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Streams and other waterbodies in poor ecological condition often require causal assessment to determine appropriate follow-up actions. However, site-specific causal assessments can be time ...

To support sound decision-making in environmental management, we need rigorous, defensible, and transparent synthesis of scientific evidence. The Association for the Sciences of Limnology and Oceanography and associated aquatic science societies are leaders in applying science to decision-making, and yet many environmental decisions are still at risk of having to be made without a comprehensive, well-synthesized evidence base to support them. In this article, we discuss two synergistic approaches that can help science inform decision-making: systematic review and evidence banking. Our aim is to promote the use of these approaches, and to enlist support and action from you, the aquatic science community. We propose that you can improve the use and uptake of science in decision-making by making your research more compatible with synthesis efforts by: considering risk of bias when designing your study and reporting results; reporting all relevant contextual information; analyzing your data using standard effect size approaches; and publishing your raw data. Awareness of how primary research feeds into informing policies can help you broaden the impact of your research, making it more directly relevant to decision-making and more likely to contribute to the protection of aquatic ecosystems.

Accurate and informative methods for evidence synthesis that are also simple and inexpensive to implement would greatly increase our ability to use scientific research results to better manage natural environments. Across the world, pressure to use evidence-based environmental management is increasing, but little guidance exists as to what ‘evidence-based’ actually means. Methods for systematic review of literature evidence have been modified from those used in medical research, but the effort involved in using these methods means that they have not been widely adopted. We compared 3 independently developed approaches to ‘rapid evidence synthesis’ methods developed in an attempt to improve efficiency and responsiveness compared to standard methods for systematic review. Each method has features that should be included in an ideal rapid evidence-synthesis method and has potential for further development. Increasing standardization of methods for evidence extraction, quality assessment, and synthesi...

Sound environmental management relies on scientific evidence to indicate whether action is warranted and, if so, which actions will be most effective. We discuss conceptual model diagrams that schematically describe how sources or human activities lead to specific stressors and ultimately to biotic effects. They are useful for describing how undesirable effects are produced and identifying where management actions may be most effective in preventing unwanted effects or improving existing conditions. We illustrate the use of conceptual model diagrams in a literature-based assessment of the impacts of mountaintop removal mining on downstream aquatic systems. The diagram-development process combined extraction and evaluation of research results with iterative refinement of diagrams depicting source-to-stressor-to-effect causal pathways. Conceptual-model diagrams provided a useful scaffold for organizing and synthesizing multiple individual pieces of evidence extracted from different studies and an ef...

Background Eutrophication of freshwater ecosystems resulting from nitrogen and phosphorus pollution is a major stressor across the globe. Despite recognition by scientists and stakeholders of the problems of nutrient pollution, rigorous synthesis of scientific evidence is still needed to inform nutrient-related management decisions, especially in streams and rivers. Nutrient stressor-response relationships are complicated by multiple interacting environmental factors, complex and indirect causal pathways involving diverse biotic assemblages and food web compartments, legacy (historic) nutrient sources such as agricultural sediments, and the naturally high spatiotemporal variability of lotic ecosystems. Determining nutrient levels at which ecosystems are affected is a critical first step for identifying, managing, and restoring aquatic resources impaired by eutrophication and maintaining currently unimpaired resources. The systematic review outlined in this protocol will compile and synthesize literature on the response of chlorophyll a to nutrients in streams, providing a state-of-the-science body of evidence to assess nutrient impacts to one of the most widely-used measures of eutrophication. This review will address two questions: “What is the response of chlorophyll a to total nitrogen and total phosphorus concentrations in lotic ecosystems?” and “How are these relationships affected by other factors?” Methods Searches for published and unpublished articles (peer-reviewed and non-peer-reviewed) will be conducted using bibliographic databases and search engines. Searches will be supplemented with bibliography searches and requests for material from the scientific and management community. Articles will be screened for relevance at the title/abstract and full text levels using pre-determined inclusion criteria; 10% (minimum 50, maximum 200) of screened papers will be examined by multiple reviewers to ensure consistent application of criteria. Study risk of bias will be evaluated using a questionnaire developed from existing frameworks and tailored to the specific study types this review will encounter. Results will be synthesized using meta-analysis of correlation coefficients, as well as narrative and tabular summaries, and will focus on the shape, direction, strength, and variability of available nutrient-chlorophyll relationships. Sensitivity analysis and meta-regression will be used to evaluate potential effects of study quality and modifying factors on nutrient-chlorophyll relationships.

