Southwest Alaska DPS of the Northern Sea Otter

(Enhydra lutris kenyoni)

5-Year Review:

Summary and Evaluation

 Photo by Randall Davis

U.S. Fish and Wildlife Service

Marine Mammals Management

Anchorage, Alaska

August 2013
5-YEAR REVIEW

Southwest Alaska DPS of the Northern Sea Otter

(Enhydra lutris kenyoni)

1.0 GENERAL INFORMATION

1.1 Reviewers

Lead Regional Office: Alaska Region 

Contact: Sonja Jahrsdoerfer, Regional Endangered Species Coordinator, 907 786-3323 

Lead Field Office: Marine Mammals Management

Contact: Frances Mann, Sea Otter Program lead, 907 786-3668. 

1.2 Methodology used to complete the review

The U.S. Fish and Wildlife Service (Service) conducts status reviews of species on the 

List of Endangered and Threatened Wildlife and Plants (50 CFR 17.11 and 17.12) as 

required by section 4(c)(2)(A) of the Endangered Species Act of 1973 (16 U.S.C. 1531 et 

seq.), as amended (ESA). The Service announced this status review via a Federal 

Register notice on April 26, 2013 (78 FR 24767), and requested new scientific or 

commercial data and information that may have a bearing on the classification of the 

southwest Alaska Distinct Population Segment (DPS) of the northern sea otter (Enhydra 

lutris kenyoni) as a threatened species.

Biologists from the Service’s Region 7 Marine Mammals Management office, located in 

Anchorage, Alaska, conducted this review. On May 3, 2013, the Service invited 37 

Alaska Native tribes within the range of the Southwest DPS of northern sea otters to 

participate via teleconference in government-to-government consultation on the status of 

this population. The Service also invited 47 Alaska Native Claims Settlement Act 

(ANCSA) regional and village corporations within the range of this sea otter population 

to provide information for the 5-year status review. Alaska Native Organizations, nongovernmental organizations, State and Federal agencies, university researchers, and other 

known interested parties were also invited to provide information. 

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1.3 Background 

1.3.1 FR Notice citation announcing initiation of this review: 78 FR 24767 

1.3.2 Listing history

FR notice: 70 FR 46366 

Date listed: August 9, 2005 

Entity listed: Southwest Alaska DPS of the northern sea otter (Enhydra lutris 

kenyoni) 

Classification: Threatened

1.3.3 Associated rulemakings

Final Rule, Designation of Critical Habitat

FR notice: 74 FR 51988 

Date: October 8, 2009 

Special Rule

FR notice: 71 FR 46864 

Date: August 15, 2006 

This special rule allows for the limited, noncommercial import and export of 

items that qualify as authentic Native articles of handicrafts and clothing that were 

derived from sea otters legally taken for subsistence purposes by Alaska Natives 

from the listed population. This special rule also allows for cultural exchange by 

Alaska Natives and activities conducted by persons registered as an agent or 

tannery under existing law. The Service also amended our definition of 

“Authentic Native articles of handicrafts and clothing” by striking the stipulation 

that such items were commonly produced on or before December 28, 1973. This 

definition change is appropriate in light of a court ruling on the Service’s 

definition of “Authentic Native articles of handicrafts and clothing” and 

consistent with our current definition of “Authentic Native articles of handicrafts 

and clothing” under the Marine Mammal Protection Act (MMPA) of 1972.

1.3.4 Review History

Draft Recovery Plan

FR notice: 75 FR 62562 

Date: October 12, 2010 

Final Recovery Plan

FR notice: 78 FR 54905 

Date: September 6, 2013 

1.3.5 Species’ Recovery Priority Number at start of 5-year review

Priority Number 6 

2
1.3.6 Recovery Plan or Outline

Name of plan or outline: Southwest Alaska Distinct Population Segment of the 

Northern Sea Otter (Enhydra lutris kenyoni) Recovery Plan 

Date issued: August 2013 

The Recovery Plan for the Southwest Alaska DPS was finalized in August 2013, 

and the Federal Register notice announcing its completion is expected to be 

published in August 2013. The Service is beginning the process of implementing 

several of the Recovery Plan’s key aspects. See section 4.0, “Recommendations 

for Future Actions.” Ongoing long-term recovery efforts are identified in the 

Recovery Plan itself, and include harvest monitoring, oil spill prevention, 

monitoring of trends in abundance, estimation of population size, disease 

screening and monitoring, and estimation of survival rates. 

2.0 REVIEW ANALYSIS

2.1 Application of the 1996 Distinct Population Segment (DPS) policy

2.1.1 Is the species under review a vertebrate?

Yes

2.1.2 Is the species under review listed as a DPS? 

Yes

2.1.3 Was the DPS listed prior to 1996? 

No

2.1.4 Is there relevant new information for this species regarding the application 

of the DPS policy? 

No

2.2 Recovery Criteria

2.2.1 Does the species have a final, approved recovery plan1

 containing objective, 

measurable criteria? 

Yes

1 Although the guidance generally directs the reviewer to consider criteria from final approved 

recovery plans, criteria in published draft recovery plans may be considered at the reviewer’s 

discretion.

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2.2.2 Adequacy of recovery criteria

2.2.2.1 Do the recovery criteria reflect the best available and most up-to date 

information on the biology of the species and its habitat?

Yes, the Recovery Plan for this DPS was finalized in August 2013, and the 

biology and habitat of this DPS are well documented. 

2.2.2.2 Are all of the 5 listing factors that are relevant to the species 

addressed in the recovery criteria (and is there no new information to 

consider regarding existing or new threats)? 

Yes, the 5 listing factors are addressed in the recovery plan, and there is no 

 new information to consider regarding threats.

2.2.3 List the recovery criteria as they appear in the recovery plan, and discuss 

how each criterion has or has not been met, citing information

 Criterion 1. Demographic. The probability of becoming endangered within 25 

years is <5%. This recovery criterion applies to all 5 Management Units (MUs). 

This recovery criterion has not been met for 4 of the 5 MUs. A Population 

Viability Analysis (PVA) indicates that current population estimates for 4 of the 5 

MUs are below the levels estimated to reduce the probability of becoming 

endangered within 25 years to <5% (Table 1). Status is expressed as a fraction of 

carrying capacity (K), where a threshold of approximately 35% of K or greater is 

required to meet this demographic criterion. See section 2.3.1 for more details. 

Only MU 5 (the Kodiak, Kamishak Bay, and Alaska Peninsula MU) currently 

meets this criterion for delisting.

Criterion 2. Ecosystem-based. Greater than 50% of islands are in the kelpdominated state. This recovery criterion applies to the Western Aleutians and 

Eastern Aleutians MUs only. 

