Wavelet Analysis of Flowering and Climatic Niche Identification

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Abstract

This chapter discusses wavelet analysis which is a robust statistical method capable of handling noisy and non-stationary data which phenological time series often are.

We used a maximal overlap discrete wavelet transform (MODWT) analysis to examine the flowering records (1940–1970) of E. leucoxylon and Eucalyptus tricarpa, E. microcarpa and E. polyanthemos. We identified four subcomponents in each flowering series: characterised as a non-flowering phase, duration, annual and intensity cycles. A decreasing overall trend in flowering was identified by the MODWT smoothed series.

Wavelet correlation found the same contemporaneous effects of climate on flow-ering for E. leucoxylon and Eucalyptus tricarpa, and for E. microcarpa and E. polyanthemos.

Wavelet cross-correlation analysis identified the cyclical influence of temperature and rainfall on peak flowering intensity. For each species there are 6 months of the annual cycle in which any given climate variable positively influences flowering intensity and 6 months of negative influence. For all species, rainfall exerts a negative influence when temperature is positive.

Keywords

Climate Cycles Flowering Wavelet analysis Wavelet cross correlation 
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© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.School of Mathematics and Statistics, University of South AustraliaAdelaideAustralia
  2. 2.Institute for Sustainable Systems and Technologies, University of South AustraliaMawson LakesAustralia
  3. 3.Department of Mathematics and StatisticsUniversity of CanterburyChristchurchNew Zealand
  4. 4.Department of forest and EcosystemSciencesUniversity of MelbourneVictoriaAustralia

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