Baseline Water Quality in Headwater Streams Near Geneva Marsh Preconstruction of Tire Burning Plant in Crawford County Pennsylvania Authors: Joseph Phelps, Ian Dempsey, Emma Fraser, Jared Balik and Prof. Casey Wilson

Stream Ecology Course Environmental Science Department

520 North Main Street Allegheny College

Meadville, PA 16335

ABSTRACT

This study was conducted in conjunction with Crawford Area Residents for the Environment (CARE) to assess the current quality of streams along Geneva Marsh in light of current land-use/land-cover (LULC) data. The goal was to provide CARE with credible baseline data that substantiates legal claims made against a proposed tire-to-energy electric generating facility due west of Geneva Marsh, which CARE deems a threat to ecosystems in the area. Watershed LULC data were assessed using Global Information Systems (GIS) technology and remote sensing. Seven streams were assessed for physical and chemical quality, as well as for diversity of benthic macroinvertebrate assemblages, specifically intolerant taxa. Results indicted urban land use in the area to be a significant factor contributing to decreased soluble reactive phosphorous (SRP). High agricultural land use was determined to be a cause of decreased sensitive (EPT) taxa abundance and overall decreased diversity of assemblages. Additionally, high water pH decreased EPT abundance and increased tolerant taxa abundance. We suggest Towpath, Shafer Run, and Williams Run watersheds to be monitored closely after construction of the tire plant. More studies should be conducted using different aquatic assemblages at different sites to give a clearer picture of water quality impacts.

INTRODUCTION

Anthropogenic activity can have measurable impacts on fluvial ecosystem functioning via

alteration to naturally-occurring abiotic (i.e., temperature, flow, nutrients, light, etc.) and biotic (i.e.,

macroinvertebrate and fish) dynamics. Excessive nutrient additions (e.g., nitrogen and phosphorous) to

lotic systems has frequently been associated with degraded stream and other aquatic habitats through

alteration of food web dynamics and other nutrient concentrations, such as dissolved oxygen (DO), in a

process called eutrophication (Ashton et al., 2014; Chambers et al., 2012; Figueroa-Nieves et al., 2006;

Robertson et al., 2006). By changing certain hydrologic aspects of a stream with varying types of land

cover (“natural”/undisturbed versus urban/agricultural) in varying proportions, land cover can have a

measurable impact on different natural flow regime components (Chang et al., 2011; Poff and

Zimmerman, 2010). Land use legacies will also play a major role in determining the state of streams and

whether their impacts are specific to that stream or translatable to other streams with similar regional

characteristics.

Increases in agricultural and developmental land cover (e.g., deforestation, pesticide runoff, etc.)

are associated with changes to channel geomorphology, increased temperature via loss of riparian buffers,

and overall loss of habitat and species diversity (Poff et al., 1997; Allan, 2004). For example, urbanization

can result in less diverse and more tolerant stream macroinvertebrate assemblages via increased sediment

transport, reduced stream bed sediment size and increased solutes (Roy et al., 2003). The natural flow

regime is important for maintaining a “dynamic equilibrium” of physical and biological components of

lotic systems, which increases buffering capacity and thus biological integrity by offering a diverse array

of habitat niches (Karr 1991; Poff et al., 1997; Poff and Zimmerman, 2010). Species extirpations and

disruptions between aquatic, riparian, and terrestrial species that have occurred as a consequence of

natural flow alteration have also proven to be socially (i.e., human health impacts) and economically

detrimental (Poff et al., 1997; Sweeney et al., 2004).

Nonpoint and point source pollution resulting in air and direct water contamination is the largest

current threat to aquatic ecosystem assemblages, namely in terms of species diversity and ecosystem

functioning (Chambers et al., 2012; Roy et al., 2003). Air pollution (e.g., smoke, gases, heavy metals,

