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Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach
Ist Teil von
Ecology letters, 2022-05, Vol.25 (5), p.1263-1276
Ort / Verlag
England: Blackwell Publishing Ltd
Erscheinungsjahr
2022
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
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.
Traditional models of community dynamics are often difficult to fit to high diversity communities as they require the estimation of a large number of pairwise interaction coefficients. Using sparse modeling techniques developed in other fields, we demonstrate a novel approach to this problem in which the model groups all species with a single ‘generic’ interaction term but adaptively allows certain species to deviate from this generic term. We show this approach, which does not require a priori knowledge or assumptions about the system, performs well with even small amounts of data for diverse communities.