validation of the california rapid assessment method
Post on 07-May-2022
7 Views
Preview:
TRANSCRIPT
1
ValidationoftheCaliforniaRapidAssessmentMethod
DepressionalWetlandModule:FinalReport
CentralCoastWetlandsGroupatMossLandingMarineLabs
Authors:CaraClark
SarahStoner-DuncanSarahPearce
KevinO’ConnorKamilleHammerstrom
RossClark
December,2017
2
TableofContents
EXECUTIVESUMMARY 5
INTRODUCTION 6
CRAMDEVELOPMENTPROCESS 7
METHODS 8
IDENTIFYTHEGRADIENTOFSTRESS 8SELECTLEVEL3DATA 8IDENTIFYMETRICSFROMLEVEL3DATA 8CONCEPTUALMODELS 9IDENTIFYFIELDSITES 10CONDUCTFIELDASSESSMENTS 11PROCESSLEVEL3DATA 12ANALYZECORRELATIONSBETWEENCRAMANDLEVEL3DATA 13
RESULTS 12
DISCUSSION 19
CONCLUSIONS 21
REFERENCES 22
3
ListofFiguresFigure1.Mapofsitesselectedforsampling..............................................................................................10
Figure2.FieldteamconductingCRAMassessmentandcollectinginvertebratesamplesatMendocinoCollegepond.......................................................................................................................................11
Figure3.SamplingregimeforLevel3protocols........................................................................................11
Figure4.FieldteamsiftinginvertebratesampleatAshCreekinpreparationfordeliverytothelabforanalysis...............................................................................................................................................12
Figure5.HistogramofCRAMIndexscores(n=45)...................................................................................13
Figure6.HistogramsshowingthedistributionofdataineachCRAMAttribute.......................................14
Figure7.HistogramsshowingthedistributionofdatafortheBMIandalgaeIBIs....................................14
Figure8.CorrelationplotofCRAMIndexscorevs.BMIIBI.......................................................................16
Figure9.CorrelationplotofCRAMIndexscorevs.AlgaeIBI.....................................................................16
Figure10.CorrelationplotofCRAMIndexscorevs.SpecificConductance...............................................17
Figure11.CorrelationplotsoftheBMIIBIversusCRAMAttributescores…………………………………………….18
Figure12.CorrelationplotsoftheAlgaeIBIversusCRAMAttributescores………………………………………….19
ListofTablesTable1.CRAMAttributesandmetricswithsummariesofeachmetric......................................................5
Table2.PredictedrelationshipbetweenCRAMandLevel3metrics..........................................................9
Table3.Spearman’srankcorrelations(ρ)andfdr-correctedp-valuesforallCRAMandCRAMmetriccomparisonstoindependentvariables.P-valuessignificantattheα=0.05levelareprintedinboldfont.....................................................................................................................................................15
4
ExecutiveSummaryDepressionalwetlandsareabundantandwidespreadinCalifornia.Stateagenciesandwetlandscientistshavedeterminedthatthereisaneedtocharacterizetheirconditioninordertoassessimpacts,establishprotectionstrategies,andinitiaterestorationefforts.TheWetlandandRiparianAreaMonitoringPlan(WRAMP)wascreatedasaframeworkformonitoringandassessmenttoachievethis.TheCaliforniaRapidAssessmentMethod(CRAM)ispartofthisframeworkandisusedtorapidlycharacterizetheoverallhealthofwetlands.
TheCRAMdevelopmentprocessincludesprescribedstepstocompletion,includingvalidationbyconfirmingcorrelationswithmoreintensiveassessmentmeasures.TheCRAMvalidationprocessfortheDepressionalCRAMwetlandmoduleisdescribedinthisreport.
TheintensivemeasuresofconditionusedtovalidateDepressionalCRAMwereabenthicmacroinvertebrateindexofbioticintegrity(IBI),analgaeIBI,andseveralwaterchemistryparameters.
TheoverallCRAMIndexscoreandindividualCRAMAttributescoresweresignificantlycorrelatedwiththemacroinvertebrateIBI,thealgaeIBI,andseveralofthewaterchemistryparameters.
TheDepressionalCRAMmoduleprovidesameaningful,repeatable,andaccurateassessmentofwetlandconditionacrossthestateofCalifornia.
