Ecological relevance of current water quality assessment unit designations in impaired rivers

Science of the Total Environment 536 (2015) 198–205

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Ecological relevance of current water quality assessment unit designations in impaired rivers Megan Layhee a,⁎, Adam Sepulveda a, Andrew Ray a,1, Greg Mladenka b, Lynn Van Every b a b

U.S. Geological Survey, Northern Rocky Mountain Science Center, 2327 University Way, Suite 2, Bozeman, MT 59715, USA Idaho Department of Environmental Quality, Pocatello Regional Office, 444 Hospital Way #300, Pocatello, ID 83201, USA

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Tested ecological relevancy of water body assessment units (AU) in impaired river. • Evaluated ecological similarity amid reaches within AUs and defined alternate AUs. • Biological conditions had greater variability within than between AUs. • Multivariate analyses identified alternative AUs that reduced this variability. • Provide a framework for identifying AU boundaries based on local-scale measures.

a r t i c l e

i n f o

Article history: Received 2 April 2015 Received in revised form 2 June 2015 Accepted 11 June 2015 Available online xxxx Keywords: Assessment unit Biological indices Impairment Multivariate analysis Spatial scale Water body assessment

a b s t r a c t Managers often nest sections of water bodies together into assessment units (AUs) to monitor and assess water quality criteria. Ideally, AUs represent an extent of waters with similar ecological, watershed, habitat and landuse conditions and no overlapping characteristics with other waters. In the United States, AUs are typically based on political or hydrologic boundaries rather than on ecologically relevant features, so it can be difficult to detect changes in impairment status. Our goals were to evaluate if current AU designation criteria of an impaired water body in southeastern Idaho, USA that, like many U.S. waters, has three-quarters of its mainstem length divided into two AUs. We focused our evaluation in southeastern Idaho's Portneuf River, an impaired river and three-quarters of the river is divided into two AUs. We described biological and environmental conditions at multiple reaches within each AU. We used these data to (1) test if variability at the reach-scale is greater within or among AUs and, (2) to evaluate alternate AU boundaries based on multivariate analyses of reach-scale data. We found that some biological conditions had greater variability within an AU than between AUs. Multivariate analyses identified alternative, 2- and 3-group, AUs that reduced this variability. Our results suggest that the current AU designations in the mainstem Portneuf River contain ecologically distinct sections of river and that the existing AU boundaries should be reconsidered in light of the ecological conditions measured at the reach scale. Variation in biological integrity within designated AUs may complicate water quality and biological assessments, influence management decisions or affect where monitoring or mitigation resources are directed. Published by Elsevier B.V.

⁎ Corresponding author. E-mail address: [email protected] (M. Layhee). 1 Present address: NPS, Greater Yellowstone Inventory and Monitoring Network, 2327 University Way, Suite 2, Bozeman, MT 59715, USA.

http://dx.doi.org/10.1016/j.scitotenv.2015.06.043 0048-9697/Published by Elsevier B.V.

