NEUS – Atlantis: 

Construction, Calibration, and 

Application of an Ecosystem Model with 

Ecological Interactions, Physiographic 

Conditions, and Fleet Behavior 

US DEPARTMENT OF COMMERCE 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

Northeast Fisheries Science Center 

Woods Hole, Massachusetts 

January 2011

NOAA Technical Memorandum NMFS-NE-218 
Recent Issues in This Series:

199. Distribution and Abundance of Fish Eggs Collected during the GLOBEC Broad-Scale Georges Bank Surveys, 1995-1999. 

By John D. Sibunka, Donna L. Johnson, and Peter L. Berrien. August 2006. iv + 72 p., 28 figs., 1 table. NTIS Access. No. PB2008-

107379. [Online publication only.] 

200. Essential Fish Habitat Source Document: Black Sea Bass, Centropristis striata, Life History and Habitat Characteristics (2nd 

ed. By Amy F. Drohan, John P. Manderson, and David B. Packer. February 2007. vi + 68 p., 33 figs., 2 tables. NTIS Access No. 

PB2008-107378. [Online publication only.]

201. U.S. Atlantic and Gulf of Mexico Marine Mammal Stock Assessments -- 2006. By Gordon T. Waring, Elizabeth Josephson, 

Carol P. Fairfield, and Katherine Maze-Foley, eds. Dana Belden, Timothy V.N. Cole, Lance P. Garrison, Keith D. Mullin, Christopher 

Orphanides, Richard M. Pace III, Debra L. Palka, Marjorie C. Rossman, and Fredrick W. Wenzel, contribs. March 2007. vi + 378 p., 92 

figs, 84 tables, 5 app., index. NTIS Access No. PB2007-112570. 

202. Evaluation of Northern Right Whale Ship Strike Reduction Measures in the Great South Channel of Massachusetts. By RL 

Merrick and TVN Cole. March 2007. NTIS Access No. PB 2008-107377. 

203. Essential fish habitat source document: Spiny dogfish, Squalus acanthias, life history and habitat characteristics, 2nd 

edition. By LL Stehlik. December 2007. NTIS Access No. PB2008-107376. 

204. An Evaluation of the Northeast Region’s Study Fleet pilot program and Electronic Logbook System: Phases I and II. By 

Michael C. Palmer, Susan E. Wigley, John J. Hoey, and Joan E. Palmer. December 2007. NTIS Access No PB2008-107374. 

205. U.S. Atlantic and Gulf of Mexico Marine Mammal Stock Assessments -- 2007. By GT Waring, E Josephson, CP Fairfield, and 

K Maze-Foley, Editors. November 2007. NTIS Access No _______. 

206. Growth of Black Sea Bass (Centropristis striata) in Recirculating Aquaculture Systems. By Dean M. Perry, David A. Nelson, 

Dylan H. Redman, Stephan Metzler, and Robin Katersky. October 2007. NTIS Access No. PB2008-107374. 

207. Analysis of Atlantic Sea Scallop (Placopecten magellanicus) Fishery Impacts on the North Atlantic Population of 

Loggerhead Sea Turtles (Caretta caretta). By Richard Merrick and Heather Haas. February 2008. NTIS Access No PB2008-107373. 

208. Global Applications of the Large Marine Ecosystem Concept 2007-2010. By Kenneth Sherman, Marie-Chirstine Aquarone, and 

Sally Adams. June 2007. NTIS Access No. PB______-_______. 

209. Impacts to Marine Fisheries Habitat from Nonfishing Activities in the Northeastern United States. By Michael R. Johnson, 

Christopher Boelke, Louis A. Chiarella, Peter D. Colosi, Karen Greene, Kimberly Lellis-Dibble, Heather Ludemann, Michael Ludwig, 

Sean McDermott, Jill Ortiz, Diane Rusanowsky, Marcy Scott, and Jeff Smith February 2008. NTIS Access No. PB______-_______. 

210. U.S. Atlantic and Gulf of Mexico Marine Mammal Stock Assessments -- 2008. By GT Waring, E Josephson, CP Fairfield, and 

K Maze-Foley, Editors. February 2009. NTIS Access No _______. 

211. Trends in Selected Northeast Region Marine Industries. By Eric M. Thunberg. July 2008. NTIS Access No. 

212. Summary of harbor porpoise (Phocoena phocoena) bycatch and levels of compliance in the northeast and mid–Atlantic 

gillnet fisheries after the implementation of the Take Reduction Plan: 1 January 1999 – 31 May 2007. By Debra Palka, Christopher 

D. Orphanides, and Melissa L. Warden. August 2008. NTIS Access No. 

213. U.S. Atlantic and Gulf of Mexico Marine Mammal Stock Assessments -- 2009. By Gordon T. Waring, Elizabeth Josephson, 

Katherine Maze-Foley, and Patricia E. Rosel. December 2009. NTIS Access No. 

214. Mortality and Serious Injury Determinations for Baleen Whale Stocks along the United States and Canadian Eastern 

Seaboards, 2004-2008. By Allison H. Glass, Timothy V.N. Cole, and Mendy Garron. May 2010.NTIS Access No. 

215. Atlantic Sturgeon Research Techniques. By Kimberly Damon-Randall, Russell Bohl, Stephania Bolden, Dewayne Fox, Christian 

Hager, Brian Hickson, Eric Hilton, Jerre Mohler, Erika Robbins, Tom Savoy, Albert Spells. May 2010. NTIS Access No. 

216. The Trophic Dynamics of 50 Finfish and 2 Squid Species on the Northeast US Continental Shelf. By Brian E. Smith and Jason 

S. Link. May 2010. NTIS Access No. 

217. General Trends and Interannual Variability in Prey Selection by Larval Cod and Haddock from the Southern Flank

of Georges Bank, May 1993­1999. By Elisabeth A. Broughton and R. Gregory Lough. May 2010. NTIS Access No. 
NEUS – Atlantis: 

Construction, Calibration, and Application of 

an Ecosystem Model with Ecological 

Interactions, Physiographic Conditions, 

and Fleet Behavior 

Jason S. Link1, Robert J. Gamble1, Elizabeth A. Fulton2

1

NOAA National Marine Fisheries Service, Northeast Fisheries Science Center 

166 Water St, Woods Hole, MA USA 02543 

2

CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research 

GPO Box Hobart, Tasmania, Australia 7001 

US DEPARTMENT OF COMMERCE 

Gary Locke, Secretary 

National Oceanic and Atmospheric Administration 

Dr. Jane Lubchenco, Administrator 

National Marine Fisheries Service 

Eric C. Schwaab, Assistant Administrator for Fisheries 

Northeast Fisheries Science Center 

Woods Hole, Massachusetts 

January 2011

NOAA Technical Memorandum NMFS-NE-218 

This series represents a secondary level of scientific publishing. All issues employ thorough internal 

scientific review; some issues employ external scientific review. Reviews are transparent collegial 

reviews, not anonymous peer reviews. All issues may be cited in formal scientific communications. 
Editorial Notes 

Information Quality Act Compliance: In accordance with section 515 of Public Law 106-554, the 

Northeast Fisheries Science Center completed both technical and policy reviews for this report. 

These predissemination reviews are on file at the NEFSC Editorial Office. 

Species Names: The NEFSC Editorial Office’s policy on the use of species names in all 

technical communications is generally to follow the American Fisheries Society’s lists of 

scientific and common names for fishes, mollusks, and decapod crustaceans and to follow the 

Society for Marine Mammalogy's guidance on scientific and common names for marine 

mammals. Exceptions to this policy occur when there are subsequent compelling revisions in the 

classifications of species, resulting in changes in the names of species. 

Statistical Terms: The NEFSC Editorial Office’s policy on the use of statistical terms in all 

technical communications is generally to follow the International Standards Organization’s 

handbook of statistical methods. 

Internet Availability: This issue of the NOAA Technical Memorandum NMFS-NE series is 

being published as a Web document in PDF format and can be accessed at: 

http://www.nefsc.noaa.gov/nefsc/publications/. 
iii 

ABSTRACT 

Ecosystem Based Fishery Management (EBFM) and ecological considerations in a 

multispecies context are increasingly being recognized as important for maintaining ecosystems 

and their associated marine resources. However there are currently relatively few fishery and 

ecosystem models which take into account EBFM- related issues and associated ecological 

considerations. One such model is Atlantis, which has been used extensively in Australia to 

inform fishery management plans. Atlantis is a series of submodels (biological, geophysical, 

fishing, assessment, and management) and as a whole simulates a Management Strategy 

Evaluation (MSE) procedure which allows for qualitative comparisons of different management 

choices. We have parameterized Atlantis for the Northeast United States Continental Shelf 

Large Marine Ecosystem (NEUS LME), including the major functional groups from an 

ecosystem perspective. We have also parameterized and simulated the physiographic dynamics 

of the ecosystem, as well as the most important fleets. Our goal was to recreate, at least in 

approximate terms, the biomass, catch, and effort trends in the NEUS LME from 1964 to the mid 

2000s. Here we document this Atlantis application for the NEUS LME and describe the various 

levels of calibration to establish a reference base for future utilizations of this particular 

application of the Atlantis model that will evaluate different management strategies and their 

tradeoffs. Our preliminary results demonstrate that Atlantis can reasonably approximate time 

series and spatial distributions for the majority of main processes and state variables of the most 

important functional groups and fisheries in the NEUS LME, given the levels of calibration 

tolerance for such a multivariate, multispecies, multifactorial modeling approach. 
1 

TABLE OF CONTENTS 

Abstract .......................................................................................................................................... iii 

Introduction ..................................................................................................................................... 2 

The Value of EBFM ............................................................................................................ 2 

The Value of Ecosystem Models ........................................................................................ 2 

The Value of This model .................................................................................................... 3 

Overview of Atlantis ....................................................................................................................... 3 

Overview of the NEUS Continental Shelf Large Marine Ecosystem ............................................. 5 

Physical Oceanography ....................................................................................................... 5

Biology ................................................................................................................................ 7 

Fisheries .............................................................................................................................. 9 

Modeling Approach and Methodology ......................................................................................... 10 

Model Structure ............................................................................................................................ 11 

Boxes, Habitat, Physics, and Chemistry ........................................................................... 11 

Biology .............................................................................................................................. 18 

Fisheries ............................................................................................................................ 29 

Model Calibration ......................................................................................................................... 35 

Biophysical ....................................................................................................................... 35 

Forced Catch ..................................................................................................................... 36 

Dynamic Catch-fixed Effort ............................................................................................. 36 

Dynamic Effort ................................................................................................................. 36 

Results ........................................................................................................................................... 36 

Biomasses ......................................................................................................................... 37 

Catches .............................................................................................................................. 38 

Catch per Fleet .................................................................................................................. 38 

Effort ................................................................................................................................. 39 

Spatial Representations ..................................................................................................... 40 

Overall Output and Outcomes .......................................................................................... 40 

Discussion ..................................................................................................................................... 41 

Lessons Learned ................................................................................................................ 41 

Value of This Approach .................................................................................................... 43 

Limitations of This Approach ........................................................................................... 43 

Next Steps for Atlantis NEUS .......................................................................................... 44 

Acknowledgements ....................................................................................................................... 45 

References Cited ........................................................................................................................... 46 

Tables...........................................................................................................53 

Figures.........................................................................................................66 

Appendix A Tables.........................................................................................100 

Appendix B Biomass Trajectory Results.................................................................140 

Appendix C Catch Trajectory Results....................................................................181 

Appendix D Catch per Fishery Trajectory Results......................................................204 

Appendix E Effort Trajectory Results....................................................................243
2 

INTRODUCTION 

The Value of EBFM 

 There have been numerous prescriptions and admonitions to implement ecosystem-based 

fisheries management (EBFM) (Larkin 1996; Link 2002a, 2002b; Garcia et al. 2003; Browman 

and Stergiou 2004, 2005). The justifications and rationales for adopting EBFM have been 

previously noted (Larkin 1996; Botsford et al. 1997; NMFS 1999, Link 2002a; Garcia et al. 

2003) as an enhancement of or improvement over the current, single stock/species (SS) 

approaches to fisheries management and science. In particular, the benefits of EBFM generally 

are: (1) categorically more conservative (precautionary) management recommendations; (2) 

explicit consideration of nontargeted species, protected species, habitats, etc. (i.e., the ecosystem 

as a whole) with appropriate precaution built in to the exploitation regimes; (3) direct 

consideration of tradeoffs among and within sectors and also tradeoffs across biomass allocation; 

(4) the potential for simpler management institutions; (5) improved short- and long-term 

economics for participating fishers; and (6) long-term sustainability for intergenerational equity. 

An ecosystem approach provides several advantages over SS approaches. As has been 

noted, EBFM: (1) addresses effects of fishing on nontarget species, habitat, ecological 

interactions, and system-wide processes; (2) recognizes that marine ecosystems provide “goods 

and services” other than fishery harvest; (3) explicitly addresses biomass tradeoffs (in our view 

the key to the entire issue); (4) increases leverage from new stakeholders; and (5) changes the 

burden of proof. Even if SS approaches were executed entirely correctly (as some have argued 

as all that is required for EBFM; e.g. Mace 2004; Hilborn 2004 (in Browman and Stergiou 

2004); Eagle 2008 (in Leslie et al. 2008)) there are still many factors that would not be 

adequately addressed. Although the debate continues (Mace 2004; Hilborn 2004 section in 

Browman and Stergiou 2004; Eagle’s perspective in Leslie 2008), there is an emerging 

recognition that EBFM and ecosystem approaches to fisheries (EAF) management are necessary. 

In simplest terms, EBFM and related ecosystem approaches to management (EAM) may provide 

a way to avoid negatively fulfilling Graham’s Law of overfishing and to also address the other, 

multiple-sector societal concerns that are currently difficult to handle in fisheries. There is a 

clearly recognized need to be holistic, coordinated, and integrated in our approach to living 

marine resources (LMR) management. 

There have been relatively few instances where such an approach has been implemented 

(Pitcher et al. 2009), but the number is growing as fisheries scientists, managers, and 

stakeholders grapple with the specific details of executing EBFM (Pitcher et al. 2009). As a 

discipline and as a practice, fisheries scientists and managers are now clearly beyond the whys 

and whats of EBFM (Murawski 2007) and squarely in the middle of the hows. That is, we are 

now well underway in the transition towards novel ways of assessing and managing LMR. 

While some have noted (Pitcher et al. 2009) that a full implementation of EBFM is still distant, 

steps to that end are extant. 

 

The Value of Ecosystem Models 

 There are numerous methodological approaches that can facilitate the scientific basis for 

the implementation of EBFM. These include: (1) development of fisheries systemic indicators, 

particularly those empirically based from longstanding fisheries and oceanographic monitoring 

surveys; (2) statistical evaluation of said surveys, both from a time series and multivariate sense; 
3 

(3) process-oriented studies (in vivo and in situ) to more fully elucidate those ecological (usually 

trophic) and environmental relationships to LMRs of interest; (4) ecosystem comparisons to 

determine unique and general marine ecosystem properties; (5) exploring the range of EBFM 

options in a process that is adaptive (e.g., Integrated Ecosystem Assessment (IEA), Management 

Strategy Evaluation (MSE), Risk Analysis); and (6) implementing the full range of ecosystem 

modeling endeavors. This latter item can be thought of as in silico studies that explore the 

relative importance of ecosystem processes and the robustness of various management strategies. 

Ecosystem models are clearly an important part of implementing EBFM, and many of the 

approaches noted above utilize ecosystem models and their outputs as a key component of those 

efforts. 

 There have been very useful summaries of the range of ecosystem models that are 

germane for EBFM (Hollowed et al. 2000; Whipple et al. 2000; Plagányi 2007; Townsend et al. 

2008; FAO 2008). These models cover the gradient noted in Link (2002a) from SS to full 

system models, with numerous modeling options along that gradient of complexity and realism. 

At various points along that gradient, multiple models can address a range of questions or issues. 

As noted in Townsend et al. (2008, their Table A.3), the types of model classes being employed 

need to correspond to the appropriate set of questions and issues. Few models are extant that can 

cover the full range of biophysical, ecological, environmental, harvest, and human management 

processes. Such a model allows one to explore a range of scenarios across ocean-use sectors and 

to simultaneously account for a wider range of processes than is usually done in more minimally 

realistic models. 

The Value of This Model 

 One approach to executing ecosystem models in an EBFM is to use the Atlantis model 

package. Atlantis has the benefits of being able to model a wide range of factors, processes, and 

considerations concurrently. It has the advantage of not locking into any one functional form of 

a given process nor requiring the application of every possible process, and as such it is rather 

modular and adaptive in design. Atlantis was originally designed to explore a range of 

management scenarios in a fisheries management context in Australia, directly accounting for 

ecosystem considerations. As such, its application to other ecosystems has similar utility. 

The purpose of this document is to provide a brief background of Atlantis models, 

describe some of the main equations and model structure used in Atlantis, provide some of the 

key parameters as used in the Atlantis NEUS modeling effort, describe the general Atlantis 

NEUS modeling and calibration process, and present some preliminary results. 

OVERVIEW OF ATLANTIS 

Atlantis (Fulton et al. 2004a, 2004c, 2004d) is an ecosystem “box” model intended for 

use in management strategy evaluation (as described in de la Mare 1996; Cochrane et al. 1998; 

Butterworth and Punt 1999; Sainsbury et al. 2000). It has been applied to multiple marine 

systems (from single bays to millions of square kilometers) in Australia and the United States. 

Atlantis incorporates multiple alternative submodels to represent each step in the 

management strategy and adaptive management cycles (Figure 1), though some steps are more 

extensive than others (e.g., the biophysical and fisheries submodels). The core of Atlantis is a 

deterministic biophysical submodel that is spatially resolved in three dimensions by using a map 

made up of boxes and slab-like layers. This model tracks the nutrient (usually nitrogen and 
4 

silicate) flows through the main biological groups found in the marine ecosystem of interest. The 

primary ecological processes considered in the model are consumption, production, waste 

production and cycling, migration, predation, recruitment, habitat dependency, and mortality. 

Atlantis treats lower (invertebrate) trophic levels as biomass pools (though cephalopods and 

prawns may have some age structure), while the vertebrates are represented with an age- and 

stock-structured formulation (which tracks the condition of average individuals). The physical 

environment is also represented explicitly with a set of polygons matched to the major 

geographical and bioregional features of the simulated marine system. Polygonal maps allow the 

model to focus the spatial attention where needed, capturing the critical dynamics while still 

achieving computational efficiency (Nihoul and Djenidi 1998; Fulton et al. 2004b). The 

biological components are replicated in each layer of each of these polygons. Movement between 

the polygons is by advective transfer or directed movements (depending on the variable), which 

are modeled as inputs from the output of a range of hydrodynamic models, used accordingly for 

each ecosystem. 

 Atlantis also features a detailed exploitation model. This model deals with the impacts of 

pollution, coastal development, and broadscale environmental change but is focused on the 

detailed dynamics of fishing fleets. It allows for multiple fleets, each with its own characteristics 

regarding gear selectivity, habitat association, targeting, effort allocation, and management 

structures. At its most complex, this exploitation submodel includes explicit handling of 

economic drivers, compliance decisions, exploratory fishing, and other complicated real world 

concerns such as quota trading. 

 The exploitation model interacts with the ecosystem, but also supplies simulated “data” 

(derived from the exploitation model) to a sampling and assessment submodel. As Atlantis is 

primarily used in Management Strategy Evaluation, it includes both operating biophysical and 

fisheries submodels and assessment submodels so that the efficacy of monitoring and assessment 

models can be considered along with any management strategies. The sampling and assessment 

submodel in Atlantis generates sector dependent and independent data with realistic levels of 

measurement uncertainty (bias and variance). These “simulated data” are based on the outputs 

from the biophysical and exploitation submodels, given a user-specified monitoring scheme. By 

handling the monitoring of the system in this way, a wide range of combinations of fisheries and 

survey information can be mimicked from landings data only to onboard catch composition 

monitoring by observers and intensive independent surveys. These “simulated data” are then fed 

into the same assessment models used in real-world stock assessments (including Surplus 

Production, Adapt Virtual Population Analysis (VPA) and Bayesian integrated assessments). In 

addition to these traditional assessment methods, a range of ecological indicators can be 

calculated with an eye to their potential role in future ecosystem-based management schemes as 

thresholds of decision criteria. 

 The output of the assessment models is fed to the management model (typically a set of 

decision rules and management levers) for action. The management model in Atlantis is 

currently only detailed for the fisheries sector. This includes an extensive list of potential 

management levers: gear restrictions, days-at-sea, quotas, spatial and temporal zoning, 

discarding restrictions, size limits, bycatch mitigation, and dynamic reference points, etc. It is 

also possible to track a crude index of the costs of management (mainly monitoring and 

enforcement costs). 

 Atlantis’ greatest strength is its modular construction. A wide range of alternative 

assumptions and model implementations is provided in Table 1. This construction allows the 
5 

user greater freedom to set the complexity at the desired level – from a few groups with simple 

trophic interactions and a simple catch equation to extensive models, with complicated stock 

structure, multiple fleets, detailed economics, and multiple management levers (input, output, 

and spatial). A lot of developmental attention has also been given to the model’s ability to 

capture age-size dependencies and temporal and spatial variation so that it produces realistic 

ecosystem dynamics, both temporally and spatially (Fulton et al. 2004c). From a user’s point of 

view, this flexibility and mechanistic basis does, unfortunately, make the model both processand parameter-intensive. This flexibility and resulting complexity can make validation quite time 

consuming and can also lead to large uncertainties. This issue is much less of a problem within 

the strategic setting of an MSE than it would be in a tactical assessment. It must be stressed that 

Atlantis was never intended to replace traditional SS assessments, rather it is a policy 

examination tool to aid in the evaluation of situations or strategies that are beyond SS models. 

OVERVIEW OF THE NEUS CONTINENTAL SHELF LARGE 

MARINE ECOSYSTEM 

Physical Oceanography 

The Northeast United States (NEUS) continental shelf large marine ecosystem (LME) 

stretches from the Gulf of Maine to Cape Hatteras, covering an area of 293,000 km2 (Sherman 

1991; Figure 2). The NEUS LME is composed of four major subregions: the Gulf of Maine 

(GoM), Georges Bank (GB), Southern New England (SNE), and Mid Atlantic Bight (MAB). 

While these four subregions are connected and exhibit flow of water and organisms across their 

boundaries (Figure 3), each has distinct geological, bathymetric, physical, chemical and 

biological properties. We modeled the NEUS LME at resolutions smaller than these four 

subregions, particularly to capture major hydrodynamic and biotic processes, but the four regions 

are well known and defined. Below we provide a description of the major oceanographic and 

physical features of each of these regions. 

The circulation of the NEUS LME can be generally characterized as being from the 

northeast to the southwest. Water from the Scotian Shelf and Northeast Channel enters the GoM 

and travels counterclockwise. A portion leaves the GoM through the Great South Channel, but 

the rest moves eastward onto the northern flank of GB, creating a clockwise circulation there. 

This circulation results in some of the water being retained on GB while the rest continues 

southwestward into the SNE and MAB regions (Figure 3). Tidal forcing plays a large role in the 

physical dynamics of the system, allowing the shallower regions of GB to remain well mixed 

even when deeper portions of GB and the GoM are well stratified (Drinkwater and Mountain 

1997). 

The Gulf of Maine 

The Gulf of Maine is a large (79,000 km2) marine basin located along the northeastern 

United States and Maritime Canada (Figure 2). It is approximately 350 km long by 200 km in 

width and averages about 160 m in depth (Uchupi and Austin 1987). This area comprises 

Northwest Atlantic Fisheries Organization (NAFO) management area 5Y. It is bounded to the 

northeast by Nova Scotia and New Brunswick, to the west by the states of Maine, New 

Hampshire, and Massachusetts, to the southwest by Cape Cod, and to the south and southeast by 

Georges Bank (Figure 2, 5). The region contains three deep basins: Georges, Jordan, and 
6 

Wilkinson, ranging from 200-400 m in depth (Uchupi and Austin 1987). Circulation patterns in 

these basins are generally clockwise, but the overall pattern in the Gulf is from east to west with 

the entry of slope water through the Northeast Channel, exiting to the southwest through the 

Great South Channel (Figure 3). Bottom sediments in the GoM are mostly silt to mud with a 

rocky coastline bordering the states (Poppe et al. 1989). Sea surface temperature ranges from 2-

5oC during winter and can be as high as 20oC during summer (Mountain and Holzworth 1989; 

Taylor and Bascunan 2001). Bottom temperatures remain relatively cool year round, ranging 

between 4-6oC (Mountain and Holzworth 1989; Taylor and Bascunan 2001). The movement of 

deep ocean slope water into the Gulf of Maine through the Northeast Channel carries a steady 

supply of nutrients, but summer stratification interrupts the nutrient cycle (Schlitz and Cohen 

1984). Occasionally, nutrient-poor Labrador Shelf water is transported from the north by an 

intense negative North Atlantic Oscillation (NAO) anomaly, and recently, fresh water from the 

Gulf of St. Lawrence and Arctic melting has made incursions into the region (Townsend et al. 

2004). 

Georges Bank 

Georges Bank, a relatively large (44,000 km2) submerged marine plateau, is an extension 

of the continental shelf along the eastern United States that thrusts out into the northwest Atlantic 

ocean in a northeasterly direction (Figure 2). It is approximately 250 km long by 125 km in 

width and is relatively shallow, averaging about 80 m in depth (Uchupi and Austin 1987). This 

area comprises NAFO management area 5Ze. The living and mineral resources of this diverse 

region are administered jointly by Canada and the USA, in accordance with a decision by the 

world court in 1984 (Backus 1987). It is bounded to the east by Nova Scotia, to the northeast by 

the Northeast Channel, to the north by the Gulf of Maine, to the west-southwest by the Great 

South Channel, and to the south by the shelf slope and Gulf Stream (Figure 2, 5). Sediments on 

Georges Bank are diverse, ranging from silt to large boulders, but much of the area is composed 

of sand and cobble (Uchupi and Austin 1987; Poppe et al. 1989). Surface temperatures during 

the winter average between 2-5oC and reach up to 20oC during the late summer (Flagg 1987). 

Bottom temperatures are relatively cool during winter at 3-5oC, but because of mixing they reach 

a low of roughly 5oC on the shallow part of the Bank (Flagg 1987; Mountain and Holzworth 

1989; Taylor and Bascunan 2001). Nutrients are plentiful because of its unique location, with 

offshore upwelling along the shelf-slope break and movements of slope water into the Gulf of 

Maine through the Northeast Channel (Townsend et al. 2004). Gulf of Maine waters to the north 

are a steady source of annual nutrients and vigorous tidal mixing promotes nutrient recycling via 

resuspension of bottom waters from the shallow portion of the Bank (Schlitz and Cohen 1984; 

Franks and Chen 2001). Georges Bank is highly productive, and because of the generally 

clockwise pattern of its currents (Butman 1987), nutrients and plankton are concentrated and 

retained on this relatively shallow plateau (Drinkwater and Mountain 1997; Cohen et al. 1982; 

Fogarty and Murawski 1998). During periods of stratification the nutrient cycle can be 

temporarily interrupted, but this occurs seasonally and only in localized areas (Drinkwater and 

Mountain 1997). 

Southern New England 

Southern New England (SNE) extends from Hudson Canyon to Great South Channel and 

is a transitional region between the Mid Atlantic Bight (MAB) and Georges Bank (GB) (Figure 

2). This area comprises NAFO management area 5Zw and 6A. Freshwater enters SNE from the 
7 

Hudson River, Long Island Sound, Narragansett Bay, and numerous small coastal watersheds, 

but the volume is much less than what enters the MAB from the Chesapeake and Delaware Bays 

(NOAA 1990). Salinity is low nearshore and increases toward the shelf break. Temperature is 

homogeneous in fall and winter but is highly stratified in spring and summer (Mountain 2003). 

Tidal forcing in SNE occurs much less frequently than on GB (Chen et al. 2001), and the 

dominant low-frequency flow is southwestward from GB to the MAB (Chapman et al. 1986). 

The area of shallow water (<30 m) in SNE is proportionally less than in MAB with the area of 

deeper water (60-100 m) proportionally greater. The average depth of SNE is about 70 m. Most 

of the SNE shelf is covered with sand, but as a result of the deeper water and relatively low 

currents, there is a greater proportion of area covered by silt and clay on SNE compared with the 

MAB (Poppe et al. 1989; Hastings et al. 2000). 

Mid Atlantic Bight 

The Mid Atlantic Bight (MAB) is the only north-south longshelf ecosystem of the four 

NEUS LME regions. It is bounded in the north by the Hudson Canyon, and in the south by 

eastward facing coasts of New Jersey, Delaware, Maryland, Virginia, and North Carolina (Figure 

2). The continental shelf is ~ 200 km wide in the New York Bight and gradually narrows south 

of New Jersey to ~ 30 km off Cape Hatteras. This area comprises NAFO management area 6BC. 

The ecosystem includes major estuaries, namely the Hudson-Raritan River, Delaware River 

estuary, Chesapeake Bay, and Pamlico Sound, and many smaller estuaries that effect hydraulics 

and productivity. The shelf has a mean depth of 58 m and is gently sloping except where 

drowned river valleys incise it. 

MAB shelf water characteristics are variable at a wide range of scales because the system 

lies in the middle latitudes at the confluence of the northward flowing Gulf Stream and 

southward flowing colder fresher waters derived from the Labrador Current (Chapman and 

Beardsley 1989). Atmospheric conditions push water derived from the Labrador Current as far 

south as the New York Bight (Loder et al. 1998). Gulf Stream meanders, and its rings impinge 

on the shelf primarily in the southern MAB. Salinities on the shelf are <34 psu and variable as a 

result of inputs of fresher water from estuaries and the Gulf of Maine. Strong surface fronts are 

formed by buoyant water off major estuaries and along the 50-m isobath (Ullman and Cornillon 

2001; Castelao et al. 2008). Seasonal heating and freshwater inputs produce strong vertical 

stratification from late spring through fall. Surface waters are warm during summer, but 

subsurface “Cold Pool” water (≤8oC) derived from winter tidal mixing in the Gulf of Maine and 

Nantucket Shoals flows southwest primarily along the 50- to 80-m isobaths. The “Cold Pool” is 

persistent north of the Hudson Shelf Valley but extends as far south as Cape Hatteras in the 

spring (Houghton et al. 1982; Bignami and Hopkins 2003). 

Southward flowing water from the Gulf of Maine is estimated to supply ~ 50% of 

nitrogen available in the MAB. Major estuaries supply ~ 20%, while ~ 30% is advected from the 

deep ocean along the shelf break (Fennel et al. 2006). 

Biology 

The NEUS LME is a highly productive ecosystem (~350 – 400 g C m-2 yr-1) which has 

supported numerous significant commercial fisheries for centuries (Sissenwine et al. 1984). 

There are large regional differences in primary production in the NEUS. The most obvious 

pattern is the general onshore-offshore decrease in primary production. The other obvious pattern 

is a general increase in primary production from north to south. At a scale smaller than the 
8 

regional ecosystems, there are areas of higher productivity including GB, inshore MAB, SNE, 

and northwestern Gulf of Maine. 

Although open in an oceanic sense, of particular interest for the NEUS LME is Georges 

Bank, which is highly productive (Cohen et al. 1982; Fogarty and Murawski 1998) and, as has 

been noted, has a high degree of nutrient recycling (Franks and Chen 2001; Schlitz and Cohen 

1984). Its clockwise flow concentrates nutrients and plankton (Drinkwater and Mountain 1997; 

Franks and Chen 2001; Ryan et al. 2001). While Georges Bank exhibits a well-mixed tidal 

current zone, it is only stratified in localized, generally shallower regions and is influenced by 

both warmer, more saline slope waters influenced by the Gulf Stream and the GoM’s colder 

water sources (Bisagni 2000; Drinkwater and Mountain 1997). Georges Bank has remained 

remarkably consistent in its overall primary production, with production and standing stock 

biomass of phytoplankton staying relatively stable over the past few decades (O’Reilly and 

Zetlin 1998). 

The NEUS LME supports a large number of tropical, subtropical, temperate, and 

subarctic species (Collette and Klein-McPhee 2002). The varied oceanography of the northwest 

Atlantic continental shelf helps to create a complex ecosystem. Georges Bank can be 

characterized by cold temperate fauna, but further to the south along the shelf, the large seasonal 

temperature fluctuations support migratory species of both cold and warm temperate fauna. 

Three quarters of the 250 shore fish species south of Georges Bank reported by Briggs (1974) are 

considered to be warm temperate or tropical in nature and either migrated from south of Cape 

Hatteras or were brought north by the Gulf Stream; whereas many of the species in the Gulf of 

Maine are considered colder-water species. Compared to other ecosystems at similar latitudes, 

the number of species and the interactions between these species are quite high (Figure 4; Link 

1999, 2002). 

The energy flow in the NEUS LME has been characterized throughout various points in 

time (Link et al. 2008a, 2008b). Clarke’s (1946) Georges Bank food web provided an integrated 

perspective for oceanographers, marine ecologists, and fisheries scientists on the classical 

grazing food chain (diatoms-zooplankton-fish). Then in the early 1970s, biological 

oceanographers developed a new paradigm for the ocean food web that emphasized the large 

pools of dissolved and particulate organic carbon (POC) and its utilization by microbes, with the 

microbial loop becoming an important focus of models constructed by this research community. 

In the 1980s, (Cohen et al. 1982; Sissenwine et al. 1984) models of Georges Bank incorporated 

some of these ideas on the potential role of detritus (nonliving POC) and bacteria in supporting 

the benthic food chain. The primary focus in the pelagic water column remained on the grazing 

food chain, using better estimates of primary production. Since then, more contemporary models 

have been developed to support an ecosystem approach to management and have begun 

reconnecting the conceptual approaches of oceanographers and fishery scientists on 

understanding how the ocean food web operates. The future challenge is to better integrate these 

perspectives. The various views and perspectives of the food web have all had varying degrees 

of species aggregation and emphasis of differing processes, yet the fundamental core 

observations remain; this is a productive ecosystem, environmental factors influence lower 

trophic levels, fishing pressures and related factors influence upper trophic levels, and most of 

the “action” is at intermediate trophic levels (Link et al. 2006, 2008b, 2009; Steele et al. 2007). 

Of most interest with respect to living marine resource issues has been what the fish community 

has done over the past several decades. 
9 

Major declines in groundfish and flounders occurred in the Gulf of Maine during 1961-

1972 (Brown et al. 1976; Gabriel 1992). Analyses by Clark and Brown (1977) suggested a 

decline in overall biomass of over 40% in this region during 1963-1974, with declines in 

groundfish and flounders ranging from 25-85%, depending on species. Cluster analysis 

classified the Gulf of Maine as a separate zoogeographic region when the entire shelf area from 

Cape Hatteras to Nova Scotia was analyzed (Gabriel 1992). 

Brown et al. (1976) documented the severe decline in biomass on Georges Bank 

following several years of intense fishery exploitation during the International Commission for 

Northwest Atlantic Fisheries (ICNAF) era of 1961-1972. Subsequent analyses showed a decline 

of over 37% in the overall fish biomass of the Georges Bank region during 1963-1974 (Clark and 

Brown 1977; Gabriel 1992). Additional analyses measured fishing effort at over 900,000 hrs/yr 

fished in the early 1960s and showed that catch per unit effort (CPUE) had declined dramatically 

during 1960-1987 on Georges Bank (Mayo et al 1992). Other studies focused on the relative 

persistence of assemblages in the region, documenting declines and recovery of several 

important groups of fishes (Overholtz and Tyler 1985; Gabriel 1992). Overholtz and Tyler 

(1985) showed that several groups of fishes consistently occupied spatial regions (depth zones) 

of the Bank over time, while Gabriel (1992) showed similarly persistent trends as well as an 

extension of these groups to the south and west into Southern New England. 

Brown et al. (1976) showed that many of the major groundfish, flounders, and other 

species declined in the Southern New England region during 1961-1972. Clark and Brown 

(1977) suggested that this decline in biomass averaged 52% during 1963-1974 with a range of 6-

99% for declining species. For example, cod (Gadus morhua) declined 53%, haddock 

(Melanogrammus aeglefinus) 99%, silver hake (Merluccius bilinearis) 43%, winter flounder 

(Pseudopleuronectes americanus) 55%, and goosefish (Lophius americanus) 18% during this 

time period. In other studies of the area, Southern New England tended to be aligned with 

Georges Bank in analyses that attempted to identify regions of persistent species composition 

during 1967-1988 (Gabriel 1992). 

