Geophytism in Monocots Leads to Higher Rates of Diversification

Research

Geophytism in monocots leads to higher rates of diversiﬁcation

Cody Coyotee Howard1,2 , Jacob B. Landis3 , Jeremy M. Beaulieu4 and Nico Cellinese1,5,6 1Florida Museum of Natural History, University of Florida, Gainesville, FL 32611, USA; 2Department of Biology, University of Florida, Gainesville, FL 32611, USA; 3University of California, Riverside, Riverside, CA 92521, USA; 4Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72731, USA; 5Biodiversity Institute, University of Florida, Gainesville, FL 32611, USA; 6Genetics Institute, University of Florida, Gainesville, FL 32611, USA

Summary Author for correspondence: Geophytes, plants with buds on underground structures, are found throughout the plant Cody Coyotee Howard tree of life. These below ground structures allow plants to inhabit highly seasonal and distur- Tel: +1 352 273 1823 bance-prone environments across ecosystems. Past researchers have hypothesised that the Email: cchoward@ufl.edu bulbous, cormous and tuberous habits promote diversification, but this had yet to be tested. Received: 24 May 2019 Using a comprehensive monocot data set of almost 13 000 taxa, we investigated the effects Accepted: 21 August 2019 of the geophytic habit on diversification using both state-dependent and state-independent models. New Phytologist (2019) We found that geophytes exhibit increased rates of diversification relative to nongeophytes. doi: 10.1111/nph.16155 State-dependent analyses recovered higher yet similar rates of diversification for bulbous, cormous and tuberous taxa compared with rhizomatous and nongeophytic taxa. However, the state-independent model returned no difference in rates among the different traits. Key words: belowground bud banks, HISSE, MUSSE, state-dependent diversification, Geophytism shows higher rates of diversification relative to nongeophytes but we found lit- underground storage organs. tle support for the hypothesis that the evolution of the bulb, corm or tuber appears to provide a diversification increase relative to rhizomatous and nongeophytic taxa. Our broad-scale analysis highlights the overall evolutionary importance of the geophytic habit (i.e. belowground bud placement). However, our results also suggest that belowground morphological diversity alone cannot explain this rate increase. In order to further test the evolutionary significance of these underground structures, future studies should consider these in combination with other biotic and abiotic factors.

Geophytes, plants with renewal buds typically located on Introduction underground structures such as a bulb, corm, tuber and/or rhi- Diversification shifts are often correlated with the evolution of zome (Raunkiaer, 1934), are large components of several ecosys- certain traits and/or the colonisation of new habitats. Examples tems (Rundel, 1996; Hoffmann et al., 1998; Parsons & Hopper, include pollinator shifts, fruit types and elevational changes in 2003; Cuellar-Martınez & Sosa, 2016), particularly those which the Andean bellflowers (Lagomarsino et al., 2016), the switch to experience a cyclic seasonal climate (e.g. wet and dry seasons) or a climbing growth form in palms (Couvreur et al., 2014), and the disturbance regime (e.g. fire), both of which are hypothesised evolution of the CAM photosynthetic pathway in Euphorbia, evolutionary drivers of geophytism (Rees, 1989; Prochesß et al., which allowed their growth and diversification in arid environ- 2006; Pausas et al., 2018; Howard et al., 2019). In addition to ments (Horn et al., 2014). In many of these cases, a key innova- belowground bud placement, many geophytes are also able to tion is hypothesised as having allowed greater survival and sequester nutrients (e.g. carbohydrates) and water in sometimes reproductive success in a new ecological niche, which subse- large, belowground structures (e.g. storage roots of yam, onion quently led to an increase in overall diversification. The use of bulbs) for use during resource limitation, dormancy and/or to large-scale phylogenies in studies of key innovations provide a fuel rapid growth once conditions are favorable (Dafni et al., critical framework and a necessary broad-eyed view to identify 1981a,b; Al-Tardeh et al., 2008; Vesely et al., 2011). These nutri- putative shifts in niche space and/or character states at a ent stores allow many geophytic taxa to be resource independent macroevolutionary scale, and foster hypotheses on how these from their environment, which has resulted in some taxa being innovations relate to changes in total diversification or diversifica- able to undergo cell division of the apical meristem during dor- tion rates (Rabosky, 2017). Therefore, large-scale analyses mancy (Grime & Mowforth, 1982; Vesely et al., 2011, 2013). provide the invaluable scaffolding to guide and inform subse- These predivided cells are then poised for rapid expansion in quent, more focussed studies, and clarify how presumed large- response to favourable conditions, which result in maximum cap- scale processes may have affected the evolution of lineages in ture of resources such as light, water and nutrients during the more specific taxa and/or geographic areas. short growing seasons that many taxa experience (Patterson &

Ó 2019 The Authors New Phytologist (2019) 1 New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com New 2 Research Phytologist

