Hierarchical classification of stream condition: a house–neighborhood framework for establishing conservation priorities in complex riverscapes

BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, researchlibraries, and research funders in the common goal of maximizing access to critical research.

Hierarchical classification of stream condition: a house–neighborhood frameworkfor establishing conservation priorities in complex riverscapesAuthor(s): George T. Merovich, Jr. and J. Todd Petty Michael P. Strager Jennifer B. FultonSource: Freshwater Science, 32(3):874-891. 2013.Published By: The Society for Freshwater ScienceDOI: http://dx.doi.org/10.1899/12-082.1URL: http://www.bioone.org/doi/full/10.1899/12-082.1

BioOne (www.bioone.org) is a nonprofit, online aggregation of core research in the biological, ecological, andenvironmental sciences. BioOne provides a sustainable online platform for over 170 journals and books publishedby nonprofit societies, associations, museums, institutions, and presses.

Your use of this PDF, the BioOne Web site, and all posted and associated content indicates your acceptance ofBioOne’s Terms of Use, available at www.bioone.org/page/terms_of_use.

Usage of BioOne content is strictly limited to personal, educational, and non-commercial use. Commercial inquiriesor rights and permissions requests should be directed to the individual publisher as copyright holder.

http://dx.doi.org/10.1899/12-082.1

http://www.bioone.org/doi/full/10.1899/12-082.1

http://www.bioone.org

http://www.bioone.org/page/terms_of_use

Hierarchical classification of stream condition: a house–neighborhood framework for establishing conservation priorities in

complex riverscapes

George T. Merovich, Jr.1 AND J. Todd Petty2

Division of Forestry and Natural Resources, West Virginia University, Morgantown,West Virginia 26506-6125 USA

Michael P. Strager3

Division of Resource Management, West Virginia University, Morgantown,West Virginia 26506-6125 USA

Jennifer B. Fulton4

Office of Monitoring and Assessment, US Environmental Protection Agency Region 3,1060 Chapline Street, Suite 303, Wheeling, West Virginia 26003-2995 USA

Abstract. Despite improved understanding of how aquatic organisms are influenced by environmentalconditions at multiple scales, we lack a coherent multiscale approach for establishing stream conservationpriorities in active coal-mining regions. We classified watershed conditions at 3 hierarchical spatial scales,following a house–neighborhood–community approach, where houses (stream segments) are embeddedwithin neighborhoods (Hydrologic Unit Code [HUC]-12 watersheds) embedded within communities(HUC-10 watersheds). We used this information to develop a framework to prioritize restoration andprotection in two HUC-8 watersheds in an intensively mined region of the central Appalachians. We usedlandscape data to predict current conditions (water chemistry and macroinvertebrate biotic integrity) forall stream segments with boosted regression tree (BRT) analysis. Mining intensity, distance to mining, andcoal type were the dominant predictors of water quality and biological integrity. A hardness–salinitydimension of the water-chemistry data was explained by land-cover features and stream elevation. Wecompiled segment-level conditions to the HUC-12 and HUC-10 watershed scales to represent aquaticresource conditions hierarchically across 3 watershed-management scales. This process enabled us to relatestream-segment watershed conditions to watershed conditions in the broader context, and ultimately toidentify key protection and restoration priorities in a metacommunity context. Our hierarchicalclassification system explicitly identifies stream restoration and protection priorities within a HUC-12watershed context, which ensures that the benefits of restoration will extend beyond the stream reach.Highest protection priorities are high-quality HUC-12 watersheds adjacent to low-quality HUC-12watersheds. Highest restoration priorities are HUC-12 watersheds in poor–fair condition within HUC-10watersheds of good–excellent condition, whereas lowest restoration priorities are isolated HUC-12watersheds. In high-priority HUC-12 watersheds, stream segments with the highest restoration priority arethose that maximize watershed-scale restorability. A similar process for classifying conditions andrestoration priorities may be valuable in other heavily impacted regions where strategic approaches areneeded to maximize watershed-scale recovery.

Key words: freshwater restoration and protection, watershed classification, landscape models, boostedregression trees, predictive models, stream ecosystem integrity, water quality, watershed management,coal mining, acid mine drainage, acid rain, benthic macroinvertebrates.

Watershed monitoring programs are important forgathering information on conditions of riverineecosystems (Mazor et al. 2006). These efforts haveexpanded greatly over the last few decades at local,

1 E-mail addresses: [email protected] [email protected] [email protected] [email protected]

Freshwater Science, 2013, 32(3):874–891’ 2013 by The Society for Freshwater ScienceDOI: 10.1899/12-082.1Published online: 2 July 2013

874

regional, and national levels for the purpose ofassessing the quality of surface water (Jones et al.2001). Attempts to sample all possible stream reachesare simply impractical because of sheer numbers, sosampling strategies of most larger-scale monitoringprograms are designed so statistical inferences arevalid across a region of interest (Carlisle et al. 2009).For example, assessment of streams within the Mid-Atlantic Highlands by the US Environmental Protec-tion Agency (EPA) indicated that .70% are severelyor moderately impaired by human-related stressors(USEPA 2000). However, despite the validity ofinferences, specific information about conditions ofunsampled streams remains unavailable. This infor-mation is necessary to identify where assessment andrestoration activities are needed (Petty and Thorne2005, Poplar-Jeffers et al. 2009, Rivers-Moore et al.2011).

Freshwaters are among the most threatened eco-systems on earth, but advanced principles forconserving these resources are only beginning to takeshape (Abell et al. 2007, Turak and Linke 2011).Impairments are too numerous, varied, and wide-spread for case-by-case or worst-first approaches torestoration to be effective (Norton et al. 2009), andlocking up pristine or unique riverine environmentsin protected areas does not guarantee success (Bar-muta et al. 2011). Riverine environments are topolog-ically complex and have a hierarchical structure withupstream–downstream connections that weaken adhoc approaches to conservation (Frissell et al. 1986,Hitt and Angermeier 2008a, Linke et al. 2011).Therefore, structured decision-support systems basedin ecological theory and riverine science are neededfor strategic prioritization of impaired stream seg-ments for restoration and protection of existing high-quality aquatic resources (Petty et al. 2010, Turak et al.2011).

The complexity that results from hierarchical,multispatial-scale influences on riverine conditionpresents challenges to watershed managers who mustprioritize restoration efforts. First, the scale must beidentified at which restoration priorities should be set.Managers cannot ignore the condition of the water-sheds within which potential restoration sites arenested (McClurg et al. 2007, Finn et al. 2011, Louhi etal. 2011, Sundermann et al. 2011). Second, informationis needed on the condition of all stream reacheswithin the stream network, subwatershed, and wa-tershed (Rivers-Moore et al. 2011). Advances inspecies-distribution modeling and predictive-model-ing algorithms (e.g., boosted regression trees [BRT],multivariate adaptive regression splines) have made itpossible to develop and validate models that predict

conditions in all river segments with predictorvariables available from remote sensing and geo-graphic information system (GIS) technologies (Stra-ger et al. 2009, Linke et al. 2011). Models that relatelandscape features to stream ecosystem condition arebecoming commonplace. For example, Merriam et al.(2011) found that large-scale surface mining stronglyimpaired water chemistry, but not stream habitatquality, whereas residential density was more strong-ly related to impaired physical conditions. Petty et al.(2010) showed that stream water quality and macro-invertebrate communities depended on interactiveeffects of coal type, mining intensity, and distance tomining in coal-mining watersheds. Jones et al. (2001)showed that the amount of agriculture, riparianforests, N deposition, and roads explained N and Ploading in streams in the Mid-Atlantic Highlands.Landscape-level variables are obviously important inexplaining, at least in part, patterns of water chem-istry and biotic communities at the stream–reachscale. Therefore, landscape-based predictive modelscould provide crucial information about unsampledstreams where in-stream data are lacking (Carlisleet al. 2009).

The ability to describe conditions at watershedscales based on information from all stream segmentsmight be even more important than the ability topredict local stream conditions. A fundamentalcharacteristic of stream networks and watersheds ishierarchical structure (Frissell et al. 1986), whichstrongly influences ecological pattern and process(Poff 1997). For example, fish species richness andindices of biotic integrity (Osborne et al. 1992,Osborne and Wiley 1992, Hitt and Angermeier2008b, 2011) depend on the position of a focal reachin the stream network. Distributions of fish speciesare influenced by hierarchical effects of environmen-tal variables (Hopkins and Burr 2009). Likewise,community composition depends on local conditionsand on conditions at larger spatial scales that mayinteract with local processes (Poff 1997, Black et al.2004). The similarity of 2 communities may dependmore on regional and historical factors than on localenvironmental conditions (Tonn et al. 1990). Benthicmacroinvertebrate communities are highly variable instreams with impaired water quality, and some of thisvariance can be explained by proximity to unimpairedstreams within the drainage network (Merovich andPetty 2010). We expect that the ability of invertebratesto recover from disturbance will depend, in part, onthe proximity and dispersal abilities of potentialcolonizers and watershed connectivity, i.e., the con-dition of the subwatershed within which a degradedstream reach exists (Strager et al. 2009, Petty et al.

