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Riverside, CA, United States

Ibekwe A.M.,United States Salinity Laboratory | Leddy M.,Fountain | Murinda S.E.,California State Polytechnic University, Pomona
PLoS ONE | Year: 2013

Current microbial source tracking (MST) methods for water depend on testing for fecal indicator bacterial counts or specific marker gene sequences to identify fecal contamination where potential human pathogenic bacteria could be present. In this study, we applied 454 high-throughput pyrosequencing to identify bacterial pathogen DNA sequences, including those not traditionally monitored by MST and correlated their abundances to specific sources of contamination such as urban runoff and agricultural runoff from concentrated animal feeding operations (CAFOs), recreation park area, waste-water treatment plants, and natural sites with little or no human activities. Samples for pyrosequencing were surface water, and sediment collected from 19 sites. A total of 12,959 16S rRNA gene sequences with average length of ≤400 bp were obtained, and were assigned to corresponding taxonomic ranks using ribosomal database project (RDP), Classifier and Greengenes databases. The percent of total potential pathogens were highest in urban runoff water (7.94%), agricultural runoff sediment (6.52%), and Prado Park sediment (6.00%), respectively. Although the numbers of DNA sequence tags from pyrosequencing were very high for the natural site, corresponding percent potential pathogens were very low (3.78-4.08%). Most of the potential pathogenic bacterial sequences identified were from three major phyla, namely, Proteobacteria, Bacteroidetes , and Firmicutes . The use of deep sequencing may provide improved and faster methods for the identification of pathogen sources in most watersheds so that better risk assessment methods may be developed to enhance public health. Source

Shange R.,Carver Integrative Sustainability Center | Shange R.,Tuskegee University | Haugabrooks E.,Iowa State University | Ankumah R.,Tuskegee University | And 3 more authors.
Diversity | Year: 2013

Wetlands provide essential functions to the ecosphere that range from water filtration to flood control. Current methods of evaluating the quality of wetlands include assessing vegetation, soil type, and period of inundation. With recent advances in molecular and bioinformatic techniques, measurement of the structure and composition of soil bacterial communities have become an alternative to traditional methods of ecological assessment. The objective of the current study was to determine whether soil bacterial community composition and structure changed along a single transect in Macon County, AL. Proteobacteria were the most abundant phyla throughout the soils in the study (ranging from 42.1% to 49.9% of total sequences). Phyla Acidobacteria (37.4%) and Verrucomicrobia (7.0%) were highest in wetland soils, Actinobacteria (14.6%) was highest in the transition area, and Chloroflexi (1.6%) was highest in upland soils. Principle Components Analysis (relative abundance) and Principle Coordinates Analysis (PCoA) (Unifrac weighted metric) plots were generated, showing distinction amongst the OPEN AC ecosystem types through clustering by taxonomic abundance and Unifrac scores at 3% dissimilarity, respectively. Selected soil properties (soil organic carbon and phosphatase enzyme activity) also differed significantly in transition soil ecosystem types, while showing predominance in the wetland area. This study suggests that with further study the structure and composition of soil bacterial communities may eventually be an important indicator of ecological impact in wetland ecosystems. © 2013 by the authors; licensee MDPI, Basel, Switzerland. Source

Scudiero E.,United States Salinity Laboratory | Corwin D.L.,United States Salinity Laboratory | Wienhold B.J.,University of Nebraska - Lincoln | Bosley B.,Colorado State University | And 2 more authors.
Precision Agriculture | Year: 2015

Crop growth and yield can be efficiently monitored using canopy reflectance. However, the spatial resolution of freely available remote sensing data is too coarse to fully understand the spatial dynamics of crop status. The objective of this study was to downscale Landsat 7 (L7) reflectance from the native resolution of 30 × 30 m to that typical of yield maps (ca. 5 × 5 m) over two fields in northeastern Colorado, USA. The fields were cultivated with winter wheat (Triticum aestivum L.) in the 2002–2003 growing season. Geospatial yield measurements were available (1 per ca. 20 m2). Geophysical (apparent soil electrical conductivity and bare-soil imagery) and terrain (micro-elevation) data were acquired (resolution <5 × 5 m) to characterize soil spatial variability. Geographically-weighted regressions were established to study the relationships between L7 reflectance and the geophysical and terrain data at the 30 × 30 m scale. Geophysical and terrain sensors could describe a large portion of the L7 reflectance spatial variability (0.83 < R2 < 0.94). Maps for regression parameters and intercept were obtained at 30 × 30 m and used to estimate the L7 reflectance at 5 × 5 m resolution. To independently assess the quality of the downscaling procedure, yield maps were used. In both fields, the 5 × 5 m estimated reflectance showed stronger correlations (average increase in explained variance = 3.2 %) with yield than at the 30 × 30 m resolution. Land resource managers, producers, agriculture consultants, extension specialists and Natural Resource Conservation Service field staff would be the beneficiaries of downscaled L7 reflectance data. © 2015 Springer Science+Business Media New York (outside the USA) Source

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