M. Layhee et al. / Science of the Total Environment 536 (2015) 198–205

1. Introduction Biological and physical monitoring and assessment efforts are used to document the integrity and track the status of inland waters worldwide. Because more waters exist than can be effectively monitored, managers in the United States and European Union (EU) often nest sections of water bodies together into assessment units (AUs) to monitor and assess water quality criteria (United States Enivonrmental Protection Agency and USEPA, 1997; Barbour et al., 1999; EU WFD, 2000). For example, over half of lotic systems in the US are included in large AUs and lack site specific monitoring (US EPA, 2015) and in the EU there is approximately two times the number of water bodies as monitoring sites (EEA, 2012). Ideally, AUs are comprised of waters with similar watershed, habitat and/or land-use characteristics in the U.S. (US United States Enivonrmental Protection Agency and USEPA, 1997) or are discrete waters with no overlapping ecological, hdyrological and geological characteristics with other waters (EU WFD, 2000). In the U.S., more often, AUs are based on political or hydrological boundaries which allow for a consistent classification system for monitoring across basins and states. However, this approach shifts the focus away from measurable changes in ecological conditions and may not capture the finer scale processes that produce biological patterns observed at smaller scales (Snelder and Biggs, 2002; CoreTeam, 2014). In the U.S., once AU boundaries are designated, biological indices (e.g., macroinvertebrate communities) and environmental features are often sampled at only a few sites within AUs and are used to make inferences about the condition of the entire unit often because of limited resources (Barbour et al., 1999). Therefore, aggregating large sections into a single AU (and only sampling a few sites within AUs) may underestimate ecological complexity present within unit boundaries, especially in impaired waters. Impairments occur at various spatial scales ranging from local point-source inputs to landscape-scale land-use patterns; these multi-scale stressors influence biological communities and ecological processes in complex ways. Current AU designations in impaired water bodies were intentionally designed to document the cumulative effects of varying impacts (Barbour et al., 1999), but may misrepresent or be too large to capture local-scale variation in biological and environmental conditions. Designation of AU boundaries based on local-scale biological and environmental patterns (e.g., reach or sampling location) may allow managers to more accurately identify impairments and changing conditions. The EU framework, with an understanding of the limitations of large-scale water body designations, recommends that AU designations should reflect changing ecological conditions and recommends that sampling sites within AUs reflect these changing conditions (European Union Water Framework Directive, 2011). Locations and quantity of sampling sites under the EU framework are meant to reflect point or non-point source inputs or physical alterations (EU WDF, 2011). Ultimately though, individual nations within the EU determine water body classifications and sampling protocols and more research is needed to address discrepancies in water body assessments including site selection because, in many instances, like in the U.S., few sampling locations are used to represent entire AUs (Birk et al., 2012; Hering et al., 2010). Here we investigate if AU designations represent local-scale ecological (e.g., biological and environmental) conditions in the Portneuf River, an impaired river in the western U.S. The Portneuf River is a 160-km long, fifth-order river in southeastern Idaho and is representative of many waters in the U.S. and Europe because it flows through a catchment with diverse hydrological and land-use characteristics. The Portneuf River is divided into three AUs, over three-quarters of the mainstem Portneuf (134 river km) are nested into two AUs, which are both listed as US EPA impaired by sediment and nutrients. These two AUs were designated by political and hydrological boundary of tribal and public lands and confluences with large tributaries, and one-two representative sampling locations (e.g., reaches) are typically sampled

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within each AU (IDEQ, 2007, 2011). Because both AUs are impaired, it is critical that AUs represent the conditions of river reaches within the unit boundaries to allow for effective detection of changes in impairment status. We hypothesized that a bottom-up approach, which uses fine-scale distinctions in biological and environmental conditions to nest reaches into AUs, will more accurately define AUs than the current top-down approach, which used political and hydrological boundaries. The specific objectives of this study were to: (1) describe biological and environmental conditions at multiple reaches within each AU, (2) test if the variability of reach-scale biological and environmental conditions is greater within or between AUs, and (3) evaluate alternate AU boundaries based on reach-scale biological and environmental data. We predict that alternate AU boundaries based on reach-scale biological and environmental data may be more suitable AU designations. These analyses provide managers with a framework for identifying more appropriate AU boundaries based on biological and environmental relevance. Though this study focuses on only one impaired river, results are germane to other regions challenged with managing water resources in catchments with diverse hydrological and land-use characteristics. 2. Methods 2.1. Study area We sampled four reaches in the middle Portneuf AU (89 km long) and three reaches in the lower Portneuf AU (47 km long) located in the Portneuf River basin (4th Field Hydrologic Unit Code 17040208; Fig. 1). Each sampled reach was 150–350 m long. To delineate reaches, existing AUs were stratified using key watershed features identified by previous studies and with Idaho Department of Environmental Quality (IDEQ) staff including natural groundwater influence, human development, land-use variations, confluences with impaired tributaries and locations of major irrigation diversions (Baldwin et al., 2004; Hopkins, 2007; IDEQ, 2010; Hopkins et al., 2011). The middle AU extends from river km 137 below Chesterfield Reservoir and end of tribal lands to river km 48 just above the confluence of Marsh Creek (a 3rd-order tributary; Fig. 1). Study reaches in the middle AU are referred to as Reach 1 (river km 118), Reach 2 (river km 109), Reach 3 (river km 85) and Reach 4 (river km 48). Rangeland (e.g., pasture, cultivated crops) was the dominant land-use in the middle AU (27.8 ± 3.1%; mean ± 1 SD) and development (e.g., impervious surface coverage) made up a smaller percentage of land-use (1.9 ± 0.1%)1. Natural springs influence flow, water temperature and nutrient loading in Reaches 1 and 2 (Minshall and Andrews, 1973; Hopkins et al., 2011). These conditions facilitate high standing stocks of aquatic macrophytes and high macroinvertebrates abundance (Hopkins et al., 2011). Reach 3 is downstream of the town of Lava Hot Springs (population 407; 2010 census data) and the town's wastewater treatment facility. Reach 4 is located downstream of an irrigation diversion dam and the town of McCammon (population 809; 2010 census data), and just upstream of the confluence of the US EPA impaired (identified pollutants: bacteria, nutrients, sediment) Marsh Creek tributary. The lower AU extends from the confluence of Marsh Creek (river km 47) to the river's mouth at American Falls Reservoir. Study reaches in the lower AU include Reach 5 (river km 25), Reach 6 (river km 9) and Reach 7 (river km 6). Rangeland was the dominant land use in the lower AU (22.1 ± 0.6%) and development made up a smaller percentage of land-use (3.4 ± 0.8%). Reach 5 is downstream of the confluence of Marsh Creek and just upstream of Pocatello, Idaho (population 54,255; 2010 census data), the largest urbanized area in the watershed. Downstream of Marsh Creek, turbidity increases and macrophytes decrease (Hopkins et al., 2011). Reach 6 is located downstream of the city center of Pocatello and adjacent to an active and former phosphorus processing facilities. In addition to urban storm water impacts, contamination from phosphorus processing facilities has leached into the

