achieving sustainable management of boreal and temperate

Achieving sustainable management of boreal and temperate forests Edited by Dr John A. Stanturf Estonian University of Life Sciences, Estonia

BURLEIGH DODDS SERIES IN AGRICULTURAL SCIENCE

E-CHAPTER FROM THIS BOOK

http://dx.doi.org/10.19103/AS.2019.0057.09Published by Burleigh Dodds Science Publishing Limited, 2020.

The impact of climate change on forest systems in the northern United States: projections and implications for forest managementW. Keith Moser, USDA Forest Service, USA; Patricia Butler-Leopold, Michigan Technological University and Northern Institute of Applied Climate Science (NIACS), USA; Constance Hausman, Cleveland Metroparks, USA; Louis Iverson, USDA Forest Service and Northern Institute of Applied Climate Science (NIACS), USA; Todd Ontl, Michigan Technological University and Northern Institute of Applied Climate Science (NIACS), USA; Leslie Brandt, USDA Forest Service and Northern Institute of Applied Climate Science (NIACS), USA; Stephen Matthews, Northern Institute of Applied Climate Science (NIACS) and The Ohio State University, USA; and Matthew Peters and Anantha Prasad, USDA Forest Service and Northern Institute of Applied Climate Science (NIACS), USA

1 Introduction 2 Climateprojectionsandtheirinfluenceonforestlandtrendsinthe

medium term—the Northern Forests Futures Project 3 Methodsofprojectioninthelongterm:modelingprojectedchangesin

habitat and potential migration 4 Ecoregional vulnerability assessments 5 Management implications 6 Conclusionandfuturetrends 7 Acknowledgements 8 References

1 IntroductionForestsplayanessential role in thesocial,economic,andecological livesoftheinhabitantsofthenorthernUnitedStates.Forestscover69.6millionha,or42%of the landareaof this region,which isboth themostheavily forestedand themost densely populated quadrant of the United States (Fig. 1). Topreserve a full rangeof forest ecosystem services into the future,managersare working to identify and implement strategies and tactics that take into

The impact of climate change on forest systems in the northern United States

The impact of climate change on forest systems in the northern United States

Chaptertakenfrom:Stanturf,J.A.(ed.),Achieving sustainable management of boreal and temperate forests, BurleighDoddsSciencePublishing,Cambridge,UK,2019,(ISBN:9781786762924;www.bdspublishing.com)

The impact of climate change on forest systems in the northern United States2

Published by Burleigh Dodds Science Publishing Limited, 2020.

account thepotentially dramatic effects of a changing climate (Nagel et al.,2010).Theregionencompassesalmost30°oflongitudeand10°oflatitudeandextendsfromtheAtlanticOceanwesttotheGreatPlains,containing20states:Connecticut, Delaware, Illinois, Indiana, Iowa, Maine, Maryland, Massachusetts, Michigan,Minnesota,Missouri,NewHampshire,NewJersey,Ohio,Maryland,Pennsylvania, New Hampshire, New York, Rhode Island, and Wisconsin.

Managersplanatseveraldifferentscalesandoverlappingtimehorizons.Inthischapter,weintegratedifferentanalysesthatprovidearangeofprojectedoutcomes over the medium and long term. In this chapter, we use several case studiestosuggestsomepotentialpathwaysformanagersseekingtoalleviateor otherwise mitigate potential climate change impacts. The intention is toprovideexamplesofstudiesat several scalesofanalysis,usingvarious toolsofanalysisandreporting.Readerscanthenmovewithintheirscaleofanalysisor interest to pursue the details cited within these case studies. We start with characterizationsofmid-andlong-termprojectedclimatechangeimpactsfortrees and forests of the northernUnited States. Particular attention is drawntotheimpactsofmorefrequentandsevereprecipitationanddroughtevents.Wethenscaledownthediscussiontoexamplesfromthethree-stateCentralAppalachians region, and last provide local examples in rural and urban landscapes. We conclude with some lessons learned and recommendations thatmanagersmightconsiderastheycrafttheirownstrategies.

Figure 1 Distributionofforest-typegroupsinthenorthernUnitedStates,2010.Source:adaptedfromGoerndtet al.(2016).


The impact of climate change on forest systems in the northern United States 3

2 Climate projections and their influence on forestland trends in the medium term—the Northern Forests Futures Project

2.1 The modeling process

A multistep process and many datasets were used to explore the potential medium-termimpactofeconomics,demographics,andchangingclimateontheforestedlandscapeofthenorthernUnitedStates.Trendsinforestdynamicsandtheresultantchangesinforestattributesintheregionwereprojectedandanalyzedfortheperiod2010–2060usingtwocyclesofdatasetsfromtheUSDAForestService,NorthernResearchStationForest InventoryandAnalysis (FIA)program (Woudenberg et al., 2010). Future forest conditions were imputedfrom the Forest Dynamics Model (Wear et al., 2011), which was previouslyemployed in the national-level analysis of future conditions in ResourcesPlanningAct(RPA)assessments(USDAFS,2012a,c).Thedataweredownscaledso that theycouldbematchedupwith individualFIAplots (USDAFS,2011;Goerndtetal.,2016).TheIntergovernmentalPanelonClimateChange(IPCC)describedasetofemissionsscenariosor‘storylines’1basedonassumptionsofpopulationgrowth,economics,andtechnologicalchanges.TheIPCCcreatedfourfamilies(A1,A2,B1,andB2)ofscenarios(Nakićenovićetal.,2000),fromwhich 12 individual storylines were developed. By using assumptions about changes in land use, population, and climate, along with modeled disturbances causedbyharvestingofforestproductsandinsect(emeraldashborer;Agrilus planipennis)attack,threestorylineswerelinkedwithclimatemodelstoprojectclimate scenarios and the associated future forest conditions at the locallevel(Goerndtetal.,2016;ShifleyandMoser,2016).Theseanalysesresultedin 13 future scenarios, ofwhich sevenwere studied in depth and three arepresentedinthefollowingdiscussion.

Analyses for the RPA assessment projected the entire US populationto increasebetween2010and2060 from309millionpeople to397millionfor the B2 scenario, 447 million for the A1B scenario, and 505 million fortheA2scenario,or increasesof29%,45%,and64%, respectively (USDAFS,2012b; Zarnoch et al., 2010). These population estimates are based on the2004 Census population series for 2000–2050, which were extrapolated to2060(USDAFS,2012b).Usinghumanpopulationprojectionsincorporatedintothe2010RPAanalyses(USDAFS,2012b),theNorthernForestFuturesProject(NFFP)projectedpopulationforthenorthernUnitedStatesandthenallocatedtheexpectedpopulationtothestatesandtheircounties(Zarnochetal.,2010;USDAFS,2012c;Goerndtetal.,2016).Thepopulationofstatesinthenorthern

1‘coordinatedgroupsofassumptionsthatdescribefuturepopulation,economicactivity,landuse,bioenergyuse,andassociatedgreenhousegasemissions’Goerndtet al.(2016).



UnitedStatesareexpectedtoincreasefrom125millionin2010to140million(B2scenario),158million(A1Bscenario),andto178million(A2scenario)by2060,or increasesof12%,25%,and39%, respectively (Fig. 2). States alongtheAtlanticOceanseaboardareprojected tohave thegreatest increases inpopulation(Fig.3)(Goerndtetal.,2016).

Scientists throughout the world have developed models that project and map changes in selected weather factors, such as precipitation andtemperature,basedoneachoftheindividualIPCCstorylines.Fromthesemanycombinations,NFFPselectedtwoversionsofa‘middle-of-the-road’model,theCanadianGlobalCirculationModel (CanadianCentre forClimateModellingandAnalysis2012a,b;ShifleyandMoser,2016).Theseversionsformthebasisforthecalculationsinthefollowingdiscussion.

TheNFFPusedtheForestDynamicsModel(Wearetal.,2013)toprojectchangesintreeandstandconditions.ProjectionsoffutureFIAplotconditionswere used to model future wood volumes, species groups, and a host ofecosystemservices(Goerndtetal.,2016;Moseretal.,2016;Taverniaetal.,2016). Plotswerepartitioned intogroupsbasedonbiophysical, standage,and climate factors, with growing stock volume per ha used as a point ofsimilarity.

Changes in landusearea functionofchanges inpopulation,economicactivity,andanypotentialclimatechangeinfluenceoverthe50-yeartimeperiod

Figure 2 Projected increases inpopulationof thenorthernUnitedStates,2010–2060.Source:adaptedfromGoerndtet al.(2016).



ofthisstudy(Wearetal.,2013;Goerndtetal.,2016).TheNFFPusedlanduseprojections fromtheRPAassessment (USDAFS,2012c),whichassumedthattherewouldbenolandusechangesonfederalforestlandacrossthenorthernUnitedStates, and that nonfederal forest landwoulddeclineby two to fourmillion ha by 2060.

ProjectedharvestinglevelswereextrapolatedfromobservedharvestinginthepriorFIAinventoriesandtiedtovariablesoftreesize,age,species,density,stand diversity, site conditions, and previous harvest types (full or partial)(Wearetal.,2013;Goerndtetal.,2016).Modelalgorithmsreplacedinventoryplots affected by harvesting with suitable replacement plots representingthe postharvest conditions (e.g. a newly regenerated plot; Goerndt et al.,2016).Atransitionmodel,whichpredictedchangesinplotageandspeciescompositionovertime,determinedforestage,harvesting,andregeneration.Theseprojectedvalues,alongwithclimatevariables,wereappliedasinputstoan imputationmodel.Thismodel selecteda replacement (updated)plotfroma subset of observedFIAplots ‘thatbestmatches conditions that areprojectedforeachplotlocation’,basedonage,speciesgroup,climate,andproportionsofhardwoodandsoftwood.Thisnewplotbecame the startingpoint for the next 5-year projection. Results for all plots were summarizedat theendofeachintervalandusedasastartingpoint forthenext interval(Goerndtetal.,2016).

Projected population density change(people per square kilometer)

Under 1 1 to 10 11 to 58 Over 58

Figure 3 Patternofprojectedpercentageincreasesinpopulation(2010–2060)undertheA2storyline.Source:adaptedfromGoerndtet al.(2016).



2.2 Northern Forest Futures Project results of the medium-term projections

According to the analyses for theRPA (USDAFS, 2012c), the areaof forestlandinthenorthernUnitedStateswilldecreasefrom70millionhain2010to66millionha(a6.4%decrease)undertheA1Bscenario,67millionha(a5.4%decrease)undertheA2storyline,and68millionhectares(a3.5%decrease)under theB2storyline (Fig.4).Thegreatestdeclinesareexpected tooccurnear urban areas and in states along the eastern seaboard, which are also highlyurbanized.Percapitaforest landareainthenorthernUnitedStates isexpectedtodeclinefromabout0.6hato0.4haasthepopulationincreasesand forest land areadeclines (Moser et al., 2016).Oak/hickory andmaple/beech/birchforest-typegroups,togethermakingup61%offorestlandareain2010,willcontinuetobethemostprominentforest-typegroupsunderallthreescenarios,withaprojected64%offorestlandareain2060(Table1).Despitethis prominence, oak/hickory is expected to decrease slightly in the area under allscenarios,alongwithelm/ash/cottonwood,spruce/fir,andaspen/birch.Themaple/beech/birchforest-typegroupisexpectedtoincreasesomewhatinthearea under all scenarios.

Figure 4 Forest land area, historical and projected, in million hectares, 1900–2060.Source:adaptedfromMoseret al.(2016).



The extent of each forest-type group is expected to change over the50-year projection period (Table 1; Moser et al., 2016). The expectation oflimited current forest products harvesting being extended into the future(Shifleyetal.,2014)isexpectedtoimpedeestablishmentofearlysuccessionalforest-type groups, such as aspen/birch, thereby reducing their proportion.Anothernotablechangeistheprojectionthat5%ofthecurrentforestlandisconvertedtononforestusesby2060(Moseretal.,2016).

Approximately70%of forests in thenorthernUnitedStates in2010wasestimatedtobe40–100yearsold(Shifleyetal.,2012).Applyingslightlydifferentdefinitions of early and late successional forests, Pan et al. (2011) observedrelativelylowpercentagesofearlyandlatesuccessionalforestsintheregion.Region-wide, thecurrentproportionof forest stands in the40–100-yearagebracket isnotexpectedtochangemuch(except for thenaturalagingof thecohort)through2060(Fig.5;Moseretal.,2016;Taverniaetal.,2016).

Using calculations based on the FIA database and transition models(USDAFS,2012b;Goerndtetal.,2016),theNFFPfoundthat,particularlyinthewesternpartoftheregion,thecurrentsubstantialpercentageofyoungerageclasses isexpected todeclineover the50yearsof theprojection. Increasedbiomassharvesting(datanotpresentedhere)wouldincreasetheproportionofearlysuccessionalforestsinthefuture.Therelativelylowpercentagesofforestin the northernUnited States in 2010 that are in the 100+ year age classesare expected to change dramatically depending upon the scenario, barring substantialincreasesinharvestingorseveredisturbances(Fig.6;Moseretal.,2016;Taverniaetal.,2016).Severedisturbances,suchaswindstorms(Nelsonand Moser, 2007; Moser and Nelson, 2009) or attacks by eastern spruce

Table 1 Projectedarea,inhectares,byforest-typegroupin2060basedonforestlandareaof2010, A2 scenario

Forest-typegroup(scientificname) 2010(%) 2060(%)

Aspen/birch(Populus spp./Betulaspp.) 6 983 038 10 5 747 767 8Elm/ash/cottonwood(Ulmus spp./Fraxinus spp./Populusspp.)

