Dataset Information

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

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.

SUBMITTER: Shang Y

PROVIDER: S-EPMC5348048 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Shang Yu Y Sikorski Johannes J Bonkowski Michael M Fiore-Donno Anna-Maria AM Kandeler Ellen E Marhan Sven S Boeddinghaus Runa S RS Solly Emily F EF Schrumpf Marion M Schöning Ingo I Wubet Tesfaye T Buscot Francois F Overmann Jörg J

PloS one 20170313 3

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. ...[more]

PMID: 28288199

Similar Datasets

Project description:UnlabelledThere is a growing recognition of the roles of marine microenvironments as reservoirs of biodiversity and as sites of enhanced biological activity and in facilitating biological interactions. Here, we examine the bacterial community inhabiting free-living and particle-associated seawater microenvironments at the Pivers Island Coastal Observatory (PICO). 16S rRNA gene libraries from monthly samples (July 2013 to August 2014) were used to identify microbes in seawater in four size fractions: >63 μm (zooplankton and large particles), 63 to 5 μm (particles), 5 to 1 μm (small particles/dividing cells), and <1 μm (free-living prokaryotes). Analyses of microbial community composition highlight the importance of the microhabitat (e.g., particle-associated versus free-living lifestyle) as communities cluster by size fraction, and the microhabitat explains more of the community variability than measured environmental parameters, including pH, particle concentration, projected daily insolation, nutrients, and temperature. While temperature is statistically associated with community changes in the <1-μm and 5- to 1-μm fractions, none of the measured bulk seawater environmental variables are statistically significant in the larger-particle-associated fractions. These results, combined with high particle-associated community variability, especially in the largest size fraction (i.e., >63 μm), suggest that particle composition, including eukaryotes and their associated microbiomes, may be an important factor in selecting for specific particle-associated bacteria.ImportanceBy comparing levels of particle-associated and free-living bacterial diversity at a coastal location over the course of 14 months, we show that bacteria associated with particles are generally more diverse and appear to be less responsive to commonly measured environmental variables than free-living bacteria. These diverse and highly variable particle-associated communities are likely driven by differences in particle substrates both within the water column at a single time point and due to seasonal changes over the course of the year.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets