United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-737

March 2008

Lichen Bioindication of Biodiversity,

Air Quality, and Climate: Baseline

Results From Monitoring in

Washington, Oregon, and California

Sarah Jovan
The Forest Service of the U.S. Department of Agriculture is dedicated to the

principle of multiple use management of the Nation’s forest resources for sustained yields of wood, water, forage, wildlife, and recreation. Through forestry

research, cooperation with the States and private forest owners, and management of the national forests and national grasslands, it strives—as directed by

Congress—to provide increasingly greater service to a growing Nation.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its

programs and activities on the basis of race, color, national origin, age, disability,

and where applicable, sex, marital status, familial status, parental status, religion,

sexual orientation, genetic information, political beliefs, reprisal, or because all or

part of an individual’s income is derived from any public assistance program. (Not

all prohibited bases apply to all programs.) Persons with disabilities who require

alternative means for communication of program information (Braille, large print,

audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice

and TDD).

To file a complaint of discrimination write USDA, Director, Office of Civil Rights,

1400 Independence Avenue, S.W. Washington, DC 20250-9410, or call (800) 795-

3272 (voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and

employer.

Author

Sarah Jovan is a research lichenologist, Forestry Sciences Laboratory,

620 SW Main, Suite 400, Portland, OR 97205.
Abstract

Jovan, Sarah. 2008. Lichen bioindication of biodiversity, air quality, and climate:

baseline results from monitoring in Washington, Oregon, and California. Gen.

Tech. Rep. PNW-GTR-737. Portland, OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station. 115 p.

Lichens are highly valued ecological indicators known for their sensitivity to a wide

variety of environmental stressors like air quality and climate change. This report

summarizes baseline results from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Lichen Community Indicator covering

the first full cycle of data collection (1998–2001, 2003) for Washington, Oregon,

and California. During this period, FIA conducted 972 surveys of epiphytic

macrolichen communities for monitoring both spatial and long-term temporal

trends in forest health. Major research findings are presented with emphasis on

lichen biodiversity as well as bioindication of air quality and climate. Considerable

effort is devoted to mapping geographic patterns and defining lichen indicator

species suitable for estimating air quality and climate.

Keywords: Acidophytes, air quality, California, climate change, cyanolichens,

forest health, gradient analysis, indicator species, neutrophytes, nitrophytes,

nonmetric multidimensional scaling, ordination, Pacific Northwest, pollution.
Contents

Part I: The FIA Lichen Community Indicator

1 Chapter 1: Introduction and Methods

1 Introduction

4 Lichens as Biological Indicators

7 Report Objectives

7 Methods

10 Lichen Survey Protocol

12 Data Analysis Overview: Gradient Models Demystified

Part II: Lichen Diversity Biomonitoring

17 Chapter 2: Epiphytic Lichen Diversity and Forest Habitats in

Washington, Oregon, and California

17 Key Findings

17 Summary of Lichen Species Richness

23 Coast Ranges

23 Large Agricultural Valleys

23 Cascade Range and Modoc Plateau

25 Sierra Nevada

25 Eastern Oregon and Washington

27 Conclusions

Part III: Air Quality Biomonitoring

29 Chapter 3: Air Quality in the West-Side Pacific Northwest

29 Key Findings

29 Introduction to the Model

32 Ancillary Data: Lichen Nitrogen and Sulfur Content

33 Interpretation of Air Quality Estimates: Nitrogen Pollutants Are Key Stressors

33 Discussion of Indicator Species

34 Direct and Indirect Nitrogen Effects on Lichens

34 Nitrophytes

36 Acidophytes

37 Neutrophytes

38 Stratified Cyanolichens

39 Indicator Synthesis
46 Summary of Air Quality Patterns

50 Conclusions

53 Chapter 4: Air Quality in the Greater Central Valley, California

53 Key Findings

53 Introduction to the Model

55 Ancillary Data: Air Quality Measurements

56 Interpretation of Air Quality Estimates: Nitrogen Is Shaping Lichen

Community Structure

58 Discussion of Indicator Species

63 Summary of Air Quality Patterns

65 Hotspots of Degraded Air Quality

67 Conclusions

69 Chapter 5: Air Quality in the Greater Sierra Nevada, California

69 Key Findings

69 Introduction to the Model

71 Model Adjustment for Elevation Effects

72 Ancillary Data: Lichen Nitrogen Content

75 Interpretation of Air Quality Estimates: Nitrogen Impacts Are Apparent

75 Summary of Air Quality Patterns in the Greater Sierra Nevada

76 Major Deposition Patterns

79 Conclusions

Part IV: Climate Mapping and Tracking

81 Chapter 6: Climate Biomonitoring in the West-Side Pacific

Northwest

81 Key Findings

81 Introduction to the Model

81 Ancillary Data: PRISM Model Climate Data

82 Interpretation of Climate Estimates

82 Review of Climate Zones and Indicator Species From Geiser and Neitlich

(2007)

83 Large Stratified Cyanolichens

83 Hypermaritime Communities

84 High-Elevation Communities

86 Summary of Climate Patterns in the West-Side Pacific Northwest

88 Conclusions
89 Chapter 7: Climate Biomonitoring in Northern and Central California

89 Key Findings

89 Introduction to the Model

91 Ancillary Data: PRISM Model Climate Data

91 Interpretation of Climate Estimates

91 Discussion of Climate Zones and Indicator Species

92 Proposed Temperature Effects

96 Proposed Moisture Effects

99 Summary of Climate Patterns in Northern and Central California

99 Spatial Temperature Patterns

99 Spatial Moisture Patterns

101 Conclusions

103 Chapter 8: Overall Conclusions

104 Acknowledgments

104 English Equivalents

105 References
Sticta fuliginosa (spotted felt lichen) found on Mount Hood National Forest in Oregon.

Roseann Barnhill

Part I: The FIA Lichen Community Indicator
1

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Chapter 1: Introduction and Methods

Introduction

Although appearing to be a single organism, a lichen (fig. 1) is actually a symbiotic

partnership between a fungus and one or more photosynthetic organisms, an alga

or cyanobacterium. Their intimate coexistence inspired the analogy (originally

conceived by Canadian lichenologist Trevor Goward) that lichens are fungi that

discovered agriculture (fig. 2). Typically the fungal partner provides most of the

composite organism’s structure and mass, thus trading physical protection for

carbohydrates manufactured by the photosynthetic partner (fig. 3). Together the

fungus and its partner(s) can inhabit a much wider variety of habitats and conditions than any could on their own.

The mountainous landscapes of Washington, Oregon, and California support

a very diverse and conspicuous lichen flora. This region is home to several rare,

endemic, old-growth-associated species such as Pseudocyphellaria rainierensis (fig.

4) and Nephroma occultum (fig. 5) as well as species that often achieve impressively high biomass in western forests, like Bryoria fremontii (fig. 6). Lichens play

many essential functional roles in these forested ecosystems. For instance, species

of Bryoria and Alectoria are important forage for elk, caribou, deer, and flying

squirrels (Maser et al. 1985, McCune 1993, Stevenson 1978). Birds, rodents, and

invertebrates also use these pendulous, hair-like species for nesting materials and

shelter (Hayward and Rosentreter 1994, Pettersson et al. 1995).

Figure 1—Hypogymnia physodes (tube lichen), one of several

Hypogymnia species occurring in the Pacific Western States.

Sarah Jovan

Lichens play many

essential functional

roles in these forested ecosystems.
2

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 2—Lobaria pulmonaria (lung lichen), a large species found in wet forests of

the Pacific Northwest.

Daphne Stone

Figure 3—Cross section of the lichen Lobaria pulmonaria, a three-way symbiosis between a

fungus, algae, and cyanobacteria. Fungal cells are located in the upper cortex (UC), medulla

(M), and lower cortex (LC). Photosynthesis occurs in the green algal layer (A) and nodules

containing cyanobacteria (CB). The cyanobacteria also fix atmospheric nitrogen into a form

that is useable to plants.

Eric Peterson from www.crustose.net
3

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 4—Pseudocyphellaria rainierensis

(Rainier pseudocyphellaria lichen). Provided

courtesy of OSU Lichen Group. 1999. Photographs of Pacific Northwest Lichens. Oregon

State University, Department of Botany and

Plant Pathology, Lichen Research Group. Jim Riley

Figure 5—Nephroma occultum (kidney lichen), a rare lichen species inhabiting old-growth forests in the Pacific Northwest.

Tim Wheeler
4

GENERAL TECHNICAL REPORT PNW-GTR-737

Cyanolichens such as Pseudocyphellaria anthraspis, P. crocata (fig. 7), and

Lobaria oregana (fig. 8) make important contributions to nutrient cycling in Pacific

Northwest forests as their cyanobacteria partners fix atmospheric nitrogen (N2) into

a form that is useable by plants. Nitrogen inputs from cyanolichens may be quite

substantial, especially in moist, old-growth forests. It is estimated that L. oregana

alone fixes as much as 16.5 kg N2

·ha-1·yr-1 at some high biomass sites in the western

Cascades of Oregon (Antoine 2004). These lichens, like many others, are critical

components of western ecosystems and directly influence forest health.

Lichens as Biological Indicators

Lichens are often likened to canaries in a coal mine because some species are

extremely sensitive to environmental change, a major reason for their popularity as

bioindicators for natural resource assessment (e.g., Nimis et al. 2002). The Forest

Inventory and Analysis (FIA) Program of the U.S. Department of Agriculture,

Forest Service (USFS), includes lichens among a suite of forest health indicators

(Stolte et al. 2002) that are monitored nationwide on a permanent sampling grid

(Will-Wolf 2005). Formally known as the FIA Lichen Community Indicator, these

Figure 6—Conifers laden with Bryoria fremontii (Freemont’s horsehair lichen) in Starkey

Experimental Forest, eastern Oregon.

Sarah Jovan
5

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

data are periodic surveys of epiphytic (“tree-dwelling”) lichen communities conducted by specially trained field crews (fig. 9).

The structure of a lichen community in a forest (i.e., species presence and

abundance) intrinsically provides a wealth of information about forest health, function, and local climatic conditions (fig. 10). Analysts can extract particular properties of the community data, such as indices of indicator species and community

gradients, to address a wide variety of monitoring questions (See sidebar 1).

At present, the three primary objectives of the Lichen Community Indicator are to

Figure 7—Pseudocyphellaria crocata (Pseudocyphellaria lichen); its bright

yellow soredia (powdery clusters of algal cells and fungal strands erupting

from the surface) are diagnostic.

Tim Wheeler

Figure 8—Lobaria oregana

(Oregon lung lichen), an

important nitrogen-fixer in

western forests. Eric Straley

The structure of a

lichen community in

a forest intrinsically

provides a wealth

of information about

forest health, function, and local

climatic conditions.
6

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 9—The Alaskan Forest Inventory and Analysis crew

training for the 2006 lichen certification exam. This was the

second year of lichen surveying in coastal Alaska.

Sarah Jovan

Figure 10—Concept behind the Lichen Community Indicator. N = nitrogen, S = sulfur.
7

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

evaluate and monitor biodiversity, air quality, and climate. Survey data may be

directly applied for biodiversity monitoring, whereas the latter two applications

involve multivariate gradient models.

Report Objectives

Four gradient models that use FIA lichen data to estimate air quality and climate

have been implemented in the study region, and three to four additional models

are scheduled for completion within the next few years (fig. 11 and table 1). The

main objective of this report is to summarize lichen biodiversity and major research

findings from the four completed gradient models. Summaries typically include

both original analysis and review of previously published articles. Air quality

results are mapped to indicate forests at greatest risk of ecological degradation,

and modeled climate estimates likewise signify current conditions affecting forests. Lichen indicator species are examined to help link air quality estimates to

specific pollutants and also to illustrate the biological underpinnings of each

gradient model. These baseline results are of great interest to forest and air managers, serving as the first large-scale comprehensive assessments of forest health for

the study region.

Methods

This section covers the survey protocol for the Lichen Community Indicator

(USDA FS 2005) as well as analytical methods used to build gradient models for

air quality and climate bioindication. These general overviews are critical to understanding the basis for results presented in the following chapters. Less essential but

more technical explanations are presented in sidebars. Any model-specific methods

are described in their respective chapters.

Sidebar 1: Why use bioindicators?

What is the rationale for biomonitoring when we are technologically capable

of directly measuring forest conditions? Perhaps the most immediate benefit

is the ability to map conditions at a sampling intensity that is prohibitively

expensive with active monitors. Moreover, bioindicators are intimately tied to

local conditions. Their responses to stressors are often more representative of

cumulative impact on the function and diversity of the surrounding ecosystem

than are active monitors.

These baseline

results are of great

interest to forest

and air managers,

serving as the first

large-scale comprehensive assessments of forest

health for the study

region.
8

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 11—Map of all sites in Washington, Oregon, and California surveyed for lichen communities. Plots are grouped in

model areas as described in table 1. Federal class 1 areas are shown because they receive special air quality protection

under the Clean Air Act (Section 162(a)). These include national parks, national wilderness areas, and national monuments. Plot locations are approximate.
9

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 1—Implementation of the Forest Inventory and Analysis Lichen Indicator in Washington, Oregon, and Californiaa

Estimated Anticipated

Model region parameters Progress Citation completion Notes

Pacific Northwest, west-side Air quality, climate Complete Geiser and Neitlich 2007 Done See chapters 3 and 6Pacific Northwest, east-side Air quality, climate In progress Fall 2007 May include plots from Montana and Idaho

California, Northwest Coast Air quality In progress Summer 2007 Region may be merged with the west-side Pacific

Northwest Model

California, Greater Central Valley Air quality Complete Jovan and McCune 2005 Done See chapter 4California, Greater Sierra Nevada Air quality Complete Jovan and McCune 2006 Done See chapter 5California, Northern and Central Climate Complete Jovan and McCune 2004 Done Encompasses greater Central Valley, greater Sierra

Nevada, and Northwest coast models; See chapter 7

California, Greater Los Angeles

Basin Air quality, climate Study plan under 2008 or 2009 Model boundaries in figure 11 are preliminary

development

California, Southeast Desert

Basin and Range Air quality, climate Study plan in Not yet May be merged with a model for the Southwest

discussion scheduled (Arizona, New Mexico, and Nevada)

a Model boundaries are shown in figure 11.
10

GENERAL TECHNICAL REPORT PNW-GTR-737

The FIA crews have completed lichen surveys at a full cycle of plots for Forest Service Regions 5 (California) and 6 (Oregon and Washington; fig. 11). Five

panels of plots were surveyed in 1998, 1999, 2000, 2001, and 2003, and resurveys

are tentatively scheduled to begin in 2010. Extensive measurements of forest stand

structure, history, and productivity are systematically collected nationwide at one

plot for every 2430 ha on the FIA phase 2 (P2) grid. Every 16th plot is also a phase

3 (P3) plot where additional data are collected for all Forest Health Indicators,

which includes the Lichen Community Indicator (Stolte et al. 2002).

Lichen Survey Protocol

Lichen community surveys are conducted on a circular, 0.378-ha plot centered

on subplot 1 in the standard FIA plot design (fig. 12). Surveys last a minimum

of 30 minutes and a maximum of 2 hours during which the crew member collects

a voucher specimen of every epiphytic macrolichen species encountered. Although

data are not a complete inventory of all macrolichen species on a plot, this costeffective survey protocol generates representative and repeatable plot data that are

comparable across the country. Abundance is estimated for each species by using

broad abundance codes (table 2). Surveyors collect from the surface of all standing woody substrates, above 0.5 m and within arm’s reach, and also freshly fallen

lichens in the litter. Vouchers are sent to professional lichenologists for identification in the lab.

Figure 12—Forest Inventory and Analysis plot design. Lichen communities are surveyed in the shaded area.

The FIA crews have

completed lichen

surveys at a full

cycle of plots for

California and

Oregon and

Washington.
11

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Surveyors are nonspecialists specially trained to differentiate between lichen

species. Every year surveyors must pass a certification exam administered by a

professional lichenologist (fig. 13). At a practice FIA plot, the number of species the trainee collects must be 65 percent or more of the number found by the

lichenologist in order to certify. The lichenologist then periodically audits crews

during the field season with hot and blind checks to ensure that species capture

continues to meet the 65 percent measurement tolerance. Hot checks are mainly

instructive, where crew members and the lichenologist survey a plot simultaneously. Blind-checked plots are usually surveyed by a lichenologist within 1

month of the crew, without knowledge of the crew’s data, and provide a measure

of data quality. Air quality estimates from FIA gradient models have been found

to be highly repeatable as long as the 65 percent criterion is met (McCune et al.

1997).

Table 2—Abundance codes used during lichen community surveys

Code Abundance

1 Infrequent (1 to 3 thalli)

2 Uncommon (4 to 10 thalli)

3 Common (>10 thalli; species occurring on less than 50 percent of all boles

and branches in plot)

4 Abundant (>10 thalli; species occurring on greater than 50 percent of boles

and branches in the plot)

Figure 13—The Alaskan Forest Inventory and Analysis crew

prepares for the 2006 lichen certification exam.

Sarah Jovan
12

GENERAL TECHNICAL REPORT PNW-GTR-737

To help build gradient models for air quality or climate bioindication, additional lichen surveys may be conducted at temporary plots off the regular FIA

grid (i.e., off-frame plots). These plots are strategically located in areas of special

interest or significance (e.g., wilderness areas and national parks) or in places expected to be highly polluted (e.g., in cities, near industry) in order to capture the

entire regional pollution gradient. Off-frame plots are often co-located with air

quality monitors.

Data Analysis Overview: Gradient Models Demystified

A model built with gradient analysis (see McCune and Grace 2002) is required to

apply the lichen community data for air quality and climate monitoring. Gradient

analysis helps the investigator detect the strongest patterns (i.e., gradients) in lichen

community composition and determine how they relate to environmental variables.

Gradient models are typically developed by using either nonmetric multidimensional scaling ordination (NMS) (Kruskal 1964) or regression techniques (or some

combination of the two). Usually at least two strong gradients can be extracted

from the FIA lichen data: one gradient in community composition relating to air

quality and a second gradient relating to macroclimate.

Analysts tailor these gradient models to meet the management objectives and

research needs of their respective coverage areas. Thus no two models are exactly

alike. Attempts are made to integrate lichen data with outside sources of information, such as measurements of pollutant concentrations or deposition at off-frame

plots, modeled climate estimates, and FIA forest mensuration data collected from

the P2 and P3 grids, as well as lichen survey data from other landscape-scale

sampling grids.

Nonmetric multidimensional scaling—

Nonmetric multidimensional scaling is a powerful tool for gradient analysis of

complex ecological data (McCune and Grace 2002) and provides the foundation

for three of the four models discussed in this report. Basically NMS simultaneously

considers the abundance of all lichen species in all plots by using a “distance measure” (e.g., Sørensen or Euclidean distance; McCune and Grace 2002). This measure is a mathematical expression that quantifies how community composition

differs between each and every plot in the data. NMS uses these differences to

Usually at least two

strong gradients

can be extracted

from the FIA lichen

data: one gradient

in community composition relating

to air quality and

a second gradient

relating to

macroclimate.
13

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

detect gradients in community composition and score plots along them (see sidebar

2). All reported NMS analyses were conducted using the PC-ORD software package1

 (McCune and Mefford 1999).

Tools including scatterplots, regression, and overlays are used to determine

how these lichen community gradients relate to environmental factors. For example, if a gradient detected by NMS is strongly correlated with pollution variables (e.g., nitrogen deposition or sulfur dioxide concentration), the score of a plot

on that gradient serves as a relative estimate of local air quality. The FIA lichen

surveys contain an enormous amount of information as lichens respond to many

Sidebar 2: Technical aspects of nonmetric multidimensional

scaling (NMS)

The data set used to build an NMS model with PC-ORD (McCune and

Mefford 1999) is an n x p matrix of Forest Inventory and Analysis plots

(rows) by species (columns), with each cell containing a lichen abundance

code (table 2). NMS is indirect ordination, meaning that detected gradients

are based solely on the ecological information given by the distributions of

species among plots. This process is technically a transformation of the plots

as points in p-dimensional species space to a lower dimensional space, which

is accomplished through hundreds of iterations. Each detected gradient is,

functionally, an independent response of lichen communities to one or more

environmental conditions (i.e., an environmental gradient). The analyst must

discover the identity of these environmental conditions by looking at ancillary

environmental data.

Because NMS generates major community gradients without consideration of environmental variables (unlike the other popular techniques, canonical correspondence analysis and redundancy analysis) (Økland 1996), it is

statistically valid to correlate environmental variables with community

gradients to make predictions. It is also statistically valid to make predictions

for new plots, which can be easily fit to the NMS axes. For further technical

detail, an excellent introduction to NMS is given by McCune and Grace

(2002, chapter 16).

1

Use of trade of firm names in this publication is for information and does not imply

endorsement of any product or service by the U.S. Department of Agriculture.
14

GENERAL TECHNICAL REPORT PNW-GTR-737

aspects of their local environment. In essence, NMS is a distillation of that information into the strongest patterns. Once community gradients are linked to the

environmental gradient(s) they reflect, indicator species for that environmental

factor can be identified.

Identifying indicator species with species scores—

Species scores were used in chapters 3 and 4 to identify pollution indicator species.

Basically each species used to build an NMS model is scored on each of the detected

lichen community gradients. The calculation involves simple weighted averaging

that considers how the species’ abundance was distributed among plots and how

those plots scored on the gradient. The species’ score is technically its average position on the gradient and a representation of the species’ optimum (i.e., for air

Sidebar 3: Calculating species scores

Species scores are calculated by using PC-ORD (McCune and Mefford 1999)

in a single weighted-averaging step, whereby species abundance is weighted

by plot scores from the nonmetric multidimensional scale air quality gradient:

∑

∑

=

= = n

i

ij

n

i

ij i

j

a

a w

v

1

1

where vj = score for species j, aij = abundance of species j in plot i, wi is

the plot score of plot i, and n is the number of species on the plot. A few

important caveats are (1) scores for infrequent species are based on less data

and so their scores are less reliable, (2) this analysis is susceptible to bias at

extremes of the air quality gradient as species scores are restricted to the range

of the data even if the species’ true optimum lies beyond the data extremes

(McCune and Grace 2002), and (3) a score can be calculated for every species

even if the species does not actually respond to air quality. Species scores

suggest pollution sensitivity or intolerance but should be supported by multiple lines of evidence.

