Dear Jacob,

May be you can use cluster sampling or adaptive cluster sampling (Design-based estimation) to get a density estimate.
Best,

Manuel Spínola

Capelle, Jacob wrote:
Dear all,
I have a kind of a theoretical question from which I hope it might interest you and hopefully can help me a bit. In order to obtain ecological (surrvey) data, I try to make a prediction about the accuracy of a sampling tool to estimate mussel density. For this reason I took a lot of samples at a certain fixed location and counted the amount of mussels in each sample. Because mussels are aggregated on the sediment, I had a lot of zero values. To estimate the sample size I used a binomial distribution and obtained the k value and the mu from the fitdistr(x,"negative binomial") (MASS). The question I have is: how can I test if this distribution accurately described my (zero inflated count) data? I am a bit familiar with the AIC but since I only have counts on one variable I cannot perform a GLS. Creating a vector with rnbinom() using the k and mu from the fitdistr() I plotted a histogram and compared it with my data, this showed that is was roughly comparable, but I want to quantify this. I have a biological background not a statistical one, so I realize I can ask silly questions. But I hope someone can give me some hints. Kind regards, Jacob Capelle PhD student
Wageningen Imares
The Netherlands
jacob.cape...@wur.nl <mailto:jacob.cape...@wur.nl>
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--
Manuel Spínola, Ph.D.
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
mspin...@una.ac.cr
mspinol...@gamil.com
Teléfono: (506) 2277-3598
Fax: (506) 2237-7036

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