Regularizing priors for Bayesian VAR applications to large ecological datasets.

Area of Science:

• Ecology
• Computational Biology
• Statistical Modeling

Background:

• Estimating inter-specific interactions in food webs is crucial for ecological understanding.
• Traditional methods like vector autoregressive (VAR) models struggle with large food webs due to increased data requirements.
• Scaling these models requires longer time series data, which is often unavailable.

Purpose of the Study:

• To investigate the benefits of Bayesian methods with regularized priors for estimating inter-specific interactions.
• To assess the performance of Laplace and regularized horseshoe priors in vector autoregressive (VAR) and state space VAR (VARSS) models.
• To improve the accuracy and efficiency of ecological network analysis.

Main Methods:

• Employed Bayesian inference with regularized priors (Laplace, regularized horseshoe) for VAR and VARSS models.
• Conducted a large-scale simulation study to evaluate prior performance under varying observation error.
• Applied the Bayesian VAR model with regularized priors to a 37-species marine food web model.

Main Results:

• The regularized horseshoe prior demonstrated minimal bias and variance in estimating inter-specific interactions, especially for sparse matrices.
• Regularization enhanced the predictive performance of the VAR model in the marine food web analysis.
• Key inter-specific interactions were successfully identified even with model regularization.

Conclusions:

• Bayesian approaches with regularized priors offer a robust solution for analyzing complex ecological networks.
• Regularized horseshoe priors are particularly effective for improving the estimation of species interactions in large food webs.
• This methodology enhances ecological modeling by improving predictive accuracy and identifying critical interactions.