachBRT
individually,wecalculatedthecross-validation(CV)devianceandthestandarderrorofCVdeviance
(se). Here,weusedCVdevianceasameasureofeachmodel’sabilitytoexplainthewithhelddatain
thetrainingdataset.
To determine model performance of the hierarchical BRTs, we calculated the area under the
receiveroperatingcharacteristiccurve(AUC)andmodeldevianceforeachhierarchicalmodel. To
quantitativelymeasuregoodness-of-fit,weusedAUCvalues,whichprovideametricforhowwellthe
modeldistinguishespresencesandabsencesbycomparingratesoftruepositivesandfalsepositives.
AmodelthatisunabletoassignpresencesmoreoftenthanrandomisindicatedbyanAUCvalueof0.5
whereasamodelthatalwaysassignspresenceswithahigherprobabilitythanabsences(perfectfit)is
indicatedbyanAUCvalueof1.0.Thepercentageoftestdataexplainedbythemodeliscalleddeviance
(D).WecalculatedDastheaveragedifferencebetweentrueobservationsofdiseaseprevalenceinthe
testdatasetandpredictedestimatesofdiseaseprevalencemadebythemodel.
RemoteSens.2016,8,93 7of15
Remote Sens. 2016, 8, 93 7 of 15
3. ResultsandDiscussion
3. Results and Discussion
3.1. DefiningDiseaseOutbreaks
3.1. Defining Disease Outbreaks
UsingthemethoddescribedinHeronetal.[17],wedefinedtheoccurrenceofadiseaseoutbreak
Using the method described in Heron et al. [17], we defined the occurrence of a disease outbreak
forMontiporawhitesyndrome,PoritesgrowthanomaliesandPoritestissuelosssyndromeinHawai’ias
for Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome in Hawai’i
anysurveywhereprevalencevaluesexceeded8.5%,45.5%and16.2%respectively. Accordingtothese
as any survey where prevalence values exceeded 8.5%, 45.5% and 16.2% respectively. According to
thresholds,outbreaksofMontiporawhitesyndromewererecordedin10surveys(1.3%),outbreaks
these thresholds, outbreaks of Montipora white syndrome were recorded in 10 surveys (1.3%),
ofPoourtibterseagkrso woft hPoarnitoesm garoliwesthw aenroemreacloiersd wederien r2e6cosrudrevde yins 2(36. 3su%r)veaynsd (3o.u3t%b)r eaankds oouftPbroeraitkess otifs sPuoreitleoss s
syntdisrsoume elowsse rseynredcroormdee dwienre4 3rescuorrvdeeyds i(n5 .443% s)u;trhveeryes w(5e.4re%7);8 t9hseurer vweyersei n78t9o tsaulrfvoeryesa icnh tdoitsael afsoer. eMaceha n
predviasleeanscee. Mvaelaune sprperveavleionuces lvyarluepeso rptreedvifoourslMy orneptioprotreadw fohri tMeosnytnipdorroa mwehiaten dsytnidssruomeleo asnsds ytinsdsuroe mloessi n
Hawsyani’dirroamngee dinf rHomaw0a%i’it ora1n%geadn dfrionmcr e0a%se–d1%se vaenrda l-ifnoclrdeadsuerdi nsgevoeurtablr-efoalkde vdeunritns,gp roeuvtiborueaslky erevpeonrtste, d
ashpirgehviaosus3l5y. 9r5ep%oratnedd a2s9 h.1i7g%h arse 3sp5.e9c5t%iv aenlyd [2296.,1377%]. rIenspceocntitvraeslyt, [m26e,3a7n]. pInre cvoanlternacset, vmaelaune spprerveavlieonucsel y
repvoartluedesf oprrePvoioriutesslyg rreopwotrhteadn foomr Paloireisteas cgrrooswstthh eanInomdoa-lPieasc aificrco(sisn cthlue dInindgo-HPaacwifaici’ i()inrcalnugdeindgf rHoamw0a%i’i)t o
13.7r%angileluds ftrroamtin 0g%r etloa t1iv3.e7l%y hililguhstreantdinegm rieclalteivveellys ohfigdhi seenadseempirce vleavleenlsc oefd deisspeaitsee dpyrnevaamleinccceh daensgpeistei n
theidrypnraemseinc ccehdaunrgiensg itnh ethseuimr pmreerseanncde fdaullr[i2n6g, 3t7h–e3 9su].mTmheerl aarngde sfiazlel o[2f6t,h3i7s–d3a9t]a. sTehtea lllaorwgee dsiuzse toofd tehfiisn e
regidoantaalsleyt saplleocwifiecdo uust btroe adkeftihnree srhegoilodnsaflolyr tshpeecfiirfisct toiumtbe.reAask wthitrhesahlollsdtsu dfoiers thine wfiirlsdt litfimede.i sAeass weeitcho laolgl y,
studies in wildlife disease ecology, these thresholds should neither be considered an exact or fixed
thesethresholdsshouldneitherbeconsideredanexactorfixedthreshold. However,theyprovideda
threshold. However, they provided a meaningful metric to distinguish locations with normal versus
meaningfulmetrictodistinguishlocationswithnormalversusaboveaveragediseaseprevalence.
