Zanne1, William D

Research

Functional biogeography of angiosperms: life at the extremes

Amy E. Zanne1, William D. Pearse2, William K. Cornwell3, Daniel J. McGlinn4, Ian J. Wright5 and Josef C. Uyeda6 1Department of Biological Sciences, George Washington University, Washington, DC 20052, USA; 2Ecology Center and Department of Biology, Utah State University, Logan, UT 84322, USA; 3Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia; 4Biology Department, College of Charleston, Charleston, SC 29424, USA; 5Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia; 6Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

Summary Author for correspondence: Nonlinear relationships between species and their environments are believed common in Amy E. Zanne ecology and evolution, including during angiosperms’ rise to dominance. Early angiosperms Tel: +1 202 994 8751 are thought of as woody evergreens restricted to warm, wet habitats. They have since Email: [email protected] expanded into numerous cold and dry places. This expansion may have included transitions Received: 7 December 2017 across important environmental thresholds. Accepted: 9 February 2018 To understand linear and nonlinear relationships between angiosperm structure and biogeographic distributions, we integrated large datasets of growth habits, conduit sizes, leaf New Phytologist (2018) 218: 1697–1709 phenologies, evolutionary histories, and environmental limits. We consider current-day pat- doi: 10.1111/nph.15114 terns and develop a new evolutionary model to investigate processes that created them. The macroecological pattern was clear: herbs had lower minimum temperature and precipi- Key words: angiosperms, conduit size, tation limits. In woody species, conduit sizes were smaller in evergreens and related to environmental limits and thresholds, growth species’ minimum temperatures. Across evolutionary timescales, our new modeling approach form, leaf phenology, macroevolution, found conduit sizes in deciduous species decreased linearly with minimum temperature limits. minimum temperature, nonlinearity. By contrast, evergreen species had a sigmoidal relationship with minimum temperature limits and an inﬂection overlapping freezing. These results suggest freezing represented an important threshold for evergreen but not deciduous woody angiosperms. Global success of angiosperms appears tied to a small set of alternative solutions when faced with a novel environmental threshold.

change points occur is a vital question to be addressed in Introduction macroevolution and macroecology (Brown, 1984; Ogle & A plant’s structural characteristics determine the size of the spatial Reynolds, 2004; Tomkins & Hazel, 2007; Andersen et al., and temporal windows over which it experiences its local envi- 2009). However, current methods in comparative biology are ronment. Small ephemeral herbs may live their short lives poorly suited to detecting these transitions. Rather, most meth- between challenging events – ﬁres, physical disturbances, ods either require researchers to discretize traits a priori (Lewis, droughts, or annual freezes – but such short lifespans preclude 2001; Felsenstein, 2012) or examine simple linear relationships tall stature. Tall woody trees are successful competitors for light between continuous predictors and traits (Butler & King, 2004; across much of the world, maintaining an aboveground presence Hansen et al., 2008). In this paper, we introduce a novel across years and changing environments (Schippers et al., 2001; approach to detecting nonlinear relationships between environ- Westoby et al., 2002). Running between these extremes is the ment and traits (i.e. abrupt changes in species distributions and breadth of morphologies that constitute our modern ﬂora (Moles trait values) that allows researchers to test for change points and et al., 2009; Cornwell et al., 2014). The ability of plants with dif- directly estimate their values in an adaptive evolutionary frame- ferent body plans to tolerate changing environments determines work. where and when we see them. In considerations of species’ trait responses to environmental While much of the variation in critical traits underpinning pressures, an additional dimension in any comparative analysis is plant ecological strategies may be continuous (e.g. adult height), how to represent the environmental space a species inhabits other key properties contain obvious discontinuities. For exam- (Brown, 1984). In most cases, a given species exists across a range ple, transitions between woody and herbaceous stems, annual of spatial and temporal environments; the existence of range lim- and perennial life histories, and deciduous and evergreen habits its themselves suggests nonlinear responses of species to their are abrupt (even while intermediate forms exist) and suggest criti- environments (Whittaker, 1965; Bridle & Vines, 2007). There cal change points (thresholds) in how organisms adapt to a given has been debate as to the degree to which a species’ presence in a set of conditions. Understanding when, where, and why these given location is driven by average vs extreme environmental

