Thirty clues to the exceptional diversification of flowering plants (original) (raw)

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Departamento de Botánica, Instituto de Biología, Universidad Nacional Autónoma de México, 3er Circuito de Ciudad Universitaria, Del. Coyoacán, Ciudad de México, México

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Posgrado en Ciencias Biológicas, Instituto de Biología, Universidad Nacional Autónoma de México, 3er Circuito de Ciudad Universitaria, Del. Coyoacán, Ciudad de México, México

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Departamento de Botánica, Instituto de Biología, Universidad Nacional Autónoma de México, 3er Circuito de Ciudad Universitaria, Del. Coyoacán, Ciudad de México, México

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Revision requested:

12 June 2018

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23 October 2018

Published:

30 October 2018

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Susana Magallón, Luna L Sánchez-Reyes, Sandra L Gómez-Acevedo, Thirty clues to the exceptional diversification of flowering plants, Annals of Botany, Volume 123, Issue 3, 15 February 2019, Pages 491–503, https://doi.org/10.1093/aob/mcy182
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Abstract

Background and Aims

As angiosperms became one of the megadiverse groups of macroscopic eukaryotes, they forged modern ecosystems and promoted the evolution of extant terrestrial biota. Unequal distribution of species among lineages suggests that diversification, the process that ultimately determines species richness, acted differentially through angiosperm evolution.

Methods

We investigate how angiosperms became megadiverse by identifying the phylogenetic and temporal placement of exceptional radiations, by combining the most densely fossil-calibrated molecular clock phylogeny with a Bayesian model that identifies diversification shifts among evolutionary lineages and through time. We evaluate the effect of the prior number of expected shifts in the phylogenetic tree.

Key results

Major diversification increases took place over 100 Ma, from the Early Cretaceous to the end of the Paleogene, and are distributed across the angiosperm phylogeny. The long-term diversification trajectory of angiosperms shows moderate rate variation, but is underlain by increasing speciation and extinction, and results from temporally overlapping, independent radiations and depletions in component lineages.

Conclusions

The identified deep time diversification shifts are clues to the identification of ultimate drivers of angiosperm megadiversity, which probably involve multivariate interactions among intrinsic traits and extrinsic forces. An enhanced understanding of angiosperm diversification will involve a more precise phylogenetic location of diversification shifts, and integration of fossil information.

INTRODUCTION

Flowering plants (Angiospermae) represent the most recent evolutionary explosion of embryophytes, a lineage that occupied land at least 470 million years ago (Ma) (Rubinstein et al., 2010), and diverged from their closest living relatives 300–350 Ma (Magallón et al., 2013). Since their first appearance in the fossil record, angiosperms have radiated exceptionally, surpassing all other plant lineages not only in sheer species richness but also to become ecologically predominant, forming the structural and energetic basis of nearly all extant terrestrial biomes. Through their ecological expansion, angiosperms have promoted the diversification of other plants (Schneider et al., 2004; Laenen et al., 2014), animals (Cardinal and Danforth, 2013; Wang et al., 2013), fungi (Guzmán et al., 2013; Kraichak et al., 2015) and bacteria (Goffredi et al., 2011). Human nutrition, culture and well-being inextricably depend on angiosperms.

With between 295 000 and 304 000 described species (Christenhusz and Byng, 2016; The Plant List, 2017) and an estimated total of >350 000 species (Joppa et al., 2010), angiosperms are among the megadiverse groups of macroscopic eukaryotes. Their exceptional diversity is distributed unequally among evolutionary lines, some of which include tens of thousands of species (e.g. orchids, composites) and others fewer than ten (e.g. lotus, London planes), indicating that the process of diversification, the balance between speciation and extinction that ultimately determines species richness (subsequently diversity), has acted differentially through angiosperm evolution.

Many studies have attempted to identify the factors that underlie the exceptional diversity of angiosperms, including intrinsic attributes (Farrell et al., 1991; Sargent, 2004), ecological interactions (van der Niet and Johnson, 2012; Weber and Agrawal, 2014), extrinsic opportunity (Moore and Donoghue, 2007; Hughes and Atchinson, 2015) or complex interactions among them (Vamosi and Vamosi, 2011; Spriggs et al., 2015). Nevertheless, little is known about the dynamics of the diversification process that underlies the acquisition of angiosperm megadiversity through time and its unequal distribution among phylogenetic branches. A long tradition considered that the myriad unique vegetative and reproductive attributes of angiosperms made them competitively superior (Stebbins, 1974). Studies based on current phylogenetic understanding have shown that it is unlikely that increased phylogenetic branching characterizes angiosperms as a whole (Sanderson and Donoghue, 1994). Groups with unexpectedly high or low diversity, given a time-homogeneous diversification rate, have been identified (Magallón and Sanderson, 2001), and diversification tests based on tree asymmetry or model selection have found that there is significant imbalance in net diversification rates among angiosperm lineages (Davies et al., 2004), that diversification shifts do not always correspond to named taxonomic entities (Smith et al., 2011), and that some occurred downstream of major genomic duplications (Tank et al., 2015).

The fossil record unequivocally documents an Early Cretaceous explosive radiation of angiosperms shortly following the appearance between the Valanginian and the Hauterivian (140–130 Ma) of pollen grains with a combination of detailed microstructural attributes found only among some angiosperm lineages (Hughes and McDougall, 1987; Brenner, 1996). Immediately younger sediments show an explosive increase in diversity of pollen types and vegetative and reproductive remains that represent early angiosperm branches (Friis et al., 2004, 2009) and their major evolutionary lineages (Doyle et al., 1977; Eklund et al., 2004; Mohr and Bernardes-de-Oliveira, 2004; Herendeen et al., 2017).

