1

Timing and Re-interpretation of 

Ringed Seal Surveys

Brendan P. Kelly

Principal Investigator 

Co-principal Investigators:

Oriana H. Badajos

Mervi Kunnasranta

and

John Moran 

Final Report

OCS Study MMS 2006-013

June 2006
2

 Contact information 

e-mail: carter@sfos.uaf.edu 

phone: 907.474.1811 

 fax: 907.474.1188 

 Coastal Marine Institute 

 P.O. Box 757220 

School of Fisheries and Ocean Sciences 

 University of Alaska Fairbanks 

 Fairbanks, AK 99775-7220 
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Table of Contents

List of Figures .................................................................................................................................4

List of Tables ...................................................................................................................................5

Abstract ...........................................................................................................................................6

Introduction .....................................................................................................................................6

Methods...........................................................................................................................................8

 Study area .................................................................................................................................8

 Locating seal holes .....................................................................................................................8

 Capturing, tagging, and tracking seals .......................................................................................8

 Spatial and temporal patterns of lair use ....................................................................................9

 Recording snow and weather conditions ...................................................................................9

 Re-interpreting past surveys ......................................................................................................9

Results ...........................................................................................................................................10

 Locating seal holes ...................................................................................................................10

 Capturing, tagging, and tracking seals .....................................................................................10

 Spatial and temporal patterns of lair use ..................................................................................10

 Relationship between snow conditions and number of seals visible ....................................... 11

 Methods for determining snow conditions and survey times .................................................. 11

 Re-analysis of ringed seal surveys in the Beaufort Sea ...........................................................12

Discussion .....................................................................................................................................12

Acknowledgements .......................................................................................................................15

Reports and publications resulting from this project: ..................................................................16

Study Products ..............................................................................................................................17

References .....................................................................................................................................18
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Figure 1. The study site on the shorefast sea ice of the Beaufort Sea between Prudhoe Bay, Reindeer Island, 

and the man-made Northstar Island.........................................................................................................22

Figure 2. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the trained 

dogs in 1999. Also shown are the minimum convex polygons delineating the home ranges of 8 ringed 

seals based on on-ice resting sites. ..........................................................................................................22

Figure 3. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the trained 

dogs in 2000. Also shown are the minimum convex polygons delineating the home ranges of 8 

ringed seals based on on-ice resting sites ................................................................................................23

Figure 4. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the trained 

dogs in 2001. Also shown are the minimum convex polygons delineating the home ranges of 14 

ringed seals based on on-ice resting sites. ...............................................................................................23

Figure 5. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the trained 

dogs in 2002. Also shown are the minimum convex polygons delineating the home ranges of 14 

ringed seals based on on-ice resting sites. ...............................................................................................24

Figure 6. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the trained 

dogs in 2003. Also shown are the minimum convex polygons delineating the home ranges of 14 ringed 

seals based on on-ice resting sites. The home range of 1 radio-tagged seal was not determined ...........24

Figure 7. Duration of net sets for 75 captures of ringed seals in the Beaufort Sea in 1999, 2000, 2001, 2002, 

and 2003. .................................................................................................................................................25

Figure 8. Mean (and SE) of the number of holes used by radio tagged ringed seals as a function of the 

number of times haulout locations were determined. The asymptote is 4.5 holes per seal. ...................25

Figure 9. Cumulative percent frequency of home range size (minimum convex polygon) for 58 ringed seals 

radio tracked in 1999, 2000, 2001, 2002, and 2003. ...............................................................................26

Figure 10. Minimum convex polygons delineating the home ranges of 3 ringed seals each tracked during two 

successive breeding seasons. IM was an adult male tracked in 2001 and 2002. SH was an adult 

female tracked in 2002 and 2003. WW was an adult male tracked in 2002 and 2003 ............................27

Figure 11. Duration (hrs) of haulout bouts in subnivean lairs and out of lairs (basking). ........................................28

Figure 12. Interval (hrs) between haulout bouts in and out of subnivean lairs. .........................................................29

Figure 13. Diel haulout patterns of radio tagged ringed seals while using subnivean lairs and while basking. Each 

graph shows the percentage of monitoring periods when the seal was out of the water or each hour of 

the day. ...................................................................................................................................................30

Figure 14 a through e. Total number of seals visible in the study area (dashed trend lines) and the percentage 

of tagged seals visible in the study area (solid trend lines) by date in 1999, 2000, 2001, 2002, and 

2003. ........................................................................................................................................................45

Figure 15. Percentages of radio tagged seals in the water in subnivean lairs and basking on the ice in 1999, 2000, 

2001, 2002, and 2003. .............................................................................................................................48

Figure 16. Air temperature (thin line) and snow temperature at the ice surface (thick line) in April, May, and 

June 1999, 2000, 2001, 2002, and 2003. .................................................................................................49

Figures 17. Close up of air temperature (thin line) and snow temperature at the ice surface (thick line) in mid 

May to mid June 1999, 2000, 2001, 2002, and 2003. Arrows indicate abrupt increases of snow 

temperatures at the ice surface from -2 to 0oC. .......................................................................................50

Figure 18. Snow temperatures over the sea ice in our study area and on the tundra at the West Dock, 

Prudhoe Bay. ...........................................................................................................................................51

Figure 19. Infl uence (based on generalized additive models) of time of day, day of year, days from snow melt 

(estimated from passive microwave emissions), air temperature, and wind speed on the probability 

that radio tagged seals were visible on the ice. .......................................................................................52

Figure 20. Probabilities (means and standard deviations) of ringed seals basking on the ice during 7 survey years 

(Frost et al. 2002) based on combined effects of time of day, day of year, days from snow melt, air 

temperature, and wind speed. ..................................................................................................................53

Figure 21. Predicted probabilities (means and standard deviations) of seals basking during aerial surveys 

conducted by Frost et. al. (2002) based on univariate models. ...............................................................54

List of Figures
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Table 1. Subnivean breathing holes and lairs located by trained dogs in 1999, 2000, 2001, 2002, and 2003...........55

Table 2. Numbers of nets set, seal holes in which nets were set, and seals captured in 1999, 2000, 2001, 2002, 

and 2003. ......................................................................................................................................................55

Table 3. Seals captured in 1999, 2000, 2001, 2002, and 2003.. .................................................................................56

Table 4. Regression equations relating the proportions of tagged seals visible to the total number of seals 

visible in the study area in 1999, 2000, 2001, 2002, and 2003. ...................................................................58

Table 5. Dates lair abandonment was complete in 1999–2003 and possible predictors of abandonment. ................58

Table 6. Comparison of generalized additive models predicting the probability of seals basking. ...........................59

Table 7. Proportions of tagged seals visible on the ice surface during aerial surveys conducted by LGL Ltd. in 

1999. .............................................................................................................................................................60

Table 8. Ringed seal densities (seals / km2) in the Prudhoe Bay area estimated by the Alaska Department of 

Fish and Game (Frost et al. 2002, 2005) and LGL Limited (Moulton et al. 2002). .....................................60

List of Tables
6

Abstract

We used VHF radio telemetry to estimate the 

proportion of ringed seals (Phoca hispida) visible 

resting on the ice surface during the spring months 

in 1999, 2000, 2001, 2002, and 2003. Changes in the 

number of seals counted during ground-based, visual 

surveys of seals resting on the ice corresponded to 

changes in the number of radio-tagged seals basking. 

Tagged seals spent 16% (SD = 10%) of their time 

out of the water while using subnivean lairs and 55% 

(SD = 22%) of their time out of water after emerging 

from those lairs. Emergence from the lairs was related 

to structural failure of the snow pack, and passive 

microwave emissions–indicative of liquid moisture in 

the snow–predicted lair abandonment (r2 = 0.982, p = 

0.001). Active microwave (Ku-band) backscatter also 

detected snow melt but responded to an anomalous rain 

event.

Inter-annual and spatial comparisons of seal 

densities based on aerial surveys have not adequately 

accounted for the proportion of seals concealed 

within subnivean lairs. Previous models of the factors 

infl uencing the density of seals detected in aerial 

surveys have been based on densities observed during 

surveys conducted under limited ranges of conditions. 

We used the probability of radio tagged seals being 

visible as our response variable to determine which 

environmental factors were most important in 

explaining the availability of seals to be counted. The 

most important variables were time of day, date, wind 

speed, air temperature, and days from snow melt as 

determined by passive microwave emissions. Modeling 

of the probability that seals were visible during past 

aerial surveys indicated that the fraction of seals visible 

varied from less than 0.40 to more than 0.75 between 

survey years. Future surveys should be conducted after 

seals have emerged from their lairs and should employ 

telemetry to determine the probability that seals are 

visible. 

Radio-tagged ringed seals remained close to their 

capture and release sites in April, May, and early 

June. The mean home range size (based on breathing 

holes and lairs visited by tagged seals) during the 

breeding season was 1.73 km2 (SD = 4.19), and 94% 

of the home ranges were less than 3 km2. Inter-annual 

fi delity to breeding sites was observed in 3 adult ringed 

seals (2 males and 1 female) and suggests fi tness costs 

to displacement of seals and an unexpected level of 

population structuring.

Introduction

Accurate estimates of the number of ringed seals 

(Phoca hispida), believed to be the most numerous 

pinniped in the northern hemisphere, remain elusive 

(Scheffer 1958; Smith 1987, Kelly 1988, Reeves 1998) 

due to inadequate knowledge of the species’ population 

structure, ecology, and behavior. Distributed throughout 

the seasonally ice covered seas (and some lakes) of 

the northern hemisphere, ringed seals are thought to 

comprise six subspecies (Scheffer 1958, Reeves 1998), 

but population structure is largely unknown.

Local densities and, by extrapolation, population 

size of ringed seals have been estimated based on 

aerial surveys (e.g., Chapman et al. 1977, Stirling et 

al. 1977, Harwood and Stirling 1992, Frost et al. 2002, 

2004; Garner et al. 1999; Moulton et al. 2002) typically 

without reference to population structure. Seasonal 

peaks in the number of seals out of the water (hauled 

out) vary by demographic class, and the timing of those 

peaks may vary from year to year depending on food 

availability as well as temporal and environmental 

variables (Green et al. 1995, Daniel et al. 1999, Jemison 

and Kelly 2001). Nonetheless, spatial and temporal 

comparisons often have been made under the implicit 

assumption that the proportion of animals visible 

is constant from survey to survey (Caughley 1977, 

Drummer 1999). In surveys of harbor seal (Phoca 

vitulina richardsi) populations, that assumption has been 

tested using radio telemetry (Withrow and Loughlin 

1995, 1996, Huber et al. 2001). Frost et al. (1999b) 

used another approach in which they ignored the unseen 

fraction of a population and analyzed trends by adjusting 

counts of harbor seals to standardized conditions 

based on environmental and temporal covariates. That 

approach assumes that a similar (unknown) proportion 

of the population is visible when survey data collected 

under those conditions are compared. The approach 

also depends on selecting covariates that can predict the 

peak number of animals and on surveys that adequately 

represent all demographic classes and span a broad 

range of conditions relative to the covariates (Ver Hoef 

and Frost 2003). Simpkins et al. (2003) suggested that, 

when harbor seal surveys are corrected for covariates, 

they account for 81–86% of the local population. 

