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Determinants of badger Meles meles sett location in Bialowieza primeval forest, northeastern Poland.

The distribution of organisms in the environment constitutes one of the basic elements of their ecology (e.g. Krebs 1994, Macdonald & Rushton 2003). The distribution of species with wide ecological niches often depends on many factors, and different factors may play the main role in different environments (e.g. Gaston & Blackburn 1999, Guisan & Zimmermann 2000). The identification of these factors and the understanding of their importance are crucial for species management or protection (e.g. Kaiser 1997, Krebs 2002).

The European badger Meles meles is a medium-sized carnivore from the Mustelidae family. Its range extends across the temperate zone of Eurasia, from Portugal and Ireland to Russia (west of the Volga River) and northern Afganistan (Wozencraft 2005). It inhabits many types of habitat, i.e. forests, agricultural areas, steppe and even suburbs (Griffiths & Thomas 1993). Because of its wide range of occurrence, both in geographical and environmental space, as well as its territorialism and environmental adaptability, the badger has become a subject of numerous studies on species-habitat relationships (review in Newton-Cross et al. 2007).

The Resource Dispersal Hypothesis (RDH; Macdonald 1983) is often applied to explain badger sett settling behaviour. It states that many aspects of the badger's ecology, e.g. social structure, are affected by the accessibility of certain limited resources. A version of the RDH is the Sett Dispersion Hypothesis (Doncaster & Woodroffe 1993). This hypothesis states that badger distribution and abundance is limited by accessibility of areas suitable for sett digging (Thornton 1988, Neal & Roper 1991, Roper 1993, Virgos 2001, Rosalino et al. 2005, Kaneko et al. 2006), determined by: geomorphological units, altitude a.s.l., slope orientation and inclination, soil type and cohesivity, accessibility of food, water and bedding material, type of vegetation cover, anthropogenic impact and many more (e.g. Dunwell & Killingley 1969, Thornton 1988, Neal & Roper 1991, Biak et al. 2000, Hammond et al. 2001, Virgos 2001, Mickevicius 2002, Jepsen et al. 2005, Prigioni & Deflorian 2005, Kaneko et al. 2006). Both of the mentioned hypotheses have been strongly criticised (Revilla & Palomares 2002, Revilla 2003, Macdonald et al. 2004), and more habitat properties may be proposed as determinants of badger population density, e.g. the level of wilderness of available habitat. Badgers live in high density populations on small territories in agriculture or suburb landscapes intensively utilised by humans (e.g. Kruuk 1978, Roper 1992, Woodroffe & Macdonald 1993, Rogers et al. 1997, Tuyttens et al. 2000, Macdonald et al. 2004). Badgers also occur in low density populations on less disturbed lowlands in northeastern Poland (Kowalczyk et al. 2000, 2003), the Iberian Peninsula (Revilla et al. 2001b, Rosalino et al. 2005) and in Scandinavia (Braseth et al. 1997, Kauhala et al. 2006). However, few studies have provided evidence for the importance of direct or indirect human-related habitat features for the badger population density or sett location (Revilla et al. 2001a, Jepsen et al. 2005, Kurek 2011, Myslajek et al. 2012a). Studies from Bialowieza Primeval Forest (BPF), situated on the borderland of Poland and Belarus, are particularly important for the investigation of wild animals' living patterns due to its long-term wilderness, uncommon in European lowlands (Falinski 1986, Jedrzejewska & Jedrzejewski 1998). Distribution of badger territories and sett location have so far only been studied in terms of habitat selection for sett excavation (Kowalczyk et al. 2003) and in terms of territory use and energy saving (Kowalczyk et al. 2006), but the importance of human pressure has not yet been analysed. Therefore, it is not clear whether data on the preferred forest habitat (i.e. English oak Quercus robur-hornbeam Carpinus betulus forest) from BPF can be extrapolated to other areas (e.g. Thornton 1988, Macdonald et al. 1996, van Apeldoorn et al. 1998, Wright et al. 2000, Hammond et al. 2001, Jepsen et al. 2005). Better understanding of the relative importance of the many determinants of badger sett location may be useful in the management of the badger population in more transformed lowland landscapes, e.g. in the vicinity of BPF, where badgers have been noted in secondary forests on abandoned fields (A. Obidzinski, pers. obs., W. Kojlo, Forestry Administration, pers. comm.).

Recent advances in methods of statistical modelling of distribution and abundance of organisms in geographic and environmental space may be helpful in such a study (Elith & Leathwick 2009). So far, most analyses of badgers' habitat preferences have used multiple regression models (Thornton 1988) or logistic regression models (e.g. Progioni & Deflorian 2005, Rosalino et al. 2005, Newton-Cross et al. 2007), sometimes coupled with ordination methods like DECORANA (Macdonald et al. 1996). Despite their wide use, these methods have many common disadvantages shared by all parametric statistical methods, e.g. they require satisfying assumptions on the shape of the relationships between predictors and dependent variable, as well as the independence of error terms (Franklin & Miller 2010). Some statisticians claim that parametric methods are not well suited for analysing data of natural experiments that cannot be fully controlled (Breiman 2001b), and propose more robust, non-prametric modelling methods, e.g. ensembles of classification and regression trees (Breiman 2001a, Cultler et al. 2007), possible to use for both regressive and classification purposes. Despite the complexity of the background algorithms, their use is growing in biogeographical modelling (Elith & Leathwick 2009, Franklin & Miller 2010).

The aim of our paper was to find natural and anthropogenic features important for sett habitat selection in BPF within the boundaries of Poland (595 [km.sup.2]), and specifically, to investigate the relative importance of food supply, geological conditions, human activity and the neighbouring badger territories for sett location. Our hypothesis was that in the low-density badger population in BPF, geological conditions allowing digging and supply of earthworms in the habitat are more important than the level of human intervention or social neighbourhood.

Material and methods

Study area

BPF, located on the Polish-Belarusian border (52[degrees]30'-53[degrees]N, 23[degrees]30'-24[degrees]15'E) is considered to be the best preserved natural forest of European lowlands. Currently, it occupies an area of ca 1,450 [km.sup.2] (595 [km.sup.2] in Poland and 855 [km.sup.2] in the Republic of Belarus; Fig. 1). The exceptional value of the BPF, expressed by high species diversity with numerous species endangered elsewhere in Europe (Falinski 1986, Jedrzejewska & Jedrzejewski 1998), derives from certain tree stands that have never been exploited for timber, despite exploitation that started during World War I and was continued to a limited extent recently (e.g. Jedrzejewska & Jedrzejewski 1998, Sokolowski 2004). Local climate displays continental-maritime characteristics. Annual average temperature (1955-2001) is 6.8[degrees]C, with an average temperature of-4.2[degrees]C and 17.7[degrees]C in January and July, respectively, and the average annual precipitation (1955-2001) is 633 mm (Pierzgalski et al. 2002). The vegetation season lasts 208 days, while snow cover lasts 92 days (Falinski 1986). The geological substrate of BPF is formed mainly by moraine upland, made of glaciofluvial sands, gravels and silts and divided by marshy river valleys. The ablation moraine has a prevalence of cambisols, the ground moraine either cambi- or luvisols and the eolic accumulation plains mainly podzols. River valleys are filled with hydrogenic soils (Kwiatkowski 1994). BPF is situated within a mixed forest zone. Its main forest-forming species are hornbeam, English oak, Norway spruce Picea abies, Scots pine Pinus sylvestris and alder Alnus glutinosa (Falinski 1986). Potential natural vegetation covering the greatest area is composed by oak-lime Tilia cordata-hornbeam forests (65%), alder wet forests (15%) and mixed pine-oak forests (10%; Kwiatkowski 1994). BPF is inhabited by four denning predators, i.e. wolf Canis lupus, badger Meles meles, red fox Vulpes vulpes and raccoon dog Nyctereutes procyonoides (the last-mentioned predator colonised BPF in the early 1950s; Jedrzejewska & Jedrzejewski 1998). Density of badgers in BPF (0.21 individuals/[km.sup.2]) and of their setts (0.055 setts/[km.sup.2]) is one of the lowest in Europe while territories of the groups (12.8 [km.sup.2]) are among the largest in Europe (Kowalczyk et al. 2003). The Polish part of BPF includes Bialowieza National Park (BNP; with a strictly protected area of 57 [km.sup.2] and a partially protected area of 48 [km.sup.2]), nature reserves of partial protection (outside BNP) of 118 [km.sup.2] and managed forests of 372 [km.sup.2].

