Maximum Caliber modeling of dynamic ecological communities

Ecological communities have been modeled using statistical methods, often represented as large networks of species with random or structured interactions. These models have typically focused on the equilibrium states of the system. However, many ecological processes in nature are expected to be non-equilibrium, requiring new methods for inferring species interactions from empirical data. We employ Maximum Caliber as a framework for modeling such dynamic ecological processes. As an extension of Maximum Entropy modeling, Maximum Caliber assigns probabilities to possible trajectories of a system rather than its equilibrium states. By focusing on state transitions, it allows us to model dynamic processes away from equilibrium. We present a probabilistic model for the presence and absence of species in a meta-community, a type of data commonly collected in field studies that can be readily analyzed using the Maximum Caliber framework. This approach allows us to estimate species interactions, migration rates, and environmental variations, enabling the prediction of the system's future trajectories.