CHAPTER 2. Mack and Others 2000)

INTRODUCTION damage the diversity, ecology, and economy of affected areas (Brockerhoff and others 2006, he impacts of insects and pathogens on CHAPTER 2. Mack and others 2000). Few forests remain forests vary from natural thinning to unaffected by invasive species, and their extraordinary levels of tree mortality, but Large-Scale Patterns of T devastating impacts in forests are undeniable, the fact that insects and diseases kill trees does including, in some cases, wholesale changes not necessarily make them enemies of the Insect and Disease Activity to the structure and function of an ecosystem forest (Teale and Castello 2011). If disturbances, (Parry and Teale 2011). pests, and diseases are viewed in their full in the Conterminous United ecological context, then some amount can be Examining pest occurrences and related considered “healthy” to sustain the structure stress factors from a landscape-scale perspective States and Alaska from of the forest (Manion 2003, Zhang and others is useful, given the regional extent of many 2011) by causing the tree mortality that culls infestations and the large-scale complexity of the National Insect and weak competitors and releases resources that are interactions among host distribution, stress needed to support the growth of the surviving factors, and the development of pest outbreaks Disease Survey, 2011 trees (Teale and Castello 2011). (Holdenrieder and others 2004). The detection of geographic clusters of disturbance is one Recognizing how much mortality is natural, such landscape-scale approach that allows and how much is excessive, is an important for the identification of areas at greater risk Kevin M. Potter task for forest managers, pathologists, and of significant impact and for the selection Jeanine L. Paschke entomologists, and relates to ecologically based of locations for more intensive monitoring or commodity-based management objectives and analysis. (Teale and Castello 2011). Diseases and insects 19 cause changes in forest structure and function, METHODS species succession, and biodiversity, which may be considered negative or positive depending Nationally compiled Forest Health Protection on management objectives (Edmonds and (FHP) low-altitude aerial survey and ground others 2011). survey data (FHM 2005) can be used to identify forest landscape-scale patterns associated Analyzing patterns of forest pest infestations, with geographic hot spots of forest insect disease occurrences, forest declines, and related and disease activity in the conterminous 48 biotic stress factors is necessary to monitor the States and to summarize insect and disease health of forested ecosystems and their potential activity by ecoregion in Alaska (Potter and impacts on forest structure, composition, Koch 2012, Potter 2012, Potter 2013, Potter biodiversity, and species distributions (Castello and Paschke 2013). In 2011, FHP surveys and others 1995). Introduced nonnative insects covered approximately 147.9 million ha of the and diseases, in particular, can extensively forested area in the conterminous United States Forest Health Monitoring 20 SECTION 1 Chapter 2 than 5000ha offorestintheconterminous defoliation agentsandcomplexes foundonmore were usedtoidentifythemortality and interpretation oftheresults. can complicatetheanalysisofdataand coding proceduresamongStatesandregions in datacollection,attributerecognition,and “aspen defoliation”).Inaddition,differences tree species(e.g.,“subalpinefirmortality”or under animpactlabelrelatedtoaspecifichost complexes ofmultipleagentssummarized agents ofmortalityordefoliationareactually such asdrought.Insomecases,theidentified area andtheconvergenceofstressfactors on thelevelofdamagetoforestinagiven defoliation-causing agentinanother, depending mortality-causing agentinonelocationanda A pathogenorinsectmightbeconsidereda defoliation thatisotherwisedifficulttodetect. tree speciesdiversityormaycausemortality widely dispersedthroughoutforestswithhigh surveys. Suchpestsmayattackhoststhatare thoroughly quantifiedthroughaerialdetection (such asoakdecline)arenoteasilydetectedor cankers disease),andmortalitycomplexes disease, whitepineblisterrust,andthousand adelgid), diseases(suchaslaurelwilt,Dutchelm (such asemeraldashborerandhemlockwoolly activity, althoughsomeimportantforestinsects and defoliationcausedbyinsectpathogen (fig. 2.1). Alaska’s forestedarea(15.6percentofthetotal) (58 percent ofthetotal),and8.0millionha The 2011mortalityanddefoliation polygons These surveysidentifyareasofmortality cover map(1-km mortality anddefoliationpolygonswithaforest calculated bymaskingthesurveyedareaand causing ordefoliation-causingagentswasthen in eachhexagonexposedtoeithermortality- county. Thepercentageofsurveyedforestarea resolution ofaboutthesameareaasatypical cell sizeallowsforanalysisatamedium-scale extent, fortheconterminousUnitedStates.This (USDA ForestService2008). Thepercentage Service RemoteSensingApplications Center (MODIS) satelliteimagery bytheForest Moderate ResolutionImaging Spectroradiometer develop alatticeofhexagonalcells,2500km (White andothers1992)wereintensifiedto Program NorthAmericanhexagoncoordinates Environmental MonitoringandAssessment defoliation-causing agentsandcomplexes.The greatest exposuretothedetectedmortality-and 2006) toidentifysurveyedforestareaswiththe Ord 1992)wasemployedinArcMap9.2(ESRI agents perpolygoninsomesituations. affected areaasaresultofreportingmultiple of agentsandcomplexesisnotequaltothetotal in theestimatesofforestareaaffected.Thesum of damagewithinthefootprintisnotreflected may existwithinthefootprint,andamount agent orcomplexispresent.Unaffectedtrees complex, outliningtheareaswithinwhich quantities are“footprint”areasfortheagentor insect anddiseasesketch-mappingprocess,all mortality agentsforAlaska.Asaresultofthe list themostwidelydetecteddefoliationand United Statesinthatyearandtoidentify A Getis-Ordhotspotanalysis(Getisand 2 resolution),derivedfrom 2

