Historical biogeography models with dispersal probability as a func7on of distance(s)

Transcription

1 Historical biogeography models with dispersal probability as a func7on of distance(s) Nicholas J. Matzke, Postdoctoral Fellow, NIMBioS (Na6onal Ins6tute of Mathema6cal, SSE symposium: Fron6ers in Parametric Biogeography 10:45 am, Nobre Room, Guarujá, Brasil, June 30, 2015 Figure: Map of stochas6cally- mapped dispersal events BioGeoBEARS model: BAYAREALIKE- d- e+a+x+n Data from: Angiosperm megatree, Zanne et al. (2013, Nature)

2 Acknowledgements Ques6ons/comments/ collabora6ons at: (also: seeking a job) Thanks especially to: Jim Albert NIMBioS Brian O Meara Jeremy Beaulieu Ka?e Massana Michael Landis Ph.D. commicee John Huelsenbeck Tony Barnosky David Jablonski Roger Byrne Systema?c Biology editors & reviewers TRY IT YOURSELF AT: hmp://phylo.wikidot.com/biogeobears Funding: NIMBioS NSF Bivalves in Time and Space UC Berkeley Wang Fellowship UC Berkeley Tien Fellowship Google Summer of Code NIMBioS

3 Outline 1. Historical biogeography: What s the point? 2. Models morghulis 3. Models in BioGeoBEARS, and valida6on 4. Adding more realism with +x and +n 5. Es6ma6ng the global angiosperm macroevolu6onary dispersal kernel

4 Outline 1. Historical biogeography: What s the point? 2. Models morghulis 3. Models in BioGeoBEARS, and valida6on 4. Adding more realism with +x and +n 5. Es6ma6ng the global angiosperm macroevolu6onary dispersal kernel

5 Historical biogeography: What s the point? Tradi6onally, back to parsimony days, the point has been Ancestral Area Reconstruc6on e.g. Hawaiian Psychotria

6 Historical biogeography: What s the point? Sugges7on: let s replace Ancestral Area Reconstruc6on with Ancestral Range Es6ma6on (credit: Brian Moore) e.g. Hawaiian Psychotria

7 Historical biogeography: What s the point? Sugges7on: let s replace Ancestral Area Reconstruc6on with Ancestral Range Es6ma6on (credit: Brian Moore)

8 Historical biogeography: What s the point? Is Ancestral Range Es6ma6on the only point of historical biogeography? Not originally. The original hope was that by looking at many taxa, we could infer common pacerns and processes. (e.g.: General Area Cladograms, Lieberman- modified Brooks Parsimony Analysis)

9 Historical biogeography: What s the point? I think sta6s6cal model choice can bring process back into historical biogeography in a big way. We can do this by implemen6ng different models and seeing what probability they confer on the data (the likelihood). We don t expect this to be perfect, of course, but it is becer than just picking one model and not tes6ng it. - Already standard procedure in PCMs.

10 George Box All models are wrong, but some models are useful. This phrase is ubiquitous, but s6ll not kept in mind enough. Perhaps drama6za6on will help George E. P. Box ( )

11 For drama, look no further than HBO, 9 pm Sundays

12 In the free city of Braavos, the tradi7onal gree7ng is:

13 In the free city of Braavos, the tradi7onal gree7ng is:

14 In the free city of Braavos, the tradi7onal gree7ng is: The Faceless Man, Jaqen H'ghar

15 George E. P. Box ( )

16 George E. P. Box ( )

17 MODELS MODELS George E. P. Box ( ) MODELS MODELS

18 MODELS MODELS The Faceless Man Jaqen H'ghar George E. P. Box ( ) MODELS MODELS

19 MODELS MODELS The Faceless Man Jaqen H'ghar George E. P. Box ( ) MODELS MODELS

20 Models doeharis: All models must serve Let s make that happen with model comparison in BioGeoBEARS

21 Outline 1. Historical biogeography: What s the point? 2. Models morghulis 3. Models in BioGeoBEARS, and valida6on 4. Adding more realism with +x and +n 5. Es6ma6ng the global angiosperm macroevolu6onary dispersal kernel

22 Model comparison in BioGeoBEARS Example: DEC vs. DEC+J on Hawaiian Psychotria DEC LnL = DEC+J LnL = (tree & geog: Ree & Smith 2008)

23 Model comparison in BioGeoBEARS (for the +* model, set include_null_range=false) DEC* LnL = DEC*+J LnL = (tree & geog: Ree & Smith 2008)

24 Model comparison in BioGeoBEARS Example: DEC* vs. DEC*+J on Hawaiian Psychotria (for the * model, set include_null_range=false) LnL d e j DEC DEC+J DEC* (+) 0 DEC*+J (+) 0.12