Analyses of ringed seal surveys generally have been 

less intensive and have not taken into account seasonal 

changes in the visibility of ringed seals resting on the 

ice. When out of the water during the winter months, 

ringed seals are concealed from view within subnivean 

lairs excavated above breathing holes in the sea ice 

(Chapskii 1940, McLaren 1958, Smith and Stirling 

1975). In late spring, the seals begin to emerge from 
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those lairs, and some are visible resting on top of the ice. 

Others, however, remain hidden from view either under 

the ice or on top of the ice but still concealed under the 

snow (Kelly 2005). Surveys of ringed seals typically 

have been conducted in late May and early June when 

the seals begin their annual molt. Regeneration of the 

epidermis requires that they elevate skin temperatures 

(Feltz and Fay 1966, King 1983), which they do by 

lying in the sun–hereafter referred to as “basking”. The 

greatest numbers of seals are visible on the ice “just 

before the ice break-up in most mid arctic localities” 

(Smith 1973a). Burns and Harbo (1972), however, 

pointed out that “extensive water on top of the fast ice, 

from melt and overfl ow of rivers” reduced the number 

of seals visible, and Frost et al. (1999a) observed that “it 

is diffi cult to predict when breakup will begin and how 

long the survey window might be in a particular year.” 

Nor has the onset of the survey window been explicitly 

defi ned. Rather, most surveys of ringed seals depend on 

the assumption that all seals have abandoned their lairs 

and, hence, are available to be counted when out of the 

water.

The Alaska Department of Fish and Game and 

LGL Ltd. surveyed ringed seals in the Beaufort Sea off 

of Alaska and corrected their counts for environmental 

infl uences using multivariate models (Moulton et al. 

2002; Frost et al. 2004). In both of those studies, the 

response variable was the number of seals visible on 

the ice during aerial surveys. Thus, their models could 

not include information about seals hidden under snow 

cover, and their analyses of covariates were limited to 

conditions suitable for fl ying in light aircraft. Thus, their 

models were constructed without benefi t of contrasting 

conditions of daylight, fog, and high winds.

Comparisons of local densities of seals visible on 

the ice have been used to assess the effects of human 

activities on ringed seals (Frost and Lowry 1988, Kelly 

et al. 1988, Richardson and Williams 2000; Moulton et 

al. 2005). Those human activities most often occur in 

winter or early spring (when snow and ice conditions 

are suitable to occupying the ice), while the seal surveys 

are conducted in late spring when seals are basking. An 

implicit assumption is that local seal distributions and 

densities are static in the interim. Some ringed seals, 

however, appear to move away from their winter range 

when they emerge from their lairs to bask. At least four 

of thirteen seals tracked telemetrically near Prudhoe 

Bay in the 1980s “hauled out at new sites . . . several 

kilometers” from those occupied prior to the beginning 

of snow melt (Kelly and Quakenbush 1990). Two of 

ten seals tracked in the Canadian arctic basked at sites 

several kilometers from lairs they occupied earlier in 

the spring (Kelly 2005). During intensive aerial surveys 

conducted near Prudhoe Bay over eight days in 1999, 

the density of seals visible near shore decreased while 

the density offshore increased (Moulton et al. 2000). 

The authors interpreted those changes in densities 

as a large-scale movement of seals, but no such shift 

was evident in similar surveys conducted in 2000 or 

2001 (M. Williams, pers. comm.). We do not know 

what determines whether ringed seals bask at the same 

breathing holes they used during their months under the 

ice and snow.

Ringed seals can be counted during visual surveys 

only if (1) they are on top of the ice, (2) they are not 

concealed in subnivean lairs, and (3) sightability is not 

impaired by fog, blowing snow, or a complex visual 

fi eld such as a mosaic of snow cover and melt ponds. 

Ringed seals tend to bask on the ice whenever the 

weather is warm and calm. Those conditions occur only 

rarely in fall and winter months, and basking seals are 

rare in those seasons. Warm, calm conditions occur 

increasingly frequently in the spring months, and seals 

begin to abandon their lairs in favor of resting on top of 

the snow and ice. 

We explored environmental factors that might 

predict when lairs are abandoned and then analyzed the 

covariates infl uencing the probability of seals being on 

top of the ice for the period after lair abandonment but 

before the development of extensive melt ponds on the 

ice surface. 

We used VHF-radio telemetry to determine where 

and when ringed seals were visible on top of the snow 

and ice near Prudhoe Bay, Alaska in April, May, and 

early June of 1999–2003. Seal behavior was monitored 

24 hours per day in all weather conditions. At the same 

time, we monitored weather and snow conditions on the 

ice adjacent to the seals to determine the relationships 

between environmental variables and the availability 

of seals for counting. Our response variable was the 

probability of tagged seals being visible on the ice, and 

we modeled the infl uence of covariates, including days 

before snow melt (hence the seals’ emergence from 

subnivean lairs), on that proportion.

Our objectives were to:

1. Determine the relationship between environmental 

variables (including snow conditions) and the number 

of seals visible during spring surveys.

Hypothesis: The transition from resting in lairs 

to resting in the open will be complete when the 

•
8

snow becomes saturated with melt water.

Hypothesis: The degree of saturation will better 

predict the abandonment of lairs than will calendar 

dates.

Hypothesis: The timing of saturation and of lair 

abandonment will vary regionally.

Hypothesis: Variation in the proportion of seals 

visible on the ice surface will decrease after seals 

cease to rest in lairs.

2. Determine the best methods for monitoring snow 

conditions and determining optimal survey times. 

Hypothesis: Ku-band backscatter data can indicate 

optimal survey times by consistently identifying 

the point at which snow on the sea ice becomes 

saturated with melt water.

Hypothesis: Optimal survey times can be determined using on-shore coastal snow data as a proxy 

for snow conditions on the sea ice.

3. Re-analyze previous ringed seal surveys from the 

Beaufort Sea of Alaska.

Hypothesis: The proportion of seals visible during 

past surveys can be estimated based on historical, 

coastal snow data used as a proxy for snow conditions on the sea ice.

Hypothesis: Relationships between seal densities 

and offshore development activities will be better 

represented when survey data are corrected for 

estimates of the proportion of seals observed.

Methods

Study area 

We collected fi eld data in the near shore Alaskan 

Beaufort Sea (70o29’23”N 148o41’52”W) between 

Prudhoe Bay, Reindeer Island, and Northstar Island 

(Figure 1) in April, May, and June 1999 through 2003. 

The area is shallow (water depths mostly less than 9 m, 

maximally 15 m), and usually covered by shorefast ice 

from October to July (Wise and Searby 1977). Snow 

falls mainly during September and October when open 

water in the Beaufort Sea provides moisture (Dingman 

et al. 1980, Walker et al. 1980) and is redistributed by 

winds throughout the winter (Benson et al. 1975). The 

snow cover is shallow over fl at ice and drifts on the 

windward and leeward sides of pressure ridges and 

other deformities in the ice surface. Subnivean lairs are 

excavated by ringed seals in wind-packed snow greater 

than 20 cm deep (Smith and Stirling 1975). 

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Locating seal holes

Specially trained Labrador retriever dogs indicated 

the location of subnivean breathing holes by digging in 

the snow where they detected the odor of ringed seals 

(Kelly and Quakenbush 1987). We used avalanche 

probes to penetrate the snow and precisely locate the 

breathing hole in the underlying ice. We also used the 

probes and visual observations to determine whether a 

subnivean lair was excavated above the breathing hole. 

Each hole was marked with a numbered wooden stake 

and its location (± 10 m) was determined using a Global 

Positioning System (GPS). 

Capturing, tagging, and tracking seals

We captured seals in nets that pursed below them 

when they entered breathing holes (Kelly 1996). We 

transported the seals to our ice camp where we recorded 

weights, estimates of age, and axillary girths. Annuli 

on the claws of the front limbs provided estimates of 

minimal age, since the earliest annuli are worn away 

from the distal end of the claws. We also photographed 

the unique pelage patterns of each seal and assigned 

identifi cation codes. We compared the pelage patterns 

of each seal captured to those previously photographed 

in this and an earlier study (Kelly and Quakenbush 

1990) to determine whether the seal had been previously 

captured.

We glued a 26 g VHF radio transmitter (Advanced 

Telemetry Systems, model MM170) with a unique 

frequency to the hair on each seal’s back. To extend 

our observations of seal haulout bouts and movements 

beyond the annual molt, we attached a second 23 g VHF 

radio transmitter (Advanced Telemetry Systems, model 

MM420) to one hind fl ipper of each seal by way of a 

cattle ear tag (Temple Tag, Inc., model 73200) in 2002 

and 2003. 

We monitored radio signals hourly from stations 

equipped with 8-element Yagi antennas on 11 m 

high masts and within 5 km of the seal capture 

sites. We rotated the antenna through 360o

 while 

monitoring and recorded the direction from which 

each signal was received. Each time a seal came out 

of the water, as indicated by the presence of its radio 

signal, we determined its location using a mobile 

receiver and hand-held directional antenna array. The 

directional antenna array consisted of two H-antennas 

communicating with the acoustic receiver by way of 

a null combiner. Thus, the bearing from the array to a 

transmitter was indicated by a null surrounded by high 

amplitude signals. Typically, fi ve or more bearings 
9

(with an accuracy of approximately ±3o) from points 

surrounding a tagged seal were obtained, and the seal’s 

position was read as the intersection of those bearings. 

Once the seal’s position was determined, we recorded 

whether each seal was concealed within a lair or visible 

on the snow surface.

Spatial and temporal patterns of lair use

The locations of breathing lairs and basking sites 

used by the tagged seals were plotted using MapSource 

(Garmin Corporation), transferred to an ArcView shape 

fi le, and the home ranges were delineated as minimum 

convex polygons (Worton 1987). The areas of those 

polygons were calculated to estimate home range sizes. 

The estimates did not account for water depth which 

did not exceed 9 m in any the areas used by seals in this 

study. 

Diel patterns of haulout bouts were examined over 

periods ranging from 22 to 52 days for each tagged 

seal. The frequency distributions of haulout bouts at 

each hour of the day were plotted and the circular mean 

and standard deviations were calculated (Batschelet 

1981). The frequency and duration of haulout bouts 

were examined separately for haulout bouts in lairs and 

haulout bouts at basking sites. The probability that the 

timing of haulout was the same when seals were in lairs 

and when they were basking was tested using Watson’s 

U2 statistic (Batschelet 1981). Rao’s Spacing Statistic 

(U) was used to evaluate deviations from uniformity 

in the times at which seals were out of the water 

(Batschelet 1981). 