West Coast

North East North Central

Interior West

South

Alaska

Figure 2.1—The extent of surveys for insect and disease activity conducted in the conterminous United States and Alaska in 2011. The black lines delineate Forest Health Monitoring regions. (Data source: USDA Forest Service, Forest Health Protection Program.) Forest Health Monitoring 22 SECTION 1 Chapter 2 masked surveyedarea. total forest-maskeddamageareabytheforest- defoliation agentswascalculatedbydividingthe of surveyedforestexposedtomortalityor G of themean(Laffan2006). Inotherwords,a be withinapproximately2 standarddeviations observations underanormal distributionshould values (p<0.025),because95percent ofthe -1.96 representingsignificantclusteringoflow clustering ofhighvaluesandlessthan representing significant(p<0.025)local deviation of1,withvaluesgreaterthan1.96 as az-scorewithmeanof0andstandard conterminous 48States.Itisthenstandardized mean ofalltheforestedhexagonalcellsin hexagons contiguoustothose6),andtheglobal (the 6adjacenthexagonsandthe12additional and its18first-second-orderneighbors moving windowconsistingofeachhexagon values inalocalsample,determinedby summed thedifferencesbetweenmean subregion ofthedata(Anselin1992). when spatialclusteringisconcentratedinone detecting nonstationaritiesinadataset,suchas location, andisthereforeparticularlysuitablefor association intoitscontributingfactors,by decomposition ofaglobalmeasurespatial expected bychance.Thisstatisticallowsforthe mortality ordefoliationagentswashigherthan the percentageofsurveyedforestexposedto identify clustersofhexagonalcellsinwhich of thepercentage offorestexposedtomortality- i * value of1.96indicatesthatthelocalmean The Getis-OrdG The Getis-OrdG i i * statisticforeachhexagon * statisticwasusedto defoliation data weresummarizedbyecoregion analyses fortheState.Instead, mortalityand in 2011(fig.2.1)precluded theuseofhotspot feature (ESRI2006). enough toincludeseveralneighborsforeach sample aroundthetargetobservationislarge long asthedistancebandusedtodefinelocal z-scores arereliable,evenwithskeweddata,as to anormaldistribution(Anselin1992).The a standardizedz-scorethatasymptoticallytends the localG the inputdatabenormallydistributedbecause The Getis-Ordapproachdoesnotrequirethat required forstatisticalsignificance(Laffan2006). data violatestheassumptionofindependence are notexactbecausethecorrelationofspatial defoliation-causing agents. percentages offorestexposedtomortality-or neither consistentlyhighnorlow -1.96 and1.96,itits18neighborshave randomization assumption,withG words, whenahexagonhasG concentration ofhighorlowvalues.Inother -1.96 and1.96havenostatisticallysignificant absence ofspatialclustering.Values between deviations lessthanthemeanexpectedin its 18neighborsisapproximately2standard mortality ordefoliationmeanforahexagonand a G in theabsenceofspatialclustering,whereas deviations greaterthanthemeanexpected its 18neighborsisapproximately2standard or defoliation-causingagentsforahexagonand The lowdensityofsurvey data fromAlaska It isworthnotingthatthethresholdvalues i * valueof-1.96indicatesthatthelocal i * valuesarecomputedundera i * valuebetween i * equatingto section (Nowacki and Brock 1995), calculated Table 2.1—Mortality agents and as the percentage of the forest within the complexes affecting more than 5000 ha in surveyed areas affected by agents of mortality or the conterminous United States in 2011 defoliation. For reference purposes, ecoregion sections (Cleland and others 2007) were also Agents/complexes displayed on the geographic hot spot maps of the causing mortality, 2011 Area conterminous 48 States. ha Mountain pine beetlea 1 542 877.2 RESULTS AND DISCUSSION Five-needle pine declinea 160 423.2 Spruce beetle 153 570.3 The FHP survey data identified 78 different Fir engraver 128 156.5 mortality-causing agents on approximately Subalpine fir mortality a 122 372.5 2.26 million ha across the conterminous United Western pine beetle 82 649.6 States in 2011, an area slightly larger than Douglas-fir beetle 64 624.5 that of the combined land area of New Jersey Spruce budworm 46 147.9 and Rhode Island. (Of these mortality-cause Ips 40 962.0 categories, three were “rollups” of multiple Bark beetles 38 585.5 agents.) By way of comparison, forests cover Sudden aspen declineb 18 622.1 about 304 million ha of the conterminous Beech bark disease 13 745.3 48 States (Smith and others 2009). Emerald ash borer 9 245.1 Mountain pine beetle (Dendroctonus Western balsam bark beetlec 9 129.3 ponderosae) was the most widespread mortality Hemlock decline 8 125.0 agent, detected on 1.54 million ha (table 2.1). Unknown 8 011.1 23 Other mortality agents and complexes detected Balsam woolly adelgid 6 703.0 across very large areas, each affecting more Decline 6 142.6 than 100 000 ha, were five-needle pine decline, Flatheaded borer 5 077.3 spruce beetle (D. rufipennis), fir engraver Scolytus( Other mortality agents (59) 38 733.9 ventralis), and subalpine fir Abies( lasiocarpa) Total, all agents 2 258 275.6 mortality. Mortality from the western bark beetle Note: All values are “footprint” areas for each group, encompassing 19 different agents in the agent or complex. The sum of the individual agents insect and disease survey data (table 2.2), was is not equal to the total for all agents because of overlapping damage polygons. detected on a total of more than 2.12 million ha a Rollup of multiple agent codes from the Insect in 2011, a large majority of the total area on and Disease Survey database. which mortality was recorded. b Includes mortality, defoliation, and decline. c Also included in the subalpine fir mortality rollup. Forest Health Monitoring 24 SECTION 1 Chapter 2 Bark beetles Bark beetle pine Western beetle bark cedar Western beetle bark balsam Western beetles Tip beetle Spruce beetle fir Silver beetle pine Roundheaded beetle engraver spruce Northern beetle pine Mountain beetle pine Jeffrey beetles engraver Ips borer Flatheaded engraver Fir beetle Douglas-fir taxa beetle bark Western group beetle” bark “western the in included taxa Table 2.2—Beetle Interior West region(asdefined bytheFHM than 100000ha. butterfly (Neophasiamenapia ) eachaffectedmore needle scale(Matsucoccusacalyptus ), andpine 2.3). Tent caterpillars(Malacosomaspp.),pinyon C. fumiferana),affecting1.95millionha(table spruce budworms(Choristoneuraoccidentalisand widespread defoliatorswerewesternandeastern