ArticleNature ecology & evolution2024
Power laws in species' biotic interaction networks can be inferred from co-occurrence data.
Article in Nature ecology & evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Biotic interactions biogeography: A framework for understanding how species interactions shape biodiversity patterns across scales.PLoS biology · 2026Review
- Ecological analysis of mosquito larval communities in Burkina Faso to inform environmental monitoring of genetic control programs.Scientific reports · 2026Article
- Global evidence of non-pyramidal and uniform ratios of animal diversity across terrestrial trophic levels.Proceedings. Biological sciences · 2025Article
- Environmental Effects on Bacterial Community Assembly in Arid and Semi-Arid Grasslands.Microorganisms · 2025Article
- Inferring Species Interactions From Co-occurrence Networks With Environmental DNA Metabarcoding Data in a Coastal Marine Food Web.Molecular ecology · 2025Article
- Generalism in species interactions is more the consequence than the cause of ecological success.Nature ecology & evolution · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring biotic interactions from species co-occurrence patterns has long intrigued ecologists. Yet recent research revealed that co-occurrences may not reliably represent pairwise biotic interactions. We propose that examining network-level co-occurrence patterns can provide valuable insights into community structure and assembly. Analysing ten bipartite networks of empirically sampled biotic interactions and associated species spatial distribution, we find that approximately 20% of co-occurrences correspond to actual interactions. Moreover, the degree distribution shifts from exponential in co-occurrence networks to power laws in networks of biotic interactions. This shift results from a strong interplay between species' biotic (their interacting partners) and abiotic (their environmental requirements) niches, and is accurately predicted by considering co-occurrence frequencies. Our work offers a mechanistic understanding of the assembly of ecological communities and suggests simple ways to infer fundamental biotic interaction network characteristics from co-occurrence data.
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