[R-sig-ME] nested random effects with temporal correlation

[Charlotte Reemts]

Mixed modelers,
I am analyzing data from a fire experiment. We counted the number of point intercepts of a grass along 3 pairs of transects (so n=6). One transect in each pair was burned. We collected data in 2006 (before burning) and for three years after burning. We calculated frequency of the grass by dividing the intercepts by the total number of contacts of all species, so I am using the binomial family for analysis. We want to know whether the frequency of the grass decreased in the burned transects compared to the unburned transects and how that frequency changes with time since burning (I am using glht contrasts, not shown, to do this).

A reviewer would like me to include nested random effects and account for temporal correlation, since we repeatedly sampled the same transects. I created the glmmPQL model below, which converges, but I am concerned that it is a very complex model for a small dataset. Do you have any suggestions for 1) the best way to simplify the model and 2) the best way to justify that simplification to the reviewer?

Thanks,
Charlotte

krdata2<-read.table(header=T, text= "
Pair

Treatment

Year

Transect

totalcontacts

freq

1

burned

2006

T1

190

0.778947

1

burned

2007

T1

231

0.337662

1

burned

2008

T1

250

0.508

1

burned

2009

T1

148

0.52027

1

unburned

2006

C1

188

0.946809

1

unburned

2007

C1

210

0.92381

1

unburned

2008

C1

214

0.878505

1

unburned

2009

C1

162

0.962963

2

burned

2006

T2

196

0.80102

2

burned

2007

T2

270

0.414815

2

burned

2008

T2

266

0.56015

2

burned

2009

T2

210

0.847619

2

unburned

2006

C2

193

0.782383

2

unburned

2007

C2

211

0.85782

2

unburned

2008

C2

194

0.938144

2

unburned

2009

C2

198

0.959596

3

burned

2006

T3

193

0.632124

3

burned

2007

T3

275

0.192727

3

burned

2008

T3

222

0.405405

3

burned

2009

T3

176

0.642045

3

unburned

2006

C3

198

0.747475

3

unburned

2007

C3

207

0.758454

3

unburned

2008

C3

207

0.772947

3

unburned

2009

C3

143

0.944056

")

krdata2$Year<-as.factor(krdata2$Year)
aus.kr.pql2<-glmmPQL(freq ~ Treatment*Year, random = ~1|Pair/Treatment,
                    correlation=corCAR1(form = ~Year|Pair/Treatment),
                    family=binomial(link="logit"), weights=totalcontacts, data=krdata2)?



___________________________________________
Charlotte Reemts, M.S.
Research and Monitoring Ecologist
creemts using tnc.org

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