Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

Contents lists available at ScienceDirect

Perspectives in Plant Ecology, Evolution and Systematics

journal homepage: www.elsevier.com/locate/ppees

The evolution of seed dispersal is associated with environmental heterogeneity in Pinus T ⁎ Diego Salazar-Tortosaa, , Bianca Saladinb, Niklaus E. Zimmermannb, Jorge Castroa, ⁎ Rafael Rubio de Casasa,c,d, a Departamento de Ecología, Facultad de Ciencias, Universidad de Granada, Granada, Spain b Swiss Federal Research Institute WSL, Birmensdorf, Switzerland c Estación Experimental de Zonas Áridas, EEZA-CSIC, Almería, Spain d CEFE UMR 5175, CNRS, Universite de Montpellier, Universite Paul-Valery, Montpellier Cedex 05, France

ARTICLE INFO ABSTRACT

Keywords: Seed dispersal is a major life history stage for plants. Because of its influence on reproductive success, dispersal is Arid environments expected to be under strong selection. Different ecological circumstances might favour dispersal towards few Anemochory suitable sites or alternatively, the random distribution of propagules among suitable and unsuitable sites. Evolutionary rates However, the evolutionary dynamics favouring specific dispersal syndromes remain a matter of speculation in Seed many cases. Here, we explore the linkage between dispersal and environmental conditions at an evolutionary Trait evolution scale. We use a comparative phylogenetic approach to investigate the evolution of dispersal morphology in the Zoochory genus Pinus and its connection with climatic variability, aridity and fire. Our results show that dispersal appears to have evolved towards two alternative strategies: seeds with vs. without wings, closely matching the dis- tribution of wind- and vertebrate- mediated dispersal within the genus. Moreover, we find a close evolutionary association between dispersal morphology and environmental conditions such that each morphology pre- dominates under particular abiotic conditions. Seeds with bigger wings are selected for primarily in environ- ments with high temperature variability and/or prone to fire, whereas wingless or remnant-winged seeds are evolutionarily linked primarily to environments that are arid or exhibit a high variability in rainfall. These findings suggest a role of seed dispersal in the adaptation to certain environmental conditions, along with the influence of such conditions on the evolution of plant functional traits.

1. Introduction diversification (Bohrer et al., 2005). The role of dispersal as a connector of different demes is expected to Plant dispersal is the movement and establishment of offspring be particularly important if environmental heterogeneity is high (normally seeds) away from the parental patch (Herrera and Herrera, (Comins et al., 1980). When conditions vary across space and time, 2002). It has profound ecological and evolutionary consequences, as it dispersal facilitates survival by spreading risk over a higher number of determines the distribution and demography of populations (e.g. the patches. This becomes particularly adaptive when conditions at the risk of local extinctions) (Gadgil, 1971; Ronce, 2007; Willis et al., maternal patch are likely to become unsuitable over time (i.e., when the 2014). For instance, gene-flow is reduced when dispersal is limited, environment is negatively autocorrelated; Duputié and Massol, 2013). which can lead to the isolation of populations and ultimately promote As a result, selection favours dispersal mechanisms that maximize the speciation (Givnish, 2010). If populations are insufficiently connected probability of reaching a suitable patch and minimize the mortality of to maintain gene flow, demographic dynamics can foster genetic drift propagules (Ronce, 2007). among the different patches, ultimately resulting in lineage In plants, individuals move generally as seeds (Kartzinel et al.,

Abbreviations: PET, potential evapotranspiration; P, precipitation; W/S, ratio between wing (W) and seed (S) length; PICs, phylogenetically independent contrasts; BM, brownian motion models; OU, Ornstein–Uhlenbeck models; QuaSSE, quantitative state speciation and extinction; BAMM, Bayesian analysis of macroevolu- tionary mixture ⁎ Corresponding authors at: Departamento de Ecología, Facultad de Ciencias, Universidad de Granada, Av. Fuentenueva SN, 18071, Granada, Spain. E-mail addresses: [email protected] (D. Salazar-Tortosa), [email protected] (B. Saladin), [email protected] (N.E. Zimmermann), [email protected] (J. Castro), [email protected] (R. Rubio de Casas). https://doi.org/10.1016/j.ppees.2019.125464 Received 17 May 2018; Received in revised form 10 July 2019; Accepted 15 July 2019 Available online 17 September 2019 1433-8319/ © 2019 Elsevier GmbH. All rights reserved. D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

2013; Levin et al., 2003; Wang and Smith, 2002). Seed dispersal is a key Johnson, 1993); ii) The seeds of around 24 species are dispersed by process for plant population dynamics. However, it is often conditioned vertebrates. These vectors collect seeds from cones or the ground, and by the participation of exogenous agents for transportation of the seeds then bury them in specific spots (caches). Dispersal distance differs (Schupp et al., 2010). These agents, or vectors, can be quite diverse among animals, being specially long in the case of birds (approx. 20 are ranging from abiotic factors (e.g., wind) to the participation of animals dispersed by corvids along with other vectors like rodents; Lanner, (e.g., by ingesting or caching the seeds). Some plant groups include 2000, 1996; Thayer and Vander Wall, 2005; Tomback and Linhart, clades that differ in the vectors of dispersal (e.g., wind and animal 1990); iii) Some additional species have mixed dispersal syndromes. dispersal; Vander Wall, 2001). This seems to indicate that evolution of Around 14 have been described to be dispersed by both vertebrates and dispersal traits can be closely coupled to the diversification of certain wind (see for example Vander Wall (2008, 2003)). lineages. The effectiveness of gene flow and the degree of connectivity To study the evolution of dispersal in Pinus and its association with can potentially vary across dispersal syndromes, as different vectors speci fic environmental conditions, we have investigated 1) whether might lead to differences in population structures and even to different selection has repeatedly favoured the emergence of two alternative rates of speciation and extinction (Goodman, 1987; Lengyel et al., 2009; (wind vs. vertebrate) strategies for dispersal, with mixed dispersal as an Levin, 2000; Qiao et al., 2016). However, in spite of its potential evo- evolutionarily transition form. Alternatively, dispersal evolution might lutionary relevance, the association between dispersal syndromes and have followed a different pattern, such as convergence towards a single plant diversification remains equivocal (Willis et al., 2014). mixed syndrome (in which case anemochory and zoochory would be The seed shadow (i.e., the dispersal kernel) is strongly influenced by just extreme cases) or the differentiation of three dispersal modes; 2) the physical characteristics of the vector that disperses the seeds. Some whether changes in dispersal syndrome can be associated with differ- vectors deposit seeds with higher probability than expected by chance ences in speciation or extinction rates across lineages, i.e., if wind or in sites where germination and early development are favoured. This animal dispersal can be linked to different diversification rates in Pinus; type of dispersal is often associated to animal vectors, and can be and 3) whether a link between environmental conditions and dispersal specially beneficial for recruitment when it also results in low density evolution can be established, such that the different dispersal syn- patterns and therefore low competition among seedlings (Howe and dromes are associated with various components of environmental het- Smallwood, 1982; Spiegel and Nathan, 2010; Wenny, 2001). Con- erogeneity, with a particular focus on the potential associations with versely, other vectors distribute seeds with stochastic probability climatic variability, aridity and fire. among suitable and unsuitable sites. For instance, when seeds are dis- persed by wind (i.e., anemochorous) the seed shadow is mostly a 2. Material and methods function of the distance to the maternal tree. This type of dispersal can be regarded as random relative to the spatial distribution of sites fa- 2.1. Functional traits, climatic and fire data vourable for recruitment (Spiegel and Nathan, 2010; although see Seiwa et al. (2008)). Under conditions of high environmental hetero- Our study system consists of 113 extant species of the genus Pinus. geneity, suitable patches would be sparse and disconnected, which Phylogenetic relationships were inferred by a Bayesian analysis using could increase the advantage of specific dispersal to suitable sites by BEAST (v1.8.0; Drummond and Rambaut, 2007) from eight plastid gene scatter-hoarders (Pesendorfer et al., 2016; Purves et al., 2007; Spiegel regions (matK, rbcL, trnV, ycf2, accD, rpl20, rpoB and rpoC1). The gene and Nathan, 2010). It has been posited that environmental factors af- tree obtained was ultrametric and was dated in BEAST using the node fecting the temporal variability in recruitment and growth, such as dating method where the fossil ages were transformed into calibration aridity and fire, likely favour dispersal to microsites where seedling densities following Leslie et al. (2012). For details about the gene tree emergence and survival is less uncertain (Wenny, 2001). On the other and distribution data see Gallien et al. (2016). Pines were classified hand, Lamont et al. (1991) argued that homogeneously empty land- according to their dispersal mechanism. This information mainly came scapes, such as those that result from fire events, are more easily co- from Richardson (2000), but also the U.S. Forest Service (https://www. lonized by wind dispersed seeds. However, the specific environmental fs.fed.us), the Gymnosperm Database (https://www.conifers.org), the conditions that favour selection of these types of dispersal remain to be American Conifer Society (http://conifersociety.org/), Benkman (1995) determined. and Keane et al. (2011). We classified as animal dispersed those pines In this study, we investigate the influence of environmental condi- whose seeds are only dispersed by vertebrates (birds and/or rodents; tions on the evolution of seed dispersal in Pinus. Pines constitute a e.g. P. sibirica is dispersed by Nucifraga caryocatactes; Tomback and genus of conifers with approx. 113 species (Farjon and Filer, 2013) that Linhart, 1990), while pines whose seeds have only been reported to be are important components of Holarctic forests, with the highest di- dispersed by wind were included in the category of wind dispersal (e.g. versity concentrated between 30–40 degrees north latitude P. sylvestris; Debain et al., 2003). Those cases in which both vectors can (Richardson, 2000). Pine species provide a multiplicity of ecosystem be implicated in seed dispersal (alone or in a two step – diplochory - services and are of high economic value (Richardson, 2000). Moreover, process) were considered to have mixed dispersal. For example, P. jef- Pinus is an excellent system to address macroevolutionary questions freyi can be primary dispersed by wind or birds, then rodents take the because its evolution includes old divergence events along with rapid seeds found on the ground or in bird caches and store them in their own and relatively shallow radiations (Saladin et al., 2017). The genus has a caches (Vander Wall, 2008). We also compiled data on seed and wing moderate size and a rich fossil record reaching back over 100 My length to define seed morphology related to dispersal (see below for (Alvin, 1960; Ryberg et al., 2012). Additionally, because of its eco- details about dispersal morphology calculations). These data were ob- nomic and ecological importance, there is a wealth of data on the dis- tained from Eckenwalder (2009), Farjon (2010), the IUCN red list tribution, morphology and ecological characteristics of most species, as (https://www.iucnredlist.org/), along with the sources previously cited well as a number of well-resolved phylogenies based on plastid DNA for the dispersal syndrome data. For all statistical analyses we used R- (Gallien et al., 2016; Parks et al., 2012). This genus is a good model 3.0.2 (R Core Team, 2016), unless indicated otherwise. system for studying the evolution of dispersal syndromes because it We used the variation of climatic conditions across time and space exhibits two dispersal mechanisms: i) Approximately 75 species dis- as proxy of environmental heterogeneity. To this end, we derived a set perse their seeds by wind. Overall, most of seeds dispersed by this of variables characterizing spatial and temporal variation in climatic vector tend to fall close to the parent trees, but in some cases they can conditions as registered in the Worldclim data (Hijmans et al., 2005). reach long distances, a difference that could be influenced by variation First, we used the absolute range of temperature and precipitation in wing-loading between species as this trait is related to terminal ve- within a species’ distribution (the range of Bioclim variables BIO1 and locity of seeds (Benkman, 1995; Caplat et al., 2012; Greene and BIO12), which we assume to be indicative of the overall variability in