5
IntroductionDepressionalwetlandsareabundantandwidespreadinCalifornia.Freshwaterwetlandscomprise60%ofallwetlandsinthestate(NRA2010).Manyofthesewetlandsareisolatedgeographicallyandvulnerabletodevelopmentpressure(Brownetal.2016).Stateagenciesandwetlandscientistshavedeterminedthatthereisaneedtocharacterizetheirconditioninordertoassessimpacts,establishprotectionstrategies,andinitiaterestorationefforts.
TheCaliforniaWaterQualityMonitoringCouncil(Council)wasconvenedin2007underamandatefromlegislation,CASenateBill1070,tocoordinateandintegratewaterqualityandrelatedecosystemmonitoring,assessment,andreporting(mywaterquality.ca.gov2017).TheCaliforniaWetlandMonitoringWorkgroup(CWMW)wasestablishedasasub-groupoftheCounciltobuildtoolsforwetlandmonitoring(CWMW2013).TheCWMWoverseestheimplementationoftheWetlandandRiparianAreaMonitoringPlan(WRAMP).TheWRAMPisacoordinatedmonitoringandassessmentstrategythatisstructuredundertheUSEPA’sthreelevelframeworkforaquaticsystemassessment.Theframeworkcategorizeswetlandmonitoringunder:Level1,GISmappingandinventoryofaquaticresources;Level2,field-basedrapidassessmentsofwetlandcondition;andLevel3,moreintensivemeasuresofspecificfunctions,suchaswaterqualityorspeciessampling.
Californiahasthehighestlossofhistoricalwetlandsinthelower48statesat91%reductionoverthe20thcentury(Dahl1990).Thestatehasprioritizedtheassessmentandprotectionofanyremainingwetlands,whicharestillverydiverse.Rapidassessmentofwetlandsallowsforcost-effectiveandrepeatablecharacterizationofwetlandhealthatscalesfromlocaltowatershedtoregionorstatewide.TheCaliforniaRapidAssessment(CRAM)wasdevelopedtosupportthesemonitoringneeds.CRAMprovidesanoverallIndexscore(rangingfrom25to100)thatindicatesthegeneralhealthofawetlandanditscapacitytoperformimportantfunctionsandservices.TheIndexscoreisanaverageoffourmain“Attributes”ofcondition.EachAttributeiscomposedoftwotofivemetricsandsubmetrics(Table1).Theassessmentofeachmetricorsubmetricisbasedonvisualindicatorssurveyedduringafieldvisitoflessthanhalfaday.
Table1.CRAMAttributesandmetricswithsummariesofeachmetric
Attributes Metrics MetricSummaryBufferandLandscapeContext
AquaticAreaAbundance
Measuresextentofwetlandswithin500m
PercentwithBuffer
Percentofareasurroundedbyatleast5mofbufferlandcover
BufferWidth Averageof8bufferwidthmeasurementsupto250mBufferCondition Vegetationquality(nativevs.non-native),degreeofsoil
disturbance,andimpactofhumanvisitationHydrology WaterSource Anthropogenicinfluenceonwatersources(extractionsor
inputs)withinlocalwatershedupto2km
6
Hydroperiod DirectanthropogenicinputsordiversionsHydrologicConnectivity
Accesstoadjacentslopeswithoutlevees,roadgrades,orotherobstructions
PhysicalStructure StructuralPatchRichness
Numberofhabitatstructurespresentfromalistofpotentialpatchtypesfordepressionalwetlands
TopographicComplexity
Complexityofmicro-andmacro-topographicfeatures
BioticStructure NumberofPlantLayers
Numberofplantheightclassesthatcoveratleast5%ofthearea
NumberofCo-dominantSpecies
Totalnumberoflivingplantspeciesthatcompriseatleast10%ofanyplantlayer
PercentInvasiveSpecies
Thepercentofthetotalnumberofco-dominantspeciesthatarelistedbyCal-IPCasinvasive
HorizontalInterspersion
Thecomplexityofplantzones(speciesassemblagesormono-specificstands)
VerticalBioticStructure
Twomethodoptions:1)wetlandswithwoodyvegetationscoreoverlapoflayers;2)wetlandswithemergentmarshplainsscoreentrainmentandvegetationcanopy
CRAMDevelopmentProcessTherearesixstepstoCRAMdevelopment,asdescribedinSutulaetal.(2006)andoutlinedontheCRAMwebsite(http://www.cramwetlands.org/about).Thesestepsinclude:
1. Definitionphase2. Basicdesignphase3. Verificationphase4. Validationphase5. Moduleproductionphase6. Ambientsurveyphase
Previouswork,fundedbytheUSFWS(Agreement#M11AF00103),accomplishedphasesonethroughthree.Verificationwascompletedin2012and2013withastatewidesurveyacrossagradientofhydroperiod(ClarkandO’Connor2014).Thisstudywasasolidfoundationtolaunchthecurrentproject,asitverifiedthatthedepressionalmodulewaseffectivelydifferentiatingbetween“good”,“fair”,and“poor”sitesascategorizedaprioribythedevelopmentteam.ThefieldbookforDepressionalCRAMwasrevisedtoimproveitsperformanceandutility.
