Aquat. Sci. 67 (2005) 403-423 1015-1621/05/040403-21 DOI 10.1007/s00027-005-0792-3 �9 Eawag, Dtibendorf, 2005

I Aquatic Sciences

Research Artic le

Macroinvertebrate response to land cover, habitat, and water chemistry in a mining-impacted river ecosystem: A GIS watershed analysis Dale A. Bruns

College of Science and Engineering, GeoEnvironmental Sciences and Engineering Department, Pennsylvania GIS Consortium, Wilkes University, Wilkes-Barre, PA 18766, USA

Received: 14 December 2004; revised manuscript accepted: 30 May 2005

Abstract. This study addressed potential land use impacts to macroinvertebrate communities and water quality from past coal mining activities in the watershed of the North Branch of the Susquehanna River (located in northeastern Pennsylvania and southern New York). Landscape tools of GIS and remote sensing (RS) were used to calculate percent land cover (forest, agriculture, barren, urban, and water) from SPOT imagery (for tributaries) and Multi-Resolution Land Characteristics (MRLC) data (for fiver sites) on 17 subcatchments in the study area. The study design included field sampling at reach locations at four first- and second-order sites with low urban and low barren (mining) land cover, four similar sized sites with high mining and high barren land cover, five sites with intermediate combinations of urban and barren, and four mainstem river sites (60 % forest and 35 % agriculture). Sites were sampled in early fall for macroinvertebrates (17 parameters, e.g., EPT rich- ness, percent filterers), benthic substrates (including deposits from mine waters), and 10 water chemistry pa- rameters. A principal component analysis (PCA) on the macroinvertebrate parameters provided plot-clustering of

subcatchments based generally on the above study design groupings; river sites clustered closer to smaller streams with low mining and urban land cover. Correlations iden- tified six macroinvertebrate parameters (e. g., EPT rich- ness, collector-gatherers) best associated with the three major axes of the PCA; each of these six indicators was analyzed in step-wise multiple regressions as dependent variables against land cover, benthic substrate, and water chemistry parameters. The strongest regressions were for percent barren land cover that explained the greatest amount of variation in both EPT richness and taxa rich- ness. This mining affect was confirmed with dissolved iron and sulfate concentrations and levels of sedimentation and iron deposition explaining variability across several mac- roinvertebrate parameters. Comparison to the published lit- erature on mining impacts indicated advantages to using a GIS watershed approach in multivariate analyses of stream ecosystem response. Also, this appears to be the first GIS watershed assessment of mining land use affects since most published studies of land use impacts to watersheds and lotic ecosystems have focused on either agriculture or urbanization.

Key words. Macroinvertebrates; water chemistry; watershed; GIS; mining.

* Corresponding author e-mail: dbruns@wilkes.edu Published Online First: November 21, 2005

404 D.A. Bruns Mining-impacted river: A GIS watershed analysis

Introduction

There has been strong scientific consensus that chang- ing land use is the single most important component of global environmental change affecting ecological sys- tems (Vitousek, 1994; National Research Council, 1993; Meyer and Turner, 1994). Estimates (Dr. Roger Hooke cited in Monastersky, 1994) indicate that people move roughly 40 billion tons of soil and rock each year as part of landscape impacts worldwide that exceed any single geomorphic agent such as water, wind, or ice. Although assessment of ecological change in land use and land cover at landscape and watershed scales of resolution has been challenging, geographic information systems (GIS) and remote sensing (RS) have emerged as critical tools to this broad-scale approach in environmental monitor- ing, assessment, and management (Wessman, 1992; Vitousek, 1994; Richards and Host, 1994; Richards et al., 1996; Wiersma and Bruns, 1996; O'Neill et al., 1997; Jones et al., 1997; Johnson and Gage, 1997).

Land use changes also are considered the dominant stressor on freshwater ecosystems (Carpenter et al., 1992) with the greatest impacts being associated with watershed modification and use, and contamination of aquatic re- sources by humans. Land use change is threatening water quality world-wide and as Vitousek (1994) has indicated, "any dichotomy between pristine ecosystems and hu- man-altered areas that may have existed in the past have vanished, and ecological research should account for this reality." More recent reviews (Gergel et al., 2002; Allan, 2004) re-enforce this perspective and highlight the use of landscape and watershed indicators to assess human impacts on stream and fiver ecosystems. However, Allan (2004) has identified several difficulties, including multi- ple scale-dependent mechanisms, in delineating "pathways of influence" based on empirical analyses of land use and stream response. A number of investigators have examined scaling factors that usually include variables encompass- ing stream reaches, stream corridors (riparian buffer), and whole catchments with variable findings from study to study (e.g., Sponseller et al., 2001; Wang et al., 2001; Gove et al., 2001).

Another problem in studies that relate land use to stream response based a GIS watershed analysis is the co-variation in both land cover classes and intermediate habitat factors (Van Sickle, 2003; King et al., 2005) such as benthic substrates and woody debris. These factors may not vary independently and bi-variate correlation analyses of land use, nutrients, habitat parameters, and macroin- vertebrate community response may yield numerous sta- tistical findings that overestimate the importance of these relationships or result in spurious associations. As a more appropriate statistical approach, Van Sickle (2003) recom- mends multiple regression when one wishes to analyze each stream response variable separately relative to land

use and other intermediate habitat factors (e. g., Lammert and Allan, 1999). Other relevant multivariate methods (Van Sickle, 2003) may include principal component analysis (e. g., Richards and Host, 1994; Richards et al., 1996), mul- tidimensional scaling (Tong, 2001), and correspondence analysis (Turner et al., 2004). Recently, King et al. (2005) employed partial correlation analysis in a similar fashion to deal in part with spatial autocorrelations of stream and watershed variables.

Watershed analyses linking land cover to the ecologi- cal response of streams and rivers have covered a range of applications based on the use of GIS and remote sens- ing data. These have included investigations of agricul- tural watersheds (Richards et al., 1996; Lammert and Al- lan, 1999; Fitzpatrick et al., 2001; King et al., 2005) and catchments affected by urbanization (Wang et al., 2000; 2001). The Mid-Atlantic Regional Landscape studies of Jones et al. (1997; 2001a,b) and Wickham et al. (1999) cover a five-state area plus portions of three other adja- cent states; these U. S. Environmental Protection Agency (EPA) watershed assessments in this region have encom- passed a range of anthropogenic impacts from agricultur- al nutrient loading, urbanization, roads, loss of riparian zones, stream impoundments, and forest fragmentation, and have identified "quality" watersheds with the highest amounts of forest and riparian cover. However, none of these watershed studies cited above, nor any cited in the recent review of Allan (2004), indicate a particular focus on evaluating and assessing mining impacts to stream and river ecosystems and their catchments.

Land use impacts associated with past and ongoing coal mining activities in the anthracite fields of northeast- ern Pennsylvania (PA) have resulted in severe ecological degradation of both the terrestrial landscape and aquatic ecosystems in this region (Stranahan, 1993). For example, Growitz et al. (1985) have estimated that through 1944 about 3.5 billion tons of coal were mined over a 150 year period in this Eastern Anthracite Field situated solely on the North Branch of the Susquehanna River. More recent- ly, federal agency testimony at Congressional hearings in 2000 have indicated that the costs of mining reclamation in the Eastern Anthracite Field alone have approached $2 billion and would take 200-300 years at current rates of state and federal funding (Bruns et al., 2001). In 1994, a newly formed land conservancy group (Earth Conserv- ancy) had just purchased 6,478 ha of abandoned mining lands in this area of the anthracite fields near Wilkes- Barre, PA (EDAW, 1996). Because the previous company owning these abandoned mining lands had declared bank- ruptcy, these lands were locked out of any focused and managed reclamation efforts; these highly impacted lands were held in the bankruptcy court for 19 years, a national record. The descriptive influence of land use on water quality for the Susquehanna River is indicated generally in environmental monitoring survey reports that provide

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descriptive aspects of the combined affects of agriculture, mining, industry, and urbanization (Edwards, 1994; 1996; Bollinger 1994; Bollinger and Sitlinger, 1996).

In this context, the objective of the present study was to address ecological land use impacts to river basin wa- ter quality and biotic communities from mostly past and some ongoing coal mining activities in the upper portion of the watershed of the North Branch of the Susquehanna River. Given the spatial extent of these landscape per- turbations, a GIS-RS approach was employed toward sampling design and analysis (Bruns et al., 1997a; Bruns and Wiersma, 2004) based on the coupling of terrestrial- aquatic ecosystems (Minshall et al., 1985; Likens, 1985; Cummins, 1992; Leopold, 1994) and a GIS watershed perspective toward analysis of land use affects on the response of stream and river ecosystems (Richards and Host, 1994; Richards et al., 1996; O'Neill et al., 1997; Johnson and Gage, 1997; Gergel et al., 2002; Van Sickle, 2003; Allan, 2004; King, 2005). A critical goal was to identify potential relationships on land use, geomorphic (substrate habitat), and chemical parameters that may affect the macroinvertebrate community response on a subcatchment and watershed basis. This approach may help to facilitate ranking and prioritization of environ- mental impacts (Bruns et al., 1997a,b) and accelerate reclamation activities in the region (Brnns et al., 2001; Bruns and Sweet, 2004a). In addition, if successful, this GIS watershed approach could provide tools for environ- mental managers to monitor stream and river water qual- ity conditions and biotic community response for regions with significant problems of mining (e. g., Herlihy et al., 1990; Bruns and Wiersma, 2004).

Study area The Susquehanna River drains the largest basin on the Atlantic coast of the U.S. and is the sixteenth largest river in the U.S. (Edwards, 1994); over half of the freshwater inflow to the Chesapeake Bay is from the Susquehanna River. There are almost 4 million people living in the drainage basin (U.S. Bureau of the Census, 1991) yet only about 9 % of the basin is in urban land use while over 63 % is forested (Ott et al., 1991). This study fo- cused on the North Branch of the Susquehanna River in the southwestern portion of the Wyoming Valley near Wilkes-Barre, PA in the Northern Anthracite Field (Fig. 1). This study area encompasses 6,478 ha of abandoned mining lands (see above) and is now part of the Upper Susquehanna-Lackawanna American Heritage River (US-L AHR). President Clinton designated heritage river status to 14 river systems in 1998 from a field of over 120 national applications (Bruns and Wiersma, 2004; Brnns and Sweet, 2004a,b).

The AHR watershed has been heavily impacted over the last 150 years not only from coal mining in the

Northern Anthracite Field but also from previous urban development including more than 200 combined sewer overflows (CSOs) that unload human sewage mixed with stormwater into the mainstem river during storm events. Also, subsurface mining has lead to an underground mine pool (Growitz et al., 1985; Wood, 1996) under the Wyoming Valley urban corridor; boreholes on selected tributaries bring these underground "mine waters" to the ground surface where they combine with stream flows or other mine seeps from surface mining waste piles (culm). Although the cities of Scranton, Wilkes-Barre, and Hazleton are recognized as a Metropolitan Statisti- cal Area, with combined populations around 150,000, the other 188 townships, municipalities, and boroughs in the watershed make up the remaining population of 350,000. These live in small towns across 10 counties with the usual problems of economic stagnation in rural areas but strongly exacerbated by the negative legacy of coal mining.

M e t h o d s

Satellite imagery A GIS (ArcView, Environmental Systems Research Institute) was used to input, store, retrieve, manipulate, and analyze collected spatial information regarding watershed conditions relative to land cover, geomorphic (substrate), chemical, and macroinvertebrate parameters of water quality (see below). For land use and land cover, a SPOT satellite image was purchased for the Summer of 1994, concurrent with field sampling for benthic sub- strates, water chemistry, and macroinvertebrates. The SPOT imagery was selected over other potential data sets (e. g., existing or newer data obtained from Landsat) for land cover in order to obtain better spatial scale resolu- tion of multi-spectral imagery (20m pixel size vs. 30m for Landsat) for the smaller subcatchments and tributary watersheds more typical of the study area.

