Statistical Code 
Code for statistical analyses in ASReml and R used to generate the results in this manuscript. 

TABLE 1 MODELS
!part 1 #  Exploration full model SIMPLE MODEL TO DEFINE AMONG-INDIVIDUAL VARIATION
##################################################################################
ExpFieldScoreWoHops~  mu PlotLevelTreatment SexCent CatchTimeSinceSunriseMC !r BirdID PlotYear 
VPREDICT !DEFINE
F VP 1+2+3       # 4 total phenotypic variation
F Residual 1  # residual variation
F Ind 2           # individual variation
F PlotYear 3	#variation in plotyear
H IndR 2 4      # ID repeatability with SE
H ResR 1 4    # Residual repeatability with SE
H Plotyear 3 4 

!part 2 # Exploration wo BirdID OR PlotYear, run twice- once each for dropping each variable to determine estimates for chi2  SIMPLE MODEL TO DEFINE AMONG-INDIVIDUAL VARIATION
##################################################################################
ExpFieldScoreWoHops~ mu PlotLevelTreatment SexCent CatchTimeSinceSunriseMC !r BirdID 

!part 3 # LD full model SIMPLE MODEL TO DEFINE AMONG-INDIVIDUAL VARIATION
##################################################################################
FirstEggDay~ mu PlotLevelTreatment !r BirdID PlotYear
VPREDICT !DEFINE
F VP 1+2+3       # 4 total phenotypic variation
F Residual 1  # residual variation
F Ind 2           # individual variation
F PlotYear 3	#variation in plotyear
H IndR 2 4      # ID repeatability with SE
H ResR 1 4    # Residual repeatability with SE
H Plotyear 3 4 

!part 4 # LD wo BirdID OR PlotYear, run twice- once each for dropping each variable to determine estimates for chi2  SIMPLE MODEL TO DEFINE AMONG-INDIVIDUAL VARIATION
##################################################################################
FirstEggDay~ mu PlotLevelTreatment !r BirdID 

!part 5 # CS full model SIMPLE MODEL TO DEFINE AMONG-INDIVIDUAL VARIATION
##################################################################################
ClutchSize~ mu PlotLevelTreatment !r BirdID PlotYear 
VPREDICT !DEFINE
F VP 1+2+3       # 4 total phenotypic variation
F Residual 1  # residual variation
F Ind 2           # individual variation
F PlotYear 3	#variation in plotyear
H IndR 2 4      # ID repeatability with SE
H ResR 1 4    # Residual repeatability with SE
H Plotyear 3 4 

!part 6 # CS wo BirdID OR PlotYear, run twice- once each for dropping each variable to determine estimates for chi2 SIMPLE MODEL TO DEFINE AMONG-INDIVIDUAL VARIATION
##################################################################################
ClutchSize~ mu PlotLevelTreatment !r PlotYear 



TABLE 2 MODELS. Example below given for Exploration model set. Full set was run twice more for lay date and clutch size. LD and CS are only for females, and are not associated with time, so those variables are dropped from their equations (i.e base models: CSCont CSPRed ~ !r Trait.BirdID Trait.PlotYear and LDCont LDPred~ !r Trait.BirdID Trait.PlotYear)

!part 1 # Exploration base model 
############################################################################
ExpCont ExpPred ~ Trait.SexCent Trait.CatchTimeSinceSunriseMC !r Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co)variance matrix, 1 random effect           
0 
Trait 0 US  !S2==1 !GPZP 
0.1
0 0.1

Trait.BirdID 2
Trait 0 US !GPUP 
0.1
0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPFP
0.1
0.0 0.1
PlotYear

