NOAA Technical Memorandum NMFS-NWFSC-123

Estimating the Impacts of 

Chinook Salmon Abundance and 

Prey Removal by Ocean Fishing 

on Southern Resident Killer Whale 

Population Dynamics

July 2013

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Northwest Fisheries Science Center
NOAA Technical Memorandum

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The Northwest Fisheries Science Center of NOAA’s

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Reference throughout this document to trade names does not 

imply endorsement by the National Marine Fisheries Service.

Reference this document as follows:

Ward, E.J., M.J. Ford, R.G. Kope, J.K.B. Ford, L.A. VelezEspino, C.K. Parken, L.W. LaVoy, M.B. Hanson, and K.C. 

Balcomb. 2013. Estimating the impacts of Chinook salmon 

abundance and prey removal by ocean fishing on Southern 

Resident killer whale population dynamics. U.S. Dept. 

Commer., NOAA Tech. Memo. NMFS-NWFSC-123.
NOAA Technical Memorandum NMFS-NWFSC-123

Estimating the Impacts of 

Chinook Salmon Abundance and 

Prey Removal by Ocean Fishing 

on Southern Resident Killer Whale 

Population Dynamics

Eric J. Ward, Michael J. Ford, Robert G. Kope, John K.B. Ford,1

L. Antonio Velez-Espino,1 Chuck K. Parken,

1 Larrie W. LaVoy,2

M. Brad Hanson, and Kenneth C. Balcomb3

Northwest Fisheries Science Center

Conservation Biology Division

2725 Montlake Boulevard East

Seattle, Washington 98112

1 Fisheries and Oceans Canada

Pacific Biological Station

3190 Hammond Bay Road

Nanaimo, British Columbia V9T 6N7

2 NMFS Northwest Regional Office

Salmon Recovery Division

510 Desmond Drive Southeast

Lacey, Washington 98503

3 Center for Whale Research

P.O. Box 1577

Friday Harbor, Washington 98250

July 2013

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service
Most NOAA Technical Memorandums

NMFS-NWFSC are available at the 

Northwest Fisheries Science Center

Web site, http://www.nwfsc.noaa.gov

Copies are also available from the

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Phone orders: 1-800-553-6847

E-mail orders: orders@ntis.fedworld.gov

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Table of Contents

List of Figures...............................................................................................................................................v

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

Executive Summary.....................................................................................................................................ix

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

Summarizing Population Dynamics of SRKWs...........................................................................................5

Estimating Differences in Survival, Fecundity, and Growth Rates between NRKWs and SRKWs ............9

Calculating Stock-specific and Aggregate Salmon Indices........................................................................15

Stock-specific Indices.............................................................................................................................15

Aggregate Indices...................................................................................................................................16

CTC Indices .......................................................................................................................................16

FRAM Indices....................................................................................................................................16

Estimating Carrying Capacity and Strength of Density Dependence in SRKWs.......................................22

Identifying Salmon Stocks Most Correlated with SRKW Population Growth...........................................28

Estimating Historical Effects of Chinook Abundance and Salmon Fishing on SRKW Population 

Growth ........................................................................................................................................................33

Estimating Future Effects of Changing Chinook Salmon Abundance on SRKW Population Growth.......37

References...................................................................................................................................................45

Appendix A: SRKW Population Summaries..............................................................................................51

Appendix B: Output from SRKW Population Projections .........................................................................63
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v

List of Figures

Figure 1. Summaries of young females, young males, and the sex ratio of young animals in the 

SRKW population, 1974–2011.....................................................................................................................7

Figure 2. Summaries of young or reproductive females, young or reproductive males, and the 

sex ratio of young or reproductive animals in the SRKW population, 1974–2011 ......................................8

Figure 3. Population effect in fecundity model. Estimated posterior distribution of the mean 

difference, in logit space, between SRKW and NRKW fecundity rates, 1980–2011.................................12

Figure 4. Estimated fecundity rates by age for SRKW and NRKW females, 1980–2011.........................12

Figure 5. Population effect in survival model. Estimated posterior distribution of the mean 

difference, in logit space, between SRKW and NRKW survival rates, 1979–2010...................................13

Figure 6. Estimated posterior distributions of survival rates, by stage, for SRKW and NRKW 

survival rates, 1979–2010 ...........................................................................................................................13

Figure 7. Estimated posterior distributions of growth rates for SRKW and NRKW populations, 

1979–2010 ..................................................................................................................................................14

Figure 8. Ocean catch and escapement indices for eight stocks, 1979–2010 ............................................19

Figure 9. Ocean catch and escapement indices for eight stocks, 1979–2010 ............................................20

Figure 10. Ocean catch and escapement indices for six stocks, 1979–2010..............................................21

Figure 11. Estimates of SRKW carrying capacity and 95% confidence intervals from the logistic 

population growth model, 1974–2010 ........................................................................................................25

Figure 12. Mean estimates of SRKW and NRKW survival rates as a function of Chinook salmon 

and respective killer whale population densities.........................................................................................27

Figure 13. Estimates of pod-specific SRKW female growth rates, using just the terminal run of 

the fall group of Chinook as a predictor .....................................................................................................35

Figure 14. Absolute improvement in SRKW female growth rate by year, calculated by comparing 

the fall Chinook salmon index with and without the fishing impact ..........................................................36

Figure 15. Percent improvement in SRKW female growth rate by year, calculated by comparing 

the fall Chinook salmon index with and without the fishing impact ..........................................................36

Figure 16. Observed and projected SRKW population size, years 1974–2040 .........................................40

Figure 17. Expected change in SRKW population size by year relative to an increase of Chinook 

salmon by 20%, from 1.2 to 1.44 million ...................................................................................................41

Figure 18. Probability of negative long-term SRKW population growth in 30 years, relative to 

the 2011 population size of 87 whales, as a function of different levels of Chinook abundance ...............41

Figure 19. Probability of meeting the PSP recovery goal of at least 95 whales in the SRKW 

population by the year 2020........................................................................................................................42
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Figure 20. Probability of meeting the recovery goals required for downlisting and delisting the 

SRKW population under the ESA ..............................................................................................................42

Figure 21. Relative changes in the probabilities of meeting the recovery goals required for 

downlisting and delisting the SRKW population under ESA.....................................................................43

Figure 22. Illustration of how two types of recovery goals might be balanced .........................................43

Figure 23. Illustration of how two types of recovery goals might be balanced .........................................44

Figure A-1. Births by pod and total births for SRKWs, 1974–2011..........................................................52

Figure A-2. Adult females by pod and total adult females for SRKWs, 1974–2011.................................53

Figure A-3. Reproductive females by pod and total reproductive females for SRKWs, 1974–2011........54

Figure A-4. Adult males by pod and total adult males for SRKWs, 1974–2011.......................................55

Figure A-5. Adult female deaths by pod and total adult female deaths for SRKWs, 1974–2011 .............56

Figure A-6. Adult male deaths by pod and total adult male deaths for SRKWs, 1974–2011....................57

Figure A-7. Juvenile deaths by pod and total juvenile deaths for SRKWs, 1974–2011............................58

Figure A-8. Births per adult female by pod and total births per adult female for SRKWs, 1974–2011....59

Figure A-9. Births per reproductive female by pod and total births per reproductive female for 

SRKWs, 1974–2011 ...................................................................................................................................60

Figure A-10. Total adult death rate versus total adults by pod and total adult death rate versus total 

adults for SRKWs, 1974–2011 ...................................................................................................................61
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List of Tables

Table 1. SRKW female ages by pod for females less than or equal to 42 years old....................................6

Table 2. Annual census numbers from surveys .........................................................................................11

Table 3. Random effect deviations in fecundity and survival by pod and clan .........................................14

Table 4. Average abundance and terminal run size for Chinook salmon stock aggregates from 

northern British Columbia south.................................................................................................................18

Table 5. Fecundity models used to evaluate hypotheses about density dependence and interactions 

with prey between NRKW and SRKW populations...................................................................................25

Table 6. Survival models used to evaluate hypotheses about density dependence and interactions 

with prey between NRKW and SRKW populations...................................................................................26

Table 7. Groupings used to evaluate hypotheses about Chinook salmon stocks.......................................30

Table 8. Model selection results for the SRKW population, 1979–2010 ..................................................31

Table 9. Model selection results for the SRKW and NRKW populations, 1979–2010.............................32

Table 10. Time series of terminal run, fishery impact, and combined total ocean index for the fall 

grouping of Chinook salmon ......................................................................................................................34

Table B-1. Mean and SD of J Pod over 30 years into the future across different levels of simulated 

fall Chinook salmon terminal run abundance .............................................................................................64

Table B-2. Mean and SD of expected changes in J Pod, relative to the status quo, over 30 years into 

the future across different levels of Chinook salmon abundance ...............................................................65

Table B-3. Mean and SD of K Pod over 30 years into the future across different levels of simulated 

Chinook salmon abundance ........................................................................................................................66

Table B-4. Mean and SD of expected changes in K Pod, relative to the status quo, over 30 years into 

the future across different levels of Chinook salmon abundance ...............................................................67

Table B-5. Mean and SD of L Pod over 30 years into the future across different levels of simulated 

Chinook salmon abundance ........................................................................................................................68

Table B-6. Mean and SD of expected changes in L Pod, relative to the status quo, over 30 years into 

the future across different levels of Chinook salmon abundance ...............................................................69

Table B-7. Mean and SD of the total SRKW population over 30 years into the future across different 

levels of simulated Chinook salmon abundance .........................................................................................70

Table B-8. Mean and SD of expected changes in the total SRKW population, relative to the status quo, 

over 30 years into the future across different levels of Chinook salmon abundance..................................71
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ix

Executive Summary

This technical memorandum describes modeling analyses done for a series of three 

bilateral workshops investigating the effects of Chinook salmon (Oncorhynchus tshawytscha) 

fishing on Southern Resident killer whales (SRKW, Orcinus orca). The workshops were held 

September 2011 in Seattle, Washington, March 2012 in Vancouver, British Columbia, and 

September 2012 in Seattle. They were jointly convened by the National Marine Fisheries 

Service (NMFS) and the Fisheries and Oceans Canada in order to evaluate scientific information 

and analyses being used by NMFS to evaluate the effects on SRKWs of prey reduction by 

salmon fisheries. Both the whales and their salmon prey inhabit transboundary waters, thus 

effective management of both species is a bilateral issue. Material presented during these 

workshops was critically evaluated by a panel of prominent biologists with expertise in ecology, 

fisheries science, and marine mammal biology, with the goal of evaluating the strength of 

evidence and inferences presented by the agencies and their partners.

The SRKW population is listed as endangered under the Canada Species at Risk Act 

(2003) and U.S. Endangered Species Act (2005). The population is relatively small, ranging

from 71 individuals in 1974 to a maximum of 97 individuals in 1996 (based on the July census). 

The workshops focused on the population status through 2010, but as of July 2012, the SRKW

population numbered 85 animals. Previous studies have shown that indices of Chinook salmon 

are correlated with SRKW population dynamics, and that prey samples collected at feeding 

events are predominately composed of Chinook salmon.

SRKWs have been shown to have lower growth rates than a neighboring population of 

resident whales, and using slightly different statistical methods, we confirmed those results with 

a longer data set. The differences in growth rates between the Southern Resident and Northern 

Resident populations are due to differences in fecundity and survival between populations. The 

exact mechanisms responsible for creating density dependence in killer whales are unknown; 

however, we found that the strength of density dependence appears to be stronger in the Northern 

Resident population. Southern Residents may be at or near carrying capacity, but because the 

population has experienced relatively small fluctuations, estimating carrying capacity is 

uncertain. Another reason why estimating density dependence for Southern Residents may be so 

difficult is that carrying capacity may be related to killer whale prey availability, which is 

variable over time.

As with previous analyses, we found strong correlations between killer whale 

demographic rates and indices of Chinook salmon abundance. In our consideration of the new 

stock-specific Chinook salmon indices, the high correlation between stock indices over time 

prevented a single stock or group of stocks from being strongly identified as most correlated with 

Southern Resident population dynamics. Of all groups considered, a seasonal fall group not 

including California stocks was most supported by the data; however, in subsequent analyses, a 

larger aggregate of all stocks performed slightly better. While these types of correlative analyses 
x

are important for identifying possible relationships, there is no direct link between correlations 

and causal mechanisms. In other words, the Chinook salmon stock or group of stocks that is 

most correlated in these types of analyses is not necessarily the stock or group that is limiting 

SRKW growth or recovery. Complicating matters, the relative importance of individual stocks 

likely changes over time.

In a retrospective analysis of potential decreases in prey availability as a result of fishing, 

we found that if fishing had been reduced at any point in the time series, the largest potential 

benefits would have been early in the time series (early 1980s) or in years when Chinook salmon 

indices were low (late 1990s). In recent years, reducing Chinook fishing likely has a smaller 

impact, because exploitation rates in ocean fisheries have generally declined since the 1970s. 

For future projections under alternative amounts of Chinook availability, we found that the 

Southern Resident population will likely have a slightly positive growth trajectory, but will likely 

not meet recovery goals in the next 30 years. For example, even if average Chinook abundance 

were increased to the maximum that has been observed historically (1979–2010), the probability 

of the Southern Resident population being ESA-downlisted to threatened status is less than 60%. 

Because of uncertainties in the demographic relationships between killer whales and Chinook 

salmon, and uncertainty resulting from demographic stochasticity, our projections become highly 

uncertain across all levels of hypothetical salmon abundance, with coefficients of variation 

exceeding 200% over 5 years. Therefore, even though reducing Chinook salmon fishing might 

have some short term benefit to killer whales, there is low confidence in our ability to detect any 

difference resulting from this increase in prey.

The objective of this technical memorandum is to summarize some of the quantitative 

analyses and relationships investigated during the workshops, with particular emphasis on the 

analyses in which NMFS played a large role. This document is not meant to be exhaustive or 

discuss all uncertainties in different data sets used. For more details, readers are directed to The 

Effects of Salmon Fisheries on Southern Resident Killer Whales: Final Report of the 

Independent Science Panel, published by NMFS and Fisheries and Oceans Canada.
Introduction

The Southern Resident killer whale (SRKW, Orcinus orca) population is one of several 

killer whale populations inhabiting waters along the west coast of the United States and Canada 

(Olesiuk et al. 1990, Ford et al. 2000, Krahn et al. 2002, Krahn et al. 2004). Since the 1970s, 

researchers have identified several distinct ecological forms of Pacific killer whale, including 

fish-eating residents, mammal-eating transients (also known as Bigg’s killer whales), and 

offshores (Ford et al. 2000). Photo identification methods have allowed researchers to identify 

and track the fates of individual animals within populations, allowing for detailed demographic 

analysis (Bigg et al. 1990, Olesiuk et al. 1990, Krahn et al. 2004, Olesiuk et al. 2005, Ward et al. 

2009a). In addition to demographic monitoring, considerable work has been conducted on the 

behavior (Noren et al. 2009, Ashe et al. 2010, Noren and Hauser 2012), social structure (Parsons 

et al. 2009, Pilot et al. 2010, Beck et al. 2012), movements (Hauser 2006), and diet (Ford et al. 

1998, Ford and Ellis 2006, Newsome et al. 2009, Hanson et al. 2010, Williams et al. 2011) of 

Pacific killer whales.

The analyses described here focus primarily on the SRKWs. This population is the most 

southerly distributed of several eastern North Pacific fish-eating killer whale populations (Krahn 

et al. 2002). Concern over the small SRKW population size and a sharp decline from 97 animals 

in 1996 to 79 animals in 2001 led to SRKWs being listed as endangered under the Canada 

Species at Risk Act in 2003 and the U.S. Endangered Species Act in 2005 (Krahn et al. 2004). 

For purposes of comparison, the analyses described here also include a second population, the 

Northern Resident killer whales (NRKW). After the SRKW, the NRKW population is the next 

most southerly distributed fish-eating killer whale population in the eastern North Pacific, and is 

ecologically very similar to the SRKW, particularly with regard to sharing much of the same diet 

and prey base. However, the NRKW population is considerably larger, with a population size of 

greater than 250 in 2012, compared to 85 for the SRKW, and has experienced a higher rate of 

population growth since the 1970s. The two populations have overlapping ranges (Ford et al. 

