Fisheries Ecosystem Model of the 

Chesapeake Bay: Methodology, 

Parameterization, and Model Exploration 

Villy Christensen, Alasdair Beattie, Claire Buchanan, Hongguang Ma, 

Steven J. D. Martell, Robert J. Latour, Dave Preikshot, Madeline B. Sigrist, 

James H. Uphoff, Carl J. Walters, Robert J. Wood, and Howard Townsend 

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

NOAA Technical Memorandum NMFS-F/SPO-106 

October 2009 

Fisheries Ecosystem Model of the 

Chesapeake Bay: Methodology, 

Parameterization, and Model Exploration

Villy Christensen1, Alasdair Beattie2, Claire Buchanan3, Hongguang Ma8, Steven J. D. Martell4, 

Robert J. Latour5, Dave Preikshot9, Madeline Sigrist2, James H. Uphoff7, Carl J. Walters1, Robert 

J. Wood6, and Howard Townsend6 

1

Fisheries Centre, University of British Columbia, 2259 Lower Mall, Vancouver BC, Canada V6T 1Z4. Telephone 

604-822-5751; fax 604-822 8934; email v.christensen@fisheries.ubc.ca 

2

NOAA/Chesapeake Research Consortium, 410 Severn Avenue, Suite 107A, Annapolis, MD 20403 

3

Interstate Commission on the Potomac River Basin. 51 Monroe Street, Suite PE-8, Rockville, MD 20850 

4

Chesapeake Biological Laboratory, University of Maryland, Center for Environmental Science, P.O. Box 38, 

Solomons, MD 20688 (present address UBC Fisheries Centre) 

5

Department of Fisheries Science, Virginia Institute of Marine Science, P.O. Box 1346, Gloucester Point, VA 

23062-1346 

6

NOAA Chesapeake Bay Office/Cooperative Oxford Laboratory, 904 South Morris Street, Oxford, MD 2165; email 

Howard.Townsend@noaa.gov 

7

Maryland Department of Natural Resources, Matapeake Work Center, 301 Marine Academy Drive, Stevensville, 

MD 21666 

8

NOAA Chesapeake Bay Office/Cooperative Oxford Laboratory/Versar, Inc., 904 South Morris Street, Oxford, MD 

21654; email Hongguang.Ma@noaa.gov 

9

Pacific Biological Station, Department of Fisheries and Oceans, 3190 Hammond Bay Road, Nanaimo, British 

Columbia, Canada V9T 6N7 

NOAA Technical Memorandum NMFS-F/SPO-106 

October 2009

U.S. Department of Commerce 

Gary Locke, Secretary 

National Oceanic and Atmospheric Administration 

Jane Lubchenco, Ph.D., Under Secretary for Oceans and Atmosphere 

National Marine Fisheries Service 

James W. Balsiger, Ph.D., Acting Assistant Administrator for Fisheries
Suggested citation: 

Christensen, Villy, and Alasdair Beattie, Claire Buchanan, Hongguang Ma, Steven J. D. Martell, Robert 

J. Latour, Dave Preikshot, Madeline B. Sigrist, James H. Uphoff, Carl J. Walters, Robert J. Wood, and 

Howard Townsend. 2009. Fisheries Ecosystem Model of the Chesapeake Bay: Methodology, 

Parameterization, and Model Explanation. U.S. Dep. Commerce, NOAA Tech. Memo. NMFS-F/SPO106, 146 p. 

A copy of this report may be obtained from: 

Howard Townsend 

NOAA Chesapeake Bay Office 

410 Severn Ave., Suite 107A 

Annapolis, MD 21403 

howard.townsend@noaa.gov 

Or online at: 

http://chesapeakebay.noaa.gov 
1 

Table of Contents

1 ABSTRACT 5

2 INTRODUCTION 5

2.1 The Chesapeake Bay 5

2.2 Multispecies management 7

3 METHODS 9

3.1 EwE General Methodology 9

3.2 Development of an Ecopath Model of the Chesapeake Bay 13

3.3 Development of an Ecosim Model of the Chesapeake Bay 16

3.4 Stock Assessment Methods for Developing Time Series 21

4 RESULTS AND DISCUSSION 29

4.1 Ecopath models 29

4.2 Ecosim simulations 29

4.3 Evaluating policy questions 34

5 CONCLUSIONS 38

5.1 Data availability 38

5.2 Stock assessment 39

5.3 Spatial modeling 39

5.4 Ecosystem boundaries and model structure 40

6 ACKNOWLEDGEMENTS 41

7 TABLES 53

7.1 Relating species and management plan entities 53

7.2 Questions 54

7.3 Basic parameters 55

7.4 Catches, species groups 1-12 57
2 

7.5 Catches, species groups 13-35 60

7.6 Vulnerabilities for species groups 62

7.7 Bay anchovy simulation – change in biomass 62

7.8 Oyster fishing moratorium simulation 63

8 LIST OF FIGURES 64

8.1 The Chesapeake Bay 64

8.2 Model development 64

8.3 Foraging arena 64

8.4 Food web components 64

8.5 Ecoranger 64

8.6 Mixed trophic impacts 64

8.7 Time-series fit, biomass 64

8.8 Time-series fit, catches 65

8.9 Impact of nutrient loading on primary production 65

9 FIGURES 66

9.1 Figure 1. 66

9.2 Figure 2. 67

9.3 Figure 3. 68

9.4 Figure 4. 69

9.5 Figure 5. 70

9.6 Figure 6. 71

9.7 Figure 7. 72

9.8 Figure 8. 73

9.9 Figure 9. 74

10 APPENDICES – DESCRIPTION OF METHODS FOR ESTIMATING BASIC INPUT 

PARAMETERS AND DETERMINING TIME SERIES FOR CBFEM 75

10.1 Appendix A: Basic Input Parameters 75
3 

10.2 Appendix B: Time Series 95

11 TABLES RELATED TO APPENDICES A AND B 107

11.1 Catches, species groups 1-12 107

11.2 Catches, species groups 13-35 109

11.3 Striped bass growth parameters 111

11.4 Striped bass biomass 112

11.5 Striped bass fishing mortality 113

11.6 Bluefish growth parameters 115

11.7 Bluefish biomass and fishing mortality 116

11.8 Weakfish growth parameters 118

11.9 Weakfish biomass and fishing mortality 118

11.10 Atlantic croaker biomass and fishing mortality 120

11.11 Summer flounder biomass and fishing mortality 122

11.12 Menhaden biomass and fishing mortality 123

11.13 American shad and blueback herring biomass 124

11.14 Bay anchovy biomass 125

11.15 White perch biomass and fishing mortality 126

11.16 Spot biomass 127

11.17 Blue crab biomass, effort, and fishing mortality 128

11.18 Eastern oyster biomass, effort, and fishing mortality 130

11.19 Zooplankton biomass 131

11.20 Diet compositions for high-trophic-level, multi-stanza, commercial fish 132

11.21 Diet composition for other commercial fish species 133

11.22 Diet composition for other fishes 134

11.23 Diet composition for commercial invertebrates 135

11.24 Diet composition for other invertebrates 135

11.25 Diet composition for birds 137
4 

11.26 Non-piscivorous bird species included in the Chesapeake Bay Fisheries Ecosystem Model 137

11.27 Piscivorous bird species included in the Chesapeake Bay Fisheries Ecosystem Model 138

11.28 Hurricanes and their assumed impact on the relative P/B for clams 139

11.29 Estimated relative P/B for clams, carried over to years subsequent to hurricane events 140

11.30 Phytoplankton chlorophyll content 142

11.31 Confidence intervals for basic input parameters 143

11.32 Prices for commercial catches 144

11.33 Multispecies Production stock assessment model parameter values 145

11.34 Submerged Aquatic Vegetation biomass index 146
5 

1 Abstract 

This report describes an ecosystem model of the Chesapeake Bay, the Chesapeake Bay Fisheries 

Ecosystem Model (CBFEM), prepared using the Ecopath with Ecosim approach and software. The 

CBFEM was created in response to a management need in the Chesapeake region for a quantified 

estimation of trophic pathways in the Bay. This information can be used to understand how one stock 

affects another within the food web and how the many Bay fisheries impact both target and nontarget 

species. Because the life histories and population dynamics of the thousands of organisms that live 

within the Bay are complicated, a model is necessary to provide an accurate estimation of the system. 

Model construction has been carried out in close consultation with Chesapeake Bay researchers through 

a series of workshops. Currently, the model includes 45 functional groups of organisms, some of which 

represent life history stanzas of individual species, representing all trophic levels. The input data 

primarily includes assessment results from the Chesapeake Bay (including biomasses, mortality rates, 

catches, and effort) supplemented with research vessel survey data (fisheries and biological 

oceanography studies), ecological studies (as available from researchers and institutions in the region), 

and parameter estimates obtained from literature where necessary to supplement local data. Activities 

are under way to refine the temporal and spatial resolution of the CBFEM and to continue to incorporate 

hydrographic data. 

This documentation is intended to facilitate use and further development of the CBFEM, so that it can 

serve as a ‘living’ model. Future revisions to the CBFEM and supporting documentation will be made 

available on the NOAA Chesapeake Bay Office web site (http://chesapeakebay.noaa.gov). 

2 Introduction 

2.1 The Chesapeake Bay 

The Chesapeake Bay is the largest estuary in the continental United States, located midway along the 

Atlantic coast of the United States. The surface area of the tidal portion of the Chesapeake Bay system is 

approximately 10,000 km2

, while the area including tributaries is estimated to 18,580 km2. More than 20 

major tributaries drain into the Bay from a watershed that stretches across six states: New York, 

Pennsylvania, Maryland, Delaware, Virginia, West Virginia, and the District of Columbia. The largest 

of these tributaries, the Susquehanna River, provides more than half of the freshwater flow to the Bay. 

The waters of the Chesapeake Bay and tidal portions of its tributaries are governed by Maryland and 

Virginia (Figure 1). 

The Bay is a partially mixed estuary, with an average tidal range of approximately 1 m at its mouth to 

less than 30 cm at its head (cited in 1989). Salinity within the Bay ranges from less than 0.5 ppt at its 

northern extreme to 32 ppt near its mouth. The Bay can be divided into three major salinity regions: 

oligohaline (0-5 ppt), mesohaline (6-18 ppt), and polyhaline (> 18 ppt). Water temperatures in the Bay 

vary greatly throughout the year, reaching 28-30oC in late summer and 1-4oC in late winter (Murdy et 

al., 1997). 
6 

The estuarine circulation pattern of a flow of deeper, more saline water from the Atlantic Ocean into the 

Bay and its tributaries and surface fresher water out of the Bay serves to transport larval fishes and crabs 

from the ocean to their nursery habitats and juvenile fishes from tributaries to the coastal waters of the 

Atlantic. This transport mechanism is very important to the population dynamics of many Bay species. 

The mixture of freshwater from the tributaries and seawater from the coastal ocean creates and 

maintains a variety of brackish habitats within the Bay. Tidally influenced habitat types in the Bay 

include: pelagic waters, nearshore littoral areas, and the benthic zone. Littoral habitats, such as marshes 

on intertidal lowlands, aquatic grass beds in the shallow flooded flatlands, and oyster reefs, are highly 

productive, serving as nursery areas to many fish and shellfish species, facilitating rapid growth under 

relatively protected conditions. The diversity of habitats within the Chesapeake Bay system enables it to 

support nearly 3,000 species of plants and animals within its waters and tidal margins. 

Finfish species inhabiting the Chesapeake Bay have a wide variety of life history strategies. The 

American eel, Anguilla rostrata, is a catadromous species, spending most of its life in tributaries of the 

Chesapeake Bay, returning to the Atlantic Ocean to spawn. Some marine fishes, like the weakfish, 

Cynoscion regalis, enter the Bay to feed and spawn seasonally and then return to the coastal ocean. 

Anadromous species, like the American shad, Alosa sapidissima, and striped bass, Morone saxatilis, 

spend most of their adult lives migrating in the Atlantic Ocean, but return to Bay tributaries to spawn. 

Other species, like the white perch, Morone americana, spend their entire lives within the Chesapeake 

Bay system, undergoing ‘semi-anadromous’ seasonal migrations within the Bay. Due to the complexity 

of the Chesapeake Bay ecosystem, it is necessary to develop modeling tools like the CBFEM to simulate 

interactions between these many different species, to quantitatively estimate how they fit together within 

the larger food web and how human impacts are likely to affect this complex system. 

The diversity of habitats within the Chesapeake Bay, combined with wide ranges of temperatures 

throughout the year, result in very dynamic seasonal changes in fish assemblages. During late summer 

and early autumn, fish diversity reaches its maximum due to a movement of tropical species into the 

lower portion of the Bay. When the cooler temperatures of autumn arrive, most marine fish within the 

Bay begin to migrate either south to Cape Hatteras, North Carolina, or offshore to the edge of the 

continental shelf. During winter, the abundance and diversity of fish in the Bay is relatively low. 

However, by early spring, abundance and diversity rebound significantly as anadromous species enter 

the Bay, followed soon after by the warm-temperate and subtropical summer residents. 

Since the early 1800s, the Chesapeake Bay has supported a variety of large-scale commercial and 

recreational fisheries of both finfish and shellfish. The predominant invertebrate fisheries in the 

Chesapeake Bay have included the eastern oyster (Crassostrea virginica), blue crab (Callinectes 

sapidus), soft clam (Mya arenaria), and hard clam (Mercenaria mercenaria). The large-scale finfish 

fisheries have included striped bass, American shad, river herring (Alosa aestivalis), white perch, 

bluefish (Pomatomus saltatrix), Atlantic menhaden (Brevoortia tyrannus), summer flounder 

(Paralichyths dentatus), weakfish, Atlantic croaker (Micropogonias undulates), and spot (Leiostomus 

xanthurus). Several species, like white perch and Atlantic croaker, have sustained significant harvest 

levels, although trends in the commercial and recreational landings have varied over the last several 

decades. Striped bass landings may be the most dramatic in terms of variability from the 1960s to 

present. Many species, such as the eastern oyster, American shad, and striped bass, have suffered 

overexploitation in the Chesapeake Bay. Overfishing and the collapse of several Bay and coastal fish 
7 

stocks during the 1900s prompted the creation of fisheries management agencies both along the Atlantic 

coast and within the Chesapeake Bay. 

In coastal areas, the Atlantic States Marine Fisheries Commission (ASMFC) serves as a deliberative 

body, coordinating the conservation and management of fisheries in near-shore state waters along the 

eastern seaboard from Maine to Florida (to 4.8 km or 3 miles off the coast). The Mid-Atlantic States 

Fishery Management Council (MASFC) is responsible for managing fisheries in federal waters, which 

occur predominantly off the mid-Atlantic coast (from 4.8 to 322 km or 3 to 200 miles offshore). Within 

the Bay, tidal fisheries are managed on a jurisdiction-specific basis, by the Virginia Marine Resources 

Commission (VMRC), the Maryland Department of Natural Resources (MD DNR), and the Potomac 

River Fisheries Commission (PRFC). The three jurisdictions have agreed upon management strategies, 

as outlined in Chesapeake Bay fisheries management plans, for commercially and recreationally targeted 

species within the tidal portion of the Chesapeake Bay. 

2.2 Multispecies management 

Traditionally, fisheries management plans have been targeted to manage a single species, (e.g., stock 

assessments designed to derive estimates of population size and fishing mortality rates, synthesis of life 

history characteristics to determine fishing seasons). However, single-species analyses have not 

traditionally considered the ecology of the species under management, (e.g., habitat requirements, 

response to environmental change), ecological interactions among species, (e.g., predation, 

competition), or technical interactions, (e.g., discards, bycatch) (NMFS, 1999; Link, 2002b; a). Basing 

fisheries management plans on both single-species characteristics and ecological processes is now 

mandated federally by the Magnuson-Stevens Act Reauthorization (NMFS, 1999; NRC, 1999; U.S. 

Commission on Ocean Policy, 2004) and regionally by the Chesapeake 2000 agreement (Chesapeake 

Bay Program, 2000). Ecosystem-based fisheries management plans are currently under development for 

five target species/species groups in the Chesapeake Bay: striped bass, alosines (shad, alewife, and 

blueback herring), blue crab, menhaden, and the eastern oyster. 

Ecosystem-based fisheries management is preferable in the Chesapeake Bay as traditional methods of 

management do not explicitly allow consideration of how fish populations and habitats are variably 

impacted by increasing human populations. To understand how one species is likely to respond to 

changing conditions, it is necessary to look at the response of all other species that affect it within its 

ecosystem. Not only do humans affect fish populations directly through fishing, but they also have 

major indirect impacts such as nutrient, sediment, and toxics pollution; disease; and physical destruction 

of habitat. All of these factors influence fish stock levels. Ecosystem-based fisheries management 

attempts to address all of these factors to lessen their impact on fish stocks. As part of this process, it is 

necessary to create tools to accurately portray how the ecosystem is currently functioning and how it is 

likely to function in the future given changes in habitat and stock management. One such tool is the 

CBFEM, which helps to understand how the food web is being impacted by these changes to the 

ecosystem and how fisheries management can be altered to address them. 

The concept that ecological processes can strongly influence stock abundance is not new to fisheries 

science. During the 1970s and 1980s, several single-species population models were extended to include 

multiple species and the implied ecological interactions (Andersen and Ursin, 1977; May et al., 1979; 

Mercer, 1982; Kerr and Ryder, 1989; Daan and Sissenwine, 1991). These models fostered awareness of 

the importance and role of ecological processes on yield performances of fish stocks, but were generally 
8 

viewed as underdeveloped. Recently, this belief has changed due to the sophistication and increased 

availability of multispecies assessment and ecosystem models (Hollowed et al., 2000; Whipple et al., 

2000; Latour et al., 2003). The analytical evolution of these modeling approaches has reached the point 

where it can now provide meaningful management advice. However, a significant deterrent to the use of 

multispecies modeling approaches is that more model parameters may need to be estimated compared 

with traditional single-species analyses, which in turn creates the need for additional types of data. 

In the Chesapeake Bay region, single-species fisheries management plans have been developed for 

numerous species (Table 1). There has been a growing interest in ecosystem approaches to fisheries 

management in the Chesapeake Bay region since the mid-1990s. Efforts dedicated to ecosystem-based 

fisheries management in the region include: 1) creation of two ASMFC multispecies subcommittees, the 

first in 1999 and the second in 2004; 2) convening of multispecies technical workshops (Miller et al., 

1996; Houde et al., 1998); 3) development of the multispecies and cross-jurisdictional sampling 

platform, the Chesapeake Multispecies Monitoring and Assessment Program (ChesMMAP) in 2002; 4) 

continued development of the Fisheries Ecosystem Model for the Chesapeake Bay; 5) development of a 

multispecies virtual population analysis for the Chesapeake Bay; 6) release in 2006 of the Fisheries 

Ecosystem Planning for the Chesapeake Bay (NOAA CBFEAP, 2006) as the first fisheries ecosystem 

plan in the U.S.; and 7) ongoing development of ecosystem-based fisheries management plans for five 

target species/groups in the Chesapeake Bay (alosines, blue crab, eastern oysters, striped bass, and 

menhaden) in response to the regional goals for multispecies fisheries management set by the 

Chesapeake 2000 agreement (Chesapeake Bay Program, 2000). 

The language of the Chesapeake 2000 agreement as it pertains to multispecies fisheries management, 

reads as follows: 

 By 2004, assess the effects of different population levels of filter feeders such as menhaden, 

oysters, and clams on Bay water quality and habitat. 

 By 2005, develop ecosystem-based multispecies management plans for targeted species. 

 By 2007, revise and implement existing fisheries management plans to incorporate ecological, 

social, and economic considerations, multispecies fisheries management and ecosystem 

approaches. 

In response to the Chesapeake 2000 agreement (Chesapeake Bay Program, 2000), the NOAA 

Chesapeake Bay Office, working through the Chesapeake Bay Stock Assessment Committee, initiated a 

project in October 2001 to develop an Ecopath with Ecosim (EwE) model of the Chesapeake Bay. This 

Technical Report summarizes the results of the first years of development. Specifically, the 1950-2002 

version of the Chesapeake Bay EwE model is presented, complete with detailed descriptions of the data 

used for model parameterization and calibration included in the appendices. 

The current Chesapeake Bay EwE model should be considered a work in progress, and will evolve and 

improve in response to the collection of new information. For many components of the model, accurate 

and precise data were not available to define key input parameters. In an effort to fill these data gaps, 

research activities have been initiated to quantify missing parameters. However, several years of work 

will be necessary before the data will become available. Consequently, this Technical Report should be 

considered descriptive of the 1950-2002 version of the Chesapeake Bay Ecosystem Model. The 
9 

Technical Report will be under continuous development in the medium term, and electronic versions of 

the report will be released periodically and will be accessible on the NOAA Chesapeake Bay Office FTP 

site: ftp://noaa.chesapeakebay.net/CB Fisheries Ecosystem Model. 

3 Methods 

3.1 EwE General Methodology 

3.1.1 The EwE ecosystem modeling approach 

The EwE software is a modeling tool used to evaluate quantitative trophic interactions within an 

ecosystem in order to assess options for ecosystem-based management of fisheries. To run the Ecopath 

model, four groups of basic input parameters must be entered into the model for each of the species 

groups: diet composition, biomass accumulation, net migration, and catch. Three of the following four 

additional input parameters must also be input: biomass, production/biomass (Z), consumption/biomass, 

and ecotrophic efficiency. Forcing functions have been developed for the system, including one for 

climate, primary production, and habitat area. The model uses the input data along with algorithms and a 

routine for matrix inversion to estimate any missing basic parameters so that mass balance is achieved. 

Basic input parameters for the Chesapeake Bay Fisheries Ecosystem Model are listed in Table 3. 

Once all basic parameters have been input or estimated, the model balances the input and output of each 

group with two linear equations of production and consumption, using varying rates of respiration for 

adjustment. The model can come up with numerous balanced scenarios, given the input data and forcing 

functions. The balancing process results in predictions of biomass, production, and consumption values 

for each functional group and measures how closely these predictions match the input data. The modeler 

chooses which model run matches time-series data most closely based on the sum of least squares test 

performed by the model. The modeler then searches the selected model run for errors, makes necessary 

adjustments and documents these adjustments thoroughly. The modeler decides which parameters to 

include in the final estimation and then re-runs the model with the appropriate changes. This process 

(Figure 2) typically repeats several times until the model closely predicts existing data for that year. 

The mass-balanced linear equations of Ecopath are then re-expressed as coupled differential equations 

so that they can be used by the Ecosim module to simulate what happens to the species groups over 

time. Model runs are compared with time-series data and the closest fit is chosen to represent the 

system. The Ecosim module can be used to simulate various management options for the system, by 

varying parameters over time to estimate potential ecosystem changes. 

3.1.2 Ecopath 

An Ecopath model uses trophically-linked biomass pools to create a mass-balanced snapshot of the 

resources and interactions in an ecosystem (Christensen and Pauly, 1992; Pauly et al., 2000; Christensen 

and Walters, 2004). The biomass pools typically represent either a single species or a group of species 

that comprise an ecological guild. These pools may be split into ontogenetic age categories (juvenile, 

subadult, adult, etc.), commonly called ‘stanzas,’ and a detailed accounting of growth and survival for 

monthly cohorts is conducted for such groups. Biomass pools are created for all major components of 

the ecosystem, regardless of trophic level. 
10 

The parameterization of an Ecopath model is based on satisfying two ‘master’ equations. The first 

equation describes how the production term for each group can be divided for an arbitrary time period: 

production = catch + predation + net migration + biomass accumulation 

+ other mortality. (1) 

More formally, equation (1) can be expressed as: 

ji

n

j

Bi P B i EEi Yi Ei BAi ∑Bj Q B jDC == + + +

1

( / ) ( / ) (2) 

where for biomass pool i = 1, ..., n:

• Bi is total biomass during the period of question 

• (P/B)i is the production to biomass ratio 

• EEi is the ecotrophic efficiency, defined as the fraction of the production that is consumed within 

or harvested from the system 

• Yi is the yield or catch in weight (note that Yi = FiBi where F is the fishing mortality rate) 

• Ei is the net migration rate (emigration – immigration) 

• BAi is the biomass accumulation rate for (i) 

• Bj is the biomass of the consumers or predators of (i); (Q/B)j is the food consumption per unit 

biomass for consumer j

• DCji is the average fraction of i in the diet of j (note that DCji = 0 when j does not eat i) 

At a minimum, Ecopath requires input of DCji, Yi, and three of the following four parameters for each 

species or biomass pool in the model: Bi, (P/B)i, (Q/B)i, and EEi (mass balance principles are used to 

estimate the fourth parameter). If all four parameters are known, then Ecopath can be used to estimate 

either BAi or Ei. Equation (2) implies that an ecosystem under study is described completely by an ndimensional system of linear equations, the solutions of which can be easily calculated (Mackay, 1981); 

the resulting estimates of biomass, production, and consumption can be used to construct a quantitative 

network diagram of energy flow for the system (Ulanowicz, 1986). 

The second ‘master’ equation is based on the principle of conservation of matter within a group and is 

designed to balance the energy flows of a biomass pool: 

consumption = production + respiration + unassimilated food (3) 

Winberg (1956) defined consumption as the sum of somatic and gonadal growth, metabolic costs, and 

waste products. Equation (3) generally follows this definition, but differs in the sense that it is used to 

estimate losses rather than to measure growth. Balance of the energy equation is achieved by estimating 

respiration from the difference between the consumption, production, and unassimilated food terms. For 

more details on Ecopath, see Christensen and Pauly (1992) and Christensen and Walters (2004). 

3.1.3 Ecosim 

Ecopath is used to describe the interactions among resources within an ecosystem. Additional modules 

are created to simulate the dynamics of the ecosystem resources and the effects of different management 
11 

strategies on the structure and function of an ecosystem. The time-dynamic module, called Ecosim, 

provides a simulation capability that facilitates policy exploration at the ecosystem level, with initial 

parameters inherited from the base Ecopath model. To construct an Ecosim model, it is necessary to reexpress the system of linear equations in (2) as a system of coupled differential equations. This 

transformation takes the following form (Walters et al., 1997; Walters et al., 2000; Christensen and 

Walters, 2004): 

i i i i i

n

j 1

ij

n

j 1

i ji

i g c c I (M F e )B

dt

dB = ∑ −∑ + − + + = =

 (4) 

where: 

• gi is growth efficiency 

• Fi is the instantaneous rate of fishing mortality 

• ei is the rate of emigration 

• Ii is the rate of immigration 

• cij (cji) is the consumption of biomass pool i (j) by biomass pool j (i) 

This system of equations is used to represent the spatially aggregated dynamics of entire ecosystems and 

is combined with explicit age/size-structured delay-difference equations to represent populations that 

have complex life histories and selective harvesting of older animals. An important aspect of Ecosim is 

the expression of the consumption or ‘flow’ rates among linked species or biomass pools. Consumption 

of prey i by predator j is modeled as: 

(2v a B )

a v B B

Q (B ,B )

ij ij j

ij ij i j

ij i j + = , (5) 

where aij is the rate of effective search for prey i by predator j, and vij is the behavioral exchange rate 

between vulnerable and invulnerable prey pools (Figure 3). Equation (5) is based on the notion that 

consumption is limited by ‘risk management’ behaviors of predators and prey at very small time scales. 

That is, predator-prey interactions are assumed to take place primarily in restricted ‘foraging arenas’ 

where prey only become vulnerable to predation through their own requirements for resource acquisition 

(Walters et al., 1997; Walters et al., 2000). 

Relative to Ecopath, Ecosim introduces a number of new parameters, of which the simulations are 

especially sensitive to the vulnerability settings (Christensen and Walters, 2004). For this we use a 

vulnerability factor, (which we often just call ‘vulnerability’). The vulnerability factor expresses how 

much the predation mortality for a given prey can increase if the predator abundance is increased. When 

the predator is close to its carrying capacity with regard to the given prey, the predation mortality cannot 

be increased any further (v = 1), and an increase in predator abundance, (e.g., due to good recruitment) 

will be compensated for by a decrease in predator consumption rates. This in turn will result in lower 

predator production, and the predator abundance will move back toward its carrying capacity. In an 

opposite response, a decline in predator population size when it is close to its carrying capacity will be 

compensated for by a comparative increase in average consumption rates, which will bring the predator 

back toward its carrying capacity. A population at its carrying capacity is a stable population. 
12 

On the other hand, if the predator is far from its carrying capacity for a given prey, the situation is very 

different. An increase in predator biomass will lead to an increase in prey mortality rate. In Ecosim 

terminology, the vulnerability factor for the prey will be high. The consumption rate of the predator will 

remain relatively constant, and the increase in its biomass will manifest itself in population growth. 

There will be only limited compensatory effects. 

In general, it is not possible to estimate vulnerability factors from field or laboratory data. However, to 

assist with identifying appropriate settings, Ecosim includes several methods of estimation (see 

Christensen et al., 2004 for details on these methods), and it is recommended that vulnerabilities be 

estimated based on time-series analyses, i.e., by evaluating how groups in the ecosystem has reacted to 

changes in the past. 

Time-series data for model calibration are thus essential for developing and validating an Ecosim model. 

Therefore, time-series data depicting trends in relative and absolute biomass, fishing effort by gear type, 

fishing and total mortality rates, and catches for as long a period as possible should be viewed as 

additional data requirements. 

Using Ecosim for stock reduction analysis 

Kimura’s ‘stock reduction analysis’ (SRA, Kimura et al., 1996) can be used to analyze long-term data in 

stock assessment. Historical catches are treated as fixed, known quantities, and are subtracted from 

simulated stock size over time to aid in estimating how large (and/or productive) the stock must have 

been in order to have sustained those catches and to have been reduced by some estimated fraction from 

its historical level. 

A drawback of treating catches as fixed values, as is commonly done in stock reduction analysis (SRA), 

is that catches arise from the interaction of fishing effort and stock abundance. Ignoring this dynamic 

interaction amounts to treating the catches as purely dispensatory impacts on stock size. Consequently, 

the fixed catches can cause progressively larger calculated fishing mortality rates (F) if simulated stock 

size declines. This can lead to a rapid collapse in the simulated stock, unreflective of what may have 

happened in the real system. 

A modified version of Kimura’s SRA is used in Ecosim. This allows catch series data to be treated as a 

forcing input (with simulated F calculated each year as [input catch] / [simulated stock size]) or be used 

for evaluating model fit where F values are available from assessments. This SRA is used where 

estimates of initial biomasses and time trends in stock size are unavailable, and where no ‘drivers’ (such 

as effort or fishing mortality) is available to force the simulations. 

3.1.4 Addressing uncertainty 

The EwE model presently incorporates several approaches for explicitly addressing uncertainty, but 

given the number of parameters involved in a complex model such as the CBFEM it is not possible to 

consider the uncertainty of all input parameters on all output parameters and all predictions. We rather 

recommend that focus is on what impact uncertainty with regard to key input parameters as well as of 

the model fitting procedure (evaluating alternative fitting procedures, see e.g., Walters et al., in press) 

have on key model predictions. 
13 

 A ‘pedigree’ routine for characterizing the origin of input data and for developing an overall 

index of model quality 

 The Ecoranger routine for explicit consideration, in a Bayesian context, of the uncertainty 

inherent in all input and its impact on estimated parameters 

 A formal sensitivity analysis for documenting the effect of inputs on estimated parameters 

 A Monte Carlo routine that can be used in the time-dynamic module to evaluate the effect of 

parameter uncertainty on policy questions 

One should keep in mind that any given model represents only one possible synopsis of the trophic and 

other ecological interactions of species and functional groups of interest to fisheries and environmental 

managers. Developers should only attempt to adjust parameters in a way that makes sound ecological 

sense, rather than relying on the software’s automated tweaking, which potentially could develop a 

model parameterization that fits the data extremely well but may produce spurious results because of 

unsound assumptions fitting the noise rather than the signal (Walters and Martell, 2004). 

Upon finalizing the CBFEM, Monte Carlo simulations (accounting for uncertainty in the 

parameterization) were run to determine if the patterns produced by the model were robust to parameter 

uncertainty. Further details on how EwE deals with parameter uncertainty are described in the software 

user guide (Christensen et al., 2005). 

3.2 Development of an Ecopath Model of the Chesapeake Bay 

The construction of an Ecopath with Ecosim model for the Chesapeake Bay has been under way for 

several years. The effort has involved a large number of scientists from the Chesapeake Bay area, 

supported by modelers from the University of British Columbia. An initial workshop was held in 

October 2001 to introduce the Ecopath/Ecosim modeling approach to the Chesapeake Bay research 

community. A review of the Fisheries Ecosystem Model prototype was conducted to look for gaps in 

parameters, missing trophic linkages, and potential data sources to address concerns. A major focus of 

the workshop was to formulate research questions that can be addressed by ecosystem modeling (Table 

2). This report addresses some of the questions in Table 2. 

An introductory seminar/lab course on the use of the Ecopath portion of the EwE software was 

conducted in February 2002, with a follow-up seminar on dynamic simulation modeling in May 2002 at 

the Smithsonian Environmental Research Center, Edgewater, Maryland. A second workshop was held in 

May 2002 at the Virginia Institute of Marine Science, Gloucester Point, Virginia, to further develop the 

Fisheries Ecosystem Model and discuss its parameterization. 

On April 28-29, 2003, a modeling workshop was held at the U.S. Fish and Wildlife Service Patuxent 

Research Refuge’s National Wildlife Visitor Center in Laurel, Maryland, to assess progress on the FEM 

to date. Workshop participants generally agreed: 

 The FEM, as was implemented with 45 major species groups, reproduces many of the important 

time-series trends well; 
14 

 There remained a need to incorporate (or link to) water-quality parameters, abiotic processes, 

and lower trophic level dynamics; and 

 There will be tradeoffs among many of the stated objectives of the Chesapeake 2000 agreement. 

With finite ecosystem resources, it is unrealistic to believe that all fish species can be returned to 

their historic peak levels of abundance. 

Other noteworthy results from the 2003 workshop include: 

 Adjustments were made to the parameterization of species at lower trophic levels to secure both 

better data quality and better resolution; 

 More reliable data were incorporated for the biomass of zooplankton and oyster groups; and 

 Abundance indices and a new life history stage were introduced for oysters. The intention was to 

capture dynamics and mortality differences influenced by oyster population ontogenetics. 

A draft of the CBFEM report was finalized in 2004, and has been subsequently subject to review and 

ensuing updating. Since the 2003 EwE modeling workshop, time-series data have been revised and 

added to the model, data sources have been verified, and data has been reviewed by a panel of Bay 

researchers, the Ecosystem Modeling Technical Advisory Panel 2. Ecopath with Ecosim 6 software 

version 6.0.4.2 was used to run this current version of the CBFEM, with a resulting goodness-of-fit 

criterion, the sum of squares, equaling 1098. The model currently uses more than 90 time series. 

3.2.1 System boundaries 

The FEM focuses on modeling fisheries stocks within the Chesapeake Bay and tidal portions of its 

tributaries. Some groups being modeled, such as black drum, reside in the Chesapeake Bay but are 

considered part of larger ‘stocks’ usually encompassed by the eastern or northeast United States. Further 

complicating matters, many of the groups spend only part of the year or different parts of their life 

histories within the Chesapeake Bay, such as weakfish or bluefish. Therefore, in order to derive time 

series for EwE time simulations, it was often necessary to develop assumptions and correction factors 

such that stock assessments for a larger population could be applied to the Chesapeake Bay EwE model. 

3.2.2 Basic parameters and catches 

The basic parameters for Ecopath models, with their units of measurement and commonly used 

abbreviations in parentheses, are: 

• biomass (t·km-2, B) 

• biomass accumulation (t · km-2 · year-1, BA, default value 0) 

• consumption per unit biomass (year-1, Q/B) 

• detritus import (t · km-2 ·year -1) 

• diet composition of species ‘i’ from prey ‘j1, j2, j3,..., jn’ (fraction, DC) 
15 

• ecotrophic efficiency (EE) 

• fishery landings and discards by gear sector ‘j’ imparted upon each species ‘j1, j2, j3,..., jn’ (t · 

km-2 · year-1). 

• fraction unassimilated food (GS, default value 0.2) 

• net migration (t · km-2 · year-1, NM, default value 0) 

• production divided by consumption (P/Q, only used if P/B or Q/B not given) 

• production per unit biomass, i.e., total mortality (year-1, P/B, i.e., Z) 

When developing an Ecopath model, the diet compositions (DC) for all species must be entered. Gear 

sectors are designated by the modeler/user, who must enter catches and discards by gear type upon 

species or species groups. 

Of the four basic input parameters B, P/B, Q/B, and EE, one may be left as unknown, because “the 

Ecopath model ‘links’ the production of each group with the consumption of all groups” (Christensen et 

al., 2004) based on the trophic relations mapped out by the DC. 

Typically, building an Ecopath model emphasizes collecting data for three of the four basic input 

parameters listed above: B, P/B, and Q/B. Other basic input parameters are usually not as well 

understood for most modeled species. In the case of fraction unassimilated food, 0.2 is set as a default 

value based on the experiments of Ivlev (1961). While this estimate may be appropriate for carnivorous 

fish, it is typically too low for many herbivorous species. This is especially true for species relying on 

low-energy food, notably zooplankton, where a value of 0.4 results in more appropriate 

respiration/biomass ratios (Christensen et al., 2004). Because P/B and Q/B are usually entered, the P/Q 

rate will be set by the ratio of inputs set by the modeler for those values. However, if the modeler is 

incapable of providing an estimate of either P/B or Q/B, then the P/Q ratio may be entered instead. 

Because of the definition of P/Q, high-trophic-level predators with low production should have low P/Q 

values, (e.g., ≈ 0.05), while low-trophic-level, highly productive organisms will tend to have high P/Q 

ratios, (e.g., ≈ 0.3) (Christensen et al., 2004). 

