Although habitat models deals with some aspects of biodiversity, but it can be used in conservation management of single or few species. These models are useful in predicting changes in habitat variables and their effects on species habitat suitability and can be used as decision support tools. Persian fallow deer is a valuable species with unknown habitat requirements in some extent and their habitat ecology are not evaluated in experimental studies. Decision makers only rely on expert knowledge. This study aimed to show how Bayesian modeling framework can be used to integrate expert knowledge and other available data in different formats. There is uncertainty about habitat data, and Bayesian modeling can handle this problem using probabilities in its calculations. In this study, key factors affecting Persian fallow deer habitat suitability and their cause and effects have been identified using expert knowledge and also available published data. In the next step, influence diagram were entered into a Bayesian belief networks. The model was populated with subjective probabilities elicited from experts. Model behavior was validated using scenario and sensitivity analysis. The model can incorporate uncertainty into its prediction, and also can be updated when new knowledge about this species and its habitat priorities emerges.
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