In this study we investigate the dynamics of angiosperm macroevolutionary diversification to (1) uncover the phylogenetic placement of major diversification shifts; (2) identify phylogenetic regions and times in which diversification shifts may be concentrated; (3) identify clades that are undergoing evolutionary radiations or depletions; and (4) estimate the diversification trajectory of angiosperms as a whole, including evaluating whether they are in decline. We use a molecular Bayesian phylogenetic tree in which ~90 % of angiosperm families are represented. This tree was dated with a relaxed molecular clock informed by 136 critically selected fossil-derived calibrations (Magallón et al., 2015), and the crown group age constrained within a confidence interval derived from a quantitative palaeobiology method (Marshall, 2008). To our knowledge, it represents the most densely calibrated molecular time-tree available. Using this comprehensive time-tree, we applied Bayesian analysis of Macroevolutionary Mixtures (BAMM) (Rabosky, 2014; Rabosky et al., 2014), a method that, through a compound Poisson process, identifies major shifts in the rate of diversification among phylogenetic branches and through time. To account for the possibility that BAMM produces posterior estimates of the number of diversification shifts that are indistinguishable from the prior (Moore et al., 2016), we conducted independent analyses covering a range of values for the prior on the number of expected shifts across the tree, and provide technical results. Our results identify the phylogenetic and temporal placement of diversification shifts associated with the origins of angiosperm megadiversity, and model the long-term diversification trajectory of angiosperms, suggesting ongoing species accumulation.

MATERIALS AND METHODS

Taxonomic sample, molecular data and dating analyses

The diversification study is based on a previously published, temporally calibrated phylogenetic tree of angiosperms (Magallón et al., 2015). The taxonomic sample includes 792 angiosperms, six gymnosperms representing Cycadophyta, Gnetophyta and Coniferae, and a fern belonging to Ophioglossaceae. The angiosperms belong to 374 families, representing 87 % of those recognized by the Angiosperm Phylogeny Website in April 2013 (Stevens, 2013), and encompass 99 % of total angiosperm species richness. The molecular data are the concatenated sequences of three plastid protein-coding genes (atpB, rbcL and matK) and two nuclear markers (18S and 26S nuclear ribosomal DNA), which form an alignment of 9089 base pairs (bp). The sampled species and families and GenBank accession numbers are shown in Supplementary Data Table S1. The molecular data set is available in the DRYAD Digital Repository (https://doi.org/10.5061/dryad.4f80hb4).

Divergence time was estimated by combining a method derived from quantitative palaeobiology to constrain the age of the angiosperm crown node (Marshall, 2008) with an uncorrelated relaxed molecular clock to estimate dates within angiosperms (Drummond et al., 2006), incorporating information on 136 critically selected and justified fossils to calibrate internal nodes. Detailed information about dating analysis, including justification for all calibrations, is provided in the original study (Magallón et al., 2015).

Diversification analyses

The macroevolutionary diversification dynamics of angiosperms were investigated with the C++ programme BAMM v2.5.0 (Rabosky, 2014, 2017; Rabosky et al., 2014, 2017; Mitchell and Rabosky, 2016), which, through a compound Poisson process (CPP) implemented as a reversible-jump Markov chain Monte Carlo (rjMCMC) estimates major shifts in the rates of speciation, extinction and diversification among the branches of a phylogenetic tree and through time. Post-run analyses were conducted with the R package BAMMtools v2.5.0 (Rabosky et al., 2014).

The BAMM method.

BAMM simulates a posterior distribution of shift configurations, each corresponding to a particular combination of the number of shifts (increases and decreases in diversification, speciation or extinction), their phylogenetic placement among the branches of the tree, and their temporal position on the branch. Given the posterior sample of shift configurations derived from the rjMCMC, it is possible to obtain a phylorate plot in which the rate of speciation, extinction or diversification averaged across all the configurations in the posterior distribution is plotted on each time unit on each branch (Rabosky, 2017).

In the BAMM model, the prior distribution of the expected number of shifts in the tree (expectedNumberOfShifts parameter) influences the number of shifts in the posterior distribution. Under the prior, the distribution of shifts across the tree is uniform, and the probability of finding a shift on a given branch depends on the specified prior for the number of shifts, and on the branch length (Rabosky, 2014, 2017). Thus, a high prior value specified for expectedNumberOfShifts will result in a high number of shifts in the posterior distribution, although many of them might be weakly supported by the data (Rabosky, 2014, 2017). Significant shifts can be distinguished by considering the marginal odds ratio (MOR) of a shift being present in a given branch. Because under the prior long branches are more likely to contain a shift, the MOR provides a measure of the amount of evidence supporting a shift on a given branch after normalizing for its length, i.e. independently of the prior (Shi and Rabosky, 2015; Rabosky, 2017).

