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Perspectives in Plant Ecology, Evolution and Systematics

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The evolution of seed dispersal is associated with environmentalheterogeneity in Pinus

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, Spainb Swiss Federal Research Institute WSL, Birmensdorf, Switzerlandc Estación Experimental de Zonas Áridas, EEZA-CSIC, Almería, Spaind CEFE UMR 5175, CNRS, Universite de Montpellier, Universite Paul-Valery, Montpellier Cedex 05, France

A R T I C L E I N F O

Keywords:Arid environmentsAnemochoryEvolutionary ratesSeedTrait evolutionZoochory

A B S T R A C T

Seed dispersal is a major life history stage for plants. Because of its influence on reproductive success, dispersal isexpected to be under strong selection. Different ecological circumstances might favour dispersal towards fewsuitable sites or alternatively, the random distribution of propagules among suitable and unsuitable sites.However, the evolutionary dynamics favouring specific dispersal syndromes remain a matter of speculation inmany cases. Here, we explore the linkage between dispersal and environmental conditions at an evolutionaryscale. We use a comparative phylogenetic approach to investigate the evolution of dispersal morphology in thegenus Pinus and its connection with climatic variability, aridity and fire. Our results show that dispersal appearsto 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 evolutionaryassociation 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 areevolutionarily linked primarily to environments that are arid or exhibit a high variability in rainfall. Thesefindings suggest a role of seed dispersal in the adaptation to certain environmental conditions, along with theinfluence of such conditions on the evolution of plant functional traits.

1. Introduction

Plant dispersal is the movement and establishment of offspring(normally seeds) away from the parental patch (Herrera and Herrera,2002). It has profound ecological and evolutionary consequences, as itdetermines the distribution and demography of populations (e.g. therisk of local extinctions) (Gadgil, 1971; Ronce, 2007; Willis et al.,2014). For instance, gene-flow is reduced when dispersal is limited,which can lead to the isolation of populations and ultimately promotespeciation (Givnish, 2010). If populations are insufficiently connectedto maintain gene flow, demographic dynamics can foster genetic driftamong the different patches, ultimately resulting in lineage

diversification (Bohrer et al., 2005).The role of dispersal as a connector of different demes is expected to

be particularly important if environmental heterogeneity is high(Comins et al., 1980). When conditions vary across space and time,dispersal facilitates survival by spreading risk over a higher number ofpatches. This becomes particularly adaptive when conditions at thematernal patch are likely to become unsuitable over time (i.e., when theenvironment is negatively autocorrelated; Duputié and Massol, 2013).As a result, selection favours dispersal mechanisms that maximize theprobability of reaching a suitable patch and minimize the mortality ofpropagules (Ronce, 2007).

In plants, individuals move generally as seeds (Kartzinel et al.,

https://doi.org/10.1016/j.ppees.2019.125464Received 17 May 2018; Received in revised form 10 July 2019; Accepted 15 July 2019

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: dftortosa@gmail.com (D. Salazar-Tortosa), bianca.saladin@wsl.ch (B. Saladin), niklaus.zimmermann@wsl.ch (N.E. Zimmermann),

jorge@ugr.es (J. Castro), rubiodecasas@ugr.es (R. Rubio de Casas).

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

Available online 17 September 20191433-8319/ © 2019 Elsevier GmbH. All rights reserved.

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2013; Levin et al., 2003; Wang and Smith, 2002). Seed dispersal is a keyprocess for plant population dynamics. However, it is often conditionedby the participation of exogenous agents for transportation of the seeds(Schupp et al., 2010). These agents, or vectors, can be quite diverseranging from abiotic factors (e.g., wind) to the participation of animals(e.g., by ingesting or caching the seeds). Some plant groups includeclades that differ in the vectors of dispersal (e.g., wind and animaldispersal; Vander Wall, 2001). This seems to indicate that evolution ofdispersal traits can be closely coupled to the diversification of certainlineages. The effectiveness of gene flow and the degree of connectivitycan potentially vary across dispersal syndromes, as different vectorsmight lead to differences in population structures and even to differentrates of speciation and extinction (Goodman, 1987; Lengyel et al., 2009;Levin, 2000; Qiao et al., 2016). However, in spite of its potential evo-lutionary relevance, the association between dispersal syndromes andplant diversification remains equivocal (Willis et al., 2014).

The seed shadow (i.e., the dispersal kernel) is strongly influenced bythe physical characteristics of the vector that disperses the seeds. Somevectors deposit seeds with higher probability than expected by chancein sites where germination and early development are favoured. Thistype of dispersal is often associated to animal vectors, and can bespecially beneficial for recruitment when it also results in low densitypatterns and therefore low competition among seedlings (Howe andSmallwood, 1982; Spiegel and Nathan, 2010; Wenny, 2001). Con-versely, other vectors distribute seeds with stochastic probabilityamong suitable and unsuitable sites. For instance, when seeds are dis-persed by wind (i.e., anemochorous) the seed shadow is mostly afunction of the distance to the maternal tree. This type of dispersal canbe regarded as random relative to the spatial distribution of sites fa-vourable for recruitment (Spiegel and Nathan, 2010; although seeSeiwa et al. (2008)). Under conditions of high environmental hetero-geneity, suitable patches would be sparse and disconnected, whichcould increase the advantage of specific dispersal to suitable sites byscatter-hoarders (Pesendorfer et al., 2016; Purves et al., 2007; Spiegeland Nathan, 2010). It has been posited that environmental factors af-fecting the temporal variability in recruitment and growth, such asaridity and fire, likely favour dispersal to microsites where seedlingemergence and survival is less uncertain (Wenny, 2001). On the otherhand, Lamont et al. (1991) argued that homogeneously empty land-scapes, such as those that result from fire events, are more easily co-lonized by wind dispersed seeds. However, the specific environmentalconditions that favour selection of these types of dispersal remain to bedetermined.

In this study, we investigate the influence of environmental condi-tions on the evolution of seed dispersal in Pinus. Pines constitute agenus of conifers with approx. 113 species (Farjon and Filer, 2013) thatare important components of Holarctic forests, with the highest di-versity concentrated between 30–40 degrees north latitude(Richardson, 2000). Pine species provide a multiplicity of ecosystemservices and are of high economic value (Richardson, 2000). Moreover,Pinus is an excellent system to address macroevolutionary questionsbecause its evolution includes old divergence events along with rapidand relatively shallow radiations (Saladin et al., 2017). The genus has amoderate size and a rich fossil record reaching back over 100 My(Alvin, 1960; Ryberg et al., 2012). Additionally, because of its eco-nomic and ecological importance, there is a wealth of data on the dis-tribution, morphology and ecological characteristics of most species, aswell as a number of well-resolved phylogenies based on plastid DNA(Gallien et al., 2016; Parks et al., 2012). This genus is a good modelsystem for studying the evolution of dispersal syndromes because itexhibits two dispersal mechanisms: i) Approximately 75 species dis-perse their seeds by wind. Overall, most of seeds dispersed by thisvector tend to fall close to the parent trees, but in some cases they canreach long distances, a difference that could be influenced by variationin wing-loading between species as this trait is related to terminal ve-locity of seeds (Benkman, 1995; Caplat et al., 2012; Greene and

Johnson, 1993); ii) The seeds of around 24 species are dispersed byvertebrates. These vectors collect seeds from cones or the ground, andthen bury them in specific spots (caches). Dispersal distance differsamong animals, being specially long in the case of birds (approx. 20 aredispersed by corvids along with other vectors like rodents; Lanner,2000, 1996; Thayer and Vander Wall, 2005; Tomback and Linhart,1990); iii) Some additional species have mixed dispersal syndromes.Around 14 have been described to be dispersed by both vertebrates andwind (see for example Vander Wall (2008, 2003)).

To study the evolution of dispersal in Pinus and its association withspecific environmental conditions, we have investigated 1) whetherselection has repeatedly favoured the emergence of two alternative(wind vs. vertebrate) strategies for dispersal, with mixed dispersal as anevolutionarily transition form. Alternatively, dispersal evolution mighthave followed a different pattern, such as convergence towards a singlemixed syndrome (in which case anemochory and zoochory would bejust extreme cases) or the differentiation of three dispersal modes; 2)whether changes in dispersal syndrome can be associated with differ-ences in speciation or extinction rates across lineages, i.e., if wind oranimal dispersal can be linked to different diversification rates in Pinus;and 3) whether a link between environmental conditions and dispersalevolution can be established, such that the different dispersal syn-dromes are associated with various components of environmental het-erogeneity, with a particular focus on the potential associations withclimatic variability, aridity and fire.

2. Material and methods

2.1. Functional traits, climatic and fire data

Our study system consists of 113 extant species of the genus Pinus.Phylogenetic relationships were inferred by a Bayesian analysis usingBEAST (v1.8.0; Drummond and Rambaut, 2007) from eight plastid generegions (matK, rbcL, trnV, ycf2, accD, rpl20, rpoB and rpoC1). The genetree obtained was ultrametric and was dated in BEAST using the nodedating method where the fossil ages were transformed into calibrationdensities following Leslie et al. (2012). For details about the gene treeand distribution data see Gallien et al. (2016). Pines were classifiedaccording to their dispersal mechanism. This information mainly camefrom Richardson (2000), but also the U.S. Forest Service (https://www.fs.fed.us), the Gymnosperm Database (https://www.conifers.org), theAmerican Conifer Society (http://conifersociety.org/), Benkman (1995)and Keane et al. (2011). We classified as animal dispersed those pineswhose seeds are only dispersed by vertebrates (birds and/or rodents;e.g. P. sibirica is dispersed by Nucifraga caryocatactes; Tomback andLinhart, 1990), while pines whose seeds have only been reported to bedispersed by wind were included in the category of wind dispersal (e.g.P. sylvestris; Debain et al., 2003). Those cases in which both vectors canbe implicated in seed dispersal (alone or in a two step – diplochory -process) were considered to have mixed dispersal. For example, P. jef-freyi can be primary dispersed by wind or birds, then rodents take theseeds found on the ground or in bird caches and store them in their owncaches (Vander Wall, 2008). We also compiled data on seed and winglength to define seed morphology related to dispersal (see below fordetails about dispersal morphology calculations). These data were ob-tained from Eckenwalder (2009), Farjon (2010), the IUCN red list(https://www.iucnredlist.org/), along with the sources previously citedfor the dispersal syndrome data. For all statistical analyses we used R-3.0.2 (R Core Team, 2016), unless indicated otherwise.

We used the variation of climatic conditions across time and spaceas proxy of environmental heterogeneity. To this end, we derived a setof variables characterizing spatial and temporal variation in climaticconditions as registered in the Worldclim data (Hijmans et al., 2005).First, we used the absolute range of temperature and precipitationwithin a species’ distribution (the range of Bioclim variables BIO1 andBIO12), which we assume to be indicative of the overall variability in

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climatic conditions across space. Then, we approximated fluctuation inclimatic conditions through time by estimating seasonal variation inseveral climatic variables. Specifically, we used the inverse of iso-thermality (BIO7/BIO2*100), temperature seasonality (BIO4), the dif-ference in precipitation between the wettest and driest months (BIO13-BIO14) and precipitation seasonality (BIO15; Table S1). Although en-vironmental heterogeneity is larger and is influenced by factors otherthan climate (e.g., soil chemistry), we assumed that these metrics ofclimate variability are meaningful components of the spatio-temporalenvironmental heterogeneity for plants (i.e., that climate is one of themain drivers of environmental heterogeneity). These variables werethen summarized by means of a PCA, and the most explicative axesresulting from this analysis (i.e., those with eigenvalues> 1) used asmetrics of climatic variability (Lê et al., 2008). Preliminary explorationof the PCA results revealed that variables of precipitation variabilitywere related positively with the most explicative axis, whilst variablesof temperature variability were negatively correlated with that axis.Consequently, we decided to model the association between climaticvariability and dispersal syndrome using three different metrics ofvariability, each the first axis of one of three separate PCA analyses of:(1) precipitation variability (range of BIO12; BIO13-BIO14; BIO15); (2)temperature variability (range of BIO1; BIO7/BIO2*100; BIO4) and (3)global climatic variability (all variables; Table S1). In all cases, only thefirst axis had an eigenvalue>1. As an aridity index, we used the dif-ference between annual potential evapotranspiration (PET; calculatedbased on average temperature and solar radiation also obtained fromWorldclim) and annual precipitation (P), i.e., PET-P. Humidity is higherwhen this index is negative (precipitation is higher than PET), valuesclose to zero (i.e., low moisture) indicate low water availability, andhigh positive values represent water deficit.

Finally, we tested the first two premises of Lamont et al.'s hypothesislinking fire and wind dispersal in pines (Lamont et al., 1991). Accordingto this idea, serotinous species are likely to be wind-dispersed becausetaxa that release seeds following fire benefit from a uniform and openlandscape. To test this hypothesis, we used fire regime and serotiny datafrom He et al. (2012) to assess if: (i) wind dispersal is prevalent inenvironments where fire is expected to be a strong selective factor, and(ii) serotinous species are wind dispersed. These authors classified fireoccurrence in four levels (no fire, crown fire, surface fire and crown/surface fire), which we simplified in only two: no fire and fire. Wefurther refined these dataset based on Tomback and Achuff (2010).Pines were also segregated according to serotiny, considering as ser-otinous the species with cones that release their seeds under hightemperatures, such as those caused by fire. Therefore, our fire datasetrepresented two discrete variables: with vs. without frequent fire oc-currence and with serotinous vs. without serotinous cones (Table S2). Insummary, we tested the association between the evolution of dispersalmorphology and three components of environmental heterogeneity:Climatic variability, aridity and fire.

2.2. Evolution of dispersal syndromes

We used the ratio between the lengths of the wing (W) and the seed(S) as a response variable to study the evolution of dispersal syndrome(W/S hereafter; see Fig. S1 for details about distribution of this vari-able). W/S is expected to be proportional to the tendency of winddispersal in our trait database; small seeds with big wings are morelikely to be dispersed farther by wind than seeds with no wings.Wingless seeds or seeds with minor wing remnants are easier to collectand pouch and therefore likely preferred by vertebrates, especially bycorvids (Tomback, 1978; Fig. 1; values of W/S and syndrome are pre-sented in Table S3). To validate this proposition, we verified that W/S isa precise proxy of disc loading, which is commonly considered as themost accurate predictor of wind dispersal in pines (Benkman, 1995).W/S was negatively associated with disc loading (p.value< 0.001,R2=0.588; See Fig. S2 for further details). Then, we validated the

applicability of W/S as a proxy of the dispersal syndrome by testingdifferences in W/S among dispersal syndromes as described in the lit-erature and whether these followed the expected trend (i.e., W/Swind > W/S mixed dispersal > W/S vertebrate dispersal) using gen-eralized linear models.

As a final validation, we studied the dependence of W/S diversifi-cation on the dispersal regime. The ancestral state was estimated withthe “rerootingMethod” function (“ER” model) for dispersal mode andthe “fastAnc” function for W/S, both from the “phytools” package(Revell, 2012). To determine if selection might have shaped the evo-lution of W/S we adjusted Ornstein-Uhlenbeck models (OU). Underneutral (Brownian) evolution, trait divergence is expected to follow astochastic process and be proportional to evolutionary time and the rateof phenotypic evolution σ2 (O’Meara et al., 2006). OU models add tothis neutral process selection towards certain trait values (i.e., optima,θ). The distance between the trait value and the optimum θ and the“pull” towards the latter (represented by the parameter α) determinethe strength of selection. In other words, in OU models evolution doesnot follow a pure stochastic process but rather is governed by selectivepressures driving the traits towards specific values (Butler and King,2004). This type of model could fit well the evolution of the dispersalmorphology (W/S) in Pinus, as dispersal syndromes (wind, animal,wind/animal) could correspond to different selective regimes (θ values)for W/S. We compared a neutral evolutionary pattern (i.e., Brownianmotion, no selective optima) with several OU models that assume si-milar or different phenotypic optima (θ) across dispersal modes (OU vs.OUM). In addition, we considered the possibility of variation in otherparameters across evolutionary regimes: strength of selection (differentα as well as θ values; OUMA), rate of stochastic evolution (different σ2and θ values; OUMV) and both (different σ2, α and θ; OUMVA). Modelselection was based on the Akaike information criterion corrected forsmall sample sizes (AICc) and uncertainty in parameter estimation ac-cording to a parametric bootstrap (Beaulieu et al., 2012; see Supple-mentary Methods for details). OU results indicated that only two op-tima exist for W/S (i.e., two values of W/S approximated by themathematical function describing phenotypic evolution). Namely W/S= 0 and W/S ∼ 2.6, which match the characteristics of vertebrateand wind dispersal syndromes described in the literature (2.6 is themean of optima associated with wind and wind/animal for OUM andOUMV, which were the models with the highest parameter reliability,see below).

Our gene tree is relatively small (113 spp.) which might have biasedresults in favour of more complex evolutionary models (Cooper et al.,2016). To control for this, we performed a parametric bootstrap(O’Meara et al., 2006). Furthermore, we used a phylogenetic MonteCarlo likelihood test using the “pmc” package (Boettiger et al., 2012) inR to confirm that our gene tree did not systematically favour OU oversimpler models, like Brownian motion. Incomplete ecological descrip-tions constitute another potential source of bias in our analyses. It ispossible that not all instances of mixed dispersal have been observed(i.e., the literature does not contain all the interaction events betweenpine trees and potential vertebrate vectors, does not always account forthe effect of gusty winds, etc.) and as a result some species might beincorrectly assigned to just one of the extreme syndromes. To ensurethat our results were robust to this sort of incomplete sampling, we rana sensitivity analysis introducing different levels of noise randomlyassigning a different dispersal syndrome to a subset of the species. Oncethe noise was introduced in the dispersal regime, the two best OUmodels according to AICc and parameter reliability (OUM and OUMV)were fitted and the phenotypic optimum associated with each dispersalregime was obtained. This process was repeated one hundred times tocalculate a confidence interval for each optimum. We considered in-tervals of up to a 50% error (i.e., incorrect dispersal vectors for half ofthe species). We did not extend our sensitivity for error rates> 50%because Pinus is one of the best studied tree genera and we hope expertknowledge to be reliable at least half of the time.

