DOI: 10.5061/dryad.j0zpc86hn
title: Dataset for: Snow limits polecat (Mustela putorius) distribution in Sweden

description: Many species show range expansions or contractions due to
 climate-change-induced changes in habitat suitability. In cold climates,
 many species that are limited by snow are showing range expansions due to
 reduced winter severity. The European polecat (Mustela putorius) occurs
 over large parts of Europe with its northern range limit in southern
 Fennoscandia. However, it is to date unknown what factors limit polecat
 distribution. We thus investigated whether climate or land-use variables
 are more important in determining the habitat suitability for polecats in
 Sweden. We hypothesized that 1) climatic factors, especially the yearly
 number of snow days, drive habitat suitability for polecats, and that, 2)
 as the number of snow days is predicted to decline in the near future,
 habitat suitability in northern Sweden will increase. We used a
 combination of sightings data and a selection of national maps of
 environmental factors to test these hypotheses using MaxEnt models. We
 also used maps of future climate predictions (2021–2050 and 2063–2098) to
 predict future habitat suitability. The number of snow days was the most
 important factor, negatively determining habitat suitability for polecats,
 as expected. Consequently, the predictions showed an increase in suitable
 habitat both in the current distribution range and in northern Sweden,
 especially along the coast of the Baltic Sea. Our results suggest that the
 polecat distribution is limited by snow and that reduced snow cover will
 likely result in a northward range expansion. However, the exact
 mechanisms for how snow limits polecats are still poorly understood.
 Consequently, we expect the Scandinavian polecat population to increase in
 numbers, in contrast to many populations elsewhere in Europe, where
 numbers are declining. Due to polecat predation, the expansion of the
 species might have cascading effects on other wildlife populations.
description: Polecat sightings
 data To determine the distribution and habitat
 suitability of polecats in Sweden, we used sightings data of polecats
 gathered by volunteers and documented to the Swedish Species Information
 Centre between 1960 and 2020 (Figure 1; Swedish Species Information Centre
 2020). We validated data
 at the edge of the distribution by contacting the person that reported the
 sighting, and removed data points if there was uncertainty about the
 sighting, we also removed data that was categorized as roadkill. As a
 result, we discarded 44 of the 425 sightings before analysis. Due to an
 increase of popularity of the sightings platform, the majority of
 sightings (78%) used in the analyses was from the period
 2010–2020. Description of
 covariates We used nine covariates distributed over four
 different categories to test our hypotheses (Table 1). We included these
 covariates based on habitat and diet preferences of the polecat found in
 previous studies  All
 parameters were rasterized and aggregated to 1-km<sup>2</sup>
 grid cells over the whole of Sweden in ArcGis Pro version
 2.6.0  Land cover and soil moisture 
 We selected several land-cover
 types and an index of soil moisture from the ungeneralised version of the
 National Land Cover Database (NMD) Sweden. This project provides a land
 cover map for the whole of Sweden divided into 24 different land-cover
 types, as well as a measure of soil moisture as a spectrum from dry to wet
 soil. We reclassified 17 of the 24 land-cover types into three different
 groups: coniferous forest, deciduous forest and open landscapes (Table
 S1.1). We selected these three groups for ease of analysis and due to
 previous studies showing that polecats selected or avoided these
 land-cover types at small spatial scales. Polecats were found to avoid
 coniferous forest (Baghli and Verhagen 2005, Zabala et al.
 2005), select for deciduous forest
 (Jedrzejewski et al. 1993, Baghli et al. 2005), and use landscapes that were characterised by
 a variety of open habitat and forest (Blandford 1987, Lodé
 1993, 2000a, Baghli et al. 2005).
 Furthermore, we used soil moisture as a variable that could further
 distinguish areas that would be wet during part of the year, which could
 result in increased amphibian populations, which are an important part of
 the polecat’s diet (Lodé 1993, 1997, 2000b, Hammershøj et al.
 2004, Malecha and Antczak 2013). After
 reclassification, we
 determined the proportion of surface covered by each land-cover group, as
 well as the average soil moisture index, for each
 1-km<sup>2</sup> grid cell.
 <strong>Table S1.1:</strong> The landcover type
 clusters and which variables are merged from the original landcover
 data. 
 <strong>Cluster of land-cover
 types</strong> 
 <strong>Land-cover type number as presented in the
 NMD</strong> 
 Coniferous forest 111
 (Pine forest not on wetland), 112 (Spruce forest not on
 wetland), 113 (Mixed coniferous not on
 wetland), 121 (Pine forest on wetland),
 122 (Spruce forest on wetland), 123 (Mixed
 coniferous on wetland) Deciduous
 forest 115 (Deciduous forest not on
 wetland), 116 (Deciduous hardwood forest not on
 wetland), 117 (Deciduous forest with deciduous hardwood
 forest not on wetland), 125 (Deciduous forest on
 wetland), 126 (Deciduous hardwood forest on
 wetland), 127 (Deciduous with deciduous hardwood forest
 on wetland) Open
 landscapes 2 (Open wetland), 3
 (Arable land), 41 (Non-vegetated other open
 land), 42 (Vegetated other open land),
 118 (Temporarily non-forest not on wetland),
 128 (Temporarily non-forest on wetland) 
    