4 915 592 7 4 033 138 6

Maple/beech/birch(Acer spp./Fagus spp./Betulaspp.) 18 203 541 26 18 872 550 27Nonforest - 0 3 817 506 5Oak/hickory(Quercus spp./Caryaspp.) 25 569 666 36 24 009 601 34Other 5 158 122 7 4 676 753 7Spruce/fir(Picea spp./Abiesspp.) 6 183 077 9 5 599 335 8White/red/jackpine(Pinus alba/P. resinosa/P. banksiana)

3 438 607 5 3 694 993 5

Total 70 451 643 70 451 643

Source:adaptedfromMoseret al.(2016).



budworm(Choristoneura fumiferana)(Robertetal.,2018),couldalsoacceleratesuccession, but there were not enough historical incidents during the study periodtoaccuratelyprojectfutureoccurrences.

Density-andage-inducedmortalitywouldhaveasignificanteffectonthenumberofalllivetrees,withthetotalnumberdecreasingby10–17%(Fig.7a).Live-treevolumeonforestlandisprojectedtostayroughlythesame(Fig.7b;Moseretal.,2016).

2.2.1 Conclusions from the future forests of the northern United States project

Incontrast tothemorelong-termprojectionspresentedlater inthischapter,theexpectationsover theperiod2010–2060 focuson thedemographicandeconomic patterns behind the three climate storylines, not the changing climates themselves. Unless natural disturbance or anthropogenic activitiessuch as biomass harvesting increase considerably, the currentmiddle-agedforestcohortwillcontinuetoagewithtime(Shifleyetal.,2014).Withoutsuchdisturbances, young forests of all forest-type groups and early successionalforesttypessuchasaspen-birchwilldeclineasapercentageofthetotalforestedarea.Managers chargedwithmaintainingorenhancing thehabitat forearly

Figure 5 Distributionofforestlandageinyears,bystoryline,2010–2060.Source:adaptedfromMoseret al.(2016).



successionalspeciesor large-scale forestbiodiversitywill face thechallengeof developing socially acceptable and economically viable approaches thatprovideforthesespeciesinanever-agingforestaswellasbuildingresiliencein response to projected changes in climate patterns.

Forest managers must deal creatively with the heightened challenges expectedinthecomingdecades.By2060,aprojected85%ofthepopulationin

Figure 6 Proportion of forest land in early successional (young; <20 years) and latesuccessional(old;>100years)habitats,2010and2060,bystorylinesandstates.Source:adaptedfromMoseret al.(2016).



thenorthernUnitedStateswillbelivinginurbanareas(NowakandGreenfield,2016).Greaterpressurewillbeexertedonforestsasprivate forest landareadecreasesduetolandconversionsandtheaccompanyingfragmentation.Thegrowing population will put evermore pressure on forest systems tomeetdemands for consumption, such as timber and fuelwood harvesting, andnonconsumptiveusessatisfiedbyecosystemservices.Withincreasedhumancontact, nonnative invasive species are projected to expand into the forest,further reducing its capacity to provide goods and services into the future.Managementactivitiesmusttakeintoaccountincreasingtheresilienceoftheforeststocopewithahighlyvariableclimate.

Decreased utilization of forests for industrial uses will have cascadingeffectsonlocaleconomiesandemploymentinruralareas.Thecontinuationofcurrentlevelsofharvestingorotherhuman-causeddisturbancewillcontinuethetrendtowardagingofthecurrently60–100-year-oldforests,exacerbatinglowage-classdiversitylevelsandreducingcarbonsequestrationrates(Shifleyetal.,2014).

Localimpactsofclimatechangearelesscertainthanexpectedregionalandglobalimpacts.Theprojectionsofincreasedfrequencyandmorepronouncedswings in precipitation and drought cycles (IPCC, 2014; Clark et al., 2016)havethepotentialtoposechallengesforforestplanningactivitiesandplacestressontheregionalecosystem.TheexpectedchangesinthenorthernUnitedStates are not expected to be uniform and, at least for the 50-year periodunder discussion, will be more highly correlated with human demographic and invasivespeciesissuesthanclimaticinfluencesperse.

Facedwithsuchchallenges, forestmanagersmayaimtostrengthen theincreasinglyurbanpopulations’connectionswiththeir forests,helpingurbanvoters and taxpayers understand the value, the possibilities, and the limitations of their forests. At larger scales, cross-ownership collaboration—an ‘all lands

Figure 7 (a)Numberoftreesonforestland,bystoryline,2010–2060.(b)Live-treevolumeon forest land in thenorthernUnitedStatesbystoryline,2010–2060.Source:adaptedfromMoseret al.(2016).



approach’—is essential to counteract the decrease in ecosystem values thatthe remaining forest land area can support as greater human pressure andland fragmentation reduce forest landareaandconnectivity.Asexemplifiedin the discussion of oak decline (see Section 2.3), older forests, particularlythose composed ofmid-seral species, are oftenmore susceptible to insectand disease attack than their younger counterparts. Furthermore, a lack ofdisturbancewillresult inlimitedearlysuccessionalforests,affectingthesuiteofanimalsandplantsthatdependonearlyandmid-successionaltreespecies(Taverniaetal.,2016).

2.3 Precipitation variability and frequency and its effects on oak health

2.3.1 Background

Mostclimatemodelsprojecta futureclimateregimewhereadverseweathereventsare likely tobemore frequentandextreme (IPCC,2014;Clarket al.,2016). These weather events have the potential to exacerbate forest healthvulnerabilities by creating destructive disturbances such as severe drought events (Wehner et al., 2011; Clark et al., 2016), derechos (Pokharel et al.,2019),hurricanes (Dinan,2017),extremeprecipitationevents (Kirtmanetal.,2013), and tornadoes (Straderetal., 2017).Suchdisturbancesmay setbackthenormalpatternsof succession (Oliver andLarson,1996;Johnson,2004)or may accelerate changes to another ecological state (IPCC, 2014). Theseclimatic events can create novel conditions that the current ecosystem has not experienced before (Bauer et al., 2016) and towhich it is not adapted.The following example shows how a forest ecosystem has been subjectedto challenging current climatic conditions and suggests how future climatescenarios may exacerbate these issues.

2.3.2 Oak decline

Sinclair(1965)andManion(1981)presentedamodelofforesttreedeclinethatidentifiedthreecategoriesoffactors:predisposing,inciting,andcontributing.Thedeclinemodelforoakforestsdefinedpredisposingfactors,suchasage,long-termclimate,airpollution,orpoorsitequality,aslong-termfactorsthatstressoakforestsbyreducingtheirvigor,andhencetheaccumulationofexcesscarbohydrate reserves,of a tree.This combinationof responsesmakesoaksmorevulnerabletothesubsequenteffectsofincitingfactorssuchasdrought,defoliatinginsects,orfrost.Theseincitingfactorscreateahigherlevelofstressinatreeandcantriggertheforesthealthcomplexcalledoakdecline.Finally,contributing factorsmaybeanaccumulationofadditional inciting factorsor



theintroductionofotherinsectanddiseasespecies.Contributingfactorsareoften the agents present during oakmortality and the oneswhich forestersfocusonthemost(Worrall,2019).

Oakdecline isa long-recognized foresthealthcomplex thatparticularlyaffectsspecies intheQuercus erythrobalanus (redoak)speciesgroup. IntheOzarkMountains ofMissouri andArkansas,Quercus species exist today on landthatwashistoricallymaintainedby frequentfireasshortleafpine (Pinus echinataMill.)forests(StarkeyandOak,1988;CunninghamandHauser,1989;Dwyeretal.,1995;Oaketal.,1996;Bateketal.,1999;GuyetteandSpetich2003).Thesepinewoodsweremostlycomposedoflarge,widelyspacedpinetreeswithanherbaceousunderstory(Schoolcraft,1821).Uponremovalofthepineoverstoryfortimberorconversiontofarmland,thesitesoftenregeneratedto Quercusspecies,suchasblackoak(Q. velutinaLam.),scarletoak(Q. coccinea Münchh.),blackjackoak(Q. marilandicaMünchh.),southernredoak(Q. falcata Michx.),andnorthern redoak (Q. rubra L.), frequently influencedbyhuman-causedfires(GuyetteandSpetich,2003;Voelkeretal.,2004).

ThislandusehistoryandsubsequentmanagementledtothedevelopmentoftheMissouriOzarkforestsintodensestandsofoaks.Standsofscarletandblackoaksbecameprevalentonridgetopsandsouth-andwest-facingslopes;sites with northern aspects containedmore northern red oak (Cunninghamand Hauser, 1989; Voelker et al., 2004). This dense stand structure createdadditional stresson the trees,which resulted instandsmoreprone to foresthealth problems than stands with widely spaced trees and high species diversity.Scarletoaks,inparticular,weremorepronetoforesthealthproblemsas they got older.

The principles of Manion’s (1981) decline complex can be applied inthis situation, where predisposing, inciting, and contributing factors are allmanifested. In theOzarks, theseveredroughtof1998–2002wasthe incitingfactor(Fig.8a).Thisextendeddroughtreducedthevigorofoaktreesandmadethemvulnerabletocontributingfactors,suchasArmillariarootrot(Armillaria spp.), hypoxylon canker (Hypoxylon spp.), two-lined chestnut borer (Agrilus bilineatus), and red oak borer (Enaphalodes rufulus; Lawrence et al., 2002;Voelkeretal.,2008).Armillaria root rot, particularly Armillaria mellea, was an especiallyseverepathogen,particularlywiththehighincidenceoftransmissionvia root-grafts between scarlet and black oaks (Jenkins and Pallardy, 1995;Bruhnetal.,2000).Byexaminingfirescars,Guyetteet al.(2007)suggestedthatmoderateorseveredroughtconditionsoccurredevery10–20years.Voelkeret al.(2008)describeda‘pulseofmortality’thatoccurredimmediatelyafterthe1999–2002drought,suggestingthatmortalitywastheresponsetotheincitingcondition(drought),inthiscaseactingasathinningagent.2

2Formorediscussion,seeGrantet al.(2013).



Some climate scenarios project precipitation and drought swings ofincreasingfrequencyandseverity(Clarketal.,2016),hypotheticallyrepresentedby Fig. 8b.Two important effects result from such a new climate norm as itpertainstooak-hickoryforestsintheOzarkMountains:

1 The rapid and dramatic oscillation and the attendant forest healthimpactsdonotallowsufficienttimefortheoakforeststorecoverfromprevious disturbance cascades. In this case, drought increases tree andforestvulnerabilitytoinsectanddiseaseattack,whichprecipitatesdeclineandmortalitybeforethenextdroughtoccurs.Thisrelativelyrapidsequenceofdisturbancesvirtuallyguaranteesthatthetreeisweakened.Its response to the subsequent drought is less robust, increasing the probabilityofmortality.

2 As shown in Fig. 8b, theprecipitation from the rain events—bybeingmoresevere—arenotlikelytobecompletelyabsorbedbytheforestsoilecosystem.Theinfiltrationrate,eveninadrysoil,maynotaccommodatethe total volume of the rainfall, with the excess sheeting off into thesurface water system (Williams, 1991; Ritchie, 1998). Assuming abalanced, closed system where the total annual precipitation may not change (whichwill notnecessarilybe thecase), the forestecosystemwill not obtain the full benefit of the precipitation (surplus) but willexperiencethefullextentofthemoisturedeficit(i.e.drought).Forthetree,theaveragelong-termwateravailabilityisnotthenominalaverage

Figure 8 (a)Hypotheticalrepresentationofhistoricalprecipitationandwateravailabilityovera10-yearperiodintheOzarkMountainsofMissouri.Thehorizontalaxisrepresentstime.Theproportionsarenotnecessarilytoscale,butrepresentahypotheticalcycleofwaterabundanceandshortageoveraperiodoftime.Forthepurposesofthisdiscussion,thepoint in timewhere thewater availability linegoesbelow zero is1998–1999. It isgenerallybelievedthat thedroughtended in2003. (b)Hypothetical representationofpotential precipitation patterns under climate change scenarios projecting increased frequency and severity of weather events. For our purposes, each cycle representsapproximatelyhalfthetimeofthecycleinFig.8a.Thered-shadedareaatthetopofthecycle represents precipitation that comes down at an amount and rate such that not all canbeabsorbedbytheecosystemandthusleavesthesiteasoverlandsurfacewater.Theblue line represents the actual soil moisture available to the trees, which is less than the nominal total moisture.



precipitationrate(theredlineinthegraph,Fig.8b),butratherthelower(blue) line, the effective average water availability. Coupled with thestressimposedbytheboom-and-bustcycleofprecipitationmentionedearlier,thislong-termreductioninavailablesoilmoisturemayresultinageneraldeclineinvigorinthecurrentOzarkoak-hickoryforeststands.Alikelyconsequenceisconversiontoasuiteofmoredrought-tolerant(xeric)speciesovertime.

2.3.3 Oak decline lessons for managers

Forestmanagershaveexperiencedtheimpactsofperiodicdroughteventsoverthelastcenturies.Theseeventsarebasedondecadalormulti-decadalcycles.Suchlongperiodsbetweensuccessivedroughtsallowedtheforestecosystemtorecoveratleastsomewhatundersufficientorevenabove-averagelevelsofsoilmoisture.Climatechangemakescurrentdroughtcyclesdifferentfromthecyclesoftherecentpast.Droughtsareexpectedtoberelativelymorefrequentandsevere(IPCC,2014;Clarketal.,2016;butseeSeageretal.,2009).Giventheprojectedincreasedvariabilityinrainfall,andtheepisodicnatureofmortality,managersmayfind it logical tomanage forests to sustain them through themorestressfultimesratherthanforlong-termaverageconditions.