Moore et al. (2016) pointed out that BAMM cannot provide reliable estimates of diversification rate shift models, or diversification rate parameters, mainly because its likelihood function does not account for rate shifts that took place on extinct branches; and, because there might be an infinite amount of equally likely CPP model parameterizations for the number and magnitude of shifts on the branches of the phylogeny, the posterior estimates are extremely sensitive to the prior number of shifts in the tree. After critically examining these claims, Rabosky et al. (2017) concluded that, although BAMM and (possibly except for the Monte Carlo likelihood in Moore et al., 2016) all models that estimate rate shifts through time and on different parts of the phylogeny do not account for rate shifts on extinct (or otherwise unobserved) branches, the effects that these unaccounted diversification shifts have in empirical cases are likely to be small. Also, although all Bayesian posterior estimates are influenced by the prior, it is possible to identify posterior estimates that are strongly supported independently of the prior (Rabosky et al., 2017). Hence, the use of the Bayes Factor to select BAMM outcomes that are well supported, independently of the prior on the expected number of shifts, is strongly advocated (Rabosky, 2017). Specifically, in BAMM and BAMMtools 2.5.0 it is possible to estimate the MOR of a shift on a given branch (Rabosky, 2017). Diversification model selection with BAMM is largely robust to choice of model prior (Mitchell and Rabosky, 2017; Rabosky et al., 2017).

Identifying diversification shifts and calculating diversification parameters across a phylogeny are complex estimation problems. We are aware that all currently available tools are imperfect, yet agree that BAMM v2.5.0 has been reasonably proven to achieve the task of providing realistic estimates of diversification parameters (Rabosky et al., 2017). While we recognize the potential problems in BAMM estimates of diversification shifts and rate parameters, to our knowledge no other currently available software can simultaneously achieve identification of rate shifts among phylogenetic branches and through time, given that the sampling density in this study [i.e. RPANDA (Morlon et al., 2011, 2016) requires at least three terminals per clade to evaluate different diversification models]. We nevertheless exercise caution regarding potential prior sensitivity when applying BAMM, and conduct several independent diversification analyses under a wide range of magnitudes for the prior on the expected number of shifts to evaluate the independence of posterior estimates on this parameter (see below). In each analysis we obtained the MOR associated with the presence of a shift on any branch, and made comparisons across analyses to identify a congruent set of highly supported shifts. As a comparison of shifts identified with BAMM, we conducted an independent analysis with MEDUSA (Alfaro et al., 2009), a maximum likelihood method that uses the Akaike information criterion to select among rate-constant or rate-variable and pure birth or birth–death models. The MEDUSA analysis and results are described in Supplementary Data Material S1 and Figure S3.

Investigating angiosperm diversification.

The angiosperm dated tree described above (Magallón et al., 2015) was used as the input chronogram. BAMM was set to conduct a speciation–extinction analysis. We performed six analyses under different magnitudes for the prior on the expected number of shifts (expectedNumberOfShifts = 0.1, 1, 5, 10, 50, 100) to assess whether the prior and posterior distributions of the number of diversification shifts are independent. Priors on rate parameters were scaled to our dated tree using the setBAMMpriors function in BAMMtools. The rate parameter of the exponential priors for the initial speciation and extinction values (lambdaInitPrior and muInitPrior) were both set to 4.66095688667462. The prior for the standard deviation of the normal distribution (mean fixed at zero) of the speciation rate regimes shift parameter (lambdaShiftPrior) was set to 0.00825917607286971. Constant diversification rate branch segments were set to 3 Ma by setting the segLength parameter at 0.02152148, given a crown node age of 139.3956 Ma. Rates were allowed to vary through time (lambdaIsTimeVariablePrior = 1).

The prior distribution on the number of rate shifts was calculated with BAMMtools v2.5.0 (Rabosky et al., 2014) by considering that the number of rate shifts follows a Poisson distribution with a rate parameter determined by an exponential hyperprior (Mitchell and Rabosky, 2017), and the finding that the probability of a given number of shifts in BAMM is the product of Poisson and exponential densities, which reduces to a simple geometric distribution on the number of rate shifts (Mitchell and Rabosky, 2016). Non-random incomplete taxon sampling of full angiosperm diversity was accounted for by indicating that clade-specific sampling probabilities would be used, and by specifying the sampled fraction of clades in the tree. Most of the clades correspond to angiosperm families recognized in the Angiosperm Phylogeny Website in April, 2013 (Stevens, 2013), with the total number of species in each family obtained from this same source. Families not represented in the dated tree were accounted for by aggregating their species richness to that of their sister clade, according to relationships in the Angiosperm Phylogeny Website. Following BAMMtools documentation, for each terminal in the tree we specified the represented fraction of the clade to which it belongs by dividing the total species richness of the clade (i.e. a family or a family plus unsampled sister families) by the number of terminals belonging to that clade. The backbone of the phylogeny is fully sampled. Clade sampling fractions indicated on each terminal are shown in Supplementary Data Table S2.

Each MCMC simulation consisted of 300 × 106 steps, sampling a shift configuration every 200 000 steps. The initial 10 % of the MCMC was discarded as burn-in, hence the total number of analysed posterior samples is 1351. The input tree, control files and the BAMM output event data files are available in the DRYAD Digital Repository (https://doi.org/10.5061/dryad.4f80hb4). For each analysis, we calculated the phylorate plot (Supplementary Data Fig. S1) and identified the best and the maximum shift credibility configurations (available from the authors).

For each analysis, we identified the shifts found in all the configurations in the posterior distribution and estimated their MOR. We sorted the identified shifts by their MOR magnitude. We considered those shifts with a MOR value that falls within 95 % of the magnitude of the maximal MOR found in that analysis (e.g. if for a given analysis the maximal observed MOR is 100, we considered all the shifts with MOR ≥5). We then compared the shifts selected in each analysis across the six analyses, and chose to discuss only those that are shared by at least four analyses. To obtain the time of a shift on a given branch in each analysis, we extracted the age of the shift in all the configurations in which it was detected, and obtained the mean, minimal and maximal values (Fig. 2, Supplementary Data Table S3).

