Exploring the Relationship Between Hydrologic Parameters and Nutrient Loads Using Digital Elevation Model and GIS – A Case Study from Sugarcreek Headwaters, Ohio, U.S.A.

Ohio is typical among the Midwestern and Eastern United States with high levels of water pollutants, the main sources being from agriculture. In this study, we used a digital elevation model in conjunction with hydrological indices to determine the role of landscape complexity affecting the spatial and temporal variation in pollutant levels, in one of the most impaired headwater streams in Ohio. More than eighty five percent of the study area is dominated by agriculture. Spatial distribution of slope (S), altitude and wetness index along with other watershed parameters such as flow direction, flow accumulation, stream networks, flow stream orders and erosion index were used within a Geographic Information Systems framework to quantify variation in nitrate and phosphate loads to headwater streams. Stream monitoring data for nutrient loads were used to correlate the observed spatial and temporal patterns with hydrological parameters using multiple linear regressions. Results from the wetness index calculated from a digital elevation model suggested a range of 0.10–16.39, with more than 35% having values less than 4.0. A Revised Universal Soil Loss Equation (RUSLE) predicted soil loss in the range of 0.01–4.0 t/ha/yr. Nitrate nitrogen levels in the study area paralleled precipitation patterns over time, with higher nitrate levels corresponding to high precipitation. Atmospheric deposition through precipitation could explain approximately 35% of total nitrate levels observed in streams. Among the different topographic variables and hydrological indices, results from the step-wise multiple regression suggested the following best predictors, (1) elevation range and upstream flow length for nitrate, (2) flow direction and upstream flow length for ammonia-nitrogen and slope, and (3) elevation range for phosphate levels. Differences in the landscape models observed for nitrate, phosphate and ammonia-nitrogen in the surface waters were attributed partly to differences in the chemical activity and source strengths of the different forms of these nutrients through agricultural management practices. The results identify geomorphologic and landscape characteristics that influence pollutant levels in the study area.     

Predicting the biological condition of streams: use of geospatial indicators of natural and anthropogenic characteristics of watersheds

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments.     

Spatial Prediction of Stream Temperatures Using Top-Kriging with an External Drift

Top-kriging is a method for estimating stream flow and stream flow-related variables on a river network. Top-kriging treats these variables as emerging from a two-dimensional spatially continuous process in the landscape. The top-kriging weights are estimated by a family of variogram models (regularisations) for different catchment areas (kriging support), which accounts for the different scales and the nested nature of the catchments. This assures that kriging weights are distributed to both hydrologically connected and unconnected sites of the stream network according to the data situation: top-kriging gives most weight to close-by sites at the same river system, but when the next hydrologically connected site is far away, more weight is given to a close-by site at an adjacent river system. The distribution of weights is in contrast to ordinary kriging and stream distance-based kriging which does not account for both spatial proximity and network connectivity. We extend the top-kriging method by incorporating an external drift function to account for the deterministic patterns of the spatial variable. We test the method for a comprehensive Austrian stream temperature dataset. The drift is modelled by exponential regression with catchment altitude. Top-kriging is then applied to the regression residuals. The variogram used in top-kriging is fitted by a semiautomatic optimisation procedure. A leave-one-out cross-validation analysis shows that the model performs well for the study domain. The residual mean squared error (cross-validation) decreases by 20 % when using top-kriging in addition to the regression model. For regions where the observed stream temperatures deviate from the expected value of the drift model, top-kriging corrects these regional biases. By exploiting the topological information of the stream network, top-kriging is able to improve the local adjustment of the drift model for the main streams and the tributaries.     

A Stream Network Model for Integrated Watershed Modeling

We describe a flexible, computationally efficient stream network model, which forms the core of a simulation framework that spatially integrates the contributions from point and nonpoint sources in a watershed. The model uses the map and stream topology information in the US Environmental Protection Agency’s Reach File 3 to generate a spatially explicit network of stream reaches. Water and materials are routed through the stream network to the watershed outlet, and the routing process accounts for transit times and for possible nutrient losses in streams. This model can be applied wherever Reach File maps or maps from the newer National Hydrography Dataset are available, and it can be combined with models of other watershed processes to create a complete watershed simulation system. We present an application of the stream network model to two watersheds of different sizes in the Patuxent River watershed of Maryland, USA. Simulated predictions of streamflow and nitrate concentrations are either very good or good according to standards developed for evaluating the widely used Hydrologic Simulation Program – Fortran (HSPF) watershed model.     

