• No results found

Explaining planted-tree survival and growth in urban neighborhoods: A social–ecological approach to studying recently-planted trees in Indianapolis

N/A
N/A
Protected

Academic year: 2021

Share "Explaining planted-tree survival and growth in urban neighborhoods: A social–ecological approach to studying recently-planted trees in Indianapolis"

Copied!
14
0
0

Loading.... (view fulltext now)

Full text

(1)

ContentslistsavailableatScienceDirect

Landscape

and

Urban

Planning

jou rn al h om ep a ge :w w w . e l s e v i e r . c o m / l o c a t e / l a n d u r b p l a n

Research

Paper

Explaining

planted-tree

survival

and

growth

in

urban

neighborhoods:

A

social–ecological

approach

to

studying

recently-planted

trees

in

Indianapolis

Jessica

M.

Vogt

a,b,f,∗

,

Shannon

Lea

Watkins

b,c

,

Sarah

K.

Mincey

c,d,e,f

,

Matthew

S.

Patterson

g

,

Burnell

C.

Fischer

c,f,b

aFurmanUniversity,DepartmentofEarthandEnvironmentalSciences,3300PoinsettHighway,Greenville,SC29613,UnitedStates

bIndianaUniversityBloomington,CenterfortheStudyofInstitutions,Population,andEnvironmentalChange,408N.IndianaAve.,Bloomington,IN48407,

UnitedStates

cIndianaUniversityBloomington,SchoolofPublicandEnvironmentalAffairs1315E.10thSt.,Bloomington,IN47405,UnitedStates dIndianaUniversityBloomington,IntegratedProgramintheEnvironment,702N.WalnutGroveAve,Bloomington,IN47405,UnitedStates eIUResearchandTeachingPreserve,702N.WalnutGroveAve,Bloomington,IN47405,UnitedStates

fTheVincentandElinorOstromWorkshopinPoliticalTheoryandPolicyAnalysis,513N.ParkAve.,Bloomington,IN57508,UnitedStates gUniversityofWashington,UrbanEcologyResearchLaboratory,432GouldHall,394915thAve.NE,Seattle,WA,UnitedStates

h

i

g

h

l

i

g

h

t

s

•Weexaminedtherelationshipbetweensocial–ecologicalsystem(SES)factors&streettreesuccess.

•VariablesfromallSESfactorsinfluencerecently-plantedtreesurvival&growth.

•Theimpactofneighborhoodwateringstrategyontreesuccessdependsonplantingseason.

•Futureresearchshouldconsidersocial–ecologicalcontextofplantedurbantrees.

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received28February2014

Receivedinrevisedform

24November2014

Accepted27November2014

Availableonline30December2014

Keywords: Treegrowth Treesurvival Streettrees Social–ecologicalsystems Communitycharacteristics Institutions

a

b

s

t

r

a

c

t

Thisresearchseekstoanswerthequestion,whatfactorsoftheurbansocial–ecologicalsystem pre-dictsurvivalandgrowthoftreesinnonprofitandneighborhoodtree-plantingprojects?TheOstrom social–ecologicalsystemframeworkandClarkandcolleagues’modelofurbanforestsustainabilityinform ourselectionofvariablesinfourcategoriesinthesocial–ecologicalsystem;thesecategoriesarethe trees,thebiophysicalenvironment,thecommunity,andmanagementinstitutions.Weusetree inven-torymethodstocollectdataonthesurvival,growth,andthesocial–ecologicalgrowingenvironmentof recently-plantedstreettreesinIndianapolis,INtoanswerourresearchquestion.Weuseaprobitmodel topredicttreesurvival,andalinearregressionmodeltopredicttreegrowthrate.Thefollowingvariables arepositivelyrelatedtotreesuccess(survivaland/orgrowth):ball-and-burlaporcontainerpackaging,a visiblerootflare,goodoverallconditionrating,thesizeofthetree-plantingproject,plantingareawidth, medianhouseholdincome,percentofrenteroccupiedhomes,residenttenure,priortreeplanting experi-ence,correctmulching,andacollectivewateringstrategy.Thefollowingvariablesarenegativelyrelated totreesuccess:caliperatplanting,crowndieback,andlowertrunkdamage.Additionalvariables mea-suredhavelessclearconnectionstotreesuccessandshouldbeexaminedfurther.Giventhatmodels includingvariablesfromallfourcategoriesofthesocial–ecologicalsystemgenerallyoutperformmodels thatexcludesomecomponents,werecommendthatfutureresearchonurbantreesurvivalandgrowth shouldconsidertheholisticsocial–ecologicalsystemscontextoftheurbanecosystem.

©2014TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/3.0/).

∗ Correspondingauthor.Tel.:+0019208502016.

E-mail addresses: jessica.m.vogt@gmail.com, jessica.vogt@furman.edu (J.M. Vogt), shawatki@indiana.edu (S.L. Watkins), skmincey@indiana.edu (S.K. Mincey),

tertiarymatt@gmail.com(M.S.Patterson),bufische@indiana.edu(B.C.Fischer).

http://dx.doi.org/10.1016/j.landurbplan.2014.11.021

(2)

1. Introduction

Inthelasttwodecades,manycitiesintheUnitedStateshave

increasedtreeplantingactivitiesandsettreeplantingorcanopy

covergoals(McPherson&Young,2010).However,relativelylittle

isknownaboutthefactorsthatinfluencethesuccessoftheseyoung

urbantrees.Treesinurbanenvironmentsfacechallengestotheir

survivalandgrowththataredifferentfromthosefacedbytreesin

forestsornurseries(Whitlow&Bassuk,1987).Treesinurban

sett-ingsareaffectednotonlybyenvironmentalconditions,butbythe

peoplewhoplant,own,maintain,passby,andbenefitfromthese

trees.However,muchresearchontreeoutcomeshastakenplace

asexperimentsingreenhousesornurseries,whichcannotsimulate

theactualgrowingconditionsofurbantreesthatgrowinsuchclose

proximitytopeople.Thispaperstudiesthesurvivalandgrowth

ofyoungtreesplantedalongcitystreets.Itusesaholistic

frame-worktoexplainrecentlyplantedurbantreesuccessthataccounts

forcharacteristicsofthetrees,thebiophysicalenvironment,the

surroundingcommunity,andmaintenanceinstitutions.Webuild

uponpreviousresearchinurbanforestryandonsocial–ecological

systemsbyconductinginsituresearchonurbantreesurvivaland

growthandbyexplicitlyconsideringthatplantedtreesarepartof

alargerurbansocial–ecologicalsystem.

1.1. Studyingurbantreesurvivalandgrowthinsitu

Ourreviewoftheliteraturefindsthatthemajorityofresearch

abouturbantreesuccesscomesfromexperimentsconductedin

relativelycontrollednurserysettingsratherthanintheurban

envi-ronmentwherestreettreesgrow.Fewstudiesattempttocontrol

fortheadditionalstressesthatcomefromtheurbanenvironment.

Fewcomprehensivelymeasurethecombinedeffectsof

biophysi-calconditionsandmanagementfactorsontreesuccess,muchless

combinesocialorcommunityinfluencewiththesebiophysical

fac-tors.OneexceptionistherecentstudybyLuetal.(2011),which

examinedtheinfluenceoflocalbiophysicalfactors(urbandesign,

biologicalcondition,etc.)andsocialfactors(e.g.,aweededtreeplot

asevidenceoftreestewardship)onthemortalityratesofyoung

streettreesinNewYorkCity.Jack-Scott,Piana,Troxel,

Murphy-Dunning,andAshton(2013)alsomakeuseofinformationabout

treesurroundingstoinformtheirstudyoftreesuccess.

1.2. Urbanforestsassocial–ecologicalsystems

Theurbanforestcanbeunderstoodasasocial–ecologicalsystem

oflinkedhumanandnaturalcomponents(Mincey,2012;Mincey

etal.,2013;Vogt&Fischer2014).Thisperspective(seeTable1)

buildsontwotheoriesofsustainableresourcemanagement:the

modelofurbanforestsustainability(Clark,Matheny,Cross,&Wake,

1997)andthesocial–ecologicalsystemframework(Ostrom,2009)

andhighlightspotentialfactorsthatmightinfluencetreesurvival

andgrowth.Themodelofurbanforestsustainabilitywas

devel-opedinthefieldofurbanforestmanagementinthemid-1990s.

Themodelidentitiesthreeelementsthatarenecessaryforanurban

foresttobesustainable(i.e.,abletocontinueproducingbenefits

atthe samelevel over time): (1)a healthy vegetative resource

(thetreesandtheirgrowingenvironment),(2)asupportive

com-munity,and (3) an adequatemanagement regime(Clarket al.,

1997).Thesocial–ecologicalsystem(SES)frameworksuggests

sim-ilarcategoriesoffactorsthatappearmostrelevanttosocialand

ecologicaloutcomesin ruralnatural resource systems.The late

NobelLaureateElinorOstromandcolleaguesdeveloped theSES

frameworkthroughdecadesof casestudyresearchoncommon

poolresourcemanagementinruralforests,fisheries,andirrigation

systems(Ostrom,2009).TheSESframeworkusesfourcoresetsof

variablestocategorizeinfluencesonoutcomesoflinkedhuman

andnaturalsystems:(1)theresourceunits(e.g.,fish,trees),(2)

theattributesofthebiophysical resourcesystem(e.g.,size ofa

lakeorforest),(3)thecharacteristicsofthecommunityofactors,

orresourceusers(e.g.,numberofusers),and(4)theinstitutional

factorsofthegovernancesystem(e.g.,rulesforfishingortimber

harvesting;Ostrom,2009).Specificvariables inthesefour

cate-goriesinteractwithone anotherandwiththelargerecological

andsocio-politicalcontexttoproducesocialandecological

out-comes(Ostrom,2009;Epstein,Vogt,Mincey,Cox,&Fischer,2013,

Vogt,2014).Ascoupledhuman-naturalsystems(Liuetal.,2007)of

treesandpeople,urbanforestsaresocial–ecologicalsystems,and

theSESframeworkcanhelpexplainobservedoutcomes.However,

theoriginalSESframeworkwasdevelopedlargelyusingresearch

conductedinextractiveresourcesystemsinruralsettings;thus,

weadaptthisframeworkforourapplicationtourbanforeststhat

providenon-extractivebenefits.