Though standard stocking charts or measures of density are based onaverage climate conditions, managers could consider voluntarily forgoingmaximizingproductivityinordertoreducepotentialsusceptibilitytodrought-inducedmortality (D’Amatoet al., 2013;Gleasonet al., 2017).Voelkeret al.(2008)suggestedthatstandswithrelativelylowstockingandhencepotentiallyless inter-tree competition may provide more resilience to drought. Forestmanagers could deliberately keep stands below full stocking. They wouldsacrifice some volume production and possibly reduce tree quality due topersistent branching, but at the same time they potentially would make them more resilient under drought conditions. The benefit gained would be theexpectedvalueofthevolumelosttomortalitymultipliedbytheprobabilitythata decline would result in that mortality.

Intermsofdensityreduction,MoserandMelick(unpublishedmemo,2002)suggestedthatapathologicalrotationofspeciespronetooakdecline,suchasscarletoak,andareductionofoakstanddensitytotheC-line(representedbythe redarrows inFig.9;Gingrich,1967)—adensity levelnormally reservedforattemptstoregeneratethestand—wouldbothreducethemoisturestresson treesand reduce thenumberofvulnerable treeson thesite.Somehaveconsideredthisstockingleveltobetoolow(Johnson,pers.comm.,2002),andinstead recommendkeeping stand stockingnear theB-line (representedbythebluearrows)andharvestoakdecline-pronespeciesaround70yearsofage(Clatterbuck andKauffman, 2006).Others haveproposed a landscape-scale



matrixofeven-anduneven-agedforests,dependingonlocalsiteconditions,and shifting forest composition to more drought-resistant species such asshortleafpine(Pinus echinata)andwhiteoak(Quercus alba),astechniquestoreducethevulnerabilitytoseveredroughtevents(Johnson,pers.comm.,2002;Guyetteetal.,2007).

For example, the prognosis for forest survival in the presence of gypsymoth(Lymantria dispar)dependsontheforeststand’sabilitytosurviveoneormultipledefoliations,throughreducingthenumberofsusceptiblespeciesorincreasingthevigorofthetreesonthesite,orboth(Gottschalk,1993).Aftera few years, thegypsymothpopulationmaydeclineormaymove to othersites(Davidsonetal.,1999).Suchamodel isagoodtemplateformanaging

Figure 9 Stocking tables for upland central hardwoods, portraying the relationshipbetween trees per hectare (horizontal axis), density (basal area per hectare, verticalaxis), and thequadraticmeandiameter (raysextending from lower left toupper rightofthediagram).Inthisfigure,adaptedfromGin(g)rich(1967),theareaabovetheA-linerepresents anoverstocked stand.Theareabetween theA- andB-lines represents fullstockingand theareabetween theB- andC-lines represents anunderstocked stand.TheA-lineisbasedonthefullystockedstandthathasneverbeenthinned.AstandontheB-lineisthoughttohavetreeswithnocompetition,yetthereisnounusedgrowingspace.TheC-line isestimatedbasedon thenormalyield tableof the loweststockingthatwillgrowtotheB-linewithin10years.Source:adaptedfromLarsenet al.(2010)andLarsen(2014).



aforest’soverallvigor.Treeswithgreatervigorstoremorecarbohydratesandsugars over winter. Consequently, they have the resources to develop more extensiverootsystemsandproduceabundantcurrent-yearcarbohydratestosupportdefenseagainstinsectanddiseaseattacksevenbeyondrequirementsforgrowthoffinerootsandleaves,heightgrowth,andreproduction.

Optionsforenhancingorshapingspeciesdiversitydependoncurrentstandconditions.Olderforestswithlimitedspeciesdiversityofferfewoptions.Suchcases point toward improving tree vigor by thinning and perhaps preparing the stand for future regeneration where there aremultiple species capableofreachingandbeingmaintainedintheoverstory.Aftertheseconditionsareachieved, more management options present themselves.

3 Methods of projection in the long term: modeling projected changes in habitat and potential migration

Modeling potential changes in habitat, and the potential migration into such habitats,requiresamajorsimplificationofrealityinanuncertainandchangingworld.There is a great deal of complexity to consider, as evidencedby themanyintrinsic(e.g.physicalhabitatspecialization,successionalstage,fecundity,dependence on particular disturbances) and extrinsic (e.g. browsing, pest/pathogens, dispersal barriers, climatic extremes) factors thatmay increase aspecies’orpopulation’sriskofextinction,extirpation,orgeneticdegradation.For models to be useful (Box and Draper, 1987), they must enhance ourunderstandingofcurrentandpotentialfuturespeciesdistributions.

Totacklethesecomplexities,ourapproachhasbeentocombineaspeciesdistribution model (SDM; DISTRIB-II, an updated version of the RandomForestDISTRIBmodel[Petersetal.,2019;Iversonetal.,2019a]),forprojectingpotentialfuturesuitablehabitats),andamigrationmodel(SHIFT,forestimatingcolonization likelihoods based on historical migration rates into projectedsuitablehabitatwithin100years).Inaddition,weusealiterature-basedsetofmodification factors forassistance in interpretation (ModFacs),andacurrentforest inventory assessment (Forest Inventory and Analysis, FIA, www.fs.fed.us/fia) to better understand current tree species abundance for a particulargeographic unit (Iverson et al., 2008, 2019a, 2019b; Iverson andMcKenzie,2013;Prasadetal.,2006).

TheresultingoutputsoftheseindividualspeciesmodelsprovideawealthofinformationacrosstheeasternUnitedStates.Asabackgroundtoourmodelingframework,theresponsevariablesarederivedfromFIAandweuseahybridgrid(Petersetal.,2019)of10 × 10or20 × 20kmcellstoaccountforthedifferentialdensityofFIAplots.TheFIAplotdataweretabulatedandaveragedwithineachofthe55nationalforeststoyieldarankedlistoftreespecies,byimportancevalue (IV)derivedequally fromtotalbasalareaandnumberof stems.These

http://www.fs.fed.us/fia

http://www.fs.fed.us/fia



IV datawere also used in conjunctionwith 45 environmental variables (e.g.climate,elevation,andsoil)inastatisticalmodel(RandomForest,Prasadetal.,2016)togeneratemodeledestimates(DISTRIB-II)ofcurrentIVforeachspeciesacrosstheeasternUnitedStates.Then,byswappingcurrentclimaticvariableswith potential future climate variables according to three models (CCSM4,GFDL-CM3,andHadGEM2-ES)andtworepresentativeconcentrationpathways(4.5and8.5),for30-yearperiodsendingin2039,2069,and2099,projectionsweremaderegardingpotentialsuitablehabitatforeachspecies(Prasadetal.,2016; Iverson et al., 2019a). Usingmultiple literature sources, each specieswas also scored on nine biological traits and 12 traits related to resilience fromdisturbances(Matthewsetal.,2011)andgivenaratingastothespecies’adaptability to thechangingclimate.TheSHIFTmodel is alsoparamount tothiseffort,toassesscolonizationlikelihoodwithinthesuitablehabitatsbasedonhabitatsuitabilityandthestrengthofthesourceabundance(Prasadetal.,2013,2016).BycombiningDISTRIB-IIandSHIFTresults,wenotonly identifypotential changes in suitable habitat under various scenarios of climatechange,butalsoprovide,foreachspeciespresentcurrentlyorpotentiallyinthefuture,estimatesofcolonization likelihoodthroughthecurrently fragmentedlandscapes (Iversonetal.2019b).Weassumedagenerousmigrationrateof50km/centurywithin100years; thismigration rate represents thehighendofaverageestimatesofmigrationduringtheHoloceneperiodthroughextantforest(Davis,1981;DavisandShaw,2001;Schwartz,1993)althoughMcLachlanet al.(2005)havedeterminedfrommolecularstudiesthat25,oreven10 km,maybemorerealisticforsomespeciesthatwereassistedbyseedsourcesinclimaticrefugia.Wecontinuetouse50km/centurybecausewedonotassumefutureformationsofclimaticrefugia.

WiththecombinationofresultsfromDISTRIB-II,SHIFT,ModificationFactors,andcurrentFIAestimatesofIV,weareabletopresentadetailedpresentationof(1)speciesimportancecurrently,(2)thepotentialchangesinsuitablehabitatby2100,(3)theadaptabilityofeachspeciestothechangingclimate,(4)thecapabilityofeachspeciestocopewiththe2100climatebasedonadaptabilityandabundancecurrentlywithintheNationalForest(NF),(5)thelikelihoodofeach species to naturallymigrate into theNF, and (6) an assessment of thepotentialforthespeciestobeusedforplantingorotherwisepromotingwithinthe NF.

In order to facilitate comparisons and quantify potential risks andopportunitiesunderclimatechange,wefocushereonthecollectiveoutputsforthefollowinggeographicunits:state,1 × 1o grid, ecoregion, hydrologic unit, and NF.Thiscanbedone,andtabulatedormapped,foranygeographiclocationintheeasternUnitedStates,solongasitoccupiesanareaofatleast8000km2 to allowsufficientFIAplotsforanalyses.WebrieflyreporthereonDISTRIB-IIandSHIFToutputsforthreeanalyses:(1)DISTRIB-IIoutputsofchangesinsuitable



habitatsfortheentireeasternUSregion;(2)DISTRIB-IIwithSHIFToutputsfor55nationalforestsinthisregion,withanemphasisonone,theChequamegon-NicoletNFinnorthernWisconsin;and(3)DISTRIB-IIwithSHIFToutputsfor4641 × 1ogridsacrossthissameregioneastofthe100thmeridian.

3.1 DISTRIB-II projections of suitable habitat by 2100

Weevaluated 125 tree species that had sufficient FIA samples formodeling.Results show potentially large impacts, especially under a high emissions trajectory (RCP8.5),on suitablehabitat for tree species in theeasternUnitedStates. Of the 45 variables used in the Random Forest modeling, the sevenclimate variables were ranked among the top nine variables, indicating an overall influenceof climateassociationswith capturingpatternsat the species rangeextent. Inserting new possible climates caused large changes in potential suitable habitat.Ouranalysisfoundthatabout88ofthe125specieswouldgainand26specieswouldloseatleast10%oftheirsuitablehabitat.Theprojectedchangeinthemeancenterforeachspeciesshowsageneralmovementtothenortheast,with thehabitatcenters for81speciespotentiallymovingover100kmunderRCP 8.5. For example, Quercus nigra(wateroak)showsapotentialmovementof377kmunderthemeanofRCP8.5scenarios(Fig.10).Overall,manytreespeciesare likely to have better success in tracking their suitable habitats under RCP 4.5 ascomparedtoRCP8.5.DetailsarepresentedinIversonet al.(2019a).

3.1.1 Chequamegon-Nicolet NF assessment

The resultsof combiningmodeloutputsofDISTRIB-II andSHIFT,alongwiththemodificationfactorsandcurrentFIAestimates,areallpresentedwithinaninformation-packed,buteasilyunpackedtable(Table2,seealsoIversonetal.2019bfor fullexplanationof tablevariablesandderivatives).Besidesasuiteofspecies-level informationrelatedtocurrentandpotential futurecapacitiesto cope with the changing climate, it also provides suggestions as to species thatare(1)rarenowbutgoodcandidatesforincreasingprominenceinfuture(Infill); (2) likely there now but missed by FIA plots (Likely); and (3) goodcandidatesforassistedmigrationbecausetheyarenearbywithgoodpotentialfornaturalmigrationintotheareawithin100years(Migrate). InourexampleChequamegon-NicoletNF,weshowsixspeciesforInfill,twoforLikely,andsixtonineforMigrate,dependingonRCP(Table2).

3.1.2 1 × 1-degree assessment

Eachofthe4641 × 1ogridswastabulatedinthesamewayasdescribedfortheChequamegon-NicoletNF.These tables allow anyone east of the 100thmeridian(easternhalfoftheUnitedStates)theabilitytodeterminetheircurrent



and potential tree species attributes during this century. All that is necessary is for theuser to knowhis/hergeographic coordinates (e.g. 41.334 latitudeand−82.201longitude)andthenameofthegridwill indicatethesoutheastcornerof thegrid for thefile touse,eitheronlineordownloaded(e.g.S41_E82.pdf,seewww.fs.fed.us/nrs/atlas).Theareaconsideredwithingridsvariesslightlynorthtosouthduetothecurvatureoftheearth,soeach1 × 1o cell was calibrated to equal 10 000 km2,whichrepresentsroughly10010 × 10kmcellsor2520 × 20kmcells(usuallysomecombinationofeach).

Bycollectivelyevaluatingall1 × 1ogrids,wecanmapthecountsofspecieswithinanyofthefieldsofthe464tables.Thesecanbeassimpleascountingthenumberof species recordedonFIAplotsor thenumberofoak speciesrecorded,tomoreadvancedqueriessuchasthenumberoftreespecieswith

Figure 10 Ellipsesofone standarddeviationandmeancenters for the currentdistri-bution and suitable habitat according to CCSM4 RCP 4.5, mean RCP 4.5, mean RCP 8.5, andHadGEM2-ESRCP8.5forwateroak(Quercus nigra).FIAActualreferstotheknownFIAplotlocationsofthespecies,whileCurrentreferstothemodeledcurrentdistributionofthespecies.

http://S41_E82.pdf,

http://S41_E82.pdf,

http://www.fs.fed.us/nrs/atlas



Tabl

e 2 DISTRIB-II/SHIFToutputtableforChequam

egon-NicoletNationalForest,sortedindecreasingorderofcurrentspeciesabundance(FIAsum)

Com

mon

na

me

Scientificname

MR

FIAs

umFI

Aiv

Chn

gCl4

5C

hngC

l85Ad

apt-

Abun

dCa

pabi

l45

Capa

bil8

5SHIFT45

SHIFT85

N

Qua

king

as

pen

Popu

lus

trem

uloi

des

Hig

h64

1.22

16.7

6Sm

. dec

.Sm

. dec

.M

ediu

mAb

unda

ntGood

Good

1

Red

map

leAc

er ru

brum

Hig

h54

8.33

13.8

4N

o ch

ange

No

chan

geH

igh

Abun

dant

Very

Good

Very

Good

2

Suga

r map

leAc

er sa

ccha

rum

Hig

h48

5.17

13.6

5Sm

. dec

.Sm

. dec

.H

igh

Abun

dant

Good

Good

3Balsamfir

Abie

s bal

sam

eaH

igh

315.