2013] HIERARCHICAL CLASSIFICATION FOR RIVER CONSERVATION 875

2010, Brown et al. 2011). The capability to quantifyconditions at multiple spatial scales would be a usefulcomponent of a system for classifying aquaticecosystem condition for purposes of managing resto-ration and protection efforts (Petty et al. 2010).

We developed a hierarchical classification system toclassify watershed condition across multiple spatialscales in an intensively mined region of north-centralWest Virginia. We used multiple indicators of streamcondition that included information on landscapeattributes, water quality, and benthic macroinverte-brates to capture anthropogenic effects in multipledimensions. Our objectives were to first develop andvalidate watershed models that relate landscapecharacteristics to in-stream water quality and ecolog-ical condition. We applied these empirical models topredict conditions in all unsampled stream segmentsand quantified water quality, biological condition,and an integrated measure of watershed conditionacross 3 important spatial scales. We then used thisinformation and a house–neighborhood–communityreal-estate analogy to develop a system for prioritiz-ing stream restoration and protection strategies atmultiple scales.

Methods

Study area

Our study area was the Cheat and Tygart Valleyriver watersheds, both 8-digit Hydrologic Unit Code(HUC-8) watersheds in north-central West Virginia(Seaber et al. 1994). Combined, the watersheds drain,7957 km2 of land in the upper Monongahela Riversystem. The region is rural (,1% urban) and landcover is dominated by forests (.80%) (Table 1).However, acid mine drainage (AMD) resulting froma long history of coal mining is a large source ofimpairment to stream water quality and biologicalcondition in the region (Demchak et al. 2004, Freundand Petty 2007). Coal is mined primarily in the lowerportion of each watershed from Pennsylvanian stratawithin the Allegheny formation (Kittanning andFreeport coal) and the Conemaugh formation (Bakers-town coal) (Merovich et al. 2007). Low bufferingcapacity in these geologic strata, mining of high-Scoals, and associated coal wastes combine to producehighly acidic mine drainage rich in dissolved metals(Skousen et al. 2000). Acid precipitation also severe-ly impairs numerous kilometers of high-elevation

TABLE 1. Mean (SD) of watershed variables in the study area (Total) and by watershed (Cheat and Tygart Valley). All datawere collected at the segment-level-watershed (SLW) scale and accumulated (except for mean elevation) to the focal SLW from allupstream watersheds connected by flow. All variables were used in boosted regression tree (BRT) analysis as predictors of waterquality and biological condition. Note: % other coal refers to the percentages of coals other than Pittsburgh and UpperFreeport coals.

Variable Cheat Tygart Valley Total

Geography

Mean elevation (m) 785.7 (216.0) 626.0 (153.9) 694.3 (199.3)Drainage area (km2) 128.6 (487.8) 77.9 (358.0) 99.6 (419.3)

Coal geology and mining

% Bakerstown 5.0 (15.6) 8.2 (24.1) 6.8 (20.9)% Pittsburgh 0.1 (2.1) 1.7 (8.7) 1.0 (6.8)% Sewell 12.4 (31.2) 3.1 (16.0) 7.1 (24.3)% Upper Freeport 14.9 (30.0) 16.6 (32.0) 15.9 (31.1)% Upper Kittanning 1.1 (7.1) 2.3 (10.2) 1.8 (9.0)% Lower Kittanning 3.6 (15.9) 17.9 (32.4) 11.7 (27.5)% other coal 22.0 (36.6) 31.4 (41.4) 27.4 (39.7)% underground mining 0.4 (3.7) 1.8 (9.7) 1.2 (7.8)% surface mining 0.4 (2.8) 0.5 (3.7) 0.5 (3.3)Minimum distance to upstream mining (km) 26.9 (10.6) 23.0 (11.8) 24.7 (11.4)Mining intensity 2.3 (5.3) 4.9 (7.5) 3.8 (6.8)

Land use

% urban 1.6 (5.9) 0.4 (2.1) 0.9 (4.2)% agriculture 5.4 (8.7) 9.7 (12.8) 7.8 (11.4)% forest 86.6 (16.0) 81.3 (20.5) 83.5 (18.9)

Surficial geology

% limestone 6.3 (12.9) 1.5 (5.3) 3.5 (9.6)% sandstone 32.2 (32.5) 40.3 (40.6) 36.9 (37.6)% shale 61.0 (33.0) 55.1 (39.5) 57.7 (37.0)

876 G. T. MEROVICH ET AL. [Volume 32

mountain streams in the region (Petty and Thorne2005). High rates of acid deposition depress pH andelevate dissolved Al concentrations in surface waters(Driscoll et al. 2001).

In-stream data

Water chemistry.—We collected water samples at123 sites across the range of stream size/watershedarea, elevation, geology, and water quality through-out the Cheat and Tygart Valley river watersheds. Wechose sites to minimize spatial connection by flow asmuch as possible, so that no sample was a function ofanother sample directly upstream. Water quality inthese mined watersheds can be highly variable amongseasons, especially for moderately impaired streams,but Petty and Barker (2004) showed that watersamples from dry and wet seasons accurately charac-terize annual water quality. Therefore, we collectedseasonal samples in spring (April 2004 and 2005) andautumn (October 2005) for a total of 3 samples persite. Details of sampling protocols and design areprovided by Merovich et al. (2007). Briefly, at each siteduring each sampling event, we measured watertemperature (uC), pH, specific conductance (mS/cm),dissolved O2 (mg/L), total dissolved solids (g/L),alkalinity (mg/L CaCO3), SO4

22 (mg/L), Cl2 (mg/L),and dissolved Al, Ba, Co, Cu, Fe, Mn, Ni, Cd, Cr, Ca,Mg, Na, and Zn (mg/L, unless otherwise noted). Weused these data to develop a stream water-qualityclassification system in which we assigned waterchemistries to 1 of 5 water-quality categories based onMerovich et al. (2007) (see Statistical analyses below).Water-quality types were Reference (R), Soft (S),Transitional (T), Hard (H), and Acidic mine drainage(A) (see Results below).

Benthic macroinvertebrates.—We followed rapidbioassessment protocols outlined by West VirginiaDepartment of Environmental Protection (WV DEP)to collect benthic macroinvertebrates at 84 of the samesites in the Cheat watershed in 2003 and 2004(Merovich and Petty 2010). At each site, we collectedmacroinvertebrates from 4 separate riffles with a kicknet (335 3 508 mm, 500-mm mesh) and combinedthem into a single sample for the site. In thelaboratory, we identified individuals to family andused abundance data to calculate the West Virginiastream condition index score (WVSCI; Gerritsen et al.2000). Scores range from 0 to 100, with higher scoresrepresenting higher ecological integrity. We dividedthese scores into 5 ecological-condition categories:.84.9 is excellent (E), 84.9 to 72 is good (G), 71.9 to 60is moderate (M); 59.9 to 40 is poor (P), and ,40 is verypoor (I). We also used WVSCI scores collected by WV

DEP Watershed Assessment Program (J. Wirts, WVDEP, personal communication) in 2001–2002 froman additional 37 sites across the Cheat watershed.WVSCI scores from duplicate sites between 2002–2003were highly correlated, so we included WVSCI datafrom 2001–2004 in our analyses (Freund and Petty2007).

Watershed characteristics: landscape data

We organized landscape data for the Cheat andTygart Valley watersheds at the stream-segment-levelwatershed scale. Segment-level watersheds (SLW) arethose delineated between stream confluences at the1:24,000 scale (Strager et al. 2009). Landscape data foreach SLW consisted of the following information:mean elevation (mean stream-segment elevationwithin the SLW [m]), drainage area (km2), coalgeology (% Pittsburgh coal, % Upper Freeport coal,and % coal other than Pittsburgh or Upper Freeportcoal; data source: West Virginia Geologic and Eco-nomic Survey 1:62,500 map; Sisler and Reger 1931),mining data (% underground mines, % surface mines;data source: WV DEP), surficial geology (% limestone,% sandstone, % shale; data source: WV DEP), landuse (% forest, % urban area, % agricultural area; datasource: 2001 National Land Cover Dataset; Homeret al. 2007), distance to nearest mining feature (surfaceflow distance upstream from the pour point of theSLW to the first mapped mining feature includingsurface mines, bond forfeiture sites, or abandonedmine lands [m]; data sources: WV DEP), and miningintensity (Strager et al. 2009) (Table 1).

