DOI: 10.5061/dryad.r7sqv9shp
title: Feast to famine: Sympatric predators respond differently to seasonal prey scarcity on the low-Arctic tundra

description: Resource fluctuation is a major driver of animal movement, influencing
 strategic choices such as residency vs nomadism, or social dynamics. The
 Arctic tundra is characterized by strong seasonality: resources are
 abundant during the short summers but scarce in winters. Therefore,
 expansion of boreal-forest species onto the tundra raises questions on how
 they cope with winter-resource scarcity. We examined a recent incursion by
 red foxes (Vulpes vulpes) onto the coastal tundra of northern Manitoba, an
 area historically occupied by Arctic foxes (Vulpes lagopus) that lacks
 access to anthropogenic foods, and compared seasonal shifts in space use
 of the two species. We used 4 years of telemetry data following 8 red
 foxes and 11 Arctic foxes to test the hypothesis that the movement tactics
 of both species are primarily driven by temporal variability of resources.
 We also predicted that the harsh tundra conditions in winter would drive
 red foxes to disperse more often and maintain larger home ranges
 year-round than Arctic foxes, which are adapted to this environment.
 Dispersal was the most frequent winter movement tactic in both fox
 species, despite its association with high mortality (winter mortality was
 9.4 times higher in dispersers than residents). Red foxes consistently
 dispersed towards the boreal forest, whereas Arctic foxes primarily used
 sea ice to disperse. Home range size of red and Arctic foxes did not
 differ in summer, but resident red foxes substantially increased their
 home range size in winter, whereas home range size of resident Arctic
 foxes did not change seasonally. As climate changes, abiotic constraints
 on some species may relax, but associated declines in prey communities may
 lead to local extirpation of many predators, notably by favoring dispersal
 during resource scarcity.
description: <em>Capture and satellite telemetry. </em>– Between
 2017 and 2019, we captured 10 red foxes and 13 Arctic foxes using Tomahawk
 (Model 208, Tomahawk Live Trap Co., WI) and padded leghold traps
 (Softcatch # 1.5, Oneida Victor Ltd, USA). Traps were placed on active
 dens or by protruding features (e.g., driftwood or spruce islets) and
 remained open continuously for up to one week. We checked the traps every
 4–6 hours and closed them during extreme weather conditions (e.g.,
 blizzard or temperatures below -25°C). We captured adult foxes from March
 to May when snow still covered the ground and facilitated travel over
 large distances, except for two adult foxes caught near our field camps in
 June 2018. We did not anesthetize the foxes, which were easily handled
 without chemical restraint. Foxes were first wrapped in a blanket and
 released from the traps, then we assessed sex and body condition, deployed
 an Iridium satellite collar (#4170 or 4270, Telonics, Mesa, Arizona, USA;
 ~100g, i.e., 2–4% of a fox body mass), and released them at the site of
 capture. All handling procedures were approved by the University of
 Manitoba Animal Care Committee (Protocol F17-012), and the research was
 conducted under Parks Canada Research and Collection Permits
 WAP-2017-25781 and WAP-2018-27938, and Manitoba Wildlife Scientific
 Permits WB20226 and WB21856. <em>Movement
 analysis.</em> – Our GPS collars used different schedules throughout
 the year (see Table S1), so we thinned all the tracks by randomly
 selecting 1 location per day (the lowest fix frequency). We defined two
 relevant contrasting periods based on goose phenology. The season of
 abundant resources (hereafter summer) thus extended from May 15, the
 approximate date of nest initiation, to the end of October, the last month
 during which geese can be considered alternative prey for the foxes of
 this area (Andersen et al. 2010; McDonald et al. 2017). The
 resource-scarcity period (hereafter winter) extended from November 1 to
 May 14, when geese are absent and foxes mostly rely on arvicoline
 rodents. We plotted all fox tracks in ArcGIS 10.3 (ESRI
 2017, Redland, CA, USA) to remove possible major erroneous locations and
 identify movement strategies: residency and long-range movements. We
 labelled a fox as a resident only if it maintained a home range (i.e.,
 showed non-directional movements within a geographically circumscribed
 area) from the start of a given season until the end of that season or
 until its death, if it occurred after the area resulting from movement
 analysis had reached an asymptote. We never included the season of capture
 in movement tactic and home range comparisons, since we could not know if
 a fox was dispersing earlier that season. Using a subset of 16 individuals
