# 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, Robe rt 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 Christensen 1 , Alasdair Beattie 2 , Claire Buchanan 3 , Hongguang Ma 8 , Steven J. D. Martell 4
, Robert J. Latour 5 , Dave Preikshot 9 , Madeline Sigrist 2 , James H. Uphoff 7 , Carl J. Walters 1 , Robert J. Wood 6 , and Howard Townsend 6

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/Cooperativ e 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/Cooperativ e Oxford Laboratory/Versar, Inc., 904 South Morris Street, Oxford, MD 21654; email Hongguang.Ma@noaa.gov
9 Pacific Biological Station, Depart ment 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 Secr etary for Oceans and Atmosphere
National Marine Fisheries Service James W. Balsiger, Ph.D., Acting A ssistant Administrator for FisheriesSuggested citation: Christensen, Villy, and Alasdair Be attie, Claire Buchanan, Hongguang Ma, Steven J. D. Martell, Robert J. Latour, Dave Preikshot, Madeline B. Sigrist, Jame s H. Uphoff, Carl J. Walte rs, 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.gov1 

### 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 man agement plan entities 53
7.2 Questions 54
7.3 Basic parameters 55
7.4 Catches, species groups 1-12 572 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 753 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-le vel, 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 comme rcial invertebrates 135
11.24 Diet composition for other invertebrates 135
11.25 Diet composition for birds 1374 11.26 Non-piscivorous bird species included in the Ch esapeake Bay Fisheries Ecosystem Model 137
11.27 Piscivorous bird species included in the Ches apeake 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 chloro phyll content 142
11.31 Confidence intervals for basic input parameters 143
11.32 Prices for commercial catches 144
11.33 Multispecies Production stock a ssessment model parameter values 145
11.34 Submerged Aquatic Vegetation biomass index 1465 

### 1 Abstract This report describes an ecosyst em model of the Chesapeake Bay, the Chesapeake Bay Fisheries Ecosystem Model (CBFEM), prepared using the Ec opath 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 info rmation can be used to understand how one stock affects another within th e food web and how the many Bay fisher ies impact both target and nontarget species. Because the life historie s and population dynamics of the t housands 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, repr esenting all trophic le vels. The input data primarily includes assessment results from the Ches apeake 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 inst itutions in the region), and parameter estimates obtained from literature wher e 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 f acilitate use and further developm ent 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 km 2 , while the area including tribut aries is estimated to 18,580 km 2 . More than 20 major tributaries drain into the Bay from a wate rshed that stretches acros s six states: New York, Pennsylvania, Maryland, Delaware, Vi rginia, West Virginia, and the District of Columbia. The largest of these tributaries, the Su squehanna River, provides more than ha lf 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 averag e tidal range of approximately 1 m at its mouth to less than 30 cm at its head (cited in 1989). Salinity with in 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, r eaching 28-30oC in late summer a nd 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 th e Bay. Tidally influenced habitat types in the Bay include: pelagic waters, nearshore litt oral areas, and the benthic zone. Li ttoral habitats, such as marshes on intertidal lowlands, aquatic gra ss beds in the shallow flooded flatla nds, and oyster reefs, are highly productive, serving as nursery areas to many fish and shellfish species, facilitating rapid growth under relatively protected conditions. The di versity of habitats within the Chesapeake Bay system enables it to support nearly 3,000 species of plants and animal s 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 seasonall y 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 Atla ntic 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 migr ations 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 differe nt species, to quantitatively estima te how they fit together within the larger food web and how human impacts ar e 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 maximu m 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-tempe rate and subtropical summer residents. Since the early 1800s, the Chesap eake Bay has supported a variety of large-scale commercial and recreational fisheries of both finf ish and shellfish. The predominant invertebrate fisheries in the Chesapeake Bay have include d 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 ba ss, 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 Atlant ic croaker, have sustai ned significant harvest levels, although trends in the comme rcial and recreational la ndings have varied over the last several decades. Striped bass landings may be the most dram atic 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 a nd the collapse of severa l Bay and coastal fish7 stocks during the 1900s prompted the creation of fish eries management agencies both along the Atlantic coast and within the Chesapeake Bay. In coastal areas, the Atlantic States Marine Fish eries 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-A tlantic coast (from 4.8 to 322 km or 3 to 200 miles offshore). Within the Bay, tidal fisheries are managed on a jurisdicti on-specific basis, by the Vi rginia Marine Resources Commission (VMRC), the Maryland Department of Natural Resources (MD DNR), and the Potomac River Fisheries Commission (PRFC). The three jurisd ictions have agreed upon management strategies, as outlined in Chesapeake Bay fisheries management pl ans, 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 esti mates of population size and fishing mortality rates, synthesis of life history characteristics to determin e fishing seasons). However, si ngle-species analyses have not traditionally considered the ecology of the species under management, (e.g., habitat requirements, response to environmental change), ecologica l interactions among species, (e.g., predation, competition), or technical interactions, (e.g., di scards, bycatch) (NMFS, 1999; Link, 2002b; a). Basing fisheries management plans on both single-species characteristics and ecologi cal 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, me nhaden, 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 unde rstand how one species is likely to respond to changing conditions, it is necessary to look at the response of all other sp ecies that affect it within its ecosystem. Not only do humans affect fish populations directly through fishi ng, but they also have major indirect impacts such as nutrient, sediment, a nd toxics pollution; disease; and physical destruction of habitat. All of these factors influence fish stock levels. Ecos ystem-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 accura tely portray how the ecosystem is currently functioning and how it is likely to function in the future given changes in ha bitat 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-spe cies population models we re 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 generally8 viewed as underdeveloped. Recently, this belief ha s changed due to the sophi stication and increased availability of multispecies assessm ent and ecosystem models (Hollowed et al. , 2000; Whipple et al. , 2000; Latour et al. , 2003). The analytical evolut ion of these modeling appro aches has reached the point where it can now provide meaningful management advi ce. 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 tu rn creates the need for a dditional types of data. In the Chesapeake Bay region, single-species fisher ies management plans have been developed for numerous species (Table 1). There has been a growi ng interest in ecosystem approaches to fisheries management in the Chesapeake Bay region since th e 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 techni cal workshops (Miller et al. , 1996; Houde et al. , 1998); 3) development of the multispecies and cross-jurisdictional sampling platform, the Chesapeake Multispecies Monitori ng and Assessment Program (ChesMMAP) in 2002; 4) continued development of the Fisheries Ecosystem M odel 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 ecosy stem-based fisheries management plans for five target species/groups in the Chesapeake Bay (alosine s, blue crab, eastern oy sters, 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, multispe cies fisheries management and ecosystem approaches. In response to the Chesapeake 2000 agreement (Chesapeake Bay Program, 2000), the NOAA Chesapeake Bay Office, working through the Chesap eake 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 fi rst years of developmen t. Specifically, the 1950-2002 version of the Chesapeake Bay EwE m odel is presented, complete with detailed descriptions of the data used for model parameterization and calib ration included in the appendices. The current Chesapeake Bay EwE model should be cons idered a work in progress, and will evolve and improve in response to the collection of new info rmation. 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 quantif y missing parameters. However, several years of work will be necessary before the data will become availa ble. Consequently, this Technical Report should be considered descriptive of th e 1950-2002 version of the Chesapeake Bay Ecosystem Model. The9 Technical Report will be under continuous development in the medium term, and electronic versions of the report will be released periodically and w ill 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 to ol used to evaluate quantitative trophic interactions within an ecosystem in order to assess options for ecosystem-bas ed 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 mi gration, and catch. Three of the following four additional input parameters must al so be input: biomass, production/bi omass (Z), consumption/biomass, and ecotrophic efficiency. Forcing functions have been developed for the system, including one for climate, primary production, and habitat area. The mode l uses the input data al ong 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 Fi sheries Ecosystem Model are listed in Table 3. Once all basic parameters have been input or estimat ed, the model balances the input and output of each group with two linear equations of production and consumption, using va rying rates of respiration for adjustment. The model can come up with numerous bala nced scenarios, given th e input data and forcing functions. The balancing process results in predicti ons of biomass, producti on, and consumption values for each functional group and measures how closely th ese predictions match the input data. The modeler chooses which model run matches time-series data most closely based on the su m 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 thor oughly. The modeler decides which parameters to include in the final estimation and then re-runs the model with the a ppropriate 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 th en 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 simula te various management options for the system, by varying parameters over time to esti mate potential ecosystem changes.

3.1.2 Ecopath An Ecopath model uses trophically-linked biomass pools to create a mass-bala nced snapshot of the resources and interactions in an ecosy stem (Christensen and Pauly, 1992; Pauly et al. , 2000; Christensen and Walters, 2004). The biomass pools typically represent either a sing le 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 detail ed accounting of growth and survival for monthly cohorts is conducted for such groups. Biom ass 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 tw o ‘master’ equations. The first equation describes how the production term for each gr oup 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 j j i i i i i i DC B Q B BA E Y EE B P B 

## ∑

= + + + = 1 ) / ( ) / ( (2)
where for biomass pool i = 1, ..., n :

• B i is total biomass during the period of question

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

• EE i is the ecotrophic efficiency, defined as the fr action of the production that is consumed within or harvested from the system

• Y i is the yield or catch in weight (note that Y i = F i B i where F is the fishing mortality rate)

• E i is the net migration rate (emigration – immigration)

• BA i is the biomass accumulation rate for ( i )

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

• DC ji is the average fraction of i in the diet of j (note that DC ji = 0 when j does not eat i ) At a minimum, Ecopath requires input of DC ji , Y i , and three of the following four parameters for each species or biomass pool in the model: B i , ( P / B ) i , ( Q / B ) i , and EE i (mass balance principles are used to estimate the fourth parameter). If al l four parameters are known, then Ecopath can be used to estimate either BA i or E i . Equation (2) implies that an ecosystem under study is described completely by an n dimensional system of linear equa tions, the solutions of which can be easily calculated (Mackay, 1981); the resulting estimates of biomass, production, and c onsumption can be used to construct a quantitative network diagram of energy flow for the system (Ulanowicz, 1986). The second ‘master’ equation is base d on the principle of conservation of matter within a group and is designed to balance the ener gy 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 defi nition, but differs in the se nse that it is used to estimate losses rather than to measure growth. Ba lance of the energy equation is achieved by estimating respiration from the difference between the cons umption, production, and unassi milated food terms. For more details on Ecopath, see Christensen and Paul y (1992) and Christensen and Walters (2004).

