Download ECOLOGICAL MODELING OF AMERICAN LOBSTER (Homarus

ECOLOGICAL MODELING OF AMERICAN LOBSTER (Homarus
americanus) POPULATION IN THE GULF OF MAINE
BY
Yuying Zhang
B.S. East China University of Science and Technology, 2003
A THESIS
Submitted in Partial Fulfillment of the
Requirements for the Degree of
Master of Science
(in Oceanography)
The Graduate School
The University of Maine
December, 2005
Advisory Committee:
Yong Chen, Associate Professor for Fisheries Population Dynamics, Advisor
James A. Wilson, Professor of Marine Science
James D. McCleave, Professor of Marine Science
ECOLOGICAL MODELING OF AMERICAN LOBSTER (Homarus
americanus) POPULATION IN T H E GULF OF MAINE
By Yuying Zhang
Thesis Advisor: Dr. Yong Chen
An Abstract of the Thesis Presented
in Partial Fulfillment of the Requirements for the
Degree of Master of Science
(in Oceanography)
December, 2005
The Gulf of Maine (GOM) ecosystem has experienced large changes over the last
several decades, switching from a groundfish species dominated system to a system
dominated by crustacean species such as the American lobster and crabs. Several
hypotheses have been developed to explain such a switch, ranging from trophic
interactions between groundfish and crustacean species to increased food availability to
crustacean species due to discarded baits in the lobster fishery.
The objective of my study is to develop a lobster ecosystem model to evaluate the
dynamics of GOM lobster ecosystem. I developed a mass-balance ecosystem model
separately for the two time periods (1980s and 1990s) using Ecopath with Ecosim (EWE).
The model has twenty-four function-groups including lobster, its key predator and prey
species, and other important groups in the ecosystem such as zooplankton, phytoplankton
and detritus. The input data were obtained from published papers and reports. Using the
models developed, I conducted a comparative analysis of trophic interactions and
community structure of the GOM American lobster ecosystem for the mid-1 980s and
mid-1990s. I also simulated ecosystem dynamics in the GOM from 1985 to 1997 using
Ecosim, evaluated the interactions of population dynamics of Atlantic cod and American
lobster, and predicted the possible response of the lobster population with respect to the
possible recovery of Atlantic cod stock in the GOM.
The study shows that the Ecopath model, which snapshots the ecosystem in a
given time, and Ecosim model, which tracks the long term dynamics of ecosystem, can
well simulate the lobster ecosystem in the GOM. The trophic structures of the ecosystem
in the mid-1 980s are different from those in the mid-1 990s with a decrease in top
predator biomass and an increase in lower trophic-level organism biomass. The derived
key ecosystem parameters suggest that the mid- 1990s ecosystem tends to be more mature
than the mid-1 980s ecosystem. The results also suggest that there is a negative
relationship between cod biomass and lobster biomass. A substantial increase in fishing
mortality in the cod fishery is likely to lead to a large decrease in cod biomass, which in
turn results in large increases in lobster biomass, and vice versa. Thus, the full-scale
recovery of cod in the GOM may have some negative impact on the lobster stock. This
study suggests that a reduced cod stock might contribute to the high lobster stock biomass
in recent years.
The ecosystem model developed in this study, although preliminary in nature,
provides us with a new approach to evaluate the trophic interactions of lobsters and other
organisms in the GOM, helps us better understand the ecosystem dynamics in the GOM,
and yields the information critical to the development of an ecosystem-based
management for the lobster fishery in the GOM. More studies are needed, however, to
reduce possible uncertainty in input data and to evaluate the performance of the model.
ACKNOWLEDGEMENTS
I would like to thank my advisor, Dr. Yong Chen, for giving me a lot of help
when I came to Maine from China, as well as countless hours of guidance, advice,
assistance, and comment during my two-year study. Great appreciate is also given to my
other committee members Dr. James Wilson and Dr. James McCleave for helping me
understand the biological aspects of fisheries and fisheries management and fruitful
discussions of my research. My thanks also go to Dr. David Townsend and Ryan
Weatherbee for offering me the satellite data for estimating phytoplankton abundance, to
Dr. Malcolm Shick for the advice on the group name, to Dr Jonathan Grabowski for the
information he provided on hening bait; and to Dr. Bing Song for inspiring me some idea
when applying the Ecosim submodel. Thanks also go to the postdoc Minoru Kanaiwa and
my fellow students Adrian Jordaan, Kate Jones, Kathleen Reardon, and Sheril
Kirshenbaum for their fhendships and help.
I am also deeply indebted to my parents Binghuang Zhang and Li Zhu, who
always encourage me to realize my goal, especially my mom who stopped her work and
flew to the United States to accompany me during my busiest time. Without their support,
I absolutely would not have made it this far.
TABLE OF CONTENTS
..
ACKNOWLEDGEMENTS. ........................................................................ -11
..
LIST OF TABLES. .................................................................................
..VII
LIST OF FIGURES.. ............................................................................... ..ix
Chapter
1.
INTRODUCTION.. .......................................................................... 1
2.
ECOPATH WITH ECOSIM FOR AMERICAN LOBSTER
...........................6
2.1.
Overview of Modeling Methods.. .................................................6
2.2.
Components of EWEModel. ...................................................... .7
2.3.
Application of EWEin the Aquatic Ecosystem.. .................................8
2.3.1. Analyzing the Energy Flows in an Ecosystem ...........................9
2.3.2. Identifying and Evaluating Impacts of Changes in
Environment on Commercial Fsheries.. ................................. 10
2.3.3. Evaluating Ecological Impacts of Fishing and Alternative
..
Management Policies.. ......................................................
10
3. THE COMPARATIVE STUDY OF TROPHIC INTERACTIONS AND
COMMUNITY STRUCTURES IN THE GULF OF MAINE
ECOSYSTEM DURING THE 1980s AND 1990s. ....................................12
3.1 .
Study Area. .......................................................................... 13
3.2.
Ecopath Model.......................................................................15
3.3.
Model Parameterization ...........................................................16
3.3.1. Estimating the Biomass of Organisms Included in the
Ecosystem .................................................................. 17
3.3.2. Production or Production/Biomass (PB) Ratio ...................... 19
Ratio .................20
3.3.3. Consumption or Consumption/Biomass (QB)
3.3.4. Ecopath Efficiency (EE) ...................................................
21
3.3.5. Diet Composition (Dcij)....................................................21
3.3.6. Unassimilated / Consumption Ratio (GS) .............................22
3.3.7. Other Input Parameters.....................................................23
3.4.
Trophic Compartments...........................................................24
3.4.1. American Lobster (Homarus americanus).............................25
3.4.2. Crab....................................................................
-26
3.4.3. Shrimp......................................................................
27
3.4.4. Echinoderm .................................................................. 28
3.4.5. ShellMollusk .................................................................29
3.4.6. Squid....................................................................... 29
3.4.7. Atlantic Herring (Clupea harengus)..................................... 30
3.4.8. Silver Hake (Merluccius bilinearis) and Red Hake
(Urophycis chuss)......................................................... -31
3.4.9. Skates......................................................................... 32
3.4.10. Cunner (Tautogolabtus adspersusand) and Tautog
(Tautoga onitis)...........................................................33
3.4.1 1. Atlantic Cod (Gadus morhua)............................................34
3.4.12. Cusk (Brosme brosme) and Atlantic Wolfish (Anarhichas
lupus)....................................................................... 36
3.4.13. Other Fishes ................................................................
40
3.4.14. Macrozooplankton and Micorzooplankton ............................41
3.4.1 5 . Microbenthos .............................................................. 42
3.4.16. Phytoplankton ..............................................................
42
3.4.17. Macroalgae .................................................................. 43
3.4.1 8. Bait (herring) ............................................................... 44
3.4.19. Detritus ....................................................................... 44
3.5.