VPREDICT !DEFINE 
#1 Residual Control
#2 Residual Predator
#3 Control.BirdID      
#4 BirdID covariance Cont - Pred    
#5 Predator.BirdID     
#6 Control.Plot        
#7 Plot covariance Cont-Pred     
#8 Predator.Plot       
F PhenVarCont 1 + 3 + 6 #9 phenotypic variance of control with se
F PhenVarPred 2 + 5 + 8 #10 phenotypic variance of pred with se
F ResVarCont 1 #11 residual variance of control with se
F ResVarPred 2 #12 residual variance of pred with se
F IndVarCont 3 #13 indivdiual variance of cont with se
F IndVarPred 5 #14 individual variance of pred with se
F PlotVarCont 6 #15 plot variance of control with se
F PlotVarPred 8 #16 plot variance of pred with se
H IndRepeatCont 3 9 #17 repeatability of cont with se
H IndRepeatPred 5 10 #18 repeatability of pred with se
H PlotRepeatCont 6 9 #19 repeatablity of control plots with se
H PlotRepeatPred 8 10 #20 repeatability of pred plots with se
F IndCovCP 4 #21 individual covariance c-p
F PlotCovCP 7 #22 plot covariance c-p
R IndCorrCP 3 4 5 #23 indivdiual correlation c-p with se
R PlotCorrCP 6 7 8 #24 plot correlation c-p with se


!part 2 # Exploration model to determine if BirdID (individual level) variance is equal in both treatments
############################################################################
ExpCont ExpPred ~ Trait.SexCent Trait.CatchTimeSinceSunriseMC !r Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co)variance matrix, 1 random effect           
0 
Trait 0 US  !S2==1 !GPZP 
0.1
0 0.1

Trait.BirdID 2
Trait 0 US !GPUP !=a0a 
0.1
0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPFP 
0.1
0.0 0.1
PlotYear

!part 3 # Exploration model to determine if Residual variance is equal in both treatments
############################################################################
ExpCont ExpPred ~ Trait.SexCent Trait.CatchTimeSinceSunriseMC !r Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co)variance matrix, 1 random effect           
0 
Trait 0 US  !S2==1 !GPZP !=a0a
0.1
0 0.1

Trait.BirdID 2
Trait 0 US !GPUP 
0.1
0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPFP  
0.1
0.0 0.1
PlotYear

!part 4 # Exploration model to determine if PlotYear variance is equal in both treatments
############################################################################
ExpCont ExpPred ~ Trait.SexCent Trait.CatchTimeSinceSunriseMC !r Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co)variance matrix, 1 random effect           
0 
Trait 0 US  !S2==1 !GPZP 
0.1
0 0.1

Trait.BirdID 2
Trait 0 US !GPUP 
0.1
0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPFP !=a0a 
0.1
0.0 0.1
PlotYear



TABLE 3 MODELS. Example below given for Exploration model set. Full set was run twice more for lay date and clutch size. LD and CS are only for females, and are not associated with time, so those variables are dropped from their equations (i.e base models: CSCont CSPRed ~ !r Trait.BirdID Trait.PlotYear and LDCont LDPred~ !r Trait.BirdID Trait.PlotYear)

!part 1 # Exploration model (compared to base model from table 2 part 1 above)to test if  individual level correlation (BirdID) = 0, chi2
############################################################################
ExpCont ExpPred ~ Trait.SexCent Trait.CatchTimeSinceSunriseMC !r Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co)variance matrix, 1 random effect           
0 
Trait 0 US  !S2==1 !GPZP 
0.1
0 0.1

Trait.BirdID 2
Trait 0 US !GPZP 
0.1
0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPFP 
0.1
0.0 0.1
PlotYear

VPREDICT !DEFINE 
#1 Residual Control
#2 Residual Predator
#3 Control.BirdID      
#4 BirdID covariance Cont - Pred    
#5 Predator.BirdID     
#6 Control.Plot        
#7 Plot covariance Cont-Pred     
#8 Predator.Plot       
F PhenVarCont 1 + 3 + 6 #9 phenotypic variance of control with se
F PhenVarPred 2 + 5 + 8 #10 phenotypic variance of pred with se
F ResVarCont 1 #11 residual variance of control with se
F ResVarPred 2 #12 residual variance of pred with se
F IndVarCont 3 #13 indivdiual variance of cont with se
F IndVarPred 5 #14 individual variance of pred with se
F PlotVarCont 6 #15 plot variance of control with se
F PlotVarPred 8 #16 plot variance of pred with se
H IndRepeatCont 3 9 #17 repeatability of cont with se
H IndRepeatPred 5 10 #18 repeatability of pred with se
H PlotRepeatCont 6 9 #19 repeatablity of control plots with se
H PlotRepeatPred 8 10 #20 repeatability of pred plots with se
F IndCovCP 4 #21 individual covariance c-p
F PlotCovCP 7 #22 plot covariance c-p
R IndCorrCP 3 4 5 #23 indivdiual correlation c-p with se
R PlotCorrCP 6 7 8 #24 plot correlation c-p with se