3.1.3 Ecosim Ecopath is used to describe the interactions amon g resources within an ecosystem. Additional modules are created to simulate the dynamics of the ecosystem resources and the effects of different management11 strategies on the structure a nd function of an ecosystem. The time-dynamic module, called Ecosim, provides a simulation capability that facilitates polic y 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 diffe rential 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

1 j ij n

1 j ji i i B ) e F M ( I c c g dt dB + +

− +

− = 

## ∑ ∑

=

= (4)

where:

• g i is growth efficiency

• F i is the instantaneous ra te of fishing mortality

• e i is the rate of emigration

• I i is the rate of immigration

• c ij ( c ji ) is the consumption of biomass pool i ( j ) by biomass pool j ( i ) This system of equations is used to represent the spatially aggregat ed dynamics of entire ecosystems and is combined with explicit age/size-structured delay- difference equations to represent populations that have complex life histories and select ive harvesting of older animals. An important aspect of Ecosim is the expression of the consumption or ‘flow’ rate s among linked species or biomass pools. Consumption of prey i by predator j is modeled as:

) B a v 2 ( B B v a ) ,B B ( Q j ij ij j i ij ij j i ij + = , (5)

where a ij is the rate of effective search for prey i by predator j , and v ij is the behavioral exchange rate between vulnerable and invulnerabl e 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 rest ricted ‘foraging arenas’ where prey only become vulnerable to predation through their own re quirements 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 of ten just call ‘vulnerability’). The vulnerability fact or expresses how much the predation mortality for a given prey can incr ease if the predator abunda nce is increased. When the predator is close to it s carrying capacity with regard to the given prey, the preda tion mortality cannot be increased any further (v = 1), and an increase in predator abundan ce, (e.g., due to good recruitment) will be compensated for by a decrease in predator consum ption 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 pred ator population size when it is clos e 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 capaci ty is a stable population.12 On the other hand, if the predator is far from its ca rrying capacity for a given pr ey, 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 cons umption rate of the predator will remain relatively constant, and the increase in it s 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 labor atory data. However, to assist with identifying appropria te settings, Ecosim includes se veral 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 esse ntial for developing and valid ating an Ecosim model. Therefore, time-series data depicti ng trends in relative and absolute bi omass, 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. Histori cal catches are treated as fixed, known quantities, and are subtracted from simulated stock size over time to aid in estimating how large (and/or productiv e) the stock must have been in order to have sustained t hose catches and to have been redu ced 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 fishi ng effort and stock abundance. Ignoring this dynamic interaction amounts to treating the ca tches as purely dispensatory im pacts on stock size. Consequently, the fixed catches can cause progressive ly larger calculated fishing mort ality 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 calcul ated each year as [input catch] / [s imulated stock size]) or be used for evaluating model fit where F values are ava ilable from assessments. This SRA is used where estimates of initial biomasses and time trends in st ock 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 incorporat es several approaches for explic itly addressing uncertainty, but given the number of parameters involved in a comp lex model such as the CBFEM it is not possible to consider the uncertainty of all input parameters on all output paramete rs and all predictions. We rather recommend that focus is on what imp act 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 characte rizing 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 th e 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 repres ents only one possible s ynopsis of the trophic and other ecological interactions of sp ecies and functional groups of intere st 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 auto mated tweaking, which potentially could develop a model parameterization that fits the data extremely well but may pr oduce spurious results because of unsound assumptions fitting the noise rather than the signal (Walters and Martell, 2004). Upon finalizing the CBFEM, Monte Carlo simula tions (accounting for uncertainty in the parameterization) were run to determine if the patt erns produced by the model were robust to parameter uncertainty. Further details on how Ew E 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 m odel for the Chesapeake Bay has been under way for several years. The effort has involved a large numb er 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 Ecopa th/Ecosim modeling approach to the Chesapeake Bay research community. A review of the Fisheries Ecosystem M odel 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 th at 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 semi nar on dynamic simulation modeling in May 2002 at the Smithsonian Environmental Res earch 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 particip ants generally agreed:

 The FEM, as was implemented with 45 major sp ecies 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 th e 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 in fluenced 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 works hop, time-series data ha ve been revised and added to the model, data sources have been verifi ed, and data has been reviewed by a panel of Bay researchers, the Ecosystem Modelin g Technical Advisory Panel 2. Ec opath with Ecosim 6 software version 6.0.4.2 was used to run this current ve rsion of the CBFEM, with a resulting goodness-of-fit criterion, the sum of squares, equaling 1098. The m odel 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 th e 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 necessa ry 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 measurem ent 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 ‘j 1 , j 2 , j 3 ,..., j n ’ (fraction, DC)15 • ecotrophic efficiency (EE)

• fishery landings and discards by gear sector ‘j’ imparted upon each species ‘j 1 , j 2 , j 3 ,..., j n ’ (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, onl y 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 compositi ons (DC) for all species must be entered. Gear sectors are designated by the mode ler/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 coll ecting data for three of the four basic input parameters listed above: B, P/B, and Q/B. Other basic input para meters 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 ma ny herbivorous species. This is espe cially 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 mode ler for those values. Howe ver, 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 producti ve organisms will tend to have high P/Q ratios, (e.g., ≈ 0.3) (Christensen et al. , 2004). Ecopath models are ‘snapshots’ that are intende d 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 th ese values only for species that have documented evidence suggesting different values. Because many sp ecies have not been studied in enough detail to yield published estimations of B, P/B, or Q/B, th e 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 theref ore are conservative for similar species in similar ecosystems. This implies that even if a reliable P/B or Q/B estimate is unavailabl e for the species or species group modeled, then estimates for similar (or the same) species in similar (or the same) ecosystem may have to16 suffice as proxies. Where possible, these may be modified up or down to reflect differences in exploitation pressure. Where biomass estimates are unava ilable, 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 pa rameterization 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 singl e-species group; or 3) as a life histor y 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 bioma ss and consumption rates for all stanza when the following parameters are supplied: the von Bertalan ffy growth (curvature) parameter K (which is available for fish species through FishBase); B for one (‘leading’) stan za; estimates of Z (= P/B) for each stanza; Q/B for one stanza; and an estimate of the ratio of the wei ght-at-maturity to the asymptotic weight, W inf . For a discussion of the calcu lations used in the Ecopath model, see Christensen and Walters (2004). In most models, there will be a highe r degree of aggregation in species that are trophically distant from the focal species. The desire to enrich the model w ith detail must be tempered by a realistic examination of the modeler’s ability to flesh out that detail and to obtain data or es timates for the required parameters. To examine specific policy issues for any particular species, detailed information must be available from surveys or assessments, and simila r information should be available for species with which the focal species likely in teracts 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 ex plore 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 / bi omass (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 eac h functional group in the model, along with a description of how diet com positions were obtained. A summary of basic input parameters of the Chesapeake Bay Fisheries Ec osystem 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 re present a snapshot of roughly what the Chesapeake Bay system may have looked like in the middle of the 20 th century. This model was then run time-dynamically using Ecosim a nd 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 Ch esapeake 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 factor s as a part of the ecosy stem 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 develope d 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 fo r the Chesapeake Bay producing similar estimates with high spatial and temporal reso lution, we have been unable to obtain information about nutrient conditions for the entire Bay over th e 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 loadi ng 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 Ches apeake Regional Estuarine Ecology Model (CBREEM), which includes three sub-models, operating with m onthly time steps: 1) a physical, or hydrodynamic, model computing current speed and direction for surf ace and deep layers; 2) a static chemistry model estimating concentration of nitrogen, salt, phytopla nkton, and suspended partic ulates; 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 hydrographi c 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 cal culates 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 nutrien t loading and physical mixing has been added to the CBREEM to calculate changes in primary pr oduction in the Chesapeake Bay. M onthly 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 bathymet ry 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 outpu ts have been compared with hi storical data to aid in model parameterization. The chemistry model calculat es concentration of nutrients and estimates phytoplankton growth as a function of nitrogen concentrations. The s ea 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 w ill include a historical reconstruction of sea grass18 communities in the Chesapeake Bay. This will serve to test hypotheses about how changes in sea grass beds have affected biota that are asso ciated and/or depende nt 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 cat ches, including catches made on the Atlantic Ocean side of the Delmarva Peninsula. The CBFEM aims to character ize stocks within the Chesapeake Bay and tidal portions of its tributaries, not coas tal 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 wa ters of Maryland and Virgin ia outside the Bay, and the catches there as a rule will be similarly limited. While the NOAA marine catch database provides es timates for commercial catches from 1950 to the present, similar time series for recr eational catches are not as readily available. Th e official recreational catch database, the Marine Recrea tional Fishery Statisti cs Survey (MRFSS), only includes information from 1982 to the most currently reported year; he nce, recreational catches for the period 1950-1981 are derived from estimates. Commercial versus recreatio nal 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 regresse d, and the pre-1982 recreational ca tches 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/r ecreational 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 ca n 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 we ighted 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 relativ e 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 asse ssment of relative data reliability. The model allows four types of analysis with the SS measure:19 1. Determine sensitivity of SS to the critical Ec osim vulnerability factors by changing each one slightly (1%) then rerunning the m odel to see how much SS is changed ( i.e. , how sensitive the time-series predictions ‘supported’ by da ta 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 exp ected to be similar (the search is typically conducted on the most sens itive interactions, as identified above);
3. Search for time-series values of annual relative primary productivity that ma y represent historical productivity shifts affecting bioma sses 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 optimi zation 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). Su ch 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 se ttings are not set such that th e 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 define d by workshop participants (Table 2). Further development and policy explorati on 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 a ssumes that the absolute values (per unit area) are estimated in the same manner fo r the original data and for the ecosystem model. Absolute data, as a rule, are ente red as relative data instead, using only the trend in the data for the fitting.

 Biomass for forcing: Used to force the si mulation 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 si mulation matches the observed data; data set need not cover all years.

o Catches.

 Comparison of model simulation and obser ved data or for estimating fishing mortalities based on stock re duction analysis; data need not cover all years; timeseries catch information is pr esented in Tables 4 and 5.

 As part of a stock reduction analysis, wh ere 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 (c atch/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; effo rt data need to be complete for the time series.

 Environmental data

o Time-forcing data: Typically relative prim ary production (monthly or annual) over the time period, but can be any kind of environmenta l 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 for ce 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 vulnerabi lity 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 exceed ing 100 were truncated at this value.

• These groups were then analyzed, and if comparis on of time series and trend from time series (or the expected trend where there were no time seri es) warranted it, the vul nerabilities 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 vulnera bilities were not changed at all). Information on time-series data is incl uded 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 rem ovals 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., certa in 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 a nd 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 de velop time series for the Chesapeake Bay Fisheries Ecosystem Model, both based on production modeling: an age-structured single-species model and a multispecies production a pproach. These two methods are described in the following two sections and are appl ied 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-structur ed 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 (unk nown) parameters that are equivalent to the maximum intrinsic rate of growth and the carrying capacity of a simple surp lus production model. These two leading parameters represent the long-term unfished biomass (B o ) and the maximum juvenile survival rate or recruitment compensation. For the estimation of the long-term unfis hed 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 relativ e abundance indices lack sufficient contrast to make it possible to estimate bot h parameters. A notable example is oysters in the current model. Without more diverse trajectories, notably decline and subse quent 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 spec ies, an age-structured assessmen t 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 as sessment model include: growth in formation (von Bertalanffy growth parameters), length-weight relationships ( i.e., w a = aL b ), parameters for a maturity cumulative frequency curve to calculate spawning stock biomass, natural mort ality 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 wa s assumed to be proportio nal 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 us ing a leading parameter setup, where the population scale (or capacity) was determined by R o (the equilibrium unfished recruits),22 and the maximum rate of population change was de fined 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 R o , M , and k. The maximum survival rate ( α ) is simply a multiple of number of recrui ts produced per unit of egg production, or:

o

o R = K E α (7)
and the asymptote of the recru itment function is defined by:

o o o aE -1 E R β ⎡ ⎤

= ⎢ ⎥

⎣ ⎦ (8)
The equilibrium egg production ( E o ) is the product of the equilibrium recruits and the number of eggs produced per recruit. The number of eggs per recruit ( e φ ) is the product of survivorship to age a times mean fecundity of age a individuals. It is not ne cessary to know the exact fecundity of any specific age group, but rather the relative differences in fecundity between separate age cla sses. Here, it is assumed that egg production is proportional to body weight. The equilibrium egg production ( E o ) 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 w a is the weight-at-age and m a is the proportion of that age class that is sexually mature. A simple logistic function is used to describe maturity-at-age:

) ( 1 1

h a l l g a e m

− − + = (10)

where g is a shape parameter that describe s the variation in maturity-at-age, l h is the length at 50% maturity, and l a is the mean length-at-age.23 3.4.1.2 Population dynamics The numbers-at-age ( N a ) matrix is initialized assuming a stable age distribution, and the oldest age class ( A ) is a plus group contai ning individuals ages A and older:

a M
o a e R N ) ( − = δ (11a)