25 Figure 1, Matzke 2013, Frontiers of Biogeography

26 Figure 1, Matzke 2013, Frontiers of Biogeography

27 Figure 1, Matzke 2013, Frontiers of Biogeography

28 Figure 1, Matzke 2013, Frontiers of Biogeography

29 Figure 1, Matzke 2013, Frontiers of Biogeography

30 Figure 1, Matzke 2013, Frontiers of Biogeography

31 Figure 1, Matzke 2013, Frontiers of Biogeography

32 Figure 1, Matzke 2013, Frontiers of Biogeography

33 Figure 1, Matzke 2013, Frontiers of Biogeography

34 Figure 1, Matzke 2013, Frontiers of Biogeography

35 Figure 1, Matzke 2013, Frontiers of Biogeography

36 Figure 1, Matzke 2013, Frontiers of Biogeography

37 Figure 1, Matzke 2013, Frontiers of Biogeography

38 DEC (LAGRANGE) Figure 1, Matzke 2013, Frontiers of Biogeography

39 DEC (LAGRANGE) Figure 1, Matzke 2013, Frontiers of Biogeography

40 DEC (LAGRANGE) Which model should we use? Figure 1, Matzke 2013, Frontiers of Biogeography

41 DEC (LAGRANGE) Figure 1, Matzke 2013, Frontiers of Biogeography

42 Model comparison in BioGeoBEARS DEC DEC+J

43 Model comparison in BioGeoBEARS DEC DEC+J DIVALIKE DIVALIKE+J

44 Model comparison in BioGeoBEARS DEC DEC+J DIVALIKE DIVALIKE+J BAYAREALIKE BAYAREALIKE+J

45 Model comparison in BioGeoBEARS DEC DEC+J DIVALIKE DIVALIKE+J BAYAREALIKE BAYAREALIKE+J DEC* DEC*+J DIVALIKE* DIVALIKE*+J BAYAREALIKE* BAYAREALIKE*+J

46 Model comparison in BioGeoBEARS AIC model weights across 14 datasets (assembled in Massana, Beaulieu, Matzke, O Meara, in prep.) (mostly island datasets from Matzke 2014) Across 14 datasets: Caecilians Cyrtandra Salamanders Leafhoppers Lonicera Drosophila Honeycreepers Megalagrion Orsonwelles Palpimanoid spiders Psychotria Plantago Scaptomyza Silverswords

47 What corresponds to the 3 models in RASP? AIC model weights across 14 datasets (assembled in Massana, Beaulieu, Matzke, O Meara, in prep.) (mostly island datasets from Matzke 2014) Across 14 datasets: Caecilians Cyrtandra Salamanders Leafhoppers Lonicera Drosophila Honeycreepers Megalagrion Orsonwelles Palpimanoid spiders Psychotria Plantago Scaptomyza Silverswords

48 What corresponds to the 3 models in RASP? AIC model weights across 14 datasets (assembled in Massana, Beaulieu, Matzke, O Meara, in prep.) (mostly island datasets from Matzke 2014) Across 14 datasets: Caecilians Cyrtandra Salamanders Leafhoppers Lonicera Drosophila Honeycreepers Megalagrion Orsonwelles Palpimanoid spiders Psychotria Plantago Scaptomyza Silverswords

49 Now we have 12 models. Is that the end? Nope. Models morghulis. Models dohaeris.

50 DEC (LAGRANGE) Figure 1, Matzke 2013, Frontiers of Biogeography

51 Anagene7c range- switching parameter, a In one sense, anagene6c range- switching is an absurd process (models morghulis) a But, it might be a decent approxima6on, if jump dispersal is the main dispersal mode, but many lineages are ex6nct or unsampled (models dohaeris) BAYAREALIKE*- d- e+a equals unordered character model (island model)

52 BAYAREALIKE*- d- e+a is a model we can use for valida7on BioGeoBEARS state probabili6es phytools state probabili6es (Revell)

53 Validate Biogeographical Stochas7c Mapping

54 BAYAREALIKE*- d- e+a is a model we can use for valida7on BioGeoBEARS, distribu6on of many Biogeographical Stochas7c Maps phytools state probabili6es (Revell)

55 Biogeographical Stochas7c Maps converge on state probabili7es BioGeoBEARS, distribu6on of many Biogeographical Stochas7c Maps DEC state probabili6es

56 DEC, DEC+J (BioGeoBEARS) vs. ClaSSE (diversitree) To make ClaSSE & DEC equivalent: DEC or DEC+J equals ClaSSE, *if*: - d and e control character change rates - all ex6nc6on rates set to zero - specia6on rate for each cladogenesis = (Yule rate) 6mes weight/sum(weights) - subtract equilibrium probabili6es at the root

57 DEC, DEC+J (BioGeoBEARS) vs. ClaSSE (diversitree) LnLs for: DEC- e = d DEC = e DEC+J = j

58 Outline 1. Historical biogeography: What s the point? 2. Models morghulis 3. Models in BioGeoBEARS, and valida6on 4. Adding more realism with +x and +n 5. Es6ma6ng the global angiosperm macroevolu6onary dispersal kernel

