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Predator-Prey Interactions

Predator-Prey Interactions

Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

Speciation Rates

Speciation Rates

Limits to Natural Selection

Limits to Natural Selection

Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

Convergent Evolution

Convergent Evolution

Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.

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Cyclic Incrementality in Competitive Coevolution: Evolvability through Pseudo-Baldwinian Switching-Genes.

Rick Janssen¹, Stefano Nolfi², Pim Haselager

¹Max Planck Institute for Psycholinguistics.

Artificial Life

|July 30, 2016

Summary

This study introduces phenotypic cluster analysis to master tournament data in coevolving systems. It reveals switching-genes, suggesting genetic memory and a shift towards an interaction-driven evolutionary perspective.

Keywords:

Coevolution cluster analysis computational modeling cyclic evolution switching-genes

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Area of Science:

Evolutionary Biology
Computational Biology
Systems Biology

Background:

Coevolving systems are complex due to the Red Queen effect, where fitness depends on interactions with other species.
Master tournaments in simulations help measure fitness but have limitations.

Purpose of the Study:

To introduce phenotypic cluster analysis for examining opponent distributions in coevolving systems.
To develop behavior-based category trees for hierarchical classification of phenotypes.
To investigate the role of switching-genes and genetic memory in evolutionary dynamics.

Main Methods:

Applied phenotypic cluster analysis, commonly used in bioinformatics, to master tournament data.
Constructed behavior-based category trees to classify evolving phenotypes.
Analyzed cluster data to identify switching-genes and their role in opponent specialization.

Main Results:

Phenotypic cluster analysis successfully created hierarchical classifications of phenotypes.
Identified switching-genes controlling opponent specialization, indicating dormant genetic adaptations (genetic memory).
Demonstrated the utility of computer simulations in understanding evolutionary dynamics.

Conclusions:

The study highlights the importance of an interaction-driven perspective over a component-driven one for coevolving systems.
Suggests that context genes may facilitate the emergence of switching-gene effects and genetic adaptability.
Emphasizes the potential of computational approaches to unravel complex evolutionary processes.