Our urban forests as social–ecological systems perspective

(Table1)containsfourbroadcategories ofvariables thatmight

influencethesuccessoftheurbanforest:(1)thetrees,(2)their

biophysicalenvironment,(3)thesurroundingcommunity,and(4)

themaintenanceinstitutionsthataffectthetree.Weusethis

theo-reticalframeworktomodeltreesuccess.Intherestofthissection,

wedescribewhatpreviousresearchtellsusabouthoweachofthese

categoriesmightinfluencetreesuccessintheurbanforest.

1.2.1. Trees

Thesurvivalandgrowthofplantedtreesisinfluencedbythe

characteristicsofthosetrees.Previoushorticulturaland

arboricul-turalresearchprovidessomeinsight here.Forinstance,thesize

of the tree when it is planted (Neal &Whitlow, 1997; Struve,

Burchfield, &Maupin,2000; Watson, 2005;Lambert,Harper, & Robinson,2010),thetypeofplantpackaging(Gilman&Beeson, 1996;Lambertetal.,2010),andthetreespecies(e.g.Iakovoglou, Thompson,Burras,&Kipper,2001;Grabosky&Gilman,2004)may

influenceitssurvivalandgrowth.Plantingdepthcanimpacttree

survival: trees thatare plantedtoo deeply, withtoomuch soil

coveringtherootball,areatgreaterrisk ofmortality(Gilman&

Grabosky,2004).Additionally, tree health and conditionreflect

overalltreevigorandshouldalsoberelatedtothesurvival(e.g.

Roman,2013)andgrowth(Berrang,Karnosky,&Stanton,1985; Achinelli,Marquina,&Marlats,1997)ofthetree.

1.2.2. Biophysicalenvironment

Thebiophysicalenvironmentalsoinfluencestreesuccess.

Evi-dencesuggeststhattreesurvivalisinfluencedbysurroundingland

usetype(MillerandMiller,1991;Rhoades&Stipes,1999;Luetal.,

2011), as wellasavailable growingspace (Lu etal., 2011)and

rootingvolume,whichconstrainsrootgrowthandthereforealso

abovegroundgrowth(Krizek&Dubik,1987;Grabosky&Gilman,

2004;Day,Wiseman,Dickinson,&Harris,2010).Treegrowthis

also impacted by water stress (Kramer, 1987; Krizek &Dubik,

1987;Graves,Joly,&Dana,1991),poorsoilconditions(Smith,May, &Moore,2001;Scharenbroch,Lloyd,&Johnson-Maynard,2005; Scharenbroch,2009)andcompetitionforspacewithotherurban

infrastructure both above and belowground (Green &Watson,

1989;Gilman,1990a;Kjelgren&Clark,1992;Grabosky&Gilman,

2004).Competitionwithothertreesforrootingspace,nutrientsand

waterbelowgroundandforspaceandlightabovegroundinfluences

growthrates(Nowak,McBride,&Beatty,1990;Rhoades&Stipes,

1999;Iakovoglouetal.,2001),ascantheseasoninwhichatreeis

planted(Solfjeld&Hansen,2004).

1.2.3. Community

Wedefinecommunitytobethepeoplewithinandsurroundinga

resourcesystemwhoprovide,use,andbenefitfromthatresource

(3)

Table1

Urbanforestsaresocial–ecologicalsystems.Thisperspectivecombinesthesocial–ecologicalsystem(SES)frameworkdevelopedinruralcommonpoolresourcemanagement

settings(Ostrom,2009)andthemodelofurbanforestsustainabilitydevelopedinthefieldofurbanforestsustainability(Clarketal.,1997).

Social–ecologicalsystem(SES)framework(Ostrom,2009) Modelofurbanforestsustainability(Clarketal.,1997) Urbanforestsassocial–ecologicalsystems

Resourcesystem Vegetativeresource Biophysicalenvironment

Resourceunits Trees

Users Supportivecommunity Community

Governancesystem Adequatemanagement Institutionsaandmanagement

a

Institutions”aredefinedasrules,normsandstrategiesthatgoverninteractionsbetweenthebiophysicalresourceandthecommunityofusersorbeneficiaries(Ostrom, 2005).

influencetreesuccess(Clarketal.,1997).ResearchbehindtheSES

frameworkhasdemonstratedthatcertaincommunity

character-isticscan belinkedtosuccessof theresource system(Ostrom,

1990).For instance, in ruralcommunity-managed forests, Yang

etal.(2013)findthatthelargestcontributionstoward resource

monitoringeffortscamefromintermediatecommunitysizeand

socialcapitalacrossgroupsandamonggroupmembers,leadingto

thebiggestgainsinforestcover(Yangetal.,2013).

Residentsofcommunities(i.e.,neighborhoods)inwhichtrees

areplantedvaryintheircapacityandresourcestomaintaintrees

andin theirlevelof commitmenttoalong-termneighborhood

improvementprojectliketreeplanting.Highertreeplanting

suc-cessshouldinpartbedrivenbyneighborhoodcharacteristicslinked

tohighercapacityfor–ornormsof–bettertreeplantingandcare.

Nowaketal.(1990)identifyapositivecorrelationbetweenpercent

unemploymentandtreemortalityrates.Theseauthorsspeculate

that,“increasedunemploymentsignifiesmoretimespentinthe

neighborhoodandincreasedactivityinthestreetenvironment,”

whichmaylead togreaterstreet treemortality(: p. 128).This

samestudyalsofinds anegativecorrelationbetweentheratios

ofowner-to-renteroccupiedhousesandtreemortalityrates,and

hypothesizesthat lower ratesof homeownership transferto a

lackofstreettree‘ownership’andthereforehighermortalityrates

(Nowaketal.,1990).RecentresearchinConnecticut,U.S.,by Jack-Scottetal.(2013)demonstratedtherelevanceofothercommunity

groupcharacteristicstotreesurvivalandgrowth(Jack-Scottetal.,

2013).Theyobservedthatthelongevityofthegroupengagingin

treeplantingand prior treeplanting experienceofa

neighbor-hoodarepositivelyrelatedtotreesurvivalandgrowth,andthat

morepeopleparticipatinginplantingduringaplantingyearalso

yieldedgreatersurvivalandgrowth(Jack-Scottetal.,2013).Other

studieshaveexaminedmotivationsforindividualinvolvementin

treeplanting.For example,Austin(2002)reportedthat

individ-ualsinvolvedintreeplantingandmaintenanceactivitiesinDetroit,

Michigan,U.S.weremotivatedbyadesiretoworkinnatureandan

opportunitytobettertheneighborhood.Treesmightfarebetterin

neighborhoodswhereresidentsaremoreinterestedinworkingin

natureandinbetteringtheneighborhood.

1.2.4. Institutionsandmanagement

Institutionsaretherules,norms,andsharedstrategies–formal

andinformal–thatstructuretheinteractionsbetweenthe

commu-nity,resources,andbiophysicalenvironment(Ostrom,2005).Any

rules,normsormanagement strategiesrelatedtoneighborhood

maintenanceofplantedtreesmightinfluencethesuccessofplanted

streettrees.Evidenceexiststhatinstitutionscaninfluenceurban

forests:Larsen etal.(2008)reportedthat rulesofhomeowners’

associationsactivelyregulatedthecompositionandmaintenance

ofresidentiallandscapes.Inadditiontoevidenceaboutstrictrules,

Groveet al.(2006)foundthat homeownersare likely to

main-tainlandscapessimilartotheirneighbors’becauseofsocialstatus

or norms. In multiple cities in Michigan, Nassauer, Wang, and

Darrell(2009)foundthatcommunitynormsregardinglandscape

appearanceinfluenced exurbanhouseholdpreferences forfront

yarddesign.

Maintenance strategies – thetype, frequency, duration, and

intensityofmaintenanceperformedontrees–canalsoinfluence

outcomesintheurbanforest(Vogtetal.inreview).Importantly,

maintenance throughout a tree’s life mayactually mitigatethe

impacts of poor biophysical growing conditions. For example,

irrigationandpropersoildrainagecanreducewaterstress(Gilman,

1990b,2001,2004).Researchinnurseriesandgreenhousesreveals

thatconsistentwateringofyoungtreesislinkedtogreatertree

sur-vivalandgrowth(Gilman,2001,2004).Thus,weexpectthatany

strategythatproducesmoreconsistentwateringwillleadtohigher

neighborhoodtreesurvivalrates.

Pruning andmulching strategies also influencetree success.

Pruningofrootsduringplanting(Gilman,1990b;Solfjeld&Hansen,

2004)orofbranches(Carvell,1978)canhaveasignificantinfluence

ontreegrowthandsurvival,dependingontheproportionofatree’s

rootsystemorcanopyremoved.Inparticular,pruningbranches

fromatreeremovesphotosyntheticareaandthusweexpecttrees

togrowmoreslowlyiftheyarepruned(Whitcomb,1979;Nowak

etal.,1990).Incorrectpruningcanleavealargeandslow-to-heal

wound,exposingatreetodiseasesthatmaydecreasetreevigor

(Clark&Matheny,2010).However,minimal,correctlyperformed

pruningatthetimeofplantingcanactuallyenhancetreegrowth

rates(EvansandKlett,1985).

