While mangroves are increasingly described as social-ecological systems (SESs), performing SES research is so much more than merely documenting local resource utilisation patterns in case studies. The aim of this paper is to review and show how ecological, human and institutional resilience could be understood and fostered in an era of uncertainty, through the adaptive cycle (AC) heuristic. Uncertainties come in many forms and shapes: climate change, social and economic dynamics, natural disasters, political and institutional disruption and ever-increasing public demands for participation. Social-ecological studies form windows of experimentation that can provide insights beyond their case-specific context. In order to synthesise and structure the cumulative knowledge base arising from existing and future studies, the need for a suitable overarching framework arose. Here, the AC heuristic represents the connectedness between variables of the mangrove SES versus the mangrove's accumulated capital (natural, built, human and social). We posit that the AC heuristic can be used to interpret spatial and temporal changes (ecological, social, economic, political) in mangrove SESs and we exemplify it by using the 2004 Indian Ocean tsunami as well as a century-long silviculture case. The AC, combined with the SES scheme, allows integration of the spato-temporal dynamics and the multi-dimensional character of mangrove SESs. We also reviewed the ecosystem functions, services and disservices of mangrove SESs, linking each of them to SES capital and variable (fast or slow) attributes, which in turn are closely linked to the different axes and phases of the AC. We call upon mangrove scientists from the natural, applied, social and human sciences to join forces in fitting diversified empirical data from multiple case studies around the world to the AC heuristic. The aim is to reflect on and understand such complex dynamic systems with stakeholders having various (mutual) relationships at risk of breaking down, and to prepare for interactive adaptive planning for mangrove forests.