Distribution and Abundance of Harbor Seals in Cook Inlet,

Alaska. Task III: Movements, Marine Habitat Use, Diving 

Behavior, and Population Structure, 2004-2006

FINAL REPORT

By

Peter L. Boveng, Josh M. London and Jay M. Ver Hoef

Alaska Fisheries Science Center

National Marine Mammal Laboratory

7600 Sand Point Way NE

Seattle, WA 98115

Prepared for

U.S. Department of Interior

Bureau of Ocean Energy Management

Alaska Outer Continental Shelf Region

Environmental Studies Section

3801 Centerpoint Drive, Suite 500

Anchorage, AK 99503-5823

This study was funded by the U.S. Department of the Interior, Bureau of Ocean Energy Management (BOEM), through 

Inter-agency Agreement No. MO7AC13296 (previously 0103RU72448) with the U.S. Department of Commerce, 

National Oceanic and Atmospheric Administration, National Marine Fisheries Service, as part of the MMS Alaska 

Environmental Studies Program. 
DISCLAIMER 

This report has been reviewed by the Bureau of Ocean Energy Management, and approved for 

publication. Approval does not signify that the contents necessarily reflect the views and policies 

of the Service, nor does mention of trade names or commercial products constitute 

endorsement or recommendation for use. 

REPORT AVAILABILITY 

This document is available to the public through: 

National Technical Information Service 

5285 Port Royal Road 

Springfield, Virginia 22161 

FAX: (703) 605-6900

And at

http://www.boem.gov/Environmental-Stewardship/Environmental-Studies/AlaskaRegion/Index.aspx

CITE REPORT AS

Boveng, P.L., J.M. London, and J.M. VerHoef. 2012. Distribution and abundance of harbor seals 

in Cook Inlet, Alaska. Task III: Movements, marine habitat use, diving behavior, and population 

structure, 2004-2006. . Final Report. BOEM Report 2012-065. Bureau of Ocean Energy 

Management, Alaska Outer Continental Shelf Region, Anchorage, Alaska, USA. 58 pp.
i

PROJECT ORGANIZATION

Peter Boveng, NMFS, Seattle, Washington:

Principal Investigator, report author

Josh London, NMFS, Seattle, Washington:

Co-Principal Investigator, database administrator, lead analysis, report author

Jay Ver Hoef, NMFS, Fairbanks, Alaska:

Statistical consultation
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ABSTRACT

Between 2004 and 2006 we conducted four harbor seal tagging trips in Cook Inlet during the months of 

October and May. In total, we captured and released 93 harbor seals, 77 of which were tagged with 

satellite transmitters. Each transmitter was glued to the hair on the back of the seal using durable 

epoxy. Fourteen of the seals were also equipped with specially developed transmitters that were 

attached to one of the rear flippers. Transmissions from the 91 tags resulted in 178,536 location 

estimates and 310,593 dive and haul-out behavior records. These data formed the basis for the 

development of novel analysis techniques. Johnson et al. (2008) described a novel continuous-time 

correlated random walk (CTCRW) method for predicting animal locations from satellite tags. Higgs and 

Ver Hoef (2011) described a new statistical method for analyzing dive behavior based on dive histogram 

recordings obtained from satellite tags. This report demonstrates the application of the CTCRW model 

to provide estimates of seasonal changes in behavior and use patterns for harbor seals in Cook Inlet and 

Gulf of Alaska. Harbor seals tagged in central and southern Cook Inlet ranged as far southwest as the 

Semidi Islands and some seals used habitats around the north and northeast side of Kodiak Island. 

There was a strong seasonal pattern of more coastal and restricted spatial use during the spring and 

summer (breeding, pupping, molting) and more wide-ranging movements within and outside of Cook 

Inlet during the winter months. Three trip statistics (trip length, trip duration and net trip distance) were 

examined as response variables in a mixed linear model. All age and sex classes showed a drop in trip 

duration and length during the pupping and breeding season. Adult female trips were the shortest in

length and duration during the month of June (when many are likely tending a pup). Analysis comparing 

seasonal dive behavior of a single adult female (AF) harbor seal and a juvenile male (JM) harbor seal

showed similar results. Dive activity for the adult female was reduced in the month of June. Haul-out 

behavior also varied seasonally with the proportion of seals hauled out peaking at 0.43 in June 

compared to 0.32 in October. Combining the previously reported survey counts with these haul-out 

proportions resulted in a peak seasonal abundance of 23,954 (SE: 1576) seals in August compared to 

10,820 (SE: 1240) in April. These insights into seasonal harbor seal movements, behavior and abundance

are critical for understanding foraging ecology, marine habitat use, and risk of impacts from industrial 

accidents such as oil spills.

KEY WORDS: harbor seal, Phoca vitulina, movements, seasonal use, Cook Inlet, Alaska
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CONTENTS

PROJECT ORGANIZATION............................................................................................................................... i

ABSTRACT..................................................................................................................................................... iii

CONTENTS..................................................................................................................................................... v

LIST OF FIGURES........................................................................................................................................... ix

LIST OF TABLES............................................................................................................................................. xi

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

OBJECTIVES ...................................................................................................................................................2

METHODS......................................................................................................................................................3

Seal Captures and Satellite Tag Deployment........................................................................................3

Hypothesis 1: Harbor seal diving behavior changes seasonally, presumably reflecting changes in the 

amount of time spent in social behaviors (e.g., courtship and mating) vs. foraging behaviors and the 

depth distribution of local prey aggregations.......................................................................................4

Hypothesis 2: Marine habitat use, as measured by at-sea home range sizes and locations, changes 

seasonally as seals move to access aggregations of migrating prey. Consequently, the risk of 

exposure to oil at sea changes as seals spend more, or less, time near potential oil hazards.............7

Hypothesis 3: The proportion of time spent hauled out changes as an estimable function of 

environmental and temporal covariates, allowing aerial survey counts to be corrected to absolute 

abundances...........................................................................................................................................9

Hypothesis 4: Two or more geographically distinct subpopulations of harbor seals are present in 

Cook Inlet, as demonstrated by genetic analyses and movement patterns of tracked animals........11

RESULTS ......................................................................................................................................................12
vi

Hypothesis 1: Harbor seal diving behavior changes seasonally, presumably reflecting changes in the 

amount of time spent in social behaviors (e.g., courtship and mating) vs. foraging behaviors and the 

depth distribution of local prey aggregations.....................................................................................12

Hypothesis 2: Marine habitat use, as measured by at-sea home range sizes and locations, changes 

seasonally as seals move to access aggregations of migrating prey. Consequently, the risk of 

exposure to oil at sea changes as seals spend more, or less, time near potential oil hazards...........14

Hypothesis 3: The proportion of time spent hauled out changes as an estimable function of 

environmental and temporal covariates, allowing aerial survey counts to be corrected to absolute 

abundances.........................................................................................................................................25

Hypothesis 4: Two or more geographically distinct subpopulations of harbor seals are present in 

Cook Inlet, as demonstrated by genetic analyses and movement patterns of tracked animals........29

DISCUSSION.................................................................................................................................................29

Hypothesis 1: Harbor seal diving behavior changes seasonally, presumably reflecting changes in the 

amount of time spent in social behaviors (e.g., courtship and mating) vs. foraging behaviors and the 

depth distribution of local prey aggregations.....................................................................................29

Hypothesis 2: Marine habitat use, as measured by at-sea home range sizes and locations, changes 

seasonally as seals move to access aggregations of migrating prey. Consequently, the risk of 

exposure to oil at sea changes as seals spend more, or less, time near potential oil hazards...........30

Hypothesis 3: The proportion of time spent hauled out changes as an estimable function of 

environmental and temporal covariates, allowing aerial survey counts to be corrected to absolute 

abundances.........................................................................................................................................31

Hypothesis 4: Two or more geographically distinct subpopulations of harbor seals are present in 

Cook Inlet, as demonstrated by genetic analyses and movement patterns of tracked animals........32

ACKNOWLEDGMENTS.................................................................................................................................33

PRESENTATIONS..........................................................................................................................................34
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LITERATURE CITED ......................................................................................................................................35

APPENDIX 1- Population structure of Cook Inlet harbor seals revealed by mitochondrial DNA and 

microsatellite analysis.................................................................................................................................37

APPENDIX 2 – Tagged Harbor Seals in Cook Inlet, 2004-2006....................................................................54

APPENDIX 3 – Abstracts of peer-reviewed publications resulting from this project:.................................57
viii
ix

LIST OF FIGURES

Figure 1 Raw dive data from two seals: (top) an adult female (AF) from April 30 to August 19, 2006 and 

(bottom) a juvenile male (JM) from May 10 to July 18, 2005. Each plot displays a time series of bars 

showing the relative proportions of dives from each time period falling in eight (top) or nine (bottom) 

defined depth classes. Lighter shades correspond to shallower depths, with white indicating either zero 

dives during that time period or a missing data. The total number of dives per time period is plotted 

along the top, and the tall red vertical lines indicate missing time periods (reproduced from Higgs and 

Ver Hoef 2011)..............................................................................................................................................6

Figure 2 Time Series of barplots showing posterior means of proportion of dives to each depth class for 

an AF (top) and a JM (bottom). Lighter shades indicate shallower depths. The total number of dives per 

time period is plotted along the top, where lines in a lighter shade give the predicted number of dives 

for missing time periods. Posterior means and 95% posterior intervals for daily weighted average depth 

(averaged over time periods within each day) are plotted in black with respect to the second y-axis 

(reproduced from Higgs and Ver Hoef 2011). ............................................................................................13

Figure 3 Predicted tracks of harbor seals in Cook Inlet and the Gulf of Alaska. Colors depict seasonal 

differences in distribution (warmer colors = April to June; cooler colors = November to December) ......15

Figure 4 Total trip distance traveled for harbor seals of various age and sex classes in Cook Inlet and the 

Gulf of Alaska. .............................................................................................................................................16

Figure 5 Total duration (in days) of trips for harbor seals of various age and sex classes in Cook Inlet and 

the Gulf of Alaska........................................................................................................................................17

Figure 6 Total net distance of trips for harbor seals of various age and sex classes in Cook Inlet and the 

Gulf of Alaska ..............................................................................................................................................18

Figure 7 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of October and November. Data are combined across years (2004-2006) and the 

cell size depicted on the maps are inversely proportional to the CV of the estimates..............................20
x

Figure 8 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of December and January. Data are combined across years (2004-2006) and the 

cell size depicted on the maps are inversely proportional to the CV of the estimates..............................21

Figure 9 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of February and March. Data are combined across years (2004-2006) and the cell 

size depicted on the maps are inversely proportional to the CV of the estimates. ...................................22

Figure 10 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of April and May. Data are combined across years (2004-2006) and the cell size 

depicted on the maps are inversely proportional to the CV of the estimates. ..........................................23

Figure 11 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of June and July. Data are combined across years (2004-2006) and the cell size 

depicted on the maps are inversely proportional to the CV of the estimates. ..........................................24

Figure 12 Fitted haul-out probability model, from two perspectives. Time from low-tide is set to 0 

minutes. ......................................................................................................................................................26

Figure 13 Contour plot depicting the seasonal and hourly changes in the proportion of harbor seals 

ashore. The black dots represent our survey dates (15 Apr, 15 June, 15 Aug and 15 October) at noon. 

