Landscape attributes, dissolved organic C, and metal bioaccumulation in aquatic macroinvertebrates (Arkansas River Basin, Colorado)

Prusha, Blair A.; Clements, William H.

doi:10.1899/0887-3593(2004)023<0327:ladoca>2.0.co;2

Cited by 18 publications

(14 citation statements)

References 49 publications

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“…Substituting watershed covariates for ecoregion covariates did not improve the accuracy of the model predictions. Although watershed slope and forest cover are correlated to DOC concentration in other studies (Eckhardt and Moore, 1990;Canham et al, 2004;Prusha and Clements, 2004), they do not appear to be strongly related to DOC concentration in Maryland at this scale of analysis. Five model covariates were included in the geostatistical model based on SLD and the Mariah autocovariance function: WATER, WOODYWET, EMERGWET, FELPERC, and MINTEMP.…”

Section: Discussioncontrasting

confidence: 60%

“…DOC is a significant energy source in aquatic food webs (Wetzel, 1992), absorbs biologically harmful ultraviolet rays that penetrate the water column (Williamson et al, 1996;Kiffney et al, 1997), acts as a weak acid (Sullivan et al, 1989), and binds dissolved substances, such as metals, making them temporarily less bioavailable (Driscoll et al, 1995;Prusha and Clements, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Yet, these models explained less variability in DOC (Eckhardt and Moore, 1990;Herlihy et al, 1998;Gergel et al, 1999;Prusha and Clements, 2004). For example, Kortelainen (1993) used latitude, catchment area/lake area ratio, and percent of watershed area covered by peatlands, fields, and upstream lakes to explain 55% of the variability in DOC collected in 978 lakes throughout Finland.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

Peterson

Urquhart

2006

Environ Monit Assess

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In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r(2) = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional stream condition can be made, and it can be used to locate potentially impaired stream segments. Further, the model results can be displayed visually, which allows results to be presented to a wide variety of audiences easily.

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Section: Discussioncontrasting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

Peterson

Urquhart

2006

Environ Monit Assess

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“…In a coastal Denmark watershed, Stedmon et al (2006) observed that average stream DOP concentrations in a subwatershed with 87% cultivated land (fertilized with animal manure) and 10% forest were four times greater than stream DOP concentrations in a subwatershed with 45% cultivated land and 45% forest. On the other hand, Prusha & Clements (2004) found within a Colorado river basin dominated by forest, agricultural grass, and shrubs, that percent forest cover was positively correlated with mean DOC (R 2 = 0.59) in 16 subwatersheds. Further, Correll et al (1999) observed higher storm discharge DOP and DON concentrations in a stream draining a 100% forested watershed than in a stream draining a watershed with 73% grazed cattle pasture and 27% forest in the coastal Maryland Rhode River Basin.…”

Section: Watershed Controls Of Dissolved Organic Matter Concentrationmentioning

confidence: 97%

“…A common approach to describe the spatial distribution of inorganic nutrients in watersheds dominated by non-point-source inputs is development of empirical models that relate nutrient concentrations or loads in rivers and streams to characteristics of their watersheds (e.g., Osborne & Wiley, 1988;Jones et al, 2001). Spatial and temporal variations of DOM concentration and export at the river basin, regional, or global scale have been attributed to variations in watershed characteristics including soil organic carbon content (e.g., Hope et al, 1997;Aitkenhead et al, 1999), soil clay content (Nelson et al, 1993), and land cover (e.g., Eckhardt & Moore, 1990;Cronan et al, 1999;Prusha & Clements, 2004).…”

Section: Introductionmentioning

confidence: 99%

Effects of land use on dissolved organic matter biogeochemistry in piedmont headwater streams of the Southeastern United States

Molinero

Burke

2009

Hydrobiologia

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We measured dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and dissolved organic phosphorous (DOP) concentrations over the course of a year in 15 headwater streams within a Georgia Piedmont watershed that is a mosaic of areas with intensive animal agriculture, forestry, and residential development. The watershed receives large non-point-source organic waste inputs, mostly from poultry and beef production, with lesser quantities from the rapidly growing human population. The organic wastes from animal agriculture are deposited on nearby pastures and the human wastes are mostly treated with septic systems. We selected the headwater catchments to fall along a gradient of organic waste inputs, and hypothesized that stream dissolved organic matter (DOM) concentrations would increase along this gradient. We used a set of readily obtainable landscape indicators (land use categories, soil carbon content, and soil clay content), factor scores derived from a principal components analysis (PCA) of the landscape indicators, and organic waste input estimates to develop simple empirical models that describe our measured DOM concentrations and elemental ratios (C:N, C:P, and N:P). The models show positive correlations of stream DOC, DON, and DOP concentrations with pasture land cover, and with estimated organic waste inputs, lending support to our hypothesis. Over the entire measurement period, buffer land cover better described mean DOC and DON concentrations [maximum adjusted R 2 (AdjR 2 ) of 0.86 and 0.90, respectively] whereas the best models for mean DOP concentration and elemental ratios were comprised of both watershed and buffer scale indicators or PCA factor scores (maximum AdjR 2 ranged from 0.54 to 0.64). Organic waste estimates were also useful DOM descriptors (maximum AdjR 2 ranged from 0.23 to 0.55), although they were less effective than the landscape indicators. Landscape indicators and organic waste estimates were useful descriptors of DOC concentration variation (maximum AdjR 2 ranged from 0.22 to 0.77) after grouping the observations by season. Watershed scale indicators were better descriptors during most of the seasons. We conclude that landscape indicators and organic waste input estimates are useful descriptors

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Development of a new toxic‐unit model for the bioassessment of metals in streams

Schmidt

Clements

Mitchell

et al. 2010

Enviro Toxic and Chemistry

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Two toxic-unit models that estimate the toxicity of trace-metal mixtures to benthic communities were compared. The chronic criterion accumulation ratio (CCAR), a modification of biotic ligand model (BLM) outputs for use as a toxic-unit model, accounts for the modifying and competitive influences of major cations (Ca²(+), Mg²(+), Na(+), K(+), H(+)), anions (HCO₃⁻, CO²⁻₃ ,SO²⁻₄, Cl⁻, S²⁻) and dissolved organic carbon (DOC) in determining the free metal ion available for accumulation on the biotic ligand. The cumulative criterion unit (CCU) model, an empirical statistical model of trace-metal toxicity, considers only the ameliorative properties of Ca²(+) and Mg²(+) (hardness) in determining the toxicity of total dissolved trace metals. Differences in the contribution of a metal (e.g., Cu, Cd, Zn) to toxic units as determined by CCAR or CCU were observed and attributed to how each model incorporates the influences of DOC, pH, and alkalinity. Akaike information criteria demonstrate that CCAR is an improved predictor of benthic macroinvertebrate community metrics as compared with CCU. Piecewise models depict great declines (thresholds) in benthic macroinvertebrate communities at CCAR of 1 or more, while negative changes in benthic communities were detected at a CCAR of less than 1. We observed a 7% reduction in total taxa richness and a 43% decrease in Heptageniid abundance between background (CCAR = 0.1) and the threshold of chronic toxicity on the basis of continuous chronic criteria (CCAR = 1). In this first application of the BLM as a toxic-unit model, we found it superior to CCU.

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Landscape attributes, dissolved organic C, and metal bioaccumulation in aquatic macroinvertebrates (Arkansas River Basin, Colorado)

Cited by 18 publications

References 49 publications

Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

Effects of land use on dissolved organic matter biogeochemistry in piedmont headwater streams of the Southeastern United States

Development of a new toxic‐unit model for the bioassessment of metals in streams

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