1. Simulation Studies of G-matrix Stability and Evolution Stevan J. Arnold (Oregon State Univ.) Adam G. Jones (Texas A&M Univ.) Reinhard Bürger (Univ. Vienna)

2. Overview Describe the rationale for the work. Outline the essential features of the simulation model. Describe the main results from five studies.

3. Rationale for simulation studies of G-matrix stability and evolution Analytical results limited. In applying response to selection and drift equations on evolutionary timescales, useful to know the conditions under which G is likely to be stable vs. unstable. Useful to understand the major features of G evolution.

4. Overall idea of the simulations Set up conditions so that a G-matrix will evolve and equilibrate under mutation-drift-selection balance. Characterize the shape, size and stability of the G-matrix at that equilibrium. Use correlational selection to establish a selective line of least resistance (45 deg line) with the expectation that mutation and G will evolve towards alignment with that line. Use biologically realistic values for other parameters (mutation rates, strength of stabilzing selection, effective population size). Determine the conditions under which the G-matrix is most and least stable.

5. Model details Direct Monte Carlo simulation with each gene and individual specified Two traits affected by 50 pleiotropic loci Additive inheritance with no dominance or epistasis Allelic effects drawn from a bivariate normal distribution with means = 0, variances = 0.05, and mutational correlation r μ = 0.0-0.9 Mutation rate = 0.0002 per haploid locus Environmental effects drawn from a bivariate normal distribution with mean = 0, variances = 1

6. Mutational effect on trait 1 Mutational effect on trait 2 Mutational effect on trait 1 Mutational effect on trait 2 (a) (b) Mutation conventions Arnold et al. 2008

7. More model details Discrete generations Life cycle: random sampling of breeding pairs from survivors in preceding generation, production of offspring (mutation & recombination), viability selection (Gaussian). ‘Variances’ of Gaussian selection function = 0, 9, 49, or 99, with off-diagonal element adjusted so that r ω = 0.0-0.9 N e = 342, 683, 1366, or 2731

8. Average value of trait 1 Value of trait 1 Value of trait 2 Average value of trait 2 (a) (b) (c) (d) Individual selection surfaces Adaptive landscapes Selection conventions Arnold et al. 2008

9. Estimates of the strength of stabilizing selection 0 40 80 120 160 -160-120-80-4004080120  2 Number of observations Strength of stabilizing selection,  2 Number of observations Strength of stabilizing selection,  Number of observations 0 40 80 120 160 -160-120-80-4004080120  Number of observations *** Data from Kingsolver et al. 2001

10. Simulation runs Initial burn-in period of 10,000 generations In each run, after burn-in, sample the next 2,000 – 10,000 generations with calculation of output parameters every generation 20 replicate runs

11. Measures of G-matrix stability Parameterization of the G-matrix: size (Σ = sum of eigenvalues), eccentricity (ε = ratio of eigenvalues), and orientation (φ = angle of leading eigenvector). G-matrix stability: average per-generation change relative to mean (ΔΣ, Δε) or on original scale (Δφ in degrees).

12. Change in size, ΔΣ Change in eccentricity, Δε Change in orientation, Δφ Three measures of G-matrix stability Jones et al. 2003

13. Overview of simulation studies A single trait, stationary AL (Bürger & Lande 1994). Two traits, stationary AL (Jones et al. 2003). Two traits, moving adaptive peak (Jones et al. 2004). Two traits, evolving mutation matrix (Jones et al. 2007). Two traits, one way migration between populations (Guillaume & Whitlock 2007). Two traits, fluctuation in orientation of AL (Revell 2007). Review of foregoing results (Arnold et al. 2008).

14. Evolution and stability of G when the adaptive landscape is stationary: results Different aspects of stability react differently to selection, mutation, and drift. The G-matrix evolves in expected ways to the AL and the pattern of mutation. Jones et al. 2003

15. The three stability measures have different stability profiles Orientation: stability in increased by mutational correlation, correlational selection, alignment of mutation and selection, and large N e Eccentricity: stability in increased by large N e Size: stability in increased by large N e Jones et al. 2003

16. r μ r ω 0 0 0.75 0.50 0 0.50 0.75 0.90 Mutational and selectional correlations stabilize the orientation of the G-matrix Jones et al. 2003 N e = 342 ω 11 =ω 22 =49

17. 200 400 6008001000120014001600 Generation The evolution of G reflects the patterns of mutation and selection Arnold et al. 2008

18. Equal Proportional CPC Unrelated Flury 1988, Phillips & Arnold 1999 eigenvalues eigenvectors same proportional same different same different The Flury hierarchy for G-matrix comparison

