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


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.


Synchrony Phenology Moran effect Eucalypts Data visualisation SOMs 



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.


  1. Abu-Asab MS, Peterson PM, Shelter SG, Orli SS (2001) Earlier plant flowering in spring as a response to global warming in the Washington DC. area. Biodivers Conserv 10:597–612CrossRefGoogle Scholar
  2. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automat Contr 19:716–723CrossRefGoogle Scholar
  3. Baragona R (2001) A simulation study on clustering time series with meta-heuristic methods. Quad Stat 3:1–26Google Scholar
  4. Bawa KS, Kang H, Grayum MH (2003) Relationships among time, frequency, and duration of flowering in tropical rain forest trees. Am J Bot 90(6):877–887. doi: 10.3732/ajb.90.6.877 CrossRefGoogle Scholar
  5. Bezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans Syst Man Cybern B: Cybern 28(3):301–315CrossRefGoogle Scholar
  6. Biernacki C, Celeux G, Govaert G (2000) Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans Pattern Anal Mach Intell 22:719–725CrossRefGoogle Scholar
  7. Borchert R, Renner SS, Calle Z, Navarrete D, Tye A, Gautier L, Spichiger R, von Hildebrand P (2005) Photoperiodic induction of synchronous flowering near the Equator. Nature 433(7026):627–629CrossRefGoogle Scholar
  8. Both C, Bouwhuis S, Lessells CM, Visser ME (2006) Climate change and population declines in a long-distance migratory bird. Nature 441(7089):81–83CrossRefGoogle Scholar
  9. Brockwell PJ, Davis RA (1991) Time series: theory and methods. Springer Series in Statistics, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  10. Brooker MIH, Kleinig DA (2001) Field guide to eucalypts Hawthorn. Bloomings Books, VictoriaGoogle Scholar
  11. Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Graph Image Process 37:54–115CrossRefGoogle Scholar
  12. Cheeseman P, Stutz J (1996) Bayesian classification (AutoClass): theory and results. In: Fayyard UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI/MIT Press, Cambridge, MAGoogle Scholar
  13. Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22(7):357–365CrossRefGoogle Scholar
  14. Costa JAF (2010) Clustering and Visualizing SOM Results. In: Intelligent Data Engineering and Automated Learning – IDEAL 2010, vol 6283. Lecture Notes in Computer Science. Springer Berlin, pp 334–343. doi: 10.1007/978-3-642-15381-5_41
  15. Davidson NJ, Reid JB, Potts BM (1987) Gene flow between threeeucalypt species at Snug Plains. Pap ProcR Soc Tasmania 121:101–108Google Scholar
  16. Delaporte KL, Conran JG, Sedgley M (2001) Interspecific Hybridization within Eucalyptus (Myrtaceae): Subgenus Symphyomyrtus, Sections Bisectae and Adnataria. Int J Plant Sci 162(6):1317–1326. doi: 10.1086/323276 CrossRefGoogle Scholar
  17. Diaz I, Dominguez M, Vega AC, Fuertes-Martinez J (2008) A new approach to exploratory analysis of system dynamics using SOM. Applications to industrial processes. Expert Syst Appl 34(4):2953–2965CrossRefGoogle Scholar
  18. Dunn JC (1974) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3:32–57Google Scholar
  19. Eldridge K, Davidson J, Harwood C, van Wyk G (1993) Eucalypt domestication and breeding, 1st edn. Oxford University Press, New YorkGoogle Scholar
  20. Fitter AH, Fitter RSR (2002) Rapid changes in flowering time in British plants, Science 296:1689–1691Google Scholar
  21. Forrest J, Miller-Rushing AJ (2010) Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos Trans R Soc Lond B 365(1555):3101–3112. doi: 10.1098/rstb.2010.0145 CrossRefGoogle Scholar
  22. Fort JC (2006) SOM's mathematics. Neural Netw 19(6–7):812–816. doi: 10.1016/j.neunet.2006.05.025 CrossRefGoogle Scholar
  23. Frankie GW, Baker HG, Opler PA (1974) Comparative phenological studies of trees in tropical wet and dry forests in the lowlands of Costa Rica. J Ecol 62(3):881–919CrossRefGoogle Scholar