The present study describes approaches to improve the performance of empirical models developed from a large nationwide data set to predict sediment toxicity from chemistry for regional applications. The authors developed 4 multiple chemical (PMax) models selected from individual chemical models developed using 1) a previously published approach applied to the nationwide data set; 2) a broader array of response and explanatory variables (e.g., different normalization approaches and toxicity classifications) applied to the nationwide data set; 3) a data set from the New York/New Jersey, USA, region; and 4) both nationwide and regional data sets. The models were calibrated using the regional data set. Performance was tested using an independent data set from the same region. The performance of the final PMax model developed using the calibration process substantially improved over that of the uncalibrated PMax model developed using the nationwide data set. The improvements were achieved by selecting the best performing individual chemical models and eliminating those that performed poorly when applied together. Although the best performing PMax model included both nationwide and region-specific models, the performance of the PMax model derived using only nationwide models was nearly as good. These results suggest that calibrating nationwide models to a regional data set may be both a more efficient and effective approach for improving model performance than developing region-specific models. Environ Toxicol Chem 2014;33:708–717. © 2013 SETAC. This article is a US Government work and is in the public domain in the USA.

When an environmental impairment has been identified, it becomes necessary to identify the cause so that an appropriate action can be planned. However, causation is difficult to establish—both conceptually and in practice. To ensure that the U.S. Environmental Protection Agency's (USEPA's) method for causal assessment is appropriate and defensible, we reviewed concepts of causation from philosophers, statisticians, epidemiologists, and others. This article summarizes the results of that review and explains how it relates to the USEPA's method. We include a five-step process: (1) identify alternative candidate causes; (2) logically eliminate when possible; (3) diagnose when possible; (4) analyze the strength of evidence for remaining candidate causes; and (5) identify the most likely cause. We also encourage three practices: (1) use a consistent process; (2) do not claim proof of causation; and (3) document the evidence and inferences. This approach allows assessors to identify the most likely cau...

We used complementary information collected using different conceptual approaches to develop recommendations for a stream nutrient criterion based on responses of algal assemblages to anthropogenic P enrichment. Benthic algal attributes, water chemistry, physical habitat, and human activities in watersheds were measured in streams of the Mid-Atlantic Highlands region as part of the Environmental Monitoring and Assessment Program of the US Environmental Protection Agency. Diatom species composition differed greatly between low- and high-pH reference streams; therefore, analyses for criterion development were limited to a subset of 149 well-buffered streams to control for natural variability among streams caused by pH. Regression models showed that TP concentrations were ;10 lg/L in streams with low levels of human activities in watersheds and that TP increased with % agriculture and urban land uses in watersheds. The 75 th percentile at reference sites was 12 lg TP/L. Chlorophyll a and ash-free dry mass increased and acid and alkaline phosphatase activities decreased with increasing TP concentration. The number of diatom taxa, evenness, proportion of expected native taxa, and number of high-P taxa increased with TP concentration in streams. In contrast, the number of low-P native taxa and % low-P individuals decreased with increasing TP. Lowess regression and regression tree analysis indicated nonlinear relationships for many diversity indices and attributes of taxonomic composition with respect to TP. Thresholds in these responses occurred between 10 and 20 lg/L and helped justify recommending a P criterion between 10 and 12 lg TP/L to protect high- quality biological conditions in streams of the Mid-Atlantic Highlands.

Discussions and applications of the policies and practices of the U.S. Environmental Protection Agency (USEPA) in ecological risk assessment will benefit from continued clarification of the concepts of assessment endpoints and of levels of biological organization. First, assessment endpoint entities and attributes can be defined at different levels of organization. Hence, an organism-level attribute, such as growth or survival, can be applied collectively to a population-level entity such as the brook trout in a stream. Second, assessment endpoints for ecological risk assessment are often mistakenly described as ‘‘individual level,’’ which leads to the idea that such assessments are intended to protect individuals. Finally, populations play a more important role in risk assessments than is generally recognized. Organism-level attributes are used primarily for population-level assessments. In addition, the USEPA and other agencies already are basing management decisions on population or community entities and attributes such as production of fisheries, abundance of migratory bird populations, and aquatic community composition.

