1. Soil moisture
> soil.moisture<-read.table("1SoilMoisture.txt",header=TRUE)
> soil.model<-aov(mu.SM~ treatment,data=soil.moisture)
> summary(soil.model)
> TukeyHSD(soil.model)

2. Predawn Water Potential
> predawn<-read.table(Ò2PredawnWaterPot.txtÓ,header=TRUE)
> predawn.model<-aov(pressure.bar~Treatment*collection.date,data=pre.dawn.test)
> anova(predawn.model)

3. Shrub Biomass
> shrubgrowth<-read.table(Ò3Shrubgrowth.txtÓ,header=TRUE)
> model<-lme(avggrowth_width~Treatment*age,random=~1|Plot,data=stems) 
> anova(model)names

4. A fasiculatum Physiology individual trait analysis
> adfa<-read.table("4physioADFAonly.txt",header=TRUE)
> adfa<-na.omit(adfa)
### Sort and Partition data into runs (data collection events)
> adfa <-adfa [order(adfa.table$run),]
> Arun1<-adfa.table[1:11,]
> Arun2<-adfa.table[12:39,]
> Arun3<-adfa.table[40:61,]
### Partition data for adonis
> AtraitsR1<-Arun1[,10:16]
> AfactorsR1<-Arun1[,1:9]
> AtraitsR2<-Arun2[,10:16]
> AfactorsR2<-Arun2[,1:9]
> AtraitsR3<-Arun3[,10:16]
> AfactorsR3<-Arun3[,1:9]
### Run Adonis for each ÔrunÕ separately (X=ÕrunÕ number)
>adonis(
         AtraitsRX~Treatment*Age_Class,
         data=AfactorsRX,
         method="euclidean",
         permutations=999
         )

5. Herbaceous biomass
> herb.bio<-read.table("5HerbBiomass.txt",header=TRUE)
> herb.model<-lme(biomass~Treatment*Origin, random=~1|Plot,data=herb.bio)
> anova(herb.model)

6. Juvenile shrub/Exotic Physiology: Individual trait analysis
### Sort and Partition data into runs (data collection events)
> Juvs<-Juvs [order(Juvs$run),]
> Jrun1<-Juvs[1:34,]
> Jrun2<-Juvs[35:83,]
> Jrun3<-Juvs[84:146,]
### Partition data for adonis
> JtraitsR1<-Jrun1[,11:16]
> JfactorsR1<-Jrun1[,1:10]
> JtraitsR2<-Jrun2[,11:16]
> JfactorsR2<-Jrun2[,1:10]
> JtraitsR3<-Jrun3[,11:16]
> JfactorsR3<-Jrun3[,1:10]
### Run Adonis for each ÔrunÕ separately (X=ÕrunÕ number)
> adonis(
         JtraitsRX~Treatment*Origin*Species/Group,
         data=JfactorsRX,
         method="euclidean",
         permutations=999
         )

8. Traits predicting growth test
> traits<-read.table(Ò7growthxTraits.txtÓ,header=TRUE)
> traits.model<-lm(
         growth_slope~trait.value*trait.msmt,
         data=traits
         )
> summary(traits.model)