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

Abstract

The depiction of populations – of humans or animals – as ‘population pyramids’ is a useful tool for the assessment of various characteristics of populations at a glance. Although these visualisations are well-known objects in various communities, formalised and algorithmic approaches to gain information from these data are less present. Here, we present an algorithm-based classification of population data into ‘pyramids’ of different shapes that can be linked to typical demographic properties. The classification accuracy of the algorithm was tested on over 50,000 population pyramids from 450 mammal species. The approach delivers plausible classifications, in particular with respect to changes in population size linked to specific series of, and transitions between, different ‘pyramid’ shapes. We believe this approach might become a useful tool for analysing and communicating historical population developments in multiple contexts and is of broad interest. Moreover, it might be useful for animal population management strategies.

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  Introducing a deterministic algorithmic approach to classify population pyramid data.
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  Data discretization step to reduce data complexity and to unify data.
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  Classification of a population pyramid into non-species-specific shape categories that are linked to specific characteristics of the population.