United States Department of Agriculture

Forest Service Northern Research Station General Technical Report NRS-165 December 2016

Monitoring Air Quality in 

Class I Wilderness Areas of the 

Northeastern United States 

Using Lichens and Bryophytes

Alison C. Dibble, James W. Hinds,

Ralph Perron, Natalie Cleavitt,

Richard L. Poirot, and Linda H. Pardo
Abstract

To address a need for air quality and lichen monitoring information for the Northeast, 

we compared bulk chemistry data from 2011-2013 to baseline surveys from 1988 and 

1993 in three Class I Wilderness areas of New Hampshire and Vermont. Plots were 

within the White Mountain National Forest (Presidential Range—Dry River Wilderness 

and Great Gulf Wilderness, New Hampshire) and the Green Mountain National Forest 

(Lye Brook Wilderness, Vermont). We sampled epiphyte communities and found 58 

macrolichen species and 55 bryophyte species. We also analyzed bulk samples for 

total N, total S, and 27 additional elements. We detected a decrease in Pb at the level 

of the National Forest and in a subset of plots. Low lichen richness and poor thallus 

condition at Lye Brook corresponded to higher N and S levels at these sites. Lichen 

thallus condition was best where lichen species richness was also high. Highest Hg 

content, from a limited subset, was on the east slope of Mt. Washington near the 

head of Great Gulf. Most dominant lichens in good condition were associated with 

conifer boles or acidic substrates. The status regarding N and S tolerance for many 

lichens in the northeastern United States is not clear, so the influence of N pollution 

on community data cannot be fully assessed. Continued monitoring of lichens and 

bryophytes, especially if integrated with IMPROVE aerosol data, may reveal changes in 

air quality, climatic conditions, and other potential stressors or stimuli. Lichen health 

was impacted by low air quality at some of our sites.

Cover Photo

Great Gulf Wilderness, White Mountain National Forest, view from head of Chandler 

Brook Trail, August 2013. Photo by Alison C. Dibble, used with permission.

Quality Assurance

This publication conforms to the Northern Research Station’s Quality Assurance 

Implementation Plan which requires technical and policy review for all scientific 

publications produced or funded by the Station. The process included a blind technical 

review by at least two reviewers, who were selected by the Assistant Director for 

Research and unknown to the author. This review policy promotes the Forest Service 

guiding principles of using the best scientific knowledge, striving for quality and 

excellence, maintaining high ethical and professional standards, and being responsible 

and accountable for what we do.

Published by

U.S. FOREST SERVICE

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NEWTOWN SQUARE PA 19073

December 2016

For additional copies:

U.S. Forest Service

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Email: nrspubs@fs.fed.us

Manuscript received for publication 23 November 2015
Monitoring Air Quality in 

Class I Wilderness Areas of the 

Northeastern United States 

Using Lichens and Bryophytes

The Authors

ALISON C. DIBBLE is an assistant research professor with the School of Biology and Ecology, 

University of Maine, 5572 Deering Hall, Orono, ME 04469, adibble2@gmail.com

Phone: (207)-359-4659.

JAMES W. HINDS is faculty associate with the School of Biology and Ecology, University of Maine, 

5572 Deering Hall, Orono, ME 04469, hinds@earthlink.net.

RALPH PERRON is an air quality specialist with the USDA Forest Service, 71 White Mountain Drive, 

Campton, NH 03223, rperron@fs.fed.us. Phone: (802) 222-1444.

NATALIE CLEAVITT is a research associate with the Dept. Natural Resources, Cornell University, 55 

Perch Pond Road Holderness, NH 03245, nlc4@cornell.edu. Phone: (603) 960-2519.

RICHARD L. POIROT (retired) is an air quality planning chief, Air Quality & Climate Division, 

Vermont Dept. of Environmental Conservation, Davis 2, One National Life Drive, Montpelier, VT 

05620-3802. rich.poirot@gmail.com.

LINDA H. PARDO is an environmental engineer with the USDA Forest Service Northern Research 

Station, 81 Carrigan Dr., Burlington, VT 05401, lpardo@fs.fed.us.

General Technical Report NRS-165
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CONTENTS

Introduction ...............................................................................................................................................1

Methods........................................................................................................................................................3

Study Locations....................................................................................................................................3

Timed Plot Surveys..............................................................................................................................3

Scoring Lichen Thallus Condition ..................................................................................................6

Bulk Samples..........................................................................................................................................6

Summarized FIA/FHM Plots.............................................................................................................7

Analytical Approaches for Bulk Chemistry and Thallus Health Scores.............................7

NMS Methods........................................................................................................................................7

IMPROVE Aerosol Chemistry Data and Analysis.......................................................................8

Results...........................................................................................................................................................8

Bulk Sample Chemistry......................................................................................................................8

Assessment of Thallus Condition for Common Macrolichens...........................................11

Species Richness and Among-Plot Patterns.............................................................................12

Epiphyte Community Data Related to Lichen Thallus Health and Air Quality.............19

Relationships With Epiphyte Richness........................................................................................20

Context of Regional Aerosol Data From 1992-2012..............................................................20

Discussion..................................................................................................................................................21

Changes Over Time ...........................................................................................................................21

Comparison to 92 Lichen Indicator Plots Across New England ........................................23

Context of Regional Aerosol Data From 1992-2013..............................................................23

Relationships Between IMPROVE Data and Lichen Data.....................................................23

Some Take-home Messages...........................................................................................................24

Conclusion.................................................................................................................................................25

Acknowledgments ................................................................................................................................26

Literature Cited.......................................................................................................................................26

Contributions of the Authors...........................................................................................................30

Appendix 1: Maps of study areas in White Mountain National Forest, NH, and 

Green Mountain National Forest, VT......................................................................................31

Appendix 2: Multivariate Correlations........................................................................................35

Appendix 3: Project Photos ..............................................................................................................36

Appendix 4: Aerosol Data..................................................................................................................41
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1

INTRODUCTION

Lichen species on trees are indicative of air quality 

both in their species composition and their chemical 

content, and lichens provide a crucial tool in assessing 

conditions that present ongoing problems for human 

health. California, Oregon, and Washington have been 

proving grounds for lichen community composition 

and chemistry analyses that have improved air quality 

monitoring and led to development of the National Air 

Quality and Lichen Monitoring program (Geiser et al. 

2010, Jovan 2008). Monitoring in the Northeast could 

be enhanced by relating lichen sensitivity ratings, N 

indicator status, and critical loads of N deposition and 

other metrics in eastern and western forests (e.g., Pardo 

et al. 2011a, Pardo et al. 2011b). To increase knowledge 

about lichens and bryophytes and their relevance for 

monitoring air quality at three Class I Wilderness Areas 

in two eastern National Forests, we revisited some of 

the locales at which elemental data had been collected 

from four lichen species (Wetmore 1989, 1995a, 1995b). 

While Wetmore did not measure N or Hg, there were 

numerous elements that we expected to compare.

Biomonitoring has been developed since the mid-1800s 

in Europe where lichens and bryophytes continue 

to be widely used for this purpose. These organisms 

are valued as bioindicators in part because they can 

concentrate air- and precipitation-borne elements 

within the lichen thallus or, in the case of mosses, in 

the plant body. Unlike vascular plants, epiphytic lichens 

reflect deposition conditions because the soils are not 

mediating pollution effects; this then makes them 

more effective as indicators than plants. In the U.S. 

Forest Service Inventory and Analysis, Forest Health 

Monitoring Program (FIA/FHM; http://www.fia.

fs.fed.us/program-features/indicators/lichen/), described 

by Geiser et al. (2010), Glavich and Geiser (2008), 

and Jovan (2008), biomonitoring employs epiphytic 

lichens but not bryophytes. Bryophytes can dominate 

the ground layer under conifers and can be abundant 

epiphytes especially in wetter habitats. Many species 

associate with bark of either conifer or deciduous trees 

(Cleavitt et al. 2009). Because of their prevalence in 

some habitats, bryophytes may be used to monitor air 

quality, often as a complement to a lichen biomonitoring 

program. Lichens alone have vast potential to detect 

chemicals with severe consequences for human health 

(e.g., Nascimbene et al. 2014). If used together, epiphytic 

lichens and bryophytes could detect air conditions that 

affect human health (Augusto et al. 2013) and serve as 

indicators of ecological responses to complex changes 

in air quality and climatic conditions.

New efforts have been mounted to standardize 

methods in lichen biomonitoring so that studies can be 

more closely related to each other (see Cristofolini et al. 

2014). Because some lichens are more tolerant of S and 

N deposition than others, their presence or absence on 

boles of forest trees indicates air pollution conditions 

(Geiser et al. 2010, McCune and Geiser 1997). In this 

paper, we follow Pardo et al. (2011a) in use of the terms 

“oligotroph,” “mesotroph,” and “eutroph” (gradient of 

N-tolerance from least to most), though there are other 

systems by which to describe this phenomenon. No 

single set of terms is yet in global use, as goals of studies 

differ, or because lichen communities offer different 

opportunities for understanding pollution tolerance 

depending on the geographical area studied, proximity 

to pollution sources such as smelters, power plants, and 

natural gas production areas, and pollutants involved.

Lichen thalli incorporate airborne particulates, where 

residence time varies according to the element and to 

some extent, with weather. Lichens of dry habitats may 

absorb elements, including heavy metals, differently 

from their counterparts in moist forests. Foliose 

lichens differ from fruticose species in this regard also. 

Samples of lichens subjected to mass spectrometry 

allow comparison of airborne elements across space and 

time, so monitoring of deposition levels can be effective. 

Accuracy, accessibility, and relatively low cost compared 

to air quality instruments are benefits to this type of 

monitoring. Lichen conservation is an underlying 

tenet, because the monitoring process uncovers rare and 

vulnerable species and provides understanding of their 

habitats. Forest management can reflect knowledge 

gained through the air quality monitoring activities. It 

can also favor tree species that harbor certain unusual 

lichens or favor the lichens themselves that may require 

forest continuity and/or closed canopies.

Categories of response to various pollutants have been 

compiled especially for N, S, and O3 at http://gis.

nacse.org/lichenair/?page/sensitivity. Some examples 

of northeastern epiphytic lichens include Lobaria 

pulmonaria, which has a low to moderate tolerance for 

N and is sensitive to S, and the widespread Hypogymnia 
2

physodes, which tolerates a wide range of N and 

ranges from intermediate to tolerant regarding S and 

acid deposition. Evernia mesomorpha has a particular 

vulnerability to SO2 while Ramalina farinacea has 

a moderate tolerance for Pb (Alvarez et al. 2012). 

Thus the use of a wide spectrum elemental analyses, 

not monitoring of percent N and percent S alone, is 

typically undertaken because of known toxicity to 

lichens of some elements.

Sources of pollutants are varied, and multiple types 

of sources are possible for many elements. Since 

enactment of the Clean Air Act of 1977 (cited below), 

major sources have been changing. For example, Pb was 

mainly from leaded gasoline prior to phase out. The 

Environmental Protection Agency noted a 94 percent 

decline in emissions of Pb between 1980 and 1999, 

but Pb continues to originate from various sources 

including piston-engine aircraft that use leaded fuel and 

from ore and metal processing plants (e.g., lead smelters 

are upwind of the Wilderness Areas we measured). 

Ni and V are from fossil fuel combustion. SO2 has 

largely been from factories and electric utilities. NOx is 

emitted by both electric utilities and the transportation 

sector, and ammonia is generated from livestock waste 

and fertilized soil. Atmospheric Hg in the United 

States is primarily from coal-fired power plants, and 

as with many of these other elements, is involved in 

long-range transport. Fluorine emissions are associated 

with power plants and with production of Al, Zn, and 

phosphate ore-reduction. This is a dynamic picture. 

Recent fracking activities involve ground disturbance, 

trucking, and leaks at gas wells that could account for 

increases in methane, butane, propane, and particulates 

yet to be well documented for the Northeast.

Class I Wilderness Areas, with their few roads, large 

areas of vegetation and minimal or no soil disturbance, 

represent air quality conditions that can be compared 

across regions and over time. As part of the Clean Air 

Amendments of 1977, Congress established a national 

goal of “preventing any future, and remedying any 

existing, impairment of visibility in mandatory class 

I Federal areas [in] which impairment results from 

manmade air pollution” (Clean Air Act of 1970, as 

amended August 1977). This national goal, along with 

the Federal Land Manager being given “affirmative 

responsibility to protect air quality related values”, 

including lichens, within Class I Areas (same CAAA 

source as previous, p. 52), creates a need to understand 

the existing health of air quality related values, including 

lichens. Air quality has improved in the United States 

from 1980 to 2012, due to the 67 percent reduction of 

aggregate emissions of six common pollutants (EPA n.d.).

Decline in some lichen species has been documented 

in some areas of the Northeast and there is concern 

that such decline is ongoing in New England (Hinds 

and Hinds 2008). This trend is despite reduced acidity 

of precipitation, e.g., at Hubbard Brook in the White 

Mountain National Forest (WMNF) where pH of 

wet deposition in 1965 was about 4.1 and is currently 

about 5.0. Also, visibility in terms of regional haze has 

improved at the rate of about 3 percent per year from 

2001 to 2008 near Great Gulf, and 3 percent per year 

from 2001 to 2009 near Lye Brook (Hand et al. 2011)

In a model to establish critical loads for atmospheric 

N, or loads below which significant harmful effects are 

expected to not occur, Geiser et al. (2010) related lichen 

community data for northwestern maritime forests to 

speciated aerosol measurements collected at 12 sites 

by the Interagency Monitoring of Visual Protected 

Environments (IMPROVE) visibility network. 

IMPROVE monitors are typically sited at ridgetops 

to measure air quality reflective of broad spatial scales, 

and it is unknown whether proximity of lichen plots 

to such aerosol sites can help explain trends in lichen 

bulk chemistry. IMPROVE data have the benefit of 

including metals as well as N and S, while sites in 

the smaller Clean Air Status and Trends Network 

(CASTNet) include gaseous as well as particulate S and 

N components and have advantages for dry deposition 

calculations of those species.