The haul-out proportion is adjusted to 0 minutes from low tide...............................................................27
xi

LIST OF TABLES

Table 1 Distribution of age and sex categories for harbor seals captured in Cook Inlet, Alaska and 

released with at least one SDR. ....................................................................................................................4

Table 2 Distribution of sex and age class among seals deployed with tags and initially included within the 

movement analysis procedures....................................................................................................................8

Table 3 Distribution of age and sex categories for a subset of harbor seal SDR deployments used within 

the statistical haul-out model.....................................................................................................................10

Table 4 Estimates of the proportion of seals hauled out (Prop. HO) and the corresponding survey 

correction factors (CF) for each of the survey months...............................................................................28

Table 5 Covariate adjusted counts and estimates of total harbor seal abundance for each of the survey 

months........................................................................................................................................................28
xii
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INTRODUCTION

Harbor seals (Phoca vitulina) are important marine predators that occupy a broad range in Alaska from 

approximately 130°W to 172°E (over 3,500 km east to west) and from 61°N to 51°N (over 1,000 km 

north to south) (Frost et al. 1982). In recent decades, their abundance has declined at several Alaska 

locations. For example, in the Aleutian Islands, counts declined 67% between the early 1980s and 1999 

(Small et al. 2008), with declines of 85% in the western islands. Counts of harbor seals at Tugidak Island 

declined 85% between 1976 and 1988 (Pitcher 1990). Counts in Prince William Sound declined

approximately 63% between 1984 and 1997 (Frost et al. 1999), and 3.3%/yr during 1988-1999 (Ver Hoef 

and Frost 2003) . More recent evidence indicates that harbor seals near Kodiak Island, including those 

at Tugidak Island, have increased 6.6%/yr during 1993-2001 (Small et al. 2003). Because Cook Inlet is 

located between regions of contrasting trends, it is important to assess the current status of the 

population and the potential impacts of oil and gas activities on harbor seal populations within Cook 

Inlet.

As harbor seals either recover from population declines in the 1970s and 1980s or continue to decline, 

they are particularly vulnerable to acute and chronic environmental impacts, such as those that could

result from industrial accidents such as oil spills (Frost and Lowry 1994, Frost et al. 1994, Spraker et al. 

1994, Hoover-Miller et al. 2001). If an oil spill were to occur, harbor seals would be at risk for direct 

exposure to oil both at sea and ashore when they haul out (Lowry et al. 1994) and secondary exposure 

caused by ingestion of prey that have been exposed to oil. 

Satellite-linked dive recorders (SDRs) deployed on seals are a common tool used by researchers to 

investigate the marine habitat use, behavior and ecology of marine mammals. It is only recently that 

statistically advanced techniques such as state-spaced modeling and state-switching models have been 

available and used to investigate movement and space utilization, while appropriately accounting for 

location error that is inherent with Argos locations from SDRs deployed on marine mammals. Similarly, 

advanced methods for analyzing behavior data recorded and transmitted from the SDRs has been 

lacking. These time-series datasets have a large number of repeated behavior measurements and 

appropriately dealing with the auto-correlation has been a challenge methodologically and 

computationally. Dive behavior data are summarized by the SDRs into discrete, unequal bins 

programmed by the researcher. This categorization and aggregation create data that are a time series of 
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ordered multi-category counts, which present challenges in terms of statistical modeling and practical 

interpretation. Similar issues exist with respect to the interpretation of haul-out timeline behavior data. 

These timeline data are critical not only for understanding harbor seal behavior, but also calculating 

proper estimates of abundance that account for seals in the water during the time of the aerial surveys. 

Here, we present statistically sound methodological advancements that overcome many of these issues. 

We have applied these methods to data obtained from SDRs deployed on harbor seals in the Cook Inlet 

region of Alaska between 2004 and 2006 in support of our original project objectives and hypotheses.

OBJECTIVES

Objective 1: To assess the seasonal patterns of abundance, distribution, and behavior of harbor 

seals in Cook Inlet, including an assessment of the seals’ diving behavior. 

Objective 2: To identify and determine the priority of importance specific marine habitats used 

by harbor seals in Cook Inlet associated with key life history events such breeding, pup rearing, 

and foraging.

Objective 3: To enhance estimates of harbor seal abundance in Cook Inlet by determining and 

applying a haul-out correction factor that adjusts aerial survey counts for the proportion of seals 

that are at-sea and not observed during aerial surveys.

Objective 4: To evaluate whether or not geographically distinct subpopulations of seals exist 

within Cook Inlet.

The project’s objectives will be addressed by testing the following hypotheses:

Hypothesis 1: Harbor seal diving behavior changes seasonally, presumably reflecting changes in 

the amount of time spent in social behaviors (e.g., courtship and mating) vs. foraging behaviors, 

and the depth distribution of local prey aggregations.

Hypothesis 2: Marine habitat use, as measured by at-sea home range sizes and locations, 

changes seasonally as seals move to access aggregations of migrating prey. Consequently, the 

risk of exposure to oil at sea changes as seals spend more, or less, time near potential oil 

hazards.

Hypothesis 3: The proportion of time spent hauled out changes as an estimable function of 

environmental and temporal covariates, allowing aerial survey counts to be corrected to 
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absolute abundances.

Hypothesis 4: Two or more geographically distinct subpopulations of harbor seals are present in 

Cook Inlet, as demonstrated by genetic analyses and movement patterns of tracked animals.

METHODS

Seal Captures and Satellite Tag Deployment

All four hypotheses developed in support of the project objectives relied on the capture and release of 

harbor seals within Cook Inlet. To accomplish this, four research cruises were conducted aboard charter 

vessels from the port of Homer, Alaska. The cruises took place in the fall of 2004, spring of 2005, fall of 

2005 and spring of 2006. Ninety-three harbor seals were captured and released in Cook Inlet from 2004 

to 2006. Seals were captured at a variety of sites widely distributed around central Cook Inlet, including 

Aurora Rock, Bradley River (both within Kachemak Bay), Yukon Island, Kamishak Bay, Iniskin Bay, 

Chinitna Bay, Tuxedni Bay, and Kalgin Island. Seals were captured using large-mesh nylon nets deployed 

from small boats near harbor seal haul-out sites. Seals were carefully removed from the nets and 

transported to the charter vessel where they were weighed, measured and sedated prior to tag 

attachment. Genetic tissue samples were collected from each seal.

During the four cruises, 77 seals of all age and sex categories (see Table 1) were captured and released

with at least one Argos satellite-linked dive recorder (SDR) manufactured by Wildlife Computers 

(Redmond, Washington, USA). A total of 91 satellite tags were deployed; 77 tags were of either the 

ST16 or SPLASH tag type, glued to a seals’ pelage, and 14 were SPOT4/5 tags, specially designed for 

attachment through the webbing of a seal’s hind-flipper. The ST16 and SPLASH tags were programmed 

to collect and transmit information on each seal’s location, haul-out time, and diving behavior. They 

were not duty cycled. The SPOT tags provided only location and haul-out timelines (no dive behavior) 

and had much smaller batteries. By attaching the SPOT tags to the rear flipper, rather than gluing them 

to the pelage, deployment lengths through and beyond the molt period was possible. The SPOT tags 

were duty cycled to transmit data only from the final half of July through the first half of September.
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Table 1 Distribution of age and sex categories for harbor seals captured in Cook Inlet, Alaska and 

released with at least one SDR.

Sex Adult Sub-Adult (SUB) Young of the Year (YOY)

Female 16 15 6

Male 23 13 4

Total 39 28 10

Hypothesis 1: Harbor seal diving behavior changes seasonally, presumably reflecting 

changes in the amount of time spent in social behaviors (e.g., courtship and mating) vs. 

foraging behaviors and the depth distribution of local prey aggregations.

Ordered categorical data are pervasive in environmental and ecological data, frequently arising from 

practical constraints imposed at the time of data collection. Often, the constraints require discretizing a 

continuous variable into ordered categories. A great deal of data have been collected toward the study 

of marine mammal dive behavior using dive recorders, which often discretize a continuous variable such 

as depth. Additionally, data storage or satellite-link transmission constraints may also necessitate the 

aggregation of data over time intervals of a specified length. Due to the bandwidth limitations of the 

Argos platform, dive behavior data in this study were transmitted as summarized histograms for 

‘maximum dive depth’, ‘dive duration’ and ‘time at depth’. This categorization and aggregation create 

data that are a time series of ordered multi-category counts, which present challenges in terms of 

statistical modeling and practical interpretation. 

As part of this study, we developed and introduced (Higgs and Ver Hoef 2011) an intuitive strategy for 

modeling such aggregated, ordered categorical data that is both statistically appropriate and practically 

useful for the scientists collecting the data. This technique allows for inference regarding the category 

probabilities and a measure of central tendency on the original scale of the data (e.g. meters), along 

with incorporation of temporal correlation and over-dispersion. The primary goal of the analysis was to 
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assess the relationship of covariates, such as time of day, with number of dives and maximum depth of 

dives. 

The computational requirements are currently too high to complete this analysis for the entire dataset 

of deployments from this study. Here we present summary graphs showing dive behavior of an adult 

female (AF) and juvenile male (JM) harbor seal across multiple seasons. Higgs and Ver Hoef (2011) is 

included as an appendix to this report and provides complete details describing the statistical theory 

and implementation of this novel strategy. Additional studies to further examine the dive behavior of 

Cook Inlet harbor seals are on-going and we will continue to publish findings from these endeavors in 

the peer-reviewed literature.
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Figure 1 Raw dive data from two seals: (top) an adult female (AF) from April 30 to August 19, 2006 and 

(bottom) a juvenile male (JM) from May 10 to July 18, 2005. Each plot displays a time series of bars

showing the relative proportions of dives from each time period falling in eight (top) or nine (bottom) 

defined depth classes. Lighter shades correspond to shallower depths, with white indicating either zero

dives during that time period or a missing data. The total number of dives per time period is plotted 

along the top, and the tall red vertical lines indicate missing time periods (reproduced from Higgs and 

Ver Hoef 2011).
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Hypothesis 2: Marine habitat use, as measured by at-sea home range sizes and locations, 

changes seasonally as seals move to access aggregations of migrating prey. Consequently, 

the risk of exposure to oil at sea changes as seals spend more, or less, time near potential oil 

hazards.

As with the dive behavior hypothesis, much of our effort related to this hypothesis focused on 

development of new statistical methodologies. A novel method using a continuous-time correlated 

random walk (CTCRW) was developed and published by Johnson et al. (2008) based, in large part, on 

data collected from this study. Additionally, Johnson maintains an R package, ‘crawl’, with a high 

download rate and diverse use-base across multiple disciplines. Here we present two research efforts in 

support of our second hypothesis, which rely heavily on the CTCRW model and ‘crawl’ package. First, we 

examined seasonal and age-related differences in movement metrics and trip characteristics. Second, 

we documented changes in seasonal use and distribution of harbor seals while at haul-out locations and 

while at sea. 

Argos locations from 77 harbor seal deployments were first pre-filtered with the procedure of Freitas et 

al. (2008) using the ‘argosfilter’ package within R. Additionally, for the trip analysis, the focus was on atsea trip behaviors and, therefore, all locations greater than 1500 meters inland (due to Argos location 

error) were removed from the dataset. Inland locations were not filtered from the dataset for the spaceuse analysis as we were interested in both haul-out (which occurs on land) and at-sea use patterns. 

Following the pre-filtering, animal paths were modeled using the CTCRW model within the ‘crawl’ R 

package. We accounted for haul-out time through a dynamic auto-regression parameter that is a

function of the collected dry-time data. The dynamic model does not allow for movement while the 

animal is hauled out. We used the ‘dry-stop’ function of the crawl package to incorporate harbor seal 

haul-out data into the movement model. These data were collected and summarized on the tag at 

hourly intervals, with some missing data due to Argos transmission errors or suboptimal timing of 

transmissions. These time gaps and uncertainty in the haul-out behavior can lead to additional 

uncertainty in the use and movement of harbor seals. For the use analysis, a seal was considered hauled 

out only if the tag reported 100% dry for the hour. CTCRW parameter estimation is accomplished by 

forming it as a state-space model on which the Kalman filter is used to obtain maximum likelihood 
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estimates, and to provide hourly location estimates. Six of the deployments did not provide enough data 

to appropriately model and were removed from final analysis.