Assessing the Relative Severity of Stressors at a Watershed Scale

Water quality monitoring data are usually used independently to report on the condition of streams and watersheds. For example, watersheds are often rated as good, fair, or poor with regard to a single stressor or with regard to an index of biotic integrity. The utility of monitoring data may be enhanced by integrating stressor-response information with the observed stressor data, and reporting stressor levels in terms of their relative effects upon valued ecological resources. We estimated stressor-response relationships at the regional scale using data collected in the Eastern Cornbelt Plains Ecoregion of Ohio. Generalized additive models were used to visualize stressor-response relationships. Piecewise linear functions and simple linear functions were then used to parameterize the observed responses. Parameters derived from the regional models were used to scale observations of stressors in the Big Darby Creek watershed, OH. After scaling, stressors were compared in terms of their spatial distribution and in terms of the severity with which they influenced the biological endpoint of interest. Stressors most strongly associated with the current ecological condition of the watershed were identified. In the Big Darby Creek watershed, decreases in substrate quality were associated with the most severe decrements in biological condition. At smaller decrements in biological condition, three stressors were important: substrate quality, riparian quality, and increased concentrations of NOx.     

Advantages of Geographically Weighted Regression for Modeling Benthic Substrate in Two Greater Yellowstone Ecosystem Streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m² cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r ². For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r ² values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.     

邕江流域有机氯农药残留问题的初步研究

采用固相萃取和GC-ECD对邕江流域水体中17种痕量有机氯农药的残留状况进行分析测定,检测结果表明,邕江干流均检出硫酸硫丹,但有机氯农药浓度较低,均未超过现有相关标准。城市内河残留的有机氯农药以六六六为主,主要来自于长期的历史残留,而硫酸硫丹除朝阳溪和相思湖丰水期未检测到外,均普遍检出。方法检出限为1~5ng/L,替代物加标回收率为103.5%~112.0%。     

Patterns of Spatial Autocorrelation in Stream Water Chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.     

Visually determined stream mesohabitats influence benthic macroinvertebrate assessments in headwater streams

Mesohabitat components such as substrate and surface flow types are intimately related to benthic macroinvertebrate assemblages in streams. Visual assessments of the distribution of these components provide a means of evaluating physical habitat heterogeneity and aid biodiversity surveys and monitoring. We determined the degree to which stream site and visually assessed mesohabitat variables explain variability (i.e., beta-diversity) in the relative abundance and presence-absence of all macroinvertebrate families and of Ephemeroptera, Plecoptera, and Trichoptera (EPT) genera. We systematically sampled a wide variety of mesohabitat arrangements as they occured in stream sites. We also estimated how much of the explanation given by mesohabitat was associated with substrate or surface flow types. We performed variation partitioning to determine fractions of explained variance through use of partial redundancy analysis (pRDA). Mesohabitats and stream sites explained together from 23 to 32 % of the variation in the four analyses. Stream site explained 8–11 % of that variation, and mesohabitat variables explained 13–20 %. Surface flow types accounted for >60 % of the variation provided by the mesohabitat component. These patterns are in accordance with those obtained in previous studies that showed the predominance of environmental variables over spatial location in explaining macroinvertebrate distribution. We conclude that visually assessed mesohabitat components are important predictors of assemblage composition, explaining significant amounts of beta-diversity. Therefore, they are critical to consider in ecological and biodiversity assessments involving macroinvertebrates.     

Local physical habitat quality cloud the effect of predicted pesticide runoff from agricultural land in Danish streams

The combination of intensive agricultural activities and the close connectivity between land and stream emphasise the potential risk of pesticide exposure in Danish streams. Benthic macroinvertebrates are applied in the assessment of stream ecological status, and some sensitive species have been shown to respond strongly to brief pulses of pesticide contamination. In this study we investigate the impact of agriculturally derived pesticides on stream macroinvertebrate communities in Denmark. As a measure of toxic pressure we apply the Runoff Potential. We investigated a total of 212 streams. These were grouped into distinct classes according to the magnitude of pesticide contamination in the period from 2003-2006. A total of 24 different macroinvertebrate indices were applied to detect effects of pesticide runoff (e.g. the SPEAR-index and the number of EPT taxa). We found high predicted pesticide runoff in 39% of the streams, but we found no significant effect of predicted pesticide exposure on stream macroinvertebrate indices. We, additionally, examined the influence of a series of environmental parameters ranging from site scale to catchment scale on the macroinvertebrate community. Relative proportions of gravel, sand and silt in bed sediments explained most of the variation in macroinvertebrate indices as well as the upstream riparian habitat quality. We suggest that the Runoff Potential model overestimate pesticide runoff contamination in Danish streams due the presence of buffer strips enforced by Danish legislation. When pesticide runoff contamination is low to moderate, poor physical properties (indirectly related to agricultural activity) are the main impediment for the ecological quality of Danish streams.     

