United States Department of Agriculture

DEPAR TMENT OF AGRICULT URE

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report

PNW-GTR-952

June

2017

Lichen Communities as Climate 

Indicators in the U.S. Pacific States

Robert J. Smith, Sarah Jovan, and Bruce McCune
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Authors

Robert J. Smith is a Ph.D. candidate and Bruce McCune is a professor, Oregon 

State University, Department of Botany and Plant Pathology, 2082 Cordley Hall, 

Corvallis, OR 97331; and Sarah Jovan is a research ecologist, U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station, Forestry Sciences 

Laboratory, 620 SW Main St., Suite 400, Portland, OR 97205.

Cover photo: Nitrogen-fixing cyanolichens (Peltigera spp.), Larch Mountain, 

western Oregon. Photo by Robert J. Smith.
Abstract

Smith, Robert J.; Jovan, Sarah; McCune, Bruce. 2017. Lichen communities as 

climate indicators in the U.S. Pacific States. Gen. Tech. Rep. PNW-GTR-952. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 44 p.

Epiphytic lichens are bioindicators of climate, air quality, and other forest conditions and may reveal how forests will respond to global changes in the U.S. Pacific 

States of Alaska, Washington, Oregon, and California. We explored climate indication with lichen communities surveyed by using both the USDA Forest Service 

Forest Inventory and Analysis (FIA) and Alaska Region (R10) methods. Across the 

Pacific States, lichen indicator species and ordination “climate scores” reflected 

associations between lichen community composition and climate. Indicator species 

are appealing targets for monitoring, while climate scores at sites resurveyed in 

the future can indicate climate change effects. Comparing the FIA and R10 survey 

methods in coastal Alaska showed that plot size affected lichen-species capture 

but not climate scores, whereas mixing data from both methods did not improve 

climate scores. Remeasurements from 1989 to 2014 in south-central and southeast 

Alaska revealed the importance of systematically random plot designs to detect 

climate responses in lichen communities. We provide an appendix of lichen species 

with climate indicator values. Lichen indicator species and community climate 

scores are promising tools for meeting regional forest management objectives.

Keywords: Bioindication, climate change, coastal Pacific Northwest, forest 

health, gradient analysis, indicator species, niche tolerance, ordination, site scores. 
Contents

1 Introduction

2 Methods

2 Lichens and Climate Data

3 Analysis 1—Super-Regional Lichen-Climate Relationships

6 Analysis 2—Alaska Regional Models: Survey Method Performance

8 Analysis 3—Lichen Community Changes 1989–2014

9 Results

9 Analysis 1—Super-Regional Lichen-Climate Relationships

20 Analysis 2—Alaska Regional Models: Survey Method Performance

25 Analysis 3—Lichen Community Changes 1989–2014

27 Discussion

27 Super-Regional Lichen-Climate Relationships

27 Survey Method Performance

28 Climate Change in Lichen Communities

28 Future Monitoring Targets

30 Conclusion

30 Acknowledgments

30 English Equivalents

31 References

35 Appendix: Species List
Introduction

This report explores lichen communities as indicators of climatic conditions in 

the U.S. Pacific States of Alaska, Washington, Oregon, and California. Earlyresponding “bioindicator” species are useful for evaluating how forests may 

respond to climate change because we can observe changes in species identities 

and abundances through time. Changes in bioindicators may precede changes in 

other forest vegetation or processes. Epiphytic (tree-dwelling) forest lichens are 

fungus-photobiont partnerships that are especially good bioindicators because their 

lack of any protective cuticle or active water uptake system directly exposes them 

to changes in temperature and atmospheric moisture (Gauslaa et al. 2014, Nimis et 

al. 2002). Furthermore, because they rely on atmospheric sources of nutrition, they 

are proportionately more sensitive to differences in climate than in soils. The U.S. 

Forest Service (USFS), National Park Service (NPS), and U.S. Fish and Wildlife 

Service (USFWS) have conducted over 8,000 lichen surveys across U.S. forests, 

including those considered here. This report is part of a larger effort to unite interagency lichen data for the purpose of developing viable climate change indicators. 

Communities of lichens have long been employed as indicators of air pollution 

(Geiser and Neitlich 2007, Jovan 2008, Schirokauer et al. 2014), and of forest health 

and function (McCune 2000, Smith et al. 2015). Recently, there has been increasing 

focus on lichens’ role as climate indicators. Lichen-climate relationships have been 

established in several regions of the continental United States (Geiser and Neitlich 

2007, Jovan and McCune 2004, McMurray et al. 2015, Root et al. 2015, Will-Wolf 

et al. 2015), and a regional focus in northern parts of the country is now warranted 

owing to projections of rapid ecological change in coastal Alaska. For example, 

climate change is expected to affect Alaskan animal populations, tree populations, 

vegetation productivity, glacier and permafrost melt, microbial decomposition, 

wildfire patterns, and other ecosystem processes (Hennon et al. 2012, Wolken et al. 

2011). There is also evidence that a changing climate will affect coastal Alaska’s 

epiphytic lichen communities (Root et al. 2014). Surveying and monitoring lichen 

communities could provide an early indication of potential ecosystem changes. 

Regional approaches within ecoregions or biogeographic provinces have previously been useful in describing lichen responses to climate (Will-Wolf and Neitlich 

2010). Indeed, a regional approach for Alaska is essential. However, many insights 

can be gained by evaluating lichen communities over large “super regions” that 

span large ecological gradients and cross biogeographic boundaries. A broad scope 

is useful, not only because lichen-climate patterns may become more apparent over 

larger climatic gradients, but also because practitioners may wish to understand 

how Alaska’s regional findings are tied to broader trends. Furthermore, a broad 

1

Lichen Communities as Climate Indicators in the U.S. Pacific States
scope anticipates the possibility that climate could change such that sites within the 

Alaska region could become more similar to sites that are now outside the region.

This report addresses several biological questions regarding lichen communities in the 

U.S. Pacific States, including southeast and south-central Alaska: How are lichen communities related to current climate? What are the best indicator species of distinct climate 

zones? Given historical data, have lichen communities changed over time? How might 

lichen communities be useful for monitoring climatic changes in the future? In addition 

to biological questions, workers in southeast and south-central Alaska also had practical 

questions about sampling methodology: Which of two sampling methods (large- vs. smallradius plots) is best for capturing lichen species and lichen community gradients? Can 

mixtures of data from the two methods improve the strength of lichen-climate models? 

How could answers to these questions inform future survey design and monitoring efforts?

Our objective was to address these questions and provide guidance on opportunities 

to use lichen community responses in interagency environmental monitoring programs 

in Alaska and other U.S. Pacific States. Knowledge of patterns and processes in lichen 

communities will be fundamental for environmental monitoring, and for anticipating 

how changing climates might affect forest ecosystems in the Western United States. 

Methods

Lichens and Climate Data

Lichen data originated from two sources: Forest Inventory and Analysis (FIA) plots 

and USFS Alaska Region (R10) plots. The FIA plots were a subset of nationwide plots, 

while the R10 plots were located in coastal south-central and southeastern Alaska on 

the Tongass and Chugach National Forests, the Glacier Bay, Sitka, and Klondike Gold 

Rush units of the National Park Service, and Kodiak National Wildlife Refuge. Complete FIA protocols are described by Will-Wolf (2010: 11–14), and R10 protocols are 

described by Geiser et al. (1994: 22–26). Observations that were incidental or otherwise 

not adhering to these protocols were excluded. Both datasets were based on timeconstrained surveys of a circular, fixed-area plot in which trained technicians collected 

and assigned abundance values for all epiphytic lichen species. However, the FIA and 

R10 datasets differed, respectively, in plot size (0.379 vs. 0.051 ha [0.936 vs. 0.127 

ac]), number of sampling rounds (one vs. many), criteria for locating plots (random vs. 

targeted), geographic coverage (all Pacific coastal U.S. states vs. south-central/southeastern Alaska only), and temporal coverage (1998 to 2014 vs. 1989 to 2014). Results of 

this report further quantify how each dataset performs in gradient models. 

Before analyses, we translated R10 abundance values to their equivalents on the FIA 

scale following the crosswalk used by Geiser (2004: 18), placing all values on the same 

approximately logarithmic 0 to 4 scale. Values on the scale represent: 0 = not present; 

2

GENERAL TECHNICAL REPORT PNW-GTR-952
1 = 1 to 3 thalli observed; 2 = 4 to 10 thalli observed; 3 = more than 10 thalli observed 

but on <50 percent of available branches and stems; and 4 = more than 10 thalli observed 

on >50 percent of available substrates. We harmonized species names per FIA analyst 

guidelines (Will-Wolf 2010). Only epiphytic macrolichen species were included. Terrestrial or crustose species were excluded based on documented substrate and growth form. 

Climate data came from the ClimateWNA database (Wang et al. 2012). For all FIA 

and R10 lichen plot locations (fig. 1), we extracted seven climate variables: mean annual air 

temperature (°C), continentality (mean annual temperature difference between warmest 

and coldest months, °C), mean annual precipitation (mm y−1), annual heat-moisture index 

(ratio of temperature to precipitation, unitless), frost-free period (d), percentage of precipitation as snow (%), and climatic moisture deficit (reference evaporation minus precipitation, mm). Because climate variables are commonly highly correlated, we converted them 

to principal components (PCs) using principal component analysis (PCA) based on the 

correlation matrix in PC-ORD version 7 (McCune and Mefford 2016). The first three (of 

seven possible) PCs were selected for interpretation based on PC-ORD’s Rnd-Lambda criterion from a test with 9,999 randomizations. The PCs are orthogonal linear combinations 

interpretable in terms of the original climate variables; for example, PC 1 corresponded 

to a thermal gradient, PC 2 represented a gradient of increasing continentality and lower 

moisture, and PC 3 was a more complex climatic gradient involving a frost-free period 

(table 1 and fig. 2). This and all subsequent analyses were performed in PC-ORD version 7 

(McCune and Mefford 2016) and R version 3.1.2 (R Development Core Team 2015). 

