Abstract: Decisions about pest species management are often confounded by uncertainty about the location and density of the species, different values held by stakeholders and conflicting objectives. Decision frameworks can help remove much of the subjectiveness around pest management by exploring the uncertainties and risks associated with alternative management strategies. The inputs thought to be essential for improving the management of camels and optimising control strategies are generally based on evidence about camel density, impact and accessibility. This requires weighting intelligence gathered about camel behaviour (movement, distribution and abundance) and environmental data (rainfall, waterpoints and land systems). After considering landholder preferences, the utility of different control strategies for a known density of camels is then dictated by cost-efficiency. This will depend on the type of harvest or culling operation. Moreover, the cost of gathering intelligence versus uncertainty associated with the information also needs to be considered in any decision framework. We describe the development of a Bayesian Belief Network (BBN) decision support tool that organises evidence and decisions into nodes, by building conditional probability relationships between each node. The conditional probabilities are learnt from empirical data and prediction models. By coupling the BBN to a spatially-explicit stochastic simulation model, we demonstrate how it can be used to help find an optimal control strategies for each of a range of different scenarios.