Table 2 Distribution of sex and age class among seals deployed with tags and initially included within the 

movement analysis procedures

AGE SEX NUMBER OF ANIMALS

ADULT FEMALE 16

SUB-ADULT FEMALE 12

YOUNG-OF-YEAR FEMALE 6

YEARLING FEMALE 3

ADULT MALE 23

SUB-ADULT MALE 11

YOUNG-OF-YEAR MALE 4

YEARLING MALE 2

Trips were defined based on the haul-out timeline, with the change-point set at 33.3% of an hour in the 

‘dry’ state. Trip distances were estimated with a parametric bootstrap approach. After processing with 

the Kalman filter, 50 paths were simulated for each animal from the path distribution conditional on the 

observed Argos data. For each animal and sampled path, total distance, net distance, and total time 

were calculated. Average travel distance and net distance were used as point estimates, and standard 

errors were estimated from the distance and net distance variances for each animal. These bootstrap 

estimates represent approximations to the minimum mean-squared error predictions for travel distance 

and net distance.

The trip data were modeled using a linear mixed model with sex, age, and month as fixed effects, a 

random effect for each seal, and temporal autocorrelation for repeated trips by an individual seal. The 

temporal autocorrelation used a first-order autoregressive model where trips were sequentially 

indexed. Total trip distance (km), trip duration (days) and net trip distance (km) were examined as 

response variables. All response variables were transformed to the log scale. For trip distance, two 

values were removed after checking residuals for outliers; no outliers were apparent for trip duration or 

net distance. After removing outliers, residuals appeared to be close to normal distributions for all three 

variables.
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This study used a Bayesian approach to estimate the spatial use of tagged seals in the Cook Inlet and 

Gulf of Alaska regions. By sampling (n=1000) from the posterior distributions of the CTCRW predicted 

tracks, we were able to encapsulate Argos location error and the full range of variability in the 

movement model in our spatial use analysis. This variability is represented by the overall distribution 

and the CVs of use for each cell (in the figures, smaller cell size is representative of higher CV values).

We examined the effects of season (fall, early winter, late winter, spring, and summer) on the spatial use 

patterns of the tagged seals. Use maps were created on a raster grid with cell size of 100 km2

. Two 

values were calculated for each cell in each simulation: the number of hours all seals spent in the cell, 

and the number of seals that spent any hour in the cell. Separate maps were made for each season. 

Additionally, the use patterns were depicted separately for periods of haul-out and at-sea behavior.

The movement model and use maps did not account for the presence of land. As shown in the space-use 

figures, the land overlay serves as an assessment of the variability associated with the movement model 

and Argos location quality.

Hypothesis 3: The proportion of time spent hauled out changes as an estimable function of 

environmental and temporal covariates, allowing aerial survey counts to be corrected to 

absolute abundances.

We used a subset 50 seals from our deployment of SDRs to examine haul-out behavior of harbor seals in 

the Cook Inlet region. SDRs were programmed to record an electrical resistance value of the surrounding 

medium (water or air). For every hour, the tags recorded the portion of time dry. These dry-time 

percentages were used to determine haul-out status. Data were transmitted by satellite to Argos (CLS 

America, Inc., Largo, MD) and downloaded for analysis. The percentage-dry data were converted to a

binary response variable with a “1” indicating a deployed tag was dry greater than 50% of the time 

during the hour. The binary series ranged from 478 to 6,336 hours per seal, for a total of 151,247

observations. There was limited storage on the SDR, and satellites were not always able to upload data 

before storage capacity was filled, resulting in some periods of missing data.

Our interest centered on factors that affect temporal haul-out patterns. We used restricted maximum 

likelihood on pseudo-data to fit GLM pseudo-models to the haul-out data. The contains all main effects 
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and two-way interactions. Additional details regarding details of the GLM pseudo-model can be found in 

Ver Hoef et al. 2010.

We were primarily interested in using this haul-out model to correct adjusted counts for the proportion 

of seals that were in the water at the time of the surveys. We encountered two problems with this 

approach. The count data from aerial surveys do not contain data on sex/age composition of the 

counts. Thus, we desire an average haul-out proportion, weighted by the population sex/age 

composition. Although the analysis (Ver Hoef et al. 2010) indicated some sex/age differences, these 

were minor, and we felt that the distribution of sex and age within our sample (Table 2) was fairly close 

to that in the population. Thus, we simply fit a pooled model without including sex and age effects. 

Table 3 Distribution of age and sex categories for a subset of harbor seal SDR deployments used within 

the statistical haul-out model.

Sex Adult Sub-Adult (SUB) Young of the Year (YOY)

Female 10 9 6

Male 11 10 4

Total 21 19 10

The second problem was that the satellite transmitters did not remain attached during August, the time 

of molt and one of our key seasonal survey periods. We addressed this problem by interpolating the 

fitted models using trigonometric covariates on date and time of day. This also resolved any issues with 

creating an arbitrary starting day-of-year, and having to use 24 categorical covariates for hour-of-day. 

We can create an index of “spring-time” by taking fs(tdoy) = sin(2π tdoy /365), and an index of “summertime” by taking fc(tdoy) = -cos(2π tdoy /365), where tdoy = day-of-year, from 1 to 365 beginning with 1 

January. Likewise, we can create an index of “6AM-time” by taking fs(ttod) = sin(2π ttod /24), and an index 

of “noon-time” by taking fc(ttod) = -cos(2π ttod /24), where ttod = time-of-day, from 1 to 24 beginning with 1 

AM. Let Yi be a random variable for haulout, which is a binary response. The models of Ver Hoef et al. 

(2010) assume Yi has expected value exp( ) /(1 exp( )) , with a temporally auto-correlated error μi + μi
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structure that has a diagonal variance structure like a Bernoulli distribution. Thus, we fit the following 

model,

where is minutes from low tide, is day-of-year, is time-of-day for the ith observation. Note 

that we do not include the and terms because the trigonometric identity 

creates a linear dependency with the overall intercept, and hence, nonestimable regression coefficients. In summary, we have 3 main effects for day-of-year and 3 main 

effects for time-of-day, and all possible interactions between them. This is similar, in a count of the 

parameters, to having a third-order polynomial for each effect, and their interaction. Here, the virtue is 

that the covariate values “wrap” on the circular values of day-of-year and time-of-day.

Hypothesis 4: Two or more geographically distinct subpopulations of harbor seals are 

present in Cook Inlet, as demonstrated by genetic analyses and movement patterns of 

tracked animals.

The methods used in the genetic analyses of population structure are provided in the contract report 

attached as Appendix 1.

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BOEM 2012-065

12

RESULTS

Hypothesis 1: Harbor seal diving behavior changes seasonally, presumably reflecting 

changes in the amount of time spent in social behaviors (e.g., courtship and mating) vs. 

foraging behaviors and the depth distribution of local prey aggregations.

The effort and results related to this hypothesis were focused on the development of analytical methods 

for the dive data, which present statistical difficulties due to their unusual form. Interesting behavioral 

patterns, however, were observed in our two example datasets. Higgs and Ver Hoef (2011) presented

analyses and results comparing seasonal dive behavior of an adult female (AF) harbor seal and a juvenile 

male (JM) harbor seal. For the AF harbor seal, the period from approximately the second week in May 

through June was characterized by relatively low number of dives and a weighted average depth often 

within the 5 to 10 meter range. The first part of May and the month of July were characterized by 

increased dive activity and weighted average depth values regularly within the 50 to 100 meter range. 

The months of May and June correspond with the seasonal peak in pupping, weaning and mating 

behavior. While we have no capability to confirm whether this AF had a pup, the observed behavior was 

consistent with a seal tending a pup.

The JM harbor seal maintained a relatively consistent dive behavior pattern throughout the record. 

There was no evidence of a prolonged period of reduced dive activity as was observed in the AF. The JM 

dive behavior, however, was more episodic with distinct periods (1-2 weeks) of high dive activity and 

weighted average depths between 100 and 200 meters followed by periods (1-2 weeks) of low dive 

activity and weighted average depths between 5 and 10 meters. This behavior is consistent with a nonbreeding animal that was focused on the location and exploitation of prey resources. As part of an 

ongoing effort to publish results from this and other satellite-telemetry studies of harbor seals 

throughout Alaska, we plan to further develop methods for linking the modeled dive behavior to 

predicted temporal-spatial locations derived from the Argos data.
BOEM 2012-065

13

Figure 2 Time Series of barplots showing posterior means of proportion of dives to each depth class for 

an AF (top) and a JM (bottom). Lighter shades indicate shallower depths. The total number of dives per 

time period is plotted along the top, where lines in a lighter shade give the predicted number of dives 

for missing time periods. Posterior means and 95% posterior intervals for daily weighted average depth 

(averaged over time periods within each day) are plotted in black with respect to the second y-axis

(reproduced from Higgs and Ver Hoef 2011).
BOEM 2012-065

14

Hypothesis 2: Marine habitat use, as measured by at-sea home range sizes and locations, 

changes seasonally as seals move to access aggregations of migrating prey. Consequently, 

the risk of exposure to oil at sea changes as seals spend more, or less, time near potential oil 

hazards.

Results from both the analysis of trip characteristics and space-use provided insights suggesting harbor 

seals in Cook Inlet and the Gulf of Alaska vary in their behavior and distribution across seasons and some 

of this variability is sex and age related. Figure 3 shows predicted track locations for harbor seals during 

the months of April, May, June, November, December and January. The tracks are colored with warmer 

values corresponding to spring and cooler values corresponding to winter. The distribution of 

movements was much broader in the winter months with some seals ranging as far as the Shumigan 

Islands. During summer months, movements and distribution was mostly confined to the west side of 

Cook Inlet and Kachemak Bay.

Trip distances ranged from approximately 10 to 250 kilometers (Figure 4). Shorter trips were evident 

during the pupping and breeding season. Adult female trips were the shortest in the month of June 

(when many were likely tending pups). Except for June, adult male trips were shorter than female trips. 

Trip duration ranged from approximately 4 hours to 4 days (Figure 5). Adult female trips were of 

shortest duration in June, which corresponds with the period of short trip distances. Lower variability in 

trip duration was observed among adults when compared to non-adult seals. A similar pattern to trip 

distance was evident in total net distance traveled during a trip. In general, seals returned to within 10 

kilometers of the starting point for the trip and adults were more consistent across months (Figure 6). 

Higher net distance values were calculated in the spring and fall seasons, which correspond with the 

seasonal movement of seals in and out of Cook Inlet. Consistent with total trip distance and trip 

duration, net trip distance was lowest for adult females in June.
BOEM 2012-065

15

Figure 3 Predicted tracks of harbor seals in Cook Inlet and the Gulf of Alaska. Colors depict seasonal 

differences in distribution (warmer colors = April to June; cooler colors = November to December)
16

Figure 4 Total trip distance traveled for harbor seals of various age and sex classes in Cook Inlet and the Gulf of Alaska.
17

Figure 5 Total duration (in days) of trips for harbor seals of various age and sex classes in Cook Inlet and the Gulf of Alaska.
18

Figure 6 Total net distance of trips for harbor seals of various age and sex classes in Cook Inlet and the Gulf of Alaska
BOEM 2012-065

19

Space use by harbor seals was divided into five seasonal periods: October-November, DecemberJanuary, February-March, April-May and June-July. August and September were not considered because 

most of the deployed tags were shed from the seals in August as the seals underwent their annual molt 

cycle. Within each seasonal period, seal use was calculated and displayed (Figures 7-11) based on 4 

distinct categories: average total hours of hauled-out presence in a grid cell, average total hours of inwater presence in a grid cell, average number of seals using a cell while hauled out, and average total 

number of seals using a cell while in water. Consistent with the findings reported in our analysis of trip 

durations, seal use was more broadly distributed in the winter months and most confined to the Cook 

Inlet region during spring and early summer. This pattern was consistent for both in-water behavior and 

haul-out behavior.

