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Response of Water-Quality Indicators to the Implementation of Best-Management Practices in the Upper Strawberry River Watershed, Arkansas
Teresa Ruth Brueggen-Boman, Seo-eun Choi, and Jennifer Louise Bouldin

Southeastern Naturalist, Volume 14, Issue 4 (2015): 697–713

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Southeastern Naturalist 697 T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 22001155 SOUTHEASTERN NATURALIST 1V4o(4l.) :1649,7 N–7o1. 34 Response of Water-Quality Indicators to the Implementation of Best-Management Practices in the Upper Strawberry River Watershed, Arkansas Teresa Ruth Brueggen-Boman1,*, Seo-eun Choi2, and Jennifer Louise Bouldin3 Abstract - This 4-y study monitored 6 sites located in the upper watershed of the Strawberry River, AR, where multiple types of best-management practices (BMPs) were implemented in an attempt to alleviate the impacts of cattle grazing on adjacent waterways. The water- quality variables we assessed included turbidity, total suspended solids (TSS), and concentrations of NO2 -, NO3 -, PO4 3-, Escherichia coli, and chlorophyll-a. We calculated the average annual sediment loading for comparison to published acceptable total-maximum annual load (TMAL). The mean values detected for most parameters that we assessed were within acceptable state limits and reference-stream values for the Ozark Highlands ecoregion. Following BMP implementation, all sites showed significant increases in at least one variable, and the concentration of E. coli for 3 of the sampling locations exceeded the maximum allowable concentration. The estimated sediment loading was within the accepted TMAL. We conclude that implementation of BMPs was not effective at improving water quality during the time-frame of our study. Our results suggest that maintaining desired water quality in this watershed may require the use of BMPs that are: (a) specifically targeted to limit the parameters of concern (e.g., E.coli), and (b) implemented in specific locations of concern rather than dispersed throughout the watershed. We also offer suggestions for future studies of this type to improve the study design in an effort to more efficiently and effectively determine the impact of BMP implementation. Introduction The United States Environmental Protection Agency (USEPA) (2009) ranked agriculture as the primary source of non-point-source pollution impairing surface waters in the US. Streams adjacent to and supporting agricultural practices have been shown to have greater sediment loads than streams in less-disturbed watersheds (Wohl and Carline 1996). Poor management of cattle grazing in riparian zones can lead to significant stream impairment (Davies-Colley et al. 2004, Kauffman and Krueger 1984, Line et al. 2000), and cattle trampling stream banks may lead to increased erosion and sedimentation (Braccia and Voshell 2006) resulting in measured increases in stream turbidity (Stednick 1991). In addition to increased sedimentation, cattle’s direct defecation and urination into waterways can cause increases in nutrient concentration, organic loads, and 1Missouri Southern State University, Department of Biology and Environmental Health, 3950 Newman Drive, Joplin, MO 64801. 2Arkansas State University, Mathematics and Statistics Department, PO Box 70, State University, AR 72467. 3Arkansas State University, Ecotoxicology Research Facility, 501 Iroquois, State University, AR 72467. *Corresponding author - Manuscript Editor: R. Eugene Turner Southeastern Naturalist T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 698 bacterial contamination (Braccia and Voshell 2006). Larsen et al. (1994) determined that instream bacterial contamination decreased by 95% when cattle were prevented from defecating directly into the stream and were moved as little as 2.5 m from a waterway. Cattle walking through or standing in streams also re-suspend bottom sediments into the water column. The concentration of Escherichia coli (Migula) Castellani and Chalmers (an enteric bacterium) has been found to be greater in bottom sediments than in overlying waters, and this bacterium can occur in greater concentrations in overlying water when bottom sediments are disturbed (Stephenson and Rychert 1982). Best management practices (BMPs) may be necessary to limit the impacts cattle have on water quality and instream communities. Exclusion fencing to keep cattle from entering streams has been promoted as one method to protect riparian and aquatic habitats (Platts and Rinne 1985). Other BMPs include providing cattle with an alternate drinking source, shade sources, forage availability, controlled grazing, and supplemental feeding (Agouridis et al. 2005). Implementation of these BMPs, however, has shown mixed results. Several studies have determined that when cattle are presented with an alternate water source, the amount of time they spend in or around a stream is reduced (Godwin and Miner 1996, Miner et al. 1992, Sheffield et al. 1997). Although Sheffield et al. (1997) determined