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

Pesticides give rise to considerable concern due to their impact on biodiversity. Amongst the vast range of compounds used as herbicides, glyphosate (GLY) is the most widely applied one at global scale and its transformation product, aminomethylphosphonic acid (AMPA) is also ubiquitous. However, the toxicity of these chemicals on non-target organisms, including mammals, is somewhat overlooked (Kissane et al., 2017). Beside these two chemicals, Fritsch et al. (2024) also considered another organophosphorus herbicide, i.e. glufosinate (GLUF). Their study examined exposure levels in rodents and shrews living in contrasted cropped and semi-natural habitats in France – i.e., conventional farmland, organic fields, and hedgerows – through the analysis of herbicide residues in their hair. The hypothesis that herbicide residues in hair reflect the exposure to multiple pesticides in wildlife is supported by several papers (i.e. Krief et al. 2017; Fritsch et al. 2022).

Results obtained by Fritsch et al. (2024) indicated that the target compounds were widespread in the investigated environments, i.e. GLY, AMPA, and GLUF were detected in 64%, 51%, and 44% of samples, respectively. Diet appeared as a major driver of contamination, as herbivorous and omnivorous voles exhibited higher contamination levels than insectivorous or omnivorous species such as shrews and wild mice. In addition, habitat was also a significant factor: GLY concentrations were particularly high in individuals collected from hedgerows, surpassing those found in crop fields. This unexpected result highlights the contamination of areas considered as ecological refuges for the investigated species. Exposure levels did not show clear differences across sites, based on farming practices or pesticide application intensity.

In addition, the measured concentrations of GLY (median 2.7 pg/mg), AMPA (median 1.4 pg/mg), and GLUF (median 3.5 pg/mg) frequently reached thresholds associated with toxic effects on small mammals. In worst case scenarios, exceedance percentages attained values as high as 94 %.

Altogether, these results definitely raise concerns about the potential impact of GLY, AMPA and GLUF on non-target wildlife species and populations. These findings by Fritsch et al. (2024) therefore emphasize the widespread presence of these chemicals in agricultural landscapes and question the safety of herbicide use, even in habitats meant to protect biodiversity. This study underscores the need for more comprehensive evaluation of the ecological effects of herbicides to guide policy and conservation efforts.

References

Kissane Z, Shephard JM (2017) The rise of glyphosate and new opportunities for biosentinel early-1068 warning studies. Conservation Biology 31: 1293–1300; https://doi.org/10.1111/cobi.12955

Krief S, Berny P, Gumisiriza F, Gross R, Demeneix B, Fini JB, et al. (2017) Agricultural expansion as risk to endangered wildlife: Pesticide exposure in wild chimpanzees and baboons displaying facial dysplasia. Science of the Total Environment 598:647–656; 1072; https://doi.org/10.1016/j.scitotenv.2017.04.113

Fritsch C, Appenzeller BM, Burkart L, Coeurdassier M, Scheifler R, Raoul F, et al. (2022) Pervasive exposure 1041 of wild small mammals to legacy and currently used pesticide mixtures in arable landscapes. 1042 Sci Rep 12:15904; https://doi.org/10.1038/s41598-022-19959-y

Fritsch C, Appenzeller BM, Bertrand C, Coeurdassier M, Driget V, Hardy EM, Palazzi P, et al. (2024) Exposure of wild mammals to glyphosate, AMPA, and glufosinate: a case for “emerging organic contaminants”?. HAL, ver.3 peer-reviewed and recommended by PCI Ecotoxicology and Environmental Chemistry https://hal.science/hal-04485797

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