Can the lichens Letharia vulpina and Usnea lapponica be used as reliable
and cost-effective bioindicators for nitrogen and sulfur deposition in the
Northern Rocky Mountains?
Student (MS): Jill Grenon
Montana State University
Department of Ecology 409A Lewis Hall
Bozeman, MT 59717
jillgrenon@yahoo.com
Project Leader: Dr. David Roberts
Montana State University
Department of Ecology Lewis Hall
Bozeman, MT 59717
dvrbtst@ecology.msu.montana.edu
Other cooperators:
John Shaw1, Sarah Jovan2, Linda Geiser3
Mark Fenn4, Terry Svalberg5
Project Duration: Fall 2009-Fall 2011
Projected Project Budget: $98,367
1
Forest Inventory and Analysis Program, RM Research Station, 801-625-5673, jdshaw@fs.fed.us
Forest Inventory and Analysis Program, PNW Research Station, 503-808-2070, sjovan@fs.fed.us.
3
USFS Ecologist, Pacific Northwest Region Air Program, lgeiser@fs.fed.us
4
Plant Pathologist, Pacific Southwest Research Station, mfenn@fs.fed.us
5
USFS Air Quality Specialist (R4), Bridger-Teton National Forest, tsvalberg@fs.fed.us
2
Project Summary/Abstract:
Deposition from air pollution can negatively alter the way ecosystems function. Nitrogen
deposition is increasing across much of the Western United States; sulfur deposition may
also be increasing in parts of the Northern Rocky Mountains due to development and
operation of oil and gas fields. The United States Forest Service (USFS) is federally
mandated to protect large tracts of public lands; in particular those designated as Class I
Wilderness Areas/airsheds. Knowledge about stresses, such as pollution deposition, on
ecosystems is vital for their protection, yet the amount and location of N and S deposition
across the N. Rockies is largely unknown. The majority of air pollution monitors
currently in use in the N. Rockies are expensive, widely spaced across the landscape, and
are sub-optimally located for accurate monitoring of class I Wilderness airsheds. Lichens
have been used successfully as bioindicators in other regions to gauge air pollution
deposition and ecosystem health. Lichens are appealing air quality monitoring tools
because they are cost effective, easy to use, and are widely spread throughout most
ecosystems: all of which allows for denser collecting (higher resolution) of actual data
directly in the area of interest/ concern. Because every region is different, the following
study is crucial to verify that lichens can be used successfully as indicators of air quality
in the N. Rockies. In this study, twelve plots will be created within or around the Bridger
Wilderness. At each plot two species of lichen tissue will be collected and analyzed for
N and S percent content and 12 Ion Exchange Resin (IER) monitors will be set up to
collect canopy throughfall N and S deposition (data will be collected three times over the
course of this study). Based on previous experiments, we expect to find a significant
positive correlation between N and S deposition extracted from IER monitors and percent
N and S content of lichen tissue. We will compare the percent tissue content of N and S
for both lichen species in the study area to known threshold values established for “clean”
areas and we will also compare the deposition data from the IER monitors to existing
deposition data in the area. Not only can this project validate the use of an important air
quality monitoring tool, but it will also collect valuable data needed to assess a current
pollution concern in a Class I airshed, and potentially serve as a gateway study that can
help answer future questions pertaining to ecosystem health and air pollution deposition
along the N. Rockies.
key words: Northern Rocky Mountains, Nitrogen and Sulfur deposition, Class I
airsheds, lichens, biomonitors, cost-effective, high resolution data.
Introduction:
Atmospheric nitrogen (N) deposition is increasing across the western United States (Fenn
et al. 2003b, NADP 2007). Despite national decreasing trends in sulfur (S) deposition,
increases may still occur on a local scale. Increases in atmospheric nitrogen and sulfur
deposition can negatively alter the way an ecosystem functions. The use of bioindicators
can help forecast early signs of ecosystem degradation and alteration (Baron 2006, Fenn
et al 2008). The appeal of bioindicators is their use as an economically feasible way to
maximize monitoring resolution (Jovan 2008). In forested landscapes, epiphytic
macrolichens are considered among of the most sensitive bioindicators of nitrogen and
sulfur increases (Blett et al. 2003, Fenn et al., 2003b, 2008, Geiser and Neitlich 2007,
Geiser 2004, Jovan 2008, Jovan and Carlberg 2007, van Herk et al. 2003). In addition to
indicating forest health, the disappearance of certain lichen species is of particular
concern because they play an integral role in nutrient cycling and food webs and are used
by a wide variety of species for habitat and nesting materials (McCune et al 2007).