Balancing the Model and Addressing Uncertainty ...........................55
3.6.
Results ................................................................................ 56
3.6.1. Base Estimation .............................................................56
3.6.2. The Trophic Structure......................................................
61
. .
3.6.3. Summary statistics ............................................................ 66
3.6.4. Food Web Analyses .......................................................... -69
3.6.5. Niche Overlap and Mixed Trophic Impact Analyses ..................71
3.7.
4.
Discussion ............................................................................89
THE SlMULATION LOBSTER ECOSYSTEM DYNAMICS IN THE
GULF OF MAINE ..........................................................................96
4.1.
Basic Equations of Ecosim Model ..................................................96
4.2.
Methods and Materials ..............................................................97
4.3.
Results
.............................................................................. 100
4.3.1. Simulation of Fish Stock Biomass Using the Proposed
Ecosystem Simulation Model
.........................................-100
4.3.2. Evaluating the Impact of Alternative Fishing Mortality
Rates of Atlantic Cod on American Lobster ......................... 113
4.4.
Discussion......................................................................... 114
REFERENCES..................................................................................... -119
APPENDIX: Results of Sensitivity Analysis...................................................129
BIOGRAPHY OF THE AUTHOR ...............................................................
135
LIST OF TABLES
Table 3.1.
Compartments/groups of the Ecopath model for the Gulf of Maine
lobster ecosystem ....................................................................................24
Table 3.2.
Percentage diet composition of other fishes ....................................
41
Table 3.3.
Input data for the Ecopath model of the mid-1980s ............................45
Table 3.4.
Input data for the Ecopath model of the mid- 1990s............................46
Table 3.5.
Diet composition for the lobster ecosystem in the Gulf of Maine
(group 3-9) ........................................................................... 47
Table 3.6.
Diet composition for the lobster ecosystem in the Gulf of Maine
(group 10-16).......................................................................-49
Table 3.7
Diet composition for the lobster ecosystem in the Gulf of Maine
(group 17-22)......................................................................... 51
Table 3.8.
Reference for the model .............................................................
53
Table 3.9.
Basic output data for the mid-1980s Ecopath model............................ 59
Table 3.10.
Basic output data for the mid- 1990s Ecopath model ............................ 60
Table 3.1 1.
Distributions of import, consumption by predators, export, and flow
to detritus, respiration, and throughput through aggregated trophic
levels in the Gulf of Maine ecosystem in the mid-1980s ......................62
Table 3.12.
Trophic decomposition of the groups in the Gulf of Maine
ecosystem in the mid-1 980s........................................................63
Table 3.13.
Distributions of import, consumption by predators, export, and flow
to detritus, respiration, and throughput through aggregated trophic
vii
levels in the Gulf of Maine ecosystem in mid- 1990s............................
-64
Table 3.14.
Trophic decomposition of the groups in the Gulf of Maine
.65
ecosystem in the mid- 1990s. ....................................................
Table 3.15.
The overall system properties of the lobster ecosystem in the Gulf
of Maine for the mid- 1980s and mid- 1990s.. ..................................68
Table 3.16.
The food chain analysis of groups in the lobster ecosystem in the
Gulf of Maine ........................................................................70
Table 3.1 7.
Mixed trophic impacts index for the mid-1 980s model (group 113) .................................................................................. .77
Table 3.18.
Mixed trophic impacts index for the mid-1 980s model (group 14-24
and fleet 1) .........................................................................79
Table 3.19.
Mixed trophic impacts index for the mid- 1990s model (group 113) .................................................................................. 81
Table 3.20.
Mixed trophic impacts index for the mid-1 990s model (group 14-24
and fleet 1). .........................................................................83
Table 3.21.
The comparison of some system index between the Gulf of Maine
in the mid-1 990s, the Northern Gulf of California, and the Gulf
of Mexico.. .........................................................................90
Table 4.1.
CSV file for Ecosim.. ..............................................................99
...
Vlll
LIST OF FIGURES
Figure 1.1.
American lobster fishery history in Maine, US.. ................................2
Figure 3.1.
The position and shape of the Gulf of Maine.. ................................14
Figure 3.2.
Atlantic cod fishery history in the Gulf of Maine, US.. ......................35
Figure 3.3.
Correlationship between production-model predicted cusk biomass
and ovserved CPUEs during 1963-1998. ....................................... .38
Figure 3.4.
Predicted cusk biomass during 1963-1998 by using production
model. .............................................................................. -38
Figure 3.5.
Correlationship between production-model predicted Atlantic
wolfish biomass and ovserved CPUEs during 1970-1999.. .................39
Figure 3.6.
Predicted Atlantic wolfish biomass during 1968-1999 by using
production model. .................................................................39
Figure 3.7.
The niche overlap index between every two groups of the lobster
ecosystem in the Gulf of Maine in the mid-1 980s.. ...........................72
Figure 3.8.
Predator overlap index between every two groups in the mid-1980s
model.. ............................................................................... .73
Figure 3.9.
Prey overlap index between every two groups in the mid-1 980s
model.. ............................................................................
Figure 3.1 0.
.74
The niche overlap index between every two groups of the lobster
ecosystem in the Gulf of Maine in the mid-1 990s.. ......................... ..75
Figure 3.1 1.
The mixed trophic impact analysis of groups in the mid-1 980s
model.. ............................................................................. .85
Figure 3.12.
The mixed trophic impact analysis of groups in the mid-1990s
model.. .............................................................................-87
Figure 3.13.
Flow diagram for the mid-1980s Ecopath model.. ..........................-92
Figure 3.14.
Flow diagram for Atlantic cod, Adult lobster, Juvenile lobster, and
Atlantic herring in the mid-1 980s Ecopath model.. ...........................93
Figure 3.15.
Flow diagram for the mid-1 990s Ecopath model.. .......................... .94
Figure 3.16.
Flow diagram for Atlantic cod, Adult lobster, Juvenile lobster, and
Atlantic herring in the mid- 1990s Ecopath model.. ...........................95
Figure 4.1.
Ecosim run form for the lobster ecosystem in the Gulf of Maine
from 1985 to 1997. ............................................................. ..lo1
Figure 4.2.
Stock biomass predicted from the Ecosim model and survey data
observed from the NMFS bottom trawl survey programs for some
groundfish species and adult lobster in the Gulf of Maine from
1985 to 1997.. .................................................................. -107
Figure 4.3.
Ecosim plots simulate the biomass oscillations for adult lobster
and Atlantic cod in the GOM from 1985 to 1997, under different
cod fishing mortality.. ........................................................ ..I 15
Chapter 1
INTRODUCTION
The American lobster, Homarus americanus, is a decapods crustacean found
throughput the Northwest Atlantic from the Strait of Belle Isle, Newfoundland to Cape
Hatteras, North Carolina (Cooper and Uzmann, 1980), with the highest population size
found in the Gulf of Maine (Palma et al., 1999). They are most abundant in relatively
shallow coastal waters where the population density can reach up to ten per square meter
(Steneck and Wilson, 1998; Wahle and Steneck, 1991).
Lobsters have a complicated life history. Female lobsters carry eggs on their
abdomen for 9 to 12 months before hatching. The female releases prelarvae over the
course of several days and the embryos travel toward the surface water and molt into the
first larval stage. They then experience three distinct planktonic larval stages. In these
stages, the movements of larval lobsters are largely determined by the direction of wind
and water currents. After the fourth molt, the lobster larvae grow into the post larval
stages and become benthic juveniles.
Zooplankton is the main food of lobster larvae and postlarvae during their first
year (Lavalli, 1988). In larval stomach analyses performed by Juinio and Cobb (1992),
nine taxonomic prey groups were found. Copepods and decapod larvae were common
prey items but cladocerans, fish eggs, nematodes and diatoms were also found.
The adult lobster is the largest mobile benthic invertebrate in the North Atlantic.
