MONITORING DISTRIBUTION AND ABUNDANCE 

OF RINGED SEALS IN NORTHERN ALASKA

Final Report 

Prepared by 

Kathryn J. Frost1

, Lloyd F. Lowry1, Grey Pendleton2 and Helen R. Nute3

1

School of Fisheries and Ocean Sciences 

University of Alaska Fairbanks 

1550 Coyote Trail 

Fairbanks, AK 99709-6009 

2

Alaska Department of Fish and Game 

Division of Wildlife Conservation 

P. O. Box 240020 

Douglas, AK 99824-0020 

 3

Mathematical Sciences Department 

University of Alaska Fairbanks 

P. O. Box 756660 

Fairbanks, AK 99775-6660 

for 

U.S. Department of the Interior 

Minerals Management Service 

949 East 36th Avenue 

Anchorage, AK 99508 

This study was funded by the Outer Continental Shelf Region of the Minerals Management 

Service, U.S. Department of the Interior, Anchorage, Alaska, under MMS Cooperative 

Agreement Number 14-35-0001-30810. 

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 U.S. Department of the 

Interior, nor does mention of trade names or commercial products constitute endorsement or 

recommendation for use by the Federal Government. 

30 September 2002 
ii 

Suggested format for citation:

Frost, K. J., L. F. Lowry, G. Pendleton, and H. R. Nute. 2002. Monitoring distribution and 

abundance of ringed seals in northern Alaska. OCS Study MMS 2002-043. Final report 

from the Alaska Department of Fish and Game, Juneau, AK, for U.S. Minerals 

Management Service, Anchorage, AK. 66 p + Appendices. 
iii 

PROJECT ORGANIZATION

This report includes data from two sets of aerial surveys for ringed seals in the Alaska 

Beaufort Sea conducted in 1985-1987 and 1996-1999. The 1985-1987 surveys were done under 

NOAA contract 84-ABC-00210 to the Alaska Department of Fish and Game (ADF&G). Those 

studies were funded by Minerals Management Service (MMS), Department of the Interior, 

through an Interagency Agreement with the National Oceanic and Atmospheric Administration, 

as part of the Alaska Outer Continental Shelf Environmental Assessment Program. The 1990s 

surveys were funded through Cooperative Agreement 14-35-0001-30810 between MMS and 

ADF&G. 

This cooperative effort involved the MMS Alaska Region Environmental Studies Section, 

ADF&G, the National Marine Fisheries Service (NMFS), the University of Alaska, and the 

North Slope Borough. ADF&G had primary responsibility for project management and 

coordination, conduct of surveys, data analysis, and reporting. The NMFS National Marine 

Mammal Laboratory assisted in the conduct of surveys, data analysis, and reporting. The 

University of Alaska Fairbanks assisted with conduct of surveys and reporting. A University of 

Alaska Anchorage graduate student worked on this project as part of a Master of Science thesis 

in biology. A University of Alaska Fairbanks graduate student assisted with data analysis as part 

of a Research Assistantship. The North Slope Borough Department of Wildlife Management 

assisted other cooperators in communicating information to people residing in the study area. 

All cooperators had input into project design, and have access to, and will be able to make use 

of, all data collected. 

Kathryn J. Frost (ADF&G, now retired and affiliated with University of Alaska Fairbanks) 

was one of the Principal Investigators for this project. She participated in all aspects of the study 

including logistics arrangements, design of aerial surveys, conduct of surveys as a primary 

observer during 1985-1987 and 1996-1999, quality control of data, budget preparation and 

tracking, data analysis, and report preparation. 

Lloyd F. Lowry (ADF&G, now retired and affiliated with University of Alaska Fairbanks) 

was one of the Principal Investigators for this project. He participated in the study design, was a 

primary observer for aerial surveys during 1996-1999, assisted with quality control of data, and 

participated in data analysis and report preparation. 

Grey Pendleton, ADF&G, was a data recorder for aerial surveys during 1996-1999. He 

participated in quality control of data, data analysis, and report preparation. In addition, he had 

primary responsibility for statistical design of surveys, multivariate statistical analysis of the 

survey data, reanalysis of data from previous surveys conducted in 1985-1987, and for design of 

the power analysis to evaluate the ability of surveys to detect trends in abundance. 

Helen Nute, University of Alaska Fairbanks, had primary responsibility for univariate 

analyses of the surveys. This included reanalysis of data from 1985-1987 as well as analysis of 

data collected during the 1996-1999 survey effort. Ms. Nute did extensive quality control of 

datasets and analytical procedures. 

Douglas DeMaster, NMFS National Marine Mammal Laboratory, participated in the design 

of aerial surveys, acted as either a primary observer or data recorder during surveys, and had 

primary responsibility for analysis of line transect data collected during 1996-1997 and reported 

in annual reports. 
iv 

Casey Hessinger, University of Alaska Anchorage, was a data recorder for aerial surveys 

conducted during 1996-1999. She assisted with data analysis and report preparation. She 

prepared an extensive annotated ringed seal bibliography, which has been submitted to MMS as 

a product of this study. Ms. Hessinger participated in this study as part of her thesis project for a 

Master of Science degree at UAA. 

Susan Hills, University of Alaska Fairbanks, participated in conduct of aerial surveys during 

1985-1987 and 1996-1999 as a data recorder and as a primary recorder of ice conditions. She 

assisted with interpretation of ice conditions and reconciliation of field observations of the fast 

ice edge with available satellite imagery. She also prepared a summary report describing 

industrial monitoring programs for ringed seals in the central Beaufort Sea that was submitted to 

MMS as an appendix to the 1998 annual report for this project. 
v 

TABLE OF CONTENTS 

Project Organization.......................................................................................................................iii 

Executive Summary ....................................................................................................................... vi 

Acknowledgements ......................................................................................................................... x 

Introduction ..................................................................................................................................... 1 

Study Rationale and Objectives ...................................................................................................... 1 

Methods........................................................................................................................................... 2 

Collection of Survey Data............................................................................................................ 2 

Data Analysis............................................................................................................................... 5 

Data Sources............................................................................................................................. 5 

Strip Transect Densities............................................................................................................ 5 

Univariate Analyses.................................................................................................................. 6 

Covariate Analysis (Poisson Regression)................................................................................. 6 

Power Analysis......................................................................................................................... 8 

Results ............................................................................................................................................. 9 

Survey Effort................................................................................................................................ 9 

Ringed Seal Distribution and Abundance.................................................................................. 11 

Observed Densities and Group Sizes...................................................................................... 11 

Factors Affecting Seal Densities ............................................................................................ 11 

Factors Affecting the Proportion of Seals Hauled out............................................................ 33 

Power Analysis .......................................................................................................................... 41 

Power Based on Observed Density Estimates........................................................................ 41 

Power Based on Modeled Densities ....................................................................................... 41 

Trends in Abundance ................................................................................................................. 47 

Discussion ..................................................................................................................................... 49 

Factors Affecting Aerial Survey Counts.................................................................................... 49 

Habitat Factors........................................................................................................................ 50 

Factors Affecting Proportion Hauled Out .............................................................................. 52 

Aerial Survey Design................................................................................................................. 53 

Measuring and Incorporating Covariate Effects..................................................................... 54 

Effects of Survey Date on Seal Counts .................................................................................. 55 

Use of Power Analysis ........................................................................................................... 58 

Variability and Trends in Ringed Seal Densities....................................................................... 60 

Conclusions and Recommendations.............................................................................................. 61

Literature Cited ............................................................................................................................. 62 

Appendix A .................................................................................................................................A-1 

Appendix B ................................................................................................................................. B-1 

Appendix C ................................................................................................................................. C-1 
vi 

 EXECUTIVE SUMMARY

This report describes results of aerial surveys conducted to determine the distribution and 

abundance of ringed seals (Phoca hispida) in the central Beaufort Sea off Alaska. This was a 

cooperative project funded by the U.S. Department of the Interior, Minerals Management 

Service, with additional support contributed by the Alaska Department of Fish and Game, the 

National Marine Fisheries Service, the University of Alaska, and the North Slope Borough. 

Specific objectives were to: 1) review and refine protocols for monitoring ringed seals by aerial 

surveys; 2) estimate relative abundance and density of ringed seals on fast ice in the Beaufort Sea 

during 1996-1999 and compare with data collected during 1985-1987; and 3) correlate observed 

ringed seal densities on fast ice with environmental parameters. 

Data from two sets of aerial surveys for ringed seals in the Alaska Beaufort Sea are analyzed 

in this report. The 1985-1987 surveys were conducted under NOAA Contract 84-ABC-00210 to 

the Alaska Department of Fish and Game as part of the Alaska Outer Continental Shelf 

Environmental Assessment Program. The 1996-1999 surveys were funded through Cooperative 

Agreement 14-35-0001-30810 between the Minerals Management Service and the Alaska 

Department of Fish and Game. 

Ringed seals are a widespread, circumpolar species, that in Alaskan waters occur in the 

Beaufort, Chukchi, and Bering seas, usually in association with sea ice. Although they also 

occur in pack ice, ringed seals are the only species of seal in Alaska that commonly lives on and 

under the extensive, largely unbroken, shorefast ice. They make and maintain breathing holes in 

the ice from freeze-up until breakup. As day length and temperature increase in the spring, 

increasing numbers of ringed seals haul out on the surface of the ice near breathing holes or lairs. 

This hauling-out or basking is associated with the annual molt, which occurs in May-July. It is 

during this time that seals are most readily observed and counted. 

The shorefast ice also provides a reasonably safe and convenient surface on which various 

human activities are conducted. Coastal residents have traditionally used the fast ice to hunt for 

seals and other marine mammals. More recently the oil and gas industry has used the fast ice for 

conducting certain phases of petroleum exploration and development. Currently, a warming 

climate trend is causing changes in the characteristics of sea ice which may affect both ringed 

seals and humans, and the ice they live on and use. Because such human activities may impact 

seal populations, and because climate change may affect ringed seal habitat, it is important to 

monitor changes in distribution and abundance over both short and long term time scales. 

We conducted aerial surveys during late May and early June 1996-1999 in the Beaufort Sea 

between Point Barrow and Kaktovik (156° 36’W to 143°42’W), an east-west extent of 

approximately 500 km. Two experienced primary observers counted seals using the strip 

transect protocols established during 1985-1987 surveys. Surveys transects were laid out along 

lines of longitude, and extended from approximately the 3 m depth contour to 40+ km offshore. 

Survey altitude was 91 m, and strip width was 0.41 km on each side of the aircraft. The survey 

aircraft had oversized bubble windows, radar altimeter, and an onboard navigation system linked 

to computers operated by data recorders. Three ice variables were recorded: ice type (fast or 

pack), percent ice deformation, and percent of the ice surface covered by melt water. Weather 

conditions were recorded at the beginning of each transect and whenever conditions changed. 

A subset of the data collected and reported in 1985-1987 was reanalyzed and incorporated in 

this report. Data used in the reanalysis included surveys conducted at 91 m altitude in sectors 

B1-B4. During the 1990s survey effort, complete coverage of the Alaskan Beaufort Sea (sectors 
vii 

B1-B4) was obtained for 1997 and 1999. During 1996 and 1998 only sectors B3 and B4 were 

surveyed. Only data within 40 km of shore in sectors B3 and B4 were included in the final 

analysis of all years. 

We calculated simple or “raw” densities of observed ringed seals by dividing the number of 

seals counted within the strata of interest by the area of that strata. Univariate analyses of the 

effects of habitat, weather, and time of day were done using chi-square goodness-of-fit tests. We 

also used Poisson regression to model the relationship of seal counts to covariates. Although 

date has a strong effect on seal counts, our 1996-1999 surveys did not include enough temporal 

coverage to include date as an explanatory variable. 

The dataset for 1985-1987 included 71-80 transect lines each year. We counted 2,189 seals 

on 2,271 km2

 of ice habitat in 1985, 2,605 seals on 2,361 km2 in 1986 and 3,867 seals on 2,524 

km2

 of ice in 1987. Total survey effort during 1996-1999 included 40-139 transect lines per 

year. We counted 1,612 seals on 1,961 km2

 in 1996, 3,429 seals on 4,288 km2 in 1997, 1,111 

seals on 1,198 km2

 in 1998 and 3,796 seals on 3,697 km2 in 1999. Ringed seals were broadly 

distributed throughout the study area in all years. Observed densities for fast ice and pack ice 

combined in the 1980s ranged from 1.01 seals/km2

 in 1985 to 1.85 seals/km2 in 1987. Observed 

densities for the 1990s were similar to 1985 and somewhat lower than in 1986 and 1987, ranging 

from 0.81 seals/km2

 in 1996 to 1.17 seals/km2 in 1999. The range in density estimates from the 

Poisson regression model was somewhat greater. There was a threefold difference between the 

modeled densities for 1985 (2.25 seals/km2 ) and 1999 (0.64 seals/km2 ). 

The habitat-related variables water depth, location relative to the fast ice edge, and ice 

deformation had substantial and consistent effects on the distribution and abundance of seals in 

the study area. The highest seal densities occurred between >5 m and 25 m depths. Similar 

depth preferences have been found in other ringed seal studies done in comparable habitats. 

Seals were most numerous near the shorefast ice edge, with densities declining both shoreward 

and seaward of the edge. Substantial variability may have been introduced into the analysis of 

density relative to distance from the edge because of the subjectivity and difficulty of visually 

determining the position of the fast ice edge. It is unclear what makes the ice edge so attractive 

to ringed seals and results in higher densities at this fast ice-pack ice interface. Seals feed at a 

reduced rate at this time of year when they are entering the molt. Higher densities near the edge 

could reflect diminished territoriality of seals breeding on shorefast ice, and/or an influx of seals 

from other regions as the ice begins to crack and break and the molt approaches . 

We found a strong and consistent relationship between seal densities and the degree of ice 

deformation in all years and for all years combined, with more seals found in flatter, less 

deformed ice. We think this difference is related to seal behavior and not to observer bias in 

highly deformed ice. Ringed seals are a primary prey of polar bears, and the constant threat of 

predation has shaped their behavior on the ice. Seals haul out to bask in areas where they can see 

and smell approaching predators, and where they can escape down holes or cracks too small for a 

polar bear to follow. Thus, it is not surprising that observed densities of basking ringed seals are 

higher in flat ice than in rough, ridged ice where polar bears hunt most commonly. 

Univariate analysis of our data from 1985-1999 suggested that observed densities of seals 

were generally highest between about 1100 and 1400 hrs local time (solar noon is about 1300 

hrs). In contrast, Poisson regression models predicted that densities would be highest at 1000 or 

1100 hrs and decline steadily throughout the day. Results of the Poisson analysis were 

inconsistent with behavioral data from tagged seals, which indicate that more seals haul out in 

mid day. 
viii 

Analyses of the effects of weather factors on seal counts were inconclusive. This was likely 

at least partially due to the fact that temperature and wind speed were measured at the survey 

altitude of 91 m rather than on the surface of the ice. Furthermore, surveys were conducted in 

weather considered suitable for hauling out, and the survey window excluded very windy or 

stormy weather. Other investigators have also concluded that multivariate regression using 

wide-area survey data is ineffective for determining the effects of weather. 

It was our hope at the beginning of this project that we might be able to use Poisson 

regression to model the effects of covariates and to "correct" the data collected in different years 

to standard conditions, thus making our estimated index of ringed seal density more accurate and 

our interpretation of trend more reliable. However, the results of our analyses demonstrated 

difficulties with such an approach. For several habitat-related covariates (ice deformation, 

distance from the fast ice edge, water depth), results of univariate and multivariate analyses 

indicated similar relationships. For others (e.g., weather), neither analysis was particularly 

informative. 

Despite attempts to standardize survey methodology, substantial intra- and interannual 

variability in survey conditions and ice characteristics is unavoidable. This makes it difficult to 

identify trends in abundance. Interannual comparisons of seal abundance may reflect the overall 

status of the population, annual differences in distribution caused by variation in habitat 

characteristics, differences in the proportion hauled out, or a combination thereof. Our surveys 

were not designed to quantify date effects on seal behavior. We had hoped that by narrowing the 

survey window and conducting surveys before breakup and melting occurred we could minimize 

the effect of date. However, recent information from radiotagged seals indicates the change from 

hauling out in lairs to basking in the open is rapid and annually variable, and such behavior may 

significantly affect the number of seals counted even over a narrow period of time. Future 

efforts to improve our estimates of trend should include quantification of the effects of within 

and between-year temporal variation on survey counts. 

We also conducted power analysis to evaluate our ability to detect changes in ringed seal 

abundance and as a means of evaluating whether Poisson regression models improved out ability 

to detect trend. Power analysis was based on simulations for two levels of effort, two time 

periods, and positive or negative annual trend changes of 0%-20% . Power to detect trend was 

low for all combinations of sample size, years, and population change. Almost no additional 

power was gained by adding 30% more lines to the survey coverage. However, power increased 

markedly when the survey period was increased from 5 years to 10 years. With 5 years of 

surveys, the power to detect a 20% annual decline was only 0.23 (P=0.05) for the raw data and 

0.51 for Poisson modeled data. With 10 years of surveys, power increased to 0.53 and 0.73 for 

raw and modeled counts. Our analysis indicated almost no power (8%-32%) to detect an annual 

trend of 5%, a more realistic rate of change for a ringed seal population. This suggests that 

current methods of collecting and analyzing aerial survey need to be improved if the data are to 

be useful for detecting trends in abundance. 

Both observed and modeled densities of ringed seals in the central Beaufort Sea indicated 

considerable annual variability on both fast ice and pack ice. The lowest observed annual 

density during the 1980s was similar to densities in 1998 and 1999 and about 25% higher than 

the lowest density during the 1990s. The highest annual density during the 1980s survey period, 

however, was more than double the highest density for the 1990s. The range in density estimates 

from the Poisson regression model was somewhat greater, with a threefold difference between 

the modeled densities for 1985 and 1999. Trend analysis based on an ANOVA comparison of 

observed density estimates suggested a marginally significant (p=0.09) but substantial decline of 
ix 

31% from the 1980s to the 1990s. The Poisson regression model indicated highly significant 

(p<0.001) declines of 72% on fast ice and 43% on pack ice over the 15-year period. However, 

the apparent decline between the 1980s and the 1990s may be due to a difference in the timing of 

surveys rather than an actual decline in abundance. The 1980s surveys were conducted 

substantially later (June 6-12) than surveys in the 1990s (May 27-June 4). If the phenology of 

seal basking was comparably earlier in the 1990s than in the 1980s then the decline in seal 

numbers may well be real. However if such a change in seal basking did not occur, then the 

1990s surveys were flown at a time when a relatively small proportion of seals were visible and 

the apparent decline could be an artifact. 

Regardless of whether or not the differences in ringed seal densities described above are real, 

there are several reasons to think that ringed seal abundance in the Beaufort Sea either has 

changed or is likely to change in the future. Beaufort Sea populations of polar bears, which prey 

on ringed seals, and bowhead whales, which compete with them for food, have increased since 

the 1970s. Furthermore, as climate warms, impacts on ringed seal distribution, abundance, and 

productivity will likely result from the combined effects of changes in physical habitats, changes 

in prey populations, and changes in inter-species interactions. 
x 

ACKNOWLEDGEMENTS 

This project was funded primarily by the U.S. Department of the Interior, Minerals 

Management Service, Alaska Region Environmental Studies Section. Additional support and inkind services were contributed by the Alaska Department of Fish and Game, the National Marine 

Fisheries Service, the University of Alaska Fairbanks, and the North Slope Borough. Cleve 

Cowles, Jane Carlson, Valerie Elliott, and Warren Horowitz assisted with implementation of the 

cooperative agreement. 

We thank Tom Blaesing, Commander Northwest, for providing the aircraft used for 1996 -

1999 surveys and for his expert piloting. These surveys would not have been so safe or efficient 

without Tom’s extensive experience on the North Slope and his commitment to providing the 

best survey aircraft possible. Doug DeMaster, Casey Hessinger, Sue Hills, Debbie Blaesing, 

Grey Pendleton, John Bengtson, and Janice Waite served as observers and data recorders on the 

surveys. 

Rob DeLong developed computer programs for manipulating aerial survey data. Casey 

Hessinger prepared the ringed seal bibliography, assisted with organization of historical data 

files, and developed a pilot project for GIS analysis to examine ringed seal distribution and 

density relative to habitat variables. Jeff Laake provided advice on study design and data 

analysis. 
1

 INTRODUCTION 

Ringed seals (Phoca hispida) are a widespread, circumpolar species, that in Alaskan waters 

occur in the Beaufort, Chukchi, and Bering seas, usually in association with sea ice (Burns 

1970). They are small phocid seals, with adult animals in Alaska averaging 115 cm in nose-tail 

length and 49 kg in weight (Frost and Lowry 1981). 

Although they also occur in pack ice, ringed seals are the only species of seal in Alaska that 

commonly lives in and under the extensive, largely unbroken, shorefast ice (Burns 1970). 

Shorefast ice begins to form in October-November, and persists until May-July, depending on 

location. At its maximum extent the shorefast ice extends seaward to about the 20 m isobath, 

which may be 40 km or more offshore (Stringer et al. 1980). Using strong claws on their front 

flippers, ringed seals make breathing holes in the newly formed ice and maintain the holes as the 

ice thickens (Smith and Stirling 1975, Smith and Hammill 1981). Later in the season some holes 

are enlarged to provide access to the ice surface on which seals excavate lairs in the accumulated 

snow. Pregnant females give birth to and nurture their single pup in the lairs during March-May 

(Smith and Stirling 1975). 

As day length and temperature increase in the spring, increasing numbers of ringed seals haul 

out on the surface of the ice near breathing holes or lairs. This hauling-out or basking is 

associated with the annual molt, which occurs in May-July (McLaren 1958) when increased skin 

temperatures are needed to promote epidermal growth (Feltz and Fay 1966). It is during this 

time that seals are most readily observed and counted. Seasonal shifts in distribution occur due 

to changes in sea ice characteristics, but the dynamics of those movements are poorly known. 

In addition to being an important habitat for ringed seals, the shorefast ice also provides a 

reasonably safe and convenient surface on which various human activities may be conducted. 

Coastal residents have traditionally used the shorefast ice to hunt for seals and polar bears (Ursus 

maritimus), trap for arctic foxes (Alopex lagopus), and to travel between villages and camps. 

More recently the oil and gas industry has used the shorefast ice for conducting certain phases of 

petroleum exploration and development. Activities that might affect ringed seals and their 

habitat include principally seismic profiling, exploratory drilling, and oil production, which 

require deployment of camps and heavy equipment, and construction of gravel or ice islands, ice 

roads, and airstrips. 

Over the last several decades, there has been a warming trend in much of the Arctic, resulting 

in a thinning of the sea ice and changes in the annual extent of sea ice coverage (Vinnikov et al. 

1999). Such changes may affect both ringed seals and humans who rely on sea ice for various 

activities (Huntington 2000). For this reason, it is important to monitor changes in distribution 

and abundance of ringed seals over both short and long term time scales. 

STUDY RATIONALE AND OBJECTIVES 

The Minerals Management Service (MMS) supports environmental studies needed to provide 

the information required for planning outer continental shelf (OCS) lease sales and monitoring 

the impacts of oil and gas industry-related activities on marine resources. Due to the possible 

impacts of OCS activities on ringed seals, especially possible conflicts on the shorefast ice, 

MMS has supported a variety of studies on them (e.g., Kelly et al. 1986, Frost and Burns 1989). 

In 1985-1987, MMS contracted with the Alaska Department of Fish and Game (ADF&G) to 
2

conduct a series of aerial surveys for ringed seals in the Chukchi and Beaufort seas, one of the 

objectives of which was to develop and initiate a program for monitoring seal distribution and 

abundance (Frost et al. 1988). In 1996, MMS and ADF&G entered into a cooperative agreement 

to repeat the aerial surveys of ringed seals in the Beaufort Sea. 

Results of the surveys conducted in 1996-1999 are reported here and compared to surveys 

conducted in 1985-1987. In addition, because both on-ice studies and previous aerial survey 

analyses have described effects of various habitat, weather, and temporal factors on survey 

results, we have included analyses of the effects of such factors on monitoring of seal 

distribution and abundance in the central Beaufort Sea. 

Specific objectives of this project identified in the Cooperative Agreement and addressed by 

this report are as follows: 

1. Review and refine the established protocol for monitoring ringed seal distribution and 

abundance by aerial surveys. 