Data collected in the field

Habitat and landscape features were assessed for two kinds of locations. One set of points called presence points were all places where setts were found. Locations of these points were obtained from the literature (Kowalczyk et al. 2000, 2003), Forest Administration (pers. comm.) and own field surveys. Field data were collected in the years of 2006-2009. Setts were surveyed 4-6 times within our study period, usually by one person with up to two aids. Out of 128 dens found, 67 were recognised as badger dens, based on direct observation of animals (R. Kowalczyk and Forest Administration, pers. comm.) and their traces or were determined on the basis of morphometry of setts according to the criteria of Thornton (1988). All 67 badger setts were divided into three categories: main, secondary or occasional according to the categorisation of Matyastik & Bicik (1999). This classification was based on former observations by Kowalczyk et al. (2000, 2003) and personal observations by Forest Administration crew and then verified by A. Obidzinski (pers. obs.). All the found setts were included in the habitat analyses because they often change their function (Neal & Cheeseman 1996, Wilson et al. 1997, Ostler & Roper 1998), and besides, it is sometimes impossible to distinguish between the main and other kinds of setts (Revilla et al. 2001b, Remonti et al. 2006). In some cases, no differences of habitat features were detected between setts of different categories (Macdonald et al. 2004). Moreover, the fact that a sett of any kind has been created indicates that habitat conditions of the location also enable digging of a main sett (Jepsen et al. 2005). Additionally, in our case, habitat properties of small setts did not diverge from habitat properties of the main setts either. Whether a small sett can change into a main sett depends on many other factors, e.g. presence of other badger groups or human activity in the neighbourhood. Field measurements were taken between May and October during 2006-2009. Surface area, height of spoil heaps, number of entrances as well as slope orientation and inclination, congruence between canopy and habitat and anthropogenic impact (e.g. settling setts in places transformed by humans, such as gravel pits) were determined for each sett in the field. Boundaries of sett areas were determined in the field on the basis of extent of earth relief distorted by the animals digging out soil. The maximum height of spoil heaps for each sett was measured with an optical lever. Geographic coordinates were measured with a GPS (GARMIN eTrex Vista) with an accuracy of at least 15 m.

The second set of points called pseudo-absence points, consisted of random points situated all over the Polish part of BPF. Coordinates of the pseudo-absence points were generated randomly with ArcGIS, four for each basic unit of Forest Administration net of 0.25 [km.sup.2] each, which made a total of 8,163 points. After elimination of points with no or incomplete predictor data, 7,630 points remained. This way there were 7,563 pseudo-absence and 67 presence points.

Habitat and landscape data

Data on geomorphologic units, soil type and texture, thickness of surface layers and habitat moisture were taken from forest numeric maps, obtained from the Regional State Forest Directorate and BNP. The GIS layers obtained from these two institutions were standardised and the number of categories was generalised. Four groups of variables were established: 1) conditions suitable for sett digging, 2) food supply (i.e. presence of habitats abundant in earthworms), 3) presence of other badger groups and 4) human pressure. Variables for each group were measured based on or taken directly from numeric maps. The first group, i.e. conditions favourable for digging setts, was geomorphological unit, soil texture, depth of soil layer, ground cohesivity and distance from surface waters. The second group, i.e. the availability of food, included type of soil, soil fertility, habitat moisture and potential vegetation. The third variable group, presence of other badger groups, included the distance to the neighbouring active main setts and the intensity of their use. Variables in the fourth group, linked with human pressure, were: a) protection status with four levels: strict reserves, partial reserves, forest of canopies congruent with habitat and forest of canopies incongruent with habitat, b) distance from open areas, c) distance from human settlements and d) distance from transportation lines.

Distances from open areas, built-up areas, transportation lines and surface waters were established according to topographic maps of scale 1: 10,000 and according to an updated orthophotomap. The Belarussian frontier zone was taken from the orthophotomap and topographic maps of scale 1:50,000. Thickets and young forests on abandoned fields adjacent to BPF oldgrowth were included in delimiting the boundary of open areas. Buildings permanently inhabited were considered as built-up areas. Public roads and railways in use were considered as transportation lines. Permanent streams marked on the topographic map of 1:10,000 were included under the category of surface waters. Potential natural vegetation was adopted after Kwiatkowski (1994). Habitat moisture was taken from forest numeric maps with the following ranks assigned to the following classes: dry = 1, moderately fresh = 2, strongly fresh = 3, moderately wet = 4, strongly wet = 5, moderately swamp = 6 and strongly swamp = 7. Soil fertility was defined according to the trophy index of forest soil (ITGL; Brozek & Zwydak 2003). Ground cohesivity was defined according to civil engineering measures (Kostrzewski 1988) based on texture of surface deposits assessed for a depth of 3 m taken from a geological numeric map. Availability of earthworms, the main food resource, was estimated for soil and vegetation types on the basis of results by Kowalczyk et al. (2003). Presence of other badger groups was assessed as: a) distance from the given point to the nearest active main setts and b) intensity of use of the nearest active main setts. Both of these measures were calculated as an average of 2nd, 3rd, 4th and 5th closest active main setts, which may be interpreted as a measure of the influence on neighbouring territories. The first nearest active main sett was omitted because it should represent the active main sett of a given territory, and it should not be attributed to neighbouring social groups. Additionally, in all presence points representing active main setts, this measure would always be equal to zero. The indices for the intensity of sett utilisation were based on sett surface size and relief. It is assumed that the time spent by badgers in a certain place is linked to their preference for the conditions of that place (Balestrieri et al. 2009).

In case of setts, it causes a growth of their size, both area and height of spoil heaps. Because of that, we used the product of multiplication of the surface of each sett and maximum height of its spoil heaps divided by 100 as a measure of utilisation intensity. In total, 18 habitat features were applied, including five based on field measurements and 13 based on data acquired from maps. Nine out of them were categorial and nine numerical (Tables 1 and 2).

Statistical analyses

The robust Random Forest (RF) method (Breiman 2001a) was used for modelling the distribution of setts, because it had no requirements of variables distribution, was resistant to multicollinearity, was well suited for the analysis of factor variables and had a good ability to detect non-linear relationships among variables, even if sharp changes in response curves occurred. The RF method was implemented in a Random Forest (version 2.6.7) package from the R statistical environment (R Development Core Team 2012). We consider it well suited for analysis of complex data from a not fully controlled natural system (Breiman 2001b, Franklin & Miller 2010). We are aware that high prevalence, i.e. high dominance of pseudo-absence point numbers over presence point numbers may decrease the predictive accuracy of the RF model by 0.1 and may increase the model specificity (Barbet-Massin et al. 2012). It may result in a more conservative prediction, i.e. the model will tend to predict fewer places where badger sett location is highly probable.

While using a robust modelling method, in order to facilitate clearer modelling results, we performed a pre-analysis by eliminating non-informative and potentially problematic variables with R package caret, version 5.15-023 (Kuhn 2008) with default settings. The initial data set, which we call a factor data set elsewhere in the article, was tested against the presence of Near Zero Variance (NZV) variables, multicollinearity and the presence of linear combination of traits (Linear Combos). The pre-analysis revealed that there were: a) no NZV variables, b) no possibility of investigating the level of correlation of all variables as a part of them had a factor form and c) no Linear Combo variables as there were no factors divided into single variables of binary type. Therefore, all habitat factors were used in the RF analyses.