2000), with the SRKW range extending from California to Southeast Alaska and the NRKW 

range from central Washington to Southeast Alaska (Ford et al. 2000, Krahn et al. 2002). Over 

the last 30 years, the NRKW and SRKW populations have experienced correlated population 

dynamics, typically experiencing good and bad years at the same time (Ford and Ellis 2005, Ford 

et al. 2010), but have remained genetically distinct, with no known exchange of individuals 

(Krahn et al. 2004, NMFS 2011b).

Several risks have been identified as potentially threatening the SRKW with extinction. 

When the SRKW and NRKW populations were first subject to serious investigation starting in 

the 1970s, their numbers were known to be reduced from historical levels (Krahn et al. 2002). 

Prior to 1974, both the SRKW and the NRKW populations were subject to a live capture fishery 

to catch animals for display in aquaria (Bigg and Wolman 1975). In addition, due to lack of 

legal protection and a tendency to interact with fisheries, the populations were likely to have 

been subject to some unknown level of direct mortality prior to implementation of the Marine 
2

Mammal Protection Act in 1972 (Krahn et al. 2002). In addition to direct mortality prior to the 

1970s, a number of other risk factors have been identified as current threats to the SRKW (Krahn 

et al. 2004, NMFS 2008). Among these are prey limitation, inbreeding as a result of the small 

population size (Ford et al. 2011), chemical contaminants (Krahn et al. 2007), acoustic 

disturbances or other negative effects of whale watch or commercial vessels (Holt et al. 2009, 

Lusseau et al. 2009, Lachmuth et al. 2011), other anthropogenic disturbances, and catastrophic 

events such as oil spills (Krahn et al. 2002, Krahn et al. 2004). Evaluating the relative 

magnitudes of these diverse threats is difficult, due to both limited direct data on some threats 

and widely different modes of action. Where long-term time series data related to particular

threats do exist, only abundance of prey has been found to be directly correlated with SRKW 

demography (Ward et al. 2009a).

In 2011 the National Marine Fisheries Service (NMFS) issued a biological opinion in 

response to a fishing plan proposed by the Washington Department of Fish and Wildlife and the 

Puget Sound Treaty Indian Tribes that would govern their Chinook salmon (Oncorhynchus 

tshawytscha) fisheries in Puget Sound. The analysis of the proposed Puget Sound harvest plan 

included a retrospective look at the effects on harvest, exploitation rates, and abundance of 

Chinook salmon returning to spawn—if the plan had been in effect during 1994 through 2008. 

This analysis was also used to estimate the effect on kilocalories of food energy of Chinook 

salmon prey for SRKWs in inland waters during July through September under three levels of 

fishing season structure: no salmon fishing on the U.S. and Canadian west coast, no salmon 

fishing in U.S. coastal waters, and no fishing within Puget Sound. The Puget Sound analysis 

also incorporated new information on correlations between certain indices of Chinook salmon

abundance and the SRKW population growth, as well as a broader range of years than previously 

had been used to represent interannual variations in Chinook salmon abundance.

Because of the potential ramifications of these analyses to salmon fisheries management 

and other activities affecting the abundance of Chinook salmon, NMFS and Fisheries and Oceans 

Canada sought to ensure that the scientific information used to inform management decisions is 

the best available. Toward this end, the agencies jointly commissioned an independent, 

multidisciplinary panel of expert scientists to review the available science in an open and 

inclusive scientific workshop process. The key question evaluated in these workshops was:

To what extent are salmon fisheries affecting recovery of SRKWs by reducing the 

abundance of their available prey, and what are the consequences to their survival 

and recovery?

A series of 3-day scientific workshops was attended by all seven members of the Independent 

Science Panel and nearly a hundred other invited scientists representing NMFS, Fisheries and 

Oceans Canada, academia, industry, conservation organizations, management agencies, tribes, 

First Nations, and others. The first workshop occurred September 21–23, 2011, in Seattle, 

Washington, and the second March 13–15, 2012, in Vancouver, Canada. The panel issued a 

draft report following the second workshop. Comments received on the panel’s draft report and 

other analyses requested by the panel were presented and considered at the third workshop, held 

September 18–20 in Seattle. The panel issued its final report on November 30, 2012. Interested

parties were encouraged to provide any comments in response to the report by January 31, 2013. 

Additional details describing the workshop process and the agendas, the pertinent scientific 
3

literature, and scientific presentations considered at the workshops can be found online.

1 In 

interpreting the results presented in this document or the Independent Science Panel report, it is 

important to highlight that the panel was not asked to perform analyses or make management 

decisions; instead, it was only asked to critique and evaluate all of the available science.

Two types of information suggest that SRKWs 1) depend on Chinook salmon, and 

2) may be prey limited. First, prey and fecal samples have been collected during observed 

predation events and later identified to the species and population level (Ford and Ellis 2006, 

Hanson et al. 2010). Because of logistical difficulties associated with prey sampling, these 

samples are concentrated in summer months and are largely from the relatively protected waters 

of the Salish Sea. Diets inferred from these studies are similar between NRKWs and SRKWs, 

with Chinook salmon representing the majority of recovered prey: 72% of scales and tissue in 

Ford and Ellis (2006) and 76% of scales and tissue in Hanson et al. (2010). Stable isotope data 

from resident killer whale blubber is also consistent with a diet dominated by Chinook and other 

salmonids (Herman et al. 2005). The second type of analysis supporting the link between 

Chinook salmon abundance and killer whale demographics relies on correlative studies between 

estimated annual population abundance of salmon and observed killer whale births and deaths 

(or birth rates and death rates) (Ford et al. 2005, Ward et al. 2009a, Ford et al. 2010). Though 

each of these studies has relied on different salmon indices and uses different responses (e.g.,

individual births versus population birth rates), each has found significant positive correlations 

between years of high Chinook salmon abundance and years of high SRKW population growth. 

Additional evidence that may support the prey limitation hypothesis are the anecdotal 

observations of SRKW individuals appearing to be in a starved condition (“peanut-head” 

syndrome) (Durban et al. 2009, Hilborn et al. 2012), and recently developed hormone indicators 

that may indicate seasonal periods of nutritional stress (Ayres et al. 2012). In this report, we 

focus on the details of the correlative studies, which made up the majority of the work for the 

bilateral workshops (Hilborn et al. 2012).

In addition to knowing SRKW births and deaths at the population level, individual 

resighting histories are known, because every individual in the population is recognizable based 

on unique natural marks that remain with it over the course of its life (Bigg et al. 1990, Ford et 

al. 2000, Center for Whale Research 2010). Since 1979 the SRKW population has been 

surveyed annually and, because every animal has been seen each year (or presumed dead if 

missing for more than a year), this annual survey represents a complete population census 

(Center for Whale Research 2010). The NRKW population has been surveyed annually over the 

same time period (Ellis et al. 2011). In addition to births and deaths, the maternal line of every 

individual is also known, due to the cohesive social structure of resident killer whales (Bigg et al. 

1990, Parsons et al. 2009, Foster et al. 2012).

In ecology and especially in the aquatic sciences, data sets recording more than 30 years 

of individual births and deaths are rare. Though unique in its length and detail, the SRKW data

set also presents some interesting statistical challenges. First, SRKWs are extremely long-lived 

(some females are thought to have reached 80–90 years of age) (Center for Whale Research 

2010). It is much more challenging to estimate interactions between environmental drivers and 

 1 Information on the workshops, including copies of all presentations, is online at http://www.nwr.noaa.gov

/protected_species/marine_mammals/killer_whale/effects_fisheries.html.
4

population growth when species are long-lived relative to species that are much shorter lived 

(Ozgul et al. 2009). Second, because the SRKW population is small and events used to estimate 

demographic rates (births, deaths) occur relatively infrequently, estimated correlations between 

environmental variables and demographic rates are inherently more uncertain than they would be 

for a larger population (Coulson et al. 2001, Stenseth et al. 2004). Statistically, this also means 

that models for binary data at the individual level (Cooch and White 2006) have to be used over 

standard regression tools for continuous responses.

Finally, because the SRKW population inhabits such a large range (central California to 

Southeast Alaska), conducting experimental studies to verify correlations are nearly 

impossible—meaning that the exact mechanisms linking prey availability to resident killer whale 

demography will likely remain unknown. This problem is not unusual in ecology or in marine 

fisheries research. One of the best examples of experimental manipulations in marine ecology 

involves research in the rocky intertidal; these experiments work well on very small spatial or 

temporal scales, but are also impossible across large scales (Underwood 2000). Conducting 

experiments on SRKWs and their prey would ultimately involve manipulations of multiple 

ecosystems across the North Pacific, a difficult task for ecologists even at the scale of individual 

ecosystems (Schindler 1998).

The purpose of this report is to document the details of the population modeling (both 

Chinook salmon and SRKWs) that were presented to the Independent Science Panel during the 

workshop process. The workshops included presentations on a wide variety of topics, such as 

diet, whale condition measured through photographs and hormones, contaminant profiles, and 

many others.

2

 Our focus, however, is solely on documenting the demographic models used to 1) 

understand correlations between Chinook salmon abundance and SRKW and NRKW population 

dynamics, and 2) use those statistical relationships to explore the hypothetical effects of 

changing salmon abundance on the SRKW population trends. While this report is meant to be a 

stand-alone document, many of the questions and hypotheses evaluated here reflect analyses that 

were done to address specific questions asked by the panel. Further details about the motivating 

questions are presented in the Independent Science Panel report (Hilborn et al. 2012).

The remaining sections of this technical memorandum are as follows: in the Summarizing 

Population Dynamics of SRKWs section, we present some overall patterns and trends in SRKW 

population dynamics over the last 30 years; in the Estimating Differences in Survival, Fecundity, 

and Growth Rates section, we provide details on several approaches that were used to estimate 

the carrying capacity of SRKWs; in the Calculating Stock-specific and Aggregate Salmon 

Indices section, we have an overview of how SRKW population growth has performed relative 

to NRKWs; in the Estimating Carrying Capacity and Strength of Density Dependence section,

we provide details for how stock-specific Chinook salmon time series are generated; in the 

Identifying Salmon Stocks section, we indicate which of those Chinook stocks are most 

correlated with SRKW population growth; in the Estimating Historical Effects section, we 

estimate the historical effects of ocean Chinook fishing on SRKW population growth; and in the 

Estimating Future Effects section, we estimate future effects of changes in Chinook salmon 

abundance on SRKW population growth. Two appendices are also provided, one on SRKW 

population summaries, the other on output from SRKW population projections.

 2 See footnote 1.
5

Summarizing Population Dynamics of SRKWs

Before conducting any population modeling or statistical comparisons between SRKWs 

and NRKWs, we performed basic population summaries, focusing on SRKWs. Previous work 

has documented trends and patterns in NRKWs (Olesiuk et al. 2005), as well as differences 

between these two populations (Olesiuk et al. 1990). Inference from these analyses has provided 

support for several important life history characteristics: 1) resident killer whales are long-lived, 

2) as with many large terrestrial and marine mammals, female killer whales have a higher life 

expectancy than males, 3) like other long-lived mammals, female killer whales experience 

reproductive senescence, and 4) survival curves for both male and female killer whales are Ushaped, with the majority of mortalities occurring for young and old individuals (Olesiuk et al. 

2005).

The SRKW population, numbering 85 animals in the July 2012 census, is divided into 

three subgroups or pods (referred to as J Pod, K Pod, L Pod). Each pod is comprised of multiple 

matrilines (a female and any offspring she has, as well as any offspring they may have). The 

social structure of killer whales is relatively static in that, from birth, individuals remain 

associated with their matriline and usually also their pod for the entire course of their lives (Ford 

et al. 2000, Parsons et al. 2009), so it was important to summarize population quantities for the 

entire SRKW population as well as at the pod level. We summarized births and deaths, as well 

as birth rates and death rates, by pods, sex, and age-class (Appendix A). The main point of this 

exercise was to create visual summaries that could later be used to generate hypotheses for other 

analyses (such as looking for evidence of density dependence). Since 1979 the SRKW 

population has experienced slightly more births than deaths (Table 1, Appendix A) and thus has 

a slightly positive net growth rate. No obvious temporal trends in birth or death rates exist, 

either by pod or for the aggregate SRKW population.

Part of the reason why the SRKW population size is small is that, from 1962 to 1973, it 

was targeted for live capture removals. Though NRKWs were also targeted, the majority of 

captures were taken from the SRKW population. Approximately 47 animals were removed 

before 1974 from the SRKW population, nearly all of them juveniles (<10 years old) (Bigg and 

Wolman 1975, Hoyt 1990, Ford et al. 2000). To examine trends related to legacy effects from 

these removals, we calculated the number of young (<21) and young or reproductively mature 

(<43) animals in each of the three SRKW pods (Figure 1 and Figure 2). These plots illustrate 

that one of the most serious trends affecting SRKWs is the change from a population initially 

biased toward females at the start of the time series to one that is biased toward males. Of the 

three pods, J Pod is biased toward females, but K Pod and L Pod are biased toward males. For 

example, of those individuals less than 43 years old, nearly 50% are female in K Pod and L Pod 

(55% and 50%, respectively), but 71% are female in J Pod (Figure 2). For the younger 

population segment (<21), K Pod and L Pod are 36% female, and J Pod is 63% female (Figure 

1). These trends and the current age distribution of females in the SRKW population (Table 1) 

suggest that future population growth for the SRKW population will be driven by females in J 

Pod. It is important to also note that these legacy effects of removals would be expected to 
6

possibly impact the total births at the population level (by reducing the number of reproductive 

females), rather than affect the birth rate of individual females. There is no suggestion of a 

postharvest decrease in total SRKW births (Appendix A).

Table 1. SRKW female ages by pod for females less than or equal to 42 years old (at the end of 2010). 

Data collected by the Center for Whale Research and NMFS.

J Pod K Pod L Pod

1 1 1

1 3 8

4 6 16

6 17 16

6 24 18

8 25 20

10 26 21

11 33 21

13 39 24

15 — 25

16 — 34

18 — 34

26 — 34

32 — 37

34 — —

37 — —

39 — —
7

Figure 1. Summaries of young females, young males, and the sex ratio (percent female) of young animals 

in the SRKW population, 1974–2011.
8

Figure 2. Summaries of young or reproductive females, young or reproductive males, and the sex ratio 

(percent female) of young or reproductive animals in the SRKW population, 1974–2011.
9

Estimating Differences in Survival, Fecundity, 

and Growth Rates between NRKWs and SRKWs

Because the SRKW data set represents a complete population census, it is equivalent to a 

mark-recapture study with a perfect detection probability (Cooch and White 2006). For the 

purposes of the workshops and this report, we can assume independence between models of 

fecundity (birth rates) and models for survival (death rates). Biologically, this means individuals 

who have recently given birth do not have reduced survival. Because of the low variability in the 

SRKW data, analyses of demographic rates and effects of covariates (density dependence, prey 

indices) can be improved by combining the SRKW census data alongside Fisheries and Oceans 

Canada’s NRKW data. In most recent years, all individuals in the NRKW population have been

seen; however, because this criteria has not been met in all years, the survey is not technically a 

complete census. For this combined analysis with the two populations, scientists from Fisheries 

and Oceans Canada standardized the SRKW data set to periods of the NRKW survey.