To verify that the behavior of tagged seals 

was representative of the behavior of the overall 

population, we counted all seals visible on the ice daily 

(approximately 16:00 Alaska Daylight Time) in May 

each year. We used binoculars (Leica 10 × 42) to make 

the counts from the roof of a building about 62 m above 

the ice at the southern edge of the study area.

Recording snow and weather conditions

Environmental variables that might infl uence when 

seals were visible on the ice were measured in the study 

area. Additional data were obtained from the airport in 

Deadhorse (70o11’41.9”N 148o27’54.6”W), the West 

Dock of Prudhoe Bay (70o24’47.5”N 148o31’57.3”W), 

the man-made Northstar Island (70o29’23.4”N 

148o41’51.9”W), as well as from satellites collecting 

active and passive microwave data. 

In the study area, we recorded air temperature, 

snow temperature (from ice surface to snow surface at 

5 cm intervals), wind speed, and wind direction every 

30 minutes. The data were stored on a CR10 data logger 

and SM192 storage module (Campbell Scientifi c). We 

examined changes in the distribution of liquid water in 

the snow pack, snow depth, the size and morphology 

of snow grains, and the overall snow landscape to 

monitor the transformation of the snow pack during 

snowmelt (Liston and Sturm 2002). We compared the 

snow temperature record for our study area with snow 

temperatures collected by V. E. Romanovsky on the 

tundra near the West Dock in Prudhoe Bay (70o 22.47’N 

148o 33.13’W) in 1999, 2000, and 2001.

Records obtained from the airport in Deadhorse 

included air temperature, wind speed, and wind 

direction. Wind speed and direction and air temperature 

data also were obtained from a seawater treatment 

plant (STP) operated by BP Alaska at the West Dock 

of Prudhoe Bay. Solar radiation data and additional 

temperature records were obtained from the Northstar 

Island. 

Snow melt in the study area is hastened by fl ooding 

of the Sagavanirtok and Kuparuk rivers on to the sea ice. 

Breakup dates of the Kuparuk River were obtained from 

the U. S. Geological Survey and were defi ned by a rapid 

increase in fl ow rates.

We also used satellite-borne Ku-band backscatter 

data to monitor changes in the liquid content of the snow 

pack in our study area in 2000, 2001, 2002, and 2003. 

The Jet Propulsion Laboratory in California provided 

active microwave data (14 GHz backscatter) reported 

from the satellite, QuikSCAT. Backscatter data were 

collected at 0600 and 1800 hrs daily for our study area. 

The microwave backscatter is highly sensitive to the 

amount of liquid moisture in snow, and the difference in 

backscatter amplitude between the morning and evening 

passes of the satellite indicated the degree of diurnal 

freezing and thawing in the snow cover (Tjuatja et al. 

1992; Nghiem et al. 1995).

We also monitored snow melt using passive 

microwave data collected from the NOAA/NASA 

Pathfi nder AVHRR Land Program satellite. The date of 

snow melt on the sea ice in our study area was estimated 

using two variants of the Advanced Horizontal Range 

Algorithm (Drobot and Anderson 2001); the Passive 

Microwave Surface Temperature Algorithm (PMSTA) 

and the Mean Differences and Standard Deviation 

Analysis (MDSDA) algorithm (Belchansky et al. 2004). 

Re-interpreting past surveys

We used generalized additive models (binomial 

family, logit link, and spline smooth) to explore the 

relationship between the probability of a seal basking 
10

and environmental variables (Hastie and Tibshirani 

1990). 

Each of the 2,163 hourly observations on 61 tagged 

seals were treated as a binomial response (the seal was 

basking and visible to an aerial observer or the seal 

was hidden from view, in a lair or under water). We 

minimized variance by confi ning the model to periods 

when aerial surveys in the Alaskan Beaufort Sea were 

conducted (26 May to 12 June) and to the shore fast 

ice area near Prudhoe Bay (sector B3) included both in 

Frost et al.’s (2002) surveys and our telemetry study. 

Frost et al. (2002) collected wind speed measurements 

from airport weather stations and from survey aircraft 

and reported them as meta data to the National 

Oceanographic Data Center. We developed a linear 

regression equation that corrected those measurements 

to wind speeds measured on the sea ice.

We reanalyzed the Alaska Department of Fish 

and Game surveys (Frost et al. 1999, 2002) estimating 

the proportion of seals visible during each of their 

surveys based on our telemetric observations and 

environmental covariates we measured in the fi eld. We 

used the GAM function in Splus to build models and 

test the signifi cance of covariates. We used univariate 

models and our best multivariate model to predict the 

probability of seals basking during each observation 

based on covariates from Alaska Department of Fish and 

Game surveys. The probabilities were averaged for each 

year and compared to the densities reported by Frost 

et al. (2002). Raw data were not available for surveys 

conducted by LGL Limited (Moulton et al. 2002). We 

compared the probability that tagged seals were visible 

with corrected density estimates obtained in LGL 

Limited’s aerial surveys. 

Results

Locating seal holes

In each year of the study, we located over 100 seal 

breathing holes, nearly half of which had subnivean lairs 

above them (Table 1). The percentage of all lairs that 

showed signs of a pup were low overall and varied from 

4% in 2000 and 2001 to 9% in 2002, but the differences 

were not signifi cant (Χ 2 = 1.95, df = 4, p > 0.50). 

Breathing holes and lairs often were distributed in 

linear arrays refl ecting the distribution of refrozen cracks 

and pressure ridges in the study area (Figures 2–6).

Capturing, tagging, and tracking seals

We set nets one or more times in 124 seal holes for 

a total of 180 sets (Table 2). Including recaptures, we 

caught seals in 60% of those holes or 41% of the nets 

set. Time to capture ranged from 15 minutes to 5 days 

with 56% of captures occurring within 48 hours of the 

time a net was set (Figure 7). Capture rates were higher 

in May (49% of 80 nets set) than in April (37% of 100 

nets set) refl ecting less frequent freezing of the nets and 

more frequent visits by seals. Seals entered 50% of the 

nets set in April and 66% of the nets set in May. 

We captured 62 individual ringed seals, and we 

tagged and tracked 31 females and 29 males (Table 3). 

The number of annuli visible on the pectoral claws of 

captured seals ranged from 4 to 9 with a median of 7 for 

both male and female seals.

Spatial and temporal patterns of lair use

The location of each tagged seal was determined 

1 to 32 (X ̄ = 12.8, SD = 7.02) times when they rested 

on the ice. The number of resting sites identifi ed for 

each seal ranged from 1 to 8 (X ̄= 3.4, SD = 1.53) and 

likely underestimates the actual number of holes used, 

as the seals’ locations were not determined for every 

resting bout. The number of holes used increased with 

the number of times sites were identifi ed and reached an 

asymptote at 4.5 holes per seal (Figure 8).

Home ranges were determined for 58 of the seals 

tracked in 1999–2003 (Figures 2–6) and ranged from 

0.03 to 28 km2 (X ̄= 1.73, SD = 4.19). Most (94%) of 

the home ranges were less than 3 km2 (Figure 9). Two 

of the larger home ranges included lairs ≥ 7 km apart 

between which seals moved repeatedly. In 2000, the 

home range of an adult female seal (OC00) included two 

lairs 8.6 km apart, and in 2001, an adult male (MK01) 

used two lairs separated by 7 km. The home ranges of 

two seals tracked in 2002 exceeded 3 km2 only in early 

June when they began basking 20 km (RO02) and 40 km 

(RN02) from the sites they used in May.

Nine seals were recaptured within the same 

breeding season, 1 to 20 days after their initial capture. 

Each was captured within 1300 m of their initial capture 

site. Three seals were recaptured in the subsequent 

breeding season, 371–377 days after their initial capture. 

Their recapture sites were 746, 1400, and 2000 m from 

the sites at which they were captured in the previous 

year. The home ranges of those three seals were similar 

in size, confi guration, and number of lairs occupied in 

successive breeding seasons (Figure 10).

Seals spent more time out of the water once they 

began emerging outside of lairs. Tagged seals spent 
11

16% (SD = 10%) of their time out of the water before 

abandoning lairs and 55% (SD = 22%) of their time 

out of the water after fi rst emerging on the ice outside 

of a lair. Twenty-six of 43 seals monitored before and 

after their fi rst emergence from lairs occupied lairs one 

or more times after emergence. They spent an average 

of 3% (95% CL: 1–4%) of their time in lairs and an 

average of 37% (95% CL: 32–41%) of their time 

basking after the fi rst emergence. Resting bouts on the 

ice (Figure 11) were of longer duration (Median = 9 hrs) 

when the seals were basking than when they occupied 

lairs (Median = 6 hrs). Intervals between on-ice resting 

bouts (Figure 12) tended to be shorter (Median = 14 hrs) 

when seals were basking than when using lairs (Median 

= 27 hrs). 

Most tagged seals exhibited a signifi cant diel 

pattern in the proportion of time spent on the ice (Figure 

13). Individuals varied in their preferred on-ice times, 

but most shifted from resting on the ice primarily 

between 1800 hrs and 0600 hrs (overall X ̄= 0110 hrs, 

SD = 6.10 hrs) when using lairs to resting on the ice 

primarily at mid day when basking (overall X ̄= 1646 

hrs, SD = 1.46 hrs). The shift to coming out of the water 

in late afternoon was highly signifi cant (U2 = 1.591, p < 

0.001). The variance in when seals were out of the water 

was greater during lair occupation (Rao’s U = 149.63, 

0.10 > p > 0.05, n = 41) than when seals were basking 

(Rao’s U = 286.78, p < 0.01, n = 59). The probability 

of a seal being out of the water during any hour rarely 

exceeded 0.50 when occupying lairs, but when the seals 

were basking, the probabilities often exceeded 0.50, 

especially in the afternoon hours.

Relationship between snow conditions and number of 

seals visible

No seals were visible resting on snow surface 

prior to 28 April in any of the fi ve years of 

observation. Tagged and untagged seals began appearing 

on the surface on 21 May 1999, 8 May 2000, 4 May 

2001, 5 May 2002, and 28 April 2003, and the number 

of seals visible generally increased thereafter until late 

May or June. The number of tagged seals visible in the 

study area generally increased with the total number 

of seals visible, although counts of tagged seals were 

less variable, because those seals were detected and 

located even when visibility was poor (Figure 14). The 

proportion of tagged seals visible explained more than 

60% of the variance in the total number of seals visible 

every year except 2002 (Table 4). In that year, an early 

snow melt forced all seals out of their lairs by 18 May.

The fi rst seals were recorded resting on the ice 

outside of lairs earlier each year from 1999 through 

2003, and the transition (time when the fi rst to last 

tagged seal began basking) varied from 14 days in 2002 

(5 May–18 May) to 36 days in 2000 (8 May–12 June) 

and 2003 (28 April–2 June) (Figure 15). 