Rate-through-time plots (for diversification, speciation and extinction) were obtained with BAMMtools v2.5.0, using the plotRateThroughTime command (Rabosky, 2017). This command models the rate trajectory from the estimated age of a user-selected internal tree node in the dated tree to the present. Consequently, rates through time can only be modelled from either the stem node or the crown node of a clade that underwent a diversification shift (but not from the time of the shift towards the present). If the stem node is selected as the starting point, the rate-through-time plots will correspond to a more inclusive clade that includes the clade that underwent the diversification shift, and its sister clade. Because these plots would inaccurately represent the rate trajectory of the clade that resulted from the diversification shift, we depict the rate-through-time plots starting from the crown node of this clade. This implies that the diversification shift itself is not represented in the rate-through-time plot.

RESULTS

Technical results

BAMM is a method that, through a compound Poisson process (CPP), allows identification of diversification shifts among branches in the phylogenetic tree, and modelling their change through time (Rabosky, 2014; Rabosky et al., 2014). It also allows estimation of the average rates of speciation, extinction and diversification of selected lineages, and the calculation of their temporal trajectories. Being aware of potential non-identifiability of the model on the number and distribution of diversification shifts, we conducted six independent analyses over a wide range of magnitudes for the prior on the expected number of shifts (expectedNumberOfShifts = 0.1, 1, 5, 10, 50 and 100). In all our analyses, the posterior distribution of the number of shifts is distinctly decoupled from the prior (Fig. 1). A larger magnitude on the prior on the expected number of shifts is less restrictive (Rabosky et al., 2017), and, as expected, leads to the recognition of a larger number of shifts in the posterior distribution, each with a lower frequency (Fig. 1). Nevertheless, shifts that are highly supported by the data, as indicated by their MOR, are largely congruent among the six analyses (Supplementary Data Tables S3 and S4).

Fig. 1.

Prior and posterior distributions of the number of rate shifts. Prior and posterior distributions (as indicated in the key) of the number of shifts in six BAMM analyses conducted under different prior values for the expectedNumberOfShifts parameter: (A) 0.1; (B) 1; (C) 5; (D) 10; (E) 50; (F) 100. In all cases, the prior and posterior distributions are distinct and non-overlapping. As expected (Rabosky et al., 2017), as the value of the prior on the expected number of shifts increases, the prior distribution becomes less informative, and the posterior distribution is wider and centred around a higher value for the number of shifts. All plots are at the same scale, but the _y_-axis in (A) and (B) is trimmed.

As the MOR allows shifts that are strongly supported by the data to be distinguished but cannot indicate the number of significant shifts in an analysis, we chose to discuss those shifts that, first, in any given analysis have a MOR that falls within 95 % of the maximum MOR value identified in that analysis, and, from the previous set, those that are shared among at least four of the six analyses. We identified 30 such shifts, and consider them as the core diversifications to be discussed (Table 1, Supplementary Data Table S4). In each analysis, the distribution of shifts sorted by their MOR is a hollow curve (Supplementary Data Fig. S2, Table S5). Eighteen core shifts are distributed on single branches, and 12 drift in two or more adjacent branches, within a particular phylogenetic region (Fig. 2, Supplementary Data Table S4). Twenty-six core shifts are towards increased diversification rates; three core shifts contain many nested shifts, and only one core shift is towards decreased diversification (Figs 2 and 3).

Table 1.

Thirty core angiosperm rate shifts. Core shifts correspond to those that are within 95 % of the magnitude of the highest marginal odds ratio (MOR) in each analysis with different priors for the expected number of shifts (i.e. 0.1, 1, 5, 10, 50, 100), and that are found in at least four (out of six) analyses. Core shifts detected on two or more adjacent branches are indicated as ‘ca. clade name’. Shift numbers followed by an asterisk are identical or closely match shifts identified in an alternative MEDUSA analysis conducted for comparison (Supplementary Data Material S1, Figure S3)

Table 1.

Thirty core angiosperm rate shifts. Core shifts correspond to those that are within 95 % of the magnitude of the highest marginal odds ratio (MOR) in each analysis with different priors for the expected number of shifts (i.e. 0.1, 1, 5, 10, 50, 100), and that are found in at least four (out of six) analyses. Core shifts detected on two or more adjacent branches are indicated as ‘ca. clade name’. Shift numbers followed by an asterisk are identical or closely match shifts identified in an alternative MEDUSA analysis conducted for comparison (Supplementary Data Material S1, Figure S3)

Fig. 2.

Phylorate plot of angiosperm diversification. Rate of diversification per time interval (3 Ma) averaged across all configurations within the 95 % credible set obtained in the BAMM analysis with the prior for expected number of shifts = 100. Major angiosperm clades (APG IV, 2016) are indicated with arrows. In chronological order, core shifts correspond to: 1. ca. Mesangiospermae; 2. Vitales + Rosids; 3. ca. Fabidae; 4. Ericales; 5. Myrtales; 6. Arecales + Commelinales + Zingiberales; 7. ca. Ranunculaceae; 8. Lamiidae; 9. ca. Fabaceae; 10. Asparagaceae + Amarillidaceae + Xanthorrhoeaceae + Asphodelaceae + Iridaceae + Tecophilaceae; 11. ca. Sapindales; 12. Polygonaceae + Plumbaginaceae; 13. ca. Asteraceae; 14. Dipsacales; 15. Montiniaceae + Hydroleaceae + Sphenocleaceae; 16. Orchidaceae; 17. Crassulaceae; 18. Moraceae + Urticaceae; 19. ca. Apiales; 20. ca. Cyperaceae; 21. ca. Euphorbiaceae; 22. Campanulaceae; 23. Celastraceae; 24. Amaranthaceae; 25. Piperaceae; 26. ca. Brassicaceae; 27. Poaceae; 28. Cucurbitaceae; 29. ca. Malvaceae; 30. ca. Cactaceae. See Table 1 and Supplementary Data Table S1 for description of the content of each clade. Some core shifts consist of moderately supported shifts on two or more adjacent branches, and are indicated with letters (e.g. 1a, 1b). The sub-shift indicated with a larger circle has the highest marginal odds ratio (MOR). The inset shows the diversification-through-time (DTT) plot for angiosperms as a whole, including graphs for diversification (blue), speciation (red) and extinction (green). Comm, Commelinales; Caryoph, Caryophyllales; Ran, Ranunculaceae; Sax, Saxifragales.

