Probability surveys, conditional probability, and ecological risk assessment.

We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency's (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over broad geographic areas. Under certain conditions (including appropriate stratification of the sampled population, sufficient density of samples, and sufficient range of exposure levels paired with concurrent response values), this empirical approach provides estimates of risk using extant field-derived monitoring data. The monitoring data were used to prescribe the exposure field and to model the exposure-response relationship. We illustrate this approach by estimating risks to benthic communities from low dissolved oxygen (DO) in freshwater streams of the mid-Atlantic region and in estuaries of the Virginian Biogeographical Province of the United States. In both cases, the estimates of risk are consistent with the U.S. EPA's ambient water quality criteria for DO.

Timing of increased autistic disorder cumulative incidence.

Autistic disorder (AD) is a severe neurodevelopmental disorder typically identified in early childhood. Both genetic and environmental factors are implicated in its etiology. The number of individuals identified as having autism has increased dramatically in recent years, but whether some proportion of this increase is real is unknown. If real, susceptible populations may have exposure to controllable exogenous stressors. Using literature AD data from long-term (approximately 10-year) studies, we determined cumulative incidence of AD for each cohort within each study. These data for each study were examined for a changepoint year in which the AD cumulative incidence first increased. We used data sets from Denmark, California, Japan, and a worldwide composite of studies. In the Danish, California, and worldwide data sets, we found that an increase in AD cumulative incidence began about 1988-1989. The Japanese study (1988-1996) had AD cumulative incidence increasing continuously, and no changepoint year could be calculated. Although the debate about the nature of increasing autism continues, the potential for this increase to be real and involve exogenous environmental stressors exists. The timing of an increase in autism incidence may help in screening for potential candidate environmental stressors.

CProb: a computational tool for conducting conditional probability analysis.

Conditional probability is the probability of observing one event given that another event has occurred. In an environmental context, conditional probability helps to assess the association between an environmental contaminant (i.e., the stressor) and the ecological condition of a resource (i.e., the response). These analyses, when combined with controlled experiments and other methodologies, show great promise in evaluating ecological conditions from observational data and in defining water quality and other environmental criteria. Current applications of conditional probability analysis (CPA) are largely done via scripts or cumbersome spreadsheet routines, which may prove daunting to end-users and do not provide access to the underlying scripts. Combining spreadsheets with scripts eases computation through a familiar interface (i.e., Microsoft Excel) and creates a transparent process through full accessibility to the scripts. With this in mind, we developed a software application, CProb, as an Add-in for Microsoft Excel with R, R(D)com Server, and Visual Basic for Applications. CProb calculates and plots scatterplots, empirical cumulative distribution functions, and conditional probability. In this short communication, we describe CPA, our motivation for developing a CPA tool, and our implementation of CPA as a Microsoft Excel Add-in. Further, we illustrate the use of our software with two examples: a water quality example and a landscape example. CProb is freely available for download at http://www.epa.gov/emap/nca/html/regions/cprob.

Using field data and weight of evidence to develop water quality criteria.

In the United States, ambient aquatic life water quality criteria are derived using guidelines developed in 1985 that include a clear and consistent methodology using data from standard toxicity tests. The methodology from these guidelines has been successful, but a broader methodology is needed because some effects of pollutants do not lend themselves to conventional toxicity testing. Criterion assessment is proposed as that methodology. In criterion assessment, a specific environmental goal is translated into a measurable benchmark of effect that is used together with a modeled exposure-response relationship to estimate a range of exposures that will achieve the specific goal. The model of the exposure-response relationships and the benchmark effect are developed from field data and laboratory data using multiple analytical methods. Then the model is solved for the effect, thereby estimating the criterion, an upper threshold for acceptable exposures. The resulting candidate criteria are synthesized to select criteria and other benchmark values, such as remedial goals. The criterion assessment process is illustrated using the US Environmental Protection Agency Framework for Developing for Suspended and Bedded Sediments Water Quality Criteria, which recommends developing alternative candidate criterion values and then evaluating them to select a final criterion. Candidate criteria may be derived from models of field observations, field manipulations, laboratory tests, or empirical and theoretical models. Final selection of a criterion uses a weight-of-evidence comparison that engenders confidence because causal associations are confirmed on the basis of different assumptions, independent data sets, and varied statistical methods, thereby compensating for the concerns raised by individual studies and methods. Thus, it becomes possible to specify criteria for agents with biological or physical modes of action, as well as those with chemical modes of action, to best achieve environmental goals.