Snow temperature at the ice surface warmed 

throughout April and May and reached 0oC in late May 

or early June (Figure 16). The rise from about -2oC to 

0oC was abrupt in contrast to the gradual rate of increase 

prior to that point and corresponded with the entire snow 

pack becoming isothermal (Figure 17). The abrupt shift 

likely refl ected liquid water running freely through the 

snow pack. While the date on which the last tagged seal 

abandoned lair occupation varied widely between years 

(ranging from 18 May in 2002 to 12 June in 2000), each 

year it was within 4–6 days of the day on which the 

snow became isothermal (Table 5). The abrupt increase 

in snow temperature to 0oC preceded the date of fi nal 

lair use in 1999, 2000, and 2003 and occurred after lair 

abandonment in 2001 and 2002. Thus, snow temperature 

was a poor predictor of lair abandonment (y = 1.1016x 

- 14.92, r2 = 0.663, p = 0.093). 

The variance in the proportion of seals visible 

decreased after the transition from lair use to basking 

was complete. During the transition period, the mean 

proportion of tagged seals basking was 0.19 and the 

coeffi cient of variation (CV) was 117.92. After lair 

abandonment was complete, the mean proportion of 

tagged seals basking was 0.75 and the CV was 27.93. 

Methods for determining snow conditions and survey 

times

While the abrupt increase in snow temperature at 

the ice surface occurred within one week of the fi nal 

date of lair abandonment each year, monitoring snow 

temperatures is impractical as a means of determining 

suitable survey times. Therefore, we explored more 

practical, remote methods of determining when 

failure of the snow cover was imminent and lair 

abandonment was complete. We regressed the date of 

fi nal lair abandonment against the date the fi rst seal was 

observed out of a lair, the break up date for the Kuparuk 

River, the date of substantial snow melt indicated by 

passive microwave backscatterometry, and the date of 

substantial snow melt indicated by active microwave 

imagery.

The date of fi rst lair abandonment was a poor 

predictor of the fi nal date of lair abandonment (y = 

0.4281x + 98.781, r2

 = 0.136, p = 0.542) refl ecting 

substantial inter-annual variability in the duration of 
12

snow melt. 

In 2000, 2001, and 2003, the diurnal difference 

in the amplitude of backscatter fi rst exceeded 2 dB 

7–8 days prior to the date on which the snow became 

isothermal. In 2002, however, an early rain event in late 

April caused the diurnal difference in the amplitude of 

backscatter to exceed 2 dB 17 days prior to the snow 

becoming isothermal. The date on which the diurnal 

difference in backscatter fi rst exceeded 2 dB preceded 

the date of fi nal lair abandonment by 3–13 days in 2000, 

2001, 2002, and 2003 (y = 0.8447x + 31.874, r2 = 0.847, 

p = 0.080).

We also examined snow temperature data from an 

on-shore monitoring site at West Dock in Prudhoe Bay 

as a potential proxy for snow conditions on the sea ice. 

Snow temperature on the tundra was consistently colder 

than the snow over the ice and it reached 0oC later in the 

season each year (Figure 18). Overall, snow temperature 

on the tundra was a good predictor of snow temperature 

on the sea ice (y = 1.0289x + 6.1609, r2

 = 0.883, p = 0).

Abrupt fl ow rates on the Kuparuk River are 

another indication of breakup, and we regressed dates 

of lair abandonment against melt dates indicated by 

river discharge. The discharge did not predict lair 

abandonment well (y = 0.7043x + 47.506, r2 = 0.3436, p 

= 0.4138).

We regressed date of fi nal lair abandonment against 

date of snow melt estimated by passive microwave data 

using two different algorithms. The PMSTA algorithm 

estimated date of snowmelt as 21–35 days after the date 

of fi nal lair abandonment (y = 0.8841x - 5.3768, r2 = 

0.697, p = 0.078). The MDSDA algorithm estimated 

snow melt dates within 3 days of the date of fi nal lair 

abandonment (y = 0.9308x + 11.221, r2 = 0.982, p = 

0.001) for each year of the study. 

Re-analysis of ringed seal surveys in the Beaufort Sea

We modeled the proportion of seals visible as a 

function of the environmental variables that signifi cantly 

infl uenced the haulout behavior of the tagged seals. 

We examined, date, time of day, air temperature, wind 

speed, wind direction, barometric pressure, solar 

radiation, and days from snow melt based on the active 

microwave radar and the MDSDA algorithm (Figure 

19).

The probability of seals basking increased as solar 

radiation increased. Because radiation was highly 

correlated with time of day, however, we dropped 

radiation from the model. The prevailing east winds 

provided little contrast for analysis, and wind direction 

was not a signifi cant covariate in the fi nal model. We 

observed no correlation between barometric pressure 

and the basking behavior of seals. 

Time of day, day of year, days from snow melt, 

air temperature, and wind speed all had signifi cant 

effects on the probability of seals basking (Figure 19). 

We found a strong diel effect during the aerial survey 

period with a peak probability of seals basking at 1500 

h Alaska daylight time. The probability of seals basking 

continued to increase with date through mid June when 

our observations ceased. The probability also increased 

during the 20 days preceding snow melt as estimated 

from the MDSDA data. There was little change in the 

probability of basking for the next 10 days and then an 

increasing probability for at least the next 20 days. We 

observed a rapid increase in the probability of basking 

as air temperature rose from -50

C to 00C and a slower 

increasing probability with further warming. The 

probability of basking changed little with wind speeds 

under 3.5 m/s but declined at higher wind speeds. 

Our best model included time of day, date, days 

before snow melt, air temperature, and wind speed 

(Table 6). Applied to the Alaska Department of Fish 

and Game surveys in the Prudhoe Bay region (Frost et 

al. 1999, 2002), the model (Figure 20) indicated that 

the mean probability that seals were visible during the 

surveys ranged from 0.39 (SD = 0.16) in 1999 to 0.76 

(SD = 0.07) in 1985. 

For most years, the error associated with the 

estimated probability that seals were visible during the 

Alaska Department of Fish and Game surveys was large, 

refl ecting the diversity of covariates that infl uence seal 

behavior. The probability of seals being visible was 

more strongly infl uenced by some covariates than by 

others (Figure 21). The surveys mostly were restricted 

to the hours between 1100 and 1700 h AST, and time of 

day appeared to have had little infl uence on probability 

of seals being visible. Survey dates were more variable 

from year to year, and our model indicated that twice 

as many seals were available to be counted on the days 

surveyed in the 1980s than in the 1990s. Late snowmelts 

reduced the probability of seals basking during the 

surveys in 1986 (X ̄= 0.24, SD = 0 .03) and in 1999 

(X ̄= 0.27, SD = 0.05).

Wind speed had minimal effect on the probability 

of seals basking during the surveys (Figure 21), most 

likely because surveys were not conducted at higher 

wind speeds (Frost et al. 1999, 2002). Air temperature, 

however, did not infl uence when surveys were 

conducted, and variability in air temperature increased 

variability in the proportion of seals visible.
13

Aerial surveys conducted by LGL Limited in 1999–

2002 encompassed our study area (Moulton et al. 2005). 

The densities they reported in each of those years were 

positively correlated (r2

 = 0.7546, p = 0.13) with the 

proportion of our tagged seals that were basking (Table 

7). The overall densities reported by LGL Limited, 

however, were substantially lower than those reported 

for the same area by the Alaska Department of Fish and 

Game (Table 8).

Discussion

Snow cover is important to ringed seals, especially 

the young, as it insulates them from cold (Smith and 

Stirling 1975; Kelly and Quakenbush 1990) and helps 

protect them from predators (Lydersen and Smith 1989; 

Smith and Lydersen 1991). In winter and early spring, 

ringed seals rest when out of the water in subnivean 

lairs. In late spring, they begin to bask on top of the 

ice after the lairs collapse or after excavating their 

way out of the lairs. Thus, snow conditions infl uence 

when ringed seals are available to be counted in visual 

surveys. Our hypothesis that the transition from resting 

in lairs to resting in the open would be completed 

when the snow became saturated with melt water was 

supported by the observation that the seals abandoned 

the last lairs within 4–6 days of the snow becoming 

isothermal. The liquid moisture content of the snow 

as measured by microwave emissions was even more 

strongly correlated with lair abandonment.

We attempted to test the hypothesis that the timing 

of saturation and of lair abandonment varies regionally 

by enlisting students and other observers to monitor 

emergence from lairs near Kaktovik and Barrow. We 

were unsuccessful, however, in recruiting help for the 

Barrow effort, and the Kaktovik site recommended 

by local hunters had insuffi cient numbers of seals for 

quantifying emergence. Other investigators, however, 

have reported regional variation in snow melt dates 

with the melt dates being consistently earlier near 

Kaktovik than near Barrow (Stone et al. 2002). Given 

the close relationship between snow melt dates and 

lair abandonment, the regional variation in snow melts 

likely translates into regional variation in when seals are 

available to be counted. Furthermore, along the northern 

coast of Alaska, snow melt dates have advanced over 

the past three decades (Stone et al. 2002), with the 

earlier melts corresponding to an increased variability 

of May air temperatures since 1990 (Arctic Climate 

Impact Assessment 2004). The combination of regional 

and temporal variation in snow melts indicates that 

the timing of ringed seal surveys should take snow 

conditions into account.

The timing of aerial surveys, however, typically has 

been based on calendar date and general assessments of 

weather conditions. Surveys in the Alaskan Beaufort Sea 

have been conducted after the onset of lair abandonment 

but not necessarily before abandonment was completed. 

Between the onset and completion of lair abandonment, 

less than 20% of the tagged seals were basking and 

available to be counted. Once abandonment was 

complete, an average of 75% of the tagged seals were 

visible basking. As hypothesized, the variance in the 

proportion of seals visible was substantially less after 

abandonment. The low numbers and high variance in 

the proportion visible before all seals have emerged 

from their lairs argues for commencing surveys only 

after emergence is completed. Comparisons of densities 

estimated from previous surveys that took place during 

the transition period when variances would have been 

high may be misleading. For example, Frost et al. (1997) 

reported that in 1985–1987, ringed seal densities in the 

industrial zone around Prudhoe Bay were similar to 

densities in surrounding areas, while in 1996, the density 

in the industrial zone was half that of the surrounding 

areas. With the addition of three more years of surveys 

(1997–1999), they reported observing 31% fewer ringed 

seals on the shore fast ice of the Alaskan Beaufort Sea 

in the 1990s than in the 1980s (Frost et al. 2002) with 

especially pronounced decreases in the Prudhoe Bay 

area (Frost et al. 1999b). They noted, however, that 

the reduction in numbers of seals observed might be 

a function of a shift to earlier survey periods in the 

1990s rather than of a population decline. Indeed, our 

modeling results suggested that the change in timing of 

the surveys could account for a 50% reduction in the 

number of seals available to be counted. Survey timing 

relative to snow melt was an especially strong infl uence 

and negatively biased density estimates in 1986 and 

1999.

