summary(mpl1)$varcor

# popu seulement
mpl1.popu <- glmer(total.fruits ~ nutrient*amd + rack + status + 
                     (1|X) +
                     (1|popu), 
                     data=dat.tf, family="poisson", control=glmerControl(optimizer="bobyqa"))

# gen seulement
mpl1.gen <-glmer(total.fruits ~ nutrient*amd + rack + status + 
                   (1|X) +
                   (1|gen), 
                   data=dat.tf, family="poisson", control=glmerControl(optimizer="bobyqa"))

# Approche AICc
ICtab(mpl1, mpl1.popu, mpl1.gen, type = c("AICc"))
#            dAICc df
# mpl1       0.0   10
# mpl1.popu  2.0   9 
# mpl1.gen   16.1  9 

# Approche fréquentiste (Likelihood Ratio Test)
anova(mpl1,mpl1.popu)
# Data: dat.tf
# Df         AIC  BIC      logLik  deviance  Chisq   Chi    Df    Pr(>Chisq)  
# mpl1.popu  9    5017.4   5057.4  -2499.7   4999.4                           
# mpl1       10   5015.4   5059.8  -2497.7   4995.4  4.0639  1    0.04381 *
#   ---
#   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

anova(mpl1,mpl1.gen)
# Df        AIC  BIC     logLik deviance  Chisq    Chi     Df   Pr(>Chisq)    
# mpl1.gen  9    5031.5  5071.5 -2506.8   5013.5                             
# mpl1      10   5015.4  5059.8 -2497.7   4995.4   18.177  1   2.014e-05 ***
#   ---
#   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1