INTRODUCTION in other studies (e.g., Bari and others 2001, Boquete and others 2009, Geiser and Neitlich lemental concentration in is a CHAPTER 6. 2007, Root and others 2015). Evemes, Flacap, popular and cost-effective tool to bioindicate and Phyaip were recommended as pollution load at plots (Donovan and others E species for the study region (Will- Wolf and Species to 2016, Paoli and others 2014, Root and others others 2017a, In press). 2015) and to complement costly instrumented Bioindicate Air Quality monitoring to help assess environmental health. Based on the upper Midwest studies and From recent development of lichen elemental 1994–2005 species frequencies in the FIA in Eastern United for in the U.S. National Lichen Database (table 6.1)2 Will- Wolf upper Midwest (Will- Wolf and others 2017a, and others (In press) recommended bioindicator States from Elemental 2017b, In press) for the U.S. Department of species for Northeastern (NE) region States Agriculture, Forest Service, Forest Inventory (Connecticut, Maine, Massachusetts, New Composition: Lessons and Analysis Program (FIA), we learned Hampshire, , Rhode Island, and important lessons about suitability of lichen Vermont), Mid-Atlantic (MidA) region States from the Midwest species in large-scale monitoring programs for (Delaware, , New Jersey, Ohio, the Eastern United States. Five macrolichen Pennsylvania, Virginia, and West Virginia), species common in Eastern North America (E and Southeastern (SE) region States (, NA) (Brodo and others 2001)1 were evaluated , , , and Susan Will-Wolf in that study: Evernia mesomorpha (code Evemes; Virginia). Virginia was included in both the Sarah Jovan small/medium size fruticose growth form), MidA and the SE regions to assist ecological Michael C. Amacher Flavoparmelia caperata (code Flacap; large foliose), evaluation of region boundaries. Parmelia sulcata (code Parsul; medium foliose), 101 Physcia aipolia and P. stellaris combined (code For this study, our objective was to evaluate Phyaip; small foliose, tightly appressed), and the recommendations of Will-Wolf and others rudecta (code Punrud; large foliose). (in press) using data from other studies. We Elemental data and multi-element Pollution evaluated 1994–2005 distribution patterns of Indices derived from them clearly represented all five tested macrolichen bioindicator species relative site pollution load better than did with respect to pollution load and nearby regionally modeled pollutant deposition (Will- forest cover in the NE and MidA regions using Wolf and others 2017a), as has also been found lichen community data for subsets of FIA plots.

1 Jovan, S.; Haldeman, M.; Will-Wolf, S. [and others]. [In 2 Jovan, S.; Will-Wolf, S.; Geiser, L.; Dillman, K. [In management review, spring 2018]. National atlas of epiphytic management review, spring 2018]. User guide for the lichens in forested habitats, U.S.A. Gen. Tech. Rep. PNW- national FIA lichen database (beta). Gen. Tech. Rep. PNW- GTR-XXX. Portland, OR: U.S. Department of Agriculture, GTR-XXX. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. XX p. Forest Service, Pacific Northwest Research Station. XX p. 102 Forest Health Monitoring SECTION 2 Chapter 6 for theseregionssupportimplementation of upper Midweststudyanditsrecommendations Comparisons ofourresultswiththosethe scattered unevenly andoftenmeasuredifferent network instrumentedmonitor sitesare has extensivelichenelemental data;government None oftheNE,MidA,or SE regionscurrently lichen elementalbioindicators inthoseregions. of plots (p) in parentheses after lichen region code. region lichen after parentheses in (p) plots of name. c after parentheses in indicated Europe Western and States United b a species additional an plus bold) in (names species indicator elemental lichen target five with regions lichen U.S. eastern in plots FIA 1994–2005 unique of Table 6.1—Percentage Lichen species Lichen (possible future target