Determining the effect of snow melt on the 

probability of seals basking requires proxy data 

from which past snow melts can be reliably inferred. 

Our preliminary results in 2000 (Kelly et al. 2000) 

encouraged us to hypothesize that active microwave 

(Ku-band) backscatter data would indicate optimal 

survey times by consistently identifying the point at 

which snow on the sea ice becomes saturated with 

melt water. An abrupt increase in the diurnal difference 

in backscatter amplitude did correspond to wet snow 

associated with snow melt and lair abandonment in 

2000, 2001, and 2003. The backscatter signal also 

indicated wet snow associated with a rain event 17 days 

prior to snow melt and lair abandonment in 2002. We 
14

conclude that backscatter is a useful indicator of snow 

conditions in seal habitat, but only when interpreted with 

appropriate meteorological information. 

We do not have a historical record of backscatter 

data, and we examined proxy data that might allow us to 

infer snow conditions on the ice during previous ringed 

seal surveys. We hypothesized that the completion of lair 

abandonment and, hence, optimal survey times could 

be determined using on-shore snow temperatures as a 

proxy for snow melt on the sea ice. Snow temperature 

on the tundra at West Dock was a good predictor of 

snow temperature on the sea ice in our study area, but 

the tundra record was limited to the years 1999–2001. 

Changes in discharge rates for the Kuparuk River did 

not correlate well with lair abandonment in our study 

area. The river discharge likely responds more to the 

conditions inland than to near shore conditions.

Snow melt determined from passive microwave 

emissions proved to be a good indicator of lair 

abandonment, especially when analyzed with the 

MDSDA algorithm (Belchansky et al. 2004). The 

relationship between snow melt and lair abandonment 

likely refl ects the importance of snow integrity to 

maintaining subnivean lairs, which conceal ringed 

seals. Other studies have ignored snow melt, implicitly 

assuming that the effect can be accounted for by date. 

We observed substantial interannual variation in the 

dates of snow melt, however, and a general trend toward 

later snow melts consistent with the climate record for 

the arctic coast of Alaska (Stone et al. 2002). Passive 

microwave data are especially attractive as proxy data as 

they are available from 1979 forward and for the entire 

Arctic Ocean. Future visual surveys of ringed seals 

should be conducted only after substantial snow melt as 

indicated by passive microwave data. 

After the snow melt, the probability of ringed 

seals being visible was most strongly infl uenced by 

time of day, wind speed, and air temperature. The time 

of day effect was more pronounced in the analysis of 

our telemetric studies than in analyses based on counts 

collected in aerial surveys. Burns and Harbo (1972) 

conducted surveys in Alaska between 1000 and 1600 

hours (presumably Alaska Daylight Time) “based 

on previous general observations” of when the most 

seals were expected to be visible. Smith (1973a,b) 

made repeated counts throughout the day to determine 

when the most seals were visible resting on the ice. 

He concluded that, for a site in the eastern Beaufort 

Sea, the optimal survey times were between 0900 and 

1500 hours (again, presumably local time). The Alaska 

Department of Fish and Game adopted a protocol that 

called for conducting ringed seal aerial surveys between 

1000 and 1600 hours local time (Burns and Kelly 1982, 

Frost et al. 1988, 1998). An analysis of the effects of 

covariates on ringed seal density in the Beaufort Sea 

during surveys in 1996–1998 suggested a nearly threefold linear decrease in densities between 1000 and 

1800 hours local time (Frost et al. 1999a). Moulton et 

al. (2002) reported highly signifi cant declines in the 

densities of seals observed between 0900 h and 1700 h 

(apparently local time) in 1997 and 1998 but a highly 

signifi cant midday peak in densities in 1999. When 

considered in a multivariate model, however, they found 

no signifi cant effect of time of day on seal densities. 

Frost et al. (2004), on the other hand, reported that in 

1996, 1997, and 1998, densities increased signifi cantly 

between 1000 h and 1200 or 1300 h (Alaska daylight 

time). In 1999, however, they observed a signifi cant 

increase in densities throughout the survey hours with 

the highest numbers after 1700 h. As with Moulton et 

al.’s (2002) model, Frost et al. (2004) found that time 

of day did not remain signifi cant in their multivariate 

model. Their samples provided little contrast in times 

of day, however, since “most surveys occurred between 

1100 and 1700 h.” 

In contrast, our monitoring of tagged seals showed 

a strong relationship between time of day and the 

probability that tagged seals were visible. Less than 

10% of tagged seals typically were visible at 0400 h and 

70% were visible on average at 1600 h. The inconsistent 

time-of-day effects reported by Moulton et al. (2002) 

and Frost et al. (2004) may refl ect the lack of contrast 

in the timing of their surveys. Our samples included all 

hours of day and night which better anchored the model. 

The analysis of time-of-day effects based on aerial 

surveys may also be confounded by visibility effects 

associated with changing sun angles. 

The probability of tagged ringed seals being visible 

decreased with increasing wind speed. Both Moulton et 

al. (2002) and Frost et al. (2004) reported inconsistent 

results in their analyses of the effects of wind speed 

on ringed seal densities. Their surveys necessarily 

were conducted in a narrow range of wind speeds, 

however, and the lack of contrast may have masked 

real effects. They attempted to minimize the infl uence 

of wind speed by limiting surveys to wind speeds of 

10 m/sec or less. Our modeling of ringed seal behavior 

indicated, however, that at wind speeds of 10 cm/sec the 

probability of seals being visible was less than half the 

probability under calm conditions.

For air temperatures between -10 and 8oC, the 

probability of tagged ringed seals being visible increased 
15

with temperature. Air temperatures vary irregularly 

during aerial survey periods, and the overall effect is to 

increase variance in survey counts. 

Frost et al. (2002), Moulton et al. (2002), and others 

modeled the infl uence of covariates on the number 

of seals observed in aerial surveys. We modeled the 

infl uence of covariates directly on the probability of 

tagged seals being visible and applied the model to each 

of the surveys conducted by Frost et al. (2002) in the 

Beaufort Sea in the 1980s and 1990s. The re-analysis 

indicated that the portion of seals that were visible 

during those surveys varied from less than 0.40 to more 

than 0.75. We had hoped that our re-analysis would 

permit us to correct the density estimates taking into 

account the probabilities of seals being visible, but such 

corrections seemed imprudent given the large variances 

around the estimated probabilities.

Moulton et al. (2002) corrected their counts of 

seals in our study area using a Poisson regression 

model that included date, ice deformation, cloud cover, 

distance from the ice edge, water depth, percentage of 

ice covered by melt water, and air temperature. The 

proportion of tagged seals visible varied from 0.08 to 

1.00 during the period in which those surveys were 

conducted (Table 6), and the resulting density estimates 

were positively correlated (r2

 = 0.75) with our estimates 

of the proportion of seals visible. Their results likely 

would be strengthened by incorporating date of snow 

melt and correcting for seals concealed in subnivean 

lairs. It is not clear why their density estimates were 

consistently lower than the Alaska Department of Fish 

and Game estimates (Frost et al. 2004). The differences 

may refl ect differences in covariate models and/or in 

observer abilities. 

Density estimates based on counts of molting seals 

are strongly infl uenced by the timing of the molt, which 

may vary among demographic classes of seals. The 

probability of ringed seals basking increased with date 

through our latest observations in mid June (Figure 19). 

Thus, our study (and the Alaska Department of Fish and 

Game and LGL Limited aerial surveys) were conducted 

prior to the peak molting period. It may also be that the 

number of ringed seals available to be counted varies 

seasonally among age classes as is the case in harbor 

seals (Daniel et al. 1999, Jemison and Kelly 2001). Our 

sample, however, did not include juvenile ringed seals. 

If, as is the case for harbor seals (Kelly 1981), juvenile 

ringed seals molt prior to the adults, the “peak” counts in 

ringed seal surveys may be indicative only of the adult 

segment of the population.

The optimal survey window for ringed seals is the 

period after lair abandonment is complete but before 

formation of melt ponds interferes with sightability. 

Both the onset and duration of snow melt vary widely 

from year-to-year (Kelly 2005), however, as do 

other factors, such as ice deformation, that infl uence 

sightability. While melt ponds and ice deformation 

appear to substantially decrease sightability, their 

infl uence has yet to be adequately quantifi ed. The 

high variability in the onset and duration of the 

optimal survey window–coupled with the array of 

variables infl uencing the probability of seals being 

visible–suggests that inter annual comparisons of 

observed ringed seal densities need to be corrected 

using simultaneous telemetrically derived estimates of 

the proportion of seals visible similar to the corrections 

made by Withrow and Loughlin (1995, 1996) for harbor 

seals surveys.

The small home ranges maintained by ringed seals 

in winter and early spring have implications for the 

potential impact of human on-ice activities and for the 

utility of aerial surveys for counting seals. The three 

seals that we recaptured in successive breeding seasons 

all came back to small home ranges within 2 km of the 

sites used the previous year. Furthermore, in each case 

the breeding home ranges used in subsequent years 

were similar in size, number of lairs, and the spatial 

arrangement of those lairs. If that site fi delity is typical 

of ringed seals, it suggests that there may be fi tness 

costs associated with displacement of ringed seals by the 

activities of humans or predators. Fidelity to breeding 

sites by adult seals also would beg the question of 

philopatry. If ringed seals, in fact, are returning to their 

own natal sites to breed, then there likely is substantial 

structuring of their populations and local impacts would 

be of greater consequence than if genes are exchanged 

over broad areas. We have begun investigating stock 

structure with movement data and analysis of micro 

satellite DNA and mtDNA extracted from skin samples 

of breeding seals. 

Acknowledgements

This project was funded by the Coastal Marine 

Institute (Minerals Management Service and the 

University of Alaska Fairbanks), the National Marine 

Mammal Laboratory (National Marine Fisheries 

Service), and the Natural Resources Fund (University 

of Alaska). Student involvement in the project was 

supported by grants from the National Science 

Foundation (Research Experience for Undergraduates 

program). 

BP Alaska, ARCO Alaska, Inc. and Phillips 
16

Alaska provided extensive logistic support in the 

form of snowmachines, generators, fuel, room and 

board, air transportation of our fi eld crew, and ground 

transportation of our fi eld gear. Bill Streever, Wilson 

Cullor, Mike Joyce, Linda Allen, Michael Stewman, and 

the crew at the Seawater Treatment Plant were extremely 

helpful with logistics in the fi eld. 