M A M

o A e e R N

− −

− = 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-specifi c fishing mortality rates. Since our interest was to develop a fishing mortality rate ti me series to force Ecosim, annual fishing mortality is conditioned on observed total catch:

t Ct F = B t (12)

where C t 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 pr edictions from equation (12), numbers-at-age are updated using: 

# ∑

= = A

a a a a t t w m N E 0 , (13a)

σ ω β α t e E E N

t t t + = + 1

1 , 1 (13b)

) (

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

) (

, ) ( 1 , , 1 A t a t v F M A t v F M A t A t e N e N N − − − −

− + + = 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 ω t may be included if σ > 0. The instantaneous natural mortality rate is represented by M , and the vulnerability-at-age ( v a ) is calculated using the same logistic function in equation (10). Howe ver, separate parameters ( g and l h ) 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-seri es 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 indi ces, the observation model is:24 t v t t Y = qX e (14)
where X t is the predicted biomass or age group or population numbers (d epending on what the observation Y t represents), and q is simply a scaling para meter 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 negativ e log-likelihood is: 

## ( ) 

# ( ) n 2 Y t i=1 n-1 lnl = - Z - Z 2 

## ∑ (15)

where Z t = ln( Y t /X t ) and ) ln( / 1 q Z n Z t = = 

## ∑ (16) In cases where catch-at-age information is availabl e, a multinomial likelihood is added to the overall objective function. Here, it is assumed that no aging e rrors exist and that the catch-at-age composition is representative of the ag e-structure in the Chesapeake Bay regi on. The negative log-likelihood for the multinomial distribution is: 

## ( ) 

# ∑∑

= = − = T

1 t ta A

1 a ta a p ln n ) l ln( (17)

where n ta is the observed numbers-at-age in the catch sampling programs and p ta 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 observati on errors are assumed and the unknown parameter set is limited to ( R o , k, and δ ). In cases where catch-at-age data were available, parameters for the selectivity function ( g and l h ) 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 (strip ed bass, weakfish, bluefish, white perch, spot, Atlantic croaker, Atlantic menhaden, bay anchovy, gi zzard shad, and oyster) were deve loped to provide estimates of exploitable biomass to compare with EwE estimates. The boundary of these anal yses, 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 cl osed Chesapeake Bay population, wh ich 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 stock25 extend beyond the limits of the fishery being analy zed, then the pattern of exploitation beyond the limit of analysis will determine whether the analysis of por tion 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 ha rvested by commercial fisheries c ould be assessed. MRFSS data are available back to 1981. Time-ser ies end points were variable ( 2000-2004) and depended on when the first version of the spreadsheet model was develope d 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 cat ch 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 (insho re 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. Pr ivate boat recreational fi shing 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 rema ins high even though the stock is declining) in these estimates; the recent dependence of the recreational st riped 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 la ndings and the index. However, division by effort in the index reduces the direct dependency in the data, a nd there is little alternat ive for a Bay-wide index. Changes in length limits can affect age/size classe s represented by these indi ces. Length limits have been fairly stable or nonexistent for spot, Atlantic croaker, white pe rch, 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 peri od of somewhat stable length limits.

3.4.2.2 Biomass dynamic models Biomass dynamic modeling (also known as surplus produc tion 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 cons idered as undifferentiated biomass and ag e, size, and sex structure are ignored26 (Haddon, 2001). Variations in exploitation and biomass are important for fitting th e model; length of the time series is not (Hilborn and Walters, 1992). The time-series fitting method was used to estimat e production model parameters (Hilborn and Walters, 1992; Haddon, 2001). A spreadsheet version of the disc rete 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:

• U t was the index of abundance in year t
• U t- 1 = index of abundance the previous year,

• r = intrinsic rate of population increase

• s = scalar for the abundance index

• K = maximum population biomass

• C t-1 = harvest (commercial and recrea tional) in the previous year

• ε is measurement error (Hilborn and Walters, 1992)
Combined recreational and commercial directed harvest was used; discar d estimates were not available. A genetic algorithm super solver (Evolver, Palisade Corporation) was used to estimate parameters r , K , s , and U 0 (an estimated abundance index in the initial year of the time series) that minimized observation error (observed l n U t - predicted ln U t ) 2 (Prager, 1994). The previous y ear’s estimated index was then used to predict the following year’s estimate. The spreadsheet version of the model combined w ith the genetic algorithm pr ovided a great deal of flexibility for trying different model variations. Auxili ary 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 obs erved 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 dete rministic 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 ( B t ) was estimated as predicted U t / s (Hilborn and Walters, 1992). Instantaneous annual fishing mo rtality rate in year t was esti mated, 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 st ocks vary widely, and these variat ions may not be explained with single-species population models (Spe ncer, 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 dram atic 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). C ontinuous 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 popul ation growth formula combined with an S-shaped predation function reproduces these fluctuations when subjected to simulated directional environmental fluctuations or changes in fish ing pressure (Steele and Henderson, 1984; Collie and Spencer, 1993). A simple predator-prey model was a pplied to examine the relative e ffects 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 attr ibuting all changes in bi omass to F under stable ecological conditions. In addition, the ‘effect’ of striped bass was c onsidered 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 predat or-prey model has reproduced the t ypes of rapid shif ts in abundance that have been exhibited by mari ne populations, and it was useful in exploring the role of dogfish predation on Georges Bank haddock recovery and ma nagement (Spencer and Collie, 1996; 1997). In the current weakfish assessment process, use of th is model has allowed for exploration of food web dysfunction as a hypothesis for their decline (Uphoff, 2006). This predat or-prey model is essentially a Schaefer biomass dynamic model with a sigmoida l type III predation function added to estimate additional predation losses (Colli e and Spencer, 1993). In this analysis, it provided a method for quantifying the extent that striped bass predati on 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 (St eele 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 cP (B ) B B = B + rB 1- - H - K A +(B ) ε ⎡ ⎤ ⎛ ⎞ ⎛ ⎞ ⎢ ⎥ + ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎢ ⎥ ⎝ ⎠ ⎣ ⎦ (20)

where:

• B t was biomass in year t

• B t-1 = index of biomass the previous year,

• r = intrinsic rate of population increase

• K = maximum population biomass;

• H t-1 = harvest (commercial and recreational, incl uding discard estimates) in the previous year

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

• A is weakfish biomass where predator satiation begins28 • 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; U t / q or U t-1

/q would be substituted for weakfish biomass ( B t or B t-1 , respectively) in the predator-prey model equation. The Haddon version of the spreadsheet mo del was easier to adapt to this predator-prey formulation because it estimated biomass as a first step and then estimated q as B t /U t (Haddon, 2001). Biomass was estimated directly for 1981 ( B 1981 ), and then the model was used to estimate subsequent years (Haddon, 2001). Estimating bi omass first allowed stri ped 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 (1 982-2003) 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 effo rt (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 e xploitable 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 desc ribe the time series was determ inistic and without error (Haddon, 2001). A genetic algorithm super solver (Evolver, Palis ade Corporation) was usedto estimate predatorprey model parameters that minimized observation error in the indices (observed log e U t - predicted log e U

t ) 2 and auxiliary data used (Haddon, 2001). The spread sheet version of the model combined with the genetic algorithm provided a great de al of flexibility for trying diffe rent 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 natu ral 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, D t , was equivalent to catch H t . 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 (Z t ) due to due to fishing and striped bass predation equaled F t + M bt . 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 pos sible configuration of the Ches apeake Bay in 1950; its groups are shown arranged by trophic level in Figure 4. The mode l 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 confi guration, and the Monte Carlo routines of EwE can be used to explore othe r possibilities. Using the Ec oranger routine of EwE, balanced models can be obtained through a resampli ng method with values dr awn 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 trop hic levels. Model construction shou ld 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 an alysis, via a process originally developed by Leontif (1951) to desc ribe the American economy and la ter modified for ecological use by Hannon and Joiris (1989) and Ulanowicz and Puccia (1990). Mixed trophic impact s are shown in Figure 6 for groups of focal impact to fisher ies, and the analysis serves to i llustrate, for example, that alewife and herring have strong e ffects 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 peri od 1950-2002 using default Ecosim settings except as noted below. Feeding time adjustment was not in cluded in the model runs; thus, feeding time was assumed constant for all groups over time. The ‘fract ion 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 desc ribed in the Methods secti on 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 us ed 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 pr edictive capabilities (Wa lters and Martell, 2004) . Vulnerabilities for species groups included in the CBFEM are presented in Table 6. The key criterion for changing vulnerabilities is that th ere must be availability of time-series information for the group in question. There must be time-ser ies data for prey where members of the group are important predators, or for predators, where memb ers of the group are import ant prey. For many groups, these criteria are not met because the data is unava ilable. This limits current possibilities for using the model’s use for predictive pur poses. A lack of historic information limits our capability to project what will happen in the future. We emphasi ze that this is a property of all modeling, not a specific feature of EwE. The vulnerabilities can be interprete d as a measure of how far a consum er is from its carrying capacity in the 1950-start situation. Thus, th e 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 abu ndance to increase to its carrying capacity. For groups at their carrying capac ity, the vulnerability s hould be closer to 1, indicating that the groups cannot increase the pred ation mortality they are causing on their prey.

4.2.2 Time-series fitting We fitted the model to the availabl e 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 se ries 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 st anzas along with the fit to catches for the same period. To obtain this fit, it was necessary to assume that for both re sident and non-reside nt striped bass, the fishing mortality was relativ ely 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 consider ably. However, we would then have been unable to make the striped bass return to their ‘historic le vel,’ as conventional wisdom dictates has occurred. A major problem for striped bass fitt ing 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 qui te high catches. If we31 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 bi omass time-series trend, but in that case we obtain quite low fishing mortal ity 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 agr ee that the increased fishing mortality in recent years is likely to have caused the stock trend to le vel 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 stripe d 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 recen t 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 genera l 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, a nd a small increase again in recent years. This trend is repeated closely by the Ecosim simulation, though the simulati ons tend to produce higher catches since the peak. The ease with which this general tren d 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 fo r the juveniles. Further, trophic interactions do not provide any confounding issues, becaus e any trend seen in prey consumption by bluefish is countered by an inverse trend demonstrated by the feeding sche me of their main competitor, striped bass.
4.2.2.1.3 Weakfish For adult weakfish, the assessment we conducted i ndicated a peak in abund ance 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 mo rtality 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 j uvenile trawl series estim ate 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 w ith increased catches), while the ASMFC series indicates an increase since the ea rly 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 si mulation time period. Indeed, we had to use forced catches to driv e the group. We ran the simulations with a low-vulnerability setting for Atlantic croaker (based on a sear ch using the time-series–fitting m odule 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 es timating fishing mortalit ies 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 erosi on of the black drum biomass over time, po ssibly 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 bur st in the late 1970s, and the juvenile trawl series indicates a sharp decline in the 1980s (whe re there were no catches, according to the catch information), followed by a marked increase in the 1990s (where the catches retu rned 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 mort ality 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 th e data for this group is inconsistent.
4.2.2.1.7 Menhaden Menhaden is the only group for which a long-term a ssessment was available from the regular stock assessments conducted in the Chesapeake Bay region (ASMFC, 2003c). The Ecosim simulation replicates the biomass trend well, ev en if there is some uncertainty a bout conclusions re garding the early 1950s. While it was a welcome surprise to find an assessme nt 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 ca tches 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 me thod 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 th e late 1950s and first half of 1960s indicated by the assessment, and ascribe this to a lack of information for the earlier pa rt 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 ca tch series, one representing coast- wide catch trends, and one trends in Bay catches. Our catch series initially matches th e 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 char acterized by very little available information on which to drive the simulati ons and evaluate the results. We chose to condition the simulations on catch for these groups, because there were no realistic time-series trends available. As33 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 cat ches up to 1970, and used an F series from one of the assessments for the period since then. Based on th is, 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 variab le). 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, a nd 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 indi cates 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 positiv ely 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 si nce the 1960s and 1970s. Total mortality for juvenile blue crab seems to be declini ng over time, due to lower predati on pressure from adult crabs. The fit to the catch series is re asonably good, indicating that the F a nd 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 oyste rs, so it is not surprising that we were able to replicate the biomass and catch tr ends throughout the time peri ods 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 th e 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 s cenarios. As an example of such a scenario, we present the potential outc ome of a fishing moratorium on the nativ e 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 s how a marked decrease over the simulation period, much in line with observations. However, time-serie s information about abundan ce 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 s hould be considered very tentative.