Analysis 1—Super-Regional Lichen-Climate Relationships

To assess how lichens were related to climate across a broad geographical scope, we 

created a super-regional gradient model for 1,118 FIA lichen plots throughout Alaska, 

Washington, Oregon, and California. This super-regional approach spans portions of 

8 of the 17 designated FIA lichen model regions (Will-Wolf and Neitlich 2010), and 

therefore incorporates both ecological and biogeographic variation. For this we used 

exclusively FIA data after first removing rare species (<three occurrences) and speciespoor plots (<five species). The central gradient model for this was nonmetric multidimensional scaling (NMS) ordination (Kruskal 1964). NMS assigns scores to sites 

based on similarity of lichen community composition; because these scores implicitly 

reflect lichen community responses to underlying gradients like climate, we interpret 

NMS scores here as “climate scores.” This and all subsequent NMS models used 

Sørensen distances based on lichen abundance, penalized ties, and PC-ORD’s “slowand-thorough autopilot” settings with 500 iterations, 250 runs with real data, 250 runs 

with randomized data, and final scores rotated to orthogonal principal axes (mutually 

independent axes). 

3

Lichen Communities as Climate Indicators in the U.S. Pacific States
Figure 1—Raw climate values at each Forest Inventory and Analysis and Region 10 lichen plot in the Pacific States area. MAT = 

mean annual temperature. MAP = mean annual precipitation. Source: ClimateWNA (Wang et al. 2012).

4

GENERAL TECHNICAL REPORT PNW-GTR-952
Table 1—Summary of climate principal components (PCs)

Item PC 1 PC 2 PC 3

Variance explained (percent) 42.3 27.6 15.5

Cumulative variance (percent) 42.3 69.9 85.4

Climate correlations:

Mean annual air temperature 0.70 −0.55 −0.37

Continentality −0.30 0.87 −0.04

Mean annual precipitation −0.46 −0.80 −0.26

Annual heat moisture index 0.87 0.17 −0.29

Frost-free period 0.41 −0.28 0.76

Precipitation as snow −0.78 0.04 −0.37

Moisture deficit index 0.80 0.35 −0.27

Figure 2—Principal components (PCs) climate values at each Forest Inventory and Analysis and Region 10 lichen plot in the Pacific 

States area. The first three (of seven possible) PCs were used in analyses. PC 1 represents a thermal gradient, PC 2 is a gradient of 

moisture and continentality, and PC 3 is a more complex climatic gradient.

To interpret lichen-climate relationships, we calculated both univariate and 

multivariate measures of how well NMS scores fit the climate variables (both raw 

variables and transformed PCs). Univariate fit was Kendall’s tau rank correlation. 

Multivariate fit was leave-one-out cross-validated R2 (xR2) from a nonparametric 

multiplicative regression (NPMR) (McCune 2006) of NMS scores in response to 

the three climate PCs. We implemented NPMR with a local mean model, Gaussian 

kernel and default settings in HyperNiche version 2.25 (McCune and Mefford 2011), 

which allows NMS scores to vary as a potentially nonlinear function of multiple 

interacting climate PCs.

5

Lichen Communities as Climate Indicators in the U.S. Pacific States
Sites can be grouped by climatic similarity within “climate zones.” To do so, we 

used optimal partitioning in R package “optpart” (Roberts 2015). This algorithm optimizes the ratio of within-group to among-group similarity (Roberts 2015). Without 

requiring lichen information, it groups each plot into 1 of 10 climate zones sharing 

similar climatic characteristics. We identified 10 climate zones based on Euclidean 

distances of the three climate PCs, using 19 random starts with 99 iterations each: 

Zone 1 = warm mesic lowlands, Zone 2 = warm dry subcontinental, Zone 3 = hot dry 

lowlands, Zone 4 = cold mesic subcontinental, Zone 5 = cold dry continental, Zone 6 

= cold mesic continental, Zone 7 = cool dry continental, Zone 8 = cool moist subcontinental, Zone 9 = mild moist suboceanic, and 10 = warm wet hypermaritime.

To identify the lichens that best characterized each climate zone or set of zones, 

we used multigroup indicator species analysis in R package “indicspecies” (de 

Cáceres et al. 2010). Multigroup indicator species analysis accounts for the fact that 

species can have broad or narrow climatic tolerances, and may therefore be indicators of multiple or single climate zones. We specified 3rd-order groupings (indicator 

values calculated across 1, 2, and 3 climate zones) and performed a significance test 

with 999 randomizations. Indicator values (IndVal) are calculated for each species 

as the product of its relative abundance in a given climate zone (or set of zones) 

multiplied by its relative frequency in that zone (or set of zones). IndVal scales from 

0 (no indicator value) to 1 (perfect indicator). A perfect indicator species would 

occur at all sites within a given zone and only within that zone. 

Analysis 2—Alaska Regional Models: Survey Method 

Performance

Given that two lichen datasets existed for the same region in southeast and southcentral Alaska, one goal was to evaluate the respective performance of the FIA vs. 

R10 survey data in regional gradient models. If the two methods were equivalent, 

then each should yield roughly identical sets of species, and each dataset should 

be interchangeable among regional lichen community models. More formally, we 

evaluated to what degree FIA-based and R10-based models were interchangeable 

under the null hypothesis of no difference in sampling methods. We were also 

interested in whether combining FIA and R10 datasets together could improve performance in climate response models over either dataset alone. For Analysis 2, we 

used either FIA or R10 data from Alaska only, after first removing rare species (<3 

occurrences), species-poor plots (<5 species), and plots that were extreme outliers 

in species composition (average Sørensen distance >3 standard deviations from the 

grand mean). Under these constraints, there were nearly twice as many R10 plots 

as FIA plots (281 vs. 155), so we downsampled the R10 dataset by selecting only 

6

GENERAL TECHNICAL REPORT PNW-GTR-952
those R10 plots that were nearest geographical neighbors to the FIA plots, yielding 

a balanced, equal number (155) of coregional plots for each.

Evaluating performance required a three-step process to (1) calibrate, (2) reciprocally fit, and (3) evaluate each dataset across both models (table 2). First, for the calibration step we performed NMS ordination for each dataset (software and settings as 

above). Hereafter we refer to these calibration models as “FIA model” or “R10 model” 

based on their source data. Second, the reciprocal fitting step used PC-ORD’s “NMS 

Scores” procedure to generate new gradient scores for each dataset when applied to 

its reciprocal model (scores were generated for all NMS axes simultaneously). In other 

words, FIA data were fit to the R10 model, and R10 data were fit to the FIA model, 

where new NMS scores were the outcome. Third, for the evaluation step, we used 

Procrustes analysis (Peres-Neto and Jackson 2001) (R package “vegan”) and examination of ordination distances to assess agreement between models. Procrustes analysis 

calculates the residual differences between two configurations of NMS scores after 

scaling and rotating them to maximum similarity, yielding a measure of “agreement” 

that is roughly analogous to a correlation coefficient. Higher agreement suggests better 

“performance” in climate gradient models. Two gradient models built with perfectly 

interchangeable lichen data would have Procrustes agreement approaching 1 on a 

scale of 0 to 1. We also evaluated how well NMS scores fit climate variables, using 

both the univariate (Kendall’s tau) and multivariate (xR2) metrics described above 

for Analysis 1. As a further comparison of the two datasets, we tested for differences 

in community composition using permutational multivariate analysis of variance 

(perMANOVA) (Anderson 2001), implemented in PCORD and based on Euclidean 

distances of the FIA model and R10 model NMS scores. To assess differences in mean 

species richness between datasets, we fit a simple linear model with orthogonal F-tests 

of coefficients (base R version 3.1.2) (R Development Core Team 2015).

Pairwise FIA vs. R10 comparisons were not strictly possible because plots were 

not exactly colocated, and they were measured by different observers. Therefore, to 

eliminate possible location and observer effects, one expert observer applied both the 

R10 and FIA survey methods in summer 2009 at each of 12 sites (2 methods × 12 sites, 

Table 2—Procedure to assess between-model agreement of the two lichen datasets

Data source Step 1: Calibration Step 2: Reciprocal fitting Step 3: Evaluation 

FIA model Use FIA data to create

FIA model →

FIA data fit to

R10 model →

Procrustes comparison of FIA plot scores 

between R10 model and FIA model

R10 model Use R10 data to create

R10 model →

R10 data fit to

FIA model →

Procrustes comparison of R10 plot scores 

between R10 model and FIA model

FIA = Forest Inventory and Analysis, R10 = Region 10 (Alaska) of the U.S. Forest Service.

7

Lichen Communities as Climate Indicators in the U.S. Pacific States
exactly colocated). We then evaluated datasets as above, except blocking by “plot” 

to account for the fact that values from either method were related within each plot. 

This included blocked perMANOVA and blocked indicator species analysis in PCORD; see Root et al. (2010) for a description and example of blocked indicator species 

analysis. We also tested for differences in mean species richness between survey 

methods using a separate-means linear mixed model (random block effect = plot; fixed 

effects = survey method; error structure correlated within plots) and orthogonal F-tests 

implemented in R package “nlme” (Pinheiro and Bates 2000 Pinheiro et al. 2016). We 

repeated all these analyses for 12 plots that had both the FIA and R10 methods performed by the same observer at the exact same location (colocated). 

Analysis 3—Lichen Community Changes 1989–2014

To more directly test for signals of climate change responses in Alaskan lichen 

communities, we evaluated a subset of R10 plots that had been sampled at least 

twice over the 25-year period 1989–2014. There were 50 such plots, ranging from 3 

to 21 years elapsed between rounds of sampling, averaging 12.9 years elapsed. Each 

plot was surveyed by one of four observers. Alaskan FIA data were not included in 

this analysis because they had not been remeasured as of 2014.

We used two approaches to assess changes in community composition over 

time: first, we calculated NMS scores for the 50 resurveyed plots (settings and software as above); then we used blocked perMANOVA in PCORD based on Euclidean 

distances of the NMS scores (blocks = plots). In a second approach, we tested for 

directional patterning in a successional vector overlay of NMS scores. Successional 

vectors connect each plot’s first-round and second-round NMS score with each end 

of a scaled and centered vector arrow. Because vectors indicate direction and magnitude of change, a consistent shift in species composition between rounds would 

be suggested by a directional trend of vectors in the ordination space. To formally 

test the null hypothesis of no change in species composition between sampling 

rounds, we used Kuiper’s test for circular uniformity (Stephens 1970) implemented 

in R package “circular” (Lund and Agostinelli 2013). Kuiper’s test evaluates vector 

directionality but not magnitude.

To assess changes in mean species richness between sampling rounds, we 

fit a separate-means linear mixed model (random effect = plot; fixed effects = 

sampling round, observer, and their interaction; error structure correlated within 

observers), followed by orthogonal F-tests of coefficients in R package “nlme” 

(Pinheiro et al. 2016). This tests the null hypothesis of no difference in mean 

species richness between sampling rounds (i.e., no change over time). Individual 

species gains and losses were evaluated with blocked indicator species analysis in 

PCORD (blocks = plots). 

8

GENERAL TECHNICAL REPORT PNW-GTR-952
Results

Analysis 1—Super-Regional Lichen-Climate Relationships

The super-regional model was an NMS ordination of 1,118 FIA plots from 

Alaska, Washington, Oregon, and California. The final solution was 2-dimensional, had stress = 21.3, and included 273 species (table 3). NMS Axis 1 and 2 

scores explained 30.8 percent and 37.7 percent of the variation in community 

composition, respectively (table 3). Higher scores on Axis 1 were associated 

with lichen compositional change related to greater mean annual temperature 

and lower continentality (within-year temperature range), while higher scores 

on Axis 2 were related to greater precipitation and lower moisture deficit (table 

3, fig. 3). In geographic space, Axis 1 scores depicted a coastal-interior gradient (fig. 4), while Axis 2 scores reflected a north-south latitudinal gradient (fig. 

5). Species richness ranged from 5 to 44 species per plot (fig. 6). Ten climate 

zones—collections of sites that shared similar climatic attributes—were based 

on optimal partitioning of the three climate PCs (table 4, figs. 7 and 8). Each 

climate zone was associated with a set of indicator species (table 5, fig. 9). 

Indicator values reach a maximum of 1 when a species is found only within a 

given climate zone at all sites of that climate zone. A complete species list and 

indicator values are in the appendix.

Table 3—Summary and climate correlations for Pacific States super-regional model

Item NMS Axis 1 scores NMS Axis 2 scores

Explained variation in community composition (percent) 30.8 37.7

Multivariate climate fit (xR2) 0.63 0.62

Univariate climate fit (tau):

Latitude 0.32 0.51

Mean annual air temperature 0.36 −0.08

Continentality −0.38 −0.11

Mean annual precipitation 0.36 0.46

Annual heat moisture index 0.15 −0.28

Frost-free period −0.07 −0.18

Precipitation as snow −0.09 0.32

Moisture deficit index −0.10 −0.49

PC 1 0.12 −0.33

PC 2 −0.46 −0.24

PC 3 −0.15 −0.05

Note: Cross-validated R2 (xR2) expresses fit of community axis to climate variables. Kendall’s tau is a nonparametric correlation coefficient.

NMS = nonmetric multidimensional scaling. PC = principal component.

9

Lichen Communities as Climate Indicators in the U.S. Pacific States
NMS 1 NMS 2

0.27 0.41

Heat moisture

25

0

100

75

50

0.13 0.18

Frost-free period

0

300

200

100

0.07 0.28

% precip as snow

0

3,000

2,000

1,000

0.04

-1 0 1

0.52

Moisture deficit

300

0

1,200

900

600

-1 0 1

0.33 0.43

Mean ann precip

2,000

1,000

0

5,000

4,000

3,000

Figure 3—Nonparametric regression of lichen community nonmetric multidimensional 

scaling (NMS) scores against climate variables. Fit lines and 10-fold cross-validated 

R2 values (from generalized additive models) reflect relationships between climate 

variables and community composition (NMS axes).

10

GENERAL TECHNICAL REPORT PNW-GTR-952
NMS 1 NMS 2

7.5 0.13 0.41

PC 1

5.0

2.5

0

-2.5

-5.0

0.45 0.34

Mean ann. temp.

20

10

0

-10

-20

0.32 0.11

Continentality

30

20

10

0.46 0.22

PC 2

4

2

0

-2

0.31 0.06

PC 3

5.0

2.5

0

-2.5

-1 0 1 -1 0 1

Figure 3—continued

11

Lichen Communities as Climate Indicators in the U.S. Pacific States
-150

NMS 1

1

0

-1

-140 -130 -120 -110

60

50

40

Latitude

Longitude

Figure 4—Lichen community nonmetric multidimensional scaling (NMS) scores (Axis 1) from the Pacific States superregional model. Similar scores (colors) represent similar lichen species composition at each site. NMS Axis 1 reflects lichen 

community responses to an oceanic-continental gradient (temperature, continentality). 

12

GENERAL TECHNICAL REPORT PNW-GTR-952
-150

NMS 2

1

0

-1

-140 -130 -120 -110

60

50

40

Latitude

Longitude

Figure 5—Lichen community nonmetric multidimensional scaling (NMS) scores (Axis 2) from the Pacific States superregional model. Similar scores (colors) represent similar lichen species composition at each site. NMS Axis 2 reflects 

responses to precipitation and moisture deficit.

13

Lichen Communities as Climate Indicators in the U.S. Pacific States
-150

Richness

40

30

20

10

-140 -130 -120 -110

60 60

40

Number of plots

Richness

20

0

0 10 20 30 40 50

50

40

Latitude

Longitude

Figure 6—Lichen species richness in Forest Inventory and Analysis plots across the Pacific States area. Richness distributions shown in geographic space (main figure) and as a frequency distribution (inset). Richness ranged from 5 to 44 species 

per plot.

14

GENERAL TECHNICAL REPORT PNW-GTR-952
Table 4—Kendall’s tau correlation with climate zones in the Pacific States super-region

Climate variables Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6 Zone 7 Zone 8 Zone 9 Zone 10

Mean annual air 

temperature

0.36 0.18 0.33 −0.22 −0.51 −0.20 −0.01 −0.12 0.04 0.26

Continentality −0.21 −0.15 −0.08 0.05 0.33 0.27 0.22 0.04 −0.18 −0.42

Mean annual 

precipitation

−0.02 −0.11 −0.23 0.03 −0.25 −0.04 −0.28 0.27 0.31 0.38

Annual heat moisture 

index

0.30 0.17 0.34 −0.20 −0.46 −0.05 0.31 −0.21 −0.13 −0.02

Frost-free period 0.13 0.17 0.23 0.12 0.33 −0.36 −0.28 −0.26 −0.14 0.15

Precipitation as snow −0.34 −0.20 −0.34 0.16 0.17 0.25 −0.10 0.33 0.25 −0.17

Moisture deficit index 0.35 0.16 0.35 −0.16 −0.21 0.02 0.16 −0.15 −0.19 −0.27

Note: Zone 1 = warm mesic lowlands, Zone 2 = warm dry subcontinental, Zone 3 = hot dry lowlands, Zone 4 = cold mesic subcontinental, Zone 5 = cold 

dry continental, Zone 6 = cold mesic continental, Zone 7 = cool dry continental, Zone 8 = cool moist subcontinental, Zone 9 = mild moist suboceanic, 

Zone 10 = warm wet hypermaritime.

15

Lichen Communities as Climate Indicators in the U.S. Pacific States
-150 -140 -130 -120 -110

60

5000

4000

MAP (mm)

MAT (°C)

3000

2000

1000

0

-20 -10 0 10 20

50

40

Latitude

Longitude

Climate zones

1 = Warm mesic lowlands

2 = Warm dry subcontinental 

3 = Hot dry lowlands 

4 = Cold mesic subcontinental

5 = Cold dry continental 

6 = Cold mesic continental

7 = Cool dry continental 

8 = Cool moist subcontinental 

9 = Mild moist suboceanic 

10 = Warm wet hypermaritime

Figure 7—Climate zones for each Forest Inventory and Analysis lichen plot in the Pacific States area. Climate zones are 

mapped in geographic space (main figure) and climate space (inset: precipitation vs. temperature).

MAP = mean annual precipitation. MAT = mean annual temperature.

16

GENERAL TECHNICAL REPORT PNW-GTR-952
-155 -150 -145 -140 -135 -130

62.5

60

5000

4000

MAP (mm)

MAT (°C)

3000

2000

1000

0

-20 -10 0 10 20

57.5

55.0

52.5

Latitude

Longitude

Climate zones

4 = Cold mesic subcontinental

5 = Cold dry continental 

6 = Cold mesic continental 

7 = Cool dry continental 

8 = Cool moist subcontinental 

9 = Mild moist suboceanic 

10 = Warm wet hypermaritime

Figure 8—Climate zones for each Forest Inventory and Analysis lichen plot in the Alaska region. Climate zones are 

mapped in geographic space (main figure) and climate space (inset: precipitation vs. temperature). Colored symbols = 

Alaska plots; grey symbols = Washington, Oregon, and California plots. MAP = mean annual precipitation. MAT = mean 

annual temperature.

17

Lichen Communities as Climate Indicators in the U.S. Pacific States
Table 5—Select indicator species for climate zone combinations of the Pacific States super-region

Indicator species

IndVal a all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Sphaerophorus aggr.

b 0.79 0.38 0.53 0.45

Platismatia norvegica 0.61 0.22 0.40 0.40

Hypogymnia enteromorpha 0.68 0.40 0.42 0.37

Platismatia glauca 0.67 0.32 0.46 0.41

Parmeliopsis hyperopta 0.62 0.22 0.49 0.33

Alectoria sarmentosa 0.61 0.31 0.46 0.31

Cetraria merrillii 0.55 0.49 0.25 0.20

Hypogymnia imshaugii 0.65 0.29 0.39 0.41

Letharia vulpina 0.72 0.31 0.49 0.39

Nodobryoria abbreviata 0.69 0.30 0.49 0.35

Bryoria fremontii 0.62 0.29 0.45 0.29

Letharia columbiana 0.61 0.37 0.46

Melanelixia californica 0.64 0.64

Physconia isidiigera 0.66 0.28 0.59

Phaeophyscia orbicularis 0.67 0.38 0.54

Candelaria pacifica 0.70 0.32 0.39 0.53

Physcia adscendens 0.67 0.30 0.41 0.48

Polycauliona polycarpa 0.61 0.31 0.34 0.40

Physcia tenella 0.61 0.32 0.39 0.38

Parmotrema perlatum 0.61 0.61

Note: See complete list in appendix. Blank spaces indicate species had nonsignificant indicator value (but may be present). Zone 1 = warm mesic 

lowlands, Zone 2 = warm dry subcontinental, Zone 3 = hot dry lowlands, Zone 4 = cold mesic subcontinental, Zone 5 = cold dry continental, Zone 6 

= cold mesic continental, Zone 7 = cool dry continental, Zone 8 = cool moist subcontinental, Zone 9 = mild moist suboceanic, Zone 10 = warm wet 

hypermaritime.

a IndVal = (relative abundance in given climate zone or set of zones × relative frequency in given zone or set of zones) 0.5. b Sphaerophorus species were aggregated owing to species concept changes over the sampling period.

