1 Abstract
Modeling 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 neighboring species from the diverse community that had non-generic interactions with our focal species. This sparse modeling approach facilitates exploration of species-interactions in diverse communities while maintaining a manageable number of parameters.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Data accessibility statement: Upon acceptance, all data will be archived on Dryad and the data DOI will be included at the end of the article. Model code is available on GitHub, with the URL included in the manuscript. Upon acceptance, model code with be archived on Zenodo and the URL will be updated with the Zenodo link.