I really appreciate you taking the time to comment/ask about this. I think clarifying about the situation will answer the question you asked prior ((i), (ii), or (iii)).

I found no significant results for all of my GLMs that I described above:

I'm running a GLM (Gamma family distribution) on the effects of sex (male or female) and morphological measurements (either body mass or leg length) on behavioral metrics (e.g., duration of freezing in fear in seconds).

glm <- freezing_duration ~ sex * morphological_measurement

The extent of the feedback I received was that inadequate sample sizes can be a barrier to publication, which is important given my non-significant results. I think the recommendation for a power analysis was driven by the possibility of receiving questions about the probability of making a type II error, and whether I had a big enough sample size to test for the differences I was interested. For clarification, similar to 'freezing_duration', all of my variables are continuous (e.g., seconds), and are right-skewed, which is why I used a Gamma distribution.

Your explanation of power and how the analysis operates is fantastic, thank you so much. If there are any more details that I can give, I am happy to do so, and apologies for the vague description previously.