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Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach
Author(s) -
WeissLehman Christopher P.,
Werner Chhaya M.,
Bowler Catherine H.,
Hallett Lauren M.,
Mayfield Margaret M.,
Godoy Oscar,
Aoyama Lina,
Barabás György,
Chu Chengjin,
Ladouceur Emma,
Larios Loralee,
Shoemaker Lauren G.
Publication year - 2022
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.13977
Subject(s) - abundance (ecology) , approximate bayesian computation , prior probability , bayesian probability , community , ecology , key (lock) , sample (material) , relative species abundance , computer science , selection (genetic algorithm) , species distribution , machine learning , biology , artificial intelligence , habitat , physics , inference , thermodynamics
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.