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Related Experiment Video

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Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

Published on: December 27, 2024

Quantifying community assembly processes and identifying features that impose them.

James C Stegen¹, Xueju Lin, Jim K Fredrickson

¹Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.

The ISME Journal

|June 7, 2013

Summary

This summary is machine-generated.

Ecological Drift, Selection, and Dispersal drive microbial community changes. A new framework quantifies these processes and identifies environmental factors influencing subsurface microbial communities.

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Area of Science:

Microbial Ecology
Geomicrobiology
Environmental Microbiology

Background:

Spatial turnover in biological communities is driven by ecological Drift, Selection, and Dispersal.
Existing statistical tools struggle to quantify these processes or link them to abiotic factors.

Purpose of the Study:

Develop an analytical framework to quantitatively estimate Drift, Selection, and Dispersal in subsurface microbial communities.
Identify abiotic variables that impose Selection or limit Dispersal.

Main Methods:

Quantitative estimation of ecological Drift, Selection, and Dispersal.
Analysis of microbial communities across two geologically distinct aquifer formations.
Linking ecological patterns to measured and unmeasured abiotic variables.

Main Results:

Drift alone accounts for ~25% of spatial turnover.
Selection is stronger (~60%) in deeper, fine-grained sediments due to unmeasured variables.
Selection is weaker (~30%) in shallower, coarse-grained sediments, influenced by hydrological factors.
Dispersal limitation (~30%) arises from sediment heterogeneity and isolation.
High dispersal in permeable sediments (~20%) leads to community homogenization.

Conclusions:

The novel framework provides quantitative insights into ecological processes governing microbial community turnover.
It surpasses existing methods in inferring ecological drivers and abiotic influences.
This approach can advance a unified understanding of microbial community dynamics in diverse environments.