13: Characters and Diversification Rates - Biology

13: Characters and Diversification Rates - Biology

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Many evolutionary models postulate a link between species characteristics and speciation, extinction, or both. These hypotheses can be tested using state-dependent diversification models, which explicitly consider the possibility that species’ characters affect their diversification rates. State-dependent models as currently implemented have some potential problems, but there are methods to deal with these critiques. The overall ability of state-dependent models to explain broad patterns of evolutionary change remains to be determined, but represents a promising avenue for future research.

  • 13.1: The Evolution of Self-Incompatibility
    Some species of angiosperms can avoid self-fertilization through self-incompatibility. In plants with self-incompatibility, the process by which the sperm meets the egg is interrupted at some stage if pollen grains have a genotype that is the same as the parent. This prevents self-fertilization – and also prevents sexual reproduction with plants that have the same genotype(s) at loci involved in the process.
  • 13.2: A State-Dependent Model of Diversification
    The models that we will consider in this chapter include trait evolution and associated lineage diversification. In the simplest case, we can consider a model where the character has two states, 0 and 1, and diversification rates depend on those states. We need to model the transitions among these states, which we can do in an identical way to what we did previously with a continuous-time Markov model.
  • 13.3: Calculating Likelihoods for State-Dependent Diversification Models
    To calculate likelihoods for state-dependent diversification models we use a pruning algorithm with calculations that progress back through the tree from the tips to the root. We have already used this approach to derive likelihoods for constant rate birth-death models on trees and this derivation is similar.
  • 13.4: ML and Bayesian Tests for State-Dependent Diversification
    Now that we can calculate the likelihood for state-dependent diversification models, formulating ML and Bayesian tests follows the same pattern we have encountered before. For ML, some comparisons are nested and so you can use likelihood ratio tests.
  • 13.5: Potential Pitfalls and How to Avoid Them
    The most serious limitation of state-dependent models as currently implemented is that they consider only a relatively small set of possible models. In particular, the approach we describe above compares two models: first, a model where birth and death rates are constant and do not depend on the state of the character; and second, a model where birth and death rates depend only on the character state.
  • 13.S: Characters and Diversification Rates (Summary)

Jill Fredericksen-Adams Endowed Lecture: Integration of traits and diversification: Lessons from small and big phylogenies

Macroevolutionary studies of trait evolution are incomplete without the integration of speciation and extinction rates. The frequency of a character state on the tips of a phylogenetic tree is not only the result of the trait change per se but is also a function of lineage diversification if the character state is linked to speciation and extinction rates. In this talk, I will show three different examples of trait evolution linked to diversification. I will discuss how character state changes, the interaction between traits, and their links to diversification enhance or hinder the speciation and extinction processes. Starting with a tree of nightshades (Solanaceae), and finalizing with a large tree with more than 3,000 passerines, I will argue the importance of assumptions for state-dependent diversification models, as well as, the relevance of the integration of ecology and natural history in these models. Finally, I will discuss recent theoretical developments about the estimation of speciation and extinction rates and how these results modify the way we think about state-dependent diversification.


Species numbers differ vastly among groups of organisms – a phenomenon observed at any taxonomic level. Differences in species richness of clades of different age are sometimes explained by the longer time that older clades had to accumulate species (e.g. [1, 2]). However, sister clades which are of the same age per definition often differ substantially in species richness. Therefore, net diversification rates (speciation minus extinction rates) must differ even among closely related groups. Indeed, it was recently suggested that diversification rates may explain most variation in species richness among organisms [3].

A range of factors that potentially affect rates of diversification are known. Climate and in particular changes of climatic niches among species are thought to be among the main causes of diversification rate differences [4,5,6,7,8,9]. On the macro-ecological level, invasions into new adaptive zones play a major role and have promoted some of the largest radiations. So is the diversity of many phytophagous insect lineages likely triggered by the rise of angiosperms in the Cretaceous [10,11,12]. Further factors that may affect rates of diversification are differences in body size and size dimorphism [13, 14], sexual selection [15,16,17,18], diet [19], habitat [20, 21], and parasitism [21]. The total rate of species production is highest in tropical biomes – either caused by increased speciation rates [22] or simply by the vast number of species that are already present there [23]. Higher rates in the tropics may be caused by increased opportunities for the evolution of reproductive isolation, faster molecular evolution, or the increased importance of biotic interactions [24].

Recently, microhabitat has been suggested as one of the most important factors that drive variation in diversification rates among vertebrates [20, 25,26,27]. Its effect may even supersede that of climatic niche [8], often changing several times within evolutionary young taxa [28]. It has been proposed that traits like microhabitat that are involved in local-scale resource use (alpha niche) may be more important in explaining patterns of diversification than those related to the broad-scale distribution of species (beta niche), as suggested in analyses across vertebrates and oribatid mites [25, 29, 30]. This might be because alpha-niche traits primarily change over deeper time scales while beta-niche traits (e.g., climate preferences) frequently change on lower time scales, which was shown for amphibians, reptiles, and birds [6, 29, 31,32,33].

Web spiders are generally stationary and specimens are predominantly hand collected. Thus, in contrast to many other groups of invertebrates, information on the microhabitat of pholcid spiders (Araneae: Pholcidae) is available for a large percentage of species. This makes them ideal candidates for the investigation of the relationship between microhabitat and diversification rate. Three main types of microhabitat can be distinguished in pholcids (Fig. 1): (i) ground, i.e. leaf litter and under objects on the ground (ii) space, i.e. sheltered spaces such as among tree buttresses, rocks, and logs and (iii) leaf, i.e. the lower surface of live leaves [34,35,36]. Pholcid spiders, commonly known as daddy-longlegs spiders, have a worldwide distribution from ca 56° N to 42° S, from sea level to 3800 m, and from deserts to tropical forests [36,37,38]. These small to medium-sized spiders are well-known because of several synanthropic species but the vast majority of species is found in tropical forests where they are often among the most abundant and diverse web-building spiders [36, 39,40,41,42]. With currently more than 1600 described species, pholcids are among the most species-rich spider families [43]. Previous studies on pholcid phylogenetics [44,45,46,47,48,49] indicate that microhabitat might frequently have changed in the evolutionary history of the group, probably with numerous convergent origins of leaf dwelling. Putative sister groups often differ dramatically in species numbers, suggesting variation in net diversification rates.

Microhabitats. Schematic drawing of the three main types of microhabitat (leaf, space, ground) that pholcid spiders inhabit, and of exemplary representatives

In the present study we inferred the evolutionary history and plasticity of pholcid spiders’ microhabitats using a newly developed molecular phylogeny based on three nuclear and three mitochondrial DNA markers. Compared to previous studies, we extended the taxon sampling to include 600 species representing more than 85% of described pholcid genera. We also collected microhabitat information first hand for 88% of the examined species. Separate analyses of leg proportions as a proxy for microhabitat allowed a near-complete species coverage. We investigated the evolutionary plasticity of microhabitats by ancestral state reconstructions. Using current species numbers and estimates of extant diversity, we analyzed diversification rates in pholcids and tested the effect of microhabitat on diversification dynamics.


Supertree construction

Using Matrix Representation with Parsimony (MRP) 24 , we inferred a phylogenetic supertree from 126 source trees taken from 66 papers published between 1984 and 2014. Although supertree methods, and MRP in particular, are not without their critics, this is still by far the most tractable approach for data sets of this size (1000 s taxa) 25 . Our resulting caridean supertree comprised 756 taxa (two Procarididea, the sister group to Caridea 26 , and 754 Caridea) and is the largest phylogeny of the group published to date (Fig. 1), being broadly consistent with recent discussions of their relationships 28,29,30 . All families are monophyletic with the exception of Oplophoridae, Pasiphaeidae and Hippolytidae. This taxonomic uncertainty is reflected in the source trees, and is not an artefact of the tree-building method. The non-monophyly of Pasiphaeidae has been suspected hitherto 31 , with a recent recalibration of the constituent genera 32 . Despite considerable progress towards resolving the problematic phylogeny of Hippolytidae sensu lato 30 , further studies found additional polyphyly 33 , consistent in generic scope with the present analysis. The division of the older concept of Oplophoridae into two families 34 has remained controversial 35 , with one genus—Systellaspis—occupying an intermediate position between two families, as in the present analysis.

Phylogenetic tree of Caridea. Maximum Agreement Subtree (MAST) shown from MRP supertree analysis, scaled to geological time. Branch colouring was assigned as follows: blue = marine, free-living red = marine, symbiotic orange = freshwater, free-living. Stars indicate the node from which the clade rates for the diversification analyses were calculated (yellow = freshwater, orange = symbiotic). Geological time scale was added using the R package “strap” 27

Ancestral state reconstructions

For our ASR we collected trait data for all 756 species in the phylogeny. Freshwater taxa were defined as those permanently residing in freshwater or requiring freshwater to complete their lifecycle 17 . Species were considered to be “free-living” if, in general, they do not live on or inside a host animal 21 . Trait data on freshwater or marine habitats followed the IUCN Red List 36 (based on De Grave et al. 17 ). Anchialine and symbiotic trait states were collected from an exhaustive literature search. Our ASR analyses were carried out in PhyTools 37 and indicated six independent transitions into a freshwater/anchialine habitat and a single reversal back to marine conditions within the genus Palaemon. Of these transitions, two resulted in speciose (>10 species) freshwater clades, namely the family Atyidae (approximately 470 spp.) and the genus Macrobrachium (Palaemonidae, approximately 240 spp.), while the remainder resulted in clades with fewer than ten species in each instance (Fig. 1). Symbioses evolved independently 13 times with a number of reversals. As with the habitat transitions, two of these instances of symbioses resulted in large speciose clades (Palaemonidae with an estimated 470 symbiotic spp. and Alpheidae with 300 spp.), while the others resulted in single isolated species or in clades with fewer than 10 species (Fig. 1). For ASR raw output see Supplementary Fig. 1.