Lichen plots established in three northeastern Class I 

Wilderness Areas by Wetmore (1989, 1995a, 1995b) 

present an opportunity to remeasure and possibly detect 

changes in air quality as evidenced in bulk chemistry 

of lichen thalli. Approaches of this project were to (1) 

investigate potential for remeasurement on Wetmore’s 

plots and establish new plots as a baseline using FIA/

FHM protocol regarding the P3 lichen indicator, (2) 

incorporate bryophyte diversity, abundance, and bulk 

chemistry in the new plots, (3) compare to lichen 

biomonitoring in the Pacific Northwest and California, 

and (4) seek to increase utility and repeatability of air 

quality assessments using lichens.
3

METHODS

Study Locations

We established lichen and bryophyte monitoring sites 

in 2011-2013 in three Class I Wilderness Areas of the 

Northeast, attempting to duplicate Wetmore’s locations 

where appropriate for collection purposes (Fig. 1). Two 

Wilderness Areas are in the WMNF: Presidential 

Range—Dry River Wilderness and Great Gulf 

Wilderness. The Lye Brook Wilderness is in the Green 

Mountain National Forest (GMNF). Four plots were 

established in Great Gulf and four at Lye Brook, while 

in Presidential Range—Dry River Wilderness three 

plots were established within the Wilderness Area 

and one plot just outside of the Wilderness but still 

within the National Forest (N=12). Exact locations, 

plot numbers, and the characteristics at each location 

are in Tables 1 and 2, and maps are in appendix 1. We 

were limited to plots that had trees because we were 

sampling epiphytes.

Timed Plot Surveys

We used a modification of the FIA lichen monitoring 

protocol as described in McCune et al. (1997). We 

sought to remeasure some locales sampled by Wetmore 

(1989, 1995a, 1995b; plot locations in Table 1), so we 

established a system of 12 circular plots, each 34.7 m 

in radius. The area of the lichen plot is approximately 

3793 m2 or ≈ 0.379 ha (40,830 ft2 or ≈ 0.937 acres).

To assess variables that could help explain lichen 

distribution and elemental load, we collected elevation, 

slope, and aspect data at each plot and characterized 

the forest stand. Coordinates were taken for plot center 

using a handheld global positioning system (GPS). 

A tree tally was conducted on all boles greater than 

10 cm diameter, at 1.37 m above the ground, by tree 

species, scored alive or dead, in a pie-shaped quartersection of the full plot (sample area 948.25 m2

) except 

at plot GR1, where tree tally was on a 7.32 m radius 

(area 168.33 m2). Trees were summarized by diameter 

size classes: 10-12.5, 12.6-25.3, 25.4-36.8, >36.8 cm, 

Figure 1.—Location of lichen and bryophyte 

sample points in three Class I Wilderness Areas, LB= 

Lye Brook (Green Mountain National Forest shown 

in gray in Vermont); DR= Presidential Range—Dry 

River; GG = Great Gulf (White Mountain National 

Forest shown in gray in New Hampshire). IMPROVE 

sites labeled as such.
4

Table 1.—Epiphyte and air quality monitoring plots established in Class I Wilderness Areas 2011-2013. * = on or near lichen plot established 

by Clifford Wetmore in 1980s-90s in which he collected bulk samples for elemental analysis. + = near plot established by Wetmore but 

elemental analysis not conducted by him.

Plot 

name

Wetmore’s 

plot, year(s)

Coordinates in UTM and location notes Date 

installed

County, 

State

Habitat Bulk samples 

DR1* NE Mt. 

Crawford

1988, 1993

19 T 0314052, 4889907 

White Mountain National Forest, 

Presidential Range—Dry River, East of 

Crawford Mtn, along Davis Path

Aug 14, 

2012

Coos, NH Mature montane red 

spruce forest, among 

granite ledges

Evernia mesomorpha, 

Hypogymnia physodes, 

Pleurozium schreberi

DR3* Rocky Branch 

Ridge

1993

19T 0317811, 4896845

White Mountain National Forest, 

Presidential Range—Dry River, Rocky 

Branch Trail, Engine Hill

Aug 13, 

2012

Coos, NH Mature montane spruce 

slope forest, balsam fir 

dominant

Evernia mesomorpha, 

Hypogymnia physodes, 

Pleurozium schreberi

DR5 19T 0310742, 4898788

White Mountain National Forest, 

Presidential Range—Dry River, below 

Mizpah Hut

Aug 15, 

2012

Coos, NH Bench on SE-facing slope, 

balsam fir dominant

Hypogymnia physodes, 

Pleurozium schreberi

DR6 19T 0310663, 4899744

White Mountain National Forest, 

Presidential Dry River, Crawford Path, 

to W of Webster Cliff Trail above Gibbs 

Brook watershed, Mt. Pierce (Mt. Clinton) 

Aug 16, 

2012

Coos, NH Mature balsam fir forest on 

NW-facing upper slope. 

Hypogymnia physodes, 

Pleurozium schreberi

GG1* Low’s (sic) 

Bald Spot

1993

19T 0320299, 4905656

White Mountain National Forest, Great 

Gulf Wilderness, Mt. Washington Auto 

Road, Mile Marker 2, Madison Gulf Trail 

and Appalachian Trail, Lowe’s Bald Knob 

Aug 2, 

2011

Coos, NH Mature red spruce-balsam 

fir with pitch pine on ledge

Evernia mesomorpha, 

Hypogymnia physodes

GG2 19 T 0320019, 4906334

White Mountain National Forest, Great 

Gulf Wilderness, Mt. Washington Auto 

Road, Mile Marker 2, Madison Gulf Trail 

and Appalachian Trail 

Aug 15, 

2013

Coos, NH Mature red spruce-balsam 

fir-yellow birch, with some 

red maple, with stream

Evernia mesomorpha, 

Hypogymnia physodes, 

Hylocomium splendens

GG3* Wamsutta 

Trail 1993

19T 0317872 4907222

White Mountain National Forest, Great 

Gulf Wilderness, Mt. Washington Auto 

Road, Mile Marker 4, Chandler Brook Trail 

Aug 16, 

2013

Coos, NH Mature red spruce, balsam 

fir, mountain paper birch

Evernia mesomorpha, 

Pleurozium schreberi, 

Hylocomium splendens

GG4 19T 0320717, 4907982

White Mountain National Forest, Great 

Gulf Wilderness, Osgood Trail ca. 0.1 m 

W of Jct with Great Gulf Trail; Great Gulf 

Trail parking lot, proceed ca. 1.8 mi. along 

Peabody River West Branch 

Aug 17, 

2013

Coos, NH Mixed hardwoods, mature 

yellow birch, eastern 

hemlock, red spruce, red 

maple, with striped maple 

and hobblebush

Evernia mesomorpha, 

Hypogymnia physodes, 

Hylocomium splendens, 

Pleurozium schreberi

LB1+ 18T 0661520, 4780539

Green Mountain National Forest, Lye 

Brook Wilderness, off Rootville Trail above 

Downer Glen Ravine, Manchester

Aug 19, 

2013

Bennington, 

VT

Mature mixed woods in 

exposed, dry site at top 

of cliff, red maple, eastern 

hemlock, yellow birch, 

some black cherry

Evernia mesomorpha, 

Hypogymnia physodes, 

no moss

LB2* Bourn (sic) 

Pond 1993

18T 0662302, 4774636

Green Mountain National Forest, 

Lye Brook Wilderness, Bourne Pond, 

Sunderland

Aug 20, 

2013

Bennington, 

VT

Forested wetland with 

mid-successional red 

spruce, balsam fir, red 

maple

Evernia mesomorpha, 

Hypogymnia physodes, 

Pleurozium schreberi

LB3+ Lye Brook 

1993

18T 0657837, 4775889

Green Mountain National Forest, Lye 

Brook Wilderness, hill W of Lye Brook, e 

of and above Rte 7 near summit of ridge 

above Lye Brook, Manchester

Aug 21, 

2013

Bennington, 

VT

Mature red spruce slope 

forest, over marble rock

Evernia mesomorpha, 

Hypogymnia physodes, 

Pleurozium schreberi

LB4* Kelly Stand 

1993

18T 0659626, 4760894

Green Mountain National Forest, Lye 

Brook Wilderness, Branch Pond Rd (FR 70) 

near jct Kelley Stand Rd, Sunderland

Aug 22, 

2013

Bennington, 

VT

Low elevation mixed 

hardwoods dominated by 

birches, beech, red maple 

with red spruce

Evernia mesomorpha, 

Hypogymnia physodes, 

Pleurozium schreberi
5

with the eight largest trees indicated as potentially 

important for assessing old growth. For 12 plots, 

plot center and at the 7.32 m radius in four cardinal 

directions, a 10 basal area factor prism was used to 

obtain additional data on basal area. Over the 12 plots, 

we measured a total of 1,242 trees for species, d.b.h., 

and status as live/dead. Percent gap in forest canopy 

was visually estimated to the nearest 10 percent.

To inventory macrolichens, we conducted two-hour 

timed surveys in which we documented macrolichen 

species on woody substrata (live and dead tree boles 

and shrubs) between 0.5 and 1.5 m from the ground 

and on recently fallen boles, branches and twigs 

(McCune et al. 1997, USDA FS 2004). Voucher 

specimens were taken for each species and abundance 

ratings were assigned based on the frequency of lichen 

occurrence (scale of 1 - 4 used by FIA protocol; 

USDA FS 2004—App. A). A one-hour survey for 

bryophyte epiphytes was also conducted in the plots 

during the same visit. On seven of the 12 plots where 

two collectors were present, the timed lichen and 

bryophyte surveys were conducted simultaneously 

totaling 1.5 hours per person for each half of the plot. 

A companion investigation of stand integrity was 

conducted alongside this project, mostly off plot, by 

Steven B. Selva, who inventoried the calicioid lichens 

or “stubble lichens.” These consist of both lichenized 

and non-lichenized fungi, with use for assessing forest 

continuity by Selva (2002, 2013, 2014) and others (e.g., 

Tibell 1992). Selva’s results from these Wilderness 

Areas are reported in Selva (2014).

Species Identification and Richness

Lichen specimens were tentatively identified during 

field data collection so that abundances could be 

assigned. Vouchers were collected for all species, 

and identifications were finalized following the 

nomenclature of Esslinger (2013) and Hinds and 

Hinds (2008). Bryophytes likewise were identified 

to genus in the field and determined under the 

microscope following the nomenclature of Allen 

(2005, and unpublished data). Lichen and bryophyte 

specimens are to be deposited at the University of 

Maine Herbarium (MAINE). Criteria for designation 

of lichens as associated with old growth were based on 

Hinds and Hinds (2008), and old-growth bryophytes 

were based on Cleavitt et al. (2009). To place our 12 

plot surveys in a regional context, we compared our 

species occurrences to those found on 92 FIA/FHM 

plots in New England (Hinds and Hinds 2008). 

Comparison to lichen community data collected by 

Wetmore (1989, 1995a and b) was not possible because 

of plot size differences, our focus on epiphytes only, 

and Wetmore’s lists by Wilderness Area over all plots 

rather than by individual plot.

Table 2.—Summary of plot characteristics and tree data taken on plots. “Hwd” = deciduous trees (versus conifers). Tree size classes are 

(1) 12.6-25.3 cm diameter at 1.37 m above the ground, (2) 25.4-36.8, and (3) >36.8.

Plot

Elev 

(m)

Aspect

(°)

Slope 

(%)

Basal 

area 

(m2

/ha)

Quadratic 

mean 

diameter 

(cm) 

Hwd 

(%)

Size classes 

of 8 largest 

trees

Visual 

gap

 (%)

Recent 

gaps

(<5yrs) Tree species in order of importance

DR1 925 70 0 14.25 18.55 4 2 5 Y Picea rubens, Abies balsamea 

DR3 959 70 0 33.98 17.50 39 1,2 5 Y A. balsamea, P. rubens, Betula spp. 

DR5 1135 80 10 39.60 15.82 36 1,2 12 Y A. balsamea, Sorbus sp., Betula spp., P. rubens

DR6 1245 70 0 42.97 16.23 1 2 0 Y A. balsamea, Sorbus sp.

GG1 857 150 5 24.92 21.10 9 1,2 40 N A. balsamea, Betula cordifolia

GG2 785 72 30 31.09 21.39 86 2,3 5 N A. balsamea, Betula alleghaniensis, Acer spp., Betula spp., 

Picea rubens

GG3 1138 60 30 45.20 19.07 17 2,3 20 Y A. balsamea, B. cordifolia, P. rubens

GG4 594 45 5 37.36 28.64 72 3 15 N B. alleghaniensis, A. balsamea, Acer spp. 

LB1 561 20 5 35.24 30.41 34 3 5 N Acer rubrum, Tsuga canadensis, Fagus grandifolia 

LB2 852 70 5 21.00 16.04 35 1,2 60 Y A. balsamea, P. rubens, B. cordifolia

LB3 809 290 30 34.46 18.70 2 2,3 2 Y P. rubens, B. cordifolia

LB4 803 170 5 30.94 19.42 85 2,3 5 N Fagus grandifolia, P. rubens, Betula spp., Acer spp.
6

Scoring Lichen Thallus Condition

Macrolichen specimens collected as vouchers were 

reviewed for scoring of outward appearance of thallus 

condition, referred to as “health.” We used a 4-point 

scale: 0 = thallus in very poor condition (more than 

one of the following symptoms: convoluted lobes, 

bleaching, black speckles, pink blotchy areas) or 

extensive other discoloration and/or damage; 1 = 

thallus in poor condition (one or two of the previously 

listed symptoms, but not as extensive on the thallus 

surface); 2 = within the normal range for the species; 

and 3 = robust specimen (Fig. 2). If a specimen was 

the primary collection in a packet, it was scored for 

health but secondary specimens in the same packet 

were not scored. Secondary specimens were not scored. 

Scores were summed for each species and divided by 

the number of collections per plot to find the average 

score for that species on the plot. All scoring was done 

to avoid variation that could occur among observers. 

A total of 632 specimens from three Wilderness Areas 

was evaluated.