In order to further eliminate non-relevant variables from the data set and to increase the precision of the models, factor variables from the initial data set were converted to indicators, i.e. to the binary form. We call this data set an indicator data set elsewhere in the article. Mean distance to and mean intensity of use of nearest active main setts were separated into single distances and intensities. The indicator data set was initially processed in R caret package. Among 65 variables, 18 NZV variables were found and eliminated (abbreviations are found in Tables 1 and 2). These included the following geomorphological variables: EOL_DUNE, DELUVIA and TER_PR_VAL; the following types of soils: ALB_PODZ, ENT_PODZ, GLPODZ, GRWA_GLE, MUD_SO, MUCKSOL and STAG_GLE; the soil texture SILTS and the following plant communities: BOG_ALD, FR_PIN, PW_FLO, WT_MX_S-O, SPR_BOG, BIR_BOG and PIN_BOG. There were eight pairs of variables with correlation values > 0.75, but we decided not to exclude them from the analysis mainly because of sequential correlation of distances to the nearest active main setts, i.e. 2nd with 3rd, 3rd with 4th and so on. Other variable pairs that correlated at a level > 0.75 were: PEATS vs HISTOS, MUCKSOL vs MOO_SO, HABIT_MOIS vs BI_AC_PLA and GROU_COHE vs LOAMS. We were aware that leaving those highly correlated variables in the analysis may weaken the importance measure of traits. No Linear Combos were found in this data set. Finally, there were 45 predictors left for analysis.

The importance of variables for both data sets was assessed in a two-stage procedure. The first stage was feature selection. Its goal was to eliminate all predictors that were not relevant for the model of sett habitat. Feature selection procedure from R Boruta package, version 1.6 (Kursa et al. 2010, Rudnicki & Kursa 2010) with default settings was applied for this sake. The procedure is based on the RF model. It uses Z-score parameters calculated as the quotient of Mean Accuracy Loss and Standard Deviation of Mean Accuracy Loss. Its value is high when the variable importance is high and invariant among all RF runs. Therefore, it is useful for elimination of variables that do not have a strong and constant impact on the distribution of analysed phenomenon. Comparative analysis of Z-score values of predictors and random variables generated by their reshuffling determines which predictors have Z-scores at random level enabling their elimination, and which of them have Z-scores significantly higher than random, enabling their classification as confirmed. In that way, only habitat predictors with nonrandom importance for sett distribution were selected for further analysis. The Boruta algorithm is an example of the 'all-relevant, even weak' feature selection strategy, and is well suited to the aim of seeking possible explanations for the distribution pattern of badger setts.

The importance of relevant variables was determined by the RF model implemented in R BIOMOD package, version 1.1-7.03 (Thuiller at al. 2009) with default settings, in the second (main) stage of the analysis. BIOMOD package provides: a) measurement of quality of the model with the Set-Aside method, for which we selected the Area Under ROC Curve (AUC) parameter, b) calculation of Variable Importance (called elsewhere in this article BIOMOD Variable Importance; BVI) as 1-correlation coefficient between the values of modelled phenomenon predicted by the model with the use of a given predictor and predicted after its randomisation, which is easy for biological interpretation and c) generation of Partial Response Plots for predictors that present changes in value of Y variable in relation to changes of value of a single X predictor, which is an implementation of the 'evaluation strip' procedure, described by Elith et al. (2005). The simple way to calculate BVI enables the calculation of average variable importance for different groups of predictors.


Out of 67 setts found, 48 were defined as main, seven as secondary and 12 as occasional setts. Main setts were 66-970 [m.sup.2] in size, usually with spoil heaps > 1m high, with 5.8 functioning entrances and 12 visible entrances on average. Secondary setts mostly did not exceed 100 [m.sup.2], their spoil heaps were not > 1 m and the average number of all entrances was 6.6. Occasional setts usually did not exceed 50 [m.sup.2], their spoil heaps were not > 1 m and the average number of all entrances was 4.2 (Table 3). Of the main setts, 33 were used continuously during our period of research while the remaining 15 were either permanently abandoned or used sporadically.

The soils at sett locations were of low cohesivity, strongly fresh moisture and mezo-eutrophic fertility. Mean distance [+ or -] SD of setts from small creeks was 0.9 [+ or -] 0.6 km, while it was 1.0 [+ or -] 0.8 km from main rivers, 1.3 [+ or -] 1.0 km from open areas, 1.8 [+ or -] 1.2 km from built-up areas and 1.5 [+ or -] 1.3 km from roads. The mean distance from four nearest active main setts was 4.3 [+ or -] 1.4 km and the mean intensity of use of four nearest setts was 4.4 [+ or -] 1.9 (see Table 1).

Setts were recorded mainly on ground moraine (61.2%) and ablation moraine (22.4%), on cambisols (49.3%) and podzols (26.9%), made of light loamy sand (32.8%) and loamy sand (22.4%), in the habitat of potential vegetation of thermophilous oak-hornbeam forest (37.3%) and typical oak-hornbeam forest (35.8%). Setts were found in forests of all forms of protection (38.8%) as well as managed forests (61.2 %). Within BNP, 20.9% of setts were recorded out of which 11.9% were within the area of strict protection and 9.0% were in the area of partial protection. In reserves outside of BNP, 17.9% of setts were found. Out of these setts, 61.2% were located in managed forests and 25.4% were situated under planted pine canopies not congruent with the habitat. Traces of human activity were recorded in 28.4% of the setts, out of which 19.4% were created in gravel pits, ditches, shell pits, storage pits for tree seedlings or potatoes and 9% were recolonised setts formerly dug out by poachers (see Table 2).

After 130 RF runs performed by the Boruta package, 14 of 15 analysed variables were found important. The DIST_TRAN variable proved to have the highest Z-score (25.6) and predominantly more so than all other variables. It was followed by a group of three other variables: DIST_SETS, DIST_BUILT and NATU_LEVE (Z-score from 12.3 to 15.2) and subsequently by a uniform group comprising the variables: DIST_OPEN, INT_SET_US, SOIL_TYPE, GEOM_UNIT, POTE_VEGE, HABIT_MOIS and DIST_CREE (Z-score from 8.6 to 10.7). A group of lesser importance included three variables: DIST_RIVE, GRND_COH and SOIL_FERT (Z-score from 5.8 to 7.2). Only one variable, SOIL_TEXT (Z-score = 2.9), remained with the status of ' tentative' (Fig. 2). Random variables generated and tested with the RF model of the Boruta package had Z-score values below all real predictors, as RAND_MIN, RAND_MAX and RAND_MEAN boxes show (see Fig. 2). Finally, the variable SOIL_TEXT was classified as important after an additional Tentative Rough Fix Procedure by Boruta package. Thus, no attributes were deemed unimportant.

The model obtained with the RF algorithm from the BIOMOD package may be regarded as good because the highest AUC value for five repetitions of the model reached higher than 0.87, and the mean AUC value reached the level of 0.86 (Table 4), which falls within the range of AUC 0.8-0.9 and thus can be considered as good model performance (Thuiler et al. 2009). The analysis of BVI of the sett presence vs pseudo-absence sites in the RF model indicates that distance from roads has the highest impact on badger distribution (BVI=0.725), followed by distance from human settlements (BVI = 0.295) and distance from the neighbouring active main setts (BVI = 0.206). Slightly lower values (BVI from 0.054 to 0.065) are connected with soil texture, distance from creeks and rivers, geomorphological landform, level of forest wilderness, habitat humidity and distance from open areas. These factors are followed by potential vegetation, intensity of neighbouring sett utilisation and soil type (BVI from 0.033 to 0.038). The lowest importance was connected with soil fertility and ground cohesivity (BVI from 0.006 to 0.012; Table 5).

Human pressure variables turned out to be the most important among variable groups (mean BVI= 0.283). They were followed by, with BVI lowered by half, neighbouring badger territories (mean BVI = 0.122), and then geological conditions impacting digging of dens (mean BVI=0.049). Soil features that determine the food supply of habitats occurred to be the least important factor (mean BVI = 0.037). This order of the factors' importance was a consistent result of both mean value of BVI as well as mean rank of BVI values of variables from analysed groups (Table 6).

Detailed distribution of probability of sett occurrence predicted by the RF model as a function of different values of particular predictors obtained in a form of BIOMOD Partial Response Plots indicated strong and evident avoidance of transportation lines by badgers. The best model predicted almost complete lack of setts within a distance < 2kmfromthe nearest transportation line, and sharp increase in the probability of setts at distances > 4 km. Predicted occurrence of setts across the distance to the built-up areas had a bimodal distribution, with slightly higher values predicted by the model in the close vicinity of built-up areas (< 0.5 km) and far from it (> 6 km). Predicted distribution of dens with regard to open areas was intermediate between the two previous ones. It had a weak peak at 0.25 km and a strong peak at 4 km from the forest boundary. Distribution of setts with reference to the neighbouring active main setts indicated the highest predicted probability of sett occurrence at a mean distance of 2 km from the first four neighbouring setts. Other factors, i.e. geomorphological landforms including ablation moraines and aeolian dunes, habitat moisture including moderately fresh habitats, soil type including cambisols and entic podzols, soil texture including loamy sands as well as potential vegetation including fresh pine-oak mixed forest and thermophilous oak-hornbeam forest appeared to have hardly any partial impact on the predicted probability of sett locations, obtained in the form of Partial Response Plots.