2. Estimate relative abundance and density of molting ringed seals on fast ice in the 

Beaufort Sea during 1996-1999 and compare with data collected during 1985-1987. 

3. Correlate ringed seal densities on shorefast ice with environmental variables. 

METHODS

Collection of Survey Data 

Aerial surveys were flown during late May and early June 1985-1987 and 1996-1999 in the 

Beaufort Sea between Point Barrow (longitude 156° 36’W) and Kaktovik (longitude 143°42’W), 

an east-west extent of approximately 500 km (Figure 1). The study area was divided into four 

sectors (designated B1-B4) to facilitate comparisons with previous ringed seal surveys done in 

this region. 

Surveys were flown at groundspeeds of approximately 222 km/hr (120 knots) and a survey 

altitude of 91 m. Most were conducted between 1100 and 1700 hrs local time (solar noon is at 

approximately 1300 hrs at Prudhoe Bay) to coincide with the time of day when maximal 

numbers of seals haul out and bask on the ice (Burns and Harbo 1972; Smith 1975; Finley 1979; 

Smith and Hammill 1981). A few transects were surveyed slightly before 1100 hrs or after 1700 

hrs. 

Surveys were flown north-south along lines of longitude, and were therefore generally 

oriented perpendicular to the coast. Possible transect lines were spaced at 3.6 km between 

centerlines (6 minutes of longitude). A subset of these was lines surveyed each year. In parts of 

some sectors in 1996, 1997 and 1999, lines were spaced at 1.8 km intervals (3 minutes of 

longitude). Transect lines extended from approximately the 3 m depth contour to 40+ km 

offshore. Only data collected within 40 km of the shoreward end of the transect were used in the 

final analyses. 

Survey strip width was 0.41 km on each side of the aircraft, with a 134 m offset from the 

transect centerline. Observers maintained the appropriate strip width by using inclinometers to 

mark survey angles (9.5° and 34° below the horizon) on the window with a grease pencil and 

periodically checking the angles throughout the day. 
3

B1

B2

B3

B4

BEAUFORT SEA

Barrow

Prudhoe Bay Kaktovik

ALASKA

Oliktok Point

U.S.-Canadademarcation 

line

155 W 150 W 145 W 140 W

70 N

0 50 100 150 200 250 Kilometers

Figure 1. Map of the Beaufort Sea study area showing sectors used in analysis of ringed seal survey data. Dashed line is the 20 m 

depth contour. 
4

All seals hauled out on the ice within the survey strip were identified to species (either ringed 

or bearded (Erignathus barbatus) seals), the number in each group was counted, and each was 

noted as being at a hole or by a crack. Seals at different holes were counted as separate groups, 

while those around a single hole were considered as part of the same group. When seals were 

spaced along cracks, the total number along a single crack (and within the survey strip) was 

recorded as a single group. Sightings of polar bears and polar bear tracks were noted as was any 

evidence of on-ice human activity such as ice roads or artificial islands. 

 Three ice variables were recorded: ice type (fast or pack), ice deformation (percent of the ice 

surface within the survey strip that was deformed by pressure ridges, ice jumbles, and snow 

drifts in 10% increments), and melt water (percent of the ice surface covered by standing water 

due to melting snow or river runoff in 10% increments). The delineation between fast and pack 

ice was indicated by a variety of features, including: a shear zone or large pressure ridge; the 

presence of open leads, broken ice and open water spots in the ice; or a large refrozen lead. In 

some areas the delineation between fast and pack ice was not clear from the aircraft, and the 

location of the edge was assigned later by examining NOAA ice maps made from satellite 

images taken during the same time period. 

Weather conditions (cloud cover, air temperature, and wind speed) were recorded at the 

beginning of each transect and whenever conditions changed. Because there were no on-ice 

weather stations and available weather reports were based on conditions over land, we based our 

weather information on conditions measured at survey altitude. The absence of open water in 

fast ice and the melted condition of the snow precluded the inference of surface winds from 

indicators such as white caps or blowing snow. Surveys were not conducted, or were 

discontinued, if wind speed exceeded 36 km/hr for more than a short time, or if the ceiling was 

below the survey altitude of 91 m. 

Surveys methods were generally similar in all years (see Frost et al. 1988, 1997, 1998, 

1999b). Primary differences were the type of aircraft used, the type of navigation system, and 

the manner in which ice conditions and seal sightings were recorded. 

The survey aircraft for 1985-1987 surveys was a Twin Otter equipped with oversized bubble 

windows, radar altimeter, and a Global Navigation System (GNS-500). A laptop computer was 

linked to the GNS-500 and radar altimeter, and was used to mark time, altitude, and latitude and 

longitude at the beginning and ending points of each transect, as well as at other points of 

interest. 

In 1985-1987, three scientific personnel participated in each survey. A navigator recorded 

weather, ice conditions (averaged for survey strips on both sides of the aircraft), and navigation 

information directly into the computer. Observers on each side of the aircraft counted and 

recorded seals on paper datasheets. All data were recorded by 1-minute intervals. Count data 

were later entered into the computer database. 

The survey aircraft for the 1996-1999 surveys was a twin-engine Aero Commander equipped 

with large bubble windows at all observation positions. On all flights two experienced primary 

observers counted seals using the strip transect protocols established during the 1985-1987 

surveys. An additional observer seated behind the right primary observer counted using either 

strip transect protocols or line transect methods. Each observer was paired with a data recorder 

who entered all sightings directly into a laptop computer. Data recorders also entered 

information on ice and weather conditions, evidence of on-ice industrial activity, and sightings of 

other animals. For the 1990s surveys, ice characteristics were recorded independently on each 

side of the aircraft. 
5

During the 1996-1999 surveys each recorder/observer pair had direct intercom 

communication, but was isolated from other observer/recorder pairs. A Global Positioning 

System unit interfaced with all three computers such that positions were recorded at start and end 

points of survey lines, each minute along a survey line, at each seal sighting, and at all changes 

in ice or weather conditions. All entries were checked and edited as necessary each evening 

following the survey flights. The approximate edge of the fast ice was reconciled by left and 

right side observers to ensure consistent coding of data. 

Data Analysis 

Data Sources 

Results of the 1985-1987 surveys were reported in a final report for that project by Frost et 

al. (1988). A subset of the data collected and reported in 1985-1987 was reanalyzed and 

incorporated in this report. Data incorporated into the reanalysis included surveys conducted 

within 40 km of shore between Point Barrow and Kaktovik. We used only data from surveys 

flown at 91 m altitude in suitable weather conditions and before breakup of the shorefast ice had 

obviously begun. A single survey of each line was included in the dataset; temporal replicates to 

examine the effects of breakup were not included. These data were the same as the "selected" 

datasets that were used for density calculations in Frost et al. (1988). 

 In the 1990s survey effort, complete coverage of the Alaskan Beaufort Sea was obtained in 

1997 and 1999, while in 1996 and 1998 only two of the four possible sectors (B3 and B4) were 

surveyed. All data collected during the 1990s surveys were used in the final analyses. 

 Bathymetry data from the National Ocean Service “Coastal Bathymetry of the Bering, 

Chukchi, and Beaufort Seas” were used to analyze seal density relative to water depth. The 

bathymetric contours began at 5 m and were then in 10 meter intervals out to 200 m. 

Strip Transect Densities 

The simple or “raw” density of observed ringed seals was calculated by dividing the number 

of seals counted within the strata of interest (sector or several sectors combined) by the area of 

that strata. To determine standard errors (SE) and confidence intervals (CI), we used a modified 

ratio estimator that considered seal counts and areas separately for each survey line. The area 

surveyed was computed from the latitude and longitude of the first and last survey points on each 

line. Areas were computed separately for each side of the plane, although these were very close 

in all cases. Mean density (R) and standard error (S(R)2

 ) were then computed for each sector 

using the Jackknife procedure (Manly 1991), and as shown below: 

i

i

x

y R

∑

∑ ˆ =

( )

( )( ) ∑

∑ ∑

−















 −

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





=

i

i

i

i

n x

R y

x

y

S R

1

ˆ

)ˆ (

2

2

where yi = number of seals in line i, xi = area of line i, and n = the number of lines used to 

compute the density. 
6

Approximate 95% confidence intervals were computed as the mean density plus or minus the 

standard error multiplied by the appropriate t-statistic with n-1 degrees of freedom, where n is 

the number of survey lines in a sector. 

Ringed seal density estimates were computed for all combinations of ice types (fast ice, pack 

ice, and all ice) and seals (seals at holes, seals at cracks, and all seals). For the fast and pack ice 

estimates, density within the portion of the strip covered by each of the two ice types was 

computed for each line. 

Univariate Analyses 

We tested the significance of observed differences in ringed seal densities relative to habitat, 

weather, and time of day using chi-square (χ2

) goodness-of-fit tests. Analyses included all seals 

(seals at holes and at cracks) on any ice (fast and pack) within 40 km of shore in sectors B3 and 

B4. Chi-square tests were conducted for each variable relative to the number of individual seals 

as well as to the number of seal groups (sightings) for every year and for all years combined. 

Bonferroni-adjusted 95% confidence intervals were calculated by stratum for each variable 

for proportion of occurrence (the observed proportion of seals within a strata relative to total 

seals in all strata) and for observed seals as: 

100 (1-α/I)%, 

where α=.05 

I = number of strata (categories) 

If the expected proportion for a stratum (based on survey area) was outside the confidence 

interval for the observed proportion, the difference was considered significant (Manly et al.

1993). 

The Pearson statistic was used to test the null hypothesis that selection of strata (categories) 

was random for a particular variable. Rejection of the null hypothesis (large χP

2 compared to 

percentage points of the chi-square distribution) indicates that selection is not random, and that 

there is an association between the variable and strata selection. 

Covariate Analysis (Poisson Regression) 

Survey counts are indicators of the actual population of animals using a particular habitat, 

area, or site. The observed count is less than or equal to the true population and can be expressed 

as follows: 

C = N * p 

where C is the observed count, N is the population of interest, and p is the probability that an 

animal in N is included in C (p < 1). The inclusion probability, p, can take on a variety of forms 

including constants, probability functions (e.g., binomial), and functions of covariates such as 

date, time of day, or weather conditions. Excluding the unlikely event that p is a constant (a 

constant fraction of the population is counted on all occasions and under all circumstances), 

analyses involving C as a surrogate for N will be imprecise and often biased (Barker and Sauer 

1992, 1995). 

We used Poisson regression (McCullagh and Nelder 1989) to model the relationship between 

seal counts and environmental covariates. Poisson regression is appropriate for these analyses 

because the Poisson distribution is a positive discrete distribution in which only positive integers 
7

are acceptable values. This is more suitable for count data, especially where there are zero 

counts, than the normal distribution where non-integers and negative values also are permissible. 

This approach is very similar to that of Manly et al. (1993) except that they use logistic 

regression to predict the probability that an animal is present rather than predicting the number of 

animals present. We modeled habitat variables that might affect the distribution and local 

abundance of seals (e.g., ice deformation, water depth, distance from the fast ice edge and 

longitude) simultaneously with factors that likely affect only the availability of seals for counting 

(e.g., weather or time of day). 

To model the effects of covariates, we used only data from sectors B3 and B4 within 40 km 

of the shoreward end of transects. These were the only sectors surveyed in all seven survey 

years. Only data from surveys conducted at the standard survey altitude of 91 m were included 

in the dataset. Replicate counts of transects conducted to examine date- or altitude-related 

differences in seal density were not included in the final dataset. 

Data from the left and right sides of the aircraft were used as independent observations in the 

regression analyses. The transects were divided into segments based on each unique 

combination of survey variables. During the 1980s surveys, all habitat factors were determined 

for the entire survey strip (left and right combined) and only at 1-minute internals. Thus, for 

example, ice deformation was averaged for the left and right sides for an entire minute to 

produce an ice deformation value that was assigned to seal sightings from both observers. 

segments. 

During the 1990s surveys, observations of ice deformation were recorded separately for left 

and right observers as they occurred instead of at 1-minute intervals. Changes in other variables 

were also noted as they occurred and location of all such changes were assigned through a direct 

computer link with the aircraft GPS. Thus, each survey transect was divided in segments based 

on ice type (pack or fast), ice deformation, air temperature, wind speed, and cloud cover. When 

any of these variables changed, a new segment was defined such that each segment was uniform 

with respect to the explanatory variables. Data from the left and right side observers were 

treated as separate transects since ice conditions differed between left and right sides. This 

resulted in segments of differing sizes on the left and right sides. For both the 1980s and 1990s, 

water depth (starting with depths <5 m then in 10 m intervals) and distance from the fast ice edge 

(in 2 km intervals) were added to the datasets prior to creating segments. Because the original 

data in the 1980s was summarized at 1 minute intervals (although changes in depth and distance 

from the fast ice edge did not always match these intervals), there were generally fewer segments 

per transect in the 1980s than the 1990s. The number of seals observed and the area surveyed 

(segment length in km multiplied by strip width of 0.41 km) were determined for each segment. 

The response variable in the regression analysis was the number of seals in a segment. The 

explanatory variables were year, ice type (pack or fast), percent ice deformation, distance from 

the fast ice edge, water depth, longitude, time of day, temperature, wind speed, and percent cloud 

cover. Water depth, longitude, and distance from the fast ice edge were included to account for 

large-scale patterns of seal abundance that were independent of local ice or weather conditions. 

Time was included to examine temporal changes in visibility. Year*longitude and 

year*distance-from-ice-edge interactions were included to account for annual large-scale 

changes in seal sightings that were unrelated to the other habitat variables in the model. These 

changes in sighting distributions could be due to changes among years in the distribution of the 

population or changes in the distribution of sighting conditions. 
8

Although date has a strong effect on seal counts (see Frost et al. 1988), our 1996-1999 

surveys did not include temporal replicates that are required to investigate this question, and date 

was therefore not included as an explanatory variable. We also chose not to include distance 

from shore in our analysis. We used depth in lieu of distance from shore because it is likely to 

influence factors affecting ringed seals such as prey availability and ice characteristics. Distance 

from shore, except as a reflection of water depth, is less likely to directly affect seal density. 

Using both in the same analysis caused problems with collinearity. 

The ln (area) of each segment was included in the regressions as an offset variable (Agresti 

1990) to account for the fact that, all other variables being equal, larger segments have more 

seals than smaller segments (adjusts analyses to a density basis). Quadratic terms and 

interactions were included for some variables or combinations of variables when we believed 

that relationships were not linear (on the log scale). 

Based on preliminary analyses, the assumption of a Poisson distribution did not ‘fit’ the data 

well. We made two adjustments to the analyses to adjust for this lack of fit . First we omitted 

segments <0.01 km2

. These tiny segments were artifacts of combining the survey data with 

depth and distance-from-fast-ice-edge bands that were not part of the original data. When any 

seals were in these segments, very high densities resulted that had undue influence on the 

regression results. To account for remaining lack of fit, probably due to the presence of large 

groups of seals which would be unexpected with the small mean densities we observed, we 

adjusted tests and standard errors using the Pearson chi-square statistic as an overdispersion 

parameter (i.e., quasi-likelihood approach, Agresti 1990). This results in somewhat larger 

standard errors and P-values than those computed without the adjustment. 

To account for possible spatial correlation in the data (i.e., residuals from the regression for 

segments close together were more similar than for residuals from segments far apart), we 

included a spatial component in the variance structure. We used a spatial exponential function 

with a nugget effect to model the dependency in the residuals based on the distance between 

segments within a survey line (Littell et al. 1996). Survey lines were treated as a random effect. 

We assumed independence for data from separate survey lines and years. 

All variables (including selected quadratic terms and interactions) were included in an initial 

model. Final regression models were then determined using a backward selection process. 

Terms were dropped from consideration one at-a-time based on the P-values from the Wald F

statistics; those with the largest P-values were dropped first. This continued until all variables 

had P-values <~0.05. Continuous variables with P-values >0.05 were retained in the model if 

they were contained in a continuous by categorical interaction (e.g., longitude*year) that had a 

small P-value. 

Power Analysis 

We estimated statistical power for detecting change in the size of the ringed seal population 

using simulation. Power is the probability of rejecting the null hypothesis of a statistical test. In 

this case, the null hypothesis was that the linear year effect from the Poisson regression of the 

ringed seal counts differed from zero. We used simulations to account for the effect of other 

variables that affected ringed seal counts (see results of the Poisson regression analysis). We 

estimated power for two levels of effort (50 or 65 survey lines), 5 or 10 years of surveys, and 

various rates of population change (-0.2, -0.15, -0.1, -0.05, 0.1, 0.15, 0.2). Simulations for the 

power analysis are based on the results of a regression using data from 1996-1999 for sectors B3 

and B4 and included only seals counted on fast ice. 
9

To account for varying conditions in each simulated year of data, we retained the data 

structure, except for the counts, for the original four years of data (1996-1999). For each year of 

simulated data, we selected one of the original years at random (with replacement). We then 

randomly selected transects, with replacement, from that year’s data. For the selected transects, 

we retained the original segments and covariates, including segment area, except that we 

randomly assigned longitude within the range for the study area and we systematically added the 

year effect (i.e., trend) at a rate as defined previously. This procedure retained in the simulated 

data the interrelationships among the covariates and the year to year variation in conditions of 

the original data. Based on the covariates and size of each transect segment, we used the 

estimated regression coefficients and overdispersion factor from our original Poisson regression 

to obtain a mean and variance for generating a gamma random variable, which was then used as 

the mean count for that segment. The simulated ‘count’ for each segment was then generated 

using a Poisson random number generator with associated gamma mean for that segment. 

Gamma distributions are positive and continuous, unlike Poisson variables that only take on 

integer values. Because they are continuous and positive, gamma random variables are useful 

for modeling the means of count data (although counts are integers, the means need not be). 

When the means are small, such as with our data, gamma distributions are asymmetric with most 

values near zero, but having a long tail to the right. This mimics the situation in the original 

data. Data were generated independently for each segment with no spatial correlation other than 

that induced by nearby segments often having similar values of covariates. 

When the desired number of years of simulated data were generated, we estimated trend, and 

hence power, in two ways, one using the Poisson model and the other using simple linear 

regression to regress the natural log of the yearly total count of seals against year. In the Poisson 

case, we estimated the regression coefficients using the final Poisson regression model we 

obtained from the analysis of our original data (the model we used to generate the data). Even 

though there was little evidence of spatial correlation in the original regression, we included 

estimation of spatial correlation in the regressions for the simulated data to maintain consistency 

with the original analysis. For each regression method, we tallied whether the year effect was 

significant (P<0.05) and whether the estimate was positive or negative. This procedure was 

repeated 201 times. The estimated power is the proportion of these 201 replicates with 

significant year effects. In some cases a significant trend is estimated when the true trend has the 

opposite sign (e.g., significant negative trend estimate when the true trend is positive). Because 

of this, we graphed the power separately for significant estimated positive and negative trends so 

that all power estimates are one tailed. 

RESULTS

Survey Effort

During the 1985-1987 surveys all four sectors of the Beaufort Sea study area (Figure 1) were 

surveyed in every year. The dataset selected for this analysis included 223 transect lines that 

covered 7,156 km2

 of ice habitat (Table 1). Annually, we surveyed 71-80 transects covering 

2,271-2,524 km2

. Total annual ringed seal counts were 2,189 in 1985, 2,605 in 1986 and 3,867 

in 1987 (Appendices A and B). The proportion of the total survey area comprised by fast ice 

varied from 98% (1985) to 68% (1987). 
10

Table 1. Area surveyed and number of seals counted on fast and pack ice within 40 km of shore 

during aerial surveys conducted in the Alaskan Beaufort Sea, 1985-1987 and 1996-1999. 

Year/Sector Fast Ice Pack Ice All Ice 

(# transects) Area (km2

 ) # Seals Area (km2 ) # Seals Area (km2 ) # Seals 

1985 

B1 (n=10) 303.2 252 3.0 1 306.2 253 

B2 (n=14) 447.9 399 - - 447.9 399 

B3 (n=34) 1117.2 1299 1.6 0 1118.8 1299 

B4 (n=14) 359.0 214 39.5 24 398.5 238 

All sectors 2227.3 2164 44.1 25 2271.4 2189 

1986 

B1 (n=20) 549.4 348 114.4 9 663.8 357 

B2 (n=21) 696.2 820 - - 696.2 820 

B3 (n=18) 519.9 668 80.6 105 600.5 773 

B4 (n=12) 197.7 534 202.5 121 400.2 655 

All sectors 1963.2 2370 397.5 235 2360.7 2605 

1987 

B1 (n=21) 510.5 497 40.2 33 550.6 530 

B2 (n=21) 649.8 920 53.4 61 703.2 981 

B3 (n=23) 377.6 909 388.9 571 766.5 1480 

B4 (n=15) 176.1 528 327.5 348 503.6 876 

All sectors 1714.0 2854 810.0 1013 2524.0 3867 

1996 

B1 (n=0) - - - - - - 

B2 (n=3) 67.5 51 25.1 51 92.6 102 

B3 (n=43) 655.2 355 658.4 560 1313.6 915 

B4 (n=18) 131.4 91 423.3 504 554.7 595 

All sectors 854.1 497 1106.8 1115 1960.9 1612 

1997 

B1 (n=30) 809.2 313 136.6 9 945.9 322 

B2 (n=21) 640.9 388 - - 640.9 388 

B3 (n=57) 1, 491.2 1152 259.2 250 1, 750.5 1402 

B4 (n=31) 582.3 732 368.2 585 950.5 1317 

All sectors 3523.6 2585 764.1 844 4287.8 3429 

1998 

B1 (n=0) - - - - - - 

B2 (n=0) - - - - - - 

B3 (n=28) 276.5 209 552.7 465 829.2 674 

B4 (n=12) 131.6 179 236.8 258 368.4 437 

All sectors 408.1 388 789.5 723 1197.6 1111 

1999 

B1 (n= 15) 253.6 194 175.9 72 429.5 266 

B2 (n=20) 338.5 234 281.3 191 619.8 425 

B3 (n=57) 918.7 803 833.7 886 1752.4 1689 

B4 (n=31) 313.5 604 581.5 812 895.0 1416 

All sectors 1824.3 1835 1872.4 1961 3696.7 3796 
11

The 1990s surveys were originally planned to include abbreviated first-year (1996) coverage 

from Oliktok to Kaktovik, followed by two years (1997 and 1998) of broader surveys extending 

from Barrow to Kaktovik. Due to early breakup and poor ice conditions, the 1998 survey 

included only the Oliktok-Kaktovik region, and an additional year of surveys extending from 

Barrow to Kaktovik was added in 1999 (Appendices A and B). The dataset for the 1996-1999 

surveys included 366 transects and 11,144 km2

 of ice habitat (Table 1). Annually, we surveyed 

40-139 transects covering 1,198-4,288 km2

. Total annual ringed seal counts were: 1996 - 1,612 

per 1,961 km2

; 1997 - 3,429 per 4,288 km2; 1998 - 1,111 per 1,198 km2; and 1998 - 3,796 per 

3,697 km2

. Fast ice made up 34%-82% of the total survey area. 

Ringed Seal Distribution and Abundance 

Observed Densities and Group Sizes 

Only sectors B3 and B4, which were surveyed in all seven years, were included in the 

complete data analyses. However, observed density estimates and standard errors for all sectors 

and all years are provided in Appendix A, and maps showing all transect lines and ringed seal 

sightings are provided in Appendix B. 

Ringed seals were broadly distributed throughout the central Beaufort Sea study area in all 

years (Figures 2-8). Observed densities for fast ice and pack ice combined in the 1980s ranged 

from 1.01 seals/km2

 in 1985 to 1.85 seals/km2 in 1987 (Table 2). Observed densities for the 

1990s were similar to 1985 and somewhat lower than in 1986 and 1987, ranging from 0.81 

seals/km2

 in 1996 to 1.17 seals/km2 in 1999. Total estimated densities were substantially higher 

on fast ice than pack ice during the 1980s surveys. The differences between pack and fast ice 

were not consistent for the 1990s surveys. In two years, pack ice densities were much greater 

than fast ice densities, and in the other two years the densities were similar. Except for 1987, 

more seals were seen at holes than at cracks on fast ice (Figure 9). On pack ice the relative 

proportions of seals at holes and cracks was more variable. 

The average group size of seals counted at holes was quite consistent across all survey years, 

ranging from 1.34-1.67 seals/group for all ice combined (Table 3). Average group size for seals 

at cracks was generally double or triple the average group size at holes, and was considerably 

more variable (3.38-5.71 seals/group). For most years, average group size was largest for seals 

along cracks in fast ice. 