Diversification dynamics

Using BAMM 14, 38 to model speciation and extinction rates across the tree, we tested for significant associations between habitat or mode of life and clade-specific diversification rates (Fig. 2). We found that speciation rates were 2.5 times higher in freshwater clades than in their marine counterparts (marine: mean = 0.08881644, SD = 0.00219055 freshwater: mean = 0.03548732, SD = 0.01136907), while extinction rates in freshwater clades were more than 3.5 times higher than those in marine clades (marine: mean = 0.006113175, SD = 0.002677831 freshwater: mean = 0.02210151, SD = 0.0129926). Net diversification rates in freshwater clades were double those found in marine clades (marine: mean = 0.02937415 freshwater: mean = 0.06671493). Speciation rates were higher in free-living clades than in symbiotic clades by 1.1 and 1.8 times, respectively (free living: mean = 0.04152562, SD = 0.002447845 symbiotic: mean = 0.03748387, SD = 0.003546246), whereas extinction rates were higher by 1.8 times (free living: mean = 008334043, SD = 0.003015885 symbiotic: mean = 0.004622885, SD = 0.003668764). Net diversification rates in free-living clades were only slightly higher than in symbiotic clades (1.01 times higher) (free-living: mean = 0.03286099 symbiotic: mean = 0.03319158). As the rates were not normally distributed we used Wilcoxon rank and two-sample Kolmogorov–Smirnov tests to compare the posterior distribution of rate differences between each set of clades (marine vs. freshwater clades and free-living vs. symbiotic clades) and to assess significance. All the analyses were based on a sample size of 9000 sets of rates (10,000 minus burn-in), for each of speciation, extinction and net diversification rate, as calculated from the BAMM analyses. A two-sample Kolmogorov–Smirnov test was used to distinguish between the distributions of the 9000 mean rates for each clade pair while a Wilcoxon rank test compared the rates across all 9000 samples, but considered the differences between each clade pair (i.e., the comparison between freshwater/marine or free-living/symbiotic for a single simulation for which we have 9000 samples). Both tests showed that the difference in distributions between each trait pair was statistically significant (P < 2.2e–16 for speciation, extinction and net diversification rates for each trait pair). Overall, transitions into freshwater habitats appear to be associated with increased net rates of diversification, whereas transitions from a free-living to a symbiotic lifestyle are associated with reduced net rates of diversification.

Clade-dependent diversification rate histograms. Frequency distributions from the BAMM analyses for speciation, extinction and net diversification rates for marine/freshwater clades (a) and free-living/symbiotic clades (b)

If the traits investigated here—habitat and mode of life—are linked to changes in diversification rate, it might be expected that these rate shifts will show a correlation with changes in habitat and mode of life within clades as revealed by the ASR analyses. The BAMM analysis identified four significant rate shifts, all of which remain remarkably consistent in their timing and placement across the top nine credible shift configurations (see Supplementary Fig. 2 for the credible shift set). Two of these rate shifts are located within or at the base of the two major freshwater clades, a third is located at the base of a symbiotic clade, while the fourth (Pandalidae) shows no obvious link to either trait. Most of the significant rate shifts across the credible shift configurations were positive (more rapid diversification), with the exception of a shift associated with a symbiotic clade in two of the most likely configurations, for which rates decreased. Overall, the credible shift set provides stronger support for shifts associated with transitions to freshwater environments than for those associated with the evolution of symbioses.


Asteraceae nuclear phylogeny reveals highly supported relationships among subfamilies

To reconstruct a tribal-level Asteraceae phylogeny, 243 Asteraceae species were sampled, representing all 13 subfamilies and 41 of the 45 recognized tribes ( Panero and Funk, 2008 Funk et al., 2009b Panero et al., 2014 Fu et al., 2016 Huang et al., 2016b ) (149 species in Asteroideae, e.g., sunflowers, daisies, and chrysanthemums 27 in Cichorioideae, e.g., lettuce and dandelion 33 in Carduoideae, e.g., artichoke and thistles and five outgroup taxa). We newly generated transcriptome and genome sequences from 121 (for one species, RNAs from two samples were sequenced) and 16 species, respectively (see Supporting Information Tables S1, S2), plus 67 previous datasets from our lab ( Zeng et al., 2014 Liu et al., 2015 Huang et al., 2016b ) and 44 publicly available datasets (Tables S1, S2). To reduce possible biases caused by the use of a particular approach, we used three separate approaches to identify low-copy nuclear genes for the phylogenetic analyses (see Figure S2A for a flow chart on gene selection and Supporting Information for details).

Phylogenetic analyses using both coalescent and maximum likelihood (ML) methods with multiple datasets comprising nuclear genes yielded highly supported and consistent Asteraceae phylogenies (Figures 1, 2, S3, S14) (see Supporting Information for details). Asteraceae were monophyletic in all analyses here, forming a sister clade to Calyceraceae (three genera sampled), and the phylogeny was mostly consistent with previously reported topologies (e.g., Panero and Funk, 2008 Panero et al., 2014 Fu et al., 2016 Panero and Crozier, 2016 ). Asteroideae and seven other subfamilies were monophyletic with 100% support in all analyses Famatinanthoideae and Hecastocleidoideae were monotypic whereas Wunderlichioideae, Cichorioideae, and Carduoideae were not monophyletic (Figure 1). Seven relatively small subfamilies formed a grade of six successive sister branches of all the other Asteraceae: (i) Barnadesioideae (

92 spp.) (ii) Famatinanthoideae (one sp.) (iii) a clade with the subfamilies Mutisioideae (

640 spp.) and Stifftioideae (35 spp.), and the tribe Hyalideae (13 spp.) of Wunderlichioideae, with the topology (Mutisioideae (Stifftioideae, Hyalideae)) (iv) the tribe Wunderlichieae (34 spp.) of Wunderlichioideae (supported by 100% bootstrap support (BS) values in four trees and 98% BS in the fifth tree Figure S15) (v) Gochnatioideae (85 spp.) and (vi) Hecastocleidoideae (one sp.).

A portion of the Asteraceae phylogeny showing all subfamilies, except Asteroideae, and summaries of character reconstruction

The phylogeny is shown for all subfamilies except Asteroideae, using coalescence analyses using four gene sets (set 4: 1,087 genes set 5: 649 genes set 7: 384 genes set 11: 192 genes) and maximum likelihood (ML) analysis using 192 genes (set 11) obtained as explained in Figure S2 (see Supporting Information for details). The individual phylogenies are shown in Figures S3–S14, with support values shown in Figure S15. To the right of generic/specific names are tribe names, with subfamily names abbreviated as follows: B, Barnadesioideae F, Famatinanthoideae S, Stifftioideae W-II, Wunderlichioideae-II M, Mutisioideae W-I, Wunderlichioideae-I Go, Gochnatioideae H, Hecastocleidoideae P, Pertyoideae Gy, Gymnarrhenoideae Co, Corymbioideae. The subfamilies Cichorioideae and Carduoideae are paraphyletic and are indicated with two vertical bars, with the clades labeled as Ci-I/Ci-II and Ca-I/Ca-II, respectively. The change of habit from woody (a) to herbaceous (b) according to ancestral character reconstruction (Figure S36) is estimated to have occurred at the root node of Gymnnarrhenoideae – Asteroidaee. The change of capitulum type is according to the analysis shown in Figure S39 and shown here from an ancestral and basal discoid with only disc florets (c) to ligulate capitulum with only ligulate florets (d) in the subfamily Cichorioideae and radiate capitulum (e in Figure 2) characterized by having both ray and disc florets and found in most members of Asteroideae and some in Cichorioideae II.

A portion of the Asteraceae phylogeny with subfamily Asteroideae and summaries of character reconstruction

The phylogeny shown here is for the largest subfamily Asteroideae, with 20 of its 21 tribes (except Feddeeae), summarized from results as described in the legend of Figure 1. The morphological change from discoid (c in Figure 1) to radiate (e) with both ray and disc florets, according to the ancestral character reconstruction is detailed in Figure S39. The latter type is found in most members of Asteroideae, with an important exception being members of the tribe Eupatorieae, which likely lost the ray florets following the separation from the small tribe Perityleae.

