Deep Hypersaline Anoxic Basins as Model Systems for Environmental Selection of Microbial Plankton

  • Alexandra Stock
  • Sabine Filker
  • Michail Yakimov
  • Thorsten Stoeck
Chapter
Part of the Cellular Origin, Life in Extreme Habitats and Astrobiology book series (COLE, volume 27)

Abstract

Biodiversity and biogeographic patterns of microbial organisms in general and microbial plankton specifically fuel a multitude of scientific surveys and discussions. In this context, a central question addresses the influence of environmental variables versus historical contingencies on the evolution and composition of microbial plankton communities. Such and similar fundamental questions in microbial ecology can be addressed with the benefits of model systems, such as deep hypersaline anoxic basins (DHABs). DHABs occur in different oceanic regions, for example, in the Gulf of Mexico, the Red Sea, and the Mediterranean Sea. They are characterized by a salt-induced stratification of the water column with a stable, polyextreme brine lake with highest salt concentrations, anoxia, and high pressure in complete darkness. Their unique hydrochemistries and physical separation for thousands of years give these brine lakes an “island character” with little or no genetic exchange with surrounding habitats. This makes DHABs ideal model systems in biogeography and evolutionary research. After the discovery of diverse planktonic life in the DHAB brines (see the chapter “ Microbial Eukaryotes in Marine Oxygen Minimum Zones” by Orsi and Edgcomb in this volume), studies have been conducted on the distribution of prokaryotes and eukaryotes in these environments. We here review our current knowledge on this subject and emphasize the model character of DHABs for future research.

Keywords

Terminal Restriction Fragment Length Polymorphism Environmental Selection Brine Lake Mediterranean Ridge Protistan Community 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Alexandra Stock
    • 1
  • Sabine Filker
    • 1
  • Michail Yakimov
    • 2
  • Thorsten Stoeck
    • 1
  1. 1.Department of EcologyUniversity of KaiserslauternKaiserslauternGermany
  2. 2.Institute for Coastal Marine Environment (IAMC), CNRMessinaItaly

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