4.2.2.3 Other groups For the remaining fish species, birds, and most inve rtebrates, we had no time -series information that could be used to constrain and validate the simulati ons. 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 di fficult 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 pe digree. 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 itera tions to find a balanced model), but were unable to find any constellation with lower sum squared residuals than we obt ained through the fitting procedure.


### 4.3 Evaluating policy questions Emphasis in this report has been on model validation—on examining m odel fit to qualify performance and to ascertain whether the model could provide pl ausible 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 questi ons. We emphasize that the examples we present in this report are for demonstration purposes—the rang e 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 ne ed to provide explicit protection fo r species whose va lue 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 Ch esapeake 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 on e of its major predators, stripe d 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 popu lation 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 me nhaden purse seine fisher y has had any significant biological effect on any other spec ies or fishery” (ASMFC, 2004).
4.3.1.1.1 Are the striped bass back at their historic level? Our simulations indicate that the resident stri ped bass have increased above their ‘historic’ ( i.e. , 1950) level, while the migratory are back at it. These findings seem ‘semi-r obust’ 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 partic ularly 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 exploi tation 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 th e simulation that the striped bass biomass will decrease under status quo ; that they would be stable under the re duced fishing scenario; and that they would increase with no fishing for menhaden. The stri ped bass are thus moderately sensitive to changes in menhaden fishing pressure in this model. The predictions for impact of menhaden fishery on st riped bass are sensitive to the assumptions about carrying capacity for both menhaden and striped bass. If we use th e default assumption for vulnerability ( v =2) for menhaden, we obtain a very good fit to me nhaden (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 cap acity in 1950, we are not able to get as clear a decline for menhaden in the early part of the time series as indicate d 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 assu mptions about carrying capaci ty for striped bass. We ended up using low vulnerabilities for the species, indi cating that it would have been close to carrying capacity in 1950. We doubt that this is the case, but hi gher vulnerabilities ( i.e. , assuming it to be further from carrying capacity) would let the fishing morta lity crash the stock much earlier than what likely happened. The problem we face with regard to evaluating the im pact of the menhaden fishery is clearly linked to our lack of knowledge ( i.e. , lack of assessments) about what happe ned to striped bass prior to 1982, as well as to the exploitation and popul ation history of menhaden pre-1958.36 4.3.1.2 Bay anchovy Bay anchovy is considered an important forage speci es 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. Th e simulation period was increased to 100 years, and two simulations were run with all biomasse s recorded at the end of the simulation: one status quo simulation maintaining the 2002 fishi ng 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-stat es 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 notewor thy result is that striped bass are predicted to benefit, which is assumed to be linked to improve d feeding conditions for menhaden. The groups that decline with the bay anchovy are ma inly the predators for which bay anchovy are an important prey. The predictions in Table 7 are in ge neral agreement with the mixed tr ophic impact analysis of EwE, confirming the finding of Libralato et al. (2006), who found a strong co rrelation between Ecosim simulation results and those of mi xed-trophic-impact analysis. Howe ver, 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 fl ounders would be impacted by bay anchovy, and we did not see that in the Ecosim simulation. The perhaps most important finding from this simula tion is that one cannot si mply assume a direct relation between what happens to a group and what ha ppens to its prey or pr edators; 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 com position due to nutrient en richment and/or differential responses to predation that may be essential to the reproductio n of historical changes in the Chesapeake Bay ecosystem. The model can easily be modified to pr ovide more detail to acco mmodate such questions; the main issue is whether there is su fficient empirical background to do so.

4.3.2.1 Blue crab When participants in a Chesapeake Bay Ecosyste m Modeling Workshop were asked to formulate policy questions (Table 2), several focuse d on blue crab. One question dealt with ecosystem manipulation: Can the crab stock be increased by control of its predat ors? 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 a dult blue crab is negligib le, 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 additional37 information about predator-prey inter actions 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 th rough 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 pre ssure back to the 1950 level. The simulation predicts that this would result in a bl ue crab biomass of 77% over the 1950 level, while catches would settle at 75% over th e 1950 level. Thus, indications are th at 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 th an 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 cr ab 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 ti me period has decreased to 4% of its 1950 value. Noting that the Chesapeake 2000 agreement (Chesapeake Bay Pr ogram, 2000) targets a ten-fold increase in oyster biomass by 2010, a ‘what-if’ scenar io 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 cu rrent 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 in fluence, much in line with what other models have shown (R. Fulford, The Univer sity of Southern Missi ssippi, pers. comm.) For the other groups, the increased oyster biomass is pr edicted to have only negl igible effect (<4%), and the impact is negative for nearly all groups, due largely to there be ing ‘one more competitor on the block’; predation pressure on adult oy sters 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 juve nile fishes and invertebrates. This could be done straightforwardly, given info rmation to that effect.

4.3.3 Ecosystem drivers: climate varia tion 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 estimat es decreased substantially when primary production rate was forced. Estimating “the relative importance of climate vari ation on fish populations ve rsus that of harvesting pressure” (see Table 2) is a more complicated matte r – or rather, it is something that cannot be estimated. There are systems where it is possible to ev aluate population trends ba sed on fishing pressure38 alone (Christensen, 1998), but it is almost alwa ys necessary to consider fisheries as well as environmental factors to explain what has happe ned 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 questi on about the effect of decreasing nut rient 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 expe rience from other models, and to some degree supported by empirical studi es (Nielsen and Richardson, 1996). It is important to keep in mind that primary production in estuaries may not be linearly related to the concen tration 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 hist ory of exploitation and tr ophic 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 li ttle systematic effort ha s been allocated in the Bay region to collecting and making availa ble fisheries data from before 1982. From a data perspective, the biggest problem is th e lack of pre-1982 recreationa l catch data. There is no doubt that older data is more di fficult 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 im portance in order to deve lop an understanding of how life in the Bay has developed and reacted to exploitation. The ecosystem model is strongly influenced by nutrie nt loading trends. Excelle nt 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 tw o-layer hydrographic model has been applied to the model, forced by river gauge data, nutrient load ing, 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 simulati on discussed above illustrates how assumptions about trophic interactions can be important for ev aluating impact of fisheries. This warrants a closer look when setting target and limit reference points as pa rt 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 databa ses with available diet information.39 

### 5.2 Stock assessment Current fisheries management practices are tactical. Th ey deal with how to best use available resources in the short term. The data analysis to determine us e of Bay fisheries tends to be quite limited in scope, often concentrating on recent catch data and single-s pecies 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 fo r inclusion in the model would be highly valuable. If possible, it would be best to input data fo r all assessments back to 1950. When taking an ecosystem perspective, more species th an just those that are exploited matter. In order to understand quantitative trophic inter actions in the Bay, we recommend that assessments include all species of ecological importance in the Bay. When evaluating a time-seri es 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 a ssessments: one for use in fisheries management, where decisions with short-term economical conseque nces must be made; and another for use in longterm management of the ecosystem, where emphasis is on increasing or unde rstanding of ecological processes and strategically eval uating management scenarios.


### 5.3 Spatial modeling EwE models, as well as their implem entation, are very open-ended; thei r 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 fleet s can be added or removed, and new fleets can be included (or invented) to test s uggested management interventions. Time-series data can be easily updated in the time steps required by the modeler/us er. 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 fi shing-effort dynamics in an exp licit spatial setti ng. The spatial component of EwE, Ecospace, is es sentially a grid or two-dimensi onal matrix of ‘cells,’ each cell incorporating an Ecosim m odel (initially identical, as Ecosim i nherits its parameters from Ecopath) and expressed at the user-inter face level as a map (Walters et al. , 1999). Each cell in th e map, excluding land cells, is linked through two processes: dispersal of organisms and the re distribution of fishing effort due to changing profit patterns and/or the creation of areas closed to fi shing. The user/modeler defines the base map (land/water areas) by sket ching it on the interface w ith the computer mouse. Over the top of the base map the following attributes can be sketched: 1) patterns of relative fi shing cost (effort ‘avoids’ high-cost cells; for example, cells fa r from their home port that require high fuel costs to reach); 2) patterns of relative primary producti on; 3) patterns of habitats to wh ich biomass pools and fishing fleets can be assigned; and 4) areas closed to fishing (fleet and season specifi c). This allows for the exploration of policies that include spatial components, includi ng the evaluation of the size and placement of marine protected areas (MPAs). A new sub-module of Ecosp ace has been developed to evaluate ecological and economic aspects of the pl acement 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 eu trophication, or nutrient enrichment, influences marine ecosystems. It is known that enrichment (and sedimentation) generally decreases the transpar ency of coastal water bodies, but the broade r indirect effects of such changes in light regimes on continental shelf ec osystems are not well explored. Sh elf systems may be particularly susceptible to pollution-related decreases in water tran sparency, as they may cause shifts in energy flows from benthos-based to plankton-based food webs. Nutr ient 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 ecosyste m 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 a nd recruitment and correlates some climate index with an aspect of th e 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 al so been studied in the Atlantic using the North Atlantic Oscillation as a climate index (Attrill and Power, 2002). Ecos im can be used to examine how primary production changes might be driven by c limate and evaluate how ‘bottom-up’ cascade of production might differentially affect mortality and st ock size of commercially important species in the Chesapeake Bay. Chlorophyll-a time series from CBREEM can be in corporated as a forcing function in Ecosim to influence phytoplankton pr oduction directly and benthic primary producers indirectly (by simulating interference in Ecosim’s ‘mediation’ tools). Simula ted shifts in the system’s food webs supported by each of these primary production realms can be scru tinized and compared with related empirical and theoretical information.


### 5.4 Ecosystem boundaries and model structure Few ecosystems are clearly demarcated; the Chesap eake 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 th e full extent of such populations in the model. For example, for menhaden, this may mean incorporat ing the entire Atlantic population and its production, cons umption, and catches. Lesser nui sances, 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 lowe ring 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. Th is is simply a question of focusing the modeling appropriately. The biggest problem is, rather, that it is necessary to ha ve good data on which to build our models and simulations. Models cannot be designed to answer all questi ons – at least not without having a very good understanding of how an ecosystem and its components func tion 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 stru cture, a model can be used to evaluate a range of scenarios and guide us to what are likely conseque nces and causes. The ecosystem model presented in this report has this potential. This report has sought to clarify what Chesapea ke Bay ecosystem resource information has been included in the Fisheries Ecosystem Model to date. H opefully, by assembling this material in one place, data gaps have been made more evident. Any fee dback that will enable the next generation of this modeling effort to go beyond what has been pres ented here is welcomed and encouraged.