 Factors Affecting Seal Densities 

Univariate chi-square analyses were done to examine the relationship between ringed seal 

density and water depth, distance from the fast ice edge, ice deformation, and longitude, for each 

year and for all years combined (Table 4 and Appendix C). 

Poisson regression models were separately constructed from ringed seal survey data for 

1985-1987, 1996-1999, and for all years combined (Table 5). Regression coefficients for 

variables that were retained in the final models are shown in Table 6. 

Depth - Univariate analysis indicated that water depth had a significant effect on observed 

ringed seal densities in each survey year, and for all years combined (P<0.001). In the 1980s, 

observed densities were lowest in water < 5 m deep (0.30-0.93 seals/km2

 ) and >35 m deep 

(0.42-0.48 seals/km2

 ) and highest in >5-25 m water depths (1.13-2.79 seals/km2; Figure 10a). 
12

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Kaktovik ALASKA

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Figure 2. Map of the Beaufort Sea study area showing sightings of ringed seals made during 9-12 June 1985. Thick dashed line is the 

approximate edge of shorefast ice. Thin dashed line is the 20m depth contour.
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Prudhoe Bay

Kaktovik ALASKA

BEAUFORT SEA

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1986

Figure 3. Map of the Beaufort Sea study area showing sightings of ringed seals made during 31 May-12 June 1986. Thick dashed line is the approximate edge of shorefast ice. Thin dashed line is the 20m depth contour.
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Prudhoe Bay

Kaktovik ALASKA

BEAUFORT SEA

0 10 20 30 40 50 K ilo m eters

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1987

Figure 4. Map of the Beaufort Sea study area showing sightings of ringed seals made during 6-7 June 1987. Thick dashed line is the approximate edge of shorefast ice. Thin dashed line is the 20m depth contour.
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Prudhoe Bay

Kaktovik ALASKA

BEAUFORT SEA

0 10 20 30 40 50 K ilo m eters

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1996

Figure 5. Map of the Beaufort Sea study area showing sightings of ringed seals made during 29-31 May 1996. Thick dashed line is the 

approximate edge of shorefast ice. Thin dashed line is the 20m depth contour.
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Kaktovik ALASKA

BEAUFORT SEA

0 10 20 30 40 50 K ilo m eters

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1997

Figure 6. Map of the Beaufort Sea study area showing sightings of ringed seals made during 27 May-1 June 1997. Thick dashed line 

is the approximate edge of shorefast ice. Thin dashed line is the 20m depth contour.
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Kaktovik ALASKA

BEAUFORT SEA

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1998

Figure 7. Map of the Beaufort Sea study area showing sightings of ringed seals made during 27-28 May 1998. Thick dashed line is the 

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Prudhoe Bay

Kaktovik ALASKA

BEAUFORT SEA

0 10 20 30 40 50 K ilo m eters

R in ged s ea ls

S 1 - 2

S 3 - 9

S 10 - 4 3

1999

Figure 8. Map of the Beaufort Sea study area showing sightings of ringed seals made during 29 May-4 June 1999. Thick dashed line is 

the approximate edge of shorefast ice. Thin dashed line is the 20m depth contour. 
19

Table 2. Densities of ringed seals at holes and at cracks on fast ice, pack ice, and all ice combined within 40 km of shore in the central 

Beaufort Sea of Alaska (149o50’ W to 143o40’ W), based on aerial surveys conducted in 1985-1987 and 1996-1999. 

 Fast Ice Pack Ice All Ice Combined 

 At Holes At Cracks All Seals At Holes At Cracks All Seals At Holes At Cracks All Seals 

1985 Seals/ km2 0.54 0.49 1.02 0.44 0.15 0.58 0.54 0.48 1.01 

SE 0.03 0.10 0.11 0.10 0.05 0.14 0.03 0.10 0.11 

 LCL 0.48 0.32 0.84 0.23 0.04 0.30 0.48 0.32 0.83  UCL 0.59 0.65 1.21 0.65 0.25 0.87 0.59 0.64 1.20 1986 Seals/ km2 1.19 0.49 1.67 0.22 0.58 0.80 0.91 0.51 1.43 

SE 0.08 0.22 0.20 0.03 0.14 0.14 0.08 0.16 0.14 LCL 1.04 0.12 1.33 0.16 0.33 0.55 0.78 0.25 1.19 UCL 1.33 0.86 2.02 0.28 0.83 1.05 1.05 0.78 1.66 

1987 Seals/ km2 1.06 1.53 2.60 0.44 0.85 1.28 0.71 1.15 1.85 

SE 0.08 0.52 0.53 0.06 0.14 0.18 0.06 0.23 0.24 LCL 0.92 0.66 1.69 0.33 0.61 0.98 0.60 0.76 1.45 UCL 1.20 2.41 3.50 0.54 1.09 1.58 0.82 1.54 2.26 

1996 Seals/ km2 0.51 0.06 0.57 0.60 0.38 0.98 0.56 0.24 0.81 

SE 0.04 0.03 0.05 0.11 0.06 0.13 0.06 0.05 0.08 LCL 0.44 0.01 0.48 0.42 0.27 0.76 0.46 0.17 0.67 UCL 0.58 0.10 0.65 0.79 0.48 1.21 0.67 0.32 0.95 

1997 Seals/ km2 0.89 0.02 0.91 0.47 0.86 1.33 0.79 0.21 1.01 

SE 0.06 0.01 0.06 0.08 0.18 0.22 0.06 0.04 0.08 LCL 0.79 0.00 0.80 0.33 0.57 0.96 0.70 0.14 0.88 UCL 0.99 0.03 1.01 0.61 1.15 1.70 0.89 0.28 1.14 

1998 Seals/ km2 0.65 0.30 0.95 0.53 0.38 0.92 0.58 0.35 0.93 

SE 0.08 0.19 0.21 0.06 0.06 0.08 0.05 0.07 0.09 LCL 0.52 -0.03 0.60 0.43 0.28 0.78 0.49 0.24 0.78 UCL 0.78 0.62 1.30 0.64 0.48 1.05 0.66 0.47 1.07 

1999 Seals/ km2 0.92 0.23 1.14 0.69 0.51 1.20 0.80 0.38 1.17 

SE 0.07 0.08 0.11 0.06 0.06 0.09 0.05 0.05 0.07 LCL 0.80 0.09 0.96 0.60 0.40 1.05 0.72 0.29 1.05 UCL 1.03 0.36 1.32 0.79 0.61 1.35 0.87 0.46 1.30 
20

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1985 1986 1987 1996 1997 1998 1999

Seals / km2

Fast-Hole

Fast-Crack

Pack-Hole

Pack-Crack

Figure 9. Estimated densities of ringed seals at holes and cracks on fast and pack ice within 40 

km of shore based on aerial surveys in the central Beaufort Sea, 1985-1987 and 1996-1999. 
21

Table 3. Number of groups and average group size of observed ringed seals based on aerial 

surveys in the central Beaufort Sea of Alaska (149o50’ W to 143o40’ W) conducted in 1985-1987 

and 1996-1999. 

 Fast Ice Pack Ice All Ice 

 Hole Crack All Hole Crack All Hole Crack All 

1985

 Number of seals 796 717 1513 18 6 24 814 723 1537 

 Number of groups 591 199 790 18 6 24 609 205 814 

 Average group size 1.35 3.60 1.92 1.00 1.00 1.00 1.34 3.53 1.89 

1986

 Number of seals 852 350 1202 62 164 226 914 514 1428 

 Number of groups 577 52 629 45 38 83 622 90 712 

 Average group size 1.48 6.73 1.91 1.38 4.32 2.72 1.47 5.71 2.01 

1987

 Number of seals 588 849 1437 313 606 919 901 1455 2356 

 Number of groups 358 115 473 220 158 378 578 273 851 

 Average group size 1.64 7.38 3.04 1.42 3.84 2.43 1.56 5.33 2.77 

1996 

 Number of seals 401 45 446 654 410 1064 1055 455 1510 

 Number of groups 285 22 307 347 112 459 632 134 766 

 Average group size 1.41 2.05 1.45 1.88 3.66 2.32 1.67 3.40 1.97 

1997

 Number of seals 1847 37 1884 297 538 835 2144 575 2719 

 Number of groups 1241 7 1248 184 125 309 1425 132 1557 

 Average group size 1.49 5.29 1.51 1.61 4.30 2.70 1.50 4.36 1.75 

1998 

 Number of seals 267 121 388 422 301 723 689 422 1111 

 Number of groups 179 17 196 275 108 383 454 125 579 

 Average group size 1.49 7.12 1.98 1.53 2.79 1.89 1.52 3.38 1.92 

1999 

 Number of seals 1129 278 1407 980 718 1698 2109 996 3105 

 Number of groups 842 45 887 661 178 839 1503 223 1726 

 Average group size 1.34 6.18 1.59 1.48 4.03 2.02 1.40 4.47 1.80 
22

Table 4. Summary of chi-square analyses of observed ringed seal density relative to habitat 

variables affecting distribution and abundance in the central Beaufort Sea of Alaska.

 

Variable Chi-square df P-value Combined 

Water Depth 

 1985 140.56 4 <0.001 

 1986 263.92 6 <0.001 

 1987 631.11 6 <0.001 

 1996 154.10 4 <0.001 

 1997 917.10 5 <0.001 

 1998 72.01 5 <0.001 

 1999 110.77 4 <0.001 

 All 730.97 6 <0.001 

Distance from Fast Ice Edge 

 1985 143.41 12 <0.001 

 1986 405.12 15 <0.001 

 1987 807.68 15 <0.001 

 1996 416.17 13 <0.001 

 1997 294.83 13 <0.001 

 1998 81.02 11 <0.001 

 1999 409.32 13 <0.001 

 All-Fast 814.21 15 <0.001 

Ice deformation 

 1985 247.03 5 <0.001 

 1986 212.59 5 <0.001 

 1987 333.09 5 <0.001 

 1996 69.27 5 <0.001 

 1997 195.34 5 <0.001 

 1998 15.03 5 0.01 

 1999 108.58 5 <0.001 

 All 469.38 5 <0.001 

Longitude 

 1985 216.58 6 <0.001 

 1986 73.42 6 <0.001 

 1987 769.48 6 <0.001 

 1996 183.21 6 <0.001 

 1997 464.61 6 <0.001 

 1998 73.27 6 <0.001 

 1999 266.80 6 <0.001 

 All 584.47 6 <0.001 
23

Table 5. Terms initially included in regression models of ringed seal densities in the central 

Beaufort Sea of Alaska. Those with P-values < ~0.05 were retained in the final model as were 

lower order terms that were included in higher order categorical interactions (e.g., dist*yr). Ice 

type and year were retained in all models. Entries in bold type were retained in the final model. 

Sample size (n) is the number of transect segments used in the analysis.

 1980s (n=6,286) 1990s (n=16,553) All (n=23,839) 

Variable F num 

df 

P F num 

df 

P F num df P 

ice type 13.89 2 <0.001 5.16 2 0.006 8.08 2 <0.001 

year 7.41 1 0.007 0.72 1 0.397 41.18 1 <0.001 

year*icetype 0.00 1 0.952 9.96 1 0.002 4.31 1 0.038 

dist-ice edge 0.87 1 0.351 0.83 1 0.370 13.91 1 <0.001 

dist2 11.14 1 0.001 26.08 1 <0.001 25.20 1 <0.001 

dist*yr 1.08 2 0.339 1.00 3 0.392 2.14 6 0.046 

dist2

*yr 5.04 2 0.007 4.74 3 0.003 3.68 6 0.001 

longitude 0.14 1 0.713 17.86 1 <0.001 11.06 1 0.001 

long2 7.80 1 0.005 3.59 1 0.058 0.77 1 0.380 

long*yr 5.30 2 0.005 1.63 3 0.180 5.11 6 <0.001 

long2

*yr 3.53 2 0.029 2.05 3 0.127 4.01 6 0.001 

icedef 17.19 1 <0001 52.51 1 <0.001 93.74 1 <0.001 

icedef2

 0.01 1 0.937 0.01 1 0.931 0.00 1 0.957 

time 0.43 1 0.513 1.47 1 0.226 6.29 1 0.012 

time2 26.44 1 <0.001 1.46 1 0.227 4.87 1 0.027 

depth 10.70 1 0.001 20.76 1 <0.001 21.54 1 <0.001 

depth2 11.41 1 0.001 16.36 1 <0.001 29.47 1 <0.001 

wind 0.57 1 0.449 2.36 1 0.125 0.97 1 0.325 

wind2

 0.41 1 0.522 1.37 1 0.241 2.17 1 0.140 

temperature 1.61 1 0.205 0.62 1 0.430 1.17 1 0.280 

temp2

 1.52 1 0.217 4.22 1 0.040 11.72 1 0.001 

cloud 0.08 1 0.780 0.30 1 0.583 0.54 1 0.461 
24

Table 6. Poisson regression coefficients from final ringed seal regression models for the 1980s, 1990s, and all years.  1985-1987 1996-1999 All Variable Est. SE t P Est. SE t P Est. SE t Pintercept (pack) 1.4961 0.5459 2.74 0.006 -0.0760 0.2180 -0.35 0.727 6.0059 1.9196 3.13 0.002 intercept (fast) 1.9511 0.4723 4.13 <0.001 -0.3119 0.1903 -1.64 0.101 6.4925 1.9128 3.39 0.001 year (pack) 0.4355 0.1600 2.72 0.009 -0.1413 0.0536 -2.64 0.009 -0.0478 0.0165 -2.90 0.004 year (fast) 0.4355 0.1600 2.72 0.009 0.0643 0.0581 1.11 0.269 -0.0917 0.0137 -6.71 <0.001 dist–edge85 0.0298 0.0145 2.05 0.041 dist-edge86 0.0119 0.0100 1.18 0.238 dist-edge87 0.0147 0.0098 1.49 0.136 dist-edge96 0.0416 0.0117 3.56 <0.001 dist-edge97 0.0258 0.0083 3.10 0.002 dist-edge98 0.0185 0.0138 1.34 0.180 dist-edge99 0.0069 0.0080 0.86 0.388 dist2 85 -0.0005 0.0001 -3.35 0.040 -0.0002 0.0003 -0.77 0.440 

dist2 86 -0.0005 0.0001 -3.35 0.040 -0.0002 0.0002 -1.01 0.311 dist2 87 -0.0005 0.0001 -3.35 0.040 -0.0012 0.0004 -2.70 0.007 dist2 96 -0.0023 0.0006 -4.02 <0.001 -0.0032 0.0007 -4.59 <0.001 dist2 97 -0.0004 0.0002 -1.56 0.120 -0.0002 0.0003 -0.49 0.622 dist2 98 -0.0012 0.0004 -2.71 0.007 -0.0011 0.0007 -1.67 0.096 dist2 99 -0.0009 0.0004 -2.50 0.012 -0.0009 0.0004 -2.55 0.011 longitude85 -0.1602 0.0688 -2.33 0.020 -0.1599 0.0580 -2.76 0.006 longitude86 0.0960 0.0756 1.27 0.204 0.0243 0.0600 0.40 0.686 longitude87 0.1073 0.0556 1.93 0.054 0.0802 0.0460 1.74 0.082 longitude96 0.0958 0.0227 4.23 <0.001 0.0765 0.0418 1.83 0.067 longitude97 0.0958 0.0227 4.23 <0.001 0.0524 0.0377 1.39 0.165 longitude98 0.0958 0.0227 4.23 <0.001 0.2122 0.0597 3.55 <0.001 longitude99 0.0958 0.0227 4.23 <0.001 0.1711 0.0345 4.96 <0.001 
25

Table 6. Continued. 1980’s 1990’s All Variable Est. SE t P Est. SE t P Est. SE t Plong2 85 0.0202 0.0425 0.48 0.635 -0.0253 0.0301 -0.84 0.402 long2 86 -0.0991 0.0345 -2.87 0.004 -0.0729 0.0273 -2.67 0.008 

long2 87 -0.1164 0.0348 -3.34 0.001 -0.0479 0.0250 -1.92 0.055 long2 96 0.0349 0.0224 1.56 0.119 long2 97 0.0557 0.0176 3.16 0.002 long2 98 -0.0025 0.0274 -0.09 0.928 long2 99 -0.0085 0.0201 -0.42 0.672 icedef -0.0228 0.0055 -4.15 <0.001 -0.0277 0.0038 -7.26 <0.001 -0.0284 0.0029 -9.68 <0.001 icedef2

time -0.6856 0.2734 -2.51 0.012 time2 -0.0073 0.0014 -5.14 <0.001 0.0214 0.0097 2.21 0.027 

depth -0.0959 0.0293 -3.27 0.001 -0.0776 0.0170 -4.55 <0.001 -0.0707 0.0152 -4.64 <0.001 depth2 -0.0022 0.0007 -3.38 0.001 -0.0015 0.0004 -4.04 <0.001 -0.0018 0.0003 -5.43 <0.001 

wind wind2

temperature temp2 -0.0031 0.0015 -2.05 0.040 -0.0058 0.0017 -3.42 0.001 

cloud 
26

In the 1990s, densities were also lowest in water < 5 m deep (0.35-0.73 seals/km2

) and deeper 

than 35 m (0-0.77 seals/km2

), and were highest in water depths of >5-35 m (1.00-1.33 seals/km2; 

Figure 10b). Seals were more broadly distributed relative to depth in the 1990s surveys than in 

the 1980s. For all years combined, the highest densities occurred in 5-15 m (1.27 seals/km2

) and 

15-25m (1.18 seals/km2

) water depths (Figure 10c). 

The Poisson regression model also indicated that seal densities were lowest in water <5 m 

and >35 m deep and highest in water 15-25 m deep (Figure 11). Model results were similar for 

the 1980s, the 1990s, and for all years combined, and were significant for depth and depth2

 at 

P<0.001. 

Fast ice edge - For all years, univariate analysis indicated that the position of the shorefast 

ice edge had a significant effect on ringed seal densities (P<0.001). In 1986, peak density was 

15-20 km from the edge (3.16 seals/km2

), and in 1987 it was 0-15 km from the edge (2.36-3.68 

seals/km2

; Figure 12a). Densities relative to the ice edge showed less of a pattern in 1985. The 

relationship between seal density and distance from the fast ice edge was quite consistent for the 

1990s surveys with peak densities (1.29-1.90 seals/km2

) occurring within 5 km of the edge, 

either on fast or pack ice (Figure 12b). For all years combined, observed densities were highest 

within 5 km of the fast ice edge (Figure 12c). 

The Poisson regression model also indicated a significant, non-linear effect of distance from 

the fast ice edge on seal densities (distance2, P<0.001). Relative densities were highest near the 

fast ice edge, and decreased both shoreward and seaward of the edge for both the 1980s and 

1990s (Figures 13 a,b). The model for all years combined also indicated that densities were 

highest near and just beyond the edge (Figure 13c). However, in the combined analysis analysis, 

the modeled densities for 1985, 1986 and 1997 did not show a peak near the ice edge. This is in 

contrast to the 1980s-only and 1990s-only analyses, and also to the results of the univariate 

analyses. 

Ice deformation - Ice deformation had a significant effect on seal densities for each 

individual survey year, and for all years combined in the univariate analyses (P<0.001 except 

P=0.01 for 1998). Seal densities in all years were highest in smoother ice (0%-10% and 10%-

20% deformation categories). In the 1980s, densities in different ice deformation categories 

ranged from 0.24-2.87 seals/km2

 (Figure 14a). Peak densities in 1986 and 1987 were in ice that 

was <10% deformed (1.93 and 2.87 seals/km2

). In contrast, observed densities on 0%-10% 

deformation ice in 1985 were low (0.57 seals/km2

). During 1996-1998 surveys, the range in 

densities across deformation categories was 0.27-1.45 seals/km2 (Figure 14b). As in the 1980s, 

densities in 1996-1998 were highest on smooth ice. In 1999, densities were highest in 10%-20% 

deformation ice (1.45 seals/km2

). In the combined analysis for all years, densities were highest 

in 0%-20% ice deformation with gradually declining densities in rougher ice categories(Figure 

14c). 

The Poisson regression model for ice deformation indicated significant relationships between 

ice deformation and observed seal densities for all data sets (P<0.001). Modeled densities were 

highest in the flattest ice, and lowest in the most highly deformed ice (Figure 15). 
27

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0-5 >5-15 >15-25 >25-35 >35-45

Water depth (m)

Seals / km2

1985

1986

1987

0.0

0.5

1.0

1.5

2.0

0-5 >5-15 >15-25 >25-35 >35-45

Water depth (m)

Seals / km2

1996

1997

1998

1999

0.0

0.5

1.0

1.5

2.0

0-5 >5-15 >15-25 >25-35 >35-45

Water depth (m)

Seals / km2

Figure 10. Observed densities of ringed seals by water depth in the central Beaufort Sea for a) 

1985-1987, b) 1996-1999 and c) all years combined.

A 

B 

C 
28

 

0.0

0.5

1.0

1.5

2.0

0 -5 -10 -15 -20 -25 -30 -35 -40 -45 -50 -55

Water depth (m)

Relative density (seals / km2

)

All

80s

90s

Figure 11. Modeled estimates of ringed seal densities relative to water depth in the central 

Beaufort Sea based on aerial surveys conducted in 1985-1987 and 1996-1999. 
29

0

0.5

1

1.5

2

2.5

3

3.5

4

<35 -35 -30 -25 -20 -15 -10 -5 5 10 15 20 25 30 35 335

Distance from fast ice edge (km)

Seals / km2

1985

1986

1987

0

0.5

1

1.5

2

<35 -35 -30 -25 -20 -15 -10 -5 5 10 15 20 25 30 35 335

Distance from fast ice edge (km)

Seals / km2

1996

1997

1998

1999

0

0.5

1

1.5

2

<35 -35 -30 -25 -20 -15 -10 -5 5 10 15 20 25 30 35 335

Distance from fast ice edge (km)

Seals / km2

 

Figure 12. Observed densities of ringed seals by distance from the fast ice edge in the central 

Beaufort Sea for a) 1985-1987, b) 1996-1999 and c) all years combined. 

A 

B 

C 

Fast 

Fast 

Fast 

Pack 

Pack 

Pack 
30

0.0

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1.0

1.5

2.0

-35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35

Distance from fast ice edge (km)

Relative density (seals / km2)

85

86

87

0.0

0.5

1.0

1.5

2.0

-35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35

Distance from fast ice edge(km)

Relative density (seals / km2)

96

97

98

99

0.0

0.5

1.0

1.5

2.0

-35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35

Distance from ice edge (km)

Relative density (seals / km2)

y85 y86

y87 y96

y97 y98

y99

Figure 13. Modeled estimates of ringed seal densities relative to distance from the fast ice edge 

based on aerial surveys in the central Beaufort Sea for a) 1985-1987, b) 1996-1999 and c) all 

years combined.

B 

A 

C 

Fast 

Fast 

Fast 

Pac

Pac

Pac
31

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0-10 10-20 20-30 30-40 40-50 >50

Percent Ice Deformation

Seals / km2

1985

1986

1987

0.0

0.5

1.0

1.5

2.0

0-10 10-20 20-30 30-40 40-50 >50

Percent ice deformation

Seals / km2

1996

1997

1998

1999

0.0

0.5

1.0

1.5

2.0

0-10 10-20 20-30 30-40 40-50 >50

Percent ice deformation

Seals / km2

Figure 14. Observed densities of ringed seals relative to ice deformation in the central Beaufort 

Sea for a) 1985-1987, b) 1996-1999 and c) all years combined. 

C 

A 

B 
32

 

0.0

0.5

1.0

1.5

2.0

2.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Ice deformation (percent)

Relative density (seals / km2)

All

80s

90s

Figure 15. Modeled estimates of ringed seal densities relative to percent ice deformation in the 

central Beaufort Sea based on aerial surveys during 1985-1987 and 1996-1999. 
33

Longitude - Univariate analysis indicated that longitude had a significant effect on seal 

densities in all years and for all years combined (P<0.001). However, the influence of longitude 

varied by year and was not consistent either within or across survey periods (Figure 16). In 1987 

and in the1990s, the highest densities generally occurred at about 144° and 145° W. For all 

years combined, the highest density occurred at 145°, from approximately Brownlow Point to 

Flaxman Island. 