Once community

gradients are linked

to the environmental gradient(s) they

reflect, indicator

species for that

environmental

factor can be

identified.
15

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

quality or climate) (McCune and Grace 2002). For a gradient linked to air quality,

species scores serve as rough estimates of how species respond individually to

pollution in the study area, given a few caveats (see sidebar 3). Species are scored

along the same gradient as plots. This means that any categorization of air quality

scores that the analyst uses to summarize plots (e.g., best, fair, worst) can be used

to gauge the implications of species scores and vice versa.

Indicator species analysis—

A second tool for identification of indicator species, Indicator Species Analysis

(ISA) (Dufrêne and Legendre 1997), is used in chapters 3, 6, and 7 to determine

air quality and climate indicators. The ISA is a nonparametric analysis available in

PC-ORD (McCune and Mefford 1999) that evaluates the faithfulness and exclusivity

of each species to predefined groups of plots (McCune and Grace 2002), which are

both fundamental characteristics of a reliable indicator. The analyst chooses how to

best divide plots into a priori groups, which often involves segmentation of the air

quality or climate gradient into useful categories (e.g., low, medium, and high pollution). To assess statistical significance of indicator species, a randomization test is

used whereby plots are randomly reassigned to the groups 1,000 times (McCune and

Grace 2002).
16

GENERAL TECHNICAL REPORT PNW-GTR-737

Part II: Lichen Diversity Biomonitoring

Evaluation of lichen species richness (i.e., a count of species encountered) is the

main, formalized application of the Forest Inventory and Analysis (FIA), U.S.

Department of Agriculture, Forest Service, Lichen Community Indicator in regions

awaiting air quality and climate bioindication models. Because lichen surveys are

timed, these species counts are technically a richness index rather than an unbiased

estimator for plot species richness. Lichen diversity is commonly used as a general

indicator of forest health and “ecological function” (Will-Wolf et al. 2002), as

lichens are key primary producers with important linkages to nutrient cycling and

forest food webs (see chapter 1 “Introduction”). High or low lichen diversity can

result from certain types of air pollution, changes to forest management or stand

structure, diversity of plant substrates available for colonization, favorability of

climate, return interval of disturbances like fire, and so on. As such, species richness is a simple parameter with a complex origin and interpretation. To answer

more specific questions about the local forest environment, analysts often parse out

richness indices by using lichen indicator species. Richness or abundance of pollution indicators, for instance, is commonly used in the FIA lichen-air quality models

(see chapters 3 to 5).

Over 20 species of lichens inhabit this small oak in Walnut Park, Corvallis, Oregon.

Sarah Jovan
17

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Chapter 2: Epiphytic Lichen Diversity and

Forest Habitats in Washington, Oregon,

and California

Key Findings

1. In total, 263 epiphytic lichen species were found among 779 plots

2. Zero to 45 species occurred per plot. Plot-level species richness was highly

heterogeneous within most ecoregions.

3. Prominent hotspots of diversity include the Klamath-Siskiyou region shared by

Oregon and California, the Oregon-side western Cascades, and the Washingtonside eastern Cascades.

4. Highest diversity was found in the Okanogan highlands of eastern Washington,

where plots supported an average richness of 21.8 species. The eastern Blue

Mountains farther south likewise supported a considerable density of speciesrich plots.

5. Plots were sparsely distributed over dry, shrubby ecoregions that lack substantial

forest cover like the Columbia Basin, Northwestern Basin and Range, Owyhee

uplands, and the agricultural Central Valley. Species richness estimates are least

reliable from these poorly sampled areas.

Summary of Lichen Species Richness

Washington, Oregon, and California are home to a wide array of forested ecosystems (fig. 14, table 3). The terrain is highly mountainous and along these steep

elevation gradients, climate and forest composition change drastically (fig. 15). The

diversity of conditions is reflected by a remarkable diversity of lichens (fig. 16).

An impressive total of 263 epiphytic lichen species were found in the region from a

full cycle of survey data (n = 779, 1998-2001, 2003) collected by the Forest Inventory and Analysis (FIA) Program, U.S. Department of Agriculture. Among all

plots surveyed in Washington (n = 199), Oregon (n = 292), and California (n =

288), plot-level species richness ranged from 0 to 45 species (tables 4 and 5). The

study region supports several sizeable clusters of moderate- (≥ 16 species) to highdiversity (> 25 species) plots although richness is, overall, fairly heterogeneous

within ecoregions (fig. 17).

The diversity

of conditions is

reflected by a remarkable diversity

of lichens.
18

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 14—Study area for lichen diversity assessment coded by Bailey’s ecoregion sections (1983).
19

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 3— Basic parameters of the species richness index summarized by Bailey’s ecoregions (1983;

see fig. 14)a

Ecoregion section Mean Min Max n SD Median

Blue Mountains (M332G) 15.3 1 31 63 7.0 15

Central California Coast (261A) 16.5 12 26 4 6.5 14

Central California Coast Ranges (M262A) 12.1 2 22 13 5.6 13

Columbia Basin (342I) 19.3 13 27 6 5.7 18

Eastern Cascades (M242C) 13.8 0 34 97 7.3 12

Great Valley (i.e., Central Valley) (262A) 13.2 7 23 5 6.3 12

High Lava Plains (342H) 10.1 1 29 15 6.8 9

Klamath Mountains (M261A) 18.6 0 45 84 9.4 19

Modoc Plateau (M261G) 10.7 0 22 37 5.5 10

Northern California Coast (263A) 10.8 1 29 14 6.5 10

Northern California Coast Ranges (M261B) 19.2 7 39 26 8.0 19

Northern California Interior Coast Ranges (M261C) 15.5 7 26 8 7.3 14.5

Northwestern Basin and Range (342B) 8.0 1 17 11 4.3 6.5

Okanogan Highlands (M333A) 21.8 5 34 42 6.2 23

Oregon and Washington Coast Ranges (M242A) 14.3 0 32 73 8.2 15

Owyhee Uplands (342C) 7.9 5 13 7 3.0 7

Sierra Nevada (M261E) 8.9 0 34 79 7.4 8

Sierra Nevada Foothills (M261F) 13.0 2 29 30 6.4 12.5

Southern Cascades (M261D) 16 4 36 30 7.6 15

Western Cascades (M242B) 18.8 0 34 68 6.7 19

Willamette Valley and Puget Trough (242A) 15.7 0 43 54 8.1 15

a

 Ecoregion sections with fewer than four plots were excluded from the analysis.

Figure 15—Three Sisters Wilderness, Oregon.

Daphne Stone
20

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 16—Some hardwood forests in

the Willamette Valley and Coast Ranges

support a high biomass of Ramalina

menziesii (Menzies’ cartilage lichen) and

several other green, hair-like species. Daphne Stone

Table 4—Lichen species richness in the Pacific Northwest (1998-2001, 2003)a

Parameter Pacific Northwest Oregon Washington W-PNW E-PNW

Number of plots 491 292 199 247 244

Number of plots by lichen

species richness category

1: 0 to 6 species 60 44 16 26 34

2: 7 to 15 species 186 118 68 81 105

3: 16 to 25 species 188 94 94 105 83

4: >25 species 57 36 21 35 22

Median 15 14 17 17 14

Range of species richness per

plot (low to high) 0 to 45 0 to 45 0 to 34 0 to 45 0 to 34

Average species richness

per plot 15.85 15.00 17.10 16.94 14.75

 (alpha diversity)

Standard deviation of lichen

species richness per plot 7.99 8.45 7.10 8.20 7.63

Species turnover rate

(beta diversity) 13.12 12.13 9.82 11.26 8.27

Total number of species per

area (gamma diversity) 208 182 168 191 122

a

 The Pacific Northwest region is broken down into two areas used for air quality and climate biomonitoring (see table 1, fig. 11):

the west-side (W-PNW) and the east-side (E-PNW).
21

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 5—Lichen species richness in California (1998-2001, 2003)a

Parameter California GCV GSN NWC SoCal DBR

Number of plots 288 76 133 68 8 3

Number of plots by lichen species

richness category

1: 0-6 species 61 7 43 3 5 3

2: 7-15 species 141 41 67 31 2 0

3: 16-25 species 62 19 18 24 1 0

4: >25 species 24 9 5 10 0 0

Median 12 13 9 16 2.5 0

Range of species richness per plot

(low to high) 0 to 39 2 to 31 0 to 34 1 to 39 0 to 16 0 to 0

Average lichen species richness

per plot 12.59 14.38 9.87 17.21 4.88 0

(alpha diversity)

Standard deviation of species

richness per plot 7.97 6.82 7.06 8.05 6.10 0

Species turnover rate (beta diversity) 16.52 9.11 16.92 9.36 5.12 0

Total number of species per area

(gamma diversity) 208 131 118 161 25 0

a

 Plots are broken down into model areas used for air quality biomonitoring (see table 1, fig. 11): greater Central Valley (GCV),

greater Sierra Nevada (GSN), Northwest Coast (NWC), greater Los Angeles Basin (SoCal), and the Southeast Desert Basin and

Range (DBR).
22

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 17—Lichen species richness index for plots.
23

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Coast Ranges (ecoregion section codes: 261A, M262A, M261A,

263A, M261B, M261C, and M242A)

The Pacific Ocean has a strong influence on climate patterns, tending to moderate

temperature fluctuation. The western border of the tri-state study region is lined

by warm coastal mountains (fig.14) with lush evergreen forests to the north (i.e.,

Pinus contorta [Dougl. ex Loud.], Picea sitchensis [(Bong.) Carr.], Tsuga

heterophylla [(Raf.) Sarg.] , Pseudotsuga menziesii [(Mirb.) Franco], Thuja

plicata [Donn ex D. Don]), giving way to oak communities and chapparal in the

drier south. Lichen diversity across these habitats is quite variable (range: 0 to 45

species) with average plot richness ranging from 10.8 to 19.2 species depending on

ecoregion (table 3). The most prominent diversity hotspot in California spans the

wet forests of the northern California Coast Ranges (M261B) and the KlamathSiskiyou region (M261A; fig. 17). Lichen communities of the Oregon part of the

Klamath Mountains are likewise species-rich and include the highest diversity site

in the data set (45 species) at a remote forest stand about 30 km north of Medford,

Oregon (see sidebar 4; fig. 18).

Large Agricultural Valleys (262A, 242A)

Just east of the Coast Ranges in Oregon and central California lie the broad, flat

Willamette Valley (Oregon half of 242A, fig. 14) and Central Valley (i.e., Great

Valley 262A), where drier and hotter conditions support oak-savanna, prairies, and

chaparral. These valleys are important agricultural foci and also include large cities

and industry: the Willamette and Central Valleys are home to about 70 percent and

10 percent of the population in their respective states. Owing to intense development and low tree density, too few on-frame plots were surveyed to reliably

characterize Central Valley diversity (table 3). Diversity of the Willamette Valley

and Puget Trough ecoregion (average: 15.7 species) is comparable to the adjacent

Coast Ranges (M242A; 14.3 species) although species dominance is fairly different

owing to differences in climate and air quality (see chapters 3 and 6).

Cascades Range and Modoc Plateau (M242C, M261D, M242B,

M261G)

Farther east lies a major landform that transects the study region, the volcanic

Cascades Range (fig. 14). This rugged network of peaks extends from the Fraser

River in British Columbia, Canada, to Lassen Peak in northern California. The

wetter west-side supports several mixed-conifer and hardwood forest types where

The most prominent

diversity hotspot in

California spans the

wet forests of the

northern California

Coast Ranges and

the Klamath-Siskiyou

region.
24

GENERAL TECHNICAL REPORT PNW-GTR-737

Pseudotsuga menziesii, Tsuga heterophylla, and Abies amabilis (Dougl. ex Forbes)

are common components. The Western Cascades is a lichen diversity hotspot

(average: 18.8 species; table 3), especially in Oregon where field crews detected 16

or more species at well over half of plots. Diversity is unmatched on the west slope

of the Washington Cascades for reasons unknown (fig. 17). East of the crest,

precipitation declines abruptly. Open stands of Pinus ponderosa (Dougl. ex Laws.)

and P. contorta predominate in the Cascades rainshadow, which supports less

diverse lichen communities (13.8 species) comprising many drought-tolerant (e.g.,

xerophytic) lichens. Diversity is noticeably higher, overall, on the Washington side

of the eastern Cascades.

A similar pattern is observed in the southern Cascades ecoregion in California

(M261D). Fairly species-rich west-side communities (average: 16 species) give

way to substantially lower diversity (10.7 species) in rainshadow forests of the

Sidebar 4: Klamath Region

The Klamath-Siskiyou region is heralded by the World Conservation Union as

a global center of biodiversity. Botanical diversity is exceptional for a temperate coniferous ecosystem (reviewed by DellaSala et al. 1999), and forests are

widely considered a conservation priority owing to a high incidence of plant

and animal endemism.

Figure 18—Serpentine soils, like those found in the Klamath Range,

often support endemic and endangered plants.

Daphne Stone
25

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Modoc Plateau (M261G). This cool desert landscape consists of open Juniperus

occidentalis (Hook.) stands alongside vast sagebrush-steppe and pockets of mixedconifer forest. Dry, open-canopied forests are unfavorable for the growth of many

lichen species (fig. 19). Thus, relatively low diversity in the eastern Cascades and

Modoc forests is not necessarily unnatural.

Sierra Nevada (M261E, M261F)

South of the Cascades lies the granitic Sierra Nevada Range in California

(fig. 14), where climate is more profoundly Mediterranean with hot dry summers and cool, wet winters. Resident ecosystems include sequoia groves

(Sequoiadendron giganteum [(Lindl.) Buchholz]) at southern mid elevations,

chaparral and dry oak scrub in the dry foothills, and a broad range of mixedconifer stands occurring throughout (fig. 20). Tourism is especially prolific in

the famous Yosemite, Sequoia, and Kings Canyon National Parks (see chapter 5).

Excepting some communities in the foothills, diversity of Sierra sites averaged low

at 8.9 species (table 3). Many of the most species-poor sites (<6 species) in the

southern two-thirds of the ecoregion are alpine where harsh climatic conditions

persist for much of the year (fig. 17).

Eastern Oregon and Washington (342I, 342H, 342B, 342C,

M332G, M333A)

Further east in Oregon and Washington are four sparsely-sampled ecoregions (figs.

14 and 17): the Northwestern Basin and Range (average: 8 species), High Lava

Plains (10.1 species), Owyhee Uplands (7.9 species), and the Columbia Basin

Figure 19—Melanelia subolivacea (Melanelia lichen) is widespread

across dry forests in eastern Washington, Oregon, and the Modoc Plateau.

Tim Wheeler
26

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 20—Letharia columbiana (wolf lichen), a chartreuse

species common in high-elevation forests of the Cascades and

Sierra Nevada.

Eric Peterson

sections (19.3 species; table 3). Minus a few peripheral sites in the Columbia

Basin that raise the average, low diversity is comparable to neighboring forests in

Oregon’s Eastern Cascades. Low lichen richness is not surprising for the nominal

forests, grasslands, and sagebrush-steppe typical of this arid landscape.

Nearer to the Idaho border in the Blue Mountain Range (M332G; 15.3 species)

and Okanogan Highlands (M333A; 21.8 species), lichen diversity abruptly increases along with forest cover and rainfall. Climate varies widely depending on

topographic position, but winters are generally cold and long with extensive

snowfal; summers are cool, dry, and brief. Both ecoregion sections support sagebrush-dominated ecosystems as well as conifer stands with Pinus ponderosa and

P. menziesii at higher elevations (fig. 21).

Low lichen richness

is not surprising for

the nominal forests,

grasslands, and

sagebrush-steppe

typical of this arid

landscape.
27

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Conclusions

The biodiversity data presented here serve as the baseline for continued monitoring

of forest health and function. Lichens are important components of all forest

habitats in the study region although species composition, ecological role, and

connections to forest health differ substantially across ecosystems. Environmental

gradients in climate and forest stand characteristics are steep enough that two

neighboring ecoregion sections may exhibit comparable diversity but possess totally

incongruent lichen communities. Species composition of the various floras of

Washington, Oregon, and California are explored more deeply in the following

chapters for all regions except the eastern Pacific Northwest, which is currently

under formal study.

Figure 21—Conifer forest on the Umatilla National Forest in

the Blue Mountains.

Daphne Stone

Lichens are important components

of all forest habitats

in the study region

although species

composition, ecological role, and

connections to

forest health differ

substantially across

ecosystems.
28

GENERAL TECHNICAL REPORT PNW-GTR-737

Part III: Air Quality Biomonitoring

Lichens are best known for their utility in monitoring air quality, a practice dating

back to the pioneering work of Nylander (1866). Since then, modern communitybased approaches, like that of the Lichen Community Indicator, have benefited

greatly from a profusion of research concerning which species are reliable indicators of what pollutants (e.g., Gilbert 1986; Hawksworth and Rose 1970; Sigal and

Nash 1983; van Herk 1999, 2001) and modern computer technology that permits

complex statistical analyses. Through the effective use of indicator species, community-based lichen models can often be tied to the deposition of a specific type of

pollution. Several common compounds affect lichens, although relationships to

sulfur dioxide, nitrogen-containing pollutants, and acidic deposition, are the best

understood.

The three chapters in part III cover recent air quality bioindication results from

the west-side Pacific Northwest, the greater Central Valley of California, and the

nearby greater Sierra Nevada. Detected air quality patterns are mapped and summarized for each study area. Each chapter also discusses indicator species to illustrate

the ecological underpinnings of the bioindication models. An attempt is made to

define and utilize two promising groups of indicator species, the acidophytes and

neutrophytes, which are not yet widely recognized by the North American lichen

biomonitoring community.

Tufted Usnea (beard lichen) hangs from a hawthorn branch. Walnut Park,

Corvallis, Oregon.

Sarah Jovan
29

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Chapter 3: Air Quality in the West-Side

Pacific Northwest

Key Findings

1. There is strong evidence that one or more nitrogen (N) pollutants are impacting

forests in the western Pacific Northwest.

2. Geiser and Neitlich (2007) found high N accumulations in lichens collected

from polluted sites.

3. Lichen communities at polluted sites are also characterized by species promoted

by or tolerant of N, whereas communities at clean sites are more heavily

dominated by species known to be N-intolerant or generally pollution-sensitive.

4. Lichen communities and N content of lichens collected from forests near urban

and agricultural areas reflect high N deposition. Air quality impacts are estimated to be most extensive in forests of the Willamette Valley and Puget Trough

ecoregion section.

5. Remote forests at mid to high elevations in the Cascades, near national parks,

and in the coast and Olympic Ranges, appear the most pristine.

Introduction to the Model

The gradient model for the west-side Pacific Northwest was designed for monitoring air quality and climate change in western Oregon and Washington forests (figs.

22 and 23). This model was developed by Geiser and Neitlich (2007) in a partnership between Forest Inventory and Analysis (FIA) and the Forest Service (FS)

Region 6 Air Resource Management Program. They used nonmetric multidimensional scaling ordination (NMS) (see chapter 1 “Methods” section) to build the

gradient model from 293 plots. These calibration plots were strategically drawn

from a pool of over 1,400 plots surveyed for lichens between 1994 and 2001 (see

sidebar 5). Calibration data included FIA plots (n = 19), plots on the FS Region 6

Current Vegetation Survey grid (CVS; n = 155; Max et al. 1996), and supplementary off-frame plots installed in both urban and remote areas (n = 119). Additional

plots surveyed in the west-side Pacific Northwest study area can be easily fit to the

model and scored for both air quality and climate.
30

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 22—West-side Pacific Northwest model area. The eastern-most boundary is delineated by county lines, for the most

part coinciding with the Cascades crest. Major cities are indicated by red asterisks.
31

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Sidebar 5: Selection of calibration plots

For bioindication models that cover mountainous landscapes, it is not uncommon for elevation and precipitation to confound air quality estimates. Pollution emissions are often concentrated at low elevations, which are also drier

than higher elevation forests (Jovan and McCune 2006, McCune et al. 1998).

Among the 1,416 plots surveyed in the west-side Pacific Northwest, Geiser

and Neitlich (2007) were able to select a calibration data set with equivalent

numbers of polluted and clean sites spanning the elevation and precipitation

gradient. (By definition, “polluted” sites possessed lichens with relatively

high accumulations of N). This extra step reassured effective isolation of air

quality versus elevation-related effects in the bioindication model. A second

strategy for dealing with confounded environmental variables is presented in

chapter 5.

Figure 23—Riparian forest in Columbia River

Gorge Scenic Area, Oregon. Sarah Jovan
32

GENERAL TECHNICAL REPORT PNW-GTR-737

Ancillary Data: Lichen Nitrogen and Sulfur Content

To provide independent verification of air quality patterns detected by the gradient

model, common epiphytic lichens, like Platismatia glauca and Hypogymnia

imshaugii (figs. 24 and 25), were collected from more than half of all surveyed

plots (n = 886; Geiser and Neitlich 2007). Lichen samples were analyzed for N and

sulfur (S) content by using protocols described in Blett et al. (2003). Both elements

are key components of several major west-side pollutants. Chemical analysis is a

Figure 24—Platismatia glauca (ragged lichen).

Roseann Barnhill, http://redskystudio.org

Figure 25—Hypogymnia imshaugii (Imshaug’s tube lichen).

Tim Wheeler
33

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

very practical biomonitoring tool because lichens accumulate both airborne and

waterborne chemicals in concentrations that mirror the local pollution environment

(Blett et al. 2003, Bruteig 1993, Geiser 2004, Jovan and Carlberg 2007, Søchting

1995).

Interpretation of Air Quality Estimates: Nitrogen Pollutants Are

Key Stressors

NMS uncovered two prominent gradients in lichen community composition; one

relating to air quality and the other relating to forest climate (chapter 6). Several

lines of evidence suggest that N compounds are a key component of the air quality

degradation detected by the model although other types of pollution may certainly

contribute. As reported by Geiser and Neitlich (2007), plots receiving high (poor)

air quality scores were associated with (1) forests near urban or agricultural areas,

(2) lichen samples with elevated N (r = 0.73) and S concentrations (r = 0.68), (3)

regionally high wet deposition of ammonium (NH4

+

) measured by nearby National

Atmospheric Deposition Program monitors (NADP; r = 0.74), and (4) lichen species known to be tolerant of or promoted by N (see “Discussion” below). Air

quality scores are relative (i.e., the higher a plot scores on the air quality gradient,

the poorer the air quality) but could be calibrated with direct pollutant measurements if more were available.

