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Updated: Feb 2, 2026

Characterization of Aquatic Biofilms with Flow Cytometry

Characterization of Aquatic Biofilms with Flow Cytometry

Published on: June 6, 2018

A Foraging Mandala for Aquatic Microorganisms.

Vicente I Fernandez¹, Yutaka Yawata^1,2, Roman Stocker³

¹Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland.

The ISME Journal

|November 18, 2018

Summary

This summary is machine-generated.

This study introduces a foraging mandala, a new biophysical framework to understand how aquatic microorganisms find resources. It simplifies microbe-environment interactions by focusing on search time and growth return.

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Area of Science:

Aquatic microbial ecology
Biophysics
Ecological modeling

Background:

Aquatic environments host diverse microorganisms with varied adaptations and life histories.
Understanding microbe-environment interactions is crucial for predicting ecosystem-level consequences.
Existing research lacks unifying frameworks to generalize microbial dynamics.

Purpose of the Study:

To propose a novel biophysical framework, the foraging mandala, for aquatic microorganisms.
To simplify the complexity of resource acquisition and microbial adaptations.
To provide a generalizable model for aquatic microbial ecology.

Main Methods:

Inspired by phytoplankton models, a foraging mandala was developed.
The framework distills resource acquisition into search time and growth return parameters.
The model was illustrated using diverse microbial adaptations and environmental conditions.

Main Results:

The foraging mandala accounts for both local environment and individual microbial adaptations.
It successfully simplifies complex microbe-environment interactions.
The framework demonstrates broad applicability across various microbial strategies.

Conclusions:

The foraging mandala offers a unifying framework for aquatic microbial ecology.
It facilitates comparison of disparate microbial strategies.
This model can help scale microscale dynamics to ecosystem-level predictions.