The Bridger-Teton (B-T) National Forest is the second largest National Forest in the
United States (outside of Alaska); it is part of the Greater Yellowstone ecosystem,
embracing over 3.4 million acres of land in Western Wyoming. Designated Class I and
II Wilderness areas make up an impressive 1.2 million acres of the B-T National Forest.
The Clean Air Act requires the Forest Service to protect air quality related values
(AQRV’s) in Class I Wilderness Areas. For this study we are concerned with N and S
deposition in The Bridger Wilderness Class I airshed. In the last decade oil and gas
development has boomed in Western WY. Two major gas producing fields have been
developed in Sublette County; one of these fields lies within 20 miles of the Bridger
Wilderness Boundary. Nitrate (N03-) and sulfate (S042-) emissions have also increased in
Sublette County over the past decade. Preliminary data suggests that the Bridger
Wilderness is experiencing an increase in N and S deposition, but we need more
information to find out where the deposition “hotspots” are and to quantify how much
deposition is occurring.
No high resolution data is available for N and S deposition in and around the Bridger
Wilderness because the current monitoring sites are too widely spaced. Furthermore,
most of the data available is from lower elevation sites outside National Forest land
(NADP, IMPROVE, and Castnet). Higher elevations in the Rocky Mountains typically
have higher precipitation and therefore higher wet deposition (Baron et al 2000). It is
important to include data collected from high elevation sites when assessing overall
deposition. Three lakes in the Bridger Wilderness have been sampled since the early
80’s. These lakes show an increase in NO3- at the inlets, but the lakes are only
representative of two locations in the Bridger Wilderness.
Epiphytic macrolichens have potential to efficiently map air quality in and around the
Bridger Wilderness at a higher resolution and in a more cost-effective manner than has
previously been done. We propose to set up 12 temporary but replicable monitoring sites
in and around the Bridger Wilderness. Because arid and snow-dominated environments
(both characteristics of the Bridger Wilderness) often require more than one approach for
a more wholesome assessment (Fenn et al 2009) of air pollution deposition, we will set
up ion-exchange resin column (IER) monitors and collect lichen tissue from two target
species at each site for elemental analysis. The plots will be set up in suspected pollution
hotspots and at locations thought to be “clean”. In California and the Pacific Northwest
lichen elemental analysis data have shown a significant positive correlation with IER
monitor data (Fenn and others 2008; Jovan and Geiser, personal comm. 2009). But we
are not sure how strong the correlation will be in a harsh arid environment.
The ultimate goals of this project are to figure out whether the percent N content and
percent S content of L. vulpina and U. lapponica can be used to predict N and S
deposition in the N. Rockies and to collect data to assess at a finer spatial scale the
quantity and location of N and S deposition in and around the Bridger Wilderness, taking
into consideration different distances from the primary oil and gas drilling operations.
These goals will be achieved by: 1) Calculating the mean and standard deviation of
throughfall N and S deposition at each site for IER monitors and for percent content of
lichens(only at plots with replicates); 2) Analyzing if there is a significant correlation
between N and S deposition collected from IER monitors and N and S percent content of
lichen tissue through linear regressions; 3) Analyzing if there is a significant correlation
between the amount of N and S found at each site with the distance from the nearest
upwind pollution source; 4) compare values of N and S percent content found in L.
vulpina and U. lapponica tissue to pre-established N and S threshold content values of
these two species from known “clean sites” in order to gauge if elevated deposition is
occurring; and 5) Comparing the deposition mean and standard deviation from IER
monitors at each site to other monitoring deposition data in the area.
Literature Review:
An increase in N deposition from ammonia in the Western United States is well
documented (Fenn and others 2003a, 2008; Galloway and Cowling 2002; Grenon and
Story 2009; Howarth and others 2002; Ingersoll and others 2008; Lehmann and others
2007; NADP 2006). Less information is known about other forms of N deposition such
as N from nitrate (Fenn and others 2003a). In the Bridger Wilderness a statistically
significant increase trend in N deposition, not just from ammonium, but also from nitrate
has been found in certain lakes, at two high elevation bulk deposition sites, and at the
NADP site in Sublette County, WY (Grenon and others—in review). The Bridger
Wilderness itself only has three locations where N and S are measured consistently
(biweekly in the summer and monthly in the winter), all other monitoring sites are
located outside of FS land, which means we don’t really know the extent or pattern of N
and S deposition in the Bridger Wilderness. Additionally, in arid states like WY, dry
deposition is thought to be the largest component of total atmospheric N deposition, yet
minimal data of actual dry deposition amounts is available (Fenn and others 2003a).