Within each seasonal period, there were distinct geographical regions and habitats where large portions 

of the seals’ time were spent. During the fall months of October and November, seal use was most 

concentrated in Kachemak Bay, across Cook Inlet toward Iniskin and Iliamna bays, and south through the 

Kamishak Bay, Cape Douglas and Shelikof Strait regions. Seals also used haul-out and marine habitats 

along the north side of Kodiak and Afognak islands. One seal traveled farther south and west along the 

Alaska Peninsula to the Semidi Islands. December and January were characterized by continued use of 

the core areas within central and southern Cook Inlet as well as expanded use of near-shore habitats 

along Shelikof Strait, the Alaska Peninsula, north and west sides of Kodiak Island, and Tugidak Island. 

February and March were similar to December and January. By April and May, harbor seal use 

contracted significantly and was concentrated mostly within Cook Inlet. Additionally, some seals 

expanded their use into the northern portion of Cook Inlet (where seals were not captured as part of 

this study). Our results suggest that most or all harbor seals that are found in our study area of central 

and lower Cook Inlet during May (our spring tagging period) remain in Cook Inlet through June and July.
BOEM 2012-065

20

 

 

Figure 7 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of October and November. Data are combined across years (2004-2006) and the 

cell size depicted on the maps are inversely proportional to the CV of the estimates.
BOEM 2012-065

21

 

 

Figure 8 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of December and January. Data are combined across years (2004-2006) and the 

cell size depicted on the maps are inversely proportional to the CV of the estimates.
BOEM 2012-065

22

 

 

Figure 9 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of February and March. Data are combined across years (2004-2006) and the cell 

size depicted on the maps are inversely proportional to the CV of the estimates.
BOEM 2012-065

23

 

 

Figure 10 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of April and May. Data are combined across years (2004-2006) and the cell size 

depicted on the maps are inversely proportional to the CV of the estimates.
BOEM 2012-065

24

 

 

Figure 11 Harbor seal area use maps depicting total hours of seal use and total number of seals using a 

cell during the months of June and July. Data are combined across years (2004-2006) and the cell size 

depicted on the maps are inversely proportional to the CV of the estimates.
BOEM 2012-065

25

Hypothesis 3: The proportion of time spent hauled out changes as an estimable 

function of environmental and temporal covariates, allowing aerial survey counts to 

be corrected to absolute abundances.

In the final model for the proportion of harbor seals ashore (hauled out), there were no 

significant year effects, but with such large amounts of data, most other effects were significant. 

We were especially interested in the probability of hauling out throughout hours of the day and 

dates of the year, and the interaction of hour and date. The significance of sex and age implied a 

separate fit of the date/hour-of-day interaction for each sex/age interaction. We held the tide 

effect constant at low tide, which corresponded to the maximum probability of hauling out 

while holding all other factors constant. The fitted model for date and time of day for adult 

females is shown in Figure 12, which shows that haul-out probabilities, in general, were highest 

in the summer and around mid-day.
26

Figure 12 Fitted haul-out probability model, from two perspectives. Time from low-tide is set to 0 minutes.
BOEM 2012-065

27

Figure 13 Contour plot depicting the seasonal and hourly changes in the proportion of harbor 

seals ashore. The black dots represent our survey dates (15 Apr, 15 June, 15 Aug and 15 

October) at noon. The haul-out proportion is adjusted to 0 minutes from low tide. 

The black dots in the filled contour plot (Figure 13) show the dates (15 Apr, 15 June, 15 Aug, and 

15 Oct) at noon, and the curve is adjusted for 0 minutes from low tide, so the black dots are at 

the same set of covariates as the adjusted counts. From this model, we made a correction factor 

for each of the adjusted counts. The estimated proportions that are hauled out, and the 

correction factors with their estimated standard errors, are shown for each date in (Table 3).
BOEM 2012-065

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Table 4 Estimates of the proportion of seals hauled out (Prop. HO) and the corresponding survey 

correction factors (CF) for each of the survey months.

Estimate

Prop. HO CF CF (SE)

Month

April 0.244 4.106 0.249

June 0.429 2.333 0.104

Aug 0.347 2.886 0.160

Oct 0.201 4.967 0.362

The correction factor is the inverse of the fitted proportion of seals that were hauled-out. The 

variance of the correction factor was obtained using the delta method (Dorfman 1938). By 

multiplying the correction factors in the table above by the adjusted count totals, we arrived at 

corrected total estimates for each season. The standard errors of the corrected total estimates 

were obtained using Goodman’s (1960) variance of a product. The estimates are given Table 4.

Table 5 Covariate adjusted counts and estimates of total harbor seal abundance for each of the 

survey months

Estimate

Count Count (SE) CF CF (SE) Total Total (SE)

Month

April 2635.258 257.15 4.106 0.249 10820.51 1240.885

June 7111.024 313.44 2.333 0.104 16586.73 1038.409

Aug 8301.03 292.39 2.886 0.160 23954.43 1576.032

Oct 2406.84 454.11 4.967 0.362 11954.31 2412.58
BOEM 2012-065

29

Hypothesis 4: Two or more geographically distinct subpopulations of harbor seals 

are present in Cook Inlet, as demonstrated by genetic analyses and movement 

patterns of tracked animals.

The results of the genetic analyses of population structure are provided in the contract report 

attached as Appendix 1.

DISCUSSION

Hypothesis 1: Harbor seal diving behavior changes seasonally, presumably reflecting 

changes in the amount of time spent in social behaviors (e.g., courtship and mating) 

vs. foraging behaviors and the depth distribution of local prey aggregations.

The focus of our work to date has been on the development of new, statistically sound methods

for interpreting dive behavior. We expect the efforts here to provide a key foundation for future 

analysis of the unique dive records obtained from satellite tags. To fully explore population level 

behavior patterns will require more time and more advances in computing power. However, the 

results from a comparison of one exemplar adult female and juvenile male suggest differing 

seasonal patterns that coincide with the timing of key social behaviors. The adult female had a 

relatively low number of dives during May and June that corresponded with the known timing of 

peak pupping. Reduced dive activity is expected for a harbor seal tending a newly born pup. The 

juvenile male, however, maintained a relatively consistent dive behavior with episodic, distinct 

periods of high dive activity. The weighted average depths for these likely foraging periods were 

between 100 and 200 meters. This was followed by periods of low dive activity and weighted 

average depths between 5 and 10 meters. This behavior is consistent with a non-breeding 

animal that is focused on the location and exploitation of prey resources.
BOEM 2012-065

30

Hypothesis 2: Marine habitat use, as measured by at-sea home range sizes and 

locations, changes seasonally as seals move to access aggregations of migrating prey. 

Consequently, the risk of exposure to oil at sea changes as seals spend more, or less, 

time near potential oil hazards.

We addressed this hypothesis in a two-stage approach. The first task involved the development 

of a novel method that relies on the estimate locations from satellite tags to inform a statespace movement model. This state-space model incorporates the both the error associated with 

the Argos location estimates and any error related to uncertainty within the underlying 

movement model. The second approach focused on novel applications of the movement model 

to examine and describe the seasonal movement and use patterns of harbor seals captured in 

Cook Inlet. This analysis provides an unprecedented and detailed examination of seasonal 

changes in harbor seal movement.

The large-scale patterns indicate a portion of harbor seals captured in Cook Inlet move out of 

the area in the fall and into habitats within Shelikof Strait, Northern Kodiak Island, and coastal 

habitats of the Alaska Peninsula. Many of these seals tend to be younger individuals and these 

larger movements are reflected in the estimates of trip distance and duration for younger age 

classes in the fall months. Animals begin moving back into Cook Inlet during the spring months 

and nearly all tagged animals are back within Cook Inlet by May and June, which coincides with 

peak breeding and pupping. Small-scale patterns of movement within Cook Inlet were also 

evident. Seals captured in Kachemak Bay were more likely to remain within Kachemak Bay and 

were rarely recorded leaving for other parts of Cook Inlet. Use of the western habitats of Cook 

Inlet was higher than the eastern coastline. Seals captured at various sites in Cook Inlet were 

observed in the Kalgin Island region on more than one occasion. The nearby sand bars may 

serve as a key hub location for seals as they move throughout Cook Inlet. Some seals moved into 

the northern portion of Cook Inlet, but, in general, seals captured in the southern portion of 

Cook Inlet tended to stay south of the Forelands.
BOEM 2012-065

31

Hypothesis 3: The proportion of time spent hauled out changes as an estimable 

function of environmental and temporal covariates, allowing aerial survey counts to 

be corrected to absolute abundances.

The haul-out behavior analysis and survey correction factors are the first attempt at a 

comprehensive, statistically sound model of harbor seal haul-out behavior and abundance 

ashore across multiple seasons. The seasonal haul-out pattern corresponds with the information

from the dive behavior and movement analysis. Seals are more likely to be hauled out during 

the pupping and breeding period and haul-out less frequently during late fall and winter. The 

importance of sex and age in the model also confirms our results from the dive and movement 

analysis that suggest different seasonal behaviors across age and sex. The haul-out model allows 

us to estimate abundance for October, April and June. The lowest seasonal abundance is 

estimated for April at 10,821 (SE: 1240.885) seals. The movement model indicates much of the 

seals’ spatial use is still outside of Cook Inlet in February and March. The dive behavior for both 

the adult female and the juvenile male indicate relatively intense diving in early May that could 

be associated with either travel or foraging.

The estimates for proportion of seals ashore in August are much lower than previous estimates 

for Alaska harbor seals. Therefore, the survey correction factor results in a higher than expected 

abundance estimate. August corresponds with the annual molt cycle and, therefore, most of our 

tags fall off during this period. For this reason, we have a very limited number of data records 

that correspond with the timing of our August survey. To compensate for this, we rely on 

trigonometric functions to create better covariates for the circular data (time-of-day and day-ofyear) and then interpolate across August. While this approach allows us to generate an estimate 

for the survey correction factor in August, it likely underestimates the proportion of seals ashore 

during the survey. Other studies have shown the proportion of seals ashore to peak during the 

molt. These values are often equal or in excess of the proportion estimated for pupping period. 

Without additional data from August and by interpolating the model across this period, we are 

unable to detect an expected increase in haul-out availability during the August surveys. Given 

this limitation, the estimate of total abundance of harbor seals in Cook Inlet for the month of 

August is likely between 16,587 (June estimate) and 23,954 (interpolated August estimate).
BOEM 2012-065

32

As part of this study, we tested the deployment of a few prototype flipper-mounted satellite 

transmitters that are designed to stay on the animal through the molt period. We have 

continued to deploy these transmitters in other regions of Alaska and hope additional analyses 

will improve our haul-out model and allow a more precise estimation of abundance in August.

Additional assumptions were made regarding the distribution of age and sex within the 

population compared to the subset of animals tagged and used in our analysis. Our analysis of 

the haul-out model shows significant effects for sex, age, and the interactions with date. 

Unfortunately, we are unable to determine sex or age from our survey photographs and must 

assume the distribution of age and sex with our tagging study is representative of the 

population surveyed. 

Hypothesis 4: Two or more geographically distinct subpopulations of harbor seals 

are present in Cook Inlet, as demonstrated by genetic analyses and movement 

patterns of tracked animals.

