Recipient Organization
OKLAHOMA STATE UNIVERSITY
(N/A)
STILLWATER,OK 74078
Performing Department
Plant & Soil Sciences
Non Technical Summary
JustificationIn Oklahoma, 676 waterbodies (rivers, streams, and lakes) do not meet water quality standards and are designated as impaired. Turbidity and issues related to excessive nutrient loading are the cause of 69% of lake impairment, while bacteria, sediment, and excessive nutrient loading are responsible for 60% of stream and river impairment.Nonpoint source pollution is the leading cause of pollution in the U.S. This is even more pronounced in Oklahoma where less than 5% of the source of river and stream impairments are attributed to wastewater plants (ODEQ, 2014). In comparison, 30% of stream and river impairment is attributed to grazing and wildlife.Differing land uses and land covers can positively or negatively influence loading impacts, but the manner in which the landscape changes water quality is not clearly understood. Furthermore, a better understanding of loadings from wildlife and grazing lands is needed to help better address water quality impairments. A sound scientific understanding of fundamental processes impacting fecal indicator bacteria in the environment will substantially reduce uncertainty associated with bacterial transport assessment and modeling and improve the management and regulation of bacterial contamination (Harmel et al. 2010).Previous work and present outlookScarcity of background loading data - Background loadings must be considered when conducting water quality assessment and implementation efforts (Wagner et al. 2012). Most existing water quality modeling efforts, TMDL projects, and other watershed assessment and planning efforts do not take background E. coli levels into account. However, because background concentrations have been found to be a significant component of total E. coli in runoff (Guzman et al. 2010, Collins et al. 2005, Wagner et al. 2012), especially as time lag between fecal deposition by livestock (i.e. grazing event or manure or litter spreading) and runoff event increases, this source should be considered as a separate source when allocating loads and assessing load reductions.Characterization of wildlife animal contributions and other "background" input sources of microbial pollution are highly uncertain and data are scarce (Jeong et al. 2019). Prior work has demonstrated that background E. coli concentrations (i.e., those from wildlife and soilborne naturalized strains) are present at consequential levels. Median background E. coli concentrations in edge of field runoff from ungrazed grassland sites in Texas ranged from 3,500 to 5,500 colony forming units per 100 mL (Wagner et al. 2012), greatly exceeding water quality standards (126 cfu/100 mL). Furthermore, bacterial source tracking studies in Texas have similarly found that wildlife contributes from 23-65% and average 51% of bacteria in samples collected across the state (Hauck 2006; Di Giovanni et al. 2006; Casarez et al. 2007a, 2007b; Di Giovanni et al. 2009).Right time and expertise to address this issues - Through funding from USGS and NSF EPSCoR, ten small watersheds (three grasslands, three juniper encroached, three deciduous oak forest and one juniper forest) were installed. These provide opportunities to study nutrient, sediment, and bacteria runoff from a range of vegetation types and land uses.ObjectivesOur goal is to better characterize and understand background E. coli loadings. Our specific aims are to:Objective 1 - Quantify nutrient, E. coli, and sediment runoff concentrations and loadings from a variety of land uses and land coversHypothesis 1 - Concentrations and loadings will vary by land use/land coverHarmel et al. (2013) categorized 13 study watersheds in central Texas into cultivated, native prairie, grazed pasture, hayed pasture, and mixed land use. Based on their analysis, the impact of land use was readily apparent with mean and median E. coli concentrations increasing in the following order: cultivated < hayed pasture < native prairie < mixed land use < grazed pasture. We will further evaluate the impacts of land use and possible spatial difference by assessing E. coli runoff from three grasslands, three juniper encroached, three deciduous oak forest and one juniper forest sites. Furthermore, we will also assess nutrient and sediment concentrations and loadings to help better understand the background loadings of those constituents and their impact/relation to E. coli runoff.Objective 2 - Assess background loading from wildlife and other natural sourcesHypothesis 2 - Concentrations and loadings will increase with habitat complexityHarmel et al. (2013) further found that E. coli runoff concentrations and loadings followed a similar gradient to that of wildlife habitat and presumably wildlife abundance and biodiversity. Cultivated fields were found to produce lower E. coli concentrations in runoff than did hayed pasture or native prairie. Evaluation of grassland, juniper encroached grassland, and forested sites will provide greater insight into how wildlife habitat/abundance impact E. coli loadings.Objective 3 - Assess impacts of grazing and other management practices on runoff volume and pollutant concentrationsHypothesis 3 - Concentrations and loadings will increase with grazing intensityHarmel et al. (2013) found that watersheds with grazing cattle produced highest E. coli concentrations. Wagner et al. (2012) found that E. coli concentrations in runoff from rotationally grazed pastures were not significantly different than those from ungrazed hay pastures. Further evaluation of the effect of grazing will be conducted to provide greater insight on how grazing intensity and management impacts E. coli, nutrient, and sediment loading.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
Monitor water quality from a variety of watersheds with a range of conditions (e.g., differing landuse and associated implemented BMPs, varying geographic/geologic conditions),
Project Methods
Research siteThe proposed project will be a field-based experiment conducted at OSURRS; however, as funding and opportunity arise, additional sites representing other land uses will be integrated.OSURRS is about 11 km southwest of Stillwater, OK. The mean annual precipitation averages 900 mm and the mean annual potential evapotranspiration averages 1400 mm for this site (Wine and Zou, 2012). Prescribed fire with a three-year return interval was introduced to portions of the research area beginning in 1983 as a management treatment whereas other areas were excluded from fire. The entire area has been in the same grazing unit with moderate stocking by beef cattle since 2006. Based on plot-level surveys in 2011, the grass watersheds have only a few or no trees while the encroached watersheds have about 75% woody canopy cover, primarily by junipers. Herbaceous vegetation is dominated by warm-season C4 grasses including little bluestem (Schizachyrium scoparium), big bluestem (Andropogon gerardii), Indiangrass (Sorghastrum nutans), and switchgrass (Panicum virgatum). Watersheds have well-drained, moderately deep soils consisting predominately of the Stephenville-Darnell complex, Grainola-Lucien complex, and Coyle soil series.MethodsAs recommended by Harmel et al. (2006b), flow-weighted composite edge-of-field runoff samples will be collected using ISCO 6712 (ISCO, Inc., Lincoln, Neb.) full-size portable samplers with single-bottle configuration into surface-disinfected polyethylene 15 L round bottles. Flow from each watershed site will be measured with ISCO 730 bubble flowmeters. Flow data will be downloaded at least monthly using an ISCO 581 Rapid Transfer Device. All sites are equipped with flumes. All ISCO samplers will be programmed to rinse the sample tubing with ambient water prior to collection of each sample. Water samples will be retrieved from the ISCO samplers within 24 h of initiation of the event and transported on ice to the lab, where they will be analyzed for E. coli, turbidity, nutrients, sediment, and other constituents. Precision will be assessed as outlined in Standard Methods, section 9020 B.8.b (APHA, 1998), and samples not meeting precision criteria will not be included in analysis. Management of each watershed will be meticulously tracked and integrated with data on wildlife habitat and populations to assess the data.