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conditions at that place (Gutschick & BassiriRad, 2003; Lloret Existence of such precise a priori biophysical limits to conduit et al., 2012; Coyle et al., 2013; Coyle & Hurlbert, 2016). How- size (at least with colder temperatures) with clear consequences for ever, focusing on distribution of a species, and not the species at a other structural traits allows us to set up tests at macroecological specific location, offers hope of synthesizing across biogeography, and macroevolutionary scales to examine the importance of ecolog- macroecology, and macroevolution. Biogeographers and other ical limits and change-point relationships in shaping trait–environ- modelers of species’ ecological niches focus on limits, not average ment relationships. First, we performed a set of macroecological conditions, to understand how species’ tolerances are shaped by analyses to detect predictors of herbaceousness and, for woody their structural features and further filtered by biotic interactions species, conduit size for species at limits of their biogeographic (Soberon & Nakamura, 2009; Araujo & Peterson, 2012). ranges and with different leaf phenologies. Building from these Macroevolutionary biologists, by comparison, often examine results, we explored macroevolution of conduit size in woody plants species’ average tolerances or environmental conditions (Felsen- in concert with leaf phenology and minimum temperature across stein et al., 2008). Focusing on modeling limits rather than species’ range limits. Both leaf phenology and minimum tempera- averages (or central tendencies) offers hope of resolving this ongo- ture help define the ‘adaptive regime’ for a given lineage, which is ing tension by focusing modeling efforts on the same properties a set of (admittedly incomplete) predictor traits mapped on the across ecological and evolutionary studies. phylogeny and used to reconstruct and assign lineages into discrete By exploring which species had minimum temperature range categorizations. These regimes attempt to capture the essence of limits crossing the freezing threshold, Zanne et al. (2014) identi- shifts in Simpsonian adaptive zones on phylogenies (Simpson, fied three functional traits that likely facilitated radiation of 1944; Hansen, 1997). However, when determining what predic- angiosperms (flowering plants) from their beginnings in warm, tors should be used to define regimes, it can be unclear what wet tropics into freezing environments. We focus on the same percentile of a species’ environmental range best captures the limit traits here: size of water-conducting conduits (average cross- to which species are responding. In combination with leaf phenol- sectional area of vessels and tracheids in wood), leaf phenology ogy, we therefore examined a range of minimum temperature (evergreen vs deciduous), and growth habit (woody vs herba- percentiles (from lower limits, when species are infrequent to ceous). The earliest angiosperms were probably woody trees with central tendencies,toupperlimits, as species are frequent at that evergreen leaves and a vascular water-transport network with minimum temperature) to best predict evolution of conduit size. larger conduits than their gymnosperm relatives (Sinnott & Bai- Then, using a novel macroevolutionary analysis, we tested the ley, 1915; Wing & Boucher, 1998; Feild et al., 2004). functional form of the relationship between minimum temperature Large conduits allow for fast flow rates, as flow increases to the and conduit size to determine whether freezing imposes a nonlin- second power of conduit cross-sectional area (and fourth power ear shift in tempo and mode of plant trait evolution. By framing of conduit diameter), but larger conduits have a greater risk of our approach in terms of limits of species’ niches, we provide an embolism formation (air bubbles blocking the vascular stream) in integrative description of ecological and evolutionary responses of freezing (as air comes out of solution), and possibly drought (as plant species to avoid or persist through environmental extremes. air is drawn into water-conducting conduits through pit pores via air seeding) (Tyree & Zimmermann, 2002). Embolisms are a Materials and Methods critical determinant of plant success as they can lead to loss of hydraulic, and therefore photosynthetic, function; when ‘run- Traits away’ emboli spread between adjacent files of conduits, plant death may even occur (Tyree & Sperry, 1989; Tyree & Ewers, Growth habits were taken from the Global Woodiness Database 1991; Cochard et al., 1996). While we lack a complete under- (Cornwell et al., 2013). For this study, only data for angiosperms standing of embolism risk (Charrier et al., 2017), evidence sug- with either woody or herbaceous growth habit were examined, gests 0.044 mm conduit diameter (or 0.0015 mm2 cross- excluding taxa considered variable or ambiguous. For woody taxa, sectional area, assuming circular conduits) forms a boundary species’ mean cross-sectional conduit area A was extracted from the above which conduits are more prone to freezing-induced angiosperm Global Vessel Anatomy Database (Zanne et al., 2010). embolisms at modest water tensions (Davis et al., 1999): larger Conduit area was natural-log transformed in all analyses. Species conduits allow more air bubbles to coalesce, increasing the risk leaf phenologies (deciduous, evergreen, variable) were taken from that they fill the conduit, causing an embolism. To avoid freez- the Global Leaf Phenology Database (Wright et al., 2014). Species ing, some lineages of woody evergreen species may have evolved with variable leaf phenologies were excluded from analyses. an herbaceous habit (Judd et al., 1994; Zanne et al., 2014). In lineages that remained woody, it may be that many became Latitudinal and environmental limits deciduous, losing their leaves annually to avoid cold, or produced small conduits – that is < 0.044 mm – to persist through cold, To determine extremes of geographic and environmental ranges embolism free. Direct links between conduit size and risk of that species occupy, we downloaded all georeferenced ‘human drought-induced embolisms are less clear theoretically and not observations’ (observations without accompanying vouchered well supported empirically, although greater vulnerability in specimens) of plants without spatial issues from the Global Bio- larger conduits has sometimes been reported (Tyree & diversity Information Facility (GBIF, 2017). These records are Zimmermann, 2002). cleaned by GBIF excluding all common spatial issues such as