ThisprojectaimedtocompletethevalidationphaseofDepressionalCRAMdevelopment.Validationhasbeendefinedas“theprocessofdocumentingrelationshipsbetweenCRAMresultsandindependentmeasuresofconditioninordertoestablishCRAM’sdefensibilityasameaningfulandrepeatablemeasureofwetlandcondition”(Steinetal.2009).ValidationoftheDepressionalmodulewillestablishitsscientificcredibilityandfurtheritsuseinlocal,state,andfederalprograms.
7
MethodsValidationoftheDepressionalCRAMmodulefollowedthesystematicprocessdescribedbySteinetal.(2009),whichprescribedseveralstepstovalidation:
1. Identifythegradientofstress2. IdentifyappropriateLevel3datatovalidatetheCRAMmodule3. IdentifymetricsthatwillbecalculatedfromthedetailedLevel3data4. Createconceptualmodelsoftheexpectedrelationshipbetweenthedetaileddataand
CRAMscores5. IdentifyfieldsiteswhereLevel3dataareavailableorpossibletocollect6. ConductCRAMassessmentsatthesitesidentified7. AnalyzecorrelationsbetweenCRAMscoresandLevel3metrics
IdentifytheGradientofStressDepressionalwetlandscanbeimpactedbysurroundinglanduse.Landscapeconditionscanbeaneffectivepredictorofwetlandhealth(Rothetal.1996,MicacchionandGara2008).Adjacentandupstreamlandcoveraffectswetlandsthroughmanyprocesses,includingpollutedrunoff,habitatloss,andalterationofhydrologicdynamics.Whendepressionalwetlandsaresurroundedbynaturalopenspacetheyaremuchmorelikelytosupportflora,fauna,andimportantwetlandfunctions.Conversely,whentheyareclosetodevelopedareassuchasurbanoragriculturallandcovers,theyaremorelikelytohavereducedfunctionanddiversity.Thisstudyselectedarangeofsitesalongagradientofdevelopmentpressure,includingsomesitesinopenspacepreservesorparks,andothersincitiesandagriculturalareas.
SelectLevel3DataThisprojectbenefitedfrompreviousworkondepressionalwetlands.LundeandResh(2012)developedamacroinvertebrateindexofbioticintegrity(IBI)fordepressionalwetlandsintheSanFranciscoBayArea.Brownetal.(2016)expandedthisworkinsouthernCaliforniatoincorporatemultipleindicatorsofwetlandhealth,includingmacroinvertebrates,algae,waterquality,andhabitatassessment.OurcolleaguesattheSouthernCaliforniaCoastalWaterResearchProject(SCCWRP)andtheBayAreaRegionalWaterQualityControlBoard(RB2)graciouslysharedtheirdatawithus.TheLevel3toolswereinplacebuthadonlybeenusedinparticularregionsofthestate.Wewereabletousethestandardprotocolsdevelopedforsamplingmacroinvertebrates,algae,andwaterquality(Fetscheratal.2015)toexpandthegeographicrangeofthevalidationsites.
IdentifyMetricsfromLevel3DataThemacroinvertebrateIBIisacombinationofseveralmetricsbasedontheassemblageofspeciesfoundineachsample.Theseincludescraperrichness(afunctionalgroup);percentEphemeroptera,Odonata,andTrichoptera(EOT);EOTrichness;Oligochaetarichness;andseveralothers.ThefinalIBIscoreisonascaleof1-100andresultsfromcombiningalloftheselectedmetrics.WeusedtheoverallIBIscoreasthecomparisonmetric.
8
ThediatomalgaeassemblagesarescoredusingtheD18index,whichcombinesseveralmetricsincluding:proportionsedimenttolerant;proportionlowPindicators;proportionNheterotrophs,proportionrequiring>50%DOsaturation;andproportionhalobiontic(Fetscheretal.2014).
Standardwaterchemistryandwaterqualityparameterswereselectedforanalysisaswell.Theseincludedturbidity,temperature,pH,specificconductance,salinity,dissolvedoxygen(DO),andalkalinity.
