Predicting Unobserved Driver of Regime Shifts in Social-Ecological Systems with Universal Dynamic Equations

Ecosystems around the world are anticipated to undergo regime shifts as temperatures rise and other climatic and anthropogenic perturbations erode resilience. Forecasting these nonlinear ecosystem dynamics can help stakeholders prepare for rapid changes. One major challenge is that regime shifts can be difficult to predict when driven by unobserved factors—such as illegal fishing from a fishery or unreported poaching in a game reserve. This paper advances scientific machine learning methods, specifically universal dynamic equations (UDEs), to identify changes in an unobserved bifurcation parameter and predict ecosystem regime shifts.

Key Innovation: UDEs combine mechanistic ecological models (encoding known biological processes) with neural networks (learning unknown relationships from data) to create a hybrid architecture that maintains physical realism while gaining flexibility. The framework can identify changes in unobserved bifurcation parameters and predict regime shift timing using only observable population data.

Demonstration: We test this approach on simulated data from a dynamic model of a species population experiencing loss due to unobserved extraction. The scenario: population density is observable, but the slowly increasing harvest rate (the hidden driver) is not. The UDE successfully identified changes in the unobserved harvest parameter and predicted the timing of the impending regime shift.

Results: Compared to alternative forecasting methods, our UDE approach provides:

• Superior accuracy in identifying unobserved parameter changes across all system regimes
• Reliable early warnings of impending regime shifts
• Better short-term predictions with fewer data
• Robust performance during rapid transitions between stable states

Impact: This approach provides ecosystem stakeholders and managers with new methods to identify unobserved changes in key parameters that drive nonlinear change—enabling proactive intervention for fisheries, game reserves, and other threatened ecosystems where the most critical drivers remain hidden from monitoring systems.

Authors: Kunal J. Rathore, John H. Buckner, Zechariah D. Meunier, James R. Watson
Affiliation: Oregon State University, College of Earth, Ocean, and Atmospheric Sciences

Recommended citation: Rathore, K. J., Buckner, J. H., Meunier, Z. D., Esquivel, J. A., & Watson, J. R. (2025). Predicting unobserved driver of regime shifts in social-ecological systems with universal dynamic equations. https://ecoevorxiv.org/repository/view/11165/