Ecopath models are ‘snapshots’ that are intended to serve as the basis for time-dynamic Ecosim 

simulations. For this reason, the BA may be entered to represent the rate at which biomass is increasing 

or decreasing for the species group modeled: Ecopath models do not assume steady-state. This may 

especially be required in order to improve Ecosim simulations. ‘Detritus import’ is only of concern to 

the detritus group, and can therefore be omitted for ‘living’ groups. 

Given these parameter characterizations, most Ecopath modelers prefer to leave the default values for 

‘fraction unassimilated food’ and BA, adjusting these values only for species that have documented 

evidence suggesting different values. Because many species have not been studied in enough detail to 

yield published estimations of B, P/B, or Q/B, the modeler may let Ecopath estimate one as an unknown 

while estimating the others. In such cases, remember that P/B and Q/B values to some degree scale with 

allometric relationships, and therefore are conservative for similar species in similar ecosystems. This 

implies that even if a reliable P/B or Q/B estimate is unavailable for the species or species group 

modeled, then estimates for similar (or the same) species in similar (or the same) ecosystem may have to 
16 

suffice as proxies. Where possible, these may be modified up or down to reflect differences in 

exploitation pressure. Where biomass estimates are unavailable, they can be left for Ecopath to estimate 

given that the modeler can provide a value for EE, i.e., the fraction of production used in the ecosystem 

(Christensen et al., 2004). 

One final aspect about general parameterization and grouping species in an Ecopath model – species 

may be modeled as one of three types: 1) an aggregation of trophically similar organisms, i.e., a 

‘functional’ group; 2) a single-species group; or 3) as a life history stage that is part of two or more 

groups representing life history stages of a ‘multi-stanza’ group. Generally, species to be examined in 

terms of policy questions are best dealt with as single-species or multi-stanza groups. Multi-stanza 

groups are preferred if there may be ontogenetic issues in the species’ ecosystem role that could play a 

part in the policy issues to be examined. To ensure that the biomasses for the different age-groups are 

consistent, Ecopath will estimate the stanza biomass and consumption rates for all stanza when the 

following parameters are supplied: the von Bertalanffy growth (curvature) parameter K (which is 

available for fish species through FishBase); B for one (‘leading’) stanza; estimates of Z (= P/B) for 

each stanza; Q/B for one stanza; and an estimate of the ratio of the weight-at-maturity to the asymptotic 

weight, Winf. For a discussion of the calculations used in the Ecopath model, see Christensen and 

Walters (2004). 

In most models, there will be a higher degree of aggregation in species that are trophically distant from 

the focal species. The desire to enrich the model with detail must be tempered by a realistic examination 

of the modeler’s ability to flesh out that detail and to obtain data or estimates for the required 

parameters. To examine specific policy issues for any particular species, detailed information must be 

available from surveys or assessments, and similar information should be available for species with 

which the focal species likely interacts in the environment. In particular, well-documented diet 

composition data and time-series data of biomass, natural and fishing mortality, fishing effort, and 

average weight are required to explore ecosystem relations when applying the time-dynamic Ecosim 

model. 

Input parameters for the models are described in Appendix A. The four basic input parameters – 

biomass (t · km-2); production / biomass (P/B, year-1, corresponding to total mortality, Z); consumption / 

biomass (Q/B, year-1); and catches (t · km-2 · year-1) – are described for each functional group in the 

model, along with a description of how diet compositions were obtained. A summary of basic input 

parameters of the Chesapeake Bay Fisheries Ecosystem Model is presented in Table 3. 

3.3 Development of an Ecosim Model of the Chesapeake Bay 

3.3.1 Time periods covered 

The strength of any model to be used for testing management action outcomes is measured by how well 

it is validated based on observed data for that system. This is true whether the model is a traditional 

single-species or a multispecies model. A 1950-Ecopath model was created to represent a snapshot of 

roughly what the Chesapeake Bay system may have looked like in the middle of the 20th century. This 

model was then run time-dynamically using Ecosim and tuned to observed data or to data estimated 

from other models for the time period 1950-2002 to estimate changes in biomass over 50 years. 

Descriptions and sources for the various time-series data used in driving, tuning, calibrating, and 

verifying the model are included in Appendix B of this report. 
17 

3.3.2 Primary production rate forcing: the Chesapeake Bay Regional Estuarine Ecology 

 Model (CBREEM) 

At the inception of the Chesapeake Bay Fisheries Ecosystem Model project, EwE did not have the 

ability to account for physical and chemical factors as a part of the ecosystem being modeled. The 

Ecospace component of the EwE modeling approach has been enhanced to alleviate this shortcoming. 

This is done by linking the Ecospace model to the Florida Bay Ecosystem Model (FBEM). The FBEM is 

a simple 2D-hydrographic model that was developed for Florida and Tampa Bays by Carl Walters 

(UBC) during a series of workshops sponsored by the South Florida Water Management District, The 

Nature Conservancy, and the Florida Keys Water Quality Joint Action Group. The FBEM was developed 

to provide a simple, calibrated model of salinity, nutrients and oxygen dynamics in Florida Bay (Walters 

and Gunderson, unpublished results). 

While there are complicated hydrographic models for the Chesapeake Bay producing similar estimates 

with high spatial and temporal resolution, we have been unable to obtain information about nutrient 

conditions for the entire Bay over the long time period considered here (i.e. back to 1950). The existing 

models either only cover short, recent time periods or are geographically limited to a portion of the Bay. 

Hagy et al., 2004 was used in the development of CBREEM. This study does cover the time period from 

1950-2001, however it only describes nitrogen loading for the Susquehanna River and not the entire 

Bay. It was therefore of interest to emulate the Florida models to simulate long-term variability in 

nutrient conditions. 

To this effect, we have developed the Chesapeake Regional Estuarine Ecology Model (CBREEM), 

which includes three sub-models, operating with monthly time steps: 1) a physical, or hydrodynamic, 

model computing current speed and direction for surface and deep layers; 2) a static chemistry model 

estimating concentration of nitrogen, salt, phytoplankton, and suspended particulates; and 3) a dynamic 

ecological model simulating growth and mortality of sea grass and epiphytic algae. CBREEM output 

provides a historical pattern in primary production, which has been used to drive the primary production 

rate in the CBFEM. CBREEM is a simple hydrographic model with two layers, deep and shallow. The 

horizontal spatial resolution is 1-2 km with monthly time steps covering 50+ years. The model solves for 

equilibrium velocity fields and calculates mass-balanced chemical concentrations. The model uses wind, 

rainfall, river inflow, and relative loading as major inputs. The outputs include spatial distributions of 

nutrient, salinity, and chlorophyll-a at monthly intervals. 

Historical information on nutrient loading and physical mixing has been added to the CBREEM to 

calculate changes in primary production in the Chesapeake Bay. Monthly chlorophyll-a output from 

CBREEM was used to drive primary production rate in the Ecosim model. Both the CBREEM and 

supporting data are presented in a manuscript by Ma et al. (in prep). 

The hydrodynamic model requires time-series data on wind vectors, river gauge data from major 

freshwater inputs, and bathymetry data, as well as some basic water chemistry information. 

Hydrodynamic model outputs include a time series of total primary production, nitrogen, oxygen, and 

salinity concentrations. These outputs have been compared with historical data to aid in model 

parameterization. The chemistry model calculates concentration of nutrients and estimates 

phytoplankton growth as a function of nitrogen concentrations. The sea grass submodel predicts total 

biomass and spatial distribution of sea grasses in response to changes in water chemistry and light 

penetration. Future efforts to improve the CBFEM will include a historical reconstruction of sea grass 
18 

communities in the Chesapeake Bay. This will serve to test hypotheses about how changes in sea grass 

beds have affected biota that are associated and/or dependent on sea grasses. 

3.3.3 Catches 

For many species, catches are extracted from the NOAA Fisheries Statistics and Economics Division 

online database (www.st.nmfs.gov) for the Chesapeake region, 1950-2002. The Chesapeake region 

includes Maryland and Virginia catches, including catches made on the Atlantic Ocean side of the 

Delmarva Peninsula. The CBFEM aims to characterize stocks within the Chesapeake Bay and tidal 

portions of its tributaries, not coastal stocks or freshwater stocks. In many cases, the catches have not 

been corrected for this discrepancy due to a lack of correction factors; however, this does not contribute 

any major bias to the analysis. Many of the species in the model for which this may potentially be of 

concern are migratory species that spend a major part of the year in the Chesapeake Bay. Typically, they 

spend only a limited part of the year in the coastal waters of Maryland and Virginia outside the Bay, and 

the catches there as a rule will be similarly limited. 

While the NOAA marine catch database provides estimates for commercial catches from 1950 to the 

present, similar time series for recreational catches are not as readily available. The official recreational 

catch database, the Marine Recreational Fishery Statistics Survey (MRFSS), only includes information 

from 1982 to the most currently reported year; hence, recreational catches for the period 1950-1981 are 

derived from estimates. Commercial versus recreational catches were plotted for 1982 to the present, 

and the data was checked for trend. If a trend was clear, which rarely was the case, commercial versus 

recreational catches were regressed, and the pre-1982 recreational catches were estimated from the 

regression. Where no trend was detected, either the arithmetic mean (when there were few outliers) or 

the median value of the commercial/recreational catch rate was used to estimate the recreational catches 

for the earlier time period. For the time period from 1982 to the present, the commercial/recreational 

catch ratio, as estimated from the catches, was always used. The estimates of commercial and 

recreational catches are Atlantic coast-wide for many species and, for lack of better estimates, 

considered representative for the Chesapeake Bay as well. Catch series data are presented in Tables 4 

and 5. 

3.3.4 Time-series information 

For many groups in the Chesapeake Bay model, there is time-series information available from catch 

monitoring, surveys, and stock assessments that can be incorporated into EwE simulations. EwE builds 

on the more traditional stock assessment, using much of the information available from traditional 

assessments, while integrating to the ecosystem level. 

The time-series fitting uses either fishing effort or fishing mortality data as driving factors for the 

Ecosim model runs. A statistical measure of goodness-of-fit to the time-series data outlined above is 

generated each time Ecosim is run. This goodness-of-fit measure is a weighted sum of squared 

deviations (SS) of log biomasses from log predicted biomasses, scaled in the case of relative abundance 

data by the maximum likelihood estimate of the relative abundance scaling factor q in the equation y = q 

· B (y = relative abundance, B = absolute abundance). Each reference data series can be assigned a 

relative weight representing a prior assessment of relative data reliability. 

The model allows four types of analysis with the SS measure: 
19 

1. Determine sensitivity of SS to the critical Ecosim vulnerability factors by changing each one 

slightly (1%) then rerunning the model to see how much SS is changed (i.e., how sensitive the 

time-series predictions ‘supported’ by data are to the vulnerability settings); 

2. Search for vulnerability factors that give better ‘fits’ of Ecosim to the time-series data (lower 

SS), with vulnerabilities ‘blocked’ by the user into sets that are expected to be similar (the search 

is typically conducted on the most sensitive interactions, as identified above); 

3. Search for time-series values of annual relative primary productivity that may represent historical 

productivity shifts affecting biomasses throughout the ecosystem; and, 

4. Estimate a probability distribution for the null hypothesis that all of the deviations between 

model and predicted abundances are due to chance alone, i.e., under the hypothesis that there are 

no real productivity anomalies. 

In addition to these nonlinear optimization routines, the fit to data can also be improved in a feedback 

process by examining some of the crucial ecological parameters in the EwE model (notably total 

mortality rates and the settings for top-down/bottom-up control). Such fitting does not include any 

‘fiddling factors’ internal to the model. Instead, the type of question addressed after each run is, “Which 

species parameters or ecological settings are not set such that the model adequately captures the 

observed trends over time?” 

The inclusion of time-series data in EwE facilitates the model’s use for exploring policy options for 

ecosystem-based management of fisheries. Analyses in this report illustrate how the model can be used 

to address some of the policy questions defined by workshop participants (Table 2). Further 

development and policy exploration activities will be carried out by NOAA Chesapeake Bay Office staff 

in cooperation with local experts and fisheries managers. 

Time-series information for use with EwE can be of the following types: 

 For functional groups 

o Biomass information (does not need to cover all years in the time series) 

 Relative biomass series: Can be from surveys, assessment, etc. 

 Absolute biomass: Rarely used, as it assumes that the absolute values (per unit 

area) are estimated in the same manner for the original data and for the ecosystem 

model. Absolute data, as a rule, are entered as relative data instead, using only the 

trend in the data for the fitting. 

 Biomass for forcing: Used to force the simulation at each time step; typically used 

for groups whose dynamics depend on processes that are not covered by the 

ecosystem model. 

o Fishing mortality: Used to ‘drive’ the Ecosim model and needs to be entered for all years 

of the time series. 
20 

o Total mortality: Used to compare how the simulation matches the observed data; data set 

need not cover all years. 

o Catches. 

 Comparison of model simulation and observed data or for estimating fishing 

mortalities based on stock reduction analysis; data need not cover all years; timeseries catch information is presented in Tables 4 and 5. 

 As part of a stock reduction analysis, where calculations in Ecosim are made for 

each time step of growth, mortality, and recruitment, and the catches subsequently 

are used to estimate a fishing mortality (catch/biomass), which is applied as well. 

o Average weight: Used to compare observed and estimated weights for multi-stanza 

groups. 

 For fleets 

o Effort data by gear type: Expressed as relative to the effort in the first year of the time 

series; used to ‘drive’ the Ecosim model; effort data need to be complete for the time 

series. 

 Environmental data 

o Time-forcing data: Typically relative primary production (monthly or annual) over the 

time period, but can be any kind of environmental data as long as it can be related to the 

productivity for a group . 

The actual procedure applied for fitting the model to time series can be summarized as follows: 

• The primary production forcing time series from the spatial, hydrodynamic model described 

elsewhere in this document was used to force the system productivity in the fitted run. 

• The ‘fit to time series’ interface was used to search for the most sensitive interactions in the 

model, i.e., those interactions for which the vulnerability setting has most impact on the summed 

squared residuals between time series and the simulation. 

• Catches and estimates from juvenile surveys were not included in the search, i.e., their weighting 

factor was set to 0. 

• The 25 most sensitive consumers were selected, and vulnerability factors were estimated for 

these groups. Vulnerability factors exceeding 100 were truncated at this value. 

• These groups were then analyzed, and if comparison of time series and trend from time series (or 

the expected trend where there were no time series) warranted it, the vulnerabilities for the group 

in question were manually changed to improve time series fit. 
21 

The vulnerabilities were always changed by consumer group, i.e., only one parameter was used per 

consumer (and for some groups vulnerabilities were not changed at all). 

Information on time-series data is included in Appendix B of this report. 

3.4 Stock Assessment Methods for Developing Time Series 

A requirement for the EwE reconstruction approach is to provide at least one (more is preferable) 

biomass input into Ecopath as well as historical information on the removals or fishing mortality rates. 

Biomass data can be obtained from single-species assessment models. In cases where model groups are 

partitioned into multiple life-history stanzas, an age-structured model is preferable so that fishing 

mortality rates for each stanza (if applicable, e.g., certain fishing gears harvest a specific stanza) can be 

calculated from the estimated age-composition. Typical statistical catch-at-age models are notorious for 

having hundreds and sometimes thousands of parameters. However, this “over-parameterization” is not 

necessary (Walters and Martell, 2004). 

For many Chesapeake Bay species, there is insufficient data to carry out detailed biomass assessments, 

and a simpler method is needed. Two stock assessment methods were used to develop time series for the 

Chesapeake Bay Fisheries Ecosystem Model, both based on production modeling: an age-structured 

single-species model and a multispecies production approach. These two methods are described in the 

following two sections and are applied to the trophic groups in the appendices of this report. 

3.4.1 Stock Reduction Analysis (SRA) 

In this section we briefly describe the derivation of an age-structured single-species assessment model, 

which requires few parameters and can be used to estimate biomass for Bay species. This model is 

parameterized with two leading (unknown) parameters that are equivalent to the maximum intrinsic rate 

of growth and the carrying capacity of a simple surplus production model. These two leading parameters 

represent the long-term unfished biomass (Bo) and the maximum juvenile survival rate or recruitment 

compensation. For the estimation of the long-term unfished biomass, we rely heavily on meta-analytical 

results of Myers et al. (1999) to provide prior information for recruitment compensation at low 

spawning abundance. This is especially important in cases where relative abundance indices lack 

sufficient contrast to make it possible to estimate both parameters. A notable example is oysters in the 

current model. Without more diverse trajectories, notably decline and subsequent recovery, we cannot 

tell if a given harvest was taken from a large population with low productivity or a small population with 

high productivity. 

For bluefish and several other species, an age-structured assessment model was used to reconstruct 

historical biomass and a time series of fishing mortality rates, which were used to force Ecosim 

simulations. Input data for the assessment model include: growth information (von Bertalanffy growth 

parameters), length-weight relationships (i.e., wa = aLb

), parameters for a maturity cumulative frequency 

curve to calculate spawning stock biomass, natural mortality rate estimates, and parameters that describe 

size selectivity. Model parameters were estimated by fitting the model to abundance data and to catch 

rate information. Each of the abundance indices was assumed to be proportional to stock size, and 

observation errors were assumed to be lognormal. The age-structured population model includes a 

Beverton-Holt type stock recruitment function. The model was parameterized using a leading parameter 

setup, where the population scale (or capacity) was determined by Ro (the equilibrium unfished recruits), 
22 

and the maximum rate of population change was defined by a recruitment compensation parameter (k). 

In most cases, only observation errors were assumed. 

3.4.1.1 Equilibrium conditions 

We start out by assuming that the age-structure is in equilibrium, i.e., that the population structure has 

been stable over the years prior to the first model year. Beginning with the Beverton-Holt recruitment 

model: 

o

o

e E

E

R

β

α

+ = 1 (6) 

the two parameters (α and β ) can be derived given initial estimates of Ro, M, and k. The maximum 

survival rate (α ) is simply a multiple of number of recruits produced per unit of egg production, or: 

o

o

R = K

E

α (7) 

and the asymptote of the recruitment function is defined by: 

o

o

o

aE -1 E

R β ⎡ ⎤ = ⎢ ⎥ ⎣ ⎦

 (8) 

The equilibrium egg production (Eo) is the product of the equilibrium recruits and the number of eggs 

produced per recruit. The number of eggs per recruit (φe ) is the product of survivorship to age a times 

mean fecundity of age a individuals. It is not necessary to know the exact fecundity of any specific age 

group, but rather the relative differences in fecundity between separate age classes. Here, it is assumed 

that egg production is proportional to body weight. The equilibrium egg production (Eo) for a population 

at equilibrium is calculated as follows: 

( )a -M

o oe o a a

a=0

E =R R e wm φ

∞

= ∑ (9) 

where wa is the weight-at-age and ma is the proportion of that age class that is sexually mature. A simple 

logistic function is used to describe maturity-at-age: 

( ) 1

1

a h a g l l

e

m − − + = (10) 

where g is a shape parameter that describes the variation in maturity-at-age, lh is the length at 50% 

maturity, and la is the mean length-at-age. 
23 

3.4.1.2 Population dynamics 

The numbers-at-age (Na) matrix is initialized assuming a stable age distribution, and the oldest age class 

(A) is a plus group containing individuals ages A and older: 

M a

a o N R (e ) − = δ (11a) 

M

M A

A o e

e N R −

−

− = 1

( ) δ (11b) 

The δ parameter is constrained to the interval [0-2] and represents the ratio of initial numbers to the 

unfished equilibrium numbers. Numbers-at-age are propagated over time using historical catch 

information and size selectivity to calculate age-specific fishing mortality rates. Since our interest was to 

develop a fishing mortality rate time series to force Ecosim, annual fishing mortality is conditioned on 

observed total catch: 

t

Ct F =

Bt

 (12) 

where Ct is the observed total catch from all fisheries combined and biomass is simply the product of 

numbers-at-age times mean weight-at-age. Given predictions from equation (12), numbers-at-age are 

updated using: 

∑=

=

A

a

Et Nt amawa

0

, (13a) 

ω σ

β

α t e

E

E

N

t

t

t + + = 1 1,1 (13b) 

( )

1, 1 , t a M F vt a t a N N e − −+ + = for a < A (13c) 

( )

,

( )

1, , 1 t a t A M F vt A

M F v

t A t A N N e N e − − − −+ = − + for a = A (13d) 

Equation (13a) represents the total egg production in year t, and equation (13b) is the Beverton-Holt 

recruitment function; note that process errors ωt may be included if σ > 0. The instantaneous natural 

mortality rate is represented by M, and the vulnerability-at-age (va) is calculated using the same logistic 

function in equation (10). However, separate parameters (g and lh) are used and unless otherwise noted 

are fixed values (i.e., not estimated). 

3.4.1.3 Estimating model parameters 

Model parameters were estimated by fitting the models to time-series data on relative abundance and 

composition information, if available. All abundance indices were assumed to be proportional to stock 

size or a specific component of the stock, such as age-0 recruitment indices. Observation errors were 

assumed to be log-normally distributed. In the case of relative abundance indices, the observation model 

is: 
24 

t v Y = qX e t t (14) 

where Xt is the predicted biomass or age group or population numbers (depending on what the 

observation Yt represents), and q is simply a scaling parameter or the slope of the regression between Y

and X. The scaling parameter, q, is a nuisance parameter (a parameter that is fundamental to the model, 

but of no particular interest in itself), and the model simply integrates over this parameter as well as the 

variance in the observation errors using the methods suggested by Walters and Ludwig (1994). The 

model uses the maximum likelihood estimates for q and the variance in the likelihood kernel; thus, each 

independent observation series is weighted by the relative standard deviation in the observation errors. 

The corresponding negative log-likelihood is: 

( ) ( ) n 2

Y t

i=1

n-1

lnl = - Z - Z

2 ∑ (15) 

where Zt = ln(Yt/Xt) and 

Z 1/ n Z ln(q) = ∑ t = (16) 

In cases where catch-at-age information is available, a multinomial likelihood is added to the overall 

objective function. Here, it is assumed that no aging errors exist and that the catch-at-age composition is 

representative of the age-structure in the Chesapeake Bay region. The negative log-likelihood for the 

multinomial distribution is: 

∑∑ ( ) = =

= −

T

t 1

ta

A

a 1

ln(la ) nta ln p (17) 

where nta is the observed numbers-at-age in the catch sampling programs and pta is the vulnerable 

proportion-at-age based on the numbers-at-age and vulnerability schedule in the population dynamics 

model. 

For the majority of the assessments, only observation errors are assumed and the unknown parameter set 

is limited to (Ro, k, and δ). In cases where catch-at-age data were available, parameters for the selectivity 

function (g and lh) are also estimated. Neither process errors nor recruitment anomalies are estimated in 

any of the assessment models. 

3.4.2 Multispecies Production Method (MSP) 

A series of Bay assessments (striped bass, weakfish, bluefish, white perch, spot, Atlantic croaker, 

Atlantic menhaden, bay anchovy, gizzard shad, and oyster) were developed to provide estimates of 

exploitable biomass to compare with EwE estimates. The boundary of these analyses, the mouth of the 

Chesapeake Bay, was not a physical boundary to many of the species modeled. This analysis was 

conducted in the context of a closed Chesapeake Bay population, which may not be realistic, but 

provides useful information nonetheless. 

Gulland (1988) considered definition of a unit stock an essentially operational matter, being tied to the 

models used, the questions asked, and the information available. When the bounds of the unit stock 
25 

extend beyond the limits of the fishery being analyzed, then the pattern of exploitation beyond the limit 

of analysis will determine whether the analysis of portion of the stock will be misleading. If the fishery 

outside the boundary is similar to that inside, correct answers may be provided (Gulland, 1988). 

3.4.2.1 Calculation of indices of abundance 

Relative biomass indices for many Bay finfish were developed from the Marine Recreational Fisheries 

Statistics Survey (MRFSS) database. Prior to 1981, recreational harvests were not estimated, and only 

species that were exclusively harvested by commercial fisheries could be assessed. MRFSS data are 

available back to 1981. Time-series end points were variable (2000-2004) and depended on when the 

first version of the spreadsheet model was developed as much as data availability. There are no longterm alternative fishery-independent surveys of exploitable biomass for the Chesapeake Bay. 

Commercial effort data in Maryland begins in 1980, but is discontinuous (lost tapes). Virginia can likely 

generate recent effort from its trip ticket system. PRFC has a long continuous catch and effort database. 

These indices were developed as a catch-effort ratio for private boat anglers in Maryland and Virginia in 

the MRFSS inland fishing area (inshore saltwater and brackish water bodies such as bays, estuaries, 

sounds, etc., excluding inland freshwater areas). These indices were generally calculated as EB = Hp / 

Ep; where EB is relative exploitable biomass, Hp is private boat harvest, and Ep is private boat trips. 

Coastal bays are included in these totals, but these fisheries are usually minor compared to those of the 

Chesapeake Bay. All private boat trips were included in the denominator, but not all private boat trips 

were directed toward the suite of species indexed. It was assumed that the composition of trips (bottom 

fishing, trolling, casting, etc.) have not changed a great deal over time, so that biases in effort were not 

great enough to influence trends for a particular species. Private boat recreational fishing occurs over the 

entire Bay, and this index would be as close to a global survey as could be obtained. There is a 

possibility of hyperstability (catch per effort remains high even though the stock is declining) in these 

estimates; the recent dependence of the recreational striped bass fishery on chumming may have resulted 

in an inverse catchability-biomass relationship. These data are fishery-dependent, and the harvest 

component is contained in the landings data as well. A general recommendation for data in stock 

assessment is that information only be used once (Cotter et al., 2004). In the case of an MRFSS harvestbased index, information is contained in both the landings and the index. However, division by effort in 

the index reduces the direct dependency in the data, and there is little alternative for a Bay-wide index. 

Changes in length limits can affect age/size classes represented by these indices. Length limits have 

been fairly stable or nonexistent for spot, Atlantic croaker, white perch, and bluefish. Moderate increases 

in size limits have been imposed on weakfish and major changes have been imposed on striped bass. 

The short time series for striped bass estimates (1991-2003) represents a period of somewhat stable 

length limits. 

3.4.2.2 Biomass dynamic models 

Biomass dynamic modeling (also known as surplus production models) is the simplest analytical method 

that provides a full stock assessment (Haddon, 2001). They are relatively simple to apply because they 

pool the overall effects of growth, mortality, and recruitment into a single production function. Their 

data needs are small; minimum data needed are an index of relative abundance and landings (both in 

weight). The stock is considered as undifferentiated biomass and age, size, and sex structure are ignored 
26 

(Haddon, 2001). Variations in exploitation and biomass are important for fitting the model; length of the 

time series is not (Hilborn and Walters, 1992). 

The time-series fitting method was used to estimate production model parameters (Hilborn and Walters, 

1992; Haddon, 2001). A spreadsheet version of the discrete form of the biomass dynamic model based 

on the logistic function was used: 

t-1

t t-1 t-1 t-1

U U =U +rU 1- - sC +e

sK

⎛ ⎞ ⎛ ⎞ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ (18) 

where: 

• Ut was the index of abundance in year t 

• Ut-1 = index of abundance the previous year, 

• r = intrinsic rate of population increase 

• s = scalar for the abundance index 

• K = maximum population biomass 

• Ct-1 = harvest (commercial and recreational) in the previous year 

• ε is measurement error (Hilborn and Walters, 1992) 

Combined recreational and commercial directed harvest was used; discard estimates were not available. 

A genetic algorithm super solver (Evolver, Palisade Corporation) was used to estimate parameters r, K, 

s, and U0 (an estimated abundance index in the initial year of the time series) that minimized observation 

error (observed ln Ut - predicted ln Ut)

2

 (Prager, 1994). The previous year’s estimated index was then 

used to predict the following year’s estimate. 

The spreadsheet version of the model combined with the genetic algorithm provided a great deal of 

flexibility for trying different model variations. Auxiliary data such as tagging estimates of F, M, or 

disease mortality were added in some versions of the biomass dynamic model. In some cases, the scalar 

s was the only parameter used from the biomass dynamic model and each observed index was divided 

by s to obtain a biomass estimate. 

An observation error model was used that assumed all residual errors were in the index observations and 

that the logistic equation used to describe the time series was deterministic and without error (Haddon, 

2001). Residuals were examined to see if they were normally distributed with a mean of zero and to see 

if serial trends were present. 

Biomass of the exploitable stock in year (Bt) was estimated as predicted Ut / s (Hilborn and Walters, 

1992). Instantaneous annual fishing mortality rate in year t was estimated, based on Ricker (1975) as: 

( ) t

t

t t-1

C F =

B +B 2 (19) 
27 

3.4.2.3 Predator-prey modeling 

Abundances of many exploited fish stocks vary widely, and these variations may not be explained with 

single-species population models (Spencer, 1997). Often environmental factors are sought as underlying 

causes for changes in status, but sustained periods of high and low abundance are often exhibited by fish 

populations that are much more dramatic than shifts in climate or other environmental factors (Steele 

and Henderson 1984). These shifts can be regarded as jumps between alternative equilibrium states of 

ecological systems (Steele and Henderson, 1984). Continuous changes in intrinsic population growth 

rate, carrying capacity, F, or rate of predation can lead to ‘flips’ into periods of high or low abundance 

(Spencer and Collie, 1996). A classical logistic population growth formula combined with an S-shaped 

predation function reproduces these fluctuations when subjected to simulated directional environmental 

fluctuations or changes in fishing pressure (Steele and Henderson, 1984; Collie and Spencer, 1993). 

A simple predator-prey model was applied to examine the relative effects of fishing and striped bass 

predation and competition on recent (1981-2003) population dynamics of selected fish species in the 

Bay. This model provided an alternative to attributing all changes in biomass to F under stable 

ecological conditions. In addition, the ‘effect’ of striped bass was considered to include collateral 

damage such as starvation, cannibalism, or inability to occupy habitat due to competition, as well as 

direct consumption by striped bass. Results of this predator-prey model were contrasted with fishing 

mortality and biomass estimates from ‘straight’ biomass dynamic models. 

When applied generally, this predator-prey model has reproduced the types of rapid shifts in abundance 

that have been exhibited by marine populations, and it was useful in exploring the role of dogfish 

predation on Georges Bank haddock recovery and management (Spencer and Collie, 1996; 1997). In the 

current weakfish assessment process, use of this model has allowed for exploration of food web 

dysfunction as a hypothesis for their decline (Uphoff, 2006). This predator-prey model is essentially a 

Schaefer biomass dynamic model with a sigmoidal type III predation function added to estimate 

additional predation losses (Collie and Spencer, 1993). In this analysis, it provided a method for 

quantifying the extent that striped bass predation and competition or fishing mortality could be 

influencing another species biomass. 

The spreadsheet version of a Schaefer biomass dynamic model formulated by Haddon (2001) was used, 

and the type III predation function was added (Steele and Henderson, 1984; Collie and Spencer, 1993; 

Spencer and Collie, 1996). The predator-prey model used the following discrete time-step equation: 

( ) 2

t-1 t-1 t-1

t t-1 t-1 t-1 2 2

t-1

B cP (B ) B = B + rB 1- - H -

K A +(B )

ε

⎡ ⎤ ⎛ ⎞ ⎛ ⎞ ⎢ ⎥ + ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎢ ⎥ ⎣ ⎦

 (20) 

where: 

• Bt was biomass in year t 

• Bt-1 = index of biomass the previous year, 

• r = intrinsic rate of population increase 

• K = maximum population biomass; 

• Ht-1 = harvest (commercial and recreational, including discard estimates) in the previous year 

• c is per capita consumption by striped bass biomass (Pt-1) in the previous year 

• A is weakfish biomass where predator satiation begins 
28 

• ε is measurement error (Hilborn and Walters, 1992; Collie and Spencer, 1993; Spencer and 

Collie, 1996) 

In fitting the model to an index of biomass (U), an additional catchability parameter (q) had to be 

estimated; Ut / q or Ut-1/q would be substituted for weakfish biomass (Bt or Bt-1, respectively) in the 

predator-prey model equation. The Haddon version of the spreadsheet model was easier to adapt to this 

predator-prey formulation because it estimated biomass as a first step and then estimated q as Bt/Ut

(Haddon, 2001). Biomass was estimated directly for 1981 (B1981), and then the model was used to 

estimate subsequent years (Haddon, 2001). Estimating biomass first allowed striped bass biomass to be 

used directly and parameter A to be estimated directly rather than converting biomass to index 

equivalents. Two estimates of 1982-2003 striped bass biomass were used—current estimates of coastal 

biomass and a Bay estimate. The Bay estimate (1982-2003) equaled annual coastal biomass estimate 

multiplied by the median percentage of coastal age 2+ biomass represented by a Bay estimate (biomass 

dynamic model using 1991-2002 MRFSS catch per unit effort (CPUE) index with auxiliary information 

on lesions, and F and M from tagging). This median percentage equaled 23% of the coastal biomass. 

This estimate tracked the coastal estimate in trend, but would represent the exploitable fraction of bass 

in the Bay. 

An observation error model was used that assumed all residual errors were in the index observations and 

the logistic equation used to describe the time series was deterministic and without error (Haddon, 

2001). A genetic algorithm super solver (Evolver, Palisade Corporation) was used to estimate predatorprey model parameters that minimized observation error in the indices (observed loge Ut - predicted loge

Ut)

2

 and auxiliary data used (Haddon, 2001). The spreadsheet version of the model combined with the 

genetic algorithm provided a great deal of flexibility for trying different model variations. Residuals 

were examined to see if they were normally distributed with a mean of zero and to see if serial trends 

were present. 

Instantaneous annual fishing mortality rate in year t was estimated from Ricker (1975) as: 

( )

t-1

t

t t-1

H F =

B +B 2 (21) 

An equivalent instantaneous natural mortality rate associated with striped bass predation and 

competition was estimated. The type III predator-prey term, 

( ) 2

t-1 t-1

2 2

t-1

cP (B )

A +(B ) (22) 

estimated loss of biomass, Dt, was equivalent to catch Ht. Instantaneous annual natural mortality 

associated with striped bass was estimated as 

( ) 2 1

1

−

−

+ =

t t

t

bt B B

D

M (23) 
29 

Total instantaneous annual mortality of (Zt) due to due to fishing and striped bass predation equaled Ft + 

Mbt. This estimate did not account for other sources of natural mortality not associated with food web 

effects represented by striped bass. 

4 Results and discussion 

4.1 Ecopath models 

The Ecopath model represents a possible configuration of the Chesapeake Bay in 1950; its groups are 

shown arranged by trophic level in Figure 4. The model configuration is mass balanced to the degree of 

ensuring internal consistency, i.e., there is enough food for the consumers in the model, and enough 

production to meet any demand. This, however, is not the only possible configuration, and the Monte 

Carlo routines of EwE can be used to explore other possibilities. Using the Ecoranger routine of EwE, 

balanced models can be obtained through a resampling method with values drawn from the confidence 

intervals dictated by the pedigree values. 

We used Ecoranger to obtain 200 balanced model parameterizations based on the confidence intervals 

obtained from the model pedigree. The resulting values were assigned to the original Ecopath 

parameters. There is a clear tendency to estimate higher available production for lower trophic levels 

and lower production for higher trophic levels. Model construction should ensure that the model is 

constrained from both the bottom up and the top down (biomasses and catches of higher trophic level 

groups). 

One of the few remarkable results indicated by the Ecoranger runs is a lower biomass for black drum. 

The tendency for lower black drum biomasses is likely caused by difficulties in balancing the hard clam 

group, which in the original model has an ecotrophic efficiency very close to 1. 

Trophic impact in the model can be explored through the mixed trophic analysis, via a process originally 

developed by Leontif (1951) to describe the American economy and later modified for ecological use by 

Hannon and Joiris (1989) and Ulanowicz and Puccia (1990). Mixed trophic impacts are shown in Figure 

6 for groups of focal impact to fisheries, and the analysis serves to illustrate, for example, that alewife 

and herring have strong effects on many other groups, yet very little is known quantitatively about their 

history in the Chesapeake. 

4.2 Ecosim simulations 

Ecosim simulations were performed for the period 1950-2002 using default Ecosim settings except as 

noted below. Feeding time adjustment was not included in the model runs; thus, feeding time was 

assumed constant for all groups over time. The ‘fraction of other mortality sensitive to changes in 

feeding time’ was set to zero for the two older stanzas of striped bass to reflect that they are unlikely to 

be impacted much by predation. Time-series data was input as described in Appendix B, and covers 

most of the important groups or species in the system. 
30 

4.2.1 Vulnerabilities 

Vulnerability factor settings were estimated as described in the Methods section of this document, and 

aimed at changing as few parameters as possible. In total, vulnerability factors were changed for 23 

groups, using only one setting for each consumer (i.e., where a group had several prey types, and hence 

several vulnerability settings, the same value was used for all consumer-prey interactions). The timeseries fitting routine of EwE could have been used to estimate vulnerability settings for all 218 diet 

components in the model. However, doing this would have increased the model’s ability to fit the time 

series to the detriment of its predictive capabilities (Walters and Martell, 2004). Vulnerabilities for 

species groups included in the CBFEM are presented in Table 6. 

The key criterion for changing vulnerabilities is that there must be availability of time-series information 

for the group in question. There must be time-series data for prey where members of the group are 

important predators, or for predators, where members of the group are important prey. For many groups, 

these criteria are not met because the data is unavailable. This limits current possibilities for using the 

model’s use for predictive purposes. A lack of historic information limits our capability to project what 

will happen in the future. We emphasize that this is a property of all modeling, not a specific feature of 

EwE. 

The vulnerabilities can be interpreted as a measure of how far a consumer is from its carrying capacity 

in the 1950-start situation. Thus, the default setting of 2 indicates that the given group would at most be 

able to double the predation mortality it is causing on its prey were its abundance to increase to its 

carrying capacity. For groups at their carrying capacity, the vulnerability should be closer to 1, 

indicating that the groups cannot increase the predation mortality they are causing on their prey. 