First, we treated SRKW and NRKW fecundity as the response variable. Following Ward 

et al. (2009a), five SRKW females with estimated age at the start of the study based on their last 

observed birth (K3, L2, L3, L11, and L21) were removed from the analysis to avoid introducing 

bias. The remaining births from 1980 to 2011 were included in our analysis and coded as a 

binary response (birth, no birth). Years immediately before and after observed births were 

treated as missing data, because with an 18-month gestation time, it is physically impossible for 

killer whale females to give birth in successive calendar years. The observed birth data was 

linked to population covariates via a link function (equivalent to logistic regression),

logit�fi,t� = B0,pop + B1agei,t + B2agei,t

2 + B3agei,t

3 + B4agei,t

4 (1)

where fi,t represents the probability of animal i giving birth at time t, the coefficient B0,pop is an 

intercept allowing SRKW and NRKW to have different mean fecundity rates, and B1,B2, B3,B4

relate the effect of age on the probability of giving birth. The observed binary data is then 

modeled as Yi,t~Bernoulli�fi,t�. It is important to note that covariates affect birth rates linearly 

in link space, but the relationship becomes nonlinear in normal space via the inverse link 

function. Our fecundity models were initially fit with age alone (no external covariates), then 

modified to include effects of Chinook salmon indices and density dependence.

Second, we examined support for density dependence in survival rates. Survival rates 

(φi,t) were also modeled with a link function as

logit�φi,t� = B0,pop + B1,stage + B2Li,t (2)

where B0,pop represents the population effect (NRKW, SRKW), B1,stage estimated stage-specific 

survival rates, B2 is an extra intercept parameter allowing mean survival rates for L Pod to be 

slightly different, and Li,t is a binary indicator covariate specifying whether each individual is a 

member of L Pod. Previous work has estimated survival rates of L Pod to be slightly smaller 
10

than those of J Pod or K Pod (Ward et al. 2011). When we applied this model to the SRKW data

set alone, the population effect was not included in the model. There are several important 

differences in the models for survival and fecundity. First, because of uncertainty in the ages of 

some older individuals at the start of the 1979 census, survival was estimated as stage based

rather than age based. Stages were slightly modified versions of those presented in Krahn et al. 

(2004): calves (age 0 animals seen in their first summer), juveniles (both sexes, animals aged 

1–9), young reproductive females (ages 11–42), young males (ages 10–21), old postreproductive 

females (43+), and older males (22+). As with fecundity, we excluded several whales from the 

analysis to avoid potential biases: the deaths of L97, L98 (Luna), and L104 were not included. 

L98 was included in survival estimation of juveniles, but his death was not included because it 

was unnatural (ship strike). The deaths of L97 and L104 were also not included, because they 

were calves that disappeared at the same time as their mothers (thus their deaths are not 

independent). Though the deaths of these three individuals were not included in our estimation, 

their births were included in estimates of fecundity.

In addition to evaluating requests from the science panel for calculating estimates of 

demographic rates (Hilborn et al. 2012), we calculated estimates of the population growth rate 

(lambda, λ) that depends on these rates. Statistically, this involved turning estimates of fecundity 

and survival from individual resighting histories into a demographic transition matrix, and 

estimating λ as the dominant eigenvalue of that matrix (Fujuwara and Caswell 2002). Another 

way to interpret λ is in terms of R, the discrete net growth rate (λ = 1 + R), or in terms of r, the 

instantaneous rate of increase (λ = exp(r)). These variables can also be linked back to the 

NMFS recovery plan, where the maximum growth rate observed over a 12-year period has been 

calculated as R = 0.023 (= ln(97/74)/12, Table 2). Because both our modeling approach and the 

data differ from the matrix-modeling approaches that have been used previously for these 

populations (Olesiuk et al. 1990, Brault and Caswell 1993), we expect our results to also differ 

slightly. First, we used Bayesian logistic regression to estimate the posterior distributions of the 

fecundity and survival models. We saved 25,000 vectors of Markov Chain Monte Carlo 

(MCMC) draws, and used the Gelman-Rubin diagnostic to indicate convergence (Gelman and 

Rubin 1992). While λ is a useful summary of population growth, it also has several limitations. 

First, it represents the average growth rate that is expected for a population at equilibrium; for 

small populations like SRKWs that are constantly affected by stochastic births and deaths, the 

population will never be at the exact equilibrium. Second, our growth rate estimate only 

includes females that are less than 43 years old; postreproductive females and all males are not 

included in the model and have no impact on λ (Caswell 2001).

Previous work has estimated significant differences in both demographic rates and 

population growth rate between SRKWs and NRKWs (Ford et al. 2005, Ward et al. 2009a), and 

those results were confirmed in this update. For fecundity modeling, the posterior distribution of 

the population effect indicated that there is a 99.7% chance that SRKW fecundity is lower than 

NRKW fecundity (Figure 3 and Figure 4). For survival modeling, the posterior distribution of 

the difference indicates that there is a 97.1% chance NRKW survival is higher than that of the 

SRKW (Figure 5). These differences in survival rates can also be interpreted in terms of the 

mean rates (Figure 6) or in terms of how these rates affect lifetime expectancy. Median female 

life expectancy in the SRKW population based on these rates is 37.8 years, versus 44.9 years in 

the NRKW population. Similarly, the median male age is 22.5 years in the SRKW population 

and 24.7 years in the NRKW population. Linking fecundity and survival, another way to express 
11

the differences in fecundity rates is in terms of how many calves a female killer whale produces 

over her lifetime. Incorporating population and stage-specific survival rates (Figure 6), we 

performed a Monte Carlo simulation with the estimates of the fecundity model (Figure 4). This 

step was necessary, instead of just summing rates over all ages, because females are not able to 

produce calves in sequential years (Ford et al. 2005), and experienced natural mortality. Over a 

female’s lifetime, this simulation approach indicates that a SRKW female would be expected to 

produce 3.1 calves, versus 3.5 in the NRKW population. For a female that lives to reproductive 

senescence, this difference is even greater (3.9 vs. 5.1).

Because fecundity and survival rates in the SRKW population are lower than in the 

NRKW population, it is not surprising that our estimates of population growth (λ) are also 

different between the two populations (Figure 7). The mean estimate of λ for SRKWs is 1.01 

(95% credible intervals = 0.997,1.023) and the mean estimate of λ for NRKWs is 1.028 (95% 

credible intervals = 1.016,1.039). In interpreting λ, remember that λ represents the average 

replacement of females by females for a population at equilibrium. It does not incorporate 

stochastic variation from year to year or the observed total SRKW or NRKW population sizes, 

the latter being what the NMFS recovery criteria are based on (NMFS 2008).

Mechanisms responsible for the difference in growth rates between populations remain 

largely unknown. One hypothesis is that SRKWs may be at the southern edge of a latitudinal 

gradient for the resident fish-eating ecotype. Differences in growth rates between segments of 

NRKWs and SRKWs have been reported previously (Ford et al. 2000). To quantify variability 

among different levels of social structure, we replicated our logistic regression analyses, 

including random effect terms either at the clan, pod, or matriline level. Matriline effects are 

difficult to estimate for some segments of the populations; however, there is large variability at 

the pod level (Table 3). When the random effects are included at the clan level, the Southern 

Resident population has the lowest fecundity and survival rates; of the Northern Resident 

population clans, G Clan (which is distributed more southerly and has a range most overlapping 

Southern Residents) has lower survival and fecundity than clans with more northern distributions 

(A Clan, R Clan, Table 3).

Table 2. Annual census numbers from surveys. Data collected by the Center for Whale Research and 

NMFS. Numbers are typically reported July 1 and at the end of the calendar year; these numbers 

are the SRKW population size at the end of the calendar year (NMFS 2008).

Year Population Year Population Year Population

1974 70 1987 84 2000 83

1975 71 1988 85 2001 81

1976 70 1989 85 2002 83

1977 79 1990 89 2003 84

1978 79 1991 92 2004 88

1979 81 1992 91 2005 88

1980 83 1993 97 2006 90

1981 81 1994 96 2007 87

1982 78 1995 98 2008 85

1983 76 1996 97 2009 87

1984 74 1997 92 2010 86

1985 77 1998 89

1986 81 1999 85
12

Figure 3. Population effect in fecundity model. Estimated posterior distribution of the mean difference, 

in logit space, between SRKW and NRKW fecundity rates, 1980–2011. The vertical dashed lines

are 95% credible intervals and the probability of a positive difference (SRKW fecundity > 

NRKW fecundity) is indicated in dark gray.

Figure 4. Estimated fecundity rates by age for SRKW and NRKW females, 1980–2011. The thick solid 

(SRKW) and dashed (NRKW) lines are median estimates as a function of age and the pairs of 

thinner solid and dashed lines on either side are 95% posterior credible intervals.
13

Figure 5. Population effect in survival model. Estimated posterior distribution of the mean difference, in 

logit space, between SRKW and NRKW survival rates, 1979–2010. The vertical dashed lines are 

95% credible intervals and the probability of a positive difference (SRKW survival > NRKW 

survival) is indicated in dark gray.

Figure 6. Estimated posterior distributions of survival rates, by stage, for SRKW and NRKW survival 

rates, 1979–2010. The thick horizontal lines and boxes represent the median and quartiles, 

respectively. The whiskers indicate the extremes of the data.
14

Figure 7. Estimated posterior distributions of growth rates (λ) for SRKW and NRKW populations, 1979–

2010. The vertical dashed lines are the 95% posterior credible intervals. For each population, the 

region of λ corresponding to negative growth (λ < 1) is shown in dark gray.

Table 3. Random effect deviations in fecundity and survival by pod and clan. All deviations are in logit 

space, but deviations <0 indicate worse than average and those >0 indicate better than average.

Population Clan Pod

No. of 

whales

Fecundity 

(pod)

Survival 

(pod)

Fecundity 

(clan)

Survival 

(clan)

NRKW A A1 22 0.53 0.35 0.11 0.03

A A4 16 0.56 −0.28 — —

A A5 13 −0.89 −0.04 — —

A B1 6 0.41 0.07 — —

A C1 17 0.25 0.09 — —

A D1 12 −0.58 −0.18 — —

A H1 5 −0.07 −0.21 — —

A I1 18 0.33 0.07 — —

A I2 3 −0.19 −0.08 — —

A I18 24 0.04 0.05 — —

G G1 34 0.05 −0.06 0.03 0.00

G G12 16 0.10 −0.20 — —

G I11 26 −0.12 0.22 — —

G I31 10 0.23 −0.12 — —

R R1 38 0.35 0.36 0.34 0.36

R W1 1 0.00 0.17 — —

SRKW J J1 26 −0.15 −0.04 −0.27 −0.15

J K1 19 −0.42 −0.04 — —

J L1 39 −0.27 −0.28 — —

Estimated SRKW (above) and NRKW (below) growth rates
15

Calculating Stock-specific and Aggregate 

Salmon Indices

Stock-specific Indices

Abundance data were compiled for Chinook salmon stocks from northern British 

Columbia to the Sacramento River in California to create ocean catch and escapement (OCE) 

indices. This included terminal run size for fish of both hatchery and natural origin and impacts 

in ocean fisheries. Data compilation focused on the Pacific Salmon Commission’s Chinook 

Technical Committee’s (CTC) Chinook model. The CTC model was selected as a data source 

because it provides continuous coverage from 1979 through 2010, includes both hatchery and 

natural production for stocks from Southeast Alaska to the central Oregon coast, and gets around 

gaps in coverage of coded wire tag (CWT) programs. Some smoothing of the terminal run data 

is done by the CTC model. For Canadian stocks, the model only has eight stocks, some of which 

aggregate over different life history types and exploitation patterns, and only four CWT indicator 

stocks during the model base period. For these reasons, Canadian stocks were disaggregated to a 

finer scale than the CTC model stocks and 10 CWT indicator stocks were used, where possible, 

to account for the impacts of ocean fisheries. For stocks not included in the CTC model, outside 

sources of data were used where available. These include upper Columbia River spring Chinook 

salmon, Klamath River fall Chinook, and Sacramento River fall Chinook.

These summaries are not exhaustive. All Canadian stock aggregates except Fraser late do 

not include all production, but they are suitable for describing abundance trends. Coastal 

Chinook stocks from the Umpqua River south; as well as spring Chinook from the Klamath 

River; spring, late-fall, and winter Chinook from the Sacramento River; and fall Chinook from 

the San Joaquin system are also omitted. In all cases, terminal runs and fishery impacts are of 

fish of ages 3 and older for ocean-type stocks, and ages 4 and older for stream-type stocks, and 

are presented on a calendar year basis. The abundance estimates (terminal run + fishery impacts) 

do not account for fish that do not mature, but remain at sea, and they also do not account for 

predation (e.g., by marine mammals), so they are minimum estimates. When killer whales are at 

inside waters, the terminal run size of stocks is most representative of the availability of large 

Chinook salmon. When they are at outside marine waters, the sum of fishery impacts and 

terminal run is probably more representative.

Comparing the averages from the first 10 years of the time series to those of the last 10 

years, there has been a modest decline in overall abundance of Chinook salmon coast wide, but 

an increase in the terminal run size as a result of reductions in ocean salmon fisheries (Table 4). 

However abundance patterns differ greatly among the stocks. As an example, the four 

aggregates of Fraser River spring and summer Chinook stocks appear to be important in the diets 

of SRKWs (Hanson et al. 2010), and these stocks are collectively incorporated in the CTC model 

as the Fraser early stock. This model stock includes both stream-type (spring runs 1.2 and 1.3
16

and summer 1.3) and ocean-type (summer 0.3) life histories, which have very different patterns 

of distribution, exploitation, and maturation. Stream-type stocks experience little impact from 

ocean fisheries and are harvested primarily in terminal fisheries, thus their availability to resident 

killer whales is not substantially impacted by ocean fisheries. Ocean-type stocks are harvested 

primarily in ocean fisheries. While there has been a modest increase in the abundance and 

availability of stream-type Chinook from the Fraser River, there has been about a 50% increase 

in abundance and more than a 300% increase in terminal run size of ocean-type Chinook. This 

has resulted in more than doubling of the terminal run size of Fraser River Chinook salmon 

migrating through the inside marine waters during the spring and summer. For other stocks coast 

wide, increases in abundance have been enhanced and declines in abundance have been 

mitigated or reversed in terminal runs by reductions in ocean fisheries (Figure 8, Figure 9, and 

Figure 10).

Aggregate Indices

CTC Indices

CTC abundance indices are intended to provide indices of the abundance of Chinook 

salmon vulnerable to fisheries over fairly large areas (e.g., Southeast Alaska, northern British 

Columbia, west coast Vancouver Island, etc.). This is done by using CWT recoveries by stock 

and age from the model base period (fishing years 1979–1982) to infer the proportion of each 

stock taken at each age by fisheries during the base period, and expanding these proportions by 

the age-specific abundance of each model stock during the base period. These catches are 

summed over stocks and age-classes to provide an estimate of the total catch. In subsequent 

years, estimates of age-specific abundance, combined with average base-period exploitation 

rates, are used to produce estimates of what the age-specific catches would have been in the base 

period with the stock abundances of that year. The abundance index value for that fishery in a 

particular year is the sum of the scaled catches from that year divided by the average catches in 

the base period. Thus the abundance indices in any year are intended to reflect what the relative 

change from the base period catches would have been in that year, if fisheries were conducted as 

they were during the base period.

FRAM Indices

The Fishery Regulation Assessment Model (FRAM) Chinook model is a single-pool 

deterministic model of 38 stocks from southern British Columbia to the Sacramento River, 

excluding some Columbia River spring-run stocks and stocks from southern Oregon including 

the Klamath River. All marine area fisheries within three time strata (October-April, May-June, 

July-September) from Southeast Alaska to southern California are included, with finer scale 

resolution for fisheries within Puget Sound. The number of Chinook salmon by landed catch and 

total fishery-related mortality is estimated by stock, age, and time period for each fishery. 

Natural mortality is applied as a constant for each age to each cohort at the start of the time 

period and does not vary across years, stock, or seasons. Unlike the CWT and CTC approaches, 

FRAM contains a growth function (vonBertalanffy curve) for mature and immature run types 

that allows for a size selectivity component to the estimate of Chinook prey for SRKWs. The 

biological opinion for the Puget Sound Resource Management Plan (NMFS 2011a) used the 

growth function feature to produce estimates of kilocalories of food energy from larger size 
17

Chinook prey, which were predominant in SRKW scat samples. Because FRAM is a single-pool 

model, abundance of prey available within a specific spatial location cannot be quantified. 

