DOI: 10.5061/dryad.mgqnk998d
title: The role of spatiotemporal variation in resources in the diverse movement strategies of temperate ungulates

description: The current framework for understanding large-scale animal movement
 strategies (i.e., migration, nomadism, or residency) suggests that each
 strategy is associated with specific combinations of resource spatial
 heterogeneity and temporal predictability. While there is support for this
 framework across ecosystems, modern tracking data has revealed that all
 three strategies can occur in a single population. Using 21 years of GPS
 data from seven populations (n = 239) of pronghorn (Antilocapra americana)
 and twelve populations (n = 283) of elk (Cervus canadensis) across
 Wyoming, USA, we examined the support for resource-based hypotheses in
 predicting the occurrence of migrants, nomads, and residents. Using model
 selection, we found support for the hypothesis that greater spatial
 homogeneity and less temporal predictability are associated with
 residency, and vice versa for migration or nomadism. However,
 spatiotemporal heterogeneity did not explain the differentiation between
 nomadic and migratory individuals. We found that climate and anthropogenic
 features influenced individual movements: elk were more likely residents
 if they experienced more mild winters, and pronghorn were more likely
 residents if they resided closer to roads. Our findings demonstrate that
 ungulate movement strategies are consistently linked to spatiotemporal
 resource variation across scales and identify additional mechanisms for
 localized behavioral differences.
description: # The role of spatiotemporal variation in resources in the diverse
 movement strategies of temperate ungulates
 [https://doi.org/10.5061/dryad.mgqnk998d](https://doi.org/10.5061/dryad.mgqnk998d) ## Description of the data and file structure Data file containing all data needed to reproduce all statistical analyses contained in the manuscript. ### Files and variables #### File: Pronghorn\_Elk\_Data.csv **Description:**  ##### Variables * species: species identify by common name (pronghorn or elk) * id: individual animal identification number * annual_overlap: the mean proportional overlap between pairs of monthly ranges * summer_overlap: the proportional overlap between summer ranges from year to year * winter_overlap: the proportional overlap between winter ranges from year to year * seasonal_overlap: the proportional overlap between summer and winter ranges * meanSWE_sd: the standard deviation of mean Snow Water Equivalent across individual year-round ranges  * maxSWE_sd: the standard deviation of maximum Snow Water Equivalent across individual year-round ranges  * meansnowDepth_sd: the standard deviation of mean snow depth across individual year-round ranges  * maxsnowDepth_sd: the standard deviation of maximum snow depth across individual year-round ranges  * INDVI_sd: the standard deviation of the integrated Normalized Difference Vegetation Index across individual year-round ranges  * peakIRG_sd: the standard deviation of the peak Instantaneous Rate of Green-up across individual year-round ranges  * peakNDVI_sd: the standard deviation of the peak Normalized Difference Vegetation Index across individual year-round ranges  * peakDryDown_sd: the standard deviation of the integrated Normalized Difference Vegetation Index across individual year-round ranges  * elev_sd: the standard deviation of elevation across individual year-round ranges * slope_sd: the standard deviation of slope across individual year-round ranges * aspect_sd: the standard deviation of aspect across individual year-round ranges  * peakIRG_temp_var: the inter-annual standard deviation of the mean peak Instantaneous Rate of Green-up across individual year-round ranges  * perc_agriculture: the percent of landcover containing agriculture within individual year-round ranges * mean_dist_roads_m: the mean distance to the nearest road (interstates, highways, or county roads) within individual year-round ranges  * overall_winter_conditions: the overall winter conditions experienced within individual year-round ranges. We estimated the mean daily snow depth over winter (1 December through 28/29 February) experienced within each individual’s range in the year of data collection and across all other years of available snow data from SNODAS (2003-2021). We then used the highest mean snow depth across the 21–23 months of data for an individual as a proxy of overall winter conditions for that individual’s year-round range.  * relative_winter_severity: the relative winter severity experienced within individual year-round ranges. We estimated relative winter severity by first quantifying the cumulative distribution of mean snow depth across all years of available remote-sensing data, other than the year of data collection. We then determined within which quantile the mean snow depth experienced in the year of data collection fell and used this quantile as a measure of relative winter severity. ## Code/software File: CodeToRunAnalyses.R  Description: annotated R script containing code to reproduce all statistical analyses contained in the manuscript. All model result figures were created in ggplot with the predict function. All map figures were created with ggmap.