The Unexpected Absence of Nickel Effects on a Daphnia Population at 3 Temperatures is Correctly Predicted by a Dynamic Energy Budget Individual-Based Model.

Abstract

Recent studies have shown that temperature affects chronic nickel (Ni) toxicity to Daphnia magna at the individual (apical) level. However, the effect of temperature on Ni toxicity to D. magna at the population level is unknown. The present study investigated whether the effect of temperature on chronic Ni toxicity to D. magna assessed on apical endpoints can be extrapolated to the population level. The results of the population experiment showed no consistent Ni effects on total D. magna population abundance at 15, 20, and 25 °C, although the Ni concentrations tested were previously reported to significantly reduce reproduction in D. magna individuals. This result supports the idea that ecological risk assessment should not extrapolate as such from apical endpoints to the population level. A dynamic energy budget individual-based model (DEB-IBM) was calibrated using apical Ni toxicity data at 15, 20, and 25 °C. The goal was to investigate whether the calibrated DEB-IBM would be able to predict the unexpected absence of effects at the population level and to further investigate the effect of temperature on Ni toxicity to a D. magna population. At the population level, the calibrated DEB-IBM correctly predicted the unexpected absence of an effect of Ni on a D. magna population. Detailed analysis of simulation output suggests that the predicted lower Ni sensitivity at the population level occurs because Ni-induced mortality is compensated by reduced starvation (less intraspecific competition). Extrapolated median effective concentration (EC50) values for population density predicted that the effect of temperature on Ni toxicity to D. magna populations was smaller (1.9-fold higher at 25 °C than at 15 °C) than on Ni toxicity to D. magna apical reproduction (the EC50 is 6.5-fold higher at 25 °C than at 15 °C). These results show that the DEB-IBM can help to replace population experiments by in silico simulations and to optimize the experimental design of population studies.