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Associations Between Fish and Benthic Macroinvertebrate Biotic Integrity and Non-Point Source Pollution Estimates in the Nolichucky River Watershed.

Introduction

Since the passage of the LISA Clean Water Act in 1972, regulatory agencies around the world have developed indices of biotic integrity (IBI) to assess the relative "health" of aquatic ecosystems (Karr, 1981; Couceiro et al., 2012; Jun et al., 2012; Huang et al., 2015). Fish and benthic macroinvertebrates tend to be the biological communities that are most often sampled (Karr, 1991; Klemm et al., 2003: Ruaro and Gubiani, 2013) due to their relative ease of capture, identification, and their strong response to site-specific environmental stressors like chemical pollutants, sedimentation, temperature or dissolved oxygen change (Sutherland et al., 2002; Wang et al., 2006; Utz et al., 2010). The Tennessee Valley Authority (TVA) and Tennessee Department of Environment and Conservation (TDEC) have developed IBI's for fish (modified from Karr, 1981) and benthic macroinvertebrates (Kerans and Karr, 1994; TDEC, 2011), respectively, which are used to assess the health of streams in Tennessee. As part of TDEC's stream monitoring program, reaches are sampled every five years and are scored based on a set of metrics that reflect community diversity, food web structure, behavioral and reproductive traits, productivity, and disease risk. Scores, and individual metrics that comprise the scores, are strongly associated with in-stream water quality, channel alteration, and riparian degradation (Simon and Lyons, 1995; Roth et al., 1996; Allan et al., 1997; Stoddard et al., 2008). However, landscape-level associations between non-point source stressors and IBI metrics have not been adequately assessed.

Sedimentation and nutrient runoff from non-point source pollution are the primary stressors of stream ecosystem health (Johnson et al., 1997; Zamor and Grossman, 2007; Paulsen et al., 2008; Kemp et al., 2011; Clapcott et al., 2012). However, variables describing pollutant loads derived from land use activities are rarely assessed, aside from studies that measure "snap shot" estimates of nutrients and sediment at local scales (e.g., a stream reach). The Soil and Water Assessment Tool (SWAT) is a publicly accessible watershed-scale model created by the USA Department of Agriculture that quantifies land use impacts on water, sediment, and nutrient yields (organic nitrogen and phosphorus). The main inputs of the SWAT model are rates of precipitation, surface runoff, return flow, percolation, evapotranspiration, transmission losses, pond and reservoir storage, crop growth and irrigation, groundwater flow, reach routing, nutrient and pesticide loading, and water transfer. These data are publicly available and can be incorporated in a geographic information system (GIS) and used to estimate nutrient and sediment yield at a particular point in a stream. To our knowledge. SWAT model estimates of pollutants have not been used to examine if the association exists between non-point source pollutants and stream biotic integrity. We used the Nolichucky watershed in east Tennessee as a case study for testing the hypothesis that SWAT model estimates of sediment and nutrient runoff transported from watersheds upstream of fish and benthic macroinvertebrate sample sites are correlated with IBI metrics that reflect stream ecosystem health at the local scale.

Materials and Methods

Sample site selection--The Nolichucky River watershed straddles the border of Tennessee and North Carolina (Figure 1). It drains the Blue Ridge Mountains and is a tributary to the French Broad River. All sampling of fish and benthic macroinvertebrates for this study occurred on the Tennessee side of the watershed, where there is a total of 3,803 stream kilometers (TDEC, 2008). The watershed lies within the U.S. EPA's level 1 ecoregion, which is defined as Eastern Temperate Forest (Omernik, 1987). This ecoregion contains two nested level III ecoregions--the Blue Ridge Mountains to the east and the Ridge and Valley to the west (Commission for Environmental Cooperation, CEC, 1997). The Blue Ridge Mountain landscape is mostly comprised of an oak-pine (Quercus-Pinus) forest with a small amount of agriculture consisting of apple orchards and fields of tobacco and hay/pasture. The Tennessee portion of the watershed is mostly in the Ridge and Valley ecoregion, which historically was covered with oak-pine forests that were interspersed with grassland barrens. Presently, 47% of the Tennessee portion of the watershed is covered by agriculture, mostly in cattle pasture and hay production (TDEC, 2008). The Ridge and Valley ecoregion is mainly underlain by highly soluble carbonate parent rock that can make the water slightly alkaline (Lloyd and Lyke, 1995). The soil type is comprised of brown loamy soils and red clay soils. In the Blue Ridge Mountain ecoregion, mean annual precipitation ranges from 1,020 mm to 1,270 mm. with about 20% being snow fall. Mean annual temperature is approximately 10[degrees]C to 16[degrees]C. In the Ridge and Valley ecoregion, mean annual precipitation ranges from 900 mm to 1,400 mm annually, and the mean annual temperature ranges from 13[degrees]C to 16[degrees]C (McNab, 1996).

For this study, 19 sample sites in the Tennessee portion of the watershed were selected to reflect a gradient in land cover types that contribute to variation in sediment and nutrient loads. Variation in benthic invertebrate and fish assemblages due to natural geomorphic factors were accounted for by selecting sites within an elevation gradient and watershed size (Table 1). This was done because benthic invertebrate and fish assemblages are different with respect to natural changes in water temperature (e.g., higher elevation, shaded canopy sites), discharge (e.g., lower in tributary sites), and substrate size (e.g., larger substrates in tributaries) (Vannote et al., 1980). One tributary (Clarks Creek) and one main stem site on the Nolichucky River were sampled in 2014 and 2015 to check for temporal changes in fish and benthic macroinvertebrate assemblages as a function of natural changes in hydrology; however, the assemblages were highly similar with respect to species composition, and no changes were detected with respect to land use or hydrology (Gotwald, 2016).

Land cover data acquisition and classification--Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) satellite data at path 18, row 35 with minimal cloud cover were acquired for 2014 during the visible growing season. Satellite images were preprocessed using the software ENVI version 4.8 (Exelis Visual Information Solutions. 2010). Radiometric correction was performed to produce reflectance values, and atmospheric correction was completed with a dark body subtraction (e.g., Level II Normalized Difference Vegetation Index. NDVI, Rouse et al., 1974). Scenes that were derived from Landsat surface reflectance images were acquired from the USGS's Earth Resources Observation and Science Center (EROS) to aid the classification and differentiation between row crops and open spaces. Aerial photographs that were coincident with the satellite data were acquired to provide training areas for supervised classifications and ground truth sample sites for accuracy assessments of the resulting thematic maps.

To help separate row crops from open spaces (i.e., hay, pasture, and fallow fields), a mask was created for the NDVI time series using 2011 National Land Cover Dataset's (NLCD) impervious surface layers, the National Hydrography Dataset's (NHD) vector data of open water, and the forested land cover identified in the supervised classification. They were applied to the satellite images with the closest corresponding acquisition year. A maximum likelihood supervised classification approach in combination with a normalized difference vegetation index calibrated density slice (NDVI, Rouse et al., 1974, Tucker, 1979) was used to produce LULC maps from the processed Landsat time series. The maximum likelihood supervised classification produced 4 classes: forest, impervious, row crop, and open space (i.e., hay, pasture, and fallow fields). Upstream catchments for each site were delineated using the Arc Hydro toolset version 2.0 (Djokic et al., 2011). Using U.S.D.A National Agriculture Imagery Program (NAIP) photos for 2014, land use was digitized and summed for each of the 19 catchments.

SWAT model estimates--To assess the amount of non-point source pollutant runoff in each catchment, the ArcSWAT 2012 model was used to simulate the amount of sediment yield for each sample site for 2014. Mandatory spatial input files needed for the model included the input digital elevation model (DEM), land use map. and soil layer. Using the DEM. a catchment was created for each sample site. The catchments were divided into hydrologic response units (HRUs) by land use/land cover (LULC), slope levels, and soil percentage. The HRUs are areas in the model that are calculated to have the same manner in which they conduct water through the system to the sample site. Land use and slope were reclassified in the SWAT model. Land use was classified as agriculture row crops, mixed forest, water, residential medium/low density, and pasture. The slope was classified into five classes based on natural breaks in the percent rise value in the raster.

