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Lichens As Bioindicators



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  • 1. Lichens as Bioindicators October 2003, p. 16-19 Printer-friendly version Feature 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 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 (1 of 8)10/13/2003 7:21:07 AM
  • 2. 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. 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 (2 of 8)10/13/2003 7:21:07 AM
  • 3. 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 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 (3 of 8)10/13/2003 7:21:07 AM
  • 4. Lichens as Bioindicators 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 (4 of 8)10/13/2003 7:21:07 AM
  • 5. Lichens as Bioindicators 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 (5 of 8)10/13/2003 7:21:07 AM
  • 6. 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. 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 (6 of 8)10/13/2003 7:21:07 AM
  • 7. Lichens as Bioindicators 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— 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, (7 of 8)10/13/2003 7:21:07 AM
  • 8. Lichens as Bioindicators and school. Washington, D.C.: National Academy Press. PathFinder Science. 2002. Lichens, Tardigrades, and S02. Available online: www. [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: [October 2002]. Copyright © 2003 NSTA Back to Top copyright © 2003 NSTA (8 of 8)10/13/2003 7:21:07 AM