Lichens as Bioindicators
October 2003, p. 16-19 Printer-friendly version
Lichens as Bioindicators
Gregory L. Smith and Thomas R. Baker
Lichens, small and unobtrusive organisms, are
seldom noticed as they cling to rocks, trees, and
soil in fragile veils or crusts of splendid color and
ornate textures. Their beauty is captivating and
fascinating, and their purpose and origins are
thought-provoking. Lichens are actually two organisms, a fungus and photosynthetic
algae, sharing symbiosis. In a lichen, fungus provides shelter, protection, and water
to the photosynthetic partner who, in turn, provides nutrients to the fungus. The
photosynthetic partner in lichen can be composed of blue-green algae, yellow-green
algae, or cyanobacteria living in relative harmony with many different kinds of fungi.
Lichens are found across the world and are environmentally tolerant organisms,
living in a wide range of habitats, often thriving in the most extreme locations, such
as the arctic tundra. People use them for a great many purposes, including dyes and
medicines in Native American cultures (Brodo, Sharnoff, and Sharnoff 2001), teas
and foodstuffs, as well as fixatives in the manufacturing process of perfumes (USDA
While lichens are taxonomically classified based on the fungal component, they are
frequently categorized into four morphological (shape) types:
q Crustose—a crust-like lichen growing flat on the substrate
q Foliose—a leafy lichen
q Squamulose—similar to the crustose but often appears as a colony of small
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Lichens as Bioindicators
cups with lobes raised
q Fruticose—the most delicate and slow growing. Often appears like hairs
hanging from tree branches or tube-like structures.
Lichens make particularly good bioindicators. Bioindicators are living organisms that
reflect environmental health and quality. A common example of a bioindicator is the
use of canaries by early coal miners to detect suitable air quality underground. While
a caged canary showed signs of activity (e.g. singing), miners were assured that air
quality was relatively good, lacking deadly levels of methane. As the small birds were
much less tolerant of such noxious gases, a canary’s death was a miner’s sure sign to
immediately evacuate. Unlike canaries, lichens are present throughout the world, live
a very long time, and can retain certain environmental pollutants and heavy metals
(Purvis 2000). Such qualities make the use of lichens ideal for classroom science.
Unlike other protocols that use mechanical or chemical indicators with a single
sampling event, many bioindicators have the advantages of long-term monitors and
can therefore span temporal or spatial boundaries that would otherwise pose great
difficulty. Lichens are bioindicators of sulfur dioxide (S02), fluoride, and ammonia.
The effect of these pollutants may be observed on the distribution and diversity of
lichens in your area. As with many bioindicators, lichen damage patterns alone
cannot establish a totally causal link to atmospheric quality, but they can lead
students in a systemic approach to studying the world without great financial cost,
while simultaneously investigating general processes of science, atmospheric quality,
and lichen biology.
Students using lichens as bioindicators
Based on the Standards, we organized a two-week unit allowing students to
investigate their local air quality using lichen from oak trees in and around our town.
Using the collaborative PathFinder Science website (www. pathfinderscience.net/
so2), we were able to construct a unit allowing students to gather background
information, collect and analyze data, and summarily draw conclusions based on
The PathFinder Science website is a virtual community dedicated to learning about
the material world through science. This collaborative research group grew from an
attempt to fulfill the National Science Education Standards (NRC 1996), the
Benchmarks for Science Literacy (AAAS), and the Kansas Science Education
Standards’ vision for “Science for All Americans” and the process of “doing science.”
PathFinder Science includes many research areas that span numerous science and
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Lichens as Bioindicators
mathematics topics. Teachers and students from 21 countries are members of this
research-sharing community that now involves over 1,000 registered classrooms. In
order to become an active member of the community, your classroom must be
registered. There is no cost for registration, but you must fill out a simple online form
to be a part of the group, upload collaborative project data, or publish student
research work. For the lichen project, the website contains guidelines for data
gathering, data sharing, ideas for further research, and more.
Working in cooperative groups of two or three, we first asked students to measure
lichen coverage of trees on and near school property. Guidelines for data collection
q As a class, select a tree species that is readily available throughout the area.
