Claddis: a new R package for automating disparity analyses based on cladistic datasets
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Claddis: a new R package for automating disparity analyses based on cladistic datasets

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Claddis: a new R package for automating disparity analyses based on cladistic datasets Presentation Transcript

  • 1. Claddis: a new R package for automating disparity analyses based on cladistic datasets Graeme T. Lloyd
  • 2. Acknowledgements Matt Friedman Roger Benson Liam Revell Mark Bell
  • 3. Extinction Random Marginal Asymmetric Korn et al. in press Intensity
  • 4. Clade comparison Sidlauskis 2008
  • 5. Center of gravity Hughes et al. 2013
  • 6. Caveat emptor Anderson and Friedman 2012
  • 7. Cladistic disparity Cladistic matrix Distance matrix Ordination ‘Morphospace’
  • 8. Cladistic disparity Cladistic matrix Distance matrix Ordination ‘Morphospace’ Tree Ancestral states ‘Phylo- Morphospace’
  • 9. Claddis Cladistic matrix Distance matrix Ordination ‘Morphospace’ Tree Ancestral states ‘Phylo- Morphospace’ Inputs Outputs
  • 10. Claddis Safe taxonomic reduction #NEXUS TNT Step matrices Continuous characters Other features Current limitations
  • 11. Data Choiniere et al. in press
  • 12. Data Choiniere et al. in press 114 taxa | 555 characters
  • 13. Distance Taxon_i 001011 Taxon_j 101101 Taxon_i 001011 Taxon_j 101101 Wills et al. 1994
  • 14. Distance Ordered vs. Unordered Gap-coding Polymorphisms Weighting
  • 15. Missing data Taxon_A 0000000000 Taxon_B 00000111?? Taxon_C 11???????? Taxon_D 00110????? Taxon_E ?????01100 A B A C 1.73 1.41 Euclidean 8 2 N 2.83 1.41 Max
  • 16. Rescaled distances Distance / N = Gower Dissimilarity Distance / Max = MOD Dissimilarity A B A C 0.22 0.71 Gower 0.61 1.00 MOD
  • 17. Gower vs. MOD Gower
  • 18. Gower vs. MOD PC2(16.0%) PC1 (34.1%) Gower
  • 19. Gower vs. MOD MOD
  • 20. Gower vs. MOD PC2(9.5%) PC1 (14.1%) MOD
  • 21. Missing data Taxon_A 0000000000 Taxon_B 00000111?? Taxon_C 11???????? Taxon_D 00110????? Taxon_E ?????01100 D E ? Euclidean 0 N 0 Max
  • 22. Prune Taxon_A 0000000000 Taxon_B 00000111?? Taxon_C 11???????? Taxon_D 00110????? Taxon_E ?????01100
  • 23. Fill gaps Butler et al. 2011 Average values: (Wills 1997) Phylogenetic prediction: (Brusatte et al. 2011; Butler et al. 2011)
  • 24. Phylogenetic prediction Estimateallstates Estimate tips No Yes NoYes ? ? = = = = = ? ?
  • 25. Phylogenetic prediction Estimateallstates Estimate tips No Yes NoYes = ? ? = = = = = ? ? = ? ? = = = = ? ? ? ?
  • 26. = ? ? = = = = ? ? ? ? Phylogenetic prediction Estimateallstates Estimate tips No Yes NoYes = = = = = ? ? = = = = = ? ?
  • 27. Phylogenetic prediction Estimateallstates Estimate tips No Yes NoYes = = = = = ? ? = = = = = ? ? = ? ? = = = = ? ? ? ? = = = = =
  • 28. Phylogenetic signal Estimateallstates Estimate tips No Yes NoYes 0.63 0.66 0.530.52
  • 29. Ordination N Y N Y Tips States PC1 PC2
  • 30. Ordination N Y N Y Tips States PC1 PC2
  • 31. Ordination N Y N Y Tips States PC1 PC2
  • 32. Ordination with ancestors PC1 PC2 N Y N Y Tips States
  • 33. Ordination with ancestors N Y N Y Tips States PC1 PC2
  • 34. Ordination with ancestors N Y N Y Tips States PC1 PC2
  • 35. Ordination with ancestors N Y N Y Tips States PC1 PC2
  • 36. Ordination with ancestors Phylogenetic distances PC1 PC2
  • 37. Disparity time series Cladistic matrix Distance matrix Ordination ‘Morphospace’ Time series
  • 38. Disparity time series Cladistic matrix Distance matrix Ordination ‘Morphospace’ Time series Time series Benson and Druckenmiller in press
  • 39. Disparity time series Tips only All nodes
  • 40. Phylogenetic distance time series Tips only All nodes
  • 41. Model residual time series Tips only Significantly +ve Significantly -ve
  • 42. Model residual time series All nodes Significantly +ve
  • 43. Recommendations MOD
  • 44. Recommendations MOD N Y N Y Tips States
  • 45. Recommendations MOD N Y N Y Tips States Ordination Time series Distance matrix Time series ✔
  • 46. Recommendations MOD N Y N Y Tips States Ordination Time series Distance matrix Time series ✔
  • 47. Recommendations MOD N Y N Y Tips States Ordination Time series Distance matrix Time series ✔ Use !