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0

15

30 km

Fig. 1. Portneuf River subbasin in southeastern Idaho, showing location of seven sampled reaches within the middle mainstem Portneuf Assessment Unit (AU; dark blue line) and the lower mainstem Portneuf AU (light blue line). Biotic quality criterion scores for River Macroinvertebrate Index (RMI; bottom left) and Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa richness (bottom right) including good, intermediate, and poor quality scores for three transects within each sampled reach are shown. See map legends for details on score categories.

downstream end of Reach 6 (Baldwin et al., 2004). Reach 7 is downstream of these contaminated groundwater inputs, although large amounts of groundwater continue to enter the river throughout its length. This section of the river has high nutrient concentrations, high standing stocks of macrophytes and periphyton, high abundance of non-native New Zealand mudsnail Potamopyrgus antipodarum and few sensitive macroinvertebrates [e.g., Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa; Hopkins et al., 2011]. 2.2. Sampling We followed IDEQ's Beneficial Use Reconnaissance Program (BURP) protocols to conduct reach-scale sampling of biological and environmental metrics at designated sampling reaches (IDEQ, 2007). In September 2011, we sampled three river-wide, riffle transects approximately 3–10 bankfull widths apart within each designated reach. Runs were used when riffles were not present. Reach length (150–350 m) also followed BURP protocol (N 100 m; IDEQ, 2007), with the exception of Reach 7. Due to inaccessibility, only one transect was sampled in Reach 7 so inferences about variability at this reach are limited. At each transect we sampled macroinvertebrates, rooted aquatic macrophytes, periphyton, canopy cover, substrate, discharge, wetted width, wetted depth and water quality parameters. We sampled macroinvertebrates at each reach using a Hess sampler (IDEQ, 2007). Samples were collected for two minutes at two equidistant

