Integrating science through Bayesian Belief Networks : case study of Lyngbya in Moreton Bay

Abstract

Bayesian Belief Networks (BBNs) are emerging\ud as valuable tools for investigating complex\ud ecological problems. In a BBN, the important\ud variables in a problem are identified and causal\ud relationships are represented graphically.\ud Underpinning this is the probabilistic framework\ud in which variables can take on a finite range of\ud mutually exclusive states. Associated with each\ud variable is a conditional probability table (CPT),\ud showing the probability of a variable attaining\ud each of its possible states conditioned on all\ud possible combinations of it parents. Whilst the\ud variables (nodes) are connected, the CPT attached\ud to each node can be quantified independently.\ud This allows each variable to be populated with the\ud best data available, including expert opinion,\ud simulation results or observed data. It also allows\ud the information to be easily updated as better data\ud become available ----- ----- This paper reports on the process of developing a\ud BBN to better understand the initial rapid growth\ud phase (initiation) of a marine cyanobacterium,\ud Lyngbya majuscula, in Moreton Bay, Queensland.\ud Anecdotal evidence suggests that Lyngbya blooms\ud in this region have increased in severity and\ud extent over the past decade. Lyngbya has been\ud associated with acute dermatitis and a range of\ud other health problems in humans. Blooms have\ud been linked to ecosystem degradation and have\ud also damaged commercial and recreational\ud fisheries. However, the causes of blooms are as\ud yet poorly understood