New Phytologist (2018) 218: 1697–1709 Ó 2018 The Authors www.newphytologist.com New Phytologist Ó 2018 New Phytologist Trust New Phytologist Research 1699 duplicate records, swapped latitude/longitude, imprecise loca- framework (Burnham & Anderson, 2003) to explore relative tions (e.g. integer), and locations recorded on water. Taxonomic importance of geographic and environmental limits (i.e. mini- names of these records were harmonized with The Plant List mum temperature, minimum precipitation, maximum tempera- (http://www.theplantlist.org/) using code provided from taxon- ture seasonality, maximum precipitation seasonality, and name-utils (Schwilk, 2017). To check known geographic and maximum and minimum latitude) in explaining growth habit environmental distributions against recovered values, maps of and conduit size respectively. We included leaf phenology in major clade distributions (mostly families) were manually com- models predicting conduit size. We fit all possible combinations pared with prior expectations based on expert opinion (D. Tank of additive models of explanatory variables and, where latitude & P. Stevens, pers. comm.), natural history knowledge, and dis- was present, only included squared terms (to account for sym- tribution maps (Stevens, 2001). Species-level estimates of envi- metric relationships across hemispheres). We report results from ronmental variables were also compared with expectations based regressions conducted on models where all explanatory variables on natural history knowledge. To address concerns that species were z-transformed (to make coefficients directly comparable; see distributions are influenced by records tied to herbaria, environ- Gelman et al., 2014) in the main text, but in Supporting Infor- mental variables were calculated while masking records found mation Tables S1, S2 we report models with raw coefficients within 10 km of herbaria in the Index Herbariorum (http://sweet (whose results are quantitatively identical). While we acknowl- gum.nybg.org/science/ih/); as these were strongly correlated with edge our explanatory variables are correlated, by comparing rela- uncorrected estimates (r2 > 0.95), estimates with all records, tive fit of multiple models with differing combinations of including those near herbaria, were used. explanatory variables we mitigated some of this potential bias. All georeference locations were queried against WorldClim Scripts for macroecological analyses can be found at https:// (Hijmans et al., 2004) 5-arc minute resolution Bioclim data github.com/Zanne-Lab/vessel_extremes. products to determine point location estimates of temperature Substantial variation exists in number of records in GBIF for and precipitation and derived ‘bioclimatic’ variables, averaging each species (median = 76, SD = 739.88). In the Supporting across monthly ‘current’ (c. 1960–1990) conditions. We expected Information (Figs S1, S2; Tables S3, S4; Methods S1), we pre- minimum temperature and precipitation and maximum season- sent additional analyses we performed to verify that variation in ality in these variables might impose important limits on species sampling does not bias our results. We did not control for spa- distributions and trait values. For each species binomial, we tial autocorrelation beyond including latitude in analyses. While extracted maximum precipitation seasonality (coefficient of varia- we acknowledge this potential source of error, we emphasize tion; BIO15), maximum temperature seasonality (SD 9 100; that we modeled range limits, not distributions or points in BIO4), maximum latitude, minimum latitude, minimum annual space; as such, it is unclear how we would statistically account temperature (BIO6), and minimum annual precipitation for potential biases. We do include latitude in models to control (BIO14; natural-log transformed after adding Euler’s constant to for potential confounding spatial effects. Additionally, we did make values of zero meaningful). For macroecological analyses not include more predictor variables in macroevolutionary anal- for each variable, we defined ‘maximum’ to be the 97.5th yses because computational limitations mean that modern com- percentile and ‘minimum’ to be the 2.5th percentile across georef- parative methods cannot be applied to a dataset of this size erenced locations for each species. For macroevolutionary analy- (37 783 species). Instead of applying older methods whose ses, we calculated multiple percentiles (1st,2.5th,5th,50th,95th, interpretations are limited and still difficult to fit (e.g. indepen- 97.5th,99th) to assess whether quantitative differences in the dent contrasts; see Felsenstein, 1985), we take the approach of value produced qualitatively different results. The resulting examining broad macroecological patterns in our data and matrix contained values for the various indices of geographic and applying cutting-edge macroevolutionary techniques to a care- environmental extremes, phenology (evergreen/deciduous), fully chosen subset of the data about which we can make strong growth habit (woody/herbaceous), and average conduit cross- inference. sectional area for 39 670 species binomials. All species contained records for growth form, georeference, and environmental Macroevolutionary analyses In phase 2, based on results from extremes, but not for conduit size and phenology, as these were the macroecological analyses and past research (Zanne et al., smaller datasets. For analyses, the dataset was trimmed to just 2014), we explored multivariate species’ trait responses to mini- angiosperms (Angiospermae; 37 783 species), except to display mum temperature limits. We joined our combined trait ancestral state reconstructions of freezing exposure (Fig. 1), which database with the phylogeny from Tank et al. (2013) that we display for all land plants (Embryophyta). includes representatives from across land plants (Embryophyta) and visualized the history of invasion of freezing habitats across species using a simple Brownian motion reconstruction of mini- Analyses mum temperature at the 2.5th percentile. We included all land Analyses were done in three phases, which we describe in the fol- plants (including nonvascular outgroups) to improve visualiza- lowing. tion and estimation of the phylogenetic history of freezing exposure. While more sophisticated reconstructions may change Macroecological analyses In phase 1, we ran logistic and linear results and we have not fully accounted for substantial uncer- regressions with an information-theoretic model-comparison tainty inherent in reconstructing ancestral states over millions

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Fig. 1 Plots of the proportion of taxa that are herbaceous vs (a) minimum latitude, (b) maximum latitude, (c) minimum temperature, (d) minimum precipitation

(loge), (e) maximum temperature seasonality, and (f) maximum precipitation seasonality. Line thickness denotes number of species at that latitude, with thicker lines denoting more species (see key in a).

years of evolution history, our goal was simply to visualize best captures environmental limits to which species are respond- across angiosperms the distribution of clades invading freezing ing, we started by determining the best combinations of predic- habitats. tors of conduit size, using a range of minimum temperature For analyzing trait evolution, we used phylogenetic compara- percentiles in combination with leaf phenology. For each model, tive models of adaptation – that is, Ornstein–Uhlenbeck (OU) – we assigned species to freezing-exposed (F) or freezing-unexposed which are well suited for testing hypotheses regarding constraints (NF) states based on each of seven temperature percentiles (1st, and adaptive shifts. These are in contrast to Brownian motion 2.5th,5th,50th,95th, 97.5th,99th) plus the mean. In addition, we models, which describe unconstrained evolution of traits without tested for effect of evergreen (EV) and deciduous (D) leaf phe- long-term adaptation to any particular phenotypic state. First, we nologies on conduit size. Finally, we tested four state models analyzed evolution of loge-transformed conduit area by using a where these two factors were combined (EV_F, EV_NF, D_F, model selection framework in which different adaptive regimes D_NF). For each assignment of predictor states, we recon- (i.e. categorical reconstructions of adaptive conditions) were structed 20 stochastic character mappings of trait history across painted on the phylogeny based on alternative hypothesized fac- the tree using the PHYTOOLS (Revell, 2012) function tors driving trait evolution. We used the software package OUWIE make.simmap with Bayesian Markov chain Monte Carlo (Beaulieu et al., 2012) to test a range of models and predictors for (MCMC) sampling under the best-ﬁtting model (either equal their inﬂuence on conduit size. As it is unclear what percentile transition rates or ‘all-rates different’) with a prior on transition

