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 process 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 analyses, 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 phylogenetic 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 | 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 sequentially to the times t1 through tn−1

p(si | si−1, ti−1, ti)P (sn | si, ti, tn) p(si | si−1, sn, ti, ti−1, tn) = p(sn | 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 | si−1)p(ki | λi) p(ki | si−1, si, λi) = p(si | 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 | 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 | 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. the transition probability under a continuous time Markov chain. Event times are sampled uniformly at random from the interval (ti−1, ti) under the Poisson process. Finally, sequentially sample the states corresponding to the ki events for the ith segment, beginning with the first event being sampled with state x conditional on the previous event’s state and the final event’s state (ki−1) z1 ∼ p(z1 = x | z0 = si−1, zki = si) ∝ Ri Ri si−1,x x,si followed by the second event being sampled in a similar manner (ki−2) z2 ∼ p(z2 = y | z1 = x, zki = si) ∝ Ri Ri x,y y,zki and continuing until all nk events have been sampled within segment i. As Rodrigue et al. note, by sequentially sampling from the distribution while conditioning on both the previous state and the final state, the procedure guarantees that a valid sample path is drawn. Applying this algorithm to each segment, from oldest to youngest, and concatenating the sampled events’ times and states yields one sampled history that may contain virtual events. Virtual events are transitions where the source and destination states are the same, s → s. When the superfluous virtual events are discarded, we simulate one valid stochastic mapping for the branch. Repeating this for all branches generates one phylogenetic stochastic mapping.

4 rbblte f01,06,10.Rslsi h antx sueepce it/et ae of rates sampling birth/death and expected assume 0.50 text 0.20, main 0.61. settings 0.10, the of diversification twelve probability 0.05, in of sampling Results of and three rates 1.00. 0.10 show de- 0.61, birth/death figure settings 0.17, expected this models of in prior biogeographic probabilities panels three considered: three the were The clade and Posterior that text. clades assumptions. the supported model in highly alternative scribed five under for ages estimates clade age alliance Silversword 1: Figure Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords Argyroxiphium Dubautia (OMH) Dubautia (K+) Wilkesia+Dubautia Silverswords

ρ= 1.00ρ= 1.00ρ= 1.00ρ= 1.00ρ= 0.61ρ= 0.61ρ= 0.61ρ= 0.61ρ= 0.17ρ= 0.17ρ= 0.17ρ= 0.17 λ = 0.50 λ = 0.20 λ = 0.10 λ = 0.05 λ = 0.50 λ = 0.20 λ = 0.10 λ = 0.05 λ = 0.50 λ = 0.20 λ = 0.10 λ = 0.05 10 Age (Myrs) 5 K+ M+H +M +O +K 05 Model −G +G1 +G4 References

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