I am a PhD student working in behavioral ecology. I am currently working on reviews for a paper, in which I aim to investigate the influence of mate familiarity on female mate choice. I presented a female with two males (one she already mated with and a "novel" male) and a neutral area, and recorded her behavior. I have a data set with the time the female spent with each male and in the neutral area. Therefore I have three lines per trial (one for each male and one for the neutral area).

I calculated the proportion of time spent with each male (

*time spent with the previous partner/time spent with both males*; and

*time spent with the novel male/time spent with both males*).

Example:

Trial___Time spent____Proportion of time___Side

1______3520.002______0.58__________________novel male

1______2501.002______0.42__________________previous partner

1______1535.002______NA___________________neutral area

2______3545.666______0.29__________________novel male

2______8570.965______0.71__________________previous partner

2______1520.002______NA___________________neutral area

I analyzed this using a beta regression model with the proportion of time as the response variable and the familiarity (previous partner or novel male) as fixed effect. I added ID as a random effect since I have two lines per trial (one for each male).

A reviewer commented that "

*random effect can only model positive correlations of non-independent data*" and that I should instead compared the proportion of time spent with one male against 0.5.

Is the reviewer's comment about the random effect true? I did not find any information on that on the internet, is there a paper/book where I could read more about that, or could anyone explain to me what they meant? I am fairly new to stats, so I really want to improve my knowledges to do proper stats.

I chose to analyze my data with beta regression models because it seems to be the best test to use for continuous proportional data (Mangiafico, 2016; Douma & Weedon, 2019), but maybe I'm wrong?

Thanks a lot for your help!