above average disease prevalence.
Many coral disease outbreaks between 2004 and 2015 occurred clustered in space and time
Many coral disease outbreaks between 2004 and 2015 occurred clustered in space and time
(Figures1and2). ThemostsevereoutbreaksforallthreediseasesoccurredintheMainHawaiian
(Figures 1 and 2). The most severe outbreaks for all three diseases occurred in the Main Hawaiian
Islands. Montipora white syndrome outbreaks primarily occurred during the winter of 2009/2010
Islands. Montipora white syndrome outbreaks primarily occurred during the winter of 2009/2010 and
and2015onOahuandin2014onMaui. GiventheimportanceofhostdensityforMontiporawhite
2015 on Oahu and in 2014 on Maui. Given the importance of host density for Montipora white
syndrome transmission [24], the high number of susceptible hosts at these specific locations may
syndrome transmission [24], the high number of susceptible hosts at these specific locations may
partiallyexplainthisspatialpattern. ThemajorityofoutbreaksforPoritesgrowthanomaliesandtissue
partially explain this spatial pattern. The majority of outbreaks for Porites growth anomalies and
losssyndromealsooccurredbetween2010and2015,withthemostsevereeventsin2010and2011.
tissue loss syndrome also occurred between 2010 and 2015, with the most severe events in 2010 and
ThirteenPoritesgrowthanomaliesoutbreaks(48%)wererecordedaroundtheislandofO’ahuwithsix
2011. Thirteen Porites growth anomalies outbreaks (48%) were recorded around the island of O’ahu
outwbrietha kssix( 2o2u%tb)rreeackosr d(2e2d%a) rroeuconrddHeda waraoiu’inIds lHanadw.aTi’hi eIsslapnadti.a Tlhcleu sspteartiinalg colufsPtoerriintegs gofr oPwortihteas ngoromwatlhie s
aroaunnodmHalaiwesa air’oiumnady Hbaewpaair’it imalalyy ebxe pplaaritniaeldlyb eyxpthlaeinceodrr beyla tthioen cobrertewlaetieonn dbiestewaeseenp dreisveaalseen pcreeavnaldenhcieg h
hosatndde nhsigithie hsowsth deerenassititehse wsphaetrieaalsc tlhues tseprastoiaflo culutbstreerask osf oonutOb’raehakusm onay Ob’aeheux pmlaaiyn bede ebxyptlhaienceodr breyl athtieo n
betwcoererneladtiisoena sbeetpwreevena lednisceeaasen dprleavrgaleenhcuem aannd plaorpguel ahtuiomnasns ipzoepsu[3la8t]i.oTnesn siPzoersi t[e3s8t]i.s Tsuene lPoosrsitseys ntidsrsoume e
outlborsesa ksysn(d29ro%m)ea losoutobcrecaukrsr e(d29o%n)O a’lashou o.cPcurirorreds tuodn ieOs’ahhauv.e Psrhioowr nstuadpieoss ihtiavveec oshrroewlant ioan pboestiwtiveee n
Porictoersrteilsastuioenl obsestwsyenend rPoomrieteas ntidsscuoer alolscso svyenr/dhroomsted aennds ictyor[a2l6 c,2o8v]e.rP/hoorsitte sdeisntshitey d[2o6m,2i8n]a. nPtorciotersa lisg tehneu s
onadlolmofinthanetM coairnalH gaewnuasi iaonn Iasllla onfd tsh,ein Mcluaidni nHgaOw’aaihiaun [3Is5l]a,nwdsh,i cinhcsluudgignegs tOs’tahheure [a3r5e], awddhiictiho nsualgfgaecsttosr s
drivthinegret aisrseu aedldoistsiosnyanl dfarcotmores pdrreivvianlge nticsesuthe alotsasr esycnudrrroemntely purenvkanleonwcen .that are currently unknown.

