Thanks for giving me the opportunity to review this very interesting manuscript. I am no native English speaker and did not attempt to make language edits, but generally I felt the English was good and I could follow the writing well.

I found the approach very interesting and have only a few suggestions that may help to improve the manuscript even further:

1. When reading the manuscript the first time, I was sometimes confused about what you mean by sample size. My confusion was whether you referred to the number of points per animal or the number of animals.
2. I would not restrict your approach to traditional kernels. It should work for any method that is able to produce a UD. For example aKDE (https://esajournals.onlinelibrary.wiley.com/doi/full/10.1890/14-2010.1), RSF/SSF (https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.1771; https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13406) or ctmc (https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12967).
3. Two points that I think would be nice to discuss a bit more:
   i. You make the point that often substantially fewer animals are required to estimate the population range. While this is good news, I would be a word of caution here. The fewer animals are sampled the more important it will be that it is a truly random sample of the population.
   ii. Did you do any testing/simulation/speculation if it would make sense to have more animals for a shorter time span or fewer animals for a longer time? This could lead to a similar total number of points, but probably very different information content.

A few specific comments:

• L28: Maybe better tracked animals instead of telemetry tags
• L60: Animal distributions, maybe better population distribution
• L61: Maybe also cite van Winkle here: Van Winkle, W. 1975. Comparison of several probabilistic home‐range models. Journal of Wildlife Management 39:118–123.
• L63: I think R package would be the appropriate term here instead library