Its size and large claws make it an important predator (Elner and Campbell, 1981; Moody
and Steneck, 1993). Adult lobsters are omnivorous, feeding largely on crabs, mollusks,
polychaetes, sea urchin, and sea stars (Carter and Steele, 1982a; Carter and Steele, 1982b;
Ennis, 1973). Live fish and macroalgae are also part of the natural diet. Lobsters are
opportunistic feeders, and their diet varies regionally depending upon the local prey
species composition and abundance. In their main fishing grounds, bait (mostly Atlantic
herring, Clupea harengus) is a very important component of the diet.
Figure 1.1. American lobster fishery history in Maine, US (Chen et al., 2005).
1 - - * - - Trao number 1
"
0
1945
1955
1965
1975
1985
1995
2005
Year
Over the last two decades, the lobster fishery experienced a substantial increase,
as shown by the increase in landings and the number of traps used in the fishery which
were almost three times as high as the landing level and trap numbers in 1980s (Figure
1.1) (Chen et al., 2005; Cook, 2005). According to the Maine Department of Marine
Resource (DMR) there were currently about 6,000 full-time and 4,300 part time lobster
licenses out of a total of 18,000 commercial fishing licenses in the state of Maine. The
recent annual landing was over 25,000 metric tons and the overall employment in the
business, including direct and indirect, was estimated at 26,000 people, with a total
economic impact on the state of Maine being around $777 million per year (Acheson and
Brewer, 2003).
As the most valuable commercial fishery in the northeastern United States with
most of the catch landed in Maine, the lobster fishery attracted a lot of attention for its
dramatic increase in the recent lobster production in Maine. Many studies suggested that
such a increase resulted from high recruitments and subsequent high stock abundance of
lobster in the Gulf of Maine (Steneck and Wilson, 2001). Contrasting to the increase in
lobster population, the groundfish populations experienced a large decrease over the same
time period in the Gulf of Maine (NEFSC, 1999; NEFSC, 2000; NEFSC, 2001b). It
seemed that the Gulf of Maine (GOM) ecosystem had experienced large changes over the
last two decades, switching from the cod and other large groundfish species dominated
ecosystem to a ecosystem dominated by crustacean species such as the American lobster
and crab species. Many studies had been done to identify potential causes for such an
increase in lobster abundance. Various hypotheses had been developed to explain such an
increase, ranging from substantial decline in its predator abundance (Hanson and
Lanteigne, 2000), warming ocean temperatures (Spees et al., 2002), and regime shift to
large amount of lobster fishery bait (herring) discarded back to the sea (Grabowski et al.,
2003). To evaluate these hypotheses, I needed to conduct a quantitative analysis to assess
the lobster population dynamics and how it might interact with other ecological variables.
The American lobster fishery was assessed using the Collie-Sissenwine (catchsurvey) model, which estimated fishing mortality from catch and an abundance index
derived from the National Marine Fisheries Service (NMFS) trawl survey program
(Collie and Sissenwine, 1983). The status of the fishery was estimated from an egg-perrecruit (EPR) model, and determined by comparing the estimated fishing mortality with
the biological reference point F10%, which is the rate of the fishing mortality that
reduces the expected egg production for a cohort of female lobsters to 10% of that
produced in the absence of a fishery (Fogarty and Idoine, 1988).
In the previous assessment, this approach had yielded a conclusion on the status
of lobster stock inconsistent with many studies and field observations (ASMFC, 2000;
NRC, 1997; NRC, 1999), raising a question of the ability of current stock assessment
models in describing the complex fishery and population biology for American lobster.
This called for the development of alternative approaches to assessing the lobster fishery
(ASMFC, 2000). The development of biologically detailed ecosystem models seemed
desirable and was also consistent with the calls by the National Research Council (NRC,
1997; NRC, 1999) to apply multiple stock assessment models of different complexities in
assessing fisheries resources. The use of an ecosystem model also allowed us to explore
the trophic interactions of lobsters with other species in the ecosystem and evaluate the
hypotheses developed in explaining recent increases in lobster abundance in GOM,
leading to a better understanding of lobster population dynamics. The development of
such an ecosystem modeling approach was also consistent with recent calls for
developing ecosystem-based fisheries management in the Gulf of Maine.
The objectives of my project were to (1) develop a mass-balanced ecosystem
model for the GOM lobster ecosystem; (2) conduct comparative analyses of trophic
interactions and community structure of lobsters and other fish species for the mid- 1980s
and mid-1 990s; (3) develop a simulation model that describes the ecosystem dynamics
from 1985 to 1997; and (4) evaluate the impact of alternative management policies on the
lobster fishery and ecosystem dynamics using the developed lobster ecosystem model.
Using the developed lobster ecosystem models for the 1980s and 1990s, I evaluated and
compared the structure and function of the lobster ecosystem in these two time periods.
Using the ecosystem dynamics model developed for the time period of 1985 to 1997, I
evaluated the possible interactions between the population dynamics of Atlantic cod and
American lobster.
Chapter 2
ECOPATH WITH ECOSIM FOR AMERICAN LOBSTER
2.1. Overview of Modeling Methods
Many modeling methods have been developed for assessing fisheries population
dynamics, including traditional single-species stock assessment, multispecies virtual
population analysis (MSVPA) and food-web based ecosystem models (Hilborn and
Walters, 1992; Quinn and Deriso, 1999; Sparre, 1991). Single-species stock assessment
models are most commonly used in current stock assessment (NRC, 1997; NRC, 1999).
These models ignore the interactions and connections between the targeted species and
other variables in their ecosystem (Hilborn and Walters, 1992), and cannot realistically
describe the dynamics of an ecosystem. Thus, they are not suitable for evaluating the
impact of fishing on ecosystems.
MSVPA, first introduced in the late 1970's, can be viewed as a series of singlespecies models that are linked by a feeding model (Quinn and Deriso, 1999). This
modeling approach integrated the dynamic change of species and the trophic relationship
within the ecosystem. A large number of parameters have to be entered as input data. The
number of parameters to be estimated increases rapidly with an increase in the number of
species in the assessment.
Ecopath with Ecosim (EWE) is a food-web based ecosystem model. Compared to
the MSVPA modeling approach, the data requirements for EWE are relatively simple, and
can be obtained from stock assessment and ecological studies (Christensen et al., 2000).
First built for estimating biomass and food consumption of the elements in an aquatic
ecosystem, EWE was subsequently improved with various approaches developed in
theoretical ecology (Odum, 1969; Ulanowicz, 1986). It is capable of analyzing energy
flows between the components of an ecosystem and evaluating the maturity and stability
of the ecosystem. The model can also be used for assessing fish stock dynamics, fisheries
spatial dynamics, and their interactions with other ecological variables(Christensen et al.,
2000). EWEhas been increasingly used in recent years in assessing the dynamics of
fisheries ecosystems.
In this study, I constructed an EWEmodel separately for the two time periods
(mid-1980s and mid-1990s) to describe trophic flows in the GOM lobster ecosystem.
Using the developed model, I evaluated trophic interactions in the ecosystem, analyzed
how the lobster population dynamics might interact with the population dynamics of
other species in the ecosystem, and compared the differences in the ecosystem between
the two time periods.
2.2. Components of EWEModel
EWEhas three main components: Ecopath, Ecosim, and Ecospace. Ecopath is a
static, mass-balanced snapshot of the system. It requires the input of biological and
fishery data of each ecological group in the ecosystem, and yields basic analyses of
ecosystem structure and function. Ecosim is used to describe the ecosystem dynamics and
can be used to explore the potential impact of changes in fisheries management policy on
the ecosystem dynamics. Ecospace can be used to describe spatial dynamics of
ecosystems and is often used for evaluating impacts of setting up marine protected areas
on the ecosystem dynamics (Christensen et al., 2000).