VOCs, PAHs, etc.) can directly affect watersheds via deposition into streams while also posing a threat to

human health (Ziadat and Stood, 2014). Globally, degraded stream ecosystems are responding to

synergistic environmental and anthropogenic stressors. Various methods of comparison (i.e., acute

toxicity indices, index of biological integrity or IBIs, riparian, channel and environmental data, or RCE

habitat evaluations, etc.) between “healthy” and degraded lotic systems are being employed by non-profit

organizations and scientific communities alike to assess the biological integrity of streams threatened with

nonpoint and point source pollutants (Hope, 2012; USEPA 2012). Sensitive macroinvertebrates are the

first to respond to water quality or other types of ecosystem disturbance and are thus used to assess

marginal ecosystem changes resulting from various LULC impacts. The absence of sensitive groups, such

as “EPTs,” or mayfly (Ephemeroptera), stonefly (Plecoptera), and caddisfly (Trichoptera) taxa, can

indicate degraded waters, and an abundance of tolerant species (e.g., Chironomid midges (Order Diptera)

or tubifex worms (Family Tubificidae)) may therefore also indicate degraded waters. The condition of a

stream can therefore be determined by sampling macroinvertebrates. Considering the riparian zone

around a stream also helps to determine and mitigate possible non point-source pollution. Riparian zones

of good quality have a few meters thick of vegetation, usually grasses, shrubs and trees. The roots of the

vegetation not only hold the stream banks together to help prevent erosion, but they also filter runoff from

the land beyond the stream. Poor riparian zones allow sedimentation and runoff possibly containing

excess nutrients or other chemicals to run directly into the stream. Riparian zones tie directly to land

cover and land use surrounding a stream. The land cover determines the amount of runoff flowing into a

stream. For example, a field of row crops would have less water uptake by roots than forested land. Land

use also determines the amount of runoff (amount of impervious surfaces) as well as the types of

pollution (fertilizers, salts, contaminated water from natural resource extraction, sewage or other organic

matter). All of these factors are important to consider when collecting baseline data and determining the

health of a stream or watershed.

In Crawford County, Pennsylvania, construction for a tire-to-energy electric generating facility

was announced in December of 2007 by Crawford Renewable Energy (Myers, 2013). In response to

redevelopment of the former International Paper site, Crawford Area Residents for the Environment

(CARE), an organization formed with support of Keep Erie’s Environment Protected, has been concerned

about the development of the tire burning facility and has additionally requested Crawford Renewable

Energy to provide emissions and discharge data, testing protocols and results, and information on permit

violations regarding the ongoing construction of the plant (Myers, 2013). CARE not only objects to the

plant on the basis of air pollution but holds doubts about the validity of numbers submitted on the air plan

approval application now being reviewed by the Pennsylvania Department of Environmental Protection

(Spicer, 2014).

The French Creek Watershed is one of the most ecologically diverse and important stream

systems in the Eastern United States. It is home to 27 species of mussels and 89 species of fishes, 15 of

those being darters, which are sensitive to stream changes. Understanding biodiversity (Karr et al. 1991)

is a key factor in determining the flow regime and water quality/chemistry of not only the French Creek

Watershed but the Allegheny River, Ohio River and ultimately the Mississippi River which empties into

the Gulf of Mexico. This implies that the water quality of French Creek has a direct effect downstream

rivers and watersheds. Increased understanding into the importance of the French Creek watershed (as

well as stream systems nationwide) has sparked dozens of local, state, legislated and grassroots

conservation organizations which aim to improve current water quality and prevent it from degrading any

further (Poff and Zimmerman et al. 2013).

Baseline data is very important to have in any field of research, especially environmental

protection and conservation. Baseline data documents environmental conditions before significant change

occurs, so that change, usually due to anthropogenic sources, can be identified, measured and assessed.

Biomonitoring practices are used to detect the health of a stream at its current state.

Our study was conducted as a watershed assessment based on existing LULC practices to provide

baseline data of nearby streams before the construction of the tire burning facility. Our objectives were

(1) to provide current land use information on sub watersheds of French Creek near the plant that empty

into Geneva Marsh by sampling water quality as a result of current point and nonpoint source pollution;

(2) to give an accurate portrayal of physical (stream and riparian habitat assessment) and biological (i.e.,

existing macroinvertebrate taxa) characteristics of sites in order to supplement water quality data.