428.

63Sm

. dec

.Sm

. dec

.Lo

wAb

unda

ntGood

Good

4Bl

ack

ash

Frax

inus

nig

raM

ediu

m22

1.35

6.96

Sm. d

ec.

Sm. d

ec.

Low

Abun

dant

Good

Good

5Re

d pi

nePi

nus r

esin

osa

Med

ium

176.

3310

.63

Sm. d

ec.

Sm. d

ec.

Low

Abun

dant

Good

Good

6Tamarack

(native)

Larix

laric

ina

Hig

h16

1.77

7.35

No

chan

geN

o ch

ange

Low

Abun

dant

Very

Good

Very

Good

7

Pape

r birc

hBe

tula

pa

pyrif

era

Hig

h15

9.6

4.41

No

chan

geN

o ch

ange

Med

ium

Abun

dant

Very

Good

Very

Good

8

Blac

k sp

ruce

Pice

a m

aria

naH

igh

124.

916.

47Sm

. dec

.Sm

. dec

.M

ediu

mAb

unda

ntGood

Good

9N

orth

ern

red

oak

Que

rcus

rubr

aM

ediu

m12

2.8

5.11

Sm. i

nc.

Sm. i

nc.

Hig

hAb

unda

ntVery

Good

Very

Good

10

Nor

ther

n white-cedar

Thuj

a oc

cide

ntal

isH

igh

113.

97.

16Sm

. dec

.N

o ch

ange

Med

ium

Abun

dant

Good

Very

Good

11

Amer

ican

ba

ssw

ood

Tilia

am

eric

ana

Med

ium

112.

454.

37Sm

. inc

.N

o ch

ange

Med

ium

Abun

dant

Very

Good

Very

Good

12

Bigt

ooth

as

pen

Popu

lus

gran

dide

ntat

aM

ediu

m10

6.74

4.85

No

chan

geSm

. dec

.M

ediu

mAb

unda

ntVery

Good

Good

13

Yello

w b

irch

Betu

la

alle

ghan

iens

isH

igh

105.

823.

51Sm

. dec

.Sm

. dec

.M

ediu

mAb

unda

ntGood

Good

14

East

ern

whi

te

pine

Pinu

s stro

bus

Hig

h96

.91

4.47

Sm. i

nc.

Sm. i

nc.

Low

Abun

dant

Very

Good

Very

Good

15

East

ern

hem

lock

Tsug

a ca

nade

nsis

Hig

h79

.45

3.85

No

chan

geN

o ch

ange

Low

Abun

dant

Very

Good

Very

Good

16

Jackpine

Pinu

s ban

ksia

naM

ediu

m77

.19

12.6

7Sm

. dec

.Lg

. dec

.H

igh

Abun

dant

Good

Good

17W

hite

spru

cePi

cea

glau

caM

ediu

m56

.88

2.4

No

chan

geN

o ch

ange

Med

ium

Com

mon

Good

Good

18

Blac

k ch

erry

Prun

us se

rotin

aM

ediu

m49

.63

1.81

Lg. i

nc.

Lg. i

nc.

Low

Com

mon

Very

Good

Very

Good

19

Amer

ican

elm

Ulm

us

amer

ican

aM

ediu

m44

.77

2.4

Sm. i

nc.

Sm. i

nc.

Med

ium

Com

mon

Very

Good

Very

Good

20

Whi

te a

shFr

axin

us

amer

ican

aM

ediu

m36

.96

1.95

Sm. i

nc.

Sm. i

nc.

Low

Com

mon

Very

Good

Very

Good

21

East

ern

hoph

ornb

eam

Ost

ryav

irgin

iana

Low

36.9

51.

48Sm

. inc

.Sm

. inc

.H

igh

Com

mon

Very

Good

Very

Good

22

Nor

ther

n pi

n oa

kQ

uerc

us

ellip

soid

alis

Med

ium

36.0

24.

63Sm

. dec

.Sm

. dec

.H

igh

Com

mon

Fair

Fair

Infill+

Infill+

23

Greenash

Frax

inus

pe

nnsy

lvan

ica

Low

24.2

21.

49Sm

. inc

.Sm

. inc

.M

ediu

mCo

mm

onVery

Good

Very

Good

24

Bur o

akQ

uerc

us

mac

roca

rpa

Med

ium

20.3

34.

29N

o ch

ange

No

chan

geH

igh

Com

mon

Good

Good

Infill++

Infill++

25

Amer

ican

ho

rnbe

amCa

rpin

us

caro

linia

naLo

w16

.52

1.28

Sm. d

ec.

Sm. d

ec.

Med

ium

Com

mon

Fair

Fair

26

Cho

kech

erry

Prun

us

virg

inia

naFI

A5.

460.

54Unknown

Unknown

Med

ium

Com

mon

FIAOnly

FIAOnly

27

Pin

cher

ryPr

unus

pe

nsyl

vani

caLo

w5.

450.

89N

o ch

ange

VeryLg.

dec

Med

ium

Com

mon

Good

Lost

28



Tabl

e 2 DISTRIB-II/SHIFToutputtableforChequam

egon-NicoletNationalForest,sortedindecreasingorderofcurrentspeciesabundance(FIAsum)

Com

mon

na

me

Scientificname

MR

FIAs

umFI

Aiv

Chn

gCl4

5C

hngC

l85Ad

apt-

Abun

dCa

pabi

l45

Capa

bil8

5SHIFT45

SHIFT85

N

Qua

king

as

pen

Popu

lus

trem

uloi

des

Hig

h64

1.22

16.7

6Sm

. dec

.Sm

. dec

.M

ediu

mAb

unda

ntGood

Good

1

Red

map

leAc

er ru

brum

Hig

h54

8.33

13.8

4N

o ch

ange

No

chan

geH

igh

Abun

dant

Very

Good

Very

Good

2

Suga

r map

leAc

er sa

ccha

rum

Hig

h48

5.17

13.6

5Sm

. dec

.Sm

. dec

.H

igh

Abun

dant

Good

Good

3Balsamfir

Abie

s bal

sam

eaH

igh

315.

428.

63Sm

. dec

.Sm

. dec

.Lo

wAb

unda

ntGood

Good

4Bl

ack

ash

Frax

inus

nig

raM

ediu

m22

1.35

6.96

Sm. d

ec.

Sm. d

ec.

Low

Abun

dant

Good

Good

5Re

d pi

nePi

nus r

esin

osa

Med

ium

176.

3310

.63

Sm. d

ec.

Sm. d

ec.

Low

Abun

dant

Good

Good

6Tamarack

(native)

Larix

laric

ina

Hig

h16

1.77

7.35

No

chan

geN

o ch

ange

Low

Abun

dant

Very

Good

Very

Good

7

Pape

r birc

hBe

tula

pa

pyrif

era

Hig

h15

9.6

4.41

No

chan

geN

o ch

ange

Med

ium

Abun

dant

Very

Good

Very

Good

8

Blac

k sp

ruce

Pice

a m

aria

naH

igh

124.

916.

47Sm

. dec

.Sm

. dec

.M

ediu

mAb

unda

ntGood

Good

9N

orth

ern

red

oak

Que

rcus

rubr

aM

ediu

m12

2.8

5.11

Sm. i

nc.

Sm. i

nc.

Hig

hAb

unda

ntVery

Good

Very

Good

10

Nor

ther

n white-cedar

Thuj

a oc

cide

ntal

isH

igh

113.

97.

16Sm

. dec

.N

o ch

ange

Med

ium

Abun

dant

Good

Very

Good

11

Amer

ican

ba

ssw

ood

Tilia

am

eric

ana

Med

ium

112.

454.

37Sm

. inc

.N

o ch

ange

Med

ium

Abun

dant

Very

Good

Very

Good

12

Bigt

ooth

as

pen

Popu

lus

gran

dide

ntat

aM

ediu

m10

6.74

4.85

No

chan

geSm

. dec

.M

ediu

mAb

unda

ntVery

Good

Good

13

Yello

w b

irch

Betu

la

alle

ghan

iens

isH

igh

105.

823.

51Sm

. dec

.Sm

. dec

.M

ediu

mAb

unda

ntGood

Good

14

East

ern

whi

te

pine

Pinu

s stro

bus

Hig

h96

.91

4.47

Sm. i

nc.

Sm. i

nc.

Low

Abun

dant

Very

Good

Very

Good

15

East

ern

hem

lock

Tsug

a ca

nade

nsis

Hig

h79

.45

3.85

No

chan

geN

o ch

ange

Low

Abun

dant

Very

Good

Very

Good

16

Jackpine

Pinu

s ban

ksia

naM

ediu

m77

.19

12.6

7Sm

. dec

.Lg

. dec

.H

igh

Abun

dant

Good

Good

17W

hite

spru

cePi

cea

glau

caM

ediu

m56

.88

2.4

No

chan

geN

o ch

ange

Med

ium

Com

mon

Good

Good

18

Blac

k ch

erry

Prun

us se

rotin

aM

ediu

m49

.63

1.81

Lg. i

nc.

Lg. i

nc.

Low

Com

mon

Very

Good

Very

Good

19

Amer

ican

elm

Ulm

us

amer

ican

aM

ediu

m44

.77

2.4

Sm. i

nc.

Sm. i

nc.

Med

ium

Com

mon

Very

Good

Very

Good

20

Whi

te a

shFr

axin

us

amer

ican

aM

ediu

m36

.96

1.95

Sm. i

nc.

Sm. i

nc.

Low

Com

mon

Very

Good

Very

Good

21

East

ern

hoph

ornb

eam

Ost

ryav

irgin

iana

Low

36.9

51.

48Sm

. inc

.Sm

. inc

.H

igh

Com

mon

Very

Good

Very

Good

22

Nor

ther

n pi

n oa

kQ

uerc

us

ellip

soid

alis

Med

ium

36.0

24.

63Sm

. dec

.Sm

. dec

.H

igh

Com

mon

Fair

Fair

Infill+

Infill+

23

Greenash

Frax

inus

pe

nnsy

lvan

ica

Low

24.2

21.

49Sm

. inc

.Sm

. inc

.M

ediu

mCo

mm

onVery

Good

Very

Good

24

Bur o

akQ

uerc

us

mac

roca

rpa

Med

ium

20.3

34.

29N

o ch

ange

No

chan

geH

igh

Com

mon

Good

Good

Infill++

Infill++

25

Amer

ican

ho

rnbe

amCa

rpin

us

caro

linia

naLo

w16

.52

1.28

Sm. d

ec.

Sm. d

ec.

Med

ium

Com

mon

Fair

Fair

26

Cho

kech

erry

Prun

us

virg

inia

naFI

A5.

460.

54Unknown

Unknown

Med

ium

Com

mon

FIAOnly

FIAOnly

27

Pin

cher

ryPr

unus

pe

nsyl

vani

caLo

w5.

450.

89N

o ch

ange

VeryLg.

dec

Med

ium

Com

mon

Good

Lost

28

(Con

tinue

d)



Com

mon

na

me

Scientificname

MR

FIAs

umFI

Aiv

Chn

gCl4

5C

hngC

l85Ad

apt-

Abun

dCa

pabi

l45

Capa

bil8

5SHIFT45

SHIFT85

N

Serv

iceb

erry

Amel

anch

ier

spp.

Low

3.94

0.53

Sm. i

nc.

No

chan

geM

ediu

mRa

reGood

Fair

29

Silv

er m

aple

Acer

sa

ccha

rinum

Low

3.7

10.9

3Sm

. dec

.Sm

. dec

.H

igh

Rare

Poor

Poor

Infill+

Infill+

30

Butte

rnut

Jugl

ans c

iner

eaFI

A1.

741.

71Unknown

Unknown

Low

Rare

FIAOnly

FIAOnly

31Ba

lsam

pop

lar

Popu

lus

balsa

mife

raM

ediu

m1.

40.

83VeryLg.

dec

VeryLg.

dec

Med

ium

Rare

Lost

Lo

st32

Slip

pery

elm

Ulm

us ru

bra

Low

0.82

0.81

Sm. i

nc.

Sm. i

nc.

Med

ium

Rare

Good

Good

Infill++

Infill++

33Bi

ttern

ut

hick

ory

Cary

a co

rdifo

rmis

Low

0.45

0.66

Sm. i

nc.

Sm. i

nc.

Hig

hRa

reGood

Good

Infill+

Infill+

34

Whi

te o

akQ

uerc

us a

lba

Med

ium

0.43

0.63

No

chan

geN

o ch

ange

Hig

hRa

reFa

irFa

irInfill+

Infill+

35

Mou

ntai

n m

aple

Acer

spic

atum

Low

0.43

0.25

Lg. d

ec.

Lg. d

ec.

