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Abstract Roadways may negatively impact wildlife species through vehicular-related mortality and spatial displacement or obstruction. Here, we investigated physiological responses, which provide insights into the animal’s perception of its environment. We deployed Global Positioning System (GPS)-collars in combination with cardiac biologgers on American black bears (Ursus americanus; 18 bear-years) in areas with differing road densities across Minnesota, USA. We tested whether bears exhibited acute stress responses, as defined by significant increases in heart rate (HR), associated with road crossings. Maximum HR between successive telemetry locations were, on average, 13 bpm higher when bears were known to cross a road. They crossed a road, on average, once per day. Different demographic groups (males, females with and without cubs) responded similarly. We found stronger HR responses when crossing high-traffic roads relative to low-traffic in half of the bear-year combinations we sampled. Bears crossed high-traffic roads mainly at night, but low traffic roads during daylight. Bear HRs first became elevated when 73−183 m away from roadways. Our findings suggest that roadways act as an acute stressor, but the magnitude of the stress response appears to be mild. Elevated HRs may reflect an increased vigilance and recognition of threat when preparing to cross a road. Bears’ recognition and alertness to human-related threats is adaptive for living in human-altered landscapes. INTRODUCTION Roads are linear features that can disrupt the behavior of wildlife via landscape fragmentation (Theobald et al. 1997; Eftestøl et al. 2014) and may cause increased mortality (Trombulak and Frissell 2000; Fahrig and Rytwinski 2009; Jackson and Fahrig 2011; Lamb et al. 2017). Above certain thresholds of traffic volume or road density, roads can cause some species to adjust their foraging and movement behaviors, potentially leading to avoidance of food-rich areas (Eldegard et al. 2012; Northrup et al. 2012; Prokopenko et al. 2017), reduced connectivity between populations (Dyer et al. 2002; Gagnon et al. 2007; Jackson and Fahrig 2011; Simek et al. 2015), and possible rapid evolutionary changes (Brady and Richardson 2017). Additionally, noise disturbance from traffic can cause difficulties for species that depend on auditory signals for communication (Rheindt 2003; Tennessen et al. 2014) and foraging (Luo et al. 2015). Terrestrial species with the most expansive home ranges (e.g., large carnivores) have demonstrated the strongest behavioral responses to roads (Rytwinski and Fahrig 2011). A host of studies have been conducted on effects of roads on bears (e.g., see review by MacHutchon and Proctor 2015 for grizzly bears [Ursus arctos] in North America) because it is widely recognized that roads are common across the global ranges of several bear species (Ceia-Hasse et al. 2017). Although high road density and traffic volume tend to deter bears (Beringer et al. 1990; Waller and Servheen 2005; McCown et al. 2009; Northrup et al. 2012), high-quality forage along roads may act as an attractant increasing the risk of vehicular-related mortality (Roever et al. 2008; Lewis et al. 2011). Selection for or against roadsides varies among populations of bears (Duquette et al. 2017). Roads with low traffic volume, such as decommissioned roads or roads used for forestry, along with other linear landscape features (e.g., seismic lines, trails), may serve as travel corridors for large mammals, especially predators that can increase their search areas for prey (Mace et al. 1996; James and Stuart-Smith 2000; McKenzie et al. 2012; Dickie et al. 2017). Most studies have quantified the impact of roads on mammals such as bears by measuring changes in movement rate, or differences in presence/absence or abundance in relation to road density (or distance to nearest road). Little is known, however, about the physiological responses of mammals to roadways. Conservation physiology is an emerging subdiscipline of conservation science that utilizes physiological concepts to better understand and predict an organism’s response to environmental change and stressors, such as human alterations to animal habitat (Cooke et al. 2013). One way of quantifying the physiological response to human activity is through monitoring levels of cortisol, a stress-related hormone, in body tissues (Romano et al. 2010; Sheriff et al. 2011). Studies involving bears have shown elevated cortisol levels in Asiatic black bears (Ursus thibetanus) when raiding agricultural crops (Malcolm et al. 2014) and in brown and American black bears (U. americanus) in response to resource availability (e.g., salmon; Bryan et al. 2014), and have linked cortisol levels in polar bears (U. maritimus) to fluctuations in climate (Bechshøft et al. 2013). Hormonal variation provides insight into prolonged stress events, but it reflects the response to multiple stressors throughout time and thus cannot provide inference about the effects of single or temporary events, known as acute stress. Moreover, interpretation of stress