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203 ARTICLE

Black ( americanus) functional resource selection relative to intraspecific competition and risk Jared F. Duquette, Jerrold L. Belant, Clay M. Wilton, Nicholas Fowler, Brittany W. Waller, Dean E. Beyer, Jr., Nathan J. Svoboda, Stephanie L. Simek, and Jeff Beringer

Abstract: The spatial scales at which make behavioral trade-offs is assumed to relate to the scales at which factors most limiting resources and increasing mortality risk occur. We used global positioning system collar locations of 29 reproductive-age female black (Ursus americanus Pallas, 1780) in three states to assess resource selection relative to bear population-specific density, an index of vegetation productivity, riparian corridors, or two road classes of and within home ranges during spring– summer of 2009–2013. Female resource selection was best explained by functional responses to vegetation productivity across nearly all populations and spatial scales, which appeared to be influenced by variation in bear density (i.e., intraspecific competition). Behavioral trade-offs were greatest at the landscape scale, but except for vegetation productivity, were consistent for populations across spatial scales. Females across populations selected locations nearer to tertiary roads, but females in Michigan and Mississippi selected main roads and avoided riparian corridors, whereas females in Missouri did the opposite, suggesting population-level trade-offs between resource (e.g., food) acquisition and mortality risks (e.g., vehicle collisions). Our study emphasizes that female bear population-level resource selection can be influenced by multiple spatially dependent factors, and that scale-dependent functional behavior should be identified for management of bears across their range.

Key words: black bear, food, hunting, mixed models, riparian, roads, Ursus americanus. Résumé : Il est présumé que les échelles spatiales auxquelles les animaux font des compromis comportementaux sont reliées aux échelles auxquelles se manifestent les facteurs qui limitent le plus les ressources et accroissent le plus le risque de mortalité. Nous utilisons des emplacements obtenus par colliers GPS de 29 ours noirs (Ursus americanus Pallas, 1780) en âge de procréer dans trois États pour évaluer la sélection des ressources par rapport a` la densité de la population d’ours, a` un indice de la productivité de la végétation, aux corridors rivulaires ou a` deux classes de routes associés dans les domaines vitaux durant les printemps et étés de 2009 a` 2013. La sélection des ressources par les femelles s’expliquait le mieux par des réponses fonctionnelles a` la productivité de la végétation pour presque toutes les populations et échelles spatiales, qui semblaient être influencées par des variations de la densité d’ours (c.-a`-d., concurrence intraspécifique). Les compromis comportementaux étaient les plus grands a`

For personal use only. l’échelle du paysage, mais, a` l’exception de la productivité de la végétation, étaient cohérents pour les populations a` toutes les échelles. Les femelles de différentes populations choisissaient des emplacements plus proches de routes tertiaires, mais les femelles au Michigan et au Mississippi choisissaient des routes principales et évitaient les corridors rivulaires, alors que les femelles aux Missouri faisaient le contraire, indiquant des compromis au niveau de la population entre l’acquisition des ressources (p. ex. de la nourriture) et les risques de mortalité (p. ex. collisions avec des véhicules). L’étude souligne le fait que la sélection des ressources par les ourses au niveau de la population peut être influencée par différents facteurs dépendants de l’emplacement et que la gestion des ours dans l’ensemble de leur aire de répartition requiert une connaissance des comporte- ments fonctionnels dépendants de l’échelle. [Traduit par la Rédaction]

Mots-clés : ours noir, nourriture, chasse, modèles mixtes, rivulaire, routes, Ursus americanus.

Introduction can vary across landscapes (Johnson and Seip 2008). Imperfect knowledge or cues to resource availability or quality can also limit Animals improve their reproductive success by selecting the resource accessibility (Basille et al. 2013). Therefore, animals im- most beneficial resources available across a landscape (Rettie and prove their reproductive success by using behavioral trade-offs Messier 2000; Manly et al. 2002). But resources may be limited by between resource selection and mortality risk avoidance (Martin factors including habitat fragmentation (e.g., roads: Shepherd and et al. 2010; Latif et al. 2011). Whittington 2006; land-use change: Hiller and Belant 2015; Hiller The spatial scales at which animals adapt their behavior are Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17 et al. 2015), intra- and inter-specific competition (Ciarniello et al. generally assumed to relate to the scales where access to resources 2007; Watts and Holekamp 2009), or direct and indirect mortality is maximized and mortality risk is minimized (Rettie and Messier risks (Creel et al. 2008; Steyaert et al. 2013; Kiffner et al. 2014) that 2000). A factor (e.g., risk) that limits resources and in-

Received 9 February 2016. Accepted 9 December 2016. J.F. Duquette,* J.L. Belant, C.M. Wilton, N. Fowler, B.W. Waller, N.J. Svoboda, and S.L. Simek. Ecology Laboratory, Forest and Wildlife Research Center, Mississippi State University, Mississippi State, MS 39762, USA. D.E. Beyer, Jr. Michigan Department of Natural Resources, Wildlife Division, Marquette, MI 49855, USA. J. Beringer. Missouri Department of Conservation, Columbia, MO 65201, USA. Corresponding author: Jared F. Duquette (email: [email protected]). *Present address: Illinois Natural History Survey, 1816 South Oak Street, Champaign, IL 61820, USA. Copyright remains with the author(s) or their institution(s). Permission for reuse (free in most cases) can be obtained from RightsLink.