Acidification reversal in low mountain range streams of Germany

This study evaluates the acidification status and trends in streams of forested mountain ranges in Germany in consequence of reduced anthropogenic deposition since the mid 1980s. The analysis is based on water quality data for 86 long-term monitored streams in the Ore Mountains, the Bavarian Forest, the Fichtelgebirge, the Harz Mountains, the Spessart, the Black Forest, the Thuringian Forest, and the Rheinisches Schiefergebirge of Germany and the Vosges of France. Within the observation period, which starts for the individual streams between 1980 and 2001 and ends between 1990 and 2009, trends in chemical water quality were calculated with the Seasonal Mann Kendall Test. About 87% of the streams show significant (p < 0.05) negative trends in sulfate. The general reduction in acid deposition resulted in increased pH values (significant for 66% of the streams) and subsequently decreased base cation concentrations in the stream water (for calcium significant in 58% and magnesium 49% of the streams). Reaction products of acidification such as aluminum (significant for 50%) or manganese (significant for 69%) also decreased. Nitrate (52% with significant decrease) and chloride (38% with significant increase) have less pronounced trends and more variable spatial patterns. For the quotient of acidification, which is the ratio of the sum of base cations and the sum of acid anions, no clear trend is observed: in 44% of the monitored streams values significantly decreased and in 23% values significantly increased. A notable observation is the increasing DOC concentration, which is significant for 55% of the observed streams.     

Use of continuous monitoring to assess stream nitrate flux and transformation patterns

Delivery of nitrogen from farmed fields to the stream network is an ongoing water quality issue in central North America and other parts of the world. Although fertilization and other farming practices have been refined to produce environmental improvements, stemming loss of nitrogen, especially in the soluble nitrate form, is a problem that has seemingly defied solution. The Iowa Nutrient Reduction Strategy is a policy initiative designed to implement conservation and other farm management practices to produce reductions in nitrate loading. The strategy does not focus on how the streams themselves may or may not be processing nitrogen and reducing downstream loading. We used continuous high-frequency nitrate and discharge monitoring over 3 years at two sites separated by 18 km in a low-order, agricultural stream in eastern Iowa to estimate how nitrogen is processed, and whether or not these processes are reducing downstream loading. We conclude that the upstream to downstream nitrate concentration decline between the two sites was not driven by denitrification. These data also show that nitrate concentrations are closely coupled to discharge during periods of adequate moisture, but decoupling of concentration from discharge occurs during dry periods. This decoupling is a possible indicator of in-stream nitrate processing. Finally, nitrate concentrations are likely diluted by water sourced from non-row crop land covers in the lower reaches of the watershed.     

长江入海口北岸单站降水组分10年变化状况探讨

分析2001—2010年南通市区城中子站降水监测点位数据,得出降水中起致酸作用的阴离子硫酸根、硝酸根、氟离子和氯离子中除硝酸根呈现显著性上升趋势,其余上升趋势不明显;氯离子、钠离子与降水量存在高度线性负相关,硫酸根、硝酸根与降水量分别为低度非线性负相关和正相关,其他项目与降水量为显著性非线性负相关;将pH分5级,pH<4.7为重酸雨频率,pH4.7～5.0为较重酸雨频率,pH5.0～5.3为中度酸雨频率,pH5.3～5.6为轻度酸雨频率,pH≥0.56为非酸雨频率,其结果表明:南通市区2009、2010年酸雨频率降低,分别为44.1%和38.2%,主要是重酸雨频率减少,非酸雨频率增加;2003、2007年酸雨频率升高,分别为67.4%和69.8%,主要是重酸雨频率增加,非酸雨频率减小;降水量大小与电导率浓度高低没有对应性;阴阳离子浓度总量逐年、逐月同步变化;阴离子中硫酸根、硝酸根浓度逐年、逐月变化量与幅度较大;阳离子中钙离子、铵离子浓度逐年、逐月变化量与幅度较大;阴阳离子浓度逐年、逐月占比呈准对称形。     

Estimation of nested spatial patterns and seasonal variation in the longitudinal distribution of Sicyopterus japonicus in the Datuan Stream, Taiwan by using geostatistical methods