ConceptualModelsTheexpectedrelationshipbetweenCRAMIndexandAttributescoresandLevel3datawerepredictedaprioriforeachLevel3indicator.BoththemacroinvertebrateanddiatomalgaeIBIsarescoredfromlowtohigh,withahigherscoreindicatingbetterqualitywetlands.SiteswithhigherIBIscoresprovidemoreintactfunctionsandhavefewerstressors.Therefore,apositiverelationshipisexpectedbetweenCRAMIndexandAttributescoresandinvertebrateandalgalIBIs.
ThewaterqualityparametersaregenerallymorevariableanditwasdifficulttopredictrelationshipswithconditionindicessuchasCRAM.Waterchemistrycanbeaffectedbymanyfactors,whichmaynotnecessarilyberelatedtothelocalconditionoralocaldisturbance.Waterchemistryintegratesalloftheupstreamlanduses,andmaybeaffectedmorebyimpactsupstreamthannearbyconditions.However,somewaterqualityindicatorsmayberelatedtotheconditionoftheadjacentaquaticsystem.Specificconductanceisageneralmeasureofwaterqualityandcanbeincreasedbyrisinglevelsofsaltsorotherinorganiccompounds,oftencausedbyhumandisturbance(EPA2017).Therefore,asspecificconductance(salinity)increases,CRAMscoresareexpectedtodecrease.Humanactionssuchaswaterextractionforirrigationoradditionsofwastewaterrunofftowetlandscanalsoaffectconductance.Increasesinsalinitycanbestressfulformanyaquaticbiota(EPA2017).WepredictedthathighersalinitylevelswouldcorrelatewithlowerCRAMscores.OtherwaterchemistrymeasureswerenotpredictedtocorrelatewithCRAMscores.
Table2.PredictedrelationshipsbetweenCRAMandLevel3metrics
CRAMIndexScore
BufferandLandscape
Hydrology Physical Biotic
BMIIBI + + + + +AlgaeIBI + + + + +Turbidity - - - Temperature - - - -pH + SpecificConductance - Salinity - DissolvedOxygen(mg/L)
9
DissolvedOxygen(%) Alkalinity
IdentifyFieldsitesTheprojectbenefitedfromdatapreviouslycollectedinSouthernCaliforniaandtheSanFranciscoBayArea.However,forfullvalidationassessmentsiteswereneededacrossthestate.Weselectedsitesinseveralregions,includingtheSierraNevada,theCentralValley,theModocregion,andtheNorthCoast.Siteswereselectedtorepresentthegradientofhumandisturbanceandstress.Siteaccesspermissionwasalsoaconsiderationinsiteselection.Mostsiteswereonpublicland,whileseveralwerelocatedwithinprivatepreservesoronschoolcampuses.Weincludedasub-setofsitesfromthepreviouslysampledareasinSouthernCaliforniaandtheBayArea.Tominimizeregionalbias,only15siteswereselectedfromeachoftheseareasforsynthesiswith15sitessampledinotherregionsofthestate.Atotalof45siteswereincludedintheanalysis(Figure1).
10
Figure1.Mapofsitesselectedforsampling
11
ConductFieldAssessmentsFieldassessmentswereconductedusingtheDepressionalWetlandCRAMmodule(version6.1)atfifteennewsitesforthisprojectduringspringandsummer2014(Figure2).CRAMhadpreviouslybeenconductedatthesitesinSouthernCaliforniaandtheBayAreausingthesameversionoftheprotocol.AllassessmentsfollowedthequalityassuranceproceduresoutlinedintheCRAMQAPlan(CWMW2016)andtheQAPPforthisproject(CCWG2014).
Ateachsite,thesamesuiteofLevel3indicatorswascollected.Thisincludedsamplingmacroinvertebrates,algae,
andwaterchemistryaccordingtostandardprotocols(Fetscheretal.2015).Samplingwasdistributedaroundeachwetlandalongten“nodes”whereindividualsampleswerecollected(Figure3).Thenodeswereevenlyspacedaroundthewetlandbymeasuringthecircumferenceofthepondanddividingthatlengthintotensegments.Eachofthesamplingnodeshasthreeparalleltransects:oneforwaterchemistry,oneformacroinvertebratesampling,andoneforalgaesampling.
