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Title: Cross-species extrapolation of chronic nickel Biotic Ligand Models. Author: Schlekat CE, Van Genderen E, De Schamphelaere KA, Antunes PM, Rogevich EC, Stubblefield WA. Journal: Sci Total Environ; 2010 Nov 15; 408(24):6148-57. PubMed ID: 20920817. Abstract: The use of Biotic Ligand Models (BLMs) to normalize metal ecotoxicity data and predict effects in non-BLM organisms should be supported by quantitative evidence. This study determined the ability of chronic nickel BLMs developed for the cladocera Daphnia magna and Ceriodaphnia dubia to predict chronic nickel toxicity to three invertebrates for which no specific BLMs were developed. Those invertebrates were the snail Lymnaea stagnalis, the insect Chironomus tentans, and the rotifer Brachionus calyciflorus. Similarly, we also determined the ability of chronic nickel BLMs developed for the alga Pseudokirchneriella subcapitata and the terrestrial vascular plant Hordeum vulgare to predict chronic nickel toxicity to the aquatic vascular plant Lemna minor. Chronic nickel toxicity to the three invertebrates and the aquatic plant were measured in five natural waters that varied in pH, Ca, Mg, and dissolved organic carbon (DOC), which are known to affect chronic nickel toxicity and are the important input variables for the chronic nickel BLMs. Nickel toxicity to the three invertebrates varied considerably among the test waters, i.e., a 14-fold variation of EC50s in L. stagnalis, a 3-fold variation in EC20s in C. tentans, and a 10-fold variation in EC20s in B. calyciflorus, but the cladoceran BLMs were able to predict nickel effect concentrations within a factor of two. Nickel toxicity (EC50s) to L. minor varied by 6-fold among the test waters. Although the P. subcapitata and H. vulgare BLMs offered reasonable predictions of nickel EC50s to L. minor, the D. magna and C. dubia BLM showed better predictions. Our results confirm the influence of site-specific pH, hardness, and DOC on chronic nickel toxicity to aquatic organisms, and support the use of chronic nickel BLMs to manage this influence through normalizations of ecotoxicity data.[Abstract] [Full Text] [Related] [New Search]