Running Header: Predicting Sediment Toxicity Using Logistic Regression

The question posed in this paper is: How useful are chemical concentration measurements for predicting the outcome of sediment toxicity tests? Using matched data on sediment toxicity and sediment chemical concentrations from a number of studies, we investigated several approaches for predicting toxicity based on multiple logistic regression with concentration-addition models. Three models were found to meet criteria for acceptability. The first model uses individual chemicals selected using stepwise selection. The second uses derived variables to reflect combined metal contamination, polycyclic aromatic hydrocarbon (PAH) contamination and the interaction between metals and PAHs. The third and final model is a separate species model with derived variables. Overall, these models suggest that toxicity may be correctly predicted approximately 77% of the time, although prediction is better for samples identified as non-toxic, than for those known to be toxic. 1