species; no usage) no species; target future (possible studies) U.S. eastern (some Punctelia rudecta Physcia aipolia Europe) studies, U.S. eastern (some sulcata Parmelia Europe) studies, U.S. eastern (several caperata Flavoparmelia Europe) in congener studies, U.S. eastern (some Evernia mesomorpha species target common most two the of Either species target common most three the of Any species target common most four the of Any species target Any FIA lichen regions are North Central (NC), Northeastern (NE), MidAtlantic (MidA), and Southeastern (SE); number number (SE); Southeastern and (MidA), MidAtlantic (NE), Northeastern (NC), Central North are regions lichen FIA The three species most frequently found in each region have values in bold. Past elemental indicator use in Eastern Eastern in use indicator elemental Past bold. in values have region each in found 2). frequently footnote most (see species three others and The Jovan from data press); (In others and Will-Wolf from permission with Reprinted P. stellaris (L.) Nyl. (no other studies) other (no Nyl. (L.) b (Ehrh. Taylor (Ach.) Krog (Ach.) Nyl. ex G. Wilh. &Ladd Wilh. G. (L.) Hale (L.) Humb.) Fürnr. and Fürnr. Humb.) Species Evemes Punmis Punrud Phyaip Flacap Parsul code FIA lichen region FIA NC 56.4% 96.6% 90.7% 39.2% 95.6% 95.1% 78.4% 69.1% 71.6% (204p) 6.4% States, USA. assess responseto climateandairqualityintheMid-Atlantic review forTheBryologist, spring2018].Lichenindexes 3 A PollutionIndexfromlichen elemental very expensive(Will-Wolf andothers2017a). pollutants, andonsiteinstrumentmonitoringis Will-Wolf, S.;Jovan,Nelsen,M.P. [andothers].[In NE 40.5% 93.9% 95.8% 63.0% 64.0% 85.6% 95.4% 97.0% 57.8% c (625p) 0 MidA 23.1% 52.4% 85.1% 58.0% 76.9% 10.8% 91.3% 91.9% 91.9% 3.3% (779p) a

SE 88.2% 88.2% 86.3% 32.8% 88.2% 60.2% 78.7% (357p) 4.2% 5.0% 0 3 analyses would greatly improve monitoring of environmental health across eastern FIA plots in most subregions.

METHODS For the upper Midwest study (fig. 6.1), trained non-specialists (FIA field staff) collected single-species composite samples for all five target species under 20–50 percent tree canopy near permanent FIA plots using rigorous protocols [details in Will-Wolf and others (2017a, footnote 3)]. An expert collected samples of the same species from temporary plots using both rigorous protocols and several variants of relaxed (and more economical) protocols. Elemental data from combustion and ICP-OES (inductively coupled plasma optical emission spectroscopy) were validated, and then field sample quality and subsequent elemental data quality were evaluated; only the most rigorous protocols were supported [details in Will-Wolf and others (2017b)] to provide reliable data. Data were converted between species with 103 General Linear Models (GLM) or regression [both in SPSS (2015)], then combined for Figure 6.1—Approximate locations of lichen analyses; two pollution indices were developed B sites and instrument monitor sites for the from multiple elements [aluminum (Al), cobalt upper Midwest study. Wisconsin, marked in (Co), chromium (Cr), copper (Cu), and iron the U.S. inset, is in the center with Minnesota (Fe) for one; nitrogen (N) and sulfur (S) for () west, Iowa southwest, and Illinois south. the other]. Estimation of relative pollution Full names of ecoregion provinces (Cleland load from lichen elemental concentrations was and others 2007, McNab and others 2007) confirmed by comparison with monitor site data indicated by background shading are: 212– (Will-Wolf and others 2017a). Lichen elemental Laurentian Mixed Forest, 222–Midwest bioindicators and protocols were recommended Broadleaf Forest, and 251–Prairie Parkland for implementation in other eastern U.S. regions (Temperate). [Adapted with permission from (Will-Wolf and others, In press). Will-Wolf and others (2017a).] 