Design and Parameterization of 

the Chesapeake Bay Atlantis Model: 

A Spatially Explicit End-to-End 

Ecosystem Model 

Thomas F. Ihde, Isaac C. Kaplan, Elizabeth A. Fulton, Iris A. Gray, 

Mejs Hasan, David Bruce, Ward Slacum, and Howard M. Townsend 

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

NOAA Technical Memorandum NMFS-F/SPO-166 

September 2016

Design and Parameterization of the Chesapeake 

Bay Atlantis Model: A Spatially Explicit End-to-End 

Ecosystem Model 

Thomas F. Ihde1, Isaac C. Kaplan2, Elizabeth A. Fulton3, Iris A. Gray2, Mejs Hasan4, David 

Bruce4, Ward Slacum5, and Howard M. Townsend4 

1

NOAA Chesapeake Bay Office, 410 Severn Avenue, Suite 207A, Annapolis, MD 21403; 

Tom.Ihde@noaa.gov 

2

Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries 

Service, NOAA, 2725 Montlake Blvd E, Seattle, WA 98112 

3

Commonwealth Scientific and Industrial Research Organization, Marine and Atmospheric 

Research, GPO Box 1538, Hobart, Tasmania 7001, Australia 

4

NOAA Chesapeake Bay Office/Cooperative Oxford Laboratory, 904 South Morris Street, 

Oxford, MD 21654 

5

Oyster Recovery Partnership, 1805A Virginia Street, Annapolis, MD 21401 

NOAA Technical Memorandum NMFS-F/SPO-166 

September 2016

U.S. Department of Commerce 

Penny S. Pritzker, Secretary 

National Oceanic and Atmospheric Administration 

Kathryn D. Sullivan, Administrator 

 

National Marine Fisheries Service 

Eileen Sobeck, Assistant Administrator for Fisheries
ii

Recommended citation:

Ihde, T. F., I. C. Kaplan, E. A. Fulton, I. A. Gray, M. Hasan, D. Bruce, W. Slacum, and H. M. 

Townsend. 2016. Design and parameterization of the Chesapeake Bay Atlantis Model: A 

spatially explicit end-to-end ecosystem model. U.S. Dept. of Commer., NOAA. NOAA 

Technical Memorandum NMFS-F/SPO-166, 145 p. 

Copies of this report may be obtained from: 

NOAA Chesapeake Bay Office 

410 Severn Avenue, Suite 207A 

Annapolis, MD 21403 

Or online at:

http://spo.nmfs.noaa.gov/tm/ 
iii

Table of Contents 

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

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

Executive Summary ....................................................................................................................... xi

Acknowledgments........................................................................................................................ xiv

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

Model Extent and Design................................................................................................................ 3

Projection Years .............................................................................................................................. 6

Physical Model................................................................................................................................ 7

Water Flux, Temperature, and Salinity ....................................................................................... 7

Nutrient and Sediment Inputs ..................................................................................................... 9

Biological Model .......................................................................................................................... 14

Nutrients .................................................................................................................................... 14

Primary Production ................................................................................................................... 15

Invertebrates .............................................................................................................................. 17

Vertebrates ................................................................................................................................ 19

Predation ................................................................................................................................... 20

Spawning and Recruitment ....................................................................................................... 21

Habitat Associations ................................................................................................................. 22

Model Calibration ......................................................................................................................... 24

Model Performance ....................................................................................................................... 24

Initial Calibration: No Fishing .................................................................................................. 24

Model Application ........................................................................................................................ 26

TMDL Scenario ........................................................................................................................ 26

TMDL with expected temperature increase .............................................................................. 26

Discussion ..................................................................................................................................... 27

Current and Future Work .......................................................................................................... 28

Figures 8a–10e and Tables ............................................................................................................ 29

Literature Cited ............................................................................................................................. 88

Appendix A: Map Information ................................................................................................... 104

Chesapeake Bay Atlantis Model ............................................................................................. 104
iv

Construction Log for Dynamic Boxes (24 AUG 2010) .......................................................... 104

Bathymetry Metadata .............................................................................................................. 114

Sediment Distribution Metadata ............................................................................................. 114

Appendix B: Biological Processes Modeled in Atlantis ............................................................. 115

Appendix C: Vertebrate Life History ......................................................................................... 117

Fish .......................................................................................................................................... 117

Mammals ................................................................................................................................ 118

Reptiles ................................................................................................................................... 119

Birds ........................................................................................................................................ 120

References—Life History ........................................................................................................... 123

Appendix D: Diets ...................................................................................................................... 133

Fish .......................................................................................................................................... 133

Mammals ................................................................................................................................ 137

Reptiles ................................................................................................................................... 137

Birds ........................................................................................................................................ 138

Invertebrates ............................................................................................................................ 139

References—Diets ...................................................................................................................... 141
v

List of Figures 

1a. Spatial structure of CAM. The model consists of 97 irregular polygons determined by 

salinity, depth, bottom type (mainstem only), and management boundaries. ..................... 4 

1b. Detailed river structure of CAM ............................................................................................... 5 

2. Vertical structure of CAM ......................................................................................................... 6 

3. Chesapeake Bay Water Quality Model (fine-scale cells) superimposed on Atlantis model 

polygons (yellow) (a), and Atlantis model polygons superimposed on Chesapeake Bay 

Water Quality Model (b). .................................................................................................... 8

4. Chesapeake Bay 2003 segmentation scheme (78 segments) ................................................... 10 

5. Nitrogen inputs for years 1991–2000, as specified by Chesapeake Bay Phase 5.3 Community 

Watershed model. For the Atlantis simulations presented here, values from 2000 were 

applied for all projection years, since Atlantis simulations began January 1, 2002. ........ 12 

6. Sediment inputs for years 1991–-2000, as specified by Chesapeake Bay Phase 5.3 

Community Watershed model. For the Atlantis simulations presented here, values from 

2000 were applied for all projection years, since Atlantis simulations began January 1, 

2002................................................................................................................................... 13 

7. Refuge status (≤ 1) to prey from cover of biogenic habitat group BFF, SG, or MA at differing 

levels of Acov. Low Acov values are appropriate for groups that benefit little in the 

presence of biogenic habitat, while high values of Acov (e.g., > 0.5) designate groups that 

depend on biogenic habitat as refuge from predators. ..................................................... 23 

8a - k. Biomass results for each functional group after initial tuning phase. Biomass is 

represented in metric tons summed over the entire model extent. X-axis shows years 

from start of simulation. Dashed line indicates biomass estimate at the start of the 

simulation. Results are plotted four times per year (at a 91.25-day interval); wide bands 

that appear solid are due to plotting the quarterly model predictions in close proximity to 

one another, and represent annual variation predicted for each group. Group names title 

each subplot, while group codes appear directly below names; Atlantis "long-name" for 

each group appears below each x-axis. ............................................................................. 30 

9a - e. Total numbers for each age class per vertebrate functional group after initial tuning 

phase. Numbers are summed over the entire model extent. In key, "R" represents the age 

class of new recruits. X-axis shows years from start of simulation. Results are plotted 

four times per year (at a 91.25-day interval); the wide range of quarterly values seen in 

some plots represents annual variation predicted for each group. Group names title each 

subplot, while group codes appear directly below names; Atlantis "long-name" for each 

group appears below each x-axis.. .................................................................................... 41 

10a - e. Ratio of reserve nitrogen to initial reserve nitrogen for each age class per vertebrate 

functional group after initial tuning phase. In key, "R" represents the age class of new 

recruits. X-axis shows years from start of simulation. Results are plotted four times per 

year (at a 91.25-day interval); the wide range of quarterly values seen in some plots 

represents annual variation predicted for each group. Reserve nitrogen represents weight-
vi

at-age that is related to muscle, fat, reproductive parts, and other soft tissue. As the ratio 

increases above 1, individuals become fat; as the ratio declines below 1, individuals begin 

to starve. X-axis shows years from start of simulation. Dashed line indicates biomass at 

the start of the simulation.. ................................................................................................ 46 

Appendix A: Map Information 

Spatial variables .......................................................................................................................... 104 

A-1. Salinity ................................................................................................................................ 104 

A-2. Bay sections ....................................................................................................................... 105 

A-3. Bathymetry ......................................................................................................................... 106 

A-4. Bottom hardness ................................................................................................................. 107 

Polygon construction .................................................................................................................. 108 

A-5. Create bathymetry polygons ............................................................................................... 108 

A-6. Extract bay sections ........................................................................................................... 109 

A-7. Intersect salinity zones ........................................................................................................ 109 

A-8. Draw model boxes .............................................................................................................. 110 

A-9. Convert final shapefile ........................................................................................................ 111 

A-10. Check winding order ......................................................................................................... 111 
vii

List of Tables 

1. CAM polygon characteristics. Boxes aggregate Chesapeake areas by location, salinity, depth, 

and bottom type (mainstem boxes only). Box 0 is a non-dynamic "boundary box" 

necessary to allow for the exchange of water nutrients and migratory groups to and from 

the dynamic model domain, but other processes are not explicitly modeled for this box. 

"---" indicates characteristic is not modeled for that box.................................................. 51 

2. Volume (m3) for each layer of each box in CAM. The occurrence of a '0' indicates that box is 

too shallow to include all possible water column layers. The first water column layer 

listed is always the deepest found in that box. For example, Box 4 is deep enough to have 

only two water column layers (≤ 5 m). Box 0 is a non-dynamic "boundary box" necessary 

to allow for the exchange of water nutrients and migratory groups to and from the 

dynamic model domain, but other processes are not explicitly modeled for this box. .. 54 

3. Initial concentrations for water quality parameters applied to the Chesapeake Atlantis Model, 

summarized by Segment (2003 segmentation scheme: 

http://www.chesapeakebay.net/maps/map/chesapeake_bay_2003_segmentation_scheme_

codes, accessed August 2013) and based on 5 years of EPA observations made during the 

months of December and January (2000–2004). The number of usable samples available 

for each Bay segment is shown (n) for each water quality parameter. Chlorophyll a 

observations (CHLA) were used to estimate initial concentrations of picophytoplankton, 

large phytoplankton, and dinoflagellates (CAM groups PS, PL, and DF, respectively). 

Dissolved inorganic nitrogen observations (DIN) were used to estimate NH3, NO2, and 

NO3 concentrations. Particulate nitrogen (PN) and dissolved organic nitrogen (DON) 

observations were used to estimate refractory and labile detritus (CAM groups DR and 

DL, respectively). Silica observations (SIF) were used directly to estimate initial silica 

concentrations for the model. Standard deviation for each Bay Segment mean is shown in 

parentheses; "---" indicates no data available.. ................................................................. 57 

4. Distribution of Bay Segments_2003 data in CAM boxes. ........................................................ 59 

5. Primary producer functional groups and basic life history parameterization. Growth, 

clearance, lysis, and mortality rates are post-calibration values. Maximum initial 

concentration and biomass (bay-wide, wet-weight) are estimates from January 1, 2002, 

used to initialize the model. "Q-mort" is quadratic mortality, used in the model to limit 

uncontrolled, explosive growth of any particular group. "Excess. DIN mort." is extra 

mortality due to excess dissolved inorganic nitrogen. "Refuge habitat" indicates whether 

the group can be used by other groups to decrease its availability to predators, see main 

text for further details; "---" indicates setting not applicable to that group.. .................... 61 

6. Habitat composition (proportions) of boxes in the Chesapeake Atlantis Model. '0' indicates 

habitat type is not found in that CAM box. "NA" = not applicable. Biogenic habitat 

proportions shown here correspond to Cover (Equation 18) available for refuge to prey as 

described in Habitat Associations. .................................................................................... 63 

7. Expected microphytobenthos (CHLA) concentrations in sediments of the Chesapeake Bay, 

based on M. Kemp (personal communication) and field observations presented in Rizzo 
viii

and Wetzel (1985). Concentrations are mg CHLA/m2. “Soft” includes all sediments other 

than clean sand. Bottom type was not identified in tributaries; therefore, intermediate 

CHLA concentrations were assumed for tributary boxes.. ............................................... 65 

8. Invertebrate functional groups and basic life history parameterization. "2D" or "3D" indicate 

how many spatial dimensions that the group is modeled in. Growth, clearance, and 

mortality rates are post-calibration values. Maximum initial concentration and initial 

biomass (bay-wide, wet-weight) are estimates from January 1, 2002, estimates used to 

initialize the model; however, biomass for each group is not available until the day of the 

year that group migrates into the model. "Q-mort" is quadratic mortality, used in the 

model to limit uncontrolled, explosive growth of any particular group. "Refuge habitat" 

indicates whether the group can be used by other groups to decrease its availability to 

predators, see main text for further details. ..................................................................... 66 

9. Biological characteristics and timing of invertebrate migrators in CAM. Sea nettles and crab 

do not actually leave the Bay system over the winter, but rather encyst (sea nettles) and 

enter torpor (crabs); both conditions effectively remove these groups from the model 

domain and are consequently modeled as migrations. "NA" = not applicable.. ............... 68 

10. Species composition of the four aggregate benthic invertebrate groups in CAM based on the 

Benthic-Index of Biological Integrity (B-IBI) of the Chesapeake Bay Program: benthic 

deposit feeders (BD), Macoma spp. (BFS), benthic carnivores (BC), and benthic filter 

feeders (BFD). These groups are a subset of species collected during the Long-Term 

Benthic survey during 2002–2009 that represented either 90% of the total abundance 

(ABUN), 90% of the total biomass (BIOM), or both. National Oceanic Data Center 

(NODC) taxonomic codes are superseded by Taxonomic Serial Number (TSN) 

designations; "---" indicates no TSN exists for that species. Abbreviations for the B-IBI 

are IN = Interface Feeder, CO = Carnivore and Omnivore, SU=Suspension Feeder, 

P=Predator, and M= Macoma clams.. ............................................................................... 69 

11. Biomass estimates (wet weight) for vertebrate functional groups at start of model run (2002), 

specific to Chesapeake Bay, and information sources used. Estimates for aggregate 

groups represent summary values weighted by relative abundance of each species in the 

group in Chesapeake Bay. Preliminary biomass estimates were based on a variety of 

available sources shown in the final column of the table. Preliminary biomass estimates 

were fed into an Ecopath model that was built expressly for balancing the initial CAM 

groups; "---" indicates no information was available to use for Ecopath; consequently, 

starting biomass for such groups was estimated solely by the Ecopath model. Balanced 

Ecopath estimates for all birds, reptiles, and bottlenose dolphin differed only slightly 

from preliminary biomass estimates, so the preliminary estimates were used as starting 

biomass for CAM in these instances. Landings data were obtained from the NOAA 

Office of Science and Technology for the calendar year 2002 (personal communication; 

commercial: M. Lewis, 1/19/11; recreational: L. Dolinger-Few, 1/31/11). Bird data 

obtained from the Cornell Lab of Ornithology at: http://bna.birds.cornell.edu/bna/species 

(accessed October 2010). SA = stock assessment.. .......................................................... 73 

12. Vertebrate life history parameterization. Parameters for aggregate groups represent summary 

values weighted by relative abundance of each species in the group. Natural mortality, M, 

was used only to set up the initial age structure for model runs. Likewise, k, Linf were 
ix

used only to parameterize initial size structure, where k is the VonBertalanffy growth 

coefficient and Linf is theoretical maximum size (cm). Growth of birds and dolphin were 

assumed to follow a Gompertz growth curve; "a" and "b" are constants of the length–

weight relationship (W=aLb). "Am" values indicate age class of group at first maturity 

and are post-calibration values. "Ar" is age at recruitment to the modeled population (i.e., 

approximate age of transformation from larval to juvenile stage). BHalpha and BHbeta 

are based on the Beverton-Holt recruitment model, but these values are specific to the 

Atlantis model, where BHalpha is the estimate of the maximum number of recruits that 

can be produced (system-wide), and BHbeta is the biomass of mature adults (both sexes) 

that produces one-half Bhalpha. RFixed is the fixed number of new recruits per 

individual (per year). Linear mortality is set at '0' for all vertebrates and is not included in 

the table. Quadratic mortality values are included in the model to limit uncontrolled, 

explosive growth of any particular group. Efficiency is the portion of food not 

immediately lost to sloppy feeding by each group. "NA" = not applicable. Additional 

details for life history parameterization and references are found in Appendix C. 

Common name corresponding to code for each vertebrate group modeled can be found in 

Table 11 or Table 13.. ....................................................................................................... 76 

13. Post-calibration growth parameter settings used in the functional response for each vertebrate 

functional group and age class (mgN/day) in CAM (corresponds to parameter "mum" in 

bio.prm file). ..................................................................................................................... 78 

14. Post-calibration clearance rates used in the functional response for each vertebrate functional 

group and age class (mg3/mgN/day). ............................................................................... 80 

15. Timing of reproductive and migration events for vertebrate groups in CAM. The terrapin 

group (FVV) remains in the Bay system throughout the year; however, for modeling, it is 

assumed the group effectively migrates when they overwinter buried in the mud, since 

they do not actively affect other groups in the model during this period. Striped bass 

(FBP) is the only population that has a partial migration in CAM (we assume one-half the 

population is resident year-round in the Bay); all other migrating populations move as a 

whole. DOY = day of the year. NA indicates that the timing parameter does not apply to 

that non-migratory group.. ................................................................................................ 81 

16. Pre-calibration prey availability matrix. A '0' indicates there can be no availability of the 

prey to the predator in the model. Predator groups are listed on the left side of the matrix 

while prey groups are listed along the top. As availability increases, a predator's impact 

on prey abundance increases, given both spatial overlap of predator and prey, and limited 

habitat populations (BFF, SG, MA), which provide refuge from predation for many prey 

groups. Where data was lacking, but the authors believe a predator-prey relationship 

exists (e.g., soft-bodied invertebrate remains are not typically identifiable in vertebrate 

gut content studies), a small, arbitrary number was added to predator diet to allow such 

groups to interact in the model. When no prey information was available for a particular 

predator, assumed prey items were all assigned a uniform availability. .......................... 82 

17. Post-calibration prey availability matrix. A '0' indicates there can be no availability of the 

prey to the predator in the model. Predator groups are listed on the left side of the matrix 

while prey groups are listed along the top. As availability increases, a predator's impact 

on prey abundance increases, given both spatial overlap of predator and prey, and limited 
x

habitat populations (BFF, SG, MA), which provide refuge from predation for many prey 

groups. Where data was lacking, but the authors believe a predator-prey relationship 

exists (e.g., soft-bodied invertebrate remains are not typically identifiable in vertebrate 

gut content studies), a very small, arbitrary number was added to predator diet to allow 

such groups to interact in the model. When no prey information was available for a 

particular predator, assumed prey items were all assigned a uniform availability. .......... 82 

18. Habitat dependencies for age-structured groups in CAM. For any group, a '1' for one or more 

of the habitat types indicates at that at least one of the indicated habitats must occur in a 

CAM box to allow the group to move into that CAM box.. ............................................. 83 

19. Scalar values (Acov) used in refuge relationships of age-structured CAM groups with 

biogenic habitat. Higher values of Acov mean more protection for prey if biogenic habitat 

is available. See text and Figure 7 for details.. ................................................................. 86 

20. Production changes predicted with TMDL nutrient and sediment reductions. Production 

values of all ecological groups of the TMDL reduction scenario were compared to those 

of the base model. Percentages shown are 11-year median differences in biomass for 

years 60 to 70 of each scenario. ........................................................................................ 87 

A1. Area summaries................................................................................................................... 112 

B1. Biological processes and factors modeled in Atlantis. Row labels refer to process modeled, 

column labels indicate groups in Atlantis. Row labels refer to process modeled, column 

labels indicate groups in Atlantis. Abbreviations not defined within table (in order of 

appearance): “Y” – indicates factor is modeled, “PAB” – pelagic attached bacteria, 

“PFB” – pelagic free-living bacteria, “AEB” – aerobic benthic bacteria, “ANB” – 

anaerobic benthic bacteria, “DO” – dissolved oxygen, “wc” – water column, “sed” – 

sediment, “N” – nitrogen, “DON” – dissolved organic nitrogen, “N2” – nitrogen gas, 

“DC” – carrion detritus, “pprey” – predator-prey availability matrix, “PN” – particulate 

nitrogen, “DIN” – dissolved inorganic nitrogen. The Chesapeake Atlantis Model 

simulates all groups except the last 2 columns (CORAL and MACROBEN). .............. 115 
xi

Executive Summary 

This document presents the basic formulations and parameterization for the biology and 

physics of the Chesapeake Atlantis Model (CAM). Our goal is to develop a robust simulation of 

the Chesapeake Bay ecosystem that will allow us to explore potential effects of natural and 

human-induced changes over a range of spatial and temporal scales. The model is designed to 

explore the ecological and socioeconomic trade-offs of alternative management strategies, and to 

understand better how the system responds to climate change and habitat loss. 

Model extent and design

The CAM domain (8,896 sq. km/3,434 sq. mi.) includes the brackish waters and 

sediments of the Chesapeake Bay and seven of its largest tributaries: in Virginia, the James, 

York (including large portions of the Mattaponi and Pamunkey), Rappahannock, and Potomac 

Rivers; and in Maryland, the Patuxent, Choptank (on the eastern shore of Maryland), and 

Nanticoke Rivers. The model area is divided into 97 irregular polygons or “boxes,” which are 

aggregated areas defined by salinity, depth, and in the main trunk of the system, by bottom type. 

Physical model 

CAM includes physical inputs for temperature, salinity, water movement, dissolved 

inorganic nitrogen, dissolved organic nitrogen, labile and refractory particulate matter, and silica. 

Water movements are driven by the Navy Coastal Ocean Model (NCOM) Relocatable Model. 

NCOM has a horizontal resolution of 1/30 degrees, which is roughly 3 km in the CAM domain 

(http://ecowatch.ncddc.noaa.gov/amseas/). The Hybrid Coordinate Ocean Model (HYCOM) 

provides boundary conditions, and atmospheric forcing is from the Coupled Ocean-Atmosphere 

Mesoscale Prediction System (COAMPS). Tidal forcing is included. Because our main goal was 

to capture the strong seasonal patterns of this shallow estuary, rather than interannual variability, 

we repeated the oceanographic conditions from 2012 for each year simulated in the CAM. 

Nutrient and sediment inputs 

Nutrient and sediment loads to the model were derived from the Chesapeake Bay Phase 

5.3 Community Watershed Model 

(http://www.chesapeakebay.net/about/programs/modeling/53/). This model predicts nitrogen, 

phosphorous, and sediment transport to the Bay system and how these substances respond to 

changes in watershed management. The model includes non-point nutrient inputs such as 

atmospheric deposition, fertilizer, and manure. We use two simulations of the Watershed Model, 

the first being a calibration scenario. The second simulation is a scenario that assumes reduced 

loads under restrictions associated with a Total Maximum Daily Load (TMDL). The TMDL 

scenario includes a 25% reduction in nitrogen and 20% reduction in sediment inputs as required 

under the federal Clean Water Act 

(http://www.epa.gov/reg3wapd/tmdl/ChesapeakeBay/tmdlexec.html). For our application, the 

limits associated with the TMDL scenario are assumed to apply to the period from 1991 through 

2000. 
xii

Biological model 

CAM includes 55 functional groups to model biological processes; of these groups, 26 

are invertebrates (including the primary producers) and 29 are vertebrates. Most invertebrates are 

modeled as single biomass pools (mgN/m3

), but two invertebrate groups (blue crab and brief 

squid) are modeled as linked juvenile and adult biomass pools. All vertebrate groups are divided 

into 10 age classes, each tracked by abundance and weight-at-age. Weights are measured through 

both structural and reserve nitrogen, with structural nitrogen representing bones and other hard 

parts and reserve nitrogen representing muscle, fat, reproductive parts, and other soft tissue. The 

separation of age classes for vertebrates allows for ontogenetic shifts in the parameterization. 

CAM uses nitrogen as the currency for nutrient exchange for all groups. 

Habitat associations 

Habitat types in CAM include both physical and biogenic habitats. We defined four 

physical habitats: mud; sand (only the portion of sand substrate that can support oyster 

recruitment); rock (or man-made structures such as artificial reefs, hardened shoreline, etc.); and 

woody debris. Biogenic habitat types are marsh, submerged aquatic vegetation (SAV), and 

oyster reef. The proportion of these seven types sums to 1 for each CAM box. Fish and other 

animal groups were assigned a “dependence” to one or more of the seven habitat types. At least 

one of those habitats must be available in a CAM box to allow a group to move into that box. 

Model application 

As an example application of the CAM, we estimated the biological effects of fully 

achieving the goals of the EPA TMDL requirements for the jurisdictions of the Chesapeake Bay 

watershed. To accomplish this goal, we by compared base model production to that of a model 

revised with TMDL-level reductions in nutrient and sediment loads. Nutrient and sediment 

inputs for the TMDL and base simulations varied and are described earlier under “Nutrient and 

sediment inputs”; all other aspects of the scenarios were identical. Though the majority of groups 

(72%) were predicted to have increased production under the TMDL, the changes were generally 

small. Realized change for most groups was less than 1%. Small differences (1–5 %) were 

relatively common as well; only four fish groups (alosines, catfish, panfish, and other flatfish) 

had slightly larger increases (5–6%) in production, and only one group had an increase in 

production greater than 10% (dinoflagellates, 12%). Similarly, few groups realized moderate or 

large decreases in productivity in the TMDL simulation compared with the base case. 

TMDL with expected temperature increase 

Significant effects of climate change are expected for the Chesapeake Bay region during 

the next 70 years (reviewed by Pyke et al., 2008). Pyke and colleagues report that, though the 

direction of precipitation trends (and consequent salinity trends) remains unclear, temperature 

increase is more certain. In addition, an increase of more than 1.5 oC should be expected for the 

Chesapeake Bay. Consequently, we modeled TMDL-specified improvements in nitrogen and 

sediment loads together with the anticipated temperature increase for the system. 
xiii

When TMDL conditions were modeled along with the anticipated temperature increase, 

changes in group productivity were more pronounced; the magnitude of both positive and 

negative changes were relatively large compared with the TMDL simulation; and modeled 

benefits of the TMDL appear likely to be lost. Positive change no longer dominates when 

temperature increase effects were modeled along with TMDL effects, and groups with predicted 

productivity gains were evenly split with losses. Of those groups that benefited with more than a 

5% increase in productivity (panfish, Atlantic croaker, weakfish, blue crab, menhaden, alosines, 

terrapins, catfish, and gizzard shad), only gizzard shad increased more than 15% over the base 

model. Most productivity increases were very small to moderate (0.2% to 15%). However, all 

primary producer groups were either negatively impacted by the increase in temperature or 

remained at extremely low levels of productivity (microphytobenthos) in this scenario. 

Consequently, the overall effect of the expected temperature increase appears to negate largely 

the moderate benefits modeled in the TMDL scenario for the Chesapeake Bay. 

Discussion 

Our work with CAM is driven by a need for more sophisticated modeling approaches to 

help characterize the efficacy of management actions within the Chesapeake ecosystem, and to 

predict cumulative effects from a large number of simultaneous stressors in this complex system. 

The most pressing management questions for the Chesapeake are nutrient concerns, 

which resource managers believe to be equal to, or exceed concerns about, fishing pressure. 

Other critical issues include the potential effects of climate change on the Chesapeake system, 

and related management questions that concern the effects of: sea level rise; habitat loss (for both 

marsh and SAV); latitudinal shifts in migratory populations; changes to the timing of migrations; 

and of the increasing frequency of major storm events. Simultaneously, managers can apply 

CAM to improve their understanding of the effects of restoration efforts for Oyster populations, 

changing disease rates for Oysters and Striped bass, and the cumulative effects of rapidly 

spreading invading populations of blue and flathead catfish—both of which eat most other 

groups, are long-lived, and can reach sizes in excess of 100 lb (45 kg). All of these issues could 

be equally important to managers as fishing mortality in this system. The current biogeophysical 

model documented here is a flexible, valuable tool to provide managers with insights concerning 

all of these critical issues and the potential trade-offs of alternative management actions. 
xiv

Acknowledgments 

Many decisions about the model were based on individual contributions of a large group 

of Chesapeake Bay scientists and managers too numerous to list here; thank you, to all. 

Especially critical input was provided by Donna Marie Bilkovic, Chris Bonzek, Linda Schaffner, 

Walt Boynton, Michael Kemp, Elizabeth Metheratta, Roberto Llanso, Jodi Dew-Baxter, Denise 

Brietburg, and Rich Fulford. Cameron Ainsworth, Jason Link, and Robert Gamble provided 

valuable insights for the creation of the model. The generous time donated, and innovative 

programming, by Charles Carleton and Scott Cross of the National Centers for Environmental 

Information made our use of the NCOM model possible. 

Bec Gorton was invaluable for modifying the Atlantis code to accommodate the extreme 

nature of the Chesapeake system. Nutrient and sediment loads were provided by Guido Yactayo, 

Jing Wu, and Gary Shenk. Initial concentrations of nutrients and chlorophyll a were provided by 

Mike Mallonee, and much guidance was provided by Mary Ellen Ley. Landings data was 

provided by Michael T. Lewis (commercial) and Lauren Dolinger-Few (recreational). 

This work was funded by the NOAA/NMFS Office of Habitat Conservation and the 

NMFS Office of Science and Technology through the NOAA Chesapeake Bay Office. 

 
1

Introduction 

Concerns over declining benefits of the Nation’s shared aquatic resources have resulted 

in a variety of recommendations1

 and ultimately, current U.S. policy (National Ocean Council, 

2013) to implement ecosystem-based management (EBM) of these various resources. Because 

EBM defines management strategies for entire systems, the approach accounts for interactions 

among ecosystem components and sectors, seeks to identify cumulative impacts of multiple 

sectors, and includes human responses as an integral part of the system (Levin et al., 2009). 

Integrating scientific information at a variety of scales to provide decision-makers with 

information concerning societal trade-offs requires computationally intensive, end-to-end 

modeling techniques. Of such techniques, Atlantis (Fulton et al., 2003; Fulton et al., 2004a) was 

identified by the U.N. Food and Agriculture Organization (Plagányi, 2007) as one of the best 

ecosystem models, particularly for evaluating management strategies. The approach has been a 

cornerstone in the development of the National Marine Fisheries Service (NOAA/NMFS) 

Integrated Ecosystem Assessment (IEA) approach for fisheries management (Levin et al., 2009; 

Levin and Schwing, 2011). Atlantis has been used to advise decision-making for nearly a decade 

in Australia and has been applied in multiple applications in the U.S. as well (Brand et al., 2007; 

Horne et al., 2010; Link et al., 2010; Fulton et al. 2011). 

Atlantis is a whole-ecosystem model that integrates a wide variety of information, 

including biophysical (nutrient input and dynamics, physical characteristics and movement of 

water), and a full suite of biological characteristics (predator–prey interactions, age, growth, 

reproduction, movement, and mortality) for the full range of groups needed to capture the 

essential dynamics of the ecosystem. These groups include phytoplankton, zooplankton, 

macroscopic benthic invertebrates, fishes, reptiles, birds, marine mammals, detritus (labile, 

refractory, and carrion), and biogenic habitats (in the Chesapeake, these include submerged 

aquatic vegetation (SAV), oyster reef, and marsh). 

The Atlantis approach is a deterministic model that projects differential equations 

forward in time. It is designed to simulate the ecosystem, with multiple spatially explicit 

submodels operating simultaneously and interacting to capture ecosystem dynamics. These 

submodels represent the physical and biogeochemical processes that drive primary production, 

the subsequent production of higher trophic levels all the way to human harvests, and the human 

responses to changes in the system. Calculations for all submodel estimates feed back to 

dynamically modify the input parameters for other submodels. This approach allows Atlantis to 

predict cumulative changes in the system as well as unforeseen and unintended consequences of 

policy change that could undermine management decisions (Link et al., 2010) that are also 

unlikely to be predicted by simpler approaches. 

 

1

Christensen et al., 1996; Larkin, 1996; Murawski, 2000; National Research Council, 2002; Garcia et al., 2003; 

Pikitch et al., 2004; U.S. Ocean Policy Task Force, 2010; and Seagraves and Collins, 2012. 
2

The Chesapeake Bay is the largest estuary in the U.S. with a very large watershed 

(> 165,760 sq. km/64,000 sq. mi.). The watershed encompasses portions of six states, the entire 

District of Columbia, and numerous metropolitan areas, the largest of which include Norfolk, 

Richmond, and Charlottesville, VA; Washington, DC; Harrisburg, PA; Baltimore, MD; and 

Cooperstown, NY. The nature of the Chesapeake Bay system is extreme in several respects, 

including size, jurisdictional complexity, human population, shallow depth, seasonal hypoxia, 

freshwater flushing, complexity of life histories of its flora and fauna, seasonal temperatures, 

extensive areas of habitat, and high exploitation rates The system bridges multiple jurisdictions 

and is therefore subject to a complex mix of regulations as well. There is a growing population of 

more than 17 million people, as well as a relatively large agricultural sector, all of which 

contribute to exceptionally high nutrient loads to the system. Residence time for nutrients and 

particulate matter is relatively high (90–180 days (d)) due to high levels of freshwater flow from 

river inputs and consequent lower-layer counterflow (Kemp et al., 2005). The Chesapeake is 

extremely shallow with a mean depth of only 6.5 m (Kemp et al., 2005); consequently, benthic 

dynamics are critical. Turbidity is high enough to limit plant growth, even in relatively shallow 

areas of the Bay. Deeper areas of the system are subject to seasonal hypoxic and anoxic events. 

There is a relatively strong freshwater influence, with multiple large river inputs in the system as 

well as numerous small tributaries (> 100,000 streams, creeks and rivers; 

www.chesapeakebay.net/faq/category/C44#inline, accessed May 2013). 

The Chesapeake Bay is just north of a major biogeographic break (Briggs and Bowen, 

2012). As a result, there is seasonal variation in animal populations, with southern populations 

present during summer and more northerly populations predominating from fall through spring. 

Most Bay populations migrate out of the system at some point during the year (Murdy et al., 

1997); consequently, their populations are subject to mortality outside the system, and the 

parameterization and simulation of such groups can be problematic. The Chesapeake has some of 

the most extreme ranges of water temperature for any coastal system, with winter extremes as 

low as 1 oC (Murdy et al., 1997). Because of the lack of deepwater refuge from such temperature 

extremes, the relatively few species that remain throughout the year become mostly inactive or 

enter torpor during this portion of the year. Marsh, SAV, and oysters all provide habitat 

important to the functionality of the Chesapeake system. However, over the last three decades, 

marsh and SAV have been declining steadily due to high nutrient loads, development, shoreline 

hardening, and coastal inundation associated with climate change. Once, abundant oysters were 

responsible for large reef tracks that dominated Bay habitat and affected circulation patterns, but 

oysters are now nearly extirpated from the system. Wilberg et al. (2011) estimates the current 

population is less than 1% of the original population in Maryland. Humans have a long history of 

exploitation in the Chesapeake, and the current system is highly modified compared to that of the 

early 1800’s due to heavy oyster harvests by dredging. Harvest rates from both the commercial 

and recreational sectors remain high for a variety of species including fish, shellfish, and birds. 

The Chesapeake Atlantis Model (CAM) is one of the most complex Atlantis models built 

to date, largely due to the need to capture the essential dynamics of a shallow system 

characterized by extremes. The first application of Atlantis was in the Port Phillip Bay estuary 

(Fulton and Smith, 2004; Fulton et al., 2004b; Fulton et al., 2004c), another relatively shallow, 

eutrophic embayment. However, that application did not have to accommodate many of the 

extreme features of the Chesapeake Bay. Most recent applications of Atlantis have been to 
3

offshore, coral, or shelf systems (Fulton et al., 2011) where the benthic influences that are so 

important to the Chesapeake have been of minimal importance. 

In this document, we present the basic formulations and parameterization for the biology 

and physics of the Chesapeake Atlantis Model (CAM). Our goal is to develop a robust 

simulation of the Chesapeake Bay ecosystem that will allow us to explore potential effects of 

natural and human-induced changes over a range of spatial and temporal scales. We intend to 

apply the model to explore ecological and socioeconomic trade-offs of alternative management 

strategies; understand how management scenarios impact the system’s response to variations in 

climate and habitat loss; and identify indicator metrics that are most effective for measuring 

ecosystem attributes and informing management decisions. 

Model Extent and Design 

The Chesapeake Atlantis Model (CAM) domain (8,896 sq. km/3,434 sq. mi.) includes the 

brackish waters and sediments of the Chesapeake Bay and seven of its largest tributaries: in 

Virginia, the James, York (including large portions of the Mattaponi and Pamunkey), 

Rappahannock, and Potomac Rivers; and in Maryland, the Patuxent, Choptank (on the eastern 

shore of Maryland), and Nanticoke Rivers. The brackish extent of the Bay varies throughout the 

year. Therefore, to define the model domain, we assumed that salinity zones (and subsequently, 

the model domain) follow those shown in Lippson and Lippson (1997; Appendix A). 

The model area is divided into 97 irregular polygons or “boxes” (Figure 1). Boxes are 

aggregated areas defined by salinity and depth throughout the model, and also by bottom type in 

the main trunk or "mainstem" of the Bay. A 98th box (number “0”) is included as a boundary 

box. This box is non-dynamic and necessary to allow for the exchange of water nutrients and 

migratory groups to and from the dynamic model domain, but other processes are not explicitly 

modeled for this box. The Bay outline and bathymetry were derived from a 10 m x 10 m 

bathymetry grid that was precise to 0.1 m (see Appendix A for details). 

The model roughly characterizes the geography of the Chesapeake Bay, but with a 

simplified, less sinuous geometry. Each CAM polygon is geo-representative of an equivalent 

physical volume of the Bay that shares the polygon characteristics in that general locality of the 

Bay (Table 1). In addition, though many critical boundaries (e.g., the state line between Virginia 

and Maryland) and specific features are georeferenced, all vertices represented in each polygon 

are not. Our goal is to capture the essential dynamics of the system while minimizing the number 

of sides for each polygon. We used this approach because the model must calculate a flux across 

each “side” of each polygon, for each factor modeled, on every time step. Thus, three-, four-, and 

five-sided polygons are preferred box shapes because they minimize the computational overhead 

required for each time step. The resulting map (Figures 1a, 1b) is a simplified, geometric 

representation of the Bay’s geography. 
4

Figure 1a. Spatial structure of CAM. The model consists of 97 irregular polygons determined by salinity, 

depth, bottom type (mainstem only), and management boundaries. 
5

Figure 1b. Detailed river structure of CAM. 

Wherever practical, the shallowest boxes were defined to follow two depth contours 

throughout the model. Areas less than 2 m deep were important to distinguish from deeper 

waters because these are the areas where SAV can be restored (Batiuk et al., 1992). Due to 

turbidity, deeper areas are not useful for restoration and will not support SAV growth. Similarly, 

areas less than 10 m deep were important to separate from deeper areas because this is the 

historically preferred habitat for oysters in the Chesapeake (U.S. Army Corps of Engineers, 

2012). 

The vertical design of the model also attempts to capture the most important functions of 

the system by depth (Figure 2). Throughout CAM, the first vertical layer extends to the first 

meter of the water column (0–1 m), both because this layer is generally well mixed and because 

it is characterized by adequate light to support phytoplankton growth (W. Boynton, University of 

Maryland Center for Environmental Science (UMCES, personal communication). 

Below the surface mixed layer, the pycnocline forms an important barrier to vertical 

water mixing. Seasonal hypoxia occurs below the pycnocline, resulting from normal 

consumption but greatly augmented by the decomposition of large phytoplankton blooms that 

grow in the nutrient-rich and well-lit surface mixed layer, only to sink below the pycnocline after 

the bloom dies out. The dissolved oxygen is depleted below the pycnocline and the strong 

density gradient prevents mixing and replenishment (Kemp and Boynton, 1992). The resulting 
6

hypoxic conditions are an important stressor to Bay animal groups, causing large-scale 

movements away from these waters for swimmers and die-offs for sedentary groups. Hypoxic 

waters may also have a short-term benefit for some populations, functioning either as a refuge 

from predators for groups that have relatively low oxygen demands, or as a “habitat squeeze” 

that concentrates a predator’s prey in the smaller water volume of well-oxygenated water 

(Costantini et al., 2008). Accordingly, the second vertical layer extends from depths deeper than 

1 m to the top of the pycnocline (5 m). 

Figure 2. Vertical structure of CAM. 

The third vertical layer is the pycnocline itself. In reality, the depth of the pycnocline 

varies with depth of the water column. CAM, however, assumes a common depth of the 

pycnocline throughout the model (> 5 m –10 m), a simplifying compromise between the deep 

mainstem and relatively shallow tributaries. The fourth vertical layer consists of depths greater 

than 10 m. The fifth vertical layer simulated in CAM represents the first meter of sediment, 

important to include for groups that bury themselves in the substrate, as well as for benthic 

bacteria, nutrient cycling, bioturbation, and resuspension of sediments. CAM boxes vary in the 

number of water column levels present (Table 2), following the depth of the Bay areas 

represented by those boxes. All boxes include at least two water column layers and the sediment 

layer. 