Mandel et al. ( 2019 ) reported that Barnadesioideae were sister to the other Asteraceae species in the supermatrix trees only, whereas their relationships with Calyceraceae and the remaining Asteraceae were poorly resolved in the coalescent tree (Figure S1). Furthermore, the phylogenetic relationships of Famatinanthoideae, Mutisioideae, Stifftioideae, and Hyalideae in the two supermatrix trees presented by Mandel et al. ( 2019 ) were the same as in our phylogeny however, in their coalescent tree, Famatinanthoideae were sister to a clade comprising Stifftioideae and Hyalideae (97% BS), with Mutisioideae being the next sister group separating them from the remaining Asteraceae (71% BS). Moreover, Wunderlichieae, Gochnatioideae, and Cyclolepis (not sampled here) formed a clade (64% BS/1.0 Bayesian posterior probability (PP) in the supermatrix trees and 100% BS in the coalescent tree), although the placement of Cyclolepis was inconsistent (Figure S1). Further studies are therefore needed to clarify the phylogenetic positions of Wunderlichieae, Gochnatieae, and Cyclolepis.

Several clades, including Hyalideae, Mutisioideae, and Stifftieae, were highly supported in all our analyses as well as in the supermatrix analyses presented by Mandel et al. ( 2019 ). Previously, Hyalideae were found to be sister to Wunderlichieae and were thus placed in Wunderlichioideae (s.l.) (52/71/84% BS 0.91/0.998/0.99 PP) in analyses performed using 10–14 plastid loci ( Panero and Funk, 2008 Panero et al., 2014 Panero and Crozier, 2016 ). By contrast, analyses using nuclear ITS sequences (91% BS/1.0 PP Funk et al. ( 2014 )) and numerous protein-coding nuclear genes (here and also by Mandel et al. ( 2019 )) all highly supported the sister relationship of Hyalideae and Stifftieae.

Among the remaining Asteraceae, Pertyoideae (

80 spp.) are sister to an extremely large clade (>24,000 spp.) that includes the three largest subfamilies, Asteroideae, Cichorioideae, and Carduoideae, as well as two very small subfamilies, Corymbioideae and Gymnarrhenoideae (Figures 1, 2). This position of Pertyoideae is also supported by recent analyses using ITS data ( Funk et al., 2014 ) and hundreds of nuclear sequences ( Funk et al., 2014 Mandel et al., 2019 ) (Figure S1). However, in plastid-based phylogenies, Pertyoideae are sister to the clade Gymnarrhenoideae–Asteroideae ( Panero and Funk, 2008 Panero et al., 2014 Panero and Crozier, 2016 ). Pertyoideae are the only subfamily in which distribution is restricted to East Asia, and that produce corollas with five irregularly split lobes intermediate between the typical ligulate (such as those of Cichorieae) and tubular flowers (such as those in Cardueae and some Asteroideae). In addition, the basal chromosome numbers in Pertyoideae are x = 12–15 ( Wang, 2009 Zhang, 2013 ), unlike the presumed ancestral Asteraceae chromosome number of nine ( Semple and Watanabe, 2009 ). Nevertheless, some morphological features of Pertyoideae, such as short style branches with papillae on the abaxial surface and relatively simple pollen surfaces, resemble those of the early-divergent branches of the Asteraceae rather than Carduoideae ( Katinas et al., 2008 ). Among the single-gene family trees of the 192 genes in set 11 (Figure S2), the position of Pertyoideae reported here was supported by 52 trees with BS values of more than 75%, and by 97 additional trees with BS values between 50% and 75%, but no trees supported the previously published placement based on the analyses of plastid genes (e.g., Panero and Funk, 2008 Panero et al., 2014 Fu et al., 2016 Panero and Crozier, 2016 ). Future studies with expanded sampling could improve our understanding of the placement of Pertyoideae.

The mostly Old World subfamilies Carduoideae and Cichorioideae, as previously defined ( Funk et al., 2014 Panero et al., 2014 Fu et al., 2016 ), were paraphyletic in all of our phylogenetic analyses. All four tribes in Carduoideae formed two clades, which are indicated, respectively, as Carduoideae I and II (Figure S15, and abbreviated, respectively, as Ca-I and Ca-II in Figures 1, S1). Carduoideae I includes three tribes, Cardueae, Oldenburgieae, and Tarchonantheae, with maximum support, whereas Carduoideae II contains the fourth small tribe, Dicomeae, which is consistently placed as sister (BS values of 100%, 90%, 89%, 87%, and 87% Figure S15) to the clade Gymnarrhenoideae–Asteroideae. Carduoideae I (Figures 1, S1, S15) is the sister lineage to the clade Carduoideae II–Asteroideae. The coalescent (ASTRAL) tree presented by Mandel et al. ( 2019 ) grouped all four tribes of Carduoideae in a single clade, whereas the supermatrix ML analyses placed the combined Tarchonantheae and Oldenburgieae clade, the Dicomeae clade, and the Cardueae clade as three separate branches in a grade of successive sisters to the clade of Gymnarrhenoideae–Asteroideae (Figure S1). On the other hand, phylogenies using plastid genes strongly supported the inclusion of Dicomeae in the Carduoideae ( Fu et al., 2016 Panero and Crozier, 2016 ). Gene flow among Dicomeae, Cardueae, Tarchonantheae, and Oldenburgieae and the underlying assumptions of the different methods used are possible explanations for the different relationships seen in these phylogenies.

The circumscription of Cichorioideae has changed several times, even very broadly, from containing only one tribe, Cichorieae, to encompassing all Asteraceae members other than Asteroideae (see review by Funk and Chan, 2009 ). The recent definition of the Cichorioideae was based on analyses using plastid sequences ( Funk et al., 2014 Panero et al., 2014 Fu et al., 2016 ), but it lacks the support of morphological synapomorphy. Among the four Cichorioideae tribes sampled here, Cichorieae (Figures 1, S1, S15) were sister to the clade Asteroideae + Corymbioideae (BS = 89–100% in the four phylogenies) (Figure S15). On the other hand, three other tribes, Vernonieae, Liabeae, and Arctotideae, form a clade (BS of 100%, 100%, 99%, and 93%) sister to the clade Asteroideae + Corymbioideae + Cichorieae (Figure 1). The paraphyly of Cichorioideae was also supported by Mandel et al. ( 2019 ) (Figure S1). Thus, Cichorieae are proposed as Cichorioideae s.s. (indicated as Cichorioideae I in Figure S15, and abbreviated as Ci-I in Figures 1, S1), whereas the other three tribes, Vernonieae, Liabeae, and Arctotideae, are proposed to form a separate subfamily (indicated as Cichorioideae II in Figure S15 and abbreviated as Ci-II in Figures 1, S1). The difference in the position of Cichorieae relative to the other tribes between the phylogenies using nuclear or chloroplast genes could be explained by a possible hybridization in the history of Cichorieae.

Resolution of the relationships among the Asteraceae tribes

In addition to the relationships among Asteraceae subfamilies, the nuclear phylogenies here also provide strong support for the relationships among tribes (Figures 1, 2, S3–S15). Among the 41 tribes in this study, 30 were monophyletic with 100% support (Figures 1, 2), but in the subfamily Asteroideae, Millerieae and Neurolaeneae were not monophyletic (Figure 2). The remaining nine tribes were represented by one species each (two corresponding to monotypic subfamilies). In addition to the relationships among subfamilies of Asteraceae, the nuclear phylogenies generated here also provide strong support for the relationships among tribes (Figures 1, 2, S3–S15). The vast majority of the tribes represented in this study were also sampled in Mandel et al. ( 2019 ) (Figure S1), except Polymnieae (Polymnia) in Asteroideae (Figures 2, S1). The sister relationship of Mutisieae and Nassauvieae, and that of Oldenburgieae and Tarchonantheae, were both also supported by phylogenies developed using plastid genes ( Panero and Funk, 2008 Panero et al., 2014 ) and nuclear genes ( Mandel et al., 2019 ). Within Cichorioideae II, the tribes Vernonieae and Liabeae are sisters, but more distant to Arctotideae (Figure 1), consistent with their positions in previous phylogenetic analyses (Figure S1). In Arctotideae, Heterolepis (Arctotideae III) is sister to Arctotideae I (Arctotidinae) in our analyses, but sister to Arctotideae II (Gorteriinae) in the analyses performed by Mandel et al. ( 2019 ) (Figure S1), and it has varying positions in previous analyses performed using different datasets and different outgroups ( Funk et al., 2004 , 2009a ). Future analyses with other methods and more taxon sampling may shed light on the relationships among Heterolepis and other Arctotideae species.