Predicting estuarine sediment metal concentrations and inferred ecological conditions: an information theoretic approach.

Empirically derived relationships associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. Resources limit the number, magnitude, and frequency of monitoring activities to acquire these data. Models that use available information and simple statistical relationships to predict sediment metal concentrations could provide an important tool for environmental assessment. We developed 45 predictive models for the total concentrations of copper, lead, mercury, and cadmium in estuarine sediments along the Southern New England and Mid-Atlantic regions of the United States. Using information theoretic model-averaging approaches, we found total developed land and percent silt/clay of estuarine sediment were the most important variables for predicting the presence of all four metals. Estuary area, river flow, tidal range, and total agricultural land varied in their importance. The model-averaged predictions explained 78.4, 70.5, 56.4, and 50.3% of the variation for copper, lead, mercury, and cadmium, respectively. Overall prediction accuracies of selected sediment benchmark values (i.e., effects ranges) were 83.9, 84.8, 78.6, and 92.0% for copper, lead, mercury, and cadmium, respectively. Our results further support the generally accepted conclusion that sediment metal concentrations are best described by the physical characteristics of the estuarine sediment and the total amount of urban land in the contributing watershed. We demonstrated that broad-scale predictive models built from existing monitoring data with information theoretic model-averaging approaches provide valuable predictions of estuarine sediment metal concentrations and show promise for future environmental modeling efforts in other regions.

Ecological condition of US Mid-Atlantic estuaries, 1997-1998.

The Mid-Atlantic Integrated Assessment (MAIA-Estuaries) evaluated ecological conditions in US Mid-Atlantic estuaries during the summers of 1997 and 1998. Over 800 probability-based stations were monitored in four main estuarine systems--Chesapeake Bay, the Delaware Estuary, Maryland and Virginian coastal bays, and the Albemarle-Pamlico Estuarine System. Twelve smaller estuaries within the four main systems were also assessed to establish variance at the local scale. A subset of the MAIA-Estuaries data is used here to estimate the extent of eutrophication, sediment contamination, and benthic degradation in mid-Atlantic estuaries. An Environmental Report Card and Index of Environmental Integrity summarize conditions in individual estuaries, the four estuarine systems, and the entire MAIA region. Roughly 20-50% of the region showed signs of eutrophication (high nutrients, excessive production of organic matter, poor water clarity, or depleted dissolved oxygen), 30% had contaminated sediments, and 37% had degraded benthic communities. Compared with the Environmental Monitoring and Assessment Program (EMAP)-Virginian Province study in 1990-1993, larger fractions of Chesapeake Bay (17%) and Delaware River (32%) had increased metals or organics in sediments.

Developing and applying an index of environmental integrity for the US Mid-Atlantic region.

Environmental conditions in the Mid-Atlantic region of United States are presently being documented in a series of reports that use 'environmental report cards' to summarize the condition of individual natural resources (e.g. estuaries, streams, forests, and landscapes) over the entire region and within major subregions. An 'index of environmental integrity' (IEI) approach has been developed and is illustrated using the information content of these report cards to evaluate the overall condition throughout the region. The IEI approach is a four-step process: (1) select individual components for the index, (2) calculate subindex values for each of the individual components, (3) aggregate the subindex values into the overall index, and (4) interpretation of index values. The IEI approach was illustrated by applying to the Mid-Atlantic estuaries and wadable streams in the Mid-Atlantic Highlands. Because the IEI approach is new, application should not be made without considering issues such as evaluation of the selection of the indicators, weighting scheme, uncertainties, and appropriate way to interpret the values.