were “rollups”ofmultipleagents.)Themost (Of thesedefoliation-causecategories,4 of NewHampshireandDelawarecombined. an areaslightlysmallerthantheland across theconterminousUnitedStatesin2011, defoliation agentsaffectingabout2.84million ha The ForestHealthMonitoring (FHM)Program In addition,thesurveyidentified68 Nonspecific Dendroctonus brevicomis Phloeosinus punctatus Dryocoetes confusus Pityogenes spp. D. rufipennis Pseudohylesinus sericeus D. adjunctus Ips perturbatus D. ponderosae D. jeffreyi Ips spp. Buprestidae Scolytus ventralis Dendroctonus pseudotsugae species and Genus Tent caterpillars T (46) agents defoliation Other sawfly Pinyon cast needle Larch worms Oak defoliator Unknown looper hemlock Western beetle leaf elm Larger tortrix aspen Large budworm pine Jack fungus leaf Birch blight needle Larch casebearer Larch Unknown leafroller Baldcypress Needlecast western) and (eastern budworm Spruce Agents/complexes causing defoliation, 2011 defoliation, causing Agents/complexes 2011 in States United conterminous the in ha 5000 than more affecting complexes and agents Table 2.3—Defoliation b database. Survey a polygons. damage overlapping of because agents all for total the to equal not is agents individual the of sum The complex. or agent each for areas “footprint” are values All Note: defoliation Aspen moth Winter Douglas-fir tussock moth tussock Douglas-fir butterfly Pine scale needle Pinyon Disease and Insect the from codes agent multiple of Rollup Includes mortality, defoliation, and decline. and defoliation, mortality, Includes otal, all agents all otal, a b a 1 954 235.3 1 954 2 837 254.8 2 837 284 609.0 284 260 652.0 260 101 339.6 101 Area Area 36 043.9 36 35 602.0 35 ha 12 404.2 17 694.3 17 10 522.3 10 47 875.0 47 48 592.1 48 36 791.0 36 10 106.4 10 12 676.1 5 358.6 5 552.0 6 266.7 7 570.6 8 507.2 7 619.3 6 737.5 8 411.0 Program) had, by far, the largest area on which Table 2.4—The top five mortality agents or complexes for each Forest Health mortality-causing agents were detected in 2011, Monitoring region in 2011 approximately 1.72 million ha (table 2.4). A large majority of mortality within that area was Mortality agents, 2011 Area Mortality agents, 2011 Area associated with mountain pine beetle, although ha ha spruce beetle, subalpine fir mortality, and Interior West South five-needle pine decline were also important Mountain pine beetlea 1 339 848.3 Hemlock woolly adelgid 1 433.5 mortality agents and complexes. Spruce beetle 147 567.0 Southern pine beetle 88.4 Subalpine fir mortalitya 118 752.1 Unknown 43.8 The Getis-Ord analysis detected the three Five-needle pine declinea 130 800.2 Ips 39.9 largest and most clustered hot spots of mortality Douglas-fir beetle 51 964.5 Bark beetles (nonspecific) 39.6 exposure in the Interior West region (fig. 2.2). Other mortality agents (18) 101 196.4 Black turpentine beetle 0.1 The most intense was centered on the M331I- Total, all agents 1 715 790.3 Total, all agents 1 645.2 Northern Parks and Ranges ecoregion section of north-central Colorado and south-central North Central West Coast Wyoming and included much of M331H-North Spruce budworm 46 147.9 Mountain pine beetlea 176 117.2 Central Highlands and Rocky Mountains and Mountain pine beetlea 26 911.1 Fir engraver 121 722.6 M331G-Southern Central Highlands. This hot Beech bark disease 13 253.1 Western pine beetle 59 443.6 spot, not surprisingly, was associated with Emerald ash borer 5 708.9 Bark beetles (nonspecific) 38 399.5 extensive mountain pine beetle mortality in Oak wilt 1 053.2 Douglas-fir beetle 12 660.0 lodgepole and ponderosa pine forests. Spruce Other mortality agents (13) 1 193.9 Other mortality agents (26) 45 985.1 beetle mortality was also present. Meanwhile, Total, all agents 94 268.1 Total, all agents 416 558.2 a very large hot spot of mortality exposure, 25 also caused primarily by mountain pine beetle, North East Alaska extended across several ecoregions in central Unknown 6 354.7 Spruce beetle 19 761.2 Idaho and western Montana, including M332E- Forest tent caterpillar 4 667.2 Alaska yellow-cedar decline 10 833.9 Beaverhead Mountains, M322B-Northern Decline 4 600.5 Northern spruce engraver beetle 2 675.5 Rockies and Bitterroot Valley, M332D-Belt Gypsy moth 3 791.2 Unspecified mortality 2 519.8 Mountains, M332A-Idaho Batholith, M332F- Emerald ash borer 3 536.2 Western balsam bark beetle 1.0 Challis Volcanics, and M331A-Yellowstone Other mortality agents (33) 7 344.2 Total, all agents 35 791.5 Highlands. This hot spot overlapped with a Total, all agents 30 013.8 third hot spot centered on M331J-Wind River Note: The total area affected by other agents is listed at the end of each section. All values are Mountains of Wyoming and that extended into “footprint” areas for each agent or complex. the neighboring M331D-Overthrust Mountains The sum of the individual agents may not equal and the Yellowstone Highlands. In addition to the total for all agents because of overlapping damage polygons. mountain pine beetle, five-needle pine decline a Rollup of multiple agent codes in the Insect and was a major cause of mortality in this area. Disease Survey database. M242D 242A M333A M333B 211A M333C 331D 331L 331E M242A 342I M333D 332A 222N212M 211B 331A M211A 242B M332B 212L 211C M242B M332D 331K 251A M332G 212N 211D 331N 331M 212S 212R M242C 212Y212J 212K 211E 342H M332A M332E 212X M331A 212T M211D M211B M332F M211C M331B 332B 212Q 212H 211J M261A 342A 342D 331G M334A 222M 222R 212Z M261G342B 222I 221A 342C M331J 331F 251B 211I221B M261D 222L 222K 211F 342F 332D 342J M331D 341G 222J 211G 263AM261B 342E 341E 342G 222U 221F M261C 332C M331E 221D M341D M341A 232A 341D 341A 341C 331H 251D 251H 251C 222H 221E M261E M221A M341B M331H M221B M261F 261A262A 341F M341C M331I 331C 341B 332E 223G 223F M262A 331I M331G 223B M221C 251F 251E 313A 331J 223D 232H 231I M331F 223A 221H 232I 322A 331B 332F 221J M221D 261B 313B 223E M262B 313D 315F M223A 315H 231G 313C 255A 234D 322C 231C 322B M231A 231H 231A M313A 315A 315B 234E 231D M313B 231B 232J 315C SECTION 1 Chapter 2 315G 231E 321A 234A 255E255B 232B 232C 255C 232F Clustering and degree of exposure 321B 315D 234C 232L 26 2 (Not clustered) 232E 232K 2.01 – 6 (Clustered, moderate exposure) 255D 232G 232D 6.01 – 12 (Clustered, high exposure) 315E 12.01 – 24 (Clustered, very high exposure) 411A > 24 (Clustered, extremely high exposure) Ecoregion section FHM region

