Southwest Alaska 

Distinct Population 

Segment of the 

Northern Sea Otter 

(Enhydra lutris kenyoni)

Recovery Plan

U.S. Fish & Wildlife Service
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Recovery Plan i

Disclaimer

Recovery plans delineate reasonable actions that are believed to be required to recover and/or protect 

listed species. We, the U.S. Fish and Wildlife Service (FWS), publish recovery plans, sometimes 

preparing them with the assistance of recovery teams, contractors, State agencies, and others. Objectives 

will be attained and any necessary funds made available subject to budgetary and other constraints 

affecting the parties involved, as well as the need to address other priorities. Recovery plans do not 

necessarily represent the views, official positions, or approval of any individuals or agencies involved in 

the plan formulation, other than the FWS. They represent the FWS official position only after they have 

been signed by the Director or Regional Director as approved. Approved recovery plans are subject 

to modification as dictated by new findings, changes in species status, and the completion of recovery 

actions.

Citation: U.S. Fish and Wildlife Service. 2013. 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.
ii Southwest Alaska DPS of the Northern Sea Otter

Recovery Team Members

Lloyd Lowry - (Team Leader), University of Alaska Fairbanks

Douglas Burn - (Agency Lead), U.S. Fish and Wildlife Service

Lance Barrett -Lennard, University of British Columbia

David Benton - Marine Conservation Alliance

James Bodkin - U.S. Geological Survey

Kathleen Burek - Alaska Veterinary Pathology Services

Jim Curland - Defenders of Wildlife

Douglas DeMaster - National Marine Fisheries Service

James Estes - U.S. Geological Survey (retired) and University of California, Santa Cruz

Dick Jacobsen - Aleutians East Borough

Ken Pitcher - Alaska Department of Fish and Game (retired)

Katherine Ralls - Smithsonian’s National Zoological Park

Margaret Roberts - The Alaska Sea Otter and Steller Sea Lion Commission

Tim Tinker - U.S. Geological Survey and University of California, Santa Cruz

Kate Wynne - University of Alaska Sea Grant Program

Acknowledgements

The Recovery Team wishes to thank the following individuals who attended and participated in team 

meetings: Greg Balogh, Leonard Corin, Angela Doroff, Verena Gill, Tracey Goldstein, Rowan Gould, 

Aaron Haines, Charles Hamilton, Lianna Jack, Judy Jacobs, Sonja Jahrsdoerfer, Karen Laing, Ellen 

Lance, Rosa Meehan, Karen Oakley, Alvin Osterback, Peggy Osterback, Leslie Slater, Robert Small, 

Kristine Sowl, Charla Sterne, Ted Swem, Doug Vincent-Lang, Jeff Williams, and Bill Wilson. The 

Team also thanks Melissa Miller, Tracey Goldstein, and Stephen Raverty for providing unpublished 

information about diseases in sea otters, and Dana Jenski, Angela Doroff, and Verena Gill for assistance 

with recording minutes during Team meetings. Rose Primmer provided technical support for both the 

public and internal Team web pages. Ellen Baier coordinated travel arrangements for out-of-town Team 

members. The North Pacific Research Board graciously provided space for Team meetings. Randy 

Reeves provided a thorough review of the Team’s draft plan, and we thank him for his helpful comments 

and suggestions. Rosa Meehan , Sonja Jahrsdoerfer, Frances Mann, and Suzann Speckman provided 

additional review and comments on the draft plan. 
Recovery Plan iii

Executive Summary

Based on survey information that indicated that 

the southwest Alaska population of northern sea 

otters (Enhydra lutris kenyoni) had declined 

in abundance by more than 50% since the mid1980s, the U.S. Fish and Wildlife Service (FWS) 

listed this distinct population segment (DPS) 

as threatened in August 2005. Section 4(f) of 

the Endangered Species Act (ESA) directs the 

Secretary of the Interior to develop and implement 

plans (generally known as “recovery plans”) for 

the conservation and survival of endangered 

species and threatened species. In March 2006, 

the Regional Director for the Alaska Region of 

the FWS formed a recovery team to serve in an 

advisory capacity to develop a draft recovery plan 

for the southwest Alaska DPS of the northern sea 

otter.

The sea otter is the largest species in the mustelid 

family, and one of the smallest marine mammals. It 

possesses a number of unique adaptations allowing 

it to exist in the nearshore marine environment. 

As the only marine mammal species that lacks a 

blubber layer, the sea otter relies on a dense coat 

of fur as insulation from the cold waters where 

it occurs. To maintain the insulative properties 

of their fur, sea otters must groom themselves 

regularly. Their reliance on fur for insulation also 

makes them highly vulnerable to oil spills. In 

addition to using fur for insulation, sea otters have 

a relatively high metabolic rate that helps them 

maintain their body temperature. This requires 

them to consume large quantities of prey, as much 

as 20-33% of their body weight per day. With 

few exceptions, sea otter prey consists of benthic 

invertebrates. Sea otter habitat is partially defined 

by physiological limitations in diving depth, and the 

animals generally occur in or near shallow waters.

The discovery of large sea otter populations in 

Alaska by the Russian Bering expedition in 1741 

resulted in a commercial fur harvest that lasted 

170 years and extirpated sea otters from much of 

their historic range. When the species was finally 

given protection under the International Fur Seal 

Treaty of 1911, the worldwide population may 

have consisted of fewer than 1,000 individuals in 

13 remnant colonies. Throughout much of the 

20th century, these remnant colonies grew and 

expanded their range, eventually recolonizing 

much of the species’ historically occupied habitat. 

In the late 1960s and early 1970s, the process of 

recolonization was enhanced by the translocation 

of otters from areas of high abundance to sites 

from which they had been extirpated by the fur 

harvest. During the 1990s, sea otter surveys in the 

Aleutian archipelago indicated that the population 

trend had shifted from growth and expansion to 

decline. Additional surveys throughout southwest 

Alaska helped define the scope and magnitude of 

the population decline, which led eventually to the 

listing of this DPS as threatened.

The southwest Alaska DPS ranges from west to 

east across more than 1,500 miles of shoreline, and 

the otters occur in a number of distinct habitat 

types. The magnitude of the population decline 

has varied over the range. In some areas, numbers 

have declined by more than an order of magnitude, 

while in other areas no decline has been detected. 

To address such differences, this recovery plan 

identifies five management units (MUs) within 

the DPS: 1) Western Aleutian Islands; 2) Eastern 

Aleutian Islands; 3) South Alaska Peninsula; 4) 

Bristol Bay; and 5) Kodiak, Kamishak, Alaska 

Peninsula. 

The cause of the overall decline is not known 

with certainty, but the weight of evidence points 

to increased predation, most likely by the killer 

whale (Orcinus orca), as the most likely cause. 

Predation is therefore considered a threat to the 

recovery of this DPS, but other threats, including 

infectious disease, biotoxins, contaminants, oil 

spills, food limitation, disturbance, bycatch in 

fisheries, subsistence harvest, loss of habitat, and 

illegal take, are also considered in this recovery 

plan. Threats are summarized in general, and 

their relative importance is assessed for each of 

the five MUs. Most threats are assessed to be 

of low importance to recovery of the DPS; the 

threats judged to be most important are predation 

(moderate to high importance) and oil spills (low to 

moderate importance). Threats from subsistence 

harvest, illegal take, and infectious disease are 

assessed to be of moderate importance in the 

Kodiak, Kamishak, Alaska Peninsula MU, but of 

low importance elsewhere. 

The goal of the recovery program is to control or 

reduce threats to the southwest Alaska DPS of 

the northern sea otter to the extent that this DPS 

no longer requires the protections afforded by the 

ESA and therefore can be delisted. To achieve this 

goal, the recovery plan identifies three objectives: 

1) achieve and maintain a self-sustaining population 

of sea otters in each MU; 2) maintain enough sea 

otters to ensure that they are playing a functional 
iv Southwest Alaska DPS of the Northern Sea Otter

role in their nearshore ecosystem; and 3) mitigate 

threats sufficiently to ensure persistence of sea 

otters. Each of these objectives includes explicit 

criteria to determine if the objective has been 

met; these are known as “delisting criteria.” They 

stipulate that in order for the DPS to be removed 

from the Endangered and Threatened Species List, 

at least three of the five MUs must have met the 

delisting criteria. The plan also contains criteria 

to determine if the DPS should be considered for 

reclassification as endangered; these are known 

as “uplisting criteria.” Delisting should not be 

considered if any MU meets the criteria specified 

for uplisting to endangered.

Specific actions to achieve recovery and delisting 

of the DPS are specified in the recovery 

action outline and narrative. As demographic 

characteristics of the population constitute one 

of the three types of delisting criteria, population 

monitoring and population modeling are high 

priorities. Monitoring the status of the kelp 

forest ecosystem in the Western Aleutian and 

Eastern Aleutian MUs is also a high priority, as 

results from such monitoring will be needed to 

evaluate the ecosystem-based delisting criteria. 

Other high-priority actions include identifying 

characteristics of sea otter habitat, and ensuring 

that adequate oil spill response capability exists 

in southwest Alaska. As predation is considered 

to be the most important threat to recovery, 

additional research on that topic is also a high 

priority. The recovery implementation schedule 

provides details regarding the timing, costs, and 

agencies or entities responsible for implementing 

each recovery action. The full cost of implementing 

this recovery plan over the next five years is 

approximately $15M, of which $2.815M is for 

Priority 1 actions. Securing adequate funding to 

implement the plan is therefore also a high priority.
Recovery Plan v

Table of Contents

Disclaimer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Recovery Team Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

A. Brief history of listing and recovery planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

B. Ecosystem context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

2. Biological Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

A. Species description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

B. Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

C. Physiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

D. Distribution and habitat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

E. Population biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5

F. Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7

G. Foraging ecology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

H. Population history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

I. Population abundance and trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

3. Threats and Impediments to Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

A. Predation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

B. Infectious diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

C. Biotoxins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17

D. Contaminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17

E. Oil spills and oiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19

F. Food limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-21

G. Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-27

H. Bycatch and entanglement in debris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-27

I. Subsistence harvest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-29

J. Habitat concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

K. Illegal take . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-34

4. Threats Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

5. Recovery Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

NOTE: this recovery plan is paginated by section, which will allow individual sections of the plan to be 

updated without requiring the entire document to be reprinted.
vi Southwest Alaska DPS of the Northern Sea Otter

6. Recovery Goals, Objectives, and Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

A. Recovery Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

B. Recovery Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

C. Criteria for delisting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

D. Criteria for reclassification to endangered (uplisting) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

E. Summary of demographic status in relation to delisting and uplisting criteria . . . . . . . . . . . . . . . . 6-5

7. Recovery Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

A. Recovery Action Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

B. Recovery Action Narrative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-3

8. Implementation Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

A. Key to Responsible Agencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

B. Recovery Implementation Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-2

9. Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1

A. Print Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1

B. Personal Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-16

Appendix A. Ecosystem-based Recovery Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B. Population Viability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1
Recovery Plan vii

Table 1. Recent sea otter population estimates for MUs within the southwest Alaska DPS 

of the northern sea otter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-23

Table 2. Occurrence of major known diseases and disease agents in sea otters by 

geographic region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

Table 3. Information on disease agents investigated in sea otters in the Gulf of Alaska and 

the Aleutian Islands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13

Table 4. Information on biotoxins found in sea otters in the Gulf of Alaska and the 

Aleutian Islands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-18

Table 5. Information on contaminants found in sea otters in the Gulf of Alaska and the 

Aleutian Islands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-20

Table 6. Summary of reported non-crude oil spills in southwest Alaska by MU, 

July 1, 1995 – June 30, 2005. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-23

Table 7. Reported sea otter subsistence harvest in Alaska, 1989-2008. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-31

Table 8. Age and sex composition of the reported sea otter subsistence harvest from the 

southwest Alaska DPS of the northern sea otter, 1989-2008. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-32

Table 9. Reported subsistence harvest of sea otters from the southwest Alaska DPS of 

the northern sea otter by MU, 1989-2008. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-33

Table 10. Threats analysis for the Western Aleutian Islands MU of the southwest Alaska 

DPS of the northern sea otter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

Table 11. Threats analysis for the Eastern Aleutian Islands MU of the southwest Alaska 

DPS of the northern sea otter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

Table 12. Threats analysis for the Bristol Bay MU of the southwest Alaska DPS of the 

northern sea otter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4

Table 13. Threats analysis for the South Alaska Peninsula MU of the southwest Alaska 

DPS of the northern sea otter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

Table 14. Threats analysis for the Kodiak, Kamishak, Alaska Peninsula MU of the 

southwest Alaska DPS of the northern sea otter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6

Table 15. Summary of importance of threats to recovery of the southwest Alaska DPS of 

the northern sea otter by management unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7

Table 16. Estimates of available habitat, equilibrium density, and carrying capacity for 

the five management units in the southwest Alaska DPS of the northern sea otter. . . . . . . . . . . . . . . . . . . 6-3

Table 17. Summary of criteria that must be met prior to delisting the southwest Alaska 

DPS of the Northern sea otter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

List of Tables
viii Southwest Alaska DPS of the Northern Sea Otter

Table 18. Examples of estimates that are developed by PVA models using available data through 2007. 

Estimates shown include carrying capacity, delisting abundance, uplisting abundance, and current status 

relative to carrying capacity for each of the five management units in the southwest Alaska DPS of the 

northern sea otter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

Table A-1. Summary of the datasets used to examine the relationship between sea otter 

density and sea urchin biomass in the Aleutian Islands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-8

Table B-1. Projection matrix used in population model to calculate demographic transitions 

(survival and reproduction) occurring between year t and year t+1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-10

Table B-2. Vital rate estimates used to parameterize the projection matrix (Table B-1) for 

sea otter populations at low density and high density (“at K”) situations. . . . . . . . . . . . . . . . . . . . . . . . . . B-11

Table B-3. Estimates (based on expert opinion of the Recovery Team) of the number of 

sea otters dispersing from a source population on one island to a remote population on 

another island, as a function of the density relative to carrying capacity at the source 

population, and the distance between the two islands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-11

Table B-4. Time series of skiff survey counts of sea otters (excluding dependent pups) for 

seven islands in the Aleutian archipelago between 1991 and 2007. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-12

Table B-5. Alternate functional forms evaluated for modeling δ, the per-capita rate of 

age-independent mortality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-13
Recovery Plan ix

Figure 1. Present distribution of three subspecies of sea otters (hatched areas). . . . . . . . . . . . . . . . . . . . . 2-2

Figure 2. Northern sea otter stock boundaries in Alaska, from Gorbics and Bodkin (2001). . . . . . . . . . . . 2-3

Figure 3. Management units for the southwest Alaska DPS of the northern sea otter. . . . . . . . . . . . . . . 2-13

Figure 4. Western Aleutian Management Unit (WA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-14

Figure 5. Sea otter survey results for the Near Island group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-14

Figure 6. Sea otter survey results for the Rat Island group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15

Figure 7. Sea otter survey results for the Andreanof Island group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16

Figure 8. Eastern Aleutian Management Unit (EA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16

Figure 9. Sea otter survey results for the Eastern Aleutian MU. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-17

Figure 10. Bristol Bay Management Unit (BB). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-17

Figure 11. Sea otter survey results for the Bristol Bay MU. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-19

Figure 12. South Alaska Peninsula Management Unit (SAP). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-19

Figure 13. Sea otter survey results for the South Alaska Peninsula MU. . . . . . . . . . . . . . . . . . . . . . . . . . 2-20

Figure 14. Kodiak, Kamishak, Alaska Peninsula Management Unit (KKAP). . . . . . . . . . . . . . . . . . . . . . . 2-21

Figure 15. Sea otter survey results for the Kodiak, Kamishak, Alaska Peninsula MU. . . . . . . . . . . . . . . 2-22

Figure A-1. Published data on kelp density vs. sea urchin biomass from islands in the 

Aleutian archipelago at which otters were abundant (Adak and Amchitka) or absent 

(Alaid, Nizki, Shemya). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-9

Figure A-2. Available data on kelp density vs. urchin biomass from the Aleutian 

archipelago, obtained from 19 islands sampled at various times between 1987 and 2006. . . . . . . . . . . . A-10

Figure A-3. Data from Figure A-2, averaged by island/year combination. . . . . . . . . . . . . . . . . . . . . . . . . A-11

Figure A-4. Canonical discriminant function fit to database of kelp density vs. sea urchin 

biomass from the 34 island/year combinations currently available from the Aleutian 

 archipelago (see Table A-1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-12

Figure A-5. Logistic regression of ecosystem phase state vs. sea otter density (using 

skiff surveys) as determined by data from 17 island/year combinations. . . . . . . . . . . . . . . . . . . . . . . . . . . A-13

Figure A-6. Sampling intensity vs. statistical power in correctly classifying ecosystem 

phase state. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-14

Figure B-1. Theta-logistic population growth typical of sea otter populations. . . . . . . . . . . . . . . . . . . . . . B-14

List of Figures
x Southwest Alaska DPS of the Northern Sea Otter

Figure B-2. Smoothed, age-specific vital rate estimates calculated for two sea otter 

populations, one at low density (parameterized from sea otters at Kodiak Island) and one 

at high density, near K (parameterized from sea otters at Amchitka Island). . . . . . . . . . . . . . . . . . . . . . . B-15

Figure B-3. Estimated average rates of inter-island dispersal (expressed as percentage of 

the source population dispersing) under two scenarios: A) a low dispersal scenario, and 

B) a high dispersal scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-16

Figure B-4. Examples of weighting functions used to randomly select values of t, which 

were then used to generate values of age-independent mortality, δ(D,t), in simulations of 

future population dynamics in southwest Alaska sea otters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-17

Figure B-5. Sample results from the maximum-likelihood fitting of age-independent 

mortality (δ) to observed skiff counts (Nobs). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-18

Figure B-6. Relative degree of support for nine different functional forms used to model δ, 

the per-capita rate of age-independent mortality, as measured by maximum-likelihood 

AIC weights (higher AIC weights indicate greater support for a given functional form). . . . . . . . . . . . . B-19

Figure B-7. Best-fit values of δ, the per-capita rate of age-independent mortality, plotted 

as a function of time (t) and relative population density (D). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-20

Figure B-8. Sample simulation results from the PVA model projections. . . . . . . . . . . . . . . . . . . . . . . . . . . B-21

Figure B-9. PVA model results for the western Aleutian management unit. . . . . . . . . . . . . . . . . . . . . . . . B-22
Recovery Plan xi

List of Acronyms

CDV canine distemper virus

DDT dichlorodiphenyltrichloroethane 

DMV dolphin morbillivirus

DPS distinct population segment

ESA Endangered Species Act

FWS U.S. Fish and Wildlife Service

GPS global positioning system

HAB harmful algal bloom

MHC major histocompatibility complex

MMM Marine Mammals Management

MMPA Marine Mammal Protection Act

mtDNA mitochondrial DNA

MTRP marine mammal marking, tagging, and reporting program 

MU management unit

NMFS National Marine Fisheries Service

OC organochlorine

OLE Office of Law Enforcement

PCB polychlorinated biphenyl

PCR polymerase chain reaction

PDV phocine distemper virus

PFCs perfluorinated compounds

PMV porpoise morbillivirus 

POP persistent organic pollutant

PSP paralytic shellfish poison

PVA population viability analysis

PWS Prince William Sound

TDR time-depth recorder

UME unusual mortality event

USGS U.S. Geological Survey

VE valvular endocarditis

VHF very high frequency
xii Southwest Alaska DPS of the Northern Sea Otter

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Recovery Plan 1-1

1. Introduction

actions relating to currently planned and future 

development activities. Recovery planning is 

dynamic, and a recovery program, including the 

recovery actions, will be reviewed periodically 

as new information becomes available. As 

appropriate, recovery plans are amended to reflect 

new information or a change in strategy.

Recovery plans identify site-specific management 

actions that, if completed, could lead to 

reclassification to a less critical status or help 

them recover to the point they can be removed 

from ESA protection. The ESA clearly envisions 

recovery plans as the central organizing tool for 

guiding each species’ recovery process. They 

should also guide Federal agencies in fulfilling 

their obligations under section 7(a)(1) of the ESA, 

which calls on all Federal agencies to “utilize their 

authorities in furtherance of the purposes of this 

Act by carrying out programs for the conservation 

of endangered species and threatened species...” 

As a result of these efforts, the ESA has been 

credited with saving species such as the California 

condor, black-footed ferret, peregrine falcon, 

and our Nation’s symbol, the bald eagle, from 

extinction.

B. Ecosystem context

Species often have strong influences on their 

associated ecosystems. The effects can occur in 

various ways. In some cases, the effect is selfevident, based on sheer numbers, as for example 

with a dominant tree species in a forest. Numerical 

dominants may influence other species through 

competition for limited resources; by affecting 

features of the physical environment such as 

light intensity, temperature, wind, and moisture; 

through the provision of habitat; or by controlling 

the flux of energy and matter through the 

ecosystem. 

Comparatively rare species can also play important 

ecological roles, in some cases having landscapelevel effects on the ecosystem that rival or exceed 

those of numerical dominants. This occurs under 

two conditions—either when per capita interaction 

strength with one or more other species is high 

(Paine 1992, Berlow et al. 1999) or when these 

direct interactions penetrate the ecosystem’s 

interaction web through indirect effects so as to 

influence other species and ecosystem processes. 

Comparatively rare but ecologically important 

species have been referred to as “keystone species” 

(Paine 1969, Power et al. 1996). Keystone species 

A. Brief history of listing and recovery 

planning 

In April 2000, the U.S. Fish and Wildlife Service 

(FWS) conducted an aerial survey of sea otters 

(Enhydra lutris) in the Aleutian archipelago. The 

results of that survey indicated that the population 

there had declined by an estimated 70% since 1992, 

which prompted the FWS to designate otters 

within that portion of their range as a candidate 

species for listing under the U.S. Endangered 

Species Act (ESA). Additional aerial surveys 

revealed that the decline extended beyond the 

Aleutians, and included much of the southwest 

Alaska population of northern sea otters (the 

region extending from the west side of Cook Inlet 

to Attu Island at the western end of the Aleutians, 

including Kodiak Island and Bristol Bay).

On 11 February 2004, FWS published a proposed 

rule (69 FR 6600) to list the northern sea otter 

in southwest Alaska as a threatened distinct 

population segment (DPS). The ESA defines 

“threatened” as “likely to become endangered 

in the foreseeable future over all or a significant 

portion of its range.” The ESA also defines 

“endangered” as “likely to become extinct over all 

or a significant portion of its range.” Following a 

120-day public comment period, the FWS published 

a final rule (70 FR 46366) on 9 August 2005, listing 

the DPS as threatened. 

The recovery planning process for the southwest 

Alaska DPS of the northern sea otter began 

in 2006, when the recovery team was formed. 

Collaborative efforts are critical to recovery 

success, and therefore, the recovery team was 

comprised of 15 people representing Federal and 

State agencies, universities, Tribal government, 

The Alaska Sea Otter and Steller Sea Lion 

Commission, and conservation organizations. 

Recovery plans are developed and implemented 

by the Service and our partners to help increase 

species’ populations and manage the threats to 

their existence. Comments on the draft recovery 

plan for the southwest Alaska DPS of the northern 

sea otter were solicited from the general public and 

from known interested groups, including Alaska 

Native Organizations and the State of Alaska.

It is important to recognize that recovery plans are 

guidance documents, not regulatory documents. 

No agency or entity is required by the ESA to 

implement the recovery strategy or to adhere to 

specific actions in a recovery plan. For example, 

recovery plans do not dictate any particular 
1-2 Southwest Alaska DPS of the Northern Sea Otter

often are apex predators and their ecosystemlevel effects frequently play out through what 

have become known as “trophic cascades1” (Paine 

1980, Carpenter and Kitchell 1993). Both keystone 

species and trophic cascades are known to occur in 

many ecosystems (Pace et al. 1999).

Where they inhabit areas with rocky substrates 

(which includes much of the range of the southwest 

Alaska DPS), sea otters provide a widely known 

and well-documented example of a keystone 

species. The ecosystem-level effects in this case 

occur through a simple food chain involving sea 

otters, sea urchins, and kelp. Sea urchins are 

among the most frequently consumed prey of 

sea otters, and when sea otters are sufficiently 

abundant they are capable of limiting sea urchin 

numbers and biomass. Sea urchins consume kelp 

and other macroalgae, and when sufficiently 

abundant are capable of preventing kelp forests 

from becoming established in extensive areas 

of shallow rocky reef habitat. These consumerprey interactions act together to define a trophic 

cascade, such that sea otters protect kelp forests 

from destructive overgrazing (Estes and Palmisano 

1974, Estes and Duggins 1995). Like forests on 

land, kelp forests exert important effects on 

numerous other species and ecosystem-level 

processes (see Appendix A). 

The recent population decline of sea otters in 

southwest Alaska’s Aleutian archipelago has 

resulted in a wholesale phase shift in the coastal 

ecosystem from kelp forests to deforested sea 

urchin barrens (Estes et al. 1998, Estes et al. 

2004, Estes et al. 2010). In view of the sea otter’s 

keystone role in coastal marine ecosystems, the 

goal of recovery must be not only to assure the 

continued survival of sea otters, but also to assure 

that they are numerous enough to maintain kelp 

forests through the otter-urchin-kelp trophic 

cascade. In other words, the objectives of recovery 

are not only to achieve a demographically viable 

population, but also to achieve an “ecologically 

effective” (sensu Soulé et al. 2003, Soulé et al. 2004) 

sea otter population density.

1

 Trophic cascades occur when predators in a food 

web suppress the abundance of their prey, thereby 

releasing the next lower trophic level from predation (or herbivory if the intermediate trophic level 

is an herbivore).
Recovery Plan 2-1

2. Biological Background

For nearly 50 years the United States Department 

of the Interior (specifically the FWS and U.S. 

Geological Survey (USGS)) has supported a 

research program directed at describing and 

understanding the natural history of sea otters. 

Research on both wild and captive sea otters 

has also been supported by the governments of 

Russia, Canada, and Japan, the state governments 

of Alaska, California, and Washington, and 

numerous academic institutions, aquaria, and 

non-governmental organizations. The scope of 

research has been broad and multi-dimensional, 

but with a focus generally directed toward: 1) 

understanding basic biology, particularly as it 

relates to conservation of the species; 2) support 

of translocations from remnant populations 

to unoccupied habitats in California, Oregon, 

Washington, British Columbia, and southeast 

Alaska; 3) understanding and describing the role 

of sea otters as keystone predators in structuring 

nearshore marine communities; 4) describing 

relations between human uses of nearshore marine 

resources and sea otters; 5) defining threats to sea 

otter recovery from human activities, particularly 

the effects of spilled oil; and 6) understanding the 

factors responsible for, or contributing to, changes 

in sea otter populations. 

Following the Bering expedition of 1741, sea 

otters were nearly extirpated for their fur 

by both Russians and Americans. After the 

International Fur Seal Treaty of 1911 protected 

sea otters from further commercial exploitation, 

sea otter populations generally displayed positive 

growth rates throughout most of the 20th century 

(although rates varied among populations), and 

range expansion along with translocations resulted 

in recolonization of some previously occupied 

habitat. The spatial pattern of occupied and 

unoccupied habitat and the variation in temporal 

patterns of sea otter recovery enabled comparisons 

of populations at various stages of recovery, and 

comparisons of habitats both with and without 

sea otters. As a result of the long-term dedicated 

research on this species, and the ability to make 

“experimental” comparisons owing to the patterns 

of sea otter presence and absence, a rich, diverse, 

and extensive body of literature exists on sea otter 

biology and ecology. Researchers will be able to 

draw on this foundation of knowledge as they seek 

to address unanswered and emerging questions 

in order to further aid sea otter conservation. 

While this document includes basic background 

information on the sea otter, it emphasizes those 

aspects of biology and ecology most likely to be 

relevant to the conservation of the southwest 

Alaska population. Other aspects of the species’ 

biology and ecology have been reviewed by 

Kenyon (1969), VanBlaricom (1988), Riedman and 

Estes (1990), and Estes and Bodkin (2002), and are 

described in other references listed in Section 9 of 

this plan. 

A. Species description

The sea otter is a mammal in the order Carnivora. 

It is the only completely marine species of the 

aquatic Lutrinae, or otter subfamily of the family 

Mustelidae (skunks, weasels, minks, badgers, 

and honey badgers) (Wozencraft 1993). Based 

on nucleotide sequences of the mitochondrial 

cytochrome b gene, Koepfli and Wayne (1998) 

placed Enhydra in one of three reorganized lutrine 

clades (a group of biological taxa or species that 

share features inherited from a common ancestor). 

Two lineages of sea otter are recognized. One 

led to the extinct Enhydriodon; the other to 

Enhydritherium and subsequently to Enhydra

(Berta and Morgan 1986). Early specimens of 

Enhydra, dating to the early Pleistocene, 1-3 

million years ago, have been found along the 

Pacific Rim and the genus apparently has remained 

confined to that basin (Riedman and Estes 1990).

Early sea otter taxonomy below the species 

level was based primarily on comparison of skull 

morphology between sea otters from Alaska 

and California. After an exhaustive systematic 

review and analysis of skull morphology, Wilson 

et al. (1991) concluded there are three subspecies, 

E. lutris lutris from Asia to the Commander 

Islands, E. l. nereis from California, and E. l. 

kenyoni from Alaska (Figure 1). This taxonomy 

is largely supported by subsequent molecular 

genetic data. Analysis of mitochondrial DNA 

(mtDNA) variation among eight geographically 

isolated populations identified four major groups 

(Cronin et al. 1996, Scribner et al. 1997). However, 

the haplotype frequency (genetic pattern) in the 

Commander Islands population of E. l. lutris is 

more similar to that observed in the AleutianKodiak grouping, E. l. kenyoni, than to the Asian 

subspecies, E. l. lutris, with which it was aligned 

by skull morphology. Additionally, the Prince 

William Sound (PWS) population differs from other 

Alaska populations in haplotype frequency. The 

distribution of mtDNA haplotypes suggests little 

or no recent female-mediated gene flow among 
2-2 Southwest Alaska DPS of the Northern Sea Otter

populations sampled. However, populations 

separated by large geographic distances shared 

some haplotypes (e.g., the Kuril Islands and Kodiak 

Island), suggestive of common ancestry and some 

level of historic gene flow. In a review of sea 

otter studies using genetic markers, Scribner et 

al. (1997) concluded that populations are highly 

differentiated genetically, although limited 

sequence divergence and lack of phylogeographic 

concordance suggest an evolutionarily recent 

common ancestor and some degree of gene flow 

throughout the range. The differences seen in 

genetic markers among contemporary sea otter 

populations likely reflect periods of habitat 

fragmentation and consolidation during Pleistocene 

glacial advance and retreat, effects of limited 

gene flow resulting from natural geographic 

barriers and limited dispersal capability, and 

the recent history of harvest-related reductions 

and subsequent recolonization. The population 

bottlenecks that resulted from the fur trade 

harvest may have caused a significant loss of 

genetic diversity, similar in magnitude to the 

loss in other species with similar recent histories 

(Ralls et al. 1983, Larson et al. 2002a, Larson 

et al. 2002b), and this could reduce long-term 

population viability. However, Aguilar et al. (2008) 

concluded that the bottleneck and subsequent 

loss of genetic diversity in the California sea otter 

population occurred prior to the onset of the fur 

trade. Genetic diversity is higher in translocated 

populations that came from two source populations 

(e.g., southeast Alaska and British Columbia) 

than in a population that originated from a single 

source population (Washington; Larson et al. 

2002a). Despite the potential for reduced fitness 

resulting from population bottlenecks and reduced 

genetic diversity, rates of increase for translocated 

populations are significantly higher than those for 

remnant populations (Bodkin et al. 1999). 

Currently, FWS recognizes three stocks of sea 

otters in Alaska: southeast Alaska, southcentral 

Alaska, and southwest Alaska (Gorbics and Bodkin 

2001; Figure 2). Available data on movements and 

home ranges of sea otters and findings of divergent 

population trends at relatively small spatial scales 

(Bodkin et al. 2002) are suggestive of population 

structuring at smaller geographic scales than 

presently recognized. 

B. Morphology

The sea otter is the largest mustelid. Adult males 

attain weights of 45 kg and total lengths of 148 cm 

and adult females attain weights of 36 kg and total 

lengths of 140 cm. Size appears to vary among 

populations and to a large extent may reflect the 

status of the population relative to available food 

resources. Weights reported from populations 

below equilibrium density exceed those from 

populations at or near equilibrium density by 28% 

for males and 16% for females (Kenyon 1969). 

At Bering Island, Russia, mean weights of adult 

male sea otters declined from 32.1 kg in 1980 

to 25.1 in 1990, coinciding with the population 

Figure 1. Present distribution of three subspecies of sea otters (hatched areas).
Recovery Plan 2-3

exceeding carrying capacity and a 41% reduction in 

population size (Bodkin et al. 2000). In California, 

otters in a small, translocated population at San 

Nicolas Island (where food is abundant) are 

significantly larger than otters in the source 

population (where food is limiting): female mass is 

31% greater and male mass is 35% greater (Bentall 

2005). At birth, pups weigh 1.7-2.3 kg and are about 

60 cm in total length. 

The sea otter skull is broad and blunt and with 

dentition (teeth) that differs from that of most 

other carnivores in being adapted to crush prey, 

as opposed to shearing. The canines are long, 

rounded, and blunt, and used to puncture and 

pry open prey. The molars are broad and flat, 

with rounded crowns effective in grinding. The 

incisors and canines are used to scrape tissues out 

of shelled prey. A vestigial premolar is present 

and can be used to estimate age based on annual 

deposits of cementum (Bodkin et al. 1997). Dental 

problems associated with tooth wear and breakage 

leading to systemic infection may be a common 

contributing cause of mortality, particularly in old 

individuals (Kenyon 1969). 

Fur and the air trapped within it provide the 

primary sources of insulation and buoyancy for 

the sea otter. In contrast to most other marine 

mammals (which rely on blubber for insulation), 

sea otters have little or no sub-cutaneous fat. The 

pelage (fur) consists of relatively sparse outer 

guard hairs and a shorter, very dense underfur at 

a ratio of about 1:70. Hair densities range from 

nearly 26,000/cm2 on the hind flipper to 65,000/cm2 

on the foreleg (Williams et al. 1988). Sebaceous 

glands secrete oil that aids in water repulsion. The 

absence of arrector pili muscles in the epidermis 

permits the guard hairs to lie nearly parallel to 

the skin, and this allows the underfur to remain 

dry even when submerged in water. The ability 

of the sea otter to thermoregulate is dependent 

on maintaining the integrity of the pelage, in 

conjunction with an extremely high metabolic 

rate. This requires a nearly constant, yet gradual, 

molt, as well as frequent and vigorous grooming. 

The color of the pelage ranges from light brown to 

nearly black. As animals age, they may attain a 

grizzled appearance, with whitening occurring in 

the head, neck, and torso regions. Newborn pups 

have a pale brown, woolly natal pelage until about 

three months of age.

The forelegs of the sea otter are short and powerful 

with sensitive paws and extrudable claws used 

to locate, acquire, and manipulate prey. Forelegs 

are not used in propulsion. A fold of skin at the 

axilla (armpit) of each forelimb is used to store 

and transport prey gathered while foraging. Prey 

organisms are always consumed at the surface, 

where they are held and manipulated with the 

forepaws. The sea otter is one of the few nonhuman species known to use tools, often using 

rocks or shells as anvils or hammers to break open 

hard-shelled prey. The hind limbs are flattened 

and flipper-like. While swimming, the posterior 

Figure 2. Northern sea otter stock boundaries in Alaska, from Gorbics and Bodkin (2001).
2-4 Southwest Alaska DPS of the Northern Sea Otter

margins of the hind flippers approximate the 

lunate (crescent-shaped) pattern and undulating 

movement of the flukes of cetaceans (whales, 

dolphins, and porpoises). The tail is long, 

horizontally flattened, and used in swimming, 

particularly during slow movements while on the 

surface. The ears are short and usually held erect 

while at the surface. While diving, the ears are 

held downward, presumably to exclude water. 

Rapid swimming occurs in a face-down position 

and generally includes diving and may include 

porpoising for brief periods. Slower swimming 

generally occurs with the otter on its back with 

propulsion provided by the hind flippers and/or tail. 

C. Physiology

The general mammalian problem of maintaining 

a constant and elevated body temperature is 

exacerbated in the sea otter because of its small 

body size (relative to other marine mammals) 

and the resulting high surface to volume ratio, as 

well as the generally cold water temperatures of 

high-latitude marine environments. The sea otter 

appears to be one of the few mammals to exist in 

ambient temperatures outside its thermal-neutral 

zone (Yeates 2006). In addition to using air in the 

pelage as an external insulator to reduce heat 

loss, metabolic heat production in the sea otter 

is 2.4–3.2 times that predicted in a terrestrial 

mammal of similar size (Costa and Kooyman 1982). 

To maintain an average body temperature of about 

38° C, a standard metabolic rate of about 0.72 cm3

O2/gm body weight/hour has been measured in 

the sea otter (Morrison et al. 1974). To maintain 

the elevated metabolic rate, energy intake must 

also be elevated, requiring consumption of prey 

equal to about 20-33% of body weight per day 

(Kenyon 1969, Costa 1982). Although the air 

layer in the fur is an efficient insulator, it is also 

inflexible, requiring a mechanism to dissipate heat 

during periods of intense exercise. This appears 

to be accomplished through the broad, highly 

vascularized, sparsely furred hind flippers.

Some of the physiological adaptations evident in 

sea otters result from their residing solely in a saltwater environment and foraging under hyperbaric 

(pressurized) conditions. Sea otters have little 

access to fresh water and feed primarily on marine 

invertebrates that are isotonic with seawater 

and may contain relatively high concentrations 

of nitrogen, iodine, and other electrolytes. The 

otters are able to cope with these conditions by 

consuming sea water (thereby increasing their 

urinary osmotic space) and producing large 

volumes of moderately concentrated urine from 

large, highly efficient kidneys (Costa 1982). The 

lungs are nearly 2.5 times larger than in other 

similar-sized mammals, serving to store oxygen 

needed for diving and buoyancy (Costa and 

Kooyman 1982). Oxygen-hemoglobin affinity is 

relatively high, thus increasing blood-oxygen 

storage capacity. Hemoglobin, red blood cell, 

and hematocrit values in sea otters are similar to 

values in pinnipeds (seals, sea lions, and walruses) 

and cetaceans (Bossart and Dierauf 1990). The 

trachea is relatively wide compared to other otters, 

allowing rapid and complete air replacement 

between dives. 

D. Distribution and habitat

The sea otter occurs only in the North Pacific 

Ocean, and its historical range includes coastal 

habitats around the Pacific rim between central 

Baja California and northern Japan. The range 

currently occupied extends from southern 

California to northern Japan, with extralimital 

sightings in central Baja California and near 

Wrangel Island in the Chukchi Sea. The northward 

limits in distribution appear related to the southern 

limits of sea ice, which can preclude access to 

foraging habitat. Seasonal and inter-annual 

variation in the southern extent of sea ice results 

in constriction and expansion of the sea otter’s 

northern range. During periods of advancing 

winter sea ice along their northern range, sea 

otters occasionally become trapped and sometimes 

die (Nikolaev 1965, Schneider and Faro 1975). Sea 

otters attempting to travel tens of kilometers over 

the Alaska Peninsula to gain access to the ice-free 

Pacific were observed in 1971 and 1972 (Schneider 

and Faro 1975) and again in 1982, 1999, and 2000 

(USGS unpublished data). Although some otters 

may succeed in such efforts, many apparently die 

from starvation or predation by wolves (Canis 

lupus), red foxes (Vulpes vulpes), and wolverines 

(Gulo gulo). Southern range limits are less 

well understood but appear to coincide with the 

southern limits of coastal upwelling, associated 

canopy-forming kelp forests, and the 20-22° C sea 

surface isotherm (Kenyon 1969). 

Sea otters occupy and use all coastal marine 

habitats within their range, from protected bays 

and estuaries to exposed outer coasts and offshore 

islands. Because they need to dive to the sea 

floor to forage (Bodkin 2001), the seaward limit of 

their usual distribution is defined by their diving 

ability and is approximated by the 100 m depth 

contour. While sea otters can be found at the 

surface in water deeper than 100 m, either resting 

or swimming, they are most commonly observed 

in waters within a few kilometers of shore 

(Riedman and Estes 1990), and higher densities are 

frequently associated with shallow water (Laidre 

et al. 2002). Bodkin and Udevitz (1999) found 80% 

of the otters in PWS over water depths of < 40 

m although the proportion of the seafloor habitat 

within this bathymetric zone was only about 33%. 

Sea otters can also be found in high densities 

where relatively shallow waters or islands occur 

far offshore (Kenyon 1969). While they periodically 

haul out on intertidal or supratidal (above the 

high tide line) shores (particularly during winter 

months) and generally remain close to the sea/land 

interface, no aspect of their life history requires 

leaving the ocean (Kenyon 1969, Riedman and 

Estes 1990). 
Recovery Plan 2-5

Sea otters forage in diverse bottom types, from 

fine mud and sand to rocky reefs. Recent research 

employing archival time-depth recorders recovered 

from sea otters in southeast Alaska showed that 

84% of foraging occurred in depths of 2-30 m, and 

that 16% of all foraging was between 30 and 100 m 

(Bodkin et al. 2004). Females dove to depths < 20 

m on 85% of their foraging dives while males dove 

to depths > 45 m on 50% of their foraging dives. 

Recent research from California suggests these 

patterns are similar among populations (Tinker et 

al. 2007), but additional research would improve 

confidence in the generality of these results. 

Although it had been speculated that sea otters do 

not occupy protected inside waters such as occur 

in southeast Alaska and Puget Sound (Kenyon 

1969; Kvitek et al. 1991, Kvitek et al. 1993), recent 

colonization of such habitat has been observed in 

PWS and southeast Alaska, including glacial fiords 

and inside passages such as Icy Strait (Esslinger 

and Bodkin 2009). Although densities of sea otters 

are clearly not uniform, within the geographic 

range currently occupied there is little evidence 

that particular habitat types are unsuitable. 

However, there is also clear evidence that some 

types of habitat are preferred and capable of 

supporting high densities over extended periods 

of time (Riedman and Estes 1990). In particular, 

where canopy-forming kelps occur (including 

species of Macrocystis, Eularia, and to a lesser 

extent Nereocystis), they provide preferred resting 

habitat (Kenyon 1969, Riedman and Estes 1990). 

However, canopy-forming kelp is not a required 

habitat element, and high densities of sea otters 

occur and persist in nearshore habitat where 

kelp is absent, either seasonally or entirely, as a 

consequence of unconsolidated substrates. This is 

particularly evident along coastal mainland Alaska 

(Kenyon 1969, Riedman and Estes 1990). There 

also appears to be a positive relationship between 

shoreline complexity and sea otter density (Kenyon 

1969, Riedman and Estes 1990). Again, although 

not obligatory, headlands, coves, and bays appear 

to offer preferred resting habitat, particularly 

to females with pups, presumably because they 

provide protection from high wind and sea 

conditions. Another recently recognized habitat 

attribute is refuge from predators. In a shallow 

lagoon that limited killer whale access at Adak 

Island, Estes et al. (1998) found relatively stable 

sea otter numbers during a period when declines 

of up to 90% were detected outside the lagoon. A 

shift in distribution toward very shallow (1-3 m), 

nearshore, highly protected areas in the Aleutian 

Islands (USGS unpublished data) that has been 

detected since the recent decline may represent a 

response to the risk of predation (see Section 3.A.).

Comparatively few data are available to describe 

relations between sea otter densities and habitat 

characteristics, however it is generally recognized 

that rocky habitats support higher sea otter 

densities compared to soft sediment habitats 

(Riedman and Estes 1990, Laidre et al. 2001, 

Laidre et al. 2002). Estimates of equilibrium 

densities (those that are approximately stable and 

consistent over time) across various populations 

within the sea otters range indicate values that 

range from about 1.5-17/km2

 and generally consider 

habitats out to the 40-55 m isobath (depth contour). 

Burn et al. (2003) and Estes (1977) provide 

estimates of equilibrium densities for the Aleutian 

Archipelago and Amchitka Island of 16 and 17/

km2 respectively. Equilibrium densities likely 

vary among habitats, with reported values specific 

to rocky habitats of about 1 to 8/km2 (Laidre et al. 

2001, Laidre et al. 2002, Lowry and Bodkin 2005, 

Gregr et al. 2008). Equilibrium densities from softsediment habitats are generally lower at < 1/ km2

. 

In predominantly soft sediment habitats in PWS 

sea otter densities vary among areas, averaging 

about 1.5/km2 and ranging from fewer than 1 to 

about 6/km2 (Bodkin and Udevitz 1999, USGS 

unpublished data), although maximum densities 

in Orca Inlet, a shallow soft-sediment habitat, are 

up to 16/km2. Other estimates of maximum sea 

otter densities of about 12/km2

 have been reported 

from the Aleutian and Commander Islands 

(Kenyon 1969, Bodkin et al. 2000) where habitats 

are predominantly rocky. In 2003, densities 

throughout the Aleutian Archipelago are estimated 

to be approximately 3% of equilibrium density 

(Estes et al. 2005).

E. Population biology

As in other sexually reproducing species, sea otter 

populations are ultimately regulated by age- and 

sex-specific rates of reproduction and survival. 

The life history patterns of sea otters are more 

similar to those of the pinnipeds and other marine 

mammals (with whom they share the ocean as a 

common environment) than to those of the other 

lutrines (with whom they share a more recent 

common ancestor) (Estes and Bodkin 2002). These 

patterns limit the potential for population increase.

Male sea otters can attain sexual maturity by age 3 

but likely do not attain the social maturity required 

for successful reproduction until >5 years old 

(Garshelis 1983). Variation in reproductive success 

among males and the different reproductive 

strategies they may employ are largely 

unexplored. Female sea otters attain sexual 

maturity as early as age 2, and by age 3 most 

females are sexually mature (Bodkin et al. 1993, 

Jameson and Johnson 1993). Where food resources 

limit population growth, sexual maturation may be 

delayed to 4-5 years of age. Reported reproductive 

rates of adult females range from 0.80 to 0.98 

births/yr (Siniff and Ralls 1991, Bodkin et al. 1993, 

Jameson and Johnson 1993, Riedman et al. 1994, 

Monson and DeGange 1995, Monson et al. 2000a, 

Tinker et al. 2006a). In areas where sea otter 

reproduction has been studied, reproductive rates 

appear to be fairly consistent despite differences 

in resource availability. Gestation, including a 

period of delayed implantation, requires about six 

months. Although copulation and pupping can take 
2-6 Southwest Alaska DPS of the Northern Sea Otter

place at any time of year, there appears to be a 

positive relation between latitude and reproductive 

synchrony. In California, pupping is weakly 

synchronous to nearly uniform across months 

(Siniff and Ralls 1991, Riedman et al. 1994, Tinker 

et al. 2006a), whereas in PWS a distinct peak in 

pupping occurs in late spring. In recently colonized 

habitats in Alaska where food is plentiful, pupping 

may be less synchronous than in populations at 

or near equilibrium density (Bodkin and Monson 

2002). 

Females give birth to a single pup, although 

rare instances of twinning have been observed 

(Jameson and Bodkin 1986). The average duration 

of birth to weaning is about six months, resulting in 

a reproductive interval of approximately one year 

from copulation to weaning. If a female loses her 

pup prior to weaning, she will soon enter estrus 

and breed again. Copulation occurs in the water, 

and a male may remain with an estrous female for 

several days (Riedman and Estes 1990), although 

a female may breed with more than one male. 

Distinctive and sometimes severe wounds can 

result from the male biting the nose of the female 

during copulation. 

Whereas reproductive output remains relatively 

constant over a broad range of ecological 

conditions, pup survival appears to be more 

strongly influenced by resource availability. At 

Amchitka Island, where the population was at 

or near equilibrium density prior to the recent 

decline, dependent pup survival ranged from 22-

40%, compared to nearly 85% at Kodiak Island, 

where food was not limiting and the population 

was increasing (Monson et al. 2000a). Female 

experience apparently is important to the survival 

of offspring, with primiparous females (new 

mothers) generally less successful at weaning their 

pups (Riedman et al. 1994, Monson and DeGange 

1995, Tinker et al. 2006a). Body condition (weight/

unit length) of the mother at the time of birth also 

influences pup survival (Bodkin and Monson 2002). 

Female sea otters must spend large amounts of 

time grooming and nursing their newborn pups, 

and keeping them warm and dry on their chests 

or hauled out on rocks. This necessarily restricts 

the amount of time they can spend foraging. A 

female in poor condition will not be able to restrict 

her feeding time to the extent a female in good 

condition can, and her pup will be exposed to 

longer periods in the water, and less grooming 

and nursing, while she feeds. The result is poorer 

pup survival during the first few weeks of life, 

the period during which most pre-weaning pup 

mortality occurs. This effect may be exaggerated 

during winter when conditions are particularly 

harsh. In sea otter populations with limited food 

resources, pups born in winter are more likely 

to die soon after birth. This trend, combined 

with the tendency for females to enter estrus 

soon after losing a pup, tends to result in greater 

synchronization of pup production. 

Sea otter populations generally consist of more 

females than males (Kenyon 1969, Bodkin et 

al. 2000). Age-specific survival of sea otters is 

generally lower among males (Kenyon 1969, 1982; 

Siniff and Ralls 1991, Monson and DeGange 1995, 

Bodkin et al. 2000), although variation in the 

post-weaning survival of females appears to be 

the primary mechanism of population regulation 

around carrying capacity. Populations living 

with an abundance of food exhibit relatively high 

survival rates in all age classes compared to foodlimited populations, with especially high relative 

survival in juvenile age-classes. Alternatively, 

populations at or above equilibrium density, with 

limited food, show high variation in survival in the 

weeks following weaning. Post-weaning survival 

is variable among populations and years, ranging 

from 18% to 86% (Monson et al. 2000a, Ballachey 

et al. 2003). In general, once a sea otter survives 

its first year of life, there appears to be a relatively 

good probability that it will survive to senescence 

(old age), where density-dependent mechanisms 

structure population abundance. Such may not 

be case where density-independent factors such 

as predation are important. Survival of sea 

otters more than 2 years of age is generally high, 

approaching or exceeding 90%, but gradually 

declines over time (Bodkin and Jameson 1991, 

Monson et al. 2000a). In Alaskan and other 

northern populations, most mortality (other than 

human-related) occurs during late winter and 

spring, presumably associated with harsh winter 

environmental conditions and seasonal declines 

in prey availability (Kenyon 1969, Bodkin and 

Jameson 1991, Bodkin et al. 2000, Watt et al. 2000). 

Maximum ages achieved by sea otters outside 

captivity are about 22 years for females and 15 

years for males.

The fetal sex ratio does not differ from 50:50 

(Kenyon 1982, Bodkin et al. 1993), yet sea otter 

populations may exhibit unequal sex ratios. 

Survival of juvenile (post-weaning) males in 

California exceeded that of juvenile females (Siniff 

and Ralls 1991) although the opposite was found at 

Amchitka Island (Kenyon 1969). 

Causes of mortality in sea otter populations are 

difficult to determine. The probability of detecting 

and assigning cause of death depends on the cause. 

