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Micro-Halocline Enabled Nutrient Recycling May Explain Extreme Azolla Event in the Eocene Arctic Ocean

Overview of attention for article published in PLOS ONE, November 2012
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Title
Micro-Halocline Enabled Nutrient Recycling May Explain Extreme Azolla Event in the Eocene Arctic Ocean
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0050159
Pubmed ID
Authors

Monique M. L. van Kempen, Alfons J. P. Smolders, Leon P. M. Lamers, Jan G. M. Roelofs

Abstract

In order to understand the physicochemical mechanisms that could explain the massive growth of Azolla arctica in the Eocene Arctic Ocean, we carried out a laboratory experiment in which we studied the interacting effects of rain and wind on the development of salinity stratification, both in the presence and in the absence of a dense Azolla cover. Additionally, we carried out a mesocosm experiment to get a better understanding of the nutrient cycling within and beneath a dense Azolla cover in both freshwater and brackish water environments. Here we show that Azolla is able to create a windproof, small-scale salinity gradient in brackish waters, which allows for efficient recycling of nutrients. We suggest that this mechanism ensures the maintenance of a large standing biomass in which additional input of nutrients ultimately result in a further expansion of an Azolla cover. As such, it may not only explain the extent of the Azolla event during the Eocene, but also the absence of intact vegetative Azolla remains and the relatively low burial efficiency of organic carbon during this interval.

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Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 18%
Student > Ph. D. Student 6 16%
Student > Bachelor 5 13%
Researcher 4 11%
Other 2 5%
Other 5 13%
Unknown 9 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 24%
Earth and Planetary Sciences 8 21%
Environmental Science 4 11%
Biochemistry, Genetics and Molecular Biology 4 11%
Engineering 2 5%
Other 2 5%
Unknown 9 24%