The United States Environmental Protection Agency (USEPA) has ongoing programs to encourage the evaluation of stream conditions based on biological indicators. Bioassessments reveal impairments but do not identify causes of impairments, a necessary step in the restoration of aquatic life. Furthermore, changes in biological condition are often caused by multiple stressors (chemical, physical, biological). To address this need, the USEPA has developed guidance to identify probable causes of biological impairments in aquatic ecosystems and to provide a structure for organizing the scientific evidence to make a credible case. The concepts and organizational structure of the process have potential applications to most ecoepi-demiological investigations.

The risk assessment framework presented in the National Research Council (NRC) Red Book played a key role in the development of ecological risk assessment (ERA). ERA frameworks have, however, developed along their own pathway and have significantly extended concepts that were introduced in the Red Book. When the U.S. Environmental Protection Agency (EPA) commissioned the Oak Ridge National Laboratory (ORNL) in 1981 to develop and apply methods for ERA, the work focused on probabilistic analysis, since that seemed to be the essence of risk. When the Red Book appeared, it suggested that the use of a logical framework to guide the process was also an important aspect of risk assessment. Therefore, the ORNL investigators developed a framework similar to the Red Book framework but more suited to ERA. When EPA initiated a project to develop an official EPA framework for ERA, the ORNL framework was presented in a colloquium intended to obtain input from scientists outside EPA. Later, the NRC sponsored a workshop...

Metrics characterizing the benthic macroinvertebrate assemblages in wadeable streams in the Mid-Atlantic region of the United States were analyzed to explore the relative responses of the metrics to different types of anthropogenic stress. The data used in our study were collected by the US Environmental Protection Agency Environmental Monitoring and Assessment Program from 1993 to 1996. Regression models were developed relating metric values at reference sites to natural sources of variability. These models were then used to predict reference values at test sites. Test site metric observations were scaled by subtracting the predicted reference value and dividing by the standard deviation of residuals at reference sites. Stressor–response relationships for each scaled metric were then estimated using generalized additive models. Metric responses to 4 groups of stressors (nutrient enrichment, habitat degradation, elevated metals concentrations, and elevated ion concentrations) were different. The p...

The question posed in this article is how useful the chemical concentration measurements for predicting the outcome of sediment toxicity tests are. Using matched data on sediment toxicity and sediment chemical concentrations from a number of studies, we investigated several approaches for predicting toxicity based on multiple logistic regression with concentration-addition models. Three models were found to meet criteria for acceptability. The first model uses individual chemicals selected using stepwise selection. The second uses derived variables to reflect combined metal contamination, polycyclic aromatic hydrocarbon (PAH) contamination, and the interaction between metals and PAHs. The third and final model is a separate species model with derived variables. Overall, these models suggest that toxicity may be correctly predicted approximately 77% of the time, although prediction is better for samples identified as nontoxic than for those known to be toxic.

Interest in causal investigations in aquatic systems has been a natural outgrowth of the increased use of biological monitoring to characterize the condition of resources. Although biological monitoring approaches are critical tools for detecting whether effects are occurring, they do not identify the cause of the observed effects. Formal approaches to causal evaluation can provide a mechanism to build on expert knowledge, increasing the likelihood that remedial efforts will achieve the desired environmental improvement. This paper examines how formal approaches to causal investigations minimize common errors. We reviewed common cognitive errors reported in the literature, and compared them with considerations suggested for strength-of-evidence approaches. Many of the causal considerations are directed toward distinguishing spurious correlations from true causal relationships. However, this is only one type of error; others include hypothesis dependence, confirmation bias, hypothesis tenacity and anchorin...

Individual chemical logistic regression models were developed for 37 chemicals of potential concern in contaminated sediments to predict the probability of toxicity, based on the standard 10-d survival test for the marine amphipods Ampelisca abdita and Rhepoxynius abronius. These models were derived from a large database of matching sediment chemistry and toxicity data, which includes contaminant gradients from a variety of habitats in coastal North America. Chemical concentrations corresponding to a 20, 50, and 80% probability of observing sediment toxicity (T20, T50, and T80 values) were calculated to illustrate the potential for deriving application-specific sediment effect concentrations and to provide probability ranges for evaluating the reliability of the models. The individual chemical regression models were combined into a single model, using either the maximum (P(Max) model) or average (P(Avg) model) probability predicted from the chemicals analyzed in a sample, to estimate the probability of toxicity for a sample. The average predicted probability of toxicity (from the P(Max) model) within probability quartiles closely matched the incidence of toxicity within the same ranges, demonstrating the overall reliability of the P(Max) model for the database that was used to derive the model. The magnitude of the toxic effect (decreased survival) in the amphipod test increased as the predicted probability of toxicity increased. Users have a number of options for applying the logistic models, including estimating the probability of observing acute toxicity to estuarine and marine amphipods in 10-d toxicity tests at any given chemical concentration or estimating the chemical concentrations that correspond to specific probabilities of observing sediment toxicity.