METHODS

i. Site Analysis

Seven, 1st order (headwater) streams were selected for this study and were found to be in close

proximity to the site of the proposed tire burning facility. Each watershed boundary was delineated using

topographical maps, and site boundaries were quantified in terms of area with Global Information

Systems ( ArcGIS) technology software ArcGIS 10.3. Streams entering Geneva Marsh through both the

north and south were selected for the purposes of collecting baseline data for both water and air pollution

from westerly winds. A Riparian, Channel, and Environmental (RCE) Inventory (Appendix 1) was used

to assess the physical and biological condition of small streams (2nd to 4th order). Land-use/land-cover

(LULC) analyses were conducted via remote sensing of aerial photographs and GIS technology to

Figure 1. Watershed delineation map indicating sampling sites along Geneva Marsh located in Western Pennsylvania. Left to right, Shafer Run, Game Lands Run, Towpath Run, and Kebert Run all enter Geneva Marsh from the north. Williams Run, Marsh’s End Run, and Rock Creek all enter Geneva Marsh from the south. Many of these sites were officially un-named and therefore given names for the purpose of this study.

delineate the percentage of each LULC type (Forest, Developed, Row Crops, Pasture/Hay, Wetlands, and

Waterbodies) within each watershed.

ii. Water Chemistry Water samples were collected from each stream to quantify baseline data sets for total stream

hardness (Hach Total Hardness Kit Model #HA71A); dissolved oxygen (DO) (Hach Dissolved Oxygen

Test Kit Model #OX-2P); pH (Fisher Accumet Model #10); conductivity (TDS); alkalinity using standard

titrations; and nitrogen using ultraviolet spectroscopy. Based on the optical density (OD) of the samples,

soluble reactive phosphorous (SRP) was measured via “molybdenum-blue” spectroscopy (Strickland and

Parsons, 1968).

iii. Macroinvertebrate Sampling Using D-frame kick net techniques, macroinvertebrates were collected in Spring of 2015 (in

addition to water samples) from Shafer Run and Game Lands on February 26; Towpath Run and Kebert

Run on March 12; and Williams Run, Rock Creek, and Marsh’s End on March 26. Specimens were

preserved in 70% ethanol solution, sorted and identified to Family under a dissection scope at Allegheny

College. An Index of Biological Integrity (IBI) was used to identify and calculate tolerant and intolerant

groups (Table 1). For the purpose of this study, tolerant groups consisted Chironomidae, or non-biting

midgles (Order Diptera) and Annelida (i.e., tubificid sludge worms). Our intolerant groups consisted of

Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa.

Metric Rating Criteria 5 3 1 0

Total taxa >20 11-20 ≤10 0 Mayfly taxa >3 2 - 3 1 0 Stonefly taxa >3 2 - 3 1 0 Caddisfly taxa >3 2 - 3 1 0 % EPT1 ≤50% 25-49% 10-24% <10% % Tolerants2 <10% 10-25% 25-35% >35%

iv. Statistical Analysis

All statistical analysis was completed using Statview 5.0.1 software. Simple linear regressions

were used to model relationships between chemistry variables, IBI scores, RCE scores, and watershed

land-cover. As a measure of model quality, a post-hoc regression analysis of variance (ANOVA) was

applied to each test. To standardize the data, land-cover values were expressed as percentages of their

total watershed area. Additional tables/graphs were created in Microsoft Excel 2010.

Table 1. Values used for macroinvertebrate Index of Biological Integrity (IBI) at each site. Note: 1 Does not include hydropsychid caddisflies; 2 only tolerant taxa used (i.e., red chironomid midges and annelids). Score were rated using the following scales: Good (30 – 24), Average (23 – 15), and Degraded (<15).

RESULTS

Consideration was given to LULC ratios when interpreting regression analyses, and thus serves as

a determinative data set regarding linear correlations recorded in this study. LULC findings (in hectares)

for each site from maps produced with GIS technology indicated a dominating trend of forest cover

among selected watersheds (Figure 2). Additionally, aerial photographs showed that most sites had more

agricultural (row crop and pasture/hay) land cover than developed land cover (Figure 2).

Among the seven sampling sites IBI indices showed 2 sites considered degraded, 4 were

considered average, and 1 was considered in good condition (Table 2). Towpath, which had the highest

percentage of agricultural and developed land cover and the lowest percentage of forest land cover,

exhibited the lowest benthic IBI score out of all sites (Table 2). This score was followed by Williams,

Shafer, and Game Lands, which also had either low total taxa collected or low sensitive (EPT) to tolerant

ratios (Table 2) coupled with considerable agricultural or urban LULC types (Figure 2).

0

10

20

30

40

50

60

70

80

90

100

Williams Towpath Shaffer Stream

Marsh's End

Game Lands

Kebert Run

Rock Creek

% L

and-

Use

/Lan

d-Co

ver

Forest

Developed

Row Crops

Pasture/Hay

Wetlands

Waterbodies

Figure 2. Percentages of land-use/land-cover (LULC), showing ratios of natural land cover (ha) and human activity per total area for each watershed site using remote sensing and GIS technology.

