Domesticated Reindeer Calves

Risk of brown bear predation on semi- domesticated reindeer calves

Predation patterns, brown bear – reindeer interactions and landscape heterogeneity

Therese Ramberg Sivertsen Faculty of Veterinary Medicine and Animal Science Department of Animal Nutrition and Management Uppsala

Doctoral thesis Swedish University of Agricultural Sciences Uppsala 2017

Acta Universitatis agriculturae Sueciae 2017:50

Cover: Reindeer calf in Udtja reindeer herding district (photo: Rune Stokke)

Risk of brown bear predation on semi-domesticated reindeer calves - Predation patterns, brown bear-reindeer interactions and landscape heterogeneity

Abstract

As large carnivore populations are recovering in northern boreal ecosystems of Europe and North America, there is a need to understand how these changes in predator communities influence prey populations and ecosystems. Moreover, human-wildlife conflicts are frequently causing challenges where large carnivores coexist with humans, often due to predation on livestock. In Sweden the brown bear (Ursus arctos) distributional range largely overlaps with the reindeer (Rangifer tarandus tarandus) herding area, but knowledge of potential losses to bear predation has been scarce. Also, little information exists on the behavioral interactions between semi-domesticated reindeer and brown bears in Fennoscandia. In this thesis I present data from two forest reindeer herding districts in Northern Sweden, showing that brown bear predation on reindeer neonates can be considerable on forested calving grounds. Also, brown bear predation was very limited in time, concentrated to the first weeks following birth of the reindeer calves. Moreover, using GPS location data to compare brown bear and reindeer resource selection on the reindeer calving ground, indicated that brown bear behavioral adjustments to search for reindeer possibly dominate over antipredator responses by reindeer in terms of altered resource selection on a daily and seasonal basis. Nevertheless, a closer investigation of the spatial distributions of reindeer calf kill sites suggested that use of clear-cuts, higher elevations and areas closer to large roads may reduce risk of bear predation. However, even though clear-cuts may provide advantages for survival in the short term, logging may eventually yield negative effects for the reindeer, as abundance of young forest increase, which is a preferred habitat by brown bears. Finally, using data on reindeer movements and brown bear density from seven herding districts in Sweden I show that reindeer females experiencing higher risk of bear predation, deviate more from optimal foraging and increase movement rates, which may lead to lower body condition and, in turn, possible consequences for population dynamics.

Keywords: Rangifer, brown bear, predation, landscape characteristics, habitat selection, antipredator behavior, green wave

Author’s address: Therese Ramberg Sivertsen, SLU, Department of Animal Nutrition and Management, P.O. Box 7024, 750 07 Uppsala, Sweden

Dedication

Til Heming og Nokve

We do not inherit the earth from our ancestors; we borrow it from our children Ancient proverb

Contents

List of publications 7

1 Introduction 9 1.1 Reindeer husbandry in Sweden 10 1.2 Rangifer foraging ecology and antipredator behavior 11 1.3 Brown bears in Sweden 13

2 Objectives 14

3 Methods 15 3.1 Study systems 15 3.1.1 Udtja and Gällivare reindeer herding districts 15 3.1.2 Herding districts in paper IV 17 3.2 GPS and predation data 17 3.2.1 Collaring of reindeer and brown bears in Udtja and Gällivare 17 3.2.2 Documentation of reindeer carcasses in Udtja and Gällivare 18 3.2.3 Processing of GPS and kill site data for paper II-IV 19 3.3 Environmental data 19 3.3.1 Landscape characterstics in paper II, III and IV 19 3.3.2 Fine scale registrations in paper III 20 3.3.3 Plant phenology in paper IV 20 3.3.4 Bear density index in paper IV 21 3.4 Brown bear predation on reindeer calves 21 3.4.1 Seasonal kill rate model 21 3.4.2 Between kill interval model 22 3.4.3 Estimation of total bear-caused calf mortality in Udtja and Gällivare 22 3.5 Habitat and movement models 23 3.5.1 Time periods in paper II and III 23 3.5.2 Resource selection functions 23 3.5.3 Spatial overlap between brown bears and reindeer 24 3.5.4 Relative probability maps and weighted RSF models 24 3.5.5 Fine – scale analysis of kill sites 25

3.5.6 Modelling CIRG and movement speeds 26

4 Results and discussion 27 4.1 Brown bear predation on reindeer calves 27 4.2 Reindeer and brown bear resource selection and kill site spatial distribution 29 4.3 Kill site fine scale characteristics 33 4.4 The green-wave and brown bear density 34

5 Concluding remarks 36

References 39

Acknowledgements 47

List of publications

This thesis is based on the work contained in the following papers, referred to by Roman numerals in the text:

I Støen O-G, Sivertsen TR, Rauset GR, Kindberg, J, Bischop, R, Skarin A, Segerström P and Frank J. Brown bear predation on neonatal semi- domestic reindeer: patterns and possible mitigations (manuscript)

II Sivertsen TR*, Åhman B, Steyaert SMJG, Rönnegård L, Frank J, Segerström P, Støen O-G and Skarin A. (2016) Reindeer habitat selection under the risk of brown bear predation during calving season. Ecosphere 7(11): e01583

III Sivertsen TR, Skarin A, Rönnegård L, Åhman B, Frank J, Segerström P and Støen O-G. Brown bear predation on semi-domesticated reindeer calves: relating kill locations to landscape heterogeneity (manuscript)

IV Rivrud IM**, Sivertsen TR**, Mysterud A, Åhman B, Støen O-G and Skarin A. Reindeer green-wave surfing constrained by predators (manuscript)

Paper II is published with open access.

* Corresponding author.

** These authors contributed equally to this work

The contribution of Therese R. Sivertsen to the papers included in this thesis was as follows:

I Participated in the planning of the work together with OGS, JF and PS. Contributed to idea and hypothesis. Participated in the field work and collection of data. Performed a substantial part of the analyses and compilation of results. Contributed significantly to the writing of the manuscript (main author O-G Støen).

II Designed the study, and formulated idea and hypothesis, together with AS, BÅ, LS and OGS. Performed a majority of the field work and collection of data. Performed the analyses and compiled the results. Wrote the manuscript with support from the co-authors, and acted as corresponding author towards the journal.

III Designed the study, and formulated idea and hypothesis, together with AS, BÅ and LS. Performed a majority of the field work and collection of data. Performed the analyses and compiled the results. Wrote the manuscript with support from the co-authors.

IV Participated in designing the study, and formulating idea and hypothesis, together with IMR, AS, O-G and AM. Contributed to idea and hypothesis. Participated in the collection of data. Performed all preparation of data, and contributed to analysis. Wrote the manuscript together with IM Rivrud (shared first author), with support from the other co-authors.

1 Introduction

Reindeer herding forms a basis for the Sámi cultural heritage. The land devoted to reindeer herding in Sweden covers more than half of the total land area (Sandström 2015). Owing to successful conservation efforts, populations of large carnivores have increased rapidly in Fennoscandia over the last century (Chapron et al. 2014). Whereas there is a management goal to sustain viable populations of large carnivores across Fennoscandia, Sweden have also committed to ensure the livelihood of the Sámi people, including a sustainable reindeer husbandry (Nilsson-Dahlström 2003). Direct losses of semi- domesticated reindeer to predation can be substantial, and depredation of reindeer causes both economical and emotional strain for the reindeer herders. The brown bear distributional range largely overlaps with the reindeer herding area in Sweden. However, knowledge so far is scarce about the impacts of brown bear predation on semi-domesticated reindeer populations. Brown bears are generally known to be efficient predator on ungulate neonates (Linnell, Aanes & Andersen 1995; Nieminen 2010), and can impose a major limiting factor on Rangifer (i.e. caribou and reindeer) population growth (Adams, Singer & Dale 1995). Moreover, integration of landscape heterogeneity in the understanding of large mammalian predator-prey interactions is experiencing increased focus. This includes identifying landscape structures that increase predation risk or prey safety, estimating the indirect costs in a prey population caused by behavioral adjustments to predation risk, and estimating possible consequences of landscape changes on predator-prey behavioral interactions. Improved knowledge of brown bear predation on semi-domesticated reindeer calves, and the predator-prey behavioral interactions in these systems, can help us better predict the impact from brown bear predation on semi- domesticated reindeer populations. It can thus aid in making informed and evidence-based management decisions, and contribute to an increased understanding of Rangifer - large carnivore interactions.

1.1 Reindeer husbandry in Sweden Reindeer husbandry represents an essential part of the Sámi culture and livelihood. Although it is a small industry on a national scale, it has great economic importance for local communities. The “Swedish Reindeer Herding Act” secures pastoral reindeer herding as an exclusive right for the Sámi people (Torp 2013). During the last decade, the number of semi-domesticated reindeer in Sweden has varied around 250 000 animals. Reindeer herding takes advantage of the natural adaptation of the species to a boreal/sub-arctic/arctic environment, and depends on large areas and high flexibility in land use to sustain productivity of the herd (Roturier & Roué 2009). The reindeer husbandry area in Sweden covers approximately 50 percent of the land area, and is divided into 51 reindeer herding districts. Of these, 33 are mountain herding districts, 10 forest herding districts and eight concession herding districts (Fig. 1). The mountain districts have their winter ranges in the forest, and the calving and summer ranges in alpine areas, whereas reindeer in the forest districts remain in forested areas year-round. The concession herding districts engage in reindeer husbandry east of the Swedish Lapland border with special permission from the administrative board of Norrbotten County. Except for occasional gatherings throughout the year, the reindeer are mostly freely ranged within the borders of the herding district. The most important events during a “reindeer herding year” is the migration from the winter ranges to the calving grounds in early spring, gathering of the herd for calf marking in the summer, and gathering for slaughter, separation into winter groups and migration to the winter ranges in early winter. Climate change, loss of grazing land and disturbance caused by infrastructure development, and increasing predator populations cause challenges to reindeer husbandry (Pape & Löffler 2012). Currently, the Swedish scheme for compensation is a “compensation-in- advance” scheme (Schwerdtner & Gruber 2007) based on the risk of economic loss by herders. This risk is estimated from the number of predators present within the herding districts (Swenson & Andrén 2005). In 2016, reindeer herders received 52.8 million SEK for estimated losses to predation, where 1.6 million SEK represented losses to brown bear predation (Sami Parliament 2017).

Figure 1. Maps showing the reindeer herding area in Sweden (left; source: iRENMARK - Sametinget, Sweden) and the brown bear distributional range in Norway and Sweden (right; darker color indicate higher bear densities, source: Scandinavian Brown Bear Research Project 2013)

1.2 Rangifer foraging ecology and antipredator behavior The foraging behavior of Rangifer reflects the seasonality of the arctic and subarctic regions, with large variations in food availability throughout the year, but where the annual phenological succession of vegetation tends to be highly predictable (Skogland 1984). Generally, the diet composition of Rangifer depends on the nutrient contents, digestibility, amount of secondary compounds and relative availability of potential food (White & Trudell 1980; Skogland 1984). Throughout spring and summer, Rangifer favor plants of early growth phase that are high in nutrients. During the leafing and flowering stages alpine and arctic plants commonly have a high level of TNC (total non- structural carbohydrates) and nitrogen and only small amounts of cell wall elements of low digestible value (Skogland 1984). In winter, Rangifer prefer to feed on lichens (Bergerud 1972; Skogland 1984; Danell et al. 1994; Kojola et al. 1995), and to a lesser degree on dwarf shrubs, mosses, sedges and grasses. Although poor in protein and most macrominerals, lichens are rich in soluble carbohydrates, which is an essential source of energy in the cold season, and due to low amounts of cellulose and lignin they are highly digestible (Klein 1990; Danell et al. 1994).

Rangifer commonly employ either a “space away” or a “space out” strategy to reduce predation risk during calving (Bergerud, Butler & Miller 1984; Bergerud & Page 1987; Seip 1991; Rayl et al. 2014). Some caribou populations in North America separate spatially from predation risk by migrating several hundred kilometers northwards to calve in areas above the tree line, thereby avoiding the high densities of predators that are present further south (Bergerud & Page 1987). Some Rangifer populations may space away from predators and alternate prey with shorter migratory movements to calving grounds in the mountains, on islands and along shore lines (Bergerud 1985; Bergerud & Page 1987). Forest – dwelling herds of woodland caribou typically persist at lower densities and space out during calving to increase searching time by predators (Bergerud & Page 1987; Seip 1991; Rayl et al. 2014), and also reduce predation risk by selecting habitats with lower encounter risk within the calving range (Rettie & Messier 2000; Mahoney & Virgl 2003; Pinard et al. 2012). Predation risk may also drive fine-scale selection of calving sites within the calving grounds. Rangifer is a typical follower species, being mobile and following its mother shortly after birth (Vos, Brokx & Geist 1967). Because Rangifer neonates grow at a maximal rate, they quickly gain the ability to flee from predators (Parker et al. 1989). Hiding may nevertheless be important immediately after birth. Indeed, during the first 48 hours, reindeer calves may adopt a prone position to avoid detection from predators (Lent 1966). Shrub cover can obscure the visibility of the calves, making it harder for predators to detect them (Bowyer, Kie & Van Ballenberghe 1998; Gustine et al. 2006), at the same time offering important spring forage for parturient females (Crête, Huot & Gauthier 1990). Also, Rangifer may choose calving sites at elevated locations for a better overview, and adjust the choice of slope directions according to the prevailing winds, to prevent the scent from reaching the predators (Bergerud et al. 1984, Gustine et al. 2006).

1.3 Brown bears in Sweden The brown bear population in Sweden has increased from an estimated number of 294 bears in 1942, to 834 bears in 1993, reaching a maximum of 3298 individuals in 2008. The most recent population estimate from 2013 suggested a decline to 2782 individuals (Swenson et al. 2017). The brown bear distributional range covers approximately two thirds of the land area in Sweden, and brown bears are only absent from the most southern parts of the country (Fig. 1). Brown bears are hunted at annual quotas in Sweden. The hunting season is in the autumn (21 August – 15 October, or until quotas are reached). Brown bears in Scandinavia are manly associated with forested areas at lower elevations (May et al. 2008; Støen et al. 2016). Brown bears hibernate, mainly from October to April (Linnell et al. 2000), and the mating season is during May and June (Dahle & Swenson 2003a). Their habitat use is largely driven by food availability, shelter opportunities, intraspecific interactions, and human avoidance (Moe et al. 2007; Martin & Basille 2010; Steyaert et al. 2013). Brown bears are generalist foragers with a broad diet, including various vegetation (e.g. grasses, sedges, herbs and berries), insects, and mammals (e.g. ungulates) (Mattson, Blanchard & Knight 1991; Dahle, Sørensen & Wedul 1998). The diet varies with availability and nutritional demands of the bears throughout the season (Mattson et al. 1991; Dahle et al. 1998). During the ungulate calving season (i.e., spring), ungulate neonates can be an important component of the brown bear´s diet (Mattson et al. 1991; Adams et al. 1995; Linnell et al. 1995; Nieminen 2010). Because brown bears are closely associated with forest habitat in Sweden, reindeer herding districts with their calving grounds located in the forest may be particularly vulnerable to brown bear predation.

2 Objectives

The aim of this thesis was to document brown bear predation patterns on semi- domesticated reindeer calves in Sweden, and to investigate the behavioral interactions of female reindeer and brown bears during the calving period. To increase understanding of the influence of brown bears predation on reindeer, the thesis evaluates individual brown bear kill rates on the calving range, reindeer and brown bear habitat selection patterns during calving, and the relation of kill site distribution to landscape characteristics in two forest reindeer herding districts in northern Sweden. Finally, on a broader scale, including seven herding districts, I investigated how the presence of brown bears may influence reindeer movement patterns and access to high quality forage. The main research questions were:

x Paper I: What are individual brown bear kill rates on the reindeer calving ground, and how do kill rates vary between individuals and over time? And further, how much of the total calf mortality in a herding district can be caused by brown bear predation? x Paper II: What are the characteristics of female reindeer and brown bear habitat selection within the reindeer calving range, and how does selection patterns and spatial overlap vary on a daily and seasonal basis, relative to temporal variations in brown bear predation risk? x Paper III: How does the spatial distribution of reindeer calf kill sites relate to landscape characteristics, and to the relative probability of reindeer habitat selection and reindeer-brown bear co-occurrence? Do fine-scale attributes of kill sites indicate effects of habitat on predation risk? x Paper IV: Do reindeer have lower access to high quality forage, and higher and more variable movement speeds, at higher bear densities? And, is this response most pronounced during the peak predation period?

3 Methods

3.1 Study systems

3.1.1 Udtja and Gällivare reindeer herding districts The study area in paper I - III was centered on the calving and post-calving ranges of Udtja (66.2° N,19.4 ° E) and Gällivare (66.6° N, 21.4 ° E ) forest reindeer herding districts, located in Norrbotten County, northern Sweden. The borders of the study areas defined in paper I (Udtja: 1283 km2, Gällivare: 2469 km2; Fig. 2), and further used as the framework for paper II and III, was delineated by a combination of the reindeer herder`s definitions of the reindeer calving range, formal herding district borders, and landscape features (i.e. rivers, roads and railways). The area is part of the European taiga, and the forest is dominated by Norway spruce (Picea abies) and Scots pine (Pinus sylvestris), interspersed with bogs, lakes and at the highest elevations subalpine birch (Betula pubescens) forest. The topography is characterized by an undulating forested landscape with elevations ranging from 13 to 714 m a.s.l. The human population is relatively low within the areas (average 0.02 per km2) with few human settlements. The densities of small roads (mainly gravel roads) and major roads (public roads with regular traffic) were approximately 0.25 and 0.02 km/km2 in Udtja, and 0.38 and 0.06 km/km2 in Gällivare, respectively. The reindeer densities in Udtja and Gällivare were between 1.1- 1.5 animals/km2. Udtja spring and summer ranges are mainly located within a closed military missile range, with the main human activities in the area being military training actions. Since 1995, a large part of the area is also a nature reserve with no logging activity allowed. In Gällivare, logging activities are more intense and road density is higher. In both districts, reindeer move freely within the district borders, and are subject to herding activities. The district

15 borders follow reindeer fences, rivers, roads and railroad tracks, which support reindeer herders to separate their herds, but do not constitute impassable barriers for wildlife. In Udtja in particular, seasonal movements by the reindeer from the winter areas to the calving ranges correspond to the elevation range following a south-north gradient, with higher elevations in the north. Prior to the study the two reindeer herding districts claimed losses of calves to bear predation. The brown bear population in Norrbotten was estimated to 713-1152 individuals in 2011 (Tyrén 2011). Bears are hunted during the annual hunting season in the autumn (21 August - 15 October or until quota are filled). In Udtja and Gällivare, the estimated brown bear population size in 2010 was 62-96 and 53-75 individuals, respectively. Wolves are absent in the study area and population densities of lynx and wolverines are low (Tyrén 2011).

Figure 2. The study area in paper I - III, located on the calving and post-calving ranges in Udtja and Gällivare forest reindeer herding districts. The proximity function in the brown bear GPS- collar was turned on when the bear was inside the defined borders of the study areas. The colored areas indicate the study areas in 2012 and the black line indicate the range where the proximity function in the brown bear GPS collars was activated all years of the study period. Black dots represent all reindeer carcasses documented killed by brown bears during the study period 2010- 2012.

3.1.2 Herding districts in paper IV In paper IV, locational data from reindeer was collected from four forest herding districts (Gällivare, Malå, Udtja and Östra Kikkejaure) and three mountain herding districts (Handölsdalen, Njaarke and Sirges), within the reindeer husbandry range in Sweden. The calving- and post-calving ranges of the forest herding districts are all characterized by undulating boreal forests interspersed with mires and lakes. Active forestry occurs in all forest districts apart from within the nature reserve in Udtja. The mountain district calving ranges are all located in the mountain region and mainly above the tree line.

3.2 GPS and predation data

3.2.1 Collaring of reindeer and brown bears in Udtja and Gällivare From 2010-2012 in Udtja and 2011-2012 in Gällivare, the majority of adult reindeer females in the study populations were equipped with proximity UHF- collars (Udtja 2010:990, 2011:1176, 2012:1235; Gällivare 2011:893, 2012:1350), and 24 brown bears with GPS collars containing UHF receivers (Vectronic Aerospace GmbH, Berlin, Germany) of which 21 bears (Udtja 2010:4, 2011:7, 2012:8; Gällivare 2011:4, 2012:8) were tracked within the calving ranges, with the proximity function activated (see explanation further down). Also, a total of 97 individual reindeer females (Udtja 2010:19, 2011:29, 2012:25; Gällivare 2011:16, 2012:21) were GPS-collared, these were mainly so-called “leading females”, considered to be most representative for the herd movements. The GPS was scheduled to take a location every 2 hours (Telespor AS, Tromsø, Norway; Followit AB, Stockholm, Sweden). All reindeer females equipped with a proximity collar were documented to be pregnant. Pregnancy status of female reindeer was determined using a rectal ultrasound probe in late March or early April. The reindeer UHF proximity collars emitted a weak UHF signal every second that could be detected by the brown bear GPS collars within the proximity of up to 100 m. The brown bear GPS collars were programmed to scan for UHF signals from the reindeer collars for 1.5 s every 8 s. Every time a UHF signal was detected, the GPS positioning schedule was altered from the standard 30-min schedule to one GPS position every 1 min and 10 s. This 1-min schedule persisted for one hour after the UHF signal was detected, and if new signals were received within this period, it lasted until 1 h after the last UHF signal. The GPS-collar sent an Iridium satellite message with the GPS locations to a database several times per day. With no Iridium coverage, the GPS locations were stored and sent at the

17 next possible occasion. The proximity function of the bear collars was activated when the bears were within the study areas during the period from 26 April to 24 September annually.

3.2.2 Documentation of reindeer carcasses in Udtja and Gällivare During 2010, all 1-min GPS locations by brown bears were visited, but since no calf carcasses were found on tracks or clusters of minute locations with less than four GPS location within a 30 m radius, only clusters with ≥ 3 1-min GPS locations within a 30 m radius were visited in 2011 and 2012. At a kill cluster, reindeer carcasses were classified according to age (calf, adult) and sex (male, female). We estimated the time of death based on carcass decomposition and other signs (e.g. in snow or vegetation) to decide whether the calf was killed by the GPS collared bear, or by other causes. The conclusion of mortality cause was determined by consensus, following the standards for provincial rangers (Skåtan & Lorentzen 2011) (Fig. 3). All clusters were inspected by one researcher and one reindeer herder. If clusters from several bears were overlapping in time on a kill site, the bear with the first GPS position at the kill site were judged to have killed the reindeer, unless the GPS 1-min locations gave clear indications that a another bear likely was responsible for the kill.

Figure 3. Remains of a reindeer calf killed by a brown bear in Gällivare reindeer herding district. Photo: Therese R. Sivertsen

3.2.3 Processing of GPS and kill site data for paper II-IV All reindeer GPS data were checked manually for obvious errors, and by the method of Bjørneraas et al. (2010). Brown bear data was automatically screened to remove location outliers when downloaded from the collar, and were also checked manually upon inclusion in analyses. To estimate resource selection functions in paper II and III, we used reindeer and brown bear location data located inside the 100% minimum convex polygon (MCP) encompassing all reindeer GPS positions within the predefined study area in paper I, from 10 May – 30 June. The data encompassed 110 adult female reindeer years and 29 brown bear years, representing 97 individual reindeer females (Udtja:67; Gällivare:30) and 19 individual brown bears (Udtja:11; Gällivare:8). The reindeer GPS data representing seven herding districts in paper IV included totally 557 542 locations from 319 GPS-collared reindeer females, collected in 2003 and from 2008 to 2015, covering the calving period (11 May - 9 June) and post-calving period (10 June - 31 August). The individual home ranges corresponding to the two sub-periods were estimated by calculating 95 % adaptive Local Convex Hull (a-LoCoH) polygons using the “adehabitatHR” package in R (Calenge 2006, R Core Team 2016). To analyze the spatial distribution of kill sites, we used all kill sites within the area where the brown bear proximity collars had been activated during all years of study (Fig. 2), and where we had brown bear and reindeer locational data. To avoid pseudo replicates in our analyses we removed one kill site by random when two sites were < 50 m apart (totally 13 sites removed), resulting in totally 305 kill sites (Udtja: 178; Gällivare: 127).

3.3 Environmental data

3.3.1 Landscape characterstics in paper II, III and IV The landscape parameters included in the resource selection models in paper II and III were extracted using Arc GIS 10.0-10.3 software (ESRI Inc., Redlands, California, USA ©2010–2015). Land cover classes included coniferous moss forest, coniferous lichen forest, deciduous forest (included in “other”-category in Gällivare, paper III), wetland, other open habitats, recent clear-cuts (0-5 years), old clear-cuts (6-12 years, or < 2 m height in the year 2000) and young forest (2-5 m height in the year 2000). Clear-cuts were merged to one category in Udtja in paper II, and for both districts in paper III. In addition we included elevation from a digital elevation model (DEM) 50 m in grid size, terrain

19 ruggedness (VRM, neighborhood parameter set to five cells; Sappington et al. 2007) calculated from DEM, and minimum Euclidean distance to the nearest large road (public road with regular traffic) and small road (typically gravel roads) roads. Large roads were not included in Udtja, due to a skewed distribution and correlation with elevation. We transformed distance to road using 1 - eDd (d=distance to feature, D was set to 0.002, approximate effect zone < 1500 m), resulting in exponential decays ranging from 0, to 1 at very large distances (Nielsen, Cranston & Stenhouse 2009). The final map was rasterized into a 50 m grid. In paper IV, maps of terrain ruggedness were made with R “raster package” (Hijmans & van Etten 2015), and slope and aspect using ArcMap 10.3.1, all derived from the DEM model. Aspect was converted to "northness" (cosine transformed) ranging from -1 (south) to 1 (north). Maps were rasterized with a resolution of 100 m. All digitized geographical data were provided by Lantmäteriet (www.lantmateriet.se), land cover data was obtained from vegetation vector maps, the Swedish Land cover Map 25 × 25 m (SMD Corine Land Cover Data 2000) and satellite image forestry data ("Utförd avverkning", Swedish Forest Agency 2015).

3.3.2 Fine scale registrations in paper III In paper III, we recorded fine-scale habitat characteristics at totally 142 kill sites and 126 control sites from 13 May to 9 June in 2012 within Udtja and Gällivare herding districts. Control sites represented sites used by bears in close vicinity to reindeer females during this period, but where no kill had occurred in instant distance or time (“encounters”; first bear GPS minute location after proximity function activation, minimum 200 m and 5 min from a known kill). We registered land cover within a 20 m radius of the kill, distance to visible habitat edge, snow depth and cover, and sightability based on i) average distance to closest visual obstructions measured with a range finder sitting in knee height in each cardinal and one random direction, and ii) distance to walk until we lost site of the 30 m high lower section of a collapsible cover cylinder, 60 cm high and 30 cm in diameter (Ordiz et al. 2009).

3.3.3 Plant phenology in paper IV Plant phenology was quantified using the satellite-derived normalized difference vegetation index (NDVI; Pettorelli et al. 2005) derived from

250x250m satellite images taken every 16 days and downloaded from the NASA Land Processes Distributed Active Archive Center (LP DAAC 2000) website. By fitting a double logistic curve to each pixels annual NDVI time series, the instantaneous rate of green-up (IRG) can be extracted by taking the first derivative of the part of the curve that covers spring (Bischof et al. 2012). By using reindeer female GPS location data and calculating the cumulative IRG (CIRG) for each reindeer individual, we could get a measure of the total amount of high quality forage experienced by the individual (Bischof et al. 2012).

3.3.4 Bear density index in paper IV Bear density in the home ranges of reindeer females was estimated from the latest scat survey (non-invasive DNA) conducted in each County (www.rovbase.no, Bellemain et al. 2005, Kindberg et al. 2011). We used all bear scats where the individual bear had been identified and calculated scat density with the density tool and 1000 m resolution in ArcGIS (ESRI 2015).

3.4 Brown bear predation on reindeer calves

3.4.1 Seasonal kill rate model We used the registered number of reindeer calves killed by individual GPS- collared bears within Udtja and Gällivare study areas to estimate kill rate as a function of bear demographic category. Because the collaring of female reindeer and registrations of kills were restricted to the defined study areas, whereas bears also stayed outside these borders, we accounted for individual differences in exposure time, using hours each bear spent within the study areas as an offset variable, log transformed to match the logit link function of the models. Alternative classifications were compared, as well as inclusion of herding district, and the best model chosen using AICc. We employed zero- inflated negative binomial models to account for over-dispersion and enable modelling of count data with more zeroes than expected from the Poisson distribution (Zeileis, Kleiber & Jackman 2008). This model was suitable to handle our data where several bears were not registered to kill any calves. It includes two separate processes; one part to model excess zeros, represented by a binomial GLM with a logit link, and a count part to model over-dispersed count outcomes, represented by a negative binomial GLM with a logit link (Zeileis et al. 2008). Here, the zero-part thus quantify the effects of variables affecting the probability of killing zero calves, and the count-part estimates the

21 number of reindeer calves killed by a bear per season (potentially corrected by the binomial part). Females with cubs of the year (FCOY) were not registered to kill calves, and were included in the binomial part, and the other demographic groups in the count part of the models. Since the frequency of repeated individual measurements was relatively low in our data set (n=8), we considered it justified to ignore the variation caused by repeated individual measurements in this model. To fit the model, we used the zeroinfl() function in the pscl package, version 1.4.9 in R (Jackman 2015). For model predictions, we calculated kill rates with “exposure time” from 0 to 991 hours (maximum observed value), divided into 100 intervals, and bootstrapped confidence intervals with 1000 replicates.

3.4.2 Between kill interval model To calculate between kill intervals, we only used intervals between successive kills when the bear had resided within the defined calving range the entire time of the interval. Since the distribution of intervals was right-skewed, we used log-transformed time (minutes) between kills as a response variable in linear mixed effects models, using R package lme4 (Bates et al. 2015). As the distribution of kills throughout the year showed a distinct peak during late May, we included “day/week of year” as potential covariates, both as first and second order, in addition to the same demographic groups of bears as used for seasonal kill rates. Females with cubs of the year was not included. To account for potential individual effects and repeated observations, we tested combinations of year, herding district and bear individual as random intercepts. Bear individual was the only random effect included in the final candidate model set. The same principles as described for seasonal kill rates were used for model selection and predictions.

3.4.3 Estimation of total bear-caused calf mortality in Udtja and Gällivare The total calf mortality caused by brown bears in Udtja and Gällivare reindeer herding districts was estimated from i) average number of bears within demographic categories expected to have home ranges overlapping with the calving ranges, ii) total calf mortality within the herding districts and iii) expected seasonal kill rate for demographic categories of brown bears. The average number of bears was determined and classified to sex from scat collection and DNA sampling within a 19.7 km buffer of the study areas, corresponding to the mean radius of the GPS-collared bear’s home ranges.

To estimate the total mortality of reindeer calves among adult reindeer females in the two study populations, female:calf ratios were registered by visual observations during summer calf marking (late June to mid-July) in the herding districts, when the females and calves are rounded up in corrals. We then used the category specific effect sizes of the best kill rate model to predict the total number of calves killed on the calving ground, based on average time spent by GPS-collared bears inside the study area, and the estimated total number of bears and demographic classifications. Finally, we calculated the proportion of total calf mortality that was caused by bear predation, by comparing this number to the estimated total calf mortality in the two study areas.

3.5 Habitat and movement models All statistical analyses were done in program R (R Core Team 2016). For linear mixed-effect models the package “lme4” (Bates et al. 2015) in R was employed.

3.5.1 Time periods in paper II and III In paper II and III we restricted the study period to 10 May until 30 June. The focus was thus on the main predation period on reindeer neonates, and the succeeding period after predation ceased, before the reindeer were gathered for calf marking in early summer. Based on information from paper I, we subdivided the study period into the predation period (10 May – 9 June; 332 out of 335 calves were killed in this time interval) and the post-predation period (10 – 30 June). Further, we classified data into high predation hours (6 PM to 6 AM) and low predation hours (6 AM to 6 PM) within the predation period, based on findings of diurnal brown bear predation patterns in paper I.

3.5.2 Resource selection functions Resource selection functions (RSFs), estimated using logistic regression and a use-availability design, is a well - established method in habitat selection studies (Johnson et al. 2006). We employed binary logistic regression (Lele & Merrill 2013) to estimate resource selection functions for reindeer and brown bears on the reindeer calving range (paper II), and the distribution of kill sites relative landscape characteristics (paper III). In paper II the binomial response represented reindeer and brown bear GPS locations versus an equal number of random location for each individual distributed within the two calving ranges,

23 respectively. The models included the environmental variables land cover, elevation, terrain ruggedness, distance to nearest road, and the interaction term time period as fixed factors, which made it possible to compare the selection patterns in relation to temporal variation in brown bear predation risk on a seasonal (predation/post-predation) and daily (high/low predation hours) basis. Using a model selection approach based on AICc and parsimony (Arnold 2010), we determined the best performing models for reindeer and brown bear resource selection. We also checked if the models explained more variation than the null-model, based on AICc. The best models were then validated using k-fold cross validation, following the approach of Boyce et al. (2002). In paper III, we employed the same set of covariates found to be important for reindeer and brown bear resource selection, to evaluate the spatial distribution of kill sites relative to random locations using resource selection functions. Here, the binomial response was kill sites versus random sites, ten times the number of kill sites, to make the analysis more robust. In all models, generalized linear mixed models were used, to account for repeated measurements across individuals (Zuur et al. 2009).

3.5.3 Spatial overlap between brown bears and reindeer In paper II, we used predicted values from the RSFs to further investigate the spatial overlap between brown bear and reindeer female resource selection in relation to temporal variation in brown bear predation risk, on a seasonal and daily basis. We determined the level of spatial autocorrelation within the RSF maps using Gaussian-fitted semivariograms and considered the average semivariogram range of the RSF maps as the distance in which locations become spatially independent (see Hiemstra et al. 2009 for detailed description of theory and methodology). Based on this distance we generated a set of random locations in each study area, and extracted RSF- values for each species-time period combination. Pearson product moment correlation was then used to quantify correlation between reindeer and bear RSF values within the respective time periods.

3.5.4 Relative probability maps and weighted RSF models In paper III, we used the best reindeer and brown bear RSF models from paper II, to estimate predictive maps with relative probability of reindeer habitat selection and reindeer – brown bear co-occurrence on the calving range during the predation period. We calculated relative probability for reindeer and brown bear selection for each 50 × 50 m grid cell from the model parameter estimates,

24 but dropped the fixed and random intercepts (Polfus, Hebblewhite & Heinemeyer 2011):

(1.)

where w(x) is the relative probability of selection and βn is the estimated coefficient for covariate xn (Manly et al. 2002). Following the procedure of Courbin et al. (2009), we then used w(x), and the smallest (wmin) and largest (wmax) RSF values for each model, to scale predicted RSF-values between 0 and 1:

(2.)

Finally, we calculated the relative probability of brown bear and reindeer co- occurrence ŵco:

(3.)

where ŵreindeer and ŵbrown bear is the relative probability of selection in each 50 × 50 m grid cell for female reindeer and brown bear, respectively.

Then, to investigate the relation between kill site distribution, and reindeer habitat selection and co-occurrence probability, we sampled random points within the study area weighted by ŵreindeer or ŵco for each 50 × 50 m raster cell, and used these to estimate resource selection functions for kill site distribution, as described above. If the distribution of kill sites were proportional to the relative probability of reindeer habitat selection or reindeer-brown bear co- occurrence, no significant effects would be present in the model, whereas significant effect for a given landscape characteristic indicated a difference in kill probability relative to the likelihood of reindeer habitat selection or reindeer-brown bear co-occurrence for this covariate.

3.5.5 Fine – scale analysis of kill sites Binomial logistic regression was used to compare fine-scale habitat characteristics between kill sites and control sites in paper III. Due to a small

25 sample size, we reduced the degrees of freedom in the models and merged land cover into “open”, “semi-open” and forest habitat. Edge was defined as a distinct visible edge between these categories and divided into four categories (“0-10 m”, “11-50 m”, “>50 m” and “no visible edge”). To avoid inclusion of extreme distances in the sightability index, 100 m was set as the maximum limit. To avoid a temporally unbalanced sample, we identified the break-point when predation decreased, and randomly removed control sites after this date so that the number of kills and control were equal. We made a snow index by multiplying mean snow depth with snow cover. We pooled data across study areas and if sites were < 50 m apart, one site was removed by random. Because sightability and snow conditions change over the season, we restricted inclusion of sightability measures within seven days after the true date, and only included snow measurements taken before the accumulated snow index was 99 %. Due to different number of observations for the covariates, we tested models separately (using AICc and compare to null-model) within each data set; “distance to edge” (kill=142, control=126), “sightability” (kill=142, control=83), and “snow” (kill=108, control=58).

