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Lichens as Indicators of Vehicle Pollution

Jeremy_header_smalldynamiclead

Research question 
How can we determine whether or not different lichen species can be used as indicators of traffic contamination in Forest Hills, New York?

Aim of project 
With even children beginning to use lichens as indicators of pollution (British Lichen Society, 2006), I believe it is extremely important that the procedure for calculating different lichen's indicator values becomes more and more accurate. This precision would allow for cheaper, simpler ways to monitor the stress humans put upon their environment and to pinpoint where the most help is needed to minimize pollution. As vehicle exhaust increases or decreases depending on our future choices, we will be able to determine its quantity in certain areas through indicative lichen species such as the ones explored in this experiment.

Punctelia rudecta

Punctelia rudecta


My work is based on the procedures of the Spanish researchers Jose-Manuel Cepeda and Jorge Garcia-Rowe, because their work on lichens had a low environmental impact and very clear results. My aim was to obtain even more results, using other lichens and a different indicative factor—vehicle pollution instead of pollution in general—and to write out a more detailed procedure than Cepeda and Garcia-Rowe. I expanded the experiment, for better results, by giving lichen abundance a value of 0.5, 1, 2, or 3 per tree instead of only "present." My data was tested for significance in two different ways with a two-sample T-test.

Introduction: Lichens as Pollution Monitors 
Lichens are not one entity but two, a symbiotic relationship between a fungus and algae. Photosynthesis and respiration only happen when the lichen absorbs water; the lichen is in a dormant state when dry. Lichens can absorb minerals and water straight through their thalli and can concentrate minerals even when there are only trace amounts in the air. If one of the entities begins to grow faster then the other entity, the lichen will fall apart (Brodo, 3-8). Because the danger of this happening is related to high mineral absorption, lichens grow very slowly.
Given these facts about lichens, and considering that they can be found in almost every part of the world, it is no wonder that they are used as indicators for various types of pollution. Some of the major pollutants that harm lichens are sulphur dioxide, sulphuric and nitric acids, fluorides, ozone, hydrocarbons, and metals such as copper, lead, and zinc (British Lichen Society, 2006).
For this experiment I used a survey technique (Cepeda, 2002) in order to determine the indicator value of specific lichen species in relation to vehicle pollution. Vehicle pollution harms lichens because airborne nitrogen compounds and phosphates make lichen substrates more alkaline, while lead and other emissions are poisonous to lichens (British Lichen Society, 2006).

Procedure, Part 1: Choosing Locations, Zones, Stations, and Trees 
Before choosing an area to survey, I determined the types and number of trees in that area. I identified trees using the book Knowing Your Trees. The area I chose for this experiment was a private residential area called Forest Gardens. I picked it because the area gets a lot of sunlight and is in a low-traffic zone, without any large roadways. I chose to survey the genus Aceraceae (maple) because a large amount of lichen grows on its deciduous bark. I only surveyed maple species with fissured and/or slightly scaly bark. Aceraceae platanoides (Norway maple) and Aceraceae saccharinum (silver maple) were the most prevalent of all the species I examined. I chose Aceraceae over lichen-covered bean trees and oak trees because the genus was more abundant and the trees were present in low-, medium-, and high-traffic zones.

Map of Forest Gardens

Map of Forest Gardens


The traffic zones were the independent variable, a measurement of the amount of vehicle pollution. I chose three zones for this experiment: Zone 3 near heavy traffic, Zone 2 near medium traffic, and Zone 1 near low traffic (see Diagram 1). Zone 3 was defined as an area less than 450 feet from major roadways with constant traffic. (The major roads were Union Turnpike and Jackie Robinson Parkway, with constant traffic and a minimum of four lanes.) Zone 2 was defined as an area 450 to 675 feet away from medium roadways and/or 675 feet to 900 feet away from major roadways. (The medium roadways were Yellowstone and Metropolitan avenues, both of which have two lanes, near-constant traffic, and a large number of traffic lights—almost one per intersection.) Zone 1 was defined as more than 900 feet away from medium roadways and not on a block of a minor roadway. (The minor roadways were Ascan and Continental avenues, which have yellow lines separating two-way traffic and some traffic lights.) Another way to identify minor, medium, and major roadways is to look at a map on Google Earth; at an altitude of 7,500 feet, major roadways show up as thick yellow lines, minor roadways show up as thin yellow lines, and all the rest of the roads show up as white lines.The traffic zones also coincide with wind patterns. The predominant wind pattern in New York City moves from south to north and west to east (more or less northeastern). I chose to survey traffic zones in which the wind generally blows from the major and medium roadways toward all three zones. Since the zones are all in a residential area, houses and buildings block some of the airflow carrying vehicle pollution from the major roadways from Zone 2 and most of the vehicle pollution from Zone 1. This controls the variable of vehicle pollution, keeping it low in Zone 1 and high in Zone 3.