Figure 2.2—Hot spots of exposure to mortality-causing insects and diseases in 2011. Values are Getis-Ord Gi* scores, with values > 2 representing significant clustering of high percentages of forest area exposed to mortality agents. (No areas of significant clustering of low percentages of exposure, <-2, were detected.) The gray lines delineate ecoregion sections (Cleland and others 2007); the blue lines delineate Forest Health Monitoring regions. Background forest cover is derived from MODIS imagery by the USDA Forest Service Remote Sensing Applications Center. (Data source: USDA Forest Service, Forest Health Protection Program.) Forest Health Monitoring Forest Mountain pine beetle was also a leading engraver beetles, contributed to widespread, cause of mortality in the West Coast region, often scattered mortality of pines and hardwoods, where mortality-causing agents and complexes which occurred in late summer and early fall were recorded on nearly 417 000 ha (table 2.4). (Alabama Forestry Commission 2012, Louisiana Several other types of bark beetles, including fir Department of Agriculture and Forestry 2012, engraver and western pine beetle (D. brevicomis), Texas Forest Service 2012) and was largely were also important causes of mortality in this undocumented by routine detection surveys. region. These bark beetles, especially mountain pine beetle and fir engraver, were associated As with mortality, the Interior West FHM with a relatively low-intensity geographic hot region encompassed the greatest area on which spot of mortality in northeastern California and defoliating agents and complexes were detected south-central Oregon (fig. 2.2). This hot spot in 2011, about 1.9 million ha (table 2.5). Western encompassed M242C-Eastern Cascades, M261D- spruce budworm was the most widely detected Southern Cascades, and M261G-Modoc Plateau. defoliator in the region, but pinyon needle scale was also important. A large complex of Mountain pine beetle was the second most geographic hot spots of defoliation, contained widely detected mortality agent in the North mostly in Idaho and Montana, was associated Central region, behind spruce budworm. Beech with western spruce budworm in the Interior bark disease (Nectria coccinea) and emerald ash West in 2011 (fig. 2.3). This complex of hot borer (Agrilus planipennis) were also important spots included: mortality agents. Mortality-causing agents and complexes were detected on approximately • A large and intense hot spot centered on 94 000 ha in the region (table 2.4). M333D-Bitterroot Mountains and extending 27 into M333A-Okanogan Highland, M333B- As in the North Central region, no mortality Flathead Valley, and M333C-Northern Rockies. hot spots occurred in the North East and South • Another large and intense hot spot centered FHM regions. In the North East, however, on M332A-Idaho Batholith and M332F- mortality was recorded on 30 000 ha, with Challis Volcanics and extended into M332E- forest tent caterpillar (Malacosoma disstria) Beaverhead Mountains. the most widely named causal agent. In the South, mortality was detected on 1600 ha, • A large hot spot taking in M332D-Belt with hemlock woolly adelgid (Adelges tsugae) Mountains and extending into M332B- the most commonly reported agent (table 2.4). Northern Rockies and Bitterroot Valley, In addition, record-setting drought in Texas, M333C-Northern Rockies, and M331A- much of Louisiana, and other areas in the Yellowstone Highlands. South, often associated with pests such as pine Forest Health Monitoring 28 SECTION 1 Chapter 2 Aspen defoliation Douglas-fir tussockmoth Pinyon needlescale Western sprucebudworm Interior West Defoliation agents, 2011 agents, Defoliation 2011 in region Monitoring Health Forest each for complexes or agents defoliation five top Table 2.5—The Total, allagents Other defoliationagents(19) Unknown Loopers Forest tentcaterpillar Birch leaffungus Winter moth North East Total, allagents Other defoliationagents(12) Larch casebearer Large aspentortrix Jack pinebudworm Forest tentcaterpillar Spruce budworm North Central Total, allagents Other defoliationagents(24) Needlecast a 1 515696.6 1 906426.2 Area 258 016.0 183 226.1 116 499.7 43 655.9 32 745.7 68 791.6 10 106.4 36 791.0 15 711.7 32 207.7 51 870.3 32 565.8 ha 9 021.2 2 469.9 4 444.6 6 913.3 4 627.2 7 771.8 8 507.2 Defoliation agents, 2011 agents, Defoliation a polygons. damage overlapping of because agents all for total the equal not may agents individual the of sum The complex. or agent each for areas “footprint” are values All section. each of end the at listed is agents other by affected area total The Note: Total, allagents Other defoliationagents(5) Spruce aphid Hemlock sawfly Willow leafblotchminer Aspen leafminer Defoliators (nonspecific) Alaska Total, allagents Other defoliationagents(16) Douglas-fir tussockmoth Ponderosa pinesawfly Larch casebearer Pine butterfly Western sprucebudworm West Coast Total, allagents Other defoliationagent(1) Unknown Oak worms Larger elmleafbeetle Baldcypress leafroller Forest tentcaterpillar South