Two stream reaches in the Little Scioto River (OH, USA) were characterized for the causes of impairments measured at two locations. By inductive inference, six candidate causes were winnowed down to three and five candidate causes for each of the two stream reaches. Using a formal strength-of-evidence process, a single cause was determined. At the most upstream location, habitat alterations, including fine-textured substrates and low DO, were characterized as the probable causes for an increased percentage of anomalies of fish, a decreased percentage of mayflies, and an increased percentage of tolerant macroinvertebrates. An increase in the relative weight of fish was attributed to an artificially narrow, deepened channel. Approximately 2 km downstream, polycyclic aromatic hydrocarbon (PAH)-contaminated sediments were identified as the cause for both fish and macroinvertebrate impairments. Causal characterization using first elimination and then a strength-of-evidence approach narrowed and defined the causes of ecological impairment even in this situation, where many complex and interacting candidate causes existed. Applying a formal method highlighted types of data and associations that can strengthen and present a more convincing determination of the causes of impairment.

Interest is increasing in using biological community data to provide information on the specific types of anthropogenic influences impacting streams. We built empirical models that predict the level of six different types of stress with fish and benthic macroinvertebrate data as explanatory variables. Significant models were found for six stressor factors: stream corridor structure; siltation; total suspended solids (TSS), biochemical oxygen demand (BOD), and iron (Fe); chemical oxygen demand (COD) and BOD; zinc (Zn) and lead (Pb); and nitrate and nitrite (NOx) and phosphorus (P). Model R2 values were lowest for the siltation factor and highest for TSS, BOD, and Fe. Model R2 values increased when spatial relationships were incorporated into the model. The models generally performed well when applied to a random subset of the data. Performance was more mixed when models were applied to data collected from a previous time period, perhaps because of a change in the spatial structure of these systems. These models may provide a useful indication of the levels of different stresses impacting stream reaches in the Eastern Corn Belt Plains ecoregion of Ohio, USA. More generally, the models provide additional evidence that biological communities can serve as useful indicators of the types of anthropogenic stress impacting aquatic systems.

The Little Scioto River in north-central Ohio, USA, is considered to be biologically impaired based on the results of fish and invertebrate surveys. The causes for these impairments were evaluated by means of a formal method. Two of the impairments identified on the stream reach were characterized in detail to support the causal assessment. A list of six candidate causes was developed that included habitat alteration, polycyclic aromatic hydrocarbon contamination, metals contamination, low dissolved oxygen, ammonia toxicity, and nutrient enrichment. Evidence for the causal evaluation was developed with data from the site that associated each candidate cause with the biological responses. Evidence was also developed that drew on data from other locations and laboratory studies, including comparisons of site exposures with screening values and criteria. The formal method increased the transparency of the assessment; candidate causes were clearly listed and the pathways by which they may have produced effects were shown. Analysis of the evidence maximized the utility of available data, which were collected as part of monitoring and research programs rather than to specifically support a causal assessment. This case study illustrates how the stressor identification method can be used to draw conclusions from available data about the most likely causes of impairment and to show what additional studies would be useful.

Biological surveys have become a common technique for determining whether aquatic communities have been injured. However, their results are not useful for identifying management options until the causes of apparent injuries have been identified. Techniques for determining causation have been largely informal and ad hoc. This paper presents a logical system for causal inference. It begins by analyzing the available information to generate causal evidence; available information may include spatial or temporal associations of potential cause and effect, field or laboratory experimental results, and diagnostic evidence from the affected organisms. It then uses a series of three alternative methods to infer the cause: Elimination of causes, diagnostic protocols, and analysis of the strength of evidence. If the cause cannot be identified with sufficient confidence, the reality of the effects is examined, and if the effects are determined to be real, more information is obtained to reiterate the process.