locations along each transect. Samples were composited from each transect and sent to EcoAnalysts, Inc. (Moscow, Idaho) for sorting, identification, and enumeration. Individuals were identified to the lowest practical level (genera or species, with a few instances of Family). We estimated macrophyte biomass by harvesting all rooted, emergent plant material within three randomly located quadrats (0.645 m2) along each transect. Macrophyte biomass was estimated as wet weight (g) after clipping materials at the substrate surface and spinning material for 60 s to remove excess water. The total number of macrophyte taxa identified along each transect was used as an estimate of macrophyte taxa richness (Hopkins, 2007). Periphyton biomass was measured within each sampled reach by carefully removing attached periphyton from three randomly selected cobbles along each transect. The entire exposed cobble surface was scrubbed and brushed into a container filled with 1 L of water. A known volume (15–30 L) was then filtered using 0.7 μm Whatman GF/F glass microfiber filters (GE Healthcare Bio-Sciences, Pittsburgh, Pennsylvania). We placed the filter and filtrate into a petri dish wrapped in foil and materials were immediately frozen. Chlorophyll-a (chl-a) biomass (mg·m2− 1) was used as a surrogate for standing stocks of periphyton (Barbour et al., 1999). A fluorometer was used to measure chla (mg) following methanol extraction. The planar surface area of each sampled cobble (m2) was determined using methods described in Bergey and Getty (2006). Samples from each transect were pooled and mean chl-a biomass for each transect was reported.

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Substrate particle size was characterized by conducting pebble counts along each transect using a gravelometer and a heel-to-toe procedure (IDEQ, 2007). Particle size categories included fine sediments (b2 mm), gravel (2–64 mm), cobble (65–180 mm) boulder and bedrock (N180 mm) and other (e.g., vegetation). We used a densiometer to estimate canopy cover (%) at three randomly selected locations along each transect in four directions (N, S, E and W). Estimates from each transect were pooled and mean canopy cover (%) was reported. Wetted width (m), wetted depth (m) and velocity (m·s−1) were measured along the middle transect using a Flo-mate (Hach/MarshMcBirney, Frederick, Maryland). We calculated river discharge (m3·s−1) with these measurements. We used an Acoustic Doppler Current Profiler (ADCP) to measure discharge at the single transect in Reach 7. Wetted width (m), wetted depth (m) and discharge (m3·s−1) were included in multivariate analyses. Continuous measurements of water temperature (°C), turbidity (NTU), dissolved oxygen (mg·L− 1), pH and specific conductance (mS·cm−1) were measured at each reach using a YSI 6920 sonde (YSI Inc./Xylem Inc., Yellow Springs, Ohio) at 10 minute intervals for the duration of 5 consecutive days during the survey season. We report the median and interquartile range in Table 1. Depth and widthintegrated water samples were collected at each reach and were analyzed for alkalinity (mg·L−1), chloride (mg·L− 1), sulfate (mg·L− 1), total Kjeldahl nitrogen (total N; mg·L−1) and total phosphorus (total P; mg·L−1) at Energy Laboratories, Inc. (Billings, Montana).

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designation or the alternate AU designations. CV across transects within each of the seven reaches were calculated as CV reach ¼ σ transect =μ transect where σtransect and μtransect are the standard deviation and mean values for each biotic metric across transects within each of the reaches, and across the middle and lower AUs using CV AU ¼ σ reach =μ reach where σreach and μreach are the standard deviation and mean values for each biotic metric across reaches within each AU. Since we collected multiple periphyton samples within each transect, we calculated CV for periphyton biomass at each transect as   CV transect 1…y ¼ σ transect1…y =μ transect 1…y where σtransect1…y and μtransect1…y are standard deviation and mean values for periphyton biomass across transect 1, 2 or 3. We then calculated CVreach for periphyton biomass as the mean of CVtransect1…y of all three transects within a reach. CVAU for periphyton biomass was the mean of CVreach across each AU. We report the CV median and range across reaches and across the current and alternate AUs (Fig. 2). 2.5. Statistical analyses

2.3. Macroinvertebrate indices We calculated corrected mean (± 1 SD) taxa abundance and taxa richness based on the lowest practical level (genera; see Appendix A), dominant taxa (percent of total abundance) (see Appendix A), EPT richness and IDEQ's River Macroinvertebrate Index (RMI; Table 1). IDEQ's RMI integrates several macroinvertebrate metrics indicative of biotic condition including taxa richness, EPT taxa richness, percent Elmidae, percent predators, which are predicted to decrease with increasing habitat degradation, from sources such as pollution and human land-use, and percent dominant taxa which is predicted to increase with decreasing habitat conditions. RMI scores of b14 indicate poor biotic quality, 14–15 as intermediate biotic quality and N15 as good biotic quality. EPT richness also was used to compare across sampling locations, where an EPT richness score of b9 indicates impairment and N17 is an indication of good biotic quality relative to Idaho reference sites (IDEQ, 2011). RMI and EPT richness were included in analysis of AU designations. 2.4. Spatial scales We calculated the coefficient of variation (CV) for four biological metrics (RMI scores, EPT richness, macrophyte richness, periphyton biomass) across reaches and across AUs. CVs were compared between the reach- and AU-scales to determine if variability in biological metrics decreased at more local spatial scales (reach) than the current AU Table 1 Biological variables (mean ± 1 SD) for seven reaches within the middle and lower Assessment Units (AUs) in the mainstem Portneuf River sampled in September 2011. AU

