forecasting sagebrush ecosystem components and greater sage grouse habitat for 2050 learning from past climate patterns and landsat imagery to predict the future

Table 1Flow diagram of all major methodological steps required to characterize sagebrush components, quantify change across years, relate change to changing precipitation and forecast fu

jou rn al h om ep a g e :w w w e l s e v i e r c o m / l o c a t e / e c o l i n d

a U.S Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, United States

b InuTeq, Contractor to the USGS EROS Center, United States

c Natural Resource Ecology Laboratory and Department of Ecosystem Sciences, Colorado State University in Cooperation with U.S Geological Survey, Fort

Collins, CO, United States

d SGT, Contractor to the USGS EROS Center, United States

e U.S Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, United States

f U.S Geological Survey, Fort Collins Science Center, Fort Collins, CO 80526, United States

a r t i c l e i n f o

Article history:

Received 18 November 2013

Received in revised form

19 December 2014

Accepted 2 March 2015

Keywords:

Sagebrush ecosystem

Sage grouse

Remote sensing

Climate forecasting

Trend analysis

a b s t r a c t

Sagebrush(Artemisiaspp.)ecosystemsconstitutethelargestsingleNorthAmericanshrubecosystem andprovidevitalecological,hydrological,biological,agricultural,andrecreationalecosystemservices Disturbanceshavealteredandreducedthisecosystemhistorically,butclimatechangemayultimately representthegreatestfuturerisk.Improvedwaystoquantify,monitor,andpredictclimate-driven grad-ualchangeinthisecosystemisvitaltoitsfuturemanagement.WeexaminedtheannualchangeofDaymet precipitation(dailygriddedclimatedata)andfiveremotesensingecosystemsagebrushvegetationand soilcomponents(bareground,herbaceous,litter,sagebrush,andshrub)from1984to2011in south-westernWyoming.Baregrounddisplayedanincreasingtrendinabundanceovertime,andherbaceous, litter,shrub,andsagebrushshowedadecreasingtrend.Totalprecipitationamountsshowadownward trendduringthesameperiod.Weestablishedstatisticallysignificantcorrelationsbetweeneach sage-brushcomponentandhistoricalprecipitationrecordsusingasimpleleastsquareslinearregression Usingthehistoricalrelationshipbetweensagebrushcomponentabundanceandprecipitationina lin-earmodel,weforecastedtheabundanceofthesagebrushcomponentsin2050usingIntergovernmental PanelonClimateChange(IPCC)precipitationscenariosA1BandA2.Baregroundwastheonly compo-nentthatincreasedunderbothfuturescenarios,withanetincreaseof48.98km2(1.1%)acrossthestudy areaundertheA1Bscenarioand41.15km2(0.9%)undertheA2scenario.Theremainingcomponents decreasedunderbothfuturescenarios:litterhadthehighestnetreductionswith49.82km2(4.1%)under A1Band50.8km2(4.2%)underA2,andherbaceoushadthesmallestnetreductionswith39.95km2(3.8%) underA1Band40.59km2(3.3%)underA2.Weappliedthe2050forecastsagebrushcomponentvalues

tocontemporary(circa2006)greatersage-grouse(Centrocercusurophasianus)habitatmodelsto eval-uatetheeffectsofpotentialclimate-inducedhabitatchange.Underthe2050IPCCA1Bscenario,11.6%

ofcurrentlyidentifiednestinghabitatwaslost,and0.002%ofnewpotentialhabitatwasgained,with 4%ofsummerhabitatlostand0.039%gained.Ourresultsdemonstratethesuccessfulabilityofremote sensingbasedsagebrushcomponents,whencoupledwithprecipitation,toforecastfuturecomponent responseusingIPCCprecipitationscenarios.Ourapproachalsoenablesfuturequantificationofgreater sage-grousehabitatunderdifferentprecipitationscenarios,andprovidesadditionalcapabilitytoidentify regionalprecipitationinfluenceonsagebrushcomponentresponse

PublishedbyElsevierLtd.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense

(http://creativecommons.org/licenses/by-nc-nd/4.0/)

∗ Corresponding author Tel.: +1 605 594 2714.

E-mail address: homer@usgs.gov (C.G Homer).

1 Work performed under USGS contract G13PC00028.

2 Work performed under USGS contract G10PC00044.

http://dx.doi.org/10.1016/j.ecolind.2015.03.002

1470-160X/Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

1 Introduction

Sagebrush (Artemisia spp.) ecosystems constitute the single

largestNorthAmericansemiaridshrubecosystem(Andersonand

Inouye, 2001) and provide vital ecological, hydrological,

bio-logical,agricultural,andrecreationalecosystemservices(Davies

etal.,2007;Connellyetal.,2004;Perforsetal.,2003).However,

disturbances suchas livestock grazing, exoticspecies invasion,

conversiontoagriculture,urbanexpansion,energydevelopment,

and other development have historically altered and reduced

these ecosystems (Leonard et al., 2000; Crawford et al., 2004;

Daviesetal.,2006,2007),causingalossintotalspatialextentof

about50%(Connellyet al.,2004;Schroederet al.,2004;Hagen

et al., 2007) Constant perturbations tothese systems are

dis-ruptingvital biological services, such as providing habitats for

numerous sagebrush-obligate species For example, ecosystem

decline has severely affected greater sage-grouse

(Centrocer-cusurophasianus; hereaftersage-grouse) populationsacrossthe

speciesrange(Connellyetal.,2004;Gartonetal.,2011),leaving

populationsthreatenedwithextirpationinsomehabitatswhere

theyhistoricallypersisted(Connellyetal.,2004;Aldridgeetal.,

2008)

