Thanks Bert!
I think they are relatively important. What I am doing is comparing 2003 with 2013 distribution and use of this species in an specific sampled area. They are currently way lower numbers than in 2003, however in both years the data are zero inflated. Most of the outliers are in 2003 when they were quite more birds.

On the other hand, the behavior of the species is very social (ruffs) so where they are 5 birds, they could be 300 in the next 10 minutes....so outliers accounting for this maybe are not that important to take into accout, and thus, I should focus more in the binomial part of the glmmadmb model that I chose (where just zeros vs no zeros are modeled).

Thanks for your reflections they are very good to me! 

> Date: Tue, 13 Aug 2013 09:07:41 -0700
> Subject: Re: [R] Outliers and overdispersion
> From: gunter.berton@gene.com
> To: lomasvega@hotmail.com
> CC: szehnder@uni-bonn.de; r-help@r-project.org
> 
> The central question is: What caused the 3 unusual values? What is
> their scientific relevance? Only you can answer that, not us.
> 
> -- Bert
> 
> On Tue, Aug 13, 2013 at 8:51 AM, Marta Lomas <lomasvega@hotmail.com> wrote:
> > Thanks for your interest and prompt answer!
> >
> > What I try to estimate is the correlation of one bird species counts with a set of environmental parameters. The count data are zero-inflated and overdispersed. I am modeling with hurdle-negative binomial-mixed effects.
> > The results are very difficult to interpret and it get easier dropping out 3 outliers. But I do not know if I should do this..
> > Thanks!
> > Marta
> >
> >
> >> Subject: Re: [R] Outliers and overdispersion
> >> From: szehnder@uni-bonn.de
> >> Date: Tue, 13 Aug 2013 17:41:10 +0200
> >> CC: r-help@r-project.org
> >> To: lomasvega@hotmail.com
> >>
> >> I do not know what you are exactly estimating, but if it is about count models and the model fit gets better when you drop the outliers, it does not say, that the model is now more correct. It just says, if the data were without the outliers, this model would fit good.
> >>
> >> Overdispersion in count data is sometimes a cue, that you have a mixture distribution as the generating process - for example instead of one, K different (sub)species of birds which were aggregated in the count data. In this case a mixture (negative binomial)- distribution with K components could fit the data better.
> >>
> >>
> >> Best
> >>
> >> Simon
> >>
> >> On Aug 13, 2013, at 5:28 PM, Marta Lomas <lomasvega@hotmail.com> wrote:
> >>
> >> >
> >> >
> >> >
> >> > Hi  again,
> >> >
> >> > I have a question on some outliers that I have in my response variable (wich are bird counts). At the beginning I did not drop them
> >> > out because they are part of the normal counts and I considered them "ecologically" correct.
> >> >
> >> > However, I
> >> > tried some of the same models without ouliers and the AICs are thus better. I
> >> > also have nice significances this way...
> >> >
> >> >
> >> > So would you say that, even though the outliers are right
> >> > observations and taking into consideration that already the negative binomial
> >> > distribution that I am using is accounting for the some of the overdispersion due to the outliers, it is
> >> > better to drop them out as the models fit better this way?
> >> >
> >> >
> >> > Thanks for your patience!
> >> >
> >> > :)
> >> >
> >> >
> >> >
> >> >
> >> >
> >> >
> >> >     [[alternative HTML version deleted]]
> >> >
> >> > ______________________________________________
> >> > R-help@r-project.org mailing list
> >> > https://stat.ethz.ch/mailman/listinfo/r-help
> >> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> >> > and provide commented, minimal, self-contained, reproducible code.
> >>
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> 
> 
> 
> -- 
> 
> Bert Gunter
> Genentech Nonclinical Biostatistics
> 
> Internal Contact Info:
> Phone: 467-7374
> Website:
> http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
 		 	   		  
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