Sustainable management of ecosystems and growth in agricultural productivity is at the heart of the United Nations’ Sustainable Development Goals for 2030. New management regimes could revolutionize agricultural production, but require an evaluation of the risks and opportunities. Replacing existing conventional weed management with genetically modified, herbicide-tolerant (GMHT) crops, for example, might reduce herbicide applications and increase crop yields, but remains controversial owing to concerns about potential impacts on biodiversity. Until now, such new regimes have been assessed at the species or assemblage level, whereas higher-level ecological network effects remain largely unconsidered. Here, we conduct a large-scale network analysis of invertebrate communities across 502 UK farm sites to GMHT management in different crop types. We find that network-level properties were overwhelmingly shaped by crop type, whereas network structure and robustness were apparently unaltered by GMHT management. This suggests that taxon-specific effects reported previously did not escalate into higher-level systemic structural change in the wider agricultural ecosystem. Our study highlights current limitations of autecological assessments of effect in agriculture in which species interactions and potential compensatory effects are overlooked. We advocate adopting the more holistic system-level evaluations that we explore here, which complement existing assessments for meeting our future agricultural needs.

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Data availability

The raw FSE data are free from intellectual property rights. The data can be requested by enquiry to the Environmental Information Data Centre of the Centre for Ecology and Hydrology (http://eidc.ceh.ac.uk/contact). Archived information about the FSEs are available from the National Archives of The Government of the United Kingdom (http://webarchive.nationalarchives.gov.uk/20080306073937/http://www.defra.gov.uk/environment/gm/fse/).

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We thank J. Bigham, P. Curtis, P. Kratina, B. Parker and R. Bailey for their comments and discussion. X.L. and C.G. were supported by Queen Mary University of London. X.L. was additionally supported by the Chinese Scholarship Council and C.G. was additionally supported by the Freshwater Biological Association. D.A.B. acknowledges the support of the FACCE SURPLUS PREAR and ANR (ANR-17-CE32-011) NGB projects.

Author information


  1. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    • Athen Ma
    •  & Xueke Lu
  2. School of Engineering, University of Warwick, Coventry, UK

    • Xueke Lu
  3. School of Biological and Chemical Sciences, Queen Mary University of London, London, UK

    • Clare Gray
  4. Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot, Berkshire, UK

    • Clare Gray
    •  & Guy Woodward
  5. Syngenta Crop Protection AG, Basel, Switzerland

    • Alan Raybould
  6. Department of Computer Science, University of Surrey, Guildford, UK

    • Alireza Tamaddoni-Nezhad
  7. Department of Computing, Imperial College London, London, UK

    • Alireza Tamaddoni-Nezhad
  8. Agroécologie, AgroSup Dijon, INRA, University of Bourgogne Franche-Comté, Dijon, France

    • David A. Bohan


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A.M. and D.A.B. designed the research. D.A.B. and A.T.-N. contributed materials and datasets. X.L. implemented the analysis. X.L. and C.G. analysed the data. A.M., X.L., C.G., A.R., G.W. and D.A.B. discussed the results. A.M. and D.A.B. led the paper writing with input from all authors.

Competing interests

A.R. is employed by Syngenta, which develops and markets genetically modified seed products.

Corresponding author

Correspondence to David A. Bohan.

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