Fig. 3.

Diversification-through-time plots for core shift clades. Plots are shown for clades resulting from core diversification shift, sorted by onset time (Ma; from bottom to top), including graphs for diversification (blue), speciation (red) and extinction (green). Polygonac, Polygonaceae; Plumbag, Plumbaginaceae; Comm, Commelinales; Zing, Zingiberales.

Angiosperm diversification

The onset of angiosperm diversification into extant lineages in the Early Cretaceous was soon followed by the differentiation of Mesangiospermae (Cantino et al., 2007) (a clade that contains the vast majority of angiosperm diversity, morphological variety and ecological breadth) and its diversification into five evolutionary lineages, including the Magnoliidae (magnoliids), Monocotyledoneae (monocots) and Eudicotyledoneae (eudicots). Most eudicots belong to the large clade Pentapetalae (Cantino et al., 2007), which in turn is composed of superrosids and superasterids (APG IV, 2016) (Fig. 2). Together, monocots, superrosids and superasterids include ~95 % of living angiosperm species.

Core diversification shifts are distributed all across the angiosperm phylogeny, and took place between the Early Cretaceous (Valanginian) and the latest Eocene (Priabonian) or earliest Oligocene (Rupelian), spanning a period of >100 million years (Fig. 2). Unless otherwise noted, shifts represent increasing diversification. Six core diversification shifts took place in the Early Cretaceous. The first is approximately associated with the origin of Mesangiospermae (1a–1b shifts 1a–1b in Fig. 2; Table 1, Supplementary Data Table S4), and is followed by shifts along the spine of superrosids, including one on the branch subtending rosids (shift 2 in Fig. 2) and another spanning from fabids to a clade formed by Oxalidales and Malpighiales (shifts 3a–3d in Fig. 2). Each of these three core shifts contains many nested shifts. Later during the Early Cretaceous one shift took place within asterids, subtending Ericales (shift 4 in Fig. 2); there was another one within rosids, in malvids, subtending Myrtales (shift 5 in Fig. 2), and a third one within monocots, subtending a clade approximately corresponding to commelinids (shift 6 in Fig. 2, Table 1, Supplementary Data Table S4).

During the Late Cretaceous, 15 core shifts gave rise to clades within eudicots and monocots. The earliest shift took place within eudicots, corresponding approximately to Ranunculaceae (shifts 7a–7b in Fig. 2). All other eudicot shifts are nested in Pentapetalae. Within superrosids, there was shift in Saxifragales, subtending Crassulaceae (shift 17 in Fig. 2), another one within malvids, corresponding approximately to Sapindales (shifts 11a–11b in Fig. 2), and three shifts within Fabidae: approximately Fabaceae (shifts 9a–9b in Fig. 2), Moraceae plus Urticaceae (shift 18 in Fig. 2), and approximately Euphorbiaceae (shifts 21a–21b in Fig. 2). Within superasterids, one shift took place in Caryophyllales, subtending Polygonaceae plus Plumbaginaceae (shift 12 in Fig. 2); there were two shifts in Lamiidae, corresponding to a clade containing Solanales, Gentianales, Boraginales and Lamiales (shift 8 in Fig. 2), and nested within it there was a shift towards decreased diversification in a clade formed by Montiniaceae, Hydroleaceae and Sphenocleaceae (shift 15 in Fig. 2); and there were three shifts in Campanulidae: approximately Asteraceae (shifts 13a–13c in Fig. 2), Dipsacales (shift 14 in Fig. 2) and approximately Apiales (shifts 19a–19b in Fig. 2). Three shifts took place within monocots: a clade containing Asparagaceae and five additional families (shift 10 in Fig. 2), Orchidaceae (shift 16 in Fig. 2) and approximately Cyperaceae (shifts 20a–20b in Fig. 2, Table 1, Supplementary Data Table S4).

Nine core shifts took place during the Palaeogene. Within rosids there were two shifts in malvids, corresponding approximately to Brassicaceae (shifts 26a–26b in Fig. 2) and approximately to Malvaceae (shifts 29a–29b in Fig. 2), and two shifts in Fabidae: Celastraceae (shift 23 in Fig. 2) and Cucurbitaceae (shift 28 in Fig. 2). There were two shifts in superasterids, both within Caryophyllales, corresponding to Amaranthaceae (shift 24 in Fig. 2) and approximately to Cactaceae (shifts 30a–30b in Fig. 2). There was a single shift within asterids, in campanulids, subtending Campanulaceae (shift 22 in Fig. 2), and also a single shift within monocots, corresponding to Poaceae (shift 27 in Fig. 2). A single core shift was detected within magnoliids, subtending Piperaceae (shift 25 in Fig. 2; Fig. 2, Table 1, Supplementary Data Table S4). However, two additional strong diversification increases, both within magnoliids, are noticeable in the phylogram (Fig. 2), corresponding to Annonaceae (Magnoliales) and to a clade that includes Cinnamomum, Sassafras and Laurus, within Lauraceae (Laurales). These diversification increases were not identified as core shifts according to our delimiting criteria.

