[R-sig-ME] how to code a hopefully simple mixed model with spatially autocorrelated variables

[Tim Richter-Heitmann | Universitaet Bremen]

Dear group,

i have the following data:

outcome: Abundance data (likely negative binomial or Poisson),  
potentially autocorrelated

environmental predictors: 8 continuous variables, potentially  
autocorrelated, potentially collinear.

A Spatial structure of the measurement: the depth of the measurement  
into a sediment ("Core Depth"). Continuous, in cm. It has only one  
dimension, like a gradient.

Location: a random effect. A factor with 7 levels with about 17 - 25  
observations each. The location of the measurement on the seafloor.  
These Locations are so far apart, that i cant imagine that they are  
autocorrelated. Thus, id like to use it as factor.

I would like to model something like:

gls(Abundance ~ Predictors + 1|Location, correlation = corGaus(~Depth))

Here are some questions:
1. Is this ok? Since depth is linear and progressive, can it be  
treated like a temporal structure? Or could Depth be also modelled as  
a fixed effect? It is clear from the data that abundance varies by  
Depth.
2. What would be the best way to select the best explaining variables?
3. How to get the negative binomial distribution of the outcome into  
the model?

I am very curious about your advise.

Best, Tim


-- 
Dr. Tim Richter-Heitmann

University of Bremen
Microbial Ecophysiology Group (AG Friedrich)
FB02 - Biologie/Chemie
Leobener Straße (NW2 A2130)
D-28359 Bremen
Tel.: 0049(0)421 218-63062
Fax: 0049(0)421 218-63069