Can. J. Zool. 95: 203–212 (2017) dx.doi.org/10.1139/cjz-2016-0031 Published at www.nrcresearchpress.com/cjz on 1 February 2017. 204 Can. J. Zool. Vol. 95, 2017

creases mortality risk modifies behavior at successively bears by introducing mortality risks (e.g., vehicle collisions) and finer scales until another factor (e.g., food) becomes more limiting fragmenting resources across the landscape (Reynolds-Hogland and further modifies behavior. Intraspecific and Mitchell 2007; Waller et al. 2014). Anthropogenic mortality can be a major factor influencing the behavioral trade-offs of risk may especially influence resource selection of adult females animals because intraspecific competition increases with popula- with dependent young while meeting increased nutritional de- tion density (e.g., Ciarniello et al. 2007). Greater intraspecific com- mands and avoiding vehicles (Wilton et al. 2014a), hunters petition can decrease the availability of ideal resources and limit (Reynolds-Hogland and Mitchell 2007), or intra-specific predation animal fitness (Zedrosser et al. 2006; van Beest et al. 2014). Al- (LeCount 1987; Garrison et al. 2007). An assessment of resource though identifying factors that influence the behavioral trade-offs selection and mortality risk trade-offs of reproductive-aged female of animals across multiple spatial scales is common (e.g., Basille black bears may therefore reveal factors that limit population-level et al. 2013), generalizing those conclusions across the geographic fitness (Mitchell and Powell 2007). Although numerous aspects of range of a can introduce bias from variability in biological black bear resource selection relative to mortality risks have been and environmental factors found within each population. Assess- reported for individual populations (e.g., Belant et al. 2010; Waller ing behavioral trade-offs among population density, resource se- et al. 2014), little is known about bear trade-offs between re- lection, and mortality risks within and across populations could sources and mortality risks across populations with varying pop- provide insights into factors affecting the range-wide reproduc- ulation density and environmental characteristics. tive success of a species (McLoughlin et al. 2000; Bojarska and Our objective was to assess reproductive-aged female black bear Selva 2012). resource selection relative to variation in population density, veg- Animals maximize their fitness by adapting to environmental etation productivity, riparian corridors, and two classes of roads variability in a functional nonlinear manner (Matthiopoulos et al. of and within home ranges during spring–summer for popula- 2011), often at multiple spatial scales (Moreau et al. 2012). For tions along a latitudinal gradient of the United States. We focused example, animals living in landscapes with patchy and fluctuat- on reproductive-aged females because this demographic is likely ing food resources and mortality risks may functionally select sensitive to behavioral trade-offs between resources and mortality habitat patches based on a trade-off between perceived resources risks (Martin et al. 2010) while maintaining nutritional condi- and mortality risks (Godvik et al. 2009; Moreau et al. 2012). This tion for reproductive success (Elowe and Dodge 1989; Dahle and concept can be extended to variability in other factors, such as Swenson 2003; Ayers et al. 2013). We established four hypotheses resource competition and anthropogenic disturbance, which di- related to differences in population density, latitude, and spatial rectly or indirectly influence animal fitness. Understanding the scale. First, we hypothesized that vegetation productivity would functional behaviors of animals relative to their population den- best explain female resource selection across populations and sity (i.e., competition) can indicate the habitat quality of the land- spatial scales because reproductive-age females require increased scape (McLoughlin et al. 2010) and assist in guiding population food intake to support their nutritional condition for breeding, management. gestation, and care of dependent young (Dahle and Swenson 2003; Behavioral trade-offs are important for many large Ayers et al. 2013). Second, we hypothesized that females would during spring, when young are dependent on maternal nutrition reduce their selection for profitable resources as population (Elowe and Dodge 1989) and vulnerable to intra- or inter-specific density increased because females would avoid intraspecific com- competition and predation (Palomares and Caro 1999; Garrison petition (van Beest et al. 2014). Third, we hypothesized female et al. 2007). Large carnivores in human-dominated landscapes of- resource selection behaviors to be more influential to the estab- For personal use only. ten adjust behaviors to reduce mortality risks or disturbance as- lishment of home ranges across the landscape than selection sociated with roads, including vehicles (Brody and Pelton 1989; within home ranges because variation in resources and mortality Dixon et al. 2006) and hunters (Milner et al. 2007; Stillfried et al. risks would be more predictable at this scale (Rettie and Messier 2015). Movement corridors are important to large carnivores be- 2000; Ordiz et al. 2011). Fourth, we hypothesized females to ex- cause they facilitate navigation through fragmented landscapes hibit greater resource–risk trade-offs from Mississippi to Michi- while minimizing mortality risks (Shepherd and Whittington gan because of increasing intraspecific competition (Dixon et al. 2006). Although large carnivores typically avoid roads (e.g., Simek 2006), shorter vegetation growing season (Ferguson and McLoughlin et al. 2015), roads with low traffic volume (Dixon et al. 2006) may 2000; Bojarska and Selva 2012), and greater mortality risks from serve as movement corridors or areas with plentiful food (Mace vehicles and hunters (Reynolds-Hogland and Mitchell 2007; Stillfried et al. 1996; Roever et al. 2008; Hiller and Belant 2015, Hiller et al. et al. 2015). 2015). Additionally, riparian areas can allow large carnivores to move among patches of food and cover (Naiman and Rogers 1997) Materials and methods while often providing food within the corridor itself (Lyons et al. 2003). Investigating frequency of large carnivore use of different Study areas movement corridors help reveal the relationship between spatial We conducted the study in Michigan, Missouri, and Mississippi, variation in resources and mortality risks. USA. The study area in the south-central Upper Peninsula of Mich- Black bears (Ursus americanus Pallas, 1780) are omnivorous carni- igan (528.9 km2; 45°34=N, 87°20=W; Fig. 1) has a mean elevation of vores found throughout most forested regions of 185 m above sea level and topography is flat. Predominant vege- Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17 (Scheick and McCown 2014) and are thought to use the smallest tation included upland and lowland hardwoods, lowland conifer amount of space necessary to acquire resources for reproduction swamps, upland conifers, aspen (species of the Populus L.) (Mitchell and Powell 2007). To access resources, bears must con- stands, row-crop and agriculture, herbaceous wetlands, tend with variation in intraspecific competition related to bear and intermittent patches of berry-producing shrubs (e.g., raspber- density, with individuals in denser populations typically having ries (species of the genus Rubus L.) and blueberries (species of greater despotic behavior (Beckmann and Berger 2003). Bears may the genus Vaccinium L.)). Human development was low density reduce intraspecific competition or human-related risks (Ordiz (0.09 km/km2) residential and recreational camps and main and et al. 2011) by using habitat corridors (e.g., riparian areas; Lyons tertiary road densities were 0.03 and 1.69 km/km2, respectively et al. 2003) to move among patches of resources (Dixon et al. 2006) (United States Bureau of the Census 2011). Riparian corridor (i.e., and adjusting their fine-scale resource selection behaviors to those rivers and streams) density was 1.17 km/km2 (United States risks. Bears may also occasionally use roads with low anthropogenic Geological Survey 2014). During May–August, daily mean temper- risk (Brody and Pelton 1989; Mace et al. 1996; Reynolds-Hogland and ature ranged from 3.0 to 32.2 °C and rainfall during the study Mitchell 2007) as movement corridors, but roads typically hinder period was 12.2–14.5 cm based on a weather station that we de-