Additionally,mulchingcanhavepositiveornegativeimpacts

ontreesuccessdependingonthedepth,placement,type,and

tim-ingofmulchapplications.Mulchthatislessthanabout5cm(2in.)

deepandispulledawayfromthebaseofthetrunkhelpsretain

soilmoistureandpreventsweedgrowthandcanimprovetree

suc-cess(Gilman&Grabosky,2004).However,impropermulching(i.e.,

mulchthatistoodeeparoundthebaseofa tree)mayincrease

atree’sirrigationneeds(Gilman&Grabosky,2004)andcanalso

encourageadventitiousrootgrowthorgirdlingroots.

2. Methods

Toexamine treesuccessin theurbangrowingenvironment,

this studyuses data collectedduring a re-inventory ofplanted

urbantreestocreatestatisticalmodelsoftreesurvivalandgrowth.

We partnered with Keep Indianapolis Beautiful, Inc. (KIB), an

urbangreening nonprofitinMarion County,Indiana.KIB works

withneighborhoodgroupsinIndianapolistoplanttreesthrough

itsNeighborWoods program.In theirproposalfor a tree

plant-ingproject,groupsthatplanttreesthroughtheNeighborWoods

programmustidentifyastrategythat theywillfollowtowater

theplantedtrees.Wesortedtree-plantingprojectsthatoccurred

between2006and2009bythetypeofwateringstrategyadoptedby

theneighborhoodandchoseastratifiedsampleof23projects(in16

neighborhoods)wheretreeswerewateredbyindividualresidents

(individualwateringstrategy)and12projects(in9neighborhoods)

whereatleastsometrees werewatered bygroupsof residents

together(collectivewateringstrategy).Overall,oursampleincluded

35projectsin25neighborhoods.

Inthesummermonthsof2011and2012,were-inventoried

street trees that were planted in our sample neighborhoods.

(4)

neighborhoods(1462trees)andsystematicallysampledand

re-inventoried20–30livingtreesperproject(673livingtreesintotal).

Someoftheseobservationshadmissinginformationabout

partic-ularvariablesandwerenotusedforanalysis.Overall,ouruseable

samplecontains1345treesforsurvivalmodelsand616treesfor

growthmodels.

Duringthesummerof2011wegatheredinformationaboutthe

tree,biophysicalinformationabouttheplantingsiteandevidence

oftreemaintenanceactivitiesaccordingtoadatagatheringguide

(Authoretal.,2014maskedforblindreview).Highschoolstudent

employeesofKIB’sYouthTreeTeamweretrainedbytwoofthe

authorsandcollectedadditionaltreedataduringthesummerof

2012.Additionalinformationaboutthebiophysicalenvironment

camefromtheCityofIndianapolisandtheNationalLandCover

Dataset(NLCD)2006.KIBprovidedinformationfromthetimeof

plantingaboutthetreesandthetree-plantingproject.

Socio-demographiccommunitycharacteristicscamefromthe

U.S.CensusBureau(U.S.DepartmentofCommerceCensusBureau,

2012).Eachtreewasassigned thevaluesforsocio-demographic

variablesofthecensustractinwhichthetreewasplanted.Sources

forallvariablescanbefoundinTable2.

Selectinformation aboutinstitutionsrelatedtotree

manage-ment (in addition to our knowledge of the watering strategy)

wasgatheredfromsemi-structuredinterviewswithneighborhood

leadersandnonprofitemployees(seeMincey&Vogt[2014]for

completeinterviewmethods).Interviewresponseswereusedto

generatehypothesesandtoinforminterpretationofthestatistical

models.

2.1. Dependentvariables

Wemeasuretreesuccessintwoways:treesurvivalandgrowth

rate.Survivalanditsconverse,mortality,arecommonmeasures

oftreesuccessinurbanforestrystudies,thoughspecificestimates

ofsurvivalrateswithinthefirstfewyearsafterplantingarerare

(Nowaketal.,1990;Roman&Scatena,2011),asaregeneral

esti-matesofurbantreemortality(L.Roman,personalcommunication,

November13,2013).Ourmeasureofsurvivalisabinaryindicatorof

whethertheplantedtreewasstillaliveatthetimeofre-inventory.

Growthrateisalsoausefulmeasureoftreesuccess;as

larger-sized(i.e.,mature)treesprovidegreaterecosystemservices,atree

withafastergrowthratethatreachesmaturitysooneryieldsfaster

rateofreturn(Nowaketal.,1990).Also,treesthatgrowwellearly

inlifeareconsideredmorelikelytobeestablishedandfreetogrow

withreducedneedforwateringorothermaintenanceduring

nor-malweatherconditions.Ourmeasure ofgrowthis the“relative

growthrate,”calculatedas

relativegrowthrate= lnC2−lnC1

(t2−t1)/365

whereC1andC2 aremeasurementsoftreecaliperatthetimeof

planting(t1)andtimeofre-inventory(t2),respectively(afterBrand,

1991asadaptedbySamynanddeVos(2002)).Thedifferenceinthe

timeofplantingandtimeofre-inventoryismeasuredindays,so

wedividethenumberofdaysinthedenominatorby365daysper

yeartoobtainrelativegrowthrateinyears.

2.2. Analysis

Wemodelsurvivalandgrowthasafunctionofthefour

cate-goriesofsocial–ecologicalvariablesdescribedabove.

Weusemaximumlikelihoodestimation(probit)topredictthe

likelihood that a plantedtree wasstill alive at thetime of

re-inventory.Weusedataonalltrees thatwere plantedbetween

2006and2009inourselectedneighborhoods(atotalsampleof

1345).Ourbasicsurvivalmodel(ModelS.1)includesindicatorsof

thesizeandtheconditionofeachtreeatplanting,indicatorsof

thesurroundingenvironmentthatarenottree-specific(the

per-centof impervious surfaceand the speed limit),and all of the

socialandinstitutionalindicatorsdescribedbelow.InModelS.2,

wealsoincludetheinteractionbetweencollectivewateringand

ifatreewasplantedinthefall.Wesuspectthattheeffectof

col-lectivewateringmightvaryacrosscircumstance.Weexpectthe

positiveeffectofcollectivewateringtobegreaterinspring

plant-ingsbecausethetreesfaceapotentiallylong,hot,drysummerand

collectivewateringmayprovidemoreconsistentwatering.

Weuselinearregressiontopredictannualcalipergrowthfor

thetrees thatwere livingat thetimeof re-inventory. Ourfirst

model (Model G.1) includes thetree, biophysical environment,

community,andinstitutionalindicatorsdescribedbelowaswell

asnurseryandtaxonomicfamilydummyvariables.Inoursecond

model(ModelG.2),weaddtheinteractionbetweenfallplanting

andcollectivewatering.We alsosuspecttheeffectofcollective

watering mightdiffer acrossoverall treecondition.Tomeasure

theseeffects,wealsointeractcollectivewateringwithourbinary

indicatorsoftreecondition.

The dataare clusteredin two ways—bytree speciesand by

neighborhood.Toaccountforobservedandunobserved

species-levelvariation,weincludedummyvariablesfortreefamilyinall

models,with an “other” category for families in ourdata with

fewerthan20trees,andthebeech/oakfamily(themost

numer-ous family in our dataset) as the baseline. Tree survival and

growthmay alsovary acrossneighborhood inways wecannot

observe.Wecontrolforsomeneighborhoodvariationby

includ-ingasuiteofcommunitycovariates(socio-demographicvariables).

Usingneighborhood-levelrandomeffectswouldcontrolfor

unob-served neighborhood-level characteristics. We tested whether

randomeffectswerenecessaryusinganintraclasscorrelation

coef-ficient (ICC). Given the low ICCs for both survival (ICC=0.087)

and growth(ICC=0.187)datasets,neighborhood randomeffects

models are not shown. Robust standard errors are used in all

models.

2.3. Independentvariables

2.3.1. Treecharacteristics

We includeindicatorsoftreesizeand conditionatplanting:

caliper-at-plantingandwhetherthetreewaspackagedin

ball-and-burlappackaging,acontainer,orothertypeofpackaging(bare-root

ornylonbags;theexcludedpackagingtype).Weincludedummy

variables for treefamily (withthe mostnumerous family,Oak,

beingtheexcluded,orbaselinefamily),andalsofornursery(i.e.,

originofplantingstock;themostnumerousnursery–nursery5

–isexcludedasthebaseline).Wecontrolforageofthetreeat

re-inventory(yearssinceplanting).Transplantedtreesmaytake

a fewgrowingseasonstobecomefullyestablishedinthe

land-scape(Gilman,Black,&Dehgan,1998; Struveetal., 2000), and

yearssinceplantinghelpscontrolforthis.ThesedatacamefromKIB

records.

We also include indicators oftree conditionat re-inventory

in ourgrowth models.Condition variables include binary

indi-catorsof leafchlorosis, a visible rootflare,and damage onthe

lowertrunkofthetree.Acanopydiebackrating(modifiedfrom

InternationalUnionofForestResearchOrganizations,International Society of Arboriculture, United States Forest Service, &Urban NaturalResourcesInstitute,2010)isalsousedtoindicatecondition

ofthetree.Weincludearatingoftheoverallconditionofthetreeas

twodummyvariablesforgoodconditionandpoorcondition(the

excludedconditionwasfair).Wealsoaccountforlight

availabil-itybyincludingacrownexposurerating(afterInternationalUnion

ofForestResearchOrganizationsetal.,2010).Post-plantingtree

(5)

Table2

Categoriesofvariablesandsourcesofdatausedinanalyses.