FiguFirgeu2re. D2.i sDeiasseeasper epvreavleanlecneceb ybyd idseisaesaes,e,y eyaera,r,s esaeasosonna anndd rereggioionn.. SSccaatttteerrppllootsts oof fMMoonntitpiporoar awwhihteit e
synsdyrnodmroe,mPeo, rPitoersitgesr ogwrotwhtahn aonmomaliaelsieas nadndP Poroirtietsest itsisssuueel loossss ssyynnddrroommee pprreevvaalleennccee tthhrroouugghh titmime.e D.Dasahsehde d
horhizoorinztoanltlailn leisnerse prerepsreensetnot uotubtrberaekakt htrhersehshooldldssd deetteerrmmiinneedd bbyy tthhee iitteerraattiivvee aannaalylysissi sddeescsrcirbiebde dini nthteh e
metmheotdhso.dMs. HMIH:MI: aMinaiHn aHwaawiiaaiinanIs lIaslnadnsd;s;N NWWHHI:I:N Noortrhthwweesstteerrnn HHaawwaaiiiiaann IIssllaannddss. .WWinintetre rininclculduedse s
survsueryvsecyos ncdonudctuecdteidn iNn Novoevmembebre–rA–Apprirli;l;s suummmmeerr iinncclluuddeess ssuurrvveeyys scocnodnudcutcetde din iMnaMy–aOy–cOtocbteorb. er.
ReRmemotoetSe eSnesn.s2. 021061,68, ,89, 393 8o8f o1f5 15
3.2. Determining Outbreak Risk
3.2. DeterminingOutbreakRisk
We used hierarchical BRT models to retrospectively predict coral disease prevalence. Optimal
paraWmeeutesresd fohri etrhaersceh imcaoldBeRlsT rmanogdeedl sfrtoomre 7tr5o0s–p3e5c0t0iv terleyesp, rterdeeic ctocmorpalledxiistieeass eofp 3r–e5v,a lleeanrcnei.nOg pratitmesa olf
p0a.r0a0m1 eotre r0s.0f0o5r athneds ea mbaogd ferlascrtaionng eodf 0fr.7o5m (T7a5b0leto 23).5 M00otdreele sc,rotrsese-vcaolmidpatlieoxnit ideesvoiafn3c–e5s, floear rtnhien gtrarianteinsg
odfa0t.a0 0r1anogre0d. 0f0ro5ma n0d.04a8b–a1g.21f3ra (cTtiaobnleo 2f).0 .C75ro(sTsa-vbalelid2a).tioMn oddeevliacnrocesss -cvlaolsidera ttioo nzedroe viinadnicceastef ogrretahteer
trparinedinicgtidvaet aarcacnugreadcyf.r oAmU0C.0 4v8atloue1s. 21ra3n(gTaedbl efr2o).mC r0o.s6s7-–v0a.l8id5.a tLioanrgdee vAiaUnCce svcaloluseesr toinzdeicroatien dhiicgahteer
gpreeartfeorrmpraendciect imveodaeclcsu. rAaclly .mAoUdCelsv aplrueedsicrtaendg eddisefraosme p0r.6e7vatole0n.c8e5 .bLetatregre thAaUnC ravnadluoems i(ni.de.i,c AatUeChi g>h 0e.r5)
pberufto trhme amncoedeml ofodre Plso.rAitelsl mgroodwetlhs apnreodmicatleieds dpiesrefaosremperde vbaelsetn ince thbee titnedrethpaenndreanntd voamlid(ia.eti.o,An UteCsts> u0s.i5n)g
btuhtet hteesmt doadtaelsefot rwPitohri atens AgrUoCw tvhaaluneo omf a0l.i8e5s (pTearbfolerm 2)e. dCboerastl dinistthreibiuntdioenp e(ni.ed.,e Mntovnatliipdoarati odnentseisttys, uPsoirnitges
thdeetnessittyd aatnads/eotrw ciotlhonany AdeUnCsitvya)l,u Weionft0e.r8 C5o(Tnadbilteio2n) .aCndor aa lmdeistrtricib ouft isounm(mi.ee.,rM thoenrtmipaolr astdreesnss i(tiy.e,.P, MoriPteSsA