For example, the carcass of a sea otter that dies of 

starvation is more likely to be recovered than that 

of one killed by a predator. Documented sources 

of mortality include predation, starvation, disease, 

oil spills, incidental take in fisheries, harvest, and 

intra-specific aggression. In California, infectious 

disease was implicated as the cause of death for 

nearly 40% of 195 carcasses analyzed between 

1992 and 1995 (Thomas and Cole 1996) and 63% 

of 105 carcasses analyzed between 1998 and 2001 

(Kreuder et al. 2003). However, these estimates 

may not accurately reflect the causes of death in 

the population as recovered carcasses are likely 

not an unbiased sample (as mentioned above) and 

the cause is unknown for the majority of deaths: an 
Recovery Plan 2-7

estimated 40-60% of carcasses are not recovered 

and the cause of death cannot be determined for 

about 72% of those that are recovered (Estes 

et al. 2003, Gerber et al. 2004). Disease factors 

contributing to mortality included peritonitis 

caused by acanthocephalan parasites, protozoan 

encephalitis, coccidioidomycosis, and bacterial 

infections. As noted previously, sea ice can be a 

mortality factor as well. Recognized sea otter 

predators include the white shark (Carcharodon 

carcharias), brown bear (Ursus arctos), wolf, 

red fox, wolverine, killer whale, and bald eagle 

(Haliaeetus leucocephalus) (Kenyon 1969, Ames 

and Morejohn 1980, Riedman and Estes 1990, 

Monson and DeGange 1995, Hatfield et al. 1998, 

Bodkin et al. 2000). Bald eagles prey primarily 

on young pups. Gelatt (1996) found that mothers 

with young pups fed less often overall, and when 

they did feed it was often nocturnally, apparently 

to avoid exposing their pup to eagle predation. 

Declining sea otter populations across the Aleutian 

archipelago during the 1990s have been attributed 

to increased predation by killer whales (Estes et al. 

1998). 

Prior to the sea otter decline in the Aleutian 

Islands late in the 20th century, population 

densities were generally thought to be limited 

by prey availability, with mortality being density 

dependent and increasing during periods of food 

shortage and severe weather. This pattern of 

elevated winter mortality, particularly among 

juveniles and old individuals, was initially observed 

at Amchitka Island in the 1950s and 1960s (Kenyon 

1969). In the Commander Islands, the sea otter 

population declined by 41% in a single year, 

following 10 years of increasing density, declining 

prey populations, and declining weights of adult 

male otters (Bodkin et al. 2000). It is possible that 

some of the disease-related mortality in California 

is ultimately linked to prey availability, and the 

cessation of population growth in the regions of 

highest sea otter density in California is associated 

with declining body condition and increased 

percentage of time spent foraging, both suggestive 

of food limitation (Bentall et al. 2005, Tinker et al. 

2006a, Tinker et al. 2006b). 

Relatively few studies have investigated the 

relations between the physical and biological 

attributes that contribute to variation in 

productivity of nearshore marine invertebrates, 

such as the clams, mussels, and crabs that sea 

otters consume, and how that variability in 

productivity affects variation in annual sea otter 

survival (Lowry and Bodkin 2005). Given the 

observed variation in sea otter survival and the 

recognized role of food in regulating sea otter 

populations, understanding those relations would 

facilitate empirical measures of the relative 

contributions of predation and primary production as controlling factors in structuring nearshore 

marine communities. Due to the relatively small 

size of their home ranges, sea otters integrate 

physical and biological attributes of the ecosystem 

over small spatial scales. Further, because they 

occur near shore, sea otters, their prey, and 

physical and biological ecosystem attributes can be 

accurately and efficiently monitored, providing a 

strong foundation for understanding mechanisms 

and interactions among factors that regulate sea 

otter populations.

Our understanding of the frequency, magnitude, 

causes, and consequences of changes in sea otter 

populations is constrained by the brief temporal 

perspectives imposed by the short window of 

human observation and written history. One 

consequence of this narrow time perspective is that 

we may view causes and consequences of change 

as novel, even when they are not. Relatively 

unexplored evidence from archeological remains 

suggests that local abundance of sea otters 

and other nearshore marine species has varied 

significantly over millennial time scales (Simenstad 

et al. 1978). An improved understanding of 

long-term population changes would provide 

added context for evaluating and responding to 

contemporary fluctuations in sea otter populations. 

F. Behavior

Reproduction

Male sea otters gain access to estrous females by 

establishing and maintaining territories from which 

other males are excluded (Kenyon 1969, Garshelis 

et al. 1984, Jameson 1989). Territories may be 

located in or adjacent to female resting or feeding 

areas, or along travel corridors between those 

areas. Territories are occupied continuously or 

intermittently over time (Loughlin 1980, Garshelis 

et al. 1984, Jameson 1989). Male occupancy in a 

territory may extend over 6 to 9 years (Riedman 

and Estes 1990). Male territoriality results in 

partial segregation of the sexes, and males that 

do not occupy territories tend to reside in dense 

“bachelor” aggregations (Kenyon 1969, Bodkin 

et al. 2000). Males that do not defend territories 

(transients) may gain access to receptive 

females by traveling through or adjacent to male 

territories and female areas. Male aggregation 

areas identified by Kenyon in 1962 at Amchitka 

Island persisted through at least 1995 (USGS 

unpublished data). In California, many males 

that defend territories for part of the year may 

periodically move to bachelor aggregation areas, 

apparently to take advantage of seasonally and 

locally abundant food resources (Jameson 1989, 

Tinker et al. 2006b, Kage 2004). Female choice in 

mate selection is facilitated by females traveling 

among male territories, although males may try to 

sequester estrous females within their territory. 

Adult male sea otters in California maintain 

territories that average about 0.4 km2

 (Jameson 

1989). Adult females apparently move freely 

among these territories, but the territory holder 

aggressively excludes juvenile males. Although the 

role of male territoriality in regulating population 

density is largely unexplored, territories likely 

serve to increase individual reproductive success. 
2-8 Southwest Alaska DPS of the Northern Sea Otter

Adult males frequently harass females with large 

pups in an apparent effort to force separation, 

thus inducing the female to enter estrus so that 

he can inseminate her and she will bear his 

offspring. Copulation occurs repeatedly during a 

brief consortship, after which the male and female 

separate. 

The sea otter’s polygynous mating system (one 

male can mate with more than one female) likely 

evolved in response to the high densities they 

achieved in prehistoric populations not affected 

by humans, which promoted male competition for 

females. Polygynous mating systems are typical 

of all otariids (eared seals) and some phocids 

(true seals) in temperate-latitude systems but 

apparently are rare or absent in other species of 

otters. As is true for many polygynous species, 

male sea otters provide no parental care (Estes 

1989). 

Diving

Diving occurs during grooming, traveling and 

foraging. Grooming dives usually occur before 

feeding or resting periods, and are of short 

duration and shallow depth. Because locomotion 

is more efficient under water than on the surface 

(Williams 1989), otters frequently make relatively 

long (30-60 s), shallow dives while traveling 

between resting and feeding areas. Foraging dives 

are predominantly to the sea floor, although canopy 

foraging in kelp forests for snails and crabs is 

common in California. Foraging dives are typically 

longer in duration with greater rates of descent 

and ascent than dives of other function. Attributes 

of dives (duration, depth, ratio of bottom time to 

dive time, and rates of descent and ascent) are 

significantly different for traveling, grooming, and 

foraging dives (Bodkin et al. 2004), and can be used 

to classify dives according to their function.

Three general types of diving data have been 

obtained through direct visual observation (Estes 

et al. 1981, Riedman and Estes 1990, Calkins 1978, 

Garshelis 1983, Doroff and Bodkin 1994, Doroff 

and DeGange 1994), radio-telemetry with remote 

information acquisition (Ralls et al. 1996), and most 

recently archival time-depth recorders (TDRs) 

(Bodkin et al. 2004, Tinker et al. 2006b). Dive 

attributes from visual observations include dive 

duration, surface intervals between dives, and 

approximate water depths at the estimated dive 

locations. These data are inherently biased against 

animals foraging well away from shore (Ralls et al. 

1996). Thus, estimates of average and maximum 

dive duration from animals instrumented with 

radio-transmitters are substantially longer than 

those obtained visually (Ralls et al. 1996, Bodkin et 

al. 2004). 

Mean swimming speeds during descent and 

ascent in foraging dives average about 1 m/s. In 

California, average dive times are longest for 

juvenile males and shortest for adult females 

with dependent pups (Ralls et al. 1996). These 

differences are likely because juvenile males 

forage in deeper water offshore while females 

with pups forage in shallower water near shore. 

Maximum reported dive durations are 246 s in 

California (Ralls et al. 1996) and 386 s in Alaska 

(USGS unpublished data). Dive times and surface 

intervals correlate with water depth, although 

the deepest dives are not necessarily associated 

with maximum dive times. Surface intervals are 

highly correlated with prey size and type, with the 

longest intervals associated with the largest prey, 

thus reflecting handling and consumption times 

(Ralls et al. 1996, Tinker et al. 2007). Sea otters 

commonly dive to depths exceeding 40 m in the 

Aleutian Islands and there is one record of a sea 

otter drowned in a crab pot set in 91 m of water 

(Newby 1975).

In California, it was recently demonstrated that 

TDR data alone are sufficient to detect dietary 

differences between individuals (Tinker et al. 2007), 

and information recently obtained from southeast 

Alaska using TDRs confirms the foraging 

specializations documented in California (Estes 

et al. 2003a, Tinker et al. 2007). Archival TDRs 

identified individual- and sex-related differences 

in mean and maximum foraging dive attributes 

and depth distributions (Bodkin et al. 2004). A 

bimodal pattern in forage depth distribution was 

detected for most of the individuals sampled, with 

peaks in foraging between 5-15 m and 30-60 m. 

Generally, adult females dove to shallower depths 

than adult males, although some females regularly 

dove to depths exceeding 60 m. Most adult males 

foraged at depths between 40 and 60 m, although 

several repeatedly dove to depths exceeding 60 

m. Maximum dive depths were 76 m for females 

and 100 m for males (Bodkin et al. 2004). Similar 

patterns appear to occur in California (Tinker et al. 

2006b, USGS unpublished data), but more research 

is needed to document the generality of these 

findings in other populations.

Activity budgets

Time budgets describe the allocation of time to 

specific categories of behavior, such as resting, 

grooming, foraging, or social interaction. The 

fundamental premise is that food resources 

frequently limit population abundance, and that the 

proportion of time individuals allocate to foraging 

reflects food availability (Gelatt et al. 2002). Sea 

otter activity budgets have been estimated at 7 

different locations and during 13 different time 

periods, including populations that were increasing, 

stable, and decreasing (Gelatt et al. 2002). Despite 

this breadth of research, conclusions about 

the utility of time budgets to assess sea otter 

population status have been inconsistent (Shimek 

and Monk 1977, Loughlin 1979, Estes et al. 1982, 

Estes et al. 1986, Garshelis et al. 1986, Ralls 

and Siniff 1990, Gelatt et al. 2002). Some of the 

divergence in results and conclusions stems from 

differences in accuracy and precision produced by 

various methods (visual vs. telemetry), sampling 

designs (day vs. day and night, and sample 
Recovery Plan 2-9

sizes), uncertainty in population status relative 

to carrying capacity at the time of study, and the 

sometimes overwhelming influence of sampling 

variance (including within-individual variation 

across time, as well as variation among individuals, 

sexes, and age cohorts).

In general, adult male sea otters spend less time 

foraging (37%) than younger males (44%) or 

females, adult females spend less time foraging 

(36-47%) than younger females (50%), and females 

with young pups spend less time feeding (21%) 

than females with older pups (52%) (Gelatt et al. 

2002). Recent calculations of time budgets based 

on TDR data from southeast Alaska (Bodkin et al. 

2007a) and comparisons between telemetry- and 

TDR-based activity budgets in California (Tinker 

et al. 2006b, Bentall 2005, USGS unpublished 

data) support the premise that the amount of time 

allocated to foraging reflects prey availability, 

and thus TDR-derived activity budgets may be 

of use in evaluating population status relative to 

equilibrium density. 

Movements and home ranges

Sea otters exhibit complex movement patterns 

related to habitat characteristics, social 

organization, and reproductive biology. It is likely 

that movements differ depending on whether 

a population is at or near carrying capacity or 

has access to unoccupied suitable habitat into 

which it can expand (Riedman and Estes 1990). 

Most research on sea otter movements has been 

conducted where unoccupied habitat is available 

to dispersing animals. For example, dominant 

adult males in California generally occupy and 

defend relatively small territories, which vary 

seasonally between summer-fall (4.0 km2

; 1.1 km 

of coastline) and winter-spring (7.8 km2; 2.2 km 

of coastline), frequently moving relatively long 

distances to reach male aggregations (average 

= 80.1 km; maximum = 418 km) (Jameson 1989, 

Ralls et al. 1996, Kage 2004, USGS unpublished 

data). Juvenile females may also occasionally 

make very long movements (maximum = 235 km; 

USGS unpublished data). Adult females rarely 

move farther than 20 km, although they generally 

occupy home ranges larger than the territories of 

territorial males (Ralls et al. 1988). Early research 

in the Aleutian Islands by Lensink (1962) and 

Kenyon (1969) found that males had larger home 

ranges than females, and those authors described 

the female sea otter’s home range as including 

8-16 km of contiguous coastline. Adult male 

home ranges in PWS are 4.6-11.0 km2

 and adult 

female home ranges are 1.0-4.8 km2 (Garshelis 

and Garshelis 1984). In PWS, a telemetry study 

documented movements by adult males of up to 100 

km between male aggregation areas and breeding 

areas (Garshelis and Garshelis 1984a). Recent 

estimates of annual home range sizes in western 

PWS were significantly smaller for territorial 

males1

 (50% kernel home range = 5.4 km2, and 

1

 The “% kernel home range” is the area where an 

animal can be found that % of the time.

90% kernel home range = 9.6 km2) than for adult 

females (50% kernel home range = 6.8 km2, and 

90% kernel home range = 23.8 km2) (Ballachey 

and Bodkin 2006). Additional telemetry studies 

on juveniles in PWS, and adult males and females 

along the Alaska Peninsula and in the Kodiak 

archipelago (USGS unpublished data, Monnet et al. 

1988), documented movements typically of 50 km 

or less. Comparable data are not available from 

other areas in Alaska. 

Although annual and lifetime home ranges suggest 

limited movements throughout much of their 

range, sea otters can move much greater distances 

when translocated. In California, translocated 

animals returned as much as 318 km from release 

sites back to capture sites (Ralls et al. 1992). In 

Alaska, a female treated and then released after 

the T/V Exxon Valdez oil spill traveled 400 km 

from her release site (Monnet et al. 1990).

While sea otters are somewhat constrained by 

the 100 m depth profile, they can navigate some 

distance over deep water. Translocated sea otters 

have traveled distances of up to 50 km, over water 

deeper than their maximum foraging depth, from 

San Nicolas Island to the mainland along southern 

California (Rathbun et al. 1990). Similar travel, 

but unrelated to translocation, was documented by 

Bodkin et al. (2000) from Medny to Bering Island in 

the Commander Islands. 

Conventional very high frequency (VHF) 

telemetry has most commonly been used to 

estimate sea otter home ranges and movements. 

Because relocation probabilities diminish as 

distance from last location increases, movement 

estimates from such studies may be negatively 

biased. Additional research employing 

technologies that reduce recognized sources of 

bias (e.g., global positioning system, GPS) should 

provide better estimates. Also, in the event of 

localized extinctions within geographically isolated 

segments of habitat (e.g., island groups in the 

Aleutian Islands), recolonization may become more 

problematic as the required dispersal distance 

increases. 

G. Foraging ecology

Sea otters are generalist predators, known to 

consume more than 150 different prey species 

(Kenyon 1969, Riedman and Estes 1990, Estes 

and Bodkin 2002). With few exceptions, their 

prey consists of sessile or slow-moving benthic 

invertebrates such as mollusks, crustaceans, and 

echinoderms. Foraging occurs in habitats with 

rocky and soft-sediment substrates from the high 

intertidal to depths slightly in excess of 100 m. 

Preferred foraging habitat is generally in depths of 

less than 40 m (Riedman and Estes 1990), although 

studies in southeast Alaska have found that some 

animals forage mostly at depths of 40-80 m (Bodkin 

et al. 2004). 
2-10 Southwest Alaska DPS of the Northern Sea Otter

The diet of sea otters is usually studied by 

observing prey items brought to the surface for 

consumption, and therefore diet composition is 

usually expressed as a percentage of all identified 

prey that belong to a particular prey species or 

type. Although the sea otter is known to prey 

on a large number of species, only a few tend to 

predominate in the diets of individual otters in 

any particular area. Prey type and size depend 

on location, habitat type, season, and length of 

occupation. In California, otters foraging over 

rocky substrates and in kelp forests mainly 

consume decapod crustaceans, gastropod and 

bivalve mollusks, echinoderms, and worms (Ebert 

1968, Estes et al. 1981, Tinker et al. 2008). In 

protected bays with soft sediments, otters mainly 

consume infaunal clams (Saxidomus nuttallii 

and Tresus nuttallii) (Kvitek et al. 1988). Along 

exposed coasts with soft sediments, the Pismo clam 

(Tivela stultorum) is a common prey (Stephenson 

1977). Important prey in Washington State 

include crabs (Cancer spp., Pugettia spp.), octopus 

(Octopus spp.), intertidal clams (Protothaca spp.), 

sea cucumbers (Cucumaria miniata), and red 

sea urchins (Strongylocentrotus franciscanus) 

(Kvitek et al. 1989). The predominantly softsediment habitats of southeast Alaska, PWS, and 

Kodiak Island support populations of clams that 

are the primary prey of sea otters. Throughout 

most of southeast Alaska, burrowing clams 

(species of Saxidomus, Protothaca, Macoma, 

and Mya) predominate in the sea otter’s diet 

(Kvitek et al. 1993). They account for more than 

50% of the identified prey, although urchins (S. 

droebachiensis) and mussels (Modiolis modiolis, 

Mytilus spp., and Musculus spp.) can also be 

important. In PWS and Kodiak Island, clams 

account for 34-100% of sea otter prey (Calkins 

1978, Doroff and Bodkin 1994, Doroff and DeGange 

1994). Mussels (Mytilus trossulus) apparently 

become more important as the duration of 

occupation by sea otters increases, ranging from 

0% at newly occupied sites at Kodiak to 22% in 

long-occupied areas (Doroff and DeGange 1994). 

Crabs (C. magister) were once important sea 

otter prey in eastern PWS, but apparently have 

been depleted by otter foraging and are no longer 

eaten in large numbers (Garshelis et al. 1986). Sea 

urchins are minor components of the sea otter diet 

in PWS and the Kodiak archipelago. In contrast, 

the diet in the Aleutian, Commander, and Kuril 

Islands is dominated by sea urchins and a variety 

of fin fish (including those in the taxomic families of 

hexagrammidae, gadidae, cottidae, cyclopteridae, 

and scorpaenidae; Kenyon 1969, Estes et al.1982). 

Sea urchins tend to dominate the diet of lowdensity sea otter populations, whereas more fish 

are consumed in populations near equilibrium 

density (Estes et al. 1982). For unknown reasons, 

fish are rarely consumed by sea otters in regions 

east of the Aleutian Islands. 

Sea otters also exploit episodically abundant prey 

such as squids (Loligo spp.) and pelagic red crabs 

(Pleuroncodes planipes) in California and smooth 

lumpsuckers (Aptocyclus ventricosus) in the 

Aleutian Islands (Watt et al. 2000). On occasion, 

sea otters attack and consume sea birds, including 

at least teals (Anas crecca), scoters (Melanita 

perspicillata), loons (Gavia immer), gulls (Larus 

spp.), grebes (Aechmophorus occidentalis ), and 

cormorants (Phalacrocorax spp.; Kenyon 1969, 

Riedman and Estes 1990). 

Diet diversity generally increases through time 

as otter populations colonize new habitats and 

grow toward resource limitation (Estes et al. 1981, 

Estes and Bodkin 2002). This has been chronicled 

in the Aleutian Islands, PWS, and California. 

Diversification is probably the consequence of 

otters reducing the abundance of their preferred 

or most abundant prey. Studies of marked sea 

otters in California have shown extreme individual 

variation in diet and foraging behavior (Estes et al. 

2003b, Tinker et al. 2007), although such individual 

variation apparently does not occur when food 

is very abundant, such as at San Nicolas Island 

(Bentall 2005, Tinker et al. 2008). In the center 

of the range where food is most limiting, adult 

otters tend to specialize on 1-3 prey types (Estes 

et al. 2003a, Tinker 2004, Tinker et al. 2007, 2008). 

This individual variation cannot be attributed to 

phenotypic or environmental variation, as different 

individuals of the same sex and age class often 

consume different prey at the same time and place. 

Dietary patterns, which appear to be passed from 

mothers to their offspring, are known to persist 

for years and may be life-long characteristics 

of individuals. The causes and consequences of 

individual foraging patterns remain uncertain.

Sea otters are well known for the effects their 

foraging has on the structure and function of 

coastal marine communities. They provide an 

important example of the “keystone species” 

concept (Power et al. 1996). In the absence of sea 

otter foraging during the 20th century, populations 

of several species of urchins became extremely 

abundant. Grazing activities of urchins effectively 

limited kelp populations, resulting in deforested 

areas known as “urchin barrens” (Lawrence 1975, 

Estes and Harrold 1988). Because sea urchins 

are a preferred prey item, as otter populations 

recovered they dramatically reduced the sizes and 

densities of urchins, as well as other prey such 

as mussels. Released from the effects of urchin 

herbivory, populations of macroalgae responded, 

resulting in diverse and abundant populations 

of understory and canopy-forming kelp forests. 

Although other factors, both abiotic and biotic, 

can also limit sea urchin populations (Foster and 

Schiel 1988, Foster 1990), the generality of the 

sea otter effect in reducing urchins and increasing 

kelp forests is widely recognized (Estes and 

Duggins 1995). Further cascading effects of sea 

otters in coastal rocky subtidal communities 

may stem from the proliferation of kelp forests. 

Kelp forests provide food and habitat for other 

species, including fin fish (Simenstead et al. 1978, 

Ebeling and Laur 1988), that in turn provide 
Recovery Plan 2-11

forage for birds and mammals. Furthermore, 

kelp constitutes a source of detrital organic carbon 

for the coastal marine community (Duggins et al. 

1989). Additional evidence of the strong trophic 

cascade precipitated by sea otter predation on 

urchins is provided by the significant declines in 

kelp abundance in the Aleutian Islands as a result 

of increased urchin grazing following the sea otter 

decline in the 1990s (Estes et al. 1998). 

Effects of sea otter foraging also are documented 

in rocky intertidal and soft-sediment marine 

communities. The size-class distribution of mussels 

in the rocky intertidal zone was strongly skewed 

toward animals < 40 mm shell length where otters 

were present, whereas mussels > 40 mm comprised 

a large component of the population where sea 

otters were absent (VanBlaricom 1988). In softsediment coastal communities, sea otters forage on 

epifauna (crustaceans, echinoderms and mollusks) 

and infauna (primarily clams). They generally 

select the largest individuals. This causes declines 

in prey abundance and reductions in size-class 

distributions, although the deepest-burrowing 

clams (e.g., Tresus nuttallii and Panopea generosa) 

may gain refuge from some sea otter predation 

(Kvitek and Oliver 1988, Kvitek et al. 1992). 

Community-level responses to reoccupation of 

habitat by sea otters are much less well studied 

in soft-sediment habitats that dominate much of 

the North Pacific, and our knowledge of sea otter 

ecology will benefit from additional research in this 

area.

H. Population history

Little is known about the long-term patterns of 

sea otter abundance prior to the 18th century, 

or the factors that contributed to variation in 

populations over pre-historic time scales. There is 

evidence that the indigenous people of the North 

Pacific made use of the sea otter as a resource, and 

possibly affected abundance. It is apparent that 

coastal people, particularly in the northern regions 

of the Pacific, had developed sophisticated and 

efficient methods for hunting sea otters long before 

the advent of modern firearms (Bodkin 2000). 

Further, skeletal remains of sea otters are common 

in coastal middens, the accumulated refuse at sites 

of human occupation. In the Aleutian Islands, 

variation in the composition and abundance of 

marine species suggests that sea otter populations 

were depleted or eliminated (at least locally) by 

human harvests (Simenstad et al. 1978). 

The discovery of large numbers of sea otters 

in Alaska by Vitus Bering in 1741 initiated a 

commercial sea otter harvest that lasted 170 years. 

By the beginning of the 20th century, the species 

was on the brink of extinction, with a pre-harvest 

population of approximately 300,000 reduced to 

perhaps a few thousand individuals, distributed 

in 13 isolated colonies. Following protection in 

1911, two of the remnant colonies disappeared, 

while the others began the process of recovery. 

Otter density at the Rat Islands in the western 

Aleutians apparently reached the pre-exploitation 

level in the mid 1940s (Kenyon 1969) and fluctuated 

around an equilibrium density until late in the 20th 

century (Estes 1990). Throughout much of the 20th 

century, remnant sea otter populations experienced 

growth, although annual rates of increase varied, 

averaging 9% and ranging from 6-13% (Bodkin et 

al. 1999). Range expansion of remnant populations 

into unoccupied habitat resulted in nearly complete 

occupation of northern habitats between the Kuril 

Islands and PWS by late in the 20th century. 

Reintroductions were undertaken in the 1960s 

and 1970s in southeast Alaska, British Columbia, 

Washington, and Oregon (Jameson et al. 1982), 

and sea otters were moved to San Nicolas Island 

(southern California) in 1987 (Rathbun et al. 1990). 

Most of the translocated populations increased 

at rates of 17-20% per year, about the theoretical 

maximum for sea otters (Estes 1990), and growth 

rates were significantly greater than the rates 

observed among remnant populations (Bodkin 

et al. 1999). The reasons for greater growth 

rates in translocated populations likely include 

high prey densities resulting from the prolonged 

absence of sea otters and minimal impacts from 

humans. Southeast Alaska, British Columbia, 

and Washington now support viable sea otter 

populations. Large amounts of habitat remain 

available for recolonization in the inside waters of 

southeast Alaska and south to central California, 

and along the Kamchatka Peninsula in Russia. 

While sea otter populations generally increased 

in number and range during the 20th century, 

there are exceptions. Most notable is the dramatic 

decline in the Aleutian archipelago (Estes et al. 

1998, Doroff et al. 2003, Burn and Doroff 2005), the 

impetus for this recovery plan. Several thousand 

sea otters were killed by the Exxon Valdez oil 

spill in 1989. While the overall impact of this event 

on the population in that area is unclear, chronic 

detrimental effects appear to have persisted 

through the 1990s (Dean et al. 2000, Monson et 

al. 2000b, Bodkin et al. 2002). The California sea 

otter population has generally increased slowly 

throughout most of the 20th century (Estes et al. 

2003b). Since the early 1990s it has grown at a 

slow and intermittent rate, averaging 2-3% per 

year overall but with periods of decline in the late 

1990s and 2007-2009 (Bentall 2005). Reintroduced 

sea otter populations in British Columbia and 

Washington have generally increased in numbers 

and range (Bodkin et al. 1999), while in southeast 

Alaska rates of increase appear to have moderated 

beginning in the 1990s, except in Glacier Bay 

where the population increased from near zero in 

1995 to >2,400 in 2004 (Esslinger and Bodkin 2009, 

Bodkin et al. 2007b).

I. Population abundance and trends

Sea otter survey methods vary in different 

locations due to habitat, geography, and logistics. 
2-12 Southwest Alaska DPS of the Northern Sea Otter

In some surveys of southwest Alaska, sea otters 

have been counted from boats or aircraft within 

a band of water adjacent to the shoreline; in 

others, transects have been used to sample an 

area, and the resulting sea otter density estimate 

has been extrapolated to generate a population 

estimate for the entire study area. Sea otters 

spend considerable time foraging and traveling 

under water, and it is often not possible to count 

all individuals that are below the surface at the 

time a survey is conducted. Also, observers may 

not see every individual present on the surface. 

Some survey designs include methods that allow 

for calculation of a correction factor to adjust for 

the estimated proportion of otters not detected 

by observers. One way to make this adjustment 

requires an independent estimate of the actual 

number of otters present in an area, also known as 

“ground-truth,” combined with the regular survey 

data in order to calculate a correction factor to 

adjust for sea otters not detected. Thus, there are 

four types of survey results: 1) censuses where it is 

assumed that all otters are counted by observers; 

2) censuses where counts are adjusted for otters 

not detected; 3) estimates extrapolated from 

sample counts made in a portion of the study area, 

without adjustment for otters not detected; or 4) 

estimates extrapolated from sample counts and 

adjusted for detection. In cases where uncorrected 

sea otter counts or estimates are compared over 

time, the assumption is made that the proportions 

of otters not detected by observers is constant.

The southwest Alaska sea otter DPS inhabits 

a vast region, with a linear distance along the 

coast from the eastern edge (Kodiak Island) to 

the western edge (Attu Island) of approximately 

2,500 km. For several reasons, the Recovery 

Team concluded that it would be impractical to 

develop a recovery plan for this region as if it were 

a single homogeneous unit. Across this range 

there are major differences in physical habitat 

features, for example: steep rocky islands with 

deep water very near shore in the Aleutians; 

extensive shallow waters with predominantly 

sandy substrates in Bristol Bay; and a mix of 

features along the southern Alaska Peninsula and 

in the Kodiak area. While biological communities 

show similarities across this region, there are 

differences in species dominance and presumably 

also in ecosystem processes. Furthermore, there 

are regional differences in the pattern of decline 

of sea otters, and perhaps also in the causes of 

decline and the threats to recovery. Therefore, the 

Team decided to develop this recovery plan using 

five management units (MUs), as listed below and 

shown in Figure 3:

 Western Aleutian Islands MU

 Eastern Aleutian Islands MU

 Bristol Bay MU

 South Alaska Peninsula MU

 Kodiak, Kamishak, Alaska Peninsula MU

In the following discussion of population abundance 

and trends, results are presented separately for 

each MU. Unless otherwise specified, the survey 

results are unadjusted for otters not detected by 

observers. Within each study area, recent surveys 

were conducted using methods similar to those 

used prior to the recent population decline, so that 

counts or estimates would be as comparable as 

possible with baseline information for that area. 

Although there may be slight differences in the 

time of year that surveys were conducted, such 

timing differences do not hinder comparisons of 

survey results because otters have relatively small 

home ranges and seasonal redistribution over large 

areas is unlikely. 

Western Aleutian Islands MU (Figure 4)

The first systematic, large-scale population 

surveys of sea otters in the Aleutian Islands were 

conducted from 1957 to 1965 (Kenyon 1969). At 

that time, the descendants of two remnant colonies 

had expanded throughout the Rat, Delarof, and 

western Andreanof Island groups. The total 

unadjusted count for this MU during the 1965 

survey was 9,657 sea otters. In 1965, sea otters 

were believed to have reached equilibrium 

densities throughout roughly one-third of the 

Aleutian archipelago, ranging from Adak Island in 

the east to Buldir Island in the west (Estes 1990). 

Within this MU, the Near Islands had few sea 

otters in 1965, and additional population growth 

and range expansion was expected.

In a 1992 aerial survey of the entire Aleutian 

archipelago, Evans et al. (1997) counted a total of 

6,518 otters in the Western Aleutian MU, which 

was 3,139 (33%) fewer than the total reported for 

the 1965 survey. Surveys conducted in the 1970s 

and 1980s documented range expansion into the 

Near Islands, in the far western Aleutians, with a 

population growth rate of nearly 20% per year in 

that island group (Estes 1990). The results of the 

1992 aerial survey provided the first indication of 

a sea otter population decline in southwest Alaska. 

Subsequent surveys conducted from skiffs during 

the mid-1990s indicated that substantial declines 

had occurred at several islands in the western 

Aleutians (Estes et al. 1998). 

In April 2000, the FWS repeated the 1992 aerial 

survey of the Aleutian archipelago and counted 

1,750 sea otters in this MU, indicating a 73% 

decline from the 1992 count (Doroff et al. 2003) 

and an 80% decline from 1965. Aerial and skiff 

survey data both indicate that the decline began 

in the late 1980s or early 1990s and proceeded at 

an average rate of -17.5% per year (Doroff et al. 

2003). Although otters declined in all island groups 

within the archipelago, the greatest declines were 

observed in island groups within the Western 

Aleutian MU. This result was unexpected, as the 

remnant colonies at those island groups had been 

the first to recover from the effects of commercial 
Recovery Plan 2-13

harvest, and sea otters were believed to have been 

at equilibrium density at most of these islands in 

the mid-1960s. 

Doroff et al. (2003) used skiff-based counts at 

six islands in the western Aleutians to develop 

a minimum correction factor, and calculated that 

aerial observers detected roughly 28% of the sea 

otters present. Adjusting for otters not detected 

by observers, the estimated population size of 

this MU in April 2000 was 6,250. Skiff surveys at 

these islands conducted in 2003 indicated that the 

population had declined by a further 63% at an 

estimated annual rate of -29% per year (Estes et 

al. 2005). FWS conducted additional skiff surveys 

of sea otters in the Near Islands and Rat Islands 

in 2005 and 2007. Counts at most islands appeared 

to have leveled off or slightly increased as of 2007. 

Skiff surveys conducted by the USGS at Adak 

Island indicated that the population size there had 

stabilized for the past 5 years at around 7-8% of 

the pre-decline value. Although this MU has not 

been surveyed comprehensively in recent years, 

trends from skiff survey data suggest that current 

sea otter abundance is considerably lower than the 

6,250 estimated during the April 2000 aerial survey.

Of the five MUs, the record of survey data for the 

Western Aleutian MU is the most detailed. In 

addition to broad-scale aerial surveys conducted 

in 1965, 1992, and 2000, there is a long history of 

skiff surveys around several islands throughout 

the area. The islands that have been surveyed 

have a wide range in physical size, available 

habitat, and sea otter populations that have been 

supported historically. It is therefore difficult to 

represent sea otter population trends graphically, 

as changes that have occurred at large islands 

tend to dwarf those at smaller islands. In order to 

standardize the changes in population size, survey 

results can be represented as the proportion of the 

population that remains, relative to some baseline 

level. Figure 5 illustrates the population trend 

in the Near Island group based on time series 

of skiff survey data collected at Attu and the 

Semichi Islands, and aerial survey data collected 

for the entire group in 1992 and 2000. The data 

indicate that the population in this island group 

underwent a rapid decline during the mid-1990s, 

with a current abundance that is approximately 5% 

of baseline levels. It is not clear, however, if these 

baseline levels from the early 1990s represent a 

population that was at equilibrium density.

Population trends for the Rat Island group are 

shown in Figure 6. Using aerial and skiff survey 

data from the early 1990s as the baseline, it is clear 

that this group also underwent rapid population 

Figure 3. Management units for the southwest Alaska DPS of the northern sea otter.
2-14 Southwest Alaska DPS of the Northern Sea Otter

Figure 4. Western Aleutian Management Unit (WA).

Figure 5. Sea otter survey results for the Near Island group.

Results expressed as the percentage of the population remaining relative to baseline levels (in 

parentheses). Solid symbols are skiff survey data; hollow symbols are aerial survey data.
Recovery Plan 2-15

declines in the first half of the 1990s. The most 

recent surveys conducted at Kiska and on the north 

side of Amchitka in 2007 suggest that sea otter 

abundance has leveled off, with current abundance 

at less than 10% of baseline values. As the sea 

otter population in the Rat Islands was believed 

to have been at equilibrium density in 1965, and 

the population had shown a marked decline by the 

time of the 1992 aerial survey, it is likely that the 

baseline levels presented in Figure 6 represent 

a population that was already below equilibrium 

density.

The most detailed skiff survey data for this MU 

pertain to Adak Island in the Andreanof Island 

group, with 14 surveys conducted between 

1991 and 2007 (Figure 7). Although the sea 

otter population in this area appears to have 

been relatively stable since 2003, the remaining 

population is less than 10% of the baseline value. 

Similar to the Rat Islands, much of the Andreanof 

Island group was believed to have been at 

equilibrium density in 1965, and it had declined 

to below that level by the time of the 1992 aerial 

survey. Therefore, it is likely that these baseline 

levels from the early 1990s represent a population 

that was below equilibrium density.

In summary, both skiff and aerial survey data 

in the Western Aleutian MU indicate rapid 

population declines during the early to mid-1990s, 

with current levels approximately an order of 

magnitude below those at the onset of the declines.

Figure 6. Sea otter survey results for the Rat Island group. 

Results expressed as the percentage of the population remaining relative to baseline levels (in 

parentheses). Solid symbols are skiff survey data; hollow symbols are aerial survey data.

Eastern Aleutian Islands MU (Figure 8)

Similar to the western Aleutians, the island groups 

in the Eastern Aleutian MU were surveyed 

from 1957 to 1965 (Kenyon 1969). No remnant 

colonies of sea otters were present in this area, 

and recolonization had barely begun by the 1965 

survey when only 43 otters were counted (Kenyon 

1969). By the time of the next aerial survey in 

1992, otters were present throughout the Fox 

Islands and Krenitzen Islands, where Evans et 

al. (1997) counted 1,530 individuals. By 2000, 

however, sea otter abundance had declined from 

the 1992 level by an estimated 55%, to 692 otters 

(Doroff et al. 2003). Adjusting this count for 

detection gives an abundance estimate of 2,492. 

Additional aerial surveys at several sites in the 

Krenitzen Islands group in July 2004 indicated 

that further population declines had occurred. 

Figure 9 illustrates sea otter population trends 

in the Eastern Aleutian MU relative to the 1992 

aerial survey baseline. Burn et al. (2003) classified 

the islands in this group as not having reached 

equilibrium density at the onset of the population 

decline.

In addition to the aerial survey data record, the 

Qawalangin Tribe of Unalaska has conducted skiff 

surveys at Unalaska Island since 2002 as part of 

a cooperative agreement between FWS and the 

Alaska Sea Otter and Steller Sea Lion Commission. 

In contrast to the Krenitzen Islands, data 

from these surveys do not indicate a pattern of 
2-16 Southwest Alaska DPS of the Northern Sea Otter

Figure 7. Sea otter survey results for the Andreanof Island group. 

Results expressed as the percentage of the population remaining relative to baseline levels (in 

parentheses). Solid symbols are skiff survey data; hollow symbols are aerial survey data.

Figure 8. Eastern Aleutian Management Unit (EA).
Recovery Plan 2-17

Figure 9. Sea otter survey results for the Eastern Aleutian MU. 

Results expressed as the percentage of the population remaining relative to initial levels (in 

parentheses).

Figure 10. Bristol Bay Management Unit (BB).
2-18 Southwest Alaska DPS of the Northern Sea Otter

continued population declines over the past several 

years (Jack et al. 2007).

Bristol Bay MU (Figure 10)

Within the Bristol Bay MU, one remnant colony in 

the area of False Pass was believed to have existed 

after commercial fur harvests ended in 1911 

(Kenyon 1969). During surveys in the late 1950s 

and early 1960s, substantial numbers of sea otters 

were observed between Unimak Island and Amak 

Island, with a maximum of 2,892 counted in 1965 

(Kenyon 1969).

Sea otter habitat within this MU is somewhat 

unusual due to the presence of a broad, shallow 

shelf that extends to the north from the Alaska 

Peninsula. In the 1970s and 1980s, it was not 

uncommon to observe large rafts of sea otters 

more than 25 km from shore (Schneider 1976, 

Brueggeman et al. 1988). For this reason, aerial 

or skiff surveys along the shoreline are not 

an appropriate survey method for this area. 

Consequently, the northern Alaska Peninsula 

offshore area has been surveyed from aircraft 

using parallel transects extending from the 

shoreline seaward over the shelf. Using this 

method, Schneider (1976) calculated an unadjusted 

estimate of 11,681 sea otters on the north side of 

the Alaska Peninsula in 1976. 

Brueggeman et al. (1988) conducted surveys of the 

same area during three time periods in 1986. Burn 

and Doroff (2005) re-analyzed the original 1986 

survey data to address computational errors in the 

original survey report; the re-calculated estimates 

range from 6,474 to 9,215 sea otters (uncorrected) 

for the three surveys (Burn and Doroff 2005). In 

May 2000, FWS replicated the survey design of 

Brueggeman et al. (1988) using identical survey 

methods. The uncorrected estimate of 4,728 from 

this survey indicated that abundance on the north 

side of the Alaska Peninsula had declined by 39% 

since 1986, and by 60% since 1976 (Burn and Doroff 

2005). Data from the Schneider (1976) survey are 

not available in GIS format, so additional analyses 

of population declines were made by comparison of 

survey data from 1986 and 2000.

The population change observed between 1986 

and 2000 was not uniform over the entire MU. 

Brueggeman et al. (1988) had divided their study 

area into two subareas, with “North Alaska 

Peninsula A” to the west of 162o W and “North 

Alaska Peninsula B” to the east. The population 

in North Alaska Peninsula A declined from an 

average estimated abundance of 6,303 in 1986 to 

374 in 2000 (Burn and Doroff 2005). In contrast, 

the estimated sea otter abundance in North Alaska 

Peninsula B increased from an average of 1,703 

in 1986 to 4,354 in 2000, which suggests that the 

population in the Bristol Bay MU underwent 

a change in distribution as well as abundance 

during the period between surveys. The largest 

aggregations of sea otters in May 2000 were 

observed in Port Moller and Herendeen Bay. This 

concentration had been described as a seasonal 

phenomenon, as surveys conducted later in the 

summer had not recorded similarly large numbers 

of sea otters (B. Murphy, personal communication). 

To test this assumption, FWS conducted sea otter 

surveys in the Port Moller, Herendeen Bay, and 

Nelson Lagoon areas in May and July 2004. A total 

of 1,472 otters were counted on May 5, and 936 

on July 13 (FWS unpublished data). The surveys 

were conducted at different tidal states; much of 

the head of Port Moller was exposed mud flats 

during the July survey. Although it is not clear to 

what degree use of these areas changes seasonally, 

the Port Moller and Herendeen Bay areas continue 

to support the highest observed sea otter densities 

within the Bristol Bay MU. Although the decline 

in this MU is not as severe as those in the Western 

and Eastern Aleutian MUs, the most recent survey 

data suggest that current abundance is around 40% 

of the 1976 value (Figure 11). Schneider (1976) 

believed that his unadjusted population estimate 

for 1976 was within the carrying capacity of this 

area, which would suggest that the 1986 estimate 

represents a population below equilibrium density. 

South Alaska Peninsula MU (Figure 12)

Two remnant colonies – one at Sandman Reefs and 

the other at the Shumagin Islands – were believed 

to have existed near the western end of the Alaska 

Peninsula after commercial fur harvests ended in 

1911 (Kenyon 1969). Several island groups within 

this MU, including the Pavlof and Shumagin 

Islands, as well as Sanak, Caton, and Deer islands, 

have been surveyed several times since the early 

1960s. In 1962, Kenyon (1969) counted 1,900 otters 

along these islands. In 1986, Brueggeman et al. 

(1988) counted 2,122 in the same area. In 1989, 

DeGange et al. (1995) counted 1,589 along the 

shores of the islands that had been surveyed in 

1962 and 1986, which was 16–28% fewer sea otters 

than reported in the earlier counts. This decrease 

was the first indication of a sea otter population 

decline in the area of the Alaska Peninsula. The 

1989 surveys were conducted using rotary-wing 

aircraft, as opposed to fixed-wing for all other 

surveys in this area; it is unclear to what degree 

the difference in survey platform may have 

affected survey results. In 2001, only 405 otters 

were counted from the air in this area of the Alaska 

Peninsula (Burn and Doroff 2005), indicating an 

81% decline from 1986. Additional aerial surveys 

at 13 of these islands were flown in 2004 and 2007 

using methods similar to those used in 2001, and 

the counts indicated continued declines (FWS 

unpublished data). 

In addition to sea otter counts at islands, similar 

data are available for the shoreline of the Alaska 

Peninsula within this MU. In 1989, DeGange et al. 

(1995) counted 866 sea otters along the southern 

shoreline of the Alaska Peninsula from False Pass 

to Castle Cape. In 2001, Burn and Doroff (2005) 

counted 536 in this same area. The width of the 

survey zones differed, but a comparison based on 
Recovery Plan 2-19

Figure 11. Sea otter survey results for the Bristol Bay MU.

Results expressed as the percentage of the population remaining relative to initial level (in 

parentheses). Error bars represent coefficients of variation.

Figure 12. South Alaska Peninsula Management Unit (SAP).
2-20 Southwest Alaska DPS of the Northern Sea Otter

sea otter density (otters/km2) indicates a decline of 

46% had occurred.

Similar to the Bristol Bay MU, offshore areas 

on the south side of the Alaska Peninsula were 

surveyed by air during three different time periods 

in 1986 (Brueggeman et al. 1988). Re-calculated 

abundance estimates for the three surveys were 

13,900–17,500 (Burn and Doroff 2005). FWS 

replicated the surveys in April 2001 and estimated 

1,005 otters for the southern Alaska Peninsula 

offshore area, indicating a decline of at least 93% 

when compared with the 1986 surveys. Specific 

areas of high sea otter concentrations in 1986, 

such as Sandman Reefs, were almost devoid of sea 

otters when surveyed in 2001 (Burn and Doroff 

2005).

Sea otter population trends for the South Alaska 

Peninsula MU are presented in Figure 13. The 

data suggest that the population underwent a rapid 

decline in the late 1980s and early 1990s, and that 

the decline continued at several island groups at 

least through 2004. Current abundance is likely 

less than 20% of the baseline values observed 

during the latter half of the 1980s. It is unclear 

if these baseline values represent a population at 

equilibrium density.

Kodiak, Kamishak, Alaska Peninsula MU (Figure 14) 

One of the remnant sea otter colonies in southwest 

Alaska occurred at the northern end of the Kodiak 

archipelago at Latax Rocks. In 1959, Kenyon 

(1969) counted 395 sea otters in the Shuyak 

Island area. Over the next 30 years, the sea otter 

population in the Kodiak archipelago grew at about 

9% per year (Bodkin et al. 1999), and its range 

expanded southward around Afognak and Kodiak 

Islands (Schneider 1976, Simon-Jackson et al. 

1986). DeGange et al. (1995) surveyed the Kodiak 

archipelago in 1989 and calculated a detectionadjusted population estimate of 13,526 sea otters. 

However, this estimate is now believed to be 

unreliable2

 (A. DeGange, personal communication) 

and has been excluded from analysis of population 

abundance and trends. In July and August 

1994, FWS conducted an aerial survey of the 

Kodiak archipelago using the methods of Bodkin 

and Udevitz (1999) and calculated an adjusted 

population estimate of 9,817. In June 2001, FWS 

surveyed the Kodiak archipelago using the same 

observer, pilot, and methods as in 1994. That 

survey resulted in an adjusted population estimate 

of 5,893 (FWS unpublished data), which represents 

a 40% decline from the 1994 estimate. In summer 

2004, FWS surveyed the Kodiak archipelago 

using the same methods as in 1994 and 2001 and 

2

 The specific concerns regarding the 1989 survey 

include the stratification of shoreline and offshore 

zones, as well as the methods used to correct for 

otters not detected by observers. 

Figure 13. Sea otter survey results for the South Alaska Peninsula MU. 

Results expressed as the percentage of the population remaining relative to baseline levels (in 

parentheses). Error bars represent coefficients of variation for southern Alaska Peninsula offshore 

estimates. All data collected by aerial survey.
Recovery Plan 2-21

estimated the population size at 11,005 sea otters 

(FWS unpublished data). Although these results 

do not suggest that the sea otter population in this 

area has experienced population declines similar 

to other MUs within the DPS, it remains unclear 

why the population has not continued to expand its 

range to occupy more of the available habitat at the 

southern end of Kodiak Island.

The shoreline of the Alaska Peninsula within this 

MU (including Sutwick Island) was surveyed in 

1989 by DeGange et al. (1995), who counted 1,766 

otters between Castle Cape and Cape Douglas. 

This area was surveyed again in 2001, when Burn 

and Doroff (2005) counted 2,115. The width of the 

survey zones differed, but a comparison based 

on sea otter density (otters/km2

) indicates that 

the population in this area increased slightly 

(5%) over this time period. In June 2008, the 

USGS Biological Resources Division conducted 

an aerial survey of all presumed sea otter habitat 

southwest from Cape Douglas to the western 

extent of Katmai National Park (Katmai Bay). The 

detection-corrected population estimate was 7,095 

(se = 922) with an estimated density of 4.9/ km2

. 

The area surveyed in 2008 was a small proportion 

of the area previously surveyed by DeGange et al. 

(1995) and Burn and Doroff (2005), and the results 

suggest rapid growth in sea otter abundance at 

least along the mainland in the eastern part of this 

MU.

In the summer of 2002, the USGS conducted an 

aerial survey of lower Cook Inlet and the Kenai 

Fiords area (Bodkin et al. 2003). The survey was 

designed, in part, to estimate sea otter abundance 

in Kamishak Bay, which is located on the west 

side of lower Cook Inlet. The methods used 

were identical to the aerial surveys of the Kodiak 

archipelago described above. Sea otters were 

abundant in Kamishak Bay during the 2002 survey, 

with an adjusted estimate of 6,918 (Bodkin et al. 

2003). As no previous estimates for Kamishak 

Bay exist, the population trend for this area is 

unknown.

Although the survey data for this MU are 

somewhat fragmentary, there appears to be 

little evidence that it has experienced population 

declines similar to the other units (Figure 15). 

Overview of population abundance and trends

Due to differences in the years of the various 

surveys for different areas, it is difficult to combine 

this information to estimate the overall size of 

the southwest Alaska sea otter population at the 

onset of the decline. Calkins and Schneider (1985) 

provided estimates from survey data collected 

as of 1976, adjusted for animals not detected by 

observers, for the Aleutian Islands (55,100–73,700), 

northern Alaska Peninsula (11,700–17,200), 

southern Alaska Peninsula (22,000–30,000), and 

Kodiak archipelago (4,000–6,000). They did not 

report a specific estimate for the Kamishak Bay 

area, which presumably was included within their 

Figure 14. Kodiak, Kamishak, Alaska Peninsula Management Unit (KKAP).
2-22 Southwest Alaska DPS of the Northern Sea Otter

estimate for the Kenai Peninsula and Cook Inlet 

area (2,500–3,500 otters). Assuming that half of the 

sea otters estimated for Kenai Peninsula and Cook 

Inlet occurred in Kamishak Bay and combining 

these estimates, the sea otter population in the 

area encompassing the range of the southwest 

Alaska population would have numbered between 

94,050 and 128,650 animals as of 1976. As sea 

otters had not yet fully recolonized southwest 

Alaska or reached equilibrium density in all areas 

in 1976, additional population growth was expected. 

Therefore, the overall population prior to the onset 

of the decline in the 1980s may have been higher 

than the population estimate for 1976.

The most recent estimate of the size of the 

southwest Alaska sea otter population, based on 

surveys in 2000-2008 and adjusted for animals 

not detected, is 53,674 (Table 1). As more recent 

site-specific surveys indicate the decline may 

have continued in the Western Aleutian, Eastern 

Aleutian, and South Alaska Peninsula MUs, it is 

Figure 15. Sea otter survey results for the Kodiak, Kamishak, Alaska Peninsula MU.

Results expressed as the percentage of the population remaining relative to baseline levels (in 

parentheses). Error bars represent coefficients of variation for Kodiak archipelago estimates. All 

data collected by aerial survey.

possible that the population size at the time this 

plan was developed (2008-2010) was actually lower.

The 1976 population estimate, based on the work of 

Calkins and Schneider (1985), may not be directly 

comparable to more recent estimates because 

of different survey approaches and estimation 

techniques. Nevertheless, the results provide the 

only basis for comparison of the overall extent 

of the decline of sea otters in southwest Alaska. 

Comparing the 1976 estimate to the estimate in 

Table 1 indicates an overall population decline of 

43 to 58%. Using the most comparable survey 

information as a baseline for each MU, the most 

severe declines appear to have occurred in the 

Western Aleutian and South Alaska Peninsula 

MUs. As the population decline appears to have 

been underway at the time of the 1992 aerial 

survey of the Aleutian archipelago, declines in the 

Western and Eastern Aleutian MUs are likely to 

have been even greater than estimated here.
Recovery Plan 2-23

Table 1. Recent sea otter population estimates for MUs within the southwest Alaska DPS of the northern sea 

otter.

Results from the Bristol Bay, South Alaska Peninsula, and portions of the Kodiak, Kamishak, Alaska 

Peninsula MUs are adjusted using a correction factor of 2.38 following Evans et al. (1997). Counts from 

the Western Aleutian, Eastern Aleutian, and portions of the Kodiak, Kamishak, Alaska Peninsula MUs 

have been adjusted using survey-specific correction factors.

