ele12289-sup-0001-AppendixS1-6
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Appendices Appendix S1 Fig. S1. Trait differences between species as a function of their phylogenetic distance with Brownian trait evolution or random assignment of traits to species. To explore the shape of the relationship between niche or fitness differences and phylogenetic distance, we simulated the 1 evolution of a generic trait, whose difference between species is their niche or fitness differences in hypothetical communities containing 50 to 1000 species. We assigned traits to species across the phylogeny in two ways: traits evolved according to a Brownian model of evolution, or they were assigned to species randomly with respect to tree topology. In the random assignment case (right column, panels b, d, f, and h), simulations show that the range of trait differences between species at the tips of the phylogeny is unrelated to their phylogenetic distance. Since any relationship between niche or fitness differences and phylogenetic distance between species must pass through the origin, a lack of niche or fitness conservatism always produces a non-linear relationship that rapidly asymptotes (right column, blue line). Note that linear relationships (red lines) anchored at the origin are always positive in slope, even when species are randomly assigned to the tips of the tree, and are thus inappropriate for the analyses in our paper. In general, the r2 values from the linear regressions (8 - 19%) and non-linear, asymptotic regressions (7 to 14 %) are low, even when the trait is conserved (different non-linear forms might perform better than the linear regression, but will still generate low r2 values). For the trait conservatism case (middle column, panels a, c, e, and g), close relatives tend to posses small trait differences from one another, while distant relatives can have large trait differences, but can also have very small differences depending on the course of trait evolution. Overall, this generates increasing variance in trait differences with phylogenetic distance. Simulations not presented here showed that this increasing variance is largely determined by the tree topology. Phylogenies with much “deeper” internal nodes (e.g. panels a, and e) showed faster variance increases and therefore faster niche/fitness differences accumulation with phylogenetic distance than phylogenies with “shallower” internal nodes (e.g. panels c, and g) (middle column, blue lines). At one extreme, a phylogeny containing many “deep” nodes from two disparate clades that diverged deeply in the past could generate relationships between trait differences and phylogenetic distance that were similar to the random pattern. Other models of trait evolution such as Ornstein-Uhlenbeck (OU) produced a similar triangular structure to the Brownian models. In all cases, the phylogenetic trees examined were ultrametric. 2 Appendix S2 This appendix provides information about the species included in the experiment, their vital rates and sensitivity to competition. Table 1. Species selected and species code assigned for the experiment. Family Species code Asteraceae AGHE Asteraceae AGRE Asteraceae CEME Asteraceae HECO Asteraceae LACA Asteraceae MICA Boraginaceae AMME Caryophyllaceae SIGA Euphorbiaceae EUPE Fabaceae LOPU Fabaceae LOWR * Species that failed to germinate Species Family Agoseris heterophylla Agoseris retrorsa Centaurea melitensis Hemizonia congesta Lasthenia californica Micropus californicus* Amsinckia menziesii Silene gallica* Euphorbia peplus Lotus purshianus Lotus wrangeliensis Fabaceae Geraniaceae Geraniaceae Geraniaceae Lamiaceae Myrsinaceae Plantaginaceae Polemoniaceae Polemoniaceae Onagraceae Onagraceae Species code MEPO ERBO ERCI GECA SACA ANAR PLER NAAT NAJA CLBO CLPU Species Medicago polymorpha Erodium botrys Erodium cicutarim Geranium carolinianum Salvia columbariae Anagallis arvensis