Fraser River sockeye salmon: data synthesis and cumulative impacts

Bayesian belief networks (BBN)BBNs are models that graphically and probabilistically represent correlative and causativerelationships among variables (Cain 2001). A particular BBN defines various events, thedependencies between them, and the conditional probabilities involved in those dependencies.This is represented as a probabilistic graphical model that represents a set of variables and theirprobabilistic independencies. BBNs lend themselves well to representing the variability ofnatural systems and uncertainty of understanding, and the implications of this to managementdecisions (Kuikka et al. 1999). A BBN could be developed from the conceptual model (Figure3.3-1) to improve our understanding of the cumulative impacts, interactions, and relative effectsof stressors across all projects.Control chartsThis is a concept taken from statistical process control theory usually applied to a manufacturingprocess. Control charts are used on an ongoing basis to monitor whether or not the system is ‘incontrol’. Both the actual value and the variability are monitored. Early detection of systematicchanges resulting in either a change to the value or increase in variability (or both) is critical,presumably the same concepts could be useful for informing managers of a natural system. Itmay be useful to assess whether or not the productivity data has been stable or increasing interms of variability. During the course of this project, we identified two other sockeye relateddata sets where there appeared to be increasing variability over time: 1) age of return (Figure 4.5-1) and the 2) returning spawner migration route (Figure 4.5-2). It would be useful to quantifythis observation and if valid determine if there is some way to assess the cause.225