Indices of Grizzly Bear Movement in the North Slope Region, Yukon Territory Meaghan Dinney, Adam Vajda, Bayne Bosquet, Deanna Mackinnon, Hillary Gao Simon Fraser University Geography 455 April, 2017

Table of Contents Introduction  .................................................................................................................................................  2   Study  Area  ................................................................................................................................................  4   Background  ..................................................................................................................................................  6   Grizzly  Bear  Ecology  .................................................................................................................................  6   GPS  Collars  ...............................................................................................................................................  8   Literature  Review  .........................................................................................................................................  9   Grizzly  Bear  Movement  Factors  ...............................................................................................................  9   Quantifying  Animal  Movement  .............................................................................................................  12   Methods  .....................................................................................................................................................  14   Results  ........................................................................................................................................................  16   Movement  Rate  .....................................................................................................................................  16   Regression  Analysis  ................................................................................................................................  18   Discussion  and  Limitations  .........................................................................................................................  19   Movement  Rate  .....................................................................................................................................  19   Regression  Analysis  ................................................................................................................................  20   Study  Limitations  ...................................................................................................................................  23   Conclusion  .................................................................................................................................................  25   References  .................................................................................................................................................  27  

List of Figures Figure  1:  Map  of  the  study  area,  outlined  in  red  .........................................................................................  5   Figure  2:  Movement  rates  for  the  total  population,  females,  and  males,  as  a  function  of  Julian  day  ......  17  

List of Tables Table  1:  Summary  of  vegetation  classes  in  General  Land  Cover/Habitat  Classification  for  Yukon  North   Slope  (Hawkings  2001)  ...............................................................................................................................  16   Table  2:  Average  movement  rate  in  m/h  for  the  total  population,  males,  and  females  ...........................  17   Table  3:  Correlation  coefficients  describing  the  relationships  between  bear  activity  levels  (movement   rate)  and  time  of  year,  divided  by  gender  .................................................................................................  18   Table  4:  R  and  R-­‐squared  values,  describing  the  strengths  of  the  models  used  to  predict  grizzly  bear   density  as  a  function  of  environmental  variables  ......................................................................................  18   Table  5:  Regression  coefficients  and  p-­‐values  describing  the  relationships  between  vegetation  classes   and  grizzly  bear  density  .............................................................................................................................  19  

Abstract Animal movement studies are important in ecological monitoring. Grizzly bear movement is linked to environmental factors, such as food availability and climate. There are variations in grizzly bear activity among different populations and within populations. This study used correlation and regression statistics to understand the underlying factors affecting grizzly bear movement. It was determined that grizzly bear activity levels are greatest in the summer when food is readily available. Movement rates were also greater for males than for females. Factors that were explicitly linked to grizzly bear density included certain vegetation classes and distance to water bodies. These vegetation classes are associated with varying degrees of shrubs, including Crowberry (Empetrum), Lingonberry (Vaccinium), snow willow (Salix Reticulata), and bearberry (Arctostaphylos), all of which are known food sources for Yukon grizzly bears. Further inferences assumed that the observed proximity to water courses is associated with predator-prey relationships. Several limitations exist due to data quality issues with the locational data and vegetation classes. However, these methods still provided a robust and simple way to understand grizzly bear movement.

Introduction As environmental landscapes become more complex, understanding animal movement is becoming increasingly important. By identifying movement factors, one can begin to predict the response of a species to changing resource availability. Grizzly bears in the North Slope region of the Yukon Territory are important to the ecosystem as apex predators. As grizzly bears inhabit different climates, the drivers influencing movement will vary. Investigations into their movement patterns will provide an idea of environmental conditions that influence their locations. Determining indices of grizzly bear movement is critical to preserving their habitat and the ecosystem of the North Slope. The aim of this project is to analyze the factors affecting movement rate on a seasonal scale. This will be completed through a combination of visual exploration and statistical analysis methods. The result will contribute to an understanding of northern grizzly bear movement patterns and responses.

