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Wiley InterScience

Insect Science

Insect Science

Volume 12 Issue 1, Pages 45 - 56

Published Online: 22 Feb 2005

© 2010 The Authors Journal compilation © Institute of Zoology, Chinese Academy of Sciences



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Forecasting Helicoverpa populations in Australia: A comparison of regression based models and a bioclimatic based modelling approach
MYRON P. ZALUCKI 1 MICHAEL J. FURLONG 1
  1 School of Integrative Biology, The University of Queensland, St Lucia, Brisbane, Queensland 4072, Australia
 Correspondence: M. P. Zalucki, School of Integrative Biology, The University of Queensland, St Lucia, Brisbane, Queensland 4072, Australia. Tel: + 617 3365 1747; fax: +617 3365 1655; email: M.Zalucki@uq.edu.au
Copyright Institute of Zoology, Chinese Academy of Sciences
KEYWORDS
Helicoverpa • migration • forecasting • pest management • CLIMEX • species

ABSTRACT

Abstract Long-term forecasts of pest pressure are central to the effective management of many agricultural insect pests. In the eastern cropping regions of Australia, serious infestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hübner)(Lepidoptera: Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches of adult moths were used to describe the seasonal dynamics of both species. The size of the spring generation in eastern cropping zones could be related to rainfall in putative source areas in inland Australia. Subsequent generations could be related to the abundance of various crops in agricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figured prominently as a predictor variable, and can itself be predicted using the Southern Oscillation Index (SOI), trap catches were also related to this variable. The geographic distribution of each species was modelled in relation to climate and CLIMEX was used to predict temporal variation in abundance at given putative source sites in inland Australia using historical meteorological data. These predictions were then correlated with subsequent pest abundance data in a major cropping region. The regression-based and bioclimatic-based approaches to predicting pest abundance are compared and their utility in predicting and interpreting pest dynamics are discussed.


Accepted October 12, 2004

DIGITAL OBJECT IDENTIFIER (DOI)
10.1111/j.1672-9609.2005.00007.x About DOI

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