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  Inferring interactions in complex microbial communities from nucleotide sequence data and environmental parameters

Shang, Y., Sikorski, J., Bonkowski, M., Fiore-Donno, A.-M., Kandeler, E., Marhan, S., et al. (2017). Inferring interactions in complex microbial communities from nucleotide sequence data and environmental parameters. PLoS One, 12(3): e0173765. doi:10.1371/journal.pone.0173765.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002C-CCD4-E Version Permalink: https://hdl.handle.net/11858/00-001M-0000-002C-CD42-0
Genre: Journal Article

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http://dx.doi.org/10.1371/journal.pone.0173765 (Publisher version)
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 Creators:
Shang, Yu, Author
Sikorski, Johannes, Author
Bonkowski, Michael, Author
Fiore-Donno, Anna-Maria, Author
Kandeler, Ellen, Author
Marhan, Sven, Author
Boeddinghaus, Runa S., Author
Solly, Emily1, 2, Author           
Schrumpf, Marion3, 4, Author           
Schöning, Ingo3, Author           
Wubet, Tesfaye, Author
Buscot, Francois, Author
Overmann, Joerg, Author
Affiliations:
1Department Biogeochemical Processes, Prof. S. E. Trumbore, Max Planck Institute for Biogeochemistry, Max Planck Society, 1497752              
2IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society, Hans-Knöll-Str. 10, 07745 Jena, DE, ou_1497757              
3Soil and Ecosystem Processes, Dr. M. Schrumpf, Department Biogeochemical Processes, Prof. S. E. Trumbore, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1497776              
4Soil Processes, Dr. Marion Schrumpf, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1938308              

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 Abstract: Interactions occur between two or more organisms affecting each other. Interactions are decisive for the ecology of the organisms. Without direct experimental evidence the analysis of interactions is difficult. Correlation analyses that are based on co-occurrences are often used to approximate interaction. Here, we present a new mathematical model to estimate the interaction strengths between taxa, based on changes in their relative abundances across environmental gradients.

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 Dates: 2017-02-272017-03-03
 Publication Status: Published online
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 Identifiers: Other: BGC2608
DOI: 10.1371/journal.pone.0173765
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 12 (3) Sequence Number: e0173765 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850