Habitat selection by an Alpine ungulate: the significance of foragecharacteristics varies with scale and season

Barbara Zweifel-Schielly, Michael Kreuzer, Klaus C. Ewald and Werner Suter

B. Zweifel-Schielly and W. Suter (werner.suter@wsl.ch), Swiss Federal Research Inst. WSL, Zuercherstrasse 111, CH-8903 Birmensdorf,Switzerland. (Present address of B. Z.-S.: Zettess Energy and Environment, Zaunplatz 4, CH-8750 Glarus, Switzerland.) � M. Kreuzer, ETHZurich, Inst. of Animal Science, Animal Nutrition, Universitatstrasse 2, CH-8092 Zurich, Switzerland. � K. C. Ewald, ETH Zurich, Natureand Landscape Conservation, Universitatsstrasse 22, CH-8092 Zurich, Switzerland, (present address: Grand Palais, CH-6440 Brunnen,Switzerland).

Habitat selection in ungulates should ensure access to abundant forage of sufficiently high quality. Species living inrugged mountain areas have to face nutritional bottlenecks regularly and should show particularly sophisticated habitatselection behaviour. However, patterns and mechanisms of such adaptations remain little studied. We analysed habitatselection and its seasonal variability of 10 GPS-collared red deer Cervus elaphus living in a topographically challenginglandscape of the Swiss Alps. We hypothesised that resource selection by red deer was scale-dependent and predicted thatscale-dependence would vary among seasons in relation to seasonal changes of available forage biomass and quality, whichwe sampled across the entire study area of 250 km2. The studied population of Alpine red deer undertook altitudinalmigrations and showed scale-dependent habitat selection that was strongest in winter and declined through spring andsummer. Selection occurred mostly at the larger (landscape/home-range location) scale and less so at the smaller (withinhome-range) scale. Topographic parameters were selected mainly at the landscape scale and mostly in winter. About 70%of all instances of preference for habitat parameters were associated with above-average forage characteristics, representedmostly by higher crude protein content, in a few cases also by higher biomass or both. The overall pattern of space use byred deer characterised by migration and seasonal habitat selection was therefore closely linked to the quality of foodresources, although some trade-offs with avoiding human disturbance may also have been involved.

Increasing awareness that ecological processes are oftenclosely connected to particular spatial or temporal scales(Wiens 1989, Levin 1992) has offered new perspectives forstudying how animals respond to challenges and constraintsimposed on them by their environment. Resource selectionin particular is increasingly accepted as being scale-specific(Boyce 2006), and hierarchical, multiscale approaches areused to explore it (Johnson et al. 2002, Graf et al. 2005).The importance of scale is immediately obvious whenconsidering large and mobile species that cover extensivespace over time. Thus, scale issues have been an importantaspect in wildlife studies since the idea gained momentum(Apps et al. 2001, Boyce et al. 2003, Hobbs 2003). Studiesof larger ungulates are faced with the problem that resourceselection can occur on a range of scales from bite (extent ofa few cm2) to landscape (up to several thousands of km2;Senft et al. 1987, Bailey et al. 1996). Decision-making forresource selection at particular scales therefore requires ananimal to assess resources at the respective scale and possiblyalso across scales (Richards and de Roos 2001).

For ruminants, good resource availability will meanreasonably safe access to abundant forage of sufficiently

high digestibility, high protein and low toxin content (VanSoest 1994, Hanley 1997). Spatial and temporal variationof the quality of forage available to ruminants is typicallypronounced, especially in regions with strongly seasonalclimates. In such areas the available forage often has lowerquality than necessary for maintaining or increasing bodyweight. Maximising fitness will therefore require the abilityto develop a system of scale-dependent spatial behaviourthat optimises access to nutritious plants (Senft et al. 1987).Seasonal migrations exhibited by many ungulate species areinterpreted as a strategy to achieve this on the large scale(Fryxell and Sinclair 1988, Mysterud et al. 2001). Availableforage usually varies in both quantity and quality also atsmaller spatial scales, and local differences should beparticularly strong in landscapes with rugged topography.Here nutritional bottlenecks are prone to occur regularly,especially in areas with intense snowfall regimes. Access toforage may be further limited through human disturbance,thus reinforcing any bottleneck effect. Ruminants living insuch conditions are expected to show particularly sophisti-cated resource selection behaviour, but mechanisms remainlittle studied.

Ecography 32: 103�113, 2009

doi: 10.1111/j.1600-0587.2008.05178.x

# 2009 The Authors. Journal compilation # 2009 Ecography

Subject Editor: John Linnell. Accepted 13 November 2008

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Red deer (elk) Cervus elaphus is an ungulate widelydistributed over the holarctic (and introduced elsewhere)and thus faces strongly varying conditions in terms ofaccessing forage of sufficient quality across its distributionrange. Red deer living in mountainous habitats typicallyundertake vertical migrations between low-elevation winterand high-elevation summer ranges (Georgii 1980, Albonand Langvatn 1992). Among several hypotheses about thefiner mechanisms of how such migratory behaviour shouldboost energy intake, the prolonged access hypothesis hasreceived most support (Mysterud et al. 2001): the animalsmoving up along the altitudinal gradient follow emergingvegetation and thus get prolonged access to high-qualityforage, since young plants are rich in both utilisable energyand protein (Van Soest 1994).

Red deer attempting to optimise nutrient and energyintake should also be selective within a spatial frameworkthat accommodates daily (as opposed to seasonal) activities,i.e. at scales on which home ranges, habitat patches, orfeeding sites are selected. So far, the few studies investigat-ing multi-scale patterns of resource selection by elk foundthat selection of habitat components was linked toparticular scales (Pearson et al. 1995, Wallace et al. 1995,Strohmeyer et al. 1999, Jones and Hudson 2002, Andersonet al. 2005), but that linkages could differ between seasons(Boyce et al. 2003) and sexes (McCorquodale 2003). Alpinered deer face a strongly heterogeneous topographic envir-onment that is much more rugged in general than the betterstudied nearctic elk habitats. Red deer should thus respondto elevated environmental challenges with strongly scale-dependent resource selection. Some general understandingof how these animals cope with such conditions can bederived from a few studies of spatial behaviour of Alpine reddeer (Georgii 1980, Georgii and Schroder 1983, Fischerand Gossow 1987, Schmidt 1993, Haller 2002). However,resource selection of red deer and its variability amongscales have never been quantified. Neither have its drivingfactors, particularly seasonal dynamics of forage biomassand quality, been measured in conjunction with observedpatterns of spatial behaviour under the specific conditionsset by a rugged Alpine environment.