levels from cortisol measurements can be difficult since these measures reflect cumulative stress levels that occurred over a length of time associated with the type of biological sample (typically hair, scat, or blood in mammals; Dantzer et al. 2014). New advances in technology, including remote-sensing techniques, and Global Positioning System (GPS) devices, combined with biologgers that record near-continuous physiological data, enable scientists to investigate stressors at a finer scale (Laske et al. 2011; Ropert-Coudert et al. 2012; Ditmer et al. 2015b). By combining these data, researchers can assess the physiological responses of animals during fine-scale behaviors, such as foraging, migration, habitat selection, interactions with conspecifics and human influences (Ditmer et al. 2015a; Wang et al. 2015; Wilmers et al. 2015). For example, biologgers have helped researchers link stress in brown bears to human activity in forests (Støen et al. 2015) and link strong acute stress responses by American black bears (hereafter black bears) to short duration unmanned aerial vehicle flights (Ditmer et al. 2015b). We evaluated black bear physiological responses to roadways across a range of landscapes that differ in the amount and type of human influences. By combining heart rate (HR) data obtained from biologgers with locations obtained from GPS collars, we sought to answer the following questions: 1) do bears exhibit a cardiac response when crossing roads?; 2) if so, does cardiac response vary with the frequency of crossing, or with traffic volume, and do bears choose to cross higher volume roads at times when traffic tends to be less?; 3) do responses differ by sex or reproductive status?; and 4) at what distance from roadways do bears begin to show a physiological response? We hypothesized that bears would exhibit elevated HRs when crossing roads, that the magnitude of response would increase with traffic volume, and that individuals who cross more frequently would exhibit less of a cardiac response, due to habituation. We posited that females with cubs of the year would have the largest responses. We further hypothesized that bears living near roads would only show a physiological response when very close to roads. We found that pairing GPS locations with cardiac biologgers enabled the elucidation of unique insights into the physiological responses of a large mammal negotiating a habitat fragmented by roadways. MATERIAL AND METHODS Study areas Black bears occupy a range of habitats, with different road densities, in northern Minnesota, USA. We chose 3 areas representing the center, southern, and northwestern extremes of this range (Figure 1): 1) Chippewa National Forest (CNF), 2) Camp Ripley (CR), and 3) northwestern MN (NW), respectively. Climatically, the study sites are similar. Winters (when bears are hibernating) are typically snowy (average annual snowfall: 102 [NW] −145 [CNF] cm) and cold (January average low temperature: −20 °C; Arguez et al. 2010). The spring is normally rainy (average precipitation in June: 102–112 mm) and the summers are warm and humid (July average high temperature: 27 °C; Arguez et al. 2010). Figure 1 View largeDownload slide Cardiac responses to roads by black bears were studied at 3 sites in Minnesota, USA: central, primary bear range, largely in the CNF; the southern fringe of the bear range, in and around CR National Guard Training Center; and the highly agricultural northwestern edge of the range, with more sparse habitat and lower bear density (mainly secondary range). The density and types of roads (e.g., forestry, highway, and agriculture, respectively), and traffic volume varied greatly among the 3 study areas (defined by counties or groups of counties). Figure 1 View largeDownload slide Cardiac responses to roads by black bears were studied at 3 sites in Minnesota, USA: central, primary bear range, largely in the CNF; the southern fringe of the bear range, in and around CR National Guard Training Center; and the highly agricultural northwestern edge of the range, with more sparse habitat and lower bear density (mainly secondary range). The density and types of roads (e.g., forestry, highway, and agriculture, respectively), and traffic volume varied greatly among the 3 study areas (defined by counties or groups of counties). We defined study areas along county boundaries, and calculated the road density (km per km2) and weighted mean traffic volume (vehicles per hour) for each area by obtaining traffic volume data from the Minnesota Department of Transportation (MNDOT; Minnesota Department of Transportation 2016). To calculate the weighted mean traffic volume, we averaged the traffic volume within each study area using the length (km) of each road segment as the weight. Traffic volume data were not collected for many secondary farm and forestry roads; so, we assumed they had a low traffic volume (assigned a value of 5 vehicles/hour). The CNF study area falls in the transition zone of the state’s boreal and temperate biomes. The large tracts of easily accessible public lands, with forests of various ages dominated by aspen (Populus