Includes mortality, defoliation, and decline. and defoliation, mortality, Includes Area 166 660.8 422 291.1 101 022.2 321 545.6 256 519.7 238 265.5 25 829.4 56 312.3 81 412.3 16 406.5 17 694.3 ha 2 977.4 1 665.7 4 514.0 4 219.1 4 640.2 7 777.0 4 265.2 6 266.7 7 619.3 4.9 pine budworm (C.pinus)andsprucebudworm extent byforesttentcaterpillar (table2.5).Jack 183 000 ha ofdefoliation. It wasfollowedin region, recordedonabout 117 000haofthe defoliator detectedintheNorth Central northern Washington. Highland andM242D-NorthernCascadesof cause ofasecondhotspotinM333A-Okanogan (fig. 2.3). Western sprucebudwormwasthe and 342H-BlueMountainsFoothillsofOregon and pinebutterflyinM332G-BlueMountains by infestationsofwesternsprucebudworm defoliation hotspotsintheregionwascaused two (table2.5).Themoreintenseofthe or complexwasnearlyaswidespreadthese second leadingdefoliationagent;nootheragent defoliation detectedthere.Pinebutterflywasthe accounting forthemajorityof422000ha important defoliatorintheWest Coastregion, 341D-Mono. M341A-East GreatBasinandMountains, Mountains, 341F-SoutheasternGreatBasin, forested partsofM341D-West GreatBasinand by pinyonneedlescaleextendingacross Finally, ahotspotinNevadawascaused 331J-Northern RioGrandeBasin(fig.2.3). Range, 313B-NavajoCanyonlands,and M331F-Southern ParksandRockyMountain mostly inM331G-SouthCentralHighlands, northern NewMexicoandsouthernColorado, with someaspendefoliation,occurredin mostly withwesternsprucebudworm,along Spruce budwormwassimilarly theleading Western sprucebudwormwasalsothemost An additionaldefoliationhotspotassociated M242D 242A M333A M333B 211A M333C 331D 331L 331E M242A 342I M333D 332A 222N212M 211B 331A M211A 242B M332B 212L 211C M242B M332D 331K 251A M332G 212N 211D 331N 331M 212S 212R M242C 212Y212J 212K 211E 342H M332A M332E 212X M331A 212T M211D M211B M332F M211C M331B 332B 212Q 212H 211J M261A 342A 342D 331G M334A 222M 222R 212Z M261G342B 222I 221A 342C M331J 331F 251B 211I221B M261D 222L 222K 211F 342F 332D 342J M331D 341G 222J 211G 263AM261B 342E 341E 342G 222U 221F M261C 332C M331E 221D M341D M341A 232A 341D 341A 341C 331H 251D 251H 251C 222H 221E M261E M221A M341B M331H M221B M261F 261A262A 341F M341C M331I 331C 341B 332E 223G 223F M262A 331I M331G 223B M221C 251F 251E 313A 331J 223D 232H 231I M331F 223A 221H 232I 322A 331B 332F 221J M221D 261B 313B 223E M262B 313D 315F M223A 315H 231G 313C 255A 234D 322C 231C 322B M231A 231H 231A M313A 315A 315B 234E 231D M313B 231B 232J 315C 315G 231E 321A 234A 255E255B 232B 232C Clustering and degree of exposure 255C 232F 321B 315D 234C 232L 2 (Not clustered) 232E 232K 29 2.01 – 6 (Clustered, moderate exposure) 255D 232G 232D 6.01 – 12 (Clustered, high exposure) 315E 12.01 – 24 (Clustered, very high exposure) 411A > 24 (Clustered, extremely high exposure) Ecoregion section FHM region