Inadditiontotheimpactsofpastdisturbances,climatechange

mayultimatelyrepresentthegreatestfuturerisktothis

ecosys-tem(Neilsonetal.,2005;Bradley,2010;Schlaepferetal.,2012a,b)

Bothwarmingtemperaturesandchangingprecipitationpatterns

(suchasincreasedwinterprecipitationfallingasrain)willlikely

favorspeciesotherthansagebrush(WestandYorks,2006;Bradley,

2010) and increase sagebrush vulnerability tofire, insects,

dis-eases,andinvasivespecies(Neilsonetal.,2005;McKenzieetal.,

2004).For each 1◦C increasein temperature, 12%of sagebrush

habitatis predictedtobereplacedbywoodyvegetation(Miller

et al.,2011).Semiarid lands suchas sagebrushecosystems are

especiallyvulnerabletoprecipitationchangesbecauseoflowsoil

moisturecontent (Reynolds et al., 1999; Weltzin et al., 2003)

Variations in precipitation and temperature strongly influence

aridandsemiaridplantcomposition,dynamics,anddistribution

because water is often the most limiting resource to

vegeta-tion abundance (Branson et al., 1976; Cook and Irwin, 1992;

Pelaezetal.,1994;Ehleringeretal.,1999;Reynoldsetal.,2000)

Anysubstantial changes in global or regional climate patterns

that influence precipitation regimes can put these ecosystems

atsubstantialrisk (Weltzinet al.,2003; Bradley, 2010)by

fun-damentally altering biome properties and ecosystem structure

(Brownetal.,1997).Developingabetterunderstandingof

poten-tial ecosystem component distribution and temporal variation

under future precipitation scenarios can provide critical

infor-mation to manage these lands Specifically, information about

long-term variations of sagebrush ecosystem components can

determinethepotentialrelationshipbetweenmagnitudesof

com-ponentchangeandtheregionalclimatebyusinginformationabout

long-termspatiotemporalvariationsinsagebrushecosystem

com-ponents

Remotesensingimagesinterpretedintofractionalvegetation

andsoilecosystemcomponentsofferawaytoquantifyand

region-alizesubtle climateprocess impacts onvegetationchange in a

sagebrushecosystemacrosstime(Xianetal.,2012a,b;Homeretal.,

2013).ThisprocesscandrawontheLandsat(LS)archive,which

offersanespeciallyrichsourceofremotesensinginformation

capa-bleofexploringhistorical patternsbackto1972,usingaglobal

recordofmillionsofimagesoftheEarth(Lovelandand Dwyer,

2012).The multispectral capabilitiesand 30-m resolution ofLS

arewellsuitedfordetectingandquantifyingarangeofvegetation

attributes,aswellasfordetectinggradualchangeandthe

under-lyingecologicalprocesses(Vogelmannetal.,2012;Homeretal.,

2013)

Whenexaminingclimatechangeimpactsonecosystem compo-nentsextrapolatedfromrelativelyhighresolutionremotelysensed information,acommonchallengeisthedifferenceinspatial reso-lutionofremotelysensedproductscomparedtoclimatedata.In ordertomakeaneffectivecomparison,rescalingofclimatedata

tobettermatchthehigherresolutionremotesensingproductsis necessary.Thisrescaling(calleddownscaling)ofclimate informa-tionsuchasprecipitationdatacanprovideforfinerscaleanalysis

ofsmallerregions(Hijmansetal.,2005;Wangetal.,2012).For his-toricalprecipitation,havingthelongertemporalrecordsavailable

infiner spatialscaleproductsprovidesadditionalopportunities fordefining therelationshipbetweenclimatechangeand sage-brushecosystemchange.Specifically,thereleaseofDaymetdaily griddedsurfaceclimatedata(ThorntonandRunning,1999) pro-videshistoricaldailyprecipitationdataat1-kmspatialresolution withopportunitytoexploreregionalscalelinksofclimatechange

toobservedecosystemchange.Linearregressionanalysisisone approachthathasbeenwidelyusedtolinkclimatedatatoremote sensingderivedvegetationconditionforlargeareabiomassand cropyieldpredictions(Quarmbyetal.,1993;Prasadetal.,2007) Forfutureprecipitationprojections,advancesinclimate fore-castingalsocontinuetoevolve,withtheuseofatmosphericgeneral circulation models (GCMs).GCMs are commonly used for sim-ulating atmospheric conditions and subsequent future climate response.TheIntergovernmentalPanelonClimateChange(IPCC) Fourth Assessment Report providesclimate change projections contributedfromdifferentGCMs(IPCC,2007).However,GCMsused

inclimatechangeexperimentsorseasonalforecastshaveatypical spatialresolutionofafewhundredkilometersforeachcelland thuscanpoorlyrepresentregionalclimateanalysis(Hannahetal.,