Bulk Samples

To increase potential for comparing our data with dry 

weight chemistry results of Wetmore (1989, 1995a, 

1995b), we sought a subset of his sampling areas so 

that we could compare results for near spots on the 

ground, and we engaged the same lab he had used for 

mass spectrometry. Bulk sample collections included 

both lichen thalli from branches and bark, and moss 

stems from the ground layer. We sought sufficient 

material off-plot for Hypogymnia physodes, with backup 

Evernia mesomorpha, and for the bryophyte Hylocomium 

splendens, with backup Pleurozium schreberi. Vinyl 

gloves were used to reduce contamination, and airdried specimens were cleaned of any twigs, spruce/

fir needles, or non-target lichens with a time-limit of 

two hours per sample, and shipped to the Research 

Analytical Lab at the University of Minnesota, St. 

Paul, Minnesota (http://ral.cfans.umn.edu/). There they 

were oven dried, ground, and analyzed simultaneously 

for total N (LECO FP-528 total N analyzer) and 

total S (LECO SC-132 S analyzer, manufactured by 

the LECO Corp., St. Joseph, Michigan), following 

methods of Geiser (2004). Totals are expressed as 

percent content of sample dry weight.

We analyzed for 27 elements using inductively coupled 

plasma atomic emission spectrometry (ICP-AES) (Al, 

As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, 

Mo, Na, Ni, P, Pb, Rb, S, Si, Sr, Ti, V, and Zn). Results 

Figure 2.—Differences in thallus health of the common species, Parmelia sulcata. The specimen on the left received a health score of 2 

(normal) while the specimen on the right received a health score of 0 (poor condition). Photos were taken at 14 times magnification with 

the decreased size of the poor specimen being actual size difference. Photos were taken by Patricia Hinds on specimens collected for this 

project and scored by Jim Hinds. Photos used with permission.
7

are expressed in ppm or ppb unless noted otherwise 

(μg/g = ppm and ng/g = ppb). Dried lichen or plant 

material of about 500-1000 mg was weighed into a 20 

mL high form silica crucible and dry ashed at 485 °C 

for 10-12 hours. Crucibles were covered during the 

ashing as a precaution against contamination. Ash was 

equilibrated with 5 mL of 20 percent HCl at room 

temperature for 30 minutes. Then 5 mL of deionized 

water was added, gently swirled, and allowed to settle 

for 3 hours. The solution was decanted into 15 ml 

plastic disposable tubes for direct determination by 

ICP-AES. Simultaneous sampling of total N, total S, 

and elements was recommended by Blett et al. (2003). 

For some Areas and samples, data sent from the lab 

did not include Ba, Be, Co, Li, Rb, Si, Ti, and V. Total 

Hg was obtained from three plots at Great Gulf 

and four at Lye Brook (lacking for the Presidential 

Range—Dry River) for the purpose of comparing Hg 

across geographic area and to establish a baseline for 

estimating changes in Hg deposition.

Summarized FIA/FHM Plots

To provide more context for data collected during 

this project, we compared data from the 12 new plots 

to a list of lichens found in 92 FIA/FHM P3 lichen 

indicator plots on National Forests, state lands, or 

private woodlots across New England (Connecticut, 

Massachusetts, Maine, New Hampshire, Rhode Island). 

All plots are part of a system of permanent plots in 

forested habitats across the United States. These had 

been analyzed by Hinds and Hinds (2008).

Analytical Approaches for Bulk 

Chemistry and Thallus Health Scores

To compare new bulk chemistry information to that 

of Wetmore’s earlier reports (1989, 1995a, 1995b), we 

sought structure in the data for seven remeasured plots 

using Excel® pivot charts and pivot tables, especially 

bar graphs for total N, total P, and elements for which a 

matchup of data were available.

Bulk chemistry samples were also analyzed by 

organism type (lichens vs. mosses), by National Forest, 

by Wilderness Area, and within plots over time. 

Differences between the group of lichens (“lichen 

thalli”: Hypogymnia physodes, Evernia mesomorpha, 

Platismatia glauca) and the group of mosses 

(“moss stems”: Hylocomium splendens, Pleurozium 

schreberi) were greater than within-group differences. 

Data for Ti and V were lacking for Presidential 

Range—Dry River and for plot GG1.

For both bulk chemistry and lichen thallus health 

scores, Welch’s test allowing unequal variances was 

used to accommodate differences in sample size 

between groups. We then used Tukey’s HSD betweenpair comparison as a post-hoc test for differentiating 

significant subsets when the Welch’s ANOVA was 

significant. Analyses were run using JMP Pro 10 (SAS 

Institute, Cary, NC).

NMS Methods

Species and site data were analyzed using Non-metric 

Multidimensional Scaling (NMS) in PC-ORD ver. 

6.0 (MJM Software, Gleneden, OR). Only species 

present at three or more plots were included and this 

left a data set with 12 sites and 60 species (15 mosses, 

8 liverworts, 37 macrolichens). The site variables 

included in the second matrix were elevation, percent 

of hardwoods, percent conifers, eastness, northness, 

percent of shrub cover, low shrubs (binary), new gaps 

(binary), chemistry data from the bulk samples for 

percent N and S, contents of Al, Cu, Fe, Ni and Pb, 

epiphyte species richness values, and mean thallus 

health scores. Aspect was decomposed into variables of 

eastness as sin (aspect) and northness cos (aspect). For 

eastness, a value of 1 is due east and a value of -1 is due 

west. For northness, a value of 1 is due north (0 degrees 

true) and a value of -1 is due south (180 degrees).

The NMS analysis is an ordination method that uses 

an iterative search for rankings and placement of the 

analyzed variables to find the solution that minimizes 

stress (McCune and Mefford 1999). Because NMS 

uses ranks, it can be considered a non-parametric 

form of ordination with relaxed assumptions on data 

structure that are usually more applicable to ecological 

data (McCune and Mefford 1999). All NMS analyses 

were run using Sorensen’s distance measure with 100 

runs using real data and 50 runs of randomized data. 

The instability criterion was 0.00001 with 500 as the 

maximum number of iterations. The final solution was 

chosen based on the dimensionality with the lowest 

mean stress from a run comparing randomized to real 

data (McCune and Mefford 1999).
8

IMPROVE Aerosol Chemistry Data and 

Analysis

We obtained elemental (x-ray fluorescence) and ionic 

(ion chromatography) aerosol data from IMPROVE 

sites adjacent to the Lye Brook Wilderness (LYBR1, 

1991-2012, and LYEB1, 2011-2012) and to the Great 

Gulf Wilderness (GRGU1, 2001-2012). These data are 

collected every 3 days, year round. They were acquired 

from the Federal Land Manager Environmental 

Database (http://views.cira.colostate.edu/fed/), with the 

concurrent 2001-2012 data used to assess differences 

between sites and the longer-term Lye Brook data used 

to assess long-term trends. More detailed exploration 

of these regional aerosol data and their relationship to 

lichen bulk chemistry will be presented in a separate 

report1

, and they are briefly summarized below in terms 

of the activity of monitoring using lichens.

RESULTS

Bulk Sample Chemistry

Comparison of present day samples across sites. Epiphytic 

lichen thalli vs. ground layer moss stems differed 

significantly for all elements summarized here in 

ANOVA comparisons by Wilderness Area using 

Welch’s test and involving percent N, percent S, and 

ppm by sample dry weight for Al, Cu, Fe, Ni, Pb, and 

Zn (Figs. 3a, b). Lichen thalli contained higher levels of 

most heavy metals (Al, Fe, Ni, Pb, Zn; Ti and V differed 

likewise but not shown) while the mosses had higher 

values for percent N and S and Cu. Subsequently, 

comparisons among areas were done separately for 

lichen thalli and moss stems, which decreased our 

sample size and power to detect among-site differences. 

For moss samples, only Al was significantly different 

among sites with higher levels at Presidential Range—

Dry River (Fig. 3a).

For lichen samples, percent N was higher at Lye Brook, 

intermediate at Great Gulf, and lowest at Presidential 

Range—Dry River, while percent S did not differ 

significantly among study sites (Fig. 3b). These are not 

all at the same elevation. Presidential Range—Dry 

River and Lye Brook tended to be higher for the heavy 

metals, while Lye Brook was also higher for percent 

N and S (Figs. 3a and b). Plot DR5 had particularly 

high levels of heavy metals (Al: 522 μg/g, Fe: 446 μg/g, 

Ni: 2.46 μg/g, Pb: 30.38, Zn: 125 μg/g), well above 

the mean for the other plots and exceeding Wetmore’s 

Figure 3.—At the level of Wilderness Area, comparison in 2011-2013 bulk chemistry for lichens and mosses regarding (A) Al, Cu, Fe, and 

Zn; (B) percent N, percent S, Ni, and Pb. Note that lichen thalli were collected from branches and bark while moss stems were collected 

from the ground layer. Wilderness Areas codes as for Figure 1. Bars are the mean value with one standard error. Bars with different letters 

are significantly different at α = 0.05 by Tukey HSD.

(A) (B)

1 Cleavitt et al., unpublished data. Available from author 

N. Cleavitt.
9

maximum value for Cu (DR5: 9.67 μg/g; Wetmore’s 

maximum: 6.70 μg/g; data not shown). Two plots at 

Lye Brook, LB1 and LB4, had higher heavy metal 

values. At LB1, the lichen thalli had Ni content of 

4.46 μg/g, which was four times the average value (not 

shown). At LB4, the values for Al (537 μg/g) and Fe 

(500 μg/g) were both notably high.

Comparison of National Forests across sample times.

We pooled plots by National Forest and examined 

differences in bulk sample chemistry. Four elements 

have changed between sample times: Pb, total S, Al, 

and Cu (Figs. 4a-d). We examined three main effects: 

lichen species (Hypogymnia physodes and Evernia 

mesomorpha), year (1988, 1993, or 2011-2013) and 

National Forest (Green vs. White Mountain National 

Forests). The lichen species differed significantly in 

their element contents for all heavy metals (all p < 

0.001) with the foliose H. physodes consistently higher 

in contents than the fruticose E. mesomorpha (Fig. 4). 

Figure 4.—At the level of two National Forests, comparison between Wetmore's 1993 samples in bulk chemistry and those of 2011-2013 

for bulk samples of two lichen species, Evernia mesomorpha and Hypogymnia physodes. Note that the 1988 sample data from Wetmore 

for the WMNF is shown but not included in the analysis because of low replication and lack of samples at GMNF. The elements with 

significant differences were: (A) Pb, (B) S, (C) Al, and (D) Cu. Bars are the mean value with one standard error. Bars with different letters 

are significantly different at α = 0.05 by Tukey HSD.

(B)

(C)

(A)

(D)
10

However, the two lichens show the same patterns in 

the elements with a decrease in Pb, and an increase in 

Al and Cu between Wetmore samples and our recent 

samples (Fig. 3).

Since 1993, both Pb content (F1,67 = 41.08; p < 0.0001) 

and percent of S (F1,67 = 5.71; p =0.0197) of the lichen 

thalli decreased significantly while Al (F1,67 = 32.70; p 

< 0.0001) and Cu (F1,67 = 92.46; p < 0.0001) content 

increased (Fig. 4). In bulk lichen samples at the 

National Forest level, the Pb content (F1,67 = 11.14; p = 

0.0014) was higher at WMNF, while percent S (F1,67 

= 10.76; p = 0.0016) was higher in samples from the 

GMNF (Fig. 4).

Plot by plot comparisons. Direct comparisons between 

Wetmore’s seven plots that we remeasured in 2011-

2013 brought out few unequivocal differences that we 

consider indicative of change over time. There was a 

decrease in Pb in two lichens, Evernia mesomorpha and 

Hypogymnia physodes (Fig. 5). An exceptionally high 

level of Mg++ in Hypogymnia physodes was found at 

Figure 5.—At the plot level, repeat measurement data for dry weight Pb in ppm within thalli of 

epiphytic lichens (A) Hypogymnia physodes and (B) Evernia mesomorpha. Plots for which there are 

multiple years are those measured initially by Wetmore; 2011-2013 data are from this study.

(B)

(A)
11

LB4 (not shown). It was more than twice as high as 

any other reading in the seven comparison plots and 

could be associated with acid rainfall. Otherwise, dry 

weight results for many elements were inconclusive or 

showed increase, and because of complications with the 

greater precision of recent instruments, we interpret 

that the only clear trend was decrease in Pb in the plotby-plot comparisons.

The Wilderness Area averages for 2011-2013 samples 

of Hg content in lichen thalli at Great Gulf (179.4 

± 76 ppb) and Lye Brook (175.7 ± 18 ppb; levels for 

Evernia mesomorpha shown in Fig. 6) were four times 

that of the moss stems (46.4 ± 6 and 38.8 ± 2 ppb, 

respectively). Plot GG3 had a much higher Hg level 

in Evernia mesomorpha than any other sample (264.95 

ppb). On an individual plot basis, mosses had much 

lower Hg levels than lichens by as much as five times 

(not shown).

Multivariate correlations for the elements we tested are 

provided in appendix 2.

Assessment of Thallus Condition for 

Common Macrolichens

There were significant differences in condition of 

lichen thalli, referred to here as “health,” among the 

plots (F11,89 = 12.42; p < 0.0001; Fig. 7a). When plots 

were lumped by Wilderness Area (F2, 167 = 24.99; 

p < 0.0001), each Area was distinct. Lichens of 

Presidential Range—Dry River plots were in the best 

condition, followed by those in Great Gulf and Lye 

Brook (Fig. 7b). Lichens of DR3 were not significantly 

different from the Lye Brook plots and had the lowest 

condition score for Presidential Range—Dry River. 

Poor condition of lichens in GG4, the lowest in 

elevation of the Great Gulf plots, approached those of 

Lye Brook (Fig. 5a). Cladonia spp. were less sensitive 

to difference among areas within the WMNF (Fig. 

7c).

Thallus condition differences were also compared by 

lichen frequency groups (common, infrequent, rare) 

across the three areas. For the 14 common species 

(occurring in at least two plots per area: F2, 94 = 11.36; 

p < 0.0001), Lye Brook lichens had significantly worse 

condition than those of both Presidential Range—Dry 

River and Great Gulf (subsets not shown). For the 

remaining less frequent or rare species, thallus health 

was significantly greater at Presidential Range—Dry 

River (F2,69 = 14.05; p < 0.0001; subsets not shown). 