Figure 2.3—Hot spots of exposure to defoliation-causing insects and diseases in 2011. Values are Getis-Ord Gi* scores, with values > 2 representing significant clustering of high percentages of forest area exposed to defoliation agents. (No areas of significant clustering of low percentages of exposure, <-2, were detected.) The gray lines delineate ecoregion sections (Cleland and others 2007); the blue lines delineate Forest Health Monitoring regions. Background forest cover is derived from MODIS imagery by the USDA Forest Service Remote Sensing Applications Center. (Data source: USDA Forest Service, Forest Health Protection Program.) Forest Health Monitoring 30 SECTION 1 Chapter 2 forest, mostly inthesouth-centralpart of the mortality agent,affectingabout 20000haof acres) offorest(Smithand others2009). contains about51.3million ha(126.9million approximately 36000ha(table 2.4).Alaska complexes werereportedforAlaska,affecting Coastal PrairieandMarshes. Mississippi AlluvialPlain,and232E-Louisiana and RedRiverAlluvialPlains,234A-Southern in southernLouisiana,234C-Atchafalaya and baldcypressleafroller(Archipsgoyerana) hot spotwascausedbyforesttentcaterpillar defoliation wasdetected(table2.5).Anintense on 238000haofthe257which defoliating agentintheSouthregion,detected most widelydetecteddefoliator. leaf fungus(Septoriabetulae)wasthesecond which defoliationwasrecorded(table2.5).Birch the region,foundon37000haof69 most commonlydetecteddefoliationagentin 221A-Lower NewEngland;wintermothwasthe by wintermoth(Operophterabrumata)in East FHMregion,meanwhile,wascaused 212J-Southern SuperiorUplands(fig.2.3). 212Y-Southwest LakeSuperiorClayPlain,and Wisconsin Uplands,212X-NorthernHighlands, Superior Uplands,212Q-NorthCentral 212K-Western SuperiorUplands,212L-Northern with theregion’s defoliationhotspotin were theassignedcausalagentsassociated Spruce beetlewasthemost widelydetected In 2011,fivemortality-causingagentsand Forest tentcaterpillarwasbyfartheleading The singledefoliationhotspotintheNorth central Alaska, including139A-Yukon Flats levels ofdefoliationdetection wereineast- (fig. 2.5).Otherecoregions withrelativelyhigh south-central partoftheState, with4.48percent defoliation wasM212B-Kenai Mountainsinthe proportion ofsurveyedforest affectedby than 2000 ha. aphid (Elatobiumabietinum)wasfoundonless was observedonabout4500ha,andspruce 26 000 ha.Hemlocksawfly(Neodipriontsugae) (Micrurapteryx salicifoliella),foundonnearly defoliator in2011waswillowleafblotchminer parts ofAlaska.Thenextmostimportant on 56000ha,mostlyintheeasternandcentral leaf miner(Phyllocnistispopuliella)wasdetected were theassignedcauseofdefoliation.Aspen approximately 81000ha,nonspecificdefoliators Alaska during2011.Fornearlyhalfofthatarea, complexes onnearly167000ha(table2.5)in percent, respectively). Lowlands ofsouth-centralAlaska(1.50and1.00 Northern AleutianRangeand213B-CookInlet east-central Alaska(1.61percent),andM213A- (fig. 2.4)wereM129A-SewardMountainsof surveyed forestaffectedbymortalityagents The ecoregionswiththehighestpercentageof central andeast-centralpartsoftheState. on about3000haofforest,mostlyinthe engraver beetle(Ipsperturbatus)wasdetected in theAlaskapanhandle.Northernspruce mortality agent,foundonabout11000ha decline wasthesecondmostwidelydetected State. Yellow-cedar (Chamaecyparisnootkatensis ) The Alaskaecoregionwiththehighest The studydetected10defoliationagentsand 121A