The overall decline in biomass in the Mid Atlantic area was apparently the largest of the 

four NEUS LME ecosystems with an estimated average 74% decrease occurring during 1963-

1974 (Clark and Brown 1977; Gabriel 1992). Declines ranging from 8-99% were recorded for 

several species of principal groundfish, flounders, and other groundfish. For example, silver 

hake declined 78%, yellowtail flounder (Limanda ferruginea) 99%, summer flounder 

(Paralichthys dentatus) 72%, winter flounder 93%, and goosefish 29% (Clark and Brown 1977). 

Other studies of this region suggest that the southern Mid Atlantic Bight should be classified as a 

unique region based on the composition of fishes in the area (Gabriel 1992). 

Fisheries 

 As noted, the NEUS LME has supported significant commercial fisheries for multiple 

centuries (Sissenwine et al. 1984; Rosenberg et al. 2005). Although there are regional 

differences, overall the response of the northwest Atlantic ecosystems to five hundred years of 

exploitation has been very similar. The recent history of the component fish stocks has exhibited 

the classic cycles of excessive effort, stock declines, and iterations thereof until the point of 

sequential stock depletion (Fogarty and Murawski 1998; Murawski et al. 1997; Serchuk et al. 

1994; Link 2007). The major fishery-related events over the past several decades can be 

characterized loosely as the following sequence. First there was an increase in small pelagic 

catches by foreign fleets, then a continued increase in demersal groundfish catches, followed by 
10 

a precipitous decline in small pelagic stocks. Next followed a decline of some groundfish stocks, 

then an effective cessation of the small pelagic fisheries (and expulsion of foreign fleets), then a 

continual series of overfishing on an ever-increasing array of groundfish. After that there was an 

increase in elasmobranch stocks, the beginnings of an increase in small pelagic stocks, and then 

an establishment of elasmobranch fisheries and an increase in benthic invertebrate fisheries and 

stocks. Finally, the persistence of groundfish stocks at moderate to low levels in the south and 

the collapse of groundfish in the north was observed, followed by the persistence of groundfish 

fisheries at suboptimal yields, then the decline of elasmobranch stocks and subsequently their 

fisheries, after which there was the effective explosion of small pelagic stocks to what are now 

record highs (Serchuk et al. 1994; Murawski and Fogarty 1998; Link and Brodziak 2002; 

Overholtz 2002). In response to these changes, fisheries have diversified to exploit a broad range 

of invertebrates and nontraditional species. 

The first fisheries management measures of any consequence were imposed by ICNAF, 

whose initial objective was to use science to maintain maximum sustainable catch (Halliday and 

Pinhorn 2002). To begin with, in the 1950s ICNAF imposed mesh size regulations in the trawl 

fishery, increasing from 73 mm to 114 mm. ICNAF then introduced catch control in the 1960s. 

However, when distant water fleets started operating in the northwest Atlantic, with the 

consequent depletion of fish stocks, there was a clear need for direct controls on fishing. The 

establishment of the “two-tier” quota management system in 1974 by ICNAF (ICNAF 1974; 

Murawski et al. 1997) provided the nucleus for recovery of depleted stocks. This approach 

included explicit recognition and allowance for bycatch, discarding practices, and interspecific 

interactions (Brown et al. 1976) and as such was a strong precursor to an ecosystem approach, 

but unfortunately the two-tier system was never fully implemented. 

In the United States, quota-based management was maintained under the early years of 

extended jurisdiction (i.e., after the 1976 Magnuson-Stevens Act) but was replaced by more 

gear-specific measures (constraints on mesh size, legal size limits for fish, and short-term areal 

and seasonal closures) in 1982. When these measures failed to adequately protect fishery 

resources, more restrictive measures (including the use of large-scale year-round closures and 

limits to days-at-sea) were added in 1994 (Murawski et al. 1997; Fogarty and Murawski 1998). 

Quota-based approaches were more commonly retained in the MAB. 

Meanwhile there have been notable changes to protected, endangered, and threatened 

species (PETS), with many in more critical condition than 50 years ago (Waring et al. 2004). 

Additionally, shifts in nontargeted fauna such as some benthos and nontargeted fishes occurred 

(Link and Brodziak 2002), with some actually persisting at relatively stable levels or even 

increasing (Link and Brodziak 2002; Link 2007). This all occurred while the regional dynamics 

in physiochemical conditions were also changing as noted above, particularly long-term warming 

(Taylor and Bascunan 2001) and NAO shifts (Drinkwater et al. 2003). 

MODELING APPROACH AND METHODOLOGY 

As noted above, Atlantis is a revised version of Bay Model 2 (BM2) (Fulton 2001; Fulton 

et al. 2004a, 2004c, 2004d). Atlantis is a deterministic model that tracks the nutrient (nitrogen 

and silicate) flow through the main biological groups found in temperate marine ecosystems, 

along with three detritus groups (labile detritus, refractory detritus, and carrion). The invertebrate 

and primary producer groups are simulated with aggregate biomass pools, while the vertebrates 

are represented with age-structured models. Again, the primary processes considered in Atlantis 
11 

are consumption, production, waste production, migration, predation, recruitment, habitat 

dependency, and natural and fishing mortality. A summary of the chief assumptions is given in 

Table 1. 

 We ran our simulations for 50 years. The runs started in 1964 and ended in 2014. The 

differential equations for the system dynamics are solved with a simple adaptive forward 

difference method, with an overall time step of 12 hours. 

MODEL STRUCTURE 

Boxes, Habitat, Physics, and Chemistry 

Hydrodynamics 

The hydrographic submodel included in Atlantis (and BM2 before it) is a transport 

model, modified from the model originally developed for the Port Phillip Bay Integrated Model 

(PPBIM) by Murray and Parslow (1999) and Walker (1999). Horizontally, it partitions the 

modeled area into discrete polygons (Figure 5). The size of each polygon reflects the spatial 

homogeneity in the physical variables represented in the model: depth, seabed type (reef or flat), 

canyon coverage, porosity, bottom stress, erosion rate, salinity, light, and temperature. 

Vertically, Atlantis partitions the modeled area into multiple layers (up to 5 water column layers, 

an epibenthic layer, and a sediment layer (Figure 5)). The fluxes driving the transport model are 

either derived from a spatially and temporally finely resolved, three-dimensional nonlinear, 

variable-density hydrodynamic model (Walker 1999) or from satellite data. The model is also 

driven by seasonal variation in irradiance and temperature, as well as by nutrient inputs from 

point sources, atmospheric deposition of dissolved inorganic nitrogen (DIN), and exchanges with 

an oceanic boundary boxes. If the deepest water column box contacts the sediments, a sediment 

chemistry submodel resolves nutrient remineralization and oxygen exchange. Otherwise, the 

bottom of the deepest water column box is treated as an open boundary. 

The only physical processes in Atlantis that differ from those in the PPBIM and BM2 

(detailed in Murray and Parslow 1999; Walker 1999) are bioturbation, bioirrigation, and the 

calculation of the light attenuation coefficient. 

The light attenuation coefficient is from Fulton (2001) and is formulated as: 

n  n  n  DON  n   DL  DR  n  PX  n  SUSP 

susp

i PX

w DON D P (EQ. 1) 

with nw the background extinction coefficient, nDON the contribution from dissolved organic 

nitrogen (DON), nD the contribution from labile detritus (DL) and refractory detritus (DR), nP the 

contribution from phytoplankton (PX), and nsusp the contribution from suspended sediments 

(SUSP). 

The equations for bioirrigation are as detailed in Walker (1999) for PPBIM. In Atlantis 

these equations are tied to the dynamical sediment fauna via an “enhancement” term similar to 

that of the European Regional Seas Ecosystem Model (ERSEM I, Ebenhöh et al. 1997). 

Atlantis uses explicit sediment layers and thus can approximate particulate diffusion, 

expulsion (whereby material at depth is moved to the surface), and exchange with the surface by 

sediment transfers between appropriate layers of the model. Only those particulate components 

(tracers) that are allowed in the sediments and are not macrobenthos (sediment grains, settled 

phytoplankton, microphytobenthos, meiobenthos, detritus, and sediment bacteria) can be acted 
12 

upon by bioturbation. The tracer concentration in the ith sediment layer (BXi(t)) at the end of a 

time step is formulated as: 

            

1 1 0

1 1 1 1 0 0

2

2

f f z f c w w

BX t f BX t f BX t z BX t f BX t c BX t w BX t w BX t t

i i i i i i

i i i i i i i i i i i i

i

     

                

 

   

 (EQ. 2) 

i

i

i

z

f        (EQ. 3) 

i i c       (EQ. 4) 

wi i    (EQ. 5) 

where fi represents the thickness transferred from i by particulate diffusion, ci is the thickness 

moved to the surface from layer i by expulsion, wi is the thickness moved from layer i by 

exchange with surface layers, and zi is the thickness of layer i. The thicknesses fi, ci, and wi only 

differ in a single parameter. For the parameters they share,  represents the base density of 

biological activity;  represents the modification to the baseline to reflect dynamic sediment 

fauna activity in the ecological submodel (calculated in much the same way as that of ERSEM 

(see Ebenhöh et al. 1997)); and i is the depth dependence of the mixing process (this is a simple 

functional form, as of PPBIM, and though usually constant, it is also possible to implement 

linear, parabolic, and half-Gaussian forms (Walker 1999)). The parameter which does differ in 

the calculation of fi, ci, and wi is the base rate of each process;  is the rate of particle diffusion 

(m2 per t per unit biomass of bioturbative benthos per m2),  is the rate of expulsion (m per t 

per unit biomass of bioturbative benthos per m2

), and  is the rate of exchange between the 

surface and deeper layers (m per t per unit biomass of bioturbative benthos per m2). A small 

amount of burial of sediments and associated detrital particles is also enabled by using a similar 

formulation. 

The Atlantis NEUS model covers the continental shelf from the Gulf of Maine to Cape 

Hatteras (i.e., the NEUS LME). The area of the NEUS LME is approximately 293,000 km2

. We 

divided the NEUS LME into 22 dynamic regions (or polygonal “boxes”), each with up to 4 depth 

zones defined by bathymetric contours (Figure 5). These regions were based on the 

biogeography of the NEUS LME. The geomorphology of the region was used to define the 

model domain and polygonal spatial structure of the model, with structures representing 

subregional scales all explicitly resolved. Depth, substrate grain size, and biological communities 

were all considered in the establishment of these boxes. We also established 8 boundary boxes 

(or islands) representing areas outside the model domain in which most processes are not 

simulated (exceptions include growth, reproduction, and migration to and from the model 

domain boxes). Atlantis NEUS divides the water column into depth layers: one layer for 

nearshore regions and up to four layers (0-50 m, 50-120 m, 120-300 m, and 300+ m) for the 

offshore boxes. Processes can also occur at the surface layer and the epibenthic layer. These 

depth layers correspond to approximate depths of stratification, the major shelf areas, the 

maximal depths in most regions, and beyond, respectively. 
13 

The transport model, used to represent advective and diffusive transport across the 

region, was based on flows derived from the Hybrid Coordinate Ocean Model (HYCOM) model 

(served by a Live Access Server (LAS) at http://www.hycom.org/ or 

http://hycom.rsmas.miami.edu/). The flows across each face (side) of the Atlantis NEUS 

polygons were calculated by interpolating the model velocity fields to a set of evenly spaced 

locations along the faces. These fields were then used to compute the perpendicular velocity at 

each depth level at each location (i.e., each depth layer interface at each of the evenly spaced 

points along the face). The base flows are then the sum of the contributions (with each 

contribution provided by the product of velocity and face area) for all above-seafloor portions of 

the face. These base flows are finally corrected for hyperdiffusion within boxes. To do this an 

east-west/north-south decomposition of flows was performed per box, with each of the pair 

components scaled down by the dimensions of the polygon in that orientation (i.e., the east-west 

flows were divided by the east-west width of the box and similarly for the north-south flows). A 

conservative tracer was then used to check flows through the system, with box specific flow 

tuning scalars used to remove any remaining hyperdiffusion effects. If this combined correction 

is not made, then flows within the larger boxes would be overstated by orders of magnitude, as 

once in a box any tracer is assumed to be equally accessible throughout the box which artificially 

inflates flows; this effect is removed by the correction. This is a fairly basic approach to 

correcting for hyperdiffusion, but to date no better method has been found for box models of this 

type (particle tracking also suffers from hyperdiffusion, and inverse methods are often ill-posed 

on such large domains). Where the vertical fluxes remained underestimated, values for upwelling 

were adapted from Lough and Manning (2001; c.f. Loder and Wright 1985; Loder et al. 1998). 

Water column properties (temperature and salinity) were also obtained from the HYCOM 

model (using the hindcast weekly time series from the Marine Environment and Security for the 

European Area (MERSEA) North Atlantic Class 1 data products). These properties were 

calculated by first vertically interpolating the HYCOM profiles to a set of depths that were 

uniformly distributed within each of the Atlantis NEUS model depth layers. The final 

temperature and salinity values were a simple average over all grid points within each polygon. 

Sediment Chemistry, Mineralization, and Associated Detrital Processing

Here we describe mineralization of detritus, sediment chemistry, and the dynamics of 

bacteria associated with those processes, particularly as they relate to the processing of various 

forms of detritus. Use of a compound effect of enhanced bioturbation (te) and porosity (POR) is 

based on observations by Alongi (1998) and the relationship detailed by Blackburn (1987). 

Coupled with equations (EQ. 45) to (EQ. 48), the utilization of labile detritus by aerobic or 

anaerobic bacteria is given by: 

XB,DL

XB XB,DL

DL,XB XB



 



    XBDL

P G (EQ. 6) 

where XB,DL is the assimilation efficiency of the bacteria (XB, both aerobic bacteria [AEB] and 

anaerobic bacteria [ANB]) on labile detritus (DL). 

The uptake of refractory detritus (DR) is calculated similarly. The natural mortality term 

(MXB) is as for the other invertebrates, but the term representing predation losses of bacteria to 

predator group i (PXB,i) is given by: 
14 

XB,i DL,i X XB,DL DR,i XB XB,DR P  P     P    B (EQ. 7) 

The waste handling equations for bacteria are also different from those for other 

invertebrates since wastes are channelled into DON, not DL. All of the equations for anaerobic 

bacteria are as for XB, except that any O2 factors in the equations (EQ. 46 and 47) are replaced 

by (1-O2). Adopting these equations for the attached bacteria made it easier to identify a method 

of introducing dynamic flexibility to the empirical nitrification-denitrification model proposed by 

Murray and Parslow (1999) for PPBIM. 

A more interactive form of the processes governing nitrification and denitrification was 

integrated into Atlantis. The empirical sediment chemistry model used in PPBIM (Murray and 

Parslow 1999) is linked directly to the activities of sediment bacteria and infauna. The amount of 

ammonia (NH) produced by the remineralization of DON (RDON) is handled as in PPBIM, that is: 

RDON  DONPOR (EQ. 8) 

where  is the temperature-dependent rate of breakdown for DON (set at 0.00176 d-1, Murray 

pers. com.). In Atlantis the production of the remainder of the ammonia is dependent upon the 

activity of sediment dwelling fauna and flora. Thus, the total ammonia available for nitrification 

and denitrification (RNET) is: 

   MB  NET DON AEB ANB OB BD NH, R  max 0 , R  E  E   E  E  P (EQ. 9) 

where PNH,MB is the uptake of NH by microphytobenthos (MB) (see equations for autotrophs; 

EQ. 42-44), EXX is the ammonia released by XX, and  is the fraction of the excreted NH by 

infauna that contributes available nitrogen for nitrification and denitrification (set to 0.95). The 

form of EXX for OB and BD is of the general form given for heterotrophs in Fulton (2001), Fulton 

et al. (2004a, 2004b, 2004c, 2004d) and Murray and Parslow (1999), but that for AEB and ANB 

is slightly different and is given by: 

    EXB PDL,XB 1 XB,DL PDR,XB 1 XB,DR  MXB WDON WDR       (EQ. 10) 

where EXB is the release of NH by XB, XB,DX is the efficiency of XB on the detritus fraction DX, 

and the production of DON (WDON) and DR (WDR) are calculated as follows: 

      DON DL,XB XB,DL DR,XB XB,DR XB XB XB,DON W  P  1  P  1  M  f (EQ. 11) 

    DR DL,XB XB,DL XB XB XB,DR W  P  1  M  f (EQ. 12) 

where XB indicates the fraction of the losses of XB from natural mortality that are not released 

as NH and fXB,DX is the fraction of the products of growth inefficiency and mortality directed to 

the detritus fraction DX. By using equation (EQ. 9), the processes of nitrification and 

denitrification were completed by using the form of the empirical model of Murray and Parslow 

(1999), giving nitrification (SNIT) as: 
15 















     

r0

NET SED

NIT NET DMAX 0 ,1   R S R max (EQ. 13) 

and denitrification (SDENIT) as: 















   

rD

NET SED

DENIT NIT 1, 

R  S S min (EQ. 14) 

where DMAX is the maximum rate of denitrification (set at 0.25, Murray pers. com.), r0 is the 

temperature-dependent minimum rate of respiration that supports nitrification (set at 200, Murray 

and Parslow 1999), and rD (set at 10, Murray and Parslow 1999) is the peak of the nitrificationdenitrification curve (as defined by Murray and Parslow 1999). This general form is adopted 

from PPBIM because of its demonstrated performance and robustness (Murray and Parslow 

1999; Fulton 2001). 

The equations for oxygen are modified from Walker (1999) because of the more 

interactive representation of the sediment processes, with oxygen dynamics governed by: 

 

 



















     



   















   



bw por

bw,t bw sed,t por

bw,t

1 1

bw por

bw,t bw sed,t por

bw,t 1

2 2 2

2 2

2

bw por

VOL VOL

O VOL O VOL

e O

VOL VOL

O VOL O VOL

O

VOL VOL irr

(EQ. 15) 

  bw, 1 bw,t

por

bw

sed,t 1 sed,t 2 2 O2 O2

VOL

VOL O   O   t  (EQ. 16) 

where irr is the exchange rate from irrigation, O2SED,t is the concentration of oxygen in the 

sediment at time t, O2bw,t is the concentration of oxygen in the bottom water at time t, VOLbw is 

the volume of the bottom water layer, and the porewater volume above the oxygen horizon is 

given by: 

sed

o2 cell

por VOL

VOL POR      (EQ. 17) 

with VOLsed being the volume of the entire sediment layer, o2 is the oxygen depth, and cell is the 

area of the cell. 

In the case of Atlantis NEUS, we have used the default parameters for these processes, 

only changing them slightly in the biophysical phase of calibration (noted below) to ensure 

adequate recycling of nutrients. 

 

Nutrients 

Atlantis can track several nutrients. We note all of these here, but Atlantis mainly tracks 

nitrogen as a common currency. The rate of change for NH in the water column is: 
16 

 

NIT PAB NET w

bacteria

i pelagic

i

i FX

i

i CX , BF

NH MB NH MA NH PFB i

i PX

NH i

w P P P P E E E S R

dt

d NH

w

w w w w

w

w    ,  ,  ,  ,        ,  ,

   

 (EQ. 18) 

and in the sediment: 

 (EQ. 19) 

  

    

w

sed sed sed

i BF CX

NH MB NH SG i NET sed NIT sed

sed R S P P E dt

d NH

,

, , , ,

where PNH,XX is the uptake of NH by the autotroph XX, ECX is the production of NH by the 

consumer CX, SNIT,XB is the amount of NH lost from nitrification by the bacteria XB, and 

RNET is the amount of NH produced by denitrification. These and similar, subsequent 

equations are presented as rate coefficients (as noted) which are multiplied by the state variables 

(in this case, NH) that results in the derivative changes noted. 

The rate of change for nitrate (NO) in the water column is: 

  NO MB NO MA NIT PAB

i PX

NO i

w P P P S

dt

d NO

w w w

w

w    ,  ,  ,  ,

 (EQ. 20) 

and in the sediment:

  NIT sed DENIT sed NO MB NO SG

sed

sed sed sed S S P P

dt

d NO  ,  ,  ,  ,

 (EQ. 21)

The rate of change of dissolved silicate (Si) in the water column is: 

  

 

w w

w

i PL MB

DSisol w Si i

w R P

dt

d Si

,

, ,

 (EQ. 22) 

and the rate of change of detrital silica (DSi) in the water column is given by:

 

DSisol w

i CX PL MB j

SiN lys i i j

w X M P R

dt

d DSi

w w w

,

,

, , 





























   

  (EQ. 23) 

where XSiN is the Redfield ratio of silicon and nitrogen (set at 3.0 (Murray and Parslow 1999)) 

and RDSisol is the amount of detrital silica remineralized. Note that the equations for Sised and 

DSised are as for (EQ. 22) and (EQ. 23) except that CXsed is used in the place of CXw and MB 

is the only PX present in the sediment that uses Si. 

The rate of change for dissolved oxygen (O2) in the water column is: 

 





















               

MZ,BG

i infauna,

DON w

bacteria

i pelagic

i

i FX

i i

SG

MB MA

i PX

ON i

w E E E R

2

G X G G G

dt

d O2 w

w

,
17 

 (EQ. 24) 

and in the sediment: 

 





















     

DON sed

MZ,BG

i infauna,

i

SG

ON MB

sed E R

2

G X G

dt

d O2 sed ,

 (EQ. 25)

where XON is the Redfield ratio of oxygen and nitrogen (set at 16.0 (Murray and Parslow 1999)) 

and RDON is the DON lost through remineralization. 

 The rate of change of DON in the water column is: 

 

DON w DON w DON PFB

w W R P

dt

d DON  ,  ,  ,

 (EQ. 26) 

and in the sediment:

 

DON sed DON sed

sed W R

dt

d DON  ,  ,

 (EQ. 27)

where WDON is the DON produced by bacteria, RDON is the DON lost through 

remineralization, and PDON,PFB is the DON taken up by pelagic free bacteria (PFB). 

The rate of change of DL in the water column is: 

 

lys MB MA DL PAB DL BF

i PX

lys i

bacteria

i pelagic

DL i

i FX

DL i

i CX

DL i

w

w w w

w

w w

w

w W W W M M M P P

dt

d DL   ,   ,   ,   ,  ,   ,  ,

   

 (EQ. 28) 

and in the sediment: 

     



  



  

       

i FX

DL i

i epifauna

DL i DL i

i infauna

nat MB lys MB SG DL i DL i

i PX

nat i

sed

sed

sed sed sed sed

sed

- P

M M M M W P W P

dt

d DL

,

, , , , , , ,

 (EQ. 29) 
18 

where WDL,CX is the amount of DL in the waste products from consumer CX and PDL,CX is 

the DL consumed by CX. 

The rate of change of DR in the water column is: 

 

DR PAB DR

i CX

DR i

i FX

DR i

w W P P J

dt

d DR w

w

w w        

, , ,

 (EQ. 30)

and in the sediment: 

 

DR

i infauna

DR i

i infauna

DR i

sed W P J

dt

d DR sed sed       

, ,

 (EQ. 31)

where WDR,CX is the DR in the wastes of consumer CX, PDR,CX is the amount of detritus consumed 

by CX, infauna includes sediment bacteria, and JDR is the amount of DR transferred from the 

water column to sediment pool from the feeding activities of the benthic filter feeders. 

In the case of Atlantis NEUS, we have used the default parameters for these processes, 

only changing them slightly in the biophysical phase of calibration (noted below) to ensure 

adequate recycling of nutrients. In terms of informing initial values of these nutrients, we used 

online data sources (University of Maine’s Gulf of Maine Region Nutrient and Hydrographic 

Database, http://grampus.umeoce.maine.edu/nutrients/; D. Townsend, pers. comm.; Townsend 

1991; Townsend et al. 2004) and Marine Resources Monitoring, Assessment and Prediction 

Program (MARMAP) nutrient data (J. O’Reilly, pers. comm.). 

Biology 

We simulate the dynamics of 49 functional groups, using nitrogen as the common 

currency between groups (Table 2, Appendix A). Living flora and fauna make up 43 of these 

functional groups, while detrital pools make up the other 6. There are 24 vertebrate groups and 

14 invertebrate groups. Within the vertebrate groups, there are 5 charismatic groups (birds and 

mammals). Individual species within each functional group have similar diet compositions, life 

histories, and distributions. 

Groups 

Primary Producers and Bacteria 

The Atlantis model tracks primary producer abundance (mg N/m3

) per region, as an 

aggregated biomass pool. Growth is limited by nutrient, light, and space availability. Biomass is 

lost to grazing (i.e., predation), lysis, and mortality (linear and quadratic). Linear mortality 

represents density-independent mortality in addition to the mortality sources explicitly modeled. 

Quadratic mortality represents density-dependent mortality. 

The rate of change for a standard water column primary producer (PX) is : 

  

  

groups

i predator

PX lys PX PX ,i

w

w w w G M P

dt

d PX , (EQ. 32) 
19 

 

PXsed nat

sed M

dt

d PX   , (EQ. 33) 

where GPX stands for the growth of PX, Mlys,PX is the loss of PX through lysis, Mnat,PX is the 

natural mortality losses of PX when in the sediments, and PPX,I are the losses of PX through 

predation. The equations for the benthic primary producers are slightly different. The rate of 

change of microphytobenthos is given by: 

   

  

groups

i water predator

MB lys MB MB ,i

w

w w w G M P

dt

d MB , (EQ. 34) 

  

  

groups

i sed predator

MB nat MB MB ,i

sed

sed sed sed G M P

dt

d MB , (EQ. 35) 

The general formulation for the dynamics of aerobic attached bacteria (where XB stands 

for Pelagic Attached Bacteria (PAB) or sediment bound AEB) is: 

  

  

 groups

i consumer

GXB MXB PXB,i dt

d XB (EQ. 36) 

Growth of primary producers is formulated as: 

GPX  PX  irr  N  space  PX (EQ. 37) 

where PX is the maximum growth rate and δX are limitation coefficients. The nutrient limitation 

factor for nitrogen is given by: 

DIN

DIN

N PX

N    ,

 (EQ. 38) 

(where DIN=NH+NO). For those primary producers that are also limited by the availability of 

Si, nutrient limitation is given by: 

















   Si

Si

DIN

DIN

N PX Si PX

N

, ,

min ,  

 (EQ. 39) 

Light limitation is given by: 















  1 IRR

irr PX

irr min ,  ,

 (EQ. 40) 

where IRR = photosynthetic available radiation at the depth of the primary producer (Wm-2). In 
20 

EQ. 38- 40,  represents the half saturation constants for the respective processes. Space 

limitation is as follows: 

substrate habdegrad

space

PX

  

    

PXmax

1 (EQ. 41) 

where δsubstrate is the proportion of the available space that is of the correct depth and substrate 

type to support this type of primary producer and δhabdegrad is the scalar for local habitat 

degradation scenarios. The terms δsubstrate and δhabdegrad only apply to macrophytes (which 

are not utilized in the NEUS application of the model). 

The nutrient uptake functions for the primary producer PX are given by using the above 

formulations for growth and nutrient limitation: 

DIN

DIN

NH

NH P G NH PX

NH PX

NH PX PX

     ,

,

,



 (EQ. 42)

DIN NH

NO P G

NH PX

NH PX

NO PX PX    

,

,

, 



 (EQ. 43)

where NH,PX is the half saturation constant for the uptake of NH. In addition, for PL and MB

there is the uptake of Si as follows: 

PSi PX XSiN GPX   , (EQ. 44) 

The growth of the bacteria (GXB) is given by: 

    

XB  XB 0 1  XB G   XBmax , (EQ. 45) 

and 

  DL,XB DR,XB O2 stim

XB

           DL DR

XB (EQ. 46) 

with XB representing the maximum temperature-dependent daily growth rate for the group XB. 

XB is the current pool of bacteria, and DL and DR are the labile and refractory detrital pools (all 

in mg N m-3); DL, XB and DR, XB represent the maximum possible biomass of XB per biomass of 

that grade of detritus;  is the exponent dictating the reduction in growth as the bacterial pool 

approaches its maximum attainable levels (set to 3); and O2 is the oxygen limitation factor, 

which is given by: 
21 











 

1 , otherwise

, XBbenthic

O2 XB

O2

O2  



 (EQ. 47) 

where XB is the half oxygen mortality depth for XB and the oxygen horizon (O2) is given by: 

bw

sed sed

O2 2

2 2

O

O      (EQ. 48) 

with O2sed the concentration of oxygen in the sediments, O2bw the concentration in the bottom 

water, and sed the depth of the sediment layer considered in the model. Finally stim indicates the 

degree of stimulation of the bacteria by bioturbation, and it is calculated as follows: 

 









  



1 , otherwise

, XB benthic

193.75

te 250 0 225

stim

 POR- .

 (EQ. 49) 

The rate of change for standard water column (w) primary producer (PX) is 

 (EQ. 50) 

 

    

i predatorgroups

PXw lys PX lin quad PXw i

w G M M M P

dt

d PX , ,

( )

 (EQ. 51) 

where GPX is the growth of PX, Mlys,PX is loss of PX through lysis, Mlin and Mquad are losses from 

linear and quadratic mortality, PPX,I are losses of PX through predation, PX is the maximum 

growth rate, δirr is light limitation, δN is nutrient limitation, and δspace is space limitation. 

Appendix A contains values for , Mlin and Mquad for our model; Mlys was set to 0. 

 In the case of Atlantis NEUS, we had three primary producer (PP) groups (Table 2); these 

include diatoms, dinoflagellates (combined with diatoms for large PP), and picophytoplankton 

(small PP). The data to initialize and parameterize these organisms came from the results of our 

EMAX efforts (Link et al. 2006, 2008b) as informed from satellite imagery (J. O’Reilly pers. 

comm.; Thomas et al. 2003) and associated sampling (MARMAP; J. O’Reilly, pers. comm.; 

O’Reilly and Zetlin 1998).

Invertebrates 

The Atlantis model tracks invertebrate abundance (mg N/m3

) per region, as an aggregated 

biomass pool within each spatial box, based on growth, predation, and linear and quadratic 

mortality. Quadratic mortality in this case represents density-dependent effects (predation, 

disease) that are not explicitly modeled; the ultimate effect of which is to impose a reasonable 

G PX  PX  irr  N  space  PX
22 

carrying capacity. In general we attempted to set linear and quadratic mortality to 0 when 

possible (Table A.2). We have attempted to explicitly include all significant ecological 

components; as such there was limited need to call upon these extra mortality terms, which 

represent ecological components not treated explicitly. 

The rate of change for a standard invertebrate consumer (CX) is: 

 

CX

groups

i predator

GCX MCX PCX,i F

dt

d CX      

 (EQ. 52) 

where FCX stands for losses through fishing on this group. Invertebrate consumers are restricted 

to existing only in the water column, as epibenthos on the sediment surface, or in the sediment 

pool. They cannot be dynamically active or mobile in two or three of these pools. Epibenthos can 

feed in the sediments and watercolumn, but do not actively move into those locations. 

The growth of CX is given by: 

  space o2

j DL DR

j,CX CX j

prey

i living

G CX  CX Pi,CX P   





















        ,

,

 (EQ. 53)

with CX the growth efficiency of CX when feeding on live prey, CX,j the efficiency when 

feeding on detritus (DL treated separately to DR), and  terms as limiting factors. Space 

limitation given by:

   

   















 

    

       



 



1 , otherwise

1 , BF and

CXthresh CXlow CXsat

CXlow

CXlow

CXlow CXsat

CXlow

CXlow

CXlow

CX

CX

substrate habdegrad

CX

CX

substrate habdegrad

space

CX CX

CX

CX



   

  



 



 



 

 (EQ. 54) 

where CXmax is the maximum biomass per area allowed for CX, CXlow is the crowding lower 

threshold, CXsat is the crowding half saturation level, CXthresh is the crowding threshold (this 

formulation is based on that of the ERSEM II [Blackford 1997]), δsubstrate is the proportion of the 

available space that is of the correct depth and substrate type to support this type of primary 

producer, and δhabdegrad is the scalar for local habitat degradation scenarios. 

Oxygen limitation is given by: 

 (EQ. 55) 

 

1 , if pelagic

 , if epifauna or infauna o2 CX Mo2

o2

o2











  , 




23 

where o2 is the depth of the oxygen horizon and CX,Mo2 is the half oxygen mortality depth. 

In the case of Atlantis NEUS, we had 14 invertebrate groups (Table 2). Data to initialize 

and parameterize these organisms largely came from the Energy Modeling and Analysis 

eXercise (EMAX; Link et al. 2006, 2008b) and associated surveys (Azarovitz 1981; NEFC 1988; 

NEFSC unpubl. data). We had 2 stages (juvenile and adults) for squids and shrimp. Details of 

these parameters and initial values are found in Tables A.2-A.11, A.13-18, and A.20. 

Vertebrates 

The Atlantis model supports multiple age classes for vertebrates. Our parameterization 

utilizes 10 age classes (Appendix A). It is possible for each vertebrate to have different time 

spans for corresponding age classes because the age classes represent different phases in the 

vertebrate’s lifecycle. For some groups the age classes might be one year each, while for other 

longer lived groups it might be a decade or more for each class. This has been determined to be a 

computationally efficient way of representing vertebrates with drastically different longevities 

within a common model framework. The lifespan of each vertebrate is detailed in Table A.1, and 

the duration of each of the 10 age classes for a given vertebrate is 10% of its lifespan. 

The following are the rates of change for a vertebrate group (FX): 

 

FXi s

i s G

dt

d FX

,

,  (EQ. 56)

 

FXi r

i r G

dt

d FX

,

,  (EQ. 57)

  i i i i FX

groups

j predator

IMM FX EM FX FX FX, j

i d T T M P F

dt

d FX       

, ,

,

 (EQ. 58)

where the subscript s stands for structural weight (skeletal and other material that can not be 

reabsorbed), r for reserve weight (fats and other tissues that can be broken down when food is 

scarce), and d for density. The subscript i represents age class or life phase; there is one equation 

for each age class included. The T terms represent the movement of vertebrates into (TIMM,Fxi) 

and out of (TEM,Fxi) the cell. In addition there are short-term spawning and recruitment events 

which effect the various FX pools. At the same point each year (the exact day dependent on the 

vertebrate and with a window of +/- 14 days), the vertebrates reproduce, and the materials 

required to do this are removed from the reserve weight of FX. At which point the proportion of 

the age class growing into the next age class is then noted (also, if in a scenario with external 

adult stocks, then the oldest age class in the system leaves the system at this time). Sometime 

later (the exact period dependent on the group) the recruits settle out, and their weights and 

density are assigned to the youngest age class. 

The amount of reserve weight (mg N per individual) that is used up during spawning is 

given by: 
24 

      

  













   















   

     

        

 





























 









 



 



 





 1 Y 1 X

Z 1 X

U 0

U 0 Z 1 X Y , 1

i,s i,r RS i,s

FX RS i,s

FX RS i,s i,s i,r

FX

FX FX RS i,s FX i,s i,r RS i,s

FX FX FX X FX FX

FX FX FX

FX FX FX X FX

s

i

i

i

max ,

max ,

,

 (EQ. 59) 

where UFxi is the proportion of age class i that is reproductively mature, ZFX is the fraction of the 

weight of FX used in spawning, YFX is the spawning function constant, and XRS is the ratio of 

structural to reserve weight in well-fed vertebrates. 

There are many formulations for recruitment. Constant recruitment is given as: 

tj t b  J (EQ. 60) 

where Jt is element t of the recruitment vector (constant spatially and temporally). More 

commonly a Beverton-Holt recruitment function was implemented by using the following 

formulation: 

x

tj

tj

tj t

L

L

b





















 



 (EQ. 61) 

where  is the Beverton-Holt  for the vertebrate group;  is the Beverton-Holt  for the 

vertebrate group (i.e., the density independent and density dependent parameters, respectively); tx

is total length of recruit period; and the biomass of the offspring of vertebrate group FX in cell j 

at time t (Ltj) is determined as follows:

 

     

     i age class

tj FX i,d 1 i L s FX t recruit 

 (EQ. 62) 

where sFXi is the spawn from age class i of FX, ωrecruit is the scalar for episodic recruitment, and 

[t] is an impulse function, which is only nonzero when time t is a multiple of the period of the 

recruitment pulses. 

The growth for each vertebrate group is calculated by equation of the same form as EQ. 

53, but per age class of each vertebrate. The result is then apportioned to structural and reserve 

weight increases such that: 

GFXi s GFXi   , (EQ. 63)

  FXi r GFXi G  1   , (EQ. 64)

where 

 
25 

















 





 

     

















  

0 , otherwise

 , if 0 and 0 i

RS i,s

i,r

pR,FX

RS

FX

RS i,s X

i,r

RS X

1

X

X

X

1

G

FX

FX

FX

FX

 (EQ. 65)

and where XRS is the maximum ratio of reserve to structural weight FX can have and XpR,FX is the 

relative degree to which FX replenishes reserves rather than undergoing structural growth when 

underweight. 