Discussion of Indicator Species

Two analyses of lichen indicator species are used to demonstrate how air quality in

the west-side Pacific Northwest manifests as a lichen community response detectable with gradient analysis. First, Geiser and Neitlich (2007) defined indicator

species of ”polluted” vs. “clean” plots in the calibration data set using Indicator

Species Analysis (ISA) (Dufrêne and Legendre 1997); (see sidebar 6). The ISA is

reviewed below but results are reconceptualized in terms of four groups of lichens

known to respond differently to N: nitrophytes, acidophytes, neutrophytes, and

stratified cyanolichens. Linkages of the air quality gradient to N provided an

excellent opportunity in this report to further develop our understanding of the N

indicator groups. So as a followup, the same data set was used to score each lichen

species along the air quality gradient to estimate how each responds individually to

N (see “Indicator Synthesis” below). These species scores will help researchers

determine which North American species belong to the understudied acidophytic

and neutrophytic groups.

N compounds are a

key component of

the air quality degradation detected by

the model.
34

GENERAL TECHNICAL REPORT PNW-GTR-737

Direct and Indirect Nitrogen Effects on Lichens

Nitrogen potentially affects lichens in two ways, directly by fertilization or other

physiological effect, and indirectly by altering bark pH. Definitive research that

clearly distinguishes between these two modes of action is lacking, in large part

because substrate pH is often closely linked to deposition of certain N compounds.

For instance, high ammonia (NH3) deposition will raise bark pH while acidic forms

of N, like nitric acid (HNO3

), may have the opposite effect. This confounding of

direct N effects by substrate pH is not necessarily problematic unless other anthropogenic or natural agents are present that introduce significant variation in bark pH

among sites. Alkaline dust is a common example of a natural agent that appears to

affect at least some of the N indicators in dry climates (see next section). Because

of potential interference like this, bioindication results for N are most reliable when

supported by one or more sources of ancillary N data like those listed below.

Nitrophytes

Geiser and Neitlich’s (2007) ISA identified several statistically significant indicators of “polluted” plots that are widely considered nitrophytes, a well-known group

of indicator species used already by FIA for N biomonitoring in California (Jovan

and McCune 2004, 2005, 2006). Nitrophyte is a term commonly used to describe

lichens that tolerate high N and high pH situations. Examples from the west-side

Pacific Northwest include Candelaria concolor, Physcia adscendens, and Xanthoria

polycarpa (fig. 26). Many studies have documented nitrophyte enhancement as a

function of NH3

 (e.g., Frati et al. 2007; Ruoss 1999; van Dobben and de Bakker

1996; van Herk 1999, 2001) although a more generalized, positive response to N

pollution is possible. In the absence of human-made pollution, these species tend

Sidebar 6: Defining a priori groups for Indicator Species Analysis

As described in chapter 1 “Indicator Species Analysis,” ISA is used to define

statistically significant indicator species for groups of plots defined by the

analyst. In this case, Geiser and Neitlich (2007) defined two groups, “polluted” and “clean.” Plots assigned to the “polluted” group had the highest air

quality scores, spanning the range in which 90 percent of plots either occurred

in an urban area or supported Platismatia glauca with enhanced N (N > 0.59

percent). Conversely, only 10 percent of plots in the “clean” group, scoring at

the other extreme of the air quality gradient, had P. glauca with enhanced N.
35

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 26—Nitrophytic indicators of polluted plots in the west-side Pacific Northwest:

(a) Candelaria concolor (concolor lemon lichen), (b) Physcia adscendens (rosette

lichen), (c) Xanthoria polycarpa (orange wall lichen).

Tim Wheeler Tim Wheeler

Sarah Jovan

A

B

C

B
36

GENERAL TECHNICAL REPORT PNW-GTR-737

to inhabit eutrophicated substrates such as bark beneath bird perches, near tree

wounds, or contacted at some point by urine (Barkman 1958, van Herk 1999).

Nitrophytic blooms may sometimes occur in the absence of any obvious inputs of

basic N compounds, such as near cement plants (Gilbert 1976) or in dry climates

(Loppi and Pirintsos 2000) where alkaline dust is prevalent.

Acidophytes

Conversely, many of the statistically significant indicators of “clean” plots are

potentially acidophytic. Acidophyte technically describes species requiring an

acidic substrate, which may explain why these indicators are characteristically

intolerant of NH3 (Sparrius 2007; van Herk 1999, 2001). Some acidophytes, however, show promise as more generalized N indicators. A recent study by van Herk

et al. (2003) in the Netherlands suggests several acidophytes are also highly sensitive to both ammonium (NH4

+

) and nitrate (NO3

-

). This N indicator group is used

primarily by the western European research community (Lambley and Wolseley

2004), and candidates have not yet been formally defined for the North American

flora. However, several “clean” indicators identified by the ISA are congeners

(e.g., species belonging to the same genus) of European acidophytes. These

include multiple species of Bryoria (e.g., fig. 6, fig. 27), Hypogymnia (e.g.,

fig. 1, fig. 25), and Usnea. Bryoria fuscescens (fig. 27), one of the strongest

indicators of clean sites in the west-side Pacific Northwest, has been largely

extirpated from its historical range in the Netherlands. High N is believed

to play a critical role (Van Herk et al. 2003).

Figure 27—Deer grazing on Bryoria fuscescens (horsehair lichen), an acidophyte

and clean air indicator in west-side forests.

Roger Rosentreter

Bryoria fuscescens,

one of the strongest

indicators of clean

sites in the westside Pacific Northwest, has been

largely extirpated

from its historical

range in the Netherlands. High N is

believed to play

a critical role.
37

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 28—Potential neutrophytes; (a) Ramalina subleptocarpha

(cartilage lichen), (b) Melanelia sp. (Melanelia lichens), (c) Parmelia

sulcata (shield lichen).

Sarah Jovan

A

B

C

Neutrophytes

Nonnitrophytic indicators of polluted plots were mostly species of Melanelia,

Parmelia, and Ramalina (fig. 28) (Geiser and Neitlich 2007). These genera are

considered “neutrophytic” by Dutch lichenologists Sparrius (2007) and van Herk

(1999) although the term is not yet widely applied in lichen biomonitoring research. Generally speaking, neutrophytes are considered intermediately sensitive to

NH3

 (i.e., more sensitive than nitrophytes, less sensitive than acidophytes). According to Sparrius (2007), neutrophytes are actually promoted by NH3

 but have a

lower critical threshold than nitrophytes (i.e., neutrophytes in a community will

begin to die off while nitrophytes continue to prosper as NH3 rises). The term
38

GENERAL TECHNICAL REPORT PNW-GTR-737

neutrophyte technically describes species preferring a substrate pH intermediate

to nitrophytes and acidophytes. Conceivably, their response to N may be largely a

function of substrate pH effects. The importance of direct stimulation or toxicity

by N requires further study, however. Among the N indicator groups, pollution

tolerances of the neutrophytes are least understood.

Stratified Cyanolichens

Other indicators identified by Geiser and Neitlich (2007) for clean sites included

large, leafy cyanolichen species like Lobaria oregana (fig. 8) and Nephroma

bellum (fig. 29; see chapter 1 “Introduction”). The result is unsurprising given

the cyanolichen reputation for being especially “stress-sensitive” (Richardson and

Cameron 2004). Compared to the nitrophytes and acidophytes, studies suggesting

linkages between cyanolichen distributions and N are more anecdotal (but see

mention of Lobaria in Farmer 1997, Lambley et al. 2004, Mitchell et al. 2005).

Unlike the other N indicator groups, the term cyanolichen is biologically based

and has no reference to specific pH or substrate preference, although this group is

particularly well-known for high sensitivity to acidic deposition (i.e., Farmer et al.

1991, Gauslaa 1985, Gilbert 1986, Goward and Arnesault 2000, Richardson and

Cameron 2004).

Figure 29—Nephroma bellum (naked kidney lichen), a stratified cyanolichen and clean

air indicator.

Jim Riley
39

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Indicator Synthesis

The ISA provides clear ecological evidence that one or more forms of N are

affecting lichen community composition in the west-side Pacific Northwest and

thus, entering the surrounding forest ecosystem. Figure 30 presents a more holistic

view of how species relate individually to the N gradient detected by NMS. The

gradient is divided into six air quality zones determined by Geiser and Neitlich

(2007) (best, good, fair, degraded, poor, and worst; see sidebar 7). A species

position along the x-axis in the diagram is determined by its score (i.e., optimum)

Figure 30—Distribution of species scores across the air quality gradient (x-axis). Scores are divided into the six air quality zones

defined by Geiser and Neitlich (2007). The y-axis is included here for display purposes only; it is a second gradient in lichen

community composition incidental to the discussion. Full species names are given in table 6.
40

GENERAL TECHNICAL REPORT PNW-GTR-737

on the air quality gradient (for methods see chapter 1 “Identifying Indicator Species”); a low species score suggests N sensitivity, whereas a high species score suggests tolerance or a stimulatory effect (table 6). It must be stressed, though, that

the relative importance of direct N effects versus pH effects in determining species’ responses is unknown at this juncture. Important caveats for interpretation

of species scores are discussed in sidebar 3.

Many species were classified a priori to one of the four N indicator groups.

Cyanolichens and nitrophytes are already well-defined from the literature, whereas

acidophyte and neutrophyte classifications are intended as a starting point for

determination of North American candidates (table 6). Tentative designations were

given to west-side Pacific Northwest species belonging to a genus exhibiting

acidophytic or neutrophytic behavior in the Netherlands (Sparrius 2007; van Herk

1999, 2001) or United Kingdom (Sutton et al. 2004). Some misclassifications are

likely given this coarse system but species scores will help steer refinements in

future studies of the N indicators (table 6).

Figure 30 shows a distinct progression from conditions favoring cyanolichens

and potential acidophytes (low estimated N and pH) towards conditions favoring

nitrophytes (relatively high estimated N and pH) along the west-side Pacific Northwest air quality gradient. Overall, 42 percent of cyanolichens and 59 percent of

Sidebar 7: Air quality zones

Geiser and Neitlich (2007) used results of the ISA to define six biologicallybased air quality zones: best, good, fair, degraded, poor, and worst. Each zone

is based on the distribution of air quality scores for plots inhabited by either

the “polluted” or the “clean” air indicator species. Upper bounds for

the best, good, and fair zones are based on the 75th, 90th, and 97.5th percentiles

of plot scores associated with the clean air indicators. For example, 75 percent

of plots hosting the clean air indicators had air quality scores < -0.11, which

serves as the upper bound of the “best” zone (as well as the lower bound of

the “good” zone). The upper bound of the “degraded” zone is the 25th percentile of air quality scores for plots hosting the polluted air indicators while

the upper bound of the “poor” zone corresponds to the 100th percentile for

clean air indicators. Thus, by definition, none of these sensitive species were

present in “worst” plots.
41

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 6—Species list for the west-side Pacific Northwest calibration data seta

N indicator Species Air quality

group Species Acronym Frequency score zone

Percent

Nitrophytes

Candelaria concolor Cndcon 8.19 0.89 Worst

Physcia adscendens Phyads 25.94 0.69 Worst

Physcia aipolia Phyaip 17.75 0.66 Worst

Physcia americana Phyame 3.41 0.82 Worst

Physcia tenella Phyten 8.53 0.82 Worst

Physconia enteroxantha Phoent 4.44 1.07 Worst

Physconia perisidiosa Phoper 7.51 0.97 Worst

Xanthomendoza fallax Xanfal 3.75 1.09 Worst

Xanthoria candelaria Xancan 3.41 0.83 Worst

Xanthoria parietina Xanpar 1.71 1.15 Worst

Xanthoria polycarpa Xanpol 25.60 0.71 Worst

Potential

acidophytes 

Bryoria capillaris Brycap 26.62 -0.27 Best

Bryoria fremontii Bryfre 2.39 -0.25 Best

Bryoria friabilis Bryfri 6.48 -0.30 Best

Bryoria fuscescens Bryfus 8.19 -0.46 Best

Bryoria glabra Brygla 9.22 -0.36 Best

Bryoria pseudofuscescens Brypse 5.46 -0.25 Best

Bryoria subcana Brysub 1.71 -0.41 Best

Bryoria trichodes Brytri 4.44 -0.46 Best

Cetraria canadensis Cetcan 7.85 -0.02 Good

Cetraria chlorophylla Cetchl 51.88 -0.01 Fair

Cetraria merrillii Cetmer 3.41 0.27 Degraded

Cetraria orbata Cetorb 61.09 -0.01 Fair

Cetraria pallidula Cetpal 7.85 -0.35 Best

Cetraria platyphylla Cetpla 16.04 -0.18 Best

Cladonia chlorophaea Clachl 2.05 0.00 Fair

Cladonia fimbriata Clafim 8.87 0.45 Poor

Cladonia ochrochlora Claoch 20.48 -0.11 Best

Cladonia squamosa Clasqu 18.09 -0.18 Best

Cladonia transcendens Clatra 14.68 0.07 Fair

Cladonia verruculosa Claver 2.05 0.17 Fair

Evernia prunastri b Evepru 52.90 0.46 Poor

Hypogymnia apinnata Hypapi 37.88 -0.33 Best

Hypogymnia enteromorpha Hypent 61.77 -0.26 Best

Hypogymnia heterophylla Hyphet 2.05 -0.48 Best

Hypogymnia imshaugii Hypims 41.30 -0.09 Good
42

GENERAL TECHNICAL REPORT PNW-GTR-737

Table 6—Species list for the west-side Pacific Northwest calibration data seta (continued)

N indicator Species Air quality

group Species Acronym Frequency score zone

Percent

Hypogymnia inactiva Hypina 67.92 -0.13 Best

Hypogymnia metaphysodes Hypmet 11.26 -0.31 Best

Hypogymnia occidentalis Hypocc 13.65 -0.06 Good

Hypogymnia oceanica Hypoce 4.10 -0.43 Best

Hypogymnia physodes Hypphy 72.01 0.07 Fair

Hypogymnia rugosa Hyprug 2.05 -0.27 Best

Hypogymnia tubulosa Hyptub 61.77 0.15 Fair

Parmeliopsis ambigua Popamb 5.80 -0.25 Best

Parmeliopsis hyperopta Pophyp 33.79 -0.32 Best

Platismatia glauca Plagla 82.94 -0.04 Good

Platismatia herrei Plaher 51.88 -0.21 Best

Platismatia lacunosa Plalac 5.12 -0.32 Best

Platismatia norvegica Planor 10.92 -0.50 Best

Platismatia stenophylla Plaste 42.32 -0.09 Good

Usnea cornuta Usncor 21.16 -0.25 Best

Usnea filipendula Usnfil 48.46 -0.11 Best

Usnea glabrata Usngla 24.57 0.22 Degraded

Usnea glabrescens Usngls 8.53 -0.04 Good

Usnea hesperina Usnhes 1.71 -0.54 Best

Usnea lapponica Usnlap 5.12 0.36 Poor

Usnea longissima Usnlon 7.51 -0.26 Best

Usnea scabrata Usnsca 23.21 -0.17 Best

Usnea subfloridana Usnsub 14.33 0.34 Degraded

Usnea wirthii Usnwir 37.20 -0.06 Good

Potential

neutrophytes

Melanelia elegantula Melele 1.37 0.63 Worst

Melanelia exasperatula Melexl 16.38 0.60 Worst

Melanelia fuliginosa Melful 13.31 0.66 Worst

Melanelia glabra Melgla 1.02 0.23 Degraded

Melanelia subaurifera Melsub 20.14 0.45 Poor

Melanelia subelegantula Melsel 8.19 0.53 Worst

Melanelia subolivacea Melsol 5.80 0.55 Worst

Parmelia hygrophilab Parhyg 41.30 0.00 Fair

Parmelia pseudosulcata Parpse 5.80 -0.22 Best

Parmelia saxatilisb Parsax 4.78 -0.04 Fair

Parmelia sulcata Parsul 87.71 0.13 Fair

Ramalina dilacerata Ramdil 18.09 0.29 Degraded

Ramalina farinacea Ramfar 60.41 0.36 Poor

Ramalina menziesii Rammen 1.71 -0.11 Best

Ramalina roesleri Ramroe 1.37 -0.31 Best

Ramalina subleptocarphab Ramsle 5.46 0.72 Worst

Ramalina thrausta Ramthr 2.39 -0.10 Good
43

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 6—Species list for the west-side Pacific Northwest calibration data seta (continued)

N indicator Species Air quality

group Species Acronym Frequency score zone

Percent

Stratified

cyanolichens

Lobaria hallii Lobhal 2.05 0.17 Fair

Lobaria oregana Lobore 22.18 -0.48 Best

Lobaria pulmonaria Lobpul 33.11 0.00 Fair

Lobaria scrobiculata Lobscr 12.29 0.36 Poor

Nephroma bellum Nepbel 8.19 -0.42 Best

Nephroma helveticum Nephel 11.95 0.03 Fair

Nephroma laevigatum Neplae 5.80 -0.25 Best

Nephroma occultum Nepocc 1.71 -0.44 Best

Nephroma parile Neppar 1.37 -0.18 Best

Nephroma resupinatum Nepres 10.24 0.25 Degraded

Peltigera britannica Pelbri 1.37 -0.07 Good

Peltigera collina Pelcol 28.33 0.31 Degraded

Peltigera membranacea Pelmem 4.44 -0.05 Good

Peltigera neopolydactyla Pelneo 2.39 -0.47 Best

Pseudocyphellaria anomala Pcyano 20.14 0.05 Fair

Pseudocyphellaria anthraspis Pcyant 19.11 -0.24 Best

Pseudocyphellaria crocata Pcycro 11.60 -0.33 Best

Sticta fuliginosa Stiful 14.33 0.32 Degraded

Sticta limbata Stilim 15.36 0.09 Fair

Unknown

Alectoria imshaugii Aleims 6.48 -0.11 Best

Alectoria sarmentosa Alesar 35.49 -0.36 Best

Alectoria vancouverensis Alevan 4.78 -0.24 Best

Cavernularia hultenii Cavhul 8.53 -0.46 Best

Cavernularia lophyrea Cavlop 13.31 -0.40 Best

Cetrelia cetrarioides Celcet 7.17 0.28 Degraded

Collema furfuraceum Colfur 1.71 0.90 Worst

Collema nigrescens Colnig 1.37 1.08 Worst

Esslingeriana idahoensis Essida 10.58 -0.09 Good

Fuscopannaria leucostictoides Panleu 1.71 -0.36 Best

Fuscopannaria saubinetii Pansau 3.75 -0.17 Best

Hypocenomyce castaneocinerea Hcecas 2.73 -0.11 Best

Hypocenomyce scalaris Hcesca 2.73 0.50 Worst

Hypotrachyna sinuosa Htrsin 32.08 -0.10 Good

Leptogium corniculatum Lepcor 1.37 0.04 Fair

Leptogium lichenoides Leplic 1.71 0.47 Poor

Leptogium polycarpum Leppol 5.46 0.23 Degraded

Leptogium saturninum Lepsat 2.39 0.92 Worst

Letharia vulpina Letvul 14.33 0.07 Fair

Menegazzia terebrata Menter 25.26 -0.19 Best
44

GENERAL TECHNICAL REPORT PNW-GTR-737

Table 6—Species list for the west-side Pacific Northwest calibration data seta (continued)

N indicator Species Air quality

group Species Acronym Frequency score zone

Percent

Niebla cephalota Niecep 1.37 0.23 Degraded

Nodobryoria abbreviata Nodabb 1.71 0.11 Fair

Nodobryoria oregana Nodore 19.45 -0.41 Best

Normandina pulchella Norpul 4.78 0.47 Poor

Parmotrema arnoldii Pmoarn 8.87 0.25 Degraded

Parmotrema chinense Pmochi 9.22 -0.19 Best

Parmotrema crinitum Pmocri 1.37 -0.36 Best

Physconia isidiigera Phoisi 4.44 0.98 Poor

Sphaerophorus globosus Sphglo 55.29 -0.35 Best

a

 A low species score suggests nitrogen (N) sensitivity and an association with low pH substrates. A high score suggests

tolerance or stimulation by N and an association with high pH. Species scores are divided into the six air quality zones

developed by Geiser and Neitlich (2007): best, good, fair, degraded, poor, and worst.

b

Species assigned to different indicator groups in the literature. Neutrophyte designations are consistent with the concept of

Sparrius (2007). Evernia prunastri is considered a neutrophyte by Gombert et al. 2005. Van Herk (1999) considers

Parmelia saxatilis acidophytic, and Jovan and McCune (2005, 2006) classified Parmelia hygrophila and Ramalina

subleptocarpha as nitrophytes.

potential acidophytes were most closely associated with forests classified as having

the “Best” air quality (table 7, fig. 31). Quite the opposite, nitrophyte scores were

exclusively associated with a narrow range of the highest (“Worst”) scores along

with a few hardy neutrophytes like Ramalina subleptocarpha and Melanelia species (tables 6 and 7). Species scores range widely among neutrophytes, although as

posited by Sparrius (2007), optimal N and substrate pH for the group appears to

be, on average, intermediate between the acidophytes and nitrophytes (figs. 30

and 31).

In summary, air quality appears to be influencing lichen communities of

the west-side Pacific Northwest in two key ways (1) a negative impact on the

acidophytic and cyanolichen floras and (2) a positive effect on nitrophytes and

at least some neutrophytes. Accordingly, results suggest that increased deposition

of N and substrate alkalinization would favor shifts in communities from lowscoring to high-scoring species, and vice-versa, should deposition improve. A

major shift in the chemical composition of N deposition affecting the study area

could cause considerable departure from this trajectory as different forms of N

have different effects on substrate pH. Chemical composition of detected N in the

A major shift in the

chemical composition of N deposition

could cause considerable departure

from this trajectory.
45

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 7—Species scores summarized by nitrogen (N) indicator group and Geiser and Neitlich’s (2007)

air quality zones

Air quality zones

N indicator

group Mean Min Max n Best Good Fair Degraded Poor Worst

 – – – – – – – – – – – – Percent– – – – – – – – – – – –

Nitrophytes 0.88 0.66 1.15 11 0.0 0.0 0.0 0.0 0.0 100.0

Acidophytes -0.13 -0.54 0.46 49 59.2 14.3 14.3 6.1 6.1 0.0

Neutrophytes 0.26 -0.31 0.72 17 17.6 5.9 17.6 11.8 11.8 35.3

Cyanolichens -0.07 -0.48 0.36 19 42.1 10.5 26.3 15.8 5.3 0.0

Unknown 0.09 -0.46 1.08 29 44.8 6.9 10.3 13.8 10.3 13.8

Figure 31—Distribution of species scores according to N indicator group. Box plots are

divided into quartiles with outliers indicated by lines. Ranges of the “Best” and “Worst” air

quality zones (depicted on the x-axis in figure 30) are indicated on the y-axis.
46

GENERAL TECHNICAL REPORT PNW-GTR-737

NMS model is ultimately unknown, although NH3 and its reaction product, NH4

+

,

seem likely to be major components. Besides correlation of the air quality gradient

to NH4

+ (see “Interpretation of Air Quality Estimates”), the distribution of

acidophytes, neutrophytes, and nitrophytes along the air quality gradient is analogous to the NH3

-dependent optimum curves published by Sparrius (2007).