Projection Years 

The Atlantis model was initialized to represent January 1, 2002, and is projected forward for 70 

years from these conditions on a 12-hour time step. Biological, physical, and biogeochemical 
7

initial conditions were taken from 2002 or recent years for which data were available, as 

described in the following sections. 

Physical Model 

The Atlantis hydrographic submodel is based on the physical transport model (Port 

Phillip Bay Integrated Model, PPBIM) developed by Murray and Parslow (1999) and Walker 

(1999). Details of modifications and enhancements of the PPBIM developed for Atlantis are 

found in Link et al. (2011). 

CAM includes physical inputs for temperature, salinity, water movement, dissolved 

inorganic nitrogen (DIN; includes ammonia [NH] and nitrite-nitrate [NO2 and NO3 are combined 

in CBP data, from now on abbreviated "NO"], dissolved organic nitrogen (DON), labile and 

refractory particulate matter, and silica. 

Water Flux, Temperature, and Salinity 

In the first version of the model, temperature, salinity, and water movements (or fluxes) 

are driven by the Navy Coastal Ocean Model (NCOM) Relocatable Model. NCOM has a 

horizontal resolution of 1/30 degrees, roughly 3 km in the CAM domain 

(http://ecowatch.ncddc.noaa.gov/amseas/). The Hybrid Coordinate Ocean Model (HYCOM) 

provides boundary conditions, and atmospheric forcing is from the Coupled Ocean–Atmosphere 

Mesoscale Prediction System (COAMPS; http://www.nrlmry.navy.mil/coamps-web/web/home). 

Tidal forcing is included. 

 This NCOM includes Chesapeake Bay but was not designed specifically for this 

application or for such a small spatial scale. A more realistic Regional Ocean Modeling System 

(ROMS) that captures realistic water movements for each time step is currently in development. 

The NCOM appears to predict very low salinities for much of Chesapeake Bay, except the mouth 

of the Bay south and east of the York River. Nonetheless, here we use NCOM to force Atlantis 

temperatures and water flux. Salinities are imported to Atlantis but do not currently drive 

biological processes in the model. 

 NCOM output for 2012 was downloaded and re-gridded onto the Atlantis model domain 

(Charles Carleton, NOAA National Coastal Data Development Center [NCDDC]). In cases 

where the Atlantis polygons did not overlap with bathymetry (Figure 3), net flux was assumed 0 

and temperature and salinity were assumed to be equal to values in an adjacent polygon and 

similar depth layer. Of the 98 Atlantis polygons, 27 polygons in the upper reaches of tributaries 

required this extrapolation. To simulate mixing and diffusion, we added small amounts of 

horizontal and vertical flux to all Atlantis polygons in each time step. Because our main goal was 

to capture the strong seasonal patterns of this shallow estuary, rather than interannual variability, 

we repeated the oceanographic conditions from 2012 for each year simulated in CAM. 
8

Figure 3. Chesapeake Bay Water Quality Model (fine-scale cells) superimposed on Atlantis model polygons (yellow) (a), a

polygons superimposed on Chesapeake Bay Water Quality Model (b). 
9 

Nutrient and Sediment Inputs 

Initial Concentrations 

 Initial nutrients and chlorophyll a (CHLA) concentrations were from the Chesapeake Bay 

Program Water Quality Monitoring Database (CBPWQMD; accessed by Mike Mallonee, March 

2011). A description of the database, parameter definitions, abbreviations, station descriptions, 

sampling scheme, collection methodology, detection limits, etc. are detailed in Olson, 2010. The 

most current version of the document is available at 

http://www.chesapeakebay.net/documents/3676/wq_data_userguide_10feb12_mod.pdf (accessed 

August 2013). Note that these data inform the initial conditions of the Atlantis model; after the 

first day of the model run (January 1 2002) nutrient and chlorophyll concentrations evolve 

dynamically within Atlantis. 

 Observations in the Bay Program dataset are segregated according to the Chesapeake Bay 

2003 Segmentation (CBSEG) scheme 

(http://www.chesapeakebay.net/maps/map/chesapeake_bay_2003_segmentation_scheme_codes, 

accessed August 2013; Figure 4). However, freshwater stations were removed from the dataset. 

In addition, individual observations recorded with problem codes were removed with guidance 

from Mary Ellen Ley (U.S Geological Survey, Chesapeake Bay Program [CBP]) and per the 

“Database Design and Data Dictionary” (U.S. Environmental Protection Agency [EPA], 2004; p. 

39). Bay Program observations recorded in the database during December and January from 

2000 to 2004 were summarized by Segment using PROC MEANS in SAS/STAT® 9.21 (SAS 

Institute, Cary, NC). It was assumed that mean values from this time period represented initial 

concentrations for a January 1, 2002, start date for the model (Table 3). Individual areas 

represented in the CBSEG scheme differ from those of CAM boxes, so we identified which 

CBSEGs should inform each CAM box (Table 4). If more than one CBSEG informed any 

particular CAM box, a mean concentration was used to populate that CAM box (by depth). The 

statistical program R (R Development Core Team, 2011) was used to populate the matrix of 

CAM boxes by depth for each water quality parameter. 

 Chlorophyll a, dissolved inorganic nitrogen (DIN), dissolved organic nitrogen (DON), 

and particulate nitrogen observations informed multiple initial inputs for CAM, while silica was 

used directly. Chlorophyll a observations (CHLA; n= 2955) were used to estimate initial 

concentrations of picophytoplankton (PS), large phytoplankton (PL), and dinoflagellates (DF), 

based on the findings of Adolf et al. (2006), where PS (cyanobacteria) make up12%, DF make 

up 13%, and cryptophytes and diatoms (together, the CAM group PL) make up the remaining 

76% of CHLA. DIN observations (DIN; n=3023) were used to estimate NH3, NO2, and NO3 

concentrations; we assume each nitrogen species makes up 33% of the total DIN. Particulate 

nitrogen (PN; n= 2567) and DON (n=3016) observations were used to estimate refractory and 

labile detritus (CAM groups DR and DL, respectively). Although all DON is assumed to be 

labile, we followed the assumption of the CBP as to the lability of PN; i.e., 30% is labile and 

70% is refractory nitrogen (Carl Cerco, U.S. Army Corps of Engineers [ACE], personal 
10

communication). Silica observations (SIF; n=2483) were used directly to estimate initial silica 

concentrations for the model. 

Figure 4. Chesapeake Bay 2003 segmentation scheme (78 segments).
11

Nutrient and Sediment Loadings 

Nutrient and sediment loads that are inputs to Atlantis derive from the Chesapeake Bay 

Phase 5.3 Community Watershed Model (from now on abbreiviated as "Watershed Model"; U.S. 

EPA, 2010). This watershed model is an application of a Fortran code base and Hydrologic 

Simulation Program, which is a deterministic, watershed simulation model 

(http://water.usgs.gov/software/HSPF/). The model predicts nitrogen, phosphorous, and sediment 

transport to the Bay system and how these substances respond to changes in watershed 

management. The model also includes non-point nutrient inputs, such as atmospheric deposition, 

fertilizer, and manure (U.S. EPA, 2010). 

For this application to Atlantis, we use two simulations of the Watershed Model. One is a 

calibration scenario (1991 to 2000; U.S. EPA, 2010); the other scenario assumes reduced loads 

under restrictions associated with a Total Maximum Daily Load (TMDL). The TMDL scenario 

includes a 25% reduction in nitrogen and 20% reduction in sediment inputs required under the 

Federal Clean Water Act (http://www.epa.gov/reg3wapd/tmdl/ChesapeakeBay/tmdlexec.html). 

In actuality, the TMDL sets Bay watershed limits of 85 million kg of nitrogen, 5.7 million kg of 

phosphorus, and 2.9 billion kg of sediment per year. For our application, these limits associated 

with the TMDL scenario are assumed to apply to the period from 1991 to 2000. 

TMDL with expected temperature increase 

In addition to the base and TMDL scenarios, we performed a third scenario that simulates 

TMDL nutrient and sediment loads in the context of expected increased temperature due to 

climate change. Expected temperature increase is based on Pyke et al. (2008), who projected 

temperature change for the system based on both downscaled climatic and hydrological forcing 

factors, and estimated that Chesapeake waters would realize a 1.5 oC increase in temperature 

over a 50-year time span. Ding and Elmore (2015) predict an even more extreme increase of 1.2 

oC per decade for the Maryland (northern) portion of the Bay, based on an analysis of 30 years of 

water surface temperatures from Landsat thermal images. We chose to be relatively conservative 

in the scenario and applied the 1.5 oC increase predicted by Pyke et al. (2008) in combination 

with nutrient and sediment loadings specified in the TMDL. The temperature increase was 

simulated by adding 1.5 oC to the physical forcing file outputs of NCOM. No biological 

parameters were altered for this scenario. 

__________________________________________ 

1

 SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS 

Institute Inc. in the USA and other countries. ® indicates USA registration. 

 
12

For both the TMDL scenarios and the calibration scenario, we import NH4, NO23, and 

total suspended solids from the Watershed Model into Atlantis for both point source and nonpoint source loads (Figures 5 and 6). Because the start date of the Atlantis model is January 1, 

2002, the most recent nutrient loads (from 2000) were used to force all Atlantis projection years, 

both for the base scenario and for the TMDL scenario. 

Figure 5. Nitrogen inputs for years 1991–2000, as specified by Chesapeake Bay Phase 5.3 Community 

Watershed model. For the Atlantis simulations presented here, values from 2000 were applied for 

all projection years, since Atlantis simulations began January 1, 2002. 
13

Figure 6. Sediment inputs for years 1991–-2000, as specified by Chesapeake Bay Phase 5.3 Community 

Watershed model. For the Atlantis simulations presented here, values from 2000 were applied for 

all projection years, since Atlantis simulations began January 1, 2002. 

Loads of nutrients and sediment from the Watershed Model must be assigned to spatial 

cells (polygons) within the Bay. Through previous efforts at the EPA, output (nutrient and 

sediment loads) from the Watershed Model had been mapped as spatial inputs into a Bay model, 

the Chesapeake Bay Water Quality Model (WQM). We identified the overlap of Atlantis cells 

with the finer-scale WQM cells (Figure 3) and then used this correspondence to assign nutrient 

and sediment loads to the Atlantis Bay model. Specifically, an overlap analysis in ArcGIS was 

used to match EPA WQM cells to corresponding Atlantis cells. In instances where EPA cells do 

not overlap with Atlantis cells, nutrients were assigned to the closest Atlantis cell along the 

shoreline. The exceptions to this were cases where the simpler Atlantis geography did not 

capture the sinuosity of main channels of the bay (e.g., upper mainstem of the Chesapeake, 

Atlantis cell 61, Figure 3). In these cases, WQM were assigned to Atlantis mainstem cells rather 

than to cells along the shoreline. 
14

Biological Model 

CAM includes 55 functional groups to model biological processes; of these groups, 26 

are invertebrate and 29 are vertebrate. Invertebrates include the following groups: 2 bacteria 

(pelagic and sediment); 6 detritus (labile, refractory, and carrion, each with a pelagic and 

sediment component); 4 algae (including 1 dinoflagellate); 4 zooplankton; 8 invertebrate 

(including 3 biogenic habitats that provide refuge from predators); and 2 age-structured 

invertebrate groups. Vertebrate groups include the following: 18 finfish, 4 elasmobranch, 4 bird, 

2 reptile, and one mammal. Most invertebrates are modeled as single biomass pools (mgN/m3

), 

but 2 invertebrate groups (blue crab and brief squid) are modeled as linked juvenile and adult 

biomass pools. All vertebrate groups are divided into 10 age classes, each tracked by abundance 

and weight-at-age. Weights are measured through both structural and reserve nitrogen (measured 

in mg N), with structural nitrogen representing bones and other hard parts and reserve nitrogen 

representing muscle, fat, reproductive parts, and other soft tissue. The separation of age classes 

for vertebrates allows for ontogenetic shifts in the parameterization. 

The processes modeled by Atlantis are summarized in Appendix B. Additional details on 

the dynamics of bacteria, detritus, and sediment chemistry, as well as specific parameterizations 

for dinoflagellates and macrophytes, are found in Fulton et al. (2004a). 

Nutrients 

CAM uses nitrogen as the currency for nutrient exchange. Initial concentrations and 

initial loadings of all nutrients for the model are based on EPA data as described earlier for the 

physical model. Dissolved inorganic nitrogen is composed of both ammonia (NH) and nitrite–

nitrate (NO). Concentrations of these two nitrogen pools are governed by uptake by autotrophs, 

excretion by consumers, and the bacteria-mediated processes of remineralization (of detritus and 

DON via respiration), nitrification, and denitrification. 

Following Fulton (2004a) and Horne et al. (2010), rates of change for NH (Equation 1) 

and NO (Equation 2) in the water column (w) are: 

	∆ܰܪ௪ ൌെ ෍ ܣேுೢ,௜

௜ୀ௉௑ೢ

െ ܣேுೢ,஽ி െ ܣேுೢ,ெ஻ೢ െ ܣேுೢ,ெ஺ െ ܣேுೢ,௉ி஻ ൅෍ܧ௜

௜ୀ஼௑ೢ,஻ி

௜ܧ෍൅ ௜ܧ෍൅

௜ୀ௣௘௟௔௚௜௖

௕௔௖௧௘௥௜௔

െ ܵேூ்,௉஺஻ ൅ ܴோ்ೢ

௜ୀி௑

 

(1) 

∆ܱܰ௪ ൌെ ෍ ܣேைೢ,௜

௜ୀ௉௑ೢ

െ ܣேைೢ,஽ி െ ܣேைೢ,ெ஻ೢ െ ܣேைೢ,ெ஺ ൅ ܵேூ்,௉஺஻

 (2) 
15

where ANx,XX is uptake of NH or NO by autotrophs: phytoplankton (i=PX), dinoflagellates (DF), 

microphytobenthos (MB; i.e., “benthic microalgae”), marsh grass (MA), and pelagic free-living 

bacteria (PFB). Ei is production of NH by consumers: general invertebrates (CXw), benthic 

suspension feeders (BF includes BFS, BFD, BFF), fish (i = FX), and pelagic bacteria. SNIT,PAB is 

the amount of NH converted to NO by pelagic attached bacteria (nitrification). RNET is the 

amount of NH that remains available for nitrification and denitrification; i.e., the NH produced 

by denitrification or remineralization of detritus or DON via respiration, minus local production. 

Rates of change for NH (Equation 3) and NO (Equation 4) in the sediment (sed) are: 

	∆ܰܪ௦௘ௗ ൌ ܴNET	sed െ ܵNIT	sed െ ܣNH	sed,MB	sed െ ܣNH	sed,SG	sed ൅ ෍ ܧ݅

്݅BF,CX	ݓ

 (3) 

 	∆ܱܰݏ݁݀ ൌ ܵ୒୍୘	౩౛ౚ െ ܵୈ୉୒୍୘	౩౛ౚ െ ܣ	୓୒౩౛ౚ,୑୆	౩౛ౚ െ ܣ	୓୒౩౛ౚ,ୗୋ	౩౛ౚ 

(4)

with abbreviations as described earlier and where SNIT and SDENIT are nitrification and 

denitrification, respectively, carried out by sediment bacteria. Unlike other invertebrate 

consumers, the BF groups do not contribute to the production of NH in the sediments in 

Equation 3 because they excrete waste directly into the water column. 

Primary Production 

CAM contains six primary producer groups: seagrass, marsh grass, dinoflagellates, 

picophytoplankton and large phytoplankton, and microphytobenthos. Estimates of initial 

biomass (Table 5) and distribution were taken from a variety of sources. 

“Seagrass,” from now on referred to as “SAV” (Atlantis code SG), represents an 

aggregate group that includes both saltwater and freshwater species, mainly the genera Zostera,

Ruppia, Potamogeton, and “freshwater mixed” as described by Moore et al. (2000). Seasonal 

SAV biomass was based on Moore et al. (2000) and Cerco and Moore (2001). Current SAV 

spatial coverage was based on that reported for the most extensive coverage observed in recent 

years (Orth et al., 1998) and the authors’ assumptions of current SAV proportions for each CAM 

box (Table 6). Table 6 is based on both physical characteristics that were aggregated into each 

CAM box (Table 1) and expert opinion. Maximum potential spatial coverage is based on Batiuk 

et al. (1992). An estimate of standing stock for SAV was based on Orth and Moore (1986) and 

estimated spatial coverage. 

“Marsh grass” (MA) is assumed to be composed mainly of two species, Spartina 

alterniflora (saltmarsh cordgrass) and Spartina cynosuroides (giant cordgrass). Chesapeake Bay 

marsh also contains substantial tracts of Phragmites australis, an invasive species; however, baywide population estimates of Phragmites spp. are not currently known and its effects on the role 

of marsh for the Bay system are still poorly understood. In contrast, there is a wealth of literature 
16

available for Spartina of the Atlantic coast. Consequently, we have assumed that the 

characteristics of the two dominant species of Spartina are sufficient to represent the marsh 

group for modeling purposes of this first Atlantis application to the Chesapeake Bay. Future 

simulations may want to incorporate an aggregate marsh group that includes characteristics of 

Phragmites spp. as well as those of Spartina spp. Initial biomass was based on Schubauer and 

Hopkinson (1984), author assumptions of current marsh proportions for each CAM box (Table 

6), and area estimates based on imagery from the National Wetlands Inventory (U.S. Fish and 

Wildlife Service, 1988 to1995 for Maryland and 1990 to2000 for Virginia). We also used 

estimates extracted by D. M. Bilkovic, Virginia Institute of Marine Science (VIMS),March 2013 

for wetland categories “salt marsh” and “brackish marsh.” To estimate winter biomass, we 

assumed that perennial Spartina marsh had seasonal losses comparable to that reported for SAV 

(Moore et al., 2000); i.e., approximately 12% of summer biomass survives winter. Standing 

stock was based on Schubauer and Hopkinson (1984). 

Biomass and distribution of dinoflagellates (DF), picophytoplankton (PS), and large 

phytoplankton (PL) were estimated jointly as described earlier. Estimates were based on 

CBPWQMD chlorophyll a (CHLA) observations made during December and January from 2000 

to 2004 and constituent composition estimates published by Adolf et al. (2006) for the 

Chesapeake Bay. Growth is driven by Michaelis–Menten dynamics and varies with nutrient, 

light, and space availability. 

To estimate current levels of initial biomass of microphytobenthos (MB), we started with 

a biomass estimate from a balanced Ecopath model. We then distributed that biomass 

proportionally (by box area) into CAM boxes that contain depths suitable for MB growth (i.e., 

boxes that contain areas < 8 m in depth (Kemp, personal communication). However, evidence 

reviewed by Kemp et al. (2005) suggests an inverse relationship between MB production and 

light availability. In addition, data presented by Rizzo and Wetzel (1985) suggest that MB 

biomass varies with sediment type. Consequently, we weighted MB biomass estimates according 

to the expected values (Kemp, personal communication) for each CAM box (Table 7). 

Specifically, MB biomass estimates were reduced in boxes with deeper water and those boxes 

were characterized by sediments other than sand (which favors MB growth from January through 

March [Rizzo and Wetzel, 1985] when the model is initialized). All biomass estimates were then 

uniformly adjusted to reflect the range of field observations for January through March reported 

by Rizzo and Wetzel (1985). Though a portion of MB occurs in the model in the water column, 

most grows in the sediment layer. For simplicity, we assume that all CHLA observed in the 

water column is phytoplankton, not MB. Accordingly, we initialized the entire biomass of MB in 

the sediment layer. 

Biomass of primary producers is lost to predation, natural mortality, and lysis. The rate of 

change for primary producers is:





n

j

lysAA linA MMMGA quadA M edA j

1

, , , Pr, (5) 

where GA is growth of autotroph (A), MA,lys is loss of A due to lysis, MA,lin is linear mortality of A, 

MA,quad is quadratic mortality of A, MA,Predi is mortality of A due to predator j, and n is the number 
17

of predators of A. Though in most applications Atlantis will model linear mortality as shown in 

Equation 5, linear mortality is not used for any autotroph in CAM. Marsh has an additional loss 

term available for the additional physical stress of scour, or bottom stress; similarly, SAV has an 

additional loss term for the physical stress of excessive nutrients (DIN). 

Growth of an autotroph is: 

GAX  AX    Nirr   space abund XA (6) 

where μA is maximum growth rate of autotroph (X); δirr is light limitation ; δspace is space 

limitation; δN is nutrient limitation (where N = NH and NO for all autotrophs, but also dissolved 

silicate [Si] for PL and MB); and Aabund is abundance of autotroph (X). See Fulton (2004a) for a 

full description and formulation of the factors limiting growth, as well as nutrient uptake 

functions for primary producers. 

Invertebrates 

CAM includes 12 invertebrate faunal groups that are each modeled as biomass pools 

(Table 8; note that bacteria are not listed in Table 8 because they are estimated inside the model 

run and not set at the start of the model). Most invertebrates are three-dimensional (3-D) groups; 

i.e., they occur in multiple depth layers of the model and their units of density are mgN/m3. 

However, five of these groups are epibenthic, living only on the sediment surface of the model 

(2-D) and their units of density are mgN/m2. Initial densities were based on a variety of available 

sources as described next. 

Initial densities of ctenophores (Mnemiopsis leidyi, ZG), sea nettles (Chrysaora 

quinquecirrha, ZL), mesozooplankton (ZM), microzooplankton (ZS), and oysters (Crassostrea 

virginica, BFF) were provided by Richard Fulford (personal communication, U.S. EPA, Gulf 

Breeze, FL). Zooplankton data originated from the Chesapeake Bay Monitoring Program and 

were originally compiled at the Academy of Natural Sciences Estuarine Research Center 

(ANSERC). Oyster data were compiled by C. Cerco for Cerco and Noel (2005) based on data 

originally collected by R. Mann (VIMS). Densities were provided as monthly bay-wide averages 

from1991 to 2001. Initial densities for ctenophores, mesozooplankton, and microzooplankton 

applied to the model were each based on an average of respective December and January values; 

we assumed this approach best approximated the densities of each group that was available for a 

January 1 start of CAM. Only an annual average density was available for sea nettles. 

Consequently, we assumed that sea nettle availability on April 1 (Table 9) was reduced from the 

average annual density while encysted over winter. We further assumed the proportion of 

biomass present in mid-winter was similar to that observed for ctenophores (i.e., 0.36 * annual 

density). Oyster data were provided as average densities per CBSEG. 

Individual areas represented in the CBSEG scheme differ from those of CAM boxes. 

Therefore, a procedure similar to that described previously to estimate initial concentrations of 

nutrients per CAM box was applied to determine initial oyster densities in each box. We first 

identified which CBSEGs should inform each CAM box (Table 4); if more than one CBSEG 

informed any particular box, a mean concentration was used to populate that box. The statistical 
18

program R (R Development Core Team, 2011) was used to populate the vector of CAM boxes 

for oyster density in the epibenthic layer of each box. 

Benthic groups (benthic filter feeders [BFD]; saltwater clams Macoma spp. [BFS]; 

benthic carnivores [BC]; and benthic deposit feeders [BD, composed of both omnivores and 

detritivores]) are all aggregate groups based on 8 years of data from the Benthic Index of 

Biological Integrity (B-IBI) of the Chesapeake Bay Program (from 2002 to 2009). Details of the 

long-term benthic monitoring program can be found at http://www.baybenthos.versar.com/

(accessed November 2013). Initial biomass estimates (ash-free dry weight) for each of these four 

groups are taken directly from spatially explicit biomass (density) measurements from field 

collections, expanded specifically to CAM box areas. Expanded biomass of each group was 

provided by Versar, Inc.1 The four faunal benthic invertebrate groups were subset from the BIBI, but the final aggregate groups of CAM were based on the top 90% (abundance and biomass) 

of the species observed in the B-IBI. This approach excludes a large number of B-IBI species 

that, though they occur in the system, are rare or inconsistently sampled, contributing little to the 

survey data and to the benthos of the Bay. Lists of the species that compose each aggregate 

group are found in Table 10. 

Changes in invertebrate biomass are affected by growth, predation, and multiple sources 

of mortality. The rate of change for invertebrate biomass is given by: 

quadIlinI FI

n

j

I predI MMMMGI

j , , ,

1

  ,  

 (7)

where GI is growth of invertebrate consumer (I), MI,pred j is mortality of I due to predator j, n is 

the number of predators of I, MI,lin is linear mortality of I, MI,quad is quadratic mortality of I, and 

M I, F is fishing mortality on I, and where: 

OjI space

n

i DR DLj

I PG  iIiI P jI   















     2 ,

1 ,

,, , (8)

where PI,i is predation by I on living prey i, εI,i is assimilation efficiency of I feeding on living 

prey (i), PI,j is predation by I on detrital prey j, εI,j is assimilation efficiency of I feeding on 

detrital prey j, δ02 is oxygen limitation, and δspace is space limitation. 

_______________________________________ 

1

Versar, Inc., Applied Ecosystem Assessment Group, Ecological Science & Application Division, 9200 Rumsey 

Road, Columbia, MD 21045. 

 
19

Oxygen and space limitation apply only to benthic invertebrates living on or in the 

sediment layer. Oxygen limitation is governed by a Michaelis–Menten relationship in which 

limitation increases with depth. We adopted half saturation constants and the depth of oxygen 

horizon for this relationship from Fulton et al. (2004a). Similarly, space limitation is driven by 

Michaelis–Menten dynamics. As the density of an invertebrate group increases beyond a lower 

threshold, the growth of that group is increasingly inhibited until it reaches a maximum allowed 

density. Lower thresholds, maximum densities, and half saturation constants for invertebrate 

space limitation were also adapted from Fulton et al. (2004a). 

Vertebrates 

CAM represents vertebrate biomass in 29 functional groups: 18 finfish, 4 elasmobranch, 

4 bird, 2 reptile, and 1 mammal (Table 11). Each vertebrate group is divided into 10 age classes, 

with each class representing one-tenth of the overall life span of the group. Estimates for 

aggregate groups represent summary values weighted by relative abundance of each species in 

the group in Chesapeake Bay. The initial biomass for most vertebrate groups was ultimately 

dependent on a preliminary, balanced Ecopath model that was developed to initialize CAM, and 

values used to parameterize that Ecopath model were specific to the Chesapeake whenever 

possible. Preliminary biomass estimates used in the Ecopath model were based on a wide variety 

of sources, including (in descending order of use): landings data (used for all groups except bay 

anchovy, other flatfish, bird, reptile, and mammal groups); stock assessments (Atlantic 

menhaden, summer flounder, weakfish, bluefish, Atlantic croaker, reef fish, and spiny dogfish); 

available literature; unpublished data; and expert opinion (Table 11). Due to a lack of original 

sources, only bay anchovy and other flatfish were based solely on Ecopath estimates. Ecopath 

estimates of biomass of birds, reptiles, and mammals differed only slightly from preliminary 

estimates. Therefore, preliminary estimates were used directly as initial values for these groups. 

Parameterization settings for initial age and size structure, reproduction, and mortality are 

found in Table 12; growth and clearance rates by age class are shown in Tables 13 and 14, 

respectively. Biological timing (reproduction and migration) are shown in Table 15. References 

for basic vertebrate life history parameterization are found in Appendix C. We estimated initial 

abundance at age using instantaneous mortality rates, total abundance, and life span estimates 

from the literature. Growth of fish and reptiles was assumed to follow von Bertalanffy age–

weight relationships, while growth of bird and dolphin groups was assumed to follow Gompertz 

age–weight curves. 

Following Fulton et al. (2004a), abundance (or availability; a) of a vertebrate of age 

group (i) in any CAM box is a function of individual movement (T), predation (Pred), fishing (F) 

mortality (M), and both linear (lin) and quadric (quad) mortality terms: 



 

n

j

TTV ViEmViImai Vi linViFVi MMMM quadVi j

1

,, , Pred, , , , (9)
20

where TIm,Vi is movement of individuals into a cell, TEm,Vi is movement of individuals out of a 

cell, MVi,Predj is mortality due to predator j, MVi,F is mortality due to fishing, MVi,lin is linear 

mortality, and MVi,Quad is quadratic mortality. The base CAM model documented here is limited 

to biogeophysical dynamics and does not include fishing. Moreover, linear mortality is set at 0 

for all vertebrates in this model. Consequently, vertebrate abundance (Equation 9) in this 

application of Atlantis simplifies to: 

 

 

n

j

ViEmViImai Vi MMTTV quadVi j

1

,, , Pred, , (10) 

Vertebrate growth follows the same form as for invertebrates (Equation 8), but following 

Fulton (2004a) and Horne et al. (2010), includes an extra term to allocate growth between 

structural and reserve nitrogen pools: 

si  GdtdV Vi / , (11)

ri dtdV   GVi )1(/ , (12)

where Λ describes the relationship between structural (s) and reserve (r) nitrogen for each 

functional group such that: 

/(()/1( ))

/(()/1( ))

, ,

, , ,

RS xiRSri

RS VpR xiRSri

VXVX

VXVXX

 

 

 if > 0 and GV,i is > 0; otherwise, Λ = 0 (13) 

with XRS the maximum ratio of reserve to structural weight V can have, and XpR,V is the relative 

degree to which V concentrates on replenishing reserves rather than undergoing structural growth 

when underweight. Spawning and recruitment also affect vertebrate fluxes, and these processes 

in CAM are described next. 

Predation 

We implemented the modified version of the Holling Type II predation response 

designed by Fulton et al. (2003) to model predation (P) by consumers in CAM: 





 





 

 



n

i

ijiji

j

j

jjiji

ij

EaB

g

C

CBaB

P

1

1

 (14) 

where Pij is ingestion of prey i by predator j (mgN), Bi is biomass of prey i (mgN/m3

), aij is 

availability of prey i to predator j (unitless), Bj is biomass of predator j (mgN/m3

), Cj is clearance 

rate of predator j (m3

/mgN/day; Table 14), gj is growth rate of predator j (per day; Table 13), and 

Eij is growth efficiency of predator j eating prey i (unitless; Table 12). 
21

The availability term a in Equation 14 is a combined measure of prey preference (i.e., 

contribution of prey in a predator’s diet) and the relative availability of the prey to the predator. 

This parameter is designed to reflect that not all prey is available to predators at all times. To 

derive availability parameters, we began by constructing a diet matrix to define the relative 

contribution of each functional group to each predator’s diet where the terms for each predator 

group in this matrix sum to 1. Atlantis, however, requires availability values for each predator 

that are not expressed as proportions and that sum to less than 1 (typically, much less). 

Consequently, we initialized CAM with a diet matrix populated with availability values that were 

one-tenth of our preliminary proportions (Table 16). Post-calibration values are shown in Table 

17. 

Diet relationships were based on published literature, FishBase.org, and unpublished 

data, in order of usage (see Appendix D for details). When parameterizing Atlantis, it is 

important to establish all possible predator–prey relationships, because if a predator–prey 

relationship is designated as a 0 in the matrix, the model will not allow the predator to consume 

that prey. Consequently, in some cases, where no information is available (a common situation 

for soft-bodied invertebrates, which are seldom recognized in gut content analyses) and a 

predator–prey relationship is possible, we added a small arbitrary value so the model would 

allow predators to consume a potential prey when it is available. See Appendix D for more 

information and for references used to establish the predator–prey matrix. 

Following Horne et al. (2010), the availability parameter a (Equation 14) is inversely 

related to the half saturation point of the functional response. The result for model dynamics is 

that predation is linearly related to prey availability when prey is scarce or availability is low. 

Higher values of availabilities or higher prey abundance lead to higher consumption rates, but 

with a nonlinear (asymptotic) relationship between prey abundance and consumption rates per 

predator. Availability of prey i in Equation 14 is further modified if the prey is associated with 

one of the biogenic habitat types, which provides some degree of protection from predators if 

that habitat is available. See “Habitat Associations” (later) for further detail. 

The maximum growth rate g in Equation 14 represents the upper bound for predator 

growth when food is abundant (Table 13). Further details for estimating maximum growth and 

maximum ingestion rate of different predator types (i.e., fish, mammals, birds, etc.) are found in 

Horne et al., 2010. 

Spawning and Recruitment 

Reproduction modeling follows Horne et al. (2010) and occurs in two distinct phases in 

CAM. Spawning occurs in a time window that is specified for each functional group (Table 15). 

The biomass (nitrogen) required for reproduction is removed from the reserve nitrogen pool for 

each group (including both gonadal and somatic tissue). There is a corresponding decrease in 

parental weight-at-age. Immediately after spawning, each age class is incremented by one year, 

and the oldest age class leaves the model domain. In the second phase of reproduction, 

individuals are recruited to the population at a user-specified time after spawning. The new 

recruits are assigned to the first age class. The lag time between spawning and recruitment 

represents larval settlement time for fish, incubation for birds, and gestation for mammals. 
22

Recruitment for fish groups again follows Horne et al. (2010) and assumes Beverton–

Holt dynamics with “alpha” and “beta” parameters taken from stock assessments and published 

literature (Meyers and Barrowman, 1996) but modified for entry in accordance with the input 

requirements of Atlantis (see Appendix C and Table 12 for further information). We estimated 

bird, reptile, elasmobranch, and mammal recruitment using a fixed number of offspring produced 

per adult. Following the methods described by Horne et al. (2010), recruit per adult for birds, 

reptiles, and elasmobranchs is the product of hatch success, broods per year, and clutch size; for 

mammals, it is the product of number of calves per female, pregnancy rate, proportion of females 

in the population, and pregnancy interval. 

Habitat Associations 

Habitat types in Atlantis include both physical and biogenic habitats. We defined four 

physical habitats: mud (which includes “silty” and “clayey” sand types; see Appendix A for 

more information); sand (only the portion of sand substrate that can support oyster recruitment); 

rock (or man-made structure such as artificial reefs, hardened shoreline, etc.); and woody debris. 

Biogenic habitat types are marsh, SAV, and oyster reef (see the earlier sections “Primary 

Production” and “Invertebrates”). The proportion of these seven types sums to 1 for each CAM 

box (Table 6). 

If a group is assigned a “dependence” (i.e., 1 in Table 18) to one or more of the seven 

habitat types, at least one of those habitats must be available in a CAM box to allow that group to 

move into that box. Though dependence on habitat associations for two or three of the habitat 

groups in CAM may be documented for some of the species in the model, quantitative estimates 

of habitat importance are not available. Consequently, we subjectively assigned quantitative 

dependence relationships in Table 18 based on our qualitative understanding. 

Biogenic habitat confers refuge for an age-structured prey group from its predatory 

groups by modifying the availability a of prey i to a predator j (following Equation 14): 

 ܽ௜௝

ᇱ ൌ ܽ௜௝ ∗ ߩ) ௜15 (

where a' is exposure and ρ is refuge status. The degree of refuge afforded to a prey group if 

biogenic habitat is present is determined by: 

 ߩ ൌ ܣܿݒ݋ ∗ ሺ݁ሺି௄௖௢௩∗஼௢௩௘௥ା஻௖௢௩ሻ ൅ ଵ

௞௖௢௩ሻ , ρ ≤ 1 (16) 

where Acov is a scalar for the response of the refuge relationship, Kcov determines the curvature 

of the refuge relationship, Bcov is the coefficient of the refuge relationship with biogenic habitat 

(ensures minimal refuge benefit for prey), and Cover is the relative portion of biogenic cover 

available to prey in a box (possible range of Cover is 0 to 1; see Table 6 for Cover available by 

box). For the base model of CAM, we assume Kcov = 3 and Bcov = 0.6 for all juveniles and 

adults. Acov, however, varies to allow a greater (or lesser) influence of refuge for age-structured 

groups to be more (or less) dependent on habitat in their juvenile or adult stages (Table 19, 

Figure 7). 
23 

Figure 7. Refuge status (≤ 1) to prey from cover of biogenic habitat group BFF, SG, or MA at differing levels of Acov. Low 

appropriate for groups that benefit little in the presence of biogenic habitat, while high values of Acov (e.g., > 0.5) 

depend on biogenic habitat as refuge from predators. 
24 

Model Calibration 

Calibration of the CAM largely follows the methods outlined in Horne et al. (2010). To 

calibrate CAM, we applied an iterative process, adjusting parameters to reproduce more 

ecologically reasonable dynamics and to fit historical observations. This process typically 

involved adjusting the most uncertain parameters (growth, clearance, mortality rates, and 

predator–prey interactions) for each functional group until populations were maintained at recent 

observed biomass levels. We took a simple approach to the calibration of the base model. We 

initialized the model with 2002 estimates of biomass and ran the model forward without fishing. 

Our initial goals in these runs were to keep functional groups from going extinct and to produce 

steady biomass through time. 

The challenges (and solutions) we experienced in calibration were similar to those 

described by Horne et al. (2010). Extinctions (or population explosions) during calibration often 

indicated extremely low (or high) productivity of the stock, or inadequate control of densitydependent factors. Following Horne et al. (2010), the primary parameters used to resolve 

problems in CAM were growth (g, see Equation 14) and clearance rates (C), and predation 

pressure (specified by the availability parameter a) for all groups. For vertebrates, recruit weight 

(parameters KWRR and KWSR for reserve and structural weight, respectively) and assimilation 

efficiencies (E) were modified to tune weight-at-age. Vertebrate numbers at age were tuned with 

Beverton-Holt recruitment parameters for fish groups (BHalpha and BHbeta), or constant 

recruitment numbers for birds, elasmobranchs, reptiles, and mammals (KDENR). Numbers were 

further tuned for all groups using quadratic mortality. As Horne et al. (2010) report, we also 

found the parameter to be especially useful for tuning top predators and other groups with small 

levels of predation (e.g., sea nettles, ctenophores). Though Atlantis includes a linear mortality 

setting for each group as well, we added this only during calibration if it became clear that a 

source of mortality was not captured by the model. In most cases we expect the explicitly 

modeled predation and food limitation to restrict population growth. 

Model Performance 

Initial Calibration: No Fishing 

During initial calibration, we aimed to keep vertebrate groups from going extinct and to 

achieve stable biomass through time (Figures 8a through 8k). In the base model of CAM, true 

equilibrium behavior is prevented by stochastic shocks from oceanographic forcing and is 

delayed by the long age span of some vertebrates.
25

Most groups (both invertebrate and vertebrate) performed well in these simulations, 

reaching a steady state after only 10 to 15 years (Figures 8a through 8j). Several groups took 

somewhat longer to stabilize (20 years for oyster, catfish, and reef associated fish; 35 years for 

sea turtles). Some groups, however, never stabilized (black drum, piscivorous seabirds, and 

bottlenose dolphin). For the groups that never stabilized, it appears the difficulty is due both to 

having few (e.g., black drum, piscivorous seabirds) or no (e.g., bottlenose dolphin) predators, 

and to these groups spending an important portion of their lives outside the system being 

modeled. 

Primary producers were the most difficult groups to calibrate initially, with the various 

populations either exploding to unreasonable levels or collapsing. Once calibrated, however, 

their production remained reliable, without being undermined by most calibration adjustments to 

higher trophic-level groups. 

During the initial calibration phase, we also aimed to maintain an age structure that 

roughly followed an exponential decline in abundance with age as might be expected based on 

natural mortality rates (Figures 9a through 9e). Most groups again performed well in the base 

model and demonstrated reasonable age structure at the unfished equilibrium. In several cases 

(bay anchovy, forage fish, butterfish), we do not see a decline with age because these groups do 

not live beyond 2 years and only a small portion of their populations age beyond the first age 

class. Because the current version of Atlantis does not allow age classes to be less than 1 year, in 

these cases we had to aggregate all biomass (parameters KWRR and KWSR) and numbers of 

animals into the first two age classes and add high quadratic mortality. The result is that, 

effectively, all animals die after 2 years in the model. Consequently, the pattern showing large 

numbers of age class 1 animals and few, if any, animals in age class 2 or older was expected. In 

other cases (gizzard shad, catfish, spiny dogfish), the very large numbers seen in the progression 

of the first age class (before the model reaches steady state) obscure the lower (but still 

sufficient) numbers of animals in the years that follow. 

