Geology Reports⌕ Search

USGS · sir20115209

Algal community characteristics and response to nitrogen and phosphorus concentrations in streams in the Ozark Plateaus, Southern Missouri, 1993-95 and 2006-07

Abstract

Nutrient and algae data were collected in the 1990s and 2000s by the U.S. Geological Survey for the National Water- Quality Assessment program in the Ozark Highlands, southern Missouri. These data were collected at sites of differing drainage area, land use, nutrient concentrations, and physiography. All samples were collected at sites with a riffle/pool structure and cobble/gravel bed material. A total of 60 samples from 45 sites were available for analyses to determine relations between nutrient concentrations and algal community structure in this region. This information can be used by the Missouri Department of Natural Resources to develop the State's nutrient criteria plan. Water samples collected for this study had total nitrogen concentrations ranging from 0.07 to 4.41 milligram per liter (mg/L) with a median of 0.26 mg/L, and total phosphorus concentrations ranging from 0.003 to 0.78 mg/L with a median of 0.007 mg/L. These nutrient concentrations were transformed into nutrient categories consisting of varying percentiles of data. Algal community data were entered into the U.S. Geological Survey's Algae Data Analysis System for the computation of more than 250 metrics. These metrics were correlated with nutrient categories, and four metrics with the strongest relation with the nutrient data were selected. These metrics were Organic Nitrogen Tolerance, Oxygen Tolerance, Bahls Pollution Class, and the Saprobien index with the 25th and 80th percentile nutrient categories. These data indicate that near the 80th percentile (Total Nitrogen = 0.84 mg/L, Total Phosphorus = 0.035 mg/L) the algae communities significantly changed from nitrogen-fixing species dominance to those species more tolerant of eutrophic conditions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Suzanne R. Femmer. 2012. Algal community characteristics and response to nitrogen and phosphorus concentrations in streams in the Ozark Plateaus, Southern Missouri, 1993-95 and 2006-07. https://doi.org/10.3133/sir20115209

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Karst hydrologic processes in carbonate fensters of the western Great Smoky Mountains, Tennessee, 2017–18

The geology of the Great Smoky Mountains in Tennessee and North Carolina is dominated by metamorphic strata. Along the western extent of the range, however, several carbonate fensters expose the Ordovician Jonesboro Limestone along the western edge of the Great Smoky Fault. The fensters (Cades Cove, Tuckaleechee Cove, and Wear Cove) create opportunities for allogenic recharge at the contact between the surrounding, relatively insoluble, strata and the underlying soluble formations, resulting in the development of karst systems. Karst environments have a high degree of connectivity between surface streams and groundwater systems; thus, karst aquifers are inherently vulnerable to contamination from surface activities. Karst environments also provide a specific environment for sensitive biota. Protection of the unique resources of karst environments requires an understanding of the behavior of karst aquifers, knowledge of karst recharge areas, and the mapping of groundwater flow paths. A study began in 2017 to characterize the hydrologic behavior of the karst systems in Great Smoky Mountains National Park through hydrologic monitoring, dye tracing, and seepage investigations. Hydrologic monitoring equipment was installed at several karst hydrogeologic features: two in-cave sites, two major springs, Abrams Creek in Cades Cove, and a karst depression wetland. Dye tracing was conducted to determine resurgences for the cave systems, to delineate spring recharge areas, and to estimate karst groundwater velocities. Seepage investigations along Abrams Creek helped determine if streams were sinking into the Jonesboro Limestone, either at the contact with insoluble strata or as streams flowed across the carbonate fenster. Study results indicate considerable differences in groundwater behavior between the conduit-dominated flow systems in Tuckaleechee Cove compared to the lower-gradient, colluvium-covered karst systems in Cades Cove. Study results also indicate that contaminants in the area of Bull sink and Whiteoak Sink could be rapidly transported through karst groundwater towards springs in Tuckaleechee Cove. Karst resources in Cades Cove are most likely to be affected by local events or activities taking place within the cove itself or in upper Abrams Creek upstream of the Great Smoky Fault. This study provides information on the vulnerability and uniqueness of the karst systems in the western Great Smoky Mountains, which are characterized by complex geology, high-gradient subterranean streams, deep caves, and carbonate fensters.

Tennessee↗

Analysis of groundwater geochemistry in the Lansing, Michigan, area

Groundwater resources in the Lansing, Michigan, area are protected by a wellhead protection plan, but previous studies have indicated that groundwater in this area may be susceptible to surface contamination. The U.S. Geological Survey, in cooperation with the Lansing Board of Water & Light, analyzed geochemical and aquifer characteristics to assess the potential for contamination of groundwater resources. Recharge age categories were determined from tritium concentrations for some wells, and by using ternary diagrams and principal component analysis for other wells. These age classifications, combined with hydrogeologic and geochemical data, were analyzed by using statistical methods to identify trends that may indicate areas of increased groundwater susceptibility. Wells with modern recharge tended to have higher sodium, chloride, and chloride:bromide ratios than did wells with mixed recharge, suggesting greater surface connectivity and potential influence from anthropogenic sources such as road salt. In contrast, wells in the mixed age category exhibited more stable geochemistry and lower constituent concentrations. Classification and regression tree models identified aquifer geometry—particularly the thickness of the uppermost bedrock unit and presence of confining layers—as key predictors of recharge age and geochemical indicators. The trends identified from these analyses could help to reveal locations in the Lansing area that are among the most susceptible to contamination.

Michigan↗

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

California, Idaho, Nevada, Oregon, Utah↗