UFasSEScomponent Variables Datasource

Outcomevariables Alive Protocol,V13

Annualcalipergrowth(cm) KIB/Protocol,V5 Tree Treespecies(aggregatedtofamilylevel) KIB/Protocol,V3

Nursery KIB

Caliperatplanting(cm) KIB

Ball-and-burlappackaging(0,1) KIB

Containerizedpackaging KIB

Age(#yearssinceplanting) KIB

Crowndiebackrating Protocol,V9

Lowertrunkdamage(0,1) Protocol,V12

Leafchlorosis(0,1) Protocol,V10

Rootflarevisible(0,1) Protocol,V11 Goodoverallconditionrating(0,1) Protocol,V13 Pooroverallconditionrating(0,1) Protocol,V13 Biophysical

environment

%Impervioussurface NLCD2006

Speedlimit Indianapolis

#Treesplantedinproject KIB

Fallplantingseason KIB

Plantingareawidth(naturallog) Protocol,V26 Treelawnplantingarea(0,1) Protocol,V24

Crownexposurerating Protocol,V9

#Treeswithin10m(naturallog) Protocol,V29 #Treeswithin10-20m(naturallog) Protocol,V29/V30 Community Neighborhoodname(usedforrandomeffects) KIB

%Unemployment ACS,2011

Medianhouseholdincome($) ACS,2011 %Lessthanhighschooleducation ACS,2011 %Singleparenthouseholds Census,2010

%Nonwhitepopulation Census,2010

%Renteroccupiedhomes Census,2010

%Movedinlast5years ACS,2011

%Vacanthouses Census,2010

#Totaltree-plantingprojects KIB Institutionsand

management

Plantingyear KIB

Correctpruning(0,1) Protocol,V35

Incorrectpruning(0,1) Protocol,V34

Correctmulching(0,1) Protocol,V34

Collectivewateringstrategy(0,1) Interviews

Protocol,Vogtetal.,2014maskedforblindreview,includingthevariablenumberfromtheProtocol(e.g.,V4).KIB,KeepIndianapolisBeautiful,Inc.datacollectedattime

ofplanting.NLCD2006,NationalLandCoverDataset2006(http://www.mrlc.gov/nlcd2006.php).ACS,2011,UnitedStatesDepartmentofCommerce,CensusBureau,2011

AmericanCommunitySurveyfromAmericanFactFinder(http://facfinder2.census.gov).Census,2010,UnitedStatesDepartmentofCommerce,CensusBureau,2010complete

U.S.CensusfromAmericanFactFinder(http://facfinder2.census.gov).Indianapolis,IndianapolisCityGovernmentgeographicinformationsystemshapefileofcitystreets,

obtainedfromKIB.Interviews,interviewswithKIBemployeesaboutthetreeplantingproject,includingreviewoftreeplantingprojectapplications.

2.3.2. Biophysicalenvironment

Weaccountforthesurroundingbiophysicalenvironmentusing

severalindicators.Forallmodels,weusethepercentofimpervious

surfaceinthe30×30mcellsurroundingthetreeasaproxyfor

additionalstressontherootzone(e.g.,limitedrainfallinfiltration).

ThesedatacomefromtheNationalLandCoverDataset2006.The

speedlimitonthestreetadjacenttothetreeservesasaproxyfor

howbusyastreetisandwhetheritislikelytobehighlysaltedinthe

winter.ThisdatacamefromCityofIndianapolisroadlayerfiles.We

alsoaccountforwhetherthetreewasplantedinthefallorspring

andincludethenumberoftreesplantedinaprojecttocontrolfor

projectsize.ThesevariablescamefromKIBrecords.

Forgrowthmodels,wealsoincludetwomeasuresofgrowing

space—abinaryindicatorofwhetherthetreewasplantedinatree

lawnanda measureofthewidth(narrowestdimension)ofthe

plantingarea.We usethenaturallogofthewidthofthe

plant-ingareaaroundthetreeasaproxyforavailablerootingvolume.

Wealsoincludemeasuresofcompetition:crownexposurerating

(reflectingshading),andthenumberofothertreeswithina10-m

radiusandbetween10and20moftheplantedtree,bothinnatural

logform.Thesevariablesweregatheredduringre-inventory.

2.3.3. Community

We use socio-demographic indicators to proxy community

capacity and commitmentto tree care. To capture community

capacityweincludethefollowingvariablesfromtheU.S.2010

Cen-sus:ameasureofthepercentageofindividualsinthelaborforce

thatareunemployed,themedianhouseholdincome,the

percent-ageofindividualsthat haveless thanahighschooldegreeand

thepercentageofhouseholdsthatareheadedbyasingleparent.

WeincludeameasurefromKIB’srecordsofwhetherthetreewas

plantedaspartofthecommunity’sfirstprojectwithKIB,the

sec-ond,thethird,etc.,tocapturecapacity specifictotree-careand

learningwithexperience.Tocapturecommitmentweinclude a

measureofthepercentageofindividualsthathavelivedinthesame

residenceforatleastfiveyears(i.e.,residenttenure)andthe

per-centageofhousingunitsthatareoccupiedbyrenters.Weinclude

thepercentofunitsthatarevacant.Vacanciesmightreflecttwo

things:anindicationofdeeperneighborhoodproblemsandlack

ofcapacityand/orconditionsinwhich neighborsareanxiousto

restoretheneighborhood’sappearanceandthusmaybemore

ded-icatedtotreecare.Wealsocontrolforthepercentofindividualsin

theneighborhoodthatarenon-white.Commitmentvariablescome

fromtheU.S.Census.

2.3.4. Institutionsandmanagement

Tomeasuretreemaintenanceinstitutions,weincludea

mea-sureofwhethertheneighborhood’sapplicationfortreesproposed

acollectivewateringstrategyinwhichatleasttwoneighborswould

(6)

Table3

Descriptivestatisticsforsurvivalmodels.Forbinaryvariables,themeanrepresentstheproportionofobservationswiththatindicator.

UFasSEScomponent Variables N Mean Median Std.dev.

Outcomevariable Alive(0,1) 1345 0.894 1 0.307

Tree Caliperatplanting(cm) 1345 3.536 3.81 0.506

Ball-and-burlappackaging(0,1) 1345 0.125 0 0.331

Containerizedpackaging(0,1) 1345 0.573 1 0.495

Age(#yearssinceplanting) 1345 4.545 4.72 0.970

Biophysicalenvironment %Impervioussurface 1345 43.903 46 19.115

Speedlimit(mph) 1345 32.093 30 6.605

#Treesplantedinproject 1345 67.297 57 36.070

Fallplantingseason(0,1) 1345 0.460 0 0.499

Community %Unemployment 1345 7.488 6.5 4.082

Medianhouseholdincome($1000s) 1345 $45.552 $43.221 $17.213 %Lessthanhighschooleducation 1345 15.716 13.8 9.883

%Singleparenthouseholds 1345 10.987 11.1 4.642

%Nonwhitepopulation 1345 29.362 28.8 14.417

%Renteroccupiedhomes 1345 43.021 49.5 18.995

%Movedinlast5years 1345 54.043 56.1 9.941

%Vacanthouses 1345 16.105 14.6 11.004

#Totaltree-plantingprojects 1345 1.317 1 0.561

Institutionsandmanagement Plantingyear 1345 2007 2007 1.149

Collectivewateringstrategy(0,1) 1345 0.410 0 0.492 Tree—nurserydummyvariables

(nursery5excluded)

Nursery3(0,1) 1345 0.093 0 0.290

Nursery6(0,1) 1345 0.371 0 0.483

Nursery7(0,1) 1345 0.082 0 0.274

Othernursery(0,1) 1345 0.062 0 0.241

Tree—familydummyvariables (beech/oak[Fagaceae]family excluded)

Maple(Aceraceae)family(0,1) 1345 0.150 0 0.357

Birch(Betulaceae)family(0,1) 1345 0.087 0 0.282 Dogwood(Cornaceae)family(0,1) 1345 0.057 0 0.231

Legume(Fabaceae)family(0,1) 1345 0.083 0 0.276

Pine(Pinaceae)family(0,1) 1345 0.029 0 0.168

Planetree(Platanaceae)family(0,1) 1345 0.029 0 0.168

Rose(Rosaceae)family(0,1) 1345 0.139 0 0.346

Other+family(0,1) 1345 0.076 0 0.265

+The“Otherfamily”categoryincludestreesofthefollowingfamilies,eachrepresentedbyfewerthan20individualsinourdataset:Altingiaceae,Apocynaceae,Celastraceae,

Cupressaceae,Ebenaceae,Ginkgoaceae,Juglandaceae,Lauraceae,Magnoliaceae,Malvaceae,Oleaceae,Styracaceae,andUlmaceae.

willyieldmoreconsistentwateringbecauseitmightsupportde

factoorformalmonitoring(Wade,1994),andbecauseAuthorand

Author(2014amaskedforblindreview)foundasignificantinfluence

ofwateringstrategyontreesurvival.Forourgrowthmodels,we

includewhethertherewasevidenceduringre-inventorythatthe

treewascorrectlypruned,incorrectlyprunedorlackedevidence

ofpruning. Weinclude abinarymeasureofwhethertherewas

evidenceofcorrectmulchingattimeofre-inventory.Weexpect

correctpruningandcorrectmulchingtoincreasegrowthrate.

