Forecasting ecological trajectories from ecological dynamic regimes to improve resilience analysis

The ecological dynamic regime (EDR) framework was recently proposed as an alternative to equilibrium‐based approaches for assessing ecological resilience in empirical systems, explicitly incorporating dynamic regimes as a reference for assessing the system's deviation during disturbances. Yet the lack of predictive capacity of the EDR framework limits its applications, especially when long‐term data are unavailable or the disturbed system is not well represented by frequently observed dynamics.

Here, we extend the EDR framework by introducing an algorithm (PETRA‐EDR: Predicted Ecological TRAjectories in Ecological Dynamic Regimes ) and a metric (MPD: Mean Predicted Deviation ) to forecast ecological dynamics and estimate prediction accuracy. Our method employs multivariate analyses and can be applied to any ecological system characterized by a set of state variables (e.g. species abundances, functional traits).

We conducted a simulation study to evaluate the performance of our method and illustrated its application using empirical data from Canadian boreal forests. Our results demonstrate the method's ability to forecast ecological dynamics and the effectiveness of distance‐weighting functions in improving predictions while mitigating the effects of insufficient sampling, observation noise and hidden variables.

Finally, we discuss the assumptions of our method and its applications for assessing ecological resilience to pulse disturbances and detecting regime shifts from a multidimensional, dynamic perspective.