Hig

hRa

rePo

orPo

or36

Nor

way

sp

ruce

Pice

a ab

ies

FIA

0.27

0.8

Unknown

Unknown

NA

Rare

NN

ISN

NIS

37

Scot

ch p

ine

Pinu

s syl

vest

risFI

A0.

180.

52Unknown

Unknown

NA

Rare

NN

ISN

NIS

38Peachleaf

will

owSa

lix

amyg

dalo

ides

FIA

0.13

0.37

Unknown

Unknown

Med

ium

Rare

FIAOnly

FIAOnly

39

Rock

elm

Ulm

us th

omas

iiFI

A0.

090.

27Unknown

Unknown

Low

Rare

FIAOnly

FIAOnly

40Bo

xeld

erAc

er n

egun

doLo

w0

0N

ew

Hab

itat

New

H

abita

tH

igh

Abse

ntN

ew

Hab

itat

New

H

abita

tLikely+

Likely+

41

Swam

p w

hite

oa

kQ

uerc

us b

icol

orLo

w0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Likely+

Likely+

42

Blac

k oa

kQ

uerc

us

velu

tina

Hig

h0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate++

Migrate++

43

East

ern

redc

edar

Juni

peru

s vi

rgin

iana

Med

ium

00

New

H

abita

tN

ew

Hab

itat

Med

ium

Abse

ntN

ew

Hab

itat

New

H

abita

tMigrate+

Migrate++

44

Amer

ican

be

ech

Fagu

s gr

andi

folia

Hig

h0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

Migrate+

45

Blac

k lo

cust

Robi

nia

pseu

doac

acia

Low

00

New

H

abita

tN

ew

Hab

itat

Med

ium

Abse

ntN

ew

Hab

itat

New

H

abita

tMigrate+

Migrate+

46

Blac

k w

alnu

tJu

glan

s nig

raLo

w0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

Migrate+

47

Shag

bark

hi

ckor

yCa

rya

ovat

aM

ediu

m0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

Migrate+

48

East

ern

cotto

nwoo

dPo

pulu

s de

ltoid

esLo

w0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

49

Hac

kber

ryCe

ltis

occi

dent

alis

Med

ium

00

New

H

abita

tN

ew

Hab

itat

Hig

hAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

50

Blac

kgum

Nys

sa sy

lvat

ica

Med

ium

00

New

H

abita

tN

ew

Hab

itat

Hig

hAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

51

Pign

ut h

icko

ryCa

rya

glab

raM

ediu

m0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

52

Syca

mor

ePl

atan

us

occi

dent

alis

Low

00

New

H

abita

tN

ew

Hab

itat

Med

ium

Abse

ntN

ew

Hab

itat

New

H

abita

t53

Yellow-poplar

Lirio

dend

ron

tulip

ifera

Hig

h0

0N

ew

Hab

itat

New

H

abita

tH

igh

Abse

ntN

ew

Hab

itat

New

H

abita

t54

Moc

kern

ut

hick

ory

Cary

a al

baM

ediu

m0

0N

ew

Hab

itat

New

H

abita

t H

igh

Abse

ntN

ew

Hab

itat

New

H

abita

t55

Tabl

e 2 (Continued)



Com

mon

na

me

Scientificname

MR

FIAs

umFI

Aiv

Chn

gCl4

5C

hngC

l85Ad

apt-

Abun

dCa

pabi

l45

Capa

bil8

5SHIFT45

SHIFT85

N

Serv

iceb

erry

Amel

anch

ier

spp.

Low

3.94

0.53

Sm. i

nc.

No

chan

geM

ediu

mRa

reGood

Fair

29

Silv

er m

aple

Acer

sa

ccha

rinum

Low

3.7

10.9

3Sm

. dec

.Sm

. dec

.H

igh

Rare

Poor

Poor

Infill+

Infill+

30

Butte

rnut

Jugl

ans c

iner

eaFI

A1.

741.

71Unknown

Unknown

Low

Rare

FIAOnly

FIAOnly

31Ba

lsam

pop

lar

Popu

lus

balsa

mife

raM

ediu

m1.

40.

83VeryLg.

dec

VeryLg.

dec

Med

ium

Rare

Lost

Lo

st32

Slip

pery

elm

Ulm

us ru

bra

Low

0.82

0.81

Sm. i

nc.

Sm. i

nc.

Med

ium

Rare

Good

Good

Infill++

Infill++

33Bi

ttern

ut

hick

ory

Cary

a co

rdifo

rmis

Low

0.45

0.66

Sm. i

nc.

Sm. i

nc.

Hig

hRa

reGood

Good

Infill+

Infill+

34

Whi

te o

akQ

uerc

us a

lba

Med

ium

0.43

0.63

No

chan

geN

o ch

ange

Hig

hRa

reFa

irFa

irInfill+

Infill+

35

Mou

ntai

n m

aple

Acer

spic

atum

Low

0.43

0.25

Lg. d

ec.

Lg. d

ec.

Hig

hRa

rePo

orPo

or36

Nor

way

sp

ruce

Pice

a ab

ies

FIA

0.27

0.8

Unknown

Unknown

NA

Rare

NN

ISN

NIS

37

Scot

ch p

ine

Pinu

s syl

vest

risFI

A0.

180.

52Unknown

Unknown

NA

Rare

NN

ISN

NIS

38Peachleaf

will

owSa

lix

amyg

dalo

ides

FIA

0.13

0.37

Unknown

Unknown

Med

ium

Rare

FIAOnly

FIAOnly

39

Rock

elm

Ulm

us th

omas

iiFI

A0.

090.

27Unknown

Unknown

Low

Rare

FIAOnly

FIAOnly

40Bo

xeld

erAc

er n

egun

doLo

w0

0N

ew

Hab

itat

New

H

abita

tH

igh

Abse

ntN

ew

Hab

itat

New

H

abita

tLikely+

Likely+

41

Swam

p w

hite

oa

kQ

uerc

us b

icol

orLo

w0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Likely+

Likely+

42

Blac

k oa

kQ

uerc

us

velu

tina

Hig

h0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate++

Migrate++

43

East

ern

redc

edar

Juni

peru

s vi

rgin

iana

Med

ium

00

New

H

abita

tN

ew

Hab

itat

Med

ium

Abse

ntN

ew

Hab

itat

New

H

abita

tMigrate+

Migrate++

44

Amer

ican

be

ech

Fagu

s gr

andi

folia

Hig

h0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

Migrate+

45

Blac

k lo

cust

Robi

nia

pseu

doac

acia

Low

00

New

H

abita

tN

ew

Hab

itat

Med

ium

Abse

ntN

ew

Hab

itat

New

H

abita

tMigrate+

Migrate+

46

Blac

k w

alnu

tJu

glan

s nig

raLo

w0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

Migrate+

47

Shag

bark

hi

ckor

yCa

rya

ovat

aM

ediu

m0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

Migrate+

48

East

ern

cotto

nwoo

dPo

pulu

s de

ltoid

esLo

w0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

49

Hac

kber

ryCe

ltis

occi

dent

alis

Med

ium

00

New

H

abita

tN

ew

Hab

itat

Hig

hAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

50

Blac

kgum

Nys

sa sy

lvat

ica

Med

ium

00

New

H

abita

tN

ew

Hab

itat

Hig

hAb

sent

New

H

abita

tN

ew

Hab

itat

Migrate+

51

Pign

ut h

icko

ryCa

rya

glab

raM

ediu

m0

0N

ew

Hab

itat

New

H

abita

tM

ediu

mAb

sent

New

H

abita

tN

ew

Hab

itat

52

Syca

mor

ePl

atan

us

occi

dent

alis

Low

00

New

H

abita

tN

ew

Hab

itat

Med

ium

Abse

ntN

ew

Hab

itat

New

H

abita

t53

Yellow-poplar

Lirio

dend

ron

tulip

ifera

Hig

h0

0N

ew

Hab

itat

New

H

abita

tH

igh

Abse

ntN

ew

Hab

itat

New

H

abita

t54

Moc

kern

ut

hick

ory

Cary

a al

baM

ediu

m0

0N

ew

Hab

itat

New

H

abita

t H

igh

Abse

ntN

ew

Hab

itat

New

H

abita

t55

(Con

tinue

d)



Com

mon

na

me

Scientificname

MR

FIAs

umFI

Aiv

Chn

gCl4

5C

hngC

l85Ad

apt-

Abun

dCa

pabi

l45

Capa

bil8

5SHIFT45

SHIFT85

N

Post

oak

Que

rcus

stel

lata

Hig

h0

0N

ew

Hab

itat

New

H

abita

t H

igh

Abse

ntN

ew

Hab

itat

New

H

abita

t56

Red

spru

cePi

cea

rube

nsH

igh

00

New

H

abita

tN

ew

Hab

itat

Low

Abse

ntN

ew

Hab

itat

New

H

abita

t57

Scar

let o

akQ

uerc

us

cocc

inea

Med

ium

00

New

H

abita

tN

ew

Hab

itat

Med

ium

Abse

ntN

ew

Hab

itat

New

H

abita

t58

Peca

nCa

rya

illin

oine

nsis

Low

00

Unknown

New

H

abita

tLo

wAb

sent

Unknown

New

H

abita

t59

Swee

tgum

Liqu

idam

bar

styr

acifl

uaH

igh

00

Unknown

New

H

abita

tM

ediu

mAb

sent

Unknown

New

H

abita

t60

Suga

rber

ryCe

ltis l

aevi

gata

Med

ium

00

Unknown

New

H

abita

tM

ediu

mAb

sent

Unknown

New

H

abita

t61

Flow

erin

g do

gwoo

dCo

rnus

flor

ida

Med

ium

00

Unknown

New

H

abita

tM

ediu

mAb

sent

Unknown

New

H

abita

t62

Bigleaf

mag

nolia

Mag

nolia

m

acro

phyl

laLo

w0

0Unknown

Unknown

Med

ium

Abse

ntUnknown

Unknown

63

MRistomodelreliability(seeIversonetal.,2019forexplanation).FIAivistheaverageimportancevalueforthespecieswhenpresentonFIAplots.ChngC

l45or85

presentsthechangeclasses(increase,decrease,ornochange)ofhabitatsuitabilityby2100,accordingtoRCP4.5(lowemissions)or8.5(highem

issions).Ad

aptis

aclassofadaptabilityofthespeciesaccordingtothemodificationfactors.Ab

undisanabundanceclassbasedonFIAsum

.Capabil45or85isthecapabilityofthe

speciestocopewiththeclimatesofRCP4.5or8.5at2100,basedonabundance,changeclasses,andadaptability.SHIFT45and85arederivedfrom

severaloutputsof

theSHIFTmodelincom

binationwiththesuitablehabitatfromDISTRIB-IItoderivetwolevelsofpotentialforthespeciestoInfill(+or++)forspeciesthatarecurrently

foundrarelyintheNFandlikelytoexpandinthenext100years,Likely(+or++)forspeciesthatarelikelyalreadypresentintheareabutnotfoundbyFIAplots,and

Migrate(+or++)forspeciesthatdidnotoccuronFIAplots,butSHIFT(RCP4.5or8.5)didindicatepotentialforcolonizationintheNFwithin100years.Finally,theN

columnsim

plyisacounter.FurtherdetailsarefoundinIversonetal.2019b..

Tabl

e 2 (Continued)



NewHabitatandwithat least somecolonizationpotentialby2100 (Fig.11).From these summaries, we can begin to address questions at the community levels where these data indicate that northern locations have more options in selectingspeciesforassistedmigrationascomparedtosouthernlocationsthatonlyhavetheGulfofMexicotothesouth(Fig.11).Byassistedmigration,wemeanthephysicalmovingofpropagulesnorthwardfrompointssouthastheclimatewarms(Dumroeseetal.,2015;IversonandMcKenzie,2013).Mapssuchas these allow regional planners, researchers, and interested publics to better understandtheforestresourcenowandpotentiallyintothefuture.

4 Ecoregional vulnerability assessmentsForest managers often seek the best available science to inform theirmanagement, and they could spend significant amounts of time sorting

Figure 11 Mapshowingthenumberoftreespecies,by1 × 1o grid, with both new habitat appearing(viaDISTRIB-II)andsomepotentialtobecolonizedwithin100years(viaSHIFT).



throughanddigestingthevastnumberofresearchpublicationsonclimateanditseffectsonecosystems.However,muchofthisliteratureisstilltoobroadscaleforsite-levelmanagement,andthereforelackinginrelevancyandconfoundedby numerous climate models, climate scenarios or representative concentration pathways, downscaling algorithms, time scales, ecological models, and sources ofuncertainty.

The Climate Change Response Framework addressed this informationchallengeby creatinga seriesof forest ecosystemvulnerability assessmentswritten specifically for landmanagers.Eachassessmentwas informedat theoutsetbyregionalexperts,includingbothscientistsandmanagers.Theseriescovers several ecological provinces and uses the same climate models and scenarios, and forest impactmodels. Each assessment also follows a similarformat.Eachassessmentdescribesthecontemporarylandscapeandidentifieskeystressors thathaveshaped forestecosystemsover thepastcentury.Pastandprojectedtrendsinclimatearethensummarizedfromclimateobservationsanddownscaled global circulationmodels. This information is then used toparameterizeforestimpactmodelsthatprojectfutureforestchange.Theresultsfromseveralforestimpactmodels,alongwithpublishedresearchontheeffectof climate on ecosystemprocesses, are consideredby an expert panel thatreliesonlocalknowledgeandexpertisetoidentifythefactorsthatcontributeto the vulnerability ofmajor forest ecosystemswithin each assessment areathrough theendof thiscentury.Afinalchapter summarizes the implicationsof these vulnerabilities on a variety of forest-related ecological, social, andeconomic topics across the region.

