OCS Study 

MMS 2004-014 

The Use of Sea Ice Habitat by Female Polar Bears in the 

Beaufort Sea

Prepared for 

U.S. Department of the Interior 

Mineral Management Service 

Alaska OCS Region 

949 East 36th Street 

Anchorage, Alaska 99508 
 OCS Study 

 MMS 2004-014 

The Use of Sea Ice Habitat by Female Polar Bears in the 

Beaufort Sea

By 

George M. Durner1

, Steven C. Amstrup1, Ryan Neilson2, and Trent McDonald2

1

U. S. Geological Survey, Alaska Science Center, 1011 East Tudor Road, Anchorage, Alaska 

99503, USA 

2

WEST, Inc., 2003 Central Avenue, Cheyenne, Wyoming 82001, USA 

Prepared for 

U.S. Department of the Interior 

Mineral Management Service 

Alaska OCS Region 

949 East 36th Street 

Anchorage, Alaska 99508 

This study was funded, in part, by the U.S. Department of the Interior, Minerals Management 

Service (MMS), U.S. Geological Survey, Biological Resources Division, as part of the MMS 

Alaska Environmental Studies Program. 

January 2004 

The opinions, findings, conclusions or recommendations expressed in this report or product are those of the authors 

and do not necessarily reflect the views of the Minerals Management Service, nor does the mention of trade names 

or commercial products constitute endorsement or recommendation for use by the Federal Government. 
PROJECT ORGANIZATION PAGE 

George M. Durner, Research Zoologist, U.S. Geological Survey, Alaska Science Center, 1011 

East Tudor Road, Anchorage, Alaska 99503, USA 

Steven C. Amstrup, Project Leader, U.S. Geological Survey, Alaska Science Center, 1011 East 

Tudor Road, Anchorage, Alaska 99503, USA 

Ryan Nielson, Statistician, WEST, Inc., 2003 Central Avenue, Cheyenne, Wyoming 82001, 

USA 

Trent McDonald, Statistician, WEST, Inc., 2003 Central Avenue, Cheyenne, Wyoming 82001, 

USA 

REPORT AVAILABILITY 

U.S. Geological Survey 

Alaska Science Center 

1011 East Tudor Road 

Anchorage, AK 99503 

Telephone: (907) 786-3512 

SUGGESTED CITATION 

Durner, G. M., S. C. Amstrup, R. Neilson, and T. McDonald. 2004. The Use of Sea Ice Habitat 

by Female Polar Bears in the Beaufort Sea. U.S. Geological Survey, Alaska Science 

Center, Anchorage, Alaska. OCS study, MMS 2004-014. 

 ii
TABLE OF CONTENTS 

PROJECT ORGANIZATION PAGE.............................................................................................II 

TABLE OF CONTENTS.............................................................................................................. III 

LIST OF TABLES........................................................................................................................IV 

LIST OF FIGURES ....................................................................................................................... V 

ABSTRACT.................................................................................................................................. VI 

ACKNOWLEDGEMENTS.........................................................................................................VII 

INTRODUCTION .......................................................................................................................... 1 

METHODS ..................................................................................................................................... 2 

 Ice Data ...................................................................................................................................... 3

 Ocean Depth............................................................................................................................... 6

 Creating Discrete Choice Habitats............................................................................................. 6

 Polar Bear Locations.................................................................................................................. 6

 Defining Habitat Available to Polar Bears................................................................................ 7

 Generating Random Locations and Attaching Habitat Variables.............................................. 8

 Generating a Resource Selection Function................................................................................ 8

RESULTS ....................................................................................................................................... 9 

 Spring....................................................................................................................................... 10

 Summer.................................................................................................................................... 11

 Autumn .................................................................................................................................... 13

 Winter ...................................................................................................................................... 14

 Evaluation of Models............................................................................................................... 14

DISCUSSION............................................................................................................................... 16 

LITERATURE CITED................................................................................................................. 21 

APPENDICES .............................................................................................................................. 25 

 iii
LIST OF TABLES 

Table 1. Descriptions and codes for ice stage and form...............................................4 

Table 2. Original codes for National Ice Center ice concentration..................................6 

Table 3. Seasonal discrete choice models predicting relative probability.........................10 

 iv
LIST OF FIGURES

Figure 1. Extent of the National Ice Center ice chart of the Beaufort Sea 

Figure 2. Example of movements of bear 20330 

Figure 3. Defined seasons for modeling polar bear habitat use 

Figure 4. Relative probability of selection as a function of variables – spring 

Figure 5. Relative probability of selection as a function of variables – summer 

Figure 6. Relative probability of selection as a function of variables – autumn 

Figure 7. Relative probability of selection as a function of variables – winter 

Figure 8. Comparing the seasonal distribution of RSF values in the Beaufort Sea 

Figure 9. Distribution of the spring RSF in the Beaufort Sea – June 

Figure 10. Distribution of the spring RSF in the Beaufort Sea – February 

 v
ABSTRACT

Polar bears (Ursus maritimus) depend on ice-covered seas to satisfy life history 

requirements. Modern threats to polar bears include oil spills in the marine environment and 

changes in ice composition resulting from climate change. Managers need practical models that 

explain the distribution of bears in order to assess the impacts of these threats. We explored the 

use of discrete choice models to describe habitat selection by female polar bears in the Beaufort 

Sea. Using stepwise procedures we generated resource selection models of habitat use. Sea ice 

characteristics and ocean depths at known polar bear locations were compared to the same

features at randomly selected locations. Models generated for each of four seasons confirmed 

complexities of habitat use by polar bears and their response to numerous factors. Bears 

preferred shallow water areas where different ice types intersected. Variation among seasons 

was reflected mainly in differential selection of total ice concentration, ice stages, floe sizes, and 

their interactions. Distance to the nearest ice interface was a significant term in models for three 

seasons. Water depth was selected as a significant term in all seasons, possibly reflecting higher 

productivity in shallow water areas. Preliminary tests indicate seasonal models can predict polar 

bear distribution based on prior sea ice charts and bathymetry data. 

KEY WORDS: discrete choice models, habitat selection, polar bear, resource selection function, 

RSF, sea ice, Ursus maritimus. 

 vi
ACKNOWLEDGEMENTS 

Principal funding for this work was provided by the United States Geological Survey, 

Alaska Science Center, and the United States Minerals Management Service. Additional support 

was provided by the following agencies: BP Exploration – Alaska, Inc.; the Canadian Wildlife 

Service, Edmonton, Alberta; ConocoPhillips Alaska, Inc.; Continental Shelf Project, Ottawa, 

Ontario; ExxonMobil, Corp.; U.S. Fish and Wildlife Service, Marine Mammals Management; 

North Slope Borough, Department of Wildlife Management; and the Northwest Territories 

Department of Resources, Wildlife and Economic Development, Inuvik, NWT. We thank the 

following for their support in the field and office: Air Logistics-Alaska, Arctic Air Alaska, D. 

Andriashek, M. Branigan, A. Fischbach, K. Simac and G. Weston-York. D. Douglas provided 

support in satellite-telemetry data processing. The following individuals provided constructive 

reviews on an earlier draft of this manuscript: E. Knudsen, W. Horowitz, M. Huso, K. Simac, I. 