Plantago erecta Navarretia atractyloides Navarretia jaredii* Clarkia bottae* Clarkia purpurea 3 Table 2. Mean and standard error of the fitted competitive effect (ej) and competitive response terms (ri) for each species described in “A simpler model of competition” from Appendix S4. Species Agoseris heretophylla Agoseris retrorsa Amsinckia menziesii Anagallis arvensis Centaurea melitensis Clarkia purpurea Erodium botrys Erodium cicutarium Euphorbia peplus Geranium carolinianum Hemizonia congesta Lasthenia californica Lotus purshianus Lotus wrangelianus Medicago polymorpha Navarretia atractyloides Plantago erecta Salvia columbariae Common Effect 3.060±0.084 4.367±0.046 0.685±0.095 0.057±0.411 1.280±0.332 0.348±0.317 0.484±0.744 0.679±0.499 0.386±0.156 0.088±0.260 1.346±0.109 1.113±0.084 0.622±0.195 0.863±0.130 0.809±0.049 1.864±0.089 0.895±0.038 0.868±0.079 Common Response 0.368±0.399 0.100±2.225 0.471±0.124 1.342±0.043 2.294±0.164 0.934±0.094 2.790±0.305 2.408±0.193 1.018±0.089 1.318±0.036 0.738±0.246 0.387±0.153 1.101±0.189 0.831±0.130 0.130±0.174 0.346±0.448 0.147±0.117 0.418±0.116 4 Table 3. Mean and standard error of each species’ germination rate, seed survival rate and per germinant seed production in the absence of competition Species Agoseris heretophylla Agoseris retrorsa Amsinckia menziesii Anagallis arvensis Centaurea melitensis Clarkia purpurea Erodium botrys Erodium cicutarium Euphorbia peplus Geranium carolinianum Hemizonia congesta Lasthenia californica Lotus purshianus Lotus wrangelianus Medicago polymorpha Navarretia atractyloides Plantago erecta Salvia columbariae Per germinant seed production in the absence of competition(π) 784.8±62.22 1946.32±181.8 289.91±38.97 1146.23±185.79 14606.58±1276.63 835.17±99.09 295.73±45.67 341.23±45.06 1092.95±114.35 60.92±4.59 991.28±76.33 3689.6±229.7 481.63±62.18 1484.84±123.11 287.72±25.13 2814.96±272.16 1393.34±59.25 3234.13±207.28 Proportion germination (g) Proportion seed survival (s) 0.082±0.010 0.082±0.010 0.047±0.008 0.002±0.001 0.036±0.004 0.004±0.001 0.003±0.003 0.011±0.002 0.030±0.005 0.046±0.006 0.015±0.002 0.004±0.001 0.025±0.007 0.085±0.011 0.028±0.002 0.005±0.001 0.115±0.013 0.077±0.007 0.56±0.07 0.56±0.07 0.71±0.05 0.15±0.01 0.29±0.07 0.24±0.07 0.4±0.03 1.00±0.01 0.19±0.03 0.13±0.03 1.00±0.14 0.26±0.06 0.39±0.03 0.26±0.03 0.55±0.01 0.11±0.01 0.62±0.08 0.34±0.01 5 Appendix S3 Phylogeny construction To estimate evolutionary relatedness and divergence times between competing species we use a Bayesian method implemented in BEAST v.1.7.5 (Drummond et al. 2012), which estimates separate rates and rate-change parameters for four DNA loci under a relaxed molecular clock, following code provided by Bell et al. (2010). Ribulose-bisphosphate carboxylase gene (rbcL), maturase K (matK), tRNA-Leu (trnL), and internal transcribed spacer (ITS) flanking the 5.8S ribosomal RNA gene sequence data were obtained from GenBank (last accessed 23 October 2012). Following Cadotte et al. (2009) and Burns & Strauss (2011), we included Amborella trichopoda and Magnolia heterophylla to root the tree and to increase the depth of taxon sampling. In cases where we had only species in a genus, and sequence data were missing, we selected a random congener with data available, following Cadotte et al. (2008). In all cases where we had more than one species in a genus, we had sequence data for those species. The underlying model of molecular evolution was set to be a standard general timereversible model (GTR + I + Γ) for each of the individual DNA loci, and rate change was set by an uncorrelated lognormal model (UCLN). We set a uniform prior distribution to the root of the tree [167, 199 Mya] and our starting tree had a topology and branch lengths satisfying prior knowledge of the major angiosperm clades (Wikstrom et al. 2001; Bell et al. 2010). The starting tree was created using r8s version 1.7 (Sandersen 2003), following Burns & Strauss’ (2011) procedure. A Markov chain Monte Carlo simulation in BEAST with 10 million generations, sampled every 1,000 generations resulted in 9,000 post-burn-in trees, which were 6 analyzed using Tracer v.1.5 (Drummond et al. 2012) to assess convergence and stationarity of each chain relative to the posterior distribution. We achieved an effective sample size (ESS) of more than 200 samples. Thus, we used TreeAnnotator v.1.7.5 (Drummond et al. 2012) to produce maximum clade credibility trees from the post-burn-in trees and to determine the 95% confidence bounds on the ages for all nodes in the tree. Phylogenetic conservatism of fitness components To test for phylogenetic signal in the species fitness components (π π , ππ , ππ , all defined in eqn. 4 from the main text and eqn. S2 from the appendix S4), and the species competitive rank, we calculated the K statistic (Blomberg et al. 2003), Pagel’s π (Pagel 1999) and Moran’s I (Moran 1950) for these traits using “picante”, “geiger”, and “adephylo” packages implemented in R (Kembel et al. 2010, Harmon et al. 2008, and Jombart et al. 2010, respectively). Briefly, values of Blomberg’s K approaching zero indicate that trait values approximate to a random distribution with respect to phylogeny, whereas values approaching one imply trait values that match expectations under a Brownian motion model of evolution (Blomberg et al. 2003). Pvalues were derived from the comparison of the observed K to a null distribution obtained by 999 randomizations of trait values across the tips of the phylogeny. Pagel’s λ multiplies all internal branches by λ to test whether eliminating phylogenetic structure has an effect in explaining the distribution of character values among terminal taxa. As with K, λ values closer to 1 are consistent with a Brownian model of evolution (Pagel 1999; Freckleton et al. 2002). We used a log likelihood ratio test to evaluate if λ was signiο¬cantly greater than zero, the value consistent with no phylogenetic signal. Moran’s I, a measure of spatial autocorrelation, can also be 7 used to assess the importance of phylogeny in explaining trait variation (Gittleman et al. 1996). A trait has phylogenetic inertia if species’ trait differences are significant correlated with phylogenetic distance. Moran’s I has proven useful as an alternative metric when a model of character evolution can not be assumed (Purvis et al. 2000), as is the case with the species’ competitive hierarchy in our study. References for Appendix S3 Blomberg, S.P., Garland, T. & Ives, A.R. (2003). Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution, 57, 717-745. Burns, J.H. & Strauss, S.Y. (2011). More closely related species are more ecologically similar in an experimental test. Proc. Natl. Acad. Sci. USA, 108, 5302-5307. Cadotte, M.W., Cardinale, B.J. & Oakley, T.H. (2008). Evolutionary history and the effect of biodiversity on plant productivity. Proc. Natl. Acad. Sci. USA, 105, 17012-17017. Cadotte, M.W., Cavender-Bares, J., Tilman, D. & Oakley, T.H. (2009). Using phylogenetic, functional and trait diversity to understand patterns of plant community productivity. PLoS One, 4, e5695. Drummond, A.J., Suchard, M.A., Xie, D. & Rambaut, A. (2012). Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol., 29, 19691973. Freckleton, R.P., Harvey, P.H. & Pagel, M. (2002). Phylogenetic analysis and comparative data: a test and review of evidence. Am. Nat., 160, 712-726. Gittleman, J.L., Anderson, C.G., Kot, M. & Luh, H.