A full description of the classification methods and analysis of field accuracy assessments with GPS has been reported in Bruns and Yang (2002) for this SPOT image. These investigators used a supervised classification of SPOT imagery based on an Anderson Level I (five cover classes: forest, agriculture, urban, barren, and water) scheme (Anderson et al., 1976). The Seed Pixel Method in ERDAS IMAGINE was employed to "grow" single seed pixels that were representative of training samples for each of the five cover classes (ERDAS, 1991). Thus, spectral signatures were created from known training samples that could then be identified with identified polygons of a particular class. Parametric decision rules of "minimum distance" were used to separate features in the classification procedure. This was an iterative proc- ess whereby homogeneous pixels were converted from

406 D.A. Bruns Mining-impacted river: A GIS watershed analysis

individual raster pixels to a polygon until the entire study area was completed. Code phase GPS (Trimble Pro-XL) was used to geo-reference the SPOT image (e.g., at bridge crossings, intersections, etc.,) and align classified features with other environmental data bases on the GIS. However, because of the nature of SPOT data, it was not necessary to rectify the image, resample pixels, or trans- form data.

The forest land cover class encompassed extensive stands of hardwoods (Quercus spp.) and moderate but patchy distributions of conifers (pines and hemlocks, Pinus spp. and Tsuga) along with mixed forest habitats. The agriculture class represented a number of land cover categories based on the Anderson classification system (Anderson et al., 1976). For example, open pastures and rangelands were common in the tributary watersheds; for the mainstem fiver sites, and associated drainage basin, both row crops and pastures-rangelands were common, along with some orchards. Forest cover was an important class for almost all study sites, including subcatchments with mining while the agriculture cover class was highest for the four fiver sites (see Results section below). For small, first and second-order (reference) stream sites out- side the mining areas, the barren cover class (at low per- cent coverage) reflected naturally exposed bedrock along mountain ridgetops within the subcatchments; similar sized streams but selected from within known mining areas (see design criteria below) had subcatchments with similar bedrock exposure along ridgetops separating drainages, but these were dominated by extensive and visually apparent land disturbances, vegetation removal, and topographic perturbations. The urban cover class for this particular study area, and historical mining region, included some older business corridors and mostly older residential neighborhoods in small towns, boroughs, and parts of the city of Wilkes-Barre (population near 43,000); generally, there was an absence of heavy or intensive industrial areas or recent suburban expansions or sprawl, the latter being more typical of adjacent moun- tain watersheds and communities just outside the mining region, not included in this study.

For the field accuracy assessments of the SPOT im- agery (see details in Bruns and Yang, 2002), reference sites for checking individual pixels within polygons of land cover classes were randomly selected in ERDAS IMAGINE (ERDAS, 1991). An initial stratified random sample size (n) of 50 reference sites was targeted for each of the five cover classes.

GPS (code phase) was used in the field to locate indi- vidual pixels used for the accuracy assessment. In some cases, it was difficult (in mountain areas) to visit a pixel physically but a nearby substitute was used following practical guidelines from the literature (Congalton and Green, 1999). Also, the signature for water was suf- ficiently accurate so that not all 50 pixels were actually

visited. In addition, because urban areas were intermin- gled across agriculture, forest, and barren categories, these sites were over represented due to field logistics (106 reference field sites) while those for agriculture (27 reference sites) and barren (36 reference sites) categories were under represented. After these logistical adjust- ments were made, 239 pixels across all five cover classes were visited in the field and logged with GPS for later ac- curacy analyses based on published methods (Congalton, 1991; Congalton and Green, 1993, 1999; Congalton et al., 1983). Overall accuracy of the processed SPOT im- age was 82 % which is comparable to other related stud- ies of fairly high accuracy (see review in Bruns andYang, 2002). Water (100%) and forest (94%) had the highest producer (Congalton and Green, 1999, terminology) ac- curacy for the SPOT imagery (Bruns and Yang, 2002) while agriculture had both the lowest producer and low- est user accuracy at 78 and 54 %, respectively. Producer's accuracy for urban (74 %) and user's accuracy for barren (73 %) were intermediate; remaining accuracy values were good and ranged from 82 to 98 %.

Obtaining SPOT coverage for the whole upper Susquehanna River watershed above the four mainstem river sampling sites (Fig. 1) was cost prohibitive due to the extensive watershed area (2.6 million hectares - gray area in Fig. 1) and given the focus on tributary streams with mining impacts in the vicinity of Wilkes- Barre. However, other data were obtained for classi- fied land use/cover data from the U. S. Environmental Protection Agency's (EPA) Environmental Monitoring and Assessment Program (EMAP); these EMAP GIS data were assessed by Bruns and Yang (2002) and were used for watershed conditions for the four fiver sites alone while SPOT data were used for all other tributary sites in the study design (Fig. 1). This EMAP data set was developed for assessments in the Chesapeake Bay watershed (including the Susquehanna River) and was later incorporated as part of an interagency consortium data set - the Multi-Resolution Land Characteristics (MRLC) data (Technology Planning and Management Corporation, 1999; Vogelmann et al., 2001; Weinberg, 1996). EMAP and MRLC land cover data are both based on Thematic Mapper data (30m pixel resolution) from the early 1990s.

Stream sampling Seventeen sampling sites representing four mainstem riv- er sites and 13 tributaries or subcatchments were selected and sampled in September and early October (1994) for this study (Fig. 1). GPS provided accurate locations for sampling sites and allowed for the delineation (digitized within a GIS) of tributary subcatchments and the fiver watershed (Bruns and Sweet, 2004a). At about half the distance between sites 12 and 13 (Fig. 1) on one of the

Aquat. Sci. Vol. 67, 2005 Research Article 407

Figure 1. Map of study site for GIS watershed study (North Branch of the Susquehanna River) near Wilkes-Barre, PA. Sites 14 grouped in study design as low mining and low urban (87-95 % forest); sites 5-8 grouped as high mining and high urban (less than 50% forest); sites 9-13 are intermediate combinations of urban and mining; sites 14-17 are river sites (60 % forest, 35 % agriculture). Circles with dots = sampling sites. Dark areas = mine lands.

tributaries, Solomon Creek, a series of large borehole outfalls from the underground mine pool unloads high amounts of dissolved iron in excess of 15 kg-day-l-km -2 (Bruns et al., 2001). Site 12 is an outlier in one of the multivariate analysis conducted in this study and this is noted and discussed in later sections below.

Flow, slopes, benthic substrates (Table 1), water chemistry (Table 1), and macroinvertebrate parameters (Table 2) for this study area were selected to conform to a drainage basin approach to sampling (Minshall et al., 1985; Cummins, 1992; Bruns and Wiersma, 2004). For the present study, a field sampling team and labora- tory technician sampled and analyzed 8 physical habitat variables, 10 chemical parameters, and 17 macroinverte- brate indicators. Geomorphic stream and river channel features (Table 1) were based on categories, parameters, and methods outlined in Dunne and Leopold (1978),

Rosgen (1996), and Leopold (1994). Substrate composi- tion (percentage boulder, cobble, gravel, sand) at each sampling reach was estimated by visual observation in the field by two technicians; relative amounts of ferric hydroxide ("yellow boy," Manahan, 1984) precipitate on substrate materials were approximated on a scale of 0-4 (0 = no deposition; 1 -- slight amounts of deposition; 2 = 2 -4cm depth up to 50 % of area within sampling reach; 3 = 2-7 cm depth up to 75 % of sampling reach; and 4 = 3-7+cm depth and nearly covering 100% of stream bed).

Standard Methods (APHA, 1989) for water quality (chemical) analyses (Table 1) were followed, including quality assurance and quality control procedures rela- tive to documentation, data entry checks, and instrument calibration and accuracy checks. Stream and river mac- roinvertebrate communities (Table 2) were sampled at

408 D.A. Bruns Mining-impacted river: A GIS watershed analysis

Table 1. Variables used to describe physical habitats and water chemistry at the sites.

Characteristic Method of Assessment

Substrate composition Iron oxide deposits Stream order Slope Flow - stream sites Flow - river sites Dissolved iron and aluminum Conductivity, pH Acidity, Alkalinity Sulfate, Phosphate, Nitrate Total dissolved solids

Visual examination of fifties (four classes, sand to boulders - %) Visual examination of fifties (relative rankings, 0-4) Topographic maps (curve fit for river sites: flow vs. order) Average values within watershed upstream of site (STATSGO) Field transect with flow meter (Standard Methods, APHA) Gauging station (U.S. Geological Survey, Wilkes-Barre, PA) Filtration; atomic absorption (Standard Methods, APHA) Portable field instrumentation (Standard Methods, APHA) Titration with standard base and acid (Standard Methods, APHA) Spectrophotometer (Standard Methods, APHA) Filtration; gravimetric after evaporation (Standard Methods, APHA)

Table 2. Variables used to describe macroinvertebrate communities at the sites.

Metric Method of Calculation

Richness EPT richness EPT abundance Diptera richness Diptera abundance Total abundance Percent by order Percent feeding group Percent gastropods Percent chironomids Percent crustaceans

Total no. of macroinvertebrate taxa (genera; family for Diptera) Total no. of Ephemeroptera, Plecoptera, and Trichoptera taxa (genera) Total no. of Ephemeroptera, Plecoptera, and Trichoptera individuals Total no. of Diptera taxa (family) Total no. of Diptera individuals Total no. of macroinvertebrate individuals Ephemeroptera, Plecoptera, Trichoptera, Diptera Filterers, collector-gatherers, scrapers, shredders Gastropoda taxa Chironomidae taxa mostly Gammaridae

each site (n = 5 at most sites) at baseflow (in September and early October, 1994) only since moderate to high flows on the mainstem can render the river inaccessible and even dangerous. A Surber sample was employed to take five replicate samples typically at one or two lateral transects on riffle habitats across each stream site (Bruns et al., 1991; 1992a,b). Al l organisms were counted in each sample and no subsampling was conducted. Stand- ard taxonomic keys (Merritt and Cummins, 1984; Peck- arsky et al., 1990) were used to identify organisms to their reasonable, lowest taxonomic level, which was usu- ally genus for Ephemeroptera, Trichoptera, and Plecop- tera, or family for Diptera. Total taxa richness, EPT rich- ness, and other macroinvertebrate indicators (Table 2) of stream and river ecosystem health were based on Plafldn et al. (1989) and other related studies (e.g., Barbour et al., 1996; Fore et al., 1996).

Study design criteria A GIS was used to define and evaluate various factors in site selection to optimize a study design focused on a range of land use and land cover conditions in various tributary subcatchments, but with a focus on potential mining impacts. Data on roads, strip mining sites (digi- t ized from U.S. Geological Survey topographic maps at

1:24,000 scale), land cover (SPOT imagery), land own- ership (Earth Conservancy parcels), and hydrography were employed and large, hard-copy maps were created for reference by the principal investigator (PI, Bruns) in conjunction with a field sampling team familiar with the study area. The goal was to select 3 -5 sampling sites on small streams (first or second order) with watersheds where significant mining impacts were known to ex- ist (based on environmental impacts that were visually prominent) and then also locate another 3-5 similar-sized stream sites for comparative reference where mining and urban development were minimal and land cover was mostly in its naturally forested state (mostly Quercus spp., some mixed conifers). To fill out the study design and increase sampling size for statistical analysis, an- other 3-5 sampling sites (first through third order) with intermediate levels of urban and/or mining development were also evaluated and selected.

Another factor in study design was the selection of four mainstem Susquehanna River sites in the urban (Wilkes-Barre) corridor that is in the immediate down- stream vicinity of the reference and mining tributary streams. This section of the river was desired as part of the study design because of environmental regulatory concern, the r iver 's potential for community recreation, its importance as a regional environmental resource, lack

Aquat. Sci. Wol. 67, 2005 Research Article 409

of data on the river, and its potential significance as an ecological endpoint for both urban and mining impacts (Bruns et al., 1997a,b; 2001). Also, reasonable access to all sites was needed from established roads but upstream of bridges or other local structures. In addition, another requirement was sampling site representation on 6,478 ha of widely dispersed lands in the valley owned by Earth Conservancy, a non-profit corporation for environmental reclamation and re-development and a co-sponsor of this study. An iterative process was used for site selection based on the study design factors and GIS data layers outlined above. On a team basis, the PI delineated study design features on hard copy GIS maps and the sampling team visited sites in the field to verify logistical access, land ownership, natural habitats, and areas of disturbance or development. Once field verification of various fac- tors reasonably matched GIS data maps, then aquatic sampling sites were recorded with GPS. From these GPS points - entered into the GIS, each subcatchment area was delineated by manually digitizing from GIS topographic maps (U.S. Geological Survey maps at 1: 24,000 scale).