that significant improvements in water quality and reduced sediment-loss from streambank erosion occurred after introduction of an alternate drinking source, they also found an increase in dissolved nutrients. Line et al. (2000) determined that alternate water sources alone were not enough to reduce pollutant loads. There is sparse literature available that examines BMPs involving cattle exclusion and use of riparian buffers or studies assessing the effectiveness of other BMPs, such as watering facilities or improved forage (Agouridis et al. 2005). Cattle production is an important agricultural practice in Arkansas. Many cattleproduction locations are adjacent to streams, including the Strawberry River and its tributaries located in north-central AR. Here we report on various water-quality collections in the Strawberry River Watershed (SRW) that tested for the effects of BMP implementation. Our objectives were to: (1) monitor chemical and bacteriological levels before, during, and/or following BMP implementation to assess the impact of BMPs applied in the upper portion of the watershed; and (2) calculate the average annual sediment load to compare it to the published total maximum annual loads (TMAL) for the assessed stream reaches (USEPA 2006). Our overall goal was to determine whether the implementation of BMPs protected the uppermost portion of the SRW. We anticipated that eliminating the adverse effects of non-point-source inputs would afford the opportunity to improve the aquatic conditions in the lower portions of the watershed. Field-site Description The SRW is situated in north-central AR, with the headwaters located in southern Fulton County (Fig. 1). The Arkansas Pollution Control and Ecology Southeastern Naturalist 699 T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 Commission (APCEC 2014) designated the Strawberry River as (1) an extraordinary resource water; (2) an ecologically sensitive waterbody; and (3) a natural and scenic waterway that is used for (1) primary contact; (2) secondary contact; (3) domestic, industrial, and agricultural water supply; and (4) fisheries. The Strawberry River supports a diverse community of aquatic macroinvertebrates including 3 federally endangered and several ranked freshwater mussels (Harp and Robison 2006). Additionally, one of the most diverse fish populations in AR occurs in this system—over 100 species of fish have been recorded (Robison and Buchanan 1992), including the endogenous Etheostoma fragi Distler (Strawberry River Orange-throat Darter). The upper portion of the SRW where this study took place is ~19,700 ha, and the land cover is reflective of the watershed as a whole—predominately forest (47%) and grassland (45%) (AWIS 2006). Forested-land cover in the upper SRW decreased and anthropogenic land-use (e.g., pasture, cropland, urban development) increased from 1999 to 2006 (AWIS 2006, Brueggen-Boman and Bouldin 2012). In 2008, 2 headwater reaches were listed as not supporting aquatic life because of excess turbidity from surface erosion, and were placed on Arkansas’ 303d list of impaired waterbodies (ADEQ 2008). Uunpaved roads, streambank erosion, and runoff from adjacent pastureland contribute to excess turbidity (ADE Q 2008). Best-management practice implementation Implementation of BMPs occurred throughout the upper watershed (Fig. 1). The Fulton County Conservation District (FCCD) provided the locations—township, range, and section—where BMPs were implemented. We converted these locations to universal transverse mercator (UTM) coordinates using Earth Point software Figure 1. Strawberry River watershed location in Arkansas ( 2010). The inset shows the approximate locations of farms where best-management practices (BMPs) were implemented, sampling sites, and confined animal feeding operations within the upper watershed of the Strawberry River. BMP implementation took place December 2008–June 2011. LSR = Little Strawberry River, SR = Strawberry River, UP = upper sampling location, MID = middle sampling location, and LO = lower sampling location. Southeastern Naturalist T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 700 (Earth Point 2012). We defined the implementation point as the center-point of the section identified and we accurately mapped the location point to within ~1.1 km. The exact points were not made available because of privacy concerns of the BMP implementers. At least 81 farms in the upper SRW implemented 1 or more BMPs (FCCD 2011). The BMPs implemented included installing cross-fencing and fencing to exclude livestock from streams; planting pastures with native grasses; providing livestock with watering tanks or ponds as alternate drinking sources in heavy-use protection areas; undertaking prescribed grazing; and brush, pest, and nutrient management (Table 1). If necessary, wells were installed to provide water as alternate drinking sources. Pest management involved mowing fields to remove undesirable plant species. Monitoring sites Members of the ANRC, Arkansas State University Ecotoxicology Research Facility (ASU-ERF), and the FCCD selected the sampling sites (Fig. 1). The sampling locations were chosen based on their accessibility and assumed proximity to future BMP implementations. We monitored these sampling sites to determine the: (1) baseline conditions and water-quality changes during implementation of BMPs, and (2) water quality changes following BMP implementation. Two sampling locations were located in the Little Strawberry River (LSR): LSR upper (UP) and LSR lower (LO), separated by a stream distance of 2.74 km. We selected 3 sites in the Strawberry River (SR): SRUP, SR middle (MID), and SRLO. The stream distance between SRUP and SRMID was 3.17 stream km, and SRMID and SRLO were separated by 8.86 stream km. The 6th sampling site was located in an unnamed tributary (UT) that enters the SR between the SRMID and SRLO sampling sites. The UT site was 2.01 stream km above the confluence with the SR (Fig. 1). Table 1. Extent of best-management practices (BMPs) implemented within the upper watershed of the Strawberry River. Implementation took place between December 2008 and June 2011. Names provided for implemented BMPs were designated by the US Department of Agriculture and Natural Resource Commission (FCCD 2011). BMP Total implementation Number of farms Fencing 27,301 m 53 Exclusion fencing 14,621 m 9 Brush Management 1317 ha 42 Pasture Establishment 170 ha 20 Watering Tank 29 22 Pond 2 2 Well 3 3 Heavy-use impact area 0.08 ha 22 Prescribed grazing 5307 ha 81 Pest management 3472 ha 63 Nutrient management 7789 ha 63 Southeastern Naturalist 701 T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 Methods Sampling regime We collected water samples semiweekly beginning 6 months before BMP implementation (June–December 2008) and at monthly intervals during the BMP implementation phase (January 2009–June 2011). We conducted 1 year of post- BMP semiweekly sampling from July 2011 to June 2012. Water-quality variables We obtained samples within the water column in Nalgene® containers prepared according to American Public Health Association (APHA 2005) protocols. At each site, we collected 1 sample in a sterile bottle with headspace for bacteriological analysis, filled 1 bottle without headspace for the chemical analyses., and filtered another water sample through a 45-μm filter and froze it for the analysis of dissolved nutrients. We tracked the following water-quality measures over the full course of the study: total suspended solids (TSS), turbidity, and dissolved nutrients (NO2 -, NO3 -, PO4 3-). We measured turbidity using a Hach 2100P turbidimeter (Hach, Loveland, CO) and dissolved nutrients with a LACHAT Quikchem 8500 Flow Injection Analysis (FIA) automated nutrient analyzer (Lachat Instruments, Loveland, CO). We assessed E. coli during BMP- and post-BMP implementation phases, and measured chlorophyll-a (chl-a) in the post-BMP phase. All analyses were conducted according to standard APHA methods (Table 2). We determined stream-discharge volumes at the sampling sites in the fall and spring, beginning in May 2009 and ending in May 2012 using a Marsh and McBirney Model 2000 portable flow meter (Frederick, MD). We recorded bank width every 5 m over a 50-m stream reach that we considered representative of the sampling site, and made 10 equidistant depth measurements across a representative section of the sampling location to determine average stream depth, The average width and depth of the channel were used to create a stream pro file. Data analysis We excluded from analysis samples from the UT site because dry conditions prevented us from collecting adequate samples there. The NO2 - results were below Table 2. Parameters and American Public Health Association (APHA) method used to determine effectiveness of best-management practices implemented in the upper Strawberry River Watershed between May 2008 and June 2012. Parameter APHA method Detection limit Total suspended solids (mg/L) 2540D Turbidity (NTU) 2130B NO2- (mg/L) 4500-NO2–B 0.10 NO3- (mg/L) 4500-NO3–I 0.10 PO43- (mg/L) 4500-PO4-3-PG 0.01 Escherichia coli (CFU/100ml) 9222B Chlorophyll-a (mg/m3) 10200H Southeastern Naturalist T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 702 the detection limit (DL) for all but 1 sample; thus, we also excluded them from our analyses. We used 0.05 for NO3 - mg/L, and 0.005 mg/L for PO4 3 when nutrient values fell below the DL, which is a conventional method of replacing half-values of the DL (Table 2; Kayhanian et al. 2002). We tested for the significance of BMP effect and location effect for each river using a 2-way multivariate analysis of variance (MANOVA). The model employed took into account the sample-site location and the phase of BMP implementation (pre- vs. post-implementation), as well as the interaction between the 2 factors and 4 variables (NO3 -, PO4 3-, TSS, and turbidity). We used these 4 variables because they had been collected before and after BMP implementation. The model was as follows: NO3 - + PO4 3- + TSS + turbidity = overall mean + BMP effect + location + interaction between BMP and