Management Unit Time Period

Most Recent 

Adjusted Count 

or Estimate

Population 

Change References

Western Aleutian 1992 - 2000 6,451 -73% Doroff et al. (2003)

Estes et al. (2005)

Eastern Aleutian 1992 - 2000 2,291 -56% Doroff et al. (2003)

Bristol Bay 1986 - 2000 11,253 -39% Burn and Doroff (2005)

South Alaska 

Peninsula 1986-2001 4,724 -74% Burn and Doroff (2005)

Kodiak, Kamishak, 

Alaska Peninsula 1994 - 2008 28,955 Stable or growing

Burn and Doroff (2005)

Bodkin et al. (2003)

USGS (unpublished data)

Total 53,674 -43 to 58%
2-24 Southwest Alaska DPS of the Northern Sea Otter

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Recovery Plan 3-1

3. Threats and Impediments to Recovery

A. Predation

Sea otters are typically viewed as apex predators 

in near-shore marine communities, yet there have 

been many reported instances in which sea otters 

have been killed and/or consumed by other top 

predators. The predators involved include bald 

eagles, white sharks, terrestrial carnivores such 

as brown bears, coyotes (Canis latrans), arctic 

foxes (Alopex lagopus), and killer whales (Orcinus 

orca). Documented instances of predator-caused 

mortality are summarized below. 

Bald eagles are fairly frequent predators of sea 

otter pups in the Aleutian Islands, with otter pup 

remains accounting for up to 20% of the prey items 

found in some eagle nests (Sherrod et al. 1975). 

However, because this predation is limited to very 

young pups (< 5 kg), a stage with naturally high 

mortality, it has generally been considered that 

eagle predation has a small demographic impact, 

at least at high sea otter densities (Riedman and 

Estes 1990, Sherrod et al. 1975). It is possible 

that eagle predation would pose a more significant 

threat at very low population densities, such as 

those found throughout much of the area affected 

by the decline. However, recent searches of 

bald eagle nests indicate that eagle diets in the 

Aleutian Islands have shifted to focus on offshore 

fish and seabirds, possibly as a result of decreases 

in nearshore fish populations associated with the 

loss of kelp (Reisewitz et al. 2006), and sea otters 

now represent a very small proportion of eagle 

diets (Anthony et al. 2008). Thus, eagle predation 

seems unlikely to pose a serious threat to sea otter 

recovery in southwest Alaska, although there is a 

potential for impacts at very low population sizes. 

White shark attacks represent a significant 

source of mortality for sea otters in California, 

particularly at the northern end of that subspecies’ 

range (Ames and Morejohn 1980, Estes et al. 

2003b, Kreuder et al. 2003). Based on analysis 

of the beachcast (deposited on shore) carcasses 

having evidence of shark bites, it appears that 

these carcasses do not represent true predation - 

that is, sharks are not actually consuming the sea 

otters but rather they are killed incidentally during 

shark predation on pinnipeds (Ames and Morejohn 

1980). This interpretation is consistent with the 

lack of sea otter remains in white shark stomachs 

(Riedman and Estes 1990). Regardless of whether 

or not fatal shark bites represent true predation 

or just cases of “mistaken identity,” sharks have 

a relatively high potential for limiting population 

growth in parts of the California sea otter’s range 

(Gerber et al. 2004). 

Information on shark predation on sea otters in 

Alaska is limited to one unconfirmed incident 

involving a salmon shark (Lamna ditropis) that 

reportedly attacked and consumed a sea otter 

in Prince William Sound (D. Burn, personal 

communication). In spite of their apparent rarity, 

shark attacks have been considered as a potential 

source of sea otter mortality in this plan. 

Martin (2004) found only two occurrences of 

white sharks in Alaskan waters west of PWS in 

40 years of records (1961-2001). Recent satellite 

telemetry studies showed that none of the 68 white 

sharks tagged in the coastal concentration area off 

central California ranged northward far enough to 

overlap with the southwest Alaska sea otter DPS 

(Jorgensen et al. 2009). White shark predation 

does not appear to be a concern for this DPS. 

Pacific sleeper sharks (Somniosus pacificus) 

occur in Alaskan waters, and in the 1990s their 

abundance increased substantially in the Gulf of 

Alaska and Bering Sea (Gaichas 2002). Sleeper 

sharks are known to feed on marine mammals 

other than sea otters (Hulbert et al. 2006, Klimley 

1985). Formerly thought to be scavengers, recent 

evidence shows that they also prey on salmon 

and other fast-moving species and sometimes 

make diurnal vertical migrations to waters of 100 

m or less in depth (Hulbert et al. 2006), bringing 

them into the maximum forage depth range of 

sea otters. Near Unimak Island, sleeper sharks 

routinely scavenge the carcasses of gray whales 

killed by killer whales in depths of 50 m or less 

(L. Barrett-Lennard, personal comunication). 

During a recent survey, the stomachs of 25 out of 

198 sleeper sharks caught in the northern Gulf of 

Alaska contained marine mammal remains, none 

of which could be positively identified as from sea 

otters or determined to have been acquired by 

predation (Sigler et al. 2006). However, several 

observations indicate that sleeper sharks do attack 

live marine mammals. Specifically, Crovetto et 

al. (1992) provided evidence that a Pacific sleeper 

shark consumed a live northern right whale 

dolphin (Lissodelphis peronii), van den Hoff and 

Morrice (2008) reported a southern elephant seal 

(Mirounga leonina) with a bite wound from a 

closely related sleeper shark species (Somniosus 

antarcticus), and Lucas and Stobo (2000) 

documented substantial shark predation on harbor 
3-2 Southwest Alaska DPS of the Northern Sea Otter

seals, some of which was likely by Greenland 

sharks (Somniosus microcephalus; see Taggart et 

al. 2005).

Abundance of salmon sharks also has increased in 

Alaska since the 1990s, with schools of hundreds 

being reported in nearshore areas (Gaichas 2002, 

Okey et al. 2007). In 2003, fishermen reported 

increasing densities of sleeper and/or salmon 

sharks at five locations in the Aleutian Islands 

(Okey et al. 2007). Salmon sharks are known 

to feed on salmon and cephalopods (Hulbert et 

al. 2005), and the only indication that they may 

prey on sea otters is the unconfirmed report 

described above. It is also worth noting that in 

PWS where there have been dramatic increases in 

salmon shark abundance, sea otter numbers have 

increased or remained stable (Bodkin et al. 2002). 

Terrestrial predators can prey on sea otters in 

areas where they haul out, although reports of such 

predation are remarkably few. In the Commander 

Islands, arctic foxes frequently scavenge sea otter 

carcasses and also occasionally kill live otters, 

although such predation is largely limited to young 

or already moribund (in a dying state) animals 

(Zagrebel’nyi 2000). In PWS, newly weaned sea 

otters are occasionally killed and eaten by coyotes 

(Siniff and Ralls 1988), and brown bears have been 

reported to be a significant source of predation 

on sea otters along the Kamchatka Peninsula in 

the late winter and early spring (Riedman and 

Estes 1990). There is no potential for predation by 

terrestrial predators in the Aleutian archipelago 

because there are no terrestrial carnivores in this 

region. However, along the Alaska Peninsula and 

in the Kodiak archipelago, predation by bears, 

coyotes, and foxes is possible, although there are 

no published reports of such predation. 

Killer whales are recognized as predators of 

sea otters, yet prior to 1990 there were only a 

handful of documented interactions between the 

two species. One aggressive interaction was 

reported in the Kuril Islands (Nikolaev 1965), 

and there were a few inconclusive reports of 

presumed attacks in the Commander Islands 

(summarized in Hatfield et al. 1998). However, 

most killer whale-sea otter interactions in Alaska 

and elsewhere were assumed to be non-aggressive 

(Kenyon 1969). In the early 1990s, this pattern 

appeared to change, based on nine documented 

observations of killer whales apparently attacking 

sea otters: three of these incidents occurred in 

PWS and six in the Aleutian Islands (Hatfield et 

al. 1998). It was speculated that these interactions 

reflected an increase in killer whale predation on 

sea otters and that this increase was the result 

of some “transient” (mammal-eating) killer 

whales adjusting their diet to include sea otters, 

presumably as a response to declines in other 

marine mammal prey, including harbor seals and 

Steller sea lions (Estes et al. 1998, Hatfield et al. 

1998). Since 1995 there have also been multiple 

anecdotal reports from USGS field staff (M. 

Tinker, personal communication) and FWS field 

personnel (J. Williams, personal communication) 

of apparent killer whale attacks on groups of sea 

otters; however, because these are unpublished 

reports, in most cases the details are difficult 

to confirm. Kuker and Barrett-Lennard (2010) 

noted that of the six interactions in the Aleutians 

reported by Hatfield et al. (1998), one was 

positively identified as a predatory attack that 

resulted in a confirmed kill of an otter, while the 

other five could be described as probable kills or 

harassment. In two of these cases the otters were 

seen after the incident and no signs of injury were 

reported, in one case the otter escaped but was 

apparently injured, and in two cases the otters 

were not seen after the attack and their fate is 

uncertain, although the observer believed that 

they were likely killed and/or consumed. In the 

most conservative sense, observations of killer 

whale attacks are classified as predation only when 

strong evidence of actual consumption is found, 

such as blood, blubber, or other tissues from the 

prey floating on the water surface after the attack 

(Ford et al. 1998). Although strong evidence for 

actual consumption was found in only one out of 

the nine observed killer whale attacks on sea otters 

(Hatfield et al. 1998), the fact that the skeletal 

remains of at least five sea otters were recovered 

from the stomach of a male killer whale carcass in 

PWS confirms that otters are at least sometimes 

eaten by killer whales (Vos et al. 2006). 

Evidence for impacts of killer whale predation on sea 

otters in southwest Alaska 

The cumulative support for predation as a primary 

cause of the decline actually consists of a number 

of independent pieces of information, representing 

both direct and indirect (inferential) evidence, and 

can be summarized as follows: 

1. There was an increase in the rate of observed 

killer whale attacks around the time the 

decline began (Estes et al. 1998, Hatfield et 

al. 1998). For the area between Kiska and 

Seguam Islands in the Western Aleutian 

MU, Estes et al. (1998) estimated a loss of 

6,788 otters per year between 1991 and 1997. 

Based on the level of observer effort over that 

time period, and assuming that any attack 

within 500 m of an observer during daylight 

hours would be observed, the expected 

number of observed attacks was 5.05, 

consistent with the small number of attacks 

reported by Hatfield et al. (1998). Thus, 

it is plausible that the level of killer whale 

predation needed to produce the observed 

sea otter decline could have occurred with 

relatively few of the attacks being observed. 

2. Energetic and demographic feasibility 

analyses indicate that predation by a small 

number of killer whales (specifically, a pod 

of five individuals) would have been capable 

of causing the sea otter population decline 

in southwest Alaska (Williams et al. 2004). 
Recovery Plan 3-3

Similar analyses show the energetic and 

demographic feasibility that killer whale 

predation has been a key factor in other 

marine mammal declines, and it has been 

hypothesized that a sequential cascade of that 

was ultimately caused by the reduction of 

“great whale” populations in the North Pacific 

by industrial whaling (Springer et al. 2003, 

Williams et al. 2004). This hypothesis has 

been the subject of considerable subsequent 

debate (DeMaster et al. 2006, Wade et al. 

2007, Trites et al. 2007, Springer et al. 2008, 

Wade et al. 2009, Estes et al. 2009).

3. Few beachcast sea otter carcasses were 

observed during the decline, which is 

inconsistent with what would be expected if 

animals were dying from disease, starvation, 

or other typical causes of sea otter mortality 

(aside from predation). Systematic searches 

of beaches at Adak and Amchitka Islands 

throughout the decline resulted in very few 

recovered carcasses or skeletal remains. At 

Amchitka alone, more than 4,000 animals 

disappeared between 1994 and 1997 (Doroff et 

al. 2003), yet fewer than 20 skeletal remains 

(and no fresh carcasses) were found during 

systematic searches of all beaches on the 

southern half of the island in the winter 

of 1993/94 and again in the early spring of 

1997 (USGS unpublished data). In contrast, 

searches of these same beaches in the 1950s 

during a much smaller mortality event 

attributed to nutritional stress and disease 

(Rausch 1953) resulted in the retrieval of 

hundreds of carcasses and moribund otters 

(Kenyon 1969). The strength of this line of 

evidence is reduced, at least somewhat, by 

the fact that no carcasses or injured otters 

bearing wounds of either killer whales or 

sharks were found. In southern Alaska and 

British Columbia, the inverted skins of seal 

pups are sometimes cast on beaches or found 

at low tide when transient killer whales have 

passed through an area (Barrett-Lennard 

unpublished data), and (as discussed above), 

otter carcasses bearing signs of attacks 

by white sharks are found commonly in 

California. 

4. The rate of premature disappearance of 

animals that were radio-tagged as part of 

telemetry-based studies at Amchitka Island 

(1992-94) and Adak Island (1995-96; Estes et 

al. 1998, Monson and Degange 1995, Tinker 

and Estes 1996) greatly exceeded that 

recorded during radio-telemetry studies in 

California that used identical techniques 

and instrumentation (e.g., Siniff and Ralls 

1991). Premature disappearance is defined 

as the unexplained loss of study animals with 

functioning VHF radio tags, occurring within 

the period of expected battery life (typically 

two years), and without confirmation 

of mortality, carcass recovery, or later 

resighting of flipper-tagged animals with 

non-working radios. In contrast to the above 

observation, the premature disappearance 

rate of radio-tagged animals in Clam Lagoon 

at Adak Island (an enclosed embayment 

with limited ocean access that may serve as 

a refuge from predators) was significantly 

lower (Estes et al. 1998) and consistent with 

the disappearance rate seen in previous 

studies (e.g., Siniff and Ralls 1991).

5. Between 1995 and 1999, the population inside 

Clam Lagoon declined only slightly, while 

numbers dropped by approximately 80% 

outside the lagoon in Kuluk Bay (Estes et 

al. 1998). It should be noted that pre-decline 

sea otter densities (i.e., in the early 1990s) 

were much higher inside Clam Lagoon, so 

this discrepancy in rate of decline cannot be 

explained as the result of a random movement 

process or of density-dependent immigration 

into Clam Lagoon.

6. The distribution pattern of sea otters 

throughout the region of southwest Alaska 

affected by the decline has shifted from that 

prior to the decline such that most otters 

are now closer to shore and tend to be 

concentrated around protected embayments 

or areas with extensive shallow reefs (USGS 

and FWS unpublished data). This likely 

affords some measure of protection from 

killer whales. 

7. There are unpublished reports in recent years 

(i.e., post-decline) of behavioral responses 

of sea otters to disturbance that are both 

atypical and suggestive of avoidance of 

marine predators. These include animals 

feeding and resting in very shallow (<2 m) 

water, and hauling out on land in response 

to both shore-based and water-based 

disturbances (USGS unpublished data).

It should be noted that points 4-7 above do not 

represent direct evidence of predation, but are 

indirect evidence insofar as they imply a causal 

factor consistent with a rapid disappearance of 

animals of all age classes over a large region, 

producing almost no beachcast carcasses, and 

affecting all areas except those with limited 

accessibility to large aquatic predators.

The available evidence that is inconsistent with 

predation as a possible cause of the decline is 

considerably more limited. The lack of published 

records of sea otter consumption by killer 

whales in southwest Alaska (i.e., as measured by 

analyses of stomach contents or blubber fatty acid 

composition) does not constitute evidence against 

the hypothesis given the paucity of information 

about killer whale diets throughout most of this 

region and the low likelihood that an otter-eating 

killer whale would have been sampled. One 

observation that is puzzling in the context of the 
3-4 Southwest Alaska DPS of the Northern Sea Otter

predation hypothesis is the almost complete lack 

of carcasses. On one hand, it might be expected 

that a killer whale would consume most or all of a 

sea otter and thus leave little in the way of skeletal 

remains or a floating carcass. If that were the case, 

predation by killer whales would be consistent 

with the lack of beachcast carcasses. On the other 

hand, it may be reasonable to expect that, at 

times, a sea otter would be only partially eaten by 

a killer whale, or it would escape an attack with a 

serious bite and then later die on the beach. If that 

were the case, we would expect to find at least a 

few partial carcasses, similar to the pattern with 

white sharks in California. The lack of even one 

recovered carcass with evidence of killer whale 

bites might thus be considered as evidence against 

the predation hypothesis. 

Whether or not the recent sea otter decline was 

caused by killer whale predation, mortality from 

predation clearly represents a potentially limiting 

factor to population recovery. Should killer whale 

predation on sea otters be expected to continue? 

On purely theoretical grounds, differing predictions 

can be made about future impacts depending on 

the nature of killer whale foraging dynamics. If 

killer whale predation on sea otters follows a typeII functional response (i.e., predator search rate 

remains constant irrespective of prey density), 

then the per-capita mortality rate for sea otters 

will increase as their density decreases, and the 

impacts of killer whale predation can be expected 

to be even more significant at low population 

densities. Alternatively, if killer whale predation 

on sea otters follows a type-III functional response 

(i.e., killer whales are likely to switch to alternative 

prey at low otter densities), then the per-capita 

mortality rate for sea otters should decrease at 

low densities, and we may thus expect a numerical 

“floor” below which killer whale predation becomes 

unimportant. These two scenarios represent 

highly simplified behavioral models, and in reality 

we should expect a more complex functional 

response that reflects spatial variation in killer 

whale habitat use and sea otter abundance, 

changes in the relative abundance of alternative 

killer whale prey, and other factors affecting killer 

whale foraging decisions. 

At the present time, data are insufficient to 

determine whether the per-capita rate of mortality 

from killer whale attacks has increased or 

decreased as a function of decreasing sea otter 

abundance. The lack of recent data reflects both a 

decrease in sea otter field research activity since 

the mid 1990s and a statistical artifact, i.e., even 

if the per-capita attack rate remained unchanged, 

the total number of attacks that could be observed 

would have decreased ten-fold due to decreasing 

otter abundance. Likewise, there are insufficient 

data on both killer whale population structure and 

foraging behavior in southwest Alaska to make 

informed predictions about functional or numerical 

responses. As more information becomes available, 

it will be possible to formulate and test specific 

hypotheses. For example, if only a small number 

of transient killer whales have recently added 

sea otters to their diets, it may be hypothesized 

that sea otters will be dropped as a diet item if a) 

more preferred prey species (e.g., Steller sea lions, 

harbor seals, dolphins, or large whales) increases 

in abundance, or b) the behavioral specialization 

for hunting sea otters is lost due to deaths of those 

few individuals currently using sea otters as prey. 

However, before any realistic predictions can 

be made about the likely future impacts of killer 

whale predation on sea otter recovery, there is a 

need for both theoretical and empirical work to 

better understand predator-prey relations between 

killer whales and sea otters in southwest Alaska. 

Predictive models should incorporate all the 

complexities mentioned above and account for the 

changing environmental conditions and food-web 

dynamics in the wider North Pacific ecosystem 

(e.g., Chavez et al. 2003, National Academy of 

Sciences 2003, Springer et al. 2003). 

Conclusions 

Predation has not been widely recognized as a 

limiting factor for sea otter populations in the 

past, but there are a few specific instances, such 

as white sharks in central California or killer 

whales in the Aleutian Islands, in which predation 

is now suspected to be a significant source of 

mortality with important population-level effects. 

In southwest Alaska, a number of predators 

could impede the recovery of depleted sea otter 

populations in certain areas, including bald eagles, 

sharks, terrestrial carnivores, and killer whales. 

Predation by killer whales has been hypothesized 

to be the primary driver of the recent decline, and 

it may pose a serious threat to timely recovery 

of sea otters. A recent review paper (Kuker and 

Barrett-Lennard 2010) postulates inconsistencies 

in the strength of evidence supporting the killer 

whale predation hypothesis, and emphasizes 

that there remain uncertainties about the cause 

of the sea otter decline in southwest Alaska. 

Such arguments should be evaluated carefully 

and critically, and it should be noted that all the 

alternative hypotheses suggested by Kuker 

and Barrett-Lennard (2010) are addressed in 

considerable detail in the following sections of this 

plan. 

There are many unanswered questions that must 

be addressed before reliable predictions can be 

made about future threats from predation. In 

particular, more information is needed to answer 

the following questions:

1. To what extent and how would it be possible 

to directly test the killer whale predation 

hypothesis? Is it feasible to deploy “mortality 

tags” on sea otters that could be used to 

measure rates and causes of mortality 

remotely? Could analysis of fatty acid or 

stable isotope signatures of killer whales be 

used to measure the intensity of predation 

on sea otters, or is this method too likely to 
Recovery Plan 3-5

result in false negatives (i.e., if only a few 

killer whales are actually consuming sea 

otters, they might not be sampled)?

2. What is an acceptable “weight of evidence” 

in support of the predation hypothesis, given 

that many of its specific predictions (and of 

other hypothesized causes of decline) are 

not amenable to direct testing, given the 

remoteness and enormous geographic range 

of the affected populations? 

3. What was the specific sequence of events that 

caused some killer whales to begin to prey on 

sea otters? 

4. How many killer whales are actually feeding 

on sea otters?

5. Can a functional response be predicted given 

what is known about killer whale foraging 

behavior and energetic requirements? If not, 

what empirical and theoretical work would be 

needed to do so? 

6. How can shark predation (e.g., sleeper or 

salmon sharks) be evaluated as a factor in the 

sea otter decline?

In the preceding review of the available evidence 

for and against predation as an important driver 

of sea otter population changes in southwest 

Alaska, and when evaluating the implications of 

increased predation (both past and future), it will 

be important to consider all information in the 

context of food-web interactions. Any attempt 

to interpret or predict predation impacts on sea 

otters without considering the broader food web 

is almost certainly bound to fail. On the other 

hand, by widening our perspective to encompass 

the southwest Alaska marine ecosystem (including 

both natural and anthropogenic perturbations), 

we can better judge the relative likelihood of a 

predator-caused decline. If recent population 

trends have been driven, in whole or in part, by 

an increase in predation on sea otters, then the 

best hope for understanding the ultimate cause of 

these changes, and for predicting how predation is 

likely to affect sea otter recovery, is to consider the 

structure and dynamics of the trophic interaction 

web of which sea otters are a part.

B. Infectious diseases

Disease is a perturbation from the “normal” 

condition in an animal and can be due to a variety 

of factors including pathogens, nutritional 

imbalances or deficiencies, toxins, degenerative 

changes, and neoplasia (tumors). Infectious 

diseases occur when a pathogen invades the 

host and alters normal cell or tissue function. 

However, host-pathogen-environment interactions 

that either increase the host’s susceptibility, the 

pathogen’s virulence, or its transmission rate may 

result in a change in the incidence of infectious 

disease. Epidemiological theory predicts that 

infectious diseases can have major population-level 

effects under specific circumstances as follows: 1) 

when pre-epidemic population sizes are small; 2) if 

a pathogen is able to use either an abiotic or biotic 

reservoir; or 3) when a pathogen is evolutionarily 

novel to a susceptible host (Smith et al. 2006). In 

many ways, disease agents can be thought of as 

similar to predators—they are part of the natural 

system but can cause negative population effects 

when the natural balance has been disturbed.

The importance of disease at the population level 

can be difficult to ascertain (Gulland and Hall 

2006); documented instances of infectious disease 

being a major factor in species endangerment or 

extinction are rare but do exist (Smith et al. 2006). 

A few examples include: local population extinction 

of African wild dogs (Lycaon pictus) due to rabies 

from domestic dogs; mortality in several African 

antelope species due to rinderpest from domestic 

cattle; mortality of African lions (Panthera leo) 

due to canine distemper virus (CDV) most likely 

acquired from domestic dogs; and epidemics of 

canine distemper in Baikal seals (Phoca sibrica) 

and Caspian seals (Pusa caspica) transmitted to 

seals from domestic dogs (Gog et al. 2002, Kennedy 

et al. 2000, Pollack 2001). Theoretical and historical 

evidence suggest that infectious disease can 

temporarily or permanently drive populations to 

low densities, thus predisposing them to extinction 

by other forces (DeCastro and Bolker 2005, Gerber 

et al. 2005). Diseases can also impact populations 

by effects more subtle than death, such as slower 

growth, lower fecundity, altered behavior, and 

lower social status (Lafferty et al. 2005).

The main sources of information on disease in 

wild sea otters are examinations of carcasses 

(necropsies) of animals found dead on shore, in 

fishing gear, or floating (all referred to below as 

“stranded”), and sampling of living animals in freeranging populations. There are two main concerns 

with regard to data from stranded carcasses: 1) the 

extent that stranded carcasses are representative 

of overall deaths in the population; and 2) when 

disease is found, deciding whether it was the 

primary cause of mortality or the resultant effect 

of some other stressor. Many samples from live 

animals are tested using serological screening, 

in which the presence of antibodies indicates 

exposure to a disease agent but not necessarily 

current infection. It is important when evaluating 

disease impacts to combine data from dead 

stranded animals with data from live-caught 

animals and information on population trends.

Infectious disease in sea otters range-wide (Table 2) 

California: Causes of mortality have been more 

thoroughly studied in sea otters in California 

than in other regions due to extensive efforts 

to recover carcasses by the public, stranding 

networks along a heavily populated coastline, 

and the presence of facilities that specialize 

in marine mammal stranding response. Some 
3-6 Southwest Alaska DPS of the Northern Sea Otter

major causes of death in southern sea otters 

include protozoal meningoencephalitis (due to 

Toxoplasma gondii and Sarcocystis neurona), 

myocarditis/cardiomyopathy syndrome, and 

peritonitis due to Profilicollis spp. acanthocephalan 

infestations (Kreuder et al. 2003). Scattered 

cases of Coccidioides immitis have also been 

reported (Ames 1983). Diseases caused by 

parasites, bacteria, or fungi, and diseases without 

a specified cause, were the primary cause of death 

in 64% of otters examined, and parasitic diseases 

alone caused death in 38% of otters examined 

(Kreuder et al. 2003). These causes were observed 

predominantly in juvenile and prime-aged adult 

otters (Thomas and Cole 1996, Estes et al. 2003b), 

which could have negative implications for the 

overall health and recovery of this population 

(Kreuder et al. 2003, Gerber et al. 2004). 

As mentioned above, of particular concern are 

infections with pathogens for which sea otters 

are not typical hosts and that have reservoirs in 

other species or in the environment because these 

are the types of situations where population-level 

effects could occur. Toxoplasmosis is presumed 

to be an example and it is a relatively common 

infection in California otters. Seroprevalence 

analysis in one study showed that 52% of 305 

freshly dead sea otters and 38% of 257 live sea 

otters sampled from 1998 to 2004 were infected 

with T. gondii (Conrad et al. 2005). These are 

similar prevalences to what is observed in domestic 

animals and humans, but most of those seropositive 

cases do not develop disease. To date, felids (both 

wild and domestic) are the only known definitive 

hosts for T. gondii, with sea otters apparently 

being highly susceptible to systemic infection as an 

intermediate host. Terrestrial runoff from urban 

and agricultural areas into streams and rivers is 

thought to transport the parasite to coastal marine 

waters (Miller et al. 2002, Conrad et al. 2005) 

where it is suspected that invertebrates that are 

eaten by otters accumulate the parasite (Miller et 

al. 2002, Arkush et al. 2003). The marine ecology 

of T. gondii is poorly understood, however, as the 

parasite is also found in pelagic cetaceans and 

pinnipeds that live far from coastal development 

(Dubey et al. 2003). Sarcocystis neurona, another 

protozoal parasite with a terrestrial definitive 

host (opossums), is also causing morbidity and 

death in southern sea otters (Kreuder et al. 

2003). Peritonitis caused by Profilicollis spp. 

has increased between the 1970s and 1990s in 

California (Thomas and Cole 1996), likely due to 

changes in sea otter diet or to differences in the 

intensity of infection in intermediate hosts that 

increase the rate of transmission (Mayer et al. 

2003). The significance of disease at the population 

level is still not entirely understood since high 

rates of disease-caused mortality have been noted 

in California otters for several decades, including 

during periods of population increase (Estes et al. 

2003b). 

The prevalence and variety of diseases are of 

concern, however, and it has been speculated 

that decreased immune function may be a factor. 

Reduced immune competence could result from 

environmental toxins, genetic factors, food 

limitation leading to inadequate diets, new hostpathogen interactions, or habitat degradation 

leading to nutritional stress (Thomas and Cole 

1996, Reeves 2002, Oftedal et al. 2007).

Washington: Sea otters were translocated 

from Alaska to Washington in the 1960s and the 

population there is growing and expanding its 

range (Laidre et al. 2009). Seventy carcasses were 

collected and necropsied in Washington during 

2000 and 2002–04, and 21 were suitable for a full 

necropsy. Acanthocephalan peritonitis, protozoal 

encephalitis, and leptospirosis infections were 

detected (Lance et al. 2004). Of 32 sea otters livecaptured in 2000 and 2001, 81% tested positive for 

exposure to morbillivirus, and there was a fatal 

case of canine distemper virus in 2004 (Lance et al. 

2004). Herpes-like lesions have been seen but are 

not thought to be clinically significant. Considering 

that the Washington sea otter population is 

growing, these findings most likely represent 

baseline levels of disease that are being balanced 

by positive population growth factors. 

British Columbia: Sea otters were translocated 

to British Columbia from Alaska in the 1960s, 

and the population has grown and expanded its 

range (Nichol et. al 2005). Health surveys are 

currently being carried out, including serology 

panels, oropharyngeal swabs for fungal and routine 

bacterial culture, and polymerase chain reaction 

(PCR). No fungi or significant bacterial pathogens 

have been recovered and PCR for herpesvirus has 

been uniformly negative. In serologic surveys of 

42 animals, 8 tested positive for CDV, 2 positive 

for Toxoplasma, and 6 positive for Brucella sp., 

and all tested negative for Neospora, Sarcocystis, 

calicivirus, influenza, and West Nile virus (S. 

Raverty, personal communication). CDV has 

been detected serologically in river otters living 

in the British Columbia marine environment 

(Mos et al. 2003), but there have been no detected 

cases of disease or mortality in sea otters due to 

morbilliviruses. Small numbers of animals had 

fibrosing cardiomyopathy (S. Raverty, personal 

communication). As was the case for Washington, 

considering that the population is growing, these 

findings most likely represent baseline levels 

of disease that are being balanced by positive 

population growth factors. 

Russia: Few comprehensive health surveys have 

been conducted on sea otters in Russia. Birkun 

and Krivokhizhyn (1991) included starvation and 

exhaustion (perhaps related to winter storms) 

as a major cause of death. They frequently 

found nematode infestations associated with 

ulcerative gastritis. Other findings included 

adrenal hemorrhage, “acute total necrotic gastroenterocolitis, chronic suppurative pancreatitis, 
Recovery Plan 3-7 Table 2. Occurrence of major known diseases and disease agents in sea otters by geographic region. Disease Russia Southwest Alaska Southcentral Alaska Southeast Alaska British Coloumbia Washington & Oregon California References BACTERIAL: Vegetative valvular endocarditis not reported moderate out of few cases high prevalence moderate out of few total cases negative negative rare K. Burek, S. Raverty, M. Miller (personal communication) Vibrio sp. associated enteritis/sepsis not reported not reported sporadic not examined not reported not reported sporadic M. Miller, K. Burek (personal communication) Leptospirosis low prevalence by serology negative negative for lesions; 1 high titer negative not reported 6 suspect cases in 2002 negative Lance et al. (2004), Goldstein et al. (in press) Brucella sp. serology moderate to high rate of positive serology; negative at Bering Island not tested not tested not tested negative moderate with sporadic lesions Ustinova et al. (2004), Golstein et al. (in press),M. Miller (personal communication)
3-8 Southwest Alaska DPS of the Northern Sea Otter Disease Russia Southwest Alaska Southcentral Alaska Southeast Alaska British Coloumbia Washington & Oregon California References PARASITIC: Protozoal meningoencephalitis (T. gondii /S. neurona) Serology positive for T. gondii at Bering Island negative rare negative (few examined) T. gondii, 1 case, genotyped to Type X, 2 animals seropositive sporadic in WA high prevalence Lance et al. (2004), S. Raverty (personal communication), Ustinova et al. (2004) Acanthocephalan perforations / peritonitis (Profilicolis sp.) Negative negative negative negative sporadic cases of acantho- cephalans, but subacute fibrinous peritonitis common reported prevalence 13%, n = 21 Thomas and Cole (1996) , Mayer et al. (2003), Lance et al. (2004) Nematode-associated peritonitis/ gastric perforation (Pseudoterranova decipiens) sporadic outbreaks (parasites and perforations reported) sporadic outbreaks sporadic outbreaks not reported sporadic not reported negative Rausch (1953), Margolis et al. (1997), Ballachey et al. (2002), Tuomi and Burek (1999), Burkin (1993) Cestode impactions (Diplogonoporus tetrapterus) present not reported moderate not reported negative not reported rare Tuomi (2001), Goldstein et al. (in prep.) Table 2. Continued.
Recovery Plan 3-9 Disease Russia Southwest Alaska Southcentral Alaska Southeast Alaska British Coloumbia Washington & Oregon California References FUNGAL: Coccidioidomycosis not reported negative negative negative negative not reported Sporadic; esp. in southern half of range Cornell et al. (1979), Ames et al. (1983) Histoplasmosis not examined rare (1 case) negative negative not reported not reported negative Burek et al. (in prep.) VIRAL: Morbillivirus serology 15% (n=20) positive on Bering Island in 1992; negative in 2004-2005 before 2004 negative; after 2004 high prevalence, low titer not reported not reported 19% (n=42) positive, with no evidence of clinical disease 81% positive in 2000/01 high prevalence, low titer Birkun and Krivokhizhyn (1991), Goldstein et al. (in prep.), S. Raverty (personal communication), Lance et al. (2004), M. Miller (personal communication), Ustinova et al. (2004) Morbillivirus PCR not tested positive nasal swabs from live- captures positive on a few dead stranded not tested dead animals all negative not tested Results pending Goldstein et al. (2009) Table 2. Continued.
3-10 Southwest Alaska DPS of the Northern Sea Otter Disease Russia Southwest Alaska Southcentral Alaska Southeast Alaska British Coloumbia Washington & Oregon California References Herpesvirus lesions low prevalence by serology (phocine herpes-1); suspicious gross lesions not reported normally sporadic; high prevalence under stress (Exxon Valdez oil spill in PWS) not reported negative sporadic sporadic and cyclical Harris et al. (1990), Lipscomb (1998), S. Raverty (personal communication), Lance et al. (2004), Burek and Miller (personal communication), Goldstein et al. (in prep.) Calicivirus serology low prevalence by serology not reported not reported not reported negative negative ? Goldstein et al. (in prep.) UNKNOWN CAUSE: Chronic myocarditis/ cardiomyopathy not reported negative rare (2 cases) negative rare, 2 cases of fibrosing cardio- myopathy not reported high prevalence Burek (personal communication), Kreuder et al. (2005), S. Raverty (personal communication) Neoplasia not reported not reported sporadic not reported not reported not reported rare Reimer and Lipscomb (1998), Burek (in press ) Table 2. Continued.
Recovery Plan 3-11

hepatitis and cholangitis, interstitial and necrotic 

hepatitis, bullous peritonitis (mesenteritis) and 

polyserositis, acute and chronic lymphadenitis, 

catarrhal and productive bronchitis, and 

ulcerative dermatosis,” the causes of which were 

undetermined. Causes of death listed in a 1935 

journal from the Commander Islands include 

old age, unknown diseases, storms, pathologic 

deliveries, rock slides, and injuries by killer whales. 

One animal examined for parasites had a light 

load of nematodes, all identified as Porrocaecum 

decipiens (renamed now as Pseudoterranova 

decipiens), and cestodes (Barabash-Nikiforov 

1947). 

On Bering Island from 2004 to 2006, health and 

disease surveys were conducted jointly by the 

Alaska SeaLife Center, FWS, and USGS in 

conjunction with the Marine Mammal Protection 

Division, Sevvostrybvod, “Komandorsky” 

Reserve and the Kamchatka Institute of Ecology 

and Nature Protection (Goldstein et al., in 

prep). General health was assessed in 89 otters 

by physical examination and measurement of 

hematology and serum chemistry parameters. 

Overall, values did not differ from published ranges 

for sea otters (Bossart et al. 1990, Hanni et al. 2003) 

except for a few parameters. 

Infectious diseases in Russian sea otters have 

been studied through serological surveys and 

examination of fecal parasites. Serologic surveys 

for common marine and terrestrial pathogens 

found exposure to Toxoplasma gondii, Sarcocystis 

neurona, morbillivirus (CDV, phocine distemper 

virus (PDV), porpoise morbillivirus (PMV), dolphin 

morbillivirus (DMV)), phocine herpesvirus-1 and 

Leptospira spp., Brucella spp. and San Miguel sea 

lion virus (calicivirus serotype 1). Serum antibodies 

to CDV were found in two otters harvested in the 

Kuril Islands in 1990 (Birkun and Krivokhizhyn, 

1991). Otters examined in 2004 and 2005 showed 

a low prevalence of exposure to T. gondii, phocine 

herpesvirus-1 and Leptospira interrogans serovar 

hardjo, a medium prevalence of Brucella, and 

negative results for morbilliviruses (Goldstein in 

review). 

Alaska (see Tables 2 and 3): An elevated level 

of mortality of sea otters was seen at Amchitka 

Island in February and March of 1951 (Rausch 

1953). Many of the animals were old, in poor 

body condition, and heavily parasitized. Two 

of the parasites were thought to be clinically 

significant, including an intestinal trematode 

associated with hemorrhagic enteritis and a gastric 

nematode, Pseudoterranova decipiens, causing 

gastrointestinal perforations. These parasitic 

infections apparently resulted from sea otters 

foraging heavily on fish. Poor body condition 

due to nutritional stress was thought to have 

contributed to the high death rate. During 1995-

96, dead otters recovered in Orca Inlet, PWS, had 

died from parasitic infections (consistent with P. 

decipiens) and fishbone impactions associated with 

consumption of waste products from fish processing 

(Ballachey et al. 2002). Heating and grinding of 

the fish waste and disposal farther offshore has 

eliminated these causes of death. Similar cases 

occurred in Resurrection Bay when fish waste was 

being dumped near the harbor, but cases were no 

longer seen once the fish waste was transported to 

deeper water (Tuomi and Burek 1999). 

Sea otter carcasses resulting from the Exxon 

Valdez oil spill were examined as part of the 

damage assessment process. The pathologic 

features of oil spill exposure were described along 

with lesions associated with stress, including oral 

ulcers from herpes infection, hemorrhagic enteritis, 

bullous emphysema, and liver pathology (Lipscomb 

et al. 1993). Two oil-spill animals that were kept in 

captivity were necropsied 3-10 years after the spill, 

and in one there was evidence of long-term liver 

disease. Rare cases of valvular endocarditis (VE) 

were described in oil-spill animals, but an etiologic 

agent was not identified. 

Large tumors have been found in sea otters 

killed by subsistence hunters in the southeast 

and southcentral Alaska stocks but the incidence 

appears to be low (Burek et al. 2012). 

A serologic survey of sea otters from the western 

Aleutian Islands and southeast Alaska in 1997 

(n=72) was negative for exposure to T. gondii, 

CDV, calicivirus, Leptospira interrogans, and 

Coccidioides immitis. The study showed some 

positive titers for Brucella spp. (5/65) (Hanni et 

al. 2003). Another serologic study of 65 Alaskan 

otters was negative for T. gondii (Fayer et al. 

2004). 

Since 2003, FWS and the USGS, in collaboration 

with the Alaska SeaLife Center, the California 

Department of Fish and Game, the University 

of California at Davis, and Alaska Veterinary 

Pathology Services, have made a concerted effort 

to obtain sea otter carcasses through a stranding 

network and have conducted full diagnostic 

necropsies on fresh carcasses. Most of the 

examined carcasses have come from Kachemak 

Bay, which is within the southcentral stock area. 

Several carcasses have also been obtained from 

the southwest stock, including at Kodiak Island 

and on the Alaska Peninsula. Preliminary results 

reveal a very different pattern of mortality than 

has been observed in California (Burek et al. 

2004). There has been no evidence of S. neurona, 

three cases of T. gondii encephalitis, no intestinal 

perforations and peritonitis due to Profilicollis 

spp. acanthocephalans, and only one case of 

chronic myocarditis/cardiomyopathy syndrome. 

In Alaskan otters examined to date, the most 

commonly diagnosed cause of death has been VE 

(Burek et al. 2005a). From 2002-06, VE was the 

primary cause of death for 26 of 64 fresh, nonfrozen beachcast cases, a prevalence of 41%. Of the 

cases that were cultured for bacteria, 80% were 

culture positive (in the heart valve and/or blood) 
3-12 Southwest Alaska DPS of the Northern Sea Otter

for Streptococcus bovis complex (Jang et al. 2005). 

A few cases of VE have been culture negative, 

a few were mixed cultures including S. bovis, 

Aeromonas spp., or other species of Streptococci. 

The nomenclature of the streptococcal organisms 

is quite complex and changes very frequently, so 

“S. bovis complex” actually represents a relatively 

large and diverse group of organisms. Most of the 

isolates identified further have been Streptococcus 

infantarius spp. coli. The same organism has 

also been found to be the cause of septicemia and 

meningoencephalitis. This combination of VE, 

septicemia, and meningoencephalitis due to S. bovis

complex is now being referred to as “Strep bovis 

syndrome.”

The epidemiology of this disease is not well 

understood. VE is normally a sporadic disease in 

human and animal populations. In most cases, a 

“primary” source of the bacteria for the valvular 

lesion is found, and is usually due to normal body 

flora or localized extra-cardiac infection, which 

results in repeated bacteremia such as with 

chronic oral infections or other chronic disease. 

Some possible routes of infection are fecal-oral, 

direct contact (with broken or unbroken skin), 

or aerosol. Generally, the sea otters that have 

died of Strep bovis syndrome were of prime age, 

male, and without other major primary sources 

of infection. Classically, with chronic VE, there is 

often a pre-existing valvular defect or other area 

of damage to the endothelium that the bacteria 

colonize, but it seems unlikely that such a large 

percentage of animals would have pre-existing 

valvular lesions. Some organisms (i.e., Bartonella 

spp. in dogs, and Erysipelothrix rhusiopathiae in 

pigs) do act as primary pathogens on heart valves. 

A possible involvement with Bartonella spp. is 

being investigated in sea otters. It is also possible 

that S. bovis complex has a strong predilection to 

colonize and damage valves/endothelium on its 

own and is a primary pathogen. Alternatively, 

there could be some other predisposing condition 

that damages the valve or endothelium and 

creates an environment for the S. bovis complex to 

colonize. Cases of VE have been documented from 

Umnak Island to PWS, with most cases currently 

originating in Kachemak Bay (the region where the 

stranding network is strongest). 

Historically VE has been reported in sea otters 

but only sporadically. Out of 282 sea otter 

carcasses necropsied after the 1989 Exxon Valdez

oil spill, only one was reported with VE, and it 

is not known which organism was involved in 

that case (Lipscomb et al. 1996). An additional 

case of VE was noted in an animal from PWS 

that died in captivity in 1990 (Joseph et al. 

1990). Approximately 100 sea otter carcasses 

were examined to various degrees in the decade 

preceding 2002, and VE was detected in four, all 

from PWS and Kachemak Bay (FWS unpublished 

data). In summer 1987 there was a mortality 

event in the Kodiak archipelago that involved 

approximately 55 animals. Four animals suitable 

for necropsy were sampled from this event and one 

had VE (DeGange and Vacca 1989). The mortality 

event was described as most likely caused by 

paralytic shellfish poisoning (PSP), because of an 

algal bloom and saxitoxin levels detected in other 

species at the time.

In August 2006, FWS submitted an application to 

the Marine Mammal Working Group to consider 

if these circumstances constituted an Unusual 

Mortality Event (UME) as defined under Section 

404 of the Marine Mammal Protection Act 

(MMPA). The Working Group made a formal 

recommendation to declare a sea otter UME in 

Alaska in September 2006, and an investigative 

team was formed. Population surveys indicated 

that sea otter numbers were increasing in 

Kachemak Bay and the adjacent Kenai Peninsula 

during the UME. This raises the possibility that 

the Strep bovis syndrome represents a densitydependent disease and not a decline-causing 

process. As a result of these findings the UME has 

been closed. Nevertheless, because the disease 

has been detected in some southwest Alaska DPS 

animals in Kodiak and the Alaska Peninsula, it 

is still of concern. Given the information from 

the Kenai Peninsula, it is unlikely that Strep 

bovis syndrome was a factor in the decline of the 

southwest stock; however, it could be a factor 

limiting population recovery. Studies are ongoing 

on the epidemiology and pathology of S. bovis, 

Bartonella, and morbilliviruses. Important 

questions that still need to be addressed include: 

 Is S. bovis complex a primary pathogen or 

secondary to another pathogen or toxin? 

 What is the source of the S. bovis complex or 

is it a normal part of the flora? 

 What is the route of transmission?

 Is it a threat to recovery of the southwest 

Alaska DPS?

 What is the extent of the disease syndrome 

and is the incidence increasing or decreasing?

Other causes of mortality documented in 

recent necropsies of Alaskan otters include 

boat strike, possible interspecific aggression 

(killer whale or Steller sea lion), intraspecific 

aggression, neoplasia, gastrointestinal impactions, 

emaciation, myocarditis/cardiomyopathy, Vibrio 

parahemolyticus septicemia/bacteremia and 

enteritis, and disseminated histoplasmosis (K. 

Burek personal communication). 

The role of morbilliviruses as a cause of morbidity 

and mortality in sea otters is currently poorly 

understood. Morbilliviruses in other species of 

marine mammals have resulted in large and rapid 

population declines, including PDV epizootics in 

harbor seals in the North Sea in 1998 and 2002 

(Kennedy 1998, Harknonen et al. 2006), CDV 
Recovery Plan 3-13 Table 3. Information on disease agents investigated in sea otters in the Gulf of Alaska and the Aleutian Islands. Agent/syndrome Data source Years Location Prevalence Comments Reference Valvular endocarditis (general syndrome) necropsy necropsy of fresh carcasses, culture 1987 1989 1990 2002-06 Kodiak PWS PWS primarily Kachemak Bay; few from AK Peninsula & Kodiak 1/4 1/282 1 26/63a no cultures no cultures no cultures many organisms cultured; 80% S. bovis complex DeGange and Vacca (1989), Lipscomb et al. (1996), Joseph et al. (1990), Burek et al. (personal communication), Jang et al. (2005), Burek et al. (in prep.), Gill et al. (in prep.), Toxoplasma gondii necropsy and specimens from fresh carcasses, serology and culture 2002-06 primarily Kachemak Bay; few from AK Peninsula & Kodiak 2/ 64a cases positive by serology and culture Gill et al. (in prep.) T. gondii Serology 2004 2004, 2005 not indicated Kodiak, S. Alaska Peninsula, eastern Aleutian Islands 0/65 2/74 Fayer et al. (2004), Goldstein et al. (in prep.) Sarcocystis neurona necropsy and specimens from fresh carcasses, serology and culture serology 2002-06 2004, 2005 primarily Kachemak Bay; few from AK Peninsula & Kodiak Kodiak, S. Alaska Peninsula, eastern Aleutians 0/63a 2/74 Gill et al. (in prep.) Goldstein et al. (in prep.)
3-14 Southwest Alaska DPS of the Northern Sea Otter Agent/syndrome Data source Years Location Prevalence Comments Reference Profilicolis sp. necropsy of fresh carcasses 2002-06 0/63* associated with gastric and intestinal perforation and peritonitis Pseudoterranova decipiens necropsy 1951 1989 1995-96 1999? 2002-06 Amchitka Island PWS Orca Inlet PWS Resurrection Bay primarily Kachemak Bay; few from AK Peninsula & Kodiak ? / up to 31 1/66 in PWS 5/9 1 case 0/63a associated with gastric and intestinal perforation and peritonitis in all cases except the 1989 survey Rausch (1953) Margolis et al. (1997) Ballachey et al. (2002) Tuomi and Burek (1999) Gill et al. (in prep.) Diplogonoporus tetrapterus necropsy necropsy of fresh carcasses 1989 2002-06 PWS primarily Kachemak Bay; few from AK Peninsula & Kodiak 8/66 0/63a impactions associated with heavy infestations in some cases Margolis et al. (1997) Gill et al. (in prep.) Coccidioides immites Serology 1997 Aleutians and southeast AK 0/72 Hanni et al. (2003) herpesvirus necropsy serology (phocine herpesvirus-1) 1989 1990 1998 2004, 2005 PWS Kodiak, S. Alaska Peninsula, eastern Aleutians sporadic and episodic 11/72 oral ulcers and characteristic lesions, no virus identified Harris et al. (1990) Reimer and Lipscomb (1998) Goldstein et al. (in prep.) Table 3. Continued.
Recovery Plan 3-15 Agent/syndrome Data source Years Location Prevalence Comments Reference CDV, calicivirus, Leptospira interrogans, Brucella sp., T. gondii, Coccidiodes immites serology 1997 2003-04 2004, 2005 western Aleutians (61) and SE AK (11) Kodiak / AK Peninsula Kodiak, S. Alaska Peninsula, eastern Aleutians 0/72 high prevalence of PDV- like virus calici 0/65 Brucella 1/72 Lepto 1/72 low titers Hanni et al. (2003) Goldstein et al. (2009) aTable 3. Continued. necropsy data are still preliminary and results approximate due to some pending analyses and the need to re-check data 
3-16 Southwest Alaska DPS of the Northern Sea Otter

epizootics in Caspian seals (Kennedy et al. 2000, 

Pollack 2001, Kuiken et al. 2006), and PMV in 

dolphins along the U.S. Atlantic coast (Schulman 

et al. 1997). Populations rebound once enough 

animals become resistant to disease due to survival 

and protective immune response. Sea otters that 

were live-captured and sampled in the Commander 

Islands, Aleutian Islands, southern Alaska 

Peninsula, and Kodiak archipelago in 2004 and 2005 

were tested by serum neutralization for exposure 

to CDV, PDV, PMV, and DMV. Preliminary results 

suggest exposure to a PDV-like morbillivirus in 

the eastern Aleutians/southern Alaska Peninsula 

and the Kodiak archipelago, but animals from the 

Commander Islands and the western Aleutians 

tested negative (Goldstein, in prep.). Archived 

serum from animals sampled at a variety of sites 

in the 1980s and 1990s were consistently negative 

for morbillivirus, and all animals sampled in 1997 

from the Aleutians were negative for CDV (Hanni 

et al. 2003); PDV serologic assays were not run 

on those animals. Taken collectively, this evidence 

supports the hypothesis of exposure of sea otters 

to a novel virus in the eastern Aleutians/Alaska 

Peninsula/Kodiak area between 1997 and 2004, 

but not in the western Aleutians where population 

declines were highest. These data may indicate 

exposure to a well-characterized morbillivirus or it 

may represent cross-reactivity to a new pathogen 

(A. Doroff, V. Gill, and T. Goldstein, personal 

communications). PCR sequences identical to 

PDV from the 2002 European harbor seal epizootic 

have been detected and characterized from livecaptured animals from Kodiak and some deadstranded carcasses from Kachemak Bay (Goldstein 

et al. 2009). Research is continuing to characterize 

the virus by culture and PCR, and to determine 

whether it is causing significant morbidity and 

mortality in sea otters or other species. 

The duration of the decline of southwest Alaska 

sea otters without signs of a rebound, and its 

broad geographic distribution, are not typical of a 

problem caused purely by disease. Additionally, 

the relative lack of carcasses recovered despite 

intensive field studies at two locations in the 

Aleutians during the decline is inconsistent with 

disease-related mortality observed elsewhere. 

