Tipping points in spatial ecosystems driven by short-range correlated noise.

Area of Science:

• Ecology
• Complex Systems Science
• Stochastic Processes

Background:

• Complex spatial systems are susceptible to critical transitions when perturbed.
• Previous research focused on white noise, leaving the impact of correlated noise under-explored.
• Understanding noise effects is crucial for predicting ecosystem stability.

Purpose of the Study:

• To investigate the influence of Ornstein-Uhlenbeck (OU) correlated noise on critical transitions in spatial ecosystems.
• To analyze both multiplicative and additive noise effects.
• To assess the reliability of spatial early warning indicators under varying noise conditions.

Main Methods:

• Modeling spatial ecosystems with Ornstein-Uhlenbeck (OU) correlated noise (multiplicative and additive).
• Analyzing three established spatial ecological models with different nonlinearities.
• Computing spatial early warning indicators: spatial variance, skewness, and correlation.

Main Results:

• Decreasing the noise correlation time of OU noise significantly increases the probability of critical transitions.
• This finding was consistent across diverse spatial ecological models.
• Spatial early warning indicators showed mixed reliability in predicting tipping points.

Conclusions:

• Correlated noise, particularly its temporal characteristics, plays a critical role in the stability of spatial ecosystems.
• The correlation time of noise is a key factor influencing the occurrence of critical transitions.
• While spatial indicators offer some predictive power, their reliability is contingent on noise properties.