Stream Benthic IBI Score

Total Taxa

Mayfly Taxa

Stonefly Taxa

Caddis Taxa % EPT %

Tolerants Williams 14 8 1 2 3 77.42% 22.58% Rock Creek 22 13 3 2 3 94.86% 1.61%

Marsh's End 22 10 2 2 8 71.43% 16.33%

Towpath 8 8 0 3 1 25.86% 80.00% Shafer 15 13 2 2 3 44.44% 53.89% Game Lands 18 10 1 3 5 71.88% 25.00%

Kebert 26 16 4 5 4 81.36% 13.56%

The Riparian, Channel, and Environmental (RCE) Inventory indicated that among the seven

sampling sites, zero were deemed Excellent, 3 Very Good, 3 Good, and 1 Fair (Table 3). Sites that were

sampled earlier in the season were more difficult to evaluate due to high snowfall, but most sites had

quality riparian buffers and were not in close proximity to agricultural or intense urban development.

Stream RCE RCE RCE

Riparian 1 - 4 Channel 5 - 12 Total Williams 85 120 205 Rock Creek 60 140 200 Marsh's End 40 71 111 Towpath 95 130 225 Shafer 65 95 160 Game Lands 110 105 215 Kebert 50 130 180

Regression analyses indicated a significant decrease (p = 0.05 at α = 0.05) in the concentration of

soluble reactive phosphorous (SRP) with an increase in the percentage of LULC developed land (Figure

3). A biologically significant trend (p = 0.07) was found regarding the percentage of LULC row crops and

the percentage of total mayfly (Ephemeroptera), stonefly (Plecoptera), and caddisfly (Trichoptera) (EPT)

Table 2. Benthic IBI scores for each sampling site. Score were rated using the following scales: Good (30 – 24), Average (23 – 15), and Degraded (<15).

Table 3. Riparian, Channel, and Environmental (RCE) Inventory scores for each sampling site. Scores were rated using the following scales: Excellent (273-340), Very Good (204-242), Good (134-203), Fair (66-133), and Poor (15-65).

versus total macroinvertebrate taxa collected. Figure 4 shows that as the percentage of row crops

decreased among sampling sites, the percentage of EPTs collected increased.

A biologically significant trend (p = 0.3) was found regarding the percentage of row crops and

Index of Biological Integrity (IBI) scores. As the percentage of row crops decreased among sampling

sites, IBI scores increased (Figure 5). Additionally, as the percentage of row crops, the percentage of

tolerant taxa, such as Diptera (red chironomid midges) and Annelida (tubificid worms), increased (p =

0.55), indicating biological significance (Figure 6).

The effects of pH on aquatic assemblages in streams were found to be statistically significant in

this study. Regression analyses from Figure 7 indicated a significant negative correlation between pH and

the percentage of EPTs collected. Moreover, regression analyses from Figure 8 indicated a significant

positive correlation between pH and the percentage of tolerant macroinvertebrate taxa collected.

-5

0

5

10

15

20

25

30

35

40

45

% R

ow C

rops

.2 .3 .4 .5 .6 .7 .8 .9 1% EPT

Y = 43.573 - 40.887 * X; R^2 = .521

Regression Plot

468

101214161820222426

SR

P u

g/L

-2 0 2 4 6 8 10 12 14 16 18% Developed Land

Y = 24.79 - .927 * X; R^2 = .62

Regression Plot

Figure 3. Regression analysis indicating a (-) correlation (p = 0.0456) between % developed land and soluble reactive phosphorous (SRP). R2 = 0.620.

Figure 4. Regression analysis indicating biologically significant (-) correlation (p = 0.0671) between % row crops and % of Ephemeropta, Plecoptera, and Trichoptera (EPT) macroinvertebrates collected. R2 = 0.620.

Figure 5. Regression analysis indicating a biologically significant (-) correlation (p = 0.3146) between % row crops and Index of Biological Integrity (IBI) scores. R2 = 0.200.

Figure 6. Regression analysis indicating a biologically significant (+) correlation (p = 0.550) between % row crops and % tolerant macroinvertebrates collected. R2 = 0.554.

