Macroinvertebrate response to land cover, habitat, and water ...

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 Article

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 richness, percent filterers), benthic substrates (including deposits from mine waters), and 10 water chemistry parameters. 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 identified six macroinvertebrate parameters (e. g., EPT richness, 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 richness. This mining affect was confirmed with dissolved iron and sulfate concentrations and levels of sedimentation and iron deposition explaining variability across several macroinvertebrate parameters. Comparison to the published literature 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: [email protected] Published Online First: November 21, 2005

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Introduction There has been strong scientific consensus that changing land use is the single most important component of global environmental change affecting ecological systems (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 monitoring, 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 resources by humans. Land use change is threatening water quality world-wide and as Vitousek (1994) has indicated, "any dichotomy between pristine ecosystems and human-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 multiple 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 encompassing 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 macroinvertebrate community response may yield numerous statistical findings that overestimate the importance of these relationships or result in spurious associations. As a more appropriate statistical approach, Van Sickle (2003) recommends multiple regression when one wishes to analyze each stream response variable separately relative to land

Mining-impacted river:A GIS watershedanalysis 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), multidimensional 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 ecological response of streams and rivers have covered a range of applications based on the use of GIS and remote sensing data. These have included investigations of agricultural watersheds (Richards et al., 1996; Lammert and Allan, 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 adjacent states; these U. S. Environmental Protection Agency (EPA) watershed assessments in this region have encompassed a range of anthropogenic impacts from agricultural 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 northeastern 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 recently, 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 Conservancy) had just purchased 6,478 ha of abandoned mining lands in this area of the anthracite fields near WilkesBarre, PA (EDAW, 1996). Because the previous company owning these abandoned mining lands had declared bankruptcy, 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

Aquat. Sci. Vol.67, 2005 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 water 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 perturbations, a GIS-RS approach was employed toward sampling design and analysis (Bruns et al., 1997a; Bruns and Wiersma, 2004) based on the coupling of terrestrialaquatic 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 environmental 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 environmental managers to monitor stream and river water quality 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 focused 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

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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 Statistical 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.

Methods 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 substrates, 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 resolution 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 process whereby homogeneous pixels were converted from

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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 transform 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 outside the mining areas, the barren cover class (at low percent 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 mountain watersheds and communities just outside the mining region, not included in this study. For the field accuracy assessments of the SPOT imagery (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 individual 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 sufficiently accurate so that not all 50 pixels were actually

Mining-impacted river: A GIS watershed analysis visited. In addition, because urban areas were intermingled 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 adjustments were made, 239 pixels across all five cover classes were visited in the field and logged with GPS for later accuracy analyses based on published methods (Congalton, 1991; Congalton and Green, 1993, 1999; Congalton et al., 1983). Overall accuracy of the processed SPOT image was 82 % which is comparable to other related studies of fairly high accuracy (see review in Bruns andYang, 2002). Water (100%) and forest (94%) had the highest producer (Congalton and Green, 1999, terminology) accuracy for the SPOT imagery (Bruns and Yang, 2002) while agriculture had both the lowest producer and lowest 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 WilkesBarre. However, other data were obtained for classified 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 river 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

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Figure 1. Map of study site for GIS watershed study (North Branch of the Susquehanna River) near Wilkes-Barre, PA. Sites 1 4 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 laboratory technician sampled and analyzed 8 physical habitat variables, 10 chemical parameters, and 17 macroinvertebrate 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 composition (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 - 4 c m 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 relative to documentation, data entry checks, and instrument calibration and accuracy checks. Stream and river macroinvertebrate communities (Table 2) were sampled at

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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). A l l organisms were counted in each sample and no subsampling was conducted. Standard taxonomic keys (Merritt and Cummins, 1984; Peckarsky et al., 1990) were used to identify organisms to their reasonable, lowest taxonomic level, which was usually genus for Ephemeroptera, Trichoptera, and Plecoptera, or family for Diptera. Total taxa richness, EPT richness, 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).

