%0 Journal Article %1 weisslehman2022disentangling %A Weiss-Lehman, Christopher P. %A Werner, Chhaya M. %A Bowler, Catherine H. %A Hallett, Lauren M. %A Mayfield, Margaret M. %A Godoy, Oscar %A Aoyama, Lina %A Barabás, György %A Chu, Chengjin %A Ladouceur, Emma %A Larios, Loralee %A Shoemaker, Lauren G. %D 2022 %J Ecology Letters %K Beverton-Holt coexistence methods shrinkage species_interactions %N 5 %P 1263-1276 %R https://doi.org/10.1111/ele.13977 %T Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach %U https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.13977 %V 25 %X 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.

@article{weisslehman2022disentangling, abstract = {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.}, added-at = {2024-07-20T17:38:33.000+0200}, author = {Weiss-Lehman, Christopher P. and Werner, Chhaya M. and Bowler, Catherine H. and Hallett, Lauren M. and Mayfield, Margaret M. and Godoy, Oscar and Aoyama, Lina and Barabás, György and Chu, Chengjin and Ladouceur, Emma and Larios, Loralee and Shoemaker, Lauren G.}, biburl = {https://www.bibsonomy.org/bibtex/2a4d62cbed56fbab402cb438c4b5b1622/peter.ralph}, doi = {https://doi.org/10.1111/ele.13977}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.13977}, interhash = {fe82876eb56893c1887526c68521afce}, intrahash = {a4d62cbed56fbab402cb438c4b5b1622}, journal = {Ecology Letters}, keywords = {Beverton-Holt coexistence methods shrinkage species_interactions}, number = 5, pages = {1263-1276}, timestamp = {2024-07-20T17:38:33.000+0200}, title = {Disentangling key species interactions in diverse and heterogeneous communities: {A} {Bayesian} sparse modelling approach}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.13977}, volume = 25, year = 2022 }