tremuloides, P. grandidentata), attract recreational users throughout the year (Garshelis et al. 2011). This area had the lowest road density of our 3 study areas (0.8 km of roadway/km2), with generally low traffic volume on unpaved forest service roads. However, a few high-volume highways transect the area yielding a weighted mean traffic volume of 374 vehicles/hour. The CR study area was composed of a 214-km2, heavily-forested National Guard training area, surrounded by a patchwork of agricultural land, forests, and a few larger resort towns. Roads within the military base were low traffic volume used primarily by military personnel, but several high traffic highways were located in the vicinity. This study area had the highest road density (1.2 km of roadway/km2) and highest weighted mean traffic volume (959 vehicles/hour). The NW study was the most agricultural. Considered part of the prairie/aspen parkland biome, over 50% had been converted to agriculture, with shrublands, wetlands, small patches of forest, and sparsely populated towns comprising the remainder of the landscape (Ditmer et al. 2015a). This area had an intermediate road density (1.0 km of roadway/km2), but many of these roadways were unpaved, agricultural service roads with the lowest weighted mean traffic volume (166 vehicles/hour) of the study areas. Animal location and HR data During 2012–2015, we captured bears and outfitted them with radio-collars having either store-on-board GPS devices (Telemetry Solutions, Concord, CA; n = 10 bear-years) or GPS collars that relayed fixes via the Iridium satellite system (Telonics Inc., Mesa, AZ; n = 5 bear-years or Vectronic Aerospace, Berlin, Germany; n = 3 bear-years). We classified bears in 3 demographic groups: males, females with cubs, and females without cubs of the year. We defined a bear-year as 1 year of data from an individual bear. In most cases, we programmed GPS collars to collect fixes at 1–6 h intervals depending on the battery life of the collar. GPS-locations not collected through the Iridium system were downloaded when we handled bears at their winter den sites. We removed locations with a horizontal dilution of precision greater than 10, and those associated with den entry during the fall or den emergence in the spring. While bears were anesthetized (Telazol®; 4.4 mg/kg) at den sites, we surgically implanted cardiac monitors developed for humans (Medtronic Inc., Reveal® XT Model 9529, Minneapolis, MN; specifications: 9 cc; 8 × 19 × 62 mm; 15 g). Monitors were implanted subcutaneously in a peristernal location using aseptic techniques (approved by the University of Minnesota’s Institutional Animal Care and Use Committees [permit no. 1002A77516]). These devices contained modified software (BearWare) developed by Medtronic to collect data more frequently than in normal human use to better link discrete events captured by the GPS collar to changes in HR (see Supplementary Figure 1 for an overview of the process). Monitors recorded each heart beat and reported average beats per min for each 2-min HR interval (see Supplementary Figure 2 for histograms of raw HR data). We downloaded the HR data noninvasively during subsequent winter den visits using transcutaneous telemetry (CareLink Model 2090 Programmer with software Model SW007, Medtronic Inc., Minneapolis, MN; see Laske et al. 2011). We recorded the timing of when the software began recording the bears’ HRs and when we downloaded the data in order to align all data values with corresponding GPS time steps (i.e., every 2 min from software start to data download). Analytical methods We addressed our 4 research questions using 2 distinct models. In the first model, we estimated the effect of a road crossing on bears’ HRs by identifying the maximum HR value between sequential bear locations that occurred within 8 h of one another (+3 min for GPS acquisition time) = MAXHRstep; hereafter, we refer to sequential locations as movement steps (average duration of movement steps = 112 min). We related MAXHRstep to whether the bear had crossed a road during that movement step. This model allowed us to test whether bears exhibited a cardiac response when crossing roads and to compare the responses to road crossings among males, females without cubs, and females with cubs of the year. We also assessed whether traffic volume of the crossed roads affected MAXHRstep. Our second model used the maximum HR during the interval 10 min prior to 10 min after a GPS location was obtained (MAXHRpt), so we could assess the relationship between bears’ MAXHR at varying distances to roads and not just during road crossing events. We used a smoother for the distance (m) of the location to the nearest road as an explanatory variable to investigate the range of distances from roadways that bears show a physiological response. Incorporating a flexible smoother enabled us to capture any nonlinear changes in the relationship between distance to road and MAXHRpt. Roadway data In our first model, we used ArcMap 10.3.1 (Environmental Systems Research Institute, Redlands, CA) and Geospatial Modeling Environment software (Beyer 2015) to connect bear