The results presented in the report attached as Appendix 1 were clear with regard to dispersal 

patterns and genetic exchange among broadly defined major concentration areas of harbor 

seals in Alaska, that female dispersal and male-mediated gene flow in and out of Cook Inlet is 

restricted. Within Cook Inlet, however, there were contrasting patterns of differentiation 

between mtDNA and nDNA markers. Low but significant differentiation between eastern and 

western Cook Inlet seals was found in mtDNA, but not in nDNA, possibly reflecting greater 

female philopatry to breeding location and greater male-mediated gene flow throughout the 

Inlet. Also, a failure to detect clear mtDNA differences between western Cook Inlet and parts of

the Kodiak archipelago may indicate female dispersal between these areas. Overall, the

differentiation between Cook Inlet harbor seals and other major groups of harbor seals in the 

Gulf of Alaska implies that the Cook Inlet population may be slow to recover from a large 

perturbation such as an oil spill or a serious disease outbreak; dispersal rates from nearby 

populations are likely to be low.
BOEM 2012-065

33

ACKNOWLEDGMENTS

The Kachemak Bay Estuarine Research Reserve (KBERR) staff, especially Ori Badajos, Steve Baird, 

Kim Donahue, and Scott Pegau were extremely helpful throughout the project. We also relied 

upon the skillful assistance of Michael Cameron, Bob Caruso, Shawn Dahle, Dave Withrow, John 

Jansen, and Robert Montgomery of the NOAA National Marine Mammal Laboratory (NMML);

Steve Jeffries and Bryan Murphie of the Washington Department of Fish and Wildlife, Shawn 

Johnson of Johnson Veterinary Services and Julie Richmond, currently of the University of North 

Floria during the logistics and field portions of this project. Mike Simpkins (NOAA) was crucial in 

developing the proposal, and mobilizing the initial tagging cruises. Capt. Jeff Johnson, the M/V 

Peregrine, Glenn Seaman, L.A. Holmes and many other kind people and merchants of Homer, 

Alaska, were also very helpful. We are extremely grateful to the Minerals Management Service

(MMS), now called the Bureau of Ocean Energy Management (BOEM), and especially Chuck 

Monnett for the support that enabled us to conduct this work.
BOEM 2012-065

34

PRESENTATIONS 

Boveng, P. A summary of harbor seal research conducted in Alaska by NOAA Fisheries. Oral 

presentation, Eighteenth Working Group Meeting, Project 02.05-61, “Marine 

Mammals,” under Area V of the U.S.-Russia Agreement on Cooperation in the Field of 

Environmental Protection Seattle, Washington, September 23-26, 2004.

Boveng, P.L., M.A. Simpkins, and J.L. Bengtson. Seasonal dynamics of harbor seals in Cook Inlet. 

Poster presentation, Alaska Marine Science Symposium, 24-26 January 2005, 

Anchorage, Alaska.

Boveng, P.L., J.L. Bengtson, and M.A. Simpkins. Distribution and abundance of harbor seals in 

Cook Inlet: Seasonal variability in relation to key life history events. Oral presentation, 

Minerals Management Service, Alaska OCS Region, Tenth Information Transfer Meeting, 

March 14-16, 2005, Anchorage, AK. OCS Study MMS 2005-036.

Boveng, P.L. A summary of harbor seal research conducted by NOAA Fisheries in Alaska. Oral 

presentation to the Alaska Harbor Seal Research Planning meeting (NOAA NMFS Alaska 

Fisheries Science Center, NOAA NMFS Alaska Region, Alaska Department of Fish and 

Game, Alaska Native Harbor Seal Commission, and Alaska Sea Life Center) in Juneau, 29 

March 2005.

Boveng, P. A summary of harbor seal research conducted in Alaska by NOAA Fisheries. Oral 

presentation, Working Group Meeting, Project 02.05-61, “Marine Mammals,” under 

Area V of the U.S.-Russia Agreement on Cooperation in the Field of Environmental 

Protection, St. Petersburg, Russia, September 2006.

Boveng, P., J. London, and R. Montgomery. Strong seasonal dynamics of harbor seals, an uppertrophic predator in Cook Inlet. Oral presentation, Alaska Marine Science Symposium, 

January 2007, Anchorage, Alaska.

Boveng, P. and J. London. Harbor seals in Cook Inlet. Oral presentation, Port Graham Wisdom 

Keepers’ Meeting, Port Graham, Alaska, September 2007.

London, J., D. Johnson, P. Boveng, J. VerHoef. Seasonal Movements of Harbor Seals Related to 

Sex and Age. 2007 Biennial Conference on the Biology of Marine Mammals, Capetown, 

South Africa (Poster); 2008 Alaska Marine Science Symposium, Anchorage, Alaska (Oral).

London, J., D. Johnson, and P. Boveng. Using a Continuous Time Correlated Random Walk and 

Bayesian Inference to Examine Spatial Use Patterns of Harbor Seals in Cook Inlet, Alaska, 

USA. 2009 Biennial Conference on the Biology of Marine Mammals, Quebec, Canada 

(Poster); 2010 Alaska Marine Science Symposium, Anchorage, Alaska (Poster).
BOEM 2012-065

35

LITERATURE CITED

Dorfman, R. 1938. A note on the delta-method for finding variance formulae. The Biometric 

Bulletin 1:129-137.

Freitas, C., C. Lydersen, M. A. Fedak, and K. M. Kovacs. 2008. A simple new algorithm to filter 

marine mammal Argos locations. Marine Mammal Science 24:315-325.

Frost, K. J. and L. F. Lowry. 1994. Assessment injury to harbor seals in Prince William Sound, 

Alaska, and adjacent areas following the Exxon Valdez oil spill. Alaska Department of 

Fish and Game, Wildlife Conservation Division, Fairbanks, AK.

Frost, K. J., L. F. Lowry, and J. J. Burns. 1982. Distribution of marine mammals in the coastal zone 

of the Bering Sea during summer and autumn. Pages 365-561 Environmental 

Assessment of the Alaskan Continental Shelf. Final Reports of Principal Investigators, 

Volume 20, December 1983. U.S. Department of Commerce, NOAA and U.S. 

Department of the Interior, Mineral Management Service, Juneau, AK.

Frost, K. J., L. F. Lowry, and J. M. Ver Hoef. 1999. Monitoring the trend of harbor seals in Prince 

William Sound, Alaska, after the Exxon Valdez oil spill. Marine Mammal Science 15:494-

506.

Frost, K. J., C. A. Manen, and T. L. Wade. 1994. Petroleum hydrocarbons in tissues of harbor 

seals from Prince William Sound and the Gulf of Alaska. Pages 331-358 in T. R. Loughlin, 

editor. Marine Mammals and the Exxon Valdez. Academic Press, Inc., San Diego, CA.

Goodman, L. A. 1960. On the exact variance of products. Journal of the American Statistical 

Association 55:708-713.

Higgs, M. D. and J. M. Ver Hoef. 2011. Discretized and aggregated: modeling dive depth of 

harbor seals from ordered categorical data with temporal autocorrelation. Biometrics.

Hoover-Miller, A., K. R. Parker, and J. J. Burns. 2001. A reassessment of the impact of the Exxon 

Valdez oil spill on harbor seals (Phoca vitulina richardsi) in Prince William Sound, Alaska. 

Marine Mammal Science 17:111-135.

Johnson, D. S., J. M. London, M.-A. Lea, and J. Durban. 2008. Continuous-Time Correlated 

Random Walk Model for Animal Telemetry Data. Ecology 89:1208-1215.

Lowry, L. F., K. J. Frost, and K. W. Pitcher. 1994. Observations of oiling of harbor seals in Prince 

William Sound. Pages 209-225 in T. R. Loughlin, editor. Marine Mammals and the Exxon 

Valdez. Academic Press, Inc., San Diego, CA.

Pitcher, K. W. 1990. Major decline in number of harbor seals, Phoca vitulina richardsi, on 

Tugidak Island, Gulf of Alaska. Marine Mammal Science 6:121-134.
BOEM 2012-065

36

Small, R. J., G. W. Pendleton, and K. W. Pitcher. 2003. Trends in abundance of Alaska harbor 

seals, 1983-2001. Marine Mammal Science 19:344-362.

Spraker, T. R., L. F. Lowry, and K. J. Frost. 1994. Gross necropsy and histopathological lesions 

found in harbor seals. Pages 281-311 in T. R. Loughlin, editor. Marine Mammals and the 

Exxon Valdez. Academic Press, Inc., San Diego, CA.

Ver Hoef, J. M. and K. J. Frost. 2003. A Bayesian hierarchical model for monitoring harbor seal 

changes in Prince William Sound, Alaska. Environmental and Ecological Statistics 10:201-

219.

Ver Hoef, J. M., J. M. London, and P. L. Boveng. 2010. Fast computing of some generalized linear 

mixed pseudo-models with temporal autocorrelation. Computational Statistics 25:39-55.
BOEM 2012-065

37

APPENDIX 1- Population structure of Cook Inlet harbor seals 

revealed by mitochondrial DNA and microsatellite analysis

G. O’Corry-Crowe

Population Biology and Behavioral Ecology Program

Harbor Branch Oceanographic Institute

Florida Atlantic University

5600 U.S. 1 Fort Pierce, FL 34946

Final report to

The National Marine Mammal Laboratory

7600 Sand Point Way NE

Seattle WA 98115

March 2011

 

 
BOEM 2012-065

38

Introduction

Harbor seals (Phoca vitulina) occupy a near-continuous distribution in the coastal and 

continental shelf waters of Alaska south of 61°N. This important marine predator utilizes a 

diverse range of habitats, hauls out at thousands of discrete coastal sites and represents a 

significant marine resource to a range of users. While the near-continuous distribution and 

recorded capacity of this pinniped to undertake long distance movements (Lowry et al 2001, 

Small et al. 2005) might suggest limited population subdivision in Alaska, the non-uniform 

abundance and differing population trends of harbor seals across their Alaskan range (Boveng et 

al. 2003, Small et al. 2003, 2008) as well as the behavioral philopatry to discrete breeding 

populations and foraging grounds observed elsewhere in this species (Stanley et al. 1996, 

Goodman 1998), identifies the potential for substantial population structure in Alaskan harbor 

seals. Resolving patterns of dispersal and population structure in such a species, however, is 

challenging. 

The limited temporal scale (i.e., months) over which telemetry can record movements means 

that such an approach will rarely confirm dispersal in long-lived species including harbor seals, 

and thus cannot provide detailed insight into dispersal patterns or population structure. 

Genetic analysis, specifically the examination of spatial patterns of variation at genetic loci, can 

reveal much about dispersal behavior and breeding patterns over time that facilitate inference 

about contemporary and historical demographic and reproductive relationships among groups 

of seals. Markers of choice in these endeavors include the mitochondrial genome (mtDNA), a 

maternally inherited genome that traces female dispersal patterns over time. As such, this 

marker is ideal for resolving the demographic relationships among groups of organisms. Highly 

polymorphic nuclear markers (nDNA), including microsatellites, are widely used to infer 

breeding patterns of both males and females and to estimate rates of genetic exchange. 

Incorporating these kinds of genetic analysis with direct assessment of movement patterns and 

habitat use via telemetry provide a comprehensive assessment of population structure in harbor 

seals that will assist in the assessment of population status and the risk of anthropogenic 

impacts and in the identification of management units. This report summarizes the findings of a 

genetic study of harbor seals in Cook Inlet, Alaska that primarily comprise seals sampled during 

satellite telemetry studies between 2004 and 2006 (Boveng et al. 2012).
BOEM 2012-065

39

Tissue samples have been collected from harbor seals across their Alaskan range, comprising 

samples from subsistence hunts and field studies including the current study. Prior to the 

present investigation, an analysis of 881 seal samples from 180 locations across Alaska for 

mtDNA variation revealed genetic subdivision indicative of restricted female dispersal at 

multiple spatial scales (Westlake and O’Corry-Crowe 2002, O’Corry-Crowe et al. 2003). Sample 

size from Cook Inlet and adjacent areas, however, was low limiting inference of population 

structure in this region. Nonetheless, initial indications suggested restricted female dispersal 

between Cook Inlet and the Gulf of Alaska. These earlier studies, while informative on larger 

geographic scales, highlighted the need for larger sample sizes, greater sample coverage and the 

inclusion of nDNA as well as mtDNA in the analysis of harbor seal population structure in Cook 

Inlet. Furthermore, the seasonal patterns of seal movements, haulout behavior and habitat use 

indicated that hypotheses about population genetic structure need to take seal biology into 

consideration.

In the current study, we analyzed all 93 samples collected during tagging operations between 

2004-2006 for patterns of variation in 435 base pairs (bp) of the mtDNA control region and for 

allelic polymorphism at 10 independent microsatellite loci. These data were compared to 

existing data from Cook Inlet and to data from across Alaska to assess patterns of gene flow and 

dispersal within Cook Inlet and between Cook Inlet and the Gulf of Alaska.