New Phytologist (2018) 218: 1697–1709 Ó 2018 The Authors www.newphytologist.com New Phytologist Ó 2018 New Phytologist Trust New Phytologist Research 1701 rates set as a gamma distribution with b = 0.1 and a mean equal than needing to estimate 10 independent optimum values for each to the empirical maximum likelihood estimate of the Q-matrix. temperature regime, our method estimates only parameters of the We fit alternative models to each history and regime painting specified function (e.g. a linear function requires estimation of b b to test hypotheses regarding whether freezing changed optimal intercept and slope to completely define optimum values for conduit conduit size and, particularly, strength of constraints around that size at each temperature). We tested these models in Bayesian and conduit size. To do so, we tested multi-optima OU models maximum likelihood frameworks. First, we ran Bayesian analyses (OUM) against OU models that allow for changes in the optimal for linear, sigmoidal, and step functions using uniform priors for trait value, as well as changes in width of an adaptive regime location parameters and normal or log-normal distributions for (OUMA and OUMV). For example, OUMA allows for changes in slope parameters (Table S5). Each MCMC was run for 100 000 the value of strength of adaptation across predictor states, with generations with stochastic mappings sampled throughout the stronger values of a representing increased pull toward an opti- MCMC. We discarded the initial 30% of the chain as burn-in. mum. Similarly, OUMV considers a constant, but allows the In addition, we tested the same models using a maximum like- 2 Brownian rate parameter r to vary across predictor states, enabling lihood model selection framework as in OUWIE analyses. The some states to have more rapid evolution and therefore wider adap- models tested include the following functions: tive regimes than others. We chose to omit OUMVA, as this tends linear bintercept + bslopex WIE ÀÁ to have frequent numerical errors when using OU and can be b ðÞb sigmoid b þ b = 1 þ e slope x center difficult to interpret. Multi-optimum OU models were compared left right ÀÁ b ðÞ b þ b = þ slope x 0 against a single-rate Brownian motion model (BM1), a single- sigmoid0 left right 1 e ! ðÞ[ b b optimum OU model, and generalized Brownian motion models step if x center right that allow different rates in different predictor states (BMS). Each ! ðÞ\b b if x center left model was fit using maximum likelihood and models were com- step0 if ! ðÞx [ 0 b pared using the corrected Akaike information criterion (AICc). A right if ! ðÞx\0 b total of 1362 models were fit: BM1 + OU1 + 20 stochastic left b + b + b b maps 9 4 models (OUM, OUMV, OUMA, BMS) 9 17 predic- linear step intercept slopex right tanh(x center) tors (phenology + 8 freezing percentiles + 8 freezing percentiles/ phenology combinations). Models were summarized by calculating We used the function nlminb in the STATS package in R with dAICc, the difference between AICc scores of each model fit and bounds to optimize all models and starting points randomly lowest AICc score for each stochastic map. We then compared dis- drawn from MCMC chains obtained as mentioned earlier. We tributions of dAICc for each model across all stochastic maps. optimized each model across all 100 stochastic character maps. Our goal was to examine shifts between two continuous vari- We summarized across stochastic maps using two approaches. ables: minimum temperature and conduit size; however, the First, we estimated Akaike weights for each model and map combination and summed across models. Second, we calculated OUWIE approach requires discretization of continuous temperatures into a few categories, leading to substantial loss of informa- dAICc values for each map and compared distributions of dAICc tion about response of conduit size to temperature. In phase 3, to across models. Because of the complexity of modeling both tran- overcome this challenge, we developed a novel method for esti- sitions between our discretized temperature traits and phenologi- mating a continuous functional form between minimum temper- cal states, we repeated analyses, dividing data into two datasets ature and value of optimum conduit size in an OU model containing only evergreen and only deciduous species. Although accounting for phylogenetic history and uncertainty in recon- this method assumes evergreen and deciduous species never tran- struction of past temperature conditions for each lineage. sition between character states (an assumption certainly violated), First, we took the best-fitting temperature percentile from modeling both transitions between discretized temperature states and phenology would result in transition matrices too large to be OUWIE analyses and discretized it into 10 equally spaced bins spanning the range of minimum temperature values in the data. We computationally tractable (at least in current implementations in then fit an Mk model (Lewis, 2001) in which transitions are only R). We therefore used the simplifying assumption that effects of allowed between neighboring bins (‘meristic’ model). For simplicity evolutionary lags resulting from transitions between phenology and to ease parameter estimation, we assumed transition rates states were sufficiently rapid to be ignored (supported by our esti- between all adjacent bins were equal, with all other transition rates mates of rapid phylogenetic half-lives in models we analyzed). Scripts for macroevolutionary analyses can be found at https:// being zero. The model was fit in the R package DIVERSITREE (FitzJohn, 2012) and maximum likelihood parameter estimates github.com/Zanne-Lab/vessel_extremes. were used to generate 100 stochastic character maps using MAKE.SIMMAP (Revell, 2012), reconstructing a semi-continuous his- Results tory of past temperature regimes across the tree. We used cus- tomized scripts to modify the R package BAYOU (Uyeda & Harmon, Macroecological analyses 2014) to calculate optima values as a deterministic function of minimum temperature. We tested linear, sigmoidal, step, and linear- In phase 1 in our model comparison framework, the strongest step functions in which the midpoint of each bin is used to predict predictor of the proportion of herbaceous species in a flora was the optimum value for a given regime (Boucher et al., 2018). Rather minimum temperature (negatively related; Table 1; Fig. 1). Both

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Table 1 Model comparison predicting growth habit from latitudinal and Table 2 Model comparison predicting conduit cross-sectional area from environmental range limits leaf phenology and latitudinal and environmental range limits