125A

M125A M139B 139A Percent forest exposed 129A M131A to mortality agents M129A M139A 1 1.01 – 5 M139C M131B 5.01 – 10 131A 131B > 10 M131C M135C Ecoregion section 135A M135B boundaries 129B 213B M244AM135A

M131D M213B 245A M244B M129B M213A M244C 213A

M245B M245A 271A M271A 31 M271B

Figure 2.4—Percent of surveyed forest in Alaska ecoregion sections exposed to mortality-causing insects and diseases in 2011. The gray lines delineate ecoregion sections (Nowacki and Brock 1995). Background forest cover is derived from MODIS imagery by the USDA Forest Service Remote Sensing Applications Center. (Data source: USDA Forest Service, Forest Health Protection Program.) 121A

125A

M125A M139B 139A 129A M131A Percent forest exposed to defoliation agents M129A M139A 1 M139C 1.01 – 5 M131B 5.01 – 10 131A 131B > 10 M131C M135C Ecoregion section 135A M135B boundaries 129B 213B M244AM135A

M131D M213B 245A M244B M129B M213A M244C 213A

M245B

SECTION 1 Chapter 2 M245A 271A M271A 32 M271B

Figure 2.5—Percent of surveyed forest in Alaska ecoregion sections exposed to defoliation-causing insects and diseases in 2011. The gray lines delineate ecoregion sections (Nowacki and Brock 1995). Background forest cover is derived from MODIS imagery by the USDA Forest Service Remote Sensing Applications Center. (Data source: USDA Forest Service, Forest Health Protection Program.) Forest Health Monitoring Forest section, with 3.37 percent; M139C-Dawson Cleland, D.T.; Freeouf, J.A.; Keys, J.E., Jr. [and others]. 2007. Range, with 3.24 percent; and M135C-Alaska Ecological subregions: sections and subsections for the conterminous United States. 1:3,500,000; Albers equal Range, with 2.28 percent. area projection; colored. In: Sloan, A.M., tech. ed. Gen. Tech. Rep. WO-76. Washington, DC: U.S. Department Continued monitoring of insect and disease of Agriculture Forest Service. Also as a GIS coverage in outbreaks across the United States will be ArcINFO format on CD-ROM or at http://fsgeodata.fs.fed. us/other_resources/ecosubregions.html. [Date accessed: necessary for determining appropriate followup March 18, 2011]. investigation and management activities. Edmonds, R.L.; Agee, J.K.; Gara, R.I. 2011. Forest health and Because of the limitations of survey efforts protection. Long Grove, IL: Waveland Press. 667 p. to detect certain important forest insects and ESRI. 2006. ArcMap 9.2. Redlands, CA: Environmental diseases, the pests and pathogens discussed in Systems Research Institute. this chapter do not include all the biotic forest Forest Health Monitoring (FHM). 2005. Aerial survey health threats that should be considered when geographic information system handbook: sketchmaps making management decisions and budget to digital geographic information. U.S. Department of Agriculture Forest Service, State and Private Forestry, allocations. As these analyses demonstrate, Forest Health Protection. http://www.fs.fed.us/ however, large-scale assessments of mortality foresthealth/technology/pdfs/GISHandbook_body_ and defoliation exposure, including geographical apndxA-C.pdf. [Date accessed: May 7, 2013]. hot spot detection analyses, offer a potentially Getis, A.; Ord, J.K. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis. 24(3): useful approach for prioritizing geographic areas 189-206. where the concentration of monitoring and Holdenrieder, O.; Pautasso, M.; Weisberg, P.J.; Lonsdale, D. management activities would be most effective. 2004. Tree diseases and landscape processes: the challenge of landscape pathology. Trends in Ecology & Evolution. LITERATURE CITED 19(8): 446-452. 33 Alabama Forestry Commission. 2012. Alabama forest health Laffan, S.W. 2006. Assessing regional scale weed highlights 2011. http://fhm.fs.fed.us/fhh/fhh_11/AL_ distributions, with an Australian example using Nassella FHH_2011.pdf. [Date accessed: June 7]. trichotoma. Weed Research. 