Figure2.FieldteamconductingCRAMassessmentandcollectinginvertebratesamplesatMendocinoCollegepond
Figure3.SamplingregimeforLevel3protocols
12
ProcessLevel3dataMacroinvertebratesamples(Figure4)wereprocessedbytheAquaticBioassessmentLaboratoryandusedthestandardtaxonomiceffortnamingconventionoftheSouthwestAssociationofFreshwaterInvertebrateTaxonomists.AlgaesampleswereprocessedbyEcoAnalysts,Inc.accordingtoSurfaceWaterAmbientMonitoringProgram(SWAMP)standards.
MacroinvertebrateIBIscoreswerecalculatedaccordingtoLundeandResh(2012).TherawmacroinvertebratedatawererunthroughanRscripttocalculatetheIBIwithgracioushelpfromJeffBrownattheSouthernCaliforniaCoastalWaterResearchProject(SCCWRP).ThediatomalgaeIBIwascalculatedusingSCCWRP’sonlinetool(SCCWRP2016)withtroubleshootingassistancefromBettyFetscher.ThediatomalgaeIBIwasdevelopedforSouthernCaliforniastreams(Fetscheretal.2014)buthasbeenusedindepressionalwetlandsaswell(Brownetal.2016).
AnalyzeCorrelationsBetweenCRAMandLevel3DataSpearmanrankcorrelationswereconductedfortheoverallCRAMIndexscoreandeachoftheCRAMAttributesagainsttheBMIIBI,algaeIBI,andallwaterqualityparameters.Percentdissolvedoxygenwasnotusedduetoerrorsinthedata.Thenon-parametricSpearmanrankcorrelationwasusedbecauseitdoesnotrequireanassumptionofbivariatenormality(Dodge2010).ToaccountforthelargenumberofcorrelationsandtocontrolforType-Ierror,p-valueswerecorrectedusingthefalsediscoveryrate(fdr,BenjaminiandHochberg1995).EachmetricwithinCRAMwastreatedasindependentofeachother,socorrectedp-valueswerecalculatedusingfdrseparatelyforeachCRAMmetricandindependentmeasures(BMIIBI,IBID18,turbidity,watertemperature,etc.).AllcalculationswereconductedusingSAS9.3software(SASInstituteInc.2011).
ResultsAneffectiverapidassessmentmethodmustberesponsivetoarangeofconditionsandbesensitivetohumandisturbance(Sutulaetal.2006,Steinetal.2009).TheCRAMIndexscoreisacompositeofthefourAttributescoresandrepresentstheoverallecologicalconditionofthewetland.TheCRAMtoolgeneratesaminimumvalueof25andamaximumvalueof100.TheCRAMIndexscorescollectedforthisprojectrangedfrom31to96,withamedianscoreof60(Figure5).Wedeterminedthatthescoresarenotbiasedtowardshighorlowvalues(skewness
Figure4.FieldteamsiftinginvertebratesampleatAshCreekinpreparationfordeliverytothelabforanalysis
13
=-0.04).ThebroadrangeofscoresconfirmstheresponsivenessofthedepressionalCRAMmodule.
CRAMIndexScore
Figure5.HistogramofCRAMIndexscores(n=45)
AnextensiverangeofscoresweremeasuredforeachCRAMAttribute:BufferandLandscapeContext25-93,Hydrology33-100,PhysicalStructure25-100,andBioticStructure25-97(Figure6).WedeterminedthateachAttributeisresponsivetovaryingconditionsinandaroundthewetlandofinterest.
14
CRAMAttributeScore
Figure6.HistogramsshowingthedistributionofdataineachCRAMAttribute
IBIScore
Figure7.HistogramsshowingthedistributionofdatafortheBMIandalgaeIBIs
15
TheLevel3indicatorswerefoundtohaveasimilarwiderangeofIBIscoresasfoundwithCRAM(Figure7).
TheoverallCRAMIndexscoreandeachAttributescoreweretestedforsignificantcorrelationswithLevel3data,includingtheBMIIBI,algaeIBI,andwaterchemistryparameters.
Table2liststheresultsofallanalyseswithsignificantcorrelations’p-valuesshowninboldfont(significantwhencomparedtoα=0.05).Interestingly,whenawaterqualityparameterwassignificantlycorrelated,ittendedtobesignificantacross3CRAMAttributes:BufferandLandscapeContext,Hydrology,andBioticStructure.BoththeBMIIBIandtheIBID18correlatedsignificantlywiththeCRAMIndexscoreaswellasmostAttributes.Waterqualityparametersofturbidity,pH,anddissolvedoxygendidnotsignificantlycorrelatewithanyAttributes,withtheexceptionofpH,whichcorrelatedwiththeBioticStructure.CRAMPhysicalStructuredidnotcorrelatesignificantlywithanyparameters,althoughitwasmarginallysignificantlycorrelatedwiththeAlgaeIBI(p=0.1).