Although red deer have successfully recolonised the Alpsafter having been extinct over large areas (Haller 2002) andtherefore shown ability to cope with human-dominatedlandscapes, they still remain wary of people except wherethere is no hunting pressure (Schutte-Krug and Filli 2006).Habitat selection by red deer may thus not only result frombehaviour geared towards maximising energy intake but alsobe constrained by human disturbance (Fischer and Gossow1987). A thorough understanding of how and at which scalered deer, or any other large herbivore, responds to resourceavailability and the constraints set by harsh mountainousenvironments, is therefore of considerable interest fromboth purely scientific and management points of view(Boyce et al. 2003, Hobbs 2003, Gordon et al. 2004).

We studied habitat selection by red deer in a particularlyrugged mountainous landscape in the Swiss Alps. Objec-tives of our study were 1) to measure habitat selection ontwo spatial scales, 2) to assess its seasonal variability amongscales, and 3) to relate patterns of habitat selection to spatialand seasonal variation in forage biomass and nutritionalvalue of forage. Identifying meaningful scales may not

always be possible a priori (Boyce et al. 2003) but requirecomparing alternative models varying in resolution(Thompson and McGarigal 2002, Graf et al. 2005).However, using the location of actual home ranges todefine one level of resolution (usually termed landscapescale, 2nd order sensu Johnson 1980) is accepted practice inungulate studies (Dussault et al. 2005), which we followedby discerning landscape (where the home-range is located)and home-range (3rd order; habitat use within the home-range) scales. We hypothesised that resource selection byred deer was strongly scale-dependent. We further predictedthat scale-dependence varied among seasons in general andthat variability was associated with seasonal changes inavailable forage biomass and quality.

Methods

Study area

The study area (ca 250 km2) was located in the northernAlps near Glarus, east-central Switzerland (47800?N,09807?E), containing the eastern side of an 8 km-stretchof the Linth valley and the entire watershed of the riverSernft (length ca 25 km) up to the zone traditionally usedby farmers as alpine pastures (Fig. 1). The study area wasthus naturally delineated and included all winter (January�March), spring (April�June) and summer (July�August)home ranges of 10 GPS-collared red deer. Most of theterrain is steep and rocky with slope gradients often�100% including all possible slope aspects. Elevationsrange from 470 m on the valley floor to 2060 m at theupper end of the alpine pasture zone (coinciding with thehighest red deer locations), although the peaks above thestudy area rise up to 3158 m. The climate is mixed oceanicand continental and varies with altitude. Annual meantemperature decreases from 88C on the valley bottom toaround 38C at 1600 m; the annual sum of precipitation isin the range of 1400�1600 mm (long-term means suppliedby MeteoSwiss unpubl.). Settlements are limited to thenarrow valley bottom and cover ca 3% of the total area.About one third of the area is farmland, mostly grassland inthe valley bottom (fertilised and cut several times a year)and above the timberline (alpine pastures and hay meadowscut once a year). Another third consists of forest andsubalpine scrubland, while the rest is sparsely vegetatedrocky area (Swiss Federal Statistical Office unpubl.). Theforest is mainly deciduous (mostly beech Fagus silvatica andsome sycamore maple Acer pseudoplatanus) between 400and 900 m. Mixed stands dominated by beech, silver firAbies alba and Norway spruce Picea abies occur between900 and 1400 m. Conifer forests (Norway spruce, somesilver fir) predominate from 1400 to 1900 m. Forest ismostly a small-scale mosaic of all possible age classes andcanopy closures with frequent small clear-cuts and naturalopenings such as wind-throw areas and stream gullies.

Densities of red deer in the study area are not wellknown but the red deer population for the entire canton ofGlarus (685 km2) is estimated by cantonal wildlifemanagers at 450 individuals, which would translate intoaverage densities of 3�4 individuals km�2 of forested area.Apart from 1 to 2 Eurasian lynx Lynx lynx occasionally

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frequenting the study area, there are no natural predators oflarger ungulates present but hunters harvest annually ca120�180 red deer.

Study animals and habitat selection analysis

We immobilised 18 red deer (9 females, 9 males) in winters2001/2002 and 2002/2003 by darting them with a Xylazin-Zoletil-mixture (Janovsky et al. 2000). All animals wereequipped with non-differentially corrected 12-channel-GPSSimplexTM collars manufactured by TVP Positioning AB(Televilt, Lindesberg, Sweden; see Zweifel-Schielly andSuter 2007 for data on precision). GPS collars wereprogrammed to attempt positioning every second hour ontwo fixed days per week over two years, the days being thesame for all animals. After downloading to an externalreceiver by a hand-held Yagi-antenna, position data weretransferred to a computer and transformed into Swissnational coordinates. These data were imported into aGeographic Information System (GIS, ESRI ArcView 3.2)and associated habitat determined using ArcView extensionsPatch Analyst (Elkie et al. 1999) and the program AnimalMovement (Hooge and Eichenlaub 1997). We focussed ouranalyses on habitat parameters that possibly influencebiomass or quality of vegetation important for red deer,such as landscape type (forest, open land), habitat type(forest, meadows, pastures), forest type, or topography, andanalysed each at two spatial levels, landscape and home-range scales (Table 1).

Due to erratic failures in several collars, we had toexclude individuals from the habitat selection analysis,

based on two criteria: 1) when number of positions seemedinadequate because the relationship between number ofpositions and home range area did not reach an asymptote;2) when B50 positions resulted per season (50 is thesample size necessary at home-range scale when there aremore than two habitat types, Otis and White 1999). Thefinal data set allowed calculating habitat selection in winter,spring and summer for 10 of the 18 individuals in 2002 (1individual) and 2003 (9 individuals). From these tenindividuals at least 68 positions per individual and seasonwere obtained, with similar seasonal sample sizes (winter:mean 1389SD 102, range 74�218; spring: 181983, 113�231; summer: 120958, 68�150), which gave 4206 posi-tions in total. Habitat selection and behaviour may differbetween sexes in red deer (Clutton-Brock et al. 1982,McCorquodale 2003). Our sample size did not justifydetailed sex-specific analyses of habitat selection, but sincethe two sexes were almost equally represented in our sample(4 females, 6 males), the results should be balanced.

