Ron Johnstone

Ciguatera fish poisoning in the Pacific Islands (1998 to 2008)

Background Ciguatera is a type of fish poisoning that occurs throughout the tropics, particularly in vulnerable island communities such as the developing Pacific Island Countries and Territories (PICTs). After consuming ciguatoxin-contaminated fish, people report a range of acute neurologic, gastrointestinal, and cardiac symptoms, with some experiencing chronic neurologic symptoms lasting weeks to months. Unfortunately, the true extent of illness and its impact on human communities and ecosystem health are still poorly understood. Methods A questionnaire was emailed to the Health and Fisheries Authorities of the PICTs to quantify the extent of ciguatera. The data were analyzed using t-test, incidence rate ratios, ranked correlation, and regression analysis. Results There were 39,677 reported cases from 17 PICTs, with a mean annual incidence of 194 cases per 100,000 people across the region from 1998–2008 compared to the reported annual incidence of 104/100,000 from 1973–1983. There has been a 60% increase in the annual incidence of ciguatera between the two time periods based on PICTs that reported for both time periods. Taking into account under-reporting, in the last 35 years an estimated 500,000 Pacific islanders might have suffered from ciguatera. Conclusions This level of incidence exceeds prior ciguatera estimates locally and globally, and raises the status of ciguatera to an acute and chronic illness with major public health significance. To address this significant public health problem, which is expected to increase in parallel with environmental change, well-funded multidisciplinary research teams are needed to translate research advances into practical management solutions.

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

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, Australia. 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.

Relationships between perceived coastal waterway condition and social aspects of quality of life

Previous research has shown that the presence of natural areas is beneficial to human well-being. However, to date there have been few published studies of the effects that the condition of natural areas have on well-being. We hypothesize that coastal waterways that are perceived to be in better condition are visited more often by local residents, and as a result, residents will develop a stronger sense of place and stronger social relations with other residents, which will in turn lead to a higher quality of life. A survey was conducted to test this hypothesis in two coastal regions in Queensland, Australia. A weak relationship was found between perceived coastal quality and the number of recreational visits. In both study areas, frequency of visits to coastal waterways was significantly related to quality of life through an increased sense of place and social contacts. In the Douglas region, sense of place and social capital were closely related, and social capital was also related to quality of life. In both study areas, residents were most likely to visit waterways that were located in close proximity to their residence, suggesting that management of all waterways is essential for human well-being, and that benefits to humans will result from rehabilitating degraded waterways.

Electromagnetic wave wireless communication in shallow water coastal environment: theoretical analysis and experimental results

Shallow water coastal environments are very important from the biological, social and economic point of view. Wireless technologies - and in particular wireless sensor networks (WSN) - are critical for enabling their efficient and pervasive monitoring. Electromagnetic (EM) communication is considered as the physical layer because, in shallow water coastal environments, it presents distinct advantages with respect to acoustic and optical communication. The benefit of lateral wave EM propagation in shallow water environment is explained. A theoretical analysis of EM propagation in a typical shallow water environment is then conducted, where the maximum distance coverable for a given transmitter power is calculated. The results are then compared with simulations and measurements: their differences with respect to theoretical predictions are assessed. A prototype of underwater communication system constituted by an underwater sensor and a hub buoy that relays data to the mainland is finally presented. Conclusions are drawn in terms of its performance, also in comparison with existing underwater EM communication systems and solutions.

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 Smart Environmental 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 SEMATs 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.

Effects of Lyngbya majuscula (Cyanophycea) blooms on sediment nutrients and meiofaunal assemblages in seagrass beds in Moreton Bay, Australia

Blooms of Lyngbya majuscula have been increasingly recorded in the waters of Moreton Bay, on the south-east coast of Queensland, Australia. The influences of these blooms on sediment infauna and the implications for sediment biogeochemical processes was studied. Sediment samples were taken from Moreton Bay banks during and after the bloom season. The deposition of L. majuscula seems to be responsible for the higher total Kjedahl nitrogen (TKN) concentrations measured during the bloom period. Total organic carbon (TOC) concentrations did not change. Lyngbya majuscula blooms had a marked influence on the meiobenthos. Nematodes, copepods and polychaetes were the most abundant groups of meiofauna, and the bloom produced a decrease in the abundance and a change in the sediment depth distribution of these organisms. The distribution of nematodes, copepods and polychaetes in sediment became shallower. Further, the bloom did not affect the abundance and distribution of polychaetes as strongly as it did copepods and nematodes. The changes observed in the distribution of meiofauna in the sediment during the bloom period indicate that L. majuscula produces oxygen depletion in sediments, and that different fauna seem to be affected to different degrees.