Study Area The study area is defined by the North Slope region in the Yukon Territory, Canada. It is comprised of the portion of the Inuvialuit Settlement Region (ISR) that falls within this territory. The ISR was settled by the Inuvialuit Final Agreement (IFA), which allows the Canadian government to work with the Inuvialuit to protect their land and culture. (Aklavik Local and Traditional Knowledge about Grizzly Bears of the Yukon North Slope 2017). The study area is bordered by Alaska to the west, the Beaufort Sea to the north, the Northwest Territories to the east, and the southern boundary of the ISR (Figure 1). The Yukon North Slope has no permanent settlements or roads, and its only

use is by the Inuvialuit people who set up seasonal camps during the summer to hunt and gather. (Aklavik Local and Traditional Knowledge about Grizzly Bears of the Yukon North Slope 2017). The low impact makes this an ideal place to study grizzly bear movement without human influence.

  Figure  1:  Map  of  the  study  area,  outlined  in  red  

 

The climate of the Yukon North Slope changes significantly between seasons due to significant variations in incoming solar radiation experienced with increased latitude. The area sees 24 hours of sunlight during a portion of the summer and 24 hours of darkness for a few weeks in winter. Low temperatures associated with the long winter season cause the permafrost to refreeze in the top layer of soil and water bodies and streams fill with ice. The area is only free of ice from June to October. (Yukon North

Slope Wildlife Conservation and Management Plan Volume 1: Environmental Overview 2008) Therefore the growing season is shortened and plant establishment is limited to the summer months. The topography of the Yukon North Slope changes drastically from east to west. The western portion of the study area is made of the British, Barn, and northern Richardson mountains which create a very complex landscape, consisting of narrow vshaped valleys and tall mountain ridges. These define local differences in climate that influence vegetation patterns in the region. The vegetation in this area varies from bare rock to sedges and shrubs with occasional trees. (Yukon North Slope Wildlife Conservation and Management Plan Volume 1: Environmental Overview 2008). The eastern portion is much less varied as it slopes gently northward towards the Beaufort Sea. The gentle topography in this area creates a consistent field of sedges and shrubs. (Yukon North Slope Wildlife Conservation and Management Plan Volume 1: Environmental Overview 2008).

Background Grizzly Bear Ecology Grizzly bears are omnivorous, so their diet is based both in plant and animal sources. In species that hibernate, a significant amount of protein must be consumed in order to restore muscle mass. This is especially relevant in northern populations of grizzly bears, where hibernation is longer. Meat sources are a more consistent form of protein, so a large portion of grizzly bear diet is animal-based. The main carnivorous foods of grizzly bears include broad whitefish, snow goose eggs, muskrat, small

mammals, and aquatic browsers (Edward et al. 2009). Grizzly bear diet also consists of a high portion of plant species. The main herbaceous foods are including horsetail, roots, and all berry species (Edward et al. 2009). Grizzly bear diet varies seasonally, as plant and animal resources fluctuate. MacHutchon and Wellwood (2003) studied extensively the seasonal variation in grizzly bear diet. Alpine hedysarum roots, overwintered crowberries and common horsetail are dominant plant food sources in May and June (MacHutchon and Wellwood 2003), while horsetail shoots and bearflower leaves are major food in June and July (MacHutchon and Wellwood 2003). Bog blueberries and alpine hedysarum roots were observed to be the most common plant foods in the fall (MacHutchon and Wellwood 2003). Grizzly bear activity levels vary greatly throughout the year. In winter, when food sources are scarce, bears enter their dens for hibernation. This period of reduced activity involves bears supressing their metabolism and lowering their body temperature in order to conserve energy (Pigeon et al. 2016). Den entry can be anytime from September to December and exit anytime from March to June. Time spent hibernating can be related to food availability, circannual rhythm, sex, reproductive status, individual fitness, and stored energy. Studies have determined that food availability is the main control on den entry, and spring temperature increases exert a primary control on den exit (Pigeon et al. 2016). The shoulder season is the period between off-peak hibernation and peak activity of the foraging season. When bears exit their dens, their activity is limited, and gradually increases as the season continues (Pigeon et al. 2016). A majority of caloric bulking occurs during the foraging season of the summer (Pigeon et al. 2016). The pre-denning season sees similar mass building, however activity levels

begin to decrease in order for grizzlies to conserve energy and ready themselves for den entry (Edwards et al. 2011).

