Entropy, or Information, Unifies Ecology and Evolution and Beyond.

Area of Science:

• Ecology and Evolutionary Biology
• Information Theory

Background:

• Ecology and evolution share four fundamental processes: innovation, transmission, movement, and adaptation.
• Macroecology and micro-evolutionary biology study these processes at different scales (species assemblages vs. genetic variants).
• Existing methods for analyzing these processes, particularly in macroecology, sometimes lack robust predictive power.

Purpose of the Study:

• To demonstrate the utility of entropy and information theory for analyzing and forecasting ecological and evolutionary processes.
• To develop a unified predictive framework applicable to both macroecological patterns and micro-evolutionary dynamics.
• To integrate diverse measures of diversity and information within a common theoretical structure.

Main Methods:

• Application of entropy and information theory concepts (e.g., Shannon entropy, mutual information) to ecological and evolutionary data.
• Derivation and testing of predictive equations for molecular diversity and other entropy measures.
• Comparison of information-theoretic approaches with traditional measures like Gini-Simpson index.

Main Results:

• Entropy/information methods provide a robust framework for analyzing and forecasting the four core processes in ecology and evolution.
• Predictive equations based on Shannon entropy and mutual information have been developed for molecular diversity.
• A general predictive approach for major entropy/information types is now achievable, unifying diverse biological studies.

Conclusions:

• Entropy and information theory offer a powerful, unified approach to understanding and predicting ecological and evolutionary dynamics.
• These methods facilitate seamless integration with studies of the physical environment and potentially evolutionary algorithms.
• The developed framework enhances predictive capabilities across various scales of biological organization.