3.5.6 Modelling CIRG and movement speeds We calculated the means of all covariates within each adult female reindeer individual 95 % a-LoCoH home range for each sub-period. Linear mixed effect models were used to model access to high quality forage (CIRG) and movement variation (SD of movement rate) in reindeer as a function of brown bear density. Candidate predictors included bear density index, subperiod (calving and post-calving), elevation (m a.s.l.), terrain ruggedness index, slope (degrees), northness (relative aspect), reindeer herding district habitat type (mountain or forest), minimum distances to power lines, railways and large and small roads (all in m), the interaction between subperiod and bear density index, and year and individual id as random factor. Mean daily movement speed of reindeer was modelled with the same set of predictors, but with Julian day instead of study period, and using generalized additive models (GAM) with package “mgcv” (Wood 2011). Final models was determined with AIC.

4 Results and discussion

4.1 Brown bear predation on reindeer calves Documentation of brown bear predation on the calving ranges of Udtja and Gällivare herding districts in paper I showed that brown bears killed mainly calves (333 out of 350 recovered reindeer carcasses documented killed by a radio-collared bear). Bears killed on average 11 calves on the calving ranges per season. Calf predation was mainly concentrated to three weeks in late May and the beginning of June (Fig. 4). This pattern was highly correlated with the abundance of new-born reindeer calves (e.g. Ropstad 2000, Holand et al. 2003), and is in accordance with previous documentation of predation on caribou (Adams et al. 1995; Jenkins & Barten 2005) and moose (Swenson et al. 2007). Reindeer calves rapidly increase mobility and locomotive ability (Lent 1974), and this is probably the main explanation for that predation is highly concentrated to the first weeks post-partum (Lent 1974; Jenkins & Barten 2005). Also, predation happened more frequently during nighttime (6 pm - 6 am), than daytime (6 am - 6 pm). Seasonal kill rate did not differ between the demographic categories of bears when controlling for time spent on the calving grounds, except for females with cubs of the year, which were not documented to kill any calves. This differs from earlier studies on bears and other carnivores where demography influenced kill rates (Young & McCabe 1997; Knopff et al. 2010; Mattison et al. 2011). However, Boertje et al. (1988) did not document differences in kill rate on caribou calves between demographic categories of bears. Perhaps, when the bear is on the calving ground, the effect of high availability of vulnerable prey during a very short time override any effects of demographic differences on kill rates. Nevertheless, a large variation in kill rates within the categories, combined with a relatively small sample size can

27 explain the lack of difference in our study. One possible explanation for the variation independent of category could be that bears not necessarily adjusted home ranges to calf availability. Since calving locations differed somewhat between years, availability within the home range would change, and thus affecting kill rates. Overall, seasonal kill rate was a positive function of time spent inside the calving ranges. Males, however, stayed on the calving ranges on average half as long as females. Males generally have larger home ranges than females (Dahle & Swenson 2003b) and also possibly move more during the mating season in May and June (Dahle & Swenson 2003a). The seasonal kill rate of adult males could thus have been underestimated in this study if their larger home ranges overlapped with calving ranges not included in this study. Sub-adult bears had larger kill intervals than adult bears, and length of intervals increased slightly throughout the season. Sub-adults probably have less experienced than adults in hunting calves, which has been seen in other carnivores (Holekamp et al. 1997; Sand et al. 2006). An explanation to why kill intervals increased with time could be that calves get more difficult to catch as they grow. Also, lower densities later in the season can play a role. The total number of bears potentially residing within the two study areas was estimated to be 71 [62-96] bears in Udtja and 58 [53-75] bears in Gällivare. Multiplying average bear seasonal kill rate, extracted from the model, with the total number of bears (excluding females with cubs of the year) indicated that brown bears were responsible for a considerable proportion (39 and 67 %) of the observed calf losses within the two reindeer herding districts. Average annual calf mortality in the herding districts was approximately 43 and 41 %, indicating that total bear caused mortality was around 29 and 16 %, in Udtja and Gällivare, respectively. In a management perspective, the short window of predation is an important finding. This imply use of interventions that separate bears and calving reindeer in space and time during this short period. Also, that time on the calving ground seemed to be more important than differences between demographic categories, imply that generally reducing bear densities on the calving grounds likely will reduce predation rates on reindeer calves.

Figure 4. Timing and frequency of predation on reindeer neonates by GPS-collared brown bears in Udtja and Gällivare reindeer herding districts reported in paper I

4.2 Reindeer and brown bear resource selection and kill site spatial distribution Estimation of reindeer and brown bear resource selection functions in paper II revealed marked differences in habitat selection between forest-living female reindeer and brown bears on the calving grounds. Reindeer mainly selected open areas and recent clear-cuts, and avoided young forest throughout the study period. Further, reindeer switched from selecting coniferous lichen forest and old clear-cuts in the predation period to selection of wetlands in the post- predation period. Brown bears mainly selected moss forest, young forest and avoided recent clear-cuts throughout the study period. However, reindeer did not seem to alter their behavior in response to spatiotemporal variations in the risk from brown bear predation. Rather, the results indicated that spatiotemporal behavioral adjustments by brown bears dominated, with a marked increase in spatial overlap between reindeer and brown bears in the predation period (versus post-predation period) and in high predation hours (versus low predation hours) (Fig. 5). The increased preference for reindeer habitat by brown bears was reflected in a distinct seasonal switch from

29 selection of less rugged terrain and higher elevations in the predation period to more rugged terrain, and lower elevations in the post-predation period, this being particularly pronounce in Udtja. Also, brown bear land cover selection was generally more similar to reindeer in the predation period. Reindeer habitat selection was nearly constant between high and low predation hours. In contrast, brown bears changed patterns in land cover selection at the daily level, more closely resembling reindeer in high compared to low predation hours. This suggest that bears might have actively searched for reindeer calves in our study areas. A comparable predator to the brown bear, black bears in North America, hunted in an opportunistic manner on caribou neonates (Bastille-Rousseau et al. 2011). Forest-dwelling woodland caribou are assumed to persist at low population densities and avoid predation by scattering out in the forest to reduce hunting efficiency by the predator (Bergerud & Page 1987; Seip 1991). The higher population densities in semi-domesticated reindeer herds, likely reduce the efficiency of such a spreading out strategy, and likely make active searching by the bears more profitable.

Figure 5. From paper II. Resource selection correlation between reindeer and brown bears, tested with Pearson’s product-moment correlation, comparing the predation (Pred) and post-predation (Post) period, and high (High) and low (Low) predation hours, in Udtja reindeer herding district (a,b) and Gällivare reindeer herding district (c,d). The figure shows correlation coefficients (Pearson’s R) and 95% confidence intervals.

The picture above was nuanced by relating calf kill sites to landscape characteristics and reindeer and brown bear resource selection functions in paper III. Comparing kill site spatial distribution to the relative probability of reindeer selection indicated that reindeer females might be able to take advantage of higher elevations in the landscape and to some degree areas closer to large roads, to reduce predation risk. Also, reindeer seemed to be at higher risk of encountering a brown bear and fall victim to predation in coniferous and young forest, and open habitat in Udtja, compared to wetlands. Moreover, the results suggested that the location of kill sites varied as a function of landscape characteristics (Fig. 6), and that this variation highly corresponded to reindeer – brown bear co-occurrence. However, we found possible evidence for a lower risk of kill in clear-cut habitats relative to co-occurrence probability in Gällivare and, despite increased co-occurrence probability close to roads during nighttime, that kill risk was unrelated to road distance in Udtja.

(! (! (! (!(! (! (! (! (! (! (! Ü (!(!(!(! (! (!(! (! (! (! (! (! (! (! Kill sites (! (! (! (! (!(! (! (! High : 1 (!(! (! (! (! (! (! (! (! (! (! (! (! Low : 0 (! (! (! (!(!(! (! (! (! (! (!(! (! (! (! (! (! (! (! (! (! (! (! (! (! (! ! (! (!(!(! (! (! (! (!(!(! (! (! ( (! (! (!(! (! (!(! (!(! (! (! (!(!(!(! ! (! (!(! ! (! (! (! (! (!(!(!(!(! ((!(! (!( (!(! (! (! (! (!(! (! (! (! (! (! (!(!(!(!(!(!(!(! (!(! (! (!(! (! (!(! (! (! (! (! (!(! (!(! (! (! (!(! (!(! (! (!(! (! (!(! (! (! (! (! (!(! (! (! (! (! (! (! (! (! (! (! (! (! (! (! ! (! (! (! (! (! (! ( (! (!(!(! (! (! (! (! (! (! (! ! (! (! ((! (! (! !( (! (! (! ! (! (! (! (!(!( (! (! (! ! (!(! (!(! ( (!(! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (!(! (! ! (! (!! (!(! (! (! (!(! ( ( (! (!(! ! (! (!(! (!( ! (! (! (!( (!(!(! (! (!(!(! (! (! (! (!(! (! (! (!(!(! (!

010205Km

Figure 6. Reindeer calf kill sites used in the analysis in paper III, and relative probability of kill site occurrence, estimated from binomial logistic regression, comparing spatial attributes of kill sites to complete random locations within the study areas.

We suggest that the discrepancy between kill sites and co-occurrence probability close to small roads may be explained by variable road response between females with and without calf at heel, or lower hunting effort closer to roads by brown bears, rather than landscape effects. Reproductive status can affect behavior, and females with calves often express stronger avoidance responses than females without a calf (Wolfe, Griffith & Wolfe 2000; Barten, Bowyer & Jenkins 2001; Hamel & Côté 2007; Skarin & Åhman 2014; Leblond

31 et al. 2016). Females without calves could have been present, due to variation in timing of birth, and mortality throughout the season. Higher road use by bears in nighttime could reflect use of roads for travelling, and a higher activity level and movement rate by brown bears to compensate for less daytime activity, in response to diurnal variation in human activity (Ordiz et al. 2014). Although brown bears generally increased preference for higher elevations in the predation period, bears may avoid ridge tops, to be less exposed. Thus, use of higher elevations by reindeer may reduce encounter risk, increase detection rates of brown bears and facilitate escape probability. Also, selection for clear-cuts may reduce bear encounter rates, provide good visibility and also, concealment cover for the calf (Dussault et al. 2012). Possibility for early detection of predators and hiding cover may reduce calf predation risk, as has been suggested both for caribou (Gustine et al. 2006; Carr, Rodgers & Walshe 2010; Pinard et al. 2012) and moose (Bowyer et al. 1999). Both brown bear habitat selection patterns documented in paper II and the analysis of kill site distribution in paper III, suggested higher bear encounter probability and predation risk in young forest habitats. Clear-cut habitats may be beneficial in terms of calf survival, but logging activity will in eventually lead to greater abundance of young regenerating forest. Thus, forestry may in the long run reduce available reindeer habitats, but increase habitat preferred by brown bears. Also, as suggested by Dussault et al. (2012), if females retain high calving site fidelity and the selection for clear-cut areas persist as the forest grow, this can give adverse effects on survival. Indeed, calving site fidelity appear to be common among several ungulate populations (Ferguson & Elkie 2004; Wittmer, McLellan & Hovey 2006; Tremblay, Solberg & Sæther 2007). Opposed to Gällivare, there were no indications of effects of clear-cuts on kill site distribution after accounting for co-occurrence probability in Udtja (i.e. no significant effects in the co-occurrence model). This may have been due to low occurrence of clear-cuts, especially recent clear-cuts, compared to Gällivare. Also, in Udtja kill site distribution relative to elevation, did not differ from that expected from reindeer selection. We wonder, however, whether an effect of elevation could have been masked by the elevation gradient that reindeer follow during spring, which is most pronounced in Udtja. Overall, the choice to pool data over years provide more robust estimates from a larger sample size, but may come at the cost of losing some information. Thus, future studies would benefit from using longer time series with the possibility to integrate climatic variation between years. Moreover, spatial variation in predation risk and antipredator responses can take place at a number of spatial

32 scale, and for example investigations of calving site selection and vigilance behavior should further improve our understanding of these systems.

4.3 Kill site fine scale characteristics The majority of kill and control sites included in the fine-scale analysis in paper III, were located inside the forest with no visible edge. Yet, compared to control sites, kills occurred more frequently close to habitat edges (0-10 m), the majority being forest edges, and tended to occur less frequently at distances of 11-50 m from a visible edge (Fig. 7). The higher kill frequency close to edges could be because reindeer select such habitats for foraging, as they can provide nutrient-rich forage in spring (Warenberg 1982). However, forest edges may also reduce the probability of detecting brown bears coming from the forest. Thus, such habitats may represent a trade-off situation for reindeer, representing both high forage quality and high risk. In addition, edges may act as obstacles for movement and increase the predators chance to catch a calf that is trying to flee. There was a slightly significant lower sightability (range finder measure) at kill sites compared to control sites (β=-0.016, 95% CI = [-0.032,-0.001]). This is in accordance with several other studies which have found that sightability plays a role for predation risk on ungulate calves (Bowyer et al. 1999, Gustine et al. 2006). There was also significantly less snow cover on kill sites compared to control sites (β=-0.05, 95% CI = [-0.09,-0.01]). We believe, however, that this most likely reflects reindeer`s preference for less snow cover. Importantly though, a bear might want to drag a kill out of deep snow or into cover, likely influencing these measures. Including field measurements of calving sites would clearly improve understanding of fine scale habitat characteristics and risk. Whereas several reports exist from North America (e.g. Gustine et al. 2006, Carr et al. 2010), data on reindeer calving sites in Fennoscandia is still scarce.

Figure 7. From paper III, showing a) data distribution between distance (m) to edge-categories for kill sites and control sites and b) predicted probability of kill compared to control sites given distance to edge category estimated from binomial generalized linear regression

4.4 The green-wave and brown bear density In paper IV, we found that semi-domesticated reindeer followed movement paths with lower access to high quality forage when bear density was high, and generally moved faster at higher bear densities (Fig. 8). Our results thus indicated that predation risk limited reindeer’s ability to follow the spring flush of nutritious forage, causing a trade-off between access to forage and avoiding predation. Nutritional demands, and availability of high quality forage, is generally assumed to be high during the ungulate lactation period (McEwan & Whitehead 1972; Crête et al. 1993; Parker, Barboza & Gillingham 2009). However, since Rangifer is recognized as a capital breeder (Taillon, Barboza & Côté 2013), largely relying on body reserves for gestation and early lactation (Stephens et al. 2009; Albon et al. 2017), they may be adapted to handle low forage quality at this time, and hence more willing to sacrifice following green- up in order to increase safety. Higher movement speeds at higher bear densities, may be due to more frequent flight responses due to bear encounters. To be on the move may also work as an antipredator strategy, to get less predictable in space (Lima & Dill 1990; Fischhoff et al. 2007). A simultaneous drop in movement speed across all populations towards the middle of the calving period, indicate the calving events (Panzacchi et al. 2013). Birth synchrony may also reduce predation risk (Rutberg 1987; Kerby & Post 2013). Opposite to what we expected, the effects of bear density on green-up response and movement speed remained throughout the growth season. This could indicate a persistent response to risk by reindeer females, as has been shown for other ungulates (Byers 1997). However, both Barten et al. (2001)

34 and Latombe et al. (2013) have showed that caribou change habitat selection in response to temporal variation predation risk. An alternative explanation could be that effects from insect harassment was confounded with bear density during the post-calving period. The most alpine habitats, Sirges and Handölsdalen, also had the lowest bear densities reported in our study. Disturbance from insects can cause, or enhance, mismatch with green-up (Hagemoen & Reimers 2002; Bergerud & Luttich 2003; Skarin et al. 2010), but this effect may be less pronounced in alpine than in forest habitats (Helle & Aspi 1984). Variation in movement rates was not affected by bear density. However, both movement speed and variation in speed was markedly higher in the forest, compared to in the mountains. This could be because brown bear predation generally is higher in forest herding districts, with the brown bear home ranges completely overlapping the calving grounds. Forest reindeer could be driven to move more between smaller patches of forage- and cover habitats to hide from predators (Mysterud & Østbye 1999), and frequently increase their speed to flee from bears. Reduced intake of high quality forage combined with higher and more variable speed, affects the energy budget, and is expected to have negative effects on body condition (Couturier et al. 2009; Bischof et al. 2012). Overall, our study thus indicates that the presence of brown bears may have indirect costs for the reindeer females and their calves.

Figure 8. From paper IV. Predicted mean daily movement speed in relation to Julian day and bear density index, based on a generalized additive model. Predictions are made for the mean bear density experienced by all individuals within each herding district. The herding district habitat is shown with solid (forested) and dashed (mountainous) lines. The vertical dashed line shows the two sub-periods calving (11 May - 9 June) and post-calving (10 June - 31 August).

5 Concluding remarks

Even though brown bears are known to be efficient predators on ungulate neonates (Adams et al. 1995; Linnell et al. 1995), only a few studies have documented brown bear predation on semi-domesticated reindeer calves in Fennoscandia (Nieminen 2010). In paper I in this thesis, we documented high predation rates by brown bears on semi-domesticated reindeer calves. That reindeer calves mainly are vulnerable to brown bear predation during the first weeks post-partum, is in accordance with previous findings (Adams et al. 1995; Linnell et al. 1995). In fact, in the Sámi language reindeer neonates less than two weeks old are traditionally called “njäbttso”, which means weak and with poor locomotive skills (Ryd 2007), indicating that these are recognized as important and closely linked attributes of the calf. Reindeer herders in Sweden are compensated for potential losses to brown bear predation based on the size of their herding district (www.sametinget.se). This differs from compensation for losses to the other large carnivores, which are based on number of individuals or reproducing pairs. The difference is due to both infrequent inventories and lack of knowledge of kill rates from brown bears, but results in inadequate compensation for herding districts with high brown bear predation. Thus, for the compensation system to work better, well- founded data on both the occurrence of brown bears and the expected losses and indirect costs are required. Overall, the high bear predation rates on reindeer calves reported in paper I suggest that brown bear predation cause considerable higher costs than what is previously been accounted for in Sweden (Karlsson et al. 2012). The baseline data on brown bear kill rates and timing of predation reported here can thus contribute significantly to improved predictions of the losses to predation caused by brown bears, and also, to better finding and evaluating possible mitigation actions. Forest reindeer herding districts are probably particularly vulnerable to brown bear predation, with their calving ranges completely overlapping with

36 the brown bear home ranges. The reindeer are also scattered out in smaller groups in the forest during calving making guarding more difficult, compared to mountain calving ranges. Furthermore, the higher densities in semi- domesticated reindeer herds, compared to forest-living wild reindeer and caribou, possibly make a space-out strategy to increase predator searching time less efficient, and active searching for reindeer calves by the brown bears more profitable, which may further increase vulnerability to predation. This shows the importance of well-grounded knowledge within different study systems. In terms of altered resource selection on a daily and seasonal basis, brown bear behavioral adjustments to search for reindeer seemed to override, at least partly, antipredator responses by reindeer. Nevertheless, a closer investigation of kill site spatial distributions suggested that female reindeer might utilize clear-cuts, higher elevations and areas closer to roads to reduce risk from bear predation. The preference by brown bears for young forest may indicate that logging activity on the calving range can have negative consequences for the reindeer in the long term. To further consider how the magnitude and the spatial arrangements of logging influence the risk landscape on the calving range would add important knowledge in this respect. It would also be of interest to investigate the degree of calving site fidelity in semi-domesticated reindeer, and how patterns of fidelity are influenced by landscape change. The broader scale examination of female reindeer movements indicated that behavioral responses to brown bear presence come at a cost of forage acquisition. It is interesting that there were generally few signs of adjustments to temporal variation in risk, though it has been documented in other Rangifer systems (Barten et al. 2001; Latombe et al. 2013). Overall, deviations from optimal foraging and increased movement rates, can lead to poorer body condition and have negative consequences for population dynamics. The results underline that indirect effects of carnivore presence should also be considered when evaluating the total costs from predation, as has been suggested in recent years across a broad range of ecosystems (Lima 1998; Brown & Kotler 2004; Creel et al. 2007; Zanette et al. 2011). To enable co-existence of viable large carnivore populations and a sustainable reindeer husbandry in Fennoscandia, the human-wildlife conflict level needs to be reduced. In 2013, the Sami Parliament and the Environmental Protection Agency in Sweden agreed on a "tolerance level" for maximum acceptable reindeer loss due to predation. It has however proved challenging to apply this in practical management, mainly due to a lack of trust and common knowledge base. Finding agreements on this, combined with development of compensations schemes that better reflects the true costs of presence of

37 predators, will hopefully facilitate the co-existence of reindeer husbandry and large carnivores. Moreover, the main challenges experienced by reindeer husbandry today arise from increasing predator populations and land use changes on the reindeer ranges, e.g. growing infrastructure development and forestry activities (Pape & Löffler 2012). Thus, future work needs to integrate the combined costs from predators, human encroachment, and also climate variations, on reindeer herd productivity and the lives and economy of the herders. A solid knowledge base is necessary in order to sustain a viable reindeer husbandry and mitigate disputes with conflicting interests in the reindeer herding area.

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Acknowledgements

Det har blitt noen år, og noen vendinger i livet, fra jeg satte føttene mine i Uppsala og ble introdusert til renforskningen for aller første gang. Jeg har møtt så mange bra mennesker på veien, og ikke minst fått bli kjent med renskötselen og bjørnene i Norrbotten. Endelig har jeg kommet i mål med denne avhandlingen, og det er mange jeg ønsker å takke for det!

Aller først vil jeg takke veilederne mine for å ha bidratt og støttet meg i dette prosjektet hele veien. Til Birgitta og Anna, for å ansette meg hos dere i Uppsala, med Alfons på kontoret og et stort hjerte for renar og renskötsel. Deres glede og engasjement for renforskningen har vært en stor inspirasjon for meg. Og tusen takk for at dere alltid har vært tilgjengelige for spørsmål og tilbakemeldinger, det har jeg satt utrolig stor pris på. Og det er heller ikke mange forunt å ha julbord med bålpanna og skitur i skogen! Ole-Gunnar, takk for at du viste meg tilliten og tok meg med i bjørnepredasjons-studiet, det har vært utrolig givende å få være en del av det. Og for at du ordnet plass på Ås de siste årene, inkludert bra diskusjoner, noen digresjoner, og en god porsjon saltstenger! Og til Lars, tusen takk for at du alltid har vært tilgjengelig, alltid tatt meg på alvor og tatt deg tid til å svare, selv om det i blant har føltes som at jeg har bombardert deg med spørsmål…!

Jeg har jo ikke vært så mye på SLU de siste årene, men jeg vil takke Sara Österman på HUV, som har hjulpet med å legge til rette så jeg kom i mål. Og til Majsan, Bengt-Ove, Margareta Norinder og Helena Wall for å følge meg opp med medarbeidersamtaler og praktiske ærend, selv om jeg har jobbet på distans. Til Helena Bylund og Matt Low for å ønske meg velkommen i Forskarskolen på Ekologi, og gi meg masse bra kurser. Og jeg vil også takke Sjur Baardsen, Ågot Aakra, Ole Wiggo Røstad og Mette Solsvik, for å tilrettelegge for at jeg har kunnet sitte på INA de siste årene. Og til Stig som

47 var en reddende engel og ordnet en ny maskin til meg, da den gamle krasjet på meget ubeleilig tidspunkt.

En stor takk til alle samebyene som vi har samarbeidet med, og som har bidratt med informasjon og data til denne avhandlingen: Udtja, Gällivare, Sirges, Njaarke, Handölsdalen, Östra Kikkejaure og Malå.

Jeg føler meg utrolig heldig som har fått være med på Bjørnepredasjons- prosjektet og fått jobbe med ren og bjørn oppe i Norrbottens dype skoger, og for å ha blitt kjent med så mange flotte mennesker. Takk til Jens, Ole-Gunnar og Peter for ta meg med på laget!! Til Peter og Solbritt for en fantastisk gjestefrihet, gode middager, samtaler, og tur til Skaite, gaupe og jervemerking, det har blitt minner for livet. Geir Rune, kunne ikke fått noen bedre feltkompanjong i Kåbdalis, vi fikk til og med ordnet med rømmegrøt og spekeskinke på 17 mai, fornøyd med det! Og tusen takk at du stilte opp og støttet nå i innspurten. Til Nils Anders for mange fine feltdager i Udtja, for at du turte bli sittende i bilen da vi skramlet av gårde på humpete veier, og for å lære meg å spore bjørn (har ikke glemt det!). Og til Jonas for fine dager på klustersøk. En stor takk til Rune og ElliKari for vennskap, og alltid med åpne dører og kokkaffen klar i Kåbdalis, for å vise oss nuorssjo og å gi et innblikk i livet og historien med renarna i Udtjas skoger. To Soléne for great field work and company, and for bringing delicious Quebecois recipes to our field station in Kåbdalis! To Pablo, the only Spaniard I know who has travelled to Kåbdalis by bike! Og til Einar, Lasse, Alf, Jon, Anton, Mattias, Ivan og alle andre jeg har blitt kjent med på klustersøk og kalvemerkning i Udtja. Til Lars Thomas for å ta meg med på en hinsides snøscootertur i Gällivare, fikk sett området men lurte en stund på om jeg kom fra med livet i behold! Og Stig for mange fine dager og kafferaster i Gällivares skoger. Heikki for verdens hyggeligste selskap fra Luleå til Nattavaara, og for Pelle Svanslös filmen (som har blitt en av Nokves favoritter)!

Stor takk til alle på Grimsö, for bra kurser, at jeg fikk henge med på scientific writing week, og alt annet fint som skjer på Grimsö selvfølgelig, det har vært så hyggelig å bli kjent med dere alle!: Lovisa, Marie (dere får lese litt lenger ned også), Örjan, Camilla, Gustaf, Geir Rune, Jenny, Malin, Jens, Johan, Andreas, Henrik

Also thanks to the IRSAE summer school at Evenstad, to Harry, Barbara, Bram van Moorter, Francesca Cagnacci and all others that have arranged seminars and courses, which have been very useful in terms of building networks, and a great introduction to movement ecology.

A big thanks to Jon Swenson for including me in the Scandinavian Brown Bear Research Project, it has meant a lot to be part of such a great and diverse research group. And thanks to all that are part of the project, Anne and Gro, I feel really lucky that have had you at INA to share ups and downs with, and also, together with Joanie and Johanna, thanks for great company on travel adventure in Denali! Sam for being a great friend, always helpful and supportive, and Shane and Marie, a special thanks for helping out when things got a bit tough in the end. Andres for being the best office mate, with coffee improver, Spanish recipes and making sure to complain about people once in a while! Richard, Andreas, Sven, Martin, Alina, Jonas, and all others in the project, it has been a pleasure to get to know you all!

To Atle and Jon for involving me in an inspiring environment at CAS, and even providing me with financial support, I am very grateful for that! Og en stor takk til Inger Maren, som brettet opp armene i mammapermen og gjorde en helt fantastisk jobb for at vi skulle få manuset i mål!

Til Berit og Gunnar Inga, som jeg har vært så heldig å bli kjent med i Jokkmokk, takk Berit for toppen reisefølge på renkonferanser, for at dere åpnet dørene under vintermarknaden, med huset fullt av folk og rengryte til alle! Og for å lære meg litt mer om den samiske kulturen gjennom dere liv, berrättelser, og Ajtte.

Og til dere jeg har blitt kjent med på SLU og i Uppsala. Til Markus og Karin for middager og førjulstreff, det har vært så hyggelig! Og til Kristina for å være der når jeg virkelig trengte det. Fuxen, for hyggelig samarbeid på renheten, og for at du tok meg med på långfärdsskridskor! Til Åsa, for mange fine dager sammen i Haknäs, klatretur og Calgary, du klarer alltid å finne de små gledene i livet; kjøre ut og høre på hjortens brunsthyl i Sheep River, eller ta med iskrem ned til brygga og se på solnedgangen, tusen takk for det! Anna, Hannes, Ebba og Sara for klatreturer, og skidåkning i Hågadalen!

Til Marie og Lovisa, hva skulle jeg gjort uten dere! Takk for alle middager, turer og godt vennskap. Og for ikke å glemme tidenes konsert i Bångbro! Jeg håper vi får tid til å sees mer nå fremover.

Og til de fine vennene mine i Norge (og Danmark), mange av dere har jeg sett altfor lite til de siste årene, takk for at dere har støttet og trodd på meg. Jon Håkon og Bodil, nå håper jeg vi snart kan få til en Danmarkstur! Maria Mørkrid for alltid å være der. Hanne og Erlend, beste naboene våre. Oda, takk for at du for delte kontoret ditt på Blindern halvåret før Nokve kom, det var så koselig! Til Gard og Tatiana, Dag og Mari, Frida og Jos, og Maria Aasen, Unni, Anja, og Annette, Marthe, Inger Maren, Mali. Jeg er så glad for å ha dere!

Til onkel Jo og Anne-Lene for å låne meg leiligheten deres i innspurten, det satte jeg utrolig stor pris på. Og til tante Elisabeth for å stille kolonihagen til disposisjon om jeg har trengt et sommerkrypinn i Oslo. Kitty og Lotte, veldig glad for å ha dere. Og til kjære tante Mari, jeg vet ikke hvordan det hadde gått med hus-oppussingen oppe i alt dette uten dine råd, tusen takk!

Og til min og Heming sin familie. At dere har stilt opp så mye og støttet oss i disse siste par årene har betydd veldig mye for oss. Grethe og Paul, Cecilie, Andrew, Vilda, Fride og Alfred, kunne ikke fått noen bedre svigerfamilie, Thomas og Ebba, nå skal vi endelig få tid til å besøke dere i Bergen! Og til mamma og pappa for å alltid tro på at jeg klarer det, og for å ha stilt opp og hjulpet det dere kunne i denne tiden.

Og til slutt, til de to viktigste personene i livet mitt, Heming og Nokve, det har vært mye, og jeg er evig takknemlig for at dere har stått ut dette sammen med meg, elsker dere, og gleder meg veldig til å ha mer tid sammen fremover.

Bear predation on neonatal semi-domestic reindeer: patterns and possible mitigations

Ole-Gunnar Støen1,2*, Therese Ramberg Sivertsen 3, Geir Rune Rauset 1, Jonas Kindberg 4, Richard Bischof 1, Anna Skarin 3, Peter Segerström 1, and Jens Frank 5

1 Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway 2 Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden 3 Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, P.O. Box 7024, 750 07 Uppsala, Sweden 4 Norwegian Institute for Nature Research, NO-7485 Trondheim, Norway 5 Grimsö Wildlife Research station, Department of Ecology, Swedish University of Agricultural Sciences, 730 91 Riddarhyttan, Sweden *Current address: Norwegian Institute for Nature Research, [email protected]; Phone: +47-97119945

Abstract Sámi reindeer husbandry in Fennoscandia is a pastoral system and an essential part of the Sámi culture, driven by both economic and ecological factors. Changed environmental policies during the past three decades have led to increased carnivore populations, as well as amplified conflicts over mitigation and compensation for livestock loss. Despite the immense interest in and controversy surround- ing carnivore management in this area, there is currently little quantitative information about the magnitude of livestock depredation. We followed GPS-collared brown bears (Ursus arctos) and semi-domesticated female reindeer (Rangifer tarandus tarandus) instrumented with UHF proximity collars in two reindeer herding districts in northern Sweden. We could thus detect calves killed by instrumented brown bears within the calving ranges and accurately determine brown bear kill rates on neonatal reindeer. Brown bears killed calves almost exclusively during and shortly after peak calving, i.e. the three last weeks of May, only 1 out of 333 calves was killed after 15 June. The estimated predation rate indicated that brown bears may have caused a major proportion (39 % and 67 %) of the observed calf loss, representing 16 % and 29 % of all calf mortality, respectively, within the two reindeer herding district. Both calves and adult females were killed more frequently during nighttime (between 6 pm and 6 am) than during daytime. Brown bears on the reindeer calving grounds killed on average 11 calves during the calving season, but time spent on the calving ground varied considerably, and was positively correlated with number of calves killed. Both individual kill rates and between-kill-interval varied with reproductive status of the bear, in addition between-kill- intervals increased over the season, and decreased with age of the bear. Synthesis and applications: Brown bear predation rate on semi-domesticated reindeer calves in forested areas is considerable and mitigation is needed to reduce conflicts and sustain viable reindeer husbandry. Spatial and temporal patterns of bear predation on reindeer calves suggests the feasibility of localized mitigation efforts, such as the use of corrals during calving, increased hunting quotas or targeted culling of bears that spend substantial time within the calving grounds.