The subfamily Asteroideae were previously divided into three supertribes: Asterodae, containing four tribes Helianthodae, containing 15 tribes and Senecionodae, with only one tribe, Senecioneae ( Robinson, 2004 ). The analyses performed here (Figures 2, S15) support the monophyly of Asterodae with 100% BS. Among the four Asterodae tribes, Astereae and Gnaphalieae were sister clades (100% BS), with Anthemideae and Calenduleae splitting successively and as sister to the other Asterodae. Although the sister relationship between Astereae and Gnaphalieae was also recovered in nuclear phylogenies performed by Liu et al. ( 2015 ) using 49 nuclear genes and by Mandel et al. ( 2019 ), Anthemideae were placed differently in previous analyses, either as sister to Astereae with high support in plastid phylogenies ( Panero and Funk, 2008 Panero et al., 2014 Panero and Crozier, 2016 ), or as sister to Senecioneae in nuclear phylogenies with limited samplings by Liu et al. ( 2015 ) and from the supermatrix analyses with 75% BS in Mandel et al. ( 2019 ) (Figure S1). The previously reported sister relationship between Anthemideae and Senecioneae suggested that Asterodae might not be monophyletic. This, in combination with Astereae and Anthemideae being sisters in the plastid phylogenies, led Liu et al. ( 2015 ) to propose a hybrid origin of Anthemideae from a cross between parental lineages related to Astereae and Senecioneae, respectively, which was also supported by some morphological characters (see discussion by Liu et al., 2015 ). By contrast, the sister relationship of Anthemideae and Senecioneae was supported by nuclear gene analyses of three species in each tribe ( Liu et al., 2015 ), and in 12 and 16 species in Anthemideae and Senecioneae, respectively ( Mandel et al., 2019 ). However, this reported phylogenetic relationship did not include the Senecioneae genus Abrotanella, which have disciform capitula with four-lobed corollas, unlike most Senecioneae, which have discoid or radiate capitula with five- and/or three-lobed corollas ( Nordenstam, 2007 ). The sampling here included 16 Anthemideae species in 12 genera and 18 Senecioneae species in 14 genera, including two Abrotanella species. Our findings placed Abrotanella as sister to the other Senecioneae with maximal support (Figure 2). The maximally supported monophyly of Asterodae (including Anthemideae) and Senecioneae (including Abrotanella) suggested that the increased sampling or inclusion of Abrotanella here was important for achieving the highly supported resolution of these groups, and does not support Senecioneae as a possible parental lineage of Anthemideae.

In addition, the genus Doronicum was resolved here as sister to the combined clade of Asterodae and other members of Senecioneae (Figures 2, S15), supporting its designation as the tribe Doroniceae proposed by Panero ( 2005 ) and adopted by Fu et al. ( 2016 ) in their systematic arrangements for Asteraceae from China. Doronicum was not sampled by Mandel et al. ( 2019 ). Doronicum was traditionally placed in Senecioneae, primarily according to gross morphology however, its relationship with members of the Senecioneae was poorly resolved in the ITS- and plastid-based phylogenies ( Pelser et al., 2010 Fu et al., 2016 ). Consequently, Doronicum was not assigned to a previously defined Senecioneae subtribe ( Nordenstam et al., 2009 ). The strongly supported position of Doronicum here clearly separates it from Senecioneae and argues for its designation as a distinct tribe. Both Doronicum and Abrotanella were members of Senecionodae ( Nordenstam, 2007 ), but the phylogeny generated here suggests that Senecionodae are not monophyletic.

The supertribe Helianthodae contains 15 tribes, all sampled here except for the monotypic Feddeeae (Figure 2). The relationships we identified among the Helianthodae tribes are consistent with those reported by Mandel et al. ( 2019 ), except for some taxa that were only sampled here, including one tribe (Polymnieae, containing Polymnia) and three genera (Enydra, Guardiola, and Jaumea, in the tribes Neurolaeneae, Millerieae, and Tageteae, respectively). All phylogenies herein provide strong support for the monophyly of the supertribe and seven of the 10 tribes containing two or more species: Bahieae, Coreopsideae, Eupatorieae, Helenieae, Heliantheae, Inuleae, and Madieae. In contrast, Tageteae were monophyletic only in the ML analysis of 192 genes (set 11) Jaumea did not group with other Tageteae genera in the coalescent phylogenies (Figures S3–S15), similar to results of the analysis using plastid sequences ( Fu et al., 2016 ). Moreover, Millerieae and Neurolaeneae were not monophyletic the genus Enydra (two species sampled here previously in Neurolaeneae ( Panero, 2007 )) was nested within the Millerieae, which were represented here by six genera. In a previous plastid phylogeny ( Fu et al., 2016 ), Enydra was sister to a clade of three other Neurolaeneae genera (also sampled here), but the grouping of Enydra with the other Neurolaeneae was not strongly supported (<50% BS/0.95 PP). Millerieae and Enydra are both pantropical, whereas the other Neurolaeneae are all restricted to the Americas ( Panero, 2007 ). Thus, the phylogenetic position of Enydra among Millerieae genera and their geographical distribution may support a proposed expansion of Millerieae to include Enydra.

Among the Helianthodae tribes, Inuleae and Athroismeae were successively diverged from the other tribes with maximum support, and the remaining tribes formed a strongly supported clade, referred to as the “Heliantheae alliance” ( Panero, 2007 Baldwin, 2009 ) (Figures 2, S15). Within the Heliantheae alliance, Helenieae were the first to diverge, with the other tribes forming three major clades. The first major clade includes Neurolaeneae (excluding Enydra), Heliantheae, and Coreopsideae. Members of these groups produce head inflorescences with bracts (referred to as paleae or receptacular bracts) subtending the florets/achenes. Heliantheae and Coreopsideae were strongly supported sisters (Figure 2), which was also supported by previous analyses using ITS data ( Baldwin et al., 2002 ) or dozens of nuclear genes ( Liu et al., 2015 ) however, different topologies were recovered using plastid sequences ( Jansen et al., 1991 Panero and Funk, 2002 Panero et al., 2014 ). The inconsistent positions of Coreopsideae between the nuclear and plastid phylogenies were potentially due to hybridization events ( Panero, 2007 Liu et al., 2015 ), which might also be true for Heliantheae. In the second clade we identified with six tribes, Eupatorieae and Perityleae were strongly supported as sisters (Figure 2), as were Bahieae and Chaenactideae, in agreement with previous studies ( Panero and Funk, 2002 Panero and Crozier, 2016 ). Tageteae were sister to Madieae and the weakly supported clade ((Chaenactideae, Bahieae), (Perityleae, Eupatorieae)). These six tribes are mostly epaleate ( Panero, 2007 ).

The third major clade was sister to the second clade and contains Millerieae + Enydra + Polymnieae, the latter of which contains only one genus, Polymnia, with three species distributed in eastern North America. The Neurolaeneae genus Enydra is pantropical, whereas Millerieae genus Guardiola is mostly found in Mexico and is aquatic (Figure 2). Polymnia is superficially similar to other genera in Millerieae but was placed in the Heliantheae subtribe Polymniinae by Robinson ( 1978 ), who considered most species previously placed in Polymnia to be members of the genus Smallanthus (Millerieae). Polymnieae were once placed in Millerieae as the subtribe Polymniinae ( Robinson, 1978 ), but in the plastid phylogenies they constituted a distinct clade separate from other Millerieae species ( Panero and Funk, 2002 Panero, 2007 ). Moreover, most members of Millerieae were closely related to Heliantheae in the plastid phylogenies, but Enydra was included in the Neurolaeneae clade ( Panero, 2007 Panero and Funk, 2008 ) these differences in nuclear and plastid phylogenies thus suggest a possible hybrid origin.

Asteraceae origin was estimated in the Late Cretaceous, while most tribes diverged before the Oligocene

Environmental factors play major roles in shaping biodiversity. To obtain clues about possible historical environmental influences on biodiversity in Asteraceae, we estimated the times of the origins of, and divergences among, the Asteraceae lineages using the newly reconstructed nuclear phylogeny (Figures 1, 2) and the 192 nuclear genes (set 11 in Figure S2). We performed molecular clock analyses using calibrations with 15 fossil constraints, including seven Asteraceae fossils (Table S2), and a secondary calibration for the crown node of the Eudicots ( Zanne et al., 2014 Tank et al., 2015 ). A fossil pollen, Tubulifloridites lilliei type A (referred as Tl-typeA hereafter) was reported as being related to early members of Asteraceae, but with uncertain placement ( Barreda et al., 2015 ). To test the effects of the use and position of Tl-typeA on age estimation, it was either not included (calibration set 1) or included with differing placements (sets 2–5) (see Supporting Information for details). The results from the r8s and BEAST analyses were nearly identical among calibration set 1 and sets 3–5 (Figures S16–S25) thus, only the results from using calibration sets 1 and 2 are discussed below.

The mean ages were similar along the backbone from the r8s and BEAST analyses using the same calibration set (Figure 3 Table S3). Particularly, the estimated ages of the most recent common ancestors (MRCAs) of Asteraceae, Calyceraceae, Goodeniaceae to Menyanthaceae, and of Heliantheae alliance varied by no more than 3 My between the two methods. Variations of 10–15 My were found in the ages of some lineages, such as Cichorioideae I, Carduoideae I, and Cichorioideae II. On the other hand, estimations with the fossil calibration set 1 (without the pollen fossil) and set 2 (with the pollen fossil at the position assigned by Barreda et al., 2015 ) performed using the same method differed by fewer than 10 My for most nodes, except those of the crown Barnadesioideae and one of its two subclades (the MRCA of Dasyphyllum and Arnaldoa) (Figures S26, S27). Thus, the results from both methods and each fossil calibration set all suggest the origin of Asteraceae in the middle of the Late Cretaceous, with the separation of the subfamilies before or near the Cretaceous–Paleocene boundary at

66 Mya, and divergence of most of the tribes before the Oligocene, or during the Eocene or Paleocene (Figure 3). Gymnarrheneae, Cardueae, Pertyeae, Hecastocleideae, Gochnatieae, Wunderlichieae, Famatinantheae, and Barnadesieae probably arose before the end of the Cretaceous (Figure 3).