In addition to these three main components, EWE has other components and
accessories. Their names and functions are listed as follows:
Pedigree - specifies the uncertainty associated with the input data according to
their sources, and evaluates the performance of the derived model;
Ecoranger - balances the model by applying Bayesian statistical method to specify
probability distribution for the input variables, and uses a Monte Carlo procedure to
generate probability distributions of the output variables;
Sensitivity Analysis - evaluates the sensitivity of modeling with respect to input
parameter values;
Econet - applies ecology theory, and analyzes the frame, trophic impact, energy
flow and cycles of the ecosystem;
Ecowrite - marks each input and output parameter to specify the sources of
estimates and describe how they were standardized; and
Ecoempire - uses one of the many published empirical relationships to estimate
input parameters.
2.3. Application of EWEin the Aquatic Ecosystem
EWE can be used to analyze the energy flows in an ecosystem, identify key
components of the ecosystem, evaluate the impact of changes in environments on
commercial fisheries, identify and evaluate ecological effects of fishing, and explore and
evaluate alternative management policies in their effectiveness in managing fish stocks.
2.3.1. Analyzing the Energy Flows in an Ecosystem
Vega-Cendejas and Arreguin-Sanchez (2001) used EWE to model the mangrove
ecosystem of the Yucatan Peninsula in Mexico. They found that detritus played an
important role in the mangrove ecosystem with 64% of the detritus being utilized and
transferred to juvenile fish by microcrustaceans. Lower ecotrophic efficiency (EE) values,
higher ratios of production and biomass (PJB), and higher ratios of food consumption and
biomass (Q/B) for fish groups indicated that the mangrove ecosystem is highly
productive (Vega-Cendejas and Arreguin-Sanchez, 2001). Similar analyses were done on
the trophic role of small pelagic fishes in a tropical upwelling ecosystem in the Caribbean
Sea (Duarte and Garcia, 2004).
This use of EWE is especially useful in evaluating the impact on marine protected
areas. Arreguin-Sanchez and Manickchand-Heileman (1 998) used EWEto construct two
mass-balanced and steady-state trophic ecosystem models to evaluate the trophic role of
snappers in the continental shelves of the southwestern Gulf of Mexico and Yucatan in
the southeastern Gulf of Mexico. They used persistence, recovery time and resilience to
measure the stability of the ecosystem, and concluded that the western Gulf of Mexico
system appeared to be more complex and more stable than the continental shelf of
Yucatan (Arreguin-Sanchez and Manickchand-Heileman, 1998). EWE was also used in
comparing the trophic flows in the southern Banguela to those in upwelling ecosystems
(Jarre-Teichrnann et al., 1998).
2.3.2. Identifying and Evaluating the Impact of Changes in Environments on
Commercial Fisheries
Changes in an ecosystem can result fkom natural variations such as seasonal
variations and climate changes or from human activities such as pollution, overfishing
and habitat destruction. Regardless of the sources, ecosystems are likely to be altered
through either a bottom-up or top-down process. If the biomass, food composition,
growth rate, production, and their changes over time can be estimated for each group of
organisms in the ecosystem, the ecosystem and its key components in different time
periods can be compared and the effects of the environmental changes on the ecosystem
can be identified.
Career et al. (2000) combined a trophic network model with an ecotoxicological
food-web model to calculate toxic concentrations of aquatic species in the northern part
of the Lagoon of Venice. They used organisms-specific output data from the energy
model as input to the ecotoxicological food chain model, and then successfully estimated
bioaccumulation of dioxins for all groups in the trophic network (Carrer et al., 2000).
Their work was not unique. Okey and Pauly (1 998) used a similar method to evaluate the
impact of the Exxon Valdez oil spill (EVOS) on the Prince William Sound ecosystem.
2.3.3. Evaluating the Ecological Impact of Fishing and Alternative
Management Policies
EWE was applied in northern Benguela (Heyrnans et al., 2004), southern Mexican
Caribbean (Arias-Gonzalez et al., 2004), South Brazil Bight Coast (Gasalla and RossiWongtschowski, 2004), Orbetello Lagoon in Italy (Brando et al., 2004), and the
Mediterranean (Pinnegar and Polunin, 2004) to identify how fishing might influence the
ecosystem dynamics. Using the developed EWEmodels, these researchers evaluated the
effectiveness of alternative management policies in fisheries management and identified
optimal management policy. Such studies provided much needed scientific information
for developing ecosystem-based fisheries management.
Cbapter 3
THE COMPARATIVE STUDY OF TROPHIC INTERACTIONS AND
COMMUNITY STRUCTURES IN THE GULF OF MAINE ECOSYSTEM
DURING 1980s AND 1990s
A comparative analysis of trophic interactions and community structure of
commercial fish species in the GOM was conducted. I developed a mass-balance
ecosystem model for the American lobster ecosystem using the Ecopath submodel of
EWE 5.0. The model included not only groundfish species and the American lobster, but
also other key groups of organisms that had trophic interactions with groundfish and
lobster in the ecosystem, such as pelagic species, invertebrate species other than lobster,
zooplankton, phytoplankton, and detritus. I developed the mass-balance model
separately for the two time periods, mid-1980s and mid-1990s. The models were used to
analyze the ecosystem structure and function for these two time periods. Then I compared
differences in the ecosystem structure and function between the two time periods to
evaluate how the ecosystem changed between the 1980s and 1990s. This study involved
the development of the model, identification and collection of input data for the model,
running and tuning the model, comparative study of the ecosystems between 1980s and
1990s, and interpretation of modeling output. They are described in subsequence sections
of this chapter.
3.1. Study Area
The GOM, located from 70.5' W to 64.5' W, 41.5" N to 45.5' N, is a semi-closed
sea extending 320 kilometers into the Atlantic Ocean with an area of 103,000 square
kilometers (Fig. 3.1) (Townsend, 1997). The 12,000 kilometers of shoreline (Stauble,
2004), including three New England states (Massachusetts, New Hampshire and Maine)
and two Canadian provinces (New Brunswick and Nova Scotia), make up its western and
northern boundaries, while the underwater banks outside the gulf, which were sculptured
during the lower sea levels of the ice ages, define the seaward edge on the other sides
(Cook, 2005).
The average depth of the GOM is about 150 meters (O'Brien, 1999), while the
maximum depth is approximately 275 meters in Georges Basin (Balch et al., 2004).
Because of the Gulf Stream, the average annual temperature of the GOM is about 8.3'C
(Maine Department of Marine Resources, DMR).
Every year, more than 60 rivers flow into the GOM, and bring in 950 million
cubic meters of freshwater. Meanwhile, cold-water currents also enter the GOM from the
North Atlantic through the northeast channel between Browns Bank and Georges Bank.
These two forces, plus the Gulf tides, which are amplified by the configuration of the
shoreline and underwater features, push the prevailing current in a counterclockwise
circle around the Gulf and help create a unique, self-contained oceanographic system.
Another factor that makes the Gulf much more different from the rest of the Atlantic
coast is the varied topographical features of the seafloor.
Figure 3.1. The position and shape of the Gulf of Maine.
<http://www.gma.org/. ../gem-bathymetry.jpg>
Latitude, O N
7 1'00'
70°00'
69'00'
68'00'
67'00'
Longitude, " W
66'00'
65'00'
64'00'
High concentrations of dissolved oxygen and carbon dioxide in the cold seawater
from the northeast, mixed with the nutrients that are carried by the rivers, make the GOM
richer in nutrients than almost any other place in the earth's oceans, and produce large
amount of photosynthetic phytoplankton that constitutes the base of the coastal marine
food web. Every spring (April-May) phytoplankton blooms in the central Gulf, and again
in the fall (September-October). High concentrations of dissolved oxygen and carbon
dioxide in the cold seawater from the northeast, mixed with the nutrients that are canied
by the rivers, make the GOM richer than almost any other place in the earth's oceans, and
produce large amount of photosynthetic phytoplankton that constitutes the base of the
coastal marine food web. This photosynthetic phytoplankton forms the basis for a large
and productive fishery; and feeds hundreds of species of fish and shellfish in the marine
waters and shoreline.