In the Poisson model, the influence of longitude was marginally significant in the 1980s. 

Some of the longitude terms were significant for the 1990s and for all years combined. Modeled 

densities were generally highest in the central part of the study area for the 1980s, and at the 

eastern end in the 1990s (Figures 17 a, b). Although there were significant within-year trends in 

density relative to longitude, it was clear from the combined year model that there was no 

consistent trend across all years (Figure 17c). 

Factors Affecting the Proportion of Seals Hauled out 

We also analyzed the observed density of ringed seals relative to time of day, the presence of 

melt water, cloud cover, temperature, and wind speed. Those are variables that we thought could 

affect either the proportion of seals hauled out and available for counting, or our ability to see 

and count them. Results of univariate chi-square analyses for each of these variables are 

presented in Table 7 and Appendix C. With the exception of melt water, the same variables were 

included in the multivariate Poisson regression analysis, and results are shown in Tables 5 and 6. 

Time of day - Univariate analyses of seal density versus time of day were significant for each 

survey year and for all years combined (P<0.001). In most years, densities increased somewhat 

until 1200 or 1300 hrs, then gradually decreased through the afternoon. However, the 

relationship between time of day and estimated densities was inconsistent. In 1985, for example, 

the estimated densities at 1000-1059 hrs and 1100-1159 hrs were over double the estimated 

densities later in the day (Figure 18a). In 1999, another atypical year, observed densities 

increased steadily through the day, and were highest after 1700 hrs (Figure 18b). When the data 

for all years are combined they show only a weak relationship between seal density and time of 

day (Figure 18c). 

The Poisson model for time of day indicated significant relationships (P<0.001) for the 

1980s and for all years combined, but not for the 1990s (P=0.226). The modeled data for the 

1980s indicated a substantial decline in seal densities during the day. However, the effect of 

time of day was smaller when the analysis included all survey years (Figure 19). 

Melt water - Univariate analyses of melt water were significant only in some cases (from 

P<0.001 to P=0.37). Little or no melt water was present in 1986, 1987, 1997, and 1999. 

Significant effects occurred in 1985, 1996, and 1998, when melt water was quite common. 

Densities tended to be somewhat higher in intermediate melt categories of 20%-40% than 

elsewhere (Figure 20). 

We did not include melt water in the Poisson regression analysis due to the nearly complete 

absence of melt water in four of seven years. 

Weather - Univariate analysis indicated significant effects of cloud cover, temperature and 

wind in most years and for all years combined. However, the effects were inconsistent for all 

three variables. Observed density was highest when skies were clear in 1999, but in 1996-1998 

densities were lowest when skies were clear (Figure 21a). Overall there seemed to be little 

pattern in the relationship between seal densities and cloud cover (Figure 21b). 
34

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

-149 -148 -147 -146 -145 -144 -143

Longitude (degrees W)

Seals / km2

85

86

87

0.0

0.5

1.0

1.5

2.0

-149 -148 -147 -146 -145 -144 -143

Longitude (degrees W)

Seals / km2

96

97

98

99

0.0

0.5

1.0

1.5

2.0

-149 -148 -147 -146 -145 -144 -143

Longitude (degrees W)

Seals / km2

Figure 16. Observed densities of ringed seals relative to longitude in the central Beaufort Sea for 

a) 1985-1987, b) 1996-1999 and c) all years combined.

A 

B 

C 
35

0.0

0.5

1.0

1.5

2.0

2.5

-150 -149 -148 -147 -146 -145 -144

Longitude (degrees W)

Relative density (seals / km2

)

y85

y86

y87

0.0

0.5

1.0

1.5

2.0

-150 -149 -148 -147 -146 -145 -144

Longitude (degrees W)

Relative density (seals / km2)

y96

y97

y98

y99

0.0

0.5

1.0

1.5

2.0

2.5

-150 -149 -148 -147 -146 -145 -144

Longitude (degrees W)

Relative density (seals / km2)

y85 y86 y87 y96

y97 y98 y99

Figure 17. Modeled estimates of ringed seal densities relative to longitude based on aerial 

surveys in the central Beaufort Sea for a) 1985-1987, b) 1996-1999 and c) all years combined. 

B

A

C 
36

Table 7. Summary of chi-square analyses of observed ringed seal density relative to habitat 

variables affecting distribution and abundance in the central Beaufort Sea of Alaska. 

Variable Chi-square df P-value Combined 

Time of Day 

 1985 147.30 6 <0.001 

 1986 98.07 6 <0.001 

 1987 723.45 6 <0.001 

 1996 256.52 6 <0.001 

 1997 159.31 6 <0.001 

 1998 51.81 6 <0.001 

 1999 116.70 7 <0.001 

 All 403.98 7 <0.001 

Melt Water 

 1985 134.41 5 <0.001 

 1986 252.45 3 <0.001 

 1987 0 0 - 

 1996 58.07 5 <0.001 

 1997 5.41 5 0.368 

 1998 102.15 5 <0.001 

 1999 12.89 2 0.002 

 All 131.69 5 <0.001 

Cloud Cover 

 1985 240.22 2 <0.001 

 1986 0 0 - 

 1987 0 0 - 

 1996 44.72 3 <0.001 

 1997 282.36 6 <0.001 

 1998 57.71 5 <0.001 

 1999 41.59 6 <0.001 

 All 396.13 10 <0.001 

Temperature 

 1985 104.69 1 <0.001 

 1986 1.66 2 0.440 

 1987 52.36 1 <0.001 

 1996 93.85 2 <0.001 

 1997 110.91 4 <0.001 

 1998 14.54 3 0.002 

 1999 244.48 1 <0.001 

 All 370.03 6 <0.001 

Wind Speed 

 1985 95.25 2 <0.001 

 1986 47.00 4 <0.001 

 1987 373.93 3 <0.001 

 1996 44.15 3 <0.001 

 1997 131.24 4 <0.001 

 1998 38.19 3 <0.001 

 1999 61.52 5 <0.001 

 All 333.51 5 <0.001 
37

0

0.5

1

1.5

2

2.5

3

3.5

10 11 12 13 14 15 16 17

Time of day (h)

Seals / km2

1985

1986

1987

 

0

0.5

1

1.5

2

10 11 12 13 14 15 16 17

Time of day (h)

Seals / km2

1996

1997

1998

1999

 

0.0

0.5

1.0

1.5

2.0

10 11 12 13 14 15 16 17

Time of day (h)

Seals / km2

Figure 18. Observed densities of ringed seals relative time of day (h) in the central Beaufort Sea 

for a) 1985-1987, b) 1996-1999 and c) all years combined. 

B

C
38

 

0.0

0.5

1.0

1.5

2.0

2.5

10 10.5 11 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18

Time of Day (h)

Relative density (seals / km2)

All

80s

Figure 19. Modeled estimates of ringed seal densities relative to time of day in the central 

Beaufort Sea based on aerial surveys during 1985-1987 and 1996-1999. Regressions for the 

1990s alone were not significant. 
39

0.0

0.5

1.0

1.5

0-10 10-20 20-30 30-40 40-50 >50

Percent melt water

Seals / km2

1985

1996

1998

0.0

0.5

1.0

1.5

0-10 10-20 20-30 30-40 40-50 >50

Percent melt water

Seals / km2

Figure 20. Observed densities of ringed seals relative to percent melt water coverage in the 

central Beaufort Sea a) 1985, 1996 and 1998 and b) all years combined. 

A 

B 
40

 

0.0

0.5

1.0

1.5

2.0

2.5

0 10 20 30 40 50 60 70 80 90 100

Percent cloud cover

Seals / km2

1985 1996

1997 1998

1999

0.0

0.5

1.0

1.5

2.0

0 10 20 30 40 50 60 70 80 90 100

Percent cloud cover

Seals / km2

Figure 21. Observed densities of ringed seals relative to percent cloud coverage in the central 

Beaufort Sea for a) 1985 and 1996-1999 and b) all years combined. 

A 

B 
41

Some years had higher densities with higher temperatures (Figure 22a,b) and/or wind speeds 

(Figure 23a,b), and for others the reverse was true. For the combined univariate analyses, 

density decreased with increasing temperature and increased with increasing wind speed, 

although neither temperature or wind demonstrated a strong pattern (Figures 22c and 23c). 

The Poisson regression model indicated no significant relationship between cloud cover or 

wind speed and seal densities. There was a significant non-linear relationship between air 

temperature and seal densities for the 1990s and for all data combined, but not for the 1980s. 

Seal densities were slightly higher around 0° C than when it was warmer or colder (Figure 24). 

Power Analysis 

Power Based on Observed Density Estimates 

For raw (observed) densities, the power to detect population declines (i.e., negative annual 

change) was greater than the power to detect increases (i.e., positive annual change) for all 

combinations of sample size and years surveyed (Figure 25). With five years of data (both 50 

and 65 lines/yr), power was low, even for extreme declines (power = 0.23 – 0.28 for -20%/yr 

change). In addition, detection of ‘false’ trends was high. When there was no trend (0%/yr 

change), approximately 10% of the simulations indicated significant (P<0.05) increases and 10% 

indicated significant declines. This is twice the expected rejection rate when there is no trend. 

In addition, ‘false’ trends occurred, where there were significant positive trend estimates for a 

true negative trend and significant negative trend estimates for true positive trends. It is expected 

that the occurrence of these types of ‘false’ trends should decline to zero as the true trend gets 

larger. This was not the case with the five year simulations where ‘incorrect sign false’ trends 

occurred about 10% of the time for both positive and negative trends irrespective of the 

magnitude of the trend. The number of lines surveyed (50 or 65) did not consistently affect 

power with five years of data. 

With 10 years of survey effort, performance was considerably improved. Power for declines 

of –20%/yr was 0.51- 0.52. For a comparable increase, power was 0.26-0.32 (Figure 25). The 

‘false’ trends when there was no true trend were at about the expected rate of 5% and ‘false’ 

trends declined as true trends increased, as expected. Power was slightly higher for 65 survey 

lines than for 50 lines for detecting population declines but was similar for the two sample sizes 

for detecting population increases. 

Based on our simulations, trend estimates were biased toward 0 for both negative and 

positive true change. Median estimates of trend were 25%-30% smaller than true trends; this 

pattern was relatively unaffected by sample size or number of survey years (Figure 26). 

Power Based on Modeled Densities 

 We also examined power for modeled densities from the Poisson regression analysis. For 

the Poisson model, increasing the number of survey lines from 50 to 65 resulted in modest 

increases in power for most combinations of trend and number of years (Figure 27). Doubling 

the number of years from 5 to 10 dramatically increased power, especially for smaller trends 

(i.e., <10%/yr). 
42

0.0

0.5

1.0

1.5

2.0

2.5

< -9 -9 to -5 -5 to 0 0 to 4 4 to 8 8 to 11 > 11

Air temperature (C)

Seals / km2

1985

1986

1987

0.0

0.5

1.0

1.5

2.0

< -9 -9 to -5 -5 to 0 0 to 4 4 to 8 8 to 11 > 11

Air temperature (C)

Seals / km2

1996

1997

1998

1999

0.0

0.5

1.0

1.5

2.0

< -9 -9 to -5 -5 to 0 0 to 4 4 to 8 8 to 11 > 11

Air temperature (C)

Seals / km2

Figure 22. Observed densities of ringed seals relative to air temperature (C) in the central 

Beaufort Sea for a) 1985-1987, b) 1996-1999 and c) all years combined. Air temperatures were 

measured at survey altitude of 91 m. 
43

 

0.0

1.0

2.0

3.0

4.0

<15 15-20 20-25 25-30 30-35 35-40

Wind speed (km/hr)

Seals / km2

1985

1986

1987

0.0

0.5

1.0

1.5

2.0

<15 15-20 20-25 25-30 30-35 35-40

Wind speed (km/hr)

Seals / km2

1996

1997

1998

1999

0.0

0.5

1.0

1.5

2.0

<15 15-20 20-25 25-30 30-35 35-40

Wind speed (km/hr)

Seals / km2

Figure 23. Observed densities of ringed seals relative wind speed (km/hr) in the central Beaufort 

Sea for a) 1985-1987, b) 1996-1999 and c) all years combined. Wind speed was measured at 

survey altitude of 91m. 

B 

C 

A 
44

0.8

1.0

1.2

1.4

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5

Air temperture (degrees C)

Relative density (seals / km2

)

All

90s

Figure 24. Modeled estimates of ringed seal densities relative to air temperature in the central 

Beaufort Sea based on aerial surveys during 1985-1987 and 1996-1999. Regression models for 

air temperature in the 1980s were not significant. 

0

0.1

0.2

0.3

0.4

0.5

0.6

-20 -15 -10 -5 0 5 10 15 20

True Change / Yr (%)

Powe

r

5-yr 50 pos

5-yr 65 pos

10-yr 50 pos

10-yr 65 pos

5-yr 50 neg

5-yr 65 neg

10-yr 50 neg

10-yr 65 neg

Figure 25. Comparison of estimated power of raw density estimates for ringed seal aerial 

surveys conducted in the central Beaufort Sea, 1996-1999. Power curves are based on 50 or 65 

transect lines surveyed for either 5 or 10 years. Power is based on 1-tailed tests for all rates of 

change not equal to zero. 
45

-25

-20

-15

-10

-5

0

5

10

15

20

25

-20 -15 -10 -5 0 5 10 15 20

True Change / Yr (%)

Median Estimated Change / Yr (%)

50-5

65-5

50-10

65-10

true change

Figure 26. Comparison of true change and estimated change based on raw density estimates for 

ringed seal aerial surveys conducted in the central Beaufort Sea, 1996-1999. Power curves are 

based on 50 or 65 transect lines surveyed for either 5 or 10 years. 
46

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

-20 -15 -10 -5 0 5 10 15 20

True Change / Yr (%)

Power

5-yr 50 pos

5-yr 65 pos

10-yr 50 pos

10-yr 65 pos

5-yr 50 neg

5-yr 65 neg

10-yr 50 neg

10-yr 65 neg

Figure 27. Estimated power to detect trend based on modeled estimates of ringed seal density 

from Poisson regression. Power curves are based on 50 and 65 transect lines surveyed for either 

5 or 10 years. Power is based on 1-tailed tests for all rates of change not equal to zero. 
47

Even with 10 years of surveys, the ‘false’ rejection rate for the Poisson model did not 

approach zero as trend increased. For declines of 10% and greater, the ‘false’ rejection rate did 

not fall below 5%-10% (Figure 28). For population increases, the ‘false’ rejection rate increased 

with increasing trend. Median estimates of trend were biased toward 0 for the Poisson model; 

number of years of data and survey lines had relatively little effect on this bias. 

-25

-20

-15

-10

-5

0

5

10

15

20

25

-20 -15 -10 -5 0 5 10 15 20

True Change / Yr (%)

Median Estimated Change / Yr (%)

50-5

65-5

50-10

65-10

true change

Figure 28. Comparison of true change and estimated change based on Poisson regression 

modeled density estimates for ringed seal aerial surveys conducted in the central Beaufort Sea, 

1996-1999. Power curves are based on 50 or 65 transect lines surveyed for either 5 or 10 years. 

Trends in Abundance 

We used linear regressions to test whether there was a significant change in the observed 

density of ringed seals over the years when surveys were conducted (see Figure 29). Regressions 

were done for fast ice, pack ice, and all ice types using the same dataset used in the univariate 

and Poisson analyses. Regressions for both ice types and for all ice combined indicated no 

statistically significant trends, increasing or decreasing, in the density of ringed seals (fast ice - 

F0.95(1,5)=2.60, P=0.17; pack ice - F0.95 (1,5)=1.69, P=0.25; all ice combined - F0.95 (1,5)=2.37, 

P=0.18). 

We also conducted an analysis of variance (ANOVA) to examine whether there was a 

significant difference in observed density of ringed seals for surveys conducted in the 1980s 

versus the 1990s. The years 1985-1987 were grouped as time period 1 and the years 1996-1999 

as time period 2. At significance level alpha=0.05, there was no significant difference between 

time periods for either fast ice, pack ice, or all ice combined. However, if the measure of 

significance is set at alpha= 0.10, the density of total seals on fast ice and all ice combined was 
48

significantly lower in the 1990s than in the 1980s (Table 8). The average density on all ice types 

combined for the 1990s, was 31% lower than the average density for the 1980s, and the average 

density on fast ice was 50% lower. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

198519861987

1996199719981999

Seals / km2

Fast

Pack

All 

Linear (All )

Figure 29. Observed densities of ringed seals on fast ice and pack ice in the central Beaufort Sea 

based on aerial surveys conducted in 1985-1987 and 1996-1999. 

Table 8. Analysis of variance statistics for a comparison of observed ringed seal densities on 

fast, pack and all ice combined based on aerial surveys flown in the 1980s (time period 1) the 

1990s (time period 2). 

Ice type Time period Average density/km2 F statistic P-value 

Fast 1 1.77 F.95(1,5)=4.61 0.08 

 2 0.89 

Pack 1 0.89 F.95(1,5)=1.13 0.34 

 2 1.11 

All 1 1.43 F.95(1,5)=4.23 0.09 

 2 0.98 

Examination of ice type and year effects (trend) using Poisson regression models indicated 

significant differences between fast ice and pack ice for all analyses (Table 5). Modeled 

densities were higher on fast ice in the 1980s and for all years combined, and higher on pack ice 

in the 1990s. There was a clear and significant increasing trend in modeled densities during 

1985-1987 for both fast and pack ice; the model indicated an increase of 55% per year (Table 9). 

For 1996-1999, there was no significant overall directional trend, with the model predicting a 

significantly increasing trend for fast ice but a decreasing trend for pack ice. 
49

Table 9. Trend estimates (percent change/year) from Poisson regression models for ringed seal 

density on the central Beaufort Sea based on aerial surveys conducted in 1985-1987 and 1996-

1999. 

 1980s 1990s All Years 

Fast Ice 6.6 (-4.8 – 19.5) -8.6 (-6.5 – -10.6) 

Pack Ice -13.2 (-3.6 – -21.8) -4.0 (-6.7 – -1.3) 

All Ice 54.6 (13.0 - 111.5) 

When both survey periods were considered in a combined analysis, modeled densities 

demonstrated a marked decline (Figure 30, Table 9). From 1985-1999, the density on fast ice 

declined at a rate of 8.6%/year (72% decline overall), compared to a 4%/year decline on pack 

ice. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

198519861987198819891990199119921993199419951996199719981999

Relative density (seals / km2)

Fast-trend

Fast-mean

Pack-trend

Pack-mean

Figure 30. Modeled estimates of ringed seal densities for all ice combined within 40 km of shore 

based on aerial surveys of the central Beaufort Sea in 1985-1987 and 1996-1999. 

DISCUSSION 

Factors Affecting Aerial Survey Counts 

Since the earliest aerial surveys for ringed seals were conducted, observers have been aware 

that habitat characteristics, weather, and temporal factors affect the distribution and abundance of 

seals and the proportion available to be counted during surveys, and therefore introduce 

variability into counts and estimates of density (Burns and Harbo 1972, Smith 1973, Finley 

1979, Smith and Hammill 1981). This is true not only for ringed seals but for other pinnipeds as 
50

well (Smith 1965, Olesiuk 1990, Thompson and Harwood 1990, Frost et al. 1999a). To improve 

the reliability of counts and density estimates, and thus improve the ability to detect spatial or 

temporal patterns in abundance, both survey methods and analytical procedures have been 

developed to either minimize, or quantify and account for, such variability. Better understanding 

of such factors (covariates) has made it possible to improve survey results and interpretation by 

either conducting surveys in a narrow range of “optimal” conditions (Burns and Harbo 1972, 

Huber et al. 2001), or by modeling effects of covariates and then adjusting the data to account 

for variability (Frost et al. 1999a, Boveng et al. in press). 

In this study, we examined the effects of two types of covariates on observed counts of 

ringed seals: those that we expected would affect the actual distribution and abundance of seals 

(habitat-related variables) and those that could affect our ability to count the seals that were 

present (temporal and weather-related factors). 

Habitat Factors 

Estimated densities of seals in our central Beaufort Sea study area were lowest in water 

shallower than 5 m and deeper than 35 m. This was true for the 1980s as well as for the 1990s, 

although distribution relative to depth was somewhat broader in the 1990s. LGL Limited 

conducted ringed seal surveys in a subset of our study area (the western half of sector B3, from 

approximately 147o to 149o W) during 1997-2001 using methods similar to ours (Miller et al.

1998, Link et al. 1999, Moulton et al. 2000, and Moulton et al. 2001). For the area they 

surveyed, the highest seal densities were generally in water 5-15 m deep. Seals were more 

common in somewhat deeper water in 1999 compared to other years (Moulton et al. 2000). 

Other investigators have also found differences in densities of ringed seals relative to water 

depth. In the East Siberian Sea, Ognetov (1993) found higher densities of seals in water 10-30 m 

deep (0.12-0.39 seals/km2

) than in water shallower than 10 m (0.10 seals/km2) or deeper than 30-

40 m (0.01 seals/km2

). In contrast, in the eastern Canadian Beaufort Sea and the Canadian High 

Arctic, ringed seal densities were generally higher in deeper water (50-100 m or 50-150 m) 

(Stirling et al. 1982, Kingsley 1990). The differences in distribution relative to depth between 

the Alaskan and East Siberian coasts on one hand and the eastern Beaufort Sea and Canadian 

High Arctic on the other may be related to coastal topography and the effects it has on both 

bathymetry and sea ice. Both the central Beaufort and East Siberian Sea coastlines are relatively 

linear features, with water depths generally getting deeper as one moves north and off shore. In 

those areas, fast ice occurs as a linear band along the coast. In the eastern Beaufort Sea and the 

Canadian Arctic, fast ice is much more extensive and extends over much deeper water because it 

is protected on all sides by land. The stable habitat offered by fast ice can be found farther 

offshore in deeper water. 

Univariate and Poisson regression analyses indicated that observed as well as modeled 

densities of ringed seals in the central Beaufort Sea were highest near the fast ice edge, and 

decreased both shoreward and seaward of the edge. Univariate analyses, which did not take into 

account the interactive effects of other factors, indicated somewhat more annual variability, 

particularly for the 1980s. However, when data were modeled in combination with other 

covariates, it was clear that densities were highest near the ice edge. Covariate analysis by 

Moulton et al. (2000) also indicated that during 1997-1999 ringed seals densities in their study 

area decreased with increasing distance from the fast ice edge. 

Some variability may have been introduced into the analysis of density relative to distance 

from the fast ice edge because of the difficulty of visually determining the position of the edge. 

Cues to observers for locating the outer edge of the fast ice included the presence of a large 
51

pressure ridge or shear area (often on the seaward side of a wide refrozen lead), or the presence 

of broken ice and open cracks (indicating pack ice). It was sometimes difficult to identify this 

boundary when a delineating pressure ridge was not visible, when there was a series of such 

ridges, and/or when no open water was present in the pack ice. Furthermore, early in the season 

there may be a substantial amount of “attached fast ice” beyond the actual edge (Stringer et al. 

1980). Until pack ice movement at the onset of breakup begins to fracture the attached fast ice, it 

is rarely possible to distinguish it from true fast ice. The effects of this problem can be seen in 

the assigned locations of the fast ice edge by survey personnel in 1985-1987 and 1996-1999 

(Figures 2-8). In 1985 and 1986, observers located the fast ice edge well seaward of the 20-m 

contour, particularly in sector B3. In 1987 and 1996, the assigned location of the fast ice edge 

closely approximated the 20-m contour, while in 1998 and 1999 it was positioned somewhat 

offshore. In fact, the “true” fast ice zone in the central Beaufort Sea is usually delineated by a 

series of offshore shoals at about the 20 m depth contour (Reimnitz and Kempema 1984). Those 

shoals cause the formation of grounded ice ridges and hummocks that protect the ice between 

there and shore from pack ice forces, and create the relatively immobile and flat fast ice zone that 

persists well into June. 

It is unclear what makes the fast ice edge so attractive to ringed seals and results in higher 

densities in that region. Seals feed at a reduced rate, as indicated by stomach contents and body 

condition, during late spring when they are entering the molt (Lowry et al. 1980, Frost and 

Lowry 1984). However, seal distribution and density in late May and early June, prior to 

breakup, are thought to reflect distribution patterns established earlier in the year. Higher 

abundance of seals in an area could indicate greater availability of prey during fall and winter 

when seals are actively feeding and when breathing holes are established. Alternatively, higher 

densities near the edge at the time of our surveys could be due to an influx of seals from other 

regions. During late winter and spring, ringed seals are thought to partition their habitat based on 

age, sex, and reproductive status, with adults predominating in and near the fast ice, subadults in 

the flaw (or edge) zone, and both occurring in drifting pack ice (McLaren 1958, Smith 1973). 