In the feature selection performed on the indicator data set, 30 variables proved to have a Z-score higher than random variables. DIST_TRAN had the highest Z-score value (25.4), by far higher than any other variable. It was followed by six other variables: HABIT_MOIS, DIST_SET_2, DIST_BUILT, DIST_OPEN, DIST_SET_3 and INT_SET_U_3 (Z-score from 11.2 to 14.9), and by a group of 23 variables with steadily diminishing values, including: INT_SET_U_2, DIST_SET_5, DIST_SET_4, DIST_CREE, ABLA_MOR, INT_SET_U_5, NATU_LEV_4, INT_SET_U_4, ARENOS, LLO_LS_SA, DIST_RIVE, NATU_LEV_1, NATU_LEV_2, CAMBIS TER_O-H, GRND_COHE, NATU_LEV_3, SOIL_FERT, GRND_MOR, LS_SA, EOL_PLAIN, FR_MX_P-O and UM_GLPODZ (Z-score from 3.1 to 9.4). Two variables: TP_O-H and LUVISOL (Z-score = 2.6) acquired the status of 'tentative'. Lack of even weak relevance was found for 13 variables: L_LOA_SA, WT_O-H, L_LOAMS, LOAMS, BI_AC_PLA, ALD_FLO, MOO_SO, WT_PIN, CHERNOZ, MUCKS, HISTOS, PEATS and LO_SA (Z-score from -1.4 to 2.2). Finally, the LUVISOL variable was categorised as important and the TP_O-H variable as non-important as result of application of an additional Tentative Rough Fix Procedure by the Boruta package based on the last 100 RF runs. Thus, 31 variables were considered important (Fig. 3).

The predictive performance of models built with the indicator data set can be considered as good because cross validation AUC values for the best of five repetitions of the model reached values > 0.9 and the mean value of cross validation reached 0.87 (Table 7), which was slightly higher than mean AUC values for the RF model for the factor data set (0.86).

This indicated a slight increase in the model quality as a result of the elimination of uninformative factor levels.

The analysis of BVI of the sett presence vs pseudo-absence sites in the RF model for the indicator data set underscored again the importance of the distance from roads (BVI = 0.688), then the distance from human settlements (BVI = 0.272), habitat moisture (BVI = 0.139) and distance from the second nearest neighbouring active main sett (BVI = 0.097). Other factors, with lower importance, did not create distinct groups, but rather their BVI values diminished gradually (BVI from 0.001 to 0.065; Table 8).

Among four variable groups, the human pressure variable group (Mean BVI = 0.147) appeared as the most important determinant of badger sett locations. It was followed by variables of neighbouring badger territories that had three times lower mean importance value (Mean BVI=0.043), and then, with just a slightly lower value, by geological conditions impacting digging of dens (Mean BVI = 0.034). Lastly, soil conditions determining food supply of the habitat (BVI = 0.004) had only a smal impact on location of badger setts. This order of variable groups resulted mainly from mean values of BVI that indicated high differences between groups (Table 9).

Detailed distribution of probability of sett occurrence, predicted by the RF model as a result of change of one particular predictor's value, obtained in a form of BIOMOD Partial Response Plots, again indicated strong and unambiguous avoidance of transportation lines by badgers (clear preference of areas distant by > 2.5 km). The less strong, but also clear avoidance of open areas (preference of areas distant > 4 km), and the bimodal distribution of setts in terms of distance from human settlements with two peaks of probability (one at 0.5 km and one at 6 km from human settlements) both resembled respective Partial Response Plots for the factor data set. The predicted probability values with regard to the distance to the 2nd, 3rd, 4th and 5th active main setts showed peaks in the distance of 1-2 km and a rise in the distance of ca 8-10 km. Other variables did not significantly affect sett location probability changes.


It can be assumed that the active main badger setts analysed in our study constituted nearly all existing setts of this type in BPF. Such conclusion can be drawn both because setts were easy to find and because ofthe asymptotic increase in their number in consecutive seasons when the research was carried out. The 33 utilised main setts found during our research were a higher number than the previously reported number of 23 utilised main setts in BPF (Kowalczyk et al. 2003). This means that the density of setts, so far considered as the highest, acquired by the use of telemetry of seven setts distributed over the area of 130 [km.sup.2] in the central part of BPF (Kowalczyk et al. 2003), is in fact average for the whole of BPF. Therefore, our results change the established idea about density of setts in the whole of BPF from 0.039 to 0.055 setts/[km.sup.2]. On the other hand, the number of abandoned setts, in particular those abandoned long ago, is probably recorded incompletely. This applies also to secondary and occasional setts as they were recorded by chance alongside the survey of the main setts.

Setts analysed in our study in terms of surface and number of entrances did not reach the sizes of setts described in England, where setts can have nearly 180 entrances and 50 chambers (Roper 1992), or in Holland, where setts can have > 100 entrances and 1 ha of surface (van Wijngaarden & van de Peppel 1964) or northern Italy with 29 entrances and 0.375 ha of surface (Remonti et al. 2006). They were, however, bigger than setts recorded in the Mediterranean scrubland areas of Spain with 2.6 entrances (Revilla et al. 2001b) or submountain areas with 1.8 entrances (Prigioni & Deflorian 2005). The setts in BPF were closest in size to setts from central Poland of 340 [m.sup.2] average surface and 11.4 entrances (Obidzinski & Glogowski 2005) or setts from the Czech Republic with an average area of 100 [m.sup.2] and 3.6 entrances (Bicik et al. 2000). Differences can result from diverse sizes of badger family groups living in distinct areas. These in turn probably depend on food resources of occupied habitats, which are partially dependent on the climate (Kowalczyk et al. 2003, 2004, 2006).

Selection of places for location of setts by badgers depends on several factors with terrain configuration, geomorphological land forms, habitat fertility, vegetation cover and man-made disturbances most often explored by researchers (e.g. Thornton 1988, Neal & Roper 1991, Bicik et al. 2000, Revila et al. 2001b, Mickevicus 2002, Macdonald et al. 2004, Prigioni & Deflorian 2005, Rosalino et al. 2005, Santos & Beier 2008, Kurek 2011, Myslajek et al. 2012a). Results published so far are difficult to compare due to a wide diversity of studied habitats and geographic localities. In our research, we attempted to include as many of these determinants as possible. We included four groups of factors: 1) habitat fertility that influences food accessibility, 2) geological conditions that influences possibility to dig dens, 3) presence of other badger setts that limits settlement by other family groups as well as 4) presence of anthropogenic land-use forms. We did not include the topography or present vegetation, because the topography of our study area was only slightly wavy and the vegetation of setts that had been actively used for > 50 years (Forest Administration, pers. comm.) may have changed since their creation.

Our results revealed very high importance of anthropogenic land-use forms for sett locations. Such factors as transportation lines, built-up areas and open areas turned out to be the most significant variables impacting localisation of badger dens in BPF. Avoidance of transportation lines was previously observed elsewhere (e.g. Skinner et al. 1991, Jepsen et al. 2005, Prigioni & Deflorian 2005, Myslajek et al. 2012a). The probable general mechanism of road impact is due to traffic noise, human penetration and increased mortality (Bennett 1991). In BPF, the lack of setts in the neighbourhood of roads (railways are scarce there) may not only be a result of vehicle noise, but also due to the more intensive penetration into natural areas by berry, herb and mushroom pickers, tourists, hunters and forest workers. Man-induced mortality is a limitation in analyses of animal distribution as it is difficult to obtain the necessary information on which such parameter could be based (Thornton 1988). Direct measures of road mortality are the best data sources to assess its real impact.