This study attempts to determine the scale-dependent hierarchical spatial variation and longitudinal distributions of Sicyopterus japonicus year round. The distribution of S. japonicus in the Datuan Stream in northern Taiwan was surveyed during the fall and winter 2007, as well as the spring and summer of 2008. The spatial structure of S. japonicus density was modeled using geostatistics. The longitudinal distributions of S. japonicus density were then estimated using kriging and hydrology distance with nested variogram models. Variography results indicate that nested variogram models could reflect the hierarchical structure in the spatial variation of seasonal S. japonicus density, with the small, median, and large ranges representing three nested scales. Models for the four seasons were consistent in that they shared the same shape of variogram models with various ranges and sill values. This model shape consistency implies stationary spatial correlations in the longitudinal fish distribution across the four seasons. The Kriging geostatistical method based on the multiple scales nested variogram models also provided robust estimates of S. japonicus densities at unsampled sections. We conclude that S. japonicus densities exhibit hierarchical patterns and variation in the four seasons along the study stream. Geostatistical methods with a nested variograms and hydrological distance are a highly effective means of delineating the hierarchical structure in longitudinal patterns of S. japonicus density in each season, providing estimates of the S. japonicus density for hierarchically structured spatial distributions and expanding knowledge of S. japonicus beyond the limits imposed by spatial and temporal scales.     

An evaluation of a bed instability index as an indicator of habitat quality in mountain streams of the northwestern United States

Managers of aquatic resources benefit from indices of habitat quality that are reproducible and easy to measure, demonstrate a link between habitat quality and biota health, and differ between human-impacted (i.e., managed) and reference (i.e., nonimpacted or minimally impacted) conditions. The instability index (ISI) is an easily measured index that describes the instability of a streambed by relating the tractive force of a stream at bankfull discharge to the median substrate size. Previous studies have linked ISI to biological condition but have been limited to comparisons of sites within a single stream or among a small number of streams. We tested ISI as an indicator of human impact to habitat and biota in mountain streams of the northwestern USA. Among 1428 sites in six northwestern states, ISI was correlated with other habitat measures (e.g., residual pool depth, percent fine sediment) and indices of biotic health (e.g., number of intolerant macroinvertebrate taxa, fine sediment biotic index) and differed between managed and reference sites across a range of stream types and ecoregions. While ISI could be useful in mountain streams throughout the world, this index may be of particular interest to aquatic resource managers in the northwestern USA where a large dataset, from which ISI can be calculated, exists.     

Evaluation of stream chemistry trends in US Geological Survey reference watersheds, 1970–2010

The Hydrologic Benchmark Network (HBN) is a long-term monitoring program established by the US Geological Survey in the 1960s to track changes in the streamflow and stream chemistry in undeveloped watersheds across the USA. Trends in stream chemistry were tested at 15 HBN stations over two periods (1970–2010 and 1990–2010) using the parametric Load Estimator (LOADEST) model and the nonparametric seasonal Kendall test. Trends in annual streamflow and precipitation chemistry also were tested to help identify likely drivers of changes in stream chemistry. At stations in the northeastern USA, there were significant declines in stream sulfate, which were consistent with declines in sulfate deposition resulting from the reductions in SO₂ emissions mandated under the Clean Air Act Amendments. Sulfate declines in stream water were smaller than declines in deposition suggesting sulfate may be accumulating in watershed soils and thereby delaying the stream response to improvements in deposition. Trends in stream chemistry at stations in other part of the country generally were attributed to climate variability or land disturbance. Despite declines in sulfate deposition, increasing stream sulfate was observed at several stations and appeared to be linked to periods of drought or declining streamflow. Falling water tables might have enhanced oxidation of organic matter in wetlands or pyrite in mineralized bedrock thereby increasing sulfate export in surface water. Increasing sulfate and nitrate at a station in the western USA were attributed to release of soluble salts and nutrients from soils following a large wildfire in the watershed.     

Small-scale temporal variation and the effect of urbanisation on extracellular enzyme activity in streams

The activity of six extracellular enzymes involved in the degradation of dissolved organic carbon compounds was measured in two highly urbanised and two minimally impacted streams east of Melbourne, Australia, using 4-methylumbelliferyl-substrates. Small-scale temporal variation in enzyme activity was determined by repeatedly sampling the same point in the water column, while the effect of flow was determined by sampling in regions of higher and lower flow in both stream types. Replicate samples showed that enzyme activity was not significantly different over small (minutes) time scales. On five of six sampling occasions the enzyme activity was unaffected by flow. On one sampling occasion in a minimally disturbed stream, the difference between the high- and low-flow regions was statistically significant (ANOSIM, Global R= 0.78, P= 0.03). Enzyme activity profiles (activities of the suite of enzymes) of the streams in urbanised catchments were different to those in minimally disturbed catchments. The measurements made in four different streams showed high reproducibility over short time periods (minutes) which lends greater credibility to analogous spatial studies. Although these results determined that small-scale temporal variability was not significant, and that the effects of flow were generally minimal, it is recommended that spatial and temporal variability in the stream be at least considered before any studies measuring extracellular enzyme activity in stream waters are carried out. Such an approach will lead to conclusions from measurements that are not likely to be confounded by variables such as flow rate or time.     

Stream ecosystem response to chronic deposition of N and acid at the Bear Brook Watershed, Maine

The Bear Brook Watershed in Maine (BBWM) is a long-term, paired watershed experiment that addresses the effects of acid and nitrogen (N) deposition on whole watersheds. To examine stream response at BBWM, we synthesized data on organic matter dynamics, including leaf breakdown rates, organic matter inputs and standing stocks, macroinvertebrate secondary production, and nutrient uptake in treated and reference streams at the BBWM. While N concentrations in stream water and leaves have increased, the input, standing stocks, and breakdown rates of leaves, as well as macroinvertebrate production, were not responsive to acid and N deposition. Both chronic and acute increases of N availability have saturated uptake of nitrate in the streams. Recent experimental increases in phosphorus (P) availability enhanced stream capacity to take up nitrate and altered the character of N saturation. These results show how the interactive effects of multiple factors, including environmental flow regime, acidification, and P availability, may constrain stream response to chronic N deposition.     

Correspondence of biological condition models of California streams at statewide and regional scales

We used boosted regression trees (BRT) to model stream biological condition as measured by benthic macroinvertebrate taxonomic completeness, the ratio of observed to expected (O/E) taxa. Models were developed with and without exclusion of rare taxa at a site. BRT models are robust, requiring few assumptions compared with traditional modeling techniques such as multiple linear regression. The BRT models were constructed to provide baseline support to stressor delineation by identifying natural physiographic and human land use gradients affecting stream biological condition statewide and for eight ecological regions within the state, as part of the development of numerical biological objectives for California’s wadeable streams. Regions were defined on the basis of ecological, hydrologic, and jurisdictional factors and roughly corresponded with ecoregions. Physiographic and land use variables were derived from geographic information system coverages. The model for the entire state (n?=?1,386) identified a composite measure of anthropogenic disturbance (the sum of urban, agricultural, and unmanaged roadside vegetation land cover) within the local watershed as the most important variable, explaining 56 % of the variance in O/E values. Models for individual regions explained between 51 and 84 % of the variance in O/E values. Measures of human disturbance were important in the three coastal regions. In the South Coast and Coastal Chaparral, local watershed measures of urbanization were the most important variables related to biological condition, while in the North Coast the composite measure of human disturbance at the watershed scale was most important. In the two mountain regions, natural gradients were most important, including slope, precipitation, and temperature. The remaining three regions had relatively small sample sizes (n?≤?75 sites) and had models that gave mixed results. Understanding the spatial scale at which land use and land cover affect taxonomic completeness is imperative for sound management. Our results suggest that invertebrate taxonomic completeness is affected by human disturbance at the statewide and regional levels, with some differences among regions in the importance of natural gradients and types of human disturbance. The construction and application of models similar to the ones presented here could be useful in the planning and prioritization of actions for protection and conservation of biodiversity in California streams.     

Preliminary Studies on the Population Genetics of the Central Stoneroller (Campostoma anomalum) from the Great Miami River Basin, Ohio

Molecular approaches are particularly useful for measuring genetic diversity and were applied to samples of central stonerollers obtained from sites along tributaries to the Great Miami River in Ohio. We used Random Amplified Polymorphic DNA (RAPD) analysis to assess the level of genetic diversity within and among these populations. RAPD analysis generates genetic profiles that were used to develop indices of genetic similarity. The RAPD method provides a cost effective means of generating an arbitrary sample of anonymous loci across the genome and generate a virtually unlimited set of loci for use in genetic analysis in the absence of specific sequence information. These attributes make RAPDs well suited for use in evaluating the diversity and assessing the potential vulnerability to exposure of populations across multiple spatial scales. The results demonstrate that a significant amount of structuring exists among populations analyzed to date and that a trend exists towards genetic diversity being an inverse function of site distance from the main stem as well as a being directly related to stream order. This indicates that populations farthest from main conduits or in lower order streams, and thereby most isolated, may be the most vulnerable populations to stressor exposure. It is hoped that information pertaining to genetic diversity, when integrated with other metrics of resource condition, will aid in making scientifically grounded decisions on resource management that enhance the probability of population survival and preserve natural evolutionary processes.