We included acidic coal seams, which are Sewell,Lower Kittanning, Upper Kittanning, Upper Freeport,Bakerstown, and Pittsburgh seams, in order ofdecreasing depth from surface. We also included %

Pittsburgh or Upper Freeport coal separately from allother coal types because they often were mined beforemining laws (i.e., Surface Mine Control and Reclama-tion Act, 1977) were implemented and, consequently,they deliver more highly polluted AMD than Kittan-ning coal seams (Petty et al. 2010). We calculated %

coal type as the length of the particular seam per totalcoal-outcrop length. Mining data included the mostcurrent spatial information at the 1:24,000 scale onabandoned mine lands, active permitted mine bound-aries, and surface and underground mining from theWV DEP.

Mining intensity was quantified with a miningindex (MI) developed by Strager et al. (2009) andapplied by Petty et al. (2010). MI measures theintensity of mining for a given SLW by includinginformation on total coal outcrop lengths, mining


activity, and total length of mapped streams. Morespecifically for each SLW, it is computed as thecumulative mine density (CMD) scaled by maximumcumulative mining density observed in the data pluscumulative coal outcrop density (COD) scaled bymaximum cumulative outcrop density in the data.CMD was calculated as the cumulative mine areadraining to a SLW per cumulative drainage area of theSLW, and COD was calculated as the cumulative coaloutcrop length per cumulative stream length drainingto the SLW. The MI is scaled to range from 0 to 100and can be interpreted as the percentage of themaximum possible mining intensity in an SLW(Strager et al. 2009). For example, MI is 0 when anSLW has no coal outcrops and no mining. All data foreach SLW were the sum of data for the SLW plusvalues from all upstream SLWs linked by flow.Therefore, all landscape variables were cumulativefor a focal SLW. We used landscape variables aspredictor variables in models relating in-stream waterquality and biological condition to these watershed-level characteristics.

Statistical analyses

Landscape models.—We developed empirical modelsrelating water quality and biological condition tolandscape-level information using BRTs, an extensionof classification and regression tree techniques (Aer-tsen et al. 2010). Classification and regression treeanalysis is a simple but robust analytical techniquewell suited to multivariate ecological data containingcomplex levels of information (De’ath and Fabricius2000), but can suffer from poor predictive abilities(De’ath 2007) because it finds a single parsimoniousmodel that overfits the data. Boosting is done toimprove model structure and predictive performanceby fitting many simple models from samples of thedata and combining them to better estimate theresponse. Boosting iteratively fits multiple classifica-tion or regression trees to the training data using arandom subset (bag fraction) of the data for each tree.Model-building subsequently focuses on observationsthat are hard to predict, thereby formulating moreaccurately the relationship between the response andpredictors and improving model performance (Elithet al. 2008). In the ecological modeling and machinelearning literature (e.g., Leathwick et al. 2006, Measeet al. 2007, Aertsen et al. 2010), BRT analysis is widelyrecognized as an excellent modeling technique be-cause of its strong descriptive and predictive perfor-mance (Elith et al. 2008).

We constructed separate BRTs for water quality andecological condition. For water quality, we used BRT

to predict principal component (PC)1 and PC2 scoresfrom a principal component analysis (PCA) of water-chemistry data. We log(x)-transformed and standard-ized all water-chemistry data (except pH) before PCA.Each site had 3 water samples, so we ran BRT on PC1and PC2 scores that were averaged for each of thesamples (n = 123). For ecological condition, we ranBRT on arcsin(x)-transformed WVSCI scores (n =

121). We used the BRT functions provided by Elithet al. (2008) to construct models with varying learningrates and interaction depths, but we chose modelswith a learning rate of 0.01 and a tree complexity of 1(no modeled interactions among predictors) becauseperformance (minimum predictive deviance) did notimprove with slower learning or greater tree depth.We set the bag fraction at 0.5. We evaluated modelperformance by calculating the mean model deviance(and standard error [SE]) and cross-validated predic-tive deviance (and SE) from 10 partitions of the data.We simplified final models by removing predictorsthat did not improve model performance using thegbm.simplify function (Elith et al. 2008). We alsoselected variables with 10-fold cross validation tochoose variables to drop without increasing cross-validation error rate. For final simplified models, wequantified relative influence of predictors on respons-es using the out-of-bag method, and we used partialdependency plots to show effects (Elith et al. 2008).

Landscape model application.—We used our land-scape models to predict water quality and ecologicalcondition given landscape data for all unsampledSLWs. For water quality, we predicted PC1 and PC2scores for each SLW and then assigned the SLW itsbest corresponding water-quality type based on thelocation of the predicted PCs in PCA space (Merovichet al. 2007). We lacked benthic macroinvertebrate datafor the Tygart Valley watershed, so we used our Cheatwatershed landscape model to predict WVSCI scoresfor all unsampled SLWs for the Cheat watershed andthe entire Tygart watershed. Predicted WVSCI scoreswere obtained by back-transforming the predictedarcsin(x) scores. We conducted all statistical analysesin R (version 2.13.1; R Development Core Team,Vienna, Austria).

Hierarchical watershed classification.—Given observa-tions and predictions for each SLW, we mappedwater-quality type and ecological condition at theSLW scale for the Cheat and Tygart Valley watershedsin ArcMap (version 9.2; Environmental SystemsResearch Institute, Redlands, California). We thenaggregated water quality and ecological condition tosequentially larger scales, at the HUC-12 and then atthe HUC-10 watershed scales, where HUC-12 water-sheds are nested within larger HUC-10 watersheds,


which are nested within HUC-8 watersheds. Forecological condition (WVSCI), this process involvedweighting the scores for each SLW by its stream-segment length standardized by the total stream-segment length in either the HUC-12 or HUC-10depending on the scale to which compilation wasinvolved. For water quality, we ranked the water-quality types from best to worst and assigned them anumeric value, which was weighted as above. Theranking system was on the same numerical scale asWVSCI (0–100) and was as follows: R = 100, S = 80, T= 60, H = 40, and A = 20. Last, we averaged thesegment-length-weighted water-quality and ecologi-cal-condition scores from their respective scale toproduce a combined, integrated measure of conditionat each scale. We created this integrated conditionindex (ICI) to capture a more complete description ofenvironmental conditions than either water-quality orecological measures could do alone. The approachdown-weights the condition of sites with an excellentmacroinvertebrate score but less-than-ideal waterquality (and vice versa). Such combinations can occuras a result of metacommunity dynamics in whichindividuals are supplied to degraded sites viadispersal (Merovich and Petty 2010). We mappedthese integrated results to show the pattern ofcondition across the region hierarchically at SLW,HUC-12, and HUC-10 scales. We refer to thesewatershed-management scales as house, neighbor-hood, and community, respectively (Strager et al.2009).

Results

The study area comprises 9215 SLWs (3938 in theCheat and 5277 in the Tygart Valley watershed).Across these SLWs, the mining index (MI) rangedfrom 0 to 64 with a mean (standard deviation [SD]) of2.3 (5.3) for the Cheat River and 4.9 (7.5) for the TygartValley River (Table 1). Over half (51.3%) of the riverlength in both watersheds had mining influence (i.e.,MI . 0). In the Cheat River watershed, WVSCI scoreswere highly variable. Two A sites lacked macroinver-tebrates and had WVSCI scores of 0, whereas severalhigh-quality streams possessed scores near 100. Waterquality also varied widely across the watersheds(Table 2). PCA on water-chemistry data explained.70% of the variation in 2 multivariate dimensions(Table 3). PC1 declined in pH and increased indissolved metals, conductance, and SO4

22, indicatingincreasingly acidic conditions. PC2 increased inalkalinity and minerals indicating a hardness–salinitygradient (Table 3, Fig. 1). We used the bivariatescatterplot of PC1 and PC2 scores to classify ourobservations into 5 water-quality types (Fig. 1) basedon Merovich et al. (2007). R water quality iscircumneutral and has low levels of dissolved ions.S water has lower pH than R, but has very low levelsof conductance and alkalinity. H water has elevatedpH, conductance, alkalinity, SO4

22, and mineral ionscompared to R, and probably originates from acombination of natural neutralization and dilution ofacidified waters. A water is characterized by extreme-

TABLE 2. Mean (SD) water-chemistry variables by water-quality type (A = acid mine drainage, H = hard, R = reference, S =

soft, T = transitional). SpC = specific conductance.