This recovery criterion has not been re-evaluated for either MU. The last new 

data on kelp coverage in the Aleutians were collected in 2000 and used in 

development of the Recovery Plan (USFWS 2013b). No new data have been 

collected since then. To our knowledge, this criterion has not been met for either 

MU. See section 4.0, “Recommendations for Future Actions.” 

Criterion 3. Threats-based. In the Recovery Plan, the threats-based criteria and 

their importance to recovery are assessed in Table 10 (USFWS 2013b). The best 

available information indicates that these assessments have not changed, and that 

these threats have not been adequately mitigated. While there is spatial 

variability, predation (Factor C) appears to have the highest potential impact to 

recovery, with high importance in 3 MUs (Western and Eastern Aleutians and 

South Alaska Peninsula MUs) and moderate importance in 2 MUs (Bristol Bay 

and Kodiak, Kamishak, Alaska Peninsula MUs). Oil spills (Factor A) are of 

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moderate importance to recovery in the Eastern Aleutians and Kodiak, Kamishak, 

Alaska Peninsula MUs. Subsistence harvest (Factor B), infectious disease (Factor 

C) and illegal take (Factor E) are of moderate importance in the Kodiak, 

Kamishak, Alaska Peninsula MU. Available evidence indicates that other 

potential threats are considered to have low importance to recovery of the listed 

DPS.

2.3 Updated Information and Current Species Status

2.3.1 Biology and Habitat 

2.3.1.1 New information on the species’ biology and life history

None 

2.3.1.2 Abundance, population trends (e.g. increasing, decreasing, stable), 

demographic features (e.g., age structure, sex ratio, family size, birth rate, 

age at mortality, mortality rate, etc.), or demographic trends

Population Sizes and Trends, and Population Viability Analysis (PVA)

An aerial survey of sea otters along the coastline of Katmai National Park and 

Preserve was completed during July 2012 by the National Park Service (NPS) and 

US Geological Survey (USGS). The survey area ranged from Cape Douglas to 

the southwest end of Cape Kubugakli at the park boundary. Although not yet 

finalized and peer-reviewed, preliminary results indicate a total estimated 

population size in 2012 of approximately 8,644 sea otters (se = 1,243), with an 

overall density of 5.96 sea otters/km2 (NPS and USGS unpublished data). This 

represents a 22% increase in sea otter population size in this area in the 4 year 

period 2008 – 2012. The estimated population size in 2008 was 7,095 sea otters 

(se = 922). With a population size at about 66% of K (carrying capacity), the 

Kodiak, Kamishak Bay, and Alaska Peninsula MU continues to meet the 

demographic criterion for delisting. 

Conducting aerial surveys for sea otters in the Aleutian Islands is not ideal for 

various reasons. First, the steep topography and narrow shallow shelf preclude a 

study design based on random stratified transects, because sea otters inhabit only 

a narrow strip along the shore. Counts in the Aleutians are made by flying the 

shoreline of each island, and the counts lack estimates of variability. 

Additionally, otter behavior in the past two decades has changed. Sea otters are 

now observed closer to shore than they were 20 years ago, often within a few 

meters of the shoreline or rocky outcrops, decreasing the detection probability of 

sighting them from the air and confounding comparisons with earlier datasets 

(USGS & USFWS unpublished data). Finally, conducting aerial surveys in this 

vast, isolated area is cost prohibitive, in the range of several hundred thousand 

dollars to survey the entire Aleutian chain.

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For these and other reasons, the Service has worked with USGS to conduct 

repeated skiff surveys to detect trends in abundance at selected islands (index 

sites) in the Aleutian Islands, and these efforts are on-going. The islands that 

have been surveyed have a wide range in physical size, available habitat, and 

historical sea otter population sizes. These surveys and preliminary results are 

discussed in detail in the Recovery Plan (USFWS 2013b; see pages 2-13 to 2-15). 

Trends in abundance based on skiff survey data from the Western and Eastern 

Aleutians MUs through 2007 were used in development of a Population Viability 

Analysis (PVA) model as part of the recovery planning process. The model and 

its results are discussed in detail in the Recovery Plan (pages 6-1 to 6-6 and 

Appendix B of the Recovery Plan). In summary, PVAs are useful for 

understanding how demographic rates (e.g., birth and mortality rates) interact to 

cause a population to increase, decrease, or remain stable. They have been used 

widely in the past decade to assess how discrete threats are affecting the 

continued survival of species of conservation concern. 

Objectives of the PVA for this DPS were to develop delisting and uplisting 

criteria that are flexible and can be modified as new information becomes 

available, and to provide support for future management decisions. As part of this 

review, trends in abundance for the Western Aleutians MU were re-examined 

using new data from 2008 through 2011, which were added to the existing time 

series. Additionally, the PVA was updated with the new trend information and 

the delisting and uplisting thresholds were re-estimated. Although not yet 

finalized and peer-reviewed, these preliminary results are the most up-to-date 

information available on population trends for the Western Aleutians, and were 

included in the section “Current Population Trend” in the revised Stock 

Assessment Report (SAR) for the southwest Alaska DPS (USFWS 2013a). For 

details of the updated trend analysis and PVA results, see Appendix A of this 

review.

Population Trend

A Bayesian state-space trend analysis (Clark and Bjornstad 2004) was conducted 

using all available data compiled from skiff surveys conducted by USGS and 

USFWS through 2011. Five islands had at least 5 or more surveys, resulting in a 

total of 42 surveys spanning the period 1993 – 2011: Attu Island (n = 7 surveys), 

Kiska Island (n = 5), Amchitka Island (n = 9), Adak Island (n = 16), and 

Kagalaska Island (n = 5). 

Updated results indicate that population trends for the Western Aleutians MU

over the 1993-2003 period were strongly negative, with an average rate of decline 

of approximately 20% per year. Population trends changed during the period 

2004 – 2011, with an average growth rate of approximately 0. In general, these 

trends were consistent among islands. Although there was some variation in 

trends among islands, in all cases the 95% credible intervals (analogous to a 

confidence interval for frequentist statistics) for the island-specific rates were 

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negative for time period 1 (1993-2003) but spanned 0 for time period 2 (2004-

2011). These results suggest that population trends have stabilized in the western 

Aleutian Islands over the last 5-8 years, although there is still no evidence of 

recovery (USGS and USFWS unpublished data; Appendix A). 

PVA Summary

The updated PVA results support an adjustment of the delisting threshold, which 

is the value above which the DPS would be removed from ESA protection, and 

the uplisting threshold, which is the value below which the DPS would be 

reclassified as endangered, for each MU (Table 1). In summary, the updated 

model results support lower delisting and uplisting abundances. For the Eastern 

and Western Aleutians MUs, we have used a new uplisting threshold of 1.1% of 

K, or an uplisting density of 0.165 sea otters per km2

 of habitat; and a new 

delisting threshold of 35% of K, or delisting density of 5.25 sea otters per km2 of 

habitat. In the absence of PVA results for the other MUs, we have set the 

uplisting and delisting threshold values for these MUs to the equivalent 

proportions of their respective estimates of carrying capacity until more 

information becomes available (USFWS 2013b; Appendix A). These updated 

estimates of delisting and uplisting abundance are presented in Table 1. As more 

information becomes available, the estimates will continue to be refined.