2 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464 climatic conditions across space. Then, we approximated fluctuation in applicability of W/S as a proxy of the dispersal syndrome by testing climatic conditions through time by estimating seasonal variation in differences in W/S among dispersal syndromes as described in the lit- several climatic variables. Specifically, we used the inverse of iso- erature and whether these followed the expected trend (i.e., W/S thermality (BIO7/BIO2*100), temperature seasonality (BIO4), the dif- wind > W/S mixed dispersal > W/S vertebrate dispersal) using gen- ference in precipitation between the wettest and driest months (BIO13- eralized linear models. BIO14) and precipitation seasonality (BIO15; Table S1). Although en- As a final validation, we studied the dependence of W/S diversifi- vironmental heterogeneity is larger and is influenced by factors other cation on the dispersal regime. The ancestral state was estimated with than climate (e.g., soil chemistry), we assumed that these metrics of the “rerootingMethod” function (“ER” model) for dispersal mode and climate variability are meaningful components of the spatio-temporal the “fastAnc” function for W/S, both from the “phytools” package environmental heterogeneity for plants (i.e., that climate is one of the (Revell, 2012). To determine if selection might have shaped the evo- main drivers of environmental heterogeneity). These variables were lution of W/S we adjusted Ornstein-Uhlenbeck models (OU). Under then summarized by means of a PCA, and the most explicative axes neutral (Brownian) evolution, trait divergence is expected to follow a resulting from this analysis (i.e., those with eigenvalues > 1) used as stochastic process and be proportional to evolutionary time and the rate metrics of climatic variability (Lê et al., 2008). Preliminary exploration of phenotypic evolution σ2 (O’Meara et al., 2006). OU models add to of the PCA results revealed that variables of precipitation variability this neutral process selection towards certain trait values (i.e., optima, were related positively with the most explicative axis, whilst variables θ). The distance between the trait value and the optimum θ and the of temperature variability were negatively correlated with that axis. “pull” towards the latter (represented by the parameter α) determine Consequently, we decided to model the association between climatic the strength of selection. In other words, in OU models evolution does variability and dispersal syndrome using three different metrics of not follow a pure stochastic process but rather is governed by selective variability, each the first axis of one of three separate PCA analyses of: pressures driving the traits towards specific values (Butler and King, (1) precipitation variability (range of BIO12; BIO13-BIO14; BIO15); (2) 2004). This type of model could fit well the evolution of the dispersal temperature variability (range of BIO1; BIO7/BIO2*100; BIO4) and (3) morphology (W/S) in Pinus, as dispersal syndromes (wind, animal, global climatic variability (all variables; Table S1). In all cases, only the wind/animal) could correspond to different selective regimes (θ values) first axis had an eigenvalue > 1. As an aridity index, we used the dif- for W/S. We compared a neutral evolutionary pattern (i.e., Brownian ference between annual potential evapotranspiration (PET; calculated motion, no selective optima) with several OU models that assume si- based on average temperature and solar radiation also obtained from milar or different phenotypic optima (θ) across dispersal modes (OU vs. Worldclim) and annual precipitation (P), i.e., PET-P. Humidity is higher OUM). In addition, we considered the possibility of variation in other when this index is negative (precipitation is higher than PET), values parameters across evolutionary regimes: strength of selection (different close to zero (i.e., low moisture) indicate low water availability, and α as well as θ values; OUMA), rate of stochastic evolution (different σ2 high positive values represent water deficit. and θ values; OUMV) and both (different σ2, α and θ; OUMVA). Model Finally, we tested the first two premises of Lamont et al.'s hypothesis selection was based on the Akaike information criterion corrected for linking fire and wind dispersal in pines (Lamont et al., 1991). According small sample sizes (AICc) and uncertainty in parameter estimation ac- to this idea, serotinous species are likely to be wind-dispersed because cording to a parametric bootstrap (Beaulieu et al., 2012; see Supple- taxa that release seeds following fire benefit from a uniform and open mentary Methods for details). OU results indicated that only two op- landscape. To test this hypothesis, we used fire regime and serotiny data tima exist for W/S (i.e., two values of W/S approximated by the from He et al. (2012) to assess if: (i) wind dispersal is prevalent in mathematical function describing phenotypic evolution). Namely W/ environments where fire is expected to be a strong selective factor, and S = 0 and W/S ∼ 2.6, which match the characteristics of vertebrate (ii) serotinous species are wind dispersed. These authors classified fire and wind dispersal syndromes described in the literature (2.6 is the occurrence in four levels (no fire, crown fire, surface fire and crown/ mean of optima associated with wind and wind/animal for OUM and surface fire), which we simplified in only two: no fire and fire. We OUMV, which were the models with the highest parameter reliability, further refined these dataset based on Tomback and Achuff (2010). see below). Pines were also segregated according to serotiny, considering as ser- Our gene tree is relatively small (113 spp.) which might have biased otinous the species with cones that release their seeds under high results in favour of more complex evolutionary models (Cooper et al., temperatures, such as those caused by fire. Therefore, our fire dataset 2016). To control for this, we performed a parametric bootstrap represented two discrete variables: with vs. without frequent fire oc- (O’Meara et al., 2006). Furthermore, we used a phylogenetic Monte currence and with serotinous vs. without serotinous cones (Table S2). In Carlo likelihood test using the “pmc” package (Boettiger et al., 2012)in summary, we tested the association between the evolution of dispersal R to confirm that our gene tree did not systematically favour OU over morphology and three components of environmental heterogeneity: simpler models, like Brownian motion. Incomplete ecological descrip- Climatic variability, aridity and fire. tions constitute another potential source of bias in our analyses. It is possible that not all instances of mixed dispersal have been observed 2.2. Evolution of dispersal syndromes (i.e., the literature does not contain all the interaction events between pine trees and potential vertebrate vectors, does not always account for We used the ratio between the lengths of the wing (W) and the seed the effect of gusty winds, etc.) and as a result some species might be (S) as a response variable to study the evolution of dispersal syndrome incorrectly assigned to just one of the extreme syndromes. To ensure (W/S hereafter; see Fig. S1 for details about distribution of this vari- that our results were robust to this sort of incomplete sampling, we ran able). W/S is expected to be proportional to the tendency of wind a sensitivity analysis introducing different levels of noise randomly dispersal in our trait database; small seeds with big wings are more assigning a different dispersal syndrome to a subset of the species. Once likely to be dispersed farther by wind than seeds with no wings. the noise was introduced in the dispersal regime, the two best OU Wingless seeds or seeds with minor wing remnants are easier to collect models according to AICc and parameter reliability (OUM and OUMV) and pouch and therefore likely preferred by vertebrates, especially by were fitted and the phenotypic optimum associated with each dispersal corvids (Tomback, 1978; Fig. 1; values of W/S and syndrome are pre- regime was obtained. This process was repeated one hundred times to sented in Table S3). To validate this proposition, we verified that W/S is calculate a confidence interval for each optimum. We considered in- a precise proxy of disc loading, which is commonly considered as the tervals of up to a 50% error (i.e., incorrect dispersal vectors for half of most accurate predictor of wind dispersal in pines (Benkman, 1995). the species). We did not extend our sensitivity for error rates > 50% W/S was negatively associated with disc loading (p.value < 0.001, because Pinus is one of the best studied tree genera and we hope expert R2 = 0.588; See Fig. S2 for further details). Then, we validated the knowledge to be reliable at least half of the time.