Lichen tissue analysis has been used successfully to monitor airquality regionally in the
US and globally, but no extensive studies have taken place in the Northern Rocky
Mountains (Blett and others 2003; Bruteig 1993; Fenn and others 2008; Fenn and Geiser
2007, Geiser and Neitlich 2007; Geiser 2004; Jackson and others 1996; Jovan 2008,
2006, Jovan and Carlberg 2007; Jovan and McCune 2005). Letharia vulpina (L. vulpina)
tissue has been used successfully across the Western U.S. in dry areas to evaluate air
quality (Fenn and others 2008; Geiser 2004; Jovan and Carlberg 2007). L. vulpina tissue
that is considered “clean” has a calculated threshold value of 1.0 to 1.03% dry weight
(dw) N (Fenn and others 2008; Jovan and Carlberg 2007) and 0.08% dw S (The United
States Forest Service National Lichen & Air Quality Database and Clearinghouse
(http://gis.nacse.org/lichenair/index.php?page=cleansite). The United States Forest
Service National Lichen & Air Quality Database and Clearinghouse lumps all Usnea
species together in its “clean” threshold value estimates. For N the estimate is 0.75% dw
and for S 0.08%.
In previous studies, regressions between %N in L. vulpina and N deposition from
throughfall IER monitors have showed a significant correlation exists (Fenn and others
2008; Fenn and Geiser 2007; Jovan personal communication). IER monitors in this study
will help validate the use of lichen tissue data in future studies if such a correlation is
found. We are unsure of the correlation between IER monitors and L. vulpina or U.
lapponica in an extreme arid environment. Lichen tissue has shown differences in
chemical composition between dry summer seasons and wet winter seasons (Boonpragob
and Nash 1990; 1991), this is of no surprise since deposition of N, S, and other pollutants
is known to vary among seasons due impart to temperature, moisture, and emissions
changes (NADP 2006).
More detailed information about N and S deposition datasets available for the Northern
Rockies and more specifically in and around the Bridger Wilderness is attached in
Appendix I.
Approach:
The FIA has established plots on a grid (ie. every 1.7 miles in the Siuslaw NF)
throughout National Forest lands, these plots are known as “on-grid” plots. On-grid plots
are permanent plots that contain stakes and other markers which establish their existence.
For purposes beyond typical forest inventory, temporary plots may be created following
the design and methodology found in the FIA Field Methods Guide:
http://fia.fs.fed.us/library/field-guides-methods-proc/docs/2007/p3_40_sec10_10_2007.pdf (also see McCune 1997 for lichen specific protocols), these plots
are known as “off-grid” plots.
The specific study area and the need for abundant lichen tissue for analysis determined
the location of our plots, which did not coincide with established FIA on-grid plots.
For this project, twelve 0.378 hectare circle (114 ft radius) off-grid plots will be
established in the Bridger-Teton National Forest (near or in the Bridger Wilderness).
Plots will primarily be set up on north-facing slopes (due to limited presence of lichens
on other aspects-facing slopes). At each plot two different target lichen species will be
collected for tissue analysis following Geiser (2004) protocol. Replicate tissue samples
will be collected for each target lichen species every fourth plot. In the case where only
one species is present two samples of the present species will be collected. Lichen
samples will be prepared for lab analysis following Geiser (2004). Samples will be
analyzed by the University of Minnesota Research Analytical Lab for analysis using ICP-
AES methods to analyze N and S concentrations in parts per million and percent content
(University of Minnesota Research Analytical Lab: http://ral.cfans.umn.edu/index.htm).
At each of the twelve sites IER monitors will be set up, with 8 monitors under Spruce or
Doug fir trees to measure N and S throughfall deposition and four monitors in the open to
measure N and S bulk deposition. Forest Service mules will be used to haul monitoring
equipment into each site. IER monitor protocols for set up and collection following
Fenn and Geiser (2007) and Fenn and Poth (2004). Each IER monitor has a resin tube
attached to the bottom of the instrument. When precipitation (rain or melted snow) filter
through the resin tubes, N and S are captured and can be extracted in a lab to give
deposition amounts (Fenn and Poth 2004). Capped resin tubes will be set at each of the
sites as a control. The extracted N and S amounts from the controls will be used for
calibration purposes.
Lichen tissue will be collected and analyzed for N and S at four different times
throughout the project duration: 1) initial establishment of plot (early summer); 2) the fall
before snow season starts; 3) in the Spring—after melt off and; 4) and in the fall when
IER monitors are removed. The resin tubes on the IER monitors will be switched out
twice corresponding with lichen tissue collection in the Fall and Spring (there will be a
resin tube on each IER monitor at setup for a total of 3 resin tube samples per IER
monitor). This will give two rainy season samples and one snowy season sample.