19. Experimental treatments Conspecific populations Different species Sexes Equal Proportional Full CPC Partial CPC Unrelated Number of comparisons Arnold et al. 2008 Conservation of eigenvectors is a common result in G-matrix comparisons

20. Stability of G when the orientation of the adaptive landscape fluctuates Fluctuation in orientation of the AL (r ω ) has no effect on the stability of G-matrix size or eccentricity. Fluctuation in orientation of the AL (r ω ) affects the stability of G-matrix orientation (larger fluctuations lead to more instability). Revell 2007

21. Evolution and stability of G when the peak of the adaptive landscape moves at a constant rate: simulation detail Direction of peak movement:,, or Rate of peak movement: 0.008 phenotypic standard deviations ( ≈ average rate in a large sample of microevolutionary studies compiled by Kinnison & Hendry 2001). Jones et al. 2004

22. Evolution and stability of G when the peak of the adaptive landscape moves at a constant rate: results Evolution along a selective line of least resistance (i.e., along the eigenvector corresponding to the leading eigenvalue of the AL) increased stability of the G-matrix orientation. A continuously moving optimum can produce persistent maladaptation for correlated traits: the evolving mean never catches up with the moving optimum. G elongates in the direction of peak movement Jones et al. 2004

23. Average value of trait 1 Average value of trait 2 Peak movement along a selective line of least resistance stabilizes the G-matrix Arnold et al. 2008

24. The flying kite effect Jones et al. 2004 r μ = 0.9 r ω = 0.0

25. Evolution and stability of G with migration between populations: simulation detail Life cycle: migraton, reproduction, viability selection One way migration from a mainland pop (constant N=10 4 ) to 5 island pops (each with constant N=10 3 ) Island optima situated 5 environmental standard deviations from the mainland optimum at angles ranging from g min to g max Migration rate varied from 0 to10 -2 Guillaume & Whitlock 2007

26. Mainland→island migration model g max g min mainland islands 1-5 Guillaume & Whitlock 2007 model

27. Evolution and stability of G with migration between populations: results Strong migration can affect all aspects of the G-matrix (size, eccentricity and orientation). Strong migration can stabilize the G- matrix, especially if peak movement during island–mainland differentiation is along a selective line of least resistance. Guillaume & Whitlock 2007

28. Effects of strong migration on the G-matrix Guillaume & Whitlock 2007 m = 0.01 Nm = 100

29. G-matrix orientation stabilized by strong migration: time series Guillaume & Whitlock 2007 r μ =r ω =0 mainland island

30. Evolution and stability of G when the mutation matrix evolves: simulation detail Each individual has a personal value for the mutational correlation, r μ The value of r μ is determined by 10 additive loci, distinct from the 50 loci that affect the two phenotypic traits r μ is transformed so that it varies between -1 and +1 No direct selection on r μ Jones et al. 2007

31. Evolution and stability of G when the mutation matrix evolves: results The M-matrix tends to evolve toward alignment with the AL. An evolving M-matrix confers greater stability on G than does a static mutational process. Jones et al. 2007

32. Mutational effect on trait 1 Mutational effect on trait 2 Mutational effect on trait 1 Mutational effect on trait 2 (a) (b) Individuals vary in the mutational correlation parameter r μ

33. Jones et al. 2007 The M-matrix tends to evolve towards alignment with the AL

34. Conclusions Simulation studies have successfully defined the circumstances under which the G-matrix is likely to be stable vs. unstable. They have also confirmed some expectations about G-matrix evolution and revealed new results. Simulation studies fill a void by providing a conceptual guide for using the G-matrix in various kinds of evolutionary applications.

35. Ongoing & future work Explore consequences of episodic vs. constant preak movement. Assess the consequences of using other, nonGaussian distributions for allelic effects Explore the consequence of dominance Explore the consequences of epistasis

36. Papers cited Arnold et al. 2008. Evolution 62: 2451-2461. Estes & Arnold 2007. Amer. Nat. 169: 227-244. Hansen & Houle 2008. J. Evol. Biol. 21: 1201-1219. Jones et al. 2003. Evolution 57: 1747-1760. Jones et al. 2004. Evolution 58: 1639-1654. Jones et al. 2007. Evolution 61: 727-745. Guillaume & Whitlock. 2007. Evolution 61: 2398-2409. Revell. 2007. Evolution 61: 1857-1872.

37. Acknowledgements Russell Lande (University College) Patrick Phillips (Univ. Oregon) Suzanne Estes (Portland State Univ.) Paul Hohenlohe (Oregon State Univ.) Beverly Ajie (UC, Davis)