For each individual, we calculated season-specific homeranges using the 95% minimum convex polygon method(White and Garrott 1990). We chose the polygon ratherthan a kernel approach because it provided an unweightedestimate of the area used by the animal and did not preemptsubsequent analysis of habitat selection within the homerange (see below). A season-specific home range (winter,spring, summer) was delineated by the positions of anindividual in a period within that season during which itmoved no further than 3 km in any one direction withinthree days, i.e. between two successive positions. If morethan one home range resulted per individual and season,only the longest occupied home range (always derived from

Figure 1. Study area in the Canton of Glarus, east-central Switzerland; boundary lines straightened for clarity (left), and ground view ofspring�summer areas stretching along the slopes of the Sernft valley (right; line of vision indicated by arrow in left figure).

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at least 68 positions) was used in the analysis of habitatselection. Migration distances were calculated as lineardistances between centre points of seasonal home ranges.We measured habitat selection on two spatial scales,landscape (2nd order sensu Johnson 1980) and home-rangescale (3rd order; note that some authors use home-rangescale for the 2nd order; Potvin et al. 2001). Availability of ahabitat category (Table 1) at the landscape scale was definedas the proportion of this category in the study area(assuming that all was accessible to deer; McClean et al.1998, Garshelis 2000), and its use were the proportions inthe home ranges. At home-range scale, the latter propor-tions became availability, while use was now the proportionof positions in a habitat category. Selection could thus beexpressed on both scales as a measure relating the respectiveavailability and use. It is important to note that our deerstudy population frequented a topographically well-markedwatershed throughout the year, and therefore the study areacould largely be delineated in a non-arbitrary way.

The set of possible habitat variables to be analysed wasrestricted to data contained in existing data bases (GIS ofthe Canton of Glarus) and available for any point in thestudy area in the form of vector and grid maps (e.g. forest/open land, Table 1). From the vector and grid dataproportions of habitat categories for each deer and seasonwere calculated. We used compositional analysis (SmithEcology Microsoft† Excel tool for Compositional Analysis;Smith 2003) to test for habitat selection. This techniqueuses the individual as sample unit instead of positions, andlog-ratio transformations of habitat proportions. Therefore,common problems of habitat selection analysis such asspatial and temporal autocorrelation of positions, datapooling across individuals and non-independence of habitatproportions were avoided (Aebischer et al. 1993). Indepen-dence among deer individuals was assured by never markingmore than one animal in a group, despite groups beingsmall and temporally unstable. Compositional analysis wasdone separately for each habitat parameter (Table 1).Parameters ‘‘altitude’’ and ‘‘slope’’ were classified intoonly two categories (slope: 5308/�308; altitude: 51200 m/�1200 m) to retain a sufficient sample size inthe analyses that included forage data, and categories were

defined pragmatically so as to cover ca 50% each of thestudy area (slope) and to divide the forest belt into twosimilarly broad zones roughly coinciding with deciduousand coniferous forest.

For the habitat analysis at home-range scale, we used thehabitat category present directly at the position rather thantrying to correct for some unknown position inaccuracy(Rettie and McLoughlin 1999). This is because �70% ofthe GPS positions had a median locational error of B70 m(Zweifel-Schielly and Suter 2007) whereas extent and grainsize of habitats as categorised in this study was far larger andthe sample size of positions per individual was relativelyhigh at ]68. Likewise, we had no need to adjust theposition acquisition rate in our study with habitat-specificcorrection factors since it was largely unbiased by habitattype (Zweifel-Schielly and Suter 2007).

Vegetation sampling and analysis

We randomly selected 65 plots for vegetation sampling inthe forested (plot size 400 m2) and 67 plots in the open area(valley bottom meadows 30, alpine hay meadows 22, alpinepastures 15; plot size 9 m2). Data for plot topography(altitude, slope inclination, aspect) and forest type weretaken from existing digital maps (elevation model andforest-type map, respectively).

In each sampling plot we cut above-ground green plantbiomass (51.70 m height) on four systematically selectedsubplots (forest 1 m2, open land 0.09 m2) in May�June(spring), July�August (summer) and October�November2001 (when the growing season was over and data could betaken as representative for winter), so that each subplot wascut only once. For the calculation of biomass in both forestand grassland, and for the nutritional analysis of grassland,all plant species of the four subplots were pooled. For thenutritional analysis of forest biomass, plants were assignedto nine different groups: graminoids, forbs, tall forbs, Rubussp., dwarf-shrubs, ferns, mosses, latest annual shoots ofconifers, deciduous leaves of trees and shrubs. We did notdifferentiate between graminoids and forbs for grasslandplots on the grounds that grazing red deer select at ‘‘bite

Table 1. Overall availability of habitat categories in the study area (%SA) and their use at landscape (% HR) and home range (% Pos) scales(mean n�10 deer). Some habitat parameters contain additional categories such as rocky areas that were not analysed; their proportions donot add up to 100%.

Habitat parameter Habitat categories Data type % SA Winter Spring Summer

% HR % Pos % HR % Pos % HR % Pos

Landscape type Open land total GIS, vector map 70 30 49 38 37 30 25Forest total GIS, vector map 30 70 51 62 63 70 75

Habitat type Forest total GIS, 100 m grid 30 70 51 62 63 70 75Valley bottom meadows GIS, 100 m grid 10 21 34 10 14 4 4Alpine meadows (once cut) GIS, 100 m grid 1 1 0 1 2 1 2Alpine pastures GIS, 100 m grid 25 1 0 13 8 13 9

Forest type Maple forest GIS, vector map 10 3 3 7 7 3 7Beech forest GIS, vector map 50 83 83 48 52 32 35Norway spruce forest GIS, vector map 28 13 13 38 34 55 45

Altitude 51200 m a.s.l. GIS, 25 m grid 29 91 93 49 51 35 33� 1200 m a.s.l. GIS, 25 m grid 71 9 7 51 49 65 67

Slope Inclination 5308 GIS, 25 m grid 55 42 52 49 46 48 44Inclination�308 GIS, 25 m grid 45 58 48 51 54 52 56