Shift times and diversification parameters of angiosperms as a whole, and of the 30 core shifts, estimated in analyses with different priors on the number of expected shifts, are very similar (Supplementary Data Tables S3 and S4). We discuss diversification parameters and times derived from the analysis with the prior for expected number of shifts equal to 100 because it includes the 30 core shifts selected through our combined criteria (Table 1). Estimated speciation (λ) and extinction (µ) rates for angiosperms as a whole are 0.0988 (0.0910–0.1086) and 0.0277 (0.0192–0.0384), respectively, which are congruent with previous estimates (Magallón and Sanderson, 2001).

Diversification-through-time (DTT) plots for angiosperms as a whole, estimated with different values for the prior on the expected number of shifts, are virtually equal. The rate of diversification is modelled as having undergone a moderate early decline (~140–100 Ma), followed by stabilization (~100–50 Ma) and moderate increase towards the present (Fig. 2). Speciation and extinction rates are modelled as having a pronounced increase towards the present (Fig. 2). Individual shift clades have widely varying increasing or decreasing DTT trajectories, which overlap through time (Fig. 3).

DISCUSSION

Implications of backbone sampling

The identified shifts provide information on major diversification changes for angiosperms as a whole at a deep phylogenetic scale. They represent an important starting point to understand major features of angiosperm evolution (Uyeda et al., 2018). The phylogenetic level at which shifts were identified is a function of the backbone sampling that was used, in which major angiosperm lineages were represented by a small number of placeholders, which convey limited information about species richness distributed among and within angiosperm clades. The taxonomic selection in the dated phylogenetic tree aimed to represent, as much as possible, all angiosperm clades recognized as families. This type of selection results in a highly biased depiction of species distribution, as members of very small clades, with a very low probability of being represented under random sampling, were selectively included. Each family, regardless of its species richness, is represented by a similar number of placeholders, which correspond to very different proportions of the total species richness in each clade. This biased sampling is necessary in order to include as many families as possible, but has important consequences when attempting to estimate macroevolutionary parameters. To mitigate the effect of biased sampling, we specified clade-specific sampling fractions in diversification analyses. Sampling fractions varied widely, from 1.0 (e.g. Amborellaceae, Trochodendraceae, Eucommiaceae) to <0.0003 (e.g. Rubiaceae, Lamiaceae, Orchidaceae) (Supplementary Data Table S2). Not many viable alternatives are available. Ideally, the difference in sampling fraction among clades could be reduced by very greatly increasing sampling within large families, although this strategy might be limited by molecular data availability and be contingent on the capability of models and computer power to handle taxonomically massive datasets in macroevolutionary analyses. Another potential strategy is to implement random sampling among angiosperms, but after including representatives of clades unlikely to be sampled randomly due to their small size (e.g. O’Meara et al., 2016). Bioinformatic methods to incorporate missing taxa into backbone phylogenies are available [e.g. PASTIS (Thomas et al., 2013)], but given the colossal species richness of many angiosperm families and the very small number of placeholders in the phylogenetic backbone, we suspect this type of approach would be unfeasible in this study.

As a consequence of extremely reduced sampling, estimates of diversification dynamics can only associate shifts with major clades, usually on their stem lineage, as information (i.e. species sampling) that could document one or more shifts within the clade is lacking. The reduced taxonomic sampling also precludes replicating diversification shifts detected in studies focused on delimited and much more densely sampled clades (e.g. Lagomarsino et al., 2016). The absence of diversification shifts younger than the Palaeogene may also be a consequence of sparse taxonomic sampling.

Previous angiosperm diversification studies

Few previous studies have investigated long-term angiosperm diversification dynamics, in particular identifying diversification shifts (Table 2). Davies et al. (2004) conducted the first study to identify diversification shifts at an angiosperm-wide scale, using a family-level supertree, in which diversification shifts were approximated with species richness imbalance among families (Fusco and Cronk, 1995; Purvis et al., 2002), from which they detected the most (top ten) imbalanced family pairs. Of these, five may correspond to shifts detected in our study (Table 2). Smith et al. (2011) applied SymmeTREE (Chan and Moore, 2005) [a method that considers the topological distribution of species richness to fit models of constant or variable rates (Chan and Moore, 2002)] to an angiosperm megaphylogeny. They identified between 16 and >2700 potential shifts, which were not named. The closest precedent to our study is the analysis of Tank et al. (2015), who investigated links between diversification shifts and whole-genome duplications, by applying MEDUSA (Alfaro et al., 2009) (a maximum likelihood stepwise method of identifying the best-fitting diversification model and detecting significant shifts in diversification and relative extinction) to a set of bootstrapped chronograms representing 325 angiosperm families. Over 140 unique shifts were identified across all bootstrapped chronograms, 27 of which occurred in at least 75 % of all the set (Table 2 in Tank et al., 2015). Of these, nine are approximately equivalent to shifts detected here (Table 2). In spite of some profound sampling and methodological differences, as well as analytical limitations associated with each method, it is noteworthy that these studies detect partially overlapping sets of diversification shifts, the most recurrent ones being associated with Piperaceae, Asparagaceae and related families, a clade containing Arecales, Commelinales and Zingiberales, Cyperaceae, Poaceae, Cactaceae, ca. Lamiidae, Asteraceae, Myrtaceae, Brassicaceae and Fabaceae.