However, estimates of Chinook prey abundance were made for coastal and inland waters 

aggregates within the SRKW range based on historical catch distribution and stock origin region 

(inland for catches and stocks originating within Puget Sound, Strait of Juan de Fuca, Georgia 

Strait, and Johnstone Strait, or coastal for catches and FRAM stocks other than inland type).

Whereas the estimates of Chinook prey abundance for the biological opinion of the Puget 

Sound Resource Management Plan were based on kilocalories of food energy of Chinook salmon 

that met size selectivity criteria under the provisions of the plan, the indices of Chinook 

abundance from FRAM for this analysis were not adjusted for size selectivity, nor were the 

fisheries modified from observed to meet the provisions of the plan. Estimating Chinook prey in 

terms of kilocalories of food energy was discontinued because of uncertainty in measuring 

kilocalories of food energy in Chinook tissue, sensitivity of size selectivity parameters in growth 

functions in FRAM, and the incompatibility of different measurement units between FRAM and 

the CTC Chinook model. The indices for this analysis were all adult Chinook salmon of ageclass 3 to age-class 5 estimated to be in inland waters during July through September of 1983 

through 2008 from postseason FRAM runs that incorporate observed catches and terminal run 

sizes by age and stock.

The stocks and fisheries covered in the FRAM and CTC Chinook models contain much 

of the same stock-age abundance and CWT base data and both use the same constant natural 

mortality rates. The structure of the two models is somewhat different, but many of the 

algorithms for catch and mature run accounting are similar between the two models. Despite 

these similarities, indices from the CTC Chinook model consistently showed a better fit than the 

FRAM indices and were used for this analysis and for most of those presented at the bilateral 

scientific workshops.
18

Table 4. Average abundance and terminal run size, in 1,000s of adult fish, for Chinook salmon stock 

aggregates from northern British Columbia south. Abundance is the sum of terminal run size and 

impacts in ocean fisheries by calendar year. Averages are presented for the first 10 years (1979–

1988) and last 10 years (2001–2010) of the time series compiled for this report.

Average abundance Average terminal run

1979–

1988

2001–

2010

Change 

(%)

1979–

1988

2001–

2010

Change 

(%)

Northern British Columbia 117.2 141.7 21 79.2 100.5 27

Central British Columbia 46.6 68.2 46 31.6 46.7 48

West coast Vancouver Island 339.5 220.0 −35 121.8 145.3 19

Upper Georgia Strait 118.2 35.2 −70 30.1 20.8 −31

Georgia Strait hatchery & natural 93.6 65.9 −30 20.2 36.0 78

Fraser late 425.7 207.2 −51 112.6 154.9 38

Fraser spring 1.2 11.2 22.8 103 9.4 21.3 128

Fraser spring 1.3 51.1 50.3 −2 40.9 38.4 −6

Fraser summer 1.3 48.5 53.6 11 34.6 44.8 30

Fraser summer 0.3 149.0 226.0 52 30.4 128.7 324

Puget Sound 641.1 396.9 −38 236.7 237.6 0

Washington coast 156.9 119.0 −24 73.2 72.0 −2

Lower Columbia River spring 130.0 118.8 −9 101.8 102.5 1

Upper Columbia River spring 80.9 222.8 176 80.9 222.8 176

Columbia River summer 36.9 94.9 157 19.2 64.4 236

Columbia River bright fall 369.0 495.0 34 231.0 371.3 61

Columbia River tule fall 461.4 290.9 −37 213.5 195.0 −9

Northern Oregon coast 252.0 188.4 −25 126.3 126.6 0

Klamath River fall 235.3 139.2 −41 100.9 114.0 13

Sacramento River fall 858.0 704.3 −18 212.5 361.7 70

Coast-wide total: 4,622.2 3,861.1 −16 1,906.6 2,605.3 37
19

Figure 8. Ocean catch (fishery impact) and escapement (terminal run) indices for eight stocks, 

1979–2010. 

0

50

100

150

200

250

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Northern BC Fishery impacts

Terminal run

0

20

40

60

80

100

120

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Central BC Fishery impacts

Terminal run

0

50

100

150

200

250

300

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Upper Georgia Strait

Fishery impacts

Terminal run

0

20

40

60

80

100

120

140

160

180

200

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Lower Georgia Strait

Fishery impacts

Terminal run

0

5

10

15

20

25

30

35

40

45

50

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Fraser Spring 1.2

Fishery impacts

Terminal run

0

10

20

30

40

50

60

70

80

90

100

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Fraser Spring 1.3

Fishery impacts

Terminal run

0

10

20

30

40

50

60

70

80

90

100

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Fraser Summer 1.3

Fishery impacts

Terminal run

0

50

100

150

200

250

300

350

400

450

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Fraser Summer 0.3

Fishery impacts

Terminal run
20

Figure 9. Ocean catch (fishery impact) and escapement (terminal run) indices for eight stocks, 

1979–2010.

0

100

200

300

400

500

600

700

800

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Puget Sound

Fishery impacts

Terminal run

0

50

100

150

200

250

300

350

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Washington Coast

Fishery impacts

Terminal run

0

50

100

150

200

250

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Columbia Lower River Spring

Fishery impacts

Terminal run

0

50

100

150

200

250

300

350

400

450

500

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Columbia Upper River Spring

Terminal run

0

20

40

60

80

100

120

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Columbia River Summer

Fishery impacts

Terminal run

0

100

200

300

400

500

600

700

800

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Columbia River Bright Fall

Fishery impacts

Terminal run

0

100

200

300

400

500

600

700

800

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Fraser Late

Fishery impacts

Terminal run

0

100

200

300

400

500

600

700

800

900

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

West Coast Vancouver Island

Fishery impacts

Terminal run
21

Figure 10. Ocean catch (fishery impact) and escapement (terminal run) indices for six stocks, 1979–2010.

0

100

200

300

400

500

600

700

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Columbia Tule Fall

Fishery impacts

Terminal run

0

50

100

150

200

250

300

350

400

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

North Migrating Oregon Coast

Fishery impacts

Terminal run

0

100

200

300

400

500

600

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Klamath River Fall

Fishery impacts

Terminal run

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Sacramento River Fall

Fishery impac

Terminal run

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Coastwide Total

Fishery impacts

Terminal run

0

100

200

300

400

500

600

1979 1984 1989 1994 1999 2004 2009

Abundance (1,000s)

Year

Fraser Early

Fishery impacts

Terminal run
22

Estimating Carrying Capacity and Strength of 

Density Dependence in SRKWs

Over the period 1979–2010, the SRKW population fluctuated between 71 and 97 

individuals, experiencing periods of positive growth and periods of decline, such as in the mid1990s (Table 2) (NMFS 2008). One of the recurring questions brought up before and during the 

bilateral workshops was whether one explanation for the small SRKW population growth rate is 

that the population was at or nearing carrying capacity. We conducted a series of analyses, using 

SRKW data alone and SRKW data combined with NRKW data to evaluate the strength of 

evidence for these hypotheses about density dependence and carrying capacity.

The simplest approach to estimating the carrying capacity of the SRKW population is to 

fit a single-stage population model to total population counts, ignoring age and sex information 

(Table 2). Two of the most common choices of models are the logistic (or Schaefer) model or 

the Gompertz growth model. The logistic model is parameterized in terms of the growth rate (r) 

and carrying capacity (K),

Nt+1 = Nt + rNt �1 − Nt

K � + et (3)

and the Gompertz model can be also expressed in terms of growth (u) and carrying capacity (K), 

log(Nt+1) = �

K−u

K � log(Nt) + u + et (4)

Both models include temporal deviations, et, making these models stochastic (process error 

models) (Hilborn and Mangel 1997). We assume these deviations are distributed Normal(0, σ). 

Unfortunately, carrying capacity estimates from both models are imprecise. The logistic model 

estimates K = 88.25, but the 95% confidence intervals include the majority of observed SRKW 

population sizes, in addition to an upper 95% interval of 112.4 (Figure 11). Estimates from the 

Gompertz model are even more uncertain. The inability of these simple models to estimate 

carrying capacity are not terribly surprising, as SRKWs have fluctuated in a narrow range and 

have not been observed across extremes of population sizes, such as the ranges observed in 

pinniped populations (e.g., Jeffries et al. 2003).

Instead of focusing estimation on SRKW carrying capacity, an alternative approach is to 

estimate the strength of density dependence in SRKWs. Mechanisms of density dependence in 

cetaceans are poorly understood, but are likely nonlinear and may affect multiple demographic 

rates (Taylor and DeMaster 2006). Because of the low variation in birth and death rates, we 

explored whether either observed births or deaths are affected by specific components of 

population size, such as the total number of mature animals or total number of adult females. 

Our a priori expectations were that, for both birth rates and survival rates, we would expect 

density dependent processes to have negative effects (or possibly zero effect). One possible 

exception is that older, potentially more experienced females, may have offspring with higher 
23

survival rates (Ward et al. 2009b). Because this estimated effect is small and uncertain, we did 

not include it in our models.

We modified our fecundity and survival models to include effects of prey (indices of 

Chinook salmon) and proxies for killer whale population density. The modified model became

logit�fi,t� = B0,pop + B1agei,t + B2agei,t

2 + B3agei,t

3 + B4agei,t

4 + B5Si,t−1 + B6Xi,t−1 (5)

where fi,t represents the probability of animal i giving birth at time t, the coefficient B0,pop is an 

intercept allowing SRKWs and NRKWs to have different mean fecundity rates, B1,B2, B3, B4

relate the effect of age on the probability of giving birth, the coefficient B5 relates 1-year lagged 

Chinook salmon abundance Si,t−1 to fecundity (Ford and Ellis 2005), and the coefficient B6

represents the effect of density dependence on birth rates through some covariate Xi,t−1. The 

observed binary data is then modeled as Yi,t~Bernoulli�fi,t�. It is important to note that 

covariates affect birth rates linearly in link space, but the relationship becomes nonlinear in 

normal space via the inverse link function. Similarly, we modified our logistic survival model to 

include covariates,

logit�φi,t� = B0,pop + B1,stage + B2Li,t + B3Si,t + B4Xi,t (6)

where B3 and B4 relate salmon abundance and measures of population size to survival.

In addition to each of the fixed-effects covariates (population, salmon, density), we 

considered interactions between each of these covariates. Interactions between population 

(SRKW, NRKW) and Chinook salmon indices were included to allow the NRKW and SRKW 

populations to have different responses to salmon. The interaction between population and 

density was included to allow for different strengths of density dependence in each killer whale 

population. The interaction between Chinook salmon and killer whale density was included to 

allow the relationship of fecundity or survival and the abundance of Chinook to have a 

interaction on density that was not simply linear, and finally the three-way interaction between 

population, killer whale density, and Chinook salmon was included to allow the strength of the

salmon–killer whale density relationship to vary by killer whale population.

For both the fecundity and survival models described above, we simultaneously 

considered CTC indices of Chinook salmon abundance that have been used in previous 

publications (Ford et al. 2005, Ward et al. 2009a, Ford et al. 2010). Specifically, we focused on 

northern British Columbia (NBC) and west coast of Vancouver Island (WCVI) indices, obtained 

from Appendix H in the Pacific Salmon Commission CTC’s annual report (Pacific Salmon 

Commission 2011). These indices were not meant to represent all possible indices of Chinook

salmon, but because they are significantly correlated with SRKW demographics, they represent a 

baseline model to examine effects of density dependence. As proxies for density-dependent 

metrics, the following population summaries were included as covariates in models of survival 

and fecundity: total population size, total number of females, total numbers of males, number of 

adult females, and total number of adult males. Each of these quantities was also tabulated at the 

pod and matriline levels and assumed unique to each population (females in the NRKW 

population were not affected by SRKW proxies for density dependence).
24

We conducted all model selection using maximum likelihood estimation. For all 

fecundity models considered, we found little support for density-dependent processes affecting 

birth rates of killer whales (Table 5). The best model in our analysis included an additive 

population effect and an additive Chinook salmon effect (CTC’s NBC index), but no effect of 

population density (Table 5). Effects of salmon and the difference between killer whale 

populations were both statistically significant, but all effects of population density were not. The 

population and salmon effects, without an interaction, indicate that the SRKW population has the

same fecundity response to salmon that the NRKW population does, but that NRKW fecundity 

rates are simply higher (this is consistent with our previous analyses that did not include salmon 

or density, Figure 4). For the survival models we considered, results were mixed, but appeared 

to show some weak effects of density dependence (Table 6). The best model included 

interactions between whale population and Chinook salmon (CTC’s NBC index), and unlike the 

fecundity model, an interaction between these terms was also supported. This interaction can be 

interpreted as NRKWs and SRKWs having slightly different survival responses to the same 

change in the Chinook salmon indices. The best survival model also included an interaction 

between whale population and total females in each population, representing density 

dependence. This second interaction indicates support for each whale population having slightly 

different survival responses to similar changes in the total number of females.

The effect size of the estimated interactions on killer whale survival can be calculated by 

varying the Chinook salmon indices included in the model and the density of adult females 

across the ranges that have been historically observed. Though we present results for juvenile 

survival (Figure 12), these results are identical for other life stages in our model, because we 

treated survival as stage based. What this type of sensitivity analysis suggests is that the NRKW 

response to density dependence in survival is much stronger than for SRKWs (Figure 12). Part 

of the challenge in detecting any effects of density dependence in SRKWs is that, over the last 30 

years, the SRKW population has exhibited small fluctuations in density when compared to 

NRKWs. Since 1979, the number of total females in the SRKW population has ranged from 45 

to 60, but over the same time period, the number of females in the NRKW population has 

doubled (from 60 to 120). Because we have not observed SRKWs over such a range of 

densities, it makes our inference about whether SRKWs are at or near carrying capacity highly 

imprecise (Figure 11). If density-dependent processes are present, however, we can conclude 

that their effects on SRKW survival appear to be smaller than variations in other covariates, such 

as Chinook salmon (Figure 12). Because the effects of density dependence appeared to be 

relatively small for SRKWs, density-dependent effects were not included in our SRKW 

population projections.
25

Figure 11. Estimates of SRKW carrying capacity (straight horizontal line) and 95% confidence intervals 

(light gray rectangular area) from the logistic population growth model, 1974–2010 (Table 5).

Table 5. Fecundity models used to evaluate hypotheses about density dependence and interactions with 

prey between NRKW and SRKW populations. This table is a subset of the best fitting models. 

Age is modeled as a fourth order polynomial in each case and the age effect alone represents the 

null model. Asterisks (*) between terms indicate the inclusion of interaction terms. For all 

models, two-way interactions also include single fixed effects, and three-way interactions include 

all pairwise two-way interactions. The best model with the lowest AIC score is in boldface.

Covariates included AIC ∆AIC

Null 1918.42 8.89

Population 1911.95 2.43

Population * totalPop 1915.27 5.75

Population * total males 1910.41 0.89

Population * total females 1911.75 2.23

Population * maturePop 1914.71 5.18

Population * total mature females 1914.53 5.01

Population * total mature males 1914.18 4.66

Population * year 1915.77 6.25

Population + CTC WCVI 1909.52 0.00

Population * CTC WCVI 1911.47 1.94

Population + CTC NBC 1910.62 1.10

Population * CTC NBC 1912.43 2.91

Population * total females * CTC WCVI 1916.50 7.00

Population * total females * CTC NBC 1915.80 6.29

Population * total males * CTC WCVI 1915.00 5.43

Population * total males * CTC NBC 1913.50 4.00

Population * total mature females * CTC WCVI 1915.40 5.90

Population * total mature females * CTC NBC 1916.20 6.71

Population * total mature males * CTC WCVI 1913.70 4.14

Population * total mature males * CTC NBC 1916.30 6.83
26

Table 6. Survival models used to evaluate hypotheses about density dependence and interactions with 

prey between NRKW and SRKW populations. This table is a subset of the best fitting models. 