Stirling, and one anonymous referee. 

 vii
INTRODUCTION 

Polar bears (Ursus maritimus) occur in most ice-covered seas throughout the Arctic basin 

(Amstrup 2003). Their range includes the southern Beaufort Sea of northern Alaska and the 

Chukchi and Bering Seas of western Alaska. This dependence on sea ice is so strong that the 

distribution of polar bear sub-populations may be determined by regional characteristics of the 

sea ice (Ferguson et al. 1998). While the presence of sea ice is a prerequisite, bears probably 

respond to a multitude of ice characteristics and the interaction of those characteristics (Stirling

et al. 1993). Polar bears depend on sea ice for hunting ringed (Phoca hispida) and bearded seals 

(Erignathus barbatus) (Stirling et al. 1993). Seal abundance and distribution in the southern 

Beaufort Sea is assumed to be dependent on the seasonal and annual variability of ice 

characteristics (Kelly 1988) and ecosystem productivity (Stirling and Oritsland 1995). While 

high prey density may be tied to areas of high seasonal variability in sea ice, the nature of ice 

may actually decrease polar bear access to prey (Ferguson et al. 2000). Female bears with new 

young are believed to utilize the stable nearshore fast ice in order to avoid adult males and to 

hunt seals occupying subnivian birth lairs (Stirling et al. 1993). The degree of success in 

locating mates during the breeding season may be dependent on ice type (Stirling et al. 1993). 

Annual dynamics of sea ice result in a pulse of polar bears into the near-shore regions of autumn

ice. Access to terrestrial maternal den habitat may be dependent on autumn sea ice 

characteristics (Stirling and Andriashek 1992, Amstrup and Gardner 1994). In the Beaufort Sea 

region as many as 50% of pregnant bears give birth to their young in snow dens on the surface of 

the sea ice (Amstrup and Gardner 1994). Maternal dens in the pelagic environment depend on 

stable ice for the duration of den tenure (Amstrup and Gardner 1994). In summary, polar bear 

distribution is not uniform in the Arctic but rather is determined by the nature, spatial and 

temporal extent of sea ice, and the specific requirements of reproductive status. 

Sea ice is composed of a complex array of ever changing structure and composition 

(MANICE 1994). The action of currents, winds and temperatures, which vary by season, 

produce a range of ice composition including rafts of new ice only several centimeters thick to 

pressure ridges of first year and old ice that rise several meters above and below the sea surface. 

Pack ice leads form and close and floes of various sizes are created. Some ice survives the 

summer’s melt to become thick and stable multiyear ice. Polar bears are the apical predator of

this environment. Perceived changes in population status and distribution of polar bears may be 

extrapolated to determine effects due to variation in the sea ice environment (Stirling and 

Derocher 1993, Stirling 1997). Conversely, observed and predicted changes in sea ice patterns 

may be used to estimate the future effects on polar bears. The relationship between polar bears 

and their primary prey, i.e., ringed seals, is so close that understanding the population status of 

one can be used to explain the population status of another (Stirling and Øritsland 1995). On the 

next level, because productivity of ringed seals is closely tied to the productivity of arctic marine 

systems, knowledge of polar bear population status and distribution may provide an 

understanding of variation in productivity in arctic seas (Stirling and Derocher 1993, Stirling 

1997). In oceans where sea ice is prevalent throughout most of the year, biological productivity 

is likely driven by presence of the continental shelf, ice edge habitat, and waters fed by nearshore 

polynyas (Stirling 1997). Ultimately, however, both prey and predator populations are driven by 

the nature of the sea ice (Stirling et al. 1977, Mauritzen et al. 2003). 

Understanding the relationship between polar bears and sea ice is useful from a 

management perspective. Future industrial development along the northern Alaska coast is 

expected to extend further into polar bear habitat and will increase the potential for 

 1
anthropogenic disturbances (Amstrup et al. 1986). Off-shore petroleum exploration and 

development and ocean-going vessels can alter sea-ice habitat and thus polar bear distribution 

(Amstrup et al. 1986, Stirling 1990). Petroleum spills can be directly fatal to polar bears or result 

in long-term negative health effects (Øritsland et al. 1981, St. Aubin 1990). Spilled oil will most 

likely accumulate in habitats frequented by seals and polar bears (Neff 1990). Evidence suggests 

that arctic climate patterns are changing (Vinnikov et al. 1999, Morison et al. 2000, Parkinson 

2000, Drobot and Maslanik 2002). Slight increases in average temperatures may cause dramatic 

changes in the sea ice on which polar bears depend. Long-term absence of sea ice will 

negatively impact polar bear populations (Stirling et al. 1999). One effect may include local 

extinction within the southern periphery of their range (Stirling and Derocher 1993, Stirling et al. 

1999). The welfare of polar bears is an international and local concern (Lentfer 1974, Nageak et 

al. 1991). Addressing this concern will necessitate a greater understanding of polar bear habitat 

use in order to prevent or mitigate negative consequences from environmental perturbations. 

We have a sound understanding of the population status and distribution of polar bears in 

the Beaufort Sea (Amstrup et al. 2000, Amstrup et al. 2001a, Amstrup et al. 2001b). Other 

agencies have developed protocols for accurate mapping of sea ice through compiling various 

remotely sensed and in situ data sources (MANICE 1994, Partington et al. 1999). Considerable 

gains have been made in understanding polar bear and sea ice relationships in other regions of 

the Arctic (Ferguson et al. 2000, Mauritzen et al. 2001, 2003) but not, however, in the Beaufort 

Sea. In particular, the coarse resolution of previous studies has been inadequate to explain the 

fine-scale aspects of sea ice habitat use (Arthur et al. 1996, Mauritzen et al. 2003). A need exists 

for practical models of polar bear/sea ice relationships that managers may use to assess the 

impacts of anthropogenic and natural changes in the Arctic. The ability to predict the response 

of polar bears to a changing Arctic and to reduce the potential negative effects of human-caused 

perturbations will increase with a better understanding of polar bear sea ice requirements. 

The objective of this study was to quantitatively describe patterns of sea ice-habitat use 

by polar bears in the southern Beaufort Sea. Furthermore, the products of this study provide the 

tools that allow resource agencies to predict the likelihood of occurrence of polar bears within a 

region based on the characteristics of the sea ice. Historical distributions of ice may be used to 

predict occurrence of polar bears prior to initiation of proposed human activity. Updates of sea 

ice charts will allow managers to adjust management plans according to changes in sea ice 

composition and the expected polar bear distribution that results. In this study we used discrete 

choice models to quantify patterns of sea ice habitat use by polar bears in the Beaufort Sea. We

tested the performance of our models against an independent set of real polar bear location data. 

The practical application of this knowledge will allow managers to predict occurrence of polar 

bears and create flexible management plans prior to initiation of proposed human activities. 

METHODS

Our study area was the extent of the National Ice Center’s (NIC, Washington, D.C.) chart 

of the Beaufort Sea (Fig. 1). This area includes 607,000 km2

 of seasonal and permanent ice 

covered waters that occur between 122 - 155° west longitude, and north of the mainland coast of 

Alaska and Canada to 76° north latitude. The region is typified by continuous sea ice coverage 

between November and May and approximately 50% ice coverage during the period of 

minimum ice extent in September. Ice movement is influenced by seasonal fluxes and a 

clockwise gyre that is centered in the Beaufort Sea (Gloersen et al. 1992). 

 2
Figure 1. Extent of the National Ice Center ice chart of the Beaufort Sea region for modeling polar bear habitat use, 

1997 – 2001. 

Ice Data 

We used ice charts created by the NIC and charts for the west Arctic from the Canadian 

Ice Service (CIS, Environment Canada, Ottawa, Ontario). NIC and CIS produce detailed charts 

of sea ice conditions from a diverse source of remotely sensed data. NIC data incorporates 

National Oceanic and Atmospheric Administration (NOAA) passive microwave Special Sensor 

Microwave Imager (SSM/I, 25 km resolution), Advanced High Resolution Radiometer 

(AVHRR, 1 km resolution), RADARSAT-1 synthetic aperture radar imagery (SAR, 100 – 200 m

resolution), and Defense Meteorological Satellite Program (DMSP) Operational Linescan 

System (OLS, 550 m resolution). CIS data incorporates AVHRR and RADARSAT, plus NOAA 

Geostationary Operational Environmental Satellite images (GOES, 4 – 6 km resolution), and 

ERS (European Space Agency) satellite data. NIC and CIS charts are geographic information 

system (GIS) ARC/INFO (ver. 8.1; ESRI, Redlands, CA) polygon coverages. NIC charts are 

projected as polar projections with the central meridian at 180o and the latitude of true scale at 

60°. CIS charts were re-projected as to be concordant with NIC charts. For the Beaufort Sea, 

NIC charts are created on average every 5.8 days (SD = 3.3, n = 265) and CIS charts every 11.1 

days (SD = 8.3, n = 136). Each habitat polygon in an NIC chart represents the aerial extent 

(partial concentration) and stage (the thickness or age) for the 3 thickest stages of ice (Table 1). 

