Predicting the climatic risk of myrtle rust during its first year in New Zealand

  • Robert M. Beresford Plant & Food Research
  • Richard Turner NIWA
  • Andrew Tait NIWA
  • Vijay Paul NIWA
  • Gregor Macara NIWA
  • Zhidong D. Yu MPI
  • Lorin Lima MPI
  • Rebecca Martin MPI
Keywords: Guava rust, eucalypt rust, disease detection, incursion response, biosecurity, climatology, numerical weather forecasting, Climex model, MaxEnt model, ensemble model, unified model

Abstract

After the first detection of myrtle rust (Austropuccinia psidii) on mainland New Zealand in May 2017, the Ministry for Primary Industries sought information about how weather conditions would affect regional and seasonal risk of disease establishment to help plan the incursion response. Using internationally published information, a pathogen-process model was developed to predict infection, latent period and sporulation in relation to weather variables (temperature, relative humidity and solar radiation). This Myrtle Rust Process Model (MRPM) was implemented by the National Institute of Water and Atmospheric Research Limited using numerical weather model data to produce weekly maps of potential risk. Predicted risk was greatest in northern North Island and decreased further south, but was still substantial in coastal areas of the north-western South Island during summer and autumn. Risk was low in southern coastal areas of the South Island and the lowest risk occurred in mountainous areas, particularly in the South Island. Retrospective analysis of surveillance data showed that the MRPM accurately predicted geographic risk and it is currently in use for tactical planning of incursion surveillance and organism management.

 

Published
2018-07-25
How to Cite
Beresford, R., Turner, R., Tait, A., Paul, V., Macara, G., Yu, Z., Lima, L., & Martin, R. (2018). Predicting the climatic risk of myrtle rust during its first year in New Zealand. New Zealand Plant Protection, 71, 332-347. https://doi.org/10.30843/nzpp.2018.71.176
Section
Vol 71 Rust Pathogens 2018

Most read articles by the same author(s)