-K. (1996). Phylogenetic lability and rates of evolution: a comparison of behavioral, morphological and life 8 history traits. In: Phylogenies and the comparative method in animal behavior (ed Martins E. P). Oxford University Press, NY, pp. 166–205 Harmon, L.J., Weir, J.T., Brock, C.D., Glor, R.E. & Challenger, W. (2008). GEIGER: investigating evolutionary radiations. Bioinformatics, 24, 129-131. Jombart, T., Balloux, F.O. & Dray, S.P. (2010). Adephylo: new tools for investigating the phylogenetic signal in biological traits. Bioinformatics, 26, 1907-1909. Kembel, S.W., Cowan, P.D., Helmus, M.R., Cornwell, W.K., Morlon, H., Ackerly, D.D., Blomberg, S.P. & Webb, C.O. (2010). Picante: R tools for integrating phylogenies and ecology. Bioinformatics, 26, 1463-1464. Moran, P.A.P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17-23. Pagel, M. (1999). Inferring the historical patterns of biological evolution. Nature, 401, 877-884. Purvis, A., Gittleman, J.L., Cowlishaw, G. & Mace, G.M. (2000). Predicting extinction risk in declining species. Proc. R. Soc. Lond. B Biol. Sci., 267, 1947-1952. Sanderson, M.J. (2003). r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics, 19, 301302. 9 Sequences used to build the phylogenetic tree with BEAST (Fig. 2, main text) to estimate phylogenetic distances. NA indicates missing data. Data gaps were filled, when possible, with substituted data from congeners, as indicated in the table below. No substitutions were made for genera with multiple species per genus in the experiment. Species in the experiment Agoseris heterophylla Agoseris retrorsa Amsinckia menziesii Anagallis arvensis Centaurea melitensis Clarkia purpurea Erodium botrys Erodium cicutarium Euphorbia peplus Geranium carolinianum Taxon in the phylogeny Agoseris heterophylla Agoseris retrorsa rbcL Species used for rbcL matK Species used for matK trnL Species used for trnL ITS1 and 5.8s NA NA NA NA NA NA AY218965 NA NA AJ633250 Amsinckia NA NA JQ388521 Erodium botrys Erodium cicutarium Euphorbia peplus Geranium carolinianum Hemizonia Hemizonia Anagallis arvensis Centaurea melitensis Clarkia HM849770 EU384954 L01896 NA HM849981 HM849993 HQ644043 NA Anagallis arvensis Centaurea melitensis Clarkia xantiana NA Erodium cicutarium Euphorbia peplus Geranium carolinianum NA HM850730 EU385332 Agoseris retrorsa Amsinckia lycopsoides Anagallis arvensis Centaurea melitensis AF208364 JF489053 AF547793 EU385047 NA NA AY264522 NA NA HE795460 HM850903 HM850919 EU922172 NA Erodium cicutarium Euphorbia peplus Geranium carolinianum NA AM397178 JN009978 AB702965 NA Agoseris retrorsa Amsinckia tessellata Anagallis arvensis Centaurea melitensis Clarkia xantiana Erodium botrys Erodium cicutarium Euphorbia peplus Geranium carolinianum NA Species used fo ITS1 and 5.8s Agoseris heterophylla Agoseris retrorsa Amsinckia menziesii Anagallis arvensis Centaurea melitensis Clarkia xantiana Erodium botrys Erodium cicutarium Euphorbia peplus AJ633461 JQ513392 AF547739 HQ540425 AY271534 EF185365 EF185393 HQ900643 NA NA AF283544 Hemizonia 10 congesta congesta Lasthenia californica Lotus purshianus Lotus wrangelianus Medicago polymorpha Navarretia atractyloides Plantago erecta Salvia carduacea Lasthenia californica Lotus purshianus Lotus wrangelianus Medicago polymorpha congesta NA NA AF467162 NA NA AF142729 NA NA NA HM850165 Medicago polymorpha HM851137 Navarretia NA NA HQ116959 Plantago HQ644063 Salvia AY570408 Plantago lanceolata Salvia columbariae HM851043 HM850803 Lasthenia californica Lotus purshianus NA Medicago polymorpha Navarretia atractyloides Plantago lanceolata Salvia verbenaca NA NA AF550685 NA NA AF467067 NA NA DQ641991 DQ311709 HQ911891 AY101962 NA Medicago polymorpha Navarretia leucocephala Plantago erecta NA Lasthenia californica Lotus purshianus Lotus wrangelianus Medicago polymorpha Navarretia atractyloides Plantago erecta Salvia miltiorrhiza DQ311981 EF199708 AJ548982 EU591975 Outgroup species Amborella trichopoda Magnolia grandiflora L12628 EF590545 Amborella trichopoda Magnolia grandiflora L12628 AY943528 Amborella trichopoda Magnolia grandiflora AY145324 FJ490796 Amborella trichopoda Magnolia grandiflora NA NA EU593550 Magnolia grandiflora 11 Newick file containing the phylogeny built Phylogeny dated in Mya and bootstrap support for each node (((clarkia:148.60395812371667,(euphorbia_peplus:137.3309375693278,(((lotus_purshianus:18.3972141649460 