Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1111/ele.13977
Licence creative commons licence
Title (Primary) Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach
Author Weiss-Lehman, C.P.; Werner, C.M.; Bowler, C.H.; Hallett, L.M.; Mayfield, M.M.; Godoy, O.; Aoyama, L.; Barabás, G.; Chu, C.; Ladouceur, E.; Larios, L.; Shoemaker, L.G.
Source Titel Ecology Letters
Year 2022
Department iDiv; PHYDIV
Volume 25
Issue 5
Page From 1263
Page To 1276
Language englisch
Topic T5 Future Landscapes
Data and Software links https://doi.org/10.5281/zenodo.5828361
Supplements https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fele.13977&file=ele13977-sup-0001-Supinfo.pdf
Keywords coexistence; environmental gradients; pairwise interactions; parameter shrinkage; plant fecundity; species diversity
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.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=25741
Weiss-Lehman, C.P., Werner, C.M., Bowler, C.H., Hallett, L.M., Mayfield, M.M., Godoy, O., Aoyama, L., Barabás, G., Chu, C., Ladouceur, E., Larios, L., Shoemaker, L.G. (2022):
Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach
Ecol. Lett. 25 (5), 1263 - 1276 10.1111/ele.13977