Statistical Methods:
N from sampled lichens in our plots can be compared with established clean and dirty
sites reported by Fenn and others (2008). Clean and dirty sites were established with a
cross-walk of throughfall N and community composition (n=15). A linear regression on
535 sites was fitted with a smooth curve (kernel standard of 0.0883) to a histogram of the
distribution of N concentrations in all samples (n=535). This method assumes that
concentrations of N in lichens from clean sites follow a normal distribution. Established
“clean” thresholds of percent N and S in the two lichen species L. vulpina and U.
lapponica can be found on the The United States Forest Service National Lichen & Air
Quality Database and Clearinghouse
(http://gis.nacse.org/lichenair/index.php?page=cleansite).
The mean throughfall and bulk N and S deposition from IER monitors will be calculated
for each site for each of the three collection periods. The standard error will also be
calculated to show how reliable the mean is. The variance of this mean will also be
tracked through the three collection periods as will the variance in percent lichen tissue
content. The relationship between the mean throughfall for N deposition from IER
monitors and L. vlupina percent N content at each site will be analyzed with a linear
regression (see Figure 1 as an example) for each of the three measurement periods. The
same type of regression will also be performed using S instead of N. The same two
regressions will be performed using U. lapponica instead of L. vulpina. Correlations will
be calculated for all the regression relationships analyzed. We will not try to extrapolate
significance among seasons, but will report the different values for possible future
research.
N and S percent content found in L. vulpina and U. lapponica tissue will be compared to
pre-established N and S threshold content values of these two species from known “clean
sites” in order to gauge if elevated deposition is occurring. The N and S deposition
means from IER monitors will be compared to other monitoring deposition data in the
area.
Sample size estimate (are 12 plots enough?):
Due to budgetary and logistical constraints we are hoping to keep our sample size around
12 plots. Is this sample size large enough to accomplish our goals? We used the
regression equation from Fenn and others (Figure 1: 2008) to see how much noise from
the residuals would be allowed for twelve samples and still produce significant results
about the relationship between N deposition from of IER throughfall monitors and
percent content N of both lichen species. The S regression equation is a variation on the
N regression equation with a slope and intercept adjusted to S based on data from
(Grenon 2008; Grenon and others—in review; and The United States Forest Service
National Lichen & Air Quality Database and Clearinghouse
(http://gis.nacse.org/lichenair/index.php?page=cleansite).
The expected percent content of N for L. vulpina vs U. lapponica is close to a 1:1 ratio
based on lichen tissue analysis from the Carabou-Targhee National Forest (Grenon 2008)
and the same is true for percent S content according to The United States Forest Service
National Lichen & Air Quality Database and Clearinghouse
(http://gis.nacse.org/lichenair/index.php?page=cleansite). This similarity in percent
content of N and S may be due to similar growth forms (fruiticose) of the two lichen
species. This approximately equal ratio allows the N and S regression equations to be
used on both lichen species without manipulating the slope or intercept.
Equation a) represents a linear regression expected with N data and equation b)
represents the expected equation with S data. These equations were run with the
statistical package R to see how much noise from the residuals each regression would
allow with a sample size of twelve to still produce significant results.
a) ln= (8.28 * t ) - 5.16
b) ls= (37.5*t) – 1.5
Because the equation is run through a loop, “t” actually has two meanings . At the
beginning of the loop t= a range of expected IER throughfall deposition recordings
(based on the available literature). In this case the range is 1.5 to 5.5 kg-1ha-1yr-1 for N
and 1 to 4 kg-1ha-1yr-1 for S. These two ranges are liberal approximations so large
amounts of noise are not expected. At the end of the loop t= the amount of noise added
to the 12 samples. For the N equation, ln= the % content of N expected and for the S
equation, ls= the % content of S expected. The 8.28 and 37.5 are the slopes and the (5.16) and (-1.5) are the intercepts of the equation.
Figure 1—Nitrogen regression equation from Fenn and others (2008), where eleven plots
were set up with Throughfall IER monitors and lichen tissue was collected and analyzed
from L. vulpina. The relationship of N found in IER monitors (kg ha-1 yr-1) to percent N
content found in lichen tissue was found to be significant.
The following figures (2 and 3) plot the residuals and the amount of noise acceptable at
the α = 0.05 level from our samples with twelve plots. In both N and S the amount of
noise allowable is approximately twenty-five percent of the predicted deposition ranges.
Liberal deposition ranges and large percentages of allowable noise show that twelve plots
are enough to accomplish our statistical analysis (Figures 2 and 3).
0.15
0.00
0.05
0.10
res
0.20
0.25
Rejection Error
0.8
1.0
1.2
1.4
noise
Figure 2—Nitrogen percent content of lichen tissue (Letharia vulpina and Usnea
lapponica) regressed with N deposition (kg-1ha-1yr-1) from throughfall IER monitors. The
graph shows how much noise from the residuals is allowed with twelve samples to have a
rejection error of < 5%.