Flavoparmelia caperata

Flavoparmelia caperata


In this experiment, a station was defined as a block less than 450 feet long. I chose six stations for each zone, moving down each street in the zone and counting the number of valid maple trees; I chose the blocks with the largest amount of valid maples. Valid maple trees were trees that received sunlight, had a 30- to 105-inch circumference, did not have vines growing on them, and had fissured and/or slightly scaly bark. In Zone 3 I picked stations with four valid maple trees that were either right on Union Turnpike, or between Union Turnpike and Metropolitan Avenue. Those stations were the closest to high vehicle pollution levels. For Zone 1, I picked alleyways with one-way traffic because of the lack of vehicles driving on them. The maple trees in each station also followed this pattern; in Zone 1, I chose maples in the center of the block over those near intersections, and vice versa for Zone 3.

Procedure, Part 2: Lichen Identification and Collecting Data 

JK_4_1

My first step was to take an initial survey of the lichen population in the area being studied. Lichens are prevalent on deciduous trees with fissured bark and an adequate amount of sunlight. Small samples of unknown lichens were collected for identification; rare lichens are not usually found near pollution, so no endangered species were taken. Even so, I did not sample the entirety of any species from any given tree. My samples were dried and kept in numbered envelopes with clear labels of where and when they were found, any chemical tests, and later, when identified, the species name. The samples were compared to the keys and then the plates of the book (Brodo, 117-750) with the help of a microscope and/or magnifying glass, but chemical tests were sometimes necessary or helpful for identification. Ten percent KOH and sodium hypochlorite were the two most helpful chemicals; commercial laundry bleach was used as sodium hypochlorite (Brodo, 103). Richard Harris, a lichenologist at the Bronx Botanical Gardens, then checked my identifications and made corrections.

JK_5

The lichens I identified for the experiment were  Physcia millegrana, Physcia Stellaris, Candelaria concolor, Flavoparmelia caperata, and Punctelia rudectaPhyscia millegrana was the most prevalent, on almost every tree in Forest Hills. Lichenologist Richard Harris called it "weed lichen." This was sadly true, as it covered or nudged most of the other lichens, making them harder to identify and spot. Physcia stellaris was the closest in resemblance to Physcia millegrana, both of which have a pale gray thallus, but it was easily identifiable in its lack of soredia and its larger, less broken-up body. None of the stations chosen had Physcia stellaris, or a big enough sample to be identifiable against the P. millegrana background.  Candelaria concolor was the second-most abundant; the lichen's bright yellow color is used to block harmful UV rays that might hurt the algae within. This lichen was helpful in controlling the variable of sunlight; the yellow color is brighter where there is more sunlight. Flavoparmelia caperata and Punctelia rudecta were both easily distinguishable from other lichens and easily identifiable.

I marked each station by the street it was on and the closest intersection. I measured tree circumferences and wrote up any specific comments for each tree. At each station I noted the amount of lichens on a single tree as 0, .5, 1, 2, or 3. I estimated these values by surveying and measuring the lichen abundance on many maple trees across all three zones. Then, using this data, I estimated which zone had a significantly larger number of lichens and which zone had a significantly lower number.

Results 

Chart 1 (Zone 1): Data collected from 6 stations near low traffic.

Chart 1 (Zone 1): Data collected from 6 stations near low traffic.


In order to obtain specific indication values for the different lichen species, I used the IndVal (short for indicator value) method (Dufrene and Legendre, 1997). This method compares a given species' abundance in one zone to its abundance in the other zones while also comparing the fidelity of that lichen species with in its own zone. Higher indicator values are found when a given species is abundant in one zone, is found in most, if not all, of the stations in that particular zone, and is scarce in the other zones.