Most vertebrate groups in the base model performed well in simulating weight-at-age 

(Figures 10a through 10e) after the model “spin-up” (i.e., approximately 15 to 20 years). The 

primary parameters adjusted to calibrate weight-at-age included maximum growth rates (g, 

Equation 14), clearance rates (C), and weight of recruits (KWSR and KWRR). We did not force 

or expect weight-at-age to be constant because Atlantis allows differential growth based on timevarying consumption rates. Some problems with highly migratory groups remain with weights at 

age by the end of the run greater than five times initial values; this is especially evident in some 

bird and elasmobranch groups. However, current performance was considered acceptable for the 

base model run. 
26

Model Application 

TMDL Scenario 

We estimated the biological effects of fully achieving the goals of the EPA TMDL 

requirements for the jurisdictions of the Chesapeake Bay watershed by comparing base model 

production to that of a model modified with TMDL-level reductions in nutrient and sediment 

loads. Nutrient and sediment inputs for the two simulations are described earlier (see “Initial 

Loadings” in the “Physical Model” section and Figures 5 and 6). However, all other aspects of 

the TMDL model were identical to the base model. Though the majority of groups were 

predicted to have increased production under the TMDL scenario, change for most groups was 

close to 0 (Table 20): 40 of 52 groups (only one refractory and one labile detrital group are 

distinguished in this count) had differences of 3% or less compared to the base model, and an 

additional 10 groups had differences that ranged between 3% and 7% change. Only two groups 

had relatively large differences between runs; of these, only the increase in dinoflagellates 

(+12%) appears to be important. In contrast, the relatively large decrease in microphytobenthos 

between the base and TMDL runs is of little consequence because the biomass of this group 

predicted for both runs was very small compared to the initial biomass; i.e., the population of 

microphytobenthos had collapsed in both the base and TMDL runs. 

TMDL with expected temperature increase 

 When TMDL conditions were modeled along with the anticipated temperature increase, 

both positive and negative changes in group productivity were more pronounced compared to the 

TMDL simulation, and modeled benefits of the TMDL appear to be lost. Positive change no 

longer dominates when temperature increase effects were modeled along with TMDL effects, 

and groups with predicted productivity gains were evenly split with losses. Of those groups that 

benefited with more than a 5% increase in productivity (panfish, Atlantic croaker, weakfish, blue 

crab, menhaden, alosines, terrapins, catfish, and gizzard shad), increases ranged from 8% to 

57%; only gizzard shad increased more than 15% over the base model. Most productivity 

increases were very small to moderate (0.2% to 15%). However, all primary producer groups 

were either negatively impacted by the increase in temperature or remained at extremely low 

levels of productivity (microphytobenthos) in this scenario. Moreover, the single, relatively large 

increase of dinoflagellates seen in the TMDL simulation (+12%) was reversed (-41%) when 

temperature increase was simultaneously accounted for. Consequently, the overall effect of the 

expected temperature increase appears to largely negate the moderate benefits modeled by the 

TMDL scenario for the Chesapeake Bay. 
27

Discussion 

Our work with CAM is driven by a need for more sophisticated modeling approaches to 

help characterize the efficacy of management actions within the Chesapeake ecosystem. We also 

aim to predict cumulative effects from a large number of simultaneous stressors in this complex 

system. CAM brings together physical, chemical, and biological processes in a 3-D framework 

that allows for exploration and testing at a variety of spatiotemporal scales. Although no such 

model will ever perfectly replicate ecosystem processes in nature, we have calibrated CAM 

under a wide variety of conditions. As a result, we believe the model produces an adequate 

representation of ecosystem dynamics for the scenarios explored to date. 

Upon testing the base model with a simple comparison of its predictions to those of a 

TMDL scenario, results appeared reasonable. In addition, predicted biological changes were on a 

comparable scale to results of simpler modeling approaches (H.Townsend, NOAA, unpublished 

Ecopath with Ecosim (EmE) comparison; S. Newbold, EPA, unpublished production model 

comparison). Specifically, the various models agreed that most groups would have a small (0 to 

3%) increase in production under TMDL conditions, but, the moderately large productivity 

changes predicted by CAM were not captured by the simpler approaches. Thus, the increased 

complexity of CAM appears to offer some important benefits in capturing cumulative changes to 

the system that simpler approaches cannot capture. 

Improvement of the physical model driving salt, heat, and water fluxes should facilitate 

more accurate predictions from the model. The current NCOM model, though fairly wellresolved, was not designed for inshore, estuarine use (as described earlier). We are currently 

working to incorporate a ROMS-type hydrodynamic model that is specifically designed for the 

Chesapeake estuary. 

Recent work in the Chesapeake has begun to improve our understanding of habitat refuge 

relationships (oyster reef, H. Townsend and D. Bruce, unpublished data). This work has also 

called for improved shallow-water sampling within coastal habitats (i.e., monitoring within 

marsh and SAV habitats; Ihde et al., 2015). Such work could greatly improve the current 

parameterization of habitat refuge and the accuracy of model predictions. 

Further calibration of CAM is planned to follow Horne et al. (2010), where after initial 

biomass and number at age calibration, the authors further calibrated vertebrate growth and 

resulting weight-at-age. Subsequently, we also plan to perform secondary and tertiary 

calibrations as described by Horne et al. (2010), further testing the model under constant fishing 

pressure and then tuning CAM to historical fishing pressure. We recognize that the development 

of the fisheries module of CAM is important, and will remain so; however, other management 

concerns have pushed the growth of CAM to examine stressors other than fishing. 

 
28

The most pressing management questions for the Chesapeake are not just about fishing 

pressure. As in Port Phillip Bay, where nutrient concerns for resource managers are on par with 

or exceed concerns about fishing pressure (Fulton and Smith 2004), resource managers in the 

Chesapeake are deeply concerned with a wide variety of issues. These issues include 

eutrophication and the seasonal hypoxic conditions that are closely related to the extreme 

nutrient loads of this system. Other critical issues include the potential effects of climate change 

on the Chesapeake system and related management questions. These questions concern the 

effects of sea level rise, habitat loss (both marsh and SAV), latitudinal shifts in migratory 

populations, changes to the timing of migrations, and of the increasing frequency of major storm 

events. Simultaneously, managers could apply CAM to advise them about the effects of 

restoration efforts for oyster populations, changing disease rates for oysters and striped bass, and 

the cumulative effects of rapidly spreading invading populations of blue and flathead catfish. 

Both species eat most other groups, are long-lived, and can reach sizes in excess of 100 pounds 

(45 kg). All of these issues could potentially be as important to managers as fishing mortality. 

Moreover, as discussed earlier, we are still able to address simple questions of changing fishing 

pressure with the current model by carefully applying linear mortality. The current 

biogeophysical model documented here is a flexible, valuable tool to provide managers with 

insights concerning all of these critical issues and the potential trade-offs of alternative 

management actions. 

Current and Future Work 

CAM is one of a growing number of Atlantis models developed for marine and estuarine 

ecosystems worldwide (see Fulton et al., 2011, for a recent review). For nearly two decades, 

Atlantis has been used to understand the dynamics of exploited marine systems; identify 

cumulative effects of multiple, simultaneous stressors; identify knowledge gaps; and rank the 

scale of effect of major processes like fishing, eutrophication, and oceanographic effects. The 

model provides a “flight simulator” to test management strategies before implementing them in 

the complex reality of an inter-jurisdictional region like the Chesapeake. CAM should assist 

natural resource managers to make the best informed decisions possible as they work to make 

Ecosystem-Based Management a reality in the Chesapeake Bay and the mid-Atlantic region. 
29

Figures 8a–10e and Tables 
30 

Figure 8a. Biomass results for each functional group after initial tuning phase: bacteria and benthic 

primary producers. Biomass is represented in metric tons summed over the entire model extent. 

X-axis shows years from start of simulation. Dashed line indicates biomass estimate at the start of 

the simulation. Results are plotted four times per year (at a 91.25-day interval); wide bands that 

appear solid are due to plotting the quarterly model predictions in close proximity to one another, 

and represent annual variation predicted for each group. Group names title each subplot, while 

group codes appear directly below names; Atlantis "long-name" for each group appears below 

each x-axis. 
31 

Figure 8b. Biomass results for each functional group after initial tuning phase: planktonic primary 

producers. Biomass is represented in metric tons summed over the entire model extent. X-axis 

shows years from start of simulation. Dashed line indicates biomass estimate at the start of the 

simulation. Results are plotted four times per year (at a 91.25-day interval); wide bands that 

appear solid are due to plotting the quarterly model predictions in close proximity to one another, 

and represent annual variation predicted for each group. Group names title each subplot, while 

group codes appear directly below names; Atlantis "long-name" for each group appears below 

each x-axis. 

 
32 

Figure 8c. Biomass results for each functional group after initial tuning phase: zooplankton and 

meiobenthos. Biomass is represented in metric tons summed over the entire model extent. X-axis 

shows years from start of simulation. Dashed line indicates biomass estimate at the start of the 

simulation. Results are plotted four times per year (at a 91.25-day interval); wide bands that 

appear solid are due to plotting the quarterly model predictions in close proximity to one another, 

and represent annual variation predicted for each group. Group names title each subplot, while 

group codes appear directly below names; Atlantis "long-name" for each group appears below 

each x-axis. 

 
33 

Figure 8d. Biomass results for each functional group after initial tuning phase: brief squid (CEP) and 

benthic invertebrates (continued on 7e). Dashed line indicates biomass estimate at the start of the 

simulation. Biomass is represented in metric tons summed over the entire model extent. X-axis 

shows years from start of simulation. Results are plotted four times per year (at a 91.25-day 

interval); wide bands that appear solid are due to plotting the quarterly model predictions in close 

proximity to one another, and represent annual variation predicted for each group. Group names 

title each subplot, while group codes appear directly below names; Atlantis "long-name" for each 

group appears below each x-axis. 

 
34 

Figure 8e. Biomass results for each functional group after initial tuning phase: benthic invertebrates 

(continued). Biomass is represented in metric tons summed over the entire model extent. X-axis 

shows years from start of simulation. Dashed line indicates biomass estimate at the start of the 

simulation. Results are plotted four times per year (at a 91.25-day interval); wide bands that 

appear solid are due to plotting the quarterly model predictions in close proximity to one another, 

and represent annual variation predicted for each group. Group names title each subplot, while 

group codes appear directly below names; Atlantis "long-name" for each group appears below 

each x-axis. 

 
35 

Figure 8f. Biomass results for each functional group after initial tuning phase: small-medium-sized 

forage fish. Biomass is represented in metric tons summed over the entire model extent. X-axis 

shows years from start of simulation. Dashed line indicates biomass estimate at the start of the 

simulation. Results are plotted four times per year (at a 91.25-day interval); wide bands that 

appear solid are due to plotting the quarterly model predictions in close proximity to one another, 

and represent annual variation predicted for each group. Group names title each subplot, while 

group codes appear directly below names; Atlantis "long-name" for each group appears below 

each x-axis. 

 
36 

Figure 8g. Biomass results for each functional group after initial tuning phase: benthic finfish groups. 

Biomass is represented in metric tons summed over the entire model extent. X-axis shows years 

from start of simulation. Dashed line indicates biomass estimate at the start of the simulation. 

Results are plotted four times per year (at a 91.25-day interval); wide bands that appear solid are 

due to plotting the quarterly model predictions in close proximity to one another, and represent 

annual variation predicted for each group. Group names title each subplot, while group codes 

appear directly below names; Atlantis "long-name" for each group appears below each x-axis. 

 
37 

Figure 8h. Biomass results for each functional group after initial tuning phase: mixed finfish groups. 

Biomass is represented in metric tons summed over the entire model extent. X-axis shows years 

from start of simulation. Dashed line indicates biomass estimate at the start of the simulation. 

Results are plotted four times per year (at a 91.25-day interval); wide bands that appear solid are 

due to plotting the quarterly model predictions in close proximity to one another, and represent 

annual variation predicted for each group. Group names title each subplot, while group codes 

appear directly below names; Atlantis "long-name" for each group appears below each x-axis. 

 
38 

Figure 8i. Biomass results for each functional group after initial tuning phase: elasmobranch groups. 

Biomass is represented in metric tons summed over the entire model extent. X-axis shows years 

from start of simulation. Dashed line indicates biomass estimate at the start of the simulation. 

Results are plotted four times per year (at a 91.25-day interval); wide bands that appear solid are 

due to plotting the quarterly model predictions in close proximity to one another, and represent 

annual variation predicted for each group. Group names title each subplot, while group codes 

appear directly below names; Atlantis "long-name" for each group appears below each x-axis. 

 
39 

Figure 8j. Biomass results for each functional group after initial tuning phase: bird and reptile groups. 

Biomass is represented in metric tons summed over the entire model extent. X-axis shows years 

from start of simulation. Dashed line indicates biomass estimate at the start of the simulation. 

Results are plotted four times per year (at a 91.25-day interval); wide bands that appear solid are 

due to plotting the quarterly model predictions in close proximity to one another, and represent 

annual variation predicted for each group. Group names title each subplot, while group codes 

appear directly below names; Atlantis "long-name" for each group appears below each x-axis. 

 
40 

Figure 8k. Biomass results for each functional group after initial tuning phase: bottlenose dolphin. 

Biomass is represented in metric tons summed over the entire model extent. X-axis shows years 

from start of simulation. Dashed line indicates biomass estimate at the start of the simulation. 

Results are plotted four times per year (at a 91.25-day interval); wide bands that appear solid are 

due to plotting the quarterly model predictions in close proximity to one another, and represent 

annual variation predicted for each group. Group names title each subplot, while group codes 

appear directly below names; Atlantis "long-name" for each group appears below each x-axis. 

 
41 

 

Figure 9a. Total numbers for each age class per vertebrate functional group after initial tuning phase: 

small-medium-sized forage fish. Numbers are summed over the entire model extent. In key, "R" 

represents the age class of new recruits. X-axis shows years from start of simulation. Results are 

plotted four times per year (at a 91.25-day interval); the wide range of quarterly values seen in 

some plots represents annual variation predicted for each group. Group names title each subplot, 

while group codes appear directly below names; Atlantis "long-name" for each group appears 

below each x-axis. 

 
42 

Figure 9b. Total numbers for each age class per vertebrate functional group after initial tuning phase: 

benthic finfish groups. Numbers are summed over the entire model extent. In key, "R" represents 

the age class of new recruits. X-axis shows years from start of simulation. Results are plotted four 

times per year (at a 91.25-day interval); the wide range of quarterly values seen in some plots 

represents annual variation predicted for each group. Group names title each subplot, while group 

codes appear directly below names; Atlantis "long-name" for each group appears below each xaxis. 

 
43 

Figure 9c. Total numbers for each age class per vertebrate functional group after initial tuning phase: 

mixed finfish groups. Numbers are summed over the entire model extent. In key, "R" represents 

the age class of new recruits. X-axis shows years from start of simulation. Results are plotted four 

times per year (at a 91.25-day interval); the wide range of quarterly values seen in some plots 

represents annual variation predicted for each group. Group names title each subplot, while group 

codes appear directly below names; Atlantis "long-name" for each group appears below each xaxis. 
44 

Figure 9d. Total numbers for each age class per vertebrate functional group after initial tuning phase: 

elasmobranchs and bird groups (continued on Fig. 8e). Numbers are summed over the entire 

model extent. In key, "R" represents the age class of new recruits. X-axis shows years from start 

of simulation. Results are plotted four times per year (at a 91.25-day interval); the wide range of 

quarterly values seen in some plots represents annual variation predicted for each group. Group 

names title each subplot, while group codes appear directly below names; Atlantis "long-name" 

for each group appears below each x-axis. 
45 

Figure 9e. Total numbers for each age class per vertebrate functional group after initial tuning phase: bird 

groups (continued from Fig. 9d), reptile, and mammal groups. Numbers are summed over the 

entire model extent. In key, "R" represents the age class of new recruits. X-axis shows years from 

start of simulation. Results are plotted four times per year (at a 91.25-day interval); the wide 

range of quarterly values seen in some plots represents annual variation predicted for each group. 

Group names title each subplot, while group codes appear directly below names; Atlantis "longname" for each group appears below each x-axis. 

 
46 

Figure 10a. Ratio of reserve nitrogen to initial reserve nitrogen for each age class per vertebrate functional 

group after initial tuning phase: small-medium-sized forage fish. In key, "R" represents the age 

class of new recruits. X-axis shows years from start of simulation. Results are plotted four times 

per year (at a 91.25-day interval); the wide range of quarterly values seen in some plots represents 

annual variation predicted for each group. Reserve nitrogen represents weight-at-age that is 

related to muscle, fat, reproductive parts, and other soft tissue. As the ratio increases above 1, 

individuals become fat; as the ratio declines below 1, individuals begin to starve. X-axis shows 

years from start of simulation. Dashed line indicates biomass at the start of the simulation. 

 
47 

 

Figure 10b. Ratio of reserve nitrogen to initial reserve nitrogen for each age class per vertebrate 

functional group after initial tuning phase: benthic finfish. In key, "R" represents the age class of 

new recruits. X-axis shows years from start of simulation. Results are plotted four times per year 

(at a 91.25-day interval); the wide range of quarterly values seen in some plots represents annual 

variation predicted for each group. Reserve nitrogen represents weight-at-age that is related to 

muscle, fat, reproductive parts, and other soft tissue. As the ratio increases above 1, individuals 

become fat; as the ratio declines below 1, individuals begin to starve. X-axis shows years from 

start of simulation. Dashed line indicates biomass at the start of the simulation. 

 
48 

Figure 10c. Ratio of reserve nitrogen to initial reserve nitrogen for each age class per vertebrate functional 

group after initial tuning phase: mixed finfish groups. In key, "R" represents the age class of new 

recruits. X-axis shows years from start of simulation. Results are plotted four times per year (at a 

91.25 day interval); the wide range of quarterly values seen in some plots represents annual 

variation predicted for each group. Reserve nitrogen represents weight-at-age that is related to 

muscle, fat, reproductive parts, and other soft tissue. As the ratio increases above 1, individuals 

become fat; as the ratio declines below 1, individuals begin to starve. X-axis shows years from 

start of simulation. Dashed line indicates biomass at the start of the simulation. 

 
49 

 

Figure 10d. Ratio of reserve nitrogen to initial reserve nitrogen for each age class per vertebrate 

functional group after initial tuning phase: elasmobranchs and bird groups (continued on Fig. 

10e). In key, "R" represents the age class of new recruits. X-axis shows years from start of 

simulation. Results are plotted four times per year (at a 91.25 day interval); the wide range of 

quarterly values seen in some plots represents annual variation predicted for each group. Reserve 

nitrogen represents weight-at-age that is related to muscle, fat, reproductive parts, and other soft 

tissue. As the ratio increases above 1, individuals become fat; as the ratio declines below 1, 

individuals begin to starve. X-axis shows years from start of simulation. Dashed line indicates 

biomass at the start of the simulation. 

 
50 

Figure 10e. Ratio of reserve nitrogen to initial reserve nitrogen for each age class per vertebrate functional 

group after initial tuning phase: birds (continued from Fig. 10d), reptiles and mammal groups. In 

key, "R" represents the age class of new recruits. X-axis shows years from start of simulation. 

Results are plotted four times per year (at a 91.25 day interval); the wide range of quarterly values 

seen in some plots represents annual variation predicted for each group. Reserve nitrogen 

represents weight-at-age that is related to muscle, fat, reproductive parts, and other soft tissue. As 

the ratio increases above 1, individuals become fat; as the ratio declines below 1, individuals 

begin to starve. X-axis shows years from start of simulation. Dashed line indicates biomass at the 

start of the simulation. 
51 

Table 1. CAM polygon characteristics. Boxes aggregate Chesapeake areas by location, salinity, depth, 

and bottom type (mainstem boxes only). Box 0 is a non-dynamic "boundary box" necessary to 

allow for the exchange of water nutrients and migratory groups to and from the dynamic model 

domain, but other processes are not explicitly modeled for this box. "---" indicates characteristic 

is not modeled for that box. 

Box Description

Aggregated 

Salinity

 Range 

(ppt)

Aggregated 

Depth 

Range

Bottom 

Type

Area 

(square km)

0 --- --- --- --- ---

1 Lower bay 10-18 10-41m Soft 482.2

2 Lower bay 18-30 10-31m Soft 299.8

3 Lower bay 18-30 10-31m Soft 61.6

4 Lower bay 18-30 0-10m Soft 111.2

5 Lower bay 18-30 0-10m Soft 40.4

6 Lower bay 18-30 0-10m Soft 48.9

7 Lower bay 18-30 10-14m Hard 159.8

8 Lower bay 18-30 10-14m Hard 171.1

9 Lower bay 18-30 0-10m Hard 89.6

10 Lower bay 18-30 0-10m Hard 216.5

11 Lower bay 18-30 0-10m Hard 75.7

12 Lower bay 18-30 0-10m Hard 672.2

13 Lower bay 10-18 0-10m Soft 97.0

14 Lower bay 10-18 0-10m Soft 139.2

15 Lower bay 10-18 2-10m Hard 307.6

16 Lower bay 10-18 2-10m Hard 331.0

17 Lower bay 10-18 0-2m Hard 63.0

18 Lower bay 10-18 0-2m Hard 129.0

19 James R. 18-30 0-23m --- 106.8

20 James R. 10-18 2-10m --- 97.7

21 James R. 10-18 0-2m --- 25.7

22 James R. 10-18 0-2m --- 32.8

23 James R. 1-10 0-2m --- 75.6

24 James R. 1-10 0-2m --- 78.7

25 James R. 1-10 2-10m --- 99.8

26 Rappahannock R. 10-18 10-22m --- 42.9

27 Rappahannock R. 10-18 2-10m --- 54.2

28 Rappahannock R. 10-18 2-10m --- 51.3

29 Rappahannock R. 1-10 0-2m --- 22.0

30 Rappahannock R. 1-10 0-2m --- 27.2

31 Rappahannock R. 1-10 2-15m --- 55.5

32 Potomac R. 10-18 10-24m --- 163.8

33 Potomac R. 10-18 2-10m --- 101.7

34 Potomac R. 1-10 2-16m --- 409.0
52 

Box Description

Aggregated 

Salinity

 Range 

(ppt)

Aggregated 

Depth 

Range

Bottom 

Type

Area 

(square km)

35 Potomac R. 1-10 0-2m --- 37.6

36 Potomac R. 1-10 0-2m --- 34.9

37 Rappahannock R. 10-18 0-2m --- 21.3

38 Rappahannock R. 10-18 0-2m --- 25.8

39 Lower bay 10-18 10-13m Hard 210.6

40 Mid-bay 10-18 10-50m Soft 738.3

41 Mid-bay 10-18 10-31m Soft 59.1

42 Mid-bay 10-18 10-47m Hard 182.5

43 Mid-bay 10-18 2-10m Soft 99.6

44 Mid-bay 10-18 2-10m Soft 44.6

45 Mid-bay 10-18 2-10m Soft 21.0

46 Mid-bay 10-18 2-10m Soft 101.8

47 Mid-bay 10-18 2-10m Soft 5.6

48 Mid-bay 10-18 2-10m Soft 13.3

49 Mid-bay 10-18 2-10m Hard 182.2

50 Mid-bay 10-18 2-10m Hard 39.0

51 Mid-bay 10-18 2-10m Hard 100.6

52 Mid-bay 10-18 2-10m Hard 273.5

53 Mid-bay 10-18 2-10m Hard 108.5

54 Mid-bay 10-18 2-10m Hard 39.5

55 Mid-bay 10-18 0-2m Hard 50.1

56 Mid-bay 10-18 0-2m Hard 47.2

57 Mid-bay 10-18 0-2m Hard 13.2

58 Mid-bay 10-18 0-2m Hard 13.3

59 Upper bay 1-10 10-28m Soft 99.1

60 Mid-bay 10-18 2-10m Hard 24.2

61 Upper bay 1-10 2-10m Soft 396.8

62 Upper bay 1-10 2-10m Hard 46.1

63 Upper bay 1-10 2-10m Hard 69.3

64 Upper bay 1-10 0-2m Hard 27.3

65 Upper bay 1-10 0-2m Hard 33.0

66 Potomac R. 10-18 2-10m --- 98.4

67 Potomac R. 10-18 0-2m --- 7.4

68 Potomac R. 10-18 0-2m --- 37.0

69 York R. 10-18 10-25m --- 25.0

70 York R. 10-18 2-10m --- 28.7

71 York R. 10-18 2-10m --- 28.7

72 York R. 10-18 0-2m --- 28.7

73 York R. 10-18 0-2m --- 29.8

74 York R. 1-10 2-18m --- 14.7

75 York R. 1-10 0-2m --- 3.9
53 

Box Description

Aggregated 

Salinity

 Range 

(ppt)

Aggregated 

Depth 

Range

Bottom 

Type

Area 

(square km)

76 York R. 1-10 0-2m --- 5.3

77 Patuxent R. 10-18 2-10m --- 7.5

78 Patuxent R. 10-18 2-10m --- 6.5

79 Patuxent R. 10-18 0-2m --- 3.2

80 Patuxent R. 10-18 0-2m --- 3.8

81 Patuxent R. 10-18 10-38m --- 11.7

82 Patuxent R. 1-10 2-24m --- 51.8

83 Patuxent R. 1-10 0-2m --- 12.4

84 Patuxent R. 1-10 0-2m --- 13.8

85 Nanticoke R. 10-18 2-13m --- 21.5

86 Nanticoke R. 1-10 2-17m --- 16.9

87 Nanticoke R. 10-18 0-2m --- 9.9

88 Nanticoke R. 10-18 0-2m --- 10.5

89 Nanticoke R. 1-10 0-2m --- 11.4

90 Nanticoke R. 1-10 0-2m --- 8.6

91 Mid-bay 10-18 0-2m Hard 24.7

92 Choptank R. 1-10 2-15m --- 31.4

93 Choptank R. 1-10 0-2m --- 10.7

94 Choptank R. 1-10 0-2m --- 8.8

95 Choptank R. 10-18 2-18m --- 149.9

96 Choptank R. 10-18 0-2m --- 36.6

97 Choptank R. 10-18 0-2m --- 37.4

 
54 

Table 2. Volume (m3

) for each layer of each box in CAM. The occurrence of a '0' indicates that box is too 

shallow to include all possible water column layers. The first water column layer listed is always 

the deepest found in that box. For example, Box 4 is deep enough to have only two water column 

layers (≤ 5 m). Box 0 is a non-dynamic "boundary box" necessary to allow for the exchange of 

water nutrients and migratory groups to and from the dynamic model domain, but other processes 

are not explicitly modeled for this box. 

Box Deepest --> --> Shallowest Sediment

0 --- --- --- --- ---

1 7,232,440,497 2,410,813,499 1,928,650,799 482,162,700 482,162,700

2 2,997,555,316 1,498,777,658 1,199,022,126 299,755,532 299,755,532

3 615,570,495 307,785,248 246,228,198 61,557,049 61,557,049

4 445,062,296 111,265,574 0 0 111,265,574

5 161,712,741 40,428,185 0 0 40,428,185

6 195,468,170 48,867,043 0 0 48,867,043

7 319,568,070 798,920,175 639,136,140 159,784,035 159,784,035

8 342,229,624 855,574,061 684,459,249 171,114,812 171,114,812

9 358,559,219 89,639,805 0 0 89,639,805

10 865,857,426 216,464,356 0 0 216,464,356

11 302,794,457 75,698,614 0 0 75,698,614

12 2,688,955,636 672,238,909 0 0 672,238,909

13 387,949,358 96,987,339 0 0 96,987,339

14 556,657,723 139,164,431 0 0 139,164,431

15 307,595,336 1,230,381,344 307,595,336 0 307,595,336

16 330,999,553 1,323,998,211 330,999,553 0 330,999,553

17 75,550,026 0 0 0 62,958,355

18 154,791,180 0 0 0 128,992,650

19 213,554,009 533,885,021 427,108,017 106,777,004 106,777,004

20 97,692,554 390,770,215 97,692,554 0 97,692,554

21 30,828,677 0 0 0 25,690,564

22 39,414,504 0 0 0 32,845,420

23 90,668,711 0 0 0 75,557,259

24 94,498,031 0 0 0 78,748,359

25 99,807,722 399,230,887 99,807,722 0 99,807,722

26 257,402,306 214,501,921 171,601,537 42,900,384 42,900,384

27 54,235,259 216,941,038 54,235,259 0 54,235,259

28 51,313,916 205,255,665 51,313,916 0 51,313,916

29 26,407,689 0 0 0 22,006,408

30 32,696,149 0 0 0 27,246,791

31 55,537,289 222,149,156 55,537,289 0 55,537,289

32 1,310,515,287 819,072,054 655,257,643 163,814,411 163,814,411

33 101,730,746 406,922,986 101,730,746 0 101,730,746

34 817,967,575 1,635,935,150 408,983,788 0 408,983,788

35 45,100,858 0 0 0 37,584,048
55 

Box Deepest --> --> Shallowest Sediment

36 41,865,444 0 0 0 34,887,870

37 25,532,397 0 0 0 21,276,998

38 31,003,173 0 0 0 25,835,978

39 210,612,703 1,053,063,515 842,450,812 210,612,703 210,612,703

40 14,765,505,492 3,691,376,373 2,953,101,098 738,275,275 738,275,275

41 591,018,059 295,509,030 236,407,224 59,101,806 59,101,806

42 3,285,326,065 912,590,574 730,072,459 182,518,115 182,518,115

43 99,621,473 398,485,893 99,621,473 0 99,621,473

44 44,647,653 178,590,613 44,647,653 0 44,647,653

45 20,986,387 83,945,548 20,986,387 0 20,986,387

46 101,779,118 407,116,473 101,779,118 0 101,779,118

47 5,591,884 22,367,537 5,591,884 0 5,591,884

48 13,277,868 53,111,471 13,277,868 0 13,277,868

49 182,160,753 728,643,013 182,160,753 0 182,160,753

50 38,994,586 155,978,345 38,994,586 0 38,994,586

51 100,645,556 402,582,223 100,645,556 0 100,645,556

52 273,536,805 1,094,147,221 273,536,805 0 273,536,805

53 108,536,308 434,145,234 108,536,308 0 108,536,308

54 39,492,938 157,971,753 39,492,938 0 39,492,938

55 60,091,729 0 0 0 50,076,441

56 56,657,022 0 0 0 47,214,185

57 15,830,311 0 0 0 13,191,926

58 15,994,325 0 0 0 13,328,604

59 891,577,464 495,320,813 396,256,651 99,064,163 99,064,163

60 24,163,439 96,653,758 24,163,439 0 24,163,439

61 396,755,756 1,587,023,025 396,755,756 0 396,755,756

62 46,074,747 184,298,989 46,074,747 0 46,074,747

63 69,251,550 277,006,199 69,251,550 0 69,251,550

64 32,773,913 0 0 0 27,311,594

65 39,584,138 0 0 0 32,986,782

66 98,389,781 393,559,126 98,389,781 0 98,389,781

67 8,915,039 0 0 0 7,429,199

68 44,451,794 0 0 0 37,043,162

69 199,982,885 124,989,303 99,991,443 24,997,861 24,997,861

70 28,667,075 114,668,300 28,667,075 0 28,667,075

71 28,683,206 114,732,823 28,683,206 0 28,683,206

72 34,380,685 0 0 0 28,650,571

73 35,791,690 0 0 0 29,826,408

74 44,011,033 58,681,378 14,670,344 0 14,670,344

75 4,709,022 0 0 0 3,924,185

76 6,346,339 0 0 0 5,288,615
56 

Box Deepest --> --> Shallowest Sediment

77 7,467,975 29,871,902 7,467,975 0 7,467,975

78 6,466,912 25,867,649 6,466,912 0 6,466,912

79 3,817,966 0 0 0 3,181,638

80 4,582,315 0 0 0 3,818,596

81 164,098,208 58,606,503 46,885,202 11,721,301 11,721,301

82 51,766,993 258,834,966 207,067,972 51,766,993 51,766,993

83 14,874,537 0 0 0 12,395,447

84 16,583,202 0 0 0 13,819,335

85 85,960,268 21,490,067 0 0 21,490,067

86 33,826,145 67,652,291 16,913,073 0 16,913,073

87 11,915,243 0 0 0 9,929,370

88 12,611,911 0 0 0 10,509,926

89 13,627,140 0 0 0 11,355,950

90 10,312,560 0 0 0 8,593,800

91 29,644,825 0 0 0 24,704,021

92 31,357,529 125,430,114 31,357,529 0 31,357,529

93 12,789,683 0 0 0 10,658,070

94 10,592,645 0 0 0 8,827,204

95 449,556,912 599,409,216 149,852,304 0 149,852,304

96 43,932,084 0 0 0 36,610,070

97 44,921,439 0 0 0 37,434,533

 
57 

Table 3. Initial concentrations for water quality parameters applied to the Chesapeake Atlantis Model, summarized by Segmen

scheme: http://www.chesapeakebay.net/maps/map/chesapeake_bay_2003_segmentation_scheme_codes, accessed Au

5 years of EPA observations made during the months of December and January (2000–2004). The number of usable s

each Bay segment is shown (n) for each water quality parameter. Chlorophyll a observations (CHLA) were used to es

concentrations of picophytoplankton, large phytoplankton, and dinoflagellates (CAM groups PS, PL, and DF, respecti

inorganic nitrogen observations (DIN) were used to estimate NH3, NO2, and NO3 concentrations. Particulate nitrogen organic nitrogen (DON) observations were used to estimate refractory and labile detritus (CAM groups DR and DL, r

observations (SIF) were used directly to estimate initial silica concentrations for the model. Standard deviation for eac

shown in parentheses; "---" indicates no data available. 

CHLA DIN PN DON 

CBSEG 

2003 n mean std n mean std n mean std n mean std n 

BACOH 11 19.94 (10.55) 11 1.99 (0.84) 11 0.49 (0.17) 11 0.96 (0.25) 1

BIGMH 18 11.27 (10.98) 23 0.03 (0.05) 18 0.20 (0.09) 23 0.36 (0.06) 1

BOHOH 12 14.98 (10.95) 12 1.21 (0.41) 12 0.30 (0.11) 12 0.38 (0.14) 1

BSHOH 11 6.81 (10.41) 11 0.99 (0.5) 9 0.19 (0.08) 11 0.41 (0.14) 9 

C&DOH 11 2.26 (0.69) 11 1.49 (0.27) 11 0.33 (0.17) 11 0.40 (0.19) 1

CB2OH 85 4.13 (3.98) 90 0.95 (0.41) 90 0.17 (0.13) 90 0.25 (0.08) 9

CB3MH 90 8.20 (6.54) 90 0.48 (0.41) 90 0.20 (0.12) 89 0.27 (0.06) 9CB4MH 180 9.71 (6.94) 194 0.21 (0.26) 180 0.18 (0.1) 194 0.29 (0.04) 18

CB5MH 221 9.90 (7.33) 243 0.12 (0.17) 222 0.17 (0.09) 242 0.29 (0.06) 22

CB6PH 97 13.14 (10.14) 100 0.05 (0.05) 96 0.14 (0.06) 100 0.20 (0.03) 9

CB7PH 196 8.21 (7.61) 204 0.05 (0.05) 133 0.14 (0.09) 204 0.19 (0.05) 13

CB8PH 60 7.21 (5.14) 61 0.03 (0.03) 59 0.11 (0.05) 61 0.17 (0.04) 6

CHKOH 8 8.37 (7.34) 18 0.24 (0.14) 16 0.20 (0.07) 18 0.27 (0.05) 1CHOOH 19 4.62 (5.25) 19 2.60 (0.92) 19 0.37 (0.17) 19 0.43 (0.15) 1CHSMH 60 8.57 (5.71) 47 0.47 (0.32) 45 0.21 (0.09) 47 0.31 (0.08) 4

CHSOH 22 8.92 (14.13) 22 3.18 (1.16) 22 0.25 (0.18) 22 0.32 (0.3) 2

CRRMH 9 11.49 (13.7) 29 0.02 (0.03) 18 0.14 (0.08) 29 0.25 (0.02) 1

EASMH 45 10.31 (7.66) 51 0.18 (0.19) 45 0.22 (0.12) 51 0.32 (0.06) 4EBEMH 28 11.09 (7.7) 38 0.25 (0.15) 36 0.16 (0.1) 38 0.27 (0.05) 1

ELIPH 46 11.86 (7.32) 76 0.17 (0.15) 69 0.16 (0.1) 76 0.25 (0.05) 5

ELKOH 10 2.25 (1.21) 10 1.46 (0.2) 10 0.25 (0.11) 9 0.38 (0.13) 1

FSBMH 22 12.74 (6.61) 23 0.12 (0.11) 22 0.34 (0.13) 23 0.50 (0.13) 2

GUNOH 8 8.63 (12.79) 10 0.98 (0.69) 10 0.20 (0.2) 10 0.38 (0.17) 1

JMSMH 17 7.75 (4.45) 49 0.09 (0.14) 32 0.13 (0.06) 49 0.22 (0.05) 3

JMSOH 16 7.63 (9.07) 31 0.33 (0.18) 30 0.19 (0.1) 31 0.24 (0.08) 3
58 

CHLA DIN PN DON 

CBSEG 

2003 n mean std n mean std n mean std n mean std n 

JMSPH 24 8.61 (4.6) 35 0.06 (0.09) 30 0.12 (0.06) 35 0.20 (0.04) 3

LAFMH 17 10.48 (4.97) 43 0.10 (0.12) 34 0.19 (0.11) 43 0.23 (0.05) 3

LCHMH 28 9.88 (7.12) 31 0.19 (0.2) 28 0.23 (0.12) 31 0.33 (0.06) 2

MAGMH 21 21.53 (36.85) 21 0.36 (0.3) 21 0.43 (0.56) 21 0.37 (0.15) 2MANMH 20 10.03 (5.81) 26 0.09 (0.17) 20 0.23 (0.09) 26 0.40 (0.08) 2

MIDOH 17 11.90 (13.18) 18 0.60 (0.38) 18 0.29 (0.23) 18 0.36 (0.08) 1

MOBPH 62 10.03 (9.9) 66 0.02 (0.02) 62 0.13 (0.08) 66 0.21 (0.03) 6

MPNOH 10 3.17 (2.24) 20 0.25 (0.08) 18 0.24 (0.13) 20 0.30 (0.08) 2

NANMH 16 6.61 (3.27) 16 1.34 (0.87) 16 0.46 (0.29) 16 0.47 (0.14) 1

PATMH 35 10.32 (8.43) 35 0.59 (0.31) 35 0.23 (0.17) 35 0.34 (0.12) 3

PAXMH 210 13.71 (10.73) 243 0.11 (0.17) 205 0.20 (0.11) 243 0.30 (0.04) 21

PAXOH 54 15.28 (43.67) 55 1.08 (0.46) 54 0.43 (0.49) 55 0.31 (0.08) 5

PIAMH 14 16.00 (12.35) 12 0.03 (0.04) 12 0.14 (0.07) 12 0.21 (0.02) 1

PMKOH 9 5.59 (4.1) 18 0.30 (0.14) 16 0.45 (0.41) 18 0.27 (0.04) 1

POCMH 10 10.58 (5.68) 12 0.04 (0.06) 10 0.21 (0.07) 12 0.34 (0.08) 1

POCOH 2 10.97 (0.7) 3 0.43 (0.31) 3 0.26 (0.07) 3 0.54 (0.1) 3 

POTMH 642 16.40 (18.62) 211 0.26 (0.3) 121 0.23 (0.12) 211 0.38 (0.1) 12

POTOH 60 4.07 (4.57) 60 1.20 (0.37) 60 0.18 (0.12) 59 0.35 (0.1) 6

RHDMH 12 12.71 (7.81) 15 0.19 (0.19) 12 0.30 (0.14) 15 0.33 (0.05) 1

RPPMH 65 11.56 (13.36) 167 0.13 (0.21) 116 0.18 (0.17) 167 0.25 (0.06) 12

RPPOH 10 4.75 (6.04) 24 0.62 (0.31) 20 0.20 (0.11) 24 0.34 (0.29) 2

SASOH 27 16.20 (9.66) 27 1.54 (0.52) 23 0.35 (0.17) 27 0.37 (0.14) 2

SBEMH 80 9.34 (9.93) 80 0.40 (0.24) 80 0.13 (0.09) 80 0.40 (0.11) -

SEVMH 18 11.09 (9.27) 18 0.31 (0.28) 18 0.24 (0.12) 18 0.36 (0.11) 1

SOUMH 16 8.91 (7.1) 16 0.22 (0.26) 16 0.24 (0.1) 16 0.32 (0.04) 1

TANMH 69 14.99 (11.44) 77 0.11 (0.13) 69 0.24 (0.11) 77 0.35 (0.07) 6

WBEMH 29 11.96 (8.23) 50 0.08 (0.1) 38 0.21 (0.13) 50 0.24 (0.03) 2

WICMH 20 5.23 (3.3) 20 1.11 (0.58) 20 0.23 (0.08) 20 0.49 (0.16) 2

WSTMH 14 7.34 (5.5) 14 0.35 (0.31) 14 0.21 (0.09) 14 0.32 (0.05) 1YRKMH 21 10.37 (8.1) 56 0.18 (0.13) 38 0.29 (0.27) 53 0.26 (0.07) 4

YRKPH 21 7.20 (3.6) 61 0.05 (0.06) 35 0.17 (0.11) 61 0.21 (0.05) 4
59 

Table 4. Distribution of Bay Segments_2003 data in CAM boxes. 

