Introducing adaptive waves as a concept to inform mental models of resilience

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Abstract

While ecological resilience is conceptually established, resilience concepts of social–ecological systems (SES) require further development, especially regarding their implementation in society. From the literature, (a) we identify the need for a revised conceptualization of SES resilience to improve its understanding for informing the development of adjusted mental models. (b) We stress the human capacity of social learning, enabling deliberate transformation of SES, for example of SES to higher scales of governance, thereby possibly increasing resilience. (c) We introduce the metaphor of adaptive waves to elucidate the differences between resilience planning and adaptation, by conceptualizing adaptation and transformation as dynamic processes that occur both inadvertently and deliberately in response to both shocks and to gradual changes. In this context, adaptive waves stress the human and social capacity to plan resilience with an intended direction and goal, and to dampen the negative effects of crises while understanding them as opportunities for innovation. (d) We illustrate the adaptive waves’ metaphor with three SES cases from tourism, forestry, and fisheries, where deliberate transformations of the governance structures lead to increased resilience on a higher governance scale. We conclude that conceptual SES resilience communication needs to clarify the role and potential of human and social capital in anticipating change and planning resilience, for example, on different scales of governance. It needs to emphasize the crucial importance of crises for innovation and transformation, relevant for the societal acceptance of crises as drivers of adaptation and transformation. The adaptive waves’ metaphor specifically communicates these aspects and may enhance the societal capacity, understanding, and willingness for planning resilience.

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Luthe, T., & Wyss, R. (2015). Introducing adaptive waves as a concept to inform mental models of resilience. Sustainability Science, 10(4), 673–685. https://doi.org/10.1007/s11625-015-0316-6

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