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.

[1]  C. Henny,et al.  FIELD STUDIES ON PESTICIDES AND BIRDS: UNEXPECTED AND UNIQUE RELATIONS , 1997 .

[2]  J. Platt Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. , 1964, Science.

[3]  Glenn W Suter,et al.  Determining probable causes of ecological impairment in the Little Scioto River, Ohio, USA: Part 1. Listing candidate causes and analyzing evidence , 2002, Environmental toxicology and chemistry.

[4]  S. Hurlbert Pseudoreplication and the Design of Ecological Field Experiments , 1984 .

[5]  Susan B. Norton,et al.  Can biological assessments discriminate among types of stress? A case study from the Eastern Corn Belt Plains ecoregion , 2000 .

[6]  Chris D. Metcalfe,et al.  Linkages Between Chemical Contaminants and Tumors in Benthic Great Lakes Fish , 1996 .

[7]  J. Grier,et al.  Ban of DDT and subsequent recovery of Reproduction in bald eagles. , 1982, Science.

[8]  Fred P. Meyer,et al.  Field Manual for the Investigation of Fish Kills , 1990 .

[9]  M Susser,et al.  Rules of inference in epidemiology. , 1986, Regulatory toxicology and pharmacology : RTP.

[10]  K. W. Thornton,et al.  Environmental monitoring and assessment program assessment framework , 1994 .

[11]  C. Sindermann,et al.  The search for cause and effect relationships in marine pollution studies , 1997 .

[12]  G A Fox,et al.  Practical causal inference for ecoepidemiologists. , 1991, Journal of toxicology and environmental health.

[13]  James R. Karr,et al.  Assessing biological integrity in running waters : a method and its rationale , 1986 .

[14]  Glenn W Suter,et al.  Determining the causes of impairments in the Little Scioto River, Ohio, USA: part 2. Characterization of causes. , 2002, Environmental toxicology and chemistry.

[15]  J. Yerushalmy,et al.  On the methodology of investigations of etiologic factors in chronic diseases. , 1959, Journal of chronic diseases.

[16]  K. Popper,et al.  The Logic of Scientific Discovery , 1960 .

[17]  Daniel W. Beyers,et al.  Causal Inference in Environmental Impact Studies , 1998, Journal of the North American Benthological Society.

[18]  M Susser,et al.  The logic of Sir Karl Popper and the practice of epidemiology. , 1986, American journal of epidemiology.

[19]  Russell Millar,et al.  The Ecological Detective: Confronting Models with Data (Monographs in Population Biology, 28). By Ray Hilborn and Marc Mangel , 1999, Reviews in Fish Biology and Fisheries.

[20]  M L Dourson,et al.  Categorical regression of toxicity data: a case study using aldicarb. , 1997, Regulatory toxicology and pharmacology : RTP.