GPS Collars Wildlife managers have made use of Global Positioning System (GPS) collars for the collection and monitoring of spatiotemporal movement of animals. These collars were used in order to collect location data for this study. Grizzly bears were located by helicopter and tranquilized. Once sedated the bears were equipped with the Telonics 3 generation collar and vital statistics were recorded. This capture technique is the safest mode for both the bear and the wildlife manager (Environment Yukon 2013). The radiocollars used are composed of four components: a GPS, an activity sensor, a temperature sensor, and a very high frequency (VHF) component. (Telonics Inc 2015). The GPS tracks location in two and three dimensions by trilateration of satellite signals. Three satellites must be in view of the GPS in order to get two dimensional fix and a fourth satellite is required to acquire a third dimension elevation record. (Bajaj et al. 2002; Swanlund et al. 2016). The coverage and spread of the satellites will dictate the accuracy of the geographic location recorded, with the 3-dimensional fix points being more accurate than 2-dimensional fix points. There is also a higher positional dilution of precision (PDOP) when satellites are close together (Adradosa et al. 2002). The temperature sensor records the ambient temperature, and the activity sensor records the percent of time in which a motion activated switch located within the collar is tripped. These GPS collars have the ability to be programmed to record data at discrete time intervals (Telonics Inc 2015). These data are stored onboard the collar, so collection

requires the bear to be relocated using the VHF component (Environment Yukon 2013). The duration a collar can operate is governed by the lifetime of the battery which is influenced by the ambient temperature and the frequency of data collection (Tomkiewicz et al. 2010). Swanlund et al. (2016) stated that factors such as battery life, overhanging vegetation, and topography can all interfere with acquisition of a satellite signal. However, tests of accuracy determined these collars are very accurate, with a mean locational error of less than 5 m and an overall fix rate of 99.7% (Swanlund et al. 2016).

Literature Review Grizzly Bear Movement Factors Studies focused on the movement patterns of grizzly bears have found three main influencing drivers: food availability, sex, body condition, and reproductive status, and climate (Pigeon et al. 2016; Edwards et al. 2011; Mowat and Heard, 2006). The primary factor influencing grizzly bear movement is food variety and availability, especially during periods of den entry and exit (Pigeon et al. 2016; Edwards et al. 2011; Mowat and Heard 2006). As grizzly bears inhabit a variety of ecosystems, the diets between populations can vary drastically (Mowat and Heard 2006). On a macro scale, grizzly bear populations found in coastal environments in British Columbia and the Northern United States rely more heavily on meat based foods than interior populations that derive much of their nutrients from plant foods (Mowat and Heard 2006). This variation in diet is a direct result of the food sources available within a grizzly bear’s home range (Pigeon et al. 2016). Shorter growing seasons experienced in high latitude home ranges have a significant effect on food availability (MacHutchon et al. 2003).