4.2.2 Time-series fitting 

We fitted the model to the available relative abundance data available for the various groups. Catch data 

were used either for fitting the Ecosim simulation or to drive Ecosim conditioning on catch. We 

generally found that where long time series of data on catches, fishing mortality, or relative abundance 

exists, the model fits well. In the absence of long-term data to drive the simulations, the ability of the 

model to explain short-term ecosystem changes is unclear. Long-term time-series information is of 

utmost importance for ecosystem-based management of fisheries. 

4.2.2.1 Commercial fish 

4.2.2.1.1 Striped bass 

For striped bass, the assessments used in the model only cover the period from 1982 through 2002. The 

fit to the biomass trend for 1982-2002 is good for all stanzas along with the fit to catches for the same 

period. To obtain this fit, it was necessary to assume that for both resident and non-resident striped bass, 

the fishing mortality was relatively high for the period prior to 1982, which leads to a marked 

overestimation of catches in recent years for both groups. We could have obtained a better fit to catches 

by increasing the 1950 biomass of striped bass considerably. However, we would then have been unable 

to make the striped bass return to their ‘historic level,’ as conventional wisdom dictates has occurred. 

A major problem for striped bass fitting is that we overestimate the catches for the last 20 years, with 

high biomass even though the currently estimated rather low F-values lead to quite high catches. If we 
31 

instead fit the time series to catches and hence ignore the uncertain, high estimates we have for fishing 

mortality in the pre-1982 period, we can obtain reasonable fit to the biomass time-series trend, but in 

that case we obtain quite low fishing mortality estimates for the pre-moratorium period (i.e., F values 

around 0.2-0.3 year-1, with a peak for migratory in the late 1970s and early 1980s where F values reached 

values of 2-3 year-1). 

The current striped bass assessment and simulation agree that the increased fishing mortality in recent 

years is likely to have caused the stock trend to level off or decrease. The striped bass assessments need 

to be carried back further than 1982 in order to maximize the accuracy of model simulation. 

Menhaden was assumed to contribute 52.5% to the diet of resident striped bass, and 68.5% to the diet of 

migrant striped bass in the 1950s. Due to decline in menhaden abundance over time, menhaden in the 

diet was reduced (dynamically by Ecosim) in recent years to around 20% to 40% for the different 

stanzas of striped bass. 

The main conclusion for striped bass is that the assessments should be carried back further than 1982, 

even if it means digging deep to obtain estimates for recreational catch prior to the establishment of the 

computerized NOAA recreational catch survey system. 

4.2.2.1.2 Bluefish 

The bluefish assessment indicates the following general trend: An increase in biomass in the 1950s and 

1960s associated with a reduction in fishing mortality, a peak in the mid-1970s, a gradual decline in the 

mid-1990s back to the 1950 level, and a small increase again in recent years. This trend is repeated 

closely by the Ecosim simulation, though the simulations tend to produce higher catches since the peak. 

The ease with which this general trend was reproduced can be attributed to the fact that the assessment 

and Ecosim are being driven by the same factor, fishing mortality; the adult group does not have 

predation mortality in the model, and it is minimal for the juveniles. Further, trophic interactions do not 

provide any confounding issues, because any trend seen in prey consumption by bluefish is countered by 

an inverse trend demonstrated by the feeding scheme of their main competitor, striped bass. 

4.2.2.1.3 Weakfish 

For adult weakfish, the assessment we conducted indicated a peak in abundance around 1970. While this 

peak is not reproduced fully in Ecosim, the trend for the rest of the time period is matched more closely. 

The driving force for the simulation is the fishing mortality estimated in the assessment. The simulations 

tend to overestimate the catches throughout the modeling period. 

4.2.2.1.4 Atlantic croaker 

The only two time series for Atlantic croaker are a juvenile trawl series estimate from VIMS going back 

to 1979, and an ASMFC 2003 stock assessment. The VIMS series is highly variable, with indications of 

a decreased biomass in recent years (associated with increased catches), while the ASMFC series 

indicates an increase since the early 1980s with a leveling off in the later years. The simulation is not 

conclusive, and there is little information on which to base the simulations, notably a lack of drivers in 

form of fishing mortality estimates from assessments covering the simulation time period. Indeed, we 

had to use forced catches to drive the group. We ran the simulations with a low-vulnerability setting for 

Atlantic croaker (based on a search using the time-series–fitting module designed for this purpose, 

indicating that the croaker would have been close to its carrying capacity in 1950. 
32 

4.2.2.1.5 Black drum 

For black drum, the catches can be used for estimating fishing mortalities only if the ecotrophic 

efficiency in 1950 is assumed to have been very low. This assumption increases the 1950-biomass, 

which was required to allow the estimated catches to be extracted from the population. The resulting 

decline is a gradual erosion of the black drum biomass over time, possibly associated with decline in soft 

clam, one of its major prey items. We have no time-series information to evaluate the legitimacy of the 

finding. 

4.2.2.1.6 Summer flounder 

The simulation for summer flounder is impacted by a burst in the late 1970s, and the juvenile trawl 

series indicates a sharp decline in the 1980s (where there were no catches, according to the catch 

information), followed by a marked increase in the 1990s (where the catches returned to a lower level). 

An NFSC assessment gives a trend very similar to the VIMS survey, and indicates that the summer 

flounder had high fishing mortality in the 1980s with a more recent declining trend. This, however, does 

not match the catch information at hand, and using the assessment-F series for 1983 on, we overestimate 

the catches for the period since then. Given that the mortality is fully dominated by fishing mortality, we 

cannot use a lower initial biomass to reduce the more recent catches, as this would make it impossible to 

balance the group. Our conclusion is that the data for this group is inconsistent. 

4.2.2.1.7 Menhaden 

Menhaden is the only group for which a long-term assessment was available from the regular stock 

assessments conducted in the Chesapeake Bay region (ASMFC, 2003c). The Ecosim simulation 

replicates the biomass trend well, even if there is some uncertainty about conclusions regarding the early 

1950s. 

While it was a welcome surprise to find an assessment going as far back as 1955, if ASMFC would take 

one more step and continue their data back to 1950, the accuracy of modeling results for menhaden 

would improve appreciably. 

In the simulations we tend to underestimate the catches of the YOY menhaden, while the biomasses are 

in line with the assessment, i.e., do not show any clear trend over time. For the older age group, the 

simulation replicates the multispecies production method assessment remarkably well, and is also in 

agreement with the ASMFC assessment from the mid-1960s onward. We were, however, not able to 

replicate the marked decline in the late 1950s and first half of 1960s indicated by the assessment, and 

ascribe this to a lack of information for the earlier part of the 1950s, or possibly to data inconsistency in 

form of underestimation of the catches for the early period. 

We compared the catches to two catch series, one representing coast-wide catch trends, and one trends 

in Bay catches. Our catch series initially matches the coast-wide series best, but since 1970 matches the 

Bay series very well. 

4.2.2.1.8 Alewife and herring, eel, catfish, white perch, and spot 

The simulations for alewife and herring, American eel, and catfish are all characterized by very little 

available information on which to drive the simulations and evaluate the results. We chose to condition 

the simulations on catch for these groups, because there were no realistic time-series trends available. As 
33 

such, the simulations mainly demonstrate the lack of information about what has happened to these 

groups in historical time. 

For white perch we had two (quite similar) multispecies production method assessments covering the 

period from 1981. We conditioned the simulations on catches up to 1970, and used an F series from one 

of the assessments for the period since then. Based on this, we overestimated the catches with a factor of 

three or so, while the biomass trends were more in line with the assessments (and perhaps the trawlseries though this is very variable). We cannot explain the catch-dilemma: Given the F-values, we 

should have lower biomasses, but with lower biomasses we would not have enough white perch for the 

striped bass. 

For spot we also needed drivers for the modeling, and we chose to use the trend from the VIMS survey 

series to fit a stock reduction model to the catch data. The Ecosim simulation fitted the biomass trend 

quite well when conditioned on the catch series. 

4.2.2.2 Commercial invertebrates 

4.2.2.2.1 Blue crab 

The adult biomass trend is associated with a marked increase in total mortality for adult blue crab, up 

above 3 year-1, which may be excessive. The simulation indicates a marked decline in the early 1980s 

associated with a sharp increase in catches at the time. This biomass decline is not reflected in the 

assessments (where biomass and catches increase in parallel), leading us to think that an external 

productivity factor may have positively impacted crab abundance at the same time as catches were 

increasing. 

Juvenile blue crab biomass does not show any time trend in the Ecosim simulations; similar results are 

obtained from the juvenile trawl survey indices since the 1960s and 1970s. Total mortality for juvenile 

blue crab seems to be declining over time, due to lower predation pressure from adult crabs. 

The fit to the catch series is reasonably good, indicating that the F and biomass estimates are internally 

consistent. 

4.2.2.2.2 Oyster 

For oysters, we had some recent trend data, and estimated the population trend back to 1950 from a 

stock reduction analysis. The model does not have predation on adult oysters, so it is not surprising that 

we were able to replicate the biomass and catch trends throughout the time periods quite closely – even 

without invoking any oyster mortality due to diseases. It is generally recognized that diseases are a 

contributing factor to the current poor stock status for oysters in the Chesapeake Bay, but we currently 

lack quantitative information that will allow us to incorporate diseases in the model. We consider 

evaluation of historical biomass, catch, and other mortality trends important for understanding the role 

of oyster in the Chesapeake Bay, and encourage such studies. 

The CBFEM allows for exploration of management scenarios. As an example of such a scenario, we 

present the potential outcome of a fishing moratorium on the native oyster since 1950 in the Chesapeake 

Bay in Table 8. 
34 

4.2.2.2.3 Soft and hard clam 

Our simulations for soft and hard clam for both show a marked decrease over the simulation period, 

much in line with observations. However, time-series information about abundance is lacking, as is 

information about mortality rates caused by diseases (which are considered important). The decline of 

clams in the model is caused by the combined impact of catches and sedimentation caused by 

hurricanes. The simulations for clams should be considered very tentative. 

4.2.2.3 Other groups 

For the remaining fish species, birds, and most invertebrates, we had no time-series information that 

could be used to constrain and validate the simulations. Hence, our model is not very illustrative for 

these groups; they serve mostly as ‘place-keepers.’ They are in the ecosystem and require resources for 

their sustenance, but their dynamic over time is difficult to evaluate. In no case do they markedly 

influence the groups for which we have more information. 

4.2.3 Uncertainty/sensitivity 

We used the Monte Carlo option in Ecosim to search for a better fit to the time-series data, drawing 

parameters from defined ranges based on the model pedigree. For this we included only time series for 

the key groups, thus excluding juvenile trawl survey estimates and catches in estimating the summed 

squared residuals (SS). We let the search routine conduct 500 Ecosim simulations (each involving up to 

several thousand iterations to find a balanced model), but were unable to find any constellation with 

lower sum squared residuals than we obtained through the fitting procedure. 

4.3 Evaluating policy questions 

Emphasis in this report has been on model validation—on examining model fit to qualify performance 

and to ascertain whether the model could provide plausible hypotheses for the ecosystem changes that 

occurred from 1950 to the present. If the model can successfully mimic system function and recreate 

historical trends, then it lends some credence to its prospective as a predictive tool. We will use the 

model to explore some policy questions. We emphasize that the examples we present in this report are 

for demonstration purposes—the range of questions that the model can be used to address is much 

greater. 

4.3.1 Predatory and forage fish ecosystem dynamics 

In a recent study, Walters et al. (2005) concluded that analysis of single-species versus ecosystem 

harvest strategies underlined the need to provide explicit protection for species whose value derives in 

part from support of other species as well as from harvesting. Harvesting all species at their singlespecies maximum sustainable yield (MSY) may lead to ecosystem erosion. With this in mind, we 

examined the role forage fishes play in the Chesapeake Bay ecosystem based on model simulations. 

4.3.1.1 Menhaden and striped bass 

There has been a menhaden fishery in the Bay for many decades, and there is still considerable interest 

in harvesting both menhaden and one of its major predators, striped bass, which relies heavily on 

menhaden for sustenance. Striped bass are said to be at their ‘historic level’ (Hartman and Margraf, 
35 

2003). Here we evaluate if their population growth may be impacted by the availability of menhaden as 

suggested in recent reports (Uphoff, 2003). Whether the menhaden fishery has any adverse impact is 

unclear, as “[no] studies have shown that the menhaden purse seine fishery has had any significant 

biological effect on any other species or fishery” (ASMFC, 2004). 

4.3.1.1.1 Are the striped bass back at their historic level? 

Our simulations indicate that the resident striped bass have increased above their ‘historic’ (i.e., 1950) 

level, while the migratory are back at it. These findings seem ‘semi-robust’ and it is indeed difficult to 

radically change the finding that striped bass will recover from the low fishing pressure it has 

experienced in recent decades. 

These results are, however, inconclusive. We particularly question what may have happened in the pre1982 period, where we had to estimate recreational catches based on post-1982 behavior. Again, this 

calls for a closer evaluation of historical exploitation and trends of striped bass biomass in the 

Chesapeake Bay. 

4.3.1.1.2 Impact of menhaden fishery on striped bass 

Using the model as fitted to the time series, we let the model run for an additional 25 years, and 

evaluated three alternative menhaden-harvest scenarios: status quo, half the fishing on menhaden, and 

no fishing on menhaden. We found at the end of the simulation that the striped bass biomass will 

decrease under status quo; that they would be stable under the reduced fishing scenario; and that they 

would increase with no fishing for menhaden. The striped bass are thus moderately sensitive to changes 

in menhaden fishing pressure in this model. 

The predictions for impact of menhaden fishery on striped bass are sensitive to the assumptions about 

carrying capacity for both menhaden and striped bass. If we use the default assumption for vulnerability 

(v=2) for menhaden, we obtain a very good fit to menhaden (and others groups’) time series, and we see 

that the reduction in menhaden fishery is having a fair impact on the menhaden population (the 

menhaden population roughly doubles if fishing is stopped altogether). On the other hand, if we assume 

that menhaden were much closer to their carrying capacity in 1950, we are not able to get as clear a 

decline for menhaden in the early part of the time series as indicated by the assessments, and a stop to 

the menhaden fishery at present is indicated to have very little impact on the striped bass. 

Likewise, the calculations are sensitive to the assumptions about carrying capacity for striped bass. We 

ended up using low vulnerabilities for the species, indicating that it would have been close to carrying 

capacity in 1950. We doubt that this is the case, but higher vulnerabilities (i.e., assuming it to be further 

from carrying capacity) would let the fishing mortality crash the stock much earlier than what likely 

happened. 

The problem we face with regard to evaluating the impact of the menhaden fishery is clearly linked to 

our lack of knowledge (i.e., lack of assessments) about what happened to striped bass prior to 1982, as 

well as to the exploitation and population history of menhaden pre-1958. 
36 

4.3.1.2 Bay anchovy 

Bay anchovy is considered an important forage species in the Chesapeake Bay, and a large number of 

predators feed on the group in the present ecosystem model. The fitted model was used to evaluate the 

impact of bay anchovy on other ecosystem groupings. The simulation period was increased to 100 years, 

and two simulations were run with all biomasses recorded at the end of the simulation: one status quo

simulation maintaining the 2002 fishing pressure for an additional 50 years and another simulation in 

which a very high fishing pressure was entered for bay anchovy. 

Comparing the ratios of the end-states in Table 7, a few groups are predicted to benefit from the decline 

in bay anchovy. These are mainly competitors (juvenile menhaden, and through them the adult 

menhaden) of the bay anchovy, not its prey. A noteworthy result is that striped bass are predicted to 

benefit, which is assumed to be linked to improved feeding conditions for menhaden. The groups that 

decline with the bay anchovy are mainly the predators for which bay anchovy are an important prey. The 

predictions in Table 7 are in general agreement with the mixed trophic impact analysis of EwE, 

confirming the finding of Libralato et al. (2006), who found a strong correlation between Ecosim 

simulation results and those of mixed-trophic-impact analysis. However, there are differences. For 

instance, mixed-trophic-impact analysis did predict the impact on striped bass, juvenile menhaden, and 

spot, but also showed that other flounders would be impacted by bay anchovy, and we did not see that in 

the Ecosim simulation. 

The perhaps most important finding from this simulation is that one cannot simply assume a direct 

relation between what happens to a group and what happens to its prey or predators; the food web is 

more intricate than that. 

4.3.2 Invertebrates 

The model may lack sufficient detail and be limited by its design regarding certain lower-level 

processes. Policy questions that concern alterations in planktonic community structure and resulting 

ramifications on trophically dependent higher trophic level species, or vice versa, cannot be addressed 

confidently due to a lack of detail and partitioning at lower levels. This lack of detail at planktonic levels 

prohibits shifts in community composition due to nutrient enrichment and/or differential responses to 

predation that may be essential to the reproduction of historical changes in the Chesapeake Bay 

ecosystem. The model can easily be modified to provide more detail to accommodate such questions; 

the main issue is whether there is sufficient empirical background to do so. 

4.3.2.1 Blue crab 

When participants in a Chesapeake Bay Ecosystem Modeling Workshop were asked to formulate policy 

questions (Table 2), several focused on blue crab. One question dealt with ecosystem manipulation: Can 

the crab stock be increased by control of its predators? The current model will not be good at answering 

this question, as no evidence could be found of blue crab being important prey for other groups in the 

system. The model says that predation pressure on adult blue crab is negligible, and the only important 

predator on young-of-year blue crab is older blue crab. Hence, no predator control mechanism can be 

identified for enhancing blue crab abundance short of providing refuge for small crabs to hide from 

bigger ones. This may well be a shortcoming in examining diet composition, and any additional 
37 

information about predator-prey interactions involving blue crab that could be added to the model inputs 

would be helpful. 

The model was used to evaluate whether the crab stock can be restored through fishery reductions. The 

fitted scenario was run for an additional 47 years, using productivity and exploitation patterns from 2002 

for all groups but blue crab, while bringing the blue crab fishing pressure back to the 1950 level. The 

simulation predicts that this would result in a blue crab biomass of 77% over the 1950 level, while 

catches would settle at 75% over the 1950 level. Thus, indications are that it is possible to restore blue 

crab abundance, and that it can be done through effort restriction with limited impact on overall catches. 

We assume the major reason for the blue crab settling at a level higher than that of 1950 is the increased 

productivity in the Bay as based on the nutrient loading patterns. 

It is worth noting at we see two- to three-year cycles in blue crab abundance within the model runs, 

somewhat like what is observed in the Bay. 

4.3.2.2 Oyster 

The Ecosim simulation shows good agreement with the biomass time-series trend available for oysters. 

The model indicates that the oyster biomass over the time period has decreased to 4% of its 1950 value. 

Noting that the Chesapeake 2000 agreement (Chesapeake Bay Program, 2000) targets a ten-fold 

increase in oyster biomass by 2010, a ‘what-if’ scenario was run. We ran a simulation for 40 years into 

the future and forced the biomass of oyster to ten times the 2002 level (i.e., from .75 to 7.5 t · km-2, 

illustrating that modeling is much easier than doing it empirically), and compared to a similar simulation 

also continuing for 40 years with current fishing patterns, but without forcing the biomass of oysters. 

The increased oyster biomass (to around half of the 1950 level) is not predicted to have any noticeable 

impact on phytoplankton in the Bay. We attribute this to spatial factors; it will have impact around 

oyster beds, but based on the numbers, it will not have any Bay-wide influence, much in line with what 

other models have shown (R. Fulford, The University of Southern Mississippi, pers. comm.) 

For the other groups, the increased oyster biomass is predicted to have only negligible effect (<4%), and 

the impact is negative for nearly all groups, due largely to there being ‘one more competitor on the 

block’; predation pressure on adult oysters is nonexistent in the model, hence, they do not contribute to 

funneling energy up the food web. Also, we do not include in the model any mediation effects related to 

the protection oyster reefs may offer as refuge for juvenile fishes and invertebrates. This could be done 

straightforwardly, given information to that effect. 

4.3.3 Ecosystem drivers: climate variation and fishing pressure 

The primary production rate for the Chesapeake Bay was estimated based on the hydrographic/climatic 

model. The resulting monthly time series is shown in Figure 9. The summed squared residuals between 

simulated values and ‘observed’ time-series estimates decreased substantially when primary production 

rate was forced. 

Estimating “the relative importance of climate variation on fish populations versus that of harvesting 

pressure” (see Table 2) is a more complicated matter – or rather, it is something that cannot be 

estimated. There are systems where it is possible to evaluate population trends based on fishing pressure 
38 

alone (Christensen, 1998), but it is almost always necessary to consider fisheries as well as 

environmental factors to explain what has happened in an ecosystem over time (Christensen and 

Walters, 2005; Walters et al., 2005). This is clearly indicated to be the case for the Chesapeake Bay, 

based on the impact of nutrient loading on the residuals as described above. 

The simulations here raise a question about the effect of decreasing nutrient loading to the Bay. This 

model will likely predict that higher-trophic level production will decrease even more than the amount 

to which phytoplankton is reduced, based on experience from other models, and to some degree 

supported by empirical studies (Nielsen and Richardson, 1996). It is important to keep in mind that 

primary production in estuaries may not be linearly related to the concentration of one nutrient, but that 

the combined effect of several factors (e.g., nutrients, light, salinity, and temperature) appears to be 

paramount. 

5 Conclusions 

5.1 Data availability 

This model attempts to reconstruct the recent history of exploitation and trophic interactions in the 

Chesapeake Bay. The simulations rely very heavily on data availability to reproduce the past. It is a 

major hindrance to the Fisheries Ecosystem Model that little systematic effort has been allocated in the 

Bay region to collecting and making available fisheries data from before 1982. 

From a data perspective, the biggest problem is the lack of pre-1982 recreational catch data. There is no 

doubt that older data is more difficult to obtain; the NOAA Recreational Catch database only starts in 

1982. Nevertheless, finding this data is a priority. It may call for more assumptions than were necessary 

for the post-1982 data, but its inclusion is of utter importance in order to develop an understanding of 

how life in the Bay has developed and reacted to exploitation. 

The ecosystem model is strongly influenced by nutrient loading trends. Excellent hydrographic/climatic 

modeling is available for more recent years, but do not cover the full time period required to understand 

what has happened in the Bay and why. A simple two-layer hydrographic model has been applied to the 

model, forced by river gauge data, nutrient loading, wind, and rainfall, and based on detailed 

bathymetry. The model runs are validated based on observed data. The hydrographic model is simple, 

but it provides the time series of estimates required for the Ecosim simulations. It would be best if in the 

future, more detailed habitat models were applied to the full time period, so that the trophic model can 

be linked to those as well. 

The striped bass/menhaden simulation discussed above illustrates how assumptions about trophic 

interactions can be important for evaluating impact of fisheries. This warrants a closer look when setting 

target and limit reference points as part of the stock assessment process. It also calls for digging into the 

archives to extract historical diet information (as done by Griffin and Margraf, 2003), for continued 

sampling of diet information, and for the creation of community-accessible databases with available diet 

information. 
39 

5.2 Stock assessment 

Current fisheries management practices are tactical. They deal with how to best use available resources 

in the short term. The data analysis to determine use of Bay fisheries tends to be quite limited in scope, 

often concentrating on recent catch data and single-species assessments. In the Chesapeake Bay, hardly 

any assessment goes back to before 1982. The lack of recreational catch data makes this a difficult task, 

but any effort toward increasing the body of historical data available for inclusion in the model would be 

highly valuable. If possible, it would be best to input data for all assessments back to 1950. 

When taking an ecosystem perspective, more species than just those that are exploited matter. In order to 

understand quantitative trophic interactions in the Bay, we recommend that assessments include all 

species of ecological importance in the Bay. When evaluating a time-series trend for a given species or 

group, the two most important data points are a biomass from the early part of the modeled time period 

and a biomass from the late part of the time period. Of course, it is also valuable to get data points in 

between or for all years. Are there old surveys that may be of use, even for non-target species? Or egg 

surveys? 

Perhaps there should be two standards for stock assessments: one for use in fisheries management, 

where decisions with short-term economical consequences must be made; and another for use in longterm management of the ecosystem, where emphasis is on increasing or understanding of ecological 

processes and strategically evaluating management scenarios. 

5.3 Spatial modeling 

EwE models, as well as their implementation, are very open-ended; their expansion is straightforward. 

The number of functional groups is not limited in any way—groups can be removed, aggregated, or 

added as new information is received. Fishing fleets can be added or removed, and new fleets can be 

included (or invented) to test suggested management interventions. Time-series data can be easily 

updated in the time steps required by the modeler/user. This requires only that the user create a new 

spreadsheet with comma-separated values (*.csv file) that contains the new data and load it into the EwE 

database. 

EwE can also model trophic and fishing-effort dynamics in an explicit spatial setting. The spatial 

component of EwE, Ecospace, is essentially a grid or two-dimensional matrix of ‘cells,’ each cell 

incorporating an Ecosim model (initially identical, as Ecosim inherits its parameters from Ecopath) and 

expressed at the user-interface level as a map (Walters et al., 1999). Each cell in the map, excluding land 

cells, is linked through two processes: dispersal of organisms and the redistribution of fishing effort due 

to changing profit patterns and/or the creation of areas closed to fishing. The user/modeler defines the 

base map (land/water areas) by sketching it on the interface with the computer mouse. Over the top of 

the base map the following attributes can be sketched: 1) patterns of relative fishing cost (effort ‘avoids’ 

high-cost cells; for example, cells far from their home port that require high fuel costs to reach); 2) 

patterns of relative primary production; 3) patterns of habitats to which biomass pools and fishing fleets 

can be assigned; and 4) areas closed to fishing (fleet and season specific). This allows for the exploration 

of policies that include spatial components, including the evaluation of the size and placement of marine 

protected areas (MPAs). A new sub-module of Ecospace has been developed to evaluate ecological and 

economic aspects of the placement of MPAs (Beattie et al., 2002). 
40 

The geographical scale of a model needs to be defined appropriately to consider the questions that are to 

be addressed to it. An example of this could be a study of how coastal eutrophication, or nutrient 

enrichment, influences marine ecosystems. It is known that enrichment (and sedimentation) generally 

decreases the transparency of coastal water bodies, but the broader indirect effects of such changes in 

light regimes on continental shelf ecosystems are not well explored. Shelf systems may be particularly 

susceptible to pollution-related decreases in water transparency, as they may cause shifts in energy flows 

from benthos-based to plankton-based food webs. Nutrient loading can be incorporated as a forcing 

function in Ecosim that directly influences phytoplankton production (as done here) and indirectly 

(causing shading effects on benthic producers and the food webs they support). This model is very 

capable of modeling ecosystem effects through food webs. 

Much effort has been devoted to showing the effects of climate changes on fish populations. Most of this 

work presupposes that there is some connection between climate and recruitment and correlates some 

climate index with an aspect of the life history of a particular stock of fish. For example, see Hollowed 

et al. (2000) for production and recruitment of various fish stocks in the North Pacific. The linkages 

between climate change and fish populations have also been studied in the Atlantic using the North 

Atlantic Oscillation as a climate index (Attrill and Power, 2002). Ecosim can be used to examine how 

primary production changes might be driven by climate and evaluate how ‘bottom-up’ cascade of 

production might differentially affect mortality and stock size of commercially important species in the 

Chesapeake Bay. 

Chlorophyll-a time series from CBREEM can be incorporated as a forcing function in Ecosim to 

influence phytoplankton production directly and benthic primary producers indirectly (by simulating 

interference in Ecosim’s ‘mediation’ tools). Simulated shifts in the system’s food webs supported by 

each of these primary production realms can be scrutinized and compared with related empirical and 

theoretical information. 

5.4 Ecosystem boundaries and model structure 

Few ecosystems are clearly demarcated; the Chesapeake Bay certainly is not. Many species spend a 

good part of their life cycle or year outside the Bay, and it is an open question how best to model the 

population dynamics of such species. We believe there is no way but to include the full extent of such 

populations in the model. For example, for menhaden, this may mean incorporating the entire Atlantic 

population and its production, consumption, and catches. Lesser nuisances, such as ensuring that 

consumption taken up outside the Bay does not lead to exaggerated estimates for the consumption in the 

Bay, can be handled by lowering the consumption ratio. 

It may be necessary to make several versions of the fitted model developed here. Different policy 

questions may call for different model structures. This is simply a question of focusing the modeling 

appropriately. The biggest problem is, rather, that it is necessary to have good data on which to build our 

models and simulations. Models cannot be designed to answer all questions – at least not without having 

a very good understanding of how an ecosystem and its components function and what has happened to 

the resources over time. A model cannot predict what will happen to parts of a system for which we 

have no information; the model is a formulation built on our knowledge. 
41 

Based on solid information and with an appropriate structure, a model can be used to evaluate a range of 

scenarios and guide us to what are likely consequences and causes. The ecosystem model presented in 

this report has this potential. 

This report has sought to clarify what Chesapeake Bay ecosystem resource information has been 

included in the Fisheries Ecosystem Model to date. Hopefully, by assembling this material in one place, 

data gaps have been made more evident. Any feedback that will enable the next generation of this 

modeling effort to go beyond what has been presented here is welcomed and encouraged. 

6 Acknowledgements 

This study was funded through the NOAA Chesapeake Bay Office’s Fisheries Research Program, 

NOAA Award No. NA17FU1654 and NA06NMF4570297, and we gratefully acknowledge this support. 

We especially thank Lowell Bahner for initiating and inspiring the modeling work and Derek Orner for 

enabling discussions, sharing information, and lending support. 

We thank the modelers at the University of British Columbia and around the Chesapeake Bay for their 

technical input; the EPA, USGS, MDNR, VIMS, and others for providing data; and the many 

researchers in the Chesapeake Bay area who participated in a series of ecosystem modeling workshops 

and shared their data and valuable insight into the Bay ecosystem. 

Finally, we would like to thank Marcia Olson, Jacqueline Johnson, and Kim Couranz for their time in 

reviewing and providing important input to the document. 

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53 

7 Tables 

7.1 Relating species and management plan entities 

Table 1. Chesapeake Bay fish and shellfish species with both common and scientific names. From 

Bonzek (2004) with updates. 

Species Common Name Species Latin Name(s) Management Plan Entity* 

American eel Anguilla rostrata ASMFC, CBP 

Atlantic croaker Micropogonias undulatus ASMFC, CBP 

Atlantic menhaden Brevoortia tyrannus ASMFC 

Sturgeon, Atlantic and shortnose Acipenser oxyrhynchus, 

A. brevirostrum 

ASMFC 

Bay anchovy Anchoa mitchilli No management plan 

Black drum Pogonius cromis CBP 

Black seabass Centropristis striata ASMFC, CBP, MAFMC 

Blue crab Callinectes sapidus CBP; MDNR (Coastal Bays) 

Bluefish Pomatomus saltatrix ASMFC, MAFMC, CBP 

Butterfish Peprilus triacanthus MAFMC 

Catfish (several closely related species) Ictaluridae No management plan 

Dogfish and coastal sharks Elasmobranchii ASMFC, MAFMC 

Eastern oyster Crassostrea virginica CBP 

Killifishes (several) Fundulus No management plan 

Gizzard shad Dorosoma cepedianum No management plan 

Hard clam Mercenaria mercenaria MDNR (Coastal Bays) 

Mackerel, king and Spanish (both) Scomberomorus cavallas, 

S. maculatus 

ASMFC (Spanish), CBP (Spanish and 

king); MAFMC (Spanish) 

Red drum Sciaenops ocellatus ASMFC, CBP 

River herring (alewife and blueback herring) Alosa pseudoharengus, 

 A. aestivalis 

ASMFC, CBP 

Scup Stenotomus chrysops ASMFC, MAFMC 

Spot Leiostomus xanthurus ASMFC, CBP 

Shad (American and hickory) Alosa sapidissima, A. mediocris ASMFC (American), CBP (both)

Spotted seatrout Cynoscion nebulosus ASMFC 

Striped bass Morone saxatilis ASMFC, CBP 

Summer flounder Paralichthys dentatus ASMFC, CBP, MAFMC 

Tautog Tautoga onitis ASMFC, CBP 

Threadfin shad Dorosoma petenense No management plan 

Weakfish Cynoscion regalis ASMFC, CBP 

White perch Morone Americana No management plan 

Yellow perch Perca flavescens MDNR (for Maryland section) 

Other species of possible interest: 

Cobia Rachycentron canadum SAFMC; however, states in the region are 

not on this council 

Horseshoe crab Limulus poytphemus ASMFC, CBP 

Squid (long-finned & short-finned) Illex illecebrosus 

Loligo pealei 

MAFMC 

* ASMFC = Atlantic States Marine Fisheries Commission; CBP = Chesapeake Bay Program; MAFMC = Mid Atlantic Fishery Management Council; and 

MDNR = Maryland Department of Natural Resources; SAFMC = South Atlantic Fishery Management Council 
54 

7.2 Questions 

Table 2. Topics that the Chesapeake Bay Fisheries Ecosystem Model (or a submodel derived from it) 

has been or can be used to address. 

At the October 2001 Chesapeake Bay EwE workshop, the following subsample of questions and issues 

to be addressed by the Chesapeake Bay EwE model were formulated: 

1. Can water quality (e.g., dissolved oxygen) be managed by top-down actions such as fishery 

regulations? 

2. What is the role of forage fish in Chesapeake Bay ecosystem dynamics? 

3. Are there too many striped bass in the Chesapeake Bay? 

4. What is the relative importance of the effects of climate variation on fish populations versus that of 

harvesting pressure? 

5. Can the crab stock be restored through fishery reductions and the use of protected areas? 

6. Can the crab stock be increased by the ‘control’ of other mortality agents, particularly predators? 

7. What are the consequences of a tenfold increase in the oyster population in the Chesapeake Bay? 

8. Can protected areas for oysters enhance abundance and aid in their restoration? 
55 

7.3 Basic parameters 

Table 3. Basic parameters for the Chesapeake Bay Fisheries Ecosystem Model. Values estimated 

by Ecopath are shown in italics. Estimated from a variety of sources as described in the text. 

EwE 

Group 

# 

Group name Trophic 

level 

Biomass (t 

· km-2) 

Prod. / 

biomass 

(year-1) 

Cons. / 

biomass 

(year-1) 

Ecotrophic 

efficiency 

Prod. / 

cons. 

1 Striped bass YOY 3.56 0.0125 1.800 23.266 0.401 0.077 

2 Striped bass resident 3.52 2.100 0.400 4.441 0.554 0.090 

3 Striped bass migratory 3.36 2.946 0.300 2.300 0.483 0.130 

4 Bluefish YOY 4.17 0.0161 5.650 18.111 0.014 0.312 

5 Bluefish adult 4.05 0.240 0.589 3.300 0.630 0.178 

6 Weakfish YOY 4.26 0.0257 4.000 13.525 0.304 0.296 

7 Weakfish adult 4.15 0.489 0.685 3.100 0.906 0.221 

8 Atlantic croaker 3.25 1.670 0.916 5.400 0.801 0.170 

9 Black drum 3.03 1.263 0.190 2.100 0.100 0.090 

10 Summer flounder 3.66 0.454 0.520 2.900 0.950 0.179 

11 Menhaden YOY 2.99 18.089 1.500 15.860 0.686 0.095 

12 Menhaden adult 2.13 33.000 0.800 7.800 0.941 0.103 

13 Alewife and herring 3.13 5.986 0.750 9.400 0.950 0.080 

14 American eel 3.38 3.220 0.250 2.500 0.500 0.100 

15 Catfish 3.09 1.155 0.280 2.500 0.950 0.112 

16 White perch YOY 3.55 0.00305 2.000 19.921 0.576 0.100 

17 White perch adult 3.55 0.300 0.500 4.200 0.886 0.119 

18 Spot 2.86 1.674 1.000 5.800 0.900 0.172 

19 American shad 3.04 0.400 0.700 3.500 0.725 0.200 

20 Bay anchovy 3.41 3.400 3.000 10.900 0.494 0.275 

21 Other flatfish 2.99 0.169 0.460 4.900 0.950 0.094 

22 Gizzard shad 2.43 2.086 0.530 14.500 0.950 0.037 

23 Reef-associated fish 3.40 0.232 0.510 3.100 0.900 0.165 

24 Non-reef-associated fish 3.05 1.228 1.000 5.000 0.900 0.200 

25 Littoral forage fish 2.85 5.210 0.800 4.000 0.950 0.200 

26 Sandbar shark 4.05 0.0240 0.230 1.400 0.217 0.164 
56 

27 Other elasmobranchs 3.33 0.500 0.150 0.938 0.112 0.160 

28 Piscivorous birds 3.98 0.300 0.163 120.000 0.000 0.001 

29 Non-piscivorous seabirds 2.73 0.121 0.511 120.000 0.000 0.004 

30 Blue crab YOY 2.80 1.580 5.000 12.057 0.879 0.415 

31 Blue crab adult 3.09 4.000 1.000 4.000 0.881 0.250 

32 Oyster YOY 2.00 3.280 6.000 8.965 0.096 0.669 

33 Oyster 1+ 2.09 20.400 0.150 2.000 0.414 0.075 

34 Soft clam 2.09 6.923 0.450 2.250 0.950 0.200 

35 Hard clam 2.00 2.626 1.020 5.100 0.950 0.200 

36 Ctenophores 3.48 3.400 8.800 35.200 0.205 0.250 

37 Sea nettles 4.13 0.583 5.000 20.000 0.000 0.250 

38 Microzooplankton 2.00 6.239 140.000 350.000 0.950 0.400 

39 Mesozooplankton 2.72 10.300 25.000 83.333 0.956 0.300 

40 Other suspension feeders 2.00 6.000 2.000 8.000 0.823 0.250 

41 Other in/epi fauna 2.10 66.675 1.000 5.000 0.900 0.200 

42 Benthic algae 1.00 1.717 80.000 - 0.900 - 

43 SAV 1.00 419.000 5.110 - 0.084 - 

44 Phytoplankton 1.00 27.000 160.000 - 0.684 - 

45 Detritus 1.00 1.000 - - 0.031 - 
57 

7.4 Catches, species groups 1-12 Table 4. Estimated catches of groups 1-12 (t · km2 · year-1) for the Chesapeake Bay since 1950 used in the Chesapeake 

Ecosystem Model. Estimated from a variety of sources as described in the text. 

