I have built a model describing how long it takes fish to digest their food (i.e., gut passage time) with temperature, diet, and a temperature-diet interaction as population effects. I included species as a group-level effect to account for repeat sampling, however; some species have been sampled many times (n = 8) whereas most have been sampled one time.
Here is the main model output:
And the loo output:
Obviously that is a lot of high Pareto K values, but all of my posterior predictive checks suggest that the model fits the data well, and p_loo < p. From what I have read elsewhere, the reason I am getting so many high Pareto K values is not because the model is badly specified but because many levels of the group effect (species) only have 1 estimate.
My question is, what are the practical implications of this for interpretation of the model? Does it mean that estimates of population effects will be highly sensitive to additional data? Should I trust estimates of population effects? Or does it just mean that its estimate of the species group effect is unreliable?
Any guidance would be much appreciated; I am trying to write up this model and I don’t want to overstate my conclusions.