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Retracing the Hawaiian Silversword Radiation Despite Phylogenetic, Biogeographic, and Paleogeographic Uncertainty

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Retracing the Hawaiian Silversword Radiation Despite Phylogenetic, Biogeographic, and Paleogeographic Uncertainty Version dated: April 16, 2018 RH: Retracing the Hawaiian silversword radiation Supplemental Information: Retracing the Hawaiian silversword radiation despite phylogenetic, biogeographic, and paleogeographic uncertainty Michael J. Landis1;∗, William A. Freyman2;3;4, and Bruce. G. Baldwin3;4 1Department of Ecology & Evolution, Yale University, New Haven, CT, 06511, USA; 2Department of Ecology, Evolution, & Behavior, University of Minnesota, Saint Paul, MN, 55108, USA; 3Department of Integrative Biology, University of California, Berkeley, CA, 94720, USA; 4Jepson Herbarium, University of California, Berkeley, CA, 94720, USA; ∗ Corresponding author: [email protected] Prior sensitivity and taxon counts While we have taken measures to apply an uninformative molecular clock prior, birth-death pro- cess priors may induce unanticipated node age densities (Warnock et al. 2015). To characterize this effect, we re-analyzed the data under all combinations of three model conditions (+G4, +G1, –G), four diversification priors (expected prior birth and death rates of 0.05, 0.10, 0.20, 0.50), and three uniform sampling probabilities (empirical sampling proportions under Madieae, 0.17; proportions under the Madia lineage, 0.61; or perfect sampling, 1.00). From the thirty-six anal- yses, we excluded results with prior expectation placed on low (0.05) or high (0.50) speciation and extinction rates, and results that assume perfect taxon sampling. When seeking empirical estimates, we further excluded the three models that encode some degree of paleogeographic ignorance (+G1 and –G). Slower prior rates will induce older silversword alliance crown age estimates, meaning that the discovery of an exceptionally young crown age is surprising relative to our prior beliefs. Of the four remaining +G4 analyses, the main text focuses on the results that assumed Madia lineage-wide sampling probabilities (ρ = 0:61) and the slower expected prior birth and death rates (0.10). Taxon counts for the Hawaiian silversword alliance were based on the online Flora of the Hawaiian Islands (Wagner et al., 2005), which follows the phylogenetically informed taxonomy of Carr and colleagues (Carr et al., 2003; Baldwin and Carr, 2005; Baldwin and Friar, 2010). For the mainland tarweeds, taxon counts were based on the taxonomy of Carr et al. (2003) except as subsequently revised for Calycadenia (Carr and Carr, 2012), Centromadia (Baldwin, 2012a), Deinandra (Baldwin, 2007, 2012b), and Lagophylla (Baldwin, 2013). Taxon counts for other members of tribe Madieae, outside the tarweed–silversword subtribe (Madiinae), followed the taxonomy of Baldwin and Panero (2007). The taxon sampling probability (ρ) is a parameter in the birth-death process (Nee et al. 1994). Typically, one sets ρ = n=m where n = 43 is the number of taxa sampled for the the phylogenetic analysis and m is the total number of taxa in the clade. We explored the sensitivity of our model under three alternative values of ρ from (0:17:0:61; 1:00) with the first two values justified below. The first value of ρ = 0:17 assumes m = 255 is the taxon count for all taxa in tribe Madieae, which includes all taxa of subtribe Madiinae and the subtribes Arnicinae, Baeriinae, Hulseinae, and Venegasiinae. This gives 209 species, and 255 species + non-redundant subspecies; ρ = 43=255. 1 The second value of ρ = 0:61 assumes m = 71 is the taxon count for the entire Madia lineage. This clade is comprised of all taxa in the silversword alliance and in the mainland genera Anisocarpus, Carlquistia, Harmonia, Hemizonella, Jensia, Kyhosia, and Madia, totaling 59 species, and 71 species + non-redundant subspecies; ρ = 43=71. Secondary dating analysis The secondardary node age calibrations used in the primary analyses dating the silversword radiation were generated by re-analysing the Asteraceae dataset of Barreda et al.(2015) with an expanded sequence alignment that included additional taxa from three tribes in subfamily Asteroideae: Madieae, Bahieae, and Heliantheae (Table1). Sequences for the three gene regions used in Barreda et al.(2015) were downloaded from GenBank and aligned using MAFFT v7.271 (Katoh and Standley 2013). The accessions added to the original dataset are listed in Table1. Taxon Tribe matK ndhF rbcL Bahia absinthifolia Bahieae AY215766.1 L39464.1 AY215086.1 Amauriopsis dissecta Bahieae GU817424.1 GU817832.1 GU817741.1 Florestina pedata Bahieae AY215799.1 AF384725.3 AY215118.1 Palafoxia arida Bahieae AY215836.1 L39463.1 AY215154.1 Chaetymenia peduncularis Bahieae AY215772.1 AF384700.3 AY215092.1 Chaenactis santolinoides Heliantheae AY215771.1 AF384699.3 AY215091.1 Chaenactis douglasii Heliantheae GU817439.1 GU817842.1 GU817747.1 Dimeresia howellii Heliantheae AY215783.1 AF384710.3 AY215102.1 Peucephyllum schottii Madieae AY215842.1 AF384764.3 AY215160.1 Lagophylla ramosissima Madieae AY215816.1 AF384740.3 AY215134.1 Madia sativa Madieae AY215823.1 AY215141.1 Raillardella argentea Madieae AY215851.1 AF384773.3 AY215169.1 Arnica dealbata Madieae AY215880.1 AF384802.3 AY215197.1 Hulsea algida Madieae AY215807.1 AF384732.3 AY215126.1 Table 1: Taxa and GenBank accessions added to the Barreda et al.