!part 2 # Exploration model (compared to base model from table 2 part 1 above)to test if  individual level (BirdID) correlation = 1, chi2 
############################################################################
ExpCont ExpPred ~ Trait.SexCent Trait.CatchTimeSinceSunriseMC !r Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co)variance matrix, 1 random effect           
0 
Trait 0 US  !S2==1 !GPZP 
0.1
0 0.1

Trait.BirdID 2
Trait 0 CORGH !GFPP 
0.999
0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPFP
0.1
0.0 0.1
PlotYear



TABLE 4 MODELS
!part 1 # Exp X LD base model
############################################################################
ExpCont LDCont ExpPred LDPred ~ Trait at(Trait,1).SexCent at(Trait,3).SexCent at(Trait,1).CatchTimeSinceSunriseMC at(Trait,3).CatchTimeSinceSunriseMC !r Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co)variance matrix, 1 random effect 
0 
Trait 0 US  !S2==1 !GPUPZZPZZUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1

Trait.BirdID 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
PlotYear

VPREDICT !DEFINE 
#1 ResidExpCont
#2 ResidExpLDCont
#3 ResidLDCont
#4 ResidExpPred
#5 ResidExpLDPred
#6 ResidLDPred
#7 ExpContrl.BirdID 
#8 IDCov.ExpC-LDC      
#9 LDC.BirdID      
#10 IDCov.ExpCExpP      
#11 IDCov.LDCExpP      
#12 ExpP.BirdID      
#13 IDCov.ExpCLDP
#14 IDCov.LDCLDP      
#15 IDCov.ExpPLDP     
#16 LDP.BirdID      
#17 same as above but for py
#18 
#19 
#20
#21
#22
#23
#24
#25
#26
F IDCov.ExpC-LDC 8 #25 individual covariance in control
F IDCov.ExpP-LDP 15 #26 individual covariance in pred
F ResidCov.ExpC-LDC 2 #27
F ResidCov.ExpP-LDP 5 #28
R IndCorrControl 7 8 9 #29 indivdiual correlation in control
R IndCorrPred 12 15 16 #30 indivdiual correlation in pred
R ResidCorrControl 1 2 3 #31 within ind correlation in control
R ResidCorrPred 4 5 6 #32 within ind correlation in pred

part 2 # Exp X LD pred corr = control? Among Individuals
############################################################################
ExpCont LDCont ExpPred LDPred ~ Trait at(Trait,1).SexCent at(Trait,3).SexCent at(Trait,1).CatchTimeSinceSunriseMC at(Trait,3).CatchTimeSinceSunriseMC !r Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co)variance matrix, 1 random effect 
0 
Trait 0 US  !S2==1 !GPUPZZPZZUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1

Trait.BirdID 2
Trait 0 US !GPUPFFPFFUP !=0a000000a0
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
PlotYear

!part 3 # Exp X CS base model
############################################################################
ExpCont CSCont ExpPred CSPred ~ Trait at(Trait,1).SexCent at(Trait,3).SexCent  at(Trait,1).CatchTimeSinceSunriseMC at(Trait,3).CatchTimeSinceSunriseMC !r  Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co) variance matrix, 1 random effect 
0 
Trait 0 US  !S2==1 !GPUPZZPZZUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1