daenndsi/toyr aHndo/t oSrncaopl)o nwyedree nimsitpyo)r,tWanint tfeorrC aolln dmitoidonelsa;n hdoawmeveetrri,c roeflastuivmem ceorntthriebrumtiaolnsst rdesifsfe(ir.ee.d, MamPSoAng
anmdo/doerlsH (oFtigSunraep 3)).w Feorre eimxapmoprtlaen, tthfeo rreallaltmivoed ienlfsl;uhenocwee ovfe rW, rienltaetri vCeocnodnittrioibnu itnio tnhsed Miffoenrteipdoaram wonhgite
msoydnedlrso(mFieg umreod3e).l Fwoarse x5a4m.1p%l e,cothmepraerleadti vteo inoflnulye n1c4e.5o%fW inin ttehreC Poonrditietsi ognroinwtthhe aMnoonmtiaploieras wmhoidteel
sysnudgrgoemsteinmg otdhealt wWasin5t4e.r1 %Cocnodmitpiaorne dpltaoyosn aly m14o.5re% iimnpthoertPaonrti treoslger oinw tdhisaenaosme adlyiensammoicdse losfu Mggoensttiipnogra
thwahtiWtei nstyenrdCroonmdeit i(oFnigpularey s3a).m Woree simhopwor ttahnet rroellaetiinved iisnefaluseendcyen aomf iecascohf Mproendtiicptoorra wvahriitaebslye nadnrdom thee
(Friegluatrieon3)s.hWipe bsehtowweetnh ethreel faittitveed imnfloudeenl cfeunocfteioacnhs apnredd picrteodrivctaorria vbalreiaabnldest hine rFeilgautiroen 3s.h ipbetweenthe
fittedmodelfunctionsandpredictorvariablesinFigure3.
Table 2. Optimal setting and predictive performance of boosted regression tree analyses for three
Table2. Optimalsettingandpredictiveperformanceofboostedregressiontreeanalysesforthree
coral diseases. Models: PA: Presence-Absence; PIP: Prevalence-If-Present; nt: number of trees; tc:
coraldiseases.Models:PA:Presence-Absence;PIP:Prevalence-If-Present;nt:numberoftrees;tc:tree
tree complexity; lr: learning rate; bf: bag fraction; cv dev: cross-validation deviance; se: standard
complexity;lr: learningrate;bf: bagfraction;cvdev: cross-validationdeviance;se: standarderror;
error; AUC: area under the operating curve; D: predictive deviance of the final BRT model. Large
AUC:areaundertheoperatingcurve;D:predictivedevianceofthefinalBRTmodel.LargeAUCvalues
AUC values indicate higher performance models.
indicatehigherperformancemodels.
Coral Disease Model nt tc lr bf cv dev se AUC D
CoralDisease Model nt tc lr bf cvdev se AUC D
Montipora white PA 3500 5 0.001 0.75 0.262 0.036
0.70 0.30
Msyonndtirpoormaew hite PIPPA 1430500 0 4 5 0.000.100 1 00..7755 00..216123 00..003866
0.70 0.30
syndrome PIP 1400 4 0.001 0.75 0.113 0.086
Porites growth PA 1350 4 0.005 0.75 1.061 0.031
Poritesgrowth PA 1350 4 0.005 0.75 1.061 0.031 0.85 0.41
anomalies PIP 1900 5 0.005 0.75 0.048 0.005 0.85 0.41
anomalies PIP 1900 5 0.005 0.75 0.048 0.005
Porites tissue PA 750 3 0.005 0.75 1.213 0.027
PA 750 3 0.005 0.75 1.213 0.027 0.67 0.44
Poritleosstsis sueloss PIPPI P 2720700 0 4 4 0.000.500 5 00..7755 00..3333 00..000044 0.67 0.44

Figure3.Cont.
Figure 3. Cont.
