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Data talking to theory, theory talking to data: how can we make the connections?

Data talking to theory, theory talking to data: how can we make the connections?. Stevan J. Arnold Oregon State University Corvallis, OR. Conclusions. The most cited scientific articles are methods, reviews, and conceptual pieces

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Data talking to theory, theory talking to data: how can we make the connections?

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  1. Data talking to theory, theory talking to data: how can we make the connections? Stevan J. Arnold Oregon State University Corvallis, OR

  2. Conclusions • The most cited scientific articles are methods, reviews, and conceptual pieces • A worthy goal in methods papers is to connect the best data to the most powerful theory • The most useful theory is formulated in terms of measureable parameters • Obstacles to making the data-theory connection can lie with the data, the theory or because the solution resides in a different field • Sometimes a good solution is worth waiting for

  3. The papers • Lande & Arnold 1983 The measurement of selection on correlated characters. Evolution • Arnold 1983 Morphology, performance, and fitness. American Zoologist • Arnold & Wade 1984On the measurement of natural and sexual selection … Evolution • Phillips & Arnold 1989Visualizing multivariate selection. Evolution • Phillips & Arnold 1999Hierarchial comparison of genetic variance- covariance matrices … Evolution • Jones et al. 2003, 2004, 2007Stability and evolution of the G- matrix … Evolution • Estes & Arnold 2007Resolving the paradox of stasis … American Naturalist • Hohenlohe & Arnold 2008MIPoD: a hypothesis testing framework for microevolutionary inference … American Naturalist

  4. Citations • Lande & Arnold 1983 ……………..1454 • Arnold 1983 …………………………413 • Arnold & Wade 1984………………..560 • Phillips & Arnold 1989 ……………..165 • Phillips & Arnold 1999 …………......123 • Jones et al. 2003, 2004, 2007 ………76 • Estes & Arnold 2007………………….24 • Hohenlohe & Arnold 2008 …………....2

  5. Format • Original goal: What we were looking for in the first place • Obstacle:Why we couldn’t get there • Epiphany:How we got past the block • New goal: What we could do once we got past the block

  6. Lande & Arnold 1983correlated characters • Original goal: Understand the selection gradient, • Obstacle:β impossible to estimate because it is the first derivative of an adaptive landscape • Epiphany:β is also a vector of partial regressions of fitness on traits, • New goal: Estimate β (and γ) using data from natural populations

  7. The selection gradient as the direction of steepest uphill slope on the adaptive landscape

  8. Arnold 1983morphology, performance, & fitness • Original goal: What is the relationship between performance studies and selection? • Obstacle: Performance measures are distantly related to fitness • Epiphany: Recognize two parts to fitness and selection (β), one easy to measure, the other difficult • New goal: Estimate selection gradients corresponding to these two parts ( )

  9. A path diagram view of the relationships between morphology, performance and fitness, showing partitioned selection gradients Arnold 1983

  10. Arnold & Wade 1984natural vs. sexual selection • Original goal: Find a way to measure sexual selection using Howard’s (1979) data • Obstacle: Howard used multiple measures of reproductive success • Epiphany: Use a multiplicative model of fitness to analyze multiple episodes of selection • New goal: Measure the force of natural vs. sexual selection

  11. Howard’s 1979 data table

  12. Arnold & Wade’s 1984 parameterization of Howard’s data

  13. Howard’s 1979 plot showing selection of body size

  14. Arnold & Wade’s 1984 analysis and plot of Howard’s data, showing that most of the selection body size is due to sexual selection

  15. Phillips & Arnold 1989visualizing multivariate selection • Original goal: How can one visualize the selection implied by a set of β- and γ-coefficients? • Obstacle: Univariate and even bivariate diagrams can be misleading, so what is the solution? • Epiphany: Canonical analysis is a long-standing solution to this standard problem • New goal: Adapt canonical analysis to the interpretation of selection surfaces

  16. The canonical solution is a rotation of axes Arnold et al. 2008

  17. Phillips & Arnold 1999comparison of G-matrices • Original goal: How can one test for the equality and proportionality of G-matrices • Obstacle: Sampling covariances (family structure) complicates test statistics • Epiphany: Use Flury’s (1988) hierarchial approach; use bootstrapping to account for family structure • New goal: Implement a hierarchy of tests that compares eigenvectors and values

  18. The G-matrix can be portrayed as an ellipse Arnold et al. 2008

  19. The Flury hierarchy of matrix comparisons Arnold et al. 2008

  20. Jones et al. 2003, 2004,2007stability and evolution of G • Original goal: What governs the stability and evolution of the G-matrix? • Obstacle: No theory accounts simultaneously for selection and finite population size • Epiphany: Use simulations • New goal: Define the conditions under which the G-matrix is least and most stable

  21. Alignment of mutation and selection stabilizes the G-matrix Arnold et al. 2008

  22. Estes & Arnold 2007paradox of stasis • Original goal: Use Gingerich’s (2001) data to test stochastic models of evolutionary process • Obstacle: Data in the form of rate as a function of elapsed time; models make predictions about divergence as a function of time • Epiphany: Recast the data so they’re in the same form as the models • New goal: Test representatives of all available classes of stochastic models using the data

  23. Gingerich’s 2001 plot, showing decreasing rates as a function of elapsed time

  24. Estes and Arnold 2007 plot of Gingerich’s data in a format for testing stochastic models of evolutionary process

  25. θ W z DISPLACED OPTIMUM MODEL p(z) z Lande 1976

  26. Hohenlohe & Arnold 2008MIPoD • Original goal: Combine data on: inheritance (G-matrix), effective population size (Ne), selection, divergence and phylogeny to make inferences about processes producing adaptive radiations • Obstacle: What theory? • Epiphany: Use neutral theory; use maximum likelihood to combine the data • New goal: Implement a hierarchy of tests that compares the G-matrix with the divergence matrix (comparison of eigenvectors and values)

  27. An adaptive landscape vision of the radiation:peak movement along a selective line of least resistance

  28. Summary

  29. Wait for it, wait for it …

  30. Conclusions • The most cited scientific articles are methods, reviews, and conceptual pieces • A worthy goal in methods papers is to connect the best data to the most powerful theory • The most useful theory is formulated in terms of measureable parameters • Obstacles to making the data-theory connection can lie with the data, the theory, or because the solution resides in a different field or needs to be invented • Sometimes a good solution is worth waiting for

  31. Acknowledgments Russell Lande (Imperial College) Michael J. Wade (Indiana Univ) Patrick C. Phillips (Univ. Oregon) Adam G. Jones (Texas A&M Univ.) Reinhard Bürger (Univ. Vienna) Suzanne Estes (Portland State Univ.) Paul A. Hohenlohe (Oregon State Univ.)

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