Bayesian inference of biogeographical histories for hundreds of discrete areas

Transcription

1 Bayesian inference of biogeographical histories for hundreds of discrete areas Michael Landis Nick Matzke Brian Moore John Huelsenbeck Evolu>on 06/23/13

2 Biogeography Every species has come into existence coincident both in space and 5me with a pre- exis5ng closely allied species. AR Wallace, 1855

3 Con>nental- scale biogeography (Mol Phylo Evol 2012) Supplemental Figure 1 a) b) ABCDEFGHI Abrocoma cinerea Abrocoma bennettii Octodontomys gliroides Octodon bridgesi Octodon lunatus Octodon degus Aconaemys porteri Aconaemys sagei Aconaemys fuscus Spalacopus cyanus Octomys mimax Pipanacoctomys aureus Tympanoctomys barrerae Ctenomys haigi Ctenomys boliviensis Ctenomys steinbachi Isothrix sinnamariensis Isothrix bistriata Isothrix barbarabrownae Makalata macrura Phyllomys brasiliensis Makalata didelphoides Phyllomys blainvillii Toromys grandis Echimys chrysurus Mesomys occultus Mesomys hispidus Lonchothrix emiliae Dactylomys dactylinus Dactylomys boliviensis Kannabateomys amblyonyx Proechimys roberti Proechimys longicaudatus Proechimys simonsi Proechimys quadruplicatus Hoplomys gymnurus Thrichomys apereoides Myocastor coypus Capromys pilorides Trinomys dimidiatus Trinomys iheringi Trinomys yonenagae Trinomys eliasi Trinomys paratus Trinomys setosus Clyomys laticeps Euryzygomatomys spinosus Dinomys branickii Lagostomus maximus Lagidium viscacia Chinchilla lanigera Sphiggurus melanurus Erethizon dorsatum Coendou bicolor Myoprocta acouchy Dasyprocta leporina Hydrochoerus hydrochaeris Kerodon rupestris Dolichotis patagonum Microcavia australis Cavia tschudii Cavia porcellus Cavia aperea Galea musteloides Cuniculus paca Cuniculus taczanowskii A B C E D H G F I Octodon degus Photo by José Cañas

4 Global- scale biogeography

5 For 8 areas

6 For 80 areas

7 For 800 areas

8 For 8 zillion areas

9 Why 9 areas? 86 occurrences used (Upham & PaYerson, 2012) a) b) A B C D E F G H I Abrocoma cinerea Abrocoma bennettii Octodontomys gliroides Octodon bridgesi Octodon lunatus Octodon degus Aconaemys porteri Aconaemys sagei Aconaemys fuscus Spalacopus cyanus Octomys mimax Pipanacoctomys aureus Tympanoctomys barrerae Ctenomys haigi Ctenomys boliviensis Ctenomys steinbachi Isothrix sinnamariensis Isothrix bistriata Isothrix barbarabrownae Makalata macrura Phyllomys brasiliensis Makalata didelphoides Phyllomys blainvillii Toromys grandis Echimys chrysurus Mesomys occultus Mesomys hispidus Lonchothrix emiliae Dactylomys dactylinus Dactylomys boliviensis Kannabateomys amblyonyx Proechimys roberti Proechimys longicaudatus Proechimys simonsi Proechimys quadruplicatus Hoplomys gymnurus Thrichomys apereoides Myocastor coypus Capromys pilorides Trinomys dimidiatus Trinomys iheringi Trinomys yonenagae Trinomys eliasi Trinomys paratus Trinomys setosus Clyomys laticeps Euryzygomatomys spinosus Dinomys branickii Lagostomus maximus Lagidium viscacia Chinchilla lanigera Sphiggurus melanurus Erethizon dorsatum Coendou bicolor Myoprocta acouchy Dasyprocta leporina Hydrochoerus hydrochaeris Kerodon rupestris Dolichotis patagonum Microcavia australis Cavia tschudii Cavia porcellus Cavia aperea Galea musteloides Cuniculus paca Cuniculus taczanowskii A C B D H E I G F 13,264 occurrences available (GBIF)

10 Transi>on between two ranges Founda>onal work: Ree et al. (Evolu5on 2005) Ree & Smith (Syst Biol 2008) Range Character Ancestral >me Observed & extant

11 Transi>on probability Q Instantaneous rate matrix Matrix exponen>a>on accounts for all intermediate events.

12 For few areas, no problem 3 areas

13 For more areas, Q explodes 3 areas 10 areas = Matrix exponen>a>on too slow for more than ten areas.

14 BayArea: Method for more areas Download BayArea: bayarea.googlecode.com Landis et al. (Syst Biol, in press)

15 BayArea: Method for more areas Inspired by Robinson et al. (Mol Biol Evol 2003) Landis et al. (Syst Biol, in press)

16 BayArea: Method for more areas Inspired by Robinson et al. (Mol Biol Evol 2003) Key concepts: 1. Sample biogeographic histories, H Landis et al. (Syst Biol, in press)

17 BayArea: Method for more areas Inspired by Robinson et al. (Mol Biol Evol 2003) Key concepts: 1. Sample biogeographic histories, H 2. Compute likelihood, L,H Landis et al. (Syst Biol, in press)

18 BayArea: Method for more areas Inspired by Robinson et al. (Mol Biol Evol 2003) Key concepts: 1. Sample biogeographic histories, L,H 2. Compute likelihood, 3. Approximate P (,H D) using H Markov chain Monte Carlo (MCMC) Landis et al. (Syst Biol, in press)