Investigations that examine land cover and land use relationships to stream ecosystem response and macroinvertebrate indicators often incorporate spatial scaling into their study design (reviewed in Allan, 2004). This may include local scale reach and habitat (channel) characteristics in conjunction with land cover in riparian buffers vs. land cover from a whole catch- ment perspective (e. g., Richards et al., 1996; Lammert and Allen, 1999; Sponseller et al., 2001; Wang et al., 2001; Gove et al., 2001). A similar design was initiated for this study but it was found that each land cover class at the riparian corridor scale (100m buffer) was highly correlated with the same class at the watershed-whole catchment scale (range of r = 0.74-0.92, P < 0.001, for barren, urban, agriculture and forest). Richards and Host (1994) found similar results and therefore focused exclusively on watershed scale analyses. This same ap- proach looking only at whole catchment relationships was followed for the present study also. Field obser- vations support this approach since both urban and mining impacts to stream corridors were pronounced and watersheds remain the main focus for regulatory agencies, resource managers, community environmen- tal groups, and a regional environmental master plan (Bruns et al., 2001).

Recently, King et al. (2005) have examined gradients of distance values in the spatial arrangement of land cover relative to stream response in nitrates and macroinver- tebrate communities. However, their study area encom- passed the Coastal Plain (12,900 km 2) of the upper Chesa- peake Bay and included many large watersheds in the Washington, DC and Baltimore, MD metropolitan areas. Impacted and reference tributary watersheds in the present

study were comparatively very small (e.g., l l 9km 2 for site 10 and 47km 2 for site 12, see Fig. 1), and urban and mining land cover classes each ranged from 20-40 percent in the impacted subcatchments; thus, these environmental disturbances were in relative close proximity to streams and an evaluation of spatial gradients and distance values within a watershed did not appear relevant to conditions in the current study area for this investigation.

Because of a requested reduction in the environmen- tal scope of work by one of the co-sponsors (Earth Con- servancy) of this research, sampling was conducted in only one season (late summer, early fall) for the purpose of characterizing the physical, chemical, and biological aspects of the reference and mining impacted streams and river in the study area and watershed. Late summer and early fall represent baseflow on the North Branch of the Susquehanna River and is probably the only time that macroinvertebrate samples can be collected in relative safety. Sampling in just one season is not an uncom- mon practice in other watershed studies on the affects of land use on stream ecosystems (e. g., Richards and Host, 1994). Also, all flowing streams at this time of year in the study area represent perennial aquatic habitats and this sampling time eliminates erroneous sampling of ephem- eral streams, especially those with mining impacted channels.

Statistical analysis Given potential problems of co-variation between land cover, habitat, and water chemistry (Van Sickle, 2003; King et al., 2005), a multivariate statistical approach was taken in data analysis. First, principal component analysis (PCA) (Richards and Host, 1994; Richards et al., 1996; Pennington et al., 2001) was conducted on macroinvertebrate data in order to reduce the number of monitoring indicators (e. g., EPT richness, ordinal com- position, and percent functional feeding groups). This allowed all 17 sites to be plotted against PCA axes for evaluation of clustering patterns relative to categories of land use groupings in the study design. And second, step-wise multiple regression analysis (Johnston et al., 1990; Johnson et al., 1997; Lammert and Allan, 1999; Sponseller et al., 2001) was used to relate substrate- habitat factors, land cover, and water chemistry to each of the seven macroinvertebrate parameters that were best correlated to the PCA axes. Both PCA and multiple regression were identified by Van Sickle (2003) as rec- ommended analyses to address relationships between biotic and abiotic variables that may co-vary and yield spurious results when assessed by means of bi-vari- ate correlations. PCA, correlations of PCA axes with macroinvertebrate parameters, and step-wise multiple regression analysis were all conducted in SPSS (13.0 for Windows).

410 D.A. Bruns Mining-impacted river: A GIS watershed analysis

Table 3. Range of percent land cover and stream order across groupings of sampling sites and watersheds. Water was 0 % for all stream sites but one, and 1% for river sites. Sampling site numbers provided in parentheses for each grouping.

S i t e G r o u p i n g U r b a n B a r r e n F o r e s t A g r i c u l t u r e

Low mining, low urban 1 4 3-9 87-96 0-1 - 4 sites (Nos. 1, 2, 3, 4) - stream order (1-2)

High mining, high urban 21-42 24--30 23-45 3-9 - 4 sites (Nos. 5, 6, 7, 8) - stream order (1-2)

Low mining, intermediate urban 12-15 6 48-73 9-20 - 2 sites (Nos. 9 and 10) - stream order (1-3)

Intermediate mining and urban 12 20 67 1 - 1 site(No. 11) - stream order (2)

Intermediate mining and high urban 30-35 15-16 47-51 1-2 - 2 sites (Nos. 12 and 13) - stream order (3)

River sites 4 0 60 35 - 4 sites (Nos. 14, 15, 16, 17) - stream order (8)

T a b l e 4. Range of percent benthic substrate composition at the stream sites (fifties). Bldrs. = Boulders and Iron-Ox. = Iron Oxide precipitation level (0-4 relative scale, see text).

S i t e G r o u p i n g B l d r s . C o b b l e s G r a v e l S a n d I r o n - O x .

Low mining, low urban 15-50 10-35 1-15 1-10 0 - 4 sites (Nos. 1, 2, 3, 4) - stream order (1-2)

High mining, high urban 2-20 0-80 0-30 1-3 0-3 - 4 sites (Nos. 5, 6, 7, 8) - stream order (1-2)

Low mining, intermediate urban 0-20 20-50 1-70 5-10 0 - 2 sites (Nos. 9 and 10) - stream order (1-3)

Intermediate mining and urban 10 50 25 5 0 - 1 site (No. 11) - stream order (2)

Intermediate mining and high urban 0 10-50 20-30 10-50 0-3 - 2 sites (Nos. 12 and 13) - stream order (3)

River sites 10-50 5-45 5-15 3-10 0-1 - 4 sites (Nos. 14, 15, 16, 17) - stream order (8)

R e s u l t s

L a n d c o v e r

In general , quant i ta t ive (GIS) analysis o f land cover

abundance supported the visual , hard-copy map ob-

servations used in the study des ign and site select ion

process descr ibed above. First and second order s t ream

sites used for compara t ive reference (Table 3) had low

land cover ( 1 - 4 percent) for urban and barren categories

and were more than 85 % forests (secondary growth) on

a watershed or subcatchment basis. These sites were in

sharp contrast to s imilar s ized subcatchments that were

selected to h ighl ight impacts f rom mining (24 -30 per-

cent barren land cover) and urban deve lopment (21-42

percent land cover) and where forest cover was always

less than 50 percent (Table 3). O n a ca tchment basis, the

four r iver sites demonst ra ted 60 percent and 35 percent

for forests and agriculture, respect ively; barren was less

than 1 percen t and urban only at 4 percent land cover for

the r iver watershed. Thus, these sites appeared to reflect

Aquat. Sci. Vol. 67, 2005 R e s e a r c h A r t i c l e 411

Table 5. Range of chemical parameters at each grouping of sampling sites, pH in standard units; Acid. = Acidity (mg/L), Alk. = Alkalinity (mg/L), Sulf. = Sulfate (mg/L), Nitr. = Nitrate-N (lag/L), Fe = Dissolved iron concentration (mg/L), TDS = Total Dissolved Solids (mg/L).

Site G r o u p i n g p H A c i d . A lk . Sulf . Nitr. Fe T D S

Low mining, low urban 6.0-7.3 10 10-40 8-16 193-352 .1 50-300 - 4 sites (Nos. 1, 2, 3, 4)

High mining, high urban 3.4-7.7 10-101 0-178 122-959 100-331 .1-5.4 470-1760 - 4 sites (Nos. 5, 6, 7, 8)

Low mining, intermediate urban 6.9-7.8 5-30 30-35 18-21 no data .1 90-100 - 2 sites (Nos. 9 and I0)

Intermediate mining and urban 6.9 4 16 13 183 .2 220 - 1 site(No. 11)

Intermediate mining and high urban 6.4-8.2 1-152 59-99 25-700 136-490 .6-37.6 290-1400 - 2 sites (Nos. 12 and 13)

River sites 7.7-8.9 1-10 40-90 59-79 8-150 .7-1.6 320-420 - 4 sites (Nos. 14, 15, 16, 17)

conditions identified earlier in the overall design of this study. However, intermediate combinations of land cover for urban and barren were more variable (Table 3) and one site was an outlier in the PCA plots from multivariate analyses (see below).

P h y s i c a l h a b i t a t

Substrate patterns (Table 4) also reflected general groupings of study sites: boulders were highest in rela- tive abundance in the small reference streams and in the river reaches; sand was highest where urban land use was greatest; and iron oxide deposits were highest in subcatchments with the high mining and high urban land cover. Cobbles and gravel made up significant por- tions of substrate materials at all sites and no distinct patterns were evident from a descriptive, observational basis.

Only first through third order tributary streams were available in this study area, including both reference and impacted streams. Therefore, intermediate sized streams from 4th-6th order were not addressed in this study due strictly to conditions in the spatial location of anthracite fields within the North Branch of the Susquehanna River. Late summer-early fall base flow conditions were similar but variable in reference and impacted streams: 0.002- 0.05 cubic ft/sec (first order) and 0.06-1.36cfs (second order). Third order streams ranged in base flow from 0.60 to 8.32cfs while the river sites ranged from 1180- 1290cfs under conditions of base flow in 1994. Stream order was significantly correlated with base flow (r = 0.97, P < 0.0001). Average slopes (from STATSGO soils data) within the catchments ranged from 3-8 percent but did not demonstrate any pattern relative to stream group- ings based on land cover.

C h e m i c a l p a r a m e t e r s

Alkalinity and pH both were at sufficiently high levels at all mining sites (Table 5) except for one "high min- ing" site (zero alkalinity, pH = 3.4) to reflect conditions of "mine drainage" but not "acid mine drainage." In general, acidity levels, turbidity, and concentrations of dissolved iron and sulfate were all highest in subcatch- ments where mining was high or intermediate as a bar- ren land cover class (Table 5). Nitrate concentrations were moderate in low order streams, with and without mining, and highest where urban land cover was high and mining (barren) intermediate; nitrates were consist- ently lower at the river sites. Table 5 provides summary ranges of these chemical variables relative to study site groupings.

M a c r o i n v e r t e b r a t e s

The range of macroinvertebrate indicators for stream assessment (Table 6) was somewhat variable but several trends were apparent. First, both richness and EPT rich- ness were highest at the four low-order reference sites with low barren and urban land cover and at the four river sites where forest and agriculture land cover constituted 95 percent of the watershed. Second, these two param- eters were lowest where the barren (mining) land cover class was high or intermediate; richness and EPT rich- ness values were moderate on a watershed where mining was low with only intermediate amounts of urban devel- opment. Third, EPT abundance was highest at the river sites, reasonably high where mining was low, and lowest in subcatchments with high urban land cover (Table 6). Fourth, Diptera richness, based on family taxonomic identifications, was generally low but highest at a couple of the low order stream sites, both with and without min- ing. And fifth, dipteran population densities were highest

412 D.A. Bruns Mining-impacted river: A GIS watershed analysis

T a b l e 6. Range of macroinvertebrate indicators for stream assessment. Richn. = Richness, EPT-R = EPT Richness, EPT-A --- EPT Abun- dance (numbers), Dipt-R = Diptera Richness, Dipt-A = Diptera Abundance (numbers).

