Development and Practical Application of Petroleum and Dispersant Interspecies Correlation Models for Aquatic Species

Abstract

Assessing the acute toxicity of oil has generally relied on existing toxicological data for a relatively few standard test species, which has limited the ability to estimate the impacts of spilled oil on aquatic communities. Interspecies correlation estimation (ICE) models were developed for petroleum and dispersant products to facilitate the prediction of toxicity values to a broader range of species and to better understand taxonomic differences in species sensitivity. ICE models are log linear regressions that can be used to estimate toxicity to a diversity of taxa based on the known toxicity value for a surrogate tested species. ICE models have only previously been developed for nonpetroleum chemicals. Petroleum and dispersant ICE models were statistically significant for 93 and 16 unique surrogate-predicted species pairs, respectively. These models had adjusted coefficient of determinations (adj-<i>R</i>²), square errors (MSE) and positive slope ranging from 0.29 to 0.99, 0.0002 to 0.311, and 0.187 to 2.665, respectively. Based on model cross-validation, predicted toxicity values for most ICE models (>90%) were within 5-fold of the measured values, with no influence of taxonomic relatedness on prediction accuracy. A comparison between hazard concentrations (HC) derived from empirical and ICE-based species sensitivity distributions (SSDs) showed that HC values were within the same order of magnitude of each other. These results show that ICE-based SSDs provide a statistically valid approach to estimating toxicity to a range of petroleum and dispersant products with applicability to oil spill assessment