Aspect North-oriented 271�908 GIS, 25 m grid 50 26 20 54 54 61 62South�oriented 91�2708 GIS, 25 m grid 50 74 80 46 46 39 38

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size’’ rather than for single plants (Collins and Urness 1983)whereas we assumed that they did so while browsing inforests. In order to get some idea of the magnitude ofannual variation of forage biomass and quality, eightrandomly selected forest plots and 13 plots on open landwere sampled again in 2002. Crude protein in forage wasselected as an indicator of quality as it is known to be alimiting nutrient for herbivores and often correlated withdigestibility or energy (Van Soest 1994). Fresh plantbiomass was oven-dried at 508C for 48 h and then cooledto ambient temperature, weighed for calculating air-drymatter content, and milled through a 0.75-mm sieve.Nitrogen concentration was measured with a CN-2000analyser (Leco Instruments, St. Joseph, MI, USA). Crudeprotein was calculated by multiplying N by 6.25 becausethe average N content of protein in plant tissue is 16%.Crude protein content was expressed as percentage of drymatter which had been determined using a TGA-500furnace (Leco Instruments, St. Joseph, MI, USA) at1058C until constant weight was reached.

Effects of habitat parameters (Table 1) on forage biomassand crude protein were tested using analysis of variance(ANOVA, general linear model GLM). We only builtmain-effect models because interaction terms would haveprevented comparisons between habitat selection, measuredwith compositional analysis, and forage characteristics.With forest data, we applied main-effect 5-way ANOVAs(GLM) on the dependent variables ‘‘total plant biomass’’and biomass of each plant group (for assessing the foragevalue of forest categories) for each season. Forest plantgroups having mean crude protein content above theaverage of all group means (calculated separately for eachseason) were defined as ‘‘high’’ in protein, those below as‘‘low’’. For open land data, we conducted main-effect 3-wayANOVAs (GLM) for the dependent variables ‘‘total plantbiomass’’ and ‘‘total crude protein content’’ for each season.

We used Pearson correlations to search for associationsamong predictor variables. Since ‘‘altitude’’ correlatedrather strongly (r�0.6) with some of the open land typesand forest types, they were omitted in the 3- and 5-wayANOVAs. Effects of these two parameters were analysedwith separate 1-way ANOVAs. While it was obvious fromthe beginning that forage biomass was lower in forest than

on open land on average, there was much more spatialvariation in forest suggesting that the richer patches mightmatch forage quantity and perhaps also quality of openland. Therefore, we also compared the highest values foundin forest with those from open land (top 10 for biomass, top30 for crude protein, because this sample size was larger),and the highest values of forest with mean values of thedifferent open land types, by means of 1-way ANOVAs foreach season. Effect of year on total plant biomass and crudeprotein content was tested using full-model 2-way ANO-VAs (GLM) with main factors year and habitat type. Weaccounted for the unequal sample size among groups in theANOVAs by using unweighted means and unique sums ofsquares (type III sum of squares). Distribution of residualswas examined for normality with Q-Q plots. We log-transformed the variables describing biomass of the forestplant groups because their distributions were skewed left.Variance was examined with Tukey-Anscombe plots andfound to be similar among categories. We performedpairwise tests between habitat categories using unweightedmeans and Bonferroni adjustments. Analyses were madeusing SPSS† (release 11.0.1, SPSS, Chicago, IL, USA).

Results

Seasonal migrations

Nine of the ten deer migrated from near the bottom of themain valley into the upper side valley during spring andthus had at least two distinct home ranges in the course ofthe year, whereas one female was almost sedentary.Horizontal distances travelled between winter and summerhome ranges varied from 3 to 25 km and did not differamong sexes (p�0.05; Fig. 2). Altitudinal moves weresomewhat less variable as most individuals shifted thealtitudinal centre of their home ranges from between 600and 1000 m in winter to between 1000 and 1300 m inspring and to between 1100 and 1500 m in summer(Fig. 2). Females tended to stay at slightly lower altitudesthan males; in summer the difference was significant(p�0.019, Mann-Whitney U test).

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Figure 2. Migration distances measured as linear distance between winter and summer home range centres (left; OV�overlapping homeranges), and altitudinal shift of red deer from winter to summer (right); circles (k: males, m: females) indicate altitude of mean homerange coordinate.

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Scale-dependent habitat selection

Much of the selection occurred already at the landscapescale, as the number of habitat categories involvingpreference or avoidance was higher on the landscape thanon the home-range scale (Table 2, 3). At both scales mostselection occurred in winter, less in spring and least insummer.

Selection for topographic features was manifest mainly atthe landscape scale in winter, when low altitudes, south-facing slopes and steep terrain were preferred (Table 2, 3,

parameter definitions in Table 1). In summer, topographicfeatures were frequented according to their availability inthe study area. Within home ranges, however, deer selectedflat terrain over steep slopes in winter. Because theincidence of flat terrain (slopes 5308) and low altitudes(51200 m) correlated with the presence of valley bottommeadows (slope: r��0.53, altitude: r��0.60), selectionfor these parameters results from the (night-time) use ofmeadows in the valley bottom in winter.

Red deer consistently selected forest over open land atthe landscape scale, and mean forest cover in their home

Table 2. Habitat selection by red deer at the landscape scale and associations with forage biomass and crude protein content. Onlyparameters with significant preference are listed (pB0.05; randomisation).