104 Forest Health Monitoring SECTION 2 Chapter 6 ( and onlycorrelationswithr with 0.05> account forexperiment-wideerror, correlations variables, andinterpretationarereported.To for regressions,adjustedr probability ( correlations, Pearsonr and nearbyforestcover(SPSS2015).For log10-transformed dataforpollutionload with linearregressionusingoriginaldataand Pearson andSpearmanrankcorrelations evaluated forNEandMidAdatasetswith to pollutionloadandnearbylandcoverwere Relationships ofbioindicatorspeciesabundance Will-Wolf andothers2015a;MidA:footnote3). Midwest: Will-Wolf andothers2017a;NE: project areasdiscussedinthisstudy(upper was evaluatedwithscatterplotsforthethree and proportionofnearbylandinforestcover indicator specieswithrespecttopollutionload bioindicator speciesandoneadditionalpossible p <0.00001)wereconsideredecologically Frequency ofoneormorethefivetested p p >0.005wereconsideredweak, ), anddirectionarereported; 2 orSpearman 2 , p 2 or , independent rho 2 ≥0.10 rho 2 , nearby forestcover, andrelativepollutionload p important. Regressionmodelswith0.05> one-quarter ofOhio(fig.6.2B). MidA States,butrepresentedonlytheeastern study (footnote3).Plotswerespreadacrossmost (including Virginia) wasavailablefromasimilar abundance at219plotsintheMidAregion the region(Will-Wolf andothers2015a);species independent climateandpollutionindicesfor available fromdevelopmentofstatistically at 218plotsacrossallStates(fig.6.2A)was region, bioindicatorlichenspeciesabundance each ofthethreeprojectareas.ForNE Pollution Indexdevelopedindependentlyfor load wasrepresentedbythelichen-basedsite Midwest, and~5km site: ~1km with forestcoverinabufferzonearoundeach Nearby forestcoverwasthepercentageofpixels were availablefromallthreeearlierstudies. > 0.01 wereconsideredweak. Frequency ofeachlichenspecies,percent 2 bufferforNE,~3km 2 forMidA.Pollution 2 forupper (A) E B Figure 6.2—Northeastern (A) and MidAtlantic (B) region project A plots (approximate locations). For comparison, the city of Newark, NJ, is at the bottom center of the map in (A) and near the upper right corner of the map in (B). On-frame plots are permanent FIA plots; off-frame plots are temporary plots surveyed only for each project. Full names of ecoregion provinces (Cleland and others 2007; McNab and others 2007) indicated by background shading in (A) are: 221–Eastern Broadleaf Forest, 211– Northeastern Mixed Forest, and M211– Adirondack-New England Mixed Forest - Coniferous Forest - Alpine (B) Meadow. Full names in (B) are: M211– Adirondack-New England Mixed 105 Forest - Coniferous Forest - Alpine Meadow, M221–Central Appalachians Broadleaf Forest - Coniferous Forest - Meadow, 221–Eastern Broadleaf Forest, 211–Northeastern Mixed Forest, and 231/232–Southeastern/ A Outer Coastal Plain Mixed Forest A B (two provinces combined, abbreviated E B as “Southeastern” in the legend). [(A) Adapted with permission from Will-Wolf and others (2015a); (B) adapted with permission from Will- Wolf and others (see footnote 3).] 106 Forest Health Monitoring SECTION 2 Chapter 6 The tightcorrespondences betweenN values calculated onlyfromlesssensitive lichenspecies. load, asestimatedfromthe sitePollutionIndex for sitesbelowthatmaximum relativepollution be areliableelementalbioindicator, butonly for thesamesitesoverlap. Thus Evemeswould of sites(fig.6.3B),EvemesandFlacapvalues polluted sites.Afterconversionandexclusion were higherthanFlacapvaluesatthesameless with Flacap.OriginalEvemesNvalues(fig.6.3A) Will-Wolf andothers2017a)forequivalence to achievesuccessfuldataconversion(viaGLM: some Evemessamplesfrommorepollutedsites of figures6.3Aand6.3Billustratesexclusion (e.g., Will-Wolf andothers2015a);comparison sensitivity consistentwithotherU.S.studies monitor sitedata)thatreflectedspecies from PollutionIndexN+Sandinstrumented a maximumrelativepollutionload(estimated Evemes sampleshadgooddataqualitybelow data conversion:Will-Wolf andothers2017a). of allfivespeciesat83sitesaftersuccessful had goodelementaldataquality(203samples and others2017b).FlacapPhyaipsamples Phyaip fromotherlichenspecies(Will-Wolf successfully distinguishedEvemes,Flacap,and helped evaluatethoserecommendations. community compositionintwoofthoseregions eastern U.S.regions.Comparisonwithlichen applications andlimitationsinthreeother generated severalrecommendationsforboth bioindicators intheupperMidweststudy RESULTS ANDDISCUSSON Forest InventoryandAnalysisfieldstaff Relative successoflichenspeciesaselemental

