In Ecotoxicology, the toxicity of chemicals is usually quantified for individuals under laboratory conditions, while in reality individuals interact with other individuals in populations and communities, and are exposed to conditions that vary in space and time. Micro- and mesocosm experiments are therefore used to increase the ecological realism of toxicological risk assessments. Such experiments are, however, labour-intensive, costly, and cannot, due to logistical reasons, implement all possible factors or interests (Henry et al. 2017). Moreover, as such experiments often include animals, the number of experiments performed has to be minimized to reduce animal testing as much as possible.

Modelling has therefore been suggested to complement such experiments (Beaudoin et al. 2012). Still, the population models of the species involved need to be parameterized and can thus require a large amount of data. However, how much data are actually needed is usually unclear. Lamonica et al. (2022) therefore focus on the challenge of “taking the most of experimental data and reducing the amount of experiments to perform”. 

Their ultimate goal is to reduce the number of experiments to parameterize their model of a 3-species mesocosm, comprised of algae, duckweed, and water fleas, sufficiently well. For this, experiments with one, two or three species, with different cadmium concentrations and without cadmium, are performed and used to parameterize, using the Bayesian Monte Carlo Markov Chain (MCMC) method, the model. Then, different data sets omitting certain experiments are used for the same parameterization procedure to see which data sets, and hence experiments, might possibly be omitted when it comes to parameterizing a model that would be precise enough to predict the effects of a toxicant.

The authors clearly demonstrate the added value of the approach, but also discuss limits to the transferability of their recommendations. Their manuscript presents a useful and inspiring illustration of how in the future models and experiments should be combined in an integrated, iterative process. This is in line with the current “Destination Earth” initiative of the European Commission, which aims at producing “digital twins” of different environmental sectors, where the continuous mutual updating of models and monitoring designs is the key idea. 

The authors make an important point when concluding that “data quality and design are more beneficial for modelling purpose than quantity. Ideally, as the use of models and big data in ecology increases […], modellers and experimenters could collaboratively and profitably elaborate model-guided experiments.”

References

Beaudouin R, Ginot V, Monod G (2012) Improving mesocosm data analysis through individual-based modelling of control population dynamics: a case study with mosquitofish (Gambusia holbrooki). Ecotoxicology, 21, 155–164. https://doi.org/10.1007/s10646-011-0775-1

Henry M, Becher MA, Osborne JL, Kennedy PJ, Aupinel P, Bretagnolle V, Brun F, Grimm V, Horn J, Requier F (2017) Predictive systems models can help elucidate bee declines driven by multiple combined stressors. Apidologie, 48, 328–339. https://doi.org/10.1007/s13592-016-0476-0

Lamonica D, Charles S, Clément B, Lopes C (2022) Chemical effects on ecological interactions within a model-experiment loop. bioRxiv, 2022.05.24.493191, ver. 6 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://doi.org/10.1101/2022.05.24.493191

Our ability to anticipate and estimate how pollution affects biota is intrumental in the field of ecotoxicology. Impact of chemical pollution by metals, drugs or pesticides was widely studied in different species using acute and chronic scenarios. Since the ban on tributyltin in antifouling paints, other copper (Cu)-based paints are on the market, including a new generation of booster biocides:metal pyrithiones such as Cu pyrithione (CuPT). Pyrithione acts as a Cu ionophore facilitating Cu transport across the membranes. Although some data show their occurrence in aquatic ecosystems and few studies on the toxicity of CuPT in fish are published, major gaps in knowledge remain about their toxicity and toxic pathway. Few studies were previously conducted in animals exposed to CuPT pointing to reprotoxicity, developmental malformation and mortality (Li et al. 2021, Mochida et al., 2011; Mohamat-Yusuff et al., 2018, Shin et al., 2022). However, its toxicokinetic and toxicodynamic remain to be characterized in details. 

