Skip to main content

Predicting time to peak occurrence of insect life-stages usingregression models calibrated from stage-frequency data and ancillary stage-mortality data

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract

1 Integrated Pest Management programmes often require predictions of peak occurrence of particular insect life-stages to optimize the timing of population monitoring and control operations.

2 Given a known or estimated starting time for a synchronously developing pest population, predictive models estimated from stage-frequency data alone can only predict the times of peak occurrence assuming a constant mortality rate across stages.

3 Here, continuation ratio regression models of relative stage frequencies estimated from stage-frequency data are combined with a stage-specific model of mortality estimated from ancillary mortality data to allow prediction of time of peak occurrence.

4 To calculate time of peak occurrence new mathematical derivations are given for continuation ratio models.

5 The models are used to predict the time of peak occurrence in degree-day units for each of the first to third larval instars of the Tasmanian Eucalyptus leaf beetle Chrysophtharta bimaculata (Olivier) (Coleoptera: Chrysomelidae), a serious defoliator of Eucalyptus regnans and E. nitens plantations in Tasmania.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: Chrysophtharta bimaculata; continuation ratios; generalized linear models; leaf beetles; mortality; phenology; stage-frequency data

Document Type: Research Article

Affiliations: Forestry Tasmania, 79 Melville St Hobart, Tasmania 7000, Australia

Publication date: 2003-02-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more