RemoteSens.2016,8,93 9of15
Remote Sens. 2016, 8, 93 9 of 15

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prperdeidctioctrovra rviaabrilaebslfeosr pfroerv aplernevcea-liefn-pcere-isfe-pntremseondte lsm.oPdloetlss.s hPolwotsth eshporowb abthileit yporfodbiasbeialistey proefv adleisnecaese
acproresvsaalernanceg eaocrfovsasl uae srafonrgeth eofp vreadluicetso rfovra rtihaeb lep,rwedhiiclteoar cvcoaurinatbinleg, fworhitlhee aacvceoruangetinefgf efcotsr othfea llaovtehrearge
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tetshteed duastainsgett ahned2 5w%erwe ittehshteeldd .uRsienlga ttihvee i2n5fl%u ewnictehhoefleda.c Rheplaretidviec tionrflvuaerniaceb loefi seaschho wprnedasicatopre vrcaerniatbalgee is
insahocowrnn ears oaf peearcchegnrtaagpeh .inW ae cdoirdnnero tofin ecaocrhp gorraatpehn. oWne-i ndfiodr mnoatt iivnecoprrpeodriacttoe rnso,wn-hinicfhorwmearteivdee pterremdiincteodrs,
uswinhgicthh ewRerfeu ndcettieornmgimnebd.s iumspinligfy ,thaen dRt hfuernecftoioren wgmebd.osimnoptlifsyh, oawndp atrhteiarelfdoerpe ewnde ednoc enpolto tsshfoowr tphaorsteial
vadreiapbelnesde(bnlcaen kpslpoatsc esf)o.rH othstodsee nsvitayriiasbslpese ci(fibeldanbky gsepnaucses()i..e .,HMoosnt tipdoernasoitryP oirsi tesspdeecnifsiietyd) .bMyP SgAenisus
M(ei.aen., PMoosnittiivpeorSau omr mPoerritAesn doemnsailtyy.). MPSA is Mean Positive Summer Anomaly.
3.32..21..1M. MonotniptioproaraW WhihteiteS ySnydnrdormomee
WWinitnetreCr oCnodnidtiiotino,nC, ColodldS nSanpa,pH, HotoSt nSanpapa nadndh ohsotsdt ednesnistyityw wereereth tehem mosotsitn ifnofromrmataivtievep rperdeidcitcotrosrs
ofoMf Monotniptioproaraw whihteites ysnydnrdormomeep rperveavlaelnecnece( F(iFgiugruere3 )3;)a; lallol tohtehrerp rperdeidcitcotrorv avraiaribalbelsesw wereered edteetremrminiended
nonno-nin-ifnofromrmataitviev.eT. Thheem meaenanp rperdeidcitcitvievea caccucruarcaycyf ofrorM Monotniptioproaraw whihtietes ysnydndrormomee( A(AUUCC)w) wasas0 .07.070a nadnd
exepxlpalianiended3 03%0%o fotfh tehed edveivainacneceu suisnigngth tehete tsetsdt adtaata(T (aTbalbele2 )2.)T. hTeheW WinitnetrerC oCnodnidtiiotinonm metertircich ahdadth tehe
hihgihgehsetsrte rlealtaivtievein iflnufleunecnecein inth tehem modoedle,lh, ihgihglhiglihgthintignga are rlealtaiotinosnhsihpipb ebtewtweeenenm mildildW WinitnetreCr Conodnidtiiotinosns
(>(0>.08.585˝ C°-Cw-eweekesk)sa)n danhdig hheigrhdeisre adsieseparseev aplerenvcae.leTnhcies. reTlhaitsio nreslhaitpioinssshimipi laisr tsoimthielarre latoti otnhseh irpelbaetitownesehnip
wbinettewreteemn wpeirnatteurr teesmapnderAatcurorepso raanwdh Aitcerospyonrdar womhietse osynnthderoGmreeast oBna trhriee rGRreeaeft wBahrerrieerW Rienetfe rwChoenred Witioinntser
ofC2o.5n–d6i.t5io˝nCs- wofe 2e.k5s–w6.5e r°eCa-swsoeceikaste wdewreit hashsoigchiaetreddi sweaitshe haibguhnedr adnicseea[1se7 ]a.bAulnsodacnonces i[s1te7n].t Awlistoh cAocnrsoipsotreant
wwhiittehs Ayncrdorpoomrae wonhitthee sGynBdRro[1m7]e, foenw tohre nGoBCRo l[d17S]n, afepwsw oerr enoa sCsooclida tSendawpsit hwheirgeh adssisoecaiasetepdr ewviatlhe nhcieg,h
podsissiebalsye supgrgevesatlienngceth, eppoastshibolgye nsiucgbgaecstetirniag ththate capuastehoMgoenntiicp obraacwtehriitae styhnadt rocmauesem aMyohnatvipeoraal owwheirte
liksyelnihdoroomdeo fmsuayrv hivavaled au lroiwngerv leirkyelciohlodowd ionft seursr.vival during very cold winters.
