Abstract: The concept of resilience has evolved considerably since Holling’s (1973) seminal paper. Different interpretations of what is meant by resilience, however, cause confusion. Resilience of a system needs to be considered in terms of the attributes that govern the system’s dynamics. Three related attributes of social– ecological systems (SESs) determine their future trajectories: resilience, adaptability, and transformability. Resilience (the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks) has four components—latitude, resistance, precariousness, and panarchy—most readily portrayed using the metaphor of a stability landscape. Adaptability is the capacity of actors in the system to influence resilience (in a SES, essentially to manage it). There are four general ways in which this can be done, corresponding to the four aspects of resilience. Transformability is the capacity to create a fundamentally new system when ecological, economic, or social structures make the existing system untenable. The implications of this interpretation of SES dynamics for sustainability science include changing the focus from seeking optimal states and the determinants of maximum sustainable yield (the MSY paradigm), to resilience analysis, adaptive resource management, and adaptive governance. INTRODUCTION An inherent difficulty in the application of these concepts is that, by their nature, they are rather imprecise. They fall into the same sort of category as “justice” or “wellbeing,” and it can be counterproductive to seek definitions that are too narrow. Because different groups adopt different interpretations to fit their understanding and purpose, however, there is confusion in their use. The confusion then extends to how a resilience approach (Holling 1973, Gunderson and Holling 2002) can contribute to the goals of sustainable development. In what follows, we provide an interpretation and an explanation of how these concepts are reflected in the adaptive cycles of complex, multi-scalar SESs. We need a better scientific basis for sustainable development than is generally applied (e.g., a new “sustainability science”). The “Consortium for Sustainable Development” (of the International Council for Science, the Initiative on Science and Technology for Sustainability, and the Third World Academy of Science), the US National Research Council (1999, 2002), and the Millennium Ecosystem Assessment (2003), have all focused increasing attention on such notions as robustness, vulnerability, and risk. There is good reason for this, as it is these characteristics of social–ecological systems (SESs) that will determine their ability to adapt to and benefit from change. In particular, the stability dynamics of all linked systems of humans and nature emerge from three complementary attributes: resilience, adaptability, and transformability. The purpose of this paper is to examine these three attributes; what they mean, how they interact, and their implications for our future well-being. There is little fundamentally new theory in this paper. What is new is that it uses established theory of nonlinear stability (Levin 1999, Scheffer et al. 2001, Gunderson and Holling 2002, Berkes et al. 2003) to clarify, explain, and diagnose known examples of regional development, regional poverty, and regional CSIRO Sustainable Ecosystems; University of Wisconsin-Madison; Arizona State University Ecology and Society 9(2): 5. http://www.ecologyandsociety.org/vol9/iss2/art5 sustainability. These include, among others, the Everglades and the Wisconsin Northern Highlands Lake District in the USA, rangelands and an agricultural catchment in southeastern Australia, the semi-arid savanna in southeastern Zimbabwe, the Kristianstad “Water Kingdom” in southern Sweden, and the Mae Ping valley in northern Thailand. These regions provide examples of both successes and failures of development. Some from rich countries have generated several pulses of solutions over a span of a hundred years and have generated huge costs of recovery (the Everglades). Some from poor countries have emerged in a transformed way but then, in some cases, have been dragged back by higher-level autocratic regimes (Zimbabwe). Some began as localscale solutions and then developed as transformations across scales from local to regional (Kristianstad and northern Wisconsin). In all of them, the outcomes were determined by the interplay of their resilience, adaptability, and transformability. There is a major distinction between resilience and adaptability, on the one hand, and transformability on the other. Resilience and adaptability have to do with the dynamics of a particular system, or a closely related set of systems. Transformability refers to fundamentally altering the nature of a system. As with many terms under the resilience rubric, the dividing line between “closely related” and “fundamentally altered” can be fuzzy, and subject to interpretation. So we begin by first offering the most general, qualitative set of definitions, without reference to conceptual frameworks, that can be used to describe these terms. We then use some examples and the literature on “basins of attraction” and “stability landscapes” to further refine our definitions. Before giving the definitions, however, we need to briefly introduce the concept of adaptive cycles. Adaptive Cycles and Cross-scale Effects The dynamics of SESs can be usefully described and analyzed in terms of a cycle, known as an adaptive cycle, that passes through four phases. Two of them— a growth and exploitation phase (r) merging into a conservation phase (K)—comprise a slow, cumulative forward loop of the cycle, during which the dynamics of the system are reasonably predictable. As the K phase continues, resources become increasingly locked up and the system becomes progressively less flexible and responsive to external shocks. It is eventually, inevitably, followed by a chaotic collapse and release phase (Ω) that rapidly gives way to a phase of reorganization (α), which may be rapid or slow, and during which, innovation and new opportunities are possible. The Ω and α phases together comprise an unpredictable backloop. The α phase leads into a subsequent r phase, which may resemble the previous r phase or be significantly different. This metaphor of the adaptive cycle is based on observed system changes, and does not imply fixed, regular cycling. Systems can move back from K toward r, or from r directly into Ω, or back from α to Ω. Finally (and importantly), the cycles occur at a number of scales and SESs exist as “panarchies”— adaptive cycles interacting across multiple scales. These cross-scale effects are of great significance in the dynamics of SESs.