Browsing by Subject "copper toxicity"

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    Data for refining copper concentrations using the Biotic Ligand Model to maximize zebra mussel control while minimizing non-target effects
    (2023-10-12) Dahlberg, Angelique D.; Waller, Diane L.; Severson, Todd J.; Barbour, Matthew T.; Meulemans, Matthew; Wise, Jeremy K.; Bajcz, Alex W.; Jankowski, Mark; Phelps, Nicholas B. D.; edge0023@umn.edu; Dahlberg, Angelique D.
    Copper in various forms can be toxic to aquatic organisms at high concentrations and has been used as a pesticide in lake management since the early 1900s. Managers have recently extended this use to control aquatic invasive species, including zebra mussels (Dreissena polymorpha). Because copper toxicity changes with changing concentrations of water chemistry parameters (e.g., pH, temperature, and other cations such as Ca2+ and Mg2+), using the same copper concentration to target the same species in two different waterbodies could have different outcomes. However, past zebra mussel control projects have selected copper concentrations irrespective of water chemistry differences. We demonstrate, in a two-part study, how measuring water chemistry parameters and using the Biotic Ligand Model (BLM) can help predict a site-specific copper concentration that will kill zebra mussels while minimizing effects on non-target species. We first tested the application of the BLM for predicting the effects of a copper concentration on non-target species. We found that Daphnia magna (daphnia) had a 50% chance of survival at 9.50 µg Cu/L (i.e., the 50% lethal concentration, LC50), within our BLM-predicted range of 3.38-16.95 µg Cu/L LC50 values. Given the accuracy of our prediction, in the future, managers could make similar predictions and tailor copper concentrations to their management goals. Secondly, we measured zebra mussel larvae (veliger) mortality at added copper concentrations ranging from 0-191 µg Cu/L and assessed exposure–response using a logistic regression model that also included water chemistry parameters. This model can be applied to future projects; using it, managers can predict the amount of copper in a particular waterbody that will kill a predetermined proportion of zebra mussels and simultaneously predict what non-target effects to monitor or expect.