 with 111 to 187 locations each, we determined that home range areas
 reached an asymptote with 38 locations on average. All our resident foxes
 exceeded this threshold with at least 61 locations. All foxes that
 underwent long-range movements (hereafter dispersals) were considered
 dispersers since none returned to their departure area (they either died
 dispersing or settled elsewhere). The dispersal events we used to compare
 movement tactics were not natal dispersal because we only included adults
 (at least 1.5 years old), unlike the track descriptions, which included
 all available tracks. For each dispersal (including
 those initiated during the season of capture that were not included in any
 other analysis) we calculated the cumulative distance travelled (i.e., sum
 of straight-line distances between successive daily relocations), the
 duration (starting with the last position within the home range
 boundaries), the cumulative to straight-line distance ratio (a proxy for
 fox behavior during dispersal), the cardinal direction (the angle of the
 vector between first and last locations, degrees from due North), the main
 substrate used for movement (sea ice or land), and the average daily
 speed. We considered that the dispersal started with the last location in
 a home range prior to dispersal initiation, or at the point of capture if
 a fox did not exhibit residency prior to dispersal (and thus was likely
 captured while already dispersing), and ended with the first location
 associated with a settlement of &gt;7 days in a new delimited ranging
 area (on land, not ice) or with the death of the fox. Although foxes can
 exhibit staged dispersal, exploring delimited areas for a temporary period
 ranging from a few days up to a few weeks (e.g., Walton et al. 2018), we
 never observed clear staging behavior. We estimated
 residents’ home ranges and core areas, defined as the 95% and the 50%
 utilization distribution isopleths, respectively, with local convex hulls
 (LoCoH) using the package T-LoCoH v.1.40.07 in R (Lyons et al. 2013).
 LoCoH are nonparametric estimates of utilization distributions and perform
 better than parametric kernel methods to identify boundaries (such as
 coastlines) and unused areas (Getz et al. 2007; Stark et al. 2017). As
 such, they are well-suited for our main objective to determine if red
 foxes were using the sea ice. We were not specifically interested in the
 temporal partition of space within seasons since we modelled space use
 using only one location per day. We, therefore, set the user-defined
 parameter s to 0, which entailed that the time-scaled distance was
 equivalent to the Euclidian distance (Getz et al. 2007) to heterogeneous
 location densities, we used the adaptive method (a-LoCoH). We selected the
 a value for each animal using the graph tools provided in the T-LoCoH
 package and following the recommendations to minimize the risks of both
 excluding used areas and including unused areas. To estimate seasonal
 home-range shifts in each fox, we measured summer and winter home range
 overlaps using the package T-LoCoH.dev v. 1.34.00/r12 and the distance
 between their centroids estimated in ArcGIS 10.3 (ESRI 2017). All home
 ranges and core areas are displayed in Supplemental file 1. Based on the
 same dataset, we also estimated home ranges (95% utilization distribution)
 using a classic bivariate kernel density estimator (KDE) with a reference
 bandwidth, with R package adehabitatHR v.0.4.20 (Calenge 2006). Although
 we decided not to use kernel density methods in this study, we provide the
 areas resulting from the KDE in Table S2, for comparison
 purposes. Many residents undertook short-distance and
 short-duration trips outside the boundaries of their home range, either on
 land or on the sea ice. We defined excursions as any exploratory movement
 &lt;7 days unusually far away from the current center of activity
 followed by a return to the home range. Home-range borders include areas
 that are already peripheral to the center of activity. Therefore, to avoid
 making arbitrary decisions on a distance threshold to the border, we
 differentiated excursions from other movements near the home range border,
 based on the distribution of the distances between a location and the
 home-range centroid. Locations that appeared to be outliers using a
 one-sided Hampel filter (upper bound = median (Tukey-transformed distance)
 + 3 median absolute deviations) were considered excursions. If a trip
 outside the boundaries of the LoCoH home range estimate consisted of
 multiple consecutive locations, we used the farthest away of the
 consecutive locations to determine if that trip was an excursion. Finally,
 we called “commuting trip” any excursion on the sea ice (Lai et al.
 2017).