Givnish, 2002; Vesely et al., 2013). Recent work has shown that For this study, we focussed on the monocots, a group rich in the geophytic habit allows for the occupation of seasonal, drier, geophytic taxa (Rundel, 1996; Prochesß et al., 2006; Cuellar- cooler climates relative to many nongeophytes (Evans et al., Martınez & Sosa, 2016; Givnish et al., 2018; Howard et al., 2014; Sosa et al., 2016; Howard et al., 2019), but many nongeo- 2019) as well as other growth forms such as trees, vines, succu- phytic growth forms such as annuals, trees and epiphytes are lents and annuals, all of which are differentially found across the commonly found in sympatry with geophytes. As of now, it phylogeny. Classic monocotyledonous clades containing geo- remains to be tested if the geophytic habit leads to higher diversi- phytes include the Liliales (e.g. lilies, colchicum, alstroemeria) fication relative to a nongeophytic one. and Asparagales (e.g. iris, hyacinths, amaryllids) (Patterson & Improvement in our phylogenetic understanding of many geo- Givnish, 2002; Zomlefer et al., 2003; Prochesß et al., 2006; Wil- phytic lineages has revealed several relevant evolutionary patterns son, 2006). Although not typically thought of as geophytic, many related to their belowground structures. Firstly, rhizomatous taxa tropical Zingiberales also possess a suite of underground struc- are regularly recovered as sister to those with different under- tures like rhizomes, corms and storage roots (Kubitzki, 1998; ground organs, for example the bulbous Liliaceae (Liliales) sister Chomicki, 2013), which could be utilised in times of resource to a rhizomatous Clintonia-Medeola clade (Patterson & Givnish, limitation and/or forced dormancy (e.g. when cultivated in more 2002), the bulbous Iris (Asparagales) subclades inclusive of a seasonal climates). Also not commonly thought of as geophytic, larger rhizomatous clade (Wilson, 2006), and tuberous Claytonia several Poales taxa have bulbs (e.g. Poa bulbosa), corms (e.g. (Caryophyllales) sister to rhizomatous taxa (Stoughton et al., Eleocharis dulcis) and rhizomes (e.g. Typha angustifolia), all of 2018). This repeated pattern has led researchers to hypothesise which could be used to withstand environmental adversity that an ancestral rhizomatous growth habit led to the evolution (Burns, 1946; Kausch et al., 1981; Cabi et al., 2016). Interest- of other underground traits (Patterson & Givnish, 2002; Prochesß ingly, this morphological diversity is accomplished by modifica- et al., 2006; Wilson, 2006; Givnish et al., 2018). These morpho- tions to the shoot system (the exception being swollen roots). logical shifts are attributed to shared characteristics (e.g. clasping Rhizomes are horizontally elongated stems, whereas bulbs, corms leaf bases in the monocots) that ease the transition from one to and tubers are typically compressed vertically (Rees, 1972; De the other (Givnish et al., 2018). Interestingly, we see far fewer Hertogh & Nard, 1993; Kamenetsky & Okubo, 2012). All struc- examples of the reverse trends (e.g. bulbs to rhizomes) (Patterson tures have their buds protected by layers of overlapping leaves; & Givnish, 2002; Howard et al., 2019), which suggests that mor- however, this is most pronounced in bulbs where the apical bud phological, anatomical or genetic barriers that regulate trait evo- is surrounded by swollen leaf bases and/or bulb scales (Rees, lution and reversal may exist. An additional observation is that 1972). Many studies have hypothesised the rhizome as the ances- many of these rhizomatous lineages are less speciose compared tral state of many geophytic groups with compounding modifica- with their sister clades (Prochesß et al., 2006). This latter observa- tions to the shoot system leading to morphological and ecological tion led to the hypothesis that these derived characters (i.e. bulb, diversification (Patterson & Givnish, 2002; Wilson, 2006; corm, tuber) provide an adaptive advantage and play a key role in Howard et al., 2019). However, the relationship between these promoting the diversification of these lineages (Patterson & morphologies and diversification have yet to be explicitly tested. Givnish, 2002; Prochesß et al., 2006). This hypothesis has been The numbers of studies using state-dependent speciation and further supported by evidence showing that rhizomatous lineages extinction (SSE) models have been rapidly climbing as researchers tend to inhabit more closed canopy, warmer, wetter environ- attempt to better understand drivers of diversification in various ments (i.e. the ancestral habitat) relative to their more derived sis- clades of interest (Maddison et al., 2007; Beaulieu & O’Meara, ter lineages, many of which inhabit more extreme landscapes 2016; Rabosky & Goldberg, 2017; Caetano et al., 2018). Despite (e.g. bulbs in open, more xeric or cold habitats; Kamenetsky, concerns associated with some diversification analyses (e.g. Binary 1996; Patterson & Givnish, 2002; Howard et al., 2019). How- State Speciation and Extinction (BiSSE); Rabosky & Goldberg, ever, comparative diversification analyses of geophytes with dif- 2015), it is important to continue broad-scale investigations in ferent morphologies have yet to be carried out. So far, most order to generate hypotheses on general patterns of diversity (Smith studies have investigated the evolution of geophytes overall (i.e. et al., 2011; Beaulieu & O’Meara, 2018, 2019; Folk et al., 2018; grouped together (Evans et al., 2014; Cuellar-Martınez & Sosa, Landis et al., 2018), although with a critical eye and thorough 2016)); few have attempted to answer evolutionary questions model comparisons (Rabosky & Goldberg, 2015; Beaulieu & related to the diversity of their underground growth forms (Pate O’Meara, 2016; Cooper et al., 2016; Caetano et al., 2018). Large & Dixon, 1982; Rundel, 1996; Hoffmann et al., 1998; Patterson phylogenetic syntheses can lead to a broader understanding of evo- & Givnish, 2002). Furthermore, most studies have focused on lutionary phenomena as well as highlight smaller clades and/or Mediterranean climates due to the diversity of geophytic taxa in hypotheses that warrant further investigation (Beaulieu & these ecosystems (Pate & Dixon, 1982; Rundel, 1996; Hoffmann O’Meara, 2018; Folk et al., 2018). For example, a large-scale phylo- et al., 1998; Parsons & Hopper, 2003; Prochesß et al., 2006), genetic analysis of the angiosperms found that taxa are likely to pos- despite geophytes occurring in regions with contrasting evolu- sess traits that allowed the occupation of cold climates before the tionary histories (Evans et al., 2014; Cuellar-Martınez & Sosa, onset of freezing temperatures, suggesting that phenotype may be 2016; Sosa et al., 2016; Sosa & Loera, 2017). Therefore, deeper more constrained than ecological niche (Zanne et al., 2014, 2015; investigations on the different geophytic structures are needed in Edwards et al., 2015). Analysing over 1500 Saxifragales taxa, Folk order to fully understand their evolutionary implications. et al. (2019) found an uncoupling of diversification from ecological