on presenceof eachspeciesfoundnearby than withpollutionload,and logisticregression strongly correlatedwithnearby forestcover pollution. Presenceofeach specieswasmore percentage ofnearbyforest coverastositeair others (2017a)qualitatively linkedasmuchto between thespecieswasinWill-Wolf and the studyarea(fig.6.4C);partitioningofsites upper Midweststudy)betweenthemcovered Phyaip (notusedbeforebutsuccessfulinthe and others2015b,2017a,footnote3) for lichenelementalanalysis,e.g.,Will-Wolf the LichenPollutionIndex.Flacap(oftenused for alllichenspecieswereincorporatedinto 6.4A, and6.4BreflectthatconvertedN data and theLichenPollutionIndexinfigures6.3B, site andfromsiteswithsimilarvalues.[DataWill-Wolf andothers (Inpress).] both Nandsulfur(S)inlichens.Manysymbolsoverlap,fromspeciesatthesame upper Midweststudyfornitrogen(N)inlichensvs.aLichenPollutionIndexfrom Figure 6.3—FlacapandEvemes(A)original(B)convertedsampledatafromthe (A) E (B) C (A) (B)

(C) (D)

107

Figure 6.4—(A, C) Flacap and Phyaip or (B, D) Flacap and Punrud pairs compared for the upper Midwest study. In A and B, species pairs are compared for converted nitrogen (N) concentrations vs. Pollution Index. In C and D, presence of each species is plotted by site Pollution Index and nearby forest cover. Many symbols overlap, from both species at the same site and from sites with similar values. [Data from Will-Wolf and others (In press).] 108 Forest Health Monitoring SECTION 2 Chapter 6 less airpollution) asprimarybioindicator species Evemes (northernandmountainous areaswith press) recommendedFlacap (widespread)and FIA plots(table6.1),Will-Wolf andothers(in Midwest studiesandonfrequency ofspeciesin others 2017a,inpress).Based ontheupper it issocost-effectivetohandle(Will-Wolf and northern areasandatlesspollutedsitesbecause a secondaryrecommendedbioindicatorin for thefullNorthCentralregion,withEvemes recommended lichenelementalbioindicators with moreisolatedforests)weretheprimary with lessisolatedforests)andPhyaip(inareas a similarpattern. range, theyweremuchlessfrequent;Parsulhad Flacap samplesalongmostofthepollution converted dataforPunrudsamplesoverlapped Punrud (figs.6.4Band6.4D)illustratethatwhile scientific context.ScatterplotsofFlacapand as importanttobioindicatorchoiceisthe programs; thehumancontextofastudyis of interestparticularlytolargemonitoring non-specialists wereanunexpectedoutcome and Punrudidentificationdifficultiesfor data qualityin~10percentofsamples.Parsul to fewsamplescollectedandpoorelemental (2017b)] fromotherlichenspecies;thisled others 1985;reviewedinWill-Wolf andothers successful inexpertstudies,e.g.,Olmezand distinguishing ParsulandPunrud[both unexpected result. variable [Will-Wolf andothers(inpress)],an forest covertobethemostsignificantpredictor Based ontheseearlierstudies,Flacap(inareas Non-specialist fieldstaffhaddifficulty Punrud (bothwidespread)wererecommended were notfound.FortheMidAregionFlacapand forests) wheresamplesofaprimaryspecies species inareas(usuallywithmoreisolated for theNEregion,withPhyaipasasecondary while statisticallystrong( Pollution Indexandnearby landinforestcover, For bothNEandMidA,correlation between negatively correlatedwith nearbyforestcover. the upperMidweststudy, PollutionIndexwas climate orpollution,and,inthesamepatternas weakly linkedtonearbyforestcoverthan each dataset,lichencompositionwasmore (Will- Index statisticallyindependentofclimate and airpollutionrepresentedbyaPollution composition variedstronglywithbothclimate NE