S i t e G r o u p i n g R i e h n . E P T - R E P T - A D i p t - R D i p t - A

Low mining, low urban 8-13 3-7 1-74 1-3 13-32 - 4 sites (Nos. 1, 2 , 3, 4 )

- stream order (1-2)

High mining, high urban 2-8 0-2 0-17 1-3 11-114 - 4 sites (Nos. 5, 6, 7, 8) - stream order (1-2)

Low mining, intermediate urban 4-15 1-6 6-77 1-2 3-6 - 2 sites (Nos. 9 and 10) - stream order (1-3)

Intermediate mining and urban 4 2 9 1 3 - 1 site (No. 11) - stream order (2)

Intermediate mining and high urban 1-6 0-2 0-7 1-2 1-29 - 2 sites (Nos. 12 and 13) - stream order (3)

River sites 14-18 5-9 22-166 1-2 10-57 - 4 sites (Nos. 14, 15, 16, 17) - stream order (g)

T a b l e 7. Range of percent composition of macroinvertebrates. Ephem. = Ephemeroptera, Plec. = Plecoptera, Tric. = Tricophtera, Chiro. = Chironomidae, Gastro. = Gastropoda, Gamm. = Gammaridae.

S i t e G r o u p i n g E p h e m . P l e e . T r i e . C h i r o . G a s t r o . G a m m .

Low mining, low urban 1-33 6-44 18-23 18-27 0 0 - 4 sites (Nos. 1, 2, 3, 4) - stream order (1-2)

High mining, high urban 0 0 2-41 18-99 0-1 0 - 4 sites (Nos. 5, 6, 7, 8) - stream order (1-2)

Low mining, intermediate urban 1-12 0-1 21-55 1-4 0-6 0-7 - 2 sites (Nos. 9 and 10) - stream order (1-3)

Intermediate mining and urban 7 7 62 11 0 0 - I site(No. 11) - stream order (2)

Intermediate mining and high urban 0 0 0-6 20-84 0-1 0-1 - 2 sites (Nos. 12 and 13) - stream order (3)

River sites 8-22 0-1 1-46 6-19 7-28 2-25 - 4 sites (Nos. 14, 15, 16, 17) - stream order (8)

in areas w i th h i g h m i n i n g l and c o v e r and lowes t w h e r e

u r b a n l and cove r was i n t e r m e d i a t e to h igh .

O the r macro inve r t eb ra t e t rends were also ev iden t f rom

the perspec t ive of how sites were g rouped accord ing to

levels o f m i n i n g and u r b a n land cover. Tables 7 and 8 sum-

mar i ze these o ther macro inve r t eb ra t e parameters . The rel-

at ive a b u n d a n c e of E p h e m e r o p t e r a genera l ly fo l lowed the

pa t t e rn for E P T r ichness whi le P lecop te ra was only mos t

p reva len t in the re fe rence smal l -o rder s t reams. Tr ichoptera

appea red to be we l l - represen ted at all si tes except where

u rban land cover was high; at these h igh ly u rban i zed sites

and subca t chmen t s , C h i r o n o m i d a e reached the i r h ighes t

re la t ive a b u n d a n c e but were represen ted at all sites and

groupings . G a s t r o p o d a and G a m m a r i d a e were dis t inct ly

h ighes t in pe rcen t compos i t i on at the four f iver sites.

P r i n c i p a l c o m p o n e n t a n a l y s i s

P C A was c o n d u c t e d o n 17 mac ro inve r t eb ra t e pa rame te r s

across the 17 s amp l ing sites and subca t chmen t s . P C A axis

Aquat. Sci. Vol. 67, 2005 R e s e a r c h A r t i c l e 413

T a b l e 8. Range of macroinvertebrate functional feeding group percent relative abundance for stream assessment. Coll.-Gath. = Collector- Gatherers.

Si te G r o u p i n g C o l l . - G a t h . S h r e d d e r s S c r a p e r s F i l t e r e r s

Low mining, low urban 34-50 0-7 1-25 10-22 - 4 sites (Nos. 1, 2, 3, 4) - stream order (1-2)

High mining, high urban 41-99 0-14 0-2 0-26 - 4 sites (Nos. 5, 6, 7, 8) - stream order (1-2)

Low mining, intermediate urban 29-65 1-6 4-14 23-33 - 2 sites (Nos. 9 and 10) - stream order (1-3)

Intermediate mining and urban 24 1 0 59 - 1 site (No. 11) - stream order (2)

Intermediate mining and high urban 80-88 0-1 0-1 0-6 - 2 sites (Nos. 12 and 13) - stream order (3)

River sites 28-53 0-1 13-42 1-33 - 4 sites (Nos. 14, 15, 16, 17) - stream order (8)

Tabl e 9. PCA axis correlations with macroinvertebrate parameters. R = correlation coefficient. Only the three highest statistically sig- nificant correlations are reported for each axis.

P C A axis M a c r o i n v e r t e b r a t e R P < p a r a m e t e r

PCA-1 Diptera richness 0.60 0.015 Taxa richness 0.58 0.015 EPT richness 0.49 0.060

PCA-2 EPT richness -0.93 0.001 Taxa richness -0.90 0.001 EPT abundance -0.85 0.001

PCA-3 Percent filterers 0.84 0.001 Percent trichoptera 0.78 0.001 Percent collector-gatherer -0.63 0.010

1 (PCA- 1) explained 62 % of the variation in these param- eters, PCA axis 2 added another 17 %, and PCA axis 3 im- proved the analysis by an additional 10 %. Table 9 high- lights the significant macroinvertebrate correlations with each of the PCA axes. Taxa richness correlated with PCA- 1 and strongly (negative) with PCA-2, even though these two axes were independent and uncorrelated (r = 0.48, P < 0.10). In addition, Diptera richness was correlated with PCA-1. Richness and EPT abundance also were strongly (negatively) correlated with PCA-2. Filter feeder and col- lective-gatherer relative abundances were positively and negatively correlated with PCA-3, respectively; percent Trichoptera was the only ordinal composit ional parameter strongly correlated with the third axis of the PCA.

When step-wise multivariate regression analysis was conducted for the three PCA axes relative to land cover

classes, only barren land percent cover explained vari- abili ty in PCA-2 (R 2 = 0.55, F = 18.5, P < 0.001) with no other relationships for other classes or axes. Similar ly for benthic substrates, only percent of boulders was related to PCA-2 (R 2 = 0.45, F = 12.4, P < 0.003), and again no other patterns were evident. When chemical factors were regressed step-wise against each PCA axis, different (weakly) statistically significant affects were demon- strated: PCA-1 was negatively related only to the con- centration of iron (R 2 = 0.31, F = 5.7, P < 0.030), PCA-2 was related to both nitrate concentrations and alkalinity (R 2 = 0.68, F = 7.1, P < 0.020), and PCA-3 was related only to the concentration of dissolved a luminum (R 2 = 0.27, F = 4.8, P < 0.050).

Clustering of the 17 sampling sites into subcatch- ment groupings based on PCA was best demonstrated in comparing PCA-2 vs. PCA-1 and in plotting PCA-3 vs. PCA-2 as shown in Figure 2a and 2b, respectively. Sev- eral patterns were apparent. First, for these diagrams, the three most distinct groupings of sites and subcatchments based on the original study design can be recognized (see enclosed clusters in Fig. 2). These included the reference sites (Sites 1-4 with low mining and low urban land cover), the river sites (Sites 14-17), and the "impacted sites" (Sites 5-8 with high mining and high urban land cover). Second, the lower order reference streams in the "low mining and low urban" grouping of sampling sites consistently clustered in the middle of these two plots (see enclosed groupings labeled Sites 1-4 in Figs. 2a and 2b). Third, the river sites (mostly forest and agriculture land cover, Sites 14-17 in Fig. 2) were clustered oppo- site the "high mining and high urban" grouping of sites relative to the reference sites. Fourth, Site 10, a third-

414 D.A. Bruns Mining-impacted river: A GIS watershed analysis

4 ~9

1 0.8 0.6 0.4 0.2

0 -0.2 -0.4 -0.6 -0.8

i

0.2

I Sites 5-8, high mining, high urban

L o:6 River Sites 14-17; F i l l ~ Site 10, 3 rd order I k~J T

Sites 1-4, low mining, low urban

PCA-1

Figure 2a. Plot of PCA-2 vs. PCA- 1 showing clustering of sampling sites into study design groupings. See text and map in Figure 1.

Sites 1-4 low 1 -1 I mini.g, low ha j / high ]

~ . ~ J I mining' high urban /

River Sites14-17; ~ -O.5 ~ _ ~ I Site 10, Ydora~' I l sit~ 13

-1 "J

PCA-2

Figure 2b. Plot of PCA-3 vs. PCA-2 showing clustering of sampling sites into study design groupings. See text and map in Figure 1.

order reference stream with low mining (6 % land cover) and intermediate urban (16% land cover) consistently clustered with the larger river sites. And fifth, all other intermediate combinations of barren and urban (Table 3)

were either "outliers" (Sites 11 and 12) or they clumped with the "high mining and high urban" grouping (Sites 9 and 13).

M u l t i p l e r e g r e s s i o n

PCA allowed for the identification and selection of seven macroinvertebrate parameters out of a total of 17 indica- tors that were correlated with one of the axes of this analy- sis: taxa richness, EPT richness, Diptera richness, EPT abundance, and the percent composition of Trichoptera, filter feeders, and collector-gatherers. Each of these was examined relative to step-wise multivariate regression analysis against a total of 18 environmental variables, including five benthic substrate parameters (Table 1), three physical factors (slope, flow, stream order), five land cover classes, and ten water chemistry variables (Table 1). Table 10 summarizes the key environmental variables and combinations of variables that best explain the variation in each of the seven dependent macroivertebrate parameters.

Four of the seven macroinvertebrate indicators were best explained in terms of mining related impacts, in- cluding the two strongest relationships (i. e., highest R 2 values) for EPT richness and taxa richness. For each of these two parameters, percent barren land cover explained 70% and 65 % of the variation (Table 10), respectively (see Fig. 3 for EPT richness); no other land cover variable was related to any of the selected mac- roinvertebrate measures. The concentration of dissolved iron added another 10 % in predicting variation for EPT richness and again reinforced the importance and pre- dominance of mining related impacts in the study area. Nitrate concentration, not an obvious indicator of mining disturbance (see discussion below), but rather nutrient runoff, explained another 20 % variation in richness. The next significant regression that reflected mining impacts was for percent collector-gatherers regarding sulfate con- centration and percent sand. A final but weak relation-

Table 10. Multiple linear regression models to predict macroinvertebrate parameters. Regressions are listed in order of strongest relation- 2 ships on the basis of R values.

Dependent variable Independent variable Coeff. R 2 AR 2 F P <

EPT richness 1 Percent barren -0.23 0.70 27.8 0.001 2 Dissolved iron -0.10 0.80 0.10 21.8 0.001

Taxa richness 1 Percent barren -0.31 0.65 22.3 0.001 2 Nitrate concentration -18.1 0.85 0.20 30.4 0.001

EPT abundance 1 Flow 0.82 0.64 21.2 0.001

Percent collector-gatherer 1 Sulfate concentration 0.001 0.52 12.9 0.004 2 Percent sand 0.011 0.81 0.29 23.9 0.001

Diptera richness 1 Iron oxide deposition -0.52 0.43 8.9 0.011

Percent trichoptera 1 Phosphate concentration -1.8 0.35 6.5 0.030

Percent filterers no significant variables

Aquat. Sci. Vol. 67, 2005 Research Article 415

10 �84

r 8 / Y = 6.70 - .23X; F = 27.8, P <0.001

�9 �9 / R~ = 0.70

.el

~ 4

~ 2

0 . . . . ~

5 10 15 20 25 30 35

-2

P e r c e n t B a r r e n L a n d C o v e r

Figure 3. EPT richness as a function of percent barren (mining) land cover in subcatchments of the North Branch of the Susque- hanna River. Several of the highest EPT richness values are from the mainstem river sites - see text. See Tables 6 and 10.

ship (accounting for 43 % of the variation) was evident for Diptera richness regarding impacts from iron oxide deposits on benthic substrates.