Molecular clock estimates of divergence times and diversification rate shifts in Asteraceae

This figure presents the subtree containing members of Asterales retrieved from the result of age estimation with calibration set 1. Green and blue dotted lines indicate the boundaries of Cretaceous-Paleocene and Eocene-Oligocene, respectively. The brown stripe corresponds to the hottest period of the Cenozoic era. Multiple specific molecular clock analyses are shown in Figures S16–S27. The positions for increases of net diversification rate resulting from individual analyses are collectively depicted with colored circles. Colored blocks indicate the analysis supporting a shift at the indicated position. Detailed results from each analysis are shown in Figures S28–S34.

Including Tl-typeA on the Dasyphyllum stem led to an estimated age of the crown Asteraceae similar to those reported by Barreda et al. ( 2015 ) however, this result (set 2) differed substantially from those with different placements of Tl-typeA (sets 3–5). The estimated ages of the crown Asteraceae from the latter three placements were similar to each other and to the age estimated without the inclusion of Tl-typeA, indicating an obvious impact of assigning Tl-typeA to Dasyphyllum. Because this placement is controversial ( Panero, 2016 ), we accept the more conservative and consistent results from the other four estimations and discuss the ages from calibration set 1 below. Using calibration set 1, Asteraceae and its sister clade, Calyceraceae, were estimated to have diverged

83 Mya. The Barnadesioideae then separated from the other Asteraceae subfamilies

81 Mya, with seven other subfamilies progressively diverging during the next 10 My. Throughout the late Cretaceous when these subfamilies separated, the climate was much warmer and more humid than the present ( Linnert et al., 2014 ), suggesting that higher temperatures might have promoted early Asteraceae diversification ( Davies et al., 2004 Jablonski et al., 2006 Jansson and Davies, 2008 ).

Except for Stifftioideae, all other subfamilies, including the three largest subfamilies (Asteroideae, Cichorioideae, and Carduoideae, comprising 69%, 16%, and 11% of Asteraceae species, respectively) ( Panero and Crozier, 2016 ), diverged before the Cretaceous–Paleocene boundary when massive extinctions occurred (at 66 Mya) (Figure 3) ( Jablonski and Chaloner, 1994 ). Also, the supertribes Asterodae and Helianthodae and the tribe Senecioneae (Figure 3) separated

62–61 Mya, with further divergences of most of the

20 Asteroideae tribes in the Eocene. Further divergences among some tribes/subtribes, and also genera, occurred after the Eocene–Oligocene boundary alongside great climate changes and numerous extinctions ( Ivany et al., 2000 ). In addition to the possible roles of climate factors, the massive extinctions at the Cretaceous–Paleocene and Eocene–Oligocene boundaries, which likely freed numerous ecological niches, could therefore also have facilitated the diversification of the largest subfamily, Asteroideae.

The ages estimated here are generally older than those reported in previous studies, including the estimated age of the Asteraceae stem of

50 My reported in studies with sampling at higher (e.g., across several families or orders) or lower (e.g., of a tribe) taxonomic levels ( Bremer et al., 2004 Barres et al., 2013 Beaulieu et al., 2013 Jabaily et al., 2014 Magallón et al., 2015 Park and Potter, 2015 Tank et al., 2015 ). The ages estimated here using calibration set 1 (Figures 3, S16, S17) are also older than those reported by Panero and Crozier ( 2016 ) using 11 plastid genes and one noncoding region (85 species in 39 tribes), such as the age estimated here for the crown Asteraceae of

65 My reported by Panero and Crozier ( 2016 ). However, our estimated age for the crown Asteraceae is close to that (83 Mya) reported by Mandel et al. ( 2019 ). The differences in estimated ages might be due to the sequence datasets (nuclear vs. plastid genes), gene numbers, and/or taxon sampling, as well as the calibrations (Table S2).

Multiple increases in the diversification rate in the Asteraceae

Changes in species richness could be due to either increases or decreases in diversity, which could be estimated by the analysis of shifts in the rate of diversification using a reference phylogeny. To better understand the history of diversity changes in Asteraceae, we estimated the diversification rates and identified the approximate positions of rate shifts during Asteraceae evolution using the MEDUSA ( Alfaro et al., 2009 ) and BAMM methods ( Rabosky, 2014 Rabosky et al., 2014b Shi and Rabosky, 2015 ). The positions with potential major accelerations in net diversification rate obtained using both methods (each with two different models) are collectively illustrated in Figure 3. For the analyses using MEDUSA (with mixed and birth–death models Figures S28, S29), we collapsed the tree tips to the tribal level and used the species number of each tribe as the species richness. The result showed six accelerations in net diversification rates (red circles) and one deceleration (blue circle) (Figure S28). Among the upshifts, four with 2.7-, four-, three-, and twofold increases relative to the background (circles 2–5), respectively, are associated with the Vernonioid clade (Asteroideae–Cichorioideae II), the Heliantheae alliance excluding Helenieae (also called the phytomelanic (dark-colored) fruit (PF) clade), the tribe Eupatorieae, and the clade comprising two supertribes (Asterodae and Senecionodae). Rate accelerations were also found at nodes leading to the Nassauvieae + Mutisieae clade and the tribe Cardueae, resulting in approximately twofold increases in the diversification rate.

We also used BAMM with the complete tree in Figure 3 and the sampling fraction data (Table S4), performing separate analyses using both time-variable and time-constant algorithms. Both algorithms produced the same best shift configurations (the one with maximum a posteriori probability MAP configuration) with three rate shifts (Figure S30): at the Vernonioid clade (circle 5), the Cardueae clade (circle 6), and the core Astereae clade (circle 1). These shifts can also be observed in the rate through time plots (Figure S30). The shifts at the Heliantheae alliance/PF (circle 4) and the Eupatorieae clade (circle 3) were also supported by BAMM under the time-constant algorithm in a further investigation of the results (Figures S32–S34 Table S10).

In summary, all four analyses performed here strongly support upshifts of the net diversification rates at the Vernonioid clade (circle 5) and at tribe Cardueae (circle 6) (Figure 3), affecting the largest subfamilies, Asteroideae, Cichorioideae II (Vernonioideae), and Carduoideae. The next likely position for a diversification rate upshift is among the nodes from the PF clade to the Heliantheae alliance, and may even include Athroismeae (circle 4) finding an event here possibly benefited from a greater sampling of this tribe. This group is positioned within the large supertribe Helianthodae and includes many tribes that further expanded after the Eocene–Oligocene boundary during the dramatic climate changes and mass extinctions. Another possible upshift took place near the divergence of Eupatorieae (circle 3), which expanded greatly after the Eocene–Oligocene boundary. Mutisieae (or the Mutisieae + Nassauvieae clade) (circle 7) is another group with a possible rate increase that expanded after the Cretaceous–Paleocene boundary. Similarly, the clade Asterodae + Senecioneae and the core Astereae also had possible diversification rate increases the earlier divergence among the tribes occurred after the Cretaceous–Paleocene boundary, whereas the later divergence within Astereae took place after the Eocene–Oligocene boundary. Among these, the upshifts at the PF and Vernonioid clades (circles 4 and 5) were also reported by Panero and Crozier ( 2016 ), and those marked with circles 2 and 4 were consistent with those proposed by Mandel et al. ( 2019 ).

Detection of multiple WGD events

Several WGDs have previously been detected in Asteraceae using genomic, phylogenomic, and Ks analyses of 70 or fewer species ( Barker et al., 2008 , 2016 Huang et al., 2016b Badouin et al., 2017 Reyes-Chin-Wo et al., 2017 Leebens-Mack et al., 2019 Zhang et al., 2020 ). The newly resolved Asteraceae phylogeny generated here from large-scale datasets of 243 species representing all subfamilies and almost every tribe provide an unprecedented opportunity to detect Asteraceae WGDs and place them phylogenetically. We investigated WGD by reconstructing trees of 5 282 orthologous groups (OGs) and comparing them with the reference phylogeny, detecting numerous clusters of gene duplications (GDs) as evidence for a WGD at one of multiple nodes of the Asteraceae phylogeny (see Materials and Methods). According to the strength of the GD evidence, we propose nine WGDs and 32 candidate WGDs (Figures 4, S35), including WGD1 shared by Calyceraceae and Asteraceae, WGD2 shared by the core Asteraceae (Asteroideae–Mutisioideae/Stifftioideae), and WGD3/WGD4 at successive nodes shared by tribes of the Heliantheae alliance (without/with Helenieae, respectively).

A summary of whole-genome duplications (WGDs) detected in Asteraceae

(A) The lineages in each of 13 subfamilies are represented by colored lines. Nine detected WGDs are marked as red pentagons, with one candidate WGD (WGD3) marked as a blue pentagon, with the detected GD numbers and percentages. (B) The node of WGDs within four tribes are marked. (C) Three types of the topologies of retained duplicates are illustrated. For the two sub-clades of taxa derived from one node, Type I has retention of both duplicates in both subclades. Type II lacks one copy in the small sub-clade (blue), and Type III lacks one copy in the large sub-clade (red). (D) The ratio of three types of the nine WGDs and the candidate WGD (WGD3). Additional information for WGD events is shown in Figure S35.