location We performed additional analysis if the location and/or BMP were determined to be significant for a stream segment. We used the univariate 2-sample t-test (α = 0.05) or analysis of variance (ANOVA) (α = 0.05) for the LSR and SR sites, respectively, for each variable. If we detected a statistical difference, we performed a post-hoc Tukey’s comparison to determine which pairwise comparison(s) were significantly different. We compared the 4 water-quality variables to the acceptable/reference limits for the Ozark highlands ecoregion (OHE) or the SRW (ADPCE 1987; APCEC 2014; USEPA 2000, 2006) and previously published values for this area of the SRW (ADEQ 2003). Exceedance of these values indicated system impairment. We compared the turbidity values to the published all-flow (all month samples combined) and base-flow (June–October samples) means, but could not compare them to the allowable percent exceedance of these values because our methodology did not meet the 24 monthly sample qualification (APCEC 2014). We performed the univariate 2-sample t-test (α = 0.05) on the log-transformed E. coli data to compare the results between during-BMP and post-BMP implementation for each sampling location. We used the univariate 2-sample t-test (α = 0.05) and ANOVA (α = 0.05) to compare sampling sites for the LSR and SR sites, respectively. If there was a statistical difference, then we performed a post-hoc Tukey’s comparison to determine which pairwise comparison(s) were significantly different. We calculated the geometric means and 95% confidence intervals for reporting purposes (Altman et al. 1983). We calculated the percentage of E. coli samples that exceeded the maximum allowable concentration (MAC) using individual sample criteria for the during-BMP sampling phase by separating the primary (1 May—30 September) and secondary (1 October–30 April) contact seasons. In this way, we were able to assess if samples exceeded the MAC by >25% of the time, which is the allowable limit as designated by the APCEC (2014). We conducted the same analyses for the post-BMP sampling phase except that we used the MAC geometric-mean criteria (APCEC 2014). E. coli values exceeding a 25% limit indicated that BMPs were not effective at limiting this source of impairment to the systems. Southeastern Naturalist 703 T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 We employed a univariate 2-sample t-test (α = 0.05) and ANOVA (α = 0.05) to compare mean concentrations of chl-a at sampling sites for the LSR and SR sites, respectively. If there was a statistical difference, then we performed a post-hoc Tukey’s comparison to determine which pairwise comparison(s) were significantly different. A site with a significantly greater chl-a concentration indicated greater primary productivity and nutrient availability. If chl-a concentrations were above values for reference-stream data for the OHE, then it indicated atypical primary productivity for the ecoregion and indirectly indicated elevated nutrient levels. We used the univariate 2-sample t-test (α = 0.05) to compare discharge by season—fall and spring—to determine if there were significant effects due to season. If there were significant differences, we tested data for significant differences among sites for fall and spring separately. If there were no effects of season, we combined data and compared sites to identify significant differences. We used the univariate 2-sample t-test (α = 0.05) and ANOVA (α = 0.05) to compare the LSR and SR sampling sites, respectively. We used the discharge data to generate a conservative estimate of annual sediment loading at the varying sample locations. Total suspended solids values represented sediment concentration. We calculated the available total-maximum daily loads (TMDLs) for the SR and LSR to obtain a total-maximum annual load (TMAL). If the total annual loading at monitoring sites exceeded the calculated TMAL, then we assumed that BMPs were not effective at limiting sediment loading in the systems. We transformed data for any variables that were not normally distributed. We performed a nonparametric test on any variable not normalized through transformation. We employed the Mann-Whitney U test (α = 0.05) for the LSR river data and the Kruskal-Wallis test and the Dunn’s multiple comparison test for SR data (α = 0.05). Results The results of 2-way MANOVA indicated that the interaction between the BMP and location was not significant for either stream segment. Individually, location was significant (P < 0.001; P < 0.047) and BMP was significant (P < 0.001; P less than 0.001) for the LSR and SR, respectively. The statistical tests based on sample location as well as comparing pre- and post-BMP implementation periods indicated that there were significant differences for most water-quality variables (Fig. 2). Nutrients The mean NO3 - concentration was significantly greater at the LSRUP site than at the LSRLO site for all sampling phases. The mean NO3 - concentration was also greater in the LSR compared to the SR. We detected no significant