Lichens As Bioindicators

Lichens as Bioindicators

October 2003, p. 16-19 Printer-friendly version

Feature

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

2002).

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

http://www.nsta.org/main/news/stories/science_scope.php?category_ID=87&news_story_ID=48645 (1 of 8)10/13/2003 7:21:07 AM


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

their data.

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



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

include:

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



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



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

research process.

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



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.

Collaborative research

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.

Conclusions

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



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.

Acknowledgement

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.

Resources

PathFinder Science Network—www.pathfinderscience.net

References

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,



and school. Washington, D.C.: National Academy Press.

PathFinder Science. 2002. Lichens, Tardigrades, and S02. Available online: www.

pathfinderscience.net/so2 [October 2002].

Purvis, W. 2000. Lichens. Washington, D.C.: Smithsonian Institute Press.

United States Department of Agriculture (USDA). 2002. The USDA Forest Service

Pacific Northwest and Alaska Regions Lichens and Air Quality Home Page.

Available: www.nacse.org/lichenair [October 2002].

Copyright © 2003 NSTA

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