The SWAT output file was a measurement of sediment yield, organic nitrogen yield, and organic phosphorus yield for all HRU's in the Nolichucky River watershed (Figure 2), as well as each of the 19 catchments that drained to a fish and benthic macroinvertebrate sample site. The model was run on a monthly time step during 2014. Sediment yield (SYLD) was reported as metric ton ha and is the sediment from the catchment that is transported to the sample site during the time step. Organic nitrogen yield (ORGN) was reported as the ORGN transported out of the catchment and into the sample site during the time step. Organic phosphorus yield (ORGP) was reported as the amount of ORGP transported (Arnold et al., 2012). The measurements for all months were totaled and the amounts of SYLD, ORGN, and ORGP transported with sediment into the sample site during 2014 are reported in Table 1.

Benthic macroinvertebrate sampling--To sample the benthic macroinvertebrate community, a 500-[micro]m mesh kick net was used to conduct two "semi-quantitative" samples in a "fast" and "slow" portion of the riffle to collect individuals. The procedures for collection, sorting, preserving, identification, and Tennessee Macroinvertebrate Index (TMI) metric calculation followed standard TDEC (2011) procedures for wadeable streams in the Blue Ridge Mountain and Ridge and Valley bioregions. Metrics from the TMI are designed to assess the overall health of wadeable streams with regard to supporting a natural benthic macroinvertebrate community. All invertebrates were identified to species, if practical, and the whole sample for each site was counted (i.e., no subsampling occurred prior to identification). Chironomid genera species and oligochaetes were mounted in CMCP-9 low viscosity mountain and identified under a compound light microscope. All other invertebrates were identified under a dissecting light microscope. The most recent available dichotomous keys were used for identification. Quality assurance of identifications occurred with a second person of equal skill re-identifying a portion of the invertebrates.

Fish sampling--During summers from 2014 to 2016. we followed the Tennessee Valley Authority's (TVA) standard sampling protocols for their stream fish IBI, which is a region-specific modification of the original fish IBI published by Karr (1981). Fish were sampled in riffle-run habitats by simultaneously kicking the stream bottom and back-pack electrofishing (60 Hz, AC) into a 3 m x 6 m seine net (untreated nylon, 6-mm mesh). Seine hauls were used to sample fish from pool habitats. Sample areas for electrofishing sets and seine hauls were standardized to 28 [m.sup.2]. and sampling continued in a habitat type until it was "depleted" of species. That is, sampling occurred until three consecutive runs yielded no new species for riffle and run habitats. At sites with stream widths >7 m, a 51-m shoreline backpack electrofishing sample was conducted to collect fishes inhabiting bank associated habitats. All fish were identified after each electrofishing sample run, counted, and released alive immediately into the stream. Fish sampling methods were approved under IACUC protocol #2257-0414 by the University of Tennessee-Knoxville.

Statistical analysis--Partial canonical correspondence analysis (pCCA) was conducted with a main matrix of the seven benthic macroinvertebrate IBI metrics. Proportion data were arcsine-square root transformed, and continuous data were In-transformed to improve multivariate normality. All transformed fish metric data were further standardized by watershed area, because the IBI scoring developed by the TVA takes into account the natural variation in stream fish assemblages as a function of drainage area and channel size (Vannote et al., 1980). The SWAT model estimate for each catchment was used as an explanatory variable in the second matrix of the pCCA. To account for effects of spatial proximity of sample sites within the watershed, geographic coordinates were used as a covariable matrix in the pCCA (Borcard et al., 1992; Grand and Cushman, 2003; Alford, 2014). This procedure was done to remove any potential confounding effects of spatial proximity on the composition of fish and benthic macroinvertebrate metrics (Borcard et al., 1992). A Monte Carlo randomization procedure was run 499 times to determine if the axes and correlations between the IBI metrics and SWAT model estimates matrix were statistically significant (P < 0.05). A preliminary pCCA was run on the 12 fish IBI metrics versus the SWAT model estimates, however the resulting ordination of species in environmental space was not canonical (P > 0.05 for all canonical axes). Therefore, an indirect gradient analysis was run (partial correspondence analysis, or pCA). This procedure differs from pCCA in that in does not constrain the effects of environmental variables prior to the ordination of metrics. Both analyses were run in CANOCO version 4.5 software (ter Braak and Smilauer, 1998; Grand and Cushman, 2003; Alford 2014).

Results

A total of 10.831 benthic macroinvertebrates representing 266 taxa were collected from riffle habitats in the Nolichucky River watershed (Table 2). A combination of the caddisfly genera Cheumatopsyche, Hydropsyche, Ceratopsyche, the mayfly genera Maccaffertium, Isonychia, Baetis, the blackfly genus Simulium, and the riffle beetle genus Stenelmis comprised half of all individuals collected. A total of 11,414 fish representing 55 species were sampled from riffle, run, and pool habitats using seine hauls and backpack electrofishing (Table 3). Over half (53%) of the individuals consisted of Highland Shiner Notropis micropteryx, Sharphead Darter Etheostoma acuticeps, Greenside Darter Etheostoma blennioides, Telescope Shiner Notropis telescopus. Central Stoneroller Campostoma anomalum, Banded Sculpin Cottus carolinae, and Bluebreast Darter Etheostoma camurum. A summary of IBI metrics for the benthic macroinvertebrate and fish assemblages as well as estimates generated by the SWAT models for sediment, organic nitrogen, and organic phosphorus for watersheds upstream of the fish and invertebrate sample sites are provided in Table 1.

There was a significant association between benthic macroinvertebrate IBI metrics and SWAT model estimates after removing confounding effects of spatial proximity among sample sites (partial CCA; 499 Monte Carlo runs; all canonical axes F = 1.90; P = 0.046, axis 1 F = 4.54; P = 0.018). More specifically, %TNUTOL showed a significant positive relationship with organic nitrogen and phosphorus yield, whereas %CLINGER was negatively associated with organic nitrogen and phosphorus (Figure 3). Sediment yield had a significant negative relationship with %CLINGER, TR, and EPT (Figure 3). With respect to fish IBI metrics, there was also a significant association with SWAT model estimates after accounting for spatial position of sample sites (partial CA; 499 Monte Carlo runs; total inertia = 0.39; cumulative explained variance in fish metric-SWAT variable for axes 1 and 2 = 95.5%). A significant positive association was detected between %HYBRID and CPUE and all three SWAT model variables (Figure 4). In contrast, native species richness (NATIVE) was negatively associated with non-point sources of sediment, nitrogen and phosphorus yields (Figure 4).

Discussion

Regulatory agencies that assess the biological health of streams as mandated by the USA Clean Water Act of 1972 utilize multi-metric techniques like the IBI to assess a stream's ability to mirror the biological condition it would exhibit in its "natural", unimpaired setting (Karr et al., 1986; Karr, 1991; Lyons et al., 1996; Hughes et al., 2004). Metrics that comprise an IBI, and the total score, should be sensitive to anthropogenic changes to water chemistry and physical habitat, as well as external sources of pollution from fecal coliforms (Stoddard et al., 2008; Hawkins et al., 2008; Ruaro and Gubiani, 2013). However, given the stochastic nature of stream fish and invertebrate populations (e.g., reproductive success, recruitment of juveniles to maturation), and their ability to disperse and colonize other nearby patches of suitable habitat, it can be challenging to demonstrate strong cause-effect relationships between some site-specific IBI metrics and physicochemical properties of streams, such as biological oxygen demand (BOD), sediment load, daily/instantaneous dissolved oxygen concentration, or nitrogen/phosphorus concentrations (Rowe et al., 2009). At any assessed stream reach, water chemistry, habitat, and pollutants are often measured as snapshots in time that may reflect disturbances that are occurring at larger spatiotemporal scales outside the stream reach (Hitt and Roberts, 2012; Alford, 2014; Midway et al., 2014). Cumulative impacts of land use change and natural geomorphic or hydrologic inputs farther up a stream's watershed may influence the water quality at a particular point in a stream (Johnson at al., 1997; Allan, 2004; Helms et al., 2009; Blevins et al., 2013). In addition, these disturbances can occur prior to the time when water or biological samples are taken.

