Dr. Roman Ashauer (right) receiving the award from Dr Tim Gant, chairman of the jury.
Dr Roman Ashauer from the Swiss Federal Institute of Aquatic Science and Technology won this year’s LRI Innovative Science Award. The winning proposal offers a novel approach to aquatic ecotoxicology based on the measurement and simulation of toxic processes over an extended time period. Dr Ashauer’s project will develop and use toxicokinetic and toxicodynamic (TK/TD) models that allow the effects of fluctuating exposure, exposure to sequential mixtures of substances and calculation of organism recovery: an important parameter for any ecotoxicological risk assessment. Using such tools should shed light on the link between organism recovery, the mechanism of action for specific pollutants and how acute toxicity is related to chronic effects in organism populations. Dr Ashauer described these as “cool tools” that are already attracting attention as potentially useful devices for use by regulatory authorities and others. However he also stressed the need for experimental development, in particular the development of quantitative tools to estimate critical model parameters. This would provide methods that would reduce the needs for other researchers to undertake fresh experiments for subsequent risk assessments. The experimental work will focus on gammarus pulex – chosen as a typical European aquatic sentinel organism – but will also link with the extensive existing data available on daphnia magna. This link should allow the developed technique to find direct application in risk assessment processes on a wide scale and help to define improved criteria for water quality. “The focus is very much on practical applications and quantitative tools for risk assessment,” said Dr. Ashauer. The two other finalists were: Dr. Beketov describing his project for “Understanding toxic effects at population and community levels: From physiological to ecological mode of action” and Dr. Jager on “Extrapolating from constant to time-varying exposure using biology-based modelling”.
