Abstract 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.

Process-based forest models are increasingly used to guide management, but few are validated against fine-scale spatial patterns that emerge from neighborhood interactions. We tested whether the spatially explicit individual-based model SORTIE-ND, which simulates growth, mortality, and recruitment as functions of neighbourhood interactions among individual trees, can reproduce observed fine-scale structure in boreal mixedwoods. Using long-term data from the Lake Duparquet Research and Teaching Forest (Québec) station, we initialized simulations from transect plots representing younger post-fire stands and compared simulated outcomes to independent hectare plots of similar ages along a 249-year chronosequence. The spatial structure was quantified with inhomogeneous l-functions for univariate and bivariate patterns, and model performance was assessed by comparing observed curves to simulation envelopes. SORTIE-ND reproduced fine-scale patterns for balsam fir and trembling aspen, showed partial agreement for white spruce, and failed to match the observed clustering of paper birch. Cross-species patterns were captured for fir–aspen but not for pairs involving white spruce. These results indicate that SORTIE-ND can approximate fine-scale spatial patterns for dominant species in boreal mixedwoods, but limitations remain where key processes (e.g., vegetative propagation, substrate dependence) are under-represented. We discuss implications for stand- to landscape-scale management and recommend model extensions and more independent validation to improve generality.