64,lotus_wrangelianus:18.397214164946064):36.3552862968933,medicago_polymorpha:54.75250046183936): 74.35848301340502,(geranium:44.748967078110695,(erodium_cicutarium:34.702939516829986,erodium_botr ys:34.702939516829986):10.04602756128071):84.36201639713371):8.21995409408342):11.27302055438886 4):5.492223499840833,((((lasthenia_californica:48.2916255917541,hemizonia_congesta:48.2916255917541):4 7.14522226424931,(centaurea_melitensis:41.78328229314735,(agoseris_retrorsa:13.202024805359372,agoseri s_heterophylla:13.202024805359372):28.581257487788008):53.65356557285604):34.73816193581824,((plant ago:64.5560104348593,salvia:64.5560104348593):48.2619323281352,amsinckia:112.8179427629945):17.357 067038827125):11.707424011654666,(navarretia:102.53918839821839,anagallis_arvensis:102.5391883982183 9):39.34324541525791):12.213747810081202):24.82054891236217,(magnolia_grandiflora:114.824931625604 26,amborella_trichopoda:114.82493162560426):64.0917989103154); clarkia euphorbia_peplus 1 lotus_purshianus 0.8566 0.8755 lotus_wrangelianus 1 medicago_polymorpha 0.7378 geranium 1 erodium_cicutarium 1 erodium_botrys 1 lasthenia_californica 1 hemizonia_congesta 1 centaurea_melitensis 0.9844 agoseris_retrorsa 1 0.999 agoseris_heterophylla plantago 1 1 0.9827 salvia 0.9993 amsinckia navarretia 1 anagallis_arvensis magnolia_grandiflora 0.9455 amborella_trichopoda 200.0 175.0 150.0 125.0 100.0 75.0 50.0 25.0 0.0 Appendix S4 12 A simpler model of competition As shown in eqn. 4 from the main text, one component of the species’ average fitness difference is their average difference in their response to competition, something separate from the potentially stabilizing effects of differing strengths of intra versus interspecific competition. If we treat the response to competition as a species trait, it can be fit with a simpler model than one allowing for interaction coefficients specific to each species pair (fitting all πΌππ ’s). This approach assumes that species’ absolute competitive ability is unaffected by the identity of the competitor species, as might arise with competition for a single limiting resource, and it necessarily generates a perfectly transitive competitive hierarchy. It allows us to estimate the missing interaction coefficients from the pairwise analysis and quantify each species fitness against all other competitors. Assuming that each species j exerts a generic competitive effect, ππ on all other species, and each species i has a generic response to competition, ππ , the per capita effect of species j on i can be expressed πΌππ = ππ ππ . By substituting these ππ and ππ values into eqns. 3 and 4 from the main text, it becomes apparent that this model does not allow for stabilizing niche differences (π = 1), and the average fitness difference becomes: π π ππ − 1 ππ =( ) π π ππ − 1 ππ (S1) As in eqn. 4, species gain a fitness advantage by being insensitive to competition (having a small competitive response (ππ ) and high demographic rates (ππ )). From this average fitness difference, we can isolate each species “fitness” against all competitors in the experiment, and this represents their average competitive ability: π π = ππ − 1 ππ 13 (S2) To fit species’ competitive effects and responses to our field data, we once again used a maximum likelihood approach, but because the effect and response terms (ππ , ππ ) are not specific to each competing pair, we simultaneously fit these for all species according to the following function: πΉπ = ππ 1 + ππ ∑π ππ ππ,π‘ 14 Appendix S5 Our competition experiment assembled communities to parameterize the annual plant population model with estimates of species’ germination fractions (ππ ), per germinant fecundities in the absence of neighbors (ππ ), seed survival in the soil (π π ), and all pairwise interaction coefficients (πΌππ ). Here we outlined each sequential step of the experiment for a single species pair- a hypothetical red species versus blue species (our results are based on 18 species, but the logic is the same): 1- Collect and clean seeds from naturally occurring individuals of species red and blue for later sowing into the experimental plots (April-August 2011). 