Application of the PVA for the Western and Eastern Aleutians MUs to the other 

MUs is highly precautionary, in that the Aleutians experienced the steepest 

population declines and greatest reductions in numbers. PVAs for the remaining 

MUs will be developed as data become available (see section 4 

“Recommendations for Future Actions”).

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Table 1. Examples of estimates that were developed by PVA models using available data 

through 2007, as described in the Recovery Plan (USFWS 2013b), and through 2011 (Appendix 

A). Estimates shown include carrying capacity, delisting abundance, uplisting abundance, and 

current status relative to carrying capacity for each of the five Management Units (MUs) in the 

southwest Alaska DPS of the northern sea otter. These estimates are not fixed, but are dynamic, 

and will be updated periodically as more data become available. 

Management 

Unit 

Date of 

Estimate

Carrying 

Capacity 

(K)

Delisting 

Abundance 

Uplisting 

Abundance 

Abundance 

Estimate

Status 

(%K)

Western 

Aleutians 

2007 63,897 31,309 2,556 6,451 0.10

2011 - 22,364 703 - -

Eastern 

Aleutians 

2007 30,697 15,042 1,228 2,291 0.07

2011 - 10,744 338 - -

Bristol Bay 2007 36,898 18,080 1,476 11,253 0.30

2011 - 12,914 406 - -

South Alaska 

Peninsula 

2007 33,476 16,403 1,339 4,724 0.14

2011 - 11,717 368 - -

Kodiak, 

Kamishak, 

Alaska Peninsula

2007 46,086 22,582 1,843 28,955 0.63

2011 - 16,130 507 30,504 0.66

Summary 2007 211,054 103,417 8,442 53,674 0.25

Summary 2011 211,054 73,869 2,322 55,223 0.26

Stock Assessment Reports and Guidelines

The National Marine Fisheries Service (NMFS) and USFWS held a workshop in 

February 2011 to make changes and clarifications to the Guidelines for Assessing 

Marine Mammal Stocks (GAMMS). GAMMS underlie NMFS policy for 

implementing provisions of MMPA section 117, and ensure consistent methods 

for development of marine mammal stock assessments nationwide. One objective 

of the 2011 workshop was to consider methods for assessing stock status (i.e., 

how to apply the Potential Biological Removal framework, or PBR) when 

abundance data are outdated, nonexistent, or only partially available. Workshop 

recommendations on this and other topics were summarized in a report entitled, 

“Guidelines for Assessing Marine Mammal Stocks: Report of the GAMMS III 

Workshop, February 15 – 18, 2011, La Jolla, California” (Moore and Merrick 

2011). Additional peer review on the guidelines was solicited in January 2012 

(77 FR 3450). 

NMFS is in the process of finalizing the changes and clarifications to the 

GAMMS III guidelines, and the new guidelines will be implemented by NMFS 

once this process has been completed. While FWS was involved in the workshop, 

at this time the GAMMS guidelines are not considered FWS policy. The FWS 

recognizes the value of these efforts and once finalized, FWS will follow the 

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guidelines to the extent applicable when revising SARs for sea otters and other 

marine mammals under FWS management authority. 

The most current SAR for the Southwest Alaska DPS of the northern sea otter 

will be finalized in summer 2013. This stock continues to be classified as 

“depleted” under the MMPA due to its listing as threatened under the ESA. The 

MMPA requires that depleted stocks are reviewed annually. If the determination 

is made that the status of the stock has not changed and cannot be more accurately 

determined, then a revision of the SAR is not warranted for that year. In 2009 and 

2010, FWS determined that the status of the southwest Alaska DPS had not 

changed and could not be more accurately determined, and the SAR for this stock 

was not revised. Upon the next review, FWS will evaluate whether 

implementation of the new GAMMS III guidelines will be appropriate for the 

southwest Alaska DPS.

2.3.1.3 Genetics, genetic variation, or trends in genetic variation (e.g., loss 

of genetic variation, genetic drift, inbreeding, etc.)

No new information is available to indicate this factor has changed.

2.3.1.4 Taxonomic classification or changes in nomenclature

None 

2.3.1.5 Spatial distribution, trends in spatial distribution (e.g., increasingly 

fragmented, increased numbers of corridors, etc.), or historical range (e.g., 

corrections to the historical range, change in distribution of the species

within its historical range, etc.)

Some anecdotal information was received during our solicitation of public 

comments. A local observer who has lived in southwest Alaska for many years 

reported seeing unusually large groups of sea otters around Unalaska Island, in 

Nantikan Bay, in Broad Bay, on the outside of Amaknak Spit, and near Eider 

Point. Groups were as large as 60-80 individuals, including mostly females with 

young. Kelp beds are also growing in new places. He stated that such high 

numbers of otters have not been seen since the 1940s. The same observer also 

reported that there has been a slight increase in sea otter numbers in Clam 

Lagoon, Shagiak Bay, and Bay of Islands in Adak. This information correlates 

well with survey data from the Aleutians, which indicates that the decline in 

numbers has leveled off and that numbers may have stabilized or even increased 

locally (see section 2.3.1.2).

2.3.1.6 Habitat or ecosystem conditions (e.g., amount, distribution, and 

suitability of the habitat or ecosystem)

No new information is available to indicate this factor has changed.

2.3.1.7 Other

None 

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2.3.2 Five-Factor Analysis (threats, conservation measures, and regulatory 

mechanisms)

2.3.2.1 Present or threatened destruction, modification or curtailment of its 

habitat or range 

Present or future destruction of habitat is not considered to be a factor that will 

limit recovery, however, the Service continues to engage in Section 7 

consultations on projects to minimize potential adverse impacts to sea otter 

habitat. The U.S. Army Corps of Engineers (Corps) has been the primary federal 

agency engaging in activities that trigger Section 7 consultation within the range 

of the listed entity. The Service worked with the Corps to identify their permit 

actions authorized under Section 10 of the Rivers and Harbors Act and Section 

404 of the Clean Water Act that may adversely affect sea otter habitat. Corps 

permits that could potentially impact sea otter critical habitat involve docks, 

harbors, shoreline fill for upland use, in-stream work, wetland fill, erosion control, 

drilling, dredging, and cable/pipe laying. Projects that involve filling shorelines 

are considered to have the most direct impact on sea otter habitat. Of 79 projects 

authorized by the Corps between 2007 and 2012, approximately 26 involved 

intertidal or marine fill within sea otter critical habitat. For those projects, the 

average loss of sea otter critical habitat per project was 1.3 acres, ranging from 

0.1 to 6.7 acres of shallow marine or intertidal habitat, and totaling approximately 

33 acres overall. If extrapolated over the next 100 years (assuming permit 

patterns remain the same), approximately 590 acres could be lost. This is a small 

fraction of the total designated critical habitat for sea otters. The importance of 

critical habitat, however, not only includes the amount available but also the 

features of the habitat. The Service currently lacks sufficient information in many 

areas to determine where these important habitat features are located, but is 

engaged in on-going efforts to identify high-use areas (“hot spots”) to improve 

recommendations on future federal activities. 

