Ana Sequeira writes:

I am using a temporal data series and I am trying specifically to understand if there is a temporal trends in the occurrence of a species, for which I need to use “Year” in my models (and from what I understood from pages 244-246 [in ARM] is that factors should always be used as random effects).

I believe that in your book the closest example to my situation is the one shown in Figure 14.3: I also have 4 different regions in my study, states in your example are replaced by years in my study, and the x axis is a specific value for a climatic factor I am using in my analysis (IOD).

The reason why I am writing you, is because I am having troubles understanding if my variable “Year” (factor), should only be added as a random effect (1|Year) or if I should include the “Years” (used not as factor) in my models as well (Species ~ …Years + (1|Year))?

My doubt lies in the fact that I am looking for a trend and if I do not include “Years” as variable I believe the variance shown in the resulting random coefficients is conditional to the variables and effects used in the model, i.e. if I am not specifically accounting for a possible trend (linear or polynomial), would my model still give me a trustworthy answer regarding yearly trends?

Also, some of my factors include only 4 and 5 levels (seasons and regions, respectively) – in which case, I understood that lmer() approximate inference is not reliable.

My reply:

Yes, you can include year + (1|year). See the graphs on p.293 for a similar example. Also, you could fit using blmer/bglmer to get more stable estimates of the group-level variances.