Ecological networks reveal resilience of agro-ecosystems to changes in farming management
- Submitting institution
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The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9024866_1
- Type
- D - Journal article
- DOI
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10.1038/s41559-018-0757-2
- Title of journal
- Nature Ecology & Evolution
- Article number
- -
- First page
- 260
- Volume
- 3
- Issue
- 2
- ISSN
- 2397-334X
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Moving agriculture away from being a contributor to climate and biodiversity degradation is a critical challenge. This paper is the first to demonstrate that machine learning can be used to predict the effects of farming management and the agro-ecological networks machine-learned and analysed here are currently the largest in the world. This work also influenced a new hypothesis-based risk assessment for product use (Raybould, 2020) and led to new industrial collaborations including a PhD project funded by Syngenta (contact:Nika.Galic@syngenta.com). This work is also supporting the ANR project (ANR-17-CE32-0011) on Next-Generation Biomonitoring aiming to learn ecological networks from DNA data.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -