NOAA Technical Memorandum NMFS-NWFSC-106

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

A Mass-balance Model

for Evaluating Food Web Structure

and Community-scale Indicators

in the Central Basin of Puget Sound

May 2010
NOAA Technical Memorandum

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Reference throughout this document to trade names does 

not imply endorsement by the National Marine Fisheries 

Service, NOAA.

This document should be referenced as follows:

Harvey, C.J., K.K. Bartz, J. Davies, T.B. Francis, T.P. Good, 

A.D. Guerry, B. Hanson, K.K. Holsman, J. Miller, M.L. 

Plummer, J.C.P. Reum, L.D. Rhodes, C.A. Rice, J.F.

Samhouri, G.D. Williams, N. Yoder, P.S. Levin, and M.H.

Ruckelshaus. 2010. A mass-balance model for evaluating 

food web structure and community-scale indicators in the 

central basin of Puget Sound. U.S. Dept. Commer., NOAA 

Tech. Memo. NMFS-NWFSC-106, 180 p. 
NOAA Technical Memorandum NMFS-NWFSC-106

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

A Mass-balance Model

for Evaluating Food Web Structure

and Community-scale Indicators

in the Central Basin of Puget Sound

Chris J. Harvey, Krista K. Bartz, Jeremy Davies,

Tessa B. Francis, Thomas P. Good, Anne D. Guerry,

Brad Hanson, Kirstin K. Holsman, Jason Miller,

Mark L. Plummer, Jonathan C.P. Reum,* Linda D. Rhodes,

Casimir A. Rice, Jameal F. Samhouri, Gregory D. Williams,

Naomi Yoder, Phillip S. Levin, and Mary H. Ruckelshaus

Northwest Fisheries Science Center

2725 Montlake Boulevard East

Seattle, Washington 98112

University of Washington

School of Aquatic and Fishery Sciences

Box 355020

Seattle, Washington 98195

May 2010

*
ii

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Table of Contents 

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

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

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

Acknowledgments........................................................................................................................................xi 

Abbreviations and Acronyms ....................................................................................................................xiii 

Introduction and Objectives..........................................................................................................................1 

Methods ........................................................................................................................................................4 

Model Domain: The Puget Sound Central Basin .....................................................................................4 

Ecopath with Ecosim................................................................................................................................5 

Functional Groups....................................................................................................................................7 

Data Assimilation and Parameter Development.......................................................................................7 

Model Balancing ....................................................................................................................................10 

Model Assessment..................................................................................................................................11 

Results.........................................................................................................................................................15 

General Characteristics and Mass Balancing of the Ecopath Model......................................................15 

Biomass, Functional Diversity, and Evenness Patterns..........................................................................25 

Throughput and Transfer Efficiency ......................................................................................................25 

Mortality.................................................................................................................................................27 

Dynamic Model Responses....................................................................................................................33 

Discussion...................................................................................................................................................44 

Objectives 1 and 2: Model Development and Assessment.....................................................................44 

Objective 3: Identifying Data Gaps........................................................................................................46 

Conclusions ............................................................................................................................................49 

References...................................................................................................................................................51 

Appendix A: Species and Functional Group Descriptions .........................................................................79 

Primary Producers..................................................................................................................................79 

Zooplankton and Pelagic Invertebrates ..................................................................................................85 

Benthic Invertebrates..............................................................................................................................93 

Pelagic Fishes.......................................................................................................................................113 

iii
Salmon..................................................................................................................................................118 

Gadiforms.............................................................................................................................................130 

Other Demersal Roundfish ...................................................................................................................135 

Flatfish..................................................................................................................................................142 

Chondrichthyans...................................................................................................................................145 

Seabirds................................................................................................................................................149 

Marine Mammals..................................................................................................................................159 

Bacterial and Detrital Pools..................................................................................................................162 

Appendix B: Omitted Groups...................................................................................................................165 

Primary Producers................................................................................................................................165 

Invertebrates.........................................................................................................................................165 

Fishes....................................................................................................................................................167 

Seabirds................................................................................................................................................168 

Marine Mammals..................................................................................................................................169 

Appendix C: Frequently Used Estimation Methods.................................................................................171 

Biomass Estimation for Bottom Fish....................................................................................................171 

Biomass Estimation for Seabirds..........................................................................................................172 

P/B Estimation for Benthic Invertebrates.............................................................................................172 

P/B Estimation for Fish ........................................................................................................................173 

Q/B Estimation for Fish........................................................................................................................173 

Use of Q10 for Temperature-dependent Rate Estimation......................................................................174 

Bottom Fish Diet Estimation ................................................................................................................175 

Q/B Estimation for Seabirds and Marine Mammals.............................................................................175 

Commercial Harvest Estimates ............................................................................................................176 

Appendix D: Representing Life History ...................................................................................................179 

iv
List of Figures 

Figure 1. Map of central Puget Sound, the spatial domain for the Ecopath with Ecosim model.................4 

Figure 2. Food web diagram for the PSCB Ecopath model.......................................................................16 

Figure 3. Biomass of coarsely pooled categories of functional groups in the PSCB Ecopath model........26 

Figure 4. Plot of Shannon diversity index against functional evenness for coarsely pooled 

categories of functional groups in the PSCB Ecopath model .....................................................................27 

Figure 5. Relationship between the rankings of functional group biomass and functional group 

throughput in the PSCB Ecopath model .....................................................................................................30 

Figure 6. Transfer efficiency as a function of trophic level for consumer groups in the PSCB 

Ecopath model ............................................................................................................................................31 

Figure 7. Allocation of mortality to fishing, predation, or other sources...................................................32 

Figure 8. Screen captures from 50-year Ecosim simulation of PSCB food web responding to 

stochastic variability in phytoplankton biomass.........................................................................................34 

Figure 9. Screen capture from 50-year Ecosim simulation of PSCB food web responding to a sharp 

reduction in raptor biomass.........................................................................................................................38 

Figure 10. Ecosim outputs representing trophic cascade dynamics caused by a decline in raptor 

biomass .......................................................................................................................................................39 

Figure 11. Screen captures from 50-year Ecosim simulations of the PSCB food web subjected to 

either baseline fishing mortality or a closure of all fisheries in year 1 .......................................................41 

Figure 12. Relationship between FMSY and P/B for targeted functional groups in the PSCB Ecosim 

model ..........................................................................................................................................................43 

v
vi
List of Tables 

Table 1. Bathymetry of the PSCB................................................................................................................5 

Table 2. Functional groups in the model with major representatives..........................................................8 

Table 3. Ecopath parameters for the functional groups in the PSCB model..............................................17 

Table 4. Ecopath diet matrix for consumers in the PSCB model...............................................................19 

Table 5. Landings by gear type of targeted groups....................................................................................24 

Table 6. Biomass, throughput, and transfer efficiency of functional groups.............................................28 

Table 7. Trophic relationships in the Ecopath model where a predator accounts for at least 25% of 

the total predation mortality experienced by a prey group .........................................................................33 

Table 8. Results of pairwise MAR analyses of relationships between phytoplankton and each 

functional group at lags of 1–5 years..........................................................................................................35 

Table 9. Groups that changed by at least 10% following the raptor decline in year 20 of the raptor 

simulation....................................................................................................................................................40 

Table 10. Groups that changed by at least 10% following a closure of all fisheries in year 1 ..................41 

Table 11. Productivity, fishing rates, and MSY for targeted groups .........................................................42 

Table A-1. Seasonal production rates of nanoplankton and net plankton in oceanic estuaries. ................80 

Table A-2. Key representatives of the rockfish functional group in the PSCB EwE model....................136 

Table A-3. Key families and representatives in the demersal fish functional group in the PSCB 

EwE model................................................................................................................................................141 

Table A-4. Key representatives of the flatfish functional groups in the PSCB EwE model....................142 

Table A-5. Key representatives of the marine bird functional groups in the PSCB EwE model.............150 

vii
viii
Executive Summary 

Scientists, policy makers, and stakeholders in coastal regions are increasingly calling for 

ecosystem-based approaches to managing marine resources and services. Such approaches, 

generally referred to as ecosystem-based management (EBM) of marine systems, recognize the 

importance of linkages between (and trade-offs among) species, processes, and stakeholder uses. 

Puget Sound is a system in which EBM methods are especially applicable, because it supports a 

diverse assemblage of species and a growing human population that derives a range of services 

from natural resources and processes. These services span far beyond fisheries. Recognizing the 

need for EBM in Puget Sound, the State of Washington established a consortium—the Puget 

Sound Partnership (PSP)—in 2007 to identify goals and actions pursuant to ecosystem 

restoration and sustainability, particularly with respect to species, habitat, water quality, water 

quantity, and human health and well-being. 

Ecosystem models have emerged as important tools in support of EBM. They provide 

frameworks for synthesizing information and evaluating the efficacy of alternative management 

actions. Here we describe the development of an ecosystem model of the contemporary food 

web in the Puget Sound Central Basin. This model, developed using Ecopath with Ecosim 

software, is ultimately intended to support PSP goals by identifying meaningful indicators that 

can be used to monitor the efficacy of management actions, quantify risk, and stimulate alternate 

ecosystem management scenarios. In this document, however, our objective was to describe the 

development of the model; we catalog the assimilation of available data that went into model 

construction, describe basic model outputs and scenario-based simulations used to assess model 

performance, and identify data gaps that could constrain further model development and the 

quality of model outputs. 

The model consists of 65 functional groups, including primary producers, invertebrates, 

vertebrates, and detrital groups from nearshore, offshore, pelagic, and demersal habitats; it also 

includes several fisheries. Using a range of sources, we developed parameter estimates for 

biomass, production, consumption, fishery losses, and diet. These parameters were input into the 

initial (Ecopath) model framework and were modified iteratively in order to achieve mass 

balance. The mass-balance model provided general, descriptive information on biomass 

allocation, functional group diversity, energy flow, and mortality. A series of scenarios were 

then run, examining model responses to temporal changes in key functional groups 

(phytoplankton and raptors) and to changes in fishing mortality. 

Our model depicts a system dominated, in terms of biomass, by demersal species, 

although high energy throughput in the pelagic community compensates for its lower biomass. 

Biomass is highly aggregated, with seven functional groups (phytoplankton, copepods, soft 

infauna, infaunal bivalves, geoducks [Panopea abrupta], small crustaceans, and ratfish 

[Hydrolagus colliei]) comprising nearly 68% of living biomass. Bottom-up dynamics strongly 

influence trophic flows, although there are examples of top-down control; for example, bald 

ix
eagles (Haliaeetus leucocephalus) appear capable of eliciting trophic cascades. Current levels of 

fishing mortality appear to be sustainable and below the maximum sustainable yield level, due in 

part to declines in commercial catches in recent decades; however, because recreational catches 

are not well accounted for in our model, some fishing mortality levels are likely underestimates. 

Many important data gaps became obvious over the course of model development. 

However, these gaps do not preclude use of this model in EBM-related applications; rather, they 

help model users to define the limits within which model output is applicable. Major gaps at this 

point include: the lack of basin-scale, scientifically robust estimates of biomass for a majority of 

functional groups; evidence of interaction strengths among different food web components; diet 

information across a representative range of seasons, sizes, depths, and habitats; and empirical 

estimates of recreational fishing mortality. 

We will continue to develop and apply this model to ask questions about the value of 

different functional groups as indicators, and also to address community–scale ecological or 

management questions that can be complemented with fieldwork, experimental research, and 

engagement with managers and stakeholders. Moreover, we will undertake further modeling 

efforts that account for spatial patchiness, biophysical coupling, and a wider range of ecological 

and societal processes critical to the functioning of the Puget Sound ecosystem. 

x
Acknowledgments 

This work draws on the research and input of many people. Many scientists from the 

Washington Department of Fish and Wildlife provided data, discussion, and comments on earlier 

drafts of this document. We especially thank Greg Bargmann, Wayne Palsson, Jim West, Scott 

Pearson, Monique Lance, Steve Jeffries, Bob Sizemore, Steve Schroeder, Kurt Stick, Dan 

Penttila, Don Velasquez, Mara Zimmerman, Dayv Lowry, Ocean Eveningsong, Lisa Hillier, Joe 

Evenson, David Price, Michael Ulrich, Henry Cheng, Rich Childers, Steve Burton, Mark 

O’Toole, and Tim Quinn for their valuable input and tremendous professional courtesy. 

Likewise, we received data, guidance, and valuable feedback from several scientists at 

the Washington Department of Natural Resources, notably Tom Mumford, Helen Berry, Pete 

Dowty, and Jeff Gaeckle. Roland Anderson, Seattle Aquarium, provided data on octopuses. 

Scientists at the University of Washington contributed data and suggestions. We especially 

thank Tim Essington, Lindsay Correa, Liz Duffy, Dave Beauchamp, Jan Newton, Jeff Cordell, 

and Si Simenstad. Tim Nelson, Seattle Pacific University, and Ron Thom and Dana Woodruff, 

Battelle’s Pacific Northwest National Laboratory, provided helpful input on parameters for 

benthic primary producers. 

Several researchers at the Northwest Fisheries Science Center provided valuable 

suggestions and data as well. We especially thank Cameron Ainsworth, Kurt Fresh, Isaac 

Kaplan, Andy Albaugh, Erin Richmond, Anne Beaudreau, Nick Tolimieri, Shallin Busch, and 

Sandie O’Neill. Ed Quimby and Bert Tarrant provided tremendous editorial guidance. Craig 

Wilson and Clara Salazar, Northwest and Alaska Fisheries Science Centers Library, assisted 

greatly with locating references. Kristin Wilkinson, NOAA Marine Mammal Stranding 

Network, provided data and comments on pinniped mortalities. Kerim Aydin, Alaska Fisheries 

Science Center, assisted with generating Figure 2. 

xi
xii
Abbreviations and Acronyms 

BA biomass accumulation 

BRT biological review team 

CSIRO Commonwealth Scientific and Industrial Research Organization (Australia) 

DPS distinct population segment 

EBM ecosystem-based management 

EE ecotrophic efficiency 

EwE Ecopath with Ecosim 

F fishing mortality rate 

HAB harmful algal bloom 

M mortality 

MAR multivariate autoregressive 

M0 other mortality 

MSY maximum sustainable yield 

NWFSC Northwest Fisheries Science Center 

NWIFC Northwest Indian Fisheries Commission 

PacFIN Pacific Fisheries Information Network 

PFMC Pacific Fishery Management Council 

P/B production:biomass ratio 

P/Q production:consumption ratio 

PSCB Puget Sound Central Basin 

PSP Puget Sound Partnership 

Q/B consumption:biomass ratio 

S/a subadult 

TL trophic level 

WDE Washington Department of Ecology 

WDFW Washington Department of Fish and Wildlife 

WDNR Washington Department of Natural Resources 

YOY young of the year 

xiii
xiv
Introduction and Objectives 

As a discipline, marine ecosystem-based management (EBM) has mostly emphasized 

fisheries applications. An ISI Web of Knowledge search1

 of the primary literature from 1998 to 

2007 found 100 citations with the terms “marine,” “ecosystem-based,” and “management.” If 

the terms “fishery” or “fisheries” were excluded, the total dropped to 26. However, as marine 

EBM evolves, scientists, agencies, organizations, and institutions are pursuing integrative 

approaches, focusing not just on fisheries but also on issues including habitat, species diversity, 

water quality, recreation and tourism, coastal commerce, human health, and sustainable energy 

development. In densely populated, highly impacted coastal ecosystems, fisheries and other 

sectors are inextricably linked. Ignoring those linkages may only increase uncertainty, risk, and 

conflict associated with single-sector decision making (Halpern et al. 2008). Truly integrated 

EBM offers the promise of greater understanding of ecosystems that support diverse sectors and 

ecosystem services, provided that tractable conceptual, empirical, theoretical, and institutional 

frameworks for integration are available. 

Puget Sound is a classic case of an imperiled marine ecosystem in which multiple sectors 

and stakeholder groups have strong and often opposing interests (Ruckelshaus and McClure 

2007). This large estuarine system supports iconic species of ecological, economic, and cultural 

significance, including Pacific salmon (Oncorhynchus spp.), killer whales (aka orcas, Orcinus 

orca), bald eagles (Haliaeetus leucocephalus), and Dungeness crab (Cancer magister). These 

and hundreds of other dynamically interacting species inhabit the sound’s diverse habitats, which 

include sandy beaches, eelgrass beds, river deltas, high-relief rocky reefs, narrow straits with 

intense tidal currents, and deep (>150 m) soft-sediment zones. Nearby mountain ranges and 

forests further contribute to the region’s considerable natural beauty. 

On the other hand, Puget Sound provides services to and faces stresses from many human 

sectors. The watershed is home to a growing human population of approximately 4 million 

people (PSP 2008), mostly in and around the major cities of Everett, Seattle, Tacoma, and 

Olympia. The ports of Seattle and Tacoma accounted for approximately 16% of the total volume 

shipped through commercial ports on the U.S. West Coast in 2007 (American Association of 

Port Authorities, online at http://www.aapa-ports.org). Fish and invertebrate populations support 

commercial, tribal, and recreational fisheries, and some fisheries have been closed due to 

declines in stock sizes (e.g., Gustafson et al. 2000). Impoundments throughout the watershed 

support hydropower, irrigation, and water provisioning for industrial and consumptive purposes 

(Ruckelshaus and McClure 2007). The hydrology of the system is further affected by land use 

changes that alter sediment porosity, vegetation, and transpiration. In many areas, runoff has 

been transformed from a slow, disseminated process to a fast, point-source process that carries 

large loads of sediment, nutrients, and pollutants. While these changes and activities affect many 

 

1

 Online at http://apps.isiknowledge.com, search conducted 3 December 2009. 
of the region’s hallmark species, they are also linked to a way of life and a level of affluence that 

many people consider desirable (Ruckelshaus and McClure 2007, PSP 2008). 

To better preserve the species and functioning of the Puget Sound ecosystem while also 

promoting sustainable use of its goods and services, the State of Washington established the 

Puget Sound Partnership (PSP) in 2007. PSP is a consortium of individuals, citizens groups, 

businesses, government agencies, tribes, and scientists that has identified a number of goals 

related to ecosystem restoration and sustainability (PSP 2008). These goals focus on achieving 

desirable, measurable progress for the species, habitat, water quality, water quantity, and human 

health and well-being in the region. The PSP Action Agenda (PSP 2008) further states that these 

goals should be achievable by the year 2020. The ecological and societal complexity inherent to 

Puget Sound presents a fundamental challenge to management agencies and policy makers 

mandated with meeting PSP goals. Moreover, it challenges scientists to develop tools that 

managers and policy makers can use to examine the effectiveness of management alternatives 

and the trade-offs associated with them. 

Ecosystem-scale simulation modeling has proven to be an extremely valuable tool for 

synthesizing large amounts of diverse information for the sake of understanding marine 

ecosystem dynamics in the context of EBM (Fulton et al. 2005, Plagányi 2007, Townsend et al. 

2008). Ecosystem models can provide conceptual frameworks in which to examine many of the 

salient questions and objectives put forth in the PSP Action Agenda (PSP 2008). They may 

range from relatively simple methods that focus in detail on a subset of important ecosystem 

components (such as single-species models with environmental drivers or “minimum realistic 

models” with a few interacting species) to full ecosystem models that incorporate entire 

communities and abiotic features (Plagányi 2007). Within these frameworks, modelers can 

compile the best available information pertinent to a particular issue, and develop thought 

experiments that quickly and inexpensively test questions or make predictions about ecological 

relationships and the effects of human activities on ecosystem components. Models also help 

quantify important variables that are difficult to measure (e.g., energy flow through a food web), 

and may reveal unanticipated interactions or knowledge gaps (Townsend et al. 2008). In short, 

models are essential tools that complement monitoring, empirical study, and experimentation in 

support of managing and restoring marine resources. 

Scientists at NOAA’s Northwest Fisheries Science Center (NWFSC), in partnership with 

other agencies and institutions, are developing several ecosystem-scale models of the Puget 

Sound marine ecosystem, with some direct links to freshwater and terrestrial processes. This 

report documents the development of one such model, a food web model of the Central Basin of 

Puget Sound using Ecopath with Ecosim (EwE) software created by scientists at the University 

of British Columbia (Christensen et al. 2005). 

The EwE model of the Puget Sound Central Basin (PSCB) was developed for reasons 

consistent with goals outlined in the PSP Action Agenda (PSP 2008) and the NOAA Sound 

Science report (Ruckelshaus and McClure 2007). Our major objectives for this document were 

to: 

1. Assimilate available data on species abundance, vital rates, diets, and habitat associations 

into a trophodynamic model framework. 

2
2. Assess model performance through a series of contrasting scenario-driven simulations. 

3. Identify significant data gaps that could potentially constrain model development or 

increase uncertainty in the outputs. 

Objectives 1 and 2 involve incorporating available information into a model that can 

serve as a caricature of a system. Such models can be used in a variety of applications, including 

simulation-based estimates of ecosystem responses to management actions (Townsend et al. 

2008). Modeling exercises of this sort can also improve understanding of the structure and 

functioning of a food web, which is important given the diversity and complexity of Puget 

Sound’s living resources (Ruckelshaus and McClure 2007). It is for this reason that we present 

output from several dynamic scenarios that realistically perturb the system at multiple trophic 

levels (i.e., variability in primary production, long-term changes in abundance of a top predator, 

and a fishery closure). Testing the model in these diverse ways is intended to provide a sense of 

how well our simulated system behaves in comparison to the real world system. 

Objective 3 is a corollary to Objective 1 and Objective 2. It is addressed implicitly in the 

Methods and Results sections and the Appendices, and is discussed explicitly in the Discussion 

section in the sense of lessons learned at this point of model development. The broader longterm objectives for this project (including evaluating the usefulness of different metrics as 

ecosystem indicators, analyzing the risk of different functional groups with respect to potential 

threats, and simulating alternate ecosystem management scenarios) are not discussed in detail in 

this document. However, the efficacy of this model as a management tool depends on how well 

Objectives 1, 2, and 3 are addressed within this document and as the model continues to evolve. 

3
Methods 

Model Domain: The Puget Sound Central Basin 

Puget Sound is a complex fjord-like ecosystem that is part of the larger Salish Sea (the 

inland marine waters of Washington and British Columbia, Figure 1). Its basins drain a total 

area of 35,500 km2

. Each basin has unique geography, land coverage, hydrology, oceanography, 

and species composition (Ruckelshaus and McClure 2007). We focused on the PSCB because it 

is where most of the human population resides and where considerable anthropogenic alterations 

have occurred; thus, it is an area where many management activities will be directed in order to 

Figure 1. Map of central Puget Sound, the spatial domain for the EwE model, and the rest of the southern 

Salish Sea, as well as catchment areas (lightly shaded) that feed directly into central Puget Sound. 

Inset shows Puget Sound in more detail. 

4
achieve PSP goals. Moreover, EwE software is not spatially explicit; it treats the model domain 

as a single biogeographic box with no clear differentiation of spatial gradients or patchiness 

(Aydin et al. 2002). That framework seems best suited for modeling a single basin of Puget 

Sound, not for modeling multiple basins simultaneously. 

PSCB domain boundaries (Figure 1) correspond to the combined north central and south 

central action areas as delineated and described by the PSP (2008). The PSCB model domain 

encompasses all marine habitats ranging from Tacoma Narrows in the south to just south of 

Whidbey Island in the north, and includes such large bays as Elliott Bay, Commencement Bay, 

Sinclair Inlet, and Dyes Inlet. It does not include Admiralty Inlet. General features of this 

system have been well summarized elsewhere (e.g., Strickland 1983, Gustafson et al. 2000, PSP 

2008). In brief, the system spans from intertidal habitats dominated by sand, gravel, and 

occasional eelgrass or algal habitats to offshore pelagic zones and muddy-bottomed and sandybottomed habitats with depths of more than 250 m. Bathymetrically, approximately one-third of 

the total area of the system is less than 40 m deep; below 60 m, the bottom gradient drops 

relatively steeply until 160 m, slows until about 210 m, then drops off again (Table 1). The 

water column is typically well mixed during the cooler, windy winters and stratified during 

warmer summers when freshwater runoff overlies deep, cool, saltier water. Tide energy is high, 

and tidal height differences can be on the order of 6 m. 

Ecopath with Ecosim 

To address our objectives, we used the EwE software (Christensen et al. 2005), which has 

been applied to dozens of freshwater and marine ecosystems worldwide in recent decades. EwE 

is a trophodynamic model in which different species or guilds are represented as biomass pools. 

The size of pools is regulated by gains (consumption, production, and immigration) and losses 

(mortality and emigration). Pools are linked to one another by predation or in some cases by 

reproduction and maturation. Fisheries extract biomass of targeted groups and bycatch groups. 

The principles of mass balance and energy conservation govern all processes. EwE enables the 

Table 1. Bathymetry of the PSCB, broken down by 10 m depth increments and proportion of the total 

area that falls within that increment. 

Depth (m) Proportion of total area Depth (m) Proportion of total area 

0–10 0.137 131–140 0.017 

11–20 0.081 141–150 0.019 

21–30 0.061 151–160 0.029 

31–40 0.050 161–170 0.053 

41–50 0.049 171–180 0.068 

51–60 0.049 181–190 0.064 

61–70 0.031 191–200 0.052 

71–80 0.028 201–210 0.032 

81–90 0.027 211–220 0.020 

91–100 0.029 221–230 0.017 

101–110 0.027 231–240 0.013 

111–120 0.024 241–250 0.004 

121–130 0.017 250+ 0.005 

5
user to simulate ecological or management scenarios, such as how the system and its components 

respond to changes in primary production, habitat availability, or fishing intensity (e.g., Field et 

al. 2006, Ainsworth et al. 2008b). 

The software consists of two main modules: Ecopath, a static (though not necessarily 

steady-state) mass-balanced model of the perceived “initial” or reference state of a food web, and 

Ecosim, a dynamic model in which biomass pools and vital rates change through time in 

response to perturbations. In Ecopath, key parameters and data are input for each functional 

group in order to describe the reference state. Inputs include biomass per unit area, rates of 

production and consumption, diet proportions, and fishery losses. In Ecosim, perturbations in 

the reference state are simulated, and additional parameters can be changed in order to influence 

the strength of trophic interactions (e.g., to vary the extent of top-down or bottom-up control), to 

characterize stock-recruitment relationships, or to integrate temporal patterns of fishing or 

climate variability. 

Ecopath is comprised of a series of linear equations that describe flows of mass into and 

out of discrete biomass pools, representing functional groups. Each linear equation has the form 

∑=

=−−−××−××

n

j

i ii j j BAEYDCBQBEEBPB iiiji

1

)/( )/( 0 (1) 

where, for species or functional group i, B is biomass, P/B is the production:biomass ratio (which 

is approximately equal to total mortality, Christensen et al. 2005), EE is ecotrophic efficiency, Y

is fishery yield, E is net emigration, and BA is biomass accumulation. EE is the proportion of 

mortality that is attributable to other model compartments (predators and fisheries), and must be 

less than one under the assumption of mass balance. The summation term in Equation 1 signifies 

predatory losses to all predators j, B is the biomass of predator j, Q/B is the consumption:biomass 

ratio of predator j, and DCji is the proportion of i in the diet of predator j. We assume that all Ei

equals 0. 

In Ecosim, biomass pools are dynamic, as governed by coupled differential equations that 

stem from Equation 1 (Christensen et al. 2005). The Ecosim master equation is 

∑ ∑ ×++−+−×=

j j

i ji iiiiiij

i BeFMICCg dt

dB ( ) (2) 

where, for each group i, B is biomass, g is growth efficiency, I is the immigration rate, M is other 

mortality not accounted for by interactions with groups or fisheries within the model, F is the 

fishing mortality rate, and e is the emigration rate. In our models, Ii and ei will always equal 0. 

The summation terms represent the consumption rate of all groups j by group i (Cji) and of group 

i by all groups j (Cij). The C terms are actually functional response terms that reflect the fact that 

at any given time, a portion of a prey biomass pool is, in effect, invulnerable to the predator 

biomass pool by virtue of spatial discontinuities, refuge habitat or behavior, or other means of 

predator avoidance (Walters et al. 1997, Christensen et al. 2005). 

6
The formulation for C is complex, taking into account prey invulnerability, predator and 

prey biomasses, predator and prey feeding times, and handling time; details are presented in 

Christensen et al. (2005, their Equation 49). Adjusting the parameters involved in determining C

can affect the predator-prey relationship (e.g., top-down vs. bottom-up vs. intermediate control, 

the role of other functional groups in mediating the predator-prey relationships between groups j

and I, etc.). In addition, some pools have age structure in order to better represent relatively 

complex life histories. Age-structured biomass pools are dynamically linked by temporally 

explicit reproduction and maturation processes; an explanation of those processes is given by 

Christensen et al. (2005). 

Functional Groups 

One of the first steps in the model development process was identifying the most 

important functional groups in the community, that is, the groups that would comprise the food 

webs we wish to represent in the model. The current list of 65 functional groups is presented in 

Table 2. Functional groups are either individual species or guilds of ecologically similar species, 

as described in some detail in Appendix A. The makeup of the list of functional groups was 

developed through a consultation process with experts from a number of agencies and 

institutions. Species or guilds were included or rejected based on criteria such as: 

• their perceived influence over basic ecosystem functions, such as primary production, 

secondary production, biogenic habitat provision, etc., 

• their standing stock biomass, relative to other species or guilds, 

• their importance in the diets of other groups, 

• the amount of time they occur in the model’s spatial domain each year, 

• their ability to integrate ecological processes (e.g., species that move between habitat 

types and thus link the ecology of those habitats), 

• their importance to commercial, tribal, or recreational fisheries, 

• their importance to society for nonconsumptive reasons, and 

• the availability of data to characterize their ecology, within the scope of EwE. 

The functional group selection process was iterative and largely based on expert opinion 

and consensus. We did not develop quantitative inclusion or rejection thresholds for the above 

criteria. In some cases, we initially included groups but subsequently removed them. Several 

omitted groups, along with the justification for their omission, are listed in Appendix B. 

Data Assimilation and Parameter Development 

We consulted many sources to gather data for the Ecopath equation (Equation 1) for each 

functional group. Direct data sources included the primary literature, single species stock 

assessments, agency reports, and documents and unpublished data. Additionally, we conducted a 

series of short workshops with experts from state and federal agencies to discuss the source data. 

Indirect sources of data included parameter estimates from related species or from published 

7
Table 2. Functional groups in the model with major representatives. Boldfaced groups have two age 

classes (e.g., juveniles and adults). See Appendix A for detailed information on each group. 

Functional group Common name Scientific classification 

Harbor seals Harbor seal Phoca vitulina 

Sea lions California sea lion Zalophus californianus

Steller sea lion Eumetopias jubatus 

Gulls Various gulls Larus spp.

Resident diving birds Various cormorants Phalacrocorax spp.

Pigeon guillemot Cepphus columba 

Migratory diving birds Western grebe Aechmophorus occidentalis 

Various loons Gavia spp.

Common murre Uria aalga 

Nearshore diving birds Various scoters Melanitta spp.

Various goldeneyes Bucephala spp.

Herbivorous birds Dabbling ducks Anas spp.

Various geese Branta spp.

Raptors Bald eagle Haliaeetus leucocephalus 

Wild salmon Chum salmon Oncorhynchus keta 

Chinook salmon O. tshawytscha 

Coho salmon O. kisutch 

Hatchery salmon Chum salmon O. keta 

Chinook salmon O. tshawytscha 

Coho salmon O. kisutch 

Pink salmon Pink salmon O. gorbuscha 

Pacific herring Pacific herring Clupea pallasii 

Forage fish Surf smelt Hypomesus pretiosus 

Pacific sand lance Ammodytes hexapterus 

Surfperches Shiner perch Cymatogaster aggregata 

Striped seaperch Embiotoca lateralis 

Spiny dogfish Spiny dogfish Squalus acanthias 

Skates Longnose skate Raja rhina 

Big skate R. binoculata 

Ratfish Whitespotted ratfish Hydrolagus colliei 

Pacific hake Pacific hake Merluccius productus 

Pacific cod Pacific cod Gadus macrocephalus 

Walleye pollock Walleye pollock Theragra chalcogramma 

Lingcod Lingcod Ophiodon elongatus 

Rockfish Copper rockfish Sebastes caurinus 

Quillback rockfish S. maliger 

Piscivorous flatfish Pacific sanddab Citharichthys sordidus 

Small-mouthed flatfish English sole Parophrys vetulus 

Rock sole Lepidopsetta bilineata 

Demersal fish Various poachers Family Agonidae 

Various eelpouts Lycodes spp.

Various small sculpins Family Cottidae 

Squid Opalescent (market) squid Loligo opalescens

Octopus Red octopus Octopus rubescens 

8
Table 2 continued. Functional groups in the model with major representatives. Boldfaced groups have 

two age classes (e.g., juveniles and adults). See Appendix A for detailed group information. 

Functional group Common name Scientific classification 

Octopus (continued) Giant Pacific octopus Enteroctopus dofleini 

Shrimp Pandalid shrimp Family Pandalidae 

Sand shrimp Crangon spp.

Cancer crabs Dungeness crab Cancer magister 

Sea stars Sunflower star Pycnopodia helianthoides 

Pink sea star Pisaster brevispinis 

Sea urchins Green sea urchin Strongylocentrotus droebachiensis 

Red sea urchin S. franciscanus 

Other grazers Various snails Class Gastropoda 

Various chitons Class Polyplacophora 

Small crustaceans Various amphipods Suborders Gammaridea, Corophiidea 

Various mysids Family Mysidae 

Various crabs Infraorders Brachyura, Anomura 

Large sea cucumbers California sea cucumber Parastichopus californicus 

Predatory gastropods Moon snail Euspira lewisii 

Hairy triton Fusitriton oregonensis 

Mussels Blue mussel Mytilus edulis 

Barnacles Various barnacles Suborder Balanomorpha 

Geoducks Geoduck Panopea abrupta 

Infaunal bivalves Various clams Subclass Heterodonta 

Soft infauna Polychaetes Class Polychaeta 

Deposit feeders Brittle stars Amphiodia urtica 

Various sea cucumbers Class Holothuroidea 

Suspension feeders Various sponges Phylum Porifera 

Various bryozoans Phylum Bryozoa 

Sea pen Ptilosarcus gurneyi 

Tunicates Various sea squirts Class Ascidiacea 

Bacteria Various bacteria 

Microzooplankton Various microzooplankton 

Copepods Various copepods Order Calanoida 

Euphausiids Pacific krill Euphausia pacifica 

Small gelatinous zooplankton Various small jellyfish, ctenophores, and other soft plankton 

Jellyfish Lion’s mane jelly Cyanea capillata 

Moon jelly Aurelia labiata 

Fried egg jelly Phacellophora camtschatica 

Macrozooplankton Various planktonic shrimp, amphipods, and larval crustaceans 

Phytoplankton Various diatoms, dinoflagellates and phytoflagellates 

Benthic microalgae Various benthic diatoms 

Benthic macroalgae Various understory algal species 

Overstory kelp Bull kelp Nereocystis luetkeanus 

Eelgrass Native eelgrass Zostera marina 

Detritus Not available 

Plant/algal material Not available 

Salmon carcasses Not available Oncorhynchus spp. 

9
Ecopath models of nearby systems in the North Pacific Ocean. Also, some consumption or 

production rate parameters were derived from correlations or mechanistic models that have been 

developed in the literature. Examples include empirically based correlative relationships 

between the longevity and the P/B ratio of benthic invertebrate species (Robertson 1979) and 

mechanistic models of bioenergetics that can be used to infer consumption rates of fishes (e.g., 

Kitchell et al. 1977). Finally, as described above (Ecopath with Ecosim subsection) some 

unknown parameters were indirectly estimated through the Ecopath mass-balancing procedure. 

The time period for the reference model state is intended to be relatively contemporary. 

We therefore attempted to restrict our data search to the time period from 1990 to present, 

particularly for information on biomass standing stocks, recent biomass trends, fishery landings, 

total mortality, and diet compositions. For Q/B ratios that were estimated through temperaturedependent bioenergetics modeling, we used water temperatures from the 1990s to present as 

well. 

Appendix A has detailed descriptions of the data assimilation and Ecopath parameter 

development for each functional group, along with citations of the major information sources and 

discussion of data gaps or limitations. Appendix C and Appendix D describe some of the 

methods that were most commonly used to develop parameters from the data. 

Model Balancing 

Equation 1, the Ecopath master equation, contains four core parameters that describe the 

basic biology of each functional group: biomass, P/B, Q/B, and EE. Typically, all but one of 

these are input parameters, and the remaining parameter is estimated by the Ecopath massbalancing algorithm; thus, each group’s Equation 1 has one unknown value. In our model, the 

unknown parameter for a particular group was typically either EE or biomass. Ecopath achieves 

mass balance by simultaneously solving for the unknowns for all groups i, which is possible 

because all of the groups are linked directly or indirectly via consumption (Christensen et al. 

2005). 

Achieving mass balance was a lengthy process. After the initial inputs of Equation 1 

parameters, the system was out of balance: the solution for the EE of several groups was greater 

than 1, indicating mortality rates that were unsustainable and would have depleted the biomass 

pools. Also, in other cases where the unknown parameter was biomass, P/B, or Q/B, the massbalance solution appeared unreasonable based on conventional wisdom (e.g., a biomass solution 

that was unrealistically large, given qualitative observations) or biological principles (e.g., a P/B

solution that was very high for a long-lived, slow-growing species). In a similar vein, sometimes 

EE was the unknown parameter and had a balanced but unreasonable solution. For example, 

some functional groups from low or intermediate trophic levels had EE solutions that seemed 

unreasonably low (<0.1), given our perception of their role in the diets of higher consumers. 

Achieving mass balance mostly involved iterative adjustments to the input values or 

revisiting data sources, following model-balancing guidelines provided by Christensen et al. 

(2005). For example, if the solution for EEi was greater than 1, we might have increased Bi or 

(P/B)i slightly, or we might have decreased the importance of the group in the diet of a predator 

that was imparting especially heavy predation mortality. To the best of our ability, we made 

10
small adjustments and focused on parameters that had the most uncertainty. Occasionally, we 

took more drastic steps. In some cases where solutions for EE were consistently greater than 

one, we elected to input EE and make another parameter (usually biomass) the unknown. We 

generally did this only for data-poor groups, such as Pacific hake and walleye pollock. Even 

more drastically, we aggregated some especially problematic groups into other functional 

groups; for example, Pacific tomcod (Microgadus proximus) and plainfin midshipman 

(Porichthys notatus) began as single-species groups, due to their perceived importance in the 

diets of larger vertebrates, but were later pooled into the demersal fish group to alleviate massbalancing issues. Descriptions of iterative steps and solutions are provided in Appendix A. 

Model Assessment 

We assessed model structure, behavior, and performance by examining several indices 

related to the reference (Ecopath) state and by running a series of simulations in the dynamic 

(Ecosim) module. These indices and simulations do not constitute formal sensitivity analyses. 

Rather, they were used by the most experienced EwE user in our group (C. J. Harvey) as an 

initial means of gauging the feasibility and stability of model estimates and predictions. The 

dynamic simulations also provide a preliminary glimpse of how the system might respond when 

some of the most ecologically important functional groups are perturbed. 

Indices from the Ecopath Model 

In assessing the Ecopath state, we compared biomass estimates among the food web’s 

major constituent groups (e.g., pelagic vs. demersal groups, primary producers vs. invertebrates 

vs. vertebrates) to get a basic sense of how mass is allocated. We also examined indices of 

functional group diversity and evenness at broad taxonomic scales (e.g., among primary 

producers, zooplankton, benthic invertebrates, etc.). We estimated these values using the final 

mass-balance biomass estimates from Ecopath. We expressed diversity within general functional 

pools by using a form of the Shannon diversity index: 

∑=

×−=

s

i

i i ppH

1

' )ln( (3) 

where diversity (H') is a function of the proportion (p) of each functional group i that makes up 

the total biomass of the s pools that make up a general functional pool (e.g., primary producers, 

zooplankton, etc.). As H' increases, species diversity increases. We expressed evenness (E), 

which indicates how uniformly the biomass of the entire functional pool is allocated across 

different functional groups i within the pool, with the following expression: 

)ln(

'

s

H E = (4) 

These indices are relevant because they may be positively related to community stability and 

resilience (e.g., Stachowicz et al. 2002, Worm et al. 2006, O’Gorman and Emmerson 2009). 

11
Ecopath automatically calculates several indices and rates in the mass-balancing step, and 

we examined several of these as well. We compiled model-derived estimates of throughput and 

trophic transfer efficiency to examine energy flow in the system. Throughput is the sum of all 

flows of mass or energy that enter and exit a food web compartment during a unit of time. In 

essence, it captures the total production, respiration, and mortality losses of a particular 

component, and thus describes the ecological footprint of the component better than does 

biomass (Christensen et al. 2005). A related quantity is transfer efficiency, which is the 

proportion of mass or energy that passes through a biomass pool and up to higher trophic levels, 

rather than being lost to respiratory costs or to detritus (Aydin et al. 2002). This metric thus 

focuses on the portion of a group’s throughput that is available to higher consumers. 

Ecopath also calculated total mortality of each group and broke it out further into its main 

components. Mortality is primarily allocated to three sources: predation, take by fisheries and 

other human activities, and other mortality (M0) from processes not modeled by EwE (e.g., 

disease). At a finer scale, Ecopath calculates predation mortality attributed to each predator and 

fishery mortality attributed to each gear type, so that relative impact can be assessed. The M0

component includes, among other things, mortality that occurs outside of the PSCB spatial 

domain. The user can control this in various ways, such as by adjusting the EE input so that 

migratory groups that spend considerable time outside the domain have relatively low EE. 

Simulations to Assess the Ecosim Model: Overview 

We assessed Ecosim performance through a series of simulations in which perturbations 

were introduced and the functional groups responded over time. Simulations were run for 50 

years at monthly time steps. Depending on the simulation, we present either the full time series 

of output, or the output from a specified year. In some cases, we relate outputs to a baseline 50-

year simulation in which no perturbation was introduced. The baseline case is slightly different 

from the Ecopath condition because the Ecopath model is not in true equilibrium: two groups 

have non-zero BA terms (raptors and tunicates, descriptions in Appendix A), and various 

juvenile/adult pools are dynamically linked through maturation/reproduction functions 

(described in Appendix D). By year 50 of the baseline simulation, only 7 out of 65 groups 

changed by more than 10% from their initial Ecopath biomass values (data not shown). 

Bottom-up Control and Stochastic Variation 

In the first simulation, we introduced stochastic variation into the food web through 

phytoplankton. The goal was to evaluate how our characterization of the food web responded to 

variability in primary production. We introduced variation by using a forcing function to vary 

annual phytoplankton P/B, according to random draws from a normal distribution with a mean of 

the initial P/B and a coefficient of variation (CV) of 10%. This resulted in a time series of 

phytoplankton biomass with an interannual CV of approximately 18%. This simulation 

demonstrated the responsiveness of the community to variability in a major bottom-up resource. 

We tested for significant interactions between the biomass of phytoplankton and the 

biomass of all other functional groups in the system using multivariate autoregressive (MAR) 

models at varying time lags. These models take account of intergroup interactions as well as 

density-dependent effects related to a group’s abundance and intrinsic rate of increase (Ives et al. 

12
2003). Time series analyses of marine food webs have found that models incorporating time lags 

of various lengths between species can effectively describe fish production (Tsitsika et al. 2007, 

Lindegren et al. 2009). For some longer-lived species, we might expect to observe higher-order 

time lags in density dependence or interspecific interactions (such as bottom-up effects of prey 

species on predator abundance), whereas for species with higher turnover rates, the time lags 

might be shorter. Similarly, we might expect the effects of changes in primary production, such 

as phytoplankton abundance, to ripple through a food web at multiple time lags. For these 

reasons, we analyzed time series of functional group biomass using MAR models at time lags 

from 1 to 5 years. 

The formulation of the MAR model is 

Xt = A + HXt-q + Et (5) 

where Xt is a p × 1 vector of (natural log) abundances of p interacting functional groups at year t; 

A is a p × 1 vector of constants equal to the intrinsic production rate of each group; H is a p × p

matrix in which elements Hij are interaction coefficients that denote the effect of phytoplankton 

variability on the per capita production rate of the jth group, with the diagonal elements (Hi=j) 

representing density-dependent effects. Because we are only doing pairwise comparisons, all H 

matrices are 2 × 2, and we will consider the elements H12 (phytoplankton effects) and H22

(density dependence). 

Coefficient values generally range from –1 to 1 (H12) and 0 to 1 (H22). H12 values closer 

to |1| indicate stronger interactions, whereas H22 values closer to 0 indicate stronger density 

dependence. Zero values indicate no significant interaction. Xt-q is a p × 1 vector of natural log 

abundances of p interacting groups at year t minus the time lag q (here varied from 1 to 5 years), 

and Et is a p × 1 vector of process error at year t, presumed to be stochastic variability not 

specified in the model, that has a multivariate distribution with mean vector 0 and covariance 

matrix ∑. For each pair of functional groups, we estimated the vectors A and E and the matrix 

biomass using conditional least squares, and calculated 95% confidence intervals around each 

parameter using 2,000 bootstrapped data sets. We assessed the fit of the model by calculating 

conditional R2 values for each functional group’s estimated time series of biomass. We limited 

the scope of our analyses to pairs of groups; therefore, the interaction coefficients H12 reported 

here should be interpreted as the maximum potential influence of phytoplankton biomass on the 

other groups. 

Simulations of Top-down Effects 

The second simulation tracked the response of the system to a dramatic change in the 

biomass of a top predator, raptors (bald eagles). We allowed the simulation to evolve normally 

(i.e., like the baseline scenario) for the first 20 years, then we increased raptor mortality by 

imposing what amounted to a harvest pressure on them, with an instantaneous annual harvest 

mortality rate of 0.2 yr-1. This pressure was maintained for the duration of the scenario and 

caused a dramatic decline in raptor biomass. We did not use the MAR model approach of the 

previous simulations, but opted instead for a simple examination of the signs and magnitudes of 

change of other functional groups following the perturbation, in order to demonstrate the 

responsiveness of the system to this apex predator. In particular, we looked for evidence of 

13
trophic cascade-like dynamics, where a change in a top predator biomass affects biomass 

dynamics at several lower trophic levels. 

In the third simulation, all fisheries were closed in year 1 and kept closed for the duration 

of the simulation. To assess the effect of the closure, we calculated the ratio of all groups’ Bi in 

year 50 of the no-fishing scenario to Bi in year 50 of the baseline scenario. This simulation 

demonstrated the response of the system to a top-down, anthropogenic stressor. 

Finally, a series of diagnostic scenarios were constructed to examine the equilibrium 

yield curves of functional groups that are currently targeted or were historically targeted by 

fisheries, that is, all groups with F greater than 0, along with Pacific hake and walleye pollock. 

We did not include hatchery salmon, due to the artificial nature of their production that 

fundamentally alters the relationship between fishing effort and sustainability. In each 

simulation, F was kept at initial values for all groups except one focal group; that group’s F was 

set at 0 for the first 50-year simulation, then was increased in small increments in successive 

simulations until we reached an F that drove the group extinct by year 50. We compiled the 

year-50 landings for all simulations for each group i. The maximum year-50 landing was 

assumed to be the maximum sustainable yield (MSY) for the group, and the associated FMSY was 

compared with other sources of mortality (e.g., Finitial, total natural mortality) to assess the 

apparent sustainability of current fisheries. Finitial/FMSY should be less than one to ensure 

sustainability in a single-species context, and some authors have further recommended that F

should be less than natural mortality (e.g., Gulland 1970, Patterson 1992, Walters and Martell 

2004). We also plotted estimated year-50 landings vs. effort for each targeted group and 

examined the shapes of each group’s curve; in general, the curves should be dome-shaped and 

not have long tails.2 

 

2

 C. Ainsworth, NWFSC, Seattle, WA. Pers. commun., 4 March 2010. 

14
Results 

General Characteristics and Mass Balancing of the Ecopath Model 

Following the iterative process of adjusting parameters, as described in the Methods 

section, the PSCB model came into balance. The missing parameter values that Ecopath 

calculated were generally credible relative to Ecopath models of nearby marine ecosystems 

(Beattie 2001, Ainsworth et al. 2002, Aydin et al. 2002, Field 2004, Priekshot 2007) and to 

available empirical data (functional group descriptions, Appendix A). Figure 2 provides a visual 

representation of the PSCB food web’s 65 biomass pools, the trophic relationships that link them 

together, and the sources of production that fuel the system. The core parameters of the massbalanced PSCB Ecopath model (including estimated trophic level, TL) are shown in Table 3; 

further descriptions of the parameters, data sources, and underlying assumptions are provided in 

Appendix A. 

The main indicator of mass balance is that all EEi are less than one, although some 

biomass pools have non-zero values for BA, which means that the Ecopath model, though 

balanced, is not in strict equilibrium. Moreover, all EEi are greater than zero, indicating at least 

some level of mortality attributable to another model component, either a predator or a fishery. 

High EE values indicate a greater degree of linkage between a group and its predators or 

fisheries, and thus a greater prospect of response to perturbation in dynamic Ecosim scenarios. 

Low EE values indicate a relative disconnect between functional groups and their predators or 

fisheries, at least in the reference model state. 

Another useful mass-balancing diagnostic is the P/Q ratio (Table 3), which is the P/B

ratio divided by the Q/B ratio and is an approximation of growth efficiency. In general, P/Q

ratios for consumers were between 0.05 and 0.3, which is reasonable (Christensen et al. 2005); 

moreover, most groups with low P/Q ratios (<0.05) were longer-lived, slower-growing species, 

for which P/Q should be low. These groups included seabirds, marine mammals, 

elasmobranchs, and geoducks. 

The finalized Ecopath diet matrix, after all minor adjustments were made for purposes of 

mass balancing, is shown in Table 4. Sources, general descriptions, and assumptions related to 

the diet values are presented in detail in the functional group profiles in Appendix A. One prey 

category of particular note is the Import category. This is a nondynamic pool comprised of all 

prey resources that are not themselves modeled functional groups. For example, a functional 

group may migrate out of PSCB for part of the year. During that time, it would be assigned 

Import for its diet, as it will continue to consume prey resources even though it is outside of the 

model domain. Or a functional group may remain within PSCB but consume rare prey or 

exogenous resources; for example, gulls scavenge human garbage, and that component of their 

diet falls under Import. 

15
Figure 2. Food web diagram for the PSCB Ecopath model, arranged along a vertical axis of trophic level. 

Sizes of boxes are scaled to the biomass densities of the functional groups. Lines link prey 

sources to predators; line thickness is scaled to the flow of material from prey to predator. 

The diet matrix and mass-balancing step give rise to Ecopath estimates of trophic levels 

(TL, Table 3) for all functional groups. With primary producers and detrital groups all assigned 

TL equals 1, the top predator in the system is sea lions (TL = 4.63). (Killer whales are 

intentionally not included in the models; see Appendix B.) In terms of the cumulative living 

biomass of the entire community, approximately 82% of the system is at TL less than 3. 

The finalized Ecopath fishery landings matrix is shown in Table 5. Estimation methods, 

data sources, and general descriptions of relevant fisheries and other removals are described in 

the functional group profiles in Appendix A and the Commercial Harvest Estimates subsection in 

Appendix C. With a few exceptions, recreational fishery landings are poorly quantified in this 

model, and should be regarded with caution. Moreover, bycatch mortality and discard across 

gear type is currently unknown. It is likely that the fishing mortalities in Table 5 are 

underestimates, although we do not know by how much. 

16
Table 3. Ecopath parameters for the functional groups in the PSCB model. This page, vertebrates only; 

all remaining functional groups continued on the next page. See the Ecopath with Ecosim 

subsection of the Methods section for description of parameters. Boldfaced values were 

calculated by the mass-balancing routine in the Ecopath model. 

Group TL B (mt km-2) P/B (yr-1) Q/B (yr-1) EE BA (yr-1) P/Q 

Harbor seals 

Sea lions 

Gulls 

Resident diving birds 

Migratory diving birds 

Nearshore diving birds 

Herbivorous birds 

Raptors 

Juvenile wild salmon 

Subadult wild salmon 

Juvenile hatchery salmon 

Subadult hatchery salmon 

Juvenile pink salmon 

Subadult pink salmon 

Juvenile Pacific herring 

Adult Pacific herring 

Forage fish 

Surfperches 

Spiny dogfish 

Skates 

Ratfish 

Pacific hake 

Pacific cod 

Walleye pollock 

Juvenile lingcod 

Adult lingcod 

Juvenile rockfish 

Adult rockfish 

Piscivorous flatfish 

Small-mouthed flatfish 

Demersal fish 

4.44 

4.63 

3.62 

4.42 

4.15 

3.13 

2.06 

3.56 

3.46 

3.73 

3.63 

3.91 

3.34 

3.32 

3.24 

3.28 

3.30 

3.20 

4.28 

3.88 

3.19 

3.51 

4.07 

3.44 

4.38 

4.51 

3.81 

3.89 

3.64 

3.19 

3.54 

0.0240 

0.0240 

0.0622 

0.0112 

0.0414 

0.0846 

0.0550 

0.00131 

0.0951 

11.918 

0.0819 

6.0710 

0.0085 

0.0772 

3.4072 

2.5310 

3.4606 

3.1700 

5.2540 

0.6800 

36.1000 

3.4050 

0.2000 

3.7040 

0.1240 

0.3000 

0.4540 

0.3270 

1.1550 

7.9620 

4.5650 

0.126 

0.076 

0.120 

0.164 

0.195 

0.291 

0.427 

0.314 

5.591 

0.989 

6.492 

1.017 

9.844 

1.577 

3.656 

1.600 

1.500 

1.300 

0.103 

0.127 

0.305 

0.405 

0.260 

0.800 

0.389 

0.280 

0.360 

0.240 

0.467 

0.345 

1.200 

24.594 

24.350 

172.533 

118.379 

118.965 

205.235 

329.082 

39.744 

30.000 

7.000 

30.000 

7.000 

32.850 

13.429 

15.984 

7.489 

7.000 

6.000 

2.692 

3.201 

1.653 

2.602 

3.784 

4.000 

3.070 

1.932 

2.672 

1.437 

6.007 

5.512 

6.000 

0.397

0.329

0.600

0.370

0.123

0.076

0.080

0.287

0.200

0.200

0.200

0.200

0.200

0.100

0.900

0.851

0.800

0.800

0.175

0.084

0.105

0.900

0.520

0.900

0.900

0.141

0.800

0.500

0.686

0.654

0.800

0 

0 

0 

0 

0 

0 

0 

0.09 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.005 

0.003 

0.001 

0.001 

0.002 

0.001 

0.001 

0.008 

0.186 

0.141 

0.216 

0.145 

0.300 

0.117 

0.229 

0.214 

0.214 

0.217 

0.038 

0.040 

0.185 

0.156 

0.069 

0.200 

0.127 

0.145 

0.135 

0.167 

0.078 

0.063 

0.200 

17
Table 3 continued. Ecopath parameters for the functional groups in the PSCB model. This page, all 

remaining functional groups, including invertebrates, primary producers, and detrital groups. 

See the Ecopath with Ecosim subsection of the Methods section for description of parameters. 

Boldfaced values were calculated by the mass-balancing routine in the Ecopath model. NA = not 

applicable. 

Group TL B (mt km-2) P/B (yr-1) Q/B (yr-1) EE BA (yr-1) P/Q 

Squid 

Octopus 

Shrimp 

YOY Cancer crab 

Age 1+ Cancer crab 

Sea stars 

Sea urchins 

Other grazers 

Small crustaceans 

Large sea cucumbers 

Predatory gastropods 

Mussels 

Barnacles 

Geoducks 

Infaunal bivalves 

Soft infauna 

Deposit feeders 

Suspension feeders 

Tunicates 

Bacteria 

Microzooplankton 

Copepods 

Euphausiids 

Sm. gelatinous zooplankton 

Jellyfish 

Macrozooplankton 

Phytoplankton 

Benthic microalgae 

Benthic macroalgae 

Overstory kelp 

Eelgrass 

Algal/plant material 

Salmon carcasses 

Detritus 

3.50 

3.37 

2.94 

3.17 

3.45 

3.06 

2.00 

2.13 

2.31 

2.16 

2.95 

2.01 

2.51 

2.03 

2.05 

2.10 

2.16 

2.10 

2.10 

2.00 

2.05 

2.21 

2.17 

2.59 

3.43 

2.77 

1.00 

1.00 

1.00 

1.00 

1.00 

1.00 

1.00 

1.00 

0.902 

1.158 

8.134 

0.757 

0.547 

0.346 

0.453 

11.431 

38.195 

0.0471 

0.979 

3.785 

2.310 

52.442 

70.506 

67.899 

7.512 

6.494 

0.157 

6.395 

5.343 

24.419 

11.152 

6.388 

8.483 

6.234 

51.0 

4.298 

3.182 

0.08 

3.558 

1 

1 

1 

3.000 

0.860 

2.250 

2.500 

1.500 

0.519 

0.500 

0.753 

3.410 

0.860 

1.010 

0.927 

1.245 

0.036 

2.059 

4.400 

1.421 

2.763 

2.059 

150.000 

100.000 

15.000 

10.683 

9.000 

3.000 

7.000 

226.300 

100.000 

15.000 

42.955 

24.542 

NA 

NA 

NA 

15.000 

2.500 

12.000 

8.197 

3.084 

2.595 

10.880 

8.859 

25.000 

11.300 

6.733 

3.090 

6.225 

2.000 

6.863 

22.000 

25.000 

13.815 

10.295 

300.000 

285.714 

75.000 

44.145 

30.000 

11.500 

35.000 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

0.800 

0.900 

0.900 

0.900 

0.900 

0.125 

0.500 

0.800 

0.900 

0.800 

0.700 

0.900 

0.800 

0.366 

0.718 

0.568 

0.344 

0.800 

0.099 

0.400 

0.800 

0.800 

0.800 

0.800 

0.500 

0.800 

0.320 

0.500 

0.400 

0.661 

0.156 

0.626 

0.002 

0.393 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.05 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.200 

0.344 

0.188 

0.305 

0.486 

0.200 

0.046 

0.085 

0.136 

0.076 

0.150 

0.300 

0.200 

0.018 

0.300 

0.200 

0.057 

0.200 

0.200 

0.500 

0.350 

0.200 

0.242 

0.300 

0.261 

0.200 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

18
Table 4. Ecopath diet matrix for consumers in the PSCB model. Each column represents the diet 

proportions of a consumer and sums to 1. Asterisk (*) < 0.001, S/a = subadult. 

Predators 

Prey

Harbor seals Sea lions Seagulls Resident diving birds Migr. diving birds N’shore diving birds Herbivorous birds Raptors Juvenile wild salmon S/a wild salmon Juv. hatchery salmon S/a hatchery salmon 

Harbor seals — — — — — — — — — — — — 

Sea lions — — — — — — — — — — — — 

Seagulls — — * — — — — 0.084 — — — — 

Resident diving birds — — * — — — — 0.011 — — — — 

Migr. diving birds — — — — — — — 0.019 — — — — 

N’shore diving birds — — — — — — — 0.036 — — — — 

Herbivorous birds — — — — — — — 0.036 — — — — 

Raptors — — — — — — — — — — — — 

Juvenile wild salmon * — 0.007 0.003 0.006 — — — — — — — 

S/a wild salmon 0.052 0.035 — — — — — 0.003 — — — — 

Juv. hatchery salmon * — 0.007 0.003 0.006 — — — — — — — 

S/a hatchery salmon 0.053 0.035 — — — — — 0.002 — — — — 

Juvenile pink salmon * — * 0.001 0.002 — — — — — — — 

S/a pink salmon 0.005 0.005 — — — — — 0.001 — — — — 

Juv. Pacific herring — — 0.155 0.034 0.218 0.009 — — 0.015 0.007 0.032 0.015 

Adult Pacific herring 0.425 0.065 — — — — — — — 0.003 — 0.015 

Forage fish 0.050 0.010 0.100 0.080 0.121 — — — 0.015 0.005 0.032 0.010 

Surfperches 0.020 0.005 — 0.065 0.094 — — — 0.010 — 0.020 — 

Spiny dogfish 0.005 0.075 — — — — — 0.008 — — — — 

Skates 0.010 0.002 — — — — — — — — — — 

Ratfish — — — — — — — — — — — — 

Pacific hake 0.050 0.150 — — 0.001 — — 0.004 — — — — 

Pacific cod * 0.010 — — — — — 0.004 — — — — 

Walleye pollock 0.200 0.025 — 0.001 — — — 0.004 — — — — 

Juvenile lingcod — — — — — — — 0.008 — — — — 

Adult lingcod * 0.003 — — — — — — — — — — 

Juvenile rockfish 0.025 0.005 — 0.002 — — — 0.008 — — — — 

Adult rockfish 0.025 0.005 — — — — — — — — — — 

Piscivorous flatfish 0.005 0.010 — 0.008 — — — — — — — — 

Sm.-mouthed flatfish 0.005 0.040 — 0.008 — — — 0.016 — — — — 

Demersal fish 0.050 0.010 0.030 0.430 0.131 — — 0.010 0.010 — 0.020 — 

Squid 0.015 0.010 — 0.001 0.001 — — — — — — — 

Octopus 0.005 — — — — — — — — — — — 

Shrimp — — 0.001 0.013 0.027 0.023 — — 0.006 — 0.016 — 

YOY Cancer crab — — — 0.001 — — — — — — — — 

Age 1+ Cancer crab — — — — — — — — — — — — 

Sea stars — — — — — — — — — — — — 

Sea urchins — — — — — — — — — — — — 

Other grazers — — 0.010 — 0.009 0.063 0.010 — — — — — 

Small crustaceans — — 0.053 0.016 0.005 0.116 0.010 0.004 0.260 — 0.195 — 

Large sea cucumbers — — — — — — — — — — — — 

Predatory gastropods — — 0.002 — — 0.007 — — — — — — 

Mussels — — 0.025 — — 0.072 — 0.004 — — — — 

Barnacles — — 0.005 — — 0.015 — — — — — — 

Geoducks — — — — — — — — — — — — 

Infaunal bivalves — — 0.025 — — 0.286 — 0.016 — — — — 

Soft infauna — — — — 0.009 0.001 — — 0.010 — 0.010 — 

Deposit feeders — — — — — — — — — — — — 

Suspension feeders — — — — — — — — — — — — 

Tunicates — — — — — — — — — — — — 

Bacteria — — — — — — — — — — — — 

Microzooplankton — — — — — — — — 0.003 — 0.001 — 

Copepods — — — — — — — — 0.122 — 0.030 — 

Euphausiids — — — 0.001 — — — — 0.050 0.010 0.058 0.008 

Sm. gelatinous zoopl. — — — — — — — — 0.047 0.032 0.006 0.009 

Jellyfish — — — — — — — — — — — — 

Macrozooplankton — — 0.075 — — — — — 0.122 0.043 0.220 0.043 

Phytoplankton — — — — — — — — — — — — 

Benthic microalgae — — — — — — — — — — — — 

Benthic macroalgae — — — — 0.017 0.012 0.230 — — — — — 

Overstory kelp — — — — — — — — — — — — 

Eelgrass — — — — 0.017 — 0.100 — — — — — 

Algal/plant material — — 0.004 — — 0.013 0.050 — — — — — 

Salmon carcasses — — — — — — — 0.095 — — — — 

Detritus — — 0.100 — — — — 0.040 — — — — 

Import — 0.500 0.400 0.333 0.333 0.383 0.600 0.588 0.330 0.900 0.360 0.900 

19
Table 4 continued horizontally. Ecopath diet matrix for consumers in the PSCB model. Each column 

represents the diet proportions of a consumer and sums to 1. Asterisk (*) < 0.001, S/a = subadult. 

Predators 

Prey (column list 

repeated from 

previous page) 

Juvenile pink salmon S/a pink salmon Juv. Pacific herring Adult Pacific herring Forage fish Surfperches Spiny dogfish Skates Ratfish Pacific hake Pacific cod Walleye pollock 

Harbor seals — — — — — — — — — — — — 

Sea lions — — — — — — — — — — — — 

Seagulls — — — — — — — — — — — — 

Resident diving birds — — — — — — — — — — — — 

Migr. diving birds — — — — — — — — — — — — 

N’shore diving birds — — — — — — — — — — — — 

Herbivorous birds — — — — — — — — — — — — 

Raptors — — — — — — — — — — — — 

Juvenile wild salmon — — — — — — — — — — — — 

S/a wild salmon — — — — — — 0.019 — — — — — 

Juv. hatchery salmon — — — — — — — — — — — — 

S/a hatchery salmon — — — — — — 0.018 — — — — — 

Juvenile pink salmon — — — — — — — — — — — — 

S/a pink salmon — — — — — — * — — — — — 

Juv. Pacific herring — 0.001 — 0.010 — — 0.090 0.040 0.025 0.040 — — 

Adult Pacific herring — 0.001 — — — — 0.100 — — 0.020 0.200 — 

Forage fish — 0.003 — 0.020 — — 0.007 0.003 — 0.003 0.050 — 

Surfperches — — — — — — 0.001 0.004 0.012 — — — 

Spiny dogfish — — — — — — — — — — — — 

Skates — — — — — — — — — — — — 

Ratfish — — — — — — 0.082 — — — — — 

Pacific hake — — — — — — 0.013 — — 0.045 — 0.030 

Pacific cod — — — — — — — — — — 0.010 — 

Walleye pollock — — — — — — 0.088 0.023 0.017 — 0.050 — 

Juvenile lingcod — — — — — — — — — — — — 

Adult lingcod — — — — — — — — — — — — 

Juvenile rockfish — — — — — — * — — — — — 

Adult rockfish — — — — — — * — — — — — 

Piscivorous flatfish — — — — — — 0.020 — — — — — 

Sm.-mouthed flatfish — — — — — — 0.072 0.010 0.004 — 0.050 — 

Demersal fish — — — — — — 0.025 0.017 0.008 — 0.250 0.009 

Squid — — — — — — — — — — 0.005 — 

Octopus — — — — — — 0.061 — — — — — 

Shrimp — — — — — 0.020 0.030 0.606 0.018 0.145 0.050 0.134 

YOY Cancer crab — — — — — — 0.005 0.001 0.014 — — — 

Age 1+ Cancer crab — — — — — — 0.031 0.008 — — 0.050 — 

Sea stars — — — — — — — — — — — — 

Sea urchins — — — — — — — — — — — — 

Other grazers — — — — — 0.075 — * 0.010 — — — 

Small crustaceans 0.030 — 0.050 — 0.150 0.200 0.019 0.083 0.200 0.080 0.150 0.378 

Large sea cucumbers — — — — — — — — — — — — 

Predatory gastropods — — — — — 0.010 — — 0.001 — — — 

Mussels — — — — — 0.050 — — 0.002 — — — 

Barnacles — — — — — 0.050 0.003 — 0.002 — — — 

Geoducks — — — — — — — — — — — — 

Infaunal bivalves — — — — — 0.325 — * 0.460 — 0.010 0.001 

Soft infauna — — — — — 0.010 * — 0.160 0.016 — 0.001 

Deposit feeders — — — — — 0.010 — — 0.025 — 0.050 — 

Suspension feeders — — — — — — — — — — — — 

Tunicates — — — — — — — — — — — — 

Bacteria — — — — — — — — — — — — 

Microzooplankton — — — — — — — — — — — — 

Copepods 0.250 — 0.800 0.500 0.650 0.250 — — * — 0.025 0.030 

Euphausiids 0.130 0.080 0.100 0.370 0.050 — — 0.016 — 0.504 0.025 0.318 

Sm. gelatinous zoopl. 0.130 — — — 0.090 — — — — — — — 

Jellyfish — — — — 0.010 — 0.010 — — — — — 

Macrozooplankton 0.060 0.015 0.050 0.100 0.050 — — 0.189 0.002 0.147 0.025 0.099 

Phytoplankton — — — — — — — — — — — — 

Benthic microalgae — — — — — — — — — — — — 

Benthic macroalgae — — — — — — — — — — — — 

Overstory kelp — — — — — — — — — — — — 

Eelgrass — — — — — — 0.005 — — — — — 

Algal/plant material — — — — — — — — 0.040 — — * 

Salmon carcasses — — — — — — — — — — — — 

Detritus — — — — — — — — — — — — 

Import 0.400 0.900 — — — — 0.300 — — — — — 

20
Table 4 continued horizontally. Ecopath diet matrix for consumers in the PSCB model. Each column 

represents the diet proportions of a consumer and sums to 1. Asterisk (*) < 0.001, S/a = subadult. 

Predators 

Prey (column list 

repeated from 

previous page) 

Juvenile lingcod Adult lingcod Juvenile rockfish Adult rockfish Piscivorous flatfish Sm. mouthed flatfish Demersal fish Squid Octopus Shrimp YOY Cancer crab Age 1+ Cancer crab 

Harbor seals — — — — — — — — — — — — 

Sea lions — — — — — — — — — — — — 

Seagulls — — — — — — — — — — — — 

Resident diving birds — — — — — — — — — — — — 

Migr. diving birds — — — — — — — — — — — — 

N’shore diving birds — — — — — — — — — — — — 

Herbivorous birds — — — — — — — — — — — — 

Raptors — — — — — — — — — — — — 

Juvenile wild salmon * — — — — — — — — — — — 

S/a wild salmon — 0.001 — — — — — — — — — — 

Juv. hatchery salmon * — — — — — — — — — — — 

S/a hatchery salmon — 0.001 — — — — — — — — — — 

Juvenile pink salmon * — — — — — — — — — — — 

S/a pink salmon — 0.001 — — — — — — — — — — 

Juv. Pacific herring 0.117 0.044 0.295 0.255 0.060 0.001 0.035 0.100 — — 0.001 0.001 

Adult Pacific herring 0.013 0.005 — 0.050 0.010 — — 0.030 — — — — 

Forage fish 0.295 0.020 0.010 0.010 0.017 * 0.002 0.020 — — 0.010 0.100 

Surfperches 0.022 0.001 0.004 0.005 0.170 0.003 0.018 — — — 0.010 0.020 

Spiny dogfish — 0.001 — — — — — — — — — — 

Skates — — — — — — — — — — — — 

Ratfish — — — — — — — — — — — — 

Pacific hake 0.005 * — — 0.005 — 0.002 — — — — — 

Pacific cod — 0.023 — — — — — — — — — — 

Walleye pollock 0.068 0.033 — — 0.010 0.001 0.001 — — — — — 

Juvenile lingcod 0.030 0.047 — — — — * — — — — 0.003 

Adult lingcod — — — — — — — — — — — — 

Juvenile rockfish 0.091 0.128 — — — — — — — — — — 

Adult rockfish 0.001 0.007 — — — — — — — — — — 

Piscivorous flatfish 0.016 0.032 — — — — — — — — — 0.025 

Sm.-mouthed flatfish 0.006 0.117 — 0.002 — — 0.012 — — — — 0.025 

Demersal fish 0.185 0.448 0.025 0.035 0.050 — 0.013 — 0.030 — 0.030 0.100 

Squid 0.002 * — — 0.013 0.004 0.019 0.100 — — — — 

Octopus 0.014 0.043 — — — — — — — — — — 

Shrimp 0.127 0.035 0.245 0.275 0.084 0.031 0.161 — 0.050 0.020 0.050 0.050 

YOY Cancer crab — — — — — * 0.004 — 0.100 — 0.050 0.050 

Age 1+ Cancer crab — — 0.030 0.060 — — — — 0.020 — — 0.010 

Sea stars — — — — — — — — — — — — 

Sea urchins — — — — — — — — — — — — 

Other grazers — — — — 0.011 0.012 0.012 — 0.250 0.010 — 0.025 

Small crustaceans * * 0.255 0.275 0.215 0.161 0.222 — 0.100 0.200 0.225 0.100 

Large sea cucumbers — — — — — — * — — — — — 

Predatory gastropods — * — — 0.001 0.001 0.001 — 0.050 — — 0.001 

Mussels — — — — — — 0.001 — 0.050 — 0.025 0.025 

Barnacles — — — — 0.005 0.002 0.001 — — — 0.050 0.010 

Geoducks — — — — — — — — — — — — 

Infaunal bivalves — — — — 0.005 0.231 0.025 — 0.350 — 0.400 0.300 

Soft infauna — — 0.048 0.031 0.080 0.472 0.109 — — 0.300 0.020 0.010 

Deposit feeders — — — — 0.006 0.011 — — — 0.010 — 0.020 

Suspension feeders — — — — — — — — — — — 0.010 

Tunicates — — — — — * — — — — — — 

Bacteria — — — — — — — — — — — — 

Microzooplankton — — — — — — — — — — — — 

Copepods — — 0.047 — 0.001 0.002 0.024 0.050 — 0.050 — — 

Euphausiids 0.007 — 0.021 0.002 0.008 0.002 0.112 0.600 — 0.100 — — 

Sm. gelatinous zoopl. — — — — — * 0.001 — — — — — 

Jellyfish — — — — — * 0.001 — — — — — 

Macrozooplankton — — 0.020 — 0.170 0.057 0.207 0.050 — 0.060 — — 

Phytoplankton — — — — — — — — — — — — 

Benthic microalgae — — — — — — — — — — — — 

Benthic macroalgae — 0.012 — — — 0.008 0.013 — — — 0.010 * 

Overstory kelp — — — — — — — — — — — — 

Eelgrass — — — — * * — — — — 0.009 0.005 

Algal/plant material — — — — 0.079 — 0.004 — — — 0.010 0.010 

Salmon carcasses — — — — — — — — — — — — 

Detritus — — — — — — — — — 0.250 0.100 0.100 

Import — — — — — — — 0.050 — — — — 

21
Table 4 continued horizontally. Ecopath diet matrix for consumers in the PSCB model. Each column 

represents the diet proportions of a consumer and sums to 1. Asterisk (*) < 0.001, S/a = subadult. 

Predators 

Other grazers Small crustaceans Large sea cucumbers Predatory gastropods Mussels Barnacles Geoducks Infaunal bivalves Soft infauna 

Prey (column list 

repeated from 

previous page) 

Sea stars Sea urchins Deposit feeders 

Harbor seals — — — — — — — — — — — — 

Sea lions — — — — — — — — — — — — 

Seagulls — — — — — — — — — — — — 

Resident diving birds — — — — — — — — — — — — 

Migr. diving birds — — — — — — — — — — — — 

N’shore diving birds — — — — — — — — — — — — 

Herbivorous birds — — — — — — — — — — — — 

Raptors — — — — — — — — — — — — 

Juvenile wild salmon — — — — — — — — — — — — 

S/a wild salmon — — — — — — — — — — — — 

Juv. hatchery salmon — — — — — — — — — — — — 

S/a hatchery salmon — — — — — — — — — — — — 

Juvenile pink salmon — — — — — — — — — — — — 

S/a pink salmon — — — — — — — — — — — — 

Juv. Pacific herring — — — — — — — — — — — — 

Adult Pacific herring — — — — — — — — — — — — 

Forage fish — — — — — — — — — — — — 

Surfperches — — — — — — — — — — — — 

Spiny dogfish — — — — — — — — — — — — 

Skates — — — — — — — — — — — — 

Ratfish — — — — — — — — — — — — 

Pacific hake — — — — — — — — — — — — 

Pacific cod — — — — — — — — — — — — 

Walleye pollock — — — — — — — — — — — — 

Juvenile lingcod — — — — — — — — — — — — 

Adult lingcod — — — — — — — — — — — — 

Juvenile rockfish — — — — — — — — — — — — 

Adult rockfish — — — — — — — — — — — — 

Piscivorous flatfish — — — — — — — — — — — — 

Sm.-mouthed flatfish — — — — — — — — — — — — 

Demersal fish — — — — — — — — — — — — 

Squid — — — — — — — — — — — — 

Octopus — — — — — — — — — — — — 

Shrimp — — — — — — — — — — — — 

YOY Cancer crab — — — — — — — — — — — — 

Age 1+ Cancer crab — — — — — — — — — — — — 

Sea stars 0.025 — — — — — — — — — — — 

Sea urchins 0.050 — — — — 0.010 — — — — — — 

Other grazers 0.100 — — — 0.025 0.100 — — — — — — 

Small crustaceans 0.020 — — 0.050 — — — — — — — — 

Large sea cucumbers 0.010 — — — — — — — — — — — 

Predatory gastropods 0.010 — — — — 0.010 — — — — — — 

Mussels 0.075 — — — — 0.020 — — — — — — 

Barnacles 0.075 — — — — 0.050 — — — — — — 

Geoducks 0.001 — — — — — — — — — — — 

Infaunal bivalves 0.350 — — 0.050 — 0.360 — — — — — — 

Soft infauna — — — 0.100 — — — 0.080 — — — 0.050 

Deposit feeders 0.100 — — — — 0.050 — — — — — — 

Suspension feeders 0.100 — 0.030 0.010 0.025 0.250 — — — — — — 

Tunicates * — — — — 0.001 — — — — — — 

Bacteria — — 0.100 0.044 0.100 — 0.005 0.160 0.025 0.050 0.100 0.100 

Microzooplankton — — — 0.001 — — 0.005 0.160 — — — — 

Copepods — — — 0.020 — — — 0.080 — — — — 

Euphausiids — — — — — — — — — — — — 

Sm. gelatinous zoopl. — — — — — — — — — — — — 

Jellyfish — — — — — — — — — — — — 

Macrozooplankton — — — — — — — — — — — — 

Phytoplankton — — — 0.050 — — 0.100 0.320 0.750 0.280 — — 

Benthic microalgae 0.025 0.200 0.450 0.010 0.050 — — — — — 0.100 0.050 

Benthic macroalgae — 0.200 0.080 0.005 — — — — — — — — 

Overstory kelp — 0.050 0.020 — — — — — — — — — 

Eelgrass — — 0.020 0.010 — — — — — — — — 

Algal/plant material — 0.500 0.100 0.050 — — — — — — — — 

Salmon carcasses — — — — — — — — — — — — 

Detritus 0.059 0.050 0.200 0.600 0.800 0.149 0.890 0.200 0.225 0.670 0.800 0.800 

Import — — — — — — — — — — — — 

22
23

Table 4 continued horizontally. Ecopath diet matrix for consumers in the PSCB model. Each column 

represents the diet proportions of a consumer and sums to 1. Asterisk (*) < 0.001, S/a = subadult. 

Predators 

Prey (column list 

repeated from 

previous page) 

Suspension feeders Tunicates Bacteria Microzooplankton Copepods Euphausiids Sm. gelatinous zoopl. Jellyfish Macrozooplankton 

Harbor seals — — — — — — — — — 

Sea lions — — — — — — — — — 

Gulls — — — — — — — — — 

Resident diving birds — — — — — — — — — 

Migr. diving birds — — — — — — — — — 

N’shore diving birds — — — — — — — — — 

Herbivorous birds — — — — — — — — — 

Raptors — — — — — — — — — 

Juvenile wild salmon — — — — — — — — — 

S/a wild salmon — — — — — — — — — 

Juv. hatchery salmon — — — — — — — — — 

S/a hatchery salmon — — — — — — — — — 

Juvenile pink salmon — — — — — — — — — 

S/a pink salmon — — — — — — — — — 

Juv. Pacific herring — — — — — — — — — 

Adult Pacific herring — — — — — — — — — 

Forage fish — — — — — — — — — 

Surfperches — — — — — — — — — 

Spiny dogfish — — — — — — — — — 

Skates — — — — — — — — — 

Ratfish — — — — — — — — — 

Pacific hake — — — — — — — — — 

Pacific cod — — — — — — — — — 

Walleye pollock — — — — — — — — — 

Juvenile lingcod — — — — — — — — — 

Adult lingcod — — — — — — — — — 

Juvenile rockfish — — — — — — — — — 

Adult rockfish — — — — — — — — — 

Piscivorous flatfish — — — — — — — — — 

Sm.-mouthed flatfish — — — — — — — — — 

Demersal fish — — — — — — — — — 

Squid — — — — — — — — — 

Octopus — — — — — — — — — 

Shrimp — — — — — — — — — 

YOY Cancer crab — — — — — — — — — 

Age 1+ Cancer crab — — — — — — — — — 

Sea stars — — — — — — — — — 

Sea urchins — — — — — — — — — 

Other grazers — — — — — — — — — 

Small crustaceans — — — — — — — — — 

Large sea cucumbers — — — — — — — — — 

Predatory gastropods — — — — — — — — — 

Mussels — — — — — — — — — 

Barnacles — — — — — — — — — 

Geoducks — — — — — — — — — 

Infaunal bivalves — — — — — — — — — 

Soft infauna — — — — — — — — — 

Deposit feeders — — — — — — — — — 

Suspension feeders — — — — — — — — — 

Tunicates — — — — — — — — — 

Bacteria 0.050 0.050 — 0.050 — 0.025 0.100 — 0.100 

Microzooplankton 0.050 0.050 — — 0.200 0.025 0.100 — 0.100 

Copepods — — — — — 0.100 0.200 0.425 0.300 

Euphausiids — — — — — — 0.050 0.200 0.100 

Sm. gelatinous zoopl. — — — — — — 0.049 0.245 0.040 

Jellyfish — — — — — — 0.001 0.100 0.010 

Macrozooplankton — — — — — — — 0.030 — 

Phytoplankton 0.700 0.700 — 0.900 0.800 0.800 0.350 — 0.300 

Benthic microalgae — — — — — — — — — 

Benthic macroalgae — — — — — — — — — 

Overstory kelp — — — — — — — — — 

Eelgrass — — — — — — — — — 

Algal/plant material — — — — — — — — — 

Salmon carcasses — — — — — — — — — 

Detritus 0.200 0.200 1.000 0.050 — 0.050 0.150 — 0.050 

Import — — — — — — — — — 
Table 5. Landings (mt km-2 yr-1) by gear type of targeted groups. S/a = subadult and asterisk (*) indicates arbitrary placeh

functional group known to experience recreational harvest until more data are available. 

Fishery/gear 

Group 

 Crab pot 

 Dip net 

 Gill net 

 Jigging 

 Longline 

 Dredge 

 Other net 

 Shrimp pot 

 Other gear 

 Pole 

 Seine 

 Set net 

Harbor se als 0.0004 Sea li ons 

hes 

002

p 93 

ers 

cks 97

S/a wild salmon 3.6E-08 0.463 0.0002 9.0E-08 1.35 0.162 

S/a hatchery salmon 9.0E-07 0.186 9.4E-05 6.3E-05 0.164 0.348 

S/a pink salmon 0.0021 5.0E-06 0.0013 0.0012 

Juv. Pacific herring 0.0025 0.202 0.0005

Adult Pacific herring 0.0002 0.02 1.0E-05

Forage fish 2.4E-07 0.007 5.4E-06 

Surfperc 

Spiny dogfish 

ates

0.0007 0.043 0.0032 

Sk 0.0

Pacific cod 6.4E-06

Adult lingcod 

Adult rockfish 2.0E-05 

Piscivorous flatfish 9.0E-08 

Sm.-mouthed flatfish 

Squid 6.8E-05 

Octopus 

m

1.5E-05 6.0E-08 

Shri 0.0 0 

Age 1+ Cancer crab 

ins

0.0905 

Sea urch 

Large sea cucumb 

sels

Mus 

Geodu 0.6 

Infaunal bivalves 0.0003 0.0297 

Sum 0.091 0.003 0.652 6.8E-05 0.043 0.697 0.222 0.009 0.03 6.3E-05 1.523 0.514 

24
Biomass, Functional Diversity, and Evenness Patterns 

Based on our parameterization, the PSCB Ecopath model reflects a system dominated by 

species and guilds associated with demersal habitats. In Figure 3, the initial biomass estimates of 

all nondetrital functional groups are coarsely aggregated by taxonomy, functional role, and 

habitat use. About 54.5% of all living standing stock biomass are comprised of benthic 

invertebrates, and 12.9% of living biomass are bony and cartilaginous fishes that spend much of 

their lives on or near the bottom. Including the relatively small amount of benthic primary 

producer biomass, bottom-associated functional groups make up about 70% of the living 

biomass in the PSCB Ecopath model. Species and functional groups that are primarily pelagic in 

nature comprise 28.7% of the total biomass, with several zooplankton pools making up the 

largest component. Phytoplankton, the dominant source of total system production, makes up 

about 10.2% of total standing biomass in the mass-balanced model. Less than 2% of total 

biomass is comprised of species and guilds that are considered to make extensive use of both 

pelagic and benthic habitats (e.g., bacteria, pinnipeds, seabirds, squid). 

In biomass terms, several individual functional groups stand out as major components of 

the PSCB food web. In terms of sheer biomass, more than 68% of living biomass are comprised 

of just 7 functional groups: infaunal bivalves, soft infauna, geoducks, phytoplankton, small 

crustaceans, ratfish, and copepods (Table 3). A plot of functional diversity (H') vs. functional 

evenness (E) shows a mildly negative relationship between the two variables (Figure 4). These 

results are heavily influenced by our biases in how we aggregated species into functional groups, 

however, particularly among the lower trophic levels. For example, benthic primary producers, 

small zooplankton, and large zooplankton are each dominated by a few catch-all groups. 

Phytoplankton and bacteria were excluded from the relationship altogether because their E

values could not be calculated (each of these groups had one representative and thus had a zero 

in the denominator of Equation 4). Diversity was greatest in the benthic invertebrate pool, 

although this pool ranked somewhat low in terms of evenness, largely because its biomass was 

dominated by four functional groups (infaunal bivalves, soft infauna, geoducks, and small 

crustaceans). Fish groups and seabirds also ranked high in diversity, reflecting our familiarity 

with their ecology and our bias to realistically represent their contribution to ecosystem services. 

Throughput and Transfer Efficiency 

At a community scale, throughput was dominated by phytoplankton and detritus, which 

together comprised 67.1% of total system throughput (Table 6). That in itself is an 

underestimate because Ecopath does not calculate the respiratory losses of primary producers 

and thus discounted the total throughput through phytoplankton. Of the total throughput 

accounted for by Ecopath (i.e., all but primary producer respiration), about 98.6% moved 

through nonvertebrate pools (i.e., primary producers, invertebrates, and detritus); this high 

proportion is typical of Ecopath models (C. J. Harvey, personal observation). 

What is perhaps more interesting is the manner in which different functional groups rank 

in terms of throughput and biomass. We ranked all functional groups from greatest to least in 

terms of throughput and biomass (except detritus, algal/plant material, and salmon carcasses, for 

which biomass estimates were not available, Appendix A) and conducted linear regression of 

25
0

0.1

0.2

0.3

0.4

0.5

0.6

Phytoplankton

Small

zooplankton

Large

zooplankton

Pelagic fish

Benth. primary

producers

Benth. invertsDemersal fish

Cephalopods

Seabirds

Pinnipeds

Bacteria

0

50

100

150

200

250

300

Proportion of total living biomass

Total biomass (mt km -2)

Figure 3. Biomass of coarsely pooled categories of functional groups in the PSCB Ecopath model. 

Biomass is expressed in relative and absolute terms. Seabird and pinniped biomasses are each 

greater than zero but are very small relative to other categories. 

these ranks (Figure 5). Groups with positive residuals had proportionally greater throughput per 

unit biomass than would be expected. Notable examples include the seabird groups, bacteria, 

benthic primary producers, and many of the pelagic fishes and invertebrates. By contrast, those 

with negative residuals were mostly demersal fishes and benthic invertebrates. Thus, even 

though most biomass in the system is in demersal pools (Figure 3), relatively high throughputs 

emphasize the importance of pelagic pools. 

Transfer efficiencies of consumer groups spanned a broad range (Table 6). The average 

transfer efficiency for all consumers, weighted by biomass, was 11.9%, which compares 

favorably to the Aydin et al. (2002) estimates for transfer efficiency in Bering Sea food webs 

(12.1 to 13.5%). In general, consumer transfer efficiency decreased with increasing trophic level 

(Figure 6), which is to be expected as higher consumers become less energetically efficient (i.e., 

higher respiration) and also less subject to predation by the relatively small biomass pools that 

occupy the uppermost trophic levels. However, the relationship was very weak, in part because 

of the large number of functional groups from lower trophic levels that do not experience much 

predation, and thus have low transfer efficiencies (e.g., herbivorous birds, geoducks, small 

benthic grazers, tunicates, sea urchins, other deposit feeders). Moreover, the relationship was 

26
Pinnipeds Cephalopods

Demersal fish Seabirds

Benthic inverts

Pelagic fish

Benthic primary 

producers Large zooplankton

Small zooplankton

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0 0.2 0.4 0.6 0.8 1

E

H'

Figure 4. Plot of Shannon diversity index (H′) against functional evenness (E) for coarsely pooled 

categories of functional groups in the PSCB Ecopath model. 

weakened by several mid-level consumers (squid, YOY and age 1+ Cancer crab) that had very 

high transfer efficiencies, a result of having both high assimilation efficiencies and also relatively 

high predation pressure. 

Mortality 

Allocation of mortality is a function of factors such as the values of P/B and EE, the Q/B

of an organism’s predators, and the magnitude of F. As explained earlier and also in Appendix 

A, we were forced to input estimates of EE for many functional groups, and inputting EE is 

based more on coarse guidelines than on empirical information (e.g., Christensen et al. 2005). 

For all such groups, the proportion of total mortality allocated to M0 (i.e., 1–EE) was therefore a 

direct result of our assumptions, and should be regarded with care until better estimates of 

missing parameters such as biomass enable mass-balance estimation of EE. 

For functional groups not targeted by fisheries, the relative importance of predation and 

M0 was taxonomically dependent (Figure 7a). Seabirds, with the exception of gulls, primarily 

experienced M0, which reflects their relative lack of predators and tendency to spend large 

portions of the year outside the model domain. Gulls and resident diving birds had higher 

predation mortality than other birds because they spend more time in PSCB and they are more 

heavily targeted by raptors. In all seabird groups except gulls, EE was the parameter estimated 

in the mass-balancing step of Ecopath (Table 3), so the EE-related issue alluded to in the 

preceding paragraph generally does not apply. 

27
Table 6. Biomass (mt km-2), throughput (mt km-2 yr-1), and transfer efficiency of functional groups. 

Transfer efficiencies are for consumers only. 

Biomass Throughput 

Functional group Value Rank Value Rank 

Transfer 

efficiency 

Harbor seals 0.024 58 0.59 59 0.0020 

Sea lions 0.024 58 0.58 60 0.0010 

Gulls 0.0622 54 10.74 39 0.0004 

Resident diving birds 0.0112 60 1.33 54 0.0005 

Migratory diving birds 0.0414 57 4.93 45 0.0002 

Nearshore diving birds 0.0846 50 17.36 33 0.0001 

Herbivorous birds 0.055 55 18.10 32 0.0001 

Raptors 0.00131 62 0.05 66 0.0002 

Juvenile wild salmon 0.0951 49 2.85 48 0.0373 

Subadult wild salmon 11.918 8 83.42 22 0.0283 

Juvenile hatchery salmon 0.0819 51 2.46 50 0.0433 

Subadult hatchery salmon 6.071 19 42.49 27 0.0291 

Juvenile pink salmon 0.0085 61 0.28 65 0.0599 

Subadult pink salmon 0.0772 53 1.04 56 0.0117 

Juvenile Pacific herring 3.407 28 54.46 24 0.2059 

Adult Pacific herring 2.531 32 18.95 31 0.1819 

Forage fish 3.461 27 24.22 29 0.1714 

Surfperches 3.170 31 19.02 30 0.1733 

Spiny dogfish 5.254 21 14.14 36 0.0067 

Skates 0.68 39 2.18 51 0.0033 

Ratfish 36.1 6 59.67 23 0.0194 

Pacific hake 3.405 29 8.86 40 0.1401 

Pacific cod 0.2 46 0.76 58 0.0357 

Walleye pollock 3.704 25 14.82 34 0.1800 

Juvenile lingcod 0.124 48 0.38 64 0.1141 

Adult lingcod 0.3 45 0.58 61 0.0204 

Juvenile rockfish 0.454 41 1.21 55 0.1078 

Adult rockfish 0.327 44 0.47 63 0.0835 

Piscivorous flatfish 1.155 35 6.94 41 0.0533 

Small-mouthed flatfish 7.962 12 43.89 26 0.0410 

Demersal fish 4.565 22 27.39 28 0.1600 

Squid 0.902 37 13.53 37 0.3096 

Octopus 1.158 34 2.89 47 0.1688 

Shrimp 8.134 11 97.61 18 0.1600 

YOY Cancer crab 0.757 38 6.21 43 0.2745 

Age 1+ Cancer crab 0.547 40 1.69 52 0.4377 

Sea stars 0.346 43 0.90 57 0.0250 

Sea urchins 0.453 42 4.93 44 0.0230 

Other grazers 11.431 9 101.26 17 0.0680 

Small crustaceans 38.195 5 954.88 7 0.1228 

Large sea cucumbers 0.047 56 0.53 62 0.0609 

Predatory gastropods 0.979 36 6.59 42 0.1050 

Mussels 3.785 24 11.70 38 0.2700 

Barnacles 2.310 33 14.38 35 0.1600 

28
29

Table 6 continued. Biomass (mt km-2), throughput (mt km-2 yr-1), and transfer efficiency of functional 

groups. Transfer efficiencies are for consumers only. 

Biomass Throughput 

Functional group Value Rank Value Rank 

Transfer 

efficiency 

Geoducks 52.442 3 104.88 15 0.0066 

Infaunal bivalves 70.506 1 483.91 9 0.2154 

Soft infauna 67.899 2 1,493.78 6 0.1137 

Deposit feeders 7.512 14 187.80 13 0.0196 

Suspension feeders 6.494 15 89.72 20 0.1600 

Tunicates 0.157 47 1.61 53 0.0151 

Bacteria 6.395 16 1,918.60 3 0.2000 

Microzooplankton 5.343 20 1,526.69 5 0.2800 

Copepods 24.419 7 1,831.45 4 0.1600 

Euphausiids 11.152 10 492.28 8 0.1936 

Sm. gelatinous zooplankton 6.388 17 191.63 12 0.2400 

Jellyfish 7.958 13 91.51 19 0.1304 

Macrozooplankton 6.234 18 218.20 11 0.1600 

Phytoplankton 51 4 11,541.0 1 – 

Benthic microalgae 4.298 23 429.84 10 – 

Benthic macroalgae 3.182 30 47.73 25 – 

Overstory kelp 0.08 52 3.44 46 – 

Eelgrass 3.558 26 87.32 21 – 

Algal/plant material – – 103.21 16 – 

Salmon carcasses – – 2.66 49 – 

Detritus – – 11,214.0 2 – 

Import – – 145.32 14 – 

Unlike seabirds, nontarget fishes and invertebrates typically experienced far greater 

predation mortality than M0 (Figure 7a). Exceptions included functional groups that spend a 

large portion of time outside of the model domain (juvenile salmon) or groups thought to have 

few predators (ratfish, sea stars, deposit feeders, tunicates). For most nontarget fish and 

invertebrate groups, however, EE was an input parameter (Table 3). Thus the EE-related caveat 

mentioned above should be kept in mind. 

Among the primary producers, grazing losses and M0 were both moderate (i.e., between 

30% and 70% of total mortality) for phytoplankton, benthic microalgae, benthic macroalgae, and 

overstory kelp (Figure 7a). The EE-related caveat applies to benthic microalgae and benthic 

macroalgae. Eelgrass experienced low grazing relative to M0. 

Among groups targeted by fisheries (or illegal take, in the case of marine mammals), F

generally accounted for a fairly small proportion of total mortality (Figure 7b). The biggest 

exceptions were large sea cucumbers, for which F was about 50% of total mortality, and three 

other groups (geoducks, harbor seals, and sea lions) for which F was 30–40% of total mortality. 

For several fished groups, M0 represented a large proportion (≥50%) of total mortality. These 

were mostly groups that are top predators (marine mammals, spiny dogfish, lingcod), groups that 
0

10

20

30

40

50

60

0 10 20 30 40 50 60

Throughput rank

Phytoplankton

Benthic primary producers

Small zooplankton

Large zooplankton

Cephalopods

Benthic invertebrates

Pelagic fish

Demersal fish

Seabirds

Pinnipeds

Bacteria

Linear regression

Biomass rank

Figure 5. Relationship between the rankings of functional group biomass and functional group 

throughput in the PSCB Ecopath model. Symbols correspond to coarsely pooled functional 

categories into which the functional groups fall (as in Figure 3 and Figure 4). 

are outside of the system for a large amount of time (subadult salmon), or mid-trophic level 

groups that have few predators (skates, geoducks). 

Among these groups, the aforementioned caveat regarding EE is once again relevant, 

most notably for pelagic fishes and several types of benthic invertebrates (Table 3). In contrast, 

the allocation of mortality in marine mammals, most demersal fish, geoducks, and infaunal 

bivalves relies more on empirical data and less on assumed values of EE. Also, the proportion of 

total pinniped mortality derived from illegal take may be an artifact. It assumes that pinniped 

populations in PSCB are closed, which seems unlikely for sea lions, which are migratory, and 

harbor seals, which are thought to be at or near carrying capacity at the scale of the entire Puget 

Sound basin (Jeffries et al. 2003). 

Of particular interest for targeted groups is the comparison between predation and fishing 

mortality, as predators and humans are often considered competitors for harvestable biomass. 

For the largest fisheries by landings (wild and hatchery Pacific salmon, geoducks), total biomass 

removed by the fishery exceeded, by one or more orders of magnitude, the amount removed by 

predators within the model domain (Figure 7b). Adult lingcod also experienced greater fishing 

mortality than predation. For all other groups targeted by both predators and fisheries, predators 

were the more important source of mortality, sometimes by several orders of magnitude. In most 

of those cases, predation mortality was inflicted by a variety of fishes and invertebrates, seabirds 

to a lesser extent, and marine mammals to a much lesser extent (data not shown). Fishery vs. 

30
y = 0.562e-0.8748x

R2 = 0.0976

0

0.1

0.2

0.3

0.4

0.5

012345

Trophic level

Transfer efficiency

Age 1+ Cancer crab

Squid

YOY Cancer crab

Figure 6. Transfer efficiency as a function of trophic level for consumer groups in the PSCB Ecopath 

model. Open symbols signify substantial outliers (see Throughput and Transfer Efficiency 

subsection above). 

predation comparisons should be viewed with some caution, however: first, recreational fishing 

and bycatch mortality have likely been underestimated for many groups due to lack of data; and 

second, as alluded to earlier, sources of mortality occurring outside the model domain are not 

accounted for, which could bias results for migratory species such as salmon. 

Some predation mortality estimates in the mass-balanced Ecopath food web imply 

particularly strong predator-prey linkages. These are interactions in which a predator accounts 

for a large portion (≥25%) of the total mortality of a prey group. Spiny dogfish are the most 

noteworthy predator in this case. They account for equal to or greater than 25% of the total 

mortality of 6 different functional groups, including 2 for which they account for equal to or 

greater than 50% and one for which they account for more than 85% (Table 7). The groups that 

they depredate include major species like Pacific herring, Dungeness crab, and the flatfish 

complex. Another notable predator is the small crustaceans group, which imparts substantial 

predation mortality on four groups (including via intraguild cannibalism); however, that output 

derives in part from small crustacean omnivore diet composition that is rather arbitrary in nature 

(Appendix A). Three groups (Pacific hake, walleye pollock, and mussels) appear twice as prey 

species in Table 7; that is, they experience substantial predation mortality from two different 

functional groups. Five groups in Table 7 (jellyfish, squid, small crustaceans, Pacific hake, and 

juvenile lingcod) experience substantial predation mortality due to intraspecific or intraguild 

cannibalism. Four groups (soft infauna, piscivorous flatfish, walleye pollock, and surfperch) 

appear in Table 7 in both the predator and prey columns in noncannibalistic relationships. 

31
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Gulls

Res. diving birds

Migr. diving birds

Nearshore birds

Herbivorous birds

Raptors

Juv. wild salmon

Juv. hatchery

Juv. pink salmon

Ratfish

Pacific hake

Walleye pollock

Juv. lingcod

Juv. rockfish

Demersal fish

YOY Cancer crab

Sea stars

Other grazers

Sm. crustaceans

Pred. gastropods

Barnacles

Soft infauna

Deposit feeders

Susp. feeders

Tunicates

Bacteria

Microzooplankton

Copepods

Euphausiids

Sm. gel. zoopl.

Jellies

Macrozooplankton

Phytoplankton

Benth microalgae

Benth macroalgae

Overstory kelp

Eelgrass

"Other" mortality

Predation

AProportion of total mortality

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Harbor seals

Sea lions

Subadult wild salmon

Subadult hatchery salmon

Subadult pink salmon

Juv. herring

Adult herring

Forage fish

Surf perches

Spiny dogfish

Skates

Pacific cod

Adult lingcod

Adult rockfish

Piscivorous flatfish

Small-mouthed flatfish

Squid

Octopus

Shrimp

Age 1+ Cancer crab

Urchins

Large sea cucumbers

Mussels

Geoducks

Infaunal bivalves

Fishing

"Other" mortality

Predation

Proportion of total mortality

B

Figure 7. Allocation of mortality to fishing (gray), predation (black) or other sources (white), that is, 

mortality sources not dynamically modeled in EwE. Panel A is nontarget functional groups and 

panel B is functional groups targeted by one or more fisheries. 

32
Table 7. Trophic relationships in the Ecopath model where a predator accounts for at least 25% of the 

total predation mortality experienced by a prey group. 

Predator Prey Predation mortality % 

Spiny dogfish Adult Pacific herring 34.9 

Small-mouthed flatfish 37.1 

Walleye pollock 42.0 

Piscivorous flatfish 52.4 

Age 1+ Cancer crabs 53.4 

Octopus 86.7 

Small crustaceans Soft infauna 32.0 

Infaunal bivalves 32.9 

Small crustaceans 36.7 

Suspension feeders 53.2 

Adult lingcod Pacific cod 25.6 

Juvenile rockfish 45.4 

Juvenile lingcod 56.6 

Raptors Resident diving birds 31.2 

Gulls 58.6 

Ratfish Walleye pollock 34.2 

YOY Cancer crabs 44.1 

Surfperch Mussels 27.1 

Barnacles 33.1 

Jellyfish Jellyfish 38.3 

Small gelantinous zooplankton 39.0 

Nearshore diving birds Mussels 35.6 

Pacific hake Pacific hake 28.9 

Walleye pollock Pacific hake 32.2 

Piscivorous flatfish Surfperch 28.6 

Squid Squid 50.0 

Predatory gastropods Sea urchins 29.1 

Copepods Microzooplankton 68.5 

Other grazers Overstory kelp 58.9 

Soft infauna Benthic microalgae 34.8 

Dynamic Model Responses 

Phytoplankton Variability 

Stochasticity in phytoplankton production resulted in variation in biomass throughout 

most of the food web (Figure 8). All but 5 groups experienced substantial displacement (i.e., a 

change of at least 10% from their initial [Ecopath] biomass values at some point during the 50-

year simulation). In fact, the average CV for the biomass of functional groups in this simulation 

was 18.2%. The magnitude and rate of change varied among groups. Groups with relatively low 

P/B rates, for example, tended to respond more slowly and to a lesser relative magnitude than 

groups with high P/B rates. In addition to P/B ratios, trophic relationships influenced the 

response of some groups to phytoplankton variability. The variability of phytoplankton rippled 

through all trophic levels, but various feedbacks caused by factors such as dietary overlap or 

33
0 10 20 30 40 50

Relative biomass

Simulation year

Figure 8. Screen captures from 50-year Ecosim simulation of PSCB food web (lower panel) responding 

to stochastic variability in phytoplankton biomass (upper panel). Each line represents a 

functional group’s relative biomass (i.e., biomass at time t relative to the original biomass from 

the Ecopath model, as in Table 3). Rates, directions, and magnitudes of change are dependent on 

inherent vital rates and on trophic linkages. 

differences in response rates of predators, prey, and competitors altered the extent of the 

responses. 

Time-series analyses (MAR modeling) confirmed significant bottom-up effects of 

phytoplankton on most functional groups in the ecosystem, at multiple time lags (Table 8). The 

best models (i.e., R2

 > 0.8) included negative (competitive) effects of phytoplankton on other 

primary producers, and positive effects of phytoplankton at 1-year time lags (on gulls, subadult 

pink and wild salmon, Pacific hake, walleye pollock, small-mouthed flatfish, and demersal fish) 

and 4-year and 5-year lags (on herbivorous birds). 

By far the highest number of significant models described effects of phytoplankton at a 1-

year time lag, and the number of significant models decreased with increasing time lag. 

However, significant interactions were observed between phytoplankton and upper trophic levels 

(e.g., pinnipeds and birds) at all time lags. Interestingly, the bottom-up effects of phytoplankton 

on some functional groups were delayed, with significant interactions only observed at 3-year, 4-

year or 5-year time lags (e.g. spiny dogfish, raptors, sea urchins, herbivorous birds). 

34
Table 8. Results of pairwise MAR analyses of relationships between phytoplankton and each functional group at lags of 1–

interaction coefficients for the effects of phytoplankton on each group (H12) and for the effect of density dependenc

values in MAR models typically range from –1 to 1 (H12) and 0 to 1 (H22). H12 values close to |1| indicate strong in

values close to 0 indicate strong density dependence. Zeros indicate no significant interactions. Only models with (i.e., either H12 or H22 ≠ 0) and R2 > 0.5 are shown; other models are left blank. 

Lag = 1 year Lag = 2 years Lag = 3 years Lag = 4 years 

Functional group H12 R2

35

H22 H R H H 2 R2 R2 12 H22 12 H22 12 H22 

Harbor seals 0.34 0.84 0.62 –0.26 0.86 0.62 0.48 0.61 0.55 Sea lions 0.25 0.92 0.51 –0.10 1.03 0.58 0.41 0.77 0.56 

Gulls 0.09 0.96 0.85 0.14 0.91 0.72 0.00 0.91 0.68 0.18 0.78 0.65 

Resident diving birds 0.30 0.79 0.56 0.05 0.75 0.61 0.46 0.52 0.58 

Migratory diving birds 0.18 0.91 0.51 0.35 0.79 0.59 0.00 0.62 0.58 0.49 0.53 0.51

Nearshore diving birds 0.23 0.89 0.60 0.43 0.76 0.69 0.00 0.74 0.58

Herbivorous birds 0.00 0.89 0.61 0.00 0.83 0.70 0.07 0.75 0.80 

Raptors 0.43 0.43 0.58 

Juvenile wild salmon 

Subadult wild salmon 0.36 0.83 0.90 0.45 0.67 0.57Juvenile hatchery salmon 0.87 0.00 0.65 –0.32 0.81 0.52

Subadult hatchery salmon 0.47 0.70 0.73 0.64 0.38 0.64

Juvenile pink salmon 

Subadult pink salmon 0.58 0.63 0.95

Juvenile Pacific herring

Adult Pacific herring 0.75 0.27 0.77

Forage fish 0.55 0.39 0.52

Surfperches 0.48 0.65 0.61

Spiny dogfish –0.03 0.98 0.53 0.47 0.68 0.52 

Skates 0.33 0.84 0.66 0.52 0.63 0.67 0.00 0.62 0.51Ratfish 0.26 0.87 0.69 0.43 0.73 0.62 0.00 0.68 0.52

Pacific hake 0.79 0.34 0.87

Pacific cod 0.42 0.70 0.57 –0.34 1.02 0.53

Walleye pollock 0.43 0.72 0.80

Juvenile lingcod 0.48 0.60 0.53 0.10 0.91 0.57

Adult lingcod

Juvenile rockfish 0.31 0.79 0.54 0.54 0.53 0.59 0.00 0.68 0.55

Adult rockfish 0.36 0.73 0.51

Piscivorous flatfish 0.27 0.84 0.66 0.41 0.68 0.51

Small-mouthed flatfish 0.32 0.85 0.88 0.40 0.70 0.53

Demersal fish 0.67 0.50 0.82Squid 0.99 –0.41 0.79
36

Table 8 continued. Results of pairwise MAR analyses of relationships between phytoplankton and each functional group a

Results are interaction coefficients for the effects of phytoplankton on each group (H12) and for the effect of density 

Coefficient values in MAR models typically range from –1 to 1 (H12) and 0 to 1 (H22). H12 values close to |1| indic

and H22 values close to 0 indicate strong density dependence. Zeros indicate no significant interactions. Only mod

effects (i.e., either H12 or H22 ≠ 0) and R2 > 0.5 are shown; other models are left blank. 

Lag = 1 year Lag = 2 years Lag = 3 years Lag = 4 years 

Functional group H12 H22 R2 H12 H22 R2 H12 H22 R2 H12 H22 R2 Octopus Shrimp 0.81 0.00 0.78 –0.29 0.54 0.68 –0.36 0.00 0.68

YOY Cancer crabs 0.53 0.64 0.78

Age-1+ Cancer crabs 0.60 0.38 0.58

Sea stars 0.28 0.84 0.56 0.53 0.62 0.66 0.00 0.90 0.54

Sea urchins –0.55 0.37 0.51

Other grazers 0.10 0.00 0.60 –0.66 0.00 0.70 

Small crustaceans 0.87 –0.20 0.56

Large sea cucumbers 0.48 0.60 0.67

Predatory gastropods 0.58 0.62 0.77 0.65 0.00 0.60 0.00 0.88 0.54

Mussels 0.55 0.51 0.68

Barnacles 0.62 0.40 0.67

Geoducks 0.17 0.93 0.61Infaunal bivalves Soft infauna Deposit feeders 0.62 0.42 0.73

Suspension feeders 

Tunicates Bacteria Microzooplankton 0.83 0.00 0.51

Copepods –0.34 0.00 0.54

Euphausiids 0.00 0.29 0.56

Sm. gelatinous zooplankton 

Jellyfish 

Macrozooplankton 

Benthic microalgae –0.99 –0.10 0.98

Benthic macroalgae –0.69 0.43 0.66 0.00 0.79 0.59 0.56 0.00 0.60

Overstory kelp 0.65 0.00 0.60 

Eelgrass –0.87 0.00 0.87 –0.23 0.66 0.63 0.30 0.00 0.50

Algal/plant material –0.86 0.00 0.87 –0.30 0.48 0.63 0.33 0.00 0.52Salmon carcasses 0.31 0.82 0.78 0.42 0.66 0.51

Detritus 
MAR models revealed generally increasing density dependence with time lag in upper 

trophic level functional groups (pinnipeds, birds), as expected in longer-lived species. By 

contrast, no density dependence was observed in most of the primary producer functional groups. 

The absence of significant models between phytoplankton and known grazers (copepods, small 

gelatinous zooplankton, euphausiids) is likely owing to a mismatch between life cycle duration 

and an annual time lag; that is, for some functional groups with very short turnover rates, a 1-

year time lag is likely too long to capture predator-prey dynamics. Some indirect effects of 

phytoplankton were observed at multiple time lags, including on shrimp at 3-year and 4-year 

lags, and on other grazers at 4-year and 5-year time lags. 

Bald Eagle Impacts 

The strong negative press perturbation on the raptor group caused trophic cascade 

dynamics (sensu stricto, Carpenter et al. 2008) in portions of the food web. The initial response 

of raptors in the scenario was a gradual increase to a new carrying capacity relative to the 

Ecopath state (Figure 9). This was the result of having a positive BA term for raptors in the 

Ecopath model (Table 3, see also the Raptors description in Appendix A for detail). Changes in 

several other groups accompanied the initial increase in raptors. The severe decline in raptor 

biomass beginning in simulation year 20 caused profound changes to other groups, and it 

generally reversed the signs of the biomass trajectories caused by the earlier increase in raptors. 

In general, raptor biomass was negatively related to biomass of all other bird groups 

(Figure 10, Table 9), but that negative relationship was complex, probably because of the 

interrelationships between the other birds. Raptors prey on all other bird groups in the model, 

but gulls also prey on resident diving birds, and there is generally high diet overlap among gulls 

and diving birds (Table 4). Thus the effect of raptors on any one bird group was mediated 

through several other bird groups. In turn, many prey items of gulls and diving birds declined, 

notably juvenile wild salmon, juvenile pink salmon, herring, forage fish, mussels, and demersal 

fish; by contrast, the trophic cascade did not extend to the major food of herbivorous birds, 

benthic macroalgae (Figure 10). Macrozooplankton and shrimp, which are preyed upon by many 

small pelagic and demersal fish, increased by at least 10% and were the only invertebrate groups 

to demonstrate a pronounced increase. 

The effects spread even further. Because juvenile wild and pink salmon declined, 

subadult wild and pink salmon declined as well, resulting in a decrease in salmon carcasses 

(Table 9). Because salmon carcasses are a major food source for raptors, the decrease in salmon 

carcasses may have further exacerbated the raptor decline and contributed more to the 

nonlinearity of the output. Finally, declines in demersal fish biomass appear to have contributed 

to declines in Pacific cod and lingcod, which rely on demersal fish for a major portion of their 

diet. That may be an artifact of lumping so many species of demersal fish into a single 

functional group, though, as it is questionable as to whether diving birds and predatory fishes 

would be feeding on the same species of demersal fishes. 

Of course, more subtle fluctuations in bald eagle biomass may not produce detectable 

changes in the rest of the food web. However, both raptors and their major prey (other seabirds) 

have very high Q/B ratios, and thus they have relatively large ecological footprints for groups 

37
Raptors, relative B

Knife-edge imposition

of raptor “harvest”

in simulation year 20

Figure 9. Screen capture from 50-year Ecosim simulation of PSCB food web responding to a sharp 

reduction in raptor (bald eagle) biomass. Each line in the central panel of the screen capture 

represents a functional group’s relative biomass (i.e., biomass at time t relative to the original 

biomass from the Ecopath model, as in Table 3). The knife-edge function at the bottom 

represents harvest mortality increasing from 0 to 0.20 yr-1 beginning in simulation year 20, 

causing precipitous decline in raptors and leading to responses in many other functional groups. 

Rates, directions, and magnitudes of change are dependent on inherent vital rates and on trophic 

linkages. 

with such small biomasses. Moreover, the seabirds have low P/B ratios, implying that any 

changes they experience will likely persist for many years. 

System-wide Fishery Closures 

Closing all fisheries in simulation year 1 caused responses in many target groups and in 

some groups that are linked to target species via predator-prey interactions or life history (Figure 

11). However, most functional groups experienced fairly small changes in biomass relative to a 

baseline simulation where fishing mortalities were held at the initial values from the Ecopath 

model. If we assume that a change must be on the order of at least 10% in order to be 

empirically distinguishable relative to background variability, then only 13 out of 65 functional 

groups experienced a distinguishable change (Table 10). Most of the groups that increased 

measurably did so either because of reduced fishing mortality (sea cucumbers, subadult wild and 

hatchery salmon, geoducks, and age 1+ Cancer crabs), an increase in prey availability (raptors), 

38
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Figure 10. Ecosim outputs representing trophic cascade dynamics caused by a decline in raptor biomass. 

The x-axis in each panel begins at year 15 (5 years prior to the onset of the raptor decline) and 

ends at year 50 of the 50-year scenario shown in Figure 9. The y-axis in each panel represents the 

relative biomass (Bt / B0) for a functional group, scaled between 0 and 2-fold greater than B0. 

Arrows link predators to prey. Solid arrows indicate relationships in which prey increased by at 

least 10%, dotted arrows indicate relationships in which prey decreased by at least 10%, and 

dashed arrows indicate relationships in which the prey changed by less than 10%. 

or a combination of both factors (harbor seals and sea lions). Some groups increased due to life 

history linkages (YOY Cancer crabs, juvenile wild salmon, and salmon carcasses). Two groups, 

walleye pollock and Pacific cod, experienced measurable decreases, likely due to increased 

predation pressure by functional groups such as spiny dogfish, adult lingcod, and pinnipeds. 

Equilibrium Yield Curves 

The equilibrium yield curves for functional groups that are now or have historically been 

targeted showed wide variation in levels of FMSY (Table 11), ranging from very low (0.01375 for 

geoducks) to quite high (1.1 for squid). As a general rule, target groups with relatively low P/B

ratios also had low FMSY. These groups were comprised, by and large, of slow-growing or longlived species evolved to have relatively low adult mortality rates (profiles in Appendix A), and 

39
Table 9. Groups that changed by at least 10% following the raptor decline in year 20 of the raptor 

simulation. Values represent the percent change in biomass from year 20 (the start of the raptor 

decline) to year 50 (the final year of the simulation). 

Functional group Percent decline Functional group Percent increase 

Raptors –83.3 Shrimp 10.0 

Forage fish –28.2 Migratory diving birds 12.4 

Mussels –23.0 Macrozooplankton 12.5 

Juvenile lingcod –19.6 Nearshore diving birds 17.8 

Adult lingcod –18.5 Herbivorous birds 36.4 

Demersal fish –16.8 Resident diving birds 48.0 

Subadult wild salmon –15.4 Gulls 120.3 

Salmon carcasses –14.9 

Adult Pacific herring –14.6 

Juvenile wild salmon –13.9 

Pacific cod –13.3 

Octopus –13.2 

Subadult pink salmon –12.6 

Juvenile Pacific herring –12.1 

Subadult hatchery salmon –11.9 

Juvenile pink salmon –11.0 

thus have a lesser capacity to handle the mortality brought on by intense fishing. Groups with 

higher P/B ratios had higher rates of FMSY (Figure 12), consistent with their shorter generation 

times or greater productivity. A linear regression of ln(FMSY) as a function of ln(P/B) was highly 

significant (r2

 = 0.899, p < 0.001). 

Most of the F values that were originally input into the Ecopath model (Finitial) fell well 

below the FMSY values (Table 11), which is consistent with our sense that fisheries are currently 

not exerting much pressure on the PSCB community. Among the groups with the highest 

landings (Table 5), the Finitial/FMSY ratios of wild salmon and geoducks were just below one and 

Cancer crabs were well below one. In just three functional groups, Finitial/FMSY was one or 

greater (harbor seals, sea lions and large sea cucumbers). As previously noted, the pinniped 

results may be an artifact because of the implicit and unlikely assumption that both pinnipeds 

populations in PSCB are closed. Large sea cucumbers appear to be slightly overfished, and were 

the only functional group in the entire model with Finitial that was greater than natural mortality 

(predation + M0). This result may suggest the need for more precautionary fishing of sea 

cucumbers. It also highlights the need to better quantify sea cucumber biomass and production, 

both of which were parameterized indirectly in the Ecopath mass-balancing process (Appendix 

A). 

One issue that arose during this exercise was the difficulty in calculating FMSY for the two 

herring groups. In initial runs, the FMSY of adult Pacific herring was unreasonably high (≈2.4) 

and skewed to the right rather than parabolic in shape (data not shown). The likely cause was 

constant replenishment of biomass from the large and productive juvenile herring biomass pool. 

Likewise in initial runs, FMSY for juvenile herring was high (≈1). Our short-term solution to this 

40
0 10 20 30 40 50

Relative biomass

Simulation year

Figure 11. Screen captures from 50-year Ecosim simulations of the PSCB food web subjected to either 

baseline fishing mortality (upper panel) or a closure of all fisheries in year 1 (lower panel). Each 

line represents a functional group’s relative biomass (i.e., biomass at time t relative to the original 

biomass from the Ecopath model, as in Table 3). Rates, directions, and magnitudes of change are 

dependent on inherent vital rates and on trophic linkages. 

Table 10. Groups that changed by at least 10% following a closure of all fisheries in year 1. Values 

represent the percent difference in biomass from year 50 (the final year of the fishery closure 

simulation) relative to year 50 of a baseline scenario in which all fisheries remained open and 

were held constant at their initial exploitation levels (Figure 11). 

Functional group Percent decline Functional group Percent increase 

Walleye pollock –10.0 Large sea cucumbers 106.8 

Pacific cod –14.4 Subadult wild salmon 81.1 

Salmon carcasses 74.0 

Sea lions 72.1 

Harbor seals 56.3 

Geoducks 53.4 

Juvenile wild salmon 48.8 

Subadult hatchery salmon 44.1 

Raptors 26.7 

Age 1+ Cancer crab 26.1 

YOY Cancer crab 16.6 

41
Table 11. Productivity (P/B), fishing rates (F), and MSY for targeted groups, according to equilibrium 

fishing scenarios as described in the text. Groups are sorted by P/B ratios. F rates are for the 

initial (Ecopath) condition and for the MSY condition, determined iteratively for each functional 

group (see Methods section). Large Finitial/FMSY ratios are boldfaced for emphasis. 

Functional group P/B (yr-1) Finitial (yr-1) FMSY (yr-1) Finitial/FMSY MSY (mt km-2 yr-1) 

Geoducks 0.0363 0.013 0.01375 0.966 0.696 

Sea lions 0.076 0.025 0.025 1.000 0.001 

Spiny dogfish 0.103 0.009 0.04 0.223 0.137 

Harbor seals 0.126 0.050 0.045 1.111 0.001 

Skates 0.127 0.0003 0.03 0.010 0.009 

Adult rockfish 0.24 0.031 0.11 0.279 0.020 

Pacific cod 0.26 0.00003 0.0625 0.000 0.007 

Lingcod 0.28 0.033 0.24 0.139 0.036 

Small-mouthed flatfish 0.345 0.0001 0.06 0.002 0.265 

Pacific hake 0.405 0 0.125 0.000 0.201 

Piscivorous flatfish 0.467 0.001 0.1 0.009 0.058 

Sea urchins 0.5 0.005 0.09 0.059 0.023 

Walleye pollock 0.8 0 0.15 0.000 0.282 

Octopus 0.86 0.00001 0.175 0.000 0.112 

Large sea cucumbers 0.86 0.439 0.375 1.171 0.022 

Mussels 0.927 0.00003 0.2 0.000 0.444 

Wild salmon 0.989 0.171 0.2 0.854 2.321 

Surfperch 1.3 0.00003 0.3 0.000 0.531 

Forage fish 1.5 0.002 0.35 0.006 0.733 

Age 1+ Cancer crab 1.5 0.193 0.55 0.351 0.186 

Pink salmon 1.577 0.072 0.25 0.286 0.011 

Infaunal bivalves 2.059 0.0005 0.45 0.001 17.938 

Shrimp 2.25 0.001 0.5 0.002 2.235 

Squid 3.0 0.0002 1.1 0.000 0.503 

Juvenile herring 3.656 0.060 0.89 0.068 1.916 

issue was to simultaneously fish both juvenile and adult herring by incrementally varying fishing 

pressure on the primary fishing gear that targets them (i.e., other net gear, Table 5) and then to 

determine the FMSY only for the juveniles, which are the major component of herring landings. 

That result is given in Table 11. Nevertheless, it is apparent that our parameterization of Pacific 

herring is currently insufficient to accurately represent all fishing situations. We may need to 

revisit our estimates for herring P/B or add BA terms in Ecopath,3

 or we may need to reconsider 

some of the values that govern the herring delay-difference relationship between the juvenile and 

adult pools (Appendix A and Appendix D). 

 

3

 See footnote 2. 

42
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F MSY (yr-1

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Figure 12. Relationship between FMSY and P/B for targeted functional groups in the PSCB Ecosim model. 

FMSY estimates derived from single-group equilibrium yield curve analyses. 

43
Discussion 

The EwE model presented here is a first attempt at meeting the objectives described in 

the introduction of this report. Though the model encompasses a considerable amount of the 

species diversity, total production, and ecological linkages in the PSCB, it remains a 

simplification of the system and it also reflects many assumptions and biases on the part of the 

model designers. Nevertheless, it is a foundational step forward in developing ecosystem-scale 

models of Puget Sound with which to ask meaningful ecological and management-related 

questions, and it can only improve with time and additional information. 

Objectives 1 and 2: Model Development and Assessment 

Objectives 1 and 2 of this project were to build a model that represents the current 

understanding of the PSCB food web, which would enable us to ask basic ecological questions 

based on the best available information. It also enables us to make broad assessments about how 

management actions might be expected to influence the community via direct effects on the 

managed species and indirect effects created through food web interactions and feedbacks. 

We addressed these objectives throughout the iterative mass-balancing phase of model 

development (i.e., the Ecopath model), and further through a series of dynamic heuristic 

simulations (the Ecosim model). The mass-balancing phase involved identifying the most 

important functional groups and compiling relevant data into a quantitative arena so that we 

could evaluate the importance of intergroup dynamics and linkages at the scale of the entire 

community, something that is profoundly difficult to evaluate empirically. The dynamic 

simulations provide an initial idea of the sensitivity of the model food web to perturbations at 

different trophic levels. This is essential for evaluating model performance. It allows us to 

answer basic questions, such as: 

• Do perturbations propagate in a believable manner, given past observations? 

• Do groups with short generation times respond and recover more quickly than groups 

with long generation times? 

• Do minor perturbations lead to dynamic instability, including explosions or collapses of 

multiple groups? 

These simulations may also offer a preview of groups that will be important indicators of 

ecosystem processes. 

In essence, the contemporary PSCB food web appears to be a community dominated in 

terms of biomass by benthic/demersal species, although the relatively high energy throughput in 

the pelagic community somewhat offsets its lower biomass. Phytoplankton is the major source 

of primary production and supplemental production (via the large amount of detritus generated 

by dead phytoplankton). Although we have represented many functional groups, overall biomass 

44
is highly aggregated within a subset of seven groups from low and intermediate trophic levels; 

only one of these groups is a vertebrate (ratfish). Bottom-up forcing, through phytoplankton 

production for example, appears to be a dominant trophic structuring mechanism. Top-down 

control was apparent in some instances and may even lead to complex trophic cascades that span 

several trophic levels. Such was the case in the raptor scenario. In all of the dynamic 

simulations, time lags related to differences in production and life history extend the influence of 

trophic interactions for many years. We intend to extend the MAR model analysis approach to 

more thoroughly examine the structure of time lags, including subannual time steps. We will 

also use the same method to stochastically vary other groups and examine their pairwise 

interaction strengths so that we can gauge the relative influence of top-down and bottom-up 

forcing in the system. 

Some initial diagnostic analyses produced encouraging outputs in terms of model 

structure and stability. Obviously we achieved mass balance in the Ecopath model, and in many 

exploratory Ecosim simulations (not described above), we saw no evidence of unreasonable 

population extinctions or explosions, or of high-amplitude oscillatory behavior. Knife-edge 

fishery closure did not cause explosions or extinctions in any group (Figure 11). The equilibrium 

yield curves for currently or formerly targeted functional groups showed that FMSY was a 

function of P/B (Figure 12), which is in line with expectations (Walters and Martell 2004). Less 

encouraging was the rather equivocal analysis of diversity and evenness (Figure 4), which 

implies that this model may not be informative in identifying useful ecosystem indicators related 

to species diversity. 

Overall, fishing mortality does not appear to be a major structuring force in the 

contemporary PSCB food web, although contemporary food web structure may be a byproduct 

of heavier fishing in previous decades. While F is substantial for Pacific salmon, the heavy 

fishing pressure on Pacific salmon does not ripple down to lower trophic levels because subadult 

Pacific salmon are outside of the model domain for most of their lives. It probably does affect 

food availability for pinnipeds and raptors. Two other groups support large landings: geoducks 

and Cancer crabs. Although an important mortality source for geoducks themselves, the 

geoduck fishery has little effect on the rest of the system because geoducks are fairly 

disconnected from the rest of the food web. Extraction of Cancer crabs, while apparently 

sustainable, likely affects the biomass of some Cancer crab prey groups. 

Our model suggests that simply terminating fishing would not restore depleted 

populations of gadoids (Pacific hake, Pacific cod, walleye pollock) to historic levels of 

abundance (e.g., Gustafson et al. 2000). That output has several possible explanations: we may 

lack the data to properly characterize current gadoid stocks or biomass trends; our model may 

not cover some ecological processes that are influential to gadoid populations; a fundamental 

system shift may have occurred, in which the carrying capacity of gadoids has changed; or some 

combination of the above. 

The simulations presented in the Results section and summarized above are certainly 

preliminary, pending testing of the model against time series data to see how effective it is in recreating historic trends. Testing Ecosim models against time series is a useful technique for 

estimating the relative importance of drivers such as primary production, fishing intensity, 

recruitment variability, and the relative strength of donor/recipient control within individual 

45
predator-prey interactions (e.g., Walters and Martell 2004, Christensen et al. 2005). Ongoing 

development of this model includes the gathering of time series data related to multiple trophic 

levels. Data include estimates or indexes of abundance, total landings, and catch-per-unit-effort, 

as well as values related to water quality and total phytoplankton. We will subsequently attempt 

to fit Ecosim model outputs to all available biomass estimates or indexes by incorporating time 

series of fishing, by incorporating primary production and recruitment, and through further finetuning by adjusting the strength of predator-prey interactions. 

Objective 3: Identifying Data Gaps 

Objective 3 of this work was “to identify significant data gaps that could potentially 

constrain model development or increase uncertainty in the outputs.” Meeting this objective is 

absolutely critical for developing an ecosystem model if it is to gain credibility with scientists, 

managers, and stakeholders who care about the ecosystem that the model is designed to simulate 

(Townsend et al. 2008, Levin et al. 2009). A key distinction, however, is that data gaps and 

uncertainty are not indications that the model is not yet ready. Models can be used responsibly 

in spite of data gaps and uncertainty, provided that users carefully consider data quality, model 

structure, and model assumptions as they interpret and apply the model. Uncertainty is an 

inherent quality of all ecosystem models, even the best ones. They are simplifications of true 

ecosystems, and thus purposefully omit many components and processes in order to efficiently 

simulate core dynamics of interest. One of our obligations is to minimize uncertainty that can be 

attributed to missing data or poorly understood model elements. Below is a summary of some 

critical data gaps that have become clear through the process of parameter development. These 

might be thought of as high priorities for future monitoring programs, and would be appropriate 

elements to examine in more formalized sensitivity analysis of this model. 

A major data gap is the absence of biomass estimates for many functional groups. We 

typically addressed this issue by using EE as an input and allowing Ecopath to estimate biomass. 

That method was applied to 39 (60%) of 65 functional groups (Table 3), including some of great 

ecological or social value. For example, we used Ecopath to estimate biomass of the salmon 

groups, most forage fishes, and several groundfish, including groups of conservation concern 

(rockfish, Pacific hake, and walleye pollock). Nearly all invertebrates were handled this way. 

Trophic linkages spanning multiple trophic levels were affected by this issue. For instance, 

juvenile herring and copepods have Ecopath-based biomass estimates. Because juvenile herring 

are a major copepod predator and copepods are major grazers of phytoplankton, the massbalance estimate of juvenile herring biomass ultimately influences phytoplankton EE and the 

production of phytoplankton-derived detritus. 

In all such cases, the mass-balancing solution for biomass places heavy reliance on our 

perception of EE, our estimates of those groups’ P/B ratios, and how well we characterized topdown (predatory and fishery) demands. There are guidelines for assigning EE values to groups 

at different trophic levels (Christensen et al. 2005), but estimating EE is obviously inexact and 

assumption laden. Having credible biomass estimates for most groups is more concrete and 

desirable. This will require expanded, prioritized monitoring, to the extent that research budgets 

allow. For some groups, biomass will be extremely difficult to estimate through empirical 

monitoring due to logistical sampling constraints (examples in Appendix A). For other groups 

46
(e.g., salmon), biomass data are available, but the data themselves have issues (e.g., interagency 

reporting compatibility) or are challenging to interpret due to complex spatial dynamics. 

A second and related data gap is the availability of multidecadal time series data on 

abundances or biomasses of major biota. Time series of biomass data are powerful assets in 

development of EwE models. First, recent trends enable the user to estimate biomass 

accumulation (BA in Equation 1 and Table 3). That leads to better description of the reference 

state of the food web by allowing functional groups to be at nonequilibrium if that is appropriate; 

we have only done so with two functional groups (raptors and tunicates). Second, time series 

biomass data allow the user to tune predator-prey interaction strengths to better fit model output 

to historic observations. Such tuning includes adjusting the vulnerability parameters in Ecosim 

that affect the rate at which predators diminish prey pools (Christensen et al. 2005). That tuning 

process will, in all likelihood, also involve further iterative changes to the parameters described 

herein. We have been successful in locating time series of fisheries-independent, survey-based 

biomass data for some functional groups, but in general there are few groups for which such data 

are available, uninterrupted, and collected at the scale of the basin for long time spans. 

Identifying and incorporating such time series are central efforts in our ongoing model 

development. 

For many groups, diet compositions are a critical data gap, as they represent the principal 

means by which functional groups are linked in this model. For many groups, we were unable to 

locate mass-based or volume-based data on diet proportions in the PSCB (Appendix A). In some 

cases, there were no quantitative data in the literature. In other cases, data were reported as 

frequency of occurrence of different prey, and frequency of occurrence data are very difficult to 

translate into diet proportions for input into the Ecopath diet matrix. For higher trophic levels, 

we generally addressed these issues by using data from adjacent basins in Puget Sound. For 

lower trophic levels, our approach ranged from using estimates drawn from field research or 

Ecopath models from nearby systems to making arbitrary assignments based on anecdotal 

information in the general literature. 

The groups for which we found quantitative diet data from research in Central Puget 

Sound were primarily fishes, including adult herring, all salmon groups, some surfperches, 

Pacific hake, walleye pollock, both flatfish groups, spiny dogfish, ratfish, skates, and the catchall demersal fish group. Other groups for which we found local, quantitative diet data were 

geoducks, resident and nearshore diving birds, and raptors. Obviously many functional groups 

and trophic levels are poorly represented in these lists, including almost all invertebrates and the 

marine mammals. Still other groups are examples of cases where we know relatively little about 

their predators. For example, we know little about how juvenile salmon contribute to the diets of 

nearshore fishes. This forces most juvenile salmon predation mortality in our model to be caused 

by bird groups (especially gulls and migratory divers), which may be a misrepresentation. 

The fact that there are gaps in diet data is not to say that the diet compositions are right or 

wrong, but rather that they may need adjustments in order to improve model performance. Some 

adjustments are likely to have pronounced effects. For example, groups with substantial 

intraguild cannibalism (squid, juvenile Cancer crab, Pacific hake, small crustaceans) may be 

responsive because changing the magnitude of cannibalism will strongly affect the mass balance 

of that group. Another example is higher predators that prey on highly aggregated prey groups, 

47
such as the small crustaceans or some of the zooplankton groups. Aggregating prey groups may 

result in competition for the aggregated prey pool when in fact no real competition exists in situ. 

Also, we should carefully examine groups that impart considerable predation mortality on one or 

more other groups. Some such predators have diet compositions based on recent seasonal 

stomach content analysis from samples collected in PSCB (e.g., spiny dogfish). By contrast, 

many have diet compositions that were derived arbitrarily (e.g., small crustaceans). The 

predator-prey linkages listed in Table 4 are potentially very strong and influential, as they 

highlight groups with large biomasses and throughputs at several trophic levels (e.g., spiny 

dogfish, ratfish, small crustaceans, copepods) or with the potential for disproportionate influence 

via trophic cascades (e.g., raptors). 

Fishery mortality is also a data gap, although perhaps of lower overall priority than others 

identified above. With the exception of a few functional groups, F values in PSCB appear to be 

fairly low relative to total mortality or historic levels of exploitation, due to fishery closures and 

diminished effort for a variety of reasons (e.g., Palsson et al. 1998, Ruckelshaus and McClure 

2007, PSP 2008). The major gaps are recreational catches of certain groups and bycatch 

mortality from all gears. 

Aside from biomass, diets, and fisheries, there are many explicit and implicit data gaps 

described in the functional group descriptions (Appendix A). Some of these gaps have to do 

with concrete quantities that possibly could be estimated with adequate study. Examples include 

questions such as: 

• What are appropriate P/B and Q/B ratios for the main zooplankton species? 

• What proportion of subadult wild, hatchery, or pink salmon reside in PSCB for the 

entirety of their marine phase? 

• Where do adult Pacific herring populations from PSCB and adjacent basins disperse 

following spawning? 

• What is the magnitude and trend of mortality experienced outside the model domain by 

migratory groups like seabirds? 

On the other hand, there are implicit information gaps related to assumptions of functional group 

structure (e.g., Christensen et al. 2005). That is, we may misrepresent the importance of some 

species by lumping them into broad functional groups. For example, we have one functional 

group that contains all phytoplankton, which deemphasizes the role of inedible species, harmful 

algal blooms, and competition for resources such as dissolved nutrients and light. Similarly, the 

roles of individual species are blurred in catch-all groups such as small crustaceans, 

macrozooplankton, infaunal bivalves, and demersal fish. 

Formal sensitivity analyses will aid in estimating the robustness of model outputs to 

groups for which we lack confidence in data. The EwE software contains limited capacity for 

sensitivity analysis (Christensen and Walters 2004, Christensen et al. 2005). Unfortunately, that 

capacity has not been applied very broadly in the literature (Plagányi and Butterworth 2004). 

Essington (2007) conducted perhaps the most formal published analysis of the sensitivity of 

Ecopath parameter estimation to data assumptions. He concluded that the precision of model 

estimates (namely estimates of biomass and EE) was similar to the precision of the inputs (P/B, 

48
Q/B, diet), that iterative adjustments during the balancing process led to only minor 

improvements in model performance, and that biomass and P/B are the most influential input 

parameters relative to estimation errors. Essington (2007) also concluded that tight food web 

linkages and cycles—especially ones involving cannibalism, a feature common in highly 

aggregated groups such as our small crustacean omnivore group—are most likely to produce 

imprecise or unstable outcomes. As Essington (2007) notes, the impacts of such data gaps are 

probably greater if the model is being used as a prediction tool to make quantitative estimates of 

future system states, as opposed to more heuristic applications where the model is being used to 

screen policy options and identify possible qualitative outcomes to management actions. 

Conclusions 

This effort documents the initial development of a food web model for the PSCB. It 

outlines our progress toward synthesizing available information and identifying important data 

gaps. The next major objective we hope to accomplish will be to formally use the model in 

support of EBM. As a first step, we hope to identify meaningful indicators of how the food web 

responds to ecosystem changes and to human-derived pressures. This objective is intended to 

support the PSP’s stated goal of identifying appropriate indicator species that will reflect the 

status of the biological community in response to management actions designed to restore Puget 

Sound ecosystem health (PSP 2008). Indicator identification will be undertaken using methods 

developed by NWFSC scientists (Samhouri et al. 2009, Samhouri et al. 2010). 

The model described here is and will continue to be a work in progress. Ideally, model 

development should be an ongoing process that is complemented by experiments, field research, 

and monitoring. Experimental and empirical approaches will provide data that improve our input 

data; in turn, model simulations will identify hypotheses and data gaps integral in devising future 

research plans. Further development and improvement of this model would also be fostered by 

broad engagement of managers and stakeholders in simulation development. That level of 

engagement increases the critical scrutiny of the model and enhances the likelihood that its major 

shortcomings will be identified and addressed. Additionally, broad engagement helps ensure that 

the most important research and management questions are taken into account as model 

development ensues. Finally, we plan to use the model in manuscripts that will be submitted to 

the primary literature, in order to ensure that the structure and performance of the model and the 

assumptions of its makers are subject to appropriate scientific peer review. This data-based, 

stakeholder-engaged, and peer-reviewed approach is essential to producing robust and reliable 

models that are qualified to evaluate alternative strategies in an ecosystem approach to 

management (e.g., Levin et al. 2009). 

Even as EwE model development continues, we are beginning work on a more complex 

ecosystem model intended to address questions at the scale of the Salish Sea (Puget Sound, the 

Strait of Georgia, and connecting waters). This next generation of ecosystem model will use the 

Atlantis software developed by scientists at CSIRO-Australia (Fulton et al. 2004, Fulton et al. 

2005, Smith et al. 2007). This model framework offers several advances relative to the EwE 

approach. It is spatial in nature, wherein the user divides the model domain into discrete threedimensional spatial units to account for spatial and temporal patchiness of processes, habitats, 

life histories, and species distributions. It incorporates abiotic drivers such as oceanographic 

fluxes, nutrient cycles, hypoxia, and terrestrial inputs of water, nutrients, and sediment. It has 

49
broad capabilities to simulate age structure, recruitment dynamics, habitat preferences, and 

dietary ontogeny in different functional groups. It also simulates human dimensions such as 

fisheries, ecosystem monitoring and assessment, and management decision rules. 

The Atlantis approach, although more sophisticated, is extremely data-intensive and will 

require much longer to develop than the model described here. It is our hope that this model will 

continue to improve as the parallel Atlantis model is developed. This will allow us to address 

relevant objectives with the EwE model even as the other model is being developed. Moreover, 

once both models are functional, we will have two distinct conceptual modeling frameworks 

with which to address important ecological and management questions. 

50
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Appendix A: Species and Functional Group 

Descriptions 

In the sections that follow, we outline the basic biology and ecology of the functional 

groups included in our Ecopath models. The descriptions and associated model parameters are 

based on published research in the Puget Sound basin where available, but are also informed by 

expert opinion, discussions among the authors and with colleagues, and Ecopath with Ecosim 

(EwE) models from neighboring ecosystems. In many cases, they reflect simplifying 

assumptions that will require additional work to validate. For the sake of organization, 

functional group descriptions are pooled at very general levels (e.g., primary producers, benthic 

invertebrates, etc.). Many species have been omitted from the model, including some iconic 

ones (e.g., killer whales), mostly because they are rare or more important to the ecology of 

neighboring basins than they are to the ecology of the Puget Sound Central Basin (PSCB). 

Those species are briefly noted in Appendix B, and will likely be incorporated into future models 

(e.g., Atlantis) built at the scale of the entire basin. 

The parameter descriptions below are meant to describe the baseline model state. In the 

body of this document, deviations from this baseline may have been considered in judging the 

model’s overall stability and sensitivity to assumptions and perturbations. 

Primary Producers 

Phytoplankton 

Phytoplankton in Puget Sound is highly productive relative to most marine environments 

on earth, due in large part to seasonal and spatial patterns of tide-, river-, and wind-driven mixing 

and stratification (Strickland 1983). The phytoplankton community is mostly comprised of 

diatoms, dinoflagellates, and phytoflagellates, each of which can play different ecological roles. 

Diatoms can be solitary or chain-forming; dominant genera are Skeletonema, Chaetoceros, 

Thalassiosira, and Coscinodiscus (Strickland 1983). These species are important components of 

spring blooms that drive production in the early part of the growing season. Dinoflagellates and 

phytoflagellates are better adapted to waters that are warm and highly stratified; phytoflagellates 

may dominate in the dark, well-mixed conditions of winter (Strickland 1983). Dinoflagellates 

include representatives such as Gymnodinium and Gonyaulax, the latter being responsible for 

paralytic shellfish poisoning, while phytoflagellates include taxa such as Pyramimonas and 

Dichtyocha (Strickland 1983). Taxonomic representation among phytoplankton can vary 

throughout the year and also over relatively short periods of time. For example, in Hood Canal 

during 2002, surface diatoms dropped from 70% to less than 10% of the abundance within a 

week in February, rose to 70% of the abundance within a week in late March and stayed at 

greater than 60% for two more weeks, then crashed to less than 10% a week later (Horner et al. 

2005). 

79
Estimates of steady-state phytoplankton biomass reflect properties such as standing stock, 

doubling rate, grazing by herbivores or predators, and loss by sinking; each of these properties 

can be highly variable in space and time. Three studies in the PSCB spanning nearly 4 decades 

were considered for a biomass estimate. One study was conducted in the spring of 1966 and 

1967 (Winter et al. 1975) and a second study was conducted in both spring and summer of 1975 

(Campbell et al. 1977). More recently, Newton and Van Voorhis (2002) monitored four sites in 

PSCB for 3 contiguous years during all seasons. All studies examined depth-integrated 

chlorophyll a levels, which we converted to biomass by assuming a chlorophyll a:carbon ratio of 

0.0200 (Robinson et al. 1993) and a total carbon:wet biomass ratio of 0.0828 (Sherr and Sherr 

1984). Biomass estimates for the two earlier studies were 14 and 22 mt km-2, respectively, 

assuming in each case that spring-summer values were applicable for 6 months and that a lightlimited value of 12 mg chlorophyll a m

-2 was applicable for the other 6 months. The Newton 

and Van Voorhis (2002) study, however, yielded a mean annual biomass of 51.0 mt km-2. We 

used this value for our initial phytoplankton biomass because it is based on more comprehensive 

spatial and temporal sampling (n=168 samples) representing contemporary conditions. 

Like biomass estimates, production:biomass ratio (P/B) estimates for phytoplankton can 

be highly variable, both in spatiotemporal terms and also by species. The size composition and 

taxonomy of the phytoplankton community, as driven by environmental conditions, affects 

productivity. For example, Table A-1 shows productivity derived for oceanic estuaries, 

comparing nanoplankton (<22 μm size) and net plankton (≥22 μm size), and illustrates a 

generalized observation on conditions that favor nanoplankton over net plankton: low nutrient 

levels, higher water temperatures, and high light levels (Malone 1980). 

Puget Sound experiences cyclical episodes of plankton blooms throughout the spring and 

summer. Blooms are often extensive; for example, a multiship survey found that the spring 

bloom in 1969 covered most of the Central Basin (Munson 1970). Individual 14C assimilation 

studies to estimate production in PSCB have shown significant variations from month to month, 

probably due to the bloom-bust cycles of growth during spring and summer. Annual production 

measurements suggest a trend of increasing production in PSCB. Mean annual production in 

1966 and 1967 was 2,462 and 2,314 g wet weight m-2, respectively (Winter et al. 1975), while in 

1975 it was 3,769 g m-2 (Campbell et al. 1977). Experiments in 1999, 2000, and 2001 found 

much higher annual production rates of 9,478, 12,334, and 15,570 g m-2, respectively (Newton 

and Van Voorhis 2002). The latter study also estimated P/B at four PSCB sites. During the 

summer, mean daily P/B averaged 0.92 (0.78 at West Point to 1.02 at Admiralty Inlet), while 

during the winter, daily P/B was a mean of 0.33 (0.27 at West Point to 0.42 at Possession 

Sound), resulting in a weighted average P/B of 0.62 d-1. This equals an annual P/B of 226.3 yr-1. 

Table A-1. Seasonal production rates of nanoplankton (<22 μm) and net plankton (≥22 μm) in oceanic 

estuaries, derived from Malone (1980). Values are mean production (with upper and lower limits 

in parentheses). 

Season 

Surface 

temperature (oC) 

Nanoplankton production 

(mg C m-2 d-1) 

Net plankton production 

(mg C m-2 d-1) 

Winter 0–8 0.16 (0.08–0.48) 0.67 (0.03–1.94) 

Spring 9–17 0.77 (0.11–1.56) 0.88 (0.04–3.80) 

Summer 18–26 1.90 (0.49–4.41) 0.23 (0.02–1.45) 

80
Given the inputs for biomass and P/B and the demands of grazers, the Ecopath massbalancing routine yielded an ectotrophic efficiency (EE) of 0.32. This is in contrast with studies 

that indicated relatively high rates of grazing compared to other sources of loss (e.g., sinking). In 

northern Puget Sound, zooplankton grazing rates were lower in fall and winter (35% d-1) and 

higher in spring and summer (71% d-1), with an annual rate of 64% (Strom et al. 2001). Peak 

rates of grazing by microzooplankton reached approximately 80% during blooms (Strom et al. 

2001). The annual rate of 64% is double our EE, but is similar to the 62% observed in 

Norwegian fjords (Archer et al. 2000). It is possible that the Strom et al. (2001) results for 

northern Puget Sound are not directly transferable to PSCB, but it is also possible that we have 

underrepresented grazing pressure (e.g., by copepods and microzooplankton), which would result 

in an underestimate of the true EE. 

Phytoplankton is grazed upon by numerous filter-feeding invertebrate groups (Table 4). 

It is also the largest contributor to the detritus pool. The benthic components of the food web are 

supported in large part by phytoplankton production that is uncoupled from the pelagic food web 

by virtue of the fact that phytoplankton production is patchy in space and time, and the fact that 

larger pelagic grazers in Puget Sound (copepods, euphausiids) do not respond to blooms as 

rapidly as is seen in some marine systems. Much of the initial production in spring blooms ends 

up sinking, as detritus, to the benthos (Strickland 1983). 

Benthic Microalgae 

The benthic microalgae group is comprised of the microphytobenthos found on a variety 

of substrates (sand, rock, mud) or on the surfaces of other primary producers such as macroalgae 

and seagrasses. Representatives such as benthic or epiphytic diatoms are highly productive 

species that support a variety of benthic grazers (Lie 1974) and underlie production of many 

higher consumers, including fish (e.g., Simenstad and Wissmar 1985). 

These species have high turnover rates and their standing stocks have rarely been 

measured in Puget Sound (e.g., Thom and Albright 1990). Thus we assumed that the most 

pragmatic approach was to make assumptions about their P/B ratio and EE, and allow the model 

to estimate biomass based on mass balance. We assumed a P/B of 100 yr-1, which is 

considerably lower than the P/B for phytoplankton. It is also conservative relative to published 

values for P/B of microphytobenthos from other systems, such as the Baltic Sea (range of annual 

means = 140–185.7 yr-1, calculated from data in Urban-Malinga and Wiktor 2003) and the St. 

Lawrence Estuary (range of values = 87.6–332.9 yr-1, calculated from data in Roux et al. 2002). 

We assumed an EE of 0.5. This implies that benthic microalgae experience modest 

grazing pressure. Our assumption is that benthic grazers are relatively slow-moving and cannot 

exert as much sustained pressure on a widespread area of benthic algal growth. In addition, 

some stands of benthic microalgal growth (e.g., epiphytes on seagrasses) may not be accessible 

to all sizes of benthic grazers. 

The mass-balancing routine of Ecopath yielded a biomass estimate of 4.298 mt km-2. 

This value should be viewed as hypothetical. We note that this biomass must be concentrated in 

waters where light penetration is sufficient to support benthic primary production. 

81
Benthic microalgae are fed upon by small crustaceans, other grazers, sea urchins, and 

deposit feeders (Table 4). Trace amounts are eaten by herbivorous birds. 

Benthic Macroalgae 

The benthic macroalgal functional group consists of intertidal and subtidal green algae 

(phylum Chlorophyta), red algae (phylum Rhodophyta), and brown algae (phylum Ochrophyta, 

class Phaeophyceae). These range from leafy or filamentous species to species that can grow 

several meters in length. Benthic macroalgae are abundant in littoral areas. They are major 

sources of food, detritus, and habitat for a wide variety of species in the euphotic zone (Mumford 

2007). Generally speaking, this functional group’s role in the system is not well-known and the 

underlying biology (e.g., biological rates, standing stocks, grazing pressure) is poorly described. 

Thus we must exercise caution when interpreting model outputs where benthic macroalgae 

experience marked changes or strongly influence other groups. 

The diversity in this functional group is quite high (Waaland 1977, Gabrielson et al. 

2006, Mumford 2007). Representative species cover a wide range of sizes, basic morphologies, 

longevities, and seasonal growth and abundance patterns. (The canopy-forming species bull kelp 

[Nereocystis luetkeana] is considered its own functional group; see Overstory Kelp subsection 

below.) Growths can be extensive; for example, Mauzey et al. (1968) report dense mats of Ulva

in sandy areas of Puget Sound out to a depth of 40 m. 

We are unaware of large-scale attempts to assess benthic macroalgae abundance or 

production in PSCB, and thus we rely on gross assumptions for the purpose of parameter 

development. We elected to allow Ecopath to estimate biomass based on mass balance, given 

our coarse estimates of P/B, EE, and grazer demands. 

We derived P/B estimates from studies of production of species that occur in Puget 

Sound or are closely related to Puget Sound species. Research on Ulva in this region suggests its 

annual production rate is 200 g C m-2

 yr

-1 (Thom 1984, Thom et al. 1984). Assuming that carbon 

is approximately 30% of Ulva dry biomass (Harvey unpubl. data), annual dry weight production 

(P) = 666.67 g m-2

 yr

-1. Early in the growing season, the dry biomass of green algae in nearshore 

Puget Sound sites is 14 g m-2

 yr

-1 (Thom et al. 1984, Thom 1989). These estimates yield a P/B

ratio of 47.619 yr-1. In contrast, published data for red and brown macroalgae suggest lower P/B

ratios. Red and brown algae in the Black Sea range from 2 to 8 yr-1 (Khailov and Burlakova 

1969), while species of Laminaria at temperate latitudes in the North Atlantic range from 3 to 

5.2 yr-1 (Brady-Campbell et al. 1984, Beavis and Charlier 1987). The P/B of Laminaria in the 

Alaskan high arctic is much lower (0.8 to 1.7 yr-1, Dunton and Schell 1986). We do not know 

the biomass ratios of Ulva to brown and red algae in Puget Sound; thus it is difficult to weight 

these disparate P/B ratios. In the interest of being conservative with our estimates, we set P/B at 

15 yr-1 for the time being. We also assumed moderate grazer pressure on benthic macroalgae by 

setting their EE at 0.4. 

Ecopath’s initial estimate of benthic macroalgal biomass, given the estimates of P/B and 

EE and the pressure of grazers, is 3.182 mt km-2. This is approximately fortyfold greater 

biomass density than overstory kelp (see Overstory Kelp subsection below), consistent with 

82
surveys in PSCB that indicate much greater coverage by small brown algae than by bull kelp 

(Mumford 2007). 

Benthic macroalgae are fed upon by sea urchins, other grazers, small crustaceans, 

herbivorous birds, migratory diving birds, and nearshore diving birds (Table 4). Trace amounts 

are eaten by various fish, possibly inadvertently. Many consumers feed on algal/plant material, a 

detrital pool to which benthic macroalgae is a major contributor. 

Overstory Kelp 

Overstory kelp is comprised of bull kelp, the only true overstory alga in the PSCB 

(Mumford 2007). It a brown alga with a life history alternating between a microscopic, haploid 

gametophyte stage and an enormous, floating, diploid sporophyte stage. In a single growing 

season in Puget Sound, bull kelp sporophytes grow from microscopic to as much as 40 m in 

length from the holdfast to the tip of the blade (Mumford 2007). The length of a sporophyte is a 

function of the depth it inhabits, with the stalk growing until the gas-filled pneumatocyst and the 

trailing blade reach the water surface. After releasing spores in the fall, most sporophytes die off 

in the high wave energy of winter storms. The die-off produces detritus that is transported via 

wave dynamics to adjacent marine systems and may support scavengers and detritus-based food 

webs. 

Recruitment of bull kelp sporophytes to an area is dependent on many features (Mumford 

2007, Springer et al. 2007). A habitat must have appropriate light penetration, temperature, 

nutrient concentrations (particularly dissolved NO3, which is the preferred form of dissolved 

nitrogen), and wave energy. It must have hard substrate to which holdfasts can firmly attach. 

Bull kelp can be controlled by grazer pressure from benthic herbivorous invertebrates; however, 

if grazers are in low numbers, then perennial understory or midwater canopy-forming algae may 

proliferate and outcompete bull kelp for settlement habitat or light. Bull kelp can tolerate lower 

salinities than many kelps, but is sensitive to sedimentation (Mumford 2007). 

Overstory kelps such as bull kelp provide important habitat for many other species 

(Mumford 2007). Epiphytic algae and invertebrates grow on them. Juvenile fishes, including 

rockfishes (Sebastes spp.), recruit to them (e.g., Bodkin 1986, Haldorson and Richards 1987). A 

variety of observational studies (summarized by Springer et al. 2007) suggest that adult fish 

densities of some species are positively correlated with bull kelp density. Calvert (2005) 

experimentally manipulated bull kelp abundance and found that its density was positively 

correlated with density of some benthic fishes, but negatively correlated with density of pelagic, 

schooling fishes. Finally, large bull kelp beds can dampen wave energy within the canopy and 

on the shoreward side of the canopy (Springer et al. 2007), which may affect species 

composition, shoreline disturbance and erosion, and other features in nearshore habitats. 

Estimating standing stock biomass of overstory kelp is difficult, owing to the tremendous 

seasonal variability in individual size (Foreman 1984, Springer et al. 2007). We developed a 

biomass estimate for overstory kelp based on both areal surveys and measures of biomass density 

in bull kelp beds. Bailey et al. (1998) surveyed the proportion of shoreline and intertidal area 

covered by a number of habitat types including “kelp,” which we used as the proxy for bull kelp. 

They estimated that kelp coverage accounted for 0.2% of Central Puget Sound’s intertidal zone. 

83
We assumed comparable coverage for the subtidal zone out to 10 m depth, which is 

approximately the lower range of depths at which most Puget Sound bull kelp attachment 

occurs.4

 

We then assumed a density of 1.5 sporophytes per m2

 in kelp beds, according to estimates 

from several studies (Thom 1978, Stober and Chew 1984, Shaffer and Parks 1994). 

Furthermore, we assumed an average individual mass of 20 g (dry weight), based on measures of 

Puget Sound bull kelp by Stober and Chew (1984), and a dry:wet ratio of 0.103, based on 

estimates by Foreman (1984). Thus 1 m2

 of kelp bed habitat would contain 1.5 × 20 / 0.103 = 

291.26 g wet weight. If that biomass density of bull kelp occupies 0.2% of the 0–10 m depth 

contour, which accounts for 13.75% of the total area of PSCB, then overstory kelp biomass 

equals 0.08 mt km-2. (Note: This implies that an average bull kelp sporophyte is a small plant 

that has not yet grown to its large, climax size; for example, in British Columbia waters, 

sporophytes with stipe lengths of 5.5–7.5 m typically weighed between 3,000 and 5,000 g 

[Foreman 1984]). The extraordinary growth capacity of an individual sporophyte is captured in 

its high P/B ratio, not its biomass estimate. 

The P/B estimate for overstory kelp comes in part from Thom (1978), who estimated that 

bull kelp can produce 10 kg m-2 of biomass in a growing season. Thus a plot with 0.291 kg m-2 

of bull kelp would have a P/B of 34.364 yr-1. We increased this rate because of the work of 

Khailov and Burlakova (1969), who found that algal production estimates based on biomass 

changes alone were underestimates, due to release of dissolved organic matter (DOM) that 

increases the overall production estimate by an additional 23–39%. We elected to use an 

increase of 25%, yielding an adjusted P/B of 42.955 yr-1. This may be a conservative estimate; 

Foreman (1984, his Figure 1) estimated that approximately one-half of total bull kelp production 

was lost to attrition or mortality. 

In the mass-balancing step, Ecopath estimated an EE of 0.661 for overstory kelp, 

indicating moderately high grazing pressure, most likely on smaller sporophytes. 

Overstory kelp is consumed by sea urchins and other grazers (Table 4). Its detrital fate is 

mixed: 75% of its dead material is assumed to ultimately become fragmentary organic matter 

that flows to the algal/plant material pool. The remaining 25% is the DOM loss described above; 

this flows to the general detritus pool. 

Eelgrass 

Eelgrass (Zostera marina) plays several vital roles in the Puget Sound ecosystem. It is a 

primary producer, a key source of fish and invertebrate habitat, and a source of detrital organic 

matter. It provides food for grazing invertebrates and many birds, and surface area on which 

many small epiphytic algae and bacteria grow. Its rhizomes and roots promote stabilization and 

influence water chemistry of underlying sediments, which promotes habitat quality for many 

sediment-dwelling species (e.g., Kendrick et al. 2005, Ferraro and Cole 2007, Mumford 2007). 

Eelgrass is an important substrate on which many fish species spawn, notably Pacific herring. 

The eelgrass functional group is primarily intended to represent the native intertidal and subtidal 

 

4

 T. Mumford, Washington Dept. Natural Resources, Olympia, WA. Pers. commun., 12 March 2009. 

84
eelgrass, although an invasive species, Japanese eelgrass (Z. japonica), has become well 

established in the upper intertidal zone in many areas of Puget Sound. Related species of 

surfgrasses (Phyllospadix spp.) also occur in Puget Sound, but primarily in rocky areas with high 

wave energy, and those habitats are essentially absent in the Central Basin (e.g., Mumford 2007). 

To estimate eelgrass biomass, we first assumed that it occupies 11.7% of the intertidal 

area of the Central Basin, which is based on an estimate that 11.7% of the coastline of the main 

basin of Puget Sound had eelgrass between 1975 and 1989 (Thom and Hallum 1990). The 

intertidal zone area is approximately 31.88 km2

 (WDF no date); 11.7% of this area = 3.73 km2. 

To convert areal coverage to biomass, we first elected to only model aboveground biomass, 

which varies seasonally from approximately 50% (winter) to 80% (summer) of total eelgrass 

biomass; we assumed a constant value of 70% biomass to be aboveground. In a subtidal 

meadow in the San Juan Islands, Nelson and Waaland (1997) estimated total eelgrass biomass to 

average 256.3 grams dry weight (gdw) m-2, or 179.4 gdw m-2 of aboveground material. 

Similarly, Webber et al. (1987) found aboveground biomass densities of 50–230 gdw m-2 in 

Padilla Bay (Whidbey Basin, Puget Sound). Both studies were of plots with 100% eelgrass 

coverage (i.e., saturated shoot density). If we multiply 179.4 gdw m-2 by 3.73 km2

 and apply 

conversion multipliers of 8.05 (to convert from dry weight to wet weight, Keser et al. 2003) and 

0.5 (assuming that 50% shoot density is more reasonable over the full range of eelgrass 

distribution), we get a total biomass estimate of 2,693.4 mt. After dividing by the total model 

domain area of 757.08 km2, our eelgrass biomass estimate is 3.558 mt km-2. This value 

represents a coarse estimate and should be confirmed via field study. 

We derived eelgrass P/B estimates from Nelson and Waaland (1997). Annual production 

in the meadow they examined was 1,767 gdw m-2

 y

-1, a weighted value that accounts for 

variation ranging from 1.5 gdw m-2 d-1 (December–January) to 9.8 gdw m-2 d-1 (August–

September). We divided the annual production rate by the mean January biomass density (72.2 

gdw m-2) to get a P/B of 24.542 y-1. This estimate may well be inaccurate: It is based on a 2-year 

study at one site, and the simplicity of our estimation method does not account for shoot 

recruitment that occurred after January or old shoot mortality (e.g., Olesen and Sand-Jensen 

1994). Production rates of eelgrass also may vary from site to site: Ruesink et al. (2006) 

calculated a 40% coefficient of variation for production in Willapa Bay eelgrass meadows. 

Eelgrass P/B is clearly a parameter for which we should evaluate model sensitivity. 

In the mass-balancing step, Ecopath estimated an EE of 0.156 for eelgrass, suggestive of 

low grazing pressure. 

Eelgrass is grazed by herbivorous birds, other grazers, and small crustaceans (Table 4). 

Small amounts of eelgrass occur in some fish stomachs and in Cancer crabs. Senescent eelgrass 

contributes to the algal/plant material pool. 

Zooplankton and Pelagic Invertebrates 

Copepods 

Copepods are among the most important grazers of phytoplankton, and thus are a 

principal means by which phytoplankton production is transferred into the planktonic food web. 

85
They have relatively rapid turnover rates, often with several generations occurring per year, 

which helps maintain the flow of phytoplankton-derived production into the food web 

(Strickland 1983). Copepods are among the most important food sources to fishes. Many 

schooling fishes feed primarily on copepods throughout their lives (Penttila 2007). Additionally, 

copepods are a major prey resource for early pelagic stages of many fishes, including benthic 

fishes such as rockfish and flatfish (Emmett et al. 1991, Love et al. 2002). 

Copepods are the largest component of the zooplankton standing stock throughout the 

year in Puget Sound (Strickland 1983). The copepod functional group in this model is made up 

primarily of calanoids. In biomass terms, the most important species is probably Calanus 

pacificus; the genera Acartia, Pseudocalanus, Paracalanus, and Eucalanus are also abundant 

(Strickland 1983, Dey et al. 1988). Most Puget Sound copepods experience peaks in biomass 

and reproductive effort just after phytoplankton blooms, notably in the spring. By contrast, all 

but the smallest species are relatively dormant in the less productive winter months, possibly 

entering diapause (Strickland 1983). This fact plays a role in the uncoupling hypothesis whereby 

the initial production in spring phytoplankton blooms is largely uneaten because copepods and 

other grazers are not present, allowing the production to sink to the benthic food web (Strickland 

1983). 

(Note: We excluded some abundant copepod species because they are mostly 

carnivorous. These include Corycaeus anglicus and Epilabidocera longipedata [Dey et al. 

1988]. We will assume that they are in the macrozooplankton pool, described below.) 

We found no extensive, survey-based copepod biomass estimates for PSCB in the 

literature, and thus allowed Ecopath to estimate copepod biomass through the mass-balancing 

procedure. To facilitate this, we assumed that copepod EE = 0.8, implying that 80% of copepod 

production is consumed by modeled predator groups in the initial state of this model. 

Similarly, we are unaware of P/B or Q/B estimates for Puget Sound copepods. We 

derived our estimates from the EwE model of the California Current by Field (2004). Field’s 

copepod group includes many of the same genera, although many of the species are cold-water 

copepods that are also common in Alaska waters. One species that is abundant in Puget Sound, 

Calanus pacificus, is referred to by Field as a warm-water species that is most common in 

California Current waters during the winter, when the warm, northerly flowing Davidson Current 

becomes dominant. It seems appropriate, then, to assume slightly greater Q/B ratios for our 

copepods than Field used (70 yr-1), given the relative warmth of Puget Sound waters; we set 

copepod Q/B at 75 yr-1. Assuming a P/Q of 0.2, we arrived at a P/B estimate of 15 yr-1. 

With these parameters in place, the mass-balancing routine of Ecopath yields an initial 

copepod biomass of 24.419 mt km-2. This value is one-third lower than a historic copepod 

biomass estimate in Puget Sound waters. Damkaer (1964) examined copepod biomass in Dabob 

Bay (Hood Canal Basin) and found a biomass density of approximately 36 mt km-2. 

Based on the general description of Puget Sound copepods by Strickland (1983), we 

assumed that their diets were primarily composed of phytoplankton and supplemented by 

microzooplankton (Table 4). This is similar to the diet assumed by Field (2004) for northern 

California Current copepods. Copepods are eaten by most of the smaller pelagic fish species, 

86
and also by walleye pollock (Theragra chalcogramma), juvenile rockfish, demersal fish, shrimp, 

squid, barnacles, euphausiids, small gelatinous zooplankton, jellyfish, and macrozooplankton. 

They are taken at trace levels by ratfish (Hydrolagus colliei), juvenile lingcod (Ophiodon 

elongatus), piscivorous flatfish, and small-mouthed flatfish. 

Microzooplankton 

The microzooplankton functional group consists of small planktonic invertebrates that 

feed primarily on small phytoplankton and respond rapidly to changes in phytoplankton 

production (Strickland 1983). The group is made up primarily of unicellular heterotrophic 

protozoans (ciliates, dinoflagellates, foraminiferans, and radiolarians), early pelagic larval stages 

of many benthic invertebrates, and a few species of rotifers. These small grazers collectively 

serve several vital roles in the pelagic ecosystem. They are a natural trophic link that connects 

phytoplankton production to higher consumers, including pelagic larvae of numerous 

invertebrates and fishes. In addition, their dissolved nitrogenous waste products may be taken up 

by phytoplankton in the upper layer of the water column. Such nutrient cycling further tightens 

the coupling between the microzooplankton and phytoplankton functional groups. 

We are unaware of any quantitative sampling of microzooplankton biomass in PSCB, and 

it is likely that any such sampling would reveal intense variability due to spatial patchiness and 

the rapid temporal responsiveness of these organisms to changes in phytoplankton availability. 

We used the Ecopath mass-balancing routine to estimate microzooplankton biomass. We first 

assumed a high EE (0.8), which implies that 80% of microzooplankton production is consumed 

by other model groups in the initial model state. 

We followed the lead of Field’s (2004) EwE model of the California Current ecosystem 

and assumed a microzooplankton P/B ratio of 100 yr-1, consistent with organisms capable of 

rapid increases in biomass and high mortality in the absence of resources. Our estimate is 

consistent with work by Landry and Calbet (2004), who observed that overall microzooplankton 

production typically is 28 to 55% of total primary production. Our microzooplankton P/B

estimate is 44.2% of the phytoplankton P/B estimate of 226.3 yr-1 (see Phytoplankton subsection 

above). 

Lacking data on Q/B ratios for these species, we instead estimated P/Q (the production to 

consumption ratio, the rough equivalent of food conversion efficiency). Our estimate of 0.35 

was taken from Landry and Calbet (2004) and results in a remarkable Q/B of 285.714 yr-1. 

Although a very high rate, this estimate is only slightly less than the 300 yr-1 estimated by Field 

(2004) for California Current microzooplankton. 

The above parameter estimates coupled with the demands of predators result in a massbalanced biomass estimate of 5.343 mt km-2 for microzooplankton. 

This group’s diet is dominated by phytoplankton (Table 4), with small contributions from 

bacteria and detritus. We arbitrarily assigned this initial diet based on descriptions of Puget 

Sound’s pelagic food web (Strickland 1983, Strom et al. 2001). Microzooplankton in turn are 

eaten by macrozooplankton, small gelatinous zooplankton, euphausiids, copepods, tunicates, 

suspension feeders, infaunal bivalves, barnacles, and mussels. 

87
Macrozooplankton 

The macrozooplankton pool includes many diverse taxa, including chaetognaths, pelagic 

shrimp (e.g., families Sergestidae and Pasiphaeidae), hyperiid amphipods, the pelagic larval 

stages of benthic macroinvertebrates, and large predatory copepods. This is something of a 

catchall group, incorporating species that are not included in other, more taxonomically defined 

large zooplankton groupings (e.g., euphausiids, small gelatinous zooplankton, jellyfish). In 

making this grouping, we acknowledge the perceived importance of these species as both 

consumers and prey. This broad grouping also belies our general lack of information on these 

taxa in Puget Sound. Pooling such diverse taxa undoubtedly means that we will lose some 

information as we try to characterize a representative macrozooplankter. Thus the scenarios that 

are sensitive to changes in macrozooplankton should be evaluated cautiously. 

To our knowledge, quantitative sampling of these species for estimating abundance has 

not been done in Puget Sound in any comprehensive manner. In fish stomach contents collected 

in multiple seasons, hyperiid amphipods and decapod larvae were highly abundant (Reum 2006, 

Reum unpubl. data). If fish are unbiased samplers of macrozooplankton, then hyperiids and 

decapod larvae are important representatives; however, this does not give us a biomass estimate. 

We therefore estimated biomass using the mass-balancing step of Ecopath, first assuming an EE

of 0.8 (reflecting the importance of these species in predator diets). 

Our estimates for macrozooplankton P/B and Q/B are poorly founded and represent an 

area of potential improvement through empirical study and monitoring. We reasoned that P/B

rates for these organisms should be equal to or greater than rates for similar benthic taxa (e.g., 

small crustaceans, P/B = 3.41 yr-1) and older stages of related taxa (e.g., YOY Cancer crabs, P/B

= 2.50 yr-1; shrimp, P/B = 2.25 yr-1), because we would assume that the mortality rates for 

younger, smaller, pelagic animals are higher. We set the macrozooplankton P/B at 7.00 yr-1, 

following the estimate used by Beamish et al. (2001) for an Ecopath model of the nearby Strait 

of Georgia ecosystem. This value is somewhat greater than a value estimated for chaetognaths 

(5.6 yr-1, Banse and Moser 1980). Based on our P/B estimate, we set Q/B at 35.00 yr-1, in order 

to hold conversion efficiency (P/Q) at 20%. Given the input parameters and the consumptive 

demand of higher trophic levels, macrozooplankton biomass was estimated by Ecopath to be 

6.234 mt km-2, a value that should certainly be confirmed by empirical study. 

We made generalizing assumptions about macrozooplankton diets. We assumed that 

phytoplankton and copepods each comprised approximately one-third of the diet, with 

substantial contributions from bacteria, microzooplankton, and euphausiids (Table 4). Lesser 

amounts were contributed by detritus, small gelatinous zooplankton, and jellyfish. 

Macrozooplankton are preyed upon by most of the fish groups in this model, as well as by gulls 

and jellyfish. 

Euphausiids 

Euphausiids represent still another critical trophic bridge between the primary producers 

and consumers on which they feed and the higher trophic levels that consume them in turn. 

Considerable research in Pacific Ocean ecosystems has demonstrated their importance in the 

marine food web structure and their value as indicators of ecosystem processes (e.g., Brodeur 

88
and Pearcy 1992, Ainley et al. 1996, Keister et al. 2005). In the Puget Sound–Georgia Basin, 

euphausiids are comprised primarily of one species, Pacific krill (Euphausia pacifica) (Bollens et 

al. 1992, Romaine et al. 2002). They occupy deep midwater habitats (≈75 m) during the day and 

then move to surface waters at night to feed, mainly on larger chain diatoms, copepods, and 

microzooplankton (Strickland 1983, Ohman 1984). 

To our knowledge, no euphausiid abundance surveys are conducted in Puget Sound. We 

used the Ecopath mass-balancing function to estimate euphausiid biomass. To facilitate 

estimation of biomass, we assumed an EE of 0.8, implying that most euphausiid production is 

consumed by other functional groups in the model. 

Euphausiid P/B was based on research by Tanasichuk (1998), who studied productivity 

of Pacific krill in Barkley Sound, British Columbia, during the 1990s. He developed annual 

estimates of P/B for adult Pacific krill, accounting for production devoted to both growth and 

molting. The average over the 6 years was 10.683 yr-1. This is nearly double the P/B ratios used 

for euphausiids in Ecopath models of several nearby systems (e.g., Beattie 2001, Ainsworth et al. 

2002, Field 2004). However, Field (2004) did not include the molting component of production 

in his model, and there is no evidence that the other models did either. Molting was 

approximately 40–60% of total production in the Pacific krill adults studied by Tanasichuk 

(1998). 

Absent actual estimates of consumption rates for euphausiids in PSCB, we derived a P/Q

estimate from work by Ikeda and Iguchi (1999), who studied Pacific krill in Toyama Bay in the 

southern Sea of Japan. They calculated total production (including somatic growth and molting) 

and daily consumption rates; their annual estimate was P/Q = 0.242. Our P/B estimate divided 

by 0.242 yields a Q/B of 44.145 yr-1; because our P/B estimate was high relative to EwE models 

in nearby models, our Q/B estimate is also greater. 

Based on these parameter estimates and the demands of euphausiid predators, Ecopath 

estimated the initial euphausiid biomass to be 11.152 mt km-2. 

Euphausiid diets (Table 4) were inferred based on the work of Ohman (1984), who found 

that Puget Sound Pacific krill grew more effectively when feeding on diatoms than on copepods. 

We allocated most of their diet to phytoplankton, with lesser amounts of copepods, detritus, 

microzooplankton, and bacteria. Euphausiids are eaten by resident diving birds, most pelagic 

and demersal fishes, shrimp, squid, small gelatinous zooplankton, jellyfish, and 

macrozooplankton. 

Small Gelatinous Zooplankton 

The small gelatinous zooplankton group is a taxonomically diverse assemblage that 

includes small hydromedusae (phylum Cnidaria); comb jellies (phylum Ctenophora); 

urochordates (phylum Chordata), including larvaceans (class Appendicularia) and pelagic salps 

(class Thaliacea); and thecosomes and gymnosomes (aka pteropods, phylum Mollusca). These 

animals are primarily filter feeders. Most are soft and gelatinous, but some pteropods have more 

solid bodies, possibly even shells, and surround themselves in a mucous net that helps them to 

trap food particles. Small gelatinous zooplankton can quickly form patches of high biomass 

89
when conditions are favorable (Strickland 1983, Field 2004). They are capable of consuming 

very large amounts of phytoplankton, microzooplankton, marine snow, and other suspended 

organic materials (Madin et al. 2006). Given their low energy content and their rarity in 

stomachs of vertebrates, some have considered them a dead end of energy flow in marine food 

webs (e.g., Greve and Parsons 1977). However, that perception has stemmed in part from the 

fact that gelatinous organisms are difficult to detect in fish stomach contents, and there is 

growing recognition of the importance of these organisms as prey for generalist consumers (Arai 

1988, Purcell and Arai 2001). 

Members of this group may also be important indicators of global climate change. 

Thecosome pteropods are a focal group in studies of ocean acidification because of the potential 

for lowering ocean pH to hinder development of their aragonite shells (Orr et al. 2005). On the 

other hand, pelagic salps may serve as a major carbon sink in areas where they swarm to 

immense numbers, consume large quantities of phytoplankton, and transport that carbon to the 

ocean floor via rapidly sinking fecal pellets (Madin et al. 2006). 

We are unaware of biomass surveys for small gelatinous zooplankton in Puget Sound, nor 

do we expect any to be forthcoming in the near future; these organisms are difficult to sample 

due to their fragility. Thus we input P/B, Q/B, and EE and allowed the Ecopath mass-balancing 

routine to estimate their initial biomass. 

Small gelatinous zooplankton are short-lived and grow rapidly, with life spans likely on 

the scale of weeks to months (e.g., Mills 1993). Growth rates can be quite rapid, particularly 

during seasonal bloom events, although those growth rates may not reflect growth or production 

trends at a full year scale (Heron and Benham 1985). Following the example of Ruzicka et al. 

(2007), who developed Ecopath models for the Oregon upwelling zone and drew particular 

attention to the role of gelatinous zooplankton, we assumed a P/B ratio of 9.0 yr-1 for this group. 

For consumption estimates, we used Q/B and assimilation efficiency estimates from 

Madin and Purcell (1992). They suggested that salps have high Q/B ratios (≈30 yr-1). They also 

found that salps lose approximately 40% of ingested food as fecal material, which is double the 

ratio of unassimilated:assimilated food assumed for most consumers. 

Given the above parameters, predation pressure from other functional groups, and our 

assumed EE of 0.8 (which implies high predation pressure, similar to Field [2004]), Ecopath 

calculated an initial biomass of 6.388 mt km-2 for small gelatinous zooplankton. 

We assigned small gelatinous zooplankton an initial diet dominated by a mix of 

phytoplankton, copepods, detritus, bacteria, and microzooplankton (Table 4). Minor prey 

include euphausiids, functional group cannibalism, and a trace of newly pelagic medusae of the 

jellyfish group. This reflects both the herbivorous tendencies of salps and also the omnivorous 

or carnivorous tendencies of hydromedusae, ctenophores, larvaceans, and pteropods. Besides 

cannibalism, small gelatinous zooplankton are preyed upon by jellyfish, macrozooplankton, 

demersal fish, forage fish, small-mouthed flatfish, juvenile and subadult wild salmon, and 

juvenile pink salmon. 

90
Jellyfish 

The jellyfish group contains large members of the cnidarian class Scyphozoa. Generally, 

species in this group produce medusae with bells tens of centimeters in diameter and trails of 

prominent, nematocyst-filled tentacles (Wrobel and Mills 1998). In recent years, scyphozoans 

have received worldwide attention due to an apparent increase in the frequency of major blooms, 

which have had negative impacts on coastal fisheries, aquaculture, industrial facilities, 

recreational activities, tourism, and public perception of ecosystem health (Mills 2001, Purcell 

and Arai 2001, Purcell et al. 2007). They may compete with or prey upon pelagic fish species 

(Purcell and Arai 2001). Human alterations of food web structure, habitat, water chemistry, and 

regional climate patterns are widely held to be responsible for large jellyfish blooms in coastal 

waters (Purcell et al. 2007). Some human impacts, such as coastal zone hypoxia, may exacerbate 

resource competition between scyphozoans and other species (Rutherford and Thuesen 2005). 

Jellyfish can comprise a major, or even dominant, component of pelagic community 

biomass in Puget Sound during summer months (Rice 2007). Their abundance in Puget Sound 

varies by location and year, is strongly influenced by latitude and Julian day, and was inversely 

related to biomass and diversity of the pelagic fish community in 2003 (Rice 2007). Prominent 

jellyfish species in Puget Sound include the crystal jelly (Aequorea victoria), moon jelly (Aurelia 

labiata), lion’s mane jelly (Cyanea capillata), and fried egg jelly (Phacellophora camtschatica). 

These species appear to be more tolerant to hypoxia than many other co-occurring scyphozoans 

(Rutherford and Thuesen 2005). Zooplankton, particularly copepods, are major prey for these 

species (Wrobel and Mills 1998, Purcell 2009). Some species have symbiotic relationships with 

macrozooplankton (e.g., pelagic amphipods, Wrobel and Mills 1998). 

The seasonal aspect of jellyfish abundance in Puget Sound (Rice 2007) and the inherent 

difficulty of quantitatively sampling these fragile animals makes direct biomass assessment 

problematic. We thus estimated jellyfish biomass by the Ecopath mass-balancing routine. We 

assumed an EE of 0.5, suggesting a moderate degree of predation, most of which is presumably 

focused on small medusae that have recently detached from the sessile polyp stage. We 

essentially took the other main parameters (P/B and Q/B) from prior Ecopath models of northeast 

Pacific waters (Pauly and Christensen 1996, Aydin et al. 2003, Field 2004), with slight 

modification. We used their P/B value of 3 yr-1 (which spreads their high summer production 

evenly over the course of a full year), but increased their Q/B estimate from 10 yr-1 to 11.5 yr-1. 

The increased Q/B stems from an assumption that Puget Sound waters are 2o warmer and a 

consumption Q10 of about 2.0 (Appendix C), which is approximately that observed for digestion 

times of scyphozoans (Purcell 2009). 

Given these parameters and the demands of predators, Ecopath estimated a jellyfish 

biomass of 8.483 mt km-2. That estimate exceeds the summer biomass estimate for large 

jellyfish in coastal waters off Oregon (3.269 mt km-2, Ruzicka et al. 2007). It is lower than the 

total estimated biomass of pelagic fishes (Pacific herring, forage fish, and juvenile Pacific 

salmon, Table 3), which contrasts with the findings of Rice (2007). At an annual time scale, 

however, this may be acceptable, given that jellyfish biomass is likely close to 0 mt km-2 in 

PSCB for many months each year. Empirical biomass estimates would certainly help the model 

to represent this functional group more reasonably. 

91
We adapted our jellyfish diet estimates from the work of Ruzicka et al. (2007). Jellyfish 

prey primarily on copepods, small gelatinous zooplankton, and eggs and larvae of euphausiids 

(Table 4). Macrozooplankton comprised a small portion of their diet. We also assumed a small 

amount of intraguild cannibalism, as we have witnessed fried egg jelly preying on small lion’s 

mane jelly in Puget Sound waters (G. Williams, NWFSC, field observation). We currently 

assume no predation by jellyfish on small fishes in Puget Sound. 

Squid 

Squid are truly nektonic (free-swimming), unlike most of the other more planktonic 

organisms presented thus far. They are another example of a key link between lower trophic 

levels and vertebrate predators in marine food webs, and they are also valued by fisheries. 

Smaller species such as the opalescent, aka market, squid (Loligo opalescens) are indicators of 

oceanographic changes, by virtue of their relatively short life spans and their strong growth 

responses to environmental conditions (Jackson and Domeier 2003). Market squid, the major 

squid species in Puget Sound, are generally dispersed and pelagic, except during spawning 

periods when they aggregate in nearshore, soft-bottomed habitats. Adults die after spawning 

(Morris et al. 1980, Jackson 1998). Their maximum life span is believed to be about 2 years, but 

in some areas they can complete their life cycle in less than a year (Jackson 1998, Jackson and 

Domeier 2003). Other squid species are also present, most often the small North Pacific bobtail 

squid (Rossia pacifica). 

Small squid such as market squid are very difficult to sample for the purpose of stock size 

estimation (Jackson 1998) and, to our knowledge, no attempts have been made to estimate squid 

biomass in Puget Sound. We chose to allow the Ecopath mass-balancing step to calculate squid 

biomass. Similarly, data on squid mortality and consumption were not available for this area, so 

we incorporated the P/B and Q/B values (3.0 yr-1 and 15.0 yr-1, respectively) for small squid in 

the Eastern Subarctic Gyre Ecopath model of the north Pacific Ocean (Aydin et al. 2003). 

We assumed that intense predation would lead to a high proportion of mortality directly 

related to other model components, and hence set the squid EE at 0.8. Given these parameters 

and the diet and fishery configuration of the model, Ecopath estimated squid biomass to be 0.902 

mt km-2 in PSCB. 

Squid diet estimates derive from work by Karpov and Cailliet (1979) on market squid 

near Monterey Bay. We especially followed their findings on stomach contents from squid 

captured in less than 40 fathoms (73 m) of water, except for samples collected on the spawning 

grounds. Karpov and Cailliet (1979) reported indices of relative dietary importance based on 

frequency of occurrence and number of prey items eaten, rather than on prey mass or volume, so 

we were forced to make some assumptions about relative mass of prey eaten. We assumed the 

majority of squid diet to be euphausiids, along with juvenile Pacific herring and minor 

contributions from copepods, macrozooplankton, and forage fish (Table 4). Squid are also 

cannibalistic (Karpov and Cailliet 1979). Other squid predators include harbor seals (Phoca 

vitulina), sea lions, resident and migratory diving birds, lingcod, piscivorous flatfish, smallmouthed flatfish, and demersal fish. Fisheries take a very small amount of squid (Table 5, also 

Appendix C). 

92
Benthic Invertebrates 

Sea Stars 

Sea stars are a diverse group of predatory and scavenging echinoderms (class Asteroidea) 

found at all depths in Puget Sound. Examples include short-spined pinkstar (Pisaster 

brevispinus), flat mud star (Luidia foliolata), six-rayed star (Leptasterias hexactis), rose star 

(Crossaster papposus), sunflower sea star (Pycnopodia helianthoides), and morning sun star 

(Solaster dawsoni). Sea stars feed on a variety of organisms, ranging from bryozoans to other 

sea stars (Morris et al. 1980, Lambert 1981). Many invertebrates have developed escape 

responses elicited by the touch or chemical stimulus of an attacking sea star. These are predatorspecific: benthic invertebrates may respond strongly to some sea stars while ignoring others 

(Mauzey et al. 1968). At patch scales, sea stars are known to be important consumers that can 

shape community composition through top-down control (e.g., Paine et al. 1985). 

We based sea star biomass estimates on data from trawl surveys of northern Puget Sound 

and southern Strait of Georgia, as described by Palsson et al. (2003). Trawls were conducted 

using a depth- and region-stratified design, and ranged from approximately 10 m to 220 m 

depths. The width of the trawl opening was between 10.8 m and 13.7 m, and the mesh size 

ranged from 10 cm (main body) to 3.2 cm (codend liner). Palsson et al. (2003) reported catching 

14 different species of sea star, most of which were predatory. They reported bathymetrically 

scaled biomass estimates for four study regions. The average biomass density (weighted by the 

total areas of their study regions) was 0.346 mt km-2, which we are going to assume is also 

consistent for PSCB. This conclusion has several caveats. First and probably most importantly, 

it implies that there is no basin effect on biomass density of sea stars in the Salish Sea, which is 

tenuous (e.g., Palsson et al. [2003] found rather large differences between their four sampling 

regions). Second, it implies that the trawl gear of Palsson et al. (2003) was an effective way to 

representatively sample sea star biomass. Third, it implies that the biomass density of sea stars 

in the intertidal and subtidal zones (i.e., shallower than the bathymetric contours sampled by 

Palsson et al. [2003]) is similar to the biomass density in deeper waters. These assumptions will 

require empirical monitoring in PSCB to validate. 

For sea star P/B, we used the Robertson (1979) method to estimate P/B based on 

longevity (Appendix C). Longevity data are sparse for the species common to Puget Sound, but 

studies suggest that both purple ochre sea star (Pisaster ochraceus) and rose star life spans 

exceed 20 years in natural conditions (Morris et al. 1980, Carlson and Pfister 1999). If we use 

20 years as our representative longevity, the Robertson equation yields a P/B of 0.519 yr-1. 

We estimated sea star Q/B based on the assumption that their conversion efficiency (P/Q) 

was equal to 0.2; this yields a Q/B ratio of 2.595 yr-1. This Q/B estimate may be too low, given 

the seemingly voracious nature of many sea stars (notably sunflower sea star) coupled with a 

presumed long life span, factors that would imply a higher Q/B ratio. On the other hand, Mauzey 

et al. (1968) observed some extremely long handling times by sea stars that were attacking prey. 

Attacks sometimes lasted hundreds of hours, and often did not result in a predation event if the 

target was too large. Mauzey et al. (1968) also observed extended periods of starvation by sea 

stars when preferred prey were not available. 

93
The Ecopath mass-balancing step estimated the EE of sea stars to be 0.125, reflecting a 

very low level of predation pressure that comes from intraguild cannibalism. 

Sea star diets are very diverse. Some species are specialists while diets of others vary by 

habitat, season, and prey community structure (Mauzey et al. 1968, Lambert 1981). Important 

prey groups across sea star species were bivalves, snails, barnacles, sea pens (Ptilosarcus 

gurneyi), chitons, sea urchins, small crabs, sponges, bryozoans, and carrion; some common 

species also specialize on everything from algae to other sea stars. We attempted to represent 

this dietary breadth by making the sea stars a very generalist group (Table 4). Bivalve groups 

(infaunal bivalves, mussels, and a small amount of geoducks) collectively made up more than 

40% of the initial sea star diet. The remainder of the diet included other grazers, deposit feeders, 

suspension feeders, barnacles, detritus, sea urchins, other sea stars, benthic microalgae, small 

crustaceans, large sea cucumbers, and predatory gastropods. A trace of predation on tunicates 

was also included (Mauzey et al. 1968). 

Large Sea Cucumbers 

Although there are many species of sea cucumber in Puget Sound (Kozloff 1983), this 

functional group has only one species, the California sea cucumber (Parastichopus californicus). 

Other holothurians, which are considerably smaller, are pooled into the deposit feeders, 

described below. The California sea cucumber is distinguished by its size, reaching a length 

(>40 cm) more than twice that of the next largest sea cucumber (Kozloff 1983). It is also the 

only sea cucumber in Puget Sound that is commercially harvested. California sea cucumbers are 

easily recognized by their ruddy, reddish-brown skin and many conical papillae. The California 

sea cucumber is found from intertidal waters down to 90 m (Morris et al. 1980). It inhabits both 

soft and hard substrates. Research in other systems has noted higher densities on substrates such 

as boulders, gravel, shell debris, and sand (Woodby et al. 2000). 

As there are no survey data for California sea cucumber biomass or abundance in PSCB, 

we used the Ecopath mass-balancing step to estimate biomass for large sea cucumbers. We 

assumed an EE of 0.8, implying that predation (particularly from sea stars such as sunflower sea 

star and morning sea star, Lambert 1981, Cameron and Fankboner 1989) and fishing pressure 

(Table 5) accounted for a substantial removal of California sea cucumber annual production. 

We inferred large sea cucumber P/B using the Robertson (1979) estimation method, 

where P/B is a function of longevity (Appendix C). According to Bruckner (2004),5

 the life span 

for California sea cucumber is 8–12 years; the middle of that range (10 years) yields a P/B of 

0.86 yr-1 in the Robertson method. 

Large sea cucumber Q/B was estimated using data from Yang et al. (2005), who 

measured daily consumption rates in the Japanese sea cucumber (Apostichopus japonicus), a 

relatively large sea cucumber from the western Pacific Ocean. When the daily consumption 

rates (in kJ d-1) of small, medium, and large Japanese sea cucumbers at temperatures of 10oC and 

 

5

 Bruckner (2004) stated that these data appeared in J. L. Cameron and P.V. Fankboner (1989, Journal of 

Experimental Marine Biology and Ecology 127:43–67), but we could not find these data in the Cameron and 

Fankboner paper; Cameron and Fankboner have published other papers and documents on this sea cucumber 

species, however, and it is possible that Bruckner (2004) simply misattributed the data. 

94
15oC were converted to mass-specific annual rates of total mass ingested, they ranged between 

approximately 12.6 yr-1 and 32.7 yr-1. An approximate middle point would be 22.6 yr-1. We 

divided this rate in half (11.3 yr-1) because the California sea cucumber does not feed for most of 

the fall, winter, and early spring months (Lambert 1997). 

Given these parameters and demands from predators, Ecopath estimated the large sea 

cucumber biomass to be 0.0471 mt km-2. This estimate is slightly greater than estimates of 

California sea cucumber biomass derived from surveys in the geoduck management tracts in 

Central Puget Sound. Those surveys were conducted by WDFW from 2005 to 2009 (Sizemore 

et al. unpubl. data). 

The California sea cucumber is a deposit-feeding detritivore that also ingests small 

infaunal organisms (Morris et al. 1980). We assumed a diet consisting mostly of detritus, but 

with measurable contributions from bacteria, benthic microalgae, suspension feeders, and other 

grazers (Table 4). Based on a review of predation on holothurians (Francour 1997), we assume 

that large sea cucumbers are preyed upon by sea stars, and at a trace level by demersal fish. 

Young California sea cucumbers are preyed upon occasionally by greenlings (family 

Hexagrammidae). 

Sea Urchins 

This group pools two sea urchins of the genus Strongylocentrotus: the green sea urchin 

(S. droebachiensis) and the red sea urchin (S. franciscanus). The green sea urchin far 

outnumbers the much larger red sea urchin in PSCB waters.6

 The two species have distinct 

feeding preferences; green sea urchins are often found climbing and feeding on live algae, 

whereas red sea urchins typically feed on detached algal detritus or crustose coralline algae. The 

nutritional quality of the algal species on which they feed directly influences rates of growth, 

maturation, and gonadal production (Vadas 1977). Inclusion of this functional group is certainly 

important, as it is capable of exerting strong grazing pressure on kelp species, and thus can affect 

habitat availability for a number of species. The role of sea urchins in regulating kelp forest 

biomass and structuring nearshore communities on the west coast of North America is well 

documented (Estes and Duggins 1995, Tegner 2001). 

Sea urchin biomass has not been surveyed in PSCB to our knowledge, and we used the 

Ecopath mass-balance procedure to estimate biomass for this group. We first assumed an EE of 

0.5, which assumes a moderate degree of production loss to predation; there are also losses of sea 

urchins to fishery removals, although these are small (Table 5). 

To estimate P/B, we used the Robertson (1979) relationship of P/B and life span 

(Appendix C). The green sea urchin, the more abundant species, is the shorter-lived of the two. 

We found no estimates of longevity for green sea urchins in Puget Sound, but in fjords around 

Greenland, where growth should be relatively slow and life span relatively long, maximum 

observed ages of green sea urchins ranged from 20 to 45 years (Blicher et al. 2007). We will 

assume that the maximum age of green sea urchings in PSCB is 20 years. By contrast, red sea 

urchins may live up to 100 years (Ebert and Southon 2003). These two life spans produce P/B

 

6

 B. Sizemore, WDFW, Olympia, WA. Pers. commun., 12 March 2009. 

95
estimates of 0.519 yr-1 and 0.161 yr-1, respectively, when applied to the Robertson equation. 

Because the green sea urchin is much more abundant, we assumed a P/B of 0.5 yr-1. 

We based our Q/B estimate on laboratory-based daily consumption rates from red sea 

urchins feeding on eelgrass material at 9–10oC (McBride et al. 2004). They estimated 

consumption rates of approximately 1 g (dry weight) per day in both spring and fall. After 

accounting for variable wet:dry weight ratios of red sea urchin and the sizes of the individuals in 

the spring and fall trials (McBride et al. 2004), then converting all units into wet weights and 

annual time scales, we concluded that Q/B was approximately 10.88 yr-1. 

The Ecopath mass-balance routine calculated that sea urchin biomass = 0.453 mt km-2, 

based on the above parameters and the demands of predators. 

We made coarse estimates of sea urchin diets (Table 4), assuming that the green sea 

urchin, a live algae feeder, is more abundant than the red sea urchin, which feeds on drifting 

algal detritus and microalgae. Benthic macroalgae were the dominant food source, followed by 

benthic microalgae and lesser amounts of overstory kelp and algal/plant material. Sea urchins 

are preyed upon by sea stars and predatory gastropods. As mentioned, a small diver-based 

fishery exists. 

Other Grazers 

This functional group primarily contains small gastropods (snails, limpets) and chitons 

(the large gumboot chiton [Cryptochiton stelleri] as well as smaller species like black katy 

[Katharina tunicata], Tonicella lineata, and Mopalia spp.). The primary food sources of these 

species are leafy algae, coralline algae, benthic or epiphytic diatoms, eelgrass, and detritus. This 

group is quite diverse along the west coast, particularly the snails (Morris et al. 1980). 

We lack information on biomass density of this group, and thus chose to initialize 

biomass using the Ecopath mass-balancing routine. We begin with an assumption that EE = 0.8, 

which implies that most annual production is lost to predation by other functional groups in the 

model. 

P/B estimates were generated using the Robertson (1979) method, where P/B is a 

function of maximum life span (Appendix C). Longevity data for several representative or 

related species were culled from a number of sources, and ranged from 3 years (the chiton black 

katy and Homalopoma sangarense, a Russian congener of the common snail H. luridum) to more 

than 20 years (the gumboot chiton, the keyhole limpet [Diodora aspera], the black turban snail 

[Tegula funebralis], and the exotic snail [Ilyanasa obsoleta]). We opted to apply an average life 

span of 12 years to this functional group; using the Robertson (1979) relationship, this produces 

a P/B estimate of 0.753 yr-1. This estimate is within the range of empirical observations from at 

least one other marine system: Taylor (1998) found that P/B values for grazing gastropods in 

New Zealand ranged from 0.262 to 1.375 yr-1. 

The P/B estimate also formed the basis for our Q/B estimate, because we opted to 

estimate P/Q rather than Q/B. A P/Q of approximately 0.1 is near the low end of the range of 

typical consumers in Ecopath (Christensen et al. 2005), which seems appropriate for consumers 

with a relatively low-quality diet of benthic algae and detritus. In a recent study of the slipper 

96
limpet (Crepidula fornicata), Arbach Leloup et al. (2008) determined dry weight values of 

consumption and production rates; the P/Q ratio from their measurements was 0.067. From 

these two sources, we assumed an intermediate P/Q ratio of 0.085; these estimates seem 

reasonable for relatively long-lived, slow-growing grazers. Our P/B estimate divided by 0.085 

yields a Q/B of 8.859 yr-1. 

With the parameters outlined above and the estimated loss of production to predation, 

Ecopath estimated the initial biomass of other grazers to be 11.431 mt km-2. This is a large 

biomass estimate, and may be an overestimate stemming from our assumption about P/B. 

Diets for other grazers (Table 4) come from anecdotal information about their feeding 

habits, as described by Morris et al. (1980) and Kozloff (1983). We assume that nearly half of 

their diet is benthic microalgae, with substantial contributions from detritus, bacteria, algal/plant 

material, and benthic macroalgae. Lesser dietary items were eelgrass and overstory kelp. We 

also assigned a small portion of the diet to suspension feeders, as there is evidence that some 

grazers, such as snails of the genus Calliostoma and chitons of the genus Mopalia, occasionally 

feed on hydroids and bryozoans (Morris et al. 1980, Kozloff 1983). In turn, other grazers are 

preyed upon by predatory gastropods, sea stars, Cancer crabs, octopuses, shrimp, large sea 

cucumbers, surfperches, demersal fish, both flatfish groups, ratfish, skates, herbivorous birds, 

nearshore diving birds, migratory diving birds, and gulls (Table 4). 

Predatory Gastropods 

The predatory gastropod group is comprised of predatory snails and nudibranchs. 

Noteworthy predatory snails include the moon snail (Polinices lewisii), the hairy triton 

(Fusitriton oregonensis), and the leafy hornmouth (Ceratostoma foliatum), as well as exotic 

species like the Japanese oyster drill (Ocinebrellus inornatus) and the eastern oyster drill 

(Urosalpinx cinerea). Predatory snails typically feed on bivalves, barnacles, ascidians, sponges, 

gastropods, and dead animal matter (Morris et al. 1980, Kozloff 1983). Nudibranchs, elaborately 

colored and ornate sea slugs, feed to a large extent on sessile consumers such as bryozoans, 

sponges, hydroids, and sea anemones, although some feed on mobile invertebrates including 

other nudibranchs (Morris et al. 1980, Kozloff 1983). Predatory gastropods are found in many 

habitats, including intertidal zones, rocky reefs, oyster beds, mud flats, kelp beds, docks and 

piers, or sand flats (Kozloff 1983). 

There is little background information for parameter development of this functional 

group. We used the Ecopath mass-balancing routine to estimate initial biomass, as we are 

unaware of comprehensive surveys of predatory gastropod biomass in Puget Sound. We began 

with the assumption that EE = 0.7; we are thereby assuming that predation by functional groups 

within the model accounts for a moderately high amount of annual production. Predation on 

nudibranchs is somewhat constrained by the fact that they are toxic or foul tasting to many 

consumers (Morris et al. 1980). 

Our P/B estimate was derived using the Robertson (1979) relationship between P/B and 

longevity among benthic invertebrates (Appendix C). This estimate required some assumptions, 

though: the age range of this group is broad, from the majority of nudibranchs at one extreme 

(typically ≈1 year) to leafy hornmouth at the other (16 years). Other known ages for 

97
representative species include moon snails (“several years”), Urosalpinx (5–8 years), and the 

intertidal predatory dire whelk (Lirabuccinum dira) (15 years) (all ages from Morris et al. 1980). 

We assigned a median longevity of 8 years, which produces a P/B of 1.01 yr-1. This assumption 

deemphasizes the relative importance of the short-lived nudibranchs on the overall parameter 

values of this group. To estimate Q/B, we assumed a P/Q ratio of 0.15, which seemed 

appropriate for small, slow-growing, moderately long-lived carnivores. Given our P/B estimate, 

the P/Q ratio yields a Q/B of 6.733 yr-1. The mass-balancing routine of Ecopath yielded a 

biomass estimate of 0.979 mt km-2. 

The diet of predatory gastropods is diverse, owing to the range of sizes and substrate 

types characteristic of the representative species (e.g., Kozloff 1983). Based on anecdotal 

information from a number of sources, particularly Morris et al. (1980) and Kozloff (1983), we 

assumed that the major prey groups were infaunal bivalves, suspension feeders, detritus, and 

other grazers (Table 4). Other prey included barnacles, deposit feeders, mussels, sea urchins, 

and tunicates. A small amount of their diet is functional group cannibalism, mainly to account 

for nudibranch predation on other nudibranchs. 

Assigning predatory impact on this group is difficult. Studies on feeding habits often 

report “gastropods” without necessarily specifying the type of gastropod that was fed upon, and 

predation on predatory gastropods could be misappropriated to the other grazers group (above), 

or vice versa. We consider our assignments to be preliminary. Functional groups that feed on 

predatory gastropods include sea stars, Cancer crabs, octopuses, surfperches, demersal fish, both 

groups of flatfish, adult lingcod, ratfish, nearshore diving birds, and gulls (Table 4). As 

mentioned previously, there is a small amount of functional group cannibalism. 

Octopuses 

There are two common species of octopus in Puget Sound: the red octopus (Octopus 

rubescens) and the Pacific giant octopus (Enteroctopus dofleini). The latter, as its name 

suggests, is considerably larger than the former. The Pacific giant octopus reportedly reaches a 

maximum of approximately 270 kg in mass (Morris et al. 1980), compared to 0.4 kg for the red 

octopus (Anderson et al. 1999). Both species are generalist predators of invertebrates and fishes 

(Morris et al. 1980, Anderson et al. 1999), and are in turn fed upon by large fishes and marine 

mammals. Although the vast difference in size implies that these two species are quite distinct in 

terms of their functional role in the Puget Sound ecosystem, we pool them into a single group for 

the sake of simplicity and under the assumption that they are more similar to one another than 

either is to other, more taxonomically distant species. Furthermore, biological data on these 

species are scant, and we have elected to compartmentalize that uncertainty into a single box. 

We opted to use the Ecopath mass-balancing step to solve for octopus biomass. There 

are surveys of octopus abundance in Puget Sound, particularly a volunteer-based annual Pacific 

giant octopus census coordinated by the Seattle Aquarium, but such data cannot be effectively 

translated into a basin-scale biomass estimate. Because octopuses spend much of their time in 

dens, they are not particularly vulnerable to gears that might otherwise allow some estimate of 

biomass per unit area sampled. 

98
Direct empirical measures of octopus P/B, such as mortality data, were not available in 

the literature for either species. Two theoretical estimation methods, based either on age of 

maturation (Rikhter and Efanov 1976) or life span (Hoenig 1983), provide natural mortality 

estimates ranging from 0.53 to 0.86 yr-1 for Pacific giant octopus (Conners and Jorgensen 2008). 

In all likelihood, the P/B for red octopus would be even greater because the red octopus has a life 

span of 2–3 years (Dorsey 1976) compared to 5–7 years for the Pacific giant octopus (Morris et 

al. 1980). For now, pending further information, we will assume a P/B of 0.86 yr-1 for octopus, 

which is the upper estimate for the Pacific giant octopus but is probably conservative for the red 

octopus. 

Consumption rate data for the red ocopus and Pacific giant octopus were also 

unavailable. We estimated Q/B using a bioenergetics model for the common octopus (O. 

vulgaris) in the Mediterranean Sea (Aguado Giménez and García García 2002). According to 

their model, a 100 g common octopus feeding on crab prey at the optimal growth temperature 

(17.5oC) has a Q/B of approximately 2.75 y-1. We assumed a Q/B of 2.5 y-1 for octopus in PSCB, 

owing to lower temperatures in Puget Sound and larger body sizes of typical Puget Sound 

octopuses; both factors should lower mass-specific bioenergetic rates. We do not know if the 

bioenergetics model of Aguado Giménez and García García (2002) fairly represents either the 

temperature-specific or the mass-specific bioenergetics of the two Puget Sound species. 

However, the estimates of P/B (0.86 y-1) and Q/B (2.5 y-1) result in a P/Q ratio of 0.344, which is 

at the upper end of the range of conversion efficiencies observed by Aguado Giménez and 

García García (2002). 

We assumed that octopus EE = 0.9, implying that a considerable amount of octopus 

production is consumed by predators in the model. A small amount of octopus is also landed in 

PSCB fisheries (Table 5). Given our general feeling that octopuses are solitary animals with 

populations limited by available den habitat, we are thus assuming that their total numbers are 

not high enough to vastly outpace the demands of higher trophic levels. The mass-balancing 

routine of Ecopath estimated the initial octopus biomass to be 1.158 mt km-2. 

Octopus diets have been characterized to different degrees for the two species. Anderson 

et al. (1999) examined the contents of middens around glass bottles occupied by red octopuses in 

Puget Sound. Most prey were small gastropods and barnacles. Red octopuses that occupied 

bottles were probably juveniles, and may have been at an early stage of their dietary ontogeny 

(Anderson et al. 1999). Other diet information is descriptive in nature. Morris et al. (1980), for 

example, describe both species as predators of shrimp, crabs, bivalves, gastropods, and fishes, 

with a possible preference for crabs. The Pacific giant octopus is clearly capable of feeding on 

larger prey (e.g., abalone, moon snails, large crabs, rockfish) than the red octopus. Given the 

largely qualitative nature of these data, we were forced to arbitrarily estimate octopus diets 

(Table 4). Major prey groups were infaunal bivalves, other grazers, Cancer crabs, and small 

crustaceans; lesser groups were shrimp, predatory gastropods, mussels, and demersal fish. 

Octopuses are eaten by spiny dogfish (Squalus acanthias), lingcod, and harbor seals 

(Table 4). 

99
Cancer Crabs7

The Cancer crab group is significant owing to its abundance, its role in the system as a 

major large-bodied consumer and scavenger throughout the benthic zone, its provision of prey to 

a wide range of other consumers, and the substantial fisheries that it supports (Pauley et al. 

1986). The focal species in this functional group is the Dungeness crab (C. magister), an 

abundant species that is highly prized for its flavor and fishery value. Several other Cancer

species inhabit Puget Sound, including the red rock crab (C. productus), the graceful crab (C. 

gracilis), and the pygmy rock crab (C. oregonensis). These species are not valued as highly as 

Dungeness crab because of their smaller size, their lower abundance, or (as is the case with red 

rock crab) their relatively heavy shell (Kozloff 1983). 

This functional group has two life history stages, which are dynamically linked in EwE 

software by a stage-based reproduction function (Appendix D). We chose to divide Cancer

crabs into those with carapace width (cw) less than 30 mm (young-of-the-year crabs, aka, YOY 

Cancer crabs) and with cw more than 30 mm (age 1+ Cancer crabs). YOY Cancer crabs settle 

from the plankton in the summer and feed on benthos in nearshore soft sediments, around beds 

of eelgrass (Zostera spp.), or on fields of bivalve shell hash. They are preyed upon by seabirds, 

fishes, and macroinvertebrates. Once cw is greater than 30 mm (typically in the spring following 

settlement, Fisher and Velasquez 2008), age 1+ Cancer crabs move out of this habitat into 

deeper waters, and may change distributions seasonally in relation to spawning timing or feeding 

and growing conditions. 

Mature individuals (generally with cw greater than 100 mm) produce larvae, and males 

with cw greater than 159 mm (typically age 4+) are fished intensively with several commercial 

and recreational gears. In fact, due to their complex life history, it may be more appropriate to 

separate them into even more stages. For example, larval stages, which are pelagic, feed on 

phytoplankton and zooplankton and are a key prey source for many fishes and seabirds. Also, 

among post-settled crabs, the primary sources of mortality (predation, fishing, senescence) 

change dramatically with increasing size. However, our initial attempts to divide Cancer crabs 

into more than two stages produced badly unrealistic outcomes from Ecopath’s mass-balancing 

algorithm. 

We opted to use the Ecopath mass-balancing function to estimate biomass for both stages 

of Cancer crab. We do not know of quantitative, basin-wide surveys for Cancer crab population 

abundance at any life history stage. We can make loose estimates of the biomass of older age 

classes by, for example, extrapolating annual harvested biomass by a factor of two (to account 

for the fact that only males are harvested) and then adjusting upward slightly to account for the 

fact that not all harvestable males are actually captured; however, this would only give us an 

estimate of a portion of the age 1+ Cancer crabs. Thus we used the model to estimate biomass. 

We assumed EE of 0.9 for both YOY and age 1+ Cancer crabs, which presumes a high degree of 

production loss each year to predation or to fishing, in the case of old, large males. 

 

7

 Recently, Ng et al. (2008) recommended reclassification of C. magister and C. gracilis into the genus 

Metacarcinus. As this nomenclature is not yet widely practiced in the research literature on either species, we use 

Cancer to avoid confusion. 

100
To calculate Cancer crab P/B, we used stage-specific mortality estimates from the 

Wainwright et al. (1992) study of Dungeness crab population biology in Grays Harbor, located 

on the outer coast of Washington. For post-settled YOY crabs, Wainwright et al. (1992) found 

an annual mortality rate of 2.5 yr-1, which we used as the P/B for the YOY stage. Older age 

classes of crabs had instantaneous natural mortality rates of 1.2 yr-1 (Wainwright et al. 1992), and 

we adjusted this upward slightly to 1.5 yr-1 for the age 1+ to account for an increase in mortality 

after maturation. This is reasonable because of the sudden onset of intense fishing pressure 

directed at male Dungeness crab with cw greater than 159 mm. 

Cancer crab Q/B was estimated using a Dungeness crab bioenergetics model originally 

developed by Holsman et al. (2003) and recently validated using data from temperature-specific 

feeding experiments (Holsman unpubl. data). The model estimated daily consumption rates as a 

function of individual crab cw and water temperatures for appropriate age-specific seasonal 

depth distributions. These temperatures were based on monitoring in the PSCB conducted by the 

Washington Department of Ecology (WDE no date). The presumed diets in the models were as 

described in Table 3. The energy density of the diets were 3,300 Joule (J) g-1 for YOY crab diets 

and 4,000 J g-1 for age 1+ crabs, based on calorimetric convervsion data (Thayer et al. 1973, 

Wacasey and Atkinson 1987, Brey et al. 1988, Perez 1994, Foy and Norcross 1999, Cauffopé 

and Heymans 2005). 

Models were run for approximately 13 molting stages, which exceeded the point at which 

crabs had reached harvestable size (158.75 mm cw). Growth in each simulation was constrained 

by predicted growth rates based on algorithms from published temperature and size-specific 

growth rates for Puget Sound (Wainwright and Armstrong 1993) by adjusting daily feeding rates, 

which were always below the maximum physiological feeding rate for a given size and 

temperature. The resulting Q/B ratios, after rescaling to an annual time step, were 8.197 yr-1 for 

YOY crabs and 3.084 yr-1 for age 1+ crabs. These Q/B ratios result in P/Q ratios of 0.305 and 

0.401 for the two stages, respectively. Although those are high growth efficiencies, they are 

consistent with the high food absorption efficiencies that Holsman et al. (2003) observed in 

Dungeness crabs. 

Given the above parameter inputs plus the demands of predators and fisheries, Ecopath’s 

mass-balancing step calculated biomass of 0.757 mt km-2 for YOY Cancer crabs and 0.547 mt 

km-2 for age 1+ crabs. 

Diet information on Puget Sound Cancer crabs is limited to qualitative observations, and 

little has been published. Studies from other systems often only report frequency of occurrence 

of prey items in Cancer crab diets (e.g., Butler 1954, Gotshall 1977, but see Stevens et al. 1982), 

which presents problems for generating Ecopath diets based on mass or volume. Some patterns 

emerge from the various studies, namely that there are differences related to size, habitat, and 

season (Butler 1954, Gotshall 1977, Stevens et al. 1982). Bivalves and crustaceans are clearly 

important, but the role of fish ranges from very important (e.g., Gotshall 1977, Stevens et al. 

1982) to almost entirely absent (Butler 1954). When present, fishes consisted largely of forage 

fish (e.g., osmerids), flatfish (sanddabs), and demersal fishes from many families. Stevens et al. 

(1982) note that many authors consider Dungeness crabs to be opportunistic generalists feeding 

on invertebrates and fish that are associated with benthic substrates. 

101
We used the aforementioned studies (Butler 1954, Gotshall 1977, Stevens et al. 1982) as 

rough guides, but made some arbitrary adjustments to account for differences among the studies 

(e.g., in the importance of fish) and to account for the relative abundance of common prey items 

in our model. For YOY Cancer crabs, we closely considered the Stevens et al. (1982) data for 

small (15–60 mm cw) crabs and Butler’s (1954) data for less than 100 mm cw crabs. Their diet 

consisted primarily of bivalves and crustaceans (primarily small species such as amphipods, 

mysids, and barnacles, plus a small amount of Crangon spp. and some cannibalism), a small 

amount of fish, and other items. We also added small amounts of detritus, algal/plant material, 

eelgrass, benthic macroalgae, and other invertebrate functional groups that were observed 

(Table 4). 

For age 1+ Cancer crabs, we considered the Stevens et al. (1982) data for medium and 

large crabs (61–100 mm cw and 100–162 mm cw, respectively), Butler’s (1954) data for crabs 

with greater than 101 mm cw, and all crabs sampled by Gotshall (cw range: 67–200 mm). The 

resulting diet was approximately 28% fishes (mainly forage fish and demersal fish, with smaller 

amounts of surfperch, piscivorous flatfish, small-mouthed flatfish, and trace amounts of juvenile 

Pacific herring [Clupea pallasii] and juvenile lingcod). Infaunal bivalves were a substantial diet 

item (30%). The balance of their diet was made up of many of the same groups as YOY Cancer

crabs, and included cannibalism on both YOY and age 1+ Cancer crabs (Table 4). 

Predators of YOY Cancer crabs include resident diving birds, spiny dogfish, skates, 

ratfish, adult rockfish, small-mouthed flatfish, demersal fish, octopus, and cannibals (Table 4). 

For age 1+ Cancer crabs, the predator pool is limited to spiny dogfish, skates, octopuses, and 

other age 1+ Cancer crabs. In addition, there is a substantial fishery take (Table 5). Commercial 

fisheries in PSCB land approximately 0.091 mt km-2

 yr

-1, according to data from 1998 to 2007 

(Appendix C). Still more are taken by recreational fisheries. Fisher and Velasquez (2008) 

reported that approximately 16.3% of Dungeness crabs landed in Puget Sound are taken by 

recreational harvest, which implies an additional 0.015 mt km-2 yr

-1 taken by recreational 

crabbing. 

In Ecosim simulations, the YOY and age 1+ pools of Cancer crabs are linked by a delaydifference equation (Appendix D) that uses several life history parameters. Key among these are 

the age at transition from YOY to age 1+ (0.75 year, derived from Pauley et al. 1986, Fisher and 

Velasquez 2008), the ratio of the weight of an average age 1+ crab to the weight of an individual 

at the transition (≈10, derived from Stevens and Armstrong 1985), and the k parameter of the von 

Bertalanffy growth equation (0.3, derived from Stevens and Armstrong 1985). All other values 

are the Ecosim default values (Appendix D). 

Shrimp 

The shrimp group consists of large decapod crustaceans that are primarily benthic. The 

group has numerous species, many of which are important prey for higher trophic levels. Seven 

of these species, all of the genera Pandalus and Pandalopsis (family Pandalidae), support 

important commercial and recreational fisheries throughout Puget Sound. This group also 

includes several species of sand shrimp (genus Crangon). Shrimp are found at most depths, 

associated with a variety of natural and man-made substrates (Wicksten 2008). Depths and 

activity levels likely vary on a diel basis, with many species more active at night and some 

102
moving up into the water column to forage. Some species may change depths as a function of 

seasonal temperature or reproductive status (Siegfried 1989). As this group is comprised of 

benthic, nonburrowing species, it does not include intertidal thalassinidean burrowing shrimps 

(Appendix B), nor does it include pelagic shrimps such as members of the family Sergestidae 

(see Macrozooplankton subsection above) or mysids (see Small Crustaceans subsection below). 

As there are no surveys for biomass of benthic shrimp species, we allowed Ecopath to 

estimate shrimp biomass via the mass-balancing routine. We assumed an initial EE of 0.9, 

recognizing the heavy predation on shrimp by fishes and other predators as well as the small 

removal pressure by fisheries (Table 5, Appendix C). 

We adapted the P/B estimates that Field (2004) used for the pandalid and benthic shrimp 

groups in his model of the northern California Current. He assumed a P/B of 2.0 yr-1 for 

pandalid shrimp and 2.5 yr-1 for benthic shrimp, a group that includes Crangon spp. We used the 

average (2.25 yr-1) of these values, although there are potential problems with this estimate. 

Field (2004) notes that there have been few attempts to measure P/B in these shrimp species. 

Hannah (1995) estimated the annual natural mortality of pandalid shrimp off the U.S. West Coast 

to be 1.82 yr-1, which is lower than our estimate, and we suspect that fishing mortality is higher 

for the West Coast shrimp than for shrimp in PSCB; thus, our P/B estimate may be too high. 

Furthermore, WDFW, on its Web site describing regulations for shrimp sportfishing (http://wdfw

.wa.gov/fish/shelfish/shrimpreg/shrimp.htm), reports that pandalid shrimp in Puget Sound live to 

be 4 to 5 years old. A life span of 4.5 years applied to the Robertson formula (Appendix C) 

produces a P/B estimate of 1.53 yr-1. However, Field (2004) notes a P/B estimate of 3.82 yr-1 for 

a crangonid from the Atlantic Coast of the United States. 

We also followed the lead of Field (2004) in our estimate of shrimp Q/B. He assumed 

Q/B values of 10 yr-1 for pandalid shrimp and 12 yr-1 for benthic shrimp, respectively, based 

primarily on the principle of keeping P/Q (growth efficiency) at or near 0.2 (20%). We used the 

value of 12 yr-1 for our shrimp group, which yields a reasonable P/Q (0.1875). A Q/B of 12 yr-1

is slightly greater than the average of Field’s values; we justify this by the fact that Puget Sound 

waters are slightly warmer than California Current waters. 

Given these parameters, plus the predatory and fishery removals from other components 

of the model, the mass-balancing routine of Ecopath yielded a biomass of 8.134 mt km-2. This 

value is approximately 2.75 times the combined biomasses of pandalid and benthic shrimp in 

Field’s (2004) model of the northern California Current, although that is an open coastal 

ecosystem with a narrow shelf and considerable slope habitat. 

As with other aspects of the shrimp group, we estimated their diets based on the literature 

surveys done by Field (2004) for shrimp in the northern California Current. Generally, shrimp 

diets are not well studied and we were unable to find anything other than anecdotal information 

about shrimp diets in Puget Sound. We assigned most of their diet to soft infauna, detritus, small 

crustaceans, and euphausiids (Table 4). Minor contributions came from copepods, 

macrozooplankton, other grazers, and deposit feeders; additionally, 2% of their diet was 

attributed to intraguild cannibalism. 

103
Shrimp are fed upon by most bird and fish groups, as well as octopuses and both stages of 

Cancer crabs (Table 4). As noted above, shrimp are also cannibalistic. 

Small Crustaceans 

This abundant and productive functional group is comprised of a variety of small, mobile 

crustaceans living on or near benthic substrates, macrophytes, pier pilings, or other structures. 

Key constituents include small brachyuran crabs such as spider crabs (family Majidae), 

anomurans such as hermit crabs (superfamily Paguroidea) and porcelain crabs (family 

Porcellanidae), mysid shrimps (family Mysidae), cumaceans (order Cumacea), benthic-oriented 

amphipods (order Amphipoda, suborders Gammaridea and Corophiidea), isopods (order 

Isopoda), and tanaidaceans (order Tanaidacea). Members of this group can be found at virtually 

all depths in Puget Sound and their species diversity is considerable (e.g., Jensen 1995). This is 

certainly among the most important functional groups in the system in terms of its contribution 

to food web structure and energy flow. They are key consumers of benthic primary producers 

and detritus, and are a major food source for a wide variety of higher invertebrates and fishes. 

The range of depths and habitats occupied by this group, coupled with a range of 

behaviors ranging from free swimming to burrowing in soft sediments to hiding under rocks, 

precludes comprehensive, quantitative biomass surveys. Thus we elected to estimate small 

crustacean biomass with the Ecopath mass-balancing routine. We began the estimation process 

with the assumption that their EE = 0.9, reflecting their great importance in the diets of numerous 

species in the community. 

We used Robertson’s (1979) method that draws on the relationship between production 

and life span (Appendix C) to estimate small crustacean P/B. A number of amphipod and mysid 

species, cataloged by McLusky and McIntyre (1988), have life spans of 1 to 2 years, and yield a 

mean P/B of 3.57 yr-1 when analyzed with the Robertson method, although the species listed by 

McLusky and McIntyre (1988) are mainly from the North Atlantic. Rudy and Rudy (1983) list 

some species of estuarine isopods in Oregon as having life spans of 1.5–2.5 years, which would 

yield P/B between 3.41 and 2.35 yr-1 with the Robertson method. The P/B of the isopod 

Cirolana harfordi, common in intertidal zones on the open coast, was empirically estimated at 

2.4 yr-1 (Johnson 1976). We made the simplifying assumption that most biomass in this 

functional group is comprised of small taxa (e.g., amphipods, mysids, cumaceans) and that their 

life span is 1.5 years, yielding a P/B of 3.41 yr-1. 

For Q/B, we drew from the work of Field (2004) and Aydin et al. (2003), who assumed 

Q/B ratios of 22 yr-1 for comparable functional groups in models from open ocean ecosystems of 

the Northeast Pacific. As Puget Sound is warmer than those systems, we increased the Q/B to 25 

yr

-1 to account for the higher energetic demands of warmer water. 

Given these parameters and the intense predatory demands of upper trophic levels, the 

mass-balancing function of Ecopath calculated a biomass of 38.195 mt km-2 for the small 

crustacean pool. That estimate implies that this group is one of the more abundant components 

of the benthic invertebrate community, the rest of which is largely infaunal (Table 3). 

104
The diversity of this group allows for the occupation of many different trophic niches, 

and quantitative diet studies on these species are rare. Based largely on anecdotal accounts and 

assumptions common to other EwE models in nearby systems, we developed an arbitrary initial 

diet composition (Table 4). The majority of the diet was detritus; the remainder was spread 

among a variety of benthic and pelagic invertebrates and benthic primary producers. They also 

engage in some functional group cannibalism. Some predation on invertebrates (e.g., on infaunal 

bivalves and soft infauna) is assumed to be done by brachyuran and anomuran crabs, and our 

assumption about the allocation of biomass to crabs (as opposed to smaller consumers like 

amphipods, mysids, and cumaceans) is crucial in properly representing the diet composition. 

Small crustaceans are a major prey pool in the food web (Table 4). They are fed upon by 

every seabird group and virtually every fish group. In addition, they are preyed upon by shrimp, 

octopuses, age 0+ Cancer crabs, sea stars, and other members of their own functional group. 

Barnacles 

Barnacles are found from intertidal waters down to at least 90 m (Morris et al. 1980, 

Kozloff 1983). Shallow-water species such as Chthalamus dalli, Balanus glandula, and the 

thatched barnacle (Semibalanus cariosus) affix to natural and artifical hard substrates. C. dalli is 

the shallowest, while B. glandula is slightly deeper and gradually gives way to the larger 

thatched barnacle in the middle and lower intertidal regions (Kozloff 1983). B. glandula and C. 

dalli may compete for habitat along rocky shores, with densities of tens to hundreds of thousands 

per m2

, although predation by the snails Nucella spp. can dramatically affect both taxa, 

particularly B. glandula, at local scales (Morris et al. 1980). At greater depths, the giant barnacle 

(B. nubilus) becomes increasingly important. Occurring subtidally to as deep as 90 m (Morris et 

al. 1980), the giant barnacle may reach a diameter of more than 10 cm and serve as habitat for a 

variety of other organisms such as sponges (Kozloff 1983) and small fishes (Demetropoulos et 

al. 1990). 

Empirical biomass estimates for the barnacle group are not available. We therefore used 

Ecopath’s mass-balancing routine to estimate barnacle biomass, first inputting an EE of 0.8, 

which implies that a large proportion of barnacle production is lost to predators. 

To estimate P/B for barnacles, we used the Robertson equation (Appendix C) that 

correlates benthic invertebrate P/B with longevity. The range of longevities among common 

Puget Sound barnacles is wide: C. dalli live an estimated 3 years, B. glandula life spans are 

estimated at 8–10 years, and thatched barnacles live up to 15 years (Morris et al. 1980, Rudy and 

Rudy 1983). No longevity data have been reported for giant barnacles. We elected to use a 

longevity of 6 years as a compromise between the larger, deeper-dwelling, long-lived species 

and the shallower, more short-lived species that may be more vulnerable to predators. Erring on 

the side of the shorter-lived, depredated species was done in order to keep the barnacle biomass 

estimate from being unreasonably large. Applying 6 years to the Robertson equation yields a 

P/B of 1.245 yr-1. 

We had no information on consumption rates by barnacles, and thus opted to estimate 

growth efficiency. We made the assumption that barnacle growth efficiency was 20% (i.e., 

105
P/Q = 0.2), as we have similarly assumed for many other primary consumers in this model. This 

corresponds to a Q/B of 6.225 yr-1, given our P/B estimate. 

Given these parameter estimates and the predation pressure exerted on barnacles by other 

model groups, the Ecopath mass-balancing routine estimated a biomass of 2.310 mt km-2. This 

estimate seems high given the large portion of the PSCB model area (e.g., soft-sediment habitats 

and all waters >90 m deep, Table 1) that is not inhabited by barnacles. We may have 

misrepresented the role of barnacles in the diets of some predators, particularly surfperches, 

which alone account for 42% of barnacle predation mortality. 

Barnacle diets were based on thatched barnacle gut and cirri contents in the San Juan 

Islands by Navarrete and Wieters (2000). The most abundant prey, diatoms and barnacle nauplii, 

made up 32% and 16% of the total observed items, respectively. Also present were 

microzooplankton (foraminiferans and mussel postlarvae), copepods, nemerteans, and 

macroalgal fragments. Based on these observations, we assigned barnacles a diverse diet with 

equal amounts of phytoplankton and microzooplankton, along with soft infauna, copepods, 

algal/plant material, and detritus (Table 4). Conversely, barnacles are preyed upon by nearshore 

diving birds, gulls, spiny dogfish, ratfish, piscivorous flatfish, small-mouthed flatfish, 

surfperches, demersal fish, predatory gastropods, sea stars, and Cancer crabs. 

Mussels 

The mussel functional group is comprised of the blue mussel (Mytilus edulis), a nearshore 

filter feeder that prefers relatively protected estuarine bays (Kozloff 1983). They are most often 

found on stable substrates, including anthropogenic structures, between the intertidal zone and 

waters as deep as 36 m (Emmett et al. 1991). They reach sizes of more than 6 cm, although that 

is rare (Kozloff 1983, Emmett et al. 1991). Mussels can be extremely dense in areas where 

conditions are favorable, although predation can drastically drive their numbers down (Emmett 

et al. 1991). Blue mussels can be cultured or harvested wild for human consumption as well. 

We found no data on blue mussel abundance in Puget Sound, and thus used the Ecopath 

mass-balancing step to solve for biomass. In order to do so, we assumed an EE of 0.9, implying 

a very high degree of annual production lost to predation. 

The P/B estimate for mussels stems from the P/B-longevity relationship described by 

Robertson (1979) (Appendix C). According to Seed (1976), blue mussels may live to be 18–24 

years, but that is primarily in very cold northern areas of slow growth (e.g., Greenland). 

Longevities in Atlantic systems of latitudes similar to Puget Sound were approximately 7–9 

years (Seed 1976). Applying a longevity of 9 years to the Robertson equation yields a P/B of 

0.927 yr-1, a value that falls within (but close to the upper end of) the range of blue mussel P/B

estimates summarized from other systems (e.g., Seed 1976, Munch-Petersen and Kristensen 

2001). 

The Q/B ratio for mussels was estimated indirectly, because direct consumption estimates 

were not available in the literature. However, according to Grant and Bacher (1998), a review of 

bivalve bioenergetics by Griffiths and Griffiths (1987) concluded that bivalves have a gross 

106
growth efficiency of 30%. If 0.30 is a reasonable estimate of production over consumption (i.e., 

P/Q), then Q/B for mussels = 3.090 yr-1. 

Given these parameters and the predation pressure of other groups, Ecopath’s massbalance function calculated an initial biomass of 3.785 mt km-2 for mussels. As with barnacles, 

this seems like a rather high biomass for mussels, given the relatively low amount of habitat 

available to them in PSCB (Table 1). If this is an overestimate, it may result from 

overestimating the amount of mussels in the diets of predators or from underestimating P/B. 

Blue mussels primarily consume organic detritus, along with phytoplankton and other 

microorganisms (Table 4, derived from Morris et al. 1980, Emmett et al. 1991). Mussels are 

preyed upon by sea stars, Cancer crabs, octopuses, predatory gastropods, surfperches, demersal 

fish, ratfish, nearshore diving birds, gulls, and raptors. We assume a trace level of fishing 

mortality from recreational shellfish extraction (Table 5). 

Infaunal bivalves 

Infaunal bivalves include all bivalve mollusks that are found at or below the surface of 

soft sediments (except for geoducks [Panopea abrupta], which are their own functional group, 

see next subsection below). This group ranges from very small clams, such as Transennella 

tantilla, amethyst gemclam (Gemma gemma), Charlotte macoma (Macoma carlottensis), and 

Baltic macoma (M. balthica), to large clams, such as the horse clam (Tresus capax) and the 

butter clam (Saxidomus giganteus). Members of this group are important filter feeders that can 

reach exceptionally high densities under favorable conditions; for example, Macoma spp. were 

among the most abundant infaunal invertebrates in deepwater benthic grab samples taken in 

PSCB from the early 1960s (Lie 1968) and again from 1989 to 2000 (Partridge et al. 2005). 

Infaunal bivalves supply food for a wide variety of predators, support lucrative fisheries, and are 

the basis for growing aquaculture programs in Puget Sound and elsewhere. 

Infaunal bivalve biomass was estimated directly from data collected by the Washington 

Department of Ecology (WDE) in benthic grab samples taken at numerous sites and depths 

between 1989 and 2000 (Partridge et al. 2005). We pooled bivalve density estimates into 20 m 

depth bins and weighted depth-specific mean abundances by the relative area represented by 

their depth bin. This yielded a grand mean abundance estimate (≈1,730 individuals m-2), which 

we multiplied by an estimated individual biomass for bivalves taken from similar benthic grab 

sampling done by Lie (1968). His mean individual mass estimate was less than 0.041 g, 

implying that the typical infaunal bivalve in Puget Sound is a small individual. This yielded a 

weighted biomass estimate of 70.506 mt km-2. We cannot be certain that this is plausible, 

because it is possible that the benthic grab did not sample deep enough to efficiently capture 

large clams that are deep burrowers. Even so, this group is one of the most abundant in the 

model in biomass terms. 

We again adopted the Robertson (1979) method (Appendix C) to estimate P/B as a 

function of the maximum age. Infaunal bivalve life spans range from relatively short (e.g., 2 

years for the exotic amethyst gemclam) to intermediate (e.g., 5–10 years for the exotic Baltic 

tellin [Macoma balthica], 7–8+ years for the Pilsbry piddock [Zirfaea pilsbryi] and the littleneck 

clam [Protothaca staminea]) to relatively long (e.g., 16 years for the heart cockle [Clinocardium 

107
nuttallii] and more than 20 years for the butter clam) (Morris et al. 1980, Chew and Ma 1987, 

Budd and Rayment 2001). Assuming that most species of infaunal bivalve are small bodied, we 

assigned a median maximum age of 3 years. This corresponds to a P/B of 2.059 yr-1. 

We estimated Q/B based on publications indicating that bivalves typically have a 

conversion efficiency (P/Q) of 0.3 (Griffiths and Griffiths 1987, Grant and Bacher 1998). Based 

on our estimate of P/B, this conversion efficiency requires a Q/B of 6.863 yr-1. 

With our estimates of the other parameters plus the demands of predators and fisheries, 

Ecopath estimated the infaunal bivalve EE to be 0.718. This implies a moderately high 

proportion of production lost to model predators or to fishing each year in the initial model state. 

We assumed that the diets of infaunal bivalves are primarily made up of suspended 

phytoplankton and detritus filtered from water drawn through their siphons. We partitioned 

these two diet items proportionally according to the proportion of area located above and below 

the depth of light extinction (≈30 m). About 28% of the PSCB domain is in the euphotic zone 

(Table 1), thus we assumed that 28% of infaunal bivalve diet is phytoplankton (Table 4). The 

remainder of their diet is detritus (67%) and bacteria associated with detritus (5%). 

Infaunal bivalves are fed upon by predatory gastropods, sea stars, small crustaceans, 

YOY and Age 1+ Cancer crabs, octopuses, demersal fish, surfperches, piscivorous and smallmouthed flatfish, walleye pollock, ratfish, skates, raptors, gulls, and nearshore diving birds. 

Furthermore, commercial fishing operations removed 0.03 mt km-2

 yr

-1 of infaunal bivalves from 

PSCB between 1998 and 2007 (Table 5, Appendix C). Additional removals must be allocated to 

recreational harvest and to aquacultural production, but we have not yet located or compiled 

those data. 

Geoducks 

Geoducks warrant their own functional group for at least two reasons: first, their unique 

growth and life history compared to other bivalves in the model domain, and second, their 

significance as a target species for fisheries and aquaculture in Puget Sound. Geoducks are the 

largest bivalve found in Puget Sound, achieving weights of more than 3 kg, shell lengths of 

greater than 200 mm and siphon lengths of more than 1 m, allowing them to burrow deeply into 

sand and mud sediments beyond the reach of most predators (Goodwin and Pease 1989). 

Geoducks reach sexual maturity between the ages of 2 and 8 (Andersen 1971, Goodwin and 

Shaul 1984) and may continue spawning beyond the age of 100 years (Sloan and Robinson 

1984). They are found at depths ranging from the lower intertidal to 110 m (Goodwin and Pease 

1989). 

Geoduck biomass estimates stem from survey work done by WDFW biologists on 

geoducks in the depth band between approximately 6 and 23 m (Goodwin and Pease 1987, 

Bradbury and Tagart 2000, Bradbury et al. 2000). Surveys have focused on tracts that are both 

harvestable and unharvestable. Tracts are classified unharvestable by virtue of having densities 

below approximately 0.4 geoducks m-2, conflicts with existing use sectors such as passenger 

ferries, or human health concerns (WDNR and WDFW 2001). Biomass in the tracts can be high: 

Goodwin and Pease (1987) estimated a mean biomass of 1,482 g m-2 in Puget Sound tracts, with 

108
a maximum of 19,651 g m-2. Limited information is available on biomass in areas outside of the 

tracts. We used data from the most recent (2009) survey of tracts in PSCB. Not all tracts are 

surveyed every year and some go many years or even decades between samplings, so it behooves 

us to use the most updated data possible. 

Surveys indicate a total geoduck biomass of 39,703 mt in the tracts, which cover an area 

of 37.01 km2 (Sizemore et al. unpubl. data). If we conservatively assume that this biomass 

constitutes the entirety of the geoduck population in PSCB (area = 757.08 km2

), we arrive at a 

biomass of 52.442 mt km-2. A recent environmental impact assessment of the commercial and 

tribal geoduck fisheries assumed that deepwater areas adjacent to surveyed tracts had 1.573 times 

as much biomass as the surveyed tracts, and that inshore areas adjacent to surveyed tracts had 0.5 

times as much biomass as the surveyed areas (WDNR and WDFW 2001). Those assumptions 

would yield a biomass of 161.154 mt km-2, but we will use the more conservative estimate until 

widespread empirical estimates of geoduck abundance outside the surveyed tracts become 

available. The conservative estimate also should promote precautionary levels of fishing 

mortality in fishing simulations. 

To estimate P/B, we began first with an instantaneous rate of natural mortality of 0.023 

yr

-1, estimated by Bradbury and Tagart (2000) for adult geoducks in Puget Sound from 1979 to 

1981. Based on current commercial and tribal harvest rates in the PSCB area (approximately 

0.697 mt km-2 annually from 1998 to 2007, Table 5, also Appendix C) and the current biomass 

estimates described above, instantaneous fishing mortality is approximately 0.0133 yr-1. The 

sum of these mortality rates is 0.0363 yr-1, which we used for our P/B estimate. 

In the absence of direct measures of geoduck consumption rates, we inferred the Q/B

ratio by comparison with the other, smaller bivalve species in this model. Their Q/B values 

generally fell between 3 and 5 yr-1, and we expect geoduck Q/B should be lower because large 

animals tend to have lower mass-specific energy requirements than small, taxonomically similar 

animals (e.g., Jobling 1994). We assumed a Q/B of 2 yr-1, which produced a very low 

conversion efficiency (P/Q = 0.018), although a low conversion efficiency is reasonable for a 

species as long-lived and slow growing as geoduck. 

The mass-balancing routine of Ecopath produced an estimated EE of 0.366 for geoducks. 

This implies that a moderate amount of geoduck production is lost to other model groups 

(primarily the geoduck fishery) each year. 

The diet of geoducks has only been characterized anecdotally, as summarized by 

Goodwin and Pease (1989). They are filter feeders and gut content studies from Puget Sound 

specimens have only revealed phytoplankton, apparently even among individuals collected from 

below the photic zone. These individuals may persist on phytoplankton that is transported into 

deep waters by currents or on dead or dying phytoplankton that has sunk (Goodwin and Pease 

1989). Here we assume that geoduck diet is mostly phytoplankton (Table 4), following the 

assumption that most geoduck biomass is found at depths where they have consistent access to 

living phytoplankton. The remainder of their diet is detritus and associated bacteria. 

Although predation on geoduck larvae and juveniles is likely quite high, predation 

mortality probably becomes inconsequential once they have reached 2 years of age, except for 

109
possible rare predation events due to larger sea star species (Lambert 1981, Goodwin and Pease 

1989). Because our model focuses on the adult phase of the geoduck life history, we assume 

only a small amount of predation pressure, from sea stars. 

Soft Infauna 

The soft infauna community includes numerous species of annelids, sipunculans, 

nemerteans, and turbellarians. Annelids, particularly polychaetes, are typically the most 

abundant taxa of worms captured in benthic grab samples in Puget Sound over the past 30–40 

years (Nichols 1975, 2003, Partridge et al. 2005). Therefore, all model parameters for the soft 

infauna group derive from studies of polychaetes. 

Biomass estimates were made using benthic grab data collected by WDE at 93 sites in 

PSCB and adjacent areas in 1998 (Long et al. 2000). Sites ranged from approximately 4 m to 

250 m in depth, and the data consisted of abundance estimates per 0.1 m2

. We converted the 

counts to areal biomass estimates by assuming a mean individual weight of 0.032 g, the average 

polychaete weight in Puget Sound benthos samples collected by Lie (1968). We pooled the areal 

biomass estimates into 20 m depth bins, averaged them by depth bin, then weighted the depthbased averages according to the proportional area of each 20 m bathymetric contour (Table 1). 

After appropriate unit conversions, the resulting biomass estimate was 67.899 mt km-2. 

Our P/B estimate was taken directly from work done on a numerically dominant 

polychaete in Puget Sound, Pectinaria californiensis. Nichols (1975) measured annual 

production of P. californiensis in deep waters (>100 m) of Puget Sound and found that P/B

ranged from 3.3 yr-1 to 5.5 yr-1. We used the mean of this range, 4.4 yr-1. 

Not having an estimate of Q/B for any polychaete species, we used a P/Q estimate to 

infer Q/B. Field (2004) in his model of the northern California Current ecosystem pooled 

polychaetes into a general group of benthic infauna, for which he assumed a P/Q of 0.2. That 

ratio coupled with our P/B estimate yields a Q/B of 22.0 yr-1. 

The mass-balancing step of Ecopath produces an EE estimate of 0.568 for this group, 

which implies moderate loss of production to predation. We would have expected a larger 

number and may have underrepresented soft infauna in predator diets. For example, there may 

be intraguild cannibalism that we have thus far omitted. 

The diet of soft infauna is assumed to be mostly detritus, with important contributions 

from benthic microalgae and bacteria (Table 4). Soft infauna are eaten by nearshore diving 

birds, migratory diving birds, spiny dogfish, ratfish, Pacific hake, walleye pollock, juvenile wild 

and hatchery salmon, adult rockfish, piscivorous flatfish, small-mouthed flatfish, demersal fish, 

surfperches, shrimp, Cancer crabs, small crustaceans, barnacles, and deposit feeders. 

Deposit Feeders 

This functional group covers deposit-feeding invertebrates apart from those already in 

other groups listed above. The group is mainly comprised of echinoderms that feed on patches 

of organic detritus on the seafloor. Some also feed on diatoms and suspended particles; some 

may opportunistically scavenge dead material or prey on small organisms (Morris et al. 1980, 

110
Kozloff 1983). Representatives include brittle stars (class Ophiuroidea), the heart urchin 

(Brisaster latifrons), a few species of sea star (e.g., mud star [Ctenodiscus crispatus]), and the 

many species of sea cucumber (except for the California sea cucumber, see Large Sea 

Cucumbers subsection above). These organisms can be found at all depths in Puget Sound on a 

wide variety of substrates; many of them are burrowing animals found in soft sediments (Nichols 

2003). 

The role of these organisms in the functioning of the Puget Sound ecosystem is not clear, 

but the roles of some key species have probably changed in recent decades. Nichols (1975, 

2003) measured abundances of two deposit feeders, the heart urchin and the burrowing sea 

cucumber (Molpadia intermedia) at deep stations in PSCB. He found that Brisaster numbers fell 

drastically between the 1960s and 1990s while Molpadia numbers increased and then leveled off 

over the same time period. Whereas Brisaster was a dominant component of the benthic 

community biomass in the 1960s (Nichols 1975, 2003), it was not abundant at any site sampled 

by the WDE between 1989 and 2000 (Partridge et al. 2005). Apparently, Brisaster recruitment 

has not occurred since approximately 1970 (Nichols 2003). This implies a major shift in benthic 

taxa and community structure. 

The most up-to-date sampling of the benthic community in PSCB is the WDE sampling 

mentioned above (Long et al. 2000, Partridge et al. 2005). Their data consist of counts of 

different species per 0.1 m2

, which we converted to biomass by multiplying by 0.037 g, and the 

estimated wet weight of brittle stars and sea cucumbers from data in Lie (1968). Expanding the 

data from g per 0.1 m2 to mt km-2, and weighting according to the bathymetric distribution of 

samples, we estimated deposit feeder biomass to be 7.512 mt km-2. 

P/B estimates for deposit feeders were made using the Robertson (1979) relationship 

between life span and production (Appendix C). The range of maximum ages in this functional 

group is potentially wide. The predominant subtidal brittle star (Amphiodia urtica), has a life 

span of 5 years in Puget Sound (Lie 1968). Small sea cucumbers of the genus Cucumaria are 

common in Puget Sound (Kozloff 1983); C. curata and C. pseudocurata have been found to 

have maximum ages of at least 4 or 5 years (Rutherford 1973, Morris et al. 1980). In contrast, 

Nichols (2003) surmised that heart urchins can live in excess of 20 years, as individuals recruited 

prior to 1970 were the only remaining individuals in 1992. However, Brisaster is no longer an 

important component of this group (Nichols 2003). If we assume a mean 5-year life span for 

organisms in this functional group, the Robertson relationship predicts a P/B of 1.421 yr-1. 

We are aware of no data on Q/B rates for these organisms. We assumed that it would be 

comparable to, but greater than, the Q/B of large sea cucumbers by virtue of the latter’s larger 

size and longer life span. We estimated a deposit feeder Q/B of 25 yr-1. This estimate, coupled 

with the P/B estimate, yields a P/Q (food conversion efficiency) of 0.057, which seems plausible 

for detritus feeders with multiyear life spans. 

The parameters above, coupled with the demands of predators, yield an initial EE of 

0.344 in the mass-balancing routine of Ecopath. This suggests relatively moderately low 

predation pressure on deposit-feeder populations. 

111
Based on anecdotal accounts in Morris et al. (1980) and Kozloff (1983), we assigned 

deposit feeders an initial diet dominated by detritus, along with smaller amounts of bacteria, soft 

infauna, and benthic microalgae (Table 4). Their predators included shrimp, sea stars, predatory 

gastropods, age 1+ Cancer crabs, surfperches, piscivorous flatfish, small-mouthed flatfish, adult 

rockfish, and ratfish. 

Suspension Feeders 

This group features a diverse range of filter-feeding benthic invertebrates not already 

represented in the functional groups described previously. Major groups include sponges 

(phylum Porifera), colonial hydrozoans (phylum Cnidaria, class Hydrozoa), sea pens (phylum 

Cnidaria, order Pennatulacea), tube-dwelling worms such as serpulids (phylum Annelida, class 

Polychaeta) and horseshoe worms (phylum Phoronida), and bryozoans (phylum Bryozoa). 

Suspension feeders fill multiple ecological roles: they feed on planktonic organisms and detritus, 

they are prey for a number of species, and they provide biogenic habitat structure for fishes and 

invertebrates. Some species of sponges and bryozoans may grow on other species (e.g., on kelp 

blades, on the backs of crabs, or on the shells of scallops); the resulting effects can be beneficial 

(e.g., camouflage) or deleterious (e.g., fouling) (Kozloff 1983). 

Not knowing quantitative biomass estimates for this diverse group, we used the Ecopath 

mass-balancing algorithm to estimate biomass. We began the mass-balancing process by 

assuming an EE of 0.8, which implies an initial model state in which 80% of production is lost to 

predation by other model groups. 

Lacking direct measures of production, we developed suspension feeder P/B estimates by 

using the Robertson (1979) method, where P/B is a function of maximum age (Appendix C). 

This was challenging because of the diversity of life histories among constituents of this group. 

For example, the sea pen (Ptilosarcus gurneyi) can live up to 15 years (Birkeland 1974). 

Published accounts of bryozoans and serpulids worldwide suggest life spans of several years 

(e.g., Rubin 1985, Medernach et al. 2000, Bianchi and Morri 2001, Hill 2006). Sponges, 

however, may live a few years in nearshore habitats to decades or centuries in the deep oceans 

(e.g., cloud sponges). We assumed that the vast majority of the biomass of this functional group 

in Puget Sound is represented by the relatively short-lived species of bryozoans, sponges, tube 

worms, and hydroids, rather than the old, deepwater sponges or sea pens; a life span of 2 years 

results in a P/B of 2.763 yr-1 in the Robertson equation. 

Q/B was estimated based on the simplifying assumption that P/Q = 0.2. Given our P/B

estimate, this assumption yielded a Q/B of 13.815 yr-1. 

The combination of the above parameter estimates and the top-down demands of 

predators resulted in an estimated initial biomass of 6.494 mt km-2 for suspension feeders. 

Suspension feeders were arbitrarily assigned a diet dominated by phytoplankton (Table 

4). This implies that their biomass is disproportionately found in the euphotic zone (≈28% of the 

basin), an assumption that should be tested. The remainder of their diet includes detritus, 

microzooplankton, and bacteria. Suspension feeders are preyed upon by predatory gastropods, 

sea stars, small crustaceans, age 1+ Cancer crabs, large sea cucumbers, and other grazers. 

112
Tunicates 

We chose to include tunicates as a separate group from the other suspension feeders 

because several nonindigenous tunicates have become established in Puget Sound, and their 

populations may eventually reach the level of full-blown invasions with measurable effects. The 

species of concern are the stalked sea squirt (Styela clava), solitary sea squirt (Ciona savignyi), 

and Didemnum spp. They were likely introduced via ballast water transfer or attached to ship 

hulls, and Didemnum may have also been introduced with aquacultural plantings of nonnative 

shellfish (PSAT 2007). These species are capable of spreading rapidly and exhibit some 

resistance to control measures and environmental conditions such as low dissolved oxygen. 

Numerous agencies, organizations, and fishery stakeholders are concerned that nonnative 

tunicates will outcompete native species and commercially valuable resources for space and 

resources. 

There are no biomass survey data available for the nonnative tunicates in PSCB; 

therefore, we used the Ecopath mass-balancing routine to estimate tunicate biomass. We input 

an EE of 0.075, assuming relatively little predation pressure; however, during the massbalancing step, Ecopath increased EE to 0.099 due to the biomass accumulation (BA) input 

(discussed below). 

We used the equation proposed by Robertson (1979) (Appendix C) to estimate tunicate 

P/B based on life span. Stalked sea squirt life span is estimated at 1–3 years, according to Cohen 

(2005). A maximum life span of 3 years, when applied to the Robertson equation, yields a P/B

of 2.059 yr-1. There were no estimates of tunicate Q/B available, so we assumed a trophic 

transfer efficiency (P/Q) of 0.2, leading to a Q/B estimate of 10.295 yr-1. (Morris et al. 1980 

report that the water filtration rate of the stalked sea squirt is 78–175 ml per gram dry weight per 

minute in flowing, 10oC seawater; this estimate may lend itself to a more empirical Q/B

estimate.) 

Given the above parameters, the Ecopath mass-balancing routine yielded a biomass 

estimate of 0.157 mt km-2, which is considerably lower than the biomass of suspension feeders. 

This reflects the status of tunicates as newly established, nonindigenous species. However, we 

included a BA term (Equation 1) because these species are expected to spread within the basin 

via transport on vessel hulls (PSAT 2007). The BA rate is assumed to be 0.05 yr-1, that is, an 

initial 5% annual increase in biomass. 

Tunicates have been characterized as “animals deeply committed to a life of filter feeding 

in the sea” (Morris et al. 1980, p. 177). Finding no quantitative diet estimates, we assumed 

tunicates to have a diet of 70% phytoplankton, 20% detritus, and 5% each of microzooplankton 

and bacteria (Table 4). Tunicates are fed upon in small amounts by predatory gastropods, sea 

stars, adult rockfish, and small-mouthed flatfish. Predation likely focuses on young, recently 

settled juveniles (Cohen 2005). 

Pelagic Fishes 

Pelagic fish species are of great importance to food web structure and function in Puget 

Sound. They are abundant and generally have rapid turnover rates, high activity levels, and 

113
broad habitat use; they therefore account for considerable energy transfer and connectivity across 

the basin. They serve as a bridge between primary consumers and the upper-trophic-level fishes, 

marine mammals, and seabirds that feed heavily on them. Some are important commercial, 

recreational, or subsistence fish, most notably the Pacific herring as well as some species of 

smelts (family Osmeridae) and surfperches (family Embiotocidae). 

Pacific Herring 

Pacific herring are one of the most ecologically and economically important species in 

Puget Sound (Stout et al. 2001, Stick 2005, Penttila 2007). They feed heavily on productive 

pelagic crustaceans such as copepods and euphausiids, and are fed upon by some of the most 

iconic species in Puget Sound, including salmon and marine mammals. They support fisheries 

for their roe (a roe-on-kelp fishery that occurs north of the PSCB model domain), their juveniles 

(for use as bait, Baraff and Loughlin 2000), and until recently their adults. Although no fisheries 

in Puget Sound currently target adult Pacific herring (owing to declines in the largest spawning 

stock, the Cherry Point stock in northern Puget Sound, Stick 2005), such fisheries were large 

historically and remain so in the Strait of Georgia. 

In addition, their spawning habits link the nearshore and offshore habitats: Pacific herring 

are open water pelagic feeders, but they spawn in intertidal or subtidal waters, usually over 

macrophytes such as eelgrass or kelp, in the winter and spring months. Their eggs are fed upon 

by many nearshore species. Pacific herring spawning aggregations are nonrandom: Puget Sound 

has at least 19 discrete stocks that spawn in predictable locations each year (Stick 2005). Two of 

these stocks (Port Orchard/Madison and Quartermaster Harbor) have spawning grounds located 

in PSCB, and several others spawn close to PSCB. After spawning, the stocks disperse to 

pelagic habitats (Penttila 2007). 

The Pacific herring pool has two age groups, juveniles and adults, that are dynamically 

linked in Ecosim via the delay difference reproduction function (Appendix D). The transition 

between these groups occurs at age 2–3. We assumed it to occur at age 2, given that, on average, 

40–50% of the Pacific herring spawning stock biomass at Quartermaster Harbor and Port 

Orchard/Madison has been age-2 in recent decades (Stick 2005). We distinguish the juvenile 

and adult stages because their ecology differs in important ways, as outlined below. 

We made separate calculations for the initial state of biomass for the juvenile and adult 

pools of Pacific herring. We used the Ecopath mass-balancing routine to estimate juvenile 

Pacific herring biomass, because no empirical data were available. We set EE at 0.9, consistent 

with considerable losses of annual production to predation and fishing. Adult biomass was 

drawn from Stick (2005), who conducted stock assessments of all Puget Sound spawning stocks 

based on annual hydroacoustic and trawl surveys of spawning aggregations. Our biomass 

estimate was equal to Stick’s 5-year average spawning stock biomasses for the Quartermaster 

Harbor and Port Orchard/Madison populations (1,916 mt, or 2.531 mt km-2). We assumed no 

dispersion of adult herring to or from basins adjacent to PSCB. This assumption, which was not 

informed by empirical evidence, should be revisited and monitored, particularly given the 

importance of Pacific herring in Puget Sound food webs and fisheries. 

114
P/B for juvenile Pacific herring was taken from a population model for Puget Sound 

herring (Guerry unpubl. data), based largely on the historic stock assessments by WDFW (e.g., 

Stick 2005). We set P/B equal to the instantaneous annual total mortality from the Guerry 

model, weighted by the estimated biomass in age classes 0 and 1. The weighted average 

mortality was 3.656 yr-1. For adults, we set P/B at 1.6 yr-1, which approximates the natural 

mortality for the Quartermaster Harbor and Port Orchard/Madison stocks in recent years, as 

estimated by Stick (2005). This is considerably higher than many published estimates of natural 

mortality in adult Pacific herring (Lassuy 1989). For example, Trumble and Humphreys (1985) 

reviewed population biology literature for this species and concluded that instantaneous natural 

mortality for adults was between 0.4 and 0.5 yr-1. A similar conclusion was reached by Lemberg 

et al. (1997). However, the age frequency time series data provided by Stick (2005) imply 

higher levels of mortality in Puget Sound Pacific herring. Stick (2005) concluded that natural 

mortality of adult herring had increased sharply since the 1970s. 

Q/B ratios for both stages are based on bioenergetics models for Pacific herring. We used 

bioenergetics parameters described by Megrey et al. (2007) to estimate consumption required for 

Pacific herring to grow according to the von Bertalanffy growth function described for Strait of 

Georgia herring by Gunderson and Dygert (1988). We assumed water temperatures for the 

upper 25 m of the water column of PSCB from WDE (no date) and diets corresponding to the 

proportions described below; resulting prey energy densities were 3,895 J g-1 for juveniles and 

4,280 J g-1 for adults. The average annual Q/B, weighted by age groups based on the P/B ratios 

described previously, was 15.984 yr-1 for juveniles and 7.489 yr-1 for adults. 

The mass-balancing step in Ecopath calculated an EE of 0.851 for adult Pacific herring 

and a biomass of 3.407 mt km-2 for juvenile herring. 

Adult Pacific herring diets were derived from stomach content analysis, largely collected 

in nearby basins (Strait of Juan de Fuca and San Juan Islands, Miller et al. 1977, Miller et al. 

1980) but also from the Central Basin (Fresh et al. 1981). From those sources, we estimated an 

initial diet dominated by copepods and euphausiids, rounded out by macrozooplankton, forage 

fish, and juvenile Pacific herring (Table 4). For juvenile Pacific herring, we arbitrarily assumed 

similar diet composition to adults, but changed some proportions to reflect likely ontogenetic 

differences and changes in habitat. The resulting diet is consistent with published observations 

(e.g., Stout et al. 2001). 

Between the two age classes, Pacific herring are preyed upon by nearly all other 

vertebrates in the food web, as well as by squid and Cancer crabs (Table 4). We currently do not 

assume measurable predation by jellyfish on Pacific herring. The commercial bait fishery for 

juvenile Pacific herring produces modest landings (Table 5, also Appendix C). 

For the delay-difference equation that links the two age groups (Appendix D), we used 

values of 2 years for the juvenile-adult transition age (Stick 2005), 2.875 for the weight ratio of 

an average adult to a transition-age individual (derived from bioenergetics modeling as described 

above), 0.36 for the von Bertalanffy k parameter (Gunderson and Dygert 1988), and default 

values (as in Appendix D) for all other parameters. 

115
Forage Fish 

Several other planktivorous fishes inhabit pelagic zones of Puget Sound. Major species 

are the Pacific sand lance (Ammodytes hexapterus), surf smelt (Hypomesus pretiosus), northern 

anchovy (Engraulis mordax), and longfin smelt (Spirinchus thaleichthys) (Monaco et al. 1990, 

Emmett et al. 1991, Penttila 2007). Sardine (Sardinops sagax) and eulachon (Thaleichthys 

pacificus) are present but rare (Monaco et al. 1990). We also include threespine stickleback 

(Gasterosteus aculeatus) in this group. Forage fish are crucial links between lower and upper 

trophic levels. They are important prey due to their abundance, high energy density, and vitamin 

content (e.g., Robards et al. 1999). They are also conduits of mass and energy flow between 

different habitats; for example, longfin smelt migrate into streams to spawn, and sand lance and 

surf smelt spawn primarily in beach areas (Emmett et al. 1991, Penttila 2007). They support 

fisheries in PSCB, though of a smaller magnitude than Pacific herring. As they have received 

less research or management attention than Pacific herring, much about their abundance and 

ecology in PSCB is unknown (Penttila 2007). 

There are no biomass data available for these species in PSCB, because agencies such as 

the WDFW have devoted most of their pelagic fish stock assessment efforts to Pacific herring 

(Penttila 2007). In addition, some species’ biology and spatial distribution make abundance 

estimation by traditional methods very difficult; for example, sand lance spend considerable time 

burrowed into sandy or gravelly bottoms (Penttila 2007) and also do not show up in acoustic 

surveys because they lack a swim bladder. We therefore used the Ecopath mass-balancing 

routine to estimate biomass for forage fish. We assumed a high EE (0.8), consistent with intense 

predation. There is but a trace level of fishing mortality (Table 5, also Appendix C). 

We adopted the P/B and Q/B estimates for forage fish species from other EwE models of 

northeastern Pacific Ocean ecosystems. We took the P/B estimate (1.50 yr-1) from the forage 

fish functional group in the northern California Current model developed by Field (2004). He 

describes that group as being primarily made up of northern anchovy, Pacific herring, eulachon, 

surf smelt, sand lance, American shad (Alosa sapidissima), and whitebait smelt (Allosmerus 

elongatus). Field (2004) aggregated natural mortality for this group over the life span of all 

species, thus representing the relatively high P/B of larvae and juveniles (P/B = 5 to 25 yr-1), the 

low P/B of adults (= 0.21 to 1.3 yr-1), and intermediate values for subadults. 

The Q/B estimate was derived from the forage fish group in the northern British 

Columbia shelf ecosystem model by Ainsworth et al. (2002). We rounded their estimate up from 

6.608 yr-1 to 7.0 yr-1 in order to reflect the slightly warmer water in Puget Sound. This estimate 

is slightly higher than Field’s (2004) estimates for the pelagic forage fish groups in his northern 

California Current model (5.0 to 6.0 yr-1), and slightly higher than the Q/B of 6.15 yr-1

determined by Gilman (1994) for Atlantic northern sand lance (Ammodytes dubius). 

The mass-balancing step of Ecopath calculated a forage fish biomass of 3.461 mt km-2

, a 

standing stock that is somewhat smaller than the standing stock of Pacific herring (Table 3). 

The estimated diet composition of forage fish comes from stomach analyses of surf smelt 

and sand lance. Forage fish stomach contents were sampled in the 1970s and 1980s, largely in 

the Strait of Juan de Fuca and the San Juan Islands (Miller et al. 1977, Miller et al. 1980), 

116
although some information from Central Puget Sound does exist (Fresh et al. 1981). Surf smelt 

diets were a mix of copepods, isopods, gelatinous zooplankton, and euphausiids. The diets of 

sand lance mostly consisted of copepods, along with some pelagic amphipods. Combining these 

general diet characteristics, we assumed a diet mainly of copepods, along with small crustaceans, 

small gelantinous zooplankton, euphausiids, macrozooplankton, and a small amount of jellyfish 

(Table 4). 

Forage fish are preyed upon by nearly all other vertebrates, along with squid and Cancer

crabs (Table 4). We assume no predation by jellyfish. 

Surfperches 

This group includes several members of the family Embiotocidae. The most notable 

representatives are pile perch (Rhacochilus vacca), shiner perch (Cymatogaster aggregata), and 

striped seaperch (Embiotoca lateralis). They are often found schooling around habitats such as 

kelp stands, rocky reefs, eelgrass beds, and man-made structures. Their preference seems to be 

areas with high relief, macrophyte cover, and rich invertebrate populations (Fritzche and Hassler 

1989). Schools may contain tens to thousands of individuals. Habitat use, depth distribution, 

and school density may change from day to night and with season (Fritzche and Hassler 1989, 

Love 1996). Embiotocids are live-bearing (viviparous) fishes with very low fecundity (<100 

larvae per female, Love 1996), and their population sustainability depends on relatively high 

survival rates of newly released larvae and juveniles (Quinnel 1986). They prey upon a variety 

of invertebrates, and the larger species in this group (pile perch and striped seaperch) have a 

feeding morphology particularly evolved to feed on benthic invertebrates (Fritzche and Hassler 

1989 and references therein). Surfperches are popular target species for recreational anglers 

fishing from piers, jetties, small boats, or shorelines (Fritzche and Hassler 1989, Love 1996). 

There are no comprehensive biomass surveys for surfperches in Puget Sound. Therefore, 

we used the Ecopath mass-balancing routine to solve for initial surfperch biomass. EE was set at 

0.8, which presumes a substantial proportion of production lost to predators. Losses to fishing 

are minor in the current initial model state (Table 5), although we lack data on catches by 

recreational anglers. 

Estimates of surfperch mortality rates on the west coast of North America are limited. 

Eckmayer (1975, cited in Lane et al. 2002) calculated instantaneous annual mortality rates (Z) for 

walleye surfperch (Hyperprosopon argenteum) and white seaperch (Phanerodon furcatus) in 

Anaheim Bay, California. The former species had an average Z of 1.280 yr-1, while the latter 

species’ Z was age-specific and ranged from 0.588 yr-1 (age 3 fish) to 1.792 yr-1 (age 4 fish). 

Estimates for the major species in PSCB are unavailable, but they are expected to have life spans 

of 6–10 years (Baltz 1984, Lane et al. 2002, Shanks and Eckert 2005), which puts them in 

approximately the same category as Pacific herring. Based on this information, we assumed a 

P/B rate of 1.3 yr-1 for surfperches. 

We estimated Q/B for surfperches based on Christensen and Pauly’s (1992) equation for 

determining consumption as a function of maximum weight and average temperature (Appendix 

C). We used maximum weights as described in Lane et al. (2002) for shiner perch (170 g), 

striped seaperch (870 g) and pile perch (1,160 g). At near-surface annual average temperature 

117
(10.68oC), these weights produce Q/B ratios of 6.83 yr-1, 5.19 yr-1, and 4.95 yr-1, respectively. 

Given this range of values, we set surfperch Q/B at 6.0 yr-1. 

Given the parameter estimates and the top-down demands of predators and fisheries, the 

Ecopath mass-balancing routine calculated that initial surfperch biomass is 3.170 mt km-2. 

Surfperches are most likely associated with benthic habitats between 0 and 30 m depth in Puget 

Sound, which restricts them to approximately 28% of the study area according to bathymetric 

data (Table 1). This translates to nearshore densities of 11.321 mt km-2, which is more than 

double the standing stock measurements made of embiotocids in Southern California kelp beds 

(Quast 1968). We lack information to determine the accuracy of our estimate. 

Surfperch diets come from numerous studies, many summarized by Fritzsche and Hassler 

(1989) and Emmett et al. (1991). Haldorson and Moser (1979) examined frequency of 

occurrence of prey in pile perch and striped seaperch; predominant prey included small benthic 

crustaceans, mussels, snails, and barnacles. Emmett et al. (1991) summarized shiner perch diets 

as dependent on copepods and a mix of benthic invertebrates. Reum (2006, unpubl. data) 

examined stomachs of shiner perch and pile perch from PSCB and found high dependence on 

copepods, small bivalves, and small crustaceans. Based on these sources, we input an initial diet 

that emphasizes infaunal bivalves, copepods, and small crustaceans (Table 4). Lesser 

importance was given to other grazers, mussels, barnacles, shrimp, predatory gastropods, soft 

infauna, and deposit feeders. 

Surfperches are preyed upon by harbor seals, sea lions, resident and migratory diving 

birds, spiny dogfish, skates, ratfish, lingcod, adult rockfish, piscivorous and small-mouthed 

flatfish, demersal fish, and Cancer crabs (Table 4). 

Salmon 

Pacific salmon (Oncorhynchus spp.) are perhaps the most significant species in Puget 

Sound and throughout the Pacific Northwest region as a whole, in terms of their ecological, 

economic, social, and cultural importance. Their complex life histories feature extended 

residences in diverse habitats and dramatic mass migrations. These habitats link multiple 

ecosystems together and provide critical sources of prey, energy, and nutrients in communities 

where salmon rear, migrate, and spawn. Fisheries targeting Pacific salmon are sources of 

revenue and employment; formerly, salmon fisheries were also vital sources of food for 

indigenous peoples. Salmon appeal to tourists through opportunities such as recreational fishing, 

viewing salmon along their spawning migrations (including whale-watching trips that track killer 

whales as they feed on salmon schools), and regional cuisine. The well-documented declines in 

many salmon populations (e.g., Ruckelshaus et al. 2002) have also placed them at the epicenter 

of numerous policy debates related to land and water use, water quality, habitat conservation and 

restoration, fisheries management, aquacultural practices, and even the nature of protected 

species legislation (Waples 1991, Waples 1998). 

Clearly, it is important that this modeling effort include Pacific salmon functional groups, 

but the complexity of salmon life histories, migrations, and habitat dependencies is very difficult 

for the Ecopath with Ecosim software to handle. Among the major constraints, first, the 

ephemeral nature of habitat use by salmon is difficult to capture in a food web model that lacks 

118
spatially explicit diets, rates of mortality, rates of growth, etc. Second, the scale of salmon 

migrations means that they may spend large amounts of time outside of the model domain, either 

in freshwater systems at the beginning and end of their lives or in marine systems during the time 

when they accumulate most of their growth. Third, the equations that link together multiple life 

history stages of functional groups (Appendix D)8 are not ideal for salmon because salmon do 

not grow according to von Bertalanffy–style growth trajectories (Lester et al. 2004), and nearly 

all Oncorhynchus species follow a semelparous reproductive strategy that the model was not 

designed to simulate. Fourth, salmon populations within an ecosystem often have considerable 

differences in life history and ecology, even when they are of the same species (e.g., Groot and 

Margolis 1991). 

Capturing the full range of life history and ecological variation among all distinct salmon 

populations might require a large number of salmon functional groups (e.g., Preikshot and 

Beattie 2001, Harvey and Kareiva 2005). It is unlikely that we would have accurate information 

on how other species in the system interact (e.g., as prey or predators) with many of these 

discrete stocks. 

Given the importance of Pacific salmon and the modeling challenges that they present, 

we chose to simplify them into three functional groups: pink salmon (O. gorbuscha), wild 

salmon, and hatchery salmon. The uniqueness of the pink salmon life history warranted 

distinction as a functional group. The wild and hatchery salmon groups are aggregations of 

several species (Chinook salmon [O. tshawytscha], coho salmon [O. kisutch], chum salmon [O. 

keta], and steelhead [O. mykiss]). As the names suggest, wild salmon are naturally sustaining 

populations, whereas hatchery salmon are artificially propagated. Sockeye salmon (O. nerka) 

were omitted altogether, as justified in Appendix B. 

Each group has two stages: a juvenile stage that represents the months following entry 

into the marine environment when they tend to use nearshore habitats (e.g., Fresh 2006), and a 

subadult stage that begins when they move into more pelagic habitats and continues until they 

return to freshwater to spawn. This compromise is far from perfect. It is an attempt to represent 

salmon as components in the ecosystem without expending exhaustive effort on a larger number 

of salmon functional groups that would be very difficult to model with an acceptable degree of 

realism. Clearly, the salmon groups are ones where improvement will be needed, and where 

caution will be necessary as output is interpreted. 

Pink Salmon 

Pink salmon have the simplest, most specialized life history of Pacific salmon (Heard 

1991). Following emergence from redds, fry rapidly move into estuarine or nearshore marine 

environments, where they remain for a period of weeks to months, then quickly pass through 

coastal systems to the open North Pacific Ocean. After approximately 18 months at sea, they 

 

8

 Difficulties with handling the discrete stages of salmon life history were compounded by problems we encountered 

with a feature of EwE that allows the user to break life history into three or more stanzas. Having that ability would 

potentially allow much greater realism in terms of representing salmon as semelparous species that spend discrete 

time periods in different habitats. However, we were not confident in many of the parameter values that Ecopath 

forcibly estimated when we used the multistanza feature. We therefore used the simpler feature that enables 

separation into two stanzas. 

119
return to their natal streams in the fall, spawn, and die. This two-year life history is fixed. 

Virtually all pink salmon in Puget Sound are odd year stocks, meaning that adults spawn in odd 

calendar years (Heard 1991). More than 99% of Puget Sound pink salmon are of wild origin 

(PFMC 2008). 

The adult population is large but variable. Recent run sizes (i.e., numbers harvested plus 

numbers that escaped to spawn in rivers and streams throughout all basins of Puget Sound) have 

ranged from 440,000 adults in 1997 to 3.5 million adults in 2001 (PFMC 2008). Most are 

destined for watersheds located north of the PSCB domain (e.g., the Skagit and Stillaguamish 

rivers in the Whidbey Basin), but several notable populations move through PSCB: the Puyallup 

River and Green River populations (both of which spawn within the PSCB drainage) and the 

Nisqually River population (which spawns in the South Puget Sound drainage), although the 

Nisqually stock has declined steeply (stock description online at http://wdfw.wa.gov/webmaps/ 

salmonscape/sasi/full_stock_rpts/4585.pdf). The Green River population has increased markedly 

over the last decade from negligible levels to a sizeable run (Hard et al. 1996, PFMC 2008). 

We divided pink salmon into juvenile and subadult stages, with the transition occurring at 

2 months of age. We assigned this transition age based on the Simenstad et al. (1982) review of 

literature on juvenile pink salmon residence times in Puget Sound estuaries and basins. The 

juvenile stage includes the estuarine and nearshore marine rearing periods following emergence. 

We assume the estuarine portion of this stage to occur outside of the model domain. The 

subadult stage includes the rest of the life history up to and including the period when the 

approximately 18-month-old pink salmon reenter Puget Sound and move toward spawning 

streams, a time at which they may also be captured by fisheries. 

Estimating biomass for pink salmon is difficult to do empirically, in part because both 

life history stages are transient. Populations from PSCB and adjacent basins reside in PSCB 

waters for varying amounts of time, while also spending substantial time outside the basin. We 

chose to use the Ecopath mass-balancing routine to estimate biomass for both life history stages. 

To facilitate this, we first set EE for juveniles at 0.2 (reasoning that a substantial component of 

their mortality occurs before they enter PSCB) and for subadults at 0.1 (reasoning that they 

experience only a small amount of mortality in PSCB, in the form of a small fishery and some 

predation, and that most of their mortality either occurs during their extended residence in the 

North Pacific or in freshwater on the spawning grounds). 

We are unaware of any data on juvenile pink salmon P/B or natural mortality rates in 

Puget Sound. We assumed rates of early marine mortality similar to those observed by Parker 

(1968) for pink salmon juveniles from the Bella Coola River, British Columbia, in the early 

1960s. Between 23% and 45% of juvenile pink salmon survived their first 40 days at sea in that 

study. From the midpoint of 34% survival in 40 days, we get a daily instantaneous mortality rate 

of 0.027, which when scaled to an annual time step yields a P/B of 9.844 yr-1 for juvenile pink 

salmon. Parker (1968) also estimated mortality for subadults during the subsequent 410 days at 

sea; that estimate is 1.351 yr-1 after rescaling to an annual time step. Fisheries add further 

mortality. Based on geometric mean catch data in PSCB from 1999 to 2007 (Appendix C) and 

geometric mean escapement data (PFMC 2008), we calculated an F of 0.226 yr-1, resulting in a 

total P/B of 1.577 yr-1 for subadults. Cumulatively, these mortality rates result in a fry-to-adult 

120
return rate of approximately 3.25%, which is well within the range reported for many pink 

salmon populations (e.g., Heard 1991). 

Estimates of juvenile pink salmon consumption rates are available from empirical studies. 

Parker and LeBrasseur (1974) estimated daily consumption rates of 7–8% body weight by 

juvenile pink salmon early in their marine residence. Healey (1982) used gastric evacuation rate 

estimates to calculate daily consumption rates of 4.6–11.2% body weight per day for juvenile 

pink salmon in the Strait of Georgia. Godin (1981) measured daily consumption of 6.6–13.1% 

body weight for juvenile pink salmon near Nanaimo, British Columbia. Simenstad et al. (1980) 

measured consumption of 30.2% body weight per day among pink salmon fry in Puget Sound. 

Because most of these estimates fell in the range of 4.6–13%, we assumed a consumption rate of 

9% body weight per day, or a Q/B rate of 32.85 yr-1 for juvenile pink salmon. This produces a 

P/Q estimate for juvenile pink salmon of 0.3, which is equal to independent growth efficiency 

estimates made by Duffy (2003) for juvenile pink salmon in the Whidbey Basin. 

For subadult Q/B, we used the pink salmon bioenergetics models of Beauchamp et al. 

(1989) and Moss et al. (2009). We ran a 16-month simulation, assuming monthly temperatures 

consistent with the upper 25 m of the water column in PSCB from WDE (no date), a prey energy 

density fixed at 3,500 J g-1 (consistent with a diet of copepods, euphausiids, larvaceans, and 

macrozooplankton, Moss et al. 2009), and a consumer energy density of 4,534 J g-1 (Moss et al. 

2009). The initial weight in the simulation (40 g) approximated the weight at age 60 days (the 

transition age from juvenile to subadult). The final weight (1,616 g) was taken from average 

weights of pink salmon captured in Puget Sound fisheries between 1999 and 2007 (PacFIN 

unpubl. data, also Appendix C). The model produced daily consumption estimates, which we 

divided by body weights to get daily Q/B ratios. We weighted daily Q/B ratios by daily body 

size and abundance in a theoretical equilibrium population with a natural mortality rate of 

1.351 yr-1, and arrived at an annual Q/B ratio of 13.429 yr-1. 

The mass-balancing routine of Ecopath arrived at biomass values of 0.00845 mt km-2 for 

juvenile pink salmon and 0.0772 mt km-2 for subadult pink salmon. These values compare 

favorably to rough calculations of pink salmon run size and reproductive output in the Central 

and South Puget Sound basins over recent decades. Pink salmon escapements to the Central and 

South Puget Sound basins of have been estimated back to 1983 by the Pacific Fishery 

Management Council (PFMC).9

 Run sizes are highly variable, ranging from approximately 

3,400 in 1997 to approximately 630,000 in 2007. The geometric mean escapement to Central 

Puget Sound plus South Puget Sound from 1983 to 2007 is approximately 30,400 individuals, 

and the geometric mean odd-year commercial harvest is approximately 3,900 individuals (PFMC 

2008). On average, these values sum to about 2% of the entire pink salmon run to Puget Sound 

over that time period (PFMC 2008). Geometric mean recreational harvest in all of Puget Sound 

is approximately 55,000 pink salmon per odd year over the same time period. Assuming that 

approximately 2% of that catch was destined for the Central or South Puget Sound basins adds 

an additional 1,100 individuals per odd year. The sum of these values (30,400 + 3,900 + 1,100) 

gives an approximate odd-year run size of 35,400 individuals. Pink salmon captured in PSCB 

between 1999 and 2007 averaged 1.616 kg (PacFIN unpubl. data). 

 

9

 PFMC run reconstructions do not include the Green River pink salmon population, which has not been monitored 

historically but has had substantial odd-year runs since 2001 (PFMC 2008). 

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This leads to a total return weight of 57.206 mt or 0.076 mt km-2. Dividing this in half 

(because they return only every other year) yields a biomass of 0.038 mt km-2, which is in the 

same order of magnitude as our subadult estimate. We might assume that the Green River 

population is approximately equal to the Puyallup River population (at least in recent years), 

which would double the biomass estimate. Moreover, a spawning population of 60,800 adults 

with a 1:1 sex ratio, a fecundity of 2,000 eggs per female, and an egg-to-fry mortality rate of 

87.3% (Parker 1968, Quinn 2005) would yield approximately 7.72 million fry that survive to 

enter the marine environment the following spring. An initial weight of 0.24 g (Heard 1991) 

leads to a juvenile biomass of 1.853 mt or 0.0024 mt km-2. Dividing by 2 for odd years results in 

a biomass of 0.0012 mt km-2, which is well below the Ecopath estimate for juveniles, although 

rapid early marine growth may make up some of the difference between the emergent fry 

biomass estimate and the Ecopath biomass estimate. 

The values described above do not reflect the recent sharp increases in adult pink salmon 

returns to the Puyallup and Green rivers: They now number in the hundreds of thousands, which 

is two orders of magnitude greater than typical runs of the 1990s and early 2000s (PFMC 2008). 

This likely means that values for biomass, P/B, and EE would be different in a model calibrated 

for 2009. We may explore the consequences of this change in later Ecosim simulations. 

Juvenile pink salmon diets in Puget Sound and nearby systems have been summarized by 

Simenstad et al. (1982), Heard (1991), and Rice (unpubl. data). We arbitrarily input 40% of their 

diet as Import to account for time spent outside the model domain, and also to account for prey 

not tracked explicitly in the model (e.g., insect larvae and fish eggs). The remainder of the diet 

was mostly copepods, euphausiids, and small gelatinous zooplankton, with minor contributions 

from macrozooplankton and small crustaceans (Table 4). Subadult pink salmon were assumed to 

spend most of their time outside of the model domain; we assumed 90% of their diet was Import. 

The remaining component—mostly euphausiids—was based on diets of juvenile and subadult 

pink salmon captured in offshore pelagic habitats in Puget Sound by Rice (unpubl. data). 

Juvenile pink salmon are preyed upon by harbor seals, gulls, resident diving birds, and 

migratory diving birds (Table 4). Other sources of predation (e.g., predatory fishes) are likely 

but we do not have data to quantify them. Subadults are preyed upon by harbor seals, sea lions, 

raptors, and spiny dogfish. A small fishery (compared to other salmon groups) comprised of 

several gear types takes pink salmon during odd years (Table 5, Appendix C). 

We linked the juvenile and subadult pools in Ecosim using the delay-difference equations 

for stage-structured populations (Appendix D). We set the recruitment power parameter at 0.85, 

the age at transition from juvenile to subadult at 0.167 years, the weight ratio of an average 

subadult relative to an individual at the transition age at 100, and the von Bertalanffy growth 

parameter k at 0.14. The weight ratio and k parameters were approximations based on size-atage data in Heard (1991) and the recruitment power parameter was lowered from one based on 

recommendations in Christensen et al. (2005). All other parameters in the delay-difference 

equations were set at the default values as listed in Appendix D. 

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Wild Salmon 

This group includes juveniles and subadults from naturally reproducing populations of 

chum, Chinook, and coho salmon and steelhead. Although these stocks are naturally 

reproducing, some may be mixed stocks that originated from both native populations and 

naturalized hatchery releases (stock descriptions online at http://wdfw.wa.gov/fish/sasi/). Chum 

salmon are the largest component in terms of total biomass and abundance. Steelhead are a very 

minor component. 