Stage-specific survival is included in every model and the stage effect alone represents the null

model. Asterisks (*) between terms indicate the inclusion of interaction terms. For all models, 

two-way interactions also include single fixed effects, and three-way interactions include all 

pairwise two-way interactions, in addition to main effects. The best model with the lowest 

Akaike information criterion (AIC) score is in boldface.

Covariates included AIC ∆AIC

Null 2130.0 25.9

Population 2128.4 24.3

Population * totalPop 2118.1 14.0

Population * total males 2126.3 22.2

Population * total females 2111.2 7.1

Population * maturePop 2111.6 7.5

Population * total mature females 2113.5 9.4

Population * total mature males 2111.7 7.6

Population * year 2118.8 14.7

Population + CTC WCVI 2117.8 13.7

Population * CTC WCVI 2119.6 15.5

Population + CTC NBC 2119.3 15.2

Population * CTC NBC 2119.3 15.2

Population * total females * CTC WCVI 2107.5 3.4

Population * total females * CTC NBC 2104.8 0.7

Population * total females + population * CTC NBC 2104.1 0.0

Population * total females + CTC NBC 2105.0 0.9

Population * total females 2111.2 7.1

Population * CTC NBC 2119.3 15.2

Population * total males * CTC WCVI 2123.0 18.9

Population * total males * CTC NBC 2118.8 14.7

Population * total mature females * CTC WCVI 2116.9 12.8

Population * total mature females * CTC NBC 2105.2 1.1

Population * total mature males * CTC WCVI 2112.3 8.2

Population * total mature males * CTC NBC 2108.6 4.5
27

Figure 12. Mean estimates of SRKW and NRKW survival rates as a function of Chinook salmon (CTC 

northern British Columbia) and respective killer whale population densities. SRKW estimates are 

shown in gray across the range of total females that have been observed (45–60, corresponding to 

the tight cluster of four gray lines) and the NRKW estimates are shown in black across the range 

of total females that have been observed (60–120).
28

Identifying Salmon Stocks Most Correlated with 

SRKW Population Growth

Previous research has shown that Chinook salmon indices calculated by the Pacific 

Salmon Commission CTC were correlated with SRKW survival and fecundity (Ford et al. 2005, 

Ward et al. 2009a, Ford et al. 2010). In work done for the 2011 Puget Sound Chinook Biological 

Opinion, correlations between SRKW growth and different Chinook indices derived from the 

FRAM Chinook abundance model were also found (NMFS 2011a). Interpreting comparisons 

between these two analyses was somewhat confusing, however, due to differences in which 

years were included in the analyses, differences in salmon stock distribution, differences in 

salmon age structure (specifically, how juveniles contributed to the indices), and differences in 

the Chinook stocks included in the indices. In an attempt to resolve any discrepancies between 

models that included these indices, we focused on how the newer stock-specific OCE indices 

correlated with SRKW demography. These stock-specific OCE indices were calculated for 21 

stock groupings (the Calculating Stock-specific and Aggregate Salmon Indices section above). It 

is important to note that in comparing the OCE, CTC, and FRAM indices, the three indices rely

heavily on CWT recoveries, and as expected, are correlated with each other.

To examine hypotheses about whether SRKW demographics could be correlated with 

regional or temporal groups of Chinook stocks, we used the 21 OCE indices. One advantage 

offered by these newer indices is that they include some stocks not included in the CTC indices 

(Columbia River spring, Sacramento River, and Klamath River Chinook salmon). A second 

advantage is that they are generated on a stock-by-stock basis, and at finer spatial scale than 

either the CTC or FRAM indices. There are several weaknesses in this type of modeling 

approach. First, the models aren’t mechanistic, so stocks that are highly correlated with SRKW 

dynamics are not necessarily the most important or limiting. Second, there is a high degree of 

correlation between individual stocks and stock aggregates; these correlations in the predictor 

variables increase the likelihood of spurious correlations and error in the relative ranking of 

competing models.

We first divided each of the 21 OCE indices into an ocean catch and terminal component 

(Table 7). The sum of these components was treated as “ocean index” and the terminal 

component was interpreted as being an “inland waters index,” after ocean fishing occurs. For all 

stocks, we formulated two hypotheses for grouping stocks. First, we were interested in 

developing spatial indices related to where Chinook salmon are thought to migrate (Groot and 

Margolis 1991, Quinn 2005, Tucker et al. 2012). We grouped stocks into the following three 

areas: far north migrating (e.g., Bering Sea), north/central migrating (e.g., Southeast Alaska), and 

California (or south migrating). Second, we grouped stocks by the run timing of migration into 

the following groups: spring migrating (peak in March-May), summer migrating (peak in JuneAugust), summer/fall migrating, and fall migrating (peak in September-November). For stocks 
29

that had multiple designations (e.g., Puget Sound Chinook may be between summer and fall with 

respect to run timing, Table 7), we explored both alternative model configurations.

In performing model selection, we used the Akaike information criterion (AIC)

(Burnham and Anderson 2002) to identify the models most supported by the data. Each of the 

models was also run in a Bayesian framework, allowing us to examine the relative magnitude of 

the effect size and the distribution of the posterior probability for each scenario. As a final 

comparison, we ran all models with just the SRKW population, then a second time with the 

SRKW and NRKW populations combined (to allow NRKW survival to be slightly different than 

SRKW, our logistic regressions added one additional parameter for a population offset).

When SRKW data are analyzed separately, all Chinook salmon groupings perform nearly 

equally well with respect to fecundity (the best model is the ocean index of the central migrating 

group, without Fraser spring Chinook included) (Table 8). More separation between models is 

apparent when survival is the response; the fall grouping of Chinook, without California stocks 

performs well, as does the north migrating group, without including Fraser spring stocks (Table 

8). The best fall group included the two stocks categorized as summer/fall (Puget Sound, Strait 

of Georgia) (Table 8). When the NRKW and SRKW are included in the same model, these 

results become stronger, with more support going to the grouping of fall migrating stocks (not 

including California stocks, and only including inland indices after fishing occurs) (Table 9).

Although the fall group (minus California) is used in the remainder of this report to 

evaluate the effects of ocean fishing on SRKW, we explored several other configurations of 

stocks to evaluate additional hypotheses. Specifically, members of the Independent Science 

Panel were interested in how each of the individual stocks compared to the best grouping, and 

whether a coast-wide aggregate across all 21 stocks performed better (Hilborn et al. 2012). In 

this comparison, we found that the coast-wide aggregate across all stocks not only performed 

better than any individual stock, but the coast-wide indices also performed slightly better than 

the fall grouping used in the remainder of the workshop presentations (delta AIC reduced by 

0.76). Although the coast-wide indices appear to be slightly more correlated with SRKW 

demographics, it is important to emphasize the uncertainty in model selection; delta AIC values 

less than 2.0 are interpreted as receiving strong data support and should not be removed from the 

list of candidate models (Burnham and Anderson 2002). Furthermore, because the fall aggregate

represents more than 50% of the total Chinook abundance indices over recent years, these

indices are highly correlated with the coast-wide aggregate. Therefore, we expect that the results 

presented here based on the fall group (minus California) will be very similar to what would be 

expected using the coast-wide indices.
30

Table 7. Groupings used to evaluate hypotheses about Chinook salmon stocks. “North” in the table 

refers to stocks that are the far north migrating stocks. Stocks with an asterisk (*) were found as a 

group to be most correlated with SRKW fecundity and survival (Table 8 and Table 9).

Stock Run timing Ocean distribution

Lower Columbia River spring Spring Central

Fraser spring 1.2 Spring North/central

Fraser spring 1.3 Spring North/central

Southeast Alaska Spring North

Upper Columbia River spring Spring Central

Areas 1–5 Summer North

Areas 6–10 Summer North

Columbia River summer Summer Central

Fraser summer 0.3 Summer North

Fraser summer 1.3 Summer North

Puget Sound* Summer/fall Central

Upper Georgia Strait* Summer/fall North

Columbia River tule* Fall Central

Columbia River brights* Fall North

Fraser River late* Fall Central

Lower Georgia Strait* Fall Central

Klamath River Fall California

Oregon coast* Fall North

Sacramento Fall California

Washington coast* Fall North

West coast Vancouver Island* Fall North
31

Table 8. Model selection results for the SRKW population, 1979–2010. CTC WCVI = CTC index from 

the west coast of Vancouver Island, CTC NBC = CTC index from northern British Columbia, and 

NA = not applicable. The delta AIC values are calculated across all models for fecundity, 

survival, and total (fecundity and survival combined). Lower delta AIC values indicate more data 

support and the best model for each comparison has a delta AIC value of 0.

Group

Include inland 

preterminal?

∆AIC

fecundity

∆AIC

survival

∆AIC

total

No salmon NA 1.7 15.4 13.6

CTC WCVI NA 2.5 4.7 3.7

CTC NBC NA 3.0 0.6 0.0

Distribution hypotheses

North (+Fraser spring) Y 1.7 2.4 0.6

North (+Fraser spring) N 3.5 2.1 2.1

North (−Fraser spring) Y 3.4 2.1 2.0

North (−Fraser spring) N 3.5 1.5 1.5

Central (−Fraser spring) Y 0.0 7.1 3.5

Central (−Fraser spring) N 3.2 7.8 7.4

Central (+Fraser spring) Y 2.7 7.4 6.6

Central (+Fraser spring) N 3.2 7.9 7.6

South NA 3.6 17.3 17.4

Run timing hypotheses

Spring Y 2.6 12.6 11.7

Spring N 2.8 13.0 12.2

Summer Y 3.5 17.0 17.0

Summer N 3.7 16.2 16.4

Summer/fall Y 2.1 12.6 11.1

Summer/fall N 1.9 12.3 10.6

Fall (+California) Y 3.7 4.4 4.5

Fall (+California) N 3.7 4.5 4.7

Fall (−California) Y 3.6 1.7 1.7

Fall (−California) N 3.7 0.0 0.1
32

Table 9. Model selection results for the SRKW and NRKW populations, 1979–2010. CTC WCVI = 

CTC index from the west coast of Vancouver Island, CTC NBC = CTC index from northern 

British Columbia, and NA = not applicable. The delta AIC values are calculated across all 

models for fecundity, survival, and total (fecundity and survival combined). Lower delta AIC 

values indicate more data support and the best model for each comparison has a delta AIC value 

of 0.

Group

Include inland 

preterminal?

∆AIC 

fecundity

∆AIC 

survival

∆AIC 

total

No salmon NA 2.4 20.6 21.1

CTC WCVI NA 0.0 10.1 8.2

CTC NBC NA 1.1 11.5 10.6

Distribution hypotheses

North (+Fraser spring) Y 2.4 7.2 7.7

North (+Fraser spring) N 2.7 9.2 9.9

North (−Fraser spring) Y 2.1 5.4 5.6

North (−Fraser spring) N 2.8 7.9 8.7

Central (−Fraser spring) Y 3.2 18.5 19.8

Central (−Fraser spring) N 0.9 22.3 21.3

Central (+Fraser spring) Y 1.6 18.2 17.8

Central (+Fraser spring) N 1.0 22.2 21.3

South NA 4.4 16.9 19.4

Run timing hypotheses

Spring Y 3.1 15.4 16.5

Spring N 3.1 15.1 16.3

Summer Y 0.7 19.2 18.0

Summer N 3.0 15.1 16.2

Summer/fall Y 3.5 12.8 14.4

Summer/fall N 3.8 19.2 21.1

Fall (+California) Y 2.7 14.6 15.4

Fall (+California) N 2.6 15.7 16.4

Fall (−California) Y 2.4 2.2 2.6

Fall (−California) N 1.9 0.0 0.0
33

Estimating Historical Effects of Chinook 

Abundance and Salmon Fishing on SRKW 

Population Growth

To explore possible historical effects of fishing on SRKW growth rates, we conducted a 

retrospective analysis, using the indices of fall Chinook stocks (Table 7, Table 8, and Table 9). 

The benefit of using these indices is that we can partition them into a time series of fisheries 

impacts and a time series of terminal run size (Table 10). To approximate the past effects of 

fishing on past SRKW population growth, we calculated the expected value of λ in each year 

1979–2010, first using the terminal run only as a predictor (to model a scenario with ocean 

fishing), then using the total ocean index (catch + escapement) as a predictor. The second 

scenario, meant to represent a situation with no ocean fishing, is somewhat unrealistic in that it 

assumes all fish forgone by fishing would be made available to the SRKW population.

Using the logistic modeling procedure described above, we first estimated the posterior 

distributions of the regression parameters using Bayesian methods, saving 25,000 vectors of 

MCMC draws. Second, for each vector of MCMC parameters, we generated the expected 

fecundity rate for each age-class (for all SRKW females of reproductive age, 10–42) and the 

expected survival rates, by stage, for animals through age 42. Because postreproductive females 

have no impact on population growth or estimation of λ, they were not included in the analysis. 

Third, these fecundity and survival rates were used to construct an age-based transition matrix

(Fujiwara and Caswell 2002) with survival rates on the subdiagonal. For each reproductive age,

we allowed females to be in a “breeding” class or “nonbreeding” class, depending on whether 

they reproduced. Females that gave birth in year t automatically entered the nonbreeding class 

the following year, because the 18-month gestation in SRKWs prevents females from giving 

birth in successive years (Ford and Ellis 2005). Calves were fractionally assigned as females, 

using a normal approximation to the binomial distribution, and given the historical SRKW data 

on sex ratio at birth 1979–2010, Pr(female) ~Normal �u = 48

96 , σ = �

48

96∙�1−4896�

96 �. Nonbreeding 

females in year t transitioned to becoming breeders with probabilities equal to the estimated agespecific fecundity rates estimated above �fi,t�. Given the transition matrix, we calculated λ as 

the dominant eigenvalue. Because this analysis was done in a Bayesian framework, we repeated 

this analysis for each of the 25,000 MCMC vectors, yielding a posterior probability distribution 

for λ.

To evaluate the effects of an increase in salmon abundance on SRKW population growth, 

we calculated the absolute and percent improvement in λ. Absolute improvement was calculated 

as λt,total − λt,terminal, or the difference in expected growth rates without and with a fishing 

impact. A more positive absolute difference translates into a greater impact of fishing. The 

percent improvement in λ was calculated as 100 ∙ λt,total/λt,terminal, where values greater than 
34

100 correspond to a greater effect of fishing. Each of these calculations was done separately for 

each of the three SRKW pods (J, K, and L) to investigate interactions between the estimated 

effects of increasing salmon abundance and the age structure of each pod.

Using terminal run alone of the aggregated fall Chinook index shows considerable 

differences between pods (Figure 13), and that in most years, the expected pod-specific growth 

rates remain slightly positive. It is important to note that these represent the expected rather than 

observed growth rates, and this only quantifies the female growth rate. The patterns or trends 

across years is largely a function of shifting age structure in each pod; for examples, J Pod has 

recruited more reproductive females than other pods and L Pod has a sex distribution currently 

skewed toward more males than females. Across the entire time series, 1979–2010, the effect of 

a 20% increase in Chinook salmon seems to be highest in periods when estimated harvest rates 

on salmon were high (e.g., before 1985, Table 10) or in years with low salmon abundance (mid1990s). As a last point, in most years, the 95% posterior credible intervals on the absolute and 

percent change are wide and even contain negative values (Figure 14 and Figure 15). This 

uncertainty is a result of demographic stochasticity, due to the SRKW population being small.

Table 10. Time series of terminal run, fishery impact, and combined total ocean index for the fall 

grouping of Chinook salmon (without California stocks, Table 7, Table 8, and Table 9). Numbers 

are in 1,000s of fish.