New Phytologist (2019) Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust New Phytologist Research 3 and phenotypic evolution and a lack of density-dependent evolu- ‘force.ultrametric’ function in the R package PHYTOOLS v.0.6.44 tionary patterns that may be characteristic of groups distributed pri- (Revell, 2012) and rewritten in newick format with rounding marily outside the tropics. In the monocots, geophytic traits have branch lengths after 20 digits. In order to investigate the effects evolved multiple, independent times and the individual traits (i.e. of geophytic traits on diversification, we used a state-dependent bulb, corm, tuber and rhizome) are not found evenly across the phy- method that incorporates a hidden state (i.e. HISSE), which allows logeny. Therefore, a large-scale phylogenetic analysis of geophytic us to investigate hypotheses related to both the effects of the monocots increases our power to uncover relevant evolutionary pat- observed traits as well as incorporate unmeasured factor(s) terns that can then guide future hypotheses testing at different geo- (Beaulieu & O’Meara, 2016; Caetano et al., 2018). Incorporat- graphical and phylogenetic scales. ing hidden states improves model adequacy in both state-depen- Here, we deployed a number of available methods to investigate dent diversification and biogeographical models (Beaulieu & the diversification of monocotyledonous geophytes in order to: (1) O’Meara, 2016; Caetano et al., 2018). Many of the geophytic understand whether geophytes have greater diversification rates traits (i.e. bulb, corm, tuber) exhibit high phylogenetic signal when compared with nongeophytes as geophytes can inhabit more (Howard et al., 2019), and since closely related taxa share similar variable and sometimes harsh environments due to belowground traits, higher rates of diversification may be due to other factors bud placement and/or underground resource allocation; and (2) not captured in our study (Maddison & FitzJohn, 2015; Rabosky test whether taxa with derived underground traits (i.e. bulb, corm & Goldberg, 2015; Beaulieu & O’Meara, 2016). and tuber) showed greater diversification rates due to a potential Character states were scored for presence (geophyte) or absence adaptive advantage compared with rhizomatous and nongeophytic (nongeophyte) of a geophytic structure (i.e. bulb, corm, rhizome taxa. We predicted that geophytes will show higher diversification or tuber). Scoring resulted in 6173 accessions of geophytes and rates compared with nongeophytes. Furthermore, we expected 6606 nongeophytes. Even though BiSSE models have been taxa with bulbs, corms and tubers to exhibit higher rates of diversi- widely used in the past to test the impacts of a trait on diversifica- fication relative to rhizomatous and nongeophytic taxa. tion, Rabosky & Goldberg (2015) demonstrated that neutral traits often are inferred to have a significant association with speciation rates. As pointed out by Beaulieu & O’Meara (2016), Materials and Methods however, this particular issue does not represent a case of ‘false positives’, but, rather, a problem of not properly accounting for Phylogenetic reconstruction and character coding rate heterogeneity when the diversification rates are not correlated We employed the 12 779-tipped monocot phylogeny built by with a trait of interest (i.e. the problem of rejecting model A, for Howard et al. (2019) as well as their character coding scheme. model B, when in fact model C that was not tested is true; also Briefly, sequences for seven gene regions (5S, 18S, atpB, matK, see Caetano et al., 2018). Therefore, both the HISSE model v.1.8.5 rbcL, trnL-trnF and ITS) were downloaded and aligned using the (Hidden State Speciation and Extinction; Beaulieu & O’Meara, PHLAWD pipeline (Smith et al., 2009). After manually adjust- 2016) and the nonparametric FiSSE model (Fast, intuitive SSE ing the alignment (i.e. removing trnK and taxa with relatively model; Rabosky & Goldberg, 2017) were used, the latter of long branches), phylogenetic reconstruction was performed using which served as a measure of robustness of our results. The HISSE the maximum-likelihood method as implemented in RAXML approach included a sampling frequency, f, (FitzJohn et al., v.8.0 (Stamatakis, 2014). Age estimates were obtained by assign- 2009) of 12 779 monocot taxa out of the c. 67 940, which was ing 15 fossil calibrations and one secondary calibration and using estimated previously from the Plant List database (Landis et al., penalised likelihood as implemented in TREEPL (Smith & 2018). In total, 25 models were tested in HISSE: four trait-inde- O’Meara, 2012). As per character assignment, Howard et al. pendent models, three BiSSE models, the full HISSE model allow- (2019) used a broad-scale morphological approach due to the ing all states to vary independently, and 17 models assuming a inconsistency among studies regarding which trait a taxon pos- hidden state associated with both observed character states with a sesses. As such, using available resources, taxa were coded as hav- variety of constrained values for turnover, extinction, and transi- ing a bulb, corm, tuber (this included stem or root tissue since tion rates (Supporting Information Table S1). For a detailed determining the developmental origins of the tuber was not often explanation of these models as well as their relationship to one clearly stated) or rhizome (this included both thick and thin another, we direct readers to Beaulieu & O’Meara (2016). To see structures). For more in-depth descriptions of these traits, we whether similar patterns are found using a smaller proportion of direct readers to Rees (1972), De Hertogh & Nard (1993) and the phylogeny, we randomly dropped terminals from the phy- Kamenetsky & Okubo (2012). If a taxon was not associated with logeny. This resulted in four different phylogenies: 50%, 65%, one of the four geophytic traits, they were coded as a nongeo- 75% and 85%. For each phylogeny, we maintained the same pro- phyte (Howard et al., 2019). portion of traits across all data sets. For example, the ‘50% phylogeny’ contained half of the original number of tips (i.e. 6389) and half of the taxa with bulbs (i.e. 447). Calculating diversification among geophytes and The resulting HISSE models were then used to calculate the nongeophytes marginal probabilities of possible states for each tip and node (ex- Before downstream analyses, the phylogenetic tree returned by plained in detail in Caetano et al., 2018). The set of reconstruc- treePL was ensured to be ultrametric by using the tions were then model-averaged to examine if the overall

Ó 2019 The Authors New Phytologist (2019) New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com New 4 Research Phytologist

diversiﬁcation rates for geophytes and nongeophytes were differ- in PHYTOOLS v.0.6.60 (Revell, 2012) was used to test for overall ent. This model-averaging analysis incorporates the weight of differences in rates among the character classiﬁcations using 1000 each model in explaining the observed data while jointly estimat- simulations and a Bonferroni correction. These analyses (MISSE ing the ancestral rates and states for each node and tip in the tree and CORHMM ) were executed on the University of Florida’s (Caetano et al., 2018). HiPerGator Computing cluster.

Diversification estimates of different geophyte Reconstructing the evolution of geophytism morphologies The ARD model for discrete categories in the ‘ace’ function of To disentangle the potential impact that the different geophytic APE v.5.1 (Paradis et al., 2004) as implemented in PHYTOOLS structures (i.e. bulb, corm, tuber and rhizome) have on diversifi- v.0.6.44 (Revell, 2012) was used for marginal ancestral state cation rates, MUSSE analyses were performed using the R pack- reconstructions. Stochastic mapping of character states was ini- age DIVERSITREE v.0.9.10 (FitzJohn, 2012) with five possible tially performed in PHYTOOLS v.0.6.44 (Revell, 2012) using the character states, which included bulb (894 taxa), corm (505 taxa), ARD model with 1000 simulations. However, based on the rhizome (3433 taxa), tuber (1342 taxa) and nongeophyte (6606 results from the MUSSE analysis, a second set of stochastic map- taxa). Given no prior expectation that traits had to evolve in a ping analyses were performed utilising the transition rates particular manner, transitions between character states were not observed from the diversification analyses as the Q matrix with constrained. A maximum-likelihood approach was first under- 1000 simulations over the tree. taken by fitting three distinct models with subsequent ANOVA testing: (1) full model allowing all variables to change indepen- Data availability dently; (2) a model constraining the speciation rate (k) values for each state to be equal; and (3) a model constraining each extinc- Phylogeny, HISSE and MUSSE outputs can be found on Dryad tion rate (l) to be equal. Further estimates for the parameters of (doi: 10.5061/dryad.jc77 m48). speciation, extinction, and net diversification rates for each state were obtained in a Bayesian approach by incorporating a MCMC Results analysis with an exponential prior with 8500 generations on the University of California Riverside High-Performance Computing Binary state-dependent analyses biocluster. To test if unmeasured factors were driving the diversification With a binary coding of geophytes as present or absent, for the of the monocots instead of, or in addition to, their underground full phylogeny, the best model of the 25 models tested was the characteristics (i.e. our character coding scheme), we employed full HISSE model with unique speciation, extinction and transition the MISSE (Missing State Speciation and Extinction) function rates between the two character states observed and the hidden found in the R package HISSE v.1.9.1 (Beaulieu & O’Meara, states; this model had a DAIC score of 6138.1, indicating a sub- 2016), which executes a trait-free diversification analysis across a stantial improvement in fit compared with the second best model phylogeny. The estimated proportion of extant species in the (Table S1). Examining differences in model-averaged rate esti- phylogeny (f) was set to 0.18 (12 779 out of 67940 taxa; calcu- mates for each extant species in our tree returned higher mean lated from the Plant List; Landis et al., 2018), the suspected rates speciation, extinction and net diversification rates for geophytes for both turnover and extinction were set to five, and default across all phylogenies with one exception being the 65% phy- options were used for all other parameters. We then modelled logeny (i.e. phylogeny with 65% of taxa randomly samples from bulb, corm, tuber, rhizome and nongeophyte trait evolution the full 12 779-tipped phylogeny), which returned a higher across the phylogeny, independent of the diversification process, extinction rate for nongeophytes relative to geophytes (Tables 1, using the ‘rayDISC’ function found in the R package CORHMM S2). Complementary results from our FISSE analysis reported a v.1.22 (Beaulieu et al., 2013). Specifically, we used the ARD speciation rate of 0.057 and 0.039 for geophytes and nongeo- model (‘all rates different’) with the root probability set to phytes, respectively, which was significantly different (P ≤ 0.044). maddfitz and default settings for all other parameters. The overall fit of this ‘character-independent’ model, which is of equal complexity to our character-dependent MUSSE model, was obtained by summing the likelihoods from the two analyses (i.e. MISSE Table 1 Mean rate values from the model-averaged marginal ancestral state reconstruction from HISSE for geophytes and nongeophytes using the and CORHMM ). The final AIC of this character-independent full phylogeny. model of diversification (which had 31 free parameters: 11 from MISSE and 20 from CORHMM) was then compared with the Trait Speciation Extinction Net diversification AIC of the state-dependent MUSSE analysis. Lastly, the resulting MISSE model fits were used to reconstruct the marginal probabil- Geophyte 0.07608 0.03029 0.04579 Nongeophyte 0.04369 0.00881 0.03488 ities of the tip and node states. The set of MISSE reconstructions were then model-averaged to examine the overall diversification See Supporting Information Table S1 for the values returned in the rates for the five traits, and a phylogenetic ANOVA implemented subsampled phylogenies.