18

GENERAL TECHNICAL REPORT PNW-GTR-952
Continental/montane High-altitude/coastal Hot/dry/eutrophic

Sphaerophorus Platismatia norvegica Hypogymnia hultenii

Letharia vulpina Nodobryoria abbreviata Hypogymnia imshaugii

Polycauliona polycarpa Candelaria pacifica Physcia adscendens

Figure 9—Indicator species’ distributions across the Pacific States area. See appendix A for complete list of climate indicator values.

19

Lichen Communities as Climate Indicators in the U.S. Pacific States
Analysis 2—Alaska Regional Models: Survey Method Performance

The goal of Analysis 2 was to evaluate the respective performance of the FIA 

vs. R10 survey data in regional gradient models to inform future choices of 

survey methodology. Based on 135 species, the FIA calibration model ordinated 

155 “large-radius” plots in a 3-dimensional solution with final stress = 19.9 

on a scale of 0 to 100 (table 6 and fig. 10). Axis 1 scores explained 49 percent 

of the variation in community composition and were correlated with decreasing continentality, decreasing moisture deficit, and increasing precipitation, 

suggesting that community change along this axis reflects an oceanic–inland 

gradient. Axis 2 scores explained 14.8 percent of the variation in community 

composition and were correlated with decreasing temperature and shorter 

frost-free period, suggesting that community change along this axis reflects 

temperature tolerances. Axis 3 explained 10.5 percent of the variation in community composition and was correlated with decreasing temperature, shorter 

frost-free period, and increasing precipitation as snow. Axis 1 scores increased 

toward lower latitudes. 

Table 6—Summary of three lichen-climate regional gradient models (FIA, R10, FIA+R10)

FIA model R10 model FIA+R10 model

Stress (%) 19.9 19.6 24.6

Explained variation (%) 74.3 78.2 68.9

Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3

Explained variation (%) 49.0 14.8 10.5 39.6 25.0 13.6 33.9 19.7 15.3

Multivariate climate fit (xR2) 0.31 0.04 0.06 0.18 0.14 0.01 0.22 0.14 0.06

Univariate climate fit (tau):

Latitude -0.27 0.03 0.03 -0.20 0.09 -0.09 -0.27 -0.13 0.03

Longitude 0.23 -0.01 0.02 0.17 -0.10 0.06 0.23 0.15 -0.04

Mean annual air temperature 0.10 0.11 -0.19 0.07 0.06 0.08 0.16 -0.06 -0.21

Continentality -0.24 -0.04 0.12 -0.17 0.07 -0.07 -0.25 -0.09 0.07

Mean annual precipitation 0.22 0.01 0.08 0.17 -0.22 0.04 0.16 0.23 0.09

Annual heat moisture index -0.17 0.05 -0.15 -0.14 0.23 -0.01 -0.10 -0.25 -0.16

Frost-free period 0.07 0.11 -0.21 0.05 0.08 0.08 0.12 -0.08 -0.21

Precipitation as snow 0.02 -0.07 0.19 0.02 -0.14 -0.06 -0.04 0.16 0.20

Moisture deficit index -0.29 -0.03 -0.04 -0.22 0.21 -0.04 -0.26 -0.25 -0.08

PC 1 -0.06 0.07 -0.18 -0.04 0.16 0.05 0.02 -0.18 -0.19

PC 2 -0.24 -0.06 0.08 -0.16 0.12 -0.08 -0.23 -0.11 0.04

PC 3 0.01 0.09 -0.20 0.01 0.12 0.06 0.07 -0.13 -0.21

Note: Climate fit statistics indicate the strength of association between nonmetric multidimensional scaling scores and climate. FIA = Forest Inventory 

and Analysis. R10 = Region 10 (Alaska) of the U.S. Forest Service.

20

GENERAL TECHNICAL REPORT PNW-GTR-952
Axis 2 scores

FIA model:

-2 -1 0 1

-2

0

1

-1

Axis 1 scores

 Plot size

 FIA

 R10

Axis 2 scores

R10 model:

-2 -1 0 1

-2

0

1

-1

Axis 1 scores

 Plot size

 FIA

 R10

Axis 2 scores

Combo model:

-1 0 1

-1

0.5

1.5

-0.5

Axis 1 scores

0

1

 Plot size

 FIA

 R10

Figure 10—Ordination scores for three models: Forest Inventory and Analysis (FIA) model (left), Region 10 (R10) model (center), 

and combined gradient models (right). Marginal density plots are frequency distributions of scores for each dataset. Similar frequency distributions in an ordination would suggest that the two datasets have similar ranges in lichen community composition.

Based on 156 lichen species, the R10 calibration model ordinated 155 “smallradius” plots in a 3-dimensional solution with final stress = 19.6 (table 6 and fig. 10). 

Axis 1 scores explained 39.6 percent of the variation in community composition. 

Correlations with climate revealed that community variation along this axis represents an oceanic-inland gradient similar to the FIA model. Axis 2 scores explained 

25.0 percent of the variation in community composition and were correlated with 

variables that suggested tolerances to the timing and availability of moisture. Axis 

3 scores were not strongly related to climate (table 6). As with the FIA model, NMS 

scores on the R10 model’s first axis showed weak geographical structuring.

21

Lichen Communities as Climate Indicators in the U.S. Pacific States
A combined model including both FIA+R10 data had higher stress (24.6) than 

either model alone, and explained less of the variation in the species data, with a 

generally weaker fit to climate (table 6). 

Evaluating the cross-model performance of each dataset (to assess the null 

hypothesis of no difference in sampling methods), we found that each dataset 

had comparable measures of fit within its own calibration model as well as when 

reciprocally fit to the evaluation model (table 7). Between-model agreement, based 

on ordination distances compared among calibration and evaluation models, 

approached 89 percent on a 0- to 100-percent scale of variance explained. The 

Procrustes analysis of each model likewise found comparable residual error and 

between-model agreement (table 8). This suggests that each dataset performed similarly when fit to opposing gradient models (Procrustes agreement, which is analogous to a correlation coefficient, was = 0.67 vs. 0.66). Following Procrustes rotation, 

Axis 1 scores for each model were more comparable than other axes (fig. 11).

Mean species capture in the group of large-radius FIA plots was an estimated 

3.2 species (about 20 percent) greater than in the group of R10 plots (95-percent 

confidence interval = 1.7 to 4.6 species; F = 18.9, p <0.0001) (fig. 12). Beta diversity 

(species turnover) was greater in R10 plots as a result of greater gamma diversity 

(observing more species regionwide) and lower alpha diversity (fewer average species per plot) (table 8). Gamma diversity differences could be due to the nonrandom 

placement of R10 plots, and the unintentional inclusion of nonepiphyte species in the 

R10 dataset (we attempted to manually remove these based on knowledge of species’ requirements and recorded substrate, but substrate was not always recorded). 

For the 12 exactly colocated plots surveyed by the single expert observer, community composition differed significantly between the R10 vs. FIA methods (blocked 

perMANOVA, pseudo-F = 20.3, p = 0.0006). From the 95 recorded lichen species, 

there were 3 significant indicator species for the large-radius FIA method and zero 

for the small-radius R10 method; these 3 indicator species are fewer than the number 

that might be expected at random (because 95 species × 0.05 assumed probability of 

false detection = 4.7 species). Despite differences in community composition, models 

agreed strongly between the methods (Procrustes agreement: FIA = 98.8 and R10 = 

98.9). After accounting for observer and site effects, mean species capture using the 

Table 7—Assessment of within-model fit and agreement between Forest 

Inventory and Analysis and Region 10 models

Dataset

Fit of 

FIA model

Fit of 

R10 model

Agreement between 

models

FIA data 78.4 55.8 88.9

R10 data 61.6 81.9 89.5

Note: Fit of each model is the percentage of variance explained from a nonmetric fit of ordination distances 

vs. original Sørensen distances. Agreement between models is the percentage variance explained from an 

orthogonal least-squares regression of evaluation vs. calibration model distances when each dataset was fit to the 

other model in the evaluation step.

22

GENERAL TECHNICAL REPORT PNW-GTR-952
Table 8—Comparison of attributes for Forest Inventory and Analysis (FIA) and Region 10 (R10) sized plots.

Attribute FIA plots R10 plots Notes

 Radius 36.6 m (120 ft) 12.8 m (42 ft) FIA 2.9 times larger

Areaa 0.379 ha 0.051 ha FIA 7.3 times larger

Time constraints 1⁄2 – 2 hr 1⁄2 – 2 hr Identical

Observer skill Trainees and experts Experts

Resampled? Not yet in Alaska At least once for 50 sites

Temporal coverage 2004–2009 (for Alaska) 1989–2014

Spatial coverage all land ownerships, 

all U.S. regions

R10 national forests; 

Glacier Bay, Klondike and 

Sitka NPS; Kodiak National 

Wildlife Refuge

Location assignment Systematic, random Targeted, nonrandom

Location dispersion Systematic, random Clustered, nonrandom

Transferability Standardized across the 

United States

Require unknown correction 

factors for scaling

Terrestrial species Never included Must manually exclude

Alpha α diversity 19.8 16.1 FIA: average 3.7 more species

Gamma γ diversity 150 171 R10: more species overall

Beta diversity (γ/α−1) 6.6 9.6 R10: more compositional 

variation among plots

Procrustes agreementb

(co-regional plots)

0.66 0.67 Similar agreement

Procrustes agreementb

(exactly colocated plots)

0.98 0.98 Similar agreement

NMS gradient scores Interpret freely Caution, if related to richness

a

 FIA lichen survey area excludes subplot areas inside the plot radius.

b

 Correlation-like statistic bounded [0–1], and approaching 1 for ordinations of identical data.

NPS = National Park Service. NMS = nonmetric multidimensional scaling. FIA model scores

Axis 1 scores

-2 -1 0 1

R^2 = 0.68

-2

0

1

-1

R10 model scores

FIA model scores

Axis 2 scores

-2 -1 0 1

R^2 = 0.20

-2

0

1

-1

R10 model scores

FIA model scores

Axis 3 scores

-1 0 1

R^2 = 0.40

-2

0

1

-1

R10 model scores

Figure 11—Direct comparison of nonmetric multidimensional scaling (NMS) scores for Forest Inventory and Analysis (FIA) and Region 

10 (R10) models after Procrustes rotation to maximum similarity. Perfect agreement between models would have points fall along a 

straight line with R2 = 1.

