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Related Experiment Video

Updated: Mar 22, 2026

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

Demographic inference under a spatially continuous coalescent model.

T A Joseph¹, M J Hickerson^1,2,3, D F Alvarado-Serrano¹

¹Biology Department, The City College of New York, City University of New York, New York, NY, USA.

|April 28, 2016

Summary

This study introduces a new statistical pipeline for demographic inference in spatially structured populations. The method accurately estimates neighborhood size and dispersal ability, advancing population genetics research.

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Area of Science:

Population genetics
Spatial modeling
Demographic inference

Background:

Classical population genetics models discrete populations, but many real populations have continuous spatial structure.
Spatially explicit coalescent models offer a more realistic framework for understanding populations in semi-continuous habitats.
Previous spatially explicit coalescent models lacked practical implementation for demographic inference.

Purpose of the Study:

To develop and demonstrate a statistical pipeline for demographic inference using a spatially explicit continuum model.
To estimate key population parameters like neighborhood size and dispersal ability.
To apply the pipeline to a real-world case study for inferring population density.

Main Methods:

Coupling a coalescent simulator for the continuum model with an approximate Bayesian computation (ABC) framework.
Utilizing empirically informed simulations for parameter estimation.
Employing ABC cross-validation to assess accuracy.

Main Results:

Accurate estimation of neighborhood size was demonstrated through simulations.
The pipeline was successfully applied to Berkheya cuneata, estimating dispersal ability and neighborhood size.
Inferred population density for Berkheya cuneata using the model outputs.

Conclusions:

Spatially explicit coalescent models can be effectively integrated into model-based demographic inference.
The developed pipeline provides a robust tool for analyzing populations with heterogeneous spatial structure.
This approach enhances our understanding of population dynamics in semi-continuous environments.