### 6 Acknowledgements This study was funded through the NOAA Chesapeak e Bay Office’s Fisherie s Research Program, NOAA Award No. NA17FU1654 and NA06NMF4570297, and we gratefully acknowledge this support. We especially thank Lowell Bahner for initiating a nd inspiring the modeling work and Derek Orner for enabling discussions, sharing in formation, and lending support. We thank the modelers at the University of Brit ish 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 participat ed in a series of ecosystem modeling workshops and shared their data and valuable insight into the Bay ecosystem. Finally, we would like to thank Marc ia Olson, Jacqueline Johnson, and Kim Couranz for their time in reviewing and providing importa nt input to the document.
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### 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 = Ch esapeake Bay Program; MAFMC = Mid Atlantic Fishery Management Co uncil; and MDNR = Maryland Department of Natural Resources; SAFMC = South Atlantic Fishery Management Council54 

### 7.2 Questions Table 2. Topics that th e 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, th e 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 im portance of the effects of climate varia tion on fish populations versus that of harvesting pressure? 5. Can the crab stock be rest ored through fishery reductions an d 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 enhan ce abundance and aid in their restoration?55 

### 7.3 Basic parameters Table 3. Basic parameters for the Chesapeake Ba y Fisheries Ecosystem Model. Values estimated by Ecopath are shown in italics . Estimated from a variety of s ources 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 0.16456 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 -577.4Catches, species groups 1-12Table 4. Estimated catches of groups 1-12 (t · km2· year-1)for the Chesapeake Bay since 1950 used in the Chesapeake Bay FisheriesEcosystem Model. Estimated from a varietyof sources as described in the text.Group SB residentSBresidentSBMigra-torySBmigra-toryBlue-fishBlue-fishWeak-fishAtl.croakerAtl.croakerBlackdrumSummerflounderMen-haden Age0-1Men-haden Age2+MenhadenASMFCMen-hadenCBGroup no. 2 2 3 3 5 5 7 8 8 9 10 11 12 12 1219500.463 0.792 0.427 0.825 0.116 0.0670.2860.656 0.5420.0050.209 12.706 12.71 7.75319510.329 0.549 0.303 0.572 0.073 0.0420.1370.434 0.3580.0060.212 13.954 13.95 5.7819520.271 0.442 0.25 0.46 0.071 0.0410.1110.321 0.2650.0040.194 16.758 16.76 4.1919530.247 0.393 0.228 0.409 0.062 0.0350.1420.323 0.2670.0050.273 22.844 22.84 7.35919540.243 0.377 0.224 0.392 0.076 0.0440.1480.431 0.3560.0340.304 24.25 24.25 13.10119550.275 0.416 0.254 0.433 0.079 0.0450.2640.818 0.6760.010.255 3.836 21.82 24.71 14.30519560.25 0.368 0.23 0.383 0.09 0.0520.2320.815 0.6730.0140.292 8.514 19.97 28.46 8.63819570.221 0.317 0.204 0.33 0.079 0.0460.1471.114 0.920.0110.26 6.951 17.161 23.75 12.1519580.351 0.489 0.324 0.509 0.052 0.030.110.894 0.7380.0050.332 3.086 17.314 20.33 14.64119590.512 0.693 0.472 0.721 0.059 0.0340.0490.607 0.5010.020.436 14.648 11.716 26.35 18.80219600.531 0.697 0.49 0.726 0.039 0.0220.0670.323 0.2660.0140.357 0.966 20.226 21.13 11.2919610.576 0.734 0.532 0.764 0.087 0.050.0910.224 0.1850.020.26 3.14 19.896 23.04 13.54819620.47 0.58 0.434 0.604 0.163 0.0940.1050.093 0.0770.0290.25 2.534 18.974 21.4 14.87419630.516 0.616 0.476 0.641 0.187 0.1080.0740.009 0.0070.0280.216 3.435 10.441 13.7 11.74919640.412 0.476 0.38 0.495 0.112 0.0640.110.028 0.0230.0060.195 3.529 7.239 10.28 15.2619650.41 0.457 0.378 0.476 0.059 0.0340.140.109 0.090.0090.258 4.488 6.448 10.42 16.3275819660.488 0.525 0.451 0.546 0.072 0.0410.0740.105 0.0860.0380.282 3.964 4.82 7.89 12.60519670.462 0.479 0.427 0.499 0.038 0.0220.0430.023 0.0190.0170.222 3.701 4.039 7.72 10.12719680.488 0.486 0.45 0.506 0.106 0.0610.0790 00.0280.239 2.579 6.813 8.98 12.43219690.616 0.589 0.568 0.613 0.077 0.0440.0650.005 0.0040.0090.163 2.53 3.934 6.04 8.2419700.457 0.419 0.422 0.436 0.199 0.1140.1530.009 0.0080.0060.239 4.61 5.766 10.33 20.40319710.315 0.276 0.29 0.287 0.209 0.120.170.019 0.0160.0080.191 1.77 8.242 9.85 18.14919720.467 0.391 0.431 0.407 0.354 0.2030.1820.035 0.0290.0020.203 4.748 9.888 14.59 25.24319730.624 0.498 0.576 0.518 0.884 0.5080.350.184 0.0820.0010.354 2.544 11.332 13.79 22.93519740.481 0.365 0.444 0.38 1.027 0.590.2160.223 0.0960.0030.363 2.552 9.136 11.32 17.45319750.336 0.241 0.31 0.251 0.99 0.5690.3090.544 0.3160.0030.411 2.169 7.839 9.77 14.60119760.226 0.153 0.209 0.16 1.301 0.7470.2740.734 0.4110.0020.38 4.007 9.613 13.42 20.23719770.21 0.134 0.194 0.14 1.027 0.590.2830.894 0.5480.0010.502 2.302 11.342 13.34 23.11819780.136 0.081 0.125 0.084 0.852 0.4890.2740.826 0.5130.0030.629 1.972 11.792 13.33 19.41119790.112 0.063 0.104 0.065 0.941 0.540.4280.333 0.1320.0031.115 2.957 12.071 13.74 20.90919800.207 0.107 0.191 0.112 0.904 0.520.4230.144 0.04200.934 3.784 12.276 15.99 24.76419810.162 0.065 0.149 0.068 0.623 0.260.1750.033 0.0220.0070.275 2.45 12.802 14.35 18.66119820.031 0.02 0.029 0.02 0.571 0.3180.1550.007 0.0070.0020.287 2.565 12.731 15.15 27.5619830.034 0.021 0.032 0.022 0.824 0.3790.2260.023 0.0080.0630.684 2.348 14.396 15.74 29.54419840.057 0.049 0.053 0.051 0.466 0.2260.150.061 0.0510.0040.616 3.873 9.179 11.92 22.28119850.016 0.009 0.015 0.009 0.776 0.5290.1440.153 0.1010.0170.309 3.415 8.853 11.74 29.25219860.007 0.001 0.007 0.001 0.796 0.3440.1560.233 0.1630.0370.232 0.625 8.895 9.43 20.4635919870.011 0.003 0.011 0.003 0.805 0.3110.1470.21 0.1640.0260.388 1.515 11.565 13.03 28.18319880.023 0.006 0.021 0.006 0.649 0.3340.1370.213 0.1210.0090.511 1.102 11.27 12.04 25.26519890.028 0.01 0.026 0.01 0.348 0.3060.0930.109 0.0610.0120.21 3.779 9.101 12.67 28.04719900.033 0.014 0.031 0.014 0.404 0.2780.0970.051 0.0120.0050.131 1.051 14.997 15.56 32.07819910.033 0.022 0.03 0.022 0.471 0.2220.0780.093 0.0130.0110.256 5.342 9.914 13.78 27.4319920.061 0.038 0.056 0.04 0.235 0.0720.050.153 0.0650.0170.317 3.413 8.491 11.41 26.17819930.085 0.052 0.079 0.054 0.24 0.0520.0670.42 0.3290.0040.216 2.007 10.817 12.67 29.1419940.112 0.062 0.104 0.064 0.208 0.070.0840.518 0.3940.0070.206 0.931 9.469 10.33 23.44119950.188 0.104 0.174 0.108 0.19 0.0490.0890.575 0.440.0110.212 2.212 11.384 13.54 31.95419960.255 0.129 0.235 0.135 0.148 0.0540.1090.725 0.5560.0080.172 0.953 10.763 11.69 26.50319970.31 0.164 0.286 0.17 0.142 0.0650.121.09 0.8320.0090.191 1.095 9.269 10.33 22.77319980.315 0.18 0.29 0.187 0.175 0.0680.1421.08 0.7970.0080.22 1.042 8.794 9.68 23.27819990.295 0.152 0.273 0.158 0.114 0.0470.1251.068 0.8340.0030.151 1.433 5.415 6.59 17.41320000.436 0.192 0.403 0.2 0.142 0.0460.1261.174 0.890.0040.183 0.631 6.057 6.61 16.87420010.403 0.166 0.372 0.173 0.181 0.0760.0921.277 0.9540.0030.262 0.348 9 9.29 22.30620020.467 0.148 0.431 0.154 0.147 0.0490.0831.166 0.8580.0030.227 1.396 5.564 6.71 16.77460 

### 7.5 Catches, species groups 13-35 Table 5. Estimated catches for species groups 13-35 ( t · km 2 · 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 0.04261 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 0.03162 

### 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 change d 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 ecosyst em 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 fi shing 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 0.4164 

### 8 List of figures

8.1 The Chesapeake Bay Figure 1. Bathymetry map of the Chesapeake Bay. The salinity provinces are defi ned 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 fr om NOAA/NOS Data Explorer Servi ce 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 ) 2 /( j ij i i B a v vB V + = .


### 8.4 Food web components Figure 4. Overview of the groups in the Chesapea ke 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 Ecor anger runs compared to the original Ecopath biomasses. Note tendency to estimate higher ava ilable production for lower trophic level and lower production for higher. The slope of the regression lin e is -0.06. Accepted Ecor anger runs tend to produce a very low biomass for black drum compar ed 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 thr ough 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 Ches apeake 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 Ches apeake Bay Fisheries Ecosystem Model. Catch time series are shown as dots, while th e catches predicted by Ec osim (from biomasses and fishing mortalities) are shown as lines. The time peri od (X-axis) is 1950-2002 for all plot s. Where a simulation matches the catches for all years, it indicates that the catches we re 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 dr ive 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 Oligohaline67 

### 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 (B 0 , B CC )

(Diet 0 )

(Z 0 )

Habitat area

Error pattern recognition Choice of parameters

to include in final estimation (e.g., climate anomalies) Judgmental evaluation Search Formal (Statistical) Estimation68 

### 9.3 Figure 3.

Unavailable prey B i - V i Available prey V

i Predator B

j

v ( B i - V i )

vV i a ij V i B

j a ij = search rate of predator B i = total prey biomass B

j = predator biomass
v = behavioral exchange rate V

i = vulnerable prey biomass69 

### 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 levelEcoranger/Ecopath

Black drum71 

### 9.6 Figure 6.72 

### 9.7 Figure 7.

1950 1975 2000 1950 1975 2000 1950 1975 200073 

### 9.8 Figure 8.

1950 1975 2000 1950 1975 2000 1950 1975 200074 

### 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 2000Nutrient 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 separate d into “Commercial” and “O ther fish.” The split is not used in the EwE model; it is onl y introduced as a matter of convenien ce in this report. Likewise, the invertebrates have been gr ouped into “Commercial” and “Other invert ebrates.” Where sources for diet compositions are omitted in the following data sections, they were based upon advice from local experts at the Ch esapeake Bay Ecopath Workshop (Sellner et al. , 2001) and general knowledge of these species’ troph ic behavior as reported in Ha gy (2002) and Baird and Ulanowicz (1989). Commercial fish diet co mpositions are listed in Tables 20 and 21. Diets for other fish, commercial invertebrates, other i nvertebrates, and birds are listed in Tables 22, 23, 24, and 25, respectively.


