More comprehensive ecological risk assessment procedures are needed as the unprecedented rate of anthropogenic disturbances to aquatic ecosystems continues. Identifying the effects of pollutants on aquatic ecosystems is difficult, requiring the individual and joint effects of a range of natural and anthropogenic factors to be isolated, often via the analysis of large, complicated datasets. Ecotoxicologists have traditionally used multiple regression to analyse such datasets, but there are inherent problems with this approach and a need to consider other potentially more suitable methods.Sediment pollution can cause a range of negative effects on aquatic animals, and these are used as the basis for toxicity bioassays to measure the biological impact of pollution and the success of remediation efforts. However, experimental artefacts can also lead to sediments being incorrectly classed as toxic in such studies. Understanding the influence of potentially confounding factors will help more accurate assessments of sediment pollution.In this study, we analysed standardised sediment bioassays conducted using the chironomid Chironomus tepperi, with the aim of modelling the impact of sediment toxicants and water physico-chemistry on four endpoints (survival, growth, median emergence day, and number of emerging adults). We used boosted regression trees (BRT), a method that has a number of advantages over multiple regression, to model bioassay endpoints as a function of water chemistry, sediment quality and underlying geology. Endpoints were generally influenced most strongly by water quality parameters and nutrients, although some metals negatively influenced emergence endpoints. Sub-lethal endpoints were generally better predicted than lethal endpoints; median emergence day was the most sensitive endpoint examined in this study, while the number of emerging adults was the least sensitive. We tested our modelling results by experimentally manipulating sediment and observing the impact on C. tepperi endpoints. For survival, experimental observations were accurately predicted by models, which highlighted the importance of conductivity and dissolved oxygen for this endpoint. In comparison, experimental median emergence day was poorly modelled, most likely due to the influence of a wider range of predictors identified as being important influences on this endpoint in models. To demonstrate how BRT model results compare to more traditional techniques, we analysed survival data using multiple regression. Both models yielded similar results, but boosted regression trees offer important advantages over multiple regression. Our results illustrate how boosted regression trees can be used to analyse complex ecotoxicological datasets, and reinforces the importance of water chemistry in sediment toxicology.
- Boosted regression tree
- Multiple regression