Reach

RMI

EPT richness

Macrophyte richness

Periphyton biomass (mg·m2 − 1)

Middle

1 2 3 4 5 6 7

16.3 (4.2) 19.7 (2.3) 15.0 (2.0) 11.0 (2.3) 14.0 (4.4) 11.0 (0) 5.0

9.3 (0.6) 11.0 (3.0) 6.0 (1.7) 6.3 (1.2) 10.0 (3) 8.7 (1.2) 5.0

9 (2) 6 (0) 2 (1) 1 (0) 1 (1) 0 (0) 4.0

82 (27.9) 21.2 (6.1) 100.0 (26.8) 78.3 (18.3) 28.2 (19.2) 16.8 (3.5) 91.4

Lower

2.5.1. Spatial analyses We used Mantel tests to assess spatial autocorrelation in the biological and environmental reach data. Spatial autocorrelated data were eliminated since they are not independent and are therefore unsuitable for further statistical analyses (Bocard et al., 2011). We ran Mantel tests with 9999 Monte-Carlo permutations to detect correlation between reach coordinates and each sampled metric, including relative abundance of individual macroinvertebrate taxa, macrophyte richness and biomass, periphyton biomass, and also physical, chemical, and water quality parameters. Results of tests were considered statistically significant at P b 0.05. 2.5.2. Exploratory multivariate analyses Non-metric multidimensional scaling (NMDS) was used to explore the relatedness of sampled reaches in the middle and lower AUs based on macroinvertebrate community data and other biological and environmental data. NMDS assessed the organization of sampled reaches in macroinvertebrate abundance ordination space (Oksanen et al., 2014). To incorporate environmental data into ordination space, we overlaid environmental variables onto macroinvertebrate data. Only macroinvertebrate taxa comprising ≥ 1.0% of total abundance for each reach were included in analyses (Poos and Jackson, 2012). Mean (weighted) relative abundance data were converted to a Bray–Curtis distance matrix prior to ordination. Permutation procedures were used to describe the significance of each environmental variable. We report only those environmental variables that were statistically significant at P b 0.05. 2.5.3. Analysis of grouping strength Permutational multivariate analysis of variance (PERMANOVA) was used to compare the grouping strengths of the current AUs to results of ordination- and clustering-informed groupings (Anderson, 2001). PERMANOVAs were used to test whether current AU designations, NMDS-informed groupings or clustering scenarios (see Appendix A) best described the relatedness of reach-scale macroinvertebrate and environmental data. Nineteen environmental metrics were used with Euclidean distance measure (data scaled prior to analysis) and 9999 permutations. Mean (weighted) relative abundance macroinvertebrate

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Fig. 2. Coefficient of variation (CV) values for River Macroinvertebrate Index (RMI), Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa richness, aquatic macrophyte richness, and periphyton biomass across the seven sampled reaches in the Portneuf River (“reach”), for the current middle and lower Assessment Units (“current AU”) and for alternate three-AU scenario (“alternate AU”). Black lines within boxes represent median CV, box edges represent 25 and 75 quartiles, and whiskers represent 10 and 90 percentiles.

data were converted to a Bray–Curtis distance matrix and ran with 9999 permutations. We conducted all statistical analyses in R (CoreTeam, 2014). Mantel tests were conducted in the ade4 package (mantel.rtest function; Dray and Dufour, 2007). The vegan package in R (Oksanen et al., 2014) was used for NMDS ordination (metaMDS function) and overlaying environmental data in ordination space (envfit function), and also PERMANOVAs (adonis function).

