Integrated Modeling of Survival Data from Multiple Stressor Ecotoxicology Experiments.

Citation data:

Environmental science & technology, ISSN: 1520-5851, Vol: 51, Issue: 12, Page: 7271-7277

Publication Year:
2017
Usage 33
Abstract Views 32
Link-outs 1
Captures 18
Readers 17
Exports-Saves 1
Social Media 4
Tweets 4
Citations 1
Citation Indexes 1
Repository URL:
https://ro.uow.edu.au/smhpapers/4923
PMID:
28517928
DOI:
10.1021/acs.est.7b02255
Author(s):
Proctor, Abigael H; King, Catherine K; Holan, Jessica R; Wotherspoon, Simon J
Publisher(s):
American Chemical Society (ACS)
Tags:
Chemistry; Environmental Science
Most Recent Tweet View All Tweets
article description
Ecotoxicological assessments often focus on the response of an organism to an individual contaminant under standardized laboratory conditions. Under more ecologically realistic conditions, however, individuals are likely to be exposed to a range of environmental conditions that have the potential to act as additional stressors. Multiple-stressor experiments improve our understanding of an organism's response to a toxicant under ecologically relevant conditions and provide realistic risk assessment data. To date, there is no standardized method for analyzing multiple-stressor data using dose-response regression. We present a reliable technique to assess for the effects of additional stressors on an LCx estimate in a consistent framework, providing interpretable results that meaningfully deal with environmental changes and their possible impacts on sensitivity estimates to a toxicant. The method is applicable to any data set where toxicity tests are conducted at varying levels of one or more additional stressors. We illustrate the method with data from an experiment that investigates the effects of salinity and temperature on the sensitivity of the subantarctic isopod Limnoria stephenseni to copper, where it is shown that the major change in the LC50 can be primarily attributed to a specific temperature increase. This method has been incorporated into an R package available at github.com/ahproctor/LC50.