Sample size guidelines for mapping migration corridors and population distributions using tracking data

 Animal distribution maps are a key tool for wildlife conservation, guiding high-profile decisions, such as legally designating priority habitat or building highway crossing structures. GPS tracking data enhances these efforts but requires balancing statistically robust sample sizes with minimizing researcher impacts on wildlife and costs. Nevertheless, rigorous guidelines that leverage a priori information are still lacking on how to determine the optimal number of tracked animals (i.e., sample size) for accurately mapping migration corridors and seasonal ranges at the population level, particularly in the context of ungulate conservation. We used a cumulative curve resampling approach to evaluate the consequences of reduced animal sample size, assessed sample size sufficiency, and extrapolated where sample size sufficiency might occur outside of the observed data. We illustrate our approach with simulated data. We then compiled GPS data from 77 ungulate populations and aggregated..., , , We have submitted simulated location data for animals (**example_data.zip**) and R scripts to recreate our analyses.

We compiled GPS data from ungulate studies throughout Colorado and Utah to examine animal sample size sufficiency for population distributions. We resampled population migration and seasonal range distributions to evaluate the consequences of reduced data, assessed sample size sufficiency, and extrapolated where sample size sufficiency might occur outside of the observed data. 

## Rarefaction R scripts

### 1a\_CorridorRarefaction.R, 1b\_SeasonalRangeRarefaction.R

These script performs a rarefaction analysis to evaluate how the estimated area of population-level utilization distributions (migration corridors and seasonal [summer or winter] range respectively) changes as more individual animals are added.

#### Key Steps:

* Loads individual UD rasters for each project.
* Generates 100 random permutations of individual inclusion order.
* Iteratively stacks rasters to bu...,