Group SB resident SB resident SB Migra- tory SB migra- tory Blue- fish Blue- fish Weak- fish Atl. croaker Atl. croaker Black drum Summer flounder Men- haden Age 0-1 Men- haden Ag

2+ 

Group no. 2 2 3 3 5 5 7 8 8 9 10 11 12 1950 

0.463 0.792 0.427 0.825 0.116 0.067 

0.286 

0.656 0.542 

0.005 

0.209 12.706 

1951 

0.329 0.549 0.303 0.572 0.073 0.042 

0.137 

0.434 0.358 

0.006 

0.212 13.954 

1952 

0.271 0.442 0.25 0.46 0.071 0.041 

0.111 

0.321 0.265 

0.004 

0.194 16.758 

1953 

0.247 0.393 0.228 0.409 0.062 0.035 

0.142 

0.323 0.267 

0.005 

0.273 22.844 

1954 

0.243 0.377 0.224 0.392 0.076 0.044 

0.148 

0.431 0.356 

0.034 

0.304 24.25 

1955 

0.275 0.416 0.254 0.433 0.079 0.045 

0.264 

0.818 0.676 

0.01 

0.255 3.836 21.82 

1956 

0.25 0.368 0.23 0.383 0.09 0.052 

0.232 

0.815 0.673 

0.014 

0.292 8.514 19.97 

1957 

0.221 0.317 0.204 0.33 0.079 0.046 

0.147 

1.114 0.92 

0.011 

0.26 6.951 17.161 

1958 

0.351 0.489 0.324 0.509 0.052 0.03 

0.11 

0.894 0.738 

0.005 

0.332 3.086 17.314 

1959 

0.512 0.693 0.472 0.721 0.059 0.034 

0.049 

0.607 0.501 

0.02 

0.436 14.648 11.716 

1960 

0.531 0.697 0.49 0.726 0.039 0.022 

0.067 

0.323 0.266 

0.014 

0.357 0.966 20.226 

1961 

0.576 0.734 0.532 0.764 0.087 0.05 

0.091 

0.224 0.185 

0.02 

0.26 3.14 19.896 

1962 

0.47 0.58 0.434 0.604 0.163 0.094 

0.105 

0.093 0.077 

0.029 

0.25 2.534 18.974 

1963 

0.516 0.616 0.476 0.641 0.187 0.108 

0.074 

0.009 0.007 

0.028 

0.216 3.435 10.441 

1964 

0.412 0.476 0.38 0.495 0.112 0.064 

0.11 

0.028 0.023 

0.006 

0.195 3.529 7.239 

1965 

0.41 0.457 0.378 0.476 0.059 0.034 

0.14 

0.109 0.09 

0.009 

0.258 4.488 6.448 
58 

1966 

0.488 0.525 0.451 0.546 0.072 0.041 

0.074 

0.105 0.086 

0.038 

0.282 3.964 4.82 

1967 

0.462 0.479 0.427 0.499 0.038 0.022 

0.043 

0.023 0.019 

0.017 

0.222 3.701 4.039 

1968 

0.488 0.486 0.45 0.506 0.106 0.061 

0.079 

0 0 

0.028 

0.239 2.579 6.813 

1969 

0.616 0.589 0.568 0.613 0.077 0.044 

0.065 

0.005 0.004 

0.009 

0.163 2.53 3.934 

1970 

0.457 0.419 0.422 0.436 0.199 0.114 

0.153 

0.009 0.008 

0.006 

0.239 4.61 5.766 

1971 

0.315 0.276 0.29 0.287 0.209 0.12 

0.17 

0.019 0.016 

0.008 

0.191 1.77 8.242 

1972 

0.467 0.391 0.431 0.407 0.354 0.203 

0.182 

0.035 0.029 

0.002 

0.203 4.748 9.888 

1973 

0.624 0.498 0.576 0.518 0.884 0.508 

0.35 

0.184 0.082 

0.001 

0.354 2.544 11.332 

1974 

0.481 0.365 0.444 0.38 1.027 0.59 

0.216 

0.223 0.096 

0.003 

0.363 2.552 9.136 

1975 

0.336 0.241 0.31 0.251 0.99 0.569 

0.309 

0.544 0.316 

0.003 

0.411 2.169 7.839 

1976 

0.226 0.153 0.209 0.16 1.301 0.747 

0.274 

0.734 0.411 

0.002 

0.38 4.007 9.613 

1977 

0.21 0.134 0.194 0.14 1.027 0.59 

0.283 

0.894 0.548 

0.001 

0.502 2.302 11.342 

1978 

0.136 0.081 0.125 0.084 0.852 0.489 

0.274 

0.826 0.513 

0.003 

0.629 1.972 11.792 

1979 

0.112 0.063 0.104 0.065 0.941 0.54 

0.428 

0.333 0.132 

0.003 

1.115 2.957 12.071 

1980 

0.207 0.107 0.191 0.112 0.904 0.52 

0.423 

0.144 0.042 

0 

0.934 3.784 12.276 

1981 

0.162 0.065 0.149 0.068 0.623 0.26 

0.175 

0.033 0.022 

0.007 

0.275 2.45 12.802 

1982 

0.031 0.02 0.029 0.02 0.571 0.318 

0.155 

0.007 0.007 

0.002 

0.287 2.565 12.731 

1983 

0.034 0.021 0.032 0.022 0.824 0.379 

0.226 

0.023 0.008 

0.063 

0.684 2.348 14.396 

1984 

0.057 0.049 0.053 0.051 0.466 0.226 

0.15 

0.061 0.051 

0.004 

0.616 3.873 9.179 

1985 

0.016 0.009 0.015 0.009 0.776 0.529 

0.144 

0.153 0.101 

0.017 

0.309 3.415 8.853 

1986 

0.007 0.001 0.007 0.001 0.796 0.344 

0.156 

0.233 0.163 

0.037 

0.232 0.625 8.895 
59 

1987 

0.011 0.003 0.011 0.003 0.805 0.311 

0.147 

0.21 0.164 

0.026 

0.388 1.515 11.565 

1988 

0.023 0.006 0.021 0.006 0.649 0.334 

0.137 

0.213 0.121 

0.009 

0.511 1.102 11.27 

1989 

0.028 0.01 0.026 0.01 0.348 0.306 

0.093 

0.109 0.061 

0.012 

0.21 3.779 9.101 

1990 

0.033 0.014 0.031 0.014 0.404 0.278 

0.097 

0.051 0.012 

0.005 

0.131 1.051 14.997 

1991 

0.033 0.022 0.03 0.022 0.471 0.222 

0.078 

0.093 0.013 

0.011 

0.256 5.342 9.914 

1992 

0.061 0.038 0.056 0.04 0.235 0.072 

0.05 

0.153 0.065 

0.017 

0.317 3.413 8.491 

1993 

0.085 0.052 0.079 0.054 0.24 0.052 

0.067 

0.42 0.329 

0.004 

0.216 2.007 10.817 

1994 

0.112 0.062 0.104 0.064 0.208 0.07 

0.084 

0.518 0.394 

0.007 

0.206 0.931 9.469 

1995 

0.188 0.104 0.174 0.108 0.19 0.049 

0.089 

0.575 0.44 

0.011 

0.212 2.212 11.384 

1996 

0.255 0.129 0.235 0.135 0.148 0.054 

0.109 

0.725 0.556 

0.008 

0.172 0.953 10.763 

1997 

0.31 0.164 0.286 0.17 0.142 0.065 

0.12 

1.09 0.832 

0.009 

0.191 1.095 9.269 

1998 

0.315 0.18 0.29 0.187 0.175 0.068 

0.142 

1.08 0.797 

0.008 

0.22 1.042 8.794 

1999 

0.295 0.152 0.273 0.158 0.114 0.047 

0.125 

1.068 0.834 

0.003 

0.151 1.433 5.415 

2000 

0.436 0.192 0.403 0.2 0.142 0.046 

0.126 

1.174 0.89 

0.004 

0.183 0.631 6.057 

2001 

0.403 0.166 0.372 0.173 0.181 0.076 

0.092 

1.277 0.954 

0.003 

0.262 0.348 9 

2002 

0.467 0.148 0.431 0.154 0.147 0.049 

0.083 

1.166 0.858 

0.003 

0.227 1.396 5.564 
60 

7.5 Catches, species groups 13-35 

Table 5. Estimated catches for species groups 13-35 (t · km2 · year-1) for the Chesapeake Bay since 1950 

used in the Chesapeake Bay Fisheries Ecosystem Model. Estimated from a variety of sources as 

described in the text. 

Group Alewife Eel Catfish White 

perch 

Spot Spot Shad Gizzard 

shad 

Blue 

crab 

Oyster Oyster Soft 

clam 

Hard 

clam 

Group 

no. 

13 14 15 17 18 18 19 22 31 33 33 34 35 

1950 1.577 0.065 0.091 0.073 0.456 0.203 128.1 4.329 1.36 0.073 0.071 

1951 1.789 0.056 0.079 0.064 0.508 0.22 140.4 4.005 1.34 0.011 0.067 

1952 1.513 0.055 0.091 0.058 0.619 0.256 165.2 3.796 1.56 0.126 0.059 

1953 1.299 0.045 0.11 0.053 0.407 0.204 225.3 3.732 1.68 0.011 0.045 

1954 1.448 0.036 0.135 0.056 0.452 0.212 240.2 3.332 1.89 0.059 0.037 

1955 1.225 0.04 0.156 0.056 0.416 0.225 1389.8 2.968 1.78 0.128 0.043 

1956 1.234 0.041 0.154 0.06 0.333 0.24 1021 3.176 1.68 0.126 0.042 

1957 1.006 0.035 0.148 0.051 0.382 0.239 579.6 3.529 1.58 0.178 0.05 

1958 1.032 0.037 0.14 0.056 0.547 0.188 422.7 3.222 1.7 0.205 0.045 

1959 0.995 0.037 0.168 0.073 0.356 0.148 150.1 2.942 1.51 0.253 0.088 

1960 0.862 0.018 0.161 0.056 0.405 0.122 113.6 3.847 1.23 0.213 0.083 

1961 0.816 0.017 0.132 0.068 0.109 0.143 51 3.937 1.25 0.307 0.105 

1962 1.218 0.015 0.124 0.095 0.215 0.172 43.8 4.337 0.9 0.311 0.094 

1963 1.251 0.026 0.092 0.067 0.134 0.142 11.9 3.52 0.83 0.37 0.117 

1964 1.269 0.023 0.084 0.035 0.288 0.161 39.1 3.952 1 0.357 0.126 

1965 1.746 0.043 0.06 0.068 0.155 0.195 65.5 4.266 0.96 0.336 0.124 

1966 1.363 0.031 0.069 0.091 0.102 0.162 20.7 4.772 0.96 0.238 0.092 

1967 1.431 0.044 0.06 0.064 0.392 0.136 13.1 4.278 1.17 0.253 0.098 

1968 1.648 0.044 0.064 0.085 0.1 0.159 16.1 3.155 1.03 0.359 0.109 

1969 1.54 0.049 0.072 0.104 0.092 0.161 8.2 3.471 1.01 0.282 0.122 

1970 0.958 0.068 0.061 0.075 0.547 0.234 6.5 3.705 1.12 0.272 0.086 

1971 0.705 0.066 0.081 0.077 0.044 0.112 30.1 3.945 1.16 0.088 0.098 

1972 0.582 0.033 0.089 0.054 0.252 0.137 26.4 3.791 1.09 0.03 0.069 

1973 0.516 0.02 0.069 0.039 0.215 0.138 56.1 3.154 1.15 0.095 0.065 

1974 0.686 0.073 0.08 0.026 0.188 0.081 71.8 3.435 1.13 0.057 0.068 

1975 0.552 0.068 0.081 0.027 0.164 0.06 69.5 3.2 1.03 0.744 0.079 0.053 

1976 0.2 0.033 0.066 0.023 0.097 0.046 36.8 2.696 0.95 0.675 0.075 0.042 
61 

1977 0.068 0.025 0.089 0.032 0.151 0.07 7.4 3.127 0.82 0.591 0.157 0.048 

1978 0.106 0.062 0.068 0.048 0.256 0.06 2.7 2.839 1.02 0.652 0.131 0.024 

1979 0.083 0.067 0.069 0.033 0.2 0.046 4.8 3.313 0.98 0.612 0.087 0.029 

1980 0.062 0.034 0.103 0.043 0.14 0.045 15.9 3.182 1.03 0.678 0.071 0.036 

1981 0.028 0.067 0.08 0.042 0.118 0.091 0.023 17.6 4.242 0.98 0.713 0.072 0.053 

1982 0.064 0.036 0.08 0.045 0.079 0.064 0.027 5.8 3.619 0.79 0.557 0.089 0.032 

1983 0.091 0.036 0.088 0.037 0.15 0.131 0.028 23.1 3.941 0.53 0.338 0.043 0.053 

1984 0.063 0.041 0.079 0.052 0.049 0.046 0.061 240.5 3.94 0.56 0.365 0.06 0.034 

1985 0.028 0.04 0.088 0.041 0.136 0.13 0.037 193.7 3.968 0.6 0.39 0.048 0.033 

1986 0.052 0.038 0.115 0.049 0.187 0.131 0.032 301.1 3.402 0.62 0.361 0.159 0.042 

1987 0.104 0.039 0.1 0.051 0.299 0.266 0.037 242.5 3.086 0.39 0.174 0.198 0.046 

1988 0.062 0.036 0.097 0.064 0.126 0.127 0.041 341.7 3.169 0.24 0.109 0.183 0.059 

1989 0.038 0.039 0.129 0.044 0.193 0.172 0.05 333.4 3.431 0.2 0.111 0.097 0.069 

1990 0.036 0.032 0.119 0.065 0.2 0.123 0.039 263.3 3.909 0.2 0.129 0.077 0.071 

1991 0.044 0.04 0.101 0.066 0.274 0.204 0.034 293.5 3.726 0.15 0.106 0.016 0.048 

1992 0.085 0.04 0.099 0.061 0.258 0.205 0.034 347.7 2.885 0.1 0.057 0.036 0.05 

1993 0.08 0.045 0.107 0.09 0.229 0.234 0.03 709.6 4.012 0.03 0.025 0.02 0.072 

1994 0.058 0.043 0.14 0.089 0.274 0.3 0.018 948.9 3.62 0.05 0.038 0.015 0.053 

1995 0.019 0.032 0.105 0.08 0.208 0.233 0.007 976.7 3.367 0.07 0.06 0.014 0.043 

1996 0.006 0.028 0.155 0.104 0.174 0.2 0.011 1314.3 3.211 0.05 0.04 0.011 0.036 

1997 0.016 0.028 0.135 0.15 0.211 0.23 0.024 694.1 3.555 0.08 0.065 0.01 0.031 

1998 0.009 0.03 0.182 0.097 0.261 0.29 0.021 1009.3 2.609 0.12 0.083 0.007 0.025 

1999 0.009 0.029 0.169 0.09 0.165 0.187 0.013 1068.7 2.843 0.13 0.123 0.008 0.03 

2000 0.009 0.025 0.138 0.124 0.215 0.231 0.009 166.9 2.24 0.11 0.108 0.003 0.023 

2001 0.013 0.025 0.162 0.222 0.014 471.7 2.154 0.07 0.01 0.028 

2002 0.015 0.018 0.147 0.199 0.008 615.7 2.264 0.03 0.002 0.031 
62 

7.6 Vulnerabilities for species groups 

Table 6. Vulnerability settings for species groups in the Chesapeake Bay Fisheries Ecosystem Model. 

Only groups for which the vulnerabilities were changed from the default value of 2 are displayed. 

EwE 

Group 

# Group Vulnerability 

2 Striped bass resident 1.20 

3 Striped bass migratory 1.01 

4 Bluefish YOY 10.00 

5 Bluefish adult 15.0 

6 Weakfish YOY 1.02 

7 Weakfish adult 1.10 

8 Atlantic croaker 1.00 

9 Black drum 1.50 

10 Summer flounder 1.00 

13 Alewife and herring 1.00 

14 American eel 1.10 

17 White perch adult 1.10 

19 American shad 1.20 

20 Bay anchovy 1.66 

22 Gizzard shad 5.00 

30 Blue crab YOY 1.20 

33 Oyster 1+ 1.50 

34 Soft clam 12.0 

35 Hard clam 7.00 

36 Ctenophores 1.16 

38 Microzooplankton 2 

39 Mesozooplankton 1.08 

41 Other in/epi fauna 91.7 

7.7 Bay anchovy simulation – change in biomass 

Table 7. Estimated change in biomass of ecosystem groups resulting from applying a strong fishing 

pressure on bay anchovy. Groups for which the predicted change was less than ±2% are omitted. 

Group 

Change 

(%) 

Menhaden adult 16% 

Menhaden 0-1 10% 

Spot 3% 

Striped bass migratory 3% 

Striped bass resident 2% 

Mesozooplankton -2% 

Sea nettles -2% 

Striped bass YOY -3% 

Atl. Croaker -4% 
63 

Sandbar shark -5% 

Piscivorous birds -7% 

Summer flounder -7% 

Other elasmobranchs -8% 

Reef assoc. fish -8% 

White perch adult -11% 

Bluefish adult -14% 

Weakfish Adult -27% 

Bluefish YOY -29% 

Weakfish YOY -38% 

Bay anchovy -46% 

7.8 Oyster fishing moratorium simulation 

Table 8. Predicted effect on group biomasses if fishing for oyster had been stopped 1950. The biomass 

ratios are expressed as current biomass (assuming no oyster fishing)/current biomass (with historic 

oyster fishing). Only groups for which the absolute difference exceeds 5% are included in the table. 

Group 

Biomass 

ratio 

Oyster 1+ 4.28 

Oyster YOY 3.14 

Weakfish adult 0.95 

Summer flounder 0.94 

Phytoplankton 0.94 

Other elasmobranchs 0.94 

Striped bass YOY 0.93 

Non-piscivorous seabirds 0.93 

Menhaden adult 0.92 

Piscivorous birds 0.92 

Menhaden YOY 0.91 

Other suspension feeders 0.90 

Striped bass migratory 0.89 

Striped bass resident 0.88 

Black drum 0.74 

Ctenophores 0.73 

Sea nettles 0.69 

Catfish 0.67 

Bluefish adult 0.67 

Soft clam 0.63 

Bluefish YOY 0.60 

Hard clam 0.41 
64 

8 List of figures 

8.1 The Chesapeake Bay 

Figure 1. Bathymetry map of the Chesapeake Bay. The salinity provinces are defined as oligohaline (S < 

10 psu), mesohaline (10 psu < S < 20 psu), and polyhaline (S > 20 psu) according to Harding and Perry 

(1997). The bathymetry data are from NOAA/NOS Data Explorer Service and the bythymetric elevation 

is referenced to the local tidal datum (Mean Lowest Low Water) averaged over a 19 year tidal epoch. 

8.2 Model development 

Figure 2. Ecopath model development process. 

8.3 Foraging arena 

Figure 3. Flow between available and unavailable biomass in Ecosim. The assumption of fast 

equilibrium between the two prey states implies that ) i i /(2 aijBj V = vB v + . 

8.4 Food web components 

Figure 4. Overview of the groups in the Chesapeake Bay Fisheries Ecosystem Model. Groups are placed 

according to their trophic level; the size of the boxes is a function of the group biomasses. 

8.5 Ecoranger 

Figure 5. Estimated mean biomasses from 200 Ecoranger runs compared to the original Ecopath 

biomasses. Note tendency to estimate higher available production for lower trophic level and lower 

production for higher. The slope of the regression line is -0.06. Accepted Ecoranger runs tend to produce 

a very low biomass for black drum compared with original Ecopath biomass. 

8.6 Mixed trophic impacts 

Figure 6. The mixed-trophic-impact analysis for the Chesapeake Bay Fisheries Ecosystem Model shows 

direct and indirect impact through the food web. Impacting groups are shown in rows, impacted in 

columns. Positive impacts are shown above the baselines, negative below. Impacts are relative but 

comparable between groups. Only selected groups are shown. 

8.7 Time-series fit, biomass 

Figure 7. Time-series fit for biomasses in the Chesapeake Bay Fisheries Ecosystem Model. Time series 

from assessments or surveys are shown as dots, while Ecosim simulation results are indicated with lines. 

The time period (X-axis) is 1950-2002 for all plots. 
65 

8.8 Time-series fit, catches 

Figure 8. Time-series fit for catches in the Chesapeake Bay Fisheries Ecosystem Model. Catch time 

series are shown as dots, while the catches predicted by Ecosim (from biomasses and fishing mortalities) 

are shown as lines. The time period (X-axis) is 1950-2002 for all plots. Where a simulation matches the 

catches for all years, it indicates that the catches were used to estimate fishing mortalities for the Ecosim 

run. 

8.9 Impact of nutrient loading on primary production 

Figure 9. Chlorophyll-a ration from CBREEM (1950-2001) used to drive primary production rate in 

Ecosim. 
66 

9 Figures 

9.1 Figure 1. 

 

 

James R. 

York R. 

Rappahannock R.

Potomac R. 

Patuxent R.

Susquehanna R.

Chester R.

Choptank R.

Nanticoke R.

Polyhaline

Mesohaline

Oligohaline
67 

9.2 Figure 2. 

Ecosystem model 

(predation, 

competition, 

mediation, 

age structured)

Climate Nutrient

loading

Fishing

Predicted C, 

B, Z, W, diets

Observed

C,B,Z,W, diets

Log 

Likelihood

(B0, BCC)

(Diet0)

(Z0)

Habitat 

area

Error

pattern 

recognition

Choice of parameters

to include in final 

estimation (e.g., climate 

anomalies)

Judgmental evaluation

Search 

Formal (Statistical) Estimation
68 

9.3 Figure 3. 

Unavailable prey

 Bi - Vi

 Available prey

 Vi

 Predator

 Bj

v(Bi - Vi)

 vVi

aijViBj

aij = search rate of predator

Bi = total prey biomass

Bj = predator biomass

v = behavioral exchange rate

Vi

 = vulnerable prey biomass
69 

9.4 Figure 4. 

 
70 

9.5 Figure 5. 

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

1 1.5 2 2.5 3 3.5 4 4.5

Trophic level

Ecoranger/Ecopath

Black drum
71 

9.6 Figure 6. 

 
72 

9.7 Figure 7. 

1950 1975 2000 1950 1975 2000 1950 1975 2000
73 

9.8 Figure 8. 

1950 1975 2000 1950 1975 2000 1950 1975 2000
74 

9.9 Figure 9. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1950 1960 1970 1980 1990 2000

Nutrient loading (relative)
75 

10 Appendices – Description of methods for estimating basic input parameters and 

determining time series for CBFEM 

In the appendices, the fish groups have been separated into “Commercial” and “Other fish.” The split is 

not used in the EwE model; it is only introduced as a matter of convenience in this report. Likewise, the 

invertebrates have been grouped into “Commercial” and “Other invertebrates.” 

Where sources for diet compositions are omitted in the following data sections, they were based upon 

advice from local experts at the Chesapeake Bay Ecopath Workshop (Sellner et al., 2001) and general 

knowledge of these species’ trophic behavior as reported in Hagy (2002) and Baird and Ulanowicz 

(1989). Commercial fish diet compositions are listed in Tables 20 and 21. Diets for other fish, 

commercial invertebrates, other invertebrates, and birds are listed in Tables 22, 23, 24, and 25, 

respectively. 

10.1 Appendix A: Basic Input Parameters 

10.1.1 Commercial fishes 

10.1.1.1 Striped bass: young of the year (YOY), resident and migratory (Morone saxatilis) (FEM 

Groups 1, 2, and 3) 

Striped bass is one of the higher trophic level predators in the Chesapeake Bay. It is a prized sport fish 

and of great value for both commercial and recreational fisheries in the Bay (Hartman, 2003). The 

present fisheries are the result of a successful recovery effort that began in the early 1980s, when catch 

levels were heavily curtailed after the stock had collapsed. By 1995, the stock was deemed to have 

recovered, and biomass is now often described as being at or near ‘historic levels’ (Hartman and 

Margraf, 2003). 

Three stanzas (life stages) were created to represent this species: young-of-the-year (YOY), resident, 

and migratory. These age divisions mirror behavioral changes exhibited by the species on the Atlantic 

coast (Walter and Austin, 2003) and were based on discussions with local striped bass experts at 

workshops sponsored by this project. YOY are aged 0-11 months. The resident component is defined as 

fish less than 711 mm, a length representing the age at which the ASMFC considers striped bass to be 

migratory. This corresponds to ages 12–83 months (1–6.9 years). The migratory component includes 

ages 84+ months (≥ 7 years). 

The leading stanza for entry of biomass for this group is the resident component, as biomass estimates 

for YOY as well as the migratory component of the stock utilizing the Bay are poorly understood. For 

Q/B, the migrant population is the leading stanza. Ecopath estimates YOY biomass and consumption 

rates, resident Q/B, and migrant B, based on the lead parameters, the von Bertalanffy growth parameter 

(annual K = 0.11, average of FishBase values, Froese and Pauly, 2004), and an estimate of the ratio of 

the weight-at-maturity to the Winf of 0.1. For a presentation of the life stage calculations used in the 

Ecopath model, see Christensen and Walters (2004). 
76 

10.1.1.1.1 1950 biomass 

We used an estimate of 2.1 t · km-2 as leading biomass for the resident part of the population based on 

the assumption that the population is back at its ‘historic level’. Migratory biomass is estimated at 2.949 

t · km-2 and young-of-the-year biomass at 0.0125 t · km-2. 

10.1.1.1.2 1950 P/B 

Estimates of Z (= P/B) for resident and migratory fish were obtained from virtual population analysis 

(VPA) (ASMFC, 2003b) and tagging results (Latour, unpublished results). The ASMFC assumes natural 

mortality M to be 0.15 year-1 (Smith et al., 2000). The average F for the reference years (ages 4-13) used 

by the ASMFC was 0.32 year-1; thus Z = P/B = 0.47 year-1. Based on this, we used an assumed P/Bestimate of 0.4 year-1 for resident striped bass, a lower P/B-estimate of 0.3 year-1 for the migrant portion 

of the population, and 1.8 year-1 for the YOY. 

10.1.1.1.3 1950 Q/B 

For striped bass and most of the fish groups, consumption (Q/B) values were determined by the 

empirical equation available in FishBase (Froese and Pauly, 2004), which requires that estimates be 

provided for Winf; average environmental temperature; fin aspect ratio (ratio of the ratio of the square of 

the height of the caudal fin and its surface area); and food type (detritivore, herbivore, omnivore, or 

carnivore, Palomares and Pauly, 1998). For the migratory striped bass, Q/B was estimated to 2.3 year-1, 

given the parameter estimates of T = 17 oC, Winf = 115,760 g, aspect ratio = 2.31, and carnivore diet. 

Q/B for resident striped bass was estimated to 4.41 year-1, and YOY at 23.266 year-1.

10.1.1.1.4 Diet compositions 

For all striped bass stanzas, a diet item contributing less than 1% to total diet in a referenced study was 

not considered for determination of the modeled diet composition. Striped bass YOY diets were derived 

from three sources: Hartman and Brandt (1995), Rudershausen (1994), and Markle and Grant (1970). 

Grass shrimp, mysids, stone crabs, and benthic invertebrates were combined as ‘other in/epi fauna.’ 

Killifish, naked gobies, silversides, and other small fishes were combined as littoral zone forage fish. 

For striped bass residents, diet data were found in Hartman and Brandt (1995) and Walter (1999). Grass 

shrimp, mysids, stone crabs, polychaetes, and other benthic invertebrates were combined as ‘other in/epi 

fauna’. Weighted averages were used to determine resident diet using three age-classes. Diet data were 

weighted 1.0 for ages 1 and 2; 4.0 for ages 3+. For the migratory stanza diets, composition data were 

based on Hartman and Brandt (1995) and Walter (1999). Grass shrimp, mysids, stone crabs, mantis 

shrimp, and other benthic invertebrates were combined as ‘other in/epi fauna.’ 

10.1.1.2 Bluefish: YOY and adult (Pomatomus saltatrix) (FEM groups 4 and 5) 

10.1.1.2.1 1950 biomass 

Bluefish are represented in the model by two stanzas: YOY (aged 0–11 months) and adults (aged 12+ 

months). The adult stanza is the lead stanza for this group. Adult biomass was based on the F derived 

from a coast-wide biomass dynamic model (Lee, 2003b) and catches in the Chesapeake Bay (Piavis, 

personal communication, 2003). M was assumed to be 0.26 year--1. The YOY biomass was derived by 

Ecopath based on estimates of K = 0.26 year-1 from FishBase (Froese and Pauly, 2004) and Wm/Winf = 

0.20 (Table 7). The YOY biomass was estimated to be 0.0161 t · km-2. No changes were made to the Z 

of YOY. The adult biomass estimate was based upon a delay difference model of the group, tuned to the 

coast-wide VPA data available from the ASMFC. The adult biomass estimate is 0.24 t · km-2. 
77 

10.1.1.2.2 1950 P/B 

A biomass dynamic model (Lee, 2003b) was used to derive coast-wide estimates of F (0.257-0.718 year1

) for adult bluefish age-classes. Note that coast-wide values for F are likely to be higher than Bayspecific Fs (Gartland, 2006). For 1950, the F for the older stanza was estimated to be 0.483 year-1, and 

an overall, assumed Z of 0.589 year-1 was used for this group. Z for the younger stanza was assumed, at 

5.65 year-1. 

10.1.1.2.3 1950 Q/B 

The adult bluefish leading parameter Q/B was estimated using the empirical relationship in FishBase as 

3.3 year-1 assuming T =17 °C, Winf =16,962 g, a fin aspect ratio of 2.55, and carnivorous diet. YOY Q/B 

was estimated to be 18.111 year-1. 

10.1.1.2.4 Diet compositions 

Adult bluefish diet was based on information contained in Hartman and Brandt (1995). Any component 

less than 1% in the diet was ignored in the model. Butterfish and harvestfish were combined as non-reef 

demersal fish. The diet data were averaged over six months (summer, fall, and part of winter), 

representing the time that they are resident in the Bay. Diet composition for the YOY stanza was derived 

from Hartman and Brandt (1995) and Gartland (2006), ignoring those elements that compose less than 

1% of the diet. Bay anchovy and striped anchovy in Gartland (2006) were combined as bay anchovy; 

‘unknown fish’ in Gartland (2006) was included with littoral forage fish; and ‘shrimp’ was placed into 

‘other in/epi fauna.’ Diet data were averaged over the six months of residency (summer, fall, and part of 

winter) and over the two studies. 

10.1.1.3 Weakfish: YOY and adult (Cynoscion regalis) (FEM groups 6 and 7) 

10.1.1.3.1 1950 biomass 

Weakfish are represented by two stanzas, YOY (0-11 months) and adults (12+ months), with adults as 

the leading stanza. Adult biomass was derived from the coast-wide VPA (Kahn, 2002) adjusted to 

reflect the Chesapeake Bay. Specifically, catch data from the Chesapeake Bay was compared to that of 

the entire coast. That fraction was then applied to the overall coast-wide population estimate to derive a 

population biomass estimate for the Bay. YOY biomass was estimated by Ecopath assuming K=0.26 

year-1 from FishBase (Froese and Pauly 2004) and Wm/Winf = 0.1. As leading biomass we used an 

estimate for the adult stanza derived from an age-structured model and estimated to 0.489 t · km-2. For 

the YOY, biomass was estimated at 0.0257 t · km-2. 

10.1.1.3.2 1950 P/B 

F was estimated to be approximately 0.2 year-1 in the late 1990s for the coast-wide weakfish stock (Spear

et al., 2003), and M was estimated to be 0.25 year-1 for all stock assessment purposes (Smith et al., 

2000). Thus, the current Z = P/B ≈ 0.45 year-1. For 1950, F was estimated at 0.585 year-1, and a Z value of 

0.685 year-1 was used, i.e., a lower natural mortality than used in the current stock assessments was 

assumed as we cannot quantify as high predation mortality as assumed in the assessments. YOY Z was 

estimated to be 4.0 year-1. 
78 

10.1.1.3.3 1950 Q/B 

The adult weakfish Q/B value was the leading parameter, and was estimated at 3.1 year-1 using the 

empirical formula from FishBase (Froese and Pauly, 2004) with T = 17 °C, Winf = 8,850 g, aspect ratio = 

1.32, and carnivorous diet. YOY Q/B was estimated to be 13.525 year-1. 

10.1.1.3.4 Diet compositions 

Both stanzas of weakfish had diet compositions derived from Hartman and Brandt (1995). For both 

stanzas, grass shrimp and mysids were added to ‘other in/epi fauna.’ Diet data were averaged over six 

months (summer, fall, and part of winter), representing residency time in the Bay. 

10.1.1.4 Atlantic croaker (Micropogonias undulatus) (FEM group 8) 

10.1.1.4.1 1950 biomass 

Little stock assessment data are available for the Atlantic croaker (Desfosse et al., 1999; Austin et al., 

2003), although they are one of the most abundant bottom fish in the Chesapeake Bay. Good yearclasses appear to have sustained relatively high catches for the commercial fishery from 1997 to 2002, 

and the stock appears resistant to growth overfishing, depending on F assumptions. Abundance 

estimates were calculated based on sampling area of trawl (5,402 km2

) and on the assumption that the 

trawl net efficiency is 0.4, based on hydroacoustic data (Hoffman, Personal communication). Abundance 

estimates were converted to biomass assuming an average weight value from trawl catches. Densities 

were calculated using an area of 5,402 km2

, and were entered into the model under the assumption that 

those densities apply to the entire Bay. These data were based on unpublished information provided by 

staff of the Virginia Institute of Marine Science (VIMS) Chesapeake Bay Multispecies Monitoring 

(ChesMMAP) survey (www.fisheries.vims.edu/chesmmap/). The resulting biomass was 1.67 t · km-2, 

and this biomass was used for the model in the absence of time trend information for croaker. 

10.1.1.4.2 1950 P/B 

An annual total mortality for the Chesapeake Bay Atlantic croaker stock was estimated to be 55 to 60% 

per year (Austin et al., 2003). Using the higher end as a conservative mortality estimate yields a P/B = 

0.916 year-1. 

10.1.1.4.3 1950 Q/B 

Q/B for Atlantic croaker was estimated from empirical relationship in FishBase to be 5.4 year-1, 

assuming that T = 17 °C, Winf = 2580 g, aspect ratio = 1.32, and carnivorous diet, (Desfosse et al., 1999) 

10.1.1.4.4 Diet compositions 

About half of the diet for Atlantic croaker was designated as ‘imported,’ representative of their sixmonth residency period in the Bay. FishBase (Froese and Pauly, 2004) suggests that their diet is made 

up mostly of demersal invertebrates and some larval fish. 

10.1.1.5 Black drum (Pogonias cromis) (FEM group 9) 

10.1.1.5.1 1950 biomass 

Black drum are managed as a single stock along the continental East Coast (Jones and Wells, 2001). No 

estimate of stock size was available for the Chesapeake Bay. Ecopath estimated biomass by using an 
79 

assumed ecotrophic efficiency for 1950 of 0.1. This low EE resulted in an initial biomass sufficient to 

balance the values of F estimated from catches. The biomass estimate is 1.263 t · km-2. 

10.1.1.5.2 1950 P/B 

Total annual mortality for black drum is estimated to range from 0.08 to 0.11 year-1 (Jones and Wells, 

2001). In the absence of other evidence, the median value of M = 0.095 year-1 was used. No reliable 

estimate of F was available, so it was assumed, conservatively, to equal M. Thus, Z = P/B ≈ 0.190 year-1. 

The model as outlined does not include any predation on black drum. 

10.1.1.5.3 1950 Q/B 

Q/B for black drum was estimated using the empirical relationship in FishBase (Froese and Pauly, 2004) 

as 2.100 year-1, assuming that T = 17 °C, Winf = 57612 g, aspect ratio = 1.32, and carnivore diet. 

10.1.1.5.4 Diet compositions 

The black drum diet composition was based on information made available by VIMS ChesMMAP. 

10.1.1.6 Summer flounder (Paralichthys dentatus) (FEM group 10) 

10.1.1.6.1 1950 biomass 

The 1950 biomass of summer flounder was estimated to be 0.454 t · km-2, based on an assumed EE of 

0.95. 

10.1.1.6.2 1950 P/B 

The summer flounder 2002 advisory report noted that this species is overfished in the Northeast, and 

that there was an 80% chance that F in 2001 was between 0.24 and 0.32 year-1, having declined from 

about 1.32 year-1 in 1994 (NFSC, 2002a). The more detailed analysis of the stock (NFSC, 2002b) 

suggests that natural mortality is about 0.2 year-1. An estimate of total mortality would therefore be Z = 

P/B ≈ 0.520 year-1. This estimate was used for the model for lack of any other information. 

10.1.1.6.3 1950 Q/B 

Summer flounder Q/B was calculated to be 2.900 year-1 using the empirical equation available in 

FishBase (Froese and Pauly, 2004) assuming T = 17 °C, Winf = 12,000 g, an aspect ratio of 1.32, and 

carnivorous diet. 

10.1.1.6.4 Diet compositions 

The diet composition of summer flounder was derived from information provided by the ChesMMAP 

2002 Bay-wide trawl survey (www.fisheries.vims.edu/chesmmap/) using samples from the main stem of 

the Bay. Anything contribution less than 1.0% of the diet was ignored. Bay anchovy and striped 

anchovy were combined as one group. The diet category ‘non-reef-associated fish’ included spotted 

hake, silver perch, and northern sea robin. ‘Other in/epi fauna’ included mantis shrimp and mysids. 
80 

10.1.1.7 Atlantic menhaden (Brevoortia tyrannus): YOY and adult (FEM groups 11 and 12) 

10.1.1.7.1 1950 biomass 

This species was represented in the model by two stanzas, juveniles (age 0-1) and adults (age 2+), with 

adults as the leading stanza. The 1950-biomass was assumed to be 33.000 t · km-2 for the adult group to 

balance the demand for the stanza, while the juvenile biomass was estimated by Ecopath to 18.089 t · 

km-2, assuming K = 0.424 year-1 based on estimated K from 1992 to 2002 (ASMFC, 2004) and Wm/Winf 

= 0.24. These weights were calculated from length data (ASMFC, 2004), which showed that menhaden 

mature when their length is 180–230 mm, (average 205 mm), and that from 1992-2002, L∞ averaged 328 

mm. The weight ratio for the multi-stanza group was approximated by cubing these values. 

