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A semi-automatic approach to study population dynamics based on population pyramids.

Max Hahn-Klimroth1, João Pedro Meireles2, Laurie Bingaman Lackey3

  • 1Goethe University Frankfurt, Frankfurt, Germany.

Methodsx
|September 23, 2025
PubMed
Summary
This summary is machine-generated.

We developed an algorithm to classify population pyramids by shape, linking these shapes to demographic properties. This method accurately analyzes population data for mammals and aids in understanding population dynamics and management.

Keywords:
ClassificationDemographyDimensionality reductionPopulation managementPopulation pyramid

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Area of Science:

  • Ecology
  • Demography
  • Computational Biology

Background:

  • Population pyramids are visual tools for assessing population characteristics.
  • Formalized algorithmic approaches for extracting information from population pyramids are limited.

Purpose of the Study:

  • To present an algorithm-based classification of population pyramid data.
  • To link different pyramid shapes to specific demographic properties.
  • To provide a tool for analyzing and communicating historical population developments.

Main Methods:

  • Developed a deterministic algorithmic approach for classifying population pyramid data.
  • Implemented a data discretization step to simplify and unify data.
  • Classified pyramids into non-species-specific shape categories.

Main Results:

  • The algorithm achieved high classification accuracy on over 50,000 population pyramids from 450 mammal species.
  • Classifications linked pyramid shapes to specific population size changes and transitions.
  • The approach demonstrated plausible classifications for diverse mammal populations.

Conclusions:

  • The algorithm offers a robust method for classifying population pyramid shapes and their demographic correlates.
  • This approach can aid in analyzing historical population trends and informing animal population management strategies.
  • The classification system provides a valuable tool for broad ecological and demographic research.