The foraging term (across all consumers CX, including vertebrates) is given by:

 

CX

refuge

prey CX

CX

CX prey P



 



CX,DL DL,CX CX,DR DR,CX

groups

j live prey

p j,CX j p ε p

CX

CX prey,CX

,

1 k

k p

   

























  

 

    



 (EQ. 66) 

where “prey” is the group being consumed by CX, kcx is the clearance rate of CX, and pprey,CX is 

preference (or availability) of that prey for the predator CX. This last parameter is similar to the 

“vulnerability” parameters in ECOSIM (Christensen et al. 2000) and represents the fact that the 

entire prey population will not be available to the predators at any one time (for instance some 

may be hiding). The availability of the food is further modified by δrefuge if the group is 

dependent on biogenic habitat refuges or size refuges (if they are physically outside the gape 

range of their vertebrate predators), or if the spatial range of the predator and prey do not 

completely overlap (and so explicit spatial refuges exist). If the group is dependent on biogenic 

habitat, then δrefuge is given by: 

  er

refuge er er er d

cov

cov cov cov

1

exp 

       (EQ. 67) 

where -κcover is the refuge magnitude coefficient, θcover is the habitat steepness coefficient, and 

dcover is the relative cover in the cell for the prey, which is calculated by: 

  canyon

j

er substrate habdegrad substrate biogenic j d       













    1 cov , , (EQ. 68) 

where δsubstrate,habdegrad is degradation in the physical habitat from coastal development (e.g., reefs 

broken up), ρsubstrate is the proportion of the cell covered with suitable substrate types, ρsubstrate,j is 

the proportion of the cell covered by biogenic habitat defining group j, and ρcanyon is the 

proportion of the cell covered by canyons (which is treated as an enhancement factor here as they 

are known to concentrate production, but their absence does not prevent the establishment and 

growth of the groups [Alan Williams pers. com.]). 

For each age class and each spatial cell, the model tracks the number of individuals and 

their average structural weight and reserve weight. Growth and abundance are functions of 

recruitment, predation, consumption, and linear and quadratic mortality. We tracked abundance, 
26 

biomass, weight-at-age, and condition (reserve weight/structural weight) of each group through 

time in each box and for the entire model domain. We evaluated model performance based upon 

how closely model-predicted values for biomass matched expected values. 

In the case of Atlantis NEUS, we had 24 vertebrate groups (Table 2). Data to initialize 

and parameterize these organisms largely came from the Northeast Fisheries Science Center 

(NEFSC) bottom trawl survey (Azarovitz 1981; NEFC 1988), length-weight studies (Wigley et 

al. 2003), and EMAX (Link et al. 2006, 2008b). Additionally, assessments for those species that 

have been formally assessed were used to inform and initialize the model 

(http://www.nefsc.noaa.gov/sos/, http://www.nefsc.noaa.gov/nefsc/saw/). As noted above, these 

functional groups were partitioned into 10 stages that were fractions of the total life span. 

Details of these parameters and initial values are found in Tables A.1, A.3-12, and A.14-19. 

For each of the functional forms, we chose to model recruitment as the standard 

Beverton-Holt stock-recruitment function. Details of our parameterization of the Beverton-Holt 

relationship are in Appendix A, Table A.1. The exception was for the seabird group, which used 

a fixed recruitment. 

Major Biological Processes 

Predation 

Atlantis bases growth of vertebrate functional groups on von Bertalanffy growth 

parameters that vary with consumption. We chose a Holling type II functional response for 

predation by most functional groups, except marine mammals and seabirds, which we 

parameterized as type III functional responses. Previous work by Fulton et al (2004c) suggests 

that this type (II) is simple to parameterize yet is as effective as other representations given the 

nature of questions to be asked of strategic models. Alternative feeding functional responses 

exist within Atlantis, and future sensitivity analyses could consider their use when evaluating 

potential model structural sensitivity. Our implementation of the Holling type II functional 

response is: 

 (EQ. 69) 

















           

   

       

r

y

y y j

r

j

d

x

x x j

d

j

f

h

h h j

f

j

l

k

k k j

l

j

j

j

i ij j j

ij

E B a E B a E B a E B a

g

C

B a B C

P

1

,

1

,

1

,

1

, 1

 

where Pij is the proportion of prey i in the diet of predator j; Bi is the biomass of prey i; and Bj is 

the biomass of predator j; aij is the availability of prey i to predator j (Tables A.14-A.18); Cj is 

the maximum ingestion rate of predator j; gj is the maximum growth rate of predator j; El

j is the 

assimilation efficiency of predator j on live food (the l superscript); Ef

j is the assimilation 

efficiency of predator j on seagrass, macroalgae, or phytoplankton; Ed

j is the assimilation 

efficiency of predator j on labile detritus; and Er

j is the assimilation efficiency of predator j on 

refractory detritus. The sums in the denominator are simply the sums of each of the available 

food biomasses (animal prey, primary producers, labile detritus, and refractory detritus). Values 

for prey availability parameters (a) are detailed in Tables A.14 – A.18. Values for g are 

contained in Tables A.12 and A.13, and values for C are contained in Tables A.19 and A.20. 
27 

The initial diets from the NEFSC food habits database and EMAX (Link et al. 2006) 

exercise were used to initialize the availability terms for the NEUS LME functional groups. A 

more formal means of calculating availability was developed as we iterated through the 

initialization of the model (R. Gamble, unpubl. data) but was largely used to provide context for 

our initial estimates. 

Migration and Movement 

The default vertebrate movement (in terms of the density d of vertebrate group FX, age 

class i, in cell j) is given by: 

   

     







    

      

 , qrt 4

 , qrt 4

, ,1, , , , ,

, 1, , , , ,

, , D

j qrt FX

D

j qrt FX

D

i tot j FX

D

j qrt FX

D

j qrt FX

D

i,tot j qrt FX

i d j

FX FX FX FX

FX FX FX FX

FX





 (EQ. 70) 

where FXi,tot is the total number of FX in age class i in the entire system (i.e., the sum over all 

cells),  is the proportion of the current quarter of the year which has already passed, and 

FXD

j,qrt,FX is the proportion of the population of FX found in cell j in the qrt quarter of the year. 

For the forage and density dependent vertebrate movement scheme, the following 

formulation is used: 









  

 , otherwise

g

g , g

roc_mult

FX,i,j

roc_mult FX,i,j FX,i,j thesh

FX,i,j,potential G

G G

G (EQ. 71) 

 

all j

GFX,i,tot GFX,i,j (EQ. 72) 

FX,i,tot

FX,i,j,potential cov

G

G d FX

FX

D

D er depth i,d,j,t,other

i,d,j,t

    

 (EQ. 73) 

    D

i d j t

D

i d j t

D FXi,d,j FXi,tot FXi,vel FXi,d , j,t  FX , , , 1  FX , , , 1    (EQ. 74) 

where GFX,i,j,potential is a measure of the potential attractiveness of the cell j based on the available 

forage, GFX,i,j is calculated as of GCX in EQ. 53, groc_mult is a constant reflecting how much more 

attractive a site with forage sufficient to support FXi is over a site with poor food resources, 

gthresh is the potential growth rate (as an index of the quality of the resources) where FXi switch 

from finding the site desirable to undesirable, dcover is the relative cover in the cell for the group 

FXi (set to 1 for all groups that are not habitat dependent), and depth is the distribution over the 

water column depth layers. To take into account other pressures on fish movement (such as 

seasonal or spawning migration), the calculation of the proportion FXD

i,d,j,t is weighted by the 

ideal distribution for those other migration factors, and then the final distribution is determined 

by interpolating between the current distribution and the ideal distribution (taking the maximum 

swim speed of the vertebrate into account so that individuals can not move further than they 

could actually swim in reality). These FXi,d,j values are then normalised so that their sum is one. 
28 

If a vertebrate group is site attached, then it only moves vertically at most, and if the group 

employs maternal care, then the movement scheme is calculated for the mothers and then applied 

to them and the juvenile age classes. 

In the case of Atlantis NEUS, we allowed density-dependent movement of nekton, sea 

birds, and marine mammals between boxes. Plankton moved via advection between boxes. In 

general, movement transfers abundance towards neighboring boxes with higher potential growth 

rates. For several functional groups of vertebrates (e.g., herring, spiny dogfish, baleen whales) 

we forced the model with seasonal migrations. These migrations are well documented (Link et 

al. 2006) and represent shifts associated with changes in feeding, spawning, or similar life 

history events. We also initialized the model distributions of abundance and biomass for most 

groups as estimated from surveys (Azarovitz 1981; NEFC 1988). 

Mortality 

The mortality terms for both invertebrate consumers and autotrophs (see above) are in 

terms of lost biomass while those for vertebrates refer to the number of individuals lost. 

Nevertheless, the general form of the equations is the same even though the units of the 

coefficients obviously differ between the vertebrates and other groups. The natural mortality 

term for group XX is given by 

 (EQ. 75)

where mlin,XX is the coefficient of linear mortality for XX, mquad,XX is the coefficient of quadratic 

mortality for the group XX, mO2,XX is the coefficient of oxygen dependent mortality, and mspecial,XX

is the special (additional) loss rate for XX. This rate of “special” mortality is usually set to zero, 

except in the following cases: 

mspecial,MA mSTRESS  STRESS  (EQ. 76) 

mspecial,SG mDIN  DIN  (EQ. 77) 

where mSTRESS and mDIN are the coefficient of mortality from mechanical stress and fouling by 

epiphytes, respectively. Lastly, for vertebrate pools: 

   

  













 

     



0 , otherwise

, if 0

1 X FX

m 1 X FX FX FX

m RS i s

starve FX starve RS i s i s i r

special FXi ,

, , , ,

,



 (EQ. 78) 

with mstarve,FX is the threshold ratio of reserve to structural weight at which death by starvation is 

likely. While all the groups in the model had a linear mortality term to represent disease and 

other, nonpredation sources of mortality, the vertebrates and higher trophic level zooplankton 
29 

and benthic groups suffered mortality described by a quadratic term. Only benthic consumers 

had oxygen dependent mortality, the vertebrate groups had special mortality as shown above, 

and mtop is only applied to the vertebrate groups. 

The final loss term is applied to the microscopic primary producers only, and it represents 

lysis. The loss rate of a primary producer (PX) to lysis is formulated as follows: 

0 1

m PX

M

N

lys PX

lys PX .

,

, 

   (EQ. 79)

with mlys,PX as the rate of lysis and N is the nutrient limitation term from EQ 51. 

 In the case of Atlantis NEUS, we used the standard loss terms and their default values for 

these groups (with minimal special mortality and no oxygen dependent mortality) as modified 

via predation and fishing rates. The one caveat is that we did alter the quadratic mortality term 

for some of the invertebrates (particularly shrimp and cephalopods) in preliminary calibrations. 

Waste 

Waste production by invertebrate and vertebrate consumers is handled in the same way, 

but in the case of vertebrates the mortality term has to be converted from a density to a biomass 

before being used in the following equations. The production of labile detritus (DL) by consumer 

group XX is given by: 

   

 

XX DL

XX DR XX DR DR XX XX XX

XX DL XX DL DL XX

group

i living prey

XX XX i,XX

DL f

1 P M

1 P 1 P

W ,

, , ,

, , ,

)























     

      





 

 

 (EQ. 80)

with XX the proportion of mortality losses assigned to detritus, XX the proportion of the growth 

inefficiency of XX when feeding on live prey that is sent to detritus, XX,DL the proportion of the 

growth inefficiency of XX when feeding on DL that is sent to detritus, XX,DR the proportion of 

the growth inefficiency of XX when feeding on refractory detritus (DR) that is sent to detritus, 

and fXX,DL is the proportion of the total detritus produced that is of the type DL. The same 

equation is used for the production of DR (WDR), except that the final multiplication by fXX,DL is 

replaced by multiplication by (1-fXX,DL). 

The other main waste product is excreted ammonia. The general formulation used for 

denoting the production of ammonia by a consumer XX (invertebrate or vertebrate) is as follows: 

       

   XX DR XX DR DR,XX

XX DL XX DL DL,XX

group

i living prey

XX XX XX XX XX i,XX

1 1 P

E 1 M 1 1 P 1 1 P

    

              

, ,

, ,



  

 (EQ. 81) 

In the case of Atlantis NEUS we used the default values for recycling of waste. 

Fisheries 

The Atlantis model includes the main types of fleets typical in most marine ecosystems. 

Each fleet is characterised by its target, byproduct and bycatch groups, gear type (and associated 

selectivity curve and habitat impacts), habitat dependency, discarding, and effort allocation 
30 

submodels. The alternative fisheries submodels are detailed below after the fleets in each 

operating model are described. Ports are also considered in a spatial context as part of the fleet 

dynamics when considering distance to travel for targeted functional groups relative to their 

abundance. 

Many different fishing mortality equations were used, depending on the scenario of 

interest. As an example of the general form (similar to what is below but with the group biomass 

replacing the ((FXs,i + FXr,i)

.

FXd,i) term), the amount of a vertebrate caught at time t is given by: 

  















        FC

escape

FC

depth FX i

FC F C FX FX FX space FX i 1 FX , , FX,t eff s,i r,i d,i  , ,  (EQ. 82)

where FC  depth,FX ,i is the proportion of FXi at the depth the fishing gear is sweeping, and FC FXescape is 

the proportional escapement (either constant or size-based). The accessibility of FXi for the 

fishery FC ( FC  space,FX ,i) is given by either simple percentage overlap of the habitats occupied by 

FXi using: 

, , , h FX,i   FC FX

FC  space FX i  (EQ. 83) 

or the distribution model developed by Ellis and Pantus (2001): 

 































 



 



    

 



        patch

FC FX

pattern

pattern

FC FX

FC

FC FX

FC

space FX i











   ,

FX,i log 1 h

, , , , 1 min 1, exp 1 (EQ. 84) 

where hFXi is the catchability of the age class i of FX (see EQ. 90 to EQ. 94), ρFC,FX is the 

proportional overlap of fishery FC and vertebrate group FX, ηFC is the cover of the fishery, 

ηpattern is the distribution of the fishing within the area, and ηpatch is the number of patch types in 

the area. Finally, the effort applied in EQ. 82 is given by 

eff , FC,FX FC,FX j,FC,FX  m  q  a C  depth FC (EQ. 85) 

with mFC,FX the current coefficient of fishing mortality for FX by fishery FC (it can change 

through time as fishing pressure changes). This is expressed as the percentage of total effort (of 

the FC fishing fleet) concentrated in cell j if the fleet was allowed to act with effectively minimal 

constraints. The term qFC,FX is the selectivity coefficient of the gear used by fishery FC on FX, 

and a,j,FC,FX is the adjustment to the final rate caused by management actions currently applied in 

cell j (this can also change through time). Atlantis has the ability to handle both selectivity and 

catchability; in the NEUS application after some initial explorations, we fixed selectivity (qFC,FX) 

to 1 and allowed the catchabilities (hFXi) to vary. As indicated by (EQ. 82 to EQ. 84), the 

realized fishing implemented for each fleet and stock combination is a variation on the simple 

catch equation. 

The effort coefficient mFC,FX can be calculated in a number of ways, ranging from 

prescribed effort matrices to dynamic processes (such as basing all effort allocation on past 

catches or making minor modifications to long term trends of effort distribution based on recent 

catches in each area) and is given by one of the following equations. If effort is a temporally 
31 

prescribed constant (i.e., effort per quarter is fixed), then: 

   

    







   

     

m m m , qrt 4

m m m , qrt 4

m

1, , ,

1, , ,

FC,FX D

qrt FC

D

qrt FC

D

FC

D

qrt FC

D

qrt FC

D

qrt FC





 (EQ. 86) 

where  is the proportion of the current quarter of the year which has already passed and 

m

D

j,qrt,FX is the effort of fishery FC in the qrt quarter of the year. This value can be used as is (i.e., 

homogeneously distributed across all cells) or spatially weighted based on the catch per unit 

effort (CPUE) from previous time step. If the spatial distribution of the effort is prescribed rather 

than the temporal component, then the equation used is the same as for EQ. 86 but with m

D

qrt,FX

replaced with m

D

j,qrt,FX the effort of fishery FC in cell j in quarter qrt. 

If the effort distribution is calculated dynamically, then mFC,FX in cell j time t is given by: 

  mFC,FX,t,j F meff,t,j  mFC,FX,t-1,j  mFC,FX,t-1,j   vel (EQ. 87) 

where the ideal new distribution meff,t,j given by: 

  

j

CPUE j

CPUE j

B

B FC

,

max , , meff,t,j



 (EQ. 88) 

in which ωFC,max is maximum allowable effort, BCPUE,j is the CPUE in the cell in the previous 

time step, and the velocity Fvel is based on the distance between cells (if CPUE based only) or the 

distance to the ports if using a fleet dynamics model. 

EQ. 86 to EQ. 88 are used for the commercial fleets, but the recreational fishery is 

represented in a slightly different way: 

   

    







   

        



m m m , qrt 4

m m m , qrt 4

m

,1, , , , ,

, 1, , , , ,

eff,t,j , D

j qrt rec

D

j qrt rec

D

j rec

D

j qrt rec

D

j qrt rec

D

j qrt rec

port

Frecvel pop Npop k





 (EQ. 89) 

where Npop,k is the human population in port k, κpop is the proportion of the population that fishes 

recreationally, Frecvel is the velocity of recreational vessels based on distances to port, and 

m

D

j,qrt,FX is the effort of the recreational fishery in cell j in quarter qrt (usually used to constrain 

recreational and charter boat effort to coastal cells). 

If the effort displacement option (where fleets are able to refocus the locale of their 

effort) is being used, then the total effort for a fishery would drop by using any of these dynamic 

formulations (for instance either through the imposition of marine protected areas (MPAs) or 

declining stocks). If used, then the difference in effort for each area is redistributed to the 

adjacent cells with the greatest biomass of the target groups. 

The selectivity coefficient qFC,FX may be given by one of five functions depending on the 

gear used by the fishery. It may be a constant proportion applied to all age classes or it may be 

size-based and calculated based on a normal, logistic, lognormal, or gamma distribution. The 

selectivity of the gear with regard to cohort i of vertebrate group FX (q,i,FX) (similarly for the 

different age classes in any age structured invertebrates) is given by one of the following 

equations (depending on the selectivity curve of the gear). If the selectivity curve is a constant 

then: 
32 

q FX κi,0 i  (EQ. 90) 

If a logistic selectivity curve is used for the gear then: 

      1

FX ,1 , ,2 q 1 exp i

    i  i FX  i  l  (EQ. 91) 

If a normal selectivity curve is used for the gear then: 

 

  

















   2

,1

2

, ,2

FX 2 q exp i

i

i FX i l



 (EQ. 92) 

If a lognormal selectivity curve is used for the gear then: 

        

 

 

 

    



2

exp log 2

q

,1

1 2

,1

2

, ,2

FXi

i

i FX i i l (EQ. 93) 

Finally, if a gamma selectivity curve is used for the gear then: 

  

     













 



 



 



 



        

















 



 



 



 



     



1

,2

2

,1

2

,2 , ,2

0.5 4

,2

,

FX q exp 0.5 4

1

,2

2

,1

2

,2 ,2

i i i FX i i i

i

i FX l

l i i i i

   



   

 

 (EQ. 94) 

where  is the selectivity constant for the fishery on the ith age class of vertebrate group FX, 

 is the selectivity coefficient (spread of the curve) for the fishery on the ith age class of 

vertebrate group FX,  is the selectivity coefficient (length at which 50% of the population is 

selected) for the fishery on the ith age class of vertebrate group FX, and the length (lj,FX) of a 

vertebrate from cohort i in vertebrate group FX is given by: 

    w FX

w FX

wet s i r i

i FX

FX FX

l

,

1

,

CN , ,

, 1000

X 























    (EQ. 95) 

with XCN as the Redfield ratio of carbon to nitrogen, wet is the conversion coefficient from 

weight to dry weight, w,FX is a scaling coefficient in the length-weight relationship for FX, and 

w,FX is the exponent in the length-weight relationship. In practice the logistic or normal 

selectivity curves are used (unless otherwise specified for that scenario). The parameters used to 

specify the selectivity curve may be adjusted in response to “management decisions” made under 

some of the alternative management scenarios (i.e., gear restrictions can translate into changes in 

selectivity). 

The final coefficient (aj,FC,FX) in EQ. 85 represents management actions that influence 

fishing, but not by modifying the gear and its selectivity. This includes effort reduction and 

spatial and temporal closures or zoning. All of these approaches involve calculating or specifying 
33 

the proportion of “at will” effort that is allowed under the current management strategy. If a 

trigger event (such as target or vulnerable stock decline) occurs, it initiates a gradual reduction in 

effort, which can be reversed later if the trigger recovers. The following equation is used to 

determine the final management coefficient (aj,FC,FX): 

   











 

   













     

 , if no trigger event

1 1 , if trigger event has occured

a zone

j,FC,FX

TAC season zone

TAC season

reduction

trigger d

t

t t

  

  

(EQ. 96) 

where δTAC = 1 normally (but = 0 if a total allowable catch (TAC) is present and has been 

exceeded), δseason = 1 if the fishery is not under a temporal closure, δzone = 1 if there is no spatial 

management in the cell (but = 0 if the cell is closed and there is no infringement), t is current 

time, ttrigger is the time a trigger event occurred, treduction is the time period over which the 

reduction in effort is to take place, and d is the final level of effort allowed relative to the original 

level when the effort reduction was triggered. Infringement of any of the management conditions 

is represented by keeping the management scalars at a user-defined level above the values they 

would drop to if there was no infringement. Management actions such as setting TACs or 

defining fishing seasons (which are scaled back as stocks decline) occur on the first time step of 

each year and are in place for the entire year. 

Three forms of discarding were included in the model, and the specific one used in any 

particular case depended on the scenario under consideration. The simplest form of discarding 

saw a set proportion of the total catch of vertebrate FX by fishery FC discarded – the proportion 

of each age class actually discarded matched the percentage make-up of the catch. Another form 

of discarding saw a fixed proportion of all age classes discarded. The final, more realistic 

formulation used was based on size, where the biomass of age class i of FX discarded is given by 

 











   

discard i FX legal

illegal i FX legal discarded

i l l

 l l

FX

,

,

FX,t 

1 F





 (EQ. 97)

and the length of the vertebrate (li,FX) is given by EQ. 95. 

Several alternative fisheries submodels are included in the operating models. These 

include alternative bycatch, habitat dependency, selectivity, discarding, and effort allocation 

models. All of these can change through time, representing changes in fishing practices, 

advances in gear, alternative management strategies, and so forth. 

A simple but effective representation of the many fleets in the NEUS region was 

achieved by using a threshold-based relative catch per unit effort (CPUE) model. For each 

fishery, at each active time step (some boats may only be active diurnally, while others may 

potentially fish at any time of the day) the relative weight (wb) for a specific spatial box b is 

given by 

wb  dp

D 

Cb

C











 (EQ. 98)

where dp is the relative costs of reaching that box from the home port (based on distance from 

home port, it is rescaled relative to the maximum costs found across the entire model domain D); 
34 

Cb is the average CPUE by that fishery in that box over the “memory period” of that fishery (this 

period is defined by the user and can range from a single day to the entire length of the run to 

that point in time; at the start of the run it is initialized with historical data); and C is the average 

total CPUE over the entire domain for the memory period. 

The initial effort for the time step (Eb,t) applied to box b is then set at 

Eb,t  Et 1 wb (EQ. 99)

This is then linearly interpolated across the spatial distribution in the last time step to produce the 

new distribution (to allow for vessel steaming time across particularly large spatial domains): 

E

b,t  max 1.0, S

Dt,t 1











   Eb,t  Eb,t 1  Eb,t 1 (EQ. 100)

where S is the maximum distance that can be steamed by an average boat in the fishery during 

the period of one timestep, and Dt, t-1 is the distance between the peak effort locations at time t

and t-1. 

Once a week CPUE is checked against thresholds to see if there will be investment in 

extra effort (or conversely a reduction in effort), with the change in effort in box b (Em,b) defined 

as 

Em,b 

1.0  Es , Cb,t 1  CU 

1.0 , CL  Cb,t 1  CU

min(0.0,1.0  Es ) , Cb,t 1  CL











 (EQ. 101) 

where Es is the magnitude of the relative shift in weekly effort for that fishery; Cb,t-1 is the current 

rate of CPUE (for the previous week at that location); CL is the lower CPUE threshold; and CU is 

the upper CPUE threshold. The final realised effort (E’’

b) is then given by 

Eb  E

b  Em,b (EQ. 102)

Note that this change will be reflected in the final total effort for this timestep, and so a change in 

overall dynamic effort will be realized. In cases where there are significant management 

changes (e.g., as happened in 1994; see below) additional forced changes can be imposed (e.g., a 

reduction in effort at a specific point in time to represent introduction of new restrictions or a 

buy-back). This then resets the total effort level, but from that point on the equations above 

would continue to modify the realized effort from timestep to timestep. 

Fleet Definitions 

In the case of Atlantis NEUS, we could have defined fleets in a number of ways. 

Originally each fleet was defined as a combination of gear type (14 possible gears), target groups 

(7 possible target groups), vessel size (5 tonnage classes, 1 shore based fishery, and 1 

recreational fishery), and port location (77 discrete ports) (c.f. Hall-Arber et al. 2001). This 

would have resulted in 52,822 fleets. Instead, we defined the fleets used in our model as 
35 

amalgamated gear and target group combinations, limiting the total number to 18 (Table 3). 

Each fleet was assigned target and discard functional groups which it could affect among the 

vertebrates and invertebrates (Table 4). Main ports were selected as the top 2 - 4 major ports in 

each state, representative of approximately equidistant segments along the coastline (Hall-Arber 

et al. 2001). 

The targets and bycatch per fishery are noted, with the observation that each fleet often 

targeted more than one functional group. We used the times series of catches (reported as 

landings) and effort to initialize and calibrate the model. These data were from the NEFSC 

commercial fisheries database (NEFSC unpubl. data), in conjunction with national fisheries 

statistics (http://www.st.nmfs.noaa.gov/st1/commercial/index.html) and stock assessments 

(http://www.nefsc.noaa.gov/nefsc/saw/, http://www.nefsc.noaa.gov/sos/). 

 Three main sets of parameters were used to define the interactions of each fleet with its 

target and discard functional groups. The first was catchability (q) (Table A.21), the second was 

the fixed proportion of catch discarded for each group by a given fishery (Table A.22), and the 

third was a set of four effort parameters (Table A.23). Briefly, a bottom and top threshold of 

catch per day was set. If the catch per day for a fishery was below the bottom threshold, effort 

would be adjusted downward by the effort rate change parameter. If the catch per day for a 

fishery was above the top threshold, effort would be adjusted upward by the effort rate change 

parameter. The fourth effort parameter constrained max effort per day. 

Additionally, we simulated the gear changes prescribed as a management action by the 

NEUS LME in 1994 by modifying q and effort at the time step in the model corresponding to the 

start of 1993 for the affected fisheries.

 

MODEL CALIBRATION 

We present results from simulations of 50-year runs of the Atlantis NEUS model. The 

runs started in 1964 and ended in 2014 (with tuning data ending in 2004). Our intent was to 

create a “base scenario” that approximates the observed time series of surveyed or assessed 

biomass for each functional group included, catches on those functional groups, and effort for 

each fleet included. With a base scenario that approximates magnitude and pattern of these time 

series, we would then be able to begin to explore the qualitative effects of various fishing and 

management strategies on the functional groups. The full range of those scenarios is not, 

however, a part of this document but will be forthcoming after the structure and calibration of 

this Atlantis NEUS application is documented and as specific applications for such scenarios are 

required. Rather, here we describe the four levels of calibration needed to get to our base 

scenario from which future scenarios and strategies will be based. 

We explored the model at four levels of calibration. These are described in detail below. 

At each level we attempted, as much as possible, to not alter the previously tuned parameters. 

Sometimes, however, new interactions were revealed when increasing levels of dynamism were 

modeled, necessitating changes in parameters tuned at a previous level. The goal of this tuning 

was to parameterize a base scenario which reasonably captured the magnitude and overall shape 

of the observed biomass, catch, and effort time series so that we can begin to explore qualitative 

effects of different management scenarios. The levels of tolerance for each level of calibration 

are shown in Table 5, and the results of tolerance testing for biomass, catch, and effort are 

presented in Tables 6-8. 

Biophysical 
36 

The first level aimed to get all the ecological, physical, and chemical processes at the 

appropriate magnitude such that no biological group was extirpated through the model run. We 

do not provide results of these model executions here as they were basically at the level of 

ensuring that no functional group collapsed too often or too quickly, most functional groups 

persisted and were not too emaciated, and the hydrodynamic model did not sweep excessive 

nutrients off the shelf. 

Forced Catch 

The second level forced catch of each fleet based on time series data. Biomass over time 

was allowed to vary dynamically according to model behavior and biological parameters, largely 

centered around growth and mortality-related parameters. These biological parameters were 

tuned at this stage so that the model’s estimated biomass for each functional group approximated 

the stock assessment or survey swept-area estimate time series in magnitude and overall shape. 

Dynamic Catch-fixed Effort 

The third level started after parameter tuning of the biological parameters was completed. 

This involved forcing effort of each fleet while allowing catch to vary dynamically. Parameters 

that affected catch were tuned (primarily catchability of each fleet on each biological group), 

until the model’s estimated catch and biomass approximated the observed time series. 

Dynamic Effort 

The fourth level started after parameter tuning of the catch parameters was completed, 

allowing effort to vary dynamically. Parameters that affected effort were tuned (a bottom and 

top threshold value based on CPUE, which determined if: a fishery increased or decreased its 

effort; a rate parameter which determined the rate of increase or decrease if it occurred; and a 

parameter which defined the level of effort per day for the fishery), until the model’s estimated 

effort, catch, and biomass approximated the observed time series. 

RESULTS 

 There is a plethora of results from any one run of an Atlantis model configuration. Here 

we present a selected set of functional groups that are representative examples of the full range 

of biota; the rest are presented in Appendices B-E. The representative groups we show here are 

Atlantic mackerel (Scomber scombrus; Atlantis NEUS code FPL); haddock (Melanogrammus 

aeglefinus; Atlantis NEUS code FDO); Atlantic cod (Gadus morhua; Atlantis NEUS code FDS); 

spiny dogfish (Squalus acanthias; Atlantis NEUS code SHB); and decapod shrimp (PWN) which 

included Pandalus borealis, other pandalids, paneids, and Crangon septemspinosa (Table 2). 

We also note general patterns of fitting for all taxa or fleets with respect to the levels of tolerance 

for each run. 

Generally speaking, at each level of calibration it was possible to achieve realistic results 

based on time series data available for biomass, overall catch, catch per fishery, and effort. More 

specifically, it was possible to recreate the most important time series at approximately the right 

orders of magnitude and general trajectories. 

In what follows, we present the biomass, catch, catch per fleet, and effort results for each 

functional group or fleet for the fixed catch, fixed effort, and dynamic effort runs. We have 

presented each graph to show the model output plotted against observed or assessed data in 5 
37 

year time blocks. Although Atlantis can output the information in a wide array of formats, we 

used 5 year time blocks to exhibit major trends and events in the times series while minimizing a 

higher degree of interannual variability and in an attempt to not draw specific attention to any 

particular annual event given the more strategic nature of this model’s outputs. We also show 

time series as fitted to data from 1964 - 2004, with an additional 10 years of projections through 

2014. We did this to ensure we encapsulated the major dynamics of these groups over time. 

Biomasses 

Selected Groups 

Results for the biomass trajectories from the fixed catch, fixed effort, and dynamic effort 

runs of the Atlantis NEUS model are all noted here, with some comment on their fit within the 

range of tolerances. Most functional groups had trajectories that were of a similar order of 

magnitude for all 3 runs (Figures 6-10). Shrimp was the notable exception, having biomasses 

generally between 10,000 – 30,000 mt after 1971 in the fixed catch run (Figure 10a) but rising to 

over 1,000,000 mt in both the fixed effort (Figure 10b) and dynamic effort (Figure 10c) runs. 

This latter discrepancy for shrimp was corroborated in a subsequent refinement in shrimp stock 

assessments (see Discussion). 

Atlantic mackerel exhibited biomass trajectories that more closely matched the 

assessment time series in the fixed effort (Figure 6b) and dynamic effort (Figure 6c) runs than 

was the case with the fixed catch (Figure 6a) run. Particularly, from 1967-1977, the fixed catch 

run showed a much higher modeled biomass than did the assessment time series, while the fixed 

effort and dynamic effort runs matched the assessment time series more closely during this 

range. 

For haddock, the biomass trajectories that most closely matched that of the assessment 

time series were the fixed and dynamic effort runs (Figures 7b,c). The fixed catch (Figure 7a) 

run showed a relative increase in biomass compared to the assessment time series after 1984. 

One obvious discrepancy is that the fixed and dynamic effort runs missed a biomass peak that 

occurred from 1975 to 1984. This discrepancy is due to two strong recruitment year classes 

(1975 and 1978) that Atlantis NEUS was not parameterized to include. 

For Atlantic cod, the fixed catch run (Figure 8a) matched the assessment trajectory 

reasonably well until about 1990 when the model predicted increasing biomass, while the 

assessment indicated decreasing and then stable biomass. Both the fixed effort (Figure 8b) and 

dynamic effort (Figure 8c) runs matched the overall assessment trajectory more closely in later 

years. 

For spiny dogfish, all three model runs performed well through 1984. The fixed catch 

(Figure 9a) run showed a higher than expected biomass compared to the assessment trajectory, 

while the other two runs tracked the assessment trajectory more closely. Again, the run which 

seemed to track the assessment trajectory most closely was the fixed effort run (Figure 9b), while 

the dynamic effort run (Figure 9c) showed a slightly higher than expected biomass level after 

1999. 

Other Groups 

 Generally speaking, Atlantis was able to approximate the assessment biomasses within an 

order of magnitude and also approximate the assessment biomass trajectories at each level of 

calibration (Tables 5 and 6; Appendix B). The fixed catch run usually performed worse than the 

fixed effort run, particularly in approximating the assessment biomass trajectories. This 
38 

difference was likely due to effort being prescribed by a box when effort was added to the model, 

as opposed to the more general “catch only” parameters included in the fixed catch run. When 

effort was modeled dynamically, the match between modeled and assessment biomass 

trajectories tended to be slightly worse than in the fixed effort runs (as would be expected), but 

there were some exceptions where the dynamic effort runs matched the assessment trajectories 

more closely than in the fixed effort run (Appendix B). However, in the dynamic effort run, 

biomasses for individual groups mostly remained within the acceptable limits of order of 

magnitude when compared to the assessment biomasses (Tables 5 and 6). 

Catches 

The fixed catch run results matched the actual catch time series for each functional group 

(by definition) and are therefore not shown. Most functional groups showed the right order of 

magnitude and general shape when compared to the observed data (Tables 5 and 7). 

 For Atlantic mackerel, both the fixed effort (Figure 11a) and dynamic effort (Figure 11b) 

results matched the general shape of the actual catch time series, but the magnitude of the fixed 

effort run appeared to be slightly closer to the observed catch than the dynamic effort run. 

 For haddock, both the fixed effort (Figure 12a) and dynamic effort (Figure 12b) runs 

approximately matched the general shape of catch of the actual time series, except for the peak 

corresponding to the strong recruitment class in the biomass figures (Figures 7a-c). The dynamic 

effort run more closely matched the magnitude of the actual time series compared to the fixed 

effort run. 

 For Atlantic cod, both the fixed effort (Figure 13a) and dynamic effort (Figure 13b) runs 

show the same order of magnitude of catch as the observed data. The fixed effort run more 

closely matches the overall shape of the actual time series, while the dynamic effort run appears 

to more closely follow the biomass trajectory of cod as predicted by the model. 

 For spiny dogfish, both the fixed effort (Figure 14a) and dynamic effort (Figure 14b) runs 

showed a lower level of catch than did the observed data, but the overall trajectories matched 

reasonably well. The fixed effort run appeared to perform slightly better when comparing 

trajectory shapes, and after 2004 (when there was no catch time series available), the two 

diverged sharply. The fixed effort run predicted stable catches, while the dynamic effort run 

predicted generally higher catches. 

 For shrimp, the fixed effort run (Figure 15a) matched the observed catch time series 

trajectory closely, although observed catch amount was somewhat lower (by about half). The 

dynamic effort run (Figure 15b) did not match the time series trajectory as closely, but the 

catches were approximately the same order of magnitude up through 2004, after which the 

dynamic effort run predicted higher catches for shrimp. 

 The remainder of the catch results are presented in Appendix C. 

Catch per Fleet 

We present two catch per fishery figures for each functional group: one for the fixed 

effort run and one for the dynamic effort run (again, by definition the fixed catch run would be 

redundant). Each figure has two panels, the top (labeled “Model”) is the modeled output and the 

bottom (labeled “Data”) is based on the actual catch time series. The panels show catch broken 

down by each fishery that targets a functional group. 