The differences among Wilderness Areas with all the 

species were a result of poorer condition of both the 

common and infrequent lichens at Lye Brook and 

poorer condition of infrequent lichens at Great Gulf.

Figure 6.—At the plot level, dry weight of Hg in ppb in samples of epiphytic lichen Evernia 

mesomorpha in seven of 12 plots established in Class I Wilderness Areas in 2011-2013. 

Wilderness Area codes as in Fig. 1.
12

Species Richness and Among-Plot 

Patterns

In the timed epiphyte surveys at 12 plots, we found 

113 species (39 mosses, 16 liverworts, and 58 

macrolichens), of which 58 species of cryptogams 

were present in sufficient frequency to be included 

in a multivariate analysis (Tables 3 and 4). For 

macrolichens, Great Gulf sites had a total of 45 

species, but plot GG4 had only 16 species. For the 

Presidential Range—Dry River plots, there were 

41 species, and DR5 had the fewest (19 species). 

Lye Brook had 33 species in total and both LB1 

(13 species) and LB3 (16 species) were particularly 

depauperate. For the mosses and liverworts, Great 

Gulf had 39 bryophyte species (28 mosses and 11 

liverworts). Presidential Range—Dry River had 

only 13 bryophyte species recorded across the four 

plots. Lye Brook had 40 species (26 mosses and 14 

liverworts), including many bryophytes that were 

unique to that Wilderness Area (Tables 3, 4).

The most common lichens, found in at least 10 of 

our 12 plots, were Cladonia coniocraea, C. ochrochlora, 

Hypogymnia physodes, Parmelia saxatilis, P. sulcata, 

and Usnocetraria oakesiana. The most common 

bryophytes were two mosses, Hypnum pallescens and 

Orthodicranum montanum, and a liverwort: Ptilidium 

pulcherrimum (Table 4).

Macrolichen species distribution was such that 36.2 

percent of species occurred at only one (16 spp.) or 

two (four spp.) plots (Table 3). The mosses were about 

twice as likely to be sporadic or rare in occurrence with 

61.5 percent of the species found at only one (15 spp.) 

or two (nine spp.) plots. Fifty percent of liverworts 

were found at only one (seven spp.) or two (one sp.) 

plots. Species unique to each site are noted in Table 4.

Figure 7.—Visual rating of thallus health on specimens 

collected during the timed surveys for (A) all 12 plots, (B) all 

species pooled by Wilderness Area, and (C) Cladonia species 

only, by Wilderness Area. The bars are mean for the plot with 

one standard error from the mean. Bars with no overlap in 

letters are significantly different at α = 0.05 by Tukey HSD.

(B)

(C)

(A)
13

Table 3.—Species richness in a summary of epiphyte surveys on 12 plots located in Class I Wilderness 

Areas of the National Forest. Old-growth indicators (Cleavitt et al. 2009, Hinds and Hinds 2008) are given 

as bryophyte species/macrolichen species. Eutroph species status derived in Table 6.

Site/plot

Epiphyte 

richness

Moss 

richness

Liverwort 

richness

Macrolichen 

richness

Old-growth

indicators

Fruticose

species

Eutroph

species

All sites 113 39 16 58 5/13 7 4

Presidential 

Range—

Dry River

DR1 35 4 1 30 0/5 5 0

DR3 34 8 2 24 2/2 1 1

DR5 26 6 1 19 0/3 0

DR6 35 3 1 31 0/3 4 0

Great Gulf

GG1 39 8 2 29 2/2 3 0

GG2 40 10 4 26 2/5 2 1

GG3 44 12 5 27 0/2 4 0

GG4 39 15 8 16 3/1 0 3

Lye Brook

LB1 26 7 6 13 4/0 0 3

LB2 45 12 9 24 4/2 1 0

LB3 39 11 4 24 2/2 2 1

LB4 44 20 8 16 5/3 1 4

Table 4.—Epiphyte species found in the timed surveys at 12 plots, with presence (“1”), frequency by Wilderness Area, and 

overall. Plot codes as in Fig. 1. See footnote for list of unique species by Wilderness Area.

Plots

Epiphyte species

DR

1

DR

3

DR

5

DR

6

DR

freq

GG

1

GG

2

GG

3

GG

4

GG

freq

LB

1

LB

2

LB

3

LB

4

LB

freq

All 

freq

Mosses

Amblystegium serpens 0 0 1 1 1

Anacamptodon splachnoides 0 0 1 1 1

Brachythecium laetum 0 1 1 1 1 2

Brachythecium roteanum 0 1 1 2 1 1 3

Brachythecium rutabulum 0 1 1 0 1

Brotherella recurvans 0 1 1 0 1

Callicladium haldanianum 0 1 1 1 1 1 3 4

Dicranum fuscescens 1 1 1 1 4 1 1 0 5

Dicranum scoparium 1 1 2 1 1 1 1 4

Dicranum viride 0 1 1 1 1 2

Drepanocladus aduncus 0 1 1 0 1

Eurhynchium pulchellum 0 1 1 0 1

Hylocomium splendens 0 1 1 0 1

Hypnum cupressiforme 0 0 1 1 1

Hypnum faurei 1 1 0 0 1

Hypnum imponens 0 1 1 0 1

Hypnum pallescens 1 1 1 3 1 1 1 1 4 1 1 1 1 4 11

continued
14

Plots

Epiphyte species

DR

1

DR

3

DR

5

DR

6

DR

freq

GG

1

GG

2

GG

3

GG

4

GG

freq

LB

1

LB

2

LB

3

LB

4

LB

freq

All 

freq

Isopterygium elegans 0 1 1 1 3 1 1 4

Leskea polycarpa 0 0 1 1 1

Neckera pennata* 0 1 1 0 1

Orthodicranum flagellare 0 0 1 1 1

Orthodicranum fulvum 0 1 1 0 1

Orthodicranum montanum 1 1 1 3 1 1 1 1 4 1 1 1 1 4 11

Orthotrichum sordidum 1 1 0 1 1 2

Orthotrichum stellatum 0 0 1 1 2 2

Paraleucobryum longifolium 0 1 1 1 3 1 1 2 5

Plagiomnium ciliare 0 1 1 0 1

Plagiomnium cuspidatum 0 1 1 2 0 2

Plagiothecium laetum 1 1 1 1 1 1 2 4

Platydictya subtile 0 0 1 1 2 2

Platygyrium repens 1 1 2 1 1 2 1 1 1 1 4 8

Pleurozium schreberi 0 1 1 1 1 2

Pylaisiella polyantha 0 0 1 1 2 2

Pylaisiella selwynii 0 1 1 1 1 2 3

Sanionia uncinata 1 1 1 1 2 0 3

Tetraphis pellucida 0 1 1 1 1 2 3

Thuidium delicatulum 0 0 1 1 2 2

Ulota coarctata* 1 1 1 1 2 1 1 1 3 6

Ulota crispa 1 1 2 1 1 1 3 1 1 1 1 4 9

Liverworts

Cephaloziella sp. 0 0 1 1 1

Frullania asagrayana 0 1 1 2 1 1 3

Frullania bolanderi* 1 1 1 1 2 1 1 1 3 6

Frullania eboracensis 0 1 1 2 1 1 1 1 4 6

Frullania inflata 0 0 1 1 1

Geocalyx graveolens 0 0 1 1 1

Jamesoniella autumnalis 0 1 1 1 1 2

Lophocolea heterophylla 0 1 1 2 1 1 2 4

Lophozia ascendens 0 1 1 0 1

Lophozia sp. 0 0 1 1 1

Nowellia curvifolia 0 1 1 2 1 1 2 4

Porella platyphylla 0 1 1 1 1 2 3

Ptilidium ciliare 0 0 1 1 1

Ptilidium pulcherimum 1 1 1 1 4 1 1 1 1 4 1 1 1 1 4 12

Radula complanata 0 1 1 1 1 1 3 4

Tritomaria sp. 0 1 1 0 1

Total bryophyte species 5 10 7 4 10 14 17 23 13 21 15 28

Table 4.—continued

continued
15

Plots

Epiphyte species

DR

1

DR

3

DR

5

DR

6

DR

freq

GG

1

GG

2

GG

3

GG

4

GG

freq

LB

1

LB

2

LB

3

LB

4

LB

freq

All 

freq

Macrolichens

Ahtiana aurescens* 1 1 0 0 1

Bryoria furcellata 1 1 1 1 0 2

Bryoria fuscescens* 1 1 0 0 1

Bryoria nadvornikiana 1 1 2 1 1 0 3

Candelaria concolor 0 0 1 1 1

Cetrelia olivetorum* 0 1 1 0 1

Cladonia caespiticia 0 0 1 1 1 3 3

Cladonia chlorophaea grp 1 1 1 3 1 1 1 3 1 1 1 3 9

Cladonia coniocraea 1 1 1 1 4 1 1 1 1 4 1 1 1 3 11

Cladonia cristatella 0 1 1 0 1

Cladonia decorticata 1 1 0 0 1

Cladonia digitata 1 1 1 3 0 0 3

Cladonia floerkeana 1 1 2 1 1 1 1 2 5

Cladonia macilenta 1 1 1 3 1 1 2 1 1 2 7

Cladonia metacorallifera 0 1 1 0 1

Cladonia ochrochlora 1 1 1 1 4 1 1 1 1 4 1 1 1 3 11

Cladonia ramulosa 1 1 0 0 1

Cladonia squamosa 1 1 1 3 1 1 1 1 4 1 1 2 9

Cladonia sulphurina 1 1 2 0 0 2

Evernia mesomorpha 1 1 1 1 4 1 1 1 3 1 1 2 9

Flavoparmelia caperata 0 1 1 1 1 1 3 4

Hypenomyce friesii* 1 1 0 0 1

Hypogymnia incurvoides 1 1 2 1 1 1 3 0 5

Hypogymnia krogiae 1 1 1 1 4 1 1 1 3 1 1 2 9

Hypogymnia physodes 1 1 1 1 4 1 1 1 3 1 1 1 3 10

Hypogymnia tubulosa* 1 1 1 1 4 1 1 1 3 1 1 8

Hypogymnia vittata* 1 1 0 0 1

Imshaugia aleurites 1 1 1 3 1 1 1 3 1 1 2 8

Lobaria pulmonaria* 0 1 1 0 1

Melanohalea halei 0 1 1 1 3 1 1 2 5

Melanohalea olivacea 0 1 1 0 1

Melanohalea septentrionalis 1 1 1 1 2 0 3

Melanelixia fuliginosa* 1 1 1 1 1 1 1 3 5

Melanelixia subaurifera 1 1 1 1 3 1 1 1 1 4 8

Myelochroa aurulenta 1 1 1 1 0 2

Parmelia saxatilis 1 1 1 1 4 1 1 1 1 4 1 1 2 10

Parmelia squarrosa 1 1 2 1 1 3 1 1 1 3 8

Parmelia sulcata 1 1 1 1 4 1 1 1 1 4 1 1 1 1 4 12

Parmeliopsis ambigua 0 1 1 0 1

Table 4.—continued

continued
16

Plots

Epiphyte species

DR

1

DR

3

DR

5

DR

6

DR

freq

GG

1

GG

2

GG

3

GG

4

GG

freq

LB

1

LB

2

LB

3

LB

4

LB

freq

All 

freq

Parmeliopsis capitata 1 1 0 1 1 2 3

Parmeliopsis hyperopta 1 1 1 1 4 1 1 2 1 1 7

Phaeophyscia pusilloides 0 1 1 1 1 2

Phaeophyscia rubropulchra 1 1 1 1 2 1 1 1 3 6

Physcia millegrana 0 1 1 1 1 2

Platismatia glauca 1 1 1 1 4 1 1 1 3 1 1 2 9

Pseudevernia cladonia* 1 1 1 1 4 1 1 1 3 1 1 8

Pseudevernia consocians 1 1 1 1 4 1 1 1 3 1 1 8

Punctelia caseana 0 1 1 1 1 2 3

Punctelia rudecta 0 1 1 2 1 1 1 1 4 6

Pyxine sorediata 0 1 1 1 1 2 3

Tuckermannopsis americana 0 1 1 0 1

Tuckermannopsis ciliaris 0 1 1 0 1

Tuckermannopsis orbata 1 1 1 1 4 1 1 2 1 1 2 8

Usnea filipendula 1 1 1 1 4 1 1 2 0 6

Usnea hirta 1 1 0 0 1

Usnea subfloridana 1 1 2 1 1 2 1 1 2 6

Usnocetraria oakesiana 1 1 1 1 4 1 1 1 1 4 1 1 1 1 4 12

Vulpicidia pinastri 1 1 1 3 1 1 2 0 5

Total macrolichens 31 24 19 31 29 26 27 16 13 24 25 16

All epiphyte species 36 34 26 35 39 40 44 39 26 45 40 44

% macro-lichen 86 71 73 89 74 65 61 41 50 53 63 36

Footnote list:

* = old-growth species, according to Cleavitt et al. (2009) for bryophytes and Hinds and Hinds (2008) for lichens.

Lichen species unique to: 

Great Gulf: Cetrelia olivetorum, Cladonia metacorallifera, Lobaria pulmonaria, Melanohalea olivacea, Parmeliopsis ambigua, Tuckermannopsis 

americana, and Tuckermannopsis ciliaris; 

Presidential Range—Dry River: Ahtiana aurescens, Bryoria fuscescens, Cladonia cristatella, Cladonia decorticata, Cladonia digitata, Cladonia 

sulphurina, Hypocenomyce friesii, Hypogymnia vittata, and Usnea hirta;

Lye Brook: Candelaria concolor and Cladonia caespiticia.

Bryophytes unique to: 

Great Gulf: Brachythecium rutabulum, Brotherella recurvans, Drepanocladus aduncus, Eurhynchium pulchellum, Hylocomium splendens, Hypnum 

imponens, Neckera pennata, Orthodicranum fulvum, Plagiomnium ciliare, Plagiomnium cuspidatum, Lophozia ascendens, and Tritomaria sp.; 

Presidential Range—Dry River: Hypnum faurei; 

Lye Brook: Amblystegium serpens, Anacamptodon splachnoides, Hypnum cupressiforme, Leskea polycarpa, Orthodicranum 

flagellare,Orthotrichum stellatum, Platydictya subtile, Pylaisiella polyantha, Thuidium delicatulum, Cephaloziella sp., Frullania 

inflata, Geocalyx graveolens, and Ptilidium ciliare.