We investigated the feasibility of using the structure of fish and benthic macroinvertebrate communities to distinguish among major types of stressors (e.g., siltation, nutrient enrichment, and stream structural degradation) using spatially and temporally matched data on stressors and responses. The 19 stressor variables addressed stream chemistry and in-stream habitat and included biological oxygen demand (BOD), total suspended solids, nitrogen, phosphorus, and components of the Qualitative Habitat Evaluation Index. The 42 response variables addressed fish and invertebrate community structure and included many of the component metrics of the Index of Biological Integrity and the Invertebrate Community Index as well as variables specifically calculated for this project. All data were collected between 1988 and 1994 by the Ohio Environmental Protection Agency in the Eastern Corn Belt Plains ecoregion. Prior to analysis, variables were transformed to near normality, and variables significantly correlated with drainage area were fit to regression models and the resulting residuals used in the analyses. Multivariate analyses included factor and discriminant analysis. The first six stressor factors explained 69% of the variation. Discriminant functions formed using the response variables significantly separated site clusters classified into high, medium, and low categories along stressor gradients. Both fish and macroinvertebrate variables were important in distinguishing site categories. For example, percentage Tanytarsini midges and percentage Glyptotendipes were important in distinguishing sites having high and low BOD. Percentage darters was associated with sites having high scores for stream corridor structure and low concentrations of inorganic nutrients, and percentage round-bodied suckers was associated with sites having low BOD and low concentrations of zinc and lead. These results indicate that diagnostic models may be developed that will be useful for site-specific and regional assessments.

The Big Darby Creek watershed, a highly valued ecosystem in central Ohio, USA, threatened by intensive agriculture and suburban encroachment, served as an example of how case specifics can be applied to refine and direct the planning and problem formulation stage of the U.S. Environmental Protection Agency's ecological risk assessment framework. Big Darby Creek was selected as one of five national pilot risk assessments designed to provide specific examples of how to perform an ecological risk assessment and, at the same time, to refine and improve the assessment process. The case study demonstrates how characteristics of the watershed were used to give direction to the components of establishing goals, identifying and characterizing the resource and threats to it, selecting appropriate assessment endpoints, and developing conceptual models. The hypotheses generated in the conceptual model describe expected relationships and interactions between the ecosystem at risk, identified potential stressors, and ecological effects and set the groundwork for the analysis phase that follows problem formulation.

This paper describes the use of logistic-regression modeling for evaluating matching sediment chemistry and toxicity data. Contaminant-specific logistic models were used to estimate the percentage of samples expected to be toxic at a given concentration. These models enable users to select the probability of effects of concern corresponding to their specific assessment or management objective or to estimate the probability of observing specific biological effects at any contaminant concentration. The models were developed using a large database (n = 2,524) of matching saltwater sediment chemistry and toxicity data for field-collected samples compiled from a number of different sources and geographic areas. The models for seven chemicals selected as examples showed a wide range in goodness of fit, reflecting high variability in toxicity at low concentrations and limited data on toxicity at higher concentrations for some chemicals. The models for individual test endpoints (e.g., amphipod mortality) provided a better fit to the data than the models based on all endpoints combined. A comparison of the relative sensitivity of two amphipod species to specific contaminants illustrated an important application of the logistic model approach.

The present study describes approaches to improve the performance of empirical models developed from a large nationwide data set to predict sediment toxicity from chemistry for regional applications. The authors developed 4 multiple chemical (PMax ) models selected from individual chemical models developed using 1) a previously published approach applied to the nationwide data set; 2) a broader array of response and explanatory variables (e.g., different normalization approaches and toxicity classifications) applied to the nationwide data set; 3) a data set from the New York/New Jersey, USA, region; and 4) both nationwide and regional data sets. The models were calibrated using the regional data set. Performance was tested using an independent data set from the same region. The performance of the final PMax model developed using the calibration process substantially improved over that of the uncalibrated PMax model developed using the nationwide data set. The improvements were achieved by selecting the best performing individual chemical models and eliminating those that performed poorly when applied together. Although the best performing PMax model included both nationwide and region-specific models, the performance of the PMax model derived using only nationwide models was nearly as good. These results suggest that calibrating nationwide models to a regional data set may be both a more efficient and effective approach for improving model performance than developing region-specific models. This article is a US Government work and is in the public domain in the USA.