Year

Terminal 

run

Fishery 

impact

Total ocean 

index Year

Terminal 

run

Fishery 

impact

Total ocean 

index

1979 993.0 1500.8 2493.8 1995 883.1 420.0 1303.1

1980 937.7 1449.7 2387.4 1996 942.3 358.2 1300.5

1981 849.8 1680.3 2530.1 1997 1028.4 545.1 1573.5

1982 1057.1 1768.8 2825.9 1998 1065.3 352.6 1417.9

1983 875.5 1766.1 2641.6 1999 1030.0 395.8 1425.8

1984 1045.9 1956.1 3002.0 2000 867.7 299.1 1166.8

1985 1223.3 1332.4 2555.7 2001 1402.9 417.8 1820.7

1986 1360.3 1634.8 2995.1 2002 1623.1 637.5 2260.6

1987 1650.6 1374.9 3025.5 2003 1952.4 667.3 2619.7

1988 1661.5 1076.0 2737.5 2004 1791.8 727.4 2519.2

1989 1422.0 1142.6 2564.6 2005 1437.3 683.6 2120.9

1990 1315.3 1352.7 2668.0 2006 1221.2 524.3 1745.5

1991 1211.8 1415.4 2627.2 2007 913.6 503.9 1417.5

1992 1119.0 1418.2 2537.2 2008 885.5 352.6 1238.1

1993 1032.2 1002.0 2034.2 2009 971.3 362.6 1333.9

1994 986.4 715.9 1702.3 2010 1396.3 475.3 1871.6
35

Figure 13. Estimates of pod-specific SRKW female growth rates, using just the terminal run of the fall 

group of Chinook as a predictor (Table 10). The thick line in each graph is the median growth 

rate for each pod and the thin lines are 95% posterior credible intervals. Horizontal dashed lines 

indicate no growth.
36

Figure 14. Absolute improvement in SRKW female growth rate by year, calculated by comparing the fall 

Chinook salmon index (Table 10) with and without the fishing impact. The three solid lines are 

the median estimates for each pod and the dashed lines are 95% posterior credible intervals.

Figure 15. Percent improvement in SRKW female growth rate by year, calculated by comparing the fall 

Chinook salmon index (Table 10) with and without the fishing impact. The three solid lines are 

the median estimates for each pod and the dashed lines are 95% posterior credible intervals.
37

Estimating Future Effects of Changing Chinook 

Salmon Abundance on SRKW Population Growth

In addition to performing the retrospective analysis, we were interested in estimating how 

changes in average Chinook abundance in the future are expected to influence the future growth 

rate of the SRKWs. These analysis were motivated by the question of whether an increase in 

Chinook abundance due to reduced fishing would have a substantial effect on SRKW recovery 

trends, but the analysis does not explicitly assume any particular cause to the change in Chinook 

abundance and obviously such changes could arise from any number of factors.

Our historical analysis of SRKW growth rates was constrained to only including females, 

because of interest in time-varying calculations of the growth rate (Hilborn et al. 2012). A 

second limitation was that our historical analysis did not include all sources of stochasticity,

because at each year, the SRKW population size and age and sex structure was known without 

error. Chance events, such as those generated by demographic stochasticity, are especially 

important for small populations like the SRKW population. As a more complete analysis, we 

wanted to perform future projections of the entire SRKW population, including all sources of 

uncertainty. This type of projection or population viability analysis allows us to evaluate not 

only the possible effects of increasing Chinook salmon abundance (similar to reducing fishing 

levels), but also allows us to explore the effects of reaching recovery thresholds that would allow 

the SRKW population to be downlisted or delisted from ESA.

We investigated several management criteria and benchmarks for SRKW recovery. 

NMFS established a quantitative downlisting (from endangered to threatened) criterion of 

SRKWs maintaining a growth rate of 2.3% per year for 14 years (NMFS 2008). Similarly, part 

of the NMFS delisting criteria for the SRKWs is a maintained growth rate of 2.3% for 28 years. 

The 2.3% growth rate in both instances represents a growth rate that the SRKW population 

sustained for 12 years (1984–1996) prior to ESA listing (Krahn et al. 2002, Krahn et al. 2004)

and that the NRKW has sustained over a period of several decades. As an additional recovery 

metric, we also evaluated the likelihood of Puget Sound Partnership’s (PSP) goal of reaching a 

SRKW population size of 95 whales by the year 2020 (PSP 2009). Though the science panel 

was also interested in probabilities of extinction or quasi-extinction (Hilborn et al. 2012), we 

focused on the aforementioned criteria because, with a slightly positive growth rate, it was 

difficult for our simulations to drive the SRKW population to extinction.

Simulations were conducted with a time horizon of 30 years into the future. This time 

horizon represents a balance between a desire to evaluate the effects of alternative management 

scenarios on long term outcomes (e.g., the delisting criteria require a minimum of 28 years of 

information) and the recognition that the validity of the simulation results becomes more 

uncertain the further they are projected into the future. Uncertainty in the future projections

arises not only because of the demographic uncertainty inherent in a small population (and 
38

accounted for in the projections), but also because the key assumption of stationarity (that the 

model estimated from past data will remain valid in the future) becomes more tenuous the further 

it is projected into the future.

The first step in our population projections involved simulating time series of future 

salmon abundance, generated by a similar stochastic process that could have produced the 

historical estimates (Table 10). Using levels of autocorrelation and a process coefficient of 

variation that is similar to that in our index of fall Chinook salmon (lag-1 autocorrelation = 

0.7, σ = 0.2), we simulated time series that were as long as each of our SRKW projections (30 

years). Each simulated Chinook time series was detrended to force a constraint of stationarity, 

and to initially have a mean equal to that of the observed time series (Table 10). We then 

considered a range of hypothetical decreases or increases in the fall Chinook OCE index, ranging 

from 600,000 to 1.7 million Chinook salmon in steps of 100,000.

Initializing the simulated whale populations from the SRKW population at the end 

of 2011, we created an individual-based model from our estimated posterior distributions of 

1) fecundity rates, 2) survival rates, and 3) distribution of sex ratio at birth. In modeling the 

dynamics of small populations, individual-based models can better capture uncertainty resulting 

from demographic stochasticity compared to other approaches. By treating each of the 

demographic parameters as a probability distribution rather than a single value, we are 

propagating our parameter uncertainty forward in these projections. Each realization of our 

projection proceeded as follows:

1. A random time series representing a salmon terminal run size was generated (starting 

with the lowest abundance, 0.6 million).

2. Unsexed SRKW individuals in 2011 were randomly assigned a sex, assuming sex ratio at 

birth is Pr(female) ~Normal �u = 48

96 , σ = �

48

96∙�1−4896�

96 �.

3. Females available to give birth were each assigned a randomly drawn MCMC vector, 

used to generate fecundity rates.

4. Calves were randomly assigned to these females, with sex assigned similarly to step 2, 

treating each as a Bernoulli draw.

5. All individuals in the SRKW population were assigned a randomly drawn MCMC vector 

used to generate survival rates.

6. Bernoulli draws were used to determine which individuals survived to the next time step.

7. Steps 3 through 6 were repeated for the remaining 29 years, representing 30 total time 

steps.

8. The randomly generated salmon time series was increased by 0.1 million salmon, and 

steps 3 through 8 repeated.

9. Steps 1 through 8 were repeated 25,000 times.

The matriline, pod, and mother of every hypothetical animal was stored in each iteration, and 

realistic rules were used to constrain reproduction and survival. First, calves whose mothers died 

during step 6 were also removed from the population. Second, females were not allowed to give 

birth in successive years. Third, females were only able to reproduce if a reproductive-aged 

male (>12 years old) was alive in a different matriline from the female in question. Mating 

within a pod was allowed, following recent information about paternity (Ford et al. 2011).
39

For each level of Chinook salmon abundance, we calculated the following quantities on a 

yearly basis across all simulations: 1) mean and variance of the SRKW population size, both in 

total, and by pod; 2) the change in SRKW population size, in total and by pod, relative to 

average under the status quo fall Chinook abundance estimate of 1.2 million; 3) the probability 

of SRKW reaching a total population size of at least 95 animals by 2020 (PSP recovery goal); 

4) the probability of negative population growth under different levels of Chinook abundance; 

5) the expected change, in whales per year, relative to the status quo terminal run size; 6) the 

probabilities of meeting the NMFS downlisting and delisting criteria; and 7) the relative change 

in the probabilities of meeting the recovery criteria.

Our projections indicated that under the status quo (a mean fall terminal run size of 1.2 

million Chinook salmon), the SRKW population as a whole is expected to continue to grow 

slowly (Figure 16, Appendix B). However, L Pod is expected to decline slightly, because of an 

age structure currently skewed toward older individuals and a sex structure biased toward males. 

Over 30 years, the projections indicate that K Pod is expected to increase by several animals and 

the majority of the growth is from J Pod (Appendix B). Under all scenarios, the projections 

become highly uncertain more than several years in the future because of demographic 

stochasticity (Figure 17, Appendix B). This complicates our ability to quantify effects of

management actions. Even modeled scenarios in which the average population size of the 

SRKWs grows compared to the status quo have a wide range of potential outcomes, including 

population declines, simply because of high levels of demographic stochasticity (Figure 16).

For recovery goals, increasing Chinook salmon had an expected impact on reducing the 

chance of negative SRKW growth (Figure 18). Our estimates of achieving the PSP goal of 95 

animals in 2020 was 47.7% under the status quo, and as high as 83% if the Chinook index was 

increased by 40% (Figure 19). In terms of reaching the recovery goals required for delisting and 

downlisting under ESA, we expect the probability of either goal to be less than 25% over the 

next 30 years if salmon abundance is maintained close to the status quo (Figure 20). As 

expected, downlisting is more likely than delisting because the growth rate of 2.3% needs to be 

maintained for a shorter time interval (14 years vs. 28 years). Uncertainty is not presented for 

these metrics or the PSP goal, because credible intervals are generated from a binomial 

distribution and are solely a function of the number of simulations. However, these simulations 

include the full range of parameter uncertainty in the modeled relationships between salmon and 

killer whales. Increasing the Chinook salmon index by 20% translates to a simulated increase in 

the delisting and downlisting probabilities by 15% and 12%, respectively (Figure 21).

Growth rate metrics like the 2.3% downlisting and delisting criteria for SRKW offer 

meaningful targets for recovery, but any single metric has limitations. An obvious criticism of 

growth rate metrics is that a population may hypothetically grow at a rate less than necessary to 

achieve delisting or downlisting, but could nonetheless eventually become large enough to 

suggest that it is not at risk of extinction. In modeling exercises related to the final workshop, we 

explored several potential two-dimensional metrics balancing growth rate metrics against 

measures of risk. In this exercise, we used 30 females (≤42 years old) to represent quasiextinction, and projected the SRKW population forward 30 years. This metric for quasiextinction is somewhat arbitrary, but unlike actual extinction (defined as 0 individuals) or 

previously used quasi-extinction metrics (e.g., 10 males and 10 females, Krahn et al. 2004), the 

probability of reaching quasi-extinction is non-zero. The benefit of using 30 females to represent 
40

quasi-extinction is that we can also assume it is proportional to other levels of quasi-extinction 

(10, 20, or 40 females), as well as actual extinction. Because it is consistently non-zero, it is a 

more informative measure of risk compared to actual extinction (which occurs with a much 

lower frequency). Given our definition of quasi-extinction, we can plot probabilities of recovery 

and metrics for quasi-extinction for our 30-year simulations on single plots as a function of 

Chinook salmon indices (Figure 22).

As a more retrospective example of balancing growth and risk metrics, we calculated 

short-term annual growth rates against the probability of the SRKW population reaching quasiextinction. Each of these quasi-extinction probabilities was estimated by projecting the SRKW 

population forward 30 years from the age structure in year t, then calculating the fraction of 

simulations that produced trajectories below this threshold. For each year included in our 

analysis, we also calculated the short-term growth rate over the year before and after each 

census, 1 + log(Nt+1/ Nt)/2. Plotting the relationship between risk and short-term growth 

(Figure 23) illustrates that, while growth rates were low in recent years, over the entire time 

series 1979–2010, the worst period for SRKWs was in the early 1980s. During this period, the 

SRKW population was at low density, had a particularly skewed age and sex structure (as an 

immediate effect of the aquarium harvest (Bigg and Woman 1975), and as a result, the SRKW 

population had both a low expected growth rate and a relatively higher risk of extinction, 

compared to periods later in the time series (such as the late 1990s).

Figure 16. Observed and projected SRKW population size, years 1974–2040. Results from the annual 

census are indicated with a circle and the thick solid line represents the projected median 

population size under the status quo level of 1.2 million Chinook salmon (with 95% posterior 

credible intervals in thin solid lines). The thick dashed line represents the median SRKW 

population size under an increase of 20% to 1.44 million Chinook salmon (with 95% posterior 

credible intervals in thin dashed lines).
41

Figure 17. Expected change in SRKW population size by year relative to an increase of Chinook salmon 

by 20%, from 1.2 to 1.44 million. The expected change is the difference under the two scenarios, 

with the median indicated by the solid thick line and 95% posterior credible intervals shown as 

thin solid lines. No change is indicated by the thin dashed line.

Figure 18. Probability of negative long-term SRKW population growth in 30 years, relative to the 2011 

population size of 87 whales, as a function of different levels of Chinook abundance. The status 

quo is represented as 1.2 million salmon. Estimates are generated from 25,000 simulations.

Terminal run size of fall Chinook (1,000s)
42

Figure 19. Probability of meeting the PSP recovery goal of at least 95 whales in the SRKW population 

by the year 2020. The status quo is represented as 1.2 million Chinook salmon. Estimates are 

generated from 25,000 simulations.

Figure 20. Probability of meeting the recovery goals required for downlisting and delisting the SRKW 

population under the ESA (2.3% growth for 14 and 28 years, respectively) by year. Estimates are 

generated from 25,000 simulations, under the status quo scenario (1.2 million Chinook salmon).
43

Figure 21. Relative changes in the probabilities of meeting the recovery goals required for downlisting 

and delisting the SRKW population under ESA (2.3% growth for 14 and 28 years, respectively).

Estimates are from 25,000 simulations, and relative to the status quo (1.2 million Chinook).

Figure 22. Illustration of how two types of recovery goals might be balanced. A measure of risk 

(probability of <30 or females <43 years of age, assumed proportional to population extinction)

on one y-axis, and the probability of downlisting (2.3% growth for 14 years) on the other. Both 

are calculated from 30-year projections. Quartiles for the terminal OCE index 1979–2010 are 

vertical solid gray lines and the mean terminal index is the vertical dashed gray line.

Terminal run size of fall Chinook (1,000s)

Terminal Chinook index
44

Figure 23. Illustration of how two types of recovery goals might be balanced. A measure of risk 

(probability of <30 females <43 years old, assumed proportional to population extinction, is 

calculated on the x-axis), and the realized growth rate (y-axis) indicate that the population was in 

its worst shape in the early 1980s. Growth rate is calculated as 1 + 0.5 × ln(N[t + 1] / N[t − 1]). 

Circles are proportional to SRKW population size, with calendar years indicated.
45

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51

Appendix A: SRKW Population Summaries

This appendix contains the following 10 figures for summaries spanning 1974 to 2011:

Figure A-1. Births by pod and total births.

Figure A-2. Adult females by pod and total adult females.

Figure A-3. Reproductive females by pod and total reproductive females.

Figure A-4. Adult males by pod and total adult males.

Figure A-5. Adult female deaths by pod and total adult female deaths.

Figure A-6. Adult male deaths by pod and total adult male deaths.

Figure A-7. Juvenile deaths by pod and total juvenile deaths.

Figure A-8. Births per adult female by pod and total births per adult female.

Figure A-9. Births per reproductive female by pod and total births per reproductive female.

Figure A-10. Total adult death rate versus total adults by pod and total adult death rate versus 

total adults.
52

Figure A-1. Births by pod and total births for SRKWs, 1974–2011.
53

Figure A-2. Adult females (>9 years old) by pod and total adult females for SRKWs, 1974–2011.
54

Figure A-3. Reproductive females (10–42 years old) by pod and total reproductive females for SRKWs, 

1974–2011.
55

Figure A-4. Adult males (>9 years old) by pod and total adult males for SRKWs, 1974–2011.
56

Figure A-5. Adult female (>9 years old) deaths by pod and total adult female deaths for SRKWs, 1974–

2011.
57

Figure A-6. Adult male (>9 years old) deaths by pod and total adult male deaths for SRKWs, 1974–2011.
58

Figure A-7. Juvenile (<10 years old) deaths by pod and total juvenile deaths for SRKWs, 1974–2011.
59

Figure A-8. Births per adult female (>9 years old) by pod and total births per adult female for SRKWs, 

1974–2011.
60

Figure A-9. Births per reproductive female (10–40 years old) by pod and total births per reproductive 

female for SRKWs, 1974–2011.
61

Figure A-10. Total adult (≥10 years old) death rate versus total adults by pod and total adult death rate 

versus total adults for SRKWs, 1974–2011.
62
63

Appendix B: Output from SRKW Population 

Projections

This appendix contains the following eight tables projecting over 30 years into the future 

across different levels of Chinook salmon abundance:

Table B-1. Mean and SD of J Pod.

