DOI: 10.5061/dryad.t1g1jwt9w
title: Patterns of island fox habitat use in sand dune habitat on San Clemente Island

description: On San Clemente Island (SCI), the island fox subspecies (Urocyon
 littoralis clementae) has been monitored annually since 1988 to track
 long-term population trends. Annual density estimates in most habitat
 types across the island range from 2–13 foxes/km2, yet unusually high
 estimates have repeatedly approached 50 foxes/km2 in a unique sand dune
 habitat area. Although sand dune habitat is restricted to one small area
 on the island, these estimates suggest sand dune habitat supports one of
 the highest population densities of any fox species in the world, and it
 may support &gt; 5% of the SCI fox population. This finding prompted
 our investigation to determine if SCI foxes captured in the sand dunes
 habitat area maintained home ranges within this habitat type. Between
 January–July 2018, we used Global Positioning System collars to track the
 movements of 12 island foxes captured in the sand dune habitat area.
 Contrary to our initial predictions, we found that island foxes captured
 in the sand dune habitat area do maintain home ranges and core areas
 centralized in sand dune habitat. All 12 island fox home ranges estimated
 contained &gt;50% sand dune habitat in either their 50% or 95% fixed
 kernel density estimate (KDE) home range, and island foxes were 3.14 times
 more likely to use active sand dune habitat when compared to the second
 most abundant habitat type, maritime desert scrub (Adjusted  =
 3.14, 95% CI = 3.07–3.12). We also found that island foxes in sand dune
 habitat maintained much smaller home ranges than reported estimates in
 other habitat types, with an average 95% KDE home range size of 0.42 km2
 (95% CI = 0.20–0.63 km2). Although sand dune habitat comprises just 2% of
 available habitat on SCI, our research highlights the importance of this
 unique habitat area for island foxes.
description: We deployed GPS collars (Advanced Telemetry Systems Model W500)
 to track the movements of 12 captured island foxes. Each collar weighed 65
 g (&lt; 5% of the body weight of a 1.5 kg fox) and was equipped with a
 VHF transmitter. Collars were programmed to record 1 GPS fix location per
 hour between 0600–1800 each day for 188 days between January–July 2018.
 Data were remotely downloaded from all foxes every 7 days using a W100 Com
 module (Advanced Telemetry Systems) and Yagi antenna from distances up to
 400 m. <strong>Home range
 estimation</strong> We calculated home range
 sizes from the GPS locations using the ‘adehabitatHR’ package in RStudio
 (RStudio Team 2022) and ArcMap 10.3 (ESRI 2015). Utilization distributions
 were generated using the ad hoc method for the estimation of the smoothing
 parameter (Worton 1989) within ‘adehabitatHR.’ We calculated spatial use
 metrics including the 95% minimum convex polygon (MCP) home ranges after
 the removal of 5% of extreme points, 95% fixed kernel density estimate
 (KDE) home ranges, and 50% KDE core areas. We compared these spatial
 parameters among sexes using a two-sample t-test and determined where
 primary core areas existed on the landscape. MCP and traditional KDE
 methods were selected to maintain consistency with Sanchez and
 Hudgens's (2015) previous home range estimates for SCI foxes. We
 conducted exploratory analyses using autocorrelated KDE (AKDE) to
 determine if the fine-scale location data in our study would be impacted
 by autocorrelation (Calabrese et al. 2016). Traditional KDE and AKDE
 performed nearly identically, so we proceeded with traditional KDE to be
 more directly comparable to Sanchez and Hudgens (2015).
 We defined a home range or a core area to be within the sand dune
 habitat if either the 50% or 95% KDE was composed of &gt; 50% sand
 dune habitat. We evaluated the dominant habitat types within each home
 range by calculating the percentage of each habitat type within the home
 range metrics. We classified habitat type based on the vegetation
 communities present in the study area: sand dune habitat (active and
 stabilized sand dunes), maritime desert scrub, grassland, coastal
 unvegetated, unvegetated, California chaparral, and developed (Tierra Data
 Systems 2010, Uyeda et al. 2020a, Uyeda et al. 2020b).
 <strong>Habitat selection
 analysis</strong> We conducted a resource
 selection function analysis to assess third order (within the home range,
 Johnson 1980) habitat selection under a Design 3 study design for
 comparing island fox habitat use and availability (Thomas and Taylor
 1990). Under a Design 3 framework, individual home ranges are identified
 and corresponding used and available points are measured within the
 individuals’ home range. Following methods described in Fieberg et al.