Ice stage includes a range from new ice (thin ice newly formed and up to several centimeters in 

thickness), to multiyear ice (ice that has survived at least one summer and may be > 2 m thick). 

NIC identifies up to 3 partial stages within an area, the extent of each partial stage indicated as a 

proportion of the total aerial coverage of ice within a region. Thus, it is possible to have, for 

example, a mix of new ice, first-year ice, and multiyear ice within a defined region, and each 

partial stage has its own respective concentration. Total concentration is the total extent of ice 

 3
Table 1. Descriptions and codes for ice stage and form for generating polar bear resource selection functions in the 

Beaufort Sea, 1997 – 2001. 

National Ice Center 

Ice stage Thickness 

(cm)

NIC code Our code 

for stage 

Ice free 00 Ice free 

No stage 80 Ice free 

New 81 Ice free

Nilas, rind < 10 82 Ice free 

Young 10 – 30 83 Young 

Grey 10 – 15 84 Young 

Grey – white 10 – 30 85 Young 

1st year 30 – 200 86 First year 

Thin 1st year 30 – 70 87 First year 

Thin 1st year 

stage 1 

30 – 50 88 First year 

Thin 1st year 

stage 2 

50 – 70 89 First year 

Medium 1st 

year 

70 – 120 91 First year 

Thick 1st year 

ice 

> 120 93 First year 

Old ice 95 Old 

2nd year 96 Old 

Multi-year 97 Old

Canadian Ice Service 

Form

description 

Width 

(m) 

CIS code Our code 

for form

Pancake < 2 0 Cake

Small cake ice < 2 1 Cake 

Ice cake 2 – 20 2 Cake 

Small floe 20 – 100 3 Small floe 

Medium floe 100 – 500 4 Small floe 

Big floe 500 – 2000 5 Big floe 

Vast floe 2000 – 10,000 6 Vast floe 

Giant floe > 10,000 7 Vast floe 

Fast ice 8 Fast ice 

coverage (expressed in tenths), and is the sum of concentrations of the 3 partial stages within the 

respective region (Table 2). Likewise, CIS charts delineate habitat polygons of the partial 

concentration and form (average floe size) of ice. Ice form is defined as a categorical average 

size (diameter) of individual ice floes within an area (Table 1). Ice form can range from small 

ice cakes that average 2 m in diameter, to giant ice floes that may be > 10 km in diameter. For 

example, an ice form coded as “2” indicates “cake ice” that is 2 – 20 m in diameter. Likewise, 

an ice form coded as “5” indicates “big floes” that range between 500 – 2000 m in diameter. 

 4
Each partial ice form has an accompanying partial concentration. As with ice stage, CIS data 

identifies as many as 3 partial ice forms within a defined region, the extent of each one a 

proportion of total area. Because we used NIC charts for determining partial and total 

concentrations, we re-calculated the extent (concentration) of CIS partial ice forms so that their 

sum would be equal to total concentration derived from NIC charts. We binned similar ice 

stages and similar ice forms to simplify models (Table 1). 

Both NIC and CIS present data of ice concentration, stage and form as categorical 

variables. With NIC data, a single code is used to represent a range of values. For example, 

consider the following NIC code for an ice polygon: 

CT91CA809599CB108399CC018199. 

In this example the 1st 4 columns (CT91) represents the total extent of ice cover, which is 90 – 

100% cover. The first thickest partial stage is represented by columns 5 – 12, where the partial 

ice concentration is ‘80’ (80% coverage), the stage of ice is ‘95’ (old ice), and the ice 

form is ‘99’ (unknown). The 2nd thickest partial stage is represented by columns 13 – 20, where 

the partial concentration is ‘10’ (10 % coverage), the stage is ‘83’ (young ice, 10 – 30 cm thick), 

and the form is ‘99’ (unknown). The 3rd thickest stage is represented by columns 21 – 28, where 

the partial concentration is ‘01’ (< 10 % coverage), the stage is ‘81’ (new ice < 10 cm thick), and 

the form is unknown. We converted NIC categorical values of partial concentration into 

continuous variables by simply defining concentrations as the midpoint of the concentration 

range of the NIC category. Using the character string presented in the prior example, the 1st, 2nd

and 3rd partial concentrations would be 0.80, 0.10, and 0.05, respectively. Total concentration is 

set to the sum of these partial concentrations, or 0.95. Converting categorical variables into 

continuous variables can introduce bias into model parameter estimates. This generally is not a 

problem, however, when the original values are continuous in nature, and have been binned into 

categories after data collection (binned by NIC and CIS). Distance to the nearest polygon edge 

of NIC charts (edge) was also calculated. The category edge denotes the change of one NIC ice 

polygon to another adjacent ice polygon and is not to be confused with a transition from ice to 

relatively open water (Ferguson et al. 2000, Mauritzen et al. 2003). Because of the many 

different combinations of ice polygons that would produce an ice interface, we did not attempt to 

categorize ice interface types. 

Both NIC and CIS data offer several advantages in modeling polar bear sea ice 

relationships. They provide a far higher resolution picture than previously available over large 

areas (Arthur et al. 1996, Mauritzen et al. 2003). Maps are generated from diverse remotely 

sensed data that are interpreted to provide information on total ice concentration, as well as 

partial ice stages and forms. NIC and CIS charts are almost real time data. The extent of 

both NIC and CIS data includes the entire distribution of polar bears in the world. Thus, near 

real time analysis of expected polar bear distribution anyplace in the polar basin may be possible 

by modeling NIC and CIS data. Lastly, these data are interpreted and readily available to 

researchers and resource managers. 

 5
Table 2. Original codes for National Ice Center ice concentration (categorical variable) and our new 

variable for ice concentration (continuous variable) for a RSF for polar bears in the Beaufort Sea. 

Original Variable New Variable 

code ice coverage (% of area) code ice coverage (% of area) 

0 ice free 0.00 ice free 

1 < 10 0.05 5

10 10 0.10 10

13 10 – 30 0.20 20 

24 20 – 40 0.30 30 

35 30 – 50 0.40 40 

46 40 – 60 0.50 50 

57 50 – 70 0.60 60 

68 60 – 80 0.70 70 

79 70 – 79 0.80 80 

81 80-100 0.90 90

91 90 – 100 0.95 95 

92 95 – 100 0.975 97.5 

Ocean Depth 

We used data provided by the International Bathymetric Chart of the Arctic Ocean 

(IBCAO, http://www.ngdc.noaa.gov/mgg/bathymetry/arctic/arctic.html) to obtain data on ocean 

depth (depth). These data are provided as a polarstereographic projection grid with 2500 m

resolution. We converted the grid into an ARC/INFO polygon coverage with the same 

projection as ice charts. 

Creating Discrete Choice Habitats

Data derived from both the NIC and CIS charts, and the bathymetry chart, were 

combined to produce units of discrete habitats. We define a discrete habitat as a point on a map 

which is composed of several layers of habitat information including partial concentrations of up 

to three different stages and forms of sea ice, total ice concentration, distance to the nearest ice 

interface, and ocean depth. 

Polar Bear Locations 

We captured adult female polar bears in the Beaufort Sea and equipped many of them

with satellite radio collars, or Platform Transmitter Terminals (PTTs; Telonics, Mesa, AZ, USA). 