23

Lichen Communities as Climate Indicators in the U.S. Pacific States
large-radius FIA method was an estimated 3.7 species (about 15 percent) greater than 

when using the R10 method (95 percent confidence interval = 1.9 to 5.4 species; F = 

22.0, p = 0.0007). The FIA method had higher within-plot diversity (alpha diversity: 

23.5 vs. 19.8 species) and collected a greater number of species across the 12 plots 

(gamma diversity: 95 vs. 79 species), but had similar beta diversity (beta diversity: 

3.0 vs. 2.9). The FIA method did not miss any of the 95 species observed collectively 

in the 12-plot subset, while the R10 method omitted 16 species.

-150 -145 -140 -135 -130

62.5

60

Latitude

Longitude

57.5 40

30

20

10

55

 Plot size

 FIA

 R10

FIA R10

Plot size

Species richness

Figure 12—Locations and species richness of co-occurring Forest Inventory and Analysis (FIA) and Region 10 (R10) plots 

used in Analysis 2. Inset: Mean species richness in FIA plots was about three species greater than R10 plots. Boxplot center 

bars = median. Box ends = upper/lower quartiles. Whisker ends = 1.5 × interquartile range. Dots = each observation.

24

GENERAL TECHNICAL REPORT PNW-GTR-952
Analysis 3—Lichen Community Changes 1989–2014

A suite of analyses revealed no signal of climate change in lichen communities of 

coastal Alaska over the 25-year period 1989–2014. The 50 remeasured plots ranged 

from 3 to 21 years that had elapsed between rounds of sampling, averaging 12.9 

elapsed years. Change in lichen species composition over time was indistinguishable from random. Successional vectors from the NMS ordination (3-dimensional 

solution, stress = 22.0) exhibited no directional patterning in species space, suggesting no consistent pattern of species replacement over time (null hypothesis not 

rejected by Kuiper’s test; fig. 13). Using another method, there were no changes in 

community composition between sampling rounds (blocked perMANOVA pseudoF = 0.49, p = 0.16). 

Species richness in the resampled plots exhibited roughly as many gains as 

losses over time (24 plots had increases, 5 remained equal, 21 saw decreases; 

fig. 14). There was an estimated mean of 18.8 species (± 6.5 SD) across all 100 

plot rounds. Minor changes in mean species richness among sampling rounds 

were attributable to observer effects rather than time effects (table 9). From the 

blocked indicator species analysis there were four significant “increaser” species and four significant “decreasers.” Yet, these 8 observed indicators (from 

the observed pool of 166 species) did not differ from the number that might be 

expected at random (because 166 species × 0.05 assumed probability of false 

detection = 8.3 species). NMS 2

NMS 3NMS 3

NMS 1 NMS 1 NMS 2

Figure 13—Centered successional vectors from nonmetric multidimensional scaling (NMS) ordination of 50 resampled plots in Alaska. 

Vector tails = lichen plot 1st-round NMS scores, vector heads = 2nd-round scores. Vectors radiate uniformly, indicating that the pattern of 

species replacement over time was not significantly different from random.

25

Lichen Communities as Climate Indicators in the U.S. Pacific States
Species richness

Year

40

30

20

10

1990 1995 2000 2005 2010 2015

Figure 14—Change in species richness, 1989–2014. Each line = one resampled plot. There were just as many gains as losses 

over time (24 increases, 5 equal, 21 decreases).

Table 9—Evaluation of species richness differences between sampling rounds 

and observers

Coefficient Num d.f. Den d.f. F-value p-value

Model intercept 1 49 446.24 <0.0001

Sampling round 1 43 0.53 0.4696

Observer 3 43 10.48 <0.0001

Sampling round × observer 3 43 0.31 0.8143

Note: Values are from orthogonal F-tests of linear mixed-effects model coefficients, blocking by site. Statistical 

degrees of freedom = “d.f.” for numerator (Num) and denominator (Den).

26

GENERAL TECHNICAL REPORT PNW-GTR-952
Discussion

Super-Regional Lichen-Climate Relationships

A major goal of ecologists and resource managers is the “reconciliation of local 

and regional perspectives” (Ricklefs 2004). Our super-regional approach integrated 

lichen responses to large ecological gradients at a subcontinental scale with regional 

implications for climate monitoring. A broad approach avoids idiosyncratic effects 

of local interactions among lichens (competition, facilitation), as these interactions 

become less important than climatic niche constraints across broad spatial areas 

(Peterson et al. 2011: 40). Though other factors like evolutionary history (diversification, extinction) should also affect single species’ patterns, the fact that lichen 

communities had clear and consistent climate relationships suggests their immediate utility for climate indication.

We identified lichen indicators of potentially multiple climate zones, rather than 

restricting indicator values to only single zones. Such an approach acknowledges 

that lichen species could have different climatic niche tolerances. This has real 

consequences for climate monitoring because species with narrow tolerances might 

be more visible as climate change indicators. Consider a hypothetical example: what 

if (say) Alaska’s eastern Kenai Peninsula were to experience a drying transition 

from moist Zone 8 to mesic Zone 6? In such a case, we might expect a decline in 

single-zone indicators, but perhaps not as much change in multizone indicators like 

Platismatia glauca (Zone 6 + 8 + 9 indicator).

Survey Method Performance

Evaluating how data perform in regional gradient models can help inform which 

survey methodology to use for regional forest assessments and inventories. Data 

from the FIA and R10 survey methods performed similarly in lichen-climate 

models, implying no clear superiority for climate modeling purposes. However, 

mixing the two datasets made models worse. For this reason, we do not recommend mixing data from the two survey methods in community analyses. Species 

capture (richness) was higher in the FIA plots, which had a physical area more than 

seven times larger than the R10 plots, suggesting that large-radius FIA plots may 

be advantageous if capturing locally rare species is the goal, for example, in simple 

inventories. Because richness indirectly affects lichen-climate gradient scores, the 

R10 plots may also warrant caution when attempting to interpret any environmental 

gradients that are related to species richness. Aside from the obvious species-area 

relationship, larger plots can also better include microhabitat diversity in patchy 

landscapes. Although smaller plot sizes are more convenient in difficult terrain or 

dense understories, users might consider how plot size ultimately affects diversity 

estimates, rare species capture, and transferability beyond focal regions. 27

Lichen Communities as Climate Indicators in the U.S. Pacific States
Climate Change in Lichen Communities

Given no substantial change in lichen community composition, species richness, 

or indicator species, we could not confirm a signal of climate change response for 

lichens in south-central/southeast Alaska. This may be due to at least two reasons. 

The first is that biological responses could lag climate change over time spans 

greater than the 25-year period we studied (interval between sampling rounds 

averaged 12.9 years). Delayed climate response is inherently tied to species’ demographic rates: any climate-driven population declines would not become evident 

until persistent, long-lived individuals die without replacement. Likewise, any range 

expansions to new sites would depend on lichen species’ dispersal capacity and 

colonization rates (Gjerde et al. 2012). Despite climate envelope forecasts (Ellis et 

al. 2007), we do not yet know enough about lichen demographic rates or about the 

expected rate of climate change to be able to predict whether lichen communities 

could “track” climate shifts.

The second, more likely reason we did not observe strong lichen community 

changes is the proximity of the Alaskan plots to the Pacific Ocean, with weak representation of high-elevation alpine habitats where change is most expected. The 

Pacific has a moderating influence on coastal climate and reduces year-to-year 

variation in temperature, moisture availability, and moisture seasonality. Lichen 

responses to climate are likely to be most visible at climatic “threshold” sites farther inland and away from the coast (Root et al. 2014). Inland sites of continental 

Europe, for example, have seen rapid gains of heat-tolerant species and declines 

of arctic-boreal lichens over just two decades (Aptroot and van Herk 2007, Ellis 

et al. 2009, van Herk et al. 2002). Our findings suggest possible ways detection 

of climate change with lichens might be improved by expanding both the habitat 

window and the time window of observation. For example, systematically random 

plot locations would encompass a broader range of habitats and cover inland 

climatic thresholds where change in lichen communities is expected.

Future Monitoring Targets

Climate-driven changes in lichen communities could be precursors of larger 

changes in forests because epiphytic lichens have generally shorter life cycles than 

trees and are more directly exposed to climatic fluctuations. What options are 

available to resource managers and practitioners who wish to capitalize on lichen 

responses as part of environmental monitoring programs? Several options could be 

used individually or in combination:

28

GENERAL TECHNICAL REPORT PNW-GTR-952
Option 1: Resurvey entire communities at existing sites—

A program could resurvey existing plots at 5- or 10-year intervals, determine new 

climate scores with the NMS Scores procedure, then assess whether lichen composition has changed, and in which “direction.” For example, in southeast Alaska will 

there be a collective trend toward gradient extremes representing warmer and drier 

conditions? Could there be a trend toward more “continental” and less “coastal” conditions? Could there be a trend toward lichen assemblages with no current analogue?

Option 2: Resurvey select indicator species at existing sites—

Systematically tracking abundance changes and range shifts of a few prominent 

indicator species may be more efficient than resurveying entire communities. For 

example, will “hot, dry” indicator species (e.g., Candelaria, Polycauliona, Phaeophyscia, Physcia spp.) expand northward or upward in elevation? Will there be 

range contractions for “moist, coastal” indicators (e.g., Sphaerophorus spp., Platismatia norvegica, coastal cyanolichens)? How far from existing sites will novel 

populations occur?

Option 3: Survey new sites—

Establishing new plots can fill in gaps in “environmental space” that are unique 

combinations of climate not currently represented, which could help anticipate 

shifts into novel climate spaces. Priority locations should include climatic thresholds where rapid biological changes are expected. Another priority is to establish 

plots at sites with contrasting disturbance and harvest histories because stand 

attributes can mediate the effects of climate (Ellis et al. 2009).