Variable A (n = 23) H (n = 19) R (n = 45) S (n = 15) T (n = 21)

pH 4.2 (1.1) 7.2 (0.3) 6.9 (0.4) 5.6 (1.0) 6.5 (0.4)SpC (mS/cm) 529 (395) 454 (345) 85 (31) 57 (33.7) 181 (78.1)Alkalinity (mg/L CaCO3) 9.7 (26.9) 81.1 (55.8) 20.8 (13.8) 3.5 (3.4) 16.9 (10.4)Al (mg/L) 6.4 (7.4) 0.05 (0.03) 0.05 (0.05) 0.17 (0.12) 0.16 (0.10)Ba (mg/L) 0.03 (0.008) 0.04 (0.008) 0.03 (0.01) 0.03 (0.01) 0.04 (0.01)Ca (mg/L) 25.7 (12.9) 42.7 (28.6) 8.7 (5.2) 2.1 (1.3) 14.2 (7.9)Cd (mg/L) 3.2 (0.8) 3.2 (0.6) 2.8 (0.6) 2.8 (0.5) 2.6 (0.8)Cl2 (mg/L) 3.5 (1.9) 38.8 (121.3) 2.1 (1.7) 1.1 (0.9) 4.8 (4.2)Co (mg/L) 55.6 (64.6) 2.0 (0.8) 1.7 (0.8) 2.2 (1.4) 6.3 (2.9)Cr (mg/L) 3.7 (2.7) 2.1 (0.6) 1.9 (0.9) 1.7 (0.6) 1.9 (0.6)Cu (mg/L) 11.7 (12.7) 1.8 (1.6) 1.5 (0.8) 2.0 (1.5 ) 2.7 (2.2)Fe (mg/L) 6.8 (12.6) 0.14 (0.2) 0.08 (0.07) 0.11 (0.08) 0.18 (0.08)Mg (mg/L) 11.7 (9.3) 10.5 (8.2) 1.6 (0.05) 0.6 (0.3) 5.0 (3.2)Mn (mg/L) 1.4 (1.6) 0.24 (0.2) 0.04 (0.06) 0.06 (0.04) 0.28 (0.24)Na (mg/L) 3.3 (2.1) 34.3 (65.3) 1.8 (1.4) 0.5 (0.6) 3.3 (2.8)Ni (mg/L) 79.1 (79.2) 5.0 (3.1) 3.6 (2.3) 3.7 (2.5) 12.9 (3.9)Zn (mg/L) 137.3 (130.9) 3.4 (2.1) 2.8 (2.0) 14.9 (25.4) 12.5 (8.7)SO4

22 (mg/L) 211.0 (205) 129.8 (144) 9.9 (5.0) 7.5 (3.5) 49.6 (26.8)


ly low pH and extremely high levels of dissolvedmetals (especially Al, Fe, and Mn), SO4

22, andconductance. T water is variable in composition andis best described as a transitional type between R andA. Detailed descriptions of each type are in Merovichet al. (2007).

Landscape models

BRT analysis fit §500 trees and made use of asubset of the 18 landscape variables to model waterquality and ecological condition (Table 4). In bothcases, landscape features associated with miningactivity were dominant predictors with high relativeinfluence on the responses. For PC1 (an acidic, metal-laden gradient) and WVSCI responses, mining fea-tures were much more important than landscapevariables not associated with mining and were theonly influential predictor variables. MI and distanceto mining strongly influenced PC1, whereas % UpperFreeport and Bakerstown coal, distance to mining,and MI strongly influenced WVSCI (Table 4). Partialdependency plots showed that PC1 increased veryrapidly at distinct points of MI (,10) and % UpperFreeport coal (,40%) in the watershed, but declinedmore continuously with greater distance of thewatershed from mining activities (Fig. 2A–C). Con-versely, water-quality PC2, a hardness–salinity gradi-ent, was influenced mostly by land use–land cover(e.g., % forest, % agriculture) and natural geographicvariables (i.e., elevation) (Table 4). PC2 decreased

with elevation and % forest (Fig. 2D, E) (indicating agreater likelihood of having S water in forestedmountain streams) and increased with higher %

agriculture (Fig. 2F). WVSCI also declined at distinctthresholds of % Upper Freeport coal (,40%), butmore constantly with greater % Bakerstown coal andMI (Fig. 2G–I). Each BRT model explained slightlymore than 70% of the variation in the data (Table 5).Cross-validation indicated that models predictingwater-quality PCs from landscape data were slightlymore certain than models predicting WVSCI: % totalmodel deviance explained by cross-validation rangedfrom 42.9 (0.04 cross-validated residual deviance) forWVSCI to .48.9% for water-quality PCs (0.47 cross-validated residual deviance) (Table 5).

Model application

We used the BRT models to provide a best estimateof current water quality and ecological condition in allunsampled SLWs given known watershed character-istics. In terms of water quality, model outputspredicted that ,½ of the mapped river length in ourstudy area has R water quality. The remainingstreams are predicted to have pollution from minedrainage or elevated levels of dissolved metals fromacid precipitation or diluted AMD. Notable differenc-es between watersheds were that Tygart Valley was

TABLE 3. Results for the first 3 principal components(PCs) of a principal components analysis of water-chemistrydata. – = loading , |0.4|, SpC = specific conductance.

Factor loadings PC1 PC2 PC3

Eigenvalue 9.1 3.7 1.4% variation explained 50.4 20.6 7.8pH 20.73 0.58 –SpC 0.83 0.47 –Alkalinity 20.41 0.82 –Al 0.84 – –Ba – – 20.63Ca 0.58 0.70 –Cd – – 0.84Cl2 – 0.64 –Co 0.90 – –Cr 0.61 – 0.43Cu 0.82 – –Fe 0.87 – –Mg 0.78 0.53 –Mn 0.86 – –Na – 0.79 –Ni 0.92 – –Zn 0.85 20.41 –SO4

22 0.89 – –

FIG. 1. Scatterplot of principal component 1 (PC1) andPC2 scores from principal component analysis, with siteslabeled by water-quality type (A = acid mine drainage, H =

hard, R = reference, S = soft, T = transitional). Axes areannotated with a description indicating the correspondingtrends in water chemistry.


predicted to have higher amounts of H and T waterquality. The Cheat watershed was predicted to haveslightly more streams with E ecological condition(WVSCI . 85) and more streams with R water quality.Water quality in most of the lower (northern) portionof the Cheat and most of the Tygart Valley ispredicted to be strongly affected by mining-relatedconstituents (Fig. 3A). Many of the stream reaches inthe upper portions of both watersheds in the high-elevation mountains are predicted to have S or Twater, probably because of acid deposition. Only alittle more than ¼ of river length overall is predictedto be in E ecological condition. About 20% of streamsare predicted to be impaired ecologically (WVSCI ,

72). Much of this impairment is focused in the lowerCheat and Tygart Valley rivers where coal mining isconcentrated.

Hierarchical watershed classification

We mapped ICI scores at the SLW-scale (Fig. 3A) torepresent environmental quality across the studyregion. Distinct geographic differences in aquaticecosystem integrity existed across the watersheds.Much of the lower M of the Cheat and most of thelower ½ of the Tygart Valley watersheds have streamsegments that are severely degraded (toward the redend of the color ramp in Fig. 3A). In the Cheat Riverwatershed, most of the upper (southern) portion ofthe watershed, except for the Blackwater Riverwatershed, has good streams (toward the green endof the color ramp in Fig. 3A). In addition, severalisolated patches of streams in the upper portions ofboth watersheds have degraded conditions.

When we compiled ICIs from the SLW scale to theHUC-12 scale (Fig. 3B), the presence of impairedstream segments decreased the ICI of the HUC-12watershed in which they were nested. For example,impaired streams in the lower part of Shavers Fork ofthe Cheat watershed (Fig. 3A) reduced the ICI of theirHUC-12 watershed (Fig. 3B), whereas most of thesurrounding HUC-12 watersheds remained in goodcondition. Likewise, lower-quality HUC-12 water-sheds decreased the ICI of the HUC-10 watershed inwhich they were nested (toward the red end of thecolor ramp in Fig. 3C). For example, low-qualityHUC-12 watersheds in the Buckhannon River area(Fig. 3B) reduced the ICI of the HUC-10 watershed inthe southwestern part of the Tygart Valley watershed(Fig. 3C).

ICIs across the study area can be classified intoenvironmental-quality categories depending on thescale of interest. Thus, the ICI at the stream-segmentscale can be related to the ICI at broader scales byconsidering all 3 watershed-management scales si-multaneously (Fig. 3A–C). As HUC-12 watershed ICIimproves, the minimum ICI of stream segmentsincreases (Fig. 4A), and as HUC-10 watershed ICIimproves, the minimum ICI of HUC-12 watershedsincreases (Fig. 4B). Thus, as the minimum ICI for thesmaller spatial scale improves, so does the ICI of thehigher spatial scale. Nevertheless, high-quality streamsegments are found even in the poorest-quality HUC-12 watersheds (Fig. 4A). More important, impairedstream segments (i.e., streams with ICI , 60) arefound across a broad range of HUC-12 watershed ICIs(Fig. 4A).