Although plant foods in high latitude environments, such as the North Yukon study area, are of similar nutritional quality to those found in southern environments, suitable growing sites, season length, and food diversity are significantly reduced due to climatic effects (MacHutchon et al. 2003; Pianka 1966). Some studies suggest that grizzly bears inhabiting northern environments alter their movement behavior to compensate for a shorter growing season (Mowat and Douglas, 2006; Pigeon et al. 2016). Others conclude that despite plant food disadvantages, it is not necessary for northern grizzly bear populations to exhibit higher rates of movement largely due to reduced human disturbance and more nutritious proteins and fats from food sources such as caribou (MacHuchton 2001). Diet specialization not only differs between distinct populations inhabiting different environments, but within populations themselves. A single population study consisting of 51 grizzly bears within the Mackenzie Delta region of the Canadian Arctic highlights micro level diet specialization (Edwards et al. 2011). Three distinct foraging groups were identified within a single population ranging from near-complete herbivory to near-complete carnivory (Edwards et al. 2011). These variations in diet can be seen to influence movement patterns of grizzly bears in a number of ways. Grizzly bears relying more heavily on plant-based diet exhibit reduced movement rates as plant based foods are more abundant and require less foraging effort (Edwards et al. 2011). Herbivore diets are able to be satisfied by slowly foraging across a landscape abundant in plant-based foods (Edwards et al. 2011). The opposite is found in bears consuming a diet predominantly based on animal foods. Although animal food sources are higher in

nutritional content, these food sources are sparser and require higher foraging efforts resulting in increased rates of movement (Edwards et al. 2011). Movement rates have also been linked to grizzly bear sex and body condition (Edwards et al. 2011; Pigeon et al. 2016). Nutritional requirements of individuals vary based on size and sex (Edwards et al. 2011). Larger body sizes of males require greater amounts of protein, resulting in higher movement rates as foraging animal foods involves increased effort (Edwards et al. 2011; Mowat and Heard 2006; Pigeon et al. 2016). In contrast, smaller body sizes of females and sub-adults allow for grizzly bears to satisfy their nutritional needs with less nutrient rich but more abundant food sources, reducing foraging efforts and movement rates (Edwards et al. 2011). The influence of body condition and sex on movement patterns has also been observed in other mammal species such as grey mouse lemur and ground squirrels (Pigeon et al. 2016; Vuarin et al. 2013). During den entry and exit periods, sex and reproductive status have significant implications on movement behavior (Pigeon et al. 2016). Gestating females enter dens approximately 2 weeks earlier than males, while females lactating during hibernation periods remain in dens longer into the spring (Pigeon et al. 2016). Females accompanied by cubs alter den entry and exit date based on cub age, where younger cubs result in earlier den entry and later emergence (Pigeon et al. 2016). Movement decisions based on minimizing risks of predation, especially during den entry and exit periods, more strongly influence females with cubs (Gardner et al. 2014). Movement rates of grizzly bears are also affected by environmental factors in the inhabited region (MacHutchon et al. 2003). Pigeon et al. (2016) suggested that climatic influences are the strongest drivers of den entry and exit. Reduced precipitation rates

and higher spring temperature associated with southern populations result in earlier den exits, while increases in snowfall seen in northern latitudes delay den exit (Pigeon et al. 2016). High latitude climates have significantly reduced food availability, increasing the importance and influence of torpor expression on movement rates (Humphries et al. 2003). According to the latitudinal species diversity gradient, trophic complexity should decrease with increasing latitude, suggesting that northern populations rely more heavily on plant based foods and thus experience reduced movement rates (Pianka 1966). Furthermore, populations in northern latitudes experience periods of 24-hour sunlight during the summer, but have much shorter active seasons, altering movement patterns to maximize nutritional intake and minimize energy expenditure (Pianka 1966; Vuarin et al. 2013). Grizzly bears in dryer areas with minimal snowfall have been observed to consume more animal foods (Mowat and Heard 2006). This is largely due to the high abundance and variety of ungulates in such climates, resulting in higher rates of movement among grizzly bears as their trophic complexity increases (Mowat and Heard 2006; Edwards et al. 2011). Environmental conditions such as ecosystem productivity, temperature, and snowpack play a large role in governing grizzly bear diets, and in turn movement patterns (Bojarska and Selva 2012).