Table 4.—continued
17

In a comparison of our plots with lichen data for 92 

FIA plots in New England (see Hinds and Hinds 

2008), we recorded six species not found in the New 

England plots (Table 5). By contrast, we did not find 

some frequent cyanobacteria-containing lichens, 

We also did not have common eutrophic species, 

Xanthomendoza hasseana or Physcia aipolia (Table 5). 

Of the species for which pollution indicator status 

was available, our plots had one eutrophic species 

(Candellaria concolor at Lye Brook), four mesotrophic 

species, and five oligotrophic species (Table 5).

Table 5.—Frequency of macrolichen species in 12 plots established by us in 2011-2013 and in 92 plots summarized by 

Hinds and Hinds (2008) that were surveyed using the FIA/FHM timed protocol, with notes about N indicator status based 

on many sources, with some contrasting categorizations from Europe (e.g., Sparrius 2007). Oligo = oligotrophic (compare 

to acidophyte in Jovan 2008 Table 6); Meso= potential mesotroph (compare to neutrophyte); Eutro = eutrophic (compare 

to nitrophyte); OG = old-growth indicator species; Cy = stratified cyanolichen; see footnote. For most, tolerance for N is 

undocumented or unpublished.

Species*

Occurrence in 

New England Species notes

Our plots 

%

New England 

%

Bryoria trichodes FREQ Sensitive to air pollution (Oligo) 0 8

Leptogium cyanescens FREQ Cy, should be common on hardwoods 0 9

Lobaria quercizans FREQ OG; tripartite with Cy component 0 17

Xanthomendoza hasseana FREQ Eutro 0 7

Physcia aipolia COM Eutro 0 8

Myelochroa galbina COM Hardwood requirement 0 28

Physconia deters COM Hardwood requirement 0 13

Physcia stellaris COM 0 25

Ahtiana aurescens UC OG; G3; R3 8 5

Bryoria fuscescens UC OG; R3 (Oligo) 8 3

Cladonia decorticata UC 8 N/A

Cladonia metacorallifera RARE 7 sites in Meso; R2; (as C. squamosa, Oligo) 8 N/A

Cladonia sulphurina UC Northern species 8 N/A

Hypocenomyce friesii UC OG 8 0

Hypogymnia vittata UC OG 8 0

Cladonia cristatella COM 8 N/A

Cladonia ramulosa FREQ 8 N/A

Lobaria pulmonaria FREQ tripartite with Cy component, OG; R3; Oligo 8 18

Parmeliopsis ambigua UC High elevation; boreal-arctic 8 0

Tuckermannopsis ciliaris FREQ 8 4

Cetrelia olivetorum FREQ OG 8 4

Melanohalea olivacea UC Boreal 8 2

Tuckermannopsis americana COM 8 33

Usnea hirta COM 8 30

Candelariella concolor COM Eutro 8 23

Bryoria furcellata COM 17 35

Myelochroa aurulenta COM OG 17 21

Phaeophyscia pusilloides COM Hardwood requirement 17 27

Physcia millegrana COM Roadside trees 17 36

continued

Leptogium cyanescens and Lobaria quercizans (Table 5). 
18

Species*

Occurrence in 

New England Species notes

Our plots 

%

New England 

%

Cladonia digitata FREQ Northern 25 N/A

Bryoria nadvornikiana FREQ Boreal 25 N/A

Cladonia caespiticia FREQ Southern and coastal Meso 25 N/A

Parmeliopsis capitata FREQ Northern Meso 25 N/A

Pyxine sorediata FREQ Mature forest 25 21

Melanohalea septentrionalis FREQ Northern Meso 25 20

Punctelia caseana FREQ Temperate 25 11

Hypogymnia incurvoides UC Recent segregate, 3 ME 42 0

Vulpicida pinastri FREQ Boreal 42 22

Cladonia floerkeana FREQ 42 N/A

Melanohalea halei COM (Meso) for other species in this genus 42 N/A

Usnea filipendula UC (Oligo) 42 3

Melanelixia fuliginosa FREQ OG 42 5

Usnea subfloridana FREQ (Oligo), Rare in VT 42 34

Flavoparmelia caperata COM 42 7

Parmeliopsis hyperopta FREQ Boreal 50 3

Cladonia macilenta COM 50 N/A

Punctelia rudecta COM 50 N/A

Phaeophyscia rubropulchra COM 50 68

Hypogymnia tubulosa FREQ OG; boreal 67 4

Imshaugia aleruites COM 67 21

Pseudevernia cladonia UC OG; G3 R3 67 0

Pseudevernia consocians FREQ 67 0

Tuckermannopsis orbata FREQ 67 20

Parmelia squarrosa COM 67 47

Melanelixia subaurifera COM (Meso) (or Eutro, Sparrius 2007) 67 53

Hypogymnia krogiae FREQ Northern Meso 75 9

Platismatia glauca COM (Oligo), Northern Meso 75 11

Cladonia chlorophaea group COM  (Oligo, Sparrius 2007) 75 N/A

Evernia mesomorpha COM (Oligo) 75 N/A

Cladonia squamosa COM (Oligo) 83 N/A

Parmelia saxatilis FREQ (Meso) 83 N/A

Hypogymnia physodes COM (Meso), toxin tolerant, or Oligo, Sparrius 2007) 83 83

Cladonia ochrochlora COM 92 N/A

Cladonia coniocraea COM Northern Meso 92 N/A

Usnocetaria oakesiana COM 100 34

Parmelia sulcata COM (Meso) (or Eutro, Sparrius 2007) 100 88

* In the Northeast, the cyanolichens are Lobaria scrobiculata and species within these genera: Collema, Degelia, Fuscopannaria, Heterodermia, 

Leptogium, Nephroma, Pannaria, Parmeliella, Parmotrema, Peltigera, Protopannaria, Pseudocyphellaria, or Sticta.

Table 5.—continued
19

Epiphyte Community Data Related to 

Lichen Thallus Health and Air Quality

Across all 12 of the new plots, the NMS ordination 

found Wilderness Area species composition to vary 

strongly along an elevation (r2 = 0.770) and percent 

hardwood tree (r2 = 0.456) single gradient (Fig. 8). 

The first axis explained 91.5 percent of the variation 

between sites. For the first axis, lichen thallus health 

(r2 = 0.820) and percent of conifers (r2 = 0.679) were 

positively related and percent N (r2 = 0.274) and 

Fe content (r2 = 0.255) of the bulk samples were 

negatively related to the axis (Fig. 8). Lichen and 

bryophyte richness separated along the first axis with 

lichen richness greatest in the conifer-dominated sites 

and bryophyte richness greatest in the hardwooddominated sites. A second axis related positively to 

epiphyte richness (r2 = 0.633) and negatively to bulk 

sample values for Ni (r2 = 0.279) and Cu (r2 = 0.263) 

(Fig. 8).The second axis explained an additional 4.7 

percent of variability in the data set (total of 96.2 

percent of the variation explained). Correlations among 

plot variables included more big trees in stands with 

a higher percentage hardwood (r2 = 0.546) (Table 2). 

The steeper sloped sites faced more south (northness 

r2 = 0.826). Note that in this study, the percent S of the 

bulk samples was not related to differences in species 

composition of the sites. Bryophytes did not figure 

prominently in the NMS.

Figure 8.—Non-metric multidimensional scaling (NMS) 

bi-plot of epiphyte species 

composition and important 

site variables at 12 collection 

locations in three Wilderness 

Areas, with plot codes as in 

Fig. 1 and with four locations 

per Area. Epiphyte species 

are abbreviated by the first 

three letters of their genus 

and specific epithet (refer to 

Table 4). The site variables 

are in order of the strength of 

their relation to the ordination 

axes (r2): Thallus health rating 

(0.820); elevation (0.770); 

conifer (% of tree tally, given 

in Table 2) (0.679); lichen spp. 

number (0.667); epiphyte spp. 

number (0.633); bryo spp. 

number (0.582); hardwood (% 

of tree tally, given in Table 2) 

(0.456); Ni (ppm of lichen dry 

weight) (0.279); N (% of lichen 

dry weight) (0.274); Cu (ppm 

of lichen dry weight) (0.263); 

and Fe (ppm of lichen dry 

weight) (0.255).
20

Relationships With Epiphyte Richness

We examined linear regression for compelling 

relationships suggested by the NMS bi-plot (Fig. 8). 

We found a significant positive relationship between 

health of lichen thalli and lichen species richness 

(Fig. 9a). Plots that deviated from the line of fit were 

DR5, which had better health than expected given the 

number of lichen species, and GG2, LB3 and LB4, 

which had poorer lichen health than expected given the 

richness of lichen epiphytes (Fig. 9a). Lichen richness 

was negatively related to Fe and Cu content of the 

lichen thalli collected for bulk chemistry (Fig. 9b, 9c). 

The content of Fe in lichen thalli co-varied strongly 

with Al (r2

 = 0.79; p = 0.0001).

Context of Regional Aerosol Data From 

1992-2012

IMPROVE data, going back to 1992 at Lye Brook, 

indicate that there have been improvements in air 

quality based on decreases of 3 percent per year or more 

in aerosol SO4

-

, S, Se, Ni, V, Pb, and Zn, with smaller 

but significant improvements in NO3

-

, Fe, and Cu, and 

no significant changes in Al, and Cl-

. A comparison 

of 2001-2012 data from concurrent sample days at the 

Lye Brook and Great Gulf sites indicates that CL- and 

V were highest at Great Gulf, while Cu, Pb, Fe, Ti, 

Se, S, SO4, and NO3 were significantly higher at Lye 

Brook than Great Gulf. The most important influence 

upon lichens is probably the highly elevated NO3 levels 

at Lye Brook, which were more than 50 percent higher 

than at Great Gulf. A large number of NOx-emitting 

sources, including urban motor vehicle traffic, industrial 

plants, and power-generation plants, operate upwind 

of New England in the region generally closer to Lye 

Brook than to Great Gulf. [Note: Supplemental tables 

available from R. Poirot, see appendix 4.]

Figure 9.—Regressions between (A) lichen thallus health scores and 

lichen species richness; (B) lichen species richness and lichen thalli 

Fe (ppm of lichen dry weight); and (C) epiphyte species richness 

and lichen thalli Cu (ppm of lichen dry weight). 

(B)

(C)

(A)
21

DISCUSSION

Changes Over Time

A major reason to conduct monitoring is to detect 

change over time. Air quality improvements can lead to 

recovery of epiphytic macrolichens on tree boles (based 

especially on photographic evidence, Hultengren 

and Gralen 2004), but such an expectation requires 

care in siting and analyzing bulk samples during 

remeasurement efforts. Decrease in Pb in lichens at 

the level of National Forest (Fig. 3a) and on seven 

individual plots (Figs. 5a and b) indicates benefits to 

human health. This apparent trend does not explain 

whether improved air quality has led to improved 

health of lichens and bryophytes in the Class I 

Wilderness Areas. For some elements such as S and Pb, 

the bulk sample comparison clearly shows the expected 

decrease over time. However, for Al and Cu, sample 

comparison suggests increases. We think this is likely 

due to improved chemical extraction and analytical 

capabilities for those metals. The IMPROVE data we 

used as a reference confirmed that there have been no 

significant changes in these elements in the Wilderness 

Areas over time. There are also no indications of 

increasing trends of AL or Cu concentrations in 

any other IMPROVE or EPA Chemical Speciation 

Network (CSN) sites in the Northeast, neither 

are there other indications of regional increases in 

emissions of these metals.

Otherwise, based on lichen and bryophyte elemental 

analysis alone, it is not clear that the overall air quality 

influencing lichens in the three Class I Wilderness 

Areas we sampled is better or worse than it was 

when Wetmore sampled between 1988 and1994. 

Incongruity between our findings and those of 

Wetmore (1989, 1995a, 1995b) became apparent 

despite our care in remeasurement of a near spot on 

the ground where Wetmore had sampled and our 

use of the same analytical lab that Wetmore used. 

Choice of sample bags is an example of where such 

discrepancy could arise. We collected bulk chemistry 

lichens into ZiplocTM Freezer bags while Wetmore 

used spunbound olefin bags (Wetmore 1989). There 

may be other seemingly minor differences for which 

we did not account. Besides differences in methods, 

there are aspects beyond researcher control, including 

technological advances in ICP that have brought about 

more precision in detecting elements in the samples. 

Between our second and third batches of samples sent 

to ARL, the ICP instrument used there was updated, 

and this could have led to incongruity even within our 

own data set. Wetmore’s samples are not available for 

reanalysis. Our bulk sample results probably represent 

at least some increases in element levels that cannot 

be ascribed solely to environmental change but could 

be due to methodology. While increases are difficult 

to assess in this situation, any decreases may be 

underrepresented due to improved precision of ICP 

instrumentation.

We detected a correlation between adverse factors on 

lichen health and mineral content. High Hg and Cu 

are particularly harmful to lichens (Blett et al. 2002). 

Plots with high levels of heavy metals, high total N 

and total S, and low species richness tended to also 

have poor condition of lichen thalli. At Lye Brook 

Wilderness especially, but also at our highest elevation 

plot at Great Gulf (GG3), we found correlation among 

high percent N and percent S, high levels of heavy 

metals (LB1 and LB4), and poor thallus condition. 

LB4 had the highest levels of Hg in Hypogymnia 

physodes, and thallus condition overall was poor. The 

vicinity of LB4 corresponds to a so-called mercury 

hotspot identified by Evers et al. (2007) at the 

Deerfield River in southern Vermont based on Hg 

levels in birds and mammals. GG3 had a high Hg 

level and lichen thallus condition was poor there also 

(Fig. 7a). GG3 may be influenced by a Hg hotspot in 

southern New Hampshire (Evers et al. 2007).

Maturity of forest stand and proportion of conifers 

to hardwoods were expected to have some influence 

on our findings. The majority of our 12 plots were 

in mature forest stands. Each plot had two or more 

old-growth indicator lichens present (Tables 4 and 5). 