Keywords: brown bear, calving ground, reindeer neonate, predation, kill rate

Introduction domesticated reindeer (Persson et al. 2001; Hobbs et al. 2012; Tveraa et al. 2014) and in some cases have Successful conservation efforts have led to the partial semi-domesticated reindeer (Rangifer tarandus taran- recovery of European large carnivore populations dus) as their main prey (Mattisson et al. 2011, 2016). during the past three decades (Chapron et al. 2014). An The first written evidence of domesticated or tamed increasing number of large carnivores close to human reindeer in Fennoscandia is from 800 A.D. (Ruong activities will in many cases, if no interventions are 1982), but exactly when domestication started is un- used, result in higher frequencies of carnivore attacks known (Bjørklund 2013). Today reindeer husbandry is on livestock, which in turn may fuel conflicts with central to the livelihoods of the Sámi people (Zabel & human livelihoods (Graham, Beckerman & Thirgood Holm-Müller 2008), and Sweden has signed several 2005; Heikkinen et al. 2011). In northern Fen- international agreements to safeguard the future of noscandia, several large predators prey on semi-

1 both the Sámi culture and viable large carnivore popu- currently suggested to manage bear predation on rein- lations (Nilsson-Dahlström 2003). Large carnivores are deer. believed to cause substantial losses for reindeer husbandry (Hobbs et al. 2012). Yet, we lack quantitative information about the magnitude and patterns of preda- Materials and methods tion on semi-domesticated reindeer that could inform the development of effective mitigation strategies. Study area In Sweden the estimated brown bear (Ursus arctos) Reindeer herding areas cover 55 % of the land area of population size has increased from 294 bears in 1942, Sweden (Fig. 1), and are organized in 51 legal and to 834 in 1993, reaching its maximum in 2008 with geographical herding districts. Thirty-three herding 3298 bears, and is now declining with an estimate of districts migrate between winter ranges in the forest 2782 bears in 2013 (Swenson et al. 2017). Other stud- and summer ranges in the mountains (mountain herd- ies have shown that during the ungulate calving season ing districts), and 18 districts are residing in the forest (i.e. spring), ungulate neonates can be an important year-round (10 forest herding districts and 8 non-Sámi component of the bear diet (Adams, Singer & Dale concession districts), although migrating between 1995; Linnell, Aagnes & Andersen 1995; Swenson et lower altitude lichen rich forests in winter to higher al. 2007; Nieminen 2010; Rauset, Kindberg & Swen- altitude high-quality pastures in the summer ranges son 2012). However, there are currently no precise data (The Sámi Parliament in Sweden, www.sametinget.se). about brown bear kill rate on semi-domesticated rein- The peak of the rutting season is early October (Esp- deer available. The impact of the increased brown bear mark 1964) and the mean gestation period is 221 days population on reindeer husbandry has thus been diffi- (Mysterud et al. 2009), which results in peak calving cult to estimate. Although reindeer herders repeatedly during the last three weeks of May (Holand et al. have claimed that losses are substantial, the absence of 2003). data on reindeer loss from brown bear predation has The study was conducted in the spring-to-autumn made compensation problematic. Not knowing the ranges of two forest reindeer herding districts, Udtja magnitude of predation has also made it difficult for and Gällivare skogssameby, in Northern Sweden (20º managers to determine if subsidies and preventive E 66º N) (Fig. 1), which reported high reindeer calf interventions are justified. Furthermore, not knowing losses, believed to be caused by bears. In the forest how predation is distributed over the year, area and reindeer herding districts, calving grounds are most reindeer categories has made it hard to decide which often located in open coniferous lichen forest at and interventions that are most likely to be effective. Earli- around bogs (Sivertsen et al. 2016). The borders of the er studies conducted on brown bear predation on rein- herding districts alternately followed reindeer fences, deer have been made using mortality transmitters on rivers, roads and railroad tracks, which allowed the reindeer calves when they are 1-2 months old (e.g. reindeer herders to separate the herds, but did not form Bjärvall et al. 1990; Nieminen 2010), thus precluding impenetrable barriers. neonate reindeer from kill rate estimates. The area belongs to the North western part of the In order to make informed and science-based deci- Eurasian taiga. The climate is continental, spanning sions, knowledge about individual kill rates of brown from cold winters with deep snow to dry and temperate bears is needed (Rauset, Kindberg & Swenson 2012). summers. The forest is mainly coniferous, dominated Such data has a better potential to be used in similar by Scots pine (Pinus sylvestries) and Norway spruce areas but with different bear densities or to predict (Picea abies), interspersed by large bogs and wetlands. effects of different bear management regimes in the The landscape is flat to gently rolling with a few hills, same area over time. In the present study, we used and the altitude ranges from 13 to 714 m a.s.l. The proximity collars on reproducing reindeer females in human population is relatively low in the areas (aver- combination with GPS-collars on brown bears to esti- age 0.02 per km2) with few human settlements and mate individual kill rates on reindeer from birth and infrastructure (0.37 km roads pr. km2), but large parts onwards, in two reindeer herding districts in northern of the forest is subject to intensive forestry. Sweden. To also attend the most important factors affecting the reindeer herding community the study Collaring of reindeer and bears was planned in close collaboration with the reindeer During 2010-2012, the majority of adult reindeer fe- herders. The aim of the study was to assess how the males in the study populations were equipped with predation varied over the year, between bear individu- proximity UHF-collars (Udtja 2010:990, 2011:1176, als and categories of bear and reindeer, and the magni- 2012:1235; Gällivare 2011:893, 2012:1350), individu- tude of calf loss caused by bear predation. Based on ally marked for identification in the field and 24 brown our findings, we discuss the potential interventions bears with GPS-collars equipped with UHF receivers (Vectronic Aerospace GmbH, Berlin, Germany). Fe-

2 male reindeer pregnancy was determined using a rectal seasonal kill rate as a function of bear category. These ultrasound probe in late March or early April (Savela models account for over-dispersion and enable model- et al. 2009). The UHF-collars emitted a weak UHF ling of count data with more zeroes than expected from signal every second that could be detected by the GPS- the Poisson distribution (Zeileis, Kleiber & Jackman collars within the proximity of up to 100 m. The GPS- 2008). The models include two separate processes; one collars were set to scan for UHF signals from the rein- part to model excess zeros, represented by a binomial deer collars for 1.5 s every 8 s. For each detection of an GLM with a logit link, and a count part to model over- UHF signal a time stamp and the ID of the UHF collar dispersed count outcomes, represented by a negative was stored in the brown bear GPS-collar. Simultane- binomial GLM with a logit link (Zeileis, Kleiber & ously, the GPS positioning schedule in the GPS-collar Jackman 2008). In the context of our study, the zero- was altered from the standard 30 min schedule to one part thus identified and quantified the effects of varia- GPS position every 1 min and 10 s. This altered sched- bles on the probability of killing zero calves, while the ule lasted for one hour after detection of the UHF count-part estimated the number of reindeer calves signal. In case new or repeated signals were received killed by a bear seasonally (potentially corrected by the within this period, it lasted until 1 h after the last UHF binomial part). Since the frequency of repeated indi- signal. This produced periods of frequent GPS posi- vidual measurements was relatively low in our data set tioning of the bears, lasting ≥1 hour (hereafter named (n=8), we considered it justified to ignore the variation 1-min trails), whenever a collared bear had been close caused by repeated individual measurements in this to a radio-collared pregnant reindeer female. The loca- model. To fit the model, we used the zeroinfl() function of the proximity event could then be visited in the tion in the pscl package, version 1.4.9 (Jackman 2008) field in search of calf carcasses. in R. An Iridium satellite message with the last 10 GPS We estimated kill rate using the number of calves positions was sent to a database immediately after the killed by individual brown bears in each calving season 10th GPS position was stored in the GPS-collar, pro- as a response, and compared the effects of alternative vided Iridium coverage. With no Iridium coverage the combinations of demographic classes of bears: sex GPS positions were stored in the collar and sent later. (male/female), age (adult/subadult) and reproductive The proximity function in the bear collars was activat- classes (female with cubs of the year (FCOY), female ed when the bears were within the study areas in the with +1 year old cubs (FY), lone female and lone period from April 26th to September 24th annually. male), also including individual reindeer herding district. Because the collaring of female reindeer and Carcass search registrations of kills were restricted to the defined study areas (the two herding districts), whereas bears During 2010, all GPS positions along the 1-min trails may roam outside theses borders, we accounted for were visited, but because no calf carcasses were found individual differences in time spent within the study on locations with less than 4 GPS positions within a 30 areas. We used time (hours) each bear spent within the m radius, and therefore only locations with ≥3 GPS study areas as an offset variable, log transformed to positions within a 30 m radius (hereafter named a match the logit link function of the models. This makes cluster) were visited in 2011 and 2012. When a carcass kill rate a linear function of time. was found we noted age (calf, adult), sex (male, fe- First, we built a set of candidate models with differ- male), and the estimated time of kill based on carcass ent sets of the demographic classes, and selected the decomposition and other signs (e.g. in snow or vegeta- most parsimonious model. Second, we compared the tion) to determine whether the perpetrator was the GPS best performing bear classification model with and collared bear or not. All visits to clusters were done by without herding district included as a fixed factor. A at least one researcher and one reindeer herder. The priori, we predicted zeroes in the demographic group conclusion of mortality cause of the carcasses was FCOY to stem from a separate process compared to the determined by consensus, following the standards for other groups, because FCOY are expected to actively provincial rangers (Skåtan & Lorentzen 2011). If two avoid the otherwise attractive “high resource” calving or more bears created a cluster overlapping in time on grounds to escape sexually selected infanticide from a kill site, the bear with the first GPS position at the male bears (Swenson et al. 2007; Steyaert et al. 2013). kill site was judged to have killed the reindeer, unless This assumption was supported by the observation that the distance between the carcass and the bear GPS none of the females with cubs killed reindeer calves, positions within the cluster indicated that only the but kept to rugged hillsides in the outskirts or outside second bear had been on the kill site. calving areas. For the other demographic groups, potential zeroes were predicted to stem from a random Seasonal kill rates and between-kill-intervals count process. Therefore, we included FCOY in the Because several bears killed no calves at all we used zero-inflated negative binomial models to estimate

3 binomial part, and the other demographic groups in the estimate based on scat collection and DNA sampling count part of the models. (see Bellemain et al. 2005) for a method description), Model selection was done using the principle of implemented by the County board in August 2010 parsimony and second-order Akaike`s Information (Fig. 1). Because some bears may only have parts of

Criteria (AICc), where the model with the lowest num- their home range within the calving grounds, and some ber of parameters with ΔAICc < 2 is considered the may have resided outside the study areas during the best (most parsimonious) model to fit the data (Arnold fall scat collection, we added buffers to the study areas 2010). For model predictions, we calculated kill rates when estimating the total number of bears that possibly with time spent within the study areas as a sequence could have killed calves in the study areas and in the from 0 to 991 hours (i.e. maximum observed value) same manner as the radio-collared bears. The buffer divided into 100 intervals, and bootstrapped confi- was set to 19,7 km which was the median radius calcu- dence intervals (1000 replicates). lated from the area of 28 annual home ranges of 19 of Between-kill-intervals were calculated using only the radio-collared bears residing within the study areas intervals between successively killed reindeer when the assuming circular home ranges (Fig. 1). We estimated bear resided within the calving ground the entire time the number of bears within the two study areas with of the interval. Intervals with time spent outside the buffers using the software MARK (White & Burnham study areas were excluded from the analysis. The dis- 1999; Kindberg et al. 2011). tribution of between-kill-intervals was right-skewed, The total number of bears was then divided into thus we estimated between-kill-intervals using log- demographic classes determined by the most parsimo- transformed time (minutes) between kills as a response nious model of seasonal kill rate on the calving ground. variable in linear mixed effects models in R package Because dependent young were included in the kill rate lme4 (Bates et al. 2015). The distribution of kills of their mothers, the number of dependent young (cubs throughout the year showed a distinct peak during of the year, yearlings and 2-year olds) was calculated calving season in late May (Fig. 2). Therefore we using population models and 50/50 sex ratio (Bischof included “day/week of year” as potential covariates, & Swenson 2012). The probable number of females both as first and second order, in addition to demo- that were accompanied by dependent young was calcu- graphic groups of bears (same as above for seasonal lated by dividing the estimated number of cubs in each kill rates, with the exception of females with cubs age category with the average litter sizes in the Scan- (FCOY) from which we did not have any observations, dinavian brown bear population from spring counts of i.e. the killed no reindeer). To account for potential 362 litters of radio-collared females in the period 2000- individual effects and repeated observations from the 2016, where the age of the cubs were known. same bear, we tested combinations of “year”, “study Finally, we used effect sizes of the best zero- area” and “bear individual” as random intercepts. Bear inflated negative binomial model and the mean ob- individual explained most of the variation among the- served time spent on the calving ground by each cate- se, and was the only random effect included in the final gory of radio-collared bears to predict the number of candidate model set. We used the same principles for calves killed. Summarising these estimates and the model selection and predictions as described for sea- estimated bear numbers we made an estimate of the sonal kill rates. total number of calves killed by brown bears within the two study areas. Total loss To assess the total influence of bears on calf loss in our Results study areas, we calculated the proportion of reindeer calves killed by all bears possibly residing within the Carcasses study areas from the total loss of reindeer calves. The total loss of calves was estimated from the observed We found 374 reindeer carcasses (30 adult females and number of radio-collared reindeer females (all judged 344 calves) within the two study areas (206 in Udtja to be pregnant prior to the calving season) with and 2010-2012 and 168 in Gällivare 2011-2012). Of these, without calf at the ordinary calf marking in late June to 350 were determined to have been killed by the radio- mid-July, when the females are rounded up in corrals collared bears (17 adult females and 333 calves). The and all calves are ear-marked. The rate of calf loss other 24 carcasses were either too old to have been among the radio-collared females was then applied to killed by the radio-collared bear (n=17) or the reindeer the total number of adult reindeer females residing was judged to have died of other reasons than preda- within the study areas (data provided by the reindeer tion by bears (n=7). owners). The number of bears within the study areas was determined using data from a County-wide population

4 Timing of predation intercept, which indicates that much of the observed The proximity function in the bear collars were acti- variation in seasonal kill rates among demographic vated within the study areas for an average of 48±42 classes of bears (except FCOY) can be attributed to (mean±SD) days per bear and year (n=52). Most of the differences in their time spent on calving grounds. calves were killed in May, with 300 of 333 killed in the Average seasonal kill rates on the calving ground for three week period from May 10th-31st, and peek around each bear category, as predicted by the model were: May 21st (Fig 2). May 1st was the earliest date a calf FCOY: 0 [0,0]; males and females: 10.2 [8.6, 11,5] was killed, and July 22nd was the latest, with only one (mean [95 % CI]), whereas a bear being on the calving calf killed after June 9th. Most of the adult females ground the whole calving season was estimated to kill were also killed between May 1st and June 15th (Fig. 26.1 [22.1,29.1] calves (Fig 3). 2), with 4 of 17 killed outside this period (April 26th, The between-kill-interval models did not include June 16th, July 8th and July 22nd). Kills of both calves observations of FCOY as they never killed reindeer and adult females were more frequent during night- calves. The top-ranked model by AICc for estimating time (6 pm-6 am) (Fig. 2). kill intervals included day-of-year as first and second order variable and age of bear as fixed effects, in addi- Kill rate tion to bear individual as random intercept. Between- kill-intervals increased throughout the season after its The bears killed almost all of the calves (332 out of first occurrence, and subadult bears of both sexes had 333) and 0.76 of adult females (13 out of 17) during larger between-kill-intervals than adults (Fig 4). An the period May 1st – June 15th. We thus calculated the adult bear within the calving ground have an estimated kill rate only during this period. Totally 21 bears were average between-kill-interval of 3.7 [2.0, 6,7] hours then followed within the study area for an average of early in the season, which increases to 7.5 [2.7, 20,5] 21±14 days or 408±315 hours. The seasonal kill rate hours at the end of the season (Fig 4). was on average 11±12 calf kill and 0.4±0.8 adult female kill per bear, resulting in a mean rate of 1.3±1.5 calf kills per day at the calving ground, and the average Total loss time interval between consecutive killed calves was The total number of bears potentially residing within 13±18 h (n= 276) (Table 1). Both kill rate and kill the two study areas was estimated to be 71 [62-96] interval varied highly among bear individuals and bears in Udtja and 58 [53-75] bears in Gällivare (mean categories, and it was also considerable variation be- [95 % CI]), and their estimated distribution among tween bears in the number of hours spent on the calv- categories are shown in Table 4. The estimated average ing ground each season (Table 1). In general the total seasonal calf loss was 43 % in Udtja and 41 % in Gäl- number of calves killed by an individual bear per sea- livare (Table 5). Calculating the number of calves son increased with the number of hours spent on the potentially killed by bears using the category specific calving ground (Fig 3). effect sizes of the predictive model, indicate that pre- Due to the relatively low kill rate on adult reindeer dation from bears could represent 67 [57, 76] % and 39 we only estimated seasonal kill rate on calves. The top- [33, 44] % of the loss of calves experienced in Udtja ranked model for estimating seasonal kill rate of calves and Gällivare, respectively (Table 6). included FCOY (Female with cubs of the year) as explanatory variable in the binomial part, whereas the count part included only intercept and the over- Discussion dispersion parameter log(theta) (Table 2). The effect of Use of proximity collars and designing the study in the offset variable time (Hours) at calving grounds was close collaboration with the reindeer herders were positive, which resulted in model predictions increas- critical components for obtaining reliable data on ing linearly from 0 for all groups at t=0 (i.e. brown bear predation. Based on our estimates of total min[Hour]) to its maximum at t=991 (max[Hour]; Fig mortality of calves in the studied reindeer herds, brown 3). In the binomial model (i.e. the probability of kill- bear seasonal kill rates, and the number of bears pre- ing a calf versus not killing a calf), the effect size for sent on the calving grounds, brown bears could be FCOY was high, indicating that the model was able to responsible for 39 % and 67 % of the observed rein- distinguish this group as having a very low probability deer calf loss in the two study areas. This result sug- of killing calves. This parameter was associated with gests that predation by brown bears is a major cause of extremely high SE values, potentially as a result of few calf mortality representing 16 % and 29 % of all calves observations in this group, which led to a non- born annually within Gällivare and Udtja study areas, significant effect for this group. Nontheless, model respectively. Due to the relatively high upper confi- AICc was improved by the inclusion of FCOY (ΔAICc dence interval in the bear number estimations, and = 4.31), and was included in the best models as ranked large variation in kill rate among bears, the bear- by AICc (Table 3). The count model only included the caused predation could be even higher. The assumption

5 that the observed time on the calving ground by the demographic category. Another reason can be that the radio-collared bears was representative for all bears in main calving area within the calving grounds shifted the buffer, yielded a general kill rate estimate that is from year to year, depending on snow conditions, useful for practical management when calculating total which again changed the availability of calves within reindeer calf predation by brown bears. the home ranges of the individual bears. E.g. in Udtja Similar predation rate on caribou calves has also the main calving areas in 2010 and 2012 were further been found in Quebec, Canada, where black bears south, than in 2011, when calving took place in the (Ursus americanus) were responsible for 94 % of the northernmost part of the calving grounds. predation on neonate caribou (Pinard et al. 2012), and Male bears stayed on the calving grounds on aver- in Alaska where predation from grizzly bears account- age half as long as females. A probable reason is that ed for 49 % of neonatal deaths (Adams, Singer & Dale adult males have larger home ranges (Dahle & Swen- 1995). Brockman et al. (2017) documented kill rates in son 2003a) and move more than females. This might Alaska on caribou calves by brown bears equipped be accentuated by the mating season, starting in late with neck-mounted cameras, and estimated 14.1 killed May, when males roam within and beyond their home caribou calves per bear, comparable with our estimate range to find mates (Dahle & Swenson 2003b). How- of 10.2 reindeer calves per bear and season. They also ever, when at the calving grounds, adult males had on found, as we did, that some bears killed more than 30 average the shortest between-kill-intervals, showing calves during one season. However, in the study from high efficiency to kill calves. The seasonal kill rate of Alaska the sampling was not random since some indi- adult males could thus be underestimated in this study, viduals were selected because they were known to if their larger home range overlaps with calving have killed calves, possibly leading to an overestima- grounds that are not included in the study and where tion of the kill rates for the brown bear population in they might kill additional calves. Furthermore, the area. Nevertheless, both the Brockman et al. (2017) subadult males disperse much farther than females, study and our study found higher kill rates than report- which establish home ranges overlapping with or near ed in earlier studies of brown bear predation on ungu- their mothers’ (Støen et al. 2005). Thus, females are late calves (Ballard, Spraker & Taylor 1981; Boertje et more likely to remain in the vicinity of a calving al. 1988; Swenson et al. 2007; Rauset, Kindberg & ground where they are born (and captured) (Støen et al. Swenson 2012), most likely due to new survey meth- 2006). ods. Both studies also show that bears can be highly We detected no significant difference in between- efficient predators when among new-born caribou or kill-intervals depending on bear category. There was, reindeer calves. Bear predation on adult caribou is however, a difference due to age, with adults killing opposed to calf predation relatively rare (Zager & calves more frequently than subadults. Subadults are Beecham 2006; Barber-Meyer, Mech & White 2008). probably less experienced hunters than adults, as ob- This is consistent with the results from our study where served among other carnivores (Sunquist & Sunquist few of the bears killed adult female reindeer and the 1989; Holekamp et al. 1997; Sand et al. 2006). The perpetrators killed on average less than two adult fe- length of the between-kill-intervals also increased over male reindeer annually. the season, from when they were first recorded on May Apart from females with cubs of the year (who 11th. A peak of calving in mid-May and a highly syn- stayed in the outskirts of the calving areas), we detect- chronized parturition in female reindeer (Adams & ed no significant difference in seasonal kill rate be- Dale 1998), imply that most of the between-kill- tween the bear categories when controlling for time intervals were recorded after the peak of parturition. spent on the calving grounds. This differs from earlier The increasingly longer between-kill-intervals over the studies on bears and other carnivores where demogra- season are thus likely caused by the reduced vulnera- phy influences kill rates (Young & McCabe 1997; bility of the calves, following their increased mobility Knopff et al. 2010; Mattisson et al. 2011). On the other and locomotive ability after birth (Lent 1974; Jenkins hand, Boertje et al. (1988) found no differences in kill & Barten 2005). rate on caribou calves between demographic categories There was a significant effect of Bear ID as a ran- of bears. Perhaps, for bears that are located on the dom effect in the model of between-kill-interval, sug- calving ground, the effect of high availability of vul- gesting consistent differences among individuals. This nerable prey during a very short period of time over- can indicate that some bears are specialists and more rides the possible effects of demography. The lack of efficient reindeer calf hunters than others, and that they effect may also be explained by the large individual could be “problem individuals” (Linnell et al. 1999). variation in kill rates and the relatively low sample size However, because time spent on the calving ground when dividing into categories. There was also large was a major factor influencing seasonal kill rates, and variation among individuals and among years for the between-kill-intervals might be high even if the time same individual, even when animals did not change on the calving ground is short, the present management

6 scheme in Sweden aiming at removing “problem which may make them more susceptible to brown bear bears” that evidently have killed calves, is thus more predation. likely to target bears spending relatively more time on From a management point of view an important the calving ground, than bears having short between- finding from our study is that the window of predation kill-intervals. is limited in time, to approximately three weeks around Methodologically it is difficult to record accurate the peak of calving in reindeer. Due to this short inter- bear kill rates on neonate reindeer, even with possibili- val, predation by bears is constrained almost entirely to ties of accurate GPS tracking of the perpetrator. Most the core calving grounds. Clearly, the spatial and tem- GPS-based predation studies underestimate true kill poral aggregation of risk offers opportunities for effec- rates especially for medium- and small-sized prey (e.g. tive mitigation. Strategies that separate bears and calv- Sand et al. 2005), due to study design and only visiting ing reindeer during this critical period are bound to be GPS clusters of a certain size and characteristics. Our particularly effective. Such strategies may include method made it possible to obtain sufficiently frequent physical exclosure or lethal removal of bears (Heik- GPS fixes when we knew that the bear had been close kinen et al. 2011; Ordiz et al. 2017). to reindeer females, at the same time saving battery Calving in corrals is an effective physical exclosure capacity by recording few positions at locations distant and has the potential to halt calf predation almost en- from female reindeer. In addition, it allowed us to be tirely, but involves high costs both in terms of herders’ efficient in the field, searching only clusters where workload and economic investments in fences and bears were in close proximity of reindeer females that feed. There is also a risk of health problems due to possibly had calves. infectious diseases or mal-adaption to feedstuffs, However, our method may also have uncertainties. which may lead to mortality among both adults and We investigated the potential role of three major calves (Tryland 2012). Thus, reindeer herders are sources of uncertainty associated with our approach: 1) generally not in favour of this mitigation measure, and adult female reindeer lacking proximity collars, 2) careful evaluation is necessary before implementation. clusters not visited, and 3) radio-collared bears accom- As predation was restricted to a short period in panied by un-marked bears, that may have led to un- spring, culling of bears within and around the calving der- or over-estimation of kill rates (see Appendix 1). grounds, and as early as possible in the season, may be We found that the potential over- and under- effective in terms of preventing predation on reindeer estimations were relatively small in our study. With the calves. After June 1st culling of bears will most likely large proportion of reindeer females radio-collared not have any impact on reindeer calf losses the same within the study areas all years, and small errors due to year. Even if there are indications that some bear indi- not visiting clusters with few GPS-positions and small viduals kill calves consistently more frequently, the errors for assumed associations among marked and un- difficulty in finding these specialists and remove them marked bears on kill sites, we feel confident that the successfully suggest that culling of “problem individu- brown bear kill rates and between-kill-intervals on the als” is a less plausible and effective intervention. Bears calving grounds reported here are sufficiently accurate. are hunted in Sweden (licenced hunting) (Swenson et Predation on reindeer calves was very limited in al. 2017), and an alternative intervention may be to time, and appeared to be highly correlated with the allow higher hunting quotas and especially female abundance of new-born reindeer calves (e.g. Ropstad quotas in the autumn, within designated reindeer calv- 2000; Holand et al. 2003). After the first week of June, ing grounds. Regulation of bear density through hunt- when the period of parturition was over, brown bear ing quotas would reduce the need for costly culling predation on reindeer practically stopped and bears operations and decrease conflicts between governmen- switched to other food items such as moose calves, that tal interventions and hunter interests. The long-term are born slightly later than reindeer calves (Solberg et effect would also be stronger if density is permanently al. 2007). The same pattern has been found in other reduced compared to specific culling of problem indi- systems where bears predate on caribou neonates in viduals. Many reindeer herders are also hunters, and if North-America (Adams, Singer & Dale 1995; Jenkins allowed, able to reduce brown bear populations on & Barten 2005) and moose calves in Sweden (Swenson calving grounds themselves. et al. 2007; Rauset, Kindberg & Swenson 2012). The Results from this study show that brown bears can reason why adult female reindeer also were killed more have high kill rates and short between-kill-intervals frequently during the calving season may be due to a when among calving reindeer females. However, there higher vulnerability during parturition. Also, the bears are large variations among individuals and among may be more actively seeking up reindeer during this years for the same individual. There is also no clear (Sivertsen et al. 2016). Furthermore, during the first difference among bear categories, and the seasonal kill days following parturition, the females are alone with rates are highly dependent on time spent at the calving the calf, and thus loose the benefits of group vigilance, ground. If the objective is to reduce bear predation on

7 neonate semi-domesticated reindeer, this study suggest Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., that successful interventions needs to reduce the gen- Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., eral bear densities on the calving grounds either by Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, culling or hunting, and/or restrict bear access to neo- P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, nates during their vulnerable time. T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Ili- opoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Acknowledgement Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., We are very grateful to Udtja and Gällivare reindeer Männil, P., Manz, R., Marboutin, E., Marucco, F., Melov- herding district for cooperation in this study. Also, we ski, D., Mersini, K., Mertzanis, Y., Mys\lajek, R.W., want to thank Heikki Sirkkola for doing a great job Nowak, S., Odden, J., Ozolins, J., Palomero, G., with the pregnancy tests, Vectronics Aerospace for Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., cooperation in developing the proximity technique, and Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., to all that helped out in the field. Skrbinšek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska-Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., References Zlatanova, D. & Boitani, L. (2014) Recovery of large carnivores in Europe’s modern human-dominated land- Adams, L.G. & Dale, B.W. (1998) Timing and Synchrony of scapes. Science, 346, 1517–1519. Parturition in Alaskan Caribou. Journal of Mammalogy, Dahle, B. & Swenson, J.E. (2003a) Home ranges in adult 79, 287–294. 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Tables

Table 1. Number of calves killed, hours on calving grounds and average between-kill-intervals for 21 radio-collared brown bears in two calving grounds in Northern Sweden from 1 May- 15 June in 2010-2012 Year Bear ID Category* Study area kill-intervals Calves killed intervals (hours) intervals (hours) Hours in study area Adult females killed Number of between- Average between-kill- BD241 Udtja 2010 FCOY 0 0 580 0 BD245 Udtja 2010 FL 0 0 21 0 BD240 Udtja 2010 FY 17 0 522 7 14 BD239 Udtja 2010 ML 6 0 58 8 5 BD240 Udtja 2011 FL 4 0 929 0** 2 BD241 Udtja 2011 FY 36 0 599 7 29 BD248 Udtja 2011 FY 0 0 11 0 BD229 Udtja 2011 SubF 23 1 955 21 22 BD186 Udtja 2011 ML 9 1 307 1 5 BD251 Udtja 2011 ML 5 0 292 4 3 BD246 Udtja 2011 ML 4 0 353 3 2 BD241 Udtja 2012 FCOY 0 0 125 0 BD240 Udtja 2012 FL 35 0 573 8 24 BD248 Udtja 2012 FY 0 0 3 0 BD277 Udtja 2012 FY 37 0 687 8 35 BD229 Udtja 2012 SubF 13 2 631 19 12 BD251 Udtja 2012 ML 0 0 32 0 BD239 Udtja 2012 ML 0 0 72 0 BD278 Udtja 2012 ML 0 0 44 0 BD247 Gällivare 2011 FL 23 0 991 19 21 BD263 Gällivare 2011 FL 0 0 296 0 BD250 Gällivare 2011 SubF 29 1 646 18 25 BD249 Gällivare 2011 ML 25 2 845 14 22 BD247 Gällivare 2012 FL 6 1 747 38 5 BD264 Gällivare 2012 FY 15 0 695 19 12 BD249 Gällivare 2012 ML 25 2 460 7 23 BD269 Gällivare 2012 ML 3 0 494 32 1 BD273 Gällivare 2012 ML 6 0 109 6 4 BD270 Gällivare 2012 SubM 0 0 54 0 BD266 Gällivare 2012 SubM 0 0 149 0 BD268 Gällivare 2012 SubM 11 3 379 22 10

*FCOY=Female with cubs of the year, FY=Female with yearlings, FL=Lone female, ML=Lone male, SubF=Subadult female, SubM=Subadult male. ** 3 and 5 min between successive kills.

10 Table 2. Ranking of candidate models (using AICc) to estimate brown bear seasonal kill rate, comparing alternative classifications of sex, age, and sex-reproductive classes, and with or without herding district (only included for the top ranked model) Binomial model Count model df AICc Δ AICc wi FCOY* Intercept 4 177.11 0.00 0.43 FCOY Area† 5 179.59 2.48 0.13 FCOY Age‡ 5 179.65 2.54 0.12 FCOY Sex§ 5 179.72 2.61 0.12 Intercept Intercept 3 181.42 4.31 0.05 FCOY Sex+Age¶ 6 181.46 4.35 0.05 FCOY Sex+FY** 6 182.51 5.39 0.03 FCOY + Sex Sex 6 182.60 5.49 0.03 Intercept Age 4 183.88 6.77 0.01 Intecept Sex 4 183.93 6.82 0.01 Intecept Sex+Age†† 5 186.57 9.46 0.00 FCOY Sex*Age +FY‡‡ 8 186.68 9.57 0.00 Intecept Sex*Age 6 189.16 12.5 0.00

* FCOY (n=3) † Udtja herding district (n=21); Gällivare herding district (n=13) ‡ Adult (n=26); Subadult (n=8) § Male (n= 16); Female (n=18) ¶ F – Adult (n=12); Female –Subadult (n=3); Male – Adult (n=11); Male – Subadult (n=5) ** FY (n=6); Lone female (n=9); Lone male (n= 16) †† Female - Adult (n=15); Female –Subadult (n=3); Male – Adult (n=11); Male – Subadult (n=5) ‡‡ FY (n=6); Lone female - Adult (n=6); Lone female –Subadult (n=3); Lone male – Adult (n=11); Lone male – Subadult (n=5); FCOY (n=3)

Table 3. Linear mixed effects models (ranked by AICc) to assess the effect of day-of-year and demographic class on between-kill- intervals for brown bears preying on reindeer calves. All models included bear individual as random intercept

Model df AICc Δ AICc wi Date2 + Age 6 983.13 0.00 0.49 Date2 + Sex +Age 7 985.27 2.14 0.17 Date2 + Sex *Age 8 985.61 2.48 0.14 Age 4 986.85 3.72 0.08 Date2 + Category2* 9 987.51 4.38 0.05 Date2 5 988.19 5.06 0.04 Date2 + Sex 6 989.51 6.38 0.02 Date2 + Category1† 7 990.61 7.48 0.01 Intercept 3 991.41 8.29 0.01 Date + Age 5 992.98 9.86 0.00

*females with yearlings, lone adult females, adult males, subadult females, subadult males †females with yearlings, lone females, males

11 Table 4. The total number of bears distributed on categories based on proportions in a normal population and average litter size of cubs of the year Udtja Gällivare Category Proportion of Females Males Females Males the population* Total number of bears 46 25 39 19 Cubs** 0,225 8 8 6 6 Yearlings** 0,175 6 6 5 5 2-yr olds*** 0,120 2 2 2 2

Litter size FCOY 2,26 (n=200) 7 6 Females 23 20 Males 9 6

* Bichof and Swenson 2012, **Together with their mothers, ***50 % with their mothers

Table 5. The estimated total calf mortality in each herding district per year, calculated from the annual average total number of adult reindeer females and the annual average proportion of calf loss in the radio-marked females Udtja Gällivare Study period 2010-2012 2011-2012 Average total number of adult female reindeer 1160 1660 Average proportion calf loss in radio-marked females 43 % 41 % Estimated total number of calves lost 498 675

Table 6. The estimated number of calves killed per bear category within the study areas and the subsequent proportion of the total loss possibly caused by bears Udtja Gällivare Bear category Number of bears Calves killed Number of bears Calves killed FCOY* 7 0 6 0 Male and Female† 32 325 (275,368) 26 264 (224,399) Total 325 (275,368) 264 (224,399) Calculated proportion of total 67 (57,76) % 39 (33,44) % loss caused by bears * Estimated seasonal kill rate on calving ground for female bears with cubs of the year = 0 (95%CI: 0 – 0) † Estimated seasonal kill rate on calving ground for lone bears (male and female) on calving ground = 10.2 (95%CI: 8.6 – 11.5)

12 Figures

Figure 1. Map of study area with a) reindeer herding districts and study areas, b) kill sites, and c) buffers and bear scats collected during a County wide brown bear population inventory in August 2010.

a) b)

c) d) Figure 2. Number of calves a) (n=333) and adult female b) (n=17) reindeer killed per day by 21 radio-collared brown bears, in relation to month, calving period and hibernation period for bears, and the distribution of calves c) and adult females d) killed in relation to time of the day in two calving grounds in Northern Sweden 2010-2012.

Figure 3. Estimated seasonal kill rates as a function of time (Hours) on the calving grounds with 95% bootstrap CIs, based on the highest ranked zero-inflated negative binomial model (Table 3). Observations are given as points, separated by demographic classes of brown bears.

Figure 4. Estimated between-kill-intervals (log-transformed) as a function of “day-of-year” with 95% CI, based on the highest ranked mixed-effects model (Table 4). Mean daily values of observed kill intervals are given as points, separated by age group (adults, sub adults).

15 Appendix 1. Kill rate over- and under-estimation The method used in this study benefits from reindeer females and their calves staying close to each other (see Esp- mark 1971 and Mathisen et al. 2003), which gives the opportunity for proximity collars on adult female reindeer to be detected by GPS collars on brown bears killing the un-marked calves. However, the method may still have three major sources of uncertainty when determining kill rates: 1) some of the adult female reindeer lacked proximity collars which may have led to underestimation, 2) not all clusters created were visited which may also have led to underestimation, and 3) radio-collared bears may have been accompanied by undetected un-marked bears killing calves which may have led to overestimation.

Adult female reindeer lacking proximity collars Individual brown bear kill rate may be under-estimated by our method if a radio-collared bear kills calves of females without proximity collars. Because adult females often go in groups of several females with calves on the heal, the probability of detecting a kill is higher than the proportion of un-marked females. Using simulations and kill data from Udtja where all females were collared in 2011 and 2012, we estimated the probability of detecting a kill as a function of the proportion of females instrumented with proximity collars in a herd. We did so by removing a random sample of females with proximity collars from the data and then reconstructing the kill data without the kills detected solely with the help of proximity collars worn by the females that had been removed during the simulation. We implemented the simulated removals as proportions of the entire adult female heard size ranging from 0 to 1, in increments of 0.01, with 1000 random repeats at each level of proportional removal. The predicted proportion of kills detected was calculated as the mean and confidence interval (95%) boundaries as the 0.025 and 0.975 quantiles of the resulting distribution of values at each proportions. The same approach was used to predict the proportion of kills that may have been missed at Udtja in 2010 and Gällivare in 2011 and 2012, given respective proportions of females marked at 79 %, 53 %, and 82 %. We note that these predictions assume similar associations in both herds and in all years. During 2011 and 2012 in Udtja, 166 calves were killed by the 7 radio-collared bears, and a total 778 adult female reindeer were detected in the vicinity of these kill sites. Most kills (80%) were detected as a result of the proximity of the perpetrating bear to multiple female reindeer (Fig. S1). Due to the high proportion of females collared in the study areas, the detection of calves killed of females without collars was high (Fig S2), with an underestimation ranging from 0-11,9 % dependent on year and study area (Table S1).

Figure S1. Cumulative number of reindeer calf kills detected in relation to the number of proximity collars involved in detection. The height of the curve is equivalent to the number of kills detected where the perpetrating bear was detected by equal or less than the number of proximity collars indicated on the x-axis. The red line indicates the number of kills that were detected through only one single proximity collar.

Figure S2. Probability of detecting a calf killed by GPS-marked bears, in relation to the proportion of reindeer does without proximity collars. The grey shaded area indicates the 95% CI band; red dots (white lines: 95% CI) mark the predictions for herds in years when <100% of reindeer does were equipped with proximity collars (LIST).

Table S1. Under-estimation of the kill rate due to un-marked adult female reindeer. Proportion Sami village Year marked Mean Lower 95% CI Upper 95% CI Udtja 2010 79 % 4.7 % 1.8 % 8.4 % 2011 100 % 0 % 0 % 0 % 2012 100 % 0 % 0 % 0 % Gällivare 2011 53 % 11.9 % 7.2 % 16.3 % 2012 82 % 4.0 % 1.2 % 7.8 %

Clusters not visited Due to practical (difficult to move in the area, e.g. on large wet mires), technical (not receiving GPS positions in time) and sometimes economic reasons, not all clusters were possible to visit in the field. During the period from 1 May to 15 June, when all except one of the reindeer calves were killed, totally 42814 GPS positions in minute cluster locations were generated within the study areas in Udtja and Gällivare reindeer herding districts (Table S2). Because clusters (i.e. locations with ≥3 GPS positions within a 30 m radius) occasionally were overlapping and close in time, we made a posthoc cluster definition, pooling overlapping consecutive clusters (including a 15m buffer), with a maximum time interval to the preceding cluster set to twelve hours. This resulted in a total of 1515 clusters within the given period. Of these, 75 % were visited and inspected, representing 96 % of all cluster locations (Table S2). Individual brown bear kill rate may be underestimated if the clusters not visited contained kills. One may generally expect that bears will stay longer in a place after a kill, and our data also indicated that the probability of finding a kill increased with the size of the cluster (Fig S3, Table S3). We therefore estimated the probability of finding a kill on the clusters based on the time spent by the bear inside the cluster, using number of minute GPS positions included in the cluster as a proxy (hereafter denoted cluster size). We used logistic regression with a binary response variable (calf carcass versus no carcass found in cluster) and a logit link function. We compared models with cluster size and log- transformed cluster size as the explanatory variable, using AICc to select the best predictor. To evaluate the predictive probability of the model we used the area under the ROC curve (AUC > 0.9 is excellent predictive ability; AUC between 0.8 and 0.9 is good predictive ability; AUC between 0.7 and 0.8 is fair predictive ability; AUC between 0.6 and 0.7 is poor predictive ability; AUC between 0.5 and 0.6 is no evidence of predictive ability). Finally, we applied the

17 estimated probability function on the clusters not visited, to estimate the probable underestimation of kill rate resulting from the missed clusters. Log-transformed cluster-size was the best performing predictor variable (Δ AICc=134). The results showed a significant positive effect of log(cluster size) on the probability of finding a calf carcass on a cluster (β=1.00 95 % CI [0.86, 1.14]). The predictive ability of the model was good with AUC = 0.8. Further, we estimated that the probability of finding a carcass ranged between 0.06 and 0.58 for the not visited clusters, as a function of cluster size (Fig S4), and that 34 [27, 42] (mean, [95 % CI]) or 9 % of the calves killed may have been missed.

Table S2. Number of cluster GPS positions and clusters in visited and unvisited minute location clusters, and proportions visited for the period 1 May to 15 June Number of cluster GPS Number of

positions clusters

All clusters 42814 1515

Visited 41096 1135

Not visited 1718 380

% Visited 96 % 75 %

Table S3. Table showing cluster time length (mean, SD) and cluster size (mean, SD) for the period 1 of May to 15 of June in Udtja and Gällivare reindeer herding districts Cluster time length * Cluster size Cluster category (minutes) (number minute GPS positions)

All clusters (n=1515) µ = 52, σ = 205 µ = 28 , σ = 104

Visit status

Visited (n=1135) µ = 68 , σ = 236 µ = 36, σ = 120

Not visit (n=380) µ = 5, σ = 12 µ = 5, σ = 6

Carcass status

Calf carcass (n=319) µ = 118, σ = 207 µ = 61 , σ = 88 No carcass (n=779) µ =32 , σ = 87 µ = 18, σ = 38

*Number of minutes from first to last cluster GPS position

Figure S3. Plot showing the first and third quartiles for cluster size (given by number of cluster GPS positions) for clusters with carcass and clusters with no carcass.

