Near future ocean acidification modulates the physiological impact of fluoxetine at environmental concentration on larval urchins

Pharmaceuticals found in human wastes are emergent pollutants that are continuously released into aquatic systems. While exposure to pharmaceuticals alone could adversely impact aquatic organisms, few studies have considered the interactive effects of pharmaceuticals and the future environmental conditions, such as decreasing pH due to ocean acidification. Given the bioavailability of many pharmaceuticals is dependent on these physical conditions, we investigated the effect of environmentally-relevant concentrations of fluoxetine (10 and 100 ng L−1) under ambient (pH 8.0) and reduced pH conditions (pH 7.7) on physiology, behavior, and DNA integrity of larval sea urchins (Heliocidaris crassispina). Notably, the negative impacts of fluoxetine exposure were attenuated by reduced pH. Larvae exposed to both reduced pH and fluoxetine exhibited lower levels of DNA damage compared to those exposed to only one of the stressors. Similar antagonistic interactions were observed at the organismal level: for example, fluoxetine exposure at 10 ng L−1 under ambient pH increased the percentage of embryos at later developmental stages, but such effects of fluoxetine were absent at pH 7.7. However, despite the modulation of fluoxetine impacts under ocean acidification, control larvae performed better than those exposed to either stressor, alone or in combination. We also observed that pH alone impacted organismal behaviors, as larvae swam slower at reduced pH regardless of fluoxetine exposure. Our findings highlight the need to consider multi-stressor interactions when determining future organismal performance and that multiple metrics are needed to paint a fuller picture of ecological risks. [Display omitted] •We exposed larval urchins to environmentally-relevant fluoxetine and future pH.•Acidification and FX exposure had an interactive effect on larval physiologies.•Multistressors and environmentally-relevant conditions are vital for predictions.