3 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

Fig. 1. Dispersal morphology in Pinus. The pictures show seeds of P. sylvestris, P. cembroides and P. pinaster with their corresponding W/S values. These species have been described as dispersed by wind, vertebrates, and vertebrates and wind, respectively.

In order to validate the OU results, we performed phenotypic evo- distance from the phenotypic optimum). Consequently, species with W/ lution analysis of W/S with BAMM and the BAMMtools package in R S values close to their optimum should exhibit lower diversification (Rabosky, 2014; Rabosky et al., 2014). This analysis estimates the rates of seed morphology than species further away from the putative evolutionary rate of change of W/S (i.e., speed of trait diversification phenotypic optimum, independent of the dispersal syndrome exhibited. over time) across Pinus lineages. Lastly, we used the function “phylolm” In other words, we corrected by distance to the optimum because (Ho and Ané, 2014) to test whether the evolutionary rates of change in species lying farther away from the corresponding phenotypic optimum W/S estimated by BAMM were different among dispersal syndromes. will exhibit a higher rate of change to converge towards said optimum We repeated calculations including species with vertebrate and wind than species lying near it. dispersal as wind dispersed and then as vertebrate dispersed. Note that in these and subsequent phenotypic evolution analyses we did not use fi organismal diversification rates, the reliability of which has been the 2.3. Dispersal traits and diversi cation in Pinus matter of some recent debate (Moore et al., 2016; Rabosky et al., 2017) but trait diversification rates. To study the potential association between dispersal morphology fi In all the phylolm regressions described in this paper, we selected and the diversi cation of Pinus (i.e., speciation and extinction rates),we ‘ the best-fitting covariance model (Brownian motion, OU with random conducted trait dependent speciation-extinction analyses with quanti- ’ root, OU with fixed root, lambda, kappa, delta, early burst) based on tative state speciation and extinction (QuaSSE; FitzJohn, 2010) and the AICc values. To analyze differences in evolutionary rates associated BAMM (Rabosky et al., 2014). BAMM output was treated with the fl with each phenotypic optimum, we corrected the W/S diversification BAMMtools package in R and the potential in uence of dispersal on the fi “ rates estimated by BAMM by calculating the distance of each taxon to diversi cation rates of Pinus was tested with the function traitDe- ” fi the phenotypic optimum of the model. Each species was assigned to an pendentBAMM in this package. QuaSSE analyses were conducted t- optimum according to the dispersal syndrome described in the litera- ting two alternative models following the methodology of Hardy and fi fi ture (i.e., wind or animal) and values of each phenotypic optimum were Otto (2014). The rst model considered diversi cation rates to be a taken from the initial OU results described above (optimum associated linear function of W/S, while the second model considered rates to be with animal = 0, optimum associated with wind and wind-an- constant, i.e., independent of trait values. Even if the actual relationship fi imal = 2.6). Next, we calculated the difference between the current between phenotypic change and lineage diversi cation is not truly fl fi value and the corresponding optimum of W/S in each species. The ra- linear, in uence of the trait on the diversi cation of the clade is likely fi ff fi tionale for this correction is that in OU models the change of phenotypic to result in signi cant di erences in the t of the two models values in each selective event is a function of its proximity to the (Felsenstein, 1985). In both models we used W/S as the dispersal trait adaptive phenotypic optimum (i.e., selection strength increases with and parameters were estimated by maximum-likelihood (ML). To esti- mate the significance of the influence of dispersal traits on

4 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

diversification rates, we compared the results of both models through optima (θ1; θ2): W/S ∼ 2.6 (corresponding to wind dispersal) and W/S an ANOVA test. ∼ 0 (vertebrate dispersal). Moreover, the better fit of OUMV appears to indicate that evolution towards each optimum is under different se- 2.4. Climatic variability, aridity, fire and dispersal morphology lective dynamics. Sensitivity analyses showed that evolutionary optima were fully We tested whether climatic variability is associated with the evo- robust up to 25% error (i.e., assuming that the syndrome of every one in lution of dispersal strategy, as measured by W/S (i.e., whether species four species was misassigned). Qualitative patterns did not deviate from with lower W/S values are associated with more climatic variability those obtained with the model fitted to data obtained from the biblio- than species with higher W/S values). We approached this question in graphy (two optima in wind and animal dispersal, respectively; Table 2) three ways. First, we investigated if adaptations to climatic variability even assuming that the dispersal mode of half of the species was in- explain evolutionary shifts in dispersal morphology. We analysed rates correctly described (50% error). Consequently, we conducted all sub- of phenotypic diversification for both climatic variability and W/S in sequent analyses of association between W/S and dispersal regime BAMM. In these analyses, we considered the climatic conditions inside considering only two categories for the latter. pine ranges as niche dimensions and treated them as a trait that can The analyses of trait diversification carried out with BAMM showed evolve along the phylogeny (Pearman et al., 2008; Wiens et al., 2010). that increases in the diversification rates of the dispersal morphology Then, we fitted a phylogenetically corrected regression using the are associated with shifts towards multiple phenotypic optima (Fig. 2; function “phylolm” with the rates of evolutionary change of climatic Table S5). A significant increase in W/S diversification was found in variability along the tree as the predictor and W/S diversification rates sect. Quinquefoliae, and resulted in the emergence of taxa with both low as the dependent variable, both previously calculated with BAMM. If and high W/S from an ancestor likely with high W/S. Conversely, sig- dispersal morphology provides adaptation to climatic variability, both nificant decreases in the diversification rate of W/S were observed variables are expected to covary. Therefore, the evolution of the cli- within sect. Parrya, subsect. Cembroides and especially in the evolution matic variability “trait” would exhibit two optima and its evolutionary of P. johannis and P. cembroides (Fig. 2; see also Fig. S3 for a detailed rates would be dependent on the distance to these optima, in parallel to version of the tree including species names). This decrease in evolu- the OU models of W/S evolution. To account for the deceleration of tionary rates represents a convergence towards the phenotypic op- rates closer to phenotypic optima, we corrected the diversification rates timum (W/S ∼ 0) from more intermediate phenotypic values (1 < W/ of both W/S and climatic variability by the distance to the corre- S < 0 approx.). In the clade including P. arizonica and P. cooperi the sponding phenotypic optimum in phylolm analyses. Third, to ascertain increase in the diversification rate of W/S resulted in a dispersal mor- whether extant W/S values are a function of climatic variability, we phology significantly beyond the phenotypic optimum (P. cooperi W/ fitted “phylolms” using the W/S values of all taxa as the response S = 4.5; Fig. 2). Phylogenetic regressions testing for differences in the variable and climatic variability as the independent variable. The re- rates of evolution of W/S among dispersal syndromes (i.e., with a factor sults of these analyses were visualized as linear regressions of the denoting wind or animal dispersal as predictor and the rate of di- phylogenetic independent contrasts (PICs; Felsenstein, 1985) extracted versification of W/S as response) further supported the existence of from each variable. A similar approach was used to estimate the asso- differences in the evolutionary regime of seed morphology. Evolu- ciation between aridity, fire regime and serotiny with the dispersal tionary rates were significantly higher in pines with vertebrate dispersal syndrome. In these cases, we performed “phylolm” analyses using ar- than in wind-dispersed pines (Table 3). These differences were even idity (PET - P) and the discrete variables of fire occurrence and serotiny higher when mixed cases were considered as exclusively wind dis- instead of the metrics of climatic variability as the predictor variables persed, while this significance was lost when the mixed group was (Table S2). collapsed with animal dispersed pines (Table 3). These results corro- borate the existence of different selective dynamics between phenotypic 3. Results optima as suggested by the OU models.

3.1. Dispersal morphology evolution 3.2. Dispersal and organismal diversification in Pinus

Our results revealed a relationship between W/S and the syndromes No clear link could be established between dispersal morphology described in the literature (Fig. S1). Dispersal by vertebrates is asso- and the speciation and extinction dynamics of Pinus (Fig. S4). Testing ciated with lower W/S values than wind dispersal, while species for the relationship between the diversification rates of the genus as a which both dispersal strategies have been described exhibited inter- function of W/S in BAMM yielded non-significant results (p.value > 0.9 mediate values (Fig. S1). The association between the trait and the for all rates). Likewise, the difference between QuaSSE models with and syndrome was also observed across the gene tree, with shifts in one without an effect of W/S on Pinus diversification did not reveal any corresponding with changes in the other (Fig. 2). They both appeared to significant difference. Constant (diversification not affected by trait be evolutionarily constrained (λ = 0.66 and 0.45 for W/S and dispersal changes) and linear models (trait changes influencing diversification) regime respectively). The species with the highest W/S value is P. had comparable fit to the data (ANOVA; p.value = 0.4309, cooperi (W/S = 4.5), while there are several species with W/S = 0. In ChiSq = 0.62031). The graphical representation of the QuaSSE results general, W/S values of subgen. Strobus are lower than those of subgen. seemed to reflect the existence of some, though non-significant, dif- Pinus. Within subgen. Strobus, the subsect. Cembroides (sect. Parrya) ferences in Pinus diversification rates (Fig. S4). contains only species with low W/S values (around 0) except P. rze- dowskii (W/S = 2.92). Conversely, all species of sect. Pinus have W/ 3.3. Climatic variability, aridity, fire and the dispersal syndrome S > 2.0, with the exception of P. pinea (subsect. Pinaster; Table S3). The comparison of OU models is presented in Table 1, along with The multivariate, PCA-based characterization of climatic variables parameter reliability in Table S4. Of the seven models we tested, the allowed us to estimate evolutionary associations between climatic one that best approximated the evolution of W/S assumed different variability and W/S using only three metrics, each the first axis of a state means and stochastic evolution for each regime (OUMV; AICc 70 different PCA (Table S1). To do so, we first tested the covariation be- units lower than the next best-fitting model, OUM, which allows only tween the diversification rates of climatic variability and those of W/S for variation in phenotypic optima across regimes). In both models, adjusted by the distance to the phenotypic optima of the latter. These estimation of the optima (θ) was reliable and revealed that the evolu- tests revealed a statistically significant positive association between the tion of W/S is state-dependent and converges towards two different rate of change of the trait and that of climatic conditions (Table S6).

