Managing with logic: A Bayesian causal network assessment using the critical list of variables for sustaining the commons in the Monarch Reserve

Abstract

There have been calls for systematic research approaches and innovative methods so that lessons from efficient common pool resource (CPR) management systems be scaled up and used as models for more efficient and strategic interventions. As an example, the Monarch Butterfly Biosphere Reserve in Mexico has a varied history of management success and requires a new assessment approach to ensure long-term and consistent success, as threats from urban sprawl and climate change press in. The well-established critical list of variables for sustaining the commons, along with Bayesian causal networks were used as an integrative method to assess what variables were most relevant to current management conditions. The critical list is a list of qualified variables that are substantive in cases of successful common pool resource management. Bayesian causal networks show individual and conditional relationships in complex systems, easily integrating both qualitative and quantitative data. The aim of this study is to evaluate and contrast four sites that represent relatively successful sustainable forest management in the Monarch Butterfly Biosphere Reserve and determine whether full compliance with the critical list is necessary for management efficiency. Forest cover change was maintained above a threshold of < 0.03% forest cover loss inside the reserve in all cases, and was the proxy measure of overall forest management success. The BCNs signaled small size and well-defined boundaries (both in the resource system category and in the social group category), as the most influential both as single influence variables and with conditional influence, in all cases. Full compliance with the theoretical qualifications of the critical variables was not necessary for management success.