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,baFurmanUniversity,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
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g
h
t
s
•Weexaminedtherelationshipbetweensocial–ecologicalsystem(SES)factors&streettreesuccess.
•VariablesfromallSESfactorsinfluencerecently-plantedtreesurvival&growth.
•Theimpactofneighborhoodwateringstrategyontreesuccessdependsonplantingseason.
•Futureresearchshouldconsidersocial–ecologicalcontextofplantedurbantrees.
a
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c
l
e
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n
f
o
Articlehistory:
Received28February2014
Receivedinrevisedform
24November2014
Accepted27November2014
Availableonline30December2014
Keywords: Treegrowth Treesurvival Streettrees Social–ecologicalsystems Communitycharacteristics Institutions
a
b
s
t
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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
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
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.
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
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
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
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
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
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
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
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
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/