Untangling the dynamics of persistence and colonization in microbial communities

Area of Science:

• Community Ecology
• Statistical Modeling
• Epidemiology

Background:

• Inferring biotic interactions from species co-occurrence is a central goal in community ecology.
• Shared environmental requirements can obscure direct species interactions, complicating statistical inference.
• Longitudinal co-occurrence data presents unique challenges for disentangling ecological processes.

Purpose of the Study:

• To develop a dynamic statistical model to quantify spatial and temporal covariance in co-occurrence data.
• To differentiate between direct species interactions and shared environmental responses.
• To apply this novel framework to understand human papillomavirus (HPV) type coexistence.

Main Methods:

• Developed a dynamic statistical model based on probit regression.
• Separated fixed pairwise effects (potential direct interactions) from latent pairwise correlations (shared environmental responses).
• Validated the model using simulation studies before applying it to HPV data.

Main Results:

• The model successfully distinguished between direct interactions and shared environmental drivers.
• Human papillomavirus (HPV) types exhibit similar responses to common host traits, indicating shared environmental influences.
• Direct interactions among HPV types were found to be sparse and weak.

Conclusions:

• HPV type diversity is largely determined by shared environmental drivers rather than direct interactions.
• The developed modeling approach is broadly applicable to microbial communities and other ecological systems.
• This framework facilitates more targeted hypothesis testing and mechanistic modeling in community ecology.