  24. Freitas L, Bolmgren K (2008) Synchrony is more than overlap: measuring phenological synchronization considering time length and intensity. Rev Bras Bot 31:721–724CrossRefGoogle Scholar
  25. Fulcher J, Jain L, Yin H (2008) The Self-Organizing Maps: Background, Theories, Extensions and Applications. In: Computational Intelligence: A Compendium, vol 115. Studies in Computational Intelligence. Springer Berlin, pp 715–762. doi: 10.1007/978-3-540-78293-3_17
  26. Gallagher RV, Hughes L, Leishman MR (2009) Phenological trends among Australian alpine species: using herbarium records to identify climate-change indicators. Aust J Bot 57(1):1–9. doi: 10.1071/BT08051 CrossRefGoogle Scholar
  27. Golay X, Kollias S, Stoll G, Meier D, Valavanis A, Boesiger P (1998) A new correlation-based fuzzy logic clustering algorithm for fMRI. Mag Resonance Med 40:249–260CrossRefGoogle Scholar
  28. Gordo O, Sanz J (2005) Phenology and climate change: a long-term study in a Mediterranean locality. Oecologia 146(3):484–495. doi: 10.1007/s00442-005-0240-z CrossRefGoogle Scholar
  29. Goutte C, Hansen LK, Liptrot MG, Rostrup E (2001) Feature space clustering for fMRI meta-analysis. Hum Brain Mapping 13:165–183CrossRefGoogle Scholar
  30. Griffin AR, Burgess IP, Wolf L (1988) Patterns of natural and manipulated hybridisation in the genus Eucalyptus L'Herit: a review. Aust J Bot 36(1):41–66. doi: 10.1071/BT9880041 CrossRefGoogle Scholar
  31. Gross CL, Mackay DA, Whalen MA (2000) Aggregated flowering phenologies among three sympatric legumes. Plant Ecol 148:13–21CrossRefGoogle Scholar
  32. Hudson I (2010) Interdisciplinary approaches: towards new statistical methods for phenological studies. Clim Change 100(1):143–171. doi: 10.1007/s10584-010-9859-9 CrossRefGoogle Scholar
  33. Hudson IL (2011) Meta analysis In Encyclopedia of Climate and Weather. Second edn. Editor in Chief: Stephen H. Schneider, Associate Editor in Chief: Michael Mastrandrea, Editor-in-chief: Terry L. Root. Oxford University Press. ISBN13: 9780199765324 ISBN10: 0199765324 (March 2011 publication)
  34. Hudson IL, Keatley MR (eds) (2010) Phenological Research: Methods for Environmental and Climate Change Analysis. Springer, DordrechtGoogle Scholar
  35. Hudson IL, Keatley MR, Roberts AMI (2005) Statistical Methods in Phenological Research. In: Francis AR, Matawie KM, Oshlack A, Smyth GK (eds) 20th International Workshop on Statistical Modelling, Sydney, Australia, 10–15 July 2005. Proceedings of the Statistical Solutions to Modern Problems, pp 259–270. ISBN 1 74108 101 7Google Scholar
  36. Hudson IL, Keatley MR, Kim SW, Kang I (2006) Synchronicity in Phenology: from PAP Moran to now. In: Australian Statistical Conference/New Zealand Statistical Association (ASC/NZSA) conference, Auckland, New Zealand, 3th-6th July 2006Google Scholar
  37. Hudson IL, Kim SW, Keatley MR (2009) Climatic influences on the flowering phenology of four Eucalypts: a GAMLSS approach. In: Anderssen RS, Braddock RD, Newham LTH (eds) 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, Cairns, Australia, 13–17 July 2009. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, pp 2611–2617. ISBN: 978-0-9758400-7-8Google Scholar
  38. Hudson IL, Keatley MR, Kim SW (2010a) Climatic Influences on the Flowering Phenology of Four Eucalypts: A GAMLSS Approach. In: Hudson IL, Keatley MR (eds) Phenological Research: Methods for Environmental and Climate Change Analysis. Springer, Dordrecht, pp 213–237. doi: 10.1007/978-90-481-3335-2-10
  39. Hudson IL, Keatley MR, Kim SW (2010b) Modelling the Flowering of Four Eucalypt Species Using New Mixture Transition Distribution Models. In: Hudson IL, Keatley MR (eds) Phenological Research: Methods for Environmental and Climate Change Analysis. Springer, Dordrecht, pp 315–340. doi: 10.1007/978-90-481-3335-2_14
