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<p>In this study, we have quantified the resilience of a socio-ecological system (SES) by considering it as a non-linear entity. Quantifying resilience in SES remains challenging due to their non-linear dynamics, interdependencies, and absence of mathematical links between risk and resilience. Existing approaches rely on linear models, static indices, or frameworks that overlook complex parametric interactions within SES. This study introduces a novel resilience quantification method that quantifies resilience as the dynamic capacity of an SES to drive instantaneous risk toward a minimized risk through coping, adaptation, transformation, and adequate diverse capitals. The minimized risk (resilient state) is achieved with adequate capital across different forms, whereas inadequate capital leads to maximized risk and tipping points. It allows communities with low economic capital to exhibit high resilience through strong human, social, natural, or physical capital. It also prevents strong substitutability assumptions where economic capital fully replaces natural capital. We used non-linear optimization to compute the resilient condition and the tipping point, biophysical models to compute the external stressors, the Bayesian Network to capture the interdependency and interconnectivity of different capitals of the complex system, and mathematical equations to calculate the recovery time and time required to reach the resilient condition or the tipping point. Results are visualized in a Resilience-Risk-Capital (RRC) diagram, which maps the pathway to resilience and represents the theoretical resilience properties of a SES. A new hybrid Participatory Rural Appraisal–Citizen Science method is proposed to collect dynamically updated community capital data for the RRC diagram. Applied to a coastal village in Bangladesh using secondary field data, scenarios demonstrate how a community achieves a stable equilibrium condition after recurring hazards by enhancing capital and reducing risk non-linearly. This approach fills a critical gap in SES literature by providing the first risk-domain-based resilience quantification for non-linear SES. It offers an actionable and scalable method—from local communities to regional levels—for disaster risk reduction, climate adaptation, and sustainable development in vulnerable settings.</p>

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Quantifying resilience in socio ecological systems using a risk based approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月25日

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  • Anisul Haque,
  • Marin Akter,
  • Md. Rayhanur Rahman,
  • Sheikh Mohiuddin Shahrujjaman,
  • Mashfiqus Salehin,
  • Mohammad Ashraful Haque Mollah,
  • Munsur Rahman

摘要

In this study, we have quantified the resilience of a socio-ecological system (SES) by considering it as a non-linear entity. Quantifying resilience in SES remains challenging due to their non-linear dynamics, interdependencies, and absence of mathematical links between risk and resilience. Existing approaches rely on linear models, static indices, or frameworks that overlook complex parametric interactions within SES. This study introduces a novel resilience quantification method that quantifies resilience as the dynamic capacity of an SES to drive instantaneous risk toward a minimized risk through coping, adaptation, transformation, and adequate diverse capitals. The minimized risk (resilient state) is achieved with adequate capital across different forms, whereas inadequate capital leads to maximized risk and tipping points. It allows communities with low economic capital to exhibit high resilience through strong human, social, natural, or physical capital. It also prevents strong substitutability assumptions where economic capital fully replaces natural capital. We used non-linear optimization to compute the resilient condition and the tipping point, biophysical models to compute the external stressors, the Bayesian Network to capture the interdependency and interconnectivity of different capitals of the complex system, and mathematical equations to calculate the recovery time and time required to reach the resilient condition or the tipping point. Results are visualized in a Resilience-Risk-Capital (RRC) diagram, which maps the pathway to resilience and represents the theoretical resilience properties of a SES. A new hybrid Participatory Rural Appraisal–Citizen Science method is proposed to collect dynamically updated community capital data for the RRC diagram. Applied to a coastal village in Bangladesh using secondary field data, scenarios demonstrate how a community achieves a stable equilibrium condition after recurring hazards by enhancing capital and reducing risk non-linearly. This approach fills a critical gap in SES literature by providing the first risk-domain-based resilience quantification for non-linear SES. It offers an actionable and scalable method—from local communities to regional levels—for disaster risk reduction, climate adaptation, and sustainable development in vulnerable settings.