Although current data suggest that disease has 

not been the primary factor in this decline, there 

has been little work on direct causes of mortality 

in this DPS and the potential for disease to impede 

recovery cannot be ruled out. Continued studies 

of diseases and causes of mortality are therefore 

warranted. Kuker and Barrett-Lennard (2010) 

provide additional discussion of the possibility of 

disease as a factor in the sea otter decline.

Immunocompetence 

Some researchers believe that the high rate of 

infectious disease observed in sea otter populations 

may be due partly to immune deficiency resulting 

from poor nutrition, anthropogenic toxicants, 

immunotoxic viruses (such as morbilliviruses, 

retroviruses, parvoviruses, etc.), or lack of 

genetic diversity. Defining immune competence 

and immune deficiency is often problematic, 

and determining underlying causes of immune 

deficiency is even more difficult. Some techniques 

for characterizing immune function parameters in 

sea otters were reported by Schwartz et al. (2005), 

but that work mostly characterized the “normal” 

values and did little to determine whether there 

actually was immune competence or deficiency. 

With all sea otter populations having experienced 

a population bottleneck in recent history, one 

possibility for the high rate of infectious disease 

is a loss of genetic diversity. The concept that 

loss of genetic diversity may lead to decreased 

disease resistance is controversial because of the 

difficulty of performing controlled experiments 

in the populations at risk. However, several 

experimental models support this concept 

(Spielman et al. 2004). Studies of genetic variation 

within some microsatellite and mitochondrial 

markers indicate that historical sea otter 

populations (prior to 1742) had significantly more 

variation than extant populations (Scribner et 

al. 1997, Larson et al. 2002a, b). An important 

question is whether the loss of genetic variation 

varies geographically in degree and quality, 

and whether that variation correlates with 

population-level immune function and/or disease 

susceptibility. A preliminary examination of major 

histocompatibility complex (MHC) II genes (which 

indicate immune system vigor) in sea otters from 

California, Washington, and Alaska indicated that 

distinct clades are demarcated by geographic 

region, but the significance of this for immune 

vigor or resistance to infectious disease has not 

been determined (Bowen et al. 2006). Sea otters in 

California have very low levels of variation at the 

MHC genes compared to other mammals, where 

these loci are typically highly variable. One of the 

MHC genes (DRA) was monomorphic in California 

sea otters and two of them (DQB and DRB) had 

only two alleles (Aguilar et al. 2008).

Discussion and Recommendations

Potential causes of the sea otter decline in 

the Aleutian Islands have been explored by 

reviewing available data on sea otter reproduction, 

survival, distribution, habitat, and environmental 

contaminants. Estes et al. (1998) concluded that 

the decline was most likely the result of increased 

adult mortality due to predation by killer whales. 

However, novel or episodic disease agents have not 

yet been thoroughly investigated as contributing 

factors. Furthermore, the number of sea otters 

remaining in some areas is small and continuing 

to decline. These remaining small populations are 

vulnerable to the effects of disease, oil spills, or 

other catastrophic events.

To determine whether the decline is spreading, 

surveys are required to estimate population sizes 

and trends, particularly for populations located 

at the geographic “margins” of the decline. 

Beachcast carcasses throughout the range should 
Recovery Plan 3-17

be collected and subjected to thorough and 

consistent necropsies, using protocols established 

by FWS and collaborators. The possibility 

that morbillivirus was introduced into a naive 

population should be confirmed or disproved, and 

an understanding of the epidemiology of S. bovis 

syndrome should be developed. Investigations 

of causes of mortality should be part of a 

comprehensive effort to improve understanding 

of the factors driving population dynamics, and 

this, in turn, should provide informed guidance for 

actions to aid the recovery of southwest Alaska 

sea otters. Key geographic areas for expanded 

research include accessible locations within the 

range of the southwest Alaska DPS (Adak, Kodiak, 

and Kamishak Bay), along with regions located 

along the margins of the decline (Commander 

Islands, Kachemak Bay, and PWS). Comparisons of 

causes of mortality between the southwest Alaska 

DPS and populations in Alaska and California 

would be beneficial. 

C. Biotoxins

The potential role of PSP toxins, domoic acid, 

ciguatoxins, brevetoxin, and other biotoxins in 

the sea otter decline is poorly understood. These 

compounds are produced by dinoflagellates and 

diatoms, which under certain environmental 

conditions experience increased numbers (blooms). 

Marine mammal mortality events associated 

with these “harmful algal blooms” (HABs) have 

been reported (Bossart et al. 1998, Hernandez et 

al. 1998, Scholin et al. 2000, O’Hara and O’Shea 

2001). There is evidence that the frequency and 

distribution of toxigenic phytoplankton blooms are 

increasing (Smayda 1990, Hallegraeff 1993, Van 

Dolah 2000, Mos 2001). Whether marine mammal 

mortality events are increasing due to HABs is 

unclear. 

Domoic acid, a biotoxin produced by the alga 

Pseudonitzschia australis, can accumulate in filterfeeding shellfish and be passed through the food 

chain, thereby affecting not only animals that prey 

on invertebrates, but fish-eating species as well. 

Domoic acid poisoning was first identified in human 

cases at Prince Edward Island (Canada) where 

over 100 people became ill and 4 died after eating 

contaminated mussels (Teitelbaum et al. 1990). 

First detected on the west coast of North America 

in 1991, domoic acid has caused several large dieoffs of sea birds (Work et al. 1993) and repeated 

mortality events in California sea lions (Zalophus 

californianus; Scholin et al. 2000). Despite these 

repeated mass mortality events, the California sea 

lion population is increasing and therefore domoic 

acid poisoning is not limiting population growth at 

this time. 

Domoic acid has caused stranding and mortality 

in California sea otters (Kreuder et al. 2003), but 

levels of exposure and effects of domoic acid in 

Alaskan sea otters are unknown (Table 4). There 

has been minimal monitoring in Alaska, with 

the exception of one study in which low levels 

of domoic acid were detected in razor clams in 

Kachemak Bay (Horner et al. 1997). Current 

studies of fresh dead otters have detected very low 

levels of domoic acid in urine, primarily from otters 

in Kachemak Bay (V. Gill, personal communication). 

Lesions typical of domoic acid intoxication in other 

animals have not been detected in Alaskan sea 

otters. Myocarditis and dilated cardiomyopathy are 

major causes of death in California sea otters, and 

exposure to domoic acid and S. neurona appears 

to be a major risk factor for development of this 

disease syndrome (Kreuder et al. 2005). Cases of 

chronic myocarditis similar to that described in 

California are very rare in sea otters in Alaska (K. 

Burek personal communication).

The compounds responsible for PSP, produced 

by certain dinoflagellates, can accumulate to 

toxic levels in filter-feeding bivalves. Butter 

clams (Saxidomus giganteus), which accumulate 

saxitoxin, are an important component of the sea 

otter’s diet in Alaska. Although PSP monitoring is 

performed in some commercial shellfish operations, 

the presence of these substances has not been 

monitored consistently throughout Alaska. A 

mortality event in sea otters at Kodiak Island 

in 1987 was attributed to PSP exposure, based 

primarily on the identification of an unknown 

toxin in two sea otters, a few cases in people at the 

same time, and the presence of PSP toxins in blue 

mussels (Mytilus trossulus; DeGange and Vacca 

1989). However, subsequent studies showed that 

sea otters are able to avoid consumption of PSPcontaminated prey (Kvitek et al. 1991). In summer 

2008, a major saxitoxin mortality event occurred 

in the St. Lawrence River estuary in Quebec, 

Canada, with deaths recorded of cetaceans, 

pinnipeds, fish, and birds (Measures 2007). The 

intensity of the bloom was thought to have been 

related to unusually warm ocean temperatures 

and large amounts of freshwater run-off due to 

unusual levels of precipitation (L. Measures, 

personal communication). Since PSP is known to be 

present in the habitat of the southwest Alaska sea 

otter DPS and in major food sources of sea otters, 

studies should be conducted to determine whether 

PSP is a primary or contributory cause of death. 

Initial testing of stranded otters for domoic acid 

and PSP began in 2009. 

D. Contaminants 

Persistent organic pollutants 

Persistent organic pollutants (POPs) may affect 

sea otters and their habitat. Potential sources 

of such contaminants include both local pointsources at specific locations in Alaska and more 

diffuse sources that may affect an area via 

long-range transport. One study found high 

levels of polychlorinated biphenyls (PCBs) and 

dichlorodiphenyltrichloroethane (DDT) in sea 

otter tissues collected near military installations 

in the Aleutian Islands (Estes et al. 1997), but 

the elevated levels were likely an artifact of the 
3-18 Southwest Alaska DPS of the Northern Sea Otter Table 4. Information on biotoxins found in sea otters in the Gulf of Alaska and the Aleutian Islands. Agent Data source Years Location Prevalence Comments Reference PSP (suspected) mortality event mortality event involving otters (12) and humpback whales (up to 8) 1987 2008 Kodiak Kodiak / AK Peninsula 55 otters involved preliminary results; 1 otter confirmed positive low levels of PSP in mussels DeGange and Vacca (1989), K. Burek (personal communication) Domoic acid analysis of urine during disease, monitoring, necropsy 2001 Kachemak Bay 1/30 (low level of toxin) scattered very low levels of domoic acid found in urine Frame et al. (in prep.)
Recovery Plan 3-19

samples having come from emaciated, beachcast 

carcasses (J. Estes, personal communication). 

Generally, reported levels of total PCBs have been 

highest in sea otters from the Aleutian Islands 

followed by California; they were negligible in 

otters from southeast Alaska (Table 5). High levels 

of DDT have been recorded in California but DDT 

levels have been negligible in Alaska (Bacon 1994, 

Jarman et al. 1996, Giger and Trust 1997, Estes et 

al. 1997, Bacon et al. 1999). 

A study of kidney and liver samples obtained 

from subsistence-collected animals throughout 

Alaska from 1993-99 indicated that organochlorine 

pesticides were either absent, or present only at 

low concentrations, in most otters. However, this 

was a biased sample in that hunters are known 

to target the healthier and “fatter” animals. A 

few individuals had elevated concentrations of 

PCB aroclors, indicating that PCB contamination 

may be occurring in local areas (Comerci et al. 

2002). More recent data on blue mussels collected 

from the Aleutian Islands and southeast Alaska 

indicated low concentrations of PCBs at most 

sample locations, with “hot spots” of high PCB 

concentrations evident at Adak, Dutch Harbor, and 

Amchitka. With the exception of these “hot spots,” 

PCB levels in mussel samples from southwest 

Alaska were lower than those from southeast 

Alaska (Reese 1998). 

It is not known whether organochlorine (OC) 

exposure is having an impact on sea otters at the 

population level. One documented effect of OCs 

is immunosuppression (O’Hara and O’Shea 2001), 

but the effects of different levels and mixtures of 

OCs in sea otters are unknown. In vitro studies 

with other species suggest that effects of OCs 

are species-specific (Mori et al. 2006). Ross (2002) 

suggested that prior POP exposure can exacerbate 

morbillivirus-related outbreaks in marine 

mammals. Immunosuppression resulting from 

contaminant exposure is considered a potential 

contributor to the incidence of disease documented 

in southern sea otters, but this remains unproven 

(Thomas and Cole 1996, Reeves 2002). The 

hypothesis that POP contaminants were a factor 

in the European harbor seal PDV epidemics has 

not been supported by studies where the samples 

were properly stratified by age and sex (Rijks et al. 

2008). 

OCs are also known to cause reproductive effects 

in a variety of species (Aulerich et al. 1973, 

Platonow and Karstad 1973). Recent analyses 

of blood contaminant levels in sea otters indicate 

much higher overall levels in California than in 

the Aleutian Islands, yet age-specific reproductive 

rates in these and other populations are similar 

(Jessup et al. 2010). Such results suggest that OC 

contamination is not of particular concern for sea 

otters in the western Alaska DPS, and decreased 

reproduction (from OC toxicity or other unrelated 

causes) does not seem to be a factor in the current 

decline.

Perfluorinated Compounds 

Elevated levels of perfluorinated contaminants 

(PFCs) in livers of sea otters in California were 

correlated with deaths due to infectious disease 

(Kannan et al. 2006). Recent studies indicate that 

levels of PFCs are similar in Alaskan otters to 

those described in California otters. Over time, 

perfluorooctanesulfonate levels are decreasing in 

response to their removal from markets, while the 

contribution of perfluorononanoate is increasing. 

No link has been found between PFC levels and 

causes of death, and further work is needed (Hart 

et al. 2009). 

Heavy Metals 

Higher concentrations of cadmium and selenium 

were present in older sea otters than in juvenile 

otters sampled in the Aleutian Islands during 

the 1990s (Giger and Trust 1997). Comerci et 

al. (2002) analyzed livers and kidneys collected 

from sea otters from southeast, southcentral, and 

southwest Alaska for a suite of trace elements and 

metals. Metals and trace element concentrations 

were highest in otters from southcentral Alaska, 

followed by southwest, and then southeast. This is 

very similar to the pattern seen in Steller sea lions 

(K. Beckmen, personal communication). Kannan et 

al. (2006) compared trace element concentrations 

among diseased, emaciated, and non-diseased 

sea otters in California. Hepatic concentrations 

of copper and cadmium were higher in otters in 

that study than have been reported for any other 

marine mammal species. Manganese, cobalt, zinc, 

and cadmium were elevated in the diseased and 

emaciated otters relative to non-diseased otters. 

Considering the pattern of low tissue accumulation 

of metals in Alaskan otters and the lack of 

industrial or urban development in these areas, 

heavy metals are unlikely to be a causal factor in 

the decline of the southwest Alaska DPS.

E. Oil spills and oiling

Sea otters are particularly vulnerable to 

contamination by oil (Cohen and Aylesworth 

1990). Oil contamination can have both immediate 

and long-term effects on sea otters and on 

population recovery (Peterson et al. 2003). Five 

characteristics of sea otter biology help explain 

their extreme vulnerability to oil contamination: 

Sea otters depend on their fur and the air trapped 

within it for thermal insulation. Oil destroys the 

water-repellent nature of the fur and it eliminates 

the air layer, thereby reducing the insulative value 

by 70% (Williams et al. 1988). The direct result is 

acute hypothermia. 

Once the fur is fouled, sea otters ingest oil as they 

groom themselves. Ingested oil damages internal 

organs, resulting in acute and chronic effects 

on animal health and survival. Based on a mink 

model, oral exposure to low doses of oil can lead 

to changes in hematology, immune function, and 
3-20 Southwest Alaska DPS of the Northern Sea Otter Table 5. Information on contaminants found in sea otters in the Gulf of Alaska and the Aleutian Islands. Agent Data source Years Location Sample size Comments Reference Polychlorinated Biphenyls Organochlorines Heavy Metals kidney and liver from subsistence animals 1993-99 throughout Alaska and some of Russia 68 (plus data from other studies) generally low background levels of all Comerci et al. (2002) Polychlorinated Biphenyls Organochlorines tissues 1991-92 1993-94 California southeast Alaska and Aleutian Islands Aleutian Islands 23 otters 25 eagle eggs high levels in Aleutian Islands, low in southeast Alaska Estes et al. (1997) Polychlorinated Biphenyls Organochlorines mussels 2005? Aleutian Islands Southeast Alaska 39 sites 5 sites 30-50 mussels/ sample hot spots in the Aleutian Islands, overall background level low Reese, Estes, and Tinker (in prep.) Anthropogenic radionuclides skulls 1950s and 1990s Amchitka 18 Pb 210 levels were higher in 1950s than 1990s Baskaran et al. (2003) Perfluorinated compounds livers 1992-2007 Prince William Sound Resurrection Bay Kachemak Bay 36734 similar to California Hart et al. (2009)
Recovery Plan 3-21

reproductive success (Mazet et al. 2001, Schwartz 

et al. 2004). 

Benthic invertebrates accumulate and store 

toxic hydrocarbons. Sea otters therefore ingest 

hydrocarbons when they feed on these organisms 

during and after an oil spill.

Sea otters are nearshore animals that exhibit 

strong site fidelity, often remaining in or returning 

to oiled areas after release. In addition, they often 

rest in kelp beds, which collect and retain spilled 

oil. 

Sea otters are often found in single-sex 

aggregations, which can include hundreds of 

individuals. Thus, large numbers of sea otters 

(representing a substantial portion of the 

reproductive potential of a population) can become 

fouled by oil simultaneously. 

The sea otter population in PWS illustrates 

both short-term and long-term effects of oil 

contamination. The oil tanker Exxon Valdez 

ran aground in PWS in March 1989, spilling 42 

million liters of crude oil. Nearly 1,000 sea otter 

carcasses were recovered within six months 

(Ballachey et al. 1994), with the total mortality 

estimated to be several thousand animals (Tuomi 

2001). Lesions described in sea otters exposed to 

Prudhoe Bay crude oil included gastric erosion 

and hemorrhage, centrilobular hepatic necrosis, 

periportal and diffuse hepatic lipidosis, and renal 

tubular lipidosis (Lipscomb et al. 1993). Several 

parameters suggest long-term consequences of 

residual oil. Sea otters in parts of PWS that were 

most heavily oiled have significantly higher liver 

levels of cytochrome P4501A, a biomarker for 

hydrocarbons, than otters from less heavily oiled 

areas (Ballachey et al. 2002). Differences in blood 

parameters suggest liver damage in oiled versus 

unoiled areas (Ballachey et al. 2003). Population 

growth remains significantly lower in heavily oiled 

areas, suggesting that population recovery may 

be constrained by the residual effects of oil, even 

when the food supply is adequate (Bodkin et al. 

2002). 

Some safeguards have been established since the 

Exxon Valdez oil spill to minimize the likelihood 

of another spill of catastrophic proportions in 

PWS. However, tankers, other vessels, and fuel 

barges are potential sources of oil and fuel spills 

that could have a catastrophic effect on sea otters 

in the southwest Alaska DPS. Since 1990 in 

Alaska, more than 4,000 spills of oil and chemicals 

into water have been reported to the U.S. Coast 

Guard National Response Center. Of these, nearly 

1,100 occurred within the range of the southwest 

Alaska DPS (Table 6). Reported spill quantities 

have ranged from a few gallons to thousands 

of gallons, including various refined products 

(primarily diesel fuel, gasoline, and lubricating oils; 

National Research Council 2008). It is important to 

examine patterns of ship traffic within the range 

of the southwest Alaska DPS, as well as existing 

regulations, to determine if appropriate controls 

are in place. A well-designed contingency plan, 

including procurement of the necessary supplies, 

protocols, and equipment, should be developed to 

respond to oil spills as rapidly as possible. 

Currently, there is no oil or gas production within 

the range of the southwest Alaska DPS, but outer 

continental shelf oil and gas lease sales are planned 

for lower Cook Inlet and Bristol Bay. Based 

on a review of the draft Environmental Impact 

Statement for these sales, it is the opinion of FWS 

that “the potential impacts of this development 

on the southwest Alaska DPS will be negligible as 

sea otters occur primarily in the near shore zone 

and the lease sale area is at least three miles off 

shore. Therefore, sea otters do not significantly 

overlap with the lease sale area.” However, within 

the Bristol Bay MU, many sea otters are found 

long distances from shore and they could occur 

within the lease sale area. Also, as demonstrated 

during the Exxon Valdez oil spill, spilled oil can 

affect wildlife at distances of up to several hundred 

miles from the initial release site (Loughlin 1994). 

Potential impacts of oil spills on sea otters could 

range from negligible to high, depending on the 

location, extent, and type of material that is 

spilled. In the event that areas within the range 

of the southwest Alaska DPS were to be opened 

for oil and gas lease sales in the future, potential 

impacts to sea otters should be given thorough 

consideration. In particular, studies to predict oil 

spill trajectories in this area would be beneficial. 

Large-volume oil spills, once they have occurred, 

are nearly impossible to contain or manage with 

current technology. When large numbers of otters 

become contaminated with spilled oil, it is not 

possible to capture and treat the great majority 

of the animals. Those that become extensively 

contaminated or ingest large quantities of oil are 

difficult, if not impossible, to rehabilitate with 

currently available technology (Estes, 1991). 

Even with those constraints, however, it should 

be possible to protect small areas that provide 

important habitat in specific parts of the southwest 

Alaska DPS from becoming oiled. 

F. Food limitation

As with many apex predators, the abundance or 

density of sea otter populations may be ultimately 

limited by the abundance of prey resources 

(Kenyon 1969, Garshelis et al. 1986, Estes 1990, 

Estes et al. 1996). It is generally believed that 

for sea otters, population carrying capacity 

(“K”, the average population density at which 

total births and total deaths are equal and thus 

net population growth is zero) is determined by 

increased mortality due to energetic/nutritional 

stress, emaciation, or other factors such as disease 

that may be triggered by energetic/nutritional 

stress (Estes et al. 1996). While food abundance is 

certainly not the only factor that can limit sea otter 
3-22 Southwest Alaska DPS of the Northern Sea Otter Table 6. Summary of reported non-crude oil spills in southwest Alaska by MU, July 1, 1995 – June 30, 2005. Quantity Spilled (gallons) Management Unit Source 0-10 10-25 25-100 100-500 500-1,000 1,000-5,000 >5,000 Total Western Aleutian Vessel 98 16 22 8 1 1 3 149 Cannery 16 3 8 1 2 0 1 31 Harbor/Port 5 0 2 0 0 0 0 7 Non-crude Terminal 3 1 3 2 0 0 0 9 Subtotal 122 20 35 11 3 1 4 196 Eastern Aleutian Vessel 74 11 14 5 1 0 2 107 Cannery 10 1 1 0 0 0 0 12 Harbor/Port 4 1 0 0 0 0 0 5 Non-crude Terminal 0 0 1 1 0 1 0 3 Subtotal 88 13 16 6 1 1 2 127 Bristol Bay Vessel 6 1 0 0 0 0 0 7 Cannery 1 0 0 1 0 0 0 2 Harbor/Port 0 0 0 0 0 0 0 0 Non-crude Terminal 0 0 0 1 0 0 0 1 Subtotal 7 1 0 2 0 0 0 10
Recovery Plan 3-23 Quantity Spilled (gallons) Management Unit Source 0-10 10-25 25-100 100-500 500-1,000 1,000-5,000 >5,000 Total South Alaska Peninsula Vessel 20 2 7 3 0 1 0 33 Cannery 3 3 1 1 0 0 0 8 Harbor/Port 1 0 0 0 0 0 0 1 Non-crude Terminal 0 0 0 0 1 0 0 1 Subtotal 24 5 8 4 1 1 0 43 Kodiak, Kamishak, Alaska Peninsula Vessel 100 7 10 0 2 0 1 130 Cannery 0 1 5 2 1 0 0 9 Harbor/Port 2 2 0 0 0 0 0 4 Non-crude Terminal 0 0 1 0 0 0 0 1 Subtotal 102 10 16 12 3 0 1 144
3-24 Southwest Alaska DPS of the Northern Sea Otter

population growth, there are examples from across 

the sea otter’s range that support the general 

scenario of resource limitation at high population 

density. These include Bering Island in the early 

1990s (Bodkin et al. 2000), Amchitka Island during 

the 1960s (Kenyon 1969) and early 1970s (Estes 

1977), areas of PWS prior to the 1989 Exxon 

Valdez oil spill (Garshelis et al. 1986), and, recently, 

the central portion of the southern sea otter’s 

range in California (Tinker et al. 2006a, b). 

As sea otter populations grow from low to high 

densities, they can have a strong impact on 

certain prey species, including sea urchins and 

other herbivorous invertebrates in rocky habitats 

(Duggins 1980, Estes and Duggins 1995), and 

filter-feeding bivalve mollusks in soft-sediment 

habitats (Kvitek et al. 1989). This tendency to 

limit abundant prey populations can lead to strong 

and often complex community-level effects (Estes 

and Palmisano 1974; Estes et al. 1978, 2004). For 

example, through a trophic cascade initiated by 

sea otter predation on urchins, there can be a 

significant increase in the standing algal biomass 

and the overall productivity of kelp forests 

(Duggins 1980), leading to an increase in the 

abundance of invertebrates and vertebrates that 

rely on kelp for food or habitat, thereby increasing 

the suite of potential prey species available to 

sea otters and other predators. This complex 

feedback loop can thus elevate the potential sea 

otter population density (Estes 1990). However, 

despite adaptive behavioral responses to increasing 

density, such as increased diet breadth and percent 

time foraging (Ostfeld 1982, Garshelis et al. 

1986, Watt et al. 2000, Gelatt et al. 2002), at high 

population densities individual sea otters can reach 

a point at which they are unable to meet their 

energetic requirements, leading to an increase in 

starvation-induced mortality. In some instances, 

mortality associated with food limitation can occur 

suddenly. Instead of a slow, incremental increase 

in food-related mortality, there will be a sudden 

die-off of animals, often at the end of a harsh winter 

or as a result of some triggering event that pushes 

many already-weakened animals past the point of 

energetic balance. Such sudden mortality events 

associated with nutritional limitation have been 

reported at Amchitka Island in the 1950s (Kenyon 

1969) and Bering Island in 1980 (Bodkin et al. 

2000), and they resulted in large numbers of beachcast sea otter carcasses. In both of these cases, the 

mortality events appeared to essentially mark the 

point at which the populations reached carrying 

capacity, insofar as sea otter numbers subsequently 

stabilized. The current sea otter population decline 

in southwest Alaska was first documented in the 

western Aleutian Islands (Estes et al. 1998, Doroff 

et al. 2003), a region in which sea otters had been 

present at high density for many years and where 

populations were thought to be at or near carrying 

capacity (Kenyon 1969, Estes 1990). Given the 

rapid onset of this decline, and the fact that there 

have been instances in the past of sudden dieoffs associated with food limitation in sea otter 

populations, food limitation must be considered a 

possible factor in the current population decline. 

Methods for assessing food limitation 

Direct measures of food abundance: Evaluating 

whether food limitation has contributed to the 

decline of sea otters in southwest Alaska would 

seem to be a simple matter of measuring the 

abundance of sea otter prey. Such a task is 

complicated however, because: 1) assessments of 

prey abundance require good information on which 

prey species are consumed by sea otters, yet diet 

often varies between locations and habitats; 2) 

any assessment of prey abundance must be made 

relative to a sea otter’s foraging ability - that is, 

sampling by researchers must reflect the depths 

and spatial scales over which sea otters typically 

forage; and 3) many important prey species are 

cryptic and difficult to count. Because of these 

and other difficulties, most studies rely heavily on 

indirect indices of prey abundance. Nonetheless, 

in those cases where there are consistent and 

broad-based sampling regimes for key prey species 

that are maintained over long periods of time, 

the resulting time series can provide insight into 

changes in the abundance of food resources relative 

to sea otter densities. 

Two examples serve to illustrate this approach. 

In the western Aleutian Islands during the 1980s, 

Estes and colleagues documented rapid declines 

in the density and size structure of green sea 

urchins after sea otters re-colonized Attu Island 

(Estes 1990, Estes and Duggins 1995). As the 

otter population approached carrying capacity at 

one location, Massacre Bay, the size-frequency 

distribution and density of sea urchins at long-term 

sampling sites began to approach that of Amchitka 

Island, where sea otters had been present at 

high densities for many years. In this case, the 

combination of direct estimates of urchin size and 

abundance in conjunction with the time series of 

sea otter population counts provided a clear picture 

of resource limitation in sea otters. A second 

example of the effective use of direct estimates of 

prey abundance comes from Glacier Bay. After 

their translocation to southeast Alaska in the 1960s 

(Jameson et al. 1982), sea otter numbers increased 

rapidly as the animals re-colonized unoccupied 

habitat, and otters began to move into Glacier Bay 

in about 1995. Bodkin et al. (2007) sampled the 

relative density of infaunal bivalves and other softsediment prey species, both before and during the 

re-colonization event. Clear decreases in bivalve 

size and abundance have already been seen, and it 

is anticipated that this time series will eventually 

provide baseline data on the relative density of 

prey that exists when this population reaches 

carrying capacity. 

The above examples demonstrate that it is possible 

to estimate food abundance directly and relate 

those estimates to carrying capacity for sea otters. 

However, such sampling programs are labor-
Recovery Plan 3-25

intensive and must be maintained in a consistent 

manner over many years in order to provide 

meaningful information. Ideally, direct sampling 

of prey abundance should be a part of a larger 

program that also measures one or more indices 

of sea otter population status, as described in the 

following section. 

Demographic, physiological, and behavioral 

indices used to detect food limitation: Because 

of their small size, elevated metabolic rate, and 

limited capacity for storing energy reserves, sea 

otters must maintain a consistently high rate 

of food intake (Costa and Kooyman 1982), and 

consequently they are vulnerable to even small 

fluctuations in prey abundance. Individuals that 

are thin or emaciated are seen in any sea otter 

population, even one that is well below carrying 

capacity. The importance of food limitation at 

the population level depends on its overall effects 

on one or more demographic processes, birth or 

death rates, age of first reproduction, immigration, 

or emigration. Research suggests that food 

limitation acts primarily via changes in per-capita 

death rates (Estes et al. 1996). Moreover, the 

increased mortality associated with food limitation 

is usually not distributed equally among sex and 

age classes, but disproportionately affects very 

young and very old age classes and may also affect 

males more than females (Monson et al. 2000), 

especially when males are concentrated spatially 

in areas of poorer habitat quality (Bodkin et al. 

2000). This age-specific pattern of mortality can 

be useful in diagnosing the status of different 

sea otter populations. One would expect that a 

population that has ceased to grow or started 

to decline because of resource limitation should 

exhibit increased mortality among juveniles and 

old adults, while prime-age adults (particularly 

females) should be buffered from such changes. 

Such a pattern can be tested for by monitoring the 

survival of individually marked animals over time, 

or by evaluating the age and sex structure of the 

living population or the death assemblage (e.g., 

Monson and DeGange 1995, Monson et al. 2000, 

Laidre et al. 2006, Tinker et al. 2006a). In either 

case, it is the change in survival or age structure 

over time that is most informative, rather than 

estimates at single points in time or space that 

are often difficult to interpret. Data sets on agespecific survival and age-frequency distributions 

from multiple populations over a range of relative 

densities (Laidre et al. 2006; USGS, unpublished 

data) provide a useful baseline for comparative 

studies. 

In addition to its effects on demographic rates, 

resource limitation also may cause physiological 

and behavioral changes in sea otters, and those 

changes can provide powerful and robust indices 

of population status (Bodkin and Ballachey 1996). 

Body condition is one such parameter because a 

reduced rate of food intake leads to a decrease in 

fat reserves and muscle tissue, with the result that 

the body weight of individuals at a given length 

is significantly lower in food-limited populations 

(Monson et al. 2000, Laidre et al. 2006). Because of 

the relatively high degree of individual variation 

in body condition, as well as the impact of 

measurement error (especially in total body length, 

which can be affected by immobilization drugs) 

and by undetected pregnancy in females (massto-length ratio is obviously greater for pregnant 

females irrespective of food abundance), the utility 

of body condition as an index of population status 

depends on obtaining sufficient sample sizes, which 

can be both costly and difficult when sea otter 

densities are low. Nonetheless, a review of data 

on mass and length obtained from a wide range of 

sea otter populations suggests that the mass-tolength ratio is a reliable indicator of relative body 

condition and population status (Monson 2009). 

A number of behavioral traits are also indicative 

of sea otter population status with respect to food 

resources. In particular, sea otters in food-limited 

populations tend to spend a high proportion of 

their time feeding (Estes et al. 1986, Garshelis 

et al. 1986, Ralls and Siniff 1990, Gelatt et al. 

2002), experience a low rate of food acquisition 

while foraging (Estes et al. 1981, Garshelis et 

al. 1986, Dean et al. 2002), and exhibit a diverse 

diet at the population level (Estes et al. 1981). 

The size of some prey items consumed by otters 

is generally smaller in food-limited populations 

(Estes and Duggins 1995). Recent evidence from 

California suggests that individual otters are 

more likely to be dietary specialists in food-limited 

populations, and that increased diet diversity at 

the population level occurs as a result of increased 

inter-individual variation in diet composition 

(Estes et al. 2003a, Bentall 2005, Tinker et al. 2007). 

Each of these behavioral traits can be readily 

measured from most sea otter populations using 

simple observational techniques, although reliable 

estimates of percent time feeding and individual 

diet composition necessitate the radio-tagging of 

individual animals and collection of longitudinal 

data using telemetric techniques (Garshelis et 

al. 1986, Ralls and Siniff 1990, Gelatt et al. 2002, 

Tinker et al. 2006b). Recent technical advances 

allow for remote measurement of activity budgets, 

and possibly even diet specialization, using archival 

time-depth recorders (TDRs; Bodkin et al. 2007a, 

Tinker et al. 2007). In the case of some prey 

species such as sea urchins, changes in the sizefrequency distribution of consumed prey can be 

measured from shell fragments collected from scats 

found at haul-out sites. 

It should be emphasized that for all of the indices 

mentioned above - demographic, physiological, 

and behavioral parameters - a comparative 

approach is most effective for assessing population 

status. Comparisons can be made between 

different locations where food abundance is 

known unambiguously for at least one of the 

populations (Bentall 2005), or they can be temporal 

comparisons for one location at multiple points 

in time, corresponding to differing sea otter 
3-26 Southwest Alaska DPS of the Northern Sea Otter

population densities. In either case, it is the 

dynamic patterns of change in these indices, 

rather than their absolute values, that are most 

informative. 

The role of food limitation in the southwest Alaska 

population decline: existing evidence 

Sampling programs for sea otter demography, 

body condition, activity budgets, diet, and prey 

abundance were ongoing in the western Aleutians 

during the early stages of the population decline. 

Data from telemetry-based studies of sea otter 

ecology at Amchitka Island (1992-94) and Adak 

Island (1995-96), when paired with direct measures 

of sea urchin abundance made before and during 

the decline, allow for a comparative analysis of 

most of the indices of food abundance described 

above. Although this comparison is limited 

spatially to one portion of the region affected 

by the decline (i.e., the western Aleutians), it 

nonetheless represents the best available means 

of evaluating the support for food limitation as a 

causal factor in the current decline.

For each parameter for which data exist, there 

have been significant changes over the course of 

the decline, but in each case these changes have 

been in the opposite direction from what would 

be expected if food were limiting (see Estes et al. 

2004 for a detailed summary of these changes). Put 

another way, the consistent pattern that emerges 

is one of increased food availability over the course 

of the decline. For example, sub-tidal surveys in 

1997 showed a five-fold increase in urchin biomass 

as compared to similar surveys in 1989 (Estes et 

al. 1998, Estes et al. 2004), a substantial increase in 

food resources considering that urchins comprise 

over 50% of sea otter diets in the Aleutian 

Islands (Estes 1990, Watt et al. 2000). The size 

(and thus energy content) of urchins consumed 

by otters also increased, a change that was 

already evident by 1995 at Adak Island based on 

observational data and collected scats (Tinker and 

Estes 1996). In addition to the increased urchin 

abundance, episodic spawning events of Pacific 

smooth lumpsuckers in the early 1990s resulted in 

subsidized winter diets at both Amchitka and Adak 

Islands (Watt et al. 2000). This sudden increase in 

prey availability was reflected by improved body 

condition, evident over the course of the 1992-94 

study at Amchitka Island (Monson et al. 2000). By 

1997, otters throughout the western Aleutians 

exhibited greater mass-length ratios than otters 

at Amchitka and Adak Islands in the 1960s and 

1970s (populations suspected to be at or near 

carrying capacity), and by 2004 the average body 

condition had improved even further (Laidre et al. 

2006). The age structure of the living population 

also differed from that of the 1970s, suggesting 

a release from the type of age-specific mortality 

associated with food limitation (i.e., higher death 

rates in very young and very old animals) and a 

shift toward age-independent mortality (Laidre 

et al. 2006). In contrast to previously documented 

sea otter declines associated with food limitation, 

where late-winter starvation resulted in many 

moribund and dead animals on the beach (Bodkin 

et al. 2000), beachcast carcasses were almost 

completely absent at Adak and Amchitka Islands 

(Estes et al. 2004).

Data on activity budgets collected at Amchitka 

in the early 1990s were equivocal with regard to 

whether percent time foraging had decreased as 

compared to 1980s estimates (Estes et al. 1982, 

Gelatt et al. 2002), although the percent time 

feeding at Adak Island in 1995 was significantly 

lower (Tinker and Estes 1996). Although 

telemetry-based data on activity budgets are not 

available from later in the decline, observational 

data on feeding otters at Adak Island show that 

the average duration of a typical feeding bout had 

declined from 180 minutes in the early 1990s to 

only 55 minutes in 2005, and daytime observational 

data indicate that the total percent time feeding 

had decreased by an equivalent amount (USGS, 

unpublished data). Diet diversity at Adak Island 

also decreased between 1995 and 2005, with urchins 

dominating the diet (USGS, unpublished data). 

Conclusions and future directions

Based on all available data, it seems that the cause 

of the population decline in the western Aleutian 

Islands was unrelated to the abundance of prey 

resources. In fact, it appears that as a by-product 

of the sea otter decline, there is a virtually limitless 

food supply for the remaining animals. It would be 

of interest to determine whether this same pattern 

holds more broadly throughout the region affected 

by the decline, for example, in the eastern Aleutian 

Islands and the Alaska Peninsula where relevant 

data are lacking. However, even in the absence 

of such data, certain geographic characteristics of 

the decline – in particular, the degree of temporal 

synchrony across a wide range of different 

habitats – seem to be at odds with almost any 

conceivable scenario of food limitation. To invoke 

food limitation as a causal factor in other areas 

of southwest Alaska, one would need to explain 

simultaneous declines of prey species at many 

different islands, in both rocky and soft-sediment 

habitats, independent of initial otter density at 

the start of the decline. Given the documented 

examples of prey increases in the central part 

of the affected region, such a scenario seems 

extraordinarily unlikely. 

Although food abundance likely has not been 

a factor in the recent decline, there are some 

conceivable scenarios in which prey abundance 

could act to limit future recovery. In particular, 

to the degree that predation by killer whales 

has altered sea otter behavior and habitat use 

patterns (see section 3.A.), those populations of 

sea otters that persist into the future may use 

only small fragments of their original range (i.e., 

those habitat areas offering some degree of refuge 

from killer whale predation). In that case, the 

effective carrying capacity at a given location 

would be considerably reduced. For example, if sea 
Recovery Plan 3-27

otter feeding habitat at a particular island were 

effectively reduced to 10% of its initial size as a 

result of altered sea otter behavior (e.g., sea otters 

only residing in shallow, protected coves), then the 

total population size that could be sustained would 

decrease by an equivalent amount. Under that 

scenario, food limitation would become a significant 

factor at relatively low overall sea otter densities. 

Although such a scenario is entirely speculative 

at present, it could become a significant issue for 

recovery and delisting in the future, and so it 

warrants investigation. Fortunately, it should be 

relatively straightforward to detect such a pattern 

through a monitoring program that makes use of 

a combination of the direct and indirect indices 

described above. It is recommended that such a 

monitoring program be incorporated into future 

recovery activities.

G. Disturbance

Since sea otters are slow swimmers relative to 

other marine mammals and spend much of their 

time at the surface resting, grooming, and nursing 

their young, they would appear to be highly 

vulnerable to disturbance by boats. However, 

there have been few systematic studies of 

behavioral responses to disturbance in sea otters, 

and there have been no studies of the effects of 

disturbance on stress levels, energy expenditures, 

foraging efficiency, or reproductive success. In the 

only published study of its type, Curland (1997) 

reported that sea otters in areas of disturbance 

by power boats, divers, and kayaks engaged in 

significantly greater amounts of travel than they 

did in areas without disturbance. It appears clear 

from anecdotal reports that the reaction of sea 

otters to disturbance: 1) is highly variable between 

seasons, sexes, and populations; and 2) may be 

modified by experience (reactions often decline 

in intensity with habituation, and may increase 

where populations are harassed or hunted). While 

the described responses to disturbance typically 

include diving and/or traveling away from the 

source of disturbance, in areas where sea otters 

rest, groom, and/or nurse in “rafts,” disturbance 

frequently also causes the animals to disperse and 

the raft to break up and not reform for many hours 

(J. Watson, personal communication, 2008). 

There is very little boat traffic in southwest 

Alaska, hence the impact of disturbance is likely 

to be very small. Similarly, injury by boat strikes 

is likely to be very rare. Indeed, even in British 

Columbia where sea otters experience boat traffic 

much more frequently than in southwest Alaska, 

the Canadian Sea Otter Recovery Team concluded 

that disturbance is “unlikely to be significant 

at this time” (L. Barrett-Lennard, personal 

communication). 

H. Bycatch and entanglement in debris

Sea otters may encounter a variety of fixed and 

mobile fishing gear throughout their range. 

The potential exists for them to entangle in 

the webbing of nets or become trapped inside 

submerged pots or traps that they enter to 

consume the catch or bait. This is called bycatch, 

and it has been monitored by State and Federal 

observers in some, but not all, of the fisheries 

that might take sea otters. U.S. commercial 

fishermen are required to self-report their takes 

of sea otters and other marine mammals to the 

National Marine Fisheries Service (NMFS) as 

a condition of their Marine Mammal Exemption 

Permit required by the MMPA. These reports are 

considered a minimum estimate of incidental take 

as these data are most likely negatively biased due 

to non-reporting (Credle et al. 1994). Information 

about the level of bycatch of sea otters in Alaska is 

therefore incomplete. Data from these programs 

suggest the frequency of lethal entanglement of 

sea otters depends on the gear involved, nature of 

the entanglement, experience of the otter, and the 

intervention (or not) of attending fishermen. 

Incidental mortality in commercial fisheries is 

currently infrequent; the NMFS 2006 List of 

Fisheries cites only two U.S. fisheries with lethal 

bycatch of sea otters. However, within the range 

of this DPS, there are a number of fisheries that 

have the potential to take otters but have had little 

or no observer coverage. Those fisheries include 

coastal gillnet fisheries along the southern Alaska 

Peninsula and in Bristol Bay and pot fisheries for 

shellfish (Angliss and Outlaw 2008). 

The majority of documented sea otter bycatch 

in U.S. fisheries has occurred in fixed gear set in 

nearshore waters. In California, hundreds of sea 

otters became entangled and drowned between 

1976 and 1984 in halibut gill and trammel nets 

set on the ocean bottom in shallow coastal waters 

(Wendell et al. 1985). In an effort to reduce bycatch 

of sea otters and other species, a series of depth 

restrictions were placed on that fishery, and after 

1991 set gillnets were restricted to waters deeper 

than 55 m (Forney et al. 2001). Although this 

action was followed by an initial decline in sea 

otter mortality (Estes 1990), subsequent changes 

in fishing effort and sea otter distribution again led 

to an estimated 17-125 sea otter deaths from 1995-

1998 in the Monterey Bay area (Forney et al. 2001). 

Subsequently in the early 2000s, this fishery was 

moved further offshore (to depths of 110 meters 

or more) along much of the area encompassing the 

southern sea otter range.

Sea otters are also known to enter and drown 

in submerged fish and shellfish traps (“pots”). 

Four California sea otters were found dead in 

rock crab, lobster, and experimental pots set in 

coastal California waters between 1987 and 1991 

(B. Hatfield, personal communication). Estes et al. 

(2003b) noted that increased California sea otter 

mortality from 1995-99 coincided with increased 

use of pots in a shallow-water live-fish fishery. 

Tests have shown that captive otters are capable 

of entering those traps (Estes et al. 2003), but later 
3-28 Southwest Alaska DPS of the Northern Sea Otter

experimental results have shown that modifications 

to reduce the size of the trap entrances could 

nearly eliminate sea otter bycatch without 

reducing target species catch (Hatfield et al. 2011). 

Potential for bycatch of southwest Alaska sea otters

Groundfish gear: In Alaska, NMFS observers 

are placed on a portion of groundfish vessels 

using pots, trawls, and longlines to fish in federal 

waters within the Gulf of Alaska, Bering Sea, and 

Aleutian Islands. In 1992, an observer documented 

the bycatch of eight sea otters in blackcod pots 

set illegally in nearshore Aleutian Islands waters 

closed to pot fishing (Perez 2003). Pots used in 

the groundfish fishery are converted tanner crab 

pots in which fingerlike obstructions are placed on 

the inside of the entrance to prevent fish escape. 

Blackcod pots are generally fished in deeper waters 

and fishing is illegal in shallow waters that include 

sea otter habitat. No other sea otter bycatch was 

documented by observers in this or other Alaskan 

groundfish fisheries through 2003 (Perez in prep). 

In 1997, a fisherman’s self-report documented the 

retrieval of a dead otter in a Bering Sea/Aleutian 

Island groundfish trawl but it was unclear whether 

the otter was dead or alive prior to entering the 

net.

Salmon Gillnets: Since 1988, observer programs 

have documented marine mammal interactions in 

six commercial salmon gillnet fisheries operating 

in coastal Alaskan waters: PWS setnet (1990), 

PWS driftnet (1988-1991), South Alaska Peninsula 

driftnet (1990), Cook Inlet setnet and driftnet 

(2000), and Kodiak Island setnet (2002, 2005) 

fisheries. Set gillnets (“setnets”) are anchored in 

place and extend seaward from their attachment 

points on shore. Drift gillnets (“driftnets”) are 

fished while attached to and tended by a drifting 

vessel. These salmon gillnet fisheries are managed 

by the Alaska Department of Fish and Game as 

limited-entry fisheries (i.e., the maximum number 

of participants is limited) and the amount, timing, 

duration, and distribution of seasonal fishing effort 

is regionally managed on a real-time basis. Alaskan 

salmon gillnets are made with multifilament line, 

set at and suspended from the water’s surface and 

limited to 272-364 m in length.

Four of the observed salmon fisheries occur 

outside the range (to the east) of the southwest 

Alaska DPS. In PWS, sea otters swam within 10 

m of 2.0-6.3% of observed set and drift gillnets but 

became entangled in <0.25% of 9,428 sets observed 

from 1988-1991 (Wynne 1990, Wynne et al. 1991, 

1992). The otters that became entangled were 

either able to free themselves or were extricated 

by the attending fisherman; none of the observed 

entanglements resulted in sea otter injury or 

death. However, beachcast sea otter carcasses 

were recovered on the adjacent Copper River 

Delta during those years with bullet holes and 

skull fractures, suggesting that the fate of live 

entangled sea otters depends on the actions of the 

fishermen who may or may not make efforts to 

release them unharmed (Wynne 1990). No sea otter 

entanglements were observed in Cook Inlet set or 

drift gillnets observed during the Alaska Marine 

Mammal Observer Program in 2000 (B. Mansfield, 

personal communication).

Two observer programs have monitored sea otter 

interactions with salmon gillnet fisheries conducted 

within the range of the southwest Alaska DPS. 

Observers in the South Alaska Peninsula (South 

Unimak) drift gillnet fishery operating near False 

Pass documented sea otters swimming within 10 

m of 2.1% of 373 observed sets but none became 

entangled (Wynne et al.1991). In Kodiak, sea 

otters were frequently seen in the vicinity of set 

gillnets but were observed to entangle in only 4 

sets in 2002 (Manly et al. 2003) and 1 set in 2005 

(B. Mansfield, personal communication.). Two 

of the sea otter entanglements in 2002 and the 

sole entanglement in 2005 involved “momentary 

snags” from which the otters were able to escape 

unassisted and unharmed. The two entangled 

otters observed in 2002 were released unharmed 

with human assistance. However, one unobserved 

sea otter death was reported by a Kodiak setnet 

fisherman through a NMFS self-report in 2002. 

The Kodiak setnet fishery is now the only Alaskan 

fishery listed on the NMFS 2006 List of Fisheries 

as having lethal sea otter bycatch.

These observer data suggest that the potential 

exists for sea otters from the southwest Alaska 

DPS to entangle in other commercial salmon 

gillnet fisheries that have not been observed, i.e., 

Bristol Bay set and driftnets and Alaska Peninsula 

setnets. In addition, an unknown number of gillnets 

are set by personal-use, or subsistence, salmon 

fishermen in coastal waters throughout the range 

of this DPS. Because these fisheries all use surfacehanging gillnets, the aforementioned observer data 

(Wynne 1990, Wynne et al. 1992) suggest that sea 

otters that become entangled in these nets are 

often able to extricate themselves or otherwise 

remain afloat and available for release by attending 

fishermen. Based on the geographic scope of these 

activities, it is unlikely that unobserved commercial 

and personal-use gillnet fisheries currently pose a 

significant threat of incidental injury or mortality 

to sea otters in southwest Alaska. 

Herring gillnets: Limited-entry herring gillnet 

fisheries occur in the Kodiak, Alaska PeninsulaAleutian Islands, and Bristol Bay Management 

Areas. Gillnets may be either anchored or allowed 

to drift but must be constantly tended. Herring 

gillnets use a mesh size of 21⁄8 to 21⁄2 inches (5.4- 6.3 

cm) and range in length from 50-150 fathoms (91-

273 m). They are suspended from a floatline but 

may be fished with the floatline and floats below 

the surface of the water. 

Unlike the submerged gill and trammel nets used 

in the California halibut fishery, Alaskan gillnets 

are short and left untended only briefly, if at all. 

Most importantly, they are set at or near the 
Recovery Plan 3-29

water surface, allowing sea otters to remain at the 

surface until released. The mandated use of floating 

nets and the prohibition on sink gillnets in Alaskan 

fisheries limit the likelihood of incidental mortality 

of sea otters in Alaskan gillnets. 

Crab pots: King, tanner, and snow crabs are 

commercially harvested in southwest Alaska, but 

most of these crab species inhabit waters that are 

deeper and/or further offshore than sea otters 

typically forage (Newby 1975) and the potential 

for sea otter entanglement is low. Accordingly, 

reports of sea otter bycatch in these fisheries are 

rare: one otter reportedly drowned in a king crab 

pot set in 100 m of water in the Aleutian Islands in 

1975 (Funk 2003). Even if sea otters encounter pots 

used to harvest these species, the gear’s design 

may limit lethal entrapment somewhat. Tanner 

crab pots must have a tunnel opening diameter of 

≤ 5 inches (12.7 cm), which would allow entry by 

juvenile sea otters but would likely exclude adults 

(Hatfield et al. 2011). Conversely, red king crab 

pots are required to have an unobstructed tunnel 

opening diameter of ≥ 5 inches (12.7 cm); based on 

observations of captive otters, they would likely 

enter such traps and some might escape while 

others might not (Hatfield et al. 2011). 

Dungeness crabs inhabit coastal waters of 

southwest Alaska, primarily in depths of less than 

50 m (Funk 2003). They are harvested with pots 

by both commercial and personal-use fishermen 

in Kodiak, North and South Alaska Peninsula, 

and Aleutian Islands districts. Dungeness pots 

must have rigid tunnel openings that can be 

up to 9.5 inches (24.1 cm) in diameter, which is 

large enough for a sea otter of any age to enter 

(Hatfield et al. 2011). There have been no observer 

programs for the dungeness fishery, and therefore 

any interactions would have to have been selfreported. Funk (2003) reported no incidental take 

of sea otters in this fishery, but subsequently there 

has been one report of a sea otter drowned in a 

dungeness pot in southeast Alaska (Hatfield et al. 

2011).

Potential for debris entanglement of southwest 

Alaska sea otters

Entanglement in marine debris has been identified 

as a significant contributor to mortality of northern 

fur seals in the Pribilof Islands (Fowler 1985, 

Swartzman et al. 1990, Fowler et al. 1994, Zavadil 

et al. 2003). Debris materials most commonly found 

to entangle fur seals include rubber packing bands 

and trawl net fragments. Despite the potential for 

encountering similar ensnaring debris throughout 

their range, sea otters are rarely seen entangled 

in marine debris, perhaps due to behavioral or 

anatomical differences between them and fur seals. 

Entanglement in marine debris is not currently 

considered a threat to the southwest Alaska DPS. 

However, there has been no directed effort to look 

for entangled otters so this assessment should be 

verified.

I. Subsistence harvest

Harvest of marine mammals by Alaska Natives 

is authorized under Section 101(b) of the MMPA, 

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. Such taking cannot be further 

restricted unless the stock has been designated as 

depleted. A similar exemption for species listed 

as threatened or endangered exists under Section 

10(e) of the ESA. The Secretary of the Interior 

may prescribe regulations to limit the taking of 

species or stocks listed as depleted, threatened, or 

endangered if such taking has been determined to 

have a material and negative effect.