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In order to validate the OU results, we performed phenotypic evo-lution analysis of W/S with BAMM and the BAMMtools package in R(Rabosky, 2014; Rabosky et al., 2014). This analysis estimates theevolutionary rate of change of W/S (i.e., speed of trait diversificationover time) across Pinus lineages. Lastly, we used the function “phylolm”(Ho and Ané, 2014) to test whether the evolutionary rates of change inW/S estimated by BAMM were different among dispersal syndromes.We repeated calculations including species with vertebrate and winddispersal as wind dispersed and then as vertebrate dispersed. Note thatin these and subsequent phenotypic evolution analyses we did not useorganismal diversification rates, the reliability of which has been thematter of some recent debate (Moore et al., 2016; Rabosky et al., 2017)but trait diversification rates.

In all the phylolm regressions described in this paper, we selectedthe best-fitting covariance model (Brownian motion, OU with randomroot, OU with fixed root, lambda, kappa, delta, early burst) based onthe AICc values. To analyze differences in evolutionary rates associatedwith each phenotypic optimum, we corrected the W/S diversificationrates estimated by BAMM by calculating the distance of each taxon tothe phenotypic optimum of the model. Each species was assigned to anoptimum according to the dispersal syndrome described in the litera-ture (i.e., wind or animal) and values of each phenotypic optimum weretaken from the initial OU results described above (optimum associatedwith animal= 0, optimum associated with wind and wind-an-imal= 2.6). Next, we calculated the difference between the currentvalue and the corresponding optimum of W/S in each species. The ra-tionale for this correction is that in OU models the change of phenotypicvalues in each selective event is a function of its proximity to theadaptive phenotypic optimum (i.e., selection strength increases with

distance from the phenotypic optimum). Consequently, species with W/S values close to their optimum should exhibit lower diversificationrates of seed morphology than species further away from the putativephenotypic optimum, independent of the dispersal syndrome exhibited.In other words, we corrected by distance to the optimum becausespecies lying farther away from the corresponding phenotypic optimumwill exhibit a higher rate of change to converge towards said optimumthan species lying near it.

2.3. Dispersal traits and diversification in Pinus

To study the potential association between dispersal morphologyand the diversification of Pinus (i.e., speciation and extinction rates), weconducted trait dependent speciation-extinction analyses with ‘quanti-tative state speciation and extinction’ (QuaSSE; FitzJohn, 2010) andBAMM (Rabosky et al., 2014). BAMM output was treated with theBAMMtools package in R and the potential influence of dispersal on thediversification rates of Pinus was tested with the function “traitDe-pendentBAMM” in this package. QuaSSE analyses were conducted fit-ting two alternative models following the methodology of Hardy andOtto (2014). The first model considered diversification rates to be alinear function of W/S, while the second model considered rates to beconstant, i.e., independent of trait values. Even if the actual relationshipbetween phenotypic change and lineage diversification is not trulylinear, influence of the trait on the diversification of the clade is likelyto result in significant differences in the fit of the two models(Felsenstein, 1985). In both models we used W/S as the dispersal traitand parameters were estimated by maximum-likelihood (ML). To esti-mate the significance of the influence of dispersal traits on

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 havebeen described as dispersed by wind, vertebrates, and vertebrates and wind, respectively.

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diversification rates, we compared the results of both models throughan ANOVA test.

2.4. Climatic variability, aridity, fire and dispersal morphology

We tested whether climatic variability is associated with the evo-lution of dispersal strategy, as measured by W/S (i.e., whether specieswith lower W/S values are associated with more climatic variabilitythan species with higher W/S values). We approached this question inthree ways. First, we investigated if adaptations to climatic variabilityexplain evolutionary shifts in dispersal morphology. We analysed ratesof phenotypic diversification for both climatic variability and W/S inBAMM. In these analyses, we considered the climatic conditions insidepine ranges as niche dimensions and treated them as a trait that canevolve along the phylogeny (Pearman et al., 2008; Wiens et al., 2010).Then, we fitted a phylogenetically corrected regression using thefunction “phylolm” with the rates of evolutionary change of climaticvariability along the tree as the predictor and W/S diversification ratesas the dependent variable, both previously calculated with BAMM. Ifdispersal morphology provides adaptation to climatic variability, bothvariables are expected to covary. Therefore, the evolution of the cli-matic variability “trait” would exhibit two optima and its evolutionaryrates would be dependent on the distance to these optima, in parallel tothe OU models of W/S evolution. To account for the deceleration ofrates closer to phenotypic optima, we corrected the diversification ratesof both W/S and climatic variability by the distance to the corre-sponding phenotypic optimum in phylolm analyses. Third, to ascertainwhether extant W/S values are a function of climatic variability, wefitted “phylolms” using the W/S values of all taxa as the responsevariable and climatic variability as the independent variable. The re-sults of these analyses were visualized as linear regressions of thephylogenetic independent contrasts (PICs; Felsenstein, 1985) extractedfrom each variable. A similar approach was used to estimate the asso-ciation between aridity, fire regime and serotiny with the dispersalsyndrome. In these cases, we performed “phylolm” analyses using ar-idity (PET - P) and the discrete variables of fire occurrence and serotinyinstead of the metrics of climatic variability as the predictor variables(Table S2).

3. Results

3.1. Dispersal morphology evolution

Our results revealed a relationship between W/S and the syndromesdescribed in the literature (Fig. S1). Dispersal by vertebrates is asso-ciated with lower W/S values than wind dispersal, while species forwhich both dispersal strategies have been described exhibited inter-mediate values (Fig. S1). The association between the trait and thesyndrome was also observed across the gene tree, with shifts in onecorresponding with changes in the other (Fig. 2). They both appeared tobe evolutionarily constrained (λ=0.66 and 0.45 for W/S and dispersalregime respectively). The species with the highest W/S value is P.cooperi (W/S=4.5), while there are several species with W/S= 0. Ingeneral, W/S values of subgen. Strobus are lower than those of subgen.Pinus. Within subgen. Strobus, the subsect. Cembroides (sect. Parrya)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/S > 2.0, with the exception of P. pinea (subsect. Pinaster; Table S3).

The comparison of OU models is presented in Table 1, along withparameter reliability in Table S4. Of the seven models we tested, theone that best approximated the evolution of W/S assumed differentstate means and stochastic evolution for each regime (OUMV; AICc 70units lower than the next best-fitting model, OUM, which allows onlyfor variation in phenotypic optima across regimes). In both models,estimation of the optima (θ) was reliable and revealed that the evolu-tion of W/S is state-dependent and converges towards two different

optima (θ1; θ2): W/S ∼ 2.6 (corresponding to wind dispersal) and W/S∼ 0 (vertebrate dispersal). Moreover, the better fit of OUMV appears toindicate that evolution towards each optimum is under different se-lective dynamics.

Sensitivity analyses showed that evolutionary optima were fullyrobust up to 25% error (i.e., assuming that the syndrome of every one infour species was misassigned). Qualitative patterns did not deviate fromthose obtained with the model fitted to data obtained from the biblio-graphy (two optima in wind and animal dispersal, respectively; Table 2)even assuming that the dispersal mode of half of the species was in-correctly described (50% error). Consequently, we conducted all sub-sequent analyses of association between W/S and dispersal regimeconsidering only two categories for the latter.

The analyses of trait diversification carried out with BAMM showedthat increases in the diversification rates of the dispersal morphologyare associated with shifts towards multiple phenotypic optima (Fig. 2;Table S5). A significant increase in W/S diversification was found insect. Quinquefoliae, and resulted in the emergence of taxa with both lowand high W/S from an ancestor likely with high W/S. Conversely, sig-nificant decreases in the diversification rate of W/S were observedwithin sect. Parrya, subsect. Cembroides and especially in the evolutionof P. johannis and P. cembroides (Fig. 2; see also Fig. S3 for a detailedversion of the tree including species names). This decrease in evolu-tionary rates represents a convergence towards the phenotypic op-timum (W/S ∼ 0) from more intermediate phenotypic values (1 < W/S<0 approx.). In the clade including P. arizonica and P. cooperi theincrease in the diversification rate of W/S resulted in a dispersal mor-phology significantly beyond the phenotypic optimum (P. cooperi W/S=4.5; Fig. 2). Phylogenetic regressions testing for differences in therates of evolution of W/S among dispersal syndromes (i.e., with a factordenoting wind or animal dispersal as predictor and the rate of di-versification of W/S as response) further supported the existence ofdifferences in the evolutionary regime of seed morphology. Evolu-tionary rates were significantly higher in pines with vertebrate dispersalthan in wind-dispersed pines (Table 3). These differences were evenhigher when mixed cases were considered as exclusively wind dis-persed, while this significance was lost when the mixed group wascollapsed with animal dispersed pines (Table 3). These results corro-borate the existence of different selective dynamics between phenotypicoptima as suggested by the OU models.