  40. Hudson IL, Keatley MR, Kang I (2010c) Wavelet characterization of eucalypt flowering and the influence of climate. Environmental and Ecological Statistics, (Published on line first: 27 June 2010 ) pp 1–21. doi: 10.1007/s10651-010-0149-5
  41. Johnson SD (1993) Climatic and phylogenetic determinants of flowering seasonality in the Cape flora. J Ecol 82:567–572Google Scholar
  42. Junker B, Klukas C, Schreiber F (2006) VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinform 7(1):109CrossRefGoogle Scholar
  43. Keatley MR (1999) The Flowering Phenology of Box-Ironbark Eucalypts in the Maryborough Region, Victoria. PhD thesis, The University of MelbourneGoogle Scholar
  44. Keatley MR, Hudson IL (1998) The influence of fruit and bud volumes on the flowering of eucalypts: an exploratory analysis. Aust J Bot 46(2):281–307CrossRefGoogle Scholar
  45. Keatley MR, Hudson IL (2000) Influences on the flowering phenology of three eucalypts. In 'Biometeorology and Urban Climatology at the Turn of the Century. Selected Papers from the Conference ICB-ICUC '99.' (Eds RJ de Dear, JD Kalma, TR Oke and A Aucliems) pp. 191–196. (World Meteorological Organisation: Geneva, Switzerland)Google Scholar
  46. Keatley M, Hudson I (2007) A comparison of long-term flowering patterns of Box-Ironbark species in Havelock and Rushworth forests. Environ Model Assess 12(4):279–292. doi: 10.1007/s10666-006-9063-5 CrossRefGoogle Scholar
  47. Keatley MR, Hudson IL (2008) Shifts and changes in a 24 year Australian flowering record. In: 18th International Congress of Biometeorology, Tokyo, Japan, 22nd-26th September 2008. Harmony within Nature. p 85.
  48. Keatley MR, Fletcher TD, Hudson IL, Ades PK (2002) Phenological studies in Australia: potential application in historical and future climate analysis. Int J Climatol 22(14):1769–1780. doi: 10.1002/joc.822 CrossRefGoogle Scholar
  49. Keatley MR, Hudson IL, Fletcher TD (2004) Long-term flowering synchrony of box-ironbark eucalypts. Aust J Bot 52(1):47–54. doi: 10.1071/BT03017 CrossRefGoogle Scholar
  50. Kim SW, Hudson IL, Keatley MR (2006) Extending Mixture Transition Distribution (MTD) methods to incorporate interactions: Links to species synchrony and phenology. In: Australian Statistical Conference/New Zealand Statistical Association (ASC/NZSA) conference, Auckland, New Zealand, 3–6 July 2006Google Scholar
  51. Kim SW, Hudson IL, Keatley MR, Agrawal M (2008) Modelling and synchronization of four Eucalypt species via Mixed Transition Distribution (MTD) and Extended Kalman Filter (EKF). In P. Eilers, editor, Proceedings of the 23 rd International Workshop on Statistical Modelling, 23rd International Workshop on Statistical Modelling, Utrecht, Netherlands, 7th -11th July, pp 287–292Google Scholar
  52. Kim SW, Hudson IL, Keatley MR (2009) Modelling the flowering of four eucalypts species via MTDg with interactions. In: Anderssen RS, Braddock RD, Newham LTH (eds) 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, Cairns, Australia, 13th -17th July 2009. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, pp 2625–2631. ISBN: 978-0-9758400-7-8Google Scholar
  53. King I, Wang J, Chan L, Wang D, Martín-Merino M, Román J (2006) A New SOM Algorithm for Electricity Load Forecasting. In: Neural Information Processing, vol 4232. Lecture Notes in Computer Science. Springer, Berlin, pp 995–1003. doi: 10.1007/11893028_111
  54. Klukas C (2006) The VANTED software system for transcriptomics, proteomics and metabolomics analysis. J Pestic Sci 31(3):289–292CrossRefGoogle Scholar
  55. Kohonen T (1995) Self-Organizing Maps. Springer Series in Information Sciences, 2nd edn. Springer, HeidelbergGoogle Scholar
  56. Kohonen T (2001) Self-Organizing Maps. Third, extended edn. Springer, HeidelbergGoogle Scholar
  57. Krebs CJ (1994) Ecology: the experimental analysis of distribution and abundance, 4th edn. Benjamin Cummings, New YorkGoogle Scholar