CBSEG_2003

Number 

of CAM 

boxes 

informed 

by 

segment

CAM boxes incorporating 

segment data Segment description

BACOH 3 61, 63, 64 Back R.-oligohaline region

BIGMH 3 41, 50, 58 Big Annemessex R.-mesohaline region

BOHOH 3 61, 62, 65 Bohemia R.-oligohaline region

BSHOH 3 61, 63, 64 Bush R.-oligohaline region

C&DOH 3 61, 62, 65 C&D canal-oligohaline region

CB2OH 2 59, 61 Chesapeake Bay-oligohaline region

CB3MH 2 59, 62 Chesapeake Bay-mesohaline region

CB4MH 5 40, 46, 51, 52, 53 Chesapeake Bay-mesohaline region

CB5MH 11

1, 16, 18, 39, 40, 42, 44, 47, 48, 

52, 54 Chesapeake Bay-mesohaline region

CB6PH 9 1, 2, 3, 4, 5, 9, 13, 16, 18 Chesapeake Bay-polyhaline region

CB7PH 6 1, 2, 7, 8, 12, 14 Chesapeake Bay-polyhaline region

CB8PH 4 7, 6, 10, 11 Chesapeake Bay-polyhaline region

CHKOH 1 23 Chickahominy R.-oligohaline region

CHOMH1 3 95, 96, 97 Choptank R.-mesohaline region 1

CHOMH2 3 95, 96, 97 Choptank R.-mesohaline region 2

CHOOH 3 92, 93, 94 Choptank R.-oligohaline region

CHSMH 3 61, 62, 65 Chester R.-mesohaline region

CHSOH 1 65 Chester R.-oligohaline region

CRRMH 2 27, 37 Corrotoman R.-mesohaline region

EASMH 4 40, 46, 51, 91 Eastern Bay-mesohaline region

EBEMH 2 20, 22 East Branch Elizabeth R.-mesohaline region

ELIPH 1 19 Elizabeth R.-polyhaline region

ELKOH 3 61, 62, 65 Elk R.-oligohaline region

FSBMH 2 43, 57 Fishing Bay-mesohaline region

GUNOH 3 61, 63, 64 Gunpowder R.-oligohaline region

HNGMH 2 44, 49 Honga R.-mesohaline region

JMSMH 3 20, 21, 22 James R.-mesohaline region

JMSOH 3 23, 24, 25 James R.-oligohaline region

JMSPH 1 19 James R.-polyhaline region

LAFMH 2 20, 22 Lafayette R.-mesohaline region

LCHMH 3 45, 52, 56 Little Choptank R.-mesohaline region

LYNPH 1 11 Lynnhaven R.-polyhaline region

MAGMH 3 46, 53, 55 Magothy R.-mesohaline region

MANMH 3 41, 50, 58 Manokin R.-mesohaline region

MIDOH 3 61, 63, 64 Middle R.-oligohaline region

MOBPH 4 3, 4, 5, 10 Mobjack Bay-polyhaline region

MPNOH 2 74, 75 Mattaponi R.-oligohaline region

NANMH 3 85, 87, 88 Nanticoke R.-mesohaline region

NANOH 3 86, 89, 90 Nanticoke R.-oligohaline region

PATMH 3 46, 55, 64 Patapsco R.-mesohaline region

PAXMH 6 47, 77, 78, 79, 80, 81 Patuxent R.-mesohaline region

PAXOH 3 82, 83, 84 Patuxent R.-oligohaline region
60 

CBSEG_2003

Number 

of CAM 

boxes 

informed 

by 

segment

CAM boxes incorporating 

segment data Segment description

PIAMH 3 26, 28, 38 Piankatank R.-mesohaline region

PMKOH 2 74, 76 Pamunkey R.-oligohaline region

POCMH 3 14, 15, 17 Pocomoke R.-mesohaline region

POCOH 3 14, 15, 17 Pocomoke R.-oligohaline region

POTMH 7 32, 33, 54, 60, 66, 67, 68 Potomac R.-mesohaline region

POTOH 3 34, 35, 36 Potomac R.-oligohaline region

RHDMH 3 46, 53, 55 Rhode R.-mesohaline region

RPPMH 5 26, 27, 28, 37, 38 Rappahannock R.-mesohaline region

RPPOH 3 29, 30, 31 Rappahannock R.-oligohaline region

SASOH 3 61, 62, 65 Sassafras R.-oligohaline region

SBEMH 2 20, 22 South Branch Elizabeth R.-mesohaline region

SEVMH 3 46, 53, 55 Severn R.-mesohaline region

SOUMH 3 46, 53, 55 South R.-mesohaline region

TANMH 4 14, 15, 17, 49 Tangier Sound-mesohaline region

WBEMH 2 20, 22 W. Branch Elizabeth R.-mesohaline region

WICMH 3 41, 50, 58 Wicomico R.-mesohaline region

WSTMH 3 46, 53, 55 West R.-mesohaline region

YRKMH 5 69, 70, 71, 72, 73 York R.-mesohaline region

YRKPH 5 69, 70, 71, 72, 73 York R.-polyhaline region
61 

Table 5. Primary producer functional groups and basic life history parameterization. Growth, clearance, lysis, and mortality 

calibration values. Maximum initial concentration and biomass (bay-wide, wet-weight) are estimates from January initialize the model. "Q-mort" is quadratic mortality, used in the model to limit uncontrolled, explosive growth of a"Excess. DIN mort." is extra mortality due to excess dissolved inorganic nitrogen. "Refuge habitat" indicates wheth

used by other groups to decrease its availability to predators, see main text for further details; "---" indicates setting group. 

Common name

Group

code Species

2D/ 

3D Units

Initial 

concentration 

(max)

Biomass 

(mt)

Maximum 

growth 

rate 

(mgN/day)

Clearance 

(mg3/mgN/

day)

Lysis 

(mgN/day)

Q-m

(/d

Marsh grass MA Saltmarsh 

cordgrass, Spartina alterniflora, and giant cordgrass Spartina cynosuroides

2D mgN/m2 28,963.0 4,005,682 0.200 -- 0.00100 -

Seagrass (SAV) SG Genera: 

Zostera, Ruppia, Potamogetonand "freshwater mixed" as described by Moore et al. (2000)

2D mgN/m2 472.6 85,140 0.020 -- 0.01000 -

Large phytoplankton

PL Phytoplankton 

in size range 2–200 um (e.g., Diatoms, cryptophyes)

3D mgN/m3 121.6 687,613 2.700 -- 0.00001 0.0
62 

Common name

Group

code Species

2D/ 

3D Units

Initial 

concentration 

(max)

Biomass 

(mt)

Maximum 

growth 

rate 

(mgN/day)

Clearance 

(mg3/mgN/

day)

Lysis 

(mgN/day)

Q-m

(/d

Picophytoplankton PS Phytoplankton 

in size range 0.2–2 um (e.g., cyanobacteria)

3D mgN/m3 18.6 105,052 1.000 -- 0.01200 0.0

Dinoflagellates DF Mixotrophs 3D mgN/m3 20.3 114,602 0.585 0.275 0.00900 0.0

Microphytobenthos MB Benthic algae 2D mgN/m2 377.5 189,720 0.100 -- 0.00100 -
63 

Table 6. Habitat composition (proportions) of boxes in the Chesapeake Atlantis Model. '0' indicates 

habitat type is not found in that CAM box. "NA" = not applicable. Biogenic habitat proportions 

shown here correspond to Cover (Equation 18) available for refuge to prey as described in 

Habitat Associations. 

Biogenic habitats

CAM 

box

Rock or 

artificial 

reef Sand Mud

Woody 

debris SAV Marsh Oyster

0 NA NA NA NA NA NA NA

1 0.005 0 0.995 0 0 0 0

2 0.005 0 0.995 0 0 0 0

3 0.005 0 0.995 0 0 0 0

4 0.055 0 0.775 0.035 0.035 0.100 0

5 0.055 0 0.775 0.035 0.035 0.100 0

6 0.055 0 0.775 0.035 0.035 0.100 0

7 0.005 0.995 0 0 0 0 0

8 0.005 0.995 0 0 0 0 0

9 0.080 0.720 0 0.050 0.025 0.075 0.050

10 0.080 0.720 0 0.050 0.025 0.075 0.050

11 0.080 0.720 0 0.050 0.025 0.075 0.050

12 0.080 0.720 0 0.050 0.025 0.075 0.050

13 0.055 0 0.775 0.035 0.035 0.100 0

14 0.055 0 0.775 0.035 0.035 0.100 0

15 0.010 0.940 0 0 0 0 0.050

16 0.010 0.940 0 0 0 0 0.050

17 0.150 0.500 0 0.100 0.050 0.150 0.050

18 0.150 0.500 0 0.100 0.050 0.150 0.050

19 0.008 0.283 0.607 0 0.035 0 0.067

20 0.010 0.400 0.490 0 0 0 0.100

21 0.100 0.080 0.250 0.150 0.100 0.300 0.020

22 0.100 0.080 0.250 0.150 0.100 0.300 0.020

23 0.100 0.080 0.250 0.150 0.100 0.300 0.020

24 0.100 0.080 0.250 0.150 0.100 0.300 0.020

25 0.010 0.400 0.490 0 0 0 0.100

26 0.005 0.050 0.945 0 0 0 0

27 0.010 0.400 0.490 0 0 0 0.100

28 0.010 0.400 0.490 0 0 0 0.100

29 0.100 0.080 0.250 0.150 0.100 0.300 0.020

30 0.100 0.080 0.250 0.150 0.100 0.300 0.020

31 0.008 0.225 0.718 0 0 0 0.050

32 0.005 0.050 0.945 0 0 0 0

33 0.010 0.400 0.490 0 0 0 0.100

34 0.008 0.225 0.718 0 0 0 0.050

35 0.100 0.080 0.250 0.150 0.100 0.300 0.020

36 0.100 0.080 0.250 0.150 0.100 0.300 0.020

37 0.100 0.080 0.250 0.150 0.100 0.300 0.020

38 0.100 0.080 0.250 0.150 0.100 0.300 0.020

39 0.005 0.995 0 0 0 0 0
64 

Biogenic habitats

CAM 

box

Rock or 

artificial 

reef Sand Mud

Woody 

debris SAV Marsh Oyster

40 0.005 0 0.995 0 0 0 0

41 0.005 0 0.995 0 0 0 0

42 0.005 0.995 0 0 0 0 0

43 0.010 0 0.990 0 0 0 0

44 0.010 0 0.990 0 0 0 0

45 0.010 0 0.990 0 0 0 0

46 0.010 0 0.990 0 0 0 0

47 0.010 0 0.990 0 0 0 0

48 0.010 0 0.990 0 0 0 0

49 0.010 0.940 0 0 0 0 0.050

50 0.010 0.940 0 0 0 0 0.050

51 0.010 0.940 0 0 0 0 0.050

52 0.010 0.940 0 0 0 0 0.050

53 0.010 0.940 0 0 0 0 0.050

54 0.010 0.940 0 0 0 0 0.050

55 0.150 0.500 0 0.100 0.050 0.150 0.050

56 0.150 0.500 0 0.100 0.050 0.150 0.050

57 0.150 0.500 0 0.100 0.050 0.150 0.050

58 0.150 0.500 0 0.100 0.050 0.150 0.050

59 0.005 0 0.995 0 0 0 0

60 0.010 0.940 0 0 0 0 0.050

61 0.010 0 0.990 0 0 0 0

62 0.010 0.940 0 0 0 0 0.050

63 0.010 0.940 0 0 0 0 0.050

64 0.150 0.500 0 0.100 0.050 0.150 0.050

65 0.150 0.500 0 0.100 0.050 0.150 0.050

66 0.010 0.400 0.490 0 0 0 0.100

67 0.100 0.080 0.250 0.150 0.100 0.300 0.020

68 0.100 0.080 0.250 0.150 0.100 0.300 0.020

69 0.005 0.050 0.945 0 0 0 0

70 0.010 0.400 0.490 0 0 0 0.100

71 0.010 0.400 0.490 0 0 0 0.100

72 0.100 0.080 0.250 0.150 0.100 0.300 0.020

73 0.100 0.080 0.250 0.150 0.100 0.300 0.020

74 0.008 0.225 0.718 0 0 0 0.050

75 0.100 0.080 0.250 0.150 0.100 0.300 0.020

76 0.100 0.080 0.250 0.150 0.100 0.300 0.020

77 0.010 0.400 0.490 0 0 0 0.100

78 0.010 0.400 0.490 0 0 0 0.100

79 0.100 0.080 0.250 0.150 0.100 0.300 0.020

80 0.100 0.080 0.250 0.150 0.100 0.300 0.020

81 0.005 0.050 0.945 0 0 0 0

82 0.008 0.225 0.718 0 0 0 0.050

83 0.100 0.080 0.250 0.150 0.100 0.300 0.020

84 0.100 0.080 0.250 0.150 0.100 0.300 0.020

85 0.008 0.225 0.718 0 0 0 0.050
65 

Biogenic habitats

CAM 

box

Rock or 

artificial 

reef Sand Mud

Woody 

debris SAV Marsh Oyster

86 0.008 0.225 0.718 0 0 0 0.050

87 0.100 0.080 0.250 0.150 0.100 0.300 0.020

88 0.100 0.080 0.250 0.150 0.100 0.300 0.020

89 0.100 0.080 0.250 0.150 0.100 0.300 0.020

90 0.100 0.080 0.250 0.150 0.100 0.300 0.020

91 0.150 0.500 0 0.100 0.050 0.150 0.050

92 0.008 0.225 0.718 0 0 0 0.050

93 0.100 0.080 0.250 0.150 0.100 0.300 0.020

94 0.100 0.080 0.250 0.150 0.100 0.300 0.020

95 0.008 0.225 0.718 0 0 0 0.050

96 0.100 0.080 0.250 0.150 0.100 0.300 0.020

97 0.100 0.080 0.250 0.150 0.100 0.300 0.020

Table 7. Expected microphytobenthos (CHLA) concentrations in sediments of the Chesapeake Bay, based 

on M. Kemp (personal communication) and field observations presented in Rizzo and Wetzel 

(1985). Concentrations are mg CHLA/m2

. “Soft” includes all sediments other than clean sand. 

Bottom type was not identified in tributaries; therefore, intermediate CHLA concentrations were 

assumed for tributary boxes. 

Water Column Depth

0–2 m > 2–8 m > 8 m

Mainstem: 

Soft 60 20 0

Sand 100 60 0

Tributaries: 80 40 0
66 

Table 8. Invertebrate functional groups and basic life history parameterization. "2D" or "3D" indicate how many spatial dimen

group is modeled in. Growth, clearance, and mortality rates are post-calibration values. Maximum initial concentratiobiomass (bay-wide, wet-weight) are estimates from January 1, 2002, estimates used to initialize the model; however, bgroup is not available until the day of the year that group migrates into the model. "Q-mort" is quadratic mortality, useto limit uncontrolled, explosive growth of any particular group. "Refuge habitat" indicates whether the group can be u

groups to decrease its availability to predators, see main text for further details. 

Group name

Group 

code

Group description, 

species included

2D/ 

3D Units

Initial 

concentration 

(max)

Initial 

biomass 

(mt)

Max. 

growth 

rate 

(mgN/day)

Clearance 

(mg3/mgN

/day)

Q-m

(/d

Ctenophores ZG Mnemiopsis leidyi 3D mgN/m3 0.08 998 0.04 0.004 0.

Sea nettles ZL Chrysaora 

quinquecirrha

3D mgN/m3 0.07 926 0.02 0.08 0.

Mesozooplankton ZM Zooplankton in size range 200–20,000 um 

(0.2–20 mm), e.g., copepods, cladocera

3D mgN/m3 2.09 26,410 2.3 0.3 1.00

Microzooplankton ZS Zooplankton in size range 20–200 um 

(0.02–0.2 mm), e.g., ciliates, dinoflagellates, nanoflagellates, gymnodioids, protozoa

3D mgN/m3 1.65 20,844 1.9 0.45 0.0

Deep benthic filter feeders

BFD Benthic filter feeders 

(e.g., hard clam, arcs, filter feeding poycheates, sessile tunicates; see Table 10 for complete list)

2D mgN/m2 680.65 829,445 0.04 0.004 1.00

Shallow benthic filter feeders

BFS Macoma spp.

(saltwater clams)

2D mgN/m2 97.25 118,507 0.02 0.008 1.00
67 

Group name

Group 

code

Group description, 

species included

2D/ 

3D Units

Initial 

concentration 

(max)

Initial 

biomass 

(mt)

Max. 

growth 

rate 

(mgN/day)

Clearance 

(mg3/mgN

/day)

Q-m

(/d

Other benthic filter feeders

BFF Oyster (Crassostrea 

virginica)

2D mgN/m2 4,176.60 181,555 0.04 0.002 1.00

Benthic carnivores

BC Benthic predators that 

compete with fish (e.g., polychaetes, nematodes, burrowing crustacea, flatworms et al.; see Table 10 for complete list)

3D mgN/m3 52.00 63,352 0.03 0.0002 1.00

Benthic deposit feeders

BD Benthic detritivores 

and omnivores (e.g., amphipods, isopods, small crustacea, snails, et al. (see Table 10 for complete list)

3D mgN/m3 60.30 73,452 0.06 0.007 1.00

Meiobenthos BO Major taxonomic 

groups include copepods, nematodes, nauplii, foraminifera, newly settled clams, polychaetes, et.al (following Metcalfe, 2005)

3D mgN/m3 5.72 35,266 0.2 0.02 1.00

Brief squid CEP Lolliguncula 

brevis

3D mgN/m3 1.80 1,027 0.1 0.0019 1.00

Juvenile squid jCEP Juvenile brief squid 3D mgN/m3 0 0 0.03 0.0015 1.00

Blue crab PWN Callinectes sapidus 3D mgN/m3 31.70 38,627 0.01 0.001 1.00

Juvenile crab jPWN Juvenile blue crab 3D mgN/m3 0 0 0.09 0.004 1.00
68 

Table 9. Biological characteristics and timing of invertebrate migrators in CAM. Sea nettles and crab do not actually leave the 

over the winter, but rather encyst (sea nettles) and enter torpor (crabs); both conditions effectively remove these groupmodel domain and are consequently modeled as migrations. "NA" = not applicable. 

Common 

name

Group 

code

Day group 

moves into 

model

Day group 

leaves the 

model

Day spawning 

begins for group

Period (days) 

spawning 

occurs

Average biomass 

of new recruits 

(mgN/m3)

Sea nettles ZL 91 319 NA NA NA

Brief squid CEP 74 335 182 90 NAJuvenile squid jCEP 91 0 NA NA 0.1Crab PWN 91 305 182 90 NAJuvenile crab jPWN 91 305 NA NA 0.0227
69

Table 10. Species composition of the four aggregate benthic invertebrate groups in CAM based on the Benthic-Index of Bi

IBI) of the Chesapeake Bay Program: benthic deposit feeders (BD), Macoma spp. (BFS), benthic carnivores (BC), feeders (BFD). These groups are a subset of species collected during the Long-Term Benthic survey during 2002–2either 90% of the total abundance (ABUN), 90% of the total biomass (BIOM), or both. National Oceanic Data Cencodes are superseded by Taxonomic Serial Number (TSN) designations; "---" indicates no TSN exists for that specthe B-IBI are IN = Interface Feeder, CO = Carnivore and Omnivore, SU=Suspension Feeder, P=Predator, and M= 

Scientific Name NODC TSN Family Group Code Name

B-IB

Trop

Grou

Benthic deposit feeders (BD):

Apoprionospio pygmaea 5001433599 66847 Spionidae Annelida : Polychaeta IN 

Asabellides oculata 5001670802 67786 Ampharetidae Annelida : Polychaeta IN Boccardiella ligerica 5001432802 67012 Spionidae Annelida : Polychaeta IN Hobsonia florida 5001670309 67755 Ampharetidae Annelida : Polychaeta IN Loimia medusa 5001682001 68015 Terebellidae Annelida : Polychaeta IN Magelona spp. 50014401 67043 Magelonidae Annelida : Polychaeta IN Marenzelleria viridis 5001430602 573739 Spionidae Annelida : Polychaeta IN Monticellina dorsobranchialis 5001509897 204530 Cirratulidae Annelida : Polychaeta IN Paraonis fulgens 5001410302 66697 Paraonidae Annelida : Polychaeta IN Paraprionospio pinnata 5001431701 66937 Spionidae Annelida : Polychaeta IN Polycirrus eximius 5001680804 67963 Terebellidae Annelida : Polychaeta IN Polydora cornuta 5001430448 204501 Spionidae Annelida : Polychaeta IN Prionospio perkinsi 5001430517 66854 Spionidae Annelida : Polychaeta IN Spiochaetopterus costarum 5001490302 67107 Chaetopteridae Annelida : Polychaeta IN Spiophanes bombyx 5001431001 66897 Spionidae Annelida : Polychaeta IN Streblospio benedicti 5001431801 66939 Spionidae Annelida : Polychaeta IN Tharyx sp. A Morris 5001500399 --- Cirratulidae Annelida : Polychaeta IN Acanthohaustorius millsi 6169220602 93982 Haustoriidae Arthropoda : Amphipoda IN Ameroculodes species complex 61693799 --- Oedicerotidae Arthropoda : Amphipoda IN Apocorophium lacustre 6169159798 656749 Corophiidae Arthropoda : Amphipoda IN Gammarus daiberi 6169210705 93779 Gammaridae Arthropoda : Amphipoda IN Gammarus fasciatus 6169210706 93780 Gammaridae Arthropoda : Amphipoda CO Lepidactylus dytiscus 6169220901 93998 Haustoriidae Arthropoda : Amphipoda IN 
70

Scientific Name NODC TSN Family Group Code Name

B-IB

Trop

Grou

Leptocheirus plumulosus 6169060701 93486 Aoridae Arthropoda : Amphipoda IN 

Listriella barnardi 6169330301 94213 Liljeborgiidae Arthropoda : Amphipoda IN 

Listriella clymenellae 6169330302 94214 Liljeborgiidae Arthropoda : Amphipoda IN Melita nitida 6169211006 93812 Melitidae Arthropoda : Amphipoda IN Monocorophium tuberculatum 6169159999 656762 Corophiidae Arthropoda : Amphipoda IN Paracaprella tenuis 6171010901 95434 Caprellidae Arthropoda : Amphipoda CO Protohaustorius wigleyi 6169221202 94010 Haustoriidae Arthropoda : Amphipoda IN Rhepoxynius epistomus 6169421501 94728 Phoxocephalidae Arthropoda : Amphipoda IN Leucon americanus 6154040110 90790 Leuconidae Arthropoda : Cumacea IN Oxyurostylis smithi 6154050801 90923 Diastylidae Arthropoda : Cumacea IN Biffarius biformis 6183049999 552845 Callianassidae Arthropoda : Decapoda CO 

Panopeus herbstii 6189020801 98778 Xanthidae Arthropoda : Decapoda CO Rhithropanopeus harrisii 6189020901 98790 Xanthidae Arthropoda : Decapoda CO 

Cyathura polita 6160010201 542646 Anthuridae Arthropoda : Isopoda CO Edotea triloba 6162020703 92627 Idoteidae Arthropoda : Isopoda CO 

Sphaeroma quadridentatum 6161020702 92339 Sphaeromatidae Arthropoda : Isopoda CO Synidotea spp. 61620202 92566 Idoteidae Arthropoda : Isopoda CO Squilla empusa 6191010101 99143 Squillidae Arthropoda : Stomatopoda CO 

Ceriantheopsis americanus 3743010201 51991 Cerianthidae Cnidaria : Anthozoa CO Diadumene leucolena 3760080103 52749 Diadumenidae Cnidaria : Anthozoa CO 

Leptosynapta tenuis 8178010202 158432 Synaptidae Echinodermata : Holothuroidea IN Microphiopholis atra 8129031201 --- Amphiuridae Echinodermata : Ophiuroidea IN Tellina agilis 5515310205 81088 Tellinidae Mollusca : Bivalvia IN Acteocina canaliculata 5110040103 76117 Scaphandridae Mollusca : Gastropoda CO 

Hydrobiidae 510313 70493 Hydrobiidae Mollusca : Gastropoda CO Littoridinops tenuipes 5103130501 70528 Hydrobiidae Mollusca : Gastropoda CO 

Nassarius trivittatus 5105080103 74109 Nassariidae Mollusca : Gastropoda CO 

Polinices duplicatus 5103760407 72918 Naticidae Mollusca : Gastropoda CO Rictaxis punctostriatus 5110010403 76083 Acteonidae Mollusca : Gastropoda CO 

Macoma spp.(BFS):Macoma balthica 5515310116 567846 Tellinidae Mollusca : Bivalvia M 

Macoma mitchelli 5515310119 81054 Tellinidae Mollusca : Bivalvia M 
71

Scientific Name NODC TSN Family Group Code Name

B-IB

Trop

Grou

Macoma tenta 5515310120 81055 Tellinidae Mollusca : Bivalvia M 

Benthic carnivores (BC):Aglaophamus verrilli 5001250303 66052 Nephtyidae Annelida : Polychaeta P 

Bhawania heteroseta 5001080302 65159 Chrysopetalidae Annelida : Polychaeta P 

Brania clavata 5001230902 65761 Syllidae Annelida : Polychaeta P Brania wellfleetensis 5001230903 65762 Syllidae Annelida : Polychaeta P Cabira incerta 5001220401 65565 Pilargidae Annelida : Polychaeta P Eteone heteropoda 5001130207 65266 Phyllodocidae Annelida : Polychaeta P Glycera americana 5001270104 66106 Glyceridae Annelida : Polychaeta P 

Glycera dibranchiata 5001270105 66107 Glyceridae Annelida : Polychaeta P 

Glycinde solitaria 5001280104 66132 Goniadidae Annelida : Polychaeta P 

Laeonereis culveri 5001240801 65965 Nereididae Annelida : Polychaeta P 

Neanthes succinea 5001240309 65918 Nereididae Annelida : Polychaeta P 

Nephtys bucera 5001250114 66027 Nephtyidae Annelida : Polychaeta P 

Nephtys picta 5001250117 66030 Nephtyidae Annelida : Polychaeta P 

Phyllodoce arenae 5001131410 65366 Phyllodocidae Annelida : Polychaeta P Podarkeopsis levifuscina 5001211999 555698 Hesionidae Annelida : Polychaeta P 

Pseudeurythoe paucibranchiata 5001100301 65176 Amphinomidae Annelida : Polychaeta P Sigambra tentaculata 5001220201 65552 Pilargidae Annelida : Polychaeta P 

Carinoma tremaphoros 4302020101 57429 Carinomidae Nemertina P 

Micrura leidyi 4303020505 57477 Lineidae Nemertina P Nemertina 43 57411 Unidentified Nemertina P 

Stylochus ellipticus 3906030101 54089 Stylochidae Platyhelminthes : Turbellaria P 

Turbellaria 3901 53964 Unidentified Platyhelminthes : Turbellaria P 

Benthic filter feeders (BFD): Chaetopterus variopedatus 5001490101 67097 Chaetopteridae Annelida : Polychaeta SU 

Ampelisca spp. 61690201 93321 Ampeliscidae Arthropoda : Amphipoda SU 

Ampelisca verrilli 6169020110 93331 Ampeliscidae Arthropoda : Amphipoda SU Molgula lutulenta 8406030120 159581 Molgulidae Chordata : Ascidiacea SU Molgula manhattensis 8406030108 159557 Molgulidae Chordata : Ascidiacea SU Branchiostoma caribaeum 8500010101 159682 Branchiostomidae Chordata : Cephalochordata SU 
72

Scientific Name NODC TSN Family Group Code Name

B-IB

Trop

Grou

Aligena elevata 5515100602 80685 Kelliidae Mollusca : Bivalvia SU 

Anadara ovalis 5506010202 79342 Arcidae Mollusca : Bivalvia SU 

Anadara transversa 5506010201 79340 Arcidae Mollusca : Bivalvia SU Corbicula fluminea 5515450201 81387 Corbiculidae Mollusca : Bivalvia SU 

Crassostrea virginica 5510020102 79872 Ostreidae Mollusca : Bivalvia SU 

Ensis directus 5515290301 81022 Solenidae Mollusca : Bivalvia SU 

Gemma gemma 5515471301 81511 Veneridae Mollusca : Bivalvia SU Geukensia demissa 5507011501 79555 Mytilidae Mollusca : Bivalvia SU 

Ischadium recurvum 5507011601 79561 Mytilidae Mollusca : Bivalvia SU Mercenaria mercenaria 5515471101 81496 Veneridae Mollusca : Bivalvia SU Mulinia lateralis 5515250301 80959 Mactridae Mollusca : Bivalvia SU 

Mytilopsis leucophaeata 5515370201 81335 Dreissenidae Mollusca : Bivalvia SU Mytilus edulis 5507010101 79454 Mytilidae Mollusca : Bivalvia SU 

Parvilucina crenella 5515010102 80388 Lucinidae Mollusca : Bivalvia SU 

Rangia cuneata 5515250401 80962 Mactridae Mollusca : Bivalvia SU Tagelus plebeius 5515330201 81272 Solecurtidae Mollusca : Bivalvia SU 

Phoronis spp. 77000102 155462 Phoronidae Phoronida SU 
73

Table 11. Biomass estimates (wet weight) for vertebrate functional groups at start of model run (2002), 

specific to Chesapeake Bay, and information sources used. Estimates for aggregate groups 

represent summary values weighted by relative abundance of each species in the group in 

Chesapeake Bay. Preliminary biomass estimates were based on a variety of available sources 

shown in the final column of the table. Preliminary biomass estimates were fed into an Ecopath 

model that was built expressly for balancing the initial CAM groups; "---" indicates no 

information was available to use for Ecopath; consequently, starting biomass for such groups was 

estimated solely by the Ecopath model. Balanced Ecopath estimates for all birds, reptiles, and 

bottlenose dolphin differed only slightly from preliminary biomass estimates, so the preliminary 

estimates were used as starting biomass for CAM in these instances. Landings data were obtained 

from the NOAA Office of Science and Technology for the calendar year 2002 (personal 

communication; commercial: M. Lewis, 1/19/11; recreational: L. Dolinger-Few, 1/31/11). Bird 

data obtained from the Cornell Lab of Ornithology at: http://bna.birds.cornell.edu/bna/species

(accessed October 2010). SA = stock assessment. 

Common name Scientific name

Group 

code

Prelim. 

biomass 

(mt)

Starting 

biomass 

(mt)

Source(s) for 

biomass 

estimates

Gizzard shad Dorosoma cepedianum FPL 57 9,399 Landings data

Bay anchovy Anchoa mitchilli FPO --- 9,710 Ecopath

Atl. menhaden Brevoortia tyrannus FPS 6,200 177,915 2006 ASMFC 

SA; landings 

data

Summer flounder Paralichthys dentatus FVD 1,088 4,965 2006 NMFS SA 

Update, age 2–

7; landings data

Weakfish Cynoscion regalis FVS 254 1,470 2009 48th SAW; 

landings data

Catfish: 

- Channel catfish

- Blue catfish

- White catfish

- Flathead catfish

- Black bullhead

Ictalurus punctatus

Ictalurus furcatus

Ameiurus catus

Pylodictis olivaris

Ameiurus melas

FVB 282 4,187 Landings data

Bluefish Pomatomus saltatrix FVT 1,630 1,365 2003 ASMFC 

SA; landings 

data

Alosines: 

- American shad

- Alewife

- Blueback shad

- Hickory shad

Alosa sapidissima

Alosa pseudoharengus

Alosa aestivalis

Alosa mediocris

FMM 53 12,494 Landings data

Butterfish Peprilus triacanthus FMN 0.23 1,234 Landings data

Striped bass Morone saxatilis FBP 15,962 15,962 Landings data
74

Common name Scientific name

Group 

code

Prelim. 

biomass 

(mt)

Starting 

biomass 

(mt)

Source(s) for 

biomass 

estimates

Atl. croaker Micropogonias 

undulatus

FDD 16,976 6,227 2010 ASMFC 

SA; landings 

data

Panfish: 

- Spot

- Yellow perch

- Bluegill

- Silver Perch

Leiostomus xanthurus

Perca flavescens

Lepomis macrochirus

Bairdiella chrysoura

FDE 721 8,662 Landings data

White perch Morone americana FDS 979 4,715 Landings data

Reef assoc. fish: 

- Atlantic spadefish

- Tautog

- Black seabass

- Oyster toadfish

Chaetodipterus faber

Tautoga onitis

Centropristis striata

Opsanus tau

FDM 119 1,027 2006 ASMFC 

Tautog SA; 

landings data

Forage fish: 

- Atlantic silverside

- Mummichog

Menidia menidia

Fundulus heteroclitus 

FDP 0.16 20,051 Landings data

Bottom fish: 

- Spotted hake

- Inshore lizardfish

- Northern searobin

Urophycis regia

Synodus foetens

Prionotus carolinus

FDC 3.35 9,308 Landings data

Black drum Pogonias cromis FDO 21 5,029 Landings data

Other flatfish: 

- Windowpane

- Hogchoker

- Blackcheek tonguefish

- Winter flounder

Scophthalmus aquosus

Trinectes maculatus

Symphurus plagiusa

Pseudopleuronectes 

americanus

FDF --- 20,691 Ecopath

Smooth dogfish Mustelus canis SHB 3 1,363 Landings data

Sandbar shark Carcharhinus plumbeus SHD 55 152 Landings data

Spiny dogfish Squalus acanthias SHC 1,363 7,099 2010 TRAC 

status report - 

Atlantic coast 

females; 

landings data

Cownose ray Rhinoptera bonasus SSK 6 480 Landings data
75

Common name Scientific name

Group 

code

Prelim. 

biomass 

(mt)

Starting 

biomass 

(mt)

Source(s) for 

biomass 

estimates

Benthic grazing 

seabirds: 

- Canada goose

- Mallard

- Redhead

- Tundra swan

- Mute swan

Branta canadensis

Anas platyrhynchos

Aythya americana

Cygnus columbianus 

Cygnus olor

FVO 1,076 1,076 D. Forsell, 

USF&W 

(unpublished 

data); Cornell 

Lab of 

Ornithology

Diving ducks: 

- Surf scoter

- Long-tailed duck

Melanitta perspicillata

Clangula hyemalis

SB 56 56 D. Forsell, 

USF&W 

(unpublished 

data); Cornell 

Lab of 

Ornithology

Piscivorous seabirds: 

- Osprey

- Great blue heron

- Brown pelican

- Double-crested 

cormorant

Pandion haliaetus

Ardea herodias

Pelecanus occidentalis

Phalacrocorax auritus

SP 218 218 D. Forsell, 

USF&W 

(unpublished 

data); Cornell 

Lab of 

Ornithology

Bald eagles Haliaeetus 

leucocephalus

PIN 24 24 D. Forsell, 

USF&W 

(unpublished 

data); Cornell 

Lab of 

Ornithology

Diamond-back terrapin Malaclemys terrapin FVV 4,132 4,132 Expert 

knowledge 

(Randy 

Chambers, 

College of 

William & 

Mary)

Sea turtles: 

- Loggerhead

- Kemp’s Ridley

- Leatherback

Caretta caretta

Lepidochelys kempii

Dermochelys coriacea

REP 193 193 Byles (1989); 

Keinath (1993); 

Plotkin (1995); 

Coles (1999); 

Kobell (2010)

Bottlenose dolphin Tursiops truncatus WHS 322 322 Trites and Pauly 

(1998); Barco et 

al. (1999); 

Neuenhoff et al. 

(2010) 
76

Table 12. Vertebrate life history parameterization. Parameters for aggregate groups represent summary values weighted by 

each species in the group. Natural mortality, M, was used only to set up the initial age structure for model runs. Lik

used only to parameterize initial size structure, where k is the VonBertalanffy growth coefficient and Linf is theore(cm). Growth of birds and dolphin were assumed to follow a Gompertz growth curve; "a" and "b" are constants of relationship (W=aLb). "Am" values indicate age class of group at first maturity and are post-calibration values. "Ar" 

the modeled population (i.e., approximate age of transformation from larval to juvenile stage). BHalpha and BHbet

Beverton-Holt recruitment model, but these values are specific to the Atlantis model, where BHalpha is the estimat

number of recruits that can be produced (system-wide), and BHbeta is the biomass of mature adults (both sexes) th

Bhalpha. RFixed is the fixed number of new recruits per individual (per year). Linear mortality is set at '0' for all vert

included in the table. Quadratic mortality values are included in the model to limit uncontrolled, explosive growth 

Efficiency is the portion of food not immediately lost to sloppy feeding by each group. "NA" = not applicable. Addhistory parameterization and references are found in Appendix C. Common name corresponding to code for each vmodeled can be found in Table 11 or Table 13. 

Code

M 

(per year) k Linf

Max 

age 

(yr) a b Am

Ar 

(days) BHalpha BHbeta Rfixed

Juvenile 

quadratic 

mortality 

(per day)

FPL 0.41 0.18 43.60 10.0 0.020 2.99 3 60 7.51E+09 1.04E+10 NA 1.0E-10 

FPO 2.30 0.52 11.00 2.0 0.010 3.00 1 60 1.05E+15 1.02E+08 NA 8.5E-12 

FPS 0.75 0.48 36.90 10.0 0.130 3.11 2 60 7.51E+10 1.21E+11 NA 1.0E-10 

FVD 0.46 0.84 137.00 10.0 0.010 3.12 2 60 1.00E+07 1.14E+09 NA 1.0E-07 

FVS 0.27 0.26 82.30 17.0 0.010 2.98 1 60 8.56E+07 6.64E+09 NA 1.0E-10 

FVB 0.16 0.06 77.60 17.8 0.011 3.08 3 60 3.77E+06 2.87E+08 NA 1.0E-20 

FVT 0.35 0.17 97.53 9.0 0.020 2.89 2 60 3.54E+06 6.19E+11 NA 1.0E-10 

FMM 0.49 0.47 49.10 10.3 0.007 2.80 4 24 3.00E+08 1.20E+11 NA 1.0E-12 

FMN 0.80 0.80 23.15 1.5 0.011 3.17 1 30 3.95E+09 2.03E+11 NA 1.0E-15 

FBP 0.14 0.15 119.25 30.0 0.010 3.09 2 60 2.92E+07 1.92E+10 NA 1.0E-10 

FDD 0.88 0.57 40.90 17.0 0.004 3.20 1 60 8.20E+08 2.65E+10 NA 1.0E-11 

FDE 0.67 1.08 26.59 7.8 0.015 3.07 2 60 4.80E+09 8.33E+09 NA 1.0E-09 

FDS 0.38 0.07 52.00 12.0 0.010 3.12 3 60 3.40E+10 1.05E+09 NA 1.0E-09 

FDM 0.33 0.33 38.73 16.5 0.040 2.74 1 26 3.72E+07 8.37E+10 NA 1.0E-10 
77

Code

M 

(per year) k Linf

Max 

age 

(yr) a b Am

Ar 

(days) BHalpha BHbeta Rfixed

Juvenile 

quadratic 

mortality 

(per day)

FDP 3.45 1.24 14.55 1.5 0.010 3.02 1 30 1.34E+13 6.24E+09 NA 1.0E-10 

FDC 0.63 0.30 34.84 7.0 0.010 3.03 2 60 5.66E+09 1.11E+10 NA 1.0E-09 

FDO 0.09 0.15 94.80 43.0 0.020 2.95 1 60 1.53E+09 5.10E+09 NA 1.0E-09 

FDF 0.56 0.31 28.74 8.5 0.024 2.96 4 60 2.74E+09 2.83E+10 NA 1.0E-09 

SHB 0.11 0.04 150.00 16.0 0.230 3.10 2 60 NA NA 2.50 2.0E-11 

SHD 0.12 0.06 267.00 32.0 0.010 3.27 5 60 NA NA 6.00 1.0E-17 

SHC 0.09 0.06 123.00 75.0 0.004 3.06 3 60 NA NA 5.00 1.0E-12 

SSK 0.17 0.08 124.00 18.0 0.005 3.19 2 60 NA NA 2.00 3.0E-10 

FVO 0.28 NA 92.15 23.5 0.118 2.19 1 68 NA NA 3.00 5.0E-15 

SB 0.27 NA 44.77 20.0 0.091 2.42 1 48 NA NA 3.65 1.0E-07 

SP 0.20 NA 113.88 15.3 0.099 2.20 1 58 NA NA 6.00 1.0E-13 

PIN 0.20 NA 87.89 28.0 0.004 2.60 2 119 NA NA 1.50 1.0E-05 

FVV 0.16 0.15 32.11 40.0 0.035 3.00 2 71 NA NA 17.00 5.0E-09 

REP 0.29 0.08 98.97 60.8 0.153 3.00 5 56 NA NA 40.00 5.0E-05 

WHS 0.11 NA 385.01 40.0 0.123 2.43 2 105 NA NA 0.13 2.0E-13 
 78

Table 13. Post-calibration growth parameter settings used in the functional response for each vertebrate functional group an

in CAM (corresponds to parameter "mum" in bio.prm file). 