Option 4: Include air quality gradients—

Interactions between climate and air quality suggest that models could be improved 

by accounting for both. This is especially relevant because the biological effects of 

atmospheric nitrogen deposition can be amplified in hotter, drier climates (Jovan 

et al. 2012, Sheppard et al. 2011). Much of Alaska currently has good air quality 

relative to the continental United States, but there are local exceptions related to ore 

processing and cruise ship emissions (Derr 2010, Schirokauer et al. 2014). Comprehensive environmental monitoring in Alaska and elsewhere would integrate lichen 

responses to both climate and air quality. 

29

Lichen Communities as Climate Indicators in the U.S. Pacific States
Conclusion

In summary, we (1) identified climate indicator species and climate scores across 

the Pacific States super-region, (2) evaluated two lichen survey methods in southcentral/southeast Alaska, and (3) evaluated community change from 1989 to 2014 

in south-central/southeast Alaska. We found that lichen-based climate scores 

demonstrated clear relationships to temperature, moisture, and other factors over 

large areas and within specific regions. Indicator species revealed affinities to 

high-latitude/coastal, continental/montane, and low-latitude/hot/dry habitats across 

the Pacific States super-region. Large- and small-radius survey plots each had 

comparable performance in regional lichen-climate models, but there was a tradeoff 

between plot size and species capture. Plot sizes should not be mixed in lichenclimate models. Climate change responses were not evident in lichen communities 

of coastal Alaska over a 25-year period, probably because of demographic response 

lags or oceanic climate buffering. From continued surveys of lichen climate scores 

and indicator species, interagency environmental monitoring programs can begin to 

anticipate how climate change will affect forests of the U.S. Pacific States.

Acknowledgments

We are grateful for valuable input and constructive comments from Karen Dillman, 

Rob DeVelice, Michael Bower, Michael Shephard, James Walton, Amy Miller, and 

Beth Schulz. Karen Dillman, Chiska Derr, Linda Geiser and colleagues collected 

the R10 dataset over many years. We thank Linda Geiser for helping to evaluate 

potential air quality work and Heather Root for comments and a central role in the 

plot size comparisons.

This work was performed under Joint Venture Agreement 12-JV-11261979-047 

between the U.S. Forest Service Pacific Northwest Research Station and Oregon 

State University, and Task Agreement P14AC01637 with the National Park Service’s 

Pacific Northwest Cooperative Ecosystem Studies Unit.

English Equivalents

When you know: Multiply by: To get:

Millimeters (mm) 0.394 Inches

Centimeters (cm) 0.394 Inches

Meters (m) 3.28 Feet

Kilometers (km) 0.621 Miles

Hectares (ha) 2.47 Acres

Square kilometers (km2) 0.386 Square miles

Degrees Celsius (°C) 1.8°C + 32 Degrees Fahrenheit

30

GENERAL TECHNICAL REPORT PNW-GTR-952
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yellow-cedar in the North Pacific coastal rainforest. BioScience. 62: 147–158.

Jovan, S. 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.

Jovan, S.; McCune, B. 2004. Regional variation in epiphytic macrolichen 

communities in northern and central California forests. The Bryologist. 

107: 328–339.

Jovan, S.; Riddell, J.; Padgett, P.E.; Nash, T.H., III. 2012. Eutrophic lichens 

respond to multiple forms of N: implications for critical levels and critical loads 

research. Ecological Applications. 22: 1910–1922.

Kruskal, J. 1964. Multidimensional scaling by optimizing goodness of fit to a 

nonmetric hypothesis. Psychometrika. 29: 1–27.

Lund, U.; Agostinelli, C. 2013. R package “circular”: Circular Statistics. Version 

0.4–7.

McCune, B. 2000. Lichen communities as indicators of forest health. Bryologist. 

23: 353–356.

McCune, B. 2006. Non-parametric habitat models with automatic interactions. 

Journal of Vegetation Science. 17: 819–830.

McCune, B.; Mefford, M.J. 2011. HyperNiche. Multiplicative habitat modeling. 

Version 2. Gleneden Beach, OR: MjM Software Design.

McCune, B.; Mefford, M.J. 2016. PC-ORD. Multivariate analysis of ecological 

data. Version 7. Gleneden Beach, OR: MjM Software Design.

McMurray, J.A.; Roberts, D.W.; Geiser, L.H. 2015. Epiphytic lichen indication 

of nitrogen deposition and climate in the Northern Rocky Mountains, USA. 

Ecological Indicators. 49: 154–161.

Nimis, P.L.; Scheidegger, C.; Wolseley, P.A. 2002. Monitoring with lichens. In: 

Nimis, P.L.; Scheidegger, C.; Wolseley, P.A., eds. Monitoring lichens. Dordrecht, 

The Netherlands: Springer: 1–4.

Peres-Neto, P.R.; Jackson, D.A. 2001. How well do multivariate data sets match? 

The advantages of a Procrustean superimposition approach over the Mantel test. 

Oecologia. 129: 169–178.

32

GENERAL TECHNICAL REPORT PNW-GTR-952
Peterson, A.T.; Soberon, J.; Pearson, R.G.; Anderson, R.P.; Martínez-Meyer, 

E.; Nakamura, M.; Araújo, M.B. 2011. Ecological niches and geographic 

distributions. Monographs in Population Biology MPB-49. Princeton, NJ: 

Princeton University Press. 328 p.

Pinheiro, J.C.; Bates, D.M. 2000. Mixed-effects models in S and S-PLUS. New 

York: Springer Science & Business Media. 528 p.

Pinheiro, J.C.; Bates, D.M.; DebRoy, S.; Sarkar, D. 2016. Package “nlme”: linear 

and nonlinear mixed effects models. http://CRAN.R-project.org/package=nlme 

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biodiversity. Ecology Letters. 7: 1–15.

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optimization algorithms. Plant Ecology. 216: 741–758.

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K.L. 2014. Monitoring air quality in the southeast Alaska network: linking 

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33

Lichen Communities as Climate Indicators in the U.S. Pacific States
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rapid method for landscape assessment of carbon storage and ecosystem function 

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34

GENERAL TECHNICAL REPORT PNW-GTR-952
Appendix: Species List

Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Ahtiana sphaerosporella 0.05 0.11 0.31 0.13 — — 0.18 0.21 — — — — —Alectoria imshaugii 0.10 0.22 0.37 — — — — 0.20 0.26 — 0.15 — —Alectoria sarmentosa 0.39 1.05 0.61 — — — — — 0.31 — 0.46 0.31 —Alectoria vancouverensis 0.02 0.04 — — — — — — — — — — —Bryocaulon pseudosatoanum 0.01 0.03 — — — — — — — — — — —Bryoria 0.05 0.12 0.36 — — — 0.16 — — — 0.32 — —Bryoria bicolor 0.05 0.13 0.40 — — — — — — — — 0.30 0.27Bryoria capillaris 0.17 0.44 0.48 — — — 0.22 — 0.32 — 0.26 — —Bryoria carlottae 0.00 0.01 — — — — — — — — — — —Bryoria cervinula 0.01 0.02 0.20 — — — — — — — — 0.20 —Bryoria fremontii 0.20 0.56 0.62 — — — 0.29 0.45 0.29 — — — —Bryoria friabilis 0.03 0.07 0.24 — — — 0.09 — — — 0.23 — —Bryoria fuscescens 0.17 0.44 0.42 — — — 0.22 — 0.26 — 0.23 — —Bryoria glabra 0.07 0.19 0.36 — — — — — — 0.17 0.22 0.26 —Bryoria implexa 0.01 0.01 — — — — — — — — — — —Bryoria lanestris 0.01 0.03 0.18 — — — — — — 0.18 — — —Bryoria nadvornikiana 0.00 0.01 — — — — — — — — — — —Bryoria pseudofuscescens 0.12 0.33 0.40 — — — — 0.24 0.22 0.22 — — —Bryoria simplicior 0.04 0.09 0.29 — — — 0.16 0.20 — 0.12 — — —Bryoria subcana 0.00 0.00 — — — — — — — — — — —Bryoria tenuis 0.04 0.10 0.34 — — — — — — — — 0.31 0.19Bryoria trichodes 0.14 0.36 0.49 — — — — — — — 0.17 0.32 0.33Bunodophoron melanocarpum 0.01 0.01 — — — — — — — — — — —Candelaria pacifica 0.27 0.75 0.70 0.32 0.39 0.53 — — — — — — —Cetraria 0.01 0.01 — — — — — — — — — — —Cetraria californica 0.00 0.00 — — — — — — — — — — —Cetraria canadensis 0.12 0.27 0.45 — — — — 0.22 0.26 0.30 — — —Cetraria chlorophylla 0.34 0.81 0.53 — — — — — 0.32 0.33 0.26 — —

35

Lichen Communities as Climate Indicators in the U.S. Pacific States
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Cetraria merrillii 0.25 0.64 0.55

—

—

—

— 0.49 0.25 0.20

—

—

—

Cetraria orbata 0.24 0.58 0.43

—

—

— 0.14

— 0.33

— 0.20

—

—

Cetraria pallidula 0.04 0.07 0.24

—

—

— 0.12

— 0.19

—

—

—

—

Cetraria pinastri 0.02 0.04 0.23

—

—

— 0.16

— 0.08 0.15

—

—

—

Cetraria platyphylla 0.25 0.66 0.55

—

—

— 0.17 0.32 0.41

—

—

—

—

Cetraria sepincola 0.01 0.04 0.30

—

—

—

—

—

— 0.30

—

—

—

Cetraria subalpina 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Cetrelia cetrarioides 0.02 0.04

—

—

—

—

—

—

—

—

—

—

—

Cladonia 0.03 0.07 0.21 0.11

—

—

—

—

—

— 0.16 0.10

—

Cladonia albonigra 0.02 0.04 0.24

— 0.09

—

—

—

—

— 0.09 0.20

—

Cladonia amaurocraea 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia arbuscula 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia bacillaris 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Cladonia bellidiflora 0.07 0.19 0.46

—

—

—

—

—

—

— 0.14 0.34 0.29

Cladonia borealis 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia carneola 0.04 0.09 0.27

—

—

—

—

—

—

— 0.23 0.15

—

Cladonia cenotea 0.02 0.03

—

—

—

—

—

—

—

—

—

—

—

Cladonia chlorophaea 0.03 0.07 0.22

—

—

—

—

—

—

— 0.09 0.17 0.12

Cladonia coniocraea 0.21 0.50 0.53

—

—

—

—

—

—

— 0.20 0.34 0.35

Cladonia cornuta 0.07 0.18 0.38

—

—

—

—

—

—

— 0.15 0.32 0.19

Cladonia deformis 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Cladonia fimbriata 0.08 0.17 0.31