andMidAdatasets,fulllichencommunity were well-representedinallthreestudies.Inthe Midwest study(fig.6.1),thoughallecoregions (fig. 6.2)wasmoreeventhanfortheupper distribution fortheNEandMidAregionprojects the availableFIAplotswithlichendata.Plot (table 6.1);theMidAdatasetis~28percentof FIA plotswithlichendatainthoseStates The NEdatasetis~35percentoftheavailable recommended bioindicatorsintheseregions. data subsetshelpedbetterpredictsuccessof and nearbyforestcoverfortheNEMidA species distributionswithpollutionindices species atplotsinlessforestedlandscapes. specialist fieldstaff),withPhyaipasasecondary intensive identificationtrainingfornon- as primarybioindicators(Punrudrequires Comparisons of1994–2005bioindicator Wolf andothers2015a,footnote 3).In p <0.0005),wassmall enough (r2 = 0.28 for NE, 0.36 for MidA) to p = 0.011) and Flacap abundance weak negative enter both variables into the same regression correlation (r2 = 0.052, p = 0.0007) with model. In both these datasets, Physcia stellaris the Pollution Index; both lacked significant was more frequent at plots than was Physcia correlation (not significant [NS]) with percent aipolia (from laboratory identifications; not of nearby land with forest cover. The strongest reliably distinguishable in the field), so Phyaip significant p( < 0.001) regression model (though represented the former species more, in contrast still weak; adj r2 = 0.07) for Flacap predicted its to the upper Midwest study where Physcia aipolia abundance declined as Pollution Index (original was the more frequent species in Phyaip based values) increased. No regression model was on laboratory identifications. Comparisons of significant for Phyaip. The NE Flacap and Parsul indicator species pairs over the subset of plots in scatterplot (fig. 6.5B) showing Parsul present at each dataset where at least one of them occurred more sites with high pollution and low nearby helped evaluate how well suites of indicator forest cover than Phyaip (fig. 6.5A) suggested species would represent the region. In addition, Parsul might complement Flacap better than the MidA project illustrated differences between Phyaip to provide broad representation of lichen species abundance at FIA plots and ability even high-pollution FIA plots. However, Parsul to collect a bulk lichen elemental sample within abundance had weak negative correlation a narrow canopy range in the vicinity of an FIA with pollution (rho2 = 0.04, p = 0.005; NS with plot. Flacap was recorded at 93 percent of 219 nearby forest cover), and a weak regression MidA plots (footnote 3), but adequate elemental model (adj r2 = 0.02, p = 0.02) predicted the samples were collected at only 85 percent of 26 same pattern from log10 of Pollution Index. temporary sites spanning the pollution gradient. Thus Parsul, while present, had very low Thus these analyses of lichen species abundances abundance at polluted sites, supporting Phyaip 109 can at best be very general estimators for the as the better complement to Flacap for NE likelihood of collecting adequate samples elemental bioindication. The scatterplot of NE for elemental analyses. Indicator species not Flacap and Evemes (fig. 6.5C) presence shows mentioned in the next two paragraphs were too Evemes was mostly restricted to plots with uncommon in a region to consider further. > 50 percent nearby forest cover and in the cleaner half of the Pollution Index. Evemes From the NE region dataset, Phyaip appeared abundance was negatively correlated with to be present at more of the polluted sites Pollution Index (rho2 = 0.54, p < 0.00001) and than did Flacap (fig. 6.5A), but the two species positively correlated (r2 = 0.152, p < 0.00001) overlapped across the gradient of nearby forest with percent nearby forest cover; the strongest cover in contrast to the upper Midwest sites regression model (adj r2 = 0.56, p < 0.0000) (compare with fig. 6.4C). Phyaip abundance predicted Evemes decline only with increasing had weak positive correlation (rho2 = 0.03, 110 Forest Health Monitoring SECTION 2 Chapter 6

