
Dear all,
 
I am trying to fit a generalized linear mixed model to deal with both temporal and spatial pseudorreplication.
 
I have repeated seasonal measurements (3 seasons during 3 years and 2 season during the last year, factor named percod, with 11 levels) of a bird presence/absence (pres.f) on 14 artificial ponds (charca.f). The ponds are integrated in 4 greater spatial units (zepa). I have 11 explanatory variables, some of them are continuous (prof1,prof2,prof4,capac,perim_veget,div_veg,div_herp) and some are factors (ungul.f,ganado.f,turb.f,peces.f). Not all artificial ponds have been monitored in all seasons, so I have some missing values in the dependent variable. 
 
I have thought that the correct model should be, ignoring the spatial pseudorreplication:
 
m1<-lmer(pres.f~ganado.f+ungul.f+turb.f+prof1+prof2+prof4+capac+perim_veget+peces.f+div_veg+div_herp+(percod|charca.f),family=binomial)
 
AIC     BIC     logLik   deviance
124.4  184.2   -42.18    84.35
Random effects:
 Groups             Name        Variance      Std.Dev.           Corr   
 charca.f     (Intercept)     4.9533e-11    7.0380e-06        
                      percod      5.3095e-13    7.2866e-07    -0.995 
Number of obs: 147, groups: charca.f, 13
 
The standard errors of the random effects are really small. I am not sure how to interpret it, but I suspect it is not a good thing. Can someone help me?
 
Besides, to take into account the spatial pseudorreplication I have thought to add a random effect more: (1|zepa/charca.f). When I run the model it appears 
 
Warning message:
In mer_finalize(ans) : singular convergence (7)
 
I have read that it means that the data does not support such a great number of parameters estimation. Am I correct? What should I do?
 
I hope someone could help me. Thanks a lot!
 
Mariana Fernandez-Olalla
Ph.D Student
ETSI Montes. UPM
Madrid (Spain)
marianaolalla@hotmail.com
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