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Fig. 1. Reproductive-age female black bear (Ursus americanus) study areas (black polygons) and locations within study areas in Michigan, Missouri, and Mississippi, USA, during 2009–2013. For personal use only.

ployed in the middle of the study area. Black bear density was southern and central regions of the state (Puckett et al. 2014; 14–19 bears/100 km2 (J.L. Belant, unpublished data). Black bears in Wilton et al. 2014a). Michigan were harvested annually during September and October, The study area in Mississippi (7004.8 km2; 33°20=N, 90°93=W; but only females without dependent young were legal for harvest Fig. 1) has elevations from 0 to 114 m above sea level and topogra- (Belant et al. 2011). phy is generally flat. Land cover included agricultural (39%), water The study area in the Ozark Highlands ecological region of and wetland (35%), forested (24%), and urban areas (2%) (Simek south-central Missouri (8033.8 km2; 35°57=N–40°35=N, 89°08=W– et al. 2015). Main and tertiary road densities were 0.14 and 95°46=W; Fig. 1) consisted of mostly hilly and steep topography 0.31 km/km2, respectively (United States Bureau of the Census with elevation from 70 to 540 m and the highest elevations in the 2011). Riparian corridor density was 0.33 km/km2 (United States Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17 Ozark Highlands (Wilton et al. 2014b). Dominant landcover types Geological Survey 2014). Climate is subtropical with mean monthly in the Ozark Highlands included forest, crop and pasture, grass- temperature ranging from 7.5 to 27.0 °C (Simek et al. 2015). Bear land, and human-developed areas (Fry et al. 2011). Forests were density was likely <1 bears/100 km2 (R. Rummell, Mississippi Depart- primarily upland oak (species of the genus Quercus L.) – hickory ment of Wildlife, Fisheries, and Parks, personal communication), but (species of the genus Carya Nutt.) and oak – pine (species of the has been increasing due to recolonization from expanding popu- genus Pinus L.) (Raeker et al. 2010). Landownership included pri- lations in adjacent states, increased restoration of native habitat, vate homesteads and farms and public lands. Main and tertiary and no legal harvest of bears (Simek et al. 2012). road densities were 0.18 and 1.28 km/km2, respectively (United States Bureau of the Census 2011). Riparian corridor density was Capture, handling, and monitoring 0.08 km/km2 (United States Geological Survey 2014). Mean daily We live-captured female bears in Michigan from late May to temperature ranged from 7.9 to 33.3 °C and mean rainfall was early July of 2009–2011, in Missouri from late May to early October 12.7 cm during the study period (NOAA 2014). Black bear density of 2010–2013, and in Mississippi -round in 2009–2011 using was 1.7 bears/100 km2 (Wilton et al. 2014b) and are recolonizing the Aldrich foot snares and culvert traps (Johnson and Pelton 1980;

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Reagan et al. 2002) baited with commercial lures and attractants. ArcGIS version 10.1 to estimate a mean NDVI score for each year. We checked traps at least daily. We immobilized females in the We imported the composite NDVI layers into ArcGIS, extracted three study areas intramuscularly with Telazol (1:1 mixture of the NDVI value of each female location corresponding to the year tiletamine hydrochloride (HCL) and zolazepam;® Fort Dodge Ani- when the location was recorded, and exported this data to a mal Health, Fort Dodge, Iowa, USA; Kreeger and Arnemo 2007) spreadsheet.

