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

A Bayesian causal network analysis of the Monarch Butterfly Biosphere Reserve reveals that small size and well-defined boundaries—both in resource systems and social groups—are critical to sustainable forest management success, even when full compliance with theoretical best practices is not achieved. The study used a combination of the critical list of variables for sustaining the commons and Bayesian causal networks (BCNs) to assess four successfully managed sites within the reserve. Despite threats from urban sprawl and climate change, all sites maintained forest cover loss below 0.03%, demonstrating that strict adherence to theoretical frameworks is unnecessary for effective conservation outcomes. This approach offers a scalable model for strategic interventions in common pool resource management.

Mishkin, M., & Kiker, G. (2021). Managing with logic: A Bayesian causal network assessment using the critical list of variables for sustaining the commons in the Monarch Reserve. Global Ecology and Conservation, 32, e01931. https://doi.org/10.1016/j.gecco.2021.e01931