The subsistence harvest of sea otters, polar 

bears (Ursus maritimus), and Pacific walruses 

(Odobenus rosmarus divergens) is monitored by 

FWS 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 FWS 

representative, known as a “tagger,” within 30 

days of harvest. The MTRP currently has sea otter 

taggers in 48 communities statewide, with 16 in 

southwest Alaska.

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. 

As there are no other assessments of sea otter 

harvest levels, compliance with the MTRP cannot 

be evaluated, but it likely varies over time and 

location. Because some animals may not be tagged, 

harvest estimates from MTRP data should be 

considered minimum values. For the remainder of 

this section, results derived from the MTRP are 

referred to as “reported” subsistence harvest.

In addition to non-compliance (i.e., tags not 

being applied to hides and/or skulls), erroneous 

information may be recorded inadvertently on the 

tagging certificate. For example, molecular genetic 

analysis indicates that 12% of sea otters (from a 

sample of 138) were reported as the wrong sex 

(Scribner et al. 2005). Most of the errors (13 of 17) 

consisted of males being identified incorrectly as 

females, resulting in a net 7% overestimate of the 

number of females harvested in that sample.

The reported subsistence harvest from the 

southwest Alaska DPS is the lowest of the three 

stocks of sea otters in Alaska, averaging 92 per 

year (range 23-187) during 1989-2011 (Table 7). 

This result is not surprising since there are few 

Alaska Native villages within the range of the 
3-30 Southwest Alaska DPS of the Northern Sea Otter

DPS. The harvest has primarily been composed of 

adults (91%), most of which were males (Table 8). 

Within the southwest Alaska DPS, the majority 

(82%) of the subsistence harvest comes from the 

Kodiak, Kamishak, Alaska Peninsula MU (Table 

9). With the exception of 42 otters taken from 

the Chignik area, virtually all of the 1,775 animals 

taken from this unit during 1989-2008 were 

harvested in the Kodiak archipelago. As there is 

no permanent human habitation along the Alaska 

Peninsula east of Chignik, or in Kamishak Bay, it is 

not surprising that the majority of the subsistence 

harvest from this MU occurs around Kodiak. 

Subsistence harvests from the Western Aleutian 

and Eastern Aleutian MUs are very small; Adak is 

the only location within the Western Aleutian MU 

where sea otters have been taken.

To put these harvest levels in context, the most 

recent estimates of sea otter abundance from 

the Aleutian Islands range from 3,311 to 8,742 

(Estes et al. 2005, Doroff et al. 2003). The annual 

reported harvest for both Aleutian MUs combined 

averages 1.7 otters, which amounts to 0.02–0.05% 

of the population. The annual reported harvest 

from the Bristol Bay MU averages 6.4 otters, or 

0.06% of the estimated population of 11,303. In 

the southern Alaska Peninsula MU, the reported 

average annual harvest of 9.6 otters is 0.21% of 

the estimated population of 4,682 (Burn and Doroff 

2005, FWS unpublished data). As expected, the 

reported average annual harvest of 82.3 otters 

from the Kodiak, Kamishak, Alaska Peninsula MU 

is proportionally the largest, accounting for 0.36% 

of the estimated population size of 22,957 (Burn 

and Doroff 2005, FWS unpublished data, USGS 

unpublished data). As noted above, the majority of 

this harvest comes from the Kodiak archipelago, 

where it accounts for 0.67% of the regional 

population.

The geographical scale at which sea otter 

populations are managed remains an important, 

although largely unexplored, issue. As noted by 

Gorbics and Bodkin (2001), the annual harvest 

rate during the commercial fur trade of the 18th 

and 19th centuries averaged only about 1.5% of 

the global sea otter population per year. Yet over 

time, range-wide reductions and extirpations 

occurred not because of excessive harvest, 

but because the harvest was not allocated 

proportionally to the abundance and distribution 

of sea otters. This resulted in the serial depletion 

of otters, beginning in the western Pacific and 

systematically expanding eastward across the 

Aleutian archipelago and southward along North 

America, as harvested populations became either 

reduced to unprofitable densities or locally extinct. 

The process of serial depletion is facilitated by the 

relatively sedentary nature of sea otters. Annual 

home range sizes of adult sea otters are relatively 

small, with male territories ranging from 4-11 km2

and adult female home ranges from a few to 24 km2 

(Garshelis and Garshelis 1984, Ralls et al. 1988, 

Jameson 1989, Ballachey and Bodkin 2006). When 

mortality is spatially concentrated in areas equal 

to or smaller than the cumulative home range of 

the managed population, local depletion, potentially 

leading to serial depletion, may occur. Therefore, 

it is essential to consider the spatial scales at which 

sea otter subsitence harvest is managed. 

Although the reported harvests represent 

relatively small proportions of the current 

estimated population sizes, it will be prudent to 

evaluate the potential consequences of future 

harvests quantitatively, particularly in the event 

that populations continue to decline and harvests 

do not. Age- and sex-specific population matrix 

models that incorporate present and projected 

population sizes, trajectories, and harvests 

should be constructed to consider how changes 

in abundance and harvests may interact over 

time and influence recovery. Because population 

abundance, trend, and harvest levels vary within 

the DPS, independent models should be developed 

that incorporate geographic variation and can 

guide management at appropriate geographic 

scales. 

J. Habitat concerns

Sea otter habitat within the range of the southwest 

Alaska DPS appears, at least superficially, to be 

nearly pristine, with little obvious anthropogenic 

degradation. The resident human population is 

small and located mostly in the eastern part of the 

range in small towns and villages. Historically, the 

military had a significant presence at a number of 

sites throughout southwest Alaska but it currently 

has only a minor presence. 

Developments that physically modify sea 

otter habitat are limited to nearshore waters 

immediately adjacent to towns, villages, and 

military bases, and are usually in the form of docks, 

piers, and boat harbors. Sea otters continue to use 

these sites. In fact, currently some of the highest 

densities of animals in the Aleutian Islands occur in 

the vicinity of Unalaska and Adak Islands, both of 

which are inhabited. The shoreline and nearshore 

waters throughout most of the range of the SW 

Alaska DPS should remain relatively free of such 

development, as much of these areas are within 

Federal and State refuges, parks, preserves, and 

sanctuaries. 

At present, sea otter abundance within the range 

of the southwest Alaska DPS appears to be far 

below the carrying capacity of the habitat (Burn et 

al. 2003) and preferred prey, such as sea urchins, 

are abundant (Watt et al. 2000, Estes et al. 2004). 

The loss of extensive kelp beds due to intensive 

urchin grazing (Estes et al. 2004) has reduced sea 

otter resting habitat, but this is not known to have 

affected sea otter population dynamics. Recent 

estimates of sea otter growth rates, asymptotic 

(maximum) values of body mass and length, body 

condition, and age composition were all indicative 
Recovery Plan 3-31

Table 7. Reported sea otter subsistence harvest in Alaska, 1989-2011.

Data are from the U.S. Fish and Wildlife Service Marine Mammal Marking, Tagging, and Reporting 

Program.

Year

Southwest

Alaska

Southcentral 

Alaska

Southeast 

Alaska Unknown Total

1989 59 60 152 6 277

1990 55 26 84 1 166

1991 23 92 114 1 230

1992 55 138 416 3 612

1993 180 200 832 4 1,216

1994 64 425 315 0 804

1995 56 374 198 2 630

1996 156 321 125 3 605

1997 149 274 332 0 755

1998 68 450 359 2 879

1999 74 219 328 1 622

2000 110 305 396 1 812

2001 80 219 353 0 652

2002 94 236 333 2 665

2003 126 264 385 2 777

2004 87 429 296 0 812

2005 111 444 361 2 918

2006 67 359 329 0 755

2007 64 205 438 3 710

2008 97 321 248 0 666

2009 30 267 584 0 881

2010 123 315 638 0 1,076

2011 187 231 702 1 1,121

Total 2,115 6,174 8,318 34 16,641

Mean 92.0 268.4 361.7 1.5 723.5

Percent of 

statewide total 12.7 37.1 50.0 0.2 100.0
3-32 Southwest Alaska DPS of the Northern Sea Otter

Table 8. Age and sex composition of the reported sea otter subsistence harvest from the southwest Alaska 

DPS of the northern sea otter, 1989-2008.

Data are from the U.S. Fish and Wildlife Service Marine Mammal Marking, Tagging, and Reporting 

Program. F=Female, M=Male, U=Unknown sex. Results are not corrected for possible errors in sex 

identification.

Year

Adult Subadult Pup Unknown All Ages

 F M U F M U F M U F M U F M U

1989 6 21 19 0 0 0 1 1 1 1 0 11 8 22 31

1990 2 42 1 2 8 0 0 0 0 0 0 0 4 50 1

1991 0 19 2 1 1 0 0 0 0 0 0 0 1 20 2

1992 8 38 7 0 3 0 0 1 0 0 0 0 8 42 7

1993 83 65 16 4 2 2 0 0 0 0 0 8 87 67 26

1994 6 23 19 4 8 4 0 0 0 0 0 0 10 31 23

1995 5 48 1 1 1 0 0 0 0 0 0 0 6 49 1

1996 29 94 18 6 6 3 0 0 0 0 0 0 35 100 21

1997 24 84 21 5 7 2 1 1 4 0 0 0 30 92 27

1998 5 36 20 5 0 1 0 0 1 0 0 0 10 36 22

1999 5 49 4 5 5 1 1 3 1 0 0 0 11 57 6

2000 23 26 44 2 6 6 0 1 2 0 0 0 25 33 52

2001 19 43 1 4 9 2 0 0 2 0 0 0 23 52 5

2002 20 62 4 1 4 0 1 2 0 0 0 0 22 68 4

2003 24 76 4 8 6 2 1 3 2 0 0 0 33 85 8

2004 8 68 2 1 2 3 1 2 0 0 0 0 10 72 5

2005 20 69 2 5 9 0 2 3 1 0 0 0 27 81 3

2006 12 51 1 0 2 1 0 0 0 0 0 0 12 53 2

2007 12 44 0 1 3 1 0 1 2 0 0 0 13 48 3

2008 13 70 1 2 1 0 0 0 0 2 4 0 17 75 1

Total

324 1,028 187 57 83 28 8 18 16 3 4 19 392 1,133 250

1,539 168 42 21 1,775
Recovery Plan 3-33 Table 9. Reported subsistence harvest of sea otters from the southwest Alaska DPS of the northern sea otter by MU, 1989-2008. Data are from the U.S. Fish and Wildlife Service Marine Mammal Marking, Tagging, and Reporting Program. One harvested sea otter could not be assigned to a MU. Year Western Aleutians Eastern Aleutians Bristol Bay South Alaska Peninsula Kodiak, Kamishak, Alaska Peninsula Total 1989 0 1 8 17 35 61 1990 0 0 2 25 28 55 1991 0 0 1 0 22 23 1992 1 1 1 10 44 57 1993 0 15 5 5 155 180 1994 0 2 0 9 53 64 1995 0 1 11 1 43 56 1996 0 0 11 0 145 156 1997 0 1 6 18 124 149 1998 0 0 5 3 60 68 1999 0 0 4 10 60 74 2000 5 0 7 5 93 110 2001 0 0 0 14 66 80 2002 0 0 5 13 76 94 2003 0 0 18 12 96 126 2004 0 1 1 6 78 86 2005 1 0 13 0 97 111 2006 1 0 2 7 57 67 2007 0 0 14 7 43 64 2008 0 0 0 8 85 93 Total 8 22 114 170 1,460 1,774 Mean 0.5 1.3 6.7 10.0 85.9 104.4 Percent 0.5 1.2 6.4 9.6 82.3
3-34 Southwest Alaska DPS of the Northern Sea Otter

of a population below nutritional carrying capacity 

(Laidre et al. 2006). 

Current or foreseen commercial fishing practices, 

with the exception of the dungeness crab and sea 

urchin fisheries in the Kodiak Island area, do not 

target important sea otter prey and are not known 

to affect sea otter habitat in any obvious way. 

Some fishing for yellowfin sole (Limanda aspera) 

overlaps with the range of sea otters in the Bristol 

Bay MU, but the trawls used are designed to have 

minimal contact with the bottom and therefore 

are likely to have little impact on sea otter prey or 

their habitat (B. Wilson, personal communication)

Abnormally high levels of OC pesticides and PCBs 

were found in sea otter tissues from the Aleutian 

Islands (Estes et al. 1997, Bacon et al. 1999). 

Probable sources of PCBs in the Aleutian Islands 

are atmospheric and oceanic transport from Asia 

and leakage from dump sites at military bases 

(Bacon et al. 1999). More recent data indicate that 

high levels of PCBs are limited to a few local areas, 

including Unalaska, Adak, and Amchitka Islands, 

and presumably originated from military activities. 

It is not thought that the widespread declines in 

pinniped and sea otter populations are related to 

these contaminants as levels are higher in other 

regions with stable or increasing populations 

(Reese 1998). 

Changes in climatic conditions, due to both 

“normal” climate variability (Hunt and Stabeno 

2005) and global warming (Schumacher and Kruse 

2005), are expected to modify both the physical 

environment and the biota within the range of the 

southwest Alaska DPS. It is difficult to predict 

how climate change will affect sea otter recovery. 

Therefore, it will be important to monitor relevant 

indices and to evaluate changes in regard to sea 

otter ecology and population dynamics. It is 

possible that global warming will have more of an 

impact on sea otters at the southern end of their 

range, but this expectation should be tempered 

by the realization that changes in climate and 

atmospheric conditions can influence ecosystems 

in many complex ways. For example, ocean 

acidification is a consequence of rising atmospheric 

CO2 levels (Solomon et al. 2007), which reduces the 

concentration of carbonate ions. In turn, decreasing 

carbonate ions may affect the ability of sea otter 

prey species such as bivalves, snails, and crabs to 

form exoskeletons (Green et al. 2004). 

K. Illegal take

In addition to the legal harvest of sea otters by 

Alaska Natives authorized by the MMPA, there 

may also be illegal take from the southwest Alaska 

DPS. Otters may be taken: 1) illegally by nonNatives for their valuable pelts; 2) wastefully by 

Alaska Natives; or 3) to reduce competition for 

sea otter prey resources such as crab and shellfish. 

As human habitation within the range of the DPS 

is sparse (and in many places non-existent), the 

extent to which sea otters may be taken illegally is 

unknown. 

Investigations of a sea otter mortality event in 

the Kodiak archipelago in 1987 documented the 

carcass of at least one male sea otter that had 

been shot (DeGange and Vacca 1989). Intact 

carcasses of sea otters that have been shot are 

tacit evidence of illegal take, whether shot by a 

non-Native or an Alaska Native and not retrieved 

(and therefore a wasteful take in accordance with 

Federal regulations). Most of the otter carcasses 

investigated by DeGange and Vacca (1989) were in 

advanced stages of decomposition with evidence 

of scavenging, making conclusive determination 

of the cause of death difficult. In a study of sea 

otter reproduction and survival in the Kodiak 

archipelago from 1986-1990 (Monson and DeGange 

1995), two instrumented animals were determined 

to have been killed by gunshot, one of which still 

had the pelt when discovered (D. Monson, personal 

communication).

The FWS’s Office of Law Enforcement (OLE) has 

no records of unlawful harvest of sea otters within 

the range of the southwest Alaska DPS. The 

information it does have comes from other areas 

where otters occur in Alaska. That information is 

offered both as factual, based on current and past 

investigations, and anecdotal, based on interviews 

of witnesses. Whether or not similar unlawful 

activities are occurring with the listed population is 

impossible to say.

The OLE has investigated 14 cases of illegal take 

and/or illegal sale of sea otters in Alaska from 

2000 to 2006. Seventy-six otters or parts of otters 

were seized during these investigations and 20 

violations were documented. The defendants were 

both Alaska Natives and non-Natives. Although 

the OLE has not documented large-scale unlawful 

activity to date, sea otters have not received the 

investigative attention that many other species 

have. Therefore, the occurrence or extent of 

unlawful take in many areas is unknown (S. 

Oberholtzer, personal communication).

In addition to the above information provided by 

the OLE, the managers of each National Wildlife 

Refuge within the range of the DPS and the Alaska 

State Troopers Bureau of Wildlife Enforcement 

were also contacted. None of these additional 

sources had any records of illegal take of sea otters 

from the DPS. 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.
Recovery Plan 4-1

4. Threats Analysis

The importance of potential threats to the 

existence and recovery of southwest Alaska sea 

otters was analyzed based on the description of 

threats in section 3, published literature, and 

the experience and professional judgement of 

Recovery Team members. Threats were organized 

according to the five ESA listing factors, and were 

evaluated based on the factors listed below.

 Potential impact—the amount of effect the 

threat could have on the population in an area 

where the threat occurs.

 Geographical scope—the geographical extent 

of the threat across the MU being considered.

 Likelihood—the likelihood that the threat will 

occur within the next 10 years.

 Level of confidence—the degree of confidence 

in the assessment of the previous three 

factors.

 Importance to recovery—an overall 

assessment of how much the threat could 

affect recovery.

 Management potential—an estimate of the 

likelihood that the threat could be managed to 

reduce or eliminate its impact.

Threats analyses for each MU are shown in Tables 

10-14. Table 15 shows the importance to recovery 

and management potential of all threats for all 

management units, and these considerations are 

further discussed below. 

Habitat Loss – low importance

Habitat loss as a threat to recovery was ranked 

low for all MUs. The physical habitat is largely 

unspoiled throughout the vast majority of the 

range of the southwest Alaska DPS. The human 

population in this area is small, and development 

has been limited to the few, widely scattered 

towns, villages, and military installations. The 

shoreline and near-shore waters throughout 

most of the region should remain relatively free 

of habitat modification in the future due to their 

protection status; much of the sea otter habitat 

is within Federal and State refuges, parks, 

preserves, and sanctuaries. The highest-density 

human population and highest degree of habitat 

modification are in the eastern portion of the DPS’s 

range, where sea otter numbers do not appear to 

have declined. 

While in general terms loss of habitat can 

have a severe impact on sea otter populations, 

development that could have an impact in this 

region is not likely except on a highly localized 

scale. Management potential is high because of a) 

the section 7 review process required under the 

ESA and b) state and federal land-use regulations. 

Other factors, in addition to development, that 

could have an impact on habitat used by the 

southwest Alaska DPS are atmospheric change 

and climate warming, either through changes in 

physical habitat attributes, ocean chemistry, or 

the prey base. However, the Team concluded 

that within the 10-year time window used in 

this evaluation, atmospheric change and climate 

warming are not likely to exert an important 

influence on the amount or suitability of sea otter 

habitat.

Oil Spills – low to moderate importance 

Sea otters are particularly vulnerable to oil spills 

because of the risks from: 1) fouling of their highly 

insulative fur; 2) toxicity of oil ingested during 

grooming; 3) ingestion of toxic hydrocarbons 

stored in benthic invertebrate prey; and 4) the 

fact that they often form large aggregations, 

creating the potential for large numbers of animals 

to be exposed to the effects of a spill. Currently, 

large, bulk oil tanker traffic within the range 

of the southwest Alaska DPS is infrequent (22 

voyages/yr) so most of the potential harm from oil 

spills comes from fuel supplies aboard freighters 

and fishing vessels. Many of those vessels use 

diesel fuel, which is less toxic and disperses and 

evaporates much more rapidly than crude oil. 

Most spills would probably be relatively small and 

have limited local impacts. Fuel storage facilities 

in ports throughout the range of the DPS are 

also a potential source of localized spills. Due to 

the large linear extent of the DPS, even a large 

spill from a crude oil tanker would be unlikely to 

affect a substantial proportion of the overall sea 

otter population. This relationship between the 

likelihood and geographic extent of oil spills (i.e., 

small spills with local impacts are more likely than 

large spills with widespread impacts) is reflected in 

the threats analysis tables (Tables 10-14).

Oil spills were rated as a moderate threat to the 

recovery of two MUs: Eastern Aleutian Islands 
4-2 Southwest Alaska DPS of the Northern Sea Otter Table 10. Threats analysis for the Western Aleutian Islands MU of the southwest Alaska DPS of the northern sea otter. Listing Factor Threat Potential Impact Geographic Scope Likelihood Level of Confidence Importance to Recovery Management Potential A. Present or Threatened Destruction, Modification, or Curtailment of its Habitat or Range Habitat Loss High Local Not Likely High Low High Oil Spills High Local/ Widespread Very/Not Likely High Low High Prey Base High Local Not Likely High Low High B. Overutilization for Commercial, Recreational, Scientific, or Educational Purposes Subsistence Harvest Moderate Local Somewhat Likely High Low High C. Disease or Predation Infectious Disease Moderate Local/ Widespread Not Likely Low Low Low Predation High Entire Unit Very Likely High High Low D. Inadequacy of Existing Regulatory Mechanisms N/A E. Other Natural or Anthropogenic Factors Affecting its Continued Existence Fishery Bycatch Moderate Local Not Likely Moderate Low High Illegal Take High Local Not Likely Moderate Low Low Disturbance Low Local Not Likely High Low Moderate Biotoxins Moderate Widespread Not Likely Low Low Low Point-Source Contaminants Low Local Very Likely High Low Low Non-Point-Source Contaminants Low Entire Unit Very Likely High Low Low
Recovery Plan 4-3 Table 11. Threats analysis for the Eastern Aleutian Islands MU of the southwest Alaska DPS of the northern sea otter. Listing Factor Threat Potential Impact Geographic Scope Likelihood Level of Confidence Importance to Recovery Management Potential A. Present or Threatened Destruction, Modification, or Curtailment of its Habitat or Range Habitat Loss High Localized Not Likely High Low High Oil Spills High Local/ Widespread Very/Somewhat Likely High Moderate Moderate Prey Base High Local Not Likely High Low High B. Overutilization for Commercial, Recreational, Scientific, or Educational Purposes Subsistence Harvest Moderate Local Somewhat Likely High Low High C. Disease or Predation Infectious Disease Moderate Local/ Widespread Not Likely Low Low Low Predation High Entire Unit Very Likely Moderate High Low D. Inadequacy of Existing Regulatory Mechanisms N/A E. Other Natural or Anthropogenic Factors Affecting its Continued Existence Fishery Bycatch Moderate Local Not Likely Moderate Low High Illegal Take High Local Not Likely Moderate Low Low Disturbance Low Local Not Likely High Low Moderate Biotoxins Moderate Widespread Not Likely Low Low Low Point-Source Contaminants Low Local Very Likely High Low Low Non-Point-Source Contaminants Low Entire Unit Very Likely High Low Low
4-4 Southwest Alaska DPS of the Northern Sea Otter Table 12. Threats analysis for the Bristol Bay MU of the southwest Alaska DPS of the northern sea otter. Listing Factor Threat Potential Impact Geographic Scope Likelihood Level of Confidence Importance to Recovery Management Potential A. Present or Threatened Destruction, Modification, or Curtailment of its Habitat or Range Habitat Loss High Local Not Likely High Low High Oil Spills High Local/

Widespread

Very/Not 

Likely High Low Moderate

Prey Base High Local Not Likely High Low High

B. Overutilization 

for Commercial, Recreational, Scientific, or Educational Purposes

Subsistence Harvest

Moderate Local Somewhat Likely High Low High

C. Disease or Predation Infectious Disease Moderate Local/

Widespread Not Likely Low Low Low

Predation High Entire Unit Somewhat Likely Moderate Moderate Low

D. Inadequacy of Existing 

Regulatory Mechanisms

N/A

E. Other Natural or Manmade 

Factors Affecting its Continued Existence

Fishery Bycatch Moderate Widespread Somewhat Likely Moderate Low High

Illegal Take High Local Not Likely Moderate Low Low

Disturbance Low Local Not Likely High Low Moderate

Biotoxins Moderate Widespread Not Likely Low Low Low

Point-Source Contaminants Low Local Very Likely High Low LowNon-Point-Source Contaminants Low Entire Unit Very Likely High Low Low
Recovery Plan 4-5 Table 13. Threats analysis for the South Alaska Peninsula MU of the southwest Alaska DPS of the northern sea otter. Listing Factor Threat Potential Impact Geographic Scope Likelihood Level of Confidence Importance to Recovery Management Potential A. Present or Threatened Destruction, Modification, or Curtailment of its Habitat or Range Habitat Loss High Local Not Likely High Low High Oil Spills High Local/ Widespread Very/Not Likely High Low Moderate Prey Base High Local Not Likely High Low High B. Overutilization for Commercial, Recreational, Scientific, or Educational Purposes Subsistence Harvest Moderate Local Somewhat Likely High Low High C. Disease or Predation Infectious Disease Moderate Local/ Widespread Not Likely Low Low Low Predation High Entire Unit Very Likely Moderate High Low D. Inadequacy of Existing Regulatory Mechanisms N/A E. Other Natural or Anthropogenic Factors Affecting its Continued Existence Fishery Bycatch Moderate Widespread Somewhat Likely Moderate Low High Illegal Take High Local Not Likely Moderate Low Low Disturbance Low Local Not Likely High Low Moderate Biotoxins Moderate Widespread Not Likely Low Low Low Point-Source Contaminants Low Local Very Likely High Low Low Non-Point-Source Contaminants Low Entire Unit Very Likely High Low Low
4-6 Southwest Alaska DPS of the Northern Sea Otter 

Table 14. Threats analysis for the Kodiak, Kamishak, Alaska Peninsula MU of the southwest Alaska DPS of the northern sea otter. Listing Factor Threat Potential Impact Geographic Scope Likelihood Level of Confidence Importance to Recovery Management Potential A. Present or Threatened Destruction, Modification, or Curtailment of its Habitat or Range Habitat Loss High Local Not Likely High Low High Oil Spills High Local/ Widespread Very/Somewhat Likely High Moderate Moderate Prey Base High Local Not Likely High Low High B. Overutilization for Commercial, Recreational, Scientific, or Educational Purposes Subsistence Harvest Moderate Local Very Likely High Moderate High C. Disease or Predation Infectious Disease Moderate Local/ Widespread Somewhat Likely Low Moderate Low Predation High Widespread Somewhat Likely Low Moderate Low D. Inadequacy of Existing Regulatory Mechanisms N/A E. Other Natural or Anthropogenic Factors Affecting its Continued Existence Fishery Bycatch Moderate Widespread Somewhat Likely Moderate Low High Illegal Take High Local Somewhat Likely Moderate Moderate Low Disturbance Low Local Not Likely High Low Moderate Biotoxins Moderate Widespread Not Likely Low Low Low Point-Source Contaminants Low Widespread Very Likely High Low Low Non-Point-Source Contaminants Low Entire Unit Very Likely High Low Low
Recovery Plan 4-7 Table 15. Summary of importance of threats to recovery of the southwest Alaska DPS of the northern sea otter by management unit. Management Unit Listing Factor Threat Western Aleutians Eastern Aleutians Bristol Bay South Alaska Peninsula Kodiak, Kamishak, Alaska Peninsula Management Potential A. Present or Threatened Destruction, Modification, or Curtailment of its Habitat or Range Habitat Loss Low Low Low Low Low High Oil Spills Low Moderate Low Low Moderate Moderate Prey Base Low Low Low Low Low High B. Overutilization for Commercial, Recreational, Scientific, or Educational Purposes Subsistence Harvest Low Low Low Low Moderate High C. Disease or Predation Infectious Disease Low Low Low Low Moderate Low Predation High High Moderate High Moderate Low D. Inadequacy of Existing Regulatory Mechanisms N/A E. Other Natural or Manmade Factors Affecting its Continued Existence Fishery Bycatch Low Low Low Low Low High Illegal Take Low Low Low Low Moderate Low Disturbance Low Low Low Low Low Moderate Biotoxins Low Low Low Low Low Low Point-Source Contaminants Low Low Low Low Low Low Non-Point-Source Contaminants Low Low Low Low Low Low
4-8 Southwest Alaska DPS of the Northern Sea Otter

and Kodiak, Kamishak, Alaska Peninsula. Major 

ocean passes in the eastern Aleutian Islands are 

transited by ships using the great circle route 

between Asia and the west coast of North America, 

thus increasing the risk of a large spill. Kodiak 

Island has the highest human population within 

the range of the DPS and is adjacent to a major 

shipping route into Cook Inlet, thus justifying 

the moderate ranking for that MU. For the other 

three management units, this threat was rated as 

low. Should oil and gas exploration and production 

take place in the North Aleutian Basin, threat 

classifications for the Eastern Aleutian Islands, 

Bristol Bay, and South Alaska Peninsula MUs 

should be re-evaluated. 

The management potential for prevention and 

containment of small oil spills is thought to be high, 

but the potential for containing or cleaning up large 

spills is low. Overall, the management potential for 

oil spills is considered moderate. 

Prey Base – low importance

The potential for effects on the sea otter’s prey 

base to become a threat to recovery was rated low 

for all MUs. While it is well established that high 

sea otter densities can dramatically reduce the 

abundances and sizes of many benthic invertebrate 

prey, the general result of such changes is a 

reduction in carrying capacity and an “equilibrium” 

sea otter population below peak size. In the 

western portion of the range of the DPS, where 

the decline has been most pronounced, indices of 

prey abundance, as well as sea otter diet, activity 

budgets, condition, and demography, all suggest a 

population well below carrying capacity. 

In the eastern MUs, where sea otter densities are 

much higher, populations would be expected to 

level off, or already would have leveled off, at sizes 

that can be supported by the prey base over the 

long term. That situation should not be viewed as 

a “threat” to recovery because at such a point, the 

population would be fully recovered. 

The primary factor affecting abundance of most 

sea otter prey is sea otter predation. Currently, 

there is little, if any, competition between humans 

and sea otters for prey within the range of the 

southwest Alaska DPS. Should new fisheries 

develop that create significant competition, this 

could be resolved through the State and Federal 

fishery regulatory processes, although such 

processes do not necessarily guarantee adequate 

protection of the sea otter prey base. Because 

human activities are not a factor currently 

affecting the prey base, the management potential 

for this threat is considered high. 

Subsistence Harvest – low to moderate importance

Subsistence harvest was rated as a low threat to 

recovery in all MUs except Kodiak, Kamishak, 

Alaska Peninsula, for which it was rated as a 

moderate threat. From east to west, the human 

population becomes progressively sparser. Kodiak 

Island has the largest human population and 

largest reported subsistence harvest. Between 

1989 and 2005, 1,857 otters were reportedly 

harvested from the southwest Alaska DPS, with 

the Kodiak archipelago accounting for 80% of 

that total. Annual harvest rates as a percentage 

of the estimated population size were low in all 

MUs and progressively increased from west to 

east, with a maximum of 0.32% in the Kodiak, 

Kamishak, Alaska Peninsula unit. In addition to 

being low, the harvest consists largely of males 

(73%), which means that it has less of an impact 

on population growth than if more females were 

taken. The current level of harvest is not excessive 

in relation to the population size, and is not thought 

to be a population-regulating factor. The threat 

classification of moderate was based on the fact 

that the human population within the range of 

the Kodiak, Kamishak, Alaska Peninsula MU is 

relatively large, and the fact that in much of that 

range the sea otter population is readily accessible 

to subsistence hunters using small boats. 

In the future, should subsistence hunting be 

determined to be a threat to recovery it could be 

regulated. Since this DPS is listed as threatened 

under the ESA, it is automatically considered 

to be depleted under the MMPA, and Alaska 

Native take may be subject to regulation under 

provisions of that Act. Under provisions of the 

ESA, Native take may be regulated if it is shown 

to be “materially and negatively” affecting the 

species. Therefore the management potential for 

subsistence hunting is considered high.

Infectious Disease – low to moderate importance

Infectious disease was rated as a low threat to 

recovery in all MUs except Kodiak, Kamishak, 

Alaska Peninsula, for which it was rated as a 

moderate threat. There is no evidence that disease 

was a factor in the sea otter decline that occurred 

in the western portion of the range of the DPS, 

but the fact that very little disease work took 

place during the peak of the decline hinders our 

ability to rule disease out completely as either 

a primary or a contributory factor. Disease is 

recognized to be a natural process in all wildlife 

populations, and thus the mere presence of disease 

should not automatically be viewed as detrimental 

or pathological. There have been numerous 

documented cases of disease in sea otters in 

California, Washington, and Alaska. Generally, 

those populations have increased, although not 

always at expected rates. Situations in which 

disease could threaten recovery include those 

involving: 1) exposure to a novel disease in an 

immunologically naïve population, 2) widespread 

immunosuppression (due to population stressors, 

immunosuppressive disease agents, or reduced 

genetic variability), 3) maintenance of a pathogenic 

disease agent in a biotic or abiotic reservoir, and 

4) extremely small population size. Introduction of 

a new epizootic disease agent into small remnant 

populations could be a very significant threat to 

recovery, and this seems to be what has occurred 
Recovery Plan 4-9

in Alaska with PDV. Because of these possibilities 

and uncertainties, the level of confidence in 

our assessment of the importance of disease to 

recovery is ranked as low in all MUs. 

Disease is ranked as a moderate threat to the 

Kodiak, Kamishak, Alaska Peninsula MU because 

of the relatively large number of carcasses 

recovered in recent years in Kachemak Bay in 

the adjacent southcentral Alaska stock. Valvular 

endocarditis caused by Streptococcus bovis

complex was the primary cause of death in 41% of 

the fresh carcasses examined during 2002-2006. 

While Kachemak Bay apparently was the center 

of the outbreak, cases were documented in the 

region from Umnak Island to PWS. This spike in 

mortality did not appear to have population-level 

effects, as the number of otters around the Kenai 

Peninsula increased at close to the maximum 

expected growth rate during the period of the 

outbreak. The management potential for disease 

is considered low, as the widespread distribution 

of animals would make capture, handling, and 

treatment extremely difficult.

Predation – moderate to high importance

Predation, specifically by killer whales but also 

potentially by other predators, was ranked as 

a high threat to recovery for the two Aleutian 

Islands MUs and the South Alaska Peninsula 

MU. Predation was rated as a moderate threat 

to recovery for the Bristol Bay and Kodiak, 

Kamishak, Alaska Peninsula MUs. The rationale 

for the different ratings was the relative extent of 

documented population declines. The populations 

of MUs that were rated high have declined greatly 

while those of the other two units either have not 

declined (Kodiak, Kamishak, Alaska Peninsula) or 

appear to have declined to a lesser extent (Bristol 

Bay). 

While predation has not been determined with 

certainty to be the main cause of the sea otter 

decline, this hypothesis is strongly supported 

through a weight-of-evidence analysis. 

Observations supporting the hypothesis include: 1) 

increases in observed predation events involving 

killer whales attacking sea otters; 2) persistence of 

sea otters in areas that appear to provide refuge 

from predation by killer whales; 3) observed 

changes in otter behavioral responses that are 

consistent with avoidance of killer whales; 4) 

analyses demonstrating that the observed numbers 

of attacks on sea otters by killer whales is similar 

to that which would be expected if killer whale 

predation were solely responsible for the decline; 

5) energetic analyses demonstrating that a small 

number of killer whales would be theoretically 

capable of causing the observed decline; 6) the 

rapid disappearance of large numbers of sea 

otters of all age classes over a broad area; 7) a 

scarcity of beachcast carcasses compared to what 

would be expected if the decline were driven by 

starvation, disease, or toxins; and 8) a high rate of 

disappearance of radio-tagged otters.

Several of the observations listed above would 

apply equally to predation by sharks, or to 

predation by both sharks and killer whales. There 

is no evidence for shark predation on sea otters in 

Alaska as there is for killer whale predation. White 

sharks are a significant cause of mortality of sea 

otters in California, but it is very unlikely that they 

occur more than rarely in the Aleutian Islands. 

However, the fact that sleeper sharks that are 

known to prey on marine mammals elsewhere are 

present in the range of the southwest Alaska DPS 

means that shark predation could be a contributing 

factor in the population decline.

Some have questioned the killer whale predation 

hypothesis because they don’t believe that a 

predator that apparently has co-existed with 

a prey species for millennia could cause such 

a drastic decline in that species. However, in 

other systems there are documented cases where 

predation has driven prey populations to low levels 

or even extinction. In many of those instances, 

anthropogenic factors such as the introduction of 

predators, modification of habitat, or disruption of 

the food web were involved. Other instances of 

drastic fluctuations in abundance, such as the lynx–

snowshoe hare multiyear cycle, appear to be within 

the normal range of non-equilibrium population 

dynamics. 

Even if one accepts the hypothesis that 

predation caused the otter decline, there are 

great uncertainties about what to expect in the 

future. One scenario is that killer whales will 

continue to exert enough predatory pressure on 

otters to prevent recovery or cause even further 

decline. In this scenario, the killer whales would 

have to obtain the majority of their energetic 

requirements from alternative prey. An alternate 

scenario is that predation on sea otters becomes 

energetically unprofitable so that killer whales 

shift their diet completely away from otters, to a 

point at which they are no longer a factor in the 

otters’ population dynamics for some period of 

time. Poorly understood factors, such as killer 

whale prey preferences, pod home ranges and 

migratory patterns, and learned forms of predatory 

behavior, make projections into the future 

extremely uncertain. The management potential 

for predation as a threat is ranked low.

Fishery Bycatch – low importance

There is a history of California sea otters becoming 

entangled and dying in sunken gillnets (a gear type 

not currently used in Alaska) and fish and shellfish 

pots set in shallow coastal waters. In areas where 

this has occurred, the issues have largely been 

resolved by limiting the use of sunken gillnets to 

waters deeper than those where sea otters usually 

feed and by modifying pot design. 

Available data suggest that sea otter mortality 

due to fishery bycatch within the range of the 

southwest Alaska DPS has been very low. 

Nearly all pot fisheries, with the exception of the 
4-10 Southwest Alaska DPS of the Northern Sea Otter

dungeness crab fishery, occur in waters deeper 

than sea otters usually dive. Salmon and herring 

gillnet fisheries occur within the eastern portions 

of the range of the DPS. However, because the 

nets have floats at the top and are closely attended, 

few otters are entangled and most of those that 

are caught are released alive. For these reasons, 

fishery bycatch is rated as a low threat to recovery 

in all MUs. Because it is possible to modify fishing 

gear and fishing practices to avoid entangling 

otters, management potential for fishery bycatch is 

considered high.

Illegal Take – low to moderate importance

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. Few people 

reside in or visit the western portion of the range, 

so opportunities for illegal take are very limited 

there. For this reason, illegal take as a potential 

threat to recovery was ranked as low for all MUs 

except for the Kodiak, Kamishak, Alaska Peninsula 

unit, for which it was ranked as moderate. Illegal 

take was considered to have a low management 

potential due to the vast range of the DPS.

Disturbance – low importance

Disturbance of sea otters results primarily from 

boat traffic. Boat traffic is light throughout most 

of the range of the southwest Alaska DPS. Traffic 

is highest in the eastern portion, where sea otter 

populations are at the highest levels. Otter 

populations have thrived in areas with much 

greater volumes of boat traffic, such as southeast 

Alaska and British Columbia. For these reasons, 

disturbance was ranked low as a threat to recovery, 

and management potential was ranked as moderate 

due to the vast range of the DPS.

Biotoxins – low importance

Biotoxins produced by HABs have been implicated 

in mortality events in marine mammals and 

marine birds. The frequency and severity of 

these events worldwide may be increasing as a 

function of both increased nitrification of nearshore 

waters, increased ocean temperatures, and the 

anthropogenic alteration of ocean food webs. 

Paralytic shellfish poisoining (PSP), a biotoxin that 

accumulates in filter-feeding bivalves, has caused 

illness and death in humans in Alaska and may 

have caused a minor mortality event in sea otters 

at Kodiak Island in 1987. There is experimental 

evidence that sea otters avoid prey with PSP 

toxins. At present there is no indication that 

biotoxins are a threat to recovery of southwest 

Alaska sea otters and they are therefore ranked as 

low importance. Due to the nature of this threat, 

the management potential for biotoxins is low. 

Point-Source Contaminants – low importance

High levels of PCBs have been found in sea 

otters and mussels in localized areas within the 

range of the southwest Alaska DPS, notably 

around Dutch Harbor, Adak Island, and Amchitka 

Island. However, overall PCB levels within the 

range of this DPS are lower than in areas such 

as southeast Alaska where otter populations are 

thriving. Potential adverse effects of exposure to 

high levels of PCBs include impaired reproduction 

and immunosuppression. Overall, throughout the 

range of the DPS there is no evidence that pointsource contaminants, in this instance PCBs, are a 

threat to the recovery of the sea otter population. 

Therefore, this threat is rated as low. Due to 

te geographic scope of this threat, management 

potential for point-source contaminants is low. 

Non-Point-Source Contaminants – low importance 

Some contaminants, particularly DDTs, are 

transported in the atmosphere from Asia to the 

northeastern Pacific Ocean and Bering Sea, where 

they precipitate out into the ocean and enter the 

food chain through phytoplankton. To date, there 

are no data indicating adverse effects of such 

contaminants on southwest Alaska sea otters. 

It would be expected that higher trophic-level 

predators are affected before otters, but there 

may be substantial inter-specific differences in 

responses to contaminants. This threat to recovery 

was rated as low, with low management potential 

due to the geographic scope of this threat. 
Recovery Plan 5-1

5. Recovery Strategy

Clearly the southwest Alaska DPS of sea otters 

has shown a major decline. The decline has been 

of such magnitude that the population is now at 

a perilously low level in much of the Aleutian 

Islands, with some island groups currently having 

less than one-tenth the number of otters they had 

in 1990. This is a region where just 20 years ago 

the population was large and probably at or near 

the environmental carrying capacity.

Changes in sea otter behavior and habitat use 

have occurred in this area concurrent with the 

decline. Shifts in otter distribution toward very 

shallow and nearshore areas and away from deeper 

and offshore areas are evident. These changes in 

habitat use may limit the ability of the population 

to recover, and require improved understanding. 

The range of the DPS is large, with regionally 

variable environmental characteristics. The 

pattern of the decline, and perhaps the cause(s) of 

it, also vary regionally. To design and manage an 

effective recovery program, the DPS should be 

treated as five “management units” (see section 

2.I). 

The only identified threat factor that is judged to 

have a high importance to recovery is predation. 

The weight of evidence suggests that killer whale 

predation is the most likely cause of the sea 

otter’s decline in the Western Aleutian Islands 

management unit, and it is presumed to be of 

moderate or high importance in the other MUs as 

well. High priority should be given to studies that 

will help understand and assess the importance and 

implications of predation. There may be few actions 

that can be taken to mitigate predation as a threat, 

but the sea otter recovery program should search 

for solutions and be open to novel ideas.

Factors unrelated to predation are known to affect 

sea otter population trends. However, nearly all 

of these other factors are judged to have a low 

importance to recovery of the southwest Alaska 

DPS. Nonetheless, additional research into other 

current and future threats to recovery, particularly 

disease, is warranted. Actions should be taken 

wherever possible to mitigate threats from any 

source, and thereby minimize mortality and 

maximize productivity.

For each MU, a comprehensive system is needed to 

monitor the population size and trend of sea otters 

(see Section 7.A.1) and the status of associated 

ecosystems (the habitat monitoring plan described 

in Appendix A). Information from the monitoring 

program needs to be incorporated on an ongoing 

basis into a quantitative analytical framework 

(the “population viability analysis (PVA) model” 

described in Appendix B). The resulting analyses 

can be used to reevaluate the current population 

status with respect to uplisting or downlisting 

criteria, and when appropriate, any relevant 

quantitative criteria can be re-set to reflect the 

new information.
5-2 Southwest Alaska DPS of the Northern Sea Otter

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Recovery Plan 6-1

6. Recovery Goals, Objectives, and Criteria

A. Recovery Goal

The goal of this recovery program is to control, 

reduce, or eliminate threats to the southwest 

Alaska DPS of the northern sea otter to the extent 

that this DPS no longer requires the protections 

afforded by the ESA and therefore warrants 

delisting. 

To that end, recovery criteria have been identified 

using the best available scientific information 

regarding the threats faced by the southwest 

Alaska DPS. It is possible, however, that the 

intent of the recovery criteria may be met without 

meeting the exact specifications of the written 

criteria. For example, one or more recovery 

criteria may have been exceeded and other 

criteria may not have been met, but we have 

evidence to indicate the species no longer requires 

the protections of the ESA. Additionally, new 

information may become available. The FWS has 

opportunities to revisit recovery criteria during 

recovery plan revisions, or when conducting 

a 5-year review of a species’ status. Specific 

recovery criteria may change over time as 

additional knowledge is gained. Recovery planning 

under the ESA provides inherent flexibility in 

determining whether recovery criteria have been 

met.

B. Recovery Objectives

The Recovery Team identified three objectives for 

the southwest Alaska sea otter recovery program, 

as follows:

1. Achieve and maintain a self-sustaining 

population of sea otters in each MU.

2. Maintain enough sea otters to ensure that 

they are playing a functional role in their 

nearshore ecosystem.

3. Mitigate threats sufficiently to ensure 

persistence of sea otters.

C. Criteria for delisting 

Demographic criteria

Based on the definition of a threatened species in 

the ESA, it is generally recognized that a species 

or DPS should be considered for delisting when 

there is an acceptably low likelihood of it becoming 

endangered (endangered meaning likely to become 

extinct) in the foreseeable future. Therefore, 

an initial step is deciding what probability of 

becoming endangered is “acceptably low” and 

what timescale (i.e., how many years) constitutes 

the “foreseeable future.” While the responsible 

management agencies (FWS and NMFS) have 

put significant effort into developing standards 

for these parameters (DeMaster et al. 2004, 

Regan et al. 2008), standard values that could be 

applied to species such as the sea otter have not 

been established. Based on expert opinion, the 

Recovery Team recommended using less than a 

5% probability of becoming endangered within 25 

years as the criterion for delisting, and more than a 

5% probability of becoming extinct within 25 years 

as the definition of endangered (Table 17). 

To meet recovery objective 1, managers must 

understand how the demographic characteristics 

of southwest Alaska sea otters (e.g., birth 

rates and mortality rates) interact to cause 

the population to increase, decrease, or remain 

stable. Population modeling is commonly used 

to integrate demographic data, and a growing 

trend in endangered species conservation is to use 

such models as the basis for Population Viability 

Analyses (PVAs) that can project risk of extinction 

over time (U.S. Marine Mammal Commission 

2007). PVA models have several advantages over 

qualitative assessments for informing ESA listing 

and other management decisions. Some of those 

are: 1) they are more objective than qualitative 

assessments; 2) they can incorporate and use all 

relevant data; and 3) assumptions and uncertainties 

in data can be clearly identified and incorporated 

into results.

At the time that the southwest Alaska sea otter 

DPS was listed as threatened under the ESA, 

models to estimate extinction risks had not been 

developed for this population. In the process of 

preparing this recovery plan, a PVA model was 

created by members of the Recovery Team, and it 

is described in detail in Appendix B. This model 

was developed using data collected in the Western 

and Eastern Aleutian Islands MUs. Data from 

the other three MUs were not robust enough to 

support the creation of unit-specific PVAs. The 

demographically-structured PVA model was run 

using time series of trend data available for various 

islands in the Western Aleutian Islands MU. 

The population simulations described in Appendix 

B were used to determine at what fraction of 

carrying capacity (K) the delisting criterion (<5% 

probability of becoming endangered within 25 
6-2 Southwest Alaska DPS of the Northern Sea Otter

years) would be satisfied for the Aleutian Islands 

MUs. Results indicated a threshold abundance of 

approximately 49% of K. Although the simulations 

were based only on Aleutian Islands data, the 

Recovery Team concluded that this value could be 

applied to the other MUs on an interim basis while 

data are collected that will allow creation of unitspecific PVAs (see item 1.1.5 in section 7.A.).

Using the definition of “high density habitat” 

described by Bodkin and Udevitz (1999) and 

adopted by Burn et al. (2003) for the Aleutian 

Islands, available sea otter habitat was estimated 

for each MU using bathymetric data contained 

in the NOAA Southern Alaska Coastal Relief 

Model (Lim et al. 2009). Burn et al. (2003) showed 

that the median density of sea otters in the 

Aleutian Islands at K is about 15 otters/km2 of 

appropriate habitat. Estimates of equilibrium 

density at K do not exist for the Bristol Bay, 

South Alaska Peninsula, and Kodiak, Kamishak, 

Alaska Peninsula MUs. In surveyed areas within 

the Kodiak, Kamishak, Alaska Peninsula MU, sea 

otters occur at densities ranging from 5 otters/km2

in the Katmai National Park area to 1.3 otters/km2

in Kamishak Bay (USGS unpublished data). The 

current status of this and the other areas relative 

to their carrying capacity is unknown, however. 

Pending acquisition of better data (see item 1.1.5 

in section 7.A.), the Recovery Team recommended 

assigning an average value for equilibrium density 

of 3 otters/km2 to the remaining three MUs. These 

values were used to produce estimates of K for 

each MU as shown in Table 16. 

Running the PVA with trend and demographic 

data that are current through 2007 for the Western 

Aleutians MU results in the abundance threshold 

for delisting this MU, expressed in terms of 

numbers of otters, as shown in Table 18. The 

abundance threshold can also be expressed as a 

percentage of K, which is 49% (Table 18). In the 

absence of unique PVA models for each MU, the 

modeled threshold for the Western Aleutians MU 

was applied to the others so that each MU meets 

the demographic criterion for delisting when its 

abundance is greater than 49% of K for that MU. 

Status of each MU, expressed as the ratio of the 

most current population estimate and K, is also 

shown in Table 18. Based on the most recent 

survey data, the Kodiak, Kamishak, and Alaska 

Peninsula MU was the only MU above the delisting 

threshold.

The PVA should be re-run as sufficient new trend 

or demographic data become available. PVAs are 

dynamic and evolving, and inclusion of updated 

trend or demographic data will influence the model 

outputs, including estimates of the delisting and 

uplisting thresholds. For example, if population 

trends were to remain neutral or even positive 

over the next 5-10 years and the PVA were to 

be re-run with those data, estimates of ageindependent mortality (e.g., predation) would 

be lower and thus the recommended delisting 

abundance threshold would also be lower, as 

the forecasted probability of declining to the 

uplisting threshold would decline. Specifically, the 

annual per-capita death rate associated with ageindependent mortality sources (as well as variation 

in this parameter) is estimated from trend data 

using maximum likelihood techniques. Values 

of this death rate parameter in PVA simulations 

are drawn randomly from the full time series of 

estimated values, with a bias towards more recent 

years. If age-independent mortality is declining 

over time, the model forecast simulations will 

have a higher probability of randomly drawing the 

lower values from more recent years, resulting in 

fewer simulations declining to extinction and thus a 

lower demographic delisting abundance threshold. 

Conversely, if trends were to become more 

negative (and thus estimated death rates remain 

high), the delisting threshold could remain at 49% 

or even increase. In this way the PVA is datadriven, and the results appropriately conservative. 

Additional analytical detail and explanation can be 

found in Appendix B.

The MMPA states that the primary objective 

of marine mammal management should be to 

maintain the health and stability of the marine 

ecosystem. Species and populations that are listed 

under the ESA are also considered “depleted” 

under the MMPA. Depletion is defined as “below 

the optimum sustainable population” level, 

which typically averages 60% of K for marine 

mammal species. Therefore, it is possible that the 

southwest Alaska DPS could meet the criteria for 

de-listing under the ESA, but still be considered 

depleted under the MMPA.

Ecosystem-based criteria

In developing the Recovery Plan for the southwest 

DPS of the northern sea otter, we followed the 

purposes of the ESA, which include conserving 

the ecosystems upon which listed species depend. 

According to our Interim Recovery Planning 

Guidance, “Recovery plans should aim to address 

threats by restoring or protecting ecosystem 

functions or processes whenever and wherever 

possible (as opposed to actions that require 

long-term and possibly expensive management 

programs). This approach is science-based and 

provides a means for required habitat to be 

maintained long-term in a dynamic way by natural 

processes. This broader perspective should be 

infused into all recovery plans, whether they be for 

single species (including subspecies and DPSs), or 

multiple species.” 