  58. Mac Nally R, Horrocks G (2000) Landscape-scale conservation of an endangered migrant: the swift parrot (Lathamus discolor) in its winter range. Biol Conserv 92(3):335–343CrossRefGoogle Scholar
  59. Martin PR, Bonier F, Moore IT, Tewksbury JJ (2009) Latitudinal variation in the asynchrony of seasons: implications for higher rates of population differentiation and speciation in the tropics. Ideas Ecol Evol 2:9–17Google Scholar
  60. Maulik U, Bandyopadhyay S (2002) Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell 24(12):1650–1654CrossRefGoogle Scholar
  61. Menzel A, Fabian P (1999) Growing season extended in Europe. Nature 397:659CrossRefGoogle Scholar
  62. Menzel A, Sparks T (2006) Temperature and plant development: phenology and seasonality. In: Morison JIL, Morecroft MD (eds) Plant growth and climate change. Blackwell, Oxford, pp 70–95Google Scholar
  63. Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-Kubler K, Bissolli P, Brasavská O, Briede A, Chmielewski F-M, Crepinse Z, Curnel Y, Dahl A, Defila C, Donnelly A, Filella Y, Jatczak K, Mage F, Mestre A, Nordli Ø, Peñuelas J, Pirinen P, Remišová V, Scheifinger H, Striz M, Susnik A, van vliet AJH, Wielgolaski F-E, Zach S, Zust A (2006) European phenological response to climate change matches the warming pattern. Glob Change Biol 12:1969–1976CrossRefGoogle Scholar
  64. Miller-Rushing AJ, Hoye TT, Inouye DW, Post E (2010) The effects of phenological mismatches on demography. Philos Trans R Soc Lond B 365(1555):3177–3186. doi: 10.1098/rstb.2010.0148 CrossRefGoogle Scholar
  65. Möller-Levet CS, Klawonn F, Cho KH, Wolkenhauer O (2003) Fuzzy clustering of short time series and unevenly distributed sampling points, Proceedings of the 5th International Symposium on Intelligent Data Analysis, Berlin, Germany, August 28–30Google Scholar
  66. Moran PAP (1953a) The statistical analysis of the Canadian lynx cycle. I. Structure and prediction. Aust J Zool 1(2):163–173CrossRefGoogle Scholar
  67. Moran PAP (1953b) The statistical analysis of the Canadian lynx cycle. II. Synchronization and meteorology. Aust J Zool 1(3):291–298CrossRefGoogle Scholar
  68. Nguyen PN, Haughton D, Hudson IL (2009) Living standards of Vietnamese provinces: a Kohonen map Case Studies in Business. Case Studies in Business, Industry and Government Statistics 2(2):109–113Google Scholar
  69. Parry M, Canziani O, Palutikof J, van der Linden P, Hanson C (2008) Climate change 2007 –impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth AssessmentReport of the IPCC, Cambridge University Press, CambridgeGoogle Scholar
  70. Pẽnelaus J, Filella I, Comas P (2002) Changed plant and animal cycles from 1952 to 2000 in theMediterranean region, Glob Change Biol 8:531–544Google Scholar
  71. Piccolo D (1990) A distance measure for classifying ARMA models. J Time Ser Anal 11(2):153–163CrossRefGoogle Scholar
  72. Post E, Forchhammer M (2008) Climate change reduces reproductive success of an Arctic herbivore through trophic mismatch. Philos Trans R Soc Lond B 363:2369–2375CrossRefGoogle Scholar
  73. Prieto P, Peñuelas J, Ogaya R, Estiarte M (2008) Precipitation-dependent flowering of Globularia alypum and Erica multiflora in Mediterranean shrubland under experimental drought and warming, and its inter-annual variability. Ann Bot 102:275–285CrossRefGoogle Scholar
  74. Primack RB, Ibáñez I, Higuchi H, Lee SD, Miller-Rushing AJ, Wilson AM, Silander JA Jr (2009) Spatial and interspecific variability in phenological responses to warming temperatures. Biol Conserv 142(11):2569–2577CrossRefGoogle Scholar
  75. Pryor LD, Johnson LAS (1971) A classification of the eucalypts. Australian National University, CanberraGoogle Scholar
  76. Rathcke B (1983) Competition and facilitation among plants for pollination. In: Real L (ed) Pollination Biology. Academic, Orlando, Florida, pp 305–329Google Scholar
