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Susan B Norton

Publications and source records attributed to Susan B Norton.

9 recordsLinked to original sources
PubMed2005

Individuals versus organisms versus populations in the definition of ecological assessment endpoints.

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.

Animals↗
PubMed2004

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.

Biodiversity↗
PubMed2003

Predicting sediment toxicity using logistic regression: a concentration-addition approach.

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.

Animals↗
PubMed2002

A methodology for inferring the causes of observed impairments in aquatic ecosystems.

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.

Animals↗
PubMed2002

Determining probable causes of ecological impairment in the Little Scioto River, Ohio, USA: part 1. Listing candidate causes and analyzing evidence.

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.

Animals↗
PubMed2002

Determining the causes of impairments in the Little Scioto River, Ohio, USA: part 2. Characterization of causes.

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.

Animals↗
PubMed2002

Predicting levels of stress from biological assessment data: empirical models from the Eastern Corn Belt Plains, Ohio, USA.

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.

Animals↗
PubMed2002

Predicting amphipod toxicity from sediment chemistry using logistic regression models.

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.

Amphipoda↗