Figure S4. Predicted probability of recovering a reindeer calf carcass as a function of cluster size (number of brown bear cluster GPS positions), estimated from logistic regression model with binomial response (carcass vs no carcass) as a function of log(cluster size). The orange area under the curve shows the range for the not visited clusters.

Several bears at the same kill site Individual brown bear kill rate may be overestimated if a radio-collared bear create a cluster on a kill site of an un- marked bear. We could estimate the proportion of this overestimation from the proportion of kill sites with clusters made by two or more radio-collared bears and assuming similar frequency among radio-marked and un-marked bears. We calculated an average proportion of marked bears based on the annual number of marked bears in relation to the estimated number of bears in each herding district. More than one radio-collared bear created clusters in 15 of the 332 kill sites found (Table S4). In 238 kill sites made by females, a male accompanied the female at 5 kill sites, 3 kill sites were later visited by a male, one kill site was visited by another female, and one kill site was visited by a female and a male in company. In 94 kill sites made by males, a female accompanied the male at 3 kill sites and 2 kill sites were visited by a male. In 8 of the kill sites the bears visited the kill site together (the clusters overlapped in time), or the second bear arrived immediately (< 2 min) after the first bear left. The bears visiting the kill sites (n=7) were on average 43 hours (min 26 min, max 7 days) after the first bear at the carcass. Assuming the same frequency of association on kill sites among radio-collared and un- marked bears, the over-estimation of the kill rate, due to company with other bears or visiting kill sites, were estimated to be 9 % for females and 7 % for males (Table S5).

19 Table S4. Number of kill sites visited by other radio-collared bears and visits to kill sites of other radio-collared bears Sex Year Bear ID Study area Visits to kill Calves killed sites of others Kill sites being visited by others BD241 Udtja 2010 F 0 BD245 Udtja 2010 F 0 BD240 Udtja 2010 F 17 BD239 Udtja 2010 M 6 BD240 Udtja 2011 F 4 2 2 BD241 Udtja 2011 F 36 6 2 BD248 Udtja 2011 F 0 BD229 Udtja 2011 F 23 1 1 BD186 Udtja 2011 M 9 1 3 BD251 Udtja 2011 M 5 2 BD246 Udtja 2011 M 4 3 4 BD241 Udtja 2012 F 0 BD240 Udtja 2012 F 35 BD248 Udtja 2012 F 0 BD277 Udtja 2012 F 37 BD229 Udtja 2012 F 13 BD251 Udtja 2012 M 0 BD239 Udtja 2012 M 0 BD278 Udtja 2012 M 0 BD247 Gällivare 2011 F 23 BD263 Gällivare 2011 F 0 BD250 Gällivare 2011 F 29 1 BD249 Gällivare 2011 M 25 1 BD247 Gällivare 2012 F 6 BD264 Gällivare 2012 F 15 BD249 Gällivare 2012 M 25 BD269 Gällivare 2012 M 3 BD273 Gällivare 2012 M 6 BD270 Gällivare 2012 M 0 BD266 Gällivare 2012 M 0 BD268 Gällivare 2012 M 11

Table S5. Over-estimation of the kill rate due to company with other bears or visiting kill sites Sex of radio-collared bear Female Male Sex of bear accompanying or visiting the kill sites of the radio-collared bear Male Female Male Female Average proportion of bears radio-collared in Udtja and Gällivare 2010-2012 (A) 16,8 % 29,1 % Percent of kill sites accompanied with or visited by other radio-collared bears (B) 3,78 % 0,84 % 2,13 % 3,19 % Estimated percent of kill sites accompanied with or visited by all bears (C)* 13,0 % 5,0 % 12,6 % 11,0 % Estimated percent of kill sites accompanied with or visited by un-marked bears (D)** 9,2 % 4,2 % 10,5 % 7,8 % Proportion killed when accompanied with or visited by other radio-collared bears (E) 75 % 50 % 50 % 25 % Percentage over-estimated kill rate (E*D) 6,9 % 2,1 % 5,3 % 1,9 % * For females: C = 1/A (males) * B (females), for males: C = 1/A (females) * B (males) ** D = C - B.

20 Potentially the most severe source of inaccuracy in the kill rate estimations is the fact that a portion of the adult females was lacking proximity collars. But, because adult reindeer females form groups, the detection of kills was still high. Even with almost half of the females lacking collars, we estimated that only 7-16 % of the calves killed my have been undetected. In most of the years in this study, all or a high proportion of the females were radio-collared, which grants little error due to this factor in our kill rate estimations. Because the GPS-collar on the bears continued to take frequent GPS positions for a full hour after the latest detection of a reindeer female radio collar, many small clusters were formed along one-minute trails if the bear moved slowly (<30 m in 4 min). Many of these small clusters with typically 3 GPS positions were not visited on purpose, because they were in the vicinity of larger clusters or kill sites already investigated, or on the one-minute trail of the bear away from a kill site, alternatively in terrain with perceived very low probability of encountering calves. These cluster were sometimes skipped to save time in the field when many cluster with more GPS-positions and thus perceived as more important to investigate were to be visited. The modelling does not take the knowledge of history and how a small cluster was formed into consideration, as a field worker would do. Even if 25 % of the clusters were not visited, the fact that these clusters were mostly small and represented only 4 % of all GPS positions in cluster locations (Table S2), we believe that the estimated errors due to unvisited clusters are probably over-rated. The associations among bears are probably also over-rated, because the bears that were radio-collared may have been more prone to associate with other radio-collared bears. All bears radio-collared were visited within the study areas during the mating season to find and capture bears associated with them. This may have led to an overestimation of the amount of association among the radio-collared bears and the un-marked bears within the buffer zones. This is further illustrated with the variation in associations registered among years and study areas and may depend on the circumstances for how these bears were captured (Table S4).

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ȱȱȱȱȱȱȱ ȱȱ ȱȱȱ ѕђџђѠђȱǯȱіѣђџѡѠђћǰŗǰ†ȱіџєіѡѡюȱ#ѕњюћǰ1ȱюњȱǯȱ ǯȱ ǯȱѡђѦюђџѡǰŘǰřȱюџѠȱҦћћђєѮџёǰ4 ђћѠȱџюћјǰ5ȱђѡђџȱђєђџѠѡџҦњǰ5ȱљђȬ ѢћћюџȱѡҫђћǰŘǰŜȱюћёȱћћюȱјюџіћ1

1Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, P.O. Box 7024, 750 07 Uppsala, Sweden 2Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway 3Department of Environmental and Health Studies, University College of Southeast Norway, NO-3800 Bø, Norway 4Section of Statistics, School of Technology and Business Studies, Dalarna University, 791 88 Falun, Sweden 5 ãȱȱȱǰȱȱȱ¢ǰȱ ȱ¢ȱȱȱǰȱŝřŖȱşŗȱ¢Ĵǰȱ 6Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden

Citation:ȱ ǰȱ ǯȱ ǯǰȱ ǯȱ #ǰȱ ǯȱ ǯȱ ǯȱ ǯȱ ¢ǰȱ ǯȱ ãªǰȱ ǯȱ ǰȱ ǯȱ ãǰȱ ǯȬ ǯȱ èǰȱ ȱ ǯȱǯȱŘŖŗŜǯȱȱȱȱȱȱȱȱ ȱȱȱȱȱǯȱȱ ŝǻŗŗǼǱŖŗśŞřǯȱŗŖǯŗŖŖŘȦŘǯŗśŞř

Abstract.ȱȱȱȱȱȱȬȱȱ ȱ ¢ȱ ȱ ȱ Ěȱ ȱ ȱ ȱȮ ȱĚȱȱǯȱȱȱȱȱȱ ȱȱǻUrsus arctos) ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻRangifer tarandus tarandusǼȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Rangiferȱȱ ȱȱȱȱȱǰȱȱĴȱȱ ȱȱȱȱȱ Ȭȱȱǯȱȱ¡ȱȱȱȱȱȱȱȱȱȱȱ ȱȱȱȱ ȱȱȱȱȱȱȱȱȱȱȱȱ ȱȱȱ ȱȱȱȱȱȱǯȱȱȱ ȱȱȱ ŗŗŖȱȱ¢ȱǻşŝȱǼȱȱŘşȱ ȱȱ¢ȱǻŗşȱǼǰȱȱ ȱȱȱ ȱȱȱȱȱȱȱ ǯȱȱȱȱȱȱȱȱȱȱȬ ȱȱȱȱȱȱȱ ȱȱȱǰȱȱȱȱȱȱȱǯȱ ǰȱ ȱȱȱȱȱȱ¢ȱ ȱȱ ȱȱȱȱ ȱ¢Ȯȱȱȱȱ¢ȱ¢ǯȱȱȱ¢ȱ ȱȱȱȱ ȱȬȱȱȱ¢ȱȱȱȱ¢ȱȱĚȱȱȱȱ¢ȱ¢ȱ ȱȱȱȱȱǯȱȱȱȬȱȱ¢ȱȱęȱȱȱȱȱǰȱ ȱ¢ȱ ȱ¢ȱȱȱȱȱ¢ȱȱǰȱȱȱ ȱȱȱȱȱȱ¢ȱ ȱǯȱȱ¢ȱȱȱȬȱ ȱȱȱȱȱȱȱȱ ȱǯȱȱǰȱȱ ȱ¢ȱȱȱȱǰȱ¢ȱȱȱȱȱȱȱǯȱǰȱȱ ¢ȱȱȱ¢ȱȱȬȱȱȱȱȱ ȱȱȱȱ ȱȱȱĜȱ¢ȱȱȱȱȱȱ¢ǯȱǰȱȱȱȱ¢ȱȱȱ ȱȱȱȱ ȱȱȱȱȱȱȱȱȱȱȱȱ¢ȱȱȬ ȱȱȱȱȱ ȱȱȱȱȱȱȱȱȱȱ¢ȱǯ

Key words:ȱȱȱ ȱǲȱȱǲȱȱǲȱȮ¢ȱǲȱRangifer;ȱȱȱ ǯ

ReceivedȱŘśȱ ¢ȱŘŖŗŜǲȱȱŘŜȱȱŘŖŗŜǯȱȱǱȱȱǯȱ ǯȱ Copyright:ȱȚȱŘŖŗŜȱȱȱǯȱȱȱȱȱȱȱȱȱȱȱȱȱȱĴȱ ǰȱ ȱȱǰȱȱȱȱȱ¢ȱǰȱȱȱȱ ȱȱ¢ȱǯ † E-mail: ȱǯȓǯ

ȲȲ ǯǯ 1 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

INTRODUCTION ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱȱȱȱȱȱȱ ȱȱȱǰȱȱ ȱ ȱȱȱȱ ȱǻ ȱȱǯȱŘŖŗŘǼǯȱ ȱ ȱ ȱ ȱ ȱ Ȭ ȱȱȱȱȱȱȱȱȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱȬȱ ȱȱȱȱȱȬ ȱȱ ȱ ȱ ȱ ȱ ¢ȱ ěȱ ȱ ǰȱ ȱ ȱ ȱ Ȭ ȱȱȱȱȬȱȱȱȱ ȱ ȱ ȱ ǻȱ ȱ ǯȱ ŗşşřǰȱ ȱ ǯ ȱǯȱŘŖŗŘǼǯȱ ȱȱȱȬȱěȱ ȱ ȱ ȱ ȱ ȱ ǰȱ Rangifer ȱęȱȱȱǰȱǰȱ ǻȱȱǼȱȱȱȱȱ ȱȱǰȱ ȱȱȱȱȱȬ ¢ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ǻȱ ȱ ȱ ǻȱŗşŞŖǰȱ£ ȱŗşşŗǼǯȱ ȱȱȱ ŗşŞŝǰȱȱȱǯȱŘŖŖŗǰȱȱȱǯȱŘŖŗŜǼǯȱȱ ǰȱȱȱȱȱȱȱ ȱȱRangiferȱȱȱȱȱ ȱȱȱȱȱȱȱȱǻ·ȱ ȱȱ ȱȱȱȱȱ ȱǯȱŘŖŗŖǼǯȱǰȱȱ¢ȱȱȱȱ ȱ ȱ ȱȱ ǻǯǯǰȱ ȱ ȱ ǯȱ ȱ ȱ ¢ȱ ¢ȱ ȱ ȱ ǻȱ ȱ ŗşşŖǰȱ¢ȱȱĴȱŗşşŗǰȱĴȱȱȱ ěȱŗşşşǼǯȱȱȱȱȱ ŘŖŖŗǰȱ ȱ ȱ ǯȱ ŘŖŖŜǰȱ ȱ ȱ ǯȱ ŘŖŗŘǼǯȱȱ ȱȱȱȱȱ¢ȱȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ Ȭ ȱǰȱȱȱȱȱȱ¢ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱȱ¡£ȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱǰȱȱȱȱȱ ¢ȱ ǯȱ ȱ ȱ Ȭȱȱ Ȭ ȱ ȱ ȱ ¢ȱ ǻ ȱ ȱ ǯȱ ŗşşşǰȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱěȱŗşşşǰȱ·ȱȱǯȱŘŖŗŖǼǯȱ ȱȱǻ ȱȱǯȱŗşşŜǰȱȱȱǯȱŘŖŖŞǰȱ ǰȱȱȱȱȱ Ȭȱȱ ȱ ǯȱ ŘŖŗśǼǯȱ ǰȱ ȱ ȱ ȱȱȱȱȱȱȱȱ ¢ȱ Ȭȱ ȱ ȱ ǰȱ ȱ¢ȱ¢ȱȱȱǻǯǯǰȱ ȱȱ ȱȱȱȱȱȱȱ ĴȱŗşŞŞǰȱ ȱȱȱŘŖŖŘǰȱ¡ȱȱǯȱ ȱȱ¢ǰȱȱȱ ȱȱȱ ŘŖŖşǰȱ·ȱŘŖŗŖǰȱȱȱǯȱŘŖŗŚǰȱȱ ȱ¢ȱ¢ȱȱ ȱȱȱȬ ȱǯȱŘŖŗŜǼǯ ȱȱȱȱǻĴȱȱȱ ȱǰȱȱ¢ȱȱȱȱ ŘŖŖŖǰȱ¢ȱȱȱŘŖŖřǰȱȱȱǯȱ (Rangifer tarandus tarandusǼȱ ȱ ǰȱ ŘŖŖśǰȱȱȱǯȱŘŖŗŗǰȱȱȱǯȱŘŖŗŘǼǯ ȱȬȱȱ ȱȱȱȱ ȬȱȱȬȱȱǰȱȱȱȬ ȱȱȱȱȱ¢ǯȱȱȬ ȱȱȱȱǰȱȱĚȬ ȱ ȱ ȱ ȱ ȱ ȱ ǻǯǯǰȱ ȱ ȱ ȱ ȱ ȱ ȱ Rangiferȱ ȱ ȱ ȱǽCanis lupusǾǰȱ ȱȱǽUrsus arctosǾǰȱ ȱǰȱȱǰȱȱ¢ȱȱȬ ¢¡ȱǽLynx lynxǾǰȱȱ ȱǽGulo guloǾǼȱȱ ǯȱ ȱ ¡ǰȱ Ȭȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ¢ȱ ǻ ȱ ȱ ȱȱȱ ȱȱȱȱȱ ȱ ·ȱ ŘŖŗŘǰȱ #ȱ ȱ ǯȱ ŘŖŗŚǼǯȱ ȱ ȱ ȱ ȱ ǻĴȱ ȱ ȱ ŘŖŖŖǰȱ ȱȱȱȱȱȱȱȬ ȱȱǯȱŘŖŖşǰȱȱȱǯȱŘŖŗŘǰȱȱȱǯȱ ȱȱȱȱȱǰȱȱȬ ŘŖŗŜǼǯȱ ȱǰȱȱȱȱȱȱ ȱȱ¢ȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ¢ȱȱȱȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ęȱ ȱǻ ȱȱ·ȱŘŖŖśǼǯȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ěȱ ȱ ȱ Ȭ ȱȱȱ ȱ¢ȱȱ ȱȱ ¢ȱȱȱȱȱǻȱȱǯȱ ȱ ȱ ȱ ǻ¡ȱ ŗǱȱ ǯȱ ŗǼǯȱ ŘŖŗŘǰȱȱȱǯȱŘŖŗŚǼǯȱěȱȱȬȱȱ ȱȱȱȱȱǻǯǯǰȱǼǰȱ ȬȱȱȱȱȮ¢ȱȱ ȱȱȱȱȱȱȬ ȱȱȱȱȱȱǰȱ ȱȱȱȱȱǻȱȱǯȱŗşşśǰȱȱ ȱ¡ȱȱȱȱ ȱ ȱȱ ȱǯȱŗşşśǰȱȱŘŖŗŖǰȱ ȱȱǯȱŘŖŗŘǼǰȱ ȱ ȱȱȱȱȱȱ

ȲȲ ǯǯ 2 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ȱȱȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭ ǻȱŘŖŖŝaǼǯ ȱ ¢ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱȱȱǰȱ ǰȱ ȱȬȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ȱ ȱ ǰȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱȱȱ ȱȱȱȱȱȱȱȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱǻȱȱěȱ ȱȱǯȱǰȱĴȱ ŗşşşǰȱ ·ȱ ŘŖŗŖǼǯȱ ȱ ȱ ȱ ǰȱ ȱ ȱ¡ȱȱȱȱȱȱ¢Ȭ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭȱȱȱ ¢ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ ǻȱ ȱ ȱȱ ȱȱȱȱ ŘŖŖśǼǯȱ ȱ ȱ ȱ ȱ ȱȱȱǯ ¢ȱ ȱ ¢ȱ ȱ ȱ Ȯ ȱ ȱ ¢ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ¢ǰȱ ȱ ȱ ȱ ¢ȱ Ěȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻȱ ŘŖŖśǼǯȱ ¢ȱ ȱȱȱȱȱǯȱȱ¢ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱ ȱȱȱȱȬ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱ ǰȱ ȱ¢ȱȱ ȱȱȱȱȱǰȱȱ ȱȱȱȱȱȱǰȱȱ ȱ ȱ ȱ ǻȱ ȱ ǯȱ ŗşşŞǼǯȱǰȱ ȱ ȱȱȱ¢ȱ ȱȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ¢ǰȱ ȱ ¢ȱ Ȭ ȱȱ¢ȱȱǻ ȱȱǯȱŘŖŗŘǼǯȱ ȱ ȱȱ¢ȱȱȱǻȱȱǯȱŘŖŖŝǰȱ ȱ¢ȱǰȱ ȱȱȱȱȬ £ȱȱǯȱŘŖŗŗǼǯȱȱ ȱȱȱȬ ȱȱȱ ȱ ȱ ęȱ ŚȮŜȱ ȱ ȱĴȱȱ ȱȱȱ¢ȱ ȱȱǻ ȱȱǯȱŘŖŗŘǼǯȱǰȱȬ ȱȱȱ¢ȱǰȱȱȱȱȬ ȱȱȱȱȱȱȱ¢ȱ ȱ ȱȱȱ ȱȱȱȱ ȱȱǰȱ ȱȱȱȱ ȱȱȱȱǯ ȱȱǻ ȱȱǯȱŘŖŗŘǼǯ ¢ǰȱ ȱ¢£ȱȱȱȬ ȱȱȱ ȱȱȱ ȱ ȱȱȱ¢ȱȱȱȱȱȬ ȱ ȱȬȱȱȬȱȱȱ ȱȱȱȱȱȱȱȱ ȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ¡ȱ ǻŗǼȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ǻȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ Ȭ ȱȱȱǼȱȱȱȱ ȱȱȱ ȱȱȱȱȱ ȱǻȱȱȱȬȱȱǼȱ ȱ ǰȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻȱ ȱ ȱ ȬȱȱȬȱȱȱǻǯǯǰȱȱȱ ȱ Ǽǰȱ ȱ ǻŘǼȱ ȱ ȱ Ğȱ ȱ ȱ Ǽǯȱ ȱ ȱ ¢ȱ ¢ǰȱ ȱ ȱ ȱ ȱ Ĵȱ Ȭ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱ ȱȱȱȱȱȱȱ ȱȱȱȱȱȱǰȱȬȱ ȱȱȱȱǯ ȱȱǯȱȱȱȱ ȱ¢ȱȱȱȱȱȱȱȬ METHODS ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱȱȱ Study area ǰȱ ȱȱȱȱȱȱȱȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ǰȱ ȱȱȱȱȱȱǻŗŘŗŖȱ2ǰȱ ȱȱȱ¢ȱȱȱȱȱȱȱ ȱ ŜŜǯŘǚȱǰȱŗşǯŚǚȱǼȱȱ §ȱǻŗŜŚŗȱ2ǰȱŜŜǯŜǚȱǰȱ ȱȱȱǯȱȱȱȱ ŘŗǯŚǚȱǼȱȱȱȱȱǻǯǯǰȱȬ ȱȱǻǼȱ¢ȱȱȱ ȱ ȱȱȱȱȱȱȱ ȱ ȱȱȱȱȱȱ¢ȱȬ ȱ ¢ȱ Ǽȱ ȱ ȱ Ĵȱ ¢ǰȱ ǰȱ ȱȱȱȱȱȱǰȱ ȱ ȱǻǯȱŗǼǯȱȱȱȱ¢ȱ ȱęȱȱȱȱȱȮ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱǯ ȱ ǯȱ ȱ ę¢ǰȱ ȱ ¢ȱ

ȲȲ ǯǯ 3 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ǯȱŗǯȳȱȱȱ¢ȱȱ ȱȱ ǰȱȱȱȱȱȱȱǻ ȱĞǼȱȱ §ȱ ǻȱǼȱȱȱȱǯȱȚ§ȱŘŖŗŚȦŝŜŚǯ

ȱ ȱęȱȱȱŗŖŖƖȱȱ¡ȱ ȱȱȱȱȱȱȱ¢ȱ ¢ȱ ǻǼȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ǰȱ ȱ ȱȱęȱǰȱȱ¢ȱ ȱȱȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ Ȃȱ ęȬ ¢ȱȱǯȱȱŗşşśǰȱȱȱȱ ȱȱȱȱȱǰȱȱȬ ȱȱȱȱȱȱȱȱȱ ȱȱ ȱȱǰȱȱȱȱǻǯǯǰȱ ȱ ¢ȱ ǯȱ ȱ §ǰȱ ȱ ǰȱ ǰȱ ȱ ¢Ǽǯȱ ȱ ȱ ȱ ȱȱȱȱȱȱȱ¢ȱȱ ȱ ¢ȱ ¢ȱ ȱ ǻPicea abiesǼȱ ȱ ǯȱȱȱȱȱȱǻ¢ȱȱ ȱ ȱ ǻPinus sylvestrisǼǰȱ ȱ ȱ Ǽȱȱȱȱǻȱȱ ȱȬ ǰȱȱǰȱȱȱȱǰȱȬ ȱ ĜǼȱ ȱ ŖǯŘśȱ ȱ ŖǯŖŘȱ Ȧ2ȱ ȱ ǰȱ ȱȱǻBetula pubescensǼȱȱǻ¡ȱŗǱȱ ȱ ŖǯřŞȱ ȱ ŖǯŖŜȱ Ȧ2ȱ ȱ §ǰȱ Ȭ ǯȱŘǼǯȱȱ¢ȱȱ£ȱ¢ȱȱ ¢ǯȱȱȱ¢ȱȱ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱǰȱȱȱȬ ȱȱŗŞŝȱȱŝŗŚȱȱǯǯǯȱȱȱȱřŞȱȱ ¢ȱȱȱȱǯȱȱ śŘŞȱ ȱ ǯǯǯȱ ȱ §ǯȱ ȱ ȱ ȱ ǰȱ ȱȱǰȱȱȱȱȱ ȱ ȱȱȱȱȱȱȱȱ ȱȱǰȱȱȱ¢ȱȱȬ ȱȱȱȱȱȱȱȱ ȱȱȱǰȱȱȱ ǰȱ ȱȱȱ ȱȱȮȱȬ ȱȱȱǰȱǯȱ ȱǰȱȱȱ ǰȱ ȱȱȱȱȱǯ ȱȱȱ ȱȱǯ

ȲȲ ǯǯ 4 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

Ĵȱ ¢ȱ ǻȱ ȱ şŝǰŘśŝȱ 2) ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ¡¢ȱ ŘǯŜȱ ȱȱ ȱȱ¢ȱŘȱȱǻȱǰȱ ȱȱ2ȱȱŘŖŗřǰȱȱ ȱȱ Ȭȱ èǰȱ ¢ǲȱ ȱ ǰȱ ǰȱ ȱȱȱȱȱȱȱ¢ȱ Ǽǯȱ ȱ ȱ ȱ ȱ ȱ ȱªǰȱȱȱȱȱȱǻĴȱ ȱ ȱ Ȭȱ ȱ ȱ ǻ ȱ ¢ȱ ȱ ȱ ŘŖŗŚǼǯȱ ȱ ȱ ȱ ǰȱǰȱ ¢Ǽȱ ȱȬ ȱ ȱ ȱ ȱ Ĵȱ ȱ Ȭ ęȱȱȱȱȱȱȱȱ ȱȱȱŝŗřȮŗŗśŘȱȱȱŘŖŗŗȱǻ¢·ȱ ȱȱȱ ȱȱǯȱȱȱ ŘŖŗŗǼǯȱ ȱȱȱȱȱȱȱ ȱǯȱǻŘŖŗŗǼȱȱȱȱȱȱǯȱ ȱ ȱ ȱ ȱ ȱ ǻŘŗȱ Ȯŗśȱ ȱ ȱȱ ȱȱ ȱȱ ȱ ȱ ȱ ȱ ȱ Ǽǯȱ ȱ ȱ ȱȱȱ¢ȱřŖȱǯ ȱ §ǰȱ ȱ ȱ ȱ ȱ Ȭ ȱȱŘŖŗŖȱ ȱŜŘȮşŜȱȱśřȮŝśǰȱ¢ȱ Environmental data ǻ ȱȱǯȱŘŖŗŘǼǯȱȱȱȱ ȱȱȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ¢ȱǰȱȱȱȱȱ¢¡ȱȱ ȱȱȱ¢ǰȱǰȱȱȬ ȱ ȱ ȱ ǻ¢·ȱ ŘŖŗŗǼǯȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ §ȱ ȱ ¡Ȭ ȱ ǯȱ ȱ ȱ ȱ ¢ȱ ŗŗŖȦŗŖŖȱ 2ǯȱ ȱ ȱ ¢ȱ ȱ ǻȱŗǼȱ ȱ¡ȱȱ ȱŗŖǯŖȮŗŖǯřȱ ǻŘŖŗŖȮŘŖŗŘǼǰȱ ȱȱȱȱȱȬ Ğ ȱ ǻ ȱ ǯǰȱ ǰȱ ǰȱ ȱ ȱȱȱ ȱȱȱ ȱȱ ȚŘŖŗŖȮŘŖŗśǼǯȱ ȱ ȱ ȱ ȱ ¢ȱ ȱȱȱȱȱȱȱ ȱȱ ȱȱ ȱȬ ȱǻ ȱȱǯȱŘŖŗŘǼǯ ȱ ȱ ǻǰȱ ȱ ȱ ǰȱȱȱȱ¢ȱȱ Ǽǯȱ Study period ȱȱȱȱȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ŝŖȱ ȱ ȱ ȱ ȱ ȱ ȱ ŘŖŗŖȱ ȱ ŘŖŗŘȱ ȱ ǰȱ ¢ȱ ȱ řŖŖȱ ǰȱ ȱ ȱ ȱ Ȭ ȱ ȱ ŘŖŗŗȱ ȱ ŘŖŗŘȱ ȱ §ǯȱ ȱ ȱ ȱ ǻŗǱŜŖǰŖŖŖǼȱ ȱ ęȱ ęȱ ȱ ¢ǰȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ŗŖȱ ¢ȱ ȱ ȱ ŗşŝŞȱ ȱ ŗşşŗǰȱ ȱ ȱ ȱ ǻȱ ȱ ȱ ȱ Ǽȱ ȱ řŖȱ ȱȱȱřȱȱȱȱ¢ȱǯȱȱ ȱ ǻȱ ȱ ȱ ȱ Ǽǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ęȱ ȱ ¡ȱȱǻřřŘȱȱřřśȱǼȱȱȱȱȬ ǻ¡ȱ ȱ ȱ ¡ȱ ȱ ǽ¢ȱ ȱ ¢ȱ Ȭȱȱ ȱ ȱ ȱ ȱ ȱĴǾǰȱ ȱ ȱȱȱȬ ¢ȱ ȱ ȱ ȱ ŗŖȱ ¢ȱ ȱ şȱ ȱ ȱȱȱȱ ȱȱȱǼǱȱ ǻ ȱ ȱ ǯȱ ŘŖŗŘǼǯȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ǰȱ ȱ ȱ ǰȱ ȱȱȱ¢ȱȱȱȱȱ ȱǰȱ ǰȱȱȱȱȬ ȱ ǻŗŖȱ ¢Ȯşȱ Ǽȱ ȱ ȱ Ȭȱȱ ȱǻȱǰȱǰȱȱǼǯȱȱ ȱ ǻŗŖȮřŖȱ Ǽǯȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ ȱ ȱ ȱ ŗŞǱŖŖȱ ȱȱȱȱȬȱȱǻȱ ȱ ȱ ŖŜǱŖŖȱ ȱ ǻ ȱ ȱ ǯȱ ŘŖŗŘǼǲȱ ȱŗǱŗŖǰŖŖŖȱȱǰȱȱȱŘŖŖŖȱȱ ¢ǰȱ ȱęȱȱȱȱȱȬ ŘŖŗŘǲȱãȱȱǰȱ ȱȱ¢ȱ ȱȱǻŗŞǱŖŖȱȱŖŜǱŖŖǼȱȱ ȱȱȱ ŘŖŗśǼȱȱȱȱȱ¢ȱǰȱĴȬ ǻŖŜǱŖŖȱȱŗŞǱŖŖǼȱ ȱȱȱǯ ǰȱȱȱȱȱȱ ȱȱ ȱȱ ȱȱŘśȱƼȱŘśȱȱǰȱ ȱȱȱ GPS location data ȱȱȱŗȱȱǻȱȱȱȱ ȱǰȱ ȱȱ ȱȱȱŗŗŖȱȱȬ ȱ ŘŖŖŖǼǯȱ ¢ȱ ȱ ȱ ȱ ȱ Ȭȱ ȱȱ¢ȱȱŘşȱ ȱȱ¢ǰȱȬ ȱ ȱ ȱȱ ¢ȱ ȱ ȱ ȱ ¢ȱ ȱşŝȱȱȱȱǻǱȱ ŘŖŖŖǰȱ ȱ ęȱ ȱ ȱ ȱ Ŝŝǲȱ §ǱȱřŖǼȱȱŗşȱȱ ȱȱ ¢ȱ ¢Ǳȱ ȱ Ȭȱȱ ǻŖȮśȱ ¢Ǽǰȱ ȱ ǻǱȱ ŗŗǲȱ §Ǳȱ ŞǼǯȱ Ȭȱȱ ȱ ȬȱȱǻŜȮŗŘȱ¢ȱȱǀŘȱȱȱȱ¢ȱŘŖŖŖǼǰȱȱ ȱ ȱ ¢ȱ Ȭȱȱ ȱ ǰȱ ¢ȱȱǻŘȮśȱȱȱȱ¢ȱŘŖŖŖǼǯȱ ȱǰȱ ȱȱȱȱȱȱȱȱ ȱ ȱ ȱ Ȭȱȱ ȱ ȱ ȱ ȱ

ȲȲ ǯǯ 5 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ȱŗǯȳ ȱȱȱȱȱȱȱ¢ȱȱȱȱ ȱȱȱȬ ǰȱ ȱȱǻȮ¡ǼȱȱȱȱȱȱȱǻǰȱǰȱȱȱǼȱ ȱȱȱȱȱȱȱȱȱȱ ȱ¢ȱǯ

ȱȱǻǰȱȱȱǼ ȱȱ §ȱȱ

ȱȱȱǻǰȱȱǯǯǯǼ ǻŗŞŝȮŝŗŚǰȱΐȱƽȱŚşŘǼ ǻřŞȮśŘŞǰȱΐȱƽȱřŘŝǼ ȱǻǼ ǻŖȮŖǯŖŗǰȱΐȱƽȱŖǯŖŗǼ ǻŖȮŖǯŖŗǰȱΐȱƽȱŖǯŖŖśǼ ȱȱȱǻǰȱǼ ǻŖȮŗŘǰřŞŚǰȱΐȱƽȱŘřŞŝǼ ǻŖȮśŚŖŘǰȱΐȱƽȱşřŜǼ ȱȱȱǻǰȱǼ ǻŖȮŗŘǰŜŖŘǰȱΐȱƽȱŚŜŚŜǼ ȱȱȱǻƖǼ ȱȱȱǻǼ řŚǯŜ řŜǯş ȱǻǼ ŘŝǯŘ řŜǯŖ ȱȱȱǻǼ ŘŗǯŖ ŗŖǯŘ ȱȬȱȱǻǼ Ŗǯŗ ŖǯŞ ȱȬȱȱǻǼ Śǯŝ śǯř ȱȱǻǼ ŘǯŜ Şǯś ȱȱǻǼ řǯŖ Ŗǯś ȱȱȱǻǼ řǯŚ ŗǯŖ

ȱȬȱ¢ȱǻǼǰȱȱȱȱ ȱȬ ȱȱȱȱȱȱ ȱȱȱȬȱȱǻŖǯŖŖŗǼǯȱȱęȱȱ ȱǻǼȱǻȱȱǯȱŘŖŖŝǼȱȱ ȱ ȱ ȱȱ ȱ£ȱȱȱśŖȬȱȱǯȱȱ ǻĴǱȦȦ ǯȬǯǼȱ ȱȱȱ ȱ ȱȱȱȱȱȱȱȱȬ ȱȱȱęȱǯȱȱȱȱ ȱ ǻ§ǰȱ ȱ ȱ ǰȱ ȱ ȱ£ȱȱȱȬ ȱȱ¢ȱȱ Ǽǯȱȱęȱ ¢ȱȱȱĜǯȱȱȬ ȱ ȱ ȱ ȱ ǻ¢ȱ ȱ Ǽȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱȱȱǻȱȱ ȱȱȬ ȱĚȱȱǻ ǼȱǁŘǲȱȱȱȬ ęǼȱȱȱȱȱȱȱȱ ȱȱȱǰȱ¢ȱ ȱȱȱ ȱȱȱȱśŖȱƼȱśŖȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ Ěȱ ȱ ¢ȱǯȱȱȱȱȱȱȱȱ ǻ ǼȱǂŘȱǻȱȱǯȱŘŖŗŖǼǯ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱȱȱȱǰȱ ȱȱ Statistical analysis ȱ ȃȱ ȱ Ȅȱ ȱ ǻȱ ȱ ȱ ȱ ȱ ȱ ȱ ŚşǰśŘŞȱ ȱ ȱ Ǽȱȱ¡ȱ¢ȱȱȱȱŗȱƺȱeαǰȱ (nȱƽȱşŝǰȱΐȱƽȱśŗŖǰȱȱƽȱŘŗşǼȱȱŘŚǰŖşŘȱ ȱȱ ȱȱȱȱȱȱȱȱȱαȱ ȱ ȱǻnȱƽȱŗşǰȱΐȱƽȱŗŘŜŞǰȱȱƽȱŗřřŝǼȱȱȱ ȱ ȱ ŖǯŖŖŘȱ ǻ¡ȱ ěȱ £ȱ ǀŗśŖŖȱ Ǽǰȱ ȱȱȱȱȱȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ǻŘŖŖşǼǯȱ ȱ ǻǼȱ ȱ ȱ ȱ ȱ ȱ ¡ȱȱȱ¢ȱȱȱŖȱ ȱ¢ȱȱȱǻȱȱȱ ȱ ȱ ȱ ȱ ŗȱ ȱ ¢ȱ ȱ ǯȱ ȱ ŘŖŗřǼǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¡ȱȱȱȱȱ¢ȱȱǰȱ ȱǰȱ ȱȱ£ȱȬ ȱǻŗǼȱȱȱȱȱ ȱ¢ȱ ȱ¡ȱȱǻ Ǽȱ ȱȱȱ ȱ ȱ ȱ ȱ ¡ȱ ȱ ȱȱȱȱěȱȱȱȱȱȱ ěȱ£ȱȱ¡¢ȱŘŖŖŖȱǰȱȱǻŘǼȱȱ ȱǻȱȱǯȱŘŖŖşǼǯȱ ȱȱȬ ȱȱ¢ȱ ȱ¢ȱȱ ȱȱȱ ȱ ȃȱ ǰȄȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱǰȱȱȱȱȱ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱ¢ȱȱȱěȱ£ȱȱǂŘŖŖŖȱȱ ȱ ȃȱ¢ǯȄȱȱȱǰȱȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻǯȱ ŗǼǯȱ ȱȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱśŖȱȱȱȱȱȱȬ ȱȱ ȱ ȱ ȱ ȱ ȱ ¢ȱȱƹŘȱȱǻ ȱȱ ǰȱȱ ǯȱȱȱȱȱ¢ȱ ȱ¢ȱȱ Ǽǯȱȱȱǻ°Ǽȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱȱ ǯȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻǯǯǰȱ

ȲȲ ǯǯ 6 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ȦȬȱȱȱ Ȧ ȱ ȱ ȱȱȃȄȱȱȱȱǻ ȱȱǯȱ Ǽȱ ȱȱȱę¡ȱěǯȱȱȬ ŘŖŖşǼǯȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱȱȱ¢ȱȱȱȱ ȱȱȱǻǱȱşŘśȱǲȱ §Ǳȱ ȱȱȱȱ¢ȱǯȱȱ ȱ ȱȬ řŗŗŞȱ Ǽȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ¢ȱǯȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȯȱȱȱǻ¡ȱȱ ǯȱ ǰȱ ȱ ȱȱ ȱ ȱ ȱ ȱ ȱ ȱ Ǽǰȱ ȱ ȱȱȱȱȱȱȱǻȬ ŗŝŗȱ ȱ ŗŚŗşȱ ¢ȱ ȱ ȱ ȱ ȦȬȱȱǼȱȱȱ¢ȱȬ §ȱȱǰȱ¢ǯȱ¢ǰȱ ȱȱ ȱ ȱ ȱ ȱȱȱ ǻȦ ȱ ȱ ȱȬȱȱȱȱ¢ȱ Ǽǰȱȱȱȱȱȱȱǻȱ ȱȱ ȱȱȱȱȱ ȱȱȱȱȱǼǯȱȱ ȱȱ ȱ ȱȱȱȱȱȱ¢ȱȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱȱǰȱȱȱȱȱȱǯ ȱȱȬȱȱȱ ȱȱȱȱ ǰȱ ȱ ȱȱȱȱ¢ȱ ȱ ȱ ȱ ȱ Ȧ ȱ ȱ ȱȱȱȱȱ ȱȱȱȱ ǯȱ ȱȱȱ¢ȱǰȱ ęȱ ȱ ǰȱ ȱ ȱ ȱ ȱęĴȱȱȱȱȱȱǻȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱŘŖŖŘǼǯȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ǯȱ ȱ ȱ ȱȃȱȄȱȱȱȱȬ ȱȱȱȱ¢ȱ ȱ ǰȱȱȃȱȱ¢ȱȄȱȱ ȱȱȱȱěȱȱǰȱ ȱȱȱȱǰȱȱȃȱ ȱȱȱȱȱȱ ȱ ȱ ¢Ȅȱ ȱ ȱ ȱ ȱȱşśƖȱęȱȱǻ ǼǯȱȱȬ ȱȱȱǰȱȱȱȱȱ ȱȱȱȱȱȱȱ ǻ¡ȱŗǱȱȱŗǼǯȱȱȱȱęȱȱ ȱ ȱȱȱȱȱȬ ȱȱȱȱȱȱ ȱǱȱǻŗǼȱ ȱȱǻȱȱȱǼǯȱȱȬ ȱȱȱȱȱ ȱ ȱȱǰȱ ȱȱȱ ȱ ȱ ǰȱ ȱ Ȭȱȱ Ȃȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱȱǻ cǼǰȱȱȱȱ Ĝȱȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱȱȱȱǱ ̇ ȱǀȱŘȱȱȱȱȱȱȱęȱ [ ] c ȱ ȱ ǻȱ ŘŖŗŖǼǰȱ ȱ ǻŘǼȱ ȱ ȱ ȱ ȱ = =β +β +…+β + +ε ǻ¡Ǽ − Ŗ ŗ¡ŗ ¡ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ŗ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱw(xǼȱȱȱȱȱȱȱȱ ȱȱ ȱ¢ȱǯȱȱȱȱ ȱȱȱǰȱpȱǰȱȱȱ¡ȱ ǰȱ ȱ ȱ kȬȱȱȬȱǰȱ Ȭ ȱȱȱǰȱȱ¢ȱȱ ȱȱȱȱ¢ȱȱǯȱǻŘŖŖŘǼǯ ȱȱȱŖȱȱŗǰȱȱZvȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱ¡ȱěȱǯȱȱ ȱȱȱȱȱȱȱȱ¢ȱȱ ȱ¢ȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱǱ ȱǰȱȱȱȱȱȱȱ ȱǯȱ ȱȱǰȱȱ¡ȱȱȱ β +β +…+β + +ε ¡ǻ Ŗ ŗ¡ŗ ¡ Ǽ ȱȱ¢ȱȱȱ¢ȱȱȱ = ŗ+¡ǻβ +β ¡ +…+β ¡ ++εǼ ȱ ȱ ȱ ȱ ȱ ǯȱ ǰȱ ȱ Ŗ ŗ ŗ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱ ȬȱęĴȱȬ ȱȱȱȱȱȱȱȱ ȱȱȱȱȱȬ ǰȱ ȱȱȱ¢ȱȱěȱ ȱȱȱȱȱȱȱȱȱȱ ȱ Ŗǯśȱ ǻǯǯǰȱ şśƖȱ ęȱ ȱ ȱ ŖǯśǼǰȱ ȱ ȱ ȱ ¢ȱ ȱ ȱȱ ȱȱ¢ȱǁŖǯśǰȱ ǻȱ ȱ ȱ ǯȱ ŘŖŖşȱ ȱ ȱ ȱ Ȭ ȱȱ ȱȱ¢ȱǀŖǯśǯȱȱ ȱȱ¢ȱȱ¢Ǽǯȱȱȱǰȱ ȱ Ĝȱȱȱȱȱ ȱ

ȲȲ ǯǯ 7 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ȱȱ¢ȱęȱ ȱşśƖȱ ȱ ȱȱȦ ȱȱȱȱǰȱ ȱȱȱ ȱ£ǯȱȱȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ řǯŖǯŘȱ ǻȱ ȱ ȱ̇ cȱǀȱŘȱ ȱȱȱȱȱȱ ȱȱŘŖŗřǼǯȱ ȱ ȱęȱȱȱȱ ȃȱȮ¢Ȅȱǯȱȱ ȱ ȱ ȱȱȱȃŚȄȱȱǻȱȱǯȱŘŖŗŚǼǯ ȱ ȱ ȱ ȱ Ĝȱ ȱ ȱ ȱ ȱ¢ȱǰȱ ȱȱȱȱȱȱȱ RESULTS ȱȱǻ¡ȱŗǱȱȱŘǼǯȱȱȬ ȱȱȱȱȱȱ ȱȱǰȱȱȱȱȱ¢ǰȱ ȱȱǻȱŘȱȱřǼǯ ȱ ǰȱ ȱ ȱ ǰȱ ȱ ȱ Ȭȱȱȱ ǻȱ Ǽȱ Spatial overlap between reindeer and brown bears ȱ¢ȱȱȱȱȱȱ¢ȱǯȱ ȱȱȱȱȱȱȱȱ ȱ¡ȱ ȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ

ȱŘǯȳȱĜȱǻȱǰȱβǼȱȱşśƖȱęȱȱǻ Ǽȱȱȱȱȱ ȱ ¡ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȦȬȱ ȱȱȱȱȱȱǯ

§ β şśƖȱ β şśƖȱ β şśƖȱ β şśƖȱ

0.47 0.40, 0.54 ƺŖǯřŚ ƺŖǯŚŜǰȱƺŖǯŘŘ ƺŖǯŗŖ ƺŖǯŘşǰȱŖǯŗŖ ƺŗǯŞŖ ƺŘǯŗŗǰȱƺŗǯśŖ ȱ† Op ƺŖǯŖř ƺŖǯŗřǰȱŖǯŖŝ ŖǯřŜ ŖǯŘŗǰȱŖǯśŖ ŖǯŚŞ ŖǯŘŖǰȱŖǯŝś ƺŖǯŗŖ ƺŖǯŜŖǰȱŖǯŚŖ ƺŖǯŜř ƺŖǯŜŞǰȱƺŖǯśŞ ŖǯŜŗ ŖǯśřǰȱŖǯŜş ƺŖǯŗř ƺŖǯŘŚǰȱƺŖǯŖř Ŗǯŝŗ ŖǯśŞǰȱŖǯŞś ƺŖǯŚś ƺŖǯśŜǰȱƺŖǯřř ŖǯŘś ŖǯŖŞǰȱŖǯŚŘ ƺŖǯśŗ ƺŖǯşśǰȱƺŖǯŖŞ ŗǯŚś ŗǯŖŝǰȱŗǯŞŚ ŖǯŖŞ ƺŖǯŖŗǰȱŖǯŗŜ ƺŖǯŗŗ ƺŖǯŘŝǰȱŖǯŖś Rc ŗǯŖŘ ŖǯŝśǰȱŗǯŘş ƺŗǯŗř ƺŗǯŞřǰȱƺŖǯŚř Oc ŖǯŞş ŖǯŝŚǰȱŗǯŖř ƺŖǯŗŖ ƺŖǯřřǰȱŖǯŗŘ ƺŖǯśŝ ƺŖǯŜŘǰȱƺŖǯśŗ ƺŖǯŚŚ ƺŖǯśřǰȱƺŖǯřś ƺŖǯśř ƺŖǯŜŚǰȱƺŖǯŚŘ ƺŖǯŝŞ ƺŖǯşŘǰȱƺŖǯŜř ƺŖǯŝś ƺŖǯŞŞǰȱƺŖǯŜŘ ŖǯŘŚ ŖǯŖŝǰȱŖǯŚŘ ƺŖǯŜŜ ƺŖǯŞŗǰȱƺŖǯśŗ ŖǯŞř ŖǯŜŜǰȱŗǯŖŖ ŖǯŖŞ ŖǯŖŜǰȱŖǯŗŖ ŖǯŗŚ ŖǯŗŖǰȱŖǯŗŞ Ŗǯřş ŖǯřŜǰȱŖǯŚŘ ŖǯŖŚ ŖǯŖŖǰȱŖǯŖŞ ƺŖǯŗŖ ƺŖǯŗŞǰȱƺŖǯŖŘ ŖǯŗŚ ŖǯŖŗǰȱŖǯŘŝ 0.70 ŖǯśşǰȱŖǯŞŘ ŖǯŜŖ 0.45, 0.75 ƺŖǯŘŘ ƺŖǯřşǰȱƺŖǯŖś ŗǯŗş ŖǯşŗǰȱŗǯŚŞ ƺŖǯŗş ƺŖǯŘŘǰȱƺŖǯŗŝ ƺŖǯŖś ƺŖǯŖŞǰȱƺŖǯŖŘ ƺŖǯřş ƺŖǯŚřǰȱƺŖǯřś ƺŖǯŘř ƺŖǯŘŞǰȱƺŖǯŗŞ ‡ ƺŗǯŖŝ ƺŗǯŘŘǰȱƺŖǯşŘ ƺŖǯşŗ ƺŗǯŗśǰȱƺŖǯŜŝ ƺŖǯŜŜ ƺŖǯşŜǰȱƺŖǯřś ŖǯŘř ƺŖǯřŗǰȱŖǯŝŝ ȱƼȱ ŖǯŜŚ ŖǯŚŚǰȱŖǯŞř ƺŖǯŚś ƺŖǯŞŖǰȱƺŖǯŗŗ ŗǯŗş ŖǯŝŚǰȱŗǯŜś Ŗǯřŗ ƺŖǯśŝǰȱŗǯŗş ȱƼȱ ŖǯŜŜ 0.55, 0.77 ŖǯŘŜ ŖǯŗŗǰȱŖǯŚŗ Ŗǯŝş ŖǯśşǰȱŖǯşş ŖǯŖŘ ƺŖǯŘŚǰȱŖǯŘŞ ȱƼȱ ŖǯŜŖ ŖǯřŝǰȱŖǯŞŚ ƺŖǯŘŝ ƺŖǯŜŚǰȱŖǯŗŗ ŖǯşŘ ŖǯŘŖǰȱŗǯŜř ŖǯśŖ ƺŖǯŗŚǰȱŗǯŗŚ ȱƼȱ ŖǯŖř ƺŖǯŗŝǰȱŖǯŘř ŖǯŚŞ ŖǯŘŗǰȱŖǯŝś ȱƼȱ ŗǯřŚ ŖǯŞşǰȱŗǯŝŞ ƺŗǯřŜ ƺřǯŚşǰȱŖǯŝŝ ȱƼȱ ŖǯŚŜ ŖǯŘŖǰȱŖǯŝř ŗǯŗŞ ŖǯŞŘǰȱŗǯśś ȱƼȱ ŗǯŗŝ ŗǯŖŝǰȱŗǯŘŞ ŖǯŚŘ ŖǯŘśǰȱŖǯŜŖ ŗǯŝŝ ŗǯśŝǰȱŗǯşŝ ƺŖǯŗŖ ƺŖǯřşǰȱŖǯŗş ȱƼȱ Ŗǯřś ŖǯŖŜǰȱŖǯŜś ŖǯŜŘ ŖǯřŖǰȱŖǯşř ŗǯŚŖ ŗǯŗśǰȱŗǯŜŜ ŗǯŘŘ ŖǯşŘǰȱŗǯśŘ ȱƼȱ Ŗǯŗŗ ŖǯŖŜǰȱŖǯŗŜ ƺŖǯŝŗ ƺŖǯŝŞǰȱƺŖǯŜŚ ƺŖǯŖř ƺŖǯŖşǰȱŖǯŖŘ ƺŖǯŗŜ ƺŖǯŘřǰȱƺŖǯŖŞ ȱƼȱ ŖǯŚŗ ŖǯŘśǰȱŖǯśŞ ŖǯŜş ŖǯŚŚǰȱŖǯşś 0.47 ŖǯŘŞǰȱŖǯŜŜ ƺŖǯŖŘ ƺŖǯŘşǰȱŖǯŘŚ ȱƼȱ ƺŗǯŖŗ ƺŗǯŘŞǰȱƺŖǯŝŚ ƺŖǯśş ƺŗǯŖŞǰȱƺŖǯŗŖ ȱƼȱ ƺŖǯřř ƺŖǯŚŗǰȱƺŖǯŘŜ ŖǯřŘ ŖǯŘŜǰȱŖǯřŞ ƺŖǯŘŖ ƺŖǯŘŞǰȱƺŖǯŗř Ŗǯŗś ŖǯŖŞǰȱŖǯŘŘ ̄ ȱ Ŗǯşş ŖǯşŘ ŖǯşŜ ŗǯŖŖ Notes:ȱȱȱȱȃȄȱȱȱȱȱȱȱȱȱȱȱȱ ȱ ȱȱȱȱȱ ȱ¢ȱǱȱȱȱȱȱȱȬȱȱǯȱȱȱȱȬ ȱęȱǻşśƖȱ ȱȱȱ ȱ£Ǽǯȱǰȱȱȱǲȱǰȱȱȱȱǲȱ ǰȱȱȱǲȱǰȱȱȱǲȱǰȱȱȱǲȱǰȱ ǲȱǰȱȬǲȱǰȱȱ ȬȱǲȱǰȱȱȬȱǲȱǰȱ¢ȱǲȱǰȱȱǲȱǰȱȱȱǯ †ȹȱ¢Ǳȱȱǯ ‡ȹȱȱǻȦȬȱǼȱ ȱȱ¢Ǳȱȱǯ

ȲȲ ǯǯ 8 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ȱřǯȳȱĜȱǻȱǰȱβǼȱȱşśƖȱęȱȱǻ Ǽȱȱȱȱȱ ȱ¡ȱȱȱȱȱȱȱ ȱǰȱȱȦ ȱȱȱȱ ȱȱȱǯ

§ β şśƖȱ β şśƖȱ β şśƖȱ β şśƖȱ

ŖǯŚŜ ŖǯřŜǰȱŖǯśś ŖǯŘŗ ŖǯŖŚǰȱŖǯřŝ ŖǯŖś ƺŖǯŘŘǰȱŖǯřŗ ƺŗǯŝŖ ƺŘǯŗŗǰȱƺŗǯŘş ȱ† Op ƺŖǯŖŚ ƺŖǯŗŞǰȱŖǯŖş ŖǯŚŜ ŖǯŘśǰȱŖǯŜŜ ŖǯŘŝ ƺŖǯŗŗǰȱŖǯŜś ŖǯŖŜ ƺŖǯŜŖǰȱŖǯŝŗ ƺŖǯŜŜ ƺŖǯŝřǰȱƺŖǯśş ŖǯŚş ŖǯřŞǰȱŖǯśş ƺŖǯŘş ƺŖǯŚŚǰȱƺŖǯŗś 0.40 ŖǯŘŘǰȱŖǯśŝ ƺŖǯŚŚ ƺŖǯŜŖǰȱƺŖǯŘŞ ƺŖǯŖř ƺŖǯŘŞǰȱŖǯŘŗ ƺŖǯŝŞ ƺŗǯřŜǰȱƺŖǯŘŖ 0.75 ŖǯŘśǰȱŗǯŘŜ ŖǯŖŝ ƺŖǯŖśǰȱŖǯŗş ƺŖǯŖř ƺŖǯŘřǰȱŖǯŗŞ Rc ŖǯŜŗ ŖǯŘŚǰȱŖǯşŞ ƺŖǯşŞ ƺŗǯŞśǰȱƺŖǯŗŗ Oc ŖǯŝŜ ŖǯśŜǰȱŖǯşŜ ƺŖǯŖř ƺŖǯřŘǰȱŖǯŘŝ ƺŖǯŜŜ ƺŖǯŝřǰȱƺŖǯśş ƺŖǯřş ƺŖǯśŗǰȱƺŖǯŘŝ ƺŖǯŝş ƺŖǯşřǰȱƺŖǯŜŚ ƺŖǯŝŚ ƺŖǯşřǰȱƺŖǯśś ƺŖǯŜś ƺŖǯŞřǰȱƺŖǯŚŝ ƺŖǯŖŗ ƺŖǯŘŜǰȱŖǯŘř ƺŖǯŝŞ ƺŖǯşŞǰȱƺŖǯśŞ Ŗǯřŗ ŖǯŖşǰȱŖǯśŚ ŖǯŗŖ ŖǯŖŝǰȱŖǯŗř ŖǯŘŘ ŖǯŗŝǰȱŖǯŘŝ ŖǯŚŜ ŖǯŚŗǰȱŖǯśŖ 0.05 ŖǯŖŖǰȱŖǯŗŗ ƺŖǯŖŝ ƺŖǯŗŞǰȱŖǯŖŚ ƺŖǯŚř ƺŖǯŜŗǰȱƺŖǯŘś Ŗǯŝř ŖǯśŝǰȱŖǯşŖ 0.40 ŖǯŘŖǰȱŖǯŜŖ ƺŖǯŘř ƺŖǯŚŝǰȱŖǯŖŖ ŗǯŚŞ ŗǯŖşǰȱŗǯŞŜ ƺŖǯŗş ƺŖǯŘřǰȱƺŖǯŗŜ ƺŖǯŖŜ ƺŖǯŗŖǰȱƺŖǯŖŘ ƺŖǯřŜ ƺŖǯŚŘǰȱƺŖǯřŗ ƺŖǯŘŚ ƺŖǯřŖǰȱƺŖǯŗŝ ‡ ŖǯŖŖ ƺŖǯŗŚǰȱŖǯŗŚ ƺŗǯŘŝ ƺŗǯśŘǰȱƺŗǯŖŘ ƺŖǯŚŗ ƺŖǯŝşǰȱƺŖǯŖř ƺŖǯŘŝ ƺŖǯŞşǰȱŖǯřś ȱƼȱ ŖǯŖŗ ƺŖǯŗşǰȱŖǯŘŗ ƺŖǯŘŖ ƺŖǯśŖǰȱŖǯŗŖ ŖǯŚŝ ƺŖǯŖŞǰȱŗǯŖŘ ƺŖǯŚŖ ƺŗǯŚŜǰȱŖǯŜŜ ȱƼȱ ŖǯŖś ƺŖǯŖśǰȱŖǯŗŜ ŖǯŘŝ ŖǯŗŗǰȱŖǯŚŘ Ŗǯřś ŖǯŗŚǰȱŖǯśŜ 0.77 ŖǯŚşǰȱŗǯŖŚ ȱƼȱ ƺŖǯŖŗ ƺŖǯŘŚǰȱŖǯŘř 0.55 ŖǯŘŗǰȱŖǯŞş ŖǯŜŗ ƺŖǯŘŞǰȱŗǯŚş ŗǯśş ŖǯŝŞǰȱŘǯřş ȱƼȱ ŖǯŖŗ ƺŖǯŗŜǰȱŖǯŗş ƺŖǯŘř ƺŖǯśśǰȱŖǯŗŖ ȱƼȱ ŖǯşŜ ŖǯŚŗǰȱŗǯśŗ ƺŖǯřŝ ƺŗǯŞŜǰȱŗǯŗř ȱƼȱ ŖǯřŘ ŖǯŖřǰȱŖǯŜŗ ƺŖǯŗş ƺŖǯŜŜǰȱŖǯŘş ȱƼȱ Ŗǯŗş ŖǯŖŞǰȱŖǯŘş ƺŖǯŗŗ ƺŖǯŘşǰȱŖǯŖŝ 0.55 ŖǯřřǰȱŖǯŝŝ ƺŖǯŖŝ ƺŖǯřŝǰȱŖǯŘŚ ȱƼȱ ƺŖǯŘŘ ƺŖǯŚŞǰȱŖǯŖŚ 0.50 ŖǯŗŚǰȱŖǯŞŜ ŖǯŘŚ ƺŖǯŖŜǰȱŖǯśŚ ŗǯŗŚ ŖǯŞŖǰȱŗǯŚŞ ȱƼȱ ƺŖǯŖŚ ƺŖǯŖşǰȱŖǯŖŗ ƺŖǯŗř ƺŖǯŘŗǰȱƺŖǯŖŜ ƺŖǯŖś ƺŖǯŗŗǰȱŖǯŖŘ ƺŖǯŖŚ ƺŖǯŗŘǰȱŖǯŖŚ ȱƼȱ ƺŖǯŖŝ ƺŖǯŘřǰȱŖǯŖş ŗǯřŜ ŗǯŖşǰȱŗǯŜř ƺŖǯŖŝ ƺŖǯřŖǰȱŖǯŗŝ ŖǯŚŘ ŖǯŗŘǰȱŖǯŝŘ ȱƼȱ ŖǯŖŞ ƺŖǯŘŝǰȱŖǯŚŘ ƺŖǯŜŚ ƺŗǯŘŘǰȱƺŖǯŖŜ ȱƼȱ ƺŖǯŖŘ ƺŖǯŖŝǰȱŖǯŖř ŖǯŖŚ ƺŖǯŖŘǰȱŖǯŖş ƺŖǯŗŗ ƺŖǯŗşȱƺŖǯŖŘ ŖǯŖŘ ƺŖǯŖŝǰȱŖǯŗŗ ̄ ȱ ŖǯşŘ ŖǯŞş ŖǯŞŞ ŖǯşŞ Notes:ȱȱȱȱȃ Ȅȱȱȱȱȱȱȱȱȱȱȱȱ ȱ ȱȱȱȱȱ¢ȱȱ ȱȱȱǱȱȱȱȱȱ ȱȱǯȱȱ ȱȱȱęȱǻşśƖȱ ȱȱȱ ȱ£Ǽǯȱǰȱȱȱǲȱǰȱȱȱ ȱǲȱǰȱȱȱǲȱǰȱȱȱǲȱǰȱȱȱǲȱǰȱ ǲȱǰȱȬ ǲȱǰȱȱȬȱǲȱǰȱȱȬȱǲȱǰȱ¢ȱǲȱǰȱȱǲȱǰȱȱȱǯ †ȹȱ¢Ǳȱȱǯ ‡ȹȱȱǻȦ ȱȱǼȱ ȱȱ¢Ǳȱȱȱǯ

ȦȬȱȱ ȱ ȱ Ȧ ȱ ǯȱ¢ǰȱȱǰȱȱȱ ȱȬ ȱǰȱ ȱęȱȱȱȱ ę¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱȱȱ ȱȱǯȱȱȱȱ ȱȱȱȱȱȱȱ ȱ ǰȱ ȱ ȱ ęǰȱ ȱ §ȱȱ ȱ ȱȱȱ ȱȱ ȱ ȱȱȱȱ ȱȱȱǻǯȱŘǼǯ ȱ ȱǻȱȱȱȱȱȱ Ǽȱ ȱ ȱ ȱ ȱ ǻȱ ȱ Habitat selection—predation and ȱȬȱȱǼȱȱȱȱȱ post- predation period ȱ ǻȱ ȱ ȱ ȱ Ǽǯȱ ȱ ȱ ȱȱȱȱ¢ȱȱ ȱ ȱȱȱ ȱȱȬ ȱȱȱȱȱ ȱȱȬ ȱ ȱ ǻǯǯǰȱ ȱ Ǽȱ ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ȱ ȱ ȱ ¢ȱ ¢ȱ ȱ ȱ ę¢ȱ ȱ ȱ ȱ Ȭ ǯȱȱȱȱȱȱȱȬ ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ęȱ ȱ ȱ ȱ ȱ ȱ

ȲȲ ǯǯ ş ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ȱȱȱȱȱȱ ȱȱ ȱȱȱȱȱȱȬ ȱ ǻȱ Řǰȱ ǯȱ ŚǼǯȱ ǰȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ¢ȱ ǰȱ ȱ ȱ ȱ ¢ȱ ȱ ȱȱ¢ȱ ȱȱȱ ȱȬȱȱǯȱ¢ȱȱǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱȱȱ ȱȱȱȱȱȱȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭȱ ȱǯȱȱȱ ȱȱȱ §ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱǯȱ ȱǰȱȱ ȱȱ ȱ ȱȱ ȱȱȱ ȱȱȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱȱȱȱȱȱȬȱȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ Řǯȳȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ Ȭȱȱ ǰȱ ȱ ȱ ȱ ǻǼȱȱȬȱȱȱǻǼǰȱȱȱȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻ ǰȱ Ǽǰȱ ȱ ȱ ȱ ȱȱ¢ȱȱǻȱŘǼǯ ȱȱǻǰȱǼȱȱ §ȱȱȱ ȱǻǰȱǼǯȱȱęȱ ȱȱĜȱ Habitat selection—high and low predation hours ǻȂȱǼȱȱşśƖȱęȱǯ ȱȱȱ ȱ¢ȱȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ Ȭ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱȱȱĴȱȱȱȱ ȱȱȱȱȱ ȱȱ ȱ ȱ ȱ ¢ȱ ȱ ǻȱ řǰȱ ǯȱ řȮǼǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱȱȱȱȱȱ ȱ¢ȱȱȱȱǯȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ĵȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱǰȱ ȱ ȱȱ ǻȱ Řǰȱ ǯȱ řȮǼǯȱ ȱ ¢ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ȱ ȱ ȱȱȱ ȱȱǯȱ ȱǰȱ ¢ȱ ȱ ȱ ȱ ¢ȱ ǯȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱȱ ȱȂȱȱȱȱǰȱ ȱȱȱȱȱȱȬȱȱȱȱ ȱ¢ȱȱȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱȱȱȱȱ ȱ Ȭȱȱ ǯȱ ȱ ȱ ȱȱȱ ȱȱȱǻȱřǼǯ ¢ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ȱ ȱ DISCUSSION ¢ȱ ǯȱ ȱ ȱ ȱ ȱ ȱȱȱȱ¢ȱȱ ȱ¢ȱȱȱěȱȱ ȱȱȱȱȱȱȱȱ ȱ ȱ ȱ Ȭȱȱȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ǰȱ ȱȱ ȱȱȱȱȱǯȱ ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ȱ ǰȱ ȱ ȱ ȱ ęȱ ȱ ȱ ȱ §ǯȱǰȱȱȱǰȱ ȱȱȬ ¢ȱȱȱȱȱȱȱ ȱȱȱȱ ȱȱȱȱȱ ȱȱȱȱȱȱȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ǰȱ ȱ ȱ ǻȱŘǰȱǯȱřȮǼǯȱȱȱȱȱ ȱ ȱ ȱ ȱ Ȭ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ¢ȱ ȱȱ ȱȱȱȱȱ

ȲȲ ǯǯ 10 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ǯȱřǯȳȱȱȱȱȱȱȱȱȱȱ ȱşśƖȱęȱȱ ȱȱȱǻȱǼȱȱȬȱȱȱǻȱǼǰȱȱȱȱǻȱǼȱȱ ȱǻȱǼȱȱȱ ȱȱȱǰȱȱǻǼȱȱǻǼȱ ȱȱȱǰȱǻǼȱȱǻǼȱ ȱȱǰȱǻǼȱȱǻǼȱ ȱȱȱ §ǰȱȱǻǼȱȱǻǼȱȱȱ §ǯȱȱ£ȱ ȱ¢ȱȱȱȱ¢ȱȱŖǯśǲȱȱȱȱȱȱȱȱȱȱ ȱǰȱȱȱȱȱȱȱȱȱǯȱǰȱȱ ȱǲȱǰȱȱȱǲȱǰȱ ǲȱǰȱȬǲȱǰȱȱȬǲȱǰȱȱȬǲȱǰȱ ¢ȱǲȱǰȱȱǲȱǰȱȱȱǯ

¢Ȯȱȱȱȱ¢ȱ¢ǯȱ ȱǯȱǰȱȱ¢ȱȱ ¢ǰȱȱȱȱȱȱȬ ȱ ȱȱ¢ȱȱȱȱ ȱȱȱǰȱȱ¢ǰȱȱȬ ȱ ȱ ǯȱ ¢ǰȱ ȱ ȱ ȱ ȱȱȱȱȱȱǰȱȱ ȱȱ¢ȱ¢ǰȱȱęȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱȱȱȱȱȱȱȱ ǯ ¢ȱ ȱ ȱ ȱ ȱ ¢ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻȬȱȱ ȱ ǯȱ ŘŖŗŗǼǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ Ȭȱȱ ȱ ȱ ȱȱȱȱȱ ȱ ȱ ȱ ǰȱ ¢ǯȱ ȱ ȱȱȱȱȱ¢ȱȱěȱȱ Ĵȱ ȱ ¢ȱ ȱ ¢ȱ ȱ ȱ ȱȱ ȱȱ ȱ¢ȱ¢Ȭ ȱ ȱ ȱ ǯȱ ȱ ȱ Ȭ ȱ ǻȱ ȱ ȱ ¢ȱ Ǳȱ ŗŗŖȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱŗŖŖȱ2ǲȱ ȱȱǱȱřǯřȱ ȱ ¢ȱ ȱ ȱ ȱ Ȭ ȱȱŗŖŖȱ2Ǽǯȱȱȱ ȱ¢ȱȱȬ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭ ȱȱȱǰȱȱȱȱȬȱ ȱȱȱȱȱ¢ȱ ȱ ǯȱ ǰȱ ȱ ȱ ȱ ȱ ęȱ ǻȬȱȱ ȱ ǯȱ ŘŖŗŗǼǰȱ ȱȱ¢ȱȱȱȱȱȬ ȱȱȱȱȱȱȱȱȱ ȱ ȱ ȱ Ȭȱǰȱ Ȭȱȱ ǰȱ ȱ ¢ȱȱȱȱȱȱ¢ȱȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱ¢ȱ ȱǯ ȱ ȱ Ȭȱȱǯȱȱ ȱ Ĵȱ ȱ Ğȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȬȱȱȱ

ȲȲ ǯǯ 11 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ǯȱŚǯȳȱȱȱȱȱȱȱȱȱȱȱȱȱ ȱȱǻȱǼȱȱȬȱȱȱǻȱǼȱ ȱşśƖȱęȱȱȱǻǼȱȱǻǼȱ ȱȱȱǰȱǻǼȱȱǻǼȱȱȱǰȱǻǼȱȱǻǼȱ ȱȱȱ §ǰȱȱǻǼȱȱǻǼȱȱ ȱ §ǯȱȱ£ȱȱ¢ȱȱȱȱ¢ȱȱŖǯśǲȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯ

ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱǻȱȱǯȱ Ğȱȱ ȱȱȱȱȱȱȱ ŘŖŗŘǰȱ ȱ ȱ ǯȱ ŘŖŗŜǼǯȱ ȱ Ȭȱȱȱ ¡ȱ ¢ȱ ȱ ¢ǯȱ ȱ ȱ ȱȱȱȱȱȱǻȱȱǯȱŘŖŗŘǼǰȱ ȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ğȱ ȱ ȱ ȱ ȱ ȱǻ ȱŗşŞŗǰȱȱŗşŞŚǼǯȱ ȱǰȱ ȱ ȱ ȱ ȱ Ȭȱȱ ȱ Ȭ ¢ȱ ȱ ȱ ¢ǰȱ ȱ ȱ ȱ¢ȱȱȱȱȱȱȱ ȱȱǰȱȱ ȱȱȱȱȬ ȱȱȱȱǯȱ ȱ¢ȱǰȱ ȱ ȱǻȱȱǯȱŘŖŗŘǼǯȱǰȱȱȱȱȱȱ ęȱȱȱȱ¢ȱȱȱȱȱȬ ȱǰȱ ȱȱȱȱȱ ȱȱǰȱȱȱȱȱȱ ȱ ¢ȱ ȱ ¢ȱ Ĵȱ ¢ȱ ȱ ȱ ȱȱȱǰȱ ȱȱȬȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ Ȭ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱǻ ȱŗşŞŗǼǯȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱ ȱȱȱȱǯȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱȱȱ ȱȱǰȱ ȱ ȱȱǰȱȱȱȱ ȱȱȱǰȱȱȱȱ Ĵȱ¢ȱȱ¢ȱĚȱȱȱȱ ȱȱȱ¢ȱȱȱȱȱȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱ¢ȱǻ ȱ ȱ ȱ ¢ȱ ȱ ǰȱ Ȭ ȱǯȱŘŖŗŗǼǯȱȱȱǯȱǻŘŖŗŘǼȱȱȱȱ ȱ ȱȱȱȱȱ¢ȱȱ ȬȱȱȱȱĴȱȱȱȱȱ ȱȱȱȱ¢ǰȱȱȱȱ ¢ȱȱȱȱȱȱȬ ȱ ¡ȱ ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ ȱǰȱȱǰȱȱȱȬȱȱȱ ȱ ȱ ȱ ¢ȱ ǯȱ ȱ ȱȱȱȱȱȱȱȬ ȱ ȱ Ȭȱȱȱ ȱ ęȱ ȱ ȱ ȱ ȱ ęǯȱ ȱ ȱ ¢ȱ ǰȱ ȱ