Male polar bears were not instrumented because their necks have a greater diameter than their 

heads and hence they do not retain collars. Polar bears were captured by injection of zolazepam

HCl and tiletamine HCl (Telazol®, Warner-Lambert Co.) through projectile darts fired from a 

Bell 206 helicopter (Stirling et al. 1989). Data transmitted by PTTs were received by polar 

orbiting NOAA satellites, which then were processed by the ARGOS Data Collection and 

Location System (Fancy et al. 1988). PTTs transmitted data hourly up to six hours per day 

 6
Figure 2. Example of movements of bear 20330 from point A to point B, circle defining available habitat, and 

distribution of random locations that are compared to location B. Note that random locations fall within the area 

defined by the NIC chart for the Beaufort Sea and not within the land portion of the availability circle. In this 

example, bear 20330 traveled 20 km but could have traveled up to 84 km (based on May movement rates for all

bears). 

every 1 – 7 days. Because this could generate a large amount of data we restricted our analysis 

to the best position per day and only when that position was estimated to have a > 68% chance of

being within 1.2 km of its true location, or an area of 4.5 km2

 (ARGOS quality locations 1, 2, 

and 3; Fancy et al. 1988, Keating et al. 1991). This potential error is much lower than the 

average size of ice polygons ( x = 18,964 ± 51,821 km2

 SD, n = 8417). Only 1 % of ice 

polygons had areas ≤ 72 km2. We retained all observations of bears that were not associated 

with maternal denning and were not located on land. We also used only those locations that 

were separated from the prior location by 2 – 7 days (44 – 172 hours) and fell within the 

temporal and spatial extent of NIC charts for the Beaufort Sea (Fig. 1). Polar bear location data 

were stored as an ARC/INFO (ver. 8.1; ESRI, Redlands, CA) point coverage in polar projection, 

Datum WGS84, with a central meridian of 180° longitude and a latitude of true scale at 60° N. 

Defining Habitat Available to Polar Bears 

Habitat data (NIC, CIS and depth) encompassed a large region, the area of which would 

not be entirely available to a polar bear during any particular period of time. We modified the 

method of Arthur et al. (1996), who defined the habitat available to a bear at a particular location 

and time as the area within a circle whose center was the location of the prior observation (Fig. 

2). The radius of that circle was determine by the duration of time between the prior 

observation and the subsequent observation and by a set distance that a bear was expected to 

travel during that time. Because movement rates of female polar bears in the southern Beaufort 

 7
Sea varies by month (Amstrup et al. 2000), we calculated radii for each unique bear/date 

observation by the following equation: 

radii of available habitat = {a + (b Χ 2)} Χ c; 

where a equals the mean hourly movement rate for all bears within each month; b is the standard 

deviation of the movement rate; and c equals the number of hours between locations (Fig. 2). 

Sometimes, however, the actual straight-line distance traveled by a bear between observations 

exceeded the expected distance. In these cases the radius of available habitat was defined as the 

straight-line distance actually traveled plus 1 meter. 

Generating Random Locations and Attaching Habitat Variables 

We compared habitat characteristics of each bear location to a set of up to 100 random

points generated with ARC/INFO tools (Fig. 2). These had a minimum spacing of 200 m and 

were generated in the portion of the availability circle that fell within the NIC chart. Both bear 

and random locations were merged with NIC and CIS charts, and the ocean depth coverage in 

order to attach habitat variables. Distance from locations and the nearest edge were calculated. 

Generating a Resource Selection Function 

Estimation of a resource selection function (RSF) followed the methods of McCracken et 

al. (1998), Arthur et al. (1996) and Cooper and Millspaugh (1999). These methods fit discrete 

choice models for polar bear site selection and keep the availability of landscape characteristics 

unique to each polar bear location/date-time combination. That is, points in one polar bear’s 

circle of available habitat on a particular day were not available for selection by another polar 

bear, unless the two circles overlapped. The discrete choice model is estimated by maximizing 

the multinomial logit likelihood (Manly et al. 2002). This was accomplished using the stratified

Cox proportional hazards likelihood maximization routine available in the SAS procedure PROC 

PHREG (SAS Institute 2000). Although PROC PHREG was not designed to fit discrete choice 

habitat selection functions, Kuhfeld (2000) describes a method by which PROC PHREG can be 

“tricked” into fitting the appropriate discrete choice likelihood function. 

Prior to model building, Pearson’s Correlation Coefficients (r; Conover 1980) were 

calculated for all main effects for each season. Main effects (Table 1) were excluded from the 

analysis if |r| ≥ 0.6. Separate models were developed that included one or the other correlated 

main effect. That is, each member of a pair of correlated main effects was not allowed to enter 

the same model building procedure. From these, we selected the best model based on how well 

models appeared to predict the RSF of an independent sample of polar bears locations. 

Stepwise model building began with developing a single-term model for each main 

effect. We set the critical level of covariate entry as α ≤ 0.1 for the adjusted score χ

2

 (Klein and 

Moeschberger 1997). The single-term model with the largest significant score χ

2

 was selected as 

the start of a forward selection process for model building. We allowed each step of the forwardselection process to add one other term only when the adjusted score χ

2

 value for that term was α

≤ 0.1. Each forward selection step was preceded by a backward removal step, where the variable 

with the smallest Wald χ

2

 value was dropped from the model, provided that α > 0.1. An 

interaction or quadratic term was not allowed in the model if the main effect involved was not 

already in the model. If a backward selection step identified a main effect for exclusion, and that 

main effect was also present in the model in an interaction with another main effect, the main 

 8
effect was not dropped from the RSF model. The RSF model was considered complete when no 

other terms could be entered or removed under the constraint of α ≤ 0.1. 

As an evaluation of our model building procedure, we compared each model to two other 

commonly used methods of RSF model building. First, we compared each step in our procedure 

to the change in the likelihood ratio χ

2

 each time a covariate was added (Manly et al. 2002). 

Each coefficient in the final model was also tested to see if it was significantly different from

zero. This was done by dividing the coefficient by its standard error, and then comparing the 

absolute value of this number (z) to a normal distribution. This is the classic Wald t-test, where a 

value of z > 1.64 indicates a significant difference from zero at α ≤ 0.1 (Manly et al., 2002). 

A primary focus of this work was to develop tools that would predict where polar bears 

may occur. Hence, we were interested in how our seasonal models would perform with real 

data. To do this we first created a RSF map from the average multi-year habitat values for each 

season. We then overlaid an independent data set of bear locations on the RSF maps and 

attached to each bear location its respective RSF value from the seasonal map. The distribution 

of RSF values assigned to bear locations was then graphically compared to the distribution of 

RSF values of the map in order to provide an index of the predictive abilities of our models. 

RESULTS 

Between 1 September 1997 and 31 December 2001, 88 PTTs were deployed on 80 polar 

bears in the Beaufort Sea. A total of 32,105 satellite observations from 77 bears were available 

for analysis. Following the imposition of temporal and spatial filters, 1780 observations from 53 

Figure 3. Defined seasons for modeling polar bear habitat use in the Beaufort Sea, 1997 – 2001. Total ice 

concentration is the mean total concentration for bear locations and random points during each day of a calendar 

year within the region between 140 – 150° west longitude, and north from the coast to 71° north latitude. 

bears remained for analysis. The time between bear observations ranged between 44 – 171 hours 

( x = 94.4 ± 45.7 SD). We subdivided data into four seasons, which were defined by major 

 9
seasonal changes in ice concentration near the southern Beaufort Sea coast (Fig. 3). Seasons 

included spring: 30 May – 23 July; summer: 24 July – 6 October; fall: 7 October – 15 November; 

and winter: 16 November – 29 May. 

Seasonal models were unique in their combination of terms (Table 3). Depth appeared in 

four models and edge appeared in three models. Small changes in depth, and edge resulted in 

large changes in the relative probability of use (Fig. 4 – 7). 

