Integrating Science Through Bayesian Belief Networks: Case Study of Lyngbya in Moreton Bay

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

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

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

Overcoming the technical problems associated with effective coastal monitoring systems

Coastal and aquatic ecosystems contain a wealth of information that is largely untapped. Understanding the complex dynamics of marine environments will enable scientists to propose sound methods for how to better manage these valuable resources in a sustainable manner. Emerging wireless sensor network technologies are now providing marine scientists with tools to gather data on key ecological factors in ways never previously thought possible. However, various technical, commercial, and logistical factors make it difficult to effectively develop and utilize coastal monitoring technology. This paper examines the main issues associated with wireless sensor network technologies for use in coastal monitoring applications. It describes the challenges faced by the developers, the conflicting push and pull influences by vendors, and the logistical/operational issues for deploying sensor networks in harsh marine environments. We describe the lesions learnt from several real-world sensor network systems currently in use on the Great Barrier Reef, Deception Bay and Heron Island. The experience gained from these deployments can be used as a blue print for future coastal monitoring applications so that cheaper, more cost effective, and user-friendly systems will result. This will enable end users to better understand the sensitive ecological factors that effect these environments, without being burdened by the underlying technical detail

SEMAT - The next generation of inexpensive marine environmental monitoring and measurement systems

There is an increasing need for environmental measurement systems to further science and thereby lead to improved policies for sustainable management. Marine environments are particularly hostile and extremely difficult for deploying sensitive measurement systems. As a consequence the need for data is greatest in marine environments, particularly in the developing economies/regions. Expense is typically the most significant limiting factor in the number of measurement systems that can be deployed, although technical complexity and the consequent high level of technical skill required for deployment and servicing runs a close second. This paper describes the SmartEnvironmental Monitoring and Analysis Technologies (SEMAT) project and the present development of the SEMAT technology. SEMAT is a "smart" wireless sensor network that uses a commodity-based approach for selecting technologies most appropriate to the scientifically driven marine research and monitoring domain/field. This approach allows for significantly cheaper environmental observation systems that cover a larger geographical area and can therefore collect more representative data. We describe SEMAT's goals, which include: (1) The ability to adapt and evolve; (2) Underwater wireless communications; (3) Short-range wireless power transmission; (4) Plug and play components; (5) Minimal deployment expertise; (6) Near real-time analysis tools; and (7) Intelligent sensors. This paper illustrates how the capacity of the system has been improved over three iterations towards realising these goals. The result is an inexpensive and flexible system that is ideal for short-term deployments in shallow coastal and other aquatic environments