2.3.2.2 Overutilization for commercial, recreational, scientific, or 

educational purposes

Subsistence harvest. Harvest of marine mammals by Alaska Natives is 

authorized under Section 101(b) of the MMPA and section 10(e) of the ESA, 

provided the taking is for subsistence purposes or for the purpose of creating and 

selling authentic native articles of handicrafts and clothing, and is not wasteful 

(MMPA) or does not materially and negatively affect the species (ESA). The 

subsistence harvest of sea otters is monitored by the Service through a marine 

mammal Marking, Tagging, and Reporting Program (MTRP). The MTRP was 

established in 1989 in accordance with Section 109(i) of the MMPA. 

Implementing regulations for the MTRP require hunters to present hides and 

skulls of sea otters to an authorized Service representative, known as a “tagger,” 

within 30 days of harvest. In addition to attaching tags to the hide and skull, 

taggers collect biological information about the otter (age class and sex) as well as 

about the harvest itself (date and location) and attach uniquely numbered plastic 

tags to the hide and skull. The information is entered into a computer database.

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Although there is recognition that some legal sea otter harvest may not be 

reported, this program is considered sufficient by the Service to indicate whether 

subsistence harvest levels are a threat to the recovery of the southwest Alaska 

DPS of sea otters. The Recovery Team concluded that subsistence harvest is a 

low threat to four of the MUs, and is a moderate threat to the Kodiak, Kamishak, 

Alaska Peninsula MU, based on the fact that Kodiak Island has the largest human 

population and the largest reported subsistence harvest (USFWS 2013b). 

New harvest data for the southwest Alaska DPS of sea otters indicate that 

subsistence harvest continues to be variable and generally low (Figure 1). For the 

past 5 years for which harvest data are complete, 2008-2012, the average reported 

subsistence harvest for the southwest Alaska DPS was 118 sea otters. For the 

same 5 year period, the average reported subsistence harvest for the Kodiak 

archipelago was 101 sea otters, representing an annual average of about 74.8% of 

the reported harvest for the southwest Alaska DPS. The harvest has been 

comprised predominantly of adults, and about 81% are males. An average of 

about 1.07% of the estimated population size of the Kodiak archipelago is 

reported as harvested for subsistence purposes each year. The current level of 

harvest is not excessive in relation to the population size, and is not thought to be 

a population-regulating factor (USFWS 2013b). 

 Figure 1. Reported subsistence harvest of northern sea otters from the southwest 

 Alaska DPS, 1989-2012. 

0

50

100

150

200

250

300

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

Sea Otters Tagged

11
Illegal harvest. The illegal take of sea otters within the range of the southwest 

Alaska DPS is thought to be low, and all known occurrences have been in the 

Kodiak, Kamishak, Alaska Peninsula MU where the largest human population 

resides (see USFWS 2013b for review). The Aleutian Islands are remote, difficult 

to access, experience weather and wind extremes, and support few communities. 

Most of the Aleutian Islands are uninhabited. The Western and Eastern Aleutians 

MUs have experienced the steepest population declines, but declines appear to be 

consistent across inhabited and uninhabited islands. The Bristol Bay and South 

Alaska Peninsula MUs are similarly remote and sparsely populated. The 

Recovery Team concluded that illegal harvest is a low threat to four of the MUs, 

and is a moderate threat to the Kodiak, Kamishak, Alaska Peninsula MU (USFWS 

2013b) where the largest human population resides. Although illegal take may 

have occurred and may occur in the future, it is likely that the numbers are small 

and any impact on the recovery of the DPS would be minimal. 

2.3.2.3 Disease or predation

Since 2002 the Service has been collecting and analyzing sea otter carcasses that 

have been obtained from the three Alaska stocks. The southwest Alaska DPS has 

yielded the fewest carcasses because sea otter concentrations in the southwest part 

of the state are not close to large centers of human population compared to the 

southeast and southcentral stocks. For example, the large number of sea otter 

carcasses recovered from the southcentral stock, which spurred the declaration of 

an Unusual Mortality Event (UME) in 2006, was later determined to be partly a 

result of increased public interest in reporting and collecting stranded marine 

mammals in Kachemak Bay rather than a spike in mortality. 

In general, causes of death and disease prevalence do not differ among the three 

Alaska stocks. In our investigations using recovered sea otter carcasses, causes of 

death range from directly human-caused (gunshot, bycatch, boat strike) to disease 

(Streptococcus bovis complex, carcinoma) to idiopathic causes (starvation, 

impaction, torsion). There have been no apparent changes in these causes over 

time. The Service is conducting a full epidemiological survey of data collected 

from stranded animals over the past decade and it is hoped that this summary will 

illustrate some patterns of mortality that can be used to further assess disease risks 

and threats. 

In addition, the health and disease parameters of all live sea otters captured across 

the three stocks has been monitored. An assessment of clinical pathology and 

pathogen exposure in sea otters in and on the borders of the southwest stock was 

conducted in 2011 (see Goldstein et. al 2011). The study determined that the 

general health of sea otters was not different on either end of the listed population. 

However, it was determined that exposure to Phocine distemper virus (PDV) has 

increased in sea otters in Kodiak since the 1990s. Because PDV can be highly 

pathogenic in naïve and susceptible marine mammal populations, the Service has 

an on-going study to examine tissues to explore the contribution of this virus to 

sea otter mortality. 

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In 2007, the Service began a joint project with the Alaska Department of Fish and 

Game (ADF&G) to examine the role of chlorinated fatty acids (CFAs) in sea otter 

disease in Alaska. While preliminary results indicated that concentrations of 

CFAs in sea otter heart muscle were high and that regional differences in 

exposure existed across the range of tested sea otters (Smith et al. 2008), sample 

sizes were too small to be conclusive. 

In 2009, the Service initiated a joint project with the Alaska Department of Fish 

and Game (ADF&G) to examine causes of mortality of sea otters in Kamishak 

Bay, Alaska. This area encompasses part of the listed DPS and is across Cook 

Inlet from Kachemak Bay, where the 2006 UME was declared. The study was 

intended to evaluate the role disease may have played in the decline of sea otters 

as well as how disease may, or may not, limit their recovery. Although a total of 

17 sea otter remains were collected over the three years of the study, only 2 were 

carcasses suitable for necropsies (Gill 2009, Gill 2010, Gill 2011). Cause of death 

was determined to be hypoglycemia or hypothermia in a 2 – 3 month old 

abandoned or orphaned pup. A second carcass was found and sent in for 

necropsy but the cause of death could not be determined due to decomposition 

In August of 2012, USGS led an effort to capture sea otters in Clam Lagoon, 

Adak. Sea otter densities within the lagoon remain relatively high compared to 

surrounding areas, presumably because the shallow, narrow entrance to the lagoon 

restricts killer whale access, providing a refugium from predation for sea otters. 