Until territoriality breaks down at the end of the breeding season, most seals seen on fast ice are 

single seals at holes. As the season progresses, average group size increases and it is much more 

common to see multiple seals at the same hole, or many seals along a crack in the ice (Smith and 

Hammill 1981, Finley et al. 1983, Frost et al. 1988). Subadults wintering outside the fast ice 

habitat may move into the fast ice to molt, resulting in high local densities just shoreward of the 

edge (Finley 1979). Seals may also move into a region as breakup progresses in other areas 

(Kingsley et al. 1985, Smith and Harwood 2001). A seal tagged at Little Diomede Island, 

Alaska, in May 2001 moved >700 km north and east into the Chukchi Sea before its tag ceased 

to transmit in June (Sheffield and Menadelook 2001). It is possible that ringed seals prefer to 

haul out and molt on fast ice rather than pack ice because they are less vulnerable to polar bear 

predation in fast ice areas. 

We found a strong and consistent relationship between seal densities and the degree of ice 

deformation in all years and for all years combined, with more seals found in flatter, less 

deformed ice. Similarly, Frost et al. (1988) reported that observed ringed seal densities during 

early June were higher in flat ice than in rough ice throughout both the Chukchi and Beaufort 

seas. However, once the ice began to crack and break up, they found that the correlation 

between ice deformation and observed seal density disappeared. Investigators in the eastern 

Beaufort Sea and the Canadian Arctic have also reported that during the molting season ringed 

seals bask in flat, open areas (Smith and Stirling 1975, Stirling et al. 1977, Smith 1980). 

Moulton et al. (2000) speculated that densities might be lower in rough ice because seals were 
52

harder to see. While that possibility cannot be entirely dismissed, the absence of a correlation 

between density and ice deformation after the beginning of breakup reported by Frost et al. 

(1988) suggests that the difference is related to seal distribution and not observer bias. 

Ringed seals are a primary prey of polar bears in most parts of their range, and the constant 

threat of predation has shaped their behavior on the ice (Smith 1980, Kingsley and Stirling 1991, 

Stirling and Øritsland 1995). Bears hunt mostly along pressure ridges, in hummocky ice, and at 

the edges of rough ice areas, but seldom in flat open areas. Seals in turn haul out to bask in areas 

where they can see and smell approaching predators, and where they can escape down holes or 

cracks too small for a polar bear to follow (Kingsley and Stirling 1991). It is not surprising that 

densities of basking ringed seals are higher in flat ice than in rough, ridged ice where polar bears 

hunt more commonly. 

We tested the effect of longitude on the distribution and abundance of seals because we 

thought it possible that there might be some east-west habitat gradient that was not reflected in 

the other variables incorporated in our analyses. While both univariate analysis and Poisson 

regression models indicated significant longitudinal gradients in some years, the trends were not 

the same in all years. This is not surprising, since factors that might be responsible for a 

latitudinal gradient may differ from one year to the next. For example, prey distribution and 

abundance might vary annually, as might the dynamics of freeze up and the subsequent 

development of fast ice. 

Factors Affecting Proportion Hauled Out 

The proportion of ringed seals hauled out at any particular time during late spring when 

surveys are conducted can be highly variable, ranging from 23% to 90% or more (Finley 1979, 

Smith and Hammill 1981, Kelly and Quakenbush 1990, Lydersen 1991, Born et al. 2002). This 

variability has important implications for interpretation of aerial survey results. Annual 

differences in survey conditions that affect the proportion hauled out can make comparisons of 

density estimates to determine trend more difficult. Standardizing conditions under which 

surveys are flown is one way to reduce variability in counts, and that has been a common 

procedure. For example, since the early 1970s most ringed seal surveys have been flown during 

midday, in June, at wind speeds less than 40 km/hr (Burns and Harbo 1972, Smith 1975, Finley 

1979, Smith and Hammill 1981). Nonetheless, even with such restricted survey windows, there 

may still be considerable variability in the proportion of seals hauled out. 

Univariate analysis of the data we collected from 1985 to 1999 suggested that observed 

densities of hauled out seals were generally highest between about 1100 and 1400 hrs local time 

(solar noon is about 1300 hrs). Some years, most notably 1999, did not follow this pattern. In 

contrast, Poisson regression models for density relative to time of day for the 1980s and all data 

combined predicted that densities would be highest at 1000 or 1100 hrs and decline steadily 

throughout the day. There was no significant relationship between time of day and density for 

the 1990s. Moulton et al. (2000, 2001) found a similarly inconsistent relationship between the 

number of ringed seals hauled out and time of day. In contrast to inconclusive results from 

analyses of aerial survey data, the results of most tagging studies indicate a strong diurnal 

component to ringed seal hauling out behavior, with most seals hauled out between mid morning 

and late afternoon (Finley 1979, Smith and Hammill 1981, Lydersen 1991, Kelly and 

Quakenbush 1990, Kelly et al. 2000). However, seals tagged in northwest Greenland showed no 

diel pattern in hauling out between June and August (Born et al. 2002). It may not be surprising 

that regression analysis did not appear to accurately predict diel seal behavior, since our surveys 

occurred only within the peak hauling out period. Kingsley et al. (1985) reported that time of 
53

day was not a significant factor in multiple regression analysis of ringed seal densities in the 

Canadian High Arctic. They noted there was good evidence of diurnal behavior from other 

studies and suggested that the lack of a significant correlation was probably because surveys 

were conducted during the middle of the day when seals were most likely to be hauled out. 

Although more ringed seals generally are seen basking on warm, sunny days with relatively 

light winds, it is difficult to statistically quantify this relationship. Any analysis of the effects of 

weather on seal counts is complicated by the lack of local, on-site information about weather 

conditions. Temperature and wind speed recorded from the survey aircraft at survey altitude or 

from weather stations on land may not accurately reflect conditions on the ice. It is not 

surprising, therefore, that our analyses of these factors relative to seal counts were inconsistent 

and inconclusive. Furthermore surveys are generally not conducted in weather considered 

unsuitable for hauling out (Lunn et al. 1997, Kingsley et al. 1985). In addition, cloud cover may 

affect seal counts in contradictory ways, thus obfuscating any relationship that may exist. For 

example, seals may prefer to haul out on warm clear days, but such conditions can also result in 

sun glare that impairs observers' ability to count. Conversely, cloudy days might be less optimal 

for hauling out but better for detecting seals. Attempts by other investigators to quantify the 

effects of weather on aerial survey results have also been inconsistent and inconclusive, with 

multivariate regression analysis often producing results that either contradict other studies, vary 

across years or survey replicates, or conflict with what is known about seal behavior (Finley 

1979, Kingsley et al. 1985). In fact, Kingsley et al. (1985) concluded that multivariate 

regression using wide-area survey data was ineffective for determining the effects of weather, 

and they did not use weather variables in their multivariate analysis. 

Notwithstanding the above, some investigators have been able to demonstrate effects of 

weather variables such as wind speed and temperature on the hauling out behavior of ringed 

seals as well as other as seal species. Densities were negatively correlated with wind speed for 

ringed seals in the Canadian Arctic (Smith and Hammill 1981, Stirling et al. 1982) and Weddell 

seals (Leptonychotes weddelli) in the Antarctic (Wartzok 1991). For ringed seals, as well as 

other ice associated seals, temperature seems to have the greatest influence when it is too warm 

and may exceed the animals' thermal tolerance. Densities decrease when conditions are too 

warm and calm (Burns and Harbo 1972, Finley 1979, Harrison and Kooyman 1968). Watts 

(1996) suggested that temperature appears to be less significant in models that take time of day 

and date into account, since to some degree they measure the same thing. 

Aerial Survey Design 

Methods for conducting aerial surveys of ringed seals have been similar for the last 30 years 

or more, and have generally been standardized to exclude very windy or stormy weather and to 

minimize the effects of diurnal haulout patterns by flying in the middle of the day (Stirling et al.

1977, Kingsley et al. 1985, Frost et al. 1988, Lunn et al. 1977). Nonetheless, investigators have 

documented substantial within and between year variability in both survey conditions and the 

characteristics of sea ice, and have recognized that it may be difficult to identify abundance 

trends in light of such variability. Interannual comparisons of seal densities within an area may 

show the true status of the population, or may instead reflect annual differences in counting 

conditions or the proportion hauled out. 

For many pinniped species, the physical attributes of habitat that influence distribution and 

abundance remain similar over time. For example, physical characteristics of the rocks and 

sandbars on which hauled out harbor seals are counted change little from year to year. Counts 
54

may change due to factors such as weather, time, or tide, but not because of changes in the 

haulout itself. This makes it possible to model the effects of factors responsible for variation in 

counts, and thus make more realistic estimates of both abundance and trend (see Frost et al. 

1999a, Boveng et al. in press, Ver Hoef and Frost in press). 

In contrast, the dynamic sea ice habitat used by ringed seals is temporally variable on short 

(days and weeks) as well as long (annual and decadal) time scales. A particular geographic 

location may have suitable ice conditions one year but not the next. Weather at the time of 

freeze-up and throughout the winter affects ice roughness and snow cover, which in turn 

determine the suitability of ice as seal habitat. Even within the same season, snow and ice 

conditions may change dramatically within just a few days, particularly around the time of 

breakup (Frost et al. 1988). Not only do ice conditions change dramatically during break-up, but 

there is substantial annual variation in when break-up occurs. This type of variability makes the 

timing of surveys and between-year comparisons very difficult. 

Measuring and Incorporating Covariate Effects 

One of the primary reasons for conducting ringed seal surveys is to detect changes in seal 

abundance over time and to quantify those changes with some degree of statistical certainty. 

Detection of trend is complicated when there is substantial within or between survey variability 

in density that is unrelated to true abundance. When some or all of the factors responsible for 

variation are known, it may be possible to adjust survey methods to minimize the effects of such 

factors, i.e. to use a relatively narrow and standard “survey window” in an attempt to hold the 

covariates constant (Burns and Harbo 1972, Stirling et al. 1977, Frost et al. 1988, Thompson and 

Harwood 1990, Watts 1996, Lunn et al. 1997, Frost et al. 1999a). Nonetheless, complete 

standardization is impractical, and some covariate effects are likely to remain. In an attempt to 

deal with this problem, Kingsley et al. (1985) did multivariate regression analyses of data from 

aerial surveys of ringed seals in the Canadian High Arctic. They then used the information from 

the regression analysis to determine optimum habitat and survey conditions, and selected a 

subset of the complete dataset for use in calculating stratum densities. 

 Some investigators have used correction factors to account for certain measurable covariate 

effects. Olesiuk et al. (1990) used a correction factor to adjust for the effects of date on harbor 

seal (Phoca vitulina) counts during the pupping season. Beaufort sea state and cloud cover had 

substantial effects on counts of harbor porpoises (Phocoena phocoena), and Forney et al. (1991) 

used those factors as covariates in their trend analysis. Similarly, DeMaster et al. (2001) 

considered effects of Beaufort state when developing correction factors for aerial surveys of 

beluga whales (Delphinapterus leucas). Garrott et al. (1995) modeled the effects of survey 

conditions and air and water temperature on counts of Florida manatees (Trichechus manatus 

latirostris), and found that about 50% of the variation in counts was explained by those factors. 

After that variation was accounted for, they were able to detect a significant increase in the 

number of manatees counted along the Florida coast in 1982-1992. Frost et al. (1999a) 

considered tide, time of day and date as covariates in their analysis of harbor seal aerial survey 

counts in Prince William Sound, Alaska. If they ignored the effects of covariates, a negative 

trend in seal counts during 1990-1997 was not significant. However, when covariates were used 

to “normalize” the data to standard conditions, the decline became highly significant. 

It was our hope at the beginning of this project that we might be able to use Poisson 

regression models to develop parameter estimates for the different covariates and use them to 

"correct" the data to standard conditions, thus making our estimated index of ringed seal density 

more accurate and our interpretation of trend more reliable. However, the results of our analysis 
55

of survey data collected over seven years using similar methods and frequently the same 

observers revealed problems with that approach. For several habitat-related covariates (ice 

deformation, distance from the fast ice edge, water depth), results of univariate and multivariate 

analysis indicated similar relationships. For others, such as weather related variables and time of 

day, neither analysis was particularly informative. The relationship between weather and time 

variables tended to be inconsistent for univariate analyses, and not surprisingly, were often not 

significant in Poisson regression models (Table 10). This may have been in part because the 

survey “window” had already been standardized to optimize the conditions under which surveys 

were flown. 

Table 10. Summary of univariate and multivariate analyses of ringed seal densities in the central 

Beaufort Sea relative to environmental and temporal variables, based on aerial surveys 

conducted in 1985-1987 and 1996-1999 (“+” = significant effect. “ns” = not significant). 

 Univariate Multivariate 

Variable 1980s 1990s All Yrs 1980s 1990s All Yrs 

Habitat 

Water depth + + + + + + 

Distance from fast ice edge + + + + + + 

Ice deformation + + + + + + 

Longitude + + + + + + 

Temporal 

Time of day + + + + ns + 

Weather 

Cloud + + + ns ns ns 

Temperature mixed + + ns + + 

Wind speed + + + ns ns ns 

Other 

Melt water + mixed + not included 

We included a spatial component in the Poisson regression to account for possible spatial 

correlation in the data, with survey line treated as a random effect. The variance structure of the 

model showed little evidence of spatial correlation among residuals within a survey line (Table 

11). However, there was substantial variation among lines, particularly for the 1980s when 

among-line variance was 39% of the total error variance. 

Effects of Survey Date on Seal Counts 

Kelly et al. (2000) monitored the hauling out behavior of 18 ringed seals tagged in the central 

Beaufort Sea in 1999 and 2000. They found that early in spring seals hauled out exclusively in 

snow covered lairs where they could not be seen by observers. As the season progressed, seals 

gradually began to haul out on the surface of the ice where they could be counted during surveys. 

Once seals began to bask, they did not return to their lairs. Field measurements indicated that 

most seals were basking when the snow temperature near the snow-ice interface had warmed to 

0o C, and that snow temperature might be a good predictor of peak haulout and therefore of the 
56

best time to conduct surveys. However, even though a snow temperature of 0o C predicted 

basking and May 31st was the day on which 50% of tagged seals were basking in both 1999 and 

2000, there was a substantial between-year difference in the length of the transition period from 

resting in lairs to hauling out on the surface. In 1999, only seven days lapsed between the time 

the first tagged seal basked outside of its lair and when 75% of the tagged seals were basking. In 

contrast, the transition period lasted 24 days in 2000. Kelly et al. estimated that only 12% of the 

seals present in their study area were hauled out on 29 May 1999 compared to 40% just six days 

later. Thus our 1999 surveys, which were flown during 29 May-4 June, very likely counted a 

rapidly changing proportion of the population. Born et al. (2002) also found that the proportion 

of time ringed seals spent hauled out changed rapidly in spring. It is unknown how much 

geographical variation there may be in the onset of basking within a region such as the Beaufort 

Sea. 

Table 11. Variance components associated with Poisson regressions of seal counts on year and 

covariates. 

 Parameter Proportion of 

Covariance parameter estimate total error variance 

1980s 

Line*yr variance 5.084 0.391 

Sill-Nugget (autocorrelation) 0 0 

Residual variance 7.931 0.609 

Total error variance 13.016 1.000 

1990s 

Line*yr variance 0.811 0.102 

Sill-Nugget (autocorrelation) 0.012 0 

Residual variance 7.123 0.896 

Total error variance 7.946 1.000 

All years 

Line*yr variance 1.807 0.188 

Sill-Nugget (autocorrelation) 0.003 0 

Residual variance 7.797 0.812 

Total error variance 9.607 1.000 

While it is useful to know when most seals are on the surface basking, that unfortunately 

does not resolve other survey-related problems. Even if snow temperatures do reliably predict 

basking, and if, as suggested by Kelly et al. (2000), a proxy can be found for actual on-ice snow 

temperatures, it is still not certain that counts from surveys will reflect only seals resident in the 

survey area. Concurrent with the increased visibility of resident seals later in the spring may be 

an influx of seals from the pack ice as well as from other geographic regions (Finley 1979, Smith 

and Harwood 2001). Whether or not this occurs, and to what degree, has not been documented 

in the Alaskan Beaufort Sea. The chronology of breakup, both within the survey area as well as 

in areas far removed, may affect what seals are present during the survey period. Also, resident 

seals may not always bask in the same location as where their winter lairs were located (Kelly 

and Quakenbush 1990, Kelly et al. 2001). 
57

In addition to the biological factors described above, annual variability in weather conditions 

may also affect timing of surveys. From the perspective of observers, the optimal timing for 

ringed seal surveys is before sea ice break up begins and when water on the ice surface from melt 

and overflow of rivers is not yet extensive (Burns and Harbo 1972, Frost and Lowry unpubl. 

obs.). In the central Beaufort Sea, such conditions generally occur in late May to early June, but 

that is not always the case. In our surveys, melt water covered <2% of the ice we surveyed in 

four years, and 38%-94% in the other three years. In 1998, 80%-90% of the ice near shore was 

covered by melt water when surveys were flown in 27-28 May, resulting in poor conditions for 

counting seals. Thus, conditions in 1998 had deteriorated before the usual scheduled date for 

surveys to begin, and may have been unsuitable for surveys before the date that most seals were 

basking. Prudhoe Bay temperature records show that weather was extraordinarily warm in 

April-May 1998 (Table 12). 

Table 12. Temperature and heating degree day data for Prudhoe Bay, 1985-1989 and 1996-

1999, and dates when aerial surveys were conducted in the central Beaufort Sea. 

Year April mean 

temperature (°C) 

May mean 

temperature (°C) 

April heating 

degree days1

May heating 

degree days1

Survey dates 

1985 9-12 June 

1986 -22.2 -7.5 2,192 1,435 31May-12 June 

1987 -22.3 -6.4 2,199 1,377 6-7 June 

1996 -19.1 -2.8 2,019 1,173 29-31 May 

1997 -16.7 -5.1 1,890 1,297 27 May-1 June 

1998 -9.2 -2.0 1,484 1,130 27-28 May 

1999 -19.3 -3.9 2,035 1,234 29 May-4 June 

1

 Calculated as the daily average temperature in degrees Fahrenheit subtracted from 65 and 

summed for all days in the month. Larger values indicate colder weather. 

 

In our survey design we tried to minimize the effect of date as much as possible by 

narrowing the survey window and conducting surveys before breakup and melting occurred. As 

a result surveys in the 1990s were flown several days earlier than those in the 1980s. A cursory 

examination of temperature records confirms that the weather in the study area in April and May 

was consistently much warmer in the 1990s (Table 12). Mean April air temperatures at Prudhoe 

Bay were 3-13°C warmer in the 1990s, while May temperatures were 2-5°C warmer. Heating 

degree days data suggest that an equivalent amount of warming occurred 5-10 days earlier in the 

1990s than in the 1980s, which is similar to the difference in our survey dates. 

Our surveys were not designed to quantify effects of date on seal behavior. Because surveys 

flown in the 1990s were done over relatively short periods of 2-7 days, it was not possible to 

model the effect of date in the covariate analysis. While surveys in the 1980s occurred over a 

broader date range and it was clear that there was a strong date effect (Frost et al. 1988), there 

was almost no overlap in survey dates between the 1980s and the 1990s that would allow us to 

distinguish date-related influences from long-term trends in abundance. While Poisson 

regression analysis enabled us to quantify and model the effects of some covariates, the final 

model did not account for a substantial proportion of the variation in seal counts. We think that 

this may be largely due to the effects of temporal variation in the proportion of seals hauling out. 
58

Efforts to improve our estimates of trend must include quantification of the effects of within and 

between-year temporal variation on survey counts. 

Use of Power Analysis 

We conducted power analysis to evaluate our ability to detect changes in ringed seal 

abundance and also as a means of evaluating whether Poisson regression models, which 

incorporated the effects of environmental, weather and temporal covariates, improved our ability 

to detect trend. Results indicated that power was low for all combinations of sample size, years, 

and population change. The low power is the result of the large variation in counts (mostly 0 or 

small counts but occasional large counts >10), the large effect of other variables on the counts 

such as ice deformation and geographic position, and the large variation in conditions from year 

to year. Such factors make detection of temporal changes in the ringed seal population difficult 

even for long term studies with intensive surveys. 

In general, power characteristics of the Poisson model were better than those of the log-linear 

regression on raw counts. Power was approximately twice as high with the Poisson models for 

most combinations of years, sample size, and trend compared with the raw count regressions 

(Figure 31). For very large declines and 10 years of data, the power of Poisson model was about 

35% higher. As for the raw count regressions, power under the Poisson model was higher for 

detecting population declines than for detecting increases of the same magnitude. In contrast 

with the 5-year raw count regression results, the performance of the Poisson model when there 

was no trend was good for both 5 and 10 years of data with rejection rates less than or equal to 

the expected rate of 5%. The one aspect of power where the raw count regression (10 years 

only) was superior to the Poisson model was with ‘false’ rejections (e.g., significant estimated 

negative trend when the true trend is positive) when there was a trend. Under the raw count 

model with 10 years of data, the ‘false’ rejection rate declined to 0 as trend increased. This did 

not occur under the Poisson model. If the Poisson model is used for estimating trend from future 

survey data, this undesirable property of ‘false’ trends will need to be balanced against the highly 

desirable trait of increased power. 

Regardless of whether we used observed or Poisson modeled densities, almost no additional 

power was gained by adding more lines to the survey coverage. Power increased markedly when 

the survey period was increased from 5 years to 10 years. With five years of simulated data, the 

regression of yearly total “raw” counts against years had poor characteristics in terms of both 

low power to detect change and high rates of ‘false’ trends. With 10 years of simulated data, 

there was moderate power to detect large changes in population (20% annually) with good (i.e., 

low) rates of ‘false’ trends. However, an annual population change of 20% is not likely for longlived, slow-reproducing mammals such as ringed seals. Our analysis indicated almost no power 

to detect an annual trend of 5%, suggesting that current survey methods must be modified if 

surveys are to be useful for detecting trends in abundance. 

Power analysis and other methods of assessing sampling adequacy or desirable sample sizes 

are difficult in complex problems such as our ringed seal surveys. For simple statistical 

situations involving only a single parameter (e.g., t-tests, 2x2 contingency tables, simple linear 

regression) exact power curves for a range of effects are easily calculated (Thomas 1997). But in 

complex multi-parameter analyses of population change based on incomplete counts, covariates, 

and possible spatial correlation, such as our survey, the number of alternative hypotheses that 

need to be considered rapidly becomes too large for exact computation. In these cases power 

must be approximated using simulation. In our power simulations, we attempted to maintain the 

inter-relationships among the covariates and natural variability in the data, while investigating 
59

how the Poisson regression model detected systematic changes in our main variable of interest, 

trend. However, to reduce these simulations to a tractable problem required a number of 

assumptions. We assumed that the relationships of the covariates to the counts were known (i.e., 

we used the estimated coefficients from our 1990s, fast ice analysis as known constants). We 

also assumed the estimated overdispersion parameter was known. The use of a gamma-Poisson 

mixture model to simulate a long-tailed distribution of counts (i.e., many 0 counts but a few large 

counts as well) gave a pattern similar to our observed data, but undoubtedly not exactly the same. 

Because of these necessary assumptions and simplifications, the estimates of power we present 

should be interpreted as rough approximations to power useful for planning future survey efforts. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2

True change / Yr (%)

Power

Raw 50-5 neg

Reg 50-5 neg 

Reg 65-10 neg

Raw 65-10 neg

0.0

0.1

0.2

0.3

0.4

0.5

0.6

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2

True Trend / Year (%)

Power

Raw 50-5 pos

Reg 50-5 pos

Raw 65-10 pos

Reg 65-10 pos

Figure 31. Comparison of estimated power to detect trend for raw and modeled densities. Power 

is based on 1-tailed tests for rates of change not equal to zero: a) negative trends and b) positive 

trends. 