Incidence of stress in benthic communities along the U.S. Atlantic and Gulf of Mexico coasts within different ranges of sediment contamination from chemical mixtures.

Synoptic data on concentrations of sediment-associated chemical contaminants and benthic macroinfaunal community structure were collected from 1,389 stations in estuaries along the U.S. Atlantic and Gulf of Mexico coasts as part of the nationwide Environmental Monitoring and Assessment Program (EMAP). These data were used to develop an empirical framework for evaluating risks of benthic community-level effects within different ranges of sediment contamination from mixtures of multiple chemicals present at varying concentrations. Sediment contamination was expressed as the mean ratio of individual chemical concentrations relative to corresponding sediment quality guidelines (SQGs), including Effects Range-Median (ERM) and Probable Effects Level (PEL) values. Benthic condition was assessed using diagnostic, multi-metric indices developed for each of three EMAP provinces (Virginian, Carolinian, and Louisianian). Cumulative percentages of stations with a degraded benthic community were plotted against ascending values of the mean ERM and PEL quotients. Based on the observed relationships, mean SQG quotients were divided into four ranges corresponding to either a low, moderate, high, or very high incidence of degraded benthic condition. Results showed that condition of the ambient benthic community provides a reliable and sensitive indicator for evaluating the biological significance of sediment-associated stressors. Mean SQG quotients marking the beginning of the contaminant range associated with the highest incidence of benthic impacts (73-100% of samples, depending on the province and type of SQG) were well below those linked to high risks of sediment toxicity as determined by short-term toxicity tests with single species. Measures of the ambient benthic community reflect the sensitivities of multiple species and life stages to persistent exposures under actual field conditions. Similar results were obtained with preliminary data from the west coast (Puget Sound).

An approach to integrated ecological assessment of resource condition: the Mid-Atlantic estuaries as a case study.

The complex and interconnected nature of ecological systems often makes it difficult to understand and prevent multiresource, multistressor problems. This article describes a process to assess ecological condition at regional scales. The article also describes how the approach was applied to the Mid-Atlantic estuaries, where it focused on characterizing the current state of the environment rather than on predicting future effects from humans. The necessity for iteration during the exercise showed how important it was to identify the purpose of the assessment and its users, that appropriate, consistent data are lacking for large-scale assessments, and that it remains a challenge to communicate succinctly the results of such an assessment.

Landscape metrics and estuarine sediment contamination in the mid-Atlantic and southern New England regions.

In a previously published study, quantitative relationships were developed between landscape metrics and sediment contamination for 25 small estuarine systems within Chesapeake Bay. These analyses have been extended to include 75 small estuarine systems across the mid-Atlantic and southern New England regions of the USA. Because of the different characteristics and dynamics of the estuaries across these regions, adjustment for differing hydrology, sediment characteristics, and sediment origins were included in the analysis. Multiple linear regression with stepwise selection was used to develop statistical models for sediment metals, organics, and total polycyclic aromatic hydrocarbons (PAHs). The landscape metrics important for explaining the variation in sediment metals levels (R2 = 0.72) were the percent area of nonforested wetlands (negative contribution), percent area of urban land, and point source effluent volume and metals input (positive contributions). The metrics important for sediment organics levels (R2 = 0.5) and total PAHs (R2 = 0.46) were percent area of urban land (positive contribution) and percent area of nonforested wetlands (negative contribution). These models included silt-clay content (metals) or total organic C (organics, total PAHs) of sediments and grouping by estuarine hydrology, suggesting the importance of sediment characteristics and hydrology in mitigating the influence of the landscape metrics on sediment contamination levels. The overall results from this study are indicative of how statistical models can be developed relating landscape metrics to estuarine sediment contamination for distributions of land cover and point source discharges.