3.2. Ecopath Model
The core component of the Ecopath submodel includes two master equations,
which describe the mass balance and energy balance of each group included in the
ecosystem model. The mass balance equation is used to describe the production term for
each group, and consists of several components. It can be written as:
q. = l ' . + B i * M 2 i + E i + B A i + ~ . * ( 1 - E E i ) ,
where i is the organism group, Pi is the total production, Y iis the total fishery landing,
M2i is the total predation rate, Bi is the biomass, Ei is the net migration rate (emigration immigration), BAi is the biomass accumulation rate, and EEi is the fraction that the total
production of a group is used in the system. MOi =
* (1 - EE,) is defined as the 'other
mortality' rate for group i, which is the natural mortality resulting from natural causes
other than predation. Because B, * M 2 , =
n
j=l
also can be rewritten as:
Q-
(Bj *l*
Dcji), the mass balance equation
Bj
where Dcji is the fraction that prey group i contributes to the overall stomach content of
predator group j (Chnstensen et al., 2000).
The energy balance equation can be written as:
Consumption = production + respiration + unassimilated food,
or:
Qi= 4. + Ri+ U i .
This equation implicitly assumes that energy input and output of all living groups must be
balanced in an ecosystem.
3.3. Model Parameterization
Data are one of the most important factors that determine the quality of the lobster
stock assessment. In order to reduce uncertainties and biased errors in stock assessment,
data should be collected from multiple sources. When there are some doubts about the
quality of data, choices may need to be made as to what data source is most reliable and
desirable in describing the fisheries. In this study, the model input data were collected
from published stock assessment reports, peer-reviewed journal publications, and
government reports. The estimation of the key input parameters are described below.
3.3.1. Estimating the Biomass of Organisms Included in the Ecosystem
Biomass is the total mass (measured in weight) of a certain group of organism per
unit of area. For the lobster model I developed, the unit for biomass was metric ton per
square kilometer ( t h 2 ) .
Estimating the biomass for different species included in the ecosystem model was
one of the most important aspects of constructing a representative model for the GOM
lobster ecosystem. In this study, biomass data were obtained from different sources. For
species such as Atlantic cod (Gadus morhua), shrimp, squids, biomass data were
collected from various Stock Assessment Workshop (SAW) reports that were published
by the Northeastern Fisheries Science Center (NEFSC). For some other species, biomass
data were estimated by fitting a production model to historical landing data and survey
abundance index data. The production model can be written as:
where It is the population abundance index in year t, r is the population intrinsic growth
rate, K is the carrying capacity, q is the catchability coefficient, and C, is the landing
(catch) in year t.
Observed catch and survey abundance index (or for some species CPUE, catch
per unit effort) data were used in the above equation to estimate parameters K and r. To
minimize the number of the parameters to be estimated, catchability coefficient, q, was
calculated from:
and was used as an important parameter to generate the predicted abundance index
( I T d ) using the equation I,?' = q * B, (Mackinson, 2001). By minimizing the sum of
squared differences between 1pbSand
IFd,we could estimate parameters K and r. The
production model described above was used to estimate the biomass of those species for
which observed CPUE data and catch data were available. These species included silver
hake (Merluccius bilinearis), red hake (Urophycis chuss), Atlantic wolfish (Anarhichas
lupus) and cusk (Brosme brosme).
For some groups such as the adult lobster for which the catch data (Ct), fishing
mortality (F) and nature mortality (M) were known, I estimated their biomass using the
catch equation:
For species having the survey index data for all years, and biomass data for most
years (but lacking the biomass data for some years), I used the following procedure to
estimate the biomass data for the years when they were missing: (1) derived a regression
model for the biomass (as the dependent variable) and CPUE data (as the independent
variable) using the available data; and (2) used the derived regression model and
abundance index to derive the stock biomass for years when biomass data were not
available.
It was also possible for Ecopath to estimate stock biomass for a given species by
inputting the species' P/B, QIB and EE values. I, however, considered this approach as
the last choice when all the approaches described above failed to yield a biomass estimate
for the species, because the values of P/B and Q/B varied among years and small
differences in these input data could lead to a large error in estimating the biomass data.
3.3.2. Production or ProductionIBiornass (PIB) Ratio
Production is the amount of tissue that a group accumulated during a defined time
period (Christensen et al., 2000). Many different approaches can be used to estimate
production. These methods, however, share a similarity in that the P/B ratio for fish is
considered as total mortality (Z) in the mass-balance model. Production consists of
predation, catch in fisheries, and natural mortality due to natural causes other than
predation, biomass accumulation and net migration. Its composition is usually simplified
into the two main parts: natural mortality due to predation and other natural causes, and
fishing mortality. Thus, P/B = landing / biomass + natural death /biomass = F + M = Z. It
should be noted that Z, F, M are all instantaneous rates.
Three approaches were used for estimating the total mortality Z: analyzing the
catch curve (Ricker, 1979, using the von Bertalanffy Growth Function (VBGF), and
estimating natural mortality M from an empirical model and then adding it to fishing
mortality. The VBGF can be written as:
Z = -P
- = K * - L, - L
B
L-L'
where L m is the mean length the individuals in the population would reach if they were to
live and grow indefinitely (Christensen et al., 2000), L' is the mean length of the
individuals in the population that enter into the fishery,
Z is the mean length of the
population, and K is the VBGF curvature parameter. The empirical equation for
estimating natural mortality M is
M = K 0.65 y ~ ~ 0 . 2 7* 9~ ~ 0 . 4 6 3
where T, is the mean habitat (water) temperature in "C, K and L- are the parameters in
VBGF. In this case, Z = M + F.
3.3.3. Consumption or Consumption/Biomass (Q/B) Ratio
Consumption, Q, is the food ingested by a group over the time period considered
(Christensen et al., 2000). Palomares and Pauly (1989, 1999) suggested that for fish the
Q/B value can be estimated by maximum weight (W-), mean surface water temperature
(T), food type and tail aspect ratio (A) using the equation:
log(-)Q = 7.964 + 0.204 * log W, + 1.965 * T' + 0.083 * A + 0.532 * h + 0.398 * d
B
where H and a are the height and area of the caudal fin of fish, h and d are variables
describing food type (Christensen et al., 2000). For the lobster model, the Q/B values of
each finfish group were obtained from a database developed by Worldfish and could be
found at www.fishbase.org. The Q/B data for non-finfish groups were gathered from
published papers, or were calculated from the derivation of P/B and P/Q, if P/Q was
known.
3.3.4. Ecotrophic Efficiency (EE)
Defined as the fraction of the total production that is utilized in the ecosystem,
"either passed up the food web, used for biomass accumulation migration or export"
(Christensen et al., 2000), the EE values for all ecological groups in the model should be
between 0 and 1 because biomass consumed must be less than the biomass produced.
Most of the time the entry of these dimensionless EE values is optional, but for a group,
if biomass value is not available the EE value becomes an important alternative input data
set.
Ecopath can estimate EE values for each group. A balanced model must have the
EE values of all groups smaller than 1. I estimated EE values for each group and finetuned the model to make sure that the EE values for all the groups were smaller than 1.
3.3.5. Diet Composition (Dcij)
Dcij is the fraction that prey group i contributes to the overall stomach contents of
predator group j (Christensen et al., 2000). As a predation index in the Ecopath model,
Dcii links the different groups together and reveals the dynamics within the ecosystems.
This fraction can be measured in weight percentage, volume percentage, occurrence
percentage or even energy content percentage. For the lobster model, I used weight
percentage for most groups. For juvenile lobster, I used volume percentage because the
only information I could locate was volume percentage. For the same reason, I used the
occurrence percentage to describe the percentage of juvenile lobster in the diet
composition of cunner (Tautogolabtus adspersus).
