Mechanism-Dependent Performance of Early Warning Signals: A Multi-Dataset Comparison of Coupling (Φ), Fisher Information, and Composite Indicators

This study evaluates the performance of early warning signals (EWS) for ecological regime shifts across multiple datasets with distinct underlying collapse mechanisms. While early warning indicators such as lag-1 autocorrelation and Fisher Information have often been treated as broadly applicable, their reliability under mechanistically heterogeneous conditions remains insufficiently understood.   We test a mechanism-dependent hypothesis comparing coupling-based indicators (Φ, mean absolute cross-correlation) with stability-based indicators (Fisher Information, AR1) across five ecological datasets, including both externally forced transitions and intrinsically driven bifurcations. The primary hypothesis — that indicator performance systematically differs between forced and intrinsic systems — is not supported by statistical testing.   However, three robust findings emerge. First, the coupling indicator Φ consistently activates earlier than Fisher Information, providing a lead-time advantage of approximately 1.4–1.9× across most datasets, albeit with reduced discriminative precision. Second, Fisher Information exhibits substantially higher variability in performance, performing well in fold-type bifurcation systems but approaching random classification in acceleration-driven collapses. Third, indicator performance is better explained by the presence of an internal fold attractor and the direction of pre-collapse dynamics (deceleration versus acceleration) than by the conventional forced versus intrinsic classification.   Based on these findings, we propose a refined 2×2 mechanism taxonomy that links system dynamics to optimal indicator selection. Within this framework, coupling-based indicators act as distal early warning signals capturing structural synchronization, while stability-based indicators provide proximal, high-precision detection near the transition point. A composite indicator (CRTI-2) is evaluated as a mechanism-agnostic compromise, demonstrating stable intermediate performance across all datasets.   The results demonstrate that early warning signals are not universally transferable across collapse mechanisms. Instead, effective monitoring requires mechanism-aware indicator selection and explicit consideration of the trade-off between early detection and predictive precision. These findings have implications for ecological monitoring, complex systems diagnostics, and the design of early warning frameworks in heterogeneous dynamical environments.     early warning signals, regime shifts, critical transitions, ecological systems, Fisher Information, cross-correlation, coupling, autocorrelation, bifurcation theory, resilience, complex systems, regime shift detection, multivariate time series, system dynamics, collapse prediction