Table3.Spearman’srankcorrelations(ρ)andfdr-correctedp-valuesforallCRAMandCRAMmetriccomparisonstoindependentvariables.P-valuessignificantattheα=0.05levelareprintedinboldfont.
OverallCRAM
CRAMBuffer&LandscapeContext
CRAMHydrology
CRAMBioticStructure
CRAMPhysicalStructure
BMIIBI ρ=0.42p=0.01
ρ=0.34p=0.03
ρ=0.47p=0.005
ρ=0.29p=0.07
ρ=0.11p=0.50
AlgaeIBI ρ=0.49p=0.001
ρ=0.36p=0.02
ρ=0.32p=0.04
ρ=0.50p=0.001
ρ=0.24p=0.10
Turbidity(NTU) ρ=-0.19p=0.63
ρ=-0.04p=0.83
ρ=-0.05p=0.83
ρ=-0.30p=0.40
ρ=-0.15p=0.63
WaterTemp(oC)
ρ=0.23p=0.52
ρ=0.05p=0.88
ρ=0.13p=0.85
ρ=0.03p=0.88
ρ=0.37p=0.20
pH ρ=-0.28p=0.10
ρ=-0.20p=0.10
ρ=-0.21p=0.24
ρ=-0.42p=0.01
ρ=-0.16p=0.30
SpecificConductance(μS/cm)
ρ=-0.38p=0.02
ρ=-0.36p=0.02
ρ=-0.33p=0.04
ρ=-0.39p=0.02
ρ=-0.09p=0.56
DO(mg/L) ρ=-0.07p=0.85
ρ=-0.13p=0.85
ρ=-0.06p=0.85
ρ=-0.13p=0.85
ρ=0.01p=0.96
Alkalinity(CaCO3average)
ρ=-0.32p=0.05
ρ=-0.34p=0.05
ρ=-0.34p=0.05
ρ=-0.25p=0.12
ρ=-0.07p=0.65
ThesignificantcorrelationsbetweentheCRAMIndexscoreandthelevel3indicatorsareofparticularinterest.TheCRAMIndexscorewassignificantlycorrelatedwithbothoftheselected
16
independentassessmentprotocols(BMIIBIandtheAlgaeIBI)aswellasexpectedwaterqualityparameters(specificconductance)(Figures8-11).
Figure9.CorrelationplotofCRAMIndexscorevs.AlgaeIBI
Figure8.CorrelationplotofCRAMIndexscorevs.BMIIBI
17
Figure10.CorrelationplotofCRAMIndexscorevs.SpecificConductance
TheindividualCRAMAttributeswerealsotestedforcorrelationwiththeIBIs.TheBMIIBIwassignificantlycorrelatedwiththeBufferandLandscapeContextAttributeandtheHydrologyAttribute(Figure12).ItwasnotsignificantlycorrelatedwithPhysicalStructureorBioticStructure.TheAlgaeIBIwassignificantlycorrelatedwiththeBufferandLandscapeContext,Hydrology,andBioticStructureAttributes,butnotthePhysicalStructureAttribute(Figure13).
18
CRAMAttributeScoreFigure11.CorrelationplotsoftheBMIIBIversusCRAMAttributescores
19
CRAMAttributeScoreFigure12.CorrelationplotsoftheAlgaeIBIversusCRAMAttributescores
DiscussionThegoalofthisprojectwastovalidatetheCRAMmoduleforDepressionalwetlands.ToensurethattheCRAMmethodmeetsestablishedCRAMdevelopmentguidelines(Steinetal.2009),theCRAMValidationteamsetouttoconfirmthataCRAMmodulefordepressionalsystemsmetasetofkeycriteria.AvalidatedCRAMmoduleshouldgeneratescoreswhichappropriately
20
representafullrangeofwetlandconditionsfoundwithinthestate.Thetoolshouldalsoberepeatableandcorrelatewithothertrophicorfunctionspecificindicatorsofcondition.
ThesiteselectionprocessensuredthatsampledwetlandsrepresentedthefullrangeofclimaticandecologicalconditionfoundinCalifornia.BypartneringwithwetlandscientiststhroughoutthestatewithextensiveexperienceinCaliforniadepressionalwetlands,wehavedevelopedatoolthatcanbeusedsuccessfullybymostCaliforniawetlandpractitioners.WecreatedaconceptualmodelfromwhichwepredictedandtestedrelationshipsbetweenCRAMscoresandvariousLevel3indicatorsofcondition.
