Hi!

 

I do not know if this is the right forum to ask this question but maybe
someone here has some ideas…

 

I have a set of historical and modern data on mysis shrimp abundances from
the Baltic Sea. The problem is that the derived abundances come from
different sampling areas most of the time although geographically close.
These stations have also been sampled over the season and differ in timing.
I also know that abundance within a station is highly correlated over season
as abundances decline towards winter. My interest is to see whether there
has been a decline in mysis abundance over time and I thought setting
“Station” as a random effect would take into account the potential effect of
station.

 

I tried different models in R going from a complete model design with
interactions and so on according to the 10-step protocol in Zuur et al.
“Mixed Effects Models and Extensions in Ecology with R” and ended up with
the following model that had the lowest AIC.

 

Variables:

Abund =log(Abundance (#/m2))

Year=as.factor(Year) (4 levels)

Sta= Station (13 levels)

Dyear=Day in the Year

 

Abund ~s(Dyear) + Year, random = list(Sta = ~1), weights = varIdent (form =
~1|Year), data = mydata, method="REML")

 

I made a variogram using the station coordinates and the residuals to see if
there was any spatial dependence. I could not see any. I added the
“varIdent” because I could see some heteroscedasity in the variances between
years. All in all the model residuals look very good and R-sq.(adj) =
0.971. 2008 was highly significant as well as “Dyear”.

 

Does my approach to this problem make sense? I am a complete novice to this
type of statistics and any ideas would be a great help.

 

Best regards, 

 

Martin

 

Martin Ogonowski,  Ph.D Student

Mobile:+46(0)708 25 48 46

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Address: 
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Stockholm University
SE-106 91 Stockholm
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e-mail:  <mailto:hedvig@ecology.su.se> martin@ecology.su.se



 


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