DOI: 10.5061/dryad.pg4f4qrxg
title: Data from: Phase-dependent red fox expansion into the tundra: Implications for management

description: Expansion of boreal species into tundra ecosystems is a consequence of
 climate change and human exploitation that threatens local species through
 increased predation, competition, and pathogen transmission. Under these
 circumstances, efficient control of expanding boreal species may be
 necessary, but the efficiency of such action depends on understanding the
 ecological influences of expansion. The red fox (Vulpes vulpes) is
 expanding into the tundra across the Arctic. In Scandinavia, red foxes
 threaten local tundra species and communities including the endangered
 Arctic fox (V. lagopus). The ecological dynamics in the tundra are
 influenced by small rodent cycles (classified into different phases based
 on seasonal abundance fluctuations), which can affect red fox expansion,
 distribution, and abundance. We used a 17-year (2004–2020) dataset from
 the tundra in Sweden, consisting of raw snow track data, to test how
 cyclic prey influenced red fox distribution and abundance, and
 subsequently red fox control. The winter abundance of red fox was
 influenced by small rodent phase, with higher abundance during high prey
 availability (i.e., increased number of prey numbers) with no support for
 a time lag between red fox and small rodent abundance. This suggests that
 high prey availability attracts red foxes to the tundra and that higher
 immigration from the boreal zone can be expected in response to increased
 prey abundances. There was no relationship between red fox control and
 small rodent availability, but control was influenced by red fox abundance
 during the previous year, which highlights an opportunistic control
 strategy. We recommend an adaptive management strategy where authorities
 include small rodent dynamics in the planning and execution of red fox
 control.
description: <strong>Rodent and red fox monitoring and
 control</strong> Västerbotten County has
 monitored abundance of predators yearly through snow track surveys since
 2004 (Stoessel et al. 2019); we used data from 2004–2020. This monitoring
 follows the wildlife triangle scheme (Lindén et al. 1996). Within the
 study area, there are 28 randomly distributed triangular transects, which
 are 12 km in length (i.e., 4 km on each side of the triangle), permanent,
 and are distributed across the nature reserve (total area of nature
 reserve is 565 000 hectare) (Stoessel et al. 2019). Only 2 out of 28
 triangles were located below the treeline. Fieldworkers on snowmobiles
 conducted monitoring in March-April, only on days after new snowfall or
 wind to ensure detection of recent tracks (Linden et al. 1996). For each
 snow track present, trained fieldworkers recorded a global positioning
 system point and identified the focal species based on the overall shape
 and pace of the tracks (Stoessel et al. 2019). Snow tracking can mainly
 detect lemming tracks and we therefore use this as a proxy for overall
 small rodent density. To estimate both red fox and small rodent abundance
 indexes, we selected 24 of the triangles based on the position and
 relevant temporal data available, with an average of 20.53 (range = 10 –
 24) triangles monitored each year (Figure 1).  We calculated an annual
 abundance index separately for foxes and lemmings based on the number of
 tracks/km; we also calculated the proportion of surveyed triangles with
 identified red fox or lemming tracks .
 <strong>                      </strong>There were 5
 small rodent cycles from 2004–2020. To classify the phase of the small
 rodent cycle at the time of monitoring, we used snow tracking data of
 lemmings. Phases were classified as low, increasing, peak, and post-peak
 following Meijer et al. (2013; Table 1). A low phase was identified when
 the previous year was a peak and the present year had a density decline.
 An increase phase was defined as a density increase relative to the
 previous year (that displayed a density decline). A peak phase was defined
 as a year when density increased, following a previous year of increase.
 There was only 1 post-peak phase in our study: in 2012 lemming densities
 had declined since 2011 but remained relatively high. This was followed by
 a rapid crash and low abundance in June 2012 (Ecke and Hörnfeldt 2020).
 The data set contained 6 low phases, 5 increase phases, 5 peak phases, and
 1 post-peak phase (Table 1; Figure 2). Voles mostly follow the demographic
 pattern of lemmings, with some inter-annual and seasonal variations (Ecke
 and Hörnfeldt 2020).
