I am trying to run a lmm to observe how CH4 and CO2 fluxes change over time. I have a randomized block design with repeated measures over time. I also have an unequal sample size as I wasn’t able to sample a block during one of my time points. I have tried fitting a lmm to my data but see that the random effect, that is controlling for my repeated measures, is causing a singularity issue. I’m afraid that if I remove this variable and run a simple linear regression  I would have pseudoreplication. Here is my code and my output.

plot_type=treatment
plot_id= unique id (block id & treatment id together)

buck.flux.co2 <- lmer(co2_flux~plot_type+ (1|plot_ID), data=bucket_data)

boundary (singular) fit: see help(‘isSingular’)

enter image description here

I have heard that having this means that my random effect variable explains none of the variation we see. However, because my ecological system is so variable I would expected my random effect variable to control for atleast a little bit of the variation.

Is there a way for me to check that this is a true response, in that my block design didnt influnce any of the variation? If I cant check, will having the singularity error influence my resutls or can I simply ignore the error? Is there another simpler model in which I can remove this random effect variable but still account for repeated and unequal measuerments?

I tried using a glm() but I couldnt find a distribution that would fit a left skewed dataset that had a large amount of negative continuous values. I have visualized the data and there doesn’t seem to be significant differences between treatments but again I would expect there to be some variation