Age class

Common name

Group 

code 1 2 3 4 5 6 7 8 

Gizzard shad FPL 6.12 21.83 42.88 132.40 179.54 224.74 266.61 304.49 Bay anchovy FPO 0.01 0.03 0.06 0.10 0.15 0.20 0.25 0.30 

Atl. menhaden FPS 123.46 123.43 196.16 250.48 287.78 312.32 328.09 338.07 

Summer flounder FVD 1000.82 8573.78 11186.32 12427.40 12983.43 13226.82 13332.33 13377.89 

Weakfish FVS 16.45 652.89 991.35 1227.54 1380.44 1475.77 1534.02 1569.21 

Catfish FVB 2.94 162.87 445.99 738.42 1066.17 1412.90 1766.09 2116.43 

Bluefish FVT 218.55 375.54 362.41 553.93 746.72 931.36 1102.58 1257.87 

Alosines FMM 6.00 13.00 20.00 25.00 28.00 31.00 32.00 32.40 

Butterfish FMN 0.25 2.41 1.00 1.00 1.00 1.00 1.00 1.00 

Striped bass FBP 170.40 693.00 1117.20 1444.80 1680.00 1839.60 1940.40 2007.60 

Atl. croaker FDD 20.19 113.02 133.74 140.80 143.11 143.86 144.10 144.17 

Panfish FDE 20.31 53.34 63.42 67.20 68.46 68.88 68.88 68.88 

White perch FDS 0.22 2.81 6.31 10.96 16.54 22.87 29.75 37.04 

Reef fish FDM 19.30 42.72 57.84 66.72 71.28 73.68 75.12 75.60 

Forage fish FDP 0.11 0.27 0.08 0.12 0.16 0.20 0.23 0.26 

Bottom fish FDC 2.09 6.78 12.13 17.15 21.47 25.00 27.80 29.98 

Black drum FDO 20.82 51.32 73.98 88.22 96.56 101.29 103.93 105.40 

Other flatfish FDF 4.92 21.50 26.88 52.50 46.44 75.10 59.28 89.04 

Smooth dogfish SHB 0.50 2.04 4.52 7.76 11.58 15.84 20.41 25.17 

Sandbar shark SHD 40.48 369.33 866.06 2806.94 3945.32 5077.72 6159.36 7163.87 

Spiny dogfish SHC 29.70 251.90 399.42 510.07 586.54 637.20 669.98 690.90 

Cownose ray SSK 4.68 56.12 118.61 193.51 274.46 356.82 437.36 513.99 

Benthic grazing seabirds FVO 2577.71 2577.71 2577.71 2577.71 2577.71 2577.71 2577.71 2577.71 

Diving ducks SB 1000.00 1100.00 1100.00 1100.00 1100.00 1100.00 1100.00 1100.00 
 7

9

Age class

Common name

Group 

code 1 2

3

4

5

6

7

8

Piscivorous seabirds SP 1226.67 404.80 404.80 1226.67 1226.67 1226.67 1226.67 1226.67 

Bald eagles PIN 4400.00 4400.00 4620.00 4620.00 4620.00 4620.00 4620.00 4620.00 

Diamond-back terrapin FVV 3.62 9.06 13.16 15.75 17.28 18.15 18.63 18.90 

Sea turtles REP 289.04 1243.58 2378.39 3357.32 4089.84 4599.00 4938.54 5159.53 

Bottlenose dolphin WHS 42234.33 52973.68 55229.68 55441.65 55469.27 55472.86 55473.33 55473.39 
80

Table 14. Post-calibration clearance rates used in the functional response for each vertebrate functional group and age clas

Age class

Common name

Group 

code 1 2 3 4 5 6 7 8 

Gizzard shad FPL 0.61 2.18 4.29 13.24 17.95 22.47 26.66 30.45 3Bay anchovy FPO 0.00 0.01 0.01 0.02 0.02 0.03 0.04 0.05 

Atl. menhaden FPS 12.35 12.34 19.62 25.05 28.78 31.23 32.81 33.81 3

Summer flounder FVD 100.08 857.38 1118.63 1242.74 1298.34 1322.68 1333.23 1337.79 13

Weakfish FVS 2.63 104.46 158.62 196.41 220.87 236.12 245.44 251.07 2

Catfish FVB 0.29 16.29 44.60 73.84 106.62 141.29 176.61 211.64 2

Bluefish FVT 21.86 37.55 36.24 55.39 74.67 93.14 110.26 125.79 1

Alosines FMM 0.60 1.30 2.00 2.50 2.80 3.10 3.20 3.24 

Butterfish FMN 0.02 0.24 0.10 0.10 0.10 0.10 0.10 0.10 

Striped bass FBP 27.26 110.88 178.75 231.17 268.80 294.34 310.46 321.22 3

Atl. croaker FDD 2.02 11.30 13.37 14.08 14.31 14.39 14.41 14.42 

Panfish FDE 3.25 8.53 10.15 10.75 10.95 11.02 11.02 11.02 

White perch FDS 0.04 0.45 1.01 1.75 2.65 3.66 4.76 5.93 

Reef fish FDM 3.09 6.84 9.25 10.68 11.40 11.79 12.02 12.10 

Forage fish FDP 0.01 0.03 0.01 0.01 0.02 0.02 0.02 0.03 

Bottom fish FDC 0.21 1.08 1.94 2.74 3.44 4.00 4.45 4.80 

Black drum FDO 2.08 5.13 7.40 8.82 9.66 10.13 10.39 10.54 

Other flatfish FDF 0.79 3.44 4.30 8.40 7.43 12.02 9.48 14.25 

Smooth dogfish SHB 0.08 0.33 0.72 1.24 1.85 2.53 3.27 4.03 

Sandbar shark SHD 4.05 36.93 86.61 280.69 394.53 507.77 615.94 716.39 8

Spiny dogfish SHC 2.97 25.19 39.94 51.01 58.65 63.72 67.00 69.09 7

Cownose ray SSK 0.47 8.98 18.98 30.96 43.91 57.09 69.98 82.24 9

Benthic grazing seabirds FVO 257.77 257.77 257.77 257.77 257.77 257.77 257.77 257.77 2

Diving ducks SB 150.00 150.00 150.00 150.00 150.00 150.00 150.00 150.00 1

Piscivorous seabirds SP 122.67 40.48 40.48 122.67 122.67 122.67 122.67 122.67 1

Bald eagles PIN 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 1

Diamond-back terrapin FVV 0.36 0.91 1.32 1.58 1.73 1.81 1.86 1.89 

Sea turtles REP 28.90 124.36 237.84 335.73 408.98 459.90 493.85 515.95 5

Bottlenose dolphin WHS 4223.43 5297.37 5522.97 5544.16 5546.93 5547.29 5547.33 5547.34 55
81

Table 15. Timing of reproductive and migration events for vertebrate groups in CAM. The terrapin group 

(FVV) remains in the Bay system throughout the year; however, for modeling, it is assumed the 

group effectively migrates when they overwinter buried in the mud, since they do not actively 

affect other groups in the model during this period. Striped bass (FBP) is the only population that 

has a partial migration in CAM (we assume one-half the population is resident year-round in the 

Bay); all other migrating populations move as a whole. DOY = day of the year. NA indicates that 

the timing parameter does not apply to that non-migratory group. 

Group 

code

Group 

migrates

Juveniles 

migrate 

in 

(DOY)

Juveniles 

migrate 

out 

(DOY)

Adults 

migrate 

in (DOY)

Adults 

spawn 

(or mate) 

(DOY)

Spawning 

(mating) 

duration 

(days)

Adults 

migrate out 

(DOY)

FPL Yes 196 46 258 60 90 46

FPO No NA NA NA 152 90 NA

FPS Yes 319 277 105 335 90 288

FVD Yes 15 319 105 244 90 319

FVS Yes 105 319 105 121 90 319

FVB No NA NA NA 91 60 NA

FVT Yes 288 319 105 182 30 196

FMM Yes 288 46 46 50 60 94

FMN Yes 196 319 105 121 90 319

FBP Yes 74 319 74 60 90 319

FDD Yes 105 288 105 182 150 288

FDE No NA NA NA 60 60 NA

FDS Yes 105 74 105 90 90 74

FDM No NA NA NA 121 60 NA

FDP No NA NA NA 91 90 NA

FDC No NA NA NA 15 90 NA

FDO Yes 105 319 105 91 60 319

FDF No NA NA NA 91 60 NA

SHB Yes 319 166 319 121 120 166

SHD Yes 135 288 135 121 30 288

SHC Yes 319 105 319 305 60 105

SSK Yes 135 288 135 180 21 288

FVO Yes 166 46 166 91 100 46

SB Yes 364 0 319 121 70 46

SP Yes 105 15 105 60 90 15

PIN No NA NA NA 74 60 NA

FVV Yes 74 288 74 121 60 288

REP Yes 135 319 135 182 180 319

WHS Yes 105 258 105 60 180 258

 
82

Table 16. Pre-calibration prey availability matrix. A '0' indicates there can be no availability of the prey to 

the predator in the model. Predator groups are listed on the left side of the matrix while prey 

groups are listed along the top. As availability increases, a predator's impact on prey abundance 

increases, given both spatial overlap of predator and prey, and limited habitat populations (BFF, 

SG, MA), which provide refuge from predation for many prey groups. Where data was lacking, 

but the authors believe a predator-prey relationship exists (e.g., soft-bodied invertebrate remains 

are not typically identifiable in vertebrate gut content studies), a small, arbitrary number was 

added to predator diet to allow such groups to interact in the model. When no prey information 

was available for a particular predator, assumed prey items were all assigned a uniform 

availability. 

[Editor’s note: Table 16, too large to be included in this print version, is available as an Excel file linked 

from the online PDF of this report.] 

Table 17. Post-calibration prey availability matrix. A '0' indicates there can be no availability of the prey 

to the predator in the model. Predator groups are listed on the left side of the matrix while prey 

groups are listed along the top. As availability increases, a predator's impact on prey abundance 

increases, given both spatial overlap of predator and prey, and limited habitat populations (BFF, 

SG, MA), which provide refuge from predation for many prey groups. Where data was lacking, 

but the authors believe a predator-prey relationship exists (e.g., soft-bodied invertebrate remains 

are not typically identifiable in vertebrate gut content studies), a very small, arbitrary number was 

added to predator diet to allow such groups to interact in the model. When no prey information 

was available for a particular predator, assumed prey items were all assigned a uniform 

availability. 

[Editor’s note: Table 17, too large to be included in this print version, is available as an Excel file linked 

from the online PDF of this report.]

 
83

Table 18. Habitat dependencies for age-structured groups in CAM. For any group, a '1' for one or more of 

the habitat types indicates at that at least one of the indicated habitats must occur in a CAM box 

to allow the group to move into that CAM box. 

Biogenic habitats

Common 

name Code

Prey 

age

Rock or 

artificial 

reef Sand Mud

Woody 

debris SAV Marsh

Oyster 

reef

Brief squid CEP Juvenile 1 1 1 0 1 0 1

Adult 1 1 1 0 1 0 1

Blue crab PWN Juvenile 0 0 1 1 1 1 0

Adult 1 1 1 1 1 1 1

Atlantic 

menhaden

FPS Juvenile 0 0 0 0 1 1 0

Adult 1 1 1 0 1 1 1

Gizzard shad FPL Juvenile 0 0 0 0 1 1 0

Adult 1 1 1 0 1 1 1

Bay anchovy FPO Juvenile 0 0 0 0 1 1 0

Adult 0 0 0 0 1 1 0

Summer 

flounder

FVD Juvenile 0 0 0 0 1 1 0

Adult 0 1 1 0 1 1 0

Terrapin FVV Juvenile 0 0 0 1 0 1 0

Adult 0 0 0 1 0 1 0

Weakfish FVS Juvenile 0 0 0 1 1 1 0

Adult 1 1 1 0 1 0 1

Bluefish FVT Juvenile 0 0 0 0 1 1 0

Adult 1 1 1 0 1 0 1

Benthic 

grazing 

seabirds

FVO Juvenile 0 0 0 0 1 1 0

Adult 0 0 1 0 1 1 0

Catfish FVB Juvenile 1 0 1 1 1 1 0

Adult 0 0 1 1 1 1 0

Alosines FMM Juvenile 0 0 0 0 1 1 0

Adult 0 1 1 0 1 1 0

Butterfish FMN Juvenile 1 1 1 0 1 0 1

Adult 1 1 1 0 1 0 1

Striped bass FBP Juvenile 0 0 0 1 1 1 0

Adult 1 1 1 0 1 1 1

Atlantic 

croaker

FDD Juvenile 0 0 0 1 1 1 0

Adult 0 1 0 0 1 1 0

White perch FDS Juvenile 0 0 0 1 1 1 0

Adult 0 0 0 1 1 1 0
84

Biogenic habitats

Common 

name Code

Prey 

age

Rock or 

artificial 

reef Sand Mud

Woody 

debris SAV Marsh

Oyster 

reef

Bottom fish FDC Juvenile 0 0 0 0 1 1 0

Adult 1 1 1 1 1 1 1

Black drum FDO Juvenile 0 0 0 1 1 1 0

Adult 0 1 1 0 1 1 0

Panfish FDE Juvenile 0 0 0 1 1 1 0

Adult 0 0 0 1 1 1 0

Other flatfish FDF Juvenile 0 0 0 0 1 1 0

Adult 0 1 1 0 1 1 0

Reef fish FDM Juvenile 1 0 0 1 1 1 1

Adult 1 1 0 0 1 0 1

Forage fish FDP Juvenile 0 0 0 1 1 1 0

Adult 0 0 0 1 1 1 0

Sandbar shark SHD Juvenile 1 1 1 0 1 1 1

Adult 1 1 1 0 1 0 1

Spiny dogfish SHC Juvenile 1 1 1 0 1 0 1

Adult 1 1 1 0 1 0 1

Smooth 

dogfish

SHB Juvenile 0 0 0 0 0 1 0

Adult 0 1 1 0 1 1 0

Cownose ray SSK Juvenile 0 1 1 0 1 1 1

Adult 1 1 1 0 1 0 1

Diving ducks SB Juvenile 0 1 1 0 1 1 0

Adult 0 1 1 0 1 1 0

Piscivorous 

seabirds

SP Juvenile 0 0 0 0 1 1 0

Adult 0 0 0 0 1 1 0

Sea turtles REP Juvenile 1 1 1 0 1 0 1

Adult 1 1 1 0 1 1 1

Bald eagles PIN Juvenile 1 1 1 0 1 1 1

Adult 1 1 1 0 1 1 1

Bottlenose 

dolphin

WHS Juvenile 1 1 1 0 1 0 1

Adult 1 1 1 0 1 0 1

Macoma 

clams

BFS All 0 1 1 0 1 1 0

Oysters BFF All 0 1 0 0 0 1 1

Benthic filter 

feeders

BFD All 1 1 1 1 1 1 1

Sea nettles ZL All 1 1 1 0 1 0 1
85

Biogenic habitats

Common 

name Code

Prey 

age

Rock or 

artificial 

reef Sand Mud

Woody 

debris SAV Marsh

Oyster 

reef

Ctenophores ZG All 1 1 1 0 1 0 1

Benthic 

carnivores

BC All 1 0 1 1 1 1 1

Bottom 

feeders

BD All 1 1 1 1 1 1 1

Marsh grasses MA All 0 0 1 0 0 1 1

SAV SG All 0 1 1 0 1 0 0

 
86

Table 19. Scalar values (Acov) used in refuge relationships of age-structured CAM groups with biogenic 

habitat. Higher values of Acov mean more protection for prey if biogenic habitat is available. See 

text and Figure 7 for details. 

Common name

Group 

code

Prey 

age Acov

Common 

name

Group 

code

Prey 

age Acov

Atl. Menhaden FPS Juvenile 0.1 Bottom fish FDC Juvenile 0.1

Adult 0.1 Adult 1.0

Gizzard shad FPL Juvenile 0.1 Black drum FDO Juvenile 0.1

Adult 0.1 Adult 0.1

Bay anchovy FPO Juvenile 0.1 Panfish FDE Juvenile 0.1

Adult 0.1 Adult 0.1

Weakfish FVS Juvenile 0.1 Other flatfish FDF Juvenile 0.1

Adult 1.0 Adult 0.1

Summer flounder FVD Juvenile 0.1 Reef fish FDM Juvenile 0.1

Adult 1.0 Adult 1.0

Terrapin FVV Juvenile 0.1 Forage fish FDP Juvenile 0.1

Adult 0.1 Adult 0.1

Bluefish FVT Juvenile 0.1 Sandbar shark SHD Juvenile 1.0

Adult 1.0 Adult 1.0

Benthic grazing 

seabirds FVO Juvenile 0.1 Spiny dogfish SHC Juvenile 1.0

Adult 0.1 Adult 1.0

Catfish FVB Juvenile 0.1 Smooth dogfish SHB Juvenile 0.1

Adult 1.0 Adult 1.0

Alosines FMM Juvenile 0.1 Cownose ray SSK Juvenile 1.0

Adult 0.1 Adult 1.0

Butterfish FMN Juvenile 0.1 Diving ducks SB Juvenile 0.1

Adult 1.0 Adult 0.1

Striped bass FBP Juvenile 0.1 Piscivorous 

seabirds SP Juvenile 0.1

Adult 1.0 Adult 0.1

White Perch FDS Juvenile 0.1 Sea turtles REP Juvenile 1.0

Adult 0.1 Adult 1.0

Atl. croaker FDD Juvenile 0.1 Bald eagles PIN Juvenile 1.0

Adult 0.1 Adult 1.0

Bottlenose 

dolphin WHS Juvenile 1.0

Adult 1.0

 
87

Table 20. Production changes predicted with TMDL nutrient and sediment reductions. Production values 

of all ecological groups of the TMDL reduction scenario were compared to those of the base 

model. Percentages shown are 11-year median differences in biomass for years 60 to 70 of each 

scenario. 

Percent 

difference 

(range) Count

Number 

negative 

(predicted 

loss) Group common name

 0–1 25 (9) (+): bald eagle, blue crab, benthic carnivores, benthic grazing 

seabirds, bottlenose dolphin, butterfish, cownose ray, 

ctenophores, forage fish, mesozooplankton, piscivorous 

seabirds, reef fish, sandbar shark, sea nettles, smooth dogfish, 

weakfish; (-): Atlantic menhaden, bluefish, benthic filter 

feeders, dissolved inorganic nitrogen, sea turtles, brief squid, 

terrapin, Macoma clams, large phytoplankton 

> 1–3 15 (5) (+): Atlantic croaker, bay anchovy, black drum, bottom fish, 

diving ducks, marsh grasses, spiny dogfish, striped bass, 

summer flounder, oysters; (-): bottom feeders, detritusrefractory, gizzard shad, meiobenthos, picophytoplankton 

> 3–5 3 (2) (+): white perch; (-): microzooplankton, pelagic bacteria 

> 5–7 7 (3) (+): alosines, catfish, other flatfish, panfish; (-): benthic bacteria, 

detritus-labile, SAV

> 7–9 0 (0) -- 

> 9–11 0 (0) --

> 11 2 (1) (+): dinoflagellates; (-): microphytobenthos
88

Literature Cited 

Adolf, J. E., C. L. Yeager, W. D. Miller, M. E. Mallonee, and L. W. Harding. 2006. 

Environmental forcing of phytoplankton floral composition, biomass, and primary 

productivity in Chesapeake Bay, USA. Estuarine, Coastal, and Shelf Science 67(1–

2):108–122. 

http://dx.doi.org/10.1016/j.ecss.2005.11.030

Alison, R. M. 1975. Breeding biology and behavior of the oldsquaw (Clangula hyemalis L.). 

Ornithological Monographs (18):1–52. [Available at 

https://sora.unm.edu/sites/default/files/journals/om/om018.pdf.] 

Arnold, T. W., M. G. Anderson, M. D. Sorenson, and R. B. Emery. 2002. Survival and 

philopatry of female redheads breeding in southwestern Manitoba. The Journal of 

Wildlife Management 66(1):162–169. 

http://dx.doi.org/10.2307/3802882

Barco, S. G., W. M. Swingle, W. A. McLellan, R. N. Harris, and D. A. Pabst. 1999. Local 

abundance and distribution of bottlenose dolphins (Tursiops truncatus) in the nearshore 

waters of Virginia Beach, Virginia. Marine Mammal Science 15(2):394–408. 

http://dx.doi.org/10.1111/j.1748-7692.1999.tb00809.x

Batiuk, R. A., R. J. Orth, K. A. Moore, W. C. Dennison, J. C. Stevenson, L. W. Staver, V. Carter, 

N. B. Rybicki, R. E. Hickman, S. Kollar, S. Bieber, and P. Heasley. 1992. Chesapeake 

Bay submerged aquatic vegetation habitat requirements and restoration targets: a 

technical synthesis. U.S. EPA Chesapeake Bay Program, Annapolis, MD, 246 p. 

[Available at 

https://nepis.epa.gov/Exe/ZyPDF.cgi/50000MSE.PDF?Dockey=50000MSE.PDF.] 

Bjorndal, K. A., A. B. Bolten, and H. R. Martins. 2000. Somatic growth model of juvenile 

loggerhead sea turtles Caretta caretta: duration of pelagic stage. Marine Ecology 

Progress Series 202:265–272. 

http://dx.doi.org/10.3354/meps202265

Blus, L. J., and J. A. Keahey. 1978. Variation in reproductivity with age in the brown pelican. 

The Auk 95 (1):128–134. 

http://dx.doi.org/10.2307/4085503

Bortolotti, G. R. 1986. Evolution of growth rates in eagles: sibling competition vs. energy 

considerations. Ecology 67(1):182–194. 

http://dx.doi.org/10.2307/1938517
89

Bozeman, E. L., Jr., and M. J. Van Den Avyle. 1989. Species profiles: Life histories and 

environmental requirements of coastal fishes and invertebrates (South Atlantic): alewife 

and blueback herring. U.S. Fish and Wildlife Service Biological Report (82)11:111. U.S. 

Army Corps of Engineers, TR El-82-4, 17 p. [Availble at 

http://aquaticcommons.org/3571/1/1989_boze.pdf.] 

Brand, E. J., I. C. Kaplan, C. J. Harvey, P. S. Levin, E. A. Fulton, A. J. Hermann, and J. C. Field. 

2007. A spatially explicit ecosystem model of the California current’s food web and 

oceanography. U.S. Dept. Commer., NOAA. NOAA Technical Memorandum NMFSNWFSC-84, 145 p. [Availble at 

https://www.nwfsc.noaa.gov/assets/25/578_12062007_152916_CalCurrentTM84Final.pd

f.] 

Brennessel, B. 2006. The Northern Diamondback Terrapin—Habitat, Management and 

Conservation. Wheaton College, Norton, MA, 9 p. [Availble at 

http://www.dtwg.org/Bibliography/Gray/Brennessel%202007.pdf.] 

Briggs, J. C., and B. W. Bowen. 2012. A realignment of marine biogeographic provinces with 

particular reference to fish distributions. Journal of Biogeography 39(1):12–30. 

http://dx.doi.org/10.1111/j.1365-2699.2011.02613.x

Brinker, D. F., J. M. McCann, B. Williams, and B. D. Watts. 2007. Colonial-nesting seabirds in 

the Chesapeake Bay region: where have we been and where are we going? Waterbirds 

30(SP1):93–104. 

http://dx.doi.org/10.1675/1524-4695(2007)030[0093:CSITCB]2.0.CO;2

Buehler, D. A., J. D. Fraser, J. K. D. Seegar, and G. D. Therres. 1991. Survival rates and 

population dynamics of bald eagles on Chesapeake Bay. The Journal of Wildlife 

Management 55(4):608–613. 

http://dx.doi.org/10.2307/3809506

Bulté, G., and G. Blouin-Demers. 2009. Does sexual bimaturation affect the cost of growth and 

the operational sex ratio in an extremely size-dimorphic reptile? Ecoscience 16(2):175–

182. 

http://dx.doi.org/10.2980/16-2-3243

Burger, J. 1976. Temperature relationships in nests of the northern diamondback terrapin, 

Malaclemys terrapin terrapin. Herpetologica 32(4):412–418. [Availble at 

https://www.jstor.org/stable/3891928?seq=1#page_scan_tab_contents.] 

Butler, R. W. 1992. Great blue heron (Ardea herodias). In The Birds of North America Online 

(A. Poole, ed.). Cornell Lab of Ornithology, Ithaca, NY. 

http://dx.doi.org/10.2173/bna.25

Byles, R. A. 1988. Behavior and ecology of sea turtles from Chesapeake Bay, Virginia. Ph.D. 

disseration, 224 p. College of William & Mary, Williamsburg, VA. [Available at 
90

https://books.google.com/books/about/Behavior_and_Ecology_of_Sea_Turtles_from.htm

l?id=5_eWbwAACAAJ.] 

Byles, R. A. 1989. Distribution and abundance of Kemp’s ridley sea turtle, Lepidochelys kempii, 

in Chesapeake Bay and nearby coastal waters. In Proceedings of the first international 

symposium on Kemp’s ridley sea turtle biology, conservation, and management; 

Galveston, 1–4 October (C. W. Caillouet, Jr., and A. M. Landry, Jr., ed.), p. 145. Texas 

A&M University Sea Grant College Program, Galveston, TX. [Availble at 

http://www.sefsc.noaa.gov/turtles/SG_Caillouet_Landry_1989.pdf.] 

Cerco, C. F., and K. Moore. 2001. System-wide submerged aquatic vegetation model for 

Chesapeake Bay. Estuaries 24(4):522–534. 

http://dx.doi.org/10.2307/1353254

Cerco, C. F., and M. R. Noel. 2005. Evaluating ecosystem effects of oyster restoration in 

Chesapeake Bay: a report to the Maryland Department of Natural Resources, Annapolis, 

MD, 49 p. [Available at 

http://www.chesapeakebay.net/publications/title/evaluating_ecosystem_effects_of_oyster

_restoration_in_chesapeake_bay-a_repo.] 

Chaloupka, M. 2002. Stochastic simulation modelling of southern Great Barrier Reef green turtle 

population dynamics. Ecological Modelling 148(1):79–109. 

http://dx.doi.org/10.1016/S0304-3800%2801%2900433-1

Chan, E. H., H. U. Salleh, and H. C. Liew. 1985. Effects of handling on hatchability of eggs of 

the leatherback turtle, Dermochelys coriacea (L.). Pertanika 8(2):265–271. [Available at 

http://www.pertanika.upm.edu.my/view_archives.php?journal=PERT-8-2-8.] 

Chesapeake Executive Council. 1990. Chesapeake Bay waterfowl policy and management 

plan— an agreement commitment report, 38 p. Chesapeake Bay Program, Annapolis, 

MD. [Available at http://www.chesapeakebay.net/content/publications/cbp_12307.pdf .] 

Christensen, N. L., A. M. Bartuska, J. H. Brown, S. Carpenter, C. D’Antonio, R. Francis, J. F. 

Franklin, J. A. MacMahon, R. F. Noss, D. J. Parsons, C. H. Peterson, M. G. Turner, and 

R. G. Woodmansee. 1996. The report of the Ecological Society of America committee on 

the scientific basis for ecosystem management. Ecological Applications 6(3):665–691. 

http://dx.doi.org/10.2307/2269460

Christensen, V., A. Beattie, C. Buchanan, H. Ma, S. J. D. Martell, R. J. Latour, D. Preikshot, M. 

B. Sigrist, J. H. Uphoff, C. J. Walters, R. J. Wood, and H. Townsend. 2009. Fisheries 

ecosystem model of the Chesapeake Bay: methodology, parameterization, and model 

exploration. U.S. Department of Commerce, NOAA. NOAA Technical Memorandum 

NMFS-F/SPO-106, 146 p. [Available at https://pifscwww.irc.noaa.gov/library/pubs/tech/NOAA_TM_NMFS_F-SPO_106.pdf.] 
91

Ciaranca, M. A., C. C. Allin, and G. S. Jones. 1997. Mute swan (Cygnus olor). In The Birds of 

North America Online (A. Poole, ed.). Cornell Lab of Ornithology, Ithaca, NY. 

[Available at http://bna.birds.cornell.edu/bna/species/273, accessed October 2010.] 

Clapp, R. B., M. K. Klimkiewicz, and J. H. Kennard. 1982. Longevity records of North 

American birds: Gaviidae through Alcidae. Journal of Field Ornithology 53(2):81–124. 

[Available at https://sora.unm.edu/node/50944.] 

Coles, W. C. 1999. Aspects of the biology of sea turtles in the mid-Atlantic Bight. Ph.D. 

dissertation, 198 p. College of William & Mary, Williamsburg, VA. [Available at 

https://digitalarchive.wm.edu/handle/10288/954.] 

Costantini, M., S. A. Ludsin, D. M. Mason, X. Zhang, W. C. Boicourt, and S. B. Brandt. 2008. 

Effect of hypoxia on habitat quality of striped bass (Morone saxatilis) in Chesapeake 

Bay. Canadian Journal of Fisheries and Aquatic Sciences 65(5):989–1002. [Available at 

http://www.ingentaconnect.com/content/nrc/cjfas/2008/00000065/00000005/art00017.] 

Costanzo, G. R., and L. J. Hindman. 2007. Chesapeake Bay breeding waterfowl populations. 

Waterbirds 30(SP1):17–24. 

http://dx.doi.org/10.1675/1524-4695(2007)030[0017:CBBWP]2.0.CO;2

DesGranges, J.-L. 1982. Weight growth of young double-crested cormorants in the St. Lawrence 

estuary, Quebec. Colonial Waterbirds 5:79–86. 

http://dx.doi.org/10.2307/1521038

Drilling, N., R. Titman, and F. Mckinney. 2002. Mallard (Anas platyrhynchos). In The Birds of 

North America Online (A. Poole, ed.). Cornell Lab of Ornithology, Ithaca, NY. 

[Available at http://bna.birds.cornell.edu/bna/species/658.] 

Dzubin, A., and J. B. Gollop. 1972. Aspects of mallard breeding ecology in Canadian parkland 

and grassland. In Population ecology of migratory birds (R. I. Smith, J. R. Palmer, and T. 

S. Baskett, ed.), p. 113–152. United States Fish and Wildlife Service Wildlife Research 

Report No. 2. 

Ferguson-Lees, J., and D. A. Christie. 2001. Raptors of the world. Houghton Mifflin, Boston, 

MA. 

Field, J. C. 2004. Application of ecosystem-based fishery management approaches in the 

Northern California Current. Ph.D. dissertation. Univ. Washington, Seattle, WA. 

[Available at https://cig.uw.edu/publications/application-of-ecosystem-based-fisherymanagement-approaches-in-the-northern-california-current/.] 

Frazer, N. B., and J. I. Richardson. 1985. Annual variation in clutch size and frequency for 

loggerhead turtles, Caretta caretta, nesting at Little Cumberland Island, Georgia, USA. 

Herpetologica 41(3):246–251. [Available at http://www.jstor.org/stable/3892269.] 
92

Froese, R., and D. Pauly. 2013. FishBase. World Wide Web electronic publication. [Available at 

http://www.fishbase.org, accessed April 2013.] 

Fulton, E. A., J. S. Link, I. C. Kaplan, M. Savina-Rolland, P. Johnson, C. Ainsworth, P. Horne, 

R. Gorton, R. J. Gamble, A. D. M. Smith, and D. C. Smith. 2011. Lessons in modelling 

and management of marine ecosystems: the Atlantis experience. Fish and Fisheries 

12(2):171–188. 

http://dx.doi.org/10.1111/j.1467-2979.2011.00412.x

Fulton, E. A., J. S. Parslow, A. D. M. Smith, and C. R. Johnson. 2004a. Biogeochemical marine 

ecosystem models II: the effect of physiological detail on model performance. Ecological 

Modelling 173(4):371–406. 

http://dx.doi.org/10.1016/j.ecolmodel.2003.09.024

Fulton, E. A., and A. D. M. Smith. 2004. Lessons learnt from a comparison of three ecosystem 

models for Port Phillip Bay, Australia. African Journal of Marine Science 26:219–243. 

http://dx.doi.org/10.2989/18142320409504059

Fulton, E. A., A. D. M. Smith, and C. R. Johnson. 2003. Mortality and predation in ecosystem 

models: is it important how these are expressed? Ecological Modelling 169(1):157–178. 

http://dx.doi.org/10.1016/S0304-3800(03)00268-0

Fulton, E. A., A. D. M. Smith, and C. R. Johnson. 2004b. Biogeochemical marine ecosystem 

models I: IGBEM—a model of marine bay ecosystems. Ecological Modelling 

174(3):267–307. 

http://dx.doi.org/10.1016/j.ecolmodel.2003.09.027

Fulton, E. A., A. D. M. Smith, and C. R. Johnson. 2004c. Effects of spatial resolution on the 

performance and interpretation of marine ecosystem models. Ecological Modelling 

176(1–2):27–42. 

http://dx.doi.org/10.1016/j.ecolmodel.2003.10.026

Garcia, S. M., A. Zerbi, C. Aliaume, T. Do Chi, and G. Lasserre. 2003. The ecosystem approach 

to fisheries. Issues, terminology, principles, institutional foundations, implementation, 

and outlook, 81 p. Food and Agricultural Organization of the United Nations, Rome. 

[Available at http://www.fao.org/publications/card/en/c/701c6108-e79d-57bb-87d8-

eeb85effb2cd.] 

Gause, G. F. 1934. The struggle for existence. Reprinted 1971 by Dover Publications. [Available 

at http://www.ggause.com/Contgau.htm, accessed May 2014.] 

Gerrard, J. M., P. N. Gerrard, G. R. Bortolotti, and E. H. Dzus. 1992. A 24-year study of bald 

eagles on Besnard Lake, Saskatchewan. Journal of Raptor Research 26(3):159–166. 

[Available at https://sora.unm.edu/node/53293.] 
93

Graham, K. 2000. A review of the biology and management of blue catfish. In Catfish 2000: 

proceedings of the international ictalurid symposium (E. R. Irwin, W. A. Hubert, C. F. 

Rabeni, J. Harold, L. Schramm, and T. Coon, eds.), p. 37–49. American Fisheries 

Society, Bethesda, MD. [Available at http://fisheries.org/docs/pub_sympsample.pdf.] 

Hanson, P. C., T. B. Johnson, D. E. Schindler, and J. F. Kitchell. 1997. Fish Bioenergetics 3.0 for 

Windows, software appl. Technical manual, 109 p. Univ. Wisconsin–Madison and Univ. 

Wisconsin Sea Grant Institute, Madison, WI. [Available at 

http://aqua.wisc.edu/publications/ProductDetails.aspx?productID=360.] 

Hatch, J. J., and D. V. Weseloh. 1999. Double-crested Cormorant (Phalacrocorax auritus). In

The birds of North America online (A. Poole, ed.), Ithaca, NY. [Available at 

http://bna.birds.cornell.edu/bna/species/441, accessed October 2010.] 

Henny, C. J. 1972. An analysis of the population dynamics of selected avian species with special 

reference to changes during the modern pesticide era. Journal of Wildlife Management 

37(4):592–593. 

http://dx.doi.org/10.2307/3800330

Henny, C. J., and H. M. Wight. 1969. An endangered osprey population: estimates of mortality 

and production. The Auk 86(2):188–198. [Available at https://sora.unm.edu/node/21848.] 

Horne, P. J., I. C. Kaplan, K. N. Marshall, P. S. Levin, C. J. Harvey, A. J. Hermann, and E. A. 

Fulton. 2010. Design and parameterization of a spatially explicit ecosystem model of the 

central California current. U.S. Dept. Commerc., NOAA. NOAA Technical 

Memorandum NMFS-NWFSC-104, 140 p. [Available at 

https://www.nwfsc.noaa.gov/assets/25/1080_03232010_145542_ModelCalCurrentTM10

4WebFinal.pdf.] 

Hunt, G. L., Jr., H. Kato, and S. M. McKinnell. 2000. Predation by marine birds and mammals in 

the subarctic North Pacific Ocean. North Pacific Marine Science Organization, PICES 

Scientific Report No.14, Sidney, BC, 164 p. [Available at 

http://aquaticcommons.org/1315/.] 

Johnson, S. A., A. L. Bass, B. Libert, M. Marshall, and D. Fulk. 1999. Kemp’s ridley 

(Lepidochelys kempii) nesting in Florida. Florida Scientist 62(3–4):194–204. [Available 

at http://ufwildlife.ifas.ufl.edu/pdfs/johnsonetal1999kempsridley.pdf.] 

Keinath, J. A. 1993. Movements and behavior of wild and head-started sea turtles. 

Ph.D.dissertation. College of William & Mary, Williamsburg, VA. 

Kemp, W. M., and W. R. Boynton. 1992. Benthic-pelagic interactions: nutrient and oxygen 

dynamics. In Oxygen dynamics in the Chesapeake Bay: A synthesis of recent research 

(D. E. Smith, M. Leffler, and G. Mackeirnan, eds.), p. 149–221. Maryland Sea Grant 

College, College Park, MD. [Available at 

http://www.gonzo.cbl.umces.edu/documents/sediments/BPInteractions_149.pdf.] 
94

Kemp, W. M., W. R. Boynton, J. E. Adolf, D. F. Boesch, W. C. Boicourt, G. Brush, J. C. 

Cornwell, T. R. Fisher, P. M. Glibert, J. D. Hagy, L. W. Harding, E. D. Houde, D. G. 

Kimmel, W. D. Miller, R. I. E. Newell, M. R. Roman, E. M. Smith, and J. C. Stevenson. 

2005. Eutrophication of Chesapeake Bay: historical trends and ecological interactions. 

Marine Ecology Progress Series 303:1–29. [Avvailable at http://www.intres.com/articles/feature/m303p001.pdf.] 

Kirby, R. E., L. M. Cowardin, and J. R. Tester. 1989. Premigrational movements and behavior of 

young mallards and wood ducks in north-central Minnesota. U.S. Fish and Wildlife 

Service No. 5, 25 p. [Available at https://pubs.er.usgs.gov/publication/81367.] 

Kirby, R. E., and G. A. Sargeant. 1999. Survival of postfledging mallards in northcentral 

Minnesota. The Journal of Wildlife Management 63(1):403–408. 

http://dx.doi.org/10.2307/3802526

Klimkiewicz, M. K., and A. G. Futcher. 1989. Longevity records of North American birds 

supplement 1 (Registros de longevidad en aves de Norte América: Primer suplemento). 

Journal of Field Ornithology 60(4):469–494. [Available at 

http://www.jstor.org/stable/4513471.] 

Kobell, R. 2010. Endangered species status proposed for loggerhead turtle. Bay Journal, 

Chesapeake Media Service, Seven Valleys, PA. [Available at 

http://www.bayjournal.com/article/endangered_species_status_proposed_for_loggerhead

_turtle.] 

Krementz, D. G., J. R. Sauer, and J. D. Nichols. 1989. Model-based estimates of annual survival 

rate are preferable to observed maximum lifespan statistics for use in comparative lifehistory studies. Oikos 56(2):203–208. 

http://dx.doi.org/10.2307/3565337

Kussman, J. V. 1977. Post-fledging behavior of the northern Bald Eagle, Haliaeetus 

leucocephalus, in the Chippewa National Forest, Minnesota. PhD. dissertation, 868 p. 

Univ. of Minnesota, Minneapolis, MN. [Available at 

http://primo.lib.umn.edu/primo_library/libweb/action/dlSearch.do?institution=TWINCIT

IES&vid=TWINCITIES&search_scope=default_scope&indx=1&dym=true&highlight=tr

ue&lang=eng&query=any,exact,62373074.] 

Larkin, P. A. 1996. Concepts and issues in marine ecosystem management. Reviews in Fish 

Biology and Fisheries 6(2):139–164. 

http://dx.doi.org/10.1007/BF00182341

Lesage, L., A. Reed, and J.-P. L. Savard. 1996. Plumage development and growth of wild Surf 

Scoter Melanitta perspicillata ducklings. Wildfowl 47:205–210. [Available at 

http://wildfowl.wwt.org.uk/index.php/wildfowl/article/view/999.] 
95

Levin, P. S., M. J. Fogarty, S. A. Murawski, and D. Fluharty. 2009. Integrated ecosystem 

assessments: developing the scientific basis for ecosystem-based management of the 

ocean. PLoS One 7(1):23–28 

http://dx.doi.org/10.1371/journal.pbio.1000014

Levin, P. S., and F. B. Schwing, eds. 2011. Technical background for an integrated ecosystem 

assessment of the California Current: groundfish, salmon, green sturgeon, and ecosystem 

health. U.S. Dept. Commerc., NOAA. NOAA Tech. Memo. NMFS-NWFSC-109, 330 p. 

