Investigating population-level toxicity of the antidepressant Citalopram in harpacticoid copepods using in vivo methods and bioenergetics-based population modeling
Koch, J.; De Schamphelaere, K.A.C. (2023). Investigating population-level toxicity of the antidepressant Citalopram in harpacticoid copepods using in vivo methods and bioenergetics-based population modeling. Environ. Toxicol. Chem. 42(5): 1094-1108. https://dx.doi.org/10.1002/etc.5599
In: Environmental Toxicology and Chemistry. Setac Press: New York. ISSN 0730-7268; e-ISSN 1552-8618, more
Recent research has revealed various lethal and sublethal effects of the selective serotonin reuptake inhibitor citalopram hydrobromide on the harpacticoid copepod Nitocra spinipes. In the present study, an individual-based model (IBM) grounded in the dynamic energy budget (DEB) theory was developed to extrapolate said effects to the population level. Using a generic DEB-IBM as a template, the model was designed to be as simple as possible, keeping model components that are outside the scope of the core DEB theory to a minimum. To test the model, a 56-day population experiment was performed at 0, 100, and 1000 μg citalopram hydrobromide L−1. In the experiment, the populations quickly reached a plateau in the control and at 100 μg L−1, which was correctly reproduced by the model and could be explained by food limitations hindering further population growth. At 1000 μg L−1, a clear mismatch occurred: Whereas in the experiment the population size increased beyond the supposed (food competition–induced) capacity, the model predicted a suppression of the population size. It is assumed that the IBM still misses important components addressing population density–regulating processes. Particularly crowding effects may have played an important role in the population experiment and should be further investigated to improve the model. Overall, the current DEB IBM for N. spinipes should be seen as a promising starting point for bioenergetics-based copepod population modeling, which—with further improvements—may become a valuable individual-to-population extrapolation tool in the future.
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