Quantifying Animal Movement An animal’s home range is defined by the area it travels to find food and other resources. A home range is dynamic and shift based on resource availability (Edwards et al. 2011). Metrics designed to quantify home range size can be applied to location data collected using GPS collars. These methods typically include polygons that give

the extent of home range or use density functions to identify the intensity of activity (Worton 1986). The minimum convex polygon (MCP) method of Odum and Kuenzler (1955) defines the minimum area necessary to contain all bear location points. It is noted for its simplicity as a robust way to identify home range (Blanchard and Knight 1991). Further analysis of home range can be carried out by observing dynamic moving polygons, in order to quantify expansion and contraction of home range over time. This method is used in many analyses, such as in the Spatial-Temporal Analysis of Moving Polygons (STAMP) model of Smulders et al. (2012). Through quantifying home range, one can begin to understand the environmental influences on its area and the movements within. Animal movement can be studied in a variety of ways. Many studies use direct observation to understand the factors affecting movement (Edwards et al. 2011; Pigeon et al. 2016; MacHutchon and Wellwood 2003). These studies have an advantage in that factors are directly observed and recorded, so influences on movement are explicit. However, strong studies can be carried out using remotely sensed data (Johnson et al. 2002; Avgar et al. 2013). The quantification of mammal movement is often done using statistical analysis of telemetric data. A majority of Grizzly bear studies have adopted the use of GPS collars for their studies. When analysis is conducted on the GPS data, spatial patterns can emerge giving insight to clarify understanding of biotic and abiotic factors. Tracey et al. (2005) compared animal movement with landscape objects. In this study, non-linear regression was used to compare distance from an object to movement distances and movement angles. Studies have also been done to compare environmental factors to mammal movement (Johnson et al. 2002; Avgar et al. 2013).

These studies report moderate success in identifying relationships between movement and the environment. More complex studies compare movement rates to environmental variables using advanced statistics, such as by using a semivariance framework (Signer and Ovaskainen 2016) or by adapting artificial neural networks (Tracey et al. 2010). Further, many studies examine these variables over changing time or with respect to changing resources (Prima et al. 2017). In most cases, a simple linear regression analysis provides a robust method for investigation of movement patterns.

Methods Bear location data were collected using GPS collars on approximately 60 bears from 2004 to 2010. These collars recorded location, ambient temperature, and activity levels typically every four hours. Collars recorded bear movement for various lengths of time, depending on the bear. The location data acquired though these collars was used in this analysis. Location information from 40 bears was provided for movement analysis. This data required editing prior to its use. Many data points had null or 0 values as their locations, as a result of satellite or GPS collar errors. These points were deleted to complete the dataset. In order to quantify movement rate, lines were interpolated between time-successive points. These lines were then measured to give an estimate of how far a bear moved over the time interval. The distances moved were summed over the course of a day. Each date was converted to Julian day, in order to standardize the datasets. The total distance a bear moved over the course of a day was divided by 24, to provide an average hourly movement rate. Due to expected seasonal differences in

movement, location points were divided into categories, from April to May, June to August, and September to November. Exploratory data analysis was carried out in order to examine potential relationships. This involved simple summary statistics, such as average moment rate, as well as visualizing the data using graphs. Simple correlation was then performed between Julian day and movement rate, to examine how movement changes over the course of the year.   To understand environmental factors affecting movement, the relationship between bear locations and factors such as distance from vegetation and distance from water courses was examined. This firstly involved analysing the data in map form to identify potential relationships. Vegetation classes were taken from a classified satellite image generated in 1995 (table 1) (Hawkings 2002). Euclidian distance from each vegetation class, as well as from water courses and water bodies, was calculated. Again, shapefiles were split into spring, summer, and fall seasons. The density of points was calculated using a kernel density tool. Kernel density was appropriate due to its ability to effectively interpolate between points, producing a smoothed surface of movement. Density images were rescaled to values between 1 and 10 in order to standardize between datasets of different sizes. Finally, the location points were assigned values from the corresponding distance factors as well as the density calculation. Simple regression models were chosen to analyze the relationship between distance to environmental factors and standardized density, in order to estimate the drivers of grizzly bear movement. Multi-variable regression was used as it was assumed that there were some relationships among variables.