Theprimarygoalof thisseriesofassessments is tosummarizepotentialchanges to the forest ecosystemsof each regionunder a rangeofpossiblefutureclimates,anddeterminethevulnerabilityofforestecosystemstothesechanges during the next century. Uncertainties in modeling and gaps inunderstanding are also addressed in each assessment.

Vulnerabilityisdefinedhereas‘thedegreetowhichasystemissusceptibleto and unable to cope with the adverse effects of climate change’. Forestecosystem vulnerability is defined here as susceptibility ‘to a reduction inhealth and productivity or a change in species composition that would alter its fundamentalidentity’.

Eachassessmentsummarizedstatisticallydownscaledclimateprojectionsforthreefuturetimeperiods,usingtwoclimatemodels(GFDLandPCM)undertwo contrasting greenhouse gas emission scenarios (A1FI: high emissionsandB1:lowemissions)fortheyears2070–2100.GFDLA1FIprojectsagreateramount of warming and hot, dry summers throughout the region. PCMB1 projects a lesser amount of warming and wetter summers with modesttemperature increases in summer.Thesemodel-scenario combinationswereselected because they had been used previously for projecting changes in



habitatsuitabilityfortreespeciesandrepresentedtheleastandmostamountofclimatechange,respectively.Bothdownscaledclimatescenarioswereusedasclimate inputs for threeforest impactmodels,whichwereusedtoprojectclimate-inducedimpactsonselectedtreespeciesorforestcovertypes.Eachassessment also synthesized published research on projected changes inforestproductivity;naturaldisturbanceregimes;forestcomposition;intensifiedstressors;sea-levelriseandsaltwaterintrusion;andinteractionsamongclimatechange and other ecosystem processes.

4.1 Impacts

Major impacts to system drivers and stressors were identified across eachassessment area. The most frequently identified impacts contributing toecosystemvulnerabilityinallassessmentareasincludedchangesinfireregime,soil moisture, pest and disease outbreaks, and nonnative invasive species. Someimpactswerespecifictocertaingeographicregions,suchassea-levelrise andhurricanes along theMid-Atlantic andNewEngland coastal areas.A recent analysisof adaptationplans across thenortheasternUnitedStatessimilarly identified changes in the frequency and amount of precipitation,and increasedvegetationmoisturestress,among themost-cited impactsofconcernamonglandmanagers.TheseregionalconcernsarealsoidentifiedbythemostrecentNationalClimateAssessment,whichconcludedthefollowing:

• Heavyrainfallhasincreasedinrecentdecades,andisexpectedtocontinuetointensify.

• Heatwaves have become more common, and annual temperatures are expected to continue to rise.

• Earlier spring melt and reduced snowpack contribute to changes in growing season hydrology.

Forest impactmodels projected significant changes in tree species’ habitatavailability, growth, and productivity within each of the areas, with differentspecies’responsesbetweenassessmentareas(specificresultsfortheCentralAppalachiansarediscussedlater).Generally,changesinclimateandhydrologytendtointensifymanyofthestressorsthatmayalreadyexistformanyspeciesand can increase their susceptibility to drought, pests, disease, or competition fromotherspecies.

4.2 Adaptive capacity

A review of ecosystem vulnerability assessments found that factors thatcontributed the most to adaptive capacity were generally consistent across



the assessment areas. Systems with high adaptive capacity had one or more ofthefollowingtraits:highdiversityofnativespeciesinboththeunderstoryandthecanopy;distributiononavarietyoflandforms,soiltypes,andgeologicsubstrates; distributionwith a large extent; high genetic diversity; and highspecies richness and/or diversity. Systems with low adaptive capacity oftenexhibited traits such as low species diversity and/or richness; low geneticdiversity; systems where the natural disturbance regime has been alteredsignificantly; systems where past management or land use reduced thediversity of species, ages, or genotypes. Although forest management caninfluencesomeoftheseadaptivecapacityfactors,futuremanagementwasnotaddressedinthevulnerabilityassessment;onlythecurrentadaptivecapacityofthe ecosystem was addressed in each assessment.

4.3 Forest ecosystem vulnerability in the Central Appalachian Mountains3

TheCentralAppalachiansregioncovers117400km2fromtheshoresofLakeErietothepeaksoftheAlleghenyMountainsandspansthreestates:Maryland,Ohio,andWestVirginia.Thisregioncontainsamosaicofhigh-elevationborealforests,uplandforestsandwoodlands,riparian,andfloodplainforeststhatareanessentialpartofthelandscape.

AspartoftheCentralAppalachiansClimateChangeResponseFrameworkproject,morethan40scientistsandforestmanagerscollaboratedtoassessthevulnerabilityofforestecosystemsinthisregiontothelikelyrangeofprojectedclimate change.

Although the annual average temperature in the Central Appalachians has remained generally the same between 1901 and 2011, minimum temperatures have increased by 0.6°C. By season, minimum temperatures have warmedthemostduringsummerandfall.BothminimumandmaximumtemperaturesincreasedinAprilandNovember,thetwofastestwarmingmonths.Acrosstheregion,precipitationhasincreasedinthefallbyanaverageof5.8cm(8%)andhasdecreasedinthewinterbyanaverageof2.5cm.Extremeraineventsof7.6 cmorgreater havebecomemore frequent,while light rain events havedecreased.

All climate models project that average temperatures will increase in the Central Appalachians. For the low emissions climate scenario, the projected changerangesfrom0.6°Cto2.2°C.Forthehighemissionsclimatescenario,projectedchangeincreasesrangefrom2.2°Cto6.7°C.Bothmodelsagreethatprecipitation is projected to increase in winter and spring, more so under the

3This sectionwas adapted from Butler-Leopold et al. (2018). https://forestadaptation.org/sites/default/files/evas_technicalsummary_centralapps_June%202016_0.pdf.

http://https://forestadaptation.org/sites/default/files/evas_technicalsummary_centralapps_June%202016_0.pdf

http://https://forestadaptation.org/sites/default/files/evas_technicalsummary_centralapps_June%202016_0.pdf



highemissionsclimatescenario.Modelsdisagreeaboutthetimingofpossibleseasonaldecreasesineithersummerorfall,dependingonscenario.Theremaybe greater moisture stress later in the growing season, especially as increasing temperaturesleadtoincreasedwaterlossfromevaporationandtranspiration.Evidence also suggests rain may occur during heavier rain events interspersed among relatively drier periods.

Two climatemodels, three forest impactmodels, hundreds of scientificpapers, and professional expertise were combined to assess the effects ofclimatechangeonregionalforestecosystems.Basedonthisinformation,thereisalargeamountofevidencetosuggestthatthefollowingimpactswilloccurin the Central Appalachians region:

• Soil moisture patterns will change, with drier soil conditions in summer andfall.Duetopotentialdecreasesinsummerandfallprecipitationandincreases in winter and spring precipitation, it is likely that soil moisture regimeswillalsoshift.Longergrowingseasonsandwarmertemperaturesmay also result in greater evapotranspiration and lower soil water availability later in the growing season.

• Fireriskswillincrease.Nationalandglobalstudiesagreethatwildfireriskwill increase across the region, especially in drier areas. Fire is expected to acceleratechangesinforestcomposition,promotingchangesinspeciesfasterthantemperatureormoistureavailability.

• Early growth and advanced regeneration will be vulnerable to changes in moisture. Predicted changes in temperature, precipitation, growing season onset,andsoilmoisturemayalterthedurationorqualityofgerminationconditions.Afterestablishment,saplingsmaystillbemoresensitivethanmaturetreestodisturbancessuchasdrought,heatstress,frost,fire,andflooding.

• Suitability for southern species will increase. Forest impact modelsproject increases in suitable habitat and volume formany specieswithranges largely south of the region, including shortleaf pine, post oak,andblackjackoak.Habitat suitabilitymay increase for species currentlyplanted in the region, such as loblolly pine. Most species are not expected tomigratefastenoughtokeepupwiththeshiftinghabitat.Development,fragmentation,andotherphysicalbarrierstoseeddispersalmayfurtherslownaturalmigrationoftrees.

• Suitabilityfornorthernspecieswilldecline.Forestimpactmodelsprojectdecreases in habitat suitability for northern species such as easternhemlock (Tsuga canadensis), and red spruce (Picea rubens), which arecurrently limited to specific landscape positions where conditions arecool and moist. These microhabitats may provide some refugia forthese species, but their presence on the landscape may become rare.



Populationsofsugarmaple(Acer saccharum)andothernorthernspeciesmaybeable topersist in southern refugia if newcompetitors from thesouthareunabletocolonize.

• Invasive plants, pests, and pathogens will increase or become more damaging. A warming climate has allowed some invasive plant species, insectpests,andpathogenstosurvivefurthernorth.Threatssuchasthesouthernpinebeetle (Dendroctonus frontalis),oakdecline,and invasiveplantssuchaskudzu (Pueraria spp.),bushhoneysuckles (Lonicera spp.),andcogongrass(Imperata cylindrica)mayincreaseinthefuture.

Climatechangewillnotaffectallforestspecies,communities,andpartsofthelandscapeinthesameway.Ofnineforestecosystemsassessed,thespruce/firandAppalachian(hemlock)/northernhardwoodforestswereconsideredhighlyvulnerable due to negative impacts on dominant species and a limited capacity toadapttodisturbancessuchasdroughtanddefoliation.Dryoakandoak/pineforestswereconsideredlessvulnerablebecausetheyhavemoredroughtandsuchheat-adaptedspeciesarebetterabletowithstandlarge-scaledisturbances.Riparianforestsarealsovulnerabletopotentialshiftsinflooddynamics.

Thesedeterminationsofvulnerabilityaregeneralacross theregion,andwillbeinfluencedbylocalconditions,forestmanagement,andlanduse.Thehighdiversityinlandforms,microclimates,hydrology,andspeciesassemblagesacrosstheregiongreatlycomplicatesassessmentofvulnerability.Itisessentialto consider local characteristics when interpreting vulnerabilities at local scales. Theassessmentdoesnotconsideradaptivemanagementactions,changesinlanduse,orothersocialoreconomicfactorsthatcouldaffectforesthealthorproductivity.

TheClimateChangeResponseFramework(https://forestadaptation.org/)alsodevelopedforestadaptationresources,withbothanadaptationworkbookandamenuofadaptationstrategiesandapproaches,tohelplandmanagersdevisehighlyrelevantadaptationactionsfortheirprojectsiteandobjectives.

4.4 Climate change adaptation at the local scale4

4.4.1 Climate-informed restoration in the Appalachian Mountains—Lambert Run Demonstration Project

ThehighelevationregionoftheAppalachianMountainswaslargelyinfluencedby over 4000 km2ofspruceforestover150yearsago.Atthattime,thehighvolumeofspruce in theoverstorywasadriverofabove-andbelow-groundecological processes. Strip coalmining and logging are local drivers of the

4This section is adapted from Butler et al. (online) https://forestadaptation.org/adapt/demonstration-projects/monongahela-national-forest-lambert-restoration-project.

https://forestadaptation.org/

http://https://forestadaptation.org/adapt/demonstration-projects/monongahela-national-forest-lambert-restoration-project

http://https://forestadaptation.org/adapt/demonstration-projects/monongahela-national-forest-lambert-restoration-project



degradationofspruce forests thathasgreatly impactedthe land,hydrology,andvegetationoftheprojectarea.

In May 2014, the Monongahela National Forest worked with the Northern Institute of Applied Climate Science to use a five-step adaptation workbookprocess (Swanston et al., 2016) to carefully consider near- and long-termrestorationgoalsanddemonstratehowmanagementactionscanenhancelong-termresiliencetoclimatechange.The1079-haprojectencompassestheLambertRun watershed and two small adjacent watersheds. In addition to the Lambert RunStripcoalmine, theprojectareacontainsapproximately405haof legacycoalmine lands (reclaimedaccording tomining lawsat the time).Theprojectis located8kmnorthwestofDurbin, inRandolphCounty,WestVirginia,USA.TheMonongahelaNationalForestworkscloselywithanumberofpartnersonthisprojectwhoprovidefundingandcollaboration,includingtheAppalachianRegionalReforestationInitiative,GreenForestsWork,CanaanValleyInstitute,theNatureConservancy,WestVirginiaDivisionofNaturalResources,USDA-NRCSPlant Materials Center, and the Central Appalachians Spruce Restoration Initiative.

Managementgoals(Step1):TheLambertRunStrip–abandonedcoalminelandswereminedinthe1970sandboughtbytheUSForestServiceinthe1980sasaportionofthe16380-haMowerTractacquisition.Rehabilitationeffortsinthe1970sconsistedofreshapingtheminedareastoamorestableconditionandplantingspecies,mostlynonnative,forerosioncontrol.Thecontemporaryresultislargeareasofheavilycompactedsoilwithlowwaterinfiltration,wherethepredominantcoverisnonnativeinvasivegrassesandNorwayspruce(Picea abies)plantedaspartoftheminingreclamationplan.Grass-dominatedareasremain inaconditioncalledarrestedsuccession.TheMonongahelaNationalForest is implementing the Lambert Restoration Project to essentially restore ecological function by improving watershed conditions, providing wildlifehabitat, and restoring native red spruce–northern hardwood ecosystems onLambert Run and adjacent lands.

Climate change impacts (Step 2): According to numerous climate andprocessmodelsandtheCentralAppalachiansForestEcosystemVulnerabilityAssessment (Butler et al., 2015), climate change impacts are expected tointensifyoverthenextcentury,including:

• Regional increase of roughly 1–4°C in mean annual temperature, withhigh-elevationareasprojectedtowarmlessthanlowelevationareas.