Undergraduate students–Ned Conway (Davidson 

College), Shannon Crowley (University of Alaska 

Southeast), Raychelle Daniel (UAS), Kristine Faloon 

(Boston University), Kyndall Hildebrandt (University 

of Alaska Fairbanks), Rachel Ingram (UAS), Amy 

Kamarainen (Michigan State University), Dusty 

McDonald (UAF), Phillipe Singer (UAS), and Daniella 

Swenton (University of Vermont)–put in many long 

hours in the fi eld. Graduate students–Karen Blejwas 

(University of California Berkeley), Zak Hoyt (UAF), 

Julie Nielsen (UAF), Michael Simpkins (UAF), Johanna 

Vollenweider (UAF), and Jamie Womble (UAF)–also 

assisted the project in the fi eld. John Bengston and Peter 

Boveng of the National Marine Mammal Laboratory, 

Lori Quakenbush (UAF), Brian Taras (UAF), and Doug 

Wartzok (University of Missouri) provided expertise 

and assistance in the fi eld. Additional help capturing and 

tagging seals was provided by Brita Bishop, Michelle 

Cronin, Jesse Dutton, Lara Dzinich, Rowenna Flinn, 

Scott Foster, Eran Hood, David O’Leary, Alan Steffert, 

Carrie Talus, and Sam Thalman. Matthew Sturm 

(Cold Regions Research and Engineering Laboratory) 

helped us establish the meteorological station. The U.S. 

Fish and Wildlife Service; U.S. Geological Survey, 

Biological Resources Division; the University of Alaska, 

Institute of Arctic Biology and the Alaska Cooperative 

Fish and Wildlife Research Unit loaned us telemetry 

equipment. Dave Neel (Arctic Wilderness Lodge) and 

Harding, O. R., B. P. Kelly, L. T. Quakenbush, and B. D. 

Taras. 2001. Importance of snow for ringed seal 

pups. In Abstract Book. Aquatic Sciences Meeting. 

American Society of Limnology and Oceanography. February 12-16, 2001. p. 64.

Kelly, B. P., L. T. Quakenbush, B. D. Taras, and O. 

Harding. 2001. Snow conditions predict optimal 

survey periods for ringed seals. 14th Biennial 

Conference on the Biology of Marine Mammals. 

28 November - 3 December 2001. Vancouver, 

British Columbia, Canada. Abstract.

Kelly, B. P. 2001. Climate change and ice breeding 

pinnipeds. Pages 43-55 in G.-R. Walther, C. A. 

Burga and P. J. Edwards, editors. “Fingerprints” 

of climate change: adapted behaviour and shifting species’ ranges. Kluwer Academic/Plenum 

Publishers, New York and London.

Kelly, B. P., O. R. Harding, and M. Kunnasranta. 2003. 

Timing and re-interpretation of ringed seal surveys. p. 32-37. In University of Alaska Coastal 

Marine Institute Annual Report No. 10. OCS 

Study MMS 2003, University of Alaska Fairbanks 

and USDOI, MMS, Alaska OCS Region.

Kelly, B. P. 2003. Early onset of snow melts and increased vulnerability of ice-inhabiting seals. Arctic 

Science Conference. American Association for 

the Advancement of Science. Fairbanks, Alaska. 

(Abstract).

Kelly, B. P. 2003. Ringed seals and changing snow 

cover on arctic sea ice. SEARCH Open Science 

Meeting. 27-30 October 2003. Seattle, Washington. The Study of Environmental Arctic Change. 

(Abstract).

Conway, J. E., B. P. Kelly, and E. Hood. 2004. Predicting lair abandonment by ringed seals based on 

physical properties of the snowpack. 34th International Arctic Workshop. March 10-13, 2004. 

Institute of Arctic and Alpine Research, Univ. of 

Colorado, Boulder. (Abstract).

Kelly, B. P., and S. Nghiem. 2004. Changes in arctic 

snow, seals, and backscatter. IEEE International 

Geoscience and Remote Sensing Symposium. 

Anchorage, Alaska. 20-24 September 2004. (Abstract).

Kelly, B. P. 2005. Correction factor for ringed seal surveys in northern Alaska. Final Report, OCS Study 

MMS 2005-006. Coastal Marine Institute, School 

of Fisheries and Ocean Sciences, University of 

Alaska Fairbanks. Fairbanks, Alaska.

Reports and publications resulting from this 

project: 

Sandy Hamilton (Arctic Air Alaska) safely piloted our 

survey fl ights. 

Kelly, B. P., L. T. Quakenbush, and B. D. Taras. 1999. 

Correction factor for ringed seal surveys in Northern Alaska. Annual Report for 1999 to Coastal 

Marine Institute, University of Alaska Fairbanks. 

Pp. 51-64.

Kelly, B. P., L. Quakenbush, and B. Taras. 2000. Correction factor for ringed seal surveys in northern 

Alaska. In V. Alexander (ed.) University of Alaska 

Coastal Marine Institute. Annual Report, Federal 

Fiscal Year 2000. University of Alaska Fairbanks.
17

VENUE DATE PRESENTER 

Univ. Alaska Southeast, Juneau Feb. 1999 O. Harding 

Coastal Marine Institute, Fairbanks Feb. 1999 B. Kelly 

Coastal Marine Institute, Fairbanks Mar. 2000 B. Kelly 

Research Experiences for Undergrad’s, 

Univ. Alaska Southeast, Juneau 

Jul. 2000 O. Harding 

Am. Soc. Limnology & Oceanography, 

Albuquerque, N.M. 

Feb. 2001 O. Harding 

Coastal Marine Institute, Fairbanks Feb. 2001 L. Quakenbush 

Biomonitoring/Global Change, Ascona, 

Switzerland 

Feb. 2001 B. Kelly 

MMS Information Transfer, Anchorage Apr. 2001 B. Kelly 

U.S.-Russia Marine Mammal Project, Santa 

Cruz, CA 

Apr. 2001 B. Kelly 

Mt. Film Festival, Telluride, CO May 2001 B. Kelly 

Biennial Conf. Biol. Marine Mammals, 

Vancouver, B.C. 

Dec. 2001 B. Kelly 

Coastal Marine Institute, Fairbanks Feb. 2003 B. Kelly 

MMS Information Transfer, Anchorage Apr. 2003 O. Harding 

Natl. Academy of Science, Anchorage Mar. 2003 B. Kelly 

MMS Scientific Committee, Anchorage Apr. 2003 B. Kelly 

Research Experiences for Undergrads, Univ. 

Alaska Southeast, Juneau 

Aug. 2003 J. Conway 

Research Experiences for Undergrads, Univ. 

Alaska Southeast, Juneau 

Aug. 2003 K. Hildebrandt 

54th Arctic Science Conference, Fairbanks, AK Sept. 2003 O. Harding 

SEARCH Open Science Meeting, Seattle, WA Oct. 2003 B. Kelly 

34th International Arctic Workshop, Boulder, CO Mar. 2004 J. Conway 

IEEE Internatl. Geosci. & Remote Sensing 

Symposium, Anchorage, AK 

Sept. 2004 S. Nghiem 

Study Products

 Presentations on this project have been delivered in several venues.
18

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298. 
22

70o29’N

148o41’W

Figure 1. The study site on the shorefast sea ice of the Beaufort Sea between Prudhoe Bay, Reindeer Island, 

and the man-made Northstar Island.

Figure 2. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the 

trained dogs in 1999. Also shown are the minimum convex polygons delineating the home ranges of 8 ringed 

seals based on on-ice resting sites.
23

Figure 3. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the 

trained dogs in 2000. Also shown are the minimum convex polygons delineating the home ranges of 8 ringed 

seals based on on-ice resting sites. The home ranges of 2 radio-tagged seals were not determined.

Figure 4. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the 

trained dogs in 2001. Also shown are the minimum convex polygons delineating the home ranges of 14 ringed 

seals based on on-ice resting sites. 
24

Figure 5. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the 

trained dogs in 2002. Also shown are the minimum convex polygons delineating the home ranges of 14 ringed 

seals based on on-ice resting sites. The home ranges of 2 radio-tagged seals were not determined. 

Figure 6. Locations of the monitoring camp and the subnivean breathing holes and lairs located by the 

trained dogs in 2003. Also shown are the minimum convex polygons delineating the home ranges of 14 ringed 

seals based on on-ice resting sites. The home range of 1 radio-tagged seal was not determined. 
25

0

1

2

3

4

5

6

7

8

1-5 6-10 11-15 16-20 >20

Number of times seal was located

Number of holes used

Figure 7. Duration of net sets for 75 captures of ringed seals in the Beaufort Sea in 1999, 2000, 2001, 2002, 

and 2003.

Figure 8. Mean (and SE) of the number of holes used by radio tagged ringed seals as a function of the number of times haulout locations were determined. The asymptote is 4.5 holes per seal.

0

10

20

30

40

50

60

70

80

Time to capture (hours)

 Cummulative frequency

24 48 72 96 120
26

0%

80%60%40%20%

100%

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Home range area (km2)

Cumulative frequency

Figure 9. Cumulative percent frequency of home range size (minimum convex polygon) for 58 ringed seals radio tracked between 1999 and 2003.
27

Figure 10. Minimum convex polygons delineating the home ranges of 3 ringed seals each tracked during two successive breeding seasons. IM was an adult male tracked in 2001 and 2002. SH was an adult female tracked in 2002 and 2003. WW was an adult male tracked in 2002 and 2003.
28

5%0%

30%25%20%15%10%

2 4 6 8 10 12 14 16 18 20 30 40 50 60 70

Duration (hours)

Percent frequency

Haulout bouts in lairs(n = 519)Haulout bouts out of lairs (basking)(n = 833)

Figure 11. Duration (hrs) of haulout bouts in subnivean lairs and out of lairs (basking).
298%

6%4%2%0%

20%18%16%14%12%10%

2 4 6 8 10 12 14 16 18 20 30 40 50 60 70 80 90100110120

Interval (hours)

Percent frequency

Haulout bouts in lairs(n = 429)Haulout bouts out of lairs (basking)(n = 774)

Fi

Figure 12. Interval (hrs) between haulout bouts in and out of subnivean lairs.
30

CH99 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

CH99 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=26

r=0.272

P=0.146 

n=86

r=0.701

P<0.001 

MA99 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

MA99 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=50

r=0.359

P=0.002 

n=96

r=0.513

P<0.001 

OR99 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

OR99 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=23

r=0.46

P=0.007 

n=105

r=0.525

P<0.001 

RI99 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

RI99 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=78

r=0.503

P<0.001 

n=136

r=0.478

P<0.001 

SM99 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=72

r=0.239

P=0.016 

SM99 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=24

r=0.813

P<0.001 

SP99 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SP99 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=33

r=0.424

P=0.002 

n=0

Figure 13. Diel haulout patterns of radio tagged ringed seals while using subnivean lairs and while basking. 