Table 3. Seasonal discrete choice models predicting relative probability, w(x), of an adult female polar bear 

selecting a point in the landscape characterized by x, in the Beaufort Sea, 1997 – 2001. 

Season Model (standard errors are in parentheses below coefficients) 

Spring w(x) = exp{-0.0002402(depth) + 0.52481(vastfloe) + 1.67410(totcon)} 

 (0.0000826) (0.27476) (0.92598) 

Summer w(x) = exp{-0.01085(edge) + 6.58263(oldice) - 4.93599(oldice2

) - 0.0003382(depth) 

 (0.00440) (1.29720) (1.40941) (0.0000924) 

 + 1.21442(firstyr) + 4.52479(youngice) - 0.29138(edge*youngice)} 

 (0.45996) (1.35200) (0.011078) 

Autumn w(x) = exp{-0.00152(depth) – 0.02968(edge) + 3.99265(totcon) 

 (0.0003056) (0.00521) (1.3244) 

 + 0.000000231511(depth2

) – 2.70505(totcon2)} 

 (0.000000094957) (1.11305) 

Winter w(x) = exp{-0.00170(depth) + 0.000000349299(depth2

) + 0.44398(vastfloe) 

 (0.0002075) (0.0000000661291) (0.11599) 

+ 1.94584(youngice) – 0.00524(edge) + 0.47312(firstyr)} 

 (0.73993) (0.00244) (0.25120) 

Spring 

Locations of 36 polar bears entered spring model building. There were a total of 234 

actual bear observations and 22,531 random observations ( x = 96.3 ± 5.3 SD random

observations per bear observation). High correlations were found between young ice (youngice) 

and first year ice (firstyr) (r = -0.74, P < 0.0001); old ice (oldice) and youngice (r = 0.74, P < 

0.0001); oldice and firstyr (r = -0.92, P < 0.0001); vast floe (vastfloe) and fast ice (fastice) (r = -

0.65, P < 0.0001); and vastfloe and big floe (bigfloe) (r = -0.62, P < 0.0001). Four permutations 

of model building resulted in two spring models. Based on the distribution of an independent 

sample of polar bear locations, our best spring model started with depth (score χ

2

 = 5.0378, P = 

0.0248). The drop in –2 times log likelihood deviance χ

2

 between the “no variable” model and 

the model including depth was 5.097 with 1 df. This is significantly large when compared to a χ

2

distribution, indicating that the addition of depth is an improvement to the “no variable” model. 

The second forward step created a two-term model with the addition of vastfloe (score χ

2

= 5.9091, P = 0.0151). Evidence of model improvement came from a significant drop in the 

likelihood ratio χ

2

 between the model with only depth and the model with depth and vastfloe

(6.41, 1 df). The entry of totcon (score χ

2

 = 3.4177, P = 0.0645) followed in the next forward 

step and was a significant improvement in the model (drop in likelihood ratio χ

2

: 4.122, 1 df). 

All three covariates in the final model were significantly different from zero (depth: z = 2.91; 

vastfloe: z = 1.91; totcon: z = 1.81). A backward step did not identify any covariates for removal 

from the model. The next forward step identified the interaction of totcon and vastfloe

 10
(tot*vfloe). The following backward look, however, dropped tot*vfloe from the model (Wald χ

2

= 2.5400, P = 0.1110). When forward steps were continued, the quadratic for vastfloe (vastfloe

2

) 

was identified for model inclusion. It too, however, was dropped by the preceding backward 

look (Wald χ

2

 = 2.5400, P = 0.1110). Additional forward steps did not identify any other 

variables that met the α ≤ 0.1 criteria for model entry. The final spring model included the main 

effect for depth, vastfloe and totcon (Table 3). During spring, polar bears used habitats over 

water depths between 5 – 3659 m deep, however 50 % of all bear observations occurred in 

waters ≤ 400 m deep. According to this model, polar bears in the Beaufort Sea select habitat in 

relatively shallow waters, where ice concentration and the proportion of vast floe is high (Fig. 4). 

Total concentration of ice

0.0 0.2 0.4 0.6 0.8 1.0 1.2

RSF

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Spring

Ocean depth (m)

0 1000 2000 3000 4000 5000

RSF

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Proportion of vast floe

0.0 0.2 0.4 0.6 0.8 1.0 1.2

RSF

0.5

0.6

0.7

0.8

0.9

1.0

1.1

A B

C

Figure 4. Relative probability of selection as a function of variables in the final polar bear sea ice RSF model for 

spring in the Beaufort Sea, 1999 - 2001. Variables in the final model not in a plot were held at their median values. 

Summer 

Locations of 36 polar bears entered summer model building. There were a total of 256 

actual bear observations and 24,531 random observations ( x = 95.8 ± 6.2 SD random

observations per bear observation). During summer, there was a significant correlation between 

oldice and totcon (r = 0.68, P < 0.0001). Model building began with edge (score χ

2

 = 20.4780, P

< 0.0001). The drop in –2 times log likelihood deviance χ

2

 between the “no variable” model and 

the model including edge was 23.062 with 1 df. This is significantly large when compared to a 

χ

2

 distribution, indicating that the addition of edge is an improvement to the “no variable” model. 

 11
The next covariate to enter the model was oldice (score χ

2

 = 21.557, P < 0.0001). The drop in 

likelihood ratio χ

2

 between the model with only edge and the model with edge and oldice

(21.832, 1df) is significant when compared to a χ

2

 distribution. The quadratic for oldice (old2) 

(score χ

2

 = 15.0429, P = 0.0001) was next to enter the model (drop in likelihood ratio χ

2

: 14.935, 

1 df), then depth (score χ

2

 = 11.9040, P = 0.0006; drop in likelihood ratio χ

2

: 11.827, 1 df), then 

firstyr (score χ

2

 = 4.7995, P = 0.0285; drop in likelihood ratio χ

2

: 4.818, 1 df), followed by 

youngice (score χ

2

 = 5.8774, P = 0.0153; drop in likelihood ratio χ

2

: 5.009, 1 df) and the 

interaction between edge and youngice (edge*youngice) (score χ

2

 = 7.1407, P = 0.0075; drop in 

likelihood ratio χ

2

: 8.583, 1 df). No additional covariates met our criteria for model entry. Our 

confidence in the summer model was bolstered by significant z-scores for all covariates (edge: z

= 2.47; oldice: z = 5.07; old2: z = 3.50; depth: z = 3.66; firstyr: z = 2.64; youngice: z = 3.35; and 

edge*youngice: z = 2.63). 

Ocean depth (m)

0 500 1000 1500 2000 2500

RSF

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Proportion of area that is old ice

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Proportion of area that is first year ice

0.0 0.2 0.4 0.6 0.8 1.0

RSF

0.2

0.4

0.6

0.8

1.0

1.2

Summer

A B

C Region outside of 

predictive capabilities

D

Distance from an ice interface (km)

0 20 40 60 80 100

Proportio

n of 

are

a

that is young ice

0.0

0.2

0.4

0.6

0.8

0.2

0.4

0.6

0.8

1.0

D C Region outside of

predictive capabilities

RS

F

Figure 5. Relative probability of selection as a function of variables in the final polar bear sea ice RSF model for 

summer in the Beaufort Sea, 1999 - 2001. Variables in the final model not in a plot were held at their median

values. 

The summer model included positive coefficients for oldice, firstyr, and youngice (Table 

3). Negative coefficients resulted from edge, old2

, depth, and edge*youngice. While our 

summer model suggests that polar bears select habitat in shallow waters (Fig. 5a), this is relative 

because 75 % of polar bear locations occurred in habitats where ocean depth was ≥ 355 m. Polar 

bears also select habitats with a high proportion of old ice (Fig. 5b) or first year ice (Fig. 5c), or a 

high proportion of young ice close to an ice interface (Fig. 5d). 