Summary of Air Quality Patterns

The gradient model was used to estimate air quality at a full cycle of FIA data (n =

243; 1998-2001, 2003) to describe baseline conditions affecting west-side Pacific

Northwest forests. About 75 percent of these plots were included in the extensive

air quality summary published by Geiser and Neitlich (2007). Air quality zones

used to categorize plot scores are the same as those used in the previous section

for discussing species scores (see sidebar 7). The spread of plot scores among air

quality zones (table 8) was similar for Oregon and Washington, as was average

score (-0.05 and -0.07, respectively).

Consistent with Geiser and Neitlich’s (2007) observations, nitrophyte-rich

sites receiving high (poor) air quality scores were prevalent near urban and agricultural areas (fig. 32). It follows that valley and low-elevation foothill forests of the

Willamette Valley and Puget Trough ecoregion (covering about 38 600 km2

) seem

proportionally the most affected by poor air quality (fig. 32). Air quality scores for

this ecoregion were varied (SD = 0.49), spanning nearly the full range of the data

(-1.08 to 1.59; table 9), but the majority of sites scored as “Degraded” or worse.

This region included 8 of the 12 plots receiving the worst 5 percent of air quality

scores (fig. 33).

Concentration of human activity and its infrastructure generate high pollution

emissions in this ecoregion compared to elsewhere in the study area. A large proportion of the human population in Washington and Oregon (> 65 percent) inhabits

the cities of the Willamette Valley and Puget Trough (Oregon Department of Fish

and Wildlife 2006, Washington Department of Fish and Wildlife 2005). Urban

density is greatest along the oftentimes congested Interstate-5 corridor (fig. 22),

which serves as the main conduit of north-south motor vehicle travel. The ecoregion possesses abundant productive farmland that supports a diversified agricultural-based economy, as well as forestry, manufacturing, and tourism (Oregon

Department of Fish and Wildlife 2006, Washington Department of Fish and Wildlife 2005). Correspondingly, agricultural, industrial, and motor vehicle pollutant N

sources are locally plentiful.

Valley and lowelevation foothill

forests of the

Willamette Valley

and Puget Trough

ecoregion seem

proportionally the

most affected by

poor air quality.
47

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 8—Air quality scores from the west-side Pacific Northwest (PNW) model by

state (1998–2001, 2003)a

West-side West-side West-side

Parameter PNW Oregon Washington

Number of plots surveyed 243 140 103

Number of plots by air quality zone:

 Best: -1.4 to -0.11 111 65 46

 Good: -0.11 to 0.02 26 14 12

 Fair: 0.02 to 0.21 40 25 15

 Degraded: 0.21 to 0.35 21 13 8

 Poor: 0.35 to 0.49 13 8 5

 Worst: 0.49 to 2.00 32 15 17

Air quality score extremes -1.28 to 1.59 -1.28 to 1.02 -1.22 to 1.59

Average air quality score -0.062 -0.05 -0.07

Standard deviation of air quality scores 0.49 0.43 0.56

a

 Plot scores are divided into Geiser and Neitlich’s air quality zones (2007). Plots were excluded

from analysis if they lacked species, had no species in common with the calibration plots, or were

duplicate surveys used for quality assurance purposes.

Table 9—Air quality scores by Bailey’s ecoregions (1983)

Ecoregion section Min Max Mean n SD Median

Eastern Cascades (M242C) -0.57 0.32 -0.34 11 0.27 -0.44

Klamath Mountains (M261A) -1.28 1.02 -0.11 39 0.43 -0.06

Oregon and Washington Coast Ranges (M242A) -1.22 0.91 -0.16 70 0.40 -0.11

Southern Cascades (M261D) -0.65 0.79 0.13 6 0.58 0.13

Western Cascades (M242B) -1.07 0.97 -0.25 64 0.42 -0.33

Willamette Valley and Puget Trough (242A) -1.08 1.59 0.36 53 0.49 0.38
48

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 32—Air quality scores for the west-side Pacific Northwest divided into Geiser and Neitlich’s air quality zones

(2007). Scores are unitless until calibrated with pollutant measurements. See figure 22 for ecoregion codes.
49

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 33—Extreme best and worst 5 percent of air quality scores. All other plots are indicated in gray. See fig. 22 for

ecoregion codes.
50

GENERAL TECHNICAL REPORT PNW-GTR-737

The model suggested that plots on the Willamette Valley fringe in the foothills

of the Cascades and Coast Ranges tend to experience worse air quality than more

remote forests (fig. 32). Regardless, most air quality scores in both the Cascades

and Coast Ranges fall in the “best” category. Some air quality degradation was

detected in the Klamath ecoregion, primarily in association with cities on or near

Interstate-5. Air quality at a few coastal plots is classified as “poor” or “worst,”

although such sites were widely distributed with no discernable clustering.

Most plots receiving the lowest (best) 5 percent of scores were near national

parks and on the immediate coast in Washington (fig. 33). Overall, lichen communities suggested relatively good air quality for forests in the northern Olympic

peninsula and mid to high Cascades. Ostensibly, these forests benefit from their

distance from west-side urban, industrial, and agricultural centers.

Conclusions

The importance of N as an ecological stressor in some west-side Pacific Northwest

forests is unmistakable given the clear association of N/pH lichen indicator groups

with the air quality gradient. Forests near cities and agriculture are potentially the

most impacted by poor air quality, as suggested by the spatial distribution of air

scores and verified by enhanced accumulations of N and S in collected lichens

(Geiser and Neitlich 2007). Estimated pollution hotspots are somewhat localized

as compared to parts of the Northeastern United States where pollution is more a

regional issue (Fenn et al. 2003a, 2003b). If emissions intensify, the geographic

scope of pollution impacts may, of course, extend in tandem.

Excessive N is a growing problem in many developed nations as documented in

many studies (Driscoll et al. 2003, Fenn et al. 1998, Holland et al. 2005, Vitousek

et al. 1997). But in the bigger picture, how dire is the status of forest air quality in

the west-side Pacific Northwest? A direct comparison to other regions is precluded

by the lack of comprehensive instrumented pollutant measurements, which means

that we hardly know how much N is actually getting into the system. As reviewed

by Fenn et al. (2003a), N deposition patterns are unknown for most of the Western

United States, which underscores the importance of lichen-air quality bioindication.

Comparison is likewise difficult because large-scale characterizations of dry N

deposition, potentially a major component of total N loading, are highly uncertain

(Holland et al. 2005). Given these caveats, the best available information does

suggest that N deposition to the Pacific Northwest is milder than that for much of

western Europe and the Eastern United States (Holland et al. 2005), although in

some localized instances deposition may be just as high.

The importance of

N as an ecological

stressor in some

west-side Pacific

Northwest forests

is unmistakable.
51

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

The story as told from the lichen perspective is that many sensitive species

remain widespread and altogether make for a “largely intact flora (Geiser and

Neitlich 2007).” Still, air quality in the west-side Pacific Northwest has much room

for improvement, most especially conditions affecting the lowland oak and mixedconifer forests in the Willamette and Puget Trough ecoregion. About 24 percent of

land area in the western Pacific Northwest is estimated to experience air quality

that is classified as “degraded” or worse (Geiser and Neitlich 2007). Spatial patterns in lichen community composition as seen in these baseline data illustrate how

communities might change should air quality, and N/bark pH effects in particular,

worsen. To reiterate from Geiser and Neitlich (2007), acidophytes and cyanolichens

tend to be our ecologically important and endemic lichen species. To their detriment, we expect to see enhancement of nitrophytes (and perhaps also neutrophytes)

with increased N and alkalinization of substrates. Resampling lichen communities

at FIA plots will help us continually evaluate risks to forest health. In the meanwhile, we hope these results will serve as a catalyst for investigation into N impacts

to other components of the forest ecosystem.
52

GENERAL TECHNICAL REPORT PNW-GTR-737
53

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Chapter 4: Air Quality in the Greater

Central Valley, California

Key Findings

1. As in the west-side Pacific Northwest, the model suggests nitrogen (N) is a

major stressor shaping lichen communities in the greater Central Valley.

2. Jovan and McCune (2005) found that air quality scores from the model were

correlated with estimates of ammonia (NH3

) and the dominance of nitrophytes

(e.g., lichen species commonly associated with high NH3

 habitats and high bark

pH).

3. Lichen species expected to be N intolerant were rare in the greater Central

Valley, most especially at plots where the model indicated high N impact. The

regional rarity of N-sensitive species, however, may be partly due to the hot and

dry climate.

4. Poor air quality was estimated for most forests in urban and agricultural areas

where lichen communities consisted of up to 100 percent nitrophytes.

5. Small hotspots of poor air quality were scattered across the study area, with the

largest hotpots detected in the Central Valley and San Francisco Bay area.

Introduction to the Model

The greater Central Valley (fig. 34) is a climatically and floristically distinct region

of northern and central California (Jovan and McCune 2004; see also chapter 7).

The study area includes the broad, agricultural Central Valley (fig. 35), the lowelevation Sierra Nevada foothills, the San Francisco Bay area, and a strip of coastal

ranges stretching from Willits, California, south to the Santa Clara River. Jovan and

McCune (2005) developed a gradient model for air quality bioindication using

nonmetric multidimensional scaling (NMS; for methods see chapter 1) and the

integration of various sources of land use and pollution data (table 10).

Lichen community surveys used to build the model came from 66 plots on the

Forest Inventory and Analysis (FIA), U.S. Department of Agriculture, sampling

grid visited between 1998 and 2001. These calibration data included 32 supplemental off-frame plots surveyed in 2002. Off-frame sites were placed in forests near

urban and agricultural areas, intentionally near key pollution emission sources like

high motor vehicle traffic, fertilizers, and animal husbandry, as well as various

industrial and domestic point sources (fig. 34).
54

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 34—Greater Central Valley model area. Off-frame plots are indicated by red asterisks and named after the city

or park in which they were located. Off-frame plots not named on the map are numbered as follows: 1 = Red Bluff,

2 = Chico, 3 = Colusa, 4 = North Highland, 5 = Placerville, 6 = San Andreas, 7 = Stockton, 8 = Pittsburg, 9 =

Merced, 10 = Visalia, 11 = Los Padres National Forest, 12 = Santa Ynez, 13 = Lompoc, 14 = Nipomo, 15 =

Atascadero, 16 = King City, 17 = Pinnacles National Monument, 18 = Gilroy, 19 = Davenport, 20 = San Jose, 21 =

Fremont, 22 = Crockett, 23 = Vallejo, 24 = Santa Rosa. The plot surveyed in Bakersfield was excluded from all

analyses because no lichens were present.
55

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 35—Aerial view of Central Valley landscape, taken somewhere between Patterson and

Merced. Griffin McKinney (pilot).

Dr. Christopher Bochna

Table 10—Pollution data for the greater Central Valley used to interpret the air quality gradient

detected by NMSa

Variable n Measurement type Data source

SO2 14 Annual arithmetic mean CARB; Data averaged over 1999 to 2002

NO2 22 Annual arithmetic mean CARB; Data averaged over 1999 to 2002

O3 30 Maximum 1-hour value CARB; Data averaged over 1999 to 2002

Kriged O3 All sites estimate; SUM60 indexb Unpublished data provided by T. Pritchard.

Interpolation across study area based on O3

measurements from CARB monitors.

NH3

Emissions All sites Estimate of emissions; California Gridded Ammonia Inventory

tons per year Modeling System; ENVIRON

International Corporation 2002

Land useNH3 All sites Estimate; proportion Index calculated with land use data from the

California Gap Analysis Project; Davis et al. 1998

a

 All California Air Resources Board (CARB) data are publicly accessible at: http://www.arb.ca.gov/aqd/aqdpage.htm.

SO2 = sulfur dioxide, NO2 = nitrogen dioxide, O3 = ozone, and NH3 = ammonia.

b

 SUM60: the sum of all hourly O3 concentrations ≥60 parts per billion from June 1 to August 31, 2002.

Ancillary Data: Air Quality Measurements

Each off-frame plot was co-located with an air quality monitor operated by the

California Air Resources Board that measures at least one major anthropogenic

pollutant (table 10): sulfur dioxide (SO2

; range = 1 to 5 parts per billion [ppb]),

nitrogen dioxide (NO2

; range = 3 to 25 ppb), and ozone (O3; range = 68 to 139
56

GENERAL TECHNICAL REPORT PNW-GTR-737

ppb). Ozone monitor data were kriged to calculate an index of O3 exposure (range

= 21-7.81 x 104 ppb hrs). Ammonia, for which measurements were not available,

was estimated for all on- and off-frame sites by using two independently derived

variables: (1) modeled NH3 emissions provided by ENVIRON International Corporation (2002), and (2) NH3

 estimated by using a land use index of urban and

agricultural development in the plot vicinity (table 10).

Interpretation of Air Quality Estimates: Nitrogen Is Shaping Lichen

Community Structure

Much like results from the west-side Pacific Northwest (chapter 3), NMS detected

an air quality gradient suggesting that one or more N compounds are impacting

lichens in the greater Central Valley (Jovan and McCune 2005). Air quality scores

determined for plots were linearly related to the proportion of nitrophyte abundance (PNA; r = -0.78; fig. 36), a simple measure of nitrophyte dominance in

the community (see sidebar 8; fig. 37). Nitrophytes are a well-studied group of

lichen indicator species known to react positively to N and the alkalinization of

substrates (see chapter 3 “Nitrophytes” section). Plot scores were also substantially

correlated to NH3 estimates from the emissions model and the land use index

(r = -0.51 and -0.63, respectively) but less so with NO2

 (r = -0.33). In the literature, nitrophytes are often linked specifically to NH3

, which is expected to be a

Figure 36—Scatterplot of the greater Central Valley calibration data showing

the relationship between air quality score and the proportion of nitrophyte

abundance at each plot.

One or more N

compounds are

impacting lichens in

the greater Central

Valley.
57

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Sidebar 8: Calculating the PNA

The proportion of nitrophyte abundance (PNA) is a semiquantitative measure

of nitrophyte dominance in a lichen community. The index is a variation on

the popular Nitrofiele Indicatie Waarde of van Herk (1999, 2001), which has

been implemented for estimating NH3

 in the Netherlands and elsewhere in

Europe. The PNA is calculated by (1) summing the abundance codes of all

nitrophytes recorded during the lichen survey and (2) dividing by the summed

abundance of all lichens found. The formula is as follows:

∑

∑

=

= = s

j

ij

j

s

j

ij

i

a

a n

PNA

1

1

where s is number of species in plot i, aij is the abundance of species j

in plot i, and nj

 indicates whether each species is a nitrophyte (nj = 1) or not

(nj = 0).

Figure 37—Colorful nitrophytes on a twig in Pinnacles National

Monument, California.

Judy Robertson
58

GENERAL TECHNICAL REPORT PNW-GTR-737

major component of total N deposition in the study area owing to the density of

agriculture. Altogether these results suggest that plots receiving low (poor) air

quality scores are the most impacted, wherein N is relatively high and lichen

communities are dominated by weedy nitrophytic species (fig. 36).

Discussion of Indicator Species

Although the PNA only distinguishes nitrophyte from nonnitrophyte, the abundance of acidophytes (species sensitive to N and high pH) and neutrophytes (a more

nebulous group of species that prefer or tolerate moderate N and circumneutral

pH) does influence the PNA denominator (see chapter 3 “Acidophytes” and

“Neutrophytes” sections). The pollution-sensitive cyanolichens are uncommon

in the greater Central Valley and so contribute little information to the PNA and

the gradient model. As in the section “Indicator Synthesis,” in chapter 3, weighted

averaging was used to calculate a score for each species in the calibration data set

to serve as a rough estimate of the species’ response to N and associated pH effects

(table 11; for methods see chapter 1 “Identifying indicator species with species

scores” section). The distribution of these species scores across the detected air

quality gradient illustrates the ecological patterns NMS uses to derive air quality

scores for plots.

Four air quality categories ranging from “best” to “worst” are used to give

species scores context. Each category corresponds with a quartile calculated from

the air quality scores determined for all plots in the calibration data set. A low species score suggests tolerance or promotion by N, whereas a high score suggests N

sensitivity, given the caveat that bark pH (as influenced by N deposition or otherwise) probably plays an important role in determining species’ responses (see

chapter 3 “Direct and Indirect Nitrogen Effects on Lichens”). Many species were

classified a priori to one of the four N indicator groups: nitrophytes, cyanolichens,

acidophytes, and neutrophytes (table 11). Species classified to the latter two groups

belong to a genus considered acidophytic or neutrophytic in Europe. Species scores

may be used to help refine these preliminary designations as North American

candidates receive further study.

The most obvious pattern emerging from species scores is the nitrophyteacidophyte dichotomy along the air quality gradient (fig. 38), as witnessed also

with the west-side Pacific Northwest data set (fig. 30). Nitrophyte species richness was high (n = 22; table 12), and species scores for both nitrophytes and

neutrophytes were spread widely across the lower three quartiles of air quality

scores (fig. 38). Cyanolichens (n = 2) and acidophytes (n = 11) were scarce by

The most obvious

pattern emerging

from species

scores is the

nitrophyteacidophyte

dichotomy along

the air quality

gradient.
59

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 11—Species list for the greater Central Valley calibration data seta

N indicator Species Air quality

group Species Acronym Frequency score category

Nitrophytes Percent

Candelaria concolor Cndcon 87.76 -0.01 2nd

Flavoparmelia caperatab Flacap 9.18 -0.02 2nd

Flavopunctelia flaventiorb Fpufla 51.02 0.13 3rd

Phaeophyscia hirsuta Phahir 10.20 -0.44 1st (Worst)

Phaeophyscia orbicularis Phaorb 41.84 -0.30 2nd

Physcia adscendens Phyads 67.35 0.00 2nd

Physcia aipolia Phyaip 32.65 0.19 3rd

Physcia dimidiata Phydim 15.31 -0.11 2nd

Physcia dubia Phydub 4.08 -0.10 2nd

Physcia stellaris Physte 20.41 0.17 3rd

Physcia tenella Phyten 36.73 -0.13 2nd

Physconia enteroxantha Phoent 27.55 0.01 2nd

Physconia perisidiosa Phoper 56.12 0.11 3rd

Punctelia perreticulatab Punper 24.49 0.21 3rd

Xanthomendoza fallax Xanfal 23.47 -0.25 2nd

Xanthomendoza fulva Xanful 11.22 -0.13 2nd

Xanthomendoza hasseana Xanhas 36.73 0.01 2nd

Xanthomendoza oregana Xanore 44.90 -0.27 2nd

Xanthoria candelaria Xancan 9.18 0.29 3rd

Xanthoria parietina Xanpar 8.16 -0.26 2nd

Xanthoria polycarpa Xanpol 50.00 0.00 2nd

Xanthoria tenax Xanten 21.43 -0.51 1st (Worst)

Potential acidophytes 

Cetraria merrillii Cetmer 11.22 0.57 4th (Best)

Cetraria orbata Cetorb 6.12 0.37 3rd

Evernia prunastrib Evepru 56.12 0.26 3rd

Hypogymnia imshaugii Hypims 20.41 0.54 4th (Best)

Hypogymnia tubulosa Hyptub 4.08 0.40 4th (Best)

Platismatia glauca Plagla 4.08 0.64 4th (Best)

Usnea arizonica Usnari 4.08 0.27 3rd

Usnea cornuta Usncor 6.12 -0.15 2nd

Usnea glabrata Usngla 5.10 -0.05 2nd

Usnea lapponica Usnlap 6.12 0.14 3rd

Usnea substerilis Usnsst 6.12 -0.09 2nd

Potential neutrophytes 

Melanelia elegantula Melele 5.10 0.05 3rd

Melanelia exasperatula Melexl 4.08 0.10 3rd

Melanelia fuliginosa Melful 6.12 0.31 3rd

Melanelia glabra Melgla 61.22 0.26 3rd

Melanelia subargentifera Melsar 11.22 -0.09 2nd

Melanelia subolivacea Melsol 31.63 0.14 3rd

Parmelia hygrophilab Parhyg 4.08 0.40 4th (Best)

Parmelia sulcata Parsul 16.33 0.42 4th (Best)

Ramalina farinacea Ramfar 25.51 0.07 3rd

Ramalina leptocarpha Ramlep 18.37 -0.33 1st (Worst)
60

GENERAL TECHNICAL REPORT PNW-GTR-737

Table 11—Species list for the greater Central Valley calibration data seta (continued)

N indicator Species Air quality

group Species Acronym Frequency score category

Percent

Ramalina menziesii Rammen 14.29 -0.02 2nd

Ramalina pollinaria Rampol 5.10 -0.25 2nd

Ramalina subleptocarphab Ramsle 21.43 -0.11 2nd

Stratified cyanolichens 

Pseudocyphellaria anthraspis Pcyant 7.14 0.61 4th (Best)

Peltigera collina Pelcol 8.16 0.62 4th (Best)

Unknown 

Collema furfuraceum Colfur 22.45 0.28 3rd

Collema nigrescens Colnig 15.31 0.07 3rd

Heterodermia Het 5.10 -0.10 2nd

Leptogium lichenoides Leplic 16.33 0.35 3rd

Leptogium pseudofurfuraceum Lepfur 14.29 0.45 4th (Best)

Letharia columbiana Letcol 7.14 0.10 3rd

Letharia vulpina Letvul 12.24 0.37 3rd

Niebla cephalota Niecep 6.12 -0.21 2nd

Parmelina quercina Pnaque 42.86 0.43 4th (Best)

Parmotrema austrosinense Pmoaus 4.08 0.03 2nd

Parmotrema chinense Pmochi 11.22 0.02 2nd

Phaeophyscia ciliata Phacil 4.08 -0.07 2nd

Physcia biziana Phybiz 30.61 0.12 3rd

Physconia americana Phoame 43.88 0.26 3rd

Physconia isidiigera Phoisi 60.20 0.01 2nd

Teloschistes chrysophthalmus Telchr 7.14 -0.29 2nd

a

 A low species score suggests tolerance or promotion by nitrogen (N) and an association with high pH substrates. A high score

suggests N sensitivity and an association with low pH substrates. Species scores are divided into four air quality categories

corresponding with quartiles of plot scores from the calibration data set: 1st (worst), 2nd, 3rd, and 4th (best).

b

 Species assigned to different indicator groups in the literature. Flavoparmelia caperata, Flavopunctelia flaventior, and

Punctelia perriticulata are not widely defined as nitrophytes in the literature. Neutrophyte designations are consistent with the

concept of Sparrius (2007). Evernia prunastri is considered a neutrophyte by Gombert et al. 2005. Jovan and McCune (2005,

2006) classified Parmelia hygrophila and Ramalina subleptocarpha as nitrophytes.
61

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 38—Display of species scores along the air quality gradient (x-axis), which is divided into four air quality categories

based on data quartiles. The y-axis is included here for display purposes only; it is a second gradient in lichen community

composition incidental to the discussion. Full species names are given in table 11.
62

GENERAL TECHNICAL REPORT PNW-GTR-737

Table 12—Species scores summarized by nitrogen (N) indicator group and air quality

category

Air quality categories

N indicator

group Mean Min Max n 1 (worst) 2 3 4 (best)

 – – – – – – – Percent – – – – – – –

Nitrophytes -0.06 -0.51 0.29 22 9.1 63.6 27.3 0.0

Acidophytes 0.26 -0.15 0.64 11 0.0 27.3 45.5 27.3

Neutrophytes 0.07 -0.33 0.42 13 7.7 30.8 53.8 7.7

Cyanolichens 0.62 0.61 0.62 2 0.0 0.0 0.0 100.0

Unknown 0.11 -0.29 0.45 16 0.0 41.2 43.8 11.8

Figure 39—Distribution of species scores according to nitrogen (N) indicator

group. Box plots are divided into quartiles with outliers indicated by lines.

Ranges of the “best” and “worst” air quality zones (depicted on the x-axis in

fig. 38) are indicated on the y-axis.

comparison. It is likely that N deposition has already marginalized some sensitive

species from these groups although low diversity may partly be a natural consequence of the hot, dry valley climate, as discussed further in chapter 4. Among the

cyanolichens and potential acidophytes found in the study area, however, most did

score within the better two air quality categories suggesting an association with low

to moderate N and substrate pH (table 12, fig. 39). Usnea species were an exception, scoring low compared to other acidophytic candidates (table 11). Because the

It is likely that

N deposition

has already

marginalized

some sensitive

species from

these groups.
63

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

genus is so large and ecologically diverse, some species may well have neutrophytic

or nitrophytic tendencies.

Summary of Air Quality Patterns

The following summary is based on air quality scores for most plots in the greater

Central Valley calibration data set (Jovan and McCune 2005; n = 94) plus an

additional 14 sites surveyed for lichens in 2003. Air quality scores are categorized

into zones based on quartiles from the on-frame data only; off-frame plots are

excluded as they will not be resampled. About a quarter of on-frame sites are thus

in the “worst” zone. This particular representation of on-frame data will become

more informative when used for assessment of air quality trends. Air quality zones

can be redefined with more biologically meaningful thresholds once N critical

loads for lichen communities are established. In this case, a “critical load” is the

deposition of one or more forms of N that, if exceeded, has a harmful effect on

lichen communities.

Differences in the distribution of air quality scores estimated for on-frame

versus off-frame plots are dramatic (table 13). Scores from off-frame plots had a

very poor average (-0.27), and 26 of these plots (81 percent) scored in the worst air

quality zone. Scores differed widely within ecoregions, with the highest N impacts

typically estimated for off-frame sites near urban and agricultural areas (fig. 40).

The variable spatial pattern of scores may be a reflection of high NH3 levels. In

contrast to most other major N pollutants, like nitrogen oxides (NOx

), nitrates

(NO3

-

), and ammonium (NH4

+

)-containing aerosols (see sidebar 9), a high proportion of gaseous NH3

 is dry deposited near the emission source. This makes a

Table 13—Air quality scores from the greater Central Valley (GCV) model

(1998–2001, 2003)a

Parameter GCV On-frame Off-frame

Number of plots surveyed 108 76 32

Number of plots by air quality zone:

1: (Worst) -0.99 to 0.13 45 19 26

 2: 0.13 to 0.55 23 19 4

 3: 0.55 to 0.85 22 20 2

 4: (Best) 0.85 to 1.58 18 18 0

Air quality score extremes -0.99 to 1.58 -0.86 to 1.58 -0.99 to 0.70

Average air quality score 0.28 0.52 -0.27

Standard deviation of air quality scores 0.61 0.50 0.46

a

 Plots were excluded from analysis if they lacked species, had no species in common with the

calibration plots, or were duplicate surveys used for quality assurance purposes.
64

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 40—Air quality scores for the greater Central Valley divided into air quality zones. Scores are unitless until calibrated

with pollutant measurements. On-frame (circles) and off-frame plots (asterisks) are indicated by a common color scheme. See

figure 34 for ecoregion codes.
65

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

heterogeneous distribution of NH3 across the landscape that is difficult to accurately

capture with networks of air quality monitors (Erisman et al. 2005).

Hotspots of Degraded Air Quality

Plots sampled in the Central Valley (Great Valley section -262A) appear heavily

impacted by N (figs. 40 and 41) although it is important to note how air quality in

this ecoregion is primarily represented by lichen communities sampled at off-frame

plots (fig. 34), which were intentionally installed in high pollution areas. Most onframe plots fell on nonforest land and so were not surveyed for lichens. Despite the

biased sample, other regional deposition and emissions studies recognize the valley

as a major N hotspot in California (ENVIRON 2002, Fenn et al. 2003b, Potter et

al. 2000, Weiss 2006).

For instance, according to the Community Multiscale Air Quality Model

(CMAQ) (Buyn and Ching 1999, Tonnesen et al. 20021

), the San Joaquin valley

receives the highest N in California excepting the Los Angeles Basin (reviewed

by Weiss 2006; resolution of model estimates = 36 km2). Maximum deposition

is estimated at 13 to 14 kg N·ha-1

·year-1 at Modesto, with the San Francisco Bay

area following as the second worst hotspot in the greater Central Valley (8 to 9 kg

N·ha-1·year-1; see sidebar 10). Arguably, the lichen bioindication model detects N on

1

 Tonnesen, G.S.; Wang, Z.S.; Omary, M.; Chien, C.J.; Wang, B. 2002. Regional aerosol

and visibility modeling using the Community Multiscale Air Quality Model for the Western U.S.: Results and model evaluation for the 1996 annual simulation. Paper presented at

the WESTAR Technical Conference on Regional Haze Modeling; 12-14 February 2002,

Riverside, California.

Sidebar 9: Particulate pollution

Ammonia and NOx

 combine to form ammonium nitrate (NH3NO3), a major

component of haze-inducing particulate pollution in California (California

Environmental Protection Agency 2005, Fenn et al. 2003b). In this form, the

N from NH3

 emissions can be transported over hundreds of miles. Besides

reducing visibility, these particulates irritate the lungs and are considered

a serious human health concern. Levels are especially high in the San

Joaquin Valley Air Basin, which is in nonattainment of the national standard

(California Environmental Protection Agency 2005).
66

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 41—Extreme best and worst 10 percent of air quality scores. Off-frame plots are symbolized by asterisks. See

figure 34 for ecoregion codes.
67

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

a finer spatial scale, and moreover, lichens may not respond equally to all forms of

N (see chapter 3 “Direct and Indirect Nitrogen Effects on Lichens” section).

Regardless, both hotspots were detected with the lichen gradient model (fig. 40).

Several FIA sites scoring among the worst 10 percent were from the San Joaquin

and Bay area (fig. 41). Lichen abundance in communities from the worst 10

percent was, on average, 72 percent nitrophytic (including two plots that are 100

percent nitrophytic).

Smaller hotspots of N impacts to lichen communities were scattered across the

study area, including forests near cities and agricultural parts of the Sacramento

Valley and downwind in forests of the southern Sierra Nevada foothills (fig. 40).

Many of the best-scoring sites (fig. 41) occurred in the northern and central Sierra

foothills and in the northern California Coast Ranges at sites more remote from

human habitation and industry (average abundance in nitrophytes at the best 10

percent of sites was 31 percent). Similar patterns were detected in the statewide N

analysis by Weiss (2006).

Sidebar 10: Nitrogen deposition estimates

For perspective, the community multiscale air quality model estimates maximum nitrogen (N) in the Los Angeles Basin at 21 kg (Weiss 2006), although

deposition levels in excess of 45 kg have been measured in that region (Fenn

et al. 2003b). The Los Angeles Basin is believed to receive the highest N

deposition in the Nation (Fenn et al. 2003b). Conversely, background N

deposition in the Western United States is commonly estimated at 0.5 kg

(Baron 2006, Holland et al. 1999).

Conclusions

Nitrogen, probably mostly in the form of NH3 and the reaction product NH4

+

, is an

important driver of lichen community composition in the greater Central Valley.

Air quality scores were substantially correlated to modeled NH3 estimates (Jovan

and McCune 2005), and geographic distribution also broadly matched the regional

N analysis by Weiss (2006). Moreover, complementary ecological evidence lies in

the distribution of N indicator species along the air quality gradient detected by

Many of the bestscoring sites

occurred in the

northern and central Sierra foothills

and in the northern

California Coast

Ranges.
68

GENERAL TECHNICAL REPORT PNW-GTR-737

NMS. Variation among current lichen communities lends insight into how composition is likely to shift in response to ameliorating or worsening N influence. To echo

the forecast presented for the west-side Pacific Northwest, increased N loading and

alkalinization of bark are anticipated to stimulate nitrophytes and neutrophytes

while marginalizing cyanolichens and acidophytic species.

The scarcity of cyanolichens and prospective acidophytes begs the question: Is

regional deposition advanced enough that some N-sensitive species have already

been lost from the greater Central Valley flora? Nitrogen-driven local extinction

is possible, although without a historical inventory it is impossible to know just

how much the current flora is actually a deviation from normalcy. Assuming

immaculate air quality, it is highly unlikely that the same abundance of

cyanolichens and acidophytes found in the western Pacific Northwest could naturally occur in the greater Central Valley for strictly climatic reasons. Nitrophytes

generally can better tolerate the hot and dry habitats characteristic of a Mediterranean climate regime (Loppi 2004, Loppi and De Dominicis 1996; see also van

Herk et al. 2002) like those of the study area (see chapter 7). Also, dry conditions

may promote alkaline dust that stimulates nitrophytes while excluding acidophytes

and perhaps other species from local forests. The prevalence of this phenomenon in

the Pacific coastal states is unknown and needs further investigation. Regardless, the

establishment of baseline conditions as presented here will allow tracking of

sensitive species as well as air quality trends.
69

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Chapter 5: Air Quality in the Greater Sierra

Nevada, California

Key Findings

1. The lichen bioindication model suggested that nitrogen (N) compounds are

impacting some forests of the greater Sierra Nevada.

2. Plots with high estimated N impact were (by definition) more dominated by

indicator species of N-enriched environments and high bark pH than low-impact

sites.

3. Lichens collected from high-impact sites likewise tended to have relatively high

N content (Jovan and Carlberg 2007).

4. Lichen community composition and N content of lichens both suggest that

forests of the southern Sierra Nevada lie in a large N hotspot, as further evidenced by other air quality studies.

5. Areas of relatively low N influence were substantial in the northern half of the

study area.

Introduction to the Model

The greater Sierra Nevada is a mountainous region in California spanning most

of the Sierra Nevada Range, the Southern Cascades, the Modoc Plateau, and highelevation sites (>1500 m) in the eastern Klamath Mountains (fig. 42). The majority

of the western boundary lies along the highly agricultural Central Valley (chapter

4), which is widely recognized as an important source of pollutants carried into

Sierran forests (Bytnerowicz and Fenn 1996; Bytnerowicz et al. 2002; Cahill

et al. 1996; Fenn et al. 2003b, 2003c). By comparison, the greater Sierra

Nevada is considerably less populated and less developed for agriculture and

industry (Momsen 2001). Much of the landscape is managed in national forests,

parks, and monuments that support an economy based on recreational tourism

(figs. 43 and 44).

Overall, long-term air quality data are limited although various short-term

monitoring campaigns help characterize conditions in the Sierra Nevada Range

(reviewed by Fenn et al. 2003c). It is well established that nitrogen (N) pollutants

can reach high levels in some Sierran forests, most especially at sites in the southwestern foothills (~15 kg N ̃ha-1·year-1) (Fenn et al. 2003c, Weiss 2006). Deposition

is apparently high enough in some cases to provoke classic ecological symptoms of
70

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 42—Greater Sierra Nevada model area. Off-frame plots are indicated by red asterisks and named after the city or

park in which they were located. Off-frame plots not named on the map are numbered as follows: 1 = Alturas, 2 =

Chester, 3 = Quincy, 4 = Portola, 5 = Truckee, 6 = Grass Valley, 7 = Colfax, 8 = Cool.
71

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

N enrichment. Elevated nitrate in stream water, for instance, has been detected in

Sequoia National Park and Mountain Home State Park (Fenn et al. 2003c). During

development of the air quality model for the Forest Inventory and Analysis (FIA)

Program, U.S. Department of Agriculture, it quickly became apparent that lichen

communities too were showing signs of N pollution (Jovan and McCune 2006).

Model Adjustment for Elevation Effects

Calibration data used to build the greater Sierra Nevada model included 91 onframe plots surveyed between 1998 and 2001 (for methods see chapter 1 “Lichen

Survey Protocol” section) and 24 off-frame plots surveyed in 2003. Off-frame

plots were located in urban, agricultural, or recreation areas including Yosemite,

Figure 43—Yosemite National Park, California.

Sarah Jovan

Figure 44—Lassen Volcanic National Park.

Sarah Jovan

Elevated nitrate in

stream water, has

been detected in

Sequoia National

Park and Mountain

Home State Park.
72

GENERAL TECHNICAL REPORT PNW-GTR-737

Sequoia, and Lassen Volcanic National Parks (figs. 43 and 44). Extra steps were

needed to ensure that the greater Sierra Nevada model isolates air quality effects

from the strong influence elevation naturally exerts on lichen communities (Jovan

and McCune 2006). The problem arises because air quality and elevation are at

least somewhat correlated with each other in the study area (Fenn et al. 2003c).

Higher elevation sites tend to be less N polluted than the foothills (fig. 42) since

there are fewer local emission sources and forests are farther from the Central

Valley with its high density of N sources (Jovan and McCune 2006).

The greater Sierra Nevada model is built around a simple index of indicator

species used for N bioindication in the greater Central Valley, the proportion of

nitrophyte abundance (PNA; see chapter 4 “Interpretation of Air Quality Estimates:

Nitrogen is Shaping Lichen Community Structure” section). The PNA is the summed abundance codes for all nitrophytes (table 14) divided by the summed abundance of all lichen species found in the survey. Nitrophytes are most frequently

linked to ammonia (NH3

), but it is unclear to what degree species are responding to

the N versus the increase in bark pH that NH3

 causes (see chapter 3 sections “Direct

and Indirect Nitrogen Effects on Lichens” and “Nitrophytes”). To isolate the lichen

response to air quality, elevation was regressed on the PNA using nonlinear regression with a generalized sigmoid curve (Jovan and McCune 2006; see sidebar 11).

The unstandardized residuals from the nonlinear regression serve as the air quality

scores for the greater Sierra Nevada.

Ancillary Data: Lichen Nitrogen Content

To complement air quality scores, Letharia vulpina (fig. 45) was collected from

38 on-frame sites intentionally selected to encompass a wide range of air quality

scores (Jovan and Carlberg 2007). Kjeldahl analysis was used to determine how

much N, as a percentage of dry weight, had accumulated in the lichen. Elemental

analysis of lichen collections is a widely practiced bioindication tool sometimes

employed to estimate N levels in air and precipitation (Bruteig 1993, Geiser and

Neitlich 2007, Søchting 1995. Collection and processing of L. vulpina samples

followed the protocols used by Geiser and Neitlich (2007) as documented in Blett

et al. (2003).
73

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 14—Species list for the greater Sierra Nevadaa

Nitrogen indicator group Species Acronym Frequency

Nitrophytes Percent

Candelaria concolor Cndcon 43.31

Flavopunctelia flaventiorb Flafla 3.82

Phaeophyscia orbicularis Phaorb 7.01

Physcia adscendens Phyads 14.65

Physcia aipolia Phyaip 13.38

Physcia dimidiata Phydim 4.46

Physcia stellaris Physte 11.46

Physcia tenella Phyten 18.47

Physconia enteroxantha Phoent 15.92

Physconia perisidiosa Phoper 21.66

Xanthomendoza fallax Xanfal 11.46

Xanthomendoza fulva Xanful 14.01

Xanthomendoza hasseana Xanhas 17.2

Xanthomendoza oregana Xanore 22.29

Xanthoria candelaria Xancan 12.74

Xanthoria polycarpa Xanpol 18.47

Potential acidophytes 

Bryoria fremontii Bryfre 17.83

Cetraria canadensis Cetcan 5.1

Cetraria chlorophylla Cetchl 8.92

Cetraria merrillii Cetmer 45.22

Cetraria orbata Cetorb 19.75

Cetraria pallidula Cetpal 3.82

Cetraria platyphylla Cetpla 28.03

Evernia prunastrib Evepru 22.93

Hypogymnia imshaugii Hypims 66.24

Hypogymnia occidentalis Hypocc 3.82

Parmeliopsis ambigua Paramb 3.18

Platismatia glauca Plagla 17.83

Usnea filipendula Usnfil 8.92

Potential neutrophytes 

Melanelia elegantula Melele 31.21

Melanelia exasperatula Melexl 19.11

Melanelia glabra Melgla 14.01

Melanelia subargentifera Melsar 3.18

Melanelia subelegantula Melsel 10.19

Melanelia subolivacea Melsol 51.59

Parmelia hygrophilab Parhyg 10.19

Parmelia sulcata Parsul 25.48

Ramalina subleptocarphab Ramsle 4.46

Stratified cyanolichens 

Lobaria hallii Lobhal 0.64

Nephroma helveticum Nephel 0.64

Nephroma resupinatum Nepres 0.64

Peltigera collina Pelcol 5.73
74

GENERAL TECHNICAL REPORT PNW-GTR-737

Table 14—Species list for the greater Sierra Nevadaa (continued)

Nitrogen indicator group Species Acronym Frequency

Percent

Pseudocyphellaria anomala Pcyano 2.55

Pseudocyphellaria anthraspis Pcyant 3.18

Unknown

Ahtiana sphaerosporella Ahtsph 22.29

Alectoria sarmentosa Alesar 3.82

Collema furfuraceum Colfur 7.01

Esslingeriana idahoensis Essida 10.19

Leptogium lichenoides Leplic 6.37

Letharia columbiana Letcol 59.87

Letharia vulpina Letvul 75.16

Nodobryoria Nod 6.37

Nodobryoria abbreviata Nodabb 43.31

Nodobryoria oregana Nodore 23.57

Parmelina quercina Pnaque 9.55

Physcia biziana Phybiz 5.73

Physconia americana Phoame 16.56

Physconia isidiigera Phoisi 15.29

a

 Tentative designations of acidophytes and neutrophytes are based on whether the species belongs to a genus exhibiting

acidophytic or neutrophytic behavior in the Netherlands (van Herk 1999, 2001; Sparrius 2007) or United Kingdom

(Sutton et al. 2004).

b

 Species assigned to different indicator groups in the literature. Flavopunctelia flaventior is not widely defined as

nitrophytic. Evernia prunastri is considered a neutrophyte by Gombert et al. 2005. Jovan and McCune (2005, 2006)

classified Parmelia hygrophila and Ramalina subleptocarpha as nitrophytes.

Sidebar 11: Calculating Greater Sierra Nevada air scores

Air quality scores for plots in the greater Sierra Nevada are derived as follows:

• Calculate the proportion of nitrophyte abundance (PNA) in the lichen

community (see sidebar 8).

• Plug elevation (in meters) into the nonlinear regression equation to

get Y.

5 91 ) 1689

1 (

0.48

Elevation .

Y

+

=

• Subtract Y from the actual PNA and multiply by 100.

Air quality score =100(PNA −Y).

• The resulting air quality score is a measure of how much the PNA is

above or below what is expected given the plot elevation. A higher

score suggests higher N influence.
75

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Interpretation of Air Quality Estimates: Nitrogen Impacts Are

Apparent

High (poor) air quality scores from the greater Sierra Nevada model are equated

with high N impact, of which NH3

 is likely a major component (Jovan and McCune

2006). Because scores are derived from the PNA, nitrophytes were abundant at

high-impact sites by definition. Letharia vulpina collected from these poorly

scoring plots tended to have higher N content (lichen N ranged from 0.6 percent

to 2.11 percent). When mapped, air quality scores and N content of lichens resolved the same major N deposition patterns (Jovan and Carlberg 2007) as discussed further in the following section.

Summary of Air Quality Patterns in the Greater

Sierra Nevada

Air quality scores are summarized for a full cycle of FIA data (n = 146; 1998-

2001, 2003) including the 24 off-frame plots. Most of these plots were included in

the published summary found in Jovan and McCune (2006) excepting 25 additional

sites surveyed in 2003. Most off-frame plots were purposefully installed in forests

expected to be pollution-stressed and so are included here to provide a basis for

Figure 45—Letharia vulpina (wolf lichen) was

collected for analysis of accumulated nitrogen. Tim Wheeler
76

GENERAL TECHNICAL REPORT PNW-GTR-737

comparison. Unsurprisingly, greater N impacts are estimated for off-frame sites.

The average off-frame air quality score was poor at 10.27 (range: -32.38 to 41.61)

compared to the on-frame average of -5.13 (range: -43.4 to 66.5; table 15). More

than two-thirds of off-frame plots were more nitrophyte-dominated than would be

expected given plot elevation. Even so, the worst air quality score was found for an

on-frame plot lying just outside Kings Canyon National Park where 66.5 percent of

lichen abundance was from nitrophytic lichen species.

Major Deposition Patterns

The highest N impacts are estimated for several plots located within or near Kings

Canyon, Sequoia, and Yosemite National Parks (fig. 46). Both on- and off-frame

sites indicate that a large deposition hotspot extends from Yosemite to the southernmost model boundary (figs. 46 and 47). Likewise, the most N-enriched Letharia

vulpina found in the greater Sierra Nevada (n = 38) came from six of the seven

collection sites in this region (range: 1.38 to 2.11 percent N; Jovan and Carlberg

2007).

