DOI: 10.5061/dryad.r2280gbq0
title: Data from: Heading west: Ecology of swift foxes in a novel landscape beyond their range

description: The swift fox (Vulpes velox) is generally associated with the short-grass
 prairie ecosystem of the North American Great Plains; a system that has
 declined by approximately 50% over the last century. Yet, swift fox
 populations seem to demonstrate regional variation in trends, with some
 populations declining while others appear stable to increasing. In
 Wyoming, USA, swift foxes have been observed and successfully reproducing
 over 100 km west of their historical range boundary in shrubland-dominated
 habitats, previously deemed unsuitable. This has created challenges in
 conservation, as it is unclear what factors are uniquely important to a
 species occupying habitats previously considered unsuitable. Therefore, we
 sought to investigate fundamental questions regarding swift fox ecology in
 this novel habitat: (1) what environmental gradients are contributing to
 suitable habitat?, (2) how is swift fox habitat suitability associated
 with competitors (American badgers [Taxidea taxus] and coyotes [Canis
 latrans]) in space?, and (3) how do swift foxes and competitors allocate
 activity in time? Between 2021 and 2023, seven swift foxes were
 GPS-collared, and 71 camera traps were deployed in central Wyoming.
 GPS-collar locations paired with environmental covariates were used to
 develop a habitat suitability model using Random Forest, predicting 30.28%
 of our study area was suitable. Important habitat resources identified
 were herbaceous biomass, shrub height, and sand content. We found swift
 foxes likely have minimal spatial and temporal separation between
 competitors, specifically coyotes. We hypothesize that swift foxes may
 successfully coexist with competitors either by being less risk-averse for
 the sake of resource acquisition or by relying on abundant escape cover,
 which reduces the need to modulate spatial or temporal activity. Our
 findings enhance our understanding of environmental factors contributing
 to suitable habitats and how swift foxes coexist in time and space with
 competitors in a novel shrubland environment. The R code is divided into
 five sections: The first section employs Random Forest modeling to assess
 the environmental gradients that characterize swift fox habitat in
 unconventional shrublands and predict suitable habitat across the study
 area. The second section validates the predicted habitat suitability map
 using reserved GPS centroids and independent data. The third section
 focuses on understanding trends in swift fox habitat suitability and its
 covariates. The final two sections explore the association between swift
 fox habitat suitability and competitors (i.e., coyotes and American
 badgers) in space, as well as how swift foxes and their competitors
 allocate activity over time.
description: # Heading west: Ecology of swift foxes in a novel landscape beyond their
 range Dataset DOI: [10.5061/dryad.r2280gbq0](10.5061/dryad.r2280gbq0) ##
 Description of the data and file structure The data were collected from
 GPS collars deployed on swift foxes and camera stations throughout the BLM
 Lander field office, Wyoming, USA, as well as various remotely sensed
 products and airborne LiDAR. Due to the status of swift foxes in Wyoming,
 as classified by the Wyoming Game and Fish Department as a species of
 greatest conservation need (NSS4 (Cb), Tier II) and by the Wyoming Bureau
 of Land Management as sensitive, we excluded the GPS and camera station
 coordinates from the datasets
 ([https://wyndd.org/species_list/](https://wyndd.org/species_list/)). The
 R script (VuVe_HSM_Spatiotemporal_Analyses.R) and accompanying RDS files
 contain all the material necessary to replicate our analyses. Below, we
 have provided additional context to RDS files, such as column names,
 column descriptions, etc. #### Random Forest modeling and predictions
 **RandomForest_TrainingData.RDS** Column names and information: * Id = 1 =
 presence; 0 = pseudo-absence * bd_pol = soil bulk density (g
 cm<sup>–3</sup>) * chm = vegetation height (m) * clay_pol =
 clay content (% (kg kg<sup>–1</sup>)) * eaglenests =
 likelihood of golden eagle nests (index) * herb = herbaceous biomass (kg
 ha<sup>-1</sup>) * hli = heat load index (index) * msavi =
 modified soil adjusted vegetation index (index) * roads = distance from
 roads (m) * sand_pol = sand content (% (kg kg<sup>–1</sup>)) *
 slope = slope (degrees) #### Validation: Reserved GPS centroids and
 independent (withheld) data **Independentwithheld_data.RDS** Column
 information: * ID = 60 independent swift fox observations across the BLM
 Lander field office * Habitat_Values = predicted swift fox habitat
 suitability values (binned 1 to 10) intersected by each ID
 **GridCentroidwithheld_data.RDS** Column information: * ID = 341 reserved
 grid centroids with two or more swift fox GPS locations * Habitat_Values =
 predicted swift fox habitat suitability values (binned 1 to 10)
 intersected by each ID #### Understanding trends in swift fox habitat
 suitability and covariates **PseudoAbsence_Covariates.RDS** Column
 information: * Column names are the same as the
 RandomForest_TrainingData.RDS, except now Habitat_Values is included. *
 Habitat_Values was created by intersecting all the pseudo-absence
 locations with the predicted swift fox habitat suitability values (binned
 1 to 10). #### Species occurrence models **Cams_HSM_values.RDS** Column
 information: * LocationName = the name/location of camera stations *
 Species = Swift fox, Coyote, and American badger * n = 1 = a camera
 station with one or more detections of that species. 0 = a camera station
 without a detection of that species * Habitat_Values was created by
 intersecting all the camera station locations with the predicted swift fox
 habitat suitability values (binned 1 to 10). #### Activity Patterns
 **ActivityPatterns.RDS** Column information: * Species = Swift fox,
 Coyote, and American badger * Location = 1; all activity patterns were
 calculated in one study area (Lander field office) * Time = time is in
 radians * Date = timestamp of species detection at the camera station *
 radsun = sun time = converted clock time (radians) to sun time