We found that the value of some metrics of riverine biotic integrity can be influenced by watershed-scale impacts from non-point sources of pollution, particularly nitrogen, phosphorus, and sediment. For benthic macroinvertebrates in the Nolichucky River watershed. SWAT model estimates of organic nitrogen and phosphorus yield were positively associated with percent nutrient-tolerant taxa, but negatively associated with percent clingers in semi-quantitative kick net samples. Sediment yield was negatively associated with percent clingers, taxa richness, and Ephemeroptera-Plecoptera-Trichoptera (EPT) richness. For fishes, sediment and nutrients were positively associated with % hybrids and catch per unit effort in backpack electrofishing and seine haul samples, but negatively associated with native species richness.

Remote sensing and GIS technology have made it easier for aquatic scientists and water resource managers to assess the physical and chemical conditions and potential non-point pollution sources of whole watersheds that may have occurred over decades (Gardiner et al., 2009). It is evident that land cover change from natural vegetation (e.g., forests, grasslands) to row-crop agriculture or impervious surfaces leads to increased inputs of nutrients, sediments, and organic pollutants (e.g., manure) to streams. The development of SWAT models has enabled aquatic ecologists and resource managers to investigate potential causes of non-point source pollution to specific stream segments at broader spatiotemporal scales than that of a stream reach at a particular point in time. To date, studies incorporating SWAT model estimates of nutrient and sediment yield have not attempted to show a link with biotic integrity. Our study shows that certain benthic macroinvertebrate and fish IBI metrics in the Nolichucky River drainage of Tennessee, using standard IBI sampling protocols developed by state and federal agencies, are associated with SWAT model estimates of organic nitrogen, organic phosphorus, and sediment yield in a stream segment's upstream watershed.

Ideally, any multi-metric IBI will reflect several aspects of an aquatic ecosystem, such as its biodiversity, food web structure, productivity, disease prevalence, hybridization, or invasive species effects. It is beneficial from an assessment perspective if metrics representing all of these attributes exhibit a strong response to chemical, physical and biological stressors. However, this is not always the case (Wellemeyer et al., 2018). especially if natural disturbance regimes are highly variable with respect to flow and runoff (e.g., desert streams), or in landscapes where agricultural land use has been prevalent for hundreds of years (e.g., Mississippi River alluvial valley streams). Nonetheless, the integration of all metrics into a final IBI score tends to provide a general sense of the relative health of a stream reach (Karr, 1991). In our study, only a few metrics of the benthic macroinvertebrate and fish IBI were sensitive to SWAT model estimates of non-point source pollution. Yet, they were metrics that described complementary properties of the aquatic ecosystem that exist in the Nolichucky River watershed. For example, the benthic macroinvertebrate metrics %TNUTOL, %CLINGER, TR, and EPT (4 out of 7) were strongly related to nitrogen, phosphorus, and sediment yield. This is sensible because %TNUTOL is reflective of trophic changes to the ecosystem brought on by inputs of nutrients. The metric %CLINGER is expected to decrease when sediment loads increase the embeddedness and reduce the availability of rocky substrata that are used by invertebrates to hold their position in the current on rocky surfaces. In addition, dingers typically are adapted to living in high-velocity riffle habitats that are dominated by cobble and boulder substrates. Therefore, when riffles are filled in by fine sediments, or when pool and run macrohabitats are prevalent at a reach, the dingers will drift downstream or fail to survive. Ginger taxa can also decrease when high nutrient loads cause benthic algae or macrophytes to increase their production and cover rock surfaces that otherwise would be occupied by clinging invertebrates. The metrics TR and EPT reflect the native biodiversity of the ecosystem. These metrics will decrease in value if water physicochemical properties (e.g., dissolved oxygen, temperature, ammonia, pH) are impaired due to point source or non-point source pollutants. The EPT in particular tend to be the most pollution-sensitive benthic macroinvertebrate taxonomic orders in streams. Thus, this metric should reflect changes in watershed land cover that ultimately impact water quality at a site by increasing the rate of sediment and nutrient inputs.

For the fish IBI, only 3 out of 12 metrics were associated with SWAT model estimates. The CPUE metric, which is a measure of relative abundance (i.e., reflects population density) is to expected to increase in streams that receive greater nutrient inputs that subsequently increase the primary production at the base of the food web. Fishes like the Bullhead Minnow (Pimephales vigilax) and Central Stoneroller (Campostoma anomalum) tend to be more abundant and encompass proportionately more of the species composition when benthic algae responds to increased nutrient loads and comprises more of the food base as opposed to allochthonous sources of organic matter. Although greater fish abundance is typically a positive indicator with regard to biotic integrity, when metrics that describe biodiversity (e.g., native species richness) concomitantly decrease in a stream and metrics like percentage omnivores or hybrids increase, then increases in the CPUE metric can be interpreted as a negative indicator of biotic integrity (Sutherland et al., 2002). For example, the CPUE metric utilized by TVA increases in Southern Appalachian streams when primary production increases due to nutrient inputs (pers. comm. D. Matthews, TVA, River and Reservoir Compliance Monitoring). Additionally, the % HYBRID metric tends to increase in streams when sedimentation and nutrient input increase, because fish species are more likely to hybridize with closely related species that exhibit similar nesting behaviors in degraded ecosystems (e.g., high sediment loads). For example, Green Sunfish (Lepomis cyanellus) tend to hybridize with other sunfishes, like Bluegill (L. macrochirus) or Redbreast Sunfish (L. auritus), especially in degraded conditions (Simon and Lyons, 1995). In our study, all hybrids encountered were sunfish hybrids, and these were in streams with high turbidity from sediment runoff. Native species richness (NATIVE) was negatively correlated with sediment and nutrient loads in our study, which is not surprising, since this measure of biodiversity is often used as one of the primary metrics to evaluate biotic integrity in aquatic ecosystems. In North American streams, native species richness tends to decrease as pollution increases, since physiological tolerances are often surpassed for most species, while a few tolerant species will increase in abundance and dominate the assemblage (Zamor and Grossman, 2007; Blevins et al., 2013).

It appears that the benthic invertebrate IBI developed by TDEC was more sensitive to estimates of non-point source pollution than the fish based IBI developed by TVA. This may have occurred because larval benthic invertebrates are more susceptible to changes in sediment load and nutrients (i.e., will move or die sooner than fish), or that the IBI metrics for fish are more sensitive to acute point-source pollution sources and fragmentation from dams that restrict adult dispersal and larval recruitment.