() () () E E (A) (C) (B) Will- [Data fromJovanandothers (see same siteandfromsiteswith similar values. Many symbolsoverlap,fromboth speciesatthe different levelsofairpollution betweenregions. Indices havethesamevalueranges,butindicate regions. NortheasternandMidAPollution and nearbyforestcoveratsitesinNEMidA species in1994–2005comparedtopollution Figure 6.5—Presenceoflichenbioindicator A Wolf andothers(2015a, footnote 3) (D) (E) footnote 2 .] ), log10 of Pollution Index. Evemes was clearly regression model (r2 < 0.01, F = 5.8, p = 0.017) more limited in the NE region by pollution than predicted Punrud abundance increasing by nearby forest cover, a reversal from the upper only with log of nearby forest cover. Phyaip Midwest study (Will- Wolf and others 2017a, abundance had weak negative correlation footnote 3); it was, however, the only NE region with Pollution Index (r2 = 0.02, p = 0.037) and bioindicator species showing a statistical link none (NS) with percent nearby forest cover. with nearby forest cover. These patterns support Thus Flacap appeared more pollution-sensitive the recommendations by Will- Wolf and others as well as preferring high-forest landscapes, (in press) for Flacap and Phyaip plus secondary Phyaip appeared slightly sensitive to pollution Evemes as bioindicator species in the NE region, but not to forest cover, and Punrud appeared though with little demonstrated influence of to slightly prefer high-forest landscapes but nearby forest cover on lichen distribution. be unresponsive to pollution. More sensitivity Species distribution patterns should be evaluated to forest cover might be noted in analyses with the full suite of NE FIA lichen plots to including plots in low-forest landscapes of further refine bioindicator recommendations. western Ohio (favors Phyaip) that were not in the 219-plot subset. Only 11 percent of the 219 Both Phyaip and Punrud were present at MidA plots had < 50 percent local forest cover, more MidA plots with high pollution than as compared with 30 percent of the 83 upper was Flacap (figs. 6.5D, 6.5E). While all three Midwest study plots. Flacap abundance was species were present across the full range negatively correlated (r2 = 0.19, p < 0.000001) of nearby forest cover, response of species’ with Pollution Index in MidA. These analyses abundances more strongly supported the support recommendation of Punrud as a rationale by Will-Wolf and others (in press) primary elemental bioindicator, despite its 111 for recommending Flacap and Punrud as need for intensive identification training of primary and Phyaip as a secondary bioindicator non-specialist field staff, to complement the species in the MidA region. Flacap abundance more environmentally sensitive Flacap and was positively correlated with forest cover the less abundant Phyaip. Sparse MidA region (r2 = 0.07, p < 0.0005) and negatively correlated 2 lichen elemental data (footnote 3) support the (r = 0.19, p < 0.0005) with Pollution Index. The recommendations and also highlight differences strongest (although still weak) regression model 2 between species presence based on even a single (r = 0.09, F = 23.3, p < 0.0005) predicted Flacap individual vs. an adequate sample for elemental increasing only with lower pollution. Punrud analysis. An adequate elemental sample is 1–2 g abundance had weak positive correlation with 2 per species from six or more different substrates percent nearby forest cover (r = 0.03, p = 0.013) under 20– 50 percent forest canopy (Will-Wolf but no correlation (NS) with Pollution Index; and others 2017a, in press). For example, Flacap its best but weak and ecologically unimportant 112 Forest Health Monitoring SECTION 2 Chapter 6 elemental bioindicatorsfor theSEregion (in press)ofFlacap,Phyaip, andPunrudas co- occurring species. for