TABLE 4. Relative (Rel.) influence (%) of landscape variables (sorted by decreasing magnitude) on water chemistry (principalcomponents 1 and 2) and ecological condition (West Virginia stream condition index) from boosted regression tree analysis. MI =

mining index, Dist mining = distance to nearest upstream mining feature, % U Free = % upper Freeport coal, % B-town = %

Bakerstown coal, Elev = elevation, % ag = % agriculture, % ss = % sandstone, % sh = % shale, Area = drainage area, % L Kitt =

% lower Kittanning coal; % ungrd mine = % underground mining.

Water quality Ecological condition

Principal component 1 Principal component 2 West Virginia stream condition index

Predictor variable Rel. influence Predictor variable Rel. influence Predictor variable Rel. influence

MI 36.4 Elev 24.6 % U Free 20.1Dist mine 27.7 % forest 23.7 % B-town 15.7% U Free 10.4 % ag 14.4 Dist mine 14.5% urban 6.5 % ss 7.3 MI 14.3% forest 6.4 % sh 7.0 Area 10.0% B-town 5.6 Dist mine 5.4 % ag 9.4Elev 5.2 MI 4.9 Elev 6.1% ag 1.7 % U Free 4.0 % surface mine 5.3

% L Kitt 3.9 % ungrd mine 4.6Area 3.5% other 1.3


Discussion

We were able to develop reliable empirical modelsusing BRT that related landscape-level attributes toin-stream, segment-level conditions in watershedsheavily influenced by coal mining. The importantvariables in these models were consistent with thoseidentified by Petty et al. (2010), but included bothnatural and anthropogenic stressors. MI, distancefrom mining, and coal geology had the strongestinfluence on observed biological condition and waterquality. Sampling sites close to mining activity or inintensely mined upstream drainages dominated byUpper Freeport or Bakerstown coal tended to have thepoorest ecological condition. Moreover, high-elevation

forested streams tended to have acidic water chemistrycaused by acid deposition over poorly buffered soiland rock.

The purpose of constructing landscape predictivemodels was to describe conditions in all unsampledstream segments within the domain of the study area.This modeling application, whereby data gaps arefilled, is a common first step in freshwater conserva-tion planning (Hermoso et al. 2011, Leathwick et al.2011, Rivers-Moore et al. 2011) and is needed tocharacterize the riverscape fully (sensu Fausch et al.2002). We used BRT, one of a few modeling strategieswell suited for our purpose (Linke et al. 2011)especially in watershed-scale studies when multiplecovarying factors, such as land cover and land use,

FIG. 2. Fitted functions (solid lines) of the most important landscape variables from boosted regression tree (BRT) analysispredicting water-quality principal component (PC)1: mining intensity (MI) (A), distance to nearest mining activity (Dist mine) (B),and % Upper Freeport coal geology (C); water-quality PC2: site elevation (D), % forest (E), and % agriculture (F); and the WestVirginia stream condition index (WVSCI): % Upper Freeport coal geology (G), % Bakerstown coal geology (H), and MI (I) in thewatershed. The dotted lines are the smoothed form of the fitted functions (Elith et al. 2008). Percentages on the x-axes indicate therelative importance of the predictor variable.


geology, and elevation, are involved. Snelder et al.(2009) also used BRT to develop a predictivehydrologic model based on watershed characteristicsto classify natural flow regimes in watersheds withoutflow data. Carlisle et al. (2009) used random forests,similar to BRT, to predict stream condition fromwatershed attributes. Leathwick et al. (2011) usedgeneralized dissimilarity modeling, and Hermosoet al. (2011) used multivariate adaptive regressionsplines for species distribution modeling to identifyconservation needs.

The certainty of predictions from our BRT modelswas within the typical range, but comparisons aredifficult to make because many investigators modeledbinary responses, such as species presence–absence.Snelder et al. (2009) found predictive devianceranging from 17 to 64% for BRT models predictingnatural flow-regime classes. Leathwick et al. (2006)found values between 48 and 60%, increasing withtree size, for models predicting marine fish speciesrichness in New Zealand. In our models, 70 to 74% ofthe deviance was explained by the fitted models, and43 to 53% of the deviance was explained in the cross-validation samples.

TABLE 5. Performance and validation statistics fromboosted regression tree (BRT) models relating landscapevariables to water quality and ecological condition. Statisticsare: number of trees (No. trees) fit by BRT models, meanresidual deviance for each model, cross-validated (X-val)residual deviance and its standard error (SE) from 10partitions of the data, total mean deviance of each model,and % variation explained by each model on training dataand on 10-fold cross-validation (X-val).

Statistic

Landscape modelresponse variable

Water qualityEcologicalcondition

PC1 PC2 WVSCI

No. trees 500 1700 1250Mean residual deviance 0.29 0.24 0.02X-val residual deviance 0.46 0.47 0.04X-val residual deviance SE 0.09 0.05 0.007Total mean deviance 0.97 0.92 0.07% deviance explained 70.1 73.9 71.4% X-val deviance explained 52.6 48.9 42.9

FIG. 3. Integrated condition index (ICI: water quality and ecological integrity) mapped in color for every segment-levelwatershed (A) and compiled to the Hydrologic Code Unit [HUC]-12 watershed (B) and HUC-10 watershed (C) scales. Redindicates poorer and green indicates better ICI. The Tygart Valley and Cheat watersheds are 8-digit HUCs in north-central WestVirginia that drain north toward the Monongahela River. Major rivers of importance within each watershed are indicated on themap, with the Cheat watershed on the east and the Tygart Valley on the west. See Fig. 6 for location within the state ofWest Virginia.


FIG. 4. Scatterplot of segment-level integrated condition index (ICI) vs Hydrologic Code Unit [HUC]-12 watershed ICI (n =

9215 data points) (A) and HUC-10 watershed ICI vs HUC-12 watershed ICI (n = 70 HUC-12 watersheds with nested segment-level watersheds) (B).


Information about conditions at broad spatial scalesis important to conservation efforts directed athydrology (Snelder et al. 2009), aquatic resources(Strayer et al. 2003, Weller et al. 2007, Carlisle et al.2009), and terrestrial biodiversity (Vallet et al. 2010,Moore et al. 2011). Weller et al. (2007) suggested thatinformation on wetland condition predicted fromlandscape indicators could be scaled up to estimateaverage wetland condition at broader scales. We useda weighting procedure to integrate information onwater quality, biological condition, and stream lengthin the ICI, and then scaled these segment-levelconditions up to higher watershed scales (HUC-12and HUC-10 watersheds). Thus, we were able torelate smaller-scale conditions to conditions in abroader regional context (cf. Martin and Petty 2009).

Information on environmental quality that spans awide spatial extent and can be classified at multiplescales is valuable from 2 related perspectives: meta-community theory and restoration science. Studies ofstream metacommunities require hierarchical classifi-cation of watershed conditions that include local vsregional factors and dispersal processes affectingspecies composition, key components of metacom-munity theory (Capers et al. 2010). For example,freshwater fish assemblages are thought to bestructured by regional-scale processes that affectdispersal along rivers (Heino 2011). Hitt and Roberts(2012) demonstrated hierarchical spatial variation incolonization and extinction dynamics for fish com-munities in a Virginia stream network. Thus, contin-uous information on riverscapes at multiple scalesmay be helpful when testing predictions of metacom-munity theory in watersheds fragmented by mining(Freund and Petty 2007).

Information on conditions at multiple scales isimportant for restoration of riverine ecosystems(Linke et al. 2011, Rivers-Moore et al. 2011). Informa-tion on whole watersheds is critically needed beforethe focus of management and planning of restorationefforts can be shifted from the local to the watershedscale (Weller et al. 2007, Flanagan and Richardson2010). Furthermore, hierarchical classification of con-ditions is needed for setting restoration priorities(Rivers-Moore et al. 2011). Early restoration effortswere based on the assumption that correction of local-scale environmental problems would lead to struc-tural and functional improvements in the stream(Brown et al. 2011). However, this ‘‘field of dreamshypothesis’’ (Hilderbrand et al. 2005) continues tohave weak empirical support. Restoration at any localscale will not guarantee structural and functionalimprovement (Hermoso et al. 2011) because thebroader context in which restoration is taking place,

such as connectivity among metacommunities, sourceof colonizers, large-scale environmental condition,and the status of the species pool, is ignored (McClurget al. 2007, Brown et al. 2011, Turak et al. 2011).