0.15
0.00
0.05
0.10
res
0.20
0.25
0.30
S deposition noise error
0.6
0.8
1.0
1.2
noise
Figure 3—Sulfur percent content of lichen tissue (Letharia vulpina and Usnea
lapponica) regressed with N deposition (kg-1ha-1yr-1) from throughfall IER monitors. The
graph shows how much noise from the residuals is allowed with twelve samples to have a
rejection error of < 5%.
Budget and student timetable:
The projected budget and timeline for this project are shown below. Two field seasons
and two and a half academic years are scheduled to complete this project. Timing of
some events may shift as the project is implemented.
Project Budget-overview*
item
Administration
Salary
Tuition
Travel
per diem
Transportation (cars and mules!)
Procurements
Field assistant
Lab assistant
Equipment
Elemental Analysis
Overhead
Year 1
Year 2
xx,xxx
0
xx,xxx
0
3,780
4,250
1,050
750
10,279
1,500
17,244
2,351
0
3,146
1,500
0
517.5
0
0
0
63,404
3,000
1,000
34,964
Other
Travel to 2 scientific conferences to present
findings
Publication costs
Total
Grand total*
98,367
Academic year 2009/2010: Complete year one of coursework for graduate program
including multivariate statistics needed for model building. Solidification of project
logistics and sampling design and assemble database of pre-existing FIA/FHM lichen
data and other literature on air pollution deposition in the N. Rockies.
Summer 2010: Establish plots and set up throughfall monitors in Bridger Wilderness (N
= 12) and collect lichen tissue for analysis at each site. Lichen tissue may be sampled at
additional sites if needed and budget allows.
Academic year 2010/2011: Clean lichen material and send to University of Minnesota
Analytical lab for tissue analysis of N and S. Send resin tubes to Mark Fenn’s lab for
extraction of N and S. Assemble pollution measurements and begin building statistical
model. Continue analysis and complete academic coursework.
Summer 2011: Gather passive monitoring equipment and re-sample lichen material for
N and S deposition. Clean second round of lichen material and send to University of
Minnesota Analytical lab for tissue analysis of N and S. Send second round of resin
tubes to Mark Fenn’s lab for extraction of N and S.
Fall 2011: Wait for result from lab, analyze data, finish writing thesis and defend thesis.
Publish thesis results in 2 or more peer-reviewed scientific articles.
Expected reports and publications:
1.Summary of air quality for study region including maps of pollution distribution
across forests, pollution hotspots, and discussion of ecological implications.
2.Publication on the relationship between IER monitors and lichen tissue analysis in
the N. Rockies with respect to N and S deposition.
3.Publication of N and S pollution deposition in Bridger Wilderness describing
pollutant distribution and potential ecological effects in relation to nearby oil and
gas drilling operations.
Restatement of Importance:
This project is a key opportunity to; 1) validate credibility of lichens as biomonitors in
the N. Rockies; 2) to establish a cost-effective method to gather high spatial resolution
data about pollution deposition in the N. Rockies; and 3), and to assess a current pollution
concern in a Class I airshed. This project is also a valuable gateway study that can help
answer future questions pertaining to ecosystem health and air pollution deposition along
the N. Rockies.
Student qualifications:
Humboldt State University, Botany degree (BS): 1999-2004
USFS Employee (Biological Technician-lichens/airquality):2003-2007
Western Airborne Contaminant Assessment Project (WACAP) participant 2005
“Coast to Crest” study field technician—set up IER monitors and collected lichen
tissue from the crest of the N. Cascades to the Pacific Ocean: 2006
USFS Employee (Air Quality Technician): 2007-present
Publications and Reports:
McCune B.; Grenon J.A.; Mutch L.S.; Martin E.P.; 2007. Lichens in relation to
management issues in the Sierra Nevada national parks. Pacific Northwest
Fungi 2, 1-39.
Grenon J.A. 2008. Biomonitoring airquality with lichens in the Jedediah Smith
Wilderness and the Winegar Hole Wilderness, Caribou-Targhee NF. Unpublished USFS
Report. Driggs, ID. pg 1-51.
Grenon J.A.; Story M. 2009. U.S. Forest Service Region 1 Lake Chemistry, NADP, and
IMPROVE air quality data analysis. Gen. Tech. Rep. RMRS-GTR-230WWW. Fort
Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research
Station. 42 p.
Grenon J.A.; Story M.; Svalberg T. in review. The Bridger-Teton National Forest
(Region 4) Lake and Bulk sampling Chemistry, NADP, and IMPROVE Air Quality Data
Analysis.