Captured sea otters were sedated, measured and weighed, and blood was drawn to 

evaluate health and body condition. Each otter received unique color-coded 

flipper tags for identification, and tooth wear was evaluated to assist in age 

estimation. Collected samples will allow characterization of diet, which will 

inform understanding of behavior and habitat use, such as whether sea otters 

venture outside the lagoon or remain there. Similar information was earlier 

collected from sea otters outside the lagoon before and after the population 

decline, and from sea otters inside the lagoon before the decline. When finalized, 

results from this study will be included in future sea otter assessments and 

management decisions as appropriate. 

Section 4.0 of this review describes future actions that will help the Service to 

further examine the roles of disease and predation in the decline and recovery of 

the southwest Alaska DPS of sea otters.

2.3.2.4 Inadequacy of existing regulatory mechanisms 

The Recovery Team concluded that the inadequacy of existing regulatory 

mechanisms, including the MMPA and the ESA, did not contribute to the sea 

otter decline. There is no information to indicate that this conclusion should be 

changed. 

2.3.2.5 Other natural or manmade factors affecting its continued existence

There is no new information to indicate that this factor has changed.

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2.4 Synthesis

The southwest Alaska DPS of the northern sea otter was listed as threatened under the ESA in 

August 2005 (70 FR 46366). At that time, the population was also recognized as depleted 

pursuant to the MMPA. This population experienced a rapid decline in abundance of more than 

50% since the late 1980s. At the time of the 2005 final listing rule, we estimated that the DPS 

consisted of approximately 42,000 sea otters. 

The PVA model developed to assist in recovery planning was updated and re-run to inform the 

5-year-review process. The model, initially developed using trend and demographic data 

through 2007, was updated to incorporate new data obtained from skiff surveys conducted in the 

Western Aleutians MU from 2007 - 2011. The new PVA results indicate that population trends 

have stabilized in the Western and Eastern Aleutians MUs, although there is no evidence of 

recovery, and numbers remain well below historical levels (USFWS 2013a). The updated model 

also supports lower delisting and uplisting abundances for the Western and Eastern Aleutians 

MUs. 

We concluded that it is also reasonable and precautionary to adjust the uplisting and delisting 

thresholds in the remaining 3 MUs, using the same model outputs used for the Western and 

Eastern Aleutians MUs, which had previously experienced the steepest population declines and 

greatest reductions in numbers. Unique outputs for the remaining MUs will be developed as data 

become available (see section 4 “Recommendations for Future Actions”).

The only threat factor identified in the Recovery Plan as having high importance to recovery is 

predation, most likely by killer whales (Orcinus orca). Other threats, including infectious 

disease, biotoxins, contaminants, oil spills, food limitation, disturbance, fisheries bycatch, 

subsistence harvest, loss of habitat, and illegal take, were identified as potential threats but 

considerable uncertainty remains about their contribution to population trends. This review 

supports these conclusions, and finds no new information to alter these conclusions. 

Based upon the best information currently available, the listing status of this DPS as threatened 

should remain and we should move forward with implementing actions identified in the recovery 

plan to the extent possible. The objective of the recovery plan is to delist this DPS through 

implementation of a variety of recovery measures which are outlined in section 4.0. 

14
3.0 RESULTS

3.1 Recommended Classification: Given your responses to previous sections, 

particularly section 2.4. Synthesis, make a recommendation with regard to the 

listing classification of the species 

____ Downlist to Threatened 

 ____ Uplist to Endangered 

 ____ Delist (Indicate reasons for delisting per 50 CFR 424.11):

 ____ Extinction

 ____ Recovery 

 ____ Original data for classification in error

 __X__ No change is needed 

15
4.0 RECOMMENDATIONS FOR FUTURE ACTIONS

1) Monitor for trends in sea otter abundance at existing index sites in the Western 

Aleutians MU, and establish index sites in the Central Aleutians. Monitoring surveys 

should be conducted every 2 to 3 years. Estimate sea otter population size in Lower Cook 

Inlet, the Kodiak Archipelago, and along the Alaska Peninsula. 

Recovery action number: 1.1. Continue to estimate sea otter population size and trends in 

southwest Alaska (1.1.2. Conduct surveys in each Management Unit and estimate abundance 

and population trends); 1.5.1. Investigate PVA models for the Bristol Bay, South Alaska 

Peninsula, and Kamishak, Kodiak, Alaska Peninsula MUs 

2) Continue ecosystem studies in the central and western Aleutians to assess the ecosystembased recovery criterion of whether greater than 50 percent of islands are in the kelpdominated state. Metrics include sea otter density and distribution; kelp diversity, density, 

and coverage; and prey diversity, density, and size, especially for sea urchins.

Recovery action number: 1.6. Monitor the functional role of sea otters in the ecosystem 

(1.6.1. Monitor status of the kelp forest ecosystem); 2.1. Identify important habitats or areas of 

special biological significance 

3) Develop a specialized telemetry transmitter suitable for implantation in sea otters that 

can help determine survival and causes of mortality. 

Recovery action number: 1.2. Estimate vital rates (1.2.1. birth rates and 1.2.2. survival rates); 

1.5.1. Investigate PVA models for the Bristol Bay, South Alaska Peninsula, and Kamishak, 

Kodiak, Alaska Peninsula MUs; 4.1.5. Evaluate population-level effects of disease; 5.1. 

Continue to evaluate the role of predation as a threat to recovery; 

4) Examine genetic structure of the southwest Alaska DPS of sea otters to determine 

whether MUs as described in the recovery plan could be uplisted or delisted separately 

from other MUs.

16
5.0 REFERENCES 

Clark, J.S. and O.N. Bjornstad. 2004. Population time series: Process variability, observation 

errors, missing values, lags, and hidden states. Ecology 85:3140-3150. 

Coletti, H., J. Bodkin, T. Dean, and K. Kloecker. 2009. Nearshore marine vital signs monitoring 

in the Southwest Alaska Network of National Parks. Natural Resource Technical Report 

NPS/SWAN/NRTR-2009/252. National Park Service, Fort Collins, Colorado. 

Doroff, A.M., J.A. Estes, M.T. Tinker, D.M. Burn, and T.J. Evans. 2003. Sea otter population 

declines in the Aleutian Archipelago. Journal of Mammalogy 84:55-64. 