Each graph shows the percentage of monitoring periods when the seal was out of the water for each hour 

of the day. Thin radial lines indicate the mean hour of haulout, and the perpendicular bar shows the 95% 

confi dence limits around the mean. The mean vector length (r) is shown along with the probability that the 

distribution of hourly haulout times is different from a random distribution.
31

SW99 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SW99 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=61

r=0.054

P=0.838 

n=155

r=0.703

P<0.001 

VR99 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

VR99 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=86

r=0.616

P<0.001

n=78

r=0.359

P<0.001 
32

CC00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

CC00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=38

r=0.526

P<0.001 

n=77

r=0.411

P<0.001 

CS00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

CS00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=203

r=0.537

P<0.001 

n=67

r=0.33

P<0.001 

EL00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

EL00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=178

r=0.653

P<0.001 

n=0

IS00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

IS00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=12

r=0.694

P=0.002 

n=19

r=0.245

P=0.323 

LA00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

LA00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=39

r=0.399

P=0.002 

n=0

LM00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

LM00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=218

r=0.138

P=0.016 

n=7

r=0.868

P=0.002 
33

OC00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

OC00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=65

r=0.511

P<0.001 

n=62

r=0.315

P=0.002 

PU00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

PU00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=82

r=0.338

P<0.001 

n=100

r=0.269

P<0.001 

RA00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

RA00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=75

r=0.429

P<0.001 

n=26

r=0.818

P<0.001 

SL00 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SL00 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=2

r=0.966

P=0.164 

n=71

r=0.262

P=0.008 
34

CL01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

CL01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=351

r=0.333

P<0.001 

n=24

HC01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

HC01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=266

r=0.241

P<0.001 

n=33

r=0.639

P<0.001 

IM01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

IM01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=0

n=112

r=0.307

P<0.001 

IS01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

IS01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=194

r=0.481

P<0.001 

n=5

r=0.933

P=0.005 

JB01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

JB01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=198

r=0.583

P<0.001 

n=73

r=0.317

P<0.001 

KC01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

KC01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=229

r=0.444

P<0.001 

n=52

r=0.485

P<0.001 
35

KK01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

KK01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=188

r=0.293

P<0.001 

n=168

r=0.156

P=0.019 

LH01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

LH01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=11

r=0.691

P=0.003

n=34

r=0.154

P=449 

MH01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

MH01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=139

r=0.237

P<0.001

n=56

r=0.268

P=0.018

MK01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

MK01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=189

r=0.282

P<0.001 

n=152

r=0.211

P=0.001 

PO01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

PO01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=144

r=0.5

P<0.001 

n=163

r=0.042

P=0.754 

SI01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SI01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=195

r=0.323

P<0.001 

n=38

r=0.05

P=0.909 
36

TS01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

TS01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=109

r=0.565

P<0.001 

n=113

r=0.622

P<0.001 

WC01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

WC01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=192

r=0.318

P<0.001 

n=25

r=0.305

P=0.097 
37

BC02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

BC02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=395

r=0.558

P<0.001 

n=16

r=0.415

P=0.062 

EF02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

EF02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=0

n=0

EV02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

EV02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=260

r=0.558

P<0.001 

n=24

r=0.15

P=0.586 

HM02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

HM02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=312

r=0.521

P<0.001 

n=34

r=0.377

P=0.007 

IM01_recapture in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

IM01_recapture basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=278

r=0.47

P<0.001 

n=28

r=0.34

P=0.038 

JN02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

JN02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=218

r=0.607

P<0.001

n=42

r=0.319

P=0.013 
38

JU02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

JU02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=285

r=0.475

P<0.001 

n=0

KK02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

KK02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=271

r=0.608

P<0.001 

n=0

MW02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

MW02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=210

r=0.424

P<0.001 

n=0

NA02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

NA02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=330

r=0.603

P<0.001 

n=17

r=0.572

P=0.003 

ND02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

ND02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=202

r=0.609

P<0.001 

n=28

r=0.36

P=0.025 

RN02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

RN02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=251

r=0.498

P<0.001 

n=0
39

RO02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

RO02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=124

r=0.62

P<0.001 

n=49

r=0.065

P=0.816 

SH02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SH02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=272

r=0.614

P<0.001 

n=19

r=0.2

P=0.474 

TR02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

TR02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=209

r=0.611

P<0.001 

n=13

r=0.361

P=0.186 

WW02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

WW02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=386

r=0.641

P<0.001 

n=11

r=0.691

P=0.003 
40

FI03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

FI03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=224

r=0.282

P<0.001 

n=51

r=0.371

P<0.001 

GR03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

GR03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=216

r=0.46

P<0.001 

n=17

r=0.332

P=0.155 

JW03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

JW03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=272

r=0.5

P<0.001 

n=60

r=0.528

P<0.001 

KA03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

KA03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=242

r=0.322

P<0.001 

n=132

r=0.308

P<0.001 

MO03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

MO03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=360

r=0.317

P<0.001 

n=21

r=0.711

P<0.001 

PF03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

PF03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=266

r=0.492

P<0.001 

n=51

r=0.336

P=0.003 
41

SE03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SE03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=312

r=0.346

P<0.001 

n=66

r=0.243

P=0.02 

SH02_recapture in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SH02_recapture basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=456

r=0.32

P<0.001 

n=95

r=0.211

P=0.015 

SQ03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SQ03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=273

r=0.476

P<0.001 

n=223

r=0.137

P=0.015 

SU03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SU03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=328

r=0.402

P<0.001 

n=32

r=0.12

P=0.636 

TA03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

TA03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=281

r=0.519

P<0.001 

n=52

r=0.109

P=0.537 

TE03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

TE03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=280

r=0.389

P<0.001 

n=0
42

VI03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

VI03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=60

r=0.542

P<0.001 

n=117

r=0.215

P=0.004 

WW02_recapture in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

WW02_recapture basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=48

r=0.282

P=0.021 

n=86

r=0.684

P<0.001 

YK03 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

YK03 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=209

r=0.471

P<0.001 

n=154

r=0.192

P=0.003 
43

RECAPTURES

IM01 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=56

r=0.268

P=0.018

IM01 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=0

IM01_recapture in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

IM01_recapture basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=28

r=0.34

P=0.038 

n=278

r=0.47

P<0.001 

SH02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SH02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=19

r=0.2

P=0.474 

n=272

r=0.614

P<0.001 

SH02_recapture in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

SH02_recapture basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=95

r=0.211

P=0.015 

n=456

r=0.32

P<0.001 
44

WW02 in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=11

r=0.691

P=0.003 

WW02 basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=386

r=0.641

P<0.001 

WW02_recapture in lair

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=48

r=0.282

P=0.021 

WW02_recapture basking

100 100

100

100

80 80

80

80

60 60

60

60

40 40

40

40

20 20

20

20

00:00

06:00

12:00

18:00

n=86

r=0.684

P<0.001 
45

0

5

10

15

20

25

30

27-Apr 7-May 17-May 27-May 6-Jun 16-Jun

Number of seals basking & visible

0%

25%

50%

75%

100%

Percent tagged seals visible & basking

Number of basking seals

% of tagged seals basking

1999

0

5

10

15

20

25

30

27-Apr 7-May 17-May 27-May 6-Jun 16-Jun

Number of seals basking & visible

0%

25%

50%

75%

100%

Percent of tagged seals visible & basking

Number of basking seals

% of tagged seals basking

2000

Figures 14 a through e. Total number of seals visible in the study area (dashed trend lines) and the percentage 

of tagged seals visible in the study area (solid trend lines) by date in 1999, 2000, 2001, 2002, and 2003.

a

b
46

0

5

10

15

20

25

30

27-Apr 7-May 17-May 27-May 6-Jun 16-Jun

Number of seals basking & visible

0%

25%

50%

75%

100%

Percent of tagged seals visible & basking

Number of basking seals

% of tagged seals basking

2001

0

5

10

15

20

25

30

27-Apr 7-May 17-May 27-May 6-Jun 16-Jun

Number of seals basking & visible

0%

25%

50%

75%

100%

Percent of tagged seals visible & basking

Number of basking seals

% of tagged seals basking

2002

c

d
47

0

5

10

15

20

25

30

27-Apr 7-May 17-May 27-May 6-Jun 16-Jun

Number of seals basking & visible

0%

25%

50%

75%

100%

Percent of tagged seals visible & basking

Number of basking seals

% of tagged seals basking

2003

e
48

0%

20%

40%

60%

80%

100%

4-

May

5-

May

6-

May

7-

May

8-

May

9-

May

10-

May

11-

May

12-

May

13-

May

14-

May

15-

May

16-

May

17-

May

18-

May

19-

May

20-

May

21-

May

22-

May

23-

May

24-

May

25-

May

26-

May

27-

May

28-

May

29-

May

30-

May

31-

May

1-

Jun

2-

Jun

3-

Jun

4-

Jun

5-

Jun

6-

Jun

7-

Jun

8-

Jun

9-

Jun

10-

Jun

0%

20%

40%

60%

80%

100%

16-May 23-May 30-May 6-Jun

0%

20%

40%

60%

80%

100%

28-Apr 5-May 12-May 19-May 26-May 2-Jun 9-Jun 16-Jun

0%

20%

40%

60%

80%

100%

3-May 10-May 17-May 24-May 31-May 7-Jun 14-Jun

0%

20%

40%

60%

80%

100%

30-Apr 7-May 14-May 21-May 28-May 4-Jun 11-Jun 18-Jun

1999

2000

2001

2002

2003

Figure 15. Percentages of radio tagged seals in the water ( ) , in subnivean lairs ( ) and basking on the ice ( ) in 1999, 2000, 2001, 2002, and 2003.
49

-40

-30

-20

-10

0

10

4/3 4/10 4/17 4/24 5/1 5/8 5/15 5/22 5/29 6/5 6/12

Date

-40

-30

-20

-10

0

10

4/3 4/10 4/17 4/24 5/1 5/8 5/15 5/22 5/29 6/5 6/12

-40

-30

-20

-10

0

10

Temperature (oC)

4/3 4/10 4/17 4/24 5/1 5/8 5/15 5/22 5/29 6/5 6/12

-40

-30

-20

-10

0

10

4/3 4/10 4/17 4/24 5/1 5/8 5/15 5/22 5/29 6/5 6/12

-40

-30

-20

-10

0

10

4/3 4/10 4/17 4/24 5/1 5/8 5/15 5/22 5/29 6/5 6/12

1999

2000

2001

2002

2003

Figure 16. Air temperature (thin line) and snow temperature at the ice surface (thick line) in April, May, and 

June 1999–2003.
50

-5

0

5

5/15 5/22 5/29 6/5 6/12

Date

-5

0

5

5/15 5/22 5/29 6/5 6/12

-5

0

5

Temperature (oC)

5/15 5/22 5/29 6/5 6/12

-5

0

5

5/15 5/22 5/29 6/5 6/12

-5

0

5

5/15 5/22 5/29 6/5 6/12

1999

2000

2001

2002

2003

Figure 17. Close up of air temperature (thin line) and snow temperature at the ice surface (thick line) in mid 

May to mid June 1999, 2000, 2001, 2002, and 2003. Arrows indicate abrupt increases of snow temperatures at 

the ice surface from -2 to 0oC.
51

1999

-16

-14

-12

-10

-8

-6

-4

-2

0

5-May 12-May 19-May 26-May 2-Jun 9-Jun 16-Jun 23-Jun

Snow temperature (degrees C)