      
      
      
      
      
      
   Snow
 cover and Minimum winter temperature
 The Swedish Meteorological and
 Hydrological Institute provides snow cover data for Sweden as the average
 number of days with snow with a depth above 20mm. The data is provided in
  4 time periods, including two future projections (P1=1961–1990, P2 =
 1991–2013, P3 = 2021–2050, P4= 2069–2098; Swedish Meteorological and
 Hydrological Institute 2021). The future projections we included are based
 on the 4.5 RCP scenario(Thomson et al. 2011). We included these data as a previous study
 showed that polecats had more difficulty catching prey when there is snow
 on the ground (Weber 1989). The data consists of interpolated data from
 200 weather stations with an average given per municipality. We have
 rasterized the data giving average values for cells crossing municipality
 boundaries. We used data averages per cell of P2 for model building, while
 we used averages of P3 and P4 per cell for model projections of future
 scenarios. Human pressure 
 We used the human footprint
 index as published by NASA in 2018 as a measure of human pressure. We did
 this as previous studies have shown that polecats tend to select for areas
 with extensive human use (Sidorovich et al. 1996, Rondinini
 et al. 2006) while avoiding urban
 centres (Zabala et al. 2005). The dataset is based on the global human
 footprint between 1995 and 2004. The human footprint is an index based on
 population density, land-use, infrastructure (buildings, lights, land
 use/cover) and human access (roads, railways; Venter et al.
 2018). This raster dataset is publicly
 available and has a resolution of 1 km<sup>2</sup>. We only
 clipped the dataset to the borders of Sweden. 
 Water
 availability We used the Water & Wetness geo data
 from Copernicus (CLMS 2018) as a measure of water availability. We did this
 as previous studies showed that polecats select for riparian habitat
 (Baghli et al. 2005) as
 amphibians are an important part of their diet (Lodé 1993,
 1997, 2000b, Hammershøj et al. 2004, Malecha and Antczak 2013). This dataset includes all waterways and
 waterbodies with a resolution of 10 m. We have outlined all waterbodies
 and then made a buffer of 30 meters around all waterlines to represent
 near-water (riparian) habitat. We then calculated the proportion of
 near-water habitat in each
 1-km<sup>2</sup> grid cell. 
 Elevation
 We used elevation data from
 the Copernicus Land
 Monitoring Service - EU-DEM project. We did this as previous studies
 showed that polecats avoid high-elevation areas. The dataset is provided
 as a raster with a spatial resolution of 25 meters. We calculated the
 average elevation for each
 1-km<sup>2</sup> grid cell. 
 Bias correction for sampling
 intensity Due to the
 nature of citizen science data, it is prone to come with a bias. This bias
 manifests itself mostly in a discrepancy in spatial sampling effort. To
 account for this bias, we created a density kernel (as recommended by
 Kramer-Schadt et al. 2013 and Morelle and Lejeune 2015 and in
 line with Rutten et al. 2019) based on
 all mustelid sightings (n = 25686) reported to the Swedish Species
 Information Centre between 1972 and 2021 (Swedish Species Information Centre
 2020), except for the
 Eurasian badger (<em>Meles</em> <em>meles</em>),
 the wolverine (<em>Gulo</em> <em>gulo</em>) and
 the polecat. We excluded the badger and wolverine as we expect this
 species to be much easier to identify and see compared to the polecat and
 other mustelids. Furthermore, badger and wolverine have a limited
 distribution in Sweden, while all other species – Eurasian otter
 (<em>Lutra</em> <em>lutra</em>), pine marten
 (<em>Martes</em> <em>martes</em>), American mink
 (<em>Neovison</em> <em>vison</em>), stoat
 (<em>Mustela</em> <em>erminea</em>), and weasel
 (<em>Mustela</em> <em>nivalis</em>) – have a
 distribution that covers the whole of Sweden (Swedish Species Information
 Centre 2020). We created the kernel with the ‘Kernel Density’ function in
 ArcGIS Pro (Esri 2021)
 with the mustelid sighting coordinates and the 1
 km<sup>2</sup> raster grid used for the covariates. The use of
 this density kernel is based on the assumption that people reporting other
 mustelids would also report a polecat if they saw one, and thus that the
 distribution of mustelid sightings is representative of the distribution
 of potential polecat reporters.