In summation, the SWAT model developed by the U.S.D.A. is an inexpensive GIS tool that aquatic resource managers and researchers can utilize to assess the impacts of non-point source pollution on freshwater ecosystems in the USA. We found that some, but not all, fish and benthic macroinvertebrate IBI metrics in the Nolichucky River watershed are significantly associated with variation in organic nitrogen, phosphorus, and sediment yields estimated from the SWAT model. In general, estimates of non-point source pollution were positively associated with increases in fish production (i.e. CPUE) and percentage of hybrids, but negatively related to metrics of biodiversity like species richness. Similarly, for benthic macroinvertebrates, metrics reflecting biodiversity, such as EPT richness and total taxa richness were negatively associated with non-point source pollution, while behavioral traits like clinging behavior and tolerance to nutrients were positively associated with non-point source inputs. Our findings are limited to the Nolichucky River watershed, but other researchers should use SWAT models and indices of biotic integrity to examine the link between sediment and nutrient loads and aquatic ecosystem health in other regions with different fauna. In addition, our study evaluated only one hydrologic year of non-point source pollution. These estimates will vary with respect to natural changes in precipitation as well as with land use change. A more comprehensive understanding of the influence that sediment and nutrient runoff has on biotic integrity in a watershed will be attained when datasets incorporate a greater variety of hydrologic periods from drought to high flow years.

Acknowledgements

We thank the University of Tennessee Institute of Agriculture and the Tennessee Water Resources Research Center for funding this research. The following people were instrumental in helping with field data collection: J. Coombs, J. Wolbert, D. Walker, T. Amacker, P. Hurst, K. Garner, and C. Savior. We thank Dr. R. Washington-Allen (UTK Dept. of Geography) and Dr. A. Ludwig (UTK Dept. of Biosystems Engineering and Soil Sciences) for their valuable advice on GIS modeling and study design.

Literature Cited

Alford, J. B. 2014. Multi-scale assessment of habitats and stressors influencing stream fish assemblages in the Lake Pontchartrain Basin. USA. Hydrobiologia 738: 129-146.

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

Allan, J. D., D. Erickson, and J. Fay. 1997. The influence of catchment land use on stream integrity across multiple spatial scales. Freshwater Biology 37: 149-161.

Arnold, J. G., J. R. Kiniry, R. Srinivasan, J. R. Williams, E. B. Haney, and S. L. Neitsch. 2012. Soil and water assessment tool: input/output documentation. Texas A&M University, Temple, Texas.

Blevins, Z. W., E. L. Effert, D. H. Wahl, and C. D. Suskia. 2013. Land use drives the physiological properties of a stream fish. Ecological Indicators 24: 224-235.

Borcard, D., P. LeGendre, and P. Drapeau. 1992. Partialling out the spatial components of ecological variation. Ecology 73: 1045-1055.

Clapcott, J. E., J. K. Collier, R. G. Death, E. O. Goodwin, J. S. Harding, D. Kelly, J. R. Leathwick, and R. G. Young. 2012. Quantifying relationships between land-use gradients and structural and functional indicators of stream ecological integrity. Freshwater Biology 57: 74-90.

Commission for Environmental Cooperation (CEC). 1997. Ecological regions of North America: toward a common perspective. Montreal, Quebec, Canada.

Couceiro, S. R. M., N. Hamada, B. R. Forsberg, T. P. Pimentel, and S. L. B. Luz. 2012. A macroinvertebrate multimetric index to evaluate the biological condition of streams in the Central Amazon region of Brazil. Ecological Indicators 18: 118-125.

Djokie, D., Z. Ye, and C. Dartiguenave. 2011. Arc Hydro tools overview. ESRI, Redlands, California.

Gardiner, E. P., A. B. Sutherland, R. J. Bixby, M. C. Scott. J. L. Meyer. G. S. Helfman, F. E. Benfield, C. M. Pringle, P. V. Bolstad, and D. N. Wear. 2009. Linking stream and landscape trajectories in the southern Appalachians. Environmental Monitoring and Assessment 156: 17-36.

Gotwald, H. S. 2016. Impacts of land use disturbance on fish and aquatic macroinvertebrate assemblages in the Nolichucky River watershed. MS thesis, University of Tennessee, Knoxville, Tennessee.

Grand. J., and S. A. Cushman. 2003. A multi-scale analysis of species-environment relationships: breeding birds in a pitch pine-scrub oak (Pinus rigida-Quercus ilicifolia) community. Biological Conservation 112: 307-317.

Hawkins, C. P., S. G. Paulsen, J. Van Sickle, and L. L. Yuan. 2008. Regional assessments of stream ecological condition: scientific challenges associated with the USA's national Wadeable Stream Assessment. Journal of the North American Benthological Society 27: 805-807.

Helms, B. S., J. E. Schoonover, and J. W. Feminella. 2009. Assessing influences of hydrology, physicochemistry, and habitat on stream fish assemblages across a changing landscape. Journal of the American Water Association 45: 157-169.

Hitt, N. P. and J. H. Roberts. 2012. Hierarchical spatial structure of stream fish colonization and extinction. Oikos 121: 127-137.

Huang. Q., J. Gao, Y. Cai, H. Yin, Y. Gao, J. Zhao. L. Liu, and J. Huang. 2015. Development and application of benthic macroinvertebrate-based multimetric indices for the assessment of streams and rivers in the Taihu Basin, China. Ecological Indicators 48: 649-659.

Hughes. R. M., S. Howlin, and P. R. Kaufman. 2004. A biointegrity index (IBI) for coldwater streams of western Oregon and Washington. Transactions of the American Fisheries Society 133: 1497-1515.

Johnson, L. B., C. Richards, G. E. Host, and J. W. Arthur. 1997. Landscape influences of water chemistry in Midwestern streams. Freshwater Biology 37: 193-208.

Jun, Y., D. Won, S. Lee, D. Kong, and S. J. Hwang. 2012. A multimetric benthic macroinvertebrate index for the assessment of stream biotic integrity in Korea. Environmental Research and Public Health 9: 3599-3628.

Karr, J. R. 1981. Assessment of biotic integrity using fish communities. Fisheries 6: 21-27.

Karr, J. R., K. D. Fausch, P. L. Angermeier, P. R. Yant, and I. J. Schlosser. 1986. Assessing biological integrity in running waters: a method and its rationale. Illinois Natural History Survey Special Publication 5. Urbana-Champaign, IL.

Karr, J. R. 1991. Biological integrity: a long-neglected aspect of water resource management. Ecological Applications 1: 66-84.

Kemp. P., D. Sear. A. Collins. P. Naden, and I. Jones. 2011. The impacts of fine sediment on riverine fish. Hydrological Processes 25: 1800-1821.

Kerans, B. L. and J. R. Karr. 1994. A benthic index of biotic integrity (B-IBI) for rivers of the Tennessee Valley. Ecological Applications 4: 768-785.

Klemm, D. J., K. A. Blocksom, F. A. Fulk, A. T. Herlihy, R.M. Hughes. P. R. Kaufmann, D. V. Peck, J. L. Stoddard, W.T. Thoeny, M. B. Griffith, and W. S. Davis. 2003. Development and evaluation of a macroinvertebrate biotic integrity index (MBII) for regionally assessing Mid-Atlantic Highlands streams. Environmental Management 31: 656-669.

Lloyd, O. B., and W. L. Lyke 1995. Ground water atlas of the United Stales: segment 10, Illinois, Indiana. Kentucky, Ohio, Tennessee. No. 730-K. US Geological Survey, Washington, DC.

Lyons, J., L. Wang, and T. D. Simonson. 1996. Development and validation of an index of biotic integrity for Coldwater Streams in Wisconsin. North American Journal of Fisheries Management 16: 241-256.

McNab W. H. 1996. Ecological subregions of the United States. US Forest Service, Washington, DC.

Midway, S. R., T. Wagner, and B.H. Tracy. 2014. A hierarchical community occurrence model for North Carolina stream fish. Transactions of the American Fisheries Society 143: 1348-1357.

Omernik, J. M. 1987. Ecoregions of the conterminous United States. Annals of the Association of American Geographers 77: 118-125.

Paulsen, S. G., A. Mayio, D. V. Peck. J. L. Stoddard, E. Tarquinio, S. M. Holdsworth, J. Van Sickle, L. L. Yuan, C. P. Hawkins, A. T. Herlihy, P. R. Kaufmann, M. T. Barbour, D. P. Larsen, and A. R. Olsen. 2008. Condition of stream ecosystems in the US: an overview of the first national assessment. Journal of the North American Benthological Society 27: 812-821.