A 

B 
60

Variability and Trends in Ringed Seal Densities 

Both observed and modeled densities of ringed seals in the central Beaufort Sea indicated 

considerable annual variability on both fast ice and pack ice. The lowest observed annual 

density during the 1980s was similar to densities in 1998 and 1999 and about 25% higher than 

the lowest density during the 1990s. The highest annual density during the 1980s survey period, 

however, was more than double the highest density for the 1990s. The range in density estimates 

from the Poisson regression model was somewhat greater, with a threefold difference between 

the modeled densities for 1985 and 1999. 

Densities reported by Moulton et al. (2000) for the western part of our study area were 

substantially lower than those calculated from our surveys (1997 – 0.43 seals/km2

 vs. 0.77 

seals/km2

 in our sector B3; 1998 – 0.39 seals/km2 vs. 0.76 seals/km2 in B3; 1999 – 0.63 

seals/km2

 vs. 0.87 seals/km2 in B3; see Appendix A). Those differences may be explained by a 

longitudinal gradient in density because the Moulton et al. surveys encompassed only the 

western portion of sector B3 while our Poisson regression analysis showed significantly higher 

densities occurring to the east. 

Densities reported for our surveys are generally within or somewhat higher than the ranges 

reported for other areas where ringed seals have been surveyed. In the eastern Beaufort Sea in 

the 1970s, ringed seal densities ranged from about 0.1-0.5 seals/km2

 (Stirling et al. 1977). In the 

Canadian High Arctic, densities in the early 1980s were between 0.06-1.16 seals/km2

, depending 

on the area and year. Densities in northwestern Baffin Bay in the early 1980s (1.3-1.7 seals/km2

; 

Finley et al. 1983) and Hudson Bay during the early 1990s (0.38-1.93 seals/km2

; Lunn et al.

1997) were comparable to results of this study. In some areas, densities as high as 7-14 

seals/km2

 have been reported (Smith and Hammill 1981). Marked between-year variations in 

density estimates are common for ringed seal surveys. Two- to four-fold annual differences have 

been reported in this and other studies. Such differences have variously been attributed to actual 

changes in seal abundance or to differences in the proportion hauled out due the timing of 

surveys relative to ice conditions and timing of the annual molt (Stirling et al. 1982, Frost et al.

1988, Lunn et al. 1997). 

Trend analysis based on an ANOVA comparison of observed seal densities in the central 

Beaufort Sea suggested a marginally significant but substantial decline of 31% from the 1980s to 

the 1990s. The Poisson regression model indicated highly significant declines of 72% on fast ice 

and 43% on pack ice over the 15-year period. However, the apparent decline between the 1980s 

and the 1990s may be due to a difference in the timing of surveys rather than an actual decline in 

abundance. The 1990s surveys were conducted several days earlier than surveys in the 1980s 

due to warmer weather (see Table 12) and its anticipated effect on ice conditions. If the 

phenology of seal basking was comparably earlier in the 1990s than in the 1980s then the decline 

in seal numbers may well be real. However if such a change in seal basking did not occur, then 

the 1990s surveys were flown at a time when a relatively small proportion of seals were visible 

and the apparent decline could be an artifact. Because of problems such as this, Stirling et al.

(1977) pointed out the value of having other evidence (reproductive rates, lair surveys, evidence 

of polar bear predation) to corroborate survey results. Unfortunately no such corroborative 

information is available for ringed seals in the central Beaufort Sea. 

Regardless of whether or not the differences in ringed seal densities described above are real, 

there are several reasons to think that ringed seal abundance in the Beaufort Sea either has 

changed or is likely to change in the future. Ringed seals are the primary prey of polar bears 

(Smith 1980), and a detailed analysis by Stirling and Øritsland (1995) suggests that in many 
61

regions ringed seal populations may be limited by polar bear predation. There is evidence that 

the southern Beaufort Sea polar bear population increased considerably during the 1970s and 

1980s (Amstrup et al. 2001), and increased predation could have reduced ringed seal abundance. 

Ringed seals are part of a relatively simple food web in the Beaufort Sea (Frost and Lowry 1984) 

where they feed primarily on crustaceans and arctic cod (Boreogadus saida). The population of 

bowhead whales, which feed in the Beaufort Sea and may compete for food with both ringed 

seals and arctic cod (Lowry 1993), has been increasing and is now much larger than it was in the 

1970s (Zeh et al. 1993). Perhaps most importantly, because of their strong dependence on sea 

ice ringed seals are very likely to be affected by climate change, which is predicted to result in 

considerable warming and loss of sea ice in the Beaufort Sea region (Vinnikov et al. 1999). As 

climate warms, impacts on ringed seal distribution, abundance, and productivity will likely result 

from the combined effects of changes in physical habitats, changes in prey populations, and 

changes in inter-species interactions (Lowry 2000). There is little information that can be used 

to predict how the Beaufort Sea ringed seal population will respond to climatic warming, but at 

least one study has shown how variation in the stability of shorefast ice or the timing of breakup 

can reduce ringed seal productivity (Smith and Harwood 2001). 

CONCLUSIONS AND RECOMMENDATIONS 

1) The apparent decline in observed and modeled ringed seal densities between the 1980s and 

the 1990s may be due to a difference in the timing of surveys rather than an actual decline in 

abundance. Surveys in the 1990s were conducted a week or more earlier than surveys in the 

1980s, and may have occurred before the maximum number of seals were hauling out to bask. If 

the phenology of seal basking was comparably earlier in the 1990s than in the 1980s then the 

decline in seal numbers may well be real. However if such a change in seal basking did not 

occur, then the 1990s surveys were flown at a time when a relatively small proportion of seals 

were visible and the apparent decline could be an artifact. 

2) Univariate and Poisson regression analyses both indicated that habitat-related factors 

including ice deformation, water depth, and distance from the fast ice edge significantly affected 

the distribution and abundance of seals. Densities decreased as ice deformation increased and 

with increasing distance from the fast ice edge. Seal densities were highest in water >5-35 m 

deep. The effects of longitude were not consistent across years or between analyses. 

3) Analyses of the effects of weather-related variables, time of day, and melt water were 

inconsistent across years and between analytical methods. Poisson regression analysis did not 

contribute significantly to our understanding of the effects of these variables, perhaps because 

the survey “window” had already been standardized to optimize the conditions under which 

surveys were flown. 

4) Date was not included as a covariate in our analyses because a representative date range 

was not surveyed in all years. An unknown proportion of annual variation in density estimates is 

likely due to the unquantified effects of date. For this reason, future efforts to improve estimates 

of trend should include quantification of the effects of within and between-year temporal 

variation on survey counts. 

5) It is apparent from these and previous surveys that the observed distribution and 

abundance of ringed seals may change not only between years but also during a selected survey 

period. It is unclear whether and to what degree such changes represent a change in hauling out 
62

behavior, immigration from the pack ice or other areas as breakup begins, or a combination of 

these factors. In order to conduct future surveys at the appropriate time and to better interpret 

results and facilitate trend analysis, it is essential to better understand temporal changes in 

distribution and haulout behavior. While progress has been made in identifying behavioral 

changes at the onset of basking, virtually nothing is known about distributional changes at this 

time of year. 

6) Based on our simulations (including their inherent weaknesses), accounting for the effect 

of covariates to the extent possible when estimating population trend should be done; this was 

especially the case for short time series. However, even with improved power from the more 

complex analyses, the ability to detect changes of the sizes expected (5% per year or less) was 

poor. In addition, both regression approaches seemingly result in conservatively biased trend 

estimates (i.e., true trends are more extreme than estimated trends). Because of the dynamic 

nature of the seals and their environment, changes in survey protocols are likely necessary to 

improve the sensitivity of monitoring programs. These changes might include the use of radiomarked animals and/or repeated surveys over smaller areas. 

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A-1 

APPENDIX A
APPENDIX A 

Densities of ringed seals 

The tables present the lines flown and the unadjusted densities of observed ringed 

seals by sector for fast ice, pack ice, and all ice combined for aerial surveys conducted in 

the Alaska Beaufort Sea during 1985-1987 and 1996-1999. 

Table A.1. Dates and lines surveyed for aerial surveys conducted in May-June 1985-

1987 and 1996-1999. All surveys were flown at an altitude of 91 m with a strip width of 

0.41 km on each side of the aircraft. 
Date Sector (Lines flown) 

1985

6/9/85 B3 (18,20,21,22,23,24,26,27,28,29,31,32,33,34) 

6/11/85 B3 (1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17) B2 (27,29,30,31,32,33) 

6/12/85 B3 (35,36,37,38) B4 (1,2,5,6,7,9,14,15,17,18,19,20,21,22) 

6/13/85 B2 (2,4,5,8,10,11,12,13) B1 (18,19,20,24,26,27,28,29,33,34) 

1986

5/31/86 B1 (1,2,4,5,8,10,12,13,14,15,16,21,22,24) 

6/1/86 B1 (28,29,30,31,32,34) 

6/4/86 B2 (1,4,6,7,9,12,13,14,15,17,18,19,20,23,26,27,30,31) 

6/6/86 B2 (28,29,34) B3 (1,3,5,7,8,10,12,15,17,19,23,25,28,31,33) 

6/12/86 B3 (38,37,36) B4 (8,10,11,12,13,14,16,18,1111119,20,23,24) 

1987

6/2/87 B1 (2,3,5,7,9,10,11,14,15,17,18,20,23,24,25,27,28,30,32,33,34) 

6/3/87 B2 (4,5,6,7,10,11,14,16,17,18,19,20,22,23,25,26,27) 

6/5/87 B2 (29,30,32,34) 

6/6/87 B3 (3,4,5,6,8,11,14,15,16,17,19,20,21,23,24,25,28,29,32,33) 

6/7/87 B3 (34,35,37) B4 (2,5,6,9,11,12, 13,15,16,18,19,20,22,23,24) 

1996

29 May B2 (33) B3 (4, 5, 6, 6.5, 8.5, 12, 14, 14.5,15, 23, 25, 30, 31, 32, 33, 35,36 ,37 ,38) 

30 May B2 (32, 34) B3 (1, 2, 3, 5.5, 7, 7.5, 8, 9, 9.5, 10, 10.5, 11, 11.5, 12.5, 13, 13.5, 16, 20, 

21, 24, 30) 

31 May B3 (17, 18, 19, 22) B4 (1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,14,15,17,20,22,23) 

1997

25 May B1 (1, 3, 5, 6, 7, 9, 11, 12, 13, 15, 17, 19, 21, 22, 23, 25, 27, 28, 29) 

26 May B1 (31, 33) B2 (1, 3, 4, 5, 7, 8, 9, 11, 13, 15, 17, 18, 19, 21, 23, 25, 27) 

27 May B3 (5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 

 28.5, 29.5, 30.5) 

28 May B3 (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 30, 31, 32) 

29 May B2 (28, 29, 31, 33) B3 (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16) 

31 May B4 ( 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24) B3 (27, 29) 

1 June B4 (1, 2, 3, 4, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5) B3 (33, 34, 35, 36, 37, 38) 

2 June B1 (15, 17, 19, 21, 22, 23, 25, 27, 28, 29) 

1998

27 May B3 (1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38) B4 (2) 

28 May B3 (4, 6, 7, 9, 11, 21, 23, 25, 27) B4 (4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24) 

1999 

29 May B2 (7, 8, 9, 11, 13, 15, 17, 18, 19, 21, 23, 25, 27, 28, 29, 31, 33) B3 (1, 2, 3) 

30 May B3 (4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 24, 26, 27, 28, 29, 30, 31) 

31 May B3 (5, 5.5, 6.5, 7, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 19, 21, 22.5, 23, 23.5, 24.5, 

 25, 25.5, 26.5, 27.5, 28.5, 29.5) 

1 June B1 (7, 11, 12, 13, 15, 17, 19, 21, 23, 27, 29, 33) B2 (1, 3, 5) 

3 June B1 (25, 28, 29, 31) B4 (4, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24) 

4 June B3 (30.5, 31, 33, 34, 35, 36, 37, 38) B4 (2, 3, 5, 7, 9, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 

15.5) 
Table A.2. Unadjusted densities of observed ringed seals, by sector, for fast ice, pack 

ice, and all ice combined within 40 km of shore based on May-June 1985 aerial surveys. 

 

1985 Fast Ice Pack Ice All Ice 

 Hole Crack All Hole Crack All Hole Crack All 

B1 (n=10) 

density 0.73 0.11 0.83 0.34 0 0.34 0.72 0.10 0.83 

SE 0.10 0.05 0.13 0.36 0 0.36 0.09 0.05 0.13 

LCL 0.55 0.01 0.60 0 0 -1.91 0.55 0.01 0.59 

UCL 0.90 0.20 1.07 2.58 0 2.58 0.90 0.20 1.06 

 

B2 (n=14) 

density 0.69 0.20 0.89 - - - 0.69 0.20 0.89 

SE 0.09 0.07 0.11 - - - 0.09 0.07 0.11 

LCL 0.54 0.08 0.70 - - - 0.54 0.08 0.70 

UCL 0.85 0.32 1.08 - - - 0.85 0.32 1.08 

 

B3 (n=34) 

density 0.56 0.61 1.16 - - - 0.56 0.61 1.16 

SE 0.04 0.13 0.14 - - - 0.04 0.13 0.14 

LCL 0.48 0.39 0.92 - - - 0.48 0.39 0.92 

UCL 0.63 0.82 1.40 - - - 0.63 0.82 1.40 

 

B4 (n=14) 

density 0.49 0.11 0.60 0.46 0.15 0.61 0.48 0.11 0.60 

SE 0.04 0.04 0.05 0.11 0.06 0.15 0.04 0.03 0.05 

LCL 0.42 0.04 0.51 0.22 0.03 0.29 0.42 0.05 0.51 

UCL 0.55 0.17 0.68 0.69 0.27 0.93 0.55 0.17 0.69 
Table A.3. Unadjusted densities of observed ringed seals, by sector, for fast ice, pack 

ice, and all ice combined within 40 km of shore based on May-June 1986 aerial surveys. 

 

1986 Fast Ice Pack Ice All Ice 

 Hole Crack All Hole Crack All Hole Crack All 

B1 (n=20) 

density 0.63 0 0.63 0.07 0.01 0.08 0.54 0 0.54 

SE 0.06 0 0.06 0.04 0.01 0.04 0.07 0 0.07 

LCL 0.54 0 0.54 0 0 0 0.42 0 0.42 

UCL 0.73 0 0.73 0.15 0.02 0.16 0.65 0 0.65 

 

B2 (n=21) 

density 1.17 0.01 1.18 - - - 1.17 0.01 1.18 

SE 0.07 0.01 0.07 - - - 0.07 0.01 0.07 

LCL 1.05 0 1.05 - - - 1.05 0 1.05 

UCL 1.28 0.03 1.30 - - - 1.28 0.03 1.30 

 

B3 (n=18) 

density 1.18 0.10 1.28 0.20 1.10 1.30 1.05 0.24 1.29 

SE 0.08 0.06 0.11 0.05 0.39 0.43 0.10 0.10 0.12 

LCL 1.04 0 1.09 0.04 0 0.03 0.88 0.06 1.08 

UCL 1.33 0.21 1.48 0.35 2.24 2.57 1.22 0.42 1.49 

 

B4 (n=12) 

density 1.20 1.50 2.70 0.23 0.37 0.60 0.71 0.93 1.64 

SE 0.20 0.56 0.45 0.04 0.10 0.10 0.11 0.33 0.29 

LCL 0.85 0.49 1.89 0.15 0.19 0.42 0.52 0.33 1.11 

UCL 1.56 2.50 3.51 0.30 0.55 0.78 0.90 1.52 2.16 
Table A.4. Unadjusted densities of observed ringed seals, by sector, for fast ice, pack 

ice, and all ice combined within 40 km of shore based on May-June 1987 aerial surveys. 

 

1987 Fast Ice Pack Ice All Ice 

 Hole Crack All Hole Crack All Hole Crack All 

B1 (n=21) 

density 0.94 0.03 0.97 0.62 0.20 0.82 0.92 0.04 0.96 

SE 0.06 0.02 0.06 0.14 0.16 0.19 0.05 0.02 0.06 

LCL 0.84 0.00 0.87 0.35 -0.11 0.45 0.82 0.01 0.86 

UCL 1.04 0.06 1.08 0.90 0.51 1.19 1.01 0.08 1.06 

 

B2 (n=21) 

density 1.41 0.01 1.42 0.86 0.21 1.07 1.37 0.02 1.39 

SE 0.08 0.01 0.08 0.44 0.22 0.66 0.09 0.02 0.10 

LCL 1.26 0 1.27 0.01 0 0 1.21 0 1.22 

UCL 1.55 0.02 1.56 1.71 0.64 2.35 1.53 0.06 1.56 

 

B3 (n=47) 

density 1.06 1.34 2.41 0.49 0.98 1.47 0.77 1.16 1.93 

SE 0.10 0.43 0.45 0.10 0.20 0.26 0.09 0.22 0.23 

LCL 0.89 0.60 1.64 0.31 0.64 1.02 0.62 0.78 1.54 

UCL 1.24 2.09 3.18 0.66 1.32 1.91 0.92 1.54 2.32 

 

B4 (n=15) 

density 1.06 1.94 3.00 0.38 0.69 1.06 0.61 1.13 1.74 

SE 0.15 1.26 1.29 0.07 0.20 0.23 0.08 0.49 0.51 

LCL 0.80 0 0.72 0.25 0.33 0.66 0.46 0.27 0.85 

UCL 1.31 4.17 5.28 0.50 1.04 1.47 0.76 1.99 2.63 
Table A.5. Unadjusted densities of observed ringed seals, by sector, for fast ice, pack ice, 

and all ice combined within 40 km of shore based on 28-31 May 1996 aerial surveys. 

 

1996 Fast Ice Pack Ice All Ice 

 Hole Crack All Hole Crack All Hole Crack All 

B2 (n=3) 

Density/km2 0.73 0.03 0.76 2.03 0 2.03 1.08 0.02 1.10 

SE 0.13 0.03 0.15 0.33 0 0.33 0.09 0.02 0.08 

LCL 0.34 0 0.31 1.08 0 1.08 0.82 0 0.87 

UCL 1.11 0.11 1.20 2.98 0 2.98 1.34 0.08 1.34 

 

B3 (n=43) 

Density/km2

 0.48 0.06 0.54 0.66 0.19 0.85 0.57 0.13 0.70 

SE 0.05 0.04 0.06 0.16 0.04 0.16 0.08 0.03 0.09 

LCL 0.40 0 0.44 0.39 0.12 0.58 0.44 0.08 0.55 

UCL 0.57 0.12 0.65 0.92 0.26 1.12 0.70 0.17 0.84 

 

B4 (n=18) 

Density/km2

 0.64 0.05 0.69 0.52 0.67 1.19 0.55 0.52 1.07 

SE 0.10 0.03 0.10 0.13 0.15 0.24 0.10 0.12 0.18 

LCL 0.47 0 0.53 0.30 0.41 0.77 0.38 0.32 0.75 

UCL 0.81 0.11 0.86 0.75 0.92 1.61 0.72 0.73 1.39 
Table A.6. Densities of ringed seals, by sector, for fast ice, pack ice, and all ice 

combined within 40 km of shore based on May/June 1997 aerial surveys. (This table 

uses 2 June data for lines in B1, where it is available and 31 May data for the remaining 

lines in B1.) 

 

1997 Fast Ice Pack Ice All Ice 

 Hole Crack All Hole Crack All Hole Crack All 

B1 (n=30) 

Density/km2 0.39 0 0.39 0.05 0.01 0.07 0.34 0 0.34 

SE 0.04 0 0.04 0.03 0.01 0.03 0.04 0 0.04 

LCL 0.32 0 0.32 0 0.00 0.02 0.26 0 0.26 

UCL 0.46 0 0.46 0.10 0.03 0.11 0.42 0.01 0.42 

 

B2 (n=21) 

Density/km2

 0.61 0 0.61 -- -- -- 0.61 0 0.61 

SE 0.06 0 0.06 -- -- -- 0.06 0 0.06 

LCL 0.50 0 0.50 -- -- -- 0.50 0 0.50 

UCL 0.71 0 0.71 -- -- -- 0.71 0 0.71 

 

-B3 (n=57) 

Density/km2

 0.76 0.01 0.77 0.54 0.42 0.96 0.73 0.07 0.80 

SE 0.07 0.01 0.07 0.12 0.08 0.15 0.06 0.02 0.06 

LCL 0.65 0 0.66 0.35 0.28 0.72 0.62 0.04 0.69 

UCL 0.87 0.03 0.89 0.74 0.56 1.21 0.83 0.11 0.91 

 

B4 (n=31) 

Density/km2

 1.23 0.03 1.26 0.42 1.17 1.59 0.92 0.47 1.39 

SE 0.10 0.02 0.11 0.12 0.35 0.44 0.10 0.10 0.17 

LCL 1.05 0 1.07 0.21 0.57 0.84 0.74 0.30 1.09 

UCL 1.40 0.06 1.44 0.63 1.76 2.34 1.09 0.64 1.68 
Table A.7. Unadjusted density estimates of ringed seals, by sector, for fast ice, pack ice, 

and all ice combined within 40 km of shore based on May 1998 aerial surveys. 

 

1998 Fast Ice Pack Ice All Ice 

 Hole Crack All Hole Crack All Hole Crack All 

B3 (n=28) 

Density/km2 0.53 0.22 0.76 0.49 0.35 0.84 0.51 0.31 0.81 

SE 0.06 0.21 0.23 0.06 0.07 0.08 0.04 0.08 0.08 

LCL 0.43 0 0.37 0.39 0.24 0.70 0.43 0.17 0.67 

UCL 0.63 0.59 1.14 0.59 0.46 0.98 0.58 0.44 0.96 

 

B4 (n=12) 

Density/km2

 0.91 0.45 1.36 0.63 0.46 1.09 0.73 0.46 1.19 

SE 0.20 0.41 0.41 0.15 0.14 0.18 0.12 0.13 0.19 

LCL 0.56 0 0.62 0.36 0.22 0.76 0.52 0.22 0.85 

UCL 1.26 1.19 2.10 0.90 0.71 1.42 0.94 0.69 1.52 

Table A.8. Unadjusted densities of observed ringed seals, by sector, for fast ice, pack 

ice, and all ice combined within 40 km of shore based on May-June 1999 aerial surveys. 

 

1999 Fast Ice Pack Ice All Ice 

 Hole Crack All Hole Crack All Hole Crack All 

B1 (n=15) 

density 0.77 0 0.77 0.36 0.05 0.41 0.60 0.02 0.62 

SE 0.08 0 0.08 0.07 0.03 0.07 0.07 0.01 0.07 

LCL 0.62 0 0.62 0.24 0 0.28 0.48 0 0.50 

UCL 0.91 0 0.91 0.49 0.09 0.54 0.72 0.04 0.73 

 

B2 (n=20) 

density 0.69 0 0.69 0.67 0.01 0.68 0.68 0 0.69 

SE 0.06 0 0.06 0.08 0.01 0.08 0.06 0 0.06 

LCL 0.58 0 0.58 0.53 0 0.54 0.57 0 0.57 

UCL 0.80 0 0.80 0.82 0.02 0.82 0.79 0.01 0.80 

 

B3 (n=57) 

density 0.77 0.11 0.87 0.69 0.37 1.06 0.73 0.23 0.96 

SE 0.04 0.04 0.06 0.05 0.06 0.09 0.03 0.03 0.05 

LCL 0.70 0.05 0.77 0.61 0.28 0.92 0.68 0.18 0.88 

UCL 0.83 0.17 0.98 0.78 0.46 1.21 0.79 0.28 1.05 

 

B4 (n=31) 

density 1.36 0.57 1.93 0.69 0.71 1.40 0.92 0.66 1.58 

SE 0.20 0.24 0.29 0.13 0.14 0.19 0.11 0.13 0.17 

LCL 1.02 0.16 1.43 0.47 0.48 1.08 0.73 0.44 1.29 

UCL 1.69 0.98 2.42 0.91 0.94 1.72 1.11 0.88 1.87 
B-1

APPENDIX B
B-1

APPENDIX B 

Maps of Beaufort Sea study area showing sightings of ringed seals 

during 1985-1987 and 1996-1999. 