WGD1 and WGD2 have also been detected in previous studies ( Barker et al., 2016 Huang et al., 2016b ), and are consistent with those reported in analyses including multiple angiosperm families and small numbers of Asteraceae species (the XASTβ event described by Leebens-Mack et al., 2019 ), and WGD #23 described by Zhang et al. ( 2020 )). It is worth noting that, following WGD2, the chromosome base number decreased from 27 to 10, and the species dispersed from South America to Africa/Asia–Eurasia ( Funk and Chan, 2009 Semple and Watanabe, 2009 ). After WGD3, the chromosome base number changed once again from 10 to 19, and the species dispersed from Africa/Asia–Eurasia to North America ( Funk and Chan, 2009 Semple and Watanabe, 2009 ). However, in our analysis, WGD3 shows much fewer GDs than WGD4 (301 vs. 782, respectively), and most of the GDs are of the Type II pattern (

87%), indicating that only one gene copy was detected in Helenieae species in most OGs (Figure 4C, 4D). WGD4 (without Helenieae) is also consistent with the XASTα event described by Leebens-Mack et al. ( 2019 ). These results have three possible explanations: (i) the WGD event occurred at the Heliantheae alliance (WGD3), but both copies were lost in Helenieae for most OGs (ii) the WGD event occurred at WGD3, but the variable substitution rates among the Helenieae and other lineages of the Heliantheae alliance caused many of the gene duplications to apparently support WGD4 or (iii) there was a hybridization event between Helenieae and the ancestor of the other Heliantheae alliance species shortly after their divergence.

There are also large numbers of GDs at the crown node of the subfamilies Gochnatioideae (WGD5) and Pertyoideae (WGD6) and within the tribes Mutisieae (WGD7), Senecioneae (WGD8), Anthemideae (WGD9), and Gnaphalieae (WGD10) (Figure 4B). We also detected 31 other clusters of GDs, providing evidence for candidate WGDs (Supporting Information text Figure S35). In summary, previous studies using a relatively small number of species ( Barker et al., 2016 Huang et al., 2016b Leebens-Mack et al., 2019 ) and the present analysis with much greater sampling reached the same conclusion about WGDs at nodes shared by many lineages (WGD1, WGD2, and WGD3/WGD4). In addition, other tribes have experienced independent WGD events, affecting groups with very high species richness (>1 000 species). Of the 32 candidate WGDs (Figure S35), a large majority were detected in the largest subfamilies, including 19 events in Asteroideae, three in Cichorioideae (including the tribe Cichorieae), and two in Carduoideae. Nevertheless, some WGDs were associated with small subfamilies or tribes, such as Gochnatioideae (

254 spp.), suggesting that a WGD alone might not be sufficient for increased diversity and that other factors, such as environmental conditions, are also important. This is consistent with a previous analysis of multiple WGDs throughout the angiosperms ( Ren et al., 2018 ).

Ancestral states of morphological characters

Morphological innovations can afford evolutionary advantages and promote divergence and biodiversity therefore, we examined the morphological evolution of Asteraceae in the context of the nuclear phylogeny presented here with the aim of identifying a link between morphological innovation and organismal diversity. We traced the ancestral states and histories of seven evolutionarily significant characters, including the habit, pappus, and five floral traits (Figures S36–S42). The Asteraceae ancestor was most likely woody, with epaleate receptacles, a solitary homogamous capitula with isomorphic and discoid florets, and a capillary/plumose pappus, which is largely consistent with the estimations of Bremer ( 1994 ) and Panero et al. ( 2014 ). One important morphological change along the backbone is from the ancestral woody habit to the herbaceous habit (Figures 1, S36) at the last common ancestor of multiple subfamilies, including the Gymnarrhenoideae and Asteroideae, with a likelihood value 0.948. This estimated change in habit is older than the root node of the Cichorioideae–Asteroideae (0.899) estimated by Panero et al. ( 2014 ). It is possible that the transition to herbaceousness could have occurred later than the estimate here, as our sampling did not include some of the woody species in Cichorioideae II ( Karis et al., 2009 Robinson, 2009 Robinson and Funk, 2009 ). Regardless of the precise position of the transition to the herbaceous habit, the woody habit in early Asteraceae history is supported by the woodiness of members of the subfamilies Barnadesioideae, Famatinanthoideae, Stifftioideae, Wunderlichioideae, Gochnatioideae, Hecastocleidoideae, and Pertyoideae in a grade of early divergent sister lineages of most of Asteraceae. This is further supported by the presence of woody members in the tribes Onoserideae and Nassauvieae, as successive sisters of other Mutisioideae, and in the tribes Oldenburgieae and Tarchonantheae that form a sister clade to the Cardueae (Figure S36). On the other hand, most members of the large subfamilies Asteroideae and Cichorioideae (s.l.) and the tribe Cardueae are herbaceous, although habit transitions have also occurred later in Asteraceae history, even among closely related species (e.g., Panero et al., 1999 ), with woody members in these groups likely derived secondarily from herbs.

Asteraceae are characterized by a head inflorescence (capitulum) with sessile florets surrounded by bract-like organs in a compact structure ( Funk et al., 2009b ), which can be solitary or part of a higher order inflorescence (capitulescence) (Figure S37). The florets in a capitulum can be uniformly bisexual (homogamous) or exhibit sexual differentiation between the outermost and inner florets (heterogamous) (Figure S38). In addition, the corolla of florets exhibits several morphologies, including the actinomorphic (radially symmetric) disc florets found in several subfamilies, the zygomorphic (bilaterally symmetric) ligulate florets of Cichorieae, and the zygomorphic ray florets on the periphery of heads exemplified by members of Asteroideae. Thus, discoid heads contain only disc florets, radiant heads have marginal disc florets with an enlarged corolla, while ligulate heads contain only ligulate florets, which have a corolla with a five-lobed outer lip. Radiate capitula are found in sunflowers (Helianthus) and most Asteroideae, and comprise outer pistillate or neutral ray florets and inner bisexual disc florets. On the other hand, in disciform heads, the outer florets are pistillate but lack the large corolla of ray florets.

The ancestral character analysis here supports an inflorescence transition from solitary to capitulescence prior to the divergence of the clade Hecastocleidoideae (0.983) (Figure S37), similar to the estimation of Panero et al. ( 2014 ). The homogamous and discoid capitulum were estimated as the ancestral state at the Asteraceae root node (with 99.36% and 99.95% likelihood values, respectively) (Figures S38, S39). Previously the ancestral discoid capitulum had a likelihood value of only 48% ( Panero et al., 2014 ), probably because of differences in the coding of capitulum type for some taxa in Mutisioideae and Hyalideae. In these groups, the outer florets have zygomorphic corollas with a tri-lobed outer lip and a much smaller bi-lobed inner lip. Thus, these capitula are referred to as radiate-like and are different from the true radiate capitula, which have no inner lip in the corolla of outer florets. The genes contributing to the formation of radiate-like capitula and radiate capitula are also different ( Chen et al., 2018 ). In most Asteroideae, heterogamous and radiate capitula are the symplesiomorphies the morphological and sexual differentiations between outer ray florets and inner disc ones make the capitulum function as a single larger flower. These large “flower-like” heads are again often arranged in a group or series. Such higher order inflorescences (capitulescences) have been recognized as having great success in attracting pollinators ( Stuessy et al., 1986 Celep et al., 2014 ), thereby contributing to the diversification of this largest Asteraceae subfamily comprising more than 15 000 species. Radiate capitula were also found to have arisen independently in Arctotideae, Liabeae, and Oldenburgieae. On the other hand, the transition of radiate to discoid or disciform capitula, the outer florets of which lack the large outer lip of rays, was estimated to have occurred independently several times, with one affecting the entire Eupatorieae tribe.

In most Asteraceae, the floret calyx persists after anthesis and is called the pappus. The pappus remains attached to the inferior fruit (achene) during fruit development and even after maturity. It has different morphological types, such as capillary (hair-like), plumose (feather-like), and scaly. Some Asteraceae also have a bract-like organ (called the palea, or receptacular bract) subtending all or some florets on a receptacle. Both the pappus and the palea serve to protect the developing fruit, and the pappus often facilitates the dispersal of achenes, such as by wind for dandelion and many others ( Stuessy and Spooner, 1988 Stuessy and Garver, 1996 ). Some Asteraceae members lack the pappus (and are therefore epappose) these taxa are found mostly in the Anthemideae and partly in the Heliantheae alliance, and generally possess receptacular bracts ( Stuessy and Garver, 1996 ). In the present study, the pappus was estimated to be capillary (64.7%) or plumose (30.0%) for the root node of Asteraceae. More specifically, the capillary pappus was supported to be the ancestral state for most nodes except the earliest divergent clade containing Barnadesioideae and the Heliantheae alliance. This is different from the Asteraceae ancestral state of a scaly pappus with a defensive function proposed by Stuessy and Garver ( 1996 ). The most common pappus type in Barnadesioideae is the plumose pappus ( Stuessy et al., 2009 ), which likely facilitates seed dispersal over a greater distance. Considering the origin and early evolution of Asteraceae in much warmer climates and more closed habitats (e.g., forest) than the conditions today, the dispersal function of a plumose/capillary pappus would be more important in the early evolution of Asteraceae. On the other hand, the defense function of a scaly pappus would be more important against herbivores, especially for many species of the Heliantheae alliance with larger achenes. For the Heliantheae alliance, various kinds of scaly pappus and paleate receptacles for the protection and dispersal of achenes might have contributed to the successes and increase in diversity of this large and diverse group containing 11 tribes.

