Human-dominated landscapes, encompassing terrestrial, freshwater, and urban ecosystems, are increasingly vulnerable to abrupt and potentially irreversible changes known as tipping points, driven by anthropogenic pressures such as climate change, habitat fragmentation, and pollution. These tipping points represent critical thresholds where gradual stressors precipitate nonlinear shifts in ecosystem states, often leading to degraded functionality and loss of biodiversity. Early warning signals (EWS) offer a promising approach to anticipate such transitions, enabling proactive management. This narrative review synthesizes recent advancements in EWS detection across diverse ecosystems, emphasizing the unification of indicators that transcend traditional boundaries. Drawing from peer-reviewed literature published between 2019 and 2025, we examine generic EWS based on critical slowing down, such as increased autocorrelation and variance, alongside system-specific metrics like spatial patterns and trait variability. In terrestrial systems, remote sensing reveals resilience losses in forests and drylands through vegetation indices. Freshwater ecosystems, particularly lakes, demonstrate mixed EWS reliability due to data limitations and non-bifurcation shifts. Urban environments, as highly modified human-dominated spaces, exhibit social-ecological tipping dynamics, with EWS incorporating socio-economic factors like polarization and displacement. By integrating these cross-ecosystem insights, we highlight commonalities in EWS performance, such as the benefits of multivariate and machine learning approaches, while addressing challenges like noise, seasonality, and cascading effects. This unification fosters a holistic framework for monitoring and mitigating tipping risks in interconnected landscapes, underscoring the need for enhanced data integration and adaptive governance to enhance ecosystem resilience in the Anthropocene.
This work is licensed under a Creative Commons Attribution 4.0 International License.