(2015) dataset. Node Offset (Myrs) Mean Standard deviation Most recent common ancestor (MRCA) of 76.0 1.5 0.25 genera Dasyphyllum and Barnadesia MRCA of all Asteraceae outside of subfamily 47.0 1.9 0.25 Barnadesioideae Eudicot crown node 125.0 1.5 0.25 Table 2: Pollen fossil node calibration lognormal priors from Barreda et al.(2015). We re-ran the expanded Barreda et al.(2015) analysis with the original model and MCMC specifications, so for detailed information see Barreda et al.(2015). Briefly, the original dataset incorporated sequences from three gene regions from 101 taxa within Asteraceae and 36 taxa from other eudicot lineages as an outgroup. To this we added the 14 taxa in Table1. Exactly as in Barreda et al.(2015) three pollen fossils were used with lognormal node densities to calibrate the phylogeny (Table2). Each of the three gene partitions were assigned the general-time-reversible (GTR) model of nucleotide substition (Tavaré 1986) with site rate heterogeneity modeled using a Γ distribution discretized into four rate categories (Yang et al. 1995). A constant rate birth- death process (Nee et al. 1994) was used as the tree prior and an uncorrelated lognormal relaxed clock (Drummond et al. 2006) modeled branch rate heterogeneity. The MCMC analysis was run 2 for 100 million generation in BEAST v1.8.4 (Drummond et al. 2012) on the CIPRES Science Gateway (Miller et al. 2010). Stochastic mapping by uniformization under an epoch model We modify the uniformization method proposed by Rodrigue et al.(2008) to generate phyloge- netic stochastic mappings under a time-homogeneous continuous-time Markov chain (CTMC) to generate samples under a time-stratified (or epoch) model (Ree and Smith 2008; Bielejec et al. 2014). Our extension closely follows Rodrigue et al.’s time-homogeneous uniformization method while also managing the bookkeeping associated with the time-heterogeneous piecewise constant rate matrices of the epoch model. To generate one stochastic mapping, we apply the following algorithm to each branch in the phylogeny. We assume the joint sample the start and end states for all nodes in the tree have been sampled under the same epoch model using the standard ancestral state estimation procedure. Then a given branch a time-calibrated phylogeny has sampled state s0 at origination time t0 and sampled state sn at termination time tn. The vector, t = (t0; t1; : : : ; tn−1; tn), lists the relevant segments of the branch, where the times t1; : : : ; tn−1 are the m = n − 1 breakpoints in the epoch model that occur while the branch exists during the interval (t0; tn). Within any time interval, (ti−1; ti), events arrive according to the rates defined by the instantaneous rate matrix, Qi, under a time-homogeneous CTMC. To compute transition probabilities over epochs, we take the product of relevant piecewise constant transition probability matrices beginning at age ta and ending at age tb, n−1 Y (ti−ti+1)Qi P(ta; tb) = e i=0 defining p(s j s ; t ; t ) = [ P(t ; t )] b a a b a b sa;sb in order to expose the conditional terms of the transition probability. Using this, we sample the ancestral states for each epoch’s breakpoint from the following conditional probability sequen- tially to the times t1 through tn−1 p(si j si−1; ti−1; ti)P (sn j si; ti; tn) p(si j si−1; sn; ti; ti−1; tn) = p(sn j si−1; ti−1; tn) This yields ancestral state samples for s = (s0; s1; : : : ; sn−1; sn) that correspond to the start of the branch, all intervening epochs, and the end of the branch, in that order. Next, we apply the time-homogeneous uniformization method to each branch segment, (ti−1; ti), under the corresponding rate matrix, Qi. To simplify notation, let vi = ti − ti+1. For each branch segment, we compute the stochastic matrix Ri = viQi/µi + I is defined in part by the time interval’s dominating rate, µi ≥ max(−diag(viQi)). Following this, we sample the number of transitions (real or virtual), during each segment, k = (k0; k1; : : : ; kn), using the probability distribution p(si j si−1)p(ki j λi) p(ki j si−1; si; λi) = p(si j si−1; λi) which is defined in terms of the probability of ki events occurring in time vi under a Poisson process with rate λi (µ λ )ki −µiλi i i p(ki j λi) = e ; ki! 3 in terms of the probability of a discrete time Markov chain transitioning from state si−1 into state si after ki events ki p(si j si−1; ki) = R ; i si−1;si and in terms the probability of a discrete time Markov chain transitioning from state si−1 into state si on an unconditional number of events, i.e.
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