Instances of high N-loading are well-documented for the southern Sierras

(reviewed in Fenn et al. 2003c). These mixed-conifer forests lie downwind of the

San Joaquin Valley where the FIA Lichen Indicator (see chapter 4) and the Community Multiscale Air Quality Model (CMAQ) (Buyn and Ching 1999, Tonnesen et

al. 2002, Weiss 2006) both suggest high N concentrations. Climate patterns in the

San Joaquin Valley are reminiscent of the Los Angeles Basin: frequent temperature

inversions cause air to stagnate, in effect trapping emissions near leeward forests.

Table 15—Air quality scores from the greater Sierra Nevada (GSN) model (1998–2001,

2003)a

Parameter GSN On-frame Off-frame

Number of plots surveyed 146 122 24

Number of plots by air quality zone:

 1: (Best) -43.36 to -15.88 35 31 4

 2: -15.88 to -8.22 31 30 1

 3: -8.22 to 4.35 33 30 3

 4: (Worst) 4.35 to 66.49 47 31 16

Air quality score extremes -43.36 to 66.49 -43.36 to 66.49 -32.38 to 41.61

Average air quality score -2.77 -5.13 10.27

Standard deviation of air quality scores 19.28 18.32 19.60

a

 Plots were excluded from analysis if they lacked species or were duplicate surveys used for quality assurance

purposes.

The highest N impacts are estimated

for several plots

located within or

near Kings Canyon,

Sequoia, and

Yosemite National

Parks.
77

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 46—Extreme best and worst 10 percent of air quality scores. Off-frame plots are symbolized by asterisks. See

figure 42 for ecoregion codes.
78

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 47—Air quality scores for the greater Sierra Nevada model area divided into air quality zones. Scores are unitless

until calibrated with pollutant measurements. Scores for on-frame (circles) and off-frame plots (asterisks) are indicated by a

common color scheme. See figure 42 for ecoregion codes.
79

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Preliminary high-resolution results (4 km2) from the CMAQ model estimate

deposition in the southern Sierra hotspot reaches up to 8.7 kg N·ha-1

·year-1, especially at lower elevations and more southerly sites.1

A smaller deposition hotspot may occur on the eastern Modoc Plateau as

indicated by poor air quality scores (figs. 46 and 47) and high N accumulation (up

to 1.32 percent) in some Letharia vulpina collections (Jovan and Carlberg 2007,

Jovan and McCune 2006). There are not, however, any local N measurements to

corroborate lichenological evidence of N impact. There are indeed agricultural N

sources in the Modoc although the area is fairly remote and sparsely populated.

Closer investigation would be needed before drawing any firm conclusions. Alkaline dust may contribute to the high abundance of nitrophytes in the dry landscape

of the plateau (see chapter 3 “Direct and Indirect Nitrogen Effects on Lichens”

section).

Relatively low N influence was otherwise estimated for many forests in the

northern half of the study area, which includes all plots scoring in the best 10 percent (figs. 46 and 47). Lichen communities from the southern Cascades and northern Sierra Nevada were frequently rich in acidophytes, neutrophytes, as well as a

variety of species of “unknown” N indicator value (table 14; see chapter 3 “Discussion of Indicator Species” section). Some of these forests likely receive N from the

Sacramento Valley, although there are few in situ N measurements to help quantify

exposure.

Conclusions

Nitrogen compounds are important constituents of air quality in the greater Sierra

Nevada, as evidenced by (1) the distribution of nitrophytes in the FIA Lichen Community Indicator data (2) accumulation of N in Letharia vulpina collections, (3) N

estimates from the CMAQ model, and (4) various short-term N monitoring campaigns (Fenn et al. 2003b, 2003c). Altogether these varied data sources make a

clear case for high N impact to forests in the southwestern Sierra Nevada, a scenic

region lavish with over 1.2 million ha of land in 17 federally protected class I

areas. Ozone is also a major stressor in these forests as evidenced by the FIA Ozone

Indicator, where 82 percent of ozone biosites in the San Joaquin Valley air basin

(all located in the southern Sierra Nevada) and 27 percent of biosites in the Mountain Counties air basin (central and northern Sierra Nevada) exhibited foliar

damage between 2000 and 2005 (Campbell et al. 2007).

1

 Tonnesen. 2007. Unpublished data. On file with: R. Johnson, Center for Conservation

Biology, 2415A Boyce Hall, University of California Riverside, Riverside, CA 92521.

Relatively low N

influence was estimated for many forests in the northern

half of the study

area.
80

GENERAL TECHNICAL REPORT PNW-GTR-737

Part IV: Climate Mapping and Tracking

Although lesser known, the Lichen Community Indicator data may also be applied

for mapping and monitoring climate change. Lichens, like many other sedentary

organisms, often arrange themselves predictably along climatic gradients (Insarov

and Schroeter 2002, Jovan and McCune 2004). Temperature is usually of central

importance to lichen distributions because it influences the rate of photosynthesis

and basic metabolic processes. Lichens are also intimately tied to moisture regime

because, as they lack a vascular system, they absorb water passively. (This passive

uptake is in fact a physiological reason why lichens are so sensitive to pollutants,

which dissolve in the water and then are transferred directly into the thallus.) A dry

lichen becomes metabolically inactive and may be able to persevere through long

periods of drought. But much like plants, some lichen species are drought-tolerant,

whereas some require mesic and hydric habitats to survive. Conversely, lichens

continuously hydrated will suffer, some species sooner than others, particularly

under low light levels. In the same way, lichen tolerances to heat and cold differ

widely across species.

The two following chapters deal with application of the FIA lichen data for

mapping baseline forest climate in the west-side Pacific Northwest and in northern

and central California. In the long-term, it is expected that warming trends will

initiate floristic shifts across forest ecosystems with temperature-sensitive species

generally shifting to higher latitudes and higher elevations (Easterling et al. 2000,

Parmesan and Yohe 2003, Walther et al. 2002). Lichenologists in Europe have

already documented the incursion of tropical and subtropical lichen species into

higher latitudes while also witnessing a net decrease in alpine species (van Herk et

al. 2002). Similar trends and species shifts will be detectable with data from the

Forest Inventory and Analysis (FIA) Lichen Community Indicator, U.S. Department of Agriculture.

Hypogymnia (tube lichen) found in Rhododendron, Oregon.

Roseann Barnhill
81

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Chapter 6: Climate Biomonitoring in the

West-Side Pacific Northwest

Key Findings

1. In the west-side Pacific Northwest, climate scores were most strongly patterned

on temperature: forests at higher scoring plots experience, on average, more

continental climate and lower minimum temperatures than low-scoring sites

(Geiser and Neitlich 2007).

2. Based on climate scores, plots can be subdivided into four geographically

cohesive climate zones: maritime (lowest scores), lowland, montane, and high

elevation (highest scores; Geiser and Neitlich 2007).

3. The analysis by Geiser and Neitlich (2007) suggests predicted warming trends

would push mean temperatures in the maritime zone (and potentially also the

lowland zone) to levels unprecedented in the study area.

4. Warming may be especially troubling for lichen communities of the highelevation zone, which have limited opportunity to migrate farther upwards in

elevation to find cooler habitat conditions. Likewise, rare species are more

likely to be extirpated than common species (Glavich and others 2005b)

5. As would be expected, the spatial distribution of climate scores based only

on data collected by the Forest Inventory and Analysis (FIA) Program, U.S.

Department of Agriculture, resembles the more extensive climate map produced

by Geiser and Neitlich (2007).

Introduction to the Model

Details of the west-side Pacific Northwest gradient model are summarized in

chapter 3. To review briefly, Geiser and Neitlich (2007) built the model with

nonmetric multidimensional scaling (NMS) and applied it to estimate both air

quality and climate at 1,416 plots in the study area. Each plot was surveyed for

epiphytic lichen communities between 1994 and 2001 by using the standardized

FIA field protocol (for methods see chapter 1 “Lichen Survey Protocol” section).

About 211 plots in their study lie on the FIA sampling grid.

Ancillary Data: PRISM Model Climate Data

Geiser and Neitlich (2007) related climate scores from the gradient model to a

series of climate variables extracted from the Parameter-elevation Regressions on
82

GENERAL TECHNICAL REPORT PNW-GTR-737

Independent Slopes Model (PRISM) (Daly and Taylor 2000). Variables were all

long-term annual averages (1969 to 1990) at 2 km2

 resolution, and included dew

point, maximum August temperature, minimum December temperature,

continentality (e.g., the difference between the latter two variables), number of wet

days, precipitation, and relative humidity. Mean annual days of marine fog and

annual temperature were obtained from Lipow et al. (2004).

Interpretation of Climate Estimates

The lichen community response to climate, as captured in the NMS model,

was strongly patterned on a temperature gradient (Geiser and Neitlich 2007).

Climate scores were most correlated with minimum December temperature and

continentality (r = - 0.79 and 0.73, respectively). In other words, high scores

indicate forest habitats that endure lower minimum temperatures and a wider

temperature range throughout the year. Despite this relation, these scores are not

necessarily a pure reflection of temperature and are more appropriately regarded

as an integrative lichen response to local climate. Predictably, other variables had

substantial linear correlations to climate scores like relative humidity (r = 0.57)

and geographic covariates such as elevation (r = 0.68), longitude (r = 0.68), and

distance from the ocean (r = 0.67).

Review of Climate Zones and Indicator Species

From Geiser and Neitlich (2007)

Geiser and Neitlich (2007) used natural breaks (Jenks 1967) to divide climate

scores from all 1,416 plots into four broad zones: maritime (lowest scores), lowland, montane, and high elevation (highest scores). A comparison of current mean

temperatures per zone to temperatures projected for 2040 led them to the troubling

observation that “even under the most conservative scenario [+1.5 °C for the

Pacific Northwest, according to Mote et al. 2003] mean maritime temperatures

would shift above any current climate zone range [in the study area]. The lowland

mean would be shifted into the maritime range under the minimum change scenario

and above any current zone under the maximum change [+3.2 °C; Mote et al.

2003] scenario” (Geiser and Neitlich 2007). That lichen community composition

could be used to classify forests into distinctive climate zones is itself a compelling

indication that some species will be highly responsive to climate change. Accordingly, Indicator Species Analysis (ISA) (Dufrêne and Legendre 1997) (for methods

That lichen community composition

could be used to

classify forests into

distinctive climate

zones is itself a

compelling indication that some species will be highly

responsive to climate change.
83

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

see chapter 1 “Indicator Species Analysis” section) was able to identify 17 to 23

lichen indicator species per zone, as reported in full by Geiser and Neitlich (2007)

and highlighted below.

Large Stratified Cyanolichens

Statistically defined indicator species of the maritime zone include the large,

stratified cyanolichen species Lobaria oregana (fig. 8), Peltigera membranacea,

Pseudocyphellaria anthraspis, and Pseudocyphellaria crocata (fig. 7) (Geiser and

Neitlich 2007). Rich cyanolichen assemblages are also a feature of moist, lowto mid-elevation forests in the montane zone; Lobaria pulmonaria, Nephroma

helveticum, N. resupinatum (fig. 48), and Pseudocyphellaria anomala are all

associated with the montane climate regime (Geiser and Neitlich 2007). Owing to

their unique physiology, lichens in this group are renowned for their susceptibility

to both thermal and moisture stress (Bjerke et al. 2003, Geiser and Neitlich 2007,

Nash and Olafsen 1995, Richardson and Cameron 2004). Cyanolichens are intimately linked to forest health in the Pacific Northwest because of their contribution

to nitrogen budgets and nutrient cycling.

Hypermaritime Communities

Another community of particular concern for the maritime zone is the suite of rare

and endemic lichen species restricted to a thin band of forest hugging the coastline

(Glavich et al. 2005b). These warm and wet hypermaritime forests are a bona fide

Figure 48—A large thallus of Nephroma resupinatum (kidney lichen).

Karen Dillman
84

GENERAL TECHNICAL REPORT PNW-GTR-737

special habitat for epiphytic lichens in the Pacific Northwest (Geiser et al. 2004;

Glavich et al. 2005a, 2005b), supporting such rarities as Bryoria pseudocapillaris,

Erioderma sorediatum (fig. 49), Leptogium brebissonii, Pseudocyphellaria

perpetua, and Usnea hesperina. Habitat models built for these species do forecast a

high sensitivity to climate fluctuation (Glavich et al. 2005b). For sedentary organisms, successful range migration to cooler habitats at higher latitudes and elevations

will depend on many factors. Along with poor dispersal ability, rarity and narrow

ecological amplitude are traits that probabilistically increase a species’ risk of

climate-driven extirpation (Thomas et al. 2004, Walther et al. 2002; sidebar 12).

High-Elevation Communities

At the extreme cool end of the west-side Pacific Northwest temperature gradient

are lichen communities of the high-elevation zone. Indicator species of this zone

include several hair-like forage species (see chapter 1 “Introduction”) such as

Alectoria sarmentosa (fig. 50), Bryoria capillaris, B. fremontii, B. fuscesens, B.

glabra, and Nodobryoria oregana (Geiser and Neitlich 2007). All of these lichens

rely, to varying extents, on thallus fragmentation for reproduction and colonization

of new habitats. Perhaps to untold advantage, two of these species do regularly

employ a second mode of asexual reproduction, soredia (small powder-like granules of a few algal cells wrapped in fungal hyphae). By virtue of small size and

abundance, soredia are propagules that are better suited for long-distance dispersal.

Figure 49—Erioderma sorediatum (mouse ear lichen), a very rare

lichen species usually found in coastal forests of Oregon and

Washington.

Sarah Jovan

Along with poor

dispersal ability,

rarity and narrow

ecological amplitude

are traits that

probabilistically

increase a species’

risk of climatedriven extirpation.
85

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 50—Trees bearded with thick

Alectoria sarmentosa (witch’s hair lichen)

in the western Cascades. Jim Riley, Courtesy of OSU

Sidebar 12: Usnea longissima

The west-side Pacific Northwest has

worldwide importance as a refuge

for the charismatic lichen Usnea

longissima (Keon and Muir 2002),

an indicator species of the “lowland”

zone (Geiser and Neitlich 2007).

A notoriously poor dispersor,

U. longissima relies entirely on

fragmentation to reproduce: chunks of thallus break off in the wind and, if

lucky, snag on a nearby tree and grow into clones. Current populations are

patchy, and colonization of new forested habitats is slow (Keon and Muir

2002). A chief concern among ecologists is that dispersal-limited species like

U. longissima will have difficulty “outrunning” climate change.

Usnea longissima (beard lichen).

Karen Dillman
86

GENERAL TECHNICAL REPORT PNW-GTR-737

Good dispersal ability may become increasingly important for species associated

with alpine and subalpine ecosystems as there is limited opportunity for upward

migration.

Summary of Climate Patterns in the West-Side

Pacific Northwest

The same plots summarized for air quality in chapter 3 are summarized here for

climate and will be utilized by the FIA Program and the Forest Service Region 6

Air Resource Program to track effects of climate change on forest communities.

Plots constitute a full cycle of FIA lichen data (n = 249; 1998 to 2003). About 75

percent of plots were included in Geiser and Neitlich’s (2007) extensive summary.

Climate scores for west-side Oregon and Washington are summarized in table

16 using Geiser and Neitlich’s (2007) climate zones. The spatial pattern of climate

scores is analogous to the more extensive map of Geiser and Neitlich (2007), with

geographically cohesive climate zones that coincide with sizeable portions of

ecoregions (fig. 51). As expected, low scores characteristic of the maritime and

lowland zones were predominantly assigned to low-elevation forests of the Oregon

and Washington Coast Ranges and the Willamette and Puget Trough ecoregions (see

fig. 14). Farther from the temperature-moderating effects of the ocean, the higher

scoring, cooler forests of the montane and high-elevation climate zones predominate in the Cascades Range and Klamath Mountains.

Table 16—Core table summarizing climate scores for the west-side Pacific Northwest (PNW)

model area by state (1998-2001, 2003)a

West-side West-side West-side

Parameter PNW Oregon Washington

Number of plots surveyed 243 140 103

Number of plots by climate zone:

Maritime/warmest (-1.41 to -0.25) 73 32 41

Lowland (-0.25 to 0.23) 54 29 25

Montane (0.23 to 0.66) 57 38 19

High elevation/coolest (0.66 to 1.73) 59 41 18

Climate score extremes -1.41 to 1.73 -1.21 to 1.73 -1.41 to 1.15

Average climate score 0.14 0.27 -0.03

Standard deviation on climate scores 0.64 0.63 0.61

a

 Categories are based on climate zones defined by Geiser and Neitlich (2007). Plots were excluded from

analysis if they lacked species, had no species in common with the calibration plots, or were duplicate surveys

used for quality assurance purposes.
87

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 51—Climate scores for the west-side Pacific Northwest categorized by Geiser and Neitlich’s (2007) four climate

zones. See figure 22 for ecoregion codes.
88

GENERAL TECHNICAL REPORT PNW-GTR-737

Conclusions

Clearly, predicted warming trends have considerable potential to alter the lichen

flora of the west-side Pacific Northwest. Changes to moisture regime are also

likely to affect the composition of communities, although this relationship is not as

strongly represented in the model. There is little agreement about how precipitation

will change in the region, if at all. As temperature and moisture trends interactively

drive range shifts, resulting patterns will be easily detectable with the FIA Lichen

Community Indicator. It is uncertain at this point how much noise will be introduced into the model if certain species, like the poor dispersers, are unable to

successfully migrate across the fragmented forest landscape. Many lichens are rare,

poor dispersers, or have strict habitat requirements, which are all qualities that

make species more susceptible to extirpation. All in all, climate change has considerable potential to depress lichen biodiversity.

Predicted warming

trends have considerable potential to

alter the lichen flora

of the west-side

Pacific Northwest.
89

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Chapter 7: Climate Biomonitoring in

Northern and Central California

Key Findings

1. Lichen community composition in northern and central California is closely

patterned on two macroclimatic gradients: the first gradient appears strongly

correlated with temperature and the second with moisture.

2. Following the analytical procedure of Geiser and Neitlich (2007) (chapter 5),

plot scores along the first gradient were divided into four temperature zones

differing in mean temperature and elevation. Scores on the second gradient

were divided into four moisture zones differing in mean number of wet days

and precipitation.

3. An Indicator Species Analysis (ISA) (Dufrêne and Legendre 1997) found

multiple statistically significant lichen indicator species for each climate zone.

Results provide a general framework suggesting how long-term temperature

and moisture fluctuation may alter lichen community composition.

4. With the advent of rising temperatures, species restricted to cooler forests at

mid to high elevations may face a regionally shrinking niche, whereas communities of warmer temperature zones may presumably migrate upwards in

elevation.

5. Predictions of precipitation changes conflict among global climate models

(California Climate Change Center 2006a, 2006b). Species associated with the

wettest and the driest zones are most likely to endure range contractions in the

study region depending on which way (if any) precipitation patterns swing.

Introduction to the Model

The U.S. Department of Agriculture Forest Inventory and Analysis (FIA) Program

uses a single lichen bioindication model to assess climate across northern and central California forests (fig. 52) (Jovan and McCune 2004). Lichen communities in

this region are strongly influenced by coast-to-inland macroclimatic gradients that

are intensified by the diversity of landforms in the mountainous terrain. The FIA

climate model was originally developed by Jovan and McCune (2004) as a tool to

delineate three smaller, more climatically homogeneous model areas for air quality

bioindication (fig. 52): the southernmost boundary of the climate study region
90

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 52—Northern and central California model area for climate bioindication showing the three nested air quality model

areas. See figure 14 for ecoregion codes.
91

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

coincides with the greater Central Valley (chapter 4) and greater Sierra Nevada

(chapter 5) model areas. Calibration data from 211 on-frame FIA plots were used

to build the climate model with nonmetric multidimensional scaling (NMS) (see

chapter 1 “Methods” section). All plots, visited once between 1998 and 2001, were

surveyed using the standard FIA lichen community sampling protocol.

Ancillary Data: PRISM Model Climate Data

Plot scores from the NMS model, hereafter referred to as “climate scores,”

were compared to climate estimates from the high-resolution (2 km2

) Parameterelevation Regressions on Independent Slopes Model (PRISM) (Daly et al. 2001).

The PRISM estimates are long-term averages (1969 to 1990) and include dew

temperature, temperature, maximum temperature, minimum temperature, precipitation, number of wet days, and relative humidity.

Interpretation of Climate Estimates

The model resolved two major gradients in lichen community composition, one

relating primarily to temperature and a second gradient relating to moisture.

Climate scores from the temperature gradient are most closely correlated with

mean temperature (r = -0.78) and elevation (r = 0.79) such that low scores indicate

warm, low-elevation forests, whereas higher scores typify colder forests at higher

elevations. Climate scores derived from the moisture gradient are strongly correlated with annual number of wet days (r = -0.71) and precipitation (r = -0.66).

Lichen communities at low-scoring sites reflect relatively wet forest conditions

versus higher scoring dry forests. As mentioned briefly in chapter 6 (“Interpretation

of Climate Estimates” section), however, climate scores are not necessarily a pure

estimate of one particular element of climate. Results are discussed in the context

of “temperature” and “moisture” because those are likely the primary determinants

of climate scores although scores are, more precisely, an integrative reflection of

local climate.

Discussion of Climate Zones and Indicator Species

Geiser and Neitlich (2007) were the first to use data collected with FIA protocols

to assess potential impacts of climate change on lichen communities. Their analysis

(see chapter 6) was used here as a guideline for the delineation of climate zones

and lichen indicator species for northern and central California. Scores from the
92

GENERAL TECHNICAL REPORT PNW-GTR-737

temperature and moisture gradients detected by NMS were each divided into four

climate zones using natural breaks in the data (Geiser and Neitlich 2007, Jenks

1967). Lichen indicator species were defined for each climate zone with ISA (for

methods see chapter 1 “Indicator Species Analysis” section); in this case the strongest possible indicator species is consistently present in its climate zone and absent

everywhere else. It is important to note that rare species cannot be significant

indicators because it is probable that all of the species’ occurrences will fall within

a particular climate zone simply by random chance.

Average temperature or moisture per zone was calculated from the PRISM

data (Daly et al. 2001) to roughly represent current conditions. Because we are

intersecting modeled climate data (from lichens) to modeled climate data (from

PRISM), there is unavoidably some noise in their relationships to one another.

Still, results provide a generalized, descriptive framework for forecasting community-based responses to climate change.

