I'm building a model to figure out how much heat sum lodgepole pine trees need to shed pollen from their male cones and have receptive female cones. I have data from trees sourced from all over BC grown together at a bunch of different sites also in BC. Some of the trees are clones of each other and I have a bunch of years of data. So I'm making a hierarchical bayesian model.

To help me understand the model and fix bugs in my code, I'm building the model one layer at a time and running it on simulated data. Yesterday I added multiple years and individual effects to the simulated data so I could add individual effects to the model. I ran a mixed model with lme4 in R just to try and it works better than a model without individual and year effects - most especially it's better at estimating the steepness of the curve.