5 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

Fig. 2. Evolution of dispersal morphology in Pinus. Top panels show a) ancestral state reconstruction for the dispersal syndrome and b) the wing to seed length ratio (W/S). Syndromes are attributed to nodes based on extant vectors (wind = green, animal = red, wind-animal = blue/violet). Dispersal modes were assigned based on literature reports. W/S evolution is represented as continuously varying along the branches of the gene tree. Terminal nodes show the current state for both variables. The lower c) panel shows the evolutionary rates of change in W/S. Red circles indicate the points with the highest probability for significant shifts in phenotypic evolutionary rates. The shift in evolutionary rate within subsect. Ponderosae includes the diversification of P. cooperi and P. arizonica. Grey bars next to the tips of this tree are proportional to the extant W/S values of each taxon, while colored dots indicate the dispersal syndrome assigned to each species. Subgenera and section names are indicated in boxes and subsections as vertical lines. A similar plot displaying all species name is available as supplementary material (Figure S3).

This means that the diversification of W/S accelerates when the di- not detect a significant correlation between serotiny and W/S (Table 4). versification rate of climatic variability (i.e., the variation in climate Given that serotiny has not evolved in subgenus Strobus (Table S2), we dimensions of the niche) increases, while a decrease of the evolutionary repeated the analysis considering only species of subgenus Pinus. The rate of W/S occurs when climate diversification slows down. In other association becomes marginally significant, but the effect size remains words, when the variability in the climate where a lineage occurs very low (estimate = 0.037; SE = 0.230; T value = 0.162; P changes faster across time so does W/S and vice versa. The models value = 0.087). including extant values of climatic variability and W/S further sup- ported the link between climatic conditions and dispersal morphology. These models showed a significant association for all variables, nega- 4. Discussion tive in the case of global and precipitation variability and positive in the ’ case of temperature variability (Fig. 3). According to our results, high Seed dispersal is a critical life history component of a plant s life- levels of precipitation variability are associated with low W/S values time reproductive success, and thus can be expected to be under strong ff fi (i.e., relatively small wings), while high levels of temperature varia- selection and even a ect species diversi cation rates (Beaulieu and bility are associated with high W/S values. Our results also supported Donoghue, 2013; Givnish, 2010; Qiao et al., 2016). In the case of Pinus, fi an influence of aridity and fire on the evolution of seed morphology in our ndings indicated that dispersal morphology has evolved towards Pinus. The two environmental factors seem to favour opposing evolu- two alternative phenotypic optima that are associated with seeds with tionary trends such that species producing seeds with relatively smaller and without well-developed wings, respectively. These two phenotypes wings are associated with higher aridity and taxa with bigger wings are predicted in turn to relate to the propensity for vertebrate and wind occupy fire-prone environments (Table 4; Fig. 3). In contrast, we did dispersal: seeds with bigger wings should disperse easily by air currents while seeds without wings are expected to rely primarily upon animals

6 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

Table 1 Ornstein-Uhlenbeck models with parameter estimates for each evolutionary optimum (see text for details). NA indicates parameters that were not calculated because of model characteristics. NaN denotes parameters that could not be computed numerically. Optima: 1 = Wind dispersal; 2 = Vertebrate dispersal; 3 = Vertebrate- Wind dispersal.

Optima Phenotypic SE Strength of SE Stochastic SE AICc mean (θ) selection (α) variation (σ2)

BM1 1 2.14 1.83 NA NA 0.17 0.13 404.76 2 0 0 NA NA 0.17 0.13 3 0 0 NA NA 0.17 0.13

OU1 1 2.16 0.18 0.09 0.24 0.31 0.21 359.97 2 0 0 0.09 0.24 0.31 0.21 3 0 0 0.09 0.24 0.31 0.21

BMS 1 3.97 1.86 NA NA 0.13 0.17 399.41 2 −4 2.69 NA NA 0.25 0.32 3 4.76 5.83 NA NA 0.06 0.75

OUM 1 2.8 0.08 4.14 0 3.95 0.13 247.41 2 0.05 0.14 4.14 0 3.95 0.13 3 2.31 0.19 4.14 0 3.95 0.13

OUMA 1 2.92 2.60E+20 4.14 NaN 0.73 NaN −1725.27 2 0 0 3.47 NaN 0.73 NaN 3 0 0 2.82 NaN 0.73 NaN

OUMV 1 2.81 0.09 3.4 0.23 4.00 0.29 177.37 2 0.04 0.02 3.4 0.23 0.06 0.38 3 2.31 0.23 3.4 0.23 4.73 0.43

OUMVA 1 2.92 9.67E+24 4.14 NaN 1.96 0 −1556.28 2 0 0.00 3.25 0 0.02 NaN 3 0 0.00 2.84 0 0.11 NaN

Table 2 Table 3 Robustness of evolutionary models to incomplete ecological data. Results of Association between the diversification rate of dispersal morphology and dis- analyses that correct for incomplete or incorrect dispersal syndrome description persal syndromes. The table shows the results of the phylolm tests comparing are presented. Confidence intervals of phenotypic optima (θ) for the best W/S diversification rates among the three dispersal syndromes described for models (OUM and OUMV) obtained from a sensitivity analysis with 100 Pinus. Wind is the reference level in all analyses, “Wind includ. W-V” indicates iterations, along with the percentage of error introduced in each analysis. Error that species considered to be dispersed by both vertebrates and wind were denotes the percentage of species that were randomly assigned a dispersal grouped with strictly wind-dispersing taxa, while in “Vert. includ. W-V”, ver- syndrome. tebrate-wind phenotypes were considered as vertebrate-dispersed.

Percentage Model Dispersal Quantile Quantile Quantile Estimate SE T value P value of error regime 2.5 50 97.5 Wind 0.08 0.045 1.766 0.08 5 OUM Wind 2.74 2.81 2.87 Vert 0.097 0.025 3.918 < 0.001 5 OUM Vert 0.01 0.11 0.38 Vert-Wind −0.019 0.023 −0.823 0.411 5 OUM Vert-Wind 1.96 2.29 2.49 Wind includ. V-W 0.075 0.044 1.679 0.096 5 OUMV Wind 2.74 2.81 3 Vert 0.102 0.024 4.268 < 0.0001 5 OUMV Vert 0.02 0.04 0.37 5 OUMV Vert-Wind 1.92 2.3 2.51 Wind 0.086 0.044 1.932 0.056 15 OUM Wind 2.65 2.81 2.95 Vert includ. V-W 0.032 0.02 1.601 0.112 15 OUM Vert −0.03 0.33 0.62 15 OUM Vert-Wind 1.86 2.22 2.55 15 OUMV Wind 2.64 2.82 3.01 variable. Although this environmental dependency may affect diversi- 15 OUMV Vert 0.01 0.3 0.64 fi 15 OUMV Vert-Wind 1.6 2.24 2.57 cation dynamics, we could not establish a clear link between the 25 OUM Wind 2.59 2.81 2.97 evolution of dispersal morphology and lineage diversification rates in 25 OUM Vert 0.02 0.45 1.56 Pinus. Nevertheless, our results denote the importance of seed dispersal 25 OUM Vert-Wind 1.61 2.19 2.64 as an adaptive trait and the influence of the environment on the mor- 25 OUMV Wind 2.62 2.8 3.02 phological evolution of dispersal structures. 25 OUMV Vert 0 0.47 1.36 25 OUMV Vert-Wind 1.62 2.24 2.65 50 OUM Wind 2.21 2.71 3.04 50 OUM Vert 0.24 1.01 2.07 4.1. Phenotypic optima: absence vs. presence of wings 50 OUM Vert-Wind 1.58 2.21 2.88 50 OUMV Wind 2.19 2.73 3.02 We defined a quantitative variable, the ratio between the wing and 50 OUMV Vert 0.22 1.05 2.26 50 OUMV Vert-Wind 1.55 2.2 2.9 seed length (W/S) to study the evolution of dispersal syndromes. We found a relationship between the dispersal syndrome and seed size in extant pine species, in agreement with previous studies (Benkman, as vectors. The emergence of either dispersal phenotype showed a close 1995; Greene and Johnson, 1993; Hutchins and Lanner, 1982; Lanner, evolutionary linkage with environmental conditions. Dispersal morphs 1990a; Leslie et al., 2017; Tomback, 1982). In fact, the close link to that exhibit poor wind dispersal are linked to environments that are specific dispersal vectors has been previously used to explain the evo- arid and/or have highly variable rainfall patterns, whereas wind dis- lution of W/S. For instance while most members of subsect. Cembroides persal appears to be more frequent in taxa adapted to fire-prone eco- have W/S ∼ 0, P. rzedowskii has winged seeds (W/S = 2.92) that clo- systems and where temperature (but not precipitation) is highly sely resemble those of sect. Pinus, a similarity that has been attributed

7 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

and wind-dispersal, respectively.