10.1.1.7.2 1950 P/B 

P/B of the two stanzas was derived from M and F values in the Atlantic coast menhaden stock 

assessment (ASMFC, 2004). M was estimated to be ~1.5 year-1 for juveniles, and assumed to be 0.3 year1

 for age 2+ fish. Since fishery on age 0-1 is negligible, the juvenile P/B is approximately 1.5 year-1. For 

the age 2+ group, a P/B of 0.8 year-1 was used. 

10.1.1.7.3 1950 Q/B 

For the adult group, a Q/B of 7.8 year-1 was used, which for the juveniles leads to a Q/B of 15.86 year-1

based on stanza-calculations. 

10.1.1.7.4 Diet compositions 

The diet composition of menhaden is poorly understood, and only qualitative knowledge of feeding 

characteristics was available. Much of this knowledge was synthesized by the Menhaden Working 

Group of the Chesapeake Research Consortium Scientific and Technical Advisory Committee (2002). 

This report suggests menhaden shifts diet from primarily zooplanktivorous as YOY to almost entirely 

phytoplanktivorous for age 1+. This qualitative ontogenetic shift was mirrored in the modeled diet, with 

one third of adult diet designated as ‘imported material’ to represent the time they spend out of the Bay 

ecosystem. 

10.1.1.8 Alewife/herring (Alosa pseudoharengus/Clupea harengus) (FEM group 13) 

10.1.1.8.1 Biomass for 1950 

This group includes alewife and blueback herring (Alosa aestivalis). Based on an annual average of the 

four seasonal models in Baird and Ulanowicz (1989), a biomass was estimated and converted to wet 

weight. The conversion factor (0.16 g DW / g WW) was determined by taking an average of weight 

carbon to dry weight and dry to wet weight in Jørgensen et al. (2000). The resulting biomass seemed 

rather low to local experts interviewed for this report. Therefore, the biomass was estimated by Ecopath 

instead at 5.986 t · km-2, assuming that the ecotrophic efficiency of these species in the Bay was 0.95. 

10.1.1.8.2 1950 P/B 

Total mortality for this group was based on the P/B of 0.75 year-1 for alewife in Randall and Minns 

(2000). 
81 

10.1.1.8.3 1950 Q/B 

The consumption ratio (9.4 year-1) for this group was the average of Q/B values listed for herring (10.1 

year-1) and alewife (8.62 year-1) in FishBase (Froese and Pauly, 2004). 

10.1.1.8.4 Diet compositions 

Alewife and herring spend a large portion of their life in the open ocean, but make annual spawning runs 

to rivers that feed the Bay and spend about half the year in the Bay. Based on qualitative information 

available from VIMS (2004), the diet of alewife and herring consists of a mix of mostly zooplankton and 

some phytoplankton. 

10.1.1.9 American eel (Anguilla rostrata) (FEM group 14) 

10.1.1.9.1 1950 biomass and EE 

Stock assessments for this group were not available, so Ecopath was made to estimate biomass at 3.22 t · 

km-2, by setting EE to 0.5. This EE estimate was based on well-known aspects of the life history of 

American eel: They can live for 25 years and leave the Bay to spawn and die in the Sargasso Sea. 

10.1.1.9.2 1950 P/B 

The total mortality, 0.25 year-1, was based on P/B for American eel in Randall and Minns (2000). 

10.1.1.9.3 1950 Q/B 

The consumption ratio, 2.5 year-1, was obtained from the empirical equation in FishBase (Froese and 

Pauly, 2004) assuming, T = 17 °C, Winf = 9,065 g, an aspect ratio of 1.32, and carnivore diet. 

10.1.1.9.4 Diet compositions 

The diet composition for American eel was based upon qualitative information found in the American 

Eel Plan Development Report (ASMFC, 2000). 

10.1.1.10 Catfishes (Ameiurus catus, A. nebulous, A. natalis, Ictalurus punctatus, I. furcatus, 

Pylodictis olivaris) (FEM group 15) 

Catfish are predominantly freshwater species, but also occur in estuarine areas. There are three native 

species in the Chesapeake Bay: white catfish (Ameiurus catus), brown bullhead (A. nebulous), and 

yellow bullhead (A. natalis). The introduced channel catfish (Ictalurus punctatus) and blue catfish (I. 

furcatus), both of which have economic importance in the Bay, and the rarer flathead catfish (Pylodictis 

olivaris), also reside in the Bay (www.chesapeakebay.net). 

10.1.1.10.1 1950 biomass 

Catfish biomass was estimated by Ecopath to be 1.155 t · km-2, assuming that ecotrophic efficiency was 

0.95, i.e., that the model explains 95% of the mortality of the catfish. 

10.1.1.10.2 1950 P/B 

Total mortality for catfish of 0.28 year-1 was based upon the P/B value for channel catfish in Randall and 

Minns (2000). 
82 

10.1.1.10.3 1950 Q/B 

Consumption/biomass ratio was estimated at 2.5 year-1 using empirical relationship in FishBase (Froese 

and Pauly, 2004) and parameters for channel catfish, T = 17 °C, Winf = 26 kg, an aspect ratio of 1.32, 

and carnivorous diet. 

10.1.1.10.4 Diet compositions 

The diet composition of the catfish group was based on knowledge of these fishes provided by local 

experts as part of the Chesapeake Bay-area modeling workshops (Sellner et al., 2001). 

10.1.1.11 White perch: YOY and adult (Morone americana) (FEM groups 16 and 17) 

10.1.1.11.1 1950 biomass 

White perch was represented in the model by two stanzas: YOY age 0–11 months, and adults aged 12+ 

months. The adult stanza was the lead stanza for this group. The biomass for the adult group for 1950 of 

0.300 t · km-2 is a guessed value. YOY biomass of 0.00305 t · km-2 was estimated by Ecopath assuming 

K = 0.10 year-1, an average of values from FishBase (Froese and Pauly, 2004), and Wm/Winf = 0.1. 

10.1.1.11.2 1950 P/B 

Otolith aging from the Choptank River indicated that M = 0.15 year-1 for adults (Casey et al., 1988). A 

biomass dynamic model of white perch suggested that fishing mortality from 1996 to 2000 averaged F = 

0.54 year-1. For 1950, an estimate of 0.500 year-1 was used. The YOY P/B was assumed to be 2.0 year-1 for 

1950. 

10.1.1.11.3 1950 Q/B 

The consumption ratio of white perch adults, 4.2 year-1, was estimated with the empirical equation in 

FishBase (Froese and Pauly, 2004) assuming T = 17°C, Winf = 2178g, an aspect ratio of 1.32, and 

carnivorous diet. YOY Q/B was estimated to be 19.921 year-1. 

10.1.1.11.4 Diet compositions 

The diet composition of YOY white perch in the model was obtained from Rudershausen (1994), who 

used beach seine and trawl sampling to collect juveniles in the James River. Anything comprising less 

than 1% of the diet was ignored, and decapods, mysids, polychaetes, amphipods, etc., were combined 

into ‘other in/epi fauna’. Fish as prey items were assumed to be littoral zone forage fish. A small portion 

of ‘imported food’ was used to account for insects and insect larvae. The diet of white perch adults 

consists almost entirely of benthic invertebrates (Luo et al., 1994). Because this preference appears to 

become greater as the fish age (St-Hilaire et al., 2002), the adult white perch diet is almost entirely 

‘other in/epi fauna,’ with some small fishes also included. 

10.1.1.12 Spot (Leistomus xanthurus) (FEM group 18) 

10.1.1.12.1 1950 biomass 

The biomass of spot was estimated by Ecopath to be 1.674 t · km-2, by setting the ecotrophic efficiency 

to an assumed value of 0.90. 
83 

10.1.1.12.2 1950 P/B 

The annual mortality rate for adult spot has been estimated to be 80%, i.e., Z = P/B ≈ 1.6 year-1, with a 

maximum life span of five years; few fish over three years old are found (Pacheco, 1962; cited by 

Homer and Mihursky, 1991). This mortality estimate was considered to be on the high side, and instead 

a lower guessed value of Z = 1.0 year-1 was used. 

10.1.1.12.3 1950 Q/B 

The consumption ratio of spot was estimated at 5.8 year-1 using the empirical equation in FishBase 

(Froese and Pauly 2004), assuming T = 17 °C, Winf = 466 g, an aspect ratio of 1.39, and carnivorous 

diet. 

10.1.1.12.4 Diet compositions 

The diet composition of spot was adapted from Homer and Mihursky (1991) and adjusted to reflect 

migration, although juveniles are present nearly all year round. 

10.1.1.13 American shad (Alosa sapidissima) (FEM group 19) 

10.1.1.13.1 1950 biomass 

Biomass estimates for American shad in 1950 are unavailable, and a biomass of 0.4 t · km-2 was 

assumed. 

10.1.1.13.2 1950 P/B 

The total mortality estimate for American shad of 0.7 year-1 was based upon P/B for alewife (Alosa 

pseudoharengus) in Randall and Minns (2000). 

10.1.1.13.3 1950 Q/B 

The consumption ratio for American shad, 3.500 year-1, was estimated with FishBase, assuming T = 

17°C, Winf = 5,500 g, an aspect ratio of 1.32, and carnivorous diet. 

10.1.1.13.4 Diet compositions 

American shad diet composition was derived from Walter and Olney (2003), which used percentage by 

weight from diet analysis of adult American shad during their spawning run in the York River, Virginia. 

Diet items contributing less than 1% of the diet were ignored. YOY American shad were assumed to eat 

100% mesozooplankton (Hoffman, pers. comm.), and to reside in the Bay from April to November. 

Therefore, YOY diet composition is one third ‘imported’ matter. For adults, calanoid copepod food 

items were included as ‘mesozooplankton’ and mysids as ‘other in/epi fauna.’ About one-third of the 

adult diet was assumed to be ‘imported’ to account for migratory behavior. To generate final input 

values, weighted averages were calculated for the diet items based on eight age-groups (juveniles plus 

seven ‘adult’ age-classes; adults ranged from age 3-9). 
84 

10.1.2 Other fishes 

10.1.2.1 Bay anchovy (Anchoa mitchilli) (FEM group 20) 

10.1.2.1.1 1950 biomass 

A recent biomass estimate for bay anchovy was based on data from Jung (2002), which showed that the 

standing stock biomass from 1995 to 2000 averaged 34,000 t. With 10,000 km2

 as the area of the 

Chesapeake Bay, the biomass in the late 1990s would have been around 3.4 t · km-2. The growth model 

of Luo and Brandt (1993) suggested a higher biomass of around 16 t · km-2. Here, the biomass of 3.4 t · 

km-2 was used for the bay anchovy group for 1950 as well (while we estimate a similar biomass for the 

late 1990s). 

10.1.2.1.2 1950 P/B 

Houde and Zastrow (1991) reported bay anchovy adult mortality rates ranging between 89% and 95% 

annually. Luo and Brandt (1993) suggested that a 95% mortality rate was appropriate for the species, 

although Jung (2002) found that mortality rates can be higher, and are in fact much higher for larvae and 

juveniles. Because the population being modeled will be dominated by the biomass of adult anchovy, 

the P/B ratio was calculated based on the 95% mortality rate, i.e., P/B ≈ 3.0 year-1. 

10.1.2.1.3 1950 Q/B 

The consumption ratio for bay anchovy, 10.900 year-1, was estimated with the empirical equation in 

FishBase (Froese and Pauly, 2004), assuming T = 17°C, Winf = 20g (based on length weight 

relationships reported by Jung, 2002), an aspect ratio of 1.32, and carnivorous diet. 

10.1.2.1.4 Diet compositions 

Bay anchovy diet was based on Houde and Zastrow (1991), which contains a general description of 

anchovy diet, and on Hagy (2002), which reported diet composition as 67% mesozooplankton, 28% 

microzooplankton, 4% meroplankton (fish larvae, etc.), and 1% suspension feeders. Juveniles were 

assumed to eat copepodites and copepod nauplii, which were included as ‘mesozooplankton.’ 

10.1.2.2 Other flatfishes (FEM group 21) 

10.1.2.2.1 1950 biomass 

The biomass of this diverse group, which includes hogchoker, tonguefish, window pane flounder, and 

winter flounder, was estimated by Ecopath to be 0.169 t · km-2, by assuming that ecotrophic efficiency 

was 0.95. 

10.1.2.2.2 1950 P/B 

The P/B estimate for this group of 0.460 year-1 is based on a value given for flatfish off the Atlantic 

seaboard in Sissenwine (1987). 

10.1.2.2.3 1950 Q/B 

The estimated consumption ratio of 4.9 year-1 was derived using the empirical equation in FishBase 

(Froese and Pauly, 2004), and is the average of Q/Bs calculated for winter flounder, windowpane, and 

hogchoker as representative species for the group, assuming that T = 17 °C; Winf = 3,600 g (for winter 
85 

flounder); Winf = 689 g (for window pane); Winf = 188g (for hog choker); an aspect ratio of 1.32; and 

carnivorous diet. 

10.1.2.2.4 Diet compositions 

The diet composition for ‘other flatfish’ was based on a synthesis of diet information for windowpane, 

winter flounder, and hogchoker in FishBase (Froese and Pauly, 2004) and diet information for 

hogchoker in Baird and Ulanowicz (1989). 

10.1.2.3 Gizzard shad (Dorosoma cepedianum) (FEM group 22) 

10.1.2.3.1 1950 biomass 

The biomass of gizzard shad of 2.086 t · km-2 was estimated by Ecopath assuming an ecotrophic 

efficiency of 0.95. 

10.1.2.3.2 1950 P/B 

Gizzard shad P/B was estimated to 0.530 year-1, based on an estimate of M from empirical equations in 

FishBase assuming T = 17 °C, K = 0.18 year-1, Linf = 43.6 cm, and no fishing mortality. We tried to use a 

lower P/B for gizzard shad from Randall and Minns (2000), but this production rate was not sufficient to 

meet predation mortality requirements without resulting in demand for a huge biomass. 

10.1.2.3.3 1950 Q/B 

The consumption ratio for gizzard shad, 14.5 year-1, was estimated using the empirical equation in 

FishBase (Froese and Pauly, 2004) assuming T = 17 °C, Winf = 1,980 g, an aspect ratio of 1.32, and 

herbivorous diet. 

10.1.2.3.4 1950 EE 

The ecotrophic efficiency was assumed to be high (0.95) for gizzard shad, as it is a common forage fish, 

with predation accounting for much of its mortality. The species is exploited commercially only as bait 

and is not important to recreational fishers. 

10.1.2.3.5 Diet compositions 

Publications cited in FishBase (Froese and Pauly, 2004) suggested that the majority of gizzard shad diet 

is phytoplankton, with some zooplankton included for very large individuals. Local experts at the 

Chesapeake Bay Workshop (Sellner et al., 2001) suggested that phytoplankton should be considered the 

vast majority of the diet. 

10.1.2.4 Reef-associated fishes (FEM group 23) 

This is a diverse group that includes several species, e.g., spadefish (Chaetodipterus faber), tautog 

(Tautoga onitis), toadfish (Opsanus tau), blennies (Blenniidae), adult gobies (Gobiidae), and black 

seabass (Centropristis striata). Tautog is considered a prized recreational species along the eastern 

seaboard; it is slow-growing and susceptible to overfishing, and is therefore subject to management as 

part of SAW/SARC and the ASMFC. 

10.1.2.4.1 1950 biomass 

The biomass was estimated by Ecopath to be 0.232 t · km-2 by assuming an ecotrophic efficiency of 0.9. 
86 

10.1.2.4.2 1950 P/B 

According to a recent coastwide assessment report (Stirratt et al., 2002a) during the period from 1995-

2000, F for tautog averaged 0.4 year-1. Assessments assumed a natural mortality rate, M, of 0.15 year-1

(Stirratt et al., 2002a; Stirratt et al., 2002b). Therefore P/B for tautog ≈ 0.55 year-1, and this value is 

assumed to be representative for the rest of the group. We used a lower estimate of 0.51 year-1 for the 

1950 model. 

10.1.2.4.3 1950 Q/B 

The Q/B for this group, 3.1 year-1, was estimated using the empirical equation in FishBase (Froese and 

Pauly, 2004), based on data for tautog and assuming that T = 17 °C, Winf = 8688 g, an aspect ratio of 

1.32, and a carnivorous diet. 

10.1.2.4.4 Diet compositions 

The diet composition of this group was estimated from the diet and food item entries cited in FishBase 

(Froese and Pauly, 2004) for tautog, toadfish, and black seabass. Tautog diet was described as benthic 

organisms, including mussels, gastropods, and crustaceans. Toadfish diet was described as 33% fish, 

25% gastropods, 25% bivalves, 5% crustaceans, and the remainder as plant matter. Black seabass diet 

was described as mostly benthic crustaceans with some clupeids and zooplankton. 

10.1.2.5 Non-reef-associated fishes (FEM group 24) 

10.1.2.5.1 1950 biomass 

This group was represented by species such as spotted hake (Urophycis regia), sea robins (Prionotus 

carolinus, P. evolans, and P. tribulus), lizard fish (Synodus foetens), butterfish (Peprilus burti and P. 

triacanthus), and harvest fish (Peprilus alepidotus and P. paru). Biomass for this group was estimated 

by Ecopath to be 1.228 t · km-2, assuming an ecotrophic efficiency of 0.9. 

10.1.2.5.2 1950 P/B 

Total mortality was estimated from mortality values empirically derived in FishBase (Froese and Pauly, 

2004): for spotted hake, 0.49 year-1; for sea robins, 0.53, 0.5, and 0.56 year-1; for butterfish, 1.85 and 1.19 

year-1; for harvest fish, 1.33 and 1.26 year-1. Values were derived by assuming T = 17 oC and that total 

length L∞ ≈ Lmax, when no estimate of L∞ was available. These values suggest a group P/B of about 1.0 

year-1. 

10.1.2.5.3 1950 Q/B 

The consumption ratio of the group was estimated by Ecopath to be 5.000 year-1 by assuming that the 

production/consumption ratio for this group was 0.2. The P/Q ratio for most species will vary from ≈

0.05 for long-lived, slow-growing creatures to ≈ 0.3 for small, fast-growing organisms (Christensen et 

al., 2004). Given that many of the species in this group tended to be small and fast-growing, e.g., 

butterfish and harvest fish, while others were slower to mature, e.g., sea robins, the P/Q estimate of 0.2 

should be reasonable. 

10.1.2.5.4 Diet compositions 

The diet composition for this group was synthesized from information for each species cited in FishBase 

(Froese and Pauly, 2004). Spotted hake adults were said to eat a mixture of fish and squid, whereas the 
87 

juveniles fed upon a mixture of benthos including filter feeders, crustaceans, and molluscs. Sea robin 

adults were described as eating mostly fish, while the juveniles targeted a variety of crustaceans. Lizard 

fish juveniles and adults were reported as eating mostly fishes. Butterfish and harvest fish were 

described as feeding on benthic invertebrates and detritus. 

10.1.2.6 Littoral forage fishes (FEM group 25) 

10.1.2.6.1 1950 biomass 

Species that made up this group included striped and rainwater killifish (Fundulus majalis and Lucania 

parva), mummichogs (Fundulus heteroclitus), silversides (Membras spp. and Menidia spp.), silverperch 

(Bairdiella chrysoura), tonguefish (Symphurus plagiusa), and gobies (Gobiidae). The biomass for the 

group was estimated to be 5.21 t · km-2, by setting ecotrophic efficiency to 0.95. 

10.1.2.6.2 1950 P/B 

Total mortality for littoral forage fish was estimated by local experts at a Chesapeake Bay Ecopath 

Workshop (Sellner et al., 2001) to be 0.8 year-1 and was assumed to be similar to other forage fish 

groups. 

10.1.2.6.3 1950 Q/B 

The consumption ratio for littoral forage fish of 4.0 year-1 was determined by setting a 

production/consumption ratio of 0.2. 

10.1.2.6.4 Diet compositions 

The diet composition for littoral forage fish was derived from data in Cicchetti (1998), which was 

reported in percent by volume. The study was conducted at Goodwin Island, at the mouth of the York 

River. In order to apply the data to the model group, anything less than 1% in the diet study was ignored. 

Grass shrimp, mysids, polychaetes, etc., were added into ‘other in/epi fauna.’ Diet data were averaged 

over the habitats (five) and time period (June to October 1995) covered by the study and were also 

averaged over species: striped and rainwater killifish, mummichogs, silversides, silver perch, tonguefish, 

and several species of gobies. 

10.1.2.7 Sandbar shark (Carcharhinus plumbeus) (FEM group 26) 

10.1.2.7.1 1950 biomass 

Using a model based upon fishing effort, Cortes et al. (2002) suggested that an Fmsy of about 0.05 was 

appropriate for sandbar shark and was representative of the likely, present-day fishing mortality. If it is 

assumed that this was the fishing rate in the Chesapeake Bay and if it is further assumed that this fishing 

mortality rate can be used to back calculate survival, then approximately 95% of sandbar sharks survive 

fishing each year. Given average recent catches in the Chesapeake Bay, from the VIMS Fisheries 

Ecosystem Modeling and Assessment Project web site 

(http://www.fisheries.vims.edu/multispecies/femap/femap.htm), the catch from 1995 to 2000 was 

approximately 12 t per year. If this corresponds to a fishing mortality of 0.05, the shark biomass 

computes to approximately 0.0240 t · km-2. 
88 

10.1.2.7.2 1950 P/B 

Cortes et al. (2002) estimated a natural mortality rate of 0.18 year-1 for sandbar sharks (> age 1). We 

thus assume a Z = P/B = 0.23 year-1 for the model. 

10.1.2.7.3 1950 Q/B 

The consumption/biomass ratio for sandbar sharks of 1.4 year-1 was estimated with FishBase (Froese and 

Pauly, 2004) assuming T = 17o, W∞ = 616,292 g, an aspect ratio of 1.63, and a carnivorous diet. 

10.1.2.7.4 Diet compositions 

The diet composition of the sandbar shark was based upon Ellis (2003), who sampled sharks in four size 

classes: ≤ 60 cm precaudal length (PCL), 61-80 cm PCL, 81-100 cm PCL, and > 100 cm PCL. Giving 

all size classes equal weight from summed diet data resulted in an average wet weight diet of: teleosts 

(47.9), crustaceans (27.075), elasmobranchs (22.1), cephalopods (1), unknown (1.275), and other (0.65). 

We used these values as guidelines for the diet (Table 22). 

10.1.2.8 Other elasmobranchs (FEM group 27) 

10.1.2.8.1 1950 biomass 

This group includes skates and rays, e.g., the cownose ray (Rhinoptera bonasus) and other sharks, e.g., 

the spiny dogfish (Squalus acanthias), which are common in the Bay. The biomass was assumed to be 

similar to that of the benthic rays and skates group of the southeast United States continental shelf model 

as reported by Okey and Pugliese (2001). The biomass estimate for that earlier model was, in turn, 

derived from the Southeast Area Monitoring and Assessment Program- South Atlantic, 

(http://www.gsmfc.org/sm_ov.html/). The biomass estimate was 0.5 t · km-2. 

10.1.2.8.2 1950 P/B 

The P/B estimate for the ‘other elasmobranchs’ group of 0.15 year-1 was based on values given for 

similar groups in other EwE models, e.g., skates in Beattie (2001) and benthic rays and skates in Okey 

and Pugliese (2001). 

10.1.2.8.3 1950 Q/B 

The Q/B value for the ‘other elasmobranchs’ was calculated by Ecopath to be 0.938 year-1 by estimating 

the P/Q ratio for the group as 0.16. 

10.1.2.8.4 Diet compositions 

Diet composition for the ‘other elasmobranchs’ was derived from the ChesMMAP 2002 Bay-wide trawl 

survey (www.fisheries.vims.edu/chesmmap/), using samples from the main stem of the Chesapeake Bay. 

Data for cownose rays, clearnose skate, bluntnose ray, bullnose ray, spiny butterfly ray, southern 

stingray, smooth dogfish, and spiny dogfish were used. Anything < 1.0% of the diet for any of the noted 

species was ignored for determination of the group diet. 

10.1.3 Birds and other vertebrates 

The EwE model includes two groups of seabirds—piscivorous and non-piscivorous. In addition, the odd 

marine mammal occurs in the Chesapeake Bay, as do turtles. However, these groups were excluded 

from the 1950-2002 version of the Chesapeake Ecosystem Model due to their perceived minimal trophic 
89 

and economic impacts. Should ecological considerations warrant explicit inclusion of such groups, this 

can be easily achieved. 

10.1.3.1 Piscivorous seabirds (FEM group 28) 

The birds included in biomass for this group were based on advice from local experts, and listed by D. 

Forsell (personal communication, workshop October 2001) (Table 27). 

10.1.3.1.1 1950 Biomass 

The biomass estimate for piscivorous seabirds of 0.3 t · km-2 was based on advice provided in a 

Chesapeake Ecopath Workshop (Sellner et al., 2001). 

10.1.3.1.2 1950 P/B 

A total mortality estimate for piscivorous seabirds of 0.163 year-1 was based on survival rate values of 

85-90% for cormorants and 80-93% for alcids in the northeast Atlantic (ICES, 2000). 

10.1.3.1.3 1950 Q/B 

The consumption ratio estimate of 120 year-1 was from data for the piscivorous seabirds group in 

Preikshot (2007). 

10.1.3.1.4 Diet compositions 

The diet composition for piscivorous seabirds was based upon advice from D. Forsell (Personal 

communication), modified to include a substantial predation on juvenile menhaden (E. Houde, personal 

communication). 

10.1.3.2 Non-piscivorous seabirds (FEM group 29) 

10.1.3.2.1 1950 Biomass 

Bird species included in this group are presented in Table 26. The biomass estimate for this group of 

0.121 t · km-2 was based on advice from local experts in a Chesapeake Ecopath Workshop (Sellner et al., 

2001). 

10.1.3.2.2 1950 P/B 

The total mortality estimate for non-piscivorous seabirds of 0.511 year-1 was based on an annual 

mortality rate of 37% for mallard males and 44% females (Anderson, 1975). 

10.1.3.2.3 1950 Q/B 

The consumption ratio for non-piscivorous seabirds of 120 year-1 was based on the estimated Q/B for the 

group in Preikshot (Preikshot, 2007) 

10.1.3.2.4 Diet compositions 

The diet composition for non-piscivorous seabirds was based upon advice from D. Forsell (pers. comm.) 
90 

10.1.4 Commercial invertebrates 

10.1.4.1 Blue crab: YOY and adult (Callinectes sapidus) (FEM groups 30 and 31) 

10.1.4.1.1 1950 biomass 

The blue crab is represented by two stanzas, YOY (0-11 months) and adults (12+ months), with adults 

as the leading stanza. A recent VPA (A. Sharov, pers. comm.) suggests a current biomass of 1.580 t · 

km-2 for juveniles. Juvenile biomass was estimated by Ecopath, assuming that K = 0.59 year-1 (1998) and 

Wm/Winf = 0.4. For 1950, we used a higher biomass of 4.0 t · km-2 for the adult blue crab. 

10.1.4.1.2 1950 P/B 

Total mortality for 1950 was assumed at 1.0 for adults and 5.0 year-1 for juveniles. 

10.1.4.1.3 1950 Q/B 

The consumption ratio was assumed to be 4.0 year-1 for the adult group and was estimated to be 12.057 

year-1 from multi-stanza calculations for the juveniles. 

10.1.4.1.4 Diet compositions 

Blue crab diet compositions were provided by R. Lipcius (pers. comm.) 

10.1.4.2 Oyster (Crassostrea virginica) (FEM groups 32 and 33) 

Eastern oysters are separated into young-of-year (YOY) and age 1+ stanzas in the FEM. 

10.1.4.2.1 1950 Biomass 

An oyster biomass of 20.4 t · km-2 for age 1+ was used as a leading biomass for 1950 based on a stock 

reduction analysis. YOY oysters were estimated to have a biomass of 3.28 t · km-2. 

10.1.4.2.2 1950 P/B 

A total mortality rate of 1.5 year-1 was available from the model of Dew et al. (2003), where the potential 

population dynamics of an introduced oyster species (Crassostrea ariakensis) in the Chesapeake Bay 

were described. Mortality rates of 6.0 year-1 and 0.15 year-1 were used for YOY and age 1+, 

respectively. 

10.1.4.3 1950 Q/B 

A Q/B of 2.0 year-1 was assumed as a leading parameter for age 1+ oysters, while the Q/B for juveniles 

were estimated to be 8.965 year-1 based on the stanza calculations. 

10.1.4.4 Soft clam (Mya arenaria) (FEM group 34) 

10.1.4.4.1 1950 biomass 

Present-day soft clam biomass was estimated from data in Homer et al. (2004). The assumption was 

made that the population is limited to mesohaline waters. The derivation was based on the average of 

densities from survey sites, assumed body mass of 20 g, and that soft clam inhabits 10% of Bay waters. 

The resulting biomass was 1.66 t · km-2 · year-1. It was assumed that the biomass of soft clams in the 
91 

Chesapeake Bay has decreased considerably over the last 50 years, but time-series data are not available. 

We estimated the 1950 biomass of soft clam from an assumed ecotrophic efficiency of 0.95 and 

obtained an estimate of 6.923 t · km-2. 

10.1.4.4.2 1950 P/B 

(Abraham and Dillon, 1986) estimate a soft clam survival rate for individuals > 30mm of 90%, i.e., M = 

0.105 year-1, whereas in exploited areas the exploitation rate alone has been estimated at 50-60% (2003), 

i.e., F = 0.69–0.91 year-1. Assuming for 1950 a low exploitation rate, a Z value of 0.45 year-1 was used. 

10.1.4.4.3 1950 Q/B 

The consumption ratio for soft clam was estimated to be 2.25 year-1 by assuming a P/Q ratio of 0.20. 

10.1.4.5 Hard clam (Mercenaria mercenaria) (FEM group 35) 

10.1.4.5.1 1950 biomass 

A present-day hard clam biomass was extrapolated from Mann et al. (2003), based on abundance and 

area surveyed divided by total Bay area, assuming that a one- to two-inch clam weighs 18-20 g and that 

25% of Bay waters were exploited. The current biomass was thus estimated as 2.24 t · km-2 · year-1. For 

1950, a higher assumed biomass of 2.626 t · km-2 was used to reflect that hard clams have decreased in 

the Bay over the last decades. 

10.1.4.5.2 1950 P/B 

A total production/biomass ratio of 1.02 year-1 was estimated from an empirical equation of Thomas 

Brey, AWI, included in the Ecopath software (see Christensen et al. (2000)] for a description of the 

algorithm), assuming an average mass of 20 g, water T = 17 °C, non-motile behavior, and an average 

water depth of 6.5 m. 

10.1.4.5.3 1950 Q/B 

The consumption ratio was estimated to be 5.1 year-1 assuming a P/Q = 0.20, the same as for soft clam. 

10.1.5 Other invertebrates 

10.1.5.1 Ctenophores (Mnemiopsis spp.) (FEM group 36) 

10.1.5.1.1 1950 biomass 

Present-day biomass of 3.4 t · km-2 was estimated from data obtained from the VIMS ChesMMAP 

survey (Sellner et al., 2001). This estimate was used 1950 in lack of earlier abundance estimates. 

10.1.5.1.2 1950 P/B and Q/B 

Shushkina et al. (1989) observed that ctenophores in their study had growth rates 1.5 to 2 times greater 

than jellies. Therefore, the P/B and Q/B values for ctenophores were the values for sea nettles multiplied 

by 1.75: P/B was 8.800 year-1 and Q/B was 35.2 year-1. 
92 

10.1.5.2 Sea nettles (Chrysaora quinquecirrha) (FEM group 37) 

10.1.5.2.1 1950 biomass 

Present-day biomass for sea nettles of 0.583 t · km-2 was based upon an average of the four seasonal 

models in Baird and Ulanowicz (1989) multiplied by a conversion factor of carbon to wet weight of 

0.3% for jellies (Shushkina et al., 2000). This biomass was used for 1950. 

10.1.5.2.2 1950 P/B 

(1999) estimated a daily growth rate for Aurelia aurita of 0.053 at 5 oC to 0.15 at 16.5 oC. The average 

conservative estimate was the basis for P/B in the Chesapeake Bay, i.e., 0.053 · 365 = 19.3 year-1. 

Because they are only in the Bay for three to four months each year, an apparent P/B ≈ 5.0 year-1 was 

used in the model to lower the available production. 

10.1.5.2.3 1950 Q/B 

Matishov and Denisov (1999) found a diurnal consumption rate of 7% of biomass for the medusa in the 

Black Sea. This would translate to an annual consumption per unit biomass of 365 · 0.07 = 25.55 year-1. 

Thus, a three- to four-month residency period in the Bay implies a Q/B ≈ 8 year-1. As this value implies a 

rather high P/Q ratio, we instead used an estimated Q/B of 20 year-1 based on an assumed P/Q of 0.25. 

10.1.5.3 Microzooplankton (FEM group 38) 

10.1.5.3.1 1950 biomass 

This group chiefly consisted of rotifers, copepod nauplii, and ciliates. A present-day estimate of 0.13 t · 

km-2 was available for the Maryland portion of the Chesapeake (www.chesapeakebay.net; C. Buchanan, 

pers. comm.). Biomass was estimated based on an assumed EE of 0.95, which leads to an estimated 

value for 1950 of 6.239 t · km-2, much higher than that estimated for Maryland. 

10.1.5.3.2 1950 P/B 

Total mortality rate for microzooplankton was estimated to be 140 year-1 by local experts at a 

Chesapeake Bay Ecopath workshops (Park and Marshall, 2000). The value was used for as the 1950 

estimate. 

10.1.5.3.3 1950 Q/B 

The consumption ratio for microzooplankton was estimated to be 350 year-1 by assigning a P/Q ratio of 

0.4 to the group. 

10.1.5.4 Mesozooplankton (FEM group 39) 

10.1.5.4.1 1950 biomass 

This group was largely made up of copepods, which have been noted to reach densities greater than 

1000 nauplii per liter at estuarine turbidity maximum zones (Sellner et al., 2001). A present-day biomass 

for the Chesapeake Bay of 10.3 t · km-2 was estimated from Maryland data provided by C. Buchanan 

(pers. comm.) from the Chesapeake Bay Program data set. This estimate was used as the 1950 estimate. 
93 

10.1.5.4.2 1950 P/B 

A total mortality rate of 25 year-1 for mesozooplankton was estimated by local experts at the Chesapeake 

Bay Ecopath Workshop (1989). 

10.1.5.4.3 1950 Q/B 

The consumption ratio for mesozooplankton of 83.333 year-1 was estimated by assigning a P/Q ratio of 

0.3 to the group. 

10.1.5.5 Other suspension feeders (FEM group 40) 

10.1.5.5.1 1950 biomass 

Biomass was based on the ‘other suspension feeders’ group in Baird and Ulanowicz (2000), converted to 

dry weight, then wet weight using ratios listed for annelids and zooplankton in Jørgensen et al. (2000). 

The 1950 estimate was 6.0 t · km-2. 

10.1.5.5.2 1950 P/B 

A P/B for ‘other suspension feeders’ of 2.0 year-1 was taken from a value for annelids in Jørgensen et al.

(2000). 

10.1.5.5.3 1950 Q/B 

The consumption ratio for ‘other suspension feeders’ was estimated to be 8.0 year-1 by assigning a P/Q 

ratio of 0.25 to the group. 

10.1.5.6 Other infauna/epifauna (FEM group 41) 

10.1.5.6.1 1950 biomass 

The biomass for ‘other infauna/epifauna’ was estimated by Ecopath to be 66.675 t · km-2, assuming a 

group ecotrophic efficiency of 0.9. 

10.1.5.6.2 1950 P/B 

The P/B for ‘other infauna/epifauna’ of 1.0 year-1 was taken from the value for annelids given in 

Jorgensen et al. (2000) 

10.1.5.6.3 1950 Q/B 

Q/B for ‘other infauna/epifauna’ was estimated to be 5.0 year-1 by assigning a P/Q ratio of 0.2. 

10.1.5.7 Primary producers 

10.1.5.7.1 Phytoplankton (FEM group 44) 

10.1.5.7.2 1950 biomass 

Phytoplankton biomass for 1950 was assumed at 27 t · km-2. 

10.1.5.7.3 1950 P/B 

Phytoplankton P/B was assumed to be 160 year-1. 
94 

10.1.5.8 Benthic algae (FEM group 42) 

10.1.5.8.1 1950 biomass 

Biomass of benthic algae was estimated to be 1.717 t · km-2 based on an assumed EE of 0.9. 

10.1.5.8.2 1950 P/B 

P/B for benthic algae was assumed at 80 year-1. 

10.1.5.9 Submerged aquatic vegetation (SAV) (FEM group 43) 

10.1.5.9.1 1950 biomass 

Four main groups dominate the macrophyte communities within the Chesapeake Bay; one group, 

eelgrass (Zostera marina), dominates these. Biomass ranges from a high in late summer of more than 

20,000 t to a low of 5,000 t in December (Sellner et al., 2001). Total SAV biomass, averaged annually 

for 1996, was 22,300 t in approximately 25,000 ha, or 2.1% of the total Chesapeake Bay area. Biomass 

is entered as 419 t · km-2, in a proportion of the total area corresponding to 0.021, and is also used for the 

1950 model due to the lack of time trend information (Sellner et al., 2001). 