using a syringe pole or CO2-powered dart pistol or rifle (Pneu-Dart, We assessed rivers and nonintermittent streams as a metric of Inc., Williamsport, Pennsylvania. USA). After induction, we re- riparian movement corridors for all populations using the United corded morphometrics, determined sex, ear-tagged individuals, States Geological Survey National Hydrography Data (United and collected blood, hair, and tissue samples. We fit females States Geological Survey 2014). We imported river and stream data with various Global Positioning System (GPS) telemetry collars in for each study area into ArcGIS and then estimated the distance Michigan (Lotek 7000MU; Lotek Wireless, Newmarket, Ontario, of each female or random location to the nearest river or nonin- Canada), Missouri (Northstar NSG-LD2: RASSL Globalstar, King termittent stream. Similarly, we used distance to primary and George, Virginia, USA; ATS M2610B: Advance Telemetry Systems secondary (main roads) and tertiary roads as two metrics of an- Inc., Isanti, Minnesota, USA; Lotek Wireless 7000MU: Lotek Wire- thropogenic mortality risk (Reynolds-Hogland and Mitchell 2007; less, Newmarket, Ontario, Canada), and Mississippi (Telonics, Inc., Martin et al. 2010; Waller et al. 2014), including hunting (Ciarniello Mesa, Arizona, USA; Advanced Telemetry Systems Inc., Isanti, et al. 2007; Czetwertynski et al. 2007; Ordiz et al. 2011). We obtained Minnesota, USA; Northstar, King George, Virginia, USA). We re- spatial road data in Michigan from the United States Bureau of the leased all females at their sites of capture. All capture and han- Census (2011), in Missouri from the Missouri Spatial Data Information dling complied with the American Society of Mammalogists Service (Curators of the University of Missouri–Columbia 2011), and in guidelines (Sikes et al. 2011) and the Institutional Animal Care and Mississippi from the Mississippi Automated Resource Information Use Committee at Mississippi State University (protocol Nos. 08- System (2014) and estimated distance of each female location or ran- 052, 09-004, and 10-037). We programmed GPS radio collars in dom point to the nearest main and tertiary road in each study area by Michigan, Missouri, and Mississippi to record a location every 15, conducting a spatial join in ArcGIS. 10, or 60 min from the time of deployment, respectively. We standardized all resource variables to z scores and centered scores to provide equal weight in multiple regression analyses Spatial and habitat selection (Zar 1999). We used variance inflation factor (VIF) analysis to assess We estimated the likelihood of female resource selection of and multicollinearity (collinearity considered ≥7) among resource within home ranges using second- and third-order selection anal- variables (Quinn and Keough 2002); no metrics were correlated yses (Manly et al. 2002) with designs 2 and 4 (Thomas and Taylor (VIF = 1.05–1.13). We used package lme4 (Bates et al. 2011) in pro- 2006), respectively. We first selected female GPS locations that gram R (R Core Team 2013) to develop six generalized linear were separated by ≥60 min to reduce spatial autocorrelation, mixed-effects models for each spatial scale in each state using a which was the minimum relocation time across populations. We maximum likelihood estimator and binomial distribution using standardized step length estimates from locations >60 min apart female radiolocations (1) and random points (0) as the response to a 60 min interval to compare parameter estimates across pop- variable. We assessed a null model incorporating only an inter- ulations and selection of home ranges across the landscape. We cept and a naïve model incorporating main roads, tertiary roads, then defined resource use as female locations from 1 day after den riparian corridors, and NDVI as fixed effects. We structured the exit or capture to cessation of monitoring (e.g., dropped collar), or remaining four models to assess a potential functional response

For personal use only. 31 August, which we imported into ArcGIS version 10.1 (ESRI, Inc. to each of the variables using the following notation: 2011) as a shape file. We used 31 August as our cut-off date because of senescence of primary vegetation productivity and limited GPS ϭ ␤ ϩ ␤ ϩ ␤ ϭ ␥ ϩ ␥ g(x) 0 1xij … nxnij njxnj 0j location data after this time. To estimate female selection home ranges across the landscape, we first created a 100% minimum ␤ ␤ convex polygon around all locations in each population using where 0 is the mean intercept, n are the fixed regression coeffi- ArcGIS. We then defined study-area availability by using the Gen- cients with covariates xn, ij are individual females clustered within ␥ erate Conditional Random Points tool in the Geospatial Modeling each year, nj is the random coefficient of covariate xn for each ␥ Environment (version 0.7.1.0; Beyer 2012) to plot the same number female (j), and 0j is the random intercept (Gillies et al. 2006). Each of random points as GPS locations for each population within of the four functional models incorporated the fixed parameters each of the polygons of used locations. used in the naïve model, but incorporated either main roads, To estimate female selection of resources within home ranges, tertiary roads, riparian corridors, or NDVI as a random covariate. we estimated the mean step length of successive locations for Individual females were clustered within year as a random effect each female in each population using Calculate Movement Path to account for variation in female behavior among (Gillies Metrics tool in the Geospatial Modeling Environment and then et al. 2006). We considered parameter significance as ␣ = 0.05. We calculated mean step length across populations. We then defined verified model fit by examining standardized versus fitted resid- resource availability using the Generate Conditional Random ual plots and ranked models using Akaike’s information criterion Points tool in the Geospatial Modeling Environment to establish a corrected for small sample size (AICc)(Burnham and Anderson Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17 paired random point within the determined standardized step 2002). We considered models competing if the difference in their ⌬ distance (1054 m) of each female location across populations. We AICc were ≤2 and presented models best explaining bear re- then copied the individual female and year information from source selection. used locations to respective random locations for each population for model development. Results We used the 16-day composite normalized difference vegetation We obtained a median of 1 677 (range = 658 – 5 051) locations index (NDVI) data from the MODIS server (250 m resolution; from 7 females in Michigan, 3 153 (range = 68 – 6 127) locations United States Geological Survey 2013) as a metric of vegetation from 11 females in Missouri, and 3 377 (range = 103 – 10 363) loca- productivity and quality (i.e., food) for all populations, which is tions from 11 females in Mississippi. Females in Mississippi used assumed to be of great nutritional value during spring and sum- locations farther from tertiary roads than did females in Michigan mer (Milakovic et al. 2012; Fowler 2014). We obtained NDVI raster or Missouri (Table 1). Females in Missouri used locations farther layers with the closest date to the beginning of each month from main roads, but closer to riparian corridors, than did females in for May–August of 2009–2013 and used the Raster Calculator in Michigan or Mississippi. Females in Michigan used locations with