Our archetypal wild salmon most closely, but not entirely, reflects the life history of 

chum salmon because they are the most numerous representative of this group. Following an 

initial freshwater phase, juvenile wild fish immigrate to nearshore marine habitats, where they 

remain for approximately 3 months (Simenstad et al. 1982, Fresh 2006). At the end of this 

phase, we assume that they weigh approximately 200 g, consistent with observations that chum 

salmon, which enter the system weighing between 2 and 3 g, grow at an exponential rate of 

approximately 5% body weight per day (Salo 1991). After leaving the nearshore area, subadults 

spend the majority of their time outside of the PSCB model domain, although a fraction remain 

resident for part or perhaps all of that period (O’Neill and West 2009). 

We assume a subadult marine phase lasting 3 years, which is the longer end of the typical 

range for chum and is also a reasonable marine life history duration for Chinook salmon (e.g., 

Myers et al. 1998). We assume that they grow to approximately 5 kg in size, that is, slightly 

larger than a typical adult chum salmon returning to Central or South Puget Sound, but smaller 

than a typical returning adult Chinook salmon (PacFIN unpubl. data). Returning subadults are 

subject to intense commercial and recreational fisheries, unless regulations protect them from 

fishing. Maturing adults eventually reach their natal creeks, spawn, and die (except for small 

numbers of iteroparous steelhead, Busby et al. 1996).

Estimating the biomass of juvenile and subadult stages of wild salmon is difficult due to 

several factors. For one thing, the biomass of this group might include transient populations 

moving through PSCB to or from other basins. Variable, population-specific migration timing, 

residency, and size add further complexity (e.g., Duffy et al. 2005). Moreover, our decision to 

pool many species and populations together in this functional group introduces another problem: 

Individual weights vary considerably, both at the juvenile and subadult stage. 

Because of these issues, we allowed Ecopath to estimate biomass for juvenile and adult 

wild salmon. To facilitate this, we arbitrarily set EE for both juveniles and subadults at 0.2. Our 

rationale for the juvenile wild salmon EE is that much of this group’s biomass spends weeks or 

months outside of the model domain (e.g., in freshwater or in South Puget Sound) before 

entering PSCB, and thus their EE should not be set too high. For subadults, we reasoned that 

most spend the vast majority of their time at sea, outside of PSCB (i.e., that the resident 

populations are of minor importance, but see O’Neill and West 2009). Thus their EE must be 

relatively low, but it must be greater than zero because they are targeted by fishing inside and 

outside of the model domain, and the developers of the EwE software recommend accounting for 

all fishing pressure directed at a modeled functional group, whether or not that fishing pressure 

occurs within the bounds of the modeled ecosystem (Christensen et al. 2005). 

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We examined the available literature for wild salmon P/B rates. The only empirical data 

for Puget Sound of which we are aware are estimates for juvenile chum salmon mortality rates in 

the Hood Canal Basin (Bax 1983, Whitmus 1985). These rates were unsustainably high when 

extrapolated beyond a few days: daily rates of mortality ranged between 31% and 46%. Instead, 

we used information on long-term survival rates of wild or naturally rearing chum, coho, and 

Chinook populations, summarized by Quinn (2005, his Table 15-1). That summary includes 

smolt-to-adult survival rates based on a large number of empirical studies. The proportion of 

smolts surviving to adulthood were 0.014, 0.031, and 0.104 for chum, Chinook, and coho, 

respectively (Quinn 2005). Assuming 36-month subadult phases, these values scale to 

instantaneous annual mortality rates of 0.949 yr-1, 0.772 yr-1, and 0.503 yr-1 for the three species. 

We assumed that mortality rates for juveniles were sixfold higher than for subadults (an arbitrary 

approximation based on the P/B ratios we calculated for pink salmon, see previous subsection), 

which yielded juvenile P/B of 5.692 yr-1 for chum salmon, 4.632 yr-1 for Chinook salmon, and 

3.018 yr-1 for coho salmon. We then weighted the mortality rates (at monthly intervals) by the 

projected abundances and weights of the three species to come up with the final weighted 

estimates of 5.591 yr-1 for juveniles and 0.989 yr-1 for subadults. 

We estimated juvenile wild salmon Q/B based on several indirect measures taken from 

the literature. Duffy (2003) estimated growth efficiency of juvenile chum, Chinook, and coho 

salmon in the Whidbey and South Puget Sound basins, using salmonid bioenergetics models and 

empirically derived diet and growth estimates. Spring and summer growth efficiencies of 

juvenile chum salmon were quite high (40%) in the Whidbey Basin, but were lower in the South 

Puget Sound Basin (26–34%). Growth efficiencies of Chinook and coho salmon juveniles were 

much lower (Chinook = 12–22%, coho = 17–25%). If the most conservative juvenile chum 

salmon growth efficiency (26%, i.e., P/Q = 0.26) serves as a reasonable compromise, then 

dividing P/B by P/Q yields a Q/B estimate of 19.662 yr-1. 

However, the above Q/B estimate implies a ration of approximately 5.4% body weight 

per day on an annual basis. That ration is at the low end of the range of estimated springtime 

daily rations for juvenile chum salmon in the Nanaimo Estuary (4.4–18% body weight per day, 

Healey 1979), and is far lower than daily ration estimates made by Simenstad and Salo (1982) 

for juvenile chum salmon in Hood Canal. Simenstad and Salo’s estimated daily rations (15–25% 

body weight) would produce extraordinary Q/B ratios (54.75 to 91.25 yr-1) over the course of a 

full year; those ratios may, in fact, be reasonable, because chum salmon grow very rapidly during 

the nearshore phase (Healey 1979, Salo 1991, Quinn 2005). For now, however, we assumed a 

relatively conservative Q/B of 30 yr-1 for this group. 

For subadult wild salmon, we used in situ daily ration estimates derived from a study of 

chum salmon feeding in continental shelf areas of the North Pacific Ocean (Kosenok and 

Naidenko 2008). They found seasonal daily rations of 2.6–2.8% body weight (summer) and 1.2–

2.5% body weight (fall). We took seasonal midpoints (2.7% and 1.85%, respectively) and also 

the lowest observed ration (1.2%), and assumed that the average of those three values (1.917%) 

was a reasonable mean overall daily ration; that equates to a Q/B of 7.00 yr-1. The resulting P/Q

(0.141) is greater than that of subadult pink salmon (0.117). 

Given these parameters and the top-down requirements of predators and fisheries, 

Ecopath calculated biomass of 0.0951 mt km-2 for juvenile wild salmon and 11.918 mt km-2 for 

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subadult wild salmon. As noted above, it is very difficult to empirically verify these biomass 

estimates. The subadult value can be compared to a rough approximation of returning chum 

salmon biomass in recent spawning runs. Naturally produced chum salmon run sizes (i.e., 

harvest plus escapement) to Central and South Puget Sound in recent years have been close to 

500,000 fish (WDFW no date). We can further assume tens of thousands more returning wild 

Chinook, coho, and steelhead (PFMC 2008). 

Assuming a mean weight of 5 kg for these fish, a run of 600,000 wild salmon would yield 

3,000 mt of biomass, or approximately 4 mt km-2 moving through the PSCB model domain. 

Although that number is substantially less than our biomass estimate, we note that the subadult 

pool spends a considerable amount of time outside of PSCB, and in any given year only a portion 

of all of the subadult biomass will actually be maturing and reentering Puget Sound. Thus the 

biomass estimate should realistically exceed the biomass of the total run, and our biomass 

estimate may be plausible. This issue highlights the desirability of finer age structure of the 

salmon pools, so that different phases of salmon life history could be modeled more discretely. 

Wild salmon diets are difficult to characterize because they can vary markedly by 

species, size, habitat type, season, and location (e.g., Levy and Levings 1978, Groot and 

Margolis 1991, Quinn 2005). We largely relied on diet data for juvenile chum, Chinook, and 

coho salmon in Washington estuaries, as summarized by Simenstad et al. (1982, their Table 5). 

We gleaned additional diet information from a report on nearshore habitat use by juvenile 

salmon in PSCB by Brennan et al. (2004). 

In calculating a grand mean diet for juvenile wild salmon, we weighted the mean diet 

data from Simenstad et al. (1982) according to the proportional abundances of juveniles, as 

derived from the P/B estimation exercise outlined previously. We also assumed that 33% of 

their diet was Import: 25% of the diet was assumed to have been consumed outside the model 

domain, and an additional 8% were prey items that were eaten in the PSCB model domain but 

are not functional groups in the model (e.g., insects). Besides Import, diets consisted mostly of 

mobile invertebrates—small crustaceans, copepods, macrozooplankton, euphausiids, small 

gelatinous zooplankton, soft infauna, shrimp, and microzooplankton (Table 4). Approximately 

5% was assigned to fish groups, which is consistent with Simenstad et al. (1982) although they 

did not specify the taxonomy of consumed fishes. We expect some predation to be on juvenile 

Pacific herring, sand lance, surfperch, and demersal fish, based on recent stomach samples from 

juvenile salmon captured in the Whidbey Basin (Rice unpubl. data). 

Subadult wild salmon diets are dominated by prey eaten outside of the model domain, 

where most subadult wild salmon growth occurs. Import accounts for 90% of their diet. For the 

remainder, we used stomach content data from salmon captured in pelagic habitats in the 

adjacent Whidbey Basin (Rice unpubl. data). Principal prey items were macrozooplankton and 

small gelatinous zooplankton, along with euphausiids, forage fish, and Pacific herring (Table 4). 

Predation on juvenile wild salmon is poorly characterized in this model. Currently, we 

only include juvenile lingcod, harbor seals, and certain seabirds as predators on juvenile wild 

salmon. It seems likely that other fishes and possibly some large invertebrates feed on them as 

well. We include harbor seals, sea lions, raptors, spiny dogfish, and adult lingcod as predators 

on subadult wild salmon. 

125
Several fisheries target subadult wild salmon passing through PSCB. In terms of biomass 

landed, this functional group supports a larger fishery than any other in the model (Table 5). 

Estimates of commercial catches come from PFMC run reconstructions of wild Chinook and 

coho salmon (2008) and from WDFW for chum salmon (no date). We used geometric mean 

landings from 1996 to 2002 (Table 5); catches were allocated among different gear types 

according to data from PacFIN (Appendix C). 

In addition, recreational anglers take many wild subadults each year. PFMC estimates 

total recreational catches of Chinook and coho salmon in Puget Sound, but the data are not 

broken down by basin nor are they separated into wild and hatchery stocks (PFMC 2008). Thus 

we made some assumptions with respect to recreational harvest. Because wild salmon 

(particularly Chinook and coho salmon) are protected from recreational harvest for much of the 

year in marine waters of Puget Sound (WDFW 2009), we arbitrarily assumed that only 20% of 

recreationally caught Chinook and coho salmon were wild fish. We also assumed that 

recreational landings were proportional to the reconstructed run sizes to the different regions of 

Puget Sound (PFMC 2008). 

During 1996–2002, geometric mean recreational harvests were 34,646 Chinook salmon 

and 74,352 coho salmon in all of Puget Sound. During that same time period, approximately 

79% of the total wild Chinook salmon and 16% of the total wild coho salmon returning to Puget 

Sound passed through PSCB (PFMC 2008). At those proportions and assuming 20% of the 

recreationally harvested fish were wild, the average recreational harvest would be 5,489 Chinook 

and 2,401 coho subadults. For chum salmon, we assumed that recreational chum salmon 

landings were approximately 90% wild (i.e., proportional to wild and hatchery run sizes). Based 

on this, we assumed approximately 1,800 wild chum salmon were taken annually in the 

recreational harvest (i.e., 90% of the long-term average of recreational chum salmon catches near 

the Nisqually River and Kennedy Creek, WDFW no date). Assuming mean weights of 6 kg, 2.5 

kg, and 4 kg for returning adult Chinook, coho, and chum salmon, respectively, total recreational 

harvest equals 0.061 mt km-2. 

We linked the juvenile and subadult pools in Ecosim using the delay-difference equations 

for stage-structured populations (Appendix D). We set the recruitment power parameter at 0.85, 

the age at transition from juvenile to subadult at 0.25 year, the weight ratio of an average 

subadult relative to an individual at the transition age at 25, and the von Bertalanffy growth 

parameter k at 0.15. The weight ratio and k parameters were approximations based on size-atage data in Salo (1991) and Healey (1991), and the recruitment power parameter was lowered 

from one based on recommendations in Christensen et al. (2005). All other parameters in the 

delay-difference equations were set at the default values as listed in Appendix D. 

Hatchery Salmon 

Hatchery salmon are produced by state, federal, and tribal hatcheries throughout the 

Pacific Northwest, mainly for the purpose of supporting salmon fisheries and more recently in a 

variety of efforts to help restore declining wild stocks. More than 20 state and tribal hatcheries 

produce salmon in tributaries to PSCB or in the South Puget Sound Basin (map of these 

hatcheries online at http://wdfw.wa.gov/hat/graphics/hat_map.jpg). Hatcheries release juvenile 

126
salmon at different ages and sizes, including many that are at the smolting stage and are 

physiologically ready to migrate to sea. 

The hatchery salmon group contains the same four species as the wild salmon group 

(Chinook, chum, coho, and steelhead), but in different relative amounts compared to the wild 

group, which was dominated by chum salmon. Between 1995 and 2001, state-run hatcheries 

alone released between 10 million and 18 million hatchery-reared juvenile Chinook, chum, coho, 

and steelhead annually into rivers feeding PSCB (WDFW unpubl. data). They released an 

additional 15 million to 22 million into South Puget Sound streams (WDFW unpubl. data), the 

survivors of which would presumably have moved through PSCB as they migrated seaward. 

These releases tended to be approximately 60% Chinook salmon, 28% coho salmon, 9% chum 

salmon, and less than 3% steelhead. 

Annual releases from tribal hatcheries during the same time period ranged from 

approximately 8 million to 12 million juveniles in PSCB tributaries and 3 million to 6 million 

juveniles in South Puget Sound tributaries. These releases were approximately 53% Chinook 

salmon, 28.5% chum salmon, 17% coho salmon, and just over 1% steelhead (RMPC unpubl. 

data). Returns were also substantial and reflected a greater importance of Chinook and coho 

salmon. From 1996 to 2002, geometric mean escapements of hatchery adults to Central and 

South Puget Sound catchments were approximately 39,900 Chinook, 73,800 coho, and 27,100 

chum adults (PFMC 2008, WDFW no date). 

Modeling hatchery salmon as a distinct group is necessary for several reasons. First, 

because they are artificially propagated, they are relatively disconnected from the ecology of the 

system. The number of juveniles in the system is a function of human decisions as much as it is 

a function of environmental conditions. Second, they are subject to certain fisheries that wild 

salmon are not; for example, some wild stocks of Chinook salmon are protected and must be 

released when caught, whereas anglers can retain hatchery-produced Chinook salmon, 

identifiable by the clipped adipose fin. Third, hatchery production is a controversial practice, 

due to the potential impacts of hatchery salmon competing with or interbreeding with wild 

salmon (e.g., Ruckelshaus et al. 2002). Having distinct hatchery salmon pools in the model 

allows us to explore scenarios in which hatchery production is changed with the intent of 

obtaining a management or conservation objective. 

Our archetypal hatchery salmon is an attempt to approximate the life histories of chum, 

coho, and ocean-type Chinook salmon, that is, Chinook salmon whose juveniles migrate from 

freshwater to sea within months of becoming free-swimming fry and being released from the 

hatchery. They represent the majority of hatchery-produced Chinook salmon in Puget Sound 

(e.g., PFMC 2008). Chum salmon juveniles similarly enter the marine environment during their 

first spring, soon after release from the hatchery. By contrast, juvenile coho salmon and streamtype Chinook salmon are reared to be much larger and typically go to sea after rearing for more 

than a year in the hatchery and stream environments. We presume that the juvenile hatchery 

salmon stage, during which the salmon lives in nearshore marine habitats, lasts approximately 3 

months (e.g., Simenstad et al. 1982). We assume a subadult marine phase lasting 2.5 years. This 

is a compromise between Puget Sound coho, which typically spend 18 months at sea (Weitkamp 

et al. 1995); chum, which typically spend 2 to 3 years at sea (Salo 1991); and Chinook, which 

typically spend at least 3 years at sea (Myers et al. 1998). 

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As with other salmon groups, we used the Ecopath mass-balancing algorithm to estimate 

biomass for both juvenile and subadult hatchery salmon. Of all salmon groups, juvenile hatchery 

salmon would perhaps be the most feasible for which to empirically derive biomass, because 

total annual releases are recorded by hatcheries. However, because variable levels of mortality 

occur before the released juveniles arrive in PSCB waters, and because hatchery releases from 

other basins including the Whidbey Basin and Hood Canal may spend a part of their time in 

PSCB,10 even the release data are not absolute measures of initial juvenile hatchery salmon 

biomass for PSCB. We set juvenile EE at 0.2, which partly accounts for the time they spend 

outside of PSCB. For subadults, we also assumed an EE of 0.2, reflecting that most of this phase 

is spent outside of PSCB, but also reflecting that an important portion of their annual production 

is lost to fisheries each year. 

To estimate hatchery salmon P/B, we used data on postrelease mortality rates of hatchery 

stocks, and then parsed that mortality into the juvenile and subadult phases. Quinn et al. (2005) 

examined release-to-adult survival rates of Chinook and coho salmon from two hatcheries in 

Puget Sound from 1969 to 1998. The data they compiled from the Soos Creek hatchery probably 

best represent the majority of Puget Sound hatcheries, in terms of the size of salmon at the time 

of release (mean Chinook weight = 5.17 g, mean coho weight = 25.25 g). Differences were 

stark: estimated marine survival for Soos Creek hatchery Chinook salmon averaged 0.77%, while 

Soos Creek hatchery coho salmon averaged 7.35% (Quinn et al. 2005). Subyearling Chinook 

survival may have been lowered during even-numbered years by the presence of large numbers 

of juvenile pink salmon in Puget Sound, a conclusion also reached by Ruggerone and Goetz 

(2004). 

Contemporary data on fry-to-adult survival of hatchery chum salmon from Puget Sound 

were not readily available in the literature. Survival reported by Salo (1991) for Hood Canal 

hatchery chum salmon ranged from 0.25 to 3.0%, although those data were from 1916 to 1971. 

For now, we assume that hatchery chum salmon have the same mean survival (1.4%) as reported 

by Quinn (2005) for naturally rearing chum salmon populations. We applied these speciesspecific survival rates to geometric means of estimated run sizes of hatchery salmon to Central 

and South Puget Sound from 1996 to 2002 (Chinook and coho salmon data from PFMC 2008, 

chum salmon data from WDFW no date) and back calculated the number of smolts required to 

produce the adult run sizes. We then solved for the necessary instantaneous annual mortality 

rates, assuming durations of 3 and 30 months for the juvenile and subadult phases, and also 

assuming (as with pink salmon and wild salmon, above) that juvenile mortality was sixfold 

greater than subadult mortality. Juvenile and adult mortality rates, respectively, were 7.300 yr-1

and 1.217 yr-1 for Chinook salmon, 3.916 yr-1 and 0.653 yr-1 for coho salmon, and 6.403 yr-1 and 

1.067 yr-1 for chum salmon. We then weighted the mortality rates (at monthly intervals) by the 

projected abundances of the three species to come up with the final weighted estimates of 6.492 

yr-1 for juveniles and 1.017 yr-1 for subadults. 

In the absence of data to demonstrate otherwise, we opted to use the same Q/B estimates 

and justifications as were used for juvenile and subadult wild salmon (30 yr-1 and 7 yr-1, 

respectively). Strictly speaking, the two groups likely have different Q/B ratios because the 

relative abundances of Chinook, coho, and chum salmon within the two groups are different, but 

 

10 K. Fresh, NWFSC, Seattle, WA. Pers. commun., 23 March 2009. 

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we assume that capturing the differences between the two salmon groups is not as important as 

capturing the relative differences between salmon, collectively, and nonsalmonid functional 

groups (i.e., intraguild differences are less important than interguild differences). 

With the above parameters and the top-down demands of predators and fisheries on 

hatchery salmon (see below), Ecopath’s mass-balancing procedure produced biomass estimates 

of 0.0819 mt km-2 for juvenile hatchery salmon and 6.071 mt km-2 for subadult hatchery salmon. 

By comparison, from 1996 to 2002, Central plus South Puget Sound run sizes averaged 56,000 

hatchery Chinook salmon and 132,000 coho salmon (geometric means, PFMC 2008). Run sizes 

of hatchery-produced chum salmon at the same time had a geometric mean of approximately 

51,500 (WDFW no date). Assuming individual body weights of 6 kg, 2.5 kg, and 4 kg for 

returning adult Chinook, coho, and chum salmon, respectively, these run sizes would have 

approximately 872 mt of salmon biomass, or 1.151 mt km-2 moving through PSCB, which is 

much lower than the Ecopath biomass estimate. However, as with wild salmon (see Wild 

Salmon subsection above), we would expect the run-size biomass to be smaller than the Ecopath 

biomass estimate because a large amount of standing subadult biomass is at sea. 

For juvenile hatchery salmon diets, we relied primarily on the nearshore juvenile salmon 

diet data provided in Simenstad et al. (1982) with further information from Brennan et al. (2004), 

and weighted the species-specific diets according to the juvenile abundance estimates generated 

during the P/B derivation exercise described above. We assume that 25% of their diet was 

consumed outside the model domain, and add a further 11% to account for prey that are 

consumed in PSCB but that are not part of the model (e.g., insects). This produces a total Import 

component of 36% of their diet. The remainder of the diet is dominated by zooplankton groups, 

small crustaceans, and small fishes (Table 4). Simenstad et al. (1982) did not specify which 

fishes appeared in juvenile salmon diets; we assume that it is mostly juvenile Pacific herring and 

forage fish, along with surfperches and demersal fish. 

For subadults, we assume that 90% of their diet is Import, prey consumed outside the 

model domain. The remaining 10% was allocated among prey observed in subadult Chinook, 

coho, and chum salmon stomachs following capture in offshore pelagic habitats in the adjacent 

Whidbey Basin (Rice unpubl. data). Species-specific diets were weighted according to relative 

run-size estimates as alluded to in the P/B calculation section, described above. Key prey 

included macrozooplankton, forage fish, juvenile and adult Pacific herring, small gelatinous 

zooplankton, and euphausiids (Table 4). 

As with other salmon groups, predation on hatchery salmon is poorly parameterized in 

this model. Currently, we include only juvenile lingcod, harbor seals, and certain seabirds as 

predators on juvenile hatchery salmon; however, it seems likely that other fishes and possibly 

some large invertebrates feed on them as well. We include harbor seals, sea lions, raptors, spiny 

dogfish, and adult lingcod as predators on subadult hatchery salmon. 

Commercial fisheries use several gear types to catch subadult hatchery salmon. In total, 

these fisheries make subadult hatchery salmon the second most important source of landed 

biomass in the system, behind only subadult wild salmon (Table 5). Estimates of the commercial 

catch are available from the PFMC run reconstructions of hatchery Chinook and coho salmon 

(PFMC 2008) and from the WDFW for hatchery chum salmon (WDFW no date). We used 

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geometric mean landings from 1996 to 2002; catches were allocated among different gear types 

according to data from PacFIN (Appendix C). 

In addition, recreational anglers take many hatchery-produced subadults each year. 

PFMC estimates total recreational catches of Chinook and coho salmon for the entirety of Puget 

Sound, but these catches are not broken down by basin nor are they separated into hatchery 

stocks and naturally reproducing stocks (PFMC 2008). Thus we made some assumptions with 

respect to recreational landings of subadult hatchery salmon. We arbitrarily assumed that 80% 

of recreational landings of Chinook and coho salmon are of hatchery fish, whereas recreational 

chum salmon landings were proportional to wild and hatchery chum salmon run sizes. Because 

PFMC’s counts of recreational Chinook and coho landings in Puget Sound are not broken down 

by basin, we also assumed that recreational landings are proportional to the reconstructed total 

run sizes to each major basin or drainage in the PFMC analysis (PFMC 2008). 

During the years 1996–2002 in all of Puget Sound, geometric mean recreational harvests 

were 34,646 Chinook salmon and 74,352 coho salmon. During that time period, approximately 

67% of the total hatchery-produced Chinook salmon and 41% of the total hatchery-produced 

coho salmon that returned to Puget Sound passed through PSCB, according to the run 

reconstruction model (PFMC 2008). These assumptions lead to an average recreational harvest 

of 18,570 Chinook and 24,387 coho subadults. We further assume approximately 200 hatcheryproduced chum salmon taken in the recreational harvest (i.e., 10% of the long-term average of 

recreational chum salmon catches near the Nisqually River and Kennedy Creek (WDFW no 

date). Assuming mean body weights of 6 kg, 2.5 kg, and 4 kg for returning adult Chinook, coho, 

and chum salmon, respectively, we are left with a recreational harvest of 0.229 mt km-2. 

We linked the juvenile and subadult pools in Ecosim using the delay-difference equations 

for stage-structured populations (Appendix D). We set the recruitment power parameter at 0.01, 

the age at transition from juvenile to subadult at 0.25 years, the weight ratio of an average 

subadult relative to an individual at the transition age at 25, and the von Bertalanffy growth 

parameter k at 0.15. The weight ratio and k parameters were approximations based on size-atage data in Salo (1991) and Healey (1991). The recruitment power parameter was lowered 

drastically from the default of one because these are hatchery fish and thus their recruitment is 

essentially disconnected from adult run size, necessitating a low recruitment power parameter 

(Christensen et al. 2005). All other parameters in the delay-difference equations were set at the 

default values as listed in Appendix D. 

Gadiforms 

Gadiform fishes have undergone dramatic declines in biomass in Puget Sound in recent 

decades and are a focus of fisheries management concern in the ecosystem. The larger species 

are protected from fishing, in hopes of rebuilding local populations. We have divided 

representatives of this order into three monospecific functional groups, based on differences in 

their feeding ecology and status. A fourth gadiform, Pacific tomcod (Microgadus proximus), is 

included in an aggregated group of small groundfish (see Demersal Fish subsection below) 

because it is much smaller and has little commercial or recreational importance. 

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Pacific Hake 

Pacific hake (Merluccius productus) are of great importance to the ecology and the 

fisheries of the west coast of North America. In particular, a massive stock of hake makes 

annual migrations between winter spawning grounds in southerly waters of the California 

Current ecosystem and summer feeding grounds off the coasts of British Columbia, Washington, 

and Oregon. That stock supports the largest fishery (in terms of biomass landed) on the U.S. 

West Coast. By contrast, two much smaller stocks with limited ranges of migration dwell yearround near the northern extent of their range, one in Puget Sound and the other in the Strait of 

Georgia (Gustafson et al. 2000). The three stocks are genetically discrete (Iwamoto et al. 2004). 

The Puget Sound stock was once far more abundant than now, but was badly depleted in the 

1980s, most likely by heavy fishing pressure or possibly by ecological effects such as predation 

by pinnipeds (Gustafson et al. 2000). 

Following a petition to list Puget Sound Pacific hake under the Endangered Species Act 

(Wright 1999), a federally appointed biological review team (BRT) concluded that the two 

Pacific hake stocks in the Puget Sound–Georgia Basin represent a distinct population segment 

(DPS) of the species as a whole (Gustafson et al. 2000). That DPS was judged by the BRT not to 

be worthy of listing as endangered, although the BRT did conclude that the DPS could possibly 

risk extinction in the near future. NOAA considers this DPS a Species of Concern (NOAA 

2009), and WDFW considers it a Candidate Species, one whose status “may meet the listing 

criteria defined for State Endangered, Threatened, or Sensitive” (http://wdfw.wa.gov/wlm/ 

diversty/soc/candidat.htm). 

There are two major spawning areas in Puget Sound: Port Susan, located in the Whidbey 

Basin, and Dabob Bay, located in Hood Canal. Pacific hake gather in these areas to spawn 

during winter months and then disperse to feeding areas throughout the sound (Gustafson et al. 

2000). Adults are primarily midwater feeders that prey on euphausiids and other crustaceans, 

cephalopods, and small fishes. They are preyed upon by many piscivorous fishes and pinnipeds. 

Fisheries targeting Pacific hake in Puget Sound ended in 1991 (Gustafson et al. 2000). 

We used the Ecopath mass-balancing routine to estimate Pacific hake biomass. 

Hydroacoustic surveys from earlier decades indicated an 88% decline in the population biomass 

between 1983 and 1999, although much of this decline is due to a marked decrease in mean 

individual mass (Gustafson et al. 2000). There have also been declines in length at maturity. 

Because the population was obviously in a state of dramatic change, we felt it unwise to simply 

default to the most recent biomass estimate (2,365 mt in 1999, Gustafson et al. 2000) as our 

initial estimate for biomass. We assumed a very high EE (0.9) for Pacific hake, implying that 

predation by other model groups currently consumes 90% of production. This is consistent with 

the notion that Pacific hake numbers have continued to dwindle even though fishing was halted 

in 1991. 

For Pacific hake P/B, we opted to use the mean production estimate from the status 

review of Puget Sound Pacific hake by Gustafson et al. (2000). Their instantaneous annual 

production estimates ranged from 0.30 to 0.51 yr-1; we used the average, 0.405 yr-1. This value 

exceeds the natural mortality estimate of 0.23 yr-1 for the outer coast stock of Pacific hake, as 

estimated by Dorn et al. (1999). However, as Gustafson et al. (2000) note, a natural mortality of 

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0.23 yr-1 coupled with a production rate of 0.30 to 0.51 yr-1 would result in an increase of 

population biomass, not the precipitous decline described above. Thus we conservatively 

assume that total mortality (P/B) must at least equal the production estimate developed by 

Gustafson et al. (2000). This parameter merits further research. 

We based the Pacific hake Q/B parameter on the bioenergetics analysis by Francis (1983) 

for Pacific hake on the outer coast. Francis estimated that Pacific hake consumption, in terms of 

percent body weight eaten each day, ranged from 0.713 (for large, age-8 individuals) to 1.094 

(for small, age-2 individuals). Although we could apply the rate for small individuals to the 

Puget Sound stock, owing to the much smaller size-at-age of the Puget Sound stock compared to 

the outer coast stock (Gustafson et al. 2000), we opted instead to apply the rate for large 

individuals, reasoning that consumption rates for Pacific hake in Puget Sound must be relatively 

low in recent decades, as evidenced by the steep decline in individual growth rate. When 

0.713% is multiplied by 365 days, the Q/B ratio for Pacific hake in PSCB is equal to 2.602 yr-1. 

This estimate, combined with the P/B estimate, produces a reasonable P/Q estimate of 0.156. 

Ecopath’s mass-balancing routine estimated Pacific hake biomass at 3.405 mt km-2, based 

on all other parameter inputs. 

Pacific hake diet composition was based on the stomach analyses by Reum (2006, 

unpubl. data). As described in Appendix C, we seasonally weighted the raw stomach content 

data collected by Reum (unpubl. data), assuming a Q10 of 2.6 for food consumption based on 

bioenergetics modeling for a related species (walleye pollock) by Ciannelli et al. (1998). 

Approximately half of their diet is euphausiids; other key prey are macrozooplankton, shrimp, 

small crustaceans, juvenile Pacific herring, smaller Pacific hake, and soft infauna (Table 4). 

Pacific hake in PSCB are eaten by harbor seals, sea lions, migratory diving birds, raptors, 

spiny dogfish, walleye pollock, lingcod, piscivorous flatfish, and demersal fish (Table 4). As 

noted in the preceding paragraph, Pacific hake are also mildly cannibalistic. 

Pacific Cod 

Pacific cod (Gadus macrocephalus) are large gadoids of great importance to the ecology 

and fisheries of the North Pacific. Puget Sound is close to the southeastern limit of this species’ 

spatial range (Pedersen and DiDonato 1982). Once abundant and sought by recreational and 

commercial fisheries, Pacific cod have declined sharply since the 1980s and are now protected 

from commercial and recreational fishing throughout Puget Sound. A status review in the late 

1990s concluded that this decline did not constitute an extinction threat to a distinct population 

segment (DPS) of the species as a whole, although the authors of the review acknowledged 

difficulty in defining DPSs at the scale of the Northeast Pacific (Gustafson et al. 2000). It is 

unclear whether protection from fishing has led to recovery for Pacific cod. Predation by 

pinnipeds, seabirds, and predatory fishes may be constraining recovery (Gustafson et al. 2000). 

Pacific cod are considered a Candidate Species by WDFW. It is because of this special status 

that we include them in this model: An ecosystem model may prove useful in exploring the 

feasibility (and ecological consequences) of restoring Pacific cod in Puget Sound. 

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For a biomass estimate, we used the most recent published information, derived from 

Gustafson et al. (2000), who cite data collected by W. Palsson (WDFW) in the 1990s. Palsson’s 

trawl survey data indicated a total biomass of 252.25 mt of Pacific cod in PSCB, Admiralty Inlet, 

and Whidbey Basin combined. The PSCB makes up approximately two-thirds of this spatial 

area, so if we assume even spatial distribution, then the PSCB portion of that biomass is 169.01 

mt, or 0.223 mt km-2. More recent trawl surveys in PSCB conducted by Reum (2006, unpubl. 

data) suggest similar biomass; based on the methods detailed in Appendix C, Reum’s data 

indicated a Pacific cod density of 0.181 mt km-2. We used a value of 0.200 mt km-2, which is 

roughly the average of the two empirical measures. 

For our P/B estimate, we considered several sources. Natural mortality of Pacific cod in 

Washington and British Columbia waters has been reported as 0.65 yr-1 (Fournier 1983, 

Gunderson 1997). More recent EwE studies from British Columbia waters imply lower rates. 

Ainsworth et al. (2002) used 0.258 yr-1 for juveniles and 0.174 yr-1 for adults, values derived 

from the method described in Appendix C. Preikshot (2007) used much greater natural 

mortalities for adults (0.37 yr-1, derived from studies in Alaska by Thompson et al. (2003) and 

juveniles (0.8 yr-1, source unknown). We elected to use 0.260 yr-1, which is similar to the 

Ainsworth et al. (2002) estimate for juveniles and is in between the adult estimates by Ainsworth 

et al. and Priekshot (2007). It is much lower than the natural mortality estimate from Fournier 

(1983) and Gunderson (1997), but a low productivity rate may be appropriate for a species that 

has remained at low numbers despite more than a decade of protection from fishing. 

We derived Pacific cod Q/B from Ainsworth et al. (2002), who estimated their values 

using methods described in Appendix C. They calculated juvenile and adult Q/B of 3.429 yr-1

and 2.286 yr-1, respectively, but their calculations assume temperatures of 4oC, which is 

considerably cooler than Puget Sound waters are likely to be in most seasons and at most depths 

(WDE no date). Using the same methods but assuming a mean temperature of 10.3oC, the 

juvenile and adult Q/B estimates are 4.540 yr-1 and 3.027 yr-1, respectively; we used the average 

of these two values, 3.784 yr-1. 

The Ecopath mass-balancing algorithm estimated Pacific cod EE to be 0.52, based on the 

above parameters and the top-down demands of predators plus a trace of fishing mortality (Table 

5, Appendix C). 

Pacific cod are predators and become increasingly piscivorous as they grow (Yang 2004). 

Our diet estimates are an attempt to represent both juvenile and adult feeding patterns, as 

described in nearby systems (Ainsworth et al. 2002, Priekshot 2007). Initial Pacific cod diets 

consist mostly of demersal fish, adult Pacific herring, small crustaceans, and forage fish (Table 

4). Lesser contributions come from walleye pollock, small-mouthed flatfish, shrimp, adult 

Cancer crabs, deposit feeders, macrozooplankton, euphausiids, copepods, infaunal bivalves, 

squid, and other Pacific cod. 

Besides cannibalism, the only explicit sources of predation mortality on Pacific cod are 

harbor seals, sea lions, and raptors (Table 4). We assume trace fishery removals (Table 5). 

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Walleye Pollock 

Puget Sound is near the southeastern limit of the range of walleye pollock around the 

North Pacific Rim (Pedersen and DiDonato 1982). They are thought to have multiple spawning 

stocks in Puget Sound, although it is likely that only one stock is represented in PSCB 

(Gustafson et al. 2000). As with other large gadiforms, walleye pollock were formerly more 

abundant in Puget Sound, and were once targets of commercial and recreational fisheries; in 

PSCB, recreational fisheries were the main source of harvest. However, they have declined 

sharply since the 1980s and are now protected from commercial and recreational fishing. There 

is no clear evidence that this decline represents an extinction threat to walleye pollock as a whole 

(Gustafson et al. 2000). It remains unclear whether protection from fishing has led to recovery in 

Puget Sound. Predation by pinnipeds, seabirds, and predatory fishes may be constraining 

recovery (Gustafson et al. 2000). 

Because no formal assessments of walleye pollock biomass have been conducted in the 

PSCB since the mid-1990s (Gustafson et al. 2000), we opted to allow the model to estimate 

walleye pollock biomass via the mass-balancing routine. We did attempt to estimate biomass 

based on recent research trawl data in PSCB (Reum 2006, unpubl. data), using the analytical 

methods described in Appendix C. However, the biomass estimates we calculated with that 

method (0.798 mt km-2) were insufficient to balance the model: Walleye pollock EE was greater 

than one unless P/B was increased to greater than 2 yr-1, which is nearly double the empirically 

derived rates for juvenile Alaska walleye pollock (Hollowed et al. 2000). The bottom-trawl gear 

used by Reum may have been inefficient at capturing walleye pollock, which spend some time 

off the bottom (Gustafson et al. 2000). For the purposes of mass balancing, we assumed a very 

high EE (0.9), implying an initial condition in which 90% of walleye pollock production is 

preyed upon by other model groups. We did so because there is speculation that pinniped 

predation is keeping gadiform populations from rebuilding in Puget Sound (e.g., Gustafson et al. 

2000). 

Our P/B estimates for walleye pollock stem from EwE models of two nearby systems 

(Beattie 2001, Priekshot 2007). In his model of the north and central coasts of British Columbia, 

Beattie (2001) estimated P/B of juvenile walleye pollock based on daily mortality estimates of 

age-1 and age-2 individuals that expand to an annual natural mortality rate of 1.061 yr-1. For 

adults, Beattie (2001) cited a natural mortality rate of 0.15 yr-1, estimated by Saunders and 

Andrews (1996). In his EwE model of the Strait of Georgia ecosystem, Priekshot (2007) cited 

several assessments of walleye pollock biology in Alaska waters, finding age-specific natural 

mortalities that ranged from 0.9 yr-1 for age-1 pollock (Ianelli et al. 2003) to 0.1 for fishable 

pollock in the Gulf of Alaska (Dorn et al. 2003). By contrast, Beamish et al. (1976) estimated a 

natural mortality rate of 0.84 yr-1 for Strait of Georgia walleye pollock. Priekshot (2007) settled 

on P/B estimates of 0.8 yr-1 and 0.4 yr-1 for juvenile and adult walleye pollock, respectively; 

again, those estimates include some fishing mortality. From this assortment of possibilities, we 

assumed a P/B of 0.8 yr-1, which is similar to the Beamish et al. (1976) estimate for walleye 

pollock in the Strait of Georgia, and which falls between the juvenile and adult walleye pollock 

estimates used by Beattie (2001), but is skewed toward juveniles. 

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Our Q/B estimate draws from a simple assumption that walleye pollock P/Q = 0.2, as was 

assumed by Beattie (2001) and Ainsworth et al. (2002) in their EwE models of nearby systems. 

This assumption, coupled with our P/B estimate of 0.8 yr-1, yielded a Q/B of 4.0 yr-1. 

With our parameter estimates plus the top-down demand of predators, Ecopath’s massbalancing function generated a biomass estimate of 3.704 mt km-2 for walleye pollock. This 

estimate clearly exceeds the estimate calculated from the trawl data of Reum (2006, but see 

caveats above), and should be treated with caution in the absence of an updated, comprehensive 

assessment of walleye pollock biomass. 

Walleye pollock diets were estimated according to stomach contents of individuals 

captured in Central Puget Sound (Reum 2006, unpubl. data) and weighted by season to account 

for temperature effects on consumption rate (see methods in Appendix C). The resulting diet 

proportions were dominated by small crustaceans, euphausiids, shrimp, and macrozooplankton 

(Table 4). Lesser amounts came from Pacific hake, copepods, demersal fish, infaunal bivalves, 

soft infauna, and algal/plant material. 

At different points of their life history, walleye pollock are fed upon by harbor seals, sea 

lions, resident diving birds, raptors, spiny dogfish, skates, ratfish, lingcod, piscivorous flatfish, 

small-mouthed flatfish, and demersal fish. 

Other Demersal Roundfish 

Rockfish 

The rockfish group contains many species of Sebastes and, for the time being, also some 

large cottids (Table A-2). We made this grouping for two reasons: first, only a few of the 

Sebastes species are actually common in PSCB; second, we believe that the differences within 

this group (in terms of individual species’ production and consumption rates, diets, predators, 

and importance to fisheries) are less important than the differences between this group and other 

functional groups. Given its broad range of sizes, ages, ecological roles, conservation status, and 

importance to fisheries, it is possible that this group may be split more finely in future model 

iterations. 

Rockfish are a source of significant and growing conservation concern. Several Sebastes 

species in Table A-2, including relatively numerous ones like copper and quillback rockfish, are 

species of concern at the state and federal levels that may warrant listing as Sensitive, 

Threatened, or Endangered species (http://wdfw.wa.gov/wlm/diversty/soc/candidat.htm). 

Moreover, NOAA recently recommended listing the Puget Sound population of bocaccio as 

endangered and the Puget Sound populations of canary and yelloweye rockfish as threatened 

(http://www.nwr.noaa.gov/Publications/FR-Notices/2009/upload/74FR18516.pdf). These 

findings are consistent with broad, coast-wide concern for Sebastes populations, which have 

been shown to be vulnerable to fishing pressure and extended regimes of environmental 

conditions that do not support recruitment (Parker et al. 2000). 

We elected to split the rockfish functional group into two life history stages, linked by a 

delay-difference equation (Appendix D). Rockfish populations typically consist of long-lived 

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Table A-2. Key representatives of the rockfish functional group in the PSCB EwE model. The order of 

appearance in this table does not necessarily indicate the order of abundance. Maximum lengths 

are also listed. 

Common name Scientific name Maximum fork length (cm) 

Copper rockfish Sebastes caurinus 66a

Quillback rockfish S. maliger 61a

Brown rockfish S. auriculatus 56a

Black rockfish S. melanops 69a

Bocaccio S. paucispinis 91a

Canary rockfish S. pinniger 76a

Yelloweye rockfish S. ruberrimus 91a

Vermilion rockfish S. miniatus 76a

Yellowtail rockfish S. flavidus 66a

Greenstriped rockfish S. elongatus 43a

Redstripe rockfish S. proriger 51a

Red Irish lord Hemilepidotus hemilepidotus 51b

Great sculpin Myoxocephalus polyacanthocephalus 90c

Cabezon Scorpaenichthys marmoratus 99c

a Love et al. (2002) 

b Love (1996) 

c FishBase (http://www.fishbase.org) 

individuals that spend most of their lives as reproductively active adults, producing large 

numbers of offspring that rarely survive to maturity (King and McFarlane 2003). Year-class 

strength is often stochastic, likely driven by circulation and production patterns that promote 

both pelagic larval growth and retention of larvae near suitable postlarval settlement habitat (e.g., 

Tolimieri and Levin 2005). To aid in capturing this dynamic, we divided the biomass pool into a 

mature adult stage and an immature juvenile stage. Assuming that rockfish in Puget Sound are 

best represented by relatively abundant species such as copper, quillback, brown, and yellowtail 

rockfish, we settled on a transition from juvenile to adult at approximately age 7 (e.g., Love et al. 

2002). 

We used Ecopath’s mass-balancing routine to determine juvenile and adult rockfish 

biomass. We did so because we lack adequate survey data for virtually all of the species in 

Table A-2, and acquiring such data is not likely in the near term because these species generally 

live near high-relief, rocky habitat that is difficult to sample. We set EE for juveniles at 0.8 in 

the initial model state, implying that 80% of their production is lost to predation each year. We 

assumed EE for adults to be 0.5. This is lower than many of the other fish groups considered so 

far. Our rationale for the low EE is that fishing on rockfish in PSCB is minor, compared to 

fishing on rockfish in systems such as the California Current (Field 2004). In addition, adults of 

these species are large, spiny, and tend to live in complex habitats rich in refugia, providing 

some defense against all but the largest piscivores (e.g., lingcod, spiny dogfish, marine 

mammals). Also, they have low natural mortalities once mature (King and McFarlane 2003). 

We assumed adult rockfish P/B = 0.240 yr-1. We derived this from Gowan (1983), who 

estimated the annual total mortality rates of three of the most common rockfish species in PSCB 

(copper, quillback, and brown rockfish) to be 0.233 yr-1, 0.227 yr-1, and 0.274 yr-1, respectively. 

136
We note that this estimate is greater, typically by a factor of 1.5 to 2, than the P/B estimates 

made by Field (2004) for several shelf rockfish species in the California Current in the 1990s. 

By contrast, we assumed a much lower P/B ratio for juvenile rockfish (0.36 yr-1) than did Field 

(1.5 yr-1). Our estimate follows the convention of Beattie (2001) and Ainsworth et al. (2002), 

where juvenile P/B is assumed to be 50% greater than adult P/B for split-pool groups for which 

juvenile mortality data are not available. The very high mortality experienced by larvae can be 

accounted for in functions used in Ecosim to alter the shape of the stock-recruitment function or 

the stochasticity of year-class strength, as described in Christensen et al. (2005). 

The Q/B estimates for rockfish were calculated via bioenergetics modeling, based on the 

generic rockfish bioenergetics model of Harvey (2005). We simulated the energetic needs of 

equilibrium populations of copper, quillback, and brown rockfish, assuming von Bertalanffy 

growth, length-weight, maturation, fecundity, and natural mortality patterns as summarized in 

Love et al. (2002). We developed ontogenetic diet histories based on data on copper rockfish 

and quillback rockfish diets (Moulton 1977, Reilly et al. 1992, Murie 1995), diet accounts for 

brown rockfish summarized by Stein and Hassler (1989), and anecdotal rockfish diet information 

in Love et al. (2002). Bioenergetics model outputs included monthly estimates of total 

consumption required to reach the size-at-age predicted by the von Bertalanffy growth models, 

which enabled us to estimate consumption per unit biomass at monthly increments. These 

monthly Q/B estimates were then weighted by year-class abundance, as inferred from the 

mortality rates described by Gowan (1983), and averaged across the three species for juveniles 

(<age 7) and adults. Juvenile Q/B was 2.672 yr-1, while adult Q/B was 1.437 yr-1. 

Based on the above parameters, the demands of predators, and some fishing mortality, 

Ecopath calculated biomass of 0.454 mt km-2 for juvenile rockfish and 0.327 mt km-2 for adult 

rockfish. 

The diet of rockfish was compiled from the sources noted above in the Q/B description, 

again assuming that copper, quillback, and brown rockfish are the most representative species in 

this functional group. For our diet estimates, we used the bioenergetics outputs from the Q/B

estimation. For each species, we estimated the consumption of each prey type by an equilibrium 

population of the rockfish (i.e., consumption was weighted by body weight and abundance as a 

function of the estimated mortality rate). Once prey-specific consumption estimates were made, 

we averaged across species for both the juvenile and the adult stages. In this way, we accounted 

for changes in size, abundance, and dietary ontogeny across the three representative species. 

Diets span a range of pelagic fishes, zooplankton, benthic invertebrates, and some demersal 

fishes, as shown in Table 4; juvenile Pacific herring, shrimp, and small crustaceans are the most 

important groups. 

Rockfish are eaten by juvenile and adult lingcod, spiny dogfish, harbor seals, sea lions, 

resident diving birds, and raptors (Table 4). Some adult rockfish are lost to fisheries, though 

likely more to recreational fishing than any commercial gear (Table 5). Unfortunately, we lack 

empirical data on recreational catches and thus made an arbitrary assumption on recreational 

harvest rates of rockfish. 

The parameters for the delay-difference equation (Appendix D) that dynamically links 

the biomasses of the juvenile and adult pools in Ecosim are: 7 years for the age at transition, 

137
0.125 for the k parameter from the von Bertalanffy growth function (based on data provided in 

Love et al. 2002), and 3.25 for the ratio of the weight of an average adult to the weight of an 

individual at the transition age (derived from the bioenergetics modeling efforts). 

Lingcod 

Lingcod are one of the Puget Sound ecosystem’s top predators. They reach considerably 

larger sizes than most other teleosts in the system, including their taxonomic cousins the 

greenlings; the L∞ of female lingcod in this area can exceed 1 m (Smith and McFarlane 1990). 

Their maximum life span in this area is between 15 and 20 years, with maturity occurring 

between the ages of 2 and 5 (Shaw and Hassler 1989, Smith and McFarlane 1990). Lingcod are 

generally solitary predators, feeding voraciously on other fishes and macroinvertebrates around 

complex habitats such as rocky reefs and outcroppings and typically staying within home ranges 

of several hundred square meters (Tolimieri et al. 2009). Their role as predators on rockfish is of 

particular interest because rockfish conservation and population rebuilding has become a major 

goal of West Coast fisheries management. Some research has suggested that marine protected 

areas designed to protect rockfish may actually fall short of their goals because lingcod may 

benefit from the fishing closures and consume rockfish in large numbers (e.g., Mangel and Levin 

2005, Beaudreau and Essington 2007). Lingcod are a prized target of recreational hook-and-line 

anglers and spearfishers, and limited seasons for both gear types occur in Puget Sound each year. 

We chose to split this functional group into two life history stages, juvenile and adult, 

with biomass dynamics linked through the delay-difference function described in Appendix D. 

We split them into these stages primarily so that we could develop scenarios in Ecosim where 

juvenile lingcod biomass is supplemented by hatchery releases. Hatchery production of juvenile 

lingcod has been proposed as a means of enhancing lingcod populations in Puget Sound and 

adjacent waters (Wespestad et al. 1994, Cook et al. 2005, Rust et al. 2005). 

Lingcod biomass has not been assessed in Central Puget Sound to our knowledge. The 

trawl surveys of Central Puget Sound conducted by Reum (2006) are not reasonable for 

estimates of lingcod biomass, because the trawl locations likely did not correspond to preferred 

habitat for juvenile or adult lingcod. We instead considered estimates from nearby inland waters 

with similar community structure. Beattie (2001) derived estimates of lingcod biomass (adults 

and juveniles, total biomass = 0.065 mt km-2) from virtual population analysis conducted by 

Martell (1999) on lingcod in Hecate Strait. Martell (1999) conducted a similar analysis on Strait 

of Georgia lingcod and estimated population biomass of approximately 1.3 mt km-2 in 1999; 

additionally, the biomass trend was on a steadily increasing trajectory (see also Priekshot 2007). 

These are quite disparate estimates. We set our initial adult lingcod biomass between 

these two values, but we were conservative, assuming that there may not be as much lingcod 

habitat in PSCB as there is in the Strait of Georgia. Our starting adult lingcod biomass was 

arbitrarily set at 0.3 mt km-2. For juvenile lingcod biomass, we initially assumed that the ratio of 

juvenile:adult biomass was approximately 0.3, based on a simulated equilibrium population of 

lingcod that follow sex-specific weight-at-age estimates and age-specific mortality estimates as 

described by Jagielo and Wallace (2005). However, we were unable to achieve mass balance for 

juvenile lingcod; their EE was consistently greater than one because of high predation mortality. 

We thus set juvenile lingcod EE at 0.9 and allowed the model to estimate juvenile lingcod 

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biomass. Both adult and juvenile lingcod biomass values should be interpreted with caution, 

pending quantitative monitoring work. 

Our estimate of P/B again drew from research on lingcod in British Columbia waters. 

We considered the EwE modeling work done by Beattie (2001), Ainsworth et al. (2002), and 

Priekshot (2007). Beattie (2001) used a P/B of 0.8 yr-1 for adults, based on tagging-based 

mortality estimates of lingcod in the Strait of Georgia by Smith et al. (1990), although lingcod in 

the Strait of Georgia experienced high fishing mortality in that time (Smith et al. 1990, Martell 

1999, Priekshot 2007). Ainsworth et al. (2002) assigned prefishing P/B ratios of 0.389 yr-1 and 

0.26 yr-1, respectively, to juvenile and adult lingcod along northern Vancouver Island; these 

estimates were made using the Pauly method (1980) (Appendix C). Priekshot’s (2007) model of 

the Strait of Georgia in 1950 (prior to the period of intense exploitation) arrived at a lingcod P/B

of 0.35 yr-1 based on the Ecopath mass-balancing routine. Assuming that lingcod in the PSCB 

model experience natural mortalities similar to those estimated by Ainsworth et al. (2002) and 

Priekshot (2007), and assuming a low level of recreational fishing mortality on adults, we arrived 

at P/B ratios of 0.389 yr-1 for juveniles and 0.28 yr-1 for adults. 

We derived estimates of lingcod Q/B from bioenergetics modeling done by Beaudreau 

and Essington (2009) for several size classes of lingcod from northern waters of Puget Sound. 

We used their bioenergetics model parameters to simulate consumption for an equilibrium 

population of male and female lingcod, where von Bertalanffy growth functions, length-weight 

relationships, and mortality rates were sex-specific (from Jagielo and Wallace 2005), initial sex 

ratio was 1:1 but changed with age owing to sex-specific mortality rates, and maturity occurred 

at 4.5 yr. Models were run in monthly time steps, with monthly temperature estimates taken 

from WDE data (no date) for the upper 25 m of the water column and prey energy density fixed 

at 5,000 J g-1. The proportion of maximum consumption (the p value in fish bioenergetics 

models, Kitchell et al. 1977) was adjusted to fit the growth curves from Jagielo and Wallace 

(2005). Monthly Q/B estimates were weighted by age-specific biomasses and summed, juvenile 

Q/B equaled 3.070 yr-1, and adult Q/B equaled 1.932 yr-1. These values are conservative relative 

to values used in Ecopath models from nearby systems (Beattie 2001, Ainsworth et al. 2002; 

juvenile range 3.3 yr-1 to 3.9 yr-1, adult range 2.8 yr-1 to 3.3 yr-1), but are consistent with the 

value (2.4 yr-1) used by Priekshot (2007) for all lingcod. 

Given the parameters outlined above and the demands of predators and fisheries, 

Ecopath’s mass-balancing routine estimated an EE of 0.141 for adult lingcod and a biomass of 

0.124 mt km-2 for juvenile lingcod. 

Lingcod are highly predatory fish that feed primarily on other fishes (Shaw and Hassler 

1989, Beaudreau and Essington 2007). We mainly based our lingcod diet on studies done by 

Beaudreau (2009) on lingcod in the San Juan Islands of Puget Sound. Her research indicates a 

size-based transition from strong dependence on pelagic fishes (sand lance, Pacific herring, small 

gadiforms) and shrimp to increasing dependence on demersal fishes (rockfish, sculpins, 

greenlings, flatfishes, and others). Beaudreau (2009) also documented cannibalism by juvenile 

and adult lingcod. For juvenile lingcod, we assume a diet comprised mostly of pelagic fishes, 

along with various demersal fish groups, shrimp, planktonic invertebrates, and cephalopods; a 

small amount of cannibalism is also included (Table 4). For adults, we assume a diet comprised 

mostly of demersal and semipelagic fishes, along with pelagic fishes, benthic invertebrates, 

139
cephalopods, and small numbers of euphausiids and salmon. Cannibalism is directed at juvenile 

lingcod. 

Juvenile lingcod are preyed upon by very few groups, mainly other lingcod and in trace 

amounts by raptors, demersal fish, and age 1+ Cancer crabs (Table 4). Adult lingcod are preyed 

upon in small amounts by harbor seals and sea lions. We arbitrarily allocated annual landings of 

0.01 mt km-2 adult lingcod biomass to the recreational fishery (Table 5). 

The parameter values used in the Ecosim delay-difference equation to dynamically link 

the juvenile and adult biomass pools (Appendix D) include a von Bertalanffy k of 0.125 (Jagielo 

and Wallace 2005), a transition age of 4.5 years (Jagielo and Wallace 2005), and a ratio of 3.0 

for the weight of an average adult relative to the weight of a fish at the transition age, as derived 

from growth curves used in the bioenergetics modeling described previously. Ecosim default 

values were used for all other delay-difference parameters (Appendix D).

