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International Journal of Biometeorology

, Volume 55, Issue 6, pp 879–904 | Cite as

Using Self-Organising Maps (SOMs) to assess synchronies: an application to historical eucalypt flowering records

  • Irene L. HudsonEmail author
  • Marie R. Keatley
  • Shalem Y. Lee
Original Paper

Abstract

Self-Organising Map (SOM) clustering methods applied to the monthly and seasonal averaged flowering intensity records of eight Eucalypt species are shown to successfully quantify, visualise and model synchronisation of multivariate time series. The SOM algorithm converts complex, nonlinear relationships between high-dimensional data into simple networks and a map based on the most likely patterns in the multiplicity of time series that it trains. Monthly- and seasonal-based SOMs identified three synchronous species groups (clusters): E. camaldulensis, E. melliodora, E. polyanthemos; E. goniocalyx, E. microcarpa, E. macrorhyncha; and E. leucoxylon, E. tricarpa. The main factor in synchronisation (clustering) appears to be the season in which flowering commences. SOMs also identified the asynchronous relationship among the eight species. Hence, the likelihood of the production, or not, of hybrids between sympatric species is also identified. The SOM pattern-based correlation values mirror earlier synchrony statistics gleaned from Moran correlations obtained from the raw flowering records. Synchronisation of flowering is shown to be a complex mechanism that incorporates all the flowering characteristics: flowering duration, timing of peak flowering, of start and finishing of flowering, as well as possibly specific climate drivers for flowering. SOMs can accommodate for all this complexity and we advocate their use by phenologists and ecologists as a powerful, accessible and interpretable tool for visualisation and clustering of multivariate time series and for synchrony studies.

Keywords

Synchrony Phenology Moran effect Eucalypts Data visualisation SOMs 

Notes

Acknowledgments

The authors are very grateful to two reviewers whose comments and insights very much improved the details pertaining to the motivation for this study, the clarity of the methods and visual displays, and the interlinking and distinctions of SOMs to more traditional methodologies.

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Copyright information

© ISB 2011

Authors and Affiliations

  • Irene L. Hudson
    • 1
    Email author
  • Marie R. Keatley
    • 2
  • Shalem Y. Lee
    • 3
  1. 1.School of Mathematical & Physical SciencesUniversity of Newcastle, CallaghanNewcastleAustralia
  2. 2.Department of Forest and Ecosystem ScienceUniversity of MelbourneMelbourneAustralia
  3. 3.School of Paediatrics and Reproductive HealthUniversity of AdelaideAdelaideSouth Australia

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