Continuity of the stands may explain this, but other 

factors could include air quality, proportion of conifers 

to hardwoods, and elevation (Table 1). Selva (2014) 

concluded that there is a likelihood that air quality 

impacts the algal partner in calicioid lichens; such may 

apply to the macrolichens in this report also. Despite 

improvements in air quality over the past few decades, 

shown in the IMPROVE data, there could be a lag in 

lichen recovery. Subsequent monitoring at these plots 

may help uncover such a trend.
22

Cyanolichens are particularly interesting because 

of their multi-party symbioses, in which the bluegreen alga is likely to be the most vulnerable. Lichen 

communities we measured lack the cyanolichen 

species (see footnote in Table 5) with a few minor 

exceptions; e.g., we found Lobaria pulmonaria on 

one of 12 plots. We expected to find Leptogium 

cyanescens, a cyanolichen that can be common on 

hardwoods (Cameron and Richardson 2006, Cleavitt 

et al. 2009). Lack of cyanolichens—a distinctive 

outcome of our results—could be explained in part 

by (1) dominance of conifers on many of our plots; 

(2) the FIA protocol that excludes bole area below 

0.5 m where cyanolichens might be found due to 

accumulation of nutrients through stemflow, increased 

contact with soil, and a stable moisture environment; 

(3) lichens not being a part of the community in 

pre-anthropogenic air conditions, though there is no 

evidence for this; or (4) bark acidification. Based on 

our observations across New England, in plots where 

hardwoods were dominant, cyanolichens should have 

been present. Unlike S pollution, N deposition tends 

to have a buffering effect that raises the pH of bark 

and branches (e.g., Lewis 2012) and would allow some 

lichens otherwise impacted by acidification to persist. 

Although buffering of bark is widely thought to explain 

changing lichen distribution with air quality, Jovan et 

al. (2012) found multiple sources of N affect lichen 

distribution, and bark was not the prevailing source. 

In California and the Pacific Northwest (Washington, 

Oregon) the SO2 effects received less notice as being 

influential upon lichen community composition 

compared to effects due to NO2 and O3 ( Jovan 2008). 

There is potential that the long-term impacts of SO4

2-

deposition in the Northeast are high relative to the 

West. In this regard the Northeast could resemble 

Europe, though to a lesser extent.

Plots in the Class I Wilderness Areas varied in their 

lichen richness and heavy metal content. A gradient 

of air quality degradation was detectable in the lichen 

data and is consistent with IMPROVE summaries 

(where Presidential Range—Dry River is lumped 

with the Great Gulf Wilderness Area due to location 

of the IMPROVE data collection site associated 

with these two Wilderness Areas). Great Gulf and 

Presidential Range—Dry River are each capable of 

ranking at the upper end of the gradient depending on 

the aspect of lichen health or the element of interest, 

while Lye Brook consistently ranked low due to poor 

conditions listed here. Highlights were the lower 

lichen species richness and simultaneous highest levels 

of heavy metals in bulk samples for plots DR5, LB1, 

and LB4. Although eutrophs were fairly rare in this 

study, they were more likely to occur at the three sites 

with lower species richness: GG4, LB1, and LB4. Two 

of these sites, GG4 and LB1, also had lower lichen 

richness than expected based on the relationship to Fe 

content. Lye Brook has higher heavy metal content, 

higher N, lower species richness, and poor condition 

of lichen thalli. The gradient detected here is also 

similar to results of Evers et al. (2007) for Hg hotspots. 

Note that LB4, though low in lichen richness, had 

high bryophyte richness (Table 4, footnote). This is 

explained in part by deciduous trees, dominant at that 

plot, that support a diverse bryoflora.

Candellaria concolor, found at LB4, is one of the 

eutrophic lichens recognized by Fenn et al. (2007, 

expressed as “nitrophilic”) in the Pacific Northwest. 

They also found Physcia adscendens and Xanthoria 

polycarpa in nitrogen-enriched forests. While neither 

of those species were found in this project, we found 

Physcia milligrana at three plots and think it likely that 

additional species in the genus Physcia might respond 

as eutrophs also.

Does the deposition at Lye Brook approach the critical 

load for N in Northern forests, as derived by Pardo et 

al. (2011a) and consistent with Geiser et al. (2010)? 

Lichen community composition was not thoroughly 

explored for indications of detrimental impacts from N 

deposition, but it will be examined for another report. 

Most Northeast lichen species lack N indicator status, 

though we tried to derive this information from many 

sources (Table 5). Ambiguity can be found between 

N indicator ratings from one geographic region to 

another. For example, Parmelia sulcata was a common, 

abundant lichen across most of our plots (Table 4) 

and was useful for assessing lichen health. The species

in Western Washington and Oregon was associated 

with “acidic, low fertility environments” (Geiser and 

Neitlich 2007); in The Netherlands, where decreasing 

ammonia deposition has led to a decrease in some 

lichens on oak bark, it was categorized as a eutroph 

(Sparrius 2007); and in Germany, the species increased 

its area on trees spray-fertilized biweekly for 21 months 

(Franzen-Reuter and Frahm 2007). Parmelia sulcata
23

was not found at all in an environment impacted by 

high HNO3 in the Los Angeles Basin, California 

( Jovan et al. 2012). Hypogymnia physodes and Evernia 

prunastri, both frequent in our study, were considered 

“sub-regional nitrophytic or tolerant species” by Geiser 

and Neitlich (2007), while Sparrius (2007) classified 

both as “acidophytes” (compare to oligotroph). In the 

Austrian Alps where eutrophication is of great concern 

and lichens are decreasing in diversity, abundance, 

and thallus size, Hypogymnia physodes behaves as a 

nitrophobe (Mayer et al. 2013; compare to acidophyte 

or oligotroph) but is not as sensitive to eutrophication 

as Bryoria and Usnea. Other examples can be found 

for common lichens. Variation in lichen response 

to pollutants such as found in this brief survey is a 

challenge in developing indicator status information 

that might be used to estimate critical load for N. 

Additional empirical studies such as that of FranzenReuter and Frahm (2007) could improve the broader 

reliability of such information.

Comparison to 92 Lichen Indicator 

Plots Across New England

Eight of the nine lichen species noted as rare in New 

England by Hinds and Hinds (2008), based on 92 

FIA/FHM plots with P3 Lichen Indicator data, were 

also rare in the 12 new plots (Table 5). On the other 

hand, Pseudevernia cladonia was notably more frequent 

in conifer-dominated plots than expected. This may be 

due to mature stands with relative lack of disturbance 

and to high elevation. Thirteen indicators of oldgrowth forests were among lichens found in this survey 

(Table 5). Most eutroph species were rare (Candelaria 

concolor in one plot) or infrequent (21-50 percent of 

plots), e.g., Phaeophyscia pusilloides and Physcia stellaris 

(Table 5).

Context of Regional Aerosol Data From 

1992-2013

IMPROVE data, going back to 1992 in summers 

at Lye Brook, indicated that there has been gradual 

improvement in most measures of air quality that 

affect regional haze, some of which are relevant to the 

lichen bulk chemistry results. First, the two Wilderness 

Areas (Presidential Range—Dry River is represented 

with Great Gulf in the IMPROVE data) are highly 

correlated with each other, likely because pollution 

sources are distant from the collecting stations, thus 

the readings for any given day are likely to be similar 

at Lye Brook and Great Gulf. Among minerals we 

measured, only Ni, V, and Cl- tended to influence 

the IMPROVE sites on different days, were higher 

at Great Gulf, and are likely to result from residual 

oil combustion with sources concentrated along the 

Northeast urban corridor. Concentrations of Cu, 

Pb, Fe, Ti, Se, S, SO4, and NO3 were significantly 

higher at LYBR than at GRGU. Probably the most 

important influence upon lichens is the highly elevated 

NO3 levels at Lye Brook. At 0.35 μg/m3, NO3 was 

more than 50 percent higher than at Great Gulf. 

Hundreds of industrial and power-generation plants 

operate upwind of Lye Brook, with airborne pollutants 

emanating from industrial areas near the Great Lakes 

and from coal burning power plants in the Ohio River 

Valley.

Relationships Between IMPROVE Data 

and Lichen Data

The data from nearby, remote IMPROVE sites are 

useful to compare with lichen data. The IMPROVE 

data indicate broad spatial patterns of pollutant 

exposures, while the lichens better capture the more 

complex spatial variability of actual deposition 

and biological uptake. Lichens are more widely 

available than IMPROVE data and can be used to 

fill in some gaps. These two types of data are similar 

in their associations with N, while they differ in 

that IMPROVE measures particles only and does 

not include potentially important gases that may 

contribute to dry deposition—such as SO2, O3, HNO3, 

NH4, and potentially toxic organics. While lichens 

have more variability associated with the complexity 

of the thalli (shape, folding, exposure history, etc.), 

lichen data we present here pointed to high elevation 

sites receiving more occult (cloud) deposition. Lichens 

in this report are associated with higher inputs of 

pollutants. While the lichens are not precise, they are 

useful indicators that offer finer-scale monitoring than 

do IMPROVE data.

Some additional limitations can be noted. The 

IMPROVE sites are intended to measure “regionally 

representative” atmospheric concentrations, but they 

are not co-located with our lichen plots, have limited 

applicability on the landscape, and may not capture 
24

the microscale variations in pollutant concentrations 

to which the lichens are exposed. IMPROVE metals 

analysis by x-ray fluorescence does not include all the 

metals analyzed by ICP AES in our lichen samples. 

A combination of both types of data appears to offer 

better understanding of air pollution than does just one 

type of data alone. Lichens were used with IMPROVE 

data to estimate critical loads by Glavich and Geiser 

(2008). See Cleavitt et al. (2015) for additional 

comparisons.

Some Take-home Messages

A lichen species included in 2011-2013 bulk samples 

analyzed for Hg was Evernia mesomorpha, and this 

consistently held more Hg than either of two mosses 

that were also analyzed for the element across National 

Forests (Fig. 2a) and a subset of plots (Fig. 6). Due to 

cost of sampling for Hg, it appears from these limited 

data that effectiveness will be increased if epiphytic, 

not ground layer, lichens and bryophytes are included 

in Hg surveys (see use of Orthodicranum montanum on 

oak boles in Pennsylvania; Davis et al. 2007). However, 

bryophytes would be difficult to clean if used as bulk 

samples. The high level of Hg in Evernia mesomorpha at 

GG3 is consistent with the expectation that a remote 

forest just below the upper slopes of Mt. Washington 

would have mercury contamination, as an elevation 

gradient for atmospheric Hg deposition is well 

established (e.g., Zhang et al. 2013). In our study, two 

other high elevation plots in WMNF were not sampled 

for Hg.

Blett et al. (2002) offered a list of benefits and 

limitations regarding use of lichens to monitor air 

quality (Table 1 in Blett et al. 2002). They recommend 

baseline sampling four to five times in the same area, 

at the same time of year, using the same lichen species. 

While we concur that this would enhance a monitoring 

effort, that intensity of effort was not feasible for this 

project.

Vouchering specimens not only enables taxonomic 

revisions and field training for those who will revisit 

a plot but may be valuable later for an unanticipated 

use in future research. Our assessment of lichen thallus 

condition was valuable in understanding air quality 

conditions that bulk chemistry made apparent.

We offer additional region-specific considerations for 

improving lichen and bryophyte biomonitoring of air 

quality in Class I Wilderness Areas of New Hampshire 

and Vermont:

1. Locate lichen plots near IMPROVE sites.

The relationship between air quality impacts 

on lichens and aerosol data might be resolved 

if proximity to the IMPROVE collectors is 

taken into account. New lichen plots situated 

immediately near IMPROVE sites could be 

especially helpful. Also suggested: add passive 

NH3 and HNO3 samplers at some IMPROVE 

sites.

2. Where FIA/FHM plots are used to assess 

air quality, include lichen thallus condition.

Even though a lichen is present, it might be in 

poor condition. Slow growth, malformations 

and necrosis could be due to pollution, and 

would relate to bulk chemistry results and 

measurable pollution loads. If thalli are small 

within the plot, then to reduce impacts on the 

plot, collections should be taken from off-plot 

for a subset of species only.

3. Archive samples for future comparisons.

Technological advances are likely to continue 

to make comparison between historic and 

current conditions a mismatch, as we found 

here. This is because priorities change (e.g., 

Wetmore did not screen for N, but that is 

a high priority now), and because precision 

and reliability can be much greater in newer 

ICP instruments or potentially in a replacing 

technology. Monitoring air quality using bulk 

chemistry in forest organic matter should 

include the collection of sufficient volume to 

accommodate at least three measurements: (a) 

current, (b) near future (e.g., within a decade), 

and (c) distant future (e.g., two decades or 

more). The latter two (or more) sets of samples 

will need to be archived, so infrastructure for 

secure storage of such material needs to be 

planned from the outset.

4. Conduct bulk chemistry analysis of tree 

bark samples. At many of our plots, we 

had difficulty locating sufficient volume of 

common lichens to run elemental analyses. 

Given climate change and pollution impacts, 
25

others may find this even more problematic as 

thalli decline in presence, size, and abundance. 

While we urge that future monitoring should 

continue to use the P3 lichen indicator to 

assess change in lichen communities, and that 

there is much potential for community analysis 

in estimation of critical loads, we suggest that 

bulk chemistry elemental analysis, including 

Hg, could be done on tree bark samples instead 

of lichens alone (or epiphytic bryophytes, 

as suggested above, but cleaning samples is 

problematic). This would potentially reduce the 

amount of lichen material to be collected, and 

bark is likely to continue to be readily available 

and abundant throughout forested plots. A vast 

literature on bark chemistry is already in place 

and will be useful in understanding how results 

from air quality monitoring applications fit 

with what is known elsewhere.

5. Expand pollution tolerance ratings for 

northeastern lichens. Several sets of 

descriptors have been developed to categorize 

lichen species, or genera, according to their 

tolerance to pollutants (e.g., Jovan 2008, 

Lewis 2012). Globally, bryophytes are not 

much categorized as to N indicator status but 

there appear to be prospects for developing 

such a system. For lichens, the northeastern 

United States has had less attention than 

some other parts of the world; N indicator 

status is unknown for many species we found 

(Table 5). We did not have sufficient sample 

size to assign pollution tolerance ratings to 

lichens or bryophytes in this study. To place our 

results in context of known tolerance ratings, 

we considered congenerics where speciesspecific ratings are not available, but given the 

variability in lichen response to N, that could 

be ineffective.