ȲȲ ǯǯ 12 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ǰȱ ȱ ȱȱȱȱ ȱ ǯȱ ȱ ¢ȱ ȱ ȱ ¡ȱ ȱǰȱȱȱȱȱȬȱȱȱ ȱ ȱ ǰȱ ȱ ȱ Ĵȱ Ȭ ȱ ȱ ǯȱ ǰȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ ȱ ȱ ǻȱ ȱ ǯȱ ¡ȱȱę¢ȱȱȱǰȱȱȱȱ ŘŖŖşǰȱȱȱǯȱŘŖŗśǼǯȱ ȱǰȱȱ¢ȱ ȱȱȱ ȱȱǻȱȱǯȱ Ĵȱ ȱ ȱ ȱ ȱ ȱ ȱ ŘŖŗŖǼǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ Ȭȱȱ¢ȱ ȱ ȱ Ȭ ȱ ȱ ȱ ȱ ȱ ȬȱȱȬ ȱȱǻȱȱǯȱŘŖŗŘǼǯȱǰȱ¢ȱ¢ȱ ǰȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ¢ȱ¢ȱȱȱȬ ¢ȱȱȱǻȱȱǯȱŘŖŗśǰȱ ȱ ȱ ȱ Ȭȱȱȱ ȱ ȱȱǯȱŘŖŗŜǼǯ ȱ ȱ ȱ ȱ ǻȱ ŘŖŖŝbǰȱ ȱȱȱ¢ȱ ȱȱȱ ȱ ȱ ǯȱ ŘŖŗŜǼǯȱ ȱ Ȭȱȱ ȱ ȱ ȱ ȱ ȱ ěȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱȱ¢ȱ¢ȱȱĚȱȬ ǻȱȱ ȱȱǼȱȱȱȱȱ Ȯ ȱ ȱ ȱ ǰȱ ȱ ěȱȱǰȱȱȱȱ ȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱȱ ȱȱǻȱȱǯȱ ěȱ ȱ ȱ ¢ǯȱ ȱ ȱ ŘŖŖŗǼǯȱǰȱȱ¢ȱȱĚȬ ȱȬȱȱ¢ȱȱȱȱȱȱ ȱȱȱ ȱȱȱ ǰȱȱȱȱȱȱȱ ¢ȱ ȱ ȱ ȱ Ȭȱȱ ȱęȱȱȱȱȱǻȱȱǯȱ ȱ ȱ ȱ ¢ǯȱ ȱ ȱ ȱ ȱ ŘŖŗŘǰȱȱȱǯȱŘŖŗŜǼǰȱȱ¢ȱ ǰȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ¢ǰȱ ȱ ȱ ȱ ȱ ¢ȱ ȱǰȱ ȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻèȱ ȱ ȱ ȱ ǯȱ ǰȱ ȱ ěȱ ȱ ŘŖŗřǼǯȱȱ¡ȱȱȱȱȱȱ Ȭȱȱȱȱȱȱȱȱ¢ȱȱ ȱȱȱȱ¢ȱȱȱȱȱ ȱǯȱȱȱȱȱȬȱ ȱ ȱ ǯȱ ǰȱ ȱ ȱ ȱ ȱȱȱȱ¢ȱȱȬ ¢ȱȱȱ¢ȱȱȱ¢ȱȱȱ ȱěȱȱȱȱȱȱȱ¢ȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȬȱȱȱȱǻȱȱǯȱŘŖŗŚǼǯȱǰȱȱ ȱ ȱ ¢ȱ ǯȱ ǰȱ ȱ ȱ Ȭȱȱ¢ȱ ȱ ȱ ȱ ȱ ȱ¢ȱȱǰȱȱȱȱȱ ȱęȱǻȱȱǯȱŘŖŗśǰȱȱȱǯȱ ȱǰȱȱȱ ¢ȱȱȱȱȱ ŘŖŗŜǼǯȱǰȱȱȱȱȱ¢ȱ ȱȱȱȱȱĴȱȱ ȱȬ ¢ȱȱȱȱȱ¢ȱȱȱ ǰȱȱȱȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱǻȱȱȱŗşŞŝǼǯȱȱȱȱ ǯȱȱȱȱȱ¢ȱȱ ¢ȱȱȱȬȱ ȱ ȱ ȱȱ£ȱȱȱȱȱȬ ȱ ȱ ȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ǻȱ ŗşŞŜǰȱ ȱ ǰȱ Ȭȱȱ ȱ ǯȱ ǻŘŖŗŗǼǰȱ ¢ȱ ȱ ǯȱ ŗşşŖǼǯȱ ¢ǰȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱǰȱ ȱ ȱ¢ȱȱȱȱȱȱȱ ȱȱȱȱȱȱȱȱȱ ȱǯȱ ǰȱȱȱȱȱ ȱ ȱ ¢ȱ ¢ȱ ȱ ȱ ¢ȱ ȱȱȱȱȱȱȱȱȬ ȱ ȱ ȱ ¢ȱ ǯȱ ȱ ȱ ȱ ȱȱǻȱȱ#ȱŘŖŗŚǼȱȱȱ ȱ ȱ ¢¢ȱ ȱ ¢ȱ ȱ ȱǰȱȱȱ ȱȱȬ Ȭȱȱ ȱ ȱ £ȱ ȱ ȱȱȱȱȱȱ ȱȬ Ĝ¢ǰȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ǰȱ¢ȱȱȱȱȱȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱǯ ȱ ȱ ȱ ȱ ¢ȱ ȱ ǻȬȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ŘŖŗŗǼǯȱ ȱ ȱ ǰȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ¢ȱ ¢ȱ ǰȱ ȱ ȱ ȱ ȱ ¢ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ £ȱȱȱȱȱȱȱȱ

ȲȲ ǯǯ 13 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ȱ ȱ ȱ ȱ ǻȱ ŗşŝŜǰȱ ȱ ȱ ȱ ȱȱ ȱ ȱ ȱ ȱ ȱǯȱŗşşŝǰȱ ȱȱǯȱŘŖŗŜǼǯȱȱȱȱ ǰȱǯȱȱ¢ȱȱȱȱ ȱ ȱȱȱȱȱȱȱȱ¡ȱ ȱ¢ȱȱ¢ȱȱ¢ȱȱȱ ȱ ȱǯȱ ǰȱȱěȱȱ ȱ ǰȱ ǯȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ ȱ ȱ Ȯȱ ȱ ȱ ȱȱ ¢ǰȱ ȱ ȱ ȱ ȱ ¢ǰȱ ȱ ȱ ȱȱȱǯȱȱȱȱȱ ȱȱ ¢ǰȱȱȱ ȱȱȱ ȱȱȱȱȱǯ ȱȱȱȱǯȱȱ ȱ ȱȱȱȱ¢ȱȬ ȱȱȱȱȱȱȱ¢ȱȱǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ǰȱȱȱȱȱȱȱȱ ȱ¢ȱȱǰȱȱȱ ȃȱěȱȱȱȱȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻĴȱ Ȅȱ ȱ ȱ ȱ ¢ȱ ŘŖŗśȮŘŖŗŜǯȱ ¢ǰȱ ȱȱŘŖŖŖǼǯȱ ȱ ȱRangiferȱȱ ȱ ȱȱȱȱȬȱ ȱȬȱȱȱ ȱȱȱȱ ȱǰȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ Ȭ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱȱǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻĴȱ ȱȱęȱȱǯȱŘŘřȱȱȱȱ ȱ ȱ ŘŖŖŖǼǯȱ ȱ ȱ ȱ ȱ ȱȱȱǯ ȱ ȱ ȱ ȱ ¢ȱ ǯȱ ȱ ȱ LITERATURE CITED ǰȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ǰȱǯȱ ǯǰȱǯȱ ǯȱǰȱȱǯȱǯȱǯȱŗşşśǯȱȬ ȱ¢ȱ¡ȱȱȱȱȱȱ ȱȱ¢ȱȱȱȱǰȱǯȱ ȱȱȱȱǰȱȱȱȬ ȱȱȱȱśşǱśŞŚȮśşŚǯ ȱȱȱȱȱ£ȱȱ #ǰȱǯǰȱ ǯȱǰȱȱǯȱãªǯȱŘŖŗŚǯȱ ȱ ȱ ȱ ęȱ ȱ ȱ ǻĴȱ ȱ ȱ ȱ¢ȱȱȱȱȱȱȬȱ ŘŖŖŖǼǯȱȱ¡ǰȱȱ ȱǰȱȬ ȱ ȱ ȱ ǻRangifer tarandus ȱ ȱ ȱ ȱ ȱ ȱ tarandusǼȱȱ ǯȱȱȱşǱŗŗŗśŖşǯ ȱ ȱ ȱ ȱ ǻ ȱ ȱ ǯȱ ŘŖŖşǼȱ ȱ ǰȱ ǯȱǯǰȱǯȱǰȱȱ#ǯȱǯȱŘŖŗŗǯȱȬ ȱęȬȱȱȱȱȱǻȱȱǯȱ ȱ ȱ ȱ Ȭȱ ȱ ǰȱ ŘŖŗŘǼǯȱ ǰȱ ȱ ȱ ¢ȱ ȱ ȱ ǰȱ ȱȱ¢¡ǯȱ ȱ¢ȱ ȱ¢ȱǰȱȱȱȱȱȱ¢Ȯ ǰȱǰȱ ¢ǯȱĴǱȦȦ ŗǯǯȦ ȱ ȱ ȱ ǻȱ ȱ ŘŖŖśȱ ȱ ȦȦƖŘŖƖŘŖ ƖŘŖŘŖŗŗǯ ·ȱ ŘŖŗŖǱȱ ȱ ȱ ȱ ȱ ǰȱǯȱŘŖŗŖǯȱȱȱȱȱȱ ȱ ȱ ȱ Ǽȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȃȱ ȱ ǯȱ ȱ ȱ ȱ Ȯ¢ȱ Ȭ ȱȱȱȱŝŚǱŗŗŝśȮŗŗŝŞǯ ȱȱȱȱȱȱȱȱǯȱ ǰȱ ǯȱ ǯǰȱ ǯȱ ǯȱ ¢ǰȱ ȱ ǯȱ ǯȱ ǯȱ ŘŖŖŗǯȱ ǰȱȱȱȱ¢ȱȱȱȱȱ ȱȱ¢ȱȱǱȱěȱȱ ȱȱ¢ȱȱ ȱȱȱ ȱǯȱ ȱȱȱȱ ȱȱȱȱȱȱ¢ȱȱȬ ŜśǱŝŝȮşŘǯ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱǯȱǯȱŗşŞŜǯȱěȱȱȱ¢ȱȱȬ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱȱǯȱȬ ȱǯ ȱȱ ȱŗǱřřřȮřŚŖǯ Ȭǰȱ ǯǰȱǯȱǰȱǯȱǰȱǯȱȬ ǰȱȱ ǯȬǯȱǯȱŘŖŗŗǯȱȱȱ¢ȱ ACKNOWLEDGMENTS Ǳȱȱȱȱ¢ȱȱȱ ȱȱȱȱ¢ȱ¢ȱȱ ȱ¢ȱ ȱȱȱȱȱȃȱȱ ǵȱ¢ȱǻǼȱřŚǱśŞŞȮśşŜǯ ȱȱȱ£ȱȱ ȱȯěȱȱȬ Ȭǰȱ ǯǰȱ ǯȱ ǯȱ Ĵǰȱ ǯȱǯȱ ǰȱ ǯȱǯȱ ǰȄȱ ȱȱȱŘŖŗŘȦŘŞŗŝǰȱȱ¢ȱȱ ǰȱǯȱ ǯȱǰȱǯȱǯȱ¢ǰȱǯȱǯȱ¢ǰȱȱ ȱȱ ǰȱ ȱ ȱ ȱ ȱ ǯȱǯȱ¢ǯȱŘŖŗśǯȱȱȬȱěȱȱȱ ȱ ȱ ȱ §ȱ ȱ ȱ Ȭ ȱȱ¢ȱȱȱȱȱ ǰȱȱ ȱȱȱǰȱ ȱ ȱȱ¢ǯȱ¢ȱřŞǱŗŖŚşȮŗŖśşǯ ¢ȱ ȱ ȱ ȱ ǻǼǰȱ ȱ ǰȱǯǰȱǯȱǰȱǯȱǰȱȱǯȱǯȱŘŖŗŚǯȱ ȱ ȱ ȱ ȱ ȱ ǻǼǰȱ ŚǱȱȱ¡Ȭěȱȱȱȱȱ

ȲȲ ǯǯ 14 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

Śǯȱ ȱ ȱ ŗǯŗȬŝǯȱ ĴǱȦȦǯȬǯȦ ǰȱ ǯǰȱ ǯȱ ǰȱ ǯȬǯȱ ǰȱ ǯȱ ǰȱ ƽŚ ǯȱǰȱ ȱ ǯȬ ǯȱ Ȭǰȱ ȱ ǯȱ ǯȱ ǰȱ ǯȱŘŖŖŝaǯȱǰȱȱȱȱȱȬ ŘŖŗŖǯȱ ȱ ę¢Ǳȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ǯȱ ȱȱ ȱ ȱ ȱ ǵȱ ȱ ¢ȱĴȱřǱŜŘŖȮŜŘřǯ ȱŗŚřǱŘŞŚŖȮŘŞśŖǯ ǰȱ ǯȱ ŘŖŖŝbǯȱ ȱ ȱ ȱ ȱ ¢ǰȱǯȱ ǯǰȱȱ ǯȱǯȱĴǯȱŗşşŗǯȱȱȱȬ ¢Ǳȱ ȱȱęȱȱȱěȬ ȱ ȱ ¢ȱ ȱ ȱ ǯȱ ȱ ǯȱȱ¢ȱŘŗǱŗŗŖśȮŗŗŗŜǯ ȱȱ¢ȱŜşǱŗŝřŜȮŗŝŚřǯ ǰȱ ǯǰȱ ǯȱ ǯȱ ǰȱ ȱ ǯȬǯȱ ǯȱ ŘŖŖŗǯȱ ǰȱǯȱǯǰȱ ǯȱǯȱǰȱǯȱ ǯȱ¢ǰȱǯȱǯȱ ǰȱ ȱ£ȱȱȱȱ¢ǱȱȬ ȱǯȱǯȱ ǯȱŘŖŖŜǯȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ¡ǯȱ ȱ ȱ ȱ ȱ Ȭȱȱ ¢ǯȱ ȱ ŘşŗǱŗŖřŜȮŗŖřşǯ ȱŗŜśǱŗȮřŘǯ ǰȱ ǯȱ ǯǰȱ ǯȱ ǰȱ ȱ ǯȱ ǯȱ ǯȱ ǰȱǯȱǯǰȱǯȱǯȱǰȱȱǯȱǯȱǯȱŗşşŜǯȱ ŗşşŖǯȱ ȱ ȱ ȱ ȱ ȱ Ȭ ȱ ȱ ȱ ȱȱ ȱ Ȭ ȱ ȱ ȱ ǯȱ ȱ ȱ řşǱ ȱȱȱ¢ȱȱȱȱȱ řŜŖȮřŜŞǯ ȱ ȱ ȱ ȱ ¢ǯȱ ȱ ǰȱ ǯȱ ǯǰȱ ǯȱ ǯȱ Ĵǰȱ ȱ ǯȱ ǯȱ ŘŖŖŞǯȱ ȱ ȱşǱřŝȮŚŚǯ ȬȂȱȱȱȱǱȱȱ ǰȱǯȱǯǰȱȱǯȱǯȱǯȱŘŖŖŘǯȱȱ¢ȱ ȱ ȱ ȱ ǯȱ ȬȂȱ¢ȱ ȱȱȱȱȱĚȱĴȱ ǰȱǰȱǰȱǯ ȱȱǯȱ¢ȱŞřǱŚŞŖȮŚşŗǯ ǰȱ ǯȱ ǯǰȱ ȱ ǯȱ ǯȱ ǯȱ ŗşŞŝǯȱ ȱ ǰȱǯȱŗşŞŗǯȱȱȱ ȱȱȱǻRangifer ȱȱȱȱȱȱȱȱ tarandus fennicusȱ ǯǼȱ ȱ ȬȱȱȬ ȱǯȱȱ ȱȱ¢ȱ ȱǻRangifer tarandus tarandusȱǯǼȱȱǯȱȱ ŜśǱŗśşŝȮŗŜŖŜǯ ȱȱȱȱŗŖŝǱȱŗŘǯ èǰȱ ǯȱŘŖŗřǯȱǰȱȱȱ¢ȱ ǰȱǯǰȱǯȱǯȱ ǯȱ ǯȱ¢ǰȱǯȱǰȱǯȱ¢Ȭ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ǰȱ ǯȱ ǯȱȬ ǰȱ ǯȱǯȱ ǰȱ ǯȱ ǰȱ ȱȱřŖǱŗŝŚȮŗŞşǯ ȱ ǯȱǯȱ ǯȱŘŖŗŜǯȱȱȱǱȱȬȱ ǰȱ ǯȱ ǯǰȱ ǯȱ ǰȱ ǯȱ ǯȱ ǯȱ ǰȱ ǯȬǯȱ ęȱȱȱȱȱ¢ȱȬ ȱǰȱ ǯȱ ǰȱ ȱ ǯȱ ·Ȭ ǯȱ ȱ ȱ ȱ ȱ ȱ Ȭȱȱ ǯȱ ŘŖŗŜǯȱ ȱ Ȭȱȱ ȱ ǻOdocoileus hemio- ȱȱ¢ȱȱ¢ȱŝŖǱŞřŗȮŞŚŘǯ nus sitkensisǼȱ ȱ ȱ ȱ ȱ ¢ȱ ǰȱǯȱ ǯǰȱǯȱ ǯȱǰȱǯȱ ǯȱǯȱ ǰȱȱ ¢ȱȱȱȱȱǯȱȱ Ȭ ǯȱǯȱǯȱ ǯȱŘŖŖşǯȱȬȱȱȱȬ ȱȱ¢ȱşŚǱřŞśȮřşŚǯ ȱȱȱȱȱȱȱȱ ¢ǰȱǯȱǯǰȱǯȱǯȱǰȱǯȱǯȱǰȱȱǯȱ ǯȱǯȱ ȱ ¢ȱ ȱ ǯȱ Ȭ ǯȱ ŘŖŖŘǯȱ ȱ ȱ ȱ ȱȱ ȱřśǱŗŝŗŗȮŗŝŘŗǯ ǯȱȱȱŗśŝǱŘŞŗȮřŖŖǯ ǰȱ ǯǰȱ ǯȬǯȱ ǰȱ ǯȱ ǰȱ ȱ ǯȬ ǯȱ ǰȱ ǯȱǯǰȱ ǯȱǯȱ·ǰȱȱǯȱ ǯȱŗşşşǯȱȱ ȱȬǯȱ ŘŖŖşǯȱ ȱ ȱ ¢ȱ Ȭȱ ¢ȱ ȱ Ǳȱ ȱ ǰȱ ȱ ¢ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ¢ȱ ȱǱȱȱȱȱȱęȬ ŞŖǱřŞśȮřşşǯ ȱěǯȱȱ¢ȱȱȱŘśŝǱ ǰȱ ǯȱǯǰȱȱǯȱǯȱǯȱŘŖŖŘǯȱȱȬ ŜřŜȮŜŚřǯ ȱȱȱǱȱȱȱȬ ǰȱǯȱǯǰȱȱ ǯȱ·ǯȱŘŖŗŘǯȱȱȱ Ȭȱǯȱȱǯȱǰȱ ȱ ȱ ȱ ȱ ¢ȱ ¤ȱ ǯȱ ȱǰȱ ȱǰȱǯ ȱȱŘŘǱŗŜŚŖȮŗŜśŚǯ ǰȱǯǰȱǯȱ ǯȱèǰȱǯȱ ǯȱǰȱ ǯȱǯȱ ǰȱ ǰȱǯȱ ǯǰȱȱǯȱǯȱĴǯȱŗşŞŞǯȱȱȱ ȱ ǯȱ ǯȱ ŗşşŞǯȱ ȱ ȱ ȱ ȱ ȱ ěȱȱȱȱȱȱ ȱȱȱ Ursus arctosȱȱȱǱȱěȱȱȱ ǯȱ¢ȱŜşǱŗŘśȮŗřŚǯ ȱȬȱȱȱȱOvis ariesǯȱȱ ǰȱ ǯȱ ǯǰȱ ǯȱ ȱ ¢ǰȱ ǯȱ ȱ ǰȱ ȱ ¢ȱŚǱŗŚŝȮŗśŞǯ ǯȱ ¡ǯȱ ŘŖŗŗǯȱ ȱ ȱ ȱ ȱ ǰȱǯǰȱǯȱǰȱ ǯȬǯȱǰȱǯȱǰȱȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ Ȭȱęȱ ǯȱǯȱŘŖŗŘǯȱȱȱȱȱȱ ǯȱȱȱŜǱŘśŝŖřǯ ȱȱȱ¢ȱȱǱȱȬȱ ǰȱ ǯǰȱ ȱ ǯȱ ŘŖŗŘǯȱ ãȱ ªȱ ȱ ȱ ȱ ȱ ȱ ǵȱ Ȭ ȱ ěȱ ȱ ȱ ã¢ȱ ȱª§ǯȱ ȱ ȱ ȱ ¢ȱ ¢ȱ Ǳȱ ȱ ȱ ȱ ªȱ ȱ Ŝǯȱ ȱ Ȭ ŘŝşǱŚŚŞŗȮŚŚŞŞǯ ȱ ǰȱ ҫȱ ǰȱ

ȲȲ ǯǯ 15 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

¢Ĵǰȱ ǯȱ ǽ ȱ ǯǾȱ ĴǱȦȦǯ ǰȱǯǰȱǯȬ ǯȱèǰȱ ǯȱ ǰȱ ǯȱǯȱ ǰȱ ǯǯȦŗřŖŚŝȦŗȦȏȏȏŗŜŖřŖŗǯ ǯȱǰȱ ǯȱǰȱ ǯȱ ǰȱǯȱǯȱ ǰȱ ǰȱǯȱǯȱǯǰȱǯȱǯȱǰȱȱǯȱǯȱ¢ǯȱŘŖŗŗǯȱ ǯȱ ǰȱ ȱ ǯȱ £ǯȱ ŘŖŖŝǯȱ ȱ ȱ ¢ȱ ȱ ȱȱȱȱȱȱȱ ȱ¡ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻUrsus americanusǼȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ Ĵǯȱ (Rangifer tarandus caribouǼȱȱȱǯȱ ȱȱŗřŞǱŗśŝȮŗŜśǯ ȱ ȱȱ¢ȱŞşǱŘŜŝȮŘŝŝǯ ǰȱǯȱǯǰȱ ǯȱǰȱȱ ǯȱǯȱǯȱŘŖŖşǯȱ ǰȱ ǯǰȱ ǯȱ ǰȱ ȱ ǯȱ Ĵǯȱ ŘŖŗŚǯȱ Ȭȱ ęȱȱ¢ȱȱȱ££¢ȱȱȬ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱȱ¢ȱȱǰȱǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ǯȱ ȱȱȱśǱřŞȮŜŖǯ ȱȱȱ¢ȱŞřǱŗŞśȮŗşŞǯ ǰȱǯȱŘŖŗŖǯȱȱȱȱȱȱȱȱ ·ǰȱ ǯȱ ǯȱ ŘŖŗŖǯȱ ȱ ȱ ǰȱ ¢ȱ ȱ Ȭȱȱ ȱ ǻȱRangifer Ȭȱ¢ȱȱ ǰȱ ¢ȱ ȱ ǰȱ ȱ tarandus tarandusȱǯǼȱȱȱ ǰȱȱ ȱȱȱȱȱȱȱ¢ǯȱȬ ȬȱȱǯȱȱřŖǱŝşȮŞŞǯ ¢ȱşŗǱŘşşśȮřŖŖŝǯ Ĵȱ¢ȱȱǯȱŘŖŗŚǯȱȱ ·ǰȱ ǯȱǯǰȱǯȱ £ǰȱȱǯȱ ǯȱǯȱŘŖŗŖǯȱ ȱ Ĵǯȱ Ĵȱ ¢ȱ Ȭ ȱ ȱ ȱ Ǳȱ ȱ ȱ ȱ ȱ ǰȱ ªǰȱ ǯȱ ĴǱȦȦ ǯ ȱǯȱȱ¢ȱ ȱřǱŗȮŝǯ ¢ǯȦĴȦȦ ǰȱ ǯǰȱ ǯȱ ǰȱ ǯȬǯȱ ǰȱ ȱ ǯȬ ǯȱ ȦȦƖŘŖ¢Ȧ Ȭǯȱ ŘŖŗŜǯȱ ȱ ȱ ȱ ȱ ƖŘŖȬƖŘŖƖŘŖƖŘŖĴǯ ȱ ȱ ¢ȱ ȱ Ȯȱ ȱ ȱ £ǰȱǯǰȱǯȬ ǯȱèǰȱǯȱǰȱȱ ǯȱǯȱ ǯȱ ¢ȱȱ¢ǯȱ ȱȱȱ¢ȱ ŘŖŗŗǯȱȱȱ¢ǵȱȬȱȱȬ śřǱŗŖŝŞȮŗŖŞŝǯ ȱ ȱ Ȭȱȱ ȱ ¢ȱ ȱ ǯȱ ǰȱǯǰȱǯȱǰȱȱǯȬ ǯȱȬǯȱŘŖŗŘǯȱ ȱŗŜŜǱśşȮŜŝǯ ȱȱȱȱȱȱȱȱ ǰȱ ǯǰȱ ǯȱ ǰȱ ǯȬǯȱ ǰȱ ǯȱ ǰȱ ȱ ȱȱȱȱȱȱ ȱȬ ǯȱǯȱŘŖŗŘǯȱȱǰȱȱȱǰȱ ǯȱȱ¢ȱȱȱŘŞŜǱśşȮŜśǯ ȱȱȱȱȬȱ ȱȱȱ ǰȱǯǰȱǯȱǰȱȱǯȬ ǯȱȬǯȱŘŖŗŚǯȱ ȱ ¢ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ȭ ȱŝŜǱŗŞşȮŗşşǯ ȱ¡ȱȱȱȱ ȱ ȱ ȱ ȱ ǯȱŘŖŗřǯȱ Ǳȱ ȱ ȱ ȱ ǯȱȱŗŝŜǱŘşŝȮřŖŜǯ ȱȱȱǯȱȱȬ ǰȱǯǰȱȱǯȱǯȱŘŖŗřǯȱǰȱǰȱȱȱ ȱ ȱ ȱ ǰȱ ǰȱ ǯȱ ¢Ǳȱ¢ȱȱȱȱȱ ĴǱȦȦ ǯȬǯȦ ǯȱ ȱȱȱ¢ȱŞŘǱŗŗŞřȮŗŗşŗǯ Ĵǰȱǯȱ ǯǰȱȱǯȱǯȱŘŖŖŖǯȱ ȱȱ ǰȱǯȱǯǰȱȱǯȱǯȱěǯȱŗşşşǯȱȱȬ ȱ¢ȱ ȱǱȱȱȱȱ ȱȱȱȱȱǱȱȱ ȱǯȱ¢ȱǻǼȱŘřǱŚŜŜȮŚŝŞǯ ȱ ȱ ȱ ¢ǯȱ ȱ Ĵǰȱǯȱ ǯǰȱȱǯȱǯȱŘŖŖŗǯȱȱȱȱȬ ȱŗśřǱŜŚşȮŜśşǯ ȱȱȱ ȱȱȱ ǯȱ ǰȱ ǯǰȱ ǯȱ ǰȱ ȱ ǯȱ ǯȱ ŗşşśǯȱ ȱ ȱ ȱȱ¢ȱŝşǱŗşřřȮŗşŚŖǯ ȱǵȱȱȱȱȱȱȱȱ £ ǰȱǯȱǯȱŗşşŗǯȱ ȱȱȱȬ ¢ȱȱȱǯȱȱ¢ȱ ȱǱȱȱȱȱǯȱȬ ŗǱŘŖşȮŘŘřǯ ȱȱŗřŝǱśȮŘŞǯ ǰȱ ǯȱ ǯǰȱ ǯȱ ǰȱ ǯȬ ǯȱ Ȭǰȱ ǰȱ ǯǰȱ ǯȱǯȱǰȱȱǯȱǯȱǯȱ ǯȱ ǰȱ ǯȱ ǰȱ ǯȱ ǰȱ ǯȱ ǰȱ ŘŖŖŝǯȱ ¢ȱ ȱ ȱ ȱ Ȭ ǯȱǯȱǰȱȱǯȱǯȱŘŖŗśǯȱȱȱ ȱ ȱ ¢Ǳȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ęȱȱȱȱȱǯȱ ȱȱ ȱŝŗǱŗŚŗşȮŗŚŘŜǯ ȱ¢ȱśŘǱŚşŜȮśŖŚǯ ǰȱ ǯȱ ŗşŞŖǯȱ ȱ Ǳȱ ȱ ȱ Ȭ ¢ǰȱǯǰȱȱ ǯȱǯȱŘŖŖřǯȱ ȱȱȱ ȱ ȱ Ěȱ ǵȱ ȱ ŘŗŖǱ ¢ȱȱȱ¢ȱ ȱȱ ŗŖŚŗȮŗŖŚřǯ ȱȱ ǯȱȱ ȱȱ ǰȱǯȱŘŖŖśǯȱȬ¢ȱȱȱȱȱȱ ¢ȱŞŗǱřŘŗȮřřŚǯ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ǰȱ ǯǰȱ ǯȱ ǯȱ ǰȱ ȱ ǯȱ ǯȱ ŘŖŖśǯȱ ŘŚŖȮŘśśȱ inȱ ǯȱȱ ȱ ǯȱ ǰȱ ǯȱ ȱȱȱ¢ȱȱȬȱȱȱ ¢ȱȱȬ¢ȱǯȱ¡ȱȬ ǯȱ ȱȱȱ¢ȱŝŚǱŝŖŗȮŝŖŝǯ ¢ȱǰȱ¡ǰȱ ǯ

ȲȲ ǯǯ 16 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583 ȱȱ ȱȱǯ

ǰȱǯǰȱ ǯȱǰȱǯȱǰȱǯȱ¢ǰȱȱ ǯȱĴǯȱŘŖŗŘǯȱ şȦŘŖŗŗȱ ǻ ȱ Ǽǯȱ ĴǱȦȦ ǯ¢ǯȦ ȱȱȱȱ¢ǯȱ ȦȦȦ ¢ȱĴȱŗśǱŘŝŞȮŘŞşǯ ȦƖŘŖƖŘŖȦȦ ǰȱ ǯǰȱ ȱ ǯȱ #ǯȱ ŘŖŗŚǯȱ ȱ ȱ ¢ȱ ŘŖŗŗ ǯ ȱȱȱȱǵȱ ¡ǰȱ ǯǰȱ ǯȱ ǯȱ ǰȱ ǯȱ ·Ȭ ǰȱ ȱȱȱȱȂȱǯȱȱȬ ǯȱǰȱǯȱǰȱ ǯȱ£ǰȱȱǯȱǯȱ ¢ȱřŝǱŗŖŚŗȮŗŖśŚǯ ǯȱŘŖŖşǯȱȱȱȱȬ ǰȱǯȱŗşŞŚǯȱȱȱȱȱȬ ȱȱȱȱȱ¢ȱ ǱȱȬ £ǯȱ¢ȱǻǼȱŝǱřŚśȮřŝşǯ ȱȱĚȱȱǯȱ¢ȱ ǰȱ ǯȱǯǰȱȱ ǯȱ·ǯȱŘŖŖśǯȱȱȱȱ ȱȬ şŖǱŘřȮřŖǯ Ǳȱȱȱȱȱȱ ǰȱǯǰȱ ǯȱ ¢ǰȱ ǯȱǯȱ ǰȱȱǯȱǯȱǯȱ ȱȱ ȱȱ ¢ǯȱȱȱřŘřȮřřşȱin ŗşşŝǯȱ ȱ ȱ ¢ȱ ¢ȱ ǯȱ ¢ȱ ǯȱěǰȱǯȱǰȱȱǯȱȱ £ǰȱȬ ŝŞǱŗŗŖśȮŗŗŗşǯ ǯȱȱȱ ǱȱĚȱȱȬ¡ǵȱ ǰȱ ǯȱ ǯȱ ŗşŝŜǯȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ¢ȱǰȱǰȱ ǯ ǯȱȱ ȱȱ¢ȱȱ¢Ȭ ǰȱ ǯȱ ǯǰȱ ǯȱ ǰȱ ǯȱ ¢ǰȱ ǯȱ ǯȱ ǰȱ ȱŝǱŞŗȮŗŘŖǯ ǯȱǯȱ¢ǰȱ ǯȱěǰȱȱ ǯȱ ǯȱ ǯȱ ¢ǯȱ ǰȱ ǯǰȱǯȱǯȱǰȱǯȱ ǰȱȱǯȱǯȱ ǯȱ ŘŖŗśǯȱȱĞȱȱ¢ȱȱ¢ȱ ȱ ŗşşřǯȱȱȱȱȱȬ ȱȱǯȱȱȬ ¢ǯȱȱŜŜǱřŜşȮřŞŖǯ ¢ȱȱ¢ȱŜşǱŗŗśřȮŗŗŜŗǯ ǰȱǯȱǯǰȱǯȱǯȱ ǰȱȱǯȱǯȱǯȱŘŖŗŖǯȱȱȬ ǰȱǯǰȱǯȱ¢ǰȱǯȱǰȱǯȱǰȱǯȱǰȱȱ ȱȱȱ¡ȱȱȱȱȬ ǯȱǯȱŘŖŖşǯȱȱȱȱȱȬ ȱǯȱȱȱ¢ȱȱȱ ȱȱȱȱǱȱȱȱȱȱȱ ŗǱřȮŗŚǯ ȱȱȱȱȱȱǯȱȱ ǰȱǯȱǯǰȱǯȱǯȱ ǰȱǯȱ ǯȱǰȱǯȱǯȱǰȱȱ ȱȱ¢ȱŞŝǱŗŗŘşȮŗŗřŝǯ ǯȱǯȱǯȱŘŖŖşǯȱ¡ȱěȱȱȱ¡Ȭ ¢·ǰȱ ǯȱ ŘŖŗŗǯȱ ȱ ŘŖŗŖȬŘŖŗŗȱ ȱ ȱȱ¢ȱ ȱǯȱǰȱ ȱǰȱ ȱ Ĵȱ §ǯȱ §¢ȱ ȱ ȱ ǰȱǯ

SUPPORTING INFORMATION

ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ Ǳȱ ĴǱȦȦ¢ǯ ¢ǯȦȦŗŖǯŗŖŖŘȦ ŘǯŗśŞřȦ

ȲȲ ǯǯ 17 ȱŘŖŗŜȲȱŝǻŗŗǼȲArticle e01583

APPENDIX S1

Fig.S1. Maps showing the reindeer herding area in Sweden (dark grey area, left) and the brown bear distributional range in Norway and Sweden (right, darker color indicates darker density)

Source: iRENMARK - Source: The Scandinavian Sametinget, Sweden Brown Bear Research Project Fig.S2. Maps showing the Udtja (left) and Gällivare (right) study areas with a) reindeer locations, b) bear locations, c) land cover distribution and d) elevation

Table S1. Candidate mixed-effect logistic regression models that were ranked according to AICc within each herding district model set, including the Full model, Road-Topography model (RT) and the Land cover-Topography model (LT). ID is individual animal ID that was included as a random effect in the models, and df is degrees of freedom. The interaction term “Time” represents the subdivision into time periods according to temporal variation in brown bear predation risk for the whole study period: the predation period versus the post-predation period, and on a daily basis within the predation period: high predation hours versus low predation hours.

Herding Model df district name Model

land cover + dem + SmRoad + vrm + time + timeൈland cover + timeൈdem + Full 21 timeൈSmRoad + timeൈvrm + (1 | ID)

RT dem + SmRoad + vrm + time + timeൈdem + timeൈSmRoad + timeൈvrm + (1 | ID) 9

Udtja

land cover + dem + vrm + time + time*land cover + timeൈdem + timeൈvrm + LT 19 (1 | ID)

land cover + dem + SmRoad + LaRoad + vrm + time + timeൈland cover + Full 25 timeൈdem + timeൈSmRoad + timeൈLaRoad + timeൈvrm + (1 | ID)

dem + SmRoad + LaRoad + vrm + timeൈdem + timeൈSmRoad + timeൈLaRoad + RT 11 timeൈvrm + (1 | ID)

Gällivare

land cover + dem + vrm + time + timeൈland cover + timeൈdem + timeൈvrm + LT 21 (1 | ID)

Table S2. Ranking of candidate models and corresponding AICc values within each model set. Sample sizes (n) are number of observations in each model and ID is the number of individual animals.

Model set Model AICc ' Weight

Udtja, reindeer, Full 88 024.9 0.00 1.0 LT 88 044.8 19.94 0.0 predation/post-predation (n=65292, ID=67) RT 89 190.0 1 165.07 0.0 Null 90 517.9 2 493.05 0.0

Udtja, bear, Full 39 991.9 0.00 1.0 LT 40 048.7 56.79 0.0 predation/post-predation (n=30418, ID=11) RT 41 211.6 1 219.75 0.0 Null 42 172.3 2 180.41 0.0

Udtja, reindeer, Full 65 299.1 0.00 0.605 LT 65 300.0 0.86 0.395 high/low predation hours (n=47918, ID=66) RT 66 172.1 873.02 0.0 Null 66 432.5 1 133.35 0.0

Udtja, bear, Full 29 093.4 0.00 1.0 Land 29 193.3 99.90 0.0 high/low predation hours (n=21912, ID=11) Road 30 025.8 932.46 0.0 Null 30 380.5 1287.13 0.0

Gällivare, reindeer, Full 43 019.1 0.00 1.0 Land 43 494.7 475.58 0.0 predation/post-predation (n=33764, ID=30) Road 44 204.7 1 185.55 0.0 Null 46 810.8 3 791.70 0.0

Gällivare, bear, Full 22 111.6 0.00 1.0 Land 22 277.6 166.02 0.0 predation/post-predation (n=17766, ID=8) Road 24 454.4 2 342.80 0.0 Null 24 632.9 2 521.30 0.0

Gällivare, reindeer, Full 25 587.7 0.00 1.0 Land 25 724.4 136.68 0.0 high/low predation hours (n=19742, ID=28) Road 26 316.0 728.29 0.0 Null 27 372.2 1 784.48 0.0

Gällivare, bear, Full 14 923.6 0.00 1.0 Land 15 058.9 135.22 0.0 high/low predation hours (n=11818, ID=8) Road 16 222.6 1 298.95 0.0 Null 16 387.2 1 463.58 0.0

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Brown bear predation on semi-domesticated reindeer calves: relating kill locations to landscape heterogeneity

Therese Ramberg Sivertsen1, Anna Skarin1, Lars Rönnegård2, Birgitta Åhman1, Jens Frank3, Peter Segerström3 and Ole-Gunnar Støen4,5

1 Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, P.O. Box 7024, 750 07 Uppsala, Sweden 2 Section of Statistics, School of Technology and Business studies, Dalarna University, 791 88 Falun, Sweden 3 Grimsö Wildlife Research station, Department of Ecology, Swedish University of Agricultural Sciences, 730 91 Riddarhyttan, Sweden 4 Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway 5 Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden

Abstract

Landscape characteristics can influence the probability of kill, both from effects on the spatial distribution of prey and predators, and through post encounter effects on the risk of predation. In this study, we investigated the spatial distribution of kill sites from brown bear (Ursus arctos) predation on semi-domesticated reindeer neonates (Rangifer tarandus tarandus). We used totally 305 kill sites collected over three years from two forest reindeer herding districts, Udtja and Gällivare, in Northern Sweden. We estimated resource selection functions (RSFs) for the spatial distribution of kill sites as a function of landscape characteristics, with a use-availability design. Furthermore, we investigated the relative importance of reindeer habitat selection and reindeer-brown bear co-occurrence probability on kill site distribution. The spatial distribution of kill sites varied with landscape covariates on the reindeer calving range, and this variation was largely explained by the relative probability of co-occurrence. However, we found possible evidence for a lower risk of kill in clear- cut habitats relative to co-occurrence probability in Gällivare, and, despite higher occurrence probability close to roads during nighttime, that the risk of kill was unrelated to road distance in Udtja. Moreover, reindeer may be able to reduce predation risk by utilizing higher elevations, clear- cuts and areas close to large roads within the calving ground, although this is apparently not sufficient to avoid high predation rates in the study areas. Finally, comparing fine-scale habitat attributes of kill sites with control locations suggested that areas within 0-10 m from a distinct habitat edge might be associated with higher probability of predation. As populations of large carnivores are increasing, and human activity and development is continuing to expand, we need to understand both direct and indirect costs of predators’ presence, and how land use changes and forestry may affect Rangifer-predator interactions.