 <strong>                      </strong>Red fox
 control is implemented in the study area by the County Administration
 Board of Västerbotten and the Environmental Protection Agency focused on
 red fox removal in tundra through shooting (Angerbjörn et al. 2013,
 Elmhagen et al. 2017<em>b</em>). Control measures have been
 carried out by Västerbotten county, during winter and spring, since the
 early 2000s, with varying intensity of control across the area because of
 limited personnel and economic resources (S. Almroth, County
 Administration Board, personal communication).
 <strong>Data analysis</strong> We
 conducted all statistical analyses in R 4.0.4 (R Core Team 2013) and the R
 Commander 2.7 package (Fox 2017). To test the relationship between small
 rodent and red fox abundance, we used a linear mixed model (LMM) where red
 fox tracks/km of each triangle  was the response variable, rodent phase
 (classified into categorical low, increasing, peak, and post-peak) was the
 fixed effect and triangle identity (geographic site) and year were random
 effects to control for repeated measurement and effects of specific years
 or localities. We log transformed the data, inspected model residuals to
 control for goodness of fit, and examined the mixed model with Tukey’s
 <em>post hoc</em> test.       
                       To assess the occurrence of a time lag in
 the response of red foxes to rodent abundance, we used 2 different
 approaches. First, we followed the procedure of Smedshaug et al. (1999)
 where we cross correlated average number of small rodent snow tracks/km
 and average number of red fox tracks/km using Spearman’s correlation test
 in R 4.0.4 (R Core Team 2013) and the R Commander 2.7 package (Fox 2017).
 We plotted correlation coefficients for red fox abundance and small rodent
 abundance with no time lag (<em>t</em> = 0) and 1-year time
 lags (<em>t</em> = −1, red fox abundance shows a response to
 previous year’s rodent abundance; and <em>t</em> = 1, red fox
 abundance shows a response to the following year’s rodent abundance).
 Second, we used an LMM, where red fox tracks/km was the response variable,
 small rodent tracks/km was the fixed effect and triangle identity
 (geographic site) and year were random effects to control for repeated
 measurement and effects of specific years. We ran analyses for no time lag
 (<em>t</em> = 0) and 1-year time lags (<em>t</em>
 = −1 and <em>t</em> = 1).
                       To assess whether rodent phase influenced
 the number of shot red foxes during control measures, we used a
 Kruskal-Wallis test. To further assess the relationship between red fox
 occurrence and number of shot red foxes, we used an LMM with average
 number of red fox tracks/km or proportion of triangles with red fox tracks
 as fixed effects and area (geographic site) and year as random effects. We
 used red fox indices for the specific mountain range within the
 Vindelfjällen nature reserve, where red fox control is reported
 separately: Björkfjället (<em>n</em> =10 triangles),
 Guvertfjället (<em>n </em>= 7 triangles), and Ammarfjället
 (<em>n </em>= 4 triangles). To account for a 1-year time lag
 between the number of shot red foxes in relation to red fox abundance the
 previous year, we repeated the same model using average red fox tracks/km
 as an estimate of previous year’s abundance. To account for differences in
 control strategies and available funding for the action, we included only
 data from 2010–2020 in the statistical analyses.  We interpreted
 significance values using a gradual notion of evidence described by Muff
 et al. (2022).
description: # Data from: Phase-dependent red fox expansion into the tundra:
 Implications for management Data consists of seven columns: **Year; Small
 rodent phase; Triangle identity; Number of red fox tracks; Number of red
 fox tracks/km; Number of small rodent tracks; Number of small rodent
 tracks/km** **Categorical values included in column B (Phase of small
 rodent cycle):** **Increase** = Small rodent abundance is higher than the
 low phase but lower than the peak phase **Peak** = The highest
 small rodent abundance over the cycle, appearing after the increase phase
 and before the low phase **Low** = A distinct decline in small rodent
 abundance relative to the other phases **Post-peak** = Small rodent
 densities had declined but remained relatively high