Gill, V.A. 2009. 2009 Trip Report for Cooperative Endangered Species Conservation Fund 

(Section 6 ESA) Project: Causes of mortality for threatened northern sea otters in Kamishak Bay, 

Alaska. Unpublished Trip Report, USFWS, Anchorage, Alaska. 

Gill, V.A. 2010. 2010 Trip Report for Cooperative Endangered Species Conservation Fund 

(Section 6 ESA) Project: Causes of mortality for threatened northern sea otters in Kamishak Bay, 

Alaska. Unpublished Trip Report, USFWS, Anchorage, Alaska. 

Gill, V.A. 2011. 2011 Trip Report for Cooperative Endangered Species Conservation Fund 

(Section 6 ESA) Project: Causes of mortality for threatened northern sea otters in Kamishak Bay, 

Alaska. Unpublished Trip Report, USFWS, Anchorage, Alaska. 

Goldstein, T., V.A. Gill, P. Tuomi, D. Monson, A. Burdin, P.A. Conrad, J.L Dunn, C. 

Field, C. Johnson, D.A. Jessup, J. Bodkin and A.M. Doroff. 2011. Assessment of clinical 

pathology and pathogen exposure in sea otters (Enhydra lutris) bordering the threatened 

population in Alaska. Journal of Wildlife Diseases 47(3): 579–592. 

Moore, J.E., and R. Merrick, editors. 2011. Guidelines for Assessing Marine Mammal Stocks: 

Report of the GAMMS III Workshop, February 15 – 18, 2011, La Jolla, California. Dept. of 

Commerce, NOAA Technical Memorandum NMFS-OPR-47. 

NOAA. 2008. Conservation Units of Managed Fish, Threatened or Endangered Species, and 

Marine Mammals. Report of a Workshop: February 14-16, 2006. Silver Spring, Maryland. 

Technical Memorandum NMFS-OPR-37. 108 pp. 

Smith, R.T., A.M. Doroff, V.A. Gill, L.D. Rea, and J.M. Kennish. 2008. Chlorinated fatty acids 

(CFAs) in Alaskan sea otters. Poster presentation at the Alaska Marine Science Symposium, 

Anchorage, Alaska, January 2-23, 2008. 

USFWS. 2013a. Sea Otter (Enhydra lutris): southwest Alaska stock. Marine Mammal 

Protection Act Stock Assessment Report. 8 pp. 

17
USFWS. 2013b. Southwest Alaska Distinct Population Segment of the Northern Sea Otter 

(Enhydra lutris kenyoni) Recovery Plan. U.S. Fish and Wildlife Service, Region 7, Alaska. 171 

pp. 

18

Southwest Alaska Distinct Population Segment of the Northern sea otter (Enhydra lutris kenyoni) 

Population Viability Analysis (PVA) Update, June 2013.

Dr. M. Tim Tinker, US Geological Survey Western Ecological Research Center, Santa Cruz Field Station 

As part of the 5-year review of the southwest Alaska DPS of the northern sea otter, the US Fish 

and Wildlife Service requested a re-analysis of the status and trends of sea otter populations, including a 

re-running of the Population Viability Analysis (PVA) model using the most up-to-date available data. 

Accordingly, data were compiled from all skiff surveys that had been conducted at index sites in the 

Western Aleutian Management Unit since 2007 (see Table 1). These more recent surveys were not 

included in the original version of the PVA model included in the draft Recovery Plan (USFWS 2010). 

However, a key advantage of the PVA model structure developed for the Recovery Plan was that it is 

both possible and in fact recommended that the model be re-run as new data or information become 

available, thereby avoiding the inevitable obsolescence of fixed listing threshold recommendations. In 

particular, it was recognized that if survey trends remained stable or positive, that the incorporation of 

more years of survey data into the PVA model parameterization would potentially lead to a higher 

proportion of simulations with positive population growth. It was recommended that this process be 

conducted at 5-year intervals, and the up-listing and de-listing thresholds be updated accordingly. In the 

re-running of the PVA model, the probabilistic definitions of the thresholds would not change, but the 

specific numerical estimates could change to reflect the most recent data. 

Methods

A re-analysis of population trends was conducted for the western Aleutian Islands, the western-most 

management unit of the SW Alaska stock. This region, consisting of 4 Island groups (from west to east, 

the Near Islands, Rat Islands, Delarof Islands, and Adreanof Islands), exhibited a pronounced population 

decline in the 1990s (Doroff et al 2003, Estes et al 2005). A Bayesian state-space trend analysis (Clark 

and Bjornstad 2004) was conducted for the Islands with the most complete survey records (5 or more 

skiff surveys spanning the period 1993 – 2010). Data were compiled from skiff surveys conducted by 

USGS and USFWS for 5 Islands: Attu Island (n = 7 surveys), Kiska Island (n = 5), Amchitka Island (n = 9), 

Adak Island (n = 16) and Kagalaska Island (n = 5), for a total of 42 surveys (Table 1). Counts were 

standardized by converting to linear densities (numbers of sea otters counted per km of coastline), and a 

hierarchical exponential model was fit that allowed for random differences between Islands, and also 

allowed for a time-varying rate of change (r). Specifically, the discreet population growth rate (defined 

as r = log(λ), where λ = Nt / Nt-1) was estimated for two periods: t(1) = 1993-2003 and t(2) = 2003-2011. 

At each Island and for each time period, the Island- and time-specific rate of growth (ri,t ) was assumed 

to be drawn from a normal distribution with mean r(t) and standard deviation σr . The parameters of 

interest were the regional growth rates for each period, r(t), and additional estimated parameters 

included the observer error (σobs), spatial variation in r (σr), and the initial population densities at each 

Island (Ni,0). Note that we assumed that observation error (σobs) was the predominant source of year-toyear variability in the counts at each Island after accounting for the difference in r between time periods 

and the random variation in r(t) among Islands. To fit the model we used a Markov Chain Monte Carlo 

(MCMC) Gibbs sampling algorithm implemented using the OpenBUGS software package, with 

20 Appendix A.
uninformed (vague) prior distributions for all parameters. We ran MCMC simulations initially for 

100,000 iterations to ensure convergence, and then for another 100,000 iterations to obtain posterior 

distributions for each parameter. We report the means and 95% credible intervals (CI95) for each 

parameter. 

The new skiff count data described above were added to existing time-series, and used to re-run the 

PVA model for the southwest Alaska DPS of the northern sea otter. A detailed description of the 

development and parameterization of this PVA model can be found in Appendix B of the Recovery Plan

(USFWS 2013). The approach to the PVA model can be summarized as five main steps:

1) We develop a matrix-based population model to simulate dynamics in a population that is 

spatially structured into demographically-distinct sub-populations, linked by dispersal. 