Keywords: Rangifer, brown bear, predation risk, landscape characteristics, antipredator behavior, relative probability map, co-occurrence

Introduction being killed given an encounter will be context- dependent and varying with predator hunting mode, Landscape heterogeneity can play a key role in shaping prey escape tactics and spatial structures (Hebblewhite behavioral interactions between prey and predator, and et al. 2005, Heithaus et al. 2009). Furthermore, spatial in turn, population dynamics (Johnson et al. 1992, Sih variation in predation risk may take place at a number 2005, Kauffman et al. 2007). Prey-predator systems are of scales from entire landscapes to habitat types, assumed to persist over the longer term due to terrain characteristics and escape impediments variability in predation risk in space and time (Ellner et (Halofsky and Ripple 2008, Hebblewhite and Merrill al. 2001), and variation in habitat and terrain may 2009, Laundré et al. 2010). influence the probability of prey encountering a Early predation on neonates is usually the most predator, and the conditional probability of being killed common cause of calf mortality where predators are given an encounter (Lima and Dill 1990, Atwood et al. present, and can act as a major limiting factor on 2009). A predator-prey encounter form the prerequisite ungulate population growth (Adams et al. 1995, for a predation event to occur, whereas the chance of

1 Linnell et al. 1995). Brown bear (Ursus arctos) adopt a prone position to avoid detection from predation on ungulate neonates can be considerable predators (Lent 1966). In moose, birth sites provide (Adams et al. 1995, Zager and Beecham 2006), and cover for the calf, but at the same time visibility for the recent findings in Northern Sweden have revealed high mother to detect an approaching predator (Bowyer et calf mortality caused by brown bears among forest- al. 1999). Similarly, habitat characteristics at caribou living semi-domesticated reindeer (Rangifer tarandus birth sites have been suggested to reflect ground cover tarandus) (Støen et al. 2017). Semi-domesticated for hiding. For example, Gustine et al. (2006) found reindeer in Fennoscandia move unconstrained within that shrub cover had a positive effect on early calf the limits of the herding district range most of the year, survival in woodland caribou, possibly because it and movements are comparable to wild Rangifer (i.e. provided increased possibility to hide. Carr et al. reindeer and caribou) populations. Similar to previous (2010) identified that caribou nursery sites provided reports concerning bear predation on caribou in North- calf concealment cover, and greater predator sensory America (Mahoney and Abbott 1990, Whitten et al. detection. One may therefore suspect that habitat and 1992, Adams et al. 1995, Zager and Beecham 2006), landscape features promoting both early predator predation on semi-domesticated reindeer neonates is detection and ground cover would be an advantage for highly concentrated to the first weeks post-partum reindeer calf survival. (Støen et al. 2017). Since the Swedish brown bear is Here, our primary objective was to investigate the mainly associated with rugged forested areas and spatial variation in brown bear predation on reindeer mountain valleys (Haglund 1964), forest reindeer neonates, and further, to assess how the distribution of herding districts (i.e. with ranges located in forested kill sites related to predicted probability of reindeer areas year-round) may be particularly vulnerable to habitat selection and reindeer-brown bear co- brown bear predation. Moreover, areal restrictions on occurrence. We used a unique spatial data set movements, combined with relatively high herd comprising 305 locations where semi-domestic densities compared to wild forest-living populations, reindeer calves had been killed by brown bears, possibly increase vulnerability to brown bear predation combined with brown bear and female reindeer during the calving season (Sivertsen et al. 2016). movement data (using GPS collars) from the same area Landscape characteristics may affect calf predation and period. Our study was performed during 2010- risk in several ways, depending on species and 2012, and located on the calving ranges of two forest- environment. The probability of encountering a reindeer herding districts in Northern Sweden. More predator may vary across habitats, and vegetation and specifically, we examined how the spatial distribution terrain can influence predator detection ability, hiding in reindeer calf kill sites related to landscape cover and escape capacity (Bergerud et al. 1984a, characteristics on the reindeer calving range, and Bergerud and Page 1987, James et al. 2004, Caro 2005, attempted to disentangle the influence from reindeer Hamel and Côté 2007, Rearden et al. 2011). For habitat selection and brown bear - reindeer co- example did the risk for losing the calf to predation occurrence probability, using resource selection increase for woodland caribou females that selected functions (RSFs). We used binomial logistic regression habitats suitable for black bears (Leblond et al. 2016). to model the risk of brown bear predation on reindeer White et al. (2010) also documented an impact of calves compared to landscape characteristics during the habitat structure on elk calf survival, through effects on calving season. Finally, we used field registrations to calf escapement and security cover, whereas Norberg investigate if fine-scale landscape attributes influence et al. 2006 showed that semi-domestic reindeer calves brown bear predation risk on reindeer calves. were more exposed to predation from golden eagles in open landscapes than in forested habitat. Rangifer is a typical follower species, being mobile Material and Methods and following its mother shortly after birth (Vos et al. 1967). Because Rangifer neonates grow at a maximal Study area rate, they quickly gain the ability to flee from predators The study was carried out on the calving ranges of (Parker et al. 1989). A rapid increase in calf Udtja (66.2°N, 19.4°E) and Gällivare (66.6°N, 21.4°E) locomotive skills is likely a major reason for the highly forest reindeer herding districts located in Norrbotten restricted period within which bear predation occurs. County, northern Sweden (Fig.1). The landscape in the Moreover, the fact that reindeer calves usually flee to area is typically dominated by coniferous forest escape danger, suggests an advantage of open areas, (Norway spruce, Picea abies, and Scots pine, Pinus with enhanced predator detection and a ground cover sylvestris) interspersed with bogs and lakes, with that facilitates movements (Pinard et al. 2012). Hiding subalpine birch (Betula pubescens) forest at the highest may nevertheless be important immediately after birth. elevations. Elevations range from 187 to 714 m a.s.l. in Indeed, during the first 48 hours, reindeer calves may Udtja and 38 to 528 m a.s.l. in Gällivare. A significant

2 portion of the Udtja calving range is located within a predation on reindeer calves was highly concentrated closed military missile range, with military training to 3-4 weeks in spring (Støen et al. 2017). We focused activities being the main human activities in the area. the study period when nearly all the calves (99%) Since 1995, a large part of the area has also been a predated by GPS-collared brown bears were killed, nature reserve with no logging activity allowed. There between 10 May until 9 June (hereafter referred to as are mainly gravel roads in Udtja (0.25 km/km2) used the predation period). During this period, the number by the military and reindeer herders, and in addition, of reindeer calves killed annually by individual brown larger public roads in the southern part of the district bears inside the study area ranged between 0 and 37 (0.02 km/km2). In Gällivare, logging activity is higher, (mean = 11, SD = 12.3). Further, the majority of the with a relatively dense network of forest roads (0.38 calves were killed between 6 PM to 6 AM (hereafter km/ km2) and larger public roads intersecting the area referred to as high predation hours) (Støen et al. 2017). (0.06 km/km2). The reindeer move freely within the borders of the herding districts, but are occasionally Field surveys subject to herding activities. Natural barriers (e.g. From 13 May to 9 June in 2012, we recorded fine-scale rivers), herding activities, and fences restrict habitat characteristics at kill sites and control sites movements outside the borders. In Udtja, the herding within the two reindeer herding districts (Fig 1). We district is fenced towards the northwest. The reindeer used the first bear GPS minute location recorded after densities are around 110 animals/100 km2 in both activation of the proximity function (“encounters”; districts. where one or more reindeer females with a proximity The total brown bear population in Norrbotten was UHF collar were less than 100 m away) as control estimated to be 713 – 1152 individuals in 2011 (Tyrén sites. We only included control sites that were 2011). Bears are hunted during the annual hunting minimum 200 m from a known kill site, where it took season in the autumn (21 August – 15 October or until more than 5 min until a kill occurred following the quotas are reached). The estimated brown bear encounter. The control sites thus represent habitat and population in 2010 was 62 – 96 and 53 – 75 in Udtja terrain use by bears in close vicinity to reindeer and Gällivare, respectively (Karlsson et al. 2012). females during the period when bears killed reindeer, Densities of lynx and wolverines are low, and there are but where no kill had occurred in instant distance or no wolves in the study area (Tyrén 2011). time. At all sites we registered the dominating land cover type within a 20 m radius, sightability, snow Kill registrations depth and snow cover, distance to edge and edge A total of 21 individual bears (Udtja 2010:4, 2011:7, habitat. We defined an edge as an abrupt change to a 2012:8; Gällivare 2011:4, 2012:8) were tracked on the different habitat type. We classified land cover based reindeer calving range, with a proximity function in the on the vegetation map provided by Lantmäteriet GPS-collar activated. During the same period, the (www.lantmateriet.se), which supplies national majority of adult reindeer females in the study geographic and land information data in Sweden. We populations were equipped with proximity UHF- included the classes bog, riparian zone of river or collars (Udtja 2010:990, 2011:1176, 2012:1235; stream, tree-rich bog, swamp forest, bare rocks, shrub Gällivare 2011:893, 2012:1350). All females with vegetation, open vegetation (grass/herbs), road, and if proximity collars were documented to be pregnant. forest; age (recent clear cut, older clear cut, young When a GPS-collared brown bear was < 100 m from a forest mature forest), dominating species, mean tree female reindeer with proximity UHF-collars, the GPS- height and field layer. To measure sightability we first collar was activated to register 1-min locations for one used a range finder to measure the distance to an hour. We searched for carcasses at all 1-min GPS obstruction in each cardinal direction and one random locations by brown bears in 2010, but because no calf direction. Each measurement was taken sitting on the carcasses were found on tracks or clusters of 1-min knees, with the arm in an upward direction and the locations with less than 4 GPS location within a 30 m elbow supported by the knee. Second, we used a 60 cm radius, only clusters with ≥3 1-min GPS locations high * 30 cm diameter collapsible cover cylinder, split within a 30 m radius were visited in 2011 and 2012 into a red and white section, each 30 cm high (Ordiz et (Støen et al.2017). A kill site was defined as the GPS al. 2009). We measured the distance we had to walk in location registered at the reindeer carcass. The area one random direction until we lost sight of the lower wherein we searched for reindeer carcasses (i.e. where red section of the cylinder. the proximity function in the reindeer GPS collar was activated) was defined by a combination of reindeer herder`s definitions of the reindeer calving range, formal herding district borders, and landscape features (i.e. rivers, roads and railways) (Fig. 1). Brown bear

3 Mapping relative probability of reindeer and landscape variables. We estimated the Euclidean brown bear occurrence, and reindeer-brown distance in meter to the nearest small road (typically bear co-occurrence gravel roads) and the nearest major road (public road We used previously developed resource selection with regular traffic) for each 50 x 50 m raster cell. function (RSF) models of female reindeer and brown Since road effects commonly diminish beyond a bears from the same period and study area (Sivertsen et certain distance, we transformed distance to road using al. 2016) to estimate the relative probability of reindeer 1 - eDd, where d is the distance to the feature and D was and brown bear habitat selection and reindeer-brown set to 0.002 (approximate effect zone < 1500 m) bear co-occurrence. To reduce spatial dependence of (Nielsen et al. 2009), resulting in exponential decays individual bears on kill sites, we removed bear ranging from 0 at the feature to 1 at very large locations within 50 m from a kill. Since there was distances. Due to a highly skewed distribution, and minimal variation in reindeer habitat selection on a correlation with elevation, we excluded large roads daily basis (Sivertsen et al. 2016), and preliminary from the analyses in Udtja. We used a digital elevation analyses suggested a similar pattern when relating kill model with 50 m resolution and a vertical accuracy of sites to reindeer habitat selection estimates, we limited ± 2 m. Terrain ruggedness was estimated using the our analysis of kill sites relative to estimates of Vector Ruggedness Measure tool (VRM) (Sappington reindeer habitat selection representing the full et al. 2007) in SAGA GIS (http://www.saga-gis.org) predation period. However, the brown bears in our with the neighborhood parameter set to five cells. study area changed habitat selection patterns Elevation and terrain ruggedness were standardized to throughout the day, with a stronger selection for facilitate comparability of regression coefficients. See reindeer habitat during night, when most calves were Sivertsen et al.2016 for more details on land cover and killed (Sivertsen et al. 2016). Also, human activity can terrain data. cause variation in daily activity patterns between day We used the parameter estimates from binomial and night in brown bears (Ordiz et al. 2011). logistic regression as described for the RSF models in Therefore, we wanted to evaluate the spatial Sivertsen et al. (2016), but dropping the fixed and distribution of kill sites in relation to reindeer – brown random intercepts (Polfus et al. 2011) to calculate the bear co-occurrence probability representing both the relative probability of habitat selection by brown bears full predation period, and high predation hours only. and reindeer each year for each 50×50m grid cell in the The landscape parameters included in the resource two study areas (Manly et al. 2002, Johnson et al. selection models were extracted using Arc GIS 10.0- 2006): 10.03 software (ESRI Inc., Redlands, California, USA ©2010–2015), and included land cover types, ǡ ሺͳሻ elevation, terrain ruggedness, and minimum distance to where w(x) is the relative probability of selection, β is the nearest large and small roads. All the digitized n the estimated coefficient for covariate x (Manly et al. geographical data were provided by Lantmäteriet, n 2002). Following Courbin et al. (2009), we then scaled except information about clear-cuts, which was predicted RSF – values between 0 and 1 using: provided by the Swedish Forest Agency. Land cover data was derived from vegetation vector maps, the Swedish Land cover Map 25 x 25 m (SMD Corine ǡ ሺʹሻ Land Cover Data 2000) and satellite image forestry where w(x) is derived from Eq. 1, w is the smallest data ("Utförd avverkning", Swedish Forest Agency min RSF value, and w the largest RSF value for the 2015). Combining these sources, we first categorized max study area. Further, we calculated the relative land cover into eight classes; coniferous moss forest, probabilityyof of brown bear and reindeer co- coniferous lichen forest, deciduous forest, wetland, occurrence : other open habitats (cultivated land, grassland, bare rocks), recent clear-cuts (0 – 5 years), old clear-cuts (6 ǡ ሺ͵ሻ – 12 years, or < 2 m height in the year 2000) and young forest (2 – 5 m height in the year 2000). Old and where and is the relative recent clear-cuts were merged into a common clear-cut probability of selection in each 50 × 50 m grid cell for category in Udtja, due to a low proportion of recent female reindeer and brown bear, respectively. Also clear-cuts (0.001). Because we found no kills in recent estimates of co-occurrence were scaled between 0 and clear cuts or other open habitats in Gällivare, we 1 (Courbin et al. 2009). The resulting maps thus merged recent clear cuts with old clear cuts to a represented the relative probability of reindeer and common clear cut category, and other open habitats brown bear habitat selection and reindeer – brown bear and deciduous forest into a deciduous/open category, co-occurrence in each herding district during the full to meet the purpose of the kill site models, which was predation period and in high predation hours, to make comparison between kill site locations and

4 respectively, to be used further in the analysis of kill Fine scale site spatial distributions (Fig. 2). We used binomial logistic regression to compare fine- scale habitat attributes between reindeer calf kill sites Kill site models and control sites. The response was thus binomial (kill versus control), and the explanatory variables included Calving range scale land cover, distance to visible edge, sightability and snow. As the sample size was relatively small and we To analyze the spatial distribution of kill sites on the wanted to reduce the degrees of freedom in the models, scale of the calving range, we used all kill sites within we merged the land cover classes into open habitat the area where the brown bear proximity collars were (wetland; heath; grassland; road), semi-open habitat activated in all years, and where we had brown bear (shrub; clear-cut; tree bog) and forest habitat. Edge and reindeer locational data (Fig 1). When two kill was defined as a distinct edge between these sites were < 50 m apart, we removed one of them by categories. Because the distance to edge variable random, to avoid pseudo replicates in our analyses included field registrations denoting the distance to a (totally 13 sites were removed). In total we used 305 distinct visible habitat edge it included the category kill sites (Udtja: 178; Gällivare: 127) (Fig. 1) in the “no visible edge”, and was thus treated as a categorical analyses. We applied binomial logistic regression to variable. We divided the distance to edge variable into model spatial variation in kill sites by comparing the four classes, based on possible expected effects and spatial attributes of kill sites with random sites within preliminary data exploration: “0-10 m”, “11-50 m”, each reindeer herding district. To make the analyses “>50 m” and “no visible edge”. To normalize the more robust, we generated a number of random sites distribution of the sightability measurements, and to equal to the number of kill sites multiplied by ten, make the analysis biologically meaningful, we used a resulting in 1780 random points in Udtja, and 1270 in maximum distance measurement limit to 100 m (i.e. Gällivare, for each model. As kill rates varied between measurements > 100, was set to 100). We made a snow individual brown bears, we included brown bear index by multiplying mean snow depth (at center of individual as a random intercept in the models, using registration site, and 20 m from the center) with the glmer function in the ”lme4” package (Bates et al. percentage snow cover (within 30 m radius of the kill 2014). We compared models with and without year as site). We pooled data from the two herding districts, fixed factor, using AICc. First, we assessed the overall included brown bear individual as a random intercept relation between the spatial distribution of kill sites and in the models, and used the glmer function in spatial heterogeneity on the reindeer calving range by the”lme4” package (Bates et al. 2014). To avoid comparing landscape attributes between kill sites and pseudo replicates we removed a location by random random points generated within the study area. when kill sites or control sites were < 50 m apart from Secondly, to more closely investigate the relation another kill site or control site, respectively (number of between landscape heterogeneity, relative probability control sites removed: 2; kill sites removed: 8). of reindeer habitat selection and reindeer-brown bear Moreover, brown bear kill rates on reindeer calves co-occurrence,currence, wew sampledamp random points weighted by decreased towards the end of the predation period. To either or for each 50 × 50 m raster cell. avoid a temporally unbalanced sample, we identified In this way, the random points included in the model the break point when predation decreased, and represent the relative probability of reindeer randomly removed control sites after this date to have occurrence or reindeer-bear co-occurrence probability. equal number of controls sites and kill sites throughout In all models, we included the same set of covariates as the observation period. The resulting sample included we used to model reindeer and brown bear resource 146 kill sites and 126 bear control sites. Both snow selection, and constructed separate models for Udtja melt and greening occur rapidly during this time of and Gällivare reindeer herding districts. If the year, which may result in environmental conditions distribution of kill sites were proportional to the diverging from the “real” condition if the time relative probability of reindeer habitat selection or difference between the real time and the time of reindeer-brown bear co-occurrence, no significant registration get too large. Whereas most field surveys effects would be present in the given model, whereas at kill sites were carried out within a couple of days significant effect for a given landscape characteristic after the true date of the kill (1–7 days, mean=2 days), indicate a difference in kill probability relative to the the time lag between true registration of bear control likelihood of reindeer habitat selection or reindeer- location and date of field survey was sometimes longer brown bear co-occurrence for this covariate. All (1–25 days, mean=9 days). We considered the two statistical analyses were carried out in R 3.3.0 (R Core sightability indexes to be potentially affected by the Team 2016). temporal changes in greening of vegetation, and therefore only included registrations within 7 days

5 after the true date. Further, we identified a breakpoint habitat selection (Table 1 and 2). When accounting for for when the snow started to disappear, by identifying the relative probability of reindeer-brown bear co- the date when the accumulated snow index was 99 % occurrence in Udtja, there was higher chance of a kill (Appendix: Fig. S1), and only included registrations to occur in coniferous lichen forest and lower in before this date (1 June) in the analysis of snow deciduous forest, relative to wetland, whereas there effects. Accordingly, we made three separate data sets were no effects of land cover in the co-occurrence including i) distance to edge (kill=142, control=126), model for high predation hours, but instead a positive ii) sightability (kill=142, control=83), and iii) snow effect of distance to small roads (Table 1, Fig. 4). In (kill=108, control=58). We built a series of models for Gällivare coniferous moss forest and clear cuts differed each data set. We restricted the number of parameters significantly from the reference category (wetland) in in the models to include either distance to edge, the model representing co-occurrence probability for sightability indexes or snow indexes, but included land the predation period as a whole, whereas there was cover type at all three levels. All models were only a significant negative effect of clear-cuts when compared using AICc and parameter estimate considering co-occurrence probability calculated from confidence intervals. habitat selection in high predation hours only (Table 2, Fig. 4).

Results Fine scale We used the full data set (n=268; kill=142, Calving range scale control=126) to compare kill sites and control sites When we analysed the overall distribution of kill sites with respect to distance to edge categories and land on the calving range, as a function of land cover, we cover. The “distance to edge”-model and “distance to found that the probability of a kill to occur varied with edge + land cover”-model performed better than the landscape characteristics in both study areas (Fig. 3, model only containing land cover (Table 3). These two Table 1 and 2: random model). We also found that, in top-ranked models diverged with Δ AICc < 2. both study areas the land cover model performed better Accordingly, land cover did not add anything to the than the null models, with Δ AICc > 2 (Udtja: Δ AICc model and we therefore present results based on the = 36; Gällivare: Δ AICc = 53). Inclusion of year did “distance to edge”-model (Table 3). Similarly, for not improve the models. There was generally a higher snow, the “snow” model and “snow + land cover“ probability of kill in forested areas compared to model diverged with Δ AICc < 2. Because the “snow” wetlands, and with higher elevations (Table 1 and 2). model contained fewer parameters, this was selected as In Udtja, there was also a significantly higher the best model. For sightability, the model only probability of kill in open habitats (Table 1), whereas containing the RF sightability index performed best. in Gällivare probability increased farther from both Importantly, all top-ranked models performed better small and large roads, and with decreasing ruggedness than the – null model, with Δ AICc > 2. However, in (Table 2). Moreover, in the models including the particular for the sightability and snow model the weighted random locations, a significant estimated divergences in AICc from the null-model were small, effect of landscape characteristics indicated a with Δ AICc = 2.40 and Δ AICc = 3.91, respectively difference in kill probability relative to the likelihood (Table 3). There was significantly higher probability of of reindeer habitat selection or reindeer-brown bear co- a kill in the edge distance class “0-10 m” compared to occurrence for that landscape characteristic. We found longer distances (Table 4, Fig. 5). A closer the patterns were rather similar between the two study examination of data revealed that 70 % of the kill sites areas. Overall, the significant effects of landscape in this category were located in forest-wetland edges. characteristics diminished when controlling for Both RF sightability and snow index had a significant reindeer habitat selection. After controlling for negative effect on kill site probability (Table 4, Fig. 6). reindeer-brown bear co-occurrence there were hardly Because brown bears in theory could carry the calf any significant effects, indicating that kill site away from where it actually was killed, we did a post distribution was largely a function of co-occurrence hoc examination of the location of the kill, relative to probability (Table 1 and 2). Relative to reindeer habitat the center point of the minute location cluster with selection, there was higher probability for a kill site which it was associated. Neither the center point of the within coniferous lichen forest, coniferous moss forest cluster nor the kill site may be the exact location where and young regenerating forest, compared to wetlands the kill occurred, but a comparison of those give us an (Table 1 and 2). In Udtja, there was also a higher impression of the probable spatial error in our data. For chance for a kill to occur in open habitats, whereas in all kill locations for which it was possible to identify a Gällivare the risk of a kill increased farther from large cluster center point (98%), 69% were < 10 m from the roads and at lower elevations, relative to reindeer center of the minute location cluster, 84% were < 20 m

6 away, and 16% were between 20 – 36 m from the Leblond et al. 2016), although there may be exceptions cluster center. Overall, this supported the assumption (e.g. female white-tailed deer, Caro et al. 1995). Even that field registrations on the kill site reflected the though only pregnant females were equipped with habitat where the calves actually had been killed. GPS-collars, females without calves could have been present, due variation in timing of birth, and losses to predation (or other causes) throughout the season. Discussion Thus, if females with calves selected areas away from Our results showed that the spatial distribution of roads, this could have resulted in a lower co- reindeer calf kill sites varied with landscape covariates occurrence probability between brown bears and calves on the reindeer calving range, and that this variation closer to roads than predicted from the population- was mainly explained by the relative probability of averaged estimates of resource selection. A third brown bear-female reindeer co-occurrence, estimated explanation may be that brown bears simply decreased from reindeer and brown bear resource selection their hunting effort in areas close to small roads in functions. However, we found possible evidence for a Udtja. For instance, tolerance towards roads may lower risk of kill in clear-cut habitats relative to co- increase at times when human activity is low (Ordiz et occurrence probability in Gällivare, and that kill risk al. 2014). In Udtja, human activity is generally higher was unrelated to road distance in Udtja, despite during the day, and the roads are then used quite increased co-occurrence probability close to roads extensively by the reindeer herders, and military during nighttime. Furthermore, kill sites were located training activity. We may suspect higher road use by at lower elevations and farther from large roads, than bears in nighttime to reflect a higher activity level and predicted from reindeer habitat selection in Gällivare. movement rate by brown bears to compensate for less Thus, reindeer may be able to reduce predation risk by daytime activity and feeding (Ordiz et al. 2014). utilizing higher elevations, clear-cuts and areas close to Moreover, roads and roaded habitats may work as large roads within the calving ground, although this is conduits for movements and be used for travel by bears apparently not sufficient to avoid high predation rates. (Roever et al. 2010). Since the brown bear movement On a finer scale, field registrations at kill sites and bear rates generally are higher at night, the overall area use locations suggested that distance to edge and will likely be more extensive at this time. If bears sightability may influence the probability of a kill. mainly used roads for travelling, and then increased Because most of the brown bear predation on hunting effort for reindeer calves farther from the road, reindeer calves occurred during night, and the spatial where these were more abundant, this would have overlap between brown bears and reindeer also skewed the distribution of kill sites away from the increased at nighttime (Sivertsen et al. 2016), we roads relative to brown bear use. expected higher correspondence between kill site In Gällivare, the only remaining significant effect, locations and co-occurrence probability during night. after accounting for co-occurrence during high Indeed, in both study areas, there were least effects of predation hours, was a lower probability of kills in land cover in the weighted co-occurrence models based clear-cuts. We did not see effects of clear-cuts in on habitat selection in high predation hours, indicating Udtja, which may be due to low quantities of clear-cuts higher correspondence. The discrepancy between kill (especially recent clear-cuts) compared to Gällivare. In sites and co-occurrence probability close to small roads early successional stages, clear-cuts may provide both may have several explanations. Overall, the increased better overview and hiding opportunities for the co-occurrence probability closer to roads in night hours reindeer calves (Licoppe 2006, Dussault et al. 2012). corresponded to the higher preference of areas closer to In particular during the first days after birth, reindeer roads by brown bears in Udtja (Sivertsen et al. 2016). calves in forested habitats may rely on hiding Terrain features associated with areas near roads could techniques to avoid detection from predators (Lent have influenced post-encounter outcomes. Roads were 1966). In this phase both overview and ground cover often located in flatter terrain providing increased can be important, since the calves may adapt a prone detection and escape probabilities for reindeer. Thus, position when detecting a predator (Lent 1966, 1974). higher brown bear encounter risk close to roads could As locomotive skills and the ability to flee from be outweighed by better escape opportunities. A predators develop rapidly (Parker 1989), early second explanation could be that reproductive status detection combined with the possibility to escape may affect sensitivity to both predation risk and human likely become more important. Moreover, as indicated disturbance, and when these factors are not in conflict, in Sivertsen et al. (2016), our results suggest that females with calves are generally expected to express environments like clear-cuts that provide early stronger avoidance responses compared to females detection and hiding possibilities for the calf may without a calf at heel (Wolfe et al. 2000, Barten et al. decrease predation risk, but that forest habitats, 2001, Hamel and Côté 2007, Skarin and Åhman 2014, including young forest, represent increased risk for

7 reindeer due to higher brown bear encounter risk. exploit. Brown bears generally increased preference Thus, although providing advantages in the short term, for higher elevations in the predation period (Sivertsen clear cuts will eventually be replaced by risky habitats et al. 2016), but bears seem to avoid walking on ridge of young forest. Also, as suggested by Dussault et al. tops and for example rather prefer to follow hill sides, (2012), selection for clear cut habitats can in the end possibly to avoid getting fully exposed (T.R. Sivertsen have adverse consequenses if females show high site pers. obs.). Thus, if reindeer females and their calves fidelity, as has been shown in several ungulates use higher elevations, this may increase detection rates (Wittmer et al. 2006, Tremblay et al. 2007). of brown bears and facilitate escape probability Kill sites were located farther away from large (Gustine et al. 2006, Pinard et al. 2012). Several works roads than predicted from reindeer habitat selection. from North America have reported higher elevations as This pattern was evidently caused by brown bear important for caribou calving sites to increase early avoidance to large roads (Sivertsen et al.2016), since detection probability (Bergerud et al. 1984b, Gustine et there was no effect of large roads when relating kill al. 2006, Carr et al. 2010, Leclerc et al. 2012, Pinard et sites to co-occurrence probability. When predators al. 2012), and avoid grizzly bear encounters (Gustine et avoid human infrastructure, prey may respond with al. 2006). In Udtja, there was no effect of elevation on increased tolerance for disturbance, using human kill site distribution relative to reindeer habitat disturbance as a shield towards predation risk (Berger selection. Generally, the home ranges of GPS-collared 2007, Lesmerises et al. 2017). Such behavior may be brown bears completely overlapped with the reindeer particular pronounced for females with offspring calving grounds, although individual brown bears used (Berger 2007, Lesmerises et al. 2017). Reindeer different parts of the calving grounds. In Udtja, response to large roads in our study area was however, the majority of the GPS-collared bears were significant, but with a weak effect, and showed no confined to the northern part of the calving ground, adjustments to temporal variation in predation risk likely skewing kill site distribution northward and to (Sivertsen et al. 2016), thus it is unclear whether higher elevations, although the reindeer movements to reindeer actively adjusted their behavior to seek refuge these areas were somewhat delayed in 2010 and 2012 closer to large roads. Nevertheless, these results due to a late spring. Thus, this may have masked a suggest that reindeer closer to large roads benefitted possible negative effect of elevation on kill site from lower predation risk. distribution relative to reindeer habitat selection. The In both study areas, reindeer calves were at higher environmental gradient that reindeer follow during risk of predation in coniferous and young forest, and in spring, which is most pronounced in Udtja, require open habitats in Udtja, relative to wetlands, clear cuts precaution when interpreting the results, and may mask and deciduous forest habitats. This corresponds well finer scale effects of habitat on kill site distribution. with the brown bear preference for forest documented We chose to pool the data over years within each in our study area and other areas (Steyaert et al. 2011, herding district to achieve more robust estimates from Sivertsen et al. 2016). In systems with woodland a larger sample size, but at the risk of losing some caribou it has been documented that wetlands can information. represent lower bear encounter risk and safer habitats The majority of both kill and control sites where we during calving (James and Stuart-Smith 2000, Latham did the fine-scale recordings were located with no et al. 2011). Although coniferous lichen forest was visible edge, inside the forest (Appendix: Fig. S2). preferred by reindeer over wetland habitat during the However, compared to control sites, kills occurred predation period in our study area, the preference for more frequently close to habitat edges (0-10 m), and, wetlands increased throughout the season (Sivertsen et although not significant, tended to occur less al. 2016). Spring was late in 2010 and 2012, and snow frequently at distances of 11-50 m from a visible edge. conditions likely delayed the use of wetlands. The higher frequency of kills relative to control sites at However, there were no signs of reduced brown bear close habitat edges might be because reindeer females kill rates in 2011, when spring was earlier and reindeer and calves feed more often in such habitats. Forest– reached higher elevations and areas with wetland wetland edges can provide valuable foraging habitats sooner. This supports the impression that brown bears for reindeer as spring progresses, with early snow melt in our study area adjusted their behavior in order to and emergence of nutrient-rich vascular plants hunt reindeer calves (Sivertsen et al. 2016). (Warenberg 1982). However, such habitats may also In Gällivare, kill sites were located at lower offer lower probability of escape for the reindeer elevations than expected from reindeer habitat calves, with less time to detect a brown bear coming selection, but corresponded, although weakly, with out of the forest. Thus, to feed at narrow forest wetland reindeer-brown bear co-occurrence. Thus, areas at edges can represent a possible trade-off situation for higher elevations may be associated with lower brown reindeer, representing both high forage quality and bear encounter risk, that the reindeer females are able high risk. On the other hand, reindeer moving at

8 intermediate distances (11-50 m) from an edge in open lose some information when averaging across years habitat will probably have a higher chance of escaping and environmental gradients. Thus, using longer time or take a prone position. A reindeer approached by a series and integrate climatic variation between years, bear in open habitat will probably also move towards a combined with methods that incorporate fine-scale more closed habitat (thus an edge) if possible. It might temporal patterns of movement behavior, can add also be that the edge acts as barrier that slows down the important information to our understanding of these movement of the prey (the small calf in particular), systems. Also, we did not measure forage quality and making it easier for the predator to catch it, thus quantity in this study, but antipredator behavior may be increasing the probability of finding a kill site within costly for the prey, if coming at a cost of reduced 10 m from the edge compared to a bit further away forage acquisition (Festa-Bianchet 1988, Creel and (11-50 m). Christianson 2008). Many populations of Rangifer, Detection probability can be important for kill risk, both wild and semi-domesticated, are experiencing which is supported by the lower sightability at kill sites increased predation pressure, with increasing numbers compared to control sites that we observed here. of large carnivores. Predator presence may cause both Studies of both moose and forest-dwelling caribou direct losses, and have indirect costs related to have suggested that high visibility, combined with antipredator behavior (Lima and Dill 1990). At the ground cover, are common characteristics of calving same time, human activity and infrastructure sites, probably as a mean to reduce predation risk development is constantly increasing in many of the (Bowyer et al. 1999, Gustine et al. 2006). We did not northern boreal ecosystems. Thus, knowledge of reveal any differences in ground cover (using the cover interactions between Rangifer, their predators and cylinder measurements) between kill sites and control landscape heterogeneity is crucial to be able to predict sites. However, we suggest that an inclusion of calving the costs of predator presence, and possible effects of site measurements would contribute to better land use changes and forestry on predator – prey understanding whether ground cover matter. We also interactions. found less snow cover on kill sites compared to control sites. Deeper snow could hinder calf movement and escape capacity, but such effects are probably less Acknowledgements important for neonates, as early detection and hiding We want to give a sincere thanks to Udtja and probably plays a larger role in escaping predation. Gällivare reindeer herding district for contribution to Therefore, we rather believe that our findings here may this study. Also, a special thanks to Solène Tremblay- reflect the general habitat choice of reindeer, selecting Gendron, and all others that participated in the field, habitats where terrain and light conditions promote for a great job. early snow melt and benefits foraging and locomotion, whereas brown bears, at this spatial scale, likely not perceive areas with deeper snow as a hinder for References locomotion. Both sightability and snow measures Adams, L., F. Singer, and B. Dale. 1995. Caribou calf however, may be sensitive to movements of the carcass mortality in Denali national park, Alaska. The Journal of by the bear, even over short distances, and it is Wildlife Management 59:584–594. believable that the bear may drag the calf. Atwood, T., E. Gese, and K. Kunkel. 2009. Spatial The brown bear home ranges overlapped partitioning of predation risk in a multiple completely with the calving grounds in our study area, predator‐multiple prey system. The Journal of Wildlife which is likely an important reason for the high bear Management 73:876–884. Barten, N. L., R. T. Bowyer, and K. J. Jenkins. 2001. Habitat predation rates reported in these districts (Støen et al. use by female caribou: tradeoffs associated with 2017). Also, the relative probabilities of brown bear parturition. The Journal of Wildlife Management 65:77– selection estimated from the resource selection 92. functions in this study, suggest that the habitat patches Bates, D., M. Maechler, B. Bolker, and S. 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11 Tables