These figures show sightings of seals in all sectors (B1-B4) surveyed during 1985-1987 and 1996-1999. Only lines include

Temporal replicates and lines flown at altitudes greater than 91 m are not included. 
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Figure B.1. Map of the Beaufort Sea study area showing sightings of ringed seals made during 9-13 June 1985.
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Figure B.2. Map of the Beaufort Sea study area showing sightings of ringed seals made during 31 May-12 June 
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Figure B.5. Map of the Beaufort Sea study area showing sightings of ringed seals made during 25 May-2 June 1
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B-8

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Figure B.7. Map of the Beaufort Sea study area showing sightings of ringed seals made during 29 May-4 June 1
C-1

APPENDIX C 
APPENDIX C 

Statistical tables for aerial surveys of ringed seals 

The tables present the results of chi-square goodness-of-fit tests and Bonferonnicorrected 95% confidence intervals used to assess the significance of observed 

differences in ringed seal densities in relation to water depth, distance from the ice edge, 

ice deformation, longitude, time of day, melt water, cloud cover, temperature, and wind 

speed. Statistics are shown for sightings of individual seals. 
C-2

Table C.1. Water depth versus observed and expected numbers of ringed seals counted during aerial surveys in tSea, 1985-1987 and 1996-1999. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits

 on Observed Seals Year Depth (meters) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval

Lower Upper Lower Upper

R

1985 5 166.31 0.11 59 153.81 0.04 0.03 0.05 39.62 78.38 

 15 534.76 0.34 464 494.55 0.32 0.29 0.35 418.20 509.80  25 548.46 0.35 693 507.22 0.48 0.44 0.51 643.91 742.09  35 292.37 0.19 217 270.39 0.15 0.13 0.17 182.00 252.00  45 32.48 0.02 23 30.04 0.02 0.01 0.02 10.74 35.26 

1986 5 111.32 0.11 117 150.66 0.08 0.06 0.10 89.12 144.88 

 15 335.83 0.32 682 454.52 0.48 0.44 0.51 631.22 732.78  25 303.04 0.29 452 410.14 0.32 0.28 0.35 404.72 499.28  35 233.38 0.22 150 315.87 0.11 0.08 0.13 118.83 181.17  45 53.21 0.05 24 72.01 0.02 0.01 0.03 10.93 37.07  55 13.43 0.01 2 18.17 0.00 0.00 0.00 -1.80 5.80  >55 4.90 0.00 1 6.63 0.00 0.00 0.00 -1.69 3.69 

1987 5 176.88 0.13 182 311.50 0.08 0.06 0.09 147.14 216.86 

 15 406.16 0.30 1325 715.29 0.56 0.54 0.59 1260.24 1389.76  25 372.85 0.28 510 656.62 0.22 0.19 0.24 456.23 563.77  35 282.31 0.21 299 497.17 0.13 0.11 0.15 255.54 342.46  45 73.92 0.06 36 130.19 0.02 0.01 0.02 19.98 52.02  55 19.96 0.01 3 35.16 0.00 0.00 0.00 -1.66 7.66  >55 5.16 0.00 0 9.08 0.00 0.00 0.00 0.00 0.00 

1996 5 282.53 0.15 101 227.95 0.07 0.05 0.08 75.99 126.01 

 15 654.12 0.35 652 527.76 0.43 0.40 0.46 602.43 701.57  25 662.46 0.35 626 534.49 0.41 0.38 0.45 576.70 675.30  35 238.88 0.13 122 192.73 0.08 0.06 0.10 94.72 149.28  45 32.31 0.02 8 26.07 0.01 0.00 0.01 0.73 15.27 
C-3

Table C.1. Depth continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year Depth (meters) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. Per Interval 

Lower Upper Lower Upper 

R

1997 5 433.52 0.16 284 436.66 0.10 0.09 0.12 241.93 326.07 

 15 965.20 0.36 770 972.18 0.28 0.26 0.31 708.02 831.98  25 861.33 0.32 1109 867.56 0.41 0.38 0.43 1041.39 1176.61  35 381.54 0.14 492 384.30 0.18 0.16 0.20 439.04 544.96  45 57.36 0.02 44 57.78 0.02 0.01 0.02 26.64 61.36  55 0.52 0.00 20 0.53 0.01 0.00 0.01 8.24 31.76 

1998 5 218.36 0.18 109 202.59 0.10 0.07 0.12 82.84 135.16 

 15 434.14 0.36 457 402.79 0.41 0.37 0.45 413.74 500.26  25 367.30 0.31 377 340.78 0.34 0.30 0.38 335.37 418.63  35 145.32 0.12 158 134.83 0.14 0.11 0.17 127.29 188.71  45 31.08 0.03 9 28.84 0.01 0.00 0.02 1.12 16.88  55 0.19 0.00 0 0.17 0.00 0.00 0.00 0.00 0.00 

1999 5 504.47 0.19 374 591.64 0.12 0.10 0.13 327.28 420.72 

 15 949.52 0.36 1256 1113.59 0.41 0.38 0.43 1185.56 1326.44  25 841.75 0.32 1095 987.20 0.35 0.33 0.38 1026.42 1163.58  35 338.45 0.13 380 396.93 0.12 0.11 0.14 332.96 427.04  45 13.33 0.01 0 15.64 0.00 - - - - 

All 5 1893.38 0.15 1226 2092.44 0.09 0.08 0.10 1136.13 1315.87 

 15 4279.73 0.35 5606 4729.67 0.41 0.40 0.42 5451.25 5760.75  25 3957.19 0.32 4862 4373.22 0.36 0.34 0.37 4711.40 5012.60  35 1912.25 0.15 1818 2113.29 0.13 0.13 0.14 1711.19 1924.81  45 293.70 0.02 144 324.58 0.01 0.01 0.01 111.89 176.11  55 34.10 0.00 25 37.68 0.00 0.00 0.00 11.56 38.44  >55 10.06 0.00 1 11.11 0.00 0.00 0.00 -1.69 3.69 
C-4

Table C.2. Distance from fast ice edge versus observed and expected numbers of ringed seals counted during aerial survey

Sea, 1985-1987 and 1996-1999.

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year 

Distance 

from ice 

edge (km) 

Area 

Surveyed 

(km2) 

Proportion 

Total Area 

Surveyed 

Observed 

Number of 

Seals 

Expected 

Number of 

Seals 

Proportion 

Total Obs. per 

Interval 

Lower Upper Lower Upper 

1985 <-35 656.61 0.42 438 607.24 0.30 0.27 0.34 387.42 488.5

 -35 168.62 0.11 244 155.94 0.17 0.14 0.20 202.81 285.1 -30 187.17 0.12 176 173.09 0.12 0.10 0.15 140.05 211.9 -25 155.01 0.10 188 143.35 0.13 0.10 0.15 151.01 224.9 -20 150.78 0.10 186 139.44 0.13 0.10 0.15 149.18 222.8 -15 96.21 0.06 93 88.98 0.06 0.05 0.08 66.03 119.9 -10 63.45 0.04 61 58.68 0.04 0.03 0.06 38.90 83.1 -5 50.47 0.03 44 46.67 0.03 0.02 0.04 25.12 62.8 5 27.61 0.02 14 25.54 0.01 0.00 0.02 3.24 24.7 10 8.65 0.01 1 8.00 0.00 0.00 0.00 -1.89 3.8 15 4.25 0.00 6 3.93 0.00 0.00 0.01 -1.07 13.0 20 4.94 0.00 3 4.57 0.00 0.00 0.01 -2.00 8.0 25 0.61 0.00 2 0.57 0.00 0.00 0.00 -2.08 6.0

1986 <-35 196.59 0.19 224 266.08 0.16 0.13 0.19 183.39 264.6

 -35 52.69 0.05 74 71.31 0.05 0.03 0.07 49.25 98.7 -30 72.86 0.07 108 98.62 0.08 0.05 0.10 78.47 137.5 -25 70.58 0.07 79 95.53 0.06 0.04 0.07 53.47 104.5 -20 94.26 0.09 298 127.58 0.21 0.18 0.24 252.62 343.3 -15 82.17 0.08 106 111.21 0.07 0.05 0.09 76.73 135.2 -10 92.72 0.09 183 125.49 0.13 0.10 0.15 145.67 220.3 -5 75.33 0.07 142 101.96 0.10 0.08 0.12 108.58 175.4 5 68.10 0.06 63 92.17 0.04 0.03 0.06 40.07 85.9 10 54.14 0.05 30 73.27 0.02 0.01 0.03 13.98 46.0 15 54.31 0.05 29 73.50 0.02 0.01 0.03 13.25 44.7 20 57.50 0.05 59 77.83 0.04 0.03 0.06 36.77 81.2 25 34.26 0.03 17 46.37 0.01 0.00 0.02 4.89 29.1 30 29.17 0.03 13 39.48 0.01 0.00 0.02 2.39 23.6 35 17.69 0.02 3 23.94 0.00 0.00 0.01 -2.11 8.1 >35 2.71 0.00 0 3.66 0.00 0.00 0.00 0.00 0.0
C-5

Table C.2. Distance from edge continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year 

Distance 

from ice 

edge (km) 

Area 

Surveyed 

(km2) 

Proportion 

Total Area 

Surveyed 

Observed 

Number of 

Seals 

Expected 

Number of 

Seals 

Proportion Total 

Obs. per 

Interval 

Lower Upper Lower Upper 

1987 <-35 9.18 0.01 6 16.17 0.00 0.00 0.01 -1.23 13.2

 -35 3.44 0.00 3 6.07 0.00 0.00 0.00 -2.12 8.1 -30 20.07 0.02 30 35.35 0.01 0.01 0.02 13.92 46.0 -25 34.05 0.03 19 59.97 0.01 0.00 0.01 6.17 31.8 -20 84.14 0.06 93 148.17 0.04 0.03 0.05 65.07 120.9 -15 107.90 0.08 351 190.01 0.15 0.13 0.17 299.93 402.0 -10 118.71 0.09 280 209.06 0.12 0.10 0.14 233.58 326.4 -5 177.01 0.13 652 311.72 0.28 0.25 0.30 587.83 716.1 5 176.23 0.13 304 310.35 0.13 0.11 0.15 255.92 352.0 10 131.46 0.10 131 231.51 0.06 0.04 0.07 98.13 163.8 15 127.32 0.10 157 224.23 0.07 0.05 0.08 121.23 192.7 20 152.41 0.11 193 268.41 0.08 0.07 0.10 153.66 232.3 25 87.59 0.07 53 154.25 0.02 0.01 0.03 31.73 74.2 30 70.20 0.05 71 123.63 0.03 0.02 0.04 46.48 95.5 35 29.93 0.02 8 52.71 0.00 0.00 0.01 -0.34 16.3 >35 7.60 0.01 4 13.38 0.00 0.00 0.00 -1.91 9.9

1996 -30 1.67 0.00 0 1.35 - - - - 

 -25 20.62 0.01 6 16.63 0.00 0.00 0.01 -1.12 13.1 -20 125.84 0.07 23 101.51 0.02 0.01 0.02 9.13 36.8 -15 175.03 0.09 67 141.19 0.04 0.03 0.06 43.69 90.3 -10 214.02 0.11 125 172.64 0.08 0.06 0.10 93.80 156.2 -5 244.12 0.13 221 196.92 0.15 0.12 0.17 180.98 261.0 5 254.18 0.14 392 205.04 0.26 0.23 0.29 342.37 441.6 10 249.84 0.13 309 201.54 0.20 0.17 0.24 263.33 354.6 15 247.12 0.13 208 199.35 0.14 0.11 0.16 168.98 247.0 20 170.86 0.09 59 137.82 0.04 0.02 0.05 37.06 80.9
C-6

Table C.2. Distance from edge continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year 

Distance 

from ice 

edge (km) 

Area 

Surveyed 

(km2) 

Proportion 

Total Area 

Surveyed 

Observed 

Number of

Seals 

Expected 

Number of

Seals 

Proportion 

Total Obs. per 

Interval 

Lower Upper Lower Upper 

96 cont 25 89.03 0.05 72 71.82 0.05 0.03 0.06 47.87 96.13 

 30 55.85 0.03 26 45.05 0.02 0.01 0.03 11.27 40.73  35 17.63 0.01 1 14.22 0.00 0.00 0.00 -1.91 3.91 

1997 <-35 21.94 0.01 9 22.10 0.00 0.00 0.01 0.27 17.73 

 -35 150.41 0.06 89 151.48 0.03 0.02 0.04 61.97 116.03  -30 230.80 0.09 152 232.45 0.06 0.04 0.07 117.10 186.90  -25 272.89 0.10 203 274.83 0.07 0.06 0.09 163.07 242.93  -20 320.44 0.12 307 322.73 0.11 0.10 0.13 258.92 355.08  -15 343.13 0.13 421 345.57 0.15 0.13 0.18 366.04 475.96  -10 366.55 0.14 357 369.16 0.13 0.11 0.15 305.69 408.31  -5 359.16 0.13 346 361.72 0.13 0.11 0.15 295.37 396.63  5 276.54 0.10 467 278.51 0.17 0.15 0.19 409.70 524.30  10 161.85 0.06 150 163.01 0.06 0.04 0.07 115.31 184.69  15 106.23 0.04 105 106.98 0.04 0.03 0.05 75.73 134.27  20 57.72 0.02 48 58.13 0.02 0.01 0.03 27.99 68.01  25 27.42 0.01 44 27.62 0.02 0.01 0.02 24.83 63.17  30 4.68 0.00 21 4.72 0.01 0.00 0.01 7.70 34.30 

1998 -25 4.35 0.00 1 4.03 0.00 0.00 0.00 -1.86 3.86 

 -20 34.97 0.03 16 32.44 0.01 0.00 0.02 4.62 27.38  -15 86.70 0.07 36 80.43 0.03 0.02 0.05 19.09 52.91  -10 124.71 0.10 126 115.69 0.11 0.09 0.14 95.72 156.28  -5 156.30 0.13 201 145.00 0.18 0.15 0.21 164.24 237.76  5 158.70 0.13 132 147.23 0.12 0.09 0.15 101.10 162.90  10 162.76 0.14 162 151.00 0.15 0.12 0.18 128.30 195.70  15 161.40 0.13 186 149.73 0.17 0.14 0.20 150.35 221.65  20 140.61 0.12 129 130.44 0.12 0.09 0.14 98.41 159.59  25 96.88 0.08 64 89.87 0.06 0.04 0.08 41.75 86.25  30 57.00 0.05 53 52.88 0.05 0.03 0.07 32.64 73.36  35 12.14 0.01 4 11.27 0.00 0.00 0.01 -1.72 9.72 
C-7

Table C.2. Distance from edge continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year 

Distance 

from ice 

edge (km) 

Area 

Surveyed 

(km2) 

Proportion 

Total Area 

Surveyed 

Observed 

Number of

Seals 

Expected 

Number of

Seals 

Proportion 

Total Obs. 

per Interval Lower Upper Lower Upper 

1999 -35 3.07 0.00 0 3.63 - - - - - 

 -30 43.38 0.02 14 51.27 0.00 0.00 0.01 3.12 24.88  -25 85.76 0.03 45 101.35 0.01 0.01 0.02 25.60 64.40  -20 147.87 0.06 89 174.75 0.03 0.02 0.04 61.91 116.09  -15 246.76 0.09 209 291.62 0.07 0.05 0.08 168.33 249.67  -10 329.53 0.13 381 389.44 0.12 0.11 0.14 327.75 434.25  -5 351.45 0.13 669 415.35 0.22 0.19 0.24 602.29 735.71  5 362.86 0.14 589 428.83 0.19 0.17 0.21 525.38 652.62  10 333.06 0.13 360 393.61 0.12 0.10 0.13 308.04 411.96  15 283.88 0.11 215 335.49 0.07 0.06 0.08 173.79 256.21  20 231.67 0.09 318 273.78 0.10 0.09 0.12 268.79 367.21  25 149.86 0.06 177 177.10 0.06 0.05 0.07 139.36 214.64  30 39.90 0.02 19 47.16 0.01 0.00 0.01 6.34 31.66  35 5.60 0.00 5 6.62 0.00 0.00 0.00 -1.51 11.51 

All <-35 884.32 0.07 677 978.76 0.05 0.04 0.06 602.04 751.96 

 -35 378.23 0.03 410 418.62 0.03 0.03 0.03 351.07 468.93  -30 555.95 0.05 480 615.32 0.04 0.03 0.04 416.40 543.60  -25 643.26 0.05 541 711.96 0.04 0.03 0.04 473.64 608.36  -20 958.3 0.08 1012 1060.64 0.07 0.07 0.08 921.54 1102.46  -15 1137.9 0.09 1283 1259.42 0.09 0.09 0.10 1182.24 1383.76  -10 1309.69 0.11 1513 1449.55 0.11 0.10 0.12 1404.60 1621.40  -5 1413.84 0.11 2275 1564.83 0.17 0.16 0.18 2146.31 2403.69  5 1324.22 0.11 1961 1465.64 0.14 0.13 0.15 1839.89 2082.11  10 1101.76 0.09 1143 1219.42 0.08 0.08 0.09 1047.36 1238.64  15 984.51 0.08 906 1089.65 0.07 0.06 0.07 820.05 991.95  20 815.7 0.07 809 902.81 0.06 0.05 0.07 727.47 890.53  25 485.65 0.04 429 537.51 0.03 0.03 0.04 368.76 489.24  30 256.8 0.02 203 284.22 0.01 0.01 0.02 161.21 244.79  35 82.99 0.01 21 91.85 0.00 0.00 0.00 7.47 34.53  >35 15.18 0.00 4 16.80 0.00 0.00 0.00 -1.91 9.91 
C-8

Table C.3. Ice deformation versus observed and expected numbers of ringed seals counted during aerial surveys in the cen

1987 and 1996-1999. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur. 

95% Bonferroni 

Confidence Limits 

on Observed Seals 

Year % Ice- deformation Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. /Interval

Lower Upper Lower Upper

Re

1985 10 407.60 0.27 233 415.57 0.15 0.13 0.17 195.89 270.12 

 20 208.00 0.14 372 212.06 0.24 0.21 0.27 327.65 416.35  30 335.78 0.22 422 342.34 0.27 0.24 0.30 375.78 468.22  40 291.99 0.19 319 297.70 0.21 0.18 0.23 277.02 360.98  50 122.72 0.08 107 125.11 0.07 0.05 0.09 80.67 133.33  >50 151.27 0.10 94 154.22 0.06 0.04 0.08 69.21 118.79 

1986 10 322.17 0.32 643 459.73 0.45 0.42 0.49 593.40 692.60 

 20 261.76 0.26 454 373.53 0.32 0.29 0.35 407.57 500.43  30 244.04 0.24 174 348.24 0.12 0.10 0.14 141.39 206.61  40 141.97 0.14 138 202.59 0.10 0.08 0.12 108.54 167.46  50 25.30 0.03 17 36.11 0.01 0.00 0.02 6.19 27.81  >50 5.47 0.01 2 7.81 0.00 0.00 0.00 -1.73 5.73 

1987 10 268.51 0.21 768 498.06 0.33 0.30 0.35 707.98 828.03 

 20 309.76 0.24 706 574.58 0.30 0.27 0.32 647.34 764.66  30 348.18 0.27 525 645.84 0.22 0.20 0.25 471.71 578.29  40 269.15 0.21 311 499.25 0.13 0.11 0.15 267.65 354.35  50 58.22 0.05 42 107.99 0.02 0.01 0.03 25.06 58.95  >50 16.33 0.01 4 30.28 0.00 0.00 0.00 -1.27 9.27 

1996 10 451.42 0.24 451 364.14 0.30 0.27 0.33 404.09 497.91 

 20 420.42 0.22 386 339.14 0.26 0.23 0.29 341.28 430.72  30 533.71 0.29 394 430.53 0.26 0.23 0.29 348.98 439.02  40 270.32 0.14 194 218.06 0.13 0.11 0.15 159.70 228.30  50 125.05 0.07 63 100.87 0.04 0.03 0.06 42.50 83.50  >50 69.75 0.04 21 56.26 0.01 0.01 0.02 8.99 33.01 
C-9

Table C.3. Ice deformation continued.

95% Bonferroni 

Confidence Limits 

on Prop. of Occur. 

95% Bonferroni 

Confidence Limits 

on Observed Seals 

Year % Ice- deformation Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. /Interval

Lower Upper Lower Upper

1997 10 1358.14 0.50 1641 1367.82 0.60 0.58 0.63 1573.71 1708.29 

 20 812.22 0.30 810 818.01 0.30 0.27 0.32 747.08 872.92  30 387.71 0.14 223 390.47 0.08 0.07 0.10 185.25 260.75  40 121.71 0.05 33 122.57 0.01 0.01 0.02 17.94 48.06  50 18.93 0.01 11 19.06 0.00 0.00 0.01 2.27 19.73  >50 1.06 0.00 1 1.07 0.00 0.00 0.00 -1.64 3.64 

1998 10 663.01 0.55 657 615.07 0.59 0.55 0.63 613.80 700.20 

 20 255.29 0.21 227 236.84 0.20 0.17 0.24 191.55 262.45  30 158.61 0.13 132 147.14 0.12 0.09 0.14 103.55 160.45  40 86.51 0.07 81 80.26 0.07 0.05 0.09 58.14 103.86  50 23.06 0.02 9 21.39 0.01 0.00 0.02 1.12 16.88  >50 10.02 0.01 4 9.30 0.00 0.00 0.01 -1.27 9.27 

1999 10 1500.11 0.57 1651 1759.23 0.53 0.51 0.56 1577.64 1724.36 

 20 832.32 0.31 1207 976.08 0.39 0.37 0.41 1135.34 1278.66  30 276.39 0.10 235 324.14 0.08 0.06 0.09 196.12 273.88  40 28.63 0.01 8 33.57 0.00 0.00 0.00 0.55 15.45  50 8.22 0.00 4 9.64 0.00 0.00 0.00 -1.27 9.27  >50 1.99 0.00 0 2.33 

All Years 10 4970.96 0.41 6044 5611.00 0.44 0.43 0.45 5890.35 6197.65 

 20 3099.77 0.25 4162 3498.88 0.30 0.29 0.31 4019.82 4304.18  30 2284.42 0.19 2105 2578.55 0.15 0.14 0.16 1993.59 2216.41  40 1210.28 0.10 1084 1366.11 0.08 0.07 0.08 1000.63 1167.37  50 381.5 0.03 253 430.62 0.02 0.02 0.02 211.42 294.58  >50 255.89 0.02 126 288.84 0.01 0.01 0.01 96.52 155.48 
C-10

Table C.4. Longitude speed versus observed and expected numbers of ringed seals counted during aerial surveys in the cen

1985-1987 and 1996-1999. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year Longitude (degrees) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. in Interval 

Lower Upper Lower Upper 

1985 -149 235.37 0.16 370 239.97 0.24 0.21 0.27 324.87 415.14 

 -148 330.90 0.22 438 337.37 0.28 0.25 0.31 390.33 485.67  -147 285.72 0.19 333 291.31 0.22 0.19 0.24 289.51 376.49  -146 269.68 0.18 168 274.95 0.11 0.09 0.13 135.08 200.92  -145 173.20 0.11 112 176.58 0.07 0.05 0.09 84.58 139.42  -144 195.14 0.13 109 198.96 0.07 0.05 0.09 81.92 136.08  -143 27.34 0.02 17 27.87 0.01 0.00 0.02 5.97 28.03 

1986 -149 165.87 0.17 251 236.70 0.18 0.15 0.20 212.31 289.69 

 -148 138.51 0.14 140 197.65 0.10 0.08 0.12 109.77 170.23  -147 128.84 0.13 163 183.85 0.11 0.09 0.14 130.68 195.33  -146 167.32 0.17 219 238.76 0.15 0.13 0.18 182.37 255.63  -145 66.43 0.07 81 94.80 0.06 0.04 0.07 57.49 104.51  -144 267.86 0.27 508 382.23 0.36 0.32 0.39 459.33 556.67  -143 65.89 0.07 66 94.02 0.05 0.03 0.06 44.66 87.34 

1987 -149 165.25 0.13 405 306.52 0.17 0.15 0.19 355.74 454.27 

 -148 184.87 0.15 261 342.92 0.11 0.09 0.13 220.02 301.98  -147 249.39 0.20 328 462.61 0.14 0.12 0.16 282.80 373.20  -146 167.04 0.13 486 309.84 0.21 0.18 0.23 433.17 538.84  -145 134.55 0.11 558 249.58 0.24 0.21 0.26 502.49 613.51  -144 267.37 0.21 245 495.96 0.10 0.09 0.12 205.14 284.86  -143 101.66 0.08 73 188.57 0.03 0.02 0.04 50.38 95.63 
C-11

Table C.4. Longitude continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

 on Observed Seals Year Longitude (degrees W) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval 

Lower Upper Lower Upper 

1996 -149 388.09 0.21 394 312.93 0.26 0.23 0.29 348.09 439.91 

 -148 479.35 0.26 279 386.51 0.18 0.16 0.21 238.43 319.57  -147 204.04 0.11 90 164.53 0.06 0.04 0.08 65.25 114.75  -146 259.00 0.14 165 208.84 0.11 0.09 0.13 132.39 197.61  -145 299.02 0.16 374 241.11 0.25 0.22 0.28 328.88 419.12  -144 181.32 0.10 179 146.20 0.12 0.10 0.14 145.21 212.79  -143 61.85 0.03 29 49.88 0.02 0.01 0.03 14.65 43.35 

1997 -149 385.64 0.14 482 387.91 0.18 0.16 0.20 428.43 535.57 

 -148 489.29 0.18 383 492.16 0.14 0.12 0.16 334.20 431.80  -147 531.35 0.20 355 534.47 0.13 0.11 0.15 307.74 402.26  -146 370.20 0.14 184 372.38 0.07 0.05 0.08 148.77 219.23  -145 351.07 0.13 366 353.13 0.13 0.12 0.15 318.12 413.88  -144 476.44 0.18 831 479.24 0.31 0.28 0.33 766.38 895.62  -143 99.12 0.04 118 99.70 0.04 0.03 0.05 89.42 146.58  Total 2703.12 1 2719 2719 1 

1998 -149 209.77 0.18 171 194.61 0.15 0.12 0.18 138.64 203.36 

 -148 191.26 0.16 121 177.43 0.11 0.08 0.13 93.07 148.93  -147 275.77 0.23 200 255.83 0.18 0.15 0.21 165.55 234.45  -146 152.43 0.13 182 141.41 0.16 0.13 0.19 148.81 215.19  -145 153.09 0.13 194 142.02 0.17 0.14 0.21 159.96 228.04  -144 154.23 0.13 177 143.07 0.16 0.13 0.19 144.18 209.82  -143 61.04 0.05 66 56.63 0.06 0.04 0.08 44.80 87.20 
C-12

Table C.4. Longitude continued. 