To meet recovery objective 2, managers need a 

metric that can be used to determine at what point 

sea otters are serving their normal functional role 

in southwest Alaska ecosystems. In an overall 

sense, the sea otter is closely linked to kelp forest 

ecosystems in the North Pacific Ocean. As a 

keystone species in those ecosystems, its presence 

or absence in a region can have a large effect on 

physical and biotic features. In rocky areas, there 
Recovery Plan 6-3

Table 16. Estimates of available habitat, equilibrium density, and carrying capacity for the five management 

units in the southwest Alaska DPS of the northern sea otter. Pending development of better estimates of 

carrying capacity, these estimates are considered provisional.

Management Unit

Available 

Habitat (km2)

Equilibrium Density 

(otters/km2)

Carrying 

Capacity (K)

Western Aleutian 4,260 15 63,897

Eastern Aleutian 2,046 15 30,697

Bristol Bay 12,299 3 36,898

South Alaska Peninsula 11,159 3 33,476

Kodiak, Kamishak, Alaska Peninsula 15,362 3 46,086

Total 211,054

are two alternative phases—kelp-dominated when 

otters are present and urchin-dominated when they 

are at low densities or absent. In soft-sediment 

areas, the relationships between otter abundance 

and ecosystem state are much less well known.

In the Aleutian Islands, sea otter habitat is largely 

rocky, and the extensive long-term datasets on sea 

otters and their ecosystem can be used to describe 

the relationship between sea otter abundance and 

the point at which an urchin-dominated ecosystem 

(that has resulted from reduced otter numbers) 

undergoes a phase shift to a kelp-dominated 

ecosystem (due to increased numbers of sea otters). 

This is described in detail in Estes et al. (2010) and 

Appendix A.

The proposed ecosystem-based recovery criterion, 

applicable only to the two Aleutian Islands MUs, 

is that sea otters must be sufficiently abundant 

to either maintain, or bring about, a phase shift 

to the kelp-dominated state. Attainment of this 

criterion will be evaluated by measuring kelp 

forest density at a number of sites at a number of 

islands as explained in Appendix A. Either of the 

Aleutian Islands MUs will be considered to meet 

the ecosystem-based recovery criterion when >50% 

of the islands sampled within each MU are judged 

to be in a kelp-dominated state (Table 17). In the 

event that a MU meets the demographic criteria 

for delisting but the ecosystem does not shift back 

to the kelp-dominated state as expected, the need 

for meeting both demographic and ecosystembased criteria will be re-examined. Recovery 

planning under the ESA provides inherent 

flexibility in determining whether recovery criteria 

have been met.

The Recovery Team believes that it is not 

appropriate to propose ecosystem-based recovery 

criteria for other MUs at this time. Within the 

Bristol Bay MU, habitats used by sea otters are 

almost entirely soft-sediment, and while there 

may be a relationship between sea otter numbers 

and ecosystem state in these circumstances, that 

relationship has not been defined. In the South 

Alaska Peninsula and Kodiak, Kamishak, Alaska 

Peninsula MUs there is a mix of rocky and softsediment habitats, and for rocky areas preliminary 

results indicate that the relationship between otter 

density and kelp cover may be different from that 

described for the Aleutian Islands (see Appendix 

A). Potentially fruitful options for developing 

ecosystem-based recovery criteria for these other 

areas are described in Section 7.B, item 1.6.2.

Threats-based criteria

To meet recovery objective 3, managers must 

evaluate whether threats determined to have 

population level effects have been adequately 

mitigated. Guidance for making such an 

evaluation is given below, organized by the five 

threat categories used in the ESA for listing 

determinations. Threats-based criteria should be 

applied to each MU separately.

Factor A: The present or threatened destruction, 

modification, or curtailment of a species’ habitat or 

range

 Sea otter habitat within the range of the 

southwest Alaska DPS appears to be nearly 

pristine, with little obvious anthropogenic 

degradation. Although habitat destruction 

or modification likely did not play a role in 

the decline of this DPS, new threats to sea 

otter habitat should be minimized. Prior 

to delisting the DPS, threats to its habitat 

should be negligible as specified below.
6-4 Southwest Alaska DPS of the Northern Sea Otter

 Aquatic habitat sufficient to support 

a recovered population of sea otters is 

protected through appropriate management 

measures. Conservation measures provide 

for: 1) adequate protection of the otters’ 

prey base; and 2) the continued availability 

of habitat that affords refuge from marine 

predators.

 Safeguards are in place to prevent spills of 

oil and hazardous materials, and state-of-theart response capabilities are available in the 

event of any spills.

Factor B: Overutilization for commercial, 

recreational, or educational purposes

 Prior to delisting the southwest Alaska DPS 

of northern sea otters, any overutilization 

for commercial, recreational, scientific, or 

educational purposes that threatens its 

continued existence should be managed as 

specified below.

 Subsistence harvest of sea otters is accurately 

monitored and managed to ensure that it does 

not compromise the recovered status of the 

DPS.

Factor C: Disease or predation

 Predation is believed to have played 

an important role in the decline of the 

southwest Alaska DPS of northern sea 

otters. Currently, disease is considered to 

have a relatively minor impact on the sea 

otter population. Prior to delisting the DPS, 

threats from disease or predation should be 

understood as specified below.

 Information is adequate to conclude that 

neither disease nor predation is currently 

compromising the recovered status of sea 

otters.

Factor D: The inadequacy of existing regulatory 

mechanisms

 Inadequacy of existing regulatory 

mechanisms, including the MMPA and the 

ESA, did not contribute to the sea otter 

decline. Prior to delisting the southwest 

Alaska DPS of northern sea otters, existing 

regulatory mechanisms should exist as 

specified below.

 Existing regulatory mechanisms that provide 

protections for sea otters should not have 

been weakened.

Factor E: Other natural or anthropogenic factors 

affecting its continued existence

 Prior to delisting the southwest Alaska 

DPS of northern sea otters, natural and 

anthropogenic threats to its continued 

existence should be minimized as specified 

below.

Table 17. Summary of criteria that must be met prior to delisting the southwest Alaska DPS of the Northern 

sea otter.

Management Unit

De-listing Criterion

Western 

Aleutian

Eastern 

Aleutian Bristol Bay

South Alaska 

Peninsula

Kodiak, 

Kamishak, 

Alaska 

Peninsula

Demographic Probability 

of becoming 

endangered 

within 25 

years is <5%

Probability 

of becoming 

endangered 

within 25 

years is <5%

Probability 

of becoming 

endangered 

within 25 

years is <5%

Probability 

of becoming 

endangered 

within 25 

years is <5%

Probability 

of becoming 

endangered 

within 25 

years is <5%

Ecosystem-based >50% of islands 

are in the 

kelp-dominated 

state

>50% of islands 

are in the 

kelp-dominated 

state

None

proposed

None

proposed

None

proposed

Threats-based Threats are 

adequately 

mitigated

Threats are 

adequately 

mitigated

Threats are 

adequately 

mitigated

Threats are 

adequately 

mitigated

Threats are 

adequately 

mitigated
Recovery Plan 6-5

 A mechanism (e.g., fishery management and 

monitoring) is in place and will remain in 

place that ensures fishery bycatch is less than 

the potential biological removal (as defined 

under the MMPA).

 Enforcement of MMPA and ESA regulations 

is adequate to restrict illegal take to 

negligible levels.

 All types of human disturbance are 

controlled sufficiently to make it unlikely that 

disturbance will jeopardize the recovered 

status of the DPS.

 Toxins, including point-source and non-pointsource pollutants and biotoxins, do not pose 

significant health risks to the recovered sea 

otter population.

 Contingency plans and response capability 

for oil spills are in place and are capable of 

dealing with a major oil spill in important 

areas used by sea otters.

Summary of criteria for delisting

Each of the five MUs identified in this plan for the 

southwest Alaska sea otter DPS will be evaluated 

independently with respect to delisting criteria as 

shown in Table 17. The status of all MUs will be 

evaluated against demographic and threats-based 

criteria, while the Western Aleutian and Eastern 

Aleutian MUs will also be evaluated against the 

ecosystem-based criterion.

To delist a species listed as threatened, the 

responsible management agency must find that the 

DPS is no longer “likely to become an endangered 

species within the foreseeable future throughout 

all or a significant portion of its range.” For this 

criterion, “foreseeable future” has been fixed at 25 

years as specified in the PVA model (Appendix B). 

The Recovery Team did not find a non-arbitrary 

way to define “significant portion of its range” for 

the southwest Alaska sea otter DPS. For this plan, 

it is recommended that delisting be considered 

when any three of the five MUs meet all of the 

recovery criteria specified for them in Table 17. 

Delisting should not be considered if any one of the 

MUs meets the criteria specified for uplisting to 

endangered as described in the following section.

D. Criteria for reclassification to endangered 

(uplisting)

Demographic criteria

Based on the definition of an endangered species in 

the ESA, it is generally recognized that a species 

or DPS should be considered for reclassification 

from threatened to endangered when there is 

an unacceptably high likelihood of it becoming 

extinct within the foreseeable future. Decisions 

must be made as to what is an unacceptably high 

probability of becoming extinct and how many 

years constitute the foreseeable future. While 

the responsible management agencies (FWS and 

NMFS) have put significant effort into developing 

Table 18. Examples of estimates that are developed by PVA models using available data through 2007. 

Estimates shown include carrying capacity, delisting abundance, uplisting abundance, and current status 

relative to carrying capacity for each of the five management units 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

Carrying 

Capacity

(K)

Delisting 

Abundance

Uplisting 

Abundance

2007

Abundance 

Estimate

Status

(%K)

Western Aleutian 63,897 31,309 2,556 6,451 0.10

Eastern Aleutian 30,697 15,042 1,228 2,291 0.07

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

South Alaska Peninsula 33,476 16,403 1,339 4,724 0.14

Kodiak, Kamishak, 

Alaska Peninsula 46,086 22,582 1,843 28,955 0.63

Total 211,054 103,417 8,442 53,674 0.25
6-6 Southwest Alaska DPS of the Northern Sea Otter

standards for these parameters (DeMaster et al. 

2004, Regan et al. 2009), standard values that could 

be applied to species such as the sea otter have 

not been established. Based on expert opinion, 

the Recovery Team has recommended using more 

than a 5% probability of becoming extinct within 25 

years as the criterion for uplisting to endangered. 

For long-lived species such as the sea otter, 

equating extinction to the point where only one 

individual remains is not appropriate (Regan et al. 

2009), and the Team therefore specified a “quasiextinction” threshold, defined as the point at which 

all islands1 within a MU have <5 female otters and 

no males. 

The population simulations described in Appendix 

B were used to determine the threshold density 

at which the reclassification criterion (>5% 

probability of becoming extinct within 25 years) 

would be satisfied. Using the most current trend 

and demographic data through 2007 results in 

the abundance thresholds for uplisting for each 

MU, expressed in terms of numbers of otters, as 

shown in Table 18. These thresholds can also be 

expressed as a percentage of K, which is 4% for 

each MU. A MU would meet the demographic 

criterion for reclassification to endangered if its 

abundance declined to less than 4% of K. While the 

simulations were based only on Aleutian Islands 

data, in the judgment of the Recovery Team 

this value can be applied to the other MUs on an 

interim basis while data are collected that will 

allow unit-specific PVAs to be developed (see item 

1.5.1 in section 7.A.). 

As with all reclassification decisions, the PVAs 

should be re-run as new trend or demographic 

data become available. Inclusion of updated trend 

or demographic data will influence the outputs, 

including estimates of the delisting and uplisting 

thresholds.

1

 In this context we use the term “island” to refer 

to a single island or island group that is geographically distinct from other such islands or island 

groups (i.e., separated by at least 10 km) and that 

has sufficient sub-tidal habitat to support an isolated sub-population of sea otters.

Ecosystem-based criteria

If the southwest Alaska sea otter DPS is being 

considered for uplisting from threatened to 

endangered, it is unlikely that the population will 

have recovered its functional role in the ecosystem 

and more likely that its functional role will have 

diminished. One would expect that less of the 

rocky habitat normally used by otters would be 

in the kelp-dominated state and more would be 

urchin-dominated. While it might be possible to 

design an ecosystem-based uplisting criterion 

based on these likely changes, such a criterion is 

not being proposed at this time.

Threats-based criteria

If the population status has deteriorated such that 

reclassification to endangered is being considered, 

then clearly some threat or threats have not been 

adequately mitigated. Any formal status review 

of the DPS should include a detailed analysis of the 

threats that may be affecting the population at that 

time, but threats-based criteria for uplisting are 

not being proposed at this time.

Summary of criteria for reclassification to 

endangered

Each of the five MUs of the southwest Alaska sea 

otter DPS identified in this plan will be evaluated 

independently with respect to reclassification 

criteria. To list a species as endangered, the 

responsible management agency must find that 

the DPS “is in danger of extinction throughout 

all or a significant portion of its range.” The 

Recovery Team did not find a non-arbitrary way 

to define “significant portion of its range” for the 

southwest Alaska sea otter DPS. For this plan, it 

is recommended that reclassification to endangered 

be considered when at least three of the MUs meet 

the demographic criteria specified above. 

E. Summary of demographic status in 

relation to delisting and uplisting criteria

The most recent estimates of sea otter abundance 

(Table 1) indicate that the four MUs that have 

shown clear evidence of population declines 

(Western and Eastern Aleutian Islands, Bristol 

Bay, and South Alaska Peninsula) are all at levels 

between their de-listing and up-listing thresholds 

(Table 18). The Kodiak, Kamishak, Alaska 

Peninsula unit is above the de-listing threshold. 

Pending the development of PVAs specific to the 

Bristol Bay, South Alaska Peninsula, and Kodiak, 

Kamishak, Alaska Peninsula MUs (see item 1.5.1 

in section 7.A.), and better estimates of carrying 

capacity (see item 1.1.5 in section 7.A.), delisting 

and uplisting thresholds for these MUs are 

considered provisional. 
Recovery Plan 7-1

7. Recovery Program

The recovery program is organized with seven major categories, each with one or more specific actions 

that promote recovery of the southwest Alaska DPS of the northern sea otter. These actions are 

presented as a step-down outline in Section 7.A. below. Each discrete recovery action is assigned a 

priority and narrative description in Section 7.B.

A. Recovery Action Outline

1. Population monitoring and research

1.1. Continue to estimate sea otter population size and trends in southwest Alaska

1.1.1. Evaluate and improve methods for abundance surveys

1.1.2. Conduct surveys in each management unit and estimate abundance and population 

trends

1.1.3. Identify areas of consistently high sea otter abundance, relative to other areas, within 

each management unit

1.1.4. Evaluate potential biases in survey data and identify methods that may be useful for 

correcting such biases

1.1.5. Refine estimates of equilibrium density and carrying capacity for the Bristol Bay, South 

Alaska Peninsula, and Kamishak, Kodiak, Alaska Peninsula MUs

1.1.6. Evaluate the overall population monitoring program on an ongoing basis to assess 

sensitivity 

1.2. Estimate vital rates

1.2.1. Estimate birth rates 

1.2.2. Estimate age- and sex-specific survival rates 

1.3. Monitor health and body condition 

1.3.1. Monitor general body condition

1.3.2. Analyze data for evidence of food limitation

1.4. Monitor distribution and movements

1.5. Continue development of population viability analysis models

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

Kodiak, Alaska Peninsula MUs

1.5.2. Revise and re-run the PVA model(s) incorporating new data

1.6. Monitor the functional role of sea otters in ecosystems

1.6.1. Monitor status of the kelp forest ecosystem

1.6.2. Develop methods to monitor sea otter impacts on marine ecosystems with soft-sediment 

substrates

1.7. Develop new research tools

2. Habitat needs and habitat protection

2.1. Identify important habitats or areas of special biological significance

2.1.1. Identify characteristics of areas that are serving as refuges for remnant groups of sea 

otters

2.1.2. Explore the feasibility of creating more areas that can serve as refuges

2.1.3. Protect important habitats

2.2. Prepare a habitat conservation plan under Section 10 of the ESA for Alaska state-managed 

fisheries
7-2 Southwest Alaska DPS of the Northern Sea Otter

3. Manage impacts of human uses

3.1. Ensure that Alaska Native subsistence harvest does not affect recovery

3.1.1. Continue to estimate harvest levels through the FWS marine mammal marking, tagging, 

and reporting program (MTRP)

3.1.2. Quantitatively evaluate the impact of sea otter harvest on recovery 

3.1.3. Develop sea otter harvest management plan(s) with Alaska Native tribes and tribally 

authorized organizations

3.1.4. Continue outreach to promote “males only” harvest 

3.1.5. Use pelts from stranded otters to reduce demand for subsistence-harvested animals

3.2. Ensure that incidental take in fisheries does not affect recovery

3.2.1. Monitor the level of incidental take in fisheries within the sea otter’s range in southwest 

Alaska

3.2.2. Quantitatively evaluate the impact of incidental take on recovery in each MU

3.2.3. Develop programs to reduce incidental take in situations where it occurs

3.3. Ensure that entanglement in marine debris does not affect recovery

3.3.1. Encourage net recycling and environmentally responsible disposal

3.4. Eliminate intentional illegal take

3.4.1. Establish an outreach program on the ecological role of sea otters in the nearshore 

marine ecosystem

3.4.2. Increase enforcement efforts to investigate and prosecute cases of illegal killing

3.5. Evaluate potential impacts of scientific research activities

4. Disease 

4.1. Evaluate the potential role of disease as a threat to recovery

4.1.1. Expand the marine mammal stranding network in southwest Alaska 

4.1.2. Use carcasses and tissues from subsistence-harvested animals to investigate disease 

processes

4.1.3. Sample carcasses and tissues

4.1.4. Conduct live-capture studies to examine health status of live sea otters 

4.1.5. Evaluate population-level effects of identified diseases 

4.2. Develop disease management plans where practical

5. Predation

5.1. Continue to evaluate the role of predation as a threat to recovery

5.2. Develop predation management plans, where practical

6. Protect from other natural or human-caused threats

6.1. Ensure that oil spills do not impede recovery of sea otters and/or negatively affect the 

nearshore marine environment in southwest Alaska

6.2. Establish an outreach program to mariners on how to avoid striking sea otters

6.3. Continue to measure and monitor contaminant levels in sea otters

6.4. Monitor occurrence of biotoxins in sea otters and their prey

6.5. Evaluate the feasibility of translocating sea otters to enhance recovery

6.6. Evaluate potential impacts of recreational activities, tourism, and other forms of direct human 

disturbance

7. Implement the recovery program for southwest Alaska sea otters

7.1. Maintain the Southwest Alaska Sea Otter Coordinator position within FWS

7.2. Continue and enhance coordination of management efforts among FWS, Alaska Natives, and 

the State of Alaska

7.3. Continue and enhance coordination of research efforts among FWS, USGS, the State of Alaska, 

Alaska Natives, academic institutions, and others

7.4. Develop and continue a program of outreach to stakeholders

7.5. Secure adequate funding for southwest Alaska sea otter management and research needs
Recovery Plan 7-3

B. Recovery Action Narrative

The following action items are generally assigned 

priority independent of one another. However, 

many of the items are in fact not independent. 

For example, marking of individuals requires 

capture and handling, during which measurements 

and samples should be obtained that can be 

used to evaluate competing hypotheses related 

to population status, a low-priority objective. 

Reproductive rates, while not assigned a high 

priority, can, and should, be estimated through 

counts of dependent pups during surveys of 

abundance. Thus, assignment of a priority lower 

than high to a given action should not necessarily 

mean that no progress is going to be made on 

that action. In many instances, multiple types of 

information can be collected for little additional 

cost while conducting high-priority studies.

Priorities have been assigned to each action item 

according to the guidance provided in the FWS 

Recovery Handbook (FWS 1990), as follows:

 Priority 1: Actions that must be taken to 

prevent extinction or to prevent the species 

from declining irreversibly. 

 Priority 2: Actions that must be taken to 

prevent a significant decline in species 

population/habitat quality or to prevent some 

other significant negative effect short of 

extinction 

 Priority 3: All other actions necessary to 

provide for full recovery of the species.

1. Population monitoring and research

1.1. Continue to estimate sea otter population 

size and trends in southwest Alaska

1.1.1. Evaluate and improve methods for 

abundance surveys—Priority 2

A variety of survey methods have been 

used over the past several decades to 

estimate the number of sea otters that 

inhabit different areas within Alaska. 

In addition to statistical sampling 

considerations, the remote nature of 

many of these areas imposes logistical 

constraints on the type of survey 

platforms that can be used to conduct 

survey operations safely. Although 

survey data exist for each of the five 

MUs in the southwest Alaska DPS, 

survey methods should continue to be reevaluated and re-designed as necessary 

to improve accuracy and precision. 

In addition to statistical sampling 

methods, an important consideration 

in survey design is how to correct for 

otters that are missed by observers 

during aerial and/or skiff surveys. One 

commonly used aerial survey method 

includes procedures for developing 

a correction factor to account for 

undetected otters. This method employs 

intensive searches within strip transects 

to estimate detection. Thus far, it 

has been tested using a small, singleengine aircraft on floats, which may be 

impractical for MUs where the use of 

twin-engine aircraft is required for safety 

reasons. For those MUs, some other 

method should be developed, tested, and 

implemented to correct survey data for 

missed (non-detected) otters.

1.1.2. Conduct surveys in each 

management unit and estimate 

abundance and population 

trends—Priority 1

The decline of sea otters throughout 

much of southwest Alaska since the mid1980s is well documented through aerial 

and skiff surveys. In order to monitor 

population trends and assess recovery, 

surveys of sea otters in each MU should 

be conducted at regular intervals (to be 

determined) using established methods 

that yield comparable results. As 

delisting criteria are based in large part 

on the population status within each MU, 

this is a priority 1 item. Specific actions 

that are needed in each MU are listed 

below.

 Western Aleutian MU: 

 Continue to conduct skiff survey 

counts at established index sites. 

 Establish an additional index site 

(aircraft-based) in the Islands of 

Four Mountains. 

 Eastern Aleutian MU: 

 Continue to conduct skiff survey 

counts at established index sites.

 Bristol Bay MU:

 Establish a system of index 

sites for monitoring trends, such 

that a representative subset of 

available habitat (stratified by 

geographic attributes) can be 

covered by aerial transects in 1-2 

days. 

 South Alaska Peninsula MU:

 Continue to conduct aerial 

survey counts at established 

index sites.
7-4 Southwest Alaska DPS of the Northern Sea Otter

 Kodiak, Kamishak, Alaska Peninsula 

MU:

 Continue to conduct aerial 

survey counts at established 

index sites.

1.1.3. Identify areas of consistently 

high sea otter abundance, relative 

to other areas, within each 

management unit—Priority 2

Repeated surveys identified in item 

1.1.2 have the potential to identify areas 

where sea otters occur in consistently 

high numbers over time. Knowledge of 

where such areas are will be very useful 

for evaluating the adequacy of habitat 

protection measures. Also, a detailed 

analysis of the characteristics of habitat 

in those areas could provide valuable 

insight into the importance of habitat in 

recovery. 

1.1.4. Evaluate potential biases in survey 

data and identify methods that 

may be useful for correcting such 

biases—Priority 2

Both aerial and skiff surveys have 

biases. With the exception of doublecounting otters, the vast majority of bias 

in survey data is negative (i.e., it causes 

counts and estimates to be lower than 

the actual number of animals). Factors 

that may contribute to bias in survey 

results include search intensity, observer 

experience and fatigue, environmental 

conditions, and habitat types. The 

overall effect of bias in survey results is 

a reduction in the accuracy of counts or 

estimates, which in turn may compromise 

assessment of population trends. 

Analyses of survey data may help identify 

the most important sources of bias and 

facilitate development of methods to 

correct for that bias. 

1.1.5. Refine estimates of equilibrium 

density and carrying capacity for 

the Bristol Bay, South Alaska 

Peninsula, and Kodiak, Kamishak, 

Alaska Peninsula MUs—Priority 1

Estimates of sea otter equilibrium 

density and carrying capacity do not 

exist in the scientific literature for the 

Bristol Bay, South Alaska Peninsula, and 

Kodiak, Kamishak, Alaska Peninsula 

MUs. The estimates of equilibrium 

density developed by Burn et al. (2003) 

were based on survey-specific data from 

the Aleutian archipelago, and are not 

considered applicable to these other 

three MUs. Along with developing PVA 

models for each specific MU (item 1.5.1), 

valid estimates of equilibrium density 

and carrying capacity will be needed to 

update demographic de-listing and uplisting criteria, and this is a high priority 

item.

1.1.6. Evaluate the overall population 

monitoring program on an ongoing 

basis to assess sensitivity—

Priority 1

The ability to detect population trends 

is largely dependent on the precision 

of the population estimates calculated 

from survey results. As new survey 

information is collected, the results 

should be analyzed to determine if 

sample size and survey frequency are 

sufficient to detect population trends 

with the desired level of confidence, or 

alternatively if adequate information 

might be obtained through reduced 

survey effort in some areas. Results of 

this analysis may also be used to guide 

survey design considerations in item 

1.1.1. As this item relates to evaluating 

the use of survey information to detect 

population status relative to delisting 

criteria, it has high priority. 

1.2. Estimate vital rates

Information on sea otter demography is needed 

to help understand the causes of changes 

in abundance, and to predict likely rates of 

population decline or recovery. Demographic 

studies are conducted in two main ways—

by taking measurements from individuals 

and by making observations at the level of 

populations. Individual-based studies generally 

require that otters be tagged or marked 

so that they are individually recognizable 

and their fates can be followed over time to 

estimate survival and reproductive rates. 

Standardized, safe techniques for sea otter 

capture, immobilization, instrumentation, 

and marking have been developed and are 

readily available. Capture operations also 

provide opportunities to collect biological 

samples and information (e.g., teeth can be 

collected for age determination, blood and 

tissue samples can be collected for health 

screening and genetic studies, and body length 

and mass can be measured for assessments of 

nutritional status). Under some circumstances, 

sampling at the level of the population may 

be necessary or preferable to longitudinal 

study of individuals (one that follows the same 

animals over a period of time). For example, 

adequate sample sizes of individually marked 

animals may be difficult to obtain because 

densities of individuals are low or because 

particular habitats have certain characteristics. 

Population-level estimates of vital rates can be 
Recovery Plan 7-5

derived from the standing age structure of the 

living population, the age-at-death distribution, 

or a combination of the two. 

1.2.1. Estimate birth rates—Priority 3

Information on sea otter reproductive 

status can be obtained from examination 

of reproductive tracts of dead animals 

(obtained from subsistence harvest or 

strandings), or potentially by analysis of 

hormones in blood taken from animals 

live-captured and released. However, 

it is unlikely that sufficient samples 

will be available from such sources 

to quantify birth rates in each of the 

various MUs of the DPS. During skiff 

surveys at index sites, pups and adults 

are recorded independently, and the ratio 

of pups to adults can provide a relative 

index of reproductive rates that can be 

monitored over time. Because a low 

rate of reproduction is not considered 

to be a causal factor in the decline, or an 

impediment to recovery, a low priority 

should be given to estimating birth rates. 

1.2.2. Estimate age- and sex-specific 

survival rates—Priority 1

An excessively high mortality rate 

appears responsible for the decline in 

southwest Alaska sea otters, at least 

in the Aleutian Islands, and this factor 

will constrain recovery until survival 

improves. Therefore, monitoring 

survival in the current population will 

be essential for projecting recovery and 

understanding constraints to recovery, 

and such monitoring is a high priority. 

Although the elevated mortality during 

the Aleutian decline was apparently 

constant across all ages, demographic 

recovery models require information 

on survival rates for specific age and 

sex classes. The most direct method 

to measure such rates is to follow 

individually identified animals and 

determine at what age they die. Given 

the geographical and environmental 

conditions, and the low density of 

animals in much of southwest Alaska, 

an intensive effort will be required to 

obtain useful data on survival by such 

a method. An alternative is to locate 

carcasses of animals that have died and 

determine their age (from teeth) and sex 

(from observing reproductive organs or 

analyzing genetic samples). However, 

such samples are clearly biased against 

sources of mortality such as predation, for 

which carcasses will not be available for 

examination. In low-density populations, 

it may be impossible to obtain enough 

carcasses to determine survival rates 

reliably. Studies of survival rates might 

be possible in MUs where otter numbers 

are still relatively high (e.g., Kodiak, 

Kamishak, Alaska Peninsula MU or 

Bristol Bay MU), but inferences for 

areas where otters are not declining are 

unlikely to be applicable for areas where 

a decline is underway. 

1.3. Monitor health and body condition

Although it is difficult to understand all of 

the specific causes of the recent sea otter 

population decline, useful inferences can 

sometimes be made based on the condition of 

living animals. Specifically, comparing a suite 

of health and morphometric variables between 

MUs that have declined greatly and units 

with relatively high abundance could provide 

useful indicators of the status of the depressed 

populations. 

Methods to assess body condition in sea otter 

populations include capturing, handling, 

measuring, and sampling of tissues from live 

animals. Such methods are well developed 

and can be employed throughout most of the 

habitat occupied by sea otters. However, in 

areas where individuals occur at very low 

densities, a high level of effort by researchers 

will be required. 

1.3.1. Monitor general body condition—

Priority 3

Recent analyses of age- and sex-specific 

weights, total lengths, and other linear 

dimensions of sea otters show that such 

measurements are sensitive indicators 

of nutritional status. Contrasts in body 

condition between animals from areas 

with stable populations and animals from 

areas with declining populations thus 

may provide supplementary evidence 

to resolve competing hypotheses on the 

cause of the decline or the constraints 

to recovery. Similarly, contrasts based 

on the screening of blood, serum, and 

other tissues for evidence of disease, 

contaminants, and immune system 

function are likely to provide further 

insights into the cause of decline and 

the status of the populations relative to 

recovery. Because food limitation is not 

thought to be a cause of the sea otter 

decline, and data on body condition have 

already been collected in the area of 

decline, this action is a low priority at 

present. Nonetheless, such data should 

be collected and archived whenever 

animals are captured for other purposes, 

as baseline data will be important for 

interpreting any changes in status over 

time.
7-6 Southwest Alaska DPS of the Northern Sea Otter

1.3.2. Analyze data for evidence of food 

limitation—Priority 2

Sea otters are unique among 

marine mammals in that they feed 

almost exclusively on large benthic 

invertebrates, usually in shallow waters 

close to shore, and bring their prey to 

the surface. Therefore, experienced 

observers can record dive attributes 

(e.g., dive times, surface times, and prey 

capture success rates) and sizes, numbers, 

and taxonomic classes (often to genus or 

species) of prey consumed. Those records 

can be used to estimate diet diversity, 

energy recovery rates, and activity-time 

budgets, which can indicate the current 

status of those units relative to prey 

availability. Because of the potential for 

additional factors to constrain recovery 

and the broad inferential capacity of these 

types of data, this action is a moderate 

priority.

1.4. Monitor distribution and movements—

Priority 1

A fundamental shift in the fine-scale 

distribution of sea otters in the Aleutian 

Islands has occurred over the period of 

population decline, with otters now associated 

mostly with very shallow-water habitats, 

often less than 2 m deep, and rarely occurring 

in deeper or offshore waters. Given the clear 

change that has occurred with the population 

decline in the Aleutians, measuring and 

monitoring distribution and habitat use 

patterns in each MU is a high priority, as 

variations in these metrics might be used to 

monitor changes in the extent of the decline or 

in the factors that have caused it.

Fine-scale distribution and habitat use can be 

quantified by systematic visual observations 

of live sea otters, their locations, and their 

activities. Observations should be made across 

broad geographic areas, and sampling protocols 

should be developed with consideration for 

potential biases (e.g., distance bias in visual 

observations). Another more powerful and 

potentially less biased approach is to attach 

telemetry instruments to animals and follow 

their movements over time. This can provide 

detailed data on habitat use by individuals over 

extended periods.

Another important aspect of distribution 

and movements is the frequency and 

nature of large-scale movements and 

inter-island dispersal. This is important 

both for understanding and explaining 

apparent changes in local abundance and for 

understanding the likelihood and possible 

rates of recolonization of depleted areas. Data 

on large-scale movements and inter-island 

dispersal are also needed for parameterization 

of the PVA model. Methods for obtaining 

such information are tracking with satellitelinked tags and detailed examination of 

genetic characteristics of animals. Both of 

these techniques may require development of 

additional tools, and they will, in any event, 

be difficult to apply in areas with very low 

densities of otters. However, given the high 

value of such data, both efforts to collect data 

and the development of new techniques for 

doing so should be considered high priorities.

1.5. Continue development of population 

viability analysis models

1.5.1. Investigate PVA models for 

the Bristol Bay, South Alaska 

Peninsula, and Kamishak, Kodiak, 

Alaska Peninsula MUs—Priority 1

As part of the development of this 

recovery plan, a PVA has been created 

for southwest Alaska sea otters 

(Appendix 2) and used to describe 

demographically-based delisting and 

reclassification criteria in this recovery 

plan (sections V.C.1 and V.D.1). The PVA 

model was developed using demographic 

data collected in the Aleutian Islands. 

At the time this plan was developed, 

demographic data for other MUs were 

limited, so results from the Aleutians 

PVA were applied provisionally to all 

MUs. As additional data are collected 

(e.g., items 1.1, 1.2, and 1.4 of this section), 

additional modeling should be carried 

out to either verify that the existing 

PVA is appropriate for application to all 

MUs, or to create specific PVAs for the 

Bristol Bay, South Alaska Peninsula, and 

Kamishak, Kodiak, Alaska Peninsula 

MUs. Because the demographic status 

of each individual MU is important for 

evaluating listing status of the DPS as a 

whole this is a high priority.

1.5.2. Revise and re-run the PVA 

model(s) incorporating new data—

Priority 1

The existing PVA model uses 

demographic data collected in the 

Aleutian Islands through the year 2008. 

It is expected that substantial new 

data will be collected in the Aleutian 

Islands and other MUs as the action 

items described in this recovery plan are 

undertaken. At appropriate intervals 

(e.g., every five years) those new data 

should be incorporated into the PVA(s) 

and the model(s) should be re-run. After 

that, target demographic thresholds for 

delisting and reclassification should be 

revised, if necessary. Because results 
Recovery Plan 7-7

from the PVA are an essential part 

of determining whether or not the 

southwest Alaska DPS is recovering this 

is a high priority.

1.6. Monitor the functional role of sea otters 

in ecosystems

1.6.1. Monitor status of the kelp forest 

ecosystem—Priority 1

Sea otter predation is an essential process 

in maintaining nearshore reef habitats 

as kelp forest ecosystems, and in rocky 

areas the status of a regional sea otter 

population can be inferred from the state 

of kelp forests. The recent collapse of 

the sea otter population in southwest 

Alaska caused a shift to the deforested 

phase state throughout all or most of 

the Aleutian archipelago. This change 

was documented through SCUBA-based 

monitoring at various islands across the 

archipelago. Further analysis of these 

data has shown that a large-scale phase 

shift, from the currently deforested 

phase state to the kelp forest phase state, 

could be detected with a high level of 

confidence by sampling as few as 5 to 10 

sites at several islands (Appendix 1). A 

sampling effort of this sort could easily 

be accomplished with four divers (two 

dive teams) in 1-2 days at each island, less 

than the time required to complete the 

skiff surveys.

Kelp forest monitoring should be 

carried out in conjunction with the 

USFWS skiff-based sea otter monitoring 

program, using the established USGS 

kelp forest monitoring protocol (as 

described by Estes and Duggins [1995] 

and in Appendix 1). The reassessment 

of ecosystem status will require samples 

from at least 4 of the following 5 specific 

islands: Attu (Near Islands), Amchitka 

(Rat Islands), Adak (Andreanoff Islands), 

Chiginadak (Islands of Four Mountains), 

and Akutan (Fox Islands). A minimum of 

10 sites should be sampled per island. 

When monitoring the status of the kelp 

forest ecosystem in southwest Alaska, 

researchers should be aware of, and pay 

attention to, ecological factors other than 

sea otters that might be affecting the 

abundance of urchins (and other major 

kelp predators) and of kelp.

1.6.2. Develop methods to monitor 

sea otter impacts on marine 

ecosystems with soft-sediment 

substrates —Priority 2

The direct effects of persistent sea otter 

foraging on their prey species generally 

include reductions in abundance and 

size distribution, and such reductions 

have been documented for a variety 

of species, including abalones, crabs, 

mussels, urchins, and clams, over a broad 

geographic area. Because sea otters 

appear to prey preferentially on larger 

individuals, there is a consistent shift 

toward smaller size classes and reduced 

mean prey size as sea otters colonize 

previously unoccupied habitat. It is 

possible that these direct effects of sea 

otter predation on prey could be used 

in ways similar to the kelp forest/urchin 

barren phase state as a tool to evaluate 

the status of sea otter populations 

(Appendix 1). 

For example, if otters at equilibrium 

density effectively maintain truncated 

prey size distributions by consuming most 

individuals above some particular size, 

then one could reasonably hypothesize 

that reduced otter density will result in 

a shift in the distribution toward larger 

individuals and an increase in the mean 

size of preferred prey species. Such 

an approach for evaluating sea otter 

populations is appealing for several 

reasons, including its relatively low cost 

and high statistical power, as well as the 

fact that it provides a way to assess the 

role of food limitation in influencing otter 

populations in the nearshore ecosystem. 

However, in contrast to the breadth of 

data supporting the idea that the sea 

otter functions as a “keystone species” in 

the nearshore, maintaining kelp forests 

through predation on herbivorous sea 

urchins, the data on direct effects of 

otter predation on prey size are both 

geographically and taxonomically limited. 

Because of the abundance of softsediment habitats across much of 

the range of this DPS, and given the 

importance of clams in the otter’s diet in 

such habitats, clams may be appropriate 

for testing such hypotheses in these 

habitats. Also, given the presence 

and consumption of intertidal mussels 

throughout the sea otter’s range, and the 

availability of historical data, mussels 

may provide similar opportunities. 

Initially, research could explore the 

functional relationship between otter 
7-8 Southwest Alaska DPS of the Northern Sea Otter

density and prey population attributes 

(e.g., mean size, size distribution, and 

biomass) and the potential generality of 

findings across broad geographic scales. 

1.7. Develop new research tools —Priority not 

assigned

There is a great deal of uncertainty about the 

factors that may act to influence population 

recovery. While marine mammals are 

inherently difficult to study, a variety of 

recently developed technologies could be used 

to obtain data relevant to sea otter recovery 

actions in southwest Alaska. The continued 

development of such tools is important for 

marine mammal science in general, and is not 

assigned a priority or cost in this recovery 

plan specific to southwest Alaska sea otters. 

Nonetheless, some potentially promising 

areas for investigation are identified below. 

Given the broad scope of these research tools, 

priority and costs could not be assigned to this 

item.

 TDRs—TDRs can provide long-term, 

high-resolution data on sea otter 

dive depths, habitat use, dietary 

specialization, activity budgets, 

female reproductive events, and 

causes of death. These instruments 

should be considered for deployment 

in any project involving live 

capture and marking. Because the 

instruments are archival, they must 

be recovered in order to obtain the 

data, and this requires recapture 

of the marked animals. However, 

pop-off TDR instruments that do 

not necessitate recapture could be 

developed for sea otters. 

 Satellite transmitters—Conventional 

radiotelemetry techniques, while 

greatly extending our knowledge of 

sea otter biology and ecology, only 

allow for tracking animals within 

a limited range. Satellite-linked 

transmitters have been used on a 

number of wide-ranging species 

and could be developed for use with 

sea otters. Because spatial and 

temporal resolution of locations 

from the ARGOS doppler system 

are relatively poor, incorporation of 

GPS technology will be essential for 

studies addressing fine-scale habitat 

use. 

 Survival pop-up tags—Estimating 

sea otter survival with visual 

observations and conventional 

radiotelemetry requires intensive 

and costly monitoring. Even then, 

some deaths go undetected, meaning 

that untestable assumptions need to 

be made about the fates of missing 

animals. Satellite-linked tags that 

transmit a specific signal when an 

animal dies would provide better 

information on the timing, location, 

and perhaps causes of mortality. 

 Stable isotope and fatty acid 

analyses—Stable isotopes in tissues 

can provide information on trophic 

status of species on various time 

scales. Analyses of stable carbon and 

nitrogen isotopes in the teeth of sea 

otters may show patterns of variation 

that are characteristic of growing and 

food-limited populations, and analyses 

of killer whale teeth could help clarify 

the role of killer whale predation in 

the sea otter decline. Analyses of 

fatty acid signatures in sea otters and 

their prey and in potential predators 

may provide similar information.

 Molecular genetics—Recent 

advances in analytical approaches 

to molecular genetic data have 

potential application to questions of 

dispersal distances and recolonization 

probabilities of sea otters. Tissue 

samples from sea otters in many 

locations in southcentral and 

southwest Alaska are available 

and could be used to evaluate, for 

example, the likelihood of dispersal 

across Aleutian Island groups 

separated by various distances. 

Advanced molecular genetic methods 

could also be applied to historical 

samples to examine the frequency 

and duration of population declines 

in the past, for example, following 

human occupation of North America. 

2. Habitat needs and habitat protection 

2.1. Identify important habitats or areas of 

special biological significance

There is virtually no evidence to suggest that 

the quality or quantity of sea otter habitat, 

as traditionally defined, has been reduced or 

degraded within the range of the southwest 

Alaska DPS. However, reasonably good 

evidence exists to suggest that use of that 

habitat, at least in the Aleutian Islands MUs, 

has shifted significantly. Prior to the decline, 

sea otters were widely distributed and most 

prevalent in canopy-forming kelp forests, 

generally in water depths from 5-20m and often 

hundreds of meters from shore, depending on 

bathymetry. Following the decline, the otters 

have become highly aggregated in very shallow 

water a few meters or less in depth and most 

often less than tens of meters from shore. This 
Recovery Plan 7-9

shift in use of habitat may be explained as a 

response to increased predation pressure. The 

observed shift in habitat use suggests that 

these very nearshore areas are important to 

the conservation of sea otters. Consequently, 

improved understanding of the relationships 

between current sea otter distribution and the 

features that define this shift in habitat use is a 

high priority.

2.1.1. Identify characteristics of areas 

that are serving as refuges for 

remnant groups of sea otters—

Priority 1

Locations of sea otters can be accurately 

determined through visual observations 

and plotted in a geographic information 

system. Efforts to date have largely 

consisted of plotting locations of otters 

encountered during population surveys. 

Those data provide little information on 

how animals are using the habitat for 

specific behaviors (e.g., foraging, resting, 

socializing), and on what features of 

that habitat are making it particularly 

suitable. Additional insights could be 

gained through following individuals over 

time and identifying specific areas and 

specific types of habitat used for specific 

kinds of behavior. Such work could 

employ visual scan sampling or telemetry. 

A variety of techniques (e.g., SCUBA 

sampling, remote sensing) should then 

be applied to describe the physical and 

biological attributes of those areas. 

2.1.2. Explore the feasibility of creating 

more areas that can serve as 

refuges—Priority 2

Certain features appear to define those 

habitats where sea otters are now most 

commonly found. These include very 

shallow constrictions leading to protected 

waters (e.g., Clam and Shagak lagoons at 

Adak Island), highly convoluted shallow 

nearshore habitats with close access to 

the supratidal, and kelp beds associated 

with shallow-water benthic features. 

Such features can be interpreted as 

providing refuge from predation by 

limiting access of killer whales. However, 

the relationships between habitat 

features and sea otter density are not 

well understood. Efforts to improve this 

understanding could include analyses 

of existing survey information on the 

current spatial distribution of sea otters 

and information on the physical attributes 

associated with that distribution. Such 

analyses would likely benefit from the 

types of work identified under 2.1.1. 

It may prove feasible to modify some 

existing habitats and thus enhance the 

features responsible for creating what is 

considered refuge from predation.

2.1.3. Protect important habitats—

Priority 2

Specific relationships between habitats 

and use (or avoidance) by sea otters in the 

Aleutian Islands following the decline are 

not well understood. The actions identified 

under 2.1.1 should improve understanding 

of habitat features and sea otter use. If it 

is determined that certain habitat features 

provide sea otters with refuge from predation, 

this could lead to the identification of habitats 

and habitat features that merit additional 

assessment and protection. Habitat protection 

could be accomplished through educational 

efforts, consultation, and regulation. It should 

be noted, however, that the identification 

and protection of habitats where sea otters 

presently persist may not be adequate, by 

itself, to achieve recovery criteria, as the 

available evidence suggests those specific 

habitats are rare and constitute a relatively 

small proportion of what is considered typical 

sea otter habitat. 

2.2. Prepare a habitat conservation plan 

under Section 10 of the ESA for Alaska 

state-managed fisheries—Priority 3

The majority of sea otter habitat in southwest 

Alaska is located within State of Alaska 

waters, i.e., from the mean high tide line 

seaward to a distance of three miles. Habitat 

conservation plans provide private landowners, 

corporations, and state or local governments 

with a means to obtain permits for activities 

that might incidentally take ESA-listed 

species. The development of a habitat 

conservation plan for sea otters would allow 

FWS and the State of Alaska to work together 

to minimize the impact of commercial fisheries 

and other human activities on the recovery of 

sea otters in southwest Alaska. Direct impacts 

from commercial fisheries and other human 

activities are not believed to be a significant 

threat to recovery of the southwest Alaska 

DPS, therefore this item is a low priority.

3. Manage impacts of human uses

3.1. Ensure that Alaska Native subsistence 

harvest does not affect recovery

3.1.1. 3.1.1. Continue to estimate 

harvest levels through the FWS 

marine mammal marking, tagging, 

and reporting program (MTRP)—

Priority 3

Section 10 of the ESA includes an 

exemption for subsistence harvest of a 
7-10 Southwest Alaska DPS of the Northern Sea Otter

listed entity by Alaska Natives, provided 

that such taking does not materially 

and negatively affect the listed entity. 

In order to evaluate the impact of the 

subsistence harvest, it is necessary 

to monitor the number, age, sex, and 

geographic distribution of harvested sea 

otters. This is an ongoing responsibility 

of FWS, which operates the marine 

mammal MTRP. This program was 

initiated in December 1988 and operates 

using FWS base funding. As this is an 

ongoing program that is regularly funded, 

this item is a low priority.

3.1.2. Quantitatively evaluate the impact 

of sea otter harvest on recovery—

Priority 2

In areas where subsistence harvest 

occurs regularly and at levels likely to 

have more than negligible impacts (i.e., 

more than one or two animals every few 

years), quantitative analyses should 

be conducted to evaluate the possible 

population-level consequences of harvest 

mortality. Such analyses are of relatively 

low priority in all MUs west of Kodiak, 

where subsistence harvest is considered 

to be of low importance to recovery, 

but they are of moderate priority in the 

Kodiak, Kamishak, Alaska Peninsula MU, 

where it is considered to be of moderate 

importance to recovery and highly 

amenable to management. In this MU, the 

potential consequences of a given level 

of harvest should be evaluated regularly, 

and the expected impacts gauged over 

a range of spatial scales (from the local 

population up to the scale of the MU) 

and time frames. Analyses should use a 

formal demographically-based population 

model such as an age- or stage-structured 

projection matrix, parameterized with 

the best available estimates of vital rates 

for the specific area or habitat in question. 

Note that the spatially structured matrix 

model developed for the PVA (Appendix 

2) constitutes just such an analytical 

framework. Use of spatially explicit 

demographic models is important because 

results can often be non-intuitive, and 

may not be well predicted simply by 

considering total mortality level as a 

fraction of existing population size. 

The procedural approach to such an 

analysis is fairly straightforward. 

Briefly, a population vector is created 

by multiplying the best estimate of 

the current population size (for the 

area in question) by the stable stage 

distribution vector associated with 

the projection matrix (see Appendix 

B for a further description), and then 

using standard matrix multiplication 

techniques to project population 

dynamics (over a 10-25 year period) with 

and without incorporating the additional 

harvest mortality. Harvest mortality 

should be apportioned to age and sex 

classes according to actual reported 

ratios, and multiple iterations of the 

simulation should be run to account 

for environmental and demographic 

stochasticity (randomness), as well as 

the uncertainty associated with harvest 

levels, vital rate estimates, and initial 

population sizes. By comparing expected 

population growth trajectories and final 

population sizes with and without the 

harvest mortality incorporated, managers 

can evaluate the likely impact of harvest 

mortality on population persistence and/

or probability of recovery. In particular, 

the relative importance of a given set of 

results can be evaluated by answering 

three questions: 1) what is the magnitude 

of the effect (i.e., the percentage decrease 

in expected growth rates associated 

with the harvest); 2) does the effect lead 

to a qualitative change in the expected 

outcome for the population (i.e., the 

population would have positive growth 

without harvest but negative growth 

with the harvest); and 3) what is the scale 

over which the effect occurs (i.e., what 

proportion of the MU would experience 

the effect in question)? Clearly, a harvest 

that results in a substantial decrease 

in population growth over a very large 

area would constitute the greatest cause 

for concern. However, in certain cases a 

significant impact that occurs only at a 

local scale – for example, at a single island 

within an island group – might still be 

important to the recovery of the entire 

management unit if that island happens 

to have a key “source population” (i.e., it 

is an area of net positive growth) and is 

surrounded by other islands with either 

declining or depleted otter populations.

3.1.3. Develop sea otter harvest 

management plan(s) with Alaska 

Native tribes and tribally 

authorized organizations—Priority 3

Although Section 10 of the ESA does not 

allow regulation of subsistence harvest 

prior to a determination that such harvest 

is materially and negatively affecting the 

species or population, FWS should work 

with individual Alaska Native tribes 

and tribally authorized organizations 

to develop local and regional sea otter 

management plans that minimize 

the impact of subsistence harvests 
Recovery Plan 7-11

on recovery. The current levels and 

geographic distribution of the harvest do 

not suggest that it is a significant threat 

to recovery of the DPS; therefore, this is 

a low-priority item.

3.1.4. Continue outreach to promote 

“males only” harvest—Priority 3

FWS has been working with MTRP 

taggers in southwest Alaska since 2006 

to distribute handouts that request sea 

otter hunters to avoid harvesting female 

sea otters, especially ones with pups. 

The FWS should take advantage of other 

outreach mechanisms and opportunities 

to distribute this message to sea otter 

hunters. 

3.1.5. Use pelts from stranded otters to 

reduce demand for subsistenceharvested animals—Priority 3

The sea otter stranding program is 

a potential source of pelts that are 

currently used for educational purposes. 

Over the past several years, the 

program has taken in far more pelts than 

have been distributed to educational 

institutions. One possible use of surplus 

pelts would be to offset the harvest of sea 

otters in southwest Alaska by making 

these pelts available to artisans for 

handicraft purposes. Current levels of 

subsistence harvest are not considered to 

represent a significant threat to recovery; 

therefore, this item is a low priority.

3.2. Ensure that incidental take in fisheries 

does not affect recovery 

3.2.1. Monitor the level of incidental take 

in fisheries within the sea otter’s 

range in southwest Alaska—

Priority 2 

The NMFS fishery observers have 

collected valuable data on the frequency 

of sea otter bycatch in numerous Alaskan 

fisheries. Their data show that trawl, pot, 

and longline fisheries conducted within 

the range of the southwest Alaska sea 

otter DPS operate in deep waters and 

rarely interact with sea otters. Data on 

bycatch collected by NMFS observers in 

southern Alaska Peninsula, Kodiak, and 

Cook Inlet waters have shown that sea 

otters encounter coastal salmon set and 

drift gillnets but that entanglement is 

infrequent and rarely lethal. Bycatch in 

Alaskan fisheries is currently considered 

a low threat to southwest Alaska sea 

otters and the monitoring of incidental 

mortality in fisheries by observers is 

therefore a low priority. However, rates 

may vary over time, and therefore FWS 

should support the continued collection 

of sea otter bycatch data in existing 

NMFS observer programs and develop 

programs for coastal fisheries in those 

parts of the southwest Alaska sea otter 

range that have had little or no prior 

observer coverage (e.g., northern Alaska 

Peninsula, Bristol Bay).