Roth, N. E., J. D. Allan, and D.L. Erickson. 1996. Landscape influences on stream biotic integrity assessed at multiple spatial scales. Landscape Ecology 11: -141-156.

Rouse, Jr., J. W., R. H. Haas, D.W. Deering, J. A Schell, and J. C. Harlan. 1974. Monitoring the vernal advancement and retro-gradation (green wave effect) of natural vegetation. NASA/GSFC. Type III, Final Report, Greenbelt, Maryland.

Rowe, D. C., C. L. Pierce, and T. F. Wilton. 2009. Physical habitat and fish assemblage relationships with landscape variables at multiple spatial scales in wadeable Iowa streams. North American Journal of Fisheries Management 29: 1333-1351.

Ruaro, R. and E. A. Gubiani. 2013. A scientometric assessment of 30 years of the index of biotic integrity in aquatic ecosystems: applications and main flaws. Ecological Indicators 29: 105-110.

Simon. T. P. and J. Lyons. 1995. Application of the index of biotic integrity to evaluate water resource integrity in freshwater ecosystems. Pp. 245-262 in Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making (W. S. Davis and T. P. Simon, eds.) CRC Press. Boca Raton. Florida.

Stoddard, J. L. A. T. Herlihy, D. V. Peck, R. M. Hughes, T. R. Whittier, and E. Tarquinio. 2008. A process for creating multimetric indices for large-scale aquatic surveys. Journal of the North American Benthological Society 27: 878-891.

Sutherland, A. B., J. L. Meyer, and E.P. Gardiner. 2002. Effects of land cover on sediment regime and fish assemblage structure in four Southern Appalachian streams. Freshwater Biology 47: 1791-1805.

Tennessee Department of Environment and Conservation (TDEC). 2008. Tennessee River and Stream Assessment Project. Nashville. Tennessee.

Tennessee Department of Environment and Conservation (TDEC). 2011. Quality system standard operating procedure for macroinvertebrate stream surveys. Nashville, Tennessee.

ter Braak, C. J. F. and P. Smilauer. 1998. CANOCO Reference manual and user's guide to Conoco for Windows, Software for canonical community ordination (version 4.5). Microcomputer Power, Ithaca, New York.

Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127-150.

Utz, R. M., R. H. Hilderbrand, and R. L. Raesly. 2010. Regional differences in patterns of fish species loss with changing land use. Biological Conservation 143: 688-699.

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

Wang, L., P. W. Seelbach, and J. Lyons. 2006. Effects of levels of human disturbance on the influence of catchment, riparian, and reach-scale factors on fish assemblages. Pp. 199-219 in Landscape Influences on Stream Habitats and Biological

Assemblages (R. M. Hughes, L. Wang, and P. W. Seelbach, eds.) American Fisheries Society. Symposium 48, Bethesda, Maryland.

Wellemeyer, J. C., J. S. Perkin, J. D. Fore, and C. Boyd. 2018. Comparing assembly processes for multimetric indices of biotic integrity. Ecological Indicators 89: 590-609.

Zamor, R. M. and G. D. Grossman. 2007. Turbidity affects foraging success of drift-feeding rosyside dace. Transactions of the American Fisheries Society 136: 167-176.

Manuscript received 15 December 2015; Manuscript accepted 10 August 2018.

J. Brian Alford (*) and Hayley S. Gotwald

Department of Forestry, Wildlife, and Fisheries, University of Tennessee, Knoxville, TN 37996 (JBA, HSG) Present Address: Tennessee Valley Authority, Knoxville, TN 37902 (HSG)

(*) Corresponding author
TABLE 1. Summary statistics for watershed size and position, non-point
source pollutant yield from SWAT model estimates during 2014, and
metrics reflecting biotic integrity of fish and benthic
macroinvertebrates in the Nolichucky River watershed. Tennessee, during
2014-2016. Detailed descriptions of each metric and how they are
calculated can be found in Gotwald (2016).

Characteristic            Mean     Max       Min

Upstream catchment area   1.798.9   4.229.0   14.2
([km.sup.2])
Elevation above mean sea    462.5     637.6  315.8
level (m)
Yield estimates (kg/ha)
from SWAT model
simulations
Sediment                  8,164.7  46,629.3  181.4
Organic nitrogen              5.3      12.9    0.6
Organic phosphorus            0.9       2.1    0.1
Fish IBI metrics
Native richness              19.3      26.0    3.0
[NATIVE]
Darter richness               6.0       8.0    0.0
[DARTER]
Sunfish richness minus        2.0       6.0    0.0
Micropterus [SUNFISH]
Sucker richness               2.0       5.0    0.0
[SUCKER]
Pollution-intolerant          3.5       5.0    0.0
richness [INTOLERANT]
% Pollution-tolerant          7.7      26.6    0.0
[% TOLERANT]
% specialized                58.1      86.8    0.0
insectivores
[% INSECTIVORE]
% omnivores                  14.0      43.0    1.9
[% OMNIVORE]
% piscivores                  1.2       7.8    0.0
[% PISCIVORE]
Catch per unit effort        19.5      43.5    5.0
(fish/[m.sup.2])
[CPUE]
% hybrids                     8.8      58.0    0.0
[% HYBRID]
% with disease,               0.5       2.7    0.0
anomalies, lesions,
tumors [% DALT]
Benthic invertebrate
IBI metrics
Taxa richness [TR]           28.5      41.0   11.0
Ephemeroptera-               14.1      25.0    2.0
Plecoptera
-Trichoptera
richness [EPT]
% EPT minus                  55.9      74.9   21.2
Cheumatopsyche
[% EPT-C]
% Oligochaeta                 5.8      16.2    0.2
and Chironomidae
[% OC]
North Carolina                4.1       4.7    3.1
Biotic Index
[NCB1]
% clingers                   50.4      78.5   14.1
[% CLINGER]
% total nutrient             19.9      36.3    5.2
tolerant organisms
[% TNUTOL]

TABLE 2. Summary of abundance and frequencies of benthic
macroinvertebrate taxa collected using semi-quantitative kick nets in
riffle habitats in the Nolichucky River watershed, Tennessee, during
2014-2016. Counts reflect the combined numbers of each taxon for 21
samples.

