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.

1. Uncovering modes of evolution: divergent selection vs. genetic drift in species diversificationAnna Kostikova and Nicolas Salamin, DEE, UNIL

3. Simple case: frog in a pondmean 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 decreasesEnvironment favors same phenotype

9. Directional selection(2) Speciation (?)(3) Mean trait value changes(4) Variance may or may not decreaseEnvironment favors 1 new phenotype

10. Divergent selection(2) Speciation certainly occurs(3) Mean trait value changes(4) Variance in trait value changesEnvironment favors 2 new phenotypes

11. To understand modes of speciation:3. Fitness gains1. Trait mean/variance2. Phenotype-environmentinteractions

12. Drift or selection?

13. Drift vs. selection

14. Drift vs. selection

15. Mathematical models

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