Compared to other parameters, Dcij is perhaps the most import parameter in the
model because any one of the biomass PA3, Q/B or EE values can be estimated if we
know the other three. However, the Dcij is an irreplaceable parameter that must be
entered as an input parameter in the model for each species/group. This information is
difficult to collect because of its measurement errors resulting from large spatial and
temporal variability in fish feeding behavior. In this study, I used the Dcij values averaged
over different studies reported in multiple studies.
Ideally I should have estimated Dcii values separately for the two time periods
(1980s and 1990s) to reflect the difference in prey and predator compositions for a
species in the ecosystem over the time period. However, because little information about
the temporal variation in Dcii was available, I had to assume the Dcii values were
unchanged during the time period fiom mid- 1980s to mid- 1990s.
3.3.6. Unassimilated / Consumption Ratio (GS)
Unassimilated / Consumption Ratio (GS) is the proportion that the nonassimilated food consists of in the total consumption. Although this dimensionless
parameter does not affect the balance of nutrition within the Ecopath model, it is an
indispensable parameter in estimating the energy-related parameters in different groups.
The Ecopath model suggests a default GS value of 0.2 for carnivorous fish groups
because 20% of the consumption is assumed to be physiologically useless and is directed
to the detritus in the form of urine and feces (Winberg, 1956). For herbivores, the
proportion of the non-assimilated food in the total consumed food is higher. For instance,
the GS value for herbivorous fish groups feeding on zooplankton is 0.41, while the GS
values for zooplankton, mollusk and decapods are 0.65, 0.4 and 0.7, respectively
(Halfom et al., 1996; Park et al., 1974; Scavia et al., 1974). These values were used in
the lobster model.
3.3.7. Other Input Parameters
In addition to the parameters described above, there are other parameters in the
Ecopath model. Although these data are not necessarily required as input data, they can
also have a large impact on modeling outputs. These parameters include biomass
accumulation (BA), detritus imports, gross efficiency of food conversion (GEi), fishing
mortality (F) or yield (Yi), nature mortality (M), and predation mortality (M2). In
principle, all these parameters described above should be collected from the
measurements done in the Gulf of Maine to minimize potential biases resulting from
spatial differences in the biological parameters. In this study, however, because
information for some species is not available in the GOM, I estimated the information for
the same species inhabiting an area with similar ecological features as that of the GOM.
These species only consist of a small proportion of the total number of groups included in
the model.
How efficient the EWEmodel is depends on how accurate and how reliable the
input data is. Thus, it is important to document data sources and standardize the input
data. When aggregating data, I used Ecowrite to document their sources and described
how they were standardized. The Pedigree was then used to specify the uncertainty
associated with the input data according to their sources and to evaluate the quality of the
model. For those parameters that could not be determined, I estimated their possible
ranges using Ecoranger. Such a systematic approach was likely to minimize errors
resulting from mis-inputting data or inputting parameters that were biologically
unrealistic.
3.4. Trophic Compartments
Table 3.1. Compartments/groups of the Ecopath model for the Gulf of Maine lobster
ecosystem.
Group
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microbenthods (polychaetes, worms)
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms (sea urchin, sea cucumber, sea star)
Squid
Shrimp
Atlantic herring (CH)
Silver hake (MB)
Skate
Cunner (TA)
Cusk (BB)
Atlantic cod (GM)
Red hake (UC)
Tautog (TO)
Atlantic wolfish (AL)
Other fishes
Bait (Herring)
Detritus
Scientific Name
Homarus americanus
Homarus americanus
Clupea harengus
Merluccius bilinearis
Tautogolabtus adspersus
Brosme brosrne
Gadus morhua
Urophycis chuss
Tautoga onitis
Anarhichas lupus
With the focus on the American lobster, the GOM trophic network was structured
in twenty-four compartments (Table 3.1). These twenty-four groups included two
primary producers, two zooplankton groups, seven groups of benthos, eleven groups of
fishes (eight of them belongs to the groundfish species), one lobster bait group (herring),
and detritus, which was the only non-living group in the model that was produced by the
living organism groups.
3.4.1. American Lobster (Homarus americanus)
As the core group in the model, I divided the lobster into two groups: adult and
juvenile. Lobster life history is usually divided into five phases after they settle down to
the bottom: shelter-restricted juvenile (-4-14 mm carapace length, CL), emergent
juvenile (CL: -15-25 mm), vagile juvenile (CL: -25mm to size of physiological
maturity), adolescent, and adult (Sainte-Marie and Chabot, 2002). In the model, based on
lobster feeding behavior, 45mm was set as the cut-off CL between juvenile and adult
lobster groups, although the ovigory in females were usually not sexually mature at this
size.
The biomass of adult lobster was obtained using a newly developed sizestructured stock assessment model at the University of Maine (Chen et al. 2005). The
average biomass was estimated at 0.537 t/km2 in the mid-1980s and 0.98 t/km2 in the
mid-1990s. I estimated that the P/B ratio and Q/B ratio of the adult lobster were 1.2
and 8.2
respectively (Okey, 2001).
Because little information was available for estimating the biomass of the juvenile
lobster for the time period of 1980s and 1990s, I did not treat the biomass of the juvenile
lobster as input data. Instead I estimated it in the Ecopath model with the assumption that
EE=0.95. This means that 95% ofjuvenile lobster biomass passed up the food web, used
for biomass accumulation, migration or export, and only 5% of juvenile biomass was lost
due to natural mortality other than predations (e.g., disease). This EE value was
commonly used in the ecological studies (Okey, 2001). I set P/B=2.4
and
~ / ~ = 1 2 . 3 ~ e afor
r - the
' juvenile lobsters, which were much higher than adult lobsters
(Mackinson, 2001).
Ojeda and Dearbom (1991) analyzed the diet composition of adult lobsters in the
rocky sub-tidal communities and found that 36.8% of the stomach contents were crab
remains, 23.2% of them were mollusks, and the remaining 40% were echinoderms, fish
remains, and detritus. Carter and Steele (1 982b) described the diet of immature lobster in
Newfoundland as 19% echinoderms, 16.7% mollusks, 14.8% crabs, 8.4% lobster, and
17% detritus and other unidentified organisms. These data were incorporated into the
model; together the herring bait (Grabowski et al., 2003). Therefore, I assumed that there
are about 15% of herring bait in the juvenile lobster's stomach content and 46.2% in the
adult lobster's stomach content, while other organisms occupied the remaining
percentages.
3.4.2. Crab (Carcinus maenas, Cancer borealis, and Cancer irroratus)
In the GOM, there are three main crab species: Carcinus maenas, Cancer borealis,
and Cancer irroratus. They were mainly bycatch in the lobster fishery. I included them as
one group in the Ecopath model because of their importance in the lobster ecosystem.
For the crab group, the biomass was estimated by Ecopath with the assumption
that EE=0.95, P/B=1.38
and Q/B=8.5
(Okey and Pugliese, 2001). Also, I
estimated the diet data from the work by Ojeda and Dearborn (1991) and averaged the
diet composition of C. borealis, and C. irroratus. The averages were 43.7% mollusks,
21.4% echinoderms, 12.9% microbenthos, 10% crabs, 0.3% Macroalgae, 4.9% other
fishes and 6.8% detritus.
3.4.3. Shrimp
One of the most important shrimp species in the northeast United States is
northern shnmp, Pandalus borealis. It inhabits cold waters and soft mud bottom (Schick,
1991) throughput boreal waters of the northern hemisphere (Shumway et al., 1985) with
the GOM as its southern boundary in the Atlantic range (Haynes and Wigley, 1969). The
northern shrimp fishery in the GOM started as early as the 1930s, expended rapidly
during the 1960s, collapsed in the late 1970s, and then recovered in 1978 (Cadrin et al.,
1999).