Thus, the ability to set restoration priorities byintegrating local and regional information within ametacommunity framework could increase the likeli-hood of success of river restoration projects (Brown etal. 2011). For example, Sundermann et al. (2011) foundthat restoration success depended on the quality ofthe species pool, and macroinvertebrate communitiesin restored reaches did not improve unless potentialcolonizers were in close range. Restoration of dispers-al-limited grassland forbs was most successful whenseed additions were close to established individuals(Moore et al. 2011). Macroinvertebrate communitieshad not improved 20 y after restoration of anotherstream, probably because large-scale disturbancelimited establishment of bryophytes needed by themacroinvertebrates (Louhi et al. 2011). Thus, condi-tions beyond the focal reach dictate the success ofriver restoration projects (Sundermann et al. 2011).Our own research indicates that success of streamrestoration may be constrained by regional conditions(Petty and Thorne 2005, McClurg et al. 2007, Merovichand Petty 2010). Restoration of short stream segmentsembedded within impaired watersheds is less effec-tive than integrated projects that seek to restorestream networks.

Few investigators have developed models thatincorporated the inherent nestedness of riverscapesinto conservation planning. Norton et al. (2009) usedrecovery metrics, including recolonization access, topredict recovery potential as a way to set restorationpriorities for impaired streams. Turak et al. (2011) usedresponses of a biodiversity persistence index tosimulate changes in catchment-level conditions toidentify restoration and protection priorities. Studiesbased on multiscale frameworks are varied. Forexample, Abell et al. (2007) proposed critical watershedmanagement zones essential for protecting importantfocal areas, such as hotspots of endemism or spawningareas for focal species. Rivers-Moore et al. (2011) used a2-tiered hierarchical approach in which criteria forsetting priorities for protecting subwatersheds wereidentified based on watershed-level conditions.Khoury et al. (2011) used a coarse–fine filter, top-downapproach. At the coarse scale, the approach is based onthe assumption that conservation of representativeecosystems conserves ecological processes, but at thefine scale, special species that are missing from therepresentative ecosystem are selected for restoration.

We propose a framework (Fig. 5) for settingconservation (restoration and protection) priorities


based on a hierarchical classification of watershedcondition that follows a house–neighborhood–com-munity analogy. In this analogy, houses (SLWs) areembedded in neighborhoods (HUC-12 watersheds),which are embedded in communities (HUC-10 wa-tersheds). The logic is that restoration and protectionare analogous to home repair, in that the improve-ment in value of a repaired house is likely to beinfluenced by neighborhood and community condi-tions, such as the condition of other houses in theneighborhood or the income and unemployment ratein the community.

We begin by establishing tiered protection andrestoration priorities at 2 spatial scales (Fig. 6): first atthe neighborhood (HUC-12) scale and then at thecommunity (HUC-10) scale. We define protection as

prevention of decline of ecological integrity. Prioritiesfor protection should focus on good HUC-12 water-sheds in poor HUC-10 watersheds because HUC-12watersheds in good condition are likely to provide apool of species for recovery of aquatic biota. Sunder-mann et al. (2011) found that stream restoration hadgreatest success when good sites were within 0 to 5 kmof the restored segment. Therefore, protection priorityat the HUC-12 watershed scale declines as thecondition of the HUC-10 watershed within which itis nested improves (Figs 4B, 5). The highest level ofprotection priority (Level 1) is assigned to excellent-quality HUC-12 watersheds in HUC-10 watershedswith fair to poor condition. These neighborhoodsshould be protected so that they can supply rawmaterial (e.g., clean water, colonizers) for restoringthe ecological condition of the broader HUC-10watershed. For example, the headwater HUC-12watershed of the Buckhannon River receives Level 1protection priority because it is a good HUC-12watershed within a relatively poor HUC-10 water-shed (Fig. 6A). The next priority for protection (Level2) is assigned to excellent-quality HUC-12 watershedsin good HUC-10 watersheds, and the last priority(Leve1 3) is assigned to good HUC-12 watersheds inexcellent HUC-10 watersheds. For example, theheadwater HUC-12 watersheds of the Tygart ValleyRiver fall into Level 3 because they are within anexcellent HUC-10 watershed (Fig. 6A).

Protection priorities at the SLW scale involve asimilar rationale, but at a smaller scale. Protectiondecisions are mutually exclusive between HUC-12watersheds and stream-segment scales because adegraded neighborhood is not protectable but mayhave excellent-quality streams that should be protect-ed. At the SLW scale, protection priority declines asthe conditions in the broader context improve. Thehighest level of stream-protection priority (Level 1) isassigned to excellent-quality streams in poor HUC-12watersheds within poor HUC-10 watersheds, becauseexcellent streams supply the raw material restorationsites need for improving HUC-12 watershed-levelquality (Fig. 6A). The western portion of the Buc-khannon River watershed has several excellentheadwater stream segments that receive Level 1protection because they are in poor HUC-12 water-sheds within a poor-quality HUC-10 watershed(Fig. 6A). The next priority for protection (Level 2) isassigned to excellent stream segments in poor HUC-12 watersheds within good HUC-10 watersheds, andthe last priority (Level 3) is assigned to excellentstreams embedded in good-quality HUC-12 water-sheds within excellent-quality HUC-10 watersheds(Fig. 6A).

FIG. 5. Conceptual model for protection and restorationpriorities at the neighborhood (Hydrologic Code Unit[HUC]-12 watershed) scale, overlaid on top of the relation-ship between community (HUC-10 watershed)-level andneighborhood (HUC-12 watershed)-level integrated condi-tion index (ICI) redrawn from Fig. 4B. The vertical dashedline represents the boundary separating restoration andprotection efforts at the neighborhood scale. Arrowsrepresent the gradients of prioritization for each ofrestoration (sloped arrow lying on the least squaresregression line, r2

= 0.64) and protection (vertical arrow).Priority levels (low to high) for each of restoration orprotection are indicated along the gradients. Restorationpriority for neighborhoods increases with increasing condi-tion at the community scale. Protection priority forneighborhoods increases with decreasing condition at thecommunity scale.


Within our analogy, restoration priorities are set tomaximize the likelihood of neighborhood and com-munity restoration. In other words, the focus ofrestoration shifts from restoration of a specific houseto the neighborhood or community scale. Priorities forrestoration are impaired neighborhoods in goodcommunities and impaired houses in good neighbor-hoods because of the advantage of having goodconditions in close proximity to restored houses. Atthe neighborhood scale, restoration priority declineswith poorer community-level condition (Fig. 4B), sothat as poor-quality neighborhoods become moreisolated, their restoration priority decreases (Fig. 5).

Therefore, Level 1 restoration priority is assignedto poor neighborhoods in excellent communities(Fig. 6B). The flow-through HUC-12 watershed ofShavers Fork receives this top-level priority because itis in a good HUC-10 watershed and is immediatelydownstream of an excellent HUC-12 watershed.Community recovery probably would proceed rapid-ly with restoration, and as a result, improvementswould reconnect fluvial environments in the entireupper Cheat River watershed (Fig. 6B).

Level 2 restoration priority is assigned to poorneighborhoods in poor communities that are adjacentto good communities (Fig. 6B). For example, the lower

FIG. 6. Protection (A) and restoration (B) priorities within the Hydrologic Unit Code (HUC)-8 Tygart Valley (west) and CheatRiver (east) watersheds at the HUC-12 watershed (neighborhood) and the stream-segment (house) scales. In A, the darker thegreen, the higher the protection priority at each scale. Protection priorities are mutually exclusive between HUC-12 watershed andstream-segment scales, so that mapped patches of protection priority represent excellent-quality stream segments in a degradedHUC-12 watershed. White indicates areas in which protection is not applicable because conditions are impaired. In B, the darkerthe red, the higher the restoration priority at the HUC-12 watershed scale. Red indicates loss of ecological condition that isrecoverable given appropriate spatially explicit restoration techniques. White indicates restoration is not needed becauseconditions are good. Inset shows the HUC-8 watersheds in the state.


Cheat River watershed has several HUC-12 water-sheds at this level because they are in a poor HUC-10watershed adjacent to a HUC-10 watershed justupstream that is in good condition. Last, Level 3restoration priority is assigned to poor neighborhoodsin poor communities surrounded by other poorcommunities. The HUC-12 watersheds near the pourpoint of the Tygart Valley River are assigned this levelbecause much of that region is in poor condition (i.e.,poor neighborhoods nested in poor communities withno good community nearby).

Restoration is further prioritized within levelsbased on the restorability of individual streams withina HUC-12 watershed. Restorability refers to the abilityof restoration technology to bring about positivechange in degraded conditions and to regain somedesirable attribute, such as water-quality standards(Norton et al. 2009). For example, acid precipitation-affected streams are relatively easy to restore withlimestone sand amendments (Petty and Thorne 2005)and receive higher restoration priority than AMD-affected streams, which are not easy to restore andreceive lower priority because removal of dissolvedpollutants is difficult (Merovich et al. 2007).