Habitat parameter x2 DF p Preference Forage

Biomassa Proteinb

WinterLandscape type 34.36 1 0.001 Forest�open landHabitat type 37.41 3 0.001 Forest�alpine meadows �

Forest�alpine pastures �Valley bottom meadows�alpine meadows �Valley bottom meadows�alpine pastures �

Forest type 14.04 2 0.003 Beech�spruce dominatedBeech�maple dominated

Altitude 13.68 1 0.003 Low�high � c

Slope 10.55 1 0.005 Steep�flat �c

Aspect 9.29 1 0.001 South�north

SpringLandscape type 26.46 1 0.001 Forest�open land �Habitat type 18.54 3 0.019 Forest�valley bottom meadows �

Forest�alpine meadows �Forest�alpine pastures �Valley bottom meadows�alpine meadows �

Altitude 3.50 1 0.012 Low�high �d

SummerLandscape type 16.66 1 0.006 Forest�open landHabitat type 20.08 3 0.011 Forest�valley bottom meadows

Forest�alpine meadows �Forest�alpine pastures �

Forest type 6.90 2 0.039 Spruce�maple dominated

a �: significantly higher biomass in the preferred habitat category within the pairwise comparison (results for biomass from Supplementarymaterial Appendix 1; forest/open land: only 10 highest values considered).b �: significantly higher crude protein content in the preferred habitat category within the pairwise comparison (forest, open land, open landtypes, topographic features in open land; forest/open land: only 30 highest values considered; results for crude protein content fromSupplementary material Appendix 2) or significantly higher biomass of at least one forest plant group with high mean crude protein contentin preferred habitat category (all habitat parameters in forest; results from Supplementary material Appendix 3). Winter forage biomass/quality was measured in October/November while winter habitat selection was studied from January to March.c In open land.d In forest.

Table 3. Habitat selection by red deer at home-range scale and associations with plant biomass and crude protein content. For explanationssee Table 2.

Habitat parameter x2 DF p Preference Forage

Biomassa Proteinb

WinterLandscape type 11.713 1 0.002 Open land�forest � �Habitat type 47.970 3 0.004 Valley bottom meadows�forest � �

Forest�alpine pastures �Valley bottom meadows�alpine pastures �

Slope 7.97 1 0.009 Flat�steep � c

SpringHabitat type 19.694 3 0.002 Forest�alpine pastures �

Valley bottom meadows�alpine pastures

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ranges was around 70% throughout the year (winter: 70%,SD 10%; spring: 62%, SD 10%; summer 70%, SD 17%;Table 1, 2). At the home-range scale, however, use of openland was strong in winter (49%) resulting in preference, butdeclined through spring (37%) to summer (25%, Table 3).Within forest, red deer exhibited some selection for foresttype only at the landscape scale, where beech forest waspreferred in winter and coniferous forest in summer (Table1, 3). Among categories of open land, red deer preferredvalley bottom meadows at both landscape and home-rangescales in winter and spring while alpine pastures andmeadows were completely avoided in winter. During thesnow-free seasons, the use of the latter was still negligible,whereas alpine pastures were visited more often yet still lessthan expected (Table 1).

Habitat selection related to forage biomassand quality

Two thirds (14 of 21) of the instances of selection at thelandscape scale and all but one (6 of 7) at the home-rangescale were associated with the presence of better forage,mostly with higher crude protein content, but in a few casesalso higher biomass (Table 2, 3). In winter, selection at thelandscape scale was mainly against higher altitudes (�1200m) and associated with poor nutrient availability on alpinemeadows and pastures (Table 2, Fig. 3), whereas at thehome-range scale, it favoured valley bottom meadows and

was associated with higher forage quality and, in comparisonto forest, also with higher biomass (Table 3). Valley bottommeadows in winter offered forage with crude protein contentca 10�80% higher than in other habitat types (Fig. 3). Inspring and summer, selection was virtually restricted to thelandscape scale where preferences for forested habitatsprevailed and were associated with higher forage quality(Table 2). The best forest patches in spring offered plantmaterial with 10�40% higher crude protein content thanwhat was to be found in open land albeit in low quantity(Fig. 3). Alpine pastures and meadows in spring�summermostly scored on average in terms of crude protein contentbut had relatively high biomass in summer (Fig. 3) yet werenever positively selected by red deer.

Since vegetation sampling was done in 2001 whereasdata of habitat use by deer were from 2002 to 2003, we hadto be aware of possible annual variation in biomassproduction and plant quality. This would not have been aproblem as long as the differences were proportional amonghabitat types, but differential response could have ques-tioned the general validity of our comparisons. We foundplant biomass to be higher and crude protein to be lower ingeneral in 2002 than in 2001 (full model ANOVA:biomass, spring: F1,17�25.175, pB0.001, summer:F1,17�4.611, p�0.046; protein, spring: F1,32�7.337,p�0.011, summer: F1,38�22.575, pB0.001). However,there were no significant interactions between year andhabitat type in any season, suggesting that growth responseswere similar in all habitat types.

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Figure 3. Seasonal mean and variation (95% confidence interval) of plant biomass (air-dry matter; top) and crude protein content(bottom) in different types of open land (solid lines) and of the highest values in open land and forest respectively (top 10 for biomass, top30 for crude protein; dashed line).

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Discussion

Function of migratory behaviour

Red deer have the most extensive distribution area of alldeer species, naturally ranging over much of the holarcticand across many different climatic zones. Consequently,there is broad variation of large-scale spatial behaviourwithin the species. Lowland populations in milder climatestend to be sedentary (Clutton-Brock et al. 1982, Carranzaet al. 1991), whereas seasonal movements between uplandsummer and lower-lying winter habitats are common inmountainous regions, both in European red deer (Georgii1980, Albon and Langvatn 1992, Homolka and Matous1999, Mysterud et al. 2001) and American elk (Boyce1991, Irwin 2002). Distances travelled depend on regionaltopography and can exceed 100 km in elk living in rollingterrain. Topography in the Alps is rather precipitous, andlinear distances between summer habitat of deer at higherelevations and suitable winter ranges along the valleybottoms may be quite short. Published migratory distancesfor Alpine red deer are mostly between ca 5 and 30 km(Haller 2002), similar as found in our study area.

The most general explanation of why large herbivoresmigrate, namely to increase protein and energy intake, isoften favoured over predator-related motivations (Fryxelland Sinclair 1988, Irwin 2002), although migrations mayhave evolved in form of a trade-off integrating responses toboth nutritional needs and predator avoidance, and otherconstraints (Bolger et al. 2008). Vertical migrations of reddeer were found to coincide with the phenological delay ofemergent vegetation on high-altitude pastures resulting inhigher nutritive value during late spring and early summercompared to valley bottom meadows (Atzler 1984, Boyce1991, Albon and Langvatn 1992). However, specifichypotheses on the mechanisms of energy gains have hardlybeen addressed. Mysterud et al. (2001) tested alternativehypotheses on whether body condition in red deer wasrelated to the absolute amount of high-altitude habitatavailable, the variety of slope aspects or the diversity ofdifferent altitudes. They found body weight to be positivelyassociated with the diversity of altitudes but negatively withthe proportion of high-lying area, and concluded that themain reason for altitudinal migrations lies in the prolongedaccess to newly emerged vegetation as the deer move upalong the altitudinal gradient. To our knowledge, wepresent the first data linking habitat selection at thelandscape scale directly with forage quality in differenthabitats spread across the altitudinal gradient (see alsoAlbon and Langvatn 1992). Our data lend support to theprolonged access hypothesis. Red deer positively selectedvalley bottom meadows and some adjacent forest habitats inwinter, lower altitude forest in spring and forest withoutlow altitudinal preference in summer. Compositionalanalysis of habitat use by red deer produced 28 pairwisecomparisons between habitat categories showing selection.In 18 of these the preferred category offered forage withsignificantly higher crude protein content, and in 4comparisons the preferred category had higher foragebiomass.