[Available at 

https://www.nwfsc.noaa.gov/assets/25/1618_07122011_125959_CalCurrentIEATM109

WebFinal.pdf.] 

Limpert, R. J., and S. L. Earnst. 1994. Tundra Swan (Cygnus columbianus). In The birds of 

North America online (A. Poole, ed.). 

http://dx.doi.org/10.2173/bna.89

Link, J. S., E. A. Fulton, and R. J. Gamble. 2010. The northeast US application of ATLANTIS: a 

full system model exploring marine ecosystem dynamics in a living marine resource 

management context. Progress in Oceanography 87(1–4):214–234. 

http://dx.doi.org/10.1016/j.pocean.2010.09.020

Link, J. S., R. J. Gamble, and E. A. Fulton. 2011. NEUS–Atlantis: construction, calibration, and 

application of an ecosystem model with ecological interactions, physiographic 

conditions, and fleet behavior. U.S. Dept. Commer., NOAA. NOAA Technical 

Memorandum NMFS-NE-218, 247 p. [Available at 

http://www.nefsc.noaa.gov/publications/tm/tm218/tm218.pdf.] 

Lippson, A. J., and R. L. Lippson. 1997. Life in the Chesapeake Bay, 2nd ed. Johns Hopkins 

University Press. 

Low, J. B. 1945. Ecology and management of the redhead, Nyroca americana, in Iowa. 

Ecological Monographs 15(1):35–69. 

http://dx.doi.org/10.2307/1943294

Lubbers, L., W. R. Boynton, and W. M. Kemp. 1990. Variations in structure of estuarine fish 

communities in relation to abundance of submersed vascular plants. Marine Ecology 

Progress Series 65:1–14. [Available at http://www.int-res.com/abstracts/meps/v65/.] 

Mathiasson, S. 1980. Weight and growth rates of morphological characters of Cygnus olor. In 2nd

international swan symposium (G. V. T. Matthews and M. Smart, eds.) p. 379–389. 

IWRB, Sapporo, Japan. 

Metcalfe, W. J. 2005. Meiofauna abundance and distribution in Chesapeake Bay: relationships 

with environmental stressors, sediment toxicity, and macrofauna. M.S. thesis, 69 p. 

College of William and Mary, Williamsburg, VA. [Available at 

https://digitalarchive.wm.edu/handle/10288/1104.] 
96

Methot, R. D. 2009. SS3 text version 3.03a (user manual). National Marine Fisheries Service, 

NOAA Fisheries Toolbox. U.S. Department of Commerc., NOAA. [Available at 

http://nft.nefsc.noaa.gov/SS3.html, accessed May 2014.] 

Monda, M. J., J. T. Ratti, and T. R. McCabe. 1994. Reproductive ecology of tundra swans on the 

Arctic National Wildlife Refuge, Alaska. The Journal of Wildlife Management 

58(4):757–773. 

http://dx.doi.org/10.2307/380969

Moore, K. A., D. J. Wilcox, and R. J. Orth. 2000. Analysis of the abundance of submersed 

aquatic vegetation communities in the Chesapeake Bay. Estuaries 23(1):115–127. 

[Available at http://www.jstor.org/stable/1353229.] 

Reed, A., J.-P. L. Savard, A. Morrier, and L. Lesage. 1997. Étude sur l'écologie de la Macreuse à 

front blanc au lac Malbaie, réserve des Laurentides-1994–1995. Canadian Wildlife 

Service, Région du Québec, 120 p. [Available at 

http://www.publications.gc.ca/pub?id=9.624613&sl=0.] 

Mowbray, T. B., C. R. Ely, J. S. Sedinger, and R. E. Trost. 2002. Canada goose (Branta 

canadensis). In The Birds of North America Online (A. Poole, ed.). 

http://dx.doi.org/10.2173/bna.682

Mrang, K. R., P. M. Payne, and V. G. Thayer. 1994. Coastal stock(s) of Atlantic bottlenose 

dolphin: status review and management. U.S. Dept. of Commer., NOAA. NOAA 

Technical Memorandum. [Available at 

http://www.nmfs.noaa.gov/pr/pdfs/species/coastalbottlenosestock.pdf .] 

Murawski, S. 2000. Definitions of overfishing from an ecosystem perspective. ICES Journal of 

Marine Science 57(3):649–658. 

http://dx.doi.org/10.1006/jmsc.2000.0738

Murdy, E. O., R. S. Birdsong, and J. A. Musick. 1997. Fishes of Chesapeake Bay, p. i–xi. 

[Available at https://www.amazon.com/Fishes-Chesapeake-Bay-EdwardMurdy/dp/1588340457.] 

Murray, A. G., and J. S. Parslow. 1999. Modelling of nutrient impacts in Port Phillip Bay—a 

semi-enclosed marine Australian ecosystem. Marine & Freshwater Research 50(6):597–

612. 

http://dx.doi.org/10.1071/MF98087

Musick, J. A., ed. 1999. Life in the slow lane: ecology and conservation of long-lived marine 

animals, 260 p. American Fisheries Society Symposium 23, American Fisheries Society, 

Bethesda, MD. [Available at http://fisheries.org/bookstore/all-titles/afssymposia/x54023xm/.] 
97

Myers, R. A., N. J. Barrowman, R. Hilborn, and D. G. Kehler. 2002. Inferring Bayesian priors 

with limited direct data: Applications to risk analysis. North American Journal of 

Fisheries Management 22(1):351–364. 

http://dx.doi.org/10.1577/1548-8675(2002)022<0351:IBPWLD>2.0.CO;2

Myers, R. A., K. G. Bowen, and N. J. Barrowman. 1999. Maximum reproductive rate of fish at 

low population sizes. Canadian Journal of Fisheries and Aquatic Sciences 56(12):2404–

2419. 

http://dx.doi.org/10.1139/f99-201

National Ocean Council. 2013. National ocean policy implementation plan, 32 p.Washington, 

DC. [Available at 

https://www.whitehouse.gov/administration/eop/oceans/implementationplan.] 

National Research Council. 2002. Science and its role in the National Marine Fisheries Service, 

98 p. The National Academies Press, Washington, DC. 

http://dx.doi.org/10.17226/10387

Secretariat of Environment & Natural Resources, Mexico, National Commission of Natural 

Protected Areas, Mexico, Federal Attorney of Environmental Protection, Mexico, U.S. 

Dept. of Commer., NOAA, and U.S. Fish and Wildlife Service. 2011. Bi-national 

recovery plan for the Kemp’s ridley sea turtle (Lepidochelys kempii), second revision, 

174 p. [Available at 

https://content.sierraclub.org/grassrootsnetwork/sites/content.sierraclub.org.activistnetwo

rk/files/teams/documents/kempsridley_revision2.pdf.] 

Neuenhoff, R. D., D. F. Cowan, H. Whitehead, and C. D. Marshall. 2010. Prenatal data impacts 

common bottlenose dolphin (Tursiops truncatus) growth parameters estimated by lengthat-age curves. Marine Mammal Science 27(1):195–216. 

http://dx.doi.org/10.1111/j.1748-7692.2010.00394.x

Nichols, J. D., J. Bart, J. L. Roland, W. J. L. Sladen, and J. E. Hines. 1992. Annual survival rates 

of adult and immature eastern population tundra swans. The Journal of Wildlife 

Management 56(3):485–494. 

http://dx.doi.org/10.2307/3808863

Olson, M. 2010. Guide to using Chesapeake Bay Program water quality monitoring data (M. 

Mallonee and M. E. Ley, eds.), 153 p. Chesapeake Bay Program, Annapolis, MD. 

[Available at 

http://www.chesapeakebay.net/documents/3676/wq_data_userguide_10feb12_mod.pdf.] 

 

Orth, R. J., and K. Moore. 1986. Seasonal and year-to-year variations in the growth of Zostera 

marina L. (eelgrass) in the lower Chesapeake Bay. Aquatic Botany 24(4):335–341. 

http://dx.doi.org/10.1016/0304-3770(86)90100-2
98

Orth, R. J., J. F. Nowak, D. J. Wilcox, J. R. Whiting, and L. S. Nagey. 1998. Distribution of 

submerged aquatic vegetation in the Chesapeake Bay and tributaries and the coastal 

bays—1997, 367 p. Collge of William & Mary, Virginia Institue of Marine Science, 

VIMS Special Scientific Report Number 138, Gloucester Point, VA. [Available at 

http://docplayer.net/17506314-Distribution-of-submerged-aquatic-vegetation-in-thechesapeake-bay-and-tributaries-and-the-coastal-bays-1997.html.] 

Owen, D. F. 1959. Mortality of the great blue heron as shown by banding recoveries. The Auk 

76(4):464–470. [Available at https://sora.unm.edu/node/20800.] 

Paige, G. R., and M. W. Luckenbach. 2008. Distribution, habitat characteristics, prey abundance, 

and diet of surf scoters (Melanitta perspicillata) and long-tailed ducks (Clangula 

hyemalis) in polyhaline wintering habitats in the mid-Atlantic region: a comparison of 

shallow coastal lagoons and Chesapeake Bay environs, 85 p. U.S. Fish and Wildlife 

Service, FWS #70181-7-R039 2008, Anchorage, AK. [Avavilable at 

http://www.vcrlter.virginia.edu/elecvol/reports/SDJV_FY08-104-

Ross%20and%20Luckenbach-Final%20Report-no_appendices.pdf.] 

Palmer, R. S., ed. 1988. Handbook of North American birds: volume 4, diurnal raptors, first 

edition, 433 p. Yale University Press, New Haven, CT. [Available at 

https://www.amazon.com/Handbook-North-American-Birds-Diurnal/dp/0300040598.] 

Pauly, D. 1980. On the interrelationships between natural mortality, growth parameters, and 

mean environmental temperature in 175 fish stocks. ICES Journal of Marine Science 

39(2):175–192. 

http://dx.doi.org/10.1093/icesjms/39.2.175

Pauly, D., and V. Christensen. 1995. Primary production required to sustain global fisheries. 

Nature 374(6519):255–257. 

http://dx.doi.org/10.1038/374255a0

Peck, G. K., and R. D. James. 1983. Breeding birds of Ontario: nidiology and distribution, 321 p. 

Royal Ontario Museum, Toronto, Canada. [Available at 

https://www.amazon.com/Breeding-Birds-Ontario-NonpasserinesMiscellaneous/dp/0888542887.] 

Perry, M. C., A. M. Wells-Berlin, D. M. Kidwell, and P. C. Osenton. 2007. Temporal changes of 

populations and trophic relationships of wintering diving ducks in Chesapeake Bay. In 

Special publication 1: Waterbirds of the Chesapeake Bay and vicinity: harbingers of 

change? Waterbirds 30(SP1):4–16. [Available at http://www.jstor.org/stable/25148272.] 

Pikitch, E. K., C. Santora, E. A. Babcock, A. Bakun, R. Bonfil, D. O. Conover, P. Dayton, P. 

Doukakis, D. Fluharty, B. Heneman, E. D. Houde, J. Link, P. A. Livingston, M. Mangel, 

M. K. McAllister, J. Pope, and K. J. Sainsbury. 2004. Ecosystem-based fishery 

management. Science 305(5682):346–347. 

http://dx.doi.org/10.1126/science.1098222
99

Plagányi, É. E. 2007. Models for an ecosystem approach to fisheries. FAO fisheries technical 

paper no. 477, 108 p. Food and Agriculture Organization of the United Nations, Rome, 

Italy. [Available at http://www.fao.org/docrep/010/a1149e/a1149e00.htm.] 

Plotkin, P. T. 1995. National Marine Fisheries Service and U.S. Fish and Wildlife Service status 

reviews for sea turtles Listed under the Endangered Species Act of 1973, 139 p. 

[Available at http://www.nmfs.noaa.gov/pr/pdfs/statusreviews/turtles.pdf .] 

Polovina, J. J. 1984. Model of a coral reef ecosystem. Coral Reefs 3(1):1–11. 

http://dx.doi.org/10.1007/BF00306135

Pratt, H. M. 1973. Breeding attempts by juvenile great blue herons. The Auk 90(4):897–899. 

[Available at https://sora.unm.edu/node/22456.] 

R Development Core Team. 2011. R: A language and environment for statistical computing., 

2.14.0 edition. R Foundation for Statistical Computing, Vienna, Austria. [Available at 

http://www.R-project.org/.] 

Reese, J. G. 1980. Demography of European mute swans in Chesapeake Bay. The Auk 

97(3):449–464. [Available at https://sora.unm.edu/node/23427.] 

Reese, J. G. 1991. Osprey Pandion halaetus. In Habitat requirements for Chesapeake Bay living 

resources, second edition (S. L. Funderburk, J. A. Mihursky, S. J. Jordan, and D. Riley, 

eds.), p. 1–11. Chesapeake Research Consortium, Inc., Solomons, MD. [Available at 

https://nepis.epa.gov/Exe/ZyNET.exe/2000WBYD.TXT?ZyActionD=ZyDocument&Clie

nt=EPA&Index=1991+Thru+1994&Docs=&Query=&Time=&EndTime=&SearchMetho

d=1&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFie

ldDay=&IntQFieldOp=0&ExtQFieldOp=0&XmlQuery=&File=D%3A\zyfiles\Index%20

Data\91thru94\Txt\00000016\2000WBYD.txt&User=ANONYMOUS&Password=anony

mous&SortMethod=h|-

&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i

425&Display=hpfr&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&Bac

kDesc=Results%20page&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL.] 

Reynolds, C. M. 1972. Mute swan weights in relation to breeding. Wildfowl 23:111–118. 

[Available at http://wildfowl.wwt.org.uk/index.php/wildfowl/article/view/443.] 

Rizzo, W. M., and R. L. Wetzel. 1985. Intertidal and shoal benthic community metabolism in a 

temperate estuary: studies of spatial and temporal scales of variability. Estuaries 

8(4):342–351. [Available at http://www.jstor.org/stable/1351871.] 

Robertson, G. J., and J.-P. L. Savard. 2002. Long-tailed duck (Clangula hyemalis). In The Birds 

of North America Online (A. Poole, ed.), Cornell Lab of Ornithology, Ithaca, NY. 

http://dx.doi.org/10.2173/bna.651
100

Rohwer, F. C., and D. I. Eisenhauer. 1989. Egg mass and clutch size relationships in geese, 

eiders, and swans. Ornis Scandinavica 20(1):43–48. [Available at 

http://www.jstor.org/stable/3676706.] 

Roosenburg, W. M. 1990. Final report: Chesapeake diamondback terrapin investigations for the 

period 1987, 1988, and 1989, 84 p. Chesapeake Research Consortium, Solomons, MD. 

[Available at http://www.vims.edu/GreyLit/CRC/crc133.pdf.] 

Savard, J.-P. L., D. Bordage, and A. Reed. 1998. Surf scoter (Melanitta perspicillata). In The 

Birds of North America Online (A. Poole, ed.). Cornell Ornithology Lab, Ithaca, NY. 

Retrieved 7 October 2010, from the Birds of North America Online: 

http://bna.birds.cornell.edu/bna/species/363. 

Schempf, P. F. 1997. Bald eagle longevity record from southeastern Alaska (Registro de 

longevidad para individuo de Haliaeetus leucocephalus de Alaska). Journal of Field 

Ornithology 68(1):150–151. [Available at http://www.jstor.org/stable/4514206.] 

Schreiber, E. A., and J. Burger, eds. 2002. Biology of marine birds, second edition, CRC marine 

biology series, 722 p. CRC Press, Boca Raton, FL. [Available at 

https://www.crcpress.com/Biology-of-Marine-Birds/SchreiberBurger/p/book/9780849398827.] 

Schubauer, J. P., and C. S. Hopkinson. 1984. Above- and belowground emergent macrophyte 

production and turnover in a coastal marsh ecosystem, Georgia. Limnology and 

Oceanography 29(5):1052–1065. 

http://dx.doi.org/10.4319/lo.1984.29.5.1052

Seagraves, R., and K. Collins, eds. 2012. Fourth national meeting of the regional fishery 

management council’s Scientific and Statistical Committees, Report of a national SSC 

workshop on scientific advice on ecosystem and social science considerations in U.S. 

federal fishery management, 82 p. Mid-Atlantic Fishery Management Council, 

Williamsburg, VA. 

Shields, M. 2002. Brown pelican (Pelecanus occidentalis). In The Birds of North America 

Online 9 (A. Poole, ed.). Cornell Lab of Ornithology, Ithaca, NY. 

http://dx.doi.org/10.2173/bna.609

Steidl, R. J., and C. R. Griffin. 1991. Growth and brood reduction of Mid-Atlantic coast ospreys. 

The Auk 108(2):363–370. [Available at https://sora.unm.edu/node/24977.] 

Terwilliger, M. R., and T. A. Munroe. 1999. Age, growth, longevity, and mortality of blackcheek 

tonguefish, Symphurus plagiusa (Cynoglossidae: Pleuronectiformes), in Chesapeake Bay, 

Virginia. Fish. Bull. 97(2):340–361. [Available at http://fishbull.noaa.gov/12terwil.pdf.] 
101

Trites, A. W., and D. Pauly. 1998. Estimating mean body masses of marine mammals from 

maximum body lengths. Canadian Journal of Zoology/Revue Canadien de Zoologie 

76(5):886–896. 

http://dx.doi.org/10.1139/z97-252

Tucker, A. D., J. W. Gibbons, and J. L. Greene. 2001. Estimates of adult survival and migration 

for diamondback terrapins: conservation insight from local extirpation within a 

metapopulation. Canadian Journal of Zoology 79(12):2199–2209. 

http://dx.doi.org/10.1139/z01-185

U.S. Army Corps of Engineers. 2012. (Draft) Chesapeake Bay oyster recovery: Native oyster 

restoration master plan—Maryland and Virginia, 240 p. U.S. Army Corps of Engineers, 

Baltimore and Norfolk Districts. [Available at 

http://www.nab.usace.army.mil/Missions/Environmental/Oyster-Restoration/OysterMaster-Plan/.] 

U.S. Envionmental Protection Agency. 2004. Water quality database: database design and data 

dictionary, 74 p. Chesapeake Bay Program Office, Region III. [Available at 

http://www.chesapeakebay.net/documents/3676/cbwqdb2004_rb.pdf.] 

U.S. Environmental Protection Agency. 2010. Chesapeake Bay Phase 5.3 Community Watershed 

Model, 558 p. [Available at 

http://www.chesapeakebay.net/about/programs/modeling/53/.] 

U.S. Ocean Policy Task Force. 2010. Final recommendations of the interagency Ocean Policy 

Task Force, 77 p. Executive Office of the President, White House Council on 

Environmental Quality, Washington DC. [Available at 

https://www.whitehouse.gov/administration/eop/ceq/initiatives/oceans.] 

Van der Veen, H. E. 1973. Some aspects of the breeding biology and demography of the doublecrested cormorants (Phalacrocorax auritus) of Mandarte Island, 25 p. Zoologisch 

Laboratorium der Rijksuniversiteit te Groningen, Groningen. [Available at 

http://agris.fao.org/agris-search/search.do?recordID=AV20120100143.] 

Van Tets, G. F. 1959. A comparative study of the reproductive behaviour and natural history of 

three sympatric species of cormorants, (Phalacrocorax auritus, P. penicillatus, & P. 

pelagicus) at Mandarte Island, B.C. University of British Columbia, Vancouver, BC, 

Canada. [Available at 

https://open.library.ubc.ca/cIRcle/collections/ubctheses/831/items/1.0106056.] 

 
102

Walker, S. J. 1999. Coupled hydrodynamic and transport models of Port Phillip Bay, a semienclosed bay in south-eastern Australia. Marine & Freshwater Research 50(6):469–481. 

http://dx.doi.org/10.1071/MF98071

Watts, B. D., and B. J. Paxton. 2007. Ospreys of the Chesapeake Bay: Population recovery, 

ecological requirements, and current threats. Waterbirds 30(sp1):39–49. 

http://dx.doi.org/10.1675/1524-4695(2007)030[0039:OOTCBP]2.0.CO;2

Watts, B. D., G. D. Therres, and M. A. Byrd. 2008. Recovery of the Chesapeake Bay bald eagle 

nesting population. The Journal of Wildlife Management 72(1):152–158. 

http://dx.doi.org/10.2193/2005-616

Wilberg, M. J., M. E. Livings, J. S. Barkman, B. T. Morris, and J. M. Robinson. 2011. 

Overfishing, disease, habitat loss, and potential extirpation of oysters in upper 

Chesapeake Bay. Marine Ecology Progress Series 436:131–144. 

http://dx.doi.org/10.3354/meps09161

Williams, B., D. F. Brinker, and B. D. Watts. 2007. The status of colonial nesting wading bird 

populations within the Chesapeake Bay and Atlantic Barrier Island-Lagoon System. 

Waterbirds 30(SP1):82–92. [Available at http://www.jstor.org/stable/25148278.] 

Williams, K. L., and M. G. Frick. 2008. Tag returns from loggerhead turtles from Wassaw 

Island, GA. Southeastern Naturalist 7(1):165–172. [Available at 

http://www.eaglehill.us/SENAonline/articles/SENA-7-1/25-Williams.shtml.] 

Winemiller, K. O., and K. A. Rose. 1992. Patterns of life-history diversification in North 

American fishes: Implications for population regulation. Canadian Journal of Fisheries 

and Aquatic Sciences 49(10):2196–2218. 

http://dx.doi.org/10.1139/f92-242

Woodin, M. C., and T. C. Michot. 2002. Redhead (Aythya americana). In The Birds of North 

America Online (A. Poole, ed.). Cornell Ornithology Lab, Ithaca, NY. [Available at 

http://bna.birds.cornell.edu/bna/species/695/.] 

Yntema, C. L., and N. Mrosovsky. 1982. Critical periods and pivotal temperatures for sexual 

differentiation in loggerhead sea turtles. Canadian Journal of Zoology/Revue Canadienne 

de Zoologie 60(5):1012–1016. 

http://dx.doi.org/10.1139/z82-141

Zammuto, R. M. 1986. Life histories of birds: clutch size, longevity, and body mass among 

North American game birds. Canadian Journal of Zoology 64(12):2739–2749. 

http://dx.doi.org/10.1139/z86-398

 
103

Zug, G. R., M. Chaloupka, and G. H. Balazs. 2006. Age and growth in olive ridley sea turtles 

(Lepidochelys olivacea) from the north-central Pacific: a skeletochronological analysis. 

Marine Ecology 27(3):263–270. 

http://dx.doi.org/10.1111/j.1439-0485.2006.00109.x

Zug, G. R., H. J. Kalb, and S. J. Luzar. 1997. Age and growth in wild Kemp's ridley sea turtles 

Lepidochelys kempii from skeletochronological data. Biological Conservation 80(3):261–

268. 

http://dx.doi.org/10.1016/S0006-3207(96)00143-7

Zug, G. R., and J. F. Parham. 1996. Age and growth in leatherback turtles, Dermochelys 

coriacea (Testudines: Dermochelyidae): A Skeletochronological Analysis. Chelonian 

Conservation and Biology 2(2):244–249. [Available at 

https://www.researchgate.net/publication/287496441_Age_and_growth_in_leatherback_t

urtles_Dermochelys_coriacea_Testudines_Dermochelyidae_a_skeletochronological_anal

ysis.] 
104

Appendix A: Map Information 

Chesapeake Bay Atlantis Model 

Construction Log for Dynamic Boxes (24 AUG 2010) 

Methods: 

Spatial variables 

1. SALINITY (follows Lippson and Lippson, 1997) 

Model covers 3 salinity zones: 0–10 ppt, 10–18 ppt, and 18–30 ppt. 
105

2. BAY SECTION/TRIBUTARY 

 
106

3. BATHYMETRY 

Bay outline and bathymetry data derived from 10 m x 10 m grid named “bathygrid” (see 

Bathymetry Metadata at end of section). 
107

4. BOTTOM HARDNESS 

Mainstem only. Data derived from sediment grab samples. Sand bottoms classed as 

“Hard”; combinations of clay, silt, and sand classed as “Soft” (see Sediment 

Distribution Metadata at end of section).
108

Polygon construction 

5. Create Bathymetry polygons from “bathygrid” 

-Extract 3 sets of grid cells: 0–2 m, 2–10 m, & >10 m 

-Convert extracted cells to polygons 

-Merge polygons: “All_CB_bathy_polygons.shp” 

 
109

6. Extract Bay Sections from bathymetry polygons by clipping. 

7. a) Intersect Salinity zones, Bathymetry polygons, and bottom hardness for each main 

Bay section with intersect tool. Intersect Tributary Sections with Salinity and 

Bathymetry only. 

 
110

b) Summarize areas of intersection polygons by Bay Section and Tributary. 

For each section calculate total area by salinity zone (1–10 ppt, 10–18 ppt, 18–30 

ppt), depth zone (0–2 m, >2–10 m, >10 m), and bottom hardness (Hard, Soft). If an 

area was less or equal to 5% of total section area it was added to areas of same 

salinity and most similar depth (See Table A1 for area summaries). 

8. Draw model boxes based on areas and bay shape. Ensure correct topology. No 

overlapping polygons and no holes. There are 97 dynamic polygons and 1 border 

polygon in the final shapefile: Final_CAM_Boxes_8.shp. 

 
111

9. Convert final shapefile to BGM file with BGMeriser script. 

BGM file is named Final_CAM_Boxes_8.BGM 

10. Ensure that winding order of the vertices in the BGM file is correct with the 

Checkwinding.exe. All windings are counterclockwise. 

 
112

Table A1. Area Summaries 

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

Main_upper 1‐10 0‐2m 0‐2m Hard 61.0 675.2 9.0

Main_upper 1‐10 2‐10m 2‐10m Hard 114.4 16.9

Main_upper 1‐10 2‐10m 2‐10m Soft 398.5 59.0

Main_upper 1‐10 >10m 10‐28m Soft 101.4 15.0

100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

Main_Middle 10‐18 0‐2m 0‐2m Hard 204.4 2170.2 9.4

Main_Middle 10‐18 2‐10m 2‐10m Hard 754.9 34.8

Main_Middle 10‐18 2‐10m 2‐10m Soft 228.1 10.5

Main_Middle 10‐18 >10m 10‐47m Hard 182.7 8.4

Main_Middle 10‐18 >10m 10‐50m Soft 800.1 36.9

total= 100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

Main_lower 10‐18 0‐2m 0‐2m Hard 185.9 3701.9 5.0

Main_lower 10‐18 >10m 10‐13m Hard 210.6 5.7

Main_lower 10‐18 2‐10m 2‐10m Hard 639.7 17.3

Main_lower 10‐18 0‐10m 0‐10m Soft 237.0 6.4

Main_lower 10‐18 >10m 10‐41m Soft 482.0 13.0

Main_lower 18‐30 >10m 10‐14m Hard 331.1 8.9

Main_lower 18‐30 0‐10m 0‐10m Hard 1055.5 28.5

Main_lower 18‐30 0‐10m 0‐10m Soft 199.6 5.4

Main_lower 18‐30 >10m 10‐31m Soft 360.6 9.7

total= 100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

James_river 1‐10 0‐2m 0‐2m 153.0 517.3 29.6

James_river 1‐10 >2m 2‐10m 101.9 19.7

James_river 10‐18 0‐2m 0‐2m 57.4 11.1

James_river 10‐18 >2m 2‐10m 98.3 19.0

James_river 18‐30 >0m 0‐23m 106.7 20.6

total= 100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

Potomac_river 1‐10 0‐2m 0‐2m 75.4 887.4 8.5

Potomac_river 1‐10 >2m 2‐16m 409.0 46.1

Potomac_river 10‐18 >10m 10‐25m 160.0 18.0

Potomac_river 10‐18 0‐2m 0‐2m 44.7 5.0

Potomac_river 10‐18 2‐10m 2‐10m 198.3 22.3
113

total= 100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

Rappahannock_river 1‐10 0‐2m 0‐2m 50.4 300.7 16.7

Rappahannock_river 1‐10 >2m 2‐15m 56.5 18.8

Rappahannock_river 10‐18 >10m 10‐22m 41.9 13.9

Rappahannock_river 10‐18 0‐2m 0‐2m 47.8 15.9

Rappahannock_river 10‐18 2‐10m 2‐10m 104.1 34.6

total= 100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

York_river 1‐10 0‐2m 0‐2m 8.7 161.9 5.3

York_river 1‐10 >2m 2‐18m 14.7 9.0

York_river 10‐18 >10m 10‐25m 24.3 15.0

York_river 10‐18 0‐2m 0‐2m 58.1 35.9

York_river 10‐18 2‐10m 2‐10m 56.3 34.8

total= 100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

Choptank_river 1‐10ppt 0‐2m 0‐2m 19.5 274.1 7.1

Choptank_river 1‐10ppt 2‐10m 2‐15m 31.5 11.5

Choptank_river 10‐18ppt 0‐2m 0‐2m 73.6 26.9

Choptank_river 10‐18ppt 2‐10m 2‐18m 149.5 54.5

total= 100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

Nanticoke_river 1‐10ppt 0‐2m 0‐2m 19.6 77.8 25.3

Nanticoke_river 1‐10ppt 2‐10m 2‐17m 17.1 22.0

Nanticoke_river 10‐18ppt 0‐2m 0‐2m 20.0 25.8

Nanticoke_river 10‐18ppt 2‐10m 2‐13m 21.0 27.0

total= 100.0

Section Salinity Depth_m_1 Depth_m_2 Bottom Area(kmsq) Total_area_sect Percent_area

Patuxent_river 1‐10ppt 0‐2m 0‐2m 26.9 110.5 24.4

Patuxent_river 1‐10ppt 2‐10m 2‐24m 51.0 46.1

Patuxent_river 10‐18ppt 0‐2m 0‐2m 7.2 6.5

Patuxent_river 10‐18ppt 2‐10m 2‐10m 14.0 12.7

Patuxent_river 10‐18ppt >10m 10‐38m 11.4 10.3

total= 100.0

 
114

Bathymetry Metadata 

 Bathymetry for CAM was based on a 10 m x 10 m grid originally created by Patrick 

Nowlan, 25 June, 1999. The grid was composed of Chesapeake Bay Program (CBP) bathymetry 

tins. After the tins were gridded they were merged using the GRID merge function. It is an 

integer grid to save space and multiplied by 10 to preserve the first decimal place. Depth units 

are meters; measures are precise to 0.1 m. The original source was the NOAA bathymetry 

soundings for the Chesapeake Bay and tidal tributaries. Soundings for the shallow waters (less 

than 2 meters) were edited when the 1- and 2-meter contours were developed at CBP. Soundings 

that looked out of place or seemed out of a reasonable range were verified using NOAA nautical 

charts. The quality checks for water greater than 2 meters was not comprehensive; it was only an 

examination of potential outliers. Bathymetry sounds are incomplete for the Elizabeth River, so 

this section of the grid coverage is known to be invalid. Grid values were standardized to the 

Mean Lower Low Water (MLLW) datum. 

Sediment Distribution Metadata 

 This dataset merges Chesapeake Bay sediment grab sample data collected by the 

Maryland Geological Survey (MGS) and the Virginia Institute of Marine Science (VIMS) during 

1976-1984 (see http://www.mgs.md.gov/coastal_geology/baysedata.html and 

http://pubs.usgs.gov/ds/2005/118/metadata/GOV/vasedsfaq.htm ). Data classifications are based 

on CMECS (Coastal and Marine Ecological Classification Standard); further information, as well 

as metadata for sediment distribution in XML format, is found at: 

http://www.habitat.noaa.gov/chesapeakebay/gis/chesapeake_bay_cmecs_geodatabase/

 Purpose: Create a Bay-wide GIS coverage of sediment distribution represented by 

interpolated polygons. 

 Supplemental Information: Percent sand, clay, and silt data in addition to coordinate data 

were taken from MGS and VIMS datasets and classified with the Shepard's ternary model 

(http://www.mgs.md.gov/coastal/vmap/shepard.html) to create the following categories: sand, 

clay, silt, sandy clay, sandy silt, clayey sand, clayey silt, silty sand, silty clay and sand-clay-silt. 

Categorical grid interpolation was done with ArcGIS/Spatial Analyst grid tools in several steps. 

 1: Rectangular neighborhood 3x3 cells, majority (most frequent category). 

 2: Circular neighborhood radius = 3 cells, majority. 

 3: Boundary clean, no sort, run expansion and shrink 2 times. 

Interpolation error was assessed with a raster calculator by subtracting the point raster from the 

interpolated raster. Of 6130 cells in the uninterpolated raster, 96% were spatially coincident with 

cells of equivalent value in the interpolated raster. 

 
115

Appendix B: Biological Processes Modeled in Atla

Table B1. Biological processes and factors modeled in Atlantis. Row labels refer to process modeled, column labels indicate groups in 

process modeled, column labels indicate groups in Atlantis. Abbreviations not defined within table (in order of appearance): ““PAB” – pelagic attached bacteria, “PFB” – pelagic free-living bacteria, “AEB” – aerobic benthic bacteria, “ANB” – anaerobidissolved oxygen, “wc” – water column, “sed” – sediment, “N” – nitrogen, “DON” – dissolved organic nitrogen, “N2” – nitro

“pprey” – predator-prey availability matrix, “PN” – particulate nitrogen, “DIN” – dissolved inorganic nitrogen. The Chesapea

groups except the last 2 columns (CORAL and MACROBEN). 

[See online version of this report to enlarge table for readability.] 
Factors & Groups modeled: 

If no "Y" 

then one 

setting for 

entire Bay

If no "Y" 

then one 

setting for 

all layers Detritus-Labile

DetritusRefractory Pelagic Bacteria Benthic Bacteria AmmoniaNitriteNitrateSilicon/ 

Silicate Marsh SAV

Phytoplankton 

Small

Phytoplankton 

Large

MicrophytobenthosDinoflagellates 

(mixotrophic)

Zooplankton 

Small

Meso 

zooplankton Ctenophores Sea Nettles

Invert 

Predators

Invert 

Omnivores Meiobenthos

Benthic Filter 

feeders 

(Macoma, 

suspension 

feeders, & 

oysters) "BFx "

General 

Consumers: 

Crab, Fish, Birds, 

Reptiles & 

Mammal Coral

Large Benthic 

Invertebrate

Process By Box? By Layer? DL DR

PB (2 types: PAB, 

PFB)

BB (2 types: AEB, 

ANB) NH3 NO Si MA SG PS PL MB DF ZS ZM ZG ZL BC BD BO BFS, BFD, BFF CX CORAL MACROBEN

Excretion (NH3, NO) Y Y Y (decreases DO) Y (NH3 + NO) Y (NH3 + NO) Y (NH3 + NO) Y Y Y Y Y Y Y Y (wc only) Y (decreases DO) Y Y

Remineralization ("Rnet"=NH produced by 

Denitrification & other pathways) Y Y Bacterial remineralization

Bacterial 

remineralization

minor (decreases 

DO)

major (decreases 

DO) Y

Denitrification (NO, not NH3) Y Y Y Y Y Y

Nitrification Y Y minor (NH3 + NO) major (NH3 + NO) Y Y wc sed wc and sed

Uptake N [also Si for PL and MB] Y Y Y wc (NH3 + NO) sed (NH3 + NO) wc wc (N + Si) wc and sed (NH, 

NO, and Si) wc

DON production Y Y Y Y

DON remineralization & uptake Y Y Y Y

N2 fixing Y Y minor major

Waste (detritus) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Detritus-Labile production Y Y

Y (a fraction is 

recycled during 

remineralization, 

plus a fraction of 

both DR and DC are 

converted to DL)

Y (a fraction is 

recycled during 

remineralization

, plus a fraction 

of DL is 

converted to 

DR)

Y Y Y (wc only via lysis) Y (sed only via 

lysis)

Y (via lysis in both 

wc and sed)

Y (via lysis in both 

wc and sed)

Y (via lysis in both 

wc and sed)

Y (via lysis in both 

wc and sed)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding, plus 

moves detritus 

from wc to 

sediments)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding)

Y (via mortality 

and sloppy 

feeding)

Detritus-Labile consumption Y Y Y Y

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Detritus-Refractory prod. Y Y Y Y Y (via mortality) Y (via mortality) Y (via mortality) Y (via mortality) Y (via mortality) Y (via mortality)

Y From 

mortality and 

sloppy feeding

Y From mortality 

and sloppy 

feeding

Y From 

mortality and 

sloppy feeding

Y From 

mortality and 

sloppy feeding

Y From 

mortality and 

sloppy feeding

Y From 

mortality and 

sloppy feeding

Y From 

mortality and 

sloppy feeding

Y From mortality 

and sloppy 

feeding

Y From mortality 

and sloppy 

feeding

Y From 

mortality and 

sloppy feeding

Y From 

mortality and 

sloppy feeding

Detritus-Refractory consump.+burial Y Y Y Y

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey + burial)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Y (consump. 

depends on 

pprey)

Nutrient limitation (N, Si) Y Y N N N N, Si N, Si N

Y 

(zooxanthellae 

component)

Oxygen limitation/ Oxygen mortality Y Y Y Y Y Y Y Y Y Y Y Y Y (host 

component) Y

Space limitation Y Y Y (optional, if use 

cubic formulation)

Y (optional, if use 

cubic formulation) Y Y Y Y Y Y Y Y Y

Turbidity Y Y Y (via potential 

shading)

Y (via potential 

shading; also - 

optional, can be 

stressed by 

fouling and 

Y (via potential 

shading)

Y (via potential 

shading)

Y (via potential 

shading)

Y (via potential 

shading)

Y (optional, 

growth can be 

slowed)

Y (as energy 

used to clean 

host)

Resuspension Y Y

Y (can be stressed 

by excess bottom 

stress degrading 

leaves)

Y Y Y Y Y (optional) Y (optional) Y (optional) Y (optional) Y (optional)

Light availability/shading Y Y

Y (result of 

sediment, 

phytoplankton, 

detritus and water 

color)

Y (result of 

sediment, 

phytoplankton, 

detritus and 

water color)

Y (result of 

sediment, 

phytoplankton, 

detritus and 

water color)

Y (result of 

sediment, 

phytoplankton, 

detritus and 

water color)

Y (result of 

sediment, 

phytoplankton, 

detritus and water 

color)

Y (result of 

sediment, 

phytoplankton, 

detritus and 

water color)

Predation Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Lysis (producer mortality, to detritus) Y Y Y Y Y Y wc Y

Mortality (linear) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Mortality (quadratic) Y Y Y Y Y Y Y Y Y Y Y Y

Mortality (Fishing on consumers = 0 for all in 

base model) Y Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional)

Other Mortality

The degree of 

bottom stress can 

degrade leaves 

(optional)

Excessive DIN 

causes mortality 

(optional, via 

impicit 

representation of 

epiphyte effects)

Cells degrade in 

sediments by 

both linear 

mortality and 

lysis

Cells degrade in 

sediments by 

both linear 

mortality and 

lysis

Cells degrade in 

sediments by both 

linear mortality 

and lysis

Cells degrade in 

sediments by 

both linear 

mortality and 

lysis

Bleaching

Movement (box to box) Y (advection) Y (advection) Y (advection) Y (advection) Y (advection)

Y (Advection and 

optional directed 

movement)

Y (Advection and 

optional 

directed 

movement)

Y (Advection 

and optional 

directed 

movement)

Y (Advection 

for 

resuspended 

animals; and 

optional 

[untested] 

directed 

movement)

Y (Advection 

for 

resuspended 

animals; and 

optional 

[untested] 

directed 

movement)

Y (Advection for 

resuspended 

animals; and 

optional 

[untested] 

directed 

movement)

Y (Directed) Larval 

(Advection)

Y (optional 

directed 

movement)

Vertical movement Y (Directed) Y (Directed) Y (Directed)

Variable type: Need to set starting value? 

