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Related Concept Videos

Naturalistic Observations02:30

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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What are Populations and Communities?00:30

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Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
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Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less likely to...

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Reconstructing community dynamics from limited observations.

Chandler Ross1, Ville Laitinen2, Moein Khalighi2

  • 1Department of Computing, University of Turku, Turku, 20014, Finland. cmross@utu.fi.

Microbiome
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new Bayesian method to analyze microbial community dynamics using short time series data. This approach predicts ecosystem stability and tipping points, even with limited data, improving our understanding of microbial ecology.

Keywords:
BistabilityExit timeGaussian processesHuman gut microbiotaMicrobial ecologyStability landscapeStochastic differential equationTipping points

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Area of Science:

  • Microbial Ecology
  • Ecological Dynamics
  • Statistical Modeling

Background:

  • Ecosystems exhibit fluctuations around stable states, but can collapse into alternative states.
  • Understanding microbial community dynamics is crucial for predicting responses to environmental changes.
  • Existing models struggle with real-world microbiome data, limited by data scarcity.

Purpose of the Study:

  • To develop a novel method for characterizing microbial community dynamics from limited time series data.
  • To predict ecosystem stability, tipping regions, and resilience in microbial communities.
  • To differentiate between bistability and bimodality in ecological systems.

Main Methods:

  • Employed Bayesian inference to combine information from multiple short time series.
  • Utilized Gaussian process priors to decompose dynamics into deterministic and stochastic components.
  • Estimated probabilistic metrics for resilience, such as expected exit time.

Main Results:

  • Successfully predicted stable and tipping regions with associated uncertainty using limited data.
  • Distinguished between bistability and bimodality, overcoming limitations of classical potential analyses.
  • Validated the approach on simulated data and re-analyzed lake cyanobacteria and human gut microbiota data.

Conclusions:

  • Introduced a robust probabilistic non-parametric approach for analyzing stationary community dynamics.
  • The method is applicable to diverse microbial ecology systems and short time series data.
  • Clarified bistable vs. bimodal dynamics and advanced understanding of microbial community stability and resilience.