Estimate SE zP Importance Estimate SE zP (%) Intercept 0.96 0.03 33.61 < 0.0001 Minimum temperature 0.87 0.03 28.54 < 0.0001 Intercept 5.22 0.12 41.95 < 0.0001 NA Minimum precipitation 0.37 0.02 16.25 < 0.0001 Minimum 0.41 0.12 3.33 0.00086 100 Maximum precipitation 0.22 0.02 9.24 < 0.0001 temperature seasonality Minimum 0.04 0.06 0.59 0.553 45 Maximum temperature 0.25 0.03 8.38 < 0.0001 precipitation seasonality Maximum 0.07 0.08 0.84 0.399 58 Minimum latitude 0.28 0.28 13.87 < 0.0001 precipitation (Minimum latitude)2 0.27 0.02 11.52 < 0.0001 seasonality Maximum latitude 0.31 0.02 14.57 < 0.0001 Maximum 0.02 0.09 0.26 0.793 29 (Maximum latitude)2 0.10 0.02 4.37 < 0.0001 temperature seasonality Only a single model was found to have a dAIC (difference between the Phenology 0.71 0.11 0.11 < 0.0001 100 AIC score of each model and the lowest AIC score observed) < 4 during Minimum latitude 0.12 0.06 1.83 0.068 100 model search; that model (null deviance = 52 983, df = 39 669; residual (Minimum latitude)2 0.48 0.10 4.84 < 0.0001 100 2 deviance = 45 325; df = 39 661; pseudo r = 0.14) is presented here. All Maximum latitude 0.32 0.10 3.21 0.0013 100 explanatory variables were z-transformed before analysis to make coeffi- (Maximum latitude)2 0.22 0.07 3.04 0.0024 100 cients directly comparable and facilitate model averaging following Grue- ber et al. (2011) and Gelman et al. (2014). All estimates are on a logit All coefficients are Akaike information criterion (AIC)-weighted averaged scale. Note that, because only a single model is presented, no variable coefficients across the eight models with dAIC (difference between the importance can be calculated (cf. Table 2). AIC score of each model and the lowest AIC score observed) < 4 following Burnham & Anderson (2003) and Barton (2017); the exception to this is ‘Importance’, which is the percentage of models containing a term maximum temperature seasonality and minimum precipitation weighted by the AIC-weight of each model. We did not consider any mod- were the next strongest environmental predictors (both negatively els without an ‘intercept’ term and so we list its importance as ‘NA’ rather related; Table 1; Fig. 1d,e), while maximum precipitation season- than 100%. All explanatory variables were z-transformed before analysis ality showed the weakest but still significant correlation (nega- to make coefficients directly comparable and facilitate model averaging following Grueber et al. (2011) and Gelman et al. (2014). tively related; Table 1; Fig. 1f). Negative associations between herbaceousness and maximum seasonality of temperature and Fig. 2c). Minimum precipitation and maximum seasonality of precipitation reflect abilities of our models (but not our figures) temperature were weakly negatively related, while maximum sea- to disentangle strong correlations among predictor variables. sonality of precipitation was weakly positively related. For species Minimum temperature was strongly negatively correlated with with minimum temperature limits above freezing, both evergreen maximum seasonality of temperature (Pearson’s r = 0.90; and deciduous species had large variation in conduit size; how- = < t39 668 422.34; P 0.0001), as was minimum precipitation ever, below freezing limits, evergreen species had smaller conduits with maximum seasonality of precipitation (Pearson’s r = 0.82; than deciduous (Fig. 2c). Few deciduous species were found at = < > t39 668 287.77; P 0.0001). Of all models, the one contain- higher minimum precipitation limits c. 40 mm (Fig. 2d). All ing all terms had the best Akaike information criterion (AIC), representations of latitude except minimum latitudinal limits and no other model had an AIC value within four units (Table 1; were significant (Table 2; Fig. 2a,b), with conduit size decreasing Fig. 1). As described in the Materials and Methods section, we in species with latitudinal limits close to the poles. Large drops in included a squared term for maximum latitude to be conserva- conduit size occurred in both hemispheres for species with range tive, but its retention should not affect model results, as we are limits outside of the tropics. These results again suggest that spa- essentially reporting the maximal model (Whittingham et al., tial distribution of species was independent from selected envi- 2006). These results suggest temperature and precipitation were ronmental limits when predicting conduit size. both related to species’ growth form, as well as additional additive terms captured by latitude. Proportion of herbaceousness Macroevolutionary analyses increased in species with limits close to the poles. Rapid shifts from a woody- to herbaceous-dominated flora occurred as latitu- In phase 2, ancestral reconstruction of species with minimum dinal ranges shifted out of tropics, as well as when species’ envi- temperature limits in freezing environments suggested only some ronmental limits shifted below freezing (Fig. 1a–c). lineages were able to invade cold places (Fig. 3). OUWIE analyses While fewer predictors were significant across all models when found that best-fitting models were those with four distinct adap- conduit size was the response (Table 2; Fig. 2), the model con- tive regimes corresponding to combinations of phenology and taining all terms again had the best AIC value: 66.68 AIC units lowest percentiles of temperature exposures (1st (tmin.01) and greater than the next best. For conduit size, leaf phenology was 2.5th (tmin.025), Fig. 4). Furthermore, OUM models performed the strongest predictor, with deciduous species having larger con- as well, if not better, than more complex OUMV and OUMA duits than evergreen. Minimum temperature was the only signifi- models using AICc (Table S6; Fig. 4). Parameter estimates were cant environmental predictor (positively related; Table 2; highly consistent across simmap reconstructions, with evergreen

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(a) (b)

(e) (f)

Fig. 2 Plots of conduit area vs (a) minimum latitude, (b) maximum latitude, (c) minimum temperature, (d) minimum precipitation

(loge), (e) maximum temperature seasonality, and (f) maximum precipitation seasonality. Both conduit area and minimum precipitation are natural-log transformed. The dashed horizontal line is at 0.0015 mm2 (i.e. the 0.044 mm diameter threshold above which freezing-induced embolisms are believed to become frequent at modest tensions; Davis et al., 1999). For point colors, see key on (a). species having a lower optimum for conduit size than deciduous adaptive optima in conduit size, we found evidence across and freezing-exposed species having a lower optimum for conduit the dataset for a sigmoidal relationship between minimum size than freezing-unexposed species (Table S7; Fig. 5). Differ- temperature and natural log of conduit cross-sectional area ences between optima appeared rough additive and without using both Akaike weights (AICw_sigmoid = 0.34, AICw_sig- interactions. For example for the 1st percentile, transition from moid0 = 0.38, AICw_stepLinear = 0.21, AICw_linear = 0.07) freezing unexposed to freezing exposed (NF ? F) resulted in and dAIC values (median dAICc_sigmoid0 = 0.01, median = = increases in optimal conduit area (2.13 and 2.12 loge conduit dAICc_sigmoid 1.82, median dAIC_linear 0.75, median 2 = units (loge[mm ]) for deciduous and evergreen species respec- dAIC_stepLinear 2.82) (Table S8; Fig. 6). That the sigmoid0 tively). Similarly, transitions from deciduous to evergreen leaf model ﬁt nearly as well as the sigmoid model with an uncon- phenology (D ? EV) decreased optimal conduit area by strained center suggests the transition occurs at or near 0°C 2 0.54 loge conduit units (loge[mm ]) for both freezing-exposed (when freely estimated, we estimated a 95% highest posterior den- and freezing-unexposed species. sity for the location of the center was between 4.1 and +8.2°C). In phase 3, using our novel approach to test alternative Furthermore, while sigmoidal models had the strongest relative functional relationships between minimum temperature and support, some support exists for linear or step linear models.