2002).GlobalGCMoutputscanbetoocoarsetoassessregional impactsonbiodiversity,ecosystemservices,speciesdistributions, andotherlandscaperelatedmatters(TaborandWilliams,2010; Salathéetal.,2007).Hence,differentdownscalingtechniqueshave beendeveloped toobtainregionalpredictionsoftheseclimatic changes(TaborandWilliams,2010;Fowleretal.,2007),but tech-niquescanvaryinaccuracyandoutputresolution.Becauseshifts

inprecipitationmayhaveagreaterimpactonecosystemdynamics thanrisingCO2ortemperature(Weltzinetal.,2003),downscaled GCMsthataccommodateregionalprocesses(e.g.,land-water inter-actionsandtopography)arekeywhenpredictingfuturesemiarid systemssuchassagebrush

Sagebrush ecosystems contain many wildlife species highly dependentuponthehabitattheyprovide.Wildlifemanagement

in thefuturewill requiretheability tounderstandandpredict future changesin habitatand associatedeffects onspecies and populations.Sage-grouse,asagebrushhabitatobligateunder con-sideration for listing as threatened or endangered, is an ideal candidate toevaluatetheeffects of future conditions based on futurehabitatscenarios.Quantitativemonitoringofhabitattrends has been identified as a key requirement to understand and reduceuncertaintyaboutclimatechangeimpactsonhabitatand associatedwildlifespecies(U.S.FishandWildlifeService,2013) Development of future habitat scenarios for sage-grouse could allowforapplicationtootherspeciesofconservationconcern Sea-sonalhabitatmodelshavebeendevelopedforsage-grouseacross thestateofWyoming(Fedyetal.,2014)usingsagebrush compo-nentsasbasehabitatlayers(seeHomeretal.,2012).Thisprovides

anidealopportunitytoevaluatehowclimate-inducedchangesin projectedfuturehabitatconditionswillaffectsage-grouse popula-tions

Wetheorizedthatdevelopmentsincapturinggradualchange acrosstimeusingremotesensingsagebrushcomponentsandthe downscalingofprecipitationcouldbecombinedtocorrelate pre-cipitationtrendswithcomponentabundanceacross28years.We furthertheorizedthattheseprecipitationtrendswouldinfluence

couldbequantifiedintopotentialfuturecomponentscenariosand

thesubsequenteffectonsage-grousehabitat.Wefirstexamined

thelong-termresponse ofsagebrush ecosystemcomponentsto

trendsinhistorical precipitationvariation anddeveloped linear

modelsexplainingthishistoricalrelationship.Second,weapplied

thesemodelsto2050IPCC precipitationprojections toforecast

changesincomponentsthoughto2050,basedonthe28-yearslope

relationships Third,we usedpredicted2050sagebrush

compo-nentstoreapply sage-grousehabitatmodelstounderstandhow

sage-grousehabitatqualityandquantitymightchangeasaresult

ofprecipitationinducedchangesinsagebrushcomponents

2 Data and methods

2.1 Overview

Weexaminedtheannualchangeoffivesagebrushecosystem

components(hereafter called components) from 1984to2011

Wecharacterizedbareground,herbaceous(herbaceousness),

lit-ter,sagebrush,andshrubcoverascontinuousfieldsinonepercent

intervals.Weused2006and2007QuickBird(QB)satellitedatawith

coincidentfieldmeasurementstotrain2006LSsatellitedatato

createa2006baseanalysisyear.We thennormalizedhistorical

LSimageryeveryyearbackto1984,withcomparisontothe2006

basetofindareasthathadchangedspectrally(Table1)

Compo-nentpredictionswereupdatedinthesespectrallychangedareas

usingunchanged2006baseareasastraining sourcesin

regres-siontreealgorithms.Daymetprecipitationdataforthesameperiod

wasdownscaledtoa30mgrid,andregressionanalysisconducted

todeveloplinearmodelsbetweencomponentestimatesand

pre-cipitationmeasurements.WethenappliedtwoIPCCprecipitation

projectionstothelinearmodelstoproduce2050predictionsfor

eachcomponent.Sagebrushandherbaceousnesscomponentsfor

2050wereusedtodevelopsage-grousehabitatpredictionsfor2050

(Table1).Weexplaineachmethodologicalstepbysectionbelow

2.2 Studyarea

Our studyareaislocatedin southwesternWyoming,United

States(Fig.1 andoccupies8330km2.Itcontainsarangeof

topog-raphywithelevationsfrom1865to2651m,andslopesupto48◦

IthaspredominantlysandysoilsandcontainstheKillpeckersand

dunes.Vegetationisdominatedbysagebrushshrubland,especially

intheuplandareas,withsaltdesertshrubspeciesdominatingin

thelowlandandsandyareas.Herbaceousareasrangefrom

typ-ical grasses and forbs interspersed among shrubs to meadows

whereahighsub-surfacewatertableinthesandyareascreates

higherbiomassproductivityfortheseselectedareas.Shruband

herbaceousvegetationoccurinarelativelywiderangeofcanopy

amounts,withsparservegetationinthelowerelevationsofthe

southwesternportionofthestudyarea,anddenservegetationin

thehigherelevationnorthernportionsofthestudyarea.Thissite

ispredominantlypubliclandadministeredbytheBureauofLand

Management,withmostareashistoricallygrazedbycattleforthe

durationofthesummer.Wealsoselectedthisstudyareabecause

itcontainedoneoftheoriginaleightQBsitesusedforthe2006

Wyomingsagebrushcharacterization(calledsite1;Homeretal.,

2012;seeFig.1).Site1isthelocationwherecomprehensivetrend

analysisresearchhasbeenon-goingformanyyears(Homeretal.,

2013)