### 10.1 Appendix A: Basi c Input Parameters

10.1.1 Commercial fishes

10.1.1.1 Striped bass: young of the year (YOY), resi dent and migratory (Morone saxatilis) (FEM Groups 1, 2, and 3) Striped bass is one of the higher trop hic level predators in the Chesapeake Bay. It is a prized sport fish and of great value for both commercial and recrea tional fisheries in the Bay (Hartman, 2003). The present fisheries are the result of a successful recovery effort that be gan in the early 1980s, when catch levels were heavily curtailed af ter the stock had collapsed. By 1995, the stock was deemed to have recovered, and biomass is now often described as being at or near ‘histo ric levels’ (Hartman and Margraf, 2003). Three stanzas (life stages) were created to represent this specie s: young-of-the-year (YOY), resident, and migratory. These age divisions mirror behavioral changes exhib ited by the species on the Atlantic coast (Walter and Austin, 2003) and were based on di scussions with local st riped 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 repr esenting the age at which the ASMF C considers striped bass to be migratory. This corresponds to ages 12–83 months (1–6.9 years). The migrat ory component includes ages 84+ months ( ≥ 7 years). The leading stanza for entry of biomass for this gr oup 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 th e leading stanza. Ecopa th estimates YOY biomass and consumption rates, resident Q/B, and migrant B, based on the lead parameters, th e von Bertalanffy growth parameter (annual K = 0.11, average of FishBase values, Froese and Pauly, 2004), and an es timate of the ratio of the weight-at-maturity to the W inf 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 reside nt part of the population based on the assumption that the population is back at its ‘historic level’. Migr atory 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 reside nt and migratory fish were obtained from virt ual population analysis (VPA) (ASMFC, 2003b) and ta gging results (Latour, unpublished resu lts). The ASMFC assumes natural mortality M to be 0.15 year -1 (Smith et al. , 2000). The average F for the re ference 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, c onsumption (Q/B) values were determined by the empirical equation available in Fi shBase (Froese and Pauly, 2004), wh ich requires that estimates be provided for W inf ; average environmental temperature; fin aspect ratio (ratio of the ratio of the square of the height of the caudal fin and its surface area); a nd food type (detritivore, herbivore, omnivore, or carnivore, Palomares and Pauly, 1998). For the migr atory striped bass, Q/B was estimated to 2.3 year -1 , given the parameter estimates of T = 17 oC, W inf = 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 to tal diet in a referenced study was not considered for determination of the modeled di et composition. Striped bass YOY diets were derived from three sources: Hartman and Brandt (1995), R udershausen (1994), and Ma rkle and Grant (1970). Grass shrimp, mysids, stone crabs, and benthic invertebrates were comb ined as ‘other in/epi fauna.’ Killifish, naked gobies, silversides, and other small fi shes 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, polychaet es, and other benthic invertebrates were combined as ‘other in/epi fauna’. Weighted averages were used to determine resi dent diet using three age-classes. Diet data were weighted 1.0 for ages 1 and 2; 4.0 for ages 3+. Fo r 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 st anzas: YOY (aged 0–11 months) and adults (aged 12+ months). The adult stanza is the lead stanza for this group. Adult bi omass was based on the F derived from a coast-wide biomass dynamic model (Lee, 2003b) and catches in the Ch esapeake 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 W m /W inf = 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 ASMF C. 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 de rive coast-wide estimates of F (0.257-0.718 year 1 ) for adult bluefish age-classes. Note that coast-wi de 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 relati onship in FishBase as 3.3 year -1 assuming T =17 °C, W inf =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 contai ned in Hartman and Brandt (1995). Any component less than 1% in the diet was ignored in the model. Butterfish and harves tfish 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 th e Bay. Diet composition for the YOY stanza was derived from Hartman and Brandt (1995) an d Gartland (2006), ignoring those el ements 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 re sidency (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 biom ass was estimated by Ecopath assuming K=0.26 year -1 from FishBase (Froese and Pauly 2004) and W m /W inf = 0.1. As leading biomass we used an estimate for the adult stanza derived from an ag e-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 co ast-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 mo rtality 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 (Fro ese and Pauly, 2004) with T = 17 °C, W inf = 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 di et compositions derive d 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 winte r), representing residency time in the Bay.

10.1.1.4 Atlantic croaker (Micropogonia s undulatus) (FEM group 8)
10.1.1.4.1 1950 biomass Little stock assessment data are availa ble 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 sustaine d relatively high catches for the commercial fishery from 1997 to 2002, and the stock appears resistan t to growth overfishing, depe nding on F assumptions. Abundance estimates were calculated based on sampling area of trawl (5,402 km 2 ) and on the assumption that the trawl net efficiency is 0.4, ba sed on hydroacoustic data (Hoffman, Personal communication). Abundance estimates were converted to biomass assuming an av erage weight value from trawl catches. Densities were calculated using an area of 5,402 km 2 , and were entered into the model under the assumption that those densities apply to the entire Bay. These data were based on unpublishe d information provided by staff of the Virginia Institute of Marine Sc ience (VIMS) Chesapeake Bay Multispecies Monitoring (ChesMMAP) survey (www.fisheries.vims.edu/ches mmap/). The resulting biomass was 1.67 t · km -2
, and this biomass was used for the model in the ab sence of time trend information for croaker.
10.1.1.4.2 1950 P/B An annual total mortality for the Chesapeake Bay Atla ntic 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 empi rical relationship in Fi shBase to be 5.4 year -1 , assuming that T = 17 °C, W inf = 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 de signated as ‘imported,’ repr esentative of their sixmonth residency period in the Bay. FishBase (Froese and Pauly, 2004) s uggests that their diet is made up mostly of demersal inverteb rates 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 sing le stock along the continental East Coast (Jones and Wells, 2001). No estimate of stock size was available for the Chesap eake Bay. Ecopath estimated biomass by using an79 assumed ecotrophic efficiency for 1950 of 0.1. This lo w EE resulted in an init ial 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 assume d, 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, W inf = 57612 g, aspect ratio = 1.32, and carnivore diet.
10.1.1.5.4 Diet compositions The black drum diet composition was based on in formation made available by VIMS ChesMMAP.

10.1.1.6 Summer flounder (Paralichthy s 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 ove rfished 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 mortal ity 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, W inf = 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 derive d from information provided by the ChesMMAP 2002 Bay-wide trawl survey (www.fisheries.vims.edu/ch esmmap/) using samples from the main stem of the Bay. Anything contribution le ss than 1.0% of the diet was ignored. Bay anchovy and striped anchovy were combined as one group. The diet ca tegory ‘non-reef-associate d fish’ included spotted hake, silver perch, and northern sea robin. ‘Other in/epi fauna’ in cluded 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 juven ile 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 W m /W inf = 0.24. These weights were calculated from lengt h 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 gr oup 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 year 1 for age 2+ fish. Since fishery on age 0-1 is ne gligible, 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 juveni les 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 menhade n is poorly understood, and only qualitative knowledge of feeding characteristics was available. Much of this know ledge was synthesized by the Menhaden Working Group of the Chesapeake Research Consortium Scie ntific and Technical Advisory Committee (2002). This report suggests menhaden shifts diet from pr imarily zooplanktivorous as YOY to almost entirely phytoplanktivorous for age 1+. This qua litative ontogenetic shift was mi rrored in the modeled diet, with one third of adult diet designated as ‘imported material’ to represent th e time they spend out of the Bay ecosystem.
10.1.1.8 Alewife/herring (Alosa pseudoharengu s/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 a nd 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 li fe in the open ocean, but make annual spawning runs to rivers that feed the Bay and spend about half th e 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 ba sed 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, W inf = 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 relations hip in FishBase (Froese and Pauly, 2004) and parameters for channel catfish, T = 17 °C, W inf = 26 kg, an aspect ratio of 1.32, and carnivorous diet.
10.1.1.10.4 Diet compositions The diet composition of the catf ish group was based on knowledge of these fishes provided by local experts as part of the Chesapeake Ba y-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 th is 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 Fish Base (Froese and Pauly, 2004), and W m /W inf = 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 th at 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, W inf = 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 juve niles 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 s pot 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 mort ality 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, W inf = 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 sapi dissima) (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, W inf = 5,500 g, an aspect ratio of 1.32, and carnivorous diet.
10.1.1.13.4 Diet compositions American shad diet composition wa s derived from Walter and Olney (2 003), 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 we re 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 ‘mesozooplankt on’ and mysids as ‘other in/epi fauna.’ About one-third of the adult diet was assumed to be ‘imported’ to accoun t 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 km 2 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 a dult 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 hi gher, and are in fact mu ch higher for larvae and juveniles. Because the population being modeled w ill 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, W inf = 20g (based on length weight relationships reported by Jung, 2002), an aspe ct ratio of 1.32, a nd carnivorous diet.
10.1.2.1.4 Diet compositions Bay anchovy diet was based on Houde and Zastrow ( 1991), which contains a ge neral description of anchovy diet, and on Hagy (2002), which reported di et composition as 67% mesozooplankton, 28% microzooplankton, 4% mer oplankton (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 incl udes hogchoker, tonguefish, wi ndow 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 fo r 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 empi rical 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; W inf = 3,600 g (for winter85 flounder); W inf = 689 g (for window pane); W inf = 188g (for hog choker); an as pect 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 in formation for windowpane, winter flounder, and hogchoker in FishBase (Fro ese 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 , L inf = 43.6 cm, and no fishing mort ality. We tried to use a lower P/B for gizzard shad from Ra ndall and Minns (2000), but this produ ction rate was not sufficient to meet predation mortality requi rements 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, W inf = 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 zoopla nkton included for very large indi viduals. Local experts at the Chesapeake Bay Workshop (Sellner et al. , 2001) suggested that phytoplankt on 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), adul t gobies (Gobiidae), and black seabass ( Centropristis striata ). Tautog is considered a prized recreational species along the eastern seaboard; it is slow-growi ng 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 coastwid e 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 th e 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 taut og and assuming that T = 17 °C, W inf = 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 wa s estimated from the diet and food item entries cited in FishBase (Froese and Pauly, 2004) for tautog, toadfish, and bl ack seabass. Tautog diet was described as benthic organisms, including mussels, gastropods, and crustace ans. Toadfish diet was described as 33% fish, 25% gastropods, 25% bivalves, 5% cr ustaceans, and the remainder as plant matter. Black seabass diet was described as mostly benthic crust aceans 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 spec ies 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 Fish Base (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 ∞ ≈ L max , when no estimate of L ∞ was available. These values s uggest 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 fr om 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 the87 juveniles fed upon a mixture of benthos including filte r feeders, crustaceans, and molluscs. Sea robin adults were described as eating mostly fish, while th e juveniles targeted a vari ety 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 includ ed 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 estim ated by local experts at a Chesapeake Bay Ecopath Workshop (Sellner et al. , 2001) to be 0.8 year -1 and was assumed to be sim ilar to other forage fish groups. 10.1.2.6.3 1950 Q/B The consumption ratio for litto ral 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) cove red by the study and were also averaged over species: striped and rainwater killifish, mummichogs, silver sides, 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 F msy of about 0.05 was appropriate for sandbar shark and was representative of the likely, presen t-day fishing mortality. If it is assumed that this was the fishing rate in the Chesapeak e Bay and if it is further assumed that this fishing mortality rate can be used to back calculate surviv al, then approximately 95% of sandbar sharks survive fishing each year. Given average r ecent 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 th is corresponds to a fishing mortal ity 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 sample d sharks in four size classes: ≤ 60 cm precaudal length (PCL), 61-80 cm PC L, 81-100 cm PCL, a nd > 100 cm PCL. Giving all size classes equal weight from su mmed diet data resulted in an aver age 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 guideli nes 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 Stat es 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 Monitori ng 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 ra ys 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 dete rmination 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, thes e groups were excluded from the 1950-2002 version of the Chesapeake Ecosyste m Model due to their perceived minimal trophic89 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 ba sed 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 Atla ntic (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 wa s 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 gr oup 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 ma les 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-piscivor ous seabirds was based upon advice from D. Forsell (pers. comm.)90 10.1.4 Commercial invertebrates

10.1.4.1 Blue crab: YOY and adu lt (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, pe rs. 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 W m /W inf = 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 pr ovided by R. Lipcius (pers. comm.)