In short, Asteraceae have experienced several morphological changes, including the transition from a woody to herbaceous habit, from homogamous capitula with isomorphic florets to heterogamous capitula with differentiated florets, from a discoid capitulum to other types with zygomorphic florets in several large subfamilies, including radiate-like (Mutisioideae), ligulate (Cichorioideae I), and radiate (most Asteroideae and tribes in Cichorioideae II/Vernonioideae). The formation of paleate receptacles and variously modified pappuses increased the defense against herbivores and/or enhanced the dispersal of achenes. These morphological changes are associated with increases in diversity, suggesting that they might have played important roles in the elevated biodiversity of these groups.

Likelihood of the Bisse Model

We assume that an accurate rooted phylogenetic tree with branch lengths is known (the “inferred tree”) and that the character state is known for each of the terminal taxa. (Alternatively, our methods could be applied to each of the fully resolved trees coming from a Bayesian MCMC analysis— Yang and Rannala, 1997 Larget and Simon, 1999.) The tree is assumed complete: all extant species in the group have been found and included. We consider only binary characters, but extending the method to multistate characters would be straightforward. All terminal taxa are contemporaneous, and the tree is ultrametric (i.e., the total root-to-tip distance is the same for all tips). Except where noted, we measure time backwards, with 0 being the present.

The parameters of the model are as follows. While a lineage has character state 0, the instantaneous speciation rate is λ0, the extinction rate is μ0, and the rate of transition to state 1 is q01. Similarly, while a lineage has character state 1, the speciation, extinction, and transition rates are λ1, μ 1, and q10. These parameters are assumed constant throughout the tree, although it would be straightforward to extend the model to explore hypotheses about changes to these parameters. We assume that the transitions happen instantaneously over the time scales considered (i.e., we ignore periods of time during which a species is polymorphic). We also assume that these events are independent of one another in particular, we assume that the character state change does not, in and of itself, cause speciation (or vice versa).

Probability of the Tree and Character States (D)

Although the rates reflect probabilities of events moving forward in time, our calculations will move backward in time, from the tips of the tree to the root. This is the well-established “pruning” ( Felsenstein, 1981) or “downpass” ( Maddison and Maddison, 1992) approach, and is used to handle more compactly all the possibilities of ancestral states at various parts of the tree ( Felsenstein, 1981). This approach uses a simple principle: if we are able to use key probabilities at any point on a tree to derive corresponding probabilities immediately ancestral (i.e., closer to the root), then it must be possible to move step-by-step down the tree toward the root. When the root is reached, the calculated probabilities will apply to the whole tree.

Our calculations make use of the full tree topology, unlike those of Nee et al. (1994b), which use only timing of branching events. We need to use the full tree topology because we are considering simultaneously the evolution of character states. In Appendix 1 we describe how removing the dependence on character states reduces our equations to those of Nee et al.

In the BiSSE model, the key probabilities, DN0(t) or DN1(t), describe the chance that a lineage beginning at time t with state 0 or 1 would evolve into a clade like that observed to have descended from node N, closer to the present ( Fig. 1). We track these probabilities back by a very short amount of time, Δ t, toward the root, accounting for all possible events that could have happened along the way. If we use a small enough time interval Δ t, we can ignore the possibility that more than one event happens during the time interval. Once we have derived equations for the change in the probability over Δ t, we then shrink the time interval and use the definition of a derivative to obtain differential equations describing the change in these probabilities as we descend toward the root. By integrating these differential equations along the branches, we are able to solve for the overall probability of the data given the BiSSE model. In the following, we carry out these calculations, first along a branch and then across nodes in the tree.

Calculation of the probabilities (D) of the observed tree and character states, along a branch of the tree. We assume that we know the D's for time t on the branch and attempt to calculate them for time tt.


How much signal is there within a phylogeny about the evolutionary processes that generated it? On the simulated trees used here, it was generally possible to infer the correct trend in the character dependence of speciation but difficult to infer the exact functional form of the trend. For instance, both the linear and sigmoidal functions capture the tendency of speciation to increase or decrease with increasing character state and the inferred linear speciation function was often a rough characterization of the true function ( Fig. 4). It is often difficult to infer ancestral states with confidence (which are needed to identify a speciation-trait correlation, even though this is only done implicitly here), as the information provided by the tips attenuates deeper into the past. Here, adding more species improved the ability to recover the more specific model, but this may be through the larger number of shallow nodes rather than through more accurate information about deep ancestral states ( Mossel and Steel 2005).

It is possible that extinction is not possible to reliably detect on real (nonsimulated) molecular phylogenies. Accurate detection of extinction requires that we determine the rate at which species fail to appear in our phylogeny, which is a difficult task. ML estimates of the extinction rates are frequently zero despite fossil evidence of nonzero extinction (e.g., Nee 2006, Purvis 2008). However, even when ML estimates are zero, the confidence intervals around extinction rate estimates may be large, allowing potentially high levels of extinction to be consistent with the observed data. Where we have strong independent evidence of high extinction rates, perhaps our analyses would be improved by including these rates directly either through a prior distribution on extinction rates in a Bayesian analysis or by using this estimated rate and not attempting to directly estimate it from the phylogeny. The likelihood calculations proposed here would hold in either case.

Many phylogenies appear to show some sort of slowdown in lineage accumulation toward the present, which will generate low extinction rate estimates. The response to this has generally been to alter the model of diversification. Most commonly, slowdowns have been interpreted as evidence that speciation rates may be density dependent (e.g., McPeek 2008, Phillimore and Price 2008), and various alternative models of cladogenesis have been proposed and tested based on this pattern (e.g., McPeek 2008, Rabosky 2009). Because of its use of the birth–death model, which does not allow interaction among lineages, it would not be straightforward to incorporate these types of dynamics directly into QuaSSE, though it is possible that they may be approximated ( Rabosky and Lovette 2008, but see Bokma 2009). Care should be taken to interpret results from QuaSSE and other birth–death-based models (e.g., Nee et al. 1994, Paradis 2005, Rabosky 2006, Maddison et al. 2007, Freckleton et al. 2008, Alfaro et al. 2009) in light of these limitations.

An alternative explanation for the observed “slowdown,” and consequent problems for estimating speciation and extinction rates, is that our methods of tree construction and ultrametricization creates trees that are incongruous with the model. Extinction rate estimates will always be sensitive to the precise lengths of terminal branches, and any consistent bias toward lengthening the terminal branches will cause problems ( Purvis 2008). Furthermore, our delineation of species is generally retrospective, with lineages counted as species once both morphological changes and reproductive isolation have occurred. However, many isolated lineages may be considered “species” in that they will never again exchange genes. Some of these would eventually become recognized species, but most will go extinct. However, simple birth–death models do not include this sort of process incorporating such lags in species recognition into tree construction or diversification models, along with information from the fossil record where available, may help with efforts to infer meaningful speciation and extinction rates.

The likelihood equations derived here provide exact solutions to the forward-time dynamics described by Paradis (2005) and Freckleton et al. (2008), and also to the early model of Slatkin (1981), but ignoring character evolution at nodes. The key advance of this work is that it treats character evolution and cladogenesis simultaneously. Though the equations cannot be solved directly, likelihoods computed using this approach will correspond exactly to those under this model of character evolution and cladogenesis. Because the likelihood method here uses all the available phylogenetic and character data, it should have higher statistical power than methods based on approximations, such as first inferring ancestral states and ignoring the character-dependent diversification process when doing so. Run on the same trees, the model of Freckleton et al. (2008) had approximately 26% of the power of QuaSSE at detecting differential speciation (data not shown). However, the factors that affect power were the same as identified by Freckleton et al. (2008) increased rates of character evolution, stronger effects of a character on speciation, and larger trees all increased power ( Fig. 3). QuaSSE does retain some ability to detect differential extinction in contrast to the method of Freckleton et al. (2008), but this power appears to be limited and parameter dependent ( Fig. 3). QuaSSE was also robust to the levels of background extinction used here (cf. Paradis 2005).

Despite their assumptions, diffusion models of character evolution and birth–death models of cladogenesis have given us insights over the last few decades into correlated character evolution ( Felsenstein 1985), evolutionary constraints ( Hansen and Martins 1996), and patterns of diversification ( Alfaro et al. 2009). Although the combination of the birth–death and diffusion methods used in QuaSSE may inherit the limitations of both methods, it presents a tractable and powerful method that will help to answer long standing questions about the correlates of diversification from phylogenetic data and current character distributions. As Freckleton et al. (2008) noted, we have no general expectation of what the relationship between speciation or extinction and character states might look like. Because QuaSSE can use arbitrary speciation and extinction functions, it allows investigation of alternative functions. However, we should not generally expect to extract more than general trends from the data, especially where variation in extinction is important in affecting patterns of diversification.