2.3 Baselinedatacollection

Severalkeystepswererequiredtocalculatecomponent

mea-surementsforthebaseyear(2006)andadditionalyearsbetween

1984and2011including:(1)collectandpre-processLSdataforall years;(2)calculatevegetationcontinuousfieldcomponentsforthe baseyear(2006);(3)normalizespectralreflectanceofallscenesto thebaseyear(2006);(4)compareyearlyLSimageswiththebase yeartoidentifypixelsthathavespectrallychanged;and(5) calcu-latenewcomponentvaluesforspectrallychangedpixelsfromeach year.Eachofthesestepsisdescribedindetailbelow

2.3.1 Imagecollectionandpre-processing

WeacquiredeightQBimages(64km2each)distributedacross

LSpath37/row31duringthesummerof2006and2007(Homer

etal.,2012).Foreachimage,fourbandsofmultispectral informa-tion(visibleblue,green,red,andnear-infrared)werecollectedat 2.4-mresolution.ImagerywasprojectedtoUniversalTransverse Mercator(UTM)usinga2×2bilinearre-samplingkernel Coin-cidentwithimagecollection,Homeretal.(2012,2013)collected fieldmeasurementsatthissiteforeachcomponent.Weestimated percentcoverforallcomponentsfromanoverheadperspective (satellite),whilestipulatingthatthetotalcoverofallvegetation andsoilcomponentssumto100%

Weacquiredleaf-on(June,July,orAugust)LSThematicMapper (TM)imageryfrom1984to2011forpath37/row31andprocessed usingtheautomatedLandsatProductGenerationSystem(LPGS)

WeselectedLSproductsbecausetheywerehistoricallyavailable forthelongestspan(1984–2011).LSimageswereconverted to at-sensor-reflectance,projectedtoAlbersEqualArea,andterrain corrected(Chanderetal.,2009;Xianetal.,2009;XianandHomer,

2010)

2.3.2 Componentbaseyearpredictions

Weproducedthespatialdistributionsoffivesagebrush com-ponents (bareground, herbaceous,litter, shrub,and sagebrush)

atonepercentintervalsforbothQBandLSusingregressiontree models.FortheeightQBscenes,groundsamplingdatawereused

inregressiontreemodels(crossvalidationcorrelationsacrossall componentsaverageda correlationof 0.86)toproduce compo-nentestimates(trainingprotocolsandaccuraciesaredescribedin Homer etal.(2012, 2013)).Inorder toensurea rigorous train-ingsample atthe LSscale,QB predictionsfromboth 2006and

2007werecombinedtocreatethe2006LSbase.Addingthesesites providedfullvariation incomponentrangesacrossanentireLS path/rowandensuredcomponentresultswererepresentativeof

alargerecosystemscaleclassificationapplication.LSbase predic-tionsweremodeledusingthreeseasonsofimagery,coupledwith

aDigitalElevationModel(DEM)andancillarydata(Homeretal.,

2012);modelcrossvalidationcorrelationsacrossallcomponents averagedacorrelationof0.92

2.3.3 Imagenormalization,changeidentification,andprediction Normalizing the spectral reflectance of the LS image dates ensures consistentcomparison,which is importantfor success-fultrendanalysis.We usedthefollowingprocedurestoidentify potentialchangeareasandthemagnitudeandtypeofchange.First, allcloud,cloudshadow,and snowand iceareaswereexcluded fromanalysis.Second,anormalizationprocedureusingalinear regressionalgorithmtorelateeachpixelofthesubjectimageto thereferenceimage(2006leaf-on)bandbybandwasconducted (Xian et al., 2012b) Third,potential change area identification wasaccomplishedusingachangevectorprocessthatcompared normalized imagesto thebase imageusing vegetationspecific thresholds to identify change (Xian et al., 2012b) Fourth, we assignedanewcomponentvaluetoLSchangeareasusinga regres-siontree(RT)modelingapproachsimilartothecreationofthe2006 baseline.WeidentifiedthecandidatetrainingdatawithintheLS basefortheRTestimatesbyexcludingpotentialchangepixelsvia thechangemaskandbinningtrainingpixelsusingnaturalbreaks

Table 1

Flow diagram of all major methodological steps required to characterize sagebrush components, quantify change across years, relate change to changing precipitation and forecast future component and Sage grouse habitat predictions.

Forstudyareawidechangeanalysis,wecompiledpredictionsby

totalareaofchange(thearealproportionofthecomponentofeach

cellintoatotalareasummaryvalue)foreachcomponentforeach

yearacrossthestudyareaonareasthatwerenotmaskedinany year(pixelsthatwerepureacrossall28years).Wealsocalculated themeanyear-to-yearpercentchangeandlineartrend.The cor-relationofannualcomponentproportionsandannualwateryear meanvalueswerecalculatedusingaPearson’scorrelation 2.4 Climatedataprocessing,historicalclimatedata TheDaymetmodelisacollectionofalgorithmsandcomputer softwaredesignedtointerpolateandextrapolatedaily meteoro-logicalobservationstoproducegriddedestimatesofdailyweather parameters over the conterminous United States, Mexico, and southern Canada (Thornton et al., 1997) The required model inputsincludeadigitalelevationmodelandobservationsof maxi-mumtemperature,minimumtemperature,andprecipitationfrom ground-basedmeteorologicalstations TheDaymetmethodwas developedattheOakRidgeNationalLaboratoryandisbasedon