Badger setts are sometimes recorded in the vicinity of built-up areas, especially in the suburbs (e.g. Wright et al. 2000, Kaneko et al. 2006, Davison et al. 2009). However, such locations result mostly from lack of woods and mid-field wood lots. Nonetheless, in general, badgers mostly avoid the vicinity of built-up areas (Hammond et al. 2001, Jepsen et al. 2005, Prigioni & Deflorian 2005, Kurek 2011, Myslajek et al. 2012a). More precisely, they shun the presence of humans as settling of setts was recorded under derelict buildings (Revila et al. 2001a, Pavlacik et al 2004, Myslajek et al. 2012a, A. Obidzinski, pers. obs. from Kampinoska Forest, Central Poland). Our observation of avoidance of the vicinity of built-up areas is, therefore, congruent with the majority of observations of other researchers.

Distribution of setts with reference to open areas looks different. Badger setts have usually been recorded at the outskirts of forests (e.g. Biciket al. 2000, Good et al. 2001, Obidzinski & Glogowski 2005, van Apeldoorn et al. 2006, Myslajek et al. 2012b) and sometimes even a majority of setts are observed at the outskirts of forests (e.g. Neal 1972, Stubbe 1965, Suminski 1989, Do Linh San et al. 2007). It is generally explained by close access to meadows and pastures with high earthworm abundance, the common and preferred food of the badger (e.g. Kruuk 1978, Brown 1983, Kowalczyk et al. 2003, Myslajek et al. 2012b). However, in other areas, a smaller proportion of setts is recorded at the outskirts of forests than inside the forest (Virgos& Casanovas 1999, Revila et al. 2001b, Mickevicus 2002, Bicik et al. 2000, Prigioni & Deflorian 2005, Kaneko et al. 2006). This aspect of distribution is usually explained by several kinds of man-made disturbances present close to the forest edge.

The high proportion of setts recorded inside BPF (away from open areas) may be explained by two causes: 1) higher anthropic pressure at the outskirts than inside of the forest due to more roads and human settlements outside than inside the forest, and 2) a small area of meadows in the neighbourhood of forests and a large area of fields and fallow fields that are not so abundant with earthworms (Kruuk 1978, Ryl 1984). On the other hand, limiting habitat properties such as swamping or poor soils are not present in the outskirts more often than inside BPF (Table 10).

It is interesting to note that the significance of forest naturalness was low compared to demonstrated avoidance of anthropogenic forms of terrain use for sett distribution in BPF. The bimodal character of badger sett distribution, located either in most natural or in most transformed forests, can show badger's tolerance to anthropogenic transformations of forests. This finding is supported by the settling of setts in small mid-field woods or hedgroves in agricultural areas elsewhere (e.g. Feore & Montgomery 1999, Baliestieri et al. 2009, Rosalino et al. 2004, Remonti et al. 2006) or in suburbs (e.g. Wright et al. 2000, Kaneko et al. 2006, Davison et al. 2009). A different situation was reported by Kurek (2011) in central Poland, where badgers preferred to settle their setts in protected parts of forests; however, it was due to a more limited human presence and large areas of pine monocultures there. Regardless of the above, the lack of importance of the level of forest transformation on sett location that we observed could have been partially caused indirectly by our forest naturalness classification, based on the forms of protection used in BPF (strict protection, partial protection and managed forests). It is possible that the importance of forest naturalness would be higher if based on field observation of biological features of the forest.

Distances between badger setts observed in diverse environments differ significantly from 0.3 to 0.5 km in England (Kruuk & Parish 1982, Clements, et al. 1988) and from 3.75 to 4.1 km in Poland (Bartmaniska & Nadolska 2003, Kowalczyk et al. 2003). They depend on a number of factors, mostly abundance of food supply, climate, size of family groups and predator pressure (Kruuk & Parish 1982, Kowalczyk et al. 2003, Sidorovich et al. 2011). The distance found most often between setts was 2 km, and it was two times smaller than the mean distance of4.28 km. Our results indicated the existence of a certain level of clumping of setts in the investigated area. There may be many possible explanations for this pattern. First of all, this can be a result of a patchy distribution of most suitable habitats. In BPF, the areas most suitable for digging setts are moraine areas and the least suitable are large swampy areas, and both habitat types are distributed in patches of a few square kilometres. In big suitable patches, many smaller territories may be placed and beside them there are larger territories containing less suitable landscape patches. This is supported by conclusions from another study of the badger population in BPF (Kowalczyk et al. 2004). There may be other causes of the low distance between setts and higher density of setts relating to the average distance between setts. In high density badger populations, some males and females migrate between groups and almost half of the young badgers have fathers from neighbouring territories (Dugdale et al. 2007). That may be a reason for neighbouring groups not to compete aggressively or outcompete the other group from suitable habitat patches. It is possible that the correlation between the genetic and physical distances between badger groups is high. While it was not tested in the badger population in BPF, as it was in other mammal species (Gliwicz & Dabrowski 2008), it could be an interesting additional explanation of the lower than the mean, most probable distance to the nearest active main sett.

Geological conditions are, beside food supply, the most frequently mentioned factor conditioning sett settling. Ground diggability (e.g. Dunwell & Killingley 1969, Neal 1972, Thornton 1988, Revila et al. 2001b, Macdonald et al 2004, Rosalino et al. 2005), exposition and slope inclination (Neal 1972, Macdonald et al 2004, Prigioni & Deflorian 2005) or moisture (Hammond et al. 2001, Mickevicus 2002, Kowalczyk et al. 2003, Macdonald et al. 2004, Remonti et al. 2006) have been indicated as significant most frequently to date. In our case, moderately fresh habitat moisture and ablation moraines were the most significant habitat conditions. Distance from creeks (0.88 km) and distance from rivers (1.03 km) were other geomorphologic factors observed as significant. Preference of fresh and avoidance of wet habitats when settling setts results from a high level of ground waters that can flood underground corridors. This phenomenon has been recognised widely by all researchers who have analysed setts settled in habitats with varied moisture (e.g. Hammond et al. 2001, Mickevicus 2002, Kowalczyk et al. 2003, Macdonald et al. 2004, Remonti et al. 2006). Observed distance from surface waters seems to be linked with a sufficiently deep level of ground waters allowing settling setts. Ground cohesiveness had minimum impact on localisation of setts in our case (> 30 times weaker than habitat moisture). It could have been caused by the fact that the entire range of ground cohesiveness observed in BPF, from loose sands to loams and clays, enables badgers to dig setts. Clays are less inhabited here, but they are not avoided. Perhaps a larger sample size might allow more exact correlation with respect to this habitat aspect.

Habitat fertility is also very often indicated as a factor conditioning sett distribution (e.g. Neal & Roper 1991, Bicik et al. 2000, Mickevicus 2002), reportedly connected with the abundance of earthworms (e.g. Kruuk 1978, Brown 1983, Da Silva et al. 1993, Kowalczyk et al. 2003,2006). However, it had a rather insignificant impact on sett distribution in BPF. An average forest habitat patch in BPF is ca 20 ha (calculated on data from Kwiatkowski 1994) while an average badger family group territory measured as a 95% minimum convex polygon is 920 ha in BPF (Kowalczyk et al. 2003). This implies that one family group territory can include over 40 forest habitat patches. Therefore, setts could have been settled in places of any fertility. Habitats of high food supply were always available in badger family territories because fertile habitats prevail all over BPF. Hence, the observed weak correlation of sett distribution with habitat fertility does not contradict earlier results (Kowalczyk et al. 2003) indicating a strong correlation of badger territory distribution with fertility of habitats in BPF. Nonetheless, differences in habitat fertility can play a role in sett distribution in areas of large-scale habitat diversity (e.g. Neal & Roper 1991, Bicik et al. 2000, Mickevicus 2002).

Therefore, the hierarchy of factors impacting the distribution of setts that we observed is an interesting and new result. Only a few studies have defined this hierarchy so far. They indicated the following sequences of significance: geological structures suitable for digging setts > high quality feeding patches (Rosalino et al. 2005), slope angle = boulder size > anthropogenic disturbances (Prigioni & Deflorian 2005), deciduous forests > cork oak Quercus suber plantations > pine plantations (Santos & Beier 2008). All of these works examined a wider scope of variables but only the factors mentioned were significant.