19 1. Sample biogeographic histories, H Nielsen (Syst Biol 2002) Landis et al. (Syst Biol, in press)

20 2. Compute likelihood, L,H Range evolu>on events from range : r = X r j re rt r i /r sum of rates leaving prob any event at >me prob next event is j L,H = product of event types & >mes over tree Landis et al. (Syst Biol, in press)

21 3. Approximate P (,H D) using MCMC high L,H low L,H P (,H D) Landis et al. (Syst Biol, in press)

22 Can we infer distance effects? Distance- dependent dispersal model Redistributes the rate of area gain Anywhere Nearby 0 ¼ ½ Collapses to independence model Simula>on: 600 areas, 50 replicates, 8 distances Landis et al. (Syst Biol, in press)

23 BayArea recovers true parameters Distance effects ¼ ½ Rate of area loss Rate of area gain Distance effects Landis et al. (Syst Biol, in press)

24 Bayes factors iden>fy distance effects 100% % of simula>ons supported 75% 5 50% 0 25% 5 Bayes factors support for distance model Favors M 0 Insubstantial Substantial Strong Very strong Decisive 0% ¼ ½ Landis et al. (Syst Biol, in press)

25 Malesian Rhododendron Vireya Re- analysis of Webb & Ree (2012) work 65 species, 20 areas Landis et al. (Syst Biol, in press)

26 Malesian Rhododendron Vireya Re- analysis of Webb & Ree (2012) work 65 species, 20 areas Wallace s Line Known dispersal barrier Vireya crossing? Landis et al. (Syst Biol, in press)

27 Malesian Rhododendron Vireya Re- analysis of Webb & Ree (2012) work 65 species, 20 areas Wallace s Line Known dispersal barrier Vireya crossing? Data from Brown et al. (J Biogeogr 2012) Webb & Ree (Chapter 8 in Bio5c Evolu5on and Environmental Change in Southeast Asia 2012) Landis et al. (Syst Biol, in press)

28 Distance mayers for Vireya dispersal Posterior Prior Rate of area loss Rate of area gain Distance Landis et al. (Syst Biol, in press)

29 B) Posterior of Branches: % of inferred range east of Wallace s Line W E Node maps: W Posterior probability ancestral of presence ranges per area E East of Wallace s Line West of Wallace s Line Wallace s Line & Lydekker s Line: 1 crossing West East Wallace s Line: 3+ crossings West East

30 Phylowood: biogeographic anima>ons

31 Future direc>ons Rate- modifiers for other traits/features Incorpora>ng on paleo- etc.- ical data Occupancy models to handle false absences Specia>on models (allopatry vs sympatry) Adding to RevBayes (easy to develop models)

32 Summary Allows hundreds of areas for analysis Joint posterior of parameters and ancestral ranges Simple distance- dependent dispersal model Efficient model tes>ng framework Open- source soqware available

33 Thanks! Ques>ons? BayArea Nick Matzke Brian Moore John Huelsenbeck Biogeography for many areas bayarea.google.code.com Phylowood Biogeographic anima>ons Trevor Bedford mlandis.github.com/phylowood Helpful folks Bas>en Boussau Tracy Heath Josh Schraiber Sebas>an Höhna Contact twiyer.com/landismj

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35 Extra slides

36 Malesian paleogeography We assume constant geography, but E 110 E 120 E 130 E Volcanoes Highlands Land Lakes Shallow sea Carbonate platforms Deep sea Trenches a 100 E 110 E 120 E 130 E b 0 10 S 10 S 20 S 60 Mya Paleocene 40 Mya Late Eocene 20 S c d S 10 S 20 S 30 Mya Middle Oligocene 20 Mya Early Miocene 20 S e f S 10 S 20 S 10 Mya Late Miocene 5 Mya Early Pliocene 100 E 110 E 120 E 130 E 100 E 110 E 120 E 130 E Lohman et al. (2011) 20 S

37 Vireya results Assume Vireya root age is 55 Mya Ancestral range posterior Joint WL and LL crossing once ~40 Mya All other WL crossings < 15 Mya Plausible biogeographical scenario Single long distance dispersal event around 40 Mya As Sundi and Sahul Shelf converge, repeated short dispersals

38 Dispersal- ex>nc>on model R (a) Y i,y j = 8 0 if Y j,a =0 >< 1 if Y j,a =1 0 if Y i and Y j di er at more than one area >: 0 if Y j =(0, 0,...,0) iid, Jukes- Cantor, forbids ex>nc>on

39 Rate- modified dispersal model 8 0 if Y j,a =0 R (a) Y i,y j = >< 1 (Y i,y j,a, ) if Y j,a =1 0 if Y i and Y j di er at more than one area >: 0 if Y j =(0, 0,...,0) Per- area rate of gain depends on current biogeographical range.

40 Compute likelihood, L,H

41 Distance- dependent dispersal model 2, the numerator). (Y i =(1, 1, 0, 0)! Y j =(1, 1, 0, 1),a=4, )= d(g 1,G 4 ) + d(g 2,G 4 ) {z } Rate- modifier d(g 1,G 3 ) + d(g 2,G 3 ) {z } + d(g 1,G 4 ) + d(g 2,G 4 ) {z } Normaliza>on

42 BayArea recovers rate of area gain Posterior rate of area gain True rate True distance effects Landis et al. (Syst Biol, in press)

43 BayArea recovers rate of area loss Posterior rate of area loss True distance effects True rate Landis et al. (Syst Biol, in press)

44 BayArea recovers distance effects Posterior of distance effects True value True distance effects Landis et al. (Syst Biol, in press)