Fig 1. Study area extent, located north of Rock Spring, WY, U.S.A The small magenta rectangle in the center of the study area is the location of site 1, where intensive monitoring work has been ongoing since 2006 (see Homer et al., 2013 ).

thespatialconvolution ofa truncatedGaussian-weightingfilter

runwiththesetofstationlocations.Sensitivitytothe

heteroge-neousdistributionofstationsincomplexterrainisaddressedwith

aniterativestationdensityalgorithm.Forouranalyses,we

consid-eredDaymetproductsofminimumand maximumtemperature,

precipitation,humidity,andincidentsolarradiationproducedon

a1km×1kmgriddedsurface.Wesummarizedthedailygridded

surfacesintomonthlytotals(precipitation)oraverages

(temper-ature),andthencompiledmonthlyprecipitationdataintowater

yeartotals(October–September)foreachyearbetween1984and

2011withinourstudyarea.Were-projectedalldatatomatchthe

mapprojectionusedforthesagebrushproductsandre-sampled

the1kmgridsto30-mspatialresolutionusingthebilinear

inter-polationmethod

2.5 Climatedataprocessing,futurepredictions

We obtainedfuture precipitationdata fromthe IPCCFourth

AssessmentReport(IPCC,2007).Weevaluated2050precipitation

datafromthreeglobalclimatemodelsincludingtheGeophysical

Fluid Dynamics Laboratory Coupled Climate Model 2.1

(GFDL-CM2.1;Delworthetal.,2004),theNationalCenterforAtmospheric

ResearchCommunityClimateSystemModel3.0(NCAR-CCSM3.0;

Collinsetal.,2005)and theUnitedKingdomMetOfficeHadley

CenterCoupledModel3.0(UKMO-HADCM3;Gordonetal.,2002)

Weevaluatedtwoofthefourfamilyscenarioswiththese

mod-els:A1B(economicgrowthwithbalanced energydevelopment)

andA2(highpopulationgrowth).Futureclimatechangesunder

theA1Band A2scenarios willresultin substantialincreasesin

surfacetemperature:1.7–4.4◦CforA1Band2.0–5.4◦CforA2.We

excludedtheothertwofamilyscenariosfromouranalysisbecause

downscaledprecipitationdatawerenotavailablefortheB2family andwejudgedtheB1familyrepresentedanunlikelyscenariofor thisarea.Weuseddownscaled30GCMmodelpredictionsforthe threemodelsmentionedaboveforbothfutureclimatechange sce-narios.ThesedownscaleddatawerecreatedusingtheDeltamethod (Hijmansetal.,2005;Ramirez-VillegasandJarvis,2010),whichwe downloadedfromtheCGIARResearchProgramonClimateChange, Agricultureand FoodSecurity(CCAFS; www.ccafts-climate.org)

Were-projectedthedatatothesameprojectionasthesagebrush componentsandresampledto30musingtheBilinearInterpolation method.Weorganizedtheoriginaldataintomonthlyprecipitation, whichwasthenrecompiledintoannualprecipitationandclipped

tofitourstudyarea

2.6 Futurecomponentchangepredictions

Wedevelopedfuturepredictionsforfivesagebrushcomponents

byfirstexploringhistoricaldatacorrelationsbetweenseveral cli-mateindicesandsagebrushcomponentstounderstandcorrelation potentialatthestudyareascale.Wethendevelopedtheclimate predictorthatbestpredictedsagebrushcovercomponentchange (annualprecipitation)asalinearmodelatthesinglepixelleveland subsequentlyappliedtheserelationshipstofutureclimate precip-itationscenarios.Thesestepsareoutlinedbelow

2.6.1 Linearregression Previous field experiments conducted in thenorthern Great Basin using rain shelters for different precipitation treatments suggested that the fractional cover of most sagebrush compo-nentshave a significantlinearresponse toannualprecipitation (Bateset al., 2006).In ourresearch, we conducted exploratory

Table 2

Nesting and summer habitat logistic regression model coefficients and standard errors (in parentheses) used to predict effects of changes in sagebrush habitat components due to climate change in 2050 Many variables were included in the original models (see Fedy et al., 2014 ) These were also applied to future scenarios analyses developed here; however, only the sagebrush habitat components within those models were changed and are shown here.

a Mean cover of all sagebrush species estimated over a 564 m radius moving window.

b Mean cover of all sagebrush species estimated over a 45 m radius moving window.

c Standard deviation of mean sagebrush cover (all species) estimated over a 45 m radius moving window.

d Mean cover of all sagebrush species estimated over a 1500 m radius moving window.

e Mean cover of all sagebrush species estimated over a 3200 m radius moving window.

f Standard deviation of mean cover of all sagebrush species estimated over a 3200 m radius moving window.

g Mean cover of herbaceous vegetation estimated over a 564 m radius moving window.

h Standard deviation of mean cover of herbaceous vegetation estimated over a 564 m radius moving window.

