ArticleEcology letters2022
Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach.
Article in Ecology letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 33 citations in OpenAlex.
- A practical guide to characterising ecological coexistence.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Population Genetics Meets Ecology: A Guide to Individual-Based Simulations in Continuous Landscapes.Ecology and evolution · 2025Article
- A Continuum From Positive to Negative Interactions Drives Plant Species' Performance in a Diverse Community.Ecology letters · 2025Article
- Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach.Ecology letters · 2022Article
Corrections and comments
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Authors and funding
12 authors at 8 institutions in 6 countries.
Funding
Abstract
Modelling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species' intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighbouring species from the diverse community that had non-generic interactions with our focal species. This sparse modelling approach facilitates exploration of species interactions in diverse communities while maintaining a manageable number of parameters.
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Registered trials
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