New Phytologist (2019) Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust New Phytologist Research 5

was better than the AIC returned from MUSSE (i.e. Diversification of the different underground organs AIC = 118 642). Therefore, the character-independent model is In our MUSSE state-dependent analysis, ANOVA calculations favored with regards to model fit, and the model-averaged tip preferred a full model compared with constraining either specia- rates from these models returned similar rates for all trait cate- tion rates (AIC = 118 642 compared with 119 073) and extinc- gories (Fig. 1d–f; Table 2). Although geophytes had higher aver- tion rates (AIC = 118 642 compared with 118 941). The full age rates compared with nongeophytes (Table 2), no significant model was then used as the starting point for a MCMC run of differences were found between the tips rates returned from the 8500 generations to more clearly estimate speciation, extinction MISSE analysis (data not shown). and net diversification rates for each character state using a state- dependent model (Fig. 1a–c). From this analysis, we see an over- Character evolution lap in speciation rates for bulbous, cormous and tuberous taxa, with rhizomatous taxa having a substantially higher speciation As the favored model was found to be trait independent, the use rate in comparison, and nongeophytes having a lower speciation of the Markov model reconstructions was acceptable. Ancestral rate compared with all of the geophytes (Fig. 1a). For extinction state reconstructions favoured the rhizomatous state as the ances- rate, only rhizomatous taxa are higher than the remaining charac- tral state of the monocots (Fig. 2a). Additionally, most internal ter states (Fig. 1b). When looking at net diversification rates, we nodes for broad taxonomic groups approximating to the ordinal see that the derived traits have overlapping rates, all of which are level (e.g. Asparagales, Liliales, Poales, etc.) were reconstructed higher than nongeophytes and rhizomatous taxa, the latter of with a similar character state (except Arecales and Pandanales; which had the lowest net diversification rate (Fig. 1c). data not shown but see Figs 2a, S1). There were on average 633 The AIC value returned from the MISSE and CORHMM anal- state changes out of 1000 simulations across the phylogeny. yses (i.e. ‘character-independent’ analyses) was 107 972, which When looking at the time spent in each trait throughout the tree,

(a)Speciation rate (b)Extinction rate (c) Net diversification 500

300 600

400 250 500

200 300 400

150 300

MuSSE 200 Probability density 100 200

100 50 100

0 0 0

0.06 0.08 0.10 0.12 0.14 0.00 0.02 0.04 0.06 0.08 0.10 0.04 0.05 0.06 0.07 0.08 (d) (e) (f) Speciation rate Extinction rate Net diversification 600 1250 15000 400 10000 1000 200 5000

0 0 750 01234 01234 600

500 15000 Density MiSSE 400 250 10000

0 200 5000 0.02 0.04 0.06 Bulb Tuber Corm 0 0 Non-geophyte 0.12 0.14 0.16 0.18 0.20 0.100 0.125 0.150 0.175 0.200 Rhizome

Fig. 1 Marginal distributions (a–c) or density plots (d–f) for speciation, extinction and net diversification rates from MUSSE (a–c) and MISSE (d–f) for taxa with bulbs (pink), corms (purple), rhizomes (green), tubers (yellow) and those characterised as nongeophytes (orange). Plots (a–c) show the MUSSE marginal distributions from the MCMC for each rate category by trait classification. (d–f) Density plots of tip rates for each trait as output from the MISSE analysis. For mean tip rates from the MISSE analysis for each trait see Table 2. Plots from MUSSE were made using the ‘profile.plots’ function in the R package DIVERSITREE v.0.9-10 (FitzJohn, 2012). The MISSE tip rate density plots were made using GGPLOT2 v.3.1.0 (Wickham, 2016), enlarged graphs were displayed using GGFORCE v.0.2.2 (Pedersen, 2016). Note the differences in axes for each graph.

Ó 2019 The Authors New Phytologist (2019) New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com New 6 Research Phytologist

Table 2 Mean tip rate values for speciation, extinction and net or times of resource deficiency, likely contributes to the diversifi- diversification from model-averaging the marginal ancestral state cation of geophytes (Prochesß et al., 2005, 2006; Ranwala & reconstructions in MISSE (found in HISSE). Miller, 2008). Attached to these belowground, resource-rich Trait Speciation Extinction Net diversification structures, the meristematic buds used for regrowth are buffered by the soil from aboveground disturbances (Tyler & Borchert, Bulb 0.34216 0.30828 0.03388 2003; Pausas et al., 2018). Conceptually, these attributes allow Corm 0.27219 0.23860 0.03359 geophytic taxa to be decoupled from the influences of the sur- Rhizome 0.34288 0.31000 0.03288 Tuber 0.28702 0.25267 0.03435 rounding topside environment during times of hardships (i.e. Nongeophyte 0.26860 0.23483 0.03277 they are like living bomb shelters). Therefore, taxa are able to occupy climatically seasonal, fire-prone habitats (Pate & Dixon, We found no significant difference between speciation, extinction and net 1982; Hoffmann et al., 1998; Prochesß et al., 2005; Evans et al., diversification for each of the traits using a phylogenetic ANOVA (data not 2014; Cuellar-Martınez & Sosa, 2016; Sosa et al., 2016; Pausas shown). et al., 2018), which are niches largely unsuitable for a number of aboveground-dwelling, monocotyledonous lineages (Zanne et al., the majority (59.1%) was spent in the nongeophyte category fol- 2014; Givnish et al., 2018). Other potential considerations, many lowed by rhizomes (23.3%). The remaining categories were more of which have been shown or hypothesised to influence geophyte uncommon with 5.4% for bulbs, 3.2% for corms, and 9% for evolution, include fire regimes (Tyler & Borchert, 2003; Prochesß tubers. Fig. 2(b) shows the transition rates observed across 1000 et al., 2006; Pausas et al., 2018) as well as edaphic heterogeneity iterations of stochastic mapping, with the highest observed transi- and specialisation (Rundel, 1996; Perret et al., 2003; Prochesß tion being from nongeophytic to rhizomatous, which occurred et al., 2006). The ability of monocotyledonous geophytes to on average 317 times across the tree. The second highest transi- inhabit these heterogeneous and sometimes unpredictable habi- tion rate was from rhizomatous to nongeophytic, which occurred tats may lead to greater competitive capability resulting in higher on average 153 times. Most of the other transitions were found rates of diversification in particular habitats relative to their non- in limited numbers across the tree; however, transitions from geophytic counterparts. tuberous to bulbous (and vice versa) and bulbous to nongeophytic (and vice versa) were not observed (Fig. 2b). Although our phy- Diversity in form, not in diversification logeny represents 18% of known monocot diversity, general patterns (i.e. reduced transitions to/from bulb, corm, tuber) would Although there is evidence for higher diversification rates in geo- likely hold true using a more densely sampled phylogeny as evi- phytes collectively, the different, individual underground traits denced by a recent, smaller phylogenetic study of geophytic appear not to be a significant factor in promoting rate increases monocots that recovered similar trends (Howard et al., 2019). (Fig. 1dÀf; Table 2). Therefore, we found little support for the hypothesis that the bulbous, cormous and tuberous habits led to greater diversification. Without a discernible advantage over one Discussion another, selection for these traits may be more related to shared An interesting consistent finding from our analyses is that mono- morphological and ecological factors since many clades show cotyledonous geophytes exhibit overall higher diversification rates high phylogenetic signal for both (Howard et al., 2019). Certain compared with nongeophytes (Tables 1, S2). The different clades may have been more prone to evolve one trait over another underground traits, though, appear to not be the significant con- in response to historical environmental stimuli such as fire or cli- tributor, suggesting that other influences, possibly climate or fire, matic shifts (Rees, 1989; Pausas et al., 2018), and have simply may be driving these trends (Fig. 1dÀf; Tables 2, S1). The maintained these characters throughout evolution. hypothesis that the bulbous, cormous or tuberous habits led to Despite many of these traits being different modifications of increased diversification is not supported here (Fig. 1dÀf; the shoot system (i.e. rhizome, bulb, corm and stem tuber), the Table 2). These results suggest that geophytism provides an evolution of a derived geophytic habit leads to reduced transi- advantage, regardless of the morphological changes that have tions or reversions out of those states (Fig. 2b), possibly due to occurred. Lastly, we found evidence supporting the hypothesis morphological specialisation and/or niche constraints. This may that the rhizomatous habit has a precursory role in the evolution be why we find no evidence for increased diversification among of other geophytic morphologies (Fig. 2b). bulbous, cormous and tuberous lineages. Ecologically, they appeared to inhabit slightly different niches but, as with diversification, there appeared to be no significant separation (Hoffmann Geophytism, as a whole, leads to higher diversification et al., 1998; Howard et al., 2019). Therefore, we hypothesised Although geophytes show higher rates of diversification, their that the bulbous, cormous and tuberous habits are specialised to particular underground morphologies do not appear to be solely particular niche spaces and as such, their ability to inhabit more responsible. Therefore, geophytism overall may provide a selec- divergent ecological niches as they become available during times tive advantage regardless of how it is accomplished. The ability to of climatic shifts is restricted (Hoffmann et al., 1998; Wiens, sequester sometimes large amounts of nutrients and water into 2004; Howard et al., 2019). These taxa may now be more underground reserves, where they are utilised during dormancy speciose compared with sister rhizomatous taxa due to an

New Phytologist (2019) Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust New Phytologist Research 7

N-GEOPHY NO TE (a) (b)

7.252 1.874 BULB CORM

1.64

29.412 1.026 8.533

317 8.1 24 .036 13.291 152.

613

31 16.187 1. 1.785 032 1.