46(3): 194-206. Anselin, L. 1992. Spatial data analysis with GIS: an Louisiana Department of Agriculture and Forestry. 2012. introduction to application in the social sciences. Tech. Louisiana forest health highlights 2011. http://fhm. Rep. 92-10. Santa Barbara, CA: National Center for fs.fed.us/fhh/fhh_11/LA_FHH_2011.pdf. [Date accessed: Geographic Information and Analysis. 53 p. June 7]. Brockerhoff, E.G.; Liebhold, A.M.; Jactel, H. 2006. The Mack, R.N.; Simberloff, D.; Lonsdale, W.M. [and others]. ecology of forest insect invasions and advances in their 2000. Biotic invasions: causes, epidemiology, global management. Canadian Journal of Forest Research. 36(2): consequences, and control. Ecological Applications. 263-268. 10(3): 689-710. Castello, J.D.; Leopold, D.J.; Smallidge, P.J. 1995. Pathogens, Manion, P.D. 2003. Evolution of concepts in forest pathology. patterns, and processes in forest ecosystems. Bioscience. Phytopathology. 93: 1052-1055. 45(1): 16-24. Forest Health Monitoring 34 SECTION 1 Chapter 2 Potter, K.M.;Koch,F.H. 2012.Large-scalepatternsofinsect Potter, K.M.2013.Large-scalepatternsofinsectanddisease Potter, K.M.2012.Large-scalepatternsofinsectanddisease Parry, D.;Teale, S.A.2011.Alieninvasions:theeffects Nowacki, G.;Brock,T. 1995.Ecoregionsandsubregionsof Station: 63-72. of AgricultureForestService,SouthernResearch Gen. Tech. Rep.SRS-158.Asheville,NC:U.S.Department Forest health monitoring2008nationaltechnicalreport. and Alaska,2006.Potter, K.M.;Conkling,B.L.,eds. and diseaseactivityintheconterminousUnitedStates Station: 15-29. of AgricultureForestService,SouthernResearch Gen. Tech. Rep.SRS-176.Asheville,NC:U.S.Department monitoring: nationalstatus,trends,andanalysis2010. 2009. In:Potter, K.M.;Conkling,B.L.,eds.Foresthealth from thenationalinsectanddiseasedetectionsurvey, activity intheconterminousUnitedStatesandAlaska Research Station:63-78. Department ofAgricultureForestService,Southern report. Gen.Tech. Rep.SRS-167.Asheville,NC:U.S. eds. Foresthealthmonitoring2009nationaltechnical database, 2007and2008.In:Potter, K.M.;Conkling,B.L., from thenationalinsectanddiseasedetectionsurvey activity intheconterminousUnitedStatesandAlaska Press: 115-162. integrated perspective.NewYork: CambridgeUniversity In: Castello,J.D.;Teale, S.A.,eds.Foresthealth:an of introducedspeciesonforeststructureandfunction. Alaska Region. AK: U.S.DepartmentofAgricultureForestService, Alaska, EcoMapVersion 2.0.1:5,000,000;colored.Juneau, Zhang, L.;Rubin,B.D.;Manion,P.D. 2011.Mortality:the White, D.;Kimerling,A.J.;Overton,W.S. 1992.Cartographic U.S. DepartmentofAgriculture(USDA)ForestService.2008. Texas ForestService.2012.Texas foresthealthhighlights Teale, S.A.;Castello,J.D.2011.Regulatorsandterminators: Smith, W.B.; Miles,P.D.; Perry, C.H.;Pugh,S.A.2009.Forest Potter, K.M.;Paschke,J.L.2013.Large-scalepatternsof Cambridge UniversityPress:17-49. eds. Foresthealth:anintegratedperspective.NewYork: essence ofahealthyforest.In:Castello,J.D.;Teale, S.A., Information Systems.19(1):5-22. environmental monitoring.CartographyandGeographic and geometriccomponentsofaglobalsamplingdesignfor May 13]. fs.fed.us/rastergateway/forest_type/. [Dateaccessed: National foresttypedatadevelopment.http://svinetfc4. [Date accessed:June7]. 2011. http://fhm.fs.fed.us/fhh/fhh_11/TX_FHH_2011.pdf. Press: 81-114. perspective. NewYork: CambridgeUniversity Castello, J.D.;Teale, S.A.,eds.Foresthealth:anintegrated the importanceofbioticfactorstoahealthyforest.In: Service, Washington Office.336p. 78. St.Paul,MN:U.S.DepartmentofAgricultureForest resources oftheUnitedStates,2007.Gen.Tech. Rep.WO- Service, SouthernResearchStation:15-28. 185. Asheville,NC:U.S.DepartmentofAgricultureForest status, trends,andanalysis2011.Gen.Tech. Rep.SRS- Conkling, B.L.,eds.Foresthealthmonitoring:national detection surveydatabase,2010.In:Potter, K.M.; States andAlaskafromthenationalinsectdisease insect anddiseaseactivityintheconterminousUnited