Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning
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Networks of trophic links (food webs) are used to describe and understand mechanistic routes for translocation of energy (biomass) between species. However, a relatively low proportion of ecosystems have been studied using food web approaches due to difficulties in making observations on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from arable fields in Great Britain. We found that 45 invertebrate species or taxa, representing approximately 25% of the sample and about 74% of the invertebrate individuals included in the learning, were hypothesized to be linked. As might be expected, detritivore Collembola were consistently the most important prey. Generalist and omnivorous carabid beetles were hypothesized to be the dominant predators of the system. We were, however, surprised by the importance of carabid larvae suggested by the machine learning as predators of a wide variety of prey. High probability links were hypothesized for widespread, potentially destabilizing, intra-guild predation; predictions that could be experimentally tested. Many of the high probability links in the model have already been observed or suggested for this system, supporting our contention that A/ILP learning can produce plausible food webs from sample data, independent of our preconceptions about "who eats whom." Well-characterised links in the literature correspond with links ascribed with high probability through A/ILP. We believe that this very general Machine Learning approach has great power and could be used to extend and test our current theories of agricultural ecosystem dynamics and function. In particular, we believe it could be used to support the development of a wider theory of ecosystem responses to environmental change.
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Bohan et al. (2011) use ILP to generate plausible and testable hypotheses for trophic relations ('who eats whom') from ecological data.
“For instance, Bohan et al. (2011) use ILP to generate plausible and testable hypotheses for trophic relations ('who eats whom') from ecological data.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Bohan et al., 2011
There has been much recent work on applying ILP in ecology.
“There has been much recent work on applying ILP in ecology (Bohan et al., 2011; Tamaddoni-Nezhad et al., 2014; Bohan et al., 2017).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Bohan et al., 2011
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References 55 · Cited by 83
Research works printed but not linked: 22
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- Authors
- Bohan, D. A., Caron-Lormier, G., Muggleton, S., Raybould, A., & Tamaddoni-Nezhad, A.
- Affiliations
- Rothamsted Research (GB)
- Imperial College London (GB)
- Syngenta (United Kingdom) (GB)
- Corresponding author
- Bohan, David A.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-12-29
- Volume
- 6
- Issue
- 12
- Pages
- e29028
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3248413
- OpenAlex
- W2083078127
- Cited by
- 83
- References
- 55
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 1 · 2023: 6 · 2022: 5 · 2021: 7 · 2020: 11 · 2019: 8 · 2018: 7 · 2017: 3 · 2016: 6 · 2015: 4 · 2014: 4 · 2013: 7 · 2012: 4
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Imperial College London
- Topics
- Life Sciences › Agricultural and Biological Sciences › Insect Science › Insect-Plant Interactions and Control
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- food web reconstruction, food webs, arable fields, Collembola, ground beetles, intraguild predation, agroecosystems
- MeSH terms
- Animals, Artificial Intelligence, Automation, Logic, Models, Biological, Predatory Behavior, Species Specificity, Statistics as Topic, Food Chain
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