Uncovering modes of evolution
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This document discusses mechanisms of species diversification through genetic drift versus divergent selection. It outlines how genetic drift can lead to speciation through random processes that increase trait variance over time without a correlation between traits and the environment. Divergent selection, which is more complex, can also lead to speciation by favoring different trait values in different environments, changing both the mean trait value and variance between populations. The author develops a mathematical model combining genetic drift and selection based on the Ornstein-Uhlenbeck process to test these mechanisms on artificial and real biological datasets.
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Uncovering modes of evolution
- 1. Uncovering modes of evolution: divergent selection vs. genetic drift in species diversification Anna Kostikova and Nicolas Salamin, DEE, UNIL
- 3. Simple case: frog in a pond mean trait value (sits on fitness optimum) trait variance
- 4. How frogs can speciate with time?We will look at mechanisms(and not at geographic mode or initial events)
- 5. Genetic drift and natural selection
- 6. By genetic drift (2) Speciation due to random processes (3) Variance in trait value increases (1) No trait-environment correlation
- 7. By natural selection, which is far more complex
- 8. Stabilizing selection (2) No speciation (3) Variance in trait value decreases Environment favors same phenotype
- 9. Directional selection (2) Speciation (?) (3) Mean trait value changes (4) Variance may or may not decrease Environment favors 1 new phenotype
- 10. Divergent selection (2) Speciation certainly occurs (3) Mean trait value changes (4) Variance in trait value changes Environment favors 2 new phenotypes
- 11. To understand modes of speciation: 3. Fitness gains 1. Trait mean/variance 2. Phenotype-environment interactions
- 16. Drift in mathematical form:
- 18. Drift + selection in mathematical form:
- 20. In this project: Code (in R) a model of drift+selection (based on Ornstein–Uhlenbeck process) with fixed parameters ( ) Develop it further to include parameter optimization with OPTIM package (R) Test on (1) artificial dataset and (2) real dataset of Amolops frogs species
- 21. Thanks for attention and I am open to any questions
Editor's Notes
- Trait mean and variancePhenotype-environment interactionFitness measures