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Dataset for: Snow limits polecat (Mustela putorius) distribution in Sweden


Osinga, Thomas (2022), Dataset for: Snow limits polecat (Mustela putorius) distribution in Sweden , Dryad, Dataset,


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.


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-km2 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-km2 grid cell.

Table S1.1: The landcover type clusters and which variables are merged from the original landcover data.

Cluster of land-cover types

Land-cover type number as presented in the NMD

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 km2. 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-km2 grid cell.


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-km2 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 (Meles meles), the wolverine (Gulo gulo) 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 (Lutra lutra), pine marten (Martes martes), American mink (Neovison vison), stoat (Mustela erminea), and weasel (Mustela nivalis) – 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 km2 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.