differences between sites in the SR. We found significant increases in the mean NO3 - and PO4 3- concentrations at 3 of the 5 sites (LSRUP, SRUP, and SRLO) pre- and post-BMP implementation. The mean PO4 3- concentration increased significantly at the LSRLO site, while the mean NO3 - concentration increased significantly at the SRMID site. Most nutrient mean-concentration values fell within previously published criteria Southeastern Naturalist T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 704 Figure 2. Mean ± standard error (SE) of nutrients, total suspended solids (TSS), and turbidity prior to implementation of best-management practices (BMPs) (Pre), May–Dec 2008, and following BMP implementation (Post), July 2011–June 2012, for the various sampling locations in the upper Strawberry River Watershed. The geometric mean ± 95% confidence interval of Escherichia coli during the implementation of BMPs (During), January 2009 to June 2011, and post-BMP implementation. Values followed by different letters indicate significant differences (P ≤ 0.05) between or among sampling locations within the same stream segment only (i.e., the Little Strawberry River [LSR] or Strawberry River [SR]). * indicates a significant difference (P ≤ 0.04) between BMP sampling time-frames in individual sampling locations, univariate 2-sample t-test (P ≤ 0.05). † indicates that the post-BMP median was significantly different (P ≤ 0.01) from the pre-BMP median, the original mean and SE. UP = upper, MID = middle, and LO = lower sampling location. Southeastern Naturalist 705 T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 limits, but the mean NO3 - concentration we detected at the LSRUP in the post- BMP phase exceeded the reference values for the OHE site (ADPCE 1987, USEPA 2000). Additionally, the mean NO3 - concentrations of pre- and post-BMP samples at LSR sites and the post-BMP samples at SR sites exceeded the previously published means of the sample sites in the upper SR W (ADEQ 2003). TSS and turbidity For the pre-BMP-implementation portion of the study, the means of all-flow and base-flow turbidity values in the LSR were significantly greater at the LSRLO site than at the LSRUP site. Conversely, on the SR, the mean all-flow and base-flowturbidity values were significantly greater at the SRLO than at the SRUP. The mean TSS values at LSRUP and LSRLO were not significantly different. The mean TSS value was significantly lower at the SRLO site compared to the o ther 2 SR sites. Following BMP implementation, the mean TSS value was significantly greater at the LSRLO site than at the LSRUP site, and the mean TSS value at SR was significantly lower than at SRMID or SRLO. Following BMP implementation the all-flow and base-flow-turbidity values were significantly lower at SRUP than at SRMID or SRLO. The mean turbidity values were significantly lower at SRMID than at SRLO. The results of the univariate 2-sample t-tests for pre- and post-BMP measurements at individual sites indicated significant increases in the mean TSS and turbidity at 3 of the 5 sites (LSRUP, LSRLO, and SRLO) in the post-BMP implementation phase (compared to pre-BMP). The mean TSS values increased at the SRUP site between pre- and post-BMP implementation, although we detected no significant change in turbidity for this site. We found a significant decrease in the mean base-flow turbidity at the SRMID site, although the all-flow turbidity and mean TSS values were not significantly different. While the mean TSS and turbidity values generally fell within previously published criteria limits and/or reference-stream data, the 2 mean TSS values in the post- BMP sampling phase (at LSRLO and SRLO) exceeded a previously published reference value for the OHE (ADPCE 1987). The LSRLO post-BMP TSS mean exceeded a previously published mean from a sample site located in the LSR in the upper SRW (ADEQ 2003). E. coli We detected no significant differences in the geometric means of the E. coli concentrations at site locations during the BMP-implementation phase. However, in the post-BMP phase, the density of E. coli was significantly lower at LSRLO compared to LSRUP and also at SRMID compared to SRLO. A significant increase in E. coli density during-BMP to post-BMP sampling intervals occurred at 3 sites (LSRUP, LSRLO, and SRUP). During-BMP implementation, which occurred during the primary contact season, 33% of samples collected from LSRUP and 44% of the samples collected from LSRLO exceeded the MAC (298 CFU/100 ml) (APCEC 2014). Twenty-eight percent of samples from LSRLO and 29% from SRUP exceeded the MAC (126 Southeastern Naturalist T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 706 CFU/100 ml) (APCEC 2014) in the post-BMP sampling phase. All sites were within acceptable limits during the secondary contact season. Chl-a The statistical analysis of the chl-a concentrations indicated that there were significant differences among sampling locations in the post-BMP phase (Fig. 3). The chl-a concentrations at LSRLO and SRUP were significantly lower than those measured at LSRUP