—

—

—

—

—

— 0.17

— 0.23 0.14

Cladonia furcata 0.01 0.03

—

—

—

—

—

—

—

—

—

—

—

Cladonia macilenta 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Cladonia maxima 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia merochlorophaea 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia multiformis 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia norvegica 0.02 0.05

—

—

—

—

—

—

—

—

—

—

—

Cladonia pyxidata 0.01 0.03 0.24

—

—

—

—

—

—

— 0.13 0.20

—

36

GENERAL TECHNICAL REPORT PNW-GTR-952
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Cladonia rangiferina 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Cladonia scabriuscula 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia squamosa 0.13 0.32 0.57

—

—

—

—

—

—

— 0.22 0.39 0.35

Cladonia squamosa var. subsquamosa 0.05 0.12 0.30

—

—

—

—

—

—

— 0.11 0.14 0.24

Cladonia straminea 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Cladonia subfurcata 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia sulphurina 0.01 0.03 0.19

—

—

— 0.09

—

—

— 0.17

—

—

Cladonia transcendens 0.14 0.38 0.42

—

—

—

—

—

—

— 0.21 0.21 0.28

Cladonia umbricola 0.08 0.18 0.37

—

—

— 0.08

—

—

—

— 0.21 0.28

Cladonia uncialis 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Cladonia verruculosa 0.01 0.03

—

—

—

—

—

—

—

—

—

—

—

Collema 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Collema curtisporum 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Collema furfuraceum 0.05 0.12 0.37

— 0.20 0.30

—

—

—

—

—

—

—

Collema nigrescens 0.03 0.08 0.30

— 0.23 0.20

—

—

—

—

—

—

—

Dendriscocaulon intricatulum 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Dendrosticta wrightii 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Erioderma sorediatum 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Esslingeriana idahoensis 0.12 0.29 0.46

—

—

—

—

— 0.38 0.22 0.17

—

—

Evernia mesomorpha 0.01 0.01

—

—

—

—

—

—

—

—

—

—

—

Evernia prunastri 0.28 0.65 0.54 0.37 0.19 0.32

—

—

—

—

—

—

—

Flavoparmelia caperata 0.01 0.04 0.44

— 0.44

—

—

—

—

—

—

—

—

Flavopunctelia flaventior 0.06 0.17 0.59

— 0.43 0.41

—

—

—

—

—

—

—

Fuscopannaria 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Fuscopannaria ahlneri 0.01 0.02 0.18

—

—

—

—

—

— 0.17

—

— 0.08

Fuscopannaria alaskana 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Fuscopannaria confusa 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Fuscopannaria laceratula 0.01 0.02 0.17

—

—

—

—

—

—

—

— 0.14 0.10

Fuscopannaria leucostictoides 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

37

Lichen Communities as Climate Indicators in the U.S. Pacific States
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Fuscopannaria mediterranea 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Fuscopannaria pacifica 0.02 0.04

—

—

—

—

—

—

—

—

—

—

—

Heterodermia 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Heterodermia japonica 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Heterodermia leucomela 0.01 0.01

—

—

—

—

—

—

—

—

—

—

—

Heterodermia speciosa 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Hypogymnia 0.03 0.06

—

—

—

—

—

—

—

—

—

—

—

Hypogymnia apinnata 0.20 0.52 0.51

—

—

—

—

—

—

— 0.32 0.23 0.32

Hypogymnia austerodes 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Hypogymnia bitteri 0.02 0.06 0.35

—

—

— 0.07

—

— 0.34

—

—

—

Hypogymnia canadensis 0.02 0.03 0.18

—

—

—

—

—

— 0.12

— 0.10 0.08

Hypogymnia duplicata 0.09 0.25 0.54

—

—

—

—

—

—

— 0.20 0.35 0.35

Hypogymnia enteromorpha 0.26 0.67 0.68

—

—

—

—

—

—

— 0.40 0.42 0.37

Hypogymnia heterophylla 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Hypogymnia hultenii 0.14 0.34 0.56

—

—

—

—

—

—

— 0.22 0.32 0.40

Hypogymnia imshaugii 0.46 1.26 0.65

—

—

— 0.29 0.39 0.41

—

—

—

—

Hypogymnia inactiva 0.16 0.43 0.47 0.18

—

—

—

—

—

— 0.38 0.30

—

Hypogymnia lophyrea 0.05 0.11 0.36

— 0.06

—

—

—

—

—

— 0.16 0.32

Hypogymnia occidentalis 0.17 0.42 0.41

—

—

— 0.15

— 0.27 0.26

—

—

—

Hypogymnia oceanica 0.02 0.04 0.25

—

—

—

—

—

—

— 0.06 0.15 0.19

Hypogymnia physodes 0.27 0.70 0.50

—

—

—

—

—

— 0.33 0.28 0.24

—

Hypogymnia pulverata 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Hypogymnia rugosa 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Hypogymnia tubulosa 0.17 0.37 0.41 0.22

—

—

—

—

— 0.24 0.26

—

—

Hypogymnia vittata 0.04 0.08 0.31

—

—

—

—

—

—

— 0.11 0.23 0.19

Hypogymnia wilfiana 0.13 0.30 0.43

—

—

— 0.20

— 0.30

— 0.19

—

—

Hypotrachyna sinuosa 0.07 0.16 0.36

— 0.16

—

—

—

—

—

— 0.19 0.24

Imshaugia aleurites 0.01 0.02 0.23

—

—

—

—

—

— 0.23

—

—

—

Lathagrium fuscovirens 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

38

GENERAL TECHNICAL REPORT PNW-GTR-952
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Leptochidium albociliatum 0.01 0.01

—

—

—

—

—

—

—

—

—

—

—

Leptogidium contortum 0.03 0.06 0.30

—

—

—

—

—

—

— 0.07 0.11 0.29

Leptogidium dendriscum 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Leptogium 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Leptogium burnetiae 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Leptogium cyanescens 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Leptogium insigne 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Leptogium pseudofurfuraceum 0.01 0.04 0.25

—

— 0.25

—

—

—

—

—

—

—

Leptogium saturninum 0.02 0.05 0.20

—

—

— 0.12

—

— 0.14 0.09

—

—

Letharia columbiana 0.26 0.72 0.61

—

—

— 0.37 0.46

—

—

—

—

—

Letharia vulpina 0.43 1.24 0.72

—

—

— 0.31 0.49 0.39

—

—

—

—

Lobaria anomala 0.07 0.16 0.28 0.15

—

—

—

— 0.17 0.17

—

—

—

Lobaria anthraspis 0.06 0.16 0.27 0.15

—

—

—

— 0.16 0.16

—

—

—

Lobaria hallii 0.02 0.03

—

—

—

—

—

—

—

—

—

—

—

Lobaria linita 0.09 0.24 0.49

—

—

—

—

—

—

— 0.23 0.32 0.29

Lobaria oregana 0.10 0.25 0.53

—

—

—

—

—

—

— 0.19 0.34 0.36

Lobaria pulmonaria 0.10 0.24 0.33 0.16

—

—

—

—

— 0.25

— 0.15

—

Lobaria retigera 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Lobaria scrobiculata 0.05 0.12 0.29

—

—

— 0.10

—

— 0.23

— 0.12

—

Melanelia 0.05 0.11

—

—

—

—

—

—

—

—

—

—

—

Melanelixia californica 0.11 0.30 0.64

—

— 0.64

—

—

—

—

—

—

—

Melanelixia fuliginosa 0.06 0.15 0.33 0.28 0.14

—

—

—

—

—

—

—

—

Melanelixia subargentifera 0.02 0.05 0.42

—

— 0.42

—

—

—

—

—

—

—

Melanelixia subaurifera 0.03 0.05 0.23

— 0.23

—

—

—

—

—

—

—

—

Melanohalea elegantula 0.14 0.39 0.44 0.25

— 0.18

— 0.31

—

—

—

—

—

Melanohalea exasperatula 0.18 0.47 0.44

—

—

— 0.22

— 0.25 0.26

—

—

—

Melanohalea olivacea 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Melanohalea septentrionalis 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Melanohalea subelegantula 0.06 0.16 0.34

—

—

— 0.24 0.20 0.16

—

—

—

—

39

Lichen Communities as Climate Indicators in the U.S. Pacific States
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Melanohalea subolivacea 0.29 0.82 0.49

—

— 0.30 0.24 0.30

—

—

—

—

—

Melanohalea trabeculata 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Menegazzia subsimilis 0.04 0.10 0.33

— 0.14

—

—

—

—

—

— 0.12 0.26

Nephroma 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Nephroma arcticum 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Nephroma bellum 0.07 0.18 0.38

—

—

—

—

—

—

— 0.23 0.23 0.21

Nephroma helveticum 0.08 0.19 0.34

—

—

—

—

— 0.19

—

— 0.21 0.20

Nephroma isidiosum 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Nephroma laevigatum 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Nephroma occultum 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Nephroma parile 0.04 0.09 0.24

—

—

—

—

— 0.10 0.21 0.10

—

—

Nephroma resupinatum 0.04 0.10 0.25

—

—

—

—

— 0.17 0.13

— 0.13

—

Niebla cephalota 0.01 0.01

—

—

—

—

—

—

—

—

—

—

—

Nodobryoria 0.01 0.03

—

—

—

—

—

—

—

—

—

—

—

Nodobryoria abbreviata 0.28 0.79 0.69

—

—

— 0.30 0.49 0.35

—

—

—

—

Nodobryoria oregana 0.16 0.43 0.54

—

—

— 0.31

— 0.26

— 0.39

—

—

Pannaria 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Parmelia 0.05 0.11

—

—

—

—

—

—

—

—

—

—

—

Parmelia hygrophila 0.22 0.54 0.43

—

—

—

—

— 0.23

— 0.27 0.26

—

Parmelia pseudosulcata 0.06 0.13 0.40

—

—

—

—

—

—

— 0.17 0.35 0.18

Parmelia saxatilis 0.06 0.15 0.33

—

—

—

—

—

—

—

— 0.19 0.26

Parmelia squarrosa 0.03 0.06 0.27

—

—

—

—

—

— 0.14

—

— 0.24

Parmelia sulcata 0.42 1.09 0.51 0.28

—

—

—

— 0.29 0.32

—

—

—

Parmeliella parvula 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Parmeliella triptophylla 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Parmelina coleae 0.08 0.20 0.51