Table 1. Log odds ratio and 95 % CI from binomial logistic regression models estimating the effects effect of landscape variables on the spatial distribution of kill sites (n = 178) for the random model, the model with relative to probability of reindeer habitat selection, the model with probability of co-occurrence calculated from RSF-values representing the whole predation period, and the model with probability of co-occurrence calculated from RSF-values representing high predation hours within the predation period in Udtja reindeer herding district

Random Reindeer occurrence Predation period Co- High predation hours occurrence Co-occurrence

Variable β 95%CI β 95%CI β 95%CI β 95%CI

Land cover†

Con Lich 1.50 0.97,2.03 0.63 0.02,1.24 0.57 0.03, 1.10 0.53 -0.01,1.06

Con Moss 0.90 0.40,1.39 1.16 0.59,1.72 -0.20 -0.70,0.30 0.05 -0.46,0.55

Dec for -0.41 -1.90,1.09 -0.44 -2.36,1.48 -1.50 -2.98,-0.01 -1.15 -2.64,0.34

Young for 1.47 0.40,2.53 1.31 0.21,2.40 0.54 -0.55,1.62 0.86 -0.23,1.95

Clear 0.84 -0.16,1.83 0.45 -0.56,1.45 0.14 -0.87,1.15 -0.19 -1.19,0.80

Open 1.45 0.73,2.17 1.04 0.26,1.83 0.26 -0.46,0.98 0.24 -0.48,0.95

DEM 0.33 0.11,0.55 0.10 -0.13,0.33 0.09 -0.10,0.29 0.02 -0.18,0.22

SmRoad 0.38 -0.34,1.10 0.44 -0.37,1.24 0.29 -0.46,1.04 0.90 0.19,1.61

VRM -0.01 -0.20,0.19 0.09 -0.07,0.25 0.12 -0.01,0.25 0.13 -0.01,0.26

Table 2. Log odds ratio and 95 % CI from binomial logistic regression models estimating the effects effect of landscape variable on the spatial distribution of kill sites (n = 127) for the random model, the model with relative to probability of reindeer habitat selection, the model with probability of co-occurrence calculated from RSF-values representing the whole predation period, and the model with probability of co-occurrence calculated from RSF-values representing high predation hours within the predation period in Gällivare reindeer herding district

Random Reindeer occurrence Predation period Co- High predation hours occurrence Co-occurrence

Variable β 95%CI β 95%CI β 95%CI β 95%CI

Land cover†

Con Lich 1.70 1.06,2.34 1.11 0.47,1.74 0.30 -0.37,0.98 0.25 -0.40,0.91

Con Moss 1.25 0.77,1.74 0.89 0.40,1.38 -0.65 -1.16,-0.15 -0.33 -0.83,0.17

Young for 0.87 0.02,1.72 1.19 0.31,2.07 -0.74 -1.61, 0.13 -0.34 -1.21,0.53

Clear 0.68 -0.44,1.80 -0.74 -1.84,0.36 -1.42 -2.55,-0.29 -1.56 -2.69,-0.43

OpenDec 0.08 -1.98,2.14 -0.19 -2.24,1.87 -1.77 -3.81,0.28 -1.40 -3.44,0.65

DEM 0.27 0.08,0.46 -0.21 -0.40,-0.01 -0.18 -0.38,0.02 -0.18 -0.38,0.01

SmRoad 1.01 0.24,1.77 0.07 -0.70,0.84 -0.26 -1.03,0.51 -0.25 -1.02,0.52

LaRoad 1.90 0.15,3.65 2.12 0.36,3.88 1.07 -0.68,2.82 0.21 -1.49,1.91

VRM -0.79 -1.26,-0.33 -0.20 -0.45,0.05 -0.01 -0.22,0.20 -0.09 -0.33,0.14

12 Table 3. Ranking of candidate models (using AICc) comparing fine-scale habitat attributes between kill sites and control sites. Model set 1 includes distance to edge and land cover as covariates (n = 268; kill = 142,control = 126), Model set 2 includes sightability indexes and land cover as covariates (n = 221; kill =142, control = 83), and Model set 3 includes snow index and land cover as covariates (n = 179; kill = 116, control = 63)

Model AICc Δ

Model set 1 DistEdge + LandCov 358.5 0.00

DistEdge 359.0 0.50 Null model 368.3 9.78 LandCov 368.8 10.32

Model set 2 RF_sight 292.5 0.00 Null model 294.9 2.40 RF_sight + LandCov 295.2 3.02 Syl_sight 296.5 3.69 Syl_sight + LandCov 297.1 4.59

Model set 3 SnowIndex 228.4 0.00 SnowIndex + LandCov 229.8 1.37 Null model 232.3 3.91

Table 4. Model estimates (log odds) representing the difference in distance to edge (n=268; kill=142, control=126), sightability (n=221; kill=142, control=83), and snow index (n=179; kill=116, control=63) between reindeer calf kill sites and bear control sites, based on data from field registrations in Udtja and Gällivare reindeer herding districts in 2012. Estimates are presented as log odds ratio with 95% CI Model β 95%CI Distance to edge Intercept 1.50 0.41, 2.60 DistEdge† 11-50 m -2.09 -3.24, -0.93 >50 m -1.57 -2.94, -0.19 No Edge 1.44 -2.55, -0.33

Sightability Intercept 0.901 0.327, 1.475 RF_sight -0.016 -0.032, -0.001

Snow Intercept 0.76 0.26, 1.26 Snow Index -0.05 -0.09, -0.01 † Reference category: 0-10 m

Figures

Figure 1. Map of study area representing the reindeer calving ranges in Udtja (right) and Gällivare (left) reindeer herding communities where the brown bear GPS collar proximity function was activated to detect minute cluster. The full line show the area wherein the proximity function was activated all years of the study, and the dotted line show the study area defined by the area with proximity activated, kill sites, and reindeer and bear GPS locations. ©Lantmäteriet, i2014/764.

Figure 2. Maps showing relative probability of reindeer habitat selection (left), brown bear habitat selection (middle) and reindeer – brown bear (right) co-occurrence during the period brown bears killed reindeer calves in Udtja (upper row) and Gällivare (lower row) forest reindeer herding districts.

15 (! (! (! (!(! (! (! (! (! (! (! Ü (!(!(!(! (! (!(! (! (! (! (! (! (! (! Kill sites (! (! (! (! (!(! (! (! High : 1 (!(! (! (! (! (! (! (! (! (! (! (! (! Low : 0 (! (! (! (!(!(! (! (! (! (! (!(! (! (! (! (! (! (! (! (! (! (! (! (! (! (! ! (! (!(!(! (! (! (! (!(!(! (! (! ( (! (! (!(! (! (!(! (!(! (! (! (!(!(!(! ! (! (!(! ! (! (! (! (! (!(!(!(!(! ((!(! (!( (!(! (! (! (! (!(! (! (! (! (! (! (!(!(!(!(!(!(!(! (!(! (! (!(! (! (!(! (! (! (! (! (!(! (!(! (! (! (!(! (!(! (! (!(! (! (!!( (! (! (! (! (!(! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (!(!(! ! (! (! (! (! (! (! ( ! (! (! ((! (! (! !( (! (! (! ! (! (! (! (!(!( (! (! (! ! (!(! (!(! ( (!(! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (! (!(! (! ! (! (!! (!(! (! (! (!(! ( ( (! (!(! ! (! (!(! (!( ! (! (! (!( (!(!(! (! (!(!(! (! (! (! (!(! (! (! (!(!(! (!

010205Km

Figure 3. Reindeer calf kill sites and relative probability of kill site occurrence, estimated from binomial logistic regression, comparing spatial attributes of kill sites to complete random locations within the study areas.

Figure 4. Odds ratio estimates for land cover categories from generalized linear model of the spatial distribution of reindeer calf kill sites relative to reindeer – brown bear co-occurrence probability representing high predation hours in a) Udtja and b) Gällivare reindeer herding districts.

Figure 5. Showing a) data distribution between distance to edge-categories (0-10 m; 11-50 m; >50 m; No edge) for kill sites and control sites and b) predicted probability of kill compared to control sites given distance to edge category estimated from binomial generalized linear regression

Figure 6. Data distribution for kill sites and control sites for a) the snow index and b) the RF sight index, and predicted probability of kill compared to control locations estimated from binomial generalized linear regression for a) the snow index and b) the RF sight index

Appendix

Figure S1. Cumulative curve of snow index for all field registrations on kill sites and control sites Udtja and Gällivare reindeer herding districts in 2012. Red dotted line indicate the breaking point at 99% of maximum cumulative value

Figure S2. Proportion of the land cover class (Fo = Forest; SO = Semi-open; O = Open) wherein kill sites and control sites were located, within the different distance to edge categories (0-10 m; 11-50 m; >50 m; No edge)

ǿ9

Reindeer green wave surfing constrained by predators

Inger Maren Rivrud1*, Therese Ramberg Sivertsen2*, Atle Mysterud1, Birgitta Åhman2, Ole-Gunnar Støen3,4 & Anna Skarin2

1 Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, P.O. Box 1066 Blindern, NO-0316 Oslo, Norway 2 Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, P.O. Box 7024, 750 07 Uppsala, Sweden 3 Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway 4 Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden

* These authors contributed equally to this work

Abstract Migratory large herbivores in seasonal environments are known to follow the onset of new growth during spring, so called green wave surfing. This ensures prolonged access to forage with an optimal balance between forage quality and quantity. Many studies have focused on herbivores’ ability to follow the spring flush, but without considering potential constraints to surfing the green wave. The presence of predators is likely to be such a limitation, which could force herbivores to deviate from the optimal movement patterns in terms of forage access. We compare how well 319 reindeer (Ran- gifer tarandus tarandus) from seven different populations follow the green-up during calving and throughout the summer, and relate this to the different densities of brown bear (Ursus arctos). We found that at higher bear densities reindeer selected movement paths with lower access to high quality forage throughout the growth season. In addition, reindeer generally moved faster at higher bear densities within both the forest and mountain habitats. Our results indicate that reindeer are forced to deviate from following the spring flush and alter their movement pattern in order to reduce risk of predation in areas with high bear densities. Increased energy expenditure due to faster movement speed, and decreased access to high quality forage may lead to lower body condition for reindeer experiencing high predation risk. With the recent recovery of large carnivores in northern ecosystems, it is critical to understand the direct and indirect effects of predators on large herbivores in order to assess potential effects on population dynamics, and possible consequences on ecosystem function.

Keywords: Movement patterns, forage maturation hypothesis, predation, semi-domesticated reindeer, trade-offs

Introduction 1988, Hebblewhite et al. 2008). Likewise, animals should respond to spatiotemporal variation in plant Understanding animal foraging is important as it de- phenology between habitat patches within their sea- termines how populations are limited and distributed. sonal ranges (van Moorter et al. 2013). The marginal value theorem (Charnov 1976) predicts The term “green wave surfing” is now established that the optimal patch residence time depends on the to emphasize how animals are expected to follow balance of depletion and renewal of resources across resource waves with an optimal balance of forage habitat patches. Similar simple principles are underly- quantity and quality (Merkle et al. 2016). How such ing most of habitat selection theory and methods such waves are utilized by herbivores is less well studied as Resource Selection Functions (Manly et al. 2002), (Armstrong et al. 2016), as it was previously logistical- and allow ranking of habitats depending on their prof- ly infeasible to measure both the resources and the itability. However, the habitat profitability may change animals at the scales that large herbivores operate on. rapidly when seasonality leads to resource waves of The recent emergence of satellite-derived measures of abundant food that changes across spatial scales (Arm- plant green-up (the Normalized Difference Vegetation strong et al. 2016). For migratory herbivores, forage Index, NDVI) and animal tracking devices (Global maturation is known to be an important driver of Positioning Systems, GPS) have opened a new era, movement and habitat selection, predicting that they allowing quantification of the temporal and spatial benefit from following the onset of fresh new growth distribution in forage quality across vast scales. Bis- along environmental gradients (Fryxell and Sinclair

1 chof et al. (2012) used NDVI time-series to calculate prey (Fischhoff et al. 2007), leading to higher energy the instantaneous rate of green-up (IRG) relative to expenditure (Parker et al. 1984) and less time for for- speed of migratory deer using GPS, which was a major aging (Colman et al. 2003). step forward (Fryxell and Avgar 2012). The develop- Here, we hypothesize that different predation re- ment of this methodology have enabled detailed track- gimes, i.e. different bear densities, affect reindeer’s ing of how well large herbivores follow the green wave ability to maximize energy gain during spring migra- during the growth season while moving from their tion. We use the cumulative IRG (CIRG), which meas- winter to their summer ranges (Bischof et al. 2012, ure the total amount of the green-up that each individ- Merkle et al. 2016, Rivrud et al. 2016, Mysterud et al. ual experiences over a given period (Bischof et al. 2017). 2012), as an index of access to high quality forage. We

So far, studies of the green wave surfing have fo- predict (P1) that reindeer have lower access to high cused on how well migratory large herbivores track the quality forage when bear density is high, as they must peak of onset of new growth, without considering invest more in predator avoidance. Furthermore, we potential limitations caused by external disturbances. predict that reindeer move (P2) faster and (P3) at more Animal foraging behavior is often the result of trade- variable speeds at higher bear densities, due to flight offs between different demands, frequently leading to responses from bear encounters, or as an antipredator habitat selection processes varying across spatiotem- strategy to get less predictable in space (Lima and Dill poral scales (Senft et al. 1987). With the recolonization 1990, Fischhoff et al. 2007). Finally, as reindeer calves of large carnivores in North-America and Europe almost exclusively are killed by brown bear during (Chapron et al. 2014), it is important to understand the three weeks around peak calving (Støen et al. 2017), herbivore trade-off between safety and access to high we predict (P4) more pronounced responses to bear quality forage in northern boreal ecosystems (Atwood density during the calving season, compared to the et al. 2009). Several herbivore movement studies have post-calving season. investigated predation risk - forage accessibility tradeoffs. For example, in Isle Royale, predation risk led female moose (Alces alces) with offspring to postpone Materials and methods the decision to migrate (Edwards 1983), parturient bighorn sheep (Ovis canadensis) ewes sacrificed for- Study area age quality for increased safety from predators when Sámi reindeer husbandry in Fennoscandia is a pastoral migrating to higher elevations (Festa-Bianchet 1988), system distributed over the northern part of the Scan- and migratory caribou (Rangifer tarandus) selected dinavian Peninsula and the Kola Peninsula; in Sweden habitats with abundant forage and reduced black bear alone it covers 55 % of the Swedish land base (Sand- (Ursus americanus) predation risk during calving ström 2015). In Sweden, it is practiced both within (Bastille-Rousseau et al. 2015). However, to our forest reindeer herding districts, migrating between knowledge, there has been no study of how green wave separated summer and winter ranges in the forest, and surfing varies across a gradient of predation risk. within mountain reindeer herding districts, migrating We take advantage of a unique dataset of 319 GPS- between summer ranges in the mountains and winter marked reindeer (Rangifer tarandus) from seven herds ranges in the forest. In this study, locational data from across a population density gradient of brown bear reindeer was gathered from four forest herding districts (Ursus arctos) in Sweden. Recent work suggests that (Gällivare, Malå, Udtja and Östra Kikkejaure) and up to 30 % of reindeer calves can be predated by three mountain herding districts (Handölsdalen, brown bears during calving (Støen et al. 2017). The Njaarke and Sirges), within the reindeer husbandry main predation period is concentrated to the first weeks range in Sweden (Fig. 1). The forest district calving following parturition (Støen et al. 2017), coinciding and post-calving ranges are characterized by undulat- with the onset of spring green-up. Calving is an ener- ing boreal forests interspersed with mires and lakes. getically demanding period for reindeer females, with Active forestry occurs in all forest districts apart from high costs of gestation and lactation (McEwan and the calving and post-calving ranges of Udtja, which is Whitehead 1972, Crête et al. 1993). Furthermore, a nature reserve since 1995. The mountain district reindeer females must utilize the short growth season calving ranges are all situated in the mountain region in spring and summer to restore body reserves after and mainly above the tree line. Reindeer density within depletion during winter (Parker et al. 2009). It is still the summer ranges (all-year land) during the years unclear how reindeer females respond to brown bear studied ranged from 1.3-3.4 reindeer/km2, with the predation risk at different spatial scales, and whether highest densities in the mountain districts (Table 1). behavior to reduce risk comes at a cost of optimal Every year in April, the reindeer herders initiate rein- foraging during this critical period. Predation may also deer spring migration to the calving ranges, which cause higher and more variable movement speed in takes place “by foot” or with trucks if migrations

2 routes are unavailable. Except for the gatherings for al. 2006, Hird and McDermid 2009, Rivrud et al. calf marking in the summer, the reindeer roam freely 2016). From this, the instantaneous rate of green-up within the borders of the calving to autumn ranges (IRG) can be extracted by taking the first derivative of until the snow arrives and autumn migration is initiat- the part of the logistic curve that covers spring (Bis- ed. chof et al. 2012). When in early growth stages, grasses have the highest nutritional value (Van Soest 1994), Reindeer data and are also more easily digestible (Langvatn and Hanley 1993). Thus, the IRG gives an index of when Our focus period was from 11 May-31 August, and forage is of highest quality. For each individual, we represents the growth season. Although it is likely that calculated the mean daily IRG experienced based on spring growth starts in April, these dates were chosen the corresponding GPS locations. From this, we calcu- based on the earliest collaring dates in some of the lated the cumulative IRG (CIRG) for each individual in herding districts. Only individuals with 95 % coverage each of the two sub-periods as a measure of the total of the focus period, and with a maximum gap in the amount of high quality forage experienced. Further logging sequence of 4 days, were retained for further details on IRG estimation can be found in Bischof et analyses. The focus period was further split into two al. (2012). sub-periods, based on the risk of calf predation by bears. The first sub-period (11 May-9 June; hereafter termed calving) represents the reindeer calving season Home range estimation and landscape features where calves are under considerably predation risk As landscape features are known to influence plant from bears (Støen et al. 2017). During the second sub- growth (Mysterud et al. 2001, 2017) and therefore also period (hereafter termed post-calving), representing the forage access, we extracted a range of landscape fea- post-calving and summer season (10 June-31 August), tures on the home range scale to control for this. Home few calves are killed by bears (Støen et al. 2017). ranges corresponding to the two sub-periods (11 May- Thus, we expect different reindeer movement respons- 9 June and 10 June-31 August) were estimated for each es relative to bear density during calving and post- individual by calculating 95 % adaptive Local Convex calving. We calculated the movement speed (in km/h) Hull (a-LoCoH) polygons using the “adehabitatHR” between two successive reindeer GPS locations as the package in R (Calenge 2006, R Core Team 2016). Euclidean distance divided by the time between the LoCoHs allow for linear movement patterns, which are locations. We discarded all locations where the indi- common when covering animals in constant motion, viduals had moved at unlikely distances or speeds (>40 such as reindeer. When the a-LoCoHs were calculated, km/h or more than 10 km between fixes; 0.11 % of the the parameter a was chosen according to Getz et al. data). The mean daily movement speed per individual (2007), stating that an a-value exceeding the two max- was calculated from all individual speed entries for imum distances between individual locations should each Julian day. As a measure of movement speed always give the 100% density isopleth. In the case of variation, we calculated the standard deviation of calculation failure, we increased the a-value using the movement speed for each individual in each of the two sum of the three longest distances, up until the sum of sub-periods within the focus period. the five longest distances to ensure success. The mean number of locations used per individual home range Plant phenology was 400 (range 28-1439) during calving and 960 (range 83-2983) during post-calving. Plant phenology was quantified using the satellite- Maps with terrain ruggedness, slope and aspect derived normalized difference vegetation index were derived from a digital elevation model (DEM) (NDVI; Pettorelli et al. 2005). The NDVI have been covering Sweden provided by Lantmäteriet shown to function well as a proxy for ungulate forage (www.lantmateriet.se). Terrain ruggedness maps were quality and quantity (Hebblewhite et al. 2008, Hamel made in R (“raster” package; Hijmans and van Etten et al. 2009, Garroutte et al. 2016). MODIS TERRA 2015), while maps on slope and aspect were made MOD13Q1 satellite images covering the study area using ArcMap 10.3.1 (ESRI, USA). Aspect was con- were downloaded from the NASA Land Processes verted to "northness" (cosine transformed) ranging Distributed Active Archive Center (LP DAAC 2000, from -1 to 1, where values close to -1 face south and website close to 1 face north. Elevation was extracted directly (https://lpdaac.usgs.gov/data_access/daac2disk). The from the DEM. Maps of minimum distances from images were taken every 16 days, with a resolution of power lines, railways and large (>5 m wide) and small 250 m. Details on processing steps including removal roads (<5 m) were made in ArcMap 10.3.1. All maps of unrealistic values and noise reduction can be found were rasterized with a resolution of 100 m. The indi- elsewhere (Hird and McDermid 2009, Bischof et al. vidual a-LoCoH home range polygons for the two sub- 2012, Rivrud et al. 2016). A double logistic curve was periods were overlaid the different maps, and the cor- fitted to each pixel’s annual NDVI time series (Beck et

3 responding means of each landscape feature were with year, individual reindeer id or both as random extracted using R. intercepts were compared. A total of 818 observations from 319 individuals Bear density index were used in the analyses of CIRG and SD of movement speed. The same individuals were available for The bear density was estimated from the latest scat the analysis of mean daily movement speed, and N = survey (non-invasive DNA) conducted in each County obs 45872. (Fig. 1, Table 1), with data from the Scandinavian database of large carnivore surveys (www.rovbase.no, Bellemain et al. 2005, Kindberg et al. 2011). We used Results all bear scats where the individual bear had been identified and calculated density of scats with the density Access to high quality forage tool in ArcGIS (ESRI 2015.ArcGIS Desktop: Release 10.1 Redlands, CA: Environmental Systems Research The final model explaining access to high quality Institute), giving a raster map with a 1000 m resolu- forage (CIRG) included elevation (log-transformed), tion. We calculated the mean of the bear density index northness (northern aspect), bear density index (log- within each adult female reindeer individual 95 % a- transformed), reindeer herding district habitat, sub- LoCoH home range within each sub-period. period, and the interaction between bear density and sub-period (Table 2a). Year and individual female id were fitted as random intercepts. The proportion of Statistical analyses variance explained by the fixed effects (marginal R2) We ran model sets with three different response varia- was 0.23, and the total variance explained by both bles to answer questions about movement in relation to fixed and random effects (conditional R2) was 0.47. bear density; access to high quality forage (CIRG), As predicted (P1), increased bear density led rein- mean daily movement speed and variation in move- deer to select movement paths with lower access to ment speed (SD of movement speed). For CIRG and high quality forage, but contrary to our predictions (P4) SD of movement speed, we ran linear mixed-effects there was no significantly different effect of bear den- models using “lme4” (Bates et al. 2015) in R. General- sity between sub-periods (Table 2a, Fig. 2). The CIRG ized additive models (GAM) were run using “mgcv” decreased by 7.1 points from the lowest to the highest (Wood 2011) in R when modelling mean daily move- bear density during calving, while CIRG decreased ment speed. with 12.2 points from lowest to highest bear density Candidate predictors for CIRG and SD of move- during post-calving. Overall, the access to high quality ment speed models were bear density index, sub-period forage was higher during calving (Table 2a; Fig. 2). (categorical; 1 or 2, representing calving and post- The model showed no effect of elevation, northness or calving respectively), elevation (m a.s.l.), terrain rug- reindeer herding district habitat on CIRG (Table 2a). gedness index, slope (degrees), northness, reindeer herding district habitat type (mountain or forest), and Mean daily movement speed the minimum distances to power lines, railways and large and small roads (all in m), as well as the interac- The final model explaining mean daily reindeer tion between sub-period and bear density index. The movement speed (log-transformed) included bear same predictors were used as candidates for the model density index (log-transformed), reindeer herding of mean daily movement speed, except sub-period, but district habitat, elevation (log-transformed), northness with the addition of the non-linear Julian day (smooth and GPS collar schedule, as well as the non-linear term) and the interaction between the non-linear Julian Julian day main effect and in interaction with bear 2 day and bear density index. In addition, for the speed density index (Table 2b). The adjusted R was 0.42. models we included a fixed effect for GPS schedule, as As predicted (P2), reindeer moved faster at higher the logging schedule varied between herds. The distri- bear densities, but the effect depended on the non- bution of the variables, including the response varia- linear effect of Julian day, i.e. there were periods when bles, were investigated and transformed when needed reindeer moved faster at higher bear densities, but also to ensure that model assumptions were met. We other days with no difference in movement speed checked correlations of all pairs of candidate predictors depending on bear density (Table 2b; Fig. 3). Overall, before running models, and only one of the variables in reindeer moved 5.3-20.9 % faster at the highest bear each pair was retained when Pearson’s r > |0.4|. After density compared to the lowest during calving, and correlations were determined, we checked the remain- 0.8-41.5 % faster during post-calving. Further, reindeer ing candidate variables for nonlinearities using GAM in forest herding districts moved faster than reindeer in plots. The need for random factors was determined mountain herding districts, regardless of bear density using the Akaike Information Criterion (AIC). Models (Fig. 3). For landscape features, both increased eleva-

4 tion and northness led to faster daily movement speed shifted to less valuable habitats for foraging, when (Table 2b). predation risk from brown bears increased. Rangifer is considered to be a capital breeder (Taillon et al. 2013), Movement speed variation implying a strategy where the females rely on body reserves for gestation and early lactation (Stephens et The final model explaining variation in reindeer al. 2009, Albon et al. 2017). It is hence possible that movement speed (SD of speed; log-transformed) in- reindeer are adapted to cope with low forage quality cluded bear density index (log-transformed), reindeer during the fairly short calving period, and therefore herding district habitat, elevation (log-transformed), less prone to adjust to the green wave during this peri- northness, sub-period and the interaction between bear od. density and sub-period, as well as GPS collar schedule We predicted a stronger effect of bear density on (Table 2c). Both year and individual id were fitted as 2 access to high quality forage and movement speed, random intercepts. The marginal R was 0.68 and con- 2 during the calving season, as the actual predation risk ditional R = 0.72. peaks during this period (Støen et al. 2017). During The effect of bear density on movement speed vari- post-calving, the calf is usually large enough to escape ation depended on sub-period (P ; Table 2c). However, 4 predation from brown bears (Adams et al. 1995), and contrary to prediction (P ), there was no significant 3 brown bears likely concentrate more on other food effect of bear density on movement speed variation items. We therefore expected reindeer to change forag- during calving (Table 2c). During post-calving, the ing strategy in relation to predation risk among sea- relationship with bear density was negative but mar- sons, to not undergo unnecessary costs to avoid preda- ginally non-significant (β = -0.078, t = 1.883, P = 804 tors (Pierce et al. 2004), as shown for several deer 0.060). Overall, the reindeer moved with more varia- species under temporal variation in predation risk tion during the post-calving period (Table 2c). In- (Lone et al. 2017). Several factors may explain why creased elevation was positively associated with higher the negative effect of brown bears on forage access, variation in movement speed, while there was no asso- and the higher movement speed, persisted in the post- ciation with northness (Table 2c). Finally, reindeer in calving period in our study. It could indicate an innate forest herding districts showed a 47.1% higher varia- and strong general risk avoidance behavior, as suggest- tion in movement speed than reindeer in mountain ed for other ungulates (Byers 1997). Indeed, even in districts (Table 2c). populations devoid of predators, females show characteristic risk-reducing behaviors around calving and Discussion high fidelity to calving sites (Tremblay et al. 2007). On the contrary, Latombe et al. (2013) documented ad- Several studies have documented that herbivores fol- justments in habitat selection by caribou in response to low the green wave during migration, increasing their temporal variations in predator presence, and Barten et access to high quality forage (Bischof et al. 2012, al. (2001) reported that caribou females moved down Merkle et al. 2016, Mysterud et al. 2017). Here we from higher elevations to areas with higher predator show how predation risk may limit the herbivores’ densities, when the calf had developed sufficient loco- ability to follow this spring flush of nutritious forage, motive skills (around 10 days old). Thus, an alterna- causing a trade-off between access to forage and avoid- tive explanation may be that bear density have been ing predation. We found that semi-domesticated rein- confounded with other environmental factors during deer followed movement paths with lower access to post-calving. Alpine habitats may experience less high quality forage when bear density was high (sup- insect harassment and have more areas for relief than porting P ), but similarly during calving and post- 1 forest habitats (Helle and Aspi 1984). The most alpine calving (not supporting P ). In addition, reindeer gen- 4 habitats, Sirges and Handölsdalen, also had the lowest erally moved faster at higher bear densities (supporting bear densities reported in our study. Insect harassment, P ) throughout the growth season both in mountain and 3 may cause or enhance a mismatch with green-up (Mör- forest habitat. We found no support for P , as bear 2 schel and Klein 1997, Hagemoen and Reimers 2002, density did not affect variation in movement speed. Skarin et al. 2010). Moreover, Bergerud and Luttich Earlier studies have shown that ungulates may shift (2003) (and many earlier works) argue that predation to less profitable habitats to avoid predation risk (Lima risk is the most important driver of female behavior and Dill 1990, Godvik et al. 2009). Predation risk may during calving, and that insect avoidance is more im- also trigger migration, i.e. selection of calving area far portant during summer. Thus, different exposure to away from predators (Bergerud et al. 1984, Bergerud insect harassment may have contributed to the ob- and Page 1987, Hebblewhite and Merrill 2009), or served trends of access to forage and movement speed postpone migration to assumed better quality foraging in the post –predation period. The simultaneous drop in areas due to higher predation risk (Edwards 1983). movement speed across all populations towards the Likewise, our results suggest that reindeer females

5 middle of the calving period, indicate the calving implying long movements between insect free areas events (Panzacchi et al. 2013). Reindeer as many other and good grazing grounds (Skarin et al. 2010). ungulates are also known to synchronize their calving, To be able to evaluate the effect of predators on also leading to reduced predation risk (Rutberg 1987, prey population dynamics it is important to understand Kerby and Post 2013). both direct and indirect costs of predator`s presence Habitat structure and forest cover is important for (Lima 1998, Lima and Bednekoff 1999). Our study understanding prey responses to predation risk (Murray indicates that increased predation risk is a factor limit- et al. 1995, Mysterud and Østbye 1999). Our setting is ing herbivores’ ability to follow the green wave, which unique in spanning over both mountain and forest may lead to decreased body condition. This adds to a reindeer populations, however with the mountain growing literature of indirect costs of predation populations typically wintering in forested areas. In our (Schmitz 1998, Creel and Christianson 2008), with study system, brown bears preferably reside in the potential repercussion for ecosystem function (Ripple forest (Haglund 1964, May et al. 2008), but may occa- and Beschta 2004, Creel and Christianson 2009, Ripple sional do forays into open mountain habitat and kill et al. 2014). reindeer calves. We could expect reindeer with access to mountain habitats to advance ahead of green-up into calving ranges in order to reduce predation by brown Acknowledgements bears during calving. In contrast, the forest reindeer are We greatly acknowledge the support of Centre for restricted to calving grounds completely overlapping Advanced Study in Oslo, Norway that funded and with the brown bear home ranges (Støen et al. 2017, hosted our research project “Climate effects on har- Sivertsen et al. 2016). The significant faster movement vested large mammal populations” during the academ- rates and higher variation in movement speed of rein- ic year of 2015/16. Our study was made possible deer in the forest compared to those in the mountains thanks to the sincere cooperation of the Sami reindeer may reflect larger brown bear predation pressure in the herders in Sirges, Njaarke, Handölsdalen, Gällivare, forest compared to mountain calving habitats. With Udtja, Östra Kikkejaure and Malå reindeer herding only small “pockets” of safer habitats to maneuver districts. Data on reindeer numbers for the study years between (Sivertsen et al. 2017), forest reindeer could was kindly provided by the Sami Parliament of Swe- be forced to move more between forage- and cover den. habitats to hide from predators (Mysterud and Østbye 1999), and adjust their speed to flee when encountering a predator. There are also more infrastructure and References roads intersecting the calving and summer ranges of Adams, L., F. Singer, and B. Dale. 1995. Caribou calf mortal- the forest districts, which may lead to more disturbance ity in Denali national park, Alaska. The Journal of Wild- and higher movement rates among the reindeer (Skarin life Management 59:584–594. et al. 2015). In addition, insect harassment possibly Albon, S., R. Irvine, O. Halvorsen, R. Langvatn, L. Loe, E. explains the generally higher movement rates in the Ropstad, V. V, R. van der Wal, E. Bjørkvoll, E. Duff, B. post-calving period (Hagemoen and Reimers 2002, Hansen, A. Lee, T. Tveraa, and A. 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Tables

Table 1. Reindeer density within the districts all-year land during the study year, the GPS data distribution among the seven reindeer herding districts, and the mean bear density index within the home ranges of the GPS-collared reindeer in Sirges, Njaarke, and Handölsdalen mountain districts, and Gällivare, Udtja, Östra Kikkejaure, and Malå forest districts

Reindeer herding Year Size of all- Reindeer densi- Number of Number of Number of Year of Mean bear district year land ty within all- reindeer positions – positions – brown bear density index km2 year land (rein- females with Calving period Post-calving density within reindeer deer/km2) GPS period survey home range

Mountain districts

Sirges 2003 6145 2.4 18 12 813 35 227 2010 4.39

Njaarke 2009-2012 836 2.2 45 18 209 79 374 2006 30.91

Handölsdalen 2003 1884 3.4 10 7 180 19 690 2006 3.53

Forest districts

Gällivare 2011-2015 3278 1.5 50 24 323 42 930 2010 17.98

Udjta 2010-2015 1671 1.5 74 71 700 121 199 2010 24.51

Östra Kikkejaure 2010-2015 2790 1.3 56 5 699 20 815 2010 12.91

Malå 2008-2011 3031 2.1 66 23 960 74 450 2009 15.93

Total 319 163 884 393 658

9 Table 2. Tables showing the best models predicting a. access to high quality forage, b. mean daily speed and c. movement speed variation in domestic reindeer. Both a. and c. are linear mixed-effects models, and b. is a generalized additive model. Reference for reindeer herding district habitat is forest. Period represents the two sub-periods within the growth season: 11 May - 9 June and 10 June - 31 August. The first sub-period (calving) is reference. SE = standard error; df = degrees of freedom. a. Plant phenology

Variable Estimate SE t-value P-value

Intercept 23.402 6.146 3.808 0 Elevation -0.131 0.99 -0.133 0.895

Northness 0.006 0.295 0.02 0.984 Bear density index -1.306 0.482 -2.707 0.007 Habitat (mountain) -0.555 0.929 -0.597 0.551 Period (post-calving) -4.785 1.77 -2.703 0.007

Bear density x period (post-calving) -0.936 0.631 -1.483 0.138

Random effects standard deviations: year = 3.87, id = 0.00. Nobs = 818 b. Mean daily movement speed

Estimate SE t-value P-value

Intercept -6.905 0.099 -70.101 <0.001 Bear density -0.042 0.069 -0.615 0.538 Habitat (mountain) -0.558 0.016 -35.854 <0.001

Elevation 0.712 0.017 43.033 <0.001 Northness 0.019 0.005 4.147 <0.001 GPS collar schedule 1 hour 1.297 0.021 61.465 <0.001 2 hour 0.959 0.009 106.984 <0.001 6 hour 0.288 0.01 27.657 <0.001 Smooth terms Est. df Ref. df F-value P-value Julian day (smooth term) 9 9 85.045 <0.001 Julian day (smooth term) x bear density 8.87 9.39 9.985 <0.001

Nobs = 45872 c. Movement speed variation

Estimate SE t-value P-value

Intercept -5.353 0.409 -13.1 <0.001

Elevation 0.444 0.067 6.601 <0.001 Northness 0.01 0.02 0.501 0.617 Bear density index 0.01 0.035 0.294 0.769 Habitat (mountain) -0.638 0.063 -10.054 <0.001 Period (post-calving) 0.899 0.12 7.484 <0.001 GPS collar schedule 1 hour 1.609 0.176 9.157 <0.001 2 hour 1.208 0.058 20.677 <0.001 6 hour 0.475 0.047 10.146 <0.001 Bear density x period (post-calving) -0.088 0.043 -2.053 0.04

Random effects standard deviations: year = 0.14, id = 0.00. Nobs = 818

10 Figures

Figure 1. Map over the distribution and boundaries of the four forest (dark grey) and three mountain (light grey) reindeer herding districts included in the study, from where GPS-data has been gathered during the years 2003 and 2008-2015. ©Lantmäteriet i1204/764.

Figure 2. Predicted access to high quality forage (measured as cumulative instantaneous rate of green-up; CIRG) in relation to bear density and sub-period. The two sub-periods calving (11 May - 9 June) and post-calving (10 June - 31 August) have presumed different calf predation risk, with higher risk in the first period. Points are raw residuals. Nobs = 818.

Figure 3. Predicted mean daily movement speed in relation to Julian day and bear density index, based on a generalized additive model. Predictions are made for the mean bear density experienced by all individuals within each reindeer herding district. The reindeer herding district habitat is shown with solid (forested) and dashed (mountainous) lines. The vertical dashed line shows the two sub-periods calving (11 May - 9 June) and post-calving (10 June - 31 August), with presumed different calf predation risk

(higher in the first period). Nobs = 45872.