In human-dominated landscapes such as our study area,farming, hunting (annual harvest here corresponds to ca33% of the estimated red deer population size), vehicletraffic, tourism and other recreational activities togetherproduce a spatially and seasonally variable yet constantsource of human disturbance to red deer, and are thus likelyto constrain migratory patterns driven by nutritional needs.Altitudinal migrations generally ended below the timberlinein summer, and deer preferably used the forest belt ataltitudes between 1100 and 1500 m but did not preferalpine pastures and meadows, although crude protein valueswere similar and forage biomass was higher there than in theforest (Fig. 3). Where alpine grassland is free of humandisturbance as is the case in the Swiss National Park, reddeer intensively use such habitat throughout the day insummer (Haller 2002, Schutte-Krug and Filli 2006). Atnight during winter, however, red deer were not deterred tofeed in close vicinity of farm buildings, and our radio-marked deer were actually caught by darting from smallfarm buildings. Therefore, and because the distribution ofhuman settlements (including the very few roads) correlatedclosely with the presence of valley bottom meadows (andconsequently, altitude as well), we did not use distance toinfrastructure as a predictive variable in our models. Inanother study area in the Alps, we had previously found thatthe spatial distribution of bark stripping by red deer wasunrelated to the spatial pattern of roads and buildings(Rheinberger and Suter 2006).

Variation in habitat selection dependson spatial scale and time

As expected, habitat selection by red deer varied withspatial scale but also in time. Because deer were spatiallyconcentrated in winter near the valley bottom but moreevenly spread across larger areas of the forest belt for muchof the year, habitat selection decreased from winter tosummer. With respect to scale, much selection wasexhibited already at landscape and less at home-range scalebut seasonal changes occurred in parallel. Deer thereforechose areas to be used as their home-ranges that differed inhabitat composition from that provided by the study area,but then mostly used the habitats as they were availablewithin their home-ranges. Although we had expectedselection to be manifest on a finer grain, the pattern foundhas also been described for other large herbivores (Pearsonet al. 1995, Wallace et al. 1995, Apps et al. 2001). Forsome habitat parameters, the selection that has happened atthe landscape scale might be sufficient and no additionalselection will be necessary within home-ranges. Otherexplanations brought forward involve the idea that theimportant factors which directly affect individual fitness aremore likely to operate at the landscape scale (Senft et al.1987, Rettie and Messier 2000, Dussault et al. 2005).Apart from large-scale patterns of predation pressure, theytend to be abiotic factors such as topography and snowcover able to severely restrict access to forage and thusaffect the coarse-grain distribution pattern of ungulates(Senft et al. 1987, Bailey et al. 1996, Boyce et al. 2003).Indeed, we found selection for topographic features to bestronger at landscape than at home-range scale and to occur

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mostly in winter, when home ranges were preferablyestablished at low altitudes, in steep terrain and onsouth-facing slopes. This pattern has been noted elsewherein the Alps (Fischer and Gossow 1987, Haller 2002) andprobably reflects selection for wintering areas that offerrelatively low snow cover, although additional advantagessuch as resting habitat free of human disturbance (steepslopes) and access to nearby valley bottom grassland (flatterterrain) might also be important. In fact, the solepreference for a topographic feature expressed at home-range scale was selection of flat terrain in winter (asopposed to selection of steep lower slopes at the landscapelevel). While winter habitats are generally characterised alsoby above-average temperatures, temperature per se may beless important than the advantage provided by lesser snowcover. In some parts of the Alps red deer overwinter onwindblown alpine pastures above the timberline (Schmidt1993, Haller 2002), thereby demonstrating cold resistancepossibly enabled by their capacity for nocturnal hypome-tabolism (Arnold et al. 2004).

Habitat selection for forage quality or quantity?

Access to sufficient forage may not only have been drivingseasonal movements of red deer but also influencing habitatselection at both investigated scales. Associations of habitatpreferences with forage characteristics differed seasonallywith respect to scale. Associations at home-range scale weremost frequent in winter when red deer distribution at thecoarser level was largely determined by topographic factors.However, the intensive use of open land resulted inpreference on the home-range scale and was due to grazingon the fertilised valley bottom meadows where the grass hadrelatively high protein content. Feeding out in the open wasrestricted to night-time, and deer retreated to forestedslopes for the day.

As soon as more habitats became accessible in spring,selection for better forage quality (and higher quantity insome instances) expressed itself almost exclusively at thelandscape scale, at which forest was preferred over all typesof open habitats. During spring and summer, the best forestpatches scored highest among all habitat types in terms ofprotein content of forage but were consistently lowest inavailable forage biomass. The strong preference for nutri-ent-rich over biomass-rich habitats was possibly due to highenergy requirements for lactation and antler growth(Clutton-Brock et al. 1982, Arnold et al. 2004) althoughsome preferred forest areas in spring were both nutrient-and biomass-rich. However, there was little selection at thehome-range scale in spring and summer, and differences inthe nutrient content of forage between habitats levelled outin summer. This means that deer used the different habitatswithin home-ranges proportionally to their extent, and deermay therefore have taken advantage also of the biomass-richbest patches in open land (Fig. 3).