TUBER RHIZOME

38.722

32.54 Bulb Corm Rhizome Tuber Non-geophyte Fig. 2 Ancestral state reconstructions (a) and transitions between states from 1000 stochastic simulations (b) between bulbs (pink), corms (purple), rhizomes (green), tubers (yellow) and nongeophytes (orange). (a) The ancestral state for monocots was favoured as rhizomatous. (b) Arrows are scaled proportionally to the number of transitions that were recovered as occurring between states; larger arrows indicate a higher number of transitions between states. Individual numbers next to arrows refer to the amount of transitions that were found to have occurred out of 1000 stochastic character simulations. inability to maintain connections between separating populations determining appropriate cutoffs for these requirements. There- during climate shifts, resulting in speciation events (i.e. niche fore, these subjective concepts and inconsistent terminology have conservatism promotes speciation; Wiens, 2004) rather than created incomparable results among studies, due to the ambiguity actual diversification increases promoted by their different under- that plagues geophytic classifications of taxa and their traits. In ground organs. Rhizomatous taxa, conversely, inhabit a wider order to generate a better understanding of geophyte evolution, ecological niche (Howard et al., 2019) and are more capable of an extended development and implementation of geophytic trait moving throughout their environment by horizontal growth, ontologies, which enable users to create definitions with precise, which may provide more opportunity to explore their surround- unambiguous and fully computable semantics, should be ings, maximising resource acquisition, as well as position clonal adopted. Unfortunately, this effort requires a substantial commu- offsets and/or renewal buds in different microenvironments (Bell nity buy-in and is a lengthy endeavor. Therefore, until these & Tomlinson, 1980; Hoffmann et al., 1998; Klimesova et al., resources are fully available, a broad-based morphological defini- 2018). These characteristics could result in weaker selective pres- tion, one that encapsulates traits that allow for a geophytic habit, sure and consequently, lower taxon diversity when compared is still valuable and will foster further comparative studies allow- with a bulbous sister lineage, for example. These factors discussed ing us to obtain a holistic understanding of trait evolution from above may be why we see the repeated pattern of higher species which more focussed efforts can clarify processes and patterns at diversity in derived geophytic lineages (i.e. those with bulbs, smaller evolutionary scales. corms or tubers) relative to rhizomatous sister lineages for some Our broad-scale morphological analysis recovered higher rates groups (e.g. Amaryllidaceae, Liliales). of diversification associated with geophytism, and coding taxa as such was a relatively straightforward undertaking since we simply determined whether a plant had a trait associated with the geo- Diving deeper into geophyte morphology and evolution phytic habit. However, within the geophytes, we used a broad- Varying definitions of what is a geophyte have thwarted thorough scale coding of taxa into four smaller morphological categories investigations into their evolution and traits. For example, some that only imperfectly captures the true diversity of their under- researchers only include taxa that undergo a dormancy period; ground traits since transitional and/or combinatorial growth therefore, they exclude evergreen taxa even though they still pos- forms exist. For example, in Veratrum (Melanthiaceae), rhizomes sess relevant traits (Parsons, 2000; Vesely et al., 2011). Others terminate in a bulb (Zomlefer et al., 2003). Additionally, many require that the underground organ participates in resource stor- rhizomatous taxa possess root tubers (e.g. Curcuma (Zingiber- age (Prochesß et al., 2006) and/or be thick and fleshy (Pate & aceae)), and most bulbs have thickened, contractile roots, all of Dixon, 1982); however, no standard measures exist for which would not have been captured in our coding scheme.

Ó 2019 The Authors New Phytologist (2019) New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com New 8 Research Phytologist

Therefore, studies at smaller phylogenetic scales, with more smaller scales may corroborate or contradict our findings; how- detailed character assignment, may uncover more nuanced, but ever, our broad-scale synthesis of geophytism now acts as a frame- perhaps similar, trait diversification and transitional patterns. work for future studies to test the evolutionary significance of More focused studies will also allow for greater model complexity these often overlooked, underground traits and taxa. and should incorporate additional hypothesised drivers of geophyte evolution in order to understand the relationship between Acknowledgements morphology, ecology and diversification. Along those lines, we found no evidence that the different geo- We would like to thank four anonymous reviewers for their con- phytic structures (i.e. bulb, corm, tuber and rhizome) explained structive comments, Rebecca Stubbs for thoughtful discussions the diversification rate variation estimated for the broad geophyte on project design and analyses, and Zachary Emberts for his com- category. We remain somewhat sceptical of these results. First, ments on an earlier version of this manuscript. we suspect that the lack of rate variation among geophytic structures was partly due to our reliance on MUSSE, which does not Author contributions allow for rate variation differences within each of the five possible states in our model (e.g. bulbs in Asparagaceae would have the CCH and JBL conceived the project, CCH and JBL ran analyses, same rates as bulbs in Liliaceae). There are extensions to MUSSE JMB and NC guided and improved project design and analyses, that allow for within state rate variation, but either the software is all authors contributed to the writing of the manuscript. not freely available and/or difficult to use (i.e. SECSSE; Herrera- Alsina et al., 2018), or the implementation is restricted to testing ORCID a maximum of four different states (i.e. MUHISSE; Nakov et al., 2018). However, for our purposes, we did not want to exclude Cody Coyotee Howard https://orcid.org/0000-0001-7662- nongeophytes from our analysis since they are important for 9102 understanding geophyte evolution from a broader context so we Jacob B. Landis https://orcid.org/0000-0002-5631-5365 did not attempt a four-state MUHISSE analysis. It is possible that our characterisation of the nongeophytic traits could also mask important diversification patterns. For References example, nongeophytic taxa inhabit niches or have traits that have been correlated with increased diversification rates (Cou- Al-Tardeh S, Sawidis T, Diannelidis B-E, Delivopoulos S. 2008. Water content and reserve allocation patterns within the bulb of the perennial geophyte red vreur et al., 2014; Givnish et al., 2014; Horn et al., 2014), and squill (Liliaceae) in relation to the Mediterranean climate. Botany-Botanique 86: some studies have shown that transitions between particular geo- 291–299. phytic and nongeophytic traits have occurred (Evans et al., 2014; Beaulieu JM, O’Meara BC. 2016. Detecting hidden diversification shifts in Sosa et al., 2016). Detailed categorisation of the nongeophytes models of trait-dependent speciation and extinction. Systematic Biology 65: – into their respective traits (e.g. trees, succulents, epiphytes) would 583 601. Beaulieu JM, O’Meara BC. 2018. Can we build it? Yes we can, but should we use highlight fine-scale patterns of diversification and trait transitions it? Assessing the quality and value of a very large phylogeny of campanulid across growth forms, resulting in an improved understanding of angiosperms. American Journal of Botany 105: 417–432. the evolutionary dynamics that exist among the diversity of Beaulieu JM, O’Meara BC. 2019. Diversity and skepticism are vital for growth forms in the monocots. Studying the evolution of geo- comparative biology: a response to Donoghue and Edwards (2019). American – phytism within the context of nongeophytes will allow for a Journal of Botany 106: 613 617. Beaulieu JM, O’Meara BC, Donoghue MJ. 2013. Identifying hidden rate greater understanding of trait evolution and the subsequent evo- changes in the evolution of a binary morphological character: the lutionary effects of these contrasting life history strategies. evolution of plant habit in campanulid angiosperms. Systematic Biology 62: 725–737. Bell AD, Tomlinson PB. 1980. Adaptive architecture in rhizomatous plants. Conclusion Botanical Journal of the Linnean Society 80: 125–160. Burns W. 1946. Corm and bulb formation in plants, with special reference to the Our comprehensive and comparative study of geophytic and Gramineae. Transactions of the Botanical Society of Edinburgh 34: 316. nongeophytic monocots provides evidence for the geophytic Cabi E, Soreng RJ, Gillespie L, Amiri N. 2016. Poa densa (Poaceae), an habit as a means towards greater diversification. Although state- overlooked Turkish steppe grass, and the evolution of bulbs in Poa. dependent analyses provided support for one hypothesis Willdenowia 46: 201–211. (Fig. 1aÀc), the best model was found to be a state-independent Caetano DS, O’Meara BC, Beaulieu JM. 2018. Hidden state models improve À state-dependent diversification approaches, including biogeographical models. model (Fig. 1d f). These contrasting results, therefore, show the Evolution 72: 2308–2324. importance of model comparisons in hypothesis testing. Our Chomicki G. 2013. Analysis of rhizome morphology of the Zingiberales in results suggest that the plethora of underground morphologies Payamino (Ecuador) reveals convergent evolution of two distinct architectural alone do not explain the overall increase in diversification rates in strategies. Acta Botanica Gallica 160: 239–254. geophytes. We hypothesise that the current diversity in derived Cooper N, Thomas GH, FitzJohn RG. 2016. Shedding light on the “dark side” of phylogenetic comparative methods. Methods in Ecology and Evolution 7: geophytic lineages may now act as a constraint on morphological 693–699. and ecological evolution, which may have historically driven spe- Couvreur TLP, Kissling WD, Condamine FL, Svenning J-C, Rowe NP, Baker ciation during times of climatic shifts. Phylogenetic studies at WJ. 2014. Global diversification of a tropical plant growth form:

