<p> Supplementary Material</p><p>Table S1. Sample size, number of sites and the sampled altitudinal ranges of different growth form and species investigated in this study</p><p>Growth Species Species Sample Number Min. Max. form code size of sites altitude altitude annual Artemisia anethifolia 6 5 2 1356 1984 annual Artemisia annua 8 18 7 377 1116 annual Artemisia 10 3 1 1290 1290 blepharolepis annual Artemisia hedinii 21 6 2 2920 3613 annual Artemisia palustris 30 3 1 1369 1369 annual Artemisia scoparia 36 104 31 259 2745 annual Artemisia 38 24 7 231 2745 sieversiana annual Neopallasia 41 24 8 280 3245 pectinata perennial Ajania breviloba 2 3 1 1659 1659 perennial Ajania parviflora 3 3 1 2674 2674 perennial Ajania przewalskii 4 3 1 1743 1743 perennial Artemisia adamsii 5 3 1 1395 1395 perennial Artemisia 7 5 2 566 1146 angustissima perennial Artemisia argyi 9 14 5 259 835 perennial Artemisia commutata 13 6 2 1347 2362 perennial Artemisia 14 3 1 997 997 desertorum perennial Artemisia frigida 16 17 5 1347 1926 perennial Artemisia igniaria 22 5 2 403 1168 perennial Artemisia japonica 23 6 2 1130 1622 perennial Artemisia kanashiroi 24 8 3 1072 1352 perennial Artemisia lancea 25 4 2 788 835 perennial Artemisia 26 11 4 314 1307 lavandulifolia perennial Artemisia 27 9 3 1113 2674 leucophylla perennial Artemisia mongolica 28 48 15 231 3245 perennial Artemisia phaeolepis 31 2 1 3635 3635 perennial Artemisia pubescens 32 12 4 1369 1984 perennial Artemisia rubripes 34 62 19 259 2920 perennial Artemisia 37 3 1 314 314 selengensis perennial Artemisia subulata 39 3 1 1116 1116 perennial Seriphidium 45 5 2 2420 3245 nitrosum perennial Seriphidium terrae- 47 8 3 1743 3635 albae semi- Ajania achilloides 1 3 1 1926 1926 shrub semi- Artemisia 11 9 3 1926 3613 shrub brachyloba semi- Artemisia capillaris 12 21 6 403 2674 shrub semi- Artemisia dubia 15 2 1 1352 1352 shrub semi- Artemisia gansuensis 17 3 1 1732 1732 shrub semi- Artemisia giraldii 18 23 8 311 2420 shrub semi- Artemisia gmelinii 19 2 1 2674 2674 shrub semi- Artemisia 20 3 1 1088 1088 shrub halodendron semi- Artemisia ordosica 29 12 4 1307 1515 shrub semi- Artemisia 33 3 1 3613 3613 shrub roxburghiana semi- Artemisia sacrorum 35 48 16 377 2920 shrub semi- Hippolytia trifida 40 3 1 1732 1732 shrub semi- Seriphidium finitum 42 2 1 902 902 shrub semi- Seriphidium 43 3 1 2851 2851 shrub gracilescens semi- Seriphidium 44 3 1 1743 1743 shrub lehmannianum semi- Seriphidium 46 5 1 1587 1587 shrub santolinum Table S2. Effects of annual climate on non-structural carbohydrates (NSC) of Artemisia species and their close relatives across northern China</p><p>Variable Predictor Estimate t- value</p><p>REML MCMC</p><p>NSC Intercept 33.573(19.532) 32.862(-5.672,68.342) 1.719 AMT -18.438(11.125) -18.028(-38.318,4.015) -1.657 AP -11.783(7.616) -11.499(-26.852,2.652) -1.547 AMT*AP 6.771(4.336) 6.607(-1.352,15.471) 1.562 Sugar Intercept 38.820(34.917) 37.705(-30.312,101.812) 1.112 AMT -21.797(19.885) -21.160(-58.032,17.313) -1.096 AP -14.086(13.640) -13.648(-38.512,13.542) -1.033 AMT*AP 8.078(7.765) 7.828(-7.711,21.872) 1.040 Starch Intercept 31.284(18.063) 32.292(-4.219, 67.801) 1.732 AMT -17.189(10.291) -17.760(-38.164,2.966) -1.670 AP -11.021(7.019) -11.422(-24.796,3.246) -1.570 AMT*AP 6.328(3.997) 6.556(-1.912,14.112) 1.583</p><p>Restricted maximum likelihood (REML, estimate with standard error in parentheses) and Markov chain Monte Carlo (MCMC, estimate with HPD 95% intervals in parentheses) parameter estimates and their statistical significance for the fixed effects. AMT, annual mean temperature; AP, annual precipitation. R script for statistical analyses in this study</p><p>#### reading data#### dat=read.csv(###data file path###,header=T)</p><p>##constructing a data frame that contains data used in this study## dat1=cbind.data.frame(dat$location,dat$life.form,dat$species,dat$C.mg.g,dat$N.mg.g ,dat$sugar.mg.g,dat$starch.mg.g,dat$protein.mg.g,dat[,7:9],dat[,44:46],dat[,48:66]) colnames(dat1) [1:8]=c("location","life.form","species","C.mg.g","N.mg.g","sugar.mg.g","starch.mg. g","protein.mg.g")</p><p>##calculating altitudinal ranges of each species### tab=c() spe=levels(dat1$species) for (i in 1:length(spe)){ lif=unique(dat1$life.form[dat1$species==spe[i]]) tab=rbind(tab,c(paste(lif),paste(spe[i]),range(dat1$altit[dat1$species==spe[i]])))</p><p>}</p><p>##calculating site number of each species### tab=c() spe=levels(dat1$species) for (i in 1:length(spe)){ dat2=subset(dat1,species==spe[i]) dat2$location=factor(dat2$location) tab=rbind(tab,paste(spe[i]),nlevels(unique(dat2$location)))</p><p>}</p><p>##calculating sample size of each species### tab=c() spe=levels(dat1$species) for (i in 1:length(spe)){ dat2=subset(dat1,species==spe[i]) dat2$location=factor(dat2$location) tab=rbind(tab,paste(spe[i]),nrow(dat2))</p><p>}</p><p>####calculating C constituents####</p><p>##Hoch et al. (2002): 0.41 g C per g sugar (mean of glucose, fructose and sucrose), 0.444 g C per g starch### dat1=na.exclude(dat1) dat1$nsc=0.41*dat1$sugar.mg.g+0.444*dat1$starch.mg.g dat1$sc=dat1$C.mg.g-dat1$nsc dat1$sug.c=0.41*dat1$sugar.mg.g dat1$star.c=0.444*dat1$starch.mg.g dat1$ratio=dat1$sugar.mg.g/dat1$starch.mg.g dat1$total=dat1$sugar.mg.g+dat1$starch.mg.g</p><p>##descriptive analyses### mean(dat1$total) mean(dat1$sugar) mean(dat1$starch) mean(dat1$ratio) sd(dat1$total) sd(dat1$sugar) sd(dat1$starch) sd(dat1$ratio)</p><p>##log transformation### dat1$C=log10(dat1$C.mg.g) dat1$sugar=log10(dat1$sugar.mg.g) dat1$starch=log10(dat1$starch.mg.g) dat1$ratio=log10(dat1$ratio) dat1$total=log10(dat1$total) dat1$nsc=log10(dat1$nsc) dat1$sc=log10(dat1$sc) dat1$sug.c=log10(dat1$sug.c) dat1$star.c=log10(dat1$star.c)</p><p>###hierarchical models### library(languageR) library(lme4) library(MCMCglmm)</p><p>##geographical varible## dat1$longit=log10(dat1$longit) dat1$latit=log10(dat1$latit) dat1$altit=log10(dat1$altit) carb=c(colnames(dat1[,34:42])) for (i in 1:length(carb)){ form=formula(paste(carb[i],"~longit+latit+altit+(1|location)",sep="")) dat.lmer=lmer(form,data=dat1) print(paste("##",carb[i],sep="")) print(summary(dat.lmer)) form=formula(paste(carb[i],"~longit+latit+altit",sep="")) dat.mc=MCMCglmm(form,, random=~location,nitt=10000,data=dat1, verbose=FALSE) print(summary(dat.mc))</p><p>}</p><p>##climate effects ### dat1$bio1=log10(dat1$bio1+50) dat1$bio12=log10(dat1$bio12) dat1$bio10=log10(dat1$bio10) dat1$bio18=log10(dat1$bio18)</p><p>#annual climate# for (i in 1:length(carb)){ form=formula(paste(carb[i],"~bio1*bio12+(1|location)",sep="")) dat.lmer=lmer(form,data=dat1) print(paste("##",carb[i],sep="")) print(summary(dat.lmer)) form=formula(paste(carb[i],"~bio1*bio12",sep="")) dat.mc=MCMCglmm(form,, random=~location,nitt=10000,data=dat1, verbose=FALSE) print(summary(dat.mc))</p><p>}</p><p>#warmest quarter climate# for (i in 1:length(carb)){ form=formula(paste(carb[i],"~bio10*bio18+(1|location)",sep="")) dat.lmer=lmer(form,data=dat1) print(paste("##",carb[i],sep="")) print(summary(dat.lmer)) form=formula(paste(carb[i],"~bio10*bio18",sep="")) dat.mc=MCMCglmm(form,, random=~location,nitt=10000,data=dat1, verbose=FALSE) print(summary(dat.mc))</p><p>}</p><p>####nested ANOVA### dat1$species=factor(dat1$species) dat1$life.form=factor(dat1$life.form) dat1$location=factor(dat1$location)</p><p> library(nlme) library(boot) library(ape)</p><p>##nested variance partition### for (i in 1:length(carb)){ form=formula(paste(carb[i],"~1",sep="")) dat.varcomp=varcomp(lme(form,random=~1|location/life.form/species,data=dat1),1) print(paste("####",carb[i],sep="")) print(dat.varcomp)</p><p>##boxplot to show relationships between carbon in sugar and starch## lf=levels(dat1$life.form) col1=c("blue","yellow","green") col2=c("light blue","light yellow","light green") dval=c(0,12,24) aval=c(30,45,60) boxplot(dat1$sug.c~dat1$species,ylim=c(-1,2),xlab="species",ylab="C concentration (log scale)",cex.lab=1.3,cex.axis=1.2,type="n") for (i in 1:length(lf)){ dat2=subset(dat1,life.form==lf[i]) outstat<-boxplot(dat2$sug.c~dat2$species,col=col1[i],labs=F,axes=F,add=T) outstat<-boxplot(dat2$star.c~dat2$species,col=col2[i],labs=F,axes=F,add=T) ngroups <- length(levels(dat2$species)) rect((1:ngroups)-.4, outstat$stats[2,], (1:ngroups)+.4, outstat$stats[4,], </p><p> density=dval[i], angle=aval[i])</p><p>}</p><p> legend("bottomright", paste(lf),pch=15,col=col1,title="Sugar") legend("topright", paste(lf),pt.bg=col2,angle=aval,density=dval,title="Starch")</p>