Table  1:  Summary  of  vegetation  classes  in  General  Land  Cover/Habitat  Classification  for  Yukon  North  Slope  (Hawkings  2001)  

Results Movement Rate Movement rate varied greatly over the course of a year and between males and females (figure 1). For both male and female bears, movement rate was greatest during the summer months and lower during the spring and fall. Movement rates were, on average, greater for male bears than for female bears (table 2). Correlation analysis suggested a strong relationship between Julian day and movement rate (table 3). In general, bears gradually increased their movement in the spring, post-den exit. In the summer movement rates decreased slowly, and in the fall movement rates decreased rapidly. This pattern was similar for male and female bears during the spring and fall.

However, during the summer males and females exhibited different patterns of movement, with male movement being more random.  

Figure  2:  Movement  rates  for  the  total  population,  females,  and  males,  as  a  function  of  Julian  day  

  Table  2:  Average  movement  rate  in  m/h  for  the  total  population,  males,  and  females  

 

Table  3:  Correlation  coefficients  describing  the  relationships  between  bear  activity  levels  (movement  rate)  and  time  of  year,   divided  by  gender.    

 

Regression Analysis Values for R and R-Squared were generally moderate (table 4), suggesting explicit differences between groups. The weakest relationship was exhibited when analysing the population as a whole (R=0.37; R-squared=0.14), whereas the strongest relationship was between male movement hotspots in the spring and various environmental factors (R=0.75, R-squared=0.56). Stronger relationships are observed when dividing the data by gender and season. Regression coefficients and p-values revealed those factors that contributed most to the observed relationships (table 5). Negative values indicate that as the distance to that particular variable decreases, the density of bear location points increases, essentially representing a preferred environment. In general, females and males showed seasonal patterns in their locations.   Table  4:  R  and  R-­‐squared  values,  describing  the  strengths  of  the  models  used  to  predict  grizzly  bear  density  as  a  function  of   environmental  variables.  Higher  values  indicate  a  stronger  relationship  

In the spring, female bears preferred vegetation classes 9, 11, and 13. In the summer, they were most commonly found near classes 12 and 14. In the fall, they were

typically located near classes 11, 13, and were also found close to water courses. On the other hand, males preferred vegetation classes 10 and 7, and showed a relationship to water courses in the spring. They were found nearest classes 11, 12, and 9 in the summer, and preferred class 12, as well as proximity to water courses, in the fall. These patterns of movement may be influenced by specific vegetation types found in each vegetation class. P-values for these classes were generally low. However, the relatively high p-values associated with female bears and vegetation class 11 bring into question the strength of those relationships. High p-values suggest the observed relationship may be due to random variation in the data set. Table  5:  Regression  coefficients  and  p-­‐values  describing  the  relationships  between  vegetation  classes  and  grizzly  bear  density.   Negative  coefficients  indicate  proximity  to  that  variable  is  related  to  denser  occurrences  of  grizzly  bears.  P  values  of  less  than   0.05  suggest  that  the  values  obtained  are  statistically  significant  

Discussion and Limitations Movement Rate Patterns of movement rate for North Slope grizzly bears follow a typical trend. Previous studies of grizzly bear movement have suggested that there is a seasonal influence on movement rates (MacHutchon 2001). The pattern of low movement rates in

the spring and fall, and higher rates in the summer, is consistent with observations made of grizzly bear activity (MacHutchon 2001). Post-den exit, in the spring, bears are lethargic and exert minimum energy to pursue limited food sources (Humphries et al. 2003). In the summer, they are most active due to a need to find adequate food for caloric bulking (MacHutchon 2001). In the fall, they decrease their activity again to preserve energy and prepare for the denning season (Pigeon et al. 2016). Additionally, average movement rates suggest that activity levels are greater for males than for females. This observation is reflected in previous studies (Edwards et al. 2010; Edwards et al. 2011; Mowat and Douglas, 2006; Pigeon et al. 2016). This is because male bears require greater amounts of food in order to maintain body size. This higher caloric need is fulfilled by increasing activity levels and by pursuing higher quality sources of food, such as protein-rich meats (Edwards et al. 2010).