The model results indicated unexpected relationships between disease prevalence and Hot
Snaps and host (i.e., Montipora) density. The Hot Snap metric had a non-linear relationship with
disease prevalence. Similar to Acropora white syndrome, there was an association between Hot Snaps
RemoteSens.2016,8,93 10of15
ThemodelresultsindicatedunexpectedrelationshipsbetweendiseaseprevalenceandHotSnaps
and host (i.e., Montipora) density. The Hot Snap metric had a non-linear relationship with disease
prevalence. SimilartoAcroporawhitesyndrome,therewasanassociationbetweenHotSnapsabove
2˝C-weeksandhigherdiseaseprevalence. IncontrasttoAcroporawhitesyndromehowever,high
disease prevalence was also associated with Hot Snaps less than 1.3 ˝C-weeks. This relationship
may help explain why several Montipora white syndrome outbreaks have occurred during winter
monthsinHawai’i(Figure2). Howeveradditionalfactorsnotincludedinthestudymaybeneeded
to better understand the seasonality of Montipora white syndrome such as increased rainfall and
associated runoff. Surprisingly, disease prevalence increased as host density decreased with the
highestdiseaseprevalenceatsiteswith<2.7colonies¨m´2.Morestudiesareneededtofurtherexamine
thisrelationship,however,becausethisresultmayreflecthostdensitiesinlocationswithpersistent
severeoutbreakssuchasKa¯ne’oheBay,O’ahu,ratherthanrealthresholdhostdensitiesneededfor
diseaseestablishment.
3.2.2. PoritesGrowthAnomalies
The model developed for Porites growth anomalies prevalence had the highest predictive
performance where depth and colony density were the strongest drivers of disease prevalence
(Figure3). TheAUCvalueforthehierarchicalPoritesgrowthanomaliesmodelwas0.85andexplained
41% of the deviance using the test data (Table 2). Given the high relative influence of site level
characteristics in predicting disease prevalence, thermal stress is most likely to increase disease
prevalenceatsitesinspecificdepthandhostdensityranges. Spatiallyexplicitmapsofsuchsitelevel
characteristicscanbedeveloped(andsubsequentlyupdated)tocomplementthermalriskassessments.
Therewerenegativerelationshipsbetweenbothdepthandcolonydensityanddiseaseprevalence,
whereshallowercoralcommunities(<12.7m)andlowercolonydensitieswereassociatedwithhigher
diseaseprevalence. AlthoughtherelationshipbetweenshallowerdepthsandPoritesgrowthanomalies
prevalenceiscurrentlyunclear,onepreviousstudydeterminedthatUVradiationdoesnotexplain
thisrelationship[27]. WithinKa¯ne’oheBay,O’ahu,Williamsetal.[28]foundanegativerelationship
betweencoralcoverandPoritesgrowthanomaliesprevalence;ourstudyincludestheWilliamsetal.
dataset and suggests a similar relationship may exist across the entire archipelago. One possible
explanationforthenegativerelationshipbetweencolonydensityanddiseaseisthathighercolony
density often reflects a community of very small colonies, and Porites growth anomalies are more
commononlargercorals[39].
WinterCondition,MPSAandHotSnapsignificantlycontributedtopredictiveaccuracyofthe
Poritesgrowthanomaliesmodel(Figure3). Abi-modalrelationshipexistsbetweenWinterCondition
and disease prevalence where cold (<´5 ˝C-weeks) and warm (>8.8 ˝C-weeks) conditions were
associated with higher disease prevalence. There was a negative relationship between disease
prevalenceandbothMeanPositiveSummerAnomaly(MPSA)andHotSnap,indicatinghighthermal
stress is associated with lower disease prevalence. This combination of colder winters and mild
summers compliments a prior study that found a negative association between Porites growth
anomaliesandaverageoccurrenceofanomalouslywarmtemperature(WSSTA)overthefouryears
priortoasurvey[26]. Thiscombinationmayalsohelpexplaintheshorter-termseasonalityassociated
withincreasedgrowthanomalydensityandsubsequentgrowthinthefall[27].
3.2.3. PoritesTissueLossSyndrome
Host and coral density, depth, Winter Condition, MPSA and Hot Snap were important for
predictingPoritestissuelosssyndromeprevalence(Figure3). ThemeanpredictiveaccuracyforPorites
tissuelosssyndrome(AUC)was0.67andexplained44%ofthedevianceusingthetestdata(Table2).
Therewasaninteractionbetweenhostdensity,coraldensityanddiseaseprevalence,inwhichsiteswith
highcoraldensitybutlowerhostdensitywereassociatedwiththehighestdiseaseprevalencevalues.
Diseaseprevalencewasalsohighestinshallowerwater(<7m). TheWinterConditionmetricshowsa