95% Bonferroni 

Confidence Limits 

 on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year Longitude (degrees W) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval

Lower Upper Lower Upper 

1999 -149 393.74 0.15 367 461.80 0.12 0.10 0.13 318.61 415.39 

 -148 495.84 0.19 448 581.55 0.14 0.13 0.16 395.33 500.67  -147 523.53 0.20 557 614.03 0.18 0.16 0.20 499.49 614.51  -146 362.64 0.14 340 425.33 0.11 0.09 0.12 293.19 386.81  -145 366.43 0.14 555 429.77 0.18 0.16 0.20 497.57 612.43  -144 462.61 0.17 818 542.57 0.26 0.24 0.28 751.97 884.03  -143 42.58 0.02 20 49.94 0.01 0.00 0.01 8.01 31.99 

All Years -149 1943.73 0.16 2440 2193.22 0.18 0.17 0.19 2319.46 2560.54 

 -148 2310.01 0.19 2070 2606.51 0.15 0.14 0.16 1957.18 2182.82  -147 2198.65 0.18 2026 2480.86 0.15 0.14 0.16 1914.17 2137.83  -146 1748.32 0.14 1744 1972.72 0.13 0.12 0.13 1639.01 1848.99  -145 1543.79 0.13 2240 1741.94 0.16 0.15 0.17 2123.49 2356.51  -144 2004.97 0.16 2867 2262.32 0.21 0.20 0.22 2738.82 2995.18  -143 459.47 0.04 389 518.45 0.03 0.02 0.03 336.70 441.30 
C-13

Table C.5. Time of day versus observed and expected numbers of ringed seals counted during aerial surveys in the central 

1987 and 1996-1999. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year Time of Day (hour) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. Per Interval 

Lower Upper Lower Upper 

1985 11 239.62 0.16 187 244.30 0.12 0.10 0.14 152.51 221.

 12 268.02 0.18 206 273.26 0.13 0.11 0.16 170.05 241. 13 311.76 0.21 473 317.85 0.31 0.27 0.34 424.25 521. 14 331.67 0.22 386 338.16 0.25 0.22 0.28 340.21 431. 15 167.40 0.11 176 170.67 0.11 0.09 0.14 142.40 209. 16 100.32 0.07 59 102.29 0.04 0.03 0.05 38.74 79. 17 98.56 0.06 60 100.49 0.04 0.03 0.05 39.57 80.

1986 11 99.35 0.10 87 141.78 0.06 0.04 0.08 62.69 111.

 12 90.95 0.09 155 129.79 0.11 0.09 0.13 123.38 186. 13 227.36 0.23 451 324.43 0.32 0.28 0.35 403.75 498. 14 186.78 0.19 271 266.54 0.19 0.16 0.22 231.14 310. 15 128.11 0.13 129 182.80 0.09 0.07 0.11 99.86 158. 16 210.42 0.21 270 300.27 0.19 0.16 0.22 230.20 309. 17 57.74 0.06 65 82.40 0.05 0.03 0.06 43.81 86.

1987 10 9.16 0.01 40 16.99 0.02 0.01 0.02 23.13 56.

 11 245.06 0.19 849 454.56 0.36 0.33 0.39 786.31 911. 12 217.87 0.17 587 404.13 0.25 0.23 0.27 530.52 643. 13 194.11 0.15 250 360.05 0.11 0.09 0.12 209.79 290. 14 232.49 0.18 185 431.26 0.08 0.06 0.09 149.88 220. 15 264.25 0.21 286 490.16 0.12 0.10 0.14 243.36 328. 16 107.20 0.08 159 198.84 0.07 0.05 0.08 126.24 191.
C-14

Table C.5. Time of day continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year Time of Day (hour) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. Per Interval Lower Upper Lower Upper 

1996 11 37.59 0.02 15 30.32 0.01 0.00 0.02 4.63 25.

 12 306.13 0.16 397 246.94 0.26 0.23 0.29 350.99 443. 13 426.25 0.23 490 343.84 0.32 0.29 0.36 441.07 538. 14 384.38 0.21 236 310.06 0.16 0.13 0.18 198.04 273. 15 373.35 0.20 198 301.17 0.13 0.11 0.15 162.72 233. 16 314.59 0.17 150 253.76 0.10 0.08 0.12 118.73 181. 17 28.39 0.02 23 22.90 0.02 0.01 0.02 10.20 35.

1997 10 52.11 0.02 17 52.49 0.01 0.00 0.01 5.94 28.

 11 379.01 0.14 454 381.71 0.17 0.15 0.19 401.68 506. 12 501.17 0.19 682 504.74 0.25 0.23 0.27 621.19 742. 13 625.74 0.23 620 630.20 0.23 0.21 0.25 561.15 678. 14 501.27 0.19 478 504.84 0.18 0.16 0.20 424.61 531. 15 375.04 0.14 312 377.72 0.11 0.10 0.13 267.29 356. 16 265.42 0.10 156 267.31 0.06 0.05 0.07 123.38 188.

1998 11 32.72 0.03 3 30.36 0.00 0.00 0.01 -1.65 7.

 12 183.85 0.15 212 170.56 0.19 0.16 0.22 176.77 247. 13 262.76 0.22 234 243.76 0.21 0.18 0.24 197.44 270. 14 202.47 0.17 182 187.83 0.16 0.13 0.19 148.82 215. 15 211.87 0.18 160 196.55 0.14 0.12 0.17 128.52 191. 16 251.11 0.21 278 232.96 0.25 0.22 0.29 239.17 316. 17 51.73 0.04 41 47.99 0.04 0.02 0.05 24.10 57.
C-15

Table C.5. Time of day continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year Time of Day (hour) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. Per Interval Lower Upper Lower Upper 

1999 10 63.18 0.02 49 74.09 0.02 0.01 0.02 30.01 67.

 11 395.55 0.15 374 463.87 0.12 0.10 0.14 324.41 423. 12 259.45 0.10 280 304.26 0.09 0.08 0.10 236.36 323. 13 358.13 0.14 416 420.00 0.13 0.12 0.15 364.10 467. 14 483.37 0.18 604 566.87 0.19 0.18 0.21 543.69 664. 15 536.25 0.20 578 628.88 0.19 0.17 0.21 518.69 637. 16 322.08 0.12 386 377.71 0.12 0.11 0.14 335.73 436. 17 229.65 0.09 418 269.32 0.13 0.12 0.15 365.99 470.

All Years 10 124.45 0.01 106 140.47 0.01 0.01 0.01 77.96 134.

 11 1428.9 0.12 1969 1612.88 0.14 0.13 0.15 1856.67 2081. 12 1827.44 0.15 2519 2062.73 0.18 0.17 0.19 2394.95 2643. 13 2406.11 0.20 2934 2715.91 0.21 0.20 0.22 2802.61 3065. 14 2322.43 0.19 2342 2621.46 0.17 0.16 0.18 2221.45 2462. 15 2056.27 0.17 1839 2321.03 0.13 0.13 0.14 1729.85 1948. 16 1571.14 0.13 1458 1773.43 0.11 0.10 0.11 1359.27 1556. 17 466.07 0.04 607 526.08 0.04 0.04 0.05 541.13 672.
C-16

Table C.6. Melt water versus observed and expected numbers of ringed seals counted during aerial surveys in the central B

and 1996-1999. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year % Melt Water Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion of Total Obs. per Interval 

Lower Upper Lower Upper 

1985 0-10 79.95 0.05 43 81.51 0.03 0.02 0.04 25.94 60.

 11-20 678.71 0.45 788 691.97 0.51 0.48 0.54 736.13 839. 21-30 318.27 0.21 428 324.49 0.28 0.25 0.31 381.58 474. 31-40 261.83 0.17 200 266.95 0.13 0.11 0.15 165.18 234. 41-50 121.09 0.08 73 123.46 0.05 0.03 0.06 51.00 95. >50 57.5 0.04 15 58.62 0.01 0.00 0.02 4.83 25.

1986 0-10 979.34 0.98 1361 1397.49 0.95 0.94 0.97 1341.04 1380.

 11-20 7.62 0.01 62 10.87 0.04 0.03 0.06 42.76 81. 21-30 10.68 0.01 3 15.24 0.00 0.00 0.01 -1.32 7. 31-40 3.08 0.00 2 4.40 0.00 0.00 0.00 -1.53 5.

1987 0-10 1270.12 1.00 2356 2356.00 1.00 - - - 1996 0-10 1158.71 0.62 1063 934.69 0.70 0.67 0.74 1016.24 1109.

 11-20 341.13 0.18 198 275.18 0.13 0.11 0.15 163.40 232. 21-30 132.63 0.07 75 106.99 0.05 0.03 0.06 52.73 97. 31-40 73.38 0.04 69 59.19 0.05 0.03 0.06 47.59 90. 41-50 22.87 0.01 19 18.45 0.01 0.01 0.02 7.57 30. >50 141.95 0.08 85 114.51 0.06 0.04 0.07 61.37 108.

1997 0-10 2683.95 0.99 2714 2703.08 1.00 1.00 1.00 2708.11 2719.

 11-20 13.42 0.00 5 13.52 0.00 0.00 0.00 -0.89 10. 21-30 0.4 0.00 0 0.40 0.00 0.00 0.00 0.00 0. 31-40 0.3 0.00 0 0.30 0.00 0.00 0.00 0.00 0. 41-50 0.26 0.00 0 0.26 0.00 0.00 0.00 0.00 0. >50 1.43 0.00 0 1.44 0.00 0.00 0.00 0.00 0.
C-17

Table C.6. Melt water continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year % Melt Water Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion of Total Obs. per Interval 

Lower Upper Lower Upper 

1998 0-10 318.94 0.27 190 295.88 0.17 0.14 0.20 156.89 223.

 11-20 65.46 0.05 45 60.73 0.04 0.02 0.06 27.66 62. 21-30 134.65 0.11 132 124.91 0.12 0.09 0.14 103.55 160. 31-40 185.38 0.15 273 171.98 0.25 0.21 0.28 235.15 310. 41-50 103.76 0.09 101 96.26 0.09 0.07 0.11 75.72 126. >50 388.32 0.32 369 360.24 0.33 0.30 0.37 327.59 410.

1999 0-10 2640.24 1.00 3089 3096.30 0.99 0.99 1.00 3079.45 3098.

 11-20 5.72 0.00 16 6.71 0.01 0.00 0.01 6.45 25. 21-30 1.7 0.00 0 1.99 0.00 0.00 0.00 0.00 0.

All Years 10 9131.25 0.75 10816 10306.97 0.79 0.78 0.79 10688.85 10943.

 20 1112.06 0.09 1114 1255.25 0.08 0.07 0.09 1029.58 1198. 30 598.33 0.05 638 675.37 0.05 0.04 0.05 572.92 703. 40 523.97 0.04 544 591.44 0.04 0.04 0.04 483.69 604. 50 247.98 0.02 193 279.91 0.01 0.01 0.02 156.61 229. >50 589.2 0.05 469 665.06 0.03 0.03 0.04 412.85 525.
C-18

Table C.7. Cloud cover versus observed and expected numbers of ringed seals counted during aerial surveys in the central 

1987 and 1996-1999. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

 on Observed Seals Year % Cloud Cover Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval 

Lower Upper Lower Upper 

1985 60 134.21 0.09 164 136.83 0.11 0.09 0.12 135.01 192.

 80 332.42 0.22 578 338.92 0.37 0.34 0.40 532.45 623. 100 1050.71 0.69 805 1071.25 0.52 0.49 0.55 757.96 852.

1986 0 1000.72 1.00 1428 1428.00 1.00 1.00 1.00 1573.00 1573.01987 0 1270.12 1 2356 2356 1 1.00 1.00 2508.00 2508.01996 0 484.68 0.26 296 390.97 0.20 0.17 0.22 257.47 334.

 10 210.89 0.11 144 170.12 0.10 0.08 0.11 115.49 172. 70 31.10 0.02 21 25.08 0.01 0.01 0.02 9.63 32. 100 1144.01 0.61 1048 922.83 0.69 0.66 0.72 1003.31 1092.6

1997 0 243.07 0.09 136 244.80 0.05 0.04 0.06 105.42 166.

 10 179.76 0.07 163 181.05 0.06 0.05 0.07 129.70 196. 20 369.08 0.14 310 371.71 0.11 0.10 0.13 265.42 354. 40 657.72 0.24 903 662.41 0.33 0.31 0.36 836.94 969. 50 182.91 0.07 272 184.22 0.10 0.08 0.12 229.91 314. 80 169.52 0.06 252 170.73 0.09 0.08 0.11 211.32 292. 100 897.69 0.33 683 904.09 0.25 0.23 0.27 622.16 743.

1998 0 674.22 0.56 528 625.47 0.48 0.44 0.52 484.10 571.

 20 10.32 0.01 23 9.58 0.02 0.01 0.03 10.48 35. 40 153.39 0.13 163 142.30 0.15 0.12 0.17 131.89 194. 50 61.73 0.05 88 57.26 0.08 0.06 0.10 64.25 111.7 80 61.61 0.05 60 57.15 0.05 0.04 0.07 40.12 79. 100 235.25 0.20 248 218.24 0.22 0.19 0.26 211.39 284.
C-19

Table C.7. Cloud cover continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year % Cloud Cover Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval 

Lower Upper Lower Upper 

O

P

R

1999 0 12.00 0.005 22 14.07 0.01 0.00 0.01 9.43 34.57 

 20 16.11 0.01 13 18.89 0.00 0.00 0.01 3.32 22.68  30 151.41 0.06 120 177.56 0.04 0.03 0.05 91.11 148.89  50 513.89 0.19 606 602.66 0.20 0.18 0.21 546.59 665.41  80 123.02 0.05 118 144.27 0.04 0.03 0.05 89.34 146.66  90 138.78 0.05 132 162.76 0.04 0.03 0.05 101.76 162.24  100 1692.45 0.64 2094 1984.79 0.67 0.65 0.70 2023.76 2164.24 

All Years 0 3684.81 0.30 4766 4159.26 0.35 0.33 0.36 4607.58 4924.42  10 390.65 0.03 307 440.95 0.02 0.02 0.03 257.84 356.16  20 395.51 0.03 346 446.44 0.03 0.02 0.03 293.88 398.12  30 151.41 0.01 120 170.91 0.01 0.01 0.01 89.05 150.95  40 811.11 0.07 1066 915.55 0.08 0.07 0.08 977.01 1154.99  50 758.53 0.06 966 856.20 0.07 0.06 0.08 880.95 1051.05  60 134.21 0.01 164 151.49 0.01 0.01 0.01 127.88 200.12  70 31.1 0.00 21 35.10 0.00 0.00 0.00 8.01 33.99  80 686.57 0.06 1008 774.97 0.07 0.07 0.08 921.27 1094.73  90 138.78 0.01 132 156.65 0.01 0.01 0.01 99.56 164.44  100 5020.11 0.41 4878 5666.49 0.35 0.34 0.37 4718.73 5037.27 
C-20

Table C.8. Air temperature versus observed and expected numbers of ringed seals counted during aerial surveys in the cen

1987 and 1996-1999. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year Temperature (degC) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval

Lower Upper Lower Upper 

1985 -5 to 0 1019.09 0.6716 1228 1039.01 0.79 0.77 0.82 1192.33 1263.6

 0 to 4 498.26 0.3284 319 507.99 0.21 0.18 0.23 283.33 354.6

1986 -5 to 0 33.57 0.0335 42 47.90 0.03 0.02 0.04 26.72 57.2

 0 to 4 866.81 0.8662 1253 1236.92 0.88 0.86 0.90 1223.33 1282.6 4 to 8 100.34 0.1003 133 143.18 0.09 0.07 0.11 106.71 159.2

1987 -5 to 0 968.52 0.7625 1946 1796.55 0.83 0.81 0.84 1904.75 1987.2

 0 to 4 301.6 0.2375 410 559.45 0.17 0.16 0.19 368.75 451.2

1996 0 to 4 150.71 0.08 220 121.57 0.15 0.12 0.17 187.18 252.8

 4 to 8 1599.23 0.85 1224 1290.04 0.81 0.79 0.84 1187.60 1260.4 8 to 11 120.74 0.06 65 97.39 0.04 0.03 0.06 46.12 83.8

1997 ≤ -9 137.46 0.05 190 138.44 0.07 0.06 0.08 155.76 224.2

 -9 to -5 1055.03 0.39 1278 1062.55 0.47 0.45 0.49 1210.96 1345.0 -5 to 0 924.96 0.34 778 931.55 0.29 0.26 0.31 717.30 838.7 0 to 4 213.22 0.08 163 214.74 0.06 0.05 0.07 131.11 194.8 4 to 8 369.08 0.14 310 371.71 0.11 0.10 0.13 267.31 352.6

1998 0 to 4 138.08 0.12 136 128.10 0.12 0.10 0.15 108.71 163.2

 4 to 8 843.41 0.70 732 782.43 0.66 0.62 0.69 692.57 771.4 8 to 11 91.68 0.08 94 85.06 0.08 0.06 0.11 70.83 117. > 11 123.33 0.10 148 114.41 0.13 0.11 0.16 119.71 176.2

1999 -5 to 0 2159.05 0.82 2194 2531.99 0.71 0.69 0.72 2137.13 2250.8

 0 to 4 488.61 0.18 911 573.01 0.29 0.28 0.31 854.13 967.8
C-21

Table C.8. Air temperature continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

on Observed Seals Year Temperature (degC) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval

Lower Upper Lower Upper 

All Years ≤ -9 137.46 0.01 190 155.16 0.01 0.01 0.02 153.18 226.8 -9 to -5 1055.03 0.09 1278 1190.87 0.09 0.09 0.10 1186.40 1369.6 -5 to 0 5105.19 0.42 6188 5762.53 0.45 0.44 0.46 6030.96 6345.0 0 to 4 2657.29 0.22 3412 2999.44 0.25 0.24 0.26 3275.71 3548.2 4 to 8 2912.06 0.24 2399 3287.01 0.17 0.17 0.18 2279.26 2518.7 8 to 11 212.42 0.02 159 239.77 0.01 0.01 0.01 125.28 192.7 > 11 123.33 0.01 148 139.21 0.01 0.01 0.01 115.45 180.5
C-22

Table C.9. Wind speed versus observed and expected numbers of ringed seals counted during aerial surveys in the central 

1987 and 1996-1999. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

 on Observed Seals Year Windspeed (km/hr) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval

Lower Upper Lower Upper 

1985 15-20 1086.09 0.72 1266 1107.31 0.82 0.79 0.84 1229.70 1302.

 20-25 32.73 0.02 43 33.37 0.03 0.02 0.04 27.52 58. 25-30 398.53 0.26 238 406.32 0.15 0.13 0.18 204.03 271.

1986 15-20 66.93 0.07 41 95.51 0.03 0.02 0.04 24.75 57.

 20-25 315.74 0.32 434 450.56 0.30 0.27 0.34 389.23 478. 25-30 457.75 0.46 681 653.19 0.48 0.44 0.51 632.38 729. 30-35 126.73 0.13 230 180.84 0.16 0.14 0.19 194.22 265. 35-40 33.57 0.03 42 47.90 0.03 0.02 0.04 25.55 58.

1987 20-25 66.84 0.05 289 123.98 0.12 0.11 0.14 249.23 328.

 25-30 434.91 0.34 549 806.74 0.23 0.21 0.25 497.75 600. 30-35 501.65 0.39 1137 930.54 0.48 0.46 0.51 1076.42 1197. 35-40 266.72 0.21 381 494.74 0.16 0.14 0.18 336.36 425.

1996 <15 1175.11 0.63 1069 947.91 0.71 0.68 0.74 1024.90 1113.

 15-20 120.74 0.06 65 97.39 0.04 0.03 0.06 45.30 84. 25-30 273.67 0.15 189 220.76 0.13 0.10 0.15 156.88 221. 30-35 301.16 0.16 186 242.93 0.12 0.10 0.14 154.10 217.

1997 <15 213.22 0.08 163 214.74 0.06 0.05 0.07 131.11 194.

 15-20 963.24 0.36 827 970.10 0.30 0.28 0.33 765.21 888. 20-25 1214.98 0.45 1488 1223.64 0.55 0.52 0.57 1421.14 1554. 25-30 246.59 0.09 228 248.35 0.08 0.07 0.10 190.77 265. 30-35 61.72 0.02 13 62.16 0.00 0.00 0.01 3.73 22.

1998 <15 429.11 0.36 479 398.08 0.43 0.39 0.47 437.78 520.

 15-20 171.18 0.14 131 158.80 0.12 0.09 0.14 104.15 157. 25-30 152.91 0.13 93 141.86 0.08 0.06 0.10 69.94 116. 30-35 443.31 0.37 407 411.26 0.37 0.33 0.40 366.90 447.
C-23

Table C.9. Wind continued. 

95% Bonferroni 

Confidence Limits 

on Prop. of Occur.

95% Bonferroni 

Confidence Limits 

 on Observed Seals Year Windspeed (km/hr) Area Surveyed (km2) Proportion Total Area Surveyed Observed Number of Seals Expected Number of Seals Proportion Total Obs. per Interval

Lower Upper Lower Upper 

1999 <15 215.87 0.08 210 253.16 0.07 0.06 0.08 173.08 246.

 15-20 872.82 0.33 1032 1023.58 0.33 0.31 0.35 962.75 1101. 20-25 1127.74 0.43 1485 1322.54 0.48 0.45 0.50 1411.56 1558. 25-30 92.64 0.03 77 108.64 0.02 0.02 0.03 54.14 99. 30-35 243.44 0.09 228 285.50 0.07 0.06 0.09 189.65 266. 35-40 95.16 0.04 73 111.59 0.02 0.02 0.03 50.73 95.

All Years <15 2033.31 0.17 1921 2295.11 0.14 0.13 0.15 1813.73 2028. 15-20 3280.99 0.27 3362 3703.44 0.24 0.23 0.25 3229.00 3495. 20-25 2910.94 0.24 3832 3285.75 0.28 0.27 0.29 3693.25 3970. 25-30 2347.4 0.19 2369 2649.65 0.17 0.16 0.18 2252.15 2485. 30-35 1234.71 0.10 1794 1393.69 0.13 0.12 0.14 1689.79 1898. 35-40 395.44 0.03 496 446.36 0.04 0.03 0.04 438.31 553.