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250

200

150

dAICc 100

Freeze.99Freeze.95Freeze.50Freeze.05Freeze.01 PhenologyFreeze.975 Freeze.025 Freeze.mean

Freeze.99Freeze.95 x PhenologyFreeze.50 x PhenologyFreeze.05 x PhenologyFreeze.01 x Phenology x Phenology Freeze.975 x PhenologyFreeze.025 x Phenology Freeze.mean x Phenology

Fig. 3 Reconstruction of species entering freezing environments across the Fig. 4 Distribution of the distance between the minimum corrected Akaike land plant (Embryophyta) tree of life. Ancestral states were reconstructed information criterion (dAICc) score and the best model across 20 stochastic for visualization purposes by assuming a Brownian motion model of character maps, with smaller values indicating less distance to the best evolution using the 2.5th percentile for minimum temperature. Branches model for a given reconstruction. Greater values indicate a relatively are colored blue if reconstructed minimum temperatures for the ancestral poorer model fit to the data; thus, that models focusing on extreme node are below freezing. Major clades (Magnoliidae, Monocotyledoneae, temperature minima have the lowest values indicates that limits, not Superrosidae, Superasteridae) are denoted with labels and colored lines on mean/percentile values, drive these data. ‘Phenology’ indicates the model the outside of the phylogeny. Earlier diverging lineages in Embryophyta includes reconstructions of deciduous/evergreen character states, while (bryophyte grade, lycophytes, monilophytes, and gymnosperms) are ‘Freeze.xx’ indicates the model includes a reconstruction of past presence/ included in the figure to contextualize freezing evolution in the absence of lineages in freezing habitats based on one of the eight Angiospermae. temperatures (seven percentiles across the environmental range of species from 0.01 to 0.99 plus the mean). Dark-gray boxplots indicate two-regime When species were divided into deciduous and evergreen and models, while light-gray boxplots indicate four-regime models. Boxplot th th analyzed separately, we found different models were favored whiskers indicate the 95 and 5 percentiles across simmap reconstructions. (Table S6; Fig. 6). Evergreen species again followed a sigmoidal (AICw = 0.71, median dAICc = 0.89) or sigmoid0 (AICw = 0.29, median dAICc = 0.00) relationship, while deciduous inability is due to data limitations – it requires a great deal of data species were better fit by a linear relationship (AICw = 0.44, to capture species range limits accurately – and lack of appropri- median dAIC = 0.00) in all reconstructions over sigmoidal ate macroevolutionary models. (AICw = 0.12, median dAIC = 3.17) or sigmoid0 (AICw = 0.26, Here, we tackle these challenges, testing for nonlinearities in median dAIC = 1.6) models (although sometimes only slightly). radiation of angiosperms into Earth’s cold and dry places. We Parameter estimates show that even when sigmoidal models were find environmental limits were strong predictors of linear and fit to deciduous species, relationships were more linear with a nonlinear shifts in species’ trait values at both macroecological substantially shallower slope than in evergreen species (Table S9). and macroevolutionary scales. In fact, minimum temperatures at percentiles where species are found but infrequent are better pre- Discussion dictors of evolutionary shifts in conduit size than percentiles where species are frequent. Furthermore, our new macroevolu- Data and theory at microevolutionary, demographic, and physio- tionary approach reveals both continuous and abrupt shifts in logical scales suggest that environmental threshold (change-point) conduit size with changing minimum temperature limits for behaviors should be relatively common in nature (Donohue species, with key differences for species with alternate leaf et al., 2015). Furthermore, ecological assembly theory is based phenologies. around ideas of limits: environments filter species according to the most extreme conditions they can tolerate, with implications Macroecology of growth form and conduit size in response for both community assembly at a given place (Keddy, 1992) to ecological gradients and species range limits (Sexton et al., 2009). Despite these predictions, we have been unable to test for such nonlinear limits Understanding how and why species array themselves along spa- across macroecological and macroevolutionary scales. This tial and environmental gradients has long been a goal in plant

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included in all top models. However, minimum temperature lim- Deciduous/Freezing -

]) Deciduous/Nonfreezing -

2 its were the strongest environmental variable, with 2.4 times and - - Evergreen/Freezing - -- Evergreen/Nonfreezing ---- 6.1 times the inﬂuence of the next strongest environmental driver [mm ---- e ------for growth form and conduit size respectively. As in previous ------work (Judd et al., 1994; Zanne et al., 2014), we recover change ------points in these traits across freezing and with shifts out of the ------tropics with more herbaceous species or when woody, smaller ------conduits especially in evergreens. In fact, evergreen species with (conduit area) (log -- e - -- minimum temperature limits below freezing largely had conduits