3.2. Dispersal and organismal diversification in Pinus

No clear link could be established between dispersal morphologyand the speciation and extinction dynamics of Pinus (Fig. S4). Testingthe relationship between the diversification rates of the genus as afunction of W/S in BAMM yielded non-significant results (p.value> 0.9for all rates). Likewise, the difference between QuaSSE models with andwithout an effect of W/S on Pinus diversification did not reveal anysignificant difference. Constant (diversification not affected by traitchanges) and linear models (trait changes influencing diversification)had comparable fit to the data (ANOVA; p.value=0.4309,ChiSq=0.62031). The graphical representation of the QuaSSE resultsseemed to reflect the existence of some, though non-significant, dif-ferences in Pinus diversification rates (Fig. S4).

3.3. Climatic variability, aridity, fire and the dispersal syndrome

The multivariate, PCA-based characterization of climatic variablesallowed us to estimate evolutionary associations between climaticvariability and W/S using only three metrics, each the first axis of adifferent PCA (Table S1). To do so, we first tested the covariation be-tween the diversification rates of climatic variability and those of W/Sadjusted by the distance to the phenotypic optima of the latter. Thesetests revealed a statistically significant positive association between therate of change of the trait and that of climatic conditions (Table S6).

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This means that the diversification of W/S accelerates when the di-versification rate of climatic variability (i.e., the variation in climatedimensions of the niche) increases, while a decrease of the evolutionaryrate of W/S occurs when climate diversification slows down. In otherwords, when the variability in the climate where a lineage occurschanges faster across time so does W/S and vice versa. The modelsincluding 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-tive in the case of global and precipitation variability and positive in thecase of temperature variability (Fig. 3). According to our results, highlevels of precipitation variability are associated with low W/S values(i.e., relatively small wings), while high levels of temperature varia-bility are associated with high W/S values. Our results also supportedan influence of aridity and fire on the evolution of seed morphology inPinus. The two environmental factors seem to favour opposing evolu-tionary trends such that species producing seeds with relatively smallerwings are associated with higher aridity and taxa with bigger wingsoccupy fire-prone environments (Table 4; Fig. 3). In contrast, we did

not detect a significant correlation between serotiny and W/S (Table 4).Given that serotiny has not evolved in subgenus Strobus (Table S2), werepeated the analysis considering only species of subgenus Pinus. Theassociation becomes marginally significant, but the effect size remainsvery low (estimate= 0.037; SE= 0.230; T value= 0.162; Pvalue= 0.087).

4. Discussion

Seed dispersal is a critical life history component of a plant’s life-time reproductive success, and thus can be expected to be under strongselection and even affect species diversification rates (Beaulieu andDonoghue, 2013; Givnish, 2010; Qiao et al., 2016). In the case of Pinus,our findings indicated that dispersal morphology has evolved towardstwo alternative phenotypic optima that are associated with seeds withand without well-developed wings, respectively. These two phenotypesare predicted in turn to relate to the propensity for vertebrate and winddispersal: seeds with bigger wings should disperse easily by air currentswhile seeds without wings are expected to rely primarily upon animals

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 basedon literature reports. W/S evolution is represented as continuously varying along the branches of the gene tree. Terminal nodes show the current state for bothvariables. 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 inphenotypic evolutionary rates. The shift in evolutionary rate within subsect. Ponderosae includes the diversification of P. cooperi and P. arizonica. Grey bars next to thetips 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 andsection names are indicated in boxes and subsections as vertical lines. A similar plot displaying all species name is available as supplementary material (Figure S3).

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as vectors. The emergence of either dispersal phenotype showed a closeevolutionary linkage with environmental conditions. Dispersal morphsthat exhibit poor wind dispersal are linked to environments that arearid and/or have highly variable rainfall patterns, whereas wind dis-persal appears to be more frequent in taxa adapted to fire-prone eco-systems and where temperature (but not precipitation) is highly

variable. Although this environmental dependency may affect diversi-fication dynamics, we could not establish a clear link between theevolution of dispersal morphology and lineage diversification rates inPinus. Nevertheless, our results denote the importance of seed dispersalas an adaptive trait and the influence of the environment on the mor-phological evolution of dispersal structures.

4.1. Phenotypic optima: absence vs. presence of wings

We defined a quantitative variable, the ratio between the wing andseed length (W/S) to study the evolution of dispersal syndromes. Wefound a relationship between the dispersal syndrome and seed size inextant pine species, in agreement with previous studies (Benkman,1995; Greene and Johnson, 1993; Hutchins and Lanner, 1982; Lanner,1990a; Leslie et al., 2017; Tomback, 1982). In fact, the close link tospecific dispersal vectors has been previously used to explain the evo-lution of W/S. For instance while most members of subsect. Cembroideshave W/S ∼ 0, P. rzedowskii has winged seeds (W/S= 2.92) that clo-sely resemble those of sect. Pinus, a similarity that has been attributed

Table 1Ornstein-Uhlenbeck models with parameter estimates for each evolutionary optimum (see text for details). NA indicates parameters that were not calculated becauseof 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 AICcmean (θ) selection (α) variation (σ2)

BM1 1 2.14 1.83 NA NA 0.17 0.13 404.762 0 0 NA NA 0.17 0.133 0 0 NA NA 0.17 0.13

OU1 1 2.16 0.18 0.09 0.24 0.31 0.21 359.972 0 0 0.09 0.24 0.31 0.213 0 0 0.09 0.24 0.31 0.21

BMS 1 3.97 1.86 NA NA 0.13 0.17 399.412 −4 2.69 NA NA 0.25 0.323 4.76 5.83 NA NA 0.06 0.75

OUM 1 2.8 0.08 4.14 0 3.95 0.13 247.412 0.05 0.14 4.14 0 3.95 0.133 2.31 0.19 4.14 0 3.95 0.13

OUMA 1 2.92 2.60E+20 4.14 NaN 0.73 NaN −1725.272 0 0 3.47 NaN 0.73 NaN3 0 0 2.82 NaN 0.73 NaN

OUMV 1 2.81 0.09 3.4 0.23 4.00 0.29 177.372 0.04 0.02 3.4 0.23 0.06 0.383 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 −1556.282 0 0.00 3.25 0 0.02 NaN3 0 0.00 2.84 0 0.11 NaN

Table 2Robustness of evolutionary models to incomplete ecological data. Results ofanalyses that correct for incomplete or incorrect dispersal syndrome descriptionare presented. Confidence intervals of phenotypic optima (θ) for the bestmodels (OUM and OUMV) obtained from a sensitivity analysis with 100iterations, along with the percentage of error introduced in each analysis. Errordenotes the percentage of species that were randomly assigned a dispersalsyndrome.

Percentage Model Dispersal Quantile Quantile Quantileof error regime 2.5 50 97.5

5 OUM Wind 2.74 2.81 2.875 OUM Vert 0.01 0.11 0.385 OUM Vert-Wind 1.96 2.29 2.495 OUMV Wind 2.74 2.81 35 OUMV Vert 0.02 0.04 0.375 OUMV Vert-Wind 1.92 2.3 2.5115 OUM Wind 2.65 2.81 2.9515 OUM Vert −0.03 0.33 0.6215 OUM Vert-Wind 1.86 2.22 2.5515 OUMV Wind 2.64 2.82 3.0115 OUMV Vert 0.01 0.3 0.6415 OUMV Vert-Wind 1.6 2.24 2.5725 OUM Wind 2.59 2.81 2.9725 OUM Vert 0.02 0.45 1.5625 OUM Vert-Wind 1.61 2.19 2.6425 OUMV Wind 2.62 2.8 3.0225 OUMV Vert 0 0.47 1.3625 OUMV Vert-Wind 1.62 2.24 2.6550 OUM Wind 2.21 2.71 3.0450 OUM Vert 0.24 1.01 2.0750 OUM Vert-Wind 1.58 2.21 2.8850 OUMV Wind 2.19 2.73 3.0250 OUMV Vert 0.22 1.05 2.2650 OUMV Vert-Wind 1.55 2.2 2.9

Table 3Association between the diversification rate of dispersal morphology and dis-persal syndromes. The table shows the results of the phylolm tests comparingW/S diversification rates among the three dispersal syndromes described forPinus. Wind is the reference level in all analyses, “Wind includ. W-V” indicatesthat species considered to be dispersed by both vertebrates and wind weregrouped with strictly wind-dispersing taxa, while in “Vert. includ. W-V”, ver-tebrate-wind phenotypes were considered as vertebrate-dispersed.

Estimate SE T value P value

Wind 0.08 0.045 1.766 0.08Vert 0.097 0.025 3.918 < 0.001Vert-Wind −0.019 0.023 −0.823 0.411

Wind includ. V-W 0.075 0.044 1.679 0.096Vert 0.102 0.024 4.268 < 0.0001

Wind 0.086 0.044 1.932 0.056Vert includ. V-W 0.032 0.02 1.601 0.112

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to convergent evolution for wind dispersal (Farjon, 2008). The values ofW/S ranged from 0 (no wing) to> 4. Intermediate values correspondedto taxa such as P. pinaster, P. aristata or P. brutia (Richardson, 2000),which have been reported to be dispersed both by wind and vertebrates(Earle, 2018; Fryer, 2004; Lanner, 2000; Vanwilgen and Siegfried,1986). These intermediate phenotypes might represent partial ane-mochory, as the models that fitted the data better were those in whichmixed dispersal taxa were treated as wind dispersed. Therefore, inter-mediate phenotypes (i.e., shorter, less bulky wings) might evolve to-wards one of the two extremes. Of course, this fact does not precludethe adaptive value of mixed dispersal like diplochory in many en-vironments where it could be maintained by diversifying selection and/or constitute a bet-hedging strategy. For instance, seeds that can beeasily carried by both wind currents and vertebrates can benefit fromdiplochory whenever hoarding by a secondary disperser increases theprobability of seedling establishment (Vander Wall and Longland,2004). Even we do not rule out the adaptive potential of mixed dis-persal, our evolutionary analyses supported the existence of only twolong-term evolutionary optima located at W/S ∼ 0-0.5 and ∼ 2.6,seemingly corresponding to the phenotypes identified as vertebrate-

and wind-dispersal, respectively.