2) Using the updated time-series of population surveys available for various islands throughout the 

Aleutian archipelago, we use maximum-likelihood estimation (MLE) techniques to estimate the annual 

per-capita death rate (δ) associated with an age-independent source of mortality (e.g., predation) 

responsible for the recent decline, as well as variation in this parameter. Nine different MLE model 

forms were evaluated for variation in δ, ranging from a constant value to more complex models 

including linear and non-linear effects of time and population density (Table 2). Information theory 

criteria (specifically, AIC values) were used to select the best model (Burnham and Anderson 2002). 

Recognizing that the particular combination of Islands selected for analysis would potentially affect 

which model provided the best fit, we took 1000 boot-strap re-samples of the Island time series and refit the MLE model to each. These iterated MLE model fits were used for subsequent steps of the PVA 

analysis, thereby accounting for sampling error and model selection uncertainty 

3) For each Management Unit, we simulate 25 years of population dynamics using model 

projections that incorporate a) density-dependent variation in vital rates and inter-island dispersal, b) 

variation in age-independent mortality (δ) and c) environmental and demographic stochasticity. To 

account for various sources of uncertainty (including sampling error and model selection uncertainty: 

see step #2), we run 1000 simulations and allow all parameters to vary according to the appropriate 

variance distributions.

4) The suite of simulations described in step #3 are re-run with 10 different starting densities, and 

for each initial density we record the proportion of simulations that go to extinction within a 25 year 

period (Ω). We then solve for the density at which Ω = 0.05, the point at which there is ≥ 5% probability 

of extinction within a 25 year period. This critical density, Ω05, is suggested as an appropriate value for 

the up-listing threshold, DU

. 

5) The suite of simulations are again re-run with 10 different starting densities, and for each initial 

density we record ω, the proportion of simulations in which population abundance drops to DU

, the uplisting threshold. We then solve for the density at which ω = 0.05; above this density there is <5% 

probability of the population becoming endangered (dropping below DU

) within 25 years. This critical 

density, ω05, is suggested as an appropriate value for the de-listing threshold, DD

. 

21
Results

The results of the Bayesian hierarchical trend analysis for 5 Islands in the western Aleutian Islands 

(Figure 1) indicated that the population trends for the western Aleutian Islands over the 1993-2003 

period were strongly negative (Figure 2a), with an average rate of decline of approximately 20% per year 

(r(1) = -0.206, CI95 = -0.3099 − -0.1023). Population trends changed during the later period (2003 – 2011; 

Figure 2b), with an average growth rate of approximately 0 (r(2) = -0.008, CI95 = -0.1262 − 0.1108). In 

general these trends were consistent among Islands (Figure 3): although there was some variation in 

trends among Islands, in all cases the 95% CI for the Island-specific rates were negative for period 1 but 

spanned 0 for period 2. These results suggest that population trends have stabilized in the western 

Aleutian Islands over the last 5-8 years, although there is still no evidence of recovery. 

The PVA model was re-run for the Western Aleutian and Eastern Aleutian Management Units using the 

updated survey time-series. Of the nine different functional forms evaluated for variation in ageindependent mortality (δ) there was greatest empirical support for model 9 (Figure 4), which included 

first and second order effects of both time and density (Table 2). In general, best-fit models suggest that 

δ increased through the 1990s but has since declined, and that the highest rate of mortality occurs at 

densities of 6-10 sea otters per km2 but decreases sharply at lower densities (Figure 5). The temporal 

trends in mortality coupled with the low densities that currently prevail throughout the Aleutians mean 

that the effect of age-independent mortality on population growth is now relatively small, and thus 

population trends have stabilized at most index sites (Figure 6). 

Incorporating these new parameter estimates (and associated uncertainty) into simulations of future 

population growth resulted in somewhat more positive projections as compared to the earlier (2007) 

PVA results. The first critical density, Ω05, below which >5% of simulations went to extinction within a 

25-year period, was 608 otters (95%CI = 543-647) in the Western Aleutian Management Unit and 310 

otters (95%CI = 293-325) in the Eastern Aleutian Management Unit. These densities correspond to 

1.05% of K (95%CI = 0.94-1.12) for the Western Aleutians (Figure 7) and 0.77% of K (95%CI = 0.72-0.80)

for the Eastern Aleutians. The second critical density, ω05, above which <5% of simulations dropped to 

Ω05 within a 25-year period, was 16,035 otters (95%CI = 12,843-20,380) in the Western Aleutian 

Management Unit and 13,644 otters (95%CI = 11,291-16,677) in the Eastern Aleutian Management Unit. 

These densities correspond to 27.80% of K (95%CI = 22.27-35.34) for the Western Aleutians (Figure 8) 

and 33.63% of K (95%CI = 27.83-41.11) for the Eastern Aleutians. 

Based on these results, it can be reasonably inferred that the updated PVA supports an adjustment of 

up-listing and de-listing thresholds. For the Aleutian management units we would suggest a new uplisting threshold of 1.1% of K, or DU = 0.165 sea otters per km2 of habitat, and a new de-listing threshold 

of 35% of K, or DD = 5.25 sea otters per km2 of habitat. In the absence of PVA results for the other 

management units, a reasonable approach would be to set the up-listing and de-listing threshold values 

for these management units to the equivalent proportions of their respective carrying capacity 

estimates. 

22
Literature Cited

Burnham, K. P. and D. R. Anderson. 2002. Model selection and inference: a practical informationtheoretic approach. 2nd edition. Springer-Verlag, New York, NY.

Clark, J. S. and O. N. Bjornstad. 2004. Population time series: Process variability, observation errors, 

missing values, lags, and hidden states. Ecology 85:3140-3150.

USFWS. 2010. Southwest Alaska Distint Population Segment of the Northern sea otter (Enhydra lutris 

kenyoni) - Draft Recovery Plan. . U.S. Fish and Wildlife Service, Region 7, Alaska, Anchorage, AK.

USFWS. 2013b. Southwest Alaska Distinct Population Segment of the Northern Sea Otter (Enhydra 

lutris kenyoni) Recovery Plan. U.S. Fish and Wildlife Service, Region 7, Alaska. 171pp.

23
Tables

Table 1. Time series of skiff counts of sea otter numbers at 5 index sites in the western Aleutian Islands.

Island Year

Shoreline 

Length 

(km)

Number 

Independ. 