Table B-2. Mean and SD of expected changes in J Pod relative to the status quo.

Table B-3. Mean and SD of K Pod.

Table B-4. Mean and SD of expected changes in K Pod relative to the status quo.

Table B-5. Mean and SD of L Pod.

Table B-6. Mean and SD of expected changes in L Pod relative to the status quo.

Table B-7. Mean and SD of the total SRKW population.

Table B-8. Mean and SD of expected changes in the total SRKW population relative to the 

status quo.
64

Table B-1. Mean and SD of J Pod over 30 years into the future across different levels of simulated fall 

Chinook salmon terminal run abundance (1,000s of fish).

Year

600 800 1,000 1,200 1,400 1,600

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

1 27.0 1.6 27.4 1.4 27.6 1.4 27.8 1.3 27.9 1.3 28.0 1.2

2 27.0 2.2 27.7 2.1 28.2 1.9 28.5 1.8 28.8 1.8 29.0 1.7

3 26.9 2.9 28.0 2.7 28.7 2.5 29.3 2.4 29.7 2.4 30.0 2.3

4 26.8 3.5 28.3 3.2 29.3 3.0 30.1 2.9 30.6 2.9 31.0 2.8

5 26.9 4.0 28.7 3.7 30.0 3.5 30.9 3.4 31.6 3.4 32.1 3.3

6 26.9 4.5 29.0 4.2 30.6 4.0 31.7 3.9 32.6 3.8 33.2 3.8

7 26.9 5.0 29.3 4.6 31.2 4.4 32.5 4.4 33.6 4.3 34.3 4.3

8 26.7 5.4 29.5 5.1 31.7 4.9 33.3 4.9 34.6 4.8 35.5 4.8

9 26.7 5.9 29.8 5.5 32.3 5.3 34.1 5.4 35.6 5.3 36.6 5.3

10 26.5 6.3 30.0 5.9 32.8 5.8 34.8 5.8 36.5 5.8 37.7 5.8

11 26.4 6.7 30.2 6.4 33.3 6.2 35.6 6.3 37.5 6.3 38.9 6.4

12 26.3 7.1 30.4 6.8 33.8 6.7 36.4 6.8 38.5 6.9 40.0 6.9

13 26.3 7.5 30.7 7.2 34.3 7.2 37.2 7.3 39.5 7.4 41.2 7.5

14 26.3 7.9 30.9 7.7 34.9 7.7 38.0 7.8 40.5 8.0 42.5 8.1

15 26.2 8.3 31.2 8.1 35.4 8.2 38.8 8.4 41.7 8.6 43.7 8.8

16 26.2 8.7 31.5 8.6 36.0 8.7 39.7 9.0 42.8 9.2 45.1 9.5

17 26.3 9.2 31.8 9.1 36.6 9.3 40.6 9.6 43.9 9.9 46.4 10.2

18 26.4 9.6 32.2 9.6 37.3 9.9 41.6 10.3 45.1 10.7 47.8 11.0

19 26.4 10.0 32.5 10.2 37.9 10.5 42.5 11.0 46.3 11.5 49.2 11.8

20 26.5 10.5 32.9 10.7 38.6 11.2 43.5 11.7 47.6 12.3 50.7 12.7

21 26.4 11.0 33.2 11.3 39.3 11.9 44.5 12.5 48.8 13.2 52.2 13.7

22 26.5 11.4 33.6 11.9 40.0 12.7 45.5 13.4 50.1 14.2 53.7 14.7

23 26.6 11.9 34.0 12.6 40.7 13.4 46.5 14.2 51.5 15.1 55.4 15.8

24 26.6 12.4 34.4 13.2 41.5 14.2 47.6 15.2 52.9 16.2 57.0 17.0

25 26.7 12.9 34.8 13.9 42.2 14.9 48.7 16.1 54.4 17.3 58.7 18.2

26 26.8 13.4 35.1 14.5 42.9 15.8 49.8 17.1 55.8 18.4 60.4 19.4

27 26.8 13.9 35.5 15.2 43.7 16.6 50.9 18.1 57.2 19.6 62.2 20.7

28 26.8 14.4 35.8 15.9 44.4 17.4 52.0 19.1 58.7 20.8 64.0 22.0

29 26.8 14.8 36.1 16.5 45.1 18.3 53.1 20.2 60.1 22.0 65.7 23.4

30 26.7 15.3 36.3 17.2 45.7 19.2 54.1 21.3 61.5 23.3 67.4 24.9
65

Table B-2. Mean and SD of expected changes in J Pod, relative to the status quo (1.2 million Chinook), 

over 30 years into the future across different levels of Chinook salmon abundance.

Year

600 800 1,000 1,400 1,600

Mean SD Mean SD Mean SD Mean SD Mean SD

1 −0.8 1.6 −0.4 1.4 −0.2 1.4 0.1 1.3 0.2 1.2

2 −1.5 2.2 −0.8 2.1 −0.3 1.9 0.2 1.8 0.4 1.7

3 −2.4 2.9 −1.3 2.7 −0.6 2.5 0.4 2.4 0.7 2.3

4 −3.2 3.5 −1.8 3.2 −0.7 3.0 0.6 2.9 0.9 2.8

5 −4.0 4.0 −2.2 3.7 −0.9 3.5 0.7 3.4 1.2 3.3

6 −4.8 4.5 −2.7 4.2 −1.1 4.0 0.9 3.8 1.5 3.8

7 −5.6 5.0 −3.2 4.6 −1.3 4.4 1.1 4.3 1.8 4.3

8 −6.6 5.4 −3.8 5.1 −1.6 4.9 1.3 4.8 2.2 4.8

9 −7.4 5.9 −4.3 5.5 −1.8 5.3 1.5 5.3 2.5 5.3

10 −8.3 6.3 −4.9 5.9 −2.1 5.8 1.7 5.8 2.9 5.8

11 −9.2 6.7 −5.4 6.4 −2.4 6.2 1.9 6.3 3.3 6.4

12 −10.1 7.1 −6.0 6.8 −2.6 6.7 2.1 6.9 3.6 6.9

13 −10.9 7.5 −6.5 7.2 −2.9 7.2 2.3 7.4 4.0 7.5

14 −11.7 7.9 −7.1 7.7 −3.2 7.7 2.5 8.0 4.5 8.1

15 −12.6 8.3 −7.6 8.1 −3.4 8.2 2.8 8.6 4.9 8.8

16 −13.5 8.7 −8.2 8.6 −3.7 8.7 3.0 9.2 5.3 9.5

17 −14.4 9.2 −8.8 9.1 −4.0 9.3 3.3 9.9 5.8 10.2

18 −15.2 9.6 −9.4 9.6 −4.3 9.9 3.5 10.7 6.2 11.0

19 −16.1 10.0 −10.0 10.2 −4.5 10.5 3.8 11.5 6.7 11.8

20 −17.0 10.5 −10.6 10.7 −4.9 11.2 4.1 12.3 7.2 12.7

21 −18.0 11.0 −11.3 11.3 −5.2 11.9 4.3 13.2 7.7 13.7

22 −19.0 11.4 −11.9 11.9 −5.5 12.7 4.6 14.2 8.2 14.7

23 −20.0 11.9 −12.6 12.6 −5.8 13.4 5.0 15.1 8.8 15.8

24 −21.0 12.4 −13.3 13.2 −6.1 14.2 5.3 16.2 9.4 17.0

25 −22.0 12.9 −14.0 13.9 −6.5 14.9 5.7 17.3 10.0 18.2

26 −23.1 13.4 −14.7 14.5 −6.9 15.8 6.0 18.4 10.6 19.4

27 −24.1 13.9 −15.5 15.2 −7.2 16.6 6.3 19.6 11.3 20.7

28 −25.2 14.4 −16.2 15.9 −7.6 17.4 6.7 20.8 11.9 22.0

29 −26.3 14.8 −17.0 16.5 −8.0 18.3 7.1 22.0 12.6 23.4

30 −27.4 15.3 −17.8 17.2 −8.4 19.2 7.4 23.3 13.3 24.9
66

Table B-3. Mean and SD of K Pod over 30 years into the future across different levels of simulated 

Chinook salmon abundance.

Year

600 800 1,000 1,200 1,400 1,600

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

1 18.0 1.4 18.3 1.3 18.5 1.2 18.6 1.1 18.7 1.1 18.8 1.1

2 17.9 1.8 18.4 1.7 18.8 1.6 19.0 1.5 19.2 1.4 19.4 1.4

3 17.7 2.3 18.4 2.1 19.0 2.0 19.4 1.9 19.7 1.8 19.9 1.8

4 17.4 2.6 18.4 2.4 19.2 2.3 19.7 2.2 20.1 2.1 20.4 2.1

5 17.1 2.9 18.3 2.7 19.2 2.6 19.9 2.5 20.4 2.4 20.8 2.4

6 16.6 3.2 18.0 2.9 19.1 2.8 20.0 2.7 20.6 2.7 21.1 2.6

7 16.2 3.4 17.8 3.2 19.1 3.1 20.1 3.0 20.9 2.9 21.4 2.9

8 15.9 3.7 17.6 3.4 19.1 3.3 20.3 3.2 21.1 3.1 21.8 3.1

9 15.6 3.9 17.5 3.6 19.1 3.5 20.4 3.4 21.4 3.4 22.1 3.4

10 15.3 4.0 17.4 3.8 19.1 3.7 20.5 3.7 21.6 3.6 22.5 3.6

11 15.0 4.2 17.2 4.0 19.1 3.9 20.7 3.9 21.9 3.8 22.8 3.8

12 14.7 4.4 17.1 4.2 19.1 4.1 20.8 4.1 22.2 4.1 23.2 4.1

13 14.4 4.6 16.9 4.4 19.1 4.4 21.0 4.4 22.4 4.3 23.5 4.3

14 14.1 4.7 16.7 4.6 19.1 4.6 21.1 4.6 22.7 4.6 23.9 4.6

15 13.7 4.9 16.4 4.8 19.0 4.9 21.2 4.9 22.9 5.0 24.2 5.0

16 13.4 5.1 16.3 5.1 19.0 5.1 21.3 5.2 23.2 5.3 24.6 5.3

17 13.1 5.3 16.1 5.3 19.0 5.4 21.5 5.5 23.4 5.7 25.1 5.7

18 12.9 5.5 16.0 5.6 19.0 5.7 21.6 5.9 23.7 6.0 25.4 6.1

19 12.7 5.6 15.8 5.8 18.9 6.0 21.7 6.2 24.0 6.4 25.8 6.5

20 12.4 5.8 15.6 6.0 18.8 6.2 21.7 6.5 24.2 6.8 26.1 6.9

21 12.1 5.9 15.5 6.2 18.8 6.5 21.8 6.8 24.4 7.2 26.5 7.4

22 11.9 6.1 15.3 6.5 18.8 6.8 22.0 7.2 24.6 7.6 26.9 7.9

23 11.7 6.3 15.2 6.7 18.8 7.2 22.1 7.6 24.9 8.0 27.3 8.4

24 11.6 6.4 15.1 7.0 18.8 7.5 22.2 8.0 25.2 8.5 27.7 8.9

25 11.4 6.6 15.0 7.2 18.9 7.8 22.4 8.4 25.5 8.9 28.2 9.4

26 11.3 6.8 15.0 7.4 18.9 8.2 22.6 8.8 25.9 9.4 28.7 10.0

27 11.2 7.0 14.9 7.7 19.0 8.5 22.8 9.3 26.2 9.9 29.2 10.5

28 11.0 7.1 14.9 8.0 19.0 8.9 23.0 9.7 26.5 10.5 29.7 11.1

29 10.9 7.3 14.8 8.2 19.1 9.2 23.3 10.2 26.9 11.0 30.2 11.7

30 10.9 7.5 14.8 8.5 19.2 9.6 23.5 10.7 27.3 11.6 30.7 12.4
67

Table B-4. Mean and SD of expected changes in K Pod, relative to the status quo (1.2 million Chinook), 

over 30 years into the future across different levels of Chinook salmon abundance.

Year

600 800 1,000 1,400 1,600

Mean SD Mean SD Mean SD Mean SD Mean SD

1 −0.6 1.4 −0.3 1.3 −0.1 1.2 0.1 1.1 0.2 1.1

2 −1.1 1.8 −0.6 1.7 −0.3 1.6 0.2 1.4 0.3 1.4

3 −1.7 2.3 −1.0 2.1 −0.4 2.0 0.3 1.8 0.5 1.8

4 −2.3 2.6 −1.3 2.4 −0.5 2.3 0.4 2.1 0.7 2.1

5 −2.8 2.9 −1.6 2.7 −0.7 2.6 0.5 2.4 0.9 2.4

6 −3.4 3.2 −2.0 2.9 −0.9 2.8 0.6 2.7 1.1 2.6

7 −3.9 3.4 −2.3 3.2 −1.0 3.1 0.8 2.9 1.3 2.9

8 −4.4 3.7 −2.6 3.4 −1.2 3.3 0.9 3.1 1.5 3.1

9 −4.9 3.9 −2.9 3.6 −1.3 3.5 1.0 3.4 1.7 3.4

10 −5.3 4.0 −3.2 3.8 −1.4 3.7 1.1 3.6 1.9 3.6

11 −5.7 4.2 −3.5 4.0 −1.6 3.9 1.2 3.8 2.1 3.8

12 −6.1 4.4 −3.7 4.2 −1.7 4.1 1.3 4.1 2.4 4.1

13 −6.6 4.6 −4.1 4.4 −1.8 4.4 1.5 4.3 2.6 4.3

14 −7.0 4.7 −4.4 4.6 −2.0 4.6 1.6 4.6 2.8 4.6

15 −7.5 4.9 −4.7 4.8 −2.2 4.9 1.7 5.0 3.1 5.0

16 −7.9 5.1 −5.0 5.1 −2.3 5.1 1.9 5.3 3.3 5.3

17 −8.3 5.3 −5.3 5.3 −2.5 5.4 2.0 5.7 3.6 5.7

18 −8.7 5.5 −5.6 5.6 −2.6 5.7 2.1 6.0 3.9 6.1

19 −9.0 5.6 −5.8 5.8 −2.8 6.0 2.3 6.4 4.1 6.5

20 −9.3 5.8 −6.1 6.0 −2.9 6.2 2.4 6.8 4.4 6.9

21 −9.7 5.9 −6.4 6.2 −3.0 6.5 2.6 7.2 4.6 7.4

22 −10.0 6.1 −6.6 6.5 −3.2 6.8 2.7 7.6 4.9 7.9

23 −10.3 6.3 −6.9 6.7 −3.3 7.2 2.9 8.0 5.2 8.4

24 −10.6 6.4 −7.1 7.0 −3.4 7.5 3.0 8.5 5.5 8.9

25 −11.0 6.6 −7.4 7.2 −3.5 7.8 3.1 8.9 5.8 9.4

26 −11.3 6.8 −7.6 7.4 −3.7 8.2 3.3 9.4 6.1 10.0

27 −11.6 7.0 −7.9 7.7 −3.8 8.5 3.4 9.9 6.4 10.5

28 −12.0 7.1 −8.2 8.0 −4.0 8.9 3.5 10.5 6.6 11.1

29 −12.3 7.3 −8.4 8.2 −4.1 9.2 3.7 11.0 6.9 11.7

30 −12.6 7.5 −8.7 8.5 −4.3 9.6 3.8 11.6 7.2 12.4
68

Table B-5. Mean and SD of L Pod over 30 years into the future across different levels of simulated 

Chinook salmon abundance.

