Dynamic environment-induced multistability and critical transition in a metacommunity ecosystem.

Area of Science:

• Ecology
• Theoretical Ecology
• Mathematical Biology

Background:

• Metacommunity dynamics are crucial for understanding species persistence across landscapes.
• Environmental variability and coupling mechanisms significantly influence ecological community structure.
• Predicting critical transitions in ecosystems remains a key challenge.

Purpose of the Study:

• To investigate multistability and critical transitions in a consumer-resource metacommunity model.
• To analyze the impact of environment-dependent coupling on complex ecological dynamics.
• To assess the predictability of critical transitions using early warning signals.

Main Methods:

• Development of a metacommunity model incorporating consumer-resource populations and dispersal.
• Application of nonlinear environmental coupling and diffusive coupling mechanisms.
• Utilizing basin stability measures to quantify the probability of alternative stable states.
• Analysis of critical slowing-down indicators (lag-1 autocorrelation, variance) for transition prediction.
• Exploration of network structures to identify synchronization and multiclustering.

Main Results:

• The coupled system exhibits bistability, multistability, and critical transitions driven by environmental dynamics.
• Basin stability analysis quantifies the likelihood of alternative community states.
• Critical transitions, both increasing and decreasing species density, were identified under stochastic fluctuations.
• Critical slowing-down indicators demonstrated robustness in forewarning transitions.
• Network analysis revealed synchronization and multiclustering patterns based on initial conditions.

Conclusions:

• Environmental heterogeneity, modulated by specific magnitudes of dynamic coupling, is a key determinant of metacommunity stability and persistence.
• The study provides a framework for predicting ecosystem stability and anticipating critical transitions.
• Understanding complex dynamics in metacommunities is essential for effective ecological management and conservation.