• Depending on the model, regional decrease of roughly 2.5–10 cm ofprecipitationinsummerorfall,withmoreseveredryinginhigh-elevationareas.

• Increasedfrequencyofintenserainevents,whichisexpectedtoincreaseerosion potential, especially on steep slopes and where hydrology has been altered.



• Projecteddeclines in redspruce,sugarmaple,bigtoothaspen (Populus grandidentata),andothernativespecies.

Challenges and opportunities (Step 3): Red spruce is currently expandingonthelandscape,recoveringfrompast logging,acidification,andwildfiretoregainan importantecologicalniche.Current restorationeffortsare focusedonrestoringsiteecological functionsrelatedtosoilandwater,andrestoringnative tree, shrub, and herb species. Although climate impact models project severedeclinesforredsprucebytheendofthecentury,thesehigh-elevationareas provide the last remaining habitat that is cool and wet enough to support redspruce.Restorationofthesesitesnowmayincreasetheabilityofredspruceforesttocopewithfuturechangesinclimatebycorrectingarrestedsuccession,reconnectingforestedlandscapes,andprovidingagreatersuiteofredsprucesiteswiththepotentialtoserveasrefugia.

Adaptation actions (Step 4): Numerous adaptation approaches andtactics were identified for the project area (Table 3). Adaptation approach1.1was selected to restore and sustain the ecological function so that thehydrologyofthesystemwillbebetterabletowithstandfutureclimate-relateddisturbances (Swanston et al., 2016); the tactic to leave thinned wood onsite isdesigned to improvenutrient inputs.Adaptationapproaches5.1–5.3were selected to enhance species and structural diversity in the spruce-firforest; tactics included releasing red spruce by removing some mid-storyhardwoods, but specifically retaining underrepresented species such asblackcherryanddisease-resistantbeech.Anothertacticistomonitornativespecies at lower elevations in order to detect and monitor upward migration, allowingspeciestoestablishnaturally.Thesetacticsaredesignedtosetuptheecosystemtofunctionasbestaspossibleintheshorttermsothatitcanbetterwithstand future climate changes.Although red spruce is projectedto decline across the region due to climate, the red spruce in this region are currently occupying the best possible habitat. Restoring the ecological functionanddiverse forestsnowmaydelayorbuffer theeffectsofclimatechange locally.

Monitoring (Step 5): Information was also gathered in order toevaluate whether the selected actions were effective and could informfuture management. Because standard monitoring is detailed within themanagement plan for this area, and the restoration of this site requires ahigh level of flexibility, staff did not identify any additionalmonitoring thatis recommended at this time. However, several monitoring variables were chosen for future consideration, includingmonitoring streamflow,pH, anddissolved oxygen in order to detect the progress made in the hydrologic restoration.



Tabl

e 3 Ad

aptationapproachesandtacticsfortheLambertRunDem

onstrationArea

Site

sM

anag

emen

t obj

ectiv

esAd

apta

tion

appr

oach

Adap

tatio

n ta

ctic

Mix

ed h

ardw

ood

fore

stTheseforestsw

ereformerly

redspruceforest.Red

spru

ce is

exp

andi

ng o

n th

e landscape.Othernative

spec

ies i

nclu

de: r

ed m

aple

, bl

ack

cher

ry, c

ucum

ber t

ree,

an

d bl

ack

birc

h.

•Im

prov

e co

mpo

sitio

n by

in

crea

sing

red

spru

ce

com

pone

nt to

30%

•En

hanc

e gr

owth

rate

and

st

and

stru

ctur

e

6.2:

Mai

ntai

n an

d re

stor

e diversityofnativespecies

6.1:

Pro

mot

e di

vers

e ag

e cl

asse

s1.

1: R

educ

e im

pact

s to

soils

an

d nu

trien

t cyc

ling

6.3:

Ret

ain

biol

ogic

al le

gaci

es

•Releaseexistingredsprucebyremoval(herbicide)

ofmidstoryhardwoodsandcreatewildlifesnags

•Protectblackcherryanddisease-resistantbeech

(othernorthernhardwoodspeciesarenota

conc

ern

at th

is tim

e du

e to

cur

rent

abu

ndan

ce o

n thelandscape)

•Leave-thinnedwoodonsiteforw

oodymaterial

Min

e be

nch

Theseareasareeither

com

pact

ed b

are

soil

or a

re

cove

red

with

Nor

way

spru

ce

andaggressivegrasses.They

aretypicallyconvexpartsof

the

land

scap

e so

soils

are

drieralready.Thereislittle

tonoinfiltrationintothesoil,

butrunoffisbeingcaptured

by m

inin

g po

ols.

•Es

tabl

ish n

ativ

e sp

ecie

s •

Incr

ease

coa

rse

woo

dy

mat

eria

l •

Rest

ore

hydr

olog

ic

function

•M

aint

ain

and

rest

ore

diversityofnative

spec

ies

•Re

duce

land

scap

e fragm

entation

•Pr

even

t the

intro

duct

ion

andestablishmentof

inva

sive

plan

t spe

cies

an

d re

mov

e ex

istin

g in

vasiv

es •

Redu

ce im

pact

s to

soils

an

d nu

trien

t cyc

ling

•M

aint

ain

or re

stor

e hy

drol

ogy

•Pl

ant n

ativ

e tre

e sp

ecie

s and

her

bace

ous s

peci

es

fromlocalnurserystock

•Prioritizenativespeciesinthislocation,whichmay

serveaslong-termrefugia,andmonitorupw

ard-

mig

ratin

g sp

ecie

s •Removenonnativetreesmechanically(Norway

spruce)andherbaceousspecies(spotted

knapweed)

•Increaseinputofcoarsewoodymaterial(leave-

upro

oted

Nor

way

spru

ce, m

ulch

tree

s on

site,

and

importcleanmulchfrom

othersites)

•Priordeep-rippedslopeshavenotshownincreased

erosion.Increaseallowableslopeto<45%to

loosensoilsandincreaserainwaterfiltrationover

mor

e la

nd a

rea

•D

ecom

miss

ion

road

s tha

t are

impe

ding

hyd

rolo

gic

functionorrepurposeforrecreation

•As

sess

road

stre

am c

ross

ings

and

upg

rade

cul

verts

tohandlehigherpeakstream

flow

andallow

aqua

tic o

rgan

ism p

assa

ge



Next steps: Climate change considerations are integrated into forestmanagement under the Lambert Restoration Project. At the time of thispublication, some areas have already been deep ripped and planted, while others are in progress. Wetland creation and road decommissioning is also ongoing. Native species will continue to be planted according to availability, with an emphasis on greater native species diversity.

4.4.2 Climate-informed development of LEAP regional biodiversity vision—Implementation project at Cleveland Metroparks

The Lake Erie Allegheny Partnership for Biodiversity (LEAP) is a consortiumof over 50 conservation-minded organizations (park districts, museums,consultants, watershed groups, state and local government agencies and nonprofits) dedicated to protecting and restoring the biodiversity of theGlaciatedLakeErieAlleghenyPlateauEcoregion.Thisincludesapproximately57000 km2oflandandwaterssouthofCanadafromSanduskyBayinOhiotothe Allegheny Mountains in Pennsylvania.

Theregion’snatural landscapewasprimarilyadeciduous forest (uplandandriparian)withextensivewetlandcomplexesreflectingtheimpactofthelastglacialretreat18000yearsago.Muchoftheregion’sforestswerecutoverandits wetlands drained over a century ago to allow industrial development and agriculturalexpansion.Today’sfragmentedlandscapecontinuestoreflecttheintersectionofurbansprawlandagriculturewithnaturalcommunitiessparselyconnectedthroughforestremnants,ripariancorridors,andrevertingfarmandpastureland.Theresultisaregionwithamosaicpatternofhumandevelopmentinterspersed with natural areas. LEAP collectively protects through ownership or easement ~126 140 ha scattered across the region.

In2018,LEAPworkedwiththeUSForestServiceandtheNorthernInstituteofAppliedClimateSciencetodevelopcustomizedclimateassessmentsfortheregion.Theseeffortswerebrokendowntocapturevariousspatialscaleswherechanging climate could affect differences in tree species distributions. Thespatialscalesinclude(1)LEAPregionalscale,(2)fiveecoregionalsubsections,(3) five largewatershed (HUC6) designations, (4) 13 smallwatershed (HUC8)5 designations, and (5) 11-1 × 1-degree grid cells. Breaking down theregional landscape into these functional units increases the likelihood thatthe fragmentedpatchesofprotected landswereaccuratelycaptured. ItalsoprovidesindividualLEAPandlocallandownersofthosevariousfragmentswithmodeledspeciesadaptationinformationtoconsiderastheylooktoimplementforestmanagementstrategies.

5The HUC stands for hydrologic unit code (HUC) consisting of two to eight digits based on the four levels ofclassificationinthehydrologicunitsystemdevelopedbytheUSGeologicalSurvey.https://water.usgs.gov/GIS/huc.html.

https://water.usgs.gov/GIS/huc.html

https://water.usgs.gov/GIS/huc.html



Climatechangeimpacts:Theprojectedregionalclimatechangeimpactsareexpectedtointensifyoverthenextcentury,andinclude:

• Regionalincreaseof3.7–6.1°Cinmeanannualtemperature. • Regional increase of 9.9–13.2 cm of precipitation with the greatestincreasesprojectedtooccurinNEOhio,NWPennsylvania,andSWNewYork.

• Planthardinesszones6:AshiftofonefullzonewithRCP4.5andtwofullzoneswithRCP8.5.

• Average number of days above 30°C are projected to increase by anadditional41–109daysannually.

• Projected declines in eastern hemlock, pin oak (Quercus palustris),bigtooth aspen, and others.

4.4.2.1 Management goals

Results from the climate data were integrated into the development of aregional biodiversity vision (Beach, 2018). This resource provides prioritiesfor protecting nature across theGlaciatedAllegheny Plateau. Five prioritieswere established such as (1) preserve large blocks of natural land, (2) linknatural areas, (3) reduce habitat fragmentation, (4) reduce other stressors(invasivespecies,pollution,overabundanceofwhite-taileddeer (Odocoileus virginianus)), and (5) prepare for a changing climate (see example project).Eachprioritycontributestoensuringsuitableopportunitiesforforestsacrosstheregiontoadapttoclimatechange(Table4).

4.4.2.2 Challenges and opportunities

While models provide possible climate scenarios with potential species range shifts, several factors should be considered when interpreting the outputs.Modelsrelyongeneralizations,andscaleresolutionmaynotbeadequatetocapture unique microsite variability or subtle landscape features that affectspeciesdistributions.Speciesrecommendationsforclimatetolerancedonotconsiderplantcommunityassemblages,andcarefulreviewbylocalexpertsisrequired fordeterminingmanagementactivities suitable foragivenhabitat.Finally,aforestmaybecompromisedbyexistingsiteconditionsandstressors(invasiveplantspecies, forestpestsorpathogens,browsepressure) thatcanbe exacerbated by climate change and mitigating those stressors are also as importanttomaintainingorincreasingforesthealth.

6Planthardiness zonesaredelineatedbyaverageannualminimumwinter temperature,divided into10-degreeFzones.https://planthardiness.ars.usda.gov/PHZMWeb/.

http://https://planthardiness.ars.usda.gov/PHZMWeb/



4.4.2.3 Example implementation

ClevelandMetroparks(CM),innortheastOhio,isapartnerwithinLEAPandisusingtheclimatedatageneratedtoassistwithforestmanagement.Theparkdistricthasover9700haofprotectedlandpredominatelyinCuyahogaCountywhichservesapopulationof1.25millionpeople.Approximately80%ofthelandisundevelopedandcomprisedofvariousnaturalcommunities(forests,wetlands,meadowsetc.).Thecurrentforestedcommunities,however,reflectthechangescausedbyvariouslanduseactivitiesandhistoric impactsfromthespreadoftheChestnutBlight(Flinnetal.,2018).Asanexampleproject,CM identified a small forested track (~10 ha) to implement managementactivitiesthatenhanceforestresiliencetoclimatechange.Originallyclearedforpasturepriortothe1930s,thestandhassincebeencolonizedbyruderaltreespeciesdominatedbypoorlyformed(multi-stemmed),single-cohortredmaple(Acer rubrum)withminorrepresentationofwildblackcherry(Prunus serotina) and sugar maple (Acer saccharum). Based on climate models,northern red oak (Quercus rubra), yellow-poplar (Liriodendron tulipifera),bitternut hickory (Carya cordiformis), shagbark hickory (Carya ovata), andAmericanelm(Ulmus americana)areallspeciesprojectedtodowellunderclimateprojections.Thesetreespeciesarealsopartofthemixedforestthatoccursinthisareaandarelimitedlyfoundonorneartheprojectsite.Forestmanagementwilltargetareductionindensityanddominanceofredmapleandotherpoorlyformedtreespeciesonsite(Table4).Theharvestwillreducebasalarea,createsmallgapopenings,releasedesirablecroptrees(i.e.oaksandhickories), and increase light reaching the forestfloor. Largeexclosurefences will allow regenerating seedlings time to establish without deerbrowse pressure.

While no tree planting projects are immediately planned at the example forestproject,CMregularlyimplementstreeplantingstomitigatetheimpactsof climate change. Special attention is provided during planning of thoseprojectstotrackprovenancesourceofthetrees.Inthisway,sourcelocationisconsideredasignificantfactordeterminingpotentiallocalgeneticadaptationsthatmayaffectclimateadaptabilityandensuringsuitablegeneflowintoourarea.

