Bayesian Belief Networks as a tool for evidence-based conservation management

Effective conservation management is dependent on accessing and integrating different forms of evidence regarding the potential impacts of management interventions. Here, we explore the application of Bayesian Belief Networks (BBN), which are graphical models that incorporate probabilistic relationships among variables of interest, to evidence-based conservation management. We consider four case studies, namely: (i) impacts of deer grazing on saltmarsh vegetation; (ii) impacts of burning on upland bog vegetation; (iii) control of the invasive exotic plant Rhododendron ponticum; and (iv) management of lowland heathland by burning. Each of these themes is currently a significant conservation issue in the UK, and yet the potential outcomes of management interventions are poorly understood. Through these examples, we demonstrate that BBNs can be used to integrate and explore evidence from a variety of sources, including expert opinion and quantitative results from research investigations. Incorporation of such information in BBNs enables different sources of evidence to be compared, the potential impacts of management interventions to be explored and management trade-offs to be identified. BBNs also offer a highly visual tool for communicating the uncertainty associated with potential management outcomes to conservation practitioners, and they can also be readily updated as new evidence becomes available. Based on these features, we suggest that BBNs have outstanding potential for supporting evidence-based approaches to conservation management.