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

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.