EPT abundance was significantly related to stream flow and indicated that these taxa reached their maximum population densities in the larger river sites of the study area. This finding was supported by descriptive trends noted above (Table 6). Trichoptera were present at most sites and groupings in the study design (Table 7) and per- cent Trichoptera was inversely related to phosphate con- centrations. Based on step-wise regression analysis, no parameters measured in this study explained the percent filter feeders even though this variable was significantly correlated with PCA-3 as noted above.

Discuss ion

The objective of this study was to address the potential relationship between land use-land cover at the watershed level (Forman and Godron, 1986; Gardner et al., 1993; Ri- chards and Host, 1994; O'Neill et al., 1997; Allan, 2004) and the response of geomorphic (Leopold, 1994), chemical (Webb and Walling, 1992), and ecological (macroinverte- brate) components of a stream-river ecosystem (Vannote et al., 1980; Minshall et al., 1985; Cummins, 1992; Bruns et al., 1992b). In addition, the overall project was intended to test the utility and application of the geospatial tools of GIS-GPS-RS for watershed environmental monitoring and assessment (O'Neill et al., 1997; Johnson and Gage, 1997; Gergel et al., 2002) with a special focus on mining impacts (Bruns and Sweet, 2004a,b). Earlier investigations in the anthracite fields of northeastern Pennsylvania have evalu- ated this approach based on ecosystem monitoring design principles (Bruns et al., 1997a; Bruns and Wiersma, 2004) and the application of the technologies of GIS-GPS-RS for river basin and ecosystem impact assessment (Bruns et al., 1997a; O'Neill et al., 1997).

Based on a review of previous investigations on min- ing and AMD impacts on streams and rivers (Table 11), it was expected that a high percentage of a watershed or subcatchment under natural vegetative cover such as for- est would promote "favorable" ecological conditions in the riverine system such as low nutrients and pollutants, while landscape features such as disturbed lands from mining would result in deteriorated environmental condi- tions as indicated in low pH and high levels of acidity, TDS, sulfates, and nutrients, along with sedimentation and physical disturbance of natural benthic substrates. In addition, the biotic response of macroinvertebrate com- munities was predicted to conform to this perspective with lowered biodiversity or loss of EPT taxa. In general, numerous such relationships were observed for forest cover and for both mining and urban related disturbances based on preliminary, exploratory bi-variate correlations among land cover, water chemistry, substrate, and mac- roinvertebrate variables (unpublished technical report of Bruns et al., 1997b). However, Van Sickle (2003) has identified critical problems of co-variation between land cover, habitat, water chemistry, and biotic response variables (see also King et al., 2005) that may overesti- mate the importance of these relationships or result in spurious correlations. Therefore, a multivariate statistical approach (Johnson and Gage, 1997) was taken for data analysis in this study based on PCA (Richards and Host, 1994; Richards et al., 1996; Pennington et al., 2001) and step-wise multiple regression (Johnston et al., 1990; Johnson et al., 1997; Lammert and Allan, 1999; Sponsel- ler et al., 2001), both highlighted by Van Sickle (2003) as more appropriate alternatives to bi-variate correlation analysis. On this basis, only six of 17 macroinvertebrate variables were related to PCA axes and some measure of land cover and/or physical and chemical parameters. In addition, barren land was the only land cover class that entered significantly into a regression analysis, for either PCA or macroinvertebrate variables. This mining affect was confirmed with other environmental indicators of impact such as dissolved iron and sulfate concentrations and levels of sedimentation and iron oxide deposition explaining variability across several macroinvertebrate community parameters (Table 10). In particular, the strongest regressions were for percent barren land cover that explained the greatest amount of variation in both EPT richness and overall taxa richness. The only other factors that entered into explaining variation in macroin- vertebrate indicators were stream flow and nitrate and phosphate concentrations at sampling point locations within stream reaches.

There are two categories of ecological literature that pertain to these findings: "conventional" (without the use of GIS or remote sensing data) studies based only on point sampling at stream locations (e. g., Table 11, re- gional AMD technical reports and published papers) and

416 D.A. Bruns Mining-impacted river: A GIS watershed analysis

Table 11. Literature review of water quality studies relative to mining impacts.

Investigation Water Quality Problem Finding(s) Comments

This study Acid mine drainage AMD impacts identified GIS watershed analysis on the (AMD) Susquehanna River

Edwards 1994, 1996 General water quality Focused on water quality of North survey Branch Susq. River (No GIS-RS)

Hawkins (1994) AMD impacts for water Western PA "re-mining" sites quality

Wood (1996)

Herlihy et al., (1990)

Growitz et al., (1985)

Ladwig et al., (1984)

Wirries and McDonnell (1983)

Helsel (1983)

Erickson et al., (1982)

Brush et al., (1979)

Brennan et al., (1977)

Gale et al., (1976)

AMD from 100 mine dis- charge in Eastern PA

Regional AMD

Mine Drainage in Eastern PA

AMD in mine pools

AMD and metals

Land use & AMD

AMD in a mine pool

Heavy Metals and acidity

Fe, pH, 02, C02, and AMD

Iron & AMD

AMD and urban impacts detected

Water quality from underground mines more degraded than

from surface mines

AMD decreased from 1975 to 1991 in many outfalls

AMD is a serious problem in coal mining regions

Total sulfate load: 1470 tons per day; total iron load:

79 tons per day

Pollutants increase with depth in boreholes, mineshafts, and mine

pools; AMD decreased over time (1968-1980)

Majority of sites exceeded federal standards

Both influenced water quality

Pollutant increase at depth with pronounced change at mined

out coal beds

AMD and urban imparts were detected

Partial correlation analysis of variables

Significant iron deposition in AMD areas & downstream

pH, DO, conductivity, sulfate, iron, manganese, alkalinity, acidity, and

metals analyzed; No GIS

AMD is as serious as atmospheric acid deposition in mining regions; no GIS

Almost all underground mines abandoned and flooded with drainage to surface waters

AMD from near surface flow system

Water quality highly variable; some metals low; calcareous

overburden affected drainage quality (Appalachian Mountains)

Reclamation improved water quality (OH)

AMD removal by simple flushing mechanism

AMD vs. urban impacts; these were difficult to separate

(Susquehanna River)

Partial correlation of coefficients vs. variables for two AMD streams (PA)

Physical, chemical, and biological factors in iron deposition discussed

(Susquehanna River)

the peer reviewed watershed literature focused on land use affects on lotic systems based on GIS analysis and use of satellite imagery for land cover (e. g., reviewed in Allan, 2004). Both categories are considered below.

Convent ional A M D asses sment The findings of this study are generally consistent with

the "conventional" (i. e., stream surveys at single sta- tions) literature and technical reports on the environmen- tal impacts of mining and acid mine drainage (AMD) to

stream ecosystems (Table 11). First, water quality was highly variable (e. g., Wirries and McDonnell, 1983) in most cases and even though AMD impacts such as high levels of dissolved iron, sulfate, and sedimentation were readily identified from the published literature, other

variables such as flow or nutrient levels may complicate interpretation or provide other affects on biotic com- munities. In some cases in the published literature on a descriptive basis, the close association between mining and urban areas complicated a thorough partitioning of urban vs. mining effects. For example, water quality sur- veys by other investigators (Brush et al., 1979; Edwards, 1994; 1996) found impacts from both AMD and urban land use but results were often confounded by one land use or the other. Helsel (1983) examined non-urban land use vs. mining for 122 stream sites in Ohio and found base flow water quality to reflect both land use (primary) and bedrock mineralogy (in non-mining areas, see also Wirries and McDonnell, 1983); reclaimed lands showed improved aspects of water quality but sulfate levels and conductivity remained high. Other investigators, such as

Aquat. Sci. Vol. 67, 2005 Research Article 417

Gale et al. (1976) found that various physical, chemical, and biological factors complicated and influenced iron deposition in the Susquehanna River in reaches down- stream ofAMD outfalls and mine discharges. Brennan et al. (1977) used partial correlation analysis among AMD variables in order to focus on specific aspects of water quality and its variability.

Another relevant aspect regarding this AMD litera- ture (Table 11) is that deep, underground, subsurfacing mining appeared to produce the most severe water qual- ity problems (Hawkins, 1994; Wood, 1996; and see also deep borehole data in Erickson et al., 1982; and Ladwig et al., 1984). Throughout eastern PA both surface and subsurface mining of coal deposits have been extensive (e.g., Growitz et al., 1985; Wood, 1996) with complicat- ing impacts to the landscape and water quality due to a complex mix of hydrology and biogeochemical factors. The study area of the present investigation is part of the Northern Anthracite Field and subsurface mining dis- charges have been studied by various investigators for almost 20 years. Although Ladwig et al. (1984) and Wood (1996) have shown improvements in water quality from subsurface borehole AMD discharges to surface waters in this northern field, the present study based on PCA did locate one site as an "outlier" even though only "interme- diate" levels of surface mining land cover were estimated for this tributary drainage (site 12, Table 3 for land cover, Fig. 2). This tributary has several major subsurface (bore- hole) discharges upstream and within several hundred meters of the sampling site (i. e., between sites 12 and 13 in Fig. 1). This site had not only very high levels of acid- ity, sulfate concentration, and TDS (Table 5) but it also exhibited the most pronounced concentration of dissolved iron (37.6 mg/L) for any site in this study. This "borehole" signature for site 12 was consistently present as an outlier in both graphs of PCA axes (Figs. 2a and 2b) and does not conform to any clustering pattern as seen in either the other "high mining" impacted sites or reference sites.

A third and final observation from the present study in conjunction with a review of "conventional" water qual- ity and AMD studies (Table 11) is that there is no com- prehensive, integrated AMD database (e. g., on GIS, see Bruns and Wiersma, 2004) and studies have tended to be fragmented or focused on only one aspect of the problem (e. g., water quality depth profiles in mining shafts and boreholes [Erickson et al., 1982; Ladwig et al., 1984]). Part of the problem is that most of the mining impacts took place long before any state or federal environmen- tal agency was established for developing and enforcing environmental regulations. Also, much of the water qual- ity work was surveyed in the mid-1970's to mid-1980's before GIS, GPS, and RS were generally and commonly available for environmental investigations; thus, oppor- tunities were extremely limited for building a common, digital database with easy access by users including

data sharing capabilities relative to regional mining, its impacts, and its extent. Even the regional study of AMD vs. atmospheric acid deposition impacts along the eastern U.S. (Herlihy et al., 1990) is not based on GIS or RS ap- plications or analyses. However, EPA's BASINS (Better Assessment Science Integrating Point and Nonpoint Sources, EPA, 2001) is a multipurpose environmental analysis system based on GIS for use by natural resource agencies to perform watershed- and water-quality-based studies. Although BASINS (EPA, 2001) represents a good first-step in providing access to a range of geospatial data on land use and water quality (Bruns and Wiersma, 2004), environmental information on AMD impacted streams is limited in comparison to other sources of aquatic pol- lution, especially "permitted" (by regulatory agencies) point sources of industrial pollution and effluents.

GIS watershed as se s sment Numerous investigators have employed GIS and remote sensing data for assessing the affects of land use on stream ecosystems (Johnson and Gage, 1997; Gergel et al., 2002; Allan, 2004) and these have addressed a range of environmental scales, settings, and problems. The im- portance of different scales from reach to riparian buffer (e. g., 100m) to entire catchment upstream of a site has been reviewed recently by Allan (2004). Since land cover for all classes in riparian corridors in the present study area strongly correlated to respective whole catchment land cover values (see also Richards and Host, 1994 for a similar finding), a riparian scale assessment could not be addressed based on the specifics of this study area. Nev- ertheless, reach scale variables as measured by macroin- vertebrate communities, substrate conditions, and water chemistry did allow some level of comparison to whole catchment patterns based on multivariate data analysis (Johnson and Gage, 1997; Van Sickle, 2003).

In this context, "whole-catchment" findings in this study were consistent with general findings in several other GIS related watershed investigations, even though a wide range of differing results have been reported (Al- lan, 2004). For example, Richards et al. (1996) concluded that macroinvertebrate "biological signatures" could be used to assess landscape-scale influences on central Michigan streams based on factors such as geology and land use. Johnson et al. (1997) reported that relatively coarse spatial scale databases were able to provide useful descriptors of regional water quality in central Michigan streams. And multivariate analyses by Stewart et al. (2001) indicated that agricultural land cover in riparian zones and forest land cover in whole watersheds were part of a suite of variables with the most influence on invertebrate and fish communities in agricultural water- sheds in eastern Wisconsin. As noted above, in the step- wise multivariate analysis of macroinvertebrate param-

418 D.A. Bruns Mining-impacted river: A GIS watershed analysis

eters in this study, barren land cover relative abundance at the watershed level was the strongest environmental predictor of both EPT richness and taxa richness, and was the only land cover class to have any statistically significant relationship.

Other studies that have focused on the ecological re- sponse of streams at different spatial scales have reported other findings. Fitzpatrick et al. (2001) found it difficult to separate the interrelated effects of geologic setting, watershed and buffer land cover, and base flow; both watershed and buffer land cover affected invertebrate di- versity while the percentage of EPT was more influenced by nutrient concentrations and flow variability. Gove et al. (2001) also found different water quality parameters to vary in their response to scale, but indicated that in- termediate scales of land use were better than riparian or total catchment scales. In contrast to the present study, Sponseller et al. (2001) found that macroinvertebrate indices were more closely related to land cover patterns evaluated in a 200 m sub-corridor scale than to a whole catchment assessment.

The two most common applications of watershed (and riparian) analysis of land use impacts to stream eco- systems has been in regard to agriculture and urbaniza- tion (Johnson and Gage, 1997; Gergel et al., 1992; Allan, 2004). Stewart et al. (2001) demonstrated from among a number of results that percent agriculture in riparian zones impacted macroinvertebrate communities, includ- ing EPT indicators. In a study of agriculturally developed watersheds, Lammert and Allan (1999) concluded that land use immediate to tributaries predicted biotic condi- tion better than regional land use but was less important than local habitat variables relative to explaining vari- ability in macroinvertebrate assemblages. More recently, King et al. (2005) found that percentage cropland was linked only to nitrate-N, and not macroinvertebrate as- semblages, after factoring out spatial autocorrelations and other variables. Other studies of agricultural land use also indicate impacts to water quality, habitat, and biotic community variables (Richards et al., 1966; Sponseller et al., 2001; Wang et al., 1997). Wang et al. (2000) found that although agricultural land use impacts degraded stream fish communities, they were generally less severe than those from urbanization on a per unit-area basis. In general, stream biological communities changed with increasing watershed area in urban land use (e. g., Booth and Jackson, 1997; Morley and Karr, 2002; Tong and Chen, 2002; and review in Allan, 2004).

In the present study, agricultural land use was only important in the catchment of the mainstem river sites. However, many of the highest EPT richness and total taxa richness values were observed for these river sites (e. g., see Table 6, and Fig. 3 for EPT richness, upper left-hand area of the graph along the y-axis). In addi- tion, variability in EPT abundance (population densities)

was best explained by stream flow with highest EPT and flow values at these mainstem river sites (Table 10). It should be noted that the mainstem Susquehanna River in the Wilkes-Barre area has been identified recently by the non-profit organization, American Rivers, as the "number one" river at risk in 2005 in the United States from environmental impacts, including mining and urban runoff (http://www.americanrivers.org/site/ PageServer; Venesky, 2005; Guydish, 2005). The present study confirms the extent of mining disturbed lands in a number of tributary streams in this area of the watershed, but the higher EPT abundance and biodiversity in mac- roinvertebrate communities in the river habitats indicate that ecological resources are at a sufficiently high level to be threatened by continued abuses in land use. The American Rivers' designation is not for the level of cur- rent or past impacts, but rather ecological resources most at risk. Currently, federal funds for clean water programs in Pennsylvania are slated for a $14M budget cut (Ve- nesky, 2005).

Based on the review of Allan (2004), agricultural land is the more common and extensive land use in most developed catchments of the U.S. (e. g., Benke and Cushing, 2004); although urban land use is at a lower percentage of U. S. catchments, this land use class has a disproportionately larger negative influence on stream ecosystems (e.g., Paul and Meyer, 2001). As indicated above in the present study, mining land use was highest in the same subcatchments with high levels of urbaniza- tion (Table 3). Preliminary, exploratory bi-variate cor- relations (Bruns et al., 1997b, unpublished technical report) indicated that both mining (barren) and urban land cover percentages were consistently associated with a number of water chemistry and macroinvertebrate in- dicators, including both EPT richness and total richness. However, these correlations with urban land cover in a subcatchment were most likely a spurious finding since the multivariate analysis in this study yielded significant relationships relative to barren land cover only. In ad- dition, results from regression analysis identified other mining related impacts due to high concentrations of dissolved iron, sedimentation, and iron oxide deposition. From these findings, in the context of other published investigations, it is concluded that mining impacts to streams in the present study area exceeded that of both urbanization and agriculture.

Although Bryce et al. (1999) reported land use affects on stream ecosystem conditions in a Mid-Appalachian regional study area due to forestry, mining, and recrea- tion, most investigations (Allan, 2004) of land use affects on streams from a watershed perspective have focused on either agriculture or urban land, or natural land cover such as forests. Generally, the percent barren land cover (includes quarry areas, coal mines, and transitional ar- eas such as clear-cut areas) in various EPA watershed

Aquat. Sci. Vol. 67, 2005 Research Article 419

investigations in the Mid-Atlantic Region (Jones et al., 2001a,b; Wickham et al., 1999) have ranged from 0 percent to 8.8 percent (mean = 0.9 percent) as measured over 123 regional watersheds generally in the size range of 5000klTl 2. Presumably, due to this comparatively low percent barren land cover class, Wickham et al. (1999) did not include this barren category in any of the 3t indica- tors of regional ecological conditions. Thus, when these investigators conducted cluster analysis to rank relative cumulative impact scores for these regional watersheds, typical impacts were associated with other anthropogenic disturbances such human population change, number of aquatic impoundments, degree of urbanization, and amount of agriculture on steep slopes.

Nevertheless, two other similar regional EPA water- shed studies (Jones et al., 2001a,b) did include analyses on barren lands. In an investigation of landscape change on ecological resources from 1973-1993, Jones et al. (2001a) reported changes in six land use categories, including a percent change in barren land cover from 0.40 to 0.92 - an overall 0.48 percent increase. However, these authors did not discuss this result relative to their focus on bird habitat conditions and changes in nitrogen export. In another study (Jones et al., 2001b), landscape metrics were used to predict nutrient and sediment load- ings to streams in this same region. The only significant finding reported here for percent barren land was in accounting for an additional 10% of the variability in dissolved phosphorus, a minor finding that was not addressed or discussed further by these investigators (Jones et al., 2001b). In the present study, both nitrate and phosphate concentrations accounted for some level of variability (negative association) in taxa richness and percent Trichoptera (Table 10). This might potentially be due to unregulated disposal of domestic sewage or wastes on mining waste piles - a practice known to occur in the past (personal communication with local sanitary author- ity personnel). For dissolved nitrogen, which can remain stable and be transported readily in water (Hem, 1985; Jones et al., 2001b), this finding may indicate the influ- ence of nitrogen fixing trees such as black locust (Ro- binia pseudoacacia) that are common colonizers along the margins of abandoned mining waste piles throughout the study area (personal field observations). It should be noted that black locust has been planted and seeded more than any other tree species on abandoned mining lands in the eastern U.S. (Ashby et al., 1985).

The use of macroinvertebrate indicators for biomoni- toting of stream and fiver ecosystem health and for de- tection of pollution impacts has had a long history (e. g., Hynes, 1980; Washington, 1984; Plafkin et al., 1989; Resh et al., 1996). Although macroinvertebrate metrics have been used in the past for assessing mining impacts (e. g., Samuel et al., 1978), later research in this field has focused on other toxic pollutants such as acid precipitation

(Sparling, 1995; Bruns et al., 1992a; 1997a), insecticides (Wallace et al., 1996), or other non-point sources of stream impacts such as logging and road construction (Fore et al., 1996), and urban land use from a GIS watershed and whole-catchment approach (Richards and Host, 1994).

As discussed above from a watershed perspective, EPT richness and total taxa richness provided the two most consistent stream and river ecological response parameters to spatial changes in barren land cover in this study. EPT richness has been used extensively in the past for stream biomonitoring (see review in Lenat and Pen- rose, 1996) and findings of the present study support the use of this parameter for both conventional applications but also in regard to GIS watershed analyses (see EPT richness responses in Stewart et al., 2001; and Fitzpatrick et al., 2001). In addition, Kiffney and Clements (1996) found EPT richness to decrease significantly in experi- mental stream exposures to heavy metals. Dissolved iron, along with barren land cover, accounted for a significant portion of the variability in EPT richness in this study. Dissolved iron and other heavy metals are known to be toxic in high concentrations associated with AMD (e. g., Vaughan et al., 1978). Integrating EPT richness with a GIS watershed analysis for regional mining assess- ment may provide a first step to developing and testing landscape indicators of ecotoxicology as developed and discussed in Holl and Cairns (1994) and Cairns and Niederlehner (1996).

Conc lus ion

Based on analyses in this study, it is concluded that the use of GIS and RS from a watershed perspective can fa- cilitate assessment of land use impacts to stream ecosys- tems from extensive mining of anthracite in northeastern PA. If an environmental investigation of this study area had relied strictly on point sampling and analysis of sub- strates, water chemistry, and macroinvertebrates, then the importance of catchment-wide impacts from mining and related land disturbances would not have been detected. This conclusion is consistent with various reviews on the ecological impacts of land use on freshwater and terres- trial ecosystems (Carpenter et al., 1992; Vitousek, 1994; National Research Council, 1993; Meyer and Turner, 1994).

A watershed approach in conjunction with multivari- ate analysis also allowed for the clear statistical determi- nation that mining impacts were significant and exceeded those for urbanization and agriculture over the study area considered. Other published surveys and studies based on point sampling in the region resulted in unclear and confounding factors associated with both mining and ur- ban runoff or agriculture (Gale et al., 1976; Brush et al., 1979; Edwards, 1994; 1996).

420 D.A. Bruns Mining-impacted river: A GIS watershed analysis

Geospatial data from a watershed perspective are also useful in the design of a monitoring and assessment pro- gram (Bruns and Wiersma, 2004). Methods and analyses outlined in this study for evaluating mining impacts in this portion of the anthracite fields were consistent with this approach and confirmed the benefits of a systems approach to environmental monitoring (Bruns et al., 1997a). It is anticipated that GIS watershed analyses of mining impacts will help facilitate the design of stream and river channel restoration efforts (e. g., Brookes and Shields, 1996) along with other landscape-scale environ- ment reclamation programs (Jordan et al., 1987; National Research Council, 1992). Benefits may include ranking and prioritization of mining impacted sites (e. g., Bruns et al., 2001), identifying key ecological endpoints for rec- lamation targets (Bruns and Wiersma, 2004), and provid- ing a comprehensive regional view to mining reclama- tion and ecological restoration rather than "piecemeal" projects on an individual site by site basis (Bruns and Sweet, 2004a).

Acknowledgements

I would like to extend my appreciation to Dr. G. Wayne Minshall for his ideas, support, and mentoring provided during my graduate studies at Idaho State University and as a colleague in subsequent studies of Rocky Mountain streams. I also wish to thank the following colleagues at Wilkes University for their contributions to the present study: Xiaoming Yang (SPOT classifications), Brian Oram (supervised water chemistry analysis), and Sam Wilkes (collection and identification of macroinverte- brate data). Writing of aspects of this paper was funded and supported by the USDA (Cooperative State Research, Education, and Extension Service) Rural GIS program and EPA's Office of Environmental Information.

References

Allan, J. D., 2004. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annual Review of Ecology and Systematics 35: 257-284.