Each cooperative team will need a single mature tree to sample. In order to
mark the tree for later identification, tie a string around the trunk 1.5 meters
above the ground. Include a cooperative team name and class period label
attached to the string.
q Using a USGS topographic map or Global Positioning Satellite Unit determine
the latitude and longitude of your samples and record on the data collection
table (see Figure 1). As an alternative, use trees that are within one kilometer
(0.6 mile) of your school and record the latitude and longitude of your school
site on the data collection table.
q Try to choose trees with alkaline bark, preferably ash, then elm or sycamore. If
need be, use trees with a more acidic bark, preferably oak, then beech or birch.
To test the acidity of the bark, scrape a small amount of bark from a sample
tree into distilled water and let it soak. After 24 hours, measure the pH.
A standardized grid from PathFinder Science (see Resources), printed on a
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transparency or an acetate sheet, is used to count lichen coverage. The grid is an
arrangement of 100 small hollow circles, placed in a 10-circle by 10-circle format.
Students hold the grid against the side of the tree and count the substrate or
organism present in each of the 100 small circles. For students in many regions, this
count usually results in a number for bare bark, moss, and one or two lichen
morphologies. This process was repeated four times for the tree, in each of the
cardinal directions as determined using a compass. Averaging the results from each
of the four sides provides an index for the tree that we called the Average Lichen
Coverage (ALC). For example, one student group’s ALC index included: 60 percent
bare bark, 15 percent crustose lichen, 10 percent moss, and 5 percent foliose lichen,
etc. On a slightly more pragmatic note, due to the multiple steps required in data
gathering, some students found this process initially difficult but, using cooperative
learning, stronger students were able to support others in their group so the
collection could be completed.
As the lichen “growing season” is typically during the winter months after the leaves
have fallen, this is the best time to measure coverage (or change in coverage). We
found that, when completing such outdoor activities during a cold time of the year,
students were very apt to stay on task so that they could return indoors as soon as
possible. Similarly, outdoor activities during the colder winter months minimize
allergy conflicts and run-ins with poison ivy and poison oak. The only safety issue
during outdoor data collection at school was crossing city streets to get to adjoining
school and community properties.
By itself, the ALC for one tree is fairly meaningless, as the protocol requires several
data points across a geographic region allowing for relative comparisons of data. In
short, the more trees that are sampled, the more accurate a depiction can be
constructed. After collecting the ALC data in class, students were then assigned to
collect the ALC for one tree at home, perferably from the same species of tree. The
following day in class, we had a very good distribution of ALC values from across the
town. To make better sense of their collected data, students then compared their
findings to other students and classes. As a way of organizing data, all students wrote
their home ALC values on a single transparency which was then viewed by the entire
class. In most cases, teachers guided students’ attention to the overhead and
everyone calculated averages (for example: bare bark, moss, crustose lichen, and
foliose lichen columns). By calculating an arithmetic mean, students had an easy way
to compare their data. Students were also directed to note the highest and lowest
value in each column. Inevitably, a student would report excessively high or low
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values for a column, leading to a barrage of questions and conjectures about the
variation in the data set.
As geography matters in this study, the location of sampling events is critical to
understanding the local air quality. Using maps freely available from the town’s travel
and tourism board, each student mapped sampling locations from all students in the
class, color coding data points that fell above the class average in blue and data points
that fell below the class average in red. (We’ve found that small blue or red stickers
worked quite well, allowing for relocation of sampling points as needed.) As the data
appeared on the map, the base data (such as streets, railroads, airports, power plants,
and factories) were correlated to the student data.
As students began looking for visual patterns in their data, a number of questions and
contentions began to arise. Often, when the map lacked the location of a facility,
students would draw it in, noting the potential effects it may have on data. As
automobile exhaust is a primary contributor to the atmospheric pollutants in
question, students also began wondering aloud whether all roads should be
considered with the same potential for atmospheric pollutions. Noting that nearby
highways had substantially more vehicles than the smaller county roads on the
opposite side of town, it was decided that all roads could not be treated equally in this
study. Students also concluded that the density of roads signs and stoplights might
also be a valuable consideration, as traffic would tend to concentrate at these points
throughout town. The more data that appeared on each student’s map, the more
questions each student seemed to vocalize. “What if” scenarios and new research
ideas abounded, most legitimately based on the data points students had mapped. To
further guide student use of the map as an analytical tool, we provided five major
themes in which questions and discussions were directed (Mitchell 1999):
1. Where are sampling events and areas of interest located? Based on our research
questions, what features and data are necessary to map?