Table 2.

Comparative summary of published studies in which shifts in the rate of diversification within angiosperms have been identified

1Lamiales II corresponds roughly to Lamiidae in APG IV, 2016.

Table 2.

Comparative summary of published studies in which shifts in the rate of diversification within angiosperms have been identified

1Lamiales II corresponds roughly to Lamiidae in APG IV, 2016.

Phylogenetic placement of radiations

The unequal distribution of species richness among lineages in different biological groups has long attracted the interest of evolutionary biologists. Angiosperms are an emblematic example, in which clades exhibit pronounced differences in the number of extant species they contain. This study conclusively confirms previous suggestions (Magallón and Sanderson, 2001; Davies et al., 2004; Tank et al., 2015) that angiosperm megadiversity is not directly associated with the origin of angiosperms as a whole, but rather results from several independent diversification shifts within the clade, and goes further by identifying particular phylogenetic regions in which diversification shifts have taken place, documenting a complex pattern of temporally overlapping radiations and depletions in different lineages that result in the present-day distribution of species richness across angiosperms.

Most core shifts subtend species-rich clades identified as distinct botanical orders and families that, while containing wide disparity in morphology and ecological function, are nevertheless each characterized by a distinctive combination of vegetative attributes and/or reproductive ground plans (e.g. Fabaceae, Asteraceae, Orchidaceae, Poaceae; Table 1). The placement of diversification shifts associated with distinct ground plans suggests that diversification shifts are associated with the evolution of integrated morphological combinations that achieve particular complex functions (O’Meara et al., 2016). Nevertheless, as discussed above, the sparse taxonomic sample in this study, in which massive clades are represented by only a few species, precludes the detection of radiations that might have taken place within those clades.

The finding of shifts on adjacent branches is technically consistent with the fact that shifts identified in different configurations by the CPP in BAMM are not independent of each other, but most likely represent a shift detected with moderate marginal probabilities on adjacent branches (Rabosky, 2017).

Have there been diversification decreases in angiosperm evolution?

The extraordinary present-day diversity of angiosperms makes it easy to overlook that some angiosperm lineages may be in decline. Our results document that, while some lineages within angiosperms represent exceptional evolutionary radiations, others are decreasing. The DTT plots show that lineages within angiosperms underwent independent radiations and depletions at different times, which overlapped and masked each other, suggesting that different lineages were predominant at different times through angiosperm history (Fig. 3).

Our results show pronounced variations in the rates of diversification among angiosperm lineages (Supplementary Data Table S3). Many high-rate clades derive from distinct diversification shifts, and are scattered across the phylogeny (Fig. 2). We observe that lineages with low diversification rates occupy two distinct types of position in the phylogeny. There are low-diversification lineages that form a grade subtending a large clade characterized by high diversification rates. These grades correspond to the ‘depauperons’ discussed by Donoghue and Sanderson (2015). There are also isolated low-diversification branches (or small clades) embedded within a speciose clade characterized by high diversification rate. We hypothesize that low-diversification lineages in these two distinct phylogenetic positions result from differential evolutionary situations, and are characterized by distinct diversification dynamics. The low-diversification grades have been explained as branches that differentiated before the evolution or during the assembly of traits or conditions associated with the increased diversification of the speciose sister clade (Donoghue and Sanderson, 2015). Thus, the rates of low-diversification grades possibly represent retained plesiomorphic conditions. On the other hand, isolated low-diversification branches are probably outcomes of diversification shifts towards decreasing rates that affected that particular branch. In fact, the single decreasing core diversification shift detected in this study (core shift 15; Fig. 2, Table 1) is associated with an isolated low-diversification clade (i.e. Montiniaceae, Sphenocleaceae, Hydroleaceae) within a high-diversification, speciose clade (i.e. Lamiidae). Although only one isolated low-diversification clade was identified as resulting from a core shift, many others can be identified in the phylorate plot (e.g. Plocospermataceae, Roridulaceae, Barbeyaceae-Dirachmaceae, Goupiaceae; Fig. 2). Interestingly, several of these low-diversification lineages were detected as significant shifts in our alternative MEDUSA analysis (Supplementary Data Material S1, Figure S3).

The relatively few detected diversification decreases probably do not reflect the paucity of lineages in decline, but rather, as the natural ultimate outcome of decreasing diversification is extinction, lineages undergoing an evolutionary depletion persist for a short time. Detection of recent diversification decreases is more likely, appearing as depauperate lineages on their way to extinction. Plocospermataceae, the sister group to the remainder of Lamiales, is a possible example (Fig. 2). Lineages that underwent an ancient diversification decrease but survive to the present are unexpected and difficult to explain (Strathmann and Slatkin, 1983; Magallón and Sanderson, 2001). These lineages might be in decline from former megadiversity and ultimate demise is taking longer, or they might have recovered after a drastic decrease. The clade containing Monitiniaceae, Hydroleaceae and Sphenocleaceae (Fig. 2, shift 15) is a possible example.

Are angiosperms in decline?

