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Leaf Litter Decomposition in Torna Stream before and after a Red Mud Disaster

Overview of attention for article published in Biologia Futura, December 2014
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Title
Leaf Litter Decomposition in Torna Stream before and after a Red Mud Disaster
Published in
Biologia Futura, December 2014
DOI 10.1556/abiol.65.2014.1.9
Pubmed ID
Authors

T. Kucserka, Kata Karádi-Kovács, M. Vass, G. B. Selmeczy, Katalin Eszter Hubai, Viktória Üveges, I. Kacsala, N. Törő, Judit Padisák

Abstract

The aim of the study was to estimate the breakdown of the allochthonous litter in an artificial stream running in an agricultural area and compare it with the same values following a toxic mud spill into the same stream. Litter bags were filled with three types of leaves (Quercus robur, Populus tremula and Salix alba) and placed to the bottom of the river. Ergosterol was used to detect fungal biomass. We supposed the absence of fungi and the retardation of leaf litter decomposition. Only pH and conductivity increased significantly. Leaf mass loss after the catastrophe was much slower than in 2009 and the decay curves did not follow the exponential decay model. Prior to the catastrophe, leaf mass loss was fast in Torna, compared to other streams in the area. The reason is that the stream is modified, the bed is trapezoid and covered with concrete stones. Fungal biomass was lower, than in the pre-disaster experiment, because fungi did not have enough leaves to sporulate. Leaf mass loss followed the exponential decay curve before the disaster, but after that it was possible only after a non-change period.

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Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 31%
Other 2 15%
Student > Ph. D. Student 2 15%
Professor 1 8%
Student > Doctoral Student 1 8%
Other 2 15%
Unknown 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 38%
Environmental Science 4 31%
Nursing and Health Professions 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Unknown 2 15%