10.1.5.9.2 1950 P/B 

Mortality for Z. marina was estimated in a similar system in Japan (Oshima et al., 1999) as Z = P/B = 

5.11 year-1. 

10.1.6 Model pedigree 

Pedigree was defined for all input parameters as described in the EwE User’s Guide (Christensen et al., 

2004). The pedigree indices were used to obtain confidence interval estimates for the input parameters 

(Table 31). The pedigree index was estimated as 0.45. 

10.1.7 Prices 

Prices for the major species exploited in the Chesapeake Bay were obtained from the Sea Around Us 

global price database (www.seaaroundus.org). The price database includes year-specific as well as 

consumer price index-corrected values for the various commodities. Prices for 2000 were used; the 

intention is to use prices for evaluating future policy options (Table 32). 

For the U.S. recreational fisheries overall, Sumaila (pers. comm., UBC Fisheries Centre) estimated that 

recreational catches were worth approximately 32 times as much per unit weight of the catch (in US 

dollars). In this model, a much more conservative factor of two was used for the value of 

recreational/commercial fisheries. 
95 

10.2 Appendix B: Time Series 

10.2.1 Commercial fishes 

10.2.1.1 Striped bass 

10.2.1.1.1 1950-2002 catches 

For the most recent striped bass catch time series, Marine Recreational Fisheries Statistics Survey 

(MRFSS) estimates of recreational catch of striped bass for Maryland and Virginia combined from 1981 

to 2002 were used. For the period from 1950 to 1980, the multispecies production method was used to 

estimate a recreational to commercial ratio to determine the recreational catch. For commercial catch of 

striped bass, NMFS estimates for the Chesapeake Bay from 1950 to 2002 were added to a discards 

estimate, determined as 33% of the commercial catches (NFSC, 2003). The catch distribution between 

resident (12-83 months) and migrant (84+ months) striped bass was estimated from the coastwide 

catches reported by ASMFC (2003b) Tables 1b, 12, and 13, estimating total weight in catch from the 

numbers in the catch, 1982-2002, and average weight. This resulted in an average distribution by weight 

in the catch of 49% for resident and 51% for migrant striped bass (standard deviation 13%). 

An earlier version of the striped bass catch time series is also included in the model. This version uses a 

recreational:commercial catch ratio for the years 1982-2002 based on Table 1 of the 2003 ASMFC 

Striped Bass Advisory Report (ASMFC, 2003a). This ratio is then applied to the NMFS commercial 

data for the Chesapeake Bay to estimate the recreational catch for these years. Prior to 1982, the median 

of the recreational to commercial catch ratio for the years 1982-2002 was used to estimate the 

recreational catch. The resident and migrant portions of the catch were determined for 1982-2002 based 

on an average percent resident striped bass calculated from values in Tables 10 and 11 of the 2003 

ASMFC Striped Bass Advisory Report (ASMFC, 2003a). 

10.2.1.1.2 Other 1950-2002 time series 

A number of time series were available for striped bass in the Chesapeake Bay. There are two striped 

bass young-of-the-year biomass estimates included in the model. The most recent version was calculated 

using values from Tables 13 and 18a of the 2005 Stock Assessment Report for Atlantic Striped Bass 

(ASMFC, 2005b). The earlier version was calculated with data from Tables 11 and 16 of the 2003 Stock 

Assessment Report for Atlantic Striped Bass (ASMFC, 2003a). Resident and migratory biomass are 

represented by data from the ASMFC striped bass stock assessment for 2003, the ASMFC striped bass 

stock assessment for 2005 (ASMFC, 2005b), and multispecies production method estimates. Biomass 

data for striped bass are presented in Table 4. Growth parameters are given in Table 3. 

The combined fishing mortality series for both stocks were estimated for 1982-2002 with data from the 

ASMFC 2005 stock assessment (ASMFC, 2005b). The data from the ASMFC are estimated from a 

virtual population analysis model (VPA), i.e., it recreates the population history by receding back in 

time and adding catches back into the population, based on an assumed natural mortality. Values of 

striped bass resident combined F for 1959–1984 are from Gibson (1993) Table 3, Run 2, and migratory 

combined F for 1959-1981 from Gibson (1993) Appendix Table 3, Run 3. The fishing mortalities for the 

period before 1959 are values estimated by Ecopath. A second migratory fishing mortality for 1988-

2002 is included for the Maryland spring spawning stock in the Chesapeake Bay. This estimate is 
96 

provided by Table 24 of the 2005 Stock Assessment Report for Atlantic Striped Bass (ASMFC, 2005b). 

Fishing mortality data for striped bass are presented in Table 5. 

10.2.1.1.3 Calculation of abundance indices 

Most of the recreational species indexed did not have fishing seasons. Striped bass was the exception, 

and the blanket application of the technique to make annual estimates probably led to some bias, 

because closed seasons were imposed until 1996 and the 18-inch minimum did not apply to resident 

stock for the entire season (a 28-inch minimum would apply early) until about 1999. Generally, it was 

only possible to model a stable or very slightly declining striped bass biomass in the Bay with the annual 

MRFSS index. A very different trend for resident striped bass was found if the September-October wave 

of the MRFSS data was used. This is the period when fishing seasons were always opened during the 

cautious period following the moratorium; an 18-inch size limit was consistently applied; and migratory 

fish had left the Bay. On a kg harvested per trip basis there has been a drop in relative abundance during 

this wave. If kg harvest per open day is used as an estimate of relative abundance, a dramatic, steady 

decline is displayed. Over the same time period, migratory biomass has increased in the Bay (May-June 

wave data). These would be much larger fish than residents. A general problem with hindcasting striped 

bass landings for the Bay has been the unknown relationship of recreational landings to commercial 

landings prior to the MRFSS survey. Eight estimates of Maryland recreational landings were compiled 

from creel surveys conducted between 1962 and 1984. These estimates were used in regression analysis 

with commercial landings to estimate the trend in recreational harvest back to 1950. The regression 

indicated that recreational landings became a decreasing fraction of total landings between 1962 and 

1984 (r2 = 0.77, P < 0.004). This relationship could be used to develop the long-term striped bass model 

described briefly below. 

A striped bass kg harvest per open day estimate of relative abundance was developed along with a 

biomass dynamic model incorporating an additional loss function (a function of lesions presenceabsence which was used as an indicator of health and subsequent mortality). It was tuned to bay 

estimates of F and M from tagging. Landings consisted of MRFSS and commercial reports for 1950-

2004, recreational landings (Maryland and Virginia) predicted from the analysis described previously 

(1962-1980), and recreational landings back to 1950 predicted by using the 1962 ratio as a constant. 

Lesions were invoked as an additional increasing loss term from 1994 to 2004 and held constant at a 

very low level for all years previous. The analysis was conducted in three stages. An initial estimate of 

production parameters (r and K) and biomass was made for 1991-2004. These estimates of biomass were 

then substituted as “observed” values and a model was run for the MD moratorium through present 

(1985-2004). A final run used these biomass values as “observations” and the predicted landings back to 

1950 to derive a history of resident biomass by running the biomass dynamic model in reverse. Bear in 

mind that regulations prior to the moratorium were minimal (12- to 14-inch size limit, creel limits were 

not in place until the 1980s, and commercial harvests were restricted minimally) and were restrictive 

afterwards (18-inch size limit, two fish creel, strict seasons, mesh restrictions). It was assumed that 

production parameters would be the same regardless of size changes, but exploitable biomass prior to 

1985 would be 12-inch fish and larger, while post-moratorium exploitable biomass would be 18-inch 

and larger striped bass. 
97 

10.2.1.2 Bluefish: YOY and adult (Pomatomus saltatrix) (FEM groups 4 and 5) 

10.2.1.2.1 1950–2002 catches 

Total catch of bluefish for the Chesapeake Bay was determined by adding the recreational catch for 

Maryland and Virginia as reported by MRFSS to commercial catch of bluefish in the tidal waters of the 

Chesapeake Bay, as reported by NMFS for the years 1982-2002. The recreational to commercial ratio 

for years prior to 1982 was estimated using the median value for years 1982-2002 from Table 1 of the 

bluefish assessment in the 41st SAW Assessment Report (NFSC, 2005), 2.52. The recreational to 

commercial ratio was then applied to the commercial catch values for the Chesapeake reported by the 

National Marine Fisheries Service (Fisheries Statistics and Economics Division, Silver Spring, 

Maryland) for 1950 through 2002 to determine recreational portions of the Chesapeake catch. These two 

values were added together to determine total catch for the Chesapeake in the time period. It was 

assumed that there were no commercial discards, based on the 41st SAW Assessment Report (NFSC, 

2005). 

A second estimate of bluefish catch was assessed using stock reduction analysis. 

10.2.1.2.2 Other 1950–2002 time series 

Bluefish time series data are presented in Table 7. A relative abundance index for bluefish in the 

Chesapeake Bay region specifically was unobtainable. Consequently, it was assumed that coast-wide 

abundance indices for bluefish are representative. Both Ecosim and single-species assessment models 

were fitted to the same information. Two different sources of time-series information were available: 

Bluefish YOY and adult biomass were estimated using data from Tables 10 and 20 of the NFSC 41st

SAW (NFSC, 2005) and YOY and adult bluefish biomass was estimated using stock reduction analysis. 

Coast-wide trawl survey information (in numbers landed per tow and kg landed per tow) were taken 

from Lee (2003a) for the period 1972-2002. Total fishing mortality rates were estimated using a singlespecies assessment model, where the combined recreational and commercial catch data was used to 

drive the assessment model. Furthermore, an index of 0- to 12-month bluefish biomass and adult 

biomass for the entire time period was constructed from the age-structured model. Growth, size 

selectivity, and maturity parameters used in the assessment model were taken from Salerno et al. (2001),

and length-weight relationships from Wigley et al. (2003) (provided in Table 6). Prior to 1972, there is 

no survey information on relative abundance, and the uncertainty associated with the bluefish abundance 

during 1950 to 1972 is high. Reported landings prior to 1970 suggest that bluefish abundance was 

relatively low and as such, the initial biomass ratio to the unfished equilibrium was estimated to be very 

low. 

10.2.1.3 Weakfish: YOY and adult (Cynoscion regalis) (FEM groups 6 and 7) 

10.2.1.3.1 1950–2002 catches 

Information about commercial catches of weakfish in the Chesapeake region was obtained from the 

NOAA Fisheries Statistics and Economics Division online database (www.st.nmfs.gov) for the years 

1950-2002. A ratio between recreational and total catches for weakfish on the Atlantic coast was 

obtained from Tables 1 and 2 of the ASMFC 2003 fishery management plan review for weakfish (Spear

et al., 2003), and estimated to average 27% (median 28%) for the period 1982-2002. This ratio was used 

for all years prior to 1982, while the actual ratio was used for 1982-2002. 
98 

10.2.1.3.2 Other 1950–2002 time series 

The multispecies production method was used to provide a biomass estimate for weakfish. Adult fishing 

mortality and biomass estimates were also estimated using stock reduction analysis. Weakfish biomass 

and fishing mortality data are presented in Table 9. Growth parameters are given in Table 8. 

10.2.1.4 Atlantic croaker (Micropogonias undulatus) (FEM group 8) 

10.2.1.4.1 1950–2002 catches 

Commercial catches of Atlantic croaker for the Chesapeake region were extracted from the NOAA 

Fisheries Statistics and Economics Division online database (www.st.nmfs.gov) for the years 1950-

2002. A ratio between recreational and commercial catches for Atlantic croaker was calculated from 

data in Table G1 of the 2003 Atlantic Croaker Stock Assessment report (ASMFC, 2005a), with an 

average value of 0.22, a standard deviation of 0.99, and a median value of 0.23 for the period 1973-

2002. This recreational to commercial catch ratio was used to determine the total catch based on NMFS 

data for the period 1950 through 1980. For 1981-2002, MRFSS estimates of recreational catch of 

Atlantic croaker for Maryland and Virginia combined from 1981-2002 were used. 

Catch data was also estimated using data from the NOAA Fisheries Statistics and Economics Division 

online database (www.st.nmfs.gov) for the years 1950-2002 with a recreational:commercial ratio from 

the ASMFC 2004 Atlantic Croaker Stock Assessment (ASMFC, 2005a) for the years 1981-2002. For the 

time period 1950-1980, the median recreational:commercial ratio for 1981-2002 was used to estimate 

recreational catch. 

The catch series was used to obtain fishing mortality rates over time using the Ecosim stock reduction 

analysis. 

10.2.1.4.2 Other 1950–2002 time series 

Time-series abundance data for Atlantic croaker were available from the VIMS Trawl Surveys 

(www.fisheries.vims.edu/trawlseine), and are presented in Table 10. Atlantic croaker biomass and 

fishing mortality estimates for the mid-Atlantic region for the years 1973 through 2002 were available 

from the ASMFC 2003 Atlantic Croaker Stock Assessment (ASMFC, 2005a). 

10.2.1.5 Black drum (Pogonias cromis) (FEM group 9) 

10.2.1.5.1 1950–2002 Catches 

To obtain time series for the black drum, commercial catches were extracted from the NMFS statistics 

for the Chesapeake region and recreational catches since 1981 for Maryland and Virginia state waters 

were extracted from the Marine Recreational Fishery Statistics Survey (MRFSS). A median 

recreational:commercial ratio value for the years 1981-2002 of 0.75 was used for the years 1950-1980. 

The catches were used to calculate F estimates using the stock reduction analysis in Ecosim. 

10.2.1.5.2 Other 1950–2002 time series 

No time-series data were available for black drum (apart from the catches discussed above). 
99 

10.2.1.6 Summer flounder (Paralichthys dentatus) (FEM group 10) 

10.2.1.6.1 1950–2002 catches 

Commercial catches for ‘flatfish’ in the Chesapeake region were extracted from the NOAA Fisheries 

Statistics and Economics Division online database (www.st.nmfs.gov) for the years 1950-1957. The 

flatfish category was assumed to be dominated by summer flounder, the main commercial flatfish. 

Recreational catches since 1981 for Maryland and Virginia state waters were extracted from the Marine 

Recreational Fishery Statistics Survey (MRFSS). Prior to 1981, a recreational:commercial ratio was 

used to estimate the recreational portion of the catch. This ratio is the median value of 

recreational:commercial ratios determined for 1998-2004 based on data from the 41st SAW (NFSC, 

2005). 

10.2.1.6.2 Other 1950–2002 time series 

The summer flounder stock assessed by NFSC (2002a) was considered to extend from Cape Hatteras to 

New England. The summer flounder in the Chesapeake Bay represent a subset of that stock and not a 

separate population. Therefore, the stock assessment for the larger population was used for indicating 

the changes in the summer flounder group in the model. 

The first biomass series (1982 and after) used in the model were taken from the results of a VPA 

(Figures A10 and A11), which used ADAPT as the calibration method (NFSC, 2002a). An additional 

biomass series was added, including data from the 41st Northeast Regional Stock Assessment Workshop 

Assessment Report (NFSC, 2005). 

Fishing mortality data from the 35th Northeast Regional Stock Assessment Workshop Assessment 

Report (NFSC, 2002b) was used for summer flounder for the period from 1982 through 1997 and from 

the 41st Northeast Regional Stock Assessment Workshop Assessment Report (NFSC, 2005) for the 

period from 1998 through 2002. Summer flounder biomass data are presented in Table 11. 

10.2.1.7 Atlantic menhaden (Brevoortia tyrannus): YOY and adult (FEM groups 11 and 12) 

10.2.1.7.1 1950–2002 catches 

There are two main menhaden fisheries, a reduction fishery (major component) and a minor bait fishery 

(pound net gear type). The proportion of the Atlantic catches taken in the Bay has increased from 20% 

before the mid 1960s, to 50% in the 1970s, to more than 80% since 1980, based on the NMFS catch 

data. There are two catch series based on the NMFS catch database: 1) assume a constant proportion 

(0.4) of the menhaden catches being taken in the Chesapeake Bay, and 2) use the Chesapeake Bay 

catches as reported. There is a catch series calculated using the values from Table 2.2 of the Analyses of 

the Status of the Atlantic Menhaden Stock (Vaughan et al., 2002). Specifically, the total weight of the 

catch on the Atlantic coast for a given year (in 1000 mt) is multiplied by the sum product of the weight 

at age in the fishery of age 1-8 menhaden in that year. There are also two catch series for the 

Chesapeake split into stanzas, one for the 0-1 age group and one for the 2+ age group, based on the 

ASMFC 2003 Menhaden Stock Assessment (ASMFC, 2004) for the Atlantic Coast, NMFS commercial 

data for the Chesapeake, and estimates of the proportion of the catch taken in the Chesapeake. 
100 

10.2.1.7.2 Other 1950–2002 time series 

Relative biomass and F series were extracted from figures in the ASMFC stock assessment report 

(ASMFC, 2004). The estimates are representative for the total Atlantic menhaden population, not just 

the fraction occurring in the Chesapeake Bay, as this fraction is difficult to estimate. This is a minor 

problem, as more than 80% of the catches have been taken in the Bay in recent decades. Since several of 

the major predators on menhaden also move in and out of the Bay, it may well be best to include the 

total populations. 

Relative biomass and fishing mortality for menhaden was estimated in several time series. From the 

ASMFC 2004 stock assessment, the Potomac River Fisheries Commission menhaden pound net index 

was used as an estimate of age 2+ abundance, and the ASMFC juvenile coast-wide index as an estimate 

of age 0-1 abundance. An estimate of both the age 0-1 and 2+ relative biomass and fishing mortality was 

made based on estimates from the ASMFC 2003 stock assessment (ASMFC, 2004). Another fishing 

mortality time series was used from Figure 7.8 of the ASMFC 2003 menhaden stock assessment 

(ASMFC, 2004). The multispecies production method was used to estimate Chesapeake Bay and 

Atlantic biomass as well as fishing mortality values for Atlantic menhaden. Menhaden biomass and 

fishing mortality data are presented in Table 12. 

10.2.1.8 Alewife/herring (Alosa pseudoharengus/Clupea harengus) (FEM group 13) 

10.2.1.8.1 1950–2002 catches 

Commercial catches of alewife for the Chesapeake region were extracted from the NOAA Fisheries 

Statistics and Economics Division online database (www.st.nmfs.gov/st1) for the years 1950-2002. It 

was assumed that there were no recreational fisheries for these species, because neither species is 

included in the NOAA recreational fisheries database available through the same link as the commercial 

catches. The catch series was used to estimate F values based on the stock reduction analysis of Ecosim. 

10.2.1.8.2 Other 1950–2002 time series 

We obtained a relative measure of blueback herring abundance from fish lifts at Conowingo Dam (St. 

Pierre, pers. comm.). American shad and blueback herring biomass estimates are presented in Table 13. 

The effectiveness of the fish lifts is less in wet years, (e.g., 2000, 2002-2004) than in years with drier 

spring months, (e.g., 1997-1999, 2001). 

10.2.1.9 American eel (Anguilla rostrata) (FEM group 14) 

10.2.1.9.1 1950–2002 catches 

Commercial catches of American eel for the Chesapeake region were extracted from the NOAA 

Fisheries Statistics and Economics Division online database (www.st.nmfs.gov) for the years 1950-

2002. The recreational catch of American eel is very limited (Munger et al., 2002); the species is not 

included in the NOAA recreational catch database, and it is therefore ignored here. It may be noted that 

from 1987 to 1996, Maryland, Virginia, and the Potomac River accounted for approximately 60% of the 

American eel catch in the United States (ASMFC, 2000). 

Catches were used to estimate F values using the stock reduction analysis of Ecosim. 
101 

10.2.1.10 Catfishes (Ameiurus catus, A. nebulous, A. natalis, Ictalurus punctatus, I. furcatus, 

Pylodictis olivaris) (FEM group 15) 

10.2.1.10.1 1950–2002 catches 

Commercial catch for the combined catfish group was based on catfishes and bullheads in the NOAA 

Fisheries Statistics and Economics Division online database (www.st.nmfs.gov) for the Chesapeake 

region for the years 1950-2002. Since catfish are limited to freshwater and estuarine areas, the 

Chesapeake region as defined in this database should be representative for the Chesapeake Bay. 

Information about recreational catches in the Bay was not available, and the assumption was made that 

the catches were miniscule and could be ignored. Hence, the commercial catches were assumed to be 

representative for the group (Tables 1 and 2). 

Fishing mortalities were estimated from the catches using the stock reduction analysis in Ecosim. 

10.2.1.11 White perch: YOY and adult (Morone americana) (FEM groups 16 and 17) 

10.2.1.11.1 1950–2002 catches 

The catch series for white perch was estimated using the multispecies production method and was based 

on commercial and recreational harvest data for Maryland, Virginia, and the Potomac River. 

10.2.1.11.2 Other 1950–2002 time series 

Biomass series for white perch age groups 0 and 1+ were available for ‘upper rivers’ from the Virginia 

Institute of Marine Science juvenile trawl survey. These were used here for comparison with the Ecosim 

simulations. The multispecies production method was used to estimate catch, biomass, and fishing 

mortality. White perch biomass and fishing mortality estimates are presented in Table 14. 

10.2.1.12 Spot (Leistomus xanthurus) (FEM group 18) 

10.2.1.12.1 1950–2002 catches 

A time series of spot catches in the Chesapeake Bay was estimated from the commercial catches in the 

NOAA Fisheries Statistics and Economics Division online database (www.st.nmfs.gov), and the ratio 

between recreational and commercial catches reported by Desfosse et al. (2001). A second catch series 

was estimated using the multispecies production method, with combined estimates of recreational and 

commercial catch from NMFS, MRFSS, and the VIMS trawl survey. 

10.2.1.12.2 Other 1950–2002 time series 

Estimates of spot relative biomass for the period 1950-2002 were obtained from a stock reduction 

analysis based on catches and tuned to the mean of the VIMS survey estimates for age groups 0 and 1+ 

(Table 15). The multispecies production method was used to estimate catch, fishing mortality, and 

biomass estimates for spot from 1981 through 2000. We were, however, not able to use the F-series as 

this would lead to a major overestimation of catches. 
102 

10.2.1.13 American shad (Alosa sapidissima) (FEM group 19) 

10.2.1.13.1 1950–2002 catches 

Commercial catches of American shad for the Chesapeake region were extracted from the NOAA 

Fisheries Statistics and Economics Division online database (www.st.nmfs.gov) for the years 1950-

2002. The recreational catch was assumed to be minimal in comparison and therefore was ignored. The 

catches were used to obtain F values based on the stock reduction analysis in Ecosim. 

10.2.1.13.2 Other 1950–2002 time series 

An estimate of relative biomass was provided by the geometric mean of fish caught at the Conowingo 

Fish Lift on the Susquehanna River in the upper Chesapeake Bay (Bob Sadzinski, MDNR, pers. comm.). 

This estimate does not adequately represent trends Bay-wide; however, these are not available at this 

time. 

10.2.2 Other fishes 

10.2.2.1 Bay anchovy (Anchoa mitchilli) (FEM group 20) 

10.2.2.1.1 1950–2002 time series 

Two time series of relative abundance for bay anchovy were used for comparison with the Ecosim 

simulations, both from surveys. One is from the Maryland DNR juvenile seine survey going back to 

1958, the other is from the VIMS trawl surveys from 1978. These time series are presented in Table 16. 

10.2.2.2 Other flatfishes (FEM group 21) 

No time-series data were included in the CBFEM for other flatfish. 

10.2.2.3 Gizzard shad (Dorosoma cepedianum) (FEM group 22) 

10.2.2.3.1 1950–2002 time series 

The multispecies production method was used to estimate fishing mortality and biomass for gizzard shad 

from 1958 through 2000. F-values were impressively low and we chose to ignore them as they were 

below detection range. Commercial catches of gizzard shad for the Chesapeake region were extracted 

from the NOAA Fisheries Statistics and Economics Division online database (www.st.nmfs.gov) for the 

years 1950-2002. 

Gizzard shad provided an additional exception to the general assessment framework. Five count-based, 

fishery-independent indices were considered for gizzard shad: the Maryland gizzard shad juvenile index 

(1958-2004; E. Durell, Maryland Department of Natural Resources, pers. comm.), two estimates of 

catch per effort at the Conowingo fish lifts (east or west catch per lift hour; 1991-2000 or 1972-2000, 

respectively; R. Sadzinski, MD DNR, pers. comm.), and Head-of-Bay or Potomac River catch per gill 

net hour (1985-2005; P. Piavis, MD DNR, pers. comm.) from the spring striped bass spawning stock 

survey (Warner et al., 2006). They likely represented different sizes on average. Various trial versions 

combining all or some of the indices were tried, but eventually a long-term model (1958-2004) was 

developed based on the Maryland juvenile index time-series (GSJI; geometric mean for Choptank, 

Nanticoke, and Potomac rivers and Head-of-Bay). This model was acceptable because it was the only 
103 

one that came close to supplying an estimate of intrinsic rate of increase (0.66) that made sense for a fish 

with a short life-span. Most model runs were suited to much more long-lived species. The GSJI was 

comprised of young-of-year through large adults; some extra variability was probably caused by the 

inclusion of YOY that would not have been present in the harvest. Exploratory analyses indicated that 

NO3 loading estimates of Hagy et al. (2004) was significantly related to the GSJI (1958-2000, r2

 = 0.38, 

P < 0.001). This relationship should be biologically significant because gizzard shad are well suited to 

taking advantage of eutrophic conditions because of their ability to feed on detritus, algae, and 

zooplankton. 

Hagy et al. (2004) provided estimates of NO3 loading through 2000. Loading was estimated for 2001-

2004 from the relationship of mean annual Susquehanna River flow at Conowingo Dam and NO3

loading estimates during 1968-2000. NO3 loading was first plotted versus river flow for 1945-2000. 

Residuals from the regression of these data (r2

 = 0.47, P < 0.001) increased steadily from positive to 

negative over the course of the time-series and intercepted the x-axis at about 1968. The NO3 loading 

data were then plotted as 1945-1968 and 1969-2000 time series. These data strongly suggested two 

distinct relationships of flow and NO3 loading (different slopes). The 1969-2000 time-series was used 

and the fit of the regression of flow and NO3 loading improved (r2

 = 0.80, P < 0.001); however, a serious 

serial trend was evident in the residuals. Autoregression analysis (Proc Autoreg in SAS) was used with a 

one-year lag, and this removed the patterning in the residuals and improved fit (r2

 = 0.94, P < 0.001). 

This adjusted relationship was used to estimate NO3 loading for 2001-2005 from annual mean flow. 

Three types of biomass dynamic models were assessed. The first was the standard Schaefer logistic 

model, the second was a logistic model with a predator function (striped bass predation as a linear 

function of bass biomass), and the third used standardized NO3 loading (standardized to the time-series 

median) as a multiplier for the intrinsic rate parameter. Rose (2004) used a similar approach to model 

changes in cod productivity. A run with both predation and changing production due to shifting NO3

loading was made as well. Ultimately, the version with changing production and without predation was 

selected for gizzard shad. This model is described by equation (18) above. 

A combined bay-wide (Maryland and Virginia) commercial harvest was used; discard estimates were 

not available. A genetic algorithm super solver (Evolver, Palisade Corporation) was used to estimate 

parameters. The previous year’s estimated index was then used to predict the following year’s estimate. 

The spreadsheet version of the model combined with the genetic algorithm provided a great deal of 

flexibility for trying different model variations. The time-series for this model was shortened to 1964-

2004, partially because a menhaden model with this time-span, was used as the basis for this model and 

partially to minimize patterning in residuals that occurs in the earliest years of the time-series. This 

patterning could reflect shifts from varying to fixed stations and from a single sampling round to two 

rounds in the seine survey. 

The GSJI model index was numeric, while landings were in weight. Landings were converted to 

numbers landed by converting the mean length of gizzard shad sampled from Potomac River pound nets 

during summer 1995 (Austin et al. 1996) to mean weight (0.26 kg) based on the length-weight 

relationship described in Fishbase. Once abundance was estimated, it was converted back to biomass by 

using the 1996 mean weight. 

Maximum biomass of gizzard shad estimated by this method was not large and was about 4,000 mt. This 

is considerably less than that estimated for Atlantic menhaden and bay anchovy. 
104 

10.2.2.4 Reef-associated fishes (FEM group 23) 

10.2.2.4.1 1950–2002 catches 

Catches for these species are very limited. For example, for tautog, the total catch reported in the NOAA 

commercial catch data is 316 t for the years 1950-2002 combined. Consequently, we did not include 

catches in the model. 

10.2.2.5 Non-reef-associated fishes (FEM group 24) 

No time-series data were included in the CBFEM for non-reef-associated fish. 

10.2.2.6 Littoral forage fishes (FEM group 25) 

No time-series data were included in the CBFEM for littoral forage fish. 

10.2.2.7 Sandbar shark (Carcharhinus plumbeus) (FEM group 26) 

No time-series data were included in the CBFEM for sandbar shark. 

10.2.2.8 Other elasmobranchs (F.E.M. group 27) 

No time-series data were included in the CBFEM for other elasmobranchs. 

10.2.3 Birds and other vertebrates 

10.2.3.1 Piscivorous seabirds (FEM group 28) 

No time-series data were included in the CBFEM for piscivorous seabirds. 

10.2.3.2 Non-piscivorous seabirds (FEM group 29) 

No time-series data were included in the CBFEM for non-piscivorous seabirds. 

10.2.4 Commercial invertebrates 

10.2.4.1 Blue crab: YOY and adult (Callinectes sapidus) (FEM groups 30 and 31) 

10.2.4.1.1 1950–2002 catch 

Catch data were included from the 2005 Stock Assessment of the Blue Crab in Chesapeake Bay (Miller 

et al., 2005). The total Chesapeake Bay catch time series were from the assessment data presented in the 

Chesapeake Bay Stock Assessment Committee report (2003a). 

10.2.4.1.2 Other 1950–2002 time series 

Effort data for 1945-1994 were available from CBSAC assessments (Rugolo et al., 1997). 

The age 0 (YOY) and age 1+ (adult biomass) time series were from the assessment data presented in the 

Chesapeake Bay Stock Assessment Committee report (2003a). Note that the original abundance indices 
105 

range around an average value of zero and may, therefore, be negative in some years, but have been rescaled for this study. Blue crab biomass, effort, and fishing mortality estimates are presented in Table 

17. 

10.2.4.2 Eastern oyster (Crassostrea virginica) (FEM groups 32 and 33) 

10.2.4.2.1 1950–2002 catch 

Oyster catches were extracted from the NOAA online database (www.st.nmfs.gov) including all oyster 

catches for Maryland and Virginia. Recreational catch estimates were not available, and were omitted 

from the analysis. These estimates are provided in the stock reduction analysis. A catch estimate for 

oysters was also estimated using the multispecies production method. 

10.2.4.2.2 Other 1950–2002 time series 

Estimates of oyster catch per unit effort (CPUE) for the Maryland harvest were made available by the 

Maryland DNR Shellfish Division and included in the oyster stock reduction analysis (Table 18). Oyster 

abundance and fishing mortalities were estimated from a stock reduction analysis tuned to a CPUE 

series and are shown in the same table. The oyster fishing mortality combined time series includes data 

from the stock reduction analysis for the years 1950 through 1974 and data from the multispecies 

production method assessment for the years 1975 through 1999. A multispecies production method 

estimate was also made of catch and biomass for the years 1975 through 2000. 

10.2.4.3 Soft clam (Mya arenaria) (FEM group 34) 

10.2.4.3.1 1950–2002 catch 

Commercial catch data were extracted from the NOAA Fisheries Commercial catch database 

(www.st.nmfs.gov), soft clam for the Chesapeake region, and used to force the simulations. 

10.2.4.3.2 Other 1950 - 2002 time series 

No actual time-series data were available for soft clam. There are indications – perhaps evidence – that 

flooding caused by hurricanes may severely affect soft and hard clams in the Chesapeake Bay. A forcing 

function was constructed to impact the P/B of the two groups based on the occurrence and severity of 

flooding caused by hurricanes in the Maryland/Virginia region. An overview of the hurricanes is 

presented (Table 28), while the time series with assumed relative P/B values for forcing the simulations 

is given in Table 29. 

10.2.4.4 Hard clam (Mercenaria mercenaria) (FEM group 35) 

10.2.4.4.1 1950–2002 catch 

Commercial catches were extracted from the NOAA Fisheries Statistics online database 

(www.st.nmfs.gov), based on quahog, or hard clam, Mercenaria mercenaria, for the Chesapeake region, 

and used to force the simulations. 
106 

10.2.5 Other invertebrates 

10.2.5.1 Ctenophores (Mnemiopsis spp.) (FEM group 36) 

No time-series data were included in the CBFEM for ctenophores. 

10.2.5.2 Sea nettles (Chrysaora quinquecirrha) (FEM group 37) 

No time-series data were included in the CBFEM for sea nettles. 

10.2.5.3 Microzooplankton (FEM group 38) 

10.2.5.3.1 1950–2002 time series 

Estimated of relative abundance of microzooplankton were based on the estimate from CRC workshop 

(Sellner, 2001) that the median rotifer and copepod nauplii biomass value is roughly 50% of the 

mesozooplankton biomass value over all station-dates available in the Chesapeake Bay Program 

database. Zooplankton biomass estimates are presented in Table 19. 

10.2.5.4 Mesozooplankton (FEM group 39) 

10.2.5.4.1 1950–2002 time series 

Estimates of relative abundance for mesozooplankton are based on data from the Chesapeake Bay 

Program database, weighed by station. Zooplankton biomass estimates are presented in Table 19. 

10.2.5.5 Other suspension feeders (FEM group 40) 

No time-series data were included in the CBFEM for other suspension feeders. 

10.2.5.6 Other infauna/epifauna (FEM group 41) 

No time-series data were included in the CBFEM for other infauna/epifauna. 

10.2.6 Primary producers 

10.2.6.1 Phytoplankton (FEM group 44) 

10.2.6.1.1 1950–2002 time series 

A relative chlorophyll abundance measure, 1950-1994, was estimated based on Harding and Perry 

(1997), and was used for comparison with the Ecosim simulations. Phytoplankton chlorophyll content is 

presented in Table 30. 

10.2.6.2 Benthic algae (FEM group 42) 

No time-series data were included in the CBFEM for benthic algae. 
107 

10.2.6.3 Submerged aquatic vegetation (SAV) (FEM group 43) 

10.2.6.3.1 1950-2002 time series 

SAV monitoring data is included from the VIMS SAV Monitoring Program by Chesapeake Bay 

Program segment from 1971-2002. Only those segments that were fully surveyed were included in the 

SAV index used in the CBFEM. For each year, SAV acreage for those segments that were fully 

surveyed was totaled and divided by the total surface area of the segments. This resulted in a single 

value for each year of the survey from 1971-2002, representing the ratio of SAV identified/SAV 

surveyed (Table 34). The year 1979 was not included in the index, as values for this year were based on 

only 2 segments and created outlier values. 

10.2.7 Nutrient loading 

An estimate of monthly primary production rate affected by nutrient loading was obtained based on a 

spatial hydrodynamic model, CBREEM (Ma et al., in prep; Ma et al., MS). The model runs are still of a 

preliminary nature, but are used in the absence of any other time-series information describing 

environmental loading factors through the modeled period. 

11 Tables related to Appendices A and B 

11.1 Catches, species groups 1-12 

Table 1. Estimated catches Groups 1-12 (t · km2 · year-1) for the Chesapeake Bay since 1950 used in the 

Chesapeake Bay Fisheries Ecosystem Model. Estimated from a variety of sources as described in the 

text. 