 For Atlantic mackerel the primary fishery was the small pelagic midwater trawl after 

1979, with strong fishing pressure by the international small pelagic midwater fleet up until 
39 

1978. There were no official catch data available for the international pelagic midwater fleet, 

which is the reason that the “Data” portion of the graph does not show the estimated catch for 

this fishery, even though we accounted for it in the model. Both the fixed effort (Figure 16) and 

dynamic effort (Figure 17) runs predicted these two fisheries as the primary fisheries (and at 

reasonable magnitudes) on Atlantic mackerel during the appropriate time frame. 

 For haddock, the primary fishery was the demersal bottom trawl, with the demersal gill 

net and demersal line fisheries contributing to the overall catch to a much lower degree. In both 

model runs (Figures 18 and 19), the demersal bottom trawl made up most of the overall catch. 

The magnitude of the catch by the demersal bottom trawl in the dynamic effort run (Figure 19) 

was closer to the actual time series than it was in the fixed effort run (Figure 18). 

For Atlantic cod, the primary fishery was the demersal bottom trawl, with the demersal 

gill net and the demersal longline fisheries also showing noticeable catches. The fixed effort run 

(Figure 20) showed the best overall proportions between these three fisheries in comparison to 

the observed data. The dynamic effort run (Figure 21) showed a much larger proportion of catch 

for the demersal longline fishery through the mid 1980s, before it dropped to a proportion closer 

to the observed data thereafter. 

 For spiny dogfish, the primary fisheries were the dogfish trawl and the demersal gill net. 

As mentioned previously, the overall catch was lower in the model than in the observed time 

series for both the fixed effort and dynamic effort runs. However, in the fixed effort run (Figure 

22), the two primary fisheries caught approximately the same proportion of spiny dogfish as the 

observed data indicated, but other fisheries (small pelagic gill net and small pelagic midwater 

trawl) caught more in the model run than in the observed data. Both of these fisheries had their 

catch proportions reduced in the dynamic effort run (Figure 23) to more appropriate levels, with 

most of the catch by the dogfish trawl and demersal gill net as was seen in the observed data. 

 For shrimp, the main fishery was the shrimp bottom trawl. The fixed effort run (Figure 

24) reasonably matched the catch per fishery time series. The dynamic effort run (Figure 25) 

was unable to match the catch per fishery time series trajectories but did catch approximately the 

right order of magnitude starting in the late 1970s until the end of the observed data. 

 The remainder of the catch per fishery results is presented in Appendix D. 

Effort 

We present the results for the fisheries that were the primary fleets targeting the 

representative groups. We also present a few examples of other minor, albeit important, 

fisheries. Each figure shows the observed time series of effort in days-at-sea for a given fishery 

from the dynamic effort model run. The forced effort model’s results, by definition, matched the 

observed data and are not shown here. In general, the effort for each fishery was within an order 

of magnitude of the observed data (Tables 5 and 8). 

The small pelagic midwater trawl fishery (Figure 26) matched the observed data in both 

overall magnitude and shape up through 2004 (the final year for which we had time series data). 

Likewise, the international small pelagic midwater trawl fishery (Figure 27) matched the 

observed data in overall magnitude and shape until 1977, when the enactment of national laws 

establishing the exclusive economic zone (EEZ) expelled this fishery from operating within the 

NEUS LME. 

 The demersal bottom trawl fishery (Figure 28) matched the observed data in both overall 

magnitude through 2004, although the initial predicted effort up through 1989 is consistently 

lower. The demersal gill net fishery (Figure 29) also matched the overall shape and magnitude 
40 

although from 1985-2004 the modeled effort was somewhat lower than the observed time series. 

As these were the major fleets in the NEUS region, it was imperative that we adequately capture 

their dynamics. 

 The benthopelagic demersal trawl fishery (Figure 30) was a modeled fishery which did 

not capture the major maxima and minima in effort that was seen in the observed data. We 

observed that minor modifications to the final dynamic effort parameters resulted in exponential 

increase of effort over the model run. Since the effort was at the right order of magnitude 

compared to the effort time series for most of the model run (with the largest discrepancy 

occurring in 1991-1992), we accepted the parameterization as a better alternative than the one 

which led to exponential effort increases. 

The dogfish trawl (Figure 31) and shrimp bottom trawl (Figure 32) fisheries were other 

examples of fisheries which did not match the specific trajectories of the observed data but 

generally matched the right order of magnitude. One probable explanation for some of the 

discrepancies between the observed data and the dynamic effort model outputs is the use of the 

“Perfect Knowledge” assumption in our effort model. This assumption involves the fleets 

operating such that they can track all known dynamics of the targeted species, whereas in reality 

there is always some uncertainty about estimates of the biomass, distribution, and so on for these 

taxa. The model simulates effort for functional groups which have well known preferred ranges 

better than it does for more wide-ranging functional groups such as the ones just mentioned. 

Within the model, this could notably change the distribution of these taxa more easily and often. 

 The remainder of the effort results are presented in Appendix E. 

Spatial Representations 

Figures 33-39 show spatially explicit output in an interactive visualization tool (Online 

Interactive Visualization Environment - OLIVE) for each of the above functional groups from 

the dynamic effort run. Each box (and depth layer within the box) is indicated on the map, and 

the time series at the bottom shows abundance over the run of the model in the box indicated by 

the red dot on the map. The map itself shows the abundance at the specific time indicated in the 

upper left of the image (the time is also referenced with red triangles at the top and bottom of the 

time series). Cooler colors (blue, purple) on the map indicate lower abundance, while warmer 

colors (yellow, orange and red) indicate higher abundance. 

 It is evident from these figures that these functional groups show reasonable patterns 

relative to their known (empirically demonstrated) distributions. Atlantic mackerel were 

predicted to have high biomass along the northern flank of Georges Bank and to a lesser degree 

in the Gulf of Maine (Figure 33) in the fall, with seasonal migrations evident in the winter 

(Figure 34). Haddock were predicted to have the highest biomass in the Gulf of Maine and the 

northern flank of Georges Bank (Figure 35). Atlantic cod (Figure 36) were predicted to have 

high biomass on the northern flank of Georges Bank. Spiny dogfish (Figure 37) were also 

predicted to have high biomass on the northern flank of Georges Bank in the fall with seasonal 

migrations evident in the winter (Figure 38). The shrimp (mainly northern shrimp Pandalus 

borealis and other pandalids) were predicted to primarily occur in the Gulf of Maine, with less 

biomass of other shrimp species such as panaeids and Crangon spp. occurring offshore of 

southern New England and the Mid Atlantic Bight (Figure 39). 

Overall Output and Outcomes 
41 

In the final model run that we would propose to use as a baseline for future dynamic 

modeling scenarios, we were within limits of tolerance (model vs. observed data) at a reasonable 

level (Tables 5-8). For final biomass outputs we achieved tolerance in 44 out of 45 functional 

groups. For final catch outputs, we achieved tolerance in 26 out of 35 targeted functional 

groups, with 27 out of 35 targeted functional groups for catches per fleet. For final effort 

outputs, we achieved tolerance for 16 out of 18 fleets. These numbers, while not perfect, 

represent a tradeoff of better fitting in forced scenarios versus allowing the model to dynamically 

respond and hence adapt to simulated novel strategies. We also note that for those functional 

groups or fleets whose biomass or catches were outside of our stated range of tolerance, they 

were generally reasonable representations of observed time series and often represented very 

minor fisheries that were hard to model or that had minimal/uncertain data inputs. These 

functional groups or fleets were represented fairly well, again just not within our limits of 

tolerance. These discrepancies were usually due to differences in timing of events more so than 

orders of magnitude or general shape of these plots. More germane, the major and most 

important fleets and biota (ecologically and economically speaking) were well modeled and well 

within the limits of tolerance. 

 

DISCUSSION 

Lessons Learned 

Here we summarize those major points that we have gleaned while parameterizing and 

calibrating the Atlantis NEUS model. We also provide some context and commentary on the 

model baseline and possible, future improvements and uses. 

While Atlantis includes a wide range of options and can be used to create quite 

complicated models, this capacity must be used carefully. So long as critical drivers and 

processes are captured, simpler component formulations have substantial predictive power. As 

an example, despite the immense complexity of the social, political, economic, and regulatory 

environment of the NEUS LME, the patterns of effort per fleet over the last 40-50 years were 

quite effectively captured for most fleets by using a model based on simple catch-per-unit effort 

(CPUE) thresholds, with days-at-sea by a fleet reduced if CPUE dropped below a minimum 

threshold and days-at-sea increased if CPUE raised above a maximum threshold. The single 

intervention required for validation and calibration was the imposition of the major regulatory 

restructuring that occurred in 1994. Thus, while it is widely recognized that all models are 

simplified approximations of reality (Box 1976, 1979), the art to modeling is to parsimoniously 

represent a system such that major processes are realistically captured without being overparameterized or structured. This will likely remain an inherent challenge for an approach such 

as Atlantis. We trust that the documentation provided demonstrates a reasonable attempt at such 

parsimony in our modeling approach, given the complexity of an approach such as Atlantis 

A major observation from this Atlantis application is that calibration of certain groups 

(particularly invertebrates such as gelatinous zooplankton, cephalopods, and shrimp) is much 

more difficult than others. Historical fishery-independent surveys are preferable for informing 

this process of calibration, and these kinds of data are often much harder to locate for some of 

the invertebrates. Yet a mismatch between Atlantis predictions and single species models can be 

insightful rather than simply uninformative or problematic from the perspective of tuning or 

calibrating. For instance, the shrimp abundance in this Atlantis NEUS model tended to increase 

under plausible parameterizations in contradiction to the assessments to which we were trying to 

calibrate. Subsequent revision of the assessment based on new data sources has also found this 
42 

pattern of increase that was predicted by Atlantis (NEFSC 2007; Link and Idoine 2009). Some 

species will likely remain underdetermined (e.g., gelatinous zooplankton) and as such, Atlantis 

models may provide a mechanism for bounding orders of magnitude on estimates of their 

abundance, as well as a source of information to feedback into the real-world monitoring 

schemes of such organisms. 

While Atlantis can simulate strong or weak year classes, we did not utilize this feature in 

model calibration to ensure the capturing of the main processes over the time series and not 

artificially force it to specific events across the broad array of taxa groups modeled. Some of our 

discrepancies between model runs and the data to which we were calibrating could be attributed 

to this phenomenon. For instance, Atlantic haddock had an observed spike in biomass (and 

catch) from 1977-1983 which could be attributed to a strong year class in prior years, but the 

Atlantis NEUS application was unable to replicate this. In turn this may have affected effort 

allocations in the dynamic effort run for fisheries which target haddock. 

In terms of calibration, and generally speaking, tuning the biology and physics was 

intuitive and reasonable; the balance among known processes resulted in rational model outputs. 

This modeling exercise in general and the level of tuning in particular reinforced the fact that one 

cannot overlook the role of nutrients, physics, primary production, and secondary production in a 

given LME for the production of LMR. That is, it takes an entire ecosystem to grow a fish. 

Furthermore, it takes the right kind of ecosystem to grow bigger fish and fisheries of a desirable 

species mix. 

By calibrating with fisheries catch and biology, the tuning was still rather intuitive, but 

once we moved to calibrating to biology, catch and effort, the tuning was, as expected, more 

difficult because of the increased number of constraints. Yet this level of constraining, although 

limiting our parameter space, was useful in that it forced us to focus on our understanding of the 

system and of which processes had the most influence on all the targeted biota. Thus, from this 

effort we conclude that calibrated predictions are now possible for LMEs. We note that the 

precision (variance, exact value, etc.) of those predictions may be less than is typically done, but 

they are no less accurate (within tolerances of orders of magnitude, directional, etc.) despite the 

potential lack of precision. 

Another thing we learned in the calibration process was that to match the historical record 

of biological, catch and effort time series, we had to explicitly address tradeoffs. A model 

application like this explicitly forced us to recognize that we cannot optimize everything 

simultaneously. As a corollary, this calibration process resulted in the observation that it is very 

likely that simpler LMR management options are usually better or at least more robust. For 

instance, tuning to a simple quota was actually more within the levels of tolerance than tuning to 

dynamic effort controls as modeled. 

Modern computing power has eased many computational constraints and facilitates the 

use of ecosystem models (Beck 1999). Nevertheless model specification, structure, 

parameterization, and system understanding remain a continued and nontrivial challenge (Silvert 

1981; Jørgensen 1994). While there are substantial tradeoffs between the volume of data 

available for model initialization and time taken to parameterize and calibrate the model (the 

more data available the longer it takes to initially parameterize the model, but the faster the 

model can be calibrated), all models benefit from time put into conceptualizing model structure 

in the first place. Following ecosystem modeling guidelines and best practices, such as those 

specified in FAO (2008) and NMFS (Townsend et al. 2008), is strongly recommended. 
43 

We conclude the lessons learned by noting that patterns are in fact detectable, repeatable, 

and predictable from complex biophysical systems such as this Atlantis NEUS application. 

Value of This Approach 

Atlantis’ main value is its ability to simulate an entire Management Strategy Evaluation 

(MSE) cycle, including stock assessments and management actions based on those stock 

assessments. This use will provide germane advice to fishery managers as to how different 

management actions will affect the ecosystem modeled, and thus the tradeoffs present with each 

potential management action. Even without the full MSE module enacted, Atlantis has 

additional value simply through increasing understanding of a given ecosystem in the 

parameterization stage. In some cases (c.f. shrimp noted above), Atlantis has predicted 

biomasses for a functional group widely divergent than was assumed to be the case, only to have 

later in situ observations confirm the simulation results. In other cases, this model has helped to 

increase our understanding of the precise interactions between functional groups and fisheries. 

The use of Atlantis can highlight misunderstanding of ecosystem structure and function 

as well as unexpected consequences of well-intentioned management options. Even without the 

fully closed MSE approach enacted, a lot can be learned from Atlantis, especially considering the 

impacts of different scenarios and forms of management (even if such management is not 

adaptive, but is in place permanently). 

From the inception of Atlantis, cumulative impacts have been an important consideration. 

The majority of these considerations have revolved around impacts of other industries on water 

quality, productivity, and the availability of suitable habitat in the coastal zone. Although not 

largely enacted for Atlantis NEUS, the majority of these are possible with the focus on the 

nutrient cycle used in Atlantis. This is something that has demarcated the Atlantis approach from 

other ecosystem models. It is important to address these cumulative impacts because they can 

also modify the system and so undermine or counteract the effects of fisheries management. 

Atlantis remains a viable tool to account for such factors.

Limitations of This Approach 

Atlantis is a strategic tool and is not appropriate for use in direct support of tactical 

management decisions. That is, we strongly recommend against setting specific quotas, TACs, 

etc. (namely, tactical biological reference points, BRPs) by using this modeling approach. None 

of the parameterizations we have calibrated above would necessarily result in useful or 

believable tactical LMR management advice. Nor should they. Rather, we emphasize that their 

purpose is to offer strategic advice from a model such as this Atlantis NEUS application. 

Atlantis has many weaknesses – poor ease of use, patchy documentation, large data 

demands, and long run and calibration times. Many of these are common to all complex models, 

and one lesson from Atlantis that may be extended to many system models has to do with 

calibration. The size of these models means that methods such as pattern-oriented modeling (as 

compared to routine sensitivity analyses) that simultaneously fit multiple parameters against 

multiple data sets are the only feasible nonheuristic means of calibrating system-level models. 

Given the observational uncertainty associated with so many data sources (Stow et al. 2009), it is 

important to focus on accuracy rather than precision. Data should be prioritized so that predictive 

error against the most trustworthy time series is minimized; so long as output variables are 

reasonably close (i.e., within the tolerances we noted, e.g. +/- 20%) to estimates from data and 

the gross patterns in time series and spatial distributions match observed series, then notable 
44 

periods of calibration time (i.e., months) should not be wasted “chasing the fourth decimal place” 

of what is ultimately false precision. 

This principle also extends to the handling of uncertainty. This consideration is a critical 

but not straightforward undertaking for system-level models. Classical sensitivity analyses 

(where parameters are varied systematically, like in Saltelli et al. 2004) are not only impractical, 

but also confounded by internal model feedbacks, which means that parameter sensitivity is 

combinatoric and dependent on the time frame being considered (Pantus 2007). While methods 

that can deal with these issues may already exist in other fields, it is unclear in this case how they 

need to be identified and adapted. Rather, bounding options in an MSE sense appears to be a 

reasonable manner, coupled with the calibration noted above, to address this uncertainty. 

Next Steps for Atlantis NEUS 

 The three primary rationales for implementing Atlantis in the NEUS LME were to: (1) 

increase our understanding of ecosystem dynamics, (2) collaborate with our Australian 

colleagues (at Commonwealth Scientific and Industrial Research Organization (CSIRO) Marine 

and Atmospheric Research Division) by using our relatively large data sets to foster the 

development of this modeling tool in particular and ecosystem modeling in general, and (3) 

develop a modeling framework that could handle multiple fisheries management scenarios 

simultaneously so as to explore all the various tradeoffs and indirect effects across a range of 

management options. Increasingly, the Atlantis NEUS implementation is also being staged for 

consideration of multiple ocean-use (or multiple sector) issues, with other, nonfishing uses to be 

included. Currently, the NEUS LME application has gone through all four rounds of model 

calibration and validation. A number of preliminary scenarios have been run, but we would 

intend to expand on the suite of scenarios to explore once the Atlantis NEUS application has 

been fully documented and peer reviewed. 

We think that Atlantis NEUS as we have parameterized it can be used to compare 

management strategies. We also think that novel data and information could allow us to 

reparameterize the model to more rigorously meet the demands of other disciplines. Examples 

of potential reparameterizations include: updating the oceanographic submodel inputs with 

newer outputs data from updated or newer hydrodynamic models, revisiting the spatial resolution 

of the cells in the model, revising definitions of functional taxa groups, or expanding the use of 

the socioeconomic modules extant in Atlantis. Yet initially exploring tradeoffs among different 

management actions, and levels of each action, with the existing Atlantis NEUS application is, 

although not without its nuances and need for improvements as just noted, likely to be robust as 

currently parameterized and calibrated. 
45 

ACKNOWLEDGEMENTS 

 

We thank past and present members of the NEFSC Ecosystems Survey Branch who have 

supported, sampled, and maintained what is arguably one of the world’s best data sets of a 

multispecies survey. We similarly thank past and present members of the NEFSC Food Web 

Dynamics Program who have supported, sampled, and maintained a similarly impressive food 

habits database. These two elements of information were core to the construction this Atlantis 

NEUS model. We also acknowledge the full range of sampling conducted at the NEFSC, 

ranging from lower trophic levels through fisheries statistics. We also thank the many NEFSC 

colleagues who participated in EMAX, from which the network models were developed to form 

the basis (and associated information) for developing the food web structure herein. 

We thank CSIRO and associated parties that provided a Frohlich fellowship to J. Link 

which formally initiated the collaboration among Australian and American scientists. We also 

thank H. Townsend at NMFS NCBO who provided support to help R. Gamble partially work on 

elements of the Atlantis NEUS application and develop tools for the easier parameterization of 

future Atlantis applications. We thank A.D.M. Smith for his general support and specifically 

arranging the connection among CSIRO and NMFS colleagues. We thank C. Harvey, H. 

Townsend, M. Fogarty, and other, anonymous colleagues at the NEFSC who provided 

constructive comments on earlier versions of this draft. 
46 

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Feature Assumptions and/or formulation notes

General features

biomass units mg N/m3

input forcing nutrients, temperature and physics on interannual, 

seasonal, tidal frequencies

level of group detail functional group (with a small number of individual species)

resolution of the formulation used for the 

invertebrate groups

follow the dynamics of the entire biomass pool of the 

functional group (or species) in the cell

resolution of the formulation used for the 

vertebrate groups

follow the biomass dynamics (structural and reserve weight) 

of the ‘average individual’ for the functional group (or 

species) in the cell and the number of individuals in the cell

time step adaptive* daily or diurnal time step

Process related 

bioturbation and bioirrigation yes, simple exchange between layers

consumption formulation type II (asymptotic), with an availability parameter which can 

be habitat dependent

equations

five general sets of rate of change equations used 

(autotrophs, invertebrate consumer, vertebrate consumer, 

bacteria, inanimate)

formulation detail general: only growth, mortality and excretion explicit

light limitation optimal irradiance fixed

mixotrophy yes, for dinoflagellates (if present)

nutrient limitation external nutrients determine uptake

nutrient ratio Redfield

oxygen limitation yes

sediment burial very low background rate included

sediment chemistry dynamic, with sediment bacteria

shading of primary producers yes

spatial (or habitat) limitation yes (for benthic or demersal groups (and species))

spatial structure flexible with the potential for multiple vertical and horizontal 

cells

temperature dependency yes

transport model used for hydrodynamics flows yes

* Time steps at this scale (daily or diurnal) may cause instability in variables with fast dynamics (e.g. phytoplankton groups). 

These groups (all at the base of the food web) use smaller time-steps (adaptive in the sense that to optimize computational 

efficiency they are as large as they can be without causing instability) which are repeated until full model level time step has 

been completed. In contrast the higher trophic level groups (and the physical submodels) employ only model level timesteps.

Table 1. The assumptions and formulations of ATLANTIS.

53
Feature Assumptions and/or formulation notes

Model closure

top predators represented by static loss terms

some top predators are included explicitly, but predators not 

explicitly included in the food web are represented using 

quadratic mortality terms

mortality terms linear and quadratic

Vertebrate and fisheries related

age structure for the vertebrate groups multiple age classes (or stages, which equate to life 

phases), with final age class of each group a “plus group”

fishery discards target and bycatch groups (and species)

incidental mortality due to fishing yes

invertebrate fisheries yes

management

variable, may be via effort limitations, gear limitations, 

minimum legal size, area or temporal closures and may be 

based on target or endangered stocks

stock-recruit relationship Beverton-Holt, productivity-based or constant recruitment

stock structure

depends on recruitment function chosen – may be internal 

(all the stock within the bay and self-seeds) or external (the 

reproductive stock outside the bay produces the recruits 

and the oldest age classes migrate out of the bay to join this 

stock)

Table 1, continued. The assumptions and formulations of ATLANTIS.

54
CODE GROUP SPECIES 

FPL Atlantic mackerel Atlantic mackerel 

FPS Atlantic herring Atlantic herring 

FVD White hake White hake FVS Bluefish Bluefish FVB Other piscivores Flounder (fourspot, windowpane, gulfstream, summer, winter, witch), Atlantic halibut, hogchok

American plaice 

FVT Large piscivores Marlin (blue, white), swordfish, tuna (albacore, big eye, blackfin, bluefin, little) 

FMM Migratory mesopelagics Myctophids (lanterfish, pearlsides) FBP Benthopelagics Argentine, bay anchovy, striped anchovy, butterfish, sandlance, harvest fish, Atlantic silverside

round herring, chub mackerel, halfbeak

FDD Goosefish Goosefish 

FDE Shallow demersal fish Alewife, gizzard shad, blueback herring, mehaden, hickory shad, American shad, smelt 

FDS Atlantic cod Atlantic cod FDB Silver hake Silver hake 

FDC Miscellaneous demersals 57 spp (Note 1) FDO Haddock Haddock 

FDF Yellowtail flounder Yellowtail flounder 

SHB Spiny dogfish Spiny dogfish 

SHD Other demersal sharks Dogfish (chain, smooth), sharks (Atlantic angel, sandbar, porbeagle, nurse, sand tiger, bull, le

sharpnose, scalloped hammerhead, thresher)

SHP Pelagic sharks Sharks (blue, dusky, great hammerhead, shortfin mako, white, blacknose, silky, blacktip, tiger

finetooth, bignose) 

SSK Skates and rays Skates (barndoor, clearnose, little, rosette, smooth, winter, thorny), Atlantic torpedo, bullnose 

cownose ray, spotted eagle ray, stingrays (bluntnose, roughtail, Atlantic, yellow)

SB Seabirds Shearwaters (Cory’s, greater, sooty), gulls (great black-backed, herring, laughing), northern 

garnet, black-legged kittiwake, northern fulmar, Wilson’s storm petrel, red phalarope

PIN Pinnipeds Seals (grey, harbor, harp, hooded) 

REP Reptiles Turtles (green, leatherback, loggerhead, ridley) 

Table 2. The functional groups are shown with their ATLANTIS NEUS code, the group name, and the species included instances (e.g., Atlantic mackerel, only one species was included in the group and thus the group and species names Additionally, the initial biomass for each group is shown.

Note 1: Offshore hake, pollock, red hake, spotted hake, rockling (fourbeard, threebeard), cusk, roundnose grenadier, snipefish, John Dory, stickleback, pipefimoonfish, lookdown, Atlantic croaker, black sea bass, scup, weakfish, northern kingfish, black drum, silver perch, spot, tilefish, scorpionfish, redfish, blackbel(longhorn, moustache, shorthorn), sea raven, sea robin (armored, northern, striped, other), striped bass, tautog, cunner, eel (American, conger and uncl.), nowolfish, wrymouth, garfish, hagfish, hogfish, lumpfish, oyster toadfish, sand perch, Atlantic needlefish, Atlantic salmon, sheepshead, spot, sturgeons, tomcod

55
CODE GROUP SPECIES 

WHB Baleen whales Fin, humpback, minke, right, sei 

WHT Toothed whales Beaked whale, dolphin (bottlenose, common, risso, spotted, striped, whiteside), harbor porpoi

pilot whales, sperm whale

CEP Squid Illex, Loligo BFS Sea scallop Sea scallop BFF Other benthic filter feeder 14 groups (Note 2) BG Benthic grazer Echinoids, selected amphipods, gastropods 

BML Lobster Lobster 

BMS Shallow macrozoobenthos 14 spp (Note 3) PWN Shrimp Northern shrimp, (Pandalus borealis & other pandalids), sand shrimp, pink shrimp, brown shr

(& other paneids)

ZL Carnivorous zooplankton Euphausiids, mysids, chaetognaths, tunicates, hyperiids BD Deposit feeder 15 taxa groups (Note 4) 

BC Benthic carnivore Selected gastropods, selected annelids, octopods, starfish ZG Gelatinous zooplankton Ctenophores, siphonophores, salps, coelenterates, scyphozoans PL Diatoms DF Dinoflagellates PS Pico-phytoplankton ZM Copepods Copepods (various calanoid spp.) ZS Microzooplankton PB Pelagic bacteria BB Sediment bacteria BO Meiobenthos 

Note 2: Bay scallop, ocean quahog, surf clam, porifera, hydrozoa, anthozoa, selected annelids, other bivalves, cirripedia, bryozoa, brachiopoda, crinoidea, heNote 4: Platyhelminthes, Nemertea, Annelida, Pogonophora, Sipuncula, Echiura, Polyplacophora, Pycnogoda, Cumacea, Isopoda, Amphipoda (selected grouDecapoda (selected groups), Holothuroidea, Ophiuroidea

Table 2, continued. The functional groups are shown with their ATLANTIS NEUS code, the group name, and the speciegroup. In some instances (e.g., Atlantic mackerel, only one species was included in the group and thus the group and identical). Additionally, the initial biomass for each group is shown.

Note 3: Crabs (blue, cancer, green, hermit, horseshoe, jonah, lady, queen snow, red, rock, spider), knobbed whelk, asteroids

56
Fishery Code Description Swept Area (m3

)

dlineFD Line fishery on demersals 33609600

dredgeBFS Scallop dredge 17334080

dtrawlCEP Demersal trawl on cephalopods 41670000

dtrawlFBP Demersal trawl on benthopelagics 435297600

dtrawlFD Demersal trawl on other deep demersals 90004000

dtrawlFDB Demersal trawl on shallow demersals 5000400

dtrawlFDO Demersal trawl on cod and haddock 1680480

midwcCEP Midwater trawl on cephalopods 4167000

midwcFD Midwater Trawl - international fleet 77500600

midwcFP Midwater trawl on small pelagics 90500600

netFD Demersal gillnet on other deep demersals 259200000

plineFVO Pelagic line on tuna and sharks 2666880

pseineFP Purse seine on small pelagics 7853981.6

pseineFVO Purse seine on tuna and sharks 370.4

ptrawlPWN Shrimp trawl 8334000

REC Recreational fishery 12090

trapBMS Lobster traps 2880000

trapFDE Trap on demersals 5334000

Table 3. This table shows the ATLANTIS NEUS fishery codes along with the 

description and swept area of gear.

57
CODE GROUP midwcCEP midwcFP dredgeBFS netFD plineFVO pseineFVO pseineFP trapBMS trapFD dtrawlCEP dtrawlF

BC Benthic carnivore B 

BD Deposit feeder B 

BFS Sea scallop T 

BFF

Other benthic filter feeder

B 

BG Benthic grazer

BMS

Shallow macrozoobenthos

T B

BML Lobster T

CEP Squid T T 

PWN Shrimp

FDD Goosefish T T T

FDS Atlantic cod 

FMM Migratory mesopelagics B

FBP Benthopelagics

FPL Atlantic mackerel T T 

FPS Atlantic herring T T

FDB Silver hake 

FDC Miscellaneous demersals T TT

FDO Haddock

FVD White hake T

FDE Shallow demersal fish 

FDF Yellowtail flounder T TFVS Bluefish FVB Other piscivores 

FVT Large piscivores T T

PIN Pinnipeds B B 

SB Seabirds B

SHB Spiny dogfish B 

SHD Other demersal sharks B 

SHP Pelagic sharks T

SSK Skates and rays B 

WHB Baleen whales B

WHT Toothed whales B

REP Reptiles 

ZG Gelatinous zooplankton B 

ZL Carnivorous zooplankton

Table 4. The targeted groups for each fishery are indicated with a ‘T’ and the fisheries which include bycatch of a group aindicated with a ‘B’ in the appropriate cell.

GEAR

58
CODE GROUP dtrawlFDO midwcFD dlineFD REC ptrawlPWN dtrawlFBP trapFDE

BC Benthic carnivore B BBD Deposit feeder B B

BFS Sea scallop T B

BFF

Other benthic filter feeder

T B

BG Benthic grazer T

BMS

Shallow macrozoobenthos

T BB

BML Lobster T

CEP Squid T

PWN Shrimp T T

FDD Goosefish T T

FDS Atlantic cod T

FMM Migratory mesopelagics B

FBP Benthopelagics T T

FPL Atlantic mackerel T

FPS Atlantic herring T

FDB Silver hake T T

FDC Miscellaneous demersals T B

FDO Haddock T

FVD White hake T TFDE Shallow demersal fish T T

FDF Yellowtail flounder TFVS Bluefish TFVB Other piscivores T T

FVT Large piscivores T

PIN Pinnipeds

SB Seabirds B

SHB Spiny dogfish B

SHD Other demersal sharks T

SHP Pelagic sharks T

SSK Skates and rays T B

WHB Baleen whales

WHT Toothed whales

REP Reptiles B T

ZG Gelatinous zooplankton

ZL Carnivorous zooplankton

Table 4, continued. The targeted groups for each fishery are indicated with a ‘T’ and the fisheries which include bycatch oare indicated with a ‘B’ in the appropriate cell.

GEAR

59
Table 5. Levels of tolerance for each step of ATLANTIS calibration

Biophysical Tuning physics and background for biota

Hydrodynamic model doesn’t sweep nutrients off shelfNothing dies too often or too quicklyMost spp persist and aren’t too emaciated

Fixed Catch, no Effort Tuning biological groups and processes to observed levels of catch

Can have exploitation so that none of the biology dies outrightMost (~>75%) biomass trajectories within +/- 1 orders of magnitude (OOM)

Dynamic Catch, Fixed Effort Tuning catch to effort

Most (~85%) biomass trajectories and shapes within +/- 0.5 OOMMost (~75%) catch trajectories within +/- 1 OOM

Dynamic Effort Letting fleet behavior be tuned to effort

Most biomass (~90%) and catch (~80%) trajectories & shapes within +/- 0.5 & 1 OOMost (~85%) effort trajectories and shapes within +/- 1 OOM

60
CODE GROUP Fixed Catch Fixed Effort Dynamic Effort

FPL Atlantic mackerel Y Y Y

FPS Atlantic herring Marginal Y Y

FVD White hake Y Y Y

FVS Bluefish Y Y Y

FVB Other piscivores Y Y Y

FVT Large piscivores No TS1 No TS1 No TS1

FMM Migratory mesopelagics Y Y Y

FBP Benthopelagics Y Y Y

FDD Goosefish Marginal Y Marginal

FDE Shallow demersal fish Y Marginal Marginal

FDS Atlantic cod Y Y Y

FDB Silver hake Y Y Y

FDC Miscellaneous demersals Y Y Y

FDO Haddock Y Y Y

FDF Yellowtail flounder Y Y Y

SHB Spiny dogfish Y Y Y

SHD Other demersal sharks Ya Ya Ya

No TS1 No TS1 No TS1

OK OK OK

SSK Skates and rays Ya Ya Ya

No TS1 No TS1 No TS1

OK OK OK

No TS1,2 No TS1,2 No TS1,2

N OK OK

No TS1,3 No TS1,2 No TS1,2

Marginal OK OK

No TS1,4 No TS1,4 No TS1,4

OK OK Marginal

No TS1,4 No TS1,4 No TS1,4

OK OK OK

CEP Squid Ya Y Y

BFS Sea scallop Y Y Y

BFF Other benthic filter feeder Y Y Y

BG Benthic grazer No TS1 No TS1 No TS1

Note a – compared to survey estimates, not assessments

Note 1 – Magnitude compared to point estimates from Link et al. (2006) and sources therein.

Note 2 – G. Waring, pers. comm.

Note 3 – H. Haas, pers. comm.

Note 4 – D. Palka, pers. comm.

Table 6. Levels of tolerance for each functional group for each ATLANTIS NEUS model run, with regards 

to biomass trajectories. Y = yes, if the group matched the tolerance criteria (Table 5) for a given model 

run; N = no if it didn’t match the tolerance criteria; Marginal = either the magnitude was generally 

acceptable, or the shape or timing of the data trajectories were acceptable, but not both. No TS = there 

was no corresponding observed data time series available to compare the model runs against, but 

magnitudes were checked against point estimates or expert opinion, as indicated by the notes.

PIN Pinnipeds

REP Reptiles

MODEL RUN

SHP Pelagic sharks

SB Seabirds

WHB Baleen whales

WHT Toothed whales

61
CODE GROUP Fixed Catch Fixed Effort Dynamic Effort

BML Lobster Y Y Y

BMS Shallow macrozoobenthos No TS1 No TS1 No TS1

PWN Shrimp Marginal Marginal Marginal

ZL Carnivorous zooplankton No TS1 No TS1 No TS1

BD Deposit feeder No TS1 No TS1 No TS1

BC Benthic carnivore No TS1 No TS1 No TS1

ZG Gelatinous zooplankton No TS1 No TS1 No TS1

PL Diatoms No TS1 No TS1 No TS1

PS Pico-phytoplankton No TS1 No TS1 No TS1

ZM Copepods No TS1 No TS1 No TS1

ZS Microzooplankton No TS1 No TS1 No TS1

PB Pelagic bacteria No TS1 No TS1 No TS1

BB Sediment bacteria No TS1 No TS1 No TS1

BO Meiobenthos No TS1 No TS1 No TS1

Within Tolerances 23 26 24

Marginal 4 2 4

Outside Tolerances 1 0 0

Percentage which matched tolerances 96.40% 100% 100%

(discounting marginals) (89.3%) (96.4%) (92.8%)

Note a – compared to survey estimates, not assessments

Note 1 – Magnitude compared to point estimates from Link et al. (2006) and sources therein.

Note 2 – G. Waring, pers. comm.

Note 3 – H. Haas, pers. comm.

Note 4 – D. Palka, pers. comm.

Table 6, continued. Levels of tolerance for each functional group for each ATLANTIS NEUS model run, 

with regards to biomass trajectories. Y = yes, if the group matched the tolerance criteria (Table 5) for a 

given model run; N = no if it didn’t match the tolerance criteria; Marginal = either the magnitude was 

generally acceptable, or the shape or timing of the data trajectories were acceptable, but not both. No 

TS = there was no corresponding observed data time series available to compare the model runs 

against, but magnitudes were checked against point estimates or expert opinion, as indicated by the 

notes.