Regression Analysis Regression analysis revealed weak to moderate relationships between environmental factors (distance to vegetation types and distance to water courses) and grizzly bear density. However, of these relationships, regression coefficients revealed some patterns of movement. These coefficients outline the differences in male and female patterns of movement on a seasonal scale. Male and female preferred environments often did not overlap. The vegetation classification used for this analysis provides only moderate indications of associated plant types, and minimal indication of their density. Nevertheless, additional sources can provide some idea of the types of vegetation typically observed in these classes. This can be related back to known

information about grizzly bear diet, in order to understand why we are observing relationships between specific classes and grizzly bear location hot spots in the regression analysis. There was little or no association of grizzly bears with land classes termed “wet” (classes 4-8). The major relationships occurred within moist to dry terrain (classes 914). This could speak to the type of preferred vegetation. Hydrophytes, such as the ones that would be found in classes 4-8, are not indicated in any of the primary literature as being a major part of grizzly bear diet. However, plants associated with moist or dry environments tend to be a major source of food for grizzly bears. For example, Hedysarum Alpinium roots comprise a major part of grizzly bear diet in the spring and a minor part in other seasons, as they have high levels of protein (MacHutchon and Wellwood 2003). These plants grow in well-drained soils (Drew and Shanks, 1985), and so are likely associated with the observed vegetative classes. Classes 11, 12, and 13 often show a strong relationship to grizzly bear communities. These classes are associated with varying degrees of shrubs, including Crowberry (Empetrum), Lingonberry (Vaccinium), snow willow (Salix Reticulata), and bearberry (Arctostaphylos) (Hawkings 2001). Vaccinuim and Empetrum species are particularly important plant contributors to grizzly bear diet (MacHutchon and Wellwood, 2003; Edwards et al. 2010), which may provide a reason for grizzly bear location. The cottongrass tussock ecosystem (class 10) is related to male movement in the spring, which may suggest that this ecosystem provides a source of food. Some studies (MacHutchon and Wellwood 2003; Phillips 1987), through direct observation, concluded that cottongrass was not a primary source of food for grizzly bears. However,

Gau and Case (1998), reported to have observed the consumption of cotton grass by grizzly bears in the Northwest Territories. Vegetation types that are associated with cotton grass species, such as crowberries and other graminoids (US Fish and Wildlife Service 1987) are also important spring sources of food for grizzly bears (MacHutchon and Wellwood, 2003). The draw to this ecosystem may be a combination of abundant cottongrass and other minor species. The association of grizzly bears with specific environmental factors can be used to infer relationships with certain types of prey. MacHutchon and Wellwood (2003) suggested that grizzly bears typically hunt caribou as they move from dense tree or shrub cover towards major rivers. This association between caribou hunting and watercourses may reflect the relationship between male bears and water courses in the spring. As Phillips (1987) suggested, a large component of grizzly bear diet in the spring is caribou. Males typically receive a higher portion of their diet from animal sources than females (Mowat and Heard 2006) which may help to explain why they are more closely following water courses than females. The use of vegetation class 7 (wet barrens) by male grizzly bears in the spring may also reflect barren-ground caribou migration patterns (Gau et al. 2002). In addition, the proximity of males and females to water courses during the fall may be associated with salmon availability. Salmon serves as a minor source of protein for grizzly bears (Mowat and Heard 2006). The understanding of predator-prey relationships in this study is limited to inferences regarding how prey might use specific vegetation classes.