Figure 5 represents our process of assigning pro-tection and restoration priorities as a conceptualmodel, overlain on our watershed-scale data. Wehave categorized discrete levels (1–3) of protectionand restoration priority, but this framework acknowl-edges that priorities will exist along a continuum (i.e.,arrows in Fig. 5). It also omits rare conditions, as inthe top left corner of model space, because we did notobserve extremely poor neighborhoods in very highquality communities. Overall, the model will ensurethat restoration builds on existing high-quality con-ditions. Poor regions are not ignored in the restorationprocess, but are set up for future restoration successby first reconnecting isolated good HUC-12 water-sheds. The process will improve the likelihood ofHUC-12 watershed and HUC-10 watershed restora-tion, using expectations derived from metacommu-nity theory as a guiding principle (e.g., Brown et al.2011, Turak et al. 2011).

In conclusion, we think that the decision-supportsystem developed here provides a structured ap-proach to watershed management challenges in coal-mining landscapes. A similar approach to modelingand classifying environmental quality at multiplescales for landscape-scale management could beapplied to any number of environments, environmen-tal stressors, or biota used as indicators of environ-mental health. We concur with Carlisle et al. (2009)that our model outputs will not replace ecologicalassessments, but they can provide a process to aid

watershed-scale strategies for restoration and protec-tion. Huge sums of money are spent on streamrestoration (Roni et al. 2005), so it is imperative thatthese funds are directed towards projects that arelikely to succeed. We believe that a house–neighbor-hood approach to setting conservation prioritieswould greatly improve management outcomes. Adhoc approaches to restoration do not work efficiently(Linke et al. 2011) and undermine efforts to restoreand maintain industrial, recreational, and biologicaluses of the nation’s waters (Barmuta et al. 2011).

Acknowledgements

We thank John Wirts and Jeff Bailey (West VirginiaDepartment of Environmental Protection) for providingwatershed assessment data, Roy Martin and EricMerriam for helpful comments on analyses, 2 anony-mous referees for comments that improved the manu-script structure, and Associate Editor Lester Yuan andEditor Pamela Silver for comments and suggestions thatgreatly improved the final version. This paper wasprepared with the support of a grant from the US EPAto JTP under Contract Agreement No. RD-83136401-0.However, any opinions, findings, conclusions, orrecommendations expressed herein are those of theauthors and do not reflect the views of the US EPA.

Literature Cited

ABELL, R., J. D. ALLAN, AND B. LEHNER. 2007. Unlocking thepotential of protected areas for freshwaters. BiologicalConservation 134:48–63.

AERTSEN, W., V. KINT, J. V. ORSHOVEN, K. OZKAN, AND B. MUYS.2010. Comparison and ranking of different modellingtechniques for prediction of site index in Mediterraneanmountain forests. Ecological Modelling 221:1119–1130.

BARMUTA, L. A., S. LINKE, AND E. TURAK. 2011. Bridging thegap between ‘planning’ and ‘doing’ for biodiversityconservation in freshwaters. Freshwater Biology 56:180–195.

BLACK, R. W., M. D. MUNN, AND R. W. PLOTNIKOFF. 2004. Usingmacroinvertebrates to identify biota–land cover optimaat multiple scales in the Pacific Northwest, USA. Journalof the North American Benthological Society 23:340–362.

BROWN, B. L., C. M. SWAN, D. A. AUERBACH, E. H. GRANT

CAMPBELL, N. P. HITT, K. O. MALONEY, AND C. PATRICK.2011. Metacommunity theory as a multispecies, multi-scale framework for studying the influence of rivernetwork structure on riverine communities and ecosys-tems. Journal of the North American BenthologicalSociety 30:310–327.

CAPERS, R. S., R. SELSKY, AND G. J. BUGBEE. 2010. The relativeimportance of local conditions and regional processes instructuring aquatic plant communities. FreshwaterBiology 55:952–966.


CARLISLE, D. M., J. FALCONE, AND M. R. MEADOR. 2009.Predicting the biological condition of streams: use ofgeospatial indicators of natural and anthropogeniccharacteristics of watersheds. Environmental Monitor-ing and Assessment 151:143–160.

DE’ATH, G. 2007. Boosted trees for ecological modeling andprediction. Ecology 88:243–251.

DE’ATH, G., AND K. E. FABRICIUS. 2000. Classification andregression trees: a powerful yet simple technique forecological data analysis. Ecology 81:3178–3192.

DEMCHAK, J., J. SKOUSEN, AND L. M. MCDONALD. 2004.Longevity of acid discharges from underground mineslocated above the regional water table. Journal ofEnvironmental Quality 33:656–668.

DRISCOLL, C. T., G. B. LAWRENCE, A. J. BULGER, T. J. BUTLER,C. S. CRONAN, C. EAGAR, K. F. LAMBERT, G. E. LIKENS, J. L.STODDARD, AND K. C. WEATHERS. 2001. Acidic depositionin the northeastern United States: sources and inputs,ecosystem effects, and management strategies. BioSci-ence 51:180–198.

ELITH, J., J. R. LEATHWICK, AND T. HASTIE. 2008. A workingguide to boosted regression trees. Journal of AnimalEcology 77:802–813.

FAUSCH, K. D., C. E. TORGERSEN, C. V. BAXTER, AND H. W. LI.2002. Landscapes to riverscapes: bridging the gapbetween research and conservation of stream fishes.BioScience 52:483–498.

FINN, D. S., N. BONADA, C. MURRIA, AND J. M. HUGHES. 2011.Small but mighty: headwaters are vital to streamnetwork biodiversity at two levels of organization.Journal of the North American Benthological Society30:963–980.

FLANAGAN, N. E., AND C. J. RICHARDSON. 2010. A multi-scaleapproach to prioritize wetland restoration for water-shed-level water quality improvement. Wetlands Ecol-ogy and Management 18:695–706.

FREUND, J. G., AND J. T. PETTY. 2007. Response of fish andmacroinvertebrate bioassessment indices to waterchemistry in a mined Appalachian watershed. Environ-mental Management 39:707–720.

FRISSELL, C. A., W. J. LISS, C. E. WARREN, AND M. D. HURLEY.1986. A hierarchical framework for stream habitatclassification: viewing streams in a watershed context.Environmental Management 10:199–214.

GERRITSEN, J., J. BURTON, AND M. T. BARBOUR. 2000. A streamcondition index for West Virginia wadeable streams.Tetra Tech, Inc., Owings Mills, Maryland. (Availablefrom: http://www.dep.wv.gov/WWE/watershed/bio_fish/Documents/WVSCI.pdf)

HEINO, J. 2011. A macroecological perspective of diversitypatterns in the freshwater realm. Freshwater Biology 56:1703–1722.

HERMOSO, V., S. LINKE, J. PRENDA, AND H. P. POSSINGHAM. 2011.Addressing longitudinal connectivity in the systematicconservation planning of fresh waters. FreshwaterBiology 56:57–70.

HILDERBRAND, R. H., A. C. WATTS, AND A. M. RANDLE. 2005.The myths of restoration ecology. Ecology and Society10:19.

HITT, N. P., AND P. L. ANGERMEIER. 2008a. Evidence for fishdispersal from spatial analysis of stream networktopology. Journal of the North American BenthologicalSociety 27:304–320.

HITT, N. P., AND P. L. ANGERMEIER. 2008b. River–streamconnectivity affects fish bioassessment performance.Environmental Management 42:132–150.

HITT, N. P., AND P. L. ANGERMEIER. 2011. Fish community andbioassessment responses to stream network position.Journal of the North American Benthological Society 30:296–309.

HITT, N. P., AND J. H. ROBERTS. 2012. Hierarchical spatialstructure of stream fish colonization and extinction.Oikos 121:127–137.

HOMER, C., J. DEWITZ, J. FRY, M. COAN, N. HOSSAIN, C. LARSON,N. HEROLD, A. MCKERROW, J. N. VANDRIEL, AND J.WICKMAN. 2007. Completion of the 2001 National LandCover Database for the conterminous United States.Photogrammetric Engineering and Remote Sensing 73:337–341.

HOPKINS, R. L., AND B. M. BURR. 2009. Modeling freshwaterfish distributions using multiscale landscape data: acase study of six narrow range endemics. EcologicalModelling 220:2024–2034.

JONES, K. B., A. C. NEALE, M. S. NASH, R. D. VAN REMORTEL,J. D. WICKMAN, K. H. RIITTERS, AND R. V. O’NEILL. 2001.Predicting nutrient and sediment loadings to streamsfrom landscape metrics: a multiple watershed studyfrom the United States Mid-Atlantic Region. LandscapeEcology 16:301–312.

KHOURY, M., J. HIGGINS, AND R. WEITZELL. 2011. A freshwaterconservation assessment of the Upper Mississippi Riverbasin using a coarse- and fine-filter approach. Freshwa-ter Biology 56:162–179.

LEATHWICK, J. R., J. ELITH, M. P. FRANCIS, T. HASTIE, AND P.TAYLOR. 2006. Variation in demersal fish species richnessin the oceans surrounding New Zealand: an analysisusing boosted regression trees. Marine Ecology ProgressSeries 321:267–281.