American Public Health Association (APHA), 1989. Standard Methods for the Analysis of Water and Wastewater. Washing- ton, DC, 1527 pp.

Anderson, J. R., E. E. Hardy, J. T. Roach and R. E. Witner, 1976. A land use and land cover classification system for use with remote sensor data. USGS Professional. Paper 964.

Ashby, W. C., W. G. Vogel and N. E Rogers, 1985. Black locust in the reclamation equation. General Technical Report NE-105. Broomall, PA: U. S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station, 12 pp.

Barbour, M. T., J. Gerritsen, G. E. Griffith, R. Frydenborg, E. Mc- Carton, J. S. White and M. L. Bastian, 1996. A framework for biological criteria for Florida streams using benthic macroinver- tebrates. Journal of the North American Benthological Society 15: 185-211.

Benke, A. C. and C. E. Cushing, eds., 2004. Rivers of North Ameri- ca. San Diego, CA. Academic/Elsevier Publishers, 1168 pp.

Bollinger, S. W., 1994. Water Quality of Interstate Streams in the Susquehanna River Basin. Monitoring Report #7. Susquehanna River Basin Commission, Harrisburg, Pa.

Bollinger, S. W. and D. L. Siflinger, 1996. Water Quality of In- terstate Streams in the Susquehanna River Basin. Monitoring Report #9. Susquehanna River Basin Commission, Harrisburg, Pa.

Booth, D. B. and C. R. Jackson, 1997. Urbanization of aquatic systems: degradation thresholds, stormwater detection, and the limits of mitigation. Journal of the American Water Resources Association 33" 311-323.

Brennan, E J., R, Shertzer, S. Corbett and J. Centofani, 1977. Partial correlation analysis of some chemical and biological param- eters of two streams receiving mine drainage. Proceedings of the Pennsylvania Academy of Science 51: 125-130.

Brooks, A. and F. D. Shields, Jr., 1996. River Channel Restoration: Guiding Principles for Sustainable Projects. John Wiley & Sons, England, 433 pp.

Bruns, D. A., G. B. Wiersma and E. J. Rykiel, Jr., 1991. Ecosystem monitoring at global baseline sites. Environmental Monitoring and Assessment 17- 3-31.

Bruns, D. A., G. B. Wiersma and G. W. Minshall, 1992a. Evaluation of community and ecosystem monitoring parameters at a high- elevation Rocky Mountain study site. Environmental Toxicol- ogy and Chemistry 11" 359-372.

Brans, D. A., G. B. Wiersma and G. W. Minshall, 1992b. Lotic ecosystems and long-term monitoring for global change. In: R Firth and S. G. Fisher, (eds.), Global Warming and Freshwater Ecosystems, Springer-Verlag, New York, pp. 285-307.

Bruns, D. A., G. B. Wiersma and G. J. White, 1997a. Testing and application of ecosystem monitoring parameters. Toxicological and Environmental Chemistry 62: 169-196.

Bruns, D. A., X. Yang, B. Toothill and S. Halsor, 1997b. System for Environmental Survey and Characterization. (Use of GIS, GPS, and Remote Sensing to Assess Coal Mining Impacts on a Water- shed Basis). Final Report to Department of Defense, Advanced Research Projects Agency, and Earth Conservancy, Ashley, PA. Wilkes University Technical Report. Wilkes-Barre, PA.

Brans, D. A., T. Sweet and B. Toothill, 2001. Upper Susquehanna- Lackawanna River Watershed, Section 206, Ecosystem Resto- ration Report. Phase I GIS Environmental Master Plan. Final Report to U. S. Army Corps of Engineers, Baltimore District, MD.

Bruns, D. A. and X. Yang, 2002. An accuracy assessment of satellite imagery used in landscape-watershed assessments: a compari- son of four databases. In: B. E. Montz and G. A. Tobin, (eds.), Papers and Proceedings of the Applied Geography Conferences 25: 230-244.

Bruns, D. A. and T. Sweet, 2004a. Geospatial tools to support water- shed environmental monitoring and reclamation: assessing min- ing impacts on the Upper Susquehanna-Lackawanna American Heritage River. U. S. Department of Interior, Office of Surface Mining. Conference Proceedings, Advanced Integration of Ge- ospatial Technologies in Mining and Reclamation, December 7-9, 2004, Atlanta, GA. http://www.tips.osmre.gov/GeoSpatial/ post-conference/Papers/Bruns_Sweet l_PA.pdf

Brnns, D. A. and T. Sweet, 2004b. PPGIS in Rural America: Inte- gration of Geospatial Technologies and Resource Leveraging in Data Acquisition for an American Heritage River community. Proceedings of the Annual PPGIS Conference of the Urban and Regional Information Systems Association (URISA). Univer- sity of Wisconsin, Madison,WI, pp. 43-70.

Bruns, D. A. and G. B. Wiersma, 2004. Design of environmental monitoring systems: a geospatial perspective. In: G. B. Wiers- ma, (ed.), Environmental Monitoring, Lewis Publishers, Boca Raton, FL, pp. 1-35.

Brush, E. J., M. Kalinowski and T. L. Miller, 1979. Heavy metals in a stretch of the Susquehanna River badly polluted with acid

Aquat. Sci. Vol. 67, 2005 R e s e a r c h Ar t ic le 421

mine effluents. Proceedings of the Pennsylvania Academy of Sciences 53: 179-188.

Bryce, S. A., D. P. Larsen, R. M. Hughes and P. Kaufmann, 1999. Assessing relative risks to aquatic ecosystems: a mid-Appa- lachian case study. Journal of the American Water Resources Association 35: 23-36.

Cairns, J., Jr. and B. R. Niederlehner, 1996. Developing a field of landscape ecotoxicology. Ecological Applications 6: 790-796.

Carpenter, S. R., S. G. Fisher, N. B. Grimm and J. E Kitchell, 1992. Global change and freshwater ecosystems. Annual Review of Ecology and Systematics 23:119-139.

Congalton, R. G., 1991. A review of assessing the accuracy of clas- sifications of remotely sensed data. Remote Sensing of Environ- ment 37: 35-46.

Congalton, R. G. and K. Green, 1993. A practical look at the sources of confusion in error matrix generation. Photogrammetric Engi- neering and Remote Sensing 59: 641-644.

Congalton, R. G. and K. Green, 1999. Assessing the Accuracy of Remotely Sensored Data: Principles and Practices. Boca Raton, FL, Lewis Publishers, 180 pp.

Congalton, R. G., R. G. Oderwald and R. A. Meade, 1983. Assess- ing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing 49: 1671-1678.

Cummins, K. W., 1992. Invertebrates. In P. Calow and G. E. Petts, (eds.), The Rivers Handbook. Volume 1. Blackwell Scientific Publications, London, pp. 234-250.

Dunne, T. and L. B. Leopold, 1978. Water in Environmental Plan- ning. W. H. Freeman and Company, San Francisco, 818 pp.

Edwards, R. E., 1994. Susquehanna River Basin Water Quality Assess- ment. Susquehanna River Basin Commission, Harrisburg, PA.

Edwards, R. E., 1996. The Susquehanna River Basin Water Quality Assessment 305(B) Report. Susquehanna River Basin Commis- sion, Harrisburg, PA. Publication 176.

Erickson, E M., R. L. E Kleinmann, E. T. Posluszny and E J. Leonard-Mayer, 1982. Hydrogeochemistry of a Large Mine Pool. Presented at the First International Mine Water Congress, Budapest, Hungary.

EDAW, Inc., 1996. Earth Conservancy Land Use Plan, Luzerne County, Pennsylvania. Final Report to Earth Conservancy, Ashley, PA.

Environmental Protection Agency (EPA), 2001. Better Assessment Science Integrating Point and Nonpoint Sources (BASINS), User's Manual, Version 3.0, U.S. EPA Office of Water (4305), Washington, DC, EPA-823-H-01-001.

ERDAS, 1991. ERDAS Field Guide. ERDAS, Inc., Atlanta, GA, 628 pp.

Fitzpatrick, E A., B. C. Scudder, B. N. Lenz and D. J. Sullivan, 2001. Effects of multi-scale environmental characteristics on agricultural stream biota in eastern Wisconsin. Journal of the American Water Resources Association 37: 1489-1507.

Fore, L. S., J. R. Karr and R. W. Wisseman, 1996. Assessing inver- tebrate responses to human activities: Evaluating alternative ap- proaches. Journal of the North American Benthological Society 15: 212-231.

Forman, R. T. T. and M. Godron, 1986. Landscape Ecology. John Wiley, New York, pp. 619.

Gale, W. E, T. V. Jacobsen and K. M. Smith, 1976. Iron and its role in a river polluted by mine effluents. Proceedings of the Penn- sylvania Academy of Science 50: 182-195.

Gardner, R. H., R. V. O'Neill and M. G. Turner, 1993. Ecological implications of landscape fragmentation. In: S. T. A. Pickett and M. J. McDonnell, (eds.), Humans as components of ecosys- tems: subtle human effects and the ecology of populated areas. Springer-Verlag, New York, New York, pp. 208-226.

Gergel, S. E., M. G. Turner, J. R. Miller, J. M. Melack and E. H. Stanley, 2002. Landscape indicators of human impacts to river- ine systems. Aquatic Sciences 64: 118-128.

Gove, N. E., R. T. Edwards and L. L. Conquest, 2001. Effects of scale on land use and water quality relationships: a longitudinal

basin-wide perspective. Journal of the American Water Re- sources Association 37: 1721-1734.

Growitz, D. J., L. A. Reed and M. M. Beard, 1985. Reconnaissance of mine drainage in the coal fields of eastern Pennsylvania. U. S. Geological Survey. Water-Resources Investigation Report 83-4274. Harrisburg, PA.

Guydish, M., 2005. Troubled waters: Susquehanna tops list of nation's 10 endangered rivers. Times Leader, April 15, Wilkes- Barre, PA, pp. 1 and 12.

Hawkins, J. W., 1994. Statistical characteristics of coal-mine discharges of Western Pennsylvania remining sites. Water Re- sources Bulletin 30: 861-869.

Helsel, D. R., 1983. Mine Drainage and Rock Type Influences on Eastern Ohio Stream Water Quality. Water Resources Bulletin - 83114, American Water Resources Association.

Hem, J. D., 1985. Study and interpretation of the chemical charac- teristics of natural water. U.S. Geological Survey Water-Supply Paper 2254, Washington, DC.

Herlihy, A. T., P. R. Kaufmann, D. Brown and M. E. Mitch, 1990. Regional estimates of acid mine drainage impact on streams in the Mid-Atlantic and Southeastern United States. Water, Air, Soil Pollution 50: 91-107.

Holl, K. D. and J. Cairns, Jr., 1994. Landscape indicators in ecotoxi- cology. In D. J. Hoffman, B. A. Rattner, G. A. Burton, Jr., and J. Cairns, Jr., (eds.), Handbook of Ecotoxicology. Lewis Publish- ers, Boca Raton, FL, pp. 185-197.

Hynes, H. B. N., 1980. The Biology of Polluted Waters, University of Toronto Press, Toronto, Ontario, 202 pp.

Johnson, L. B. and S. H. Gage, 1997. Landscape approaches to the analysis of aquatic ecosystems. Freshwater Biology 37: 113-132.

Johnson, L. B., C. Richards, G. E. Host and A. W. John, 1997. Landscape influences on water chemistry in Midwestem stream ecosystems. Special Applied Issues Section - Freshwater Biol- ogy 37: 193-208.

Johnston, C. A., N. E. Detenbeck and G. J. Niemi, 1990. The cumulative effect of wetlands on stream water quality and quantity: a landscape approach. Biogeochemistry 10: 105- 141.

Jones, K. B., A. C. Neale, T. G. Wade, J. D. Wickham, C. L. Cross, C. M. Edmonds, T. R. Loveland, M. S. Nash, K. H. Riitters and E. R. Smith, 2001a. The consequences of landscape change on ecological resources: an assessment of the United States Mid- Atlantic Region, 1973-1993. Ecosystem Health 7: 229-242.