Group SB resident SB 

resident 

SB 

Migratory 

SB 

migratory 

Bluefish Bluefish Weakfish Atl. 

croaker 

Atl. 

croaker 

Black 

drum 

Summer 

flounder 

Menhaden 

Age 0-

1 

Menhaden 

Age 

2+ 

Menhaden 

ASMFC 

Menhaden 

CB 

Group no. 2 2 3 3 5 5 7 8 8 9 10 11 12 12 12 

1950 

0.463 0.792 0.427 0.825 0.116 0.067 

0.286 

0.656 0.542 

0.005 

0.209 12.706 12.71 7.753 

1951 

0.329 0.549 0.303 0.572 0.073 0.042 

0.137 

0.434 0.358 

0.006 

0.212 13.954 13.95 5.78 

1952 

0.271 0.442 0.25 0.46 0.071 0.041 

0.111 

0.321 0.265 

0.004 

0.194 16.758 16.76 4.19 

1953 

0.247 0.393 0.228 0.409 0.062 0.035 

0.142 

0.323 0.267 

0.005 

0.273 22.844 22.84 7.359 

1954 

0.243 0.377 0.224 0.392 0.076 0.044 

0.148 

0.431 0.356 

0.034 

0.304 24.25 24.25 13.101 

1955 

0.275 0.416 0.254 0.433 0.079 0.045 

0.264 

0.818 0.676 

0.01 

0.255 3.836 21.82 24.71 14.305 

1956 

0.25 0.368 0.23 0.383 0.09 0.052 

0.232 

0.815 0.673 

0.014 

0.292 8.514 19.97 28.46 8.638 

1957 

0.221 0.317 0.204 0.33 0.079 0.046 

0.147 

1.114 0.92 

0.011 

0.26 6.951 17.161 23.75 12.15 

1958 

0.351 0.489 0.324 0.509 0.052 0.03 

0.11 

0.894 0.738 

0.005 

0.332 3.086 17.314 20.33 14.641 
108 

1959 

0.512 0.693 0.472 0.721 0.059 0.034 

0.049 

0.607 0.501 

0.02 

0.436 14.648 11.716 26.35 18.802 

1960 

0.531 0.697 0.49 0.726 0.039 0.022 

0.067 

0.323 0.266 

0.014 

0.357 0.966 20.226 21.13 11.29 

1961 

0.576 0.734 0.532 0.764 0.087 0.05 

0.091 

0.224 0.185 

0.02 

0.26 3.14 19.896 23.04 13.548 

1962 

0.47 0.58 0.434 0.604 0.163 0.094 

0.105 

0.093 0.077 

0.029 

0.25 2.534 18.974 21.4 14.874 

1963 

0.516 0.616 0.476 0.641 0.187 0.108 

0.074 

0.009 0.007 

0.028 

0.216 3.435 10.441 13.7 11.749 

1964 

0.412 0.476 0.38 0.495 0.112 0.064 

0.11 

0.028 0.023 

0.006 

0.195 3.529 7.239 10.28 15.26 

1965 

0.41 0.457 0.378 0.476 0.059 0.034 

0.14 

0.109 0.09 

0.009 

0.258 4.488 6.448 10.42 16.327 

1966 

0.488 0.525 0.451 0.546 0.072 0.041 

0.074 

0.105 0.086 

0.038 

0.282 3.964 4.82 7.89 12.605 

1967 

0.462 0.479 0.427 0.499 0.038 0.022 

0.043 

0.023 0.019 

0.017 

0.222 3.701 4.039 7.72 10.127 

1968 

0.488 0.486 0.45 0.506 0.106 0.061 

0.079 

0 0 

0.028 

0.239 2.579 6.813 8.98 12.432 

1969 

0.616 0.589 0.568 0.613 0.077 0.044 

0.065 

0.005 0.004 

0.009 

0.163 2.53 3.934 6.04 8.24 

1970 

0.457 0.419 0.422 0.436 0.199 0.114 

0.153 

0.009 0.008 

0.006 

0.239 4.61 5.766 10.33 20.403 

1971 

0.315 0.276 0.29 0.287 0.209 0.12 

0.17 

0.019 0.016 

0.008 

0.191 1.77 8.242 9.85 18.149 

1972 

0.467 0.391 0.431 0.407 0.354 0.203 

0.182 

0.035 0.029 

0.002 

0.203 4.748 9.888 14.59 25.243 

1973 

0.624 0.498 0.576 0.518 0.884 0.508 

0.35 

0.184 0.082 

0.001 

0.354 2.544 11.332 13.79 22.935 

1974 

0.481 0.365 0.444 0.38 1.027 0.59 

0.216 

0.223 0.096 

0.003 

0.363 2.552 9.136 11.32 17.453 

1975 

0.336 0.241 0.31 0.251 0.99 0.569 

0.309 

0.544 0.316 

0.003 

0.411 2.169 7.839 9.77 14.601 

1976 

0.226 0.153 0.209 0.16 1.301 0.747 

0.274 

0.734 0.411 

0.002 

0.38 4.007 9.613 13.42 20.237 

1977 

0.21 0.134 0.194 0.14 1.027 0.59 

0.283 

0.894 0.548 

0.001 

0.502 2.302 11.342 13.34 23.118 

1978 

0.136 0.081 0.125 0.084 0.852 0.489 

0.274 

0.826 0.513 

0.003 

0.629 1.972 11.792 13.33 19.411 

1979 

0.112 0.063 0.104 0.065 0.941 0.54 

0.428 

0.333 0.132 

0.003 

1.115 2.957 12.071 13.74 20.909 

1980 

0.207 0.107 0.191 0.112 0.904 0.52 

0.423 

0.144 0.042 

0 

0.934 3.784 12.276 15.99 24.764 

1981 

0.162 0.065 0.149 0.068 0.623 0.26 

0.175 

0.033 0.022 

0.007 

0.275 2.45 12.802 14.35 18.661 

1982 

0.031 0.02 0.029 0.02 0.571 0.318 

0.155 

0.007 0.007 

0.002 

0.287 2.565 12.731 15.15 27.56 

1983 

0.034 0.021 0.032 0.022 0.824 0.379 

0.226 

0.023 0.008 

0.063 

0.684 2.348 14.396 15.74 29.544 

1984 

0.057 0.049 0.053 0.051 0.466 0.226 

0.15 

0.061 0.051 

0.004 

0.616 3.873 9.179 11.92 22.281 

1985 

0.016 0.009 0.015 0.009 0.776 0.529 

0.144 

0.153 0.101 

0.017 

0.309 3.415 8.853 11.74 29.252 

1986 

0.007 0.001 0.007 0.001 0.796 0.344 

0.156 

0.233 0.163 

0.037 

0.232 0.625 8.895 9.43 20.463 

1987 

0.011 0.003 0.011 0.003 0.805 0.311 

0.147 

0.21 0.164 

0.026 

0.388 1.515 11.565 13.03 28.183 
109 

1988 

0.023 0.006 0.021 0.006 0.649 0.334 

0.137 

0.213 0.121 

0.009 

0.511 1.102 11.27 12.04 25.265 

1989 

0.028 0.01 0.026 0.01 0.348 0.306 

0.093 

0.109 0.061 

0.012 

0.21 3.779 9.101 12.67 28.047 

1990 

0.033 0.014 0.031 0.014 0.404 0.278 

0.097 

0.051 0.012 

0.005 

0.131 1.051 14.997 15.56 32.078 

1991 

0.033 0.022 0.03 0.022 0.471 0.222 

0.078 

0.093 0.013 

0.011 

0.256 5.342 9.914 13.78 27.43 

1992 

0.061 0.038 0.056 0.04 0.235 0.072 

0.05 

0.153 0.065 

0.017 

0.317 3.413 8.491 11.41 26.178 

1993 

0.085 0.052 0.079 0.054 0.24 0.052 

0.067 

0.42 0.329 

0.004 

0.216 2.007 10.817 12.67 29.14 

1994 

0.112 0.062 0.104 0.064 0.208 0.07 

0.084 

0.518 0.394 

0.007 

0.206 0.931 9.469 10.33 23.441 

1995 

0.188 0.104 0.174 0.108 0.19 0.049 

0.089 

0.575 0.44 

0.011 

0.212 2.212 11.384 13.54 31.954 

1996 

0.255 0.129 0.235 0.135 0.148 0.054 

0.109 

0.725 0.556 

0.008 

0.172 0.953 10.763 11.69 26.503 

1997 

0.31 0.164 0.286 0.17 0.142 0.065 

0.12 

1.09 0.832 

0.009 

0.191 1.095 9.269 10.33 22.773 

1998 

0.315 0.18 0.29 0.187 0.175 0.068 

0.142 

1.08 0.797 

0.008 

0.22 1.042 8.794 9.68 23.278 

1999 

0.295 0.152 0.273 0.158 0.114 0.047 

0.125 

1.068 0.834 

0.003 

0.151 1.433 5.415 6.59 17.413 

2000 

0.436 0.192 0.403 0.2 0.142 0.046 

0.126 

1.174 0.89 

0.004 

0.183 0.631 6.057 6.61 16.874 

2001 

0.403 0.166 0.372 0.173 0.181 0.076 

0.092 

1.277 0.954 

0.003 

0.262 0.348 9 9.29 22.306 

2002 

0.467 0.148 0.431 0.154 0.147 0.049 

0.083 

1.166 0.858 

0.003 

0.227 1.396 5.564 6.71 16.774 

11.2 Catches, species groups 13-35 

Table 2. Estimated catches for species groups 13 - 35 (t · km2 · year-1) for the Chesapeake Bay since 1950 

used in the Chesapeake Bay Fisheries Ecosystem Model. Estimated from a variety of sources as 

described in the text. 

Group Alewife Eel Catfish White 

perch 

Spot Spot Shad Gizzard 

shad 

Blue 

crab 

Oyster Oyster Soft 

clam 

Hard 

clam 

Group 

no. 

13 14 15 17 18 18 19 22 31 33 33 34 35 

1950 1.577 0.065 0.091 0.073 0.456 0.203 128.1 4.329 1.36 0.073 0.071 

1951 1.789 0.056 0.079 0.064 0.508 0.22 140.4 4.005 1.34 0.011 0.067 

1952 1.513 0.055 0.091 0.058 0.619 0.256 165.2 3.796 1.56 0.126 0.059 

1953 1.299 0.045 0.11 0.053 0.407 0.204 225.3 3.732 1.68 0.011 0.045 

1954 1.448 0.036 0.135 0.056 0.452 0.212 240.2 3.332 1.89 0.059 0.037 

1955 1.225 0.04 0.156 0.056 0.416 0.225 1389.8 2.968 1.78 0.128 0.043 

1956 1.234 0.041 0.154 0.06 0.333 0.24 1021 3.176 1.68 0.126 0.042 
110 

1957 1.006 0.035 0.148 0.051 0.382 0.239 579.6 3.529 1.58 0.178 0.05 

1958 1.032 0.037 0.14 0.056 0.547 0.188 422.7 3.222 1.7 0.205 0.045 

1959 0.995 0.037 0.168 0.073 0.356 0.148 150.1 2.942 1.51 0.253 0.088 

1960 0.862 0.018 0.161 0.056 0.405 0.122 113.6 3.847 1.23 0.213 0.083 

1961 0.816 0.017 0.132 0.068 0.109 0.143 51 3.937 1.25 0.307 0.105 

1962 1.218 0.015 0.124 0.095 0.215 0.172 43.8 4.337 0.9 0.311 0.094 

1963 1.251 0.026 0.092 0.067 0.134 0.142 11.9 3.52 0.83 0.37 0.117 

1964 1.269 0.023 0.084 0.035 0.288 0.161 39.1 3.952 1 0.357 0.126 

1965 1.746 0.043 0.06 0.068 0.155 0.195 65.5 4.266 0.96 0.336 0.124 

1966 1.363 0.031 0.069 0.091 0.102 0.162 20.7 4.772 0.96 0.238 0.092 

1967 1.431 0.044 0.06 0.064 0.392 0.136 13.1 4.278 1.17 0.253 0.098 

1968 1.648 0.044 0.064 0.085 0.1 0.159 16.1 3.155 1.03 0.359 0.109 

1969 1.54 0.049 0.072 0.104 0.092 0.161 8.2 3.471 1.01 0.282 0.122 

1970 0.958 0.068 0.061 0.075 0.547 0.234 6.5 3.705 1.12 0.272 0.086 

1971 0.705 0.066 0.081 0.077 0.044 0.112 30.1 3.945 1.16 0.088 0.098 

1972 0.582 0.033 0.089 0.054 0.252 0.137 26.4 3.791 1.09 0.03 0.069 

1973 0.516 0.02 0.069 0.039 0.215 0.138 56.1 3.154 1.15 0.095 0.065 

1974 0.686 0.073 0.08 0.026 0.188 0.081 71.8 3.435 1.13 0.057 0.068 

1975 0.552 0.068 0.081 0.027 0.164 0.06 69.5 3.2 1.03 0.744 0.079 0.053 

1976 0.2 0.033 0.066 0.023 0.097 0.046 36.8 2.696 0.95 0.675 0.075 0.042 

1977 0.068 0.025 0.089 0.032 0.151 0.07 7.4 3.127 0.82 0.591 0.157 0.048 

1978 0.106 0.062 0.068 0.048 0.256 0.06 2.7 2.839 1.02 0.652 0.131 0.024 

1979 0.083 0.067 0.069 0.033 0.2 0.046 4.8 3.313 0.98 0.612 0.087 0.029 

1980 0.062 0.034 0.103 0.043 0.14 0.045 15.9 3.182 1.03 0.678 0.071 0.036 

1981 0.028 0.067 0.08 0.042 0.118 0.091 0.023 17.6 4.242 0.98 0.713 0.072 0.053 

1982 0.064 0.036 0.08 0.045 0.079 0.064 0.027 5.8 3.619 0.79 0.557 0.089 0.032 

1983 0.091 0.036 0.088 0.037 0.15 0.131 0.028 23.1 3.941 0.53 0.338 0.043 0.053 

1984 0.063 0.041 0.079 0.052 0.049 0.046 0.061 240.5 3.94 0.56 0.365 0.06 0.034 

1985 0.028 0.04 0.088 0.041 0.136 0.13 0.037 193.7 3.968 0.6 0.39 0.048 0.033 

1986 0.052 0.038 0.115 0.049 0.187 0.131 0.032 301.1 3.402 0.62 0.361 0.159 0.042 

1987 0.104 0.039 0.1 0.051 0.299 0.266 0.037 242.5 3.086 0.39 0.174 0.198 0.046 

1988 0.062 0.036 0.097 0.064 0.126 0.127 0.041 341.7 3.169 0.24 0.109 0.183 0.059 

1989 0.038 0.039 0.129 0.044 0.193 0.172 0.05 333.4 3.431 0.2 0.111 0.097 0.069 

1990 0.036 0.032 0.119 0.065 0.2 0.123 0.039 263.3 3.909 0.2 0.129 0.077 0.071 

1991 0.044 0.04 0.101 0.066 0.274 0.204 0.034 293.5 3.726 0.15 0.106 0.016 0.048 
111 

1992 0.085 0.04 0.099 0.061 0.258 0.205 0.034 347.7 2.885 0.1 0.057 0.036 0.05 

1993 0.08 0.045 0.107 0.09 0.229 0.234 0.03 709.6 4.012 0.03 0.025 0.02 0.072 

1994 0.058 0.043 0.14 0.089 0.274 0.3 0.018 948.9 3.62 0.05 0.038 0.015 0.053 

1995 0.019 0.032 0.105 0.08 0.208 0.233 0.007 976.7 3.367 0.07 0.06 0.014 0.043 

1996 0.006 0.028 0.155 0.104 0.174 0.2 0.011 1314.3 3.211 0.05 0.04 0.011 0.036 

1997 0.016 0.028 0.135 0.15 0.211 0.23 0.024 694.1 3.555 0.08 0.065 0.01 0.031 

1998 0.009 0.03 0.182 0.097 0.261 0.29 0.021 1009.3 2.609 0.12 0.083 0.007 0.025 

1999 0.009 0.029 0.169 0.09 0.165 0.187 0.013 1068.7 2.843 0.13 0.123 0.008 0.03 

2000 0.009 0.025 0.138 0.124 0.215 0.231 0.009 166.9 2.24 0.11 0.108 0.003 0.023 

2001 0.013 0.025 0.162 0.222 0.014 471.7 2.154 0.07 0.01 0.028 

2002 0.015 0.018 0.147 0.199 0.008 615.7 2.264 0.03 0.002 0.031 

11.3 Striped bass growth parameters 

Table 3. Striped bass growth parameters. 

Model group: Striped bass resident 

Growth parameters Stock assessment parameters 

Loo (maximum length) 95.5 Parameter Value Estimated 

K (growth constant) 0.110 

Log_Ro (unfished 

recruits) 6.095 1 

To (time at length 0) -1.126 

Compensation 

(recruitment) 14.24 2 

A (length-weight 

coefficient) 9.99E-06 natural mortality (M) 0.15 -1 

B (length-weight 

exponent) 3.0851167 

length @ 50% 

vulnerability 34.53 -1 

Wm/Woo 0.1 Shape 0.5 -1 

Maturity ogive parameters Delta 0.309 3 

length @ 50% mature 

(lh) 71.1 

shape (g) 0.25 
112 

11.4 Striped bass biomass 

Table 4. Biomass (relative) of striped bass in the Chesapeake Bay Fisheries Ecosystem Model. Resident 

striped bass includes year classes 1 to 6 (12 to 83 months of age). Estimated from a variety of sources as 

described in the text. 

Group 

SB YOY B 

(kg) 

*SB YOY B 

(kg) SB Res B (kg) 

*SB Res B 

(kg) 

*SB Res B 

MSP(kg) 

SB Mig B 

(kg) 

*SB Mig B 

(kg) 

*SB Mig B 

MSP 

(mt/km2) 

Data Source 

Calculated 

with data from 

tables 11 and 

16 of ASMFC 

2003 SB SA 

Calculated 

with data from 

tables 13 and 

18a of the 

2005 ASMFC 

SB SA 

Calculated 

with data from 

tables 11 and 

16 of ASMFC 

2003 SB SA 

Calculated 

with data from 

tables 13 and 

18a of the 

2005 ASMFC 

SB SA MSP estimate 

Calculated 

with data from 

tables 11 and 

16 of ASMFC 

2003 SB SA 

Calculated 

with the data 

from tables 13 

and 18a of the 

2005 ASMFC 

SB SA MSP estimate 

1950 5054056.086 

1951 4404263.816 

1952 4242201.800 

1953 4414565.898 

1954 4855072.339 

1955 5097882.750 

1956 6080353.035 

1957 8361671.314 

1958 9978378.041 

1959 10005523.960 

1960 9747647.087 

1961 8584217.538 

1962 8406517.611 

1963 7454031.873 

1964 7683335.424 

1965 8229713.024 

1966 8025512.177 

1967 8212299.446 

1968 8268757.809 

1969 6482731.512 

1970 5891137.404 

1971 7193377.369 

1972 7329748.884 

1973 5289128.147 

1974 3605957.530 

1975 2546979.554 

1976 2153180.469 

1977 1555873.213 

1978 1525929.522 

1979 1937919.104 

1980 1319880.278 

1981 577713.883 

1982 185 199 3690 3830 632694.641 2293 2563 

1983 586 636 3570 3755 906548.780 1925 2630 

1984 529 576 5306 5715 231288.372 2973 3183 
113 

1985 195 215 5091 5611 250802.393 2909 2794 

1986 366 387 8551 9470 672590.965 2116 2327 

1987 679 789 11447 12514 1723760.466 3048 3813 

1988 1432 1618 15562 17163 4052464.804 3064 3693 

1989 807 897 19014 20855 8278235.282 6280 7868 

1990 636 674 20537 22679 14758505.323 8548 7644 

1991 1518 1815 24407 27194 21566483.107 12860 14625 0.561 

1992 731 871 28354 32404 27712441.373 14393 17579 0.777 

1993 622 775 34141 39108 32400576.860 17929 21303 1.032 

1994 3114 3975 43587 51573 35593992.442 20859 25426 1.305 

1995 3024 3735 45656 55683 35656745.204 24614 29366 1.546 

1996 1489 1810 57632 72594 33612075.364 33920 40582 1.727 

1997 1366 2026 52344 66915 29747954.775 28690 36612 1.823 

1998 1054 4144 55402 61640 28034679.814 30038 38254 1.856 

1999 1228 6809 56180 56721 25790968.200 29295 37498 1.896 

2000 866 3057 63586 51959 22614237.572 40178 46929 1.911 

2001 1958 2478 46825 51771 21146081.449 34950 52135 1.901 

2002 2163 44382 47699 17671866.443 31939 58406 1.918 

11.5 Striped bass fishing mortality 

Table 5. Fishing mortality estimates, 1950-2000, for striped bass in the Chesapeake Bay used in the 

Chesapeake Bay Fisheries Ecosystem Model. 

Group 

*SB Res 

Combined F

*SB Mig 

Combined F *SB Mig F 

Data 

Source 

For 1950-1958 an 

Ecopath 

estimation was 

used; for 1959-

1981 Gibson 

1993, Appendix 

Table 3, Run 2 

was used; 1982-

2002 from file 

entitled, "2005 

striped bass 

processing 

ASMFC_VPA 

2005 ; MSP 

estimate 

For 1950-1958 an 

Ecopath 

estimation was 

used;1959-

1981from Gibson 

1993, Appendix 

Table 3, Run 3; 

1982-2002 from 

file entitled, "2005 

striped bass 

processing 

ASMFC_VPA 

2005 ; MSP 

estimate 

From Table 

24 ASMFC 

2005 SB SA 

for MD CB 

Age 7+ 

1950 0.360 

0.370 

1951 0.360 

0.370 

1952 0.360 

0.370 

1953 0.360 

0.370 

1954 0.360 

0.370 

1955 0.360 

0.370 

1956 0.360 

0.370 
114 

1957 0.360 

0.370 

1958 0.360 

0.370 

1959 0.480 

0.560 

1960 0.630 

0.920 

1961 0.610 

0.930 

1962 0.720 

0.990 

1963 0.730 

0.880 

1964 0.660 

0.800 

1965 0.620 

0.510 

1966 0.660 

0.520 

1967 0.640 

0.520 

1968 0.800 

0.600 

1969 0.910 

0.630 

1970 0.730 

0.500 

1971 0.750 

0.810 

1972 1.420 

1.560 

1973 1.220 

1.330 

1974 1.150 

1.170 

1975 1.080 

1.070 

1976 1.140 

1.390 

1977 1.200 

1.520 

1978 0.850 

0.860 

1979 1.210 

1.250 

1980 1.640 

1.890 

1981 1.330 

1.920 

1982 0.354 

0.426 

1983 0.354 

0.092 

1984 0.354 

0.121 

1985 0.273 

0.113 
115 

1986 0.078 

0.070 

1987 0.035 

0.140 

1988 0.043 

0.092 0.070 

1989 0.040 

0.174 0.040 

1990 0.073 

0.196 0.110 

1991 0.063 

0.168 0.150 

1992 0.050 

0.239 0.140 

1993 0.043 

0.208 0.130 

1994 0.070 

0.227 0.120 

1995 0.098 

0.256 0.240 

1996 0.130 

0.290 0.200 

1997 0.143 

0.320 0.280 

1998 0.200 

0.330 0.240 

1999 0.178 

0.353 0.390 

2000 0.208 

0.353 0.210 

2001 0.208 

0.353 0.130 

2002 0.208 

0.353 0.120 

11.6 Bluefish growth parameters 

Table 6. Bluefish growth and maturity parameters used in the single-species stock assessment, as well as 

ratio of weight at maturity and asymptotic weight used in multi-stanza calculations. Estimated parameter 

values from the stock assessment model are listed on the right (positive phase values indicate parameter 

was estimated by fitting the model to time-series data; negative values indicate parameter was fixed). 

Model Group: Bluefish 

Growth Parameters Stock assessment parameters 

Loo (cm) (maximum length) 87.2 Parameter Value Estimated 

K (year-1) (growth constant) 0.26 Log_Ro (unfished recruits) 7.729 1 

T0 (year) (time at length 0) -0.93 Compensation (recruitment) 5.84 2 

A (length-weight coefficient) 1.09E-05 natural mortality (M) 0.26 -1 

B (length-weight exponent) 3.0548 length @ 50% vulnerability 34 -1 

Wm/Woo 0.2007328 shape 0.5 -1 

Maturity ogive Parameters delta 0.01 3 

length @ 50% mature (lh) 35.433408 

shape (g) 0.3544103 
116 

11.7 Bluefish biomass and fishing mortality 

Table 7. Bluefish biomass (B, relative) and fishing mortality (F, year-1) used in the Chesapeake Bay 

Fisheries Ecosystem Model. 

Group 

Bluefish YOY B 

SRA (t·km-2) 

*Bluefish YOY 

B (kg) 

Bluefish Ad B SRA 

(t·km-2) 

*Bluefish Ad B 

(kg) Bluefish Ad F SRA 

Data Source SRA estimate 

Calculated from 

tables 10 and 20 

of the NFSC 

41st SAW SRA estimate 

Calculated from 

tables 10 and 20 

of the NFSC 

41st SAW SRA estimate 

1950 0.010 0.240 0.483 

1951 0.010 0.153 0.481 

1952 0.054 0.129 0.551 

1953 0.035 0.150 0.409 

1954 0.028 0.171 0.446 

1955 0.033 0.174 0.451 

1956 0.038 0.181 0.499 

1957 0.039 0.185 0.429 

1958 0.040 0.200 0.261 

1959 0.041 0.244 0.242 

1960 0.044 0.286 0.136 

1961 0.053 0.358 0.242 

1962 0.062 0.403 0.405 

1963 0.077 0.403 0.465 

1964 0.086 0.407 0.274 

1965 0.086 0.491 0.120 

1966 0.086 0.639 0.112 

1967 0.103 0.791 0.048 

1968 0.131 1.012 0.105 

1969 0.158 1.217 0.063 

1970 0.195 1.515 0.131 
117 

1971 0.226 1.765 0.118 

1972 0.269 2.081 0.170 

1973 0.301 2.343 0.377 

1974 0.339 2.225 0.462 

1975 0.368 2.065 0.479 

1976 0.354 1.980 0.657 

1977 0.335 1.674 0.613 

1978 0.325 1.535 0.555 

1979 0.287 1.492 0.630 

1980 0.268 1.340 0.675 

1981 0.263 1.183 0.526 

1982 0.242 3250 1.204 85488 0.474 

1983 0.219 2255 1.234 78837 0.667 

1984 0.222 2764 1.056 70434 0.442 

1985 0.227 1659 1.131 67245 0.686 

1986 0.199 1259 0.978 100391 0.815 

1987 0.211 1269 0.795 68569 1.013 

1988 0.187 2542 0.616 50927 1.054 

1989 0.156 2704 0.497 48886 0.702 

1990 0.124 3320 0.515 57459 0.784 

1991 0.102 2104 0.453 49542 1.041 

1992 0.106 733 0.332 51727 0.707 

1993 0.095 1019 0.347 58201 0.692 

1994 0.070 851 0.338 43040 0.616 

1995 0.074 1424 0.326 49101 0.584 

1996 0.072 1740 0.324 40323 0.458 

1997 0.070 971 0.352 35916 0.404 

1998 0.070 1546 0.383 27546 0.457 

1999 0.075 1744 0.390 28951 0.291 
118 

2000 0.082 1623 0.458 27784 0.310 

2001 0.083 2997 0.514 28273 0.353 

2002 0.096 2254 0.546 35576 0.270 

11.8 Weakfish growth parameters 

Table 8. Weakfish growth parameters. 

Model Group: Weakfish 

Growth Parameters Stock assessment parameters 

Loo 68.6 Parameter Value Estimated 

K 0.35 Log_Ro 6.095 1 

To -0.051 Compensation 14.24 2 

A 1.10E-05 natural mortality (M) 0.25 -1 

B 2.9575 

length @ 50% 

vulnerability 34.53 -1 

Wm/Woo 0.1382508 Shape 0.5 -1 

Maturity Ogive Parameters Delta 0.309 3 

length @ 50% mature 

(lh) 25 

shape (g) 1 

11.9 Weakfish biomass and fishing mortality 

Table 9. Weakfish relative biomass (B) and fishing mortality (F) estimates used in the Chesapeake Bay 

Fisheries Ecosystem Model. 

Group 

*Weakfish Ad F 

MSP 

Weakfish B SRA 

(t·km-2) 

*Weakfish B 

MSP(t) 

Weakfish F SRA 

(t·km-2) 

Data Source MSP estimate SRA estimate MSP estimate SRA estimate 

1950 0.489 0.585 

1951 0.290 0.473 

1952 0.212 0.524 

1953 0.201 0.707 

1954 0.220 0.671 
119 

1955 0.232 1.140 

1956 0.174 1.331 

1957 0.142 1.035 

1958 0.156 0.707 

1959 0.190 0.258 

1960 0.270 0.248 

1961 0.332 0.274 

1962 0.372 0.282 

1963 0.402 0.184 

1964 0.465 0.237 

1965 0.501 0.279 

1966 0.513 0.144 

1967 0.582 0.074 

1968 0.681 0.116 

1969 0.739 0.088 

1970 0.803 0.191 

1971 0.783 0.217 

1972 0.751 0.243 

1973 0.714 0.490 

1974 0.565 0.383 

1975 0.523 0.591 

1976 0.428 0.640 

1977 0.365 0.776 

1978 0.302 0.907 

1979 0.250 1.709 

1980 0.147 2.878 

1981 0.410 0.084 50961 2.080 

1982 0.400 0.095 34440 1.625 

1983 0.500 0.105 29047 2.153 
120 

1984 0.630 0.076 21060 1.973 

1985 0.450 0.073 24840 1.983 

1986 0.560 0.073 27562 2.126 

1987 0.500 0.066 23432 2.239 

1988 0.850 0.059 15408 2.337 

1989 1.060 0.056 7656 1.659 

1990 0.800 0.067 6801 1.440 

1991 0.650 0.073 8602 1.068 

1992 0.610 0.086 7917 0.580 

1993 0.460 0.120 9770 0.558 

1994 0.320 0.146 16875 0.575 

1995 0.250 0.162 23805 0.549 

1996 0.270 0.182 25841 0.600 

1997 0.260 0.196 28037 0.611 

1998 0.270 0.209 29229 0.678 

1999 0.220 0.212 28436 0.590 

2000 0.260 0.230 23436 0.549 

2001 0.310 0.249 17118 0.369 

2002 0.350 0.297 12765 0.279 

11.10 Atlantic croaker biomass and fishing mortality 

Table 10. Atlantic croaker relative biomass (B) and fishing mortality (F) estimates used in the 

Chesapeake Bay Fisheries Ecosystem Model. 

Group 

Atl. croaker B 

(VIMS biomass 

index value) 

*Atl. Croaker B 

(t) *Atl. Croaker F 

Data Source

Estimated in: 

Croaker 

Atlantic.xls 

Calculated with 

data from table 

G9 of the 

ASMFC 2003 

Atlantic Croaker 

SA (Nov 05) 

From Table G8 

of ASMFC 2003 

Atlantic Croaker 

SA (Nov 05) 

1973 13196 0.170 

1974 13196 0.280 
121 

1975 21261 0.270 

1976 38338 0.220 

1977 45165 0.270 

1978 39883 0.330 

1979 0.310 30057 0.500 

1980 0.080 22389 0.410 

1981 0.200 18566 0.280 

1982 1.100 15387 0.350 

1983 0.310 11746 0.230 

1984 1.590 27920 0.150 

1985 2.220 37804 0.120 

1986 0.910 49070 0.090 

1987 0.400 57373 0.080 

1988 0.720 63800 0.090 

1989 1.950 62221 0.060 

1990 1.720 60099 0.050 

1991 1.350 57085 0.040 

1992 2.460 73274 0.030 

1993 0.460 82924 0.040 

1994 0.930 90023 0.050 

1995 0.340 94131 0.060 

1996 1.620 96686 0.060 

1997 0.930 89624 0.100 

1998 0.980 81590 0.100 

1999 0.510 84412 0.090 

2000 0.280 91040 0.090 

2001 0.690 88773 0.110 

2002 0.290 80328 0.110 
122 

11.11 Summer flounder biomass and fishing mortality 

Table 11. Summer flounder relative biomass (B) and fishing mortality (F) estimates used in the 

Chesapeake Bay Fisheries Ecosystem Model. 

Group 

Summer 

flounder B 

(t·km-2)

*Summer 

flounder B (t) *Summer flounder F 

Data 

Source 

Estimated 

by Ecopath 

based on 

an EE of 

0.95 

From Table 

3-5 of the 

NFSC 41st 

SAW report 

For 1982-1997, 

values are from 

the Stock 

Assessment of 

Summer 

Flounder for 

2003, NFSC 

Doc. 03-09; For 

1998-2002, 

values are from 

the NFSC 41st 

SAW (2005) 

table entitled, " 

Catch & Status 

Table: Summer 

Flounder" on pg. 

11 

1982 41939 0.940 

1983 48802 17501 2.150 

1984 44553 18837 1.240 

1985 40196 16087 1.360 

1986 38453 14972 1.590 

1987 35403 13934 1.060 

1988 29412 14424 2.070 

1989 16122 8130 1.900 

1990 16449 5217 1.650 

1991 17102 7453 1.580 

1992 17647 6007 1.410 

1993 21351 7303 0.980 

1994 28214 9249 1.320 

1995 35948 11960 1.240 

1996 36928 15611 1.150 

1997 32244 15886 1.220 

1998 37800 15669 0.910 

1999 36275 17794 0.970 

2000 36819 16497 0.990 

2001 43137 19381 0.860 

2002 25544 0.650 
123 

11.12 Menhaden biomass and fishing mortality 

Table 12. Menhaden biomass (B, relative values) and fishing mortality (F) estimates used in the 

Chesapeake Bay Fisheries Ecosystem Model. 

Group 

*Menh 

PRFC 

pound net 

index 

*Menhjuv 

ASMFC 

coastwide 

index 

*Menh 

Chesapeake 

Age 0-1 B 

(t·km-2)

*MenhChesAg

e 2+ B (t·km-2)

*Menh Atlantic 

B MSP (t) 

*Menh Ages 

0-1 F 

*Menh Ages 

2+ F Menh. F2 

*Menhad

en F 

MSP 

Data Source

Fig. 7.6 

ASMFC 

Menhaden 

Stock 

Assessme

nt 2004 

Fig. 7.5 of 

ASMFC 

Menhaden 

Stock 

Assessment 

2004 

Estimated in 

Menhaden 

ASMFC 

assessment 

1993 extracts 

07 revis - see 

green 

highlighted 

worksheets 

Estimated in 

Menhaden 

ASMFC 

assessment 

1993 extracts 

07 revis - see 

green 

highlighted 

worksheets MSP estimate 

Estimated in 

Menhaden 

ASMFC 

assessment 

1993 extracts 

07 revis - see 

green 

highlighted 

worksheets 

Estimated in 

Menhaden 

ASMFC 

assessment 

1993 extracts 

07 revis - see 

green 

highlighted 

worksheets 

From Fig. 

7.8 in 

ASMFC 

2004 

Menhaden 

Stock 

Assessmen

t 

MSP 

estimate 

1950 0.030 0.379 0.510 

1951 0.033 0.416 0.560 

1952 0.040 0.500 0.680 

1953 0.054 0.682 0.920 

1954 0.058 0.724 0.980 

1955 274.253 55.841 0.023 0.651 0.996 

1956 119.871 59.941 0.118 0.555 1.597 

1957 189.130 67.519 0.061 0.424 1.117 

1958 232.391 57.330 0.022 0.503 0.821 

1959 2.455 240.063 118.991 0.102 0.164 0.992 

1960 1.681 113.551 88.831 0.014 0.379 0.500 

1961 1.453 143.217 76.415 0.037 0.434 0.905 

1962 4.133 192.628 53.086 0.022 0.596 1.273 

1963 2.359 134.977 30.621 0.042 0.568 1.653 

1964 50285.390 0.131 143.998 22.553 0.041 0.535 1.739 

1965 50285.390 1.357 123.087 19.910 367789.300 0.061 0.540 2.463 2.300 

1966 38856.900 0.720 228 17.601 326579.775 0.029 0.456 2.180 2.262 

1967 20571.300 0.946 123.205 23.780 445263.858 0.050 0.283 1.392 1.546 

1968 19428.450 2.672 215.266 21.093 383623.587 0.020 0.538 1.637 1.636 

1969 12571.350 2.762 432.059 24.751 440292.429 0.010 0.265 0.972 0.576 

1970 38856.900 2.125 162.579 31.780 593658.464 0.047 0.302 1.242 1.013 

1971 36571.200 3.760 279.069 32.125 579451.496 0.011 0.428 0.860 0.983 

1972 73142.390 3.895 55.111 33.484 611307.975 0.144 0.492 1.388 1.417 

1973 

139427.70

0 4.576 212.670 34.902 635443.007 0.020 0.541 1.215 1.555 

1974 

143999.10

0 5.348 190.825 29.136 531375.140 0.022 0.523 1.227 1.103 

1975 

115427.80

0 5.301 212.988 27.389 500866.402 0.017 0.477 1.017 0.739 

1976 

100570.80

0 5.709 170.534 34.677 636569.546 0.039 0.462 1.188 0.793 

1977 

150856.20

0 5.435 143.594 35.740 585195.680 0.027 0.529 1.052 0.930 

1978 

147427.60

0 4.344 220.396 35.458 644519.508 0.015 0.554 1.175 0.878 

1979 

109713.60

0 5.070 277.266 37.563 680746.024 0.018 0.536 1.162 0.667 
124 

1980 

149713.30

0 4.796 166.659 43.095 792437.012 0.038 0.475 1.284 1.185 

1981 

167998.90

0 5.067 223.388 38.133 686248.524 0.018 0.560 1.012 0.269 

1982 

158856.10

0 5.067 185.835 47.511 877809.128 0.023 0.447 1.196 0.796 

1983 

177141.70

0 4.611 233.383 36.565 551590.460 0.017 0.656 1.318 1.063 

1984 

105142.20

0 5.292 236.089 30.344 555868.213 0.027 0.504 1.502 0.607 

1985 

117713.50

0 4.973 200.706 40.007 704552 0.028 0.369 1.034 0.802 

1986 84570.890 4.971 169.876 42.689 778336.459 0.006 0.347 0.590 0.703 

1987 

157713.30

0 3.834 170.380 49.914 898323.903 0.015 0.386 0.651 1.084 

1988 

145141.90

0 4.833 217.910 37.059 507110.261 0.008 0.507 0.860 1 

1989 87999.450 4.196 173.684 45.654 844022.896 0.036 0.332 1.092 1.141 

1990 52571.100 4.376 268.177 48.116 862412.886 0.007 0.519 0.919 1.127 

1991 65142.440 4.375 416.227 43.657 805783.953 0.021 0.378 1.410 1.227 

1992 69713.850 3.329 184.199 43.740 806222.868 0.031 0.324 0.893 1.244 

1993 

111999.30

0 2.691 296.487 57.952 1062836.038 0.011 0.311 0.708 1.757 

1994 68570.990 3.236 113.533 41.616 743953.921 0.014 0.379 0.597 1.109 

1995 71999.550 2.871 72.026 47.377 854719.103 0.051 0.400 0.780 1.652 

1996 52571.100 2.597 34.788 41.640 739099.464 0.046 0.431 0.558 1.760 

1997 58285.350 2.914 67.222 35.626 636683.833 0.027 0.434 0.730 1.789 

1998 39999.750 2.959 219.277 31.866 575809.970 0.008 0.460 0.963 2.070 

1999 50285.390 2.776 135.181 26.429 482852.894 0.018 0.341 0.900 1.881 

2000 50285.390 2.411 37.519 28.900 522824.527 0.028 0.349 0.617 1.316 

2001 34285.500 3.092 141.848 35.859 643162.852 0.004 0.418 0.739 1.961 

2002 31999.800 2.546 128.452 27.273 491494.918 0.018 0.340 0.738 1.605 

11.13 American shad and blueback herring biomass 

Table 13. American shad and blueback herring relative biomass in the Conowingo Dam fish lifts, 

Susquehanna River, Maryland, 1972-2002, used in the Chesapeake Bay Fisheries Ecosystem Model. 