Proposed Temperature Effects

Indicator species, mean temperature, and mean elevation per climate zone from

the first gradient detected by NMS are displayed in table 17. Despite overlapping

ranges, mean temperatures decrease by 1.2, 3.1, and 2.0 °C between consecutive

zones. Predicted warming trends could potentially have a powerful impact on

lichen assemblages given that average temperatures in California are expected to

increase by 1.7 to 5.8 °C by 2100 (California Climate Change Center 2006a,

2006b). This wide temperature range was derived from multiple climate scenarios

differing by anticipated magnitude of greenhouse gas emissions and assumed

climatic sensitivity (California Climate Change Center 2006a, 2006b) (see sidebar

13). Depending on how quickly warming proceeds, shorter-term impacts to lichens

and the surrounding forest ecosystem are indeed possible.

Migrations of temperature-sensitive lichen species are expected to proceed

from lower to higher numbered climate zones as defined with the FIA Lichen

Community Indicator data (table 17); how this trend might play out geographically

is demonstrated in section 7.4 below. The warmer zones, 1 through 3, are each

characterized by numerous indicator species spanning a variety of lichen genera

and functional groups. The comparatively low diversity of zone 4 indicators is not

surprising considering the harsh climatic conditions of high-elevation forests.

Ahtiana sphaerosporella (fig. 53) and Parmeliopsis ambigua (fig. 54; table 17) are

both hardy boreal species. Like high-elevation species in the west-side Pacific

Northwest (see chapter 6 “High-Elevation Communities” section), these indicators

Predicted warming

trends could potentially have a powerful impact on lichen

assemblages.
93

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Table 17—Indicator species of temperature zones in northern and central Californiaa

Zone 1: Warm, low Candelaria concolor, Dendriscocaulon sp., Flavopunctelia

elevation forests flaventior, Hypogymnia tubulosa, Leptogium lichenoides, Melanelia

fuliginosa, Melanelia glabra, Melanelia subargentifera,

Temperature: 14.0 (2.0) Phaeophyscia orbicularis, Physconia americana, Physconia

Elevation: 584 (376) isidiigera, Physconia perisidiosa, Physcia adscendens, Physcia

biziana, Physcia dimidiata, Physcia stellaris, Parmotrema

chinense, Parmelina quercina, Punctelia perriticulata, Ramalina

farinacea, Ramalina menziesii, Ramalina subleptocarpha, Sticta

limbata, Usnea arizonica, Usnea glabrata, Usnea substerilis,

Xanthoria fallax

Zone 2 Collema furfuraceum, Collema nigrescens, Esslingeriana

idahoensis, Evernia prunastri, Hypogymnia inactiva, Hypogymnia

Temperature: 12.8 (2.3) physodes, Parmelia sulcata, Pseudocyphellaria anomala,

Elevation: 815 (389) Pseudocyphellaria anthraspis, Peltigera collina, Physconia

enteroxantha, Physcia aipolia, Platismatia herrei, Platismatia

stenophylla, Usnea lapponica, Usnea subfloridana, Usnea wirthii

Zone 3 Bryoria capillaris, Bryoria fremontii, Cetraria chlorophylla, Cetraria

merrillii, Cetraria platyphylla, Hypogymnia imshaugii, Letharia

Temperature: 9.6 (2.2) collina, Letharia vulpina, Melanelia elegantula, Melanelia

Elevation: 1476 (424) exasperatula, Melanelia subelegantula, Melanelia subolivacea,

Nodobryoria abbreviata, Nodobryoria oregana, Platismatia glauca,

Usnea filipendula

Zone 4: Cool, high- Ahtiana sphaerosporella, Parmeliopsis ambigua

elevation forests

Temperature: 7.6 (2.2)

Elevation: 1943 (461)

a

 Average temperature (°C) and plot elevation (m) are reported, followed by standard deviations in parentheses.

Only species that are statistically significant indicators (p < 0.05), according to Indicator Species Analysis, are

reported. Nitrophytes are in bold and stratified foliose cyanolichens are underlined.
94

GENERAL TECHNICAL REPORT PNW-GTR-737

Sidebar 13: Feedback loops

Warming predictions were compiled from three global climate models that

differ in climate sensitivity (i.e., how sensitive temperature will be to greenhouse gas concentrations). An important determinant of climate sensitivity is

the influence of environmental feedback loops wherein warming stimulates a

biological or physical process that has positive feedback on temperature. One

increasingly famous example with potentially global consequences is warming

of the extensive Siberian permafrost. Thawing causes the release of greenhouse gases (methane and carbon dioxide) from the decomposing peat, which

accelerates atmospheric warming, which then accelerates thawing, and so on.

Based on the reconstruction of prehistoric climate trends from the study of ice

cores, Torn and Harte (2006) argued that feedback loops will have considerable impact on future climate.

Figure 53—Ahtiana sphaerosporella (Ahtiana lichen).

Jim Riley, OSU lichen group
95

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Figure 54—Both species of Parmeliopsis occurring in the Western United

States: P. hyperopta (on left, bran lichen) and P. ambigua (on right, ambiguous bran lichen).

Eric Peterson, OSU lichen group

and their respective communities face a shrinking niche that may force their

distributions polewards. Zones 1 through 3 may shift to higher elevations, although

the more extreme climate scenarios (California Climate Change Center 2006a,

2006b) suggest it is plausible that zone 3 communities may also face a locally

shrinking niche before 2100.

Nitrophytic indicator species identified by the ISA were asymmetrically

distributed among climate zones, being primarily associated with the warm, lowelevation forests of zone 1 (table 17). Nitrophytes are most commonly used as

nitrogen (N) indicators (see chapter 3 “Nitrophytes” section) although many species

are known for their high tolerance to heat and light exposure. It has been proposed

elsewhere (van Herk et al. 2002) that nitrophytes will benefit from warming trends

with increased abundance in temperate regions. The apparent association of some

nitrophytes with the warmest climate zone in northern and central California tentatively seems to support this notion. More investigation is needed, however, to rule

out confounding by the high N deposition in the greater Central Valley (see chapter

4) where most zone 1 forests are located.

Nitrophytes will

benefit from warming trends with

increased abundance in temperate

regions.
96

GENERAL TECHNICAL REPORT PNW-GTR-737

Proposed Moisture Effects

Indicator species, mean number of wet days, and mean precipitation per climate

zone from the second gradient detected by NMS are displayed in table 18. Ranges

overlapped for both moisture variables, although means steadily declined between

zones. Wet days decreased by 10, 19, and 8 days between consecutive zones while

precipitation decreased by 27, 39, and 8 cm, respectively. Three of the four moisture zones had 11 or more indicator species. Unlike temperature, there is no clear

outlook for precipitation trends in California. According to the three main climate

models summarized by the California Climate Change Center (2006a, 2006b), precipitation patterns may remain stable, increase, or decrease. Even if precipitation

itself does not change, however, temperature changes will affect relative humidity

and thus increase the amount of moisture available to lichens.

Table 18—Indicator species of moisture zones in northern and central Californiaa

Zone 1: Alectoria sarmentosa, Alectoria vancouverensis, Bryoria capillaris, Bryoria

Wet forests trichodes, Cladonia fimbriata, Cladonia furcata, Cladonia ochrochlora,

Cladonia subsquamosa, Cladonia transcendens, Cladonia verruculosa,

Wet days: 99 (21) Esslingeriana idahoensis, Hypogymnia enteromorpha, Hypogymnia

Precip: 153.2 (55.2) inactiva, Hypogymnia occidentalis, Hypogymnia physodes, Lobaria

pulmonaria, Parmeliopsis hyperopta, Parmotrema arnoldii, Platismatia

glauca, Platismatia herrei, Platismatia stenophylla, Pseudocyphellaria

anthraspis, Ramalina dilacerata, Sphaerophorus globosus, Usnea

filipendula, Usnea scabrata, Usnea wirthii

Zone 2 Alectoria imshaugii, Bryoria fremontii, Bryoria pseudofuscescens, Cetraria

chlorophylla, Cetraria merrillii, Cetraria orbata, Cetraria platyphylla,

Wet days: 89 (18) Dendriscocaulon sp., Hypogymnia imshaugii, Leptogium cellulosum,

Precip: 126.7 (57.8) Letharia vulpina, Nephroma helveticum, Nephroma resupinatum,

Nodobryoria abbreviata, Nodobryoria oregana, Parmelia hygrophila,

Parmelia sulcata, Pseudocyphellaria anomala, Peltigera collina,

Parmeliopsis ambigua, Usnea glabrescens, Usnea substerilis

Zone 3 Leptogium pseudofurfuraceum, Letharia columbiana, Physconia isidiigera

Wet days: 70 (19)

Precip: 87.6 (36.5)

Zone 4: Candelaria concolor, Flavopunctelia flaventior, Melanelia glabra,

Dry forests Phaeophyscia orbicularis, Physcia adscendens, Physcia biziana,

Parmelia quercina, Punctelia perriticulata, Xanthoria fallax, Xanthoria

Wet days: 62 (18) fulva, Xanthoria oregana

Precip: 79.7 (39.3)

a Average numbers of wet days per year and precipitation (cm) are reported, followed by standard deviations in

parentheses. Only species that are statistically significant indicators (p < 0.05), according to Indicator Species

Analysis, are reported. Nitrophytes are bolded and stratified foliose cyanolichens are underlined.
97

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Species associated with the wettest zone (zone 1) and the driest zone (zone 4)

are most likely to endure range contractions in the study region depending on

which way (if any) precipitation patterns swing. The high moisture requirements

of cyanolichens are met primarily in the wet zones 1 and 2 (see chapter 6 “Large

Stratified Cyanolichens” section), and based on the specialized ecology of this

group, it is suspected that species will be relatively inflexible to both moisture and

temperature regime changes. Nitrophytic indicator species are again asymmetrically

distributed among climate zones, being associated with dry forests of zone 4. Many

nitrophytes and most especially Xanthoria species (fig. 26c) are considered highly

xerophytic (Loppi and Pirintsos 2000, Loppi et al. 1998, Pirintsos et al. 1998).

But as mentioned in the previous section, part of the observed pattern could result

from the high N impact in the greater Central Valley where zone 4 plots are most

common.

Table 19—Temperature and moisture scores for northern and central California by air quality model

area (1998–2001, 2003): greater Central Valley (GCV), greater Sierra Nevada (GSN) and Northwest

Coast (NWC)a

Parameter Total GCV GSN NWC

Number of plots surveyed 264 76 121 67

Number of plots by temperature

zone:

Warmest (-2.59 to -1.04) 67 44 6 17

Warm (-1.04 to 0.01) 65 25 15 25

Cool (0.01 to 0.87) 66 5 43 18

Coolest (0.87 to 2.14) 66 2 57 7

Temperature score extremes -2.59 to 2.14 -2.59 to 2.10 -2.07 to 2.14 -2.46 to 1.27

Average temperature score -0.02 -0.96 0.73 -0.32

Standard deviation of temperature 1.13 0.79 0.88 0.92

scores

Number of plots by moisture zone:

Wettest (-2.28 to -0.71) 66 5 16 45

Wet (-0.71 to 0.13) 66 11 39 16

Dry (0.13 to 0.89) 68 25 40 3

Driest (0.89 to 2.22) 64 35 26 3

Moisture score extremes -2.28 to 2.22 -1.17 to 2.22 -2.20 to 2.13 -2.28 to 1.57

Average moisture score 0.08 0.77 0.21 -0.92

Standard deviation of moisture

scores 1.04 0.83 0.82 0.83

a

 The ranges of temperature and moisture zones are based on natural breaks in the data as described in chapter 7. Plots

were excluded from analysis if they lacked species, had no species in common with the calibration plots, or were duplicate

surveys used for quality assurance purposes.
98

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 55—Temperature scores for northern and central California categorized by temperature zone. See figure 14 for

ecoregion codes.
99

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Summary of Climate Patterns in Northern and

Central California

The northern and central California climate model was applied to a full cycle of

FIA Lichen Community Indicator data (n = 264; 1998 to 2001, and 2003) to

describe landscape-scale macroclimatic patterns. About 80 percent of these plots

were summarized by Jovan and McCune (2004) although the former study had a

different objective and analytical approach. Large-scale climate patterns are displayed in table 19. Compare these patterns with those for the smaller model areas

defined by Jovan and McCune (2004) for air quality studies (fig. 53). The distribution of climate scores reflects how forests in the greater Central Valley tend to be

hot and dry compared to the warm and wet conditions of the marine-influenced

Northwest Coast. Forests in the greater Sierra Nevada are relatively cool overall,

whereas moisture levels are more variable.

Spatial Temperature Patterns

The spatial heterogeneity of lichen-derived temperature zones in northern and central California roughly echoes the underlying topography. The warm forests of

zone 1 are predominantly distributed among the low-elevation landforms of the

nitrophyte-rich greater Central Valley model area but extend, to a lesser extent,

into low elevations in the Northwest Coast (figs. 52 and 55). Plots in the latter

region include a higher diversity of temperature regimes with forests classified

from all four zones. Cooler zone 3 and 4 forests are distributed widely outside

the greater Central Valley, primarily in the mid to high elevations of the various

mountain ranges. A more-or-less continuous aggregation of these cooler forests lies

within the greater Sierra Nevada model area, beginning in the eastern, highelevation Klamath Range, and incorporating sites on the Modoc Plateau, southern

Cascades Range, Tahoe Basin, and Sierra Nevada Range.

Spatial Moisture Patterns

The wettest (zone 1) forests are mostly concentrated in the Northwest Coast,

whereas forests of the greater Central Valley are almost entirely classified among

the dry zones 3 and 4 (fig. 56). Moisture differs latitudinally in the greater Sierra

Nevada, with forests getting drier to the south. The northern half of this region has

forests with a wider variety of moisture regimes, including sizeable pockets of wet

zone 2 forests and more isolated instances of wetter zone 1 forest.
100

GENERAL TECHNICAL REPORT PNW-GTR-737

Figure 56—Moisture scores for northern and central California categorized by moisture zone. See figure 14 for ecoregion

codes.
101

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Conclusions

The current composition of lichen communities in northern and central California

is strongly patterned on macroclimatic gradients in forest temperature and moisture. Based on the framework built with NMS and ISA, it is apparent how temperature and moisture changes might affect community composition and the arrangement of forest climate zones across the landscape. Being an indicator species for a

particular climate zone implicitly suggests a greater sensitivity to climate change

than that of species with wide distributions spanning multiple zones. Additional

study is needed to assess potential impacts to rare species, however, which are

neglected by the ISA. Odds are that rare species are inherently at greater risk of

climate-driven extinction than abundant species simply for having low numbers.

Ultimately the ecological outcome of climate change by 2100 will be determined

by the complex interplay of many environmental factors, including but not limited

to the rate and amount of change in temperature and moisture. Sensitive species

will need to reach a habitat that is not only climatically suitable, but one that also

meets all the species’ nutrient requirements, provides adequate substrate, and has

tolerable air quality. Factors affecting the likelihood of successful colonization,

like dispersal ability, ecological amplitude, adaptability, and habitat fragmentation

are bound to be important predictors of change at the species level. Although not

accounted for in these baseline data, the influence of these factors are expected to

surface as the FIA Program begins assessment of climate trends with the Lichen

Community Indicator data.

Lichen communities

in northern and central California are

strongly patterned

on macroclimatic

gradients in forest

temperature and

moisture.
102

GENERAL TECHNICAL REPORT PNW-GTR-737
103

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Chapter 8: Overall Conclusions

The Lichen Community Indicator of the Forest Inventory and Analysis (FIA)

Program, U.S. Department of Agriculture, is an important scientific resource.

These data document the distributions of epiphytic lichens in western forests with

unprecedented detail and scope. This report explores only a few examples of the

many applications these data have for environmental monitoring, namely, the use of

lichen community parameters for indicating biodiversity, air quality, and forest

climate. These baseline assessments will help natural resource managers identify

forests at high risk of degradation as well as areas of high conservation importance.

Moreover, the gradient models themselves are tools that can benefit anyone who

surveys lichens using the FIA protocol and desires to evaluate forest health within

the model’s geographic boundaries. If coverage by the systematic grid of FIA plots

is insufficient for a particular monitoring objective, new plots can be surveyed and

scored using the local model.

As we continue refining biomonitoring tools, FIA and collaborators will be

able to obtain ever more accurate and specific information on local conditions

affecting lichens and the surrounding forest. Considerable progress has been made

applying and identifying nitrogen (N) indicator species in the Pacific Western

States. As stated in part III, however, designations are preliminary and meant to

serve as a basis for more rigorous inquiry. Statistical indicators of climate were

identified for the western Pacific Northwest and a large chunk of California in part

IV, although, again, indicator designations are meant to serve as a starting point for

further investigation.

Upcoming projects include use of the FIA Lichen Community Indicator data

for developing critical loads for lichens and N, completion of a gradient model for

the eastern Pacific Northwest, completion of a gradient model for the Northwest

Coast in California, and planning for development of a gradient model for southern

California in and near the Los Angeles Basin. Work will begin shortly on the next

phase of the FIA Lichen Indicator, trend analysis. Besides allowing managers and

scientists to evaluate changes to forest health in the short term, continual monitoring of epiphytic lichen communities will ultimately allow an invaluable long-term

ecological perspective on forest health in the region.

These baseline

assessments will

help natural resource managers

identify forests at

high risk of degradation as well as areas

of high conservation

importance.
104

GENERAL TECHNICAL REPORT PNW-GTR-737

Acknowledgments

This report is the culmination of many people’s efforts. The Lichen Community

Indicator in the Pacific coastal states is made possible by the hard work of over

35 skilled lichen surveyors, lichenologists, and lichen identification specialists.

Funding was provided by the Pacific Northwest Research Station Forest

Inventory and Analysis (PNW-FIA) through the Oregon State University (OSU)

Department of Botany and Plant Pathology. Bruce McCune (OSU) and Sally

Campbell (PNW-FIA) reviewed the report, in addition to providing considerable

advice and input throughout report development. Linda Geiser (PNW Air Program), Susan Will-Wolf (University of Wisconsin Madison), Peter Neitlich (National Park Service), Paul Patterson (Interior West-FIA), and Olaf Kuegler

(PNW-FIA) graciously provided formal reviews and invaluable guidance. All

maps were developed by geogrphic information systems specialist Elaina Graham

(PNW-FIA), and John Chase (PNW-FIA) advised on the early stages of mapmaking

Linda Geiser, Peter Neitlich, Dale Weyermann (PNW-FIA), Ron Wanek

(PNW-FIA), and Susan Will-Wolf all provided assistance obtaining or processing

data while J. Robert Johnson (UCLA-Riverside) provided preliminary modeled N

data from the CMAQ model. Photo illustrations were provided by many talented

photographers: Roseann Barnhill (www.redskystudio.org), Dr. Christopher Bochna,

Karen Dillman, Griffen McKinney, Eric Peterson (www.crustose.net), Jim Riley,

Judy Robertson, Roger Rosentreter, Daphne Stone, Eric Straley, and Tim Wheeler.

English Equivalents

When you know: Multiply by: To get:

Centimeters (cm) 0.394 Inches

Meters (m) 3.28 Feet

Kilometers (km) 0.621 Miles

Kilograms (kg) 2.205 Pounds

Hectares (ha) 2.47 Acres

Square kilometers (km2

) 0.386 Square miles

Degrees celsius (°C) 1.8ˆ°C + 32 Degrees Farenheit
105

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

References

Antoine, M.E. 2004. An ecophysiological approach to quantifying nitrogen fixation

by Lobaria oregana. The Bryologist. 107(1): 82–87.

Bailey, R.G. 1983. Delineation of ecosystem regions. Environmental Management.

7: 365–373.

Barkman, J.J. 1958. Phytosociology and ecology of cryptogamic epiphytes. Assen,

The Netherlands: Van Gorcum. 628 p.

Baron, J.S. 2006. Hindcasting nitrogen deposition to determine an ecological

critical load. Ecological Applications. 16: 433–439.

Bjerke, J.W.; Zielke, M.; Solheim, B. 2003. Long-term impacts of simulated

climatic change on secondary metabolism, thallus structure and nitrogen

fixation activity in two cyanolichens from the Arctic. New Phytologist. 159:

361–367.

Blett, T.; Geiser, L.; Porter, E. 2003. Air pollution-related lichen monitoring in

national parks, forests, and refuges: guidelines for studies intended for regulatory and management purposes. Tech. Rep. NPS-D2292. Denver, CO: U.S.

Department of the Interior, National Park Service, Air Resources Division and

U.S. Fish and Wildlife Service, Air Quality Branch; Corvallis, Oregon: U.S.

Department of Agriculture, Forest Service. 26 p.

Bruteig, I.E. 1993. The epiphytic lichen Hypogymnia physodes as a biomonitor

of atmospheric nitrogen and sulphur deposition in Norway. Environmental

Monitoring and Assessment. 26: 27–47.

Buyn, D.W.; Ching J.K.S. 1999. Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Report EPA/600/R99/030. Washington, DC: U.S. Environmental Protection Agency, Office of

Research and Development.

Bytnerowicz, A.; Fenn, M. 1996. Nitrogen deposition in California forests: a

review. Environmental Pollution. 92: 127–146.

Bytnerowicz, A.; Tausz, M.; Alonso, R.; Jones, D.; Johnson, R.; Grulke, N.

2002. Summer-time distribution of air pollutants in Sequoia National Park,

California. Environmental Pollution. 118: 187–203.
106

GENERAL TECHNICAL REPORT PNW-GTR-737

Cahill, T.A.; Carroll, J.J.; Campbell, D.; Gill, T.E. 1996. Air quality in status of

the Sierra Nevada, U.S. Geological Survey Digital Data Series, Sierra Nevada

Ecosystem Project; final report to Congress. Davis, CA: University of California Centers for Water and Wildland Resources: 1227–1261. Vol. II. Chapter 48.

http://ceres.ca.gov/snep/pubs/web/PDF/VII_C48.PDF. (August 22, 2007).

California Climate Change Center. 2006a. Our changing climate: assessing the

risks to California. http://www.energy.ca.gov/2006publications/CEC-500-2006-

077/CEC-500-2006-077.PDF. (August 22, 2007).

California Climate Change Center. 2006b. Scenarios of climate change in

California: an overview. http://www.energy.ca.gov/2005publications/CEC-500-

2005-186/CEC-500-2005-186-SF.PDF. (August 22, 2007).

California Environmental Protection Agency, Air Resources Board. 2005.

Characterization of ambient PM10 and PM2.5 in California. Tech. Rep.

June 2005. http://www.arb.ca.gov/pm/pmmeasures/pmch05/pmch05.htm.

(August 22, 2007).