Air Quality Plans:
Story M.; Schlenker K.; Grenon J.A. 2008. Wilderness Air Quality Value (WAQV) Class
2 Monitoring Plan. Absaroka Beartooth Wilderness, Lee Metcalf Wilderness: Gallatin,
Custer, Beaverhead, Shoshone NF, Butte District BLM.
Story M.; Grenon J.A.; Botello A. 2008. Wilderness Air Quality Value (WAQV) Class 2
Monitoring Plan. Gospel Hump Wilderness Area, Nez Perce National Forest
Story M.; Dzomba T.; Grenon J.A. 2008. USFS R1 Wilderness Air Quality Monitoring
Plan
Story M.; Grenon J.A.; 2007. Wilderness Air Quality Value (WAQV) Class 2 Monitoring
Plan. Rattlesnake and Welcome Creek Wilderness Areas, Lolo National Forest
References:
Baron J.S. 2006. Hindcasting nitrogen deposition to determine an ecological critical
load. Ecological Application 16(2): 433-439.
Baron J.S.; Rueth H.M.; Wolfe A.M.; Nydick K.R.; Allstott E.J.; Minear J.T.; Moraska
B. 2000. Ecosystem responses to nitrogen deposition in the Colorado Front Range.
Ecosystems 3: 352-368.
Bevenger G. 2008. Shoshone National Forest. Comprehensive Evaluation Report,
Version 2.0: 1-104.
Blett T., L. Geiser, & E. Porter. 2003. Air pollution-related lichen monitoring in
National Parks, Forests, and Refuges: Guidelines for studies intended for regulatory and
management purposes. USDA National Park Service Air Resources Division and US
Fish & Wildlife Service Air Quality Branch, USDA Forest Service. NPS D2202.
Boonpragob K.; Nash T.H. 1990. Seasonal variation of elemental status in the lichen
Ramalina menziesii Tayl. from two sites in southern California: evidence for dry
deposition accumulation. Environmental and experimental Botany. 30(4): 425-428.
Boonpragob K.; Nash T.H. 1991. Physiological responses of the lichen Ramalina
menziesii Tayl. to the Los Angeles urban environment. Environmental and experimental
Botany. 31(2): 229-238.
Bridger-Teton National Forest Wind River Mountains Air Quality Monitoring Program
Methods Manual. 2002.
Bruteig I.E. 1993. The epiphytic lichen Hypogymnia physodes as a biomonitor of
atmospheric nitrogen and sulphur deposition in Norway. Environmental Monitoring and
Assessment 1-26.
Burns, D.A. 2003. Atmospheric nitrogen deposition in the Rocky Mountains of Colorado
and Southern Wyoming- A review and new analysis of past study results. Atmospheric
Environment 37: 921-932.
Campbell D.H. 2004. Atmospheric Deposition and Its Effects in the Intermountain West.
Session V. North American Regional Impacts Panel.
http://www.ceinfo.org/resources/Acidrain/53_Campbell.pdf
Fenn M.E.; Jovan S.; Yuan F.; Geiser L.; Meixner T.; Gimeno B.S. 2008. Empirical and
simulated critical loads for nitrogen deposition in California mixed conifer forests.
Environmental Pollution (155) 492-511.
Fenn M.E.; Geiser L.; Bachman R.; Blubaugh T.J.; Bytnerowicz A. 2007. Atmospheric
deposition inputs and effects on lichen chemistry and indicator species in the Columbia
River Gorge, USA. Environmental Pollution. 146: 77-91.
Fenn M.E. and Poth M.A. 2004. Monitoring nitrogen deposition in throughfall using ion
exchange resin columns: a field test in the San Bernardino Mountains. J. Environ. Qual.
33: 2007-2014.
Fenn M.E.; Haeuber R.; Tonnesen G.S.; Baron J.S.; Grossman-Clarke S.; Hope D.; Jaffe
D. A.; Copeland S.; Geiser L.; Rueth H.M.; Sickman J. O. 2003a. Nitrogen Emissions,
Deposition, and Monitoring in the Western United States. Bioscience. Vol. 53 (4): 391404.
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. 2003b. Ecological effects of
Nitrogen Deposition in the Western United States. Bioscience. Vol. 53 (4): 404-420.
FIA Field Methods Guide. 2007: http://fia.fs.fed.us/library/field-guides-methodsproc/docs/2007/p3_4-0_sec10_10_2007.pdf
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.
Galloway J.N.; Cowling E.B. 2002. Reactive nitrogen and the world: 200 years of
change. Ambio 31: 62-71.
Geiser, L. 2004. Manual for Monitoring Air Quality Using Lichens on National Forests
of the Pacific Northwest. USDA-Forest Service Pacific Northwest Region Technical
Paper, R6-NR-AQ-TP-1-04. 126 pp.
