Our ability to anticipate and estimate how pollution affects biota is of paramount importance 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. While environmental factors such as temperature are also often considered, noise is largely ignored in these models despite the knowledge of its detrimental effects in vertebrates. Studies of noise impacts included behavior and fitness endpoints and showed no effect to death depending on intensity, frequency and the distance from the noise source (Peng et al., 2015). Nonetheless, the impact of noise in biota is not well-understood, which impairs its effective mitigation. 

Noise or acoustic pollution due to boat traffic produce low-frequency stationary noise. It is a pervasive and ubiquitous pollutant found in aquatic ecosystems. In this context, Prosnier et al. (2023) addresses how intermittent and random noise impacted Daphnia magna, a representative of zooplankton model, widely used in ecotoxicology. Endpoints of lifespan and clonal offspring production were measured in the presence or absence of motorboat noises, in animals reared from birth to death. Noise consisted in a playlist of 15 sounds of motorboat recorded in the Grangent lake (Loire, France). Their intensity ranged from 0 to -25 dB Re 1 μPa by 5 dB to create 75 sounds from 103 to 150 dB RMS Re 1 μPa – a range of levels occurring in lakes. Treatment had no effect on analyzed endpoints, contrary to a continuous broadband noise (100-20,000 Hz) that caused higher survival and fecundity, and reduced speed of motion compared to control (Prosnier et al., 2022). Data point that temporal (continuous, regular, random) and frequency of noise are instrumental for its effects. 

References

Peng, C., X. Zhao and G. Liu (2015). "Noise in the Sea and Its Impacts on Marine Organisms." Int J Environ Res Public Health 12(10): 12304-12323. https://doi.org/10.3390/ijerph121012304

Prosnier, L., E. Rojas and V. Médoc (2023). "No evidence for an effect of chronic boat noise on the fitness of reared water fleas." bioRxiv: 2022. ver. 4 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://doi.org/10.1101/2022.11.20.517267

Prosnier, L., E. Rojas, O. Valéro and V. Médoc (2022). "Chronic noise unexpectedly increases fitness of a freshwater zooplankton." bioRxiv: 2022. https://doi.org/10.1101/2022.11.19.517212

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

Over the last decades, pesticides have been massively used in agriculture and their impacts on both the environment and human health are a major growing concern (Humann-Guilleminot et al., 2019; 2019 Boedeker et al., 2020). Improving the prediction of wildlife exposure to pesticides and the associated impacts on ecosystems is therefore crucial. In general, ecotoxicological studies addressing the effects of pesticides include compounds that are selected based on general use over large areas (e.g. regions, country) or specific crop types. Such a selection does not necessarily reflect the mixtures to which species of wildlife are exposed in a particular ecosystem.

In this context, Cairo et al. (2023) present an original approach to identify relevant mixtures of current-use pesticides. Their approach relies on public data concerning pesticide sales and cropping, available at a nationwide scale in France and at a relatively high resolution (i.e. postcode of the buyer). Based on a number of clearly exposed and discussed assumptions (e.g. “pesticides were used in the year of purchase and in the postcode of purchase”), their approach allowed for identifying 18 groups that were discriminated by a reduced number of pesticides. Some compounds were found in most or all groups and were termed “core substances” (e.g. deltamethrin and lambda-cyhalothrin). Other compounds, however, were associated with a limited number of groups and termed “discriminant substances” (e.g. boscalid and epoxiconazole).

The authors identified groups of molecules that are probably associated with the same mixtures, which warrants the investigation of potential synergetic effects. In addition, their approach allowed for the identification of areas where aquatic biota may be exposed to similar mixtures, which is might prove of interest to further investigate in situ the actual impacts of pesticide mixtures on ecosystems. Note that the approach taken by the authors might be applied by others in other countries, provided a database of pesticide sales is available.

REFERENCES

Boedeker W, Watts M, Clausing P, Marquez E (2020) The global distribution of acute unintentional pesticide poisoning: estimations based on a systematic review. BMC Public Health, 20, 1875. https://doi.org/10.1186/s12889-020-09939-0

Cairo M, Monnet A-C, Robin S, Porcher E, Fontaine C (2023) Identifying pesticide mixtures at country-wide scale. HAL, ver. 2 peer-reviewed and recommended by Peer Community in Ecotoxicology and Environmental Chemistry. https://hal.science/hal-03815557

Humann-Guilleminot S, Tassin de Montaigu C, Sire J, Grünig S, Gning O, Glauser G, Vallat A, Helfenstein F (2019) A sublethal dose of the neonicotinoid insecticide acetamiprid reduces sperm density in a songbird. Environmental Research, 177, 108589. https://doi.org/10.1016/j.envres.2019.108589

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