AtleastoneLevel-3metriccorrelatedsignificantly,andintheexpecteddirection,foreachdepressionalAttribute,withtheexceptionofPhysicalHabitat,althoughitsrelationshiptoAlgaeIBIscoreswasnearlysignificant(ρ=0.24,p=0.10).OuranalysisfoundthatsiteCRAMIndexscoresweresignificantlycorrelatedwiththebenthicinvertebrateindex(BMIIBI),thealgaeindex(AlgaeIBI),andseveralwaterqualityparameters(specificconductanceandalkalinity).TheCRAMAttributesBufferandLandscapeContextandHydrologyweresignificantlycorrelatedwiththesameLevel3parameters.Sincethealgaeandmacroinvertebrateindexesweredevelopedtorespondtodisturbanceandstressinthewetland,CRAMattributesthatevaluatesimilarfunctionsandareasofconditionshouldreflectasimilargradientofimpacts.BoththeoverallIndexscoreandseveralpredictedCRAMAttributescoreswerecorrelatedwiththeIBIscores.TheBufferandLandscapeContextAttributemeasuresanthropogenicimpactsfromsurroundinglanduse.Similarly,themacroinvertebratesandalgaeindexesaresensitivetothosesameimpacts.TheHydrologyAttributeevaluatesthesourcesofwaterandpotentialcontamination,theartificialmanipulationofwaterflow,andtheconnectiontoadjacenttransitionalhabitat.Thesefactorssimilarlyaffectthecompositionofthealgaeandmacroinvertebratecommunities.
ThewaterqualityparametersthatweremoststronglycorrelatedwithCRAMscoreswerespecificconductanceandalkalinity.Inaddition,pHwascorrelatedwiththeBioticStructureAttribute.Intheabsenceofmarinesources,higherlevelsofconductivityoftenindicateanincreaseinhumandisturbance,whichresultsinrunoffhighinsaltsanddissolvedsolids(EPA2017).ThesignificantcorrelationbetweenCRAMandtheseparametersshowsthatCRAMisabletodetectadjacentlanduseswhichleadtoimpairedwaterquality.
InvalidatingthisCRAMmodule,thegoalwastohavebroadcorrelationwithmultipleL3metricsthatrepresentarangeofecologicalfunctionsandservices.However,wedidnotexpectthosecorrelationstobetight,withhighSpearmans’Rhovalues,asthiswouldnegatetheneedfordevelopinganewmethodofassessment.CRAMismeanttomeasuremultiplepotentialwetlandfunctions,notanysinglefunction,asrepresentedbytheL3data.
Anumberofthewaterchemistrymeasurementsdidn’tcorrelatewithCRAM,includingturbidity,temperature,anddissolvedoxygen,whilepHonlycorrelatedwithoneCRAMAttribute(BioticStructure).WeexpectedanegativecorrelationbetweenCRAMAttributesand
21
turbidityandtemperaturemeasurements,becausebothindicateuplanddisturbances.TurbidityandtemperaturewereexpectedtocorrelatenegativelywithCRAMIndexscores,asareductioninripariancovercanleadtohighertemperatures,increasederosion(turbidity)andlowerCRAMIndexscores.SimilartofindingsbyCRAMvalidationeffortsforothermodules,otherfactorsappeartobeaffectingthequalityofwaterflowingintothesampledwetlands,suchasspecificupstreamlanduses,localclimateandgeography(Sutulaetal.2006).
MostofthesiteswithhighwatertemperatureswereintheCentralValley,buthadarangeofCRAMIndexscoresreflectingsiteconditions.Otherwaterqualityparametersincludingdissolvedoxygenalsoseemstobeinfluencedbyfactorsthatdon’trelatetotheoverallconditionofthewetland.Dissolvedoxygencanvarywidelyatasinglewetlandinresponsetodynamicfactorssuchasdailyrespirationfluctuations,soitmaynotbethebestindicatorofwetlandcondition.ThepHofasystemismoreinfluencedbylocalgeologyandrainfallthanoverallwetlandhealth.
ConclusionsThisworkwaspresentedtotheLevel2/RapidAssessmentCommitteeoftheCWMWinJuly,2017,andtheiradvicecontributedtofurtheranalyses.TheLevel2/RapidAssessmentCommitteeapprovedthevalidationoftheDepressionalCRAMmoduleattheOctober,2017meeting.