Class/order     Family             Genus

Amphipoda       Crangonyctidae     Crangonyx
                Crangonyctidae
Coleoptera      Chrysomelidae
                Curculionidae      Stenopelmus
                Dryopidae          Helichus
                Elmidae            Dubiraphia
                                   Gonielmis
                                   Macronychus
                                   Macronychus
                                   Microcylloepus
                                   Microcylloepus
                                   Optioservus
                                   Optioservus
                                   Oulimnius
                                   Oulimnius
                                   Promoresia
                                   Promoresia
                                   Promoresia
                                   Promoresia
                                   Stenelmis
                                   Stenelmis
                                   Stenelmis
                Gyrinidae          Dineutus
                                   Gyretes
                Helophoridae       Helophorus
                Hydrochidae        Hydrochus
                Hydrophilidae      Cerycon
                Psephenidae        Psephenus
                                   Psephemus
                Ptilodactylidae    Anchytarsus
                Scirtidae          Sacodes
                Scirtidae
                Hydrphilidae       Sperchopsis
Diptera         Athericidae        Atherix
                                   Atherix
                Ceratopogonidae    Prionocera
                Chironomidae       Ablabesmyia
                                   Cardiocladius
                                   Cardiocladius
                                   Cladotanytarsus
                                   Cladotanytarsus
                                   Cricotopus
                                   Cricotopus
                                   Cricotopus
                                   Cricotopus
                                   Cricotopus
                                   Cricotopus
                                   Cricotopus
                                   Cryptochironomus
                                   Demicryptochironomus
                                   Eukiefferiella
                                   Eukiefferiella
                                   Eukiefferiella
                                   Eukiefferiella
                                   Euryhapsis
                                   Microtendipes
                                   Nanocladius
                                   Nanocladius
                                   Natarsia
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Orthocladius
                                   Pagastia
                                   Polypedilum
                                   Polypedilum
                                   Polypedilum
                                   Polypedilum
                                   Polypedilum
                                   Polypedilum
                                   Polypedilum
                                   Potthastia
                                   Rheotanytarsus
                                   Rheotanytarsus
                                   Stempellinella
                                   Stenochironomus
                                   Sublettea
                                   Tanytarsus
                                   Tanytarsus
                                   Thienemanniella
                                   Thienemannimyia
                                   Tokunagaia
                                   Tvetenia
                                   Tvetenia
                                   Dicrotendipes
                                   Apedilum
                                   Rheocricotopus
                   Empididae       Hemerodromia
                   Simuliidae      Cnephia
                                   Simulium
                   Tabanidae       Haematopota
                   Tanyderidae     Protoplasa
                                   Protoplasa
                   Tipulidae       Antocha
                                   Hexatoma
                                   Limonia
                                   Pedicia
                                   Tipula
Ephemeroptera   Baetidae           Acentrella
                                   Acerpenna
                                   Baetis
                                   Heterocloeon
                                   Heterocloeon
                                   Iswaeon
                                   Plauditus
                                   Procloeon
                                   Pseudocentroptiloides
                Caenidae           Caenis
                Ephemerellidae     Drunella
                                   Ephemerella
                                   Serratella
                                   Serratella
                                   Serratella
                                   Serratella
                                   Serratella
                                   Teloganopsis
                                   Teloganopsis
                Ephemeridae        Ephemera
                Heptageniidae      Epeorus
                                   Epeorus
                                   Epeorus
                                   Leucocruta
                                   Leueoeruta
                                   Maccaffertium
                                   Maccaffertium
                                   Maccaffertium
                                   Maccaffertium
                                   Maccaffertium
                                   Maccaffertium
                                   Maccaffertium
                                   Rhithrogena
                                   Stenacron
                                   Stenacron
                                   Stenonema
                                   Stenonema
                                   Heptagenia
                Isonychidae        Isonychia
                                   Isonychia
                Leptohyphidae      Tricorythodes
                Leptophlebiidae    Leptophlebia
                                   Paraleptophlebia
                Polymitarcydae     Ephoron
                                   Ephoron
Megaloptera     Corydalidae        Corydalus
                                   Nigronia
                                   Nigronia
                Sialidae           Sialis
Odonata         Aeshnidae          Triacanthagyna
                Calopterygidae     Hetaerina
                                   Hetaerina
                Coenagrionidae     Argia
                Gomphidae          Arigomphus
                                   Hetaerina
                                   Lanthus
                                   Lanthus
                                   Stylogomphus
                                   Ophiogomphus
                Macromiidae        Macromia
Plecoptera      Chloroperlidae     Haploperla
                                   Suwallia
                Leuctridae         Leuctra
                Peltoperlidae      Tallaperla
                                   Peltoperla
                Perlidae           Acroneuria
                                   Acroneuria
                                   Agnetina
                                   Neoperla
                                   Paragnetina
                                   Paragnetina
                                   Paragnetina
                                   Perlesta
                                   Perlesta
                                   Hansonoperla
                Pteronarcyidae     Pteronarcys
                                   Pteronarcys
                Perlodidae         Diploperla
                Taeniopterygidae   Strophopteryx
Trichoptera     Brachycentridae    Brachycentrus
                                   Brachycentrus
                                   Brachycentrus
                                   Brachycentrus
                                   Micrasema
                                   Micrasema
                                   Micrasema
                Glossosomatidae    Glossosoma
                                   Glossosoma
                                   Protoptila
                Goeridae           Goera
                Helicopsychidae    Helicopsyche
                Hydropsychidae     Ceratopsyche
                                   Ceratopsyche
                                   Ceratopsyche
                                   Ceratopsyche
                                   Cheumatopsyche
                                   Diplectrona
                                   Hydropsyche
                                   Hydropsyche
                                   Hydropsyche
                                   Hydropsyche
                                   Hydropsyche
                                   Hydropsyche
                                   Psychomyia
                                   Psychomyia
                Hydroptilidae      Hydroptila
                                   Leucotrichia
                                   Oxyethira
                                   Stactobiella
                Lepidostomatidae   Lepidostoma
                Leptoceridae       Nectopsyche
                                   Oecetis
                                   Oecetis
                                   Oecetis
                                   Setodes
                Limephilidae       Pycnopsyche
                Odontoceridae      Psilotreta
                Philopotamidae     Chimarra
                                   Chimarra
                                   Dolophilodes
                                   Dolophilodes
                                   Wormaldia
                Polycentropodidae  Cernotina
                                   Cyrnellus
                                   Neureclipsis
                                   Neureclipsis
                Psychomyiidae      Lype
                                   Psychomyia
                                   Psychomyia
                Rhyacophilidae     Rhyacophila
                                   Rhyacophila
                                   Rhyacophila
                Thremmatidae       Neophylax
                                   Neophylax
Limnophila      Ancylidae          Ferrissia
Veneroida       Corbiculidae       Corbicula
Lumbriculida    Lumbricidae
                Lumbriculidae      Eclipidrilus
                                   Lumbriculis
                                   Lumbriculis
                                   Stylodrilus
                Naididae
                Sparganophilidae   Sparganophilus
                Tubificidae
Mesogastropoda  Pleuroceridae      Leptoxis
                                   Pleurocera
                                   Pleurocera
Decapoda        Cambaridae         Cambarus
                                   Cambarus
                                   Cambarus
                                   Faxonius
                                   Faxonius
Lepidoptera     Pyralidae          Petrophila
Acarina         Acarina            Acarina
Tricladida      Dugesiidae         Dugesia
                Planaridae
Isopoda         Asselidae          Lirceus

Class/order     Species/               Count   % Occurrence
                species group

