Here, we synthesize conceptual frameworks, applied modeling approaches, and as case studies to highlight complex social-ecological system (SES) dynamics that inform environmental policy, conservation and management. Although a set of “good practices” about what constitutes a good SES study are emerging, there is still a disconnection between generating SES scientific studies and providing decision-relevant information to policy makers. Classical single variable/hypothesis studies rooted in one or two disciplines are still most common, leading to incremental growth in knowledge about the natural or social system, but rarely both. The recognition of human dimensions is a key aspect of successful planning and implementation in natural resource management, ecosystem-based management, fisheries management, and marine conservation. The lack of social data relating to human-nature interactions in this particular context is now seen as an omission, which can often erode the efficacy of any resource management or conservation action. There have been repeated calls for a transdisciplinary approach to complex SESs that incorporates resilience, complexity science characterized by intricate feedback interactions, emergent processes, non-linear dynamics and uncertainty. To achieve this vision, we need to embrace diverse research methodologies that incorporate ecology, sociology, anthropology, political science, economics and other disciplines that are anchored in empirical data. We conclude that to make SES research most useful in adding practical value to conservation planning, marine resource management planning processes and implementation, and the integration of resilience thinking into adaptation strategies, more research is needed on (1) understanding social-ecological landscapes and seascapes and patterns that would ensure planning process legitimacy, (2) costs of transformation (financial, social, environmental) to a stable resilient social-ecological system, (3) overcoming place-based data collection challenges as well as modeling challenges.