article · Journal of Applied Ecology
Global ecosystem restoration efforts have expanded under initiatives such as the Bonn Challenge and the UN Decade on Ecosystem Restoration. Despite science-based guidelines, project outcomes vary widely, partly because ecosystem components respond unpredictably to planned interventions. Ecosystems operate as complex systems characterised by non-linearity, regime shifts, ecological resilience, and feedback mechanisms that shape degradation and recovery pathways. A framework named Explore Before You Restore incorporates these complex systems science concepts into standard restoration project cycles through a dedicated assessment phase. This approach provides indicators and methods enabling restoration teams to evaluate complex dynamics before intervening. Integrating complex systems assessment into project workflows and international restoration guidelines addresses underlying causes of project failure, potentially improving ecological recovery outcomes while identifying key science and policy tasks required for full operationalisation.
Millions of hectares are slated for ecological recovery globally, yet many restoration projects fail to achieve expected results due to unexpected ecosystem shifts. By accounting for ecological complexity, regime shifts, and non-linear feedbacks prior to intervention, decision-makers can design more reliable interventions, avoid costly failures, and improve long-term environmental outcomes.
This work provides an early-stage methodological framework and assessment guidance for environmental practitioners, restoration project managers, and policy bodies. It enables practitioners to evaluate non-linear dynamics and resilience thresholds before deploying interventions. Because the work identifies indicators, methods, and outstanding science and policy tasks rather than a packaged commercial product, it appears to be at an early conceptual stage requiring further operationalisation.
AI-generated from the published abstract. Always read the original work before citing.
Abstract The global movement for ecosystem restoration has gained momentum in response to the Bonn Challenge (2010) and the UN Decade on Ecosystem Restoration (UNDER, 2021–2030). While several science‐based guidelines exist to aid in achieving successful restoration outcomes, significant variation remains in the outcomes of restoration projects. Some of this disparity can be attributed to unexpected responses of ecosystem components to planned interventions. Given the complex nature of ecosystems, we propose that concepts from Complex Systems Science (CSS) that are linked to non‐linearity, such as regime shifts, ecological resilience and ecological feedbacks, should be employed to help explain this variation in restoration outcomes from an ecological perspective. Our framework, Explore Before You Restore, illustrates how these concepts impact restoration outcomes by influencing degradation and recovery trajectories. Additionally, we propose incorporating CSS concepts into the typical restoration project cycle through a CSS assessment phase and suggest that the need for such assessment is explicitly included in the guidelines to improve restoration outcomes. To facilitate this inclusion and make it workable by practitioners, we describe indicators and methods available for restoration teams to answer key questions that should make up such CSS assessment. In doing so, we identify key outstanding science and policy tasks that are needed to further operationalize CSS assessment in restoration. Synthesis and applications . By illustrating how key Complex Systems Science (CSS) concepts linked to non‐linear threshold behaviour can impact restoration outcomes through influencing recovery trajectories, our framework Explore Before You Restore demonstrates the need to incorporate Complex Systems thinking in ecosystem restoration. We argue that inclusion of CSS assessment into restoration project cycles, and more broadly, into international restoration guidelines, may significantly improve restoration outcomes.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1111/1365-2664.14614
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.