10.1.4.2 Oyster (Crassostrea virginic a) (FEM groups 32 and 33) Eastern oysters are separated into young-of-year (YOY) and ag e 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 ag e 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 wa s estimated from data in Homer et al. (2004). The assumption was made that the population is limited to mesohaline wa ters. 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 the91 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 th e exploitation rate alone has be en estimated at 50-60% (2003), i.e. , F = 0.69–0.91 year -1 . Assuming for 1950 a low exploitati on 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 curr ent 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 ha rd 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 de scription of the algorithm), assuming an average mass of 20 g, wate r 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 ctenophore s 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 lowe r the available production.
10.1.5.2.3 1950 Q/B Matishov and Denisov (1999) found a diur nal consumption rate of 7% of biomass for the medusa in the Black Sea. This would translat e to an annual consumption per unit biomass of 365 · 0.07 = 25.55 year -1

. Thus, a three- to four-month reside ncy 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 naupl ii, 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 a ssumed 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 microz ooplankton was estimated to be 140 year -1 by local experts at a Chesapeake Bay Ecopath workshops (Park and Marsha ll, 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 ha ve been noted to reach densities greater than 1000 nauplii per liter at estuarine tu rbidity 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 Ulanow icz (2000), converted to dry weight, then wet weight us ing ratios listed for annelid s 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 suspensi on 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 tota l area corresponding to 0.021, and is also used for the 1950 model due to the lack of tim e 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 es timates for the input parameters (Table 31). The pedigree i ndex was estimated as 0.45.

10.1.7 Prices Prices for the major species exploited in the Ch esapeake Bay were obtained from the Sea Around Us global price database (www.seaaroundus.org). The pric e database includes year-specific as well as consumer price index-corrected values for the va rious commodities. Prices for 2000 were used; the intention is to use prices for evaluati ng 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 conserva tive 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 Fish eries 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 ca tches (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, a nd 13, estimating total weight in catch from the numbers in the catch, 1982-2002, and average weight. Th is resulted in an aver age distribution by weight in the catch of 49% for resident and 51% for migrant striped bass (sta ndard deviation 13%). An earlier version of the striped bass catch time series is also included in the m odel. 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 recreati onal 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 a nd migrant portions of the catch were determined for 1982-2002 based on an average percent resident striped bass calcula ted 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 stripe d bass in the Chesapeake Ba y. There are two striped bass young-of-the-year biomass estimate s included in the model. The most recent version was calculated using values from Tables 13 and 18a of the 2005 St ock Assessment Report for Atlantic Striped Bass (ASMFC, 2005b). The earlier version was calculated with data from Ta bles 11 and 16 of the 2003 Stock Assessment Report for Atlantic Striped Bass (ASM FC, 2003a). Resident and migratory biomass are represented by data from the ASMF C striped bass stock assessment for 2003, the ASMFC striped bass stock assessment for 2005 (ASMFC, 2005b), and multis pecies 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 stoc ks were estimated for 1982-2002 with data from the ASMFC 2005 stock assessment (ASMFC, 2005b). The da ta from the ASMFC are estimated from a virtual population analysis model (VPA), i.e. , it recreates the population hi story by receding back in time and adding catches back into the population, ba sed on an assumed natural mortality. Values of striped bass resident combined F for 1959–1984 are from Gibson (1993) Ta ble 3, Run 2, and migratory combined F for 1959-1981 from Gibson (1993) Appendix Ta ble 3, Run 3. The fish ing mortalities for the period before 1959 are values estimated by Ecopath. A second migratory fi shing mortality for 1988- 2002 is included for the Maryland sp ring spawning stock in the Chesap eake Bay. This estimate is96 provided by Table 24 of the 2005 Stock Assessment Re port 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 i ndexed did not have fishing seasons . Striped bass was the exception, and the blanket application of th e technique to make annual estimat es 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 woul d apply early) until about 1999. Generally, it was only possible to model a stable or ve ry slightly declining striped bass biomass in the Bay with the annual MRFSS index. A very different trend for resident st riped bass was found if the September-October wave of the MRFSS data was used. This is the period wh en 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 ba sis 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, mi gratory biomass has increas ed in the Bay (May-June wave data). These would be much la rger fish than residents. A gene ral problem with hindcasting striped bass landings for the Bay has been the unknown relati onship of recreational la ndings to commercial landings prior to the MRFSS survey. Eight estimates of Maryland recreational landings were compiled from creel surveys conducted between 1962 and 1984. Th ese estimates were used in regression analysis with commercial landings to estimate the trend in recreational harvest b ack to 1950. The regression indicated that recreational landings became a d ecreasing fraction of tota l 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 incorporatin g an additional loss function (a function of lesions presenceabsence which was used as an indicator of health and subsequent mortalit y). It was tuned to bay estimates of F and M from tagging. Landings cons isted of MRFSS and commercial reports for 1950- 2004, recreational landin gs (Maryland and Virginia) predicted fr om 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 additi onal increasing loss term from 1994 to 2004 and held constant at a very low level for all years previ ous. The analysis was conducted in th ree stages. An initial estimate of production parameters (r and K) a nd 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 pred icted landings back to 1950 to derive a history of reside nt 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 fi sh creel, strict seasons, mesh re strictions). It was assumed that production parameters would be the same regardless of size changes, but exploi table biomass prior to 1985 would be 12-inch fish and larger, while post-mo ratorium exploitable bi omass 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 wa s determined by adding the recreational catch for Maryland and Virginia as reported by MRFSS to commerc ial catch of bluefish in the tidal waters of the Chesapeake Bay, as reported by NMFS for the y ears 1982-2002. The recreati onal to commercial ratio for years prior to 1982 was estimated using the medi an value for years 1982- 2002 from Table 1 of the bluefish assessment in the 41 st SAW Assessment Report (NFSC, 2005), 2.52. The recreational to commercial ratio was then applied to the commerci al catch values for the Chesapeake reported by the National Marine Fisherie s Service (Fisheries Statistics and Economics Division, Silver Spring, Maryland) for 1950 through 2002 to dete rmine recreational portions of the Chesapeake catch. These two values were added together to determine total ca tch for the Chesapeake in the time period. It was assumed that there were no comme rcial discards, based on the 41 st SAW Assessment Report (NFSC, 2005). A second estimate of bluefish catch wa s 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 us ing data from Tables 10 and 20 of the NFSC 41 st 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 fishi ng mortality rates were estimated using a singlespecies assessment model, where th e combined recreational and commer cial catch data was used to drive the assessment model. Furthermore, an inde x of 0- to 12-month bluefish biomass and adult biomass for the entire time period was constructe d 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 relati onships from Wigley et al. (2003) (provided in Table 6). Prior to 1972, there is no survey information on relative ab undance, and the uncertainty associ ated 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 on line database (www.st.nmfs.gov) for the years 1950-2002. A ratio between recreationa l and total catches for weakfi sh 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 th e 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 pr esented in Table 9. Growth para meters are given in Table 8.

10.2.1.4 Atlantic croaker (Micropogonia s undulatus) (FEM group 8)
10.2.1.4.1 1950–2002 catches Commercial catches of Atlantic croaker for th e Chesapeake region were extracted from the NOAA Fisheries Statistics and Economics Division online data base (www.st.nmfs.gov) for the years 1950- 2002. A ratio between recreational a nd commercial catches for Atlantic croaker was calculated from data in Table G1 of the 2003 Atlantic Croaker St ock Assessment report (A SMFC, 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 commercia l catch ratio was used to determ ine the total catch based on NMFS data for the period 1950 through 1980. For 1981-2002, MRFSS estimates of r ecreational 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 Assessme nt (ASMFC, 2005a) for the years 1981-2002. For the time period 1950-1980, the median recr eational: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 Croake r 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 si nce 1981 for Maryland and Virginia state waters were extracted from the Marine Recreational Fi shery 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 dr um (apart from the catc hes discussed above).99 10.2.1.6 Summer flounder (Paralichthy s dentatus) (FEM group 10)
10.2.1.6.1 1950–2002 catches Commercial catches for ‘flatfish’ in the Chesapea ke region were extracted from the NOAA Fisheries Statistics and Economics Division online databa se (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 Ma ryland and Virginia state waters were extracted from the Marine Recreational Fishery Statistics Survey (MRFSS). Pr ior to 1981, a recreati onal:commercial ratio was used to estimate the recreational portion of th e catch. This ratio is the median value of recreational:commercial ratios determined for 1998-2004 based on data from the 41 st 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 fl ounder 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 41 st Northeast Regional Stock Assessment Workshop Assessment Report (NFSC, 2005). Fishing mortality data from the 35 th Northeast Regional Stock Assessment Workshop Assessment Report (NFSC, 2002b) was used for summer flounde r for the period from 1982 through 1997 and from the 41 st Northeast Regional Stock Assessment Work shop Assessment Report (NFSC, 2005) for the period from 1998 through 2002. Su mmer flounder biomass data ar e 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 Ba y has increased from 20% before the mid 1960s, to 50% in the 1970s, to mo re 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 th e Chesapeake Bay, and 2) use the Chesapeake Bay catches as reported. There is a catc h 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 10 00 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 tw o 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 pr oportion 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 re presentative for the total Atlan tic 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 Rive r Fisheries Commission menhaden pound net index was used as an estimate of age 2+ abundance, and th e 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 st ock 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 fish ing mortality values for Atlantic menhaden. Menhaden biomass and fishing mortality data ar e presented in Table 12.