All factors impacting sett distribution taken into account in our study occurred to be significant; however, their hierarchic sequence was contrary to the one we had expected. Human pressure occurred as the most significant, followed by presence of other actively used setts, then, much less, geological conditions and lastly, habitat fertility. Even if the distinction between geological and soil factors may be considered somewhat contrived, the anthropogenic factor and space structure of badger populations will still occur as the primary habitat aspect, in comparison to other properties.

It is important to note, however, that while our model is a robust one, it also has some statistical limitations. Our data sets have very low prevalence, i.e. low number of presence points. While our models qualified as good in terms of predictive power (AUC > 0.8), it has recently been revealed that for best results, RF should have the same number of presence and pseudo-absence points (Barbet-Massin et al. 2012). In our cases, due to the high variability and low size of habitat patches, we decided to stay with the high number of pseudo-absence points. That probably lowered the predictive power, but drastically increased the accordance of results in subsequent model runs. Barbet-Massin et al. (2012) also warned that RF models with low prevalence would have higher specificity, i.e. in our case, they should have a weaker tendency to classify a given point as suitable for settling a sett. In other words, there may be more presence points than predicted, but all predicted presences are very probable to be true ones. However, relative importance of habitat features should not be affected by these methodological aspects of RF models. Another statistical limitation is an a priori assumption that absence points represent really unsuitable habitat forms. In a case of unsaturated habitat, it is difficult to determine whether the unoccupied areas are really unsuitable or whether the population is just not saturated (Schadt et al. 2002). This is another reason why we stayed with a more conservative sampling approach.

Demonstrated hierarchy of factors impacting badger sett distribution in BPF may result from: 1) the species' anthropophobia to presence of various man-made disturbances, 2) the species' territorialism, 3) diversification of the forest into fresh habitats (52%) that enable setts digging and moist or wet habitats (48%) not suitable for this purpose, as well as 4) prevalence of fertile habitats (48 %) and medium fertile habitats (37%) over poor ones (15%). However, badgers' anthropophobia observed as the prime factor still remains unanswered. Levels of anthropophobia are diverse across the whole species range and they seem to depend on the level of transformation of the environment inhabited by a particular population. It has been observed that badgers avoid settling their setts close to human land-form use in areas with a high proportion of forest cover (Bicik et al. 2000, Mickevicus 2002, Kowalczyk et al. 2003, Prigioni & Deflorian 2005, Myslajek et al. 2012a). On the other hand, badgers demonstrate distinct synanthropisation in agricultural areas or suburbs (Wright et al. 2000, Rosalino et al. 2004, Kaneko et al. 2006, Davison et al. 2009). A higher density of setts inside BPF can more likely result from higher pressure of poachers at the outskirts of the forest than from road kills as the traffic around BPF is low. Also traces of destruction of setts are present more often at the outskirts than inside of the forest (A. Obidzinski, pers. obs.). Smaller numbers of badgers at the outskirts of the forest caused by the pressure of poachers and road kills was observed also in Don ana National Park, southwestern Spain (Revilla et al. 2001a) and by hunters in Beskidy Mountains, southern Poland (Myslajek 2012a,b). It can, therefore, be assumed that the lack of or smaller numbers of badgers at the outskirts of BPF is caused rather by their physical extermination than aetiological anthropophobia.

It seems that there is a sort of balance of costs and benefits for badgers inhabiting the outskirts of forests. They have easier access to meadows abundant in earthworms, but a higher risk of mortality at the outskirts of forests. Traditional use of forests, including poaching, is more common in regions less developed in terms of economy and farming, such as BPF (Radecki 1996), than in developed regions (e.g. Wilfred & MacColl 2010). Therefore, inhabiting the outskirts of the forests is an advantage for badgers living in regions with well developed agriculture. Inhabiting the forests outskirts is dangerous or even impossible in regions of extensive agriculture (Revilla et al. 2001a, Wilfred & MacColl 2010). In some cases, badgers have developed a survival strategy where they live inside the forest and feed at the outskirts (Myslajek 2012a).

Fortunately, poaching gradually disappears in BPF since the last poachers gradually pass away and new ones are not recruited (Forests Administration, pers. comm.). Only old traces of destruction of setts were observed during our research. Moreover, we recorded the creation of new setts and reinhabiting of formerly abandoned ones. Anti-rabies vaccines distributed in the forest may have an additional impact on the growth of the badger population. All this seems to have an influence on the current recolonisation of badger setts at the outskirts of the forest (A. Obidzinski, pers. obs.). The regeneration of badger populations is recorded all over Europe (e.g. Griffiths & Thomas 1997, Macdonald et al. 2004, van Apeldoorn et al. 2006). It happens in Poland as well, e.g. the number of badgers increased from 386 to 1,361 individuals and the number of hunting harvest increased from 5 to 119 individuals/year during 2000-2011 in the Bialystok Region where BPF is situated (Kamieniarz & Panek 2008, Research Station of Polish Hunting Association, unpubl. data).

Recognition of habitat properties shaping badger sett distribution in BPF may facilitate protection of this species through landscape management. Our results show that forest refuges for badgers should ideally represent mesotrophic deciduous canopies on cambisols, consisting of loamy sands, on moderately fresh habitats. Ecological corridors and road passes should be aimed at linking such places. Due to low importance of naturalness level, our results should be applicable to less natural areas with similar spatial extent of suitable habitats and the similar mean distance to roads and open areas as well as local badger population density. Our results may also be helpful in predicting recolonisation of the badger in areas of BPF that are still unoccupied by this species but which seem suitable for settling. The same applies to post-agricultural areas being overgrown with thickets and pole-timber woods adjacent to BPF as well as in other forest complexes. The obtained results may also be helpful for understanding the distribution of other taxa for which the badger plays the role of engineer species (sensu Jones et al. 1994), e.g. the raccoon dog, partially the red fox and occasionally the wolf (Kowalczyk et al. 2008) as well as a number of rodent species (Neal & Roper 1991) offering them shelter in abandoned burrows, and even several plant species usually considered as nonforest species may grow in the soil conditions offered by the digging activity of the badger (Obidzinski & Glogowski 2005, Obidzinski & Kieltyk 2006).


* Random Forest models of good predictive power enabled us to calculate and rank the habitat feature importance (BIV) and possible impact for habitat features and their groups.

* Contrary to our expectations, human impact features with the distance to roads in the first place were the most important factor determining badger sett distribution, then the presence of neighbouring badger territories, then digging suitability conditions and the habitat fertility was the least important feature.

* Badgers in Bialowieza Primeval Forest prefer cambisols or entic podzols of fresh moisture, made of loamy sands on ablation moraines or eolic dunes, with potential vegetation of thermophilous oak-hornbeam forest or fresh pine-oak mixed forest in the distance of 2 km from the nearest active main setts, and they avoid vicinity of roads, open areas or built-up areas for settling their setts.

* Our results emphasise the role of large, intact, not necessarily highly natural, forest preserves as the refuge areas for badger populations, when the mortality on the more open areas is higher than the benefits of easy food availability.

DOI: 10.2981/11-074

Acknowledgements--we would like to thank the State Forests and the Bialowieza National Park Administration for information about localisation of setts and access to numeric maps. We also thank Dr. Rafal Kowalczyk from the Mammal Research Institute in Bialowieza for data on localisation of some setts and other valuable information, Professor Jacek Goszczynski[dagger] from the Warsaw University of Life Sciences for his valuable comments on the first draft of the paper, as well as three anonymous reviewers for their detailed and valuable remarks. The research was partially funded by the WULS grant No 504 030 800 18.


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Artur Obidzinski, Department of Forest Botany, Warsaw University of Life Sciences, 159 Nowoursynowska Street, 02-776 Warsaw, Poland--e-mail:

Piotr Pabjanek, Department of Geoinformatics and Remote Sensing, University of Warsaw, 30 Krakowskie Przedmiescie Street, 00-927 Warsaw, Poland--e-mail:

Piotr Medrzycki, Laboratory of Applied Plant Ecology, University of Ecology and Management in Warsaw, 14 Wawelska Street, 02-061 Warsaw, Poland--e-mail:

Corresponding author: Artur Obidzinski

Received 9 August 2011, accepted 2 October 2012

Associate Editor: Klaus Hacklander

Table 1. Abbreviations and mean values ([+ or -] SD) of numeric
variables in sett pseudo-absence and presence points in
Bialowieza Primeval Forest, Poland. The variables of
geomorphological unit, soil texture, soil type, potential
vegetation and naturalness level are presented in Table 2.