There are many small shrimp species in the Gulf of Maine. These species play
important roles in the ecosystem and should not be ignored in modeling the lobster
ecosystem. Thus, I included all shrimp species in one group and estimated the shrimp
biomass in the Ecopath model with the assumption that their EE value was 0.95.
The PA3 and Q/B values for shrimps were obtained from published papers. For
P/B data, I used Okey's value of 4
For the Q/B value, I averaged the value of
9.667 year" for the shrimp species in Newfoundland (Bundy et al., 2000) and the value of
19.2
in the South Atlantic States continental shelf (Okey and Pugliese, 2001). The
Ecopath yielded the preliminary estimates of all shrimp biomass, 3.256 tkm2for the mid1980s and 3.93 1 t/km2 for the mid- 1990s. These values were much larger than 0.198
tlkrn2 and 0.13 8 t/km2predicted at the 36thStock Assessment Workshop (NEFSC, 2002)
for the biomass of northern shrimp. This difference might suggest that my estimate was
for all the shrimp species in the GOM.
The diet composition of shrimp group I used in the model was 52% detritus, 9%
phytoplankton, 24% microzooplankton, 12% macrozooplankton and 3% microbenthos
obtained from Heyrnans (2001).
3.4.4. Echinoderm
The Echinoderm group included sea urchins, sea stars, and sea cucumbers. The
green sea urchin (Strongylocentrotus droebachiensis) is the only sea urchin fishery in the
GOM. The population of the green sea urchin was small in the 1970s in the region, but
grew dramatically up to the early 1990s, followed by a substantial decrease. In its peak
time, this sea urchin wiped out the kelp bed and transformed the seafloor into urchin
barrens. The biomass of sea urchin decreased significantly since 1992 under the pressure
of heavy fishing and/or increased abundance of crabs that are the main predator of sea
urchin. The present biomass was about one tenth of its peak abundance. Because of its
important ecological role to the benthic community, as well as its roles as a significant
diet for crabs, lobster, and fishes, the green sea urchin was included in the Ecopath model
(Taylor, 2004).
A recent stock assessment estimated the sea urchin biomass for the time period of
1987 to 2004 (Chen and Hunter, 2003; Kanaiwa et al., 2005). From this assessment, the
sea urchin stock biomass was estimated as 55,000 tons (0.606 t/km2) in the mid-1 980s
and 28,830 tons (0.31 8 t/km2) in the mid-1990s.
Sea cucumber (Cucumariafrondosa) is a fast-developed fishery in the GOM,
which began in 1988 and started expanding around 1994 (Chenoweth and McGown,
1997). There was no estimate of its stock biomass, production or consumption in the
GOM. IVo biomass, production, and consumption data were available for sea stars and
other echinoderm species. Thus, I had to estimate the total biomass of echinoderm from
the Ecopath model.
Steimle (1987) estimated the biomass of the benthic macrofauna of Georges
Banks in 1977. In that study, the wet weight of echinoderm was estimated as 8.4 t/km2.
The PA3 ratio and QA3 ratio were 1.2
and 3.7
respectively (Okey, 2001).
The diet of echinoderm was from (Okey, 2001) consisted of 20 % miceobenthos,
24% macrozooplankton, 47% detritus and 9% other things.
3.4.5. Shelled mollusk
In the GOM, there are many species of shelled mollusk such as Mahogany quahog,
softshell clams, blue mussel, periwinkle and sea scallop. These bivalvia and gastropoda
species were integrated into one group in my model. The biomass of shelled mollusk was
estimated as 195.5 t/krn2, with 50% of the wet weight being shell (Heymans, 2001). The
P/I3 and Q/B ratios are 1.22
and 23
respectively (Okey, 2001). The diet
composition of the shelled mollusk was 25% zooplankton, 25% microbenthos, and 50%
detritus (Okey, 2001).
3.4.6. Squid
The longfin inshore squid (Loligo paeleii) is the most common squid species in
the GOM. The Northern shortfin squid (Illex illecebrosus) is also found in the GOM, but
most of them are distributed fkom Georges Banks to Cape Hatteras (Collette and KleinMacPhee, 2002).
There was little information for squid in the GOM. The latest survey for the
longfin inshore squid stock was in 2001 .Its biomass estimate was only available from
1987-2000 (NEFSC, 2001b), and was 22,100 tons (0.244 t/km2) for the mid-1990s. The
same biomass was estimated for the mid-1980s. Sissenwine (1987) estimated P/B and
Q B values of 1.5 and 7
respectively, for squid. They were used in the Ecopath
models for the mid-1980s and for the mid-1990s. Many studies had been done for the
stomach analysis of squid in different locations, such as Bowman et al. (2000) for the
Northwest Atlantic, and Bundy et al. (2000) for the Newfoundland. Many studies
estimated the diet composition of squid in the areas close to the GOM (Heymans, 2001;
Okey, 2001). I averaged the diet composition data from these studies and used a diet of
49.7% macrozooplankton, 8.6% microbenthos, 8.6% echinoderms, 3.9% squid, 5.4%
shrimp, 3.3% cod, 3.2% other fishes and 16.2% detritus in the Ecopath model.
3.4.7. Atlantic Herring (Clupea harengus)
The Atlantic herring is widely distributed in the Northwest Atlantic continental
shelf waters from Labrador to Cape Hatteras (Overholtz, 2000). In 1976, the herring
stock in the GOM and Georges Bank collapsed and the stock biomass dropped to below
100,000 tons. The stock started rebuilding in the mid- 1980s (Overholtz, 2000). Thus, it
was reasonable to assume the biomass for the mid-1980s was 100,000 tons (0.696 t/km2).
The biomass for the mid-1990s was estimated as 1,900,000 tons (13.287 t/km2) from
survey data using a linear regression model derived for the survey index data and biomass
data in the earlier years.
Sissenwine (1987) estimated an average annual P/B ratio of 0.29
for the
herring that were older than 1 year in the time period when herring biomass was lower. I
set the PA3 value for the mid-1980s at 0.29
Because commercial landings during
1990-1998 ranged from 8 1,000 to 124,000 tons (Overholtz, 2000), the P/B value for the
mid-1990s was calculated as P/B = F + M = 0.519
value of 10.1
For Q B , I estimated an annual
from the Worldfish Fishbase website (www.fishbase.org).
Darbyson et al. (2003) estimated the diet composition of Atlantic herring as
86.58% copepods (one of microzooplankton), 6.92% other planktons, 4.3% fishes and
2.3% detritus. The copepod composes between 30% and 83% of the diet composition
(Fishbase, www.fishbase.org). I used these data and assumed the diet compositions of
71.5% microzooplankton, 22.5% macrozooplankton, 4% fishes and 2% detritus for the
Atlantic herring in the Ecopath model.
3.4.8. Silver Hake (Merluccius bilinearis) and Red Hake (Urophycis chuss)
The silver hake, also called whiting hake, is a widely distributed gadid (Brodziak,
2001), and is also an important commercial fishery in the GOM. According to the 1lth
SAW report (NEFSC, 1990), the biomass of silver hake in the mid-1980s was about
30,250 tons (0.333 t/km2). Because the survey index in the mid-1990s was 1.37 times as
that in the mid-1980s (Brodziak, 2001), the biomass of silver hake in the mid-1990s was
estimated as 0.456 t/krn2.