and SRLO, respectively. We detected the highest monthly means in August for LSRUP (4.13 ± 0.94 mg/m3), SRLO (1.06 ± 0.35 mg/m3), SRUP (0.15 ± 0.10 mg/m3), and SRMID (1.13 ± 0.44mg/m3). We measured the highest monthly mean value for LSRLO (1.67 0.41mg/m3) in September 2011. The only site monthly mean that reached or exceeded reference-stream criteria (2.67 ± 0.53 mg/m3) was the LSRUP site in the months of July (2.91 ± 1.38 mg/m3), August (4.13 ± 0.94 mg/m3), and September (2.67 ± 4.80 mg/m 3) 2011. Discharge We employed a nonparametric approach to compare spring and fall discharge. The median discharge was 0.39 m3/s lower in the fall, and was significantly different from the spring discharge. We detected no significant differences among the sampling locations in each river for either fall or spring median discharges. The estimated annual sediment load was greatest at SRLO (Table 3). All estimated loading values fell below the TMAL determined for the upper portion of the Strawberry River and the Little Strawberry River. Figure 3. Mean Chlorophyll-a concentration ± standard error (SE) for the post-BMP implementation, July 2011–June 2012. The different letters indicate the significant differences (P < 0.001) between/ among sampling locations within the same stream segment only, for the Little Strawberry River (LSR) or the Strawberry River (SR). UP = upper, MID = middle, and LO = lower sampling location. Southeastern Naturalist 707 T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 Discussion The quantity and/or type of BMPs selected for implementation in this study were not effective at improving water quality during the time frame within which we conducted our assessment. We measured numerous water-quality variables across multiple site locations and and found that values for many of them exceeded published recommendations or historical values. It should be noted, however, that the majority of the detected differences, measured over the entirety of this study, were within limits deemed acceptable for the OHE. The mean turbidity levels and the mean TSS concentrations were significantly greater at the downriver sampling locations compared to in the upriver sample sites within the same river. Our results agree with those of a previous study in the SRW that found an increasing trend in a downstream direction for these 2 parameters (ADEQ 2003). The findings of both studies suggest that attempts to decrease sediments may benefit from BMP implementation because TSS and turbidity results indicated a cumulative effect with tributary input and increased river size, but not site-specific inputs. The mean concentration of TSS and turbidity at these lower sampling sites exceeded the historic and/or reference-stream values (ADEQ 2003, ADPEC 1987), and there were significant increases from the pre- to the post-BMP sampling phase. Additional implementation of BMPs could benefit this region of the SRW to alleviate the cumulative downstream effect. Table 3. Median instantaneous discharge, median instantaneous-total suspended solids (TSS), and estimated annual sediment load for sampling locations in the upper Strawberry River Watershed from 2009 to 2012. LSR= Little Strawberry River, SR = Strawberry River, UP = upper, MID = middle, and LO = lower sampling locations. Base-flow total maximum daily load (TMDL) for Strawberry River 11010012-011 = 0.49 tons TSS/day (USEPA 2006); base-flow TMDL for Little Strawberry River 11010012-010 = 0.23 tons TSS/day (USEPA 2006). Total maximum Median discharge Median TSS Median annual TSS annual load Site (m3/sec) (mg/L) load (metric tons) (metric tons) Little Strawberry River 85.10 LSRUP Median 0.13 5.30 17.18 Range 0.01–0.51 2.1–15.6 LSRLO Median 0.25 4.40 14.95 Range 0.01–0.72 0.5–19.6 Upper Strawberry River 178.24 SRUP Median 0.26 2.80 17.58 Range 0.04–0.57 0.7–6.1 SRMID Median 0.09 5.63 11.26 Range 0.01–1.22 0.87–10.9 SRLO Median 0.85 6.30 72.01 Range 0.13–1.18 0.03–32.6 Southeastern Naturalist T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 708 We noted throughout the study that cattle continued to have unrestricted access at 3 sampling sites. We saw hoof prints, excrement, or cattle in water at the LSRUP, SRLO, and SRUP sites. These locations had the greatest estimated annual TSS loading as well as the greatest E. coli concentrations in the post-BMP sampling phase. Both the LSRUP and SRUP had geometric E. coli means that exceeded the AR acceptable limits during the primary-contact season and had significant increases in their TSS concentrations from the pre- to post-BMP sampling phase. Turbidity also increased significantly at LSRUP. SRLO had both the greatest mean TSS and significant increases in all variables when we compared pre- and post- BMP implementation. The unrestricted cattle access at these sites may have contributed to the elevated concentrations of E. coli and increased sediment loads compared to the other sampling sites without cattle access. The potential for further decreases in water quality at these upper-watershed sites are of concern because they could