it varied within individual reaches (Table 1). Mean periphyton biomass varied in the middle and lower AUs (Table 1). Several other biological and environmental features were variable in the middle and lower AUs (see Appendix A). Four factors were spatially autocorrelated including percent development (r = 0.77, P b 0.01), percent rangeland (r = 0.70, P b 0.01), dissolved oxygen (r = 0.69, P b 0.01) and total P (r = 0.21, P b 0.01). 3.2. Exploratory multivariate analyses

3. Results 3.1. Spatial scales There was less variation in RMI scores and EPT richness at the reach scale than at the current AU scale (Fig. 2). Median CV of macrophyte richness was lower at the reach scale than the current AU scale, but the range of CVs for macrophyte richness was greater at the reach scale (Fig. 2). Periphyton biomass was more variable at the reach scale than at the current AU scale (Fig. 2). In the middle AU, mean RMI scores were “good” in Reaches 1 and 2, “intermediate” in Reach 3 and “poor” in Reach 4 (Table 1), but scores were variable within individual reaches including Reaches 1 and 3 (Fig. 1). In the lower AU, mean RMI scores were “intermediate” in Reach 5 and “poor” in Reaches 6 and 7 (Table 1), and scores were consistent within Reaches 6 and 7, but variable within Reach 5 (Fig. 1). Mean EPT richness scores were “intermediate” in Reaches 1 and 2 and “poor” in Reaches 3 and 4 (Table 1) and scores were fairly consistent within individual reaches in the middle AU (Fig. 1). In the lower AU, mean EPT richness scores were “intermediate” in Reach 5 and “poor” in Reaches 6 and 7 (Table 1), but scores ranged from “poor” to “intermediate” within Reaches 5 and 6 (Fig. 1). Mean macrophyte richness was greater in Reach 1, 2 and 7 than other sampled reaches, though

NMDS ordination of macroinvertebrate abundance data using two axes resulted in a final stress of 0.003 (Fig. 3). Three potential groups formed in ordination space: Reaches 1 and 2, Reaches 3, 4, 5 and 6, and Reach 7. NMDS axis 1 was negatively associated with taxa sampled in Reaches 1, 2 and 7 and Reaches 3, 4, 5 and 6 positively associated with axis 1. Taxa negatively associated with NMDS axis 1 included spiny crawler mayflies Ephemerella and Helicopsyche (dominant taxa in Reaches 1 and 2), and long-horned casemaker caddisflies Oecetis avara, several non-biting midges Micropsectra, Orthocladius (dominant taxa in Reach 1), Rheotanytarsus, Thienemanniella and Thienemannimyia, dance flies Hemerodromia and water mites Hygrobates, Lebertia and Sperchon. Taxa positively associated with NMDS axis 1 included netspinner caddisflies Hydropsyche (dominant taxa in Reaches 3, 5 and 6), saddle-case maker caddisflies Protoptila, riffle beetles Microcylloepus, non-biting midges Microtendipes pedellus and moths Petrophila. Along NMDS axis 2, Reaches 3 and 7 were positively associated with axis 2 and Reaches 1, 2, 4, 5 and 6 were negatively associated with axis 2, but Reach 3, 5 and 6 were near zero. Taxa positively associated with NMDS axis 2 included the dominant taxa sampled in Reach 7 and second dominant taxa sampled in Reach 3, P. antipodarum, and also longhorned casemaker caddisflies Nectopsyche (second dominant taxa in Reach 7) and aquatic worms Tubificidae (third dominant taxa in Reach

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status in water quality data sets (Cooter, 2004). Current AU boundaries within the Portneuf River encompass variable biological conditions (e.g., RMI, EPT richness) and disparate physical (e.g., fine substrate) and water quality patterns that current sampling protocols (1–2 sampled locations per AU) may missed. We found that alternative AU classification scenarios based on a bottom-up approach, where fine-scale data are used to group larger river stretches (Brierley and Fryirs, 2000), may be more effective at monitoring river condition over space and time. 4.1. Local- and landscape-scale processes

Fig. 3. Non-metric multidimensional scaling (NMDS) of macroinvertebrate community composition at each of the seven sampled reaches in the Portneuf River, including only taxa that comprised N1% of community (not shown in figure). Environmental variables (black vectors) including macrophyte richness (MR), river discharge (DS; m3·s−1), water temperature (WT; °C), pH (PH), chloride (CL; mg·L−1) and sulfate (SL; mg·L−1) were overlaid onto macroinvertebrate data.