4.2. Evolutionary rates of phenotypic diversification

Our analyses suggested that the selective regimes governing seed dispersal are dependent on the specific syndrome. In other words, the evolutionary rates of change in seed morphology are significantly dif- ferent between vertebrate and wind-dispersed lineages. At this point, it is not possible to establish the causes of these differences. The evolution of seed morphology is a complex phenomenon, influenced both by so- phisticated developmental regulatory networks (Gramzow et al., 2014; Pabón-Mora et al., 2014) and exogenous selective agents, including dispersal vectors (Beaulieu and Donoghue, 2013; Mazer and Wheelwright, 1993; Rubio de Casas et al., 2012). Nevertheless, our results highlight the differences that can exist in the evolution of a functional trait, even among closely related taxa. Seed morphology appeared to have evolved at significantly higher rates in lineages with vertebrate dispersal. Conversely, wind- and mixed- dispersal lineages seemed to have similar evolutionary rates and phenotypic optima. These results corroborate the idea that selective dynamics are different between syndromes, as indicated by the OU models. Moreover, these patterns appear to support the hypothesis that animal dispersal in pines is the derived condition, as suggested by its Fig. 3. Evolutionary association between dispersal morphology and climatic late appearance (Miocene) in the fossil record (Axelrod, 1986; Lanner, variability in Pinus spp. The plots represent regressions of PICs 1990a, 1990b), because attaining the new phenotypic values requires (Phylogenetically Independent Contrasts) of W/S against the PICs of variables higher divergence away from the ancestral state and thus relatively representing climatic variability (PCA axes of global, precipitation and tem- higher evolutionary rates. This is the case of sect. Quinquefoliae (a perature variability) and aridity. Slope estimates and p.values are extracted heterogeneous group including East Asian pines and white pines of from the phylolm tests of untransformed variables. R2 values were low in all North America) that exhibited a remarkable increase in morphological 2 associations (R < 0.05). In every case, W/S is considered to be the dependent evolutionary rates in which diversifying selection led from a likely variable and environmental variables are the predictors. winged ancestor to a diversity of W/S values, including several extant taxa with W/S ∼ 0(Lanner, 1990a, 1990b). Other cases of accelerated Table 4 evolutionary rates seemed to result from directional selection, as in the Coefficients of the phylolms testing for the association between W/S with fire divergence of P. cooperi and P. arizonica. These two species exhibit regime and serotiny. Extant W/S is the dependent variable in every case, while winged seeds and are very closely related (P. cooperi is often considered fire regime and serotiny were considered as independent factors with reference a subspecies of P. arizonica; Farjon and Styles, 1997) but the phenotype “ fi ” “ ” levels No re and Non-serotinous , respectively. of P. cooperi is very extreme (W/S = 4.5). Although the causes of the Estimate SE T value P value rapid evolution of P. cooperi and morphological divergence between the two species remain to be investigated, the differentiation might be Intercept 1.74 0.64 2.718 < 0.01 linked to environmental conditions, as P. cooperi grows at higher alti- Fire regime 0.677 0.244 2.777 < 0.01 tudes and in colder environments than P. arizonica (Bannister and Intercept 2.106 0.673 3.131 < 0.01 Neuner, 2001; Farjon et al., 2015; González-Elizondo et al., 2007). Serotiny 0.021 0.295 0.071 0.944 The conservation of the ancestral phenotype (i.e., stabilizing selec- tion) might have also resulted in shifts in evolutionary rates, albeit of a ff to convergent evolution for wind dispersal (Farjon, 2008). The values of di erent nature. While evolution towards new trait values resulted in W/S ranged from 0 (no wing) to > 4. Intermediate values corresponded higher evolutionary rates, canalization appeared to lead to decelera- to taxa such as P. pinaster, P. aristata or P. brutia (Richardson, 2000), tion. In subsect. Cembroides, the maintenance of the ancestral unwinged ∼ which have been reported to be dispersed both by wind and vertebrates phenotype (W/S 0) led to a substantial reduction in the evolutionary (Earle, 2018; Fryer, 2004; Lanner, 2000; Vanwilgen and Siegfried, rates of W/S. In conclusion, while changes in the evolutionary rates of 1986). These intermediate phenotypes might represent partial ane- W/S are indicative of selection, their sign depends on the type of se- ff mochory, as the models that fitted the data better were those in which lection a ecting the trait. Disruptive (sect. Quinquefoliae) or directional mixed dispersal taxa were treated as wind dispersed. Therefore, inter- (P. cooperi - P. arizonica) selection leads to accelerated evolutionary mediate phenotypes (i.e., shorter, less bulky wings) might evolve to- rates, while stabilizing selection (sect. Cembroides) resulted in a de- wards one of the two extremes. Of course, this fact does not preclude crease in the rate of morphological evolution. the adaptive value of mixed dispersal like diplochory in many en- The evolution of dispersal morphology appeared to be very dynamic vironments where it could be maintained by diversifying selection and/ in Pinus, but these morphological changes did not seem to correlate fi or constitute a bet-hedging strategy. For instance, seeds that can be with shifts in the diversi cation patterns of the group. The results of the easily carried by both wind currents and vertebrates can benefit from analyses performed with BAMM and QuaSSE did not reveal any sig- fi ff diplochory whenever hoarding by a secondary disperser increases the ni cant e ect of dispersal traits on speciation and extinction rates. The probability of seedling establishment (Vander Wall and Longland, power of these analyses might have been limited by the relatively small 2004). Even we do not rule out the adaptive potential of mixed dis- size of the group or by the shortcomings of the algorithms (Moore et al., fl persal, our evolutionary analyses supported the existence of only two 2016; Rabosky et al., 2014). In any case, an in uence of the dispersal fi long-term evolutionary optima located at W/S ∼ 0-0.5 and ∼ 2.6, syndrome on the diversi cation of Pinus cannot be supported based on seemingly corresponding to the phenotypes identified as vertebrate- our results.

