The current work was conducted in State Key Laboratory of Marine Geology, Tongji University.
Environmental controls on the distribution of archaeal lipids in Tibetan hot springs: insight into the application of organic proxies for biogeochemical processes
Version of Record online: 18 AUG 2013
© 2013 John Wiley & Sons Ltd and Society for Applied Microbiology
Environmental Microbiology Reports
Volume 5, Issue 6, pages 868–882, December 2013
How to Cite
Li, F., Zhang, C. L., Dong, H., Li, W. and Williams, A. (2013), Environmental controls on the distribution of archaeal lipids in Tibetan hot springs: insight into the application of organic proxies for biogeochemical processes. Environmental Microbiology Reports, 5: 868–882. doi: 10.1111/1758-2229.12089
- Issue online: 14 NOV 2013
- Version of Record online: 18 AUG 2013
- Accepted manuscript online: 25 JUL 2013 04:41AM EST
- Manuscript Accepted: 21 JUL 2013
- Manuscript Revised: 20 JUL 2013
- Manuscript Received: 20 OCT 2012
- National Natural Science Foundation of China. Grant Number: # 40972211
- US National Science Foundation. Grant Numbers: # ETBC-1024614, #OISE-0968421
- International Cooperation of Ministry of Science & Technology (MOST), China. Grant Numbers: S2012ZR0069, 2013DFA31980
- ‘Thousand Talents Program’
- State Key Laboratory of Marine Geology at Tongji University
Fig. S1. Redundancy analysis (RDA) ordination diagram of major archaeal lipids with all environmental variables from hot springs.
Fig. S2. Cluster analyses of archaeal IP-GDGTs from Tibetan hot springs by the Euclidean and complete method (A), the Canberra and complete method (B), the Euclidean and ward method (C), the Canberra and ward method (D), the Euclidean and median method (E) and the Canberra and median method (F).
Table S1. Absolute concentrations of total GDGTs; RI and TEX86 calculations for archaeal lipids; and amoA- and nirS gene copy numbers for Tibetan hot springs (#1–37) and soil samples (#38–42).
Table S2. Bivariate correlations between individual IP-GDGTs or C-GDGTs and environmental variables. The significant correlations are highlighted in bold.
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