(if no "Y" initial value internally calculated) Y (DON + PN) Y (PN) Y (DIN) Y (DIN) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Water column Y Y Y Y Y Y Y (optional) Y Y

Y (if resuspended, 

but not active; can 

be eaten)

Y Y Y Y Y Y Y

Epibenthic Y Y Y

Y (there is a 

variant that 

allows for above 

ground, below 

ground and 

epiphyte biomass 

Y Y Y Y

Sediment Y Y Y Y Y Y Y (optional)

Can exist here 

once settled 

waiting for 

resuspension, but 

not active (can be 

eaten)

Can exist here 

once settled 

waiting for 

resuspension, but 

not active (can be 

eaten)

Y Y Y Y

Other processes

Toxic effects on 

other species 

(optional)

Rugosity, 

calcification

Persistent through time Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Explicit overwintering behaviour (Optional - 

encysted, or inactive overwintering stage) Y Y Y Y Y Y Y Y Y Y Y Y Y Y

bioturbation Y Y Y Y Y Y (If a benthic 

invertebrate) Y

bioirrigation Y Y Y Y Y Y Y (If a benthic 

invertebrate) Y

Explicit reproduction Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y (optional) Y Y Y (optional)

Growth Y Y Y (tied to magnitude 

of detritus pool)

Y (tied to magnitude 

of detritus pool)

Y (increases DO in 

wc)

Y (increases DO in 

both wc and sed)

Y (increases DO in 

wc)

Y (increases DO in 

wc)

Y (increases DO in 

both wc and sed)

Y (increases DO in 

wc) Y Y Y Y Y Y Y Y Y Y Y

Predator - consumes other groups Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Other Factors and Processes Modeled

Refuge for consumers (prey) Y Y (epi) Y Y Y (BFF) Y

Detritus Bacteria Nutrients Consumers

Nutrient Cycling Mortality Growth Limiting Factors Modeled Domain

Primary Producers (PX )

Movement
117

Appendix C: Vertebrate Life History 

CAM represents vertebrate biomass in 29 functional groups: 18 finfish, 4 elasmobranch, 

4 bird, 2 reptile, and 1 mammal (Table 11). Functional group types include both individual 

species and aggregate groups of species; in total, 58 vertebrate species are included in CAM. 

Each vertebrate group is divided into 10 age classes, with each class representing one-tenth of 

the overall life span of the group. Initial biomass for most vertebrate groups was based on a 

preliminary, balanced Ecopath model for the system (with no fishing) that was developed for this 

purpose. Preliminary biomass estimates used in the Ecopath model were, in turn, based on a 

wide variety of sources including (in descending order of use): landings data, stock assessments, 

available literature, unpublished data, and expert opinion (Table 11). Weight-length conversions 

were based on the relationship 

ܹ ൌ ܽܮ) ௕C.1) 

where W is weight in grams, L is length in mm, and a and b are constants. 

Fish 

Life history parameters for finfish and sharks came primarily from FishBase (Froese and 

Pauly, 2013). When multiple species occurred within a functional group, each parameter value 

was weighted according to the biomass of each constituent species or group of species. Life 

history parameters are summarized primarily in Tables 12, 13, 14, and 15. When life history 

parameter estimates for fish were not available from FishBase, estimates were taken directly 

from the literature. Estimates of k, a, and b (for length-weight relationship) for blueback shad 

come from Bozeman and Van Den Avyle (1989); k for blackcheek tonguefish (in aggregate 

group FDF) comes from Terwilliger and Munroe (1999); Winemiller and Rose (1992) provided 

maximum ages for American shad and tautog (in aggregate groups FMM and FDM, 

respectively), age at maturity for winter flounder (in aggregate group FDF), and both maximum 

age and age at maturity for white perch, summer flounder, bay anchovy, small forage fish (FDP); 

and for spot, blueback shad, black seabass, from aggregate groups FDE, FMM, and FDM, 

respectively. 

For species for which the natural mortality rate (M) was not available but a longevity 

estimate (Tmax) was available, and we calculated M assuming that only 1% of the population 

should reach maximum longevity: 

ܯ ൌ ୪୬	ሺ଴.଴ଵሻ

ି்௠௔௫ (C.2) 

When no Tmax value was present, but the asymptotic length ܮஶ and the von Bertalanffy 

K were available (by Fishbase or otherwise), we calculated the natural mortality (Pauly, 1980) 

as: 
118

ஶܮ ∙ ହ଺.଴ܭൌܯ

ି଴.ଶ଻ଽ ∙ ܶ଴.ସ଺ଷ (C.3) 

where T is the average water temperature. For Chesapeake Bay, we approximated 

average water temperature as 15.75	Ԩ (Christensen et al., 2009). 

Tmax was provided by FishBase for only two (bluefish and winter flounder) of our 41 

species of fish. For the remaining species that did not already have a longevity value from 

Winemiller and Rose (1992), we calculated Tmax following Equation C.2, but now using 

reported M and again assuming 1% of the population should reach maximum longevity: 

ܶ݉ܽݔ ൌ ୪୬	ሺ଴.଴ଵሻ

ିெ (C.4) 

The age class length was calculated by simply dividing the lifespan (Tmax) by 10, the 

number of age classes for all of our vertebrate groups in CAM. 

Reproduction for finfish was assumed to follow Beverton-Holt dynamics; parameter 

estimates (unfished adult biomass [Bo], maximum reproduction of recruits [in numbers, Ro] from 

an unfished population, and steepness) used to calculate "BHalpha" and "BHbeta" (Table 12) 

came largely from the most recent available stock assessments. When no Bo values could be 

found, we estimated unfished biomass using a preliminary Ecopath model with no fishing. Ro

values for unassessed small (forage fish, bay anchovy) and medium (panfish) forage fish were 

estimated based on Lubbers et al. (1990), while the value used for catfish was based on 

Winemiller and Rose, (1992) and Graham (2000). Steepness estimates for Atlantic menhaden, 

Alosines, and bottomfish came from Myers et al. (1999); weakfish, gizzard shad, bay anchovy, 

white perch, and panfish were from Myers et al. (2002). When no information on Ro or 

steepness was found, values were used from closely related species. If still no information was 

available for a particular group, overall mean values from groups with estimates were used. 

Mammals 

Bottlenose Dolphin (WHS) 

Life history parameters for the bottlenose dolphin (Tursiops truncatus) are largely based 

on the Chesapeake Bay Program, Field Guide: bottlenose dolphin, 

(http://www.chesapeakebay.net/fieldguide/critter/bottlenose_dolphin; accessed May 2014). 

These parameters include lifespan, average adult length, average adult weight, age at maturity, 

recruitment age, and timing of reproduction and migration. The abundance estimate for the 

Chesapeake Bay came from Barco et al. (1999). The annual natural mortality rate came from 

Mrang et al. (1994). Our length-weight relationship parameters a and b (Equation C.1) were 

taken from Trites and Pauly (1998). We required weights at age for all age classes, and the 

length-weight relationship was used to calculate weights at age. Age at length was based on 

Neuenhoff et al. (2010), and growth was assumed to follow a Gompertz function. Though we 

assumed the productivity to biomass ratio (P/B) applied in initial Ecopath balancing is assumed 

to be approximately equal to the natural mortality rate, following earlier works (Field, 2004; 

Brand et al., 2007), balanced Ecopath estimates of initial biomass differed only slightly from our 

initial estimate. Therefore, our original preliminary estimate was used to initialize CAM (Table 

11). 
119

Reptiles 

The length-weight relationship parameters a and b (Equation C.1) were not found in 

literature for the reptiles. We assumed a b value of 3 and estimated the value of a based on the 

average adult length and weight using Solver in Excel for each species of reptile. Following 

methodology described for the bottlenose dolphin (above), we assumed P/B is equal to natural 

annual mortality for purposes of balancing our preliminary Ecopath model, but because balanced 

values differed little from preliminary estimates, again applied our preliminary biomass estimate 

to initialize CAM (Table 11). 

Diamondback Terrapin (FVV) 

Life history parameters for the diamondback terrapin (Malaclemys terrapin) were taken 

from a variety of online and published sources. Average adult size (SCL), lifespan, and clutch 

frequency were from the online resource of the Virginia Institute of Marine Science (VIMS) 

(http://www.vims.edu/research/units/programs/sea_turtle/va_sea_turtles/terps.php, accessed 6-6-

13). Average adult weight was taken from Brennessel (2006). Abundance was based on 

personal communication with R. Chambers (The College of William & Mary). Average clutch 

size for diamondback terrapins specific to the Chesapeake Bay area was from Roosenburg 

(1990). The von Bertalanffy growth parameter k was given by Bulté and Blouin-Demers (2009). 

The recruitment age was given by Burger (1976). Annual natural mortality was from Tucker et 

al. (2001). 

Sea Turtles (REP) 

The sea turtle group is an aggregate group composed of three species of sea turtle that 

occur in the Chesapeake: the loggerhead (Caretta caretta), Kemp's ridley (Lepidochelys kempii), 

and leatherback (Dermochelys coriacea). Life history characteristics for the REP group are 

based on a weighted average of the characteristics for these species. The relative weight 

attributed to each species is based on biomass estimates (reported numbers * average weight). 

This approach results in an aggregate group characterized mainly by the relatively common 

loggerhead species (94% of the total by weight). The seasonal abundance of loggerheads in the 

Chesapeake is based on a quote by Jack Musick (VIMS) as reported by Kobell (2010). The 

population estimate for Kemp’s ridley comes from field observations reported by Byles (1989). 

Leatherbacks are known to enter the Chesapeake (Keinath, 1993), but no abundance estimate is 

available for the Bay. Leatherbacks are considered uncommon in the Bay (Coles, 1999), so we 

assume their abundance to be 10% of the Kemp’s ridley population estimate. Average weight 

for loggerhead was from an online resource of VIMS 

(http://www.vims.edu/research/units/programs/sea_turtle/va_sea_turtles/loggerhead.php; 

accessed 6-6-13); average weight for Kemp's ridley and leatherback was given in Plotkin (1995). 

Other size parameters were based on both literature and online resources. Average adult 

straight carapace length (SCL) for the loggerhead sea turtle was taken from an online resource 

provided by VIMS 

(http://www.vims.edu/research/units/programs/sea_turtle/va_sea_turtles/loggerhead.php; 

accessed 6-6-13). SCL for Kemp's ridley and leatherback were from Plotkin (1995). Size-at-age 

was assumed to follow the von Bertalanffy growth function; associated k values were taken from 
120

Bjorndal et al. (2000) for loggerheads, Zug et al. (1997) for Kemp's ridley, and Musick (1999) 

for the leatherback. 

The timing of critical life history events was taken from the literature. Incubation period 

for each species was used as indicated by Yntema and Mrosovsky (1982) for the loggerhead, 

Johnson et al. (1999) for Kemp’s ridley, and Chan et al. (1985) for the leatherback. Age at 

maturity values were provided by Bjorndal et al. (2000); the National Marine Fisheries Service, 

U.S. Fish and Wildlife Service, and SEMARNAT (2010); and Chaloupka (2002) for the 

loggerhead, Kemp’s ridley, and leatherback, respectively. Maximum age was based on Plotkin 

(1995) for loggerhead and on Zug and Parham (1996) for leatherback. We assumed maximum 

age for Kemp's ridley to be similar to that estimated for olive ridley sea turtles (Lepidochelys 

olivacea) reported by Zug et al. (2006). 

Remigration interval (the time period between successive nesting events) and other 

reproductive parameters were taken from the literature. Remigration interval, clutch size, and 

clutch frequency was based on: Williams and Frick (2008) [remigration], and Frazer and 

Richardson (1985) [clutch size and frequency] for loggerheads; the National Recovery Plan 

(National Marine Fisheries Service et al., 2010) for Kemp's ridley; and Plotkin (1995) for 

leatherback. 

Annual natural mortality rates were taken from Byles (1988), National Marine Fisheries 

Service et al. (2010), and Chaloupka (2002) for the loggerhead, Kemp’s ridley, and leatherback 

sea turtles, respectively. 

Birds 

Whenever possible, we took life history parameters from Birds of North America Online 

(http://bna.birds.cornell.edu/bna; accessed 18 October 2010), a database maintained by the Cornell 

Lab of Ornithology. Parameters not included in the database were based on published literature 

and are listed below for each group. We assume the growth of all birds in our model follow the 

Gompertz growth curve. Since fledgling weight is often equal to or greater than asymptotic 

weight in seabirds, we assume that no additional structural growth occurs in the model beyond 

the rapid initial growth of young-of-the-year for all birds. The weighting of our bird species 

parameters within their groups was based on the annual migratory proportional biomass of each 

species, based on unpublished data collected by Doug Forsell (Coastal Program biologist with 

the U.S. Fish and Wildlife Service, Chesapeake Bay Field Office). We estimated the lengthweight parameters a and b (Equation C.1) by building a power regression using as many lengthweight pairs as we could find for all species within a group. As described above, the 

productivity to biomass ratio (P/B) applied in initial Ecopath balancing is assumed to be 

approximately equal to the natural mortality rate for all birds. As described above, however, the 

preliminary biomass estimates were used to initialize CAM (Table 11) for all bird groups, rather 

than the balanced estimates resulting from Ecopath. 

Benthic Grazing Seabirds (FVO) 

Benthic grazing seabirds is an aggregate group of five species: Atlantic Canada goose, 

mallard, redhead, tundra swan, and mute swan. 

Life history rates for the Atlantic Canada goose (Branta canadensis) could not often be 
121

found on the subspecies level. In that event, we took values belonging to subspecies of Branta 

canadensis, and in the event that multiple values were available, we opted to use those belonging 

to subspecies of similar size and/or from similar regions whenever possible. Annual natural 

mortality was calculated as an average between the annual natural mortalities of two subspecies, 

B. c. moffitti and B. c. hutchinsii. They represented the two extremes in mortality among all the 

subspecies of Canada geese. Average clutch size was determined by Rohwer and Eisenhauer 

(1989) for B. c. minima. A lifespan estimate was from Krementz et al. (1989) for the species but 

information by subspecies was not provided. Abundance for Branta canadensis specific to 

Chesapeake Bay is from Costanzo and Hindman (2007). Age at maturity, recruitment age, 

natural annual mortality, and average adult length and weight were from Birds of North America 

Online (Mowbray et al., 2002). 

The mallard duck (Anas platyrhynchos) annual natural mortality was taken from Kirby 

and Sargeant (1999). Average clutch size was from Dzubin and Gollop (1972). An estimate of 

maximum age came from Krementz et al. (1989). Recruitment age was found in Kirby et al. 

(1989). Costanzo and Hindman (2007) provided the abundance estimate specific to Chesapeake 

Bay. Age at maturity and average adult length and weight were from Birds of North America 

Online (Drilling et al., 2002). 

Annual natural mortality for the redhead duck (Aythya americana) was found in Arnold 

et al. (2002). Zammuto (1987) provided the average clutch size. Lifespan estimates came from 

Clapp et al. (1982). Ages at maturity and recruitment were taken from Low (1945). Perry et al. 

(2007) provided the abundance estimate specific to Chesapeake Bay. Average adult length and 

weight were taken from Birds of North America Online (Woodin and Michot, 2002). 

Tundra swan (Cygnus columbianus) annual natural mortality was taken from Nichols et 

al. (1992). Average clutch size was provided by Monda (1991). Average adult length was found 

within an online resource for the International Bird Rescue Research Center (IBRRC website: 

http://www.ibrrc.org/tundra_swan.html, accessed 1 October 2010). The abundance estimate for 

tundra swan came from a Chesapeake Bay Program report (1990). Maximum age, age at 

maturity, and average adult weight are from Birds of North America Online (Limpert and Earnst, 

1994). We did not find a reference for age at recruitment, so we assumed the same value for 

mute swan, below. 

Mute swan (Cygnus olor) abundance was taken from Costanzo and Hindman (2007). 

The estimate of annual natural mortality was found in Reese (1980). Average clutch size was 

provided by Reynolds (1972). Klimkiewicz and Fletcher (1989) estimated maximum age for this 

species. Age at recruitment was from Mathiasson (1980). Age at maturity and average adult 

length and weight were from Birds of North America Online (Ciaranca et al., 1997). 

Piscivorous Seabirds (SP) 

Piscivorous seabirds is an aggregate group of four species: osprey, great blue heron, 

brown pelican, and double-crested cormorant. 

The average clutch size and ages at recruitment and maturity for the osprey (Pandion 

haliaetus) was described by Reese (1991). Annual natural mortality and lifespan were found in 

Henny and Wight (1969). Abundance specific to the Chesapeake Bay region was taken from 

Watts and Paxton (2007). Average adult length was found in Ferguson-Lees and Christie (2001), 
122

while average adult weight was found in Steidl and Griffin (1991). 

Annual natural mortality for the great blue heron (Ardea herodias) was provided by 

Henny (1972). Maximum age was from Owen (1959). Abundance specifically for Chesapeake 

Bay was found in Williams et al. (2007). Age at maturity was found in Pratt (1973). Clutch size, 

recruitment age, and average adult length and weight were taken from Birds of North America 

Online (Butler, 1992). 

The brown pelican (Pelecanus occidentalis) annual natural mortality was given by 

Schreiber and Burger (2002). Average clutch size was from Blus and Keahey (1978). 

Abundance specific to the Chesapeake Bay region was provided by Brinker et al. (2007). 

Maximum age, age at maturity, age at recruitment, and average adult length and weight were 

from Birds of North America Online (Shields, 2002). 

Age at maturity and annual natural mortality values for the double-crested cormorant 

(Phalacrocorax auritus) were found in Van Der Veen (1973). Average clutch size was from 

Peck and James (1983). Lifespan estimate comes from Klimkiewicz and Futcher (1989). 

Average adult weight was taken from DesGrange (1982). An abundance estimate specific to 

Chesapeake Bay was found in Brinker et al. (2007). Recruitment age was taken from Van Tets 

(1959). Average adult length was taken from Birds of North America Online (Hatch and 

Weseloh, 1999). 

Diving Ducks (SB) 

Morrier et al. (1997) provided the average clutch size for surf scoters (Melanitta 

perspicillata). The recruitment age was found in Lesage et al (1996). Abundance for the 

Chesapeake Bay region was from Paige and Luckenbach (2008). Age at maturity and average 

adult length and weight were from Birds of North America Online (Savard et al., 1998). 

Maximum age and mortality could not be found for this species; consequently, we assumed these 

values were similar to those of the long-tailed duck, referenced below. 

The long-tailed duck (Clangula hyemalis) mortality, lifespan, age at maturity, and 

average adult length and weight were all taken from Birds of North America Online (Robertson 

and Savard, 2002). The mortality rate was taken as an average of the two rates the website 

provided for ducks in Alaska and Iceland. Though these locations differ from the Chesapeake 

Bay, these were the only rates found by the authors. Clutch size and recruitment age were found 

in Alison (1975). An estimate of abundance specific to Chesapeake Bay was taken from Perry et 

al. (2007). 

Bald Eagle (PIN) 

For the bald eagle (Haliaeetus leucocephalus), annual natural mortality rate and clutch 

size were from Buehler et al. (1991). Our lifespan estimate came from Schempf (1997) for 

Alaskan bald eagles. Average adult length was taken from Palmer (1988), while average adult 

weight was found in Bortolotti (1986). Age at maturity was given by Gerrard et al. (1992) for 

eagles in Saskatchewan, and age at recruitment was provided by Kussman (1977) for eagles in 

Minnesota. Our abundance estimate specific to the Chesapeake Bay region was from Watts et al. 

(2008). 
123

References—Life History 

Alison, R. M. 1975. Breeding biology and behavior of the oldsquaw (Clangula hyemalis L.). 

Ornithological Monographs (18):1–52. 

http://dx.doi.org/10.2307/40166735

Arnold, T. W., M. G. Anderson, M. D. Sorenson, and R. B. Emery. 2002. Survival and 

philopatry of female redheads breeding in southwestern Manitoba. The Journal of 

Wildlife Management 66(1):162–169. 

http://dx.doi.org/10.2307/3802882

Barco, S. G., W. M. Swingle, W. A. McLellan, R. N. Harris, and D.A. Pabst. 1999. Local 

abundance and distribution of bottlenose dolphins (Tursiops truncatus) in the nearshore 

waters of Virginia Beach, Virginia. Marine Mammal Science 15(2):394–408. 

http://dx.doi.org/10.1111/j.1748-7692.1999.tb00809.x

Bjorndal, K. A., A. B. Bolten, and H. R. Martins. 2000. Somatic growth model of juvenile 

loggerhead sea turtles Caretta caretta: Duration of pelagic stage. Marine Ecology 

Progress Series 202:265–272. 

http://dx.doi.org/10.3354/meps202265

Blus, L. J., and J. A. Keahey. 1978. Variation in reproductivity with age in the brown pelican. 

The Auk 95(1):128–134. [Available at 

https://sora.unm.edu/node/23100.] 

Bortolotti, G. R. 1986. Evolution of growth rates in eagles: sibling competition vs. energy 

considerations. Ecology 67(1):182–194. 

http://dx.doi.org/10.2307/1938517

Bozeman, E. L., and M. J. Van Den Avyle. 1989. Species profiles: Life histories and 

environmental requirements of coastal fishes and invertebrates (South Atlantic): Alewife 

and blueback herring, 17 p. [Available at 

https://www.researchgate.net/publication/235162309_Species_Profiles_Life_Histories_a

nd_Environmental_Requirements_of_Coastal_Fishes_and_Invertebrates_South_Atlantic

_Alewife_and_Blueback_Herring.] 

Brand, E. J., I. C. Kaplan, C. J. Harvey, P. S. Levin, E. A. Fulton, A. J. Hermann, and J. C. Field. 

2007. A spatially explicit ecosystem model of the California Current’s food web and 

oceanography. U.S. Dept. of Commer., NOAA. NOAA Technical Memorandum NMFSNWFSC-84, 145 p. [Available at 

https://www.nwfsc.noaa.gov/assets/25/578_12062007_152916_CalCurrentTM84Final.pd

f.] 

 
124

Brennessel, B. 2006. The northern diamondback terrapin—habitat, management, and 

conservation, 9 p. Wheaton College, Norton, MA. [Available at 

http://www.dtwg.org/Bibliography/Gray/Brennessel%202007.pdf.] 

Brinker, D. F., J. M. McCann, B. Williams, and B. D. Watts. 2007. Colonial-nesting seabirds in 

the Chesapeake Bay Region: Where have we been and where are we going? Waterbirds 

30(SP1):93–104. 

http://dx.doi.org/10.1675/1524-4695(2007)030[0093:CSITCB]2.0.CO;2

Buehler, D. A., J. D. Fraser, J. K. D. Seegar, and G. D. Therres. 1991. Survival rates and 

population dynamics of bald eagles on Chesapeake Bay. The Journal of Wildlife 

Management 55(4):608–613. 

http://dx.doi.org/10.2307/3809506

Bulté, G., and G. Blouin-Demers. 2009. Does sexual bimaturation affect the cost of growth and 

the operational sex ratio in an extremely size-dimorphic reptile? Ecoscience 16(2):175–

182. 

http://dx.doi.org/10.2980/16-2-3243

Burger, J. 1976. Temperature relationships in nests of the northern diamondback terrapin, 

Malaclemys terrapin terrapin. Herpetologica 32(4):412–418. [Available at 

http://www.jstor.org/stable/3891928.] 

Butler, R.W. 1992. Great Blue Heron (Ardea herodias). In The Birds of North America Online 

(A. Poole, ed.). Cornell Lab of Ornithology, Ithaca. 

http://dx.doi.org/10.2173/bna.25

Byles, R. A. 1988. Behavior and ecology of sea turtles from Chesapeake Bay, Virginia. Ph.D. 

dissertation. College of William & Mary, Williamsburg, VA. [Available at 

https://www.researchgate.net/publication/35076742_Behavior_and_ecology_of_sea_turtl

es_from_Chesapeake_Bay_Virginia.] 

Byles, R. A. 1989. Distribution and abundance of Kemp's ridley sea turtle, Lepidochelys kempii, 

in Chesapeake Bay and nearby coastal waters. In Proceedings of the first international 

symposium on Kemp's ridley sea turtle biology, conservation, and management; 

Galveston, p.145. Texas A&M University Sea Grant College Program, Galveston, TX. 

Chaloupka, M. 2002. Stochastic simulation modelling of southern Great Barrier Reef green turtle 

population dynamics. Ecological Modelling 148(1):79–109. 

http://dx.doi.org/10.1016/S0304-3800(01)00433-1

Chan, E. H., H. U. Salleh, and H. C. Liew. 1985. Effects of handling on hatchability of eggs of 

the leatherback turtle, Dermochelys coriacea (L.). Pertanika 8(2):265–271. [Available at 

http://www.pertanika.upm.edu.my/view_archives.php?journal=PERT-8-2-8.] 

Chesapeake Bay Program. 1990. Chesapeake Bay waterfowl policy and management plan—an 

agreement commitment report from the Chesapeake Executive Council. Annapolis, MD, 

38 p. [Available at http://www.chesapeakebay.net/content/publications/cbp_12307.pdf.] 
125

Christensen, V., A. Beattie, C. Buchanan, H. Ma, S. J. D. Martell, R. J. Latour, D. Preikshot, M. 

B. Sigrist, J. H. Uphoff, C. J. Walters, R. J. Wood, and H. Townsend. 2009. Fisheries 

ecosystem model of the Chesapeake Bay: Methodology, parameterization, and model 

exploration. U.S. Dept. of Commer., NOAA. NOAA Technical Memorandum NMFSF/SPO-106, 146 p. [Available at http://spo.nmfs.noaa.gov/tm/TM106%20FINAL.pdf.] 

Ciaranca, M. A., C. C. Allin, and G. S. Jones. 1997. Mute swan (Cygnus olor). In The Birds of 

North America Online (A. Poole, ed.), Cornell Lab of Ornithology, Ithaca, NY. 

http://dx.doi.org/10.2173/bna.273

Clapp, R. B., M. K. Klimkiewicz, and J. H. Kennard. 1982. Longevity records of North 

American birds: Gaviidae through Alcidae. Journal of Field Ornithology 53(2):81–124. 

[Available at http://www.jstor.org/stable/4512701.] 

Coles, W. C. 1999. Aspects of the biology of sea turtles in the mid-Atlantic Bight. Ph.D. 

disseration. College of William & Mary, Williamsburg, VA. [Available at 

http://www.vims.edu/library/theses/Coles99.pdf.] 

Costanzo, G. R., and L. J. Hindman. 2007. Chesapeake Bay breeding waterfowl populations. In 

Special Publication 1:Waterbirds of the Chesapeake Bay and Vicinity: Harbingers of 

Change? Waterbirds 30(SP1):17–24. 

http://dx.doi.org/10.1675/1524-4695(2007)030[0017:CBBWP]2.0.CO;2

DesGranges, J.-L. 1982. Weight growth of young double-crested cormorants in the St. Lawrence 

estuary, Quebec. Colonial Waterbirds 5:79–86. 

http://dx.doi.org/10.2307/1521038

Drilling, N., R. Titman, and F. Mckinney. 2002. Mallard (Anas platyrhynchos). In The Birds of 

North America Online (A. Poole, ed.). Cornell Lab of Ornithology, Ithaca, NY. 

http://dx.doi.org/10.2173/bna.658

Dzubin, A., and J. B. Gollop. 1972. Aspects of mallard breeding ecology in Canadian parkland 

and grassland. In Population ecology of migratory birds (R. I. Smith, J. R. Palmer, and T. 

S. Baskett, eds.), p. 113–152. United States Fish and Wildlife Service Wildlife Report 

No. 2. 

Ferguson-Lees, J., and D.A. Christie. 2001. Raptors of the world, 320 p. Houghton Mifflin. 

[Available at https://www.amazon.com/Raptors-World-Princeton-FieldGuides/dp/0691126844.] 

Field, J. C. 2004. Application of ecosystem-based fishery management approaches in the 

northern California Current. Ph.D. dissertation. School of Aquatic and Fishery Sciences, 

University of Washington, Seattle, WA. [Available at 

https://digital.lib.washington.edu/researchworks/handle/1773/34523.] 
126

Frazer, N. B., and J. I. Richardson. 1985. Annual variation in clutch size and frequency for 

loggerhead turtles, Caretta caretta, nesting at Little Cumberland Island, Georgia, USA. 

Herpetologica 41(3):246–251. [Available at http://www.jstor.org/stable/3892269.] 

Froese, R., and D. Pauly. 2013. FishBase. World Wide Web electronic publication. 

www.fishbase.org, accessed April 2013. 

Gerrard, J. M., P. N. Gerrard, G. R. Bortolotti, and E. H. Dzus. 1992. A 24-year study of bald 

eagles on Besnard Lake, Saskatchewan. Journal of Raptor Research 26:159–166. 

[Available at https://sora.unm.edu/node/53293.] 

Graham, K. 2000. A review of the biology and management of blue catfish. In Catfish 2000: 

Proceedings of the International Ictalurid Symposium (E. R. Irwin, W. A. Hubert, C. F. 

Rabeni, J. Harold, L. Schramm, and T. Coon, eds.), 24:37–49. American Fisheries 

Society, Bethesda, MD. [Available at http://fisheries.org/docs/pub_sympsample.pdf.] 

Hatch, J. J., and D. V. Weseloh. 1999. Double-crested cormorant (Phalacrocorax auritus). In

The Birds of North America Online (A. Poole, ed.). Cornell Ornithology Lab, Ithaca, NY. 

http://dx.doi.org/10.2173/bna.441

Henny, C. J. 1972. An analysis of the population dynamics of selected avian species—with 

special reference to changes during the modern pesticide era. Bureau of Sport Fisheries 

and Wildlife, U.S. Fish and Wildlife Service, Wildlife Research Report No. 1, 

Washington, D.C., 99 p. [Available at 

https://books.google.com/books/about/An_Analysis_of_the_Population_Dynamics_o.ht

ml?id=3PmmnQEACAAJ.] 

Henny, C. J., and H. M. Wight. 1969. An endangered osprey population: Estimates of mortality 

and production. The Auk 86(2):188–198. [Available at https://sora.unm.edu/node/21848.] 

Johnson, S. A., A. L. Bass, B. Libert, M. Marshall, and D. Fulk. 1999. Kemp's ridley 

(Lepidochelys kempi) nesting in Florida. Florida Scientist 62(3–4):194–204. [Available at 

http://ufwildlife.ifas.ufl.edu/pdfs/johnsonetal1999kempsridley.pdf.] 

Keinath, J. A. 1993. Movements and behavior of wild and head-started sea turtles. Ph.D. 

dissertation. College of William & Mary, Williamsburg, VA. 

Kirby, R. E., L. M. Cowardin, and J. R. Tester. 1989. Premigrational movements and behavior of 

young mallards and wood ducks in north-central Minnesota. U.S. Fish & Wildlife Service 

5, 25 p. [Available at https://pubs.er.usgs.gov/publication/81367.] 

Kirby, R. E., and G. A. Sargeant. 1999. Survival of postfledging mallards in northcentral 

Minnesota. The Journal of Wildlife Management 63(1):403–408. 

http://dx.doi.org/10.2307/3802526

 
127

Klimkiewicz, M. K., and A. G. Futcher. 1989. Longevity records of North American birds 

supplement 1 (Registros de longevidad en aves de Norte América: primer suplemento). 

Journal of Field Ornithology 60(4):469–494. [Available at 

http://www.jstor.org/stable/4513471.] 

Kobell, R. 2010. Endangered species status proposed for loggerhead turtle. Bay Journal. 

Chesapeake Media Service, Seven Valleys, PA. [Available at 

http://www.bayjournal.com/article/endangered_species_status_proposed_for_loggerhead

_turtle.] 

Krementz, D. G., J. R. Sauer, and J. D. Nichols. 1989. Model-based estimates of annual survival 

rate are preferable to observed maximum lifespan statistics for use in comparative lifehistory studies. Oikos 56(2):203–208. 

http://dx.doi.org/10.2307/3565337

Kussman, J. V. 1977. Post-fledging behavior of the northern bald eagle, Haliaeetus 

leucocephalus alascanus Townsend, in the Chippewa National Forest, Minnesota. PhD. 

disseration, 868 p. Univ. of Minnesota, Minneapolis, MN. [Available at 

https://books.google.com/books/about/Post_fledging_Behavior_of_the_Northern_B.html

?id=hlcJSwAACAAJ.] 

Lesage, L., A. Reed, and J.-P. L. Savard. 1996. Plumage development and growth of wild surf 

scoter Melanitta perspicillata ducklings. Wildfowl 47:205–210. [Available at 

http://wildfowl.wwt.org.uk/index.php/wildfowl/article/view/999.] 

Limpert, R. J., and S. L. Earnst. 1994. Tundra swan (Cygnus columbianus). In The Birds of 

North America Online (A. Poole, ed.). Cornell Ornithology Lab, Ithaca, NY. [Available 

at http://bna.birds.cornell.edu/bna/species/089/articles/.] 

Low, J. B. 1945. Ecology and Management of the redhead, Nyroca americana, in Iowa. 

Ecological Monographs 15(1):35–69. 

http://dx.doi.org/10.2307/1943294

Lubbers, L., W. R. Boynton, and W. M. Kemp. 1990. Variations in structure of estuarine fish 

communities in relation to abundance of submersed vascular plants. Marine Ecology 

Progress Series 65:1–14. [Available at http://www.intres.com/articles/meps/65/m065p001.pdf.] 

Mathiasson, S. 1980. Weight and growth rates of morphological characters of Cygnus olor. In

2nd International Swan Symposium (G. V. T. Matthews and M. Smart, eds.), p. 379–389. 

IWRB, Sapporo, Japan. 

Monda, M. J., J. T. Ratti, and T. R. McCabe. 1994. Reproductive ecology of tundra swans on the 

Arctic National Wildlife Refuge, Alaska. The Journal of Wildlife Management 

58(4):757–773. 

http://dx.doi.org/10.2307/3809691
128

Morrier, A., L. Lesage, A. Reed, and J.-P. L. Savard. 1997. Étude sur l'écologie de la Macreuse à 

front blanc au lac Malbaie, Réserve des Laurentides-1994–1995. Canadian Wildlife 

Service, Région du Québec, 133 p. [Available at 

http://www.publications.gc.ca/site/eng/73710/publication.html.] 

Mowbray, T. B., C. R. Ely, J. S. Sedinger, and R. E. Trost. 2002. Canada goose (Branta 

canadensis). In The Birds of North America Online (A. Poole, ed.). Cornell Ornithology 

Lab, Ithaca, NY. 

http://dx.doi.org/10.2173/bna.682

Musick, J. A., ed. 1999. Life in the slow lane: Ecology and conservation of long-lived marine 

animals. American Fisheries Society Symposium 23, 260 p. American Fisheries Society, 

Bethesda, MD. [Available at http://fisheries.org/bookstore/all-titles/afssymposia/x54023xm/.] 

Myers, R. A., N. J. Barrowman, R. Hilborn, and D. G. Kehler. 2002. Inferring Bayesian priors 

with limited direct data: Applications to risk analysis. North American Journal of 

Fisheries Management 22(1):351–364. 

http://dx.doi.org/10.1577/1548-8675(2002)022<0351:IBPWLD>2.0.CO;2

Myers, R. A., K. G. Bowen, and N. J. Barrowman. 1999. Maximum reproductive rate of fish at 

low population sizes. Canadian Journal of Fisheries and Aquatic Sciences 56(12):2404–

2419. 

http://dx.doi.org/10.1139/f99-201

National Marine Fisheries Service, U.S. Fish and Wildlife Service, and SEMARNAT (Mexico). 

2010. Bi-national recovery plan for the Kemp’s ridley sea turtle (Lepidochelys kempii), 

second revision, 174 p. [Available at 

http://www.nmfs.noaa.gov/pr/pdfs/recovery/turtle_kempsridley_draft2.pdf.] 

Neuenhoff, R. D., D. F. Cowan, H. Whitehead, and C. D. Marshall. 2011. Prenatal data impacts 

common bottlenose dolphin (Tursiops truncatus) growth parameters estimated by lengthat-age curves. Marine Mammal Science 27(1):195–216. 

http://dx.doi.org/10.1111/j.1748-7692.2010.00394.x

Nichols, J. D., J. Bart, J. L. Roland, W. J. L. Sladen, and J. E. Hines. 1992. Annual survival rates 

of adult and immature eastern population tundra swans. The Journal of Wildlife 

Management 56(3):485–494. 

http://dx.doi.org/10.2307/3808863

Owen, D. F. 1959. Mortality of the great blue heron as shown by banding recoveries. The Auk 

76(4):464–470. [Available at https://sora.unm.edu/node/20800.] 

Paige, G. R., and M. W. Luckenbach. 2008. Distribution, habitat characteristics, prey abundance, 

and diet of surf scoters (Melanitta perspicillata) and long-tailed ducks (Clangula 

hyemalis) in polyhaline wintering habitats in the mid-Atlantic region: a comparison of 

shallow coastal lagoons and Chesapeake Bay environs, FWS # 70181-7-R039 2008. U.S. 

Fish and Wildlife Service, Anchorage, AK, 85 p. [Available at 
129

http://www.vcrlter.virginia.edu/elecvol/reports/SDJV_FY08-104-

Ross%20and%20Luckenbach-Final%20Report-no_appendices.pdf.] 

Palmer, R. S., ed. 1988. Handbook of North American birds: volume 4, diurnal raptors, 433 p. 

Yale University Press. [Available at https://www.amazon.com/Handbook-NorthAmerican-Birds-Diurnal/dp/0300040598.] 

Pauly, D. 1980. On the interrelationships between natural mortality, growth parameters, and 

mean environmental temperature in 175 fish stocks. ICES Journal of Marine Science 

39(2):175–192. 

http://dx.doi.org/10.1093/icesjms/39.2.175

Peck, G. K., and R. D. James. 1983. Breeding birds of Ontario: nidiology and distribution, 406 p. 

Royal Ontario Museum, Toronto. [Available at 

https://archive.org/details/breedingbirdsofo02peck.] 

Perry, M. C., A. M. Wells-Berlin, D. M. Kidwell, and P. C. Osenton. 2007. Temporal changes of 

populations and trophic relationships of wintering diving ducks in Chesapeake Bay. In 

Special ublication 1: Waterbirds of the Chesapeake Bay and Vicinity: Harbingers of 

Change? Waterbirds 30(SP1):4–16. [Available at 

http://www.bioone.org/toc/cowa/30/sp1.] 

Plotkin, P. T (ed.). 1995. National Marine Fisheries Service and U. S. Fish and Wildlife Service 

Status Reviews for Sea Turtles Listed under the Endangered Species Act of 1973, 139 p. 

[Available at http://www.nmfs.noaa.gov/pr/pdfs/statusreviews/turtles.pdf .] 

Pratt, H. M. 1973. Breeding attempts by juvenile great blue herons. The Auk 90(4):897–899. 

[Available at https://sora.unm.edu/node/22456.] 

Reese, J. G. 1980. Demography of European mute swans in Chesapeake Bay. The Auk 

97(3):449–464. [Available at https://sora.unm.edu/node/23427.] 

Reese, J. G. 1991. Osprey Pandion haliaetus. In Habitat requirements for Chesapeake Bay living 

resources, second edition (S. L. Funderburk, J. A. Mihursky, S. J. Jordan, and D. Riley, 

eds.), p. 1–11. Chesapeake Research Consortium, Inc., Solomons, MD. [Available at 

https://nepis.epa.gov/Exe/ZyNET.exe/2000WBYD.TXT?ZyActionD=ZyDocument&Clie

nt=EPA&Index=1991+Thru+1994&Docs=&Query=&Time=&EndTime=&SearchMetho

d=1&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFie

ldDay=&IntQFieldOp=0&ExtQFieldOp=0&XmlQuery=&File=D%3A\zyfiles\Index%20

Data\91thru94\Txt\00000016\2000WBYD.txt&User=ANONYMOUS&Password=anony

mous&SortMethod=h|-

&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i

425&Display=hpfr&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&Bac

kDesc=Results%20page&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL.] 

Reynolds, C. M. 1972. Mute swan weights in relation to breeding. Wildfowl 23:111–118. 

[Avvailable at http://wildfowl.wwt.org.uk/index.php/wildfowl/article/view/443.] 
130

Robertson, G. J., and J.-P. L. Savard. 2002. Long-tailed duck (Clangula hyemalis). In The Birds 

of North America Online (A. Poole, ed.). Cornell Lab of Ornithology, Ithaca, NY. 

http://bna.birds.cornell.edu/bna/species/651. 

Rohwer, F. C., and D. I. Eisenhauer. 1989. Egg mass and clutch size relationships in geese, 

eiders, and swans. Ornis Scandinavica 20(1):43–48. 

http://dx.doi.org/0.2307/3676706

Roosenburg, W. M. 1990. Final report: Chesapeake diamondback terrapin investigations for the 

period 1987, 1988, and 1989. Chesapeake Research Consortium, 84 p. [Available at 

http://www.vims.edu/GreyLit/CRC/crc133.pdf.] 

Savard, J.-P. L., D. Bordage, and A. Reed. 1998. Surf scoter (Melanitta perspicillata). In The 

Birds of North America Online (A. Poole, ed.). Cornell Lab of Ornithology, Ithaca, NY. 

[Available at http://bna.birds.cornell.edu/bna/species/363.] 

Schempf, P. F. 1997. Bald eagle longevity record from southeastern Alaska (Registro de 

longevidad para individuo de Haliaeetus leucocephalus de Alaska). Journal of Field 

Ornithology 68(1):150–151. [Available at http://www.jstor.org/stable/4514206.] 