5 Management implicationsGuidance on incorporating information on projected climate changes andimpacts to natural ecosystems into planning efforts can aid on-the-groundimplementationofforestmanagementactions(Keenan,2015;WoodruffandStultz,2016).Tohelpovercomebarrierstoimplementingclimateadaptationactions,planningeffortsmustbeabletodeployresourcesonclimatechange



Tabl

e 4 Ad

aptationapproachesandtacticsfortheClevelandMetroparks

Land

scap

e sc

ale

Man

agem

ent o

bjec

tives

Adap

tatio

n ap

proa

chAd

apta

tion

tact

ic

Gla

ciat

ed A

llegh

eny

Plat

eau

Thisecoregionishighly

fragm

entedandincludes

intactforestcom

munities

like

Beec

h/M

aple

and

successionalforestsfrom

la

nd u

se c

onve

rsio

n

•Preservelargeblockofnatural

land

•Li

nk n

atur

al a

reas

•Reducehabitatfragm

entation

•Re

duce

oth

er st

ress

es in

nat

ure

•Prepareforclim

atechange(see

below)

•Expandsizeofexistingoradd

newlargeforestedareasfor

refugia

•C

reat

e m

igra

tion

corr

idor

s by

link

ing

key

land

scap

e fragm

ents

•En

cour

age

tree

rege

nera

tion

•Identifyunprotectedland(public)and

determineeasementorfeepurchase

pote

ntia

l •Buildlinkagebuffersalongphysicaland

physiographicfeatures(ripariancorridors,

wat

ersh

ed d

ivid

es, L

ake

Erie

shor

elin

e andPortageEscarpm

ent)

•M

anag

e in

vasiv

e pl

ant s

peci

es

com

petit

ion

and

miti

gate

dee

r bro

wse

pressureonyoungseedlings(fence

protection,cullingorhunting)

Clev

elan

d M

etro

park

sEx

ampl

e Fo

rest

Pro

ject

: Fo

rmer

cle

ared

pas

ture

convertedtoforestand

dom

inat

ed b

y ru

dera

l treespecies-redmaple.

•Eliminatenon-nativewoody

plan

t com

petit

ion

•In

crea

se tr

ee sp

ecie

s div

ersit

y •

Enco

urag

e yo

ung

tree

rege

nera

tion

•Re

duce

inva

sive

plan

t po

pula

tions

•M

anag

e ex

istin

g in

vasiv

e sp

ecie

s po

pula

tions

. Com

bine

phy

sical

and

ch

emic

al c

ontro

l with

prio

r and

subsequenttreatmentsfollowingforest

man

agem

ent

•Reducestem

densityofred

map

le •

Sing

le tr

ee se

lect

ion

rem

oval

•Reduceim

pactsofdeer

brow

se •Installfencedexclosureareas

•Co

nsid

er im

plem

entin

g cu

lling

in c

ut

zone

•In

crea

se li

ght g

aps

•Re

mov

e de

ad/d

ying

ash

tree

s •Removepoorlyformed(double/triple

trunk)trees

•C

reat

e sm

all g

roup

ope

ning

s



impacts and adaptation responses that are at the same spatial scales as those used in management decisions. For example, interviews with state agency land managers suggested that adaptation actions that were needed at a local scale were limited because adaptation planning tended to occur at a regional scale (Anhalt-Depies et al., 2016). The AdaptationWorkbook is astructured adaptation planning process designed to help managers consider thepotentialeffectsofclimatechangeinidentifyingmanagementactionsthathelp reduce risks and increase the ability to cope with changing conditions (Janowiak et al., 2014). Managers use resources such as ecoregionalvulnerability assessments to understand the broad-scale climate changesandecosystem impacts inorder to ascertain site-level impacts thatpresentimportant risks tomeetingmanagementgoals andobjectives forparticularprojects or parcels of land. The process of ‘stepping down’ regional-scaleinformationtothesalientclimate impactsallowsforestmanagersto identifyspecificandtangibleadaptationactionstominimizeclimateriskstoachievingmanagementobjectives(Swanstonetal.,2016).ManycasestudiesofclimateadaptationinforestmanagementhavebeendevelopedthroughtheClimateChangeResponseFrameworkusingtheAdaptationWorkbook.Theseserveasimportantexamplesofhowmanagersarerespondingtoachangingclimate.Theyprovide insights intohowadaptation in forestryandnatural resourcesmanagement is occurring and highlight similarities across regions and types oflandownershipsaswellasfactorsthatinfluencedifferencesinadaptationresponses.

Theoverarchingsimilarityacrossadaptationprojectsdevelopedthroughthe Climate Change Response Framework is that adaptation decision-making is significantly shapedbypeople’svaluesof the land theymanageand the conditions of the site that they are managing. The recognitionof the critical importance of the uniqueness of both people and place toadaptation planning emphasizes that adaptation is not a one-size-fits-allprocess.This is evident in thebroad array of landmanagers’ goals acrossthe various ownership types and the climate-driven changes with whichthey are primarily concerned. Despite this diversity, regional trends suggest thatadaptationprojectsandparcelsreflectthepredominant—aswellastheunique—ecosystems and resources of a place (Ontl et al., 2018). Similarly,the climate impacts that concern managers the most vary depending on siteconditions,but suggest the importanceof regional climate trendsandimpacts. For example, based on regional forest vulnerability assessments(Janowiak et al., 2018), forestmanagers in northernNewEngland showedthe greatest concern with declines in northern and boreal tree species, while managers working in southern New England were most concerned about the impactsonsoilmoisturestressforthehealthandregenerationofforestsontheir management units.



Theseregionaltrendsareapparentinthegeneralpatternsofemphasisoneitherresistingclimatechange,enhancingresilience,ortransitioningforeststoconditions thataredifferent fromcurrentconditionsandbetteradapted toafutureclimate (Millaret al., 2007). In thecentralhardwoods region (southernMissouri,Illinois,andIndiana),thelowadaptivecapacityofforestsresultingfromtheencroachmentofmesicspeciessuchasmaplesandalteredforeststructurefromclosureofthecanopyintheabsenceoffire,combinedwithaconcernwithchanges in precipitation patterns, resulted in an emphasis on adaptation actions thataimtotransitionforeststowardamorehistoricalspeciescomposition(e.g.increasedcoverofoakandhickoryspecies)andstructuralconditions(increasedcanopyopenings;Ontletal.,2018).InnorthernregionsofboththeMidwestandNewEngland,recognitionofthegreateradaptivecapacityofforestsresultingfrom higher species diversity or reduced sensitivity to projected changes inmany forest typesseems tocontribute toanemphasisonadaptationactionsthatenhanceresilienceofforeststoimportantsystemstressors,suchasinsectpestsandforestdiseases.Whileactionsaimingtoenhanceresilienceofforestswere the most common in southern New England as well, there was a greater relativeemphasisontransitionactionscomparedtonorthernNewEngland.Thisdifferenceinemphasisisverylikelyaresultofdifferingrelativelevelsofconcernoverforesthealthimpactsfromclimatechangeandnonnativeinsectpests(e.g.gypsymothdefoliationandtreemortality;Kretchumetal.,2014)andclimateinfluencesonpotentialtreeregenerationfailuresintheregion.

Whiletheseadaptationcasestudieshighlighttheimportanceofregionaldifferencesinclimateconcernsandadaptationresponses,theyalsoillustrateimportantcommonalities in forestadaptation.Acrossalladaptationprojects,managersidentifiednumerousadaptationstrategiesappropriateforindividualprojectsthatspannedthecontinuumofresistingclimate impacts,enhancingsystem resilience, and transitioning systems to be better adapted to futureconditions (Ontl et al., 2018). Similar to diversifying an investment portfolioto spreadfinancial risk, thesemanagersmaybe seeing thevalue inusingamultitude of adaptation tactics that address both near-term challenges andlong-term climate impacts to enhance the capacity of the system to copewithchange. Identifyingan ‘adaptationportfolio’ that includesactionsacrossthis resistance-resilience-transition continuum may also reduce risk whenplanningforarangeofpossiblefutureconditionsataparticularsite.Despitethis diversified approach to adaptation planning, there were strong linksbetween concern about particular climate impacts and adaptation responses, assummarizedinthefollowingexamples:

• Identifying increased stressors associated with warming winters andgreater pest and disease pressures correlated with actions that aimed to increase species and structural diversity.



• Concern over altered precipitation patterns and related impacts were linkedtotacticsthataimedtofacilitatespeciestransitions.

• Impactsofextremeprecipitationeventscorrelatedwithactionsthatincreasedlandscape connectivity, generally in streams and aquatic ecosystems.

• Increasedriskofwildfirewasassociatedwithafocusonactionsthatrealignsystemsfollowingdisturbance.

Theprecedingsectionsonclimatechangeandnear-orlonger-termimpactspointtohighertemperatureandincreasedpossibilityofdroughtasacommonprojectedoutcome.Withepisodesof loweramountsof rainfallapossibility,andhighertemperatures—whichcanfurtherreducesoilmoistureavailabilitydue tohigher transpiration (Clarket al., 2016)— therewouldbeperiodsofreduced growing space for trees and other plants.Although some speciesmay be more adaptable to these swings, the moisture stress can reduce the vigor of a tree andmake itmore vulnerable tootherdisturbances, such asinsectordiseaseattack.Treeswithreducedvigorstoreloweramountsofplantsugars over the winter, so the plant starts out the new year in an even more vulnerable state. As water becomes limiting, current stand densities are no longer adaptive.

Theongoingreductioninanthropogenicdisturbances,suchasharvesting,willperpetuatethedeclineintheproportionofforestsinanearlysuccessionalstage.Withthisdecreaseinearlytomid-successionaltreespeciescomesalossofhabitat for theanimals,birds, insects,andotherco-occurringspecies thatrely on them.

6 Conclusion and future trendsThischapter isdesigned to inform landmanagersandpolicymakersas theythink about what their forests might look like in the future and considerapproachestopreparingforthatfuture.Intheshortterm—thenextdecadeortwo—thehuman-causeddemographicandeconomicfactorsthatcontributetofutureclimatechangescenarioshavemoreinfluencethanachangingclimateontheextentandcompositionof forests.Theanthropogenicvariablesaffectforestextentandfragmentation.Meanwhile,thebiologicaltrendsofsuccessionandcompositionreflectthehistoricaldevelopmentandcurrentstructuretoagreaterdegreethandonear-termclimateinfluences.Eventually,however,thisecological momentum is projected to be overwhelmed by the outside abiotic influencesofalteredtemperatureandprecipitation.

The information in this chapter provides context for climate changeconsiderationsandlaysthefoundationforthemanagementactionsdiscussedin the case studies. While there is deserved attention paid to the potential impact of climate change on forest land extent, composition, and health,



readersareurgedtobear inmind theother forces thathelp toshape theseaspects of forests. These other influences, such as land use change, maymagnifytheprojectedclimatechangeeffectormaysloworotherwisemitigateit.Theoutcomedependsontheattributesoftheforest.Inturn,thenatureandtimelineoftheeffectsdependnotonlyonforestcharacteristics,butalsoonthetypeofstressor.Forexample,nonnativeinvasivespecieshaveanimpactthatismore pronounced in the near term than over the long term, but they can also reducethelonger-termresilienceoftheforeststowithstandchangingclimaticconditions.

Thesocialandeconomicfactorsthatdriveclimatechangealsohaveadirecteffecton theextent,composition,andstructureof the forest. Insomecases,managerscanemploythesefactorstobringaboutthedesiredoutcomes,butthepotentiallonger-termimpactsofachangingclimatewillgraduallyincreasein importance.

Ecologists speakof resistanceand resilience in the faceofdisturbance,usually with the former being a more pre-disturbance response and thelatter being post-disturbance response. Such a model is predicated on adiscrete event or block of time.Whatmanagers are urged to comprehendgoing forward is that the future forests may face both kinds of influencessimultaneously. Managers need to consider both resistance and resilience in their management plans. Some disturbances will set back the successional clockwithinthecurrentecologicalprogression(OliverandLarson,1996).Giventherightcircumstances,however,thesamedisturbancemaysettheforestedlandscape on a new trajectory.

Resiliency in future forests depends upon the collective response ofindividual tree vigor. As mentioned in the oak decline section mentioned previously,treeswithadequateorevenasurplusofresourcesshouldbebetterable towithstandperiodsofdroughtor forest health attacks. Leaving someunoccupied growing space, some ‘slack’ in the system gives room for theforest to absorb temporary resource restrictions. Fromamanagementpointofview,modelsofproductivitybasedon full stockingoftendonotconsidertheexpectedimpactsofattacksonforesthealth(Moseretal.,2003).Ifamoredemandingclimatemakes recovery frommortalityeventsmorechallenging,expectationsoffutureproductivitymustbereducedandmanagementactionsmustreflectthisnewrealitybydeliberatelykeepingforestsbelowthehistorical,nominallevelsoffullstocking.Suchforestsareanticipatedtobeabletobetterwithstandclimateinfluencesthanmightthepreviousdenseforests.Regardlessof the extent of climate change impacts, the forests of the northern UnitedStateswillrequiremanagementtomaintaintheirvigorandhealth.Toolsandapproachesforguidingwisemanagementareavailabletodaytohelpensurethat forests continue to provide the values andbenefits that people expectfromthem.



7 AcknowledgementsWethankDr.StephenR.Shifleyforhisvaluablecommentsonearlierversionsof this manuscript and Cynthia F. Moser for her wide-ranging editorialcontributions. We are grateful to both for giving their insight and time sogenerously. Reviewers of earlier versions of this chapter provided manyvaluable comments and suggestions.

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