Reconstructing community dynamics from limited observations

Area of Science:

• Microbial Ecology
• Ecological Dynamics
• Statistical Modeling

Background:

• Ecosystems exhibit fluctuations around stable states, but can collapse into alternative states.
• Understanding microbial community dynamics is crucial for predicting responses to environmental changes.
• Existing models struggle with real-world microbiome data, limited by data scarcity.

Purpose of the Study:

• To develop a novel method for characterizing microbial community dynamics from limited time series data.
• To predict ecosystem stability, tipping regions, and resilience in microbial communities.
• To differentiate between bistability and bimodality in ecological systems.

Main Methods:

• Employed Bayesian inference to combine information from multiple short time series.
• Utilized Gaussian process priors to decompose dynamics into deterministic and stochastic components.
• Estimated probabilistic metrics for resilience, such as expected exit time.

Main Results:

• Successfully predicted stable and tipping regions with associated uncertainty using limited data.
• Distinguished between bistability and bimodality, overcoming limitations of classical potential analyses.
• Validated the approach on simulated data and re-analyzed lake cyanobacteria and human gut microbiota data.

Conclusions:

• Introduced a robust probabilistic non-parametric approach for analyzing stationary community dynamics.
• The method is applicable to diverse microbial ecology systems and short time series data.
• Clarified bistable vs. bimodal dynamics and advanced understanding of microbial community stability and resilience.