Study Limitations Moderate to low regression scores indicate that some factors that drive grizzly bear movement have been left out. An obvious limitation of this study was the inability to include data regarding the distribution of grizzly bear prey, such as caribou or small vertebrates. Predator-prey relationships form an important component of movement in these species. However, a lack of appropriate data has made it difficult to include this aspect of movement into the analysis. Indications of prey contributions were inferred due to specific habitat considerations, but a more robust study would include direct observations of these species. Further influences of movement include interactions among bears (competition, mating), as well as the reproductive status of female bears. These relationships were not examined in this study, but are likely to contribute to grizzly bear movement. The quality of data used in this study may have influenced results. The primary bear location data was flawed in that it only recorded location typically every four hours. Distance between points was calculated linearly, which is likely not the case for true movement. Therefore, distances calculated in the initial GIS analysis are certainly underestimates of the true distance covered. Further, bear locations may have occurred closer to specific vegetation or water courses than what was recorded in this study, but were simply not recorded due to timing. In addition, often location was not recorded due to issues connecting with satellites, creating gaps in the data. Factors that affect GPS collar performance, such as dense overhanging vegetation and topography (Swanlund et al. 2016), may be associated with a specific type of vegetative class. Because these points were empty and not considered in analysis, there may be a bias associated with

missing data. However, these blank data points comprised only a small portion of total observations and therefore the data used in analysis is likely highly reflective of true conditions Additional uncertainty was created using the vegetation classes as an indicator for movement. Firstly, the classes used are vague and provide little understanding of the true species types associated with them. Plant species that are commonly consumed by grizzly bears may have occurred in a variety of vegetative classes. This may help to explain the weak relationships recorded between vegetation and bear location, as this layer provides no indication of the relative occurrence of plant species that are preferred by grizzly bears. Further, this image was created in 1995. Though the area is not actively influenced by human activity, it is possible that there has been a change in species distribution over time. Moreover, the image was created during a specific time of year. This analysis considers bear locations to have a temporal or seasonal influence. An appropriate analysis would consider how vegetation classes also change over the course of a year. This is especially relevant for class 1, which represents ice cover. It is very likely that this changes based on season, and either reveals or obscures other classes. Splitting the population into males and females, as well as by seasons, revealed explicit differences in the way these populations use space. Further analysis may look to divide the population in different ways. Initially, this study aimed to identify differences between adult and sub-adult groups. However, due to a very small amount of sub-adult bears, the data set became too sparse for analysis. Another possible division includes looking at differences between encumbered and unencumbered females, but this

information was not provided. Future work may identify differences in movement among these additional groups. A second research question that was left unexplored was aimed at understanding how northern populations of grizzly bears compensate for a shortened growing season. This could be accomplished by comparing the results of this study to information regarding a southern population of grizzly bears. However, differences in data collection methods and vegetation types create difficulties in locating a comparable study. Therefore, running similar analyses on a second population of grizzly bears would be the most effective method to provide insight into the differences between northern and southern populations. In this way, a more complete understanding of grizzly bear movement would be formed.

Conclusion The aim of this study was to identify the factors that drive northern grizzly bear movement. By using simple correlation analyses, it was concluded that there is significant variation in grizzly bear movement rate related to the time of year. Further, activity levels were determined to vary between males and females. Regression analysis outlined some likely relationships between environmental variables and bear density. This model predicted areas that consist of moderate to dense shrubs and were close to water courses comprise preferred foraging environment for grizzly bears, but these preferred classes changed over the course of a year. This seasonal variation in grizzly bear environment is expected and predicted in many other studies.

There were some limitations introduced due to data quality. Namely, the collection of bear locations using GPS collars produced gaps in the data set due to the time interval over which data was collected and because satellite unavailability meant some points were not recorded. Further uncertainty was introduced due to the land cover map that was used, as it may be outdated and the vegetation classes were notably vague. Finally, the model failed to properly integrate grizzly bear prey data. Prey information was only inferred as a proxy, which may have limited the success of this model. Fundamentally, this analysis was successful as a relatively simple way to identify movement factors. By understanding current patterns of grizzly bear movement, we can begin to predict how they would shift with changing resources and changing climate. Future studies can contribute to an understanding of northern grizzly bear movement by finding additional ways to divide the populations. This will provide information on how different groups use home range, and indicate how they might respond differently to a changing environment. In addition, it would beneficial to do a comparative analysis on a second population of grizzly bears from a southern region, in order to understand how these populations differ.

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