log - - - below the 0.044 mm diameter (i.e. 0.0015 mm2 cross-sectional - –10 –8 –6 –4 –2 - area) threshold (Davis et al., 1999), while deciduous species below freezing had conduits spanning this threshold. These 0.00 0.25 0.50 0.75 1.00 results support suggestions that freezing-induced embolism risk Relative time shapes how plants are constructed (Cochard & Tyree, 1990; Fig. 5 Distribution of trait values and model fit predictions for Ornstein– Sperry & Sullivan, 1992) when vascular pathways need to be Uhlenbeck models fit to natural log of conduit area. The best-fitting model maintained for long-lived leaves. was the OUM model with a freezing threshold determined by tmin.025 While other environmental variables explained unique varia- combined with leaf phenology. The distribution of regimes across 20 tion in plant traits (based on effect sizes), only minimum precipi- simmap reconstructions is shown in the phenogram (left) and raw trait values (far right). Normal distributions for the OUM and OUMV models tation in growth form models had a reasonably large influence, illustrate the predicted trait distributions at stationarity (r2/2a) for each albeit much smaller than minimum temperature. More herba- regime across all simmap estimates. Note that deciduous and nonfreezing ceous species were found as minimum precipitation declined, regimes are higher than equivalent evergreen and freezing regimes. The 2 consistent with possibilities that many species solved problems of black dashed line is at 0.0015 mm (i.e. the 0.044 mm diameter threshold existing in dry conditions by avoiding these extremes with an above which freezing-induced embolisms are believed to become frequent at modest tensions; Davis et al., 1999). herbaceous habit. Strikingly, we did not find evidence for a relationship between conduit size and minimum precipitation (Tyree & Zimmermann, 2002). ecology (Whittaker, 1965; Moles et al., 2009; Higgins et al., As leaf phenology is recorded for woody, not herbaceous, 2016; Weiser et al., 2017). Across our contemporary angiosperm species, it was only included as a predictor in conduit size mod- flora, we found species’ environmental and latitudinal limits were els. Much like minimum temperature, it was highly influential strong predictors of functional traits of those species. Representa- with deciduous species having larger conduits than evergreen. tions of temperature, precipitation, and latitudinal extremes were Others have found relationships between leaf phenology, ]) 2 [mm e (conduit area) (log e log –10–8–6–4

–40 –30 –20 –10 0 10 20 Minimum temperature (°C) Fig. 6 Bayesian posteriors of estimates of best-fitting functional relationships between macroevolutionary adaptive optima for natural log of conduit area and minimum temperature at the 2.5th percentile of geographic distribution. Functions are fit separately for deciduous (gold) and evergreen (green) species. Best-fitting models for deciduous species are linear, whereas evergreen species follow a sigmoid relationship with the center around freezing (vertical dashed line). The horizontal black dashed line is at 0.0015 mm2 (i.e. the 0.044 mm diameter threshold above which freezing induced embolisms are believed to become frequent at modest tensions; Davis et al., 1999).

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seasonality, and freezing (Preston & Sandve, 2013; Vitasse Furthermore, we show that model fits were substantially et al., 2014); our results point to mechanisms for this. Our improved when the models included leaf phenology in combina- results imply that when plants do not need an intact vascular tion with minimum temperature at lower limits of species’ ranges network throughout the year, they optimize that network for (Fig. 4). Such results suggest relatively simple scenarios where warmer, wetter conditions. In this case, larger conduits found in only the adaptive optimum shifts in correspondence with leaf deciduous species should allow for faster hydraulic flow, transpi- phenology and species’ minimum temperature, while variation in ration, and photosynthetic rates, permitting plants to grow taller width of adaptive regimes remains relatively constant (Fig. 4). In and/or carry greater leaf area (Tyree & Ewers, 1991; Tyree & particular, deciduous species and species with temperature Zimmermann, 2002; Gleason et al., 2012). While both ever- extremes above freezing had higher adaptive optima. Addition- green and deciduous species were found at all latitudes, seasonally, both freezing exposure and deciduous leaf phenology had alities, and minimum temperatures, deciduousness was rare for roughly equivalent and additive effects on conduit size evolution, species with high minimum precipitation limits. Additionally, with deciduous species in freezing having an optimum very close few species were deciduous and had small conduits (Fig. 2). to predicted thresholds of 0.044 mm diameter (0.0015 mm2 Together these results suggest that when rainfall is high, decidu- cross-sectional area), and evergreen species were substantially ousness is likely costly; for example, lost opportunities for fixing below that (Fig. 4). Interestingly, our results imply no differences carbon (Kikuzawa, 1991; Sobrado, 1991; Valladares & Pug- occurred in width of adaptive regimes for conduit size when the naire, 1999; Givnish, 2002). trait is considered on logarithmic scales (meaning, on the raw scale, evergreen species in freezing habitats should have narrower adaptive regimes). We had some expectation that when the Macroevolutionary change points in conduit optima as a strong selective pressure of freezing is absent for freezing- freezing response unexposed lineages, we would see greater variation in conduit size From our macroecological models of conduit size, we found leaf evolution (i.e. wider adaptive regimes). While we consider loga- phenology was the strongest predictor and minimum tempera- rithmic scales as appropriate for evolutionary size variation (Gin- ture was the strongest environmental predictor. We selected these gerich, 1993), such changes may not be equivalent in organismal variables from macroecological models to include in our two performance or functional constraints. macroevolutionary model sets. In our first set, we examined Our OUWIE analyses provide strong phylogenetic support that importance of representing species’ distributions based on limits conduits are larger in tropical than in temperate locations and central tendencies. We determined whether, in combination (Wheeler et al., 2007). However, to date, it has been unclear with leaf phenology, minimum temperature was a stronger pre- whether differences are related to discontinuous (e.g. freezing) or dictor of conduit size evolution when estimated at lower limits continuous (e.g. other effects of temperature on water transport, when individuals of a species were infrequent (1st, 2.5th or 5th such as viscosity) increases in conduit sizes with rising tempera- percentile) vs when individuals were frequent at central tendencies tures. Leaf phenology is especially relevant, as evergreen species or upper limits (50th,95th, 97.5th or 99th percentile). We also must maintain leaves and vascular networks across environmental tested for differences in optima and width of adaptive regimes conditions where they grow, while deciduous species shed leaves among different groups in data based on leaf phenologies and as environments become seasonally harsh (cold or dry). Our sec- whether they crossed freezing. In our second set, we tested for ond set of models provides convincing evidence for an overall sig- change points in trait evolution in coordination with a continu- moidal relationship between minimum temperature and conduit ous predictor. Using our new evolutionary approach, we took size, with a change point indistinguishable from 0°C. Further- results from the first set to select the percentile of minimum tem- more, when evergreen and deciduous species were analyzed sepa- perature to include when estimating functional form of relation- rately, only evergreen species drive this overall pattern, while ships between conduit size and minimum temperature across deciduous species follow a simple linear relationship. These evergreen and deciduous species. results indicate leaf phenology may mitigate effects of freezing Our first set showed that in macroevolution, as in macroecol- temperatures on adaptive requirements of conduit size, but an ogy, it was the limits that best estimated shape of adaptive land- overall positive relationship between conduit size and minimum scapes (Kraft et al., 2014). In predicting conduit size optima, temperature remains. Indeed, when comparing estimated curves models incorporating information on lower limits as species when minimum temperatures were above freezing, the two curves became infrequent at a given minimum temperature (Fig. 4) had appear similar (Fig. 6). significantly better performance than models incorporating information as species became common (central tendencies or upper A new comparative method for estimating nonlinear limits). This is despite limits of distributions being inherently relationships and change points more difficult to estimate than central tendencies; this potential source of error makes our results, if anything, conservative esti- Our new model estimates evolutionary optima in response to a mates of relative importance of species’ limits. Moving forward, continuous predictor, allowing optima to vary according to any it will be important to examine what parts of species’ environ- type of continuous function specified by users. Our approach is mental regimes (upper vs lower limits vs central tendencies) are similar to Hansen et al. (2008) but lets users compare different best coordinated with evolutionary shifts in trait values. functional forms in relationships beyond linear fits. For instance,

