EGU24-20216, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Agent-based modelling for understanding the socio-ecological resilience in alpine mountain communities

Andreas Mayer1,2, Claudine Egger2, and Veronika Gaube2
Andreas Mayer et al.
  • 1Coupled human landscape systems - risk and resilience, Department of Geography, University of Innsbruck, Innsbruck, Austria
  • 2Institute of Social Ecology, Department of Economics and Social Sciences, University of Natural Resources and Life Sciences (Boku), Vienna, Austria

European mountain regions are becoming more vulnerable to natural hazards due to global change, climate change, and land-use change. Therefore, it is essential to understand their resilience. Currently, quantitative and dynamic models of coupled human-landscape interactions are in their infancy. However, agent-based modelling (ABM) approaches have high potential to advance the analysis of the interplay of natural and social factors affecting socio-ecological resilience in European mountain communities. The Socio-Ecological Land Agent-Based Model (SECLAND) integrates information from qualitative interviews and spatial data into a quantitative modelling environment. This enriches the diversity of scenario modelling beyond economic rationales by incorporating individual agent's motivations for land-use decisions. The outputs from this model have been used as input to hydrological or ecological models on multiple occasions.

SECLAND has been used to model the potential success of various adaptation strategies for coping with climate-induced natural hazards. In a study conducted in the department of Ariège, France, we analysed the potential impacts of intensified livestock grazing on mountain pastures under scenarios with strong climate change effects and increased extreme events. In this scenario, farmers use mountain pastures to seek additional forage resources in specific years. However, these grazing areas require considerate management in years when they are not needed for food provision. Our study also found that the utilization patterns of mountain pastures are strongly influenced by farm succession, vegetation regrowth on unused mountain pastures, and the search for cost-efficient forage resources. In a case study conducted in Eastern Austria, we found that adaptive learning moderates the decline in the number of active farms and farmland, regardless of the scenario conditions, compared to scenarios without adaptive learning. However, the results also indicate that adaptation increases the workload of farmers. This highlights the importance of considering more than just simplistic economic rationales when making land-use decisions. Agent-based models can be used to model socio-ecological responses and help cope with adaptation in complex socio-ecological systems.

Both studies emphasise that in the context of risk management and socio-ecological resilience, learning and managing additional workload are key factors for achieving adaptive success. To further improve, it is necessary to couple agent-based models with climatic and landscape models, allowing for bi-directional feedback between social and natural systems. SECLAND has been adapted to integrate adaptive learning processes, demonstrating the possibility of capturing mutual system dynamics and feedback loops. This allows the full capacity of agent-based models to be used to assess the resilience of mountain communities to cope with natural hazards, using a scenario approach that includes heterogeneous agents, different trajectories of socio-economic conditions, as well as global and climate change dynamics. This presentation outlines a conceptual framework for operationalizing an interdisciplinary effort within a modelling environment that integrates human decision-making, socio-economic conditions, and climatic and landscape dynamics.

How to cite: Mayer, A., Egger, C., and Gaube, V.: Agent-based modelling for understanding the socio-ecological resilience in alpine mountain communities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20216,, 2024.