2- Lay landscape fabric in the experimental area previously cleared of vegetation, and cut rectangular holes for the experimental plots (October 2011). 3- Sow seed of the red and blue species as focal individuals into plots either with or without neighbors (November 2011). The neighbor plots subject the focal species to a single neighbor species sown at 2, 4, 8, or 16 g/m2. In each plot, a single species (red 15 or blue) is the neighbor species, but both species (red and blue) are sown in as focal species. When the identity of the focal and the neighbor species match, the declining fecundity of the focal individuals with increasing neighbor density (across multiple plots) estimates intra-specific competition. When the species are different, the fecundity decline of the focal individual reflects interspecific competition. 4- Burry seeds of each species in mesh bags at 3 cm depth in an area close to the plots for later estimates of buried seed survival (November 2011). 5- Count the number of neighbors surrounding each focal individual in a radius of 7cm to estimate the actual neighbor density around each focal individual. In addition, this number of germinants, divided by the number of viable seeds added to a circle of 7 cm radius gives a measure of species’ germination fractions (ππ ) (March-April 2012). 6- Measure seed production of each focal individual (April-August 2012). 7- Recover mesh bags with buried seeds to estimate seed survival in the soil (π π ) (August 2012). 8- Combine data on the seed production of focal individuals with the number of surrounding neighbors to estimate per germinant fecundity in the absence of neighbors (ππ ), and pairwise interaction coefficients (πΌππ ) using maximum likelihood methods (September 2012). 16 Appendix S6 Here, we report the results for the four different non-linear functions relating species niche or fitness differences to phylogenetic distance (as in Figs. 3 and 4 from main text). We used AIC (Akaike Information Criterion) to compare several models, each shown in the table below that describe an exponential rise to a maximum value. For niche differences, all of these functions provided similarly poor fits (βAIC<10, although the exponential function including a maximum show the lowest AIC) due to the lack of a relationship between niche differences and phylogenetic distance (Fig. 4). The same exponential function including a maximum proved the best supported (βAIC>10) for the relationship between fitness differences and phylogenetic distance (Fig. 3). It was also plotted for the relationship between niche differences and phylogenetic distance (Fig. 4) to ease comparison with the fitness difference relationships. Pseudo-R2 values show the % of deviance explained. Function Exponential function including a maximum Power Hyperbola Logistic AIC, pseudo-R2 Fitness Differences AIC, pseudo-R2 Niche differences π¦ = π(1 − π −ππ₯ ) 445.1, 8.3% 127.0, 2.1% π¦ = ππ₯ π π₯ π¦= π + ππ₯ ππ₯ π¦= π−π₯ 1 + π( π ) 461.6, 7.1% 131.0, 2.0% 464.1, 6.8% 131.1, 1.8% 488.7, 8.6% 133.6, 2.4% Formula The lack of a relationship between niche differences and phylogenetic distance was similar regardless of whether cases with zero stabilizing niche difference (when intraspecific effects were smaller than interspecific effects) were included or excluded. 17 The asymptotic pattern held when considering each of the three different functions with the lowest AIC. The black line corresponds to the exponential rise to a maximum, the red line to 0.6 0.4 0.2 0.0 Niche Differences 0.8 1.0 the power function and the blue line to hyperbolic function. 0 50 100 150 200 250 300 Phylogenetic Distance 18