GPS locations to create bear movement steps. We then intersected the bear movement steps with GIS layers of roadways. We combined a publically available GIS layer of roads in MN developed by the MNDOT with another GIS layer of roads within Camp Ripley National Guard base provided by MN Department of Natural Resources staff at the base Minnesota Department of Transportation 2012. The resulting map contains all roadways within the state of Minnesota including rarely used farm, forest, and military training dirt roads in the regions inhabited by the collared bears. If a bear movement step intersected any road segment, the bear movement step received a value of 1 for the ROADX variable (regardless of the number of road crossings); if a road was not intersected, ROADX was assigned a value of 0. We calculated the proportion of days that bears crossed a road during a single bear-year, and examined whether bears that crossed roads more frequently exhibited a stronger or weaker cardiac response. We used another MNDOT GIS layer that provided an estimate of vehicle counts at specific road segments on each day of the year, although not specific to time of day. Sensors on roads collected short-duration raw counts of traffic passing in both directions, which were corrected for season, weekday versus weekend, and number of truck axels to obtain an adjusted count of traffic volume (see http://www.dot.state.mn.us/traffic/data/coll-methods.html for further details). Roads with low traffic volumes such as farm and forestry roads were not included in this GIS layer. We assigned traffic volume (TRAFFVOL) at the roadway segment of the intersection of a bear’s movement step and the roadway. Movement steps that crossed a roadway without a recorded traffic volume were assigned a TRAFFVOL of 5 (including military base roads in Camp Ripley) because they appeared to be secondary roads. Movement steps with multiple roadway crossings were assigned the average TRAFFVOL for these roads. For the second model, we used the package spatstat (Baddeley et al. 2015) in program R (R Core Team 2016) to calculate the distance between each bear GPS location and the nearest roadway contained in the complete GIS layer (i.e., road layer containing secondary roads, not just those with measured traffic volumes). Other variables We assigned the Julian date and hour of the day to all bear locations based on the timestamp of the GPS location. We calculated the altitude of the sun in the sky based on the associated GPS location and timestamp of the bear data using the package maptools (Bivand and Lewin-Koh 2017). Altitude values above zero are associated with daylight and negative values are associated with nighttime observations. We assigned a habitat type by overlaying the bear locations on a raster of the 2011 National Land Cover Database (Homer et al. 2015) along with crop type for any agricultural locations based on the corresponding annual CropScape layer (United States Department of Agriculture 2016). We extracted the habitat classification of each raster cell with the package raster (Hijmans 2016). We created the binary variable AG-URB to distinguish locations in agricultural fields that bears do not use for foraging (e.g., soybeans, wheat) or developed/urban areas, versus locations within natural vegetation and agricultural fields containing crops that bears consume (e.g., corn, sunflowers, and oats—see Ditmer et al. 2016). We made this distinction based on previous observations that HRs tended to be higher for bears in agricultural fields not associated with forage, but HRs were low, or at least not significantly elevated when bears foraged on crops (Ditmer et al. 2015a). Modeling the effect of roadways on bears Both models employed generalized additive models (GAMs) using package mgcv (Wood 2011) in program R (R Core Team 2016). We fit the models to each bear-year of data separately. We modeled the maximum bear HR during each movement step (MAXHRstep) in response to the effects of ROADX to test whether a road crossing, regardless of traffic volume, resulted in a higher maximum HR. We report the coefficients for the effect of ROADX and we considered a P-value less than 0.05 to be significant. To account for confounding factors that may also influence HR, we included: 1) movement rate of the bear (calculated from GPS data), 2) whether the second sequential location in each movement step was classified as developed/agricultural (AG-URB = 1) or natural vegetation or crops consumed by bears (AG-URB = 0), 3) seasonality, using a smoothing spline based on Julian date, and 4) elevation of the sun using acyclic smoother (cubic regression splines where the ends must meet). We then refit the same models to each bear-year, replacing ROADX with TRAFFVOL. We modeled MAXHRpt for all bear locations occurring within 1000 m of any roadway, regardless of whether the bear actually crossed the road, using the same variables as the model for MAXHRstep except that we used a smoothing spline to quantify the effect of distance between each location and the nearest roadway (DISTROAD). From each GAM, we identified the value of DISTROAD at