The science of ecology has been the subject of considerable criticism recently, much of which centers on the gap between ecological theory and its practical application. A major difficulty with integrating current ecological thinking into adaptive management and sustainable policy of goods and services in SESs is a lack of solid scientific understanding, on one side, and knowledge transfer to managers and policymakers, on the other, in four areas within complex systems science: (1) the time- and context-dependent nature of problems; (2) types and role of interactions in systems theory and human system analysis (e.g., how feedbacks can determine system behavior); (3) types of mechanisms of transformation and adaptation in interrelated human and natural systems (e.g., how general is the adaptive cycle model); and (4) how to identify and address mismatches of scale between human actions and responsibility and natural interactions.

One way of dealing with these problems is to look retrospectively at the experienced evolution of an SES and by observing trends of effects caused by past exposure to stressors, deriving information concerning systems' dynamics and responses. A retrospective approach can deal with both short-term processes due mainly to human activities or slow processes involving a long time span (decades to centuries), thus revealing fast and slow dynamics and shedding light on the role of feedback mechanisms in SESs. So far, there is little field experience with estimating system properties of SESs, and little understanding of their sensitivity to changes in both natural and social components. This shortage coupled with inherent difficulties of carrying out practical field experience are barriers to building understanding through empirical study of coupled human and natural systems. But providing long time series of observations and studying the history of a system we may be better able to deepen our understanding of scale-dependent regulatory mechanisms acting within a complex system and to broaden our scientific and institutional memory for understanding system change. This will increase our adaptive capacity by helping to take into account the anticipated changes of the driving forces at work and of their consequent disturbances, supporting modeling of the present situation and predictability of future scenarios.

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