Two Australian scientists have helped reveal the "evolutionary trick" which makes the diamondback moth one of the world's worst agricultural pests.

 

Charles Sturt University Professor Geoff Gurr, from the EH Graham Centre for Agricultural Innovation, and University of Adelaide Ramsay research fellow Dr Simon Baxter were part of an international consortium which today revealed the genetic blueprint of the moth in a paper in the international journal Nature Genetics.

 

Diamondback moth wreaks billions of dollars worth of damage to crops around the world each year, costing producers $4-5 billion in crop loss and control measures, and has caused major problems for the Australian canola industry.

 

The caterpillars feed on cabbage and related plants and are difficult to control because they can quickly develop resistance to all types of insecticide.

 

Professor Gurr said the successful sequencing of the moth's genome revealed the moth's "evolutionary trick"; its ability to detoxify the defence compounds produced by plants in the cabbage family.

 

"These are the same compounds that make mustard so pungent and cabbage so smelly," Professor Gurr said.

 

"Remarkably, it appears that the very genetic adaptations that allow diamondback moth to cope with these natural compounds also allow it to detoxify the insecticides used against it."

 

Dr Baxter said the moth had spread throughout the world and could be found in vegetable gardens and farms across Australia.

 

"They have an incredible ability to migrate long distances and to quickly adapt to the environments they encounter, making outbreaks of these insects difficult to predict and control," Dr Baxter said.

 

"This project has helped identify the genes that make diamondback moth such a successful pest and will enable new insecticide resistance monitoring techniques and pest management strategies to be developed."

 

Research team leader Professor You Minsheng, from the Fujian Agriculture and Forestry University in China, said the genetic blueprint had taken more than 40 scientists several years to develop using specially designed software and had identified more than 18,000 separate genes.