New methodologies for the use of cladistic-type matrices to measure morphological disparity and evolutionary rate
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Slides from my talk at the Radiation-Extinction meeting (Linn. Soc., November 2014)
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New methodologies for the use of cladistic-type matrices to measure morphological disparity and evolutionary rate
- 1. New methodologies for the use of cladistic-type matrices to measure morphological disparity and evolutionary rate @GraemeTLloyd
- 2. Acknowledgements Matt Friedman Liam Revell Mark Bell Peter Smits Steve Brusatte Roger Benson Steve Wang Rich Fitzjohn
- 3. Cladistic-type data - Discrete morphological data
- 4. Cladistic-type data - Discrete morphological data - Limited to 32 states (often less)
- 5. Cladistic-type data - Discrete morphological data - Limited to 32 states (often less) - Frequently non-Euclidean
- 6. Cladistic-type data - Discrete morphological data - Limited to 32 states (often less) - Frequently non-Euclidean - Missing data common
- 7. Acladistic analyses Disparity Rates
- 8. Acladistic analyses Disparity Rates Common Rare
- 9. Acladistic analyses Disparity Rates Common No models Rare Simple models
- 10. Acladistic analyses Disparity Rates Common No models Single approach (GED) Rare Simple models N approaches ≈ N studies
- 11. Acladistic analyses Disparity Rates Common No models Single approach (GED) Rare Simple models N approaches ≈ N studies Time series an issue
- 13. Disparity
- 14. Toljagic and Butler 2013 Disparity studies Brusatte et al 2008 Thorne et al 2011 Butler et al. 2011
- 15. Cladistic disparity Cladistic matrix Distance matrix Ordination ‘Morphospace’
- 16. Cladistic disparity Cladistic matrix Distance matrix Ordination ‘Morphospace’ Distance metric
- 17. Desiderata An ideal distance metric should:
- 18. Desiderata An ideal distance metric should: 1. have high fidelity
- 19. Desiderata An ideal distance metric should: 1. have high fidelity 2. be normally distributed
- 20. Desiderata An ideal distance metric should: 1. have high fidelity 2. be normally distributed 3. be Euclidean
- 21. Desiderata An ideal distance metric should: 1. have high fidelity 2. be normally distributed 3. be Euclidean 4. be calculable
- 22. Desiderata An ideal distance metric should: 1. have high fidelity 2. be normally distributed 3. be Euclidean 4. be calculable 5. be easily visualised
- 23. Generalised Euclidean Distance Wills 2001
- 24. Generalised Euclidean Distance But: Sijk is incalculable if k values for i or j (or both) are missing Wills 2001
- 25. Generalised Euclidean Distance But: Sijk is incalculable if k values for i or j (or both) are missing Wills 2001
- 27. Alternate distances Raw GED
- 28. Alternate distances Raw GED Gower
- 29. Alternate distances Raw GED Gower MOD
- 30. Simulations Input 20 taxa 50 binary characters 0-80% missing data
- 31. Simulations Input Output 20 taxa 50 binary characters 0-80% missing data N taxa retained Variance of first two PCA axes Shapiro-Wilk test Mantel test
- 32. Calculable Raw GED Gower MOD Incompleteness 100% % taxa retained 0% 80% 0%
- 33. Visualisation Raw GED Gower MOD Incompleteness 45% % variance axes 1 & 2 0% 80% 15%
- 34. Normalcy Raw GED Gower MOD Incompleteness 1.00 Shapiro-Wilk test 0% 80% 0.75
- 35. Fidelity Raw GED Gower MOD Incompleteness +30% Correlation 0% 80% -30%
- 36. Fidelity Gower Raw GED MOD Incompleteness 100% % highest fidelity 0% 80% 0%
- 37. % missing data Incompleteness 30 N data sets 0% 80% 0
- 38. Rates
- 39. Rate studies Derstler 1982 Forey 1988 Ruta et al 2006 Brusatte et al 2008
- 40. Rate calculation Rate = N changes / Δt × Completeness
- 41. Null hypothesis H0 = equal rates
- 42. Alternate hypothesis Halt = +
- 44. Lungfish Devonian high rates Lloyd et al 2012
- 45. Lungfish post-Devonian low rates Lloyd et al 2012
- 46. Parsimony problem DELTRAN ACCTRAN ? ? ? Change early Change late
- 47. Parsimony problem Lloyd et al 2012
- 48. Parsimony problem Lloyd et al 2012
- 49. Parsimony problem ? ? ? ? ? ?
- 50. Parsimony problem ? ? ? ? ? ? ? ? ? ? ? ? No changes
- 51. Internal vs. terminal Rate >
- 52. Internal vs. terminal Changes ≈
- 53. Internal vs. terminal Duration <
- 54. Internal vs. terminal Solution
- 55. Rates revisited high rates low rates Brusatte et al 2014
- 56. Rates revisited high rates low rates Brusatte et al 2014
- 58. Toljagic and Butler 2013 Disparity time series Brusatte et al 2008 Thorne et al 2011 Butler et al. 2011
- 59. Toljagic and Butler 2013 Disparity time series 4 time bins 4 time bins Brusatte et al 2008 Thorne et al 2011 14 time bins 2 time bins Butler et al. 2011
- 60. Rate time series Lloyd et al 2012 Ruta et al 2006 Branch-binning No completeness
- 61. Rate time series N changes | Δt | Completeness
- 62. Conclusions Raw GED Gower MOD
- 63. Conclusions Raw GED Gower MOD PCO 1 ? PCO 2
- 64. Conclusions Raw GED Gower MOD PCO 1 ? PCO 2
- 65. Conclusions Raw GED Gower MOD Rate ? ? t PCO 1 PCO 2
Notes de l'éditeur
- Fidelity (distances in ordination = true distances) Normality (no outliers) Distances should be Euclidean (low/no negative eigenvalues) High variance on first two ordination axes (visualisation) Retain as many taxa as possible (calculable distances)
- Fidelity (distances in ordination = true distances) Normality (no outliers) Distances should be Euclidean (low/no negative eigenvalues) High variance on first two ordination axes (visualisation) Retain as many taxa as possible (calculable distances)
- Fidelity (distances in ordination = true distances) Normality (no outliers) Distances should be Euclidean (low/no negative eigenvalues) High variance on first two ordination axes (visualisation) Retain as many taxa as possible (calculable distances)
- Fidelity (distances in ordination = true distances) Normality (no outliers) Distances should be Euclidean (low/no negative eigenvalues) High variance on first two ordination axes (visualisation) Retain as many taxa as possible (calculable distances)
- Fidelity (distances in ordination = true distances) Normality (no outliers) Distances should be Euclidean (low/no negative eigenvalues) High variance on first two ordination axes (visualisation) Retain as many taxa as possible (calculable distances)
- Fidelity (distances in ordination = true distances) Normality (no outliers) Distances should be Euclidean (low/no negative eigenvalues) High variance on first two ordination axes (visualisation) Retain as many taxa as possible (calculable distances)