MODEL RUN

62
CODE GROUP Fixed Effort Dynamic Effort

FPL Atlantic mackerel Y Y

FPS Atlantic herring Marginal Y

FVD White hake Y Y

FVS Bluefish Y Y

FVB Other piscivores Marginal Marginal

FVT Large piscivores Y Marginal

FMM Migratory mesopelagics N/A N/A

FBP Benthopelagics Y Y

FDD Goosefish N Y

FDE Shallow demersal fish Y N 

FDS Atlantic cod Y Y

FDB Silver hake Y Y

FDC Miscellaneous demersals Y Y

FDO Haddock Y Y

FDF Yellowtail flounder Y Y

SHB Spiny dogfish Y Y

SHD Other demersal sharks Y Marginal

SHP Pelagic sharks No TS1 No TS1

SSK Skates and rays Marginal Marginal

SB Seabirds No TS1 No TS1

PIN Pinnipeds No TS1 No TS1

REP Reptiles No TS1 No TS1

WHB Baleen whales No TS1 No TS1

WHT Toothed whales No TS1 No TS1

CEP Squid Y Y

BFS Sea scallop Y N

BFF Other benthic filter feeder N N

BG Benthic grazer N/A N/A

BML Lobster Y N

BMS Shallow macrozoobenthos Y N 1 Bycatch, no directed catches on these groups. Statistics only for targeted groups.

 MODEL RUN

Table 7. Levels of tolerance for each functional group for each ATLANTIS NEUS model run, with 

regards to catch trajectories. Y = yes, if the group matched the tolerance criteria (Table 5) for a given 

model run; N = no if it didn’t match the tolerance criteria; Marginal = either the magnitude was generally 

acceptable, or the shape or timing of the data trajectories were acceptable, but not both. No TS = there 

was no corresponding observed data time series available to compare the model runs against; N/A 

means that it was not appropriate for the group to be considered as catch, even if it was incidental 

bycatch. N/A and No TS values were not considered in the calculations.

63
CODE GROUP Fixed Effort Dynamic Effort

PWN Shrimp Marginal Marginal

ZL Carnivorous zooplankton N/A N/A

BD Deposit feeder N/A N/A

BC Benthic carnivore N/A N/A

ZG Gelatinous zooplankton N/A N/A

PL Diatoms N/A N/A

PS Pico-phytoplankton N/A N/A

ZM Copepods N/A N/A

ZS Microzooplankton N/A N/A

PB Pelagic bacteria N/A N/A

BB Sediment bacteria N/A N/A

BO Meiobenthos N/A N/A

Within Tolerances 17 13

Marginal 4 5

Outside Tolerances 2 5

91.30% 78.20%

(82.6%) (67.4%) 1 Bycatch, no directed catches on these groups. Statistics only for targeted groups.

Percentage which matched tolerances (discounting marginals)

Table 7, continued. Levels of tolerance for each functional group for each ATLANTIS NEUS model run, 

 MODEL RUN

64
MODEL RUN

FISHERY Dynamic Effort1

midwcCEP Marginal

midwcFP Y 

dredgeBFS Y

netFD Y

plineFVO Y

pseineFVO Y

pseineFP Y

trapBMS Marginal

dtrawlCEP Y

dtrawlFD Y

dtrawlFDB N

dtrawlFDO Marginal

midwcFD Y

dlineFD Y

ptrawlPWN Y

dtrawlFBP Marginal

trapFDE Y

Within Tolerances 12

Marginal 4

Outside Tolerances 1

94.10%

(82.3%)

Percentage which matched tolerances (discounting marginals)

Table 8. Levels of tolerance for each fishery for each ATLANTIS NEUS model 

run, with regards to effort trajectories. Y = yes, if the group matched the 

tolerance criteria (Table 5) for a given model run; N = no if it didn’t match the 

tolerance criteria; Marginal = either the magnitude was generally acceptable, or 

the shape or timing of the data trajectories were acceptable, but not both.

Note 1- recreational fisheries were not included in this calculation due to data input uncertainties, but were 

a part of the model.

65
FIGURES 

Figure 1. The specific steps of the management strategy and adaptive management cycles are 

explicitly modelled within Atlantis. Adapted from Fulton et al. (2004a, 2004c, 2004d). MSE = 

management strategy evaluation, RBC = recommended biological catch. 

Figure 2. The NEUS LME is composed of four separate subregions: Gulf of Maine, Georges Bank, 

Southern New England, and Mid Atlantic Bight. 

66
Figure 3. The arrows show the general pattern of circulation in the NEUS LME. Water enters the 

system from the colder (blue) Labrador Shelf (A) and deep ocean slope wamer (red) water through 

the Northeast Channel (B), and leaves the northern part of the system primarily through the Great 

South Channel (C) and offshore flow fields near Cape Hatteras (D). 

Figure 4. The NEUS LME food web adapted from Link (2002). Red flows indicate predation on fish 

and upper trophic levels, black on invertebrates and lower trophic levels. The numbers represent 

various species or species groups in the ecosystem (see Link 2002 for further details). 

67
68 

Figure 5. The NEUS LME as modeled spatially by Atlantis. There are 22 dynamically modeled 

horizontal regions (or boxes). Each region is further modeled as having multiple depth layers: up 

to four water column layers in the case of Atlantis NEUS, with an epibenthic layer and a sediment 

layer. There are also 8 boundary boxes which primarily control migration of groups into and out 

of the system, and are not shown here. Locations of several prominent geographic features are 

noted. 
a.

b.

c.

Figure 6. Atlantic mackerel (Scomber scombrus) biomass trajectories for both Atlantis NEUS (a. 

fixed catch run, b. fixed effort run, c. dynamic effort run) and observed time series (assessment 

and survey). Modeled and assessment data are scaled to the left Y-axis while survey data are 

scaled to the right Y-axis. 

69
a.

b.

c.

Figure 7. Haddock (Melanogrammus aeglefinus) biomass trajectories for both Atlantis NEUS (a. 

fixed catch run, b. fixed effort run, c. dynamic effort run) and observed time series (assessment 

and survey). Modeled and assessment data are scaled to the left Y-axis while survey data are 

scaled to the right Y-axis. 

70
a.

b.

c.

Figure 8. Atlantic cod (Gadus morhua) biomass trajectories for both Atlantis NEUS (a. fixed catch 

run, b. fixed effort run, c. dynamic effort run) and observed time series (assessment and survey). 

Modeled and assessment data are scaled to the left Y-axis while survey data are scaled to the 

right Y-axis. 

71
a. 

b. 

c. 

Figure 9. Spiny dogfish (Squalus acanthias) biomass trajectories for both Atlantis NEUS (a. fixed 

catch run, b. fixed effort run, c. dynamic effort run) and observed time series (assessment and 

survey). Modeled and assessment data are scaled to the left Y-axis while survey data are scaled 

to the right Y-axis. 

72
a.

b.

c.

Figure 10. Shrimp biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed effort 

run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and 

assessment data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

73
a.

b.

Figure 11. Atlantic mackerel (Scomber scombrus) catch trajectories for both Atlantis NEUS (a. 

fixed effort run, b. dynamic effort run) and the actual observed time series. 

74
a.

b.

Figure 12. Haddock (Melanogrammus aeglefinus) catch trajectories for both Atlantis NEUS (a. 

fixed effort run, b. dynamic effort run) and the actual observed time series. 

75
a.

b.

Figure 13. Atlantic cod (Gadus morhua) catch trajectories for both Atlantis NEUS (a. fixed effort 

run, b. dynamic effort run) and the actual observed time series. 

76
a.

b.

Figure 14. Spiny dogfish (Squalus acanthias) catch trajectories for both Atlantis NEUS (a. fixed 

effort run, b. dynamic effort run) and the actual observed time series. 

77
a.

b.

Figure 15. Shrimp catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort 

run) and the actual observed time series. 

78
Figure 16. Atlantic mackerel (Scomber scombrus) catch per fishery trajectories for both Atlantis 

NEUS (fixed effort run) and the actual observed time series. 

79
Figure 17. Atlantic mackerel (Scomber scombrus) catch per fishery trajectories for both Atlantis 

NEUS (dynamic effort run) and the actual observed time series. 

80
Figure 18. Haddock (Melanogrammus aeglefinus) catch per fishery trajectories for both Atlantis 

NEUS (fixed effort run) and the actual observed time series. 

81
Figure 19. Haddock (Melanogrammus aeglefinus) catch per fishery trajectories for both Atlantis 

NEUS (dynamic effort run) and the actual observed time series. 

82
Figure 20. Atlantic cod (Gadus morhua) catch per fishery trajectories for both Atlantis NEUS (fixed 

effort run) and the actual observed time series. 

 

83
Figure 21. Atlantic cod (Gadus morhua) catch per fishery trajectories for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

84
Figure 22. Spiny dogfish (Squalus acanthias) catch per fishery trajectories for both Atlantis NEUS 

(fixed effort run) and the actual observed time series. 

 

85
Figure 23. Spiny dogfish (Squalus acanthias) catch per fishery trajectories for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

86
Figure 24. Shrimp catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and the 

actual observed time series. 

 

87
Figure 25. Shrimp catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) and 

the actual observed time series. 

88
Figure 26. Effort trajectory for the midwater trawl on small pelagics for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

Figure 27. Effort trajectory for the international fleet - midwater trawl for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

89
Figure 28. Effort trajectory for the demersal trawl on other deep demersals for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

Figure 29. Effort trajectory for the demersal gillnet on other deep demersals for both Atlantis 

NEUS (dynamic effort run) and the actual observed time series. 

90
Figure 30. Effort trajectory for the demersal trawl on benthopelagics for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

Figure 31. Effort trajectory for the demersal trawl on shallow demersals for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

91
Figure 32. Effort trajectory for the shrimp trawl for both Atlantis NEUS (dynamic effort run) and the 

actual observed time series. 

92
Figure 33. Atlantic mackerel (Scomber scombrus) spatial distribution on September 14, 2001 of 

the dynamic model run. The red dot indicates the currently selected box and the time series 

below shows the biomass in mt / km2

. Each box is indicated on the map, and the time series at 

the bottom shows abundance over the run of the model in the box indicated by the red dot on the 

map. The map itself shows the abundance at the specific time indicated in the upper left of the 

image (the time is also referenced with red triangles at the top and bottom of the time series). 

Cooler colors (blue, purple) on the map indicate lower abundance, while warmer colors (yellow, 

orange and red) indicate higher abundance. 

93
Figure 34. Atlantic mackerel (Scomber scombrus) spatial distribution on December 13, 2001 of 

the dynamic model run. The red dot indicates the currently selected box and the time series 

below shows the biomass in mt / km2

. The dramatic change in distribution compared to Figure 33 

is due to seasonal migrations built into the model. Each box is indicated on the map, and the time 

series at the bottom shows abundance over the run of the model in the box indicated by the red 

dot on the map. The map itself shows the abundance at the specific time indicated in the upper 

left of the image (the time is also referenced with red triangles at the top and bottom of the time 

series). Cooler colors (blue, purple) on the map indicate lower abundance, while warmer colors 

(yellow, orange and red) indicate higher abundance. 

94
Figure 35. Haddock (Melanogrammus aeglefinus) spatial distribution on September 14, 2001 of the 

dynamic model run. The red dot indicates the currently selected box and the time series below 

shows the biomass in mt / km2

. Each box is indicated on the map, and the time series at the 

bottom shows abundance over the run of the model in the box indicated by the red dot on the 

map. The map itself shows the abundance at the specific time indicated in the upper left of the 

image (the time is also referenced with red triangles at the top and bottom of the time series). 

Cooler colors (blue, purple) on the map indicate lower abundance, while warmer colors (yellow, 

orange and red) indicate higher abundance. 

95
Figure 36. Atlantic cod (Gadus morhua) spatial distribution on September 14, 2001 of the dynamic 

model run. The red dot indicates the currently selected box and the time series below shows the 

biomass in mt / km2

. Each box is indicated on the map, and the time series at the bottom shows 

abundance over the run of the model in the box indicated by the red dot on the map. The map 

itself shows the abundance at the specific time indicated in the upper left of the image (the time is 

also referenced with red triangles at the top and bottom of the time series). Cooler colors (blue, 

purple) on the map indicate lower abundance, while warmer colors (yellow, orange and red) 

indicate higher abundance. 

96
Figure 37. Spiny dogfish (Squalus acanthias) spatial distribution on September 14, 2001 of the 

dynamic model run. The red dot indicates the currently selected box and the time series below 

shows the biomass in mt / km2

. Each box is indicated on the map, and the time series at the 

bottom shows abundance over the run of the model in the box indicated by the red dot on the 

map. The map itself shows the abundance at the specific time indicated in the upper left of the 

image (the time is also referenced with red triangles at the top and bottom of the time series). 

Cooler colors (blue, purple) on the map indicate lower abundance, while warmer colors (yellow, 

orange and red) indicate higher abundance. 

97
Figure 38. Spiny dogfish (Squalus acanthias) spatial distribution on December 13, 2001 of the 

dynamic model run. The red dot indicates the currently selected box and the time series below 

shows the biomass in mt / km2

. Each box is indicated on the map, and the time series at the 

bottom shows abundance over the run of the model in the box indicated by the red dot on the 

map. The map itself shows the abundance at the specific time indicated in the upper left of the 

image (the time is also referenced with red triangles at the top and bottom of the time series). 

Cooler colors (blue, purple) on the map indicate lower abundance, while warmer colors (yellow, 

orange and red) indicate higher abundance. 

98
Figure 39. Shrimp spatial distribution on September 14, 2001 of the dynamic model run. The red 

dot indicates the currently selected box and the time series below shows the biomass in mt / km2

. 

Each box is indicated on the map, and the time series at the bottom shows abundance over the 

run of the model in the box indicated by the red dot on the map. The map itself shows the 

abundance at the specific time indicated in the upper left of the image (the time is also referenced 

with red triangles at the top and bottom of the time series). Cooler colors (blue, purple) on the 

map indicate lower abundance, while warmer colors (yellow, orange and red) indicate higher 

abundance. 

99
Code Group Age mature Max age Initial biomass a b Bev-Holt alpha Bev-Holt beta Adult Mlin Juvenile 

(years) (years) (tons/m3) (linear mortality) (linear mo

FPL Atlantic mackerel 2 10 6637.535 0.0126 3.3 3.55E+09 2.00E+12 0 0 

FPS Atlantic herring 2 10 4886.576 0.0115 2.9 7.55E+09 3.00E+11 0 0 

FVD White hake 3 10 32303.92 0.0126 3.2 1.70E+07 1.60E+10 3.80E-09 3.80EFVS Bluefish 1 10 2697.025 0.011 3 1.85E+08 1.00E+11 0 0 

FVB Other piscivores 6 20 94801.77 0.0124 3.2 5.80E+08 3.00E+10 0 0 

FVT Large piscivores 9 30 4837.667 0.0214 2.96 2.80E+07 6.00E+11 0 0 

FMM Migratory mesopelagics 2 10 20.87382 0.011 3.01 1.25E+06 1.00E+07 7.00E-07 7.00EFBP Benthopelagics 3 10 23718.14 0.0116 3 4.50E+08 3.00E+11 8.00E-12 8.00EFDD Goosefish 3 10 38974.77 0.0107 2.91 9.40E+07 1.00E+11 0 0 

FDE Shallow demersal fish 2 10 6329.917 0.0123 3.2 1.91E+09 2.00E+10 0 9.00EFDS Atlantic cod 4 20 93206.04 0.0118 3.1 3.70E+07 2.50E+10 0 8.25EFDB Silver hake 3 10 36660.84 0.0123 3.1 3.20E+08 6.00E+10 0 0 

FDC Miscellaneous demersals 2 20 261600.6 0.012 3.1 1.45E+09 1.00E+11 0 0 

FDO Haddock 2 20 126222.4 0.0118 3.1 2.18E+08 7.00E+11 0 0 

FDF Yellowtail flounder 2 10 30212.81 0.012 3.1 4.05E+08 2.50E+10 5.00E-19 5.00ESHB Spiny dogfish 12 40 306781.6 0.0129 3 5.80E+07 3.50E+12 0 0 

SHD Other demersal sharks 10 50 18788.62 0.00396 3.2 9.90E+05 2.00E+12 0 0 

SHP Pelagic sharks 14 70 7183.533 0.00524 3.141 2.00E+04 1.56E+10 0 0 

SSK Skates and rays 6 30 138910.8 0.0127 3.1 6.10E+07 2.00E+13 1.00E-10 1.00ESB Seabirds 2 20 1808.545 0.02 3 3.00E+06 1.05E+07 0 0 

PIN Pinnipeds 9 30 7660.843 0.035 2.9 4.00E+04 1.50E+09 9.00E-07 9.00EREP Reptiles 16 80 803.2799 0.00396 3.004 3.40E+04 1.50E+09 2.00E-07 2.00EWHB Baleen whales 27 90 90230.75 0.2 3 7.00E+00 3.00E+11 1.10E-08 1.10EWHT Toothed whales 21 70 19997.48 0.01 3 1.00E+03 5.00E+08 2.00E-08 2.00E

Table A1. Vertebrate life history parameters. Parameters a and b are from the length-weight relationship (W = aLb, as adapte

The remainder were informed from various stock assessments (http://www.nefsc.noaa.gov/sos/), fits to survey data (Azarovitopinion (J. Burnett, pers. comm.).

100
Table A2. Invertebrate life history parameters, as used in the final, calibrated versions of the model.

Code Group ML (Linear Mortality) Mquad (Quadratic Mortality)

CEP Squid 1.16E-11 1.70E-08

BFS Sea scallop 3.47E-12 0.00E+00

BFF Other benthic filter feeder 5.79E-12 0.00E+00

BG Benthic grazer 1.16E-11 0.00E+00

BML Lobster 2.31E-11 0.00E+00

BMS Shallow macrozoobenthos 3.47E-11 0.00E+00

PWN Shrimp 7.81E-11 2.05E-08

ZL Carnivorous zooplankton 5.79E-11 1.16E-10

BD Deposit feeder 1.16E-12 0.00E+00

BC Benthic carnivore 9.26E-10 4.63E-11

ZG Gelatinous zooplankton 3.47E-11 5.79E-11

PL Diatoms 1.74E-08 0.00E+00

DF Dinoflagellates 5.79E-09 0.00E+00

PS Pico-phytoplankton 1.85E-07 0.00E+00

ZM Copepods 1.16E-09 1.16E-11

ZS Microzooplankton 9.26E-10 4.63E-10

PB Pelagic bacteria 1.16E-09 0.00E+00

BB Sediment bacteria 1.16E-10 0.00E+00

BO Meiobenthos 4.63E-12 1.16E-12

101
CODE GROUP REGIONS

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.1171547 0.029229 0.02635 0.035685 0.012294 0.012681 0.030716 0.023108 0.018FDD Goosefish 0.04978 0.08661 0.16588 0.15961 0.17914 0.27066 0.13708 0.16799 0.06FDC Miscellaneous demersals 1.00746 0.61552 1.02307 0.19687 1.07917 0.50473 0.52409 0.61559 0.29FDO Haddock 0 0 0 0.00053 0.00333 0.00294 0.12768 1.19756 0.19FDF Yellowtail flounder 0.00675 0.08468 0.03176 0.22144 0.48798 0.27651 0.25722 0.07985 0.0FDE Shallow demersal fish 0.01 0.02099 0.01442 0.03162 0.04607 0.07694 0.06146 0.00529 0.00FDS Atlantic cod 0.03299 0.03647 0.00065 0.13027 0.19032 0.09644 0.15213 1.29409 0.09FDB Silver hake 0.02699 0.07124 0.09998 0.15429 0.11174 0.31658 0.14032 0.04203 0.12FMM Migratory mesopelagics 0 0 0.00039 0.00106 0 0.00008 00

FPL Atlantic mackerel 0.01716 0.02998 0.0468 0.12642 0.03099 0.11895 0.01276 0.00167 0.0FPS Atlantic herring 0.01461 0.01214 0.0018 0.02026 0.0178 0.05968 0.01127 0.03248 0.0FVD White hake 0.00034 0.00058 0.00412 0.00704 0.01259 0.01898 0.0234 0.05139 0.02FVS Bluefish 0.05955 0.01301 0.01212 0.01688 0.02009 0.01547 0.01331 0.00204 FVB Other piscivores 0.21618 0.15846 0.08666 0.32813 0.72087 0.45959 0.5505 0.54613 0.33FVT Large piscivores 0.0061327 0.006133 0.006133 0.006133 0.006133 0.006133 0.006133 0.040486 0.040PIN Pinnipeds 0.0001453 0.000145 0.000145 0.000145 0.01382 0.01382 0.01382 0SB Seabirds 0.0036315 0.003631 0.003631 0.003631 0.006303 0.006303 0.006303 0.014212 0.014SHD Other demersal sharks 0.52146 0.21479 0.02589 0.0771 0.10913 0.03937 0.0091 0.28002 SHB Spiny dogfish 0.2845 0.8562 3.1788 2.8987 1.9556 3.154 1.5015 1.84529 0.5SHP Pelagic sharks 0.026886 0.026886 0.026886 0.026886 0.026886 0.026886 0.026886 0.039443 0.039SSK Skates and rays 0.58863 0.5704 0.09202 0.24382 1.34725 0.46329 0.66319 1.42784 0.2REP Reptiles 0.0093287 0.009329 0.009329 0.009329 0.002498 0.002498 0.002498 0.000319 0.000WHB Baleen whales 0.0661091 0.066109 0.066109 0.066109 0.210102 0.210102 0.210102 0.234384 0.234WHT Toothed whales 0.0409291 0.040929 0.040929 0.040929 0.0759 0.0759 0.0759 0.159909 0.159CEP Squid 0.07413 0.23718 0.40625 0.21005 0.20565 0.22411 0.19327 0.11829 0.09PWN Shrimp 0.0002 0.0003 0.0006 0.0007 0.0003 0.0003 0.0006 0.0025 0.0BML Lobster 0.00222 0.00335 0.05851 0.02132 0.03433 0.03874 0.05405 0.03371 0.06ZL Carnivorous zooplankton 0.2856923 0.285692 0.285692 0.285692 6.335461 6.335461 6.335461 3.713601 3.713

Table A3. Initial biomass (g/m2) per Atlantis NEUS region (See Figure 5) for each functional group. The boundary regions 

initial biomass assigned to them.

102
CODE GROUP REGIONS

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.001074 0.003007 0.002229 0.001622 5.12E-05 9.64E-06 0.000112 0 8.00

FDD Goosefish 0.25625 0.1656 0.17226 0.22522 0.22385 0.13471 0.07443 0.05979 0.28FDC Miscellaneous demersals 1.30699 0.95555 0.53489 0.40182 2.32087 2.50604 0.8964 0.62997 2.04FDO Haddock 0.6739 1.10575 0.29424 0.23266 0.16983 0.31493 1.78728 1.27686 0.85

FDF Yellowtail flounder 0.06872 0.00382 0.00729 0.00939 0.00038 0.00031 0.02635 0.23119 0.00FDE Shallow demersal fish 0.02198 0.0017 0.10802 0.06479 0.00675 0.00497 0.00617 0.00692 0.00FDS Atlantic cod 1.02943 0.76876 0.70157 0.48356 0.50887 0.21427 0.63433 0.62475 0.56FDB Silver hake 0.23859 0.11488 0.22793 0.12311 0.19232 0.1247 0.02952 0.05299 0.29

FMM Migratory mesopelagics 0.00001 0000000FPL Atlantic mackerel 0.01108 0.00004 0.01248 0.00024 0.00141 0.00095 0.00053 0.00926 0.0

FPS Atlantic herring 0.11635 0.00247 0.00154 0.00372 0.00429 0.00303 0.01037 0.01555 0.00FVD White hake 0.2321 0.10892 0.43244 0.20891 0.48373 0.49921 0.05655 0.01534 0.32FVS Bluefish 0 0 0.00043 0.00015 0 0 0 0.00274 

FVB Other piscivores 0.58589 0.02958 0.56918 0.56914 0.38442 0.2822 0.23891 0.40809 0.33FVT Large piscivores 0.022342 0.022342 0.022342 0.022342 0.022342 0.022342 0.022342 0.040486 0.022PIN Pinnipeds 0.070454 0.070454 0.070454 0.070454 0.070454 0.070454 0.070454 0 0.070

SB Seabirds 0.005828 0.005828 0.005828 0.005828 0.005828 0.005828 0.005828 0.014212 0.005

SHD Other demersal sharks 0000 0.00051 000

SHB Spiny dogfish 1.1283 0.7989 0.6749 0.52803 0.24271 0.4522 0.56666 0.25401 0.35

SHP Pelagic sharks 0.021766 0.021766 0.021766 0.021766 0.021766 0.021766 0.021766 0.039443 0.02SSK Skates and rays 0.25762 0.27265 0.22389 0.16455 0.5102 0.28063 0.21384 0.76513 0.49REP Reptiles 8.32E-05 8.32E-05 8.32E-05 8.32E-05 8.32E-05 8.32E-05 8.32E-05 0.000319 8.32WHB Baleen whales 0.650984 0.650984 0.650984 0.650984 0.650984 0.650984 0.650984 0.234384 0.650WHT Toothed whales 0.06074 0.06074 0.06074 0.06074 0.06074 0.06074 0.06074 0.159909 0.06CEP Squid 0.06857 0.02536 0.1371 0.76683 0.01907 0.01344 0.01263 0.06397 0.0PWN Shrimp 0.1345 0.0038 0.0323 0.051 0.3424 0.0191 0.0038 0.0005 0.0BML Lobster 0.02205 0.04164 0.02929 0.03667 0.00775 0.00887 0.01491 0.03697 0.00ZL Carnivorous zooplankton 0.185756 0.185756 0.185756 0.185756 0.185756 0.185756 0.185756 3.713601 0.185

Table A3, continued. Initial biomass (g/m2) per Atlantis NEUS region (See Figure 5) for each functional group. The bounda

have no initial biomass assigned to them.

103
CODE GROUP REGIONS

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.139 0.071 0.226 0.087 0.069 0.093 0.107 0.08 FDD Goosefish 0.014 0.026 0.05 0.048 0.054 0.082 0.041 0.051 FDC Miscellaneous demersals 0.047 0.03 0.05 0.01 0.052 0.025 0.025 0.03 FDO Haddock 000000 0.011 0.102 

FDF Yellowtail flounder 0.004 0.035 0.013 0.093 0.204 0.116 0.108 0.033 FDE Shallow demersal fish 0.019 0.038 0.026 0.058 0.084 0.141 0.113 0.01 FDS Atlantic cod 0.003 0.004 0 0.014 0.02 0.01 0.016 0.138 FDB Silver hake 0.009 0.023 0.032 0.05 0.036 0.102 0.045 0.014 FMM Migratory mesopelagics 0 0 0.188 0 0.107 0.12 0 0 FPL Atlantic mackerel 0.1 0.1 0.105 0.284 0.1 0.23 0.03 0.01 FPS Atlantic herring 0.1 0.1 0.105 0.284 0.1 0.23 0.03 0.01 FVD White hake 0 0 0.003 0.002 0.004 0.007 0.008 0.018 FVS Bluefish 0.23 0.178 0.097 0.119 0.104 0.08 0.069 0.011 FVB Other piscivores 0.026 0.019 0.01 0.038 0.084 0.054 0.064 0.064 FVT Large piscivores 0 0 0.101 0.003 0.105 0.2 0.01 0.05 PIN Pinnipeds 000000 0.061 0.05 SB Seabirds 0.022 0.022 0.022 0.022 0.039 0.039 0.039 0.087 SHD Other demersal sharks 0.348 0.143 0.017 0.051 0.073 0.026 0.006 0.187 SHB Spiny dogfish 0.1 0.1 0.105 0.284 0.1 0.23 0.03 0.01 SHP Pelagic sharks 0.1 0.1 0.105 0.284 0.1 0.23 0.03 0.01 SSK Skates and rays 0.049 0.047 0.008 0.02 0.112 0.039 0.055 0.119 REP Reptiles 0.299 0.162 0.088 0.108 0.095 0.073 0.063 0.01 WHB Baleen whales 0.008 0.008 0.008 0.008 0.008 0.026 0.026 0.029 WHT Toothed whales 0.023 0.023 0.023 0.023 0.043 0.043 0.043 0.09 CEP Squid 0.083 0.083 0.083 0.083 0.083 0.095 0.083 0.083 PWN Shrimp 0 0 0.001 0 0.001 0 0 0.004 BML Lobster 0.004 0.005 0.093 0.029 0.034 0.062 0.055 0.054 ZL Carnivorous zooplankton 0.018 0.009 0.002 0.337 0.003 0.063 0.205 0.078 

Table A4. Distribution of adult stages of each vertebrate and mobile invertebrate group, given as a proportion, for each reNEUS at each time step, from January to March.

104
CODE GROUP REGIONS

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.026 0 0 0.001 0.001 0 0.001 0.011 FDD Goosefish 0.077 0.05 0.052 0.068 0.068 0.041 0.022 0.018 FDC Miscellaneous demersals 0.064 0.046 0.026 0.02 0.113 0.122 0.044 0.031 FDO Haddock 0.057 0.094 0.025 0.02 0.014 0.027 0.152 0.108 FDF Yellowtail flounder 0.029 0.002 0.003 0.004 0 0 0.011 0.097 FDE Shallow demersal fish 0.04 0.003 0.198 0.119 0.012 0.009 0.011 0.013 FDS Atlantic cod 0.11 0.082 0.075 0.052 0.054 0.023 0.068 0.067 FDB Silver hake 0.077 0.037 0.073 0.04 0.062 0.04 0.01 0.017 FMM Migratory mesopelagics 0.035 0 0.2 00000 

FPL Atlantic mackerel 0 0 0.01 00000FPS Atlantic herring 0 0 0.01 00000FVD White hake 0.08 0.037 0.149 0.072 0.166 0.172 0.019 0.005 FVS Bluefish 00000000FVB Other piscivores 0.068 0.003 0.066 0.066 0.045 0.033 0.028 0.048 FVT Large piscivores 0 0 0.17 0.16 0000PIN Pinnipeds 0.104 0.001 0 0 0.15 0.15 0.154 0SB Seabirds 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.087 SHD Other demersal sharks 00000000

SHB Spiny dogfish 0 0 0.01 00000SHP Pelagic sharks 0 0 0.01 00000SSK Skates and rays 0.021 0.023 0.019 0.014 0.042 0.023 0.018 0.064 REP Reptiles 00000000WHB Baleen whales 0.029 0.029 0.029 0.029 0.081 0.081 0.081 0.081 WHT Toothed whales 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.09 CEP Squid 0.03 0.03 0.03 0.008 0.008 0.008 0.008 0.008 PWN Shrimp 0.012 0 0.001 0.001 0.515 0.049 0.077 0.029 BML Lobster 0.038 0.07 0.1 0.059 0.012 0.047 0.058 0.014 ZL Carnivorous zooplankton 0.037 0.083 0.029 0.04 0.001 0.002 0.002 0.001 

Table A4, continued. Distribution of adult stages of each vertebrate and mobile invertebrate group, given as a proportion, 5) in Atlantis NEUS at each time step, from January to March.

105
CODE GROUP

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.139 0.071 0.226 0.087 0.069 0.093 0.107 0.08 FDD Goosefish 0.014 0.026 0.05 0.048 0.054 0.082 0.041 0.051 FDC Miscellaneous demersals 0.047 0.03 0.05 0.01 0.052 0.025 0.025 0.03 FDO Haddock 000000 0.011 0.102 FDF Yellowtail flounder 0.004 0.035 0.013 0.093 0.204 0.116 0.108 0.033 FDE Shallow demersal fish 0.019 0.038 0.026 0.058 0.084 0.141 0.113 0.01 FDS Atlantic cod 0.003 0.004 0 0.014 0.02 0.01 0.016 0.138 FDB Silver hake 0.009 0.023 0.032 0.05 0.036 0.102 0.045 0.014 FMM Migratory mesopelagics 0 0 0.188 0 0.107 0.12 0 0 FPL Atlantic mackerel 0.001 0.001 0.001 0.234 0.1 0.268 0.1 0.07 FPS Atlantic herring 0.001 0.001 0.001 0.234 0.1 0.268 0.1 0.07 FVD White hake 0 0 0.003 0.002 0.004 0.007 0.008 0.018 FVS Bluefish 0.202 0.042 0.038 0.14 0.095 0.073 0.123 0.01 FVB Other piscivores 0.026 0.019 0.01 0.038 0.084 0.054 0.064 0.084 FVT Large piscivores 0 0 0.101 0.003 0.105 0.1 0.01 0.05 PIN Pinnipeds 000000 0.061 0.05 SB Seabirds 0.022 0.022 0.022 0.022 0.039 0.039 0.039 0.087 SHD Other demersal sharks 0.348 0.143 0.017 0.051 0.073 0.026 0.006 0.187 SHB Spiny dogfish 0.001 0.001 0.001 0.234 0.1 0.268 0.1 0.07 SHP Pelagic sharks 0.001 0.001 0.001 0.234 0.1 0.268 0.1 0.07 SSK Skates and rays 0.049 0.047 0.008 0.02 0.112 0.039 0.055 0.119 REP Reptiles 0.202 0.042 0.038 0.14 0.095 0.073 0.123 0.01 WHB Baleen whales 0.008 0.008 0.008 0.008 0.008 0.026 0.026 0.029 WHT Toothed whales 0.023 0.023 0.023 0.023 0.043 0.043 0.043 0.09 CEP Squid 0.071 0.072 0.072 0.072 0.072 0.083 0.072 0.072 PWN Shrimp 0 0 0.001 0 0.001 0 0 0.004 BML Lobster 0.004 0.005 0.093 0.029 0.034 0.062 0.055 0.054 ZL Carnivorous zooplankton 0.018 0.009 0.002 0.337 0.003 0.063 0.205 0.078 

Table A5. Distribution of adult stages of each vertebrate and mobile invertebrate group, given as a proportion, for each reAtlantis NEUS at each time step, from April to June.

REGIONS

106
CODE GROUP

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.026 0 0 0.001 0.001 0 0.001 0.011 FDD Goosefish 0.077 0.05 0.052 0.068 0.068 0.041 0.022 0.018 FDC Miscellaneous demersals 0.064 0.046 0.026 0.02 0.113 0.122 0.044 0.031 FDO Haddock 0.057 0.094 0.025 0.02 0.014 0.027 0.152 0.108 FDF Yellowtail flounder 0.029 0.002 0.003 0.004 0 0 0.011 0.097 FDE Shallow demersal fish 0.04 0.003 0.198 0.119 0.012 0.009 0.011 0.013 FDS Atlantic cod 0.11 0.082 0.075 0.052 0.054 0.023 0.068 0.067 FDB Silver hake 0.077 0.037 0.073 0.04 0.062 0.04 0.01 0.017 FMM Migratory mesopelagics 0.035 0 0.2 00000 FPL Atlantic mackerel 0.025 0 0.07 0.001 0.003 0 0.001 0FPS Atlantic herring 0.025 0 0.07 0.001 0.003 0 0.001 0FVD White hake 0.08 0.037 0.149 0.072 0.166 0.172 0.019 0.005 FVS Bluefish 0 0 0.002 0.001 0 0 0 0.013 FVB Other piscivores 0.068 0.003 0.066 0.066 0.045 0.033 0.028 0.038 FVT Large piscivores 0 0 0.22 0.16 0000PIN Pinnipeds 0.104 0.001 0 0 0.15 0.15 0.154 0SB Seabirds 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.087 SHD Other demersal sharks 00000000

SHB Spiny dogfish 0.025 0 0.07 0.001 0.003 0 0.001 0SHP Pelagic sharks 0.025 0 0.07 0.001 0.003 0 0.001 0SSK Skates and rays 0.021 0.023 0.019 0.014 0.042 0.023 0.018 0.064 REP Reptiles 0 0 0.002 0.001 0 0 0 0.013 WHB Baleen whales 0.029 0.029 0.029 0.029 0.081 0.081 0.081 0.081 WHT Toothed whales 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.09 CEP Squid 0.05 0.05 0.05 0.008 0.008 0.008 0.008 0.008 PWN Shrimp 0.012 0 0.001 0.001 0.515 0.049 0.077 0.029 BML Lobster 0.038 0.07 0.1 0.059 0.012 0.047 0.058 0.014 ZL Carnivorous zooplankton 0.037 0.083 0.029 0.04 0.001 0.002 0.002 0.001 

Table A5, continued. Distribution of adult stages of each vertebrate and mobile invertebrate group, given as a proportion, 5) in Atlantis NEUS at each time step, from April to June.

