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Agrochemicals interact synergistically to increase bee mortality

Nature volume 596, pages 389–392 (2021)Cite this article

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An Addendum to this article was published on 24 April 2023

Abstract

Global concern over widely documented declines in pollinators1,2,3 has led to the identification of anthropogenic stressors that, individually, are detrimental to bee populations4,5,6,7. Synergistic interactions between these stressors could substantially amplify the environmental effect of these stressors and could therefore have important implications for policy decisions that aim to improve the health of pollinators3,8,9. Here, to quantitatively assess the scale of this threat, we conducted a meta-analysis of 356 interaction effect sizes from 90 studies in which bees were exposed to combinations of agrochemicals, nutritional stressors and/or parasites. We found an overall synergistic effect between multiple stressors on bee mortality. Subgroup analysis of bee mortality revealed strong evidence for synergy when bees were exposed to multiple agrochemicals at field-realistic levels, but interactions were not greater than additive expectations when bees were exposed to parasites and/or nutritional stressors. All interactive effects on proxies of fitness, behaviour, parasite load and immune responses were either additive or antagonistic; therefore, the potential mechanisms that drive the observed synergistic interactions for bee mortality remain unclear. Environmental risk assessment schemes that assume additive effects of the risk of agrochemical exposure may underestimate the interactive effect of anthropogenic stressors on bee mortality and will fail to protect the pollinators that provide a key ecosystem service that underpins sustainable agriculture.

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Fig. 1: The interaction effects of parasites, agrochemicals and nutritional stressors on bee mortality.
Fig. 2: The interaction effects of parasites, agrochemicals and nutritional stressors on non-mortality response measures.
Fig. 3: Reversal interactions.

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Data availability

All data used in this analysis are available at OSF (https://osf.io/8xnua/).

Code availability

All code used in this analysis is available at OSF (https://osf.io/8xnua/).

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Acknowledgements

We thank all authors who made data available to us upon request (C. Alaux, K. Antunez, B. Baer, B. Blochtein, C. Botias, B. Dainat, M. Diogon, A. G. Dolezal, V. Doublet, K. Fent, D. C. de Graaf, G. de Grandi-Hoffman, P. Graystock, E. Guzman-Novoa, R. M. Johnson, E. G. Klinger, I. M. de Mattos, N. A. Moran, M. Natsopoulou, F. Nazzi, P. Neumann, R. Odemer, R. Raimets, G. Retschnig, R. M. Roe, E. Ryabov, B. M. Sadd, C. Sandrock, F. Sgolastra, R. Siede, H. V. V. Tome, I. Toplak, S. Tosi, M. Tritschler, V. Zanni and Y. C. Zhu); and J. Bagi and A. J. Folly for helping with the initial screening of titles and abstracts. H.S. was supported by a Royal Holloway University of London Reid PhD Scholarship and by contributions from the High Wycombe Beekeeper’s Association. This project has received funding from the European Horizon 2020 research and innovation programme under grant agreement no. 773921 and ERC Starting Grant BeeDanceGap 638873, and from the Biotechnology and Biological Sciences Research Council, grant/award number BB/N000668/1.

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Author notes

  1. These authors contributed equally: Harry Siviter, Emily J. Bailes

Authors and Affiliations

  1. Department of Biological Sciences, Royal Holloway University of London, Egham, UK

    Harry Siviter, Emily J. Bailes, Callum D. Martin, Thomas R. Oliver, Julia Koricheva, Ellouise Leadbeater & Mark J. F. Brown

  2. Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA

    Harry Siviter

  3. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, UK

    Emily J. Bailes

  4. School of Natural Sciences, Bangor University, Bangor, UK

    Emily J. Bailes & Thomas R. Oliver

  5. Rothamsted Research, Harpenden, UK

    Thomas R. Oliver

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Contributions

H.S., E.J.B., C.D.M., T.R.O. and M.J.F.B. conceived the idea for the study in a discussion group. H.S. and E.B. oversaw and managed the data collection. H.S., E.B., C.D.M. and T.R.O. carried out the literature search and collected the data. H.S. and E.L. conducted the statistical analysis and H.S. wrote the first version of the manuscript. H.S., E.J.B., J.K., E.L. and M.J.F.B. contributed to the writing of subsequent drafts.

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Correspondence to Harry Siviter.

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The authors declare no competing interests.

Additional information

Peer review information Nature thanks Antica Culina, Adam Vanbergen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Distribution of Hedges’ d values for the individual effect sizes included for the interaction effects of parasites, agrochemicals and nutritional stressors for bee response variables.

a–e, Distributions are shown for mortality (a), behaviour (b), fitness (c), parasite load (d) and immune responses (e). Data are shown as Hedges’ d values ± 95% CI. Effect sizes are sorted for each response variable from most negative to most positive. Each mean ± 95% CI represents a different data point, hence there are more effect sizes than number of studies. Interactions are synergistic when the effect size is positive and the 95% CI does not include zero, antagonistic when the effect size is negative and the 95% CI does not include zero and additive when the 95% CI includes zero. Note that each panel is presented on a different scale.

Extended Data Fig. 2 Hedges’ d values for interactions between specific stressors on bee mortality.

a, Interactions between combinations of parasite stressors. b, Interactions between combinations of parasite and nutritional stressors. Data are shown as Hedges’ d values ± 95% CI. The interactions are synergistic when the effect size is positive and the 95% CI does not include zero, antagonistic when the effect size is negative and the 95% CI does not include zero and additive when the 95% CI includes zero. Numbers next to the 95% CIs indicate the number of effect sizes in each category. Asterisks indicate that the 95% CI does not include zero.

Extended Data Fig. 3 Hedges’ d values for different bee genera.

a–e, Data are shown as Hedges’ d values ± 95% CI for mortality (a), behaviour (b), fitness proxies (c), parasite load (d) and immune responses (e). The genus is indicated by the colour and shape of the symbol. Interactions are synergistic when the effect size is positive and the 95% CI does not include zero, antagonistic when the effect size is negative and the 95% CI does not include zero, and additive when the 95% CI includes zero. Numbers next to the 95% CIs indicate the number of effect sizes in each category. Asterisks indicate that the 95% CI does not include zero. Note that each panel is presented on a different scale.

Extended Data Fig. 4 The interaction effects of different agrochemical classes on bee mortality response measures.

Hedges’ d values  ± 95% CI are shown. Asterisks indicate that the 95% CI does not include zero. Numbers next to the 95% CIs indicate the number of effect sizes in each category. Note that effect sizes for azole fungicide × pyrethroid are included in both groups.

Extended Data Fig. 5 Modified PRISMA flowchart.

A flowchart depicting the number of studies included or excluded at each stage of the literature search.

Extended Data Fig. 6 Funnel plots of the full models of the interactions between specific stressors.

a–e, Plots represent the models for mortality (a), behaviour (b), fitness proxies (c), parasite load (d) and immune responses (e).

Supplementary information

Supplementary Information

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Siviter, H., Bailes, E.J., Martin, C.D. et al. Agrochemicals interact synergistically to increase bee mortality. Nature 596, 389–392 (2021). https://doi.org/10.1038/s41586-021-03787-7

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