Trait.BirdID 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
PlotYear

!part 4 # Exp X CS pred corr = control?
############################################################################
ExpCont CSCont ExpPred CSPred ~ Trait at(Trait,1).SexCent at(Trait,3).SexCent  at(Trait,1).CatchTimeSinceSunriseMC at(Trait,3).CatchTimeSinceSunriseMC !r  Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co) variance matrix, 1 random effect          
0 
Trait 0 US  !S2==1 !GPUPZZPZZUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1

Trait.BirdID 2
Trait 0 US !GPUPFFPFFUP !=0a000000a0
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
PlotYear

!part 5 # LD X CS base model
############################################################################
LDCont CSCont LDPred CSPred ~ Trait  !r  Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co) variance matrix, 1 random effect 
0 
Trait 0 US  !S2==1 !GPUPZZPZZUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1

Trait.BirdID 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
PlotYear

!part 5 # LD X CS pred corr = control?
############################################################################
LDCont CSCont LDPred CSPred ~ Trait  !r  Trait.BirdID Trait.PlotYear
1 2 2 !STEP 0.001       # 1 residual variance component, 2 dimensional (co) variance matrix, 1 random effect          
0 
Trait 0 US  !S2==1 !GPUPZZPZZUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1

Trait.BirdID 2
Trait 0 US !GPUPFFPFFUP !=0a000000a0
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
BirdID

Trait.PlotYear 2
Trait 0 US !GPUPFFPFFUP
0.1
0.1 0.1
0 0 0.1
0 0 0.1 0.1
PlotYear














Data input and R script for SEM analysis in figure 2:


     ExpC LDC  CSC  ExpP  LDP   CSP
ExpC  1   0.32 0.20 0.94  0.17  0.21
LDC  0.32  1  -0.41 -0.02 0.40 -0.35
CSC  0.20 -0.41  1  -0.03 -0.21 0.82
ExpP 0.94 -0.02 -0.03  1  -0.15 0.21
LDP. 0.17  0.40 -0.21 -0.15  1 -0.35
CSP  0.21 -0.35  0.82 0.21 -0.35  1

library(lme4)
library(arm)
require(sem)

#Among Individual correlation matrix Control
#"ExpC","LDC", "CSC",    
Cont = matrix(
  c( 1.00,  0.32,  0.20,   
     0.32,  1.00, -0.41,   
     0.20, -0.41,  1.00      
  ), # the data elements
  nrow=3,              # number of rows
  ncol=3,              # number of columns
  byrow = TRUE)        # fill matrix by rows
Cont                    # print the matrix
v.namesCont<- c("ExpC","LDC", "CSC")
model.C <-specifyModel()
ExpC<->ExpC,e1,NA
LDC<->LDC,e2,NA
CSC<->CSC,e3,NA
ExpC->LDC,c1,NA
LDC->CSC,c2,NA
ExpC->CSC,c3,NA
model.C.sem<-sem(model.C, S=Cont, N=262,obs.variables=v.namesCont)
summary(model.C.sem)  

#Among Individual correlation matrix Predator
#"ExpP","LDP", "CSP",    
Pred = matrix(
  c( 1.00,  -0.15,  0.21,   
     -0.15,  1.00, -0.35,  
     0.21, -0.35,  1.00   
  ), # the data elements
  nrow=3,              # number of rows
  ncol=3,              # number of columns
  byrow = TRUE)        # fill matrix by rows
Pred                    # print the matrix
v.namesPred<- c("ExpP","LDP", "CSP")
model.P <-specifyModel()
ExpP<->ExpP,e1,NA
LDP<->LDP,e2,NA
CSP<->CSP,e3,NA
Exec->LDP,c1,NA
LDP->CSP,c2,NA
ExpP->CSP,c3,NA
model.P.sem<-sem(model.P, S=Pred, N=262,obs.variables=v.namesPred)
summary(model.P.sem)  
#t.test
t.test2<-function(m1,m2,s1,s2,n1,m0=0,equal.variance=FALSE)
{
  se <-s1+s2
  df <-n1-1 
  t<-(m1-m2-m0)/se
  dat<-c(m1-m2, se, t, 2*pt(-abs(t),df))
  names(dat) <- c('difference of means', 'se', 't', 'p')
  return(dat)
}