non-specialiststodistinguishfromother Punmis mightbeasdifficultParsulorPunrud been tested,but,basedonitsformandcolor, Identification successbynon-specialistshasnot likely mostaddedsitesinlow-forestlandscapes. including therestofOhio(table6.1),with with thefullsuiteofMidAFIAlichenplots will becomemoreapparentafterevaluation there. Thebioindicatorpotentialofthisspecies could makeitausefulelementalbioindicator the PollutionIndex;suchpollutiontolerance (r abundance hadweakpositivecorrelation However, evenfromthosefewsites,Punmis present atonlyfiveofthe219subsetplots. States endemic:Brodoandothers2001),was forest landscapes(itisaMidwesternUnited bioindicator speciesfortheMidAregioninlow and others(inpress)asanotherpossible missouriensis) suite ofFIAlichenplots.Punmis( evaluated intheMidAregionwithfull pollution gradient. sites (footnote3)selectedtorepresentthe Flacap camefrom85 percentof26supplemental plots, whileadequateelementalsamplesof (table 6.1) and93percentof219 MidAsubset was presentat~77percentofallMidAplots 2 Recommendations byWill-Wolf andothers Species distributionpatternsshouldbe = 0.02, p =0.021;regressionNS)with (table6.1),suggestedbyWill- Punctelia Wolf will beparticularlyimportanttofurtherrefine species distributionpatternsforallSEplots both theMidAandSEregions.Evaluationof analyses, basedoninclusionofVirginia in are alsosupportedtoadegreefromMidA mostly onlesscommonlichen species. such impactswerepreviously documented lichen recolonization(Gilbert 1992),though and distancefrompropagule sourcecanaffect has longbeenknownthatforestfragmentation become relativelymoreimportantbydefault.It and theimpactofnearbyforestcovermayhave distribution mayhavedeclinedintheseregions, the impactofairpollutiononlichenspecies declined about35–40percent.Soby2017 (PM2.5; correlatedwithpollutionmetals)have has declined~70–75percent,andparticulates declined ~25–30percent,sulfurdioxide(SO regions since2005,nitrogendioxide(NO pollution (USEPA 2017).IntheNEandMidA have beenoffsetbyregionaldeclinesinair impacts onforests(Drohanandothers2012) from expandedfracking,recentlocalpollution production acceleratedstartingabout2000 coal tonaturalgasformucheasternU.S.energy discussed here.Probablybecauseshiftsfrom the 1994–2005FIAlichencommunitydata changed somewhatfromthatrepresentedby for theNE,MidA,andSEregionsmayhave of FIAplots,basedondataintable6.1. species tofallshortofavailabilityat90percent the mostlikelylocationforrecommendedtarget bioindicator recommendations.Thisregionis Distribution ofelementalbioindicatorspecies 2 ) has 2 ) CONCLUSIONS ACKNOWLEDGMENTS Distribution patterns of lichen elemental This project was funded by cooperative bioindicator species with respect to pollution agreement SRS09-CA1130145101, between the and landcover in NE and MidA U.S. regions Forest Service Forest Inventory and Analysis supported recommendations from an upper Program (FIA) and University of Wisconsin- Midwest study, but also suggested the need Madison. We thank reviewers Peter Neitlich, for additional evaluations. One additional Thomas Hall, and report editors for constructive recommendation from this study is to evaluate comments that helped us improve the chapter. Punmis as an elemental bioindicator for the Robert (Wes) Hoyer created the figure 6.1 map MidA region. It appears from evaluation of in ArcGIS. Sarah Friedrich modified all maps and 1994–2005 species distributions that considering prepared the graphs in Adobe Illustrator. nearby forest cover will be more helpful to predict bioindicator species representation at LITERATURE CITED plots in the MidA region than in the NE region. Bari, A.; Rosso, A.; Minciardi, M.R. [and others]. 2001. Additional FIA lichen data from 1994–2005 are Analysis of heavy metals in atmospheric particulates in relation to their bioaccumulation in explanted Pseudevernia available for several eastern regions to conduct furfuracea Thalli. 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