DOI: 10.5061/dryad.gmsbcc2tc
title: Data from: Sample size guidelines for mapping migration corridors and population distributions using tracking data

description:  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 individuals’ spatial distributions in each study area to create
 population-level migration and seasonal distributions, and examined
 whether known explanatory variables (e.g., population abundance,
 environmental metrics) could predict sufficient sample sizes to map
 population distribution. Our simulated and empirical analyses to assess
 and model sample size sufficiency demonstrated that sample size varies
 depending on the species, season, population-level percent volume contour
 of interest, and population abundance. For example, for migration
 distributions at the 95% volume contour, the interquartile range for the
 number of individuals needed to reach an adequate sample was 10 – 23 for
 bighorn sheep, 51 – 93 for elk, and 58 – 164 for mule deer. For
 existing datasets, the resampling approach quantifies the sensitivity of
 population distribution maps to sample size. To guide study design for
 future GPS tracking projects aimed at mapping population distributions,
 our models provide specific sample size recommendations incorporating
 known population covariates. If adequate model training data are
 available, our approach can be extended across a wide range of taxa and
 populations to inform sample size requirements for estimating robust
 distribution patterns.
description: 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 build cumulative population-level
 UDs. * Calculates smoothed volume contours (50%, 75%, 90%, 95%, 99%) and
 associated areas. * Tracks data duration, number of migrations, and
 animal-years per step. * Runs in parallel to speed up processing. * Saves
 `.rda`, `.csv`, and `.RData` outputs per project. #### Requirements: * R
 packages: `raster`, `spatialEco`, `stringr`, `arrangements`, `foreach`,
 `doSNOW`, `parallel` #### Outputs: * Rarefaction curves showing how
 corridor area scales with sample size. * Summary metrics for individual
 contributions and data richness. ### 2\_CompileAddCovariates.R This script
 compiles rarefaction results from multiple projects, adds ecological and
 management covariates, and prepares both wide and long-format datasets for
 analysis. #### Requirements: * R packages: `dplyr`, `reshape2` ###
 3\_areaAccumulation.R This R script estimates the number of animals needed
 for utilization distribution (UD) area estimates to stabilize using
 asymptotic regression. It models UD area vs. sample size across projects,
 seasons, and contour levels. #### Requirements: * R packages: `nlme`,
 `aomisc`, `stringr`, `drc`, `ggplot2`, `statforbiology` ##
 example\_data.zip These folders contain simulated input data for the
 rarefaction workflow. ```text example_data/ │ ├── RarefactionLONGWCovs.rda
 │ # Final cleaned and formatted dataset used as input for modeling. │ #
 Generated from `2_CompileAddCovariates.R` and ready for analysis in | #
 `3_areaAccumulation.R`. │ # This data frame (object named `rare_long`)
 includes the following variables: │ # - anID : Character ID for the
 individual animal. │ # - nAnimals : Number of animals included in the
 rarefaction sample. │ # - simulation : Simulation replicate number. │ # -
 projName : Project or scenario name (e.g., "sim2"). │ # - UD :
 Season of UD (e.g., "mig", "winter",
 "summer"). │ # - Contour : Isopleth value used to define the UD
 (e.g., 50 for 50% UD). │ # - area_sqkm : Area of the estimated UD isopleth
 in square kilometers. │ # - [additional covariates] : Variables to be
 added in `2_CompileAddCovariates.R` | # and used for modeling, such as: │
 # - spp : Species code (e.g., "elk", "deer"). │ # -
 abundance : Simulated or real population abundance value (e.g., 250). │ #
 - allDuration : Project duration (numerical). │ ├── outputs/ │ #
 Intermediate outputs from `1a_CorridorRarefaction.R` and
 `1b_WinterRarefaction.R`. │ ├── *.rda │ ├── *.RData │ └── *.csv │ └──
 projFolders/ # Raw input data (simulated utilization distribution [UD]
 rasters). # All rasters are in 500 m resolution and share a common
 coordinate system and extent. | └── pop250/ # Example population of 250
 individuals (other populations could be included # similarly). ├──
 footprints_pop/ │ └── *.asc # Migration 99% contour footprint rasters | #
 Filenames follow the convention: AnimalID_99pct_contour.asc | # For
 example, `14_99pct_contour.asc` indicates: AnimalID = 14. └── UDsWinter/
 └── *.asc # Seasonal (winter) utilization distribution rasters # Filenames
 follow the convention: AnimalID_SeasonYear_ASCII.asc # `1_wi22_ASCII.asc`:
 AnimalID = 1, SeasonYear = wi22 (winter 2022). ``` ### Simulation details
 Each folder under pop250/ includes UD rasters at 500 m resolution,
 simulated for 250 individuals. To generate the data: * **Seasonal range
 movements:** We created 250 random starting points drawn from a normal
 distribution centered on a mean centroid, with a standard deviation of 10
 km on both the x- and y-axes. * **Migratory movements:** We defined two
 seasonal range centroids 99.24 km apart (based on average empirical mule
 deer migration data). Start and end points were randomly drawn around
 these centroids from a normal distribution with a 10 km standard
 deviation. UD rasters were generated using Brownian Bridge Movement Models
 (see manuscript for full methodological details).