-5

0

5

10

15

20

25

30

35

40

45

% R

ow C

rops

6 8 10 12 14 16 18 20 22 24 26 28IBI Score

Y = 33.855 - .984 * X; R^2 = .2

Regression Plot

-5

0

5

10

15

20

25

30

35

40

45

% R

ow C

rops

0 .1 .2 .3 .4 .5 .6 .7 .8 .9% Tolerant

Y = 5.129 + 36.66 * X; R^2 = .554

Regression Plot

Figure 7. Regression analysis indicating a (-) correlation (p = 0.0225) between pH and % of Ephemeropta, Plecoptera, and Trichoptera (EPT) macroinvertebrates collected. R2 = 0.680.

8

8.2

8.4

8.6

8.8

9

9.2

9.4

pH

.2 .3 .4 .5 .6 .7 .8 .9 1% EPT

Y = 9.262 - 1.335 * X; R^2 = .68

Regression Plot

8

8.2

8.4

8.6

8.8

9

9.2

9.4

pH

.2 .3 .4 .5 .6 .7 .8 .9 1% EPT

Y = 9.262 - 1.335 * X; R^2 = .68

Regression Plot

Figure 8. Regression analysis indicating a (+) correlation (p = 0.0153) between pH and % tolerant macroinvertebrates collected. R2 = 0.723.

DISCUSSION

This water quality assessment was conducted to assist environmental protection groups in

opposition of a proposed tire-to-energy electric generating facility, and it has provided a suitable baseline

data set that can be expanded upon and refined in later studies of the potentially affected watersheds.

Although Towpath and Williams Run received the lowest benthic IBI scores (Table 2), they had some of

the highest RCE scores (Table 3), indicating the possibility of nonpoint source pollution from surrounding

development, especially agricultural LULC types. These two streams should be highly monitored after

construction of the power plant. A study conducted in Michigan (Roth et al., 1996) concluded that basin

land use was a prime determinant of stream water quality, indicating a negative correlation between

agricultural land use and IBI scores. Another stream with confounding scores was Marsh’s End, which

received the lowest RCE value (Table 3) but the highest benthic IBI score (Table 2) which could indicate

lesser impact of nonpoint source pollutants on benthic macroinvertebrates as a result of relatively low

agricultural land cover (Figure 2). This trend coincides with data collected from Shafer, which had the

second lowest RCE value (Table 3) and a marginally degraded benthic IBI score (Table 2) coupled with

relatively high agricultural and urban land cover (Figure 2). This stream should also be highly monitored

after construction of the power plant.

IBI and RCE scores were taken into consideration when interpreting regression analyses on a

regional scale (i.e., among all sampling sites), especially those related to agricultural/urban LULC values

and pH. A statistically significant negative correlation (p = 0.0456) was found between the concentration

of soluble reactive phosphorous (SRP) and the percentage of developed land within all watersheds (Figure

3). At first this trend appears to be confounding (high percentages of agricultural land cover also existed

in areas with development), but upon further analysis of LULC values it is possible that watersheds with

high percentages of developed land had altered the natural flow regime by changing minimum and

maximum flows in addition to increased flashiness from channelization and more likely, higher degrees

of impervious surfaces (Poff et al., 1997; Poff et a., 2006). During high flow events, these areas may have

deposited excess runoff into riparian buffer zones or may have swept sticky phosphorous downstream

after sediment loading events (Jones et al., 2001).

A biologically significant negative correlation (p = 0.0671) was found between the percentage of

sensitive (EPT) taxa collected and the percentage of row crops (Figure 4). There was also biological

significance found between decreased percentage of row crops with increased IBI scores (p = 0.3146) and

increased percentage of row crops with increased percentage of tolerant taxa collected (p = 0.550), all

three of which report the same trend (Figures 5 and 6). This correlates to numerous studies regarding

agricultural land cover and sensitive versus tolerant taxa ratios. Earlier analyses of sites throughout the

United States have found that streams draining agricultural runoff versus forest runoff are more likely to

result in degraded aquatic habitat and lower abundance of sensitive versus tolerant macroinvertebrates

(Omernik, 1976). The amount of agricultural land use in a given watershed is positively correlated with

the concentration of soluble nitrogen and phosphorous and thus eutrophication in streams (Allan, 2004;

Figueroa-Nieves, 2006; Chambers et al., 2012).

