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

Advanced monitoring systems for biological applications in marine environments

The increasing need to manage complex environmental problems demands a new approach and new technologies to provide the information required at a spatial and temporal resolution appropriate to the scales at which the biological processes occur. In particular sensor networks, now quite popular on land, still poses many difficult problems in underwater environments. In this context, it is necessary to develop an autonomous monitoring system that can be remotely interrogated and directed to address unforeseen or expected changes in such environmental conditions. This system, at the highest level, aims to provide a framework for combining observations from a wide range of different in-situ sensors and remote sensing instruments, with a long-term plan for how the network of sensing modalities will continue to evolve in terms of sensing modality, geographic location, and spatial and temporal density. The advances in sensor technology and digital electronics have made it possible to produce large amount of small tag-like sensors which integrate sensing, processing, and communication capabilities together and form an autonomous entity. To successfully use this kind of systems in under water environments2 , it becomes necessary to optimize the network lifetime and face the relative hindrances that such a field imposes, especially in terms of underwater information exchange

The loss of seagrass in cockburn sound, Western Australia. III. The effect of epiphytes on productivity of Posidonia australis Hook. F.

The hypothesis was examined that increased epiphyte growth was responsible for a reduction in seagrass meadows in Cockburn Sound during the discharge of nutrient-rich effluent. One study site was in a deteriorating meadow near an effluent outfall, the other at similar depth in an unaffected meadow in more oceanic water. Seagrass production at the first site was less than that at the second, with 33% lower growth per shoot and 29% less dense meadow. Water at the former site had higher mean concentrations of chlorophyll and phosphate than the latter, but light reaching the seagrass meadows was not significantly different. Epiphyte loads (as dry weight or chlorophyll per unit leaf area) were 2–8 times higher at the former site. Seasonal changes in epiphyte loads were well correlated with periphyton biomass on glass slides or plastic seagrass. Photosynthesis of leaf segments, with and without epiphytes, was measured using an oxygen meter in the laboratory; epiphyte photosynthetic rates were similar to those of periphyton on plastic, expressed per unit chlorophyll. The percentage reduction in light by known periphyton loads was measured, and used to calculate light reduction by epiphytes in the field, which was estimated to be 63% on average at the first site and 15% at the second. Pooling data for sites and seasons, there was a negative log-linear relationship between leaf production and epiphyte load. The observations provide support for the suggestion that seagrass loss in the Sound may be attributed to enhanced epiphyte loads following nutrient enrichment

The loss of seagrass in Cockburn Sound, Western Australia. II. Possible causes of seagrass decline

This paper examines possible reasons for the extensive loss of seagrass in Cockburn Sound following industrial development. Transplanted seedlings survived poorly in Cockburn Sound compared with an adjoining embayment. Altered temperature, salinity, sedimentation and water movement do not explain the death of seagrass over wide areas, and there is no evidence for a role of pathogens. Oil refinery effluent reduced seagrass growth in aquaria at concentrations similar to those at the point of discharge, but could not account for the widespread deterioration observed in the field. Severe grazing by sea urchins was observed on meadows already under stress and does not appear to be a primary cause of decline; caged, transplanted seedlings also deteriorated. Increased light attenuation by phytoplankton blooms may have affected the ddepth to which seagrasses could survive, but would have had little significant effect in shallow water; marked phytoplankton blooms were recorded only after extensive seagrass decline had taken place. Light reduction by enhanced growth of epiphytes and loose-lying blankets of filamentous algae in nutrient enriched waters is suggested as the most likely cause of decline. Heavy epiphyte fouling was consistently observed on seagrasses in deteriorating meadows, as well as on declining, transplanted seedlings, and is known to significantly impair photosynthesis in other systems. Extensive seagrass decline coincided with the discharge of effluents rich in plant nutrients

Smart Integrated Sensor Networks for the Marine Environment

The sustainable management of coastal and offshore ecosystems, such as coral reef environments, requires the collection of accurate data across various temporal and spatial scales. Accordingly, monitoring systems are seen as central tools for ecosystem-based environmental management, helping on one hand to accurately describe the water column and substrate biophysical properties, and on the other hand to correctly steer sustainability policies by providing timely and useful information to decision-makers. A robust and intelligent sensor network that can adjust and be adapted to different and changing environmental or management demands would revolutionize our capacity to wove accurately model, predict, and manage human impacts on our coastal, marine, and other environments. Underwater measurements are greatly influenced by environmental conditions; especially in shallow waters. Temperature, salinity, turbidity, oxygen, pH and many other parameters still need optimization due to the difficulty in performing the process in situ in such an environment. Notably however, modern developments in wireless network technology and miniaturization now make it possible to realistically monitor the aquatic environment in situ using smart devices that are completely autonomous. However, to successfully use these kinds of systems in under water environments it is necessary from the outset to define the specific requirements and relative hindrances that such a field imposes; especially in terms of underwater information exchange. The aim of this paper is to examine these issues and to propose strategies for the cost effective and scientifically robust integration of remote sensor network technologies for the monitoring and management of critical marine environments such as coral reefs

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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