Coral reefs are dynamic ecosystems that support enormous biodiversity. They play a vital role in protecting the coastline from harmful effects of waves and tropical storms, provide habitat to numerous marine organisms and maintain the carbon level in the water. The Great Barrier Reef (GBR) is the world's largest coral reef system, stretching over 348,000 square kilometres of Australia’s north-eastern coast. It contains over 3000 individual coral reefs. In addition to the environmental benefits, the GBR is an international tourist destination, which creates thousands of job opportunities in Australia. Therefore, long-term resilience of this world heritage site is a key concern in many aspects.
Coral reefs are under many environmental threats. The world’s first major coral bleaching occurred in 1998. It was an eye opener for scientists to establish monitoring programmes to gather spatially extensive information on reef conditions. Such reef monitoring programs play a key role in identifying patterns, trends and threats to coral reef systems. However, it is understood that implementation and maintenance of such programs is expensive.
In this project, we will develop new Bayesian adaptive design methods for improving the effectiveness of reef monitoring. For example, coral cover is strongly affected by environmental impacts such as cyclones. Therefore, it is crucial to obtain information about current status and trends after a particular environmental disturbance occurred on the reef to quantify its impact. In order to accommodate this, additional samples may be needed from an affected area on the reef system. Bayesian adaptive design methods can be used to determine when and where samples should be collected on the GBR based on a particular environmental disturbance. Such methods have been shown to be cost effective and yield highly informative data. Thus, these methods should help to improve the effectiveness of reef monitoring programs.