Socio-economic factors boosting the effectiveness of marine protected areas: A Bayesian network analysis

Marine protected areas (MPAs) represent an example of nature-based solutions for the conservation and sustainable management of marine biodiversity. Despite the number of MPAs growing worldwide, many of them fail to achieve their goals, sometimes up to the point of becoming the so-called “paper parks”: protected areas without real protection or enforcement that are virtually non-existent in terms of their effectiveness in achieving the ecological and socioeconomic goals for which they have been set up. Following the Kunming–Montreal Biodiversity Agreement (COP 15), the EU Biodiversity Strategy for 2030, and the Biodiversity Beyond National Jurisdiction treaty, global MPA coverage should increase substantially in the coming years. Hence, identifying the factors that contribute to raising the effectiveness of MPAs is pivotal to informing their planning and management. Our study introduces a model based on the Bayesian network that allows testing how different socioeconomic factors (e.g., stakeholder involvement, increased communication and enforcement) can impact the effectiveness of MPAs. The system is a user-friendly decision-support tool to quantify the contribution of each factor in the creation of a successful MPA, thus narrowing the gap between science and decision-making. We modelled the evolution of the effectiveness of MPAs under three contrasting policy-relevant scenarios based on the Intergovernmental Panel on Climate Change frameworks. Our results indicate that the highest and lowest the effectiveness of MPAs is achieved under the “global sustainability” and “national enterprise” scenarios, respectively. Our work sheds light on the complexity of the interactions among the different factors underpinning the effectiveness of MPAs and supports the growth process of MPAs at the global level on the pathway towards the sustainable exploitation of marine living resources. • Many marine protected areas worldwide fail to achieve their goals. • A Bayesian network model was built to predict marine protected area effectiveness. • The model uses a global literature review as a proxy of expert judgement. • Effectiveness is highest under the “global sustainability” policy-relevant scenario.