impact water quality in lower portions of the SRW. Parkyn et al. (2003) concluded that achieving water-quality goals for a watershed may not be possible when the upstream areas and tributaries remain impacted. Although some impacts observed during our study seem to be directly related to anthropogenic sources (e.g., cattle access to waterways), others may have been related to climatic events and are, therefore, not directly related to BMP implementation. Various severe climatic events after the pre-BMP data-collection phase may have impacted the water-quality results by causing natural damage to the stream banks, potentially increasing sediment input into the rivers. For example, a severe ice storm in the area in late January 2009 (NWS 2010) resulted in trees on unstable banks falling into streams with their full root wads. This event led to the increased potential for sedimentation in waterways because banks were less stable due to loss of soil-holding vegetation, and the sediments from root wads were subjected to stream flow. Various studies have noted that increased stream-bank erosion, and hence, increases in TSS and turbidity, are possible when streamside trees fall (Harmon et al. 1986, Keller and Swanson 1979, Trimble 1997). In some areas of the stream segments sampled in this study, trees were later removed to prevent log-jams (D. Hall, Fulton County Conservation District, AR, pers. comm.). This tree-removal potentially exposed even more bank surface, and released sediment built up behind this coarse woody debris. In 2011, the wettest month on record occurred in April causing widespread flooding in the area (NOAA 2012) and bank collapse at multiple sampling sites (T.R. Brueggen- Boman, 15 May 2011 pers. observ.). The nutrient concentrations we measured at our sample sites were below threshold levels for impairment outlined in AR water-quality regulations (APCEC 2014). It should be noted, however, that multiple sites had significant increases in nutrients over the course of the study and that these values exceeded historic and referencestream values (ADEQ 2003, ADPCE 1987, USEPA 2000). We would expect that nutrient levels in the upper SRW would be low because there is low urban-land usage, minimal crop-land cover, and, with the exception of 1 confined animal-feeding operation (CAFO) near SRUP, no major point-source dischargers (ADEQ 2003, Southeastern Naturalist 709 T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 AWIS 2006). BMPs, such as cattle exclusion, were designed to affect nutrient concentrations in waterways, but thus far the results are mixed. For example, Sheffield et al. (1997) determined that there was an increase in dissolved nutrients with reduced cattle presence in a stream, but they compared measurements made during winter pre-BMP implementation to those made during summer post-BMP implementation. Other studies have determined that there were no significant differences in NO3 - and PO4 3- concentrations when comparing reaches with and without cattlegrazing (Campbell and Allen-Diaz 1997, Ranganath et al. 2009). Chl-a concentration is used to estimate phytoplankton concentration and is an indirect indicator of nitrogen and phosphorous concentrations (YSI Environmental 2005). The majority of the levels we detected in this study fell below historically published values and support the conclusion that NO3 - and PO4 3- concentrations were not at levels of concern at most sampling locations. Both nutrient and chl-a concentrations indicated that agricultural activities in the upper portion of the SRW were not causing excessive nitrogen and phosphorous concentrations in the waterways at most sampling locations. The BMPs implemented within this study area were not specifically targeted to properties adjacent to waterways and were implemented on a strictly volunteer basis. Thus, it is difficult to determine if a single type and/or locat ion of BMP was primarily responsible for the results we obtained. However, multiple findings in this study indicate that future site-specific or goal-specific BMPs to target variables that impact water-quality should be implemented to maintain and/or improve water quality in this portion of the SRW. Modeling of agricultural watersheds indicates that sensitive areas characterized by erosive soils and steep slopes contribute excess sediments and nutrients to adjacent streams (Mostaghimi et al. 1997, Wright 2015). Wright (2015) reported that implementation of conservation practices on as little as 5% of sensitive areas in a watershed can significantly decrease non-pointsource contaminant inputs. Identifying and targeting for management such areas in the SRW may be key to the success of future water-quality initiatives. It is possible that the threshold of BMP implementation necessary to limit increases in sediment and bacterial impairment of these sub-watersheds was not met, or that our study was not long enough to truly assess the impact of the implemented BMPs because of lag time from implementation to measurable effect. Spooner (1991) suggested that a minimum