which the predicted effect of the smoother on the change in MAXHRpt, defined as θ, was no longer positive (Figure 2). We considered this value to be the distance at which DISTROAD had no effect on MAXHRpt. We report 2 values of θ: 1) the point estimate for the distance from the road where MAXHRpt was detected (positive influence, > zero), and 2) a more conservative estimate using the point estimate minus 1 SE where DISTROAD began to influence MAXHRpt (θ1se; Figure 2). To account for other possible confounding factors influencing MAXHRpt, we included variables for movement rate where the MAXHRpt is the terminal location of the movement step, AG-URB, and a smoother for the sun’s elevation, both described for the first model above. Figure 2 View largeDownload slide Distance to road versus predicted maximum HR of a black bear, derived from a GAM smoother, accounting for movement rate, habitat, time of day and year. We fit models separately to each bear-year. This enabled us to estimate θ and θ1se for each bear-year individually and pinpoint the distance from a road where there was no longer a relationship with MAXHRpt (HR change = 0, based on the mean and mean minus 1 SE). This illustration is an example from one bear-year. Figure 2 View largeDownload slide Distance to road versus predicted maximum HR of a black bear, derived from a GAM smoother, accounting for movement rate, habitat, time of day and year. We fit models separately to each bear-year. This enabled us to estimate θ and θ1se for each bear-year individually and pinpoint the distance from a road where there was no longer a relationship with MAXHRpt (HR change = 0, based on the mean and mean minus 1 SE). This illustration is an example from one bear-year. We used the regression coefficients from our models as data points for further hypothesis testing (Murtaugh 2007). To provide averages and estimates of uncertainty for both the coefficients of the model outputs and summary statistics of pertinent metrics, we calculated averages based on bear-years and estimated 95% bootstrapped confidence intervals (using package boot, Canty and Ripley 2016). Bootstrap estimates were based on the adjusted bootstrap percentile method (Davison and Hinkley 1997) using 10,000 bootstrap samples of either the estimated regression parameters or average values associated with a bear-year for a given summary (e.g., road crossings per year, traffic volume of roadways crossed). We used ANOVA to test whether demographic groups (i.e., males, females with cubs, females without cubs) differed in their response to road crossings based on their regression coefficients for ROADX. We tested whether bears tended to cross roads with higher traffic volumes during daylight or nighttime hours. We used the TRAFFVOL count of the road segment crossed by a bear as our response variable and regressed an explanatory variable based on the sun’s elevation at the time of the terminal GPS-location of each movement step. For sun elevations ≤0 we assigned a value of 0 (nighttime), for elevations >0 we assigned a value of 1 (daytime). We used a linear mixed model and incorporated a random intercept for bear-year ID using package nlme (Pinheiro et al. 2017). RESULTS We obtained 35,648 locations aligned with corresponding 2-min HR data, representing 18 bear-years: 9 bear-years for females with cubs of the year (8 different bears), 7 bear-years for females without cubs (5 different bears), and 2 bear-years for a single male bear. Bear-years averaged 1980 locations (range: 481–5,124). We obtained 10 bear-years of data in the NW study area, 6 in CR, and 2 in CNF. Movement steps averaged 112 minutes (range: 20–483 min). From the GPS data, we observed 3346 road crossing events with an average of 186 (95% CI: 132–255; range: 18–495) roads crossed per individual bear-year. Bears averaged one road crossing per day (i.e., total crossings/total days of GPS monitoring per bear-year: X¯ = 1.04; range: 0.09–2.72), but these were clustered through time; on average, road crossings occurred only once every 3–4 days (days with a crossing/days of GPS monitoring per bear-year: X¯ = 0.35; 95% range: 0.04–0.59), often with multiple crossings/day. There was high variability among individuals. Two bears living in the NW study area rarely moved outside a large patch of natural cover, resulting in the lowest average road crossing rate X¯ = 0.09 and 0.15 road crossings per day; crossing roads, on average, every 12.5–25 days). In contrast, the 2 bears living in CR crossed roads most frequently, averaging 1.7 road crossings per day during 3 years of monitoring, or crossing at least one road every 2.6 days. The average traffic volume where bear crossings occurred was also highly variable among bear-years X¯ = 130.5; 95% CI: 41.6–332.4 vehicles per hour; range: 5–1025). Do bears exhibit a cardiac response when crossing roads? In 83% of the bear-years, we detected significant differences between the maximum HRs recorded for movement steps that crossed roads versus those that did not (Figure 3), after accounting for the effects of movement rate, agricultural or urban land cover, and time of day and year (Supplementary Figures 3−5). Most bears exhibited higher HRs during intervals when they crossed roadways (88.9% positive regression coefficients; 72.2% positive and statistically significant at α = 0.05; Figure 3). In bear-years with a positive significant response, the average increase in maximum HR was 12.9 bpm (95% CI: 9.87–17.21 bpm). Bear HRs, excluding movement step periods with a road crossing, were lowest in spring and fall X¯April = 52 bpm; X¯Oct. = 61 bpm), and peaked in the mid-summer X¯July = 87 bpm). Correspondingly, bears’ average HR response to road crossings (13 bpm) was an increase of approximately 25% in spring, 21% in fall, and 15% during the summer. The magnitude of the increase within these bear-years appeared to be unrelated to the frequency with which the bear crossed roads, suggesting that experience, at least above a certain threshold, had little impact on HR response (Figure 4). In 2 bear-years (11.1%), maximum HRs associated with movement steps that crossed roads were lower than those associated with movement steps that did not cross roads, and the effect size was relatively large (β = −15.57 and β = −22.4; Figure 3). These 2 bears were the same individuals mentioned previously that had a very low encounter rate with roads and passing vehicles. Figure 3 View largeDownload slide Estimated regression parameters quantifying the effect of road crossings on the maximum heart rate (MAXHRstep) for 3 demographic classes of bears in Minnesota, USA. Estimates were calculated by fitting generalized additive models to each of 18 bear-years of GPS movement data, using a binary variable for whether or not a road was crossed during a movement step (ROADX). Regression coefficients were positive and statistically significant for 13 bear years, positive, but not statistically significant in 3 bear years, and negative and statistically significant in 2 bear years. Different shaped symbols (square, circle, triangle) represent different demographic groups of bears. Figure 3 View largeDownload slide Estimated regression parameters quantifying the effect of road crossings on the maximum heart rate (MAXHRstep) for 3 demographic classes of bears in Minnesota, USA. Estimates were calculated by fitting generalized additive models to each of 18 bear-years of GPS movement data, using a binary variable for whether or not a road was crossed during a movement step (ROADX). Regression coefficients were positive and statistically significant for 13 bear years, positive, but not statistically significant in 3 bear years, and negative and statistically significant in 2 bear years. Different shaped symbols (square, circle, triangle) represent different demographic groups of bears. Figure 4 View largeDownload slide Estimated regression parameters quantifying the effect of road crossings on the maximum HR of bears between sequential locations (MAXHRstep) plotted against the frequency of crossings (calculated as the number of days a bear crossed a road divided by the total number of GPS days bears were monitored for each of 18 bear-years). Figure 4 View largeDownload slide Estimated regression parameters quantifying the effect of road crossings on the maximum HR of bears between sequential locations (MAXHRstep) plotted against the frequency of crossings (calculated as the number of days a bear crossed a road divided by the total number of GPS days bears were monitored for each of 18 bear-years). Does traffic volume influence the cardiac response to road crossings? In 56.3% of bear-year models, maximum HRs associated with road-crossings were related to the volume of traffic (TRAFFVOL). We could not consider traffic volume for one bear during 2 bear-years because there was no variability in traffic volume among the roads in its home range. Among bears with significant regression coefficients, 8 of 9 (88.9%) had positive coefficients, suggesting that HRs were higher when crossing roads with more traffic volume. These bears also tended to cross higher traffic volume roads X¯ = 265.7; 95% CI = 73.7–629.9 vehicles per hour; n = 8 bear-years) and experienced more variation in traffic of the roadways they crossed (SD = 648.8; 95% CI = 124–1499) than bears that did not respond to traffic volume X¯ = 26.7; 95% CI =19–37 vehicles per hour; SD: X¯ = 58.3, 95% CI = 37.8–83.3; n = 8 bear-years). Bears appeared to avoid crossing busier road segments during the day, when traffic volume was highest. Although bears crossed roads at nearly an equal rate during daylight (50.7%) and nighttime (49.3%) hours, the road segments they crossed during nighttime had an average of 164 more vehicles per hour (SE = 43.3; df = 2076; groups = 18; P ≤ 0.001) than road segments crossed during daytime. Do different demographic groups respond to road crossings differently? Bear cardiac response (MAXHRstep) to road crossings (ROADX) did not differ among females with cubs of the year, females without cubs, and males (F2 = 1.79; P = 0.20; Figure 3). However, the bear-years with the 2 largest positive responses were females with cubs of the year (β = 24.1 and β = 27.7 for ROADX). At what distance do bears respond to roads? Bears’ maximum HRs (MAXHRpt) first began to increase when bears were, on average, 73 m (95% CI = 35.0–157.4 m) to 184 m (95% CI = 131.6–254.7 m) from roadways (DISTROAD) based on the conservative standard error estimate and the point estimate, respectively (θ1se, θX¯, Figure 2). One adult female bear (2 bear-years) showed no consistent change in HR related to distance from roadways, yet this individual had some of the largest changes in HR associated with road crossings (β = 17.4, P ≤ 0.001; β = 24.1, P ≤ 0.001). Bears’ HRs also increased when they crossed agricultural/urban lands β¯=8.06, SE = 1.73; 94% of individual bear-year GAMs had positive coefficients; 69% were statistically significant) and with high rates of movement β¯=1.033, SE = 0.17; positive influence in 94% of bear-year models; 83% statistically significant; Supplementary Figure 3). Bear HRs also varied based on the time of year and day (smoothers for Julian date and time of day were significant in 100% and 94% of bear-year models, respectively; the effects of Julian date and time of day influenced MAXHRpt (Supplementary Figure 6) and MAXHRstep [Supplementary Figures 3−5]). DISCUSSION Numerous studies have documented changes in mammal behavior and space use near roadways—we increased the understanding of animal reactions to roadways by investigating changes in HRs of a large, highly mobile mammal that crosses roads frequently (approximately daily in this study). Black bears exhibited elevated HRs when crossing roads, after controlling for the habitat in the vicinity (i.e., normally increased HR in agricultural and urban areas), their rate of travel, time of day, and time of year. The effect of roadways on HR was discernible >100 m away, suggesting that bears responded to traffic noise or anticipation of danger, and not just the road itself. An important question is whether the elevated HRs that we observed represented acute stress or just increased vigilance. If a bear is stressed each time it approaches a road, then road crossings might not only be dangerous, but also generally unhealthy. But if the increased HR simply represents vigilance, or wariness around roads, then it suggests that bears are aware of the danger and try to avoid it. Here, we observed 3300 road crossings in 18 bear-years of GPS data. With an estimated 15,000 bears in Minnesota, that would equate to 2.8 million road crossings by bears per year. Despite this huge number of crossings, an average of <50 bears are killed in vehicle collisions each year in Minnesota (Garshelis and Tri 2016), suggesting that bears are generally aware of the danger, and cross with some vigilance. Stress hormones may promote increased vigilance (Sapolsky et al. 2000), which encompasses both an internal state as well as a suite of behaviors, including heightened watchfulness, wariness, attentiveness, and apprehension, all involving the detection and avoidance of a threat (Beauchamp 2015). Taken together, this information suggests that increased HRs related to road crossings signal the bear’s awareness of danger, and also may prime its body to act quickly. The 2 bears in our study that showed reduced HRs along roadways (Figure 3) rarely crossed roads, and the roads they crossed were primarily secondary dirt roads in the agricultural NW study area; possibly due to this low level of exposure, these 2 bears were less cognizant of the danger that roads could pose. They could have found abundant forage along the roadsides, and become comfortable feeding there or used the roads as conduits for movement. Presently, the only comparative data by which to judge the degree to which road crossing heightened stress in bears is previous work in our NW study area in which we investigated bears’ reactions, measured using the same biologger technology as in this study, to low-altitude unmanned aerial vehicles flying over them (Ditmer et al. 2015b). The spikes in HR documented in that study (maximum of 47–123 beats per minute above the expected baseline for individual bears), where the stimulus was foreign to them (noise above the forest canopy), lingered longer and produced different noises than vehicular traffic, far exceeded the HR increases found in this study (~13 bpm), indicating that bears are more accustomed to roads, and can better judge and avoid the threats. Other researchers have also begun using HR monitors to discover bears’ perceptions of threats in their environment. Støen et al. (2015) used cardiac biologgers and discovered that brown bears exhibited a stress response, as measured by decreased HR variability, when located closer to human settlements and during times of year when human activities in the forest increased. Ordiz et al. (2012) also found a strong and sudden behavioral response in brown bears, which they associated with the onset of the hunting season. Laske et al. (2017), using HR monitors, found that, each year over a period of 6 years, a black bear became predominantly nocturnal (higher HR at night) when hunters in Minnesota put out bait sites several weeks before hunting began; it appeared that the bear, which was known to feed at such bait sites, was alert to the