ArticleEcology letters2020
A Bayesian network approach to trophic metacommunities shows that habitat loss accelerates top species extinctions.
Article in Ecology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 26 citations in OpenAlex.
- Global soybean trade dynamics: Drivers, impacts, and sustainability.Innovation (Cambridge (Mass.)) · 2026Review
- Metapopulation persistence can be inferred from incomplete surveys.Proceedings. Biological sciences · 2022Article
- Multispecies coexistence in fragmented landscapes.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Predictors of psychological stress and behavioural diversity among captive red panda in Indian zoos and their implications for global captive management.Scientific reports · 2022Article
- Global relationships between crop diversity and nutritional stability.Nature communications · 2021Article
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
Abstract
We develop a novel approach to analyse trophic metacommunities, which allows us to explore how progressive habitat loss affects food webs. Our method combines classic metapopulation models on fragmented landscapes with a Bayesian network representation of trophic interactions for calculating local extinction rates. This means that we can repurpose known results from classic metapopulation theory for trophic metacommunities, such as ranking the habitat patches of the landscape with respect to their importance to the persistence of the metacommunity as a whole. We use this to study the effects of habitat loss, both on model communities and the plant-mammal Serengeti food web dataset as a case study. Combining straightforward parameterisability with computational efficiency, our method permits the analysis of species-rich food webs over large landscapes, with hundreds or even thousands of species and habitat patches, while still retaining much of the flexibility of explicit dynamical models.
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