This review assesses the state of the science on the effects of mountaintop mines and valley fills (MTM-VF) on the physicochemical characteristics of streams in the central Appalachian coalfields of West Virginia, Kentucky, Virginia and Tennessee, USA. We focus on the impacts of mountaintop removal coal mining, which involves removing all - or some portion - of the top of a mountain or ridge to expose and mine one or more coal seams. Excess overburden is disposed in constructed fills in small valleys adjacent to the mining site. MTM-VF leachate persistently increases the downstream concentrations of major ions. Conductivity is a coarse measure of these ions, which are dominated by a distinct mixture of SO(4)(2-), HCO(3)(-), Ca(2+) and Mg(2+), that reflects their source, the oxidation of pyrite to form acid followed by neutralization of the acidity by carbonate minerals within the valley fills. This results in neutral to alkaline pHs, a range at which many metals are relatively insoluble. Other compounds within coal or overburden are solubilized and occur at elevated albeit lower concentrations, including K(+), Na(+), Cl(-), Se and Mn. In terms of physical characteristics, the valley fills act like headwater aquifers, baseflows increase in streams below valley fills and water temperatures exhibit reduced seasonal variation. Peak discharges may be increased in response to intense precipitation events, because of compaction of base surfaces of the MTM-VF areas, but newer approaches to reclamation reduce this compaction and may ameliorate these peak flows. Although the sedimentation pond is intended to capture fine particles that wash downstream from the valley fill, some studies found increased fine sediments in streams downstream from valley fills. However, a proportion of these fines may be eroded from stream banks rather than the valley fills. This is probably a result of the alterations in stream flows.

Ecological risk assessment is becoming an increasingly important tool for ranking, assessing, reducing, and managing environmental risks. To pro- vide Agency-wide guidance in this area in the U.S., EPA's Risk Assessment Forum has begun a multi-year guidelines development program. The first step in this program was the publication of the report “Framework for Ecological Risk Assessment” which describes the principles, concepts, terminology, and structure of ecological risk assessments.

Ecological risk assessments evaluate the likelihood of adverse ecological effects caused by stressors related to human activities such as draining of wetlands or release of chemicals. The term stressor is used to describe any chemical, physical, or biological entity that can induce adverse effects on ecological components (i.e., individuals, populations, communities, or ecosystems). In this review article, a historical perspective on ecological risk assessment activities at the U.S. Environmental Protection Agency (EPA) is followed by a discussion of the EPA's “Framework Report,” which describes the basic elements for conducting an ecological risk assessment. The “Framework Report” is neither a procedural guide nor a regulatory requirement within the EPA. Rather, it is intended to foster a consistent approach to ecological risk assessments within the Agency, identify key issues, and define terminology.

Discussions and applications of the policies and practices of the U.S. Environmental Protection Agency (USEPA) in ecological risk assessment will benefit from continued clarification of the concepts of assessment endpoints and of levels of biological organization. First, assessment endpoint entities and attributes can be defined at different levels of organization. Hence, an organism-level attribute, such as growth or survival, can be applied collectively to a population-level entity such as the brook trout in a stream. Second, assessment endpoints for ecological risk assessment are often mistakenly described as "individual level," which leads to the idea that such assessments are intended to protect individuals. Finally, populations play a more important role in risk assessments than is generally recognized. Organism-level attributes are used primarily for population-level assessments. In addition, the USEPA and other agencies already are basing management decisions on population or community entities and attributes such as production of fisheries, abundance of migratory bird populations, and aquatic community composition.

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.

Individual chemical logistic regression models were developed for 37 chemicals of potential concern in contaminated sediments to predict the probability of toxicity, based on the standard 10-d survival test for the marine amphipods Ampelisca abdita and Rhepoxynius abronius. These models were derived from a large database of matching sediment chemistry and toxicity data, which includes contaminant gradients from a variety of habitats in coastal North America. Chemical concentrations corresponding to a 20, 50, and 80% probability of observing sediment toxicity (T20, T50, and T80 values) were calculated to illustrate the potential for deriving application-specific sediment effect concentrations and to provide probability ranges for evaluating the reliability of the models. The individual chemical regression models were combined into a single model, using either the maximum (P(Max) model) or average (P(Avg) model) probability predicted from the chemicals analyzed in a sample, to estimate the probability of toxicity for a sample. The average predicted probability of toxicity (from the P(Max) model) within probability quartiles closely matched the incidence of toxicity within the same ranges, demonstrating the overall reliability of the P(Max) model for the database that was used to derive the model. The magnitude of the toxic effect (decreased survival) in the amphipod test increased as the predicted probability of toxicity increased. Users have a number of options for applying the logistic models, including estimating the probability of observing acute toxicity to estuarine and marine amphipods in 10-d toxicity tests at any given chemical concentration or estimating the chemical concentrations that correspond to specific probabilities of observing sediment toxicity.