LEATHWICK, J. R., T. SNELDER, W. L. CHADDERTON, J. ELITH, K.JULIAN, AND S. FERRIER. 2011. Use of generalised dissim-ilarity modelling to improve the biological discrimina-tion of river and stream classifications. FreshwaterBiology 56:21–38.

LINKE, S., E. TURAK, AND J. NEL. 2011. Freshwater conservationplanning: the case for systematic approaches. Freshwa-ter Biology 56:6–20.

LOUHI, P., H. MYKRA, R. PAAVOLA, A. HUUSKO, T. VEHANEN, A.MAKI-PETAYS, AND T. MUOTKA. 2011. Twenty years ofstream restoration in Finland: little response by benthicmacroinvertebrate communities. Ecological Applica-tions 21:1950–1961.

MARTIN, R. W., AND J. T. PETTY. 2009. Local temperature andthermal topology interact to influence the distribution ofbrook trout and smallmouth bass in a central Appala-chian watershed. Journal of Freshwater Ecology 23:497–508.

MAZOR, R. D., T. B. REYNOLDSON, D. M. ROSENBERG, AND V. H.RESH. 2006. Effects of biotic assemblage, classification,


and assessment method on bioassessment performance.Canadian Journal of Fisheries and Aquatic Sciences 63:394–411.

MCCLURG, S. E., J. T. PETTY, P. M. MAZIK, AND J. L. CLAYTON.2007. Stream ecosystem response to limestone treatmentin acid impacted watersheds of the Allegheny Plateau.Ecological Applications 17:1087–1104.

MEASE, D., A. J. WYNER, AND B. ANDREAS. 2007. Boostedclassification trees and class probability/quantile esti-mation. Journal of Machine Learning Research 8:409–439.

MEROVICH, G. T., AND J. T. PETTY. 2010. Continuous responseof benthic macroinvertebrate assemblages to a discretedisturbance gradient: consequences for diagnosingstressors. Journal of the North American BenthologicalSociety 29:1241–1257.

MEROVICH, G. T., J. M. STILES, J. T. PETTY, J. FULTON, AND P. F.ZIEMKIEWICZ. 2007. Water chemistry based classificationof streams and implications for restoring minedAppalachian watersheds. Environmental Toxicologyand Chemistry 26:1361–1369.

MERRIAM, E. R., J. T. PETTY, G. T. MEROVICH, J. B. FULTON, AND

M. P. STRAGER. 2011. Additive effects of mining andresidential development on stream conditions in acentral Appalachian watershed. Journal of the NorthAmerican Benthological Society 30:399–418.

MOORE, K. A., S. P. HARRISON, AND S. C. ELMENDORF. 2011. Canspatial isolation help predict dispersal-limited sites fornative species restoration? Ecological Applications 21:2119–2128.

NORTON, D. J., J. D. WICKHAM, T. G. WADE, K. KUNERT, J. V.THOMAS, AND P. ZEPH. 2009. A method for comparativeanalysis of recovery potential in impaired watersrestoration planning. Environmental Management 44:356–368.

OSBORNE, L. L., S. L. KOHLER, P. B. BAYLEY, D. M. DAY, W. A.BERTRAND, M. J. WILEY, AND R. SAUER. 1992. Influence ofstream location in a drainage network on the index ofbiotic integrity. Transactions of the American FisheriesSociety 121:635–643.

OSBORNE, L. L., AND M. J. WILEY. 1992. Influence of tributaryspatial position on the structure of warmwater fishcommunities. Canadian Journal of Fisheries and Aquat-ic Sciences 49:671–681.

PETTY, J. T., AND J. BARKER. 2004. Water quality variability intributaries of the Cheat River, a mined Appalachianwatershed. Pages 1484–1504 in 2004 National Meeting ofthe American Society of Mining and Reclamation andthe 25th West Virginia Surface Mine Drainage TaskForce. American Society of Mining and Reclamation,Morgantown, West Virginia.

PETTY, J. T., J. B. FULTON, M. P. STRAGER, G. T. MEROVICH, J. M.STILES, AND P. F. ZIEMKIEWICZ. 2010. Landscape indicatorsand thresholds of stream ecological impairment in anintensively mined Appalachian watershed. Journal ofthe North American Benthological Society 29:1292–1309.

PETTY, J. T., AND D. THORNE. 2005. An ecologically basedapproach to identifying restoration priorities in an acid-impacted watershed. Restoration Ecology 13:348–357.

POFF, N. L. 1997. Landscape filters and species traits: towardmechanistic understanding and prediction in streamecology. Journal of the North American BenthologicalSociety 16:391–409.

POPLAR-JEFFERS, I. O., J. T. PETTY, J. T. ANDERSON, S. J. KITE,M. P. STRAGER, AND R. H. FORTNEY. 2009. Culvertreplacement and stream habitat restoration: implica-tions from brook trout management in an Appalachianwatershed, U.S.A. Restoration Ecology 17:404–413.

RIVERS-MOORE, N. A., P. S. GOODMAN, AND J. L. NEL. 2011.Scale-based freshwater conservation planning: towardsprotecting freshwater biodiversity in KwaZulu-Natal,South Africa. Freshwater Biology 56:125–141.

RONI, P., M. C. LIERMANN, C. JORDAN, AND E. A. STEEL. 2005.Steps for designing a monitoring and evaluationprogram for aquatic restoration. Page 350 in P. Roni(editor). Monitoring stream and watershed restoration.American Fisheries Society, Bethesda, Maryland.

SEABER, P. R., F. P. KAPINOS, AND G. L. KNAPP. 1994.Hydrologic unit maps. Water-Supply Paper 2294. USGeological Survey, Reston, Virginia.

SISLER, J. D., AND D. B. REGER. 1931. Maps of general andeconomic geology. West Virginia Economic and Geo-logic Survey, Morgantown, West Virginia.

SKOUSEN, J. G., A. SEXSTONE, AND P. F. ZIEMKIEWICZ. 2000. Acidmine drainage control and treatment. Pages 131–168 inR. I. Barnhisel, R. G. Darmody, and W. L. Daniels(editors). Reclamation of drastically disturbed lands.Agronomy No. 41. American Society of Agronomy andAmerican Society for Surface Mining and Reclamation,Madison, Wisconsin.

SNELDER, T. H., N. LAMOUROUX, J. R. LEATHWICK, H. PELLA, E.SAUQUET, AND U. SHANKAR. 2009. Predictive mapping ofthe natural flow regimes of France. Journal of Hydrol-ogy 373:57–67.

STRAGER, M. P., J. T. PETTY, J. M. STRAGER, AND J. BARKER-FULTON. 2009. A spatially explicit framework forquantifying downstream hydrologic conditions. Journalof Environmental Management 90:1854–1861.

STRAYER, D. L., R. E. BEIGHLEY, L. C. THOMPSON, S. BROOKS, C.NILSSON, G. PINAY, AND R. J. NAIMAN. 2003. Effects of landcover on stream ecosystems: roles of empirical modelsand scaling issues. Ecosystems 6:407–423.

SUNDERMANN, A., S. STOLL, AND P. HAASE. 2011. Riverrestoration success depends on the species pool of theimmediate surroundings. Ecological Applications 21:1962–1971.

TONN, W. M., J. J. MAGNUSON, M. RASK, AND J. TOIVONEN. 1990.Intercontinental comparison of small-lake fish assem-blages: the balance between local and regional process-es. American Naturalist 136:345–375.

TURAK, E., S. FERRIER, T. BARRETT, E. MESLEY, M. DRIELSMA, G.MANION, G. DOYLE, J. STEIN, AND G. GORDON. 2011.Planning for the persistence of river biodiversity:exploring alternative futures using process-based mod-els. Freshwater Biology 56:39–56.

TURAK, E., AND S. LINKE. 2011. Freshwater conservationplanning: an introduction. Freshwater Biology 56:1–5.


USEPA (US ENVIRONMENTAL PROTECTION AGENCY). 2000. Mid-Atlantic Highlands streams assessment. EPA/903/R-00/015. Environmental Monitoring and AssessmentProgram, US Environmental Protection Agency Region3, Philadelphia, Pennsylvania.

VALLET, J., V. BEAUJOUAN, J. PITHON, F. ROZE, AND H. DANIEL.2010. The effects of urban or rural landscape context anddistance from the edge on native woodland plantcommunities. Biodiversity Conservation 19:3375–3392.

WELLER, D. E., M. N. SNYDER, D. F. WHIGHAM, A. D. JACOBS,AND T. E. JORDAN. 2007. Landscape indicators of wetlandcondition in the Nanticoke River watershed, Marylandand Delaware, USA. Wetlands 27:498–514.

Received: 23 May 2012Accepted: 15 May 2013