 12
Autumn 

Thirty-nine individual bears entered autumn model building. There were a total of 311 

actual bear observations and 29,805 random observations ( x = 95.8 ± 5.4 SD random

observations per bear observation). During autumn, there was a significant correlation of oldice

with youngice (r = -0.70, P < 0.0001). The highest score χ

2

 in a single term model resulted with 

depth (score χ

2

 = 108.8619, P < 0.0001). The drop in –2 times log likelihood deviance between 

the “no variable” model and the model including depth was 126.304 with 1 df. This is highly 

significant when compared to a χ

2

 distribution, indicating that the addition of depth is an 

improvement to the “no variable” model. The sequence of covariate entry followed with edge

(score χ

2

 = 41.2362, P < 0.0001). The drop in likelihood ratio χ

2

 (48.186, 1 df) was significant, 

indicating model improvement from the model with only depth, to the model including both 

depth and edge. The next covariate to enter the model was totcon (score χ

2

 = 7.9508, P = 0.0048; 

drop in likelihood ratio χ

2

: 174.49, 1 df). This was followed by the quadratic for depth (depth2) 

(score χ

2

 = 6.3139, P = 0.0120; drop in likelihood ratio χ

2

: 6.42, 1 df), and lastly the quadratic for 

Total ice concentration

0.0 0.2 0.4 0.6 0.8 1.0 1.2

RSF

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Ocean depth (m)

0 1000 2000 3000 4000 5000

RSF

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Distance to ice interface (km)

0 20 40 60 80 100 120

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Autumn

A B

C

RSF

Figure 6. Relative probability of selection as a function of variables in the final polar bear sea ice RSF model for 

autumn in the Beaufort Sea, 1999 - 2001. Variables in the final model not in a plot were held at their median values. 

totcon (totcon

2

) (score χ

2

 = 5.9884, P = 0.0144; drop in likelihood ratio χ

2

: 6.199, 1 df). Our 

confidence in model correctness was strengthened by z-scores for the final model (depth: z = 

4.97; edge: z = 5.70; totcon: z = 3.01; depth2

: z = 2.46; totcon2: z = 2.43). 

 13
The autumn model (Table 3) had a positive coefficient for totcon and depth2

, and 

negative coefficients for depth, edge, and totcon2

. The quadratic term for depth results in a 

curvilinear form of the RSF with increasing depth (Fig. 6a). This initially causes a decrease in 

the RSF with increasing depth. The RSF function, however, approaches an asymptotic pattern 

when depth was > 1000 m. During autumn, polar bear locations occurred over waters as deep as 

3729 m. Of those, 75 % occurred in waters ≤ 189 m deep and 50 % occurred in waters ≤ 30.5 m

deep. Our autumn model indicates that polar bears use habitat in relatively shallow water (Fig. 

6a) close to an ice interface (Fig. 6b), and with high total ice coverage (Fig. 6c). 

Winter 

Locations of 47 polar bears entered winter model building. There were a total of 864 

actual bear observations and 82,094 random observations included in the analysis ( x = 95.0 ± 

5.7 SD random observations per bear observation). Significantly large correlations were found 

between firstyr and oldice (r = -0.83, P < 0.0001), and between vastfloe and bigfloe (r = -0.65, P

< 0.0001). Our winter model began with depth (score χ

2

 = 179.6092, P < 0.0001). The drop in 

–2 times log likelihood deviance between the “no variable” model and the model including depth

was 208.481 with 1 df. This is highly significant when compared to a χ

2

 distribution, indicating 

that the addition of depth is an improvement to the “no variable” model. The sequence of 

covariate entry continued with depth2 (score χ

2

 = 30.5267, P < 0.0001). The drop in likelihood 

ratio χ

2

 (28.609, 1 df) was significant, indicating improvement from the model with only depth, 

to the model including both depth and depth2

. The next covariate to enter the model was vastfloe

(score χ

2

 = 9.6772, P = 0.0019; drop in likelihood ratio χ

2

: 9.898, 1 df), followed by youngice

(score χ

2

 = 5.1145, P = 0.0237; drop in likelihood ratio χ

2

: 5.03, 1 df), edge (score χ

2

 = 4.4296, P

= 0.353; drop in likelihood ratio χ

2

: 4.51, 1 df), and firstyr (score χ

2

 = 3.5582, P = 0.0593; drop 

in likelihood ratio χ

2

: 3.686, 1 df). Our confidence in model correctness was strengthened by zscores for the final model (depth: z = 8.19; depth2

: z = 5.29; vastfloe: z = 3.83; youngice: z = 

2.63; edge: z = 2.15; firstyr: z = 1.88). Positive coefficients included the quadratic for depth, 

vastfloe, youngice, and firstyr (Table 3). Negative coefficients were depth, and edge. 

During winter polar bears select habitat over shallow water (Fig. 7a) and close to an ice 

interface (Fig. 7b). Polar bears also select vast floe ice (Fig. 7c) that is composed of a high 

proportion of first year ice (Fig. 7d) or young ice (Fig. 7e). The quadratic term for depth results 

in a curvilinear form of the RSF with increasing depth (Fig. 7a). This initially causes a decrease 

in the RSF with increasing depth. The RSF function, however, approaches an asymptotic pattern 

when depth was > 1000 m. During winter, polar bear locations occurred over waters as deep as 

3640 m. However, 75 % of observations occurred in waters ≤ 107 m deep and 50 % occurred in 

waters ≤ 38 m deep. 

Evaluation of Models 

Our stepwise approach to building seasonal models by using the score χ

2

 was perfectly 

concordant with the change in likelihood χ

2

 (Manly et al., 2002). Also, all coefficients in the 

final models were significantly different from zero (Manly et al., 2002). These results boosted 

our confidence in our choice of model building procedures. 

 14
Winter

E

Ocean depth (m)

0 1000 2000 3000 4000 5000

RSF

0.0

0.2

0.4

0.6

0.8

1.0

1.2

A

Distance to ice interface (km)

0 20 40 60 80 100 120

0.5

0.6

0.7

0.8

0.9

1.0

1.1

B

Proportion of vast floe

0.0 0.2 0.4 0.6 0.8 1.0 1.2

RSF

0.6

0.7

0.8

0.9

1.0

1.1

C

Proportion of first year ice

0.0 0.2 0.4 0.6 0.8 1.0 1.2

0.6

0.7

0.8

0.9

1.0

1.1

D

Proportion of young ice

0.0 0.2 0.4 0.6 0.8 1.0

RSF

0.0

0.2

0.4

0.6

0.8

1.0

1.2

E

RSF RSF

Figure 7. Relative probability of selection as a function of variables in the final polar bear sea ice RSF model for 

winter in the Beaufort Sea, 1999 - 2001. Variables in the final model not in a plot were held at their median values. 

The real test of our models came through a comparison with an independent set of real 

polar bear location data collected in 2002. These location data were not used in the generation of 

models. During spring, 81 % of bear locations from 2002 occurred in the highest 30 % of

mapped RSF values in the study area (Fig. 8a). During summer (Fig. 8b), the predictive abilities 

 15
of our model was evident, however, much less than the spring model. Fifty-eight percent of bear 

locations during summer 2002 occurred within 30 % of the highest mapped RSF values in the 

study area. Autumn showed an improvement of prediction, where 74 % of bear locations fell 

within the highest 30 % of mapped RSF values (Fig. 8c). Finally, winter showed the best 

predictive results, with 85 % of polar bear locations occurring in the highest 30 % of mapped 

RSF values (Fig. 8d). Mapped RSF values and polar bear locations clearly demonstrate a 

tendency for polar bear locations to fall within areas of greatest RSF values for both spring (Fig. 

9) and winter (Fig. 10). 

