Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1371/journal.pone.0173765
Title (Primary) Inferring interactions in complex microbial communities from nucleotide sequence data and environmental parameters
Author Shang, Y.; Sikorski, J.; Bonkowski, M.; Fiore-Donno, A.-M.; Kandeler, E.; Marhan, S.; Boeddinghaus, R.S.; Solly, E.F.; Schrumpf, M.; Schöning, I.; Wubet, T. ORCID logo ; Buscot, F.; Overmann, J.
Journal PLOS ONE
Year 2017
Department BOOEK; iDiv
Volume 12
Issue 3
Page From e0173765
Language englisch
UFZ wide themes RU1
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.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=18581
Shang, Y., Sikorski, J., Bonkowski, M., Fiore-Donno, A.-M., Kandeler, E., Marhan, S., Boeddinghaus, R.S., Solly, E.F., Schrumpf, M., Schöning, I., Wubet, T., Buscot, F., Overmann, J. (2017):
Inferring interactions in complex microbial communities from nucleotide sequence data and environmental parameters
PLOS One 12 (3), e0173765