REGIONS

107
CODE GROUP REGIONS

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.139 0.071 0.226 0.087 0.069 0.093 0.107 0.08 FDD Goosefish 0.014 0.026 0.05 0.048 0.054 0.082 0.041 0.051 FDC Miscellaneous demersals 0.047 0.03 0.05 0.01 0.052 0.025 0.025 0.03 FDO Haddock 000000 0.011 0.102 

FDF Yellowtail flounder 0.004 0.035 0.013 0.093 0.204 0.116 0.108 0.033 FDE Shallow demersal fish 0.019 0.038 0.026 0.058 0.084 0.141 0.113 0.01 FDS Atlantic cod 0.003 0.004 0 0.014 0.02 0.01 0.016 0.138 FDB Silver hake 0.009 0.023 0.032 0.05 0.036 0.102 0.045 0.014 FMM Migratory mesopelagics 0 0 0.188 0 0.107 0.12 0 0 FPL Atlantic mackerel 0000000 0.001 

FPS Atlantic herring 0000000 0.001 

FVD White hake 0 0 0.003 0.002 0.004 0.007 0.008 0.018 FVS Bluefish 0.202 0.042 0.038 0.14 0.095 0.073 0.123 0.01 FVB Other piscivores 0.026 0.019 0.01 0.038 0.084 0.054 0.064 0.064 FVT Large piscivores 0 0 0.101 0.003 0.105 0.1 0.01 0.05 PIN Pinnipeds 000000 0.061 0.05 SB Seabirds 0.022 0.022 0.022 0.022 0.039 0.039 0.039 0.087 SHD Other demersal sharks 0.348 0.143 0.017 0.051 0.073 0.026 0.006 0.187 SHB Spiny dogfish 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.001 SHP Pelagic sharks 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.001 SSK Skates and rays 0.049 0.047 0.008 0.02 0.112 0.039 0.055 0.119 REP Reptiles 0.202 0.042 0.038 0.14 0.095 0.073 0.123 0.01 WHB Baleen whales 0.008 0.008 0.008 0.008 0.008 0.026 0.026 0.029 WHT Toothed whales 0.023 0.023 0.023 0.023 0.043 0.043 0.043 0.09 CEP Squid 0.071 0.072 0.072 0.072 0.072 0.083 0.072 0.072 PWN Shrimp 0 0 0.001 0 0.001 0 0 0.004 BML Lobster 0.004 0.005 0.093 0.029 0.034 0.062 0.055 0.054 ZL Carnivorous zooplankton 0.018 0.009 0.002 0.337 0.003 0.063 0.205 0.078 

Table A6. Distribution of adult stages of each vertebrate and mobile invertebrate group, given as a proportion, for each reAtlantis NEUS at each time step, from July to September.

108
CODE GROUP

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.026 0 0 0.001 0.001 0 0.001 0.011 FDD Goosefish 0.077 0.05 0.052 0.068 0.068 0.041 0.022 0.018 FDC Miscellaneous demersals 0.064 0.046 0.026 0.02 0.113 0.122 0.044 0.031 FDO Haddock 0.057 0.094 0.025 0.02 0.014 0.027 0.152 0.108 FDF Yellowtail flounder 0.029 0.002 0.003 0.004 0 0 0.011 0.097 FDE Shallow demersal fish 0.04 0.003 0.198 0.119 0.012 0.009 0.011 0.013 FDS Atlantic cod 0.11 0.082 0.075 0.052 0.054 0.023 0.068 0.067 FDB Silver hake 0.077 0.037 0.073 0.04 0.062 0.04 0.01 0.017 FMM Migratory mesopelagics 0.035 0 0.2 00000 

FPL Atlantic mackerel 0.09 0.08 0.06 0.227 0.02 0.05 0.05 0.021 FPS Atlantic herring 0.09 0.08 0.06 0.227 0.02 0.05 0.05 0.021 FVD White hake 0.08 0.037 0.149 0.072 0.166 0.172 0.019 0.005 FVS Bluefish 0 0 0.002 0.001 0 0 0 0.013 FVB Other piscivores 0.068 0.003 0.066 0.066 0.045 0.033 0.028 0.048 FVT Large piscivores 0 0 0.22 0.16 0000PIN Pinnipeds 0.104 0.001 0 0 0.15 0.15 0.154 0SB Seabirds 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.087 SHD Other demersal sharks 00000000

SHB Spiny dogfish 0.09 0.08 0.08 0.12 0.02 0.05 0.05 0.021 SHP Pelagic sharks 0.09 0.08 0.08 0.12 0.02 0.05 0.05 0.021 SSK Skates and rays 0.021 0.023 0.019 0.014 0.042 0.023 0.018 0.064 REP Reptiles 0 0 0.002 0.001 0 0 0 0.013 WHB Baleen whales 0.029 0.029 0.029 0.029 0.081 0.081 0.081 0.081 WHT Toothed whales 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.09 CEP Squid 0.05 0.05 0.05 0.008 0.008 0.008 0.008 0.008 PWN Shrimp 0.012 0 0.001 0.001 0.515 0.049 0.077 0.029 BML Lobster 0.038 0.07 0.1 0.059 0.012 0.047 0.058 0.014 ZL Carnivorous zooplankton 0.037 0.083 0.029 0.04 0.001 0.002 0.002 0.001 

Table A6, continued. Distribution of adult stages of each vertebrate and mobile invertebrate group, given as a proportion, 5) in Atlantis NEUS at each time step, from July to September.

REGIONS

109
CODE GROUP REGIONS

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.139 0.071 0.226 0.087 0.069 0.093 0.107 0.08 FDD Goosefish 0.014 0.026 0.05 0.048 0.054 0.082 0.041 0.051 FDC Miscellaneous demersals 0.047 0.03 0.05 0.01 0.052 0.025 0.025 0.03 FDO Haddock 000000 0.011 0.102 

FDF Yellowtail flounder 0.004 0.035 0.013 0.093 0.204 0.116 0.108 0.033 FDE Shallow demersal fish 0.019 0.038 0.026 0.058 0.084 0.141 0.113 0.01 FDS Atlantic cod 0.003 0.004 0 0.014 0.02 0.01 0.016 0.138 FDB Silver hake 0.009 0.023 0.032 0.05 0.036 0.102 0.045 0.014 FMM Migratory mesopelagics 0 0 0.188 0 0.107 0.12 0 0 

FPL Atlantic mackerel 0.039 0.067 0.105 0.284 0.07 0.268 0.029 0.004 FPS Atlantic herring 0.036 0.03 0.004 0.05 0.044 0.149 0.028 0.081 FVD White hake 0 0 0.003 0.002 0.004 0.007 0.008 0.018 

FVS Bluefish 0.23 0.178 0.097 0.119 0.104 0.08 0.069 0.011 

FVB Other piscivores 0.026 0.019 0.01 0.038 0.084 0.054 0.064 0.064 FVT Large piscivores 0 0 0.101 0.003 0.105 0.2 0.01 0.05 

PIN Pinnipeds 000000 0.061 0.05 

SB Seabirds 0.022 0.022 0.022 0.022 0.039 0.039 0.039 0.087 SHD Other demersal sharks 0.348 0.143 0.017 0.051 0.073 0.026 0.006 0.187 

SHB Spiny dogfish 0.039 0.067 0.105 0.284 0.07 0.268 0.029 0.004 SHP Pelagic sharks 0.039 0.067 0.105 0.284 0.07 0.268 0.029 0.004 SSK Skates and rays 0.049 0.047 0.008 0.02 0.112 0.039 0.055 0.119 REP Reptiles 0.299 0.162 0.088 0.108 0.095 0.073 0.063 0.01 

WHB Baleen whales 0.008 0.008 0.008 0.008 0.008 0.026 0.026 0.029 WHT Toothed whales 0.023 0.023 0.023 0.023 0.043 0.043 0.043 0.09 CEP Squid 0.083 0.083 0.083 0.083 0.083 0.095 0.083 0.083 

PWN Shrimp 0 0 0.001 0 0.001 0 0 0.004 

BML Lobster 0.004 0.005 0.093 0.029 0.034 0.062 0.055 0.054 ZL Carnivorous zooplankton 0.018 0.009 0.002 0.337 0.003 0.063 0.205 0.078 

Table A7. Distribution of adult stages of each vertebrate and mobile invertebrate group, given as a proportion, for each reAtlantis NEUS at each time step, from October to December.

110
CODE GROUP REGIONS

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.026 0 0 0.001 0.001 0 0.001 0.011 

FDD Goosefish 0.077 0.05 0.052 0.068 0.068 0.041 0.022 0.018 FDC Miscellaneous demersals 0.064 0.046 0.026 0.02 0.113 0.122 0.044 0.031 FDO Haddock 0.057 0.094 0.025 0.02 0.014 0.027 0.152 0.108 FDF Yellowtail flounder 0.029 0.002 0.003 0.004 0 0 0.011 0.097 

FDE Shallow demersal fish 0.04 0.003 0.198 0.119 0.012 0.009 0.011 0.013 FDS Atlantic cod 0.11 0.082 0.075 0.052 0.054 0.023 0.068 0.067 FDB Silver hake 0.077 0.037 0.073 0.04 0.062 0.04 0.01 0.017 FMM Migratory mesopelagics 0.035 0 0.2 00000 

FPL Atlantic mackerel 0.025 0 0.028 0.001 0.003 0.002 0.001 0.021 FPS Atlantic herring 0.29 0.006 0.004 0.009 0.011 0.008 0.026 0.039 FVD White hake 0.08 0.037 0.149 0.072 0.166 0.172 0.019 0.005 FVS Bluefish 00000000FVB Other piscivores 0.068 0.003 0.066 0.066 0.045 0.033 0.028 0.048 FVT Large piscivores 0 0 0.17 0.16 0000

PIN Pinnipeds 0.104 0.001 0 0 0.15 0.15 0.154 0

SB Seabirds 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.087 SHD Other demersal sharks 00000000

SHB Spiny dogfish 0.025 0 0.028 0.001 0.003 0.002 0.001 0.021 SHP Pelagic sharks 0.025 0 0.028 0.001 0.003 0.002 0.001 0.021 SSK Skates and rays 0.021 0.023 0.019 0.014 0.042 0.023 0.018 0.064 REP Reptiles 00000000WHB Baleen whales 0.029 0.029 0.029 0.029 0.081 0.081 0.081 0.081 WHT Toothed whales 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.09 CEP Squid 0.03 0.03 0.03 0.008 0.008 0.008 0.008 0.008 

PWN Shrimp 0.012 0 0.001 0.001 0.515 0.049 0.077 0.029 

BML Lobster 0.038 0.07 0.1 0.059 0.012 0.047 0.058 0.014 ZL Carnivorous zooplankton 0.037 0.083 0.029 0.04 0.001 0.002 0.002 0.001 

Table A7, continued. Distribution of adult stages of each vertebrate and mobile invertebrate group, given as a proportion, Figure 5) in Atlantis NEUS at each time step, from October to December.

111
CODE GROUP REGIONS

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.139 0.071 0.226 0.087 0.069 0.093 0.107 0.08 FDD Goosefish 0.014 0.026 0.05 0.048 0.054 0.082 0.041 0.051 FDC Miscellaneous demersals 0.047 0.03 0.05 0.01 0.052 0.025 0.025 0.03 FDO Haddock 000000 0.011 0.102 

FDF Yellowtail flounder 0.004 0.035 0.013 0.093 0.204 0.116 0.108 0.033 FDE Shallow demersal fish 0.019 0.038 0.026 0.058 0.084 0.141 0.113 0.01 FDS Atlantic cod 0.003 0.004 0 0.014 0.02 0.01 0.016 0.138 FDB Silver hake 0.009 0.023 0.032 0.05 0.036 0.102 0.045 0.014 FMM Migratory mesopelagics 0 0 0.188 0 0.107 0.12 0 0 FPL Atlantic mackerel 0.1 0.1 0.105 0.284 0.1 0.23 0.03 0.01 FPS Atlantic herring 0.1 0.1 0.105 0.284 0.1 0.23 0.03 0.01 FVD White hake 0 0 0.003 0.002 0.004 0.007 0.008 0.018 FVS Bluefish 0.299 0.162 0.088 0.108 0.095 0.073 0.063 0.01 FVB Other piscivores 0.026 0.019 0.01 0.038 0.084 0.054 0.064 0.064 FVT Large piscivores 0 0 0.101 0.003 0.105 0.2 0.01 0.05 PIN Pinnipeds 000000 0.061 0.05 SB Seabirds 0.022 0.022 0.022 0.022 0.039 0.039 0.039 0.087 SHD Other demersal sharks 0.348 0.143 0.017 0.051 0.073 0.026 0.006 0.187 SHB Spiny dogfish 0.1 0.1 0.105 0.284 0.1 0.23 0.03 0.01 SHP Pelagic sharks 0.1 0.1 0.105 0.284 0.1 0.23 0.03 0.01 SSK Skates and rays 0.049 0.047 0.008 0.02 0.112 0.039 0.055 0.119 REP Reptiles 0.299 0.162 0.088 0.108 0.095 0.073 0.063 0.01 WHB Baleen whales 0.008 0.008 0.008 0.008 0.008 0.026 0.026 0.029 WHT Toothed whales 0.023 0.023 0.023 0.023 0.043 0.043 0.043 0.09 CEP Squid 0.083 0.083 0.083 0.083 0.083 0.095 0.083 0.083 PWN Shrimp 0 0 0.001 0 0.001 0 0 0.004 

Table A8. Distribution of juvenile stages of each vertebrate and mobile invertebrate group, given as a proportion, for each Atlantis NEUS at each time step, from January to March.

112
CODE GROUP REGIONS

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.026 0 0 0.001 0.001 0 0.001 0.011 FDD Goosefish 0.077 0.05 0.052 0.068 0.068 0.041 0.022 0.018 FDC Miscellaneous demersals 0.064 0.046 0.026 0.02 0.113 0.122 0.044 0.031 FDO Haddock 0.057 0.094 0.025 0.02 0.014 0.027 0.152 0.108 FDF Yellowtail flounder 0.029 0.002 0.003 0.004 0 0 0.011 0.097 FDE Shallow demersal fish 0.04 0.003 0.198 0.119 0.012 0.009 0.011 0.013 FDS Atlantic cod 0.11 0.082 0.075 0.052 0.054 0.023 0.068 0.067 FDB Silver hake 0.077 0.037 0.073 0.04 0.062 0.04 0.01 0.017 FMM Migratory mesopelagics 0.035 0 0.2 00000 FPL Atlantic mackerel 0 0 0.01 00000FPS Atlantic herring 0 0 0.01 00000FVD White hake 0.08 0.037 0.149 0.072 0.166 0.172 0.019 0.005 FVS Bluefish 00000000FVB Other piscivores 0.068 0.003 0.066 0.066 0.045 0.033 0.028 0.048 FVT Large piscivores 0 0 0.17 0.16 0000PIN Pinnipeds 0.104 0.001 0 0 0.15 0.15 0.154 0SB Seabirds 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.087 SHD Other demersal sharks 00000000SHB Spiny dogfish 0 0 0.01 00000SHP Pelagic sharks 0 0 0.01 00000SSK Skates and rays 0.021 0.023 0.019 0.014 0.042 0.023 0.018 0.064 REP Reptiles 00000000WHB Baleen whales 0.029 0.029 0.029 0.029 0.081 0.081 0.081 0.081 WHT Toothed whales 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.09 CEP Squid 0.03 0.03 0.03 0.008 0.008 0.008 0.008 0.008 PWN Shrimp 0.012 0 0.001 0.001 0.515 0.049 0.077 0.029 

Table A8, continued. Distribution of juvenile stages of each vertebrate and mobile invertebrate group, given as a proportioFigure 5) in Atlantis NEUS at each time step, from January to March.

113
CODE GROUP REGIONS

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.139 0.071 0.226 0.087 0.069 0.093 0.107 0.08 FDD Goosefish 0.014 0.026 0.05 0.048 0.054 0.082 0.041 0.051 FDC Miscellaneous demersals 0.047 0.03 0.05 0.01 0.052 0.025 0.025 0.03 FDO Haddock 000000 0.011 0.102 

FDF Yellowtail flounder 0.004 0.035 0.013 0.093 0.204 0.116 0.108 0.033 FDE Shallow demersal fish 0.019 0.038 0.026 0.058 0.084 0.141 0.113 0.01 FDS Atlantic cod 0.003 0.004 0 0.014 0.02 0.01 0.016 0.138 FDB Silver hake 0.009 0.023 0.032 0.05 0.036 0.102 0.045 0.014 FMM Migratory mesopelagics 0 0 0.188 0 0.107 0.12 0 0 FPL Atlantic mackerel 0.001 0.001 0.001 0.234 0.1 0.268 0.1 0.07 FPS Atlantic herring 0.001 0.001 0.001 0.234 0.1 0.268 0.1 0.07 FVD White hake 0 0 0.001 0.002 0.004 0.007 0.008 0.018 FVS Bluefish 0.202 0.042 0.038 0.14 0.095 0.073 0.123 0.01 FVB Other piscivores 0.026 0.019 0.01 0.038 0.084 0.054 0.064 0.084 FVT Large piscivores 0 0 0.101 0.003 0.105 0.1 0.01 0.05 PIN Pinnipeds 000000 0.061 0.05 SB Seabirds 0.022 0.022 0.022 0.022 0.039 0.039 0.039 0.087 SHD Other demersal sharks 0.348 0.143 0.017 0.051 0.073 0.026 0.006 0.187 SHB Spiny dogfish 0.001 0.001 0.001 0.234 0.1 0.268 0.1 0.07 SHP Pelagic sharks 0.001 0.001 0.001 0.234 0.1 0.268 0.1 0.07 SSK Skates and rays 0.049 0.047 0.008 0.02 0.112 0.039 0.055 0.119 REP Reptiles 0.202 0.042 0.038 0.14 0.095 0.073 0.123 0.01 WHB Baleen whales 0.008 0.008 0.008 0.008 0.008 0.026 0.026 0.029 WHT Toothed whales 0.023 0.023 0.023 0.023 0.043 0.043 0.043 0.09 CEP Squid 0.071 0.072 0.072 0.072 0.072 0.083 0.072 0.072 PWN Shrimp 0 0 0.001 0 0.001 0 0 0.004 

Table A9. Distribution of juvenile stages of each vertebrate and mobile invertebrate group, given as a proportion, for each Atlantis NEUS at each time step, from April to June.

114
CODE GROUP REGIONS

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.026 0 0 0.001 0.001 0 0.001 0.011 

FDD Goosefish 0.077 0.05 0.052 0.068 0.068 0.041 0.022 0.018 FDC Miscellaneous demersals

0.064 0.046 0.026 0.02 0.113 0.122 0.044 0.031 

FDO Haddock 0.057 0.094 0.025 0.02 0.014 0.027 0.152 0.108 

FDF Yellowtail flounder 0.029 0.002 0.003 0.004 0 0 0.011 0.097 

FDE Shallow demersal fish 0.04 0.003 0.198 0.119 0.012 0.009 0.011 0.013 FDS Atlantic cod 0.11 0.082 0.075 0.052 0.054 0.023 0.068 0.067 FDB Silver hake 0.077 0.037 0.073 0.04 0.062 0.04 0.01 0.017 FMM Migratory mesopelagics 0.035 0 0.2 00000 FPL Atlantic mackerel 0.025 0 0.07 0.001 0.003 0 0.001 0

FPS Atlantic herring 0.025 0 0.07 0.001 0.003 0 0.001 0FVD White hake 0.08 0.037 0.149 0.072 0.166 0.172 0.019 0.005 

FVS Bluefish 0 0 0.002 0.001 0 0 0 0.013 

FVB Other piscivores 0.068 0.003 0.066 0.066 0.045 0.033 0.028 0.038 FVT Large piscivores 0 0 0.22 0.16 0000PIN Pinnipeds 0.104 0.001 0 0 0.15 0.15 0.154 0SB Seabirds 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.087 SHD Other demersal sharks 00000000SHB Spiny dogfish 0.025 0 0.07 0.001 0.003 0 0.001 0

SHP Pelagic sharks 0.025 0 0.07 0.001 0.003 0 0.001 0SSK Skates and rays 0.021 0.023 0.019 0.014 0.042 0.023 0.018 0.064 REP Reptiles 0 0 0.002 0.001 0 0 0 0.013 

WHB Baleen whales 0.029 0.029 0.029 0.029 0.081 0.081 0.081 0.081 WHT Toothed whales 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.09 CEP Squid 0.05 0.05 0.05 0.008 0.008 0.008 0.008 0.008 PWN Shrimp 0.012 0 0.001 0.001 0.515 0.049 0.077 0.029 

Table A9, continued. Distribution of juvenile stages of each vertebrate and mobile invertebrate group, given as a proportioFigure 5) in Atlantis NEUS at each time step, from April to June.

115
CODE GROUP REGIONS

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.139 0.071 0.226 0.087 0.069 0.093 0.107 0.08 FDD Goosefish 0.014 0.026 0.05 0.048 0.054 0.082 0.041 0.051 FDC Miscellaneous demersals 0.047 0.03 0.05 0.01 0.052 0.025 0.025 0.03 FDO Haddock 000000 0.011 0.102 FDF Yellowtail flounder 0.004 0.035 0.013 0.093 0.204 0.116 0.108 0.033 FDE Shallow demersal fish 0.019 0.038 0.026 0.058 0.084 0.141 0.113 0.01 FDS Atlantic cod 0.003 0.004 0 0.014 0.02 0.01 0.016 0.138 FDB Silver hake 0.009 0.023 0.032 0.05 0.036 0.102 0.045 0.014 FMM Migratory mesopelagics 0 0 0.188 0 0.107 0.12 0 0 FPL Atlantic mackerel 0000000 0.001 

FPS Atlantic herring 0000000 0.001 FVD White hake 0 0 0.003 0.002 0.004 0.007 0.008 0.018 FVS Bluefish 0.203 0.042 0.038 0.14 0.095 0.073 0.123 0.01 FVB Other piscivores 0.026 0.019 0.01 0.038 0.084 0.054 0.064 0.064 FVT Large piscivores 0 0 0.101 0.003 0.105 0.1 0.01 0.05 PIN Pinnipeds 000000 0.061 0.05 

SB Seabirds 0.022 0.022 0.022 0.022 0.039 0.039 0.039 0.087 SHD Other demersal sharks 0.348 0.143 0.017 0.051 0.073 0.026 0.006 0.187 SHB Spiny dogfish 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.001 SHP Pelagic sharks 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.001 SSK Skates and rays 0.049 0.047 0.008 0.02 0.112 0.039 0.055 0.119 REP Reptiles 0.202 0.042 0.038 0.14 0.095 0.073 0.123 0.01 WHB Baleen whales 0.008 0.008 0.008 0.008 0.008 0.026 0.026 0.029 WHT Toothed whales 0.023 0.023 0.023 0.023 0.043 0.043 0.043 0.09 CEP Squid 0.071 0.072 0.072 0.072 0.072 0.083 0.072 0.072 PWN Shrimp 0 0 0.001 0 0.001 0 0 0.004 

Table A10. Distribution of juvenile stages of each vertebrate and mobile invertebrate group, given as a proportion, for eacAtlantis NEUS at each time step, from July to September.

116
CODE GROUP REGIONS

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.026 0 0 0.001 0.001 0 0.001 0.011 FDD Goosefish 0.077 0.05 0.052 0.068 0.068 0.041 0.022 0.018 FDC Miscellaneous demersals 0.064 0.046 0.026 0.02 0.113 0.122 0.044 0.031 FDO Haddock 0.057 0.094 0 0.02 0.014 0.027 0.152 0.108 

FDF Yellowtail flounder 0.029 0.002 0.003 0.004 0 0 0.011 0.097 

FDE Shallow demersal fish 0.04 0.003 0.198 0.119 0.012 0.009 0.011 0.013 FDS Atlantic cod 0.11 0.082 0.075 0.052 0.054 0.023 0.068 0.067 FDB Silver hake 0.077 0.037 0.073 0.04 0.062 0.04 0.01 0.017 FMM Migratory mesopelagics 0.035 0 0.2 00000 FPL Atlantic mackerel 0.09 0.08 0.06 0.227 0.02 0.05 0.05 0.021 FPS Atlantic herring 0.09 0.08 0.06 0.227 0.02 0.05 0.05 0.021 FVD White hake 0.08 0.037 0.149 0.072 0.166 0.172 0.019 0.005 

FVS Bluefish 0 0 0.002 0.001 0 0 0 0.013 

FVB Other piscivores 0.068 0.003 0.066 0.066 0.045 0.033 0.028 0.048 FVT Large piscivores 0 0 0.22 0.16 0000

PIN Pinnipeds 0.104 0.001 0 0 0.15 0.15 0.154 0SB Seabirds 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.087 SHD Other demersal sharks 00000000

SHB Spiny dogfish 0.09 0.08 0.08 0.12 0.02 0.05 0.05 0.021 SHP Pelagic sharks 0.09 0.08 0.08 0.12 0.02 0.05 0.05 0.021 SSK Skates and rays 0.021 0.023 0.019 0.014 0.042 0.023 0.018 0.064 REP Reptiles 0 0 0.002 0.001 0 0 0 0.013 

WHB Baleen whales 0.029 0.029 0.029 0.029 0.081 0.081 0.081 0.081 WHT Toothed whales 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.09 CEP Squid 0.05 0.05 0.05 0.008 0.008 0.008 0.008 0.008 PWN Shrimp 0.012 0 0.001 0.001 0.515 0.049 0.077 0.029 

Table A10, continued. Distribution of juvenile stages of each vertebrate and mobile invertebrate group, given as a proportFigure 5) in Atlantis NEUS at each time step, from July to September.

117
CODE GROUP REGIONS

1 2 3 4 5 6 7 8 

FBP Benthopelagics 0.139 0.071 0.226 0.087 0.069 0.093 0.107 0.08 FDD Goosefish 0.014 0.026 0.05 0.048 0.054 0.082 0.041 0.051 FDC Miscellaneous demersals 0.047 0.03 0.05 0.01 0.052 0.025 0.025 0.03 FDO Haddock 000000 0.011 0.102 

FDF Yellowtail flounder 0.004 0.035 0.013 0.093 0.204 0.116 0.108 0.033 FDE Shallow demersal fish 0.019 0.038 0.026 0.058 0.084 0.141 0.113 0.01 FDS Atlantic cod 0.003 0.004 0 0.014 0.02 0.01 0.016 0.138 FDB Silver hake 0.009 0.023 0.032 0.05 0.036 0.102 0.045 0.014 FMM Migratory mesopelagics 0 0 0.188 0 0.107 0.12 0 0 FPL Atlantic mackerel 0.039 0.067 0.105 0.284 0.07 0.268 0.029 0.004 FPS Atlantic herring 0.036 0.03 0.004 0.05 0.044 0.149 0.028 0.081 FVD White hake 0 0 0.001 0.002 0.004 0.007 0.008 0.018 FVS Bluefish 0.299 0.162 0.088 0.108 0.095 0.073 0.063 0.01 FVB Other piscivores 0.026 0.019 0.01 0.038 0.084 0.054 0.064 0.064 FVT Large piscivores 0 0 0.101 0.003 0.105 0.2 0.01 0.05 PIN Pinnipeds 000000 0.061 0.05 

SB Seabirds 0.022 0.022 0.022 0.022 0.039 0.039 0.039 0.087 SHD Other demersal sharks 0.348 0.143 0.017 0.051 0.073 0.026 0.006 0.187 SHB Spiny dogfish 0.039 0.067 0.105 0.284 0.07 0.268 0.029 0.004 SHP Pelagic sharks 0.039 0.067 0.105 0.284 0.07 0.268 0.029 0.004 SSK Skates and rays 0.049 0.047 0.008 0.02 0.112 0.039 0.055 0.119 REP Reptiles 0.299 0.162 0.088 0.108 0.095 0.073 0.063 0.01 WHB Baleen whales 0.008 0.008 0.008 0.008 0.008 0.026 0.026 0.029 WHT Toothed whales 0.023 0.023 0.023 0.023 0.043 0.043 0.043 0.09 CEP Squid 0.083 0.083 0.083 0.083 0.083 0.095 0.083 0.083 PWN Shrimp 0 0 0.001 0 0.001 0 0 0.004 

Table A11. Distribution of juvenile stages of each vertebrate and mobile invertebrate group, given as a proportion, for eacAtlantis NEUS at each time step, from October to December.

118
CODE GROUP REGIONS

12 13 14 15 16 17 18 19 

FBP Benthopelagics 0.026 0 0 0.001 0.001 0 0.001 0.011 FDD Goosefish 0.077 0.05 0.052 0.068 0.068 0.041 0.022 0.018 FDC Miscellaneous demersals 0.064 0.046 0.026 0.02 0.113 0.122 0.044 0.031 FDO Haddock 0.057 0.094 0.025 0.02 0.014 0.027 0.152 0.108 FDF Yellowtail flounder 0.029 0.002 0.003 0.004 0 0 0.011 0.097 FDE Shallow demersal fish 0.04 0.003 0.198 0.119 0.012 0.009 0.011 0.013 FDS Atlantic cod 0.11 0.082 0.075 0.052 0.054 0.023 0.068 0.067 FDB Silver hake 0.077 0.037 0.073 0.04 0.062 0.04 0.01 0.017 FMM Migratory mesopelagics 0.035 0 0.2 00000 FPL Atlantic mackerel 0.025 0 0.028 0.001 0.003 0.002 0.001 0.021 FPS Atlantic herring 0.29 0.006 0.004 0.009 0.011 0.008 0.026 0.039 FVD White hake 0.08 0.037 0.149 0.072 0.166 0.172 0.019 0.005 FVS Bluefish 00000000FVB Other piscivores 0.068 0.003 0.066 0.066 0.045 0.033 0.028 0.048 FVT Large piscivores 0 0 0.17 0.16 0000

PIN Pinnipeds 0.104 0.001 0 0 0.15 0.15 0.154 0SB Seabirds 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.087 SHD Other demersal sharks 00000000

SHB Spiny dogfish 0.025 0 0.028 0.001 0.003 0.002 0.001 0.021 SHP Pelagic sharks 0.025 0 0.028 0.001 0.003 0.002 0.001 0.021 SSK Skates and rays 0.021 0.023 0.019 0.014 0.042 0.023 0.018 0.064 REP Reptiles 00000000WHB Baleen whales 0.029 0.029 0.029 0.029 0.081 0.081 0.081 0.081 WHT Toothed whales 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.09 CEP Squid 0.03 0.03 0.03 0.008 0.008 0.008 0.008 0.008 PWN Shrimp 0.012 0 0.001 0.001 0.515 0.049 0.077 0.029 

Table A11, continued. Distribution of juvenile stages of each vertebrate and mobile invertebrate group, given as a proportFigure 5) in Atlantis NEUS at each time step, from October to December.

119
CODE GROUP AGE 

CLASS

1 2 3 4 5 6 7 

FPL Atlantic mackerel 0.68 0.687 4.822 16.88 18.323 20.368 20.382 20FPS Atlantic herring 3.374 3.032 3.602 4.853 5.346 5.425 6.46 6.4FVD White hake 2.012 5.441 18.643 27.782 75.584 138.218 228.255 308.0

FVS Bluefish 3.022 45.739 80.307 195.188 195.841 195.673 195.018 195.0

FVT Large piscivores 2.667 666.667 1266.667 3760.667 5796.667 8806.667 8836.667 8806.6FVB Other piscivores 3.312 4.126 5.77 7.447 18.7 29.665 40.663 40.7

FMM Migratory mesopelagics 0.0307 0.0173 0.00487 0.00487 0.00487 0.00487 0.004873 0.004FBP Benthopelagics 0.8 1 1.2 1.7 1.9 2.1 2.2 FDD Goosefish 1.84 5.048 6.669 18.428 29.21 75.896 109.143 152.7FDS Atlantic cod 2.766 33.826 78.983 155.83 252.431 305.03 305 304.3

FDB Silver hake 0.1255 2.345 3.13 6.878 15.069 28.334 44.06 FDC Miscellaneous demersals 0.73 3.902 5.88 8.412 12.319 18.033 21.9 20.9FDO Haddock 2.1 20.112 28.615 48.252 55.844 60.49 61.625 61.01FDE Shallow demersal fish 6.03 9.309 9.511 9.3 9.289 9.315 9.315 FDF Yellowtail flounder 0.754 2.863 5.584 7.75 11.232 15.656 15.391 15.3SHD Other demersal sharks 1 9 10 15 15 20 20 SHP Pelagic sharks 3 52 300 1230.5 1550.5 1850.5 1850.5 185SHB Spiny dogfish 0.5 10.5 25.5 80.5 270.5 320.5 460.5 52SSK Skates and rays 3 9 10 26 40 50 50 SB Seabirds 200 400 500 500 500 800 800 8REP Reptiles 10 150 100 100 100 150 205 2PIN Pinnipeds 100 500 500 500 500 500 500 5WHB Baleen whales 2000000 3500000 3500000 3500000 3500000 3500000 3500000 35000WHT Toothed whales 52000 18000 18000 18000 18000 18000 18000 180Table A12. Growth rates (mg / day) for vertebrate groups in Atlantis NEUS for each age class. Each class is the maximdivided by 10 (number of stages), distinct for each group.

120
CODE GROUP Rate (mg 

N / d)

ZG Gelatinous zooplankton 0.017

ZL Carnivorous zooplankton 0.1

ZM Copepods 0.18

ZS Microzooplankton 0.55

BFS Sea scallop 0.05

BFF Other benthic filter feeder 0.0035

BD Deposit feeder 0.004

BC Benthic carnivore 0.0008

BG Benthic grazer 0.002

BMS Shallow macrozoobenthos 0.0001

BML Lobster 0.00012

BO Meiobenthos 0.04

PB Pelagic bacteria 1.5

BB Sediment bacteria 1.5

CEP Squid 0.0444

jCEP Juv. Squid 0.046

PWN Shrimp 0.003043

jPWN Juv. Shrimp 0.003043

Table A13. Growth rates for invertebrate groups in Atlantis NEUS.