                                                     of setts
Variable                        Abbreviation        (N = 7630)

Habitat moisture                 HABIT_MOIS     3.72 [+ or -] 1.74

Ground cohesiveness              GROU_COHE      7.18 [+ or -] 11.55

Soil fertility                   SOIL_FERT     33.55 [+ or -] 1.29

Distance from creeks (km)        DIST_CREE     0.796 [+ or -] 0.614

Distance from rivers (km)        DIST_RIVE     1.241 [+ or -] 0.873

Distance from four nearest       DIST_SETS      4.63 [+ or -] 1.44
setts (km)

Mean intensity of use of         INT_SET_US     4.12 [+ or -] 1.89
four nearest setts

Distance from open areas (km)    DIST_OPEN     1.200 [+ or -] 0.871

Distance from built-up           DIST_BUILT    1.201 [+ or -] 1.245
areas (km)

Distance from transportation     DIST_TRAN     0.709 [+ or -] 0.651
lines (km)

Geomorphological unit            GEOM_UNIT

Soil texture                     SOIL_TEXT

Soil type                        SOIL_TYPE

Potential vegetation             POTE_VEGE

Naturalness level                NATU_LEVE

                                      of sett
Variable                              (N = 67)

Habitat moisture                 2.34 [+ or -] 0.64

Ground cohesiveness              8.44 [+ or -] 9.96

Soil fertility                   34.7 [+ or -] 0.00

Distance from creeks (km)       0.879 [+ or -] 0.576

Distance from rivers (km)        1.03 [+ or -] 0.812

Distance from four nearest       4.28 [+ or -] 1.40
setts (km)

Mean intensity of use of         4.38 [+ or -] 1.89
four nearest setts

Distance from open areas (km)   1.340 [+ or -] 0.959

Distance from built-up           1.76 [+ or -] 1.17
areas (km)

Distance from transportation    1.499 [+ or -] 1.293
lines (km)

Geomorphological unit

Soil texture

Soil type

Potential vegetation

Naturalness level

Table 2. Abbreviations and frequencies of sett pseudo-absence and
presence points (where categories of indicator variables' value
is '1')and distances and use intensity indices of the n-th
nearest active main sett. Variables that were later excluded are
indicated by *.

                                                   Absence of setts
Variable                           Abbreviation       (N = 7630)

Geomorphological unit

Deluvia *                          DELUVIA                 75

Ablation moraine                   ABLA_MOR               526

Ground moraine                     GRND_MOR              4275

Plains of biogenic accumulation    BI_AC_PLA             1791

Plains of eolic accumulation       EOL_PLAIN              779

Proglacial valley terrace *        TER_PR_VAL             191

Eolic dunes *                      EOL_DUNE                60

Soil texture

Loams, clays                       LOAMS                  684

Light loams                        L_LOAMS                421

Mucks                              MUCKS                 1050

Loamy sand                         LO_SA                  603

Loose sand                         LS_SA                  874

Light loamy loose sand             LLO_LS_SA             1101

Light loamy sand                   L_LOA_SA              2136

Silts *                            SILTS                   88

Peats                              PEATS                  740

Soil type

Albic podzols *                    ALB_PODZ                 3

Entic podzols *                    ENT_PODZ               211

Cambisols                          CAMBIS                1988

Chernozems                         CHERNOZ                392

Gleyic podzols *                   GLPODZ                  50

Umbri-gleyic podzols               UM_GLPODZ              951

Groundwater gleys *                GRWA_GLE               371

Mud soils *                        MUD_SO                   2

Moorsh soils                       MOO_SO                 843

Muck soils *                       MUCKSOL                 91

Stagnogleys *                      STAG_GLE               309

Luvisols                           LUVISOL                827

Arenosols                          ARENOS                1021

Histosols                          HISTOS                 638

Potential vegetation

Bog alder forest *                 BOG_ALD                160

Alder-flood forests                ALD_FLO                900

Termophilous oak-hornbeam forest   TER_O-H               1639

Wet pine forest                    WT_PIN                 452

Fresh pine forest *                FR_PIN_                141

Fresh mixed pine-oak forest        FR_MX_P-O              513

Wet mixed spruce-oak forest *      WT_MX_S-O              324

Poplar-willow flood forest *       P-W_FLO                 18

Spruce bog forest *                SPR_BOG                127

Birch bog forest *                 BIR_BOG                116

Typical oak-hormbeam forest        TP_O-H                1679

Wet oak-hormbeam forest            WT_O-H                1541

Pine bog forest *                  PIN_BOG                 87

Natural level

Strict reserves                    NATU_LEV_1            1195

Partial reserves                   NATU_LEV_2            3517

Canopies congruent with soil       NATU_LEV_3            2218

Canopies incongruent with soil     NATU_LEV_4             628

Distance from nearest setts (km)

Distance from nearest 2nd sett     DIST_SET_2     3.61 [+ or -] 1.35

Distance from nearest 3rd sett     DIST_SET_3     4.73 [+ or -] 1.60

Distance from nearest 4th sett     DIST_SET_4     5.75 [+ or -] 1.73

Distance from nearest 5th sett     DIST_SET_5     6.58 [+ or -] 1.81

Intensity of nearest sett use

Intensity of use of nearest        INT_SET_U_2    1.01 [+ or -] 1.49
2nd set

Intensity of use of nearest        INT_SET_U_3    0.86 [+ or -] 1.33
3rd sett

Intensity of use of nearest        INT_SET_U_4    1.25 [+ or -] 1.66
4th sett

Intensity of use of nearest        INT_SET_U_5    0.98 [+ or -] 1.29
5th sett

                                   Presence of setts
Variable                                (N = 67)

Geomorphological unit

Deluvia *                                  0

Ablation moraine                          15

Ground moraine                            41

Plains of biogenic accumulation            0

Plains of eolic accumulation               7

Proglacial valley terrace *                0

Eolic dunes *                              4

Soil texture

Loams, clays                               9

Light loams                                0

Mucks                                      0

Loamy sand                                15

Loose sand                                 8

Light loamy loose sand                    13

Light loamy sand                          22

Silts *                                    0

Peats                                      0

Soil type

Albic podzols *                            0

Entic podzols *                           18

Cambisols                                 33

Chernozems                                 0

Gleyic podzols *                           0

Umbri-gleyic podzols                       6

Groundwater gleys *                        0

Mud soils *                                0

Moorsh soils                               0

Muck soils *                               0

Stagnogleys *                              0

Luvisols                                  10

Arenosols                                  0

Histosols                                  0

Potential vegetation

Bog alder forest *                         0

Alder-flood forests                        0

Termophilous oak-hornbeam forest          25

Wet pine forest                            2

Fresh pine forest *                        5

Fresh mixed pine-oak forest                6

Wet mixed spruce-oak forest *              0

Poplar-willow flood forest *               0

Spruce bog forest *                        0

Birch bog forest *                         0

Typical oak-hormbeam forest               24

Wet oak-hormbeam forest                    5

Pine bog forest *                          0

Natural level

Strict reserves                           17

Partial reserves                          24

Canopies congruent with soil              18

Canopies incongruent with soil             8

Distance from nearest setts (km)

Distance from nearest 2nd sett     3.20 [+ or -] 1.39

Distance from nearest 3rd sett     4.25 [+ or -] 1.46

Distance from nearest 4th sett     5.39 [+ or -] 1.68

Distance from nearest 5th sett     6.25 [+ or -] 1.74

Intensity of nearest sett use

Intensity of use of nearest        1.71 [+ or -] 2.02
2nd set

Intensity of use of nearest        1.05 [+ or -] 1.51
3rd sett

Intensity of use of nearest        1.50 [+ or -] 1.76
4th sett

Intensity of use of nearest        0.54 [+ or -] 0.10
5th sett

Table 3. Size and intensity of use of badger setts in the
Bialowieza Primeval Forest, Poland.

