title: Integrated Bayesian network framework for modeling complex ecological issues.
abstract: The management of environmental problems is multifaceted, requiring varied and sometimes conflicting objectives and perspectives to be considered. Bayesian network (BN) modeling facilitates the integration of information from diverse sources and is well suited to tackling the management challenges of complex environmental problems. However, combining several perspectives in one model can lead to large, unwieldy BNs that are difficult to maintain and understand. Conversely, an oversimplified model may lead to an unrealistic representation of the environmental problem. Environmental managers require the current research and available knowledge about an environmental problem of interest to be consolidated in a meaningful way, thereby enabling the assessment of potential impacts and different courses of action. Previous investigations of the environmental problem of interest may have already resulted in the construction of several disparate ecological models. On the other hand, the opportunity may exist to initiate this modeling. In the first instance, the challenge is to integrate existing models and to merge the information and perspectives from these models. In the second instance, the challenge is to include different aspects of the environmental problem incorporating both the scientific and management requirements. Although the paths leading to the combined model may differ for these 2 situations, the common objective is to design an integrated model that captures the available information and research, yet is simple to maintain, expand, and refine. BN modeling is typically an iterative process, and we describe a heuristic method, the iterative Bayesian network development cycle (IBNDC), for the development of integrated BN models that are suitable for both situations outlined above. The IBNDC approach facilitates object-oriented BN (OOBN) modeling, arguably viewed as the next logical step in adaptive management modeling, and that embraces iterative development. The benefits of OOBN modeling in the environmental community have not yet been fully realized in environmental management research. The IBNDC approach to BN modeling is described in the context of 2 case studies. The first is the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia where 3 existing models are being integrated, and the second case study is the viability of the free-ranging cheetah (Acinonyx jubatus) population in Namibia where an integrated OOBN model is created consisting of 3 independent subnetworks, each describing a particular aspect of free-ranging cheetah population conservation.
authors.affiliation: Discipline of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia. sandra.johnson@qut.edu.au
authors.forename: Sandra
authors.initials: S
authors.lastname: Johnson
authors.forename: Kerrie
authors.initials: K
authors.lastname: Mengersen
mesh_terms: D002612:Acinonyx; D000818:Animals; D001499:Bayes Theorem; D000458:Cyanobacteria / Q000254:growth & development; D055669:Ecological and Environmental Phenomena; D005068:Eutrophication; D008962:Models, Theoretical; D009276:Namibia; D011381:Programming Languages