8 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

4.3. Influence of environmental heterogeneity on dispersal evolution (dry) periods. There is a direct correlation between the size of seeds and that of seedlings (Castro et al., 2006; Kitajima and Fenner, 2000; Our analyses revealed a strong link between the evolution of dis- Tomback and Linhart, 1990). Simultaneously, there are biomechanical persal morphology and environmental conditions, specifically with constraints on the potential size of wings, and consequently the de- climatic variability. Variability in temperature conditions was asso- velopment of very large seeds is necessarily associated with a reduction ciated with longer wings (higher W/S), whereas precipitation varia- in W/S (Richardson, 2000). Indeed, it has been hypothesized that harsh bility and aridity were associated with the evolution of seeds with very conditions to which many bird-dispersed pines are exposed could have small or no wings. The environments exhibiting a wider variation in resulted in selection for larger seeds, which in turn may have favoured temperatures are those located at higher latitudes, where seasonal mutualism between pines and animal dispersers (Lanner, 1990b; fluctuations are more extreme. Thus, our results could be regarded as Tomback and Linhart, 1990). This idea is supported by the results ob- confirmation of the adaptive value of wind dispersal in boreal en- tained by Debain et al. (2003) in P. sylvestris where an increase in seed vironments, hypothesized by Willson et al. (1990). mass was negatively associated with wind-dispersal capacity and po- Seasonal variability in precipitation is expected in environments sitively with seedling biomass. At this point, however, these remain with markedly dry and wet seasons, such as Mediterranean ecosystems. hypotheses and further research will be necessary to disentangle the It seems that in these habitats selection has favoured seeds without different selective processes that influence the evolution of seed mor- wings, which may indicate a higher adaptive value of zoochory. A si- phology in drought-prone environments. milar pattern was found by Leslie et al. (2017), who observed a positive In addition to variability in temperatures, fire was another en- association between seed volume and precipitation seasonality in Cu- vironmental component that tended to select for wind dispersal. Our pressaceae and Podocarpaceae, but not in Pinaceae. This disagreement analyses reflected a significant association between the binary occur- between results of Leslie et al. (2017) and our own could be caused by rence of fire (based on the classification of He et al. (2012)) and dis- the different response variables considered, as we used a more direct persal morphology. This indicates that the evolution of relatively longer proxy for dispersal, including wing in addition to seed size. An addi- wings was favoured in fire-prone environments. It has been proposed tional explanation could be the fact that we considered a broader range that fire opens and homogenizes the landscape and accelerates nutrient of climatic conditions including precipitation variability across space, mineralization, making conditions more suitable for wind dispersal. not only seasonality. We deem this multivariate approach to be more Additionally, the heat generated by blackened soil following fire pro- realistic because environmental factors are not orthogonal and tend to duces updraughts and small whirlwinds, which may foster the dispersal co-vary and this non-independence might influence their selective ef- of anemochorous diaspores (Lamont et al., 1991). It is worth noting that fects. Finally, the phylogenetic scale could be another cause for the the two factors that seem to favour the development of large wings are disagreement, as we focus only on Pinus, while Leslie et al. (2017) had a closely correlated, since fire is a major disturbance in boreal environ- broader scope. ments where temperature variability is also very high (Rowe and Other theoretical and microevolutionary studies have shown the Scotter, 1973). Thus even if our results are quite preliminary, the selective power of environmental heterogeneity on plant phenotypes in possibility that large W/S values and wind dispersal are adaptive in fire- general and on seed dispersal in particular (Baythavong, 2011; prone environments seems plausible and ecologically meaningful. Baythavong et al., 2009; Paccard et al., 2013; Schurr et al., 2008). For In a somewhat conflicting result, wind dispersal was not associated example, Gómez (2004, 2003) showed that acorn burial by Garrulus with serotiny, even though this association had been previously hy- glandarius leads to increased emergence and survival of Quercus ilex pothesized to exist and serotiny is one of the strongest predictors of seedlings in a landscape where favourable microsites were disconnected adaptation to fire (Lamont et al., 1991). The exclusion of subgenus and sparse. Similarly, using a stochastic occupancy model (SPOM) Strobus, in which serotiny has not evolved, returned a marginally sig- Purves et al. (2007) showed that under random habitat loss (i.e., under nificant association between serotiny and W/S. This result suggests a spatial and temporal negative autocorrelation) only long distance dis- positive relationship between serotiny and wind dispersal. Support for persal by animals to suitable sites was able to maintain viable popu- this tendency comes from the fact that extant pine species without lations in three species of Quercus, because other types of dispersal al- wings are usually non-serotinous and vertebrate-dispersed (Procheş ways resulted in overly high seed loss. Interestingly, these examples et al., 2012). A similar pattern has been observed in other groups such favouring dispersal to specific suitable sites all refer to Mediterranean as Leucadendron (Bond, 1984). The ambiguous association between ecosystems, where both precipitation seasonality and animal dispersal serotiny and anemochory could also be influenced by other factors, are relatively relevant ecological factors (Jordano, 2000; Willson et al., such as post-dispersal seed predation. Serotiny implies that part of the 1990, 1989). seeds remain on the tree, which could make them easier to find and It has been posited that arid environments are particularly hetero- more susceptible to predation. Therefore, pre-dispersal seed predation geneous and vertebrate dispersal would thus be especially favourable and fire frequency might be opposing selective pressures on serotiny there (Wenny, 2001). This is congruent with the association we ob- (Benkman and Siepielski, 2004; Talluto and Benkman, 2013). The lack served between lower W/S and increased aridity across pine species. of a robust association between serotiny and wind dispersal in our data Leslie et al. (2017) also reported an association between animal dis- might be also attributable to a technical limitation: it is very hard to persal and lower precipitation across conifers, which further supports rule out co-ancestry as the main cause of similarity in any trait among this result. However, it is important to point out that although sig- serotinous Pinus because they are all closely related. Therefore, it is nificant, the slope of this association is two orders of magnitude lower possible that this inconclusive result is a statistical artifact of our than the slope for the association between W/S and any of the metrics phylogenetically corrected analyses. of climatic variability. It also must be pointed out that other authors have failed to find a positive association between aridity and vertebrate 5. Conclusions dispersal and Willson et al. (1990) even showed that the frequency of vertebrate dispersal was negatively correlated with aridity. Maybe In conclusion, our work demonstrates that the evolution of seed constant aridity acts differently from variable precipitation regimes on dispersal morphology is environment-dependent in Pinus. In this group, dispersal. seeds with small or no wings, likely dispersed by vertebrates, seem to Uniformly unfavourable conditions might cause selection not so have been selected preferentially under high precipitation variability much on the ability of seeds to reach favourable sites (i.e., on traits and/or aridity. Conversely, seeds with bigger wings and thus likely affecting the transient stage of dispersal) but on post-dispersal traits anemochorous, appear to have been selected for in environments with favouring large seedlings better capable of withstanding unfavourable high temperature variability and/or prone to fire.

