Aquatic Toxicology 100 (2010) 112–119 Contents lists available at ScienceDirect Aquatic Toxicology journal homepage: www.elsevier.com/locate/aquatox Toxicity of proton–metal mixtures in the field: Linking stream macroinvertebrate species diversity to chemical speciation and bioavailability Anthony Stockdale a , Edward Tipping a,∗ , Stephen Lofts a , Stephen J. Ormerod b , William H. Clements c , Ronny Blust d a Centre for Ecology and Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster LA1 4AP, UK b Catchment Research Group, Cardiff School of Biosciences, Cardiff University, Cardiff CF10 3US, UK c Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO 80523, USA d Ecophysiology, Biochemistry and Toxicology Group, Department of Biology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium article info Article history: Received 4 June 2010 Received in revised form 14 July 2010 Accepted 15 July 2010 Keywords: Acidification Bioavailability Macroinvertebrates Metals Modelling Quantile regression Streamwaters Toxicity abstract Understanding metal and proton toxicity under field conditions requires consideration of the complex nature of chemicals in mixtures. Here, we demonstrate a novel method that relates streamwater con- centrations of cationic metallic species and protons to a field ecological index of biodiversity. The model WHAM-F TOX postulates that cation binding sites of aquatic macroinvertebrates can be represented by the functional groups of natural organic matter (humic acid), as described by the Windermere Humic Aque- ous Model (WHAM6), and supporting field evidence is presented. We define a toxicity function (F TOX ) by summing the products: (amount of invertebrate-bound cation) × (cation-specific toxicity coefficient, ˛ i ). Species richness data for Ephemeroptera, Plecoptera and Trichoptera (EPT), are then described with a lower threshold of F TOX , below which all organisms are present and toxic effects are absent, and an upper threshold above which organisms are absent. Between the thresholds the number of species declines lin- early with F TOX . We parameterised the model with chemistry and EPT data for low-order streamwaters affected by acid deposition and/or abandoned mines, representing a total of 412 sites across three conti- nents. The fitting made use of quantile regression, to take into account reduced species richness caused by (unknown) factors other than cation toxicity. Parameters were derived for the four most common or abundant cations, with values of ˛ i following the sequence (increasing toxicity) H + < Al < Zn < Cu. For waters affected mainly by H + and Al, F TOX shows a steady decline with increasing pH, crossing the lower threshold near to pH 7. Competition effects among cations mean that toxicity due to Cu and Zn is rare at lower pH values, and occurs mostly between pH 6 and 8. © 2010 Elsevier B.V. All rights reserved. 1. Introduction Assessment of the toxic effects of metals and protons in the environment would benefit from an ability to deal with mix- tures. Despite this, recent treatises on metals contamination of aquatic systems give scant treatment to the topic of mixture tox- icity (Adams and Chapman, 2007; Luoma and Rainbow, 2008). To interpret the results of laboratory single-metal toxicity studies, the concept of multi-substance Potentially Affected Fractions has been developed, in which results from experiments with single toxicants are used to describe mixtures, assuming either concentration or response additivity (De Zwart and Posthuma, 2005; De Zwart et al., ∗ Corresponding author. Tel.: +44 0 1524 595866. E-mail address: et@ceh.ac.uk (E. Tipping). 2006). The Biotic Ligand Model has been applied in a meta-analysis study of metal mixtures, by assessing competition for binding at the biotic ligand between toxic metals (Zn, Cu, Cd), calcium and other major cations (Kamo and Nagai, 2008). A ‘saturation’ model, where toxicity is assumed to be a function of the amount of metal bound to a specific binding site on the organism, has also been tested for mix- tures (e.g. Borgmann et al., 2004; Norwood et al., 2007). Expanding these approaches to create predictive models for the full range of conditions, mixtures and organisms in environmental systems would be a formidable task as a large quantity of new data would be required. A pragmatic means of combining single-metal Environ- mental Quality Standards (EQS) for the field assessment of mixtures is through the use of Cumulative Criterion Units (Clements et al., 2008), calculated as the sum of the ratios of the concentration of each metal to its individual EQS. However, this only predicts an acceptable limit, not a concentration–response relationship, and 0166-445X/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.aquatox.2010.07.018