Without this information, the potential for 

lichen communities to indicate air quality 

in the Northeast cannot be fully realized. 

Among common lichens, such patterns may 

be detected from surveys such as those cited 

here. For rare lichens, multiple factors probably 

contribute to their absence: they might always 

have been rare, their habitat could be altered 

(e.g., old forest), there may be poor dispersal, 

or they may have a low rate of reproduction. 

Pollution might contribute, but pollution 

alone may not be the factor that explains their 

absence. Although there is temptation to infer 

pollution tolerance from their absence, this 

would be a difficult hypothesis to test.

6. Continue to test bryophytes as bioindicators.

While some of our data indicate only weak 

utility for bryophytes (e.g., low influence of 

bryophytes in the NMS, see Fig. 8), that might 

be explained in part by our bulk samples, 

which were of ground layer bryophytes and 

not epiphytic species. Three species were found 

at almost all our plots (Hypnum pallescens, 

Orthodicranum montanum, and Ptilidium 

pulcherrimum) and could be present in 

sufficient quantity that they can be collected 

for bulk chemistry from off-plot. Their 

diversity and the conservation of their habitats 

can be considered along with lichens in the 

same locales, as indeed they are habitat for and 

may influence the establishment of some lichen 

species on branches and boles.

CONCLUSION

While this monitoring project is a start toward 

integrating data from the Northwest with greater effort 

in the northeastern United States, there are unresolved 

areas for further exploration that we think will greatly 

enhance the ability of the IMPROVE monitoring 

program to strengthen what is learned from lichen 

monitoring, and vice versa. Yet to be developed are 

the pollution sensitivity ratings for lichens of the 

Northeast. Despite evidence from the IMPROVE data 

that air quality is better now than in the recent past, we 

cannot be sure from lichen data alone that there have 

been substantial changes in air quality since Wetmore’s 

sampling, apart from our result for Pb. If there is to be 

a lag in lichen recovery, then evidence might be sought 

from lichen health data and increases in abundance and 

richness. We found a gradient of air quality conditions 

that can be followed using lichens (and bryophytes), 

with implications for human health, improving the 

environment, and cryptogam conservation. Finally, we 

identified that air quality at Lye Brook Wilderness 

Area is in need of improvement for the sake of people, 

lichens, and all their various habitats.
26

ACKNOWLEDGMENTS

This is a report of a monitoring project undertaken 

with support from the U.S. Department of Agriculture, 

Forest Service. The monitoring project was supported 

by the Forest Service (National Air Program, WMNF, 

and GMNF). We appreciate advice in the early phase 

of this project from Linda Geiser and Susan WillWolf. Steve Selva helped us in many ways and visited 

all plots with us while he assessed the value of calicioid 

lichens in monitoring air quality. Thank you to Bennet 

Leon for help with summaries of IMPROVE data 

referred to here. Jake Maier assisted with summaries of 

basal area and quadratic mean diameter. Thank you to 

two reviewers of an earlier version of this manuscript, 

Heather T. Root of Weber State University, Ogden, 

Utah, and Aaron Weiskittel of the University of Maine, 

Orono, Maine. Thank you also to two anonymous 

reviewers who improved the manuscript in its final 

stages. We are grateful to our field helpers Maurane 

Bourjouin, Stephanie Freeman, Erin Lane, Stacy 

Lemieux, Mary Beth Poli, and Sarah Thorne. Thank you 

to Louise Roy for help with cleaning moss and lichen 

samples. Photos from the project are in appendix 3.

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30

CONTRIBUTIONS OF THE AUTHORS

A.C. Dibble designed the project, led all aspects of 

writing this report, assisted in location of field sites, 

assisted with field work organization, collected lichens 

and bryophytes on all 12 plots, cleaned bulk samples, 

compiled data, helped develop the lichen thallus 

condition ratings, and took photographs.

J.L. Hinds examined lichen specimens and prepared 

final identifications for all lichen vouchers, helped 

develop and carried out lichen thallus condition ratings, 

helped develop the manuscript, and contributed to 

report revisions.

R.L. Perron conceived and initiated the project, led the 

proposal writing and field logistics, located and visited 

all 12 plots, led measurement of trees on plots, cleaned 

bulk samples, and helped with revisions on this report.

N.L. Cleavitt assisted in designing project, collected 

lichens and bryophytes at 7 of the 12 plots, prepared 

final identifications for all bryophyte specimens, 

oversaw and took part in most of the bulk specimen 

cleaning, helped develop the lichen thallus condition 

ratings, did most of the statistical analyses, wrote 

the initial results section, and contributed to report 

revisions.

L.H. Pardo contributed to project planning and 

oversight, and contributed to report revisions.

R.L. Poirot provided air quality data, provided insight 

on deposition data relevant to interpretation of our 

results, and contributed to report revisions.

L. Geiser contributed to project planning and to report 

revisions.
31

APPENDIX 1

Maps of study areas in White Mountain National Forest, NH, 

and Green Mountain National Forest, VT

Lichen plot locations at White Mountain National Forest, NH, by Christine Perron, used 

with permission. "GG" are plots in Great Gulf, and "DR" are those in the Presidential 

Range—Dry River Area.
32

Lichen plot locations at Great Gulf Wilderness Area, NH, by Christine Perron, used with permission.

Lichen plot locations at Presidential Range—Dry River Area, NH, by Christine Perron, used with permission.
33

Presidential River—Dry Range Wilderness, detail for lichen plot DR-1, by Christine Perron, used with permission. 

Presidential Range—Dry River Wilderness, detail for lichen plot DR-3, by Christine Perron, used with permission.
34

Presidential Range—Dry River Wilderness, detail for lichen plots DR-5 and -6, by Christine Perron, used with permission.

Locations of lichen plots lin Lye Brook Wilderness, Green Mountain National Forest, VT, by Thomas M. Tenyah, used with permission. "LBW" corresponds to plots labeled "LB" in the text.
35

APPENDIX 2

Multivariate Correlations

Multivariate correlations for S and N (as percent) plus 16 additional elements. There are 22 missing values. 

The correlations are estimated by Pairwise method.

S percent N percent Al B Ca Cd Cr Cu Fe

S percent 1.0000 0.5087 -0.2307 0.1846 -0.1707 -0.2878 -0.0142 0.2471 -0.1480

N percent 0.5087 1.0000 -0.5753 -0.1968 -0.1063 -0.1224 0.1241 0.5590 -0.3921

Al -0.2307 -0.5753 1.0000 0.1407 0.5739 0.4148 0.1083 -0.0630 0.9392

B 0.1846 -0.1968 0.1407 1.0000 0.0617 0.1121 -0.3169 0.1902 -0.0327

Ca -0.1707 -0.1063 0.5739 0.0617 1.0000 0.7612 0.3117 0.4176 0.6526

Cd -0.2878 -0.1224 0.4148 0.1121 0.7612 1.0000 0.2327 0.5377 0.4594

Cr -0.0142 0.1241 0.1083 -0.3169 0.3117 0.2327 1.0000 0.1966 0.3327

Cu 0.2471 0.5590 -0.0630 0.1902 0.4176 0.5377 0.1966 1.0000 0.0639

Fe -0.1480 -0.3921 0.9392 -0.0327 0.6526 0.4594 0.3327 0.0639 1.0000

K 0.2798 0.8363 -0.7520 -0.1764 -0.2005 -0.0980 -0.0899 0.5452 -0.6509

Mg 0.3000 0.8351 -0.5644 -0.1180 0.0599 0.1040 -0.0744 0.5587 -0.4443

Mn -0.1199 0.2145 0.1920 0.2646 0.6606 0.6688 0.4059 0.6804 0.2816

Na -0.2164 -0.1971 -0.0809 0.2525 -0.1408 -0.1311 -0.1001 -0.1366 -0.1608

Ni 0.0812 0.0593 0.1582 0.0206 0.1069 0.3703 0.6320 0.3761 0.3190

P 0.3939 0.8274 -0.6154 -0.0415 -0.0627 -0.0132 -0.1011 0.5656 -0.5233

Pb -0.0785 -0.2437 0.6263 0.4439 0.6637 0.6174 0.0854 0.3980 0.6077

Zn -0.2096 -0.0208 0.5092 0.1614 0.7907 0.7900 -0.0268 0.4492 0.5477

Hg -0.2932 -0.6914 0.8308 -0.4727 -0.4674 -0.0487 0.2652 -0.6156 0.8503

Continued

K Mg Mn Na Ni P Pb Zn Hg

S percent 0.2798 0.3000 -0.1199 -0.2164 0.0812 0.3939 -0.0785 -0.2096 -0.2932

N percent 0.8363 0.8351 0.2145 -0.1971 0.0593 0.8274 -0.2437 -0.0208 -0.6914

Al -0.7520 -0.5644 0.1920 -0.0809 0.1582 -0.6154 0.6263 0.5092 0.8308

B -0.1764 -0.1180 0.2646 0.2525 0.0206 -0.0415 0.4439 0.1614 -0.4727

Ca -0.2005 0.0599 0.6606 -0.1408 0.1069 -0.0627 0.6637 0.7907 -0.4674

Cd -0.0980 0.1040 0.6688 -0.1311 0.3703 -0.0132 0.6174 0.7900 -0.0487

Cr -0.0899 -0.0744 0.4059 -0.1001 0.6320 -0.1011 0.0854 -0.0268 0.2652

Cu 0.5452 0.5587 0.6804 -0.1366 0.3761 0.5656 0.3980 0.4492 -0.6156

Fe -0.6509 -0.4443 0.2816 -0.1608 0.3190 -0.5233 0.6077 0.5477 0.8503

K 1.0000 0.8673 0.1874 -0.0468 -0.1609 0.8543 -0.3220 -0.1255 -0.8813

Mg 0.8673 1.0000 0.2497 -0.0968 -0.1092 0.8529 -0.1630 0.1901 -0.7774

Mn 0.1874 0.2497 1.0000 -0.0631 0.2530 0.1532 0.6580 0.5378 -0.6235

Na -0.0468 -0.0968 -0.0631 1.0000 -0.0907 -0.0625 -0.1497 -0.1203 -0.2202

Ni -0.1609 -0.1092 0.2530 -0.0907 1.0000 -0.0913 0.1783 0.1233 0.2740

P 0.8543 0.8529 0.1532 -0.0625 -0.0913 1.0000 -0.1698 0.0454 -0.6727

Pb -0.3220 -0.1630 0.6580 -0.1497 0.1783 -0.1698 1.0000 0.6309 0.6692

Zn -0.1255 0.1901 0.5378 -0.1203 0.1233 0.0454 0.6309 1.0000 -0.2273

Hg -0.8813 -0.7774 -0.6235 -0.2202 0.2740 -0.6727 0.6692 -0.2273 1.0000
36

APPENDIX 3

Project Photos

Plot GG-3, view to N from plot center, Aug. 16, 2013. Photo by A.C. Dibble, used with permission.

Plot GG-4, view toward plot center, Aug. 17, 2013. Photo by A.C. Dibble, used with permission.
37

PD-3, view to W from plot center, Aug. 13, 2012. Photo by A.C. Dibble, used with permission.

PD-5, view from plot center (below Mizpah Hut), Aug. 15, 2012. Photo by A.C. Dibble, used with permission.
38

PD-6, view from plot center, Aug. 16, 2013. Photo by A.C. Dibble, used with permission.

LB-3, hill W of Lye Brook, Aug. 21, 2013. Photo by A.C. Dibble, used with permission.
39

LB-4, view to S from plot center, above Downer Glen Ravine, Aug. 19, 2013. Photo by A.C. Dibble, used 

with permission.

Cladonia floerkeana, PD-6, near Crawford Path, Aug. 16, 2012. Photo by A.C. Dibble, used with permission.
40

Imshaugia aleurites, lichen of conifer forests, PD-1, Aug. 14, 2012. Photo by A.C. Dibble, used with permission.

Pseudevernia cladonia, ghost antler lichen, at PD-1, Aug. 14, 2012. Photo by A.C. Dibble, used with permission.
41

APPENDIX 4

Aerosol Data

Aerosol measurements from sites in the Interagency 

Monitoring of Protected Visual Environments 

(IMPROVE) network (Eldred et al. 1990, Hand 

et al. 2011) can provide an indication of relative 

concentrations of, and exposures to, various air 

pollutants in and around the Great Gulf National 

Wilderness (GRGU1 site) and the Lye Brook National 

Wilderness (LYBR1 and LYEB1 sites). The GRGU1 

IMPROVE site (44.308-71.218, 454 m elev.) was 

established in 1995 and began year-round operation 

in 2001 (sampling in earlier years was summer only). 

The LYBR1 IMPROVE site (43.148-73.127, 1015 m 

elev.) was established in 1991 and began year-round 

operation in 1992. The LYEB1 site (42.953-72.910, 

943 m elev.) was established in 2012, to replace the 

LYBR1 site, which was terminated in September 2012 

due to recurring power supply problems.

IMPROVE aerosol sites collected 24-hour filter 

samples twice a week (Wednesdays and Saturdays) 

prior to 9/1/2000, after which the network switched to 

an every 3rd day schedule. Four samples are collected 

each sample day, including a coarse particle (PM10) 

sample on a Teflon filter and three fine particle (PM2.5) 

samples collected on Teflon, nylon, and quartz filters 

respectively. The PM10 and PM2.5 Teflon filters are 

analyzed for mass (gravimetrically). The Teflon PM2.5

filters are also analyzed for light absorption (by laser 

integrating sphere) and for elemental composition by 

proton induced x-ray emission (PIXE – used for lighter 

elements, Na through Mn, prior to 12/1/2001) and 

x-ray florescence (XRF – used for heavier elements 

prior to 12/1/2001 and for all elements after that date). 

Nylon PM2.5 filters are analyzed for major inorganic 

ions (Cl-, NO3

-

, and SO4

=

) by ion chromatography (IC), 

and quartz PM2.5 filters are analyzed for elemental and 

organic carbon by thermal optical reflectance (TOR). 

