Quantifying resilience of socio-ecological systems through dynamic Bayesian networks

Abstract

Quantifying resilience of socio-ecological systems (SES) can be invaluable to delineate management strategies of natural resources and aid the resolution of socio-environmental conflicts. However, resilience is difficult to quantify and the factors contributing to it are often unknown. We provide a theoretical and conceptual framework to quantify resilience in a long-term context. Our approach uses elements from interdisciplinarity and network perspectives to establish links and causalities between social and ecological variables and resilience attributes. The evaluation and modeling of SES structure and function are established from the analysis of dynamic Bayesian networks (DBN). DBN models allow quantifying resilience through probabilities and offer a platform of interdisciplinary dialogue and an adaptive framework to address questions on ecosystem monitoring and management. The proposed DBN is tested in Monquentiva, a SES located in the high Andes of Colombia. We determined historical socio-ecological resilience from paleoecological evidence (palynological diversity, forest cover, fires, and precipitation) and social-economic factors (governance, social organization, and connectivity) between 1920 and 2019. We find that transformation processes in Monquentiva are mainly related to social change (e.g., social organization) and increased ecological diversity that in turn have fostered SES resilience between 1980 and 2019. The ability to predict the SES response over time and under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management. We conclude with a series of management and policy-relevant applications of the DBN approach for SES resilience assessment.