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NDVI values 14%–15% less than females in Missouri or Mississippi, which had similar values. — — — —

Selection of home ranges Variation in resource selection of females in Michigan was best <1 0.31 0.14 0.33 explained (Table 2) by the functional NDVI (i.e., vegetation produc- tivity) model, suggesting that females selected tertiary and main roads and locations with greater vegetation productivity, but — — — — avoided riparian corridors (Table 3; Fig. 2a). Females in Michigan showed functional selection of tertiary roads between about 300 and 500 m and NDVI of about 7000–8000. Variation in resource 1.7 1.28 0.18 0.08 selection of females in Missouri was best explained by the func- tional riparian corridors model, suggesting that females selected — — — — riparian corridors, tertiary roads, and locations with greater veg- etation productivity, but avoided main roads (Fig. 2b). Females in Missouri showed functional selection of tertiary roads between

Michigan Missouri Mississippi about 300 and 2000 m and riparian corridors between about 500 and 6500 m. Variation in resource selection of females in Missis-

) 1.69 ) 0.03 ) 1.17 sippi was best explained the by functional NDVI model, suggest- 2 2 2 ing that females selected tertiary and main roads and locations ) in Michigan, Missouri, and Mississippi, USA, during with greater vegetation productivity, but avoided riparian corri- dors (Fig. 2c). Females in Mississippi showed functional selection of tertiary roads between about 1 500 and 4 500 m, riparian corri- dors between about 5 000 and 10 000 m, and NDVI of about 7 000 Population density 14–19 Density (km/km Used locationsLS availabilityHR availability 496 653Used locations 386 625LS availability 518HR 511 availability 414 2142 823Density (km/km 611 5808 316Used 1323 locations 5914 767LS 2948 availability 7689 3702 325 3058HR availability 1914 5476 2686 3251 4172 5833 3799 1716 2156 5637 3260LS 3097 availability 1799 3256 2271 4792HR 1293 availability 4064 2125 6460 1349 3585 9429 1784 2685 6160 7792 6522 6366 5961and 1240 2690 4542 1007 2683 1814 614310 7583 3709 1925000. 1301 530 5169Greater 7595 2248 1228 coefficient values indicated the resource se-

Ursus americanus lection behaviors of females in Missouri were more pronounced than females in Michigan or Mississippi. Females in Mississippi and Michigan had analogous resource avoidance behaviors, but the resource selection behaviors of females in Michigan were more pronounced.

Selection within home ranges competition movement corridor or areas of food resources Females across populations had similar resource selection be- Resource Mortality risk or Movement corridors Mortality risk Density (km/km haviors to those they used to establish home ranges across the landscape. But coefficient values suggested that female resource selection behaviors were less pronounced within home ranges than those used to establish home ranges across the landscape,

For personal use only. with the exception of main roads and riparian corridors in Missis- sippi. Although the functional NDVI model best explained varia- tion of resource selection across populations (Table 2), females used locations that avoided or were indifferent to areas with Wilton et al.

) greater vegetation productivity (Table 3; Figs. 2d, 2e, 2f). Females in Michigan showed functional selection of riparian corridors be- tween about 7 000 and 11 000 m and females in Missouri showed ) functional selection of NDVI of about 4000–7000. Missouri: ) Discussion Reproductive-aged female bear resource selection was best ex- plained by a functional response to vegetation productivity across nearly all populations and spatial scales, supporting our first hy- pothesis. Similar to previous bear studies (Milakovic et al. 2012;

R. Rummell, MDWFP, personal communication Fowler 2014), we presume vegetation productivity correlated with food resources reproductive females needed to maintain nutri- tional condition for maximizing their reproductive success (Dahle

United States Geological Survey 2014 and Swenson 2003; Ayers et al. 2013). Marked support for a func- Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17

J.L. Belant, unpublished data; tional response to vegetation productivity emphasizes females Mississippi:

; were adjusting their behavior based on variation in vegetation b productivity across the landscape, and to a lesser extent within United States Bureau of the Census 2011 United States Bureau of the Census 2011 ( 2014 highways or main arteries andeach have direction, one and or typically more have lanes( intersections of with traffic many in other roads lake edges ( their home ranges. Compared with females in Missouri and Mis- Local county or city roads, off-road vehicle trails, and private roads Primary and secondary (main) roads are generally divided, limited-access Streams and rivers, braided streams, canals, ditches, and pond and Michigan: sissippi, females in Michigan had the broadest functional selec- tion of vegetation productivity, which were also the least NDVI

) values across populations, despite similar or greater availability 2 across all landscapes. We suspect this trend in functional behavior was due to greater intraspecific competition for areas with pro- Mean and standard deviation (SD) of variables related to locations of reproductive female black bear ( ductive vegetation (i.e., food and cover), which increased despotic MDWFP, Mississippi Department of Wildlife, Fisheries, and Parks; LS, landscape scale; HR, home range. behavior (Beckmann and Berger 2003) from Mississippi to Michi- gan. For example, the low-density population in Mississippi would tertiary road (m) bears/100 km Note: main road (m) riparian corridor (m) Distance to nearest VariableBear density (no. of Definition Representation Estimate Mean SD Mean SD Mean SD spring–summer of 2009–2013. Table 1. Distance to nearest Distance to nearest NDVIhave 16-day composite normalized difference vegetation index minimal intraspecific Food resources Used locations competition 6449 985 for 7478 those 503 7576 females 1049 and

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Table 2. Model rankings of generalized linear mixed-effect models assessing resource selection of reproductive female black bear (Ursus americanus; n = 29) across the study landscape and within home ranges in Michigan, Missouri, and Mississippi, USA, during spring–summer of 2009–2013. Landscape Home range

AICc AICc ⌬ ⌬ State Model K AICc AICc weight LL Model K AICc AICc weight LL Michigan (n = 7) NDVI 8 14 233.0 0.0 1.0 –7 108.5 NDVI 8 12 522.6 0.0 1.0 –6 253.3 Riparian corridor 8 20 368.1 6 135.1 0.0 –10 176.0 Riparian corridor 8 19 403.0 6 880.4 0.0 –9 693.5 Main roads 8 20 838.6 6 605.6 0.0 –10 411.3 Main roads 8 19 395.8 6 873.2 0.0 –9 689.9 Tertiary roads 8 20 882.9 6 649.9 0.0 –10 433.5 Tertiary roads 8 19 391.6 6 869.0 0.0 –9 687.8 Naïve 7 20 949.1 6 716.1 0.0 –10 467.6 Naïve 7 19 489.0 6 966.5 0.0 –9 737.5 Null 3 42 886.8 28 653.8 0.0 –21 440.4 Null 3 42 864.4 30 341.8 0.0 –21 429.2 Missouri (n = 11) Riparian corridor 8 18 270.1 0.0 1.0 –9 127.1 NDVI 8 12 522.6 0.0 1.0 –6 253.3 NDVI 8 18 365.8 95.7 0.0 –9 174.9 Tertiary roads 8 19 391.6 6 869.0 0.0 –9 687.8 Main roads 8 18 870.7 600.6 0.0 –9 427.3 Main roads 8 19 395.8 6 873.3 0.0 –9 689.9 Tertiary roads 8 25 841.9 7 571.8 0.0 –12 912.9 Riparian corridor 8 19 403.0 6 880.4 0.0 –9 693.5 Naïve 7 26 346.3 8 076.2 0.0 –13 166.2 Naïve 7 19 489.0 6 966.5 0.0 –9 737.5 Null 3 95 957.0 77 686.9 0.0 –47 975.5 Null 3 42 864.4 30 341.8 0.0 –21 429.2 Mississippi (n = 11) NDVI 8 43 575.4 0.0 1.0 –21 779.7 NDVI 8 30 313.8 0.0 1.0 –15 148.9 Riparian corridor 8 44 157.3 581.9 0.0 –22 070.7 Riparian corridor 8 38 815.4 8 501.6 0.0 –19 399.7 Main roads 8 49 918.9 6 343.5 0.0 –24 951.5 Main roads 8 38 997.6 8 683.9 0.0 –19 490.8 Tertiary roads 8 51 588.6 8 013.2 0.0 –25 786.3 Tertiary roads 8 40 040.5 9 726.8 0.0 –20 012.3 Naïve 7 60 549.4 16 974.0 0.0 –30 267.7 Naïve 7 40 520.6 10 206.9 0.0 –20 253.3 Null 3 128 997.9 85 422.5 0.0 –64 496.0 Null 3 95 957.0 65 643.2 0.0 –47 975.5 Note: Models included individual bear and year as random effects on the intercept. Models presented Akaike’s information criterion corrected for small sample size ⌬ (AICc), difference in AIC relative to minimum AIC ( AICc), AIC weight of the model relative to other models (AICc weight), number of parameters (K), and log-likelihood (LL). The model NDVI is the normalized difference vegetation index.

allowed them to select choice patches of food and cover (Beyer hypothesis. Similar to vegetation productivity, broad-scale knowl- et al. 2013) as we observed at the landscape scale. Although fe- edge of variation in resources (McDonald and Fuller 2005) and males in Missouri also exhibited a threshold of vegetation pro- mortality risks was likely more beneficial for females than at- ductivity selection, the prominence of riparian corridor use by tempting to monitor and respond to smaller scale changes in their Missouri females was probably due to the steep terrain of the home ranges (Ordiz et al. 2011, 2012). For example, females in study area and anthropogenic risk (e.g., vehicles; Brody and Pelton Mississippi increased their selection of main roads and avoidance 1989) on hilltops, which filtered animals nearer to streams or of riparian areas from the landscape to home-range scale, plausi- rivers (Garneau et al. 2008; Waller et al. 2013). Areas near riparian bly because of a 35% increase and 40% decrease in the mean dis-

