1. Effects of light and temperature fluctuations on the growth of Myriophyllum spicatum in toxicity tests—a model-based analysis
- Author
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Walter Schmitt, Thomas G. Preuss, G. Görlitz, S. Heine, and Andreas Schäffer
- Subjects
Myriophyllum ,biology ,Health, Toxicology and Mutagenesis ,Design of experiments ,Temperature ,Environmental engineering ,General Medicine ,Growth model ,biology.organism_classification ,Pollution ,Magnoliopsida ,Toxicity ,Environmental Chemistry ,Environmental science ,Biochemical engineering ,GROWTH ALTERATIONS ,Water Pollutants, Chemical - Abstract
Laboratory toxicity tests are a key component of the aquatic risk assessments of chemicals. Toxicity tests with Myriophyllum spicatum are conducted based on working procedures that provide detailed instructions on how to set up the experiment, e.g., which experimental design is necessary to get reproducible and thus comparable results. Approved working procedures are established by analyzing numerous toxicity tests to find a compromise between practical reasons (e.g., acceptable ranges of ambient conditions as they cannot be kept completely constant) and the ability for detecting growth alterations. However, the benefit of each step of a working procedure, e.g., the random repositioning of test beakers, cannot be exactly quantified, although this information might be useful to evaluate working procedures. In this paper, a growth model of M. spicatum was developed and used to assess the impact of temperature and light fluctuations within the standardized setup. It was analyzed how important it is to randomly reassign the location of each plant during laboratory tests to keep differences between the relative growth rates of individual plants low. Moreover, two examples are presented on how modeling can give insight into toxicity testing. Results showed that randomly repositioning of individual plants during an experiment can compensate for fluctuations of light and temperature. A method is presented on how models can be used to improve experimental designs and to quantify their benefits by predicting growth responses.
- Published
- 2014
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