A. Spring

RSF interval

0246 8 10

Proportion of observations 0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

RSF grid (n = 606120)

RSF bear (n = 133)

RSF intervals

 1: 0.0000-3.3320

 2: 3.3320-3.4070

 3: 3.4070-3.5190

 4: 3.5190-3.7280

 5: 3.7280-4.1020

 6: 4.1020-4.5670

 7: 4.5670-5.1870

 8: 5.1870-6.0270

 9: 6.0270-7.0760

10: 7.0760-8.4910

B. Summer

RSF interval

0246 8 10

Proportion of observations 0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

RSF grid (n = 606120)

RSF bear (n = 123)

RSF intervals

 1: 0.1590-0.4320

 2: 0.4320-0.5560

 3: 0.5560-0.6760

 4: 0.6760-0.8350

 5: 0.8350-1.1130

 6: 1.1130-1.4810

 7: 1.4810-1.8900

 8: 1.8900-2.3560

 9: 2.3560-3.3230

10: 3.3230-6.6590

C. Autumn

RSF interval

0 2 468 10

Proportion of observations 0.0

0.1

0.2

0.3

0.4

RSF grid (n = 606120)

RSF bear (n = 244)

RSF intervals

 1: 0.0000-0.0060

 2: 0.0060-0.0280

 3: 0.0280-0.0710

 4: 0.0710-0.1140

 5: 0.1140-0.1690

 6: 0.1690-0.2890

 7: 0.2890-0.7700

 8: 0.7700-1.5970

 9: 1.5970-2.3990

10: 2.3990-4.3880

D. Winter

RSF interval

02468 10

Proportion of observations 0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

RSF grid (n = 606120)

RSF bear (n = 52)

RSF intervals

 1: 0.0710-0.1070

 2: 0.1070-0.1400

 3: 0.1400-0.1710

 4: 0.1710-0.2040

 5: 0.2040-0.2390

 6: 0.2390-0.3600

 7: 0.3600-1.0060

 8: 1.0060-1.4280

 9: 1.4280-1.7720

10: 1.7720-2.4410

Figure 8. Comparing the seasonal distribution of RSF values in the Beaufort Sea to RSF values at known polar bear 

locations during 2002. Grid RSF values were calculated from averaged ice data derived from NIC and CIS charts 

between 1997-2001. 

DISCUSSION 

The presence of depth and edge in most models indicates the importance of these 

variables to polar bears throughout the year. The extent of ice cover and the characteristics of 

form and stage of sea ice also were important in habitat selection by polar bears. Throughout the 

 16
Figure 9. Distribution of the spring RSF in the Beaufort Sea, derived from average ice characteristics during June, 

1999 – 2001, and known polar bear locations during June 2002 (n = 133). 

Figure 10. Distribution of the winter RSF in the Beaufort Sea, derived from average ice characteristics during 

February, 1999 – 2001, and known polar bear locations during February 2002 (n = 52).

year, selection of different habitats is likely driven by the abundance and accessibility of prey 

and the availability of safe resting and refuge habitat. Ringed seal densities in the Beaufort Sea 

are greatest in water with > 80 % ice cover and depths between 50 – 100 m, while bearded seals 

prefer relatively low ice cover in waters 25 – 50 m deep (Stirling et al. 1982). Hence, a high 

RSF value in shallow waters where ice coverage is nearly complete appears reasonable. 

 17
Spring marks a period of transition in the Beaufort Sea. Warming temperatures bring 

about ice melt in the near shore regions, where total ice concentration decreases from an average 

of 100% to approximately 75% ice cover (Fig. 3). Naïve juvenile ringed seals become important 

prey for polar bears during this time. Prey densities are highest in shallow regions and where the 

proportion of multiyear ice is low (Kingsley et al. 1985, Stirling et al. 1982). Polar bears in the 

Beaufort Sea respond to this changing environment by using regions that are relatively shallow 

with ice concentrations > 90 %, and composed of ice floes 2 – 10 km in diameter. The use of 

floes > 2 km in spring is similar to that observed in the Canadian high Arctic (Ferguson et al. 

2000) but differs from the use of open ice in the Chukchi Sea (Arthur et al. 1996). The use of 

large floe ice may be related to an attempt by polar bears to adjust their hunting strategies in 

response to prey behavior. Ringed seals haul-out on the ice surface during spring in order to 

facilitate molting, the peak period of which corresponds to our defined spring season (Smith 

1973). Preferred haul-out locations generally include the interior of large floes distant from large 

lead systems (Kingsley and Stirling 1991). Ringed seals haul-out next to breathing holes that 

they have created themselves, or they use cracks in the ice that average 46 cm in width (Kingsley 

and Stirling 1991). The small diameter of breathing holes and cracks in the ice are assumed to 

prevent a polar bear from following its prey into the water (Kingsley and Stirling 1991). Thus, 

bears may spend more time hunting ringed seals in habitats composed of relatively unbroken ice 

and away from the major lead systems than what they use during other times of year. This is 

supported in our research by the highest RSF resulting when the proportion of floes 2 – 10 km in 

diameter was also high. Selection for large ice floes was also observed in the Canadian high 

Arctic during spring (Ferguson et al., 2000). Despite loss of cover through ice melt, polar bears 

continue to use habitat that is composed of high ice concentration. Less than 10 % of bear 

observations during spring occurred in areas where total ice concentration < 90 %. Because the 

majority of melt is in near shore regions, polar bears must use habitats in deeper water where 

prey may not be as abundant. Fifty percent of bear observations occurred in water ≥ 395 m deep. 

Summer melt brings minimal ice extent to the near shore regions of the Beaufort Sea 

(Fig. 3) and sea ice is often absent within several hundred kilometers of the coast. Selection of 

habitats with high proportion of old ice resulted in the highest RSF values far from the coast 

(Appendix 1i – 1k). Hence 75 % of bear locations occurred in waters ≥ 355 m deep and outside 

of areas of greatest prey abundance (Stirling et al. 1977, Stirling et al. 1982, Harwood and 

Stirling 1992, Gjertz et al. 2000). Surveys conducted during late summer in the Beaufort Sea 

indicate that seals aggregate in open water < 500 m deep where primary production is thought to 

be high (Harwood and Stirling 1992), thus placing them well out of reach of polar bears 

summering on the pack ice. However, some ringed seals persist in deep waters during summer 

(Stirling et al. 1977, Gjertz et al. 2000), indicating that polar bears are not entirely without prey. 

Three different ice types are used by polar bears for different reasons. The use of old ice is 

similar to that observed in two regions of the Canadian Arctic (Ferguson et al. 2000) and may 

reflect refuge selection (Mauritzen et al. 2003) because seals seldom use old (multi-year) ice 

(Kingsley et al. 1985). At concentrations of old ice > 60 %, however, our RSF actually begins to 

decline (Fig. 5b). The negative coefficient for distance to ice interface and the positive 

coefficient for young ice suggest an affinity for edge habitat. This apparent use of edge habitat 

may be driven by the high use of old ice during summer (Fig. 5b). Because seals prefer annual 

ice to old (multi-year) ice (Kingsley et al. 1985), the use of edge habitat may be evidence of 

bears attempting to track the distribution of the few prey that may be available. Conversely, the 

patterns of habitat selection by bears and the distribution of seals in the Beaufort Sea may also 

 18
suggest that most polar bears do not feed extensively during summer. This is supported by 

reports of seasonal activity levels of polar bears. Minimum ice cover and low prey availability 

resulted in a tendency for polar bears in Canada to decrease their activity in order to conserve 

energy (Ferguson et al. 2001). Amstrup et al. (2000) showed that polar bears in the Beaufort Sea 

have their lowest level of movements in September, the time when ice used by polar bears is 

beyond the preferred habitat of seals. 

