Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach

Area of Science:

• Ecology
• Computational Biology
• Statistical Modeling

Background:

• Modeling species interactions in diverse communities is computationally challenging due to the large number of interaction coefficients required.
• Environmental dependence further complicates these models, increasing the need for parameter reduction techniques.

Purpose of the Study:

• To develop and evaluate a Bayesian variable selection method for simplifying non-linear species abundance models.
• To reduce the number of parameters in ecological models by identifying essential species interactions and averaging non-essential ones.
• To differentiate direct environmental effects from indirect interaction effects on species growth rates.

Main Methods:

• Implemented Bayesian variable selection with sparsity-inducing priors on non-linear species abundance models.
• Evaluated model performance using simulated communities, assessing predictive accuracy and parameter recovery.
• Applied the method to a diverse empirical community to analyze species interactions and environmental influences.

Main Results:

• The sparse modeling approach successfully reduced the number of parameters while maintaining predictive accuracy.
• The method effectively disentangled direct environmental effects from indirect competitive interactions in an empirical community.
• Identified specific neighboring species with non-generic interactions within the diverse community.

Conclusions:

• Sparse modeling offers a computationally tractable way to explore species interactions in complex, diverse ecological communities.
• This approach facilitates the identification of key interactions and the understanding of environmental impacts on community dynamics.
• The method allows for the manageable analysis of large-scale ecological interaction networks.