JK_8

The formula for the IndVal is simple; using the data from Charts 1, 2, and 3, the mean number of lichen species in a zone is found by adding species values and dividing by the number of stations in that zone. The species values range from 0 to 3, and the number of stations was always six. This mean is divided by the sum of all the means of this species for all the zones. The answer will be the measurement of abundance. Fidelity is measured by dividing the number of stations with the species present in a particular zone by the total number of stations in that zone. Then, in order to get the IndVal, you multiply the abundance and fidelity values together, and then multiply by 100 to get the percentage.

JK_9

Although IndVal deals with differences between zones in the relative abundance part, the two-sample T-test is still helpful. The two-sample T-test can be used to discern the differences between lichen values, making sure that there are significant differences in values. This is useful because without significant differences in values from one zone to another, species may be an indicator for two or three zones. This nullifies the purpose of using them to differentiate one zone of vehicular traffic exhaust from another.
A value of p less than 0.05 sufficed as a sufficient difference between zones. There were three values of p for each lichen species: a comparison of Zone 1 to 2, Zone 2 to 3, and Zone 1 to 3. The two-sample T-test was calculated for significant differences between two samples.

JK_10

Analyzing the IndVal data for the four species, Physcia millegrana is the most constant, with the greatest IndVal difference between zones being 3%. Although it seems that there is a pattern of the percentage slowly declining from low vehicular traffic exhaust to high, this is probably coincidence. According to the two-sample T-test results, there are no significant differences between Physcia millegrana values between any two of the zones. Hence, Physcia millegrana is an indicator in all the zones, being widespread and abundant among them all, but cannot help differentiate between them. The only differences between zones was that in some stations of Zone 3 there was a lot of bleaching on the lichens, some turning white and others a khaki brown.

JK_11

Candelaria concolor is similar, as it doesn't have any p value for the two-sample T-test that shows a significant difference between any two zones. It's interesting to note that Cepeda's experiment (Cepeda, 2002) came up with an IndVal of 63.6% for the low pollution zone, while the other pollution zone IndVals were both below 25%. This is not a mistake, as Cepeda found no specimens of Candelaria concolor in any of his high pollution stations, while even in Station 1 and 2 of my high vehicular pollution zones (the stations right near Union Turnpike), there were many clusters of Candelaria concolor (more on this in my conclusion).

JK_12

Flavoparmelia caperata has a significant IndVal in Zone 1 only. The 54%, 19%, and 9% values show a dramatic decrease in the IndVal of this species as vehicular traffic exhaust is increased. There were obvious stresses on the Flavoparmelia caperata in Zone 3, Station 2, as the lichens were bleached and some were fully gray in color instead of green. Some of the lichens were dead and peeling off the bark. All this information shows that Flavoparmelia caperata is sensitive to vehicular exhaust. The sole significant p value for the two-sample T-test was between Zone 1 and Zone 3. This signifies that although there is a very significant difference between the two extremes of low and high vehicle exhaust on this species, the different lichen values in medium levels of vehicle exhaust and the two extremes are not significantly different from one another. Hence, Flavoparmelia caperata is an indicator of areas near low vehicular traffic, and distinguishes this area from areas with high vehicular traffic. Punctelia rudecta is the most prominent indicator species in this experiment. With the highest IndVal of all the species, Punctelia rudecta is an indicator for Zone 1. It does not differentiate Zone 1 from Zone 2, since it didn't show a significant value of p. According to the two-sample T-test, only Zone 1 is significantly different from Zone 3. But in this case, Punctelia rudecta shows a small but significant difference between Zones 2 and 3, with a p value of 0.0340. This does not mean that this species is significant in Zone 2, however, because it has an IndVal of more than 25%.

The two-sample T-test not only signifies differences in lichen species, but differences in overall zones as well. With no significant p values for any lichen species between Zones 1 and 2, it seems that Zones 1 and 2 were not significantly different from each other. Zones 2 and 3 only had one significant p-value difference; as such, it is questionable whether these zones were significantly different. Zones 1 and 3 had two very significant p values, meaning that Zones 1 and 3 were significantly different in the quality of air due to traffic exhaust.
 

Conclusion

My research question was: How can we determine whether or not different lichen species can be used as indicators of traffic contamination in Forest Hills, New York?

