Predicted mixture toxic pressure relates to observed fraction of benthic macrofauna species impacted by contaminant mixtures.
Species sensitivity distributions (SSDs) quantify fractions of species potentially affected in contaminated environmental compartments using test species sensitivity data. The present study quantitatively describes associations between predicted and observed ecological impacts of contaminant mixtures, based on monitoring data of benthic macroinvertebrates. Local mixture toxic pressures (multisubstance potentially affected fraction of species [msPAF]) were quantified based on measured concentrations of 45 compounds (eight metals, 16 chlorinated organics, mineral oil, 16 polycyclic aromatic hydrocarbons, four polychlorinated biphenyls), using acute as well as chronic 50%-effective concentration-based SSD-modeling combined with bioavailability and mixture modeling. Acute and chronic toxic pressures were closely related. Generalized linear models (GLMs) were derived to describe taxon abundances as functions of environmental variables (including acute toxic pressure). Acute toxic pressure ranged from 0 to 42% and was related to abundance for 74% of the taxa. Habitat-abundance curves were generated using the GLMs and Monte Carlo simulation. Predicted abundances for the taxa were associated with acute mixture toxic pressure in various ways: negative, positive, and optimum abundance changes occurred. Acute toxic pressure (msPAF) was associated almost 1:1 with the observed fraction of taxa exhibiting an abundance reduction of 50% or more. The findings imply that an increase of mixture toxic pressure associates to increased ecological impacts in the field. This finding is important, given the societal relevance of SSD model outputs in environmental policies.
Rijks Instituut voor Volksgezondheid en Milieu (Dutch National Institute for Public Health and the Environment), Bilthoven, The Netherlands. leo.posthuma@rivm.nl
Leo
L
Posthuma
Dick
D
de Zwart
D000818:Animals; D004784:Environmental Monitoring / Q000379:methods*; D007448:Invertebrates / Q000187:drug effects*; D016014:Linear Models; D008670:Metals / Q000633:toxicity; D008899:Mineral Oil / Q000633:toxicity; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011078:Polychlorinated Biphenyls / Q000633:toxicity; D011084:Polycyclic Aromatic Hydrocarbons / Q000633:toxicity
D008670:Metals; D011084:Polycyclic Aromatic Hydrocarbons; D008899:Mineral Oil; D011078:Polychlorinated Biphenyls