Grenon J.A. 2008. Biomonitoring airquality with lichens in the Jedediah Smith
Wilderness and the Winegar Hole Wilderness, Caribou-Targhee NF. Unpublished USFS
Report. Driggs, ID. pg 1-51.
Grenon J.A.; Story M. 2009. U.S. Forest Service Region 1 Lake Chemistry, NADP, and
IMPROVE air quality data analysis. Gen. Tech. Rep. RMRS-GTR-230WWW. Fort
Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research
Station. 42 p.
Grenon J.A.; Story M.; Svalberg T. in review. The Bridger-Teton National Forest
(Region 4) Lake and Bulk sampling Chemistry, NADP, and IMPROVE Air Quality Data
Analysis.
Howarth, R.W.; Boyer E.W.; Pabich W.J.; Galloway J.N. 2002. Nitrogen use in the
United States from 1961-2000 and potential future trends. Ambio 31: 88-96.
Jackson L.L., Geiser L., Blett T., Gries C., Haddow D. 1996. Biogeochemistry of Lichens
& Mosses in and near Mt. Zirkel Wilderness, Routt National Forest, Colorado: Influences
of coal-fired power plant emissions. Open-file Report, USGS.
Jovon S., 2008. Lichen bioindication of biodiversity, air quality, and climate: Baseline
results from monitoring in Washington, Oregon, and California. USDA-FS, PNW
Research Station. General Technical Report: PNW-GTR-737.
Jovon S. and Carlberg T. 2007. Nitrogen content of Letharia vulpina tissue from forests
of the Sierra Nevada, California: Geographic patterns and relationship to ammonia
estimates and climate. Environ Monit Assess. 129: 243-251.
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.; McCune B. 2005. Air-quality bioindication in the greater Central
Valley of California, with epiphytic macrolichen communities. Ecological
Applications. 15: 1712–1726.
IMPROVE (Interagency Monitoring or Protected Visual Environments):
http://vista.cira.colostate.edu/improve/.
Ingersoll G.P.; Mast M.A.; Campbell D.H.; Clow D.W.; Nanus L.; Turk J.T. 2008.
Trends in Snowpack Chemistry and Comparison to National Atmospheric Deposition
Program Results for the Rocky Mountains, US 1993-2004. Atmos Environ 42: 60986113.
Lehmann C.M.B.; Bowersox V.C.; Larson R.S.; Larson S.M. 2007. Monitoring Longterm Trends in Sulfate and Ammonium in US Precipitation: Results from the National
Atmospheric Deposition Program/National Trends Network. Water Air SoilPollut (2007)
7:59-66
McCune B.; Grenon J.A.; Mutch L.S.; Martin E.P.; 2007. Lichens in relation to
management issues in the Sierra Nevada national parks. Pacific Northwest
Fungi 2, 1-39.
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.
National Atmospheric Deposition Program 2006 annual summary. NADP Data Report
2007-01. Illinois State Water Survey. Champaign, IL. 2007.
National Atmospheric Deposition Program (NADP): http://nadp.sws.uiuc.edu.
National Atmospheric Deposition Program (NADP) Protocols.
http://nadp.sws.uiuc.edu/documentation/completeness.asp.
NPElement: A Database of Lichen Elemental Concentrations in the U.S. National Parks
http://www.nwhc.usgs.gov/our_research/np_element.jsp
The United States Forest Service National Lichen & Air Quality Database and
Clearinghouse (http://gis.nacse.org/lichenair/index.php?page=cleansite)
Svalberg T. and Porwoll T. 2008. Wind River Bulk Deposition Program
Bridger-Teton National Forest Summary of 2007 and 2008 Data.
van Dobben H.F. and ter Braak C.J.F. 1999. Ranking of epiphytic lichen sensitivity to air
pollution using survey data: a comparison of indicator scales. Lichenologist 31:27-39.
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: 347-359.
APPENDIX I:
Inventory overview of existing data:
Overall data about airquality from in and around the Bridger Wilderness includes 4 high
alpine lakes (in the Bridger Wilderness), 2 bulk sampling sites at two of the lakes, 2
NADP sites in Sublette County (5 NADP sites in the region). This data has been
analyzed with the Mann-Kendall, Kruskal-Wallis, and seasonal Mann-Kendall tests. In
addition there is existing analyzed data about N and S deposition in the snow pack along
the Rocky Mountains (Ingersoll 2008), limited data about diatoms in Colorado lakes,
limited lichen surveys and tissue analysis from Yellowstone NP, Grand Teton NP, Grand
Targhee NF, and The Bridger-Teton NF.
Lichen Data:
Yellowstone and Grand Teton NP have limited elemental analysis data of the two target
species used for this study. The elemental content of lichen tissue from these two parks
is expected to be clean since the location is away from urban and industrial centers.
