Data from: Phase-dependent red fox expansion into the tundra: Implications for management
Data files
May 15, 2024 version files 26.69 KB
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README.md
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Wilkinson_J_Wildl_Man_240515.xlsx
Abstract
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.
README: 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
Methods
Rodent and red fox monitoring and control
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 .
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).
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. 2017b). 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).
Data analysis
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 post hoc 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 (t = 0) and 1-year time lags (t = −1, red fox abundance shows a response to previous year’s rodent abundance; and t = 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 (t = 0) and 1-year time lags (t = −1 and t = 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 (n =10 triangles), Guvertfjället (n = 7 triangles), and Ammarfjället (n = 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).