2. Reductionism & the Modern Synthesis “Change in allele frequency over time” Wright Dobzhansky Stebbins Fisher Simpson Haldane

3. Microevolution Macroevolution

4. Microevolutionary patterns We now know that we can study evolution in real time 16.06 g (1976) 17.13 g (1978) Average weight Response to selection 6.66% change in body size in 1 generation (2 years) Conservatively, let’s assume that only a fraction is due to evolution: i.e. 1% change in body size in 1 generation Grant and Grant, 2002

5. Let’s do the calculations If a population increased by 1% every generation starting from ~16 g: In 200 years In 500 years In 1,000 years In 2,000 years 43 g 193 g 2.3 kg 335 kg In 10,000 years 6.47 x 10 19 kg

6. Thousands of years

9. The Paradox of Stasis (Hansen & Houle 2004) : Organisms seem to be able to evolve far more than they do over macroevolutionary scales Empirical studies often find: Strong (and often persistent) directional selection (Hereford et al. 2004, Morrissey & Hadfield 2012) High levels of additive genetic variance (Mousseau & Roff 1987, Houle 1992) Rapid evolutionary rates (Hendry & Kinnison 1999, Kinnison & Hendry 2002) …yet stasis in the fossil record (Gingerich 1983, 2002) …and BM in comparative data? How do we reconcile patterns at different scales?

10. All studies of phenotypic evolution measure comparable quantities How can we see the pattern across scales of time? We measure two quantities: (1) “time for evolution” (2) Δ mean body size Pop B mean(z) Pop A mean(z) Time interval Pop X Pop B mean(z) Pop A mean(z) Time interval

11. Phenotypic divergence database (Uyeda, Hansen, Arnold & Pienaar, 2011. PNAS. ) Only animals, mostly vertebrates, but also some inverts Only traits related to linear body size change Field, Fossil and Phylogenetic comparative data >8000 data points from > 150 studies Intervals from < 1 yr to 360 my

12. Thousands of years

13. Microevolutionary data Fossil data Phylogenetic comparative data Uyeda et al., PNAS, 2011

14. 2 years Microevolutionary data Fossil data Phylogenetic comparative data Uyeda et al., PNAS, 2011

15. 800,000 years Microevolutionary data Fossil data Phylogenetic comparative data Uyeda et al., PNAS, 2011

16. 332.4 my MRCA of all mammals Microevolutionary data Fossil data Phylogenetic comparative data Uyeda et al., PNAS, 2011

17. Average size change for all monotremes vs. all other mammals Microevolutionary data Fossil data Phylogenetic comparative data Uyeda et al., PNAS, 2011

18. Microevolutionary data Fossil data Phylogenetic comparative data Uyeda et al., PNAS, 2011

19. Microevolutionary data Fossil data Phylogenetic comparative data Uyeda et al., PNAS, 2011

20. Microevolutionary data Fossil data Phylogenetic comparative data Uyeda et al., PNAS, 2011

21. Simpson’s Adaptive Zones

22. Model fits: Brownian Motion (BM) Multiple-burst (MB) – Poisson Point Process

23. AIC = -9142.5AIC = -9018.0 AIC = -7878.0

24. Time Phenotype Million-year waiting times “Ephemeral” divergence over short timescales (Futuyma 1987)

25. Time Phenotype Adaptive peak shifts, stabilizing selection & genetic drift Adaptive Zones/Niches

28. θ1θ1 θ2θ2 θ3θ3 α σ2σ2 ln2/α = Phylogenetic Half-Life

29. Compare models (e.g. by AIC)

30. The “best” model may still be bad OU models are not always well- behaved statistically Problems: OU models are valuable because of their biological realism

31. …but where did the biology go?

32. Bayesian Reversible-Jump OU Modeling Flexible Can infer adaptive shifts and compare to a priori hypotheses Can incorporate additional data/realism through informative priors Customize to test specific hypotheses

35. Turtles and Tortoises (Jaffe et al. 2011)

36. (Uyeda and Harmon, 2014 ) Habitat Model bayou Model

37. 2 ln BF = 15.24 surface (Ingram & Mahler, 2012) surface: 92.6my (Uyeda and Harmon, submitted ) habitat bayou Habitat Model bayou Model surface Model bayou Model Phylogenetic half-life (my) prior

38. We can generate better hypotheses Jaffe et al. (2011) Marine, Freshwater, Terrestrial and Island Only Marine was found by bayou. Better hypothesis? Aquatic life history + high environmental temperature

39. But is it stabilizing selection or adaptive zone shifts or….? 85 taxa Body size (SVL) Use 3 different priors: Weakly informative (Free) Blunderbuss model (Stasis shifts) Quantitative genetic model (Peak shifts) Anolis

40. Parameterize with the “Blunderbuss” σ 2 /(2α) ln2/α

41. Quantitative Genetics Model (Lande 1976) Stabilizing Selection Genetic Drift α = h 2 V P /(V P + ω 2 ) σ 2 = h 2 V P /N e

42. QG model priors h 2 =estimated in anoles to be ~0.55 for body size V P =estimated from the data Ne = 99% CI between 1000 and 400,000 (mean 22,000) ω 2 = Stabilizing & directional selection gradients estimated from wild populations

43. QG Model Posteriors Posterior Prior

44. ParameterizationMarginal lnL2 ln BF: Free vs. _____ Free-16.40 Blunderbuss-24.917.0 Lande (QG)-37.721.3 Both Blunderbuss and Lande models perform much more poorly than free parameterization Can also combine these interpretations with ecotype-based hypotheses…..

45. RegimesParameterizationConvergenceMarginal lnL Fixed- EcotypeFreeYes9.83 Fixed- EcotypeBlunderbussYes2.73 Fixed- EcotypeFreeNo-3.62 Fixed- EcotypeLande (QG)Yes-6.70 Fixed- EcotypeBlunderbussNo-14.16 Free No-16.41 Fixed- EcotypeLande (QG)No-18.66 FreeBlunderbussNo-24.93 FreeLande (QG)No-37.73 Convergent, Ecotype-based regimes fit tree best, but should not be interpreted as either Blunderbuss or Lande model

46. Time Phenotype Adaptive zone (e.g. Anolis ecotypes) QG Adaptive peak shifts More to come (Species scale shifts?)

47. A broader framework: Combining fossils, microevolution and phylogenetic comparative data Fossil timeseries Microevolutionary timeseries Quantitative genetic & selection parameters

48. Goal: Powerful, customizable phylogenetic comparative methods for testing user- specific biological hypotheses http://www.arborworkflows.com/

49. Acknowledgements Funding and Support National Science Foundation Research Council of Norway BEACON Center iBEST, U. of Idaho OSU Zoology department CEES, U. of Oslo, Norway Other assistance Phil Gingerich Andrew Hendry Lynne Houck Paul Joyce Joe Felsenstein Coauthors and collaborators Thomas Hansen Jon Eastman Luke Harmon Stevan J Arnold Jason Pienaar Matt Pennell Aaron Liston Mike Blouin David Lytle Harmon Lab Hansen Lab Arnold Lab