Year

600 800 1,000 1,200 1,400 1,600

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

1 38.9 2.4 39.9 2.2 40.6 1.9 41.1 1.8 41.5 1.7 41.8 1.6

2 37.1 3.5 38.9 3.1 40.3 2.8 41.4 2.6 42.1 2.4 42.7 2.3

3 35.5 4.3 38.0 3.8 40.0 3.5 41.5 3.3 42.6 3.2 43.5 3.0

4 33.9 4.9 37.0 4.4 39.6 4.1 41.5 3.9 43.0 3.7 44.1 3.6

5 32.2 5.5 35.9 4.9 39.0 4.6 41.3 4.4 43.2 4.3 44.7 4.2

6 30.7 5.9 34.9 5.3 38.3 5.0 41.1 4.9 43.4 4.8 45.1 4.7

7 29.3 6.2 33.8 5.6 37.7 5.4 40.8 5.2 43.4 5.2 45.4 5.2

8 27.8 6.5 32.6 6.0 36.9 5.7 40.4 5.6 43.3 5.6 45.6 5.6

9 26.6 6.7 31.6 6.2 36.2 6.0 40.0 6.0 43.2 6.0 45.8 6.1

10 25.4 6.9 30.6 6.4 35.4 6.2 39.5 6.2 43.0 6.3 45.8 6.4

11 24.2 7.0 29.6 6.6 34.6 6.5 39.0 6.5 42.8 6.7 45.9 6.8

12 23.1 7.2 28.7 6.8 34.0 6.7 38.7 6.8 42.7 7.0 45.9 7.2

13 22.2 7.4 27.9 7.1 33.4 6.9 38.3 7.2 42.6 7.4 46.1 7.7

14 21.4 7.5 27.2 7.3 32.9 7.3 38.1 7.5 42.6 7.8 46.4 8.2

15 20.8 7.7 26.7 7.6 32.6 7.6 38.0 7.9 42.8 8.3 46.8 8.7

16 20.2 7.9 26.3 7.8 32.3 8.0 38.0 8.4 43.1 8.9 47.3 9.3

17 19.7 8.2 25.8 8.2 32.2 8.4 38.0 8.9 43.4 9.5 47.8 10.0

18 19.2 8.4 25.5 8.5 32.0 8.8 38.1 9.4 43.8 10.1 48.5 10.8

19 18.7 8.6 25.1 8.8 31.8 9.2 38.2 10.0 44.1 10.8 49.0 11.6

20 18.2 8.8 24.7 9.1 31.6 9.6 38.3 10.5 44.4 11.4 49.6 12.4

21 17.8 9.0 24.3 9.5 31.5 10.1 38.4 11.1 44.8 12.2 50.3 13.2

22 17.3 9.2 24.0 9.8 31.4 10.6 38.4 11.7 45.1 12.9 51.0 14.1

23 16.9 9.4 23.6 10.1 31.1 11.0 38.5 12.3 45.4 13.6 51.5 14.9

24 16.4 9.5 23.2 10.4 30.9 11.4 38.4 12.9 45.7 14.3 52.0 15.7

25 15.8 9.6 22.8 10.6 30.6 11.8 38.3 13.4 45.9 15.0 52.5 16.6

26 15.4 9.7 22.4 10.9 30.3 12.2 38.3 14.0 46.1 15.8 53.0 17.5

27 14.9 9.8 22.0 11.1 30.0 12.6 38.2 14.5 46.4 16.5 53.5 18.4

28 14.5 9.9 21.6 11.4 29.7 13.0 38.2 15.1 46.6 17.3 54.0 19.4

29 14.0 10.0 21.1 11.6 29.4 13.4 38.1 15.7 46.8 18.1 54.5 20.3

30 13.6 10.0 20.8 11.8 29.2 13.8 38.1 16.3 47.1 18.9 55.0 21.4
69

Table B-6. Mean and SD of expected changes in L Pod, relative to the status quo (1.2 million Chinook), 

over 30 years into the future across different levels of Chinook salmon abundance.

Year

600 800 1,000 1,400 1,600

Mean SD Mean SD Mean SD Mean SD Mean SD

1 −2.3 2.4 −1.3 2.2 −0.5 1.9 0.4 1.7 0.7 1.6

2 −4.3 3.5 −2.4 3.1 −1.1 2.8 0.8 2.4 1.3 2.3

3 −6.0 4.3 −3.5 3.8 −1.5 3.5 1.1 3.2 2.0 3.0

4 −7.6 4.9 −4.5 4.4 −1.9 4.1 1.5 3.7 2.6 3.6

5 −9.1 5.5 −5.5 4.9 −2.4 4.6 1.9 4.3 3.3 4.2

6 −10.4 5.9 −6.3 5.3 −2.8 5.0 2.2 4.8 4.0 4.7

7 −11.6 6.2 −7.0 5.6 −3.1 5.4 2.6 5.2 4.6 5.2

8 −12.6 6.5 −7.8 6.0 −3.5 5.7 2.9 5.6 5.2 5.6

9 −13.4 6.7 −8.4 6.2 −3.9 6.0 3.2 6.0 5.7 6.1

10 −14.1 6.9 −8.9 6.4 −4.1 6.2 3.5 6.3 6.3 6.4

11 −14.9 7.0 −9.5 6.6 −4.4 6.5 3.8 6.7 6.8 6.8

12 −15.5 7.2 −10.0 6.8 −4.7 6.7 4.0 7.0 7.3 7.2

13 −16.1 7.4 −10.4 7.1 −4.9 6.9 4.3 7.4 7.8 7.7

14 −16.7 7.5 −10.9 7.3 −5.2 7.3 4.5 7.8 8.3 8.2

15 −17.2 7.7 −11.3 7.6 −5.4 7.6 4.8 8.3 8.8 8.7

16 −17.8 7.9 −11.8 7.8 −5.7 8.0 5.0 8.9 9.3 9.3

17 −18.3 8.2 −12.2 8.2 −5.9 8.4 5.4 9.5 9.8 10.0

18 −18.9 8.4 −12.6 8.5 −6.1 8.8 5.6 10.1 10.3 10.8

19 −19.5 8.6 −13.1 8.8 −6.4 9.2 5.9 10.8 10.8 11.6

20 −20.0 8.8 −13.6 9.1 −6.6 9.6 6.2 11.4 11.4 12.4

21 −20.6 9.0 −14.0 9.5 −6.8 10.1 6.4 12.2 11.9 13.2

22 −21.1 9.2 −14.4 9.8 −7.1 10.6 6.7 12.9 12.5 14.1

23 −21.6 9.4 −14.8 10.1 −7.3 11.0 7.0 13.6 13.0 14.9

24 −22.0 9.5 −15.2 10.4 −7.5 11.4 7.3 14.3 13.6 15.7

25 −22.5 9.6 −15.6 10.6 −7.8 11.8 7.6 15.0 14.2 16.6

26 −22.9 9.7 −15.9 10.9 −8.0 12.2 7.8 15.8 14.7 17.5

27 −23.3 9.8 −16.3 11.1 −8.3 12.6 8.1 16.5 15.3 18.4

28 −23.7 9.9 −16.6 11.4 −8.5 13.0 8.4 17.3 15.8 19.4

29 −24.1 10.0 −17.0 11.6 −8.7 13.4 8.7 18.1 16.4 20.3

30 −24.5 10.0 −17.3 11.8 −8.9 13.8 9.0 18.9 16.9 21.4
70

Table B-7. Mean and SD of the total SRKW population over 30 years into the future across different 

levels of simulated Chinook salmon abundance.

Year

600 800 1,000 1,200 1,400 1,600

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

1 83.9 3.7 85.6 3.2 86.7 3.0 87.6 2.8 88.2 2.7 88.6 2.6

2 82.0 5.4 85.0 4.8 87.3 4.4 88.9 4.2 90.1 4.0 91.0 3.9

3 80.0 7.2 84.4 6.3 87.7 5.8 90.2 5.6 92.0 5.4 93.4 5.3

4 78.2 8.5 83.7 7.5 88.1 7.0 91.3 6.8 93.7 6.7 95.6 6.6

5 76.1 9.6 82.8 8.5 88.1 8.1 92.1 7.9 95.2 7.8 97.5 7.8

6 74.2 10.7 81.9 9.5 88.1 9.0 92.9 8.9 96.6 8.9 99.4 9.0

7 72.3 11.6 80.9 10.4 87.9 9.9 93.4 9.9 97.8 10.0 101.1 10.1

8 70.4 12.5 79.8 11.2 87.6 10.8 93.9 10.9 99.0 11.0 102.8 11.2

9 68.8 13.3 78.9 12.1 87.5 11.7 94.5 11.8 100.2 12.1 104.5 12.3

10 67.3 13.9 77.9 12.8 87.2 12.4 94.9 12.7 101.2 13.1 106.0 13.4

11 65.6 14.7 77.0 13.5 87.0 13.2 95.4 13.5 102.2 14.0 107.6 14.4

12 64.2 15.4 76.2 14.3 86.9 14.0 95.9 14.5 103.3 15.1 109.1 15.6

13 62.9 16.1 75.5 15.1 86.8 14.9 96.5 15.5 104.5 16.2 110.9 16.8

14 61.8 16.8 74.9 15.9 86.9 15.9 97.2 16.6 105.9 17.4 112.7 18.1

15 60.7 17.5 74.4 16.7 87.0 16.9 98.0 17.7 107.4 18.7 114.8 19.6

16 59.8 18.3 74.0 17.7 87.3 18.0 99.0 19.0 109.0 20.1 117.0 21.2

17 59.1 19.1 73.8 18.7 87.8 19.2 100.1 20.4 110.8 21.7 119.3 22.9

18 58.5 19.9 73.7 19.7 88.3 20.4 101.3 21.8 112.6 23.3 121.7 24.7

19 57.8 20.6 73.4 20.7 88.7 21.6 102.4 23.3 114.3 25.1 124.0 26.7

20 57.1 21.4 73.2 21.7 89.1 22.9 103.5 24.8 116.1 26.9 126.4 28.6

21 56.3 22.1 73.0 22.7 89.6 24.3 104.7 26.4 118.0 28.7 128.9 30.8

22 55.8 22.9 72.9 23.8 90.1 25.6 105.9 28.1 119.9 30.7 131.5 33.1

23 55.2 23.7 72.8 24.9 90.7 27.0 107.1 29.8 121.9 32.7 134.1 35.3

24 54.5 24.4 72.7 26.0 91.2 28.4 108.2 31.6 123.8 34.8 136.7 37.7

25 54.0 25.1 72.6 27.0 91.6 29.8 109.4 33.3 125.8 37.0 139.4 40.2

26 53.4 25.8 72.5 28.1 92.1 31.2 110.7 35.2 127.8 39.2 142.1 42.7

27 52.9 26.5 72.4 29.1 92.6 32.6 112.0 37.0 129.8 41.4 144.9 45.3

28 52.3 27.2 72.2 30.2 93.2 34.1 113.2 38.9 131.9 43.8 147.6 48.0

29 51.7 27.8 72.0 31.2 93.6 35.6 114.4 40.8 133.8 46.2 150.3 50.8

30 51.2 28.5 71.9 32.3 94.1 37.1 115.7 42.9 135.9 48.7 153.1 53.8
71

Table B-8. Mean and SD of expected changes in the total SRKW population, relative to the status quo 

(1.2 million Chinook), over 30 years into the future across different levels of Chinook salmon 

abundance.

Year

600 800 1,000 1,400 1,600

Mean SD Mean SD Mean SD Mean SD Mean SD

1 −3.6 3.7 −2.0 3.2 −0.8 3.0 0.6 2.7 1.0 2.6

2 −6.9 5.4 −3.9 4.8 −1.7 4.4 1.2 4.0 2.1 3.9

3 −10.2 7.2 −5.8 6.3 −2.5 5.8 1.8 5.4 3.2 5.3

4 −13.1 8.5 −7.6 7.5 −3.2 7.0 2.4 6.7 4.3 6.6

5 −16.0 9.6 −9.3 8.5 −4.0 8.1 3.1 7.8 5.4 7.8

6 −18.6 10.7 −11.0 9.5 −4.8 9.0 3.7 8.9 6.5 9.0

7 −21.1 11.6 −12.6 10.4 −5.5 9.9 4.4 10.0 7.7 10.1

8 −23.5 12.5 −14.1 11.2 −6.3 10.8 5.0 11.0 8.9 11.2

9 −25.7 13.3 −15.6 12.1 −7.0 11.7 5.7 12.1 10.0 12.3

10 −27.7 13.9 −17.0 12.8 −7.7 12.4 6.3 13.1 11.1 13.4

11 −29.8 14.7 −18.4 13.5 −8.3 13.2 6.9 14.0 12.2 14.4

12 −31.7 15.4 −19.7 14.3 −9.0 14.0 7.4 15.1 13.2 15.6

13 −33.6 16.1 −21.0 15.1 −9.7 14.9 8.1 16.2 14.4 16.8

14 −35.5 16.8 −22.4 15.9 −10.3 15.9 8.6 17.4 15.5 18.1

15 −37.3 17.5 −23.7 16.7 −11.0 16.9 9.3 18.7 16.7 19.6

16 −39.2 18.3 −25.0 17.7 −11.7 18.0 10.0 20.1 17.9 21.2

17 −41.0 19.1 −26.3 18.7 −12.4 19.2 10.6 21.7 19.2 22.9

18 −42.8 19.9 −27.6 19.7 −13.0 20.4 11.3 23.3 20.4 24.7

19 −44.6 20.6 −28.9 20.7 −13.7 21.6 12.0 25.1 21.7 26.7

20 −46.4 21.4 −30.3 21.7 −14.4 22.9 12.6 26.9 22.9 28.6

21 −48.3 22.1 −31.7 22.7 −15.1 24.3 13.3 28.7 24.3 30.8

22 −50.1 22.9 −33.0 23.8 −15.8 25.6 14.0 30.7 25.7 33.1

23 −51.9 23.7 −34.3 24.9 −16.4 27.0 14.8 32.7 27.1 35.3

24 −53.7 24.4 −35.5 26.0 −17.1 28.4 15.6 34.8 28.5 37.7

25 −55.5 25.1 −36.9 27.0 −17.8 29.8 16.4 37.0 30.0 40.2

26 −57.3 25.8 −38.2 28.1 −18.6 31.2 17.1 39.2 31.4 42.7

27 −59.1 26.5 −39.6 29.1 −19.3 32.6 17.8 41.4 32.9 45.3

28 −60.9 27.2 −41.0 30.2 −20.1 34.1 18.6 43.8 34.4 48.0

29 −62.7 27.8 −42.4 31.2 −20.8 35.6 19.4 46.2 35.9 50.8

30 −64.5 28.5 −43.7 32.3 −21.5 37.1 20.2 48.7 37.5 53.8

Recent NOAA Technical Memorandums

published by the

Northwest Fisheries Science Center

NOAA Technical Memorandum NMFS-NWFSC122 Frame, E., and K. Lefebvre. 2013. ELISA methods for domoic acid quantification in multiple marine 

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number PB2013-101722.

119 Waples, R.S., K. Hindar, and J.J. Hard. 2012. Genetic risks associated with marine aquaculture. 

U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-119, 149 p. NTIS number PB2013-101344.

118 Stout, H.A., P.W. Lawson, D.L. Bottom, T.D. Cooney, M.J. Ford, C.E. Jordan, R.G. Kope, 

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116 Lian, C.E. 2012. West Coast open access groundfish and salmon troller survey: Protocol and results 

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115 Plummer, M.L., W. Morrison, and E. Steiner. 2012. Allocation of fishery harvests under the 

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Most NOAA Technical Memorandums NMFS-NWFSC are available at the

Northwest Fisheries Science Center Web site, http://www.nwfsc.noaa.gov