Group 

Blueback B 

(t·km-2)

Am. Shad 

Conowingo fish 

lift GM 

Data Source 

Dick St. Pierre, 

pers. comm. 

Conowingo fish 

lift Geometric 

Mean 1984-

2002 (From 

Bob Sadzinski, 

MDNR) 

1972 58198 

1973 330341 

1974 340084 

1975 69916 

1976 35519 

1977 24395 

1978 13098 
125 

1979 2282 

1980 502 

1981 618 

1982 25249 

1983 517 

1984 311 0 

1985 6763 0.510 

1986 6327 1.740 

1987 5861 3.400 

1988 14570 2.490 

1989 3598 5.040 

1990 9658 6.620 

1991 15616 9.310 

1992 27533 8.760 

1993 8626 5.550 

1994 2851 15.610 

1995 97863 14.470 

1996 1132 20.090 

1997 376072 38.780 

1998 6211 7.810 

1999 138625 21.230 

2000 29289 31.440 

2001 301240 59.620 

2002 2465 79.210 

11.14 Bay anchovy biomass 

Table 14. Bay Anchovy biomass indices, provided by MD DNR and VIMS. 

Year 

Bay Anchovy B 

MDNR (index) 

Bay anchovy B 

VIMS (index) 

1958 0.460 

1959 4.400 

1960 1.190 

1961 0.990 

1962 1.920 

1963 3.140 

1964 1.980 

1965 4.300 

1966 7.670 

1967 3.870 

1968 3.210 

1969 4 

1970 1.090 

1971 0.460 

1972 1.880 
126 

1973 1.160 

1974 4.960 

1975 1.160 

1976 1.190 

1977 0.730 

1978 0.660 0.570 

1979 1.520 1.090 

1980 0.860 1.140 

1981 2.780 0.890 

1982 1.290 0.690 

1983 2.080 1.130 

1984 1.650 1.210 

1985 2.940 0.770 

1986 2.740 1.530 

1987 1.650 2.080 

1988 0.830 1.350 

1989 1.420 1.120 

1990 2.020 0.570 

1991 3.310 1.270 

1992 1.820 1.240 

1993 1.820 1.090 

1994 0.630 1.030 

1995 0.400 0.740 

1996 0.200 0.720 

1997 0.130 0.890 

1998 0.400 0.790 

1999 0.660 0.740 

2000 0.200 0.860 

2001 0.260 0.310 

2002 0.230 

11.15 White perch biomass and fishing mortality 

Table 15. White perch abundance (B) and fishing mortality (F, year-1) estimates used in the Chesapeake 

Bay Fisheries Ecosystem Model. 

Group 

White Perch 

B Age 0 

(VIMS Trawl 

index) 

White Perch 

B Age 1+ 

(VIMS Trawl 

index) 

White Perch 

B MSP(t) 

White Perch 

F MSP 

Data 

Source

From the 

VIMS Trawl 

Survey 

From the 

VIMS Trawl 

Survey 

MSP 

estimate 

MSP 

estimate 

1978 3.300 

1979 15.800 

1980 18.900 

1981 15.900 2467.087 0.167 

1982 26.600 2578.735 0.174 
127 

1983 10 23.800 2677.583 0.134 

1984 13.300 36.800 2880.024 0.176 

1985 1.900 9.500 2979.551 0.133 

1986 1.800 21.900 3202.804 0.147 

1987 42.100 35.100 3395.362 0.146 

1988 5.300 25.900 3600.671 0.175 

1989 13.300 32 3714.726 0.114 

1990 3.300 29.500 4048.144 0.156 

1991 2.300 15.800 4236.807 0.151 

1992 1.200 15 4449.518 0.132 

1993 17.900 18.800 4749.236 0.189 

1994 8.400 40.800 4805.407 0.185 

1995 4.600 12.500 4881.603 0.160 

1996 20.600 20.200 5069.711 0.207 

1997 10 27.400 5041.325 0.314 

1998 7.100 22.200 4548.173 0.214 

1999 16.100 16.800 4503.868 0.200 

2000 6 17.100 4519.615 0.284 

2001 9.480 20.600 4201.165 0.265 

2002 9.160 18.500 3982.242 0.256 

 

11.16 Spot biomass 

Table 16. Estimates of spot abundance (B) used in the Chesapeake Bay Fisheries Ecosystem Model. 

Group 

Spot B SRA 

(index) Spot B MSP (t) 

Data Source SRA estimate MSP estimate 

1950 1.400 

1951 1.260 

1952 1.120 

1953 0.920 

1954 0.870 

1955 0.780 

1956 0.710 

1957 0.700 

1958 0.640 

1959 0.480 

1960 0.410 

1961 0.290 

1962 0.340 

1963 0.330 

1964 0.380 

1965 0.330 

1966 0.360 
128 

1967 0.460 

1968 0.360 

1969 0.460 

1970 0.610 

1971 0.440 

1972 0.630 

1973 0.670 

1974 0.750 

1975 0.860 

1976 1 

1977 1.190 

1978 1.320 

1979 1.340 

1980 1.390 

1981 1.480 7468.157 

1982 1.580 1045.165 

1983 1.670 1669.975 

1984 1.670 2096.694 

1985 1.730 3736.686 

1986 1.710 4221.186 

1987 1.700 3772.859 

1988 1.600 2596.898 

1989 1.630 3566.087 

1990 1.620 3591.417 

1991 1.650 3530.030 

1992 1.610 2764.064 

1993 1.580 2408.359 

1994 1.550 2329.427 

1995 1.480 1788.350 

1996 1.470 1708.007 

1997 1.490 1918.693 

1998 1.490 1868.895 

1999 1.440 1295.811 

2000 1.480 1297.523 

2001 1.480 

2002 1.480 

11.17 Blue crab biomass, effort, and fishing mortality 

Table 17. Blue crab abundance (B), effort, and fishing mortality (F) estimates used in the Chesapeake 

Bay Fisheries Ecosystem Model. 

Group *Blue Crab F 

*Blue crab 1+ B 

(avg z-score rescaled) 

*Blue crab 0 B 

(avg z-score rescaled) 

Data 

Source 

Estimated in: 

Table 23 Blue 

Crab Stock 

Assessment for 

Chesapeake 

values are zscores 

averaged from 

up to 4 surveys 

(Fig. 13 of 2005 

values are zscores 

averaged from 

up to 4 surveys 

(Fig. 13 of 2005 
129 

Bay 2005 Blue Crab 

Stock 

Assessment) 

and re-scaled to 

get rid of 

negative 

numbers by 

dividing by the 

standard 

deviation 

Blue Crab 

Stock 

Assessment) 

and re-scaled to 

get rid of 

negative 

numbers by 

dividing by the 

standard 

deviation 

1950 

1951 

1952 

1953 

1954 

1955 

1956 1.793 2.129 

1957 0.991 2.281 

1958 1.348 0.400 

1959 1.091 0.374 

1960 0.444 0.054 

1961 1.611 0.257 

1962 2.041 0.784 

1963 2.094 0.125 

1964 1.090 1.087 

1965 1.187 1.250 

1966 3.053 0.951 

1967 0.990 0.220 

1968 0.649 1.632 

1969 2.413 0.317 

1970 2.126 3.313 

1971 3.948 2.296 

1972 1.542 0.610 

1973 1.038 0.655 

1974 1.350 0.135 

1975 1.116 0.273 

1976 1.075 0.468 

1977 1.810 1.125 

1978 1.199 0.846 

1979 1.241 0.376 

1980 1.392 1.908 

1981 3.858 1.827 

1982 1.866 1.016 

1983 2.755 1.873 

1984 2.934 1.276 

1985 2.792 1.269 

1986 2.101 1.046 

1987 1.381 0.928 

1988 1.679 0.756 

1989 2.243 3.190 

1990 0.775 2.851 3.654 
130 

1991 0.667 1.803 1.253 

1992 1.120 2.051 2.728 

1993 0.877 1.990 1.940 

1994 1.241 1.742 1.281 

1995 1.214 1.373 2.370 

1996 0.772 2.243 2.111 

1997 0.915 1.868 1.092 

1998 1.483 0.947 1.738 

1999 2.487 1.441 1.082 

2000 1.726 1.028 1.048 

2001 1.738 1.022 1.139 

2002 1.315 1.273 1.143 

11.18 Eastern oyster biomass, effort, and fishing mortality 

Table 18. Eastern oyster effort (relative), fishing mortality (F), and abundance estimates (B) used in the 

Chesapeake Bay Fisheries Ecosystem Model. 

Group Oyster F Comb Oyster SRA F 

*Oyster F 

MSP 

*Oyster F 

ASPIC 

*Oyster 

B SRA 

(index) 

Oyster 

CPUE SRA 

*Oyster 

B MSP 

(mt) 

Data 

Source MSP estimate SRA estimate 

MSP 

estimate 

MSP 

estimate 

SRA 

estimate 

SRA 

estimate 

MSP 

estimate 

1950 0.060 0.070 20.415 

1951 0.074 0.070 19.561 

1952 0.088 0.080 18.756 

1953 0.102 0.090 17.855 

1954 0.116 0.110 16.922 

1955 0.130 0.110 15.901 

1956 0.144 0.110 14.977 

1957 0.158 0.110 14.138 

1958 0.172 0.120 13.380 

1959 0.186 0.110 12.568 

1960 0.200 0.100 11.882 

1961 0.214 0.100 11.370 

1962 0.228 0.080 10.853 

1963 0.242 0.070 10.544 

1964 0.256 0.090 10.283 

1965 0.270 0.090 9.919 

1966 0.284 0.100 9.581 

1967 0.298 0.130 9.243 

1968 0.312 0.110 8.785 

1969 0.326 0.120 8.409 

1970 0.340 0.140 8.045 

1971 0.354 0.150 7.616 

1972 0.368 0.150 7.162 

1973 0.382 0.170 6.743 
131 

1974 0.391 0.180 6.288 

1975 0.391 0.180 0.417 0.391 5.838 16.100 

19643.78

0 

1976 0.380 0.170 0.446 0.380 5.443 15.500 

16061.60

6 

1977 0.345 0.160 0.429 0.345 5.084 12.100 

14183.75

3 

1978 0.397 0.210 0.514 0.397 4.793 14.700 

13371.47

8 

1979 0.397 0.220 0.533 0.397 4.389 14.700 

11981.56

2 

1980 0.485 0.260 0.674 0.485 3.997 13.700 

10956.48

8 

1981 0.622 0.280 0.898 0.622 3.574 14.300 9170.802 

1982 0.624 0.250 0.947 0.624 3.173 13.100 6707.325 

1983 0.439 0.180 0.682 0.439 2.858 9.400 5046.854 

1984 0.519 0.210 0.796 0.519 2.675 8.100 4859.913 

1985 0.677 0.250 1.034 0.677 2.470 7.800 4311.051 

1986 0.920 0.290 1.426 0.920 2.242 9.800 3230.150 

1987 0.643 0.200 1.064 0.643 1.995 6.700 1834.414 

1988 0.462 0.130 0.773 0.462 1.860 5.200 1430.695 

1989 0.497 0.110 0.820 0.497 1.803 5.500 1396.055 

1990 0.678 0.110 1.113 0.678 1.770 5.200 1317.199 

1991 0.732 0.070 1.240 0.732 1.731 5.200 1004.117 

1992 0.473 0.060 0.840 0.473 1.721 5.300 701.602 

1993 0.183 0.010 0.310 0.183 1.742 4.600 662.355 

1994 0.227 0.020 0.353 0.227 1.811 6.200 921.641 

1995 0.304 0.040 0.429 0.304 1.867 5.700 1236.115 

1996 0.168 0.030 0.207 0.168 1.912 6.900 1558.448 

1997 0.219 0.040 0.233 0.219 1.974 5.800 2304.557 

1998 0.228 0.060 0.206 0.228 2.019 6.500 3296.258 

1999 0.292 0.060 0.219 0.292 2.040 5.800 4725.387 

2000 0.292 0.060 2.058 7.900 6525.113 

2001 0.292 0.057 2.085 

2002 0.292 0.057 2.142 

11.19 Zooplankton biomass 

Table 19. Estimate of relative abundance (B) for mesozooplankton and microzooplankton used in the 

Chesapeake Bay Fisheries Ecosystem Model. 

Group 

Microzoo B (ug C 

/ liter) 

Mesozoo B 

(ug C / 

liter) 
132 

Data Source 

Based on an 

estimate that 

"median rotifer 

and cop naup 

biomass value is 

roughly 50% of 

the 

mesozooplankton 

biomass value 

over all stationdates available in 

the CBP 

database." 

Based on 

CBP 

database 

weighted 

by station 

1985 18.086 36.171 

1986 7.171 14.341 

1987 5.381 10.762 

1988 4.348 8.695 

1989 10.878 21.755 

1990 7.430 14.860 

1991 5.781 11.563 

1992 7.913 15.827 

1993 6.442 12.883 

1994 6.133 12.266 

1995 6.179 12.358 

1996 5.332 10.664 

1997 4.580 9.160 

1998 3.241 6.482 

1999 7.214 14.428 

2000 5.336 10.672 

2001 4.509 9.018 

2002 

11.20 Diet compositions for high-trophic-level, multi-stanza, commercial fish 

Table 20. Diet compositions for high-trophic-level, multi-stanza, commercial fish in the Chesapeake 

Bay Fisheries Ecosystem Model. Diet compositions are expressed as proportions, and are expressed on a 

volume or wet weight basis. See text for sources. 

EwE 

Group 

# 

Prey \ Predator Striped 

bass 

YOY 

Striped bass 

resident 

Striped bass 

migratory 

Bluefish 

YOY 

Bluefish 

adult 

Weakfish 

YOY 

Weak-fish 

adult 

White 

perch 

YOY 

White 

perch 

adult 

1 Striped bass YOY 0.000498 050 

6 Weakfish YOY 0.000856 

7 Weakfish adult 0.000994 0.000995 

8 Atlantic croaker 0.00994 0.0498 0.077 0.131 0.035 

10 Summer flounder 0.010 

11 Menhaden 0-1 0.0855 0.140 0.480 0.464 
133 

12 Menhaden adult 0.439 0.682 

13 Alewife and blueback 

herring 

 0.0944 0.123 

14 American eel 0.0129 

16 White perch YOY 0.001 

17 White perch adult 0.00497 0.00199 

18 Spot 0.0676 0.0109 0.014 0.224 0.031 

19 American shad 0 0.00498 0.00498 0 0 0 0 0 0 

20 Bay anchovy 0.105 0.00895 0.0110 0.546 0.168 0.768 0.429 0.167 

22 Gizzard shad 0.0279 

23 Reef associated fish 0.00795 0.016 

24 Non reef-associated fish 0.0855 0.022 

25 Littoral forage fish 0.321 0.0597 0.286 0.028 0.022 0.146 

30 Blue crab YOY 0.00298 0.0229 

39 Mesozooplankton 0.124 0.549 0.167 

40 Other suspension 

feeders 

 0.0587 

41 Other in/epi fauna 0.449 0.0547 0.0637 0.014 0.101 0.031 0.221 0.504 

46 Import 0.208 

11.21 Diet composition for other commercial fish species 

Table 21. Diets for other commercial fish species. Diets are expressed as proportions. (Numbers in the 

first column refer to EwE group numbers.) 

EwE 

Group 

# Prey \ Predator 

Atlantic 

croaker 

Black 

drum 

Summer 

flounder 

Menhaden 

YOY 

Menhaden 

adult 

Alewife 

and 

herring 

American 

eel 

American 

eel 

Catfish Spot 

1 

Striped bass 

YOY 

0.0001000901 0 0 0 0 0 0 0 0 0.0001000901 

4 Bluefish YOY 

0 0 0 0 0 0 0 0 0 0 

6 Weakfish YOY 

0 0 0 0 0 0 0 0 0 0 

8 Atlantic croaker 

0 0 0.0009990011 0 0 0 0 0 0 0 

13 

Alewife and 

blueback herring 

0 0 0 0 0 0 0 0 0 0 

16 White perch YOY 

0 0 0.009990009 0 0 0 0.000998004 0 0 0 

18 Spot 

0 0 0 0 0 0 0 0 0 0 
134 

19 American shad 

0 0 0.05694306 0 0 0 0 0 0 0 

20 Bay anchovy 

0 0 0 0 0 0 0 0 0 0 

22 Gizzard shad 

0 0 0 0 0 0 0 0 0 0 

24 

Non reefassociated fish 

0 0 0 0 0 0 0 0 0 0 

25 

Littoral forage 

fish 

0 0 0 0 0 0 0 0 0 0 

30 Blue crab YOY 

0 0 0 0 0 0 0.0998004 0 0 0 

32 Oyster YOY 

0 0 0 0 0 0 0 0 0 0 

34 Soft clam 

0 0 0 0 0 0 0 0 0 0 

35 Hard clam 

0 0 0 0 0 0 0 0.001107663 0 0 

38 Microzooplankton 

0 0 0 0 0 0 0 0 0 0 

39 Mesozooplankton 

0 0 0.07392608 0 0 0 0 0 0 0 

40 

Other suspension 

feeders 

0 0 0 0 0 0 0.001996008 0.001996008 0 0 

41 Other in/epi fauna 

0.05004504 0 0.1578422 0 0 0 0 0 0 0.05004504 

42 Benthic algae 

0 0 0 0 0 0 0 0 0 0 

43 SAV 

0 0 0 0 0 0 0 0.1107663 0 0 

44 phytoplankton 

0 0 0 0 0 0 0 0 0 0 

45 Detritus 

0 0 0.2137862 0 0 0 0 0 0 0 

46 Import 

0.5224702 0 0 0 0.2 0.4 0.038 0 0.208 0.5224702 

11.22 Diet composition for other fishes 

Table 22. Diet compositions for other fishes, expressed as proportions (sum = 1) and evaluated on a 

weight or volume basis. For sources, see text. 

EwE 

Group 

# Prey \ Predator 

American 

shad Bay 

anchovy 

Other 

flatfish 

Gizzard 

shad 

Reefassociated 

fish 

Non-reef 

associated. 

fish 

Littoral 

forage 

fish 

Sandbar 

shark 

Other 

elasmobranchs

7 Weakfish adult 0.04 

8 Atlantic croaker 0.35 0.011 

12 Menhaden adult 0.03 

13 Alewife and herring 0.05 0.05 0.01 

14 American eel 0.01 

18 Spot 0.034 

20 Bay anchovy 0.101 0.073 

21 Other flatfish 0.06 

24 Non-reef-associated fish 0.011 

25 Littoral forage fish 0.07 0.05 0.015 
135 

27 Other elasmobranchs 0.25 

30 Blue crab YOY 0.1 0.04 0.05 

31 Blue crab adult 0.1 0.05 

38 Microzooplankton 0.130 0.365 

39 Mesozooplankton 0.130 0.562 0.25 0.05 0.041 

40 Other suspension feeders 0.05 0.202 0.1 0.13 

41 Other in/epi fauna 0.310 0.073 0.85 0.296 0.6 0.598 0.15 0.324 

42 Benthic algae 0.031 

43 SAV 0.05 

44 Phytoplankton 0.75 

45 Detritus 0.100 0.1 0.15 0.283 

46 Import 0.330 0.023 0.417 

11.23 Diet composition for commercial invertebrates 

Table 23. Diet compositions for commercial invertebrates. Diet compositions are expressed as 

proportions and are on a volume or weight basis. For sources, see text. 

EwE 

Group 

# Prey \ Predator 

Blue crab 

YOY 

Blue crab 

adult 

Oyster 

YOY Oyster 1+ Soft clam Hard clam 

30 Blue crab YOY 0.25 

32 Oyster YOY 0.050 0.05 

34 Soft clam 0.050 0.075 

35 Hard clam 0.050 0.075 

38 Microzooplankton 0.09 0.09 

40 Other suspension feeders 0.150 0.05 

41 Other in/epi fauna 0.450 0.35 

42 Benthic algae 0.075 0.025 0.5 

43 SAV 0.075 0.025 

44 Phytoplankton 1 0.9 0.9 0.25 

45 Detritus 0.100 0.100 0.01 0.01 0.25 

11.24 Diet composition for other invertebrates 

Table 24. Diet compositions for other invertebrates expressed as proportions on a weight or volume 

basis. The diet compositions are based on general knowledge about the groups. 

EwE 

Group 

# Prey \ Predator Ctenophores 

Sea 

nettles 

Microzooplankton Mesozooplankton 

Other 

suspension 

feeders 

Other 

in/epi 

fauna 

23 Reef-associated fish 0.001 

25 Littoral forage fish 0.053 

36 Ctenophores 0.525 

38 Microzooplankton 0.334 0.72 0.08 

39 Mesozooplankton 0.666 0.421 

41 Other in/epi fauna 0.02 

42 Benthic algae 0.25 0.3 
136 

44 Phytoplankton 1 0.28 0.5 0.4 

45 Detritus 0.25 0.2 
137 

11.25 Diet composition for birds 

Table 25. Diets for piscivorous and non-piscivorous birds. Both diet compositions are based on general 

knowledge for the groups, and are expressed as proportions (wet weight or volume). Import includes 

food taken outside the Chesapeake Bay model area. 

EwE 

Group 

# Prey \ Predator 

Piscivorous 

birds 

Non-piscivorous 

seabirds 

1 Striped bass YOY 011 

2 Striped bass resident 0056 

6 Weakfish YOY 0056 

11 Menhaden YOY 0.457 

12 Menhaden adult 0.022 

13 Alewife and herring 0.028 

14 American eel 0.006 

15 Catfish 0.006 

20 Bay anchovy 0.074 

21 Other flatfish 0.002 

22 Gizzard shad 0.015 

25 Littoral forage fish 0.017 

30 Blue crab YOY 0.006 

35 Hard clam 0.01 

38 Microzooplankton 056 

39 Mesozooplankton 0.053 

40 Other suspension feeders 0.041 

41 Other in/epi fauna 0.235 

42 Benthic algae 

43 SAV 0.128 

45 Detritus 0.011 

46 Import 0.313 0.575 

11.26 Non-piscivorous bird species included in the Chesapeake Bay Fisheries Ecosystem 

 Model 

Table 26. List of bird species (common name and scientific names given) included in the nonpiscivorous group of the Chesapeake Bay Fisheries Ecosystem Model. 

Common Name Scientific Name 

Brant Branta bernicla 

Canada Goose Branta Canadensis 

Snow Goose Chen caerulescens 

Mute Swan Cygnus olor 

Tundra Swan Cygnus columbianus 

Wood Duck Aix sponsa 

Gadwall Anas stepera 

American Wigeon Anas americana 

American Black Duck Anas rubripes 

Mallard Anas platyrhynchos 

Blue-winged Teal Anas discors 

Northern Shoveler A. Clypeata 

Northern Pintail Anas acuta 

Green-winged Teal Anas crecca 

Canvasback Aythya valisineria 
138 

Redhead Aythya americana 

Ring-necked Duck Aythya collaris 

Greater and Lesser Scaup Aythya marila A. affinis 

Surf Scoter Melanitta perspicillata 

White-winged Scoter Melanitta fusca 

Black Scoter Melanitta nigra 

Long-tailed Duck Clangula hyemalis 

Bufflehead Bucephala albeola 

Common Goldeneye Bucephala clangula 

Hooded Merganser Lophodytes cucullatus 

Common Merganser Mergus merganser 

Red-Breasted Merganser Mergus serrator 

Ruddy Duck Oxyura jamaicensis 

Bald Eagle Haliaeetus leucocephalus 

Osprey Pandion haliaetus 

American Coot Fulica americana 

American Oystercatcher Heamatopus palliatus 

11.27 Piscivorous bird species included in the Chesapeake Bay Fisheries Ecosystem 

 Model 

Table 27. List of piscivorous birds included in the Chesapeake Bay Fisheries Ecosystem Model. 

Common names and scientific names are presented. Based on input from mesotrophic levels group in 

Chesapeake Bay Workshop 1 (October 22-24, 2001). (D. Forsell, pers. comm.) 

Common Name Scientific Name 

Common Loon Gavia immer 

Red-throated Loon Gavia stellata 

Horned Grebe Podiceps auritus 

Red-necked Grebe Podiceps grisegena 

Northern Gannet Sula bassanus 

Brown Pelican Pelecanus occidentalis 

Double-crested Cormorant Phalacrocorax auritus 

Great Blue Heron Ardea herodias 

Great Egret Casmerodius albus 

Snowy Egret Egretta thula 

Little Blue Heron Egretta caerulea 

Tricolored Heron Egretta tricolor 

Yellow-crowned Night Heron Nyctanassa violaea 

Black-crowned Night Heron Nycticorax nycticorax 

Green Heron Butorides virescenes 

Brant Branta bernicla 

Canada Goose Branta canadensis 

Snow Goose Chen caerulescens 

Mute Swan Cygnus olor 

Tundra Swan Cygnus columbianus 

Wood Duck Aix sponsa 

Gadwall Anas stepera 

American Wigeon Anas americana 

American Black Duck Anas rubripes 

Mallard Anas platyrhynchos 
139 

Blue-winged Teal Anas discors 

Northern Shoveler A. Clypeata 

Northern Pintail Anas acuta 

Green-winged Teal Anas crecca 

Canvasback Aythya valisineria 

Redhead Aythya americana 

Ring-necked Duck Aythya collaris 

Greater and Lesser Scaup Aythya marila A. affinis 

Surf Scoter Melanitta perspicillata 

White-winged Scoter Melanitta fusca 

Black Scoter Melanitta nigra 

Long-tailed Duck Clangula hyemalis 

Bufflehead Bucephala albeola 

Common Goldeneye Bucephala clangula 

Hooded Merganser Lophodytes cucullatus 

Common Merganser Mergus merganser 

Red-Breasted Merganser Mergus serrator 

Ruddy Duck Oxyura jamaicensis 

Bald Eagle Haliaeetus leucocephalus 

Osprey Pandion haliaetus 

American Coot Fulica americana 

American Oystercatcher Heamatopus palliatus 

Laughing Gull Larus atricilla 

Bonaparte's Gull Larus philadelphia 

Ring-billed Gull Larus delawarensis 

Herring Gull Larus argentatus 

Great Black-backed Gull Larus marinus 

Black-legged Kittiwake Rissa tridactyla 

Royal Tern Sterna maxima 

Common Tern Sterna hirundo 

Least Tern Sterna antillarum 

Sandwich Tern Sterna sandvicensis 

Black Skimmer Rynchops niger 

11.28 Hurricanes and their assumed impact on the relative P/B for clams 

Table 28. Hurricanes in the Maryland/Virginia region, and assumed impact on the relative P/B for soft 

and hard clam. Data sources: www.vdem.state.va.us/library/vahurr/va-hurr.htm and 

www.nhc.noaa.gov/HAW2/english/history.shtml#iris. 

Year Date Name 

Hurricane 

category VA/MD flooding 

Assumed P/B 

(relative) 

1952 31-Aug Able Weak Minor 0.7 

1953 14-Aug Barbara Weak 0.9 

1954 15-Oct Hazel 4 0.9 

1955 12-Aug Connie Weak Minor 0.7 

1955 17-Aug Diane 1 Minor - 
140 

1955 19-Sep Ione Weak - 

1956 27-Sep Flossy Weak 0.9 

1959 10-Jul Cindy Weak 0.9 

1959 30-Sep Gracie 3 0.9 

1960 12-Sep Donna 4 Major 0.1 

1964 01-Sep Cleo weak heavy rains 0.7 

1969 20-Aug Camille 5 

historic record for 

Virginia 0.1 

1971 27-Aug Doria TS Minor 0.7 

1972 21-Jun Agnes 1 Major 0.1 

1979 05-Sep David 2 0.9 

1985 25-Jul Bob 2 - 

1985 27-Sep Gloria 3 Moderate 0.4 

1986 17-Aug Charley weak Minor 0.7 

1996 12-Jul Bertha weak - 

1996 05-Sep Fran 3 Major 0.1 

1997 24-Jul Danny Minor 0.7 

1998 27-Aug Bonnie 2 Moderate 0.4 

1999 04-Sep Dennis Minor - 

1999 15-Sep Floyd 2 Major 0.1 

2001 14-Jun Allison TS 0.9 

2003 18-Sep Isabel 3 

historic record for 

Maryland 0.1 

11.29 Estimated relative P/B for clams, carried over to years subsequent to hurricane 

 events 

Table 29. Estimated relative P/B for soft and hard clams assuming that the impacts of hurricanes are 

carried over to subsequent years, although with less severity (last column). 

Year P/B (relative) 

P/B (relative), 

used to force 

simulations 

1950 1 1 

1951 1 1 

1952 0.7 0.7 

1953 0.9 0.8 

1954 0.9 0.9 

1955 0.7 0.7 

1956 0.9 0.8 

1957 1 0.9 

1958 1 1 

1959 0.9 0.9 

1960 0.1 0.1 

1961 1 0.2 

1962 1 0.3 

1963 1 0.4 

1964 0.7 0.5 

1965 1 0.6 
141 

1966 1 0.7 

1967 1 0.8 

1968 1 0.9 

1969 0.1 0.1 

1970 1 0.2 

1971 0.7 0.3 

1972 0.1 0.1 

1973 1 0.2 

1974 1 0.3 

1975 1 0.4 

1976 1 0.5 

1977 1 0.6 

1978 1 0.7 

1979 0.9 0.8 

1980 1 0.9 

1981 1 1 

1982 1 1 

1983 1 1 

1984 1 1 

1985 0.4 0.4 

1986 0.7 0.5 

1987 1 0.6 

1988 1 0.7 

1989 1 0.8 

1990 1 0.9 

1991 1 1 

1992 1 1 

1993 1 1 

1994 1 1 

1995 1 1 

1996 0.1 0.1 

1997 0.7 0.2 

1998 1 0.3 

1999 0.1 0.1 

2000 1 0.2 

2001 0.9 0.3 

2002 1 0.4 

2003 0.1 0.1 

2004 1 0.2 
142 

11.30 Phytoplankton chlorophyll content 

Table 30. Estimate of relative chlorophyll content for the Chesapeake Bay, 1950-1994 (Harding and 

Perry, 1997). The series is used for comparison with Ecosim simulation results, not for driving the 

model. (Data not available for 1953-1963.) 

Year Chlorophyll-a 

1950 5.068869

1951 4.770248

1952 1.9

1964 8.551069

1965 15.1605

1966 13.72456

1967 4.316667

1968 32.7125

1969 16.43147

1970 13.70561

1971 27.84041

1972 37.13371

1973 17.15287

1974 26.38086

1975 14.55696

1976 17.46835

1977 19.6949

1978 9.791241

1979 10.01648

1980 15.0723

1981 8.022222

1982 -------------- 

1983 4.856979

1984 9.241438

1985 9.210595

1986 8.347284

1987 12.11841

1988 9.780312

1989 8.656951

1990 9.735246

1991 7.954487

1992 6.10325

1993 7.92401

1994 7.7015
143 

11.31 Confidence intervals for basic input parameters 

Table 31. Estimate of confidence intervals for basic input parameters of the Chesapeake Bay Fisheries 

Ecosystem Model. The confidence intervals are derived from the model pedigree. See the EwE User’s 

Guide for information (Christensen et al., 2004). 

EwE 

Group 

# Group B P/B Q/B Diet Catch 

1 Striped bass YOY 50 50 50 10 --- 

2 Striped bass resident 50 10 50 10 50 

3 Striped bass migratory 50 10 50 10 50 

4 Bluefish YOY 50 50 50 10 --- 

5 Bluefish adult 50 50 50 10 50 

6 Weakfish YOY 50 50 50 10 --- 

7 Weakfish adult 50 50 50 10 50 

8 Atlantic croaker 50 10 50 60 50 

9 Black drum 80 10 50 30 50 

10 Summer flounder 80 40 50 30 50 

11 Menhaden YOY 50 50 50 50 50 

12 Menhaden adult 50 50 50 50 50 

13 Alewife and herring 80 20 50 50 50 

14 American eel 80 20 50 60 50 

15 Catfish 80 20 50 60 50 

16 White perch YOY 50 50 50 10 --- 

17 White perch adult 50 50 50 60 50 

18 Spot 80 10 50 30 50 

19 American shad 50 30 50 10 50 

20 Bay anchovy 50 30 50 50 --- 

21 Other flatfish 80 50 50 60 --- 

22 Gizzard shad 80 50 50 60 --- 

23 Reef-associated fish 80 10 50 60 --- 

24 Non-reef-associated fish 80 70 50 60 --- 

25 Littoral forage fish 80 70 50 10 --- 

26 Sandbar shark 50 70 50 60 50 

27 Other elasmobranchs 50 70 50 30 --- 

28 Piscivorous birds 50 40 50 60 --- 

29 Non-piscivorous seabirds 50 40 50 60 --- 

30 Blue crab YOY 50 70 50 10 --- 

31 Blue crab adult 50 10 50 30 50 

32 Oyster YOY 50 70 50 60 --- 

33 Oyster 1+ 50 50 50 60 50 

34 Soft clam 80 50 50 60 50 

35 Hard clam 50 50 50 30 50 

36 Ctenophores 80 40 50 60 --- 

37 Sea nettles 80 40 50 60 --- 

38 Microzooplankton 80 70 50 60 --- 

39 Mesozooplankton 50 40 50 60 --- 

40 Other suspension feeders 80 50 50 60 --- 
144 

41 Other in/epi fauna 80 50 50 60 --- 

42 Benthic algae 80 70 --- --- --- 

43 SAV 80 20 --- --- --- 

44 Phytoplankton 80 40 --- --- --- 

11.32 Prices for commercial catches 

Table 32. Year 2000 prices ($US) for commercial catches of exploited groups in the Chesapeake Bay. 

For group 13, alewife/herring, price for alewife was used as these dominated the landings (98.6%). For 

group 15, catfish, average price for the two most common species (blue and channel catfish) was used. 

Source: www.seaaroundus.org. 

EWE 

Group # Name $/kg 

2, 3 Striped bass 3.91 

5 Bluefish 0.77 

7 Weakfish, grey 1.51 

8 Atlantic croaker 0.83 

9 Black drum 1.56 

10 Summer flounder 3.94 

12 Atlantic menhaden 0.14 

13 Alewife 0.44 

13 Atlantic herring 0.14 

14 American eel 1.80 

15 Catfish, blue 1.06 

15 Catfish, channel 1.13 

15 Catfish, flathead 1.03 

17 White perch 1.14 

18 Spot 1.14 

19 American shad 0.63 

26 Sandbar shark 0.71 

31 Blue crab 1.79 

33 Oyster, American cupped 0.74 

34 Soft clam 5.48 

35 Hard clam (quahog) 9.70 
145 

11.33 Multispecies Production stock assessment model parameter values Table 33. MSP models and their extensions. B = biomass; r = intrinsic rate of increase; B0 = biomass estimate to initi

in years; H = harvest; c is a constant defined in “Additional Mortality Source” column; and A = prey biomass at satiaprey function. Note that units of measure (Unit) vary among species. If “Additional Mortality Source” is blank, fishisource of loss. 

Species Equation Unit Bo r K c A Additional Mortality Atlantic croaker 

Bt = Bt-1 + rBt-1 (1-(Bt-1 / K)) - Ht-1 Mt 4.42 

103

1.96 1.19 105 

Spot Bt = Bt-1 + rBt-1 (1-(Bt-1 / K)) - Ht-1

– [(cPt-1(Bt-1)2)/(A2+ (Bt-1)2)] 

Lbs 8.85 

108

1.09 4.80 108 3.72 1.48 

108

P = striped bass bay bestimated as below; predation losses; c=maximum per capiconsumption. 

White perch Bt = Bt-1 + rBt-1 (1-(Bt-1 / K)) - Ht-1 Lbs 4.05 

106

0.48 1.64 107 

Striped bass Bt = Bt-1 + rBt-1 (1-(Bt-1 / K)) - Ht-1 

– (Bt-1 L c) 

Kg 5.78 

106

0.64 6.62 107 3.94 L = lesions; disease / 

starvation losses; c = for lesions to loss 

Weakfish Bt = Bt-1 + rBt-1 (1-(Bt-1 / K)) - Ht-1

– [(cPt-1(Bt-1)2)/(A2+ (Bt-1)2)] 

Mt 1.62 

104

2.83 5.54 103 12.92 4.99 

105

P = striped bass coast biomass; predation, competition, migratiolosses; c=maximum pcapita consumption 

Bluefish Bt = Bt-1 + rBt-1 (1-(Bt-1 / K)) - Ht-1

– [(cPt-1(Bt-1)2)/(A2+ (Bt-1)2)] 

Mt 1.17 

105

1.54 3.05 104 7.42 4.32 104 P = striped bass coast 

biomass; predation, competition, migratiolosses; c=maximum pcapita consumption 
146 

Atlantic menhaden 

Bt = Bt-1 + rBt-1 (1-(Bt-1 / K)) - Ht-1 Mt 2.29 

105

2.27 4.54 104 

Gizzard shad 

Bt = Bt-1 + rCBt-1 (1-(Bt-1 / K)) - Ht1 N 1.58 107 0.66 1.51 106 C = NO3 load standar

to median 

Oyster Bt = Bt-1 + rBt-1 (1-(Bt-1 / K)) 

 - Ht-1 – Dt-1Bt-1

Lbs meat 

5.85 107

0.57 7.23 107 D = estimate percent 

oysters killed by dise

11.34 Submerged Aquatic Vegetation biomass index Table 34. Submerged Aquatic Vegetation Index based on the VIMS SAV Monitoring Program data 

Year 

SAV Biomass Index (ID/surveyed) 

1971 0.025337 1972 1973 1974 0.015884 1975 1976 1977 1978 0.014696 1979 1980 0.010436 1981 0.011951 1982 1983 1984 0.017739 1985 0.017519 1986 0.017449 
147 

1987 0.017709 1988 1989 0.021291 1990 0.021414 1991 0.02259 1992 0.025183 1993 0.026083 1994 0.023348 1995 0.021379 1996 0.022652 1997 0.024711 1998 0.022051 1999 0.022423 2000 0.024009 2001 0.030924 2002 0.031126