title: Using Self-Organising Maps (SOMs) to assess synchronies: an application to historical eucalypt flowering records.
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.
authors.affiliation: School of Mathematical & Physical Sciences, University of Newcastle, Callaghan, Newcastle, NSW, Australia. Irene.Hudson@newcastle.edu.au
authors.forename: Irene L
authors.initials: IL
authors.lastname: Hudson
authors.forename: Marie R
authors.initials: MR
authors.lastname: Keatley
authors.forename: Shalem Y
authors.initials: SY
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D057231:Climate Change; D016000:Cluster Analysis; D003625:Data Collection; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D005052:Eucalyptus / Q000145:classification / Q000254:growth & development / Q000502:physiology*; D016571:Neural Networks, Computer; D012621:Seasons; D013045:Species Specificity; D013997:Time Factors