danger of being near human-related food sources. Bears in our study had an analogous response to crossing roads with high traffic, selecting times of day that mitigated risk. The behavioral adaptiveness of this species, and its awareness of risk, may explain, in part, why bears have been successful expanding their range into human-dominated landscapes (Scheick and McCown 2014). Bears are drawn into these areas by the calorically-rich, human-related foods (Beckmann and Berger 2003; Ditmer et al. 2016). However, these areas pose more risks (of human-caused mortality) than a densely-forested habitat, so to live there, a bear must identify these risks, become more vigilant, and alter its behavior accordingly. Our NW study site was an area recently colonized by bears, having only small patches of forest within large expanses of agriculture; crossing open fields entailed a risk, which bears perceived, as evidenced by increased HRs (Ditmer et al. 2015a). But even within this risky environmental matrix, where bears were frequently reacting to human-related stimuli, they responded to road-crossings with further elevated HRs, suggesting that they viewed them as especially hazardous. Surprisingly, bears that crossed roads frequently exhibited similar physiological responses to those that crossed roads less frequently (e.g., bears that crossed roads once every 10 days had maximum HRs that were similar to individuals that crossed roads every 2 days). This finding was the opposite of our initial hypothesis that frequent road crossings might habituate bears to the sights, sounds, and smells (e.g., exhaust, road chemicals) of traffic, resulting in a diminished HR response with greater exposure to roads. However, we found no evidence of habituation (reduced fear or alertness) among our study animals. We offer one interesting anecdote of a 16-year-old female bear in this study that frequently denned near a busy highway, apparently unbothered by the noise, which posed no immediate danger. Yet this same bear, who crossed heavily-trafficked roads repeatedly (2-year average: 1 road crossing every 1.6 days), still exhibited an increased HR when crossing roads. Nevertheless, despite this bear’s awareness of the danger, it was eventually struck by a vehicle and killed (Supplementary Figure 7). GPS-technology combined with cardiac biologgers enabled us to ascertain some novel insights into the relationship between bears and roadways. Biologgers are being heralded as the new frontier in ecology (see Wilmers et al. 2015), providing researchers with a greater understanding of how animals perceive their environment (Ditmer et al. 2015b). An obvious limitation of our study was that, due to the lengthy time step (~2 h) between GPS locations, we could not actually know when a bear crossed a road. We only knew that HR increased during time steps when roads were crossed, and in at least some cases, elevated HRs during road-crossing time steps may have been due to something other than the road or one of our other covariates (e.g., conspecifics, human presence). Improvements to GPS-technology will enable more precise linking of events with physiological data, such as the use of geofencing to increase GPS fix attempts when animals approach or are located in areas of interest (Wall et al. 2014), and GPS collars with accelerometers (Pagano et al. 2017) or cameras (e.g., Brockman et al. 2017), thus providing even more insights into how animals perceive and negotiate environmental risks. The expanding human footprint requires researchers to understand how human actions negatively influence wildlife, how species find ways to coexist with humans, and how they may alter behavior in response to human activities (e.g., Stillfried et al. 2015). We caution that whereas physiological information (e.g., stress hormones, HRs) may improve our understanding, interpretations of such data are not necessarily clear cut. Here we found that bears had elevated HRs during intervals when they crossed roads and times when they were in close proximity to roads. Although sudden increases in HRs may portend an acute stress response, the magnitude of the response was relatively minor, and we found evidence that the response may be more an indication of increased alertness. At the same time, we recognize that roads can seriously fragment bear habitats and are a major source of bear mortality in areas with higher road densities and/or traffic volume than in Minnesota (Dixon et al. 2007; Hostetler et al. 2009; McCown et al. 2009). That is, there are likely thresholds of road density and traffic volume above which increased vigilance is not enough to prevent impacts on bear survival and habitability of the landscape for bears. SUPPLEMENTARY MATERIAL Supplementary data are available at Behavioral Ecology online. FUNDING This work was supported by the Minnesota Department of Natural Resources. We thank B. Dirks, T. Iles, A. Tri, L. Mattison, and H. Martin for field assistance. J. Cowles provided editorial suggestions. 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Published: Mar 23, 2018
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