Measure of sett use                           Sett type

                                             Main (N = 48)

                                      Mean [+ or -] SD       Range

Area ([m.sup.2])                    313.7 [+ or -] 239.8   66.0-970.0
Denivelation (m)                      1.1 [+ or -] 0.4     0.25-1.75
Area x denivelation x [10.sup.-2]     3.8 [+ or -] 3.4      0.5-15.0
All entrances                        12.0 [+ or -] 6.8        4-28
Entrances in use                      5.8 [+ or -] 4.0        0-18

Measure of sett use                           Sett type

                                          Secondary (N = 7)

                                    Mean [+ or -] SD       Range

Area ([m.sup.2])                    82.7 [+ or -] 52.4   40.0-180.0
Denivelation (m)                     0.9 [+ or -] 0.2     0.5-1.0
Area x denivelation x [10.sup.-2]    0.7 [+ or -] 0.6     0.25-2.0
All entrances                        6.6 [+ or -] 2.2       4-9
Entrances in use                     5.4 [+ or -] 1.7       3-7

Measure of sett use                           Sett type

                                         Temporary (N = 12)

                                    Mean [+ or -] SD       Range

Area ([m.sup.2])                    37.5 [+ or -] 23.6   10.0-100.0
Denivelation (m)                     0.7 [+ or -] 0.2     0.5-1.0
Area x denivelation x [10.sup.-2]    0.2 [+ or -] 0.2     0.1-0.6
All entrances                        4.2 [+ or -] 2.2       2-8
Entrances in use                     2.9 [+ or -] 1.3       1-6

Table. 4. Predictive performance of Random Forestmodel for the
factor data set run in Rpackage BIOMOD. Area Under ROC Curve
(AUC) values are cross validation values in lowest row. An
overall model performance in the last column and row is the
arithmetic mean of AUC values for all five repetitions (1-5).

Performance measure                            Repetition

                                         1          2          3

Total score                             0.97       0.98       0.98
Cut-off                                13.69      12.84      12.61
Sensitivity (true positive fraction)   92.54      92.54      92.54
Specificity (true negative fraction)   93.24      92.22      92.67
Cross validation                        0.812      0.862      0.871

Performance measure                            Repetition

                                         4          5         Full

Total score                             0.99       0.98        1
Cut-off                                13.68      19.54      600.7
Sensitivity (true positive fraction)   92.54      94.03      100
Specificity (true negative fraction)   92.98      94.99      100
Cross validation                        0.877      0.867       0.85

Table 5. BIOMOD Variable Importance (BVI) for factor and numeric
variables from different variable groups in factor data set.

Variable abbrieviation    BVI    Rank of BVI     Variable group

DIST_TRAN                0.725       15        Anthropic pressure

DIST_BUILT               0.295       14        Anthropic pressure

DIST_SETS                0.206       13        Neighbouring badger

SOIL_TEXT                0.065       12        Feeding conditions

DIST_RIVE                0.064       11        Denning conditions

DIST_CREE                0.061       10        Denning conditions

GEOM_UNIT                0.057        9        Denning conditions

NATU_LEVE                0.057        8        Anthropic pressure

HABIT_MOIS               0.055        7        Denning conditions

DIST_OPEN                0.054        6        Anthropic pressure

POTE_VEGE                0.038        5        Feeding conditions

INT_SET_US               0.037        4        Neighbouring badger

SOIL_TYPE                0.033        3        Feeding conditions

SOIL_FERT                0.012        2        Feeding conditions

GRND_COH                 0.006        1        Denning conditions

Mean                     0.118        7        -

Table. 6. Mean BIOMOD Variable Importance (BVI) values for different
variable groups in factor data set.

Variable group        Mean BVI   Mean rank of BVI   Variable group
                                                    importance rank

Anthropic pressure     0.283           10.8                 I

Neighbouring badger    0.122            8.5                II

Denning conditions     0.049            7.6               III

Feeding conditions     0.037            5.5                IV

Table 7. Predictive performance of Random Forest model for the
indicator data set run in R package BIOMOD. Area Under ROC Curve
(AUC) values are cross validation values in lowest row. An
overall model performance in the last column and row is the
arithmetic mean of AUC values for all five repetitions (1-5).

Performance measure                         Repetition

                                       1        2        3

Total score                             0.99     0.98     0.99
Cut-off                                19.12     9.66    15.38
Sensitivity (true positive fraction)   95.52    92.54    94.03
Specificity (true negative fraction)   95.39    91.43    94.26
Cross validation                        0.923    0.825    0.890

Performance measure                         Repetition

                                       4        5        Full

Total score                             0.98     0.97      1
Cut-off                                13.78    11.58    599.88
Sensitivity (true positive fraction)   94.03    92.54    100
Specificity (true negative fraction)   94.33    92.28    100
Cross validation                        0.861    0.833     0.867

Table 8. BIOMOD Variable Importance (BVI) for numeric and indicator
variables from different groups in the indicator data set.

Variable       BVI     Rank of BVI   Variable group

DIST_TRAN      0.688       31        Anthropic pressure
DIST_BUILT     0.272       30        Anthropic pressure
HABIT_MOIS     0.139       29        Denning conditions
DIST_SET_2     0.097       28        Neighbouring badger territories
DIST_SET_5     0.065       27        Neighbouring badger territories
DIST_SET_4     0.052       26        Neighbouring badger territories
DIST_OPEN      0.047       25        Anthropic pressure
ABLA_MOR       0.038       24        Denning conditions
DIST_SET_3     0.036       23        Neighbouring badger territories
INT_SET_U_2    0.030       22        Neighbouring badger territories
INT_SET_U_3    0.030       21        Neighbouring badger territories
DIST_CREE      0.027       20        Denning conditions
DIST_RIVE      0.024       19        Denning conditions
INT_SET_U_5    0.019       18        Neighbouring badger territories
INT_SET_U_4    0.012       17        Neighbouring badger territories
NATU_LEV_2     0.011       16        Anthropic pressure
SOIL_FERT      0.009       15        Feeding conditions
ARENOS         0.009       14        Feeding conditions
NATU_LEV_1     0.007       13        Anthropic pressure
NATU_LEV_4     0.006       12        Anthropic pressure
TER_O-H        0.005       11        Feeding conditions
LLO_LS_SA      0.005       10        Feeding conditions
GRND_COHE      0.004        9        Denning conditions
FR_MX_P-O      0.003        8        Feeding conditions
CAMBIS         0.002        7        Feeding conditions
GRND_MOR       0.002        6        Denning conditions
NATU_LEV_3     0.001        5        Anthropic pressure
LS_SA          0.001        4        Feeding conditions
UM_GLPODZ      0.001        3        Feeding conditions
EOL_PLAIN      0.001        2        Denning conditions
LUVISOL        0.001        1        Feeding conditions
Mean           0.053       16        -

Table 9. Mean BIOMOD Variable Importance (BVI) values for different
variable groups in the indicator data set.

Variable group                    Mean    Mean rank   Variable group
                                  BVI     of BVI      importance rank

Anthropic pressure                0.147     18.9             I
Neighbouring badger territories   0.043     22.8            II
Denning conditions                0.034     15.6            III
Feeding conditions                0.004      8.1            IV

Table 10. Avoided habitat properties in relation to distances
from open areas and built-up areas by badgers in the Bialowieza
Primeval Forest, Poland. Data did not match the assumptions for
the [chi square]-test for swamps and oligotrophy at a distance
of 250 m from built-up areas.

Avoided habitat   In distance of   From             Area observed
properties                                          ([km.sup.2])

Swamps            500 m            Open areas       36
                  250 m                             20
                  500 m            Built-up areas   6

Oligotrophy       500 m            Open areas       24
                  250 m                             11
                  500 m            Built-up areas   5

Avoided habitat   In distance of   Area expected   [chi square]
properties                         ([km.sup.2])

Swamps            500 m            29              2.21
                  250 m            15              2.46
                  500 m            11              2.38

Oligotrophy       500 m            24              1 59
                  250 m            12              0.95
                  500 m            9               4.49

Avoided habitat   In distance of   df   P-value

Swamps            500 m            1    0.1373
                  250 m            1    0.1168
                  500 m            1    0.1230

Oligotrophy       500 m            2    0.4522
                  250 m            2    0.6233
                  500 m            2    0.1060
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Title Annotation:Original article
Author:Obidzinski, Artur; Pabjanek, Piotr; Mcdrzycki, Piotr
Publication:Wildlife Biology
Article Type:Report
Geographic Code:4EXPO
Date:Mar 1, 2013
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