Significant results regarding pH were found in Figures 7 and 8, which indicated that a decreased

pH (on a scale of 8 to 9.4) resulted in an increased percentage of sensitive (EPT) taxa (p = 0.02) and that

an increased pH resulting in an increased percentage of tolerant taxa (p = 0.02). This indicates sensitive

taxa were more abundant in waters that were closer to a pH of 7, whereas tolerant were able to survive in

water with pH greater than 7. This alkaline system can be a result of naturally-occurring dolomite or

calcite deposits leaching into water from nearby soil erosion via agricultural land use and limestone from

urban development (Curtis et al., 1986; Keener and Sharpe, 2005). Further decreases in pH from the

development of the tire burning facility may require increasing acid neutralizing capacity (ANC) via

limestone remediation projects to protect Geneva Marsh and adjacent headwater stream assemblages from

diversity loss (Keener and Sharpe, 2005).

This study gives clearer insight into the current quality of Geneva Marsh, a rich 500+ acre

ecosystem that is extremely valuable to many breeding and migrating birds, and its surrounding

headwater streams (Gross and Korber, 2011). Further studies may focus on improving data collection

from this assessment by examining water quality and aquatic assemblages in other streams. Different

habitat evaluations, such as a Water Quality Habitat Network Assessment (WQHNA), and more extensive

testing for soluble levels of nitrogen, phosphorous, dissolved oxygen (DO), and total dissolved solids

(TDS) with higher sample sizes may prove beneficial. Fish should also be sampled and fish diversity

indices calculated to gain a better understanding and monitoring of long-term changes in these headwater

streams.

The goal is to gather as much data on the streams as possible but also to evaluable the current

condition of Geneva Marsh itself, which holds a great variety of different aquatic assemblages not

included in this study. It will be important to see the gradient of human impact as one travels from

headwaters the mouth of each stream and into the center axis of Geneva Marsh. To do this, different IBI

indices should be used, such as other sensitive taxa (Megaloptera, Molluska, etc.) and taxa used for

diversity counts (Coleoptera, Hemiptera, Odonata, Crustacea, etc.) year round. Overall, the Allegheny

College ES 344 stream quality assessment team encourages further testing to be conducted with the

synergistic effects of current land use in mind. Even streams with seemingly higher quality can quickly

become degraded when terrestrial land use is changing within the watershed and altering ground water

and surface run-off that can cause non-point source pollution in these headwater streams, Geneva Marsh

and the French Creek watershed.

Acknowledgements:

This project was conducted in Allegheny College's Stream Ecology course, and would not have been

possible without every student's hard work and participation throughout the entire semester. The authors listed were a part of this course and contributed to the final written report, however, every student in the course worked on this project and should be sincerely thanked. Additional students include: Corey R. Baumgardner, Ronald A. Borne, II , Naisy E. Flannery , Ryan L. Koerbel, Charles J. Miller, Josie R.

Niovich, Kirsten A. Oravec, Meghan E. Pierce, Julia M. Schock, Joseph W. Schultz, Katherine R. Snively and Kaitlin M. Walsh.

LITERATURE CITED

Allan, J. (2004, January 1). Influence of land use and landscape setting on the ecological status of rivers. Retrieved February 21, 2015, from http://www-personal.umich.edu/~dallan/pdfs/limnetica.pdf.

Ashton, M. J., Morgan, R. P., & Stranko, S. (2014). Relations between macroinvertebrates, nutrients, and

water quality criteria in wadeable streams of Maryland, USA. Environmental Monitoring and Assessment, 186(2), 1167-82.

Chambers, P. A., McGoldrick, D. J., Brua, R. B., Vis, C., Culp, J. M., & Benoy, G. A. (2012).

Development of environmental thresholds for nitrogen and phosphorus in streams. Journal of Environmental Quality, 41(1), 7-20.

Chang, F., Tsai, W., Wu, T., Chen, H., & Herricks, E. E. (2011). Identifying natural flow regimes using

fish communities. Journal of Hydrology (Amsterdam), 409(1-2), 328-336. Curtis, C. D., Coleman, M. L., & Love, L. G. (1986). Pore water evolution during sediment burial from

isotopic and mineral chemistry of calcite, dolomite and siderite concretions. Geochimica et Cosmochimica Acta, 50(10), 2321-2334.

Figueroa-Nieves, D., Royer, T. V., & David, M. B. (2006). Controls on chlorophyll-a in nutrient-rich

agricultural streams in Illinois, USA. Hydrobiologia, 568(1), 287-298. Gross, D. & Korber, K. (2011). Eagle-Watching in Pennsylvania Conneaut Marsh, Geneva Marsh, SGL

213, Crawford County. Pennsylvania Game Commission. Retrieved April 21, 2015 from http://www.portal.state.pa.us/portal/server.pt?open=514&objID=1728779&mode=2.