Otters

Number 

Otter 

Pups

Total Sea 

Otters

Otter 

Density 

(#/km)

Adak 1993 117.3898 718 149 867 7.38565

1995 359.5835 929 156 1085 3.01738

1996 354.0461 725 146 871 2.460131

1997 372.311 688 127 815 2.18903

1998 372.311 525 86 611 1.641101

1999 372.311 635 124 759 2.038618

2000 372.311 713 148 861 2.312583

2001 372.311 515 117 632 1.697506

2002 372.311 461 112 573 1.539036

2003 372.311 306 91 397 1.066313

2004 372.311 277 75 352 0.945446

2005 361.261 209 41 250 0.692021

2006 372.311 272 62 334 0.8971

2007 372.311 204 57 261 0.701027

2008 353.448 158 35 193 0.546049

2010 357.8286 184 69 253 0.707042

Amchitka 1993 49.37514 641 159 800 16.20249

1999 141.3271 356 75 431 3.049664

2000 30.26423 117 40 157 5.187642

2003 39 294 7.538462

2004 38.54918 65 15 80 2.075271

2005 39 82 2.102564

2007 39 155 3.974359

2009 39 159 4.076923

2011 39 74 1.897436

Attu 1994 272.3576 4156 1250 5406 19.84891

2000 272.3576 436 170 606 2.225016

2003 272.3576 132 0.484657

2005 272.3576 147 0.539732

2007 272.3576 319 1.171254

2009 272.3576 318 1.167583

2011 272.3576 292 1.07212

Kagalaska 1993 88.78112 191 36 227 2.55685

1997 88.78112 26 6 32 0.360437

2000 88.78112 43 11 54 0.608237

2003 88.78112 11 4 15 0.168955

2006 88.78112 11 2 13 0.146428

Kiska 1993 93.87849 516 146 662 7.051669

2003 93.87849 230 2.449976

2005 93.87849 159 1.693679

2007 93.87849 186 1.981285

2009 93.87849 260 2.769538

24
Model ID Functional Form for f(D,t) 

1 f(D,t) = ψ1

2 f(D,t) = ψ1 + ψ2

 t

3 f(D,t) = ψ1 + ψ2

t + ψ3 t2

4 f(D,t) = ψ1 + ψ2

 D

5 f(D,t) = ψ1 + ψ2 D + ψ3

 D2

6 f(D,t) = ψ1 + ψ2 D + ψ3

 t

7 f(D,t) = ψ1 + ψ2 D + ψ3 D2 + ψ4

t

8 f(D,t) = ψ1 + ψ2 D + ψ3

t + ψ4t

2

9 f(D,t) = ψ1 + ψ2 D + ψ3 D2 + ψ4

t + ψ5t

2

Table 2. Alternate functional forms evaluated for modeling δ, the per-capita rate of ageindependent mortality. In each case δ was calculated as a logit of f, the function listed in 

column 2, using the equation: 

δ(D , t) = ef(D,t) / 1 + ef(D,t) 

The simplest function evaluated (Model 1) corresponds to a constant rate of mortality, while 

the remaining eight models represent linear or non-linear functions of density (D), time (t), or 

density and time. The symbols ψ1, ψ2... ψn

represent parameters fit by maximum-likelihood 

(see text for further details). 

25
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

5

10

Adak

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

10

20

Amchitka

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

10

20

Attu

Otters per km

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

5

Kagalaska

Year

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

5

10

Kiska

Figure 1. Fitted exponential trends in sea otter populations at 5 Islands in the western 

Aleutian Islands

26
Figure 2. Posterior probability distribution for r, the discreet instantaneous rate of growth 

in sea otter populations, for two time periods: A) 1993 – 2003, and B) 2004-2011. 

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

0

5

10

Frequency

Regional growth rate, 1993-2003

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

0

5

10

r (rate of population growth)

Frequency

Regional growth rate, 2004-2011

A

B

27
Figure 3. Temporal variation in the island-specific rate of population growth at 5 different 

Islands in the western Aleutian Island management unit. The rate of growth is shown for 

two different time periods at each Island, with error bars indicating the 95% credible 

intervals for each estimate.

Adak Amchitka Attu Kagalaska Kiska

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

rate of population growth

1993-2003

2004-2011

28
0 1 2 3 4 5 6 7 8 9 10

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Model ID (see Table 2)

Relative model support (AIC weight)

Figure 4. Relative degree of empirical support for each of 9 different functional forms for 

maximum likelihood estimation (MLE) of “age-independent mortality” () based on timeseries of skiff counts of sea otters at index sites in the Aleutians. See Table 2 for 

definitions of each model. MLE solutions were found and AIC weights were estimated for 

each of 1000 boot-strap samples of the skiff count time series. Mean AIC weights for 

each model are shown, with error bars corresponding to standard deviations of AIC 

weights. 

29
0

5

10

15

20 0

2

4

6

8

10

0

0.1

0.2

0.3

0.4

0.5

Age-Independent Mortality (

)

Figure 5. Variation in “age-independent mortality” () as a function of time and sea otter 

density, as estimated by maximum likelihood analysis of time-series of skiff counts (see 

text and Table 2, model 9). 

30
N observed

N expected (Model 9)

Model 8 estimated 

Model 9 estimated 

0

0.1

0.2

0.3

0.4

0.5

0

1000

2000

3000

4000

1990 1995 2000 2005 2010

Adak

0

0.1

0.2

0.3

0.4

0

500

1000

1500

1990 1995 2000 2005 2010

Amchitka

0

0.1

0.2

0.3

0.4

0.5

0

1000

2000

3000

4000

5000

6000

1990 1995 2000 2005 2010

Number of sea otters counted or estimated

Attu

Estimated value of 

, Age-Independent Mortality

Figure 6. Trends in skiff counts of sea otter abundance at three index sites in the western 

Aleutians, Adak Island (top panel), north-eastern Amchitka Island (middle panel) and Attu 

Island (bottom panel). Also shown are the expected trends based on maximum likelihood 

analysis (see text) and the corresponding point estimates of age-independent mortality () 

responsible for driving these trends (estimates shown for model 8 and 9; see Table 2). 

31
Percent of Carrying Capacity (K) at Year 0

Probability of Extinction within 25 years

0 1 2 3 4 5 6 7 8 9 10

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Critical Density = 1.05% K

or 608 otters

Figure 7. Estimated probability of reaching quasi-extinction within a 25 year period for 

sea otters in the Western Aleutian Management Unit, plotted as a negative exponential 

function of sea otter density at year 0 (density is shown as % of carrying capacity, defined 

as K=15 sea otters per km2 of habitat). The 95% confidence limits for the function are 

shown as dashed lines. The critical density shown represents the point at which 5% of 

simulations go to quasi-extinction. 

32
0 10 20 30 40 50 60

0

0.1

0.2

0.3

0.4

0.5

Critical Density = 27.8% K

or 16,035 otters

Percent of Carrying Capacity (K) at Year 0

Probability of reaching endangered threshold within 25 years

Figure 8. Estimated probability of becoming endangered (see text and Figure 7) within a 

25 year period for sea otters in the Western Aleutian Management Unit, plotted as a 

negative exponential function of sea otter density at year 0 (density is shown as % of 

carrying capacity, defined as K=15 sea otters per km2 of habitat). The 95% confidence 

limits for the function are shown as dashed lines. The critical density shown represents 

the point at which 5% of simulations drop to the point of becoming endangered within 25 

years.

33