Therearesometypesofrulesanddecision-makingprocesses

thataffecttreesuccessthatoccuratthenonprofitlevel,suchas

choiceoftreespecies,depthoftreeplanting(whichaffects

pres-enceofarootflare),andothermethodsofplanningandorganizing

treeplantingactivities.Manynonprofitdecisionsarerepresented

in tree,biophysical environment, and community categories of

variables. The single nonprofit-level institution we included in

our model is year of planting, which we believe may

repre-sentorganizationallearningandinstitutionalchange,andvaries

acrosstrees.InterviewsandinformalconversationswithKeep

Indi-anapolisBeautiful,Inc. employeesrevealedthat over time they

changedtree-plantingstrategies,adaptingandrespondingto

infor-malobservations that KIB employees weremaking about their

tree-plantingprojects.Forthisreason,weincludeyearof

plant-ingasaseparatevariable,distinctfrom–althoughstillcorrelated

with–theageofthetree.Nonprofitrulesanddecisionsthatdonot

varyacrossprojectshavenovariationinourdatasetandsoarenot

includedinthissingle-cityanalysis.

3. Results

Descriptive statistics for variables included in survival and

growthmodels are displayed in Tables 3 and 4. Overall, 89.4%

oftreesplantedbetween2006and2009werealiveatthetime

ofre-inventory. Averagecaliper growthrateof livingtrees was

1.12cm/year.

3.1. Treesurvivalmodels

Table5presentscompleteresultsfromtreesurvivalprobit

mod-els.Coefficientsinprobitmodelsaredifficulttointerpret,sowe

relyheavilyonsignificanceandexpecteddirectiontointerpretour

results.Positive,significantcoefficientsinTable5indicatethatan

increaseinthevalueofavariableincreasestheprobabilityofa

tree’ssurvival;negativecoefficients indicatethatanincreasein

thevalueofavariablereducestheprobabilityofatree’ssurvival.

Fig.1presentstheoddsratios(exponentialformofthecoefficients

inModelS.2inTable5)foreachindependentvariable.Theodds

ratioistheoddsofatreesurvivinggivenaone-unitchangeinthe

meanoftheindependentvariable.Forpresence/absence

indepen-dentvariables,theoddsratioistheoddsofatreesurvivinggiventhe

presenceofthatvariablerelativetotheoddsofatreesurvivingin

theabsenceofthatvariable.Oddsratiosgreaterthanoneindicate

increasedprobabilityoftreesurvival,whileoddsratioslessthan

oneindicatedecreasedprobabilityoftreesurvival.Oddsratiosnot

significantlydifferentfromoneindicateanindependentvariable

thatdoesnotaffecttheoddsoftreesurvival.

Treeandbiophysicalvariablesarenotasstronglyrelatedaswe

expectedtosurvival.Inourbestmodelsthatcontrolfortree

fam-ily,wefindnosignificantrelationshipsbetweensurvivalandthe

characteristicsofthetreeexceptfornurserywhereitwasgrown.

However,wefindsomerelationshipbetweensurvivalandthe

bio-physical environment:impervioussurface negatively influences

survival,andthenumberoftreesplantedinagivenproject

(7)

Table4

Descriptivestatisticsforvariablesingrowthmodels.Forbinaryvariables,themeanrepresentstheproportionofobservationswiththatindicator.

UFasSEScomponent Variables N Mean Median Std.dev.

Outcomevariable Annualcalipergrowth(cm) 675 1.126 1 0.605

Relativegrowthrate 675 0.192 0.189 0.070

Tree Caliperatplanting(cm) 675 3.477 3.81 0.530

Ball-and-burlappackaging(0,1) 675 0.079 0 0.269

Containerizedpackaging 675 0.607 1 0.489

Age(#yearssinceplanting) 675 4.472 4.69 1.067

Crowndiebackrating 673 0.201 0 0.816

Lowertrunkdamage(0,1) 656 0.474 0 0.500

Leafchlorosis(0,1) 630 0.144 0 0.352

Rootflarevisible(0,1) 658 0.274 0 0.446

Goodoverallconditionrating(0,1) 646 0.853 1 0.354 Pooroverallconditionrating(0,1) 646 0.023 0 0.151 Biophysical

environment

%Impervioussurface 675 43.947 47 18.361

Speedlimit 675 31.785 30 5.710

#Treesplantedinproject 675 59.939 49 35.339

Fallplantingseason 675 0.447 0 0.498

Plantingareawidth(naturallog) 650 1.806 1.5 1.241

Treelawnplantingarea(0,1) 675 0.535 1 0.499

Crownexposurerating 674 4.733 5 0.782

#Treeswithin10m(naturallog) 675 1.372 1.39 0.467 #Treeswithin10–20m(naturallog) 675 1.813 1.79 0.482

Community %Unemployment 675 7.557 6.8 3.923

Medianhouseholdincome($1000s) 675 $45.609 $39.375 $16.798 %Lessthanhighschooleducation 675 16.316 15 9.438

%Singleparenthouseholds 675 11.110 11.1 4.397

%Nonwhitepopulation 675 28.347 25.8 14.986

%Renteroccupiedhomes 675 41.620 46.8 19.546

%Movedinlast5years 675 55.641 58.1 10.005

%Vacanthouses 675 15.954 14.6 10.214

#Totaltree-plantingprojects 675 1.320 1 0.572

Institutionsandmanagement Plantingyear 675 2007.23 2007 1.209

Correctpruning(0,1) 658 0.169 0 0.375

Incorrectpruning(0,1) 658 0.207 0 0.405

Correctmulching(0,1) 658 0.105 0 0.307

Collectivewateringstrategy(0,1) 675 0.450 0 0.498 Tree—nurserydummyvariables

(nursery5excluded)

Nursery3(0,1) 675 0.062 0 0.242

Nursery6(0,1) 675 0.419 0 0.494

Nursery7(0,1) 675 0.044 0 0.206

Othernursery(0,1) 675 0.030 0 0.170

Tree—familydummyvariables (beech/oak[Fagaceae]family excluded)

Maple(Aceraceae)family(0,1) 675 0.099 0 0.299

Birch(Betulaceae)family(0,1) 675 0.096 0 0.295

Dogwood(Cornaceae)family(0,1) 675 0.052 0 0.222

Legume(Fabaceae)family(0,1) 675 0.080 0 0.271

Pine(Pinaceae)family(0,1) 675 0.031 0 0.174

Planetree(Platanaceae)family(0,1) 675 0.041 0 0.200

Rose(Rosaceae)family(0,1) 675 0.159 0 0.365

Other+family(0,1) 675 0.062 0 0.242

+The“Otherfamily”categoryincludestreesofthefollowingfamilies,eachrepresentedbyfewerthan20individualsinourdataset:Altingiaceae,Apocynaceae,Celastraceae,

Cupressaceae,Ebenaceae,Ginkgoaceae,Juglandaceae,Lauraceae,Magnoliaceae,Malvaceae,Oleaceae,Styracaceae,andUlmaceae.

Somecommunitycharacteristicsare relatedtotree survival.

Medianhouseholdincome,thepercentofrenter occupiedunits

andthepercentofpeoplewhohavemovedinwithinthelast5

yearsareallsignificantlyandpositivelyrelatedtotreesurvivalin

ModelS.2(ourtheoreticallypreferredmodel).

Weseeevidencethattreesthatwereplantedinlateryearshave

ahigherprobabilityofsurviving,evenaftercontrollingforageofthe

tree.Thiscouldbearesultoforganizationlearningand

improve-mentsinplantingmethodsbyKIB.However,thesummerof2007

wasaparticularlydryyearandrainfallduringthesummermonths

(May–August)wasmorethan7inchesbelowaverage(National

WeatherServiceIndianapolis,INWeatherForecastOffice,2013).

Wecannotdisentanglewhetherapositivecoefficientontheyear

ofplantingpicksuptheimpactofadrysummerontreesplantedin

earlieryearsoroforganizationallearningresultinginimproved

plantingtechniquesinlater years. Additionally,mortalitycould

simplybehigherforearlierplantingyearsbecauseearliercohorts

oftreeshavehadmoretimetoaccruehighermortality(Roman,

2013).Whateverthecausalmechanism,mortalityratesdodiffer

significantlybyyearofplanting,rangingfromahighof16.4%for

treesplantedin2007toalowof7.5%fortreesplantedin2008.

Thesignificanceoffallplantingseason,collectivewatering

strat-egy,andtheinteractiontermbetweenthesevariablesmeansthat

thecombinedimpactofthesevariablesisdifferentforeachofthe

foursub-populationsoftreesdividedbythesevariables(Table7).

Holdingallothervariablesconstant,wefindthattreesplantedin

thespringandwateredcollectivelyhavegreateroddsofsurvival

thantreesplantedinthespringandwateredindividuallyandthan

alltreesplantedinthefall.However,treesplantedinthefalland

wateredcollectivelyhaveslightlyloweroddsofsurvivalthantrees

plantedinthefallandwateredindividually.Wenotethatthe

inter-actionterm(collectivewatering×fallplanting)ishighlynegatively

correlated with ball-and-burlap planting packaging (polychoric

correlation coefficient of −0.96; see Supplementary material),

and highly positivelycorrelated withexperience (project

num-ber;polychoriccorrelationcoefficientof0.90;seeSupplementary

material);thesevariablescouldbeconfoundingtherelationship

(8)

Table5

Survivalmodelresults.Coefficientsshownwithstandarderrorsinparentheses.VariablesthataresignificantinModelS.2,themosttheoreticallysoundsurvivalmodel,are inbold.ModelS.1:probitmodel(nointeractionterm,noneighborhoodrandomeffects).ModelS.2:probitmodelwithinteractionterm(noneighborhoodrandomeffects).

ReducedModel:significantvariablesfromModelS.2only(includingallnurserydummyvariables).