121
PREDATOR PREY

Stages Code Group FPL FPS FVD FVS FVB FVT FMM FBP FDD

j-->j FPL Atlantic mackerel 0.0001 0.00005 0 0 0.008 0 0 0.01 0

a-->j FPL Atlantic mackerel 0.0001 0.00005 0 0 0.01 0 0 0.007 0

j-->a FPL Atlantic mackerel 0.00001 0 0 0 0.0005 0 0 0.01 0

a-->a FPL Atlantic mackerel 0.00001 0 0 0 0.0005 0 0 0.01 0

j-->j FPS Atlantic herring 0 0.0005 0 0 0 0 0.008 0.008 0

a-->j FPS Atlantic herring 0 0.00005 0 0 0 0 0.008 0.004 0

j-->a FPS Atlantic herring 0 0.0001 0 0 0 0 0.003 0.00165 0

a-->a FPS Atlantic herring 0 0 0 0 00 0.003 0.0001 0

j-->j FVD White hake 0.15 0.08 0.035 0 0.15 0 0.025 0.05 0.015

a-->j FVD White hake 0.03 0.08 0.035 0 0.1 0 0.025 0.04 0.06

j-->a FVD White hake 0.01 0.075 0.035 0 0.1 0 0.03 0.2 0.0025

a-->a FVD White hake 0.01 0.075 0.035 0 0.1 0 0.03 0.2 0.045

j-->j FVS Bluefish 0.06 0.02 0.00225 0.0095 0.026 0 0.1 0.04 0

a-->j FVS Bluefish 0.06 0.02 0.0025 0.008 0.09 0 0.1 0.04 0

j-->a FVS Bluefish 0.007 0.005 0.005 0.0017 0.026 0 0.15 0.03 0

a-->a FVS Bluefish 0.007 0.005 0.005 0.0007 0.08 0 0.15 0.03 0

j-->j FVB Other piscivores 0.085 0.008 0.007 0.0525 0.013 0 0.02 0.05 0

a-->j FVB Other piscivores 0.085 0.008 0.007 0.05 0.015 0 0.02 0.05 0.03

j-->a FVB Other piscivores 0.005 0.008 0.017 0.015 0.05 0 0.06 0.12 0

a-->a FVB Other piscivores 0.005 0.008 0.009 0.025 0.05 0 0.06 0.15 0.015

j-->j FVT Large piscivores 0.003 0.09 0 0 0 0.02 0.1 0.08 0

a-->j FVT Large piscivores 0.003 0.09 0 0 0 0.03 0.1 0.08 0

j-->a FVT Large piscivores 0.01 0.04 0 0 0 0.003 0.2 0.06 0

a-->a FVT Large piscivores 0.01 0.04 0 0 0 0.02 0.2 0.06 0

j-->j FMM Migratory mesopelagics 0 0 0 0 000 0 0

a-->j FMM Migratory mesopelagics 0 0 0 0 000 0 0

j-->a FMM Migratory mesopelagics 0 0 0 0 000 0 0

a-->a FMM Migratory mesopelagics 0 0 0 0 000 0 0

j-->j FBP Benthopelagics 0 0 0 0 000 0 0

a-->j FBP Benthopelagics 0 0 0 0 000 0 0

j-->a FBP Benthopelagics 0 0 0 0 000 0 0

a-->a FBP Benthopelagics 0 0 0 0 000 0 0

j-->j FDD Goosefish 0.005 0.05 0.004 0 0.05 0 0.06 0.02 0.01

a-->j FDD Goosefish 0.0006 0.05 0.0045 0 0.08 0 0.06 0.02 0.01

j-->a FDD Goosefish 0.005 0.01 0.01 0 0.05 0 0.08 0.005 0.004

a-->a FDD Goosefish 0.005 0.01 0.0035 0 0.06 0 0.08 0.005 0.0075

j-->j FDE Shallow demersal fish 0 0 0 0 000 0 0

a-->j FDE Shallow demersal fish 0 0 0 0 000 0 0

j-->a FDE Shallow demersal fish 0 0 0 0 000 0 0

a-->a FDE Shallow demersal fish 0 0 0 0 000 0 0

j-->j FDS Atlantic cod 0.0048 0.07 0.0045 0.008 0.05 0 0.06 0.065 0.03

a-->j FDS Atlantic cod 0.048 0.07 0.008 0.008 0.03 0 0.06 0.065 0.03

j-->a FDS Atlantic cod 0.005 0.02 0.0035 0.0015 0.05 0 0.03 0.28 0.03

a-->a FDS Atlantic cod 0.05 0.02 0.0035 0.0025 0.02 0 0.03 0.28 0.025

j-->j FDB Silver hake 0.028 0.005 0 0 0.005 0 0.06 0.04 0

a-->j FDB Silver hake 0.033 0.005 0 0 0.005 0 0.06 0.04 0

j-->a FDB Silver hake 0.035 0.003 0 0 0.005 0 0.07 0.008 0

a-->a FDB Silver hake 0.035 0.005 0 0 0.005 0 0.07 0.007 0

j-->j FDC Miscellaneous demersals 0.006 0.004 0 0.007 0.021 0 0.01 0.02 0.012

a-->j FDC Miscellaneous demersals 0.006 0.004 0 0.0035 0.025 0 0.01 0.02 0.012

Table A14. Consumption parameters (a in Equation 69) in the calibrated model for predation on some fish 

groups. Prey groups are listed by column. A value of 0 indicates cases where that predator does not eat 

that prey. In the left hand column, j-->j signifies juvenile predation on juveniles, a-->j signifies adult 

predation on juveniles, j-->a signifies juvenile predation on adults, and a-->a signifies adult predation on 

adults

122
PREDATOR PREY

Stages Code Group FPL FPS FVD FVS FVB FVT FMM FBP FDD

j-->a FDC Miscellaneous demersals 0.008 0.0025 0 0.0005 0.0275 0 0.02 0.03 0.005

a-->a FDC Miscellaneous demersals 0.008 0.0025 0 0.0015 0.0275 0 0.02 0.03 0.005

j-->j FDO Haddock 0.13 0 0 0 0.00393 0 0.01 0 0

a-->j FDO Haddock 0.13 0 0 0 0.0045 0 0.01 0 0

j-->a FDO Haddock 0.01 0 0 0 0.00393 0 0.008 0 0

a-->a FDO Haddock 0.01 0 0 0 0.00393 0 0.008 0 0

j-->j FDF Yellowtail flounder 0 0 0 0 000 0 0

a-->j FDF Yellowtail flounder 0 0 0 0 000 0 0

j-->a FDF Yellowtail flounder 0 0 0 0 000 0 0

a-->a FDF Yellowtail flounder 0 0 0 0 000 0 0

j-->j SHB Spiny dogfish 0.023 0.0003 0.0004 0.04 0.0025 0 0.01 0.015 0.0025

a-->j SHB Spiny dogfish 0.008 0.0003 0.0005 0.04 0.0025 0 0.01 0.015 0.0025

j-->a SHB Spiny dogfish 0.001 0.0001 0.0007 0.00015 0.001 0 0.015 0.05 0.0008

a-->a SHB Spiny dogfish 0.001 0.0001 0.0007 0.0005 0.001 0 0.015 0.05 0.0015

j-->j SHD Other demersal sharks 0.09 0.14 0.0007 0.31 0.08 0 0.07 0.25 0.003

a-->j SHD Other demersal sharks 0.09 0.12 0.001 0.27 0.08 0 0.1 0.25 0.012

j-->a SHD Other demersal sharks 0.1 0.07 0.0005 0.015 0.081 0 0.5 0.3 0.075

a-->a SHD Other demersal sharks 0.1 0.055 0.0005 0.015 0.081 0 0.5 0.35 0.075

j-->j SHP Pelagic sharks 0.11 0.1 0 0.46 0.075 0.15 0.1 0.3 0.125

a-->j SHP Pelagic sharks 0.09 0.1 0 0.46 0.075 0.3 0.1 0.7 0.125

j-->a SHP Pelagic sharks 0.08 0.2 0 0.005 0.06 0.15 0.9 0.3 0.15

a-->a SHP Pelagic sharks 0.08 0.2 0 0.02 0.06 0.6 0.7 0.7 0.15

j-->j SSK Skates and rays 0.04 0.1 0 0 0.08 0 0.1 0.01 0.015

a-->j SSK Skates and rays 0.025 0.1 0 0 0.065 0 0.1 0.01 0.017

j-->a SSK Skates and rays 0.0025 0.06 0 0 0.06 0 0.08 0.15 0.0275

a-->a SSK Skates and rays 0.004 0.06 0 0 0.07 0 0.08 0.15 0.0275

j-->j SB Seabirds 0.23 0.5 0 0.31 0.01 0 0 0.3 0

a-->j SB Seabirds 0.23 0.5 0 0.31 0.01 0 0 0.3 0

j-->a SB Seabirds 0.1 0.1 0 0.015 0.015 0 0 0.65 0

a-->a SB Seabirds 0.1 0.1 0 0.015 0.015 0 0 0.65 0

j-->j PIN Pinnipeds 0.1 0.2 0 0.3 0.01 0.3 0.1 0.75 0

a-->j PIN Pinnipeds 0.1 0.2 0 0.3 0.01 0.3 0.1 0.75 0

j-->a PIN Pinnipeds 0.08 0.05 0 0.015 0.015 0.1 0.9 0.85 0

a-->a PIN Pinnipeds 0.08 0.05 0 0.07 0.03 0.1 0.9 0.85 0

j-->j REP Reptiles 0 0 0 0 000 0 0

a-->j REP Reptiles 0 0 0 0 000 0 0

j-->a REP Reptiles 0 0 0 0 000 0 0

a-->a REP Reptiles 0 0 0 0 000 0 0

j-->j WHB Baleen whales 0 0.025 0 0 0 0 0.08 0.02 0

a-->j WHB Baleen whales 0 0.025 0 0 0 0 0.08 0.02 0

j-->a WHB Baleen whales 0 0.005 0 0 0 0 0.05 0.17 0

a-->a WHB Baleen whales 0 0.005 0 0 0 0 0.05 0.17 0

j-->j WHT Toothed whales 0.006 0.06 0.0015 0.21 0.01 0.15 0.1 0.19 0

a-->j WHT Toothed whales 0.006 0.06 0.002 0.21 0.01 0.15 0.1 0.19 0

j-->a WHT Toothed whales 0.03 0.025 0.0025 0.0025 0.008 0.5 0.3 0.37 0

a-->a WHT Toothed whales 0.03 0.025 0.0025 0.005 0.012 0.5 0.3 0.37 0

Table A14, continued. Consumption parameters (a in Equation 69) in the calibrated model for predation 

on some fish groups. Prey groups are listed by column. A value of 0 indicates cases where that predator 

does not eat that prey. In the left hand column, j-->j signifies juvenile predation on juveniles, a-->j signifies 

adult predation on juveniles, j-->a signifies juvenile predation on adults, and a-->a signifies adult predation 

on adults

123
PREDATOR PREY

Stages Code Group FDE FDS FDB FDC FDO FDF

j-->j FPL Atlantic mackerel 0.01 0 0.00625 0.0125 0 0

a-->j FPL Atlantic mackerel 0.00175 0 0.00625 0.006 0 0

j-->a FPL Atlantic mackerel 0 0 3.00E-07 1.00E-08 0 0

a-->a FPL Atlantic mackerel 0 0 6.50E-07 1.00E-08 0 0

j-->j FPS Atlantic herring 0.00075 0 0.0175 0.0025 0 0

a-->j FPS Atlantic herring 0.00075 0 0.0175 0.0025 0 0

j-->a FPS Atlantic herring 0 0 1.00E-07 1.00E-08 0 0

a-->a FPS Atlantic herring 0 0 1.00E-07 1.00E-08 0 0

j-->j FVD White hake 0.13 0.04 0.338 0.078 0.0025 0.18

a-->j FVD White hake 0.13 0.04 0.363 0.078 0.0025 0.18

j-->a FVD White hake 0.09 0.005 0.378 0.05 0.002 0.57

a-->a FVD White hake 0.175 0.008 0.35 0.05 0.18 0.57

j-->j FVS Bluefish 0.025 0 0.0151 0.02 0 0

a-->j FVS Bluefish 0.03 0 0.0203 0.02 0 0

j-->a FVS Bluefish 0.015 0 0.0375 0.01 0 0

a-->a FVS Bluefish 0.035 0 0.0243 0.01 0 0

j-->j FVB Other piscivores 0.05 0.025 0.08 0.01 0 0.125

a-->j FVB Other piscivores 0.05 0.03 0.0925 0.03 0.02 0.125

j-->a FVB Other piscivores 0.09 0.001 0.129 0.0075 0 0.8

a-->a FVB Other piscivores 0.09 0.002 0.138 0.008 0.08 0.8

j-->j FVT Large piscivores 0.061 00000

a-->j FVT Large piscivores 0.061 00000

j-->a FVT Large piscivores 0.0475 00000

a-->a FVT Large piscivores 0.061 00000

j-->j FMM Migratory mesopelagics 000000

a-->j FMM Migratory mesopelagics 000000

j-->a FMM Migratory mesopelagics 000000

a-->a FMM Migratory mesopelagics 000000

j-->j FBP Benthopelagics 000000

a-->j FBP Benthopelagics 000000

j-->a FBP Benthopelagics 000000

a-->a FBP Benthopelagics 000000

j-->j FDD Goosefish 0.05 0.002 0.0114 0.05 0.001 0.0125

a-->j FDD Goosefish 0.05 0.002 0.0114 0.05 0.001 0.0125

j-->a FDD Goosefish 0.01 0.002 0.0104 0.02 0.01 0.1

a-->a FDD Goosefish 0.03 0.005 0.0105 0.02 0.06 0.1

j-->j FDE Shallow demersal fish 0 0 0.0005 0 0 0

a-->j FDE Shallow demersal fish 0 0 0.0015 0 0 0

j-->a FDE Shallow demersal fish 0 0 0.03 0 0 0

a-->a FDE Shallow demersal fish 0 0 0.00875 0 0 0

j-->j FDS Atlantic cod 0.075 0.005 0.0655 0.01 0.035 0.03

a-->j FDS Atlantic cod 0.075 0.015 0.0655 0.01 0.035 0.03

j-->a FDS Atlantic cod 0.1 0.0012 0.157 0.0075 0.03 0.075

a-->a FDS Atlantic cod 0.25 0.0045 0.155 0.0075 0.03 0.075

j-->j FDB Silver hake 0.07 0.00375 0.106 0.05 0 0.002

a-->j FDB Silver hake 0.07 0.00406 0.106 0.08 0 0.002

j-->a FDB Silver hake 0.03 0.00225 0.119 0.07 0 0.045

a-->a FDB Silver hake 0.0375 0.0035 0.208 0.01 0 0.045

Table A15. Consumption parameters (a in Equation 69) in the calibrated model for predation on the rest of the 

fish groups. Prey groups are listed by column. A value of 0 indicates cases where that predator does not eat 

that prey. In the left hand column, j-->j signifies juvenile predation on juveniles, a-->j signifies adult predation 

on juveniles, j-->a signifies juvenile predation on adults, and a-->a signifies adult predation on adults. 

124
PREDATOR PREY

Stages Code Group FDE FDS FDB FDC FDO FDF

j-->j FDC Miscellaneous demersals 0.03 0.0015 0.00625 0.15 0.005 0.013

a-->j FDC Miscellaneous demersals 0.025 0.002 0.00625 0.09 0.005 0.013

j-->a FDC Miscellaneous demersals 0.065 0.0007 0.0335 0.08 0.02 0.045

a-->a FDC Miscellaneous demersals 0.025 0.000638 0.036 0.02 0.01 0.045

j-->j FDO Haddock 0.08 0 0.041 0.018 0 0

a-->j FDO Haddock 0.08 0 0.041 0.02 0 0

j-->a FDO Haddock 0.004 0 0.143 0.003 0 0

a-->a FDO Haddock 0.006 0 0.123 0.003 0 0

j-->j FDF Yellowtail flounder 0 0 0.0155 0.0008 0 0

a-->j FDF Yellowtail flounder 0 0 0.0165 0.012 0 0

j-->a FDF Yellowtail flounder 0 0 0.0875 0.0008 0 0

a-->a FDF Yellowtail flounder 0 0 0.055 0.012 0 0

j-->j SHB Spiny dogfish 0.004 0.0035 0.128 0.008 0.03 0.075

a-->j SHB Spiny dogfish 0.003 0.0035 0.15 0.008 0.03 0.075

j-->a SHB Spiny dogfish 0.035 0.0008 0.208 0.003 0.2 0.41

a-->a SHB Spiny dogfish 0.035 0.002 0.208 0.003 0.4 0.41

j-->j SHD Other demersal sharks 0.55 0 0.335 0.0235 0.015 0.09

a-->j SHD Other demersal sharks 0.55 0 0.335 0.0235 0.015 0.08

j-->a SHD Other demersal sharks 0.45 0 0.418 0.01 0.17 0.45

a-->a SHD Other demersal sharks 0.35 0 0.418 0.01 0.1 0.45

j-->j SHP Pelagic sharks 0.575 0.0406 0.324 0 0.1 0

a-->j SHP Pelagic sharks 0.575 0.0406 0.324 0 0.1 0

j-->a SHP Pelagic sharks 0.375 0.023 0.412 0 0.25 0

a-->a SHP Pelagic sharks 0.475 0.08 0.557 0 0.25 0

j-->j SSK Skates and rays 0.085 0.001 0.0535 0.05 0.0035 0.05

a-->j SSK Skates and rays 0.085 0.002 0.0675 0.015 0.0035 0.05

j-->a SSK Skates and rays 0.095 0.009 0.0688 0.005 0.03 0.09

a-->a SSK Skates and rays 0.15 0.03 0.112 0.005 0.05 0.09

j-->j SB Seabirds 0.475 0.19 0.45 0 0 0

a-->j SB Seabirds 0.375 0.2 0.45 0 0 0

j-->a SB Seabirds 0.575 0.009 0.513 0 0 0

a-->a SB Seabirds 0.675 0.15 0.538 0 0 0

j-->j PIN Pinnipeds 0.575 0 0.375 0 0 0

a-->j PIN Pinnipeds 0.475 0 0.375 0 0 0

j-->a PIN Pinnipeds 0.28 0 0.463 0 0 0

a-->a PIN Pinnipeds 0.575 0 0.475 0 0 0

j-->j REP Reptiles 0 0 0 0.003 0 0

a-->j REP Reptiles 0 0 0 0.003 0 0

j-->a REP Reptiles 0 0 0 0.001 0 0

a-->a REP Reptiles 0 0 0 0.001 0 0

j-->j WHB Baleen whales 0.125 00000

a-->j WHB Baleen whales 0.065 00000

j-->a WHB Baleen whales 0.04 00000

a-->a WHB Baleen whales 0.05 00000

j-->j WHT Toothed whales 0.2 0.02 0.375 0.01 0 0

a-->j WHT Toothed whales 0.275 0.02 0.375 0.01 0 0

j-->a WHT Toothed whales 0.2 0.03 0.48 0.0045 0 0

a-->a WHT Toothed whales 0.275 0.05 0.492 0.0045 0 0

Table A15, continued. Consumption parameters (a in Equation 69) in the calibrated model for predation on the 

rest of the fish groups. Prey groups are listed by column. A value of 0 indicates cases where that predator 

does not eat that prey. In the left hand column, j-->j signifies juvenile predation on juveniles, a-->j signifies 

adult predation on juveniles, j-->a signifies juvenile predation on adults, and a-->a signifies adult predation on 

adults. 

125
PREY

Stages Code Group SHB SHD SHP SSK SB PIN REj-->j FPL Atlantic mackerel 000000a-->j FPL Atlantic mackerel 000000j-->a FPL Atlantic mackerel 000000a-->a FPL Atlantic mackerel 000000j-->j FPS Atlantic herring 000000a-->j FPS Atlantic herring 000000j-->a FPS Atlantic herring 000000a-->a FPS Atlantic herring 000000j-->j FVD White hake 0 0 0 0.005 00a-->j FVD White hake 0 0 0 0.005 00j-->a FVD White hake 0 0 0 0.00005 00a-->a FVD White hake 0 0 0 0.00005 00j-->j FVS Bluefish 0.055 0 0 0.001 00a-->j FVS Bluefish 0.002 0 0 0.001 00j-->a FVS Bluefish 0.0005 0 0 0.001 00a-->a FVS Bluefish 0.0005 0 0 0.001 00j-->j FVB Other piscivores 0.008 00000

a-->j FVB Other piscivores 0.001 0 0 0.01 00j-->a FVB Other piscivores 0.0003 00000a-->a FVB Other piscivores 0.0003 0 0 0.008 00j-->j FVT Large piscivores 000000a-->j FVT Large piscivores 000000j-->a FVT Large piscivores 000000a-->a FVT Large piscivores 000000j-->j FMM Migratory mesopelagics 000000a-->j FMM Migratory mesopelagics 000000j-->a FMM Migratory mesopelagics 000000a-->a FMM Migratory mesopelagics 000000j-->j FBP Benthopelagics 000000a-->j FBP Benthopelagics 000000j-->a FBP Benthopelagics 000000a-->a FBP Benthopelagics 000000j-->j FDD Goosefish 0.0095 0 0 0.0015 00

PREDATOR

Table A16. Consumption parameters (a in Equation 69) in the calibrated model for predation on the elasmobranch and mPrey groups are listed by column. A value of 0 indicates cases where that predator does not eat that prey. In the left hand juvenile predation on juveniles, a-->j signifies adult predation on juveniles, j-->a signifies juvenile predation on adults, and predation on adults. 

126
PREY

Stages Code Group SHB SHD SHP SSK SB PIN REa-->j FDD Goosefish 0.0008 0 0 0.0006 00j-->a FDD Goosefish 0.0005 0 0 0.0015 00a-->a FDD Goosefish 0.0005 0 0 0.0006 00j-->j FDE Shallow demersal fish 000000a-->j FDE Shallow demersal fish 000000j-->a FDE Shallow demersal fish 000000a-->a FDE Shallow demersal fish 000000j-->j FDS Atlantic cod 0.005 0 0 0.011 00

a-->j FDS Atlantic cod 0 0 0 0.009 00j-->a FDS Atlantic cod 0.00115 0 0 0.0008 00

a-->a FDS Atlantic cod 0 0 0 0.0008 00j-->j FDB Silver hake 000000a-->j FDB Silver hake 000000j-->a FDB Silver hake 000000a-->a FDB Silver hake 000000j-->j FDC Miscellaneous demersals 0.0035 0 0 0.01 00a-->j FDC Miscellaneous demersals 0.0035 0 0 0.0015 00j-->a FDC Miscellaneous demersals 0.00004 0 0 0.00015 00a-->a FDC Miscellaneous demersals 0.00004 0 0 0.00015 00j-->j FDO Haddock 000000a-->j FDO Haddock 000000j-->a FDO Haddock 000000a-->a FDO Haddock 000000j-->j FDF Yellowtail flounder 000000a-->j FDF Yellowtail flounder 000000j-->a FDF Yellowtail flounder 000000a-->a FDF Yellowtail flounder 000000j-->j SHB Spiny dogfish 0.0005 0 0 0.0075 00

a-->j SHB Spiny dogfish 0.0005 0 0 0.0075 00

j-->a SHB Spiny dogfish 0.002 0 0 0.0002 00a-->a SHB Spiny dogfish 0.002 0 0 0.0002 00j-->j SHD Other demersal sharks 0.0025 0.2 0 0.03 0 0 0.0000a-->j SHD Other demersal sharks 0.0015 0.21 0 0.03 0 0 0.000j-->a SHD Other demersal sharks 0.0002 0.25 0 0.03 0 0 0.0000Table A16, continued. Consumption parameters (a in Equation 69) in the calibrated model for predation on the elasmobramammal groups. Prey groups are listed by column. A value of 0 indicates cases where that predator does not eat that prcolumn, j-->j signifies juvenile predation on juveniles, a-->j signifies adult predation on juveniles, j-->a signifies juvenile pand a-->a signifies adult predation on adults. 

PREDATOR

127
PREY

Stages Code Group SHB SHD SHP SSK SB PIN REa-->a SHD Other demersal sharks 0.0002 0.275 0 0.03 0 0 0.0000j-->j SHP Pelagic sharks 0.08 0.51 0.035 0.025 0 0 0.0000a-->j SHP Pelagic sharks 0.08 0.51 0.035 0.025 0 0 0.0000j-->a SHP Pelagic sharks 0.05 0.61 0.008 0.01 0 0 0.0000a-->a SHP Pelagic sharks 0.02 0.61 0.008 0.02 0 0 0.0000j-->j SSK Skates and rays 0 0 0 0.051 00a-->j SSK Skates and rays 0 0 0 0.051 00j-->a SSK Skates and rays 0 0 0 0.0015 00a-->a SSK Skates and rays 0 0 0 0.0015 00j-->j SB Seabirds 000000a-->j SB Seabirds 000000j-->a SB Seabirds 000000a-->a SB Seabirds 000000j-->j PIN Pinnipeds 000000a-->j PIN Pinnipeds 000000j-->a PIN Pinnipeds 000000a-->a PIN Pinnipeds 000000j-->j REP Reptiles 000000a-->j REP Reptiles 000000j-->a REP Reptiles 000000a-->a REP Reptiles 000000j-->j WHB Baleen whales 000000a-->j WHB Baleen whales 000000j-->a WHB Baleen whales 000000a-->a WHB Baleen whales 000000j-->j WHT Toothed whales 0 0.015 0.0015 0.015 0 5.00E-07 a-->j WHT Toothed whales 0 0.015 0.0015 0.015 0 5.00E-07 j-->a WHT Toothed whales 0 0.06 0.0045 0.002 0 0.000001 a-->a WHT Toothed whales 0 0.06 0.0045 0.002 0 0.000001 

PREDATOR

Table A16, continued. Consumption parameters (a in Equation 69) in the calibrated model for predation on the elasmobramammal groups. Prey groups are listed by column. A value of 0 indicates cases where that predator does not eat that prcolumn, j-->j signifies juvenile predation on juveniles, a-->j signifies adult predation on juveniles, j-->a signifies juvenile pand a-->a signifies adult predation on adults. 

128
PREDATOR PREY

Stages Code Group BFS BFF BG BML BMS BD BC

j-->j FPL Atlantic mackerel 0 0.0005 0.00115 0 2.83E-05 0.01 0

a-->j FPL Atlantic mackerel 0 0.0005 0.01 0 2.83E-05 0.01 0

j-->a FPL Atlantic mackerel 0 0.0005 0.00115 0 2.83E-05 0.01 0

a-->a FPL Atlantic mackerel 0 0.0005 0.01 0 2.83E-05 0.01 0

j-->j FPS Atlantic herring 0 0.0001 0.0114 0 0.000178 0.01 0

a-->j FPS Atlantic herring 0 0.0001 0.0114 0 0.000178 0.1 0

j-->a FPS Atlantic herring 0 0.0001 0.0114 0 0.000178 0.01 0

a-->a FPS Atlantic herring 0 0.0001 0.0114 0 0.000178 0.1 0

j-->j FVD White hake 0.0002 0.000623 0.00006 0.000226 0.007 0.002 0

a-->j FVD White hake 0.0002 0.000623 0.00006 0.000226 0.007 0.002 0

j-->a FVD White hake 0.0002 0.000623 0.00006 0.000226 0.007 0.002 0

a-->a FVD White hake 0.0002 0.000623 0.00006 0.000226 0.007 0.002 0

j-->j FVS Bluefish 0 0.000845 0.000262 0 0.002 0.001 0

a-->j FVS Bluefish 0 0.000845 0.000262 0 0.002 0.001 0

j-->a FVS Bluefish 0 0.000845 0.000262 0 0.002 0.001 0

a-->a FVS Bluefish 0 0.000845 0.000262 0 0.002 0.001 0

j-->j FVB Other piscivores 0.000003 0.001 0.01 0.00002 0.05 0.04 0

a-->j FVB Other piscivores 0.000003 0.001 0.003 0.00002 0.04 0.03 0

j-->a FVB Other piscivores 0.000003 0.001 0.01 0.00002 0.05 0.04 0

a-->a FVB Other piscivores 0.000003 0.001 0.003 0.00002 0.04 0.03 0

j-->j FVT Large piscivores 0 0 0 0 0 00

a-->j FVT Large piscivores 0 0 0 0 0 00

j-->a FVT Large piscivores 0 0 0 0 0 00

a-->a FVT Large piscivores 0 0 0 0 0 00

j-->j FMM Migratory mesopelagics 0000 0.05 0.01 0

a-->j FMM Migratory mesopelagics 0000 0.05 0.01 0

j-->a FMM Migratory mesopelagics 0000 0.05 0.01 0

a-->a FMM Migratory mesopelagics 0000 0.05 0.01 0

j-->j FBP Benthopelagics 0 0.015 0.01 0 0.000678 0.05 0

a-->j FBP Benthopelagics 0 0.015 0.01 0 0.000678 0.05 0

j-->a FBP Benthopelagics 0 0.015 0.01 0 0.000678 0.05 0

a-->a FBP Benthopelagics 0 0.015 0.01 0 0.000678 0.05 0

j-->j FDD Goosefish 0.0003 0.004 3.82E-05 0.000022 0.000753 0.000646 0

a-->j FDD Goosefish 0.0003 0.004 3.82E-05 0.000022 0.000753 0.000646 0

j-->a FDD Goosefish 0.0003 0.004 3.82E-05 0.000022 0.000753 0.000646 0

a-->a FDD Goosefish 0.0003 0.004 3.82E-05 0.000022 0.000753 0.000646 0

j-->j FDE Shallow demersal fish 0 0 0.005 0 0.001 0.08 0

a-->j FDE Shallow demersal fish 0 0 0.005 0 0.001 0.08 0

j-->a FDE Shallow demersal fish 0 0 0.005 0 0.001 0.08 0

a-->a FDE Shallow demersal fish 0 0 0.005 0 0.001 0.08 0

j-->j FDS Atlantic cod 0.00005 0.001 0.007 0.0003 0.12 0.08 0

a-->j FDS Atlantic cod 0.00005 0.001 0.007 0.0003 0.12 0.08 0

j-->a FDS Atlantic cod 0.00005 0.001 0.007 0.0003 0.12 0.08 0

a-->a FDS Atlantic cod 0.00005 0.001 0.007 0.0003 0.12 0.08 0

j-->j FDB Silver hake 0 0.01 5.85E-05 2.46E-05 0.000176 0.04 0

a-->j FDB Silver hake 0 0.000749 5.85E-05 2.46E-05 0.000176 0.02 0

j-->a FDB Silver hake 0 0.01 5.85E-05 2.46E-05 0.000176 0.04 0

a-->a FDB Silver hake 0 0.000749 5.85E-05 2.46E-05 0.000176 0.02 0

Table A.17. Consumption parameters (a in Equation 69) in the calibrated model for predation on the benthic 

invertebrate groups. Prey groups are listed by column. A value of 0 indicates cases where that predator does 

not eat that prey. In the left hand column, j-->j signifies juvenile predation on juveniles, a-->j signifies adult 

predation on juveniles, j-->a signifies juvenile predation on adults, and a-->a signifies adult predation on adults.

129
PREDATOR PREY

Stages Code Group BFS BFF BG BML BMS BD BC

j-->j FDC Miscellaneous demersals 0.00003 0.01 0.008 0.00003 0.08 0.07 0

a-->j FDC Miscellaneous demersals 0.00003 0.0105 0.008 0.00003 0.09 0.08 0

j-->a FDC Miscellaneous demersals 0.00003 0.01 0.008 0.00003 0.08 0.07 0

a-->a FDC Miscellaneous demersals 0.00003 0.0105 0.008 0.00003 0.09 0.08 0

j-->j FDO Haddock 0.0001 0.0005 0.0007 0.0001 0.355 0.08 0

a-->j FDO Haddock 0.0001 0.0005 0.0007 0.0001 0.332 0.07 0

j-->a FDO Haddock 0.0001 0.0005 0.0007 0.0001 0.355 0.08 0

a-->a FDO Haddock 0.0001 0.0005 0.0007 0.0001 0.332 0.07 0

j-->j FDF Yellowtail flounder 0.0001 0.008 0.009 0 0.0119 0.08 0

a-->j FDF Yellowtail flounder 0.0001 0.008 0.009 0 0.0119 0.08 0

j-->a FDF Yellowtail flounder 0.0001 0.008 0.009 0 0.0119 0.08 0

a-->a FDF Yellowtail flounder 0.00001 0.008 0.009 0 0.0119 0.08 0

j-->j SHB Spiny dogfish 0.000005 0.001 0.0008 0.00005 0.0242 0.005 0

a-->j SHB Spiny dogfish 0.000005 0.001 0.0008 0.00005 0.002 0.002 0

j-->a SHB Spiny dogfish 0.000005 0.001 0.0008 0.00005 0.0242 0.005 0

a-->a SHB Spiny dogfish 0.000005 0.001 0.0008 0.00005 0.002 0.002 0

j-->j SHD Other demersal sharks 0.0001 0.001 0.007 0.0003 0.3 0.008 0

a-->j SHD Other demersal sharks 0.0001 0.001 0.007 0.0003 0.3 0.008 0

j-->a SHD Other demersal sharks 0.0001 0.001 0.007 0.0003 0.35 0.008 0

a-->a SHD Other demersal sharks 0.0001 0.001 0.007 0.0003 0.35 0.008 0

j-->j SHP Pelagic sharks 0 0 0 0 0 00

a-->j SHP Pelagic sharks 0 0 0 0 0 00

j-->a SHP Pelagic sharks 0 0 0 0 0 00

a-->a SHP Pelagic sharks 0 0 0 0 0 00

j-->j SSK Skates and rays 0.000075 0.025 0.001 0.003 0.167 0.08 0

a-->j SSK Skates and rays 0.000075 0.025 0.001 0.003 0.167 0.08 0

j-->a SSK Skates and rays 0.000075 0.025 0.001 0.003 0.167 0.08 0

a-->a SSK Skates and rays 0.000075 0.025 0.001 0.003 0.167 0.08 0

j-->j SB Seabirds 0 0 0 0.0025 0.00575 0 0

a-->j SB Seabirds 0 0 0 0.0025 0.00575 0 0

j-->a SB Seabirds 0 0 0 0.0025 0.00575 0 0

a-->a SB Seabirds 0 0 0 0.0025 0.00575 0 0

j-->j PIN Pinnipeds 0 0.01 0.001 0.00115 0.00115 0 0

a-->j PIN Pinnipeds 0 0.01 0.001 0.00115 0.00115 0 0

j-->a PIN Pinnipeds 0 0.01 0.001 0.00115 0.00115 0 0

a-->a PIN Pinnipeds 0 0.01 0.001 0.00115 0.00115 0 0

j-->j REP Reptiles 0 0.0575 0.01 0.0275 0.1 0 0

a-->j REP Reptiles 0 0.0575 0.01 0.0275 0.1 0 0

j-->a REP Reptiles 0 0.0575 0.01 0.0275 0.1 0 0

a-->a REP Reptiles 0 0.0575 0.01 0.0275 0.1 0 0

j-->j WHB Baleen whales 0 0 0 0 0 00

a-->j WHB Baleen whales 0 0 0 0 0 00

j-->a WHB Baleen whales 0 0 0 0 0 00

a-->a WHB Baleen whales 0 0 0 0 0 00

j-->j WHT Toothed whales 0000 0.01 0 0

a-->j WHT Toothed whales 0000 0.01 0 0

j-->a WHT Toothed whales 0000 0.01 0 0

a-->a WHT Toothed whales 0000 0.01 0 0

Table A17, continued. Consumption parameters (a in Equation 69) in the calibrated model for predation on the 

benthic invertebrate groups. Prey groups are listed by column. A value of 0 indicates cases where that predator 

does not eat that prey. In the left hand column, j-->j signifies juvenile predation on juveniles, a-->j signifies adult 

predation on juveniles, j-->a signifies juvenile predation on adults, and a-->a signifies adult predation on adults.

130
PREDATOR PREY

Stages Code Group CEP PWN ZL ZG ZM ZS DC

j-->j FPL Atlantic mackerel 0 0.008 0.003 0.00175 0.0025 0.03 0

a-->j FPL Atlantic mackerel 0.00001 0.008 0.003 0.00175 0.0025 0.03 0

j-->a FPL Atlantic mackerel 0 0.008 0.003 0.00175 0.0025 0.01 0

a-->a FPL Atlantic mackerel 0.00001 0.008 0.003 0.00175 0.0025 0.01 0

j-->j FPS Atlantic herring 0 0.0025 0.02 0.0075 0.02 0 0

a-->j FPS Atlantic herring 0 0.0025 0.015 0.0075 0.02 0 0

j-->a FPS Atlantic herring 0 0.0025 0.02 0.0075 0.02 0 0

a-->a FPS Atlantic herring 0 0.0025 0.015 0.0075 0.02 0 0

j-->j FVD White hake 0.02 0.14 0.175 0.000126 1.88E-05 0 0

a-->j FVD White hake 0.02 0.14 0.175 0.000126 1.88E-05 0 0

j-->a FVD White hake 0.01 0.1 0.175 0.000126 1.88E-05 0 0

a-->a FVD White hake 0.01 0.1 0.175 0.000126 1.88E-05 0 0

j-->j FVS Bluefish 0.003 0.052 0.1 0.001 0 0 0

a-->j FVS Bluefish 0.003 0.052 0.1 0.001 0 0 0

j-->a FVS Bluefish 0.002 0.07 0.1 0.001 0 0 0

a-->a FVS Bluefish 0.002 0.07 0.1 0.001 0 0 0

j-->j FVB Other piscivores 0.001 0.21 0.03 0.015 0.000726 0 0

a-->j FVB Other piscivores 0.001 0.21 0.03 0.015 0.000726 0 0

j-->a FVB Other piscivores 0.001 0.31 0.03 0.015 0.000726 0 0

a-->a FVB Other piscivores 0.001 0.31 0.03 0.015 0.000726 0 0

j-->j FVT Large piscivores 0.01 0 0.06 0.00025 0 0 0

a-->j FVT Large piscivores 0.01 0 0.06 0.00025 0 0 0

j-->a FVT Large piscivores 0.01 0 0.06 0.00025 0 0 0

a-->a FVT Large piscivores 0.01 0 0.06 0.00025 0 0 0

j-->j FMM Migratory mesopelagics 0 0 0.3 0 0.015 0 0

a-->j FMM Migratory mesopelagics 0 0 0.3 0 0.015 0 0

j-->a FMM Migratory mesopelagics 0 0 0.3 0 0.015 0 0

a-->a FMM Migratory mesopelagics 0 0 0.3 0 0.015 0 0

j-->j FBP Benthopelagics 0.01 0.25 0.27 0.01 0.06 0 0

a-->j FBP Benthopelagics 0.01 0.25 0.27 0.01 0.06 0 0

j-->a FBP Benthopelagics 0.011 0.2 0.27 0.01 0.06 0 0

a-->a FBP Benthopelagics 0.011 0.2 0.27 0.01 0.06 0 0

j-->j FDD Goosefish 0.02 0.08 0.01 0 0 0 0

a-->j FDD Goosefish 0.03 0.08 0.01 0 0 0 0

j-->a FDD Goosefish 0.012 0.06 0.01 0 0 0 0

a-->a FDD Goosefish 0.012 0.06 0.01 0 0 0 0

j-->j FDE Shallow demersal fish 0 0.062 0.18 0.0125 0.004 0 0

a-->j FDE Shallow demersal fish 0 0.062 0.18 0.0125 0.004 0 0

j-->a FDE Shallow demersal fish 0 0.062 0.18 0.0125 0.004 0 0

a-->a FDE Shallow demersal fish 0 0.062 0.18 0.0125 0.004 0 0

j-->j FDS Atlantic cod 0.01 0.06 0.035 0.002 0.000144 0 0

a-->j FDS Atlantic cod 0.01 0.06 0.035 0.002 0.000144 0 0

j-->a FDS Atlantic cod 0.005 0.1 0.035 0.002 0.000144 0 0

a-->a FDS Atlantic cod 0.005 0.1 0.035 0.002 0.000144 0 0

j-->j FDB Silver hake 0.009 0.15 0.06 0.0017 0.01 0 0

a-->j FDB Silver hake 0.009 0.15 0.02 0.0017 0.0001 0 0

j-->a FDB Silver hake 0.007 0.18 0.06 0.0017 0.01 0 0

a-->a FDB Silver hake 0.015 0.18 0.02 0.0017 0.0001 0 0

j-->j FDC Miscellaneous demersals 0.0005 0.0494 0.019 0.00225 0.000722 0 0

Table A18. Consumption parameters (a in Equation 69) in the calibrated model for predation on the plankton, 

shrimp and squid groups. Prey groups are listed by column. A value of 0 indicates cases where that predator 

does not eat that prey. In the left hand column, j-->j signifies juvenile predation on juveniles, a-->j signifies 

adult predation on juveniles, j-->a signifies juvenile predation on adults, and a --> a signifies adult predation on 

adults. 