Jones, K. B., A. C. Neale, M. S. Nash, R. D. Van Remortel, J. D. Wickham, K. H. Riitters and R. V. O'Neill, 200lb. Predicting nutrient and sediment loadings to streams from landscape met- tics: A multiple watershed study from the United States Mid- Atlantic Region. Landscape Ecology 16: 301-312.

Jones, K. B., K. H. Riitters, J. D. Wickham, R. D. Tankersley Jr., R. V. O'Neill, D. J. Chaloud, E. R. Smith and A. C. Neale, 1997. An Ecological Assessment of the United States Mid-Atlantic Region: A Landscape Atlas. U. S. EPA, Office of Research and Development. Washington, DC. EPA/600/R-97/130.

Jordan, W. R., M. E. Gilpin and J. D. Aber, 1987. Restoration Ecol- ogy. Cambridge University Press, Cambridge, 342 pp.

Kiffney, P. M. and W. H. Clements, 1996. Effects of heavy metals on stream macroinvertebrate assemblages from different eleva- tions. Ecological Applications 6: 472-481.

King, R. S., M. E. Baker, D. E Whigham, D. E. Weller, T. E. Jordan, P. E Kazyak and M. K. Hurd, 2005. Spatial considerations for linking watershed land cover to ecological indicators in streams. Ecological Applications 15: 137-153.

Ladwig, K. J., P. M. Erickson, R. L. P. Kieinmann and E. T. PUs- luszny, 1984. Stratification in Water Quality in Inundated Anthracite Mines, Eastern Pennsylvania. United States Depart- ment of the Interior. Bureau of Mines Report of Investigations, 8837.

Lammert, M. and J. D. Allen, 1999. Environmental auditing - as- sessing biotic integrity of streams: effects of scale in measuring

422 D.A. Bruns Mining-impacted river: A GIS watershed analysis

the influence of land use/cover and habitat structure on fish and macroinvertebrates. Environmental Management 23: 257-270.

Lenat, D. R. and D. L. Penrose, 1996. History of the EPT taxa richness metric. Bulletin of the North American Benthological Society 13: 305-307.

Leopold, L. B., 1994. A View of the River. Harvard University Press, Cambridge, Mass., London, England, 298 pp.

Likens, G. E., 1985. The aquatic ecosystem and air-land-water in- teractions. In: G. E. Likens (ed.), An Ecosystem Approach to Aquatic Ecology: Mirror Lake and Its Environment. Springer- Verlag New York, New York, pp. 430-435.

Manahan, S. E., 1984. Environmental Chemistry. Willard Grant Press, Boston, MA, 612 pp.

Merritt, R. W. and K. W. Cummins, (eds,), 1984. An Introduction to the Aquatic Insects of North America. Kendall/Hunt, Dubuque, Iowa, 722 pp.

Meyer, W. B. and B. L. Turner, (eds.), 1994. Changes in Land Use and Land Cover: A Global Perspective. New York, New York. Cambridge University Press, 554 pp.

Minshall, G. W., K. W. Cummins, R. C. Petersen, C. E. Cushing, D. A. Bruns, J. R. Sedell and R. L. Vannote, 1985. Developments in stream ecosystem theory. Canadian Journal of Fisheries and Aquatic Sciences 42" 1045-1055.

Monastersky, R., 1994. Earthmovers: humans take their place along- side wind, water, and ice. (Humans' impact on Earth's surface). Science News 146" 432-433.

Morley, S. A. and J. R. Karr, 2002. Assessing and restoring the health of urban streams in the Puget Sound Basin. Conservation Biology 16: 1498-1509.

National Research Council, 1992. Restoration of Aquatic Ecosys- tems. National Academy Press. Washington, DC, 576 pp.

National Research Council, 1993. The role of terrestrial ecosystems in global change. National Academy Press. Washington, DC.

O'NeiU, R. V., C. T. Hunsaker, K. B. Jones, K. J. Riitters, J. D. Wickham, R Schwarz, I. A. Goodman, B. Jackson and W. S. Baillargeon, 1997. Monitoring environmental quality at the landscape scale. Bioscience 47:513-519.

Ott, A. N., C. S. Takita, R. E. Edwards and S. W. Bollinger, 1991. Loads and Yields of Nutrients and Suspended Sediment Trans- ported in the Susquehanna River Basin, 1985-1989, Publ. #136, Susquehanna River Basin Commission, Resource Quality Man- agement and Protection Division, Harrisburg, PA.

Paul, M. J. and J. L. Meyer, 2001. Streams in the urban landscape. Annual Review of Ecology and Systematics 32" 333-365.

Peckarsky, B. L., R R. Fraissinet, M. A. Penton and D. J. Conklin, Jr., 1990. Freshwater Macroinvertebrates of Northeastern North America. Cornell University Press. Ithaca, New York, 442 pp.

Pennington, A. T., A. K. Harding, C. W. Hendricks and H. M. K. Campbell, 2001. Environmental auditing: evaluating microbial indicators of environmental condition in Oregon rivers. Envi- ronmental Management 28: 833-841.

Plafkin, J. L., M. T. Barbour, K. D. Porter, S. K. Gross and R. M. Hughes, 1989. Rapid Bioassessment Protocols for Use in Streams and Rivers: Benthic Macroinvertebrate and Fish. U. S. EPA Office of Water (WH-553), Washington, DC. EPA/444/4- 89-001.

Resh, V. H., M. J. Meyers and M. J. Hannaford, 1996. Macroinver- tebrates as biotic indicators of environmental quality. In: E R. Hauer and G. A. Lamberti, (eds.), Methods in Stream Ecology. Academic Press, New York, New York. pp. 647-667.

Richards, C. and G. E. Host, 1994. Examining land use influences on stream habitats and macroinvertebrates: a GIS approach. Water Resources Bulletin 30: 729-737,

Richards, C., L. B. Johnson and G. E. Host, 1996. Landscape-scale influences on stream habitats and biota. Canadian Journal of Fisheries and Aquatic Sciences 53 (Suppl. 1): 295-311.

Rosgen, D., 1996. Applied River Morphology. Willdland Hydrol- ogy, CO, 390 pp.

Samuel, D. E., J. R. Stauffer, C. H. Hocutt and W. T. Mason, Jr., (eds.), 1978. Surface Mining and Fish/Wildlife Needs in the

Eastern United States. Proceedings of a Symposium. West Vir- ginia University Fish and Wildlife Service, U. S. Department of the Interior. FWS/OBS-78/81.

Sparling, D. W., 1995. Acidic deposition: a review of biological effects. In: D. J. Hoffman, B. A. Rattner, G. A. Burton, Jr. and J. Cairns, Jr., (eds.), Handbook of Ecotoxicology. CRC Press, Lewis Publishers, Boca Raton, FL, pp. 301-329.

Sponseller, R. A., E, E Benfield and H. M. Valett, 2001. Relation- ships between land use, spatial scale and stream macroinverte- brate communities. Freshwater Biology 46: 1409-1424.

Stewart, J. S., L. Wang, J. Lyons, J. A. Horwatich and R. Banner- man, 2001. Influences of watershed, riparian-corridor, and reach scale characteristics on aquatic biota in agricultural wa- tersheds. Journal of the American Water Resources Association 37: 1475-1487.

Stranahan, S. Q., 1993. Susquehanna, River of Dreams. The John Hopkins University Press. Baltimore and London, 322 pp.

Technology Planning and Management Corporation, 1999. A Guide to Land Cover and Land Use Information in the MAIA Region. EPA Contract No. 68-D-98-002 Work Assignment 99-05. Fort Meade, MD. U.S. Environmental Protection Agency.

Tong, S. T. Y., 2001. An integrated exploratory approach to ex- amining the relationships of environmental stressors and fish responses. Journal of Aquatic Ecosystem Stress and Recovery 9: 1-19.

Tong, S. T. Y. and W. L. Chen, 2002. Modeling the relationship between land use and surface water quality. Journal of Environ- mental Management 66: 377-393.

Turner, M. G., S. E. Gergel, M. D. Dixon and J. R. Miller, 2004. Distribution and abundance of trees in floodplain forests of the Wisconsin River: environmental influences at different scales. Journal of Vegetation Science 15: 729-738.

Van Sickle, J., 2003. Analyzing correlations between stream and watershed attributes. Journal of the American Water Resources Association 39: 717-726.

Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell and C. E. Cushing, 1980. The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37: 130-137.

Vaughan, G. L., A. Talak and R. J. Anderson, 1978. The chronol- ogy and character of recovery of aquatic communities from the effects of strip mining for coal in East Tennessee. In: D. E. Samuel, J. R. Stauffer, C. H. Hocutt and W. T. Mason, Jr., (eds.), Surface Mining and Fish/Wildlife Needs in the Eastern United States: Proceedings of a Symposium. West Virginia University Fish and Wildlife Service, U. S. Department of the Interior. FWS/OBS-78/81, pp. 119-125.

Venesky, T., 2005. Susquehanna tabbed "most endangered:" sew- age, mine acid drainage woes cited. Citizens' Voice, April 14, Wilkes-Barre, PA, pp. 4 and 52.

Vitousek, R M., 1994. Beyond global warming: Ecology and global change. Ecology 75:1861-1876.

Vogelmann, J. E., S. M. Howard, L. Yang, C. R. Larson, B. K. White and N. Van Driel, 2001. Completion of the 1990s National Land Cover Data Set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogrammetric Engineering and Remote Sensing 67: 650-662.

Wallace, J. B., J. W. Grubangh and M. R. Whiles, 1996. Biotic indi- ces and stream ecosystem processes: results from an experimen- tal study. Ecological Applications 6: 140-151.

Wang, L., J. Lyons, P. Kanehl, R. Bannerman and E. Emmons, 2000. Watershed urbanization and changes in fish communities in southeastern Wisconsin streams. Joumal of the American Water Resources Association 36: 1173-1189.

Wang, L., J. Lyons, P. Kenehl and R. Bannerman, 2001. Impacts of urbanization on stream habitat and fish across multiple spatial scales. Environmental Management 28: 255-266.

Wang, L., J. Lyons, P. Kenehl and R. Gatti, 1997. Influences of watershed land use on habitat quality and biotic integrity in Wisconsin streams. Fisheries 22: 6-12.

Aquat. Sci. Vol. 67, 2005 Research Ar t ic le 423

Washington, H. G., 1984. Diversity, biotic and similarity indices: a review with special reference to aquatic ecosystems. Water Resources 18: 653-694.

Webb, B. W. and D. E. Wailing, 1992. Water Quality. II. Chemical Characteristics. In: R Calow and G.E. Petts, (eds.), The Rivers Handbook.Volume 1. Blackwell Scientific Publications, Lon- don, pp. 73-100.

Weinberg, H., 1996. EMAP Data. Memorandum to Xiaoming Yang (Wilkes University). Chesapeake Bay Program Office. Annapo- lis, MD.

Wessman, C. A., 1992. Spatial scales and global change: bridging the gap from plots to GCM grid cells. Annual Review of Ecol- ogy and Systematics 23: 175-200.

Wickham, J. D., K. B. Jones, K. H. Riitters, R. V. O'Neill, R. D. Tankersley, E. R. Smith, A. C. Neale and D. J. Chaloud, 1999.

Environmental auditing: an integrated environmental assess- ment of the US Mid-At/antic Region. Environmental Manage- ment 24: 553-560.

Wiersma, G. B. and D. A. Bruns, 1996. Monitoring for ecological assessment. In: C. A. Bravo, (ed.), North American Workshop on Monitoring for Ecological Assessment of Terrestrial and Aquatic Ecosystems. U. S. Department of Agriculture, Forest Service. General Technical Report RM-GTR-284.

Wirries, D. L. and A. J. McDonnell, 1983. Drainage quality at deep coal mine sites. Water Resources Bulletin 19: 235-240.

Wood, C. R., 1996. Water Quality of the 100 Largest Mine Dis- charges in the Anthracite Region of Eastern Pennsylvania. U. S. Geological Smwey. Water Resources Investigation Report 95-4243. Pennsylvania Department of Environmental Resources, Bureau of Topographic and Geologic Survey, Lemoyne, PA.

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