A Bayesian Mixture Model for Estimating Intergeneration Chronic Toxicity

Rhodes, Jonathan R., Grist, Eric P. M., Kwok, Kevin W. H. and Leung, Kenneth M. Y. (2008) A Bayesian Mixture Model for Estimating Intergeneration Chronic Toxicity. Environmental Science & Technology, 42 21: 8108-8114. doi:10.1021/es801030t

Author Rhodes, Jonathan R.
Grist, Eric P. M.
Kwok, Kevin W. H.
Leung, Kenneth M. Y.
Title A Bayesian Mixture Model for Estimating Intergeneration Chronic Toxicity
Journal name Environmental Science & Technology   Check publisher's open access policy
ISSN 0013-936X
Publication date 2008-11-01
Sub-type Article (original research)
DOI 10.1021/es801030t
Volume 42
Issue 21
Start page 8108
End page 8114
Total pages 7
Editor Jerald Schnoor
Place of publication Washingto DC, USA
Publisher American Chemical Society
Collection year 2009
Language eng
Subject C1
0502 Environmental Science and Management
9608 Flora, Fauna and Biodiversity
Formatted abstract
Understanding toxic effects on biological populations across generations is crucial for determining the long-term consequences of chemical pollution in aquatic environments. As a consequence, there is considerable demand for suitable statistical methods to analyze complex multigeneration experimental data. We demonstrate the application of a Bayesian mixture model (with random-effects) to assess the effect of intergeneration copper (Cu) exposure on the reproductive output of the copepod, Tigriopus japonicus, using experimental data across three generations. The model allowed us to appropriately specify the nonstandard statistical distribution of the data and account for correlations among data points. The approach ensured more robust inferences than standard statistical methods and, because of the model’s mechanistic formulation, enabled us to test more subtle hypotheses. We demonstrate intergeneration Cu exposure effects on both components of reproductive output (1) the ovisac maturation rate, and (2) the number of nauplii per ovisac. Current and parent generation Cu exposures negatively affected current generation reproductive output. However, in terms of reproductive output, there was also some evidence for adaptation to parental Cu exposures, but with an associated cost under Cu concentrations different from the parental exposure. Bayesian mixture and random-effects models present a robust framework for analyzing data of this kind and for better understanding chemical toxicity.
Q-Index Code C1
Q-Index Status Confirmed Code

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Access Statistics: 282 Abstract Views  -  Detailed Statistics
Created: Tue, 07 Apr 2009, 15:47:23 EST by Helen Smith on behalf of School of Geography, Planning & Env Management