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Quantifying the configurational complexity of biological systems in multivariate 'complexity space'.

Tim Rock¹, Matthew A Wills¹

¹Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath BA2 7AZ, UK.

Journal of the Royal Society, Interface

|January 28, 2025

Summary

Researchers propose "complexity spaces" to measure organismal morphological complexity. This multidimensional framework quantifies part number, differentiation, and regularity, aiding evolutionary studies of complex biological systems.

Keywords:

biological complexity information theory macroevolution morphology morphospace

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Area of Science:

Evolutionary biology
Morphological complexity
Developmental biology

Background:

Quantifying morphological complexity is crucial for evolutionary studies, especially in organisms with serially homologous elements.
Existing frameworks often conflate multiple dimensions of complexity, hindering a nuanced understanding.
A multidimensional approach is needed to accurately capture the concept of biological complexity.

Purpose of the Study:

To introduce and advocate for the use of 'complexity spaces' as a multidimensional framework for quantifying morphological complexity.
To identify key axes defining complexity in biological systems: part number, part differentiation, and regularity of differentiation.
To demonstrate the application of complexity spaces across different hierarchical levels of biological organization.

Main Methods:

Conceptual development of multidimensional 'complexity spaces'.
Identification of three primary complexity axes: part number, part differentiation, and regularity.
Application of the framework to case studies: trilobite body plans and ant colonies (superorganisms).

Main Results:

Complexity spaces provide a framework to distinguish different facets of morphological complexity.
The three identified axes (part number, differentiation, regularity) effectively characterize complexity in the studied systems.
The framework is applicable to diverse biological systems, from individual organisms to superorganisms.

Conclusions:

Complexity spaces offer a robust, multidimensional approach to quantifying morphological complexity in evolutionary studies.
This framework clarifies patterns of complexity evolution by separating distinct components of complexity.
The information-theoretic basis of complexity spaces allows for rigorous analysis of evolutionary trends.