Objectives

Objective 1: To evaluate whether or not seals within Cook Inlet are genetically distinct 

from other seal populations

Objective 2: To evaluate whether or not geographically distinct subpopulations of seals 

exist within Cook Inlet
BOEM 2012-065

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The project’s objectives were formally tested as the following hypotheses:

Hypotheses

Hypothesis 1: Harbor seals within Cook Inlet are genetically distinct from seals in the 

Gulf of Alaska for –

1a: mtDNA, indicating limited female dispersal over time

1b: nDNA, indicating limited genetic exchange via male-mediated gene flow

Hypothesis 2: Seals in western Cook Inlet are genetically distinct from seals in eastern 

Cook Inlet for -

2a: mtDNA and indicating limited female dispersal over time

2b: nDNA, indicating limited genetic exchange via male-mediated gene flow

Methods

DNA extraction

Small biopsies of skin and blubber tissue were collected from each seal during capture and 

tagging operations and preserved in 95% EtOH. Total DNA was successfully isolated from all 

samples using standard cell lysis-protein digestion methods followed by silica-based DNA 

extraction and recovery protocols. Tissue lysis and digestion steps were automated using the 

FastDNATM kit and the FastPrepTM instrument (BIO 101, Carlsbad, California, USA), and DNA was 

recovered using the DNeasyTM Tissue Kit (QIAGEN, Valencia, California, USA). The concentration 

and quality of the purified DNA from all samples were estimated by spectrophotometry.

mtDNA analysis

A 588 base-pair (bp) region of the mitochondrial genome was amplified by the polymerase chain 

reaction (PCR) with custom designed primers (Kocher et al, 1989, Rosel et al., 1994; Westlake 
BOEM 2012-065

41

and O’Corry-Crowe, 2002). The amplified PCR products were purified by membrane-based 

filtration using Microcon® (Millipore, Bedford, Massachusetts) or QIAquick® (Qiagen, Valencia, 

California) columns. A total of 435 bp of the mtDNA control region and adjacent proline tRNA 

gene were sequenced by direct dideoxy sequencing using 4-dye fluorescent technology of 

Applied Biosystems (ABI, 1992). Excess dye-labeled terminators were removed from sequencing 

reactions using Centri-SepTM spin columns (empBiotech GmbH, Berlin, Germany) or by ethanol 

precipitation. Sequences were electrophoresed on an ABI 373A or ABI 377 automated 

sequencer, and were edited and aligned with the SeqEdTM multiple-sequence editor program 

(ABI, 1992).

Microsatellite analysis

All samples were also analyzed for variation within ten pinniped microsatellite loci. Forward 

primers were labeled with a fluorescent tag and PCR amplification of target DNA was performed 

in 25 or 50μl reactions in either a 9600 thermal cycler (Perkin-Elmer, Norwalk, Connecticut, 

USA), a GeneAmp 2700 PCR system (Applied Biosystems, Foster City, California, USA) or a PTC100 Thermal Controller (MJ Research, Inc., Watertown, Massachusetts, USA). Reactions 

contained approximately 10ng template DNA, 10 mM Tris-HCl (pH 8.3), 50 mM KCl, 2.0 mM 

MgCl2, 0.01% gelatin, 150 μM of each dNTP, 0.3 μM of each primer, and 2.5 units of Taq DNA 

polymerase. Following denaturation at 90°C for 2 min., DNA was amplified by 35 cycles 

consisting of denaturation at 94°C for 45 sec., annealing at 48°C for one min., and extension at 

72°C for 1.5 min. A final extension period of 5 min at 72°C was followed by cooling of the PCR 

product to 4°C. PCR products were run on an ABI 377 gel or 3100/3130xl Genetic Analyzer. 

Comparisons of observed to expected genotypic frequencies using the Micro-Checker program 

(version 2.2.3; van Oosterhout et al. 2004) found no evidence of null alleles (i.e., homozygote 

excess), large allelic dropout or scoring errors due to stuttering.

Data Analysis

To assess patterns of genetic subdivision on a number of spatial scales tissue samples were 

stratified into regional and sub-regional groupings using information on seal distribution and 

abundance. At the regional scale all the Cook Inlet samples were compared to harbor seals from 

other broadly defined regions across Alaska, including the Kodiak Archipelago, Prince William 
BOEM 2012-065

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Sound, Southeast Alaska and Bristol Bay. At the sub-regional level, we used the criteria and 

groupings defined in O’Corry-Crowe et al. (2003). At this level, Cook Inlet samples were stratified 

into an eastern Cook Inlet (ECI) group comprising samples from Kachemak Bay and Yukon Island, 

and a western Cook Inlet (WCI) group comprising the samples from Kamishak Bay, Iniskin Bay, 

Chinitna Bay, Tuxedni Bay and Kalgin Island. Seal samples from Japan, Russia and California were 

used as outgroups in all analyses.

Both equilibrium and non-equilibrium methods were used to investigate patterns of genetic 

differentiation, dispersal and population structure. Traditional F-statistics, homogeneity tests 

and likelihood-based methods, as well as a priori stratification and landscape genetic 

approaches were used to infer seal behavior from patterns of genetic variation. Details of all 

methods used will be available in subsequent manuscripts. Frequency (Fst , Chi-Square) and 

distance-based (Rst and Φst) analysis of genetic heterogeneity with permutation-based 

estimation of statistical significance were conducted using Arlequin and GENEPOP software. For 

microsatellite data, the Bayesian model-based clustering method STRUCTURE (v. 2.3.3) was also 

used to infer population structure and assign individuals to population of origin (Pritchard et al. 

2000, 2009). Under the assumption of HW equilibrium within clusters (“populations”) and 

complete linkage equilibrium between loci STRUCTURE infers the most likely number of 

population clusters, K, given the data, Pr (K|X). Analyses were run both with and without 

admixture ancestry models. In most analyses we used the models of Hubisz et al. (2009) that use 

sampling location as prior information. The authors found that weighting clustering outcomes 

that correlate with grouped sample location can reveal further underlying structure without the 

risk of detecting structure that is not present.

Results

Hypothesis 1: Harbor seals within Cook Inlet are genetically distinct from seals in the Gulf of 

Alaska for - 1a: mtDNA, indicating limited female dispersal over time

A total of 85 seal samples collected during capture operations between 2004 and 2006 yielded 

high quality sequence data. These data were combined with 50 other samples from Cook Inlet in 
BOEM 2012-065

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a comparison of regional mtDNA differentiation across Alaska comprising 1,308 seals. A further 

70 samples from Russia, Japan and California were used as outgroups. F statistics and 

homogeneity tests revealed that Cook Inlet was genetically differentiated from other major 

regions in Alaska (Table 1). All other analysis gave similar results (not shown).
BOEM 2012-065

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Table 1. Estimates of mtDNA differentiation (Fst) and significance (p) values from 50,175 

permutations among major centers of harbor seal abundance in Alaska.

Southeast 

AK

Gulf 

Coast PWS

Cook 

Inlet Kodiak

Bristol 

Bay Pribilofs Russia Japan California

n= 371 110 297 135 254 125 16 26 14 30

Southeast AK

Gulf Coast 0.000

PWS 0.003 0.001

Cook Inlet 0.010 0.007 0.010

Kodaik 0.007 0.004 0.005 0.007

Bristol Bay 0.040 0.031 0.039 0.042 0.035

Pribilofs 0.063 0.061 0.066 0.057 0.045 0.081

Russia 0.098 0.097 0.102 0.103 0.099 0.125 0.154

Japan 0.094 0.092 0.099 0.101 0.097 0.121 0.147 0.191

California 0.056 0.054 0.060 0.061 0.057 0.080 0.097 0.140 0.133

Dark shading indicates statistical significance at p<0.05

Light shading indicates statistical significance at p<0.10

PWS = Prince William Sound

Hypothesis 1: Harbor seals within Cook Inlet are genetically distinct from seals in the Gulf of 

Alaska for - 1b: nDNA, indicating limited genetic exchange via male-mediated gene flow

A total of 126 seals from Cook Inlet were successfully screened for allelic polymorphism in 10 

independent microsatellite loci. Cook Inlet genotypes were compared to 488 seals genotyped 

from the other major regions of seal distribution across Alaska and a further 58 from north 

Pacific outgroups Russia, Japan and California. Both frequency-based and distance based F-
BOEM 2012-065

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statistics reveled substantial genetic differentiation between Cook Inlet and other regions (not 

shown). The Bayesian model-based cluster analysis, STRUCTURE, incorporating the LOCPRIOR 

option, found the most likely number of population clusters to be K= 5. The same value of K and 

groupings of individuals into the K clusters was found for analyses that allowed admixture and 

did not allow admixture. One of the genetically distinct clusters corresponds to all the seals 

sampled in Cook Inlet (Figure 1). 

Figure 1. Model-based cluster analysis of nDNA data from harbor seals in the North Pacific using 

STRUCTURE 2.3.3. The analysis using prior sample region information (LOCPRIOR) revealed K=5 

clusters as the most likely. Each of 672 individuals is represented by a vertical line with 

estimated membership, Q, in each of K clusters denoted by the ratio of the 5 different colors. 

The analysis was based on ten microsatellite loci, MCMC reps of 50,000 burn-in followed by a 

run of 1x106 reps, and used both the admixture and no admixture (shown) models of ancestry. 

The Pribilof Islands, Russia, Japan and California samples are denoted by P., R., J. and Ca.

 Southeast Gulf PWS Cook Kodiak Bristol P R. J. Ca.

 Alaska coast Inlet Bay 

Hypothesis 2: Seals in western Cook Inlet are genetically distinct from seals in eastern Cook Inlet 

for - 2a: mtDNA and indicating limited female dispersal over time

The 135 Cook Inlet samples were partitioned into two geographic sub-regions, eastern (n=56) 

and western (n=79) Cook Inlet, respectively, based on the location of coastal sampling sites to 

determine whether there was population subdivision on smaller spatial scales. Low, but 
BOEM 2012-065

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statistically significant differentiation was found between the two Cook Inlet groupings for 

mtDNA (Table 2). When compared with other similarly scaled sub-regions across Alaska for this 

marker, both the eastern and western groupings differed from most other areas. A notable 

exception involved western Cook Inlet where we failed to detect differences between these 

seals and seals sampled in Northern Kodiak (Fst = -0.001). Low levels of differentiation were also 

documented between western Cook Inlet and the outer coast of the Kenai Peninsula, western 

Kodiak and eastern Kodiak (Table 2). All other analysis methods gave similar results (not shown).
BOEM 2012-065

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Table 2. Estimates of mtDNA differentiation (Fst) and significance (p) values from 50,175 permutations among sub-regions harbor seal abundance 

from northern Southeast Alaska to the Pribilof Islands.