9 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

Acknowledgements Farjon, A., 2010. A Handbook of the World’s Conifers. Brill Academic Publishers. Farjon, A., 2008. A Natural History of Conifers. Timber press, Portland, OR, USA. Farjon, A., Filer, D., 2013. An Atlas of the World’s Conifers: an Analysis of Their We would like to thank R. O. Wüest for sharing aridity data, and E. Distribution, Biogeography, Diversity and Conservation Status. Brill Academic W. Schupp and J. M. Gómez for their helpful comments. DST was Publishers, Boston. https://doi.org/10.5860/choice.51-5353. supported by an FPU fellowship from the Spanish Ministry of Farjon, A., Gardner, M., Thomas, P., 2015. Conifer database (version Jan 2014). Digital resource at www.catalogueoflife.org/col. ISSN 2405-8858 In: Roskov, Y., Abucay, L., Education, Culture and Sport (grant number FPU13/03410). RRC was Orrell, T., Nicolson, D., Kunze, T., Flann, C., Bailly, N., Kirk, P., Bourgoin, T., DeWalt, supported by the Talentia program of the Junta de Andalucía and by the R.E., Decock, W., De Wever, A. (Eds.), Species 2000: Naturalis, Leiden, the Ministry of Economy and Competitiveness of Spain (grant number Netherlands. CGLC2016-79950-R). NEZ and BS are grateful for the support of the Farjon, A., Styles, B.T., 1997. Pinus(Pinaceae). Flora Neotropica Monograph 75. The New York Botanical Garden, New York, NY. Swiss SNF (grant number #31003A_149508/1). Analyses were run on Felsenstein, J., 1985. Phylogenies and the comparative method. Am. Nat. 125, 1–15. the Scientific Supercomputing Center of the Universidad de Granada https://doi.org/10.1086/284325. fi – and on the Sewall server funded by the Ministry of Economy and FitzJohn, R.G., 2010. Quantitative traits and diversi cation. Syst. Biol. 59, 619 633. https://doi.org/10.1093/sysbio/syq053. Competitiveness of Spain (grant number CGL2013-47558-P). Four Fryer, J.L., 2004. Pinus aristata, in: Fire Effects Information System, [Online]. Available:. anonymous reviewers and the subject editor provided insightful com- U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, ments and helpful suggestions that improved the quality and clarity of Fire Sciences Laboratory. https://www.fs.fed.us/database/feis/plants/tree/pinari/ all.html. the manuscript. Gadgil, M., 1971. Dispersal: population consequences and evolution. Ecology 52, 253–261. https://doi.org/10.2307/1934583. Appendix A. Supplementary data Gallien, L., Saladin, B., Boucher, F.C., Richardson, D.M., Zimmermann, N.E., 2016. Does the legacy of historical biogeography shape current invasiveness in pines? New Phytol. 209, 1096–1105. https://doi.org/10.1111/nph.13700. Supplementary material related to this article can be found, in the Givnish, T.J., 2010. Ecology of plant speciation. Taxon 59, 1326–1366. online version, at doi:https://doi.org/10.1016/j.ppees.2019.125464. Gómez, J.M., 2004. Importance of microhabitat and acorn burial on Quercus ilex early recruitment: non-additive effects on multiple demographic processes. Plant Ecol. 172, 287–297. References Gómez, J.M., 2003. Spatial patterns in long-distance dispersal of Quercus ilex acorns by jays in a heterogeneous landscape. Ecography 26, 573–584. https://doi.org/10. 1034/j.1600-0587.2003.03586.x. Alvin, K.L., 1960. Further conifers of the Pinaceae from the Wealden formation of González-Elizondo, M.S., González-Elizondo, M.L., Márquez-Linares, M.A., 2007. Belgium. Mémoires du Musée R. d’histoire Nat. Belgique 146, 1–39. Vegetación Y Ecorregiones De Durango. Plaza y Valdés, S.A., México, D.F. Axelrod, D.I., 1986. Cenozoic history of some Western American pines. Ann. Missouri Bot. Goodman, D., 1987. The demography of chance extinction. In: Soulé, M.E. (Ed.), Viable Gard. 73, 565–641. https://doi.org/10.2307/2399194. Populations for Conservation. Cambridge University Press, Cambridge, pp. 11–34. Bannister, P., Neuner, G., 2001. Frost resistance and the distribution of conifers. In: In: Gramzow, L., Weilandt, L., Theißen, G., 2014. MADS goes genomic in conifers: towards Bigras, F.J., Colombo, S.J. (Eds.), Conifer Cold Hardiness. Tree Physiology 1. determining the ancestral set of MADS-box genes in seed plants. Ann. Bot. 114, Springer, Dordrecht, pp. 3–21. https://doi.org/10.1007/978-94-015-9650-3_1. 1407–1429. https://doi.org/10.1093/aob/mcu066. Baythavong, B.S., 2011. Linking the spatial scale of environmental variation and the Greene, D.F., Johnson, E.A., 1993. Seed mass and dispersal capacity in wind-dispersed evolution of phenotypic plasticity: selection favors adaptive plasticity in fine-grained diaspores. Oikos 67, 69–74. https://doi.org/10.2307/3545096. environments. Am. Nat. 178, 75–87. https://doi.org/10.1086/660281. Hardy, N.B., Otto, S.P., 2014. Specialization and generalization in the diversification of Baythavong, B.S., Stanton, M.L., Rice, K.J., 2009. Understanding the consequences of seed phytophagous insects: tests of the musical chairs and oscillation hypotheses. Proc. R. dispersal in a heterogeneous environment. Ecology 90, 2118–2128. https://doi.org/ Soc. London B Biol. Sci. 281. 10.1890/08-0307.1. He, T., Pausas, J.G., Belcher, C.M., Schwilk, D.W., Lamont, B.B., 2012. Fire-adapted traits Beaulieu, J.M., Donoghue, M.J., 2013. Fruit evolution and diversification in campanulid of Pinus arose in the fiery Cretaceous. New Phytol. 194, 751–759. https://doi.org/10. angiosperms. Evolution 67, 3132–3144. https://doi.org/10.1111/evo.12180. 1111/j.1469-8137.2012.04079.x. Beaulieu, J.M., Jhwueng, D.C., Boettiger, C., O’Meara, B.C., 2012. Modeling stabilizing Herrera, C.M., 2002. Seed dispersal by vertebrates. In: Herrera, C.M., Pellmyr, O. (Eds.), selection: expanding the Ornstein-Uhlenbeck model of adaptive evolution. Evolution Plant–Vertebrate Interactions: An Evolutionary Approach. Blackwell Science, 66, 2369–2383. https://doi.org/10.1111/j.1558-5646.2012.01619.x. Hoboken, New Jersey, USA, pp. 185–208. Benkman, C.W., 1995. Wind dispersal capacity of pine seeds and the evolution of different Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high re- seed dispersal modes in pines. Oikos 73, 221–224. solution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, Benkman, C.W., Siepielski, A.M., 2004. A keystone selective agent? Pine squirrels and the 1965–1978. https://doi.org/10.1002/joc.1276. frequency of serotiny in lodgepole pine. Ecology 85, 2082–2087. Ho, Lsi T., Ané, C., 2014. A linear-time algorithm for Gaussian and non-Gaussian trait Boettiger, C., Coop, G., Ralph, P., 2012. Is your phylogeny informative? Measuring the evolution models. Syst. Biol. 63, 397–408. https://doi.org/10.1093/sysbio/syu005. power of comparative methods. Evolution 66, 2240–2251. https://doi.org/10.1111/ Howe, H.F., Smallwood, J., 1982. Ecology of Seed Dispersal. Annu. Rev. Ecol. Syst. 13, j.1558-5646.2011.01574.x. 201–228. https://doi.org/10.1146/annurev.es.13.110182.001221. Bohrer, G., Nathan, R., Volis, S., 2005. Effects of long-distance dispersal for metapopu- Hutchins, H.E., Lanner, R.M., 1982. The central role of Clark’s nutcracker in the dispersal lation survival and genetic structure at ecological time and spatial scales. J. Ecol. 93, and establishment of whitebark pine. Oecologia 55, 192–201. https://doi.org/10. 1029–1040. https://doi.org/10.1111/j.1365-2745.2005.01048.x. 1007/BF00384487. Bond, W.J., 1984. Fire survival of Cape Proteaceae-influence of fire season and seed Jordano, P., 2000. Fruits and frugivory. In: Fenner, M. (Ed.), Seeds: The Ecology of predators. Vegetatio 56, 65–74. https://doi.org/10.1007/BF00033048. Regeneration in Plant Communities. CABI Publ, Wallingford, UK, pp. 125–166. Butler, M.A., King, A.A., 2004. Phylogenetic comparative analysis: a modeling approach Kartzinel, T.R., Shefferson, R.P., Trapnell, D.W., 2013. Relative importance of pollen and for adaptive evolution. Am. Nat. 164, 683–695. https://doi.org/10.1086/426002. seed dispersal across a neotropical mountain landscape for an epiphytic orchid. Mol. Caplat, P., Nathan, R., Buckley, Y.M., 2012. Seed terminal velocity, wind turbulence, and Ecol. 22, 6048–6059. https://doi.org/10.1111/mec.12551. demography drive the spread of an invasive tree in an analytical model. Ecology 93, Keane, R.E., Tomback, D.F., Murray, M.P., Smith, C.M., 2011. The future of high-eleva- 368–377. tion, five-needle white pines in Western North America. In: Proceedings of the High Castro, J., Hódar, J.A., Gómez, J.M., 2006. Seed size. In: Basra, A.S. (Ed.), Handbook of Five Symposium. 28-30 June 2010; Missoula, MT. Proceedings RMRS-P-63. Fort Seed Science and Technology. Haworth’s Food Products Press, New York, pp. Collins, CO: U.S. Department of Agriculture, Forest Service. Rocky Mountain. 397–427. Kitajima, K., Fenner, M., 2000. Ecology of seedling regeneration. In: Fenner, M. (Ed.), Comins, H.N., Hamilton, W.D., May, R.M., 1980. Evolutionarily stable dispersal strate- Seeds: The Ecology of Regeneration in Plant Communities. CABI, Wallingford, pp. gies. J. Theor. Biol. 82, 205–230. 331–360. https://doi.org/10.1079/9780851994321.0331. Cooper, N., Thomas, G.H., Venditti, C., Meade, A., Freckleton, R.P., 2016. A cautionary Lamont, B.B., Le Maitre, D.C., Cowling, R.M., Enright, N.J., 1991. Canopy seed storage in note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J. woody plants. Bot. Rev. 57, 277–317. https://doi.org/10.1007/BF02858770. Linn. Soc. 118, 64–77. https://doi.org/10.1111/bij.12701. Lanner, R.M., 2000. Seed dispersal in Pinus. In: Richardson, D.M. (Ed.), Ecology and Debain, S., Curt, T., Lepart, J., 2003. Seed mass, seed dispersal capacity, and seedling Biogeography of Pinus. Cambridge University Press, pp. 281–295. performance in a Pinus sylvestris population. Ecoscience 10, 168–175. https://doi. Lanner, R.M., 1996. Made for Each Other: a Symbiosis of Birds and Pines. Oxford org/10.1080/11956860.2003.11682764. University Press, New York. Drummond, A.J., Rambaut, A., 2007. BEAST: bayesian evolutionary analysis by sampling Lanner, R.M., 1990a. Morphological differences between wind-dispersed and bird-dis- trees. BMC Evol. Biol. 7, 214. https://doi.org/10.1186/1471-2148-7-214. persed pines of subgenus Strobus (Compilers) In: Schmidt, W.C., McDonald, K.J., Duputié, A., Massol, F., 2013. An empiricist’s guide to theoretical predictions on the Schmidt, W.C., McDonald, K.J. (Eds.), Proceedings-Symposium on Whitebark Pine evolution of dispersal. Interface Focus 3, 20130028. Ecosystems: Ecology and Management of a High-Mountain Resource. U.S. Earle, C., 2018. Pinus brutia. The Gymnosperm Database. [Online].. https://www. Department of Agriculture Forest Service General Technical Report INT-270, pp. conifers.org/pi/Pinus_brutia.php. 371–372 Bozeman, Mont. Eckenwalder, J.E., 2009. Conifers of the World: the Complete Reference. Timber press, Lanner, R.M., 1990b. Biology, taxonomy, evolution, and geography of stone pines of the Portland, OR, USA. world. In: Schmidt, W.C., McDonald, K.J. (Eds.), Proceedings, Symposium on