Demersal Fish 

This functional group is a catch-all pool of relatively small, round-bodied teleosts that 

spend most of their lives associated with benthic habitats throughout the system. In general, 

these fish can be found over a wide range of substrate types, mostly feeding on invertebrates 

while providing an important prey resource for higher trophic levels. Many families are 

represented, including but not limited to those listed in Table A-3. 

As a whole, these species have not been monitored in a way that would allow empirical 

biomass estimation. Some species in Table A-3, such as plainfin midshipmen, poachers, and 

eelpouts, were captured in the bottom trawl survey work by Reum (2006). Using Reum’s data 

and the methods outlined in Appendix C, for example, we estimated season-weighted and depthweighted biomass densities of 0.124 mt km-2, 0.023 mt km-2, and 0.023 mt km-2 for midshipmen, 

poachers, and eelpouts, respectively. However, that represents a partial component of this 

assemblage. Also, the bottom trawl survey sampled only soft sediments, not the complex 

substrates (e.g., rocky reefs, macrophytes beds) inhabited by many demersal fish species. 

Moreover, many of these fishes are small-bodied and could conceivably pass through or burrow 

under bottom trawl gear. We therefore estimated demersal fish biomass with the Ecopath mass 

balance routine. We set EE at 0.8 to facilitate this. 

Given this group’s taxonomic diversity (Table A-3) and large number of relatively poorly 

studied species, we were not confident about how to characterize P/B or Q/B ratios. We opted to 

set Q/B at 6.0 yr-1, which is greater than Q/B estimates from comparable groups in other 

Northeast Pacific Ecopath models (e.g., 2.5 yr-1 for benthic fish in Field [2004], 5.256 yr-1 for 

Priekshot [2007]). It is similar, however, to species-specific estimates for plainfin midshipmen 

(5.703 yr-1) and sturgeon poachers (6.934 yr-1), which we calculated using the Q/B estimation 

methods described in Appendix C. If we assume a growth efficiency (P/Q) of 0.2, which is a 

fairly standard value for fishes, then P/B must be 1.2 yr-1. 

With the parameter estimates provided above, and the top-down pressure from predators, 

Ecopath estimated an initial biomass of 4.565 mt km-2 for demersal fish. This is comparable to 

biomass estimates for small demersals in the Strait of Georgia Ecopath model (5.5 mt km-2, 

140
Table A-3. Key families and representatives in the demersal fish functional group in the PSCB EwE 

model. The order of appearance in this table does not necessarily indicate the order of 

abundance. 

Family Representative species Scientific name 

Cottidae Roughback sculpin Chitonotus pugetensis 

Sailfin sculpin Nautichthys oculofasciatus 

Scalyhead sculpin Artedius harringtoni 

Slim sculpin Radulinus asprellus 

Spinyhead sculpin Dasycottus setiger 

Spotfin sculpin Icelinus tenuis 

Staghorn sculpin Leptocottus armatus 

Threadfin sculpin I. filamentosus 

Hexagrammidae Whitespotted greenling Hexagrammos stelleri 

Kelp greenling H. decagrammus 

Rock greenling H. lagocephalus 

Painted greenling Oxylebius pictus 

Batrachoididae Plainfin midshipmen Porichthys notatus 

Agonidae Bigeye poacher Bathyagonus pentacanthus 

Blacktip poacher Xeneretmus latifrons 

Bluespotted poacher X. triacanthus 

Pygmy poacher Odontopyxis trispinosa 

Smootheye poacher X. leiops 

Sturgeon poacher Podothecus accipenserinus 

Zoarcidae Bigfin eelpout Lycodes cortezianus 

Black eelpout L. diapterus 

Blackbelly eelpout L. pacificus 

Shortfin eelpout L. brevipes 

Snakehead eelpout Lycenchelys crotalinus 

Bathymasteridae Northern ronquil Ronquilus jordani 

Pholidae Various gunnels Pholis spp., Apodichthys spp.

Stichaeidae Various pricklebacks Anoplarchus spp., Chirolophis spp., etc. 

Gadidae Pacific tomcod Microgadus proximus 

Gobiidae Blackeye goby Rhinogobiops nicholsii 

Syngnathidae Bay pipefish Syngnathus leptorhynchus 

Aulorhynchidae Tube-snout Aulorhynchus flavidus 

Priekshot 2007) and benthic fish in the California Current Ecopath model (4.1 mt km-2, Field 

2004). 

The diet proportions of this group represent an amalgamation of stomach content data 

from representative species captured in recent trawl surveys of PSCB conducted by Reum (2006, 

unpubl. data). From several representative species groups captured in the survey (Pacific 

tomcod, plainfin midshipmen, various poachers, various eelpouts, and various small sculpins), 

diets were weighted by season and depth to account for temperature changes and bathymetric 

effects (see methods in Appendix C) and averaged across all taxa. The resulting diet was a 

highly diverse mix of both planktonic and benthic invertebrates, plus small amounts of fish, 

squid, and microalgae (Table 4). To a small extent, they are intraguild cannibals. 

141
Demersal fish are preyed upon by virtually every other vertebrate group, except for some 

of the pelagic fishes (Table 4). In addition, octopus and Cancer crab feed on this group. 

Flatfish 

We identified two separate functional groups of flatfish in the PSCB model. The main 

criterion by which we separated flatfish into groups was diet: one group clearly had higher 

amounts of fish in their diets, while the other fed almost exclusively on benthic invertebrates. 

The key representatives are listed in Table A-4 and are described in greater detail below. 

Piscivorous Flatfish 

This group is comprised of at least two species, Pacific sanddab and sand sole, that had 

notably higher amounts of fish in their diets than the other common, small flatfish examined by 

Reum (2006) and Reum and Essington (2008). There are two other species of piscivorous 

flatfish that we considered for this group: arrowtooth flounder (Atheresthes stomias) and petrale 

sole (Eopsetta jordani). Both species are found in PSCB and are known piscivores (e.g., Love 

1996), but we currently lack data on their relative abundance or diets in PSCB. How this affects 

the role of this functional group in the model is unknown, but it is reassuring to note that in their 

recent trawl surveys in PSCB, Reum (2006) and Reum and Essington (2008) caught Pacific 

sanddabs and sand sole, but no arrowtooth flounder or petrale sole. 

Our estimate of piscivorous flatfish biomass is based on Pacific sanddab and sand sole 

biomass per unit area data derived from trawl casts in PSCB (Reum unpubl. data, see estimation 

methods in Appendix C). We weighted the data according to season and depth-area data for 

PSCB, then summed the data for both species. The final estimated biomass was 1.155 mt km-2, 

the vast majority (>97%) of which was Pacific sanddabs. However, there was high variability 

around the biomass estimate (coefficient of variation = 85%) due to seasonally variable catches 

of Pacific sanddab. On average, Pacific sanddab biomass in summer and fall was 4 to 10 times 

greater than in winter. 

Table A-4. Key representatives of the flatfish functional groups in the PSCB EwE model. The order of 

appearance in this table does not necessarily indicate the order of abundance. 

Functional group Common name Scientific name 

Piscivorous flatfish Pacific sanddab Citharichthys sordidus 

Sand sole Psettichthys melanostictus 

Small-mouthed flatfish English sole Parophrys vetulus 

Dover sole Microstomus pacificus 

C-O sole Pleuronichthys coenosus 

Starry flounder Platichthys stellatus 

Speckled sanddab Citharichthys stigmaeus 

Rock sole Lepidopsetta bilineata 

Rex sole Glyptocephalus zachirus 

Flathead sole Hippoglossoides elassodon 

Butter sole Isopsetta isolepis 

Slender sole Lyopsetta exilis 

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We used the estimation method described in Appendix C to derive a P/B estimate for 

piscivorous flatfish; the method calculates P/B as a function of von Bertalanffy growth 

parameters and temperature. Unfortunately, von Bertalanffy growth parameters are not available 

for either Pacific sanddab or sand sole from the Puget Sound area; in fact, we could only derive 

estimates of k and L∞ from length-at-age data for Pacific sanddab captured in central California 

waters in the middle of the last century (Arora 1951). Based on data in Arora’s tables, we 

calculated von Bertalanffy k = 0.251 and L∞ = 31.7 (both sexes combined). These values and a 

mean annual temperature estimate of 10.68oC for PSCB waters at depths between 0 and 25 m 

(WDE no date) yield an estimated P/B of 0.467 yr-1. This may be an overestimate, as we might 

expect Pacific sanddabs in Puget Sound to have lower k and larger L∞ than Pacific sanddabs off 

central California. 

We are unaware of bioenergetics models for Pacific sanddabs or sand sole (or, in fact, for 

any flatfishes common to the west coast of North America). Thus to quantify piscivorous flatfish 

Q/B, we used the Christensen and Pauly (1992) estimation method, described in Appendix C. 

We again applied an annual average temperature of 10.68oC (see previous paragraph) and we 

calculated maximum weight with the von Bertalanffy growth parameters and length-weight 

conversions that we derived from Arora (1951). These values produced a Q/B estimate of 6.007 

yr-1 using the equation described in Appendix C. The P/B and Q/B estimates yield a P/Q of 0.08, 

which is plausible but lower than expected. 

The mass-balancing routine in Ecopath calculated an EE of 0.686 for piscivorous flatfish, 

implying that in the initial model state, two-thirds of the group’s annual production is lost to 

predation by other functional groups or to fisheries. 

We used stomach content data collected by Reum (2006, unpubl. data) and Reum and 

Essington (2008) to describe piscivorous flatfish diets. Raw diet data were weighted by depth 

and season (Appendix C) and by species, with the vastly more abundant Pacific sanddab 

weighted more than sand sole. Their diets were quite diverse; approximately 35% were fish 

groups, primarily surfperches, juvenile Pacific herring, and demersal fish (Table 4). Planktonic 

and benthic invertebrates comprised most of the remainder of their diet. 

Piscivorous flatfish are preyed upon by harbor seals, sea lions, resident diving birds, 

spiny dogfish, adult lingcod, and age 1+ Cancer crabs (Table 4). They are captured at very low 

rates by commercial longline gear (Table 5, Appendix C), and although they are certainly 

captured by recreational anglers, we do not have empirical estimates for recreational landings. 

Small-mouthed Flatfish 

This group is comprised of several species of small- to medium-sized flatfish (Table 

A-4). English sole is probably the most abundant single species, while substantial numbers of 

rock sole and C-O sole are also present (Reum 2006). Starry flounder are relatively abundant in 

shallow waters while Dover and rex sole occur in the deeper waters of Puget Sound. This group 

is highly abundant and forms (along with ratfish and spiny dogfish) the bulk of demersal fish 

biomass in PSCB (Reum 2006). Small-mouthed flatfish species are grouped primarily due to 

their trophic similarity, as they all feed on infaunal organisms (e.g., polychaetes, clams) and 

small benthopelagic crustaceans (Reum and Essington 2008). 

143
Our estimate of biomass is based on trawl casts in PSCB (Reum unpubl. data, see 

biomass estimation methods described in Appendix C). We weighted the catch data according to 

season and depth-area data for PSCB, then summed the data for all small-mouthed flatfish 

species. The final estimated biomass was 7.962 mt km-2, which was mostly English sole (4.571 

mt km-2), followed by rock sole (1.500 mt km-2), and C-O sole (1.215 mt km-2). 

Given the relative importance of English sole in this functional group, we used an 

estimate of natural mortality for English sole as our initial value for P/B. An instantaneous 

annual mortality estimate of 0.26 yr-1 comes from work by Forrester and Ketchen (1963), who 

examined characteristics of bottomfish in nearby Strait of Georgia. That value was also used in a 

recent stock assessment of English sole along the U.S. West Coast (Stewart 2007). Holland 

(1969) estimated much higher English sole mortality rates in Central Puget Sound in the 1950s; 

an unexploited population dominated by age 3–5 individuals had an instantaneous annual 

mortality of approximately 0.43 yr-1. We assumed the average of these two regional estimates, 

that is, P/B = 0.345 yr-1. This value is much lower than that of piscivorous flatfish (0.467 yr-1), 

which is consistent with the fact that English sole are longer-lived than Pacific sanddabs. 

According to FishBase (http://www.fishbase.org), English sole have been aged at 17 years, 

whereas Pacific sanddabs have been aged at 9 years. The two species grow to relatively similar 

sizes, implying that English sole productivity must be somewhat lower. In equating P/B to 

natural mortality, we assume that fishing mortality on this group is minor. 

We found no bioenergetics modeling information on small-mouthed flatfish species, and 

thus used the Christensen and Pauly method (1992) described in Appendix C to estimate smallmouthed flatfish Q/B. This method requires estimates of theoretical maximum size, as derived 

from von Bertalanffy growth relationships and length-weight conversions. Of the three abundant 

species in this functional group, the only one for which we could find growth and length-weight 

relationships was English sole. Based on von Bertalanffy growth parameters and length-weight 

conversions described by Stewart (2007), the theoretical maximum weight of an English sole is 

580.11 g. 

The Christensen and Pauly (1992) method also requires a temperature estimate. As 

English sole were ubiquitous and abundant at all depths sampled by Reum (2006), we used the 

average bottom temperature (10.48oC) of all offshore stations in PSCB that are monitored 

monthly by WDE (no date). When these values were applied to the equation discussed in 

Appendix C, the resulting Q/B was 5.512 yr-1. This value along with the P/B estimate yields a 

P/Q of 0.047, which is quite low but potentially in keeping with the slow growth and long life of 

English sole. 

The Ecopath mass-balancing routine produced an EE of 0.654 for small-mouthed flatfish, 

indicative of a moderately high degree of predation pressure on annual production. 

We characterized diet based on stomach content data for the majority of the smallmouthed flatfish listed in Table A-4 (English sole, rock sole, C-O sole, slender sole, speckled 

sanddab, Dover sole, and rex sole). Stomach contents were analyzed by Reum (2006) and Reum 

and Essington (2008). We aggregated the raw data from these studies (Reum unpubl. data) and 

weighted them by season as described in Appendix C. The resulting diet was dominated by soft 

144
infauna, infaunal bivalves, and small crustaceans (Table 4). Also present were a variety of 

pelagic and benthic invertebrates and traces of benthic primary producers and fish. 

Small-mouthed flatfish are eaten by age 1+ Cancer crabs, demersal fish, adult rockfish, 

juvenile and adult lingcod, Pacific cod, spiny dogfish, ratfish, skates, raptors, resident diving 

birds, harbor seals, and sea lions. There is no viable commercial fishery for them in PSCB, 

although some recreational harvest occurs for nearshore species. Unfortunately, we lack data on 

recreational harvest and thus input an arbitrary placeholder level of recreational catch (Table 5). 

Chondrichthyans 

Chondrichthyans are very abundant in Puget Sound. The spotted ratfish (Hydrolagus 

colliei) and the spiny dogfish may account for most of the demersal fish biomass in deeper 

waters of the system (Reum 2006). Collectively, the sharks, skates, and ratfish in Puget Sound 

span multiple trophic levels and are common at nearly all depths. 

Spiny Dogfish 

The spiny dogfish is an abundant shark found throughout the world’s temperate and 

subarctic oceans, from nearshore habitats to shelf and slope waters (Love 1996). They rarely 

achieve lengths greater than 1.5 m, but can live beyond 75 years. They are notably slow to 

mature, with many females reaching first maturity at more than 20 years of age. Once mature, 

they produce litters of 1 to 20 pups every 2 years. Their ecological importance in Puget Sound is 

clear, based on their abundance, high trophic position, diverse diet, and direct trophic links to 

several high-profile functional groups (e.g., Pacific herring, salmon, marine mammals). They are 

also targeted in fisheries, although not to the extent that they were historically. During World 

War II, millions of spiny dogfish were harvested in Puget Sound because of the high nutritional 

value of their livers. This market diminished quickly when laboratory synthesis of vitamin A 

became feasible, and their main value is now as a food fish (Bonham et al. 1949, Taylor and 

Gallucci 2009). Some hypothesize that spiny dogfish abundance in Puget Sound has declined 

relative to historic levels (e.g., Palsson 2009), and that major demographic shifts have occurred 

between the 1940s and the present day (Taylor and Gallucci 2009). 

Spiny dogfish biomass estimates were derived from depth-stratified seasonal trawl 

surveys conducted in PSCB by Reum (2006, unpubl. data). The estimation methods are 

described in Appendix C. We averaged the summer and fall biomass estimates to produce the 

final biomass of 5.254 mt km2

. The variability around the biomass estimates for given depths 

and seasons was high; coefficients of variation ranged from 45% to 100% (data not shown). We 

omitted the winter samples from this calculation because spiny dogfish were nearly absent from 

Reum’s winter data, suggesting that most spiny dogfish were not present in the system for at 

least one-third of the year (Reum 2006). We handled this in Ecopath by assuming that their diet 

is 30% Import. 

Spiny dogfish P/B was approximated from natural and fishing mortality estimates in 

Puget Sound and adjacent waters. Wood et al. (1979) estimated instantaneous annual natural 

mortality of 0.094 yr-1 for all age groups of spiny dogfish in waters around Vancouver Island, 

and we assumed the same rate of natural mortality for spiny dogfish in PSCB. Based on the total 

145
biomass estimate and total commercial landings in PSCB (Table 5, Appendix C), the 

instantaneous annual fishing mortality (F) of spiny dogfish is 0.009 yr-1. The sum of natural and 

fishing mortality, 0.103 yr-1, is our P/B estimate. 

We generated the Q/B ratio using a spiny dogfish bioenergetics model (Harvey 2009). A 

bioenergetics simulation was run for an equilibrium population of sharks, ages 0 to 75, with von 

Bertalanffy growth parameters and maturation schedules consistent with spiny dogfish in coastal 

Washington waters (Ketchen 1972, 1975) and an instantaneous mortality of 0.103 yr-1. The 

model used a monthly time step and temperatures consistent with monthly mean Puget Sound 

water temperatures at depths of 50–75 m (WDE no date) and size-dependent diets based on 

Jones and Geen (1977). The model generated annual estimates of total prey biomass consumed 

per unit of dogfish biomass, which were then weighted by the abundance of the age class relative 

to the overall population. The sum of the weighted age-specific estimates was the overall Q/B

estimate of 2.692 yr-1. 

The above parameters plus the top-down demands of predators and fisheries led to an EE

of 0.175 following mass-balancing in Ecopath. 

Spiny dogfish diets were estimated based on stomach contents examined by Reum (2006, 

unpubl. data) as described in Appendix C. We assumed a Q10 of 2.3 when making temperaturedependent corrections to prey contributions (Harvey 2009). As noted above, we attributed 30% 

of spiny dogfish diet to Import, because Reum (2006) captured almost no spiny dogfish during 

the winter. We assumed that most spiny dogfish emigrated from the system in winter, although 

that assumption is tenuous.11 The remainder of their diet is remarkably diverse, with principal 

dependence on fish (Table 4). Important prey categories include Pacific herring, walleye 

pollock, ratfish, flatfish, octopuses, salmon, and Cancer crabs. 

Spiny dogfish are preyed upon in small amounts by harbor seals, sea lions, raptors, and 

adult lingcod (Table 4). Spiny dogfish are captured in gill net, longline, set net, and recreational 

fisheries (Table 5, Appendix C). 

Skates 

Two species of skate are common in Puget Sound: the longnose skate (Raja rhina) and 

the big skate (Raja binoculata) (Reum 2006). Like most skates, these species live on or over soft 

sediments, feeding on a variety of invertebrates and small fishes. Little is known about their 

ecological role in Puget Sound, and they are not particularly targeted by fisheries in this region. 

Skates have been implicated as important components of groundfish communities in other 

systems, such as the continental shelf off the northeastern United States (Fogarty and Murawski 

1998, Frisk et al. 2008, Link and Sosebee 2008). On the west coast of North America, longnose 

skate populations have declined 30–40% over the last century, possibly because their life history 

traits (slow growth, late maturation, low fecundity) leave them vulnerable to fishing mortality 

(Gertseva 2009). The present modeling effort may help shed light on the potential community 

role of skates in PSCB. 

 

11 W. Palsson, WDFW, Mill Creek, WA. Pers. commun., 25 March 2009. 

146
We estimated skate biomass using seasonal trawl survey data collected in PSCB in 2004 

and 2005 (Reum 2006, unpubl. data). Trawl data were converted to biomass density estimates 

weighted by depth and season, as described in Appendix C. Estimated biomass was 0.680 mt 

km-2; based on catch data, approximately 96% of the biomass was longnose skate (Reum 2006, 

unpubl. data). 

We do not have empirical estimates of P/B for skates in Puget Sound. Gertseva (2009) 

assumed that natural mortality of longnose skates on the outer U.S. West Coast was 0.2 yr-1, and 

previous Ecopath models from the Northeast Pacific Ocean have used P/B ratios ranging from 

0.15 yr-1 (Ainsworth et al. 2002) to 0.31 yr-1 (Beattie 2001, Preikshot 2007). Ainsworth et al. 

(2002) calculated their value by applying the Pauly (1980) method, described in Appendix C, to 

an unfished skate population. As skates in PSCB likely receive little fishing pressure, we used 

the Pauly (1980) method as well, drawing longnose skate von Bertalanffy growth equation 

parameter estimates from McFarlane and King (2006) and using annual average temperature data 

for PSCB provided by WDE (no date). We used a temperature of 10.3oC, taken from 50 to 75 m 

waters, the deepest water for which data were available from all months. Most skate biomass 

was captured in deeper waters by the survey (Reum 2006), thus the mean annual temperature we 

used may be a slight overestimate. When these values were entered into the P/B estimation 

equation, the resulting value was 0.127 yr-1. 

We derived our Q/B estimates using the Christensen and Pauly (1992) method, outlined 

in Appendix C, where Q/B is a function of temperature and maximum weight. We again set 

temperature at 10.3oC, and determined maximum weight using von Bertalanffy parameters from 

McFarlane and King (2006) and the length-weight conversion equation of Gertseva (2009). The 

resulting Q/B estimate was 3.201 yr-1. Dividing P/B by Q/B yields a P/Q of 0.04, a low growth 

efficiency that is consistent with a long-lived, slow-growing fish. 

The above parameter estimates, coupled with predatory and fishery demands, produced 

an EE of 0.084 for skates after mass-balancing. This implies a low degree of top-down control 

on skates in Puget Sound, as the model is currently structured. 

Skate diets were estimated based on stomach contents examined by Reum (2006, unpubl. 

data). Skates were captured by bottom trawl in PSCB from 2004 to 2005. Raw diet data were 

weighted by depth and season, as described in Appendix C; we assumed a Q10 of 2.3 when 

making temperature-dependent corrections to prey contributions. Benthic and benthopelagic 

crustaceans were the major source of prey (Table 4). Minor prey items included various fish 

groups, infaunal bivalves, and other grazers. 

Skates form a minor part of the diets of harbor seals and sea lions (Table 4). A small 

amount of skate biomass is taken by longline fisheries (Table 5, Appendix C). 

Ratfish 

The spotted ratfish may be the most abundant demersal fish species in Puget Sound, in 

terms of biomass per unit area. This is especially true in deeper waters of the sound (Reum 

2006), although at night some part of the population spreads into shallower waters (Love 1996). 

Their numbers in Central Puget Sound remain high in all seasons, unlike many other species that 

147
experience seasonal shifts in abundance (Reum 2006). They primarily feed on infaunal and 

epifaunal benthic invertebrates; a small amount of their diet is fishes. Although they are 

frequently caught in groundfish capture gears on the U.S. West Coast, ratfish are not targeted by 

sport or commercial fisheries, and the closure of groundfish trawling in PSCB affords them 

protection from incidental fishing mortality. 

Ratfish biomass estimates were derived from depth-stratified seasonal trawl surveys 

conducted in PSCB during 2004 and 2005 (Reum 2006, unpubl. data), according to the methods 

described in Appendix C. The final biomass calculation was done by unweighted averaging 

across all seasons, because ratfish showed no evidence of moving into or out of the system on a 

seasonal basis. The initial estimate of biomass was 36.1 mt km-2. Ratfish biomass is noteworthy 

not only because it is considerably greater than that of most other fish groups (Table 3), but also 

for its seasonal consistency. The estimates derived from Reum’s raw data for summer, fall, and 

winter were 38.329, 35.495, and 34.474 mt km-2, respectively. 

Direct measures of ratfish P/B are not available from Puget Sound. Therefore, we used 

the Pauly (1980) method, as described in Appendix C, where P/B is a function of water 

temperatures and the growth rate (k) and theoretical maximum length (L∞) in the von Bertalanffy 

growth equation. We assumed a temperature of 10.3oC, the annual mean at 50–75 m in PSCB 

(WDE no date), and von Bertalanffy parameters reported by Ainsworth et al. (2002). This 

yielded a P/B estimate of 0.305 yr-1. 

Ratfish Q/B has not been quantified directly. Beattie (2001) and Ainsworth et al. (2002) 

estimated that Q/B is 1.4 yr-1for ratfish in waters around British Columbia. However, that 

calculation assumed an average temperature of 6oC, whereas an average temperature of 10.3oC is 

more reasonable for ratfish living near the bottom of Puget Sound (WDE no date). We rescaled 

the previous Q/B estimate to a temperature of 10.3oC by applying a consumption Q10 of 2.3 

(Appendix C), which is similar to Q10 values used for other small chondrichthyans (e.g., Brett 

and Blackburn 1978). Using this method yielded a Q/B of 1.653 yr-1 for PSCB ratfish. 

In the mass-balancing step, Ecopath calculated ratfish EE = 0.105, implying that 

relatively little ratfish production is lost to predation by other functional groups. 

Ratfish diet composition was estimated based on stomach content analysis on ratfish 

captured in the depth-stratified and season-stratified bottom trawl surveys (Reum and Essington 

2008, Reum unpubl. data). Raw stomach content data were converted to diet proportions 

weighted by season, using methods described in Appendix C; we again assumed a consumption 

Q10 of 2.3. The resulting diet composition for ratfish was dominated by three highly aggregated 

groups: infaunal bivalves, small crustaceans, and soft infauna (Table 4). The balance of their 

diet included a wide variety of primary producers, pelagic and benthic invertebrates, and a small 

amount of fish; however, the sheer biomass of ratfish may make even these small diet allotments 

important sources of mortality to the prey groups. 

Ratfish are preyed upon by spiny dogfish (Table 4). 

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Seabirds 

Seabirds are a special class of organisms in the context of a marine ecosystem model and 

the Puget Sound model in particular. Relatively speaking, they are a small component of 

community biomass. However, their high activity levels and endothermic nature result in very 

high consumption rates, which increase their ecological footprint. They can more or less be 

regarded as apex predators, as very few species (apart from other birds or terrestrial nest 

predators) feed on them. Many species are protected to at least some extent by general 

legislation and treaties (e.g., the Migratory Bird Treaty Act), and a small number of species are 

covered by state or federal endangered species legislation. They are also regarded as leading 

indicators of marine ecosystem change (e.g., Cairns 1987, Piatt et al. 2007, Mallory et al. 2010). 

They interact with a wide range of marine species in many habitats, ranging from nearshore to 

pelagic zones. Finally, the Puget Sound area is recognized as important nesting or feeding 

grounds for marine birds, many of which appear to have experienced marked declines in recent 

decades (Nysewander et al. 2005, Bower 2009). For these reasons, ecosystem models must 

account for the functional diversity of seabirds. We represent them in six functional groups, 

based on their trophic position, their primary foraging habitat, and whether they are primarily 

Puget Sound residents or migratory species that spend one or more seasons outside of Puget 

Sound for either feeding or breeding purposes. Groups and representative species are listed in 

Table A-5. 

Gulls 

This group consists mainly of gulls (Larus spp.), representing a range of sizes, but also 

includes surface feeders such as the Caspian tern (Hydroprogne caspia). Some are year-round 

residents while others are seasonal migrants (Table A-5). These birds are familiar due to their 

gregarious nature and close associations with humans. They are important consumers of nearsurface or shoreline animals, carrion, refuse, and even other seabirds. 

We used the Ecopath model to estimate gull biomass, due to problems in mass balancing. 

Estimates of gull biomass were available from winter aerial surveys of Puget Sound, as described 

in Appendix C. The survey counts were adjusted with a bias-correction factor of 1.33 (Evenson 

unpubl. data), and assumed individual weights of 0.95 kg (based on average weights in Hunt et 

al. 2000). This yielded a biomass estimate of 0.036 mt km-2. However, early attempts to balance 

the model resulted in EE >> 1 for gulls, due to predation by raptors (see Raptors subsection 

below). We therefore chose to make EE, rather than biomass, an input variable and set it at 0.6, 

assuming that raptor predation was a moderate source of total gull mortality. 

The P/B ratio for gulls was taken from the Ecopath model of the northern California 

Current developed by Field (2004). Field set the P/B of gulls at 0.12 yr-1, based on Schreiber 

and Burger’s (2002) suggestion of a taxonomic order-level survival rate for gulls of 

approximately 74–97% per year. 

Consumption rates for gulls were estimated using relationships between body size and 

energy requirements (Hunt et al. 2000, see methods in our Appendix C). We estimated energy 

intake for 10 gull species common to Puget Sound. To convert energy consumed to mass of prey 

consumed, we assumed that gull diets have an energy density of 5,000 J g-1, consistent with a 

149
Table A-5. Key representatives of the marine bird functional groups included in the PSCB EwE model. 

Season indicates the season in which they are most abundant in Puget Sound, according to the 

Seattle Audubon Society (http://www.birdweb.org/birdweb/index.aspx) and WDFW.12 The order 

of appearance does not necessarily indicate the order of abundance. 

Functional group Common name Scientific name Season(s) 

Gulls Glaucous-winged gull Larus glaucescens Year-round 

Thayer’s gull L. thayeri Fall, winter, spring 

Heermann’s gull L. heermanni Summer, fall 

California gull L. californicus Summer, fall 

Ring-billed gull L. delawarensis Fall, winter, spring 

Mew gull L. canus Fall, winter, spring 

Bonaparte’s gull L. philadelphia Fall, winter, spring 

Caspian tern Hydroprogne caspia Spring, summer 

Migratory divers Western grebe Aechmophorus occidentalis Fall, winter, spring 

Red-necked grebe Podiceps grisegena Fall, winter, spring 

Horned grebe P. auritus Fall, winter, spring 

Eared grebe P. nigricollis Fall, winter, spring 

Common merganser Mergus merganser Fall, winter, spring 

Hooded merganser Lophodytes cucullatus Fall, winter, spring 

Red-breasted merganser M. serrator Fall, winter, spring 

Common loon Gavia immer Fall, winter, spring 

Pacific loon G. pacifica Fall, winter, spring 

Red-throated loon G. stellata Fall, winter, spring 

Common murre Uria aalga Fall, winter, spring 

Resident divers Rhinoceros auklet Cerorhinca monocerata Year-round 

Pigeon guillemot Cepphus columba Year-round 

Double-crested cormorant Phalacrocorax auritus Year-round 

Pelagic cormorant P. pelagicus Year-round 

Brandt’s cormorant P. penicillatus Year-round 

Marbled murrelet Brachyramphus marmoratus Year-round 

Nearshore divers Surf scoter Melanitta perspicillata Fall, winter, spring 

Black scoter M. nigra Fall, winter, spring 

White-winged scoter M. fusca Fall, winter, spring 

Greater scaup Aythya marila Fall, winter, spring 

Lesser scaup A. affinis Fall, winter, spring 

Canvasback A. valisineria Fall, winter, spring 

Common goldeneye Bucephala clangula Fall, winter, spring 

Barrow’s goldeneye B. islandica Fall, winter, spring 

Bufflehead B. albeola Fall, winter, spring 

Harlequin duck Histrionicus histrionicus Fall, winter, spring 

Ruddy duck Oxyura jamaicensis Fall, winter, spring 

Long-tailed duck Clangula hyemalis Fall, winter, spring 

Herbivorous birds Tundra swan Cygnus columbianus Fall, winter 

Canada goose Branta canadensis Year-round 

Black brant B. bernicla Fall, winter, spring 

Cackling goose B. hutchinsii Fall, winter, spring 

Mallard Anas platyrhynchos Year-round 

Gadwell A. strepera Year-round 

 

12 S. Pearson, WDFW, Olympia, WA. Pers. commun., 5 May 2009. 

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Table A-5 continued. Key representatives of the marine bird functional groups included in the PSCB 

EwE model. Season indicates the season in which they are most abundant in Puget Sound, 

according to the Seattle Audubon Society (http://www.birdweb.org/birdweb/index.aspx) and 

WDFW.13 The order of appearance does not necessarily indicate the order of abundance. 

Functional group Common name Scientific name Season(s) 

Herbivorous birds American wigeon A. americana Fall, winter, spring 

(continued) Northern shoveler A. clypeata Year-round 

Northern pintail A. acuta Fall, winter, spring 

Green-winged teal A. crecca Fall, winter, spring 

Raptors Bald eagle Haliaeetus leucocephalus Year-round 

diet of planktonic and benthic invertebrates, fish, and scavenged items. Daily prey consumption 

was multiplied by 365 days and divided by the body mass of an average individual (0.95 kg, see 

above) to yield an annual Q/B ratio of 172.533 yr-1. 

In the mass-balancing step of Ecopath, the biomass of gulls was calculated at 0.0622 mt 

km-2, which is almost double our empirical estimate (see above). This may be reasonable, as gull 

densities in Puget Sound are as much as three times greater in summer than in winter, when the 

survey data we used were collected (Nysewander et al. 2005). 

Quantitative diet studies for gulls in PSCB generally do not exist, nor are there data on 

seasonal differences in diet. Summer diet data for glaucous-winged gull chicks on Vancouver 

Island (Vermeer 1982a) suggest strong reliance (≈70%) on planktivorous fishes such as Pacific 

herring, sand lance, and saury (Cololabis saira), whereas adults fed more on human refuse. 

Vermeer et al. (1987) examined seasonal diets of mew and Bonaparte’s gulls at Active Pass 

(Strait of Georgia) and found much heavier reliance on euphausiids and macrozooplankton in 

upwelling areas and nearshore and intertidal organisms outside of upwelling areas. Gulls may 

also raid nests of other seabirds during the breeding season (e.g., Parrish 1995, Good 2002). 

While this may be a minor component of gull diets, it may be an important source of mortality 

for the depredated species. 

We attempted to represent this functional group diversity as follows. We assumed 40% 

of their diet was Import, which lessens the ecological footprint of gulls by accounting for 

external food resources (seasonal migration outside of the model domain, plus feeding on refuse 

or terrestrial food sources). The balance of the initial diet is dominated by pelagic groups 

(juvenile Pacific herring, euphausiids, macrozooplankton, forage fish, surfperches, and juvenile 

salmon) and further supplemented by benthic invertebrates, demsersal fishes, and detrital groups 

(Table 4). Trace levels of their diet are attributed to gulls and resident divers, to represent nest 

predation. 

In addition to the intraguild nest predation, gulls are preyed upon by raptors (Table 4). 

We are assuming no loss of gulls to incidental mortality in active or derelict fishing gear. 

 

13 See footnote 12. 

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In the 1990s gull densities measured in Puget Sound surveys were highly variable 

(Nysewander et al. 2005). Although they appeared to decline overall, it is difficult to quantify an 

actual trend existed that would translate to a BA term. It does appear that their numbers declined 

by more than 40% between the 1970s and the 1990s (Nysewander et al. 2005, Bower 2009). The 

cause of the long-term decline is unknown. 

Migratory Divers 

Migratory divers are diving birds that mostly forage in waters deeper than the subtidal 

zone and that nest in the summer outside of the model domain. Major representatives are grebes, 

mergansers, loons, and common murres (Table A-5). Distinguishing migratory divers from other 

diving birds is important: they experience distinct, external sources of mortality during their 

breeding seasons outside of the model compared to resident divers; also, they tend to feed in 

different habitats of Puget Sound compared to nearshore divers. 

Estimates of biomass relied on the methods described in Appendix C. Aerial density 

estimates were made separately for western grebes, red-necked grebes, small grebes (i.e., horned 

and eared grebes), mergansers, loons, and murres. Each density estimate was multiplied by a 

bias correction factor (ranging from 1.16 to 1.54, Evenson unpubl. data) and a mean individual 

mass appropriate for the region, obtained from Poole (2005). These corrected biomass densities 

were then summed to yield an overall biomass estimate of 0.0414 mt km-2 for migratory divers. 

Western grebes were the dominant species, comprising more than 60% of the functional group 

biomass, while mergansers and loons each comprised 15–20% of the group. 

P/B estimates come from survival rates (S), summarized in Poole (2005); we set P/B

equal to –ln(S). Species for which no data could be found were assigned the average S of 

congeners. P/B ratios were weighted by the density estimates and summed to generate the final 

P/B estimate of 0.195 yr-1. 

Consumption rates were based on allometric functions of daily energy requirements 

(Hunt et al. 2000, see methods in Appendix C). Estimates of daily prey intake were converted 

from units of joules to grams of prey by assuming a prey energy density of 6,500 J g-1, consistent 

with a rich diet of pelagic fish and some pelagic invertebrates. Mass-specific daily prey intakes 

were multiplied by 365 days, weighted by the relative aerial biomass density estimates, and 

summed to produce a Q/B of 118.965 yr-1. 

Ecopath calculated the EE via the mass-balancing routine. EE = 0.123, which implies a 

relatively low level of predation mortality in the initial model state. 

With a few exceptions, quantitative diet studies for migratory divers in Central Puget 

Sound do not exist, nor are there data on seasonal differences in diet. We characterized their 

initial diets based on a variety of reports on diets of grebes, mergansers, and loons in marine 

waters of British Columbia (Wetmore 1924, Cottam and Knappen 1939, Munro 1941, Reimchen 

and Douglas 1984, Pauly and Christensen 1996, Gillespie and Westrheim 1997, McIntyre and 

Barr 1997, Mallory and Metz 1999, Barr et al. 2000). We characterized common murre diets 

from studies of incidentally taken murres from gill net fisheries in Central Puget Sound (Lance 

and Thompson 2005). These reports include some quantitative estimates of adult diets and prey 

152
delivered to chicks. These species are primarily piscivores with a small amount of feeding on 

invertebrates and vegetation. The initial diet, compiled from the reports above and weighted 

according to the relative biomasses of the bird species, is dominated by juvenile Pacific herring, 

forage fish, demersal fish, and surfperch. Minor contributors include benthic primary producers, 

shrimp, small crustaceans, other grazers, soft infauna, squid, and several fish groups including 

juvenile salmon. An additional 33% comes from the Import pool to account for food consumed 

when these birds seasonally migrate outside of the model domain. 

Migratory divers are preyed upon by raptors. They also experience incidental loss to 

fishing gear, but as data are not yet available, we will assume no fishing mortality rate (F) at 

present. 

In the 1990s numbers of some of these species appeared to decline in southern Puget 

Sound (Nysewander et al. 2005), although those authors were cautious in interpreting the 

apparent decline in western grebes, one of the most numerous species in this functional group. 

Their caution owes to the highly aggregated nature of western grebes, and the possibility that 

lower numbers in later years might simply have been due to large aggregations of grebes being 

out of the area when the surveys occurred, not to a drop in actual population. Loon biomass 

appeared to increase during the 1990s, while common murres and mergansers were highly 

variable (Nysewander et al. 2005). Given the unclear nature of these trends, we did not assign a 

BA term to this group. 

Resident Divers 

Resident divers are diving birds that mostly forage in waters deeper than the subtidal 

zone and that may nest in the PSCB. Major representatives are pigeon guillemots, cormorants, 

and rhinoceros auklets (Table A-5). We also included the marbled murrelet, a special case 

because it is an endangered species under the U.S. Endangered Species Act. We say that they 

may nest in the PSCB because many cormorants, guillemots, and auklets actually nest outside of 

the PSCB during summer months, but then immigrate to the Central Basin to feed for many 

months of the year (i.e., only a subset of the mature biomass is resident year-round). For 

example, seasonal bird counts by WDFW in the 1990s indicated that cormorant numbers were 

500% more abundant in winter than summer (Nysewander et al. 2005). Further complicating 

this functional grouping is the difficulty of identifying cormorants species from aerial surveys. 

The WDFW aerial surveys identify cormorants in aggregate rather than at the species level, and 

two of the species (Brandt’s cormorant and the pelagic cormorant) nest primarily along the outer 

coast (species profiles online at http://www.birdweb.org/birdweb/index.aspx). We treat all 

coromorants in aggregate as members of this functional group. 

Estimating biomass for resident divers follows the seabird biomass methodology 

described in Appendix C. Surveyors recorded pigeon guillemots, rhinoceros auklets, and 

cormorants in aggregate. Marbled murrelets were aggregated with ancient murrelets 

(Synthliboramphus antiquus). Because the latter are very rare in PSCB, we assumed that all 

murrelets in PSCB are marbled murrelets. Species or aggregate counts were multiplied by a bias 

correction factor of 1.33 (Evenson unpubl. data), then by the mean individual weights of the 

species (from Poole 2005) to produce biomass densities. The sum of those densities equaled the 

153
initial biomass for resident divers, 0.0112 mt km-2. Cormorants comprised nearly 97% of the 

biomass. 

We used published annual survival rates (S) to calculate P/B for resident divers. 

Estimates of S for most of the resident divers were available in a number of studies, albeit from 

systems other than Puget Sound (Bertram et al. 2000, Wires et al. 2001, Schreiber and Burger 

2002, Peery et al. 2006); -ln(S) was assumed to equal P/B. Using the relative biomass densities 

from the aerial surveys, we calculated a weighted average P/B of 0.164 yr-1, which seems 

appropriate for birds with maximum ages ranging from 10 to 18 years (Schreiber and Burger 

2002). 

We again used the Hunt et al. (2000) allometric scaling function to estimate the Q/B ratio 

for resident divers (Appendix C). Daily energy intake was converted from joules to grams by 

assuming a diet energy density of 6,500 J g-1, consistent with a high-quality diet rich in pelagic 

fish. Multiplying daily intake by 365 days and dividing by average individual mass yielded 

annual Q/B estimates, which were then weighted by relative abundance and summed to produce 

a weighted mean Q/B of 118.379 yr-1. 

Ecopath’s mass-balancing algorithm provided the estimate of EE, based on rates of 

predation relative to the P/B rate. The EE = 0.37. 

Several published and unpublished reports have described diets of resident divers in 

Puget Sound or in nearby waters. The high proportion of cormorants in the functional group’s 

biomass drives the diet patterns of the group (Table 4). Cormorant diets reported by Robertson 

(1974), Wiens and Scott (1975), and Couch and Lance (2004) reflect a diet dominated by 

nearshore demersal fishes, surfperch, and some pelagic fishes. Other resident divers are also 

overwhelmingly piscivorous, with pigeon guillemots feeding most heavily on nearshore 

demersal fishes while auklets and murrelets feed more on Pacific herring, sand lance, and other 

pelagic species. We assigned 33.3% to the Import box for food eaten outside of the model 

domain, in order to account for the summer emigration of most of the cormorants. 

Resident divers are preyed upon by raptors and gulls. Resident divers suffer incidental 

mortality in fishing gear, but as data are not yet available, we will assume no F at present. 

We did not assign a BA term to this group, as there was no clear density trend in the 

1990s for the most dominant species, the cormorants (Nysewander et al. 2005). 

Nearshore Divers 

The nearshore divers are primarily comprised of scoters (Melanitta spp.) and goldeneyes 

(Bucephala spp.), as well as ruddy ducks, harlequin ducks, long-tailed ducks, and scaups (Table 

A-5). Birds in this group are diving ducks that spend much of the year in Puget Sound; breeders 

depart for the summer to nest elsewhere. They mostly feed on benthic invertebrates in nearshore 

waters, and many of them eat Pacific herring roe off submerged vegetation during the springtime 

herring spawning period (e.g., Lewis et al. 2007). 

We estimated biomass for nearshore divers based on the aerial survey methods described 

in Appendix C. Surveyors recorded species aggregates (scoters, goldeneyes, scaups) and readily 

154
identifiable individual species (harlequin ducks, buffleheads). Counts were multiplied by 

species-specific or aggregate-specific bias correction factors ranging from 1.28 to 1.61 (Evenson 

unpubl. data) and converted to biomass estimates by multiplying by estimated mean individual 

weights (range: 0.42 kg for buffleheads to 1.115 kg for scoters, Poole 2005). The sum of these 

biomasses, averaged across the area surveyed, was 0.0846 mt km-2. 

Nearshore diver P/B comes from several studies of annual survival rates for the 

representative species (e.g., Savard et al. 1991, Krementz et al. 1997, Ludwichowski et al. 2002, 

Mowbray 2002, Rotella et al. 2003, Flint et al. 2006, Rodway 2007); most of these studies were 

for migratory North American populations. Annual survival rates (S) were converted to P/B

ratios according to the relationship P/B = –ln(S), then weighted according to the empirical 

estimates of biomass densities described above. The weighted average P/B of all species and 

aggregates was 0.291 yr-1. 

We estimated nearshore diver Q/B using the same methodology described for other birds, 

where daily energetic requirements are a function of body size (Hunt et al. 2000, see methods in 

our Appendix C). Nearshore diver body sizes, on average, range from 0.4 kg (bufflehead) to 1.7 

kg (white-winged scoter) (Hunt et al. 2000). We weighted the daily energy demands according 

to the relative abundances and body sizes of each species or aggregate, and assumed dietary 

energy density of 4,200 J g-1, consistent with a diet of bivalves. The final weighted average Q/B

for this functional group was 205.235 yr-1. 

We allowed Ecopath to estimate EE in the mass-balancing step. The estimated value 

(0.076) reflects low pressure applied by predators and human causes in the model domain. 

Information on nearshore diver diets comes from several sources. Henny et al. (1991) 

studied surf scoter diets in Commencement and Elliott bays, both of which are in PSCB. 

Vermeer and Bourne (1984) described white-winged scoter diets in the Strait of Georgia from 

the 1960s and 1970s. Scoters are known to feed on Pacific herring roe during herring spawning 

events (Lewis et al. 2007). Scaup diets in southern British Columbia have been described by 

Munro (1941) and Vermeer and Levings (1977). Vermeer and Levings (1977) measured diets of 

common goldeneye, while Vermeer (1982b) measured diets of Barrow’s goldeneyes and 

buffleheads. Harlequin duck diets have been measured in Puget Sound (Gaines and Fitzner 

1987) and coastal British Columbia (Vermeer 1983, Rodway et al. 2003). The latter authors 

noted feeding by harlequin ducks on herring roe. From this variety of sources, we derived 

biomass-weighted estimates of the functional group’s overall diet. To account for feeding 

outside of PSCB and opportunistic scavenging of nonmarine food sources while in PSCB, we 

assigned just over 38% of nearshore diver diets to the Import category. For dynamic prey pools, 

the most important groups are infaunal bivalves, small crustaceans, mussels, and other grazers 

(Table 4). Minor contributions come from juvenile Pacific herring, predatory gastropods, soft 

infauna, shrimp, barnacles, benthic macroalgae, and algal/plant material. 

Nearshore divers are preyed upon by raptors. We assume no incidental fishing mortality. 

According to analysis of winter aerial counts, densities of surf scoter, goldeneyes, and 

scaups—all important representatives of this group—declined by 20–40% in the Puget Sound 

region between the winters of 1993–1994 and 1999–2000 (Nysewander et al. 2005). No 

155
mechanism for the decline was given by Nysewander et al. (2005), and we do not know if the 

decline results from processes occurring within Puget Sound or in other areas of the birds’ 

geographic ranges. When we attempted to input a proportional BA term (–0.03 yr-1), the model 

went out of balance because the nearshore diver EE declined to less than 0 (i.e., the 

compensatory production related to the BA terms was greater than the predation mortality 

imposed by raptors). Because of this, we did not input a BA term that might affect massbalancing results in Ecopath; we instead recommend handling this decline using forcing 

functions in Ecosim. 

Herbivorous Birds 

The herbivorous bird functional group includes species for which plant or algal material 

is the major food source. This group is primarily comprised of dabbling ducks, geese, and 

brants, many of which are migratory birds that are most abundant in PSCB during the fall, 

winter, and spring months (Table A-5). 

We estimated biomass for herbivorous birds using the aerial survey data and methods 

described in Appendix C. Surveyors recorded counts of aggregate groups for the most part (e.g., 

dabbling ducks, geese). Counts were corrected for the bias associated with aerial counts, using 

aggregate-specific bias correction multipliers ranging from 1.33 to 1.47 (Evenson unpubl. data). 

The corrected counts were multiplied by aggregate-specific estimates of mean individual 

biomass (from Poole 2005) and summed, yielding an estimated biomass of 0.055 mt km-2. 

As with other bird functional groups, we estimated P/B using published data on annual 

survival rates, mostly from Hestbeck (1993), Poole (2005), Eichholz and Sedinger (2007), and 

Hines and Brook (2008). Annual survival probabilities (S) were converted to instantaneous 

annual mortality rates (i.e., estimates of P/B) by the formula P/B = –ln(S) and weighted by the 

relative biomasses of the species and aggregates. Although geese typically have higher annual 

survival rates than dabbling ducks, the latter made up 85–90% of the total biomass of the group 

based on the aerial survey data. The weighted average P/B for herbivorous birds was 0.427 yr-1. 

For the Q/B ratio, we again used the allometric method for estimating daily energy 

requirements of seabirds as developed by Hunt et al. (2000) and described in Appendix C. We 

calculated daily energy intake for the average individual body weights of representative 

herbivorous birds listed by Hunt et al. (2000), and converted the food requirements from units of 

joules to grams by assuming a dietary energy density of 2,500 J g-1, consistent with a diet 

dominated by plant material. These daily requirements were then expanded to annual 

requirements and weighted according to the relative biomass of dabbling ducks and geese, based 

on the bias-corrected aerial survey counts. The weighted average Q/B was 329.082 yr-1. 

We allowed Ecopath to calculate the EE for herbivorous birds as a function of predatory 

demand and direct or indirect mortality due to human activities. The EE was 0.080, reflecting 

the relatively low amount of top-down pressure currently imposed on this group. 

Diets of herbivorous birds in Puget Sound are poorly studied. These birds likely forage 

both in nearshore marine habitats and inland or highly disturbed habitats while they reside in the 

Puget Sound area. For example, geese and swans that overwinter in the area primarily forage 

156
terrestrially, in fallow fields. Some dabbling ducks may also feed heavily on larvae of terrestrial 

insects, which are not model components. In addition, we assume that they spend approximately 

one-third of the year outside the model domain. Thus we have assumed 60% of their diet to be 

Import (Table 4). The remaining diet is mostly benthic macroalgae, eelgrass, and algal/plant 

material, with a small amount of other grazers and small crustaceans. 

Herbivorous birds are eaten by raptors. We assume no losses due to hunting, and no 

incidental losses to fishing gear. 

We assume no BA term for this group. According to Nysewander et al. (2005), the 

density of these birds has changed for a variety of reasons in recent decades. Populations of 

dabbling ducks increased by 100–200% in the late 1980s as a result of feeding programs and 

management actions; however, their densities in the 1990s were variable and may have declined, 

as measured in aerial surveys (Nysewander et al. 2005, see methods in Appendix C). Geese and 

brants were far more abundant in the 1990s than in the 1970s (Bower 2009). However, we do 

not have conclusive evidence of a contemporary trend that would justify a BA input. 

Raptors 

Raptors are birds of prey, nondiving birds whose principal prey items are vertebrates, 

including fish, other birds, and land mammals. In this model, the major representative of the 

raptor group is the bald eagle. Other raptors such as the osprey (Pandion haliaetus) occur in the 

area, but we assume their roles to be minor relative to the bald eagle. Bald eagles are iconic 

birds of high social value. Throughout much of their North American range, bald eagle 

populations declined to dangerously low levels in the twentieth century, due to habitat loss, 

illegal poaching, and high contaminant loads in their prey. They were declared federally 

endangered in 1976. Since then, state and federal protection and improvements in environmental 

quality have promoted increases in nearly all populations throughout North America, including 

the Pacific Northwest (Stinson et al. 2007). Their status was upgraded to threatened in 1995, and 

they were delisted altogether in 2007. Despite delisting, bald eagles remain protected by federal 

legislation, including the Bald and Golden Eagle Protection Act and the Migratory Bird Treaty 

Act, and by state rules (WAC-232-12-292). 

As of 2000, an estimated 4,500 eagles spend part of the year in Washington, up from less 

than 1,500 in 1983. Most of these eagles overwinter in Washington and spend the balance of the 

year in Canada and Alaska, while the remainder are year-round residents that nest in the summer 

months. Based on available nesting habitat, the current carrying capacity of bald eagles in 

Washington state is likely closer to 6,000 (Stinson et al. 2007). 

To estimate biomass of raptors, we used recent data from bald eagle counts in the study 

area (Blatz unpubl. data). Between 1998 and 2002, an annual average of 73.75 nesting adults 

were observed along Puget Sound shorelines (i.e., within ≈0.8 km of the shoreline). At an 

average of 4.5 kg per adult bird (Stalmaster and Gessaman 1984), that would imply a resident 

adult population biomass of 0.332 mt, or a biomass density of 0.00044 mt km-2. The statewide 

bald eagle population increases by threefold to fourfold during the winter (Stinson et al. 2007). 

Assuming a conservative threefold increase for purposes of estimation, we get an estimated 

biomass of 0.00131 mt km-2. This result ignores some potential sources of bias. For example, 

157
we assume only pairs of adult birds in the nests, but no juveniles. Also, we do not know whether 

the winter increase noted by Stinson et al. (2007) is similar in all areas. 

Raptor P/B estimates stem from annual survival rate data on bald eagles in Washington, 

as reported by Stinson et al. (2007), citing unpublished data by J. W. Watson (WDFW). Those 

data suggest very high survival (93–100%) of hatchlings through age 2, followed by lower 

annual survival (70–73%) of subadults and adults. By fitting an exponential curve to the survival 

data, we calculated an instantaneous annual mortality rate of 0.314 yr-1, which we used for P/B. 

Raptor Q/B estimates were taken from a bioenergetics model developed for 

overwintering bald eagles in the Nooksack River area of Puget Sound by Stalmaster and 

Gessaman (1984). They used their model to estimate daily food requirements of bald eagles as a 

function of temperature, activity level, rainfall, and prey quality. Daily energy intake during 

winter months was approximately 2,068 kJ bird-1 day-1, or 490 g bird-1day-1 if chum salmon 

carcasses were the main food source. We simply extrapolated this daily food intake rate over the 

course of a year for a 4.5 kg eagle and found a Q/B rate of 39.744 yr-1. This may represent an 

overestimate for year-round residents, whose consumption rates may drop during the warmer, 

drier months of summer and fall. Alternatively, resident birds have the added costs of parental 

care during the summer. 

There are no predators on raptors in the model, and we assume no losses due to 

accidental entanglement in gear or to illegal activities (e.g., shooting or poaching). However, we 

have included a BA rate, reflecting the steady population increase that bald eagles have 

experienced in recent decades. We assumed a BA of 0.000118 mt km-2

 yr

-1, that is, an annual 

biomass increase of 9%, consistent with observations by Stinson et al. (2007). That represents 

28.7% of the P/B rate; by virtue of mass balance, the EE for raptors thus is 0.287. 

In compiling their diets (Table 4), we first assumed that raptors acquire nearly 60% of 

their diet from the Import category, following the assumptions that 1) approximately 75% of the 

total biomass spends 75% of the year outside of the system, and 2) while in the system, eagle 

diets include at least some items that are not model groups (e.g., terrestrial mammals, freshwater 

fishes, birds not associated with marine habitats). Beyond the Import category, bald eagles eat a 

variety of birds, invertebrates, and fish. They feed heavily on salmon carcasses after salmon 

spawning occurs in fall and winter. 

A survey of Puget Sound area nests (n = 17) during breeding season in June and July 

1980 found their diet to include grebes, common murres, glaucous-winged gulls, scoters, ducks, 

auklets, pigeon guillemots, loons, herons, pheasants, rockfish, lingcod, Pacific hake, sculpins, 

Pacific cod, flatfish, plainfin midshipmen, freshwater fish, terrestrial mammals, and bivalves 

(Knight et al. 1990). A 1986–1997 study of bald eagles nesting in 78 territories in tidally 

influenced marine and estuarine ecosystems and along freshwater lakes and rivers of Puget 

Sound and Hood Canal revealed dependence on (in descending order of importance) surfacefeeding birds, piscivorous birds, waterfowl, small mammals, bivalves, flatfish, lingcod, spiny 

dogfish, rockfish, sculpins, salmonids, and crabs (Watson 2002). We summarized available data 

(Table 4) to the best of our ability and weighted the relative importance of certain groups based 

on seasons; for example, migratory diving birds are most abundant in the winter, when eagles are 

also most abundant, and thus have a different relative importance than resident diving birds.

158
Marine Mammals 

Twelve species of marine mammals (six cetaceans, five pinnipeds, and one mustelid) 

occur in the waters of Puget Sound. Of these, three pinnipeds (harbor seal, California sea lion 

[Zalophus californianus], and Steller sea lion [Eumetopias jubatus]) are relatively common in 

the Central Basin and are included in the model. The remaining marine mammals, including 

killer whales, aka orcas (Orcinus orca), are quite rare or occur seasonally in low numbers in the 

PSCB. We chose to exclude them from this model (Appendix B). 

Harbor Seals 

Harbor seals are the most common pinniped in the Puget Sound–Georgia Basin, with a 

population in the late 1990s numbering close to 13,000 (Jeffries et al. 2003). This followed a 

substantial increase from a population of 3,000 seals in the late 1970s, and the relatively stable 

population since the late 1990s may indicate that the population is at carrying capacity. Harbor 

seals are year-round residents, generally displaying high haul-out site fidelity and localized home 

ranges (Suryan 1995, Hardee 2008). They are widespread throughout all basins of Puget Sound, 

but the majority of the population is found among and around the San Juan Islands and in the 

Strait of Juan de Fuca. More than 1,000 individuals are believed to reside in the Central and 

South Puget Sound basins (Jeffries et al. 2003), but most (>700) are close to Gertrude Island, a 

haul-out site in the South Puget Sound Basin (Lambourn et al. 2001). Breeding sites in the 

Central Basin include Commencement Bay, Liberty Bay, and Sinclair Inlet. 

Absent an accurate count of harbor seals in PSCB, we estimated biomass by first 

arbitrarily assuming a total resident population of 300 individuals. Following Hunt et al. (2000), 

we assumed an average mass of 0.06 mt per seal, which expands to a total population biomass of 

18 mt or 0.024 mt km-2. 

We set the P/B estimate equal to the maximum observed rate of population increase 

(Rmax) for harbor seals in inland waters of Washington State. Between 1978 and 1999, the harbor 

seal population increased dramatically and eventually stabilized; Rmax during that period equaled 

12.6% (Jeffries et al. 2003). We thus set P/B at 0.126 yr-1. This exceeds P/B estimates in other 

models. For example, Field (2004) set harbor seal P/B at 0.084 yr-1 in his model of the northern 

California Current, based on results from a life history model and corroborated by regional 

population growth rates of 7% yr-1. Similarly, Ainsworth et al. (2002) estimated P/B for 

pinnipeds to be 0.06 yr-1, half of the maximum theoretical rate of population increase of 12%. 

We estimated Q/B based on allometric relationships summarized by Hunt et al. (2000, see 

methods in our Appendix C) for a 0.06 mt individual. The relationship estimated daily energy 

intake, which we converted to prey consumption by assuming a prey energy density of 5.935 

kJ g-1 (see harbor seal diet composition description below). Rescaling to annual prey intake 

produced an annual consumption estimate of 1.476 mt of prey or a Q/B ratio of 24.594 yr-1. 

We allowed the model to estimate EE based on mass balance. The EE for harbor seals 

derives not from predation but from anthropogenic sources of mortality (bycatch and gunshot, 

see below). The initial EE estimate for harbor seals is 0.397. 

159
Harbor seal diets are comprised primarily of fish, although there is seasonal- and sitespecific variation. In the Strait of Georgia, Pacific hake and Pacific herring comprised 70% of 

their diet, with nine other fish and one squid species making up the rest (Olesiuk 1993). In Hood 

Canal, Pacific hake, Pacific herring, and salmon were the most important species (London et al. 

2001). In South Puget Sound, harbor seals consumed mostly Pacific tomcod, Pacific herring, 

English sole, plainfin midshipmen, Pacific hake, and a variety of other fish and cephalopods 

(Cullon et al. 2005). Around the San Juan Islands, the most frequently occurring prey were 

Pacific herring, walleye pollock, salmon, and sand lance (Lance and Jeffries 2007). Adding 

further complexity, harbor seal diets are reported in terms of frequency of occurrence of prey in 

scat samples, rather than in terms of proportion of total gut content mass or volume, and Ecopath 

diets must be expressed in proportions. The initial diets we input, which are dominated by 

Pacific herring, walleye pollock, forage fish, salmon, and assorted other fish (Table 4), must 

therefore be considered assumptions. Harbor seal diets in PSCB are clearly an area where more 

research is desired. 

Harbor seals are not preyed upon by any other species in this model, but some mortality 

occurs as a result of human activities. Harbor seal mortality has been documented in the 

nontreaty chum salmon gill net fishery in PSCB and Hood Canal, but the most recent observer 

data from that fishery are from 1994 (Carretta et al. 2008). Although losses likely occur in 

contemporary salmon fisheries, they cannot be quantified accurately because no observer 

program exists.14 Occasionally, dead harbor seals are found in PSCB, either entangled in 

derelict gear or with wounds suggestive of entanglement (Wilkinson unpubl. data). Additional 

mortality occurs when seals are illegally shot, probably to prevent them from damaging gear or

stealing fish. At least three harbor seals were reported with gunshot wounds in PSCB waters 

between 2001 and 2005; all were found dead or died soon thereafter (Wilkinson unpubl. dat

is unlikely that all harbor seals killed by entanglement or gunshot are recorded. As a firs

approximation, we will assume that 10 harbor seals are killed in PSCB each year due to bycatch 

or gunshot. If each individual is an average of 0.06 mt, total bycatch and gunshot mortality 

amounts to 0.0008 mt km-2 each year. 

a). It 

t 

 

Sea Lions 

California sea lions and Steller sea lions are common in Central Puget Sound. Although 

their overall numbers are relatively low, they are included in the model due to their large 

individual size, trophic position, and role as predators on salmon and gadoids. 

California sea lions were first observed in Puget Sound in the mid-1970s and their peak 

springtime abundance increased to more than 1,000 animals throughout the sound. Recent 

counts number in the low hundreds despite the sharp increase of this population along the North 

American west coast since the 1970s (Carretta et al. 2008). Puget Sound animals are primarily 

adult and subadult males that migrate north from breeding islands off Southern California; they 

arrive in September and remain through May. California sea lions are known to aggregate for 

opportunistic foraging. For example, small groups in Shilshole Bay nearly decimated spawning 

runs of steelhead that concentrated below the Hiram M. Chittenden Locks (NMFS 1997). Other 

California sea lion aggregations near Port Gardner fed primarily on Pacific hake (Everitt et al. 

14 M. Lance, WDFW, Lakewood, WA. Pers. commun., 12 March 2009. 

160
1981). The continued presence of sea lions has been implicated in the failure of the Puget Sound 

hake stock to recover from overfishing (Schmitt et al. 1995). 

Sightings of adult male Steller sea lions have increased in Puget Sound in recent years 

(Lance and Jeffries 2007). These are large animals with individual weights in excess of 450 kg. 

Sightings occur in late winter to early spring, after the males have dispersed from northern 

breeding grounds. They appear to be using areas of Puget Sound, as well as waters around 

Vancouver Island and the outer coast of Washington, to forage for elasmobranchs, small- to 

medium-sized schooling fishes, and cephalopods (Lance and Jeffries 2007). 

We estimated biomass of sea lions by first arbitrarily assuming that only 50 individuals 

are regular occupants of PSCB. We further assumed that 60% of these are California sea lions 

and 40% are Steller sea lions.15 We assumed the average mass of an individual subadult/adult 

male California sea lion to be 0.225 mt (Heath 2002) and an individual adult male Steller sea lion 

to be 0.566 mt (Loughlin 2002); this results in a biomass density of 0.024 mt km-2. 

Sea lion P/B was assumed to be between the P/B ratios for harbor seals (0.0826 yr-1) and 

Steller sea lions (0.074 yr-1) as estimated by Barlow and Boveng (1991). Presumably, P/B for 

California sea lions will lie between these estimates, owing to their intermediate size relative to 

harbor seals and Steller sea lions. If we assume that their P/B ratio is the average of the two 

estimates from Barlow and Boveng (0.0783 yr-1), and weight the California and Steller sea lion 

P/B estimates by their biomass estimates (described in the previous paragraph), then the 

weighted average P/B is 0.076 yr-1. 

The Q/B calculation for sea lions was based on allometric relationships as described by 

Hunt et al. (2000) and summarized in Appendix C. The relationships provided estimates of daily 

energy intake for California sea lions (mean weight = 0.225 mt) and Steller sea lions (mean 

weight = 0.566 mt), which we converted to mass intake by assuming a dietary energy density of 

6.338 kJ g-1, based on data from Gearin et al. (1999). Based on these calculations, California sea 

lions have a daily requirement of 17.35 kg of food, which scales to a Q/B of 28.15 yr-1, while 

Steller sea lions have a daily requirement of 34.24 kg or a Q/B of 22.08 yr-1. The weighted 

average of these two values is 24.35 yr-1. 

Ecopath calculated the EE based on the above parameters and the top-down demands on 

sea lion production. There is no predation on sea lions in this model, because we did not include 

transient (mammal-eating) orcas as a functional group. Some mortality occurs as a result of 

human activities, however. A small number of sea lions are shot illegally each year (see final 

paragraph in this subsection). Ecopath estimated that sea lion EE = 0.329. 

Because sea lions spend much of their year outside of PSCB, we assumed that 50% of 

their diet came from outside the model domain (i.e., Import = 0.5). The remainder of their diet 

was classified using some published data and some assumptions owing to the nature of the 

available data. Much of the available data are frequency of occurrence data, based on analysis of 

scat samples. Frequency of occurrence data must be converted into proportions of total diet mass 

or volume, a conversion for which there is no clear methodology. Furthermore, a major source 

 

15 M. Lance and S. Jeffries, WDFW, Lakewood, WA. Field observation, 12 March 2009. 

161
of data, a Gearin et al. (1999) study of California sea lion scat in the Whidbey Basin of Puget 

Sound, examined samples collected in the late 1980s. Those data are outdated because the prey 

community has changed considerably in that time, particularly the declining gadoid populations. 

Thus these diet estimates are best viewed with healthy caution. Local studies will be required to 

improve these estimates, but again those studies will likely produce frequency of occurrence 

data. 

Concerning available data, Gearin et al. (1999) concluded that California sea lions in the 

late 1980s primarily fed on Pacific hake, with spiny dogfish, salmonids, walleye pollock, and 

Pacific herring also occurring fairly commonly. Rare prey included squid, plainfin midshipmen, 

surfperches, cottids, skates, and lingcod. Thus California sea lions in Puget Sound seem to 

prefer (primarily but not exclusively) pelagic, schooling prey. For Steller sea lions, the best 

available information comes from scat samples collected in 2006–2007 from 12 individuals in 

the nearby San Juan Islands (Lance and Jeffries 2007). Those scat samples revealed very high 

frequencies of occurrence of spiny dogfish and skates in wintertime Steller sea lion diets, along 

with notable contributions from Pacific herring and other schooling fish as well as some 

demersal fish. Weighted according to the size and abundance of California and Steller sea lions, 

our estimated initial sea lion diet is 50% Import and the rest is made up of a diversity of fish 

groups and cephalopods (Table 4). The major fish prey are spiny dogfish, skates, gadiforms, 

forage fish, and Pacific herring. As implied above, these estimates are tenuous. 

Essentially no predation on sea lions occurs in the model domain. We are unaware of 

incidental sea lion mortality in fishing gear, and thus assume that direct fishing mortality equals 

zero. Some sea lions are illegally shot, however, to prevent them from foraging around fishing 

gear. At least 10 California sea lions were killed by gunshot in the PSCB area from 1996 to 

2005 (Wilkinson unpubl. data), and it seems likely that more were killed in this manner but were 

not reported or discovered. There are no records of Steller sea lions shot to death in PSCB 

during that time period. We will assume that two California sea lions in PSCB are killed by 

gunshot each year; at an average mass of 0.225 mt, this corresponds to an annual biomass loss of 

0.0006 mt km-2. 

Bacterial and Detrital Pools 

The final groups in the model fuel the enormous amount of production in Puget Sound 

that is supported by recycled materials (see Throughput and Transfer Efficiency subsection of 

Results section). Bacteria play the critical role of recycling nutrients, decomposing detrital 

organic matter, and providing a food source for lower trophic level consumers. Little published 

literature exists for Puget Sound bacteria in either the pelagic or benthic zones, thus the 

information presented below is largely speculative and best viewed as placeholder information 

until better data are available. Detrital pools in EwE are repositories for organic material that has 

died within the model domain due to other mortality, that is, mortality that is not due to predation 

by a model group nor to being landed by a fishery. Most EwE models feature one detrital pool: a 

generic detritus box. We recognize three distinct detrital groups in this model, because there is 

evidence that two additional detrital pools (algal/plant material and salmon carcasses) each play 

distinct ecological roles in the food web. 

162
As a general rule, detrital pools in Ecopath have only biomass and EE terms, and the 

actual value of the biomass term is unimportant. The more important aspects are production and 

fate, that is, flows of material into and out of a detrital pool (Christensen et al. 2005). Detrital 

production is a function of the biomass, P/B, and EE values of all primary producer and 

consumer groups in the model. Detrital fate is controlled by the user and indicates the detrital 

pool into which dead material flows. At the end of each model time step, detrital materials can 

be set to either accumulate or, as is more typically done, be exported from the system. 

Bacteria 

In the absence of available data, we made arbitrary estimates of P/B, Q/B, and EE for 

bacteria and allowed Ecopath to calculate biomass based on mass balance. For P/B we assumed 

a very high rate (150 yr-1), reflecting the tendency of bacteria to rapidly turn over in response to 

changing conditions. Following the suggestion of Christensen et al. (2005), we assumed a 

growth efficiency (P/Q) ratio of 0.5 for bacteria, which leads to a Q/B of 300 yr-1. We set EE = 

0.4, implying a modest degree of consumption of annual productivity by consumers that feed on 

bacteria. In the mass-balancing step, Ecopath calculated bacteria biomass is 6.395 mt km-2. 

Compared to empirical data from other Pacific systems, these estimates seem reasonable. 

For example, pelagic bacterial biomass estimates from the subarctic north Pacific to the 

equatorial Pacific average 1.327 mt C km-2 (range 1.142–1.500), and worldwide range between 

0.5 and 2.0 mt C km-2 (Ducklow 2000). Applying a conversion factor for C:dry cells (0.4748, 

Bratbak and Dundas 1984) and a range of dry weight:wet weight of 0.22 (Watson et al. 1977) to 

0.43 (Ducklow 2000), estimated wet weight biomass of bacteria could vary between 2.449 and 

19.147 mt km-2. The Ecopath-calculated biomass falls into the lower third of that range. 

Bacterial production from the subarctic to equatorial Pacific ranges from 100.01 to 509.2 mt km-2

yr

-1 (Ducklow 2000), which given the range of weight wet biomass would lead to a P/B range of 

5.22 to 207.92 yr-1. The assigned P/B value of 150 is near the upper end for pelagic production 

alone. These estimates do not account for benthic bacterial biomass or production, however. 

We assume that the bacterial diet is 100% detritus. 

Many organisms feed on bacteria, either actively (e.g., microzooplankton) or passively 

(e.g., filter feeders or organisms feeding on particulate detritus on which bacteria occur). 

Detritus 

This pool represents generic detritus—dead and decaying organic matter—and it receives 

input from all other groups (including, ultimately, the other two detrital groups listed below). It 

is a critical food resource for many consumers at most trophic levels, from bacteria to scavenging 

vertebrates. 

The detritus biomass was arbitrarily set at 1.0 mt km-2. In the mass-balancing step, 

Ecopath calculated the EE of detritus to be 0.393, a result of consumption by a range of 

functional groups. Summary descriptions of the detritus pool, provided by Ecopath following 

the mass-balancing step, indicate that 69% of the detritus comes from phytoplankton. Other 

contributors include bacteria (8.5%), copepods (3.9%), soft infauna (3.8%), microzooplankton 

(3.6%), benthic microalgae (1.9%), small crustaceans (1.8%), infaunal bivalves (1.2%), and 

163
euphausiids (1.1%). Clearly, most detritus originates in the pelagic realm via phytoplankton or 

consumers of phytoplankton. The major consumers of detritus, in terms of total detritus 

consumed per year, are soft infauna, bacteria, small crustaceans, and infaunal bivalves. Thus the 

phytoplankton-driven detritus pool is a major subsidy of benthic production, consistent with 

Strickland’s (1983) description of energy flow in Puget Sound. 

Algal/Plant Material 

The algal/plant material group was created for the purpose of more directly linking the 

dynamics of large benthic primary producers (eelgrass, benthic macroalgae, and overstory kelp) 

to consumers that are known to graze on detached algal/plant material (e.g., sea urchins, small 

crustaceans). Moreover, as ecosystem models of Puget Sound are developed further, we may 

find that this pool represents an important inshore-offshore linkage: in demersal fish surveys 

conducted in PSCB during 2004–2005, large amounts of detached algal/plant material were 

collected in bottom trawls at several sites at depths ranging from 20 to 160 m.16 This material 

could be a trophic subsidy for members of the demersal community. 

Algal/plant material biomass was arbitrarily set at 1.0 mt km-2. In the mass-balancing 

step, Ecopath calculated the EE of algal/plant material is 0.626, as a result of consumption by a 

wide range of functional groups. According to the Ecopath summary output, most algal/plant 

material is generated by eelgrass, with a substantial amount produced by benthic macroalgae. 

Overstory kelp adds a small amount. Consumption of algal/plant material is dominated by small 

crustaceans and other grazers; sea urchins and herbivorous birds are also notable consumers. 

Salmon Carcasses 

Salmon carcasses were added as a detritus pool in order to link Pacific salmon dynamics 

to raptors. Overwintering bald eagles derive much of their diet from spawned out salmon 

carcasses, and this has some potential significance because raptors are a top predator that feeds 

on a variety of other groups, including many seabirds. This creates the potential for year-class 

strength of salmon to have indirect feedbacks to the bird community. In addition, salmon 

carcasses represent marine-derived food and nutrient subsidies for a wide array of freshwater and 

terrestrial organisms, a topic that may be of interest as ecosystem models of Puget Sound 

develop further. 

Salmon carcass biomass was arbitrarily set at 1.0 mt km-2. In the mass-balancing step, 

Ecopath calculated the EE of salmon carcasses is 0.002, as a result of consumption by raptors. 

Most input to salmon carcasses comes from subadult wild salmon and subadult pink salmon. A 

small amount also comes from subadult hatchery salmon, some of which will stray onto natural 

spawning habitat rather than returning to the hatchery from which they were produced. 

 

16 T. Essington, Univ. Washington, School of Aquatic and Fishery Sciences, Seattle. Pers. commun., 23 May 2008. 

164
Appendix B: Omitted Groups 

Many species and functional groups were not included in the Puget Sound Central Basin 

(PSCB) model. Some are rare or spend only a small amount of time in the system. Some are 

poorly studied and lack suitable information to characterize them in food web terms. Some are 

thought not to be very tightly linked to other functional groups and thus would show little 

response to food web perturbations. Many of these groups will likely be incorporated into future 

models that operate on broader spatial scales or that consider a broader array of ecosystem 

processes. 

Several notable omitted species are described briefly below, including the reasons why 

they were excluded from the present model. 

Primary Producers 

We think that the included primary producers represent the most significant sources of 

marine autochthonous production in the model. Species of one group that has been omitted and 

that may play a significant ecological and management role, however, are the phytoplankton 

species that produce harmful algal blooms (HABs). Several taxa, including the genera 

Alexandrium, Pseudo-nitzschia, Heterosigma, and Chaetoceros, are found in Puget Sound and 

occasionally experience blooms under the right combinations of light, temperature, nutrient 

levels, and stratification (Ruckelshaus and McClure 2007). Some HAB species contain toxins 

that can render bivalves unsafe for human consumption, due to conditions such as paralytic 

shellfish poisoning or amnesic shellfish poisoning; the shellfish themselves are not affected by 

the toxins. Other HAB species produce toxins that can lead to fish kills. HAB dynamics could 

clearly be important in model simulations, for example, through reducing harvest rates on 

bivalves or increasing mortality on vulnerable fish species. We have not included them at this 

point simply in the interest of streamlining the model process, but we could mimic the effects of 

HAB-related closures on bivalve harvest in Ecosim by reducing the fishing mortality rate on 

infaunal bivalves, geoducks, or mussels. 

Invertebrates 

Invertebrates are so diverse and abundant in Puget Sound that some groups inevitably are 

overlooked due to either lack of data or a presumption that the groups are less essential to the 

functioning of the overall system. Key examples are discussed briefly. 

Sea Anemones 

Numerous species of sea anemone (order Actinaria) live in Puget Sound, particularly on 

hard substrates. Some, such as the plumose anemone (Metridium senile), can achieve very high 

biomasses in local patches. Sea anemones feed on a variety of species, ranging from small 

165
planktonic crustaceans to benthic invertebrates to fishes (Kozloff 1983). We chose to omit them 

because of a general lack of data on their biological rates and diets.

Burrowing Shrimp 

Two types of burrowing shrimp (infraorder Thalassinidea) inhabit Puget Sound: blue 

mud shrimp (Upogebia pugettensis) and bay ghost shrimp (Neotrypaea californiensis). Both are 

nearshore species that build extensive tunnels in soft sediments. Blue mud shrimp form 

semipermanent tunnels and draw large volumes of water into the tunnel by continuous 

movement of their pleopods, straining out phytoplankton and detritus for food (Hornig et al. 

1989). By contrast, ghost shrimp reshape their tunnels continuously, feeding on detritus sifted 

from the sediments and, to a lesser extent, detritus and phytoplankton filtered from the 

circulating water (Morris et al. 1980). 

Burrowing shrimp can be considered ecosystem engineers because their burrowing and 

filtering activities affect sediment porosity and stability (Dumbauld et al. 1997), subsurface 

biogeochemistry (Waldbusser and Marinelli 2006), and habitat suitability for other species, 

including eelgrass (Zostera marina) and other invertebrates (Dumbauld and Wyllie-Echeverria 

2003). They are also important prey for higher consumers, notably gray whales (Eschrichtius 

robustus) (Weitkamp et al. 1992). Biomass concentrations can be enormous; in northern Puget 

Sound, Weitkamp et al. (1992) estimated ghost shrimp biomass densities equivalent to 1,488 mt 

km-2. However, these aggregations are highly patchy and the biomass of burrowing shrimp at 

the scale of the entire basin is small. 

Oysters 

Oysters are very important to the ecology of estuarine ecosystems where they are 

abundant. They filter feed tremendous volumes of water, which may have feedbacks to water 

quality. The large reefs that they form are used as habitat by many species. Furthermore, they 

are an extremely valuable fishery resource for both commercial aquacultural producers and also 

recreational harvesters (e.g., Emmett et al. 1991). Most oysters in Puget Sound are either the 

nonnative Pacific oyster (Crassostrea gigas) or the native Olympia oyster (Ostrea conchaphila). 

The Pacific oyster, introduced from Japan in the early 1900s for aquacultural purposes, has 

become abundant in some parts of Puget Sound, within areas of active culture as well as in 

naturalized beds in the intertidal zone (Pauley et al. 1988). The Olympic oyster has declined 

markedly throughout much of its range, due largely to overharvest, habitat degradation, 

competition with introduced oysters, and nonnative predators that were introduced with 

nonnative oysters. It now exists primarily as small remnant populations in lower tidal zones 

(Couch and Hassler 1989, Gillespie 2009). Neither species is especially abundant in the PSCB, 

however, and hence they are not featured in this model. 

Scallops 

Puget Sound is home to several scallop species. Scallops are epibenthic, free-swimming, 

filter-feeding bivalves. Some, like the giant rock-scallop (Crassadoma gigantea), are associated 

with rocky substrates or shell hash, while others may be found on soft sediments or around the 

bases of man-made structures (Morris et al. 1980, Kozloff 1983). Maximum sizes range from 6 

166
to 8 cm in diameter for reddish scallop (Chlamys rubida) and spiny scallop (C. hastata) to more 

than 15 cm in diameter for the giant rock-scallop and the weathervane scallop (Patinopecten 

caurinus) (Kozloff 1983, MacDonald et al. 1991). Although all of these species are prized for 

their culinary value, fisheries are no longer practical owing to low populations. Commercial 

scallop fishing in PSCB essentially ceased in the late 1990s. The State of Washington permits a 

limited take of rock and weathervane scallops for broodstock purposes in Puget Sound (WAC 

220-52-069). Biomasses of scallops in PSCB are lower than in other Puget Sound basins,17 and 

we assume that their abundances are low enough to warrant exclusion from this model. 

Fishes 

As with the above groups, fishes are so diverse that many species are inevitably omitted 

from an analysis such as this. Several relatively high-profile species deserve specific mention. 

Sixgill Sharks 

Bluntnose sixgill sharks (Hexanchus griseus) may be the largest fish that regularly 

inhabits Puget Sound. Their ecology and habits in Puget Sound have only recently been studied 

in depth. Most individuals captured in Puget Sound are believed to be juveniles, although 

mature, pregnant females appear to enter Puget Sound in order to pup (Andrews et al. 2007). 

Interest in the shark has surged in recent years, due to frequent sightings by divers, destructive 

interactions between sixgills and fisheries (e.g., depredation of catches from longlines, raiding of 

crab pots), discoveries of dead specimens on beaches, and some interest in a recreational fishery, 

though that fishery was closed by state fisheries managers because the ability of the population 

to sustain a fishery was not known. Much of the recent study on sixgill sharks has been focused 

on seasonal and diel movement patterns, but little is known about core aspects of biology or 

ecology (e.g., population size, individual growth rates, diets). Our belief is that the population is 

currently too small to have a major effect on the ecological function of Puget Sound, and that our 

lack of basic ecological knowledge prevents us from developing reasonable parameter sets that 

could inform us of how they respond to ecosystem changes. This could change as more 

information is collected.

Sockeye Salmon 

Sockeye salmon (Oncorhynchus nerka) are an abundant species in the PSCB, primarily 

owing to populations that spawn in the Lake Washington watershed. Spawning runs to that 

system may number in the hundreds of thousands, although variability is high. In years with 

adequate adult escapement, they support commercial and recreational fishing, and are also 

important for tourism, as they are the most abundant salmonid that passes through the public 

viewing area at the fish ladder located at the Chittenden Locks in Seattle. We omitted them from 

the model on two accounts. First, their life history is most distinct compared to the other species, 

given their extended lacustrine residence during their juvenile freshwater stage. That made it 

difficult for us to include them with other wild salmon species in a generic salmon pool. Second, 

they pass quickly through the marine waters of Puget Sound as they go to sea and return to 

spawn, minimizing their interaction with other model groups. 

 

17 W. Palsson, WDFW, Mill Creek, WA. Field observation, 25 March 2009. 

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Sturgeons 

Sturgeons (family Acipenseridae) have two representatives in this area: the white 

sturgeon (Acipenser transmontanus) and the green sturgeon (A. medirostris). Both receive 

considerable attention due to their rather exceptional biology, large size, wide geographic ranges 

and dramatic anadromous migrations, value as fishery targets, conservation status, and cultural 

significance. Ecologically, they are of potential interest as predators of benthic invertebrates. 

However, due to both species’ rarity in Puget Sound (e.g., Monaco et al. 1990, Emmett et al. 

1991), we omitted them from the PSCB EwE model. 

Halibut 

Pacific halibut (Hippoglossus stenolepis) are the largest flatfish that occur in Puget 

Sound, and are another iconic species of the region. They are capable of reaching more than 100 

kg in weight. They are highly predatory and prized by sport anglers. WDFW records the fishing 

zones in which halibut are captured, and virtually all catch in Puget Sound occurs in basins to the 

north of PSCB. We therefore assume essentially no halibut biomass in our model domain. 

Sablefish 

Sablefish (Anoplopoma fimbria), also called black cod, are predatory demersal fish that 

are abundant in the Northeast Pacific Ocean. Their diets are diverse and opportunistic, and their 

rapid growth and large size suggest high rates of consumption of their prey resources. They are 

one of the most valuable components of the west coast and Alaska groundfish fisheries (Love 

1996). Sablefish also occur in Puget Sound. Currently, their numbers are low, to the point that 

harvests have limits under Washington state law (e.g., WAC 220-48-005). Due to their current 

rarity, which dates back several decades, we exclude them from this model. 

Wolf Eels 

Wolf eels (Anarrhichthys ocellatus) are large predators most often associated with rocky 

habitats such as subtidal, high-relief boulder fields typical of northern Puget Sound. They feed 

on benthic invertebrates and are occasionally piscivorous. They are a high-profile species for 

recreational scuba divers, owing to their distinctive physical appearance and their courtship and 

nest-guarding behaviors (Love 1996). Occasionally taken by commercial or recreational 

fisheries, they are considered quite palatable. In the absence of any quantifiable information on 

their abundance, production, consumption, or diet, we have chosen to omit them. We presume 

them to be too rare in PSCB to be of great functional significance at the community scale or of 

great enough management importance to drive policy. 

Seabirds 

Shorebirds are the most substantial group of seabirds that we omitted in the current 

model. Some shorebirds are year-round residents but most are present from fall through spring. 

Examples include the great blue heron (Ardea herodias), belted kingfisher (Megaceryle alcyon), 

blackbellied plover (Pluvialis squatarola), killdeer (Charadrius vociferous), dunlin (Calidris 

alpina), and sanderling (Calidris alba). Shorebirds are generally waders that feed very close to 

168
shore on invertebrates and fishes. We opted not to include them at this time because there are no 

comprehensive surveys of their abundances and we are unsure about the nature of their direct 

interactions with the biomass pools in the model. 

Marine Mammals 

The marine mammals that were omitted are species that typically spend very little time in 

PSCB. We opted to omit them rather than to try to judge the validity of scenarios in which they 

responded strongly (or elicited strong responses in other groups) following a perturbation. 

Orcas 

Transient and resident killer whales, aka orcas (Orcinus orca), are among the most iconic 

of species that occur in Puget Sound, but are rarely sighted in the Central Basin. The two types 

of killer whale are distinct, sympatric ecotypes that are most readily distinguished in ecological 

terms by their feeding habits; transient whales feed primarily on other marine mammals, while 

resident whales feed mostly on fish. 

Transient killer whales are seen regularly in the fall throughout the Salish Sea, most 

frequently off southeastern Vancouver Island in small groups ( n = 3, Baird and Dill 1995, 1996). 

They only occasionally enter the PSCB. However, prolonged foraging visits to Hood Canal have 

occurred in recent years: in 2003, 11 transient whales were present for 59 consecutive days; in 

2005, 6 whales were present for 172 consecutive days (London 2006). These whales were 

primarily targeting harbor seals. 

Three pods of the Southern Resident killer whale (SRKW) stock frequent waters of Puget 

Sound (Caretta et al. 2008). The SRKW population, monitored by photo identification since 

1974, is believed to number 83, and is listed as endangered under the U.S. Endangered Species 

Act. SRKW occupy the Salish Sea most days during the summer and fall, foraging mostly on 

Chinook salmon runs bound for spawning rivers in the Puget Sound–Georgia Basin. They 

primarily inhabit major channels around the San Juan Islands in the summer, but may move all 

the way down to South Puget Sound in pursuit of salmon. Extended foraging in the Central 

Basin is quite rare. 

Baleen Whales 

Gray whales are the largest animals that regularly occur in Puget Sound. They are baleen 

whales that reach up to 15 m in length and a mass of over 30 mt. Recent studies projected their 

west coast population at approximately 18,000 (Rugh et al. 2005). Each year, during the 

springtime migration from Baja California to Alaska, a small fraction of the population enters 

Puget Sound south of Admiralty Inlet. The total number is variable but apparently not very large 

(e.g., Calambokidis et al. 2002). Unlike most baleen whales, gray whales typically feed on 

benthic invertebrates in soft sediments. They enter shallow waters and gouge large troughs from 

the bottom, expelling water and sediment through their baleen and retaining the invertebrates. 

Gray whales feed heavily in ghost shrimp beds in Puget Sound, as documented by Weitkamp et 

al. (1992). We elected to omit them from the present model, assuming their ecosystem 

influences to be highly localized. 

169
170

Two other baleen whale species, minke whale (Balaenoptera acutorostrata) and 

humpback whale (Megaptera novaeangliae), make very occasional visits to Central Puget 

Sound. 

Porpoises 

Two species of porpoise live in Puget Sound: harbor porpoise (Phocoena phocoena), the 

most abundant cetacean in the system, and Dall’s porpoise (Phoecoenoides dalli). Harbor 

porpoises feed on squid and pelagic fishes, while Dall’s porpoises are primarily piscivorous and 

especially target demersal and midwater species (Walker et al. 1998). Recent assessments put 

the Washington Inland Waters stock of harbor porpoises at 10,700 (Carretta et al. 2008); most 

are found in the straits of Georgia and Juan de Fuca. While recent observations of live animals 

and strandings suggest that harbor porpoise occurrence in PSCB is increasing, their numbers are 

low and far below records from the 1940s, when harbor porpoises were very common in Puget 

Sound (Scheffer and Slipp 1948). Survey counts in Washington Inland Waters indicated about 

3,500 Dall’s porpoises present in 1991, although considerably fewer (900) were seen in the most 

recent surveys, conducted in 1996 (Carretta et al. 2008). Dall’s porpoises are found throughout 

the Strait of Juan de Fuca and Puget Sound, with the main concentration in the Haro Strait area. 

They are regularly observed in PSCB in small numbers. No current estimates of the number of 

Dall’s porpoises present in PSCB exist. In the late 1980s, it was estimated that as many as 80 

Dall’s porpoises were present in this region, although 20 individuals was the more frequent count 

(Miller 1990). 

Sea Otters 

Sea otters (Enhydra lutris) were once far more abundant in Washington state waters than 

they are now, although we lack definitive information that they have ever been abundant in Puget 

Sound. Sea otter bones have been reported at archaeological sites within Puget Sound, but these 

findings are rare (Lance et al. 2004). Currently, the sea otter population on the outer coast of 

Washington State and in the western Strait of Juan de Fuca is increasing at an annual rate of 

8.2%, and individuals are occasionally seen in Puget Sound basins, so it is possible that 

continued population increases will lead to expansion into PSCB (Lance et al. 2004). This may 

have substantial effects on ecology of nearshore communities because sea otters are widely 

considered keystone species that can control populations of benthic herbivorous invertebrates 

and release kelp from grazing pressure (Estes and Palmisano 1974). 
Appendix C: Frequently Used Estimation 

Methods 

The functional group descriptions in Appendix A provide general summaries of the 

methods used to develop estimates for the parameters in Equation 1. In some cases, the same 

basic field methods and data analyses were employed for multiple functional groups. For 

example, biomass and diet estimation followed a common process for many of the groundfish. 

Or in cases where P/B (production:biomass ratio [which is roughly equal to total mortality, 

Christensen et al. 2005]) or Q/B (consumption:biomass ratio of a predator) estimates were 

unavailable in the literature, we used estimation equations developed for a range of species and 

systems. 

Rather than repeatedly describing frequently used methods in each functional group 

description where they were applied, they are summarized here. 

Biomass Estimation for Bottom Fish 

Much of our data on bottom fish biomass comes from the work of Reum (unpubl. data) 

and Reum and Essington (2008). They conducted a series of bottom trawl surveys in October 

2004 (fall), March 2005 (winter), and July 2005 (summer) on the eastern side of the Puget Sound 

Central Basin (PSCB). Sampling was done at six stations. Trawls were conducted along 

bathymetric contours at 20 m, 40 m, 80 m, and 160 m depths, and total area swept was measured 

based on the width of the trawl mouth (which was depth dependent and empirically monitored), 

the vessel speed, and the time the trawl was believed to be in contact with the bottom (Reum 

2006). Catches in each trawl were sorted to species and counted, and each species’ total 

aggregate mass was determined and expressed as units of mass per unit area swept. For the 

purposes of demersal fish parameter development in the Ecopath with Ecosim (EwE) model, we 

assumed that trawl selectivity = 1, that is, that the trawl sampled all demersal fish that it 

encountered. (By extension, we did not use the trawl data for invertebrates or pelagic fish to 

perform estimations of biomass.) Mean individual mass of a species in each trawl was equal to 

the aggregate mass divided by the number captured. 

Using the raw trawl survey data (Reum unpubl. data), we determined the species-specific 

mean biomass density at each depth stratum in each season using delta distribution, a modified γdistribution that accounts for samples with many zeros and occasional, exceptionally large 

catches (Pennington 1996). The depth-specific biomass density estimates were then expanded to 

the scale of the basin according to the total area of bottom habitat of the adjacent bathymetric 

contours. For example, the trawl data for the 20 m bathymetric contour were assumed to be 

representative of the area between the 10 m and 30 m bathymetric contours, and the mass-perarea-swept estimate was multiplied by the total area of bottom between 10 m and 30 m, 

according to bathymetry data for PSCB (Table 1). Biomass densities at nonsampled depth strata 

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were estimated via linear interpolation relative to the two most proximate sampled strata, except 

for depths shallower than 10 m and deeper than 170 m; biomass densities at those depths were 

assumed to be equal to the nearest sampled stratum. The stratum-specific biomass densities were 

summed and divided by the total model area (757.08 km2

) to generate season-specific biomass 

estimates for each demersal species or functional group. 

This method was applied to biomass estimation for spiny dogfish (Squalus acanthias), 

ratfish (Hydrolagus colliei), skates (Raja spp.), Pacific cod (Gadus macrocephalus), demseral 

fish, piscivorous flatfish, and small-mouthed flatfish. 

Biomass Estimation for Seabirds 

Many of the key species or genera that are in our seabird functional groups (Appendix A, 

Seabirds) have been monitored by WDFW since 1992. Monitoring is conducted by summer and 

winter aerial surveys from a float plane, as described by Nysewander et al. (2005). In brief, the 

plane flies surveys at low altitude (65 m) and slow speed (approximately 150 km hr-1) over two 

habitat strata: nearshore waters (<20 m depth) and offshore waters (>20 m depth). Nearshore 

surveys are flown parallel to shore, while offshore survey tracks are zigzagged. Survey tracks 

cover 13% to 15% of the nearshore and 3% to 5% of the offshore waters in Puget Sound up to 

the Canadian border and out to the entrance to the Strait of Juan de Fuca. Two onboard 

observers identify and count all birds seen within a 50-m wide strip on each side of the plane, 

and the time and GPS location of each observation is recorded. Correction factors are applied to 

counts, given the propensity of some birds to dive and hence be missed by the surveyors. These 

correction factors are based on differences between concurrent observations by aerial surveys 

and ship-based or land-based surveys (Nysewander et al. 2005). The corrected values provide 

the basis for estimating population size and error. Mean individual weights can then be used to 

convert abundance estimates into biomass estimates. 

For our purposes, we used 2005 and 2006 winter density estimates from surveys in the 

Central Basin, Whidbey Basin, and Admiralty Inlet, and taxon-specific correction factors 

provided by Evenson (unpubl. data). Taxon-specific average individual body weights were taken 

from several sources, mainly Hunt et al. (2000) and Poole (2005). Mean total biomass estimates 

were divided by the total swept area to yield biomass estimates. 

P/B Estimation for Benthic Invertebrates 

Because secondary production of benthic marine invertebrates is difficult to study 

empirically, a theoretical relationship that links benthic invertebrate P/B to some life history or 

environmental characteristic is desirable. This is particularly true in Puget Sound, where benthic 

invertebrates are diverse and abundant. Life span should be a good predictor of P/B, because 

longer-lived benthic invertebrates will have many larger-bodied, slower-growing individuals in 

their populations and these individuals lower overall mass-specific productivity. As summarized 

by Robertson (1979), benthic invertebrate ecologists have long recognized the general 

relationship between life span and P/B, and there have been many attempts to quantify it at both 

limited and broad taxonomic scope. Robertson (1979) performed a comprehensive literature 

review of life span and P/B ratios for annelids, benthic gastropods, bivalves, crustaceans, and 

172
echinoderms. From a data set of 49 studies conducted throughout the world, he fit the following 

relationship between measured life span and empirically estimated P/B ratios: 

log10(P/B) = 0.66 – 0.726 log10(λ) (6) 

where λ is the maximum life span in years. 

We used the Robertson (1979) equation to calculate P/B for sea stars, large sea 

cucumbers, sea urchins, other grazers, predatory gastropods, small crustaceans, barnacles, 

mussels, infaunal bivalves, deposit feeders, suspension feeders, and tunicates. 

P/B Estimation for Fish 

In some cases where P/B estimates were not available from empirical data or population 

assessments, we estimated P/B using a generalized fish natural mortality predictive equation. 

Pauly (1980) developed the relationship empirically from 175 estimates of natural mortality, 

distributed throughout the world and representing 40 different families of pelagic and demersal 

fishes. Importantly, this relationship does not predict total mortality (Z), which is the sum of 

mortality (M) and the rate of fishing mortality (F); Z is assumed to approximate P/B in Ecopath. 

Thus the Pauly (1980) method must be combined with F estimates to yield P/B (Christensen et 

al. 2005). However, as Pauly (1980) notes, M is very difficult to measure, and a robust 

theoretical predictor would be of great value. 

As Pauly (1980) describes, the growth parameter k from the von Bertalanffy growth 

equation has long been recognized as a reasonable predictor of M. In his analysis, Pauly (1980) 

also found that including temperature explained substantial variation in M. His interpretation 

was that increasing temperature induced greater predation mortality because of higher metabolic 

demands of predators. According to Pauly (1980), the estimation function is: 

M = k

0.65 × L∞

-0.279 × T 0.463 (7) 

and k is the von Bertalanffy growth parameter, L∞ is the theoretical maximum length (cm), and T

is the mean annual temperature at which the species lives (oC). The correlation coefficient 

between observed and predicted values was 0.847 (Pauly 1980). 

We used this method as the basis for P/B estimation of lingcod (Ophiodon elongatus), 

ratfish (Hydrolagus colliei), and skates. von Bertalanffy parameters came from published 

estimates, and T estimates were based on monthly temperature data collected by the Washington 

Department of Ecology throughout Central Puget Sound (WDE no date). We used temperature 

data from depths corresponding to expected depth distributions of the species or group in 

question. 

Q/B Estimation for Fish

Many of the Q/B estimates for fish groups come from bioenergetics model predictions, 

but for many fish, credible bioenergetics models are not available. Moreover, empirical 

estimates of consumption rates, through field measures or laboratory studies of gastric 

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evacuation rates, are quite difficult to obtain. Thus theoretical predictive relationships are 

desirable. Two reviews by Palomares and Pauly (1989, 1998) described empirical relationships 

between Q/B and several variables, including body weight, temperature, aspect ratio of the 

caudal fin, and prey type. Christensen and Pauly (1992) developed a simplified predictive 

equation without the aspect ratio measure, and we used this equation for many functional groups 

of fishes. The equation is: 

Q/B = 106.37 × 0.0313Tk × W∞

-0.168 × 1.38Pf × 1.89Hd (8) 

where Tk is the mean annual temperature (expressed as 1,000/(T + 273.15), where T is 

temperature in oC, W∞ is the theoretical maximum weight of an individual of the functional 

group, Pf is a dummy variable (1 if the fish is a predator or planktivore, otherwise 0) and Hd is a 

dummy variable (1 if the fish is primarily an herbivore, otherwise 0). W∞ estimates came from 

published length-at-age and length-weight relationships for representative species, and T

estimates were based on temperature data collected monthly by WDE at multiple depths 

throughout Central Puget Sound (no date); we used temperature data from depths corresponding 

to expected depth distributions of the species or group in question. 

We used this method to estimate Q/B for surfperches, piscivorous flatfish, small-mouthed 

flatfish, and skates. 

Use of Q10 for Temperature-dependent Rate Estimation 

Many metabolic rates are temperature dependent, and the relationship is often not a 

simple linear function. In several cases described in Appendix A, we were forced to rescale a 

rate (e.g., consumption) as a function of temperature. Often this was because consumption rates 

or diet data were available seasonally and we know that temperature varies on a seasonal basis. 

In other instances, we had consumption rates from a nearby ecosystem where water temperatures 

were different (often cooler) than temperatures experienced by PSCB functional groups. To do 

this rescaling, we used Q10, a theoretical magnitude of increase in a rate when temperature 

increases over a small range. Q10 can be calculated as: 

12

10

1

2

10

TT

R

R Q −

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = (9) 

where T1 and T2 are the cooler and warmer temperatures, respectively, and R1 and R2 are the 

corresponding rates at those temperatures. Typically, a taxonomy-specific Q10 rate is available 

from the general scientific literature (particularly literature relating to bioenergetics) and the 

equation is rearranged to solve for the unknown R. T estimates were based on temperature data 

collected monthly by WDE throughout Central Puget Sound (no date); we used temperature data 

from depths corresponding to expected depth distributions of the species or group in question. 

The Q10 calculation was a critical part of bottom fish diet estimation (see next subsection 

below). It was also used in Q/B estimations for jellyfish and ratfish. 

174
Bottom Fish Diet Estimation 

Predator-prey interactions are the main way that functional groups are linked to one 

another in EwE. Thus reasonable diet proportions are an important part of initial model 

development. Reum (2006) examined the stomach contents of many bottom-dwelling fish 

species in several seasons, depths, and sites in PSCB between 2004 and 2005. His data represent 

the best set of information on contemporary feeding habits of the bottom fish community. We 

made extensive use of his data and developed diet proportions for a number of functional groups, 

weighted by season as described below. 

Reum (2006) conducted a series of bottom trawl surveys in October 2004 (fall), March 

2005 (winter) and July 2005 (summer) on the eastern side of PSCB. Sampling was done at six 

stations; trawls were conducted along bathymetric contours at 20 m, 40 m, 80 m, and 160 m 

depths. For each functional group, raw stomach content data (Reum unpubl. data) were sorted 

taxonomically, and prey items were pooled to correspond to our functional groups (Table 2). 

The proportion of total stomach content weight of each prey group was averaged across all 

sampled fish for a given season. We pooled across all depths and sites because of the 

considerable spatial variability of stomach contents (Reum and Essington 2008). To consolidate 

seasonal diet proportions into an annual diet, we took the weighted average of the seasonal 

proportions. Weighting was based on the temperature differences experienced from season to 

season. Assuming consumption rate is positively correlated with temperature, more 

consumption should occur during warm months, giving the diets during those months more 

weight than diets in cold months. We accomplished this by using taxonomy-specific 

consumption Q10 values, applied to seasonal temperature data (see previous subsection, Use of 

Q10 for Temperature-dependent Rate Estimation). 

We used this method to characterize diets of surfperch, Pacific hake (Merluccius 

productus), walleye pollock (Theragra chalcogramma), demersal fish, piscivorous flatfish, 

small-mouthed flatfish, spiny dogfish, skates, and ratfish. 

Q/B Estimation for Seabirds and Marine Mammals 

Hunt et al. (2000) developed methods to estimate daily energy requirements for a vast 

range of seabirds and marine mammals that inhabit large marine ecosystems throughout the 

northern Pacific Ocean. The scope of their study included many of the species represented in our 

model’s seabird and pinniped functional groups. We used their methods for our Q/B

calculations, relying specifically on their parameter values for seabirds (except raptors) and 

marine mammals in the northern California Current. 

For seabirds, Hunt et al. (2000) adopted an allometric equation developed by Birt-Friesen 

et al. (1989), which estimates daily energy requirements as a function of mean body mass. Body 

mass is an appropriate predictor because it is related to factors such as total metabolic activity 

(which is greater in large than in small birds) and the specific rate of metabolic heat loss (which 

is greater in small than large birds). The equation is equal to the function: 

Y = 11.455 × W 0.727 (10) 

175
where Y is daily energy required (in kilojoules [kj]) and W is body weight (in g). We used 

weights provided by Hunt et al. (2000) for representative species in our functional groups. 

Equation 10 predicts the energy needs for basic functions (growth, metabolism, activity, and 

reproduction). It does not account for assimilation efficiency and Hunt et al. (2000) assumed 

that seabirds assimilate approximately 75% of their ingested prey energy. Thus we must 

multiply Y by the reciprocal of 0.75 (i.e., 1.333) to calculate daily energy consumption. Dividing 

this value by the energy density of the aggregate diet yields the total mass consumed per day. 

We used energy density estimates provided by Hunt et al. (2000). They intended their estimates 

to represent daily energy requirements during the summer (June–September). We assumed no 

change in daily energy requirements in other months, and thus multiplied the daily mass 

consumed by 365 days and divided by body mass to generate Q/B for representative species. 

The same basic method was used for marine mammals, this time drawing from allometric 

relationships for phocids and otariids (including California [Zalophus californianus] and Steller 

[Eumetopias jubatus] sea lions). These relationships were originally developed by Perez et al. 

(1990) and were summarized and applied by Hunt et al. (2000). The phocid equation, which was 

the basis for estimating Q/B in harbor seals (Phocoena phocoena), was equivalent to: 

Y = 0.8411 × W 0.749 (11) 

while the otariid equation, which was the basis for estimating Q/B in sea lions, was equivalent to: 

Y = 1.5266 × W 0.737 (12) 

In both cases, Y is daily energy required (in kJ) and W is body weight (in kg). As with seabirds, 

daily estimates of Y were adjusted for assimilation efficiency (again assuming 75% efficiency, 

Hunt et al. 2000), and were converted to daily prey intake by dividing by aggregate diet energy 

density. Multiplying by 365 days and dividing by body weight yielded annual Q/B. 

Commercial Harvest Estimates 

Unless otherwise noted, commercial harvest levels in PSCB were based on data collected 

by WDFW and reported by the Pacific Fisheries Information Network (PacFIN) for the years 

1998–2007. PacFIN data consisted of number and weight of species caught by a particular gear 

type in a specific geographic region, according to commercial logbook data. Commercial 

harvest data included both tribal and nontribal sectors. For classifying harvest information, 

WDFW divides Puget Sound into individual management and catch reporting areas. Several 

WDFW areas are nested within the boundaries of the two Puget Sound Partnership (PSP) areas 

that make up PSCB, so all of the harvest in those areas is attributed to Central Puget Sound. In a 

few cases, the PSP boundaries split a WDFW area; in those cases, we assumed that the catch 

reported by PacFIN was distributed uniformly throughout the WDFW area. The proportion of 

the WDFW area in each PSP region was then used to allocate the catch. For example, WDFW 

Area 9 (Admiralty Inlet) spans four PSP regions, so the PacFIN catch is allocated using the 

proportion of the WDFW area in each region: Hood Canal (36.2%), Whidbey Island (41.7%), 

north Central Puget Sound (14.3%), and south Central Puget Sound (7.8%). For this area, then, 

22.0% of the catch reported by PacFIN for WDFW Area 9 is attributed to the Central Puget 

Sound. 

176
All PacFIN data were obtained in retrievals, dated 25 February 2008 and 18 March 2008, 

from the PacFIN Web site (http://pacfin.psmfc.org/pacfin_pub/data.php). 

Catches for each year were summed by gear type, converted into units of mt km-2, and 

allocated to the appropriate functional groups. We took the annual average for the period 

examined in order to fairly represent long-term fishing mortality. 

177
178
Appendix D: Representing Life History 

Ecopath with Ecosim (EwE) has many specialized methods for simulating biological and 

ecological relationships that fall outside the basic processes described by the master equations 

(Equation 1 and Equation 2). These include life history representation (age structure), 

controlling the strength of top-down and bottom-up predator-prey interactions, and mediation of 

trophic effects due to behavioral or habitat-related interactions. All are described in detail in the 

EwE support literature (Christensen et al. 2005) and a variety of EwE-related publications (e.g., 

Christensen and Walters 2004, Ainsworth et al. 2008a). We have run, or foresee the need to run, 

many simulations in which these special functions are used. The only specialized function that is 

extensively used in this document is the life history structuring; we briefly describe it here. 

For some functional groups, incorporating life history is necessary because ontogenetic 

changes (e.g., in vital rates, diet, habitat use, or fishery value) lead to substantially different 

ecology for the life history stages. EwE handles life history in two ways: split-pool dynamics, 

where a taxonomic group has two dynamically linked groups, typically juveniles and adults; or 

multistanza dynamics, where a group has greater than two dynamically linked stages 

(Christensen et al. 2005). We opted to use only split-pool dynamics in this application, after 

attempts to use the multistanza method (which is recommended by Christensen et al. 2005) 

produced unsatisfactory results during mass balancing of Ecopath. 

The equations used for the split-pool life history produce dynamics according to a 

Deriso-Schnute delay-difference model, which tracks the number of individuals in the juvenile 

and adult pools (Deriso 1980, Schnute 1987, Walters et al. 2000). Individuals in a cohort grow, 

age, and mature according to eight input parameters, plus the dynamic values of biomass, 

mortality rate, and consumption rate as determined in Ecosim. These terms determine the 

numbers of adults and juveniles according to five relevant equations, which are described by 

Christensen et al. (2005). Collectively, these terms enable the model to simulate changes in 

growth, mortality, and reproduction that derive from changes in feeding conditions (Walters et 

al. 2000). 

The eight new input parameters mentioned above are: 

• minimum relative time spent as a juvenile (default value = 1) 

• maximum time spent as a juvenile (default = 1.0001) 

• a recruitment power parameter (default = 1.0) 

• age of transition from the juvenile to adult pool 

• the weight ratio of an average adult to a newly matured adult 

• the von Bertalanffy k parameter 

179
180

• the base ratio of food intake used for reproduction (default = 0.8) 

• the proportion of an increase in food intake that is used for growth (default = 0.3) 

Typically, users only enter the von Bertalanffy k, the age at transition, and the weight ratio. The 

other values are generally left at their default values, unless circumstances dictate otherwise 

(Christensen et al. 2005). 
Most NOAA Technical Memorandums NMFS-NWFSC are available online at the

Northwest Fisheries Science Center web site (http://www.nwfsc.noaa.gov).

NOAA Technical Memorandum NMFS-NWFSC105 Gustafson, R.G., M.J. Ford, D. Teel, and J.S. Drake. 2010. Status review of eulachon (Thaleichthys pacificus) in Washington, Oregon, and California. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-105, 

360 p. NTIS number pending. 

104 Horne, P.J., I.C. Kaplan, K.N. Marshall, P.S. Levin, C.J. Harvey, A.J. Hermann, and E.A. Fulton. 2010. 

Design and parameterization of a spatially explicit ecosystem model of the central California Current. U.S. 

Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-104, 140 p. NTIS number PB2010-110533.

103 Dufault, A.M., K. Marshall, and I.C. Kaplan. 2009. A synthesis of diets and trophic overlap of marine species in the California Current. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-103, 81 p. NTIS 

number PB2010-110532. 

102 Reppond, K.D. 2009. Biochemistry of red king crab (Paralithodes camtschaticus) from different locations 

in Alaskan waters. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-102, 16 p. NTIS number

PB2010-110531.

101 Sands, N.J., K. Rawson, K.P. Currens, W.H. Graeber, M.H. Ruckelshaus, R.R. Fuerstenberg, and J.B. 

Scott. 2009. Determination of independent populations and viability criteria for the Hood Canal summer chum 

salmon evolutionarily significant unit. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-101, 58 p. 

NTIS number PB2010-110530. 

100 Linbo, T.L. 2009. Zebrafish (Danio rerio) husbandry and colony maintenance at the Northwest Fisheries Science Center. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-100, 62 p. NTIS number PB2009-

113299.

 99 Rawson, K., N.J. Sands, K.P. Currens, W.H. Graeber, M.H. Ruckelshaus, R.R. Fuerstenberg, and J.B. 

Scott. 2009. Viability criteria for the Lake Ozette sockeye salmon evolutionarily significant unit. U.S. Dept. 

Commer., NOAA Tech. Memo. NMFS-NWFSC-99, 38 p. NTIS number PB2009-113298. 

 98 Johnson, L.L., G.M. Ylitalo, M.S. Myers, B.F. Anulacion, J. Buzitis, W.L. Reichert, and T.K. Collier. 

2009. Polycyclic aromatic hydrocarbons and fish health indicators in the marine ecosystem in Kitimat, 

British Columbia. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-98, 123 p. NTIS number 

PB2009-110295. 

Recent NOAA Technical Memorandums

published by the

Northwest Fisheries Science Center