Dealing with spatial scales in habitat selection studies

The need of considering multiple scales in habitat selectionstudies has been widely emphasized and several aspects have

received in-depth discussion (Boyce et al. 2003, Hobbs2003). Our study emphasises the importance of multi-scaleapproaches by showing that deer selected habitat parameters(e.g. landscape type and slope inclination) on differentscales contrastingly. Disparate responses to environmentalcomponents according to the extent of scale have also beendescribed for other ungulates (Rettie and Messier 2000,Apps et al. 2001, Jones and Hudson 2002, Boyce et al.2003, McCorquodale 2003), but were found only when theextents compared were sufficiently different (Strohmeyer etal. 1999). Whether habitat selection analyses identifycertain characteristics as being preferred or avoided on agiven scale also depends on how availability is calculated.The method used should account for whether animalschoosing a home range can freely select from the full rangeof habitat types, which is not the case if habitats areclumped in large discrete blocks rather than more evenlydispersed (Garshelis 2000, Potvin et al. 2001, Katnik andWielgus 2005). Outcomes are also susceptible to whichhabitat parameters are chosen for analysis, reflecting theinfluence of parameters that may identify a spuriousselection when the animal in reality is responding toanother (correlated) feature of the landscape (Boyce et al.2003). This reminds us that results from habitat selectionanalysis often tell less on cause and effect than we wouldwish, because spatial behaviour of wild ungulates at thelandscape level usually reflects trade-offs between manyvariables (Parker 2003) that can affect individual fitness.Measuring parameters that characterise habitat quality ofknown immediate relevance to the study animal andrelating them to the selection pattern at the appropriatescale, as we did with forage quality and biomass, will oftenlead a step further. However, concomitant collection of datafor all relevant aspects is usually well beyond the possibi-lities of a normal study. In our case, this would haveentailed not only measuring forage availability in differenthabitat categories, but also assessing their properties withrespect to snow accumulation, thermal protection offered tothe red deer, or risk of human disturbance, while predationrisk would have been a paramount factor to account for inmany other areas (Johnson et al. 2001, Dussault et al.2005). However, what may appear as limitations of habitatselection studies in general, is in fact the breeding groundfor testable hypotheses on how spatial behaviour contributesto maximising individual fitness.

Acknowledgements � We thank the Canton of Glarus and the SwissFederal Office for the Environment FOEN for generous financialsupport. We are grateful to A. Schuler for his experience andtireless enthusiasm in immobilising deer, E. Weber, J. Zweifel-Schielly and H. Schielly for assistance with fieldwork, F. Bandau,N. Hemmi, M. Walser, R. Koechli, C. Kunz and M. Mergani forhelp with the vegetation analyses, R. Fankhauser for programmingsoftware, R. Haller, F. Filli and R. Eyholzer for advice on GPStelemetry, R. Graf for support in GIS analysis, B. Tona and B.Moser for statistical advice, and T. Alanko from Televilt Swedenfor technical support. Advice from D. Keppie, A. Risch and threereviewers greatly helped to improve a former version of themanuscript. We also thank all people from the research program

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Forest-Wildlife-Landscape of the Swiss Federal Research Inst.WSL for insightful discussions.

References

Aebischer, N. J. et al. 1993. Compositional analysis of habitat usefrom animal radio-tracking data. � Ecology 74: 1313�1325.

Albon, S. D. and Langvatn, R. 1992. Plant phenology and thebenefits of migration in a temperate ungulate. � Oikos 65:502�513.

Anderson, D. P. et al. 2005. Scale-dependent summer resourceselection by re-introduced elk in Wisconsin, USA. � J. Wildl.Manage. 69: 298�310.

Apps, C. D. et al. 2001. Scale-dependent habitat selection bymountain caribou, Columbia Mountains, British Columbia.� J. Wildl. Manage. 65: 65�77.

Arnold, W. et al. 2004. Nocturnal hypometabolism as anoverwintering strategy of red deer (Cervus elaphus). � Am. J.Physiol. Regul. Integr. Comp. Physiol. 286: R174�R181.

Atzler, R. 1984. Nahrungsangebot und Wanderverhalten vonRothirschen in den Ammergauer Bergen. � Z. Jagdwiss. 30:73�81.

Bailey, D. W. et al. 1996. Mechanisms that result in largeherbivore grazing distribution patterns. � J. Range Manage.49: 386�400.

Bolger, D. T. et al. 2008. The need for integrative approaches tounderstand and conserve migratory ungulates. � Ecol. Lett. 11:63�77.

Boyce, M. S. 1991. Migratory behaviour and management of elk(Cervus elaphus). � Anim. Behav. Sci. 29: 239�250.

Boyce, M. S. 2006. Scale for resource selection functions. � Divers.Distrib. 12: 269�276.

Boyce, M. S. et al. 2003. Scale and heterogeneity in habitatselection by elk in Yellowstone National Park. � Ecoscience 10:421�431.

Carranza, J. et al. 1991. Space use by red deer in a Mediterraneanecosystem as determined by radio-tracking. � Appl. Anim.Behav. Sci. 30: 363�371.

Clutton-Brock, T. H. et al. 1982. Red deer: behavior and ecologyof two sexes. � Univ. Chicago Press.

Collins, W. B. and Urness, P. J. 1983. Feeding behaviour andhabitat selection of mule deer and elk on northern Utahsummer range. � J. Wildl. Manage. 47: 646�663.

Dussault, C. et al. 2005. Linking moose habitat selection tolimiting factors. � Ecography 28: 619�628.

Elkie, P. C. et al. 1999. Patch analyst user’s manual: a tool forquantifying landscape structure. � Ontario Ministry of NaturalResources, Northwest Science and Technology, Thunder Bay,ON, Canada.

Fischer, A. and Gossow, H. 1987. Untersuchungen zur Raum-Zeit-Nutzung des Rotwildes angesichts menschlicher Storein-flusse, unter besonderer Berucksichtigung der Wintersituation:Fallstudie St. Anton am Arlberg. � Cbl. Ges. Forstwes. 104:191�218.

Fryxell, J. M. and Sinclair, A. R. E. 1988. Causes andconsequences of migration by large herbivores. � TrendsEcol. Evol. 3: 237�241.

Garshelis, D. L. 2000. Delusions in habitat evaluation: measuringuse, selection, and importance. � In: Boitani, L. and Fuller,T. K. (eds), Research techniques in animal ecology� controversies and consequences. Columbia Univ. Press, pp.111�164.

Georgii, B. 1980. Home range patterns of female red deer (Cervuselaphus L.) in the Alps. � Oecologia 47: 278�285.