59 Standard approach: manual dispersal matrix One of the revolu6onary features of Lagrange was adding manual dispersal modifiers E.g., You might try a constrained model, where * dispersal from North America - > South America gets a mul6plier of 1 * dispersal from Africa - > South America gets a mul6plier of 0.1 * dispersal from Africa - > North America gets a mul6plier of 0.01

60 Standard approach: manual dispersal matrix These mul6pliers are subjec6ve, but I think such constrained models are useful. If you get improvement, it indicates some improved fit between the phylogene6c da6ng, distribu6ons, and observed geography at the 6ps If LnL gets worse, it indicates at least one of these is off (probably the da6ng really)

61 Distances Geographic distance = Great Circle Distance between area centroids (rescaled by dividing by min. observed distance) Environmental distance = Difference in absolute value of la7tude

62 New approach: es7mate dispersal matrix Let s try it. Phylogeny: Zanne et al. (2013), Nature, 15,000+ angiosperms Geography: median lat/long of species ranges Regions: Realms x Biomes (58 regions total) Assump7on: everything lives in 1 area

63 Realms x Biomes

64 Realms x Biomes

65 Realms x Biomes

66 Realms x Biomes

67 Realms x Biomes

68 Model comparison Base model: BAYAREALIKE-d-e +a means base rate of dispersal +x means dispersal prob. modified by distance^x +n means dispersal prob. modified by enviromental distance^n +J means weight of cladogenetic jump dispersal Param.LnL +a

69 Model comparison Base model: BAYAREALIKE-d-e +a means base rate of dispersal +x means dispersal prob. modified by distance^x +n means dispersal prob. modified by enviromental distance^n +J means weight of cladogenetic jump dispersal Param.LnL +a a+x

70 Model comparison Base model: BAYAREALIKE-d-e +a means base rate of dispersal +x means dispersal prob. modified by distance^x +n means dispersal prob. modified by enviromental distance^n +J means weight of cladogenetic jump dispersal Param.LnL +a a+x a+x+n

71 Model comparison Base model: BAYAREALIKE-d-e +a means base rate of dispersal +x means dispersal prob. modified by distance^x +n means dispersal prob. modified by enviromental distance^n +J means weight of cladogenetic jump dispersal Param.LnL a x n +a a+x a+x+n

72 Model comparison Base model: BAYAREALIKE-d-e +a means base rate of dispersal +x means dispersal prob. modified by distance^x +n means dispersal prob. modified by enviromental distance^n +J means weight of cladogenetic jump dispersal Param.LnL a x n +a a+x a+x+n J models -- no significant improvement (probably due to many missing species within genera?)

73 Model comparison Base model: BAYAREALIKE-d-e +a means base rate of dispersal +x means dispersal prob. modified by distance^x +n means dispersal prob. modified by enviromental distance^n +J means weight of cladogenetic jump dispersal Param.LnL a x n +a a+x a+x+n J models -- no significant improvement (probably due to many missing species within genera?) Both distance and environmental distance have big effects on angiosperm dispersal

74 Looking at the global angiosperm macroevolu7onary dispersal kernel

75 Looking at the global angiosperm macroevolu7onary dispersal kernel

76 Looking at the global angiosperm macroevolu7onary dispersal kernel

77 Looking at the global angiosperm macroevolu7onary dispersal kernel

78 Looking at the global angiosperm macroevolu7onary dispersal kernel

79 Looking at the global angiosperm macroevolu7onary dispersal kernel

80 Looking at the global angiosperm macroevolu7onary dispersal kernel

81 Looking at the global angiosperm macroevolu7onary dispersal kernel

82 Biogeographical stochas7c mapping

83 Biogeographical stochas7c mapping mapping

84 Biogeographical stochas7c mapping mapping

85 Biogeographical stochas7c mapping mapping

86 Outline 1. Historical biogeography: What s the point? ancestral ranges < learning about process 2. Models morghulis Models dohaeris 3. Models in BioGeoBEARS Test your hypotheses with model choice 4. Adding more realism with +x and +n Distance and environmental distance marer 5. Es6ma6ng the global angiosperm macroevolu6onary dispersal kernel

87 Acknowledgements Ques6ons/comments/ collabora6ons at: (also: seeking a job) Thanks especially to: Jim Albert NIMBioS Brian O Meara Jeremy Beaulieu Ka?e Massana Michael Landis Ph.D. commicee John Huelsenbeck Tony Barnosky David Jablonski Roger Byrne Systema?c Biology editors & reviewers TRY IT YOURSELF AT: hmp://phylo.wikidot.com/biogeobears Funding: NIMBioS NSF Bivalves in Time and Space UC Berkeley Wang Fellowship UC Berkeley Tien Fellowship Google Summer of Code NIMBioS