Depending on their location, lichens may or may not have the same indicator value. This conclusion is based on Cepeda's (Cepeda, 2002) and my own results for  Candelaria concolor. Cepeda attained a high indicator value for Candelaria concolor  only the low pollution zone, while I found that was one of the highest of all the lichen species surveyed. This may mean that the Candelaria concolor in Forest Hills have grown resistant to vehicle pollution, and the  Candelaria concolor  in Seville, Spain, have not. It also may be that in the high-pollution zones of Cepeda's experiment (he was testing air purity in general, not just vehicle pollution), something such as a facility that burns coal wiped out all the Candelaria concolor nearby. In this case, vehicle pollutants did not affect Candelaria concolor, but some other air impurity would. If not, and Candelaria concolor has grown a resistance not acquired in Seville, then IndVals will have to be set differently depending on the geographic location of the lichens and may change as a lichen species adapts.

The most difficult part of constructing experiments like these is dealing with controlled variables. Variables such as sunlight and tree types were kept at a constant, while other variables were either overlooked or turned out not to be very useful in the survey. For instance, it was very difficult to find enough maples with adequate bark, so tree circumference was overlooked, although it might have had a slight effect on lichen values. Also, midway through the experiment, I observed that a disease was affecting the maple trees. The symptoms were twigs, branches, and leaves falling off, and the leaves turning brownish black. Also, parts of some trees were rotting in the trunk area, and many trees had harmful inhabitants such as carpenter ants. This was probably a result of Kabatiella apocrypta , a fungus that causes these symptoms in many types of maples; it also weakens the tree, allowing more parasites to infect the tree. This did not affect the experiment, as the fungus did not seem to hurt the lichens but instead helped them. This is probably because it let more sunlight through as the leaves and branches of the maple died. All the trees in the experiment were affected by this blight.
One variable that should be changed in future experiments is the independent variable. Although Zones 1 and 3 were significantly different, Zone 2 didn't have dramatically different lichen values from the other zones and could be removed.

Taking everything into consideration, the procedure used in this experiment worked well in determining which lichens are indicative and which are not indicative of vehicle pollution. The most indicative lichen, Punctelia rudecta, has been described as "fairly tolerant of pollution" (Brodo, 608). Even with the similar zones set up in this experiment, Punctelia rudecta showed indicative values for one zone but not for the other two. Hence, Punctelia rudecta is a good indicator of minor changes in urban areas. This could be helpful in assessing the damage of newly built or expanded roadways by observing whether or not the population of Punctelia rudecta in the area begins to decline. It could also be helpful for assessing the effect of roadways that have been demolished, or the effect of changing cars to energy sources such as electricity, in which case the number of Punctelia rudecta should go up. Although it is not very sensitive to small pollution differences,  Flavoparmelia caperata may be more abundant in an area and could be used as an indicator of vehicular pollution levels. As human impact on the environment continues to take its toll, lichens such as these will be able to cheaply monitor where the most help is needed.

Bibliography

Brodo, Irwin, Sylvia Sharnoff, and Stephen Sharnoff.  Lichens of North America. New Haven and London: Yale University Press, 2001.

Cepeda, Jose-Manuel, and Jorge Garcia-Rowe. "Identification and quantification of bio-indicator values of lichens under urban conditions."  Bibliotheca Lichenologica  Band 82 (2002): 187-195.

Collingwood, G.H., and Warren Brush.  Knowing Your Trees. Washington, D.C.: The American Forestry Association, 1947.
Dufrêne, M., and P. Legendre. "Species assemblages and indicator species: The need for a flexible, asymmetrical approach."  Ecological Monographs  67 (1997): 345-366.
Google Earth . 2006. Google Earth Explore, Search and Discover. Retrieved from the World Wide Web on 29 September 2006. www.earth.google.com.
Harris, Richard. Personal interview. 6 May 2006.
MapQuest.com: Maps, Directions and More. 2006. MapQuest. Retrieved from the World Wide Web on 29 September 2006. www.mapquest.com.
Richardson, D.H.S., and Claire Dalby (illustrator).  Pollution Monitoring with Lichens. England: Richmond Publishing Company, 1992.
Smith, Clifford.  The British Lichen Society. 2006. Retrieved from the World Wide Web on 24 September 2006. www.thebls.org.uk.
Yang, Jen:  An Outline of Scientific Writing. Singapore: World Scientific Publishing Company, 1995.

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