New Phytologist (2019) Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust New Phytologist Research 9

environmental correlates and historical contingencies in climbing palms. Lagomarsino LP, Condamine FL, Antonelli A, Mulch A, Davis CC. 2016. The Frontiers in Genetics 5: 452. abiotic and biotic drivers of rapid diversification in Andean bellflowers Cuellar-Martınez M, Sosa V. 2016. Diversity patterns of monocotyledonous (Campanulaceae). New Phytologist 210: 1430–1442. geophytes in Mexico. Botanical Sciences 94: 699. Landis JB, Soltis DE, Li Z, Marx HE, Barker MS, Tank DC, Soltis PS. 2018. Dafni A, Cohen D, Noy-Mier I. 1981a. Life-cycle variation in geophytes. Annals Impact of whole-genome duplication events on diversification rates in of the Missouri Botanical Garden 68: 652. angiosperms. American Journal of Botany 105: 348–363. Dafni A, Shmida A, Avishai M. 1981b. Leafless autumnal-flowering geophytes in Maddison WP, FitzJohn RG. 2015. The unsolved challenge to phylogenetic the Mediterranean region—phytogeographical, ecological and evolutionary correlation tests for categorical characters. Systematic Biology 64: 127–136. aspects. Plant Systematics and Evolution 137: 181–193. Maddison WP, Midford PE, Otto SP. 2007. Estimating a binary character’s De Hertogh A, Nard ML. 1993. The physiology of flower bulbs. Amsterdam, the effect on speciation and extinction. Systematic Biology 56: 701–710. Netherlands: Elsevier. Nakov T, Beaulieu JM, Alverson AJ. 2018. Freshwater diatoms diversify faster Edwards EJ, de Vos JM, Donoghue MJ. 2015. Doubtful pathways to cold than marine in both planktonic and benthic habitats. BioRxiv. https://doi.org/ tolerance in plants. Nature 521:E5–E6. 10.1101/406165. Evans M, Aubriot X, Hearn D, Lanciaux M, Lavergne S, Cruaud C, Lowry PP, Paradis E, Claude J, Strimmer K. 2004. APE: analyses of phylogenetics and Haevermans T. 2014. Insights on the evolution of plant succulence from a evolution in R language. Bioinformatics 20: 289–290. remarkable radiation in Madagascar (Euphorbia). Systematic Biology 63:697–711. Parsons RF. 2000. Monocotyledonous geophytes: comparison of California with FitzJohn RG. 2012. Diversitree: comparative phylogenetic analyses of Victoria, Australia. Australian Journal of Botany 48:39–43. diversification in R. Methods in Ecology and Evolution/British Ecological Society Parsons RF, Hopper SD. 2003. Monocotyledonous geophytes: comparison of 3: 1084–1092. south-western Australia with other areas of Mediterranean climate. Australian FitzJohn RG, Maddison WP, Otto SP. 2009. Estimating trait-dependent Journal of Botany 51: 129–133. speciation and extinction rates from incompletely resolved phylogenies. Pate JS, Dixon KW. 1982. Tuberous, cormous and bulbous plants. Biology of an Systematic Biology 58: 595–611. adaptive strategy in Western Australia. Beaverton, OR, USA: University of Folk RA, Stubbs RL, Mort ME, Cellinese N, Allen JM, Soltis PS, Soltis DE, Western Australia Press. Guralnick RP. 2019. Rates of niche and phenotype evolution lag behind Patterson TB, Givnish TJ. 2002. Phylogeny, concerted convergence, and diversification in a temperate radiation. Proceedings of the National Academy of phylogenetic niche conservatism in the core Liliales: insights from rbc L and Sciences, USA 116: 10874–10882. ndh F sequence data. Evolution 56: 233–252. Folk RA, Sun M, Soltis PS, Smith SA, Soltis DE, Guralnick RP. 2018. Pausas JG, Lamont BB, Paula S, Appezzato-da-Gloria B, Fidelis A. 2018. Challenges of comprehensive taxon sampling in comparative biology: wrestling Unearthing belowground bud banks in fire-prone ecosystems. New Phytologist with rosids. American Journal of Botany 105: 433–445. 217: 1435–1448. Givnish TJ, Barfuss MHJ, Van Ee B, Riina R, Schulte K, Horres R, Gonsiska Pedersen TL. 2016. ggforce: Accelerating ‘ggplot20’. R package v.0.1.2. [WWW PA, Jabaily RS, Crayn DM, Smith JAC et al. 2014. Adaptive radiation, document] URL https://ggforce.data-imaginist.com/. correlated and contingent evolution, and net species diversification in Perret M, Chautems A, Spichiger R, Kite G, Savolainen V. 2003. Systematics Bromeliaceae. Molecular Phylogenetics and Evolution 71:55–78. and evolution of tribe Sinningieae (Gesneriaceae): evidence from phylogenetic Givnish TJ, Zuluaga A, Spalink D, Soto Gomez M, Lam VKY, Saarela JM, Sass analyses of six plastid DNA regions and nuclear ncpGS. American Journal of C, Iles WJD, de Sousa DJL, Leebens-Mack J et al. 2018. Monocot plastid Botany 90: 445–460. phylogenomics, timeline, net rates of species diversification, the power of Prochesß Sß, Cowling RM, du Preez D. 2005. Patterns of geophyte diversity and multi-gene analyses, and a functional model for the origin of monocots. storage organ size in the winter-rainfall region of southern Africa. Diversity and American Journal of Botany 105: 1888–1910. Distributions 11: 101–109. Grime JP, Mowforth MA. 1982. Variation in genome size—an ecological Prochesß Sß, Cowling RM, Goldblatt P, Manning JC, Snijman DA. 2006. An interpretation. Nature 299: 151–153. overview of the Cape geophytes. Biological Journal of the Linnean Society 87: Herrera-Alsina L, van Els P, Etienne RS. 2018. Detecting the dependence of 27–43. diversification on multiple traits from phylogenetic trees and trait data. Rabosky DL. 2017. Phylogenetic tests for evolutionary innovation: the Systematic Biology 68: 317–328. problematic link between key innovations and exceptional diversification. Hoffmann AJ, Liberona F, Hoffmann AE. 1998. Distribution and ecology of Philosophical Transactions of the Royal Society of London. Series B: Biological geophytes in Chile. Conservation threats to geophytes in Mediterranean-type Sciences 372: 20160417. regions. In: Rundel PW, Montenegro G, Jaksic FM, eds. Ecological studies. Rabosky DL, Goldberg EE. 2015. Model inadequacy and mistaken inferences of Landscape disturbance and biodiversity in Mediterranean-type ecosystems. Berlin/ trait-dependent speciation. Systematic Biology 64: 340–355. Heidelberg, Germany: Springer Berlin Heidelberg, 231–253. Rabosky DL, Goldberg EE. 2017. FiSSE: a simple nonparametric test for the Horn JW, Xi Z, Riina R, Peirson JA, Yang Y, Dorsey BL, Berry PE, Davis CC, effects of a binary character on lineage diversification rates. Evolution 71: 1432– Wurdack KJ. 2014. Evolutionary bursts in Euphorbia (Euphorbiaceae) are 1442. linked with photosynthetic pathway. Evolution 68: 3485–3504. Ranwala AP, Miller WB. 2008. Analysis of nonstructural carbohydrates in Howard CC, Folk RA, Beaulieu JM, Cellinese N. 2019. The monocotyledonous storage organs of 30 ornamental geophytes by high-performance anion- underground: global climatic and phylogenetic patterns of geophyte diversity. exchange chromatography with pulsed amperometric detection. New Phytologist American Journal of Botany 106: 850–863. 180: 421–433. Kamenetsky R. 1996. Life cycle and morphological features of Allium L. species Raunkiaer C. 1934. The life forms of plants and statistical plant geography; being the in connection with geographical distribution. Bocconea 5: 251–257. collected papers of C. Raunkiaer. Oxford, UK: Clarendon Press. Kamenetsky R, Okubo H, eds. 2012. Ornamental geophytes: from basic science to Rees AR. 1972. The Growth of Bulbs: applied aspects of the physiology of ornamental sustainable production. Boca Raton, FL, USA: CRC Press. bulbous crop plant. New York, NY, USA: Academic Press. Kausch AP, Seago JL, Marsh LC. 1981. Changes in starch distribution in the Rees AR. 1989. Evolution of the geophytic habit and its physiological advantages. overwintering organs of Typha latifolia (Typhaceae). American Journal of Herbertia 45: 104–110. Botany 68: 877–880. Revell LJ. 2012. phytools: an R package for phylogenetic comparative biology Klimesova J, Martınkova J, Herben T. 2018. Horizontal growth: an overlooked (and other things). Methods in Ecology and Evolution 3: 217–223. dimension in plant trait space. Perspectives in Plant Ecology, Evolution and Rundel PW. 1996. Monocotyledonous geophytes in the California flora. Systematics 32:18–21. Madrono~ 43: 355–368. Kubitzki K. 1998. The families and genera of vascular plants. 4. Flowering plants- Smith SA, Beaulieu JM, Donoghue MJ. 2009. Mega-phylogeny approach for Monocotyledons. Alismatanae and Commelinanae (except Gramineae). Berlin, comparative biology: an alternative to supertree and supermatrix approaches. Germany: Springer-Verlag. BMC Evolutionary Biology 9: 37.

