Background/Question/Methods Systematic yet flexible tools for common pool resource management are imperative considering the dynamic nature of local management. The Monarch Butterfly Biosphere Reserve (MBBR) in Mexico has a varied history of management efficiency, even to the present, and requires a new assessment approach to ensure long-term and consistent efficiency as threats from urban sprawl and climate change press in that facilitates monitoring. What variables are influential in four preliminary cases of efficient forest management in the MBBR? Are there conditional relationships? The critical list of variables for sustaining the commons is a means to determine the factors that seem to be generally present in all cases of efficient CPR management, so it was used as the parameters and operationalized based on established, theoretical context. Bayesian Causal Networks (BCN) show individual and conditional relationships and easily integrate both qualitative and quantitative data for quantitative outputs for more informative assessments. BCNs were used to determine the influence and conditional relationships present in the four preliminary cases in the MBBR among the critical list of variables for sustaining the commons. Forest cover change was determined using Google Earth Pro and ArcGIS. Results/Conclusions FCC-- Dense forest cover has not only been maintained but has increased in the reserve-area since 2006 for all four, generally gaining from areas that were previously degraded or disturbed forest during the 2006 to 2015 period. BCNs-- Singular most influential variable: matching harvest restrictions. When combined with the other influential variables, it resulted in significant conditionality. The most impactful conditional variable was WDB (resource characteristics), over all. These were consistent for all ANs. This was followed closely by resource small size. This preliminary study was an attempt to test an analytical approach that would indicate the range of influence a given variable in the critical list has on each variable category and then on the system of critical variables for a given settlement. Knowing the higher order variance allows us to ensure that intervention foci are not ignoring conditional relationships. The BCN models are easily adjusted, once established, to incorporate changes in establish parameters as well as introduce new variables.This is important in the face of pressing shifts and challenges posed by climate change.