For personal use only. corridors may have also provided suitable patches of food (Lyons tances of those resources at the home-range scale, respectively. et al. 2003) or cover in that landscape. The scale-dependent behaviors of female bears in our study cor- The shift in selection to avoidance of vegetation productivity roborate those of other large (Rettie and Messier 2000; from the landscape to home-range scale further suggests that Latif et al. 2011; Beyer et al. 2013) and similarly suggest females intraspecific competition influenced female resource selection functionally adjusted their behavior to the spatial scale at which (van Beest et al. 2014). This shift supported our second hypothesis each factor most limited their reproductive success (Rettie and that females would reduce their selection for profitable resources Messier 2000). as population density (i.e., intraspecific competition) increased. The breadth of resource selection trade-offs was greatest for The negative relationship between profitable resource access and females in Missouri, followed by Michigan and Mississippi, not intraspecific competition was verified by greater selection of veg- supporting our fourth hypothesis that females would exhibit etation productivity by lower bear density populations in Mis- greater behavioral trade-offs from Mississippi to Michigan. Varia- souri and Mississippi compared with Michigan. With greater bear tion in resource selection across populations appeared to reflect density, females in Michigan would have maximized their - resource availability in each landscape, rather than bear popula- time reproductive success by occupying areas with reduced mor- tion density (Bojarska and Selva 2012). Although population-level tality risk to themselves or cubs (LeCount 1987; Garrison et al. variation in selection of vegetation productivity appeared to be 2007), even if those habitats had poorer food resources (Ben-David influenced by bear density, females likely maximized their re- et al. 2004; McDonald and Fuller 2005). But females in Michigan productive success by secondarily adapting to other landscape- likely offset these constraints by establishing suitable home specific resources or mortality risks. (Ursus arctos L., Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17 ranges across the landscape using prior knowledge of food distri- 1758) life-history strategies were similarly shown to be primarily bution (McDonald and Fuller 2005). This strategy can allow fe- influenced by variation in environmental pressures across a large males to minimize their space use while providing flexibility to portion of their range (Ferguson and McLoughlin 2000). Differ- move among patches of food and avoid risks (Mitchell and Powell ences in resources and mortality risks among populations may 2007), as indicated by the functional selection of vegetation pro- explain why females in Missouri had greater behavioral trade-offs ductivity by each population. than in Michigan, despite bear density being 8.2–11.2 times greater in With the exception of vegetation productivity, females in each Michigan than in Missouri. population established home ranges across the landscape based Within populations, female trade-offs in resources and mortality on similar resource selection patterns as those within home risks were likely related to landscape-specific variation in those fac- ranges. Although consistency in resource selection behaviors sug- tors. Tertiary road avoidance increased as bear population density gests that these resources were perceived similarly at each spatial increased from Mississippi to Michigan. This trend in avoidance scale, the strength of these behaviors were greater at the land- was closely associated with landscape availability, as tertiary road scape scale than within the home range, supporting our third density was 5.5 and 4.0 times greater in Michigan and Missouri,

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Table 3. Generalized linear mixed-effect models Table 3 (concluded). best explaining resource selection of reproductive Landscape Home range female black bear (Ursus americanus; n = 30) across the study landscape or within home ranges in VarNDVI 5.367 3.154 Michigan, Missouri, and Mississippi, USA, during SD 2.317 1.776 spring–summer of 2009–2013. Varbear 1.104 0.387 SD 1.051 0.622 Landscape Home range Varyear 0.029 0.307 Michigan SD 0.169 0.554 ␤ 0 –4.223 –3.501 Note: Models presented with standardized parameter SE 0.344 0.358 estimates (␤), standard errors (SE) or standard deviations P <0.001 <0.001 (SD), probability values (P), and the variance (Var) of the ␤ functional response parameter (normalized difference tertiary roads –5.286 –2.568 SE 0.249 0.213 vegetation index (NDVI) or riparian), individual bear, and P <0.001 <0.001 year included as random effects on the intercept. ␤ main roads –3.717 –3.275 SE 0.072 0.078 respectively, than in Mississippi. By adjusting avoidance behavior P <0.001 <0.001 to landscape-specific tertiary road density, females in each popu- ␤ riparian 3.232 2.919 lation could have maximized accessed resources (e.g., food) be- SE 0.073 0.072 tween tertiary roads with human mortality risk (Ciarniello et al. P <0.001 <0.001 2007; Reynolds-Hogland and Mitchell 2007) and interior habitats ␤ NDVI 1.766 –0.065 with greater risk of conflicts with male bears (Garrison et al. 2007). SE 0.102 0.071 Our results were corroborated by Stillfried et al. (2015) who con- P <0.001 <0.001 ducted an independent analysis of female black bear resource VarNDVI 7.901 6.226 selection in the Michigan study area. Similarly, avoidance of pri- SD 2.811 2.495 mary roads by females in Missouri was likely due to main roads Varbear 0.539 0.294 being 1.3–6.0 times denser than in Mississippi and Michigan, re- SD 0.734 0.543 spectively. The functional selection of riparian corridors between Var 0.049 0.170 year about 1000 and 6000 m by females in Missouri could represent a SD 0.221 0.412 similar scenario as in Michigan, whereby females used areas (e.g., Missouri hillsides) between main roads with human risks (e.g., vehicles) ␤ 0 –0.511 –2.979 and riparian corridors where conspecifics tend to concentrate SE –0.170 0.283 (Lyons et al. 2003). Given the choice, use of areas near riparian P 0.865 <0.001 corridors were likely safer than areas near main roads for females ␤ tertiary roads –4.316 –2.872 in Missouri. In contrast, landscape terrain in Michigan and Missis- SE 1.143 0.162 sippi was similarly flat and had low-density human development P <0.001 <0.001 that could have allowed females to use habitats closer to main ␤ 8.959 2.021 main roads roads with less human disturbance. These landscape characteristics SE 0.602 0.029 For personal use only. P <0.001 <0.001 could have allowed females to access food and cover between main ␤ roads with human risk (Jonkel and Cowan 1971; Reynolds-Hogland riparian –9.321 –2.226 SE 0.493 0.030 and Mitchell 2007; Roever et al. 2008) and riparian areas with poten- P <0.001 <0.001 tially greater conspecific risk (Garneau et al. 2008; Waller et al. ␤ 2013) or thick vegetation that impeded movement. NDVI 8.378 –0.104 SE 0.569 0.074 P <0.001 0.160 Conclusions We demonstrated variation in spatially dependent resource se- VarNDVI 6.204 7.281 SD 2.560 2.698 lection across black bear populations that allowed females to pro-