Hope, B. K. (2012). Using legacy data to relate biological condition to cumulative aquatic toxicity in the

Willamette river basin (Oregon, USA). Archives of Environmental Contamination and Toxicology, 62(3), 424-37.

Jones, K. B., Neale, A. C., Nash, M. S., Van Remortel, R. D., Wickham, J. D., Riitters, K. H., & O'Neill,

R. V. (2001). Predicting nutrient and sediment loadings to streams from landscape metrics: a multiple watershed study from the United States Mid-Atlantic Region. Landscape Ecology, 16(4), 301-312.

Karr, J. R. (1991). Biological integrity: a long-neglected aspect of water resource

management. Ecological Applications, 66-84. Keener, A., & Sharpe, W. (2005). The effects of doubling limestone sand applications in two acidic

southwestern Pennsylvania streams. Restoration Ecology, 13(1), 108-119. Myers, V. (2013, September 25). Settlement ends legal challenge to Crawford tires-to-energy plant. Erie

Times-News. Retrieved April 19, 2015 from http://www.goerie.com/settlement-ends-legal-challenge-to-crawford-tires-to-energy-plant#.

Omernik, J. M. (1976). The influence of land use on stream nutrient levels. Environmental Protection

Agency Ecological Research Series EPA- 600/3-76-014, U.S. Environmental Protection Agency, Environmental Research Laboratory, Corvallis, OR.

Poff, N. L., Allan, J. D., Bain, M. B., Karr, J. R., Prestegaard, K. L., Richter, B. D., Sparks, R.E., & Stromberg, J. C. (1997). The natural flow regime. BioScience, 769-784.

Poff, N. L., Bledsoe, B. P., & Cuhaciyan, C. O. (2006). Hydrologic variation with land use across the

contiguous United States: geomorphic and ecological consequences for stream ecosystems. Geomorphology, 79(3), 264-285.

Poff, N. L., & Zimmerman, J. K. (2010). Ecological responses to altered flow regimes: a literature review

to inform the science and management of environmental flows. Freshwater Biology, 55(1), 194-205.

Robertson, D., Graczyk, D., Garrison, P., Wang, L., LaLiberte, G., & Bannerman, R. (2006, January 1).

Nutrient Concentrations and Their Relations to the Biotic Integrity of Wadeable Streams in Wisconsin. Retrieved February 21, 2015, from http://pubs.usgs.gov/pp/pp1722/pdf/PP_1722.pdf.

Roth, N. R., J. D. David, and D. L. Erickson. 1996. Landscape influences on stream biotic integrity

assessed at multiple spatial scales. Landscape Ecology, 11, 141-156 Roy, A. H., Rosemond, A. D., Leigh, D. S., Paul, M. J., & Wallace, J. B. (2003). Habitat-specific

responses of stream insects to land cover disturbance: biological consequences and monitoring implications. Journal of the North American Benthological Society, 22(2), 292-307.

Spicer, M. (2014, September 4). Group looks to local law to fight proposed energy plant. The Meadville

Tribune. Retrieved April 19, 2015 from http://www.meadvilletribune.com/news/local_news/group-looks-to-local-law-to-fight-proposed-energy-plant/article_8a559cf8-d695-576c-927b-3e309baca02f.html.

Strickland, J. D. H., Parsons, T. R. (1968). Fisheries Research Board of Canada Bulletin 167, Queens

Printer, Ottawa. Sweeney, B. W., Bott, T. L., Jackson, J. K., Kaplan, L. A., Newbold, J. D., Standley, L. J., Hession, W.

C., & Horwitz, R. J. (2004). Riparian deforestation, stream narrowing, and loss of stream ecosystem services. Proceedings of the National Academy of Sciences of the United States of America, 101(39), 14132-14137.

United States Environmental Protection Agency. Chapter 3: Elements of Biomonitoring. (2012, March 6).

Retrieved April 19, 2015, from http://water.epa.gov/scitech/monitoring/rsl/bioassessment/ch03main.cfm#Section%3.2.2.

Ziadat, A. H. and Sood, E. (2014). An environmental impact assessment of the open burning of scrap

tires. Journal of Applied Sciences, 14(21): 2695-27.

Appendix 1: RCE Data Sheet Used for Habitat Evaluations.