Researchers develop tools to help manage seagrass survival
Marine Ecology

Researchers develop tools to help manage seagrass survival

Researchers develop tools to help manage seagrass survival
Credit: QUT

A new QUT-led study has developed a statistical toolbox to help avoid seagrass loss which provides shelter, food and oxygen to fish and at-risk species like dugongs and green turtles.

The research has been published in Methods in Ecology and Evolution run by the British Ecological Society.

The paper describes key monitoring and management designs to maximise seagrass resilience to human activities, to better inform seagrass dredging operations and development of coastal areas.

Seagrasses are a critical habitat that have been declining rapidly globally.

Led by statistical data researcher and lecturer Dr. Paul Wu, from QUT’s School of Mathematical Sciences, the study identified and analysed factors that drove variations in a global seagrass dredging .

“Real world ecosystems like seagrasses are dynamic and ever changing,” Dr. Wu said.

“Successful implementation of any seagrass management plan requires effective, efficient and timely monitoring and adaptation to changing circumstances.”

Through statistical modelling, researchers can mimic what is happening in real time and predict what is going to happen on the sea bed.

Dr. Wu said the objectives of the study included:

  • identifying the set of most influential variables to monitor eg. genus, light, growth and seed
  • finding similar sites and environmental and/or dredging scenarios
  • understanding how often to monitor and the time lag between action and effect.

The toolbox of methods included new applications of functional Principal Components Analysis (fPCA) and boosted trees to discover patterns over time relevant to environmental monitoring and management.

In 2017, Dr. Wu led a study into predicting when coastal dredging was least likely to stress seagrass by pinpointing ‘ecological windows’ for timing of dredging operations in ports.

It predicted ahead of time h

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