Varbear 0.869 0.054 cure resources while reducing apparent mortality risks. Females SD 0.932 0.233 in our study adjusted their behavior to establish home ranges

Varyear 0.441 0.176 with a threshold of vegetation productivity, under the confines of SD 0.664 0.419 interspecific competition and mortality risk within their popula- Mississippi tion. Although this behavior decreased within home ranges, fe- ␤ males maintained behavioral trade-offs among resources and 0 –2.241 –0.981 SE 0.330 0.299 mortality risks across two spatial scales. This behavior empha- P <0.001 0.001 sizes the importance of considering the functional behavior of ␤ tertiary roads –0.215 0.038 populations at multiple spatial scales (Rettie and Messier 2000)to Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17 SE 0.015 0.016 provide the best knowledge to guide population management. P <0.001 0.019 Similar functional trade-offs have been reported for other large ␤ main roads –2.525 –4.477 mammals (Godvik et al. 2009; Matthiopoulos et al. 2011; Moreau SE 0.031 0.053 et al. 2012), emphasizing that behavioral flexibility allows animals P <0.001 <0.001 to meet their life-history requirements under spatially variable ␤ riparian 1.583 4.180 intraspecific competition and environmental conditions. As black SE 0.024 0.051 bear populations in Missouri and Mississippi increase, greater P <0.001 <0.001 competition for food and space will likely greater constrain bear ␤ NDVI 2.668 –0.142 trade-offs between resources and mortality risks, as we observed SE 0.048 0.029 between bear density and vegetation productivity at the land- P <0.001 <0.001 scape scale. Increased competition from conspecifics may in- crease female use of areas with greater mortality risks (e.g., food nearer to tertiary roads), which could limit population growth

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Fig. 2. Coefficient relationships of the best generalized linear mixed-effect model explaining the resource selection of reproductive-age female black bears (Ursus americanus) across the study landscape or within home ranges in Michigan (a, d), Missouri (b, e), and Mississippi (c, f), USA, during spring–summer of 2009–2013. Solid thick line is main roads, solid thin line is tertiary roads, long-dashed line is riparian corridors, and dotted lineis the normalized difference vegetation index (NDVI). Gray shading around lines represent ±1 standard error of the mean of each resource. For personal use only. Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17

(Simek et al. 2015). We suggest that maintenance and establish- computational abilities should allow greater exploration of mod- ment of movement corridors linking high-quality food and cover els incorporating multiple functional relationships and improved within and across bear populations is important for maintaining information for management. or restoring demographic and genetic connectivity of bears (Dixon et al. 2006; Wilton et al. 2014a; Simek et al. 2012, 2015). In addition Acknowledgements to yielding important information for guiding black bear manage- This project was supported by the Federal Aid in Wildlife Res- ment, our study underscores how multiple factors can influence toration Act under Pittman–Robertson. We thank the Michigan broad variation in spatially dependent functional resource selec- Department of Natural Resources; Mississippi Department of Wild- tion of a species across large portions of its range. Advances in life, Fisheries, and Parks; Missouri Department of Conservation;

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For personal use only. Simek, S.L., Belant, J.L., Young, B.W., Shropshire, C., and Leopold, B.D. 2012. Zedrosser, A., Dahle, B., and Swenson, J.E. 2006. Population density and food History and status of the American black bear in Mississippi. Ursus, 23: conditions determine adult female body size in brown bears. J. Mammal. 87: 159–167. doi:10.2192/URSUS-D-11-00031.1. 510–518. doi:10.1644/05-MAMM-A-218R1.1. Can. J. Zool. Downloaded from www.nrcresearchpress.com by MISSISSIPPI STATE UNIV LIB on 03/06/17

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