Modeling and inferring metacommunity dynamics with Maximum Caliber

Area of Science:

• Ecology
• Statistical Physics
• Computational Biology

Background:

• Inferring parameters of ecological dynamical models from spatiotemporal data is challenging without extensive experiments.
• Existing methods often focus on system states rather than system trajectories.

Purpose of the Study:

• To present a novel framework, Maximum Caliber, for characterizing temporal dynamics and inferring parameters of ecological systems using spatiotemporal data.
• To demonstrate the framework's ability to model ecological processes across a spectrum from equilibrium to far-from-equilibrium conditions.

Main Methods:

• Utilized the Maximum Caliber framework, an extension of Maximum Entropy modeling, to analyze system trajectories.
• Applied logistic regression to estimate model parameters from spatiotemporal species occupancy data in metacommunities.
• Introduced 'entropy production' as a measure of irreversibility and 'pseudo-R^2' for predictability.

Main Results:

• The Maximum Caliber framework accurately captures ecological processes including various species interaction motifs and dispersal.
• Parameters such as migration rates, species interactions, and environmental suitabilities were estimated without bias across different system sizes and time periods.
• The model demonstrated predictive power for metacommunities, including those far from equilibrium.

Conclusions:

• The Maximum Caliber framework offers a breakthrough for estimating parameters of dynamical metacommunity models from empirical spatiotemporal data.
• This approach overcomes limitations of experimental approaches and provides insights into complex ecological dynamics.
• The findings have significant applications in conservation and restoration ecology for managing and restoring ecological communities.