In this context, Bourdon et al. (2023) compared in juveniles of rainbow trout (Oncorhynchus mykiss), the effects of exposure to CuPT and ionic Cu2+ at equivalent Cu2+ molar concentrations. Presented data allow to compare the toxicity threshold, the accumulation of Cu and mechanisms of toxicity of both compounds. Acute and chronic exposures showed a higher bioaccumulation of Cu in the gills, and a higher toxicity of CuPT than that of ionic Cu2+, e.g. mortality , transcription levels of genes related to oxidative stress, detoxification and Cu transport. Intriguingly, the activities of enzymatic biomarkers used as proxy of antioxidant capacity were not significantly altered, although Cu is generally expected to trigger oxidative stress. In conlusion, this study brings new knowledge pointing that the presence of CuPT in the environment could induce toxic effects in non-target species. Moreover, it support the need to study in detail the toxicity of Cu-based paints to adapt regulations concerning their use and release in aquatic environments. Because of its low solubility in water, CuPT is expected to adsorb to suspended matter and food pellets. Future research should study this route of exposure.

References

Bourdon, C., Cachot, J., Gonzalez, P., Couture, P., 2023. Characterization of the bioaccumulation and toxicity of copper pyrithione, an antifouling compound, on juveniles of rainbow trout,  bioRxiv  ver. 3 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://doi.org/10.1101/2023.01.31.526498

Li, X., S. Ru, H. Tian, S. Zhang, Z. Lin, M. Gao and J. Wang, 2021. Combined exposure to environmentally relevant copper and 2,2′-dithiobis-pyridine induces significant reproductive toxicity in male guppy (Poecilia reticulata). Science of the Total Environment 797, https://doi.org/10.1016/j.scitotenv.2021.149131

Mochida, K., Amano, H., Onduka, T., Kakuno, A., Fujii, K., 2011. Toxicity and metabolism of copper pyrithione and its degradation product, 2,2’-dipyridyldisulfide in a marine polychaete. Chemosphere 82, 390–397, https://doi.org/10.1016/j.chemosphere.2010.09.074

Mohamat-Yusuff, F., Sarah-Nabila, Ab.G., Zulkifli, S.Z., Azmai, M.N.A., Ibrahim, W.N.W., Yusof, S., Ismail, A., 2018. Acute toxicity test of copper pyrithione on Javanese medaka and the behavioural stress symptoms. Marine Pollution Bulletin 127, 150–153, https://doi.org/10.1016/j.marpolbul.2017.11.046

Shin, D., Y. Choi, Z. Y. Soon, M. Kim, D. J. Kim and J. H. Jung, 2022. Comparative toxicity study of waterborne two booster biocides (CuPT and ZnPT) on embryonic flounder (Paralichthys olivaceus). Ecotoxicology and Environmental Safety 233, https://doi.org/10.1016/j.ecoenv.2022.113337

Our ability to anticipate and estimate how pollution affects components of ecosystems is of paramount importance in the field of ecotoxicology. Dose-response modeling is instrumental, as it allows deriving sensitivity thresholds used at the basis of regulatory risk assessment. In recent years, omics have highly influenced how the impacts of stressors are understood by revealing molecular changes at all levels of biota biological organization (Ebner et al., 2021). To allow analysis of omics data obtained using a typical dose-response design, DRomics a freely available tool for dose-response was proposed composed of both an R package and a free web application (Larras et al. 2018). Advances in this field depend both on theoretical concepts, technology and data integration.

In this context, Delignette-Muller et al. (2023) address the question of how to better integrate omics information in dose-response questions. The paper lists previous possibilities of DRomics and presents new features. It is now able to handle all types of continuous omic and continuous non-omic data (e.g. growth data). This new version proposes new visualization tools, functional annotation and improved modeling workflow for a better robustness of analysis of data with few replicates. New features are meant to help for biological interpretation at the metabolic pathway level, to compare different measurements, biological materials or experimental settings.

References

Delignette-Muller, M. L., A. Siberchicot, F. Larras and E. Billoir (2023), DRomics, a workflow to exploit dose-response omics data in ecotoxicology. bioRxiv, 2023.2002.2009.527852, ver. 4 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://doi.org/10.1101/2023.02.09.527852

Ebner JN. (2021) Trends in the Application of "Omics" to Ecotoxicology and Stress Ecology. Genes, 12(10):1481. https://doi.org/10.3390/genes12101481

Larras F, Billoir E, Baillard V, Siberchicot A, Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M and Delignette-Muller ML (2018). DRomics: a turnkey tool to support the use of the dose-response framework for omics data in ecological risk assessment. Environmental science & technology, 52(24):14461.  https://doi.org/10.1021/acs.est.8b04752