4.2. Evolutionary rates of phenotypic diversification

Our analyses suggested that the selective regimes governing seeddispersal are dependent on the specific syndrome. In other words, theevolutionary rates of change in seed morphology are significantly dif-ferent between vertebrate and wind-dispersed lineages. At this point, itis not possible to establish the causes of these differences. The evolutionof 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, includingdispersal vectors (Beaulieu and Donoghue, 2013; Mazer andWheelwright, 1993; Rubio de Casas et al., 2012). Nevertheless, ourresults highlight the differences that can exist in the evolution of afunctional trait, even among closely related taxa.

Seed morphology appeared to have evolved at significantly higherrates in lineages with vertebrate dispersal. Conversely, wind- andmixed- dispersal lineages seemed to have similar evolutionary rates andphenotypic optima. These results corroborate the idea that selectivedynamics are different between syndromes, as indicated by the OUmodels. Moreover, these patterns appear to support the hypothesis thatanimal dispersal in pines is the derived condition, as suggested by itslate appearance (Miocene) in the fossil record (Axelrod, 1986; Lanner,1990a, 1990b), because attaining the new phenotypic values requireshigher divergence away from the ancestral state and thus relativelyhigher evolutionary rates. This is the case of sect. Quinquefoliae (aheterogeneous group including East Asian pines and white pines ofNorth America) that exhibited a remarkable increase in morphologicalevolutionary rates in which diversifying selection led from a likelywinged ancestor to a diversity of W/S values, including several extanttaxa with W/S ∼ 0 (Lanner, 1990a, 1990b). Other cases of acceleratedevolutionary rates seemed to result from directional selection, as in thedivergence of P. cooperi and P. arizonica. These two species exhibitwinged seeds and are very closely related (P. cooperi is often considereda subspecies of P. arizonica; Farjon and Styles, 1997) but the phenotypeof P. cooperi is very extreme (W/S=4.5). Although the causes of therapid evolution of P. cooperi and morphological divergence between thetwo species remain to be investigated, the differentiation might belinked to environmental conditions, as P. cooperi grows at higher alti-tudes and in colder environments than P. arizonica (Bannister andNeuner, 2001; Farjon et al., 2015; González-Elizondo et al., 2007).

The conservation of the ancestral phenotype (i.e., stabilizing selec-tion) might have also resulted in shifts in evolutionary rates, albeit of adifferent nature. While evolution towards new trait values resulted inhigher evolutionary rates, canalization appeared to lead to decelera-tion. In subsect. Cembroides, the maintenance of the ancestral unwingedphenotype (W/S ∼ 0) led to a substantial reduction in the evolutionaryrates of W/S. In conclusion, while changes in the evolutionary rates ofW/S are indicative of selection, their sign depends on the type of se-lection affecting the trait. Disruptive (sect. Quinquefoliae) or directional(P. cooperi - P. arizonica) selection leads to accelerated evolutionaryrates, while stabilizing selection (sect. Cembroides) resulted in a de-crease in the rate of morphological evolution.

The evolution of dispersal morphology appeared to be very dynamicin Pinus, but these morphological changes did not seem to correlatewith shifts in the diversification patterns of the group. The results of theanalyses performed with BAMM and QuaSSE did not reveal any sig-nificant effect of dispersal traits on speciation and extinction rates. Thepower of these analyses might have been limited by the relatively smallsize of the group or by the shortcomings of the algorithms (Moore et al.,2016; Rabosky et al., 2014). In any case, an influence of the dispersalsyndrome on the diversification of Pinus cannot be supported based onour results.

Fig. 3. Evolutionary association between dispersal morphology and climaticvariability in Pinus spp. The plots represent regressions of PICs(Phylogenetically Independent Contrasts) of W/S against the PICs of variablesrepresenting climatic variability (PCA axes of global, precipitation and tem-perature variability) and aridity. Slope estimates and p.values are extractedfrom the phylolm tests of untransformed variables. R2 values were low in allassociations (R2< 0.05). In every case, W/S is considered to be the dependentvariable and environmental variables are the predictors.

Table 4Coefficients of the phylolms testing for the association between W/S with fireregime and serotiny. Extant W/S is the dependent variable in every case, whilefire regime and serotiny were considered as independent factors with referencelevels “No fire” and “Non-serotinous”, respectively.

Estimate SE T value P value

Intercept 1.74 0.64 2.718 < 0.01Fire regime 0.677 0.244 2.777 < 0.01

Intercept 2.106 0.673 3.131 < 0.01Serotiny 0.021 0.295 0.071 0.944

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4.3. Influence of environmental heterogeneity on dispersal evolution

Our analyses revealed a strong link between the evolution of dis-persal morphology and environmental conditions, specifically withclimatic variability. Variability in temperature conditions was asso-ciated with longer wings (higher W/S), whereas precipitation varia-bility and aridity were associated with the evolution of seeds with verysmall or no wings. The environments exhibiting a wider variation intemperatures are those located at higher latitudes, where seasonalfluctuations are more extreme. Thus, our results could be regarded asconfirmation of the adaptive value of wind dispersal in boreal en-vironments, hypothesized by Willson et al. (1990).

Seasonal variability in precipitation is expected in environmentswith markedly dry and wet seasons, such as Mediterranean ecosystems.It seems that in these habitats selection has favoured seeds withoutwings, which may indicate a higher adaptive value of zoochory. A si-milar pattern was found by Leslie et al. (2017), who observed a positiveassociation between seed volume and precipitation seasonality in Cu-pressaceae and Podocarpaceae, but not in Pinaceae. This disagreementbetween results of Leslie et al. (2017) and our own could be caused bythe different response variables considered, as we used a more directproxy for dispersal, including wing in addition to seed size. An addi-tional explanation could be the fact that we considered a broader rangeof climatic conditions including precipitation variability across space,not only seasonality. We deem this multivariate approach to be morerealistic because environmental factors are not orthogonal and tend toco-vary and this non-independence might influence their selective ef-fects. Finally, the phylogenetic scale could be another cause for thedisagreement, as we focus only on Pinus, while Leslie et al. (2017) had abroader scope.

Other theoretical and microevolutionary studies have shown theselective power of environmental heterogeneity on plant phenotypes ingeneral and on seed dispersal in particular (Baythavong, 2011;Baythavong et al., 2009; Paccard et al., 2013; Schurr et al., 2008). Forexample, Gómez (2004, 2003) showed that acorn burial by Garrulusglandarius leads to increased emergence and survival of Quercus ilexseedlings in a landscape where favourable microsites were disconnectedand sparse. Similarly, using a stochastic occupancy model (SPOM)Purves et al. (2007) showed that under random habitat loss (i.e., underspatial and temporal negative autocorrelation) only long distance dis-persal by animals to suitable sites was able to maintain viable popu-lations in three species of Quercus, because other types of dispersal al-ways resulted in overly high seed loss. Interestingly, these examplesfavouring dispersal to specific suitable sites all refer to Mediterraneanecosystems, where both precipitation seasonality and animal dispersalare relatively relevant ecological factors (Jordano, 2000; Willson et al.,1990, 1989).

It has been posited that arid environments are particularly hetero-geneous and vertebrate dispersal would thus be especially favourablethere (Wenny, 2001). This is congruent with the association we ob-served between lower W/S and increased aridity across pine species.Leslie et al. (2017) also reported an association between animal dis-persal and lower precipitation across conifers, which further supportsthis result. However, it is important to point out that although sig-nificant, the slope of this association is two orders of magnitude lowerthan the slope for the association between W/S and any of the metricsof climatic variability. It also must be pointed out that other authorshave failed to find a positive association between aridity and vertebratedispersal and Willson et al. (1990) even showed that the frequency ofvertebrate dispersal was negatively correlated with aridity. Maybeconstant aridity acts differently from variable precipitation regimes ondispersal.

Uniformly unfavourable conditions might cause selection not somuch on the ability of seeds to reach favourable sites (i.e., on traitsaffecting the transient stage of dispersal) but on post-dispersal traitsfavouring large seedlings better capable of withstanding unfavourable

(dry) periods. There is a direct correlation between the size of seeds andthat of seedlings (Castro et al., 2006; Kitajima and Fenner, 2000;Tomback and Linhart, 1990). Simultaneously, there are biomechanicalconstraints on the potential size of wings, and consequently the de-velopment of very large seeds is necessarily associated with a reductionin W/S (Richardson, 2000). Indeed, it has been hypothesized that harshconditions to which many bird-dispersed pines are exposed could haveresulted in selection for larger seeds, which in turn may have favouredmutualism between pines and animal dispersers (Lanner, 1990b;Tomback and Linhart, 1990). This idea is supported by the results ob-tained by Debain et al. (2003) in P. sylvestris where an increase in seedmass was negatively associated with wind-dispersal capacity and po-sitively with seedling biomass. At this point, however, these remainhypotheses and further research will be necessary to disentangle thedifferent selective processes that influence the evolution of seed mor-phology in drought-prone environments.

