By the same authors

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From the same journal

A method to predict and understand fish survival under dynamic chemical stress using standard ecotoxicity data

Research output: Contribution to journalArticlepeer-review

Published copy (DOI)


  • R. Ashauer
  • P. Thorbek
  • J.S. Warinton
  • J.R. Wheeler
  • S. Maund


Publication details

JournalEnvironmental Toxicology and Chemistry
DateE-pub ahead of print - 30 Jan 2013
DatePublished (current) - Apr 2013
Issue number4
Number of pages12
Pages (from-to)954-965
Early online date30/01/13
Original languageEnglish


The authors present a method to predict fish survival under exposure to fluctuating concentrations and repeated pulses of a chemical stressor. The method is based on toxicokinetic-toxicodynamic modeling using the general unified threshold model of survival (GUTS) and calibrated using raw data from standard fish acute toxicity tests. The model was validated by predicting fry survival in a fish early life stage test. Application of the model was demonstrated by using Forum for Co-ordination of Pesticide Fate Models and Their Use surface water (FOCUS-SW) exposure patterns as model input and predicting the survival of fish over 485 d. Exposure patterns were also multiplied by factors of five and 10 to achieve higher exposure concentrations for fish survival predictions. Furthermore, the authors quantified how far the exposure profiles were below the onset of mortality by finding the corresponding exposure multiplication factor for each scenario. The authors calculated organism recovery times as additional characteristic of toxicity as well as number of peaks, interval length between peaks, and mean duration as additional characteristics of the exposure pattern. The authors also calculated which of the exposure patterns had the smallest and largest inherent potential toxicity. Sensitivity of the model to parameter changes depends on the exposure pattern and differs between GUTS individual tolerance and GUTS stochastic death. Possible uses of the additional information gained from modeling to inform risk assessment are discussed.

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Copyright 2013 Elsevier B.V., All rights reserved.

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