Ó 2019 The Authors New Phytologist (2019) New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com New 10 Research Phytologist

Smith SA, Beaulieu JM, Stamatakis A, Donoghue MJ. 2011. Understanding Zanne AE, Tank DC, Cornwell WK, Eastman JM, Smith SA, FitzJohn RG, angiosperm diversification using small and large phylogenetic trees. American McGlinn DJ, O’Meara BC, Moles AT, Reich PB et al. 2014. Three keys to Journal of Botany 98: 404–414. the radiation of angiosperms into freezing environments. Nature 506:89–92. Smith SA, O’Meara BC. 2012. treePL: divergence time estimation using Zanne AE, Tank DC, Cornwell WK, Eastman JM, Smith SA, FitzJohn RG, penalized likelihood for large phylogenies. Bioinformatics 28:2689– McGlinn DJ, O’Meara BC, Moles AT, Reich PB et al. 2015. Zanne et al. 2690. reply. Nature 521:E6–E7. Sosa V, Cameron KM, Angulo DF, Hernandez-Hernandez T. 2016. Life form Zomlefer WB, Whitten WM, Williams NH, Judd WS. 2003. An overview of evolution in epidendroid orchids: ecological consequences of the shift from Veratrum sl (Liliales: Melanthiaceae) and an infrageneric phylogeny based on epiphytism to terrestrial habit in Hexalectris. Taxon 65: 235–248. ITS sequence data. Systematic Botany 28: 250–269. Sosa V, Loera I. 2017. Influence of current climate, historical climate stability and topography on species richness and endemism in Mesoamerican geophyte plants. Peer J 5: e3932. Supporting Information Stamatakis A. 2014. RAxML version 8: a tool for phylogenetic analysis and post- analysis of large phylogenies. Bioinformatics 30: 1312–1313. Additional Supporting Information may be found online in the Stoughton TR, Kriebel R, Jolles DD, O’Quinn RL. 2018. Next-generation Supporting Information section at the end of the article. lineage discovery: a case study of tuberous Claytonia L. American Journal of Fig. S1 Ancestral state reconstructions of bulb, corm, tuber, rhi- – Botany 105: 536 548. zome and nongeophytes. Tyler C, Borchert M. 2003. Reproduction and growth of the chaparral geophyte, Zigadenus fremontii (Liliaceae), in relation to fire. Plant Ecology 165:11–20. Table S1 Results from the HISSE model selection showing log Vesely P, BuresP,Smarda P. 2013. Nutrient reserves may allow for genome size likelihood values and AIC scores for each model tested. increase: evidence from comparison of geophytes and their sister non-geophytic relatives. Annals of Botany 112: 1193–1200. Table S2 Mean rate values from the model-averaged marginal Vesely P, BuresP,Smarda P, Pavlıcek T. 2011. Genome size and DNA base ancestral state reconstructions for each subsampled phylogenetic composition of geophytes: the mirror of phenology and ecology? Annals of Botany 109:65–75. data set. Wickham H. 2016. ggplot2: elegant graphics for data analysis. Amsterdam, the Netherlands: Springer. Wiens JJ. 2004. Speciation and ecology revisited: phylogenetic niche – Please note: Wiley Blackwell are not responsible for the content conservatism and the origin of species. Evolution 58: 193 197. or functionality of any Supporting Information supplied by the Wilson CA. 2006. Patterns in evolution in characters that define iris subgenera and sections. Aliso: A Journal of Systematic and Evolutionary Botany 22: 425– authors. Any queries (other than missing material) should be 433. directed to the New Phytologist Central Office.

New Phytologist (2019) Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust