Investigating the Use of a Bayesian Network to Model the Risk of Lyngbya majuscula Bloom Initiation in Deception Bay, Queensland

Abstract

Modelling the risk factors driving an environmental problem can be problematic when published data describing variables and their interactions are sparse. In such cases, expert opinion forms a vital source of information. Here we demonstrate the utility of a Bayesian Net (BN) model to integrate available information in a risk analysis setting. As an example, we use this methodology to explore the major factors influencing initiation of Lyngbya majuscula blooms in Deception Bay, Queensland. Over the past decade Lyngbya blooms have increased in both frequency and extent on seagrass beds in Deception Bay, with a range of adverse effects.
This model was used to identify the main factors that could trigger a Lyngbya bloom. The five factors found to have the greatest effect on Lyngbya bloom initiation were: the available nutrient pool, water temperature, redox state of the sediments, current velocity and light. Scenario analysis was also conducted to determine the sensitivity of the model to different combinations of variable states.
The model has been used to identify knowledge gaps and therefore to direct additional research efforts in Deception Bay. With minor changes the model can be used to better understand the factors triggering Lyngbya blooms in other coastal regions.