During autumn, polar bears return to areas of water depths ≤ 189 m, with a high total 

concentration of ice and close to an ice interface (Fig. 6). In the Beaufort Sea during autumn, 

near-shore ice concentrations change quickly. Ice concentrations average between 30 – 90 % at 

the beginning of autumn and increase to ~ 95 % (Fig. 3). Highest RSF values resulted form ice 

concentrations at approximately 70 – 80 % (Fig. 6c), suggesting that polar bears may select ice 

of relatively high concentration but interspersed with leads, cracks and other openings. Selection 

for high ice cover during autumn was observed in two regions in Canada (Ferguson et al. 2000). 

Selection of habitat near an ice edge may be a response by polar bears to anticipate changes in 

ice conditions (Ferguson et al. 2000) in a push to get to productive near shore waters where prey 

abundance is greatest. This is consistent with field observations of polar bears near Prudhoe Bay 

during autumn (Durner, pers. obs.). In summary, conditions that contribute to a high relative 

probability of use during autumn include shallow areas with high total ice concentration and 

when the distance to a polygon edge is < 20 km. 

Winter habitat use relative to depth is similar to that observed in all other seasons, with 

the exception that beyond waters > 1000 m deep, an asymptotic pattern appears (Fig. 7a). Only 

9.8 % (86 of 875) of all winter bear observations occurred in habitat where depth was > 1000 m. 

Habitat use by bears in deep water areas is probably little influenced by additional changes in 

depth. Winter habitat use shows the greatest tendency of bears to use shallow water areas in the 

Beaufort Sea. Seventy-five percent of bear observations in winter occurred in waters < 130 m

deep. Formation of fast ice and consolidation of off shore ice result in the majority of active ice 

and leads at shear zones close to and parallel to coastlines (Smith and Rigby 1981). This narrow 

band of moving ice creates openings that are used by seals. Hence, accessibility of prey is 

highest there and polar bears respond by selecting active ice (Ferguson et al., 2001). This is 

reflected in the selection of ice interfaces in our winter model. Thus, polar bears during winter 

use a relatively small area of the Beaufort Sea where prey abundance is greatest and most 

accessible. This is concordant with the general pattern of small home range sizes observed for 

polar bears during most winter months in the Beaufort Sea (Amstrup et al. 2000). 

Ferguson et al. (2000) found that polar bears in two regions of the Canadian Arctic select 

high concentration ice composed of 1st year ice during winter. Multiyear ice generally had a low 

selection index. Bears also selected landfast ice and large flow ice (> 2000 m) in stable regions 

in the Arctic Archipelago, but did not select fastice in Baffin Bay (Ferguson et al. 2000). Our 

winter model also identified vast floe ice (2 – 10 km) as important, with increasing proportions 

of vast floe resulting in greater selection by polar bears (Fig. 7c). Selection for edge, which was 

strong in the Beaufort Sea (Fig. 7b), was absent in both regions in the Canada study. 

Our models demonstrate the utility of discrete choice models for predicting the seasonal 

distribution of polar bears in the Beaufort Sea. Both industrial expansion and climate change 

may impact the sea ice environment that polar bears depend on. Short-term forecasting of polar 

bear use of sea ice habitat may allow managers to predict effects of industry and oil spills on 

polar bears and take appropriate remediation. By knowing what ice conditions to expect at a 

proposed development site we may extrapolate how many polar bears might be affected. Long-

 19
term forecasting will allow prediction of sea ice characteristics resulting from climate change. If 

change can be predicted in total ice concentration, stage and form of ice, and the duration of the 

ice-free season then we can also predict the resulting distribution of polar bears. The methods 

and models that we present here are a promising tool that will allow researchers and resource 

managers to understand the use of sea ice by polar bears in order to make sound management 

decisions. 

 20
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 24
APPENDICES 

 25
Prudhoe

Bay

Barrow

Appendix 1a. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

31 January (winter). Data were derived from National Ice Center sea ice charts for the Beaufort 

Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears 

equipped with satellite radio collars, during 1999 – 2001. 

 26
Prudhoe

Bay

Barrow

Appendix 1b. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

28/29 February (winter). Data were derived from National Ice Center sea ice charts for the 

Beaufort Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar 

bears equipped with satellite radio collars, during 1999 – 2001. 

 27
Prudhoe

Bay

Barrow

Appendix 1c. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

31 March (winter). Data were derived from National Ice Center sea ice charts for the Beaufort 

Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears 

equipped with satellite radio collars, during 1999 – 2001. 

 28
Prudhoe

Bay

Barrow

Appendix 1d. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

30 April (winter). Data were derived from National Ice Center sea ice charts for the Beaufort 

Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears 

equipped with satellite radio collars, during 1999 – 2001. 

 29
Prudhoe

Bay

Barrow

Appendix 1e. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

29 May (winter). Data were derived from National Ice Center sea ice charts for the Beaufort 

Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears 

equipped with satellite radio collars, during 1999 – 2001. 

 30
Prudhoe

Bay

Barrow

Appendix 1f. A resource selection function (RSF) for polar bears in the Beaufort Sea during 30 

May – 30 June (winter). Data were derived from National Ice Center sea ice charts for the 

Beaufort Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar 

bears equipped with satellite radio collars, during 1999 – 2001. 

 31
Prudhoe

Bay

Barrow

Appendix 1g. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

23 July (spring). Data were derived from National Ice Center sea ice charts for the Beaufort Sea, 

Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears equipped 

with satellite radio collars, during 1999 – 2001. 

 32
Prudhoe

Bay

Barrow

Appendix 1h. A resource selection function (RSF) for polar bears in the Beaufort Sea during 24 

– 31 July (summer). Data were derived from National Ice Center sea ice charts for the Beaufort 

Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears 

equipped with satellite radio collars, during 1999 – 2001. 

 33
Prudhoe

Bay

Barrow

Appendix 1i. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

31 August (summer). Data were derived from National Ice Center sea ice charts for the Beaufort 

Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears 

equipped with satellite radio collars, during 1999 – 2001. 

 34
Prudhoe

Bay

Barrow

Appendix 1j. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

30 September (summer). Data were derived from National Ice Center sea ice charts for the 

Beaufort Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar 

bears equipped with satellite radio collars, during 1999 – 2001. 

 35
Prudhoe

Bay

Barrow

Appendix 1k. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

6 October (summer). Data were derived from National Ice Center sea ice charts for the Beaufort 

Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears 

equipped with satellite radio collars, during 1999 – 2001. 

 36
Prudhoe

Bay

Barrow

Appendix 1l. A resource selection function (RSF) for polar bears in the Beaufort Sea during 7 – 

31 October (autumn). Data were derived from National Ice Center sea ice charts for the Beaufort 

Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar bears 

equipped with satellite radio collars, during 1999 – 2001. 

 37
Prudhoe

Bay

Barrow

Appendix 1m. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 

– 15 November (autumn). Data were derived from National Ice Center sea ice charts for the 

Beaufort Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar 

bears equipped with satellite radio collars, during 1999 – 2001. 

 38
Prudhoe

Bay

Barrow

Appendix 1n. A resource selection function (RSF) for polar bears in the Beaufort Sea during 16 

– 30 November (winter). Data were derived from National Ice Center sea ice charts for the 

Beaufort Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar 

bears equipped with satellite radio collars, during 1999 – 2001. 

 39
Prudhoe

Bay

Barrow

Appendix 1o. A resource selection function (RSF) for polar bears in the Beaufort Sea during 1 – 

31 December (winter). Data were derived from National Ice Center sea ice charts for the 

Beaufort Sea, Canadian Ice Service ice charts for the West Arctic, and locations of female polar 

bears equipped with satellite radio collars, during 1999 – 2001. 

 40
Appendix 2. List of project publications, reports, and presentations 

Durner, G. M., S.C. Amstrup, R. Neilson, and T. McDonald. In press. Using discrete choice 

modeling to create resource selection functions for female polar bears in the Beaufort 

Sea. 1st International Conference on Resource Selection by Animals. 13-15 January 

2003, University of Wyoming, Laramie, WY. Proceedings from presentation. 

 41