Hi Tom,
I think that you should specify in your model the non-indipendence of the unit of analysis (permanent plots) in the random effects model, because the same plot was sampled more times... something like this (random= ~ 1 | plot)
I suggest you to read this interesting topic written by Philippi Tom 
https://science.nature.nps.gov/im/datamgmt/statistics/r/advanced/GLiMM_Trends.cfm

                                                                                                                                
Ludovico
Frate

PhD student (University of Molise - Italy)
Environmetrics Lab
http://www.distat.unimol.it/STAT/environmetrica/organico/collaboratori/ludovico-frate-1
Department of Biosciences and Territory - DiBT
Università del Molise.
Contrada Fonte
Lappone, 
86090 -  Pesche (IS)
ITALIA.
Cel: ++39
3333767557
Fax: ++39 (0874) 404123
E-mail ludovico.frate@unimol.it
ludovicofrate@hotmail.it


> Date: Tue, 14 May 2013 17:09:37 +0100
> From: T.R.Bishop@liverpool.ac.uk
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] Properly dealing with study design
> 
> Hi list, I hope you are all well.
> 
> I have hierarchical dataset and am having difficulty deciding whether I 
> have specified my random terms correctly.
> 
> I am trying to determine which environmental factors are influencing 
> species richness. My data are structured like so:
> 
> I have a dataset of insect communities sampled from permanent plots 
> along an altitudinal gradient for 7 years and for 2 seasons in each 
> year. There are 8 altitudinal bands at which samples have been taken and 
> 4 independent replicate communities sampled within each band. This gives 
> me 4 replicates * 8 altitudinal bands * 2 seasons * 7 years = 448 rows 
> in my dataframe. So essentially, the same 32 plots have been sample 
> twice yearly for a number of years.
> 
> I also have variable describing available area of the altitudinal bands 
> (hence, there are only 8 unique values of area) and the temperature of 
> the sites at the sampling time (at the replicate level).
> I am not interested in the effect of year on species richness and 
> understand that a mixed effects modelling approach may be appropriate to 
> deal with this. I am, however, interested in how season, altitude, 
> temperature and available area influence my species richness values.
> 
> I have been reading Zuur et al and scouring the web but have come up 
> against a bit of a wall with specifying my model correctly. I think I 
> should be starting with a full model as follows:
> 
> model <- lme(species.richness ~ 1 + altitude * season * area * 
> temperature, random = ~ 1 | year)
> 
> I am unsure if I am fully accounting for the structure in my dataset 
> here. Do I need to somehow specify the structure more explicitly? For 
> example, if season was dropped from the model following simplification 
> then my data will surely be temporally pseudoreplicated within year. I 
> suspect that I should add in the "replicate" variable in the model 
> somehow. Each of the 32 replicates is uniquely coded (1a, 1b, 1c, 1d, 
> 2a, 2b etc).
> 
> Any thoughts, or links to similar examples, on how to correctly specify 
> my full model would be much appreciated.
> 
> Thanks in advance,
> 
> Tom
> 
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