S t u d y 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 (digitized from U.S. Geological Survey topographic maps at

1:24,000 scale), land cover (SPOT imagery), land ownership (Earth Conservancy parcels), and hydrography were employed and large, hard-copy maps were created for reference b y 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 exist (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 m i x e d conifers). To fill out the study design and increase sampling size for statistical analysis, another 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 downstream vicinity o f 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 river's potential for community recreation, its importance as a regional environmental resource, lack

Aquat. Sci. Wol.67, 2005 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 factors 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 catchment 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 approach looking only at whole catchment relationships was followed for the present study also. Field observations 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 environmental 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 macroinvertebrate communities. However, their study area encompassed the Coastal Plain (12,900 km 2) of the upper Chesapeake Bay and included many large watersheds in the Washington, DC and Baltimore, MD metropolitan areas. Impacted and reference tributary watersheds in the present

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study were comparatively very small (e.g., l l 9 k m 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 environmental scope of work by one of the co-sponsors (Earth Conservancy) 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 uncommon 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 ephemeral 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 composition, 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 substratehabitat 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 recommended analyses to address relationships between biotic and abiotic variables that may co-vary and yield spurious results when assessed by means of bi-variate correlations. PCA, correlations of PCA axes with macroinvertebrate parameters, and step-wise multiple regression analysis were all conducted in SPSS (13.0 for Windows).

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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. Site Grouping

Urban

Barren

Forest

Agriculture

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

14

3-9

87-96

0-1

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

21-42

24--30

23-45

3-9

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

12-15

6

48-73

9-20

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

12

20

67

1

30-35

15-16

47-51

1-2

4

0

60

35

-

Intermediate mining and high urban - 2 sites (Nos. 12 and 13) - stream order (3) River sites 4 sites (Nos. 14, 15, 16, 17) - stream order (8) -

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). Table

Site Grouping

Bldrs.

Cobbles

Gravel

Sand

Iron-Ox.

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

15-50

10-35

1-15

1-10

0

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

2-20

0-80

0-30

1-3

0-3

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

0-20

20-50

1-70

5-10

0

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

10

50

25

5

0

Intermediate mining and high urban 2 sites (Nos. 12 and 13) - stream order (3)

0

10-50

20-30

10-50

0-3

10-50

5-45

5-15

3-10

0-1

-

-

-

-

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

Results

Land

cover

In general, quantitative (GIS) analysis o f land c o v e r abundance supported the visual, h a r d - c o p y m a p observations used in the study design and site selection process described above. First and second order stream sites used for c o m p a r a t i v e r e f e r e n c e (Table 3) had low land c o v e r ( 1 - 4 percent) for urban and barren categories and w e r e m o r e than 85 % forests (secondary growth) on

a w a t e r s h e d or subcatchment basis. T h e s e sites w e r e in sharp contrast to similar sized subcatchments that were selected to h i g h l i g h t impacts f r o m m i n i n g ( 2 4 - 3 0 percent barren l a n d cover) and urban d e v e l o p m e n t ( 2 1 - 4 2 percent land cover) and w h e r e forest c o v e r was always less than 50 p e r c e n t (Table 3). O n a c a t c h m e n t basis, the four river sites demonstrated 60 percent and 35 percent for forests and agriculture, respectively; barren was less than 1 p e r c e n t and urban only at 4 percent land c o v e r for the river watershed. Thus, these sites appeared to reflect

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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

Low mining, low urban - 4 sites (Nos. 1, 2, 3, 4) High mining, high urban - 4 sites (Nos. 5, 6, 7, 8) Low mining, intermediate urban - 2 sites (Nos. 9 and I0) Intermediate mining and urban - 1 site(No. 11) Intermediate mining and high urban - 2 sites (Nos. 12 and 13) River sites - 4 sites (Nos. 14, 15, 16, 17)

pH

Acid.

Alk.

Sulf.

Nitr.

Fe

TDS

6.0-7.3

10

10-40

8-16

193-352

.1

50-300

3.4-7.7

10-101

0-178

122-959

100-331

.1-5.4

470-1760

6.9-7.8

5-30

30-35

18-21

no data

.1

90-100

6.9

4

16

13

183

.2

220

6.4-8.2

1-152

59-99

25-700

136-490

.6-37.6

290-1400

7.7-8.9

1-10

40-90

59-79

8-150

.7-1.6

320-420

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).

Physical

habitat

Substrate patterns (Table 4) also reflected general groupings of study sites: boulders were highest in relative 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 portions 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: 0.0020.05 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 11801290cfs 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 groupings based on land cover.

Chemical

parameters

Alkalinity and pH both were at sufficiently high levels at all mining sites (Table 5) except for one "high mining" 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 subcatchments where mining was high or intermediate as a barren 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 consistently lower at the river sites. Table 5 provides summary ranges of these chemical variables relative to study site groupings.