SE Alaska 

5

Gulf Coast 

1

PWS 

1

PWS 

2

PWS 

3

E 

Kenai

E Cook 

Inlet

W Cook 

Inlet

N 

Kodiak

W 

Kodiak

E 

Kodiak

S 

Kodiak

Bristol Bay 

1

Bristol Bay 

2 Pribilofs

96 72 79 52 166 47 56 79 29 37 68 51 77 32 16

SE Alaska 5

Gulf Coast 1 0.001

PWS 1 0.005 0.001

PWS 2 0.006 0.002 -0.005

PWS 3 0.004 0.000 0.000 -0.001

E Kenai 0.003 -0.003 0.000 -0.003 -0.002

E Cook 0.043 0.036 0.036 0.038 0.039 0.040

W Cook 0.010 0.007 0.013 0.009 0.008 0.004 0.050

N Kodiak 0.004 -0.004 0.003 0.004 0.000 0.000 0.037 -0.001

W Kodiak 0.015 0.007 0.017 0.015 0.012 0.008 0.046 0.005 -0.006

E Kodiak 0.012 0.004 0.005 0.005 0.004 0.001 0.036 0.003 -0.005 0.001

S Kodiak 0.012 0.012 0.008 0.004 0.009 0.008 0.043 0.021 0.013 0.021 0.008

Bristol Bay 0.047 0.033 0.038 0.036 0.039 0.038 0.063 0.046 0.035 0.043 0.034 0.040

Bristol Bay 0.043 0.029 0.035 0.036 0.038 0.036 0.057 0.045 0.027 0.036 0.030 0.033 -0.009

Pribilofs 0.073 0.061 0.062 0.068 0.067 0.070 0.075 0.068 0.045 0.044 0.042 0.060 0.086 0.064

Dark shading indicates statistical significance at p<0.05

Light shading indicates statistical significance at p<0.10
BOEM 2012-065

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Hypothesis 2: Seals in western Cook Inlet are genetically distinct from seals in eastern Cook Inlet 

for -2b: nDNA, indicating limited genetic exchange via male-mediated gene flow

The 126 genotyped Cook Inlet samples were partitioned into the two geographic sub-regions, 

eastern (n=45) and western (n=81) Cook Inlet and, as with mtDNA, were compared with seals 

from sub-regions across Alaska and the North Pacific Ocean. Overall, microsatellite 

differentiation at these spatial scales was lower than that observed for mtDNA. Unlike the 

mtDNA analysis we were unable to detect statistically significant differentiation at nDNA 

between the two Cook Inlet groupings. As with the macrogeographic scale of the regional 

comparisons the model-based cluster analysis found the most likely number of population 

clusters to be K= 5. The same value of K and groupings of individuals into the K clusters was 

found for analyses that allowed admixture and did not allow admixture. One of the genetically 

distinct clusters comprises seals from the east and west Cook Inlet sub-regions (Figure 2). 
BOEM 2012-065

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Figure 2. Model-based cluster analysis of nDNA data from harbor seals in the North Pacific using 

STRUCTURE 2.3.3. The analysis using prior sample sub-region information (black vertical lines 

demarcate the different sub-regions) revealed K=5 clusters as the most likely. Each of 672 

individuals is represented by a vertical line with estimated membership, Q, in each of K clusters 

denoted by the ratio of the 5 different colors. The analysis was based on ten microsatellite loci, 

MCMC reps of 50,000 burn-in followed by a run of 1x106 reps, and used both the admixture and 

no admixture (shown) models of ancestry. The Pribilof Islands, Russia, Japan and California 

samples are denoted by P., R., J. and Ca.

East West

Cook Cook

 Southeast Gulf PWS Cook Kodiak Bristol P. R. J. Ca.

 Alaska coast Inlet Bay 

Discussion

The results presented here provide new insights into the dispersal patterns, breeding behavior 

and population structure of harbor seals in Cook Inlet and across Alaska. The findings of 

significant genetic differentiation between seals in Cook Inlet and other regions and sub-regions 

indicate limited dispersal and gene flow in and out of the Inlet over contemporary or ecological 

time frames, and possibly over even longer time scales. The findings from the clustering analysis 

of the nDNA data were quite surprising revealing that Cook Inlet seals were among the most 

distinct of any groupings within the Gulf of Alaska. An initial phylogeographic analysis of the 
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mtDNA data, using genetic distance among mtDNA haplotypes (not shown), also indicates a 

unique genetic signature for these animals that may date back thousands of years to the early 

Holocene of even the Pleistocene. More analysis, however, is required, including Bayesian and 

likelihood-based coalescent methods, to fully ascertain the demographic and evolutionary 

import of these genetic findings. It is already clear however, with regard to dispersal patterns 

and genetic exchange among broadly defined major concentration areas of harbor seals in 

Alaska, that female dispersal and male-mediated gene flow in and out of Cook Inlet is restricted.

The analysis of genetic heterogeneity on smaller spatial scales that are more likely to 

correspond to dispersal distance in this species revealed a more varied picture that may reflect a 

more complex pattern of dispersal and reproductive interactions involving Cook Inlet seals. The 

contrasting patterns of differentiation between mtDNA and nDNA markers within Cook Inlet, for 

example, may reflect greater female philopatry to breeding location and greater male-mediated 

gene flow throughout the Inlet. While both the eastern and western Cook Inlet groupings were 

differentiated from most other sub-regions across Alaska, they differed in the observed degree 

of mtDNA differentiation with a number of nearby areas. The failure to detect clear mtDNA 

differences between western Cook Inlet and parts of the Kodiak archipelago may indicate 

female dispersal between these areas. Furthermore, the nDNA cluster analysis, while clearly 

placing all Cook Inlet seals into a separate cluster, revealed that several seals from western Cook 

Inlet may be of mixed origins (Figure 2).

The findings on Cook Inlet harbor seal movements and habitat use presented in this report by 

Boveng, London and ver Hoef are especially interesting in this light. The majority of satellite 

tracks of Cook Inlet seals moving into the Gulf of Alaska were in waters surrounding the 

northern and western Kodaik archipelago, including Shelikov Strait (Figure 3, Boveng et al. 

2012). That some of these movements may reflect some demographic relationship between 

areas is intriguing but conjecture at this point. Most of the tagged seals provided tracks 

spanning just a few seasons and even then were typically observed to return to their capture 

location. Furthermore many (but not all) of the movements into the Gulf were recorded in the 

winter outside the breeding season.
BOEM 2012-065

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Another observation worth noting is that the north Kodiak sub-region was not clearly 

differentiated from most other areas across Alaska for mtDNA (Table 2). While this may have 

some biological basis it should be pointed out that north Kodiak had a small sample size relative 

to most other areas. 

Finally, analysis is ongoing into the potential role sampling season and the age and gender of 

seals may have on our ability to resolve population structure in harbor seals in Cook Inlet. Initial 

analysis of the data by season, age and sex largely confirm the pattern of genetic structure 

reported on here.

Acknowledgments

Funding for this project was provided by a grant from the National Marine Fisheries Service 

(NMFS), NOAA. Samples were collected and kindly provided by Peter Boveng and his team at the 

National Marine Mammal Laboratory, NMFS, Seattle. Many thanks to Carolina Bonin, Derek 

Campbell, Vicky Pease and Amy Frey who conducted much of the lab work. Laboratory facilities, 

administrative and archival assistance were provided by the Southwest Fisheries Science Center, 

NMFS, La Jolla. Thanks to Barb Taylor, Karen Martien and Robin Westlake for their assistance 

with much of the earlier genetic work on harbor seals in Alaska that helped make this study 

possible. Finally, a word of thanks to all who provided samples from the reference populations 

and to Harbor Branch Oceanographic Institute and Florida Atlantic University for providing the 

facilities to complete this report.
BOEM 2012-065

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References

Boveng P, Bengtson J, Withrow D, Cesarone J, Simpkins M, Frost K and Burns J. 2003. The 

abundance of harbor seals in the Gulf of Alaska. Marine Mammal Science 19(1): 111-127.

Boveng PL, London JM and Ver Hoef JM (2012) Distribution and abundance of harbor seals in 

Cook Inlet, Alaska. Task III: Movements, marine habitat use, diving behavior, and population 

structure, 2004-2006. Final Report. BOEM Report 2012-xxx. Bureau of Ocean Energy 

Management, Alaska Outer Continental Shelf Region Anchorage, Alaska. 57 pp.

Goodman SJ. 1998. Patterns of extensive genetic differentiation and variation among European 

harbor seals (Phoca vitulina vitulina) revealed using microsatellite DNA polymorphins. Mol Biol 

Evol 15(2): 104-118.

Hubisz M, Falush D, Stephens M and Pritchard J. 2009. Inferring weak population structure with 

the assistance of sample group information. Molecular Ecology Resources.

Kocher TD et al. 1989. Dynamics of mitochondrial DNA evolution in animals: amplification and 

sequencing with conserved primers. Proceedings of the National Academy of Sciences 86:6196-

6200

Lowry LF, Frost KJ, Ver Hoef JM and DeLong RA. 2001. Movements of satellite-tagged subadult 

and adult harbor seals in Prince William Sound, Alaska. Marine Mammal Science 17(4): 835-861.

O’Corry-Crowe GM Martien KK and Taylor BL. 2003. The analysis of population genetic structure 

in Alaska harbor seals, Phoca vitulina, as a framework for the identification of management 

stocks. Southwest Fisheries Science Center, National Marine Fisheries Service Administrative 

Report LJ-03-08. 54pp.

Pritchard JK, Stephens M and Donnelly P. 2000. Inference of population structure using

multilocus genotype data. Genetics 155:945–959.

Pritchard JK, Wen X and Falush D. 2010. Documentation for STRUCTURE software: version 2.3. 

Rosel PE, Dizon AE and Heyning JE. 1994. Genetic analysis of sympatric morphotypes of common 

dolphins (genus Delphinus). Marine Biology 119:159-167.
BOEM 2012-065

53

Small RJ, Pendleton GW and Pitcher KW. 2003. Trends in abundance of Alaska harbor seals, 

1983-2001. Marine Mammal Science 19(2): 344-362.

Small RJ, Lowry LF, Ver Hoef JM, Frost KJ, DeLong RA and Rehberg MJ. 2005. Differential 

movements by harbor seal pups in contrasting Alaska environments. Marine Mammal Science 

21(4): 671-694.

Small RJ, Boveng PL, Byrd GV and Withrow DE. 2008. Harbor seal population decline in the 

Aleutian Archipelago. Marine Mammal Science 24(4): 845-863.

Stanley HF, Casey S, Carnahan JM, Goodman S, Harwood J and Wayne RK. 1996. Worldwide 

patterns of mitochondrial DNA differentiation in the harbor seal (Phoca vitulina). Mol Biol Evol 

13(2): 368-382.

Van Oosterhout C, Hutchinson WF, Wills DPM and Shipley PF. 2004. Micro-Checker User Guide. 

The University of Hull. 28pp.

Westlake RL and O’Corry-Crowe GM. 2002. Macrogeographic structure and patterns of genetic 

diversity in harbor seals (Phoca vitulina) from Alaska to Japan. Journal of Mammalogy 

83(4):1111-1126.
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APPENDIX 2 – Tagged Harbor Seals in Cook Inlet, 2004-2006

