Journal of Paleolimnology

, Volume 46, Issue 4, pp 525–542 | Cite as

A diatom dataset and diatom-salinity inference model for southeast Australian estuaries and coastal lakes

  • Krystyna M. Saunders
Original paper


To quantify the relationship between diatom species assemblages and the water chemistry of southeast Australian estuaries and coastal lakes, a new dataset of 81 modern diatom samples and water chemistry data was created. Three hundred and ninety-nine species from 53 genera were identified in 36 samples from 32 coastal water bodies in eastern Tasmania and 45 samples from 13 coastal water bodies in southern Victoria. Multivariate statistical analyses revealed that the sampling sites were primarily distributed along salinity and nutrient gradients, and that salinity, nitrate + nitrite, phosphate and turbidity explained independent portions of variance in the diatom data. Species salinity optima and tolerances were determined and a diatom-salinity inference model (WAinv r 2 = 0.72, r 2jack = 0.58, RMSEP = 0.09 log ppt) was developed. This new information on diatom species’ salinity preferences provides a useful tool for quantitatively reconstructing salinity changes over time from diatom microfossils preserved in the sediments of a range of estuaries and coastal lakes in southeast Australia. This is valuable for studies investigating long-term human impacts and climate change in the region.


Diatoms Multivariate analyses Transfer function Australia Water quality Coast 



This study was funded by an Australian Postgraduate Award, with additional funding from the Australian Institute of Nuclear Science and Engineering, Project Aware and the PADI Foundation. I would particularly like to thank Dr. Dom Hodgson for his advice throughout the project and preparation of this manuscript. I would also like to thank Kathryn Taffs, Kaarina Weckström, John Gibson and Andrew McMinn for project advice and taxonomic assistance; Steve Juggins for statistical advice; and Kate Dziegielewska, Jessie Webb and Sarah Lovibond for field assistance. Special thanks are also due to the two anonymous reviewers of the original version of this manuscript for their very helpful suggestions, which resulted in a much improved presentation of the results.

Supplementary material

10933_2010_9456_MOESM1_ESM.pdf (328 kb)
Supplementary material 1 (PDF 328 kb)
10933_2010_9456_MOESM2_ESM.pdf (66 kb)
Supplementary material 2 (PDF 65 kb)


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Institute of Geography and Oeschger Centre for Climate Change ResearchUniversity of BernBernSwitzerland
  2. 2.Institute of Antarctic and Marine StudiesUniversity of TASHobartAustralia

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