131
PREDATOR PREY

Stages Code Group CEP PWN ZL ZG ZM ZS DC

a-->j FDC Miscellaneous demersals 0.0005 0.0494 0.019 0.00225 0.000722 0 0

j-->a FDC Miscellaneous demersals 0.0004 0.0297 0.019 0.00225 0.000722 0 0

a-->a FDC Miscellaneous demersals 0.0004 0.0297 0.019 0.00225 0.000722 0 0

j-->j FDO Haddock 0.0125 0.08 0.02 0.018 0.000566 0 0

a-->j FDO Haddock 0.0125 0.1 0.02 0.018 0.000566 0 0

j-->a FDO Haddock 0.018 0.15 0.02 0.018 0.000566 0 0

a-->a FDO Haddock 0.018 0.15 0.02 0.018 0.000566 0 0

j-->j FDF Yellowtail flounder 0 0.08 0.15 0.002 0.00161 0 0

a-->j FDF Yellowtail flounder 0 0.08 0.15 0.002 0.00161 0 0

j-->a FDF Yellowtail flounder 0 0.1 0.15 0.002 0.00161 0 0

a-->a FDF Yellowtail flounder 0 0.1 0.15 0.002 0.00161 0 0

j-->j SHB Spiny dogfish 0.0005 0.43 0.0016 0.0005 3.32E-05 0 0

a-->j SHB Spiny dogfish 0.0005 0.43 0.002 0.0005 0.00001 0 0

j-->a SHB Spiny dogfish 0.0003 0.35 0.0016 0.0005 3.32E-05 0 0

a-->a SHB Spiny dogfish 0.0003 0.35 0.003 0.0005 0.00001 0 0

j-->j SHD Other demersal sharks 0.01 0.5 0.05 0.00002 1.06E-05 0 0

a-->j SHD Other demersal sharks 0.07 0.45 0.05 0.00002 1.06E-05 0 0

j-->a SHD Other demersal sharks 0.05 0.4 0.05 0.00002 1.06E-05 0 0

a-->a SHD Other demersal sharks 0.05 0.4 0.05 0.00002 1.06E-05 0 0

j-->j SHP Pelagic sharks 0.08 0 0.058 0 0 0 0.115

a-->j SHP Pelagic sharks 0.08 0 0.0075 0 0 0 0.1

j-->a SHP Pelagic sharks 0.1 0 0.058 0 0 0 0.115

a-->a SHP Pelagic sharks 0.1 0 0.0075 0 0 0 0.1

j-->j SSK Skates and rays 0.007 0.18 0.00812 0.005 0.000597 0 0

a-->j SSK Skates and rays 0.007 0.18 0.00812 0.005 0.000597 0 0

j-->a SSK Skates and rays 0.01 0.2 0.00812 0.005 0.000597 0 0

a-->a SSK Skates and rays 0.01 0.2 0.00812 0.005 0.000597 0 0

j-->j SB Seabirds 0.05 0.78 0.155 0.005 0.00575 0 0.23

a-->j SB Seabirds 0.05 0.78 0.155 0.005 0.00575 0 0.23

j-->a SB Seabirds 0.03 0.8 0.155 0.005 0.00575 0 0.23

a-->a SB Seabirds 0.03 0.8 0.155 0.005 0.00575 0 0.23

j-->j PIN Pinnipeds 0.125 0.8 0 0 0 0 0.0575

a-->j PIN Pinnipeds 0.125 0.8 0 0 0 0 0.0575

j-->a PIN Pinnipeds 0.075 0.8 0 0 0 0 0.0575

a-->a PIN Pinnipeds 0.075 0.8 0 0 0 0 0.0575

j-->j REP Reptiles 0 0.8 0.05 0.25 0 0 0.23

a-->j REP Reptiles 0 0.8 0.05 0.25 0 0 0.23

j-->a REP Reptiles 0 0.8 0.05 0.25 0 0 0.23

a-->a REP Reptiles 0 0.8 0.05 0.25 0 0 0.23

j-->j WHB Baleen whales 0.0015 0.09 0.004 0 0.003 0 0

a-->j WHB Baleen whales 0.0015 0.09 0.004 0 0.003 0 0

j-->a WHB Baleen whales 0.003 0.15 0.004 0 0.003 0 0

a-->a WHB Baleen whales 0.003 0.15 0.004 0 0.003 0 0

j-->j WHT Toothed whales 0.03 0 0 0 00 0.115

a-->j WHT Toothed whales 0.03 0 0 0 00 0.115

j-->a WHT Toothed whales 0.03 0 0 0 00 0.115

a-->a WHT Toothed whales 0.03 0 0 0 00 0.115

Table A18, continued. Consumption parameters (a in Equation 69) in the calibrated model for predation on the 

plankton, shrimp and squid groups. Prey groups are listed by column. A value of 0 indicates cases where that 

predator does not eat that prey. In the left hand column, j-->j signifies juvenile predation on juveniles, a-->j 

signifies adult predation on juveniles, j-->a signifies juvenile predation on adults, and a --> a signifies adult 

predation on adults. 

132
CODE GROUP AGE CLASS

1 2 3 4 5 6 7 

FPL Atlantic mackerel 0.01 0.05 0.2 0.3 0.3 0.3 0.3 0FPS Atlantic herring 0.15 0.4 12 15 15 17 17 FVD White hake 30 150 400 600 600 600 600 6FVS Bluefish 30 60 105 180 250 250 815 8FVT Large piscivores 380 17000 28500 43000 44000 44000 44000 440FVB Other piscivores 1 1.8 5 10.5 25.5 25.5 25.5 25FMM Migratory mesopelagics 0.3 0.2 0.1 0.1 0.1 0.1 0.1 0FBP Benthopelagics 5.3 10.6 16.6 25.7 25.7 25.8 25.8 25FDD Goosefish 600 800 900 1600 1800 1900 2100 22FDS Atlantic cod 50 250 850 1500 1800 2100 2100 25FDB Silver hake 5 130 160 150 150 180 125 1FDC Miscellaneous demersals 1 5 15 25 45 45 45 FDO Haddock 5 15 25 70 105 105 105 1FDE Shallow demersal fish 2.5 7.5 12 20.8 28.8 28.8 30.8 30FDF Yellowtail flounder 2.5 7.5 15 17.8 18.8 19.8 20.8 2SHD Other demersal sharks 5 20 40 50 50 70 70 SHP Pelagic sharks 30 150 350 850 1450 1450 1850 18SHB Spiny dogfish 5 7.5 20 45 50 80 130 1SSK Skates and rays 900 1080 1020 1040 1060 1070 1075 10SB Seabirds 8000 8000 8000 5000 5000 5000 4500 40REP Reptiles 10 12 25 25 25 45 48 PIN Pinnipeds 25 1400 1400 1400 1200 1200 1000 10WHB Baleen whales 50000000 15000000 15000000 15000000 15000000 15000000 15000000 150000WHT Toothed whales 6000 5000 5000 5000 8000 8000 8000 80

Table A19. Clearance rates (mg3 N / day) for each vertebrate group in Atlantis NEUS for each age class.

133
CODE GROUP C (mg3 N / day)

ZG Gelatinous zooplankton 0.003

ZL Carnivorous zooplankton 0.02

ZM Copepods 0.008

ZS Microzooplankton 0.15

DF Dinoflagellates 0.01

BFS Sea scallop 0.02

BFF Other benthic filter feeder 0.00002

BD Deposit feeder 0.0005

BC Benthic carnivore 0.0006

BG Benthic grazer 0.000075

BMS Shallow macrozoobenthos 0.00005

BML Lobster 0.000065

BO Meiobenthos 0.001

CEP Squid 0.01455

jCEP Juv. Squid 0.01455

PWN Shrimp 0.0015

jPWN Juv. Shrimp 0.0015

Table A20. Clearance rates for each invertebrate group in Atlantis NEUS.

134
CODE GROUP midwcCEP midwcFP dredgeBFS netFD plineFVO pseineFVO midwcFD dlineFD recfish p

FPL Atlantic mackerel 0 0.07 0 0 0 0 0.3 0 0 FPS Atlantic herring 0 0.3 0 0 0 0 0.4 0 0 FVD White hake 0 0 0 0.1 0.0001 0 0 0.5 1.00E-07 FVS Bluefish 0 0 0 0.1 0 0 0 0 1.00E-07 FVT Large piscivores 0 0 0 0 0.9 0.9 0 0.04 8.00E-09 FVB Other piscivores 0 0 0 0.1 0 0 0 0.00002 5.00E-12 FMM Migratory mesopelagics 0 0.04 0 0 0 0 0.04 0 0 FBP Benthopelagics 0 0.05 0 0.005 0 0 0 0 5.00E-10 FDD Goosefish 0 0 0.001 0.002 0 0 0 0 0 FDS Atlantic cod 0 0 0 0.002 0 0 0 0.02 2.00E-09 FDB Silver hake 0 0.05 0 0 0 0 0.6 0 0 FDC Miscellaneous demersals 0 0 0.6 0.003 0 0 0 0.0001 1.00E-09 FDO Haddock 0 0 0 0.00002 0 0 0 0.005 0 FDE Shallow demersal fish 0.9 0.1 0 0 0 0 0.000002 0 0.00002 FDF Yellowtail flounder 0 0 0.0001 0.0002 0 0000 

SHB Spiny dogfish 0 0.0006 0 0.0006 0 0 0 0.00001 0 SHD Other demersal sharks 0 0 0 0.00003 0.5 0.000003 0.0002 0.00003 3.00E-09 SHP Pelagic sharks 0 0 0 0 0.0001 0 0 0.000001 1.00E-07 SSK Skates and rays 0 0 0 0.001 0 0 0 0.002 4.00E-07 SB Seabirds 0 0 0 0.000001 0.1 0 0 0.000001 0 PIN Pinnipeds 0 0 0 0.01 0 0000 0

REP Reptiles 0 0 0 0.01 0.05 0 0 0.000001 0 0WHB Baleen whales 0 0 0 0 0 0000 WHT Toothed whales 0 0 0 0.05 0.01 0 0 0.0001 0 CEP Squid 0.9 0 0.9 0 0 0 0 0 0.00002 BFS Sea scallop 0 0 0.9 0 0 0000 BG Benthic grazer 0 0 0.25 0 0 0000 BMS Shallow macrozoobenthos 0 0 0.000005 0 0 0000 

BML Lobster 0 0 0 0 0 0000 BD Deposit feeder 0 0 0.0015 0 0 0000 PWN Shrimp 0 0 0 0.9 0 0 0 0 0 BFF Other benthic filter feeder 0 0 0.25 0 0 0000 ZL Carnivorous zooplankton 0 0.001 0.01 0 0 0 0.001 0 0 

Table A21. q (catchability) parameters for each gear on each functional group for the dynamic effort run of Atlantis NEUS

GEAR

135
GEAR GEAR

CODE GROUP pseineFP trapBMS dtrawlCEP dtrawlFD dtrawlFDB dtrawlFDO midwcFD dlineFD recfish ptrawFPL Atlantic macke 0.01 0 0 0.008 0 0 0.3 00

FPS Atlantic herrin 0.3 0 0 0 0 00.400

FVD White hake 0 0 0.00008 0.2 0.04 0 0 0.5 1.00E-07 

FVS Bluefish 0.8 0 0 0.01 0.02 0.5 0 0 1.00E-07 

FVT Large piscivor 0000000 0.04 8.00E-09 

FVB Other piscivor 0 0.004 0.001 0.2 0.0003 0 0 0.00002 5.00E-12 

FMM Migratory mes 0.04 0 0.04 0 0 0 0.04 00

FBP Benthopelagic 0.005 0.0005 0.002 0.04 0.3 0 0 0 5.00E-10 

FDD Goosefish 0 0 0 0 0 0 0 0 0 

FDS Atlantic cod 0 0 0.00002 0.01 0.9 0.4 0 0.02 2.00E-09 

FDB Silver hake 0 0 0.00005 0.08 0.2 0.6 0.6 0 0 

FDC Miscellaneous 0 0 0.00001 0.04 0.002 0.002 0 0.0001 1.00E-09 

FDO Haddock 0 0 0 0.05 0.1 0 0 0.005 0 

FDE Shallow deme 0.99 00000 0.000002 0 0.00002 

FDF Yellowtail flou 0 0.0001 0.0001 0.1 0.001 0000 

SHB Spiny dogfish 0 0 0.00001 0 0.8 0.00001 0 0.00001 0 SHD Other demers 0 0 0 0.0006 0.06 2.00E-07 0.0002 0.00003 3.00E-09 

SHP Pelagic shark 0.00003 000000 0.000001 1.00E-07 SSK Skates and ra 0 0 0.004 0.002 0.4 0 0 0.002 4.00E-07 

SB Seabirds 0000000 0.000001 0 PIN Pinnipeds 0 0 0.000001 0.000001 0.000001 0000 0.0

REP Reptiles 0 0 0.000001 0.000001 0.000001 0 0 0.000001 0 0.0WHB Baleen whale 0 0.001 0000000WHT Toothed whal 0 0 0 0.0001 0.0001 0 0 0.0001 0 

CEP Squid 0 0 0.9 00000 0.00002 

BFS Sea scallop 0 0 0.0003 0.0001 0.005 0000 

BG Benthic graze 0 0.001 0.1 0.05 0.1 0000 

BMS Shallow macr 0 0 0 0.001 0.02 0000 

BML Lobster 0 0.01 0 0.01 00000BD Deposit feede 0 0.0001 0.001 0.001 0.001 0000 

PWN Shrimp 0 0 0 0 0 0 0 0 0 BFF Other benthic 0 0 0.01 0.01 0.01 0000 ZL Carnivorous z 000000 0.001 0 0 

Table A21, continued. q (catchability) parameters for each gear on each functional group for the dynamic effort run of At

136
FISHERY REGION

1 2 3 4 5 6 7 8 9 

midwcCEP 0.07 0.1 0.1 0.05 0.07 0.1 0.05 0.08 0.1 midwcFP 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0

dredgeBFS 0 0.15 0.05 0 0 0.15 0 0.1 0.15 netFD 0 0 0 0 0 0 0 0.2 0 plineFVO 0 0 0.2 0 0.1 0.2 0 0.05 0.2 pseineFVO 0 0 0.2 0 0 0.25 0 0 0.3 pseineFP 0 0 0 0 0 0 0 0.2 0.2 trapBMS 0.01 0.01 0.01 0.01 0.01 0.01 0.08 0.01 0.01 

dtrawlCEP 0.07 0.07 0.07 0.07 0.07 0.07 0.1 0.1 0.1 dtrawlFD 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0dtrawlFDO 0 0 0 0 0 0 0 0.3 0 

dtrawlFDB 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0midwcFD 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0dlineFD 0000000 0.13 0 

REC 0.11 0.05 0.01 0.11 0.04 0.05 0.11 0.05 0.025 

ptrawlPWN 0 0 0 0 0 0 0 0 0 dtrawlFBP 0.15 0 0 0.1 0 0 0.15 0 0 trapFDE 0 0.05 0.05 0.05 0.05 0.05 0.05 0 0.1 Table A22. Effort (in days per day) for each region for the fixed effort run of Atlantis NEUS. Boxes 0 and 23-29 were boundeffort assigned, and are not included in the below table.

137
FISHERY REGION

13 14 15 16 17 18 19 20 21 22

midwcCEP 0.07 0.07 00000000

midwcFP 0.055 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045dredgeBFS 0.2 0.03 0.1 0000000

netFD 0 0 0 0 0.2 0.2 0 0 0 0plineFVO 0 0.2 0.05 0000000pseineFVO 0 0.25 00000000

pseineFP 0.15 0.15 00000000trapBMS 0.01 0.01 0.01 0.15 0.15 0.09 0.08 0.01 0.01 0.01dtrawlCEP 0.07 0.07 00000000dtrawlFD 0.055 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045dtrawlFDO 0 0 0 0 0.2 0.2 0 0 0 0dtrawlFDB 0.055 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045midwcFD 0.055 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045dlineFD 0.01 0.01 0.02 0.09 0.02 0.02 0.09 0.09 0.05 0.05REC 0.01 0.025 0.01 0.01 0.11 0.11 0.01 0.01 0.01 0.01ptrawlPWN 0 0 0 0.2 0 0 0.2 0.2 0.1 0dtrawlFBP 0 0 0 0 0.2 0.2 0 0 0 0trapFDE 0.05 0.1 0.1 0.05 0.05 0 0 0.1 0.05 0Table A22, continued. Effort (in days per day) for each region for the fixed effort run of Atlantis NEUS. Boxes 0 and 23-29 which had no effort assigned, and are not included in the below table.

138
CODE FISHERY

Minimum Threshold CPUE (kg / d)

Maximum Threshold CPUE (kg / d)

Proportional Change in Effort

dlineFD Line fishery on demersals 200 340 0.4 dredgeBFS Scallop dredge 1500 5400 0.2 

dtrawlCEP Demersal trawl on cephalopods 1000 2800 0.3 

dtrawlFBP Demersal trawl on benthopelagics 1000 20000 0.6 

dtrawlFD Demersal trawl on other deep demersals 1650 2450 0.1 

dtrawlFDB Demersal trawl on shallow demersals 100 14000 0.9 dtrawlFDO Demersal trawl on cod and haddock 40 600 0.3 midwcCEP Midwater trawl on cephalopods 50 560000 0.65 

midwcFD Midwater Trawl - international fleet 1000 6800 0.2 

midwcFP Midwater trawl on small pelagics 2500 75000 0.55 

netFD Demersal gillnet on other deep demersals 40 250 0.25 

plineFVO Pelagic line on tuna and sharks 25 190 0.9 

pseineFP Purse seine on small pelagics 120 880 0.5 

pseineFVO Purse seine on tuna and sharks 1E-18 1E-13 0.7 

ptrawlPWN Shrimp trawl 10 1300 0.1 

REC Recreational fishery 0 0 1 

trapBMS Lobster traps 1 10 0.4 

trapFDE Trap on demersals 30 440 0.3 

Table A23. Effort parameters for each fishery for the dynamic effort run of Atlantis NEUS. If the CPUE was below the miniabove the maximum threshold, then effort was changed by the proportional change in effort (a decrease of 1 - proportionaincrease of 1 + proportional change respectively). Maximum effort per day is a constraint on effort allowed for the fishery

139
APPENDIX B: Atlantis NEUS – BIOMASS TRAJECTORY RESULTS 

 

140
a.

b.

c.

Figure B1. Atlantic herring (Clupea harengus) biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. 

fixed effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and 

assessment data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

141
a.

b.

c.

Figure B2. White hake (Urophycis tenuis) biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed 

effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and assessment 

data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

142
a.

b.

c.

Figure B3. Bluefish (Pomatomus saltatrix) biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed 

effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and assessment 

data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

143
a. 

b. 

c.

Figure B4. Other demersal flatfish biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed effort 

run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and assessment data are 

scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

144
a.

b.

c.

Figure B5. Large pelagics biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

145
a.

b.

c.

Figure B6. Migratory mesopelagics biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed effort 

run, c. dynamic effort run) and observed time series (survey only, no assessment time series was available). 

Modeled data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

146
a.

b.

c.

Figure B7. Other pelagics biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run) and observed time series (assessment and survey). Modeled and assessment data are 

scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

147
a.

b.

c.

Figure B8. Goosefish (Lophius americanus) biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. 

fixed effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and 

assessment data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

148
a.

b.

c.

Figure B9. Anadromous small pelagics biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed 

effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and assessment 

data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

149
a. 

b.

c.

Figure B10. Silver hake (Merluccius bilinearis) biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. 

fixed effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and 

assessment data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

150
a.

b.

c.

Figure B11. Other demersals biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run) and observed time series (assessment and survey). Modeled and assessment data are 

scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

151
a. 

b.

c.

Figure B12. Yellowtail flounder (Limanda ferruginea) biomass trajectories for both Atlantis NEUS (a. fixed catch 

run, b. fixed effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and 

assessment data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

152
a.

b.

c.

Figure B13. Demersal sharks biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run) and observed time series (survey only, no assessment time series was available). Modeled 

data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

 

153
a.

b.

c.

Figure B14. Pelagic sharks biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

154
Figure B15. Skates and rays biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run) and observed time series (assessment and survey). Modeled and assessment data are 

scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

 

155
a.

b.

c.

Figure B.16. Seabirds biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. dynamic 

effort run). No observed time series were available. 

156
a.

b.

c.

Figure B17. Pinnipeds biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. dynamic 

effort run). No observed time series were available. 

157
Figure B18. Reptiles biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. dynamic 

effort run). No observed time series were available. 

158
a.

b.

c.

Figure B19. Baleen whales biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

159
a.

b.

c.

Figure B20. Toothed whales biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

160
a.

b.

c.

Figure B21. Squid biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. dynamic 

effort run) and observed time series (assessment and survey). Modeled and assessment data are scaled to the 

left Y-axis while survey data are scaled to the right Y-axis. 

161
a.

b.

c.

Figure B22. Sea scallop (Placopecten magellanicus) biomass trajectories for both Atlantis NEUS (a. fixed catch 

run, b. fixed effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and 

assessment data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

162
a.

b.

c.

Figure B23. Other benthic filter feeders biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed 

effort run, c. dynamic effort run) and observed time series (assessment only, no survey time series was 

available). 

163
a.

b.

c.

Figure B24. Benthic herbivorous grazers biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort 

run, c. dynamic effort run). No observed time series were available. 

164
a.

b.

c.

Figure B25. Lobster (Homarus americanus) biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. 

fixed effort run, c. dynamic effort run) and observed time series (assessment and survey). Modeled and 

assessment data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

165
a.

b.

c.

Figure B26. Shallow macrozoobenthos biomass trajectories for both Atlantis NEUS (a. fixed catch run, b. fixed 

effort run, c. dynamic effort run) and observed time series (survey only, no assessment time series was 

available). Modeled data are scaled to the left Y-axis while survey data are scaled to the right Y-axis. 

166
a. 

b. 

c.

Figure B27. Large zooplankton biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

167
a.

b.

c.

Figure B28. Deposit feeders biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

168
a.

b.

c.

Figure B29. Carnivorous infauna biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

169
a.

b.

c.

Figure B30. Gelatinous zooplankton biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, 

c. dynamic effort run). No observed time series were available. 

170
a.

b.

c.

Figure B31. Large phytoplankton biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

171
a.

b.

c.

Figure B32. Small phytoplankton biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

172
a.

b.

c.

Figure B33. Mesozooplankton biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

173
a.

b.

c.

Figure B34. Microzooplankton biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

174
a.

b.

c.

Figure B35. Pelagic bacteria biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

175
a.

b.

c.

Figure B36. Benthic bacteria biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

176
a.

b.

c.

Figure B37. Meiobenthos biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. dynamic 

effort run). No observed time series were available. 

177
a.

b.

c.

Figure B38. Labile detritus biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

178
a.

b.

c.

Figure B39. Refractory detritus biomass trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

179
a.

b.

c.

Figure B40. Dissolved inorganic nitrogen trajectories for Atlantis NEUS (a. fixed catch run, b. fixed effort run, c. 

dynamic effort run). No observed time series were available. 

180
APPENDIX C: Atlantis NEUS – CATCH TRAJECTORY RESULTS 

a.

b.

Figure C1. Atlantic herring (Clupea harengus) catch trajectories for both Atlantis NEUS (a. fixed effort run, b. 

dynamic effort run) and the actual observed time series. 

181
a.

b.

Figure C2. White hake (Urophycis tenuis) catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic 

effort run) and the actual observed time series. 

182
a.

b.

Figure C3. Bluefish (Pomatomus saltatrix) catch trajectories for both Atlantis NEUS (a. fixed effort run, b. 

dynamic effort run) and the actual observed time series. 

183
a.

b.

Figure C4. Other demersal flatfish catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort 

run) and the actual observed time series. 

184
a.

b.

Figure C5. Large pelagics catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort run) and 

the actual observed time series. 

185
a.

b.

Figure C6. Migratory mesopelagics catch trajectories for Atlantis NEUS (a. fixed effort run, b. dynamic effort run). 

No observed time series was available. 

186
a.

b.

Figure C7. Other pelagics catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort run) and 

the actual observed time series. 

187
a.

b.

Figure C8. Goosefish (Lophius americanus) catch trajectories for both Atlantis NEUS (a. fixed effort run, b. 

dynamic effort run) and the actual observed time series. 

188
a.

b.

Figure C9. Anadromous small pelagics catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic 

effort run) and the actual observed time series. 

189
a.

b.

Figure C10. Silver hake (Merluccius bilinearis) catch trajectories for both Atlantis NEUS (a. fixed effort run, b. 

dynamic effort run) and the actual observed time series. 

190
a.

b.

Figure C11. Other demersals catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort run) 

and the actual observed time series. 

191
a.

b.

Figure C12. Yellowtail flounder (Limanda ferruginea) catch trajectories for both Atlantis NEUS (a. fixed effort run, 

b. dynamic effort run) and the actual observed time series. 

192
a.

b.

Figure C13. Demersal sharks catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort run) 

and the actual observed time series. 

193
a.

b.

Figure C14. Pelagic sharks catch trajectories for Atlantis NEUS (a. fixed effort run, b. dynamic effort run). No 

observed time series was available. 

194
a.

b.

Figure C15. Skates and rays catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort run) 

and the actual observed time series. 

195
a.

b.

Figure C16. Reptiles catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort run) and the 

actual observed time series. 

196
a.

b.

Figure C17. Squid catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic effort run) and the 

actual observed time series. 

197
a.

b.

Figure C18. Sea scallop (Placopecten magellanicus) catch trajectories for both Atlantis NEUS (a. fixed effort run, 

b. dynamic effort run) and the actual observed time series. 

198
a.

b.

Figure C19. Other benthic filter feeders catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic 

effort run) and the actual observed time series. 

199
a.

b.

Figure C20. Benthic herbivorous grazers catch trajectories for Atlantis NEUS (a. fixed effort run, b. dynamic effort 

run). No observed time series was available. 

200
a.

b.

Figure C21. Lobster (Homarus americanus) catch trajectories for both Atlantis NEUS (a. fixed effort run, b. 

dynamic effort run) and the actual observed time series. 

201
a.

b.

Figure C22. Shallow macrozoobenthos catch trajectories for both Atlantis NEUS (a. fixed effort run, b. dynamic 

effort run) and the actual observed time series. 

202
a.

b.

Figure C23. Deposit feeders catch trajectories for Atlantis NEUS (a. fixed effort run, b. dynamic effort run). No 

observed time series was available. 

203
APPENDIX D: Atlantis NEUS – CATCH PER FISHERY TRAJECTORY RESULTS 

204
Figure D1. Atlantic herring (Clupea harengus) catch per fishery trajectories for both Atlantis NEUS (fixed effort 

run) and the actual observed time series. 

205
Figure D2. Atlantic herring (Clupea harengus) catch per fishery trajectories for both Atlantis NEUS (dynamic 

effort run) and the actual observed time series. 

206
Figure D3. White hake (Urophycis tenuis) catch per fishery trajectories for both Atlantis NEUS (fixed effort run) 

and the actual observed time series. 

207
Figure D4. White hake (Urophycis tenuis) catch per fishery trajectories for both Atlantis NEUS (dynamic effort 

run) and the actual observed time series. 

208
Figure D5. Bluefish (Pomatomus saltatrix) catch per fishery trajectories for both Atlantis NEUS (fixed effort run) 

and the actual observed time series. 

209
Figure D6. Bluefish (Pomatomus saltatrix) catch per fishery trajectories for both Atlantis NEUS (dynamic effort 

run) and the actual observed time series. 

210
Figure D7. Other demersal flatfish catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and the 

actual observed time series. 

211
Figure D8. Other demersal flatfish catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) and 

the actual observed time series. 

212
Figure D9. Large pelagics catch per fishery trajectory for Atlantis NEUS (fixed effort run). No observed time 

series was available. 

Figure D10. Large pelagics catch per fishery trajectory for Atlantis NEUS (dynamic effort run). No observed time 

series was available. 

213
Figure 

D11. Migratory mesopelagics catch per fishery trajectory for Atlantis NEUS (fixed effort run). No observed time 

series was available. 

Figure D12. Migratory mesopelagics catch per fishery trajectory for Atlantis NEUS (dynamic effort run). No 

observed time series was available. 

214
Figure D13. Other pelagics catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and the actual 

observed time series. 

215
Figure D14. Other pelagics catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) and the 

actual observed time series. 

216
Figure 

D15. Goosefish (Lophius americanus) catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and 

the actual observed time series. 

217
Figure D16. Goosefish (Lophius americanus) catch per fishery trajectories for both Atlantis NEUS (dynamic effort 

run) and the actual observed time series. 

218
Figure D17. Anadromous small pelagics catch per fishery trajectories for both Atlantis NEUS (fixed effort run) 

and the actual observed time series. 

219
Figure D18. Anadromous small pelagics catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) 

and the actual observed time series. 

220
Figure D19. Silver hake (Merluccius bilinearis) catch per fishery trajectories for both Atlantis NEUS (fixed effort 

run) and the actual observed time series. 

221
Figure D20. Silver hake (Merluccius bilinearis) catch per fishery trajectories for both Atlantis NEUS (dynamic 

effort run) and the actual observed time series. 

222
Figure D21. Other demersals catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and the actual 

observed time series. 

223
Figure D22. Other demersals catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) and the 

actual observed time series. 

224
Figure D23. Yellowtail flounder (Limanda ferruginea) catch per fishery trajectories for both Atlantis NEUS (fixed 

effort run) and the actual observed time series. 

225
Figure D24. Yellowtail flounder (Limanda ferruginea) catch per fishery trajectories for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

226
Figure D25. Demersal sharks catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and the actual 

observed time series. 

227
Figure D26. Demersal sharks catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) and the 

actual observed time series. 

228
Figure D27. Pelagic sharks catch per fishery trajectory for Atlantis NEUS (fixed effort run). No observed time 

series was available. 

Figure D28. Pelagic sharks catch per fishery trajectory for Atlantis NEUS (dynamic effort run). No observed time 

series was available. 

229
Figure D29. Skates and rays catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and the actual 

observed time series. 

230
Figure D30. Skates and rays catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) and the 

actual observed time series. 

231
Figure D31. Squid catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and the actual observed 

time series. 

232
Figure D32. Squid catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) and the actual 

observed time series. 

233
Figure D33. Sea scallops (Placopecten magellanicus) catch per fishery trajectories for both Atlantis NEUS (fixed 

effort run) and the actual observed time series. 

234
Figure D34. Sea scallops (Placopecten magellanicus) catch per fishery trajectories for both Atlantis NEUS 

(dynamic effort run) and the actual observed time series. 

235
Figure D35. Other benthic filter feeders catch per fishery trajectory for Atlantis NEUS (fixed effort run). No 

observed time series was available. 

Figure D36. Other benthic filter feeders catch per fishery trajectory for Atlantis NEUS (dynamic effort run). No 

observed time series was available. 

236
Figure D37. Benthic herbivorous grazers catch per fishery trajectory for Atlantis NEUS (fixed effort run). No 

observed time series was available. 

Figure D38. Benthic herbivorous grazers catch per fishery trajectory for Atlantis NEUS (dynamic effort run). No 

observed time series was available. 

237
Figure D39. Lobster (Homarus americanus) catch per fishery trajectories for both Atlantis NEUS (fixed effort run) 

and the actual observed time series. 

238
Figure D40. Lobster (Homarus americanus) catch per fishery trajectories for both Atlantis NEUS (dynamic effort 

run) and the actual observed time series. 

239
Figure D41. Shallow macrozoobenthos catch per fishery trajectories for both Atlantis NEUS (fixed effort run) and 

the actual observed time series. 

240
Figure D42. Shallow macrozoobenthos catch per fishery trajectories for both Atlantis NEUS (dynamic effort run) 

and the actual observed time series. 

241
Figure D43. Deposit feeders catch per fishery trajectory for Atlantis NEUS (fixed effort run). No observed time 

series was available. 

Figure D44. Deposit feeders catch per fishery trajectory for Atlantis NEUS (dynamic effort run). No observed time 

series was available. 

242
APPENDIX E: Atlantis NEUS – EFFORT TRAJECTORY RESULTS 

Figure E1. Effort trajectory for the midwater trawl on cephalopods for both Atlantis NEUS (dynamic effort run) 

and the actual observed time series. 

243
Figure E2. Effort trajectory for the scallop (Placopecten magellanicus) dredge for both Atlantis NEUS (dynamic 

effort run) and the actual observed time series. 

Figure E3. Effort trajectory for the pelagic line fishery on tunas and sharks for both Atlantis NEUS (dynamic effort 

run) and the actual observed time series. 

Figure E4. Effort trajectory for the purse seine on tunas and sharks for both Atlantis NEUS (dynamic effort run) 

and the actual observed time series. 

244
Figure E5. Effort trajectory for the purse seine on small pelagics for both Atlantis NEUS (dynamic effort run) and 

the actual observed time series. 

Figure E6. Effort trajectory for lobster (Homarus americanus) traps for both Atlantis NEUS (dynamic effort run) 

and the actual observed time series. 

245
Figure E7. Effort trajectory for the demersal trawl on cephalopods for both Atlantis NEUS (dynamic effort run) 

and the actual observed time series. 

Figure E8. Effort trajectory for the demersal trawl on cod (Gadus morhua) and haddock (Melanogrammus 

aeglefinus) for both Atlantis NEUS (dynamic effort run) and the actual observed time series. 

246
Figure E9. Effort trajectory for the demersal line fishery for both Atlantis NEUS (dynamic effort run) and the 

actual observed time series. 

Figure E10. Effort trajectory for the trap on demersals fishery for both Atlantis NEUS (dynamic effort run) and the 

actual observed time series. 

247
Publishing in NOAA Technical Memorandum NMFS-NE

Manuscript Qualification

This series represents a secondary level of scientific

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For all issues, the series employs thorough internal scientific

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issues, the series employs rigorous technical and copy editing.

Manuscripts that may warrant a primary level of scientific

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Identical, or fundamentally identical, manuscripts should

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series. Manuscripts which have been rejected by any primary

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Manuscripts by Northeast Fisheries Science Center

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The ethics of scientific research and scientific publishing

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contained in Chapter 2 ("Publication Policies and Practices") of

the Scientific Style and Format: the CSE Manual for Authors,

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of Science Editors). Copies of the manual are available at

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edition of the United States Government Printing Office Style

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For in-text citations, use the name-date system. A special

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Organization; such guidance is easily accessed through the various

Cambridge Scientific Abstracts’ serials source lists (see http://

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For spelling of scientific and common names of fishes, mollusks, and decapod crustaceans from the United States and Canada,

use Special Publications No. 29 (fishes), 26 (mollusks), and 17

(decapod crustaceans) of the American Fisheries Society (Bethesda

MD). For spelling of scientific and common names of marine

mammals, use Special Publication No. 4 of the Society for Marine

Mammalogy (Lawrence KS). For spelling in general, use the most

recent edition of Webster’s Third New International Dictionary of

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or "Conclusions," "Acknowledgments," and "References Cited."

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Northeast Fisheries Science Center

Operations, Management & Information Division

Research Communications Branch

Editorial Office

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Send all materials and address all correspondence to: Jarita A.

Davis (Editor), Editorial Office, NMFS Northeast Fisheries Science Center, 166 Water Street, Woods Hole, MA 02543-1026.
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Publications and Reports

of the

Northeast Fisheries Science Center

The mission of NOAA's National Marine Fisheries Service (NMFS) is "stewardship of living marine resources 

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