7) and Limnodrilus hoffmeisteri. Most other taxa were negatively related to axis 2 or near zero. We found pH (P b 0.01) and water temperature (P b 0.01) was positively associated with NMDS axis 1 and Reaches 3, 4, 5 and 6. Chloride concentrations were positively associated with NMDS axis 1 and 2 and Reaches 3, 4, 5, 6 and 7 (P = 0.01). Discharge (P = 0.04) and sulfate (P b 0.01) were positively associated with NMDS axis 2 and Reach 7. Macrophyte richness was negatively associated with NMDS axis 1 pertaining to Reaches 1 and 2 (P b 0.01). 3.3. Alternate grouping scenarios Macroinvertebrate abundance data (mean weighted relative abundance) and environmental data differed across the 3-group scenario (Reaches 1 and 2, Reaches 3, 4, 5 and 6, and Reach 7) based on NMDS and clustering (macroinvertebrate: F1 = 2.72, P b 0.01; environmental: F1 = 2.83, P b 0.01). Macroinvertebrate taxa abundance data and environmental data also differed across the 2-group scenario (Reaches 1, 2 and 7, and Reaches 3, 4, 5 and 6) based on clustering of macroinvertebrate data (see Appendix A for clustering results; macroinvertebrate: F1 = 2.75, P = 0.03; environmental: F1 = 1.95, P b 0.01). Macroinvertebrate taxa abundance data and environmental data did not differ across groups within the current AU grouping scenario (macroinvertebrate: F1 = 1.28, P = 0.17; environmental data: F1 = 1.71, P = 0.09). There was less variation in RMI scores, EPT richness, macrophyte richness and periphyton biomass with the alternate three-AU scenario than the current AU scenario (Fig. 2). Median CVs for RMI scores and EPT richness were similar with the alternate AU scenario and at the reach-scale (Fig. 2). 4. Discussion Since there are more flowing inland waters than managers can effectively monitor, it is critical that AU classifications are developed so that variation in water body conditions are accurately depicted. Using the Portneuf River in Idaho, USA as an example, we show that the current AU designations based on political and hydrological boundaries did not capture watershed features that affect ecological patterns observed at the reach scale. The inability to capture key features or patterns could produce information gaps and misinformation in reporting impairment

Local-scale biological patterns are the result of complex processes occurring at multiple spatial scales (Poff, 1997; Verdonschot, 2006). Geologic, hydrologic and climatic processes influence the physical and biological patterns in lotic systems and change in a predictable downstream manner (Vannote et al., 1980; Minshall et al., 1985). In medium-sized rivers (stream order 4–6) like the Portneuf, features like moderate stream gradient, high solar loading and instream fine particulate organic matter inputs are predicted by the River Continuum Concept to influence primary and secondary productivity. As a result, macroinvertebrate communities should be dominated by algal grazers and benthic filter feeders (Vannote et al., 1980). In our study, reaches within the middle and lower AUs were dominated by algal grazers (Protoptila, P. antipodarum) and filter feeders (Hydropsyche). However, other biological and environmental patterns within the middle and lower AUs, including primary productivity, were less consistent across reaches and lacked a predictable downstream pattern, suggesting local-scale processes influence these patterns. Local disturbances can complicate the influence of large-scale processes on local-scale patterns (Gerritsen et al., 2000). Local-scale conditions, like groundwater and point-source inputs (e.g., wastewater) and significant water diversions, can greatly influence local-scale primary and secondary production. Groundwater inputs moderate flow and temperature fluctuations and provide steady, year round nutrient inputs benefiting macrophytes (Madsen et al., 2001; Bertrand et al., 2012) and while wastewater nutrient inputs also influence productivity (Barbour et al., 1999). In our study, macrophyte richness and biomass were highly variable within the current AUs but arguably linked to local-scale influences; reaches with the highest richness and biomass of macrophytes (Reaches 1, 2 and 7) were located in areas of groundwater influence (Minshall and Andrews, 1973; Hopkins et al., 2011). Relatively high concentrations of periphyton (Reaches 1, 3 and 7) were located in areas that receive point source pollution from urban centers and wastewater treatment facilities and/or groundwater contributions (IDEQ, 2010). In addition to primary productivity, local-scale processes like groundwater and point-source pollution also influence macroinvertebrate community composition (Barbour et al., 1999). In our study, reaches with the highest levels of macrophytes and algal stocks (Reaches 1, 2 and 7) also had the highest biological quality scores or greatest macroinvertebrate taxa abundance. 4.2. Multi-scale patterns to classify river segments A hierarchical framework that uses measurable local-scale biological features to determine higher-level organization may help managers to designate river AUs (Poff, 1997; Gerritsen et al., 2000). This strategy has been used to inform ecological-derived classification scenarios for both U.S. (Barbour et al., 1999), Australian (Brierley and Fryirs, 2000; Simpson and Norris, 2000) and European water body assessment programs (Verdonschot, 1995; Wright et al., 1993). Here, we applied a similar framework for analyzing and integrating biological and environmental data to classify reaches within the Portneuf River. Our classification strategy considered the similarity of local-scale measures using multivariate techniques. In this study, ordination and clustering procedures using empirical macroinvertebrate and environmental

