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Evaluating sediment chemistry and toxicity data using logistic regression modeling

Environmental Toxicology and Chemistry

By:
, , , , and
DOI: 10.1897/1551-5028(1999)018<1311:ESCATD>2.3.CO;2

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Abstract

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.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Evaluating sediment chemistry and toxicity data using logistic regression modeling
Series title:
Environmental Toxicology and Chemistry
DOI:
10.1897/1551-5028(1999)018<1311:ESCATD>2.3.CO;2
Volume
18
Issue:
6
Year Published:
1999
Language:
English
Publisher:
SETAC Press
Publisher location:
Pensacola, FL, United States
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Environmental Toxicology and Chemistry
First page:
1311
Last page:
1322
Number of Pages:
12