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Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

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Grand Views of Evolution.

Harold P de Vladar¹, Mauro Santos², Eörs Szathmáry³

¹Center for the Conceptual Foundations of Science, Parmenides Foundation, Kirchplatz 1, Pullach 82049, Germany; Institute of Advanced Studies Kőszeg, Chernel Utca 14, Kőszeg 9730, Hungary.

Trends in Ecology & Evolution

|March 2, 2017

Summary

This summary is machine-generated.

Evolutionary progress may not halt without environmental shifts, as ecological dynamics drive innovation. This research explores open-ended evolution, complexity, and the non-algorithmic nature of evolutionary processes.

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Area of Science:

Evolutionary biology
Ecology
Theoretical biology

Background:

Major advances in evolutionary theories exist.
Certain aspects of evolution, like its potential halt without abiotic change, unboundedness, or progressive nature, remain neglected.

Purpose of the Study:

To discuss ecological and evolutionary models.
To argue that ecological change, specifically Red Queen dynamics, facilitates innovation.
To distinguish and define three forms of open-endedness in evolution.

Main Methods:

Discussion of ecological and evolutionary models.
Analysis of Red Queen dynamics' role in innovation.
Distinction between weak, strong, and ultimate open-endedness.

Main Results:

Ecological change, leading to Red Queen dynamics, promotes but does not guarantee innovation.
Weak open-endedness allows indefinite novel phenotypes.
Strong open-endedness requires continuous evolutionary novelties and innovations.
Ultimate open-endedness necessitates unlimited heredity for indefinite complexity increase.
Open-ended innovation relies on exaptations creating novel niches.

Conclusions:

Evolution is not fully algorithmic.
Ecological dynamics and exaptations are crucial for open-ended innovation and increasing complexity.
Evolutionary processes can generate new traits and rules dynamically.