Amphipoda                               1        4.8
                                        1        4.8
Coleoptera                              7        4.8
                rufinasus               1        4.8
                Jastigiatus             5        4.8
                                        1        4.8
                                        3        4.8
                glabratus              13        9.5
                                        3        9.5
                pusillus               10        9.5
                                       16       19.0
                ovalis                 26        4.8
                                       55        4.8
                latiusculus            40        4.8
                                        9        4.8
                tardella               17        4.8
                elegans                32       19.0
                tardella                1        4.8
                                       10       19.0
                mera                  247       14.3
                                      414       81.0
                sandersoni              5        4.8
                                        6       14.3
                                        2        4.8
                                        3        4.8
                                        2        9.5
                                        7        4.8
                herricki              210       38.1
                                       69       19.0
                bicolor                 3        4.8
                                        7        4.8
                                        2        4.8
                tesselata               1        4.8
Diptera         lantha                 39       23.8
                                       13        9.5
                                        2        4.8
                mallochi                8       19.0
                albiplumus             17       14.3
                obscurus              173       19.0
                davesi                  1        4.8
                                        3        4.8
                absurdius               1        4.8
                fugax                  12       19.0
                politus                 3        4.8
                tibialis                1        4.8
                triannulatus            2        4.8
                trifascia               1        4.8
                                       16       19.0
                                        1        4.8
                                        2        9.5
                brehmi                  1        4.8
                claripennis             1        4.8
                devonica                5        4.8
                gracei                  9        9.5
                                        1        4.8
                pedellus                2        4.8
                downesi                 7        9.5
                                        3        4.8
                                       12       28.6
                annectens               4       14.3
                annulator               9        4.8
                dubitatus              90       28.6
                frigidus                1        4.8
                lignicola               5        4.8
                luteipes                3        9.5
                obumbratus             17        4.8
                oliveri                 3        9.5
                rivicola                1        4.8
                robacki                 1        4.8
                rubicundus              1        4.8
                saxosus                 6        9.5
                thienemanni             4       14.3
                                        1        4.8
                                        2        4.8
                aviceps                11       28.6
                fallax                  1        4.8
                flavum                  6       19.0
                illinoense              3        9.5
                nubeculosum             3        4.8
                scalaenum               1        4.8
                tritum                 12        9.5
                goedii                  4        4.8
                exiguus                33       19.0
                                        2        9.5
                                        6        4.8
                                        2        9.5
                coffmani                1        4.8
                brundini                3        4.8
                                        6        9.5
                similis                 1        4.8
                                        2        4.8
                                        1        4.8
                vitracies               3        4.8
                                       21       23.8
                                        2        4.8
                                        1        4.8
                unidentatus             1        4.8
                                        6       14.3
                                      244       38.1
                                      450       76.2
                                        1        4.8
                fitchii                 1        4.8
                                        1        4.8
                                       60       42.9
                                        9       19.0
                                        1        4.8
                                        3        4.8
                                       25       14.3
Ephemeroptera                          11       19.0
                                       17        4.8
                                      504       81.0
                jubilatum               1        4.8
                                      268       52.4
                                       42       23.8
                                       74        4.8
                nelsoni                 1       42.9
                                        1        4.8
                                       12       23.8
                                        2        9.5
                excruciens              3        4.8
                deficiens              57       19.0
                frisoni                10        9.5
                serrata                14        9.5
                serratoides             3        4.8
                                       59       23.8
                deficiens               1        4.8
                                        5       14.3
                                        2        4.8
                rubidus/                1        4.8
                subpallidus
                vitreus                10        4.8
                                       19       14.3
                aphrodite               3        4.8
                                       64       19.0
                carlsoni                1        4.8
                ithaca                  1        4.8
                mediopunctatum        133       52.4
                modestum               39       19.0
                pudicum                11        9.5
                terminatum              3        4.8
                                      585       52.4
                                        1        4.8
                interpunctatum          2        9.5
                                       46       23.8
                femoratum              17       19.0
                                      182       28.6
                                        2        4.8
                bicolor                 3        4.8
                                    1,026       76.2
                                       45       23.8
                                       17        4.8
                                       19       14.3
                leukon                 29        4.8
                                       47       19.0
Megaloptera     cornutus              108       61.9
                serricornis            41       33.3
                                        6        9.5
                                        9        4.8
Odonata                                 5        4.8
                americana               1        4.8
                                        1        4.8
                                       10       14.3
                                        8        4.8
                americana               1        4.8
                vernalis                2        4.8
                                        2        9.5
                                       14       23.8
                mainensis               3        4.8
                                        1        4.8
Plecoptera                              1        4.8
                marginata               1        4.8
                                      123       19.0
                                      148       14.3
                                        1        4.8
                abnormis               15        9.5
                                       35       23.8
                                       29       23.8
                                        6        9.5
                ichusa                 12        4.8
                media                   7        4.8
                                        1        4.8
                frisoni                 5        4.8
                                       47       14.3
                                        2        4.8
                proteus                25        4.8
                                        9       14.3
                duplicata               1        4.8
                fasciata                8        4.8
Trichoptera     nigrosoma             188       23.8
                numerosus              14       23.8
                spinae                  1        4.8
                                      199        9.5
                rusticum                1        4.8
                wataga                  5       14.3
                                        2        9.5
                nigrior                 3        9.5
                                        4        9.5
                                        1        4.8
                                        1        4.8
                                        1        4.8
                etnieri                 7        9.5
                morosa                255       23.8
                sparna                 83       19.0
                                      412       42.9
                                    1,482      100.0
                modesta                30       14.3
                alvata                 17        4.8
                betteni/depravata      10        4.8
                mississippiensis        1        4.8
                phalerata               1        4.8
                venularis              45       14.3
                                      514       66.7
                flavida                 1        4.8
                                       41       33.3
                                       36       14.3
                pictipes                1        4.8
                                      155        9.5
                                        3        9.5
                                        6       19.0
                                        1        4.8
                avara                   1        4.8
                persimilis             10       14.3
                                        9       14.3
                                        4       14.3
                                        1        4.8
                                       43        4.8
                aterrima               18        4.8
                                        6        9.5
                distincta              15        4.8
                                       67        9.5
                                        3        4.8
                                        l        4.8
                                        2        9.5
                crepuscularis           2        9.5
                                        3        9.5
                diversa                 1        4.8
                flavida                 3        4.8
                                        2        9.5
                formosa                 1        4.8
                fuscula                 2        4.8
                                        7        9.5
                etnieri                 2        9.5
                                        5       19.0
Limnophila      fragilis                3        9.5
Veneroida       fluminea               64       52.4
Lumbriculida                            2        4.8
                lacustris               7       14.3
                variegatus              6        9.5
                                        8        9.5
                wahkeenensis            8        9.5
                                        1        4.8
                                        2        9.5
                                        1        4.8
Mesogastropoda  praerosa               87       19.0
                clavaeformis           90       33.3
                troostiana              2        4.8
Decapoda        girardianus            10        9.5
                longirostris           16        9.5
                                        5       14.3
                juvenilis              11        4.8
                                        4        4.8
Lepidoptera                            11       14.3
Acarina                                 5       19.0
Tricladida                              5        4.8
                                        1        4.8
Isopoda                                19       14.3

Class/order      % Composition

Amphipoda         0.01
                  0.01
Coleoptera        0.06
                  0.01
                  0.05
                  0.01
                  0.03
                  0.12
                  0.03
                  0.09
                  0.15
                  0.24
                  0.51
                  0.37
                  0.08
                  0.16
                  0.3
                  0.01
                  0.09
                  2.28
                  3.82
                  0.05
                  0.06
                  0.02
                  0.03
                  0.02
                  0.06
                  1.94
                  0.64
                  0.03
                  0.06
                  0.02
                  0.01
Diptera           0.36
                  0.12
                  0.02
                  0.07
                  0.16
                  1.6
                  0.01
                  0.03
                  0.01
                  0.11
                  0.03
                  0.01
                  0.02
                  0.01
                  0.15
                  0.01
                  0.02
                  0.01
                  0.01
                  0.05
                  0.08
                  0.01
                  0.02
                  0.06
                  0.03
                  0.11
                  0.04
                  0.08
                  0.83
                  0.01
                  0.05
                  0.03
                  0.16
                  0.03
                  0.01
                  0.01
                  0.01
                  0.06
                  0.04
                  0.01
                  0.02
                  0.1
                  0.01
                  0.06
                  0.03
                  0.03
                  0.01
                  0.11
                  0.04
                  0.3
                  0.02
                  0.06
                  0.02
                  0.01
                  0.03
                  0.06
                  0.01
                  0.02
                  0.01
                  0.03
                  0.19
                  0.02
                  0.01
                  0.01
                  0.06
                  2.25
                  4.15
                  0.01
                  0.01
                  0.01
                  0.55
                  0.08
                  0.01
                  0.03
                  0.23
Ephemeroptera     0.10
                  0.16
                  4.65
                  0.01
                  2.47
                  0.39
                  0.68
                  0.01
                  0.01
                  0.11
                  0.02
                  0.03
                  0.53
                  0.09
                  0.13
                  0.03
                  0.54
                  0.01
                  0.05
                  0.02
                  0.01
                  0.09
                  0.18
                  0.03
                  0.59
                  0.01
                  0.01
                  1.23
                  0.36
                  0.10
                  0.03
                  5.40
                  0.01
                  0.02
                  0.42
                  0.16
                  1.68
                  0.02
                  0.03
                  9.47
                  0.42
                  0.16
                  0.18
                  0.27
                  0.43
Megaloptera       1.00
                  0.38
                  0.06
                  0.08
Odonata           0.05
                  0.01
                  0.01
                  0.09
                  0.07
                  0.01
                  0.02
                  0.02
                  0.13
                  0.03
                  0.01
Plecoptera        0.01
                  0.01
                  1.14
                  1.37
                  0.01
                  0.14
                  0.32
                  0.27
                  0.06
                  0.11
                  0.06
                  0.01
                  0.05
                  0.43
                  0.02
                  0.23
                  0.08
                  0.01
                  0.07
Trichoptera       1.74
                  0.13
                  0.01
                  1.84
                  0.01
                  0.05
                  0.02
                  0.03
                  0.04
                  0.01
                  0.01
                  0.01
                  0.06
                  2.35
                  0.77
                  3.80
                 13.68
                  0.28
                  0.16
                  0.09
                  0.01
                  0.01
                  0.42
                  4.75
                  0.01
                  0.38
                  0.33
                  0.01
                  1.43
                  0.03
                  0.06
                  0.01
                  0.01
                  0.09
                  0.08
                  0.04
                  0.01
                  0.4
                  0.17
                  0.06
                  0.14
                  0.62
                  0.03
                  0.01
                  0.02
                  0.02
                  0.03
                  0.01
                  0.03
                  0.02
                  0.01
                  0.02
                  0.06
                  0.02
                  0.05
Limnophila        0.03
Veneroida         0.59
Lumbriculida      0.02
                  0.06
                  0.06
                  0.07
                  0.07
                  0.01
                  0.02
                  0.01
Mesogastropoda    0.80
                  0.83
                  0.02
Decapoda          0.09
                  0.15
                  0.05
                  0.10
                  0.04
Lepidoptera       0.10
Acarina           0.05
Tricladida        0.05
                  0.01
Isopoda           0.18