A system dynamics simulation model for sustainable water resources management and agricultural development in the Volta River Basin, Ghana

In a rapidly changing water resources system, dynamic models based on the notion of systems thinking can serve as useful analytical tools for scientists and policy-makers to study changes in key system variables over time. In this paper, an integrated system dynamics simulation model was developed using a system dynamics modelling approach to examine the feedback processes and interaction between the population, the water resource, and the agricultural production sub-sectors of the Volta River Basin in West Africa. The objective of the model is to provide a learning tool for policy-makers to improve their understanding of the long-term dynamic behaviour of the basin, and as a decision support tool for exploring plausible policy scenarios necessary for sustainable water resource management and agricultural development. Structural and behavioural pattern tests, and statistical test were used to evaluate and validate the performance of the model. The results showed that the simulated outputs agreed well with the observed reality of the system. A sensitivity analysis also indicated that the model is reliable and robust to uncertainties in the major parameters. Results of the business as usual scenario showed that total population, agricultural, domestic, and industrial water demands will continue to increase over the simulated period. Besides business as usual, three additional policy scenarios were simulated to assess their impact on water demands, crop yield, and net-farm income. These were the development of the water infrastructure (scenario 1), cropland expansion (scenario 2) and dry conditions (scenario 3). The results showed that scenario 1 would provide the maximum benefit to people living in the basin. Overall, the model results could help inform planning and investment decisions within the basin to enhance food security, livelihoods development, socio-economic growth, and sustainable management of natural resources.

Wireless sensor networks in coastal marine environments: a study case outcome

In this paper the results of an experimental wireless sensor network application in a coastal shallow water marine environment are presented. The study focuses on the practical aspects of deployment, data gathering and retrieval events. The trial sensor network was used to retrieve temperature and illuminance data from the seabed of Moreton Bay, Australia. The application described possesses features and implements technical solutions that distinguish it from previous deployments. For example, the particular mooring system maintains the buoys horizontal on the waters surface even in strong tidal current conditions, thus enabling reliable communication at 2.4 GHz. In this application, the underwater sensors were wired to surface wireless nodes, and this arrangement led to various difficulties in the networks deployment, maintenance and retrieval phases. For this reason, the knowledge acquired through this experience is presented in this paper to provide insight into, and to further stress, the importance of using fully wireless systems in monitoring applications for the marine environment.

Developing low-cost intelligent wireless sensor networks for aquatic environments

Aquatic environments are extremely difficult for developing, deploying, and maintaining wireless sensor networks. Networks deployed in aquatic settings face multiple challenges, such as marine fowling of equipment, limited power supply, communications difficulties, and restricted accessibility for maintaining and updating sensor nodes. The SEMAT project is an initiative to create “smart”, low-cost, heterogeneous wireless sensor networks, tailored to alleviating the aforementioned constraints. Networks can be instantly deployable with minimal setup overheads and can utilise equipment from multiple vendors. This paper presents our experiences with developing the initial technologies to establish SEMAT for field tests. We present the design methodology and challenges faced for creating a marinebased heterogeneous wireless sensor network platform. The result is a low cost solution, with sufficient accuracy for undertaking a study into the factors contributing to Lyngbya algae blooms in Deception Bay, Queensland. The platform builds a case for the merits of the final SEMAT system, as ultimately many of the software and basic hardware challenges for future aquatic deployments have been overcome. This is significant as it allows researchers to focus on the area under study, rather than the specifics of setting up and managing the network.

Semantically Enabling the SEMAT Project: Extending Marine Sensor Networks for Decision Support and Hypothesis Testing

The SEMAT project is a multi-institution/multi-discipline program developing advanced wireless sensor networks to collect, store, process and interpret data in coastal systems. The marine environment, specifically coral reefs within the Great Barrier Reef, is one of the initial deployments for a prototype SEMAT network. Wireless sensor networks are being deployed to extract environmental data for research into environmental issues such as climate change, water quality and ecosystem health. Remote monitoring networks in remote marine locations are logistically challenging. However, the interpretation of the complex multidimensional data generated is a problem of at least equal complexity. Application of the semantic tools and methods developed in the Semantic Reef project are being mapped onto the SEMAT use-cases with the goal to develop a data model capable of complex inference, as well as conventional data storage and analysis.