of 2–3 y of pre- and post-BMP samples should be collected to account for the annual variability in environmental conditions. Other studies have determined that multiple years of data are necessary to detect the effects of physical changes, such as stream bank stability, to occur from BMP implementation because there may be a lag before the resulting changes in water-quality variables, such as TSS, and turbidity are detectable (Agouridis et al. 2005, Ranganath et al. 2009). Other studies have determined that there are positive impacts of BMP implementation in less than 4 y (Edwards et al. 1997, Larsen et al. 1994, Miller et al. 2009). We suggest that the occurrence of severe climatic events could have affected the variability in our results beyond what can be accounted for by BMP implementation alone. Southeastern Naturalist T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 710 Our study design was unique among published studies on BMP effects because the BMPs were not implemented only at sites of concern or near streams, and our sampling sites were not directly adjacent to BMP-implementation areas. Our approach yielded results that are likely more reflective of the reality of current BMP implementation in which scattered and limited sections of a watershed are improved. This study method potentially allows for a better understanding of what may occur in stream segments not directly adjacent to implemented BMPs and which are a closer representation of streams in the majority of the watershed. Overall, we conclude that BMPs as implemented at our sites did not result in improved water quality in the time-frame assessed. While the BMPs implemented in this study may not have been effective at decreasing concentrations of various measured variables, they may have been a positive preemptive measure that prevented increases in water-quality variables to levels of concern over a 4-y time period. Meals et al. (2010) noted that it is common to find little or no improvement in water quality in most watershed nonpoint-source projects that have taken place over the past 4 decades. They suggest that variability in weather, low landowner- participation, inappropriate BMPs selected for implementation, inadequate number of BMPs implemented or poor distribution of BMPs, and lag time from implementation of BMPs may impede researchers’ ability to detect measurable effects of BMPs. Our study suggests that additional BMPs specifically designed to limit the concentration of the parameters of concern (e.g., E. coli) and implementation of those BMPs in targeted locations may be required to maintain the water quality desired in this watershed. We recommend that additional monitoring in the upper SRW would likely allow land managers to gain a better understanding of the long-term impact of the implemented BMPs. We also recommend that future studies attempting to determine the effectiveness of BMPs perform a minimum-detectable–change analysis to account for environmental variability in the specific area being studied and to determine the sampling frequency necessary to obtain statistically sound results for the specific parameters being studied (Spooner et al. 2011). Nonpoint-source models should be used to identify critical areas to maximize BMP effectiveness and reduce costs by focusing on areas indicated as large contributors of pollutants to the waterways (Mostaghimi et al. 1997). Lag time (which can vary from <1 to >50 years depending on the size of the study area), BMPs implemented, and the parameter of concern should be taken into serious consideration when planning additional studies (Meals et al. 2010). Meals et al. (2010) noted that lag time is an unfortunate fact of watershed management, but must be recognized so that vital restoration efforts do not become discouraged due to the inability to document improvements in water quality. Following these recommendations would save time and money, and assist land managers in determining the most effective BMPs to be implemented to achieve the established water-quality improvement goals. Our results and those of others like it should help guide watershed- monitoring programs that aim to implement watershed-conservation actions that will result in measurable results within a short time period. Southeastern Naturalist 711 T.R. Brueggen-Boman, S. Choi, and J.L. Bouldin 2015 Vol. 14, No. 4 Acknowledgments We thank the US Environmental Protection Agency for a 319 grant and the Arkansas Water Resource Center for a 104-B US Geological Service grant. Our gratitude also goes to all the Arkansas State University Ecotoxicology Research Facility staff and student employees, Arkansas State University students, and Fulton County, AR, Conservation District employees who helped with data collection. We also thank Katie Kilmer who assisted in the editing process of this manuscript. Literature Cited Agouridis, C.T, S.R. Workman, R.C. Warner, and G.D. Jennings. 2005. Livestock-grazing management impacts on stream-water quality: A review. 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