In addition to variability in temperatures, fire was another en-vironmental component that tended to select for wind dispersal. Ouranalyses reflected a significant association between the binary occur-rence of fire (based on the classification of He et al. (2012)) and dis-persal morphology. This indicates that the evolution of relatively longerwings was favoured in fire-prone environments. It has been proposedthat fire opens and homogenizes the landscape and accelerates nutrientmineralization, making conditions more suitable for wind dispersal.Additionally, the heat generated by blackened soil following fire pro-duces updraughts and small whirlwinds, which may foster the dispersalof anemochorous diaspores (Lamont et al., 1991). It is worth noting thatthe two factors that seem to favour the development of large wings areclosely correlated, since fire is a major disturbance in boreal environ-ments where temperature variability is also very high (Rowe andScotter, 1973). Thus even if our results are quite preliminary, thepossibility that large W/S values and wind dispersal are adaptive in fire-prone environments seems plausible and ecologically meaningful.

In a somewhat conflicting result, wind dispersal was not associatedwith serotiny, even though this association had been previously hy-pothesized to exist and serotiny is one of the strongest predictors ofadaptation to fire (Lamont et al., 1991). The exclusion of subgenusStrobus, in which serotiny has not evolved, returned a marginally sig-nificant association between serotiny and W/S. This result suggests apositive relationship between serotiny and wind dispersal. Support forthis tendency comes from the fact that extant pine species withoutwings are usually non-serotinous and vertebrate-dispersed (Procheşet al., 2012). A similar pattern has been observed in other groups suchas Leucadendron (Bond, 1984). The ambiguous association betweenserotiny and anemochory could also be influenced by other factors,such as post-dispersal seed predation. Serotiny implies that part of theseeds remain on the tree, which could make them easier to find andmore susceptible to predation. Therefore, pre-dispersal seed predationand fire frequency might be opposing selective pressures on serotiny(Benkman and Siepielski, 2004; Talluto and Benkman, 2013). The lackof a robust association between serotiny and wind dispersal in our datamight be also attributable to a technical limitation: it is very hard torule out co-ancestry as the main cause of similarity in any trait amongserotinous Pinus because they are all closely related. Therefore, it ispossible that this inconclusive result is a statistical artifact of ourphylogenetically corrected analyses.

5. Conclusions

In conclusion, our work demonstrates that the evolution of seeddispersal morphology is environment-dependent in Pinus. In this group,seeds with small or no wings, likely dispersed by vertebrates, seem tohave been selected preferentially under high precipitation variabilityand/or aridity. Conversely, seeds with bigger wings and thus likelyanemochorous, appear to have been selected for in environments withhigh temperature variability and/or prone to fire.

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Acknowledgements

We would like to thank R. O. Wüest for sharing aridity data, and E.W. Schupp and J. M. Gómez for their helpful comments. DST wassupported by an FPU fellowship from the Spanish Ministry ofEducation, Culture and Sport (grant number FPU13/03410). RRC wassupported by the Talentia program of the Junta de Andalucía and by theMinistry of Economy and Competitiveness of Spain (grant numberCGLC2016-79950-R). NEZ and BS are grateful for the support of theSwiss SNF (grant number #31003A_149508/1). Analyses were run onthe Scientific Supercomputing Center of the Universidad de Granadaand on the Sewall server funded by the Ministry of Economy andCompetitiveness of Spain (grant number CGL2013-47558-P). Fouranonymous reviewers and the subject editor provided insightful com-ments and helpful suggestions that improved the quality and clarity ofthe manuscript.

Appendix A. Supplementary data

Supplementary material related to this article can be found, in theonline version, at doi:https://doi.org/10.1016/j.ppees.2019.125464.

References

Alvin, K.L., 1960. Further conifers of the Pinaceae from the Wealden formation ofBelgium. Mémoires du Musée R. d’histoire Nat. Belgique 146, 1–39.

Axelrod, D.I., 1986. Cenozoic history of some Western American pines. Ann. Missouri Bot.Gard. 73, 565–641. https://doi.org/10.2307/2399194.

Bannister, P., Neuner, G., 2001. Frost resistance and the distribution of conifers. In: In:Bigras, F.J., Colombo, S.J. (Eds.), Conifer Cold Hardiness. Tree Physiology 1.Springer, Dordrecht, pp. 3–21. https://doi.org/10.1007/978-94-015-9650-3_1.

Baythavong, B.S., 2011. Linking the spatial scale of environmental variation and theevolution of phenotypic plasticity: selection favors adaptive plasticity in fine-grainedenvironments. Am. Nat. 178, 75–87. https://doi.org/10.1086/660281.

Baythavong, B.S., Stanton, M.L., Rice, K.J., 2009. Understanding the consequences of seeddispersal in a heterogeneous environment. Ecology 90, 2118–2128. https://doi.org/10.1890/08-0307.1.

Beaulieu, J.M., Donoghue, M.J., 2013. Fruit evolution and diversification in campanulidangiosperms. Evolution 67, 3132–3144. https://doi.org/10.1111/evo.12180.

Beaulieu, J.M., Jhwueng, D.C., Boettiger, C., O’Meara, B.C., 2012. Modeling stabilizingselection: expanding the Ornstein-Uhlenbeck model of adaptive evolution. Evolution66, 2369–2383. https://doi.org/10.1111/j.1558-5646.2012.01619.x.

Benkman, C.W., 1995. Wind dispersal capacity of pine seeds and the evolution of differentseed dispersal modes in pines. Oikos 73, 221–224.

Benkman, C.W., Siepielski, A.M., 2004. A keystone selective agent? Pine squirrels and thefrequency of serotiny in lodgepole pine. Ecology 85, 2082–2087.

Boettiger, C., Coop, G., Ralph, P., 2012. Is your phylogeny informative? Measuring thepower of comparative methods. Evolution 66, 2240–2251. https://doi.org/10.1111/j.1558-5646.2011.01574.x.

Bohrer, G., Nathan, R., Volis, S., 2005. Effects of long-distance dispersal for metapopu-lation survival and genetic structure at ecological time and spatial scales. J. Ecol. 93,1029–1040. https://doi.org/10.1111/j.1365-2745.2005.01048.x.

Bond, W.J., 1984. Fire survival of Cape Proteaceae-influence of fire season and seedpredators. Vegetatio 56, 65–74. https://doi.org/10.1007/BF00033048.

Butler, M.A., King, A.A., 2004. Phylogenetic comparative analysis: a modeling approachfor adaptive evolution. Am. Nat. 164, 683–695. https://doi.org/10.1086/426002.

Caplat, P., Nathan, R., Buckley, Y.M., 2012. Seed terminal velocity, wind turbulence, anddemography drive the spread of an invasive tree in an analytical model. Ecology 93,368–377.

Castro, J., Hódar, J.A., Gómez, J.M., 2006. Seed size. In: Basra, A.S. (Ed.), Handbook ofSeed Science and Technology. Haworth’s Food Products Press, New York, pp.397–427.

Comins, H.N., Hamilton, W.D., May, R.M., 1980. Evolutionarily stable dispersal strate-gies. J. Theor. Biol. 82, 205–230.

Cooper, N., Thomas, G.H., Venditti, C., Meade, A., Freckleton, R.P., 2016. A cautionarynote on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J.Linn. Soc. 118, 64–77. https://doi.org/10.1111/bij.12701.

Debain, S., Curt, T., Lepart, J., 2003. Seed mass, seed dispersal capacity, and seedlingperformance in a Pinus sylvestris population. Ecoscience 10, 168–175. https://doi.org/10.1080/11956860.2003.11682764.

Drummond, A.J., Rambaut, A., 2007. BEAST: bayesian evolutionary analysis by samplingtrees. BMC Evol. Biol. 7, 214. https://doi.org/10.1186/1471-2148-7-214.

Duputié, A., Massol, F., 2013. An empiricist’s guide to theoretical predictions on theevolution of dispersal. Interface Focus 3, 20130028.

Earle, C., 2018. Pinus brutia. The Gymnosperm Database. [Online].. https://www.conifers.org/pi/Pinus_brutia.php.

Eckenwalder, J.E., 2009. Conifers of the World: the Complete Reference. Timber press,Portland, OR, USA.

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

Distribution, Biogeography, Diversity and Conservation Status. Brill AcademicPublishers, Boston. https://doi.org/10.5860/choice.51-5353.

Farjon, A., Gardner, M., Thomas, P., 2015. Conifer database (version Jan 2014). Digitalresource at www.catalogueoflife.org/col. ISSN 2405-8858 In: Roskov, Y., Abucay, L.,Orrell, T., Nicolson, D., Kunze, T., Flann, C., Bailly, N., Kirk, P., Bourgoin, T., DeWalt,R.E., Decock, W., De Wever, A. (Eds.), Species 2000: Naturalis, Leiden, theNetherlands.