2. Map the high and low values of data. Do the highest lichen values exist in a
particular part of town? Are the lowest values clustered or spread? Are the high and
low lichen coverage values close or far apart?
3. Where is the greatest density of sampling points (providing more accuracy). Where
is the least dense area? This is a good way to introduce uncertainty into the scientific
4. What regions or boundaries do the sampling points fall within? Determining
regions is a criteria-based process, typically tied to previous knowledge on the
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Lichens as Bioindicators
subject. In the case of lichens, we might ask where and what the bounding regions are
that contribute to the development of atmospheric pollutants. Does the town have an
urban core, industrial complex, or series of highways that could affect regional
atmospheric pollutants from cars or industrial processes? Are lichen coverages
typically higher or lower in regions deemed as agricultural or rural in nature?
5. What is nearby the sampling location? How far is the nearest airport or highway
from your sampling point? Are changes in lichen coverage apparent between your
point and another feature? Is a sampling point too close or too far away to provide
reliable data? Is proximity confounding your data or lending strength to it?
Together the tools of math and geography allow students to gain a good deal of
insight into the relative air quality patterns that exist in their town. The use of paper
mapping or Geographic Information Systems, and digital mapping systems that
easily allow the incorporation of field data, helped our students visualize the trend of
increasingly better air quality as one moves away from town—a realization that
otherwise would not have been possible (see the October 2000 issue of The Science
Teacher in the online archives for information on GIS). Furthermore, students can
readily incorporate the locations of suspected pollutants and, with the aid of the GIS,
determine whether lichen coverage is statistically less in suspected regions.
Because this was the first time we engaged students in a study of atmospheric quality,
it seemed best to work within a pre-existing, structured framework. The PathFinder
Science website allowed students to upload collected data and retrieve class-wide
data sets already formatted in a spreadsheet. Because the protocol establishes a
relative indicator of atmospheric air quality, it is important to have a large data set.
With the aid of the PathFinder Science network, we were able to compare student
results to other schools in the county and city, that had previously participated in the
study. This large, multi-school data set helped us to create a better context for our
data, essentially extending the meaning of the data our students collected by
providing a basis for comparison and analysis. This makes for an extremely rich
learning environment, encouraging students to ask and begin to answer reasonable
questions related to the geographic, mathematic, or scientific rationale for various
data trends, irregularities, or skeptical observations.
The use of the lichen study at the middle school level is developmentally and
scientifically appropriate. Many young students are idealistic and want to have a
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positive impact on their communities and to know how they are connected to the
world around them. Students at this age require explorations that challenge them to
hypothesize, collect data, manipulate that data, and make conclusions. Too often, pre-
established science labs do not address all of these matters. The lichen study
encourages students to conduct investigations, use various higher-level thinking
skills, improve active participation in group work, and apply conclusions to work
towards improvements in their community. Additionally, it may be beneficial to
spend more time examining plant growth before beginning the lichen study.
Understanding the classification of trees based on their bark will most certainly help
students collect usable data.
Ultimately, using lichens as bioindicators is a powerful classroom activity that
employs a cost-effective means for measuring local air quality, while simultaneously
teaching aspects of plant biology, air chemistry, weather, and science process.
Gregory L. Smith is a teacher at Wheatridge Middle School in Gardner, Kansas.
Thomas R. Baker is project coordinator at the Center for Research on Learning
and the Center for Science Education at the University of Kansas in Lawrence.
The authors wish to acknowledge contributions to the study of lichens as
bioindicators for the classroom from Barbara Tinker of The Global Lab Project and
Dr. Steven B. Case of the PathFinder Science Network.
PathFinder Science Network—www.pathfinderscience.net
American Association for the Advancement of Science (AAAS). 1993. Benchmarks for
science literacy. New York, N.Y.: Oxford University Press Baker, T.R. and S.B. Case.
2000. Let GIS be your guide. The Science Teacher 67(8), 24–26.
Brodo, I.M., S.D Sharnoff, and S.S. Sharnoff. 2001. Lichens of North America. New
Haven, Conn.: Yale University Press.
Mitchell, A. 1999. The ESRI guide to GIS analysis: Geographic patterns and
relationships. Redlands, Ca.: ESRI Press.
National Research Council (NRC). 1996. National Science Education Standards.
Washington, D.C.: National Academy Press.
National Research Council (NRC). 1999. How people learn: Brain, mind, experience,
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