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data assigned reaches differently than the current AU designations. Our clustering results indicate macroinvertebrates in Reach 1, 2 and 7 differ from assemblages in Reaches 3, 4, 5 and 6. However, ordination results demonstrated that dominant macroinvertebrate communities (P. antipodarum, Nectopsyche, Tubificidae) and physical characteristics (flow, fine substrate, sulfate) in Reach 7 differed from most other reaches. As a result, NMDS ordination of biological and environmental data demonstrated that the seven reaches were better organized into three groups. However, additional transects are needed to fully describe biological and environmental conditions in Reach 7. In addition, the high variability we documented in biological indices among reaches and between current AUs suggest that designation of AU boundaries would benefit from sampling additional reaches. 4.3. Constraints on framework Managers must assess the integrity of lotic systems with limited resources. Identifying reach-scale patterns based on empirical data (Brierley and Fryirs, 2000) may be more effective for water body classification scenarios than the current AU designation criteria based on political and hydrological boundaries. However, attaining reach-scale data is costly, and most monitoring programs are constrained by resources (MacDonald, 1994). In addition, redefining AU boundaries or proposing additional AUs to better represent local-scale patterns can also be costly for managers and require additional sampling efforts. We demonstrate that alternative two or three AU scenarios receive stronger statistical support than the current classification. In our alternative three-AU scenario, more AUs are proposed but some AUs are shorter in river length. Moreover, because the alternative approaches lump similar reaches, a single sampling location may be able to characterize and track trends for the entire AU — something that may be more financially and logistically feasible for managers. However, since processes and local patterns are dynamic in lotic systems and change over time, the temporal relevancy of these alternate AU boundaries is unknown. Therefore, data from additional sampling years may be needed to determine if these alternate AUs remain appropriate over time. 5. Conclusions Overarching goals of water quality monitoring are to characterize the integrity of a water body and to detect changing conditions. Results of our work demonstrate that AU designations informed by biological and environmental data may better detect variations in biological integrity and human influence. These variations in biological integrity complicate water quality and biological assessments of a given AU, and could influence decisions to list or delist a particular AU as impaired and affect where monitoring or mitigation resources are directed. Our use of multivariate clustering techniques on reach-scale data allowed us to better characterize the processes driving observed biological patterns and define AU boundaries based on ecologically relevant features. Disclaimer This draft manuscript is distributed solely for purposes of scientific peer review. Its content is deliberative and predecisional, so it must not be disclosed or released by reviewers. Because the manuscript has not yet been approved for publication by the U.S. Geological Survey (USGS), it does not represent any official USGS finding or policy. Acknowledgments Funding was provided by the Idaho Department of Environmental Quality (#C874). This manuscript benefited from the comments of Reviewers and Robert Gresswell. We would like to thank Mark AbbeyLambertz and IDEQ field technicians for assistance with sample

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