SPENO SPECIES AGE SEX TAGTYPE DEPLOYDATE ENDDATE

PV2004_0201 Harbor seal YRLG F ST16 2004-08-27 UTC 2005-02-15 UTC

PV2004_0206 Harbor seal ADULT M ST16 2004-08-27 UTC 2005-02-07 UTC

PV2004_0208 Harbor seal SUB F ST16 2004-08-28 UTC 2005-05-02 UTC

PV2004_0216 Harbor seal ADULT M ST16 2004-08-30 UTC 2005-05-25 UTC

PV2004_0217 Harbor seal ADULT F ST16 2004-08-30 UTC 2005-07-16 UTC

PV2004_0219 Harbor seal ADULT F ST16 2004-08-30 UTC 2005-07-09 UTC

PV2004_0215 Harbor seal SUB F ST16 2004-08-30 UTC 2009-01-01 UTC

PV2004_0221 Harbor seal ADULT M ST16 2004-08-30 UTC 2005-02-15 UTC

PV2004_0223 Harbor seal ADULT F ST16 2004-08-30 UTC 2005-01-25 UTC

PV2004_0214 Harbor seal SUB F ST16 2004-08-30 UTC 2005-05-17 UTC

PV2004_0226 Harbor seal SUB F ST16 2004-08-31 UTC 2005-02-11 UTC

PV2004_0233 Harbor seal YOY F SPLASH 2004-08-31 UTC 2005-01-03 UTC

PV2004_0231 Harbor seal ADULT F ST16 2004-08-31 UTC 2005-04-25 UTC

PV2004_0230 Harbor seal ADULT M ST16 2004-08-31 UTC 2005-04-01 UTC

PV2004_0229 Harbor seal SUB F ST16 2004-08-31 UTC 2004-09-03 UTC

PV2004_0227 Harbor seal SUB M ST16 2004-08-31 UTC 2004-10-30 UTC

PV2004_0224 Harbor seal ADULT F ST16 2004-08-31 UTC 2004-10-13 UTC

PV2004_0235 Harbor seal ADULT M ST16 2004-09-01 UTC 2004-09-08 UTC

PV2004_0234 Harbor seal ADULT M ST16 2004-09-01 UTC 2005-04-28 UTC

PV2004_0001 Harbor seal YOY F SPLASH 2004-09-15 UTC 2005-02-28 UTC

PV2005_0236 Harbor seal ADULT M ST16 2005-04-30 UTC 2005-05-11 UTC

PV2005_0238 Harbor seal ADULT M ST16 2005-04-30 UTC 2005-07-11 UTC

PV2005_0239 Harbor seal ADULT M ST16 2005-04-30 UTC 2005-05-16 UTC

PV2005_0237 Harbor seal SUB F SPLASH 2005-04-30 UTC 2005-06-26 UTC

PV2005_0240 Harbor seal SUB F SPLASH 2005-05-05 UTC 2005-06-30 UTC

PV2005_0241 Harbor seal ADULT M ST16 2005-05-08 UTC 2005-07-08 UTC

PV2005_0246 Harbor seal SUB F SPLASH 2005-05-09 UTC 2005-06-25 UTC

PV2005_0245 Harbor seal SUB M ST16 2005-05-09 UTC 2005-06-18 UTC

PV2005_0244 Harbor seal SUB M SPLASH 2005-05-09 UTC 2005-06-18 UTC

PV2005_0243 Harbor seal YRLG M SPLASH 2005-05-09 UTC 2005-07-17 UTC

PV2005_0247 Harbor seal SUB F SPLASH 2005-05-09 UTC 2005-07-18 UTC

PV2005_0249 Harbor seal ADULT M ST16 2005-05-09 UTC 2005-07-01 UTC

PV2005_0248 Harbor seal ADULT M ST16 2005-05-09 UTC 2005-07-09 UTC

PV2005_0250 Harbor seal SUB M SPLASH 2005-05-10 UTC 2005-06-05 UTC

PV2005_0251 Harbor seal ADULT M SPLASH 2005-05-10 UTC 2005-07-04 UTC

PV2005_0252 Harbor seal ADULT F SPLASH 2005-09-13 UTC 2006-05-23 UTC

PV2005_0253 Harbor seal ADULT M SPLASH 2005-09-13 UTC 2006-04-21 UTC

PV2005_0254 Harbor seal YOY M SPLASH 2005-09-13 UTC 2006-01-28 UTC
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PV2005_0255 Harbor seal ADULT M SPLASH 2005-09-15 UTC 2006-06-10 UTC

PV2005_0259 Harbor seal ADULT F SPLASH 2005-09-15 UTC 2006-05-21 UTC

PV2005_0256 Harbor seal ADULT F SPLASH 2005-09-15 UTC 2006-08-11 UTC

PV2005_0257 Harbor seal ADULT M SPLASH 2005-09-15 UTC 2006-06-19 UTC

PV2005_0258 Harbor seal ADULT M SPLASH 2005-09-15 UTC 2006-08-04 UTC

PV2005_0260 Harbor seal YOY F SPLASH 2005-09-16 UTC 2006-05-27 UTC

PV2005_0261 Harbor seal ADULT M SPLASH 2005-09-16 UTC 2006-07-25 UTC

PV2005_0262 Harbor seal YRLG M SPLASH 2005-09-18 UTC 2006-04-13 UTC

PV2005_0263 Harbor seal ADULT M SPLASH 2005-09-18 UTC 2006-06-14 UTC

PV2005_0264 Harbor seal ADULT M SPLASH 2005-09-18 UTC 2006-06-14 UTC

PV2005_0270 Harbor seal YOY F SPLASH 2005-09-19 UTC 2006-06-14 UTC

PV2005_0269 Harbor seal ADULT M SPLASH 2005-09-19 UTC 2006-06-19 UTC

PV2005_0268 Harbor seal YRLG F SPLASH 2005-09-19 UTC 2006-05-19 UTC

PV2005_0267 Harbor seal SUB M SPLASH 2005-09-19 UTC 2006-06-06 UTC

PV2005_0266 Harbor seal SUB F SPLASH 2005-09-19 UTC 2006-05-27 UTC

PV2005_0265 Harbor seal YRLG F SPLASH 2005-09-19 UTC 2006-06-11 UTC

PV2005_0275 Harbor seal ADULT M SPLASH 2005-09-21 UTC 2006-08-08 UTC

PV2005_0276 Harbor seal ADULT F SPLASH 2005-09-21 UTC 2006-06-02 UTC

PV2005_0274 Harbor seal ADULT M SPLASH 2005-09-21 UTC 2006-06-14 UTC

PV2005_0273 Harbor seal YOY F SPLASH 2005-09-21 UTC 2006-07-01 UTC

PV2005_0272 Harbor seal ADULT F SPLASH 2005-09-21 UTC 2006-02-03 UTC

PV2005_0277 Harbor seal ADULT F SPLASH 2005-09-21 UTC 2006-06-26 UTC

PV2005_0271 Harbor seal ADULT F SPLASH 2005-09-21 UTC 2006-05-29 UTC

PV2006_0280 Harbor seal YOY F SPLASH 2006-04-26 UTC 2006-07-22 UTC

PV2006_0280 Harbor seal YOY F SPOT5 2006-04-26 UTC 2009-01-01 UTC

PV2006_0278 Harbor seal YOY M SPLASH 2006-04-26 UTC 2006-07-09 UTC

PV2006_0278 Harbor seal YOY M SPOT5 2006-04-26 UTC 2009-01-01 UTC

PV2006_0279 Harbor seal YOY M SPLASH 2006-04-26 UTC 2006-07-05 UTC

PV2006_0279 Harbor seal YOY M SPOT5 2006-04-26 UTC 2009-01-01 UTC

PV2006_0281 Harbor seal SUB M SPLASH 2006-04-29 UTC 2006-06-24 UTC

PV2006_0281 Harbor seal SUB M SPOT5 2006-04-29 UTC 2009-01-01 UTC

PV2006_0287 Harbor seal SUB F SPLASH 2006-04-29 UTC 2006-06-08 UTC

PV2006_0286 Harbor seal ADULT F SPOT5 2006-04-29 UTC 2009-01-01 UTC

PV2006_0286 Harbor seal ADULT F SPLASH 2006-04-29 UTC 2006-08-18 UTC

PV2006_0285 Harbor seal SUB M SPLASH 2006-04-29 UTC 2006-06-18 UTC

PV2006_0284 Harbor seal SUB F SPOT5 2006-04-29 UTC 2009-01-01 UTC

PV2006_0284 Harbor seal SUB F SPLASH 2006-04-29 UTC 2006-06-14 UTC

PV2006_0283 Harbor seal SUB M SPOT5 2006-04-29 UTC 2009-01-01 UTC

PV2006_0283 Harbor seal SUB M SPLASH 2006-04-29 UTC 2009-01-01 UTC

PV2006_0282 Harbor seal SUB M SPOT5 2006-04-29 UTC 2009-01-01 UTC

PV2006_0282 Harbor seal SUB M SPLASH 2006-04-29 UTC 2006-06-24 UTC

PV2006_0288 Harbor seal ADULT F SPOT5 2006-04-30 UTC 2009-01-01 UTC
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PV2006_0288 Harbor seal ADULT F SPLASH 2006-04-30 UTC 2006-07-03 UTC

PV2006_0289 Harbor seal ADULT F SPLASH 2006-05-04 UTC 2006-07-27 UTC

PV2006_0289 Harbor seal ADULT F SPOT5 2006-05-04 UTC 2009-01-01 UTC

PV2006_0290 Harbor seal SUB M SPLASH 2006-05-04 UTC 2006-06-20 UTC

PV2006_0290 Harbor seal SUB M SPOT5 2006-05-04 UTC 2009-01-01 UTC

PV2006_0293 Harbor seal SUB M SPOT5 2006-05-05 UTC 2009-01-01 UTC

PV2006_0291 Harbor seal YOY M SPOT5 2006-05-05 UTC 2009-01-01 UTC

PV2006_0292 Harbor seal ADULT F SPLASH 2006-05-05 UTC 2006-06-28 UTC

PV2006_0292 Harbor seal ADULT F SPOT5 2006-05-05 UTC 2009-01-01 UTC

PV2006_0293 Harbor seal SUB M SPLASH 2006-05-05 UTC 2006-06-26 UTC

PV2006_0291 Harbor seal YOY M SPLASH 2006-05-05 UTC 2006-06-14 UTC
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APPENDIX 3 – Abstracts of peer-reviewed publications resulting 

from this project:

Johnson, D. S., J. M. London, M.-A. Lea, and J. Durban. 2008. Continuous-Time Correlated 

Random Walk Model for Animal Telemetry Data. Ecology 89:1208-1215.

ABSTRACT: We propose a continuous-time version of the correlated random walk model for 

animal telemetry data. The continuous-time formulation allows data that have been 

nonuniformly collected over time to be modeled without subsampling, interpolation, or 

aggregation to obtain a set of locations uniformly spaced in time. The model is derived from a 

continuous-time Ornstein-Uhlenbeck velocity process that is integrated to form a location 

process. The continuous-time model was placed into a state–space framework to allow 

parameter estimation and location predictions from observed animal locations. Two previously 

unpublished marine mammal telemetry data sets were analyzed to illustrate use of the model, 

by-products available from the analysis, and different modifications which are possible. A harbor 

seal data set was analyzed with a model that incorporates the proportion of each hour spent on 

land. Also, a northern fur seal pup data set was analyzed with a random drift component to 

account for directed travel and ocean currents.

http://www.esajournals.org/doi/abs/10.1890/07-1032.1

Ver Hoef, J. M., J. M. London, and P. L. Boveng. 2010. Fast computing of some generalized linear 

mixed pseudo-models with temporal autocorrelation. Computational Statistics 25:39-55.

ABSTRACT: This paper considers ways to increase computational speed in generalized linear 

mixed pseudo-models for the case of many repeated measurements on subjects. We obtain 

linearly increasing computing time with number of observations, as opposed to O(n 3

) increasing 

computing time using numerical optimization. We also find a surprising result; that incomplete 

optimization for covariance parameters within the larger parameter estimation algorithm 

actually decreases time to convergence. After comparing various computing algorithms and 

choosing the best one, we fit a generalized linear mixed model to a binary time series data set 

with over 100 fixed effects, 50 random effects, and approximately 1.5 × 105

observations.

http://www.springerlink.com/content/w87058283420x1p5/
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Higgs, M. and J. M. Ver Hoef. 2011. Discretized and aggregated: Modeling dive depth of harbor 

seals from ordered categorical data with temporal autocorrelation. Biometrics DOI: 

10.1111/j.1541-0420.2011.01710.x 

ABSTRACT: Ordered categorical data are pervasive in environmental and ecological data, and 

often arise from constraints that require discretizing a continuous variable into ordered 

categories. A great deal of data have been collected toward the study of marine mammal dive 

behavior using satellite depth recorders (SDRs), which often discretize a continuous variable 

such as depth. Additionally, data storage or transmission constraints may also necessitate the 

aggregation of data over time intervals of a specified length. The categorization and aggregation 

create a time series of ordered multicategory counts for each animal, which present challenges 

in terms of statistical modeling and practical interpretation. We describe an intuitive strategy for 

modeling such aggregated, ordered categorical data allowing for inference regarding the 

category probabilities and a measure of central tendency on the original scale of the data (e.g., 

meters), along with incorporation of temporal correlation and overdispersion. The strategy 

extends covariate-specific cutpoint models for ordinal data. We demonstrate the method in an 

analysis of SDR dive-depth data collected on harbor seals in Alaska. The primary goal of the 

analysis is to assess the relationship of covariates, such as time of day, with number of dives and 

maximum depth of dives. We also predict missing values and introduce novel graphical 

summaries of the data and results.

http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01710.x/full