3.2.2. Quantitatively evaluate the impact 

of incidental take on recovery in 

each management unit—Priority 3

The number, age, sex, and reproductive 

status of animals killed incidentally in 

fisheries will dictate the overall impact 

of bycatch on the population. Also, 

because the potential exposure of sea 

otters to entanglement in Alaskan 

fisheries varies regionally, FWS should 

evaluate the frequency and demographic 

characteristics of lethal sea otter bycatch 

within each management unit. The data 

can then be used in a model as described 

under 3.1.2 to quantitatively evaluate 

the impact of incidental take on recovery. 

Existing observer data suggest that 

mortality rates are low; therefore, 

quantitative assessment of the impact of 

incidental take on recovery in each MU 

should be considered a low priority.

3.2.3. Develop programs to reduce 

incidental take in situations where 

it occurs—Priority 2

Alaskan salmon fisheries may operate 

in shallow coastal waters where sea 

otters aggregate. Once entangled, sea 

otters are often able to free themselves 

from surface-hung nets but they 

sometimes require human intervention. 

In those situations, the sea otter’s fate is 

dependent on the fisherman’s response. 

Therefore, FWS should develop outreach 

material for distribution to Alaskan 

salmon fishermen that 1) discourages 

them from setting gear in areas of high 

sea otter concentration and 2) encourages 

rapid response and proper techniques 

for safely releasing entangled otters. 

Because this represents an inexpensive 

means of reducing the mortality rate for 

entangled otters, it should be considered 

a moderate priority. 

3.3. Ensure that entanglement in marine 

debris does not affect recovery

3.3.1. Encourage net recycling and 

environmentally responsible 

disposal—Priority 3
7-12 Southwest Alaska DPS of the Northern Sea Otter

While there are no data to suggest that 

entanglement in debris has had an impact 

on the southwest Alaska sea otter DPS, 

it is well known that marine debris 

frequently causes deaths of other wildlife. 

In some other regions (e.g., Hawaii), 

public-private partnerships have formed 

that encourage and facilitate the proper 

disposal of nets and other potentially 

entangling materials. A similar program 

should be established in southwest 

Alaska.

3.4. Eliminate intentional illegal take

There is little evidence that illegal take of sea 

otters is a widespread threat in southwest 

Alaska. However, efforts to minimize and 

eventually eliminate this source of sea otter 

mortality should be pursued.

3.4.1. Establish an outreach program on 

the ecological role of sea otters in 

the nearshore marine ecosystem—

Priority 3

Sea otters are often viewed as 

competitors for crab and shellfish 

resources that are taken for commercial 

and/or subsistence purposes. The role of 

sea otters in structuring the nearshore 

marine ecosystem is not well-known to 

coastal human communities in Alaska. 

The FWS should develop outreach 

materials for distribution throughout 

southwest Alaska that emphasize the 

importance of sea otters to overall health 

of the ecosystem. 

3.4.2. Increase enforcement efforts to 

investigate and prosecute cases of 

illegal killing—Priority 3

The FWS OLE currently relies on 

citizens to report incidents of sea otters 

being killed illegally. FWS should 

increase law enforcement activity in 

southwest Alaska to investigate and 

prosecute cases of illegal killing of sea 

otters, in an effort to minimize and 

eventually eliminate this source of 

mortality. Illegal killing is not believed to 

be a significant threat to recovery of the 

DPS, so this is a low-priority item.

3.5. Evaluate potential impacts of scientific 

research activities—Priority 3

Permits for scientific research are processed by 

the FWS Division of Management Authority 

based in Arlington, Virginia. The potential 

impacts of research activities on sea otters 

in southwest Alaska should continue to be 

considered in the context of other human 

activities that have the potential to affect the 

DPS. 

4. Disease 

4.1. Evaluate the potential role of disease as a 

threat to recovery 

To understand the possible role of disease in 

the decline, both living and dead sea otters 

should be examined. Examination of carcasses 

provides information on the demographic 

characteristics of the mortality event, and 

insights into the significance of different 

disease processes, anthropogenic contaminants, 

biotoxins, human interaction, predation, and 

nutritional factors (although some factors such 

as predation may be underestimated due to 

carcass consumption and loss). Concurrent 

study of live-captured animals provides critical 

information on temporal and spatial exposure 

to infectious disease, potential for isolation 

and characterization of infectious agents, 

measurement of physiological parameters of 

stress or disease, general health and body 

condition, contaminant exposure, and diet. 

Ideally, results of live-animal investigations 

should be integrated with the necropsy data 

to determine the relevance of the described 

disease agents and possible effects at the 

population level. Modeling efforts should be 

undertaken to both better understand and 

predict the influences of pathogens on sea otter 

population decline and recovery.

4.1.1. Expand the marine mammal 

stranding network in southwest 

Alaska—Priority 2

Examination of carcasses of dead sea 

otters can provide information on the 

causes of their deaths, and help to assess 

the importance of potential threats 

such as predation (action 5.1) as well 

as disease. Currently, most of the sea 

otter carcasses found in Alaska are from 

the Kachemak Bay area where there is 

a strong and well-supported stranding 

program and a relatively large human 

population. Other parts of Alaska, 

particularly the southwest, lack the 

human population base; also, the value 

of carcasses and mechanisms to collect, 

store, and transport them may have 

not been adequately communicated in 

such areas. The FWS should make sure 

that local residents and researchers 

working in southwest Alaska are 

aware of the interest in carcasses, and 

if transportation of carcasses is not 

practical, FWS should train local people 

on necropsy protocols. This could include 

not only sea otter researchers, but also 

other marine mammal scientists, FWS 

refuge staff, Alaska Natives, and others. 
Recovery Plan 7-13

Fresh carcasses could be frozen for later, 

more extensive, necropsies. 

4.1.2. Use carcasses and tissues from 

subsistence-harvested animals to 

investigate disease processes—

Priority 3

Currently, there is some collaborative 

work with Native hunters in the 

Kachemak and PWS areas to collect 

carcasses and tissues. Since very few sea 

otters from the southwest Alaska DPS 

are taken by Alaska Natives, effort in 

this area might not be very productive. 

Nonetheless, Native people who are on 

the water or the shoreline engaged in 

other subsistence activities could be very 

helpful in finding and collecting carcasses, 

and their participation in such efforts 

should be encouraged. 

4.1.3. Sample sea otter carcasses and 

tissues—Priority 2

Full necropsies of sea otter carcasses, 

with subsequent analyses for diseases 

and contaminants, are time-consuming 

and expensive. Base support to continue 

this work should be provided through 

FWS and the USGS. 

4.1.4. Conduct live-capture studies to 

examine health status of live sea 

otters—Priority 2

Morphometric and health screening 

information can be collected in 

conjunction with behavioral and 

demographic studies or whenever sea 

otters are captured and sedated. During 

capture and handling, researchers 

should adhere to a detailed and rigorous 

protocol for collecting morphological 

and health data. Such data are available 

from several sea otter populations over 

various time scales, and from populations 

of varying status relative to resource 

availability. A comprehensive analysis of 

existing datasets should be conducted, 

and the results should be used to aid 

in developing procedures for future 

data collection and interpretation. 

Additionally, specific efforts should 

be initiated to acquire morphological 

and health data from all Alaska sea 

otter stocks. Because health issues 

are evidently not a major factor in the 

southwest Alaska population decline 

but may impede recovery, this action is 

considered a moderate priority.

4.1.5. Evaluate population-level effects 

of identified diseases—Priority 2

Determining whether disease has 

an effect at a population level is 

extremely important and will require 

close collaboration between biologists 

working on population demography and 

pathologists/veterinarians working on 

health and disease topics. For example, 

information on what segment of the 

population appears to contribute most 

to a decline, either through mortality or 

reduced reproductive success, can help 

identify which potential disease processes 

to investigate and which can be ruled out. 

Comparisons of a disease agent across 

populations that are in states of decline 

and increase, along with retrospective 

and prospective studies, can help 

determine whether the disease agent is 

most likely endemic or epidemic. 

4.2. Develop disease management plans 

where practical—Priority 3

In most cases, very little can be done to 

alter a naturally occurring disease situation. 

However, understanding the epidemiology of a 

disease agent can sometimes point to potential 

management options. For example, restricting 

access of sea otters to fish waste was 

effective in reducing the incidence of gastric 

perforations due to Pseudoterranova decipiens. 

Other strategies might be available for other 

disease agents if they were better understood. 

Disease management will be particularly 

important if translocations of otters are 

to occur. Precautions would have to be 

taken to avoid introducing disease agents 

to naïve groups of animals, particularly if 

those groups are small and concentrated in 

small areas. Major considerations in such 

disease management plans should include 

understanding what disease agents are 

endemic in the southwest Alaska DPS as 

well as thoroughly screening any introduced 

animals to avoid introducing a new disease 

agent to the area. Populations that are 

reduced to small numbers in restricted areas 

are particularly susceptible to effects from 

the introduction of a new disease agent. 

If morbilliviruses are determined to be a 

significant risk in these cases, vaccination of 

the remnant populations should be considered.

5. Predation

5.1. Continue to evaluate the role of predation 

as a threat to recovery—Priority 1

Because elevated mortality from predation has 

been identified as the most likely cause of the 

decline in southwest Alaska sea otters, and 

because predation has been ranked high as a 

threat to recovery for three of the five MUs (it 

is ranked as a moderate threat to the Bristol 
7-14 Southwest Alaska DPS of the Northern Sea Otter

Bay and Kodiak, Kamishak, Alaska Peninsula 

MUs – see section IV), continued research 

aimed at understanding patterns, causes, 

and consequences of predation on sea otters 

in southwest Alaska is a high priority. While 

predation by killer whales is thought to be the 

primary driver of the decline in the Western 

and Eastern Aleutian MUs, it is important 

to note that the persistence and eventual 

recovery of these already-depleted stocks 

could be affected by predation mortality from 

species other than killer whales. In particular, 

the potential impacts of predation by sharks 

(on all age classes) and eagles (on pups) should 

be evaluated. 

Some basic information on the occurrence of 

predation may be gathered by examination 

of sea otter carcasses (item 4.1.1). A more 

complete understanding of the patterns, 

causes, and consequences of predation will 

require two types of dedicated research: 

studies focused on sea otter populations 

themselves and studies focused on the 

predators. In the case of sea otter-focused 

studies, research on behavior, habitat use, 

dispersal characteristics, and population 

biology/demography (including measurements 

of survival and reproductive parameters) 

will be important for answering five general 

questions: 1) when and where predation 

mortality has occurred or is occurring, and 

the per-capita rate of predation mortality in 

those areas; 2) how sea otter behavior and 

habitat use patterns have changed (or are 

changing) in response to predation; 3) whether 

refuge from predation mortality is afforded 

by low population density or occupation of 

certain types of habitat, and thus a predictable 

functional relationship exists between density 

and per-capita predation rate; 4) whether 

Allee (underpopulation) effects occur at very 

small population sizes, and the nature of such 

effects; and 5) whether small or low-density 

populations occupying refuge habitats are 

sustainable, and at what rate dispersal occurs 

between such remnant populations. 

The second focal area for further research 

involves studies aimed at better understanding 

the predators (or potential predators) of sea 

otters, especially killer whales. In particular, 

data are needed on the population structure 

of the predators, as well as their range 

use patterns, diet composition, and prey 

consumption rates. It has been demonstrated 

that only a small number of killer whales (one 

or a few pods) could account for the majority of 

the sea otter mortality needed to explain the 

decline. If that is the case, it may be difficult 

to identify those individuals from the total 

population of transient (mammal-eating) killer 

whales in the North Pacific Ocean. Diet studies 

of killer whale pods may be informative here, 

although given the currently low numbers of 

sea otters remaining in the area of decline, 

the potential for failing to detect sea otter 

consumption would be high, even if it were 

still occurring at a high enough frequency 

to be demographically significant for the 

greatly reduced population. Perhaps more 

importantly, a clear understanding is needed of 

how the population of transient killer whales 

is structured spatially and of the typical 

movement patterns of transient pods. Such 

information will be important for interpreting 

the geographic scope of the decline and 

predicting likely recovery patterns. 

5.2. Develop predation management plans, 

where practical—Priority 1

For the most part, the number of feasible 

management actions that could be undertaken 

to mitigate predation mortality on sea otters 

is quite limited. In the unlikely event that 

it could be determined exactly which killer 

whales or other predators were responsible for 

the majority of the sea otter predation, and if 

the decline continued to the point of uplisting 

the DPS to endangered or spread to other 

DPSs, then the option of lethal or non-lethal 

removal of the specific individuals might be 

considered. However, this option is likely to 

be inadvisable under almost any imaginable 

scenario, chiefly due to the likelihood of 

misidentifying the responsible individuals, or 

failing to detect all the responsible individuals . 

Another management approach would involve 

natural or man-made “predation refuges,” 

defined as locations where sea otters are either 

free from predation risk or at much lower 

predation risk due to the limited ability of 

killer whales (and/or other predators) to gain 

access or to hunt there effectively. Potential 

refuges include shallow lagoons or rocky-reef 

areas, as well as embayments with limited or 

only shallow-water access to the open ocean. 

Survey data suggest that some such areas 

within the area of decline already function as 

refuge habitats; sea otter numbers have either 

not declined or have declined more slowly in 

those areas. If sea otter numbers continue 

to drop in southwest Alaska, management 

plans could include the construction of more 

refuge habitats (for instance, by constructing 

“predator exclusion devices” across narrow 

channels, or otherwise altering certain areas of 

habitat to restrict access by killer whales and/

or other sea otter predators). 

6. Protect from other natural or human-caused threats

6.1. Ensure that oil spills do not impede 

recovery of sea otters and/or negatively 

affect the nearshore marine environment 

in southwest Alaska—Priority 1

The risk posed to sea otters by exposure to 

spilled oil is well known: surface contact and 
Recovery Plan 7-15

ingestion of oil from grooming and consuming 

contaminated prey will have both immediate 

and chronic impacts on otters. Thousands of 

sea otters died due to the Exxon Valdez oil spill 

in PWS, and at least a few from the southwest 

Alaska DPS died from the Selendang Ayu 

spill in the Aleutian Islands. Ensuring that 

oil spills do not have an impact on southwest 

Alaska sea otters or their habitat is a high 

priority. Oil spill planning and response are 

primarily the responsibility of the U.S. Coast 

Guard and the State of Alaska Department 

of Environmental Conservation. The FWS 

should work with those agencies and others 

on further development of oil spill response 

plans that 1) identify essential sea otter 

habitats for protection; 2) describe personnel 

and equipment needed to protect those areas 

from contact with oil and to respond to oiled 

otters; and 3) identify the cost of purchasing 

and establishing equipment caches to meet 

sea otter-specific response needs, including 

cleaning and caring for otters that become 

contaminated. Additional measures to 

protect sea otters and the nearshore marine 

environment from oil spills may also be 

developed, such as routing tankers and other 

vessel traffic away from nearshore waters used 

by sea otters.

6.2. Establish an outreach program to 

mariners on how to avoid striking sea 

otters—Priority 3

Available data suggest that otters are seldom 

struck by boats, and such instances would 

be expected to be rare in most of southwest 

Alaska because of the low level of vessel traffic. 

Therefore, actions to avoid such strikes are a 

low priority. However, there is some potential 

for strikes near boat harbors, popular fishing 

areas, etc., and actions should be taken to 

prevent them. The FWS should develop 

outreach materials to inform mariners about 

the potential for hitting sea otters, and provide 

suggestions for ways to minimize the likelihood 

of strikes (e.g., traveling at slow speed in areas 

where otters are sighted). This information 

should be made available to the public by 

posting at boat harbors, in trade journals, in 

fishing regulation books, etc. 

6.3. Continue to measure and monitor 

contaminant levels in sea otters—

Priority 3

There is no evidence that contaminants are 

having an impact on sea otters in southwest 

Alaska at the present time. Measuring 

contaminant levels in otter tissues is therefore 

a low priority. However, tissue samples should 

be collected whenever possible and archived 

for possible future contaminant analysis. It 

is essential that proper protocols be used 

for sample collection and storage. Sample 

materials for this item would be collected 

during necropsies listed under item 4.1.3.

6.4. Monitor occurrence of biotoxins in sea 

otters and their prey—Priority 3

Biotoxins are not known to cause mortality 

in southwest Alaska sea otters but very little 

testing has been conducted. Biotoxins such 

as domoic acid from phytoplankton blooms 

occur off the west coast of the U.S., where 

they have caused mortality in both sea otters 

and pinnipeds. Monitoring for potential 

biotoxin impacts on sea otters in the southwest 

Alaska DPS is a low priority. However, those 

responsible for managing Alaska’s coastal 

marine resources and ecosystems should be 

vigilant for signs of biotoxin impacts on sea 

otters or their prey. The FWS should consult 

with appropriate researchers and produce a 

summary of likely symptoms of biotoxins in 

sea otters, and make that information available 

to people who are in a position to observe 

otters regularly in the wild (e.g., biologists, 

stranding network participants, and Alaska 

Native hunters). People should be prepared to 

collect and submit samples to evaluate whether 

an algal bloom is occurring where wildlife 

mortality is observed. Sample materials for 

this item would be collected during necropsies 

listed under item 4.1.3.

6.5. Evaluate the feasibility of translocating 

sea otters to enhance recovery—Priority 2

If the decline of sea otters in southwest 

Alaska continues, it is possible that densities 

in some areas will become low enough to 

depress reproductive success, and that gaps 

in the range will form that are difficult to 

fill in by natural immigration. Methods for 

capturing and translocating sea otters have 

been developed, and translocation has been 

used to re-establish populations in areas 

where sea otter were extirpated. Evaluation 

of the feasibility of translocating sea otters 

to enhance recovery of the southwest Alaska 

population is a medium priority. The first step 

in such an evaluation would be to develop 

a decision matrix to help determine when 

circumstances are such that translocation 

should be considered. The decision matrix 

should be used in combination with a 

geographic and demographic model of the 

population decline to project when and where 

translocation would be a useful recovery tool. 

If a decision to translocate is made, additional 

feasibility factors will need to be considered, 

such as availability of potential source 
7-16 Southwest Alaska DPS of the Northern Sea Otter

populations, potential genetic consequences of 

moving animals, and whether threats to otters 

in the potential translocation area have been 

mitigated sufficiently. 

6.6. Evaluate potential impacts of recreational 

activities, tourism, and other forms of 

direct human disturbance—Priority 3

Human activities that occur in nearshore 

marine waters throughout southwest Alaska 

have the potential to take sea otters through 

disturbance. The nature and level of activities 

are not well known, and therefore the potential 

impacts are unclear. Comprehensive studies 

of boating, tourism, and other human activities 

that could cause disturbance would help 

identify areas where mitigation measures 

may be necessary. At present, however, such 

studies are a low priority.

7. Implement the recovery program for southwest 

Alaska sea otters

Implementation of the recovery actions in this 

plan will require coordination between multiple 

agencies and organizations, including Federal and 

State agencies, private groups, Alaska Native 

tribes, and tribally authorized organizations. 

7.1. Maintain the Southwest Alaska Sea Otter 

Coordinator position within FWS—

Priority 2

As the federal agency with management 

responsibility under the ESA and MMPA, 

FWS should designate a Recovery Coordinator 

and ensure that this position is adequately 

funded. This item is a medium priority.

7.2. Continue and enhance coordination of 

management efforts among FWS, other 

Federal agencies, Alaska Natives, and 

the State of Alaska—Priority 2

The Recovery Coordinator should work closely 

with various divisions within FWS, other 

federal agencies, the State of Alaska, and 

Alaska Natives to implement management 

actions for the recovery of the southwest 

Alaska DPS. This includes implementation 

of Section 7 (Interagency Cooperation) and 

Section 6 (Cooperation with the States) of the 

ESA and Section 119 of the MMPA (Marine 

Mammal Cooperative Agreements in Alaska), 

killer whale predation and bycatch of sea otters 

(NOAA Fisheries) and oil spill contingency 

planning (USCG). This item is a medium 

priority.

7.3. Continue and enhance coordination of 

research efforts among FWS, USGS, 

other Federal agencies, the State of 

Alaska, Alaska Natives, academic 

institutions, and others—Priority 2

Additional research into threats to the 

recovery of the southwest Alaska DPS is 

needed to help guide future management 

actions. The Recovery Coordinator should 

facilitate communication and cooperation 

among the various research agencies and 

institutions conducting studies of sea otters 

in southwest Alaska. This item is a medium 

priority.

7.4. Develop and continue a program of 

outreach to stakeholders—Priority 3

The results of research studies and 

management actions should be made 

available to agencies and organizations that 

are not directly involved in these activities. 

Management and policy decisions made 

by FWS relative to this DPS should be 

regularly communicated to stakeholders. The 

Recovery Coordinator should take a lead 

role to develop outreach and educational 

materials for distribution to stakeholders and 

other interested parties. Potential outreach 

opportunities include the use of internet web 

pages and list servers, presentations at public 

meetings, and FWS Refuge Information 

Technicians, to name a few. Because at this 

time there are few detrimental interactions 

between people and sea otters, this item is a 

low priority.

7.5. Secure adequate funding for southwest 

Alaska sea otter management and 

research needs—Priority 1

This recovery plan identifies research and 

management actions with annual total costs 

ranging from $2.665M -$3.650M over the first 

five years. One of the greatest challenges will 

be to secure funding needed to implement the 

plan. The principal Federal agencies involved 

(FWS and USGS) should commit adequate 

funding to support their in-house management 

and research activities as identified in this plan. 

The Recovery Coordinator should work with a 

broad range of partners to develop proposals 

for research and management activities 

identified in the plan and ensure that the 

highest-priority activities receive funding. As 

the ultimate success or failure of the recovery 

program depends on the ability to implement 

the plan, this item has high priority.
Recovery Plan 8-1

8. Implementation Schedule

A. Key to Responsible Agencies

ADEC Alaska Department of Environmental Conservation

ADFG Alaska Department of Fish and Game

ANO Alaska Native Organizations

ASLC Alaska SeaLife Center

AVPS Alaska Veterinary Pathology Services

FWS U.S. Fish and Wildlife Service

NMFS National Marine Fisheries Service

USGS U.S. Geological Survey
8-2 Southwest Alaska DPS of the Northern Sea Otter 

B. Recovery Implementation Schedule Plan Action Priority Responsible Agency Action Duration Fiscal Year Costs ($K) Comments FY 1 FY 2 FY 3 FY 4 FY 5 1. Population Monitoring 1.1. Continue to estimate sea otter population size and trends in southwest Alaska 1.1.1. Evaluate and improve methods for abundance surveys 2 USGS 2 years 125 125 Estimate detection 1.1.2. Conduct surveys in each management unit and estimate abundance and population trends 1 FWS USGS annually 200 200 200 200 200 Surveys will be conducted in different management units 1.1.3. Identify areas of consistently high sea otter abundance, relative to other areas, within each management unit 2 FWS USGS annually 5 5 5 5 5 This action will rely on survey data collected under 1.1.2 1.1.4. Evaluate potential biases in survey data and identify methods that may be useful for correcting such biases 2 USGS 2 years 10 10 1.1.5. Refine estimates of equilibrium density and carrying capacity for the Bristol Bay, South Alaska Peninsula, and Kamishak, Kodiak, Alaska Peninsula MUs 1 FWS USGS Timing is dependent on collection of additional survey data for these MUs 1.1.6. Evaluate overall monitoring program on an ongoing basis to assess sensitivity 1 FWS USGS annually 5 5 5 5 5 This action will rely on survey data collected under 1.1.2
Recovery Plan 8-3 B. Recovery Implementation Schedule Plan Action Priority Responsible Agency Action Duration Fiscal Year Costs ($K) Comments FY 1 FY 2 FY 3 FY 4 FY 5 1.2. Estimate vital rates 1.2.1. Estimate birth rates 3 USGS 4 years Obtained in 1.2.2 at no additional cost 1.2.2. Estimate age- and sex-specific survival rates 1 USGS 4 years 375 375 375 175 Includes $100K charter costs in years 1-3. Year 4 is for analysis and reporting 1.3. Monitor health and body condition 1.3.1. Monitor general body condition 3 USGS FWS Obtained in 1.2.2 at no additional cost 1.3.2. Analyze data for evidence of food limitation 2 USGS 3 years 60 60 60 1.4. Monitor distribution and movements 1 USGS FWS 4 years Obtained in 1.2.2 at no additional cost 1.5. Continue development of population viability analysis models 1.5.1. Investigate PVA models for the Bristol Bay, South Alaska Peninsula, and Kamishak, Kodiak, Alaska Peninsula MUs 1 USGS Timing is dependent on collection of additional survey data for these MUs 1.5.2. Revise and re-run the population viability analysis incorporating new data 1 USGS 5
8-4 Southwest Alaska DPS of the Northern Sea Otter 

B. Recovery Implementation Schedule Plan Action Priority Responsible Agency Action Duration Fiscal Year Costs ($K) Comments FY 1 FY 2 FY 3 FY 4 FY 5 1.6. Monitor the functional role of sea otters in ecosystems 1.6.1. Monitor status of the kelp forest ecosystem 1 USGS 3 years 125 125 125 1.6.2. Develop methods to monitor sea otter impacts on marine ecosystems with soft- sediment substrates 2 USGS 2 years 125 125 1.7. Develop new research tools Priority and costs not assigned 2. Habitat needs and habitat protection 2.1. Identify important habitats or areas of special biological significance 2.1.1. Identify characteristics of areas that are serving as refuges for remnant groups of sea otters 1 USGS FWS 3 mo. 30 2.1.2. Explore feasibility of creating more areas that can serve as refuges 2 USGS FWS 6 mo. 50 2.1.3. Protect important habitats 2 FWS ADFG annually 20 20 20 20 20 2.2. Prepare a habitat conservation plan under Section 10 of the ESA for Alaska State-managed fisheries 3 FWS ADFG 2 yr. 50 50 3. Manage impacts of human uses 3.1. Ensure that Alaska Native subsistence harvest does not affect recovery
Recovery Plan 8-5 B. Recovery Implementation Schedule Plan Action Priority Responsible Agency Action Duration Fiscal Year Costs ($K) Comments FY 1 FY 2 FY 3 FY 4 FY 5 3.1.1. Continue to estimate harvest levels 3 FWS annually 100 100 100 100 100 3.1.2. Quantitatively evaluate the impact of sea otter harvest on recovery 2 FWS USGS 6 mo. 50 3.1.3. Develop sea otter harvest management plan(s) 3 FWS ANO annually 50 50 50 50 50 3.1.4. Continue outreach to promote “males only” harvest 3 FWS ANO annually 5 5 5 5 5 3.1.5. Use pelts from stranded otters to reduce demand for subsistence-harvested animals 3 FWS annually 10 10 10 10 10 3.2. Ensure that incidental take in fisheries does not affect recovery 3.2.1. Monitor the level of incidental take in fisheries within the sea otter’s range in southwest Alaska 2 NMFS annually 1,200 1,200 1,200 1,200 1,200 Cost to conduct a fishery observer project 3.2.2. Quantitatively evaluate the impact of incidental take on recovery in each management unit 3 FWS 1 mo. 10 3.2.3. Develop programs to reduce incidental take in situations where it occurs 2 NMFS FWS 3 mo. 25 3.3. Ensure that entanglement in marine debris does not affect recovery 3.3.1. Encourage net recycling and environmentally responsible disposal 3 NMFS annually 20 20 20 20 20
8-6 Southwest Alaska DPS of the Northern Sea Otter 

B. Recovery Implementation Schedule Plan Action Priority Responsible Agency Action Duration Fiscal Year Costs ($K) Comments FY 1 FY 2 FY 3 FY 4 FY 5 3.4. Eliminate intentional illegal take 3.4.1. Establish an outreach program on the ecological role of sea otters in the nearshore marine ecosystem 3 FWS ASLC 1 year 25 10 10 3.4.2. Increase enforcement efforts to investigate and prosecute cases of illegal killing 3 FWS annually 75 75 75 75 75 3.5. Evaluate potential impacts of scientific research activities 3 FWS annually 10 10 10 10 10 4. Disease 4.1. Evaluate the potential role of disease as a threat to recovery 4.1.1. Expand the marine mammal stranding network in southwest Alaska 2 FWS ASLC annually 10 10 10 10 10 4.1.2. Use carcasses and tissues from subsistence-harvested animals to investigate disease processes 3 FWS AVPS annually 30 30 30 30 50 Increased costs in FY5 for analysis and reporting 4.1.3. Sample sea otter carcasses and tissues 2 FWS annually 20 20 20 20 70 Increased costs in FY5 for analysis and reporting 4.1.4. Conduct live-capture studies to examine health status of live sea otters 2 FWS USGS ASLC 6 mo. Per year 400 400 400 500 Increased costs in FY5 for analysis and reporting
Recovery Plan 8-7 B. Recovery Implementation Schedule Plan Action Priority Responsible Agency Action Duration Fiscal Year Costs ($K) Comments FY 1 FY 2 FY 3 FY 4 FY 5 4.1.5. Evaluate population-level effects of identified diseases 2 FWS 20 Postponed due to limited sample size at present 4.2. Develop disease management plans where practical 3 FWS 25 Postponed due to limited sample size at present 5. Predation 5.1. Continue to evaluate the role of predation as a threat to recovery 1 USGS ASLC 2 years 150 150 5.2. Develop predation management plans, where practical 1 FWS NMFS 3 mo. 20 6. Protect from other natural or human-caused threats 6.1. Ensure that oil spills do not impede recovery of sea otters and/or negatively affect the nearshore marine environment in southwest Alaska 1 FWS USGC ADEC annually 30 50 30 30 30 Costs in FY2 include incorporation of sea otter critical habitat into spill response plans 6.2. Establish an outreach program to mariners on how to avoid striking sea otters 3 FWS annually 10 10 10 10 10 6.3. Continue to measure and monitor contaminant levels in sea otters 3 FWS 1 yr. 100 Postponed due to limited sample size at present 6.4. Monitor occurrence of biotoxins in sea otters and their prey 3 FWS 3 yr. 100 100 100
8-8 Southwest Alaska DPS of the Northern Sea Otter 

B. Recovery Implementation Schedule Plan Action Priority Responsible Agency Action Duration Fiscal Year Costs ($K) Comments FY 1 FY 2 FY 3 FY 4 FY 5 6.5. Evaluate the feasibility of translocating sea otters to enhance recovery 2 FWS USGS ASLC 6 mo. 40 6.6. Evaluate potential impacts of recreational activities, tourism, and other forms of direct human disturbance 3 FWS annually 5 5 5 5 5 7. Implement the recovery program for southwest Alaska sea otters 7.1. Maintain the Southwest Alaska Sea Otter Coordinator position within FWS 2 FWS annually 100 100 100 100 100 7.2. Continue and enhance coordination of management efforts among FWS, other federal agencies, Alaska Natives, and the State of Alaska 2 FWS annually 15 15 15 15 15 7.3. Continue and enhance coordination of research efforts among FWS, USGS, other federal agencies, the State of Alaska, Alaska Natives, academic institutions, and others 2 FWS USGS ADFG ANO annually 15 15 15 15 15 7.4. Develop and continue a program of outreach to stakeholders 3 FWS annually 10 10 10 10 10 7.5. Secure adequate funding for southwest Alaska sea otter management and research needs 1 FWS USGS annually 10 10 10 10 10
Recovery Plan 8-9 B. Recovery Implementation Schedule Plan Action Priority Responsible Agency Action Duration Fiscal Year Costs ($K) Comments FY 1 FY 2 FY 3 FY 4 FY 5 SUBTOTAL 1 785 760 885 410 365 2 515 1075 655 620 765 3 1,525 1,675 1,710 1,635 1,680 TOTAL 2,825 3,510 3,250 2,665 2,810
8-10 Southwest Alaska DPS of the Northern Sea Otter

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Recovery Plan 9-1

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2008. The distribution of nuclear genetic 

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Ames, J.A. 1983. Tagging materials and methods 

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abundance. Pages 13-26 in G.W. Garner et 

al., editors. Marine Mammal Survey and 

Assessment Methods. Balekema, Rotterdam, 

Netherlands. 

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Johnson, and G.M. Matson. 1997. Accuracy 

and precision in estimating age of sea otters 

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B. Personal Communications

Lance Barrett-Lennard - University of British Columbia

Kimberlee Beckmen - Alaska Department of Fish and Game

Kathleen Burek - Alaska Veterinary Pathology Services

Douglas Burn - U.S. Fish and Wildlife Service

Anthony DeGange - U.S. Geological Survey

James Estes - University of California, Santa Cruz

Verena Gill - U.S. Fish and Wildlife Service

Tracey Goldstein - University of California, Davis

Brian Hatfield - U.S. Geological Survey

Bridget Mansfield - National Marine Fisheries Service

Lena Measures - Department of Fisheries and Oceans Canada

Melissa Miller - California Department of Fish and Game

Daniel Monson - U.S. Geological Survey

Steven Oberholtzer - U.S. Fish and Wildlife Service

Steve Raverty - Abbotsford Agricultural Centre

M. Tim Tinker - U.S. Geological Survey

Jane Watson - Vancouver Island University

Bill Wilson - North Pacific Fisheries Management Council

Jeff Williams - U.S. Fish and Wildlife Service
Recovery Plan A-1

Appendix A. Ecosystem-based Recovery Criteria 

Background

Recovery criteria for ESA-listed species are 

commonly based on estimates or measures of 

population size, status, and viability. These criteria 

can be as simple as the minimum number of 

individuals required to achieve a low probability 

of extinction over some sufficiently long period 

of time. Typically though, delisting criteria are 

more complex than this, including considerations 

of distribution and range, population age and sex 

structure, meta-population structure and dynamics, 

unforeseen or anticipated future risks, and trends 

in these various metrics and concerns over time. 

Regardless of the details, the key determinants 

for delisting are nearly always fundamentally 

demographic in nature. 

Suitable habitat is also a necessary condition for 

the recovery of any species (Noss et al. 1997). 

This unarguable fact has been codified in the 

ESA through the requirement to designate 

critical habitat in the ESA listing process. The 

essential underlying notion for a critical habitat 

designation, however, is for the protection of 

physical and biological features that are essential 

to the conservation of the listed entity. Although 

habitat considerations are necessary components of 

any ecosystem-based recovery criterion, they are 

insufficient in and of themselves. This is because 

while the designation of critical habitat makes 

allowance for effects of the environment on a listed 

species, it makes no allowance for the species’ 

effects on its environment.

There is growing evidence for the importance of 

individual species in ecosystems (Karieva and 

Levin 2004). Each species can exert a variety of 

effects on the wider biological community, not only 

by providing material resources and energy to the 

next-higher trophic level, but also through such 

well-known processes as competition, predation, 

mutualism, and the alteration of physical habitat. 

In some cases, a species’ ecological role may be 

so strong that it influences entire landscapes and 

associated patterns of biodiversity. Such species 

are known as “strong interactors” (Paine 1992, 

Power et al. 1996) and have been variously referred 

to as “keystone species” (Paine 1969), “foundation 

species” (Dayton 1972), and “ecosystem engineers” 

(Jones et al. 1994), and in various other terms that 

imply functional importance (Soulé et al. 2003). 

Not all species are strong interactors. However, 

strong interactors are often large vertebrate 

predators (Bascompte et al. 2005). These tend 

to be the same species that are most vulnerable 

to extinction because of their intrinsically 

low population densities, extensive habitat 

requirements, and generally low reproductive 

potential, and that occur in disproportionately 

large numbers on the list of threatened and 

endangered species (Ray et al. 2005). The so-called 

minimum viable population size is an inadequate 

recovery criterion for such species because in many 

cases, such a population size is likely insufficient to 

restore the species’ ecological functionality. What 

is needed instead is recovery to an “ecologically 

effective population size” (Soulé et al., 2003, 2004). 

Establishing delisting criteria based on ecological 

effectiveness presents additional challenges to 

the recovery planning process. At a minimum, the 

functional effects of the species on the ecosystem 

must be understood, and the relationship between 

the species’ abundance and its functional role must 

be both known and measurable.

Sea otters have well-documented and widely 

known effects on coastal ecosystems. Lowry 

and Pearse (1973) were the first to demonstrate 

significant impacts of sea otter predation on the 

distribution and abundance of sea urchins and 

abalones. These direct effects were subsequently 

found to extend across successively lower trophic 

levels, indirectly influencing the distribution and 

abundance of kelp (Estes and Palmisano 1974). This 

process of interaction occurs eastward along the 

North Pacific rim from at least the Commander 

Islands through the Aleutian archipelago and 

across southeast Alaska and British Columbia 

(Estes and Duggins 1995, Watson 1993). Top-down 

forcing processes of this general kind, known as 

“trophic cascades” (Paine 1980, Carpenter and 

Kitchell 1993), occur widely in nature (Pace et al. 

1999).

The presence or absence of sea otter predation 

(and the resulting trophic cascade) drives rocky 

reef ecosystems to two distinctive “phase states” 

(sensu Hughes 1994, Steneck et al. 2003)—kelp 

forests where sea otters are present in sufficient 

abundance to limit urchin biomass, and deforested 

sea urchin barrens where they are not. The 

discrete and dichotomous nature of these phase 

states on shallow rocky reefs is maintained by the 

fact that the endpoints in the potential continuum 

of variation between a kelp forest and a sea 

urchin barren are stable, whereas intermediate 
A-2 Southwest Alaska DPS of the Northern Sea Otter

community configurations are not. Urchin barrens 

tend to be self-sustaining because urchins switch 

their foraging behavior from being sessile, “sitand-wait” consumers of detrital fallout from the 

kelp canopy when a kelp forest is present, to 

being mobile hunters when it is not (Harrold and 

Reed 1985, Konar and Estes 2003). Additionally, 

individual kelp plants, once established, physically 

repel marauding urchins via a “whiplash effect” 

that occurs through the interplay of wave surge 

and the plants’ flexible morphology (Konar 2000, 

Konar and Estes 2003).

The sea otter-induced trophic cascade has 

numerous effects on other species and ecosystem 

processes that permeate coastal food webs in 

diverse ways. For example, the surface tension 

exerted by kelp on the water column attenuates 

waves and coastal currents, thus influencing 

sediment deposition and rate of coastal erosion 

(Jackson 1984, 1997). Kelp forests are more 

productive than urchin barrens, fixing an estimated 

3-4 times more inorganic carbon per unit area 

through photosynthesis (Duggins et al. 1989). 

This process in turn fuels elevated secondary 

production, resulting in increased growth rates 

and elevated population levels of various consumer 

species (Duggins et al. 1989, Estes 1996, Estes 

et al. 2004). For instance, the rock greenling, 

a numerically dominant kelp forest fish in the 

western Aleutian Islands, occurs at 10-fold higher 

population densities in sea otter-dominated kelp 

forest systems than in sea otter-impoverished 

urchin barrens (Reisewitz et al. 2005). The 

indirect effects of sea otter predation affect the 

behavior and foraging ecology of other coastal 

marine wildlife, for example glaucous-winged gulls 

(Irons et al. 1986) and bald eagles (Anthony et 

al. 2008). Many additional indirect effects may be 

unrecognized or are undocumented.

Sea otters also forage in soft-sediment (sand, 

gravel, and mud) habitats. Their known effects in 

these systems are mainly reductions in infaunal 

prey populations (especially bivalve mollusks) and 

sediment bioturbation. Such effects have been 

reported from central California (Kvitek et al. 

1988), southeast Alaska (Kvitek and Oliver 1992, 

Bodkin et al. 2007) and the Kodiak archipelago 

(Kvitek et al. 1992). Indirect effects of sea otter 

predation in soft-sediment systems, while likely 

important, are largely unstudied. 

The questions

Development of recovery criteria based on a 

species’ impact on its ecosystem requires the 

ability to 1) identify the process or processes of 

primary interest, 2) define a functional relationship 

between the outcomes of this/these process/

processes and the focal species’ population size, 3) 

establish a state or range of states of the ecosystem 

that constitutes/constitute ecosystem recovery, and 

4) develop quantitative measures for classifying a 

given region as “recovered” or “not recovered”. 

Even for sea otters and kelp forests, this process is 

fraught with complexity. For example, should the 

delisting criterion be based on rocky reef systems, 

soft-sediment systems, or some combination of the 

two? Should the delisting criterion be founded on 

a simple ecosystem metric (e.g., prey abundance) 

or a more complex one involving all or some 

combination of the known indirect effects of sea 

otter predation? For those cases in which the 

response is a graded function of sea otter density, 

how does one choose a specific point along this 

response function as the delisting criterion? And 

finally, how does one account for the spatial and 

temporal variation in the chosen ecosystem state 

variable that is independent of sea otter predation?

Many of these difficulties can be resolved by 

focusing the ecosystem-based delisting criterion on 

the sea otter-urchin-kelp trophic cascade, or more 

specifically on the abundance of kelp and urchins in 

rocky reef habitats. There are a number of reasons 

for this. One is that kelps and urchins are broadly 

distributed, abundant, and easily measured and 

monitored. Another is that while the abundance 

of kelps and urchins vary substantially within and 

among sites (Estes and Duggins 1995) and even 

at specific sites through time (Watson and Estes, 

unpubl. manuscript), the configuration of the 

ecosystem is defined by two distinct phase states—

kelp forests or urchin barrens. This latter aspect of 

temperate reef ecosystem behavior simplifies the 

task of defining a delisting criterion. One simply 

needs to answer two straightforward, qualitative 

questions, as follows. For downlisting, has the 

ecosystem recovered to a kelp-dominated state?.

For uplisting, has the ecosystem further degraded 

in terms of an increase in the extent of urchin 

barrens?

These questions are pertinent for several reasons. 

The predominant phase state in rocky reef 

habitats varies as a function of sea otter predation 

throughout and even well beyond the geographic 

range of the population decline in southwest 

Alaska. A database is under development on kelp 

and urchin abundance from the Aleutian Islands 

and elsewhere within the range of the decline. 

This database could easily be used to establish a 

monitoring program to define ecosystem phase 

states. Finally, rocky reef habitat is sufficiently 

common in shallow coastal waters from lower 

Cook Inlet through the western Aleutian Islands 

to permit analyses based on this habitat type 

throughout the current range of the sea otter 

population decline in southwest Alaska.

Approach

Information on the abundance of kelp and urchins 

in rocky reef systems of the western Aleutian 

Islands has been obtained from numerous islands 

and at various times since the mid-1980s. The 

sampling protocol for all of these data followed 

well-established procedures (Duggins and Estes 

1995, Estes et al. 2004) as follows. A 1 km-square 

grid was superimposed on a map of each island, 
Recovery Plan A-3

and the intersections between the grid lines and 

the island perimeter were identified as potential 

sampling sites. A random subset of locations was 

then selected from this set of potential locations 

for field sampling. Sand- or mud-bottom habitats, 

which are relatively uncommon in the Aleutians, 

were not sampled and sampling was restricted to 

waters 6-8m deep. Although samples have been 

obtained from a range of depths, the patterns 

across depths are generally similar (except at 

depths < 1-2 m, where intense wave surge favors 

kelp assemblages, and at depths > 10-15m, where 

light attenuation limits the settlement and growth 

of kelps). Three to 31 sites were sampled per 

island, depending on weather and time restrictions. 

Samples of the benthic community were taken at 

each site by placing a 0.5 x 0.5 m square quadrat 

at randomly selected intervals (range—1-10 diver 

kicks) along a transect parallel to the bathymetric 

contour on the seafloor. Attached kelps within the 

quadrat were counted and identified to species. 

Although size and morphology vary among kelp 

species, for simplicity the data were pooled and 

species or age differences in kelp biomass were 

not considered in the present analysis. Twenty 

quadrats were sampled per site, as determined 

by SCUBA-diving bottom-time constraints. All 

urchins within the quadrats were counted and 

collected, and their individual test diameters were 

later measured. Urchin sampling at each site 

continued until 200 individuals were obtained or 20 

plots were sampled, at which point the collections 

from that particular quadrat were considered 

complete and further sampling was terminated. 

An allometric function relating urchin mass to test 

diameter was developed so that urchin biomass 

density could be estimated from animal density and 

population size structure (see Estes and Duggins 

1995 for details). 

The intent of these procedures was to obtain a 

representative view of reef communities at islands 

with varying sea otter densities in the Aleutian 

archipelago. Early data from islands with and 

without sea otters have been analyzed and are 

reported by Estes and Duggins (1995). Most of 

the data collected after 1990 are still unpublished, 

except for those reported by Estes et al. (1998) 

and Estes et al. (2004). The data in this Appendix 

were obtained between 1987 and 2006, during 

which time 34 island–by-year combinations were 

sampled (Table A-1). Average urchin biomass 

and kelp densities were computed for each of 

these island-year combinations. K-means Cluster 

Analysis (Jain and Dubes 1988) was used to analyze 

intrinsic groupings of the data, and to assign each 

island to one of the identified groupings. Linear 

Discriminant Analysis was then used to assess the 

effectiveness of the classification scheme, and to 

create a canonical discriminant function that could 

be used to assign new data points to one cluster 

(ecosystem state) or the other.

A logistic regression was used to analyze the 

relationship between ecosystem state, a discrete 

response variable categorized as 0 (urchindominated) or 1 (kelp-dominated), and sea otter 

density (number of animals counted per km 

surveyed). This analysis was restricted to islands 

and years for which there were both benthic 

survey data and sea otter skiff counts (N = 17). 

A bootstrapped re-sampling analysis was then 

applied to the full database (N = 463 sites at 34 

island/year combinations, 10,000 iterations per 

sampling scenario) to determine the probability 

of correctly assigning islands to their ecosystem 

phase states with increased numbers of sample 

sites per island (beginning with 1). Because 

sea otter collapse or recovery may occur 

asynchronously among islands, the re-sampling 

analysis was expanded to determine the number of 

islands that must be sampled to be assured, with 

less than 5% probability of error, that the majority 

of islands (i.e., > 50%) were either kelp-dominated 

or urchin-dominated. We assume a goal of being 

able to estimate the true proportion of recovered 

islands to ±10% with >95% accuracy. A threshold 

of 50% was selected as the only non-arbitrary 

criterion for ecosystem recovery short of requiring 

recovery at every island (in which case the 

necessary sampling effort would be less). 

Results

Information on kelp density and urchin biomass 

collected in 1987 from five islands in the Aleutian 

archipelago at which sea otters were either at or 

near carrying capacity (Amchitka 1987; Adak 1987) 

or completely absent (Shemya 1987, Nizki 1987, 

Alaid 1987) sorted into essentially non-overlapping 

areas of the two-dimensional phase space (Figure 

A- 1). Islands with abundant sea otters consistently 

had low urchin biomass densities and high but 

variable kelp densities. Islands lacking sea otters, 

in contrast, consistently had low kelp densities and 

high but variable urchin biomass densities. These 

findings demonstrate that the rocky reef ecosystem 

structure (i.e., phase state) in the Aleutian Islands 

is predictable, depending on the presence or 

absence of sea otters (Figure A-1).

Similar information is now available for 33 

additional island/year combinations across the 

Aleutian archipelago, bringing the total to 38. 

This database was reduced to 34 (Table A-1) by 

excluding the 4 island/time combinations with 

fewer than 3 sample sites. Some of these data were 

obtained from the original study sites at Shemya, 

Nizki, Alaid, Amchitka, and Adak islands; the 

remaining data were obtained from 12 new islands 

that have been sampled one or more times during 

the past two decades. These latter data are from 

islands at which the sea otter populations were in 

various stages of recovery or decline when they 

were sampled. 

A notable pattern in the data is that the phase 

space distribution is very similar for all of the 
A-4 Southwest Alaska DPS of the Northern Sea Otter

datasets, thus indicating the rarity or absence of 

intermediate community configurations associated 

with phase state transformations. The pattern 

holds regardless of whether the data are plotted as 

averages within sites (Figure A-2) or by averages 

within islands (Figure A-3).

These results lend further support to the 

conclusion that kelp forest phase state can be 

determined relatively easily with a high degree of 

confidence. Using K-means cluster analysis, each 

island/year combination was classified into one of 

two distinct clusters, corresponding to alternate 

phase states (Figure A-3). Discriminant analysis 

indicated that this classification was highly robust 

(jackknife classification accuracy = 100%, F2,31 = 

44.644, P<0.001), suggesting that the canonical 

discriminate function (DF = -1.8391+ 0.0087 × 

Urchin biomass density - 0.0593 × Kelp density) 

provides a reliable algorithm for assigning any 

future data to the appropriate ecosystem phase 

state (urchin-dominated vs. kelp-dominated)

An initial estimate of the population density of 

sea otters needed to bring about a phase shift 

from the kelp-dominated to the urchin-dominated 

phase state was determined by fitting a logistic 

regression to phase state (urchin barren = 0, kelpdominated = 1) vs. sea otter density for the 17 

island/year combinations for which all the required 

information was available. This analysis provided 

a threshold estimate of 6.3 sea otters km-1 of 

coastline (Figure A-5). 

The sample sizes (number of sites per island 

and number of islands) required to detect the 

ecosystem phase state was determined by 

bootstrap re-sampling, drawing sites randomly 

with replacement from the entire dataset while 

incrementing both the number of islands sampled 

and the number of sites sampled per island. The 

resample was iterated 10,000 times for each island 

by site combination (beginning with 1 site at 1 

island). Under the simplest scenario--that the 

entire archipelago is in one phase state or the other 

(approximately the current situation)--only 6 sites 

from any given island (or combination of islands) 

would be sufficient to provide a 95% probability 

of correct classification (Figure A-6, left panel). 

Additional islands would have to be sampled under 

the more complex scenario of different islands 

recovering at different rates (as might be expected, 

for example, if recovery occurred through 

outgrowth from one or several points of origin 

as opposed to uniformly across the archipelago). 

Under this latter scenario, the optimal sampling 

strategy will vary depending on the geographic 

pattern of recovery and the proportion of 

islands in each of the two phase states. The most 

conservative assumption (in terms of statistical 

power) would be that islands recover randomly and 

asynchronously, and that we wish to determine the 

point at which half of the islands have recovered: 

in this case, 23 islands would have to be sampled to 

provide a 95% probability of correctly estimating 

the true proportion within ±10% (Figure A-6, right 

panel).

Recommendations

The proposed ecosystem-based recovery criterion 

is that sea otters must be sufficiently abundant 

to either maintain or bring about a phase shift to 

the kelp-dominated state. The important question 

is “what sort of field monitoring effort would 

be required to establish the community phase 

state with a sufficiently high level of statistical 

confidence to serve as a recovery criterion?” The 

cluster analysis indicates little or no ambiguity 

in assigning islands to phase states with as few 

as three sites sampled per island (Fig 3). An 

independent resampling analysis supports this 

view by establishing that only 6 sample sites from 

a given island would be sufficient to determine the 

ecosystem phase state with 95% certainty for any 

given island (Figure 5). 

The question of how many islands to sample is 

more complicated. Under a scenario of phase 

state uniformity throughout the archipelago, 

samples from just one island would be adequate 

to characterize the entire region. This is probably 

more or less the situation that presently occurs 

due to the collapse of sea otters across the Aleutian 

archipelago. However, spatial (i.e., inter-island) 

variability in phase state, as might occur if otter 

populations declined or recovered in a temporally 

asynchronous manner, would require the sampling 

of multiple islands. Here the question is how many 

islands to sample to determine the proportion 

that are kelp-dominated. If the true proportion is 

close to zero or one, the precision of the estimate 

is unimportant and thus a small number of sample 

islands would suffice. If, on the other hand, the 

true proportion is near 0.5, then a relatively large 

number of islands would need to be sampled to 

determine, with a reasonable degree of confidence, 

whether that proportion is greater than or less 

than 0.5. The resample analysis for this latter 

scenario indicates that 23 islands would have to 

be sampled to estimate the true proportion within 

± 10%. Given that 6 sites can easily be sampled 

by 2 or 3 dive teams in a single day, this would 

require 23 days of ship time. However, with proper 

planning, this estimate could probably be halved (to 

12 days) by sampling two or more nearby islands 

in one day. Furthermore, this entire hypothetical 

scenario is founded on the assumption that islands 

with different phase states occur randomly across 

the archipelago. This is a highly unlikely situation. 