Evolutionary rates and adaptive radiations

The term adaptive radiation has been recurrently used to describe evolutionary patterns of several lineages, and has been proposed as the main driver of biological diversification. Different definitions and criteria have been proposed to distinguish an adaptive radiation, and the current literature shows disagreements as to how radiating lineages should be circumscribed. Inconsistencies increase when authors try to differentiate a clade under adaptive radiation from clades evolving under ‘regular’ speciation with adaptation, a pattern anticipated and predicted by the evolutionary theory in any lineage. The most important disagreement is as to which evolutionary rate (phenotypical or taxonomical) authors analyze to characterize a radiation a discussion embedded in a prevailing inability to provide mechanistic explanations of the relationship among evolutionary rates. The union of pattern and process in the same term, the inadequacy of reported null hypotheses, and the frequent use of ad hoc comparisons between lineages have also contributed to the lack of consensus. A rigorous use of available terms and the articulation of solid criteria with objective methodologies in distinguishing evolutionary patterns are imperative. Given the difficulties in detecting adaptation, the use of the ‘adaptive’ term to qualify a radiation should be avoided unless methodologically tested. As an unambiguous method to distinguish radiating lineages, the statistical detection of significant increases in taxonomic diversification rates on monophyletic lineages can be considered a distinctive signature of a radiation. After recognizing this pattern, causal hypotheses explaining them can be stated, as well as correlates with other rates of evolution.

The authors declare that the code supporting the findings of this study is available at 58 .

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Taxon sampling and species richness

Plant material from herbarium specimens or silica dried samples were chosen for 100 species (and 3 subspecies) representing the distribution range of all major lineages present in Hypericaceae [30,37] (see Additional file 1: Voucher). Following Xi et al. [30], Garcinia xanthochymus Hook.f. ex T.Anderson (Clusiaceae Lindl.) was chosen as outgroup in the phylogenetic analyses.

The monograph of Hypericum [31,32,42,64-72] was used for data on species richness and distribution (for Hypericum sensu Robson 2012). Stevens [34] lists information for the remaining taxa of Hypericeae Choisy (Triadenum Raf., Thornea Breedlove & E.M.McClint., and Lianthus N.Robson included in Hypericum in Ruhfel et al. [33]), as well as the tropical genera of the family Hypericaceae. Xi et al. [30] provides information on distributions for Clusiaceae and Calophyllaceae J.Agardh (Additional file 1: Voucher). Total species numbers with evidence from taxonomy [32], morphological cladistics [36], and molecular phylogenetic analyses [37,38] were used to assign species richnesses to the major clades defined in the diversification rate analyses.

Molecular marker and sequencing

We sequenced two fragments from the chloroplast genome, namely petD (including the petB–petD intergenic spacer, the petD-5′-exon, and the petD intron) and trnL–trnF (including the trnL UAA intron and the intergenic spacer between the trnL UAA 3′ exon and trnF GAA gene), and the nuclear rDNA internal transcribed spacer region (including ITS-1, 5.8S rDNA, and ITS-2). Extraction of DNA was done according to Nürk et al. [37]. In the case of well preserved herbarium material or silica dried samples, entire regions were amplified using the primers ITS-A(F) and ITS-B(R) [73], PIpetB1411F and PIpetD738R [74], c(F) and f(R) designed by Taberlet [75] for the trnL–trnF region. In the case of degraded herbarium materials, ITS-1 and ITS-2 were amplified separately using in addition two internal primers, ITS-C(R) and ITS-D(F) binding in the conserved 5.8S rDNA [73]. Similarly for petD, using the two internal primers SALpetD599F and OpetD897R designed by Korotkova et al. [76]. PCR amplification of ITS was performed as described in Nürk et al. [37]. PCR reaction mixes for petD and trnL–trnF were chosen according to Nürk et al. [37], but without adding MgCl2, and PCR profiles consisted of an initial denaturation at 96°C for 1.5 min, followed by 35 cycles of 95°C for 30s, 50°C for 60s, 73°C for 90s and a final step at 72°C for 10 min. Primer combinations as described above for poorly preserved samples were used for cycle sequencing. DNA sequencing was done by Eurofins MWG Operon (Ebersberg, Germany). All newly generated sequences have been submitted to the to the EMBL nucleotide database (Accession No. LK871650–LK871782).

Phylogenetic inference

Sequences were assembled and edited with Geneious v5.4 [77], aligned using the automatic selection of an appropriate strategy in Mafft v6.903b [78,79] and manually adjusted using PhyDE v0.996 (available online: In order to remove poorly aligned or length variable data partitions the alignments were subjected to Gblocks 0.91b sever [80] with the ‘less stringent’ options selected.

Phylogenetic analyses were performed under maximum likelihood (ML) [81] and Bayesian inference (BI) [82] to reveal confidence limits of the data. ML analyses were performed with the RAxML GUI v1.1 [83,84] and BI in MrBayes 3.2.2 [85]. To test for discordance we analyzed the nuclear (ITS) and chloroplast data partitions (petD, trnL–trnF) separately by ML search under the GTRCAT model of sequence evolution. Clade support was evaluated with 1000 rapid bootstrap replicates [86].

The combined data set (ITS + petD + trnL–trnF) was analyzed under ML with the partitions defined and the settings chosen as described for the ML analysis above. For BI we started 4 simultaneous runs, each with 4 chains, set to run 10 8 cycles, with sampling every 10 4 cycle, setting temperature to 0.01, and with the appropriate model of sequence evolution specified per partition: GTR + I + Γ for ITS and petD and HKY + I + Γ for trnL–trnF selected in MrModeltest [87] under the Akaike Information Criterion (AIC) [88]. We used the ML tree as a starting tree, but introduced random perturbations into it to enable detection of possible convergence problems (using the command “mcmcp nperts = 5”). A ‘corrected’ exponential prior on a branch length of 1/λ = 0.1 [“prset brlenspr = Unconstrained:Exp(100)”] was specified [89]. Convergence of parameter estimates was monitored using Tracer v1.5 [90]. After discarding 25% of the sampled trees as burnin, posterior probabilities were calculated on the BI stationary sample. Trees and alignments are available at TreeBASE study number 16298.

Divergence time estimation and fossil assignment

The likelihood ratio test [91] conducted on the BI consensus tree in PAUP* [92] rejected a global molecular clock (P < 0.05) for the combined data set. Therefore, divergence times were estimated under a relaxed molecular clock employing the uncorrelated lognormal model [93] in BEAST v1.7 [94]. Eight external time-constraints were imposed for calibration, comprising six fossils [52,95-97] and two secondary calibrations [30] (for details see Additional file 1: Age estimation, calibration). Fossil calibrations were constrained by hard minimum bounds and secondary calibrations by lognormal distributions to incorporate the uncertainty reported in the original study [30]. Two approaches were designed differing only in the assignment of the seed fossil Hypericum antiguum Balueva & V.P. Nikitin [97]: (i) to the stem node of Hypericum in analysis A, and (ii) to the crown node of Hypericum in analysis B (other calibrations remained unchanged in the two analyses for a discussion of fossil assignment see Additional file 1: Age estimation, calibration).

A birth and death model of speciation considering incomplete species sampling [98] was set as tree prior. Both divergence time estimations (A and B) were started with two independent Monte Carlo Markov Chain (MCMC) runs, each set to run 10 8 cycles with sampling every 10 4 cycles. The substitution and clock models were not linked between the partitions. The ML tree was used as starting tree. To ensure that the prior branching times of the starting tree fulfilled the constraints imposed by the calibration priors we transformed branch length into absolute time using penalized likelihood [99] with the chronopl command in the R [100] package ape [101]. Convergence of parameter estimates was monitored using Tracer [90]. The resulting trees were combined in LogCombiner with a burnin of 50%. On the remaining 10,002 trees means and confidence intervals were calculated in TreeAnnotator [94] to obtain the final consensus tree, the ultrametric time calibrated maximum clade credibility (MCC) chronogram that has 95% of the highest posterior density (HPD). Because missing data can have deleterious effects on analyses that depend on branch length [102], we tested the effect on age estimates of missing sequences in our data set. We repeated both analyses (A and B) using a data set that did not contain missing data (‘no missing’ data) and that had therefore a reduced species sampling containing only 73 accessions (xml input files are available at the Dryad repository [103]).

Ancestral area estimation

Historical biogeography was analyzed by classifying the species to be distributed within six biogeographic regions, following Brummit et al. [104] for area subdivision. The region were defined to reflect general biogeographic entities, and to be meaningful for the study group: (A) Afrotropical [central Africa, the southern Arabian peninsula, Madagascar, and the West Indian Ocean islands], (WP) western Palearctic, (EP) eastern Palearctic, (IP) Indo-Pacific [SE tropical Asia, Australasia, and the Pacific], (NA) North America [Nearctic], (SA) South America [Neotropic].


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