 (2021), we assessed availability by generating 10 random (i.e., available)
 points within the home range of each collared island fox for every 1 used
 point using ArcMap 10.3 (ESRI 2015). Habitat type for used and available
 points was assigned using a fine-scale SCI Vegetation Map with a mapping
 unit of 0.25 m<sup>2</sup> (Uyeda et al. 2020a, Uyeda et al.
 2020b) in ArcMap 10.3 (ESRI 2015). We used the vegetation alliances and
 macrogroups described in Uyeda et al. (2020a, 2020b) to reclassify habitat
 type based on the vegetation communities present in the study area: active
 sand dune habitat, stabilized sand dune habitat, maritime desert scrub,
 grassland, coastal unvegetated, unvegetated, California chaparral, and
 developed (Uyeda et al. 2020a, Uyeda et al. 2020b). Sand dune habitat was
 classified as either active or stabilized sand dune habitat to determine
 if selection differed between the two types of sand dune
 habitat. We compared habitat type between used and
 available points using a generalized linear mixed model (GLMM) with a
 logit link using the ‘glmer’ function in the ‘lme4’ package in RStudio
 (Bates et al. 2015, RStudio Team 2022). Habitat type was a categorical
 variable with maritime desert scrub set as the reference level. Fox ID was
 included as a random effect in the model to account for non-independence
 of individual foxes (Fieberg et al. 2021, Johnson-Bice et al. 2023).
 Available points were assigned a larger weight of 1000 and used points
 were assigned a weight of 1 to ensure the results hold more generally
 (Fieberg et al. 2021, Johnson-Bice et al. 2023). The
 model coefficients associated with habitat type provide a comparison of
 use between each categorical habitat type (i.e., active sand dune,
 stabilized sand dune, grassland, etc.) and the reference level maritime
 desert scrub, with the assumption that each habitat type is equally
 available within the home range. However, since each habitat type is not
 equally available within the home ranges, an adjusted ratio of habitat use
 (Adjusted ) was calculated following methods described in Fieberg et al.
 (2021) by multiplying the habitat type coefficient estimate,<em>
 </em>  (calculated assuming equal availability) by the ratio of
 overall habitat type availability relative to maritime desert scrub
 availability within each home range:  
 We also compared the distribution of raw used and available
 points for the dominant habitat types (i.e., the habitat classifications
 for &gt; 95% of raw used and available data points) to assess
 disproportionate use of the habitat types, if any.
description: # Patterns of island fox habitat use in sand dune habitat on San Clemente
 Island
 [https://doi.org/10.5061/dryad.t1g1jwt9w](https://doi.org/10.5061/dryad.t1g1jwt9w) We have submitted our raw data and R scripts associated with all home range and resource selection function analyses. The "DunesHRAnalysis" R file includes code for estimating Minimum Convex Polygons and Kernel Density Estimates for the 12 island foxes monitored in this study. It requires the csv file "GPSPoints_ALL_csv," which includes all GPS data points for the foxes monitored. The "RSFAnalysis" R file includes code for estimating the resource selection functions reported. It requires the csv files "all_used" and "all_rand_kde," which includes the vegetation data associated with each used and randomly generated GPS point.  ## Description of the data and file structure ###### **all\_used** * FoxID: unique identifier for each fox * East_X: easting for each used data point (Projected Coordinate System: NAD 1983) * North_Y: northing for each used data point (Projected Coordinate System: NAD 1983) * Alliance: alliance of associated vegetation * Group: group of associated vegetation * Macrogroup: macrogroup of associated vegetation  ###### **all\_rand\_kde** * FoxID: unique identifier for each fox * Alliance: alliance of associated vegetation * Group: group of associated vegetation * Macrogroup: macrogroup of associated vegetation  ###### **GPSPoints\_ALL\_csv** * Collar: unique identifier for each fox * East_X: easting for each used data point (Projected Coordinate System: NAD 1983) * North_Y: northing for each used data point (Projected Coordinate System: NAD 1983) ## Sharing/Access information Vegetation data was derived from the following sources: * [ArcGIS - SCI Vegetation Map](https://www.arcgis.com/home/webmap/viewer.html?webmap=4344322f66884267adc7d959a743e40e&extent=-118.7419,32.7945,-118.2368,33.0441) * [ArcGIS - San Clemente Island iNaturalist assessment map](https://www.arcgis.com/home/webmap/viewer.html?webmap=5737c67a68fe4394ba8c8744f21ec232&extent=-118.8019,32.712,-118.1238,33.0635) ## Code/Software R is required to run both scripts provided. Annotations are provided throughout the scripts through 1) library loading, 2) dataset loading and cleaning, and 3) analyses.