Disastrous resonances: Social memory dynamics and extreme events. Key questions for cultural loss and landscape sustainability

Sustainability research has mostly ignored the resonant effects of the loss of key cultural information on disaster reduction, and disasters eroding such knowledge. This gap in the literature is especially salient in a rapidly changing world, where we often find that he relevant memory on how to cope with Extreme Events (EE) is not accessible to decisionmakers. In this perspective piece we focus on the combined effect of loss of skill, and experience and the strikes of disasters can act in a complex, resonant way to precipitate unforeseen effects that cascade across interconnected systems. This destructive resonance can potentially generate sudden collapses, difficult or impossible to recover from. This non-linear (unpredictable) susceptibility and lack of relevant experience is especially meaningful in landscapes where both environments and management practices are changing rapidly. In these cases, the access to accumulated memory and skill is easily disconnected. In our present world big --but also relatively small-- perturbations can trigger a complex interconnected cascading failure at even bigger scales. To address this complex and unexplored scenario, we propose a framework focusing on the interconnection between two social memory scales and two EE scales through four management stages: Mitigation, Preparation, Action, Recovery (MPAR). At the social level, community and administrative structures differently maintain, acquire and lose expertise on MPAR practices. At the EE level fast and slow-paced impacts (e.g. floods, tsunamis vs droughts, harvest failures), need different MPAR strategies. This basic framework allows to simplify the resonant scenario, allowing to initially model independently how loss of key MPAR practices increases susceptibility to disasters, and how disasters trigger the cultural loss of key MPAR practices. To this end we propose two strategies: 1) adapt coevolutionary biological and flooding models for coupling cultural loss affecting MPAR to disasters, 2) adapt network cascading science to model how impacts on social networks percolate to MPAR memory networks.

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