Quantitative relationships between benthic diatom assemblages and water chemistry in Macquarie Island lakes and their potential for reconstructing past environmental changes

• Published in: Antarctic Science
• Main Authors: Saunders, KM, Hodgson, DA, McMinn, A
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2008
• Subjects: 260112 Palaeontology; climate change; limnology; management; palaeolimnology; sub-Antarctic; transfer function; Antarctic; Island Lakes; Antarc*; Antarctic Science; Macquarie Island
• Online Access: https://eprints.utas.edu.au/6910/; https://eprints.utas.edu.au/6910/1/Macquarie.pdf; https://doi.org/10.1017/S0954102008001442

This study is the first published survey of diatom-environment relationships on sub-Antarctic Macquarie Island. Fifty-eight sites in 50 coastal and inland lakes were sampled for benthic diatoms and water chemistry. 208 diatom species from 34 genera were identified. Multivariate analyses indicated that the lakes were distributed along nutrient and conductivity gradients. Conductivity, pH, phosphate (SRP), silicate and temperature all explained independent portions of the variance in the diatom data. Transfer functions provide a quantitative basis for palaeolimnological studies of past climate change and human impacts, and can be used to establish baseline conditions for assessing the impacts of recent climate change and the introduction of non-native plants and animals. Statistically robust diatom transfer functions for conductivity, phosphate and silicate were developed, while pH and temperature transfer functions performed less well. The lower predictive abilities of the pH and temperature transfer functions probably reflect the broad pH tolerance range of diatoms on Macquarie Island and uneven distribution of lakes along the temperature gradient. This study contributes to understanding the current ecological distribution of Macquarie Island diatoms and provides transfer functions that will be applied in studies of diatoms in lake sediment cores to quantitatively reconstruct past environmental changes.