New Phytologist (2018) 218: 1697–1709 Ó 2018 The Authors www.newphytologist.com New Phytologist Ó 2018 New Phytologist Trust New Phytologist Research 1707 here we compared linear, sigmoidal, step, and linear-step func- distributions (GBIF, 2017), and environmental regimes (Hij- tions using both Bayesian and maximum likelihood model selec- mans et al., 2004). In our study, we examined up to c. 38 000 tion frameworks. This flexibility comes at a slight cost. While angiosperm species, which, while considerable, is still only c.1/ Hansen et al. (2008) modeled predictors as evolving continuously 10th of known angiosperm diversity. As we still have large data by Brownian motion, we discretize predictors into bins, model- gaps, we consider our results strong but preliminary evidence that ing them using discrete Markov models. This coarser treatment minimum temperature limits, especially across freezing, are of predictors combined with limits on computational efficiency major predictors of functional evolution. As more species, traits, may cause some lost power (Boucher et al., 2018). However, we and distribution data are added, we can further test for coordina- still detected differences in overall fit and parameter estimates tion between freezing and trait evolution, as well as how freezing among different models. may limit species distributions. Arguably more important than We should note that we tested for functional forms that best the organismal insights gained, we think approaches developed to describe data across all species, as well as when species were explore nonlinearities, such as limits and change points in separated by leaf phenology. Examining species as evergreen species’ responses to environmental regimes, should better align and deciduous separately assumes transitions between these adap- fields of biogeography and macroecological and macroevolution- tive regimes never occurred or, at least, adaptation occurred ary biology. instantaneously at these transitions. This assumption was made for the practical reason that accounting fully for two predictors Acknowledgements would result in doubling the size of the (sparse) transition matrix; we currently cannot efficiently perform simmap reconstructions We thank Tim Robertson and Andrea Hahn at GBIF for provid- for > 10 character states using the R implementation in PHYTOOLS ing advice about use of GBIF data. We thank Dylan Schwilk for (Huelsenbeck et al., 2003; but see Irvahn & Minin, 2014). Our code matching plant names in GBIF to accepted names in the observation of low phylogenetic half-lives in model fits after Plant List. We also thank Daniel Caetano for discussions on our accounting for dependency on freezing percentiles suggests this novel comparative method. Support for this work was provided assumption is reasonable, as one would expect inertia from transi- to the working group Tempo and Mode of Plant Trait Evolution tions between leaf phenologies to have phylogenetic signal not by the National Evolutionary Synthesis Center (NESCent), NSF accounted for by freezing percentiles alone. Furthermore, we did #EF-0905606, and Macquarie University Genes to Geoscience not consider models in which width of adaptive regimes varied as Research Centre. Members of NESCent working group provided a function of minimum temperature, as these models would be useful discussions on early versions of this work. substantially more difficult to estimate and implement. Neverthe- less, our OUWIE analyses provide support for this assumption, as Author contributions discrete adaptive regimes were not found to differ substantially in width. A.E.Z. and W.K.C. conceived of the project. A.E.Z. designed the The strength of our approach is it provides considerable flexibil- study. A.E.Z., W.D.P. and JCU wrote the manuscript. J.C.U. ity in models we fit to comparative datasets. By utilizing ‘data- analyzed and made figures for the macroevolutionary questions. augmentation’ approaches, we greatly simplify the ability of W.D.P. analyzed and made figures for the macroecological ques- researchers to design flexible models for which likelihood calcula- tions. D.J.M. provided an earlier version of a dataset, and A.E.Z., tions may be difficult (Landis et al., 2013). Furthermore, by fitting W.D.P., W.K.C. and I.J.W. supplied final datasets. All authors functions to discretized predictors, we reduce the number of param- provided comments on the text. eters that need to be estimated. While here we examine optimum of an OU model as a continuous function of minimum temperature, References any model for which a likelihood can be calculated across a simmap reconstruction can in principle be fit to any continuous function. Andersen T, Carstensen J, Hernandez-Garcıa E, Duarte CM. 2009. Ecological Furthermore, further development of hierarchical Bayesian models thresholds and regime shifts: approaches to identification. Trends in Ecology & Evolution 24:49–57. may allow joint modeling of evolution of intraspecific climatic Araujo MB, Peterson AT. 2012. Uses and misuses of bioclimatic envelope niches, rather than relying on a two step-process for selecting modeling. Ecology 93: 1527–1539. percentiles as we have done (Kostikova et al., 2016). Barton K. 2017. MuMIn: Multi-Model Inference. 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Supporting Information Methods S1 Accounting for sampling bias in GBIF.

Additional Supporting Information may be found online in the Please note: Wiley Blackwell are not responsible for the content Supporting Information tab for this article: or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be Fig. S1 Plots of the proportion of taxa that are herbaceous vs directed to the New Phytologist Central Ofﬁce. minimum latitude, maximum latitude, minimum temperature, minimum precipitation (loge), maximum temperature seasonality, and maximum precipitation seasonality.

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