UFasSEScomponent Variables ModelS.1 ModelS.2 ReducedModel

Tree Caliperatplanting(cm) −0.142(0.167) −0.141(0.170) Ball-and-burlappackaging(0,1) −0.081(0.394) −0.212(0.408) Containerizedpackaging(0,1) 0.063(0.225) −0.003(0.228) Age(#yearssinceplanting) 0.492*(0.273) 0.430(0.286) Biophysical

environment

%impervioussurface −0.007**(0.004) −0.007**(0.004) −0.009***(0.003)

Speedlimit(mph) 0.005(0.016) 0.006(0.016)

#treesplantedinproject 0.009***(0.003) 0.011***(0.003) 0.010***(0.002)

Fallplantingseason(0,1) 0.336(0.220) 0.681***(0.262) 0.307**(0.127)

Community %unemployment 0.003(0.022) −0.001(0.023)

Medianhouseholdincome($1000) 0.016(0.010) 0.017*(0.010) 0.018***(0.005)

%lessthanhighschooleducation −0.017(0.012) −0.020(0.012)

%singleparenthouseholds 0.066*(0.039) 0.049(0.040)

%nonwhitepopulation 0.005(0.008) 0.009(0.008)

%renteroccupiedhomes 0.036***(0.011) 0.033***(0.011) 0.024***(0.007)

%movedinlast5years 0.015(0.013) 0.024*(0.012) 0.017**(0.008)

%vacanthouses −0.033*(0.018) −0.009(0.019)

#totaltree-plantingprojects −0.128(0.165) 0.078(0.182)

Institutionsand management

Plantingyear 0.752***(0.262) 0.681**(0.273) 0.372***(0.059)

Collectivewateringstrategy(0,1) 0.466**(0.201) 1.030***(0.241) 0.853***(0.154)

Interactions Collectivewatering×Fallplanting −1.193***(0.313) −0.986***(0.262)

Tree—nurserydummy variables(nursery5 excluded) Nursery3(0,1) −0.430*(0.254) −0.486*(0.262) −0.309(0.197) Nursery6(0,1) 0.055(0.245) −0.025(0.250) 0.179(0.113) Nursery7(0,1) −0.124(0.313) −0.190(0.316) 0.146(0.240) Othernursery(0,1) −0.944***(0.270) −0.957***(0.279) −0.734***(0.214) Tree—familydummy variables(beech/oak familyexcluded) Maplefamily(0,1) 0.272(0.275) 0.334(0.266) Birchfamily(0,1) −0.264(0.244) −0.067(0.255) Dogwoodfamily(0,1) −0.082(0.233) −0.089(0.238) Legumefamily(0,1) 0.329(0.273) 0.361(0.275) Pinefamily(0,1) −0.287(0.368) −0.240(0.382) Planetreefamily(0,1) 0.088(0.324) 0.033(0.322) Rosefamily(0,1) −0.247(0.210) −0.324(0.213) Otherfamily(0,1) 0.131(0.219) 0.053(0.225) Constant −1512.816***(526.900) −1372.498**(549.493) −749.010***(118.372) No.ofobservations 1345 1345 1345 Log-likelihood −389.192 −384.031 −396.350

Modelsignif.(p-value) 0.000 . 0.000

Pseudo-R2 0.142 0.153 0.126 AIC 840.383 832.063 820.699 BIC 1001.712 998.595 893.557 *p<0.10. **p<0.05. ***p<0.01.

Ourmodelisfairlyrobusttochangesinmodelspecification;

thecoefficientsare quiteconsistentacrossmodels.While there

are a few changes in significance across models, there are no

signchangesinsignificantvariables.Inthereducedmodelwhere

insignificantvariables aredropped, coefficients change slightly,

althoughnotinsignorsignificance.

3.2. Relativegrowthratemodels

Table6presentscompleteresultsfromthetreegrowthmodels.

Asexpected,wefindthatnearlyalltreecharacteristicsincludedin

ourmodelssignificantlyinfluencegrowth.Treesthataresmaller

atplantingappeartogrowfaster.Wefindthattreespackagedas

ball-and-burlaporcontainersgrewfasterrelativetoother

packag-ingtypes.Treesfromothernurseriesalsoexhibitedslowergrowth

ratesrelativetothebaselinenursery(nursery5,themostcommon).

Manyofthefamilydummyvariablesarealsosignificantandinthe

expecteddirectiongivenpreviousworkontreegrowth:treesinthe

dogwood,pine,androsefamilygrowmoreslowlythanoaktrees

(thebaselineandmostcommonfamilyoftrees),whereastreesin

thelegumesand planetreefamiliesexhibithighergrowthrates,

holdingallothervariablesconstant.

Indicatorsoftreeconditionatthetimeofre-inventoryhelp

pre-dictrelativegrowthrate.Treesexhibitinghigherdiebackratings

andtreeswithdamagetothelowertrunkgrewmoreslowly,and

treeswithavisiblerootflareandthoseratedingoodcondition

grewmorequickly.

Generalcharacteristicsofthesurroundingenvironmentimpact

treegrowthlessthantreecharacteristics.Widerplantingareais

associatedwithfastergrowth.Wefindnoevidenceofarelationship

betweentreegrowthandimpervioussurface,speedlimit,project

size,treelawnplantingarea,crownexposure,ornumberofnearby

trees.

Thereissomeevidenceofarelationshipbetweencommunity

characteristicsandtreegrowth.Wefindthatthepercentofsingle

parenthouseholdsisnegativelyrelatedtotreegrowth,whichmay

beindicativeofthecapacityfortreecarebysingleparent

house-holds.Thenumberofpreviousprojectsthattheneighborhoodhas

undertakenisalsosignificant—treesthatareplantedaspartofa

laterprojectgrowfaster.Noneoftheotherdemographicorhousing

variablesappearedtoinfluencetreegrowth.

Wefindevidencethatsomeinstitutionandmanagement

vari-ablesmatter.Plantingyearisnegativelyandsignificantlyrelated

togrowthrate(seefollowingparagraph).Correctmulchingalsois

positivelyandsignificantlyrelatedtogrowthrate,asexpected.

Boththe ageof thetreeand the yearof plantingare

nega-tivelyandsignificantlyrelatedtotreegrowthrate;thesefindings

(9)

Fig.1. OddsratiosforvariablesincludedinsurvivalModelS.2,themosttheoreticallysoundmodel.Oddsratiosgreaterthan1(circles)indicateavariablewithapositive effectonsurvival;oddsratiosoflessthan1(squares)indicateavariablewithanegativeeffectonsurvival.+Confidenceintervalextendsoutsideoftherangeshownonthe graphto4.16.

betweenplantingandre-inventory)andtheyearofplanting(2006,

2007,etc.)arenegativelycorrelated.Anegativecoefficientforage

ofthetreeindicatesthat oldertrees(i.e.,plantedless recently)

havesloweraveragegrowthrates.Anegativecoefficientforyear

ofplantingmeansthat trees plantedinmore recent years(i.e.,

youngertrees)havegrownmoreslowly.Becauseofthis,wesuspect

yearofplantingdoesnotindicatelearningandinsteadmight

cap-turetheimpactofanestablishmentperiodcharacterizedbyslower

growth.

Thesignificanceofthefallplantingseasonindicatorandthe

interactionbetweencollectivewateringstrategyandfallplanting,

butnotofcollectivewateringstrategymeansthatthewatering

strategyonlyhasasignificantimpactongrowthfortreesplantedin

thefall(Table7).Holdingallothervariablesconstant,fallplanting

hasanegativeimpactongrowth,butitsimpactismagnifiedby

choiceofacollectivewateringstrategy.

Ourmodelfortreegrowthisfairlyrobust.Mostsignificant

envi-ronmentalandinstitutioncoefficientsaresignificantacrossboth

ModelG.1andG.2,andremainsignificantinthereducedmodel.

PercentvacanthousesaresignificantinModelG.1,butnotwhen

theinteractionterms areaddedinModel G.2.Collective

water-ingstrategyishighlysignificantinModelG.1,buttheadditionof

theinteractionbetweenwateringstrategyandplantingseasonin

ModelG.2completelycapturestheeffectofcollectivewatering.

CoefficientschangeslightlybetweenModelG.2andthereduced

model,whenallinsignificantvariablesaredropped,butno

vari-ableschangesign.

3.3. JointsignificanceofSEScategoriesandmodelselection

Oneofourobjectivesistodeterminewhetherincluding

charac-teristicsofthecommunityandofinstitutionsimprovesour

under-standingof treesuccessinurbanenvironments.We jointlytest

thesignificanceofcategoriesofSESvariablesbycomparingAkaike

andBayesianinformationcriteria(AIC,BIC)valuesbetween

mod-elsexcludingonecategoryofvariables(restrictedmodels—results

shown in Supplementary material) to Model S.2 and G.2, the

theoreticallysuperiormodels.Forexample,wecomparetheAIC

of Model S.2 to a model that excludes all of the community

(10)

Table6

Relativegrowthratemodelresults.Standardizedcoefficientsshownwithstandarderrorsinparentheses;coefficientswithlargermagnitudehaveagreaterrelativeinfluence ongrowthrate.VariablesthataresignificantinModelG.2,themosttheoreticallysoundgrowthmodel,areinbold.Notethatselectvariablesareincludedinnaturallog

formtoachieveamorenormaldistribution.ModelG.1:ordinaryleastsquares(OLS)regressionmodel(nointeractionterms,noneighborhoodrandomeffects).ModelG.2:

OLSmodelwithinteractionterms(noneighborhoodrandomeffects).Reducedmodel:onlysignificantvariablesfromModelG.2(includingallnurseryandfamilydummy

variables).

UFasSEScomponent Variables ModelG.1 ModelG.2 ReducedModel

Tree Caliperatplanting(cm) −0.496***(0.00740) −0.485***(0.00731) −0.450***(0.00578)

Ball-and-burlappackaging(0,1) 0.302***(0.0252) 0.290***(0.0246) 0.293***(0.0220)

Containerizedpackaging(0,1) 0.205***(0.00870) 0.185***(0.00907) 0.136***(0.00701)

Age(#yearssinceplanting) −0.922***(0.0112) −0.934***(0.0113) −0.690***(0.00900)

Crowndiebackrating −0.107***(0.00598) −0.108***(0.00641) −0.096**(0.00585)

Lowertrunkdamage(0,1) −0.101***(0.00497) −0.098***(0.00497) −0.097***(0.00484)

Leafchlorosis(0,1) 0.067*(0.00722) 0.080**(0.00720) 0.087**(0.00691)

Rootflarevisible(0,1) 0.061*(0.00554) 0.066*(0.00547) 0.073**(0.00530)

Goodoverallconditionrating(0,1) 0.131***(0.00700) 0.173***(0.0110) 0.139***(0.00657)

Pooroverallconditionrating(0,1) −0.001(0.0223) 0.068(0.0349)

Biophysical environment

%impervioussurface 0.031(0.000203) 0.038(0.000200)

Speedlimit −0.054(0.000762) −0.069(0.000759)

#treesplantedinproject 0.059(0.000122) 0.092(0.000124)

Fallplantingseason −0.350***(0.00919) −0.261***(0.0101) −0.141***(0.00618)

Plantingareawidth(naturallog) 0.131*(0.00376) 0.141**(0.00379) 0.079**(0.00209)

Treelawnplantingarea(0,1) −0.001(0.00748) 0.012(0.00755)

Crownexposurerating 0.015(0.00309) 0.020(0.00303)

#treeswithin10m(naturallog) 0.024(0.00646) 0.023(0.00657)

#treeswithin10–20m(naturallog) 0.053(0.00510) 0.030(0.00523)

Community %unemployment 0.034(0.00106) 0.026(0.00106)

Medianhouseholdincome($1000) −0.028(0.000531) 0.045(0.000532)

%lessthanhighschooleducation 0.093(0.000639) 0.111(0.000640)

%singleparenthouseholds −0.203*(0.00181) −0.207*(0.00182) −0.152***(0.000546)

%nonwhitepopulation 0.077(0.000397) 0.116(0.000409)

%renteroccupiedhomes −0.085(0.000490) −0.070(0.000491)

%movedinlast5years −0.013(0.000567) 0.016(0.000594)

%vacanthouses −0.197*(0.000781) −0.126(0.000812)

#totaltree-plantingprojects 0.202***(0.00729) 0.259***(0.00781) 0.314***(0.00610)

Institutionsand management Plantingyear −1.095***(0.0107) −1.086***(0.0107) −0.820***(0.00750) Correctpruning(0,1) −0.040(0.00631) −0.043(0.00628) Incorrectpruning(0,1) −0.013(0.00599) −0.024(0.00611) Correctmulching(0,1) 0.065(0.00994) 0.079*(0.00989) 0.072*(0.00952)

Collectivewateringstrategy(0,1) −0.270***(0.00847) −0.025(0.0150)

Interactionterms Collectivewatering×Fallplanting −0.201***(0.0155) −0.281***(0.0111)

Collectivewatering×Goodcondition −0.107(0.0133)

Collectivewatering×Poorcondition −0.084(0.0421)

Tree—nurserydummy variables(nursery5 excluded) Nursery3(0,1) −0.086*(0.0133) −0.097**(0.0134) −0.100**(0.0117) Nursery6(0,1) 0.021(0.0109) 0.014(0.0113) −0.039(0.00942) Nursery7(0,1) 0.055(0.0146) 0.050(0.0148) 0.061(0.0130) Othernursery(0,1) −0.162***(0.0241) −0.168***(0.0237) −0.204***(0.0220) Tree—familydummy variables(beech/oak familyexcluded) Maplefamily(0,1) −0.077(0.0118) −0.073(0.0116) −0.043(0.0113) Birchfamily(0,1) −0.061(0.0111) −0.034(0.0113) −0.020(0.00932) Dogwoodfamily(0,1) −0.124***(0.0127) −0.117***(0.0131) −0.089**(0.0117) Legumefamily(0,1) 0.106**(0.0133) 0.103*(0.0132) 0.120**(0.0124) Pinefamily(0,1) −0.097**(0.0280) −0.096**(0.0273) −0.109**(0.0275) Planetreefamily(0,1) 0.194***(0.0152) 0.191***(0.0156) 0.177***(0.0147) Rosefamily(0,1) −0.314***(0.0102) −0.318***(0.0104) −0.331***(0.0100) Otherfamily(0,1) 0.105**(0.0136) 0.101**(0.0136) 0.109**(0.0129) No.ofobservations 605 605 605 F 14.07 14.21 .

Modelsignificance(p-value) 6.13E−66 4.50e−69 .

Adjusted-R2(overallR2forr.e.models) 0.471 0.436 0.435

AIC −1796.3 −1801.5 −1816.9

BIC −1598.1 −1590.0 −1689.2

*p<0.10. **p<0.05. ***p<0.01.

jointlysignificant.AICandBICvaluesweighthebenefitsof

addi-tionalinformationwhenmorevariablesareaddedtothemodel

againstthecostsoffewerdegreesoffreedomandtheadditionof

irrelevantvariables.SignificantlylowerAICandBICvaluesindicate

bettermodels;BICpenalizesmoreharshlythanAICforaddition

ofirrelevantvariables.However,AICandBICshouldonlybeused

toguidemodelselection,andnottodefinethe“best”modelwith

absolutecertainty.

Forsurvivalmodels,wefindcoefficientestimatesforsignificant

variablestobefairlyconsistentacrossmodels(seeSupplementary

material).AICandBICvaluesindicatethatModelS.2outperforms

mostmodelsexcludingentirecategoriesofSESvariables.Wefind

thatalthoughBICvaluesforrestrictedmodelsareslightlylower

thanforModelS.2,AICvaluesaretypicallymuchhigher.Thefour

modelsthatexcludealltreecharacteristics,allenvironmental

vari-ables, allcommunity variables, or allinstitutional variables are

outperformed by ourtheoretically preferablemodel (S.2); only

therestrictedmodelthatexcludesprojectcharacteristics(a

sub-setofcommunityvariables)performsbetterthanourtheoretically

(11)

Table7

Interactionbetweenfallplantingandcollectivewateringstrategy.Coefficients

forthe combinedeffects ofplanting seasonand wateringstrategy, basedon

coefficientsfromModelS.2(survival,Table5andModelG.2(growth,Table6).

Plantingseason Wateringstrategy Collective Individual Survival(probitmodel

oddsratios) Fall 1.679 1.976 Spring 2.801 1.000 Growth(standardized coefficients) Fall −0.462 −0.261 Spring 0 0

Forgrowthmodels,coefficients acrossmodelsareconsistent

insignandsignificance,exceptforageandyearvariables,eachof

whichflipsign(topositive)andlosetheirsignificancewhentheSES

componentcontainingtheotherisexcludedfromthemodel(see

Supplementarymaterial).AICandBICvaluesindicatethatour

the-oreticallybestmodel(ModelG.2)outperformsrestrictedgrowth

modelsthatexcludealltreecharacteristics,allcommunity

vari-ables,orsubsetsofcommunityvariables;amodelthatexcludes

allbiophysicalenvironmentvariablesperformsslightlybetterthan

ModelG.2(seeSupplementarymaterial).

4. Discussion

Ourresultssuggestthatbiophysical,socialandinstitutional

fac-torsareallimportant inexplaining thesuccessofyoungurban

plantedtrees,butthatthefactorsthataffectsurvivalandgrowth

aredifferent.Fig.2comparesthemosttheoreticallysound

mod-elsforsurvivalandgrowth(ModelsS.2andG.2).Sometimesthe

samefactorsignificantlyinfluencesbothoutcomes,butinopposite

directions.

Ingeneral,treevariablesappeartomattermorefortreegrowth

thanforsurvival.Theonlytreecharacteristicthataffectssurvivalis

thenursery,whiletreegrowthissignificantlyinfluencedbynearly

alltreevariables,includingseveraltreefamilyvariables(Fig.2).

Thisisnotsurprising,becausewedonothaveinformationon

per-hapsthemostvitaltree-levelfactorthatmightinfluencesurvival—a

tree’sconditionwhile itwasalive andpresumably decliningin

healthandvigor.Otherauthorshavelinkedtreeconditionto

prob-abilityofsurvival(e.g.,Koeser,Hauer,Norris,&Krouse,2013).

Contrary to expectations, trees exhibiting leaf chlorosis

experienced faster growth. We recognize this could be from

misidentificationofchlorosisbythedatacollectors.While

chloro-sis–lackofchlorophyll–istheoretically linkedtostuntedtree

growth(Graves,1994),othercausesofleafdiscolorationappearing

aschlorosistotheuntrainedeyeandthatmighthavebeenrecorded

assuchinthere-inventorymaynotbelinkedtostuntedgrowth.We

alsorecognizethatthepresenceofchlorosisisametricofpresent

treeconditionandmaybemorereflectiveofrecenttreestresses

ratherthanthestressesoverthe(albeitshort)lifetimeofthetree.

Wefindevidencethatthebiophysicalenvironmentaffects

sur-vivalandgrowth.Plantingwidth(aproxyforarea)limitsgrowth

butnotsurvivalinourmodels.Plantingareamayrepresent

avail-ablerootingvolume,whichlimitsgrowthasotherauthorshave

found(Kopinga,1991; Kjelgren&Clark,1992;although Nowak

etal.,1990findnosignificantimpactofplantingareaongrowth).