Schreiber, E. A., and J. Burger, eds. 2002. Biology of marine birds, second edition. CRC marine 

biology series, 722 p. CRC Press, Boca Raton, FL. [Available at 

https://www.crcpress.com/Biology-of-Marine-Birds/SchreiberBurger/p/book/9780849398827.] 

Shields, M. 2002. Brown pelican (Pelecanus occidentalis). In The Birds of North America 

Online (A. Poole, ed.). Cornell Lab of Ornithology, Ithaca, NY. [Available at 

http://bna.birds.cornell.edu/bna/species/609.] 

Steidl, R. J., and C. R. Griffin. 1991. Growth and brood reduction of Mid-Atlantic coast ospreys. 

The Auk 108(2):363–370. [Available at https://sora.unm.edu/node/24977.] 

Terwilliger, M. R., and T. A. Munroe. 1999. Age, growth, longevity, and mortality of blackcheek 

tonguefish, Symphurus plagiusa (Cynoglossidae: Pleuronectiformes), in Chesapeake Bay, 

Virginia. Fish. Bull. 97(2):340–361. [Available at http://fishbull.noaa.gov/12terwil.pdf.] 

Trites, A. W., and D. Pauly. 1998. Estimating mean body masses of marine mammals from 

maximum body lengths. Canadian Journal of Zoology/Revue Canadien de Zoologie 

76(5):886–896. 

http://dx.doi.org/10.1139/z97-252

Tucker, A. D., J. W. Gibbons, and J. L. Greene. 2001. Estimates of adult survival and migration 

for diamondback terrapins: conservation insight from local extirpation within a 

metapopulation. Canadian Journal of Zoology 79(12):2199–2209. 

http://dx.doi.org/10.1139/z01-185

 
131

Van der Veen, H. E. 1973. Some aspects of the breeding biology and demography of the doublecrested cormorants (Phalacrocorax auritus) of Mandarte Island. Zoologisch 

Laboratorium der Rijksuniversiteit te Groningen, Groningen. [Available at 

http://agris.fao.org/agris-search/search.do?recordID=AV20120100143.] 

Van Tets, G. F. 1959. A comparative study of the reproductive behaviour and natural history of 

three sympatric species of cormorants (Phalacrocorax auritus, P. penicillatus, and P. 

pelagicus) at Mandarte Island, B.C. University of British Columbia, Vancouver. 

[Available at 

https://open.library.ubc.ca/cIRcle/collections/ubctheses/831/items/1.0106056.] 

Wang, K. R., P. M. Payne, and V. G. Thayer. 1994. Coastal stock(s) of Atlantic bottlenose 

dolphin: status review and management. U.S. Dept. of Commer., NOAA. NOAA 

Technical Memorandum NMFS-OPR-4, 126 p. [Available at

http://www.nmfs.noaa.gov/pr/pdfs/species/coastalbottlenosestock.pdf.] 

Watts, B. D., and J. P. Barton. 2007. Ospreys of the Chesapeake Bay: Population recovery, 

ecological requirements, and current threats. Waterbirds:39–49. [Available at 

http://www.bioone.org/toc/cowa/30/sp1.] 

Watts, B. D., G. D. Therres, and M. A. Byrd. 2008. Recovery of the Chesapeake Bay bald eagle 

nesting population. The Journal of Wildlife Management 72(1):152–158. 

http://dx.doi.org/10.2193/2005-616

Williams, B., D.F. Brinker, and B.D. Watts. 2007. The status of colonial nesting wading bird 

populations within the Chesapeake Bay and Atlantic Barrier Island-Lagoon System. In 

Waterbirds of the Chesapeake Bay and vicinity: harbingers of change? Waterbirds 

30(SP1):82–92. [Available at http://www.bioone.org/toc/cowa/30/sp1.] 

Williams, K.L., and M.G. Frick. 2008. Tag returns from loggerhead turtles from Wassaw Island, 

GA. Southeastern Naturalist 7(1):165–172. 

http://dx.doi.org/10.1656/1528-7092(2008)7[165:TRFLTF]2.0.CO;2

Winemiller, K.O., and K.A. Rose. 1992. Patterns of life-history diversification in North 

American fishes: Implications for population regulation. Canadian Journal of Fisheries 

and Aquatic Sciences 49(10):2196–2218. 

http://dx.doi.org/10.1139/f92-242

Woodin, M.C., and T.C. Michot. 2002. Redhead (Aythya americana). In The Birds of North 

America Online (A. Poole, ed.) Cornell Ornithology Lab, Ithaca, NY. 

http://bna.birds.cornell.edu/bna/species/695

Yntema, C.L., and N. Mrosovsky. 1982. Critical periods and pivotal temperatures for sexual 

differentiation in loggerhead sea turtles. Canadian Journal of Zoology/Revue Canadienne 

de Zoologie 60(5):1012–1016. 

http://dx.doi.org/10.1139/z82-141
132

Zammuto, R.M. 1986. Life histories of birds: clutch size, longevity, and body mass among North 

American game birds. Canadian Journal of Zoology 64(12):2739–2749. 

http://dx.doi.org/10.1139/z86-398

Zug, G.R., M. Chaloupka, and G.H. Balazs. 2006. Age and growth in olive ridley seaturtles 

(Lepidochelys olivacea) from the North-central Pacific: a skeletochronological analysis. 

Marine Ecology 27(3):263–270. 

http://dx.doi.org/10.1111/j.1439-0485.2006.00109.x

Zug, G.R., H.J. Kalb, and S.J. Luzar. 1997. Age and growth in wild Kemp's ridley seaturtles 

Lepidochelys kempii from skeletochronological data. Biological Conservation 80(3):261–

268. 

http://dx.doi.org/10.1016/S0006-3207(96)00143-7

Zug, G.R., and J.F. Parham. 1996. Age and Growth in leatherback turtles, Dermochelys coriacea

(Testudines: Dermochelyidae): A skeletochronological analysis. Chelonian Conservation 

and Biology 2(2):244–249. [Available at 

https://www.researchgate.net/publication/287496441_Age_and_growth_in_leatherback_t

urtles_Dermochelys_coriacea_Testudines_Dermochelyidae_a_skeletochronological_anal

ysis.] 
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Appendix D: Diets 

 Diet relationships were based primarily on published literature (both peer-reviewed and 

grey literature), and supplemented with information provided by FishBase.org, unpublished data, 

and expert opinion, in order of preference. In Atlantis, it is important to establish possible 

predator-prey relationships as well as known relationships, so the model will allow a predator to 

preferentially consume prey based on availability of all possible prey. Availability is a function 

of a predator's "preference" (determined by magnitude of prey availability as specified in the diet 

matrix; see Tables 16 and 17), its spatial and temporal overlap with the prey, and whether habitat 

groups (marsh grass [MA], oysters [BFF], and SAV [SG]) are available as refuge for those prey 

groups that use these habitats to decrease their availability to predators (Equations 17 and 18, 

Figure 7). Consequently, in some cases, where data were lacking but the authors believe a 

predator-prey relationship exists (e.g., soft-bodied invertebrate remains are not typically 

identifiable in vertebrate gut content studies), an arbitrary number was added to predator diet to 

allow such groups to be consumed by a predator if it is available. One of four values (0.001, 

0.0001, 0.00001, 0.000005) was applied for each arbitrary addition to the diet matrix. Predatorprey connections believed to be important received larger values, while relatively unimportant 

connections (but important enough not to be excluded entirely from the matrix) were assigned 

small values. When literature or data were incomplete (or none were found) for the diet of a 

trophic group included in CAM, the authors chose the prey groups that they believed were most 

likely consumed by the predator, and uniform availabilities were assigned for the prey groups. 

 The species included in each aggregate functional group are described below and are 

listed in Table 11 of the main text. 

Fish 

 Most fish species’ diets were developed using a combination of diet data documented in 

annual reports (available online) for the ongoing ChesMMAP project 

(www.vims.edu/research/departments/fisheries/programs/multispecies_fisheries_research/chesm

map/), and supplemental information found in other published literature. Few diets had to be 

developed with supplemental information sources only. 

 

 The comprehensive list of all documented prey items was reduced to exclude any prey 

items that were not included in the 55 functional groups of species or aggregate groups modeled 

in CAM. Remaining fish diet items (i.e., those modeled in CAM) were normalized to equal 

100% for each predator diet. Relevant details on the development of the diet of each CAM fish 

functional group are presented next. 
134

Alosines 

Alosines is an aggregate functional group. Alosines in the ChesMMAP data included alewife, 

American shad, blueback herring, and hickory shad. The final diets for each of these species 

were combined to develop one composite Alosine diet. 

Atlantic Croaker 

Initial diet data were derived from ChesMMAP data and supplemented with data presented on 

page 15 in the ASMFC benchmark stock assessment (Atlantic States Marine Fisheries 

Commission, 2010). 

Atlantic Menhaden 

Initial diets for juvenile and adult Atlantic menhaden were based on Christensen et al. (2009). 

As in CAM, Christensen et al. included two functional groups, or life-history "stanzas," for 

Atlantic menhaden. However, they assumed an ontogenetic shift from zooplanktivory to 

phytoplankivory (juvenile to adult stages, respectively), a shift now understood to be reversed 

(Lynch et al., 2010) from that assumed by Christensen et al. (2009). Consequently, juvenile and 

adult diets in CAM were refined based on Lynch et al. (2010). 

Bay Anchovy 

The most important forage fish in the Bay (Ihde et al. 2015; analysis based on ChesMMAP data), 

bay Anchovy are relied on as prey by most predators in the Chesapeake. The diet of this forage 

fish, in turn (based on Christensen et al., 2009), consists mostly of mesozooplankton, with 

additional important contributions from microzooplankton and lesser contributions by large 

phytoplankton, picophytoplankton, and dinoflagellates. 

Black Drum 

Initial diet data were derived from ChesMMAP data and supplemented with data presented on 

page 5 in Sutter et al. (1986).

Bluefish 

CAM bluefish diets generally followed Christensen et al. (2009), where juveniles depend mainly 

on bay anchovy, pelagic forage fish, Atlantic menhaden, and panfish (in order of importance), 

and adult fish feed mainly on Atlantic menhaden. However, adult diets in CAM are believed to 

be more generalized than assumed by Christensen et al. (2009), so authors assumed adult 

bluefish also feed heavily on panfish, Alosines, and Atlantic croaker, and to a lesser extent on 

bay anchovy, weakfish, reef fish, other flatfish, and blue crab. 

Bottom Fish 

Bottom Fish is an aggregate group that includes spotted hake, northern searobin, and lizardfish. 

The ChesMMAP data set includes information only for spotted hake and northern searobin. The 

final diet for spotted hake was supplemented with data from table 1 in Bowman (1979), and 

Table 1 in Rachlin and Warkentine (1987). Northern searobin diet data from ChesMMAP was 
135

supplemented with data from Byron and Link (2010). Lizardfish diet was derived from Table 7 

in Jeffers (2007). These data were combined to develop one composite diet. 

Butterfish (and Harvestfish) 

Diets for this aggregate group were representative of a combination of butterfish and harvestfish. 

Initial diet data were derived from ChesMMAP data and supplemented by data presented for 

butterfish on page 2 in Cross et al. (1999), and additional information on harvestfish diet from 

Cargo and Schultz (1966). 

Catfish 

The diet of this aggregate group generally follows that described by Christensen et al. (2009) but 

is more generalized. Catfish feed mainly on soft-bodied benthic invertebrates in CAM (i.e., 

benthic carnivores, benthic deposit feeders, and benthic filter feeders), and gizzard shad are an 

important part of their diet (following the Christensen et al. (2009) EwE model). Important 

dietary contributions are also made by panfish and meiobenthos. In addition, it is assumed that 

catfish opportunistically consume a much wider variety of additional prey in CAM (refer to 

Table 17) compared to the EwE model by Christensen et al. (2009). No differences in diets were 

modeled between juvenile and adult catfish. 

Cownose Ray 

Initial diet data were derived from ChesMMAP data and supplemented by data presented in 

Table 1 of Smith and Merriner (1985). 

Gizzard Shad 

The diet of gizzard shad in CAM was initially based on that of Christensen et al. (2009), but the 

Christensen diet was extremely limited and included only phytoplankton (75%) and 

mesozooplankton (25% of total diet). In CAM, the gizzard shad diet is expanded to include prey 

important to other Alosa spp. (see Alosine group diet, above), including important dietary 

contributions by meiobenthos, dinoflagellates, picophytoplankton, and soft-bodied benthic 

invertebrate groups (i.e., benthic carnivores, benthic deposit feeders, and benthic filter feeders), 

in order of decreasing assumed importance. No differences in diets were modeled between 

juvenile and adult gizzard shad. 

Other Flatfish 

The diet of this aggregate group represents a composite diet of hogchoker, windowpane flounder, 

blackcheek tonguefish, and winter flounder. The composite diet was based on ChesMMAP data 

supplemented by information presented in Curti (2005), Table 2 in Toepher and Fleeger (1995), 

page 2 in Pereira et al. (1999), and from data presented on page 9 of Link et al. (2002). 

Panfish 

Panfish is an aggregate group that includes silver perch, spot, yellow perch, and bluegill. Only 

silver perch diet data is found in the ChesMMAP data set. The final diet for silver perch in the 

ChesMMAP data were supplemented with information from Figure 7 of Parthree et al. (2006), 
136

and Figure 7 in Latour et al. (2006). Diet data for spot was developed from Figure 4 in Latour et 

al. (2006), and from data in Horvath (1997). The diet for yellow perch was taken from the 

Chesapeake Bay Foundation field guide 

(http://www.chesapeakebay.net/fieldguide/critter/yellow_perch , accessed 18 January, 2011). 

Diet of the bluegill was derived from the website publication by C. Parr, 2002, at 

http://animaldiversity.ummz.umich.edu/site/accounts/information/Lepomis_macrochirus.html, 

accessed 18 January 2011). These data were combined to develop one composite panfish diet. 

Pelagic Forage Fish 

The diet for this aggregate group, that includes both the Atlantic silverside and mummichog, is 

assumed to be composed mainly of mesozooplankton with important contributions of benthic 

filter feeders; large phytoplankton; microzooplankton; meiobenthos; other soft-bodied benthic 

invertebrates (i.e., benthic carnivores, benthic deposit feeders, and benthic filter feeders); 

dinoflagellates; and picophytoplankton. 

Reef Associated Fish 

Reef associated fish is an aggregate group that includes oyster toadfish, black seabass, tautog, 

and Atlantic spadefish. Of these, diet information was available from the ChesMMAP data for 

oyster toadfish, black seabass, and tautog. Final diets for each of these three species were 

supplemented by: oyster toadfish—Adams (1976) and Fishbase 

(http://www.fishbase.org/trophiceco/DietCompoSummary.php?dietcode=1817&genusname=Ops

anus&speciesname=tau , accessed 18 January, 2011); black seabass—Steimle et al. (1999) and 

Sedberry (1988); and tautog—Steimle and Shaheen (1999). Diet data for the Atlantic spadefish 

was derived using information found in the web publication of the Florida Museum of Natural 

History, "Biological Profile, Atlantic Spadefish" available at 

http://www.flmnh.ufl.edu/fish/Gallery/Descript/Atlanticspadefish/Atlanticspadefish.html, 

accessed 18 January 2011). These data were combined to develop one composite reef associated 

fish diet. 

Sandbar Shark 

In CAM, the diet for the top carnivore in the Bay, sandbar shark, is assumed to be varied and 

opportunistic. We initially (pre-calibration) assumed a uniform distribution of sandbar shark 

prey, not dominated by any particular fish or invertebrate (see Table 16), that included blue crab 

and most macroscopic, pelagic prey groups available in the model (excepting bay anchovy and 

pelagic forage fish). No differences in diets were modeled between juvenile and adult sandbar 

sharks. 

Smooth Dogfish 

Initial diet data for smooth dogfish was derived from ChesMMAP data and supplemented based 

on food habits data summarized at the NMFS Apex predator website: 

http://na.nefsc.noaa.gov/sharks/ (accessed 18 January 2011). 
137

Spiny Dogfish 

Initial diet data for spiny dogfish was derived from ChesMMAP data and supplemented by data 

provided in Figure 3 of McMillan and Morse (1999). 

Striped Bass 

Both juvenile and adult striped bass diets were based on Christensen et al. (2009). However, 

"resident" and "migratory" adults of that model were combined into one adult stock in CAM, and 

dietary contributions for adults were expanded to reflect that published in Water and Austin 

(2003). 

Summer Flounder 

Initial diet data for summer flounder was derived from ChesMMAP data and supplemented with 

data presented in Figure 32 in Packer et al. (1999). 

Weakfish 

Initial diet data for weakfish were derived from ChesMMAP data and supplemented based on 

data presented in Figure C11.3-3 (page 691) of the 48th Northeast Regional Stock Assessment 

Workshop (Northeast Fisheries Science Center, 2009). 

White Perch 

Initial diet data for white perch was derived from ChesMMAP data and supplemented with data 

presented in Figure 5 in Neuman et al., (2004). 

Mammals 

The only mammal stock modeled in CAM is the bottlenose dolphin. The diet of the dolphin in 

CAM follows Mrang et al. (1994), which specifies that fish and squid are important in the diets 

of coastal bottlenose, but we assume that dolphin also consume blue crab, as reported in Lippson 

and Lippson (1997). Lacking specific proportions of prey from gut content analyses, we 

assumed that a uniform distribution of all pelagic finfishes and blue crab comprise the bottlenose 

dolphin diet. 

Reptiles 

Diamondback Terrapins 

A detailed diet analysis for wild-caught diamondback terrapins was not found in the literature; 

consequently, a uniform distribution of probable prey was used for this group in the initial 

model. This was adjusted somewhat, based on spatial distribution of terrapins and their prey. 

Terrapins were assumed to prey mainly on fish (e.g., bay anchovy, weakfish, striped bass, white 

perch, pelagic forage fish, other flatfish); benthic invertebrates (e.g., benthic filter feeders, 

benthic deposit feeders, benthic carnivores, meiobenthos); algae (microphytobenthos); and SAV 
138

(seagrass), but feed on many additional groups as well (see Table 17). There were no differences 

between the prey of juvenile and adult terrapins in the model. 

Sea Turtles 

As specified earlier (see Appendix C), the aggregate sea turtle group consists mainly of 

loggerhead characteristics, while leatherback and Kemp's ridley characteristics add to the 

diversity of the group. The modeled sea turtle diet is consistent with this approach. 

Consequently, like the loggerhead diet, the sea turtle diet is characterized by crustaceans, finfish 

(Seney and Musick, 2007), SAV (Musick, 1988), and other benthic components. Since the 

leatherback and Kemp's ridley sea turtle diets (the other two common Chesapeake species 

included in the sea turtle group) are understood to be composed mostly of jellyfish and benthic 

invertebrates (mollusks and crustaceans), respectively (Musick 1988), these prey are included in 

the aggregate sea turtle diet as well. Because the loggerhead's preferred diet appears to vary 

significantly with prey availability (Seney and Musick, 2007), the aggregate sea turtle diet 

modeled here is assumed to be uniformly distributed among the available benthic invertebrates, 

finfish, and SAV and includes the detrital and bacterial components of the ecosystem. 

Birds 

Recent work suggests that bird population trends could be a useful indicator of forage health in 

the system (Piatt et al., 2006; Pikitch et al., 2012). In addition, recent trends in bird populations 

due to climate change could have an important effect on forage in the Chesapeake Bay (Ihde et 

al., 2015). Four bird groups are modeled in CAM. Three are multi-species aggregate groups 

(benthic grazing seabirds, piscivorous seabirds, diving ducks), and one is composed of a single 

species (bald eagle). 

Bald Eagle 

The modeled bald eagle diet consists mainly of larger species of available finfish. Lacking a 

regional diet study on which to base a system-wide bald eagle diet, the authors assumed a 

uniform distribution for the larger finfish species, blue crab, and herbivorous birds (Table 16). 

These assumptions were based on a general description available at the Cornell Lab of 

Ornithology website (https://www.allaboutbirds.org/guide/Bald_Eagle/lifehistory, last accessed 

1-4-16), with assumed preferences for gizzard shad, bluefish, catfish, and weakfish. 

Benthic Grazing Seabirds 

Benthic grazing seabirds is an aggregate group that includes Canada goose, tundra swans, 

mallard ducks, mute swans, and redhead ducks. Diet of each species was based on literature 

summarized on Birds of North America Online (http://bna.birds.cornell.edu/bna; accessed 18 

October 2010). The aggregate benthic grazing seabird diet was combined with individual 

species diets weighted in accordance with biomass estimates from D. Forsell (unpublished data, 

Coastal Program biologist with the U.S. Fish and Wildlife Service, Chesapeake Bay Field 

Office), with Canada goose dominating the diet characteristics of the aggregate group (0.8). 

Tundra swan, mallard duck, mute swan, and redhead ducks each contribute relatively small 

proportions to the diet (0.08, 0.063, 0.05, and 0.006, respectively). The resulting benthic grazing 
139

seabird diet is mostly herbivorous: about 45% is composed of SAV, approximately 35% marsh 

grass, and a small percentage of microphytobenthos. However, the diet also includes a variety of 

benthic invertebrates, plankton, meiobenthos, detritus, and bacteria. The modeled diet of this 

group pertains only to the aquatic portion of the diet of this group; i.e., diet was restricted to the 

model domain, and thus the contribution agricultural grains and freshwater wild rice were not 

included. 

Diving Ducks 

Diving ducks is an aggregate group. Though life history parameters are based mainly on 

"seaducks" (i.e., surf scoter and long-tailed ducks), detailed diet information was available for a 

broader group of regional diving ducks in data provided by Perry et al. (U.S.G.S. Patuxent 

Wildlife Research Center) in an online resource: 

http://www.pwrc.usgs.gov/resshow/perry/foodhabits.htm (accessed 1-4-16). Consequently, the 

authors decided to incorporate this detailed diet information for a more generalized predatory 

duck functional group. Diving duck diet, therefore, includes that of surf scoter, black scoter, 

long-tailed duck, ruddy duck, bufflehead, canvasback, lesser scaup, and greater scaup. Modeled 

diving ducks are primarily predatory birds that prey mostly on benthic invertebrates, where more 

than 60% of their diet consists of benthic filter feeders (BFD) and 25% of their diet is Macoma 

clams (BFS). They also consume submerged plant matter while foraging, and nearly 10% of 

their diet consists of SAV. 

Piscivorous Seabirds 

Piscivorous seabirds is an aggregate group consisting of brown pelican, double-crested 

cormorant, great blue heron, and osprey. Diet of each species was based on literature 

summarized on Birds of North America Online (http://bna.birds.cornell.edu/bna; last accessed 18 

October 2010). The aggregate piscivorous seabird diet was combined with individual species 

diets weighted in accordance with biomass estimates from D. Forsell (unpublished data, Coastal 

Program biologist with the U.S. Fish and Wildlife Service, Chesapeake Bay Field Office), with 

great blue heron dominating the characteristics of the diet (0.71), double-crested cormorant 

contributing a relatively large proportion (0.15), and with osprey and brown pelican both 

contributing about (0.07). Modeled piscivorous seabirds are predators and mainly consume the 

wide variety of available finfish species, but this group's diet also includes terrapins, blue crab, 

herbivorous birds and brief squid if available. 

Invertebrates 

Of the 26 groups classified as "invertebrates" in CAM, 12 are heterotrophic: dinoflagellates, 

zooplankton (4), brief squid, blue Crab, aggregate benthic invertebrate groups (4), and oyster. 

See Tables 17 and 18 for additional details. 

Dinoflagellates and Zooplankton 

Dinoflagellates are modeled as mixotrophs in Atlantis, both producing their own nutrients 

through photosynthesis and acting as heterotrophs opportunistically. Though the size of 

dinoflagellates modeled in the Chesapeake Bay ranges widely (5 um–2 mm), a simple size-based 
140

diet was assumed to be representative for this diverse group. Dinoflagellates heterotrophy 

includes (in descending order of importance) picophytoplankton, microphytobenthos, bacteria, 

microzooplankton, and dinoflagellates. 

Zooplankton are composed of four diverse and distinct groups in CAM. Ctenophores and sea 

nettles are modeled separately to allow for the modeling of the complex interactions with oysters 

currently understood to occur during different portions of the lifecycle of all three organisms 

(Breitburg and Fulford, 2006). In recognition of this interaction in the model, ctenophores are 

assumed to consume available oyster larvae, and sea nettles consume small ctenophores. Other 

prey for both groups are based on Christensen et al. (2009) and assumed to be mostly similar, 

except that a small portion of the sea nettle diet also includes fish. The two remaining 

zooplankton groups are size-based aggregate groups, mesozooplankton (0.2–20 mm, including 

copepods) and microzooplankton (0.02–0.2 mm, including rotifers, ciliates, and copepod 

nauplii). The largest portion of the mesozooplankton diet consists of (in order of decreasing 

importance) microzooplankton, cryptophyes and diatoms (one group), picophytoplankton, 

dinoflagellates, and microphytobenthos; and is based on the relative size of prey and Christensen 

et al. (2009). Likewise, the largest portion of the microzooplankton diet is picophytoplankton, 

cryptophyes/diatoms, and dinoflagellates. 

Brief Squid 

Brief squid were assumed to feed mainly on fish found in the polyhaline portions of the 

Chesapeake (Bartol et al., 2002). Consequently, the squid diet is modeled as that of an 

opportunistic piscivore, and its diet consists of bluefish, bay anchovy, pelagic forage fish, striped 

bass, white perch, other flatfish, brief squid, and smaller proportions of Atlantic menhaden and 

Alosines. 

Blue Crab 

Blue crabs are largely opportunistic feeders in the Chesapeake Bay (Seitz et al., 2011). 

Therefore, diets were assumed to be composed mostly of widely available benthic invertebrate 

groups (in order of decreasing importance: Macoma sps., deposit feeders, and benthic filter 

feeders), blue crabs, and detritus. Gross diet is largely similar to that described in Christensen et 

al. (2009), though prey groups available were fewer and less varied in that study. 

(Aggregate) Benthic Invertebrate Groups 

Aggregate benthic invertebrate groups were based on species observed in the CBP Benthic Index 

of Biological Integrity (2002–2009, see main text for details). Groups were structured to model 

ecological function; e.g., "Benthic filter feeders (BFD)," "Benthic carnivores (BC)," and 

"Benthic deposit feeders (BD)." An additional aggregate group, "Macoma (BFS)," was included 

in recognition of the current understanding of the importance of clams in general (Hines et al., 

1990) and of Macoma sps. specifically, to the blue crab diet (Seitz et al., 2005). Diets of these 

four groups were based mainly on Baird and Ulanowicz (1989), but were supplemented by 

expert knowledge (personal communication, R. Llanzo, Versar, Inc., and W. Slacum, Oyster 

Recovery Partnership). Information applied from Baird and Ulanowicz (1989) included Table 4 

(page 344) and Figure 7 (page 347), representing the annual percent of carbon at individual 

nodes from donor nodes in the network flow diagram (Figure 7 on page 347). 
141

Oyster 

The diet of oyster is based largely on Baird and Ulanowicz (1989) as described previously for the 

aggregate benthic invertebrate groups. The largest portion of the oyster diet consists of diatoms 

and cryptophyes (~40%), but the diet also includes a substantial proportion of 

picophytoplankton, detritus, and microzooplankton. 

References—Diets 

Adams, S. M. 1976. Feeding ecology of eelgrass fish communities. Transactions of the American 

Fisheries Society 105(4):514–519. 

http://dx.doi.org/10.1577/1548-8659(1976)105<514:FEOEFC>2.0.CO;2

Atlantic States Marine Fisheries Commission. 2010. Atlantic croaker 2010 benchmark stock 

assessment, 366 p. [Available at 

http://www.asmfc.org/uploads/file/5282798aatlanticCroaker2010BenchmarkStockAssess

ment.pdf.] 

Baird, D., and R. E. Ulanowicz. 1989. The seasonal dynamics of the Chesapeake Bay ecosystem. 

Ecological Monographs 59(4):329–364. 

http://dx.doi.org/10.2307/1943071

Bartol, I. K., R. Mann, and M. Vecchione. 2002. Distribution of the euryhaline squid 

Lolliguncula brevis in Chesapeake Bay: Effects of selected abiotic factors. Marine 

Ecology Progress Series 226:235–247. 

http://dx.doi.org/10.3354/meps226235

Bowman, R. E. 1979. Feeding habits of ten northwest Atlantic juvenile groundfish, 22 p. U.S. 

Dept. Commer., NOAA. Northeast Fisheries Science Center, Ref. No. 79–43, Woods 

Hole, MA. [Available at 

http://www.nefsc.noaa.gov/publications/series/whlrd/whlrd7943.pdf.] 

Breitburg, D. L., and R. S. Fulford. 2006. Oyster-sea nettle interdependence and altered control 

within the Chesapeake Bay ecosystem. Estuaries and Coasts 29(5):776–784. 

http://dx.doi.org/10.1007/BF02786528

Byron, C. J., and J. S. Link. 2010. Stability in the feeding ecology of four demersal fish predators 

in the U.S. Northeast Shelf Large Marine Ecosystem. Marine Ecology Progress Series 

406:239–250. 

http://dx.doi.org/10.3354/meps08570
142

Cargo, D. G., and L. P. Schultz. 1966. Notes on the biology of the sea nettle, Chrysaora 

quinquecirrha, in Chesapeake Bay. Chesapeake Science 7(2):95–100. 

http://dx.doi.org/10.2307/1351129

Christensen, V., A. Beattie, C. Buchanan, H. Ma, S. J. D. Martell, R. J. Latour, D. Preikshot, M. 

B. Sigrist, J. H. Uphoff, C. J. Walters, R. J. Wood, and H. Townsend. 2009. Fisheries 

ecosystem model of the Chesapeake Bay: methodology, parameterization, and model 

exploration. U.S. Department of Commer., NOAA. NOAA Technical Memorandum 

NMFS-F/SPO-106, 146 p. [Available at 

http://spo.nmfs.noaa.gov/tm/TM106%20FINAL.pdf.] 

Cross, J. N., C. A. Zetlin, P. L. Berrien, D. L. Johnson, and C. McBride. 1999. Butterfish, 

Peprilus triacanthus, life history and habitat characteristics. U.S. Department of 

Commer., NOAA. NOAA Technical Memorandum NE-145, 44 p. [Available at 

http://www.nefsc.noaa.gov/publications/tm/tm145/.] 

Curti, K. L. 2005. Patterns in the distribution, diet, and trophic demand of the hogchoker, 

Trinectes maculatus, in the Chesapeake Bay, USA. M.S. thes. Univ. of Maryland, 

College Park, MD. [Available at http://hdl.handle.net/1903/2572.] 

Hines, A. H., A. M. Haddon, and L. A. Wiechert. 1990. Guild structure and foraging impact of 

blue crabs and epibenthic fish in a subestuary of Chesapeake Bay. Marine Ecology 

Progress Series. Oldendorf 6(2):105–126. [Available at 

http://hdl.handle.net/10088/17859.] 

Horvath, M. A. 1997. Effects of spot (Leiostomus xanthurus) induced bioturbation and 

suspension on the transport and fate of sediments and a particle-associated organic 

contaminant. M.S. thes. College of William & Mary, Williamsburg, VA. 

Ihde, T. F., E. D. Houde, C. F. Bonzek, and E. Franke. 2015. Assessing the Chesapeake Bay 

forage base: existing data and research priorities, 198 p. Scientific and Technical 

Advisory Committee, Chesapeake Bay Program, STAC Publication Number 15-005, 

Annapolis, MD. [Available at http://www.chesapeake.org/pubs/346_Ihde2015.pdf.] 

Jeffers, S. A. B. 2007. Ecology of inshore lizardfish, Synodus foetens, in the northern Gulf of 

Mexico. M.S. thes., University of West Florida, Pensacola, FL. [Available at 

http://etd.fcla.edu/WF/WFE0000070/Jeffers_Sarah_Ann_Branson_200705_MS.pdf.] 

Latour, R. J., J. van Montfrans, and D. Combs. 2006. Final report to the VMRC and RFAB: The 

value of seagrass beds to Chesapeake Bay. RF 05-06, 22 p. [Available at 

http://www.mrc.virginia.gov/vsrfdf/pdf/RF05-06_May06.pdf.] 

Link, J. S., K. Bolles, and C. G. Milliken. 2002. The feeding ecology of flatfish in the northwest 

Atlantic. Journal of Northwest Atlantic Fishery Science 30:1–17. [Available at 

http://journal.nafo.int/dnn/Volumes/Articles/ID/375/The-Feeding-Ecology-of-Flatfish-inthe-Northwest-Atlantic.] 
143

Lippson, A. J., and R. L. Lippson. 1997. Life in the Chesapeake Bay, 2nd edition. Johns Hopkins 

University Press. [Available at http://www.barnesandnoble.com/p/life-in-the-chesapeakebay-alice-janelippson/1117176264/2671186804430?st=PLA&sid=BNB_DRS_Marketplace+Shopping

+Professional_00000000&2sid=Google_&sourceId=PLGoP4747&k_clickid=3x4747.] 

Lynch, P. D., M. J. Brush, E. D. Condon, and R. J. Latour. 2010. Net removal of nitrogen 

through ingestion of phytoplankton by Atlantic menhaden Brevoortia tyrannus in 

Chesapeake Bay. Marine Ecology Progress Series 401:195–209. 

http://dx.doi.org/10.3354/meps08389

McMillan, D. G., and W. W. Morse. 1999. Spiny dogfish, Squalus acanthias, life history and 

habitat characteristics. U.S. Department of Commer., NOAA. NOAA Technical 

Memorandum NMFS-NE-150, 19 p. [Available at 

http://www.nefsc.noaa.gov/publications/tm/tm150/.] 

Musick, J. A. 1988. The sea turtles of Virginia: with notes on identification and natural history. 

Educational Series Number 24, second edition. Virginia Sea Grant College Program, 

Gloucester Point, Virginia. 29 p. [Available at https://www.amazon.com/sea-turtlesVirginia-identification-Educational/dp/B00071LBU0.] 

Mrang, K. R., P. M. Payne, and V. G. Thayer. 1994. Coastal stock(s) of Atlantic bottlenose 

dolphin: status review and management. U.S. Dept. of Commer., NOAA. NOAA 

Technical Memorandum NMFS-OPR-4. [Available at 

http://www.nmfs.noaa.gov/pr/pdfs/species/coastalbottlenosestock.pdf.] 

Neuman, M. J., G. Ruess, and K. W. Able. 2004. Species composition and food habits of 

dominant fish predators in salt marshes of an urbanized estuary, the Hackensack 

Meadowlands, New Jersey. Urban Habitats 2(1):62–82. [Available at 

http://www.urbanhabitats.org/v02n01/saltmarsh_abs.html.] 

Northeast Fisheries Science Center. 2009. 48th Northeast Regional Stock Assessment Workshop 

(48th SAW): assessment summary report, appendix C. U.S. Dept. of Commer., NOAA. 

NEFSC Ref. Doc. 09-10, 50 p. [Available at http://www.nefsc.noaa.gov/nefsc/saw/.] 

Packer, D. B., S. J. Griesbach, P. L. Berrien, C. A. Zetlin, D. L. Johnson, and W. W. Morse. 

1999. Summer flounder, Paralichthys dentatus, life history and habitat characteristics. 

U.S. Dept. of Comm., NOAA. NOAATechnical Memorandum NMFS-NE-151, 91 p. 

[Available at http://www.nefsc.noaa.gov/publications/tm/tm151/tm151.pdf.] 

Parthree, D. J., C. F. Bonzek, and R. J. Latour. 2006. Chesapeake Bay Trophic Interactions 

Laboratory Services (CTILS): Project RF 05-12, June 2003–June 2006, Final Report. 

Virginia Marine Resources Commission, Marine Recreational Fishing Advisory Board, 

21 p. [Available at http://www.vims.edu/GreyLit/VIMS/CTILS2006.pdf.] 
144

Pereira, J. J., R. Goldberg, J. J. Ziskowski, P. L. Berrien, W. W. Morse, and D. L. Johnson. 1999. 

Winter flounder, Pseudopleuronectes americanus, life history and habitat characteristics. 

U.S. Dept. of Commer., NOAA. NOAA Technical Memorandum NMFS-NE-138, 42 p. 

[Available at http://www.nefsc.noaa.gov/publications/tm/tm138/tm138.pdf.] 

Piatt, J. F., W. J. Sydeman, and F. Wiese. 2006. Introduction: a modern role for seabirds as 

indicators. In Seabirds as indicators of marine ecosystems (W. J. Sydeman and J. F. Piatt, 

eds.), p. 199–204. Marine Ecology Progress Series, Girdwood, AK. 

http://dx.doi.org/10.3354/meps07070

Pikitch, E., P. D. Boersma, I. L. Boyd, D. O. Conover, P. Cury, T. Essington, S. S. Heppell, E. D. 

Houde, M. Mangel, D. Pauly, É. Plagányi, K. Sainsbury, and R. S. Steneck. 2012. Little 

fish, big impact: Managing a crucial link in ocean food webs. Lenfest Ocean Program, 

Washington, DC, 108 p. [Available at 

http://www.oceanconservationscience.org/foragefish/files/Little%20Fish,%20Big%20Im

pact.pdf.] 

Rachlin, J. W., and B. E. Warkentine. 1987. Dietary preference of the spotted hake, Urophycis 

regia, from the Inner New York Bight. Annals of the New York Academy of Sciences 

494:434–437. 

http://dx.doi.org/10.1111/j.1749-6632.1987.tb29593.x

Sedberry, G. R. 1988. Food and feeding of black sea bass, Centropristis striata, in live bottom 

habitats in the South Atlantic bight. The Journal of the Elisha Mitchell Scientific Society 

104(2):35–50. [Available at 

http://dc.lib.unc.edu/cdm/singleitem/collection/jncas/id/3265/rec/2.] 

Seitz, R. D., K. E. Knick, and M. Westphal. 2011. Diet selectivity of juvenile blue crabs 

(Callinectes sapidus) in Chesapeake Bay. Integrative and Comparative Biology 

51(4):598–607. 

http://dx.doi.org/10.1093/icb/icr098

Seitz, R. D., R. N. Lipcius, and M. S. Seebo. 2005. Food availability and growth of the blue crab 

in seagrass and unvegetated nurseries of Chesapeake Bay. Journal of Experimental 

Marine Biology and Ecology 319(1–2):57–68. 

http://dx.doi.org/10.1016/j.jembe.2004.10.013

Seney, E. E., and J. A. Musick. 2007. Historical diet analysis of loggerhead sea turtles (Caretta 

Caretta) in Virginia. Copeia 2007(2):478–489. [Available at 

http://www.jstor.org/stable/25140651.] 

Smith, J. W., and J. V. Merriner. 1985. Food habits and feeding behavior of the cownose ray,

Rhinoptera bonasus, in lower Chesapeake Bay. Estuaries 8(3):305–310. [Available at 

http://www.jstor.org/stable/1351491.] 
145

Steimle, F. W., and P. A. Shaheen. 1999. Tautog (Tautoga onitis) life history and habitat 

requirements. U.S. Dept. of Commer., NOAA. NOAA Technical Memorandum, NMFSNE-118, 23 p. [Available at http://www.nefsc.noaa.gov/publications/tm/tm118/.] 

Steimle, F. W., C. A. Zetlin, P. L. Berrien, and S. Chang. 1999. Black sea bass, Centropristis 

striata, life history and habitat characteristics. U.S. Dept. of Commer., NOAA. NOAA 

Technical Memorandum NMFS-NE-143, 42 p. [Available at 

http://www.nefsc.noaa.gov/publications/tm/tm143/.] 

Sutter, F. C., R. S. Waller, and T. D. McIlwain. 1986. Species profiles. Life histories and 

environmental requirements of coastal fishes and invertebrates (Gulf of Mexico): black 

drum. U.S. Fish and Wildlife Service, U.S. Army Corps of Engineers, Biological Report 

82(11.51)/TR EL-82-4, Washington, DC, 20 p. [Available at 

http://www.nwrc.usgs.gov/wdb/pub/species_profiles/82_11-051.pdf.] 

Toepfer, C. S., and J. W. Fleeger. 1995. Diet of juvenile fishes Citharichthys spilopterus, 

Symphurus plagiusa, and Gobionellus boleosoma. Bulletin of Marine Science 56(1):238–

249. [Available at 

http://www.ingentaconnect.com/contentone/umrsmas/bullmar/1995/00000056/00000001/

art00015.] 

Walter, J. F., III, and H. M. Austin. 2003. Diet composition of large striped bass (Morone 

saxatilis) in Chesapeake Bay. Fishery Bulletin 101:414–423. [Available at 

http://fishbull.noaa.gov/1012/18walter.pdf.]