Ecosystem risk analysis: A new methodology

O’Neill, Robert V.; Gardner, Robert H.; Barnthouse, Lawrence W.; Suter, Glenn W.; Hildebrand, S.G.; Gehrs, C.W.

doi:10.1002/etc.5620010208

Cited by 98 publications

(31 citation statements)

References 8 publications

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“…Thus, the methods to assess ecological risks to the structure and function of ecosystems will be more complicated than for HRA (O'Neill et al 1982;Sheehan et al 1984;Sheehan 1984aSheehan , 1984bSheehan , 1984c. New methodologies must be developed for combining the methods and indices ecologists use (National Research Council 1986;Sheehan et al 1984) to create a risk analysis process that can be applied by ecologists, ecosystem and risk managers and policy-makers to provide on-going evaluation of hazards, and biomonitoring of the system to detect changes.…”

Section: Ecological Risk Assessment: Complexity Of Exposuresmentioning

confidence: 99%

“…More recently, the applicability of the HRA paradigm to Ecological Risk Assessment (hereafter referred to as ERA) has been the subject of much discussion (Barnhouse 1992). HRA evaluates the risks to humans of potential exposure to environmental hazards, whereas ERA evaluates the risks to ecosystems with their component parts (O'Neill et al 1982;National Research Council 1986). Thus, ERA is a broader assessment with inherently more complexity, and may include humans as an integral part of a system.…”

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confidence: 99%

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Temporal scales in ecological risk assessment

Burger

Gochfeld

1992

Arch. Environ. Contam. Toxicol.

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E A r c h i v e s o f n v i r o n m e n t a l c o n t a m i n a t i o n Temporal Scales in Ecological Risk AssessmentJoanna Burger,*'** and Michael Gochfeld**'*** *Department of Biological Sciences, Rutgers University, Piscataway, New Jersey 08855, USA; **Environmental and Occupational Health Sciences Institute, Piscataway, New Jersey 08855, USA, and ***Environmental and Occupational Medicine, UMDNJ-Robert Wood Johnson Medical School, Piscataway, New Jersey 08854, USA Abstract.

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Section: Ecological Risk Assessment: Complexity Of Exposuresmentioning

confidence: 99%

mentioning

confidence: 99%

Temporal scales in ecological risk assessment

Burger

Gochfeld

1992

Arch. Environ. Contam. Toxicol.

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“…The use of Monte Carlo simulation, either simply to calculate the distributions (variability) of water quality characteristics that would result from alternative management scenarios [Whitehead and Young, 1979 ;O'Neill et al, 1982cO'Neill et al, , 1983 or to assess the risk of violating a prescribed standard [Fontaine, 1984 ;Chapra and Reckhow, 1983].…”

Section: Decision Making Under Uncertaintymentioning

confidence: 99%

Water quality modeling: A review of the analysis of uncertainty

Beck¹

1987

Water Resources Research

871

431

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This paper reviews the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction. More specifically, four problem areas are examined in detail: uncertainty about model structure, uncertainty in the estimated model parameter values, the propagation of prediction errors, and the design of experiments in order to reduce the critical uncertainties associated with a model. The review is rather lengthy, and it has therefore been prepared in effect as two papers. There is a shorter, largely nontechnical version, which gives a quick impression of the current and future issues in the analysis of uncertainty in water quality modeling. Enclosed by this shorter discussion is the main body of the review dealing in turn with (1) identifiability and experimental design, (2) the generation of preliminary model hypotheses under conditions of sparse, grossly uncertain field data, (3) the selection and evaluation of model structure, (4) parameter estimation (model calibration), (5) checks and balances on the identified model, i.e., model “verification” and model discrimination, and (6) prediction error propagation. Much time is spent in discussing the algorithms of system identification, in particular, the methods of recursive estimation, and in relating these algorithms and the subject of identification to the problems of prediction uncertainty and first‐order error analysis. There are two obvious omissions from the review. It is not concerned primarily with either the development and solution of stochastic differential equations or the issue of decision making under uncertainty, although clearly some reference must be made to these topics. In brief, the review concludes (not surprisingly) that much work has been done on the analysis of uncertainty in the development of mathematical models of water quality, and much remains to be done. A lack of model identifiability has been an outstanding difficulty in the interpretation and explanation of past observed system behavior, and there is ample evidence to show that the “larger,” more “comprehensive” models are easily capable of generating highly uncertain predictions of future behavior. For the future of the subject, it is speculated that there is the possibility of progress in the development of novel algorithms for model structure identification, a need for new questions to be posed in the problem of prediction, and a distinct challenge to the conventional views of this review in the new forms of knowledge representation and manipulation now emerging from the field of artificial intelligence.

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“…3 plified to linear solutions. Uncertainty of model predictions (O'Neill et al 1982) is recognized as an essential factor in evaluating the relative merit of models in a formal environmental risk assessment. However, uncertainty will tend to increase with scale.…”

Section: Simulation Modelsmentioning

confidence: 99%

Developing a Field of Landscape Ecotoxicology

Cairns¹,

Niederlehner²

1996

Ecological Applications

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Abstract. Since toxicants are spread over ecological landscapes, it seems likely that they have effects at that level of ecological organization. Landscape ecotoxicology examines the effects of toxic chemicals on larger scales than traditional environmental toxicology. This approach is characterized by the use of endpoints appropriate to the spatial scale across which a toxicant is dispersed, attention to interactions between physical and temporal patterns and the process of ecological impairment, and integration of multiple lines of evidence for toxicity at various scales. In addition, landscape ecotoxicology seeks predictive models in order to influence human actions before environmental damage occurs. Integrating information from damaged systems, toxicity tests, simulation models, and biomonitoring of healthy systems provides the best basis for decisions. Rapid progress in landscape ecotoxicology is expected as scientists incorporate tools, such as remote sensing and spatially explicit simulation models, and then calibrate these models using data from long.-term biomonitoring of large areas. Further integration into combined socio-economic-ecological models is also possible. Ecological Society of America

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Ecosystem risk analysis: A new methodology

Cited by 98 publications

References 8 publications

Temporal scales in ecological risk assessment

Temporal scales in ecological risk assessment

Water quality modeling: A review of the analysis of uncertainty

Developing a Field of Landscape Ecotoxicology

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