Alternatively,itmightbethatplantingwidthisaproxyforwater

availability(i.e.,asmallerplantingareameansasmallerareainto

whichrainfallmayinfiltrate),whichmaybemoretightlytiedtothe

successofsmall,recentlyplantedtreesthatmaynotyetbelimited

byavailablerootingvolume

We find evidence that people (i.e., characteristics of the

communityandmaintenanceinstitutionsusedbypeopleto

man-age trees) influence the survival and growth of trees. Some

(12)

socio-demographiccharacteristicsofneighborsarounda

planted-treearesignificantlyrelatedtotreesurvivalandgrowth.However,

apositivecoefficientonpercentrentersinsurvivalmodelsis

con-trarytothefindingofNowaketal.(1990).Wealsofindevidence

thatneighborhood experiencewithtreeplantingcontributesto

treesuccess—treesplantedinlaterprojectsinthesame

neighbor-hoodhadfastergrowth.Thesefindingssuggestthatmoredetailed

researchabout maintenance motivationsmight illuminate why

particularsocio-demographiccharacteristicsmightberelatedto

treesuccess.

Thereisevidencethatinstitutionsmatter,particularlyfortree

survival,butalsothathowinstitutionsimpacttreeoutcomesdepends

onplantingseason. We expectedtrees plantedin the falltobe

morelikelytosurvivebecausetheydonotexperienceasummerof

hot,dryweatherimmediatelyaftertransplanting(although,adry

fallwithoutsufficientwateringcanbepotentiallydeadlyfortrees,

sincethetreesarebeingplantedintoapotentiallyverydrysoil).

However,whenplantingseasonisconsideredincombinationwith

wateringstrategy,wefindthatafallplantingseasononlyimproves

survivalratesfortreesthatareindividuallywatered(Table7).A

col-lectivewateringstrategywaspositivelyrelatedtotreesurvivalfor

springplantings.Forgrowthmodels,collectivewateringstrategy

compoundsthenegativeimpactoffallplantingontreegrowth,

butwateringstrategyhasnoimpactontreeoutcomesforspring

plantings.Wesuspectthesefindingsmayreflectdifferencesinhow

acollectivewateringstrategymaybeimplementedforplantings

indifferentseasons.Recallthatourcollectivewatering strategy

variableisanindicatorofthewateringstrategychosenbythe

neigh-borhood,andthatwecanonlyinfertowhatextentthisstrategywas

actuallyimplementedbytheneighborhood(i.e.,howconsistently

treeswerewatered).Treesplantedinthespringmustbewatered

immediatelyduringthesummerfollowingplanting.However,for

treesplantedinthefall,wateringactivitiesmightnotcommence

untilthefollowingspringandsummer.Itmaybethatcollective

wateringiseasiertoimplementwhenwateringactivities

imme-diatelyfollowplantingactivities,andthereforewateringhappens

moreconsistentlyduringthefirstsummerafterplanting.

Weincludedplantingyearasavariablewethoughtmight

indi-cateinstitutionallearningandchangeintreeplantingmethodsby

thenonprofit.However,ourresultsindicatethatthisvariablemay

actuallybecapturingmoreinformationabouttreeageand

estab-lishmentthanaboutinstitutions.Treesplantedinlateryearsexhibit

ahigherlikelihoodofsurvivalandlowergrowthrates.Higher

sur-vivalratesareexpectedifwethinkthenonprofithasimproved

itsplanting techniquesover time. Buthighersurvival rates are

alsoexpectedduetothefactthatcumulativemortalityislower

forcohortsoftrees(i.e.,treesplantedinthesameyear)thathave

beeninthegroundforlesstime(Roman,2013).Lowergrowthrates

makessensein thecontext oftreebiology:trees plantedmore

recentlyarealsoyoungerandless likelytobeoutofthe

estab-lishmentperiodduringwhichtrunkdiametergrowthisslowed

(Gilmanetal.,1998).

5. Conclusion

Wefindthatattributesofthetree,biophysicalenvironment,

andsurroundingcommunity,aswellasmanagementinstitutions

appeartoimpacturbantreeplantingoutcomes.Severalfindings

fromthisworkcaninformdecision-making.Nonprofitshavea

rea-sonabledegreeofchoiceoverthesizeofplantedtrees:planting

smallertrees willyieldtrees thatbecomeestablishedand grow

more quickly in thelandscape. They also have somechoice in

plantinglocation,includingthesizeoftheplantingarea:

choos-inglarger,widerplantingareaswherepossiblemayyieldhigher

treegrowthrates.Andlocatingtreesinareaswithloweramountsof

impervioussurfacecovermayimprovesurvivalrates.However,we

alsorecognizethatareaswithnarrowtreelawnsandhigh

impervi-ouscoverarealsosomeoftheareasinhighestneedofthebenefitsof

trees(Wilson&Lindsey,2009).Wealsofindthatmany

characteris-ticsofthesurroundingenvironmentdonotsignificantlyaffecttree

growthforthetreesinoursample,whichsuggestsnonprofitsmight

notneedtobeconcernedaboutthesecharacteristicswhen

choos-ingplantinglocations:forinstance,thenumberofothernearby

trees,thespeedlimitoftheadjacentroad,andwhetherthelocation

isatreelawnorothertypeofplantingspotappearnottoinfluence

treesuccess.Maintenancepractices,however,domatter:Correct

mulchingpracticespositivelyimpacttreegrowthrates,suggesting

thatinvestmentinmulchingortrainingthecommunitytomulch

willyieldimprovementstotreesuccess.

Thereare otherpotential decision pointsfor which the

evi-dence is less clear.Although we observe a negative coefficient

on therelationship between fall planting and tree growth,we

hesitatetosaythat fallplantingsshouldbeavoideddue toour

inability to disentangle the relationship between water

avail-ability – a combination of neighborhood-determined watering

strategy(known),precisewateringfrequency/amount(unknown),

anduncontrollableweather/rainfallconditions–andplanting

sea-son.Theunderlyingrelationshipbetweencollectivewateringand

treesuccessisunclearanddependentontheseasonofplanting.

Morefine-grainedinformationonthefrequencyofwateringand

variationinseasonalrainfallwillhelptodetailtherelationships

between watering strategy, environmentalconditions, and tree

outcomes.Nurseryalsoappearstoinfluencesurvivalandgrowth,

butthisvariablemaybeconfoundedbyrelationshipswithplant

packagingtreeandspecies;itisalsonotsomethingthatcanbe

controlledeasilybynonprofitspurchasing treeswhere theyare

available.

Thispaperoffersamoreholisticmodelofthesurvivalandearly

growthoftreesplantedincities.Futureresearchthatexamines

treesuccessshouldexaminetreesacrossmultiplecitiestotestthe

generalizabilityoftheseresults.Longitudinalmonitoringoftree

populationswouldhelpbuildastrongercausalcaseforthefactors

thatcontributetotreesurvivalandgrowthovertime.

Acknowledgements

Thismanuscripthasbenefittedgreatlyfromdiscussionswith

manycolleagues,includingDr.DavidGood,Dr.RichardJ.Hauer,

and LukeShimek.Additionally, theauthorswould liketothank

currentandpastemployeesofKeepIndianapolisBeautiful,Inc.for

theirtimeandenthusiasticandpatientsupportofthisresearch,

inparticularDaveForsell,JeromeDelbridge,NateFaris,Andrew

Hart,BobNeary,andMollyWilson.Inaddition,wethanktheKIB

YouthTreeTeamsummer2012DataTeamandteamleaderJennifer

Swilikfortreedatacollection.Additionalfinancialsupportwas

pro-videdbytheEfroymsonFamilyFund,aswellastheGardenClubof

America’sUrbanForestryFellowshipawardedtoJMVin2012and

2013.Administrativeandfinancialsupportwasprovidedbythe

CenterfortheStudyofInstitutions,Populationand

Environmen-talChange;TheOstromWorkshopinPoliticalTheoryandPolicy

Analysis;and,theSchoolofPublicandEnvironmentalAffairsat

IndianaUniversity,Bloomington.Thisresearchfulfilledpartofthe

dissertationrequirementsoftheSchoolofPublicand

Environmen-talAffairsatIndianaUniversityforauthorJMV.Finally,wethank

twoanonymousreviewersforhelpfulcomments.

AppendixA. Supplementarydata

Supplementary data associated with this article can be

found, in the online version, at http://dx.doi.org/10.1016/

References

Related documents

affiliation with a Big 4 international accounting firm may not improve the quality of the audit provided by the local affiliate vis-à-vis other local audit firms

To assess the effects of HAdV infection on global H2B-ub levels, we infected human A549 lung adenocarcinoma cells with either wild-type HAdV type 5 (WT) expressing the full-length

“ On my very first day as a Program Manager in Google, I immediately interacted with various internal  stakeholders, getting up to speed with our products and how we execute in a

3.2 The internal review of the Census ICT service is welcome but alongside this the Director for Digital and Resources proposes to begin staged work on a digital road

• We may provide the services agreed at a fixed monthly fee or on the basis of time spent, and the scope and extent of our services may be adjusted as we progress, cf. the

In order to bridge temporary delays in the dissemination of official foreign trade statistics compiled by the National Board of Customs after the Finland's entry into the EU in

DM chromosome idiogram fi gures were reproduced from the potato reference genome publication (Potato Genome Sequencing Consortium 2011) and were aligned by orienting the short arms

In the last few years (we write in 1998) many researchers realized that at least a subfamily of fea- sible methods (those based on the barrier approach) was perhaps