Georgii, B. and Schroder, W. 1983. Home range and activitypatterns of male red deer (Cervus elaphus L.) in the Alps.� Oecologia 58: 238�248.

Gordon, I. J. et al. 2004. The management of wild large herbivoresto meet economic, conservation and environmental objectives.� J. Appl. Ecol. 41: 1021�1031.

Graf, R. F. et al. 2005. The importance of spatial scale in habitatmodels: capercaillie in the Swiss Alps. � Landscape Ecol. 20:703�717.

Haller, H. 2002. Der Rothirsch im Schweizerischen Nationalparkund dessen Umgebung. Eine alpine Population von Cervuselaphus zeitlich und raumlich dokumentiert. � Nat. park-Forsch. Schweiz 91: 1�144.

Hanley, T. A. 1997. A nutritional view of understanding andcomplexity in the problem of diet selection by deer (Cervidae).� Oikos 79: 209�218.

Hobbs, N. T. 2003. Challenges and opportunities in integratingecological knowledge across scales. � For. Ecol. Manage. 181:223�238.

Homolka, M. and Matous, J. 1999. Density and distribution ofred deer and chamois in subalpine meadow habitats in theJeseniky Mountains (Czech Republic). � Fol. Zool. 48: 1�10.

Hooge, P. N. and Eichenlaub, B. 1997. Animal movementextension to ArcView, ver. 1.1. � Alaska Biological ScienceCenter, U.S. Geological Survey.

Irwin, L. L. 2002. Migration. � In: Toweill, D. E. and Thomas,J. W. (eds), North American elk: ecology and management.Smithsonian Inst. Press, pp. 493�513.

Janovsky, M. et al. 2000. A Zoletil †�Rompun † mixture as analternative to the use of opioids for immobilization of feral reddeer. � J. Wildl. Dis. 36: 663�669.

Johnson, C. J. et al. 2001. Foraging across a variable landscape:behavioral decisions made by woodland caribou at multiplespatial scales. � Oecologia 127: 590�602.

Johnson, C. J. et al. 2002. Movement parameters of ungulates andscale-specific responses to the environment. � J. Anim. Ecol.71: 225�235.

Johnson, D. H. 1980. The comparison of usage and availabilitymeasurements for evaluating resource preference. � Ecology61: 65�71.

Jones, P. F. and Hudson, R. J. 2002. Winter habitat selection atthree spatial scales by American elk, Cervus elaphus, in west-central Alberta. � Can. Field-Nat. 116: 183�191.

Katnik, D. D. and Wielgus, R. B. 2005. Landscape proportionsversus Monte Carlo simulated home ranges for estimatinghabitat availability. � J. Wildl. Manage. 69: 20�32.

Levin, S. A. 1992. The problem of pattern and scale in ecology.� Ecology 73: 1943�1967.

McClean, S. A. et al. 1998. Evaluation of resource selectionmethods with different definitions of availability. � J. Wildl.Manage. 62: 793�801.

McCorquodale, S. M. 2003. Sex-specific movements and habitatuse by elk in the Cascade range of Washington. � J. Wildl.Manage. 67: 729�741.

Mysterud, A. et al. 2001. Plant phenology, migration andgeographical variation in body weight of a large herbivore:the effect of a variable topography. � J. Anim. Ecol. 70: 915�923.

Otis, D. L. and White, G. C. 1999. Autocorrelation of locationestimates and the analysis of radiotracking data. � J. Wildl.Manage. 63: 1039�1044.

Parker, K. L. 2003. Advances in the nutritional ecology of cervidsat different scales. � Ecoscience 10: 395�411.

Pearson, S. M. et al. 1995. Winter habitat use by large ungulatesfollowing fire in northern Yellowstone National Park. � Ecol.Appl. 5: 744�755.

112

Potvin, F. et al. 2001. How to test habitat selection at the homerange scale: a resampling random windows technique.� Ecoscience 8: 399�406.

Rettie, W. J. and McLoughlin, P. D. 1999. Overcoming radio-telemetry bias in habitat selection studies. � Can. J. Zool. 77:1175�1184.

Rettie, W. J. and Messier, F. 2000. Hierarchical habitat selectionby woodland caribou: its relationship to limiting factors.� Ecography 23: 466�478.

Rheinberger, C. and Suter, W. 2006. Bark stripping by red deer: acase study in the Pre-Alps of northeastern Switzerland.� Schweiz. Z. Forstwes. 157: 147�156.

Richards, S. A. and de Roos, A. M. 2001. When is habitatassessment an advantage when foraging? � Anim. Behav. 61:1101�1112.

Schmidt, K. 1993. Winter ecology of nonmigratory Alpine reddeer. � Oecologia 95: 226�233.

Schutte-Krug, K. and Filli, F. 2006. Diurnal patterns of red deerCervus elaphus activity in three areas of the Swiss NationalPark. � Nat.park-Forsch. Schweiz 93: 105�116.

Senft, R. L. et al. 1987. Large herbivore foraging and ecologicalhierarchies. � Bioscience 37: 789�799.

Smith, P. G. 2003. Compos analysis vers. 5.0 user’s guide. � SmithEcology Microsoft† Excel tool for Compositional Analysis.

Strohmeyer, D. C. et al. 1999. Wapiti bed sites in Idaho sagebrushsteppe. � Wildl. Soc. Bull. 27: 547�551.

Thompson, C. M. and McGarigal, K. 2002. The influence ofresearch scale on bald eagle habitat selection along the lowerHudson River, New York (USA). � Landscape Ecol. 17: 569�586.

Van Soest, P. 1994. Nutritional ecology of the ruminant, 2nd ed.� Cornell Univ. Press.

Wallace, L. L. et al. 1995. Scale of heterogeneity of forageproduction and winter foraging by elk and bison. � LandscapeEcol. 10: 75�83.

White, G. C. and Garrott, R. A. 1990. Analysis of wildliferadiotracking data. � Academic Press.

Wiens, J. A. 1989. Spatial scaling in ecology. � Funct. Ecol. 3:385�397.

Zweifel-Schielly, B. and Suter, W. 2007. Performance of GPStelemetry collars for red deer in rugged Alpine terrain undercontrolled and free-living conditions. � Wildl. Biol. 13:299�312.

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