  77. Reusch DB, Alley RB, Hewitson BC (2007) North Atlantic climate variability from a self-organizing map perspective. J Geophys Res 112 (D2):D02104. doi: 10.1029/2006jd007460
  78. Roddick JF, Spiliopoulou M (2002) A survey of temporal knowledge discovery paradigms and methods. IEEE Trans Knowledge Data Eng 14(4):750–767CrossRefGoogle Scholar
  79. Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA (2003) Fingerprints of global warming on wild animals and plants. Nature 421(6918):57–60CrossRefGoogle Scholar
  80. Royama T (2005) Moran effect on nonlinear population processes. Ecol Monogr 75(2):277–293. doi: 10.1890/04-0770 CrossRefGoogle Scholar
  81. Schwartz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464CrossRefGoogle Scholar
  82. Shaw CT, King GP (1992) Using cluster analysis to classify time series. Physica D 58:288–298CrossRefGoogle Scholar
  83. Shoichet BK, Chen Y (2009) Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat Chem Biol 5(5):358–364. doi: 10.1038/nchembio.155 CrossRefGoogle Scholar
  84. Shumway RH (2003) Time–frequency clustering and discriminant analysis. Stat Probab Lett 63:307–314CrossRefGoogle Scholar
  85. Singhal A, Seborg DE (2005) Clustering multivariate time-series data. J Chemom 19(8):427–438CrossRefGoogle Scholar
  86. Sparks TH, Jeffree EP, Jeffree CE (2000) An examination of the relationship between flowering times and temperature at the national scale using long-term phenological records from the UK. Int J Biometeorol 44:82–87CrossRefGoogle Scholar
  87. Sparks TH, Górska-Zajączkowska M, Wójtowicz W, Tryjanowski P (2010) Phenological changes and reduced seasonal synchrony in western Poland. Int J Biometeorol. doi: 10.1007/s00484-010-0355-8 Google Scholar
  88. Staggemeier VG, Diniz-Filho JAF, Morellato LPC (2010) The shared influence of phylogeny and ecology on the reproductive patterns of Myrteae (Myrtaceae). J Ecol 98:1409–1421CrossRefGoogle Scholar
  89. Thackeray SJ, Sparks TH, Frederiksen M, Burthe S, Bacon PJ, Bell JR, Botham MS, Brereton TM, Bright PW, Carvalho L, Clutton-Brock TIM, Dawson A, Edwards M, Elliott JM, Harrington R, Johns D, Jones ID, Jones JT, Leech DI, Roy DB, Scott WA, Smith M, Smithers RJ, Winfield IJ, Wanless S (2010) Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob Change Biol 16(12):3304–3313. doi: 10.1111/j.1365-2486.2010.02165.x CrossRefGoogle Scholar
  90. Thomson JD (2010) Flowering phenology, fruiting success and progressive deterioration of pollination in an early-flowering geophyte. Philos Trans R Soc Lond B 365:3187–3199. doi: 10.1098/rstb.2010.0115 CrossRefGoogle Scholar
  91. Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11(3):586–600. doi: 10.1109/72.846731 CrossRefGoogle Scholar
  92. Visser ME, Both C (2005) Shifts in phenology due to global climate change: the need for a yardstick. Proc R Soc Lond B 272(1581):2561–2569. doi: 10.1098/rspb.2005.3356 CrossRefGoogle Scholar
  93. Vlachos M, Lin J, Keogh E, Gunopulos D (2003) A wavelet based anytime algorithm for k-means clustering of time series. Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, May 1–3, 2003Google Scholar
  94. Wilson JA (2002) Flowering ecology of a Box-Ironbark Eucalyptus community. PhD thesis, Deakin UniversityGoogle Scholar
  95. Xiong Y, Yeung D-Y (2002) Mixtures of ARMA models for model-based time series clustering, Proceedings of the IEEE International Conference on Data Mining, Maebaghi City, Japan, 9–12 December, 2002Google Scholar
  96. Yin H, Gorban AN, Kégl B, Wunsch DC, Zinovyev AY (2008) Learning Nonlinear Principal Manifolds by Self-Organising Maps. In: Principal Manifolds for Data Visualization and Dimension Reduction, vol 58. Lecture Notes in Computational Science and Engineering. Springer, Berlin, pp 68–95. doi: 10.1007/978-3-540-73750-6-3

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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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