Farjon, A., Styles, B.T., 1997. Pinus(Pinaceae). Flora Neotropica Monograph 75. The NewYork Botanical Garden, New York, NY.

Felsenstein, J., 1985. Phylogenies and the comparative method. Am. Nat. 125, 1–15.https://doi.org/10.1086/284325.

FitzJohn, R.G., 2010. Quantitative traits and diversification. Syst. Biol. 59, 619–633.https://doi.org/10.1093/sysbio/syq053.

Fryer, J.L., 2004. Pinus aristata, in: Fire Effects Information System, [Online]. Available:.U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station,Fire Sciences Laboratory. https://www.fs.fed.us/database/feis/plants/tree/pinari/all.html.

Gadgil, M., 1971. Dispersal: population consequences and evolution. Ecology 52,253–261. https://doi.org/10.2307/1934583.

Gallien, L., Saladin, B., Boucher, F.C., Richardson, D.M., Zimmermann, N.E., 2016. Doesthe legacy of historical biogeography shape current invasiveness in pines? NewPhytol. 209, 1096–1105. https://doi.org/10.1111/nph.13700.

Givnish, T.J., 2010. Ecology of plant speciation. Taxon 59, 1326–1366.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.

Gómez, J.M., 2003. Spatial patterns in long-distance dispersal of Quercus ilex acorns byjays in a heterogeneous landscape. Ecography 26, 573–584. https://doi.org/10.1034/j.1600-0587.2003.03586.x.

González-Elizondo, M.S., González-Elizondo, M.L., Márquez-Linares, M.A., 2007.Vegetación Y Ecorregiones De Durango. Plaza y Valdés, S.A., México, D.F.

Goodman, D., 1987. The demography of chance extinction. In: Soulé, M.E. (Ed.), ViablePopulations for Conservation. Cambridge University Press, Cambridge, pp. 11–34.

Gramzow, L., Weilandt, L., Theißen, G., 2014. MADS goes genomic in conifers: towardsdetermining the ancestral set of MADS-box genes in seed plants. Ann. Bot. 114,1407–1429. https://doi.org/10.1093/aob/mcu066.

Greene, D.F., Johnson, E.A., 1993. Seed mass and dispersal capacity in wind-disperseddiaspores. Oikos 67, 69–74. https://doi.org/10.2307/3545096.

Hardy, N.B., Otto, S.P., 2014. Specialization and generalization in the diversification ofphytophagous insects: tests of the musical chairs and oscillation hypotheses. Proc. R.Soc. London B Biol. Sci. 281.

He, T., Pausas, J.G., Belcher, C.M., Schwilk, D.W., Lamont, B.B., 2012. Fire-adapted traitsof Pinus arose in the fiery Cretaceous. New Phytol. 194, 751–759. https://doi.org/10.1111/j.1469-8137.2012.04079.x.

Herrera, C.M., 2002. Seed dispersal by vertebrates. In: Herrera, C.M., Pellmyr, O. (Eds.),Plant–Vertebrate Interactions: An Evolutionary Approach. Blackwell Science,Hoboken, New Jersey, USA, pp. 185–208.

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high re-solution interpolated climate surfaces for global land areas. Int. J. Climatol. 25,1965–1978. https://doi.org/10.1002/joc.1276.

Ho, Lsi T., Ané, C., 2014. A linear-time algorithm for Gaussian and non-Gaussian traitevolution models. Syst. Biol. 63, 397–408. https://doi.org/10.1093/sysbio/syu005.

Howe, H.F., Smallwood, J., 1982. Ecology of Seed Dispersal. Annu. Rev. Ecol. Syst. 13,201–228. https://doi.org/10.1146/annurev.es.13.110182.001221.

Hutchins, H.E., Lanner, R.M., 1982. The central role of Clark’s nutcracker in the dispersaland establishment of whitebark pine. Oecologia 55, 192–201. https://doi.org/10.1007/BF00384487.

Jordano, P., 2000. Fruits and frugivory. In: Fenner, M. (Ed.), Seeds: The Ecology ofRegeneration in Plant Communities. CABI Publ, Wallingford, UK, pp. 125–166.

Kartzinel, T.R., Shefferson, R.P., Trapnell, D.W., 2013. Relative importance of pollen andseed dispersal across a neotropical mountain landscape for an epiphytic orchid. Mol.Ecol. 22, 6048–6059. https://doi.org/10.1111/mec.12551.

Keane, R.E., Tomback, D.F., Murray, M.P., Smith, C.M., 2011. The future of high-eleva-tion, five-needle white pines in Western North America. In: Proceedings of the HighFive Symposium. 28-30 June 2010; Missoula, MT. Proceedings RMRS-P-63. FortCollins, CO: U.S. Department of Agriculture, Forest Service. Rocky Mountain.

Kitajima, K., Fenner, M., 2000. Ecology of seedling regeneration. In: Fenner, M. (Ed.),Seeds: The Ecology of Regeneration in Plant Communities. CABI, Wallingford, pp.331–360. https://doi.org/10.1079/9780851994321.0331.

Lamont, B.B., Le Maitre, D.C., Cowling, R.M., Enright, N.J., 1991. Canopy seed storage inwoody plants. Bot. Rev. 57, 277–317. https://doi.org/10.1007/BF02858770.

Lanner, R.M., 2000. Seed dispersal in Pinus. In: Richardson, D.M. (Ed.), Ecology andBiogeography of Pinus. Cambridge University Press, pp. 281–295.

Lanner, R.M., 1996. Made for Each Other: a Symbiosis of Birds and Pines. OxfordUniversity Press, New York.

Lanner, R.M., 1990a. Morphological differences between wind-dispersed and bird-dis-persed pines of subgenus Strobus (Compilers) In: Schmidt, W.C., McDonald, K.J.,Schmidt, W.C., McDonald, K.J. (Eds.), Proceedings-Symposium on Whitebark PineEcosystems: Ecology and Management of a High-Mountain Resource. U.S.Department of Agriculture Forest Service General Technical Report INT-270, pp.371–372 Bozeman, Mont.

Lanner, R.M., 1990b. Biology, taxonomy, evolution, and geography of stone pines of theworld. In: Schmidt, W.C., McDonald, K.J. (Eds.), Proceedings, Symposium on

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

10

Whitebark Pine Ecosystems: Ecology and Management of a High-Mountain Resource.U.S. Department of Agriculture Forest Service General Technical Report INT-270.Bozeman, Mont.. pp. 14–24.

Lê, S., Josse, J., Husson, F., 2008. FactoMineR: an r package for multivariate analysis. J.Stat. Softw. 25. https://doi.org/10.18637/jss.v025.i01.

Lengyel, S., Gove, A.D., Latimer, A.M., Majer, J.D., Dunn, R.R., 2009. Ants sow the seedsof global diversification in flowering plants. PLoS One 4, 1–6. https://doi.org/10.1371/journal.pone.0005480.

Leslie, A.B., Beaulieu, J.M., Mathews, S., 2017. Variation in seed size is structured bydispersal syndrome and cone morphology in conifers and other nonflowering seedplants. New Phytol. 216, 429–437. https://doi.org/10.1111/nph.14456.

Leslie, A.B., Beaulieu, J.M., Rai, H.S., Crane, P.R., Donoghue, M.J., Mathews, S., 2012.Hemisphere-scale differences in conifer evolutionary dynamics. Proc. Natl. Acad. Sci.109, 16217–16221. https://doi.org/10.1073/pnas.1213621109.

Levin, D.A., 2000. The Origin, Expansion, and Demise of Plant Species. Oxford UniversityPress, Oxford.

Levin, S.A., Muller-Landau, H.C., Nathan, R., Chave, J., 2003. The ecology and evolutionof seed dispersal: a theoretical perspective. Annu. Rev. Ecol. Evol. Syst. 34, 575–604.https://doi.org/10.1146/annurev.ecolsys.34.011802.132428.

Mazer, S.J., Wheelwright, N.T., 1993. Fruit size and shape: allometry at different taxo-nomic levels in bird-dispersed plants. Evol. Ecol. 7, 556–575. https://doi.org/10.1007/BF01237821.

Moore, B.R., Höhna, S., May, M.R., Rannala, B., Huelsenbeck, J.P., 2016. Criticallyevaluating the theory and performance of Bayesian analysis of macroevolutionarymixtures. Proc. Natl. Acad. Sci. U. S. A. 113, 9569–9574. https://doi.org/10.1073/pnas.1518659113.

O’Meara, B.C., Ané, C., Sanderson, M.J., Wainwright, P.C., 2006. Testing for differentrates of continuous trait evolution using likelihood. Evolution 60, 922–933. https://doi.org/10.1111/j.0014-3820.2006.tb01171.x.

Pabón-Mora, N., Wong, G.K.-S., Ambrose, B.A., 2014. Evolution of fruit developmentgenes in flowering plants. Front. Plant Sci. 5, 300. https://doi.org/10.3389/fpls.2014.00300.

Paccard, A., Vance, M., Willi, Y., 2013. Weak impact of fine-scale landscape heterogeneityon evolutionary potential in Arabidopsis lyrata. J. Evol. Biol. 26, 2331–2340. https://doi.org/10.1111/jeb.12220.