TABLE 3. Summary of fish species from 21 samples collected by backpack
electroshocking and seining in riffle, run, and pool habitats in the
Nolichucky River watershed, Tennessee, during 2014-2016.

Scientific name              Common name            Count   % Occurrence

Notropis micropteryx         Highland Shiner        1,126   66.7
Etheostoma acuticeps         Sharphead Darter         932   47.6
Etheostoma blennioides       Greenside Darter         905   85.7
Notropis telescopus          Telescope Shiner         897   66.7
Campostoma anomalum          Central Stoneroller      854   95.2
Cottus carolinae             Banded Sculpin           699   90.5
Etheostoma camurum           Bluebreast Darter        685   57.1
Etheostoma zonale            Banded Darter            660   71.4
Cyprinella spiloptera        Spotfin Shiner           434   66.7
Notropis rubricroceus        Saffron Shiner           392   23.8
Etheostoma simoterum         Snubnose Darter          368   76.2
Notropis volucellus          Mimic Shiner             356   42.9
Cottus bairdi                Mottled Sculpin          353   28.6
Notropis photogenis          Silver Shiner            343   47.6
Hypentelium nigricans        Northern Hogsucker       273   90.5
Cyprinella galactura         Whitetail Shiner         263   38.1
Luxilus chrycocephalus       Striped Shiner           257    9.5
Rhinichthys atratulus        Blacknose Dace           203   33.3
Nocomis micropogon           River Chub               165   71.4
Percina evides               Gilt Darter              158   52.4
Luxilus coccogenis           Warpaint Shiner          147   33.3
Semotilus atromaculatus      Creek Chub               147    9.5
Etheostoma rufilineatum      Redline Darter           129   28.6
Moxostoma breviceps          Smallmouth Redhorse      107    9.5
Notropis straminea           Sand Shiner               83    4.8
Oncorhyncus mykiss           Rainbow Trout             79   28.6
Micropterus dolomieu         Smallmouth Bass           52   33.3
Notropis leuciodes           Tennessee Shiner          49   23.8
Hybopsis amblops             Bigeye Chub               45   23.8
Phenacobius uranops          Stargazing Minnow         44   42.9
Pimephales vigilax           Bullhead Minnow           41   23.8
Rhinichthys cataractae       Longnose Dace             36    9.5
Percina squamata             Olive Darter              18   14.3
Moxostoma erythrurum         Golden Redhorse           17    4.8
Etheostoma vulneratum        Wounded Darter            14   14.3
Moxostoma carinatum          River Redhorse            10    4.8
Moxostoma duquesnei          Black Redhorse             9    4.8
Percina aurantiaca           Tangerine Darter           9   23.8
Noturus eleutherus           Mountain Madtom            8    4.8
Percina caprodes             Common Logperch            7   14.3
Gambusia affinis             Western Mosquitofish       6    4.8
Erimystax insignis           Blotched Chub              5    4.8
Ambloplites rupestris        Rock Bass                  4   14.3
Ictiobus bubalus             Smallmouth Buffalo         4    4.8
Ichthyomyzon bdellium        Ohio Lamprey               3    4.8
Lepomis auritus              Redbreast Sunfish          3    4.8
Lepomis cyanellus            Green Sunfish              3    4.8
Lepomis macrochirus          Bluegill                   3    4.8
Etheostoma chlorobranchium   Greenfin Darter            2    4.8
Etheostoma kennicotti        Stripetail Darter          2    9.5
Ameiurus natalis             Yellow Bullhead            1    4.8
Catastomus commersoni        White Sucker               1    4.8
Etheostoma jessiae           Blueside Darter            1    4.8
Notemigonus chrysoleucas     Golden Shiner              1    4.8
Pylodicitis olivaris         Flathead Catfish           1    4.8

Scientific name              % Composition

Notropis micropteryx         9.87
Etheostoma acuticeps         8.17
Etheostoma blennioides       7.93
Notropis telescopus          7.86
Campostoma anomalum          7.48
Cottus carolinae             6.12
Etheostoma camurum           6.00
Etheostoma zonale            5.78
Cyprinella spiloptera        3.80
Notropis rubricroceus        3.43
Etheostoma simoterum         3.22
Notropis volucellus          3.12
Cottus bairdi                3.09
Notropis photogenis          3.01
Hypentelium nigricans        2.39
Cyprinella galactura         2.30
Luxilus chrycocephalus       2.25
Rhinichthys atratulus        1.78
Nocomis micropogon           1.45
Percina evides               1.38
Luxilus coccogenis           1.29
Semotilus atromaculatus      1.29
Etheostoma rufilineatum      1.13
Moxostoma breviceps          0.94
Notropis straminea           0.73
Oncorhyncus mykiss           0.69
Micropterus dolomieu         0.46
Notropis leuciodes           0.43
Hybopsis amblops             0.39
Phenacobius uranops          0.39
Pimephales vigilax           0.36
Rhinichthys cataractae       0.32
Percina squamata             0.16
Moxostoma erythrurum         0.15
Etheostoma vulneratum        0.12
Moxostoma carinatum          0.09
Moxostoma duquesnei          0.08
Percina aurantiaca           0.08
Noturus eleutherus           0.07
Percina caprodes             0.06
Gambusia affinis             0.05
Erimystax insignis           0.04
Ambloplites rupestris        0.04
Ictiobus bubalus             0.04
Ichthyomyzon bdellium        0.03
Lepomis auritus              0.03
Lepomis cyanellus            0.03
Lepomis macrochirus          0.03
Etheostoma chlorobranchium   0.02
Etheostoma kennicotti        0.02
Ameiurus natalis             0.01
Catastomus commersoni        0.01
Etheostoma jessiae           0.01
Notemigonus chrysoleucas     0.01
Pylodicitis olivaris         0.01
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Author:Alford, J. Brian; Gotwald, Hayley S.
Publication:Journal of the Tennessee Academy of Science
Date:Jun 1, 2019
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