Previous work (Vamosi and Vamosi, 2011) has suggested that extrinsic factors can set limits to angiosperm diversification. The long-term diversification trajectory of angiosperms modelled here includes a stable to slightly increasing trend towards the present. This sustained diversification trajectory indicates that angiosperms as a whole are not undergoing a diversification decline, but rather that species richness will continue to accumulate. The trends of increasing speciation and extinction (Fig. 2) imply a higher species turnover. Although angiosperms are today the most diverse group of plants in terrestrial ecosystems, where they display exceptional morphological, functional and ecological complexity and innovation, the estimated diversification trajectory indicates that angiosperm evolutionary expansion remains unmitigated. The existence of limits to species accumulation in angiosperms as a whole remains to be evaluated, but our results suggest that, if such limits exist, they have not yet been reached. These results are congruent with the finding of a non-equilibrium phase in the acquisition of floral structure diversity associated with increasing diversification rates (O’Meara et al., 2016).

Angiosperm DTT plots were modelled here considering a sample of lineages that diverged tens of million of years ago, and as such cannot predict potential changes in trajectory in response to drastic environmental changes (e.g. climate change) that were not modelled in the simulation. These plots could be complemented with graphs estimated with methods that can directly incorporate the fossil record (Stadler, 2011), or information about relevant palaeoenvironmental variables, such as temperature (Condamine et al., 2013; Sauquet and Magallón, 2018).

Are there phylogenetic regions or times in which diversification increases or decreases are concentrated?

The origin of angiosperm megadiversity cannot be traced to a few events or to a restricted time interval, but rather results from many independent diversification shifts through most of its evolutionary history and across its phylogenetic spectrum; a concentration of diversification shifts in response to major global events such as the K/T mass extinction of the end-Eocene cooling event is not observed. While floristic shifts are detected in local palaeofloras (Nichols and Johnson, 2008; Barreda et al., 2012), the absence of a distinct concentration of diversification shifts, or marked changes in direction in DTT plots around any particular time suggests that responses in angiosperm composition to global events most likely took place at lower phylogenetic scales. This question needs to be investigated with models designed to detect shifts in diversification through time, whether based on phylogenetic information (e.g. Rabosky, 2006; Stadler, 2011; Morlon et al., 2011) or fossil data (Silvestro et al., 2014).

CONCLUSIONS

In this study, we detected diversification shifts at a deep macroevolutionary level. The identified diversification shifts represent clues to recognizing possible causes and ultimate drivers of megadiversity, which likely combined intrinsic attributes, ecological functions and abiotic conditions. Most variables that have been postulated as drivers of angiosperm diversification are complex structures and functions resulting from additive integration of simpler attributes through the phylogenetic history of different lineages (O’Meara et al., 2016). The clades here detected as resulting from diversification increases lack shared potential key attributes that could commonly underlie their respective diversifications. Furthermore, each of these clades includes many intrinsic attributes deployed in a great variety of extrinsic conditions that could potentially be related to increased diversification (Sauquet and Magallón, 2018). While this study is not intended to recognize the causal factors underlying angiosperm diversification, it provides a critical framework (Uyeda et al., 2018) by pointing to particular phylogenetic regions to investigate for diversification associated with intrinsic or extrinsic factors (Bouchenak-Khelladi et al., 2015). A further improved understanding of angiosperm radiations, and of the causes that drive them, should necessarily be based on a more precise phylogenetic location of incremental and decremental diversification shifts, derived from a much denser taxonomic sampling, namely species-level phylogenies and, ideally, direct integration of fossil information.

SUPPLEMENTARY DATA

Supplementary data are available online at https://academic.oup.com/aob and consist of the following. Figure S1: phylorate plots of mean diversification rate. Phylorate plots showing the mean rate of diversification for each time interval (3 Ma) in each branch, averaged across the configurations in the posterior distribution resulting from diversification analyses conducted with a different magnitude for the prior of expected number of shifts: A = 0.1; B = 1; C = 5; D = 10; E = 50; F = 100. Figure S2: distribution of shift marginal odds ratio (MOR). Distribution of MOR of shifts detected in diversification analyses conducted with different prior magnitudes for expected number of shifts: A = 0.1; B = 1; C = 5; D = 10; E = 50; F = 100. The magnitude of MORs varies among analyses, but in all cases their distribution corresponds to a hollow curve in which few shifts have high MORs and most have low MORs. Figure S3: diversification shifts identified with MEDUSA. Diversification shifts identified with MEDUSA on the angiosperm phylogenetic tree. The terminals in the complete time-calibrated phylogenetic tree were collapsed to include a single placeholder per family (or a clade with two or more families). Numbers on branches correspond to shifts listed in the table in Supplementary Materials S1. Table S1: species list and GenBank accession numbers. Table S2: sampling fraction associated with each terminal. Table S3: thirty core angiosperm rate shifts, including shifts in adjacent branches in a distinct phylogenetic region. Table S4: thirty core angiosperm rate shifts, including shifts in adjacent branches in a distinct phylogenetic region. Table S5: values of the MOR and associated parameters. Material S1: diversification analysis with MEDUSA.

ACKNOWLEDGEMENTS

We thank H. Sauquet, A. Benítez-Villaseñor, R. Hernández-Gutiérrez, A. López-Martínez and S. Ramírez-Barahona for comments, and G. Ortega-Leite, A. Wong-León, J. C. Montero-Rojas, D. Martínez-Almaguer and A. Luna for technical assistance. L.L.S.R. thanks the Consejo Nacional de Ciencia y Tecnología México for a scholarship (CONACyT 262540) and the Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México (UNAM) for support. S.L.G.A. thanks the Dirección General de Asuntos del Personal Académico, UNAM, for postdoctoral funding. S.M. thanks PASPA-DGAPA, UNAM and CONACYT for support.

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