Information is available at NPElement: A Database of Lichen Elemental Concentrations
in the U.S. National Parks http://www.nwhc.usgs.gov/our_research/np_element.jsp.
In the Pacific NW the Forest Service has a database that includes all analyzed species
tissue and “clean” background levels for N and S in lichen tissue have been established.
These calculations are based on the 97.5% quantile of 159 samples taken in R6. The
background levels for L. vulpina in R6 may be useful because the habitat and
environmental conditions where Letharia vulpina exists from R6 and the N. Rockies is
similar. For more details on background N levels see Jovon and Carlberg (2007).
Information can be downloaded from the The United States Forest Service National
Lichen & Air Quality Database and Clearinghouse
(http://gis.nacse.org/lichenair/index.php?page=cleansite).
Two different background levels of Usnea lapponica were calculated from two separate
studies areas; Medicine bow-Routt and White River National Forests (Jackson et.al 1996)
and Grand Teton National Park
(NPElement at: http://www.nwhc.usgs.gov/our_research/np_element.jsp). Background
levels were calculated using 97.5% quantiles of the cleanest sites.
Lary St Clair, a professor at Brigham Young University has sampled lichens and
analyzed tissue analysis in the Bridger Wilderness and the Jedediah Smith Wilderness
(2004 and 2000 respectively). Grenon (2008) revisted St Clair lichen plots in the
Jedediah Smith Wilderness (5 sites-3 revists 2-newly established plots) and has
reanalyzed lichen tissue and resampled lichen community.
Lake data
The Lake Monitoring Program on the B-T National Forest began in 1984. The four
Lakes currently monitored were chosen based on elevation (all are above 2900 meters),
depth (deep), size (large), and their low buffering capacities. Lake data for the B-T
National Forest can be downloaded from the USFS website for chemistry of lakes,
streams, and bulk deposition on and near the National Forests, (USFS NRIS-Air
database) http://www.fs.fed.us/waterdata/. The four lakes analyzed in the B-T were:
Black Joe, Hobbs, Deep, and Upper Frozen. Each lake was analyzed for trends (ueq/L
unless stated otherwise) in field and lab specific conductance (μS/cm) (Fcond and Lcond
respectively), ANC (acid neutralizing capacity), Ca2+, Cl- , K+, Mg2+, Na+, NH4+, NO3-,
field pH, lab pH, and SO42-. Each lake was tested for trends in chemistry from the lake’s
inlet, outlet, hypolimnion, and epilimnion. Analysis used Mann-Kendall, Kruskal-Wallis,
and Seasonal Mann-Kendall tests. (Grenon and others, in review).
Bulk sampling
The bulk deposition program in the Bridger Wilderness was established at two sites in
1985, near Hobbs and Black Joe Lakes to gather additional information about deposition
in and around the lakes. Both lakes are part of the long-term lake monitoring program
mentioned above and their location can be seen in appendix I. The purpose is to
determine chemical deposition (both wet and dry) of particles in the air, being washed
out with precipitation, both rain and snow. The sites are sampled about every 2 weeks in
the summer, and 4 weeks in the winter. Sampling and data methods can be found in the
Bridger-Teton National Forest Wind River Mountains Airquality Monitoring Program
Methods Manual (2002). Data was analyzed with the Mann-Kendall test (Grenon and
others, in review).
NADP:
The National Atmospheric Deposition Program (NADP) data can be downloaded from
the NADP website: http://nadp.sws.uiuc.edu. Five sites have been analyzed for trends in
annual concentrations (ueq/L) and deposition (Kg/ha) and for trends among each season.
IMPROVE:
The IMPROVE program was established in 1985 as a tool to help monitor and protect
visibility in Class I airsheds (a stipulation from the 1977 amendments to the Clean Air Act).
IMPROVE monitoring is important for to discover and establish existing visibility and
aerosol conditions around Class I airsheds, to identify the man-made chemical components
which are impairing visibility, to provide regional haze monitoring in compliances with the
Regional Haze Rule, and to record long-term trends to assess progress towards national
visibility goals. Data can be downloaded and more information can be found at:
(http://vista.cira.colostate.edu/improve/).
There are two IMPROVE sites in WY that have sufficient period of record to run statistical
analysis: YELL1&2 in Yellowstone National Park and BRID1 (located near the boundary of
the Bridger Wilderness ten miles east of Pinedale, WY). Both sites were started in 1988.
Air Quality Data available in the N. Rockies outside of WY:
MT
Available and analyzed air quality data for National Forests in R1 (MT) can be reviewed
in th U.S. Forest Service Region 1 Lake Chemistry, NADP, and IMPROVE air quality
data analysis (Grenon 2009).