Experiences in applying Bayesian integrative models in interdisciplinary modeling: the computational and human challenges

We review the experience obtained in using integrative Bayesian models in interdisciplinary analysis focusing on sustainable use of marine resources and environmental management tasks. We have applied Bayesian models to both fisheries and environmental risk analysis problems. Bayesian belief networks are flexible tools that can take into account the different research traditions and the various types of information sources. We present two types of cases. With the Baltic salmon stocks modeled with Bayesian techniques, the existing data sets are rich and the estimation of the parameters is possible by data. In the environmental risk analysis we have mainly focused on the oil spills, where the data sets are poor and also the published papers are scarce. Therefore, some of our applications are based mainly on the use of expert knowledge. The salmon models were the first Bayesian models to be used in the scientific advisory process in ICES (International Council for the Exploration of the Sea). Long discussions were needed to make the results accepted by all actors, especially in cases, where overfishing had been so severe that there was no chance to estimate the carrying capacity of the rivers by observed data only. In the case of oil spills, there is much less data than in the case of fisheries and the role of existing publications and the expert knowledge is more important. Moreover, legislation does not specifically define the aims of the spill management (whether to safeguard human capital, large populations or populations of rare and threatened species). We have chosen the state of the threatened species as the decision criteria, as they have a status in other parts of Finnish legislation. One of the scientific quality criteria for using the Bayesian decision analysis for management is that the uncertainty estimates are scientifically justified. Especially in cases where society is assumed to be highly risk averse, the uncertainty estimates related to alternative management options may have a crucial role. However, the use of Bayesian parameter estimation techniques may be time consuming and research projects can be difficult to manage due to unpredictable technical problems related to parameter estimation. Biology, sociology and environmental economics have their own scientific traditions. Bayesian models are becoming traditional tools in fisheries biology, where uncertainty estimates of management options are frequently required. In sociology, the traditions allow the subjective treatment of the information, which supports the use of prior information, an elementary part of Bayesian techniques, in the models. Many of the environmental risks have also economic components, which favors the use of quantitative risk analysis. However, the traditions and quality criteria of these scientific fields are in many respects different. This creates both technical and human challenges to the modeling tasks.