# Crossed or nested random effects structure or both for mixed effects model using glmer?

I conducted an experiment where i walked transects down polytunnels on a farm. On each transect i was recording the number of flower visitation events that i saw. The farm is divided up into 3 fields, and I walked 3 transects down 3 seperate polytunnels in each field (so 9 transects in total). I then repeated these transect walks on a weekly basis for 6 weeks.

I using mixed effects models to analyse the data, with 'number of flower visits' as the response variable, and 'temperature' as a fixed effect. I am wondering how the random effects structure should look. I want to nest the transect within the field, but then each transect was repeatedly sampled on a weekly basis, so this aspect would be crossed. To represent this, i currently have the following random effects structure in R:

(1|Field/Transect) + (1|Week)


Is this correct for what i want to represent, as explained above?

Any help would be greatly appreciated.

This looks reasonable. (1|Field/Transect) + (1|Week) is equivalent to (1|Field) + (1|Field:Transect) + (1|Week). In principle given your experimental design you could fit the interaction of week with your experimental blocking factors (field and transect), i.e.
(1|Field/Transect) + (1|Week) + (1|Week:Field) + (1|Week:Field:Transect)

assuming you have one observation per week/field/transect combination, the last term in this would be an observation-level random effect (which would be unidentifiable in a model that includes a dispersion parameter, such as a linear (Gaussian) mixed model or a negative binomial model, but which can help account for overdispersion in a Poisson model). (In theory this model could be specified as (1|(Field/Transect)*Week), but I don't think lme4 is actually quite smart enough to handle that syntax ...)