Continental scale analysis of bird migration timing

Influences of climate and life history traits-a generalized mixture model clustering and discriminant approach

Lynda E. Chambers, Linda J. Beaumont, Irene L. Hudson

Research output: Contribution to journalArticle

18 Citations (Scopus)


There is substantial evidence of climate-related shifts to the timing of avian migration. Although spring arrival has generally advanced, variable species responses and geographical biases in data collection make it difficult to generalise patterns. We advance previous studies by using novel multivariate statistical techniques to explore complex relationships between phenological trends, climate indices and species traits. Using 145 datasets for 52 bird species, we assess trends in first arrival date (FAD), last departure date (LDD) and timing of peak abundance at multiple Australian locations. Strong seasonal patterns were found, i.e. spring phenological events were more likely to significantly advance, while significant advances and delays occurred in other seasons. However, across all significant trends, the magnitude of delays exceeded that of advances, particularly for FAD (+22.3 and -9.6 days/decade, respectively). Geographic variations were found, with greater advances in FAD and LDD, in south-eastern Australia than in the north and west. We identified four species clusters that differed with respect to species traits and climate drivers. Species within bird clusters responded in similar ways to local climate variables, particularly the number of raindays and rainfall. The strength of phenological trends was more strongly related to local climate variables than to broad-scale drivers (Southern Oscillation Index), highlighting the importance of precipitation as a driver of movement in Australian birds.

Original languageEnglish
Pages (from-to)1147-1162
Number of pages16
JournalInternational Journal of Biometeorology
Issue number6
Publication statusPublished - Aug 2014


  • Australia
  • Climate
  • Generalized mixture model clustering
  • Phenology

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