Snow over tundra

Snow over sea ice

2000

-16

-14

-12

-10

-8

-6

-4

-2

0

4-May 11-May 18-May 25-May 1-Jun 8-Jun

Snow temperature (degrees C)

Snow over sea ice

Snow over tundra

2001

-16

-14

-12

-10

-8

-6

-4

-2

0

26-Apr 3-May 10-May 17-May 24-May 31-May 7-Jun 14-Jun

Snow temperature (degrees C)

Snow over tundra

Snow over sea ice

Figure 18. Snow temperatures over the sea ice in our study area and on the tundra at the West Dock, Prudhoe Bay.
52

0

0.80.60.40.2

1

0 6 12 18 24

Time of day (Alaska daylight)

Proportion of tagged seals basking

0

0.80.60.40.2

1

26-May 31-May 5-Jun 10-Jun 15-Jun 20-Jun

Date

Proportion of tagged seals basking

0

0.80.60.40.2

1

-15 -10 -5 0 5 10 15

Air temperature (0C)

Proportion of tagged seals basking

0

0.80.60.40.2

1

-20 -10 0 10 20 30 40

Days from snow melt

Proportion of tagged seals basking

0

0.80.60.40.2

1

0 1 2 3 4 5 6 7 8 9 10

Wind speed (m/sec)

Proportion of tagged seals basking

Figure 19. Infl uence (based on generalized additive models) of time of day, day of year, days from snow melt (estimated from passive microwave emissions), air temperature, and wind speed on the probability that radio tagged seals were visible on the ice.
53

0.0

0.2

0.4

0.6

0.8

1.0

1985 1986 1987 1996 1997 1998 1999 85-87 96-99

Year

Mean propability of seals basking

Figure 20. Probabilities (means and standard deviations) of ringed seals basking on the ice during 7 survey 

years (Frost et al. 2002) based on combined effects of time of day, day of year, days from snow melt, air temperature, and wind speed.
54

Time of day

0

0.80.60.40.2

1

1985 1986 1987 1996 1997 1998 1999 85-87 96-99

Year

ility of seals basking Mean propab

Days from snowmelt

0

0.80.60.40.2

1

1985 1986 1987 1996 1997 1998 1999 85-87 96-99

Year

Mean propability of seals basking

Air temperature

0

0.80.60.40.2

1

1985 1986 1987 1996 1997 1998 1999 85-87 96-99

Year

ility of seals basking Mean propab

Day of year

0

0.80.60.40.2

1

1985 1986 1987 1996 1997 1998 1999 85-87 96-99

Year

ility of seals basking Mean propab

Wind speed

0

0.80.60.40.2

1

1985 1986 1987 1996 1997 1998 1999 85-87 96-99

Year

ility of seals basking Mean propab

Figure 21. Predicted probabilities (means and standard deviations) of seals basking during aerial surveys conducted by Frost et. al. (2002) based on univariate models.
55

Year Breathing holes Lairs Birth lairs N

1999 53% 47% 5% 120

2000 47% 53% 4% 154

2001 45% 55% 4% 108

2002 60% 40% 9% 119

2003 55% 45% 7% 110

Average 52% 48% 6% 611 (Total)

Table 1. Subnivean breathing holes and lairs located by trained dogs in 1999, 2000, 2001, 2002, and 2003.

Year Nets set Seal holes Captures

1999 24 19 9

2000 41 27 15

2001 31 23 16

2002 33 26 19

2003 51 29 16

Totals 180 124 75

Table 2. Numbers of nets set, seal holes in which nets were set, and seals captured in 1999, 2000, 2001, 2002, 

and 2003.
56

Capture Date Seal ID Sex Min. age First observed out of lair

6-May-99 SW99 F 6 21-May-99

6-May-99 RI99 M 6 30-May-99

13-May-99 MA99 F 7 28-May-99

14-May-99 VR99 F 6 29-May-99

15-May-99 SM99 M 7 3-June-99

21-May-99 SP99 F 6 –

23-May-99 CH99 F 5 3-Jun-99

24-May-99 OR99 F 5 2-Jun-99

25-Apr-00 CS00 M 7 17-May-00

27-Apr-00 LM00 M 6 10-Jun-00

30-Apr-00 CC00 M 6 24-May-00

1-May-00 SL00 M 7 –

2-May-00 RA00 F 6 31-May-00

3-May-00 EL00 M 5 < 3-May-00

6-May-00 OC00 F 4 3-Jun-00

6-May-00 PU00 M 7 5-Jun-00

17-May-00 LS00 F 5 >4-June-00

18-May-00 IS00 M 7 –

15-Apr-01 PO01 M 7 24-May-01

19-Apr-01 SI01 M 7 11-May-01

20-Apr-01 TS01 M 5 3-Jun-01

21-Apr-01 IS01 M 7 12-May-01

23-Apr-01 HC01 F 7 13-May-01

23-Apr-01 JB01 M 5 15-May-01

25-Apr-01 MH01 M 8 19-May-01

25-Apr-01 MK01 M 6 1-Jun-01

29-Apr-01 KK01 F 5 2-Jun-01

1-May-01 CL01 M 6 4-May-01

1-May-01 IM01 M 7 12-May-01

8-May-01 KC01 M 5 19-May-01

9-May-01 LH01 F 5 30-May-01

13-May-01 WC01 F 8 2-Jun-01

13-Apr-02 SH02 F 6 6-May-02

16-Apr-02 EV02 F 6 10-May-02

21-Apr-02 TR02 F 8 18-May-02

25-Apr-02 WW02 M 6 5-May-02

27-Apr-02 BC02 F 7 7-May-02

Table 3. Seals captured in 1999, 2000, 2001, 2002, and 2003. Minimal ages were determined from the number 

of annuli visible on the claws of the pectoral limbs.
57

Table 3, cont.

Capture Date Seal ID Sex Min. age First observed out of lair

27-Apr-02 JN02 M 5 12-May-02

28-Apr-02 HM02 F 7 9-May-02

3-May-02 NA02 F 8 8-May-02

3-May-02 RO02 F 8 6-Jun-02

7-May-02 RN02 M 7 8-May-02

7-May-02 IM01* M 8 12-May-02

8-May-02 ND02 F 7 16-May-02

11-May-02 JU02 F 9 12-May-02

14-May-02 MW02 F 5 15-May-02

14-May-02 EF02 M 6 -

15-May-02 KK02 M 7 16-May-02

20-May-02 JJ02 F 8 (not tagged)

20-May-02 MA02 F 7 (not tagged)

9-Apr-03 YK03 M 9 2-Jun-03

13-Apr-03 KA03 F 6 1-Jun-03

16-Apr-03 SU03 F 6 18-May-03

18-Apr-03 SQ03 F 9 18-May-03

22-Apr-03 VI03 F 7 28-May-03

23-Apr-03 SH02* F 8 14-May-03

23-Apr-03 SE03 M 6 21-May-03

25-Apr-03 JW03 M 8 18-May-03

26-Apr-03 TA03 F 7 16-May-03

26-Apr-03 TE03 M 6 28-Apr-03

29-Apr-03 PF03 F 7 13-May-03

5-May-03 MO03 M 8 20-May-03

7-May-03 WW02* M 7 14-May-03

10-May-03 GR03 F 7 18-May-03

10-May-03 FI03 F 6 31-May-03

*

Recapture
58

Table 4. Regression equations relating the proportions of tagged seals visible to the total number of seals visible in the study area in 1999, 2000, 2001, 2002, and 2003.

Year Regression r2 F Degrees freedom p

1999 7.3935x2

 + 2.3501x + 0.1298 0.63 10.16 2, 12 0.003

2000 26.883x2 + 8.2731x + 2.1142 0.72 34.12 2, 26 < 0.0001 

2001 15.563x2 + 29.801x + 0.5151 0.61 13.33 2, 17 0.0003

2002 -70.34x2

 + 76.52x - 5.9268 0.10 0.80 2, 14 0.47

2003 -23.958x2 + 38.163x + 0.6111 0.63 23.86 2, 28 < 0.0001 

Table 5. Dates lair abandonment was complete in 1999–2003 and possible predictors of abandonment including date the fi rst seal abandoned a lair, date the snow at the ice surface reached 0oC, breakup date for the 

Kuparuk River, date that the diurnal difference in backscatter amplitude exceeded 2 dB, snow melt date 

indicated by the PMSTA algorithm, and snow melt date indicated by the MDSDA algorithm.

Year Last lair 

abandoned

First lair 

abandoned

0o snow Kup. R. 

breakup

Delta

bcksct > 2 

Microw.

PMSTA

Microw.

MDSDA

1999 7 June 21 May 3 June 23 May -- 2 July 6 June 

2000 12 June 8 May 8 June 6 June 31 May 3 July 12 June 

2001 3 June 4 May 8 June 8 June 31 May 26 June 4 June 

2002 18 May 5 May 24 May 22 May 7 May 14 June 19 May 

2003 2 June 29 April 27 May -- 20 May 7 July 5 June 
59

Table 6. Comparison of generalized additive models predicting the probability of seals basking.

Covariates included in model

non parametric 

χ

2

non parametric

degrees of freedom p

time of day, days from snow melt, wind 

speed, air temperature, day of year

time of day, days from snow melt, wind 

speed, air temperature 21 3 0

time of day, days from snow melt, wind 

speed, day of year 71 2.8 0

time of day, days from snow melt, air 

temperature, day of year 111 3.1 0

time of day, wind speed, air temperature, 

day of year 313 3.2 0

days from snow melt, wind speed, air 

temperature, day of year 2217 2.8 0

time of day 2452 3 0

days from snow melt 617 2.9 0

wind speed 230 3.1 0

air temperature 348 2.8 0

day of year 569 3.1 0

The χ

2

 values at the 5% significance level are 5.99 for 2 degrees of freedom and 7.82 for 3 degrees of freedom.
60

Date 1999 2000 2001 2002

28-May 0.08

29-May 0.15

30-May

31-May 0.5 0.36 0.64

1-Jun 0.25 0.79

2-Jun 0.14 0.86

3-Jun 0.71 0.14 0.57

4-Jun 0.13 0.43 0.71

5-Jun 0.43 0.5 0.77

6-Jun 0.63 0.75 0.92

7-Jun 0.57 0.43 1.00

8-Jun

9-Jun --

Mean 0.49 0.39 0.20 0.78

SD 0.23 0.21 0.15 0.14

Table 7. Proportions of tagged seals visible on the ice surface during aerial surveys conducted by LGL Ltd. in 

1999–2002.

Year ADFG LGL

1996 0.78

1997 0.83 0.43

1998 0.89 0.39

1999 0.95 0.63

2000 0.47

2001 0.54

2002 0.83

Table 8. Yearly ringed seal densities (seals / km2) in the Prudhoe Bay area estimated by the Alaska Department of Fish and Game (Frost et al. 2002, 2005) and LGL Limited (Moulton et al. 2002).