Macroinvertebrates

The range of macroinvertebrate indicators for stream assessment (Table 6) was somewhat variable but several trends were apparent. First, both richness and EPT richness 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 parameters were lowest where the barren (mining) land cover class was high or intermediate; richness and EPT richness values were moderate on a watershed where mining was low with only intermediate amounts of urban development. 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 mining. 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 Abundance (numbers), Dipt-R = Diptera Richness, Dipt-A = Diptera Abundance (numbers). Site Grouping

Riehn.

EPT-R

EPT-A

Dipt-R

Dipt-A

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

8-13

3-7

1-74

1-3

13-32

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

2-8

0-2

0-17

1-3

11-114

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

4-15

1-6

6-77

1-2

3-6

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

4

2

9

1

3

1-6

0-2

0-7

1-2

1-29

14-18

5-9

22-166

1-2

10-57

-

-

Intermediate mining and high urban - 2 sites (Nos. 12 and 13) - stream order (3) River sites 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. Site Grouping

Ephem.

Plee.

Trie.

Chiro.

Gastro.

Gamm.

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

1-33

6-44

18-23

18-27

0

0

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

0

0

2-41

18-99

0-1

0

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

1-12

0-1

21-55

1-4

0-6

0-7

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

7

7

62

11

0

0

Intermediate mining and high urban - 2 sites (Nos. 12 and 13) - stream order (3)

0

0

0-6

20-84

0-1

0-1

8-22

0-1

1-46

6-19

7-28

2-25

-

-

-

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

in areas w i t h h i g h m i n i n g l a n d c o v e r a n d l o w e s t w h e r e u r b a n l a n d c o v e r w a s i n t e r m e d i a t e to h i g h . O t h e r m a c r o i n v e r t e b r a t e trends were also e v i d e n t f r o m the p e r s p e c t i v e o f h o w sites w e r e g r o u p e d a c c o r d i n g to levels o f m i n i n g a n d u r b a n land cover. Tables 7 a n d 8 summ a r i z e t h e s e o t h e r m a c r o i n v e r t e b r a t e p a r a m e t e r s . T h e relative a b u n d a n c e o f E p h e m e r o p t e r a g e n e r a l l y f o l l o w e d the p a t t e r n for E P T r i c h n e s s w h i l e P l e c o p t e r a was only m o s t p r e v a l e n t in the r e f e r e n c e s m a l l - o r d e r streams. T r i c h o p t e r a a p p e a r e d to b e w e l l - r e p r e s e n t e d at all sites e x c e p t w h e r e

u r b a n land c o v e r was high; at t h e s e h i g h l y u r b a n i z e d sites and subcatchments, Chironomidae reached their highest relative a b u n d a n c e but w e r e r e p r e s e n t e d at all sites a n d groupings. G a s t r o p o d a and G a m m a r i d a e w e r e distinctly h i g h e s t in p e r c e n t c o m p o s i t i o n at t h e four fiver sites.

Principal

component

analysis

P C A w a s c o n d u c t e d o n 17 m a c r o i n v e r t e b r a t e p a r a m e t e r s across the 17 s a m p l i n g sites a n d s u b c a t c h m e n t s . P C A axis

Aquat. Sci. Vol.67, 2005

Research

Article

413

8. Range of macroinvertebrate functional feeding group percent relative abundance for stream assessment. Coll.-Gath. = CollectorGatherers. Table

Site Grouping

Coll.-Gath.

Shredders

Scrapers

Filterers

34-50

0-7

1-25

10-22

41-99

0-14

0-2

0-26

Low mining, intermediate urban - 2 sites (Nos. 9 and 10) - stream order (1-3) Intermediate mining and urban - 1 site (No. 11) - stream order (2)

29-65

1-6

4-14

23-33

24

1

0

59

Intermediate mining and high urban - 2 sites (Nos. 12 and 13) - stream order (3)

80-88

0-1

0-1

0-6

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

28-53

0-1

13-42

1-33

Low mining, low urban 4 sites (Nos. 1, 2, 3, 4) - stream order (1-2) High mining, high urban - 4 sites (Nos. 5, 6, 7, 8) - stream order (1-2)

-

-

T a b l e 9. PCA axis correlations with macroinvertebrate parameters. R = correlation coefficient. Only the three highest statistically significant correlations are reported for each axis. PCA axis

Macroinvertebrate parameter

R

P