It is much more likely that any such variation in 

island phase state would occur in large spatial 

aggregates, in which case many fewer islands 

would have to be sampled. A monitoring program 

in which 6 sites were sampled from 5-7 islands 

distributed more or less uniformly across the 

archipelago will probably be more than sufficient 

for future decision-making.

It is also likely that sites in close proximity to 

localized refuges with high sea otter densities 
Recovery Plan A-5

will not be representative of larger areas. Such 

areas should be avoided in order to obtain a 

representative view of ecosystem recovery. In 

addition, the development of a sea otter delisting 

criterion based on the kelp forest/urchin barrens 

phase state would be most easily done as a 

time series of measurements from established 

monitoring sites. Such an approach would provide 

information on intrinsic spatial and temporal 

variation in rocky reef phase state structure and 

on temporal trends of the system toward becoming 

either more extensively urchin-dominated or more 

extensively kelp-dominated. It would also provide 

a spatially explicit understanding of ecosystem 

recovery. 

Implications for monitoring and population 

assessment

The determination of sea otter population status 

based on counts or surveys is an expensive and 

time-consuming endeavor, producing data that 

are often difficult to interpret. Long time series of 

counts are required to establish population trends 

with reasonable levels of statistical confidence, 

and the criteria or criterion chosen for delisting 

will always be arbitrary to a certain degree 

because key demographic variables—distribution, 

abundance, and age structure—are continuous in 

nature. Although some direct measures of sea otter 

abundance are needed to confirm the status and 

trends of populations, the characterization of kelp 

forest phase state provides an easier, less costly, 

less arbitrary, more sensitive, and more reliable 

means of assessing recovery. This is due in part to 

the discrete nature of the phase states and in part 

to the fact that only a modest field-sampling effort 

is required to define the phase state in any given 

area with a high level of confidence. 

Additional benefits of an ecosystem-based recovery 

criterion

Using the kelp forest phase state as a delisting 

criterion offers two further benefits. There 

are uncertainties in estimating the density of 

otters required to have a population that is 

demographically viable over time, and therefore 

incorporating an ecosystem-based recovery 

criterion provides an independent measure of 

how well the population is doing and serves as a 

further safeguard against extinction. In addition, 

kelp forests likely have been the “natural” state 

of coastal reef ecosystems in Alaska over recent 

evolutionary time (Steinberg et al. 1995, Estes 

et al. 2005). Of the two phase states, the kelpdominated state should benefit the greater number 

of species and ecosystem processes. 

Caveats

In June 2008 researchers extensively resampled 

long-term rocky reef monitoring sites at islands 

across the Aleutian archipelago from the Near 

Islands in the west to the Islands of Four 

Mountains in the east. Although the data from 

this effort have not yet been analyzed, all of the 

islands visited were extensively urchin-dominated, 

as expected from the low sea otter densities and 

the results reported herein. In August 2008 eight 

additional locations were sampled (using the same 

sampling protocol) from the Fox Islands in the 

eastern Aleutians to Katmai National Park on the 

eastern end of the Alaska Peninsula. Although 

these data also have not yet been analyzed, the 

sample sites east of the Fox Islands were almost 

entirely kelp-dominated, despite low sea otter 

densities comparable to the Aleutian Islands. 

These observations indicate that the relationship 

between sea otter density and the phase state 

varies considerably between the Aleutian Islands 

and the southern Alaska Peninsula, thereby 

suggesting that the proposed ecosystem-based 

delisting criterion might be achieved along the 

Alaska Peninsula at a sea otter density below that 

required for demographic viability (in contrast 

with the Aleutian Islands situation).

Most of the information used to define the 

relationship between sea otter density and 

ecosystem phase state (i.e., the logistic regression 

in Figure 5) is from areas in which the otters were 

either extinct, at or near K, or in decline. This 

function could very well differ for systems in which 

the sea otter populations were recovering. 

Forces other than sea otter predation also cause 

phase state shifts. Such factors as storm surge 

(Ebeling et al. 1985), other predators (Steneck 

et al. 2003), fisheries (Steneck 1997), and urchin 

wasting disease (Lafferty 2004) have been shown 

to drive kelp forest phase shifts in other regions. 

Despite decades of research, none of these factors 

has been seen to operate in southwest Alaska, 

whereas a large body of research attributes phase 

shifts to changes in the intensity of sea otter 

predation. In the seemingly unlikely event that 

a phase shift did occur for reasons other than sea 

otter predation, this would probably be evident 

from the associated evidence. For example, urchin 

wasting disease is not size-selective; storm effects 
A-6 Southwest Alaska DPS of the Northern Sea Otter

are restricted to exposed shorelines; effects of 

fisheries are evident from landings and other 

records; and any new urchin predator capable of 

driving a phase shift would likely be observed. In 

conclusion, the likelihood of failure to meet this 

ecosystem-based recovery criterion when the sea 

otter population had in fact recovered is minimal.

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A-8 Southwest Alaska DPS of the Northern Sea Otter

Table A-1. Summary of the datasets used to examine the relationship between sea otter density and sea 

urchin biomass in the Aleutian Islands.

Island/Site Year N_Sites Urchin biomass Kelp density

Adak 1987 28 29.99 9.09

1997 27 258.34 0.97

1999 30 269.12 1.49

2000 28 310.6 1.59

Agattu 1993 3 70.11 2.93

Akutan 1994 12 53.51 11.85

Alaid 1987 9 371.07 2.24

1994 8 324.85 1.59

Amchitka 1987 29 31.19 5.33

1999 31 454.81 0.36

2000 11 361.53 0.24

Anangula 1994 4 1.15 17.01

Attu 1987 9 208.8 1.83

1990 14 127.31 2.08

1993 15 59.83 4.75

2000 15 35.14 3

Bering 2006 19 31.65 3.76

Chuginadak 1994 12 450.01 0.79

Kiska 1993 11 30.57 18.51

2000 20 581.89 1.61

Nizki 1987 7 561.93 1.19

1994 7 249.99 3.34

1997 7 245.33 4.36

Ogliuga 1994 4 211.89 1.01

Rat 1994 10 611.9 1.06

Seguam 1994 10 514.66 0.24

Shemya

1987 16 413.81 0.79

1994 12 250.48 4.28

1997 16 426.44 3.3

Tanaga Bay 1994 7 118.79 3.74

Tanaga, Hot Sp 1994 6 45.91 8.88

Umnak 1994 4 3.77 9.08

Unalaska 1994 12 4.73 25.25

Yunaska 1994 10 515.27 0.77
Recovery Plan A-9

Figure A-1. Published data on kelp density vs. sea urchin biomass from islands in the Aleutian archipelago at 

which otters were abundant (Adak and Amchitka) or absent (Alaid, Nizki, Shemya). 

This is a reproduction of Figure 7 from Estes and Duggins (1995). Lower panel provides comparable 

data from southeast Alaska.
A-10 Southwest Alaska DPS of the Northern Sea Otter

Figure A-2. Available data on kelp density vs. urchin biomass from the Aleutian archipelago, obtained from 19 

islands sampled at various times between 1987 and 2006. 

Symbol shape and shading represent different islands and times; open symbols are from lower otter 

density systems (<5 km); closed symbols are from higher otter density systems (>5/km).
Recovery Plan A-11

Figure A-3. Data from Figure A-2, averaged by island/year combination. 

Dashed lines represent 90% confidence ellipses around the two data aggregates identified by 

K-means cluster analysis, which correspond with kelp-dominated (green, square symbols) and urchin 

barren (purple, diamond symbols) phase states. Note that each data point represents the average 

of all sites sampled at one island during one year. N = 34 island/year combinations representing 463 

sites in total.
A-12 Southwest Alaska DPS of the Northern Sea Otter

Figure A-4. Canonical discriminant function fit to database of kelp density vs. sea urchin biomass from the 34 

island/year combinations currently available from the Aleutian archipelago (see Table A-1). 

The function has a jackknife classification accuracy of 100% (F2,31 = 44.644, P<0.001). Thus, if DF > 

-0.295, the ecosystem state should be classified as 0 (urchin-dominated), otherwise the ecosystem 

state should be classified as 1 (kelp-dominated).
Recovery Plan A-13

Figure A-5. Logistic regression of ecosystem phase state vs. sea otter density (using skiff surveys) as 

determined by data from 17 island/year combinations. 

The one anomalous data point (Attu Island, 2000) is explained by the fact that all of the diving 

surveys that year were carried out in Massacre Bay, due to inclement weather and the short 

time window available for sampling. Massacre Bay receives an inordinately large input of fresh 

water from the Peaceful and Henderson river drainages, in turn apparently inhibiting sea urchin 

recruitment (and thus the phase shift from the kelp- to the urchin-dominated phase state) at the 

more protected inner-bay sites that were accessible to sampling.
A-14 Southwest Alaska DPS of the Northern Sea Otter

Figure A-6. Sampling intensity vs. statistical power in correctly classifying ecosystem phase state.

The left panel shows the number of sites per island. The right panel shows the number of islands 

that would have to be sampled to estimate the phase state proportion within ± 10%, assuming the 

actual proportion was 50%.
Recovery Plan B-1

Appendix B. Population Viability Analysis

Introduction

Population viability analysis (PVA) consists of a 

wide range of quantitative methods used to predict 

the probable future status of a population or 

populations of conservation concern (McCullough 

and Beissinger 2002, Morris and Doak 2002). 

PVA can be used to assess extinction risk within 

any specified time frame which can help guide 

management decisions. Since its inception in 

the late 1980s (Gilpin and Soulé 1986), PVA has 

developed rapidly and is a continually evolving 

process, incorporating new analytical methods as 

they become available. PVA is typically based on a 

computer model that simulates annual population 

processes such as births and deaths, incorporates 

various forms of uncertainty, and provides insights 

into the likelihood of attaining a specific goal, such 

as a specified population size, by some future date. 

PVA can help meet many common objectives 

of endangered species management such as 

synthesizing all available information on the 

population or species of interest, ranking risks, and 

assessing impacts of habitat loss (Ralls et al. 2002). 

The interagency Quantitative Working Group 

has explored the possibility of using standardized 

PVA models as a guide to listing decisions (Regan 

et al. 2009). The Marine Mammal Commission 

has recommended the use of PVA for several 

purposes including the determination of species 

status under the Endangered Species Act and the 

Marine Mammal Protection Act, evaluating the 

effectiveness of past management actions, and 

predicting effects of proposed management actions 

(Marine Mammal Commission 2008).

Although PVAs can be performed by incorporating 

species-specific data into a generic PVA model such 

as VORTEX (Miller and Lacy 2005), it is generally 

preferable to develop a custom, species-specific 

model when there are sufficient data on a species 

or population (Marine Mammal Commission 2007). 

Sea otters in SW Alaska are an excellent candidate 

for developing a custom PVA because of the long 

history of study by academic groups and state and 

federal agencies, the existence of multiple time 

series on population trends, and information on 

abundance, demographic parameters, and densitydependent variation in vital rates.

The Recovery Team decided that a custom PVA 

for SW Alaska sea otters would be a useful 

tool both for developing delisting and uplisting 

criteria and for making future management 

decisions. Importantly, once a PVA framework 

has been designed, the model can be re-run as 

new survey data and other information become 

available, which could result in re-classification 

criteria that are more up-to-date and relevant. 

In this way the target numbers for delisting can 

be updated to reflect new information, while 

the underlying delisting criteria (e.g., less than 

5% chance of becoming endangered within 25 

years) is maintained. Because of the way in 

which a simulation-based PVA incorporates 

uncertainty, critical threshold values tend to be 

more conservative (higher) when there is more 

uncertainty in population parameters. Thus, the 

PVA is inherently precautionary, and also provides 

a strong incentive for regular data collection 

on the population and an adaptive management 

approach whereby the model is re-run as more 

information and better data become available. A 

recent example of a similar approach is the PVA 

developed for the draft Island Fox Recovery 

Plan (Bakker et al. 2009). Finally, a PVA provides 

a quantitative framework within which specific 

new threats can be evaluated with respect to 

their effect on overall population persistence, and 

specific management actions can be evaluated with 

respect to their efficacy. An excellent example of 

such uses is the recently completed PVA model for 

the Steller sea lion (Goodman 2008), which allows 

for a probabilistic evaluation of recovery scenarios 

and explicitly considers the role of uncertainty in 

making recommendations. 

Methods

A variety of methods have been used in the 

design of PVA models, depending on the causes 

and patterns of variation in the focal population, 

the nature of threats to its persistence, and the 

availability of data (Beissinger and McCullough 

2002). The simplest approach consists of the 

evaluation of variation in population abundance, 

and implications of this variation for viability, 

without any reference to population structure 

(variation in age, sex, size or life-history stage): 

this is sometimes referred to as a “count-based” 

PVA. A second approach involves the incorporation 

of some level of population structure (for example, 

a matrix model that incorporates stage-specific 

transition probabilities); this approach can be 

referred to as a “demographically-structured” 

PVA (Morris and Doak 2002). The benefit of the 

former type of model is that it involves very few 

parameters, requires little or no information 

about underlying population structure, and can 
B-2 Southwest Alaska DPS of the Northern Sea Otter

be developed with a minimum of information 

(e.g., a time series of survey results). The benefit 

of the latter approach is that it can provide 

more robust and illuminating results in certain 

instances, assuming that there are sufficient data 

available with which to parameterize a structured 

population model. In particular, a demographicallystructured PVA may be more appropriate in cases 

where population structure itself plays a role in 

determining the probability of persistence, as is 

the case when vital rates vary as a function of age 

or sex, and/or when demographic stochasticity 

becomes prominent at very small population sizes. 

In the case of the sea otter in SW Alaska, 

demographic stochasticity is a very real concern 

due to the fragmented nature of small, remnant 

populations scattered throughout the Aleutian 

archipelago (Estes et al. 2005). Fortunately there 

are multiple sources of information about sea otter 

demography, age and sex-specific vital rates, and 

the ways in which these rates tend to vary as a 

function of population density (Eberhardt and 

Siniff 1988, Siniff and Ralls 1991, Eberhardt 1995, 

Monson 1995, Monson and Degange 1995, Monson 

et al. 2000, Tinker et al. 2006). Moreover, there is 

additional information on the nature of the elevated 

mortality that caused the recent population decline 

(Laidre et al. 2006); specifically, the decline was 

apparently driven by a dramatic increase in ageindependent mortality, representing a fundamental 

and rapid switch from the age-varying mortality 

(primarily focused on juveniles and aged-adults) 

more typically associated with density-dependent 

population regulation in sea otters (Monson et al. 

2000). In light of these facts, it was concluded that 

developing a demographically-structured PVA was 

both feasible and appropriate for this population.

Our overall approach to developing the SW Alaska 

sea otter PVA model can be broken down into five 

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 time-series of population surveys 

available for various islands throughout the 

Aleutian archipelago, we use maximumlikelihood techniques to estimate the annual 

per-capita death rate associated with an 

age-independent source of mortality (e.g., 

predation) responsible for the recent decline, 

as well as variation in this parameter. 

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, 

we run a large number of simulations and 

allow all parameters to vary according to the 

appropriate variance distributions.

4. The suite of simulations 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 density represents 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 up-listing 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 density 

represents the de-listing threshold, DD. 

Step 1. Developing a Population Model 

A spatially-structured demographic model has 

been recently developed for the southern sea 

otter, and provides a template for developing a 

PVA model for SW Alaska. The California model 

is based around a two-sex, age-structured Leslie 

matrix model with a yearly time-step (Tinker 

et al. 2006). A modification of this basic model 

incorporates spatial complexity by allowing for 

dispersal between a series of semi-discreet subpopulations (Tinker et al. 2008); below we describe 

how we have adapted this model for the SW Alaska 

sea otter DPS. 

The model tracks annual demographic transitions 

for males and females, with two types of transitions 

possible: reproduction (R) and survival (s) – we 

refer to female survival as sf and male survival as 

sm. Reproduction in sea otters, while occurring 

year-round (Jameson and Johnson 1993), tends to 

be somewhat seasonal in SW Alaska (Monson 1995, 

Tinker and Estes 1996, Monson et al. 2000), with 

most adult females annually producing a single 

pup in late spring or early summer. Because of 

this seasonality, we are able to simplify the birthflow life history to a birth-pulse model that tracks 

reproduction and survival from winter of one year 

to winter of the next. Specifically, we assume that 

reproductive females begin gestation at the start 

of the year, give birth to pups half way through 

the year (after a 6-month gestation period), rear 

their pups for the next 6 months, and, assuming 

the pup survives, wean at the end of the year. 

Following Tinker et al. (2006), the reproduction 

term in our model, R, represents the probability 

that a female of age i produces a single viable 

juvenile of either sex by the end of the year, and 

thus incorporates the age-specific annual birth 

rate (bi), the probability that the pup is male or 

female (we assume a 1:1 sex ratio at birth), the 
Recovery Plan B-3

likelihood the pup survives until weaning (given 

by the age-specific weaning success rate, wi), 

and the probability that the mother survives the 

entire year (i.e., 6 months gestation plus 6 months 

pup dependency). Accordingly, we calculate 

reproduction for females of age i as: 

(1)

where sf,i is the age-specific survival rate of the 

mother. Note that equation 1 implies that juveniles 

actually begin their first year of independence 

at 0.5 years old; however, for simplicity of 

presentation we refer to this juvenile class as 

age 0 (thus sf,0 represents survival from 0.5 to 1.5 

years-of-age for recently-weaned juvenile females, 

sf,1 represents survival of females from 1.5 to 2.5 

years-of-age, and so on). Because there are few 

data available with which to parameterize vital 

rates for older animals in Alaska, we simplified 

our model to track just 12 age classes, with the 

12th age class accounting for aged-adults ≥ 11.5 

years-of-age (collapsing older age classes has little 

effect on the resulting projections of population 

dynamics because of the relatively small proportion 

of the population occurring in these age classes). 

The resulting survival and reproduction rates are 

combined into a population projection matrix, Pj,i, 

used to model demographic transitions to age class 

j from age class i for each sex (Table B-1). 

In the Aleutian archipelago, each island or island 

group is assumed to represent a semi-discreet 

sub-population. The number of animals of each sex 

and age-class that are alive at the start of year t in 

sub-population x are tracked in a 24×1 population 

vector, nx(t), the first 12 cells of which tabulate 

individuals in each female age class, while the 

last 12 cells tabulate individuals in each male age 

class. The total abundance of the sub-population 

can be calculated as the sum of this vector, Nx(t) = 

Σnx(t). When the total abundance of a given subpopulation is large (Nx(t) ≥100), annual population 

dynamics are calculated using standard matrix 

multiplication (Caswell 2001):

(2)

where Pj,i is the demographic transition matrix 

(Table B-1). When population sizes drop below 

100, demographic stochasticity can result in a 

lower population growth rate than that predicted 

by matrix multiplication: accordingly, for small 

population sizes (<100) we use methods described 

by Morris and Doak (2002) to make calculations 

that explicitly incorporate demographic 

stochasticity in all vital rates. 

To parameterize the above-described population 

projection model, we used published estimates 

of age- and sex-specific vital rates reported from 

radio-telemetry studies of sea otters in SW Alaska; 

in the few cases where parameter estimates were 

not available from Alaskan sea otter studies, we 

used comparable data collected from California 

sea otters. As with many species, sea otter vital 

rates exhibit negative density-dependence, with 

the result that population growth can be described 

approximately as a theta-logistic function of 

density (Figure B-1; Siniff and Ralls 1991, 

Eberhardt 1995, Tinker et al. 2006). Certain lifehistory stages are particularly labile: for example, 

weaning success and survival rates of juvenile 

age-classes tend to decrease substantially at high 

population density, while survival rates of primeage adults show much less variation (Monson et 

al. 2000). Birth rates (bi) also show little or no 

variation with density: females generally reach 

reproductive maturity between 2 and 4 years of 

age, and thereafter maintain a relatively constant 

rate of pup production irrespective of density. 

Most previous studies of reproduction in sea otters 

report a birth rate of 0.88 - 0.98 pups per female 

per year, with a slightly lower rate for younger 

females (Jameson and Johnson 1993, Eberhardt 

and Schneider 1994, Riedman et al. 1994, Monson et 

al. 2000, Tinker et al. 2006). For the current model 

we assume the age of first reproduction is 2.5 years 

(i = 2), and we set birth rate to 0.4 pups per year 

for i = 2, 0.7 pups per year for i = 3, 0.9 pups per 

year for 3 > i < 11, and 0.8 pups per year for the 

final adult age class. 

We incorporated density-dependent variation in 

weaning success and survival into our model by 

using published data sets on sea otter survival 

and reproduction to parameterize vital rates at 

low density and high density populations, and 

interpolating between these extremes to obtain 

appropriate values for intermediate densities. We 

used vital rate estimates from Kodiak Island in the 

late 1980s to characterize a low density, rapidly 

growing population (Monson and Degange 1995), 

while estimates from Amchitka Island in the early 

1990s were used to characterize a high density 

population near carrying capacity, K (Monson 

1995, Monson et al. 2000). Because the vital rate 

estimates for these populations were available for 

only certain year-classes, and were reported by 

life-history stage – juveniles, adults, aged-adults 

– rather than by annual year-classes, we first 

generated smoothed age- and sex-specific survival 

rate estimates by fitting 3-parameter logistic 

functions to all available data from each location:

(3)

where i is female age, and α1, α2 and α3 are 

parameters fit by maximum likelihood (similar 

functions were used to re-calculate male survival 

and weaning success). Fitting equation 3 resulted 

in “inverted U” survivorship schedules, typical of 

sea otters and other large carnivores (Caughley 

1966), with highest survival among prime 

age adults and density-dependent mortality 

concentrated in juvenile and aged-adult animals. 

The resulting age-specific vital rate estimates for a 

low density population (designated by superscript 

“L”) and a high density population (designated 

by a superscript “H”) are illustrated in Figure 
B-4 Southwest Alaska DPS of the Northern Sea Otter

2. When incorporated into the matrix model, the 

low-density parameter estimates values produced 

an algebraically-derived expected rate of growth 

(λ) of slightly less than 1.15, while the high-density 

values produced a rate of growth of slightly less 

than 1.00. By adjusting all weaning success values 

upwards slightly (by 1.1% for Kodiak data and by 

1.5% for Amchitka data) we obtained λL = 1.15 and 

λH = 1.00: these two sets of vital rates were then 

used as bounding values for low density and high 

density populations (Table B-2). At intermediate 

densities we used a multiplier, ν, to interpolate 

between the two sets of vital rates. Specifically, we 

describe the density of each sub-population, Dx, as 

a ratio of carrying capacity (Dx=Nx/Kx), and for a 

given value of D we interpolate female survival as

(4)

where:

(5)

Male survival and weaning success were 

interpolated in an analogous fashion. Equation 

5 was parameterized such that variation in λ

describes a theta-logistic function of density, with 

λmax = 1.15, K = 15 sea otters per km2

 of shallow 

sub-tidal habitat (< 40m depth) (Burn et al. 2003) 

and theta = 2 (Figure B-1). 

In addition to tracking within-population dynamics, 

the model must also account for age- and sexspecific dispersal that can demographically link 

sub-populations. Specifically, the numbers of 

animals of each sex and age-class that move from 

sub-population x to sub-population y in year 

t is tracked in a 24×1 vector, my,x(t), and total 

dispersal is given by My,x(t) = Σmy,x(t). Equation 

2 is modified to account for dispersal, such that 

my,x(t) is subtracted from nx(t) (and added to ny(t)) 

prior to matrix multiplication, thereby assuming 

(for computational simplicity) that dispersal 

occurs at the beginning of the year, followed by 

reproduction and survival at the new location. 

While this approach is computationally simple, 

a much greater challenge was parameterization 

of my,x(t). It is known that sea otters are capable 

of making movements of hundreds of kilometers, 

although dispersal distance appears to follow 

a leptokurtic distribution such that very longdistance movements are rare (Tinker et al. 2008). 

Unfortunately, there is little information on 

the rate of long distance dispersal between sea 

otter populations in SW Alaska. No inter-island 

movements were observed over the course of 

2-3 year radio telemetry studies at Adak and 

Amchitka Islands (Monson 1995, Tinker and 

Estes 1996). This observation, together with the 

spatially staggered pattern of population recovery 

in the Aleutian archipelago following cessation of 

the fur trade (Kenyon 1969), suggest that interisland movements are relatively rare, stochastic 

events. Furthermore, it can be inferred from the 

pattern of sea otter re-colonization of the Aleutian 

archipelago (Kenyon 1969) and the Commander 

Islands (Bodkin et al. 2000) that the likelihood of 

inter-island movements is a decreasing function 

of the distance between islands and an increasing 

function of the relative density of the source 

population, Dx. Given the above observations, we 

concluded that a reasonable approach to modeling 

sea otter dispersal was to represent the total 

number of animals dispersing from island x to 

island y in year t (My,x(t)) as a random integer 

drawn from a Poisson distribution with parameter 

γy,x, where γy,x is a function of inter-island distance 

(Δy,x) and relative population density (D) at island 

x:

(6)

Parameters β1, β2 and β3 together determine 

the shape and slope of the relationship between 

dispersal rate, population density and inter-island 

distance. Equation 6 is flexible enough to produce 

a wide range of functional forms that generally 

decrease with distance and increase with density, 

and can be parameterized such that the total 

number of animals moving from island x to y in 

any given year will usually be 0 unless the islands 

are relatively close together and/or the population 

density at island x is near K. To estimate values 

for β1, β2 and β3, we polled the recovery team to 

obtain expert opinion on the range of “biologically 

reasonable” dispersal rates under various 

combinations of inter-island distance and density 

(Table B-3). We then fit equation 6 to the upper 

and lower extremes of these ranges to obtain 

parameter estimates for high dispersal and low 

dispersal scenarios (Figure B-3). 

When dispersal is non-zero (My,x(t) >0), it is 

necessary to specify the age/sex distribution of the 

dispersing animals: the simplest way to achieve 

this is to scale my,x(t) proportionally to nx(t), thereby 

assuming that each individual in the population 

is equally likely to disperse. However, it is well 

established that sea otter dispersal rates vary 

based on age and sex (Ralls et al. 1996, Tinker et 

al. 2008), and therefore we adjusted my,x(t) such 

that sub-adult males were two times more likely 

to disperse than adult males or sub-adult females, 

and five times more likely to disperse than adult 

females (sub-adults are defined as ages 0 ≤ i ≤

2, and adults are defined as i ≥3). These ratios 

correspond roughly to published age/sex-specific 

dispersal kernels estimated for southern sea otters 

(Ralls et al. 1996, Tinker et al. 2008).

Step 2. Estimating age-independent mortality, δ

Time series of sea otter abundance were available 

for seven islands in the western Aleutian Islands, 

with each time series consisting of four or more 

skiff surveys conducted between 1991 and 2007 

(Table B-4). A number of lines of evidence suggest 

that the sea otter populations at all these islands 

declined at approximately equal rates over this 

period (Estes et al. 2005), and that this decline 

was caused by the introduction of a new source 
Recovery Plan B-5

of age-independent mortality (Laidre et al. 2006). 

We reasoned that the per-capita death rate (δ) 

associated with this new mortality source could be 

quantified using the population model developed 

in Step 1. Specifically, we simulated population 

dynamics at each island from 1990-2007, with agespecific survival rates adjusted to reflect δ, and 

used maximum likelihood methods to minimize 

the deviation between expected and observed 

population trends by adjusting δ. This maximum 

likelihood approach allowed us to evaluate 

alternate scenarios of variation in δ within an 

information theoretic framework. 

The skiff counts of otter abundance provided us 

with estimates of Nx(t)obs, the observed abundance 

of population x at time t. For simplicity we 

designated 1990 as t=0, since the first population 

count was conducted in 1991. We initialized 

population vectors for each island population (nx(0)) 

by multiplying the expected abundance in 1990 

(Nx(0)exp, itself a free parameter to be fitted) by the 

algebraically-derived stable stage distribution at K

(thereby assuming that most of these populations 

were still near equilibrium densities in 1990). For 

each year after 1990 (t = 1, 2, ...17) we generated 

expected values for population abundance, 

Nx(t)exp, using matrix model projections (described 

in Step 1) and adjusting age-specific survival rates 

as follows:

(7)

where δ(D, t) represents per-capita ageindependent mortality, calculated as a logitfunction of population density (D) and time (t). 

Equation 7 represents the joint probability of 

surviving density-dependent mortality factors 

(disease, starvation, etc.) and the new ageindependent mortality source (hypothesized to 

be predation), and these two types of mortality 

are assumed to act independently. We evaluated 

nine different functional forms for δ(D, t), allowing 

for scenarios of constant mortality, time-varying 

mortality (both linear and non-linear functions), 

density-varying mortality (both linear and nonlinear functions), and time/density interactions 

(Table B-5). For each functional form we solved for 

parameter values by minimizing deviation between 

expected and observed counts using maximum 

likelihood. Specifically, for each year at which 

a given island was surveyed, we calculated the 

negative log likelihood as:

(8)

where σ2

t represents the variance due to observer 

error at time t (Hilborn and Mangel 1997), solved 

for iteratively following Pascual et al. (1997). We 

summed equation 8 across islands and survey 

years, and solved for the parameter values that 

minimized the total negative log likelihood. For 

this analysis we made the simplifying assumption 

that the primary source of variance in population 

estimates was observer-based error (sampling 

error), rather than process error (Tinker et al. 

2006). Finally, we used Akaike’s Information 

Criterion (AIC) to evaluate the relative level of 

support for each functional form (Burnham and 

Anderson 2002). The functional form with the 

lowest associated AIC was assumed to provide 

the best fit to the available survey data, but to 

account for model uncertainty we calculated AIC 

weights (Burnham and Anderson 2002) as measure 

of relative support for each functional form (higher 

AIC weights indicate greater support for a given 

functional form).

Step 3: Population Simulations

Sea otter population dynamics for the Eastern 

and Western Aleutian Island management units 

were simulated for 25-year periods using the 

above-described population model. Simulations 

were iterated 1000 times for each parameter set, 

incorporating demographic stochasticity and 

dispersal stochasticity as described in Step 1, and 

with low-dispersal and high-dispersal scenarios 

(Table B-3) equally represented. The principal 

source of variance in simulation results was 

age-independent mortality (δ), which typically 

accounted for the majority of sea otter deaths 

in any given simulation. For this analysis we 

assumed that the range of past variation in δ

provided the best available estimate of future 

variation; however, our estimates of δ included 

both sampling uncertainty and model-selection 

uncertainty. To account for these sources of 

uncertainty we used a boot-strap re-sampling 

technique, whereby 1,000 sets of 7 time series 

were drawn randomly with replacement (i.e., such 

that the same time series could be drawn multiple 

times) from the full set of 7 time series, and the 

model fitting described in Step 2 was repeated. 

The “best model” (the functional form with the 

highest AIC weight) differed between bootstrap iterations, as did the maximum likelihood 

parameter estimates, and the results from each 

iteration were used to generate estimates of δ

for one 25-year simulation. The relative support 

for each functional form (averaged across all 

simulations) was calculated as the mean AIC 

weight. In those cases where the optimal 

functional form included one or more time-varying 

terms (Table B- 5), the value of “t” in δ(D, t) was 

represented as a random value between 1 and 17 

(the total number of survey years), weighted such 

that more recent years had greater representation. 

Specifically, for each year of a given simulation, 

we drew a random value from a left-skewed 

beta-distribution, and re-scaled this value to 

obtain the number of years before present: the 

two parameters of the beta-distribution (φ1 and 

φ2) were themselves drawn randomly for each 

simulation (such that 0.5 < φ1 <1.0 and 1.5 < φ2 

< 3.0) to allow for a range of different weighting 

functions (Figure B-4). The value of D in δ(D, t) 

was itself an output of the population simulation, 

and thus varied between islands depending on 

local population dynamics. To allow for stochastic 

differences in annual per-capita mortality among 
B-6 Southwest Alaska DPS of the Northern Sea Otter

islands (i.e., environmental stochasticity), we added 

a random error term, εx(t), to our estimate of δ(D, 

t) at each island, where εx(t) had a mean of 0 and 

standard deviation of 0.02 (corresponding to the 

variance in estimates of δ(D, t)obtained when Step 

2 was repeated for each island separately).

Step 4: Determining the Up-listing Threshold

Up-listing from threatened to endangered status is 

recommended when there is an unacceptably high 

likelihood of extinction (defined by the Recovery 

Team as ≥ 5% probability) within the foreseeable 

future (defined by the Team as 25 years). We 

used population simulations to determine the 

threshold density (DU) at which this condition is 

satisfied for a given management unit. Density 

was calculated as the number of otters per area 

of sea otter habitat, scaled relative to K, where 

sea otter habitat is defined as sub-tidal area 

between the coastline and the 40m isobath, and 

K was assumed equal to 15 otters per km2 (as 

defined by Burn et al. 2003). The full suite of 1000 

simulations (described in Step 2) was re-run 10 

times, with starting density (D0

) ranging between 

1% and 50% of K. Note that D0 was specified as 

the average density for the management unit, but 

the actual distribution of otters between islands 

was randomized for each simulation. For each 

of the 10 values of D0 we recorded Ω, defined as 

the proportion of simulations that went to quasiextinction within the 25 year period. We define 

quasi-extinction as the point at which there are no 

islands remaining1 with ≥5 females and ≥1 males. 

We then plotted Ω versus D0, and fit an exponential 

function from which we calculated DU, the value 

D0 at which Ω = 0.05 and thus the density at which 

there is ≥ 5% probability of extinction within a 25 

year period. 

Step 5: Determining the De-listing Threshold

De-listing from threatened status is recommended 

when there is an acceptably low likelihood of 

becoming endangered (< 5% probability) within 

the foreseeable future (which we have defined 

as 25 years). We used population simulations to 

determine the threshold density at which this 

condition is satisfied for a given management unit. 

The full suite of 1,000 simulations (described in 

Step 2) was re-run 10 times, with starting density 

(D0

) ranging between DU and 80% of K (as defined 

by Burn et al. 2003). For each of the 10 values of 

D0 we recorded ω, defined as the proportion of 

simulations in which final density was less than or 

equal to DU. Using the same approach described 

in Step 4, we solved for the critical density, DD, 

at which ω = 0.05; above this density there is < 5% 

probability of the population becoming endangered 

within 25 years. 

1

 In this context we use the term “island” to 

refer to a single island or island group that is 

geographically distinct from other such islands or 

island groups (i.e., separated by at least 10 km) and 

that has sufficient sub-tidal habitat to support an 

isolated sub-population of sea otters.

Results

A per-capita annual rate of age-independent 

mortality (δ) varying between 0.15 and 0.35 

resulted in expected population dynamics that 

closely fit observed time series of skiff surveys 

(Figure B-5). Note that age-independent mortality 

was estimated in addition to age-dependent 

mortality from all other sources. Of the nine 

functional forms evaluated for δ(D,t), the three 

models with most empirical support all included 

terms for both density and time (Figure B-6). 

The best-supported models consistently showed a 

decrease in δ over time, and an inverse relationship 

between δ and density (Figure B-7). However, 

the two functional forms with greatest support 

(Models 5, 7 and 9) also included a 2nd order 

density term that resulted in decreased values of 

δ at very low densities (Figure B-7,A and B). This 

dip in mortality at low densities may represent a 

“refuge effect”, potentially resulting from localized 

habitat areas at some islands that provide a degree 

of refuge from predators (and/or other mortality 

sources) for small, local populations. 

Simulations of future population dynamics 

generally indicated continued decreases in 

abundance, although stochastic differences 

between islands, as well as temporal variation in 

δ and density-dependent refuge effects, allowed 

for predictions of population stability or even 

increases at some islands (Figure B-8). Because 

of the prevailing negative trends in abundance, 

however, simulations with low starting densities 

frequently resulted in quasi-extinction of sea otters 

within a management unit during the 25-year 

simulation period. The relationship between Ω (the 

proportion of simulations going to quasi-extinction 

within 25 years) and D0

 (population density at the 

start of the simulations) was well described by a 

negative exponential trend (Figure B-9 A). The 

value of DU, the threshold density at which Ω=0.05, 

did not differ significantly between the Eastern 

Aleutian Islands management unit (DU = 3.65% 

of K, 95% confidence limits = 3.58-3.95) and the 

Western Aleutian Islands management unit (DU

= 3.84% of K, 95% confidence limits = 3.57-4.12). 

Using the average upper 95% confidence limit as a 

conservative estimate of DU, these results support 

an up-listing threshold density of approximately 

4% of K, or 0.6 otters per km2 of habitat. 

We conducted a second set of simulations to 

determine an appropriate de-listing threshold 

density. As was the case for the up-listing 

simulations, the relationship between ω (the 

proportion of simulations in which population 

density for a management unit dropped below 

DU) and D0

 (population density at the start of 

the simulations) was described by a negative 

exponential trend (Figure B-9 B). The value of 

DD, the threshold density at which ω=0.05, did not 

differ significantly between the Eastern Aleutian 

Islands (DD = 48.69% of K, 95% confidence limits = 

47.87-49.51) and the Western Aleutian Islands (DD 

= 47.36% of K, 95% confidence limits = 46.06-48.64). 
Recovery Plan B-7

Using the average upper 95% confidence limit as 

a conservative estimate of DD, our results support 

a de-listing density of 49% of K, or 7.35 otters per 

km2 of habitat.

Discussion

The results of the SW Alaska PVA model described 

here can be used as a basis for recommending 

conservative, demographically-based up-listing and 

de-listing thresholds. The recommendations are 

conservative for two main reasons: first, because 

the many sources of uncertainty that have been 

explicitly incorporated into the model lead to 

substantial variation in simulation results (e.g., 

see Figure 8), and when combined with stringent 

probability requirements (e.g., less than 5% of 

simulations go to extinction in 25 years) this tends 

to elevate the up-listing and de-listing thresholds. 

The second main reason that the results are likely 

conservative is that the survey time-series used 

to parameterize mortality rates were collected for 

the most part during a period of population decline 

(although there have been slightly positive growth 

rates for the last few years at some islands). 

It is therefore not surprising that a fairly high 

proportion of future population projections also 

exhibit negative population growth, a fact that acts 

to elevate the up-listing and de-listing thresholds. 

It is worth emphasizing that the conservative 

nature of the PVA-based recommendations is 

entirely appropriate at this time, given the high 

degree of uncertainty and the prevailing negative 

population trends over the past 15-20 years. Both 

of these factors may change over the next 5-10 

years. That is to say, the collection and analysis of 

more demographic and dispersal data may reduce 

the level of uncertainty in many parameters, 

and the incorporation of more years of survey 

data into model parameterization will potentially 

lead to a higher proportion of simulations with 

positive population growth. A key advantage of 

the PVA model structure developed and presented 

here is that it is both possible and advisable to 

update parameters and re-run the model as new 

data or information become available, thereby 

avoiding the inevitable obsolescence of fixed listing 

threshold recommendations. We recommend that 

this process be conducted at 5-year intervals, 

and the up-listing and de-listing thresholds be 

updated accordingly. It is worth noting that the 

probabilistic definitions of the thresholds will 

remain the same, it is only the numerical estimates 

that will change to reflect the most recent data.

The model results highlight a number of 

interesting aspects of the recent population decline 

that may have important implications for recovery. 

One of these aspects is the high degree of temporal 

and spatial consistency in the pattern of decline 

and (by extension) the source of mortality driving 

the decline (e.g., Figure B-5). Such patterns 

can be informative when evaluating various 

potential mortality sources in terms of their likely 

contribution to past declines and future population 

recovery. A second important pattern to consider 

is that the per-capita rate of mortality appears 

to have been greater at lower densities, but at 

very low densities there is indication of reduced 

mortality (Figure B-7). Again, this pattern can 

provide insight into potential sources of mortality, 

but also must be considered with respect to 

implications for recovery. Given the relative degree 

of support for predation as a causal factor, the 

density-dependent nature of the per-capita rate of 

mortality may be used to provide insight into the 

nature of predator functional responses. Moreover, 

if there is indeed a “refuge effect” at very low 

densities, population abundance should be expected 

to stabilize at many islands, particularly those with 

abundant protected habitat, with encouraging 

implications for the likelihood of population 

persistence. Survey results over the next 5-10 

years will be crucial in evaluating whether this 

pattern continues to hold.

There are a number of additional benefits to the 

demographically-based PVA model presented here 

that have not been fully realized at this time. One 

of the benefits is that a comprehensive sensitivity 

analysis can be performed to explore the relative 

importance of various parameters in driving model 

results. We recommend that this exercise be 

conducted, however this will require additional 

time for programming, computation, and analysis. 

A second important benefit of a demographically 

structured, spatially-explicit model is that it is 

very amenable for use in exploring the populationlevel impacts of specific management actions or 

newly-emerging threats. For example, if there 

were a concern about an emerging threat (e.g., 

a new nearshore fishery or mineral resource 

extraction project), the model could be re-run 

under various scenarios of direct mortality at the 

location in question, as well as scenarios of habitat 

loss, and the effects on probabilities of population 

persistence at the level of the management 

unit could be evaluated. Incorporating such 

modifications into the existing model structure can 

be accomplished with relatively minor investment 

of time in additional programming and computer 

simulations, and will provide a means of objective 

evaluation of the importance of threats or the 

benefits of alternative management actions. 

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B-8 Southwest Alaska DPS of the Northern Sea Otter

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Recovery Plan B-9

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94-LT-4026, Santa Cruz, CA.
B-10 Southwest Alaska DPS of the Northern Sea Otter Table B-1. Projection matrix used in population model to calculate demographic transitions (survival and reproduction) occurring between year t and year t+1. Female Age Classes Male Age Classes

i = age at time t i = age at time t

Sex

j = age at 

time t+1 0 1 2 3 4 5 6 7 8 9 10

≥11 0 1 2 3 4 5 6 7 8 9 10

≥11

F 0 0 0

R2 R3 R4 R5 R6 R7 R8 R9 R10

R11 0 0 0 0 0 0 0 0 0 0 0 0

F 1 sf,1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

F 2 0 sf,2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

F 3 0 0 sf,3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0F 4 0 0 0 sf,4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0F 5 0 0 0 0 sf,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

F 6 0 0 0 0 0 sf,6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0F 7 0 0 0 0 0 0 sf,7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

F 8 0 0 0 0 0 0 0 sf,8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

F 9 0 0 0 0 0 0 0 0 sf,9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0F 10 0 0 0 0 0 0 0 0 0 sf,10 0 0 0 0 0 0 0 0 0 0 0 0 0 0

F

≥11 0 0 0 0 0 0 0 0 0 0 sf,11 sf,12 0 0 0 0 0 0 0 0 0 0 0 0

M 0 0 0

R2 R3 R4 R5 R6 R7 R8 R9 R10

R11 0 0 0 0 0 0 0 0 0 0 0 0

M 1 0 0 0 0 0 0 0 0 0 0 0 0 sm,1 0 0 0 0 0 0 0 0 0 0 0

M 2 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,2 0 0 0 0 0 0 0 0 0 0M 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,3 0 0 0 0 0 0 0 0 0

M 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,4 0 0 0 0 0 0 0 0M 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,5 0 0 0 0 0 0 0

M 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,6 0 0 0 0 0 0

M 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,7 0 0 0 0 0

M 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,8 0 0 0 0

M 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,9 0 0 0M 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,10 0 0

M

≥11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sm,11 sm,12
Recovery Plan B-11

Table B-2. Vital rate estimates used to parameterize the projection matrix (Table B-1) for sea otter populations 

at low density and high density (“at K”) situations. 

The low density rates (estimated from sea otters at Kodiak Island in the late 1980s) result in an 

expected annual growth rate (λ) of 1.15, while the high density rates (estimated from sea otters at 

Amchitka Island in the early 1990s) result in an expected λ of 1.00. For populations at intermediate 

densities, vital rate parameter estimates are obtained by interpolation between these extreme 

values, such that population growth rates exhibit negative density-dependence as described by a 

theta-logistic model of population dynamics (see text for further details). 

Low-Density Population High-Density Population

Age Class

Birth Rate 

(bi)

Wean Rate 

(wL

i)

Female 

Survival 

(sL

i)

Male 

Survival 

(sL

i)

Wean Rate 

(wH

i)

Female 

Survival 

(sH

i)

Male 

Survival 

(sH

i)

0 0.000 0.000 0.912 0.904 0.000 0.742 0.736

1 0.000 0.000 0.937 0.910 0.000 0.827 0.803

2 0.400 0.588 0.953 0.917 0.201 0.878 0.845

3 0.700 0.727 0.963 0.924 0.314 0.908 0.871

4 0.900 0.817 0.968 0.928 0.432 0.925 0.887

5 0.900 0.871 0.970 0.930 0.534 0.933 0.895

6 0.900 0.902 0.969 0.929 0.611 0.934 0.895

7 0.900 0.918 0.965 0.923 0.659 0.930 0.889

8 0.900 0.923 0.957 0.913 0.678 0.918 0.875

9 0.900 0.919 0.944 0.897 0.671 0.896 0.850

10 0.900 0.905 0.923 0.874 0.636 0.856 0.811

11 0.900 0.878 0.889 0.847 0.573 0.790 0.753

12 0.900 0.829 0.835 0.818 0.482 0.684 0.670

Table B-3. Estimates (based on expert opinion of the Recovery Team) of the number of sea otters dispersing 

from a source population on one island to a remote population on another island, as a function of the density 

relative to carrying capacity at the source population, and the distance between the two islands. 

In all cases, the source population is assumed to consist of exactly 100 animals (before dispersal), so 

that the number of animals dispersing corresponds to the % of the source population. The two sets 

of estimates were used to fit functions corresponding to a low dispersal scenario and a high dispersal 

scenario, as described in the Methods section.

Inter-Island Distance

25 km 50 km

Percent of K Lower Upper Lower Upper

50 0.5 5 0.01 0.5

100 2 15 0.2 2.5
B-12 Southwest Alaska DPS of the Northern Sea Otter

Table B-4. Time series of skiff survey counts of sea otters (excluding dependent pups) for seven islands in the 

Aleutian archipelago between 1991 and 2007. 

These counts were conducted through the period of decline in this region, and were used in the 

current analysis to fit age-independent mortality rates.

Island

Year Adak Amchitka Attu Kiska

Little 

Kiska

Semichi 

Islands Kagalaska

1991 3,494 - - - - - -

1992 - - - - - - -

1993 2,200 1,308 - 949 146 191

1994 - - 2,078 - - 178 -

1995 898 - - - - - -

1996 733 - - - - - -

1997 688 - - 237 38 - 26

1998 525 - - - - - -

1999 635 246 - - - - -

2000 713 340 436 - - 62 43

2001 515 - - - - - -

2002 461 - - - - - -

2003 306 235 106 170 13 19 11

2004 277 167 - - - - -

2005 209 74 118 114 14 5 -

2006 272 - - - - - 11

2007 231 155 262 114 26 18 -
Recovery Plan B-13

Table B-5. Alternate functional forms evaluated for modeling δ, the per-capita rate of age-independent 

mortality. 

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).

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 × t

2

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 + ψ4 × t

2

9 f(D,t) = ψ1 + ψ2 × D + ψ3 × D2

 + ψ4 × t + ψ5 × t

2

In each case δ was calculated as a logit of f, the function listed in column 2 using the equation:
B-14 Southwest Alaska DPS of the Northern Sea Otter

Figure B-1. Theta-logistic population growth typical of sea otter populations. 

A) Annual rate of population growth, λ, plotted as a function of local population density, D (where 

D is calculated as a fraction of carrying capacity, K). The curve can be described by a theta-logistic 

function, λ(D) = exp[(rmax)*(1-Dθ

)], where rmax represents the maximum instantaneous growth rate 

(ln(rmax)=1.15, the value of λ at low population densities) and θ controls the shape of the negative 

density-dependent function (θ=2). B) Trends in population abundance over time for a sea otter 

population where growth follows the theta-logistic pattern shown in A).
Recovery Plan B-15

Figure B-2. Smoothed, age-specific vital rate estimates calculated for two sea otter populations, one at low 

density (parameterized from sea otters at Kodiak Island) and one at high density, near K (parameterized from 

sea otters at Amchitka Island). 

A) Annual rate of survival for females. B) Annual rate of survival for males. C) Annual rate of 

weaning success for females of age i. The 95% confidence limits are also shown for each weaning 

success function (dashed lines): low density and high density weaning success rates were adjusted 

upwards slightly (i.e., within the plotted confidence limits) in order to obtain predicted λ values of 

1.15 and 1.00, respectively (Table B-2).
B-16 Southwest Alaska DPS of the Northern Sea Otter

Figure B-3. Estimated average rates of inter-island dispersal (expressed as percentage of the source 

population dispersing) under two scenarios: A) a low dispersal scenario, and B) a high dispersal scenario.

The plotted curves represent the mean values predicted by Poisson distributions having parameter 

γ, where γ was calculated using equation 6, which was parameterized by fitting to the values shown 

in Table B-3. In both cases, the parameterized function leads to reduced dispersal with increases 

in the distance between Islands and higher dispersal with higher density of the source population 

relative to carrying capacity, K.
Recovery Plan B-17

Figure B-4. Examples of weighting functions used to randomly select values of t, which were then used 

to generate values of age-independent mortality, δ(D,t), in simulations of future population dynamics in 

southwest Alaska sea otters. 

A different beta distribution was used for each simulation (see Methods for further details).
B-18 Southwest Alaska DPS of the Northern Sea Otter

Figure B-5. Sample results from the maximum-likelihood fitting of age-independent mortality (δ) to observed 

skiff counts (Nobs). 

The best-fit values for δ, along with resulting expected trends in sea otter abundance (Nexp), are 

shown for three sites in the western Aleutians: A) Adak Island, B) the north-east half of Amchitka 

Island, and C) Attu Island.
Recovery Plan B-19

Figure B-6. Relative degree of support for nine different functional forms used to model δ, the per-capita rate 

of age-independent mortality, as measured by maximum-likelihood AIC weights (higher AIC weights indicate 

greater support for a given functional form). 

Details on each functional form are summarized in Table B-5.
B-20 Southwest Alaska DPS of the Northern Sea Otter

Figure B-7. Best-fit values of δ, the per-capita rate of age-independent mortality, plotted as a function of time 

(t) and relative population density (D). 

Maximum-likelihood results are shown for three alternate model fits, corresponding to the three 

function forms of δ (Table B-5) with the greatest empirical support (Figure B-6). A) Model 7, 

B) Model 9, C) Model 6.
Recovery Plan B-21

Figure B-8. Sample simulation results from the PVA model projections. 

Each of the seven plotted lines represents the projected abundance over time for one island in the 

western Aleutian management unit, as recorded from a different iteration of the model. Because of 

demographic and environmental stochasticity, as well as uncertainty in model parameters (including 

the rate of age-independent mortality), possible population trends for this single island range from 

slow growth, to stability, to rapid decline.
B-22 Southwest Alaska DPS of the Northern Sea Otter

Figure B-9. PVA model results for the western Aleutian management unit. 

A) The proportion of simulations in which total population abundance declined to quasi-extinction 

within the 25-year simulation period, plotted as a function of starting population density (D0

). The 

average density at which 5% or more of the simulations declined to quasi extinction for the western 

Aleutian Islands was 3.8% of K, however the upper 95% Confidence Limit for both western and 

eastern Aleutians was 4% of K, which we recommend as an appropriate uplisting criteria. 

B) The proportion of simulations in which total population abundance declined to DU, the up-listing 

threshold, by the end of the 25-year simulation period, plotted as a function of starting population 

density (D). The average density at which 5% or fewer of the simulations declined to DU for the 

western Aleutian Islands was 47% of K, however the upper 95% Confidence Limit for both western 

and eastern Aleutians was 49% of K, which we recommend as an appropriate delisting criteria. 

U.S. Department of the Interior

U.S. Fish & Wildlife Service

Region 7 - Alaska

http://alaska.fws.gov

August 2010