correlationanalysisbetweenthestudyareameanfractionalcover

ofsagebrushcomponents(dependentvariable)andseveralclimate

indices(independentvariables),includingtotalannual

precipita-tion,annualmeantemperature,totalseasonalprecipitation,total

snowwaterequivalent,andmeanincidentsolarradiation.Because

thefractionalcoverofsagebrushcomponentsandannual(water

year)precipitationhadthehighestcorrelation,thiscomponentwas

selectedforfurtherdevelopment.Therefore,linearregression

mod-elsrelyingontheleastsquaresestimatorweredevelopedusingthe

fractionalcoverofthefivesagebrushcomponentsandannual

pre-cipitationatthepixellevel.Forallannualrecordsinapixellocation,

thelinearregressionapproachfitsastraightlinethroughthesetof

npointsthatminimizesthesumofsquaredresiduals(deviationof

observedandtheoreticalvalues):

whereXisanindependentvariable(e.g.,annualprecipitation),Y

isadependentvariable(sagebrushcomponent),bistheslopeof

thefittedline(equaltothecorrelationbetweenYandXcorrected

bytheratioofstandarddeviationsbetweenYandX),andaisthe

y-interceptterm

Fivelinearregressionanalyseswereconductedindependently

usingdatabetween1984and2011includingbaregroundcover

andannualprecipitation,herbaceouscoverandannual

precipita-tion,litter cover andannual precipitation,sagebrush cover and

annualprecipitation, andshrub cover andannual precipitation

Ournullhypothesisisthatthereisnosignificantlinear

relation-shipbetweenthe sagebrushcomponentsand precipitation.We

testedournullhypothesisusingatwo-sidedt-testforeach

com-ponent,whichcanrevealbothpositiveandnegativecorrelations

betweenXandYin Eq.(1).We evaluatedthep-valuefor three

significancelevels:0.05<p≤0.1,0.01<p≤0.05,andp≤0.01and

selected0.05<p≤0.1asthesignificancethreshold.Onlypixelsthat

hadsignificantpositiveornegativecorrelationswereretainedfor

calculating thefuture changeprediction at theindividual pixel

level.Forpixelswithnon-significantcorrelations,wedevelopeda

modifiedlinearregressionmodelbasedontheaverageslopevalue

ofallnon-significantpixels.Thisensuredthat extremechanges

infutureprecipitationvaluesoccurringovernon-significantpixel

areaswould still berepresentedin thefuturecomponent

fore-casts.Althoughthelinearmodelissimple,easilydeveloped,and

presentedareasonablestartingpoint,therearelimitationswith

usingthisapproach.Alinearmodelmaynotadequatelyrepresent

physicalprocessesofsagebrushcomponentsrespondingtoclimate variations

2.6.2 Futurechangeprediction Futurechangepredictionsforeachsagebrushcomponentwere performedusingcomponentspecificlinearregressionequations:

Yi,j(k,2050)=Yi,j(k,2006)+bi,j(k)(Xi,j(2050)−Xi,j(2006)) (2) whereiandjrepresentpixellocations,Yi,j(k,2050)representsthe fractionalcoverofthesagebrushcomponentkforapixellocated

atiandj,bi,j(k)isaslopeforthecomponentk,Xi,j(2050)isthe annualprecipitationfor2050,andXi,j(2006)istheannual precipi-tationfor2006.The2050annualprecipitationpredictionservesas theindependentvariableinEq.(2)toprojectthefractionalcoverof thefivesagebrushcomponentsto2050.Forpixelsthathave non-significantcorrelations(negativeforbaregroundandpositivefor othercomponents),ameanslopefortheentirestudyarea(all pix-els)isusedtoreplacebi,j(k)inEq.(2),forthatspecificcomponent Sincefutureprecipitationchangemaynotfollowtheexactsame patternsinareasthatexperiencesignificantcorrelations,theuse

ofmeanslopefornon-significantpixelsallowsimpactsofmore extremefutureprecipitationvaluesovernon-significantareasto

becapturedinthefuturecomponentprojections.Wedeveloped predictionsusingannualprecipitationamountsfromeachofthe twoclimatechangescenarios

2.7 Sage-grousehabitatmodelsand2050habitatpredictions Contemporarymodelsevaluatingsage-grousehabitat require-mentswererecentlydevelopedfor thestateofWyoming (Fedy

etal.,2014).Sage-grouseresponsetoanthropogenic,abiotic, ter-rain,andvegetationcharacteristicswasassessedusingGeneralized LinearModel (GLM)ResourceSelection Functions(RSFs; Manly

etal.,2002)appliedtotelemetrydatafrommultiplestudiesacross thestate.These modelspredict probabilityof selection forany givenpixel(30m)onthelandscape,andthiscontinuoussurface wassubsequentlythresholdedintoabinarysurfacedepicting habi-tatand non-habitatfor sage-grouse; seeFedy et al (2014) for details.Vegetationlayersevaluatedforsage-grousehabitat selec-tionwerethesamebaseyear(2006)sagebrushcomponentsused forclimateanalysespresentedhere,makingforrelativelysimple evaluationoffuturechanges insagebrushcomponentson sage-grouse habitatchange.Fedyetal (2014)developed modelsfor nesting,late-summer, andwinter, usingdifferentspatialextent

Table 3

Total annual percent proportional cover change compiled as a total study area value, by component This metric was calculated using only valid pixel values cloud free in all

28 years If cloud cover precluded the inclusion of valid pixels from any year, that area was excluded from all years The resulting area represented here consisted of 39% of the study area (3288 km 2 ).

etal.,2014)

Intheoriginalstatewidesagebrushcomponentproducts,edge

matchinginLSoverlapzonesandstandardizationwasrequiredto

stitchtogethermodelsdevelopedforindividualLSscenes(Homer

etal.,2012).Ourtargetstudyareawaspartiallywithintheoverlap

zoneofLSPath37/Row31andPath37/Row32,soforthisstudywe

chosetodevelophistoricalclimateprojectionsbasedondatafrom

asinglescene(Path37/Row31).Thisallowedforconsistencywith

theclimateanalysesusingspectralinformationfromoneLSscene

overtime.Asaresult,wereappliedtheoriginalGLMsage-grouse

RSFhabitatmodelequationsusingbaselayercomponentvalues

foreachpixeldevelopedfromthesingleLSscenepresentedhere

Thisresultedinaconsistentsage-grousebaseyear(2006)

habi-tatmodeltobuilduponforprojections.Wefirstregeneratedthe

appropriatemodelcovariatesrequiredforthesage-grousemodel

usingthesamespatialextent(movingwindow)foundtobe

impor-tantintheoriginalsage-grousemodels.Forinstance,ifmeancover

ofbigsagebrush(Artemisiatridentatassp.)overa6.4kmradius

win-dowwasintheoriginalmodel(Fedyetal.,2014),wetookthenew

pixelestimatesforthe2006baseyeargeneratedfromthesingle

LSPath/Rowsagebrushcomponentmodelsandre-calculatedthe

meanvaluesoverthesamespatialextent.Thisallowedfor

reappli-cationofthemodelusingmodifiedinputs,generatingconsistent

andcompatiblemodelsthatidentifiedsage-grousehabitat

require-mentsfornestingandlatesummerintheoriginalbaseyou(2006)

Weappliedthethresholdingvalues usedintheoriginalmodels

todevelopabinaryhabitat/non-habitatbasemap.Original

habi-tatmodelsweredeveloped attwoscales(patchand landscape;

seeFedyetal., 2014), and coefficients forallsagebrush habitat

componentscontainedwithintheoriginalGLMlogisticregression RSFmodelresponsesareshowninTable2.Wefollowedthesame stepstodevelopthepredicted2050sage-grousehabitatmodels, simplysubstituting in2050habitatcomponent predictionsand generatingtheappropriatemovingwindowcovariateswhere nec-essary,allowingustogeneratehabitatpredictionmapsfor2050

3 Results

3.1 Historicalcomponentandprecipitationchangeand correlation

We measuredannual change in five sagebrush components (bareground, herbaceous, litter, sagebrush,and shrub) over 28 years (1984–2011)fromthebaseyearof 2006.Measuredareas neededtobeavailableinall28years(ifcloudcoveredinanyone year,thisareawasexcludedfromallyears)with40%ofthestudy area(3288km2)cloudfreeinallyears.Baregroundisbyfarthe mostdominantcomponentofthelandscapewithmeanproportion coverage of 59.1%, followed by litter at 16.16%, herbaceous at 13.56%,shrubat11.21%,andsagebrushat9.4%(Table3).When analyzedforvariationbetweenindividualyears,bareground dis-playedthehighestannualvariationwithameanannualchangeof 0.54%,andsagebrushthelowestat0.17%(Table3).Whenanalyzed acrossall28years,baregroundshowedanoverallincreasingtrend

inabundance, withherbaceousness,litter,shrub,andsagebrush showing a decreasing trend Litter displayed the most obvious decreasingtrend

We calculatedmeanannualwater yearprecipitationineach yearovertheentirestudyareafromDaymetobservations Pre-cipitation varied from a low of 125mm in 2001 to a high of

404mmin1986(Fig.2a).Overall,thereisadownwardtrendinthe

Fig 2.Mean annual precipitation from 1984 to 2011 over the study area calculated from Daymet data by water year with the linear trend line (a), the average annual precipitation between 1984 and 2011 (b), and mean annual precipitation predicted from NCAR-CCSM3.0 under the A2 scenario for the year 2050 (c) The linear regression equation of mean annual precipitation displayed in (a) is expressed as y = -0.8761x + 275.71 in which x is the time and y is annual precipitation, with r 2 = 0.01 The white in (b) and (c) represent the areas without sagebrush cover.

historicalamountofprecipitationreceived(Fig.2a).Fig.2 shows

the1984–2011meanannualDaymetprecipitationforthestudy

areaandFig.2cshowsmeanannualprecipitationfortheyear2050

Theprecipitationissomewhatgreaterinthenortheastpartofthe

areathaninthesouthwest.Pearson’scorrelationsbetween

compo-nentstudyareameansandannualprecipitationstudyareameans

rangedfrom0.56 forherbaceous,to0.48forsagebrush,0.43for

shrub,0.42 forlitter,and0.38 forbareground Herbaceousand

sagebrushcorrelationvaluesweresignificantatthe0.01level,and

allotherssignificantatthe0.05level

3.2 2050componentforecasting

We excluded non-sagebrush component landscapes within

thestudyareafromfuturecomponentforecasting(areas

perma-nentlyconvertedtoagricultureandurbanlanduse),leaving91%

(7580km2)ofthestudyareafor analysis.We calculated future

change predictions for each sagebrush component 30-m pixel displayingasignificantlinearregression(p<0.1)resultbetween historicalcomponentandprecipitationchange.Mostpixelsdidnot haveasignificantlinearregressionandremainedunchangedinthe

2050predictions(Table4).Forbareground–precipitation regres-sion,thenumberofpixelsthathadnegativecorrelationswasabout threetimeslargerthanthenumberofpixelsthathadpositive cor-relations.Forothercomponents,twotothreetimesmorepixels hadpositivecorrelationsthanthosethathadnegativecorrelations Herbaceouscoverhadthelowestproportionofindividualpixels qualifyingfor futureupdatingat 22.3%,andlitterhad the high-estproportionofindividualpixelsqualifyingforfutureupdatingat 24.6%(Table4)

We evaluated2050 precipitationdata fromthree global cli-matemodels(GFDL-CM2.1,NCAR-CCSM3.0,andUKMO-HADCM3) acrosstwooffourfamilyscenarios(A1BandA2seeTable5).The NCAR-CCSM3.0modelpresentedthemostdivergentprecipitation

Table 4

The percentage of the total pixels that presented significant correlations (p < 0.1) to annual precipitation, listed by component These amounts include both positive and negative correlations Pixels with significant correlations had individual linear models developed to forecast each component, while pixels with non-significant correlations required a mean slope value from the entire study area.

Component % pixels with significant

positive correlation

% pixels with significant negative correlation

% pixels with both positive and negative correlations

% pixels with no significant correlations

Table 5

The comparison of 2050 mean study area precipitation projections calculated for

two families of three IPCC models For comparison, the total mean study area

pre-cipitation historically from 1984 to 2011 was 263 mm.

Fig 3.Spatial distrubution of bare ground and shrub component prediction change

between 2006 and 2050 for the A1B scenario across the entire study area

Com-ponent reductions are represented in red and orange tones and increases in green

tones.

lin-earmodelingimplementation Theannualprecipitationin2050

predictedbyNCAR-CCSM3.0undertheA2scenarioisrepresented

inFig.2c.Thispredictioncaptures asimilarspatialdistribution

Fig 4.Spatial distrubtion of bare ground and shrub component prediction change between 2006 and 2050 for the A2 scenario across the entire study area Component reductions are represented in red and orange tones and increases in green tones.

patterntothehistoricalpatternalthoughthemagnitudeissmaller

in manyareas Whenforecastprecipitation amountsfrom IPCC scenarioswereinputintoequationsandcomponentsurfaces calcu-latedin2050,baregroundwastheonlycomponentthatincreased underboth future scenarios Bareground had anet increaseof 48.98km2 (1.1%) acrossthestudy areaunder theA1B scenario and a net increase of 41.15km2 (0.9%) under the A2 scenario (Table 6, Figs 3 and 4) The remaining components decreased under both future scenarios, with litter havingthe highest net reductionsunderbothscenarios(A1Bscenarioat49.82km2(4.1%), and the A2 scenario at 50.8km2 (4.2%)), and herbaceous the smallest net reductions under both scenarios (A1Bscenario at 39.95km2(3.8%),andtheA2scenarioat40.59km2(3.3%))(Table6, Figs.3and4)

Table 6

Positive and negative total component change amounts in km 2 for 2050 IPCC A1B and A2 scenario forecast change results compared to the 2006 component base predictions.

− change (km 2 ) + change (km 2 ) Net change (km 2 ) − change (km 2 ) + change (km 2 ) Net change (km 2 )

Table 7

Total amount of study area that contained sage-grouse nesting and summer habitat in the 2006 base year and in 2050 using sagebrush components from two different climate scenarios (A1B and A2) Habitat losses are based on 2050 landscapes relative to identified habitat in the 2006 base year Habitat gains represent novel areas (pixels) in the

2050 landscape predicted to be suitable for sage-grouse, whereas habitat losses represent areas that were identified as habitat in 2006 but in 2050 are no longer habitat.

cov-eredroughly21%ofthesage-grousestudyarea(∼1669km2;see

Table7).Fornestinghabitat,the2050modelforIPCCA1Bhabitat

estimatesapplied tothesage-grousemodel predicteda loss of

Fig 5. Predicted changes in sage-grouse nesting habitat from 2006 to 2050 from climate scenerio A1B Changes are based on the original sage-grouse habitat models from

Fedy et al (2014) for the 2006 base year, which were then predicted to 2050 based on changes in sagebrush vegetation characteristics linked to the A1B climate projection scenario A small number of pixels changed to habitat in 2050 habitat (blue), which are difficult to see at the mapped scale The no habitat class represents areas where one

or more sage-grouse model data inputs were not available, preventing model prediction (For interpretation of the references to color in this figure legend, the reader is

355km2 ofadequate sage-grousehabitat,resulting inan 11.6% lossfromhabitatidentifiedin2006,andtheIPCCA2hadaloss

of∼361km2ofsage-grousehabitat,or11.8%(Table7,Fig.5).For summerhabitat,the2050modelforIPCCA1Bscenariospredicteda lossof∼67.5km2ofhabitatidentifiedin2006(∼4.0%loss),andthe IPCCA2hadalossof∼68.1km2ofhabitatidentifiedin2006(∼4.1% loss;Table7,Fig.6).InbothIPCCscenariosforeachlifestage,a smallnumberofpixelsacrossthestudyareaimprovedinhabitat quality,butthegaininidentifiedhabitatwaslessthan0.08km2