# Which statistical analysis is most appropriate to determine differences between 3 treatments and control for monthly and yearly variation?

I have a 4-year dataset of zooplankton biomass that was sampled three times a summer (i.e. June, July, August) for all four years. There are 6 sites that were sampled in triplicate (took 3 reps): two sites in Treatment A, two sites in Treatment B, and two sites in Treatment C. There are 72 data points per zooplankton functional group once I average the reps for each site giving one measurement of biomass per site for a given month and year.

I want to see if there are differences in biomass between treatments controlling for year and monthly variation. What was suggested to me was the following model:

   Bbos.model <- lmer(log(Bos.Biomass+1) ~ Treatment + Year +
(1|Site), data=mydata)


However, as I understand it, this treats the monthly sampling events as independent events which I don't think I can do. Is the correct analysis of this data a repeated measures analysis? Or would a mixed model approach be more appropriate?

• Repeated measure analysis is a special case of mixed model. Jan 5, 2019 at 3:34