IMPROVE aerosol data are stored in the Federal Land 

Manager Environmental Database (FED).

IMPROVE data for the GRGU1, LYBR1 and LYEB1 

sites were obtained using the FED Data Query Wizard 

(http://views.cira.colostate.edu/fed/DataWizard/

Default.aspx). The PM2.5 elemental and ionic data are 

most relevant to interpreting the measurements of 

similar species in lichens and mosses. The 2001 to 2012 

period with concurrent results from both sites can be 

used to compare “current” concentrations of pollutants 

between sites, while the 1992 to 2012 Lye Brook data 

are more appropriate for exploring changes over time. 

The raw IMPROVE XRF and IC data were adjusted 

by substituting values equal to half the detection limit 

for species where the attempted measurement was 

below the minimum detection limit. For the 2012 

Lye Brook samples, data from the long-term LYBR1 

site were used when available, and LYEB1 samples 

were used for days when LYBR1 data were missing. 

For comparing data between sites, the data were 

also constrained to include only sample dates where 

valid elemental and ionic samples were collected and 

analyzed at both GRGU1 and LYBR1 (or LYEB1) 

sites, resulting in 1,132 paired XRF and IC samples 

from both sites over the 2001 to 2012 period.

Comparative results for selected species are displayed 

in Table 6, including means and standard errors, 

with bold font indicating species and sites for which 

concentrations were significantly higher at one site 

than the other. Because the data for all species were 

highly skewed, a Wilcoxon Signed Rank test was 

applied to identify significant differences in species 

between the sites, and the significance levels (p) are 

reported. Table 6 also shows the percentage differences 

between the means and the correlations (R) between 

the sites for the paired samples for each species.

The daily concentrations are moderately well correlated 

(0.50 < R < 0.90) between the two sites for most 

species in Table 6, with the exceptions of Ni (R= 0.34), 

V (R = 0.37) and Cl- (R = 0.32). This is consistent 

with the remote nature of these IMPROVE sites and 

suggests that regional concentrations of most species 

are influenced by relatively distant sources that are 

likely to influence both sites on the same sample days. 

Species more heavily influenced by nearby sources such 

as sea salt (Cl-) and residual oil combustion (Ni and V 

from Northeast urban corridor power plants) would be 

more likely to influence the two IMPROVE sites on 

different days, and so tend to be less correlated between 

the sites.
42

Concentrations of Cu, Pb, Fe, Ti, Se, S, SO4

=

, and NO3

- 

were significantly higher at LYBR than at GRGU. 

No significant differences were observed for Zn and 

Ni. Concentrations of Cl- and V were significantly 

higher at GRGU than at LYBR. Ni was higher at 

GRGU, but not significantly so. The initial comparison 

for Al showed no significant difference between sites. 

However, Al had the largest fraction of below-detects 

of these reported species (45 percent were below MDL, 

while more than 75 percent or more of samples were 

above MDL for all other species in Table 6). A problem 

has also been identified with miscalculated MDLs 

in historical IMPROVE Al data (Poirot et al. 2001, 

White 2006). To eliminate the possible influence of 

MDL errors on the Al comparison, a second test (Al ́) 

was run on a much smaller subset of sample days (n = 

471) when Al was above MDL at both sites. The aboveMDL Al ́ data were slightly but significantly higher at 

LYBR, and the inter-site Al correlation (R) increased 

from 0.59 when below-detects were included to 0.88 

when only paired above-detects were considered.

The somewhat higher concentrations at LYBR than 

GRGU for most species are consistent with influence 

from relatively distant sources to the West and 

Southwest of both sites, but closer to LYBR – for 

example industrial sources along the Great Lakes 

and coal burning power plants along the Ohio River 

Lye Brook 2001-2012 Great Gulf 2001-2012

Mean concentration 

(μg/m3) ± 95% CI

Mean concentration

(μg/m3) ± 95% CI

p value 

(different 

means)

% difference in 

12-year means 

(1-GG/LB)*100

LYBR:GRGU 

Correlation (R)

Cu 0.00047 ± 1.55e-05 0.00039 ± 1.13e-05 <0.001 18% 0.51

Pb 0.00130 ± 3.68e-05 0.00112 ± 2.96e-05 <0.001 14% 0.60

Zn 0.00339 ± 0.00011 0.00333 ± 0.00010 0.247 2% 0.52

Fe 0.01633 ± 0.00076 0.01427 ± 0.00062 <0.001 13% 0.81

Ti 0.00174 ± 0.00014 0.00163 ± 0.00012 0.004 6% 0.76

Al 0.01449 ± 0.00108 0.01331 ± 0.00093 0.055 8% 0.59

Al’ 0.02386 ± 0.00197 0.02219 ± 0.00172 0.002 7% 0.88

Ni 0.00029 ± 1.68e-05 0.00034 ± 2.55e-05 0.967 -14% 0.34

V 0.00061 ± 3.52e-05 0.00089 ± 7.48e-05 <0.001 -44% 0.37

Se 0.00039 ± 1.45e-05 0.00026 ± 4.26e-06 <0.001 35% 0.80

S 0.64151 ± 0.03429 0.54885 ± 0.02608 <0.001 14% 0.84

SO4

= 1.79018 ± 0.10123 1.54045 ± 0.07817 <0.001 14% 0.85

NO3

- 0.35000 ± 0.01925 0.15392 ± 0.00842 <0.001 56% 0.63

Cl- 0.02929 ± 0.00357 0.03902 ± 0.00461 <0.001 -33% 0.32

Table 6.—Summary of selected PM2.5 species for Lye Brook and Gt. Gulf IMPROVE Sites, 2001-2012. Means 

of species that were significantly higher at one site than the other are in bold. CI = confidence interval.

Valley. NO3

-

 (56 percent higher at LYBR) and Se 

(35 percent higher at LYBR) were the species that 

showed the greatest increases in mean concentrations 

at LYBR compared to GRGU. Fine particle nitrate 

concentrations at remote IMPROVE sites are 

predominantly in the form of ammonium nitrate, 

formed from atmospheric reactions of nitric acid and 

ammonia. Previous studies have implicated ammonia 

emissions from midwestern agricultural regions 

as an important source region for aerosol nitrate 

concentrations at IMPROVE sites throughout the 

eastern United States (Pitchford et al. 2009, Poirot 

2009). This distant westerly regional origin and relative 

absence of sources of either ammonia or nitric acid 

between LYBR and GRGU are consistent with the 

much higher LYBR NO3

-

 concentrations. Se has 

been demonstrated to be a good tracer for midwestern 

coal combustion at IMPROVE sites throughout the 

eastern United States (Hopke et al. 2006, Poirot 2009), 

and this relatively distant southwesterly origin and 

absence of coal sources between LYBR and GRGU 

is consistent with higher Se concentrations at LYBR. 

The higher concentrations of Cl-, V, and Ni at GRGU 

and the lower inter-site correlations for these species 

are consistent with influences from relatively nearby 

emissions in closer proximity to GRGU, including 

marine sea salt (Cl-) and northeast urban corridor 

residual oil combustion (V and Ni).
43

Longer-term (1992-2012) trends in annual average 

concentrations of selected species at LYBR are plotted in 

Figure 10, along with more recent 2001-2012 data from 

GRGU. All of these species exhibit downward trends 

in the longer-term LYBR data. Sulfate and nitrate 

concentrations were highest in LYBR in most years, with 

nitrate showing weakest long-term trend but the largest 

between-site difference. Downward trends are also 

evident for Zn (no significant between-site difference) 

and V (steep downward trend and highest at GRGU.

To compare trends among species and characterize 

rates of change over time, the 1992-2012 annual 

average concentrations ay LYBR were “normalized” 

by dividing annual average concentrations by the 1992 

concentrations of that species, and expressing each 

year’s concentration as a percent of 1992 concentrations. 

A linear trend line was fit to the normalized annual 

concentrations, with the resulting slope expressed 

as “percentage change per year.” The correlations, 

slopes (if significant), F values, and significance levels 

of the regressions of average annual aerosol species 

concentrations vs. time are summarized in Table 7. 

Elemental Ti, included in the Table 6 comparison 

between sites for 2001-2012, is not included here, 

as a significant positive bias has been noted in the 

IMPRPOVE (PIXE) Ti measurements prior to 

January 1, 2001.

No significant trends were indicated for Al and Cl-, as 

might be expected for species resulting primarily from 

natural sources, such as wind-blown soil (Al) and sea 

salt (Cl-). Limiting the Al data to only above-detects 

resulted in a similarly insignificant change over time. All 

other species summarized in Table 7 show significant 

improvements averaging at least 2.5 percent/yr, with the 

largest improvements for Pb (-3.8 percent/yr), Ni (-4.1 

percent/yr), Se (-5.1 percent/yr), and V (-6.7 percent/yr).

Figure 10.—Annual average concentrations of selected aerosol species at the Lye Brook and Gt. Gulf IMPROVE Sites.
44

The 1992-2012 reductions in Pb occured after the 

phase-out of leaded gasoline had been completed 

and are consistent with more recent controls on 

various other Pb source categories, including mining, 

smelting, and metal working activities (U.S. EPA 

2013). Reductions in Se, Ni, and V are consistent 

with emission controls on coal and oil-fired power 

plants resulting from the acid rain provisions of the 

1990 Clean Air Act Amendments, EPA’s Clean Air 

Interstate Rule, and other regional emissions control 

programs.

LITERATURE CITED

Eldred, R.A.; Cahill, T.A.; Wilkinson, L.K.; Feeney, 

P.J.; Chow, J.C.; Malm, W.C. 1990. Measurement 

of fine particles and their chemical components 

in the NPS/IMPROVE Networks. In: Mathai, 

C.V., ed. Transactions: Visibility and Fine Particles. 

A&WMA. Pittsburgh, PA.

Hand, J.L.; Copeland, S.A.; Day, D.E.; Dillner, A.M.; 

Indresand, H.; Malm, W.C.; McDade, C.E.; Moore, 

C.T.; Pitchford, M.L.; Schichtel, B.A.; Watson, 

J.G. 2011. Spatial and seasonal patterns and 

temporal variability of haze and its constituents in 

the United States: IMPROVE Report V. http://

vista.cira.colostate.edu/improve/Publications/

Reports/2011/2011.htm.

Hopke, P.K.; Zhou, L.; Poirot, R.L. 2005. Reconciling 

trajectory ensemble receptor model results with 

emissions. Environmental Science and Technology. 

39: 7980-7983.

Pitchford, M.; White, W.; Poirot, R.; Schichtel, B.; 

Malm, W. 2009. Characterization of the winter 

midwestern particulate nitrate bulge. Journal of Air 

& Waste Management Association. 59: 1061-1069.

Poirot, R.L.; Wishinski, P.R.; Hopke, P.K.; Polissar, 

A.V. 2001. Comparative application of multiple 

receptor methods to identify aerosol sources in 

northern Vermont. Environmental Science & 

Technology. 35: 4622-4636.

Poirot, R.L. Use of trace metals as source fingerprints. 

In: Legge, A.H., ed. Air quality and ecological 

impacts: relating sources to effects. 2009. Oxford, 

UK: Elsevier Ltd.

US EPA. 2013. Integrated science assessment for 

lead. EPA/600/R-10/075F.

White, W.H. 2006. Elemental concentrations above 

the MDL can go undetected. IMPROVE Data 

Advisory, Doc #: da0010.

White, W.H. 2008. Positive interference in PIXE 

titanium determinations. IMPROVE Data 

Advisory, Doc #: da0018.

Table 7.—Trends in annual means of selected PM2.5 species at Lye Brook, 

1992-2012

PM2.5 Species

Correlation vs. 

date (R2)

Slope 

(% / yr)

Regression 

F

Significance 

of F

Cu 0.54 -2.9 21.9 <0.001

Pb 0.62 -3.8 30.8 <0.001

Zn 0.73 -3.1 52.6 <0.001

Fe 0.45 -2.6 15.3 <0.001

Al 0.00 NS 0.02 0.886

Ni 0.56 -4.1 24.4 <0.001

V 0.61 -6.7 29.5 <0.001

Se 0.33 -5.1 9.2 0.007

S 0.71 -3 47.5 <0.001

SO4= 0.84 -3.2 100.5 <0.001

NO3- 0.64 -2.5 13.2 0.002

Cl- 0.02 NS 0.3 0.588
Printed on Recycled Paper

Dibble, Alison C.; Hinds, James W.; Perron, Ralph; Cleavitt, Natalie; Poirot, Richard L.; 

Pardo, Linda H. 2016. Monitoring air quality in class I wilderness areas of the 

northeastern United States using lichens and bryophytes. Gen. Tech. Rep. NRS165. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. 44 p.

To address a need for air quality and lichen monitoring information for the Northeast, we 

compared bulk chemistry data from 2011-2013 to baseline surveys from 1988 and 1993 

in three Class I Wilderness areas of New Hampshire and Vermont. Plots were within the 

White Mountain National Forest (Presidential Range—Dry River Wilderness and Great 

Gulf Wilderness, New Hampshire) and the Green Mountain National Forest (Lye Brook 

Wilderness, Vermont). We sampled epiphyte communities and found 58 macrolichen 

species and 55 bryophyte species. We also analyzed bulk samples for total N, total S, and 

27 additional elements. We detected a decrease in Pb at the level of the National Forest 

and in a subset of plots. Low lichen richness and poor thallus condition at Lye Brook 

corresponded to higher N and S levels at these sites. Lichen thallus condition was best 

where lichen species richness was also high. Highest Hg content, from a limited subset, 

was on the east slope of Mt. Washington near the head of Great Gulf. Most dominant 

lichens in good condition were associated with conifer boles or acidic substrates. The 

status regarding N and S tolerance for many lichens in the northeastern United States is 

not clear, so the influence of N pollution on community data cannot be fully assessed. 

Continued monitoring of lichens and bryophytes, especially if integrated with IMPROVE 

aerosol data, may reveal changes in air quality, climatic conditions, and other potential 

stressors or stimuli. Lichen health was impacted by low air quality at some of our sites.

KEY WORDS: lichens, bryophytes, air quality, monitoring, lead, mercury, nitrogen, thallus 

condition, IMPROVE, National Forest, Wilderness Area

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