Parks, M., Cronn, R., Liston, A., 2012. Separating the wheat from the chaff: mitigating theeffects of noise in a plastome phylogenomic data set from Pinus L. (Pinaceae). BMCEvol. Biol. 12, 100. https://doi.org/10.1186/1471-2148-12-100.

Pearman, P.B., Guisan, A., Broennimann, O., Randin, C.F., 2008. Niche dynamics in spaceand time. Trends Ecol. Evol. 23, 149–158. https://doi.org/10.1016/j.tree.2007.11.005.

Pesendorfer, M.B., Sillett, T.S., Koenig, W.D., Morrison, S.A., 2016. Scatter-hoardingcorvids as seed dispersers for oaks and pines: a review of a widely distributed mu-tualism and its utility to habitat restoration. Condor 118, 215–237. https://doi.org/10.1650/condor-15-125.1.

Procheş, Ş., Wilson, J.R.U., Richardson, D.M., Rejmánek, M., 2012. Native and natur-alized range size in Pinus : relative importance of biogeography, introduction effortand species traits. Glob. Ecol. Biogeogr. 21, 513–523. https://doi.org/10.1111/j.1466-8238.2011.00703.x.

Purves, D.W., Zavala, M.A., Ogle, K., Prieto, F., Benayas, J.M.R., 2007. Environmentalheterogeneity, bird-mediated directed dispersal, and oak woodland dynamics inMediterranean Spain. Ecol. Monogr. 77, 77–97. https://doi.org/10.1890/05-1923.

Qiao, H., Saupe, E.E., Soberón, J., Peterson, A.T., Myers, C.E., 2016. Impacts of nichebreadth and dispersal ability on macroevolutionary patterns. Am. Nat. 188, 149–162.https://doi.org/10.1086/687201.

R Core Team, 2016. R: A Language and Environment for Statistical Computing [WWWDocument]. URL. https://www.r-project.org/.

Rabosky, D.L., 2014. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS One 9, e89543. https://doi.org/10.1371/journal.pone.0089543.

Rabosky, D.L., Grundler, M., Anderson, C., Title, P., Shi, J.J., Brown, J.W., Huang, H.,Larson, J.G., 2014. BAMMtools: an R package for the analysis of evolutionary dy-namics on phylogenetic trees. Methods Ecol. Evol. 5. https://doi.org/10.1111/2041-210X.12199.

Rabosky, D.L., Mitchell, J.S., Chang, J., 2017. Is BAMM flawed? Theoretical and practicalconcerns in the analysis of multi-rate diversification models. Syst. Biol. 66, 477–498.https://doi.org/10.1093/sysbio/syx037.

Revell, L.J., 2012. Phytools: an R package for phylogenetic comparative biology (andother things). Methods Ecol. Evol. 3, 217–223. https://doi.org/10.1111/j.2041-210X.2011.00169.x.

Richardson, D.M., 2000. Ecology and Biogeography of Pinus. Cambridge University Press.Ronce, O., 2007. How does it feel to be like a rolling stone? Ten questions about dispersal

evolution. Annu. Rev. Ecol. Evol. Syst. 38, 231–253. https://doi.org/10.1146/annurev.ecolsys.38.091206.095611.

Rowe, J.S., Scotter, G.W., 1973. Fire in the boreal forest. Quat. Res. 3, 444–464. https://doi.org/10.1016/0033-5894(73)90008-2.

Rubio de Casas, R., Willis, C.G., Donohue, K., 2012. Plant dispersal phenotypes: a seedperspective of maternal habitat selection. In: Clobert, J., Baguette, M., Benton, T.G.,Bullock, J.M. (Eds.), Dispersal Ecology and Evolution. Oxford University Press, pp.171–184.

Ryberg, P.E., Rothwell, G.W., Stockey, R.A., Hilton, J., Mapes, G., Riding, J.B., 2012.Reconsidering relationships among stem and crown group Pinaceae: oldest record ofthe genus Pinus from the early Cretaceous of Yorkshire, United Kingdom. Int. J. PlantSci. 173, 917–932. https://doi.org/10.1086/667228.

Saladin, B., Leslie, A.B., Wüest, R.O., Litsios, G., Conti, E., Salamin, N., Zimmermann,N.E., 2017. Fossils matter: improved estimates of divergence times in Pinus revealolder diversification. BMC Evol. Biol. 17. https://doi.org/10.1186/s12862-017-0941-z.

Schupp, E.W., Jordano, P., Gómez, J.M., 2010. Seed dispersal effectiveness revisited: aconceptual review. New Phytol. 188, 333–353. https://doi.org/10.1111/j.1469-8137.2010.03402.x.

Schurr, F.M., Steinitz, O., Nathan, R., 2008. Plant fecundity and seed dispersal in spatiallyheterogeneous environments: models, mechanisms and estimation. J. Ecol. 96,628–641. https://doi.org/10.1111/j.1365-2745.2008.01371.x.

Seiwa, K., Tozawa, M., Ueno, N., Kimura, M., Yamasaki, M., Maruyama, K., 2008. Roles ofcottony hairs in directed seed dispersal in riparian willows. Plant Ecol. 198, 27–35.https://doi.org/10.1007/s11258-007-9382-x.

Spiegel, O., Nathan, R., 2010. Incorporating density dependence into the directed-dis-persal hypothesis. Ecology 91, 1538–1548.

Talluto, M.V., Benkman, C.W., 2013. Landscape-scale eco-evolutionary dynamics: selec-tion by seed predators and fire determine a major reproductive strategy. Ecology 94,1307–1316. https://doi.org/10.1890/12-2058.1.

Thayer, T.C., Vander Wall, S.B., 2005. Interactions between Steller’s jays and yellow pinechipmunks over scatter-hoarded sugar pine seeds. J. Anim. Ecol. 74, 365–374.https://doi.org/10.1111/j.1365-2656.2005.00932.x.

Tomback, D.F., 1982. Dispersal of whitebark pine seeds by Clark’s nutcracker: a mutu-alism hypothesis. J. Anim. Ecol. 51, 451. https://doi.org/10.2307/3976.

Tomback, D.F., 1978. Foraging strategies of Clark’s nutcracker. Living Bird 16, 123–161.Tomback, D.F., Achuff, P., 2010. Blister rust and western forest biodiversity: ecology,

values and outlook for white pines. For. Pathol. 40, 186–225. https://doi.org/10.1111/j.1439-0329.2010.00655.x.

Tomback, D.F., Linhart, Y.B., 1990. The evolution of bird-dispersed pines. Evol. Ecol. 4,185–219.

Vander Wall, S.B., 2008. On the relative contributions of wind vs. animals to seed dis-persal of four Sierra Nevada pines. Ecology 89, 1837–1849. https://doi.org/10.1890/07-0409.1.

Vander Wall, S.B., 2003. Effects of seed size of wind-dispersed pines (Pinus) on secondaryseed dispersal and the caching behavior of rodents. Oikos 100, 25–34.

Vander Wall, S.B., 2001. The evolutionary ecology of nut dispersal. Bot. Rev. 67, 74–117.https://doi.org/10.1007/BF02857850.

Vander Wall, S.B., Longland, W.S., 2004. Diplochory: are two seed dispersers better thanone? Trends Ecol. Evol. 19, 155–161. https://doi.org/10.1016/j.tree.2003.12.004.

Vanwilgen, B.W., Siegfried, W.R., 1986. Seed dispersal properties of three pine species asa determinant invasive potential. S. Afr. J. Bot. 52, 546–548. https://doi.org/10.1016/S0254-6299(16)31489-2.

Wang, B.C., Smith, T.B., 2002. Closing the seed dispersal loop. Trends Ecol. Evol. 17,379–386. https://doi.org/10.1016/S0169-5347(02)02541-7.

Wenny, D.G., 2001. Advantages of seed dispersal: a re-evaluation of directed dispersal.Evol. Ecol. Res. 3, 51–74.

Wiens, J.J., Ackerly, D.D., Allen, A.P., Anacker, B.L., Buckley, L.B., Cornell, H.V.,Damschen, E.I., Davies, T.J., Grytnes, J.-A., Harrison, S.P., Hawkins, B.A., Holt, R.D.,McCain, C.M., Stephens, P.R., 2010. Niche conservatism as an emerging principle inecology and conservation biology. Ecol. Lett. 13, 1310–1324. https://doi.org/10.1111/j.1461-0248.2010.01515.x.

Willis, C.G., Hall, J.C., Rubio de Casas, R., Wang, T.Y., Donohue, K., 2014. Diversificationand the evolution of dispersal ability in the tribe Brassiceae (Brassicaceae). Ann. Bot.114, 1675–1686. https://doi.org/10.1093/aob/mcu196.

Willson, M.F., Irvine, A.K., Walsh, N.G., 1989. Vertebrate dispersal syndromes in someaustralian and New Zealand plant communities, with geographic comparisons.Biotropica 21, 133–147. https://doi.org/10.2307/2388704.

Willson, M.F., Rice, B.L., Westoby, M., 1990. Seed dispersal spectra: a comparison oftemperate plant communities. J. Veg. Sci. 1, 547–562. https://doi.org/10.2307/3235789.

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

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