CRC30039

This project developed a dynamic, strategic trapping system which provides a similar or higher level of confidence as current static, passive grid systems in areas free from Queensland and Mediterranean fruit flies. It provides a more cost-effective and widespread fruit fly trapping network and more accurate information about fruit fly populations in remote areas of Australia. 

Research outcomes

Fruit fly area freedom is vital for market access. Since 1990 it has been managed through codes of practice under national and international agreements. The standard practice is based on the deployment of static trapping grids covering orchards, towns and urban areas. The grids are relatively effective when numbers are high, but are an inefficient strategy to detect early fruit fly incursions and are becoming increasingly expensive to deploy and maintain due to the prescribed fixed distances between traps.

The aim of this project was to develop a science based rationale that will optimise trap placement for the detection of fruit fly. The strategy is to deploy traps in hosts at the time when they are most attractive to fruit flies, with the aim of gaining in efficiency in early detection of the incursive population. More effective and earlier detection will minimise the number of undetected incursions which lead to breeding populations, thereby reducing eradication costs and more effectively managing area freedom for market access.

Research was conducted in WA and NSW to determine if new methods termed ‘dynamic trapping’ would provide an equivalent proof of area freedom at lower cost. The standard trapping method where traps are placed in a grid system 0.4 - 1km apart (static trapping), was tested against a method of strategic trap deployment (dynamic trapping) in hosts at the time hosts held mature fruit whenever possible.

The dynamic trapping method was demonstrated to be more effective in the capture of C. capitata than the static trapping method in Donnybrook. The dynamic method detected fruit fly infestations earlier than the static method in Donnybrook (with low fly numbers) and required one-third to one-half the number of traps used in a static grid to obtain the equivalent information on detecting itinerant or established fly numbers required for the fruit fly code of practice. This result was consistent over the three seasons where population level was quite different in each season.

In areas with very low fly density (Manjimup, Pemberton) and in the area free region of Kununurra, there was no difference in fly detection between the static and dynamic trapping methods.

With Queensland fruit fly (Bactrocera tryoni), results were variable and inconclusive in three areas (Cootamundra, Junee and Gundagai) which had low-high fly densities. Similarly, the data for the Tumut orchard was limited and it is difficult to draw any conclusions. However, in Ganmain, a town of low fruit fly density, dynamic traps were more effective than static traps in capturing B. tryoni, in terms of both proportion of traps which detected flies and proportion of flies caught in traps.

Data mining research in South Australia showed how archival trapping data can be combined with modern spatial data mapping methods to improve the trapping processes. While historical data-sets present some problems relating to data consistency among locations, future detection data could be digitised and added to the data-set to expand and improve the analysis. This research has the potential to identify areas of low fruit fly establishment potential where trapping effort could be reduced, thereby saving on monitoring costs in some parts of designated fruit fly free areas. The results of this study indicated that the establishment of a breeding population occurred where the immediate surroundings of the property with the trap were characterised by a low proportion of fruit-tree free properties, and a higher proportion of properties with moderate fruit tree densities.

Research implications

The most critical implication of this study is that strategic placement of traps in hosts at times when they are most attractive leads to a greater likelihood of detecting flies and likelihood of earlier detection. With the dynamic trapping method therefore, fewer traps can be used to achieve a detection level similar to that of the current static method without sacrificing efficiency.

The number of traps can be reduced by 50 percent where suitable hosts are available. In trap deployment, the selection of host type should follow the preferred host type available in a given season, with larger trees with high fruit volume given preference. Results in WA indicated that traps should be placed in citrus in winter and thereafter moved to apricots or early peaches, then nectarines, plums and later peaches, followed by apples, pears, olives, figs and loquats, moving back to citrus in June.

The results obtained in this project also provide the scientific basis for quantifying Areas of Low Pest Prevalence (ALPP) thus enabling places that lose Area Freedom, or those places that cannot achieve area freedom to seek more favourable consideration for market access based on diminished fruit fly risk to trade. Since fewer numbers of traps are required to prove ALPP the costs of such a trapping regime may be affordable for growers.

With Queensland fruit fly, further research in the Fruit Fly Exclusion Zone is recommended.

An assessment of cost effectiveness of the dynamic trapping method (with reduced number of traps) compared with the current static grid system is needed to quantify the cost benefit.

Recommendations arising from this study are to:

• review trap deployment strategies in area free zones with a view to adopting dynamic trapping methods to reduce costs and aid early detection
• review the advantage of adopting dynamic trapping methods in classifying areas under active control as Areas of Low Pest Prevalence for market access, and develop appropriate methodology
• fund R&D projects on further development and verification of area freedom methods in area free zones
• adopt the techniques developed in this project in future area wide management programs.

Acknowledgments

This project was jointly supported by Cooperative Research Centre for Biosecurity (CRC NPB Ltd) and Horticulture Australia Limited (HAL).

Mrs Jane Speijers (DAFWA) provided invaluable guidance in the planning stages and carried out the statistical analyses for this project in WA. The following DAFWA staff assisted with field work in Western Australia at various stages of the project: Ms Mirjana Banovic, Ms Helen Collie, Ms Linda Fernihough, Ms Emma Mansfield, Ms Sandra Wellington and Ms Candice Wong, Ms Natalie Bort and Ms Jessica Paterson.

David Heaven, Manager of Primary Industries and Resources South Australia’s Plant Health Operations provided the fruit fly detection data. Ann Frodsham and Jo-Anne Ragless carried out the digitisation of the handwritten data. Catherine Smallridge of SARDI and Adam Caldwell of PIRSA Spatial Information Services contributed to the analysis of data in this project.