—

— 0.51

—

—

—

—

—

—

—

Parmeliopsis 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Parmeliopsis ambigua 0.14 0.34 0.45

—

—

— 0.20

— 0.27 0.29

—

—

—

Parmeliopsis hyperopta 0.25 0.64 0.62

—

—

— 0.22

—

—

— 0.49 0.33

—

40

GENERAL TECHNICAL REPORT PNW-GTR-952
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Parmotrema 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Parmotrema arnoldii 0.02 0.04 0.24 0.16 0.19

—

—

—

—

—

—

—

—

Parmotrema austrosinense 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Parmotrema perlatum 0.02 0.05 0.61

— 0.61

—

—

—

—

—

—

—

—

Peltigera 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Peltigera aphthosa 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Peltigera britannica 0.06 0.14 0.42

—

—

—

—

—

—

—

— 0.34 0.26

Peltigera collina 0.11 0.25 0.30 0.19

—

—

—

— 0.15

—

— 0.19

—

Peltigera degenii 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Peltigera elisabethae 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Peltigera horizontalis 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Peltigera leucophlebia 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Peltigera membranacea 0.03 0.07 0.28

—

—

—

—

—

—

— 0.07 0.11 0.25

Peltigera neopolydactyla 0.06 0.15 0.42

—

—

—

—

—

—

— 0.18 0.32 0.23

Peltigera pacifica 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Peltigera polydactylon 0.01 0.01 0.18

—

—

—

—

—

—

— 0.05 0.09 0.15

Peltigera praetextata 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Peltigera scabrosa 0.02 0.04 0.22

—

—

—

—

—

—

—

— 0.11 0.18

Phaeophyscia ciliata 0.01 0.02 0.23

—

— 0.23

—

—

—

—

—

—

—

Phaeophyscia hirsuta 0.01 0.03 0.34

— 0.30 0.21

—

—

—

—

—

—

—

Phaeophyscia kairamoi 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Phaeophyscia orbicularis 0.08 0.21 0.67

— 0.38 0.54

—

—

—

—

—

—

—

Physcia 0.02 0.05 0.22

—

— 0.22

—

—

—

—

—

—

—

Physcia adscendens 0.18 0.47 0.67 0.30 0.41 0.48

—

—

—

—

—

—

—

Physcia alnophila 0.11 0.28 0.40 0.24

— 0.31

—

— 0.17

—

—

—

—

Physcia biziana 0.07 0.19 0.56

—

— 0.56

—

—

—

—

—

—

—

Physcia caesia 0.01 0.01

—

—

—

—

—

—

—

—

—

—

—

Physcia dimidiata 0.04 0.11 0.29

— 0.28 0.16

—

—

— 0.13

—

—

—

Physcia dubia 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

41

Lichen Communities as Climate Indicators in the U.S. Pacific States
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Physcia leptalea 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Physcia stellaris 0.05 0.12 0.38 0.21

— 0.34

—

—

—

—

—

—

—

Physcia tenella 0.13 0.35 0.61 0.32 0.39 0.38

—

—

—

—

—

—

—

Physcia tribacia 0.02 0.04 0.33 0.33

—

—

—

—

—

—

—

—

—

Physciella melanchra 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Physconia 0.02 0.04

—

—

—

—

—

—

—

—

—

—

—

Physconia americana 0.11 0.28 0.49

—

— 0.49

—

—

—

—

—

—

—

Physconia enteroxantha 0.10 0.28 0.50 0.25

— 0.49

—

—

—

—

—

—

—

Physconia fallax 0.02 0.06 0.42

—

— 0.42

—

—

—

—

—

—

—

Physconia isidiigera 0.12 0.32 0.66

— 0.28 0.59

—

—

—

—

—

—

—

Physconia leucoleiptes 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Physconia perisidiosa 0.16 0.41 0.57

—

— 0.57

—

—

—

—

—

—

—

Platismatia glauca 0.51 1.40 0.67

—

—

—

—

— 0.32

— 0.46 0.41

—

Platismatia herrei 0.19 0.45 0.53

—

—

—

—

—

—

— 0.28 0.36 0.28

Platismatia lacunosa 0.08 0.19 0.48

—

—

—

—

—

—

—

— 0.33 0.35

Platismatia norvegica 0.15 0.39 0.61

—

—

—

—

—

—

— 0.22 0.40 0.40

Platismatia stenophylla 0.10 0.23 0.44

—

—

—

—

—

—

— 0.44

—

—

Polycauliona candelaria 0.06 0.14 0.27

—

— 0.19

— 0.13

— 0.15

—

—

—

Polycauliona polycarpa 0.16 0.43 0.61 0.31 0.34 0.40

—

—

—

—

—

—

—

Polycauliona tenax 0.02 0.05 0.46

— 0.46

—

—

—

—

—

—

—

—

Polychidium muscicola 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Protopannaria pezizoides 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Pseudocyphellaria crocata 0.03 0.06 0.22

—

—

—

—

—

— 0.17

— 0.11 0.09

Pseudocyphellaria mallota 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Pseudocyphellaria rainierensis 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Punctelia jeckeri 0.03 0.08 0.40

— 0.28 0.29

—

—

—

—

—

—

—

Ramalina 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Ramalina dilacerata 0.06 0.14 0.29 0.14

—

—

—

—

— 0.25 0.11

—

—

Ramalina farinacea 0.20 0.48 0.47 0.27 0.41

—

—

—

— 0.19

—

—

—

42

GENERAL TECHNICAL REPORT PNW-GTR-952
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Ramalina leptocarpha 0.02 0.04 0.60

— 0.60

—

—

—

—

—

—

—

—

Ramalina menziesii 0.01 0.03 0.42

— 0.42

—

—

—

—

—

—

—

—

Ramalina obtusata 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Ramalina pollinaria 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Ramalina roesleri 0.04 0.11 0.33

—

—

— 0.15

—

— 0.28

—

—

—

Ramalina subleptocarpha 0.03 0.08 0.44

— 0.41 0.25

—

—

—

—

—

—

—

Ramalina thrausta 0.02 0.04 0.19 0.08

—

—

—

—

— 0.19

—

—

—

Scytinium cellulosum 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Scytinium lichenoides 0.03 0.06 0.22 0.10

— 0.17

—

— 0.12

—

—

—

—

Scytinium palmatum 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Scytinium polycarpum 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Scytinium teretiusculum 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Sphaerophorus aggr.c 0.27 0.74 0.79

—

—

—

—

—

—

— 0.38 0.53 0.45

Sticta 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Sticta beauvoisii 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Sticta fuliginosa 0.03 0.06 0.25 0.12

—

—

—

—

—

—

— 0.15 0.16

Sticta limbata 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Sulcaria badia 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Teloschistes chrysophthalmus 0.01 0.01

—

—

—

—

—

—

—

—

—

—

—

Usnea 0.14 0.33 0.31 0.17

—

—

—

—

— 0.19

— 0.17

—

Usnea cavernosa 0.01 0.03

—

—

—

—

—

—

—

—

—

—

—

Usnea cornuta 0.04 0.10 0.46

— 0.46

—

—

—

—

—

—

—

—

Usnea cylindrica 0.00 0.00

—

—

—

—

—

—

—

—

—

—

—

Usnea filipendula 0.21 0.55 0.40

—

—

—

—

— 0.24

— 0.23 0.22

—

Usnea flavocardia 0.08 0.23 0.40 0.24

—

—

—

—

—

—

— 0.19 0.26

Usnea glabrata 0.05 0.12 0.32 0.24 0.21

—

—

—

—

—

—

—

—

Usnea glabrescens 0.00 0.01

—

—

—

—

—

—

—

—

—

—

—

Usnea hirta 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Usnea intermedia 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

43

Lichen Communities as Climate Indicators in the U.S. Pacific States
Summary of species from the Forestry Inventory and Analysis dataset: relative frequency, mean abundance, and significant indicator values (across all climate 

zones, and for single climate zones)—continuedSpecies

Relative 

frequencyb

Mean abundance

(0–4)

IndVala all 

zones

IndVala for single climate zones

1 2 3 4 5 6 7 8 9 10

Usnea lapponica 0.09 0.23 0.33 0.16 0.14

—

—

—

— 0.25

—

—

—

Usnea longissima 0.04 0.09 0.36

— 0.09

—

—

—

—

—

—

— 0.33

Usnea pacificana 0.02 0.05

—

—

—

—

—

—

—

—

—

—

—

Usnea scabrata 0.08 0.19 0.28

—

—

—

—

— 0.15 0.18

— 0.16

—

Usnea subfloridana 0.06 0.16 0.26 0.16

—

—

—

—

—

— 0.12 0.16

—

Usnea substerilis 0.02 0.04

—

—

—

—

—

—

—

—

—

—

—

Xanthomendoza fallax 0.06 0.18 0.45

— 0.17 0.41

—

—

—

—

—

—

—

Xanthomendoza fulva 0.11 0.29 0.42 0.22 0.14 0.34

—

—

—

—

—

—

—

Xanthomendoza galericulata 0.01 0.02

—

—

—

—

—

—

—

—

—

—

—

Xanthomendoza hasseana 0.11 0.29 0.41

— 0.27 0.28

—

— 0.18

—

—

—

—

Xanthomendoza oregana 0.13 0.36 0.55

— 0.51 0.33

—

—

—

—

—

—

—

Xanthoria 0.02 0.04

—

—

—

—

—

—

—

—

—

—

—

Xanthoria parietina 0.01 0.02 0.27

— 0.27

—

—

—

—

—

—

—

—

Note: Zone 1 = warm mesic lowlands, Zone 2 = warm dry subcontinental, Zone 3 = hot dry lowlands, Zone 4 = cold mesic subcontinental, Zone 5 = cold dry continental, Zone 6 = cold mesic continental, Zone 7 = cool dry continental, Zone 8 = cool moist subcontinental, Zone 9 = mild moist suboceanic, and Zone 10 = warm wet hypermaritime.a IndVal = (relative abundance in given climate zone or set of zones × relative frequency in given zone or set of zones) 0.5.b proportion of 1,118 plots in which the species occurred.c Sphaerophorus species were aggregated owing to species concept changes over the sampling period.

44

GENERAL TECHNICAL REPORT PNW-GTR-952


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