Understanding the decline and resilience loss of a long-lived social-ecological system: insights from system dynamics

摘要

Collapse of social-ecological systems (SESs) is a common process in human history. Depletion of natural resources, scarcity of human capital, or both, is/are common pathways toward collapse. We use the system dynamics approach to better understand specific problems of small-scale, long-lived SESs. We present a qualitative (or conceptual) model using the conceptualization process of the system dynamics approach to study the dynamics of an oasis in Mexico that has witnessed a dramatic transition to decline in recent decades. We used indepth interviews, participant observation, expert opinions, and official statistical data sets to define the boundaries, and structure in a causal loop diagram of our studied system. We described historical trends and showed the reference mode for the main system variables (observed data), and analyzed the expected system behavior according to the system structure. We identified the main drivers that changed the system structure, as well as structural changes, and the effects of these changes on the dynamics, resilience, and vulnerability of this SES. We found that the tendency of this SES toward collapse was triggered by exogenous factors (growth of modern agriculture in nearby valleys, and socio-political relocation), and was maintained by an endogenous structure. These structural changes weakened the resilience of this SES. One of these changes resulted in a long-term maladaptation of the SES, which increased its vulnerability to frequent system disturbances (hurricanes and droughts). The conceptual model developed provides an in-depth qualitative description of the system, with an important amount of qualitative and quantitative information, to establish the structural hypothesis of the observed behavior. Using this qualitative model, the next research steps are to develop a quantitative model to test the qualitative theories, and to explore future scenarios of system resilience for decision-making processes to improve local conditions and restore the well-functioning of the system.