Campbell, S.J.; Wanek, R.; Coulston, J.W. 2007. Ozone injury in west coast

forests: 6 years of monitoring. Gen. Tech. Rep. PNW-GTR-722. Portland, OR:

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research

Station. 53 p.

Daly, C.; Taylor, G. 2000. United States average monthly or annual precipitation, temperature, and relative humidity 1961-90. Arc/INFO Coverages.

Spatial Climate Analysis Service, Oregon State University (SCAS/OSU),

Corvallis, OR.

Daly, C.; Taylor, G.H.; Gibson, W.P.; Parzybok, T.W.; Johnson, G.L.;

Pasteris, P. 2001. High-quality spatial climate data sets for the United States

and beyond. Transactions of the American Society of Agricultural Engineers.

43: 1957–1962.

Davis, F.W.; Stoms, D.M; Hollander, A.D.; Thomas, K.A.; Stine, P.A.; Odion,

D.; Borchert, M.I.; Thorne, J.H.; Gray, M.V.; Walker, R.E.; Warner, K.;

Graae, J. 1998. The California Gap Analysis Project; final report. Santa

Barbara, CA: University of California. http://www.biogeog.ucsb.edu/projects/

gap/gap_rep.html. (August 22, 2007).
107

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

DellaSala, D.A.; Reid, S.B.; Frest, T.J.; Strittholt, J.R.; Olson, D.M. 1999. A

global perspective on the biodiversity of the Klamath-Siskiyou ecoregion.

Natural Areas Journal. 19: 300–319.

Driscoll, C.T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan,

C.; Goodale, C.L.; Groffman, P.; Hopkinson, C.; Lambert, K.; Lawrence,

G.; Ollinger, S. 2003. Nitrogen pollution in the Northeastern United States:

sources, effects, and management options. Bioscience. 53: 357–374.

Dufrêne, M.; Legendre, P. 1997. Species assemblages and indicator species:

the need for a flexible asymmetrical approach. Ecological Monographs. 67:

345–366.

Easterling, D.R.; Meehl, G.A.; Parmesan, C.; Changnon, S.A.; Karl, T.R.;

Mearns, L.O. 2000. Climate extremes: observations, modeling, and impacts.

Science. 289: 2068–2074.

ENVIRON International Corporation. 2002. Ammonia emissions improvement

projects in support of CRPAQS aerosol modeling and data analyses: draft

ammonia inventory development. Report to the California Air Resources Board,

Sacramento, CA. On file with: ENVIRON International Corporation 101

Rowland Way, Suite 220, Novato, CA 94945.

Erisman, J.W.; Hensen, A.; Mosquera, J.; Sutton, M.; Fowler, D. 2005. Deposition monitoring networks: What monitoring is required to give reasonable

estimates of ammonia/ammonium? Environmental Pollution. 135: 419–431.

Farmer, A.M.; Bates, J.W.; Bell, J.N.B. 1991: Comparisons of three woodland

sites in NW Britain differing in richness of the epiphytic Lobarion pulmonariae

community and levels of wet acidic deposition. Holarctic Ecology. 14: 85–91.

Farmer, A. 1997. Impacts of air pollution on Lobaria species. British Lichen

Society Bulletin. 80: 25–26.

Fenn, M.E.; Poth, M.A.; Aber, J.D.; Baron, J.S.; Bormann, B.T.; Johnson,

D.W.; Lemly, A.D.; McNulty, S.G.; Ryan, D.F.; Stottlemyer, R. 1998.

Nitrogen excess in North American ecosystems: predisposing factors, ecosystem

responses, and management strategies. Ecological Applications. 8: 706–733.
108

GENERAL TECHNICAL REPORT PNW-GTR-737

Fenn, M.E.; Baron, J.S.; Allen, E.B.; Rueth, H.M.; Nydick, K.R.; Geiser, L.;

Bowman, W.D.; Sickman, J.O.; Meixner, T.; Johnson, D.W.; Neitlich, P.

2003a. Ecological effects of nitrogen deposition in the Western United States.

BioScience. 53: 404–420.

Fenn, M.E.; Haeuber, R.; Tonnesen, G.S.; Baron, J.S.; Grossman-Clarke, S.;

Hope, D.; Jaffe, J.A.; Copeland, S.; Geiser, L.; Rueth, H.M.; Sickman,

J.O. 2003b. Nitrogen emissions, deposition, and monitoring in the Western

United States. BioScience. 53: 391–403.

Fenn, M.E.; Poth, M.A.; Bytnerowicz, A.; Sickman, J.O.; Takemoto, B.K.

2003c. Effects of ozone, nitrogen deposition, and other stressors on montane

ecosystems in the Sierra Nevada. In: Bytnerowicz, A.; Arbaugh, M.J.; Alonso,

R., eds. Ozone air pollution in the Sierra Nevada—distribution and effects on

forests. Oxford, United Kingdom: Elsevier Science Ltd.: 111–155.

Frati, L.; Santoni, S.; Nicolardi, V.; Gaggi, C.; Brunialti, G.; Guttova, A.;

Gaudino, S.; Pati, A.; Pirintsos, S.A.; Loppi, S. 2007. Lichen biomonitoring

of ammonia emission and nitrogen deposition around a pig stockfarm. Environmental Pollution. 146: 311–316.

Gauslaa, Y. 1985. The ecology of Lobaria pulmonariae and Parmelion caperatae in

Quercus dominated forests in south-west Norway. Lichenologist. 17: 117–140.

Geiser, L. 2004. Manual for monitoring air quality using lichens on national forests

of the Pacific Northwest. Tech. Pap. R6-NR-AQ-TP-1-04. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Region. 126 p.

Geiser, L.; Neitlich, P.N. 2007. Air pollution and climate gradients in western

Oregon and Washington indicated by epiphytic macrolichens. Environmental

Pollution. 145: 203–218.

Geiser, L.H.; Glavich, D.A.; Mikulin, A.G.; Ingersoll, A.R.; Hutten, M. 2004:

New records of rare and unusual coastal lichens from the U.S. Pacific Northwest. Evansia. 21(3): 104–110.

Gilbert, O.L. 1976. An alkaline dust effect on epiphytic lichens. The

Lichenologist. 8:173–178.

Gilbert, O.L. 1986. Field evidence for an acid rain effect on lichens. Environmental Pollution. 40: 227–231.
109

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Glavich, D.A.; Geiser, L.H.; Mikulin, A.G. 2005a. The distribution of some rare

coastal lichens in the Pacific Northwest and their association with late-seral and

federally-protected forests. The Bryologist. 108(2): 241–254.

Glavich, D.A.; Geiser, L.H.; Mikulin, A.G. 2005b. Rare epiphytic coastal lichen

habitats, modeling, and management in the Pacific Northwest. The Bryologist.

108(3): 377–390.

Gombert, S.; Asta, J.; Seaward, M.R.D. 2005. The use of autecological and

environmental parameters for establishing the status of lichen vegetation in a

baseline study for a long-term monitoring survey. Environmental Pollution.

135: 501–514.

Goward, T.; Arsenault, A. 2000. Cyanolichens and conifers: implications for

global conservation. Forest, Snow and Landscape Research. 75(3): 303–318.

Hawksworth, D.L.; Rose, F. 1970. Quantitative scale for estimating sulfur dioxide

air pollution in England and Wales using epiphytic lichens. Nature (London).

227: 145–148.

Hayward, G.D.; Rosentreter, R. 1994. Lichens as nesting material for northern

flying squirrels in the northern Rocky Mountains. Journal of Mammalogy.

75: 663–673.

Holland, E.A.; Dentener, F.J.; Braswell, B.H.; Sulzman, J.M. 1999. Contemporary and pre-industrial global reactive nitrogen budgets. Biogeochemistry.

46: 7–43.

Holland, E.; Braswell, B.H.; Sulzman, J.; Lamarque, J. 2005. Nitrogen deposition onto the United States and western Europe: synthesis of observations and

models. Ecological Applications. 15: 38–57.

Insarov, G.; Schroeter, B. 2002. Lichen monitoring and climate change. In: Nimis,

P.L.; Scheidegger, C.; Wolseley, P.A., eds. Monitoring with lichens—monitoring lichens. Amsterdam, Kluwer: 183–201.

Jenks, G.F. 1967. The data model concept in statistical mapping. International

Yearbook of Cartography. 7: 186–190.

Jovan, S.; McCune, B. 2004. Regional variation in epiphytic macrolichen communities in northern and central California forests. The Bryologist. 107: 328–339.
110

GENERAL TECHNICAL REPORT PNW-GTR-737

Jovan, S.; McCune, B. 2005. Air-quality bioindication in the greater Central

Valley of California, with epiphytic macrolichen communities. Ecological

Applications. 15: 1712–1726.

Jovan, S.; McCune, B. 2006. Using epiphytic macrolichen communities for

biomonitoring ammonia in forests of the greater Sierra Nevada, California.

Water, Air, and Soil Pollution. 170: 69–93.

Jovan, S.; Carlberg, T. 2007. Nitrogen content of Letharia vulpina tissue from

forests of the Sierra Nevada, California: geographic patterns and relationships to

ammonia estimates and climate. Environmental Monitoring and Assessment.

129: 243–251.

Keon, D.B.; Muir, P.S. 2002. Growth of Usnea longissima across a variety of

habitats in the Oregon Coast Range. The Bryologist. 105: 233–242.

Kruskal, J.B. 1964. Non-metric multidimensional scaling: a numerical method.

Psychometrika. 29: 115–129.

Lambley, P.; Wolseley, P.; eds. 2004. Lichens in a changing pollution environment

English Nature Research Reports 525. Peterborough, United Kingdom: English

Nature, Northminster House. 121 p. http://www.english-nature.org.uk/pubs/

publication/PDF/525.pdf. (August 22, 2007).

Lambley, P.; Wolseley, P.; James, P. 2004. The impact of a changing pollution

climate on the conservation of lichen-rich sites in England and the UK. In:

Lambley, P.; Wolseley, P., eds. 2004. Lichens in a changing pollution environment. English Nature Research Reports 525. Peterborough, United Kingdom:

English Nature, Northminster House: 107–116. http://www.englishnature.org.uk/pubs/publication/PDF/525.pdf. (August 22, 2007).

Lipow, S.R.; Vance-Borland, K.; St. Clair, J.B.; Henderson, J.; McCain, C.

2004. Gap analysis of conserved genetic resources for forest trees. Conservation

Biology. 18: 412–423.

Loppi, S. 2004. Mapping the effects of air pollution, nitrogen deposition, agriculture and dust by the diversity of epiphytic lichens in central Italy. In: Lambley,

P.; Wolseley, P., eds. 2004. Lichens in a changing pollution environment

English Nature Research Reports 525. Peterborough, United Kingdom: English

Nature, Northminster House: 37–41. http://www.english-nature.org.uk/

pubspublication/PDF/525.pdf. (August 22, 2007).
111

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Loppi, S.; De Dominicis, V. 1996. Effects of agriculture on epiphytic lichen

vegetation in central Italy. Israel Journal of Plant Science. 44: 297–307.

Loppi, S.; Pirintsos, S.A. 2000. Effect of dust on epiphytic lichen vegetation in

the Mediterranean area (Italy and Greece). Israel Journal of Plant Sciences.

4: 91–95.

Loppi, S.; Pirintsos, S.; Sforzi, B.; De Dominicis, V. 1998. Effects of climate and

agriculture on epiphytic lichen vegetation in the Mediterranean area (Tuscany,

central Italy). Acta Botanica Croatica. 55/56: 17–27.

Maser, C.; Maser, Z.; Trappe, J.M. 1985. Food habits of the northern flying

squirrel (Glaucomys sabrinus) in Oregon. Canadian Journal of Zoology.

63: 1085–1088.

Max, T.A.; Schreuder, H.T.; Hazard, J.W.; Oswald, D.O.; Teply, J.; Alegria, J.

1996. The Pacific Northwest Region vegetation inventory and monitoring

system. Res. Pap. PNW-RP-493. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 22 p.

McCune, B. 1993. Gradients in epiphyte biomass in three Pseudotsuga-Tsuga

forests of different ages in western Oregon and Washington. Bryologist.

96: 405–411.

McCune, B.; Mefford, M.J. 1999. Multivariate analysis on the PC-ORD system.

Version 4. Gleneden Beach, OR: MjM Software.

McCune, B.; Grace, J.B. 2002. Analysis of ecological communities. Gleneden

Beach, OR: MjM Software. 300 p. 

McCune, B.; Rogers, P.; Ruchty, A.; Ryan, B. 1998. Lichen communities for

forest health monitoring in Colorado, USA. Report to the USDA Forest Service. Corvallis, OR: Department of Botany and Plant Pathology, Oregon State

University. 30 p.

McCune, B.; Dey, J.P.; Peck, J.E.; Cassell, D.; Heiman, K.; Will-Wolf, S.;

Neitlich, P.N. 1997. Repeatability of community data: species richness versus

gradient scores in large-scale lichen studies. Bryologist. 100: 40–46.

Mitchell, R.J.; Truscot, A.M.; Leith, I.D.; Cape, J.N.; Van Dijk, N.; Tang,

Y.S.; Fowler, D.; Sutton, M.A. 2005. A study of the epiphytic communities of

Atlantic oak woods along an atmospheric nitrogen deposition gradient. Journal

of Ecology. 93: 482–492.
112

GENERAL TECHNICAL REPORT PNW-GTR-737

Momsen, J.H. 2001. Agriculture in the Sierra in Status of the Sierra Nevada, U.S.

Geological Survey Digital Data Series, Sierra Nevada Ecosystem Project; final

report to Congress. Davis, CA: University of California Centers for Water and

Wildland Resources: 497–528. Vol. II. Chapter 17. http://ceres.ca.gov/snep/

pubs/web/PDF/VII C17.PDF. (August 22, 2007).

Mote, P.W.; Parson, E.A.; Hamlet, A.F.; Keeton, W.S.; Lettenmaier, D.;

Mantua, N.; Miles, E.L.; Peterson, D.W.; Slaughter, R.; Snover, A.K.

2003. Preparing for climatic change: the water, salmon, and forests of the

Pacific Northwest. Climatic Change. 61: 45–88.

Nash, T.H., III; Olafsen, A.G. 1995: Climate change and the ecophysiological

response of Arctic lichens. Lichenologist. 27: 559–565.

Nimis, P.L.; Scheidegger, C.; Wolseley, P.A.; eds. 2002. Monitoring with lichensmonitoring lichens. NATO Science Series. IV. Earth and Environmental Sciences, 7. The Netherlands: Dordrecht, Kluwer Academic Publishers. 408 p.

Nylander, W. 1866. Les lichens du Jardin de Luxembourg. Bulletin de la Société

Botanique de France. 13: 364–372.

Økland, R.H. 1996. Are ordination and constrained ordination alternative or

complementary strategies in general ecological studies? Journal of Vegetation

Science. 7: 289–292.

Oregon Department of Fish and Wildlife. 2006. The Oregon conservation

strategy. Oregon Department of Fish and Wildlife. http://www.dfw.state.or.us/

conservationstrategy/document_pdf.pdf. (August 22, 2007).

Parmesan, C.; Yohe, G. 2003. A globally coherent fingerprint of climate change

impacts across natural systems. Nature. 421: 37–42.

Pettersson, R.B.; Ball, J.P.; Renhorn, K.E.; Esseen, P.A.; Sjöberg, K. 1995.

Invertebrate communities in boreal forest canopies as influenced by forestry

and lichens with implications for passerine birds. Biological Conservation.

74: 57–63.

Pirintsos, S.A.; Loppi, S.; Dalaka, A.; De Dominicis, V. 1998. Effects of

grazing on epiphytic lichen vegetation in a Mediterranean mixed evergreen

sclerophyllous and decidous shrubland (northern Greece). Israel Journal of

Plant Sciences. 46: 303–307.
113

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Potter, C.; Krauter, C.; Klooster, S. 2000. Statewide inventory estimates of

ammonia emissions from native soils and chemical fertilizers in California.

Final report prepared for the California Air Resources Board. On file with:

California Air Resources Board, Sacramento, California, USA.

Richardson, D.H.S.; Cameron, R.P. 2004. Cyanolichens: their response to

pollution and possible management strategies for their conservation in northeastern North America. Northeastern Naturalist. 11: 1–22.

Ruoss, E. 1999. How agriculture affects lichen vegetation in central Switzerland.

Lichenologist. 31: 63–73.

Sigal, L.L.; Nash, T.H., III. 1983. Lichen communities on conifers in southern

California: an ecological survey relative to oxidant air pollution. Ecology.

64: 1343–1354.

Søchting, U. 1995. Lichens as monitors of nitrogen deposition. Cryptogamic

Botany. 5: 264–269.

Sparrius, L.B. 2007. Response of epiphytic lichen communities to decreasing

ammonia air concentrations in a moderately polluted area of the Netherlands.

Environmental Pollution. 146: 375–379.

Stevenson, S.K. 1978. Distribution and abundance of arboreal lichens and their use

as forage by blacktailed deer. Vancouver, BC: University of British Columbia.

200 p. M.S. thesis.

Stolte, K.; Conkling, B.; Campbell, S.; Gillespie, A. 2002. Forest health indicators: Forest Inventory and Analysis Program. U.S. Department of Agriculture,

Forest Service. 726 p. http://www.fia.fs.fed.us/library/briefings-summariesoverviews/docs/ForestHealthIndicators.pdf. 13 p. (August 22, 2007).

Sutton, M.A.; Pitcairn, C.E.R.; Leith, I.D.; Sheppard, L.J.; van Dijk, N.;

Tang, Y.S.; Skiba, U.; Smart, S.; Mitchell, R.; Wolseley, P.; James, P.;

Purvis, W.; Fowler, D. 2004. Bioindicator and biomonitoring methods for

assessing the effects of atmospheric nitrogen on statutory nature conservation

sites. In: Sutton, M.A.; Pitcairn, C.E.R.; Whitfield, C.P., eds. Joint Nature

Conservation Committee Report No: 356. Peterborough, United Kingdom: Joint

Nature Conservation Committee. 230 p. http://www.jncc.gov.uk/page-3236.

(August 22, 2007).
114

GENERAL TECHNICAL REPORT PNW-GTR-737

Thomas, C.D.; Cameron, A.; Green, R.E.; Bakkenes, M.; Beaumont, L.J.;

Collingham, Y.C.; Erasmus, B.F.N.; Ferreira de Siqueira, M.; Grainger,

A.; Hannah, L.; Hughes, L.; Huntley, B.; van Jaarsveld, A.S.; Midgley,

G.F.; Miles, L.; Ortega-Huerta, M.A.; Peterson, A.T.; Phillips, O.L.;

Williams, S.E. 2004. Extinction risk from climate change. Nature. 427:

145–148.

Torn, M.S.; Harte, J. 2006. Missing feedbacks, asymmetric uncertainties, and the

underestimation of future warming. Geophysical Research Letters. 33: L10703.

U.S. Department of Agriculture, Forest Service. [USDA FS]. 2005. Field

instructions for the annual inventory of Washington, Oregon, California, and

Alaska: supplement for phase 3 (FHM) indicators. Portland, OR: Pacific Northwest Research Station. 136 p. http://www.fs.fed.us/pnw/fia/local-resources/pdf/

field_manuals/2005_annual_manual_supplement.pdf. (May 3, 2007).

Van Dobben, H.F.; de Bakker, A.J. 1996. Re-mapping of lichen biodiversity in

The Netherlands: effects of decreasing SO2

 and increasing NH3. Acta Botanica

Neerlandica. 45: 55–71.

Van Herk, C.M. 1999. Mapping of ammonia pollution with epiphytic lichens in

the Netherlands. Lichenologist. 31: 9–20.

Van Herk, C.M. 2001. Bark pH and susceptibility to toxic air pollutants as independent causes of changes in epiphytic lichen composition in space and time.

Lichenologist. 33: 419–441.

Van Herk, C.M.; Aptroot, A.; van Dobben, H.F. 2002. Long-term monitoring in

the Netherlands suggests that lichens respond to global warming. Lichenologist.

34(2): 141–154.

Van Herk, C.M.; Mathijssen-Spiekman, E.A.M.; de Zwart, D. 2003. Long

distance nitrogen air pollution effects on lichens in Europe. Lichenologist.

35(4): 347–359.

Vitousek, P.M.; Aber, J.D.; Howarth, R.W.; Likens, G.E.; Matson, P.A.;

Schindler, D.W.; Schlesinger, W.H.; Tilman, G.D. 1997. Human alteration

of the global nitrogen cycle: causes and consequences. Ecological Applications.

7: 737–750.
115

Lichen Bioindication of Biodiversity, Air Quality, and Climate: Baseline Results From Monitoring...

Walther, G.R.; Post, E.; Convey, P.; Menzel, A.; Parmesan, C.; Beebee, T.J.C.;

Fromentin, J.M.; Hoegh-Guldberg, O.; Bairlein, F. 2002. Ecological

responses to recent climate change. Nature. 416: 389–395.

Washington Department of Fish and Wildlife. 2005. Washington’s comprehensive wildlife conservation strategy. Washington Department of Fish and

Wildlife, Olympia, WA. http://wdfw.wa.gov/wlm/cwcs/cwcs.htm.

(August 22, 2007).

Weiss, S.B. 2006. Impacts of nitrogen deposition on California ecosystems and

biodiversity. California Energy Commission, PIER Energy-Related Environmental Research. CEC-500-2005-165. http://www.energy.ca.gov/pier/

final_project_reports/CEC-500-2005-165.html. (August 22, 2007).

Will-Wolf, S. 2005. Forest Health Monitoring, Fact Sheet Series: lichen communities indicator. http://www.fia.fs.fed.us/library/fact-sheets/p3-factsheets/

lichen.pdf. (August 22, 2007).

Will-Wolf, S.; Scheidegger, C.; McCune, B. 2002. Methods for monitoring

biodiversity and ecosystem function. In: Nimis, P.L.; Scheidegger, C.; Wolseley,

P.A., eds. Monitoring with lichens—monitoring lichens. NATO Science Series.

IV. Earth and Environmental Sciences, 7. Dordrecht, The Netherlands: Kluwer

Academic Publishers. 408 p.
Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw

Telephone (503) 808-2592

Publication requests (503) 808-2138

FAX (503) 808-2130

E-mail pnw_pnwpubs@fs.fed.us

Mailing address Publications Distribution

Pacific Northwest Research Station

P.O. Box 3890

Portland, OR 97208-3890
U.S. Department of Agriculture

Pacific Northwest Research Station

333 S.W. First Avenue

P.O. Box 3890

Portland, OR 97208-3890

Official Business

Penalty for Private Use, $300