10.2.1.8 Alewife/herring (Alosa pseudoharengu s/Clupea harengus) (FEM group 13)
10.2.1.8.1 1950–2002 catches Commercial catches of alewife for the Chesapeak e 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 fisher ies for these species, becau se neither species is included in the NOAA recreational fish eries 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 herri ng 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) th an 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 data base (www.st.nmfs.gov) for the years 1950- 2002. The recreational ca tch 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 Poto mac River accounted for approximately 60% of the American eel catch in the United States (ASMFC, 2000). Catches were used to estimate F values us ing 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 li mited to freshwater and estuarine areas, the Chesapeake region as defined in th is 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. He nce, 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 reducti on 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 us ing 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+ we re available for ‘upper ri vers’ from the Virginia Institute of Marine Scien ce juvenile trawl survey. These were used here for comparison with the Ecosim simulations. The multispecies production method was us ed to estimate catch, biomass, and fishing mortality. White perch biomass and fishing mo rtality 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 on line database (www.st.nmfs.gov), and the ratio between recreational and commercia l catches reported by Desfosse et al. (2001). A second catch series was estimated using the multispecies production method, with combined estimat es 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 us ed to estimate catch, fishing mortality, and biomass estimates for spot from 198 1 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 s apidissima) (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 data base (www.st.nmfs.gov) for the years 1950- 2002. The recreational catch was assu med to be minimal in comparis on and therefore was ignored. The catches were used to obtain F values base d 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 th e geometric mean of fish caught at the Conowingo Fish Lift on the Susquehanna Rive r in the upper Chesapeake Bay (Bob Sadzinski, MDNR, pers. comm.). This estimate does not adequately represent trends Bay-wi de; 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 abunda nce for bay anchovy were used fo r 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 estimat e fishing mortality and biomass for gizzard shad from 1958 through 2000. F-values were impressively lo w and we chose to ignore them as they were below detection range. Commercial catches of giz zard 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 gizza rd shad: the Maryland gizza rd shad juvenile index (1958-2004; E. Durell, Maryland Department of Natu ral Resources, pers. comm.), two estimates of catch per effort at the Conowingo fish lifts (east or west catc h 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 spri ng 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 trie d, but eventually a long- term model (1958-2004) was developed based on the Maryland ju venile index time-series (GSJI; geometric mean for Choptank, Nanticoke, and Potomac rivers and Head-of-Bay). This model was acceptable because it was the only103 one that came close to supplying an es timate of intrinsic rate of increase (0.66) that made sense for a fish with a short life-span. Most model runs were suite d to much more long-lived species. The GSJI was comprised of young-of-year through la rge adults; some extr a variability was probably caused by the inclusion of YOY that would not have been present in the harvest. Exploratory analyses indicated that NO 3 loading estimates of Hagy et al. (2004) was significantly relate d to the GSJI (1958-2000, r 2 = 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 NO 3 loading through 2000. Loading was estimated for 2001- 2004 from the relationship of mean annual Susq uehanna River flow at Conowingo Dam and NO 3 loading estimates during 1968-2000. NO 3 loading was first plotted versus river flow for 1945-2000. Residuals from the regression of these data (r 2 = 0.47, P < 0.001) increased steadily from positive to negative over the course of the time-series and intercepted th e x-axis at about 1968. The NO 3 loading data were then plotted as 1945-1968 and 1969-2000 time series. Thes e data strongly suggested two distinct relationshi ps of flow and NO 3 loading (different slopes). Th e 1969-2000 time-series was used and the fit of the regression of flow and NO 3 loading improved (r 2 = 0.80, P < 0.001); however, a serious serial trend was evident in the residuals. Autoregre ssion analysis (Proc Autoreg in SAS) was used with a one-year lag, and this rem oved the patterning in the re siduals and improved fit (r 2 = 0.94, P < 0.001). This adjusted relationship was used to estimate NO 3 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 pred ator function (striped bass predation as a linear function of bass biomass), and th e third used standardized NO 3 loading (standardized to the time-series median) as a multiplier for the intrinsic rate parame ter. Rose (2004) used a similar approach to model changes in cod productivity. A run with both pred ation and changing produc tion due to shifting NO 3 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 an d Virginia) commercial harvest wa s used; discard estimates were not available. A genetic algorithm super solver (Evolve r, Palisade Corporation) was used to estimate parameters. The previous year’s estimated index was th en used to predict the following year’s estimate. The spreadsheet version of the model combined w ith the genetic algorithm pr ovided a great deal of flexibility for trying different model variations. Th e time-series for this model was shortened to 1964- 2004, partially because a menhaden model with this tim e-span, was used as the basis for this model and partially to minimize patterning in residuals that occurs in the earlie st years of the time-series. This patterning could reflect sh ifts 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 lim ited. For example, for tautog, the total catch reported in the NOAA commercial catch data is 316 t fo r the years 1950-2002 combined. Cons equently, 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 include d 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 adu lt (Callinectes sapidus) (FEM groups 30 and 31)
10.2.4.1.1 1950–2002 catch Catch data were included from the 2005 Stock Assessm ent of the Blue Crab in Chesapeake Bay (Miller et al ., 2005). The total Chesapeake Bay cat ch time series were from the a ssessment 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 availa ble 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 orig inal abundance indices105 range around an average value of zero and may, therefor e, be negative in some years, but have been rescaled for this study. Blue crab biomass, effort, a nd fishing mortality estimates are presented in Table 17.

10.2.4.2 Eastern oyster (Crassostrea virg inica) (FEM groups 32 and 33)
10.2.4.2.1 1950–2002 catch Oyster catches were extracted from the NOAA onlin e 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) fo r the Maryland harvest were made available by the Maryland DNR Shellfish Division and included in the oys ter stock reduction analys is (Table 18). Oyster abundance and fishing mortalities were estimated fr om a stock reduction analysis tuned to a CPUE series and are shown in the same table. The oyster fi shing 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 occu rrence 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 th e 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 include d 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 ab undance of microzooplankton were ba sed 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-da tes 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, S AV acreage for those segments that were fully surveyed was totaled and divided by the total surface area of the segm ents. 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 pr oduction 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 · km 2 · 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 SBMigratory SBmigratory Bluefish Bluefish Weakfish Atl. croaker Atl. croaker Black drum Summerflounder 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 14.641108 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 28.183109 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 · km 2 · 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 0.042110 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 0.048111 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 L oo (maximum length) 95.5 Parameter Value Estimated K (growth constant) 0.110 Log_R o (unfished recruits) 6.095 1
T o (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 W m /W oo 0.1 Shape 0.5 -1 Maturity ogive parameters Delta 0.309 3 length @ 50% mature (lh) 71.1 shape (g) 0.25112 

### 11.4 Striped bass biomass Table 4. Biomass (relative) of striped bass in the Chesapeake Bay Fisheries Ec osystem Model. Resident striped bass includes year classes 1 to 6 (12 to 83 months of age). Esti mated 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 3183113 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 0.370114 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 0.113115 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 us ed 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 L oo (cm) (maximum length) 87.2 Parameter Value Estimated K (year -1 ) (growth constant) 0.26 Log_R o (unfished recruits) 7.729 1 T 0 (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 W m /W oo 0.2007328 shape 0.5 -1 Maturity ogive Parameters delta 0.01 3 length @ 50% mature (lh) 35.433408 shape (g) 0.3544103116 

### 11.7 Bluefish biomass an d fishing mortality Table 7. Bluefish biomass (B, re lative) 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 0.131117 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 0.291118 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 L oo 68.6 Parameter Value Estimated K 0.35 Log_R o 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 W m /W oo 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 0.671119 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 2.153120 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 bioma ss 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 0.280121 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 0.110122 

### 11.11 Summer flounder bioma ss and fishing mortality
Table 11. Summer flounder relative biomass (B) and fishing mortal ity (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 0.650123 

### 11.12 Menhaden biomass an d fishing mortality Table 12. Menhaden biomass (B, relative values) a nd 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 0.667124 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 re lative 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 13098125 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 indi ces, 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 1.880126 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 0.174127 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 0.360128 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 fishi ng 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 2005129 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 3.654130 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, e ffort, and fishing mortality Table 18. Eastern oyster effort (rela tive), 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 6.743131 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 a bundance (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 valueover 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 ar e 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 0.464133 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 othe r commercial fish species
Table 21. Diets for other commercia l fish species. Diets are expresse d 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 0134 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, expre ssed 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 elasmo
branch s 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 0.015135 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 comm ercial invertebrates Table 23. Diet compositions for commercial inve rtebrates. Diet compositions are expressed as proportions and are on a volume or wei ght 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 invertebrate s expressed as proportions on a weight or volume basis. The diet compositions are base d 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 0.3136 44 Phytoplankton 1 0.28 0.5 0.4 45 Detritus 0.25 0.2137 

### 11.25 Diet composition for birds Table 25. Diets for piscivorous a nd non-piscivorous birds. Both diet compositions are based on general knowledge for the groups, and are expressed as propor tions (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 gi ven) included in the nonpiscivorous group of the Chesapeak e 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 valisineria138 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 Fish eries Ecosystem Model. Common names and scientific names are presented. Based on input from meso trophic 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 platyrhynchos139 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 imp act on the relative P/B for soft and hard clam. Data sources: www.vdem.stat e.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 cl ams, 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, alt hough 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 0.6141 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 0.2142 

### 11.30 Phytoplankton chlo rophyll content
Table 30. Estimate of relative chlorophyll content for the Ches apeake 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 7.7015143 

### 11.31 Confidence intervals fo r 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 cat ches of exploited groups in the Chesapeake Bay. For group 13, alewife/herring, price for alewife was us ed as these dominated th e landings (98.6%). For group 15, catfish, average price fo r the two most common species (blu e 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) 9.7014511.33Multispecies Production stock assessment model parameter valuesTable 33. MSP models and their extensions. B =biomass; r = intrinsic rate of increase; B0= biomass estimate to initiate time-series; t = timein years; H = harvest; c is a constant defined in “AdditionalMortality Source” column; and A = prey biomass at satiation in Type III predatorprey function. Note that units of measure (Unit) vary among species. If“Additional Mortality Source” is blank, fishing mortality is the mainsource of loss.Species Equation Unit Bo r K c A Additional Mortality Source Time-seriesAtlanticcroakerBt= Bt-1+ rBt-1(1-(Bt-1/ K)) - Ht-1Mt 4.421031.96 1.19 1051981-2003Spot Bt= Bt-1+ rBt-1(1-(Bt-1/ K)) - Ht-1– [(cPt-1(Bt-1)2)/(A2+ (Bt-1)2)]Lbs 8.851081.09 4.80 1083.72 1.48108P = striped bass bay biomassestimated as below;predation losses;c=maximum per capitaconsumption.1981-2004White perch Bt= Bt-1+ rBt-1(1-(Bt-1/ K)) - Ht-1Lbs 4.051060.48 1.64 1071981-2003Striped bass Bt= Bt-1+ rBt-1(1-(Bt-1/ K)) - Ht-1– (Bt-1L c)Kg 5.781060.64 6.62 1073.94 L = lesions; disease /starvation losses; c = scalarfor lesions to loss1950-2004Weakfish Bt= Bt-1+ rBt-1(1-(Bt-1/ K)) - Ht-1– [(cPt-1(Bt-1)2)/(A2+ (Bt-1)2)]Mt 1.621042.83 5.54 10312.92 4.99105P = striped bass coastbiomass; predation,competition, migrationlosses; c=maximum percapita consumption1981-2003Bluefish Bt= Bt-1+ rBt-1(1-(Bt-1/ K)) - Ht-1– [(cPt-1(Bt-1)2)/(A2+ (Bt-1)2)]Mt 1.171051.54 3.05 1047.42 4.32 104P = striped bass coastbiomass; predation,competition, migrationlosses; c=maximum percapita consumption1981-2003146AtlanticmenhadenBt= Bt-1+ rBt-1(1-(Bt-1/ K)) - Ht-1Mt 2.291052.27 4.54 1041965-2004GizzardshadBt= Bt-1+ rCBt-1(1-(Bt-1/ K)) - Ht1N 1.581070.66 1.51 106C = NO3 load standardizedto median1964-2004Oyster Bt= Bt-1+ rBt-1(1-(Bt-1/ K))- Ht-1– Dt-1Bt-1Lbsmeat5.851070.57 7.23 107D = estimate percent ofoysters killed by disease1975-200611.34Submerged Aquatic Vegetation biomass indexTable 34. Submerged Aquatic Vegetation Indexbased on the VIMS SAVMonitoring Program dataYearSAV BiomassIndex(ID/surveyed)1971 0.025337197219731974 0.0158841975197619771978 0.01469619791980 0.0104361981 0.011951198219831984 0.0177391985 0.0175191986 0.0174491471987 0.01770919881989 0.0212911990 0.0214141991 0.022591992 0.0251831993 0.0260831994 0.0233481995 0.0213791996 0.0226521997 0.0247111998 0.0220511999 0.0224232000 0.0240092001 0.0309242002 0.031126