TheDepressionalCRAMmoduleisnowvalidatedandmeetsthegoalsdefinedbytheLevel2Committee.OuranalysisshowsthatthereisasignificantcorrelationbetweenCRAMIndexandAttributescoresandLevel3intensivemeasuresofconditionandfunction.Therefore,weconcludethattheDepressionalCRAMmoduleprovidesameaningful,repeatable,andaccurateassessmentofwetlandconditionacrossthestateofCalifornia.
22
ReferencesBenjaminiY,YHochberg.1995.Controllingthefalsediscoveryrate:apracticalandpowerfulapproachtomultipletesting.JRStatSocB57:289-300.
Brown,J.S.,E.D.Stein,C.Solek,A.E.Fetscher.2016.AssessmentoftheConditionofSouthernCaliforniaDepressionalWetlands.CostaMesa,CA.SCCWRPTechnicalReport921.
Clark,C.andK.O’Connor.2014.VerificationoftheDepressionalCRAMWetlandModule,CIAPTask3SummaryReport.MossLanding,CA.https://ccwg.mlml.calstate.edu/sites/default/files/documents/finalreport-depressionalCRAM.pdf(accessed11/20/17).
[CCWG]CentralCoastWetlandsGroup.2014.QualityAssuranceProjectPlanforSupportforL2CommitteePriorityToolDevelopment:ValidationofThreeCRAMModulesTask3:ValidationoftheDepressionalWetlandCRAMModule.FundingNumber:CD-99T05801-0
[CWMW]CaliforniaWetlandsMonitoringWorkgroup.2016.DataQualityAssurancePlan:CaliforniaRapidAssessmentforWetlands.Sacramento,CA.http://www.cramwetlands.org/sites/default/files/CRAM%20data%20QA%20plan%20v7-2016.9.19.pdf(accessed11/30/2017)
[CWMW]CaliforniaWetlandMonitoringWorkgroup.2013.CaliforniaRapidAssessmentMethod(CRAM)forWetlandsUser’sManual,Version6.1pp.67.http://www.cramwetlands.org/sites/default/files/2013-04-22_CRAM_manual_6.1%20all.pdf
Dahl,T.(1990)WetlandslossesintheUnitedStates1780’sto1980's.U.S.DepartmentoftheInterior,FishandWildlifeService,Washington,D.C.13pp.
Dodge,Y.2010.TheConciseEncyclopediaofStatistics.NewYork(NY):Springer.pp502-505.
[EPA]EnvironmentalProtectionAgency.2017.NationalAquaticResourceSurveys.https://www.epa.gov/national-aquatic-resource-surveys.(accessed11/14/17).
Lunde,K.B.,V.H.Resh.2012.Developmentandvalidationofamacroinvertebrateindexofbioticintegrity(IBI)forassessingurbanimpactstoNorthernCaliforniafreshwaterwetlands.EnvironmentalMonitoringandAssessment,v.184,pp3653-3674.
Mywaterquality.ca.gov.2017.CaliforniaWaterQualityMonitoringCouncil.http://www.mywaterquality.ca.gov/monitoring_council/index2.html.(accessed11/20/17).
[NRA]NaturalResourcesAgency.2010.StateoftheState’sWetlands:TenYearsofChallengesandProgress.Sacramento,CA.http://www.resources.ca.gov/docs/SOSW_report_with_cover_memo_10182010.pdf(accessed11/20/17).
23
SASInstituteInc.2011.BaseSAS®9.3ProceduresGuide.Cary,NC:SASInstituteInc.
[SCCWRP]SouthernCaliforniaCoastalWaterResearchProject.2016.AlgaeIBIAssessmentTool.http://www.sccwrp.org/Data/DataTools/algaeIBI.aspx.(accessed2/24/15).
Sutula,M.A.,E.D.Stein,J.N.Collins,A.E.FetscherandR.Clark.2006.Apracticalguideforthedevelopmentofawetlandassessmentmethod:TheCaliforniaexperience.JournaloftheAmericanWaterResourcesAssociation42:157-175.
SteinE.D.,A.E.Fetscher,R.P.Clark,A.Wiskind,J.L.Grenier,M.Sutula,J.N.Collins,C.Grosso.2009.Validationofawetlandrapidassessmentmethod:UseofEPA’slevel1-2-3frameworkformethodtestingandrefinement.Wetlands29:648-665.
top related