10 D. Salazar-Tortosa, et al. Perspectives in Plant Ecology, Evolution and Systematics 41 (2019) 125464

Whitebark Pine Ecosystems: Ecology and Management of a High-Mountain Resource. evolution. Annu. Rev. Ecol. Evol. Syst. 38, 231–253. https://doi.org/10.1146/ U.S. Department of Agriculture Forest Service General Technical Report INT-270. annurev.ecolsys.38.091206.095611. Bozeman, Mont.. pp. 14–24. Rowe, J.S., Scotter, G.W., 1973. Fire in the boreal forest. Quat. Res. 3, 444–464. https:// Lê, S., Josse, J., Husson, F., 2008. FactoMineR: an r package for multivariate analysis. J. doi.org/10.1016/0033-5894(73)90008-2. Stat. Softw. 25. https://doi.org/10.18637/jss.v025.i01. Rubio de Casas, R., Willis, C.G., Donohue, K., 2012. Plant dispersal phenotypes: a seed Lengyel, S., Gove, A.D., Latimer, A.M., Majer, J.D., Dunn, R.R., 2009. Ants sow the seeds perspective of maternal habitat selection. In: Clobert, J., Baguette, M., Benton, T.G., of global diversification in flowering plants. PLoS One 4, 1–6. https://doi.org/10. Bullock, J.M. (Eds.), Dispersal Ecology and Evolution. Oxford University Press, pp. 1371/journal.pone.0005480. 171–184. Leslie, A.B., Beaulieu, J.M., Mathews, S., 2017. Variation in seed size is structured by Ryberg, P.E., Rothwell, G.W., Stockey, R.A., Hilton, J., Mapes, G., Riding, J.B., 2012. dispersal syndrome and cone morphology in conifers and other nonflowering seed Reconsidering relationships among stem and crown group Pinaceae: oldest record of plants. New Phytol. 216, 429–437. https://doi.org/10.1111/nph.14456. the genus Pinus from the early Cretaceous of Yorkshire, United Kingdom. Int. J. Plant Leslie, A.B., Beaulieu, J.M., Rai, H.S., Crane, P.R., Donoghue, M.J., Mathews, S., 2012. Sci. 173, 917–932. https://doi.org/10.1086/667228. Hemisphere-scale differences in conifer evolutionary dynamics. Proc. Natl. Acad. Sci. Saladin, B., Leslie, A.B., Wüest, R.O., Litsios, G., Conti, E., Salamin, N., Zimmermann, 109, 16217–16221. https://doi.org/10.1073/pnas.1213621109. N.E., 2017. Fossils matter: improved estimates of divergence times in Pinus reveal Levin, D.A., 2000. The Origin, Expansion, and Demise of Plant Species. Oxford University older diversification. BMC Evol. Biol. 17. https://doi.org/10.1186/s12862-017- Press, Oxford. 0941-z. Levin, S.A., Muller-Landau, H.C., Nathan, R., Chave, J., 2003. The ecology and evolution Schupp, E.W., Jordano, P., Gómez, J.M., 2010. Seed dispersal effectiveness revisited: a of seed dispersal: a theoretical perspective. Annu. Rev. Ecol. Evol. Syst. 34, 575–604. conceptual review. New Phytol. 188, 333–353. https://doi.org/10.1111/j.1469- https://doi.org/10.1146/annurev.ecolsys.34.011802.132428. 8137.2010.03402.x. Mazer, S.J., Wheelwright, N.T., 1993. Fruit size and shape: allometry at different taxo- Schurr, F.M., Steinitz, O., Nathan, R., 2008. Plant fecundity and seed dispersal in spatially nomic levels in bird-dispersed plants. Evol. Ecol. 7, 556–575. https://doi.org/10. heterogeneous environments: models, mechanisms and estimation. J. Ecol. 96, 1007/BF01237821. 628–641. https://doi.org/10.1111/j.1365-2745.2008.01371.x. Moore, B.R., Höhna, S., May, M.R., Rannala, B., Huelsenbeck, J.P., 2016. Critically Seiwa, K., Tozawa, M., Ueno, N., Kimura, M., Yamasaki, M., Maruyama, K., 2008. Roles of evaluating the theory and performance of Bayesian analysis of macroevolutionary cottony hairs in directed seed dispersal in riparian willows. Plant Ecol. 198, 27–35. mixtures. Proc. Natl. Acad. Sci. U. S. A. 113, 9569–9574. https://doi.org/10.1073/ https://doi.org/10.1007/s11258-007-9382-x. pnas.1518659113. Spiegel, O., Nathan, R., 2010. Incorporating density dependence into the directed-dis- O’Meara, B.C., Ané, C., Sanderson, M.J., Wainwright, P.C., 2006. Testing for different persal hypothesis. Ecology 91, 1538–1548. rates of continuous trait evolution using likelihood. Evolution 60, 922–933. https:// Talluto, M.V., Benkman, C.W., 2013. Landscape-scale eco-evolutionary dynamics: selec- doi.org/10.1111/j.0014-3820.2006.tb01171.x. tion by seed predators and fire determine a major reproductive strategy. Ecology 94, Pabón-Mora, N., Wong, G.K.-S., Ambrose, B.A., 2014. Evolution of fruit development 1307–1316. https://doi.org/10.1890/12-2058.1. genes in flowering plants. Front. Plant Sci. 5, 300. https://doi.org/10.3389/fpls. Thayer, T.C., Vander Wall, S.B., 2005. Interactions between Steller’s jays and yellow pine 2014.00300. chipmunks over scatter-hoarded sugar pine seeds. J. Anim. Ecol. 74, 365–374. Paccard, A., Vance, M., Willi, Y., 2013. Weak impact of fine-scale landscape heterogeneity https://doi.org/10.1111/j.1365-2656.2005.00932.x. on evolutionary potential in Arabidopsis lyrata. J. Evol. Biol. 26, 2331–2340. https:// Tomback, D.F., 1982. Dispersal of whitebark pine seeds by Clark’s nutcracker: a mutu- doi.org/10.1111/jeb.12220. alism hypothesis. J. Anim. Ecol. 51, 451. https://doi.org/10.2307/3976. Parks, M., Cronn, R., Liston, A., 2012. Separating the wheat from the chaff: mitigating the Tomback, D.F., 1978. Foraging strategies of Clark’s nutcracker. Living Bird 16, 123–161. effects of noise in a plastome phylogenomic data set from Pinus L. (Pinaceae). BMC Tomback, D.F., Achuff, P., 2010. Blister rust and western forest biodiversity: ecology, Evol. Biol. 12, 100. https://doi.org/10.1186/1471-2148-12-100. values and outlook for white pines. For. Pathol. 40, 186–225. https://doi.org/10. Pearman, P.B., Guisan, A., Broennimann, O., Randin, C.F., 2008. Niche dynamics in space 1111/j.1439-0329.2010.00655.x. and time. Trends Ecol. Evol. 23, 149–158. https://doi.org/10.1016/j.tree.2007.11. Tomback, D.F., Linhart, Y.B., 1990. The evolution of bird-dispersed pines. Evol. Ecol. 4, 005. 185–219. Pesendorfer, M.B., Sillett, T.S., Koenig, W.D., Morrison, S.A., 2016. Scatter-hoarding Vander Wall, S.B., 2008. On the relative contributions of wind vs. animals to seed dis- corvids as seed dispersers for oaks and pines: a review of a widely distributed mu- persal of four Sierra Nevada pines. Ecology 89, 1837–1849. https://doi.org/10.1890/ tualism and its utility to habitat restoration. Condor 118, 215–237. https://doi.org/ 07-0409.1. 10.1650/condor-15-125.1. Vander Wall, S.B., 2003. Effects of seed size of wind-dispersed pines (Pinus) on secondary Procheş, Ş., Wilson, J.R.U., Richardson, D.M., Rejmánek, M., 2012. Native and natur- seed dispersal and the caching behavior of rodents. Oikos 100, 25–34. alized range size in Pinus : relative importance of biogeography, introduction effort Vander Wall, S.B., 2001. The evolutionary ecology of nut dispersal. Bot. Rev. 67, 74–117. and species traits. Glob. Ecol. Biogeogr. 21, 513–523. https://doi.org/10.1111/j. https://doi.org/10.1007/BF02857850. 1466-8238.2011.00703.x. Vander Wall, S.B., Longland, W.S., 2004. Diplochory: are two seed dispersers better than Purves, D.W., Zavala, M.A., Ogle, K., Prieto, F., Benayas, J.M.R., 2007. Environmental one? Trends Ecol. Evol. 19, 155–161. https://doi.org/10.1016/j.tree.2003.12.004. heterogeneity, bird-mediated directed dispersal, and oak woodland dynamics in Vanwilgen, B.W., Siegfried, W.R., 1986. Seed dispersal properties of three pine species as Mediterranean Spain. Ecol. Monogr. 77, 77–97. https://doi.org/10.1890/05-1923. a determinant invasive potential. S. Afr. J. Bot. 52, 546–548. https://doi.org/10. Qiao, H., Saupe, E.E., Soberón, J., Peterson, A.T., Myers, C.E., 2016. Impacts of niche 1016/S0254-6299(16)31489-2. breadth and dispersal ability on macroevolutionary patterns. Am. Nat. 188, 149–162. Wang, B.C., Smith, T.B., 2002. Closing the seed dispersal loop. Trends Ecol. Evol. 17, https://doi.org/10.1086/687201. 379–386. https://doi.org/10.1016/S0169-5347(02)02541-7. R Core Team, 2016. R: A Language and Environment for Statistical Computing [WWW Wenny, D.G., 2001. Advantages of seed dispersal: a re-evaluation of directed dispersal. Document]. URL. https://www.r-project.org/. Evol. Ecol. Res. 3, 51–74. Rabosky, D.L., 2014. Automatic detection of key innovations, rate shifts, and diversity- Wiens, J.J., Ackerly, D.D., Allen, A.P., Anacker, B.L., Buckley, L.B., Cornell, H.V., dependence on phylogenetic trees. PLoS One 9, e89543. https://doi.org/10.1371/ Damschen, E.I., Davies, T.J., Grytnes, J.-A., Harrison, S.P., Hawkins, B.A., Holt, R.D., journal.pone.0089543. McCain, C.M., Stephens, P.R., 2010. Niche conservatism as an emerging principle in Rabosky, D.L., Grundler, M., Anderson, C., Title, P., Shi, J.J., Brown, J.W., Huang, H., ecology and conservation biology. Ecol. Lett. 13, 1310–1324. https://doi.org/10. Larson, J.G., 2014. BAMMtools: an R package for the analysis of evolutionary dy- 1111/j.1461-0248.2010.01515.x. namics on phylogenetic trees. Methods Ecol. Evol. 5. https://doi.org/10.1111/2041- Willis, C.G., Hall, J.C., Rubio de Casas, R., Wang, T.Y., Donohue, K., 2014. Diversification 210X.12199. and the evolution of dispersal ability in the tribe Brassiceae (Brassicaceae). Ann. Bot. Rabosky, D.L., Mitchell, J.S., Chang, J., 2017. Is BAMM flawed? Theoretical and practical 114, 1675–1686. https://doi.org/10.1093/aob/mcu196. concerns in the analysis of multi-rate diversification models. Syst. Biol. 66, 477–498. Willson, M.F., Irvine, A.K., Walsh, N.G., 1989. Vertebrate dispersal syndromes in some https://doi.org/10.1093/sysbio/syx037. australian and New Zealand plant communities, with geographic comparisons. Revell, L.J., 2012. Phytools: an R package for phylogenetic comparative biology (and Biotropica 21, 133–147. https://doi.org/10.2307/2388704. other things). Methods Ecol. Evol. 3, 217–223. https://doi.org/10.1111/j.2041- Willson, M.F., Rice, B.L., Westoby, M., 1990. Seed dispersal spectra: a comparison of 210X.2011.00169.x. temperate plant communities. J. Veg. Sci. 1, 547–562. https://doi.org/10.2307/ Richardson, D.M., 2000. Ecology and Biogeography of Pinus. Cambridge University Press. 3235789. Ronce, O., 2007. How does it feel to be like a rolling stone? Ten questions about dispersal

11