The weighted average natural mortality (M) of silver hake was estimated as 0.37
using the data provided at Fishbase (www.fishbase.org). Thus the average PA3 = F
+ M was 0.735
for the mid-1 980s and 0.505
value was 4.26
(Fishbase, www.fishbase.org)
for the mid-1 990s. The QA3
Silver hake is an important predator that feeds on shrimp and squid (Brodziak,
2001), but little information was available on the trophic interactions of lobster and silver
hake. In a stomach analysis study of cod and hake by Okey (2001), 2% of the stomach
contents were found to be lobster. As suggested by Bowman et al. (2000), stomach
contents of silver hake included 17.9% Atlantic herring, 8.2% silver hake, 24.1% other
fishes, 6.8% shrimp, and 43% macrozooplankton. Therefore, I adjusted the diet
composition to 42.6% macrozooplankton, 16.8% Atlantic hemng, 6.7% silver hake, 6.7%
shrimp, 25% other fishes and 1.1% detritus. I assumed that the percentage of lobster in
the silver hake stomach contents ranged between 0 to 4%, with the default value of 1.1%.
Using the same method, I estimated the Ecopath input data for red hake. The
biomass was set at 0.1 17 t/km2 for the mid- 1980s and 0.1 19 t/km2 for the mid- 1990s. The
PIE3 values and Q/B values were 0.323
and 2.7
and 2.6
for the mid-1980s, and 0.343
for the mid-1990s, respectively. The diet composition for the red
hake was estimated as 49.4% macrozooplankton, 2.5% microbenthos, 2.5% shelled
mollusk, 2.8% crab, 1.5% squid, 15.5% shrimp, 18.3% other fishes and 5.6% detritus. I
also assumed the percentage of lobster in the red hake stomach contents ranged between
0 to 4%, with a default value of 1.9%.
3.4.9. Skates
There was little information about the skate in the GOM. According to the 3oth
SAW (NEFSC, 1999), the NEFSC did spring surveys in the whole northeast region from
the GOM to Chesapeake Bright in the Mid-Atlantic. Seven skate species (barndoor,
winter, thorny, little, clearnose, rosette, smooth) were identified in the survey. They were
all included as a group in the lobster model, although the skates distributed in the GOM
were mainly the thorny skate and smooth skate. The biomass survey index of these skates
declined steadily to historic lows from 1980 to 2000, probably as a result of overfishing.
During 1977-1986 the total biomass of various skates species from the GOM to
the Mid-Atlantic area was estimated to be around 100,000 metric tons. This biomass
estimate, multiplied by the percentage of the stock biomass index and the percentage of
the population present in the GOM (including Georges Bank), led to the derivation of the
total skate biomass estimate, which was 42,000 tons or 0.292 t/krn2 (Heymans, 2001).
The weighted average natural mortality (M) of the skate species was 0.18
as estimated from data at Fishbase (www.fishbase.org). Because the fishing mortality was
low before the mid-1980s, I assumed the catchlbycatch was 10% of the biomass. The
average PA3 for the mid-1980s was 0.296
and the QA3 value at www.fishbase.org
was 1.4
From the 30"' SAW (NEFSC, 1999), the survey indices in the 1990s for thorny
skate and smooth skate were about half of those in the 1980s. Thus, I assumed that the
skate biomass was 0.15 t/krn2 in 1990s. The total catch of skate in the 1990s was 11,300
tons. The P/B was then estimated as 1.049
(F + M = 0.869 + 0.1 8) and the Q/B
value was 1.5
Because the thorny skate is the most common skate species in the GOM, I took
the stomach analyses of the thorny skate as the diet composition of the skate group in the
Ecopath model. The diet compositions included 31.4% other fishes, 36.8% squid, 9.8%
microbenthos, 19.8% other groups and 0-4% lobster, with a default value of 2.2%
(Bowman et al., 2000).
3.4.10. Cunner (Tautogolabtus adspersus) and Tautog (Tautoga onitis)
Cunner is widespread along the Atlantic coast and offshore banks of North
America, from the eastern coast of northern Newfoundland to Chesapeake Bay (Collette
and Klein-MacPhee, 2002). As a recreational fishery, there was little information
available about the population structure of this species. I estimated the biomass of the
cunner to be about one-fifth of the biomass of the tautog and let Ecopath adjust this value
when balancing the model.
The natural mortality was 0.37 (Fishbase, www.fishbase.org). PA3 was estimated
as F + M = 0 + 0.37 = 0.37
was 4.1 yeail and 4.2
for both the mid-1980s and mid-1990s. The Q/B value
for the mid- 1980s and mid-1 990s, respectively.
Cunner is one of the main predators of large crustaceans. Lobster consisted of
3.6% of the stomach contents (in weight) of cunner. The cunner stomach contents also
included 50.2% crab and 12.3% shrimp, and shelled mollusks, echinoderms and other
groups consisted of the other 33.9% stomach contents (Bowman et al., 2000).
Tautog looks like cunner, but is much bigger. In fact, both cunner and tautog belong to
the Labridae family. Tautog is also a main target species in the recreational fishery. The
catch in the mid-1990s was 2,040 tons (NEFSC, 1999); only 10% was commercial
landings. The average exploitation rate was 42.3% for this period. The average biomass
of tautog was estimated at 4,823 tons (0.053 t/km2)in the 1990s. The PIE3 value was
estimated as F + M = 0.75
for the mid-1990s, and the Q/B value was 2 year"
(Fishbase, www.fishbase.org). Because the commercial landings were small compared to
other groundfish species, I assumed that the B, P/B, and QIB values did not change from
the mid-1980s to mid-1990s. For tautog, the diet composition was 32% crab, 32% shrimp,
and 34% detritus (Bowman et al., 2000). I also assumed that the lobster consisted of
0-4% of the tautog diets, with a default value of 2%.
3.4.1 1. Atlantic Cod (Gadus morhua)
The Atlantic cod is a cold-water, demersal gadoid species that is found in the
northwest Atlantic from Greenland to North Carolina (Heymans, 2001). According to the
history data, the lengths of cod may achieve up to 130 cm and the weights up to 25 to 35
kg. Maximum age is in excess of 20 years. Sexual maturity is attained between ages 2 to
4; spawning occurs during winter and early spring (Mayo and O'Brien, 2000).
In the U.S. waters area, Atlantic cod are assessed and managed as two stocks:
Gulf of Maine, and Georges Bank and Southward. Both of these stocks support important
commercial and recreational fisheries, which conducted year round (Mayo and O'Brien,
2000). During the last two decades, the U.S. cod population suffered a great decrease. For
example, the total commercial landings (exclusively US.) in 1998 were 4,200 tons, a
77% decrease from the record-high 1991 total of 17,800 tons. It was the lowest since
1966. Also the recreational catch of cod equaled 824 tons in 1998, well below the 1991
level (2,900 tons). These data are consistent with the Northeast Fisheries Science Center
(NEFSC) trawl survey on cod's abundance and biomass (Figure 3.2) (NEFSC, 2001a).
Figure 3.2. Biomass change of Atlantic cod in the Gulf of Maine, US (NEFSC, 2001a).
1980
1985
1990
1995
Year
2000
2005
In my model, the average biomass in the GOM was estimated as at 23,500 tons
and 18,700 tons for the mid- 1980s and mid- 1990s, respectively (NEFSC, 200 1a), which
is equivalent to 0.259 t/km2and 0.206 t/km2. The fishing mortality F was 0.82 during the
mid-1980s (NEFSC, 2001a) and the natural mortality M was 0.23. Thus, P/B could be
calculated as F + M = 1.05 year-'. As reported in the 33rdSAW (NEFSC, 2001a), the
average landing for cod during the mid-1990s in the GOM was about 8,500 tons. Thus
PA3 = F + M = 0.697 + 0.23 = 0.927
Compared with the PA3 value of 0.65 1
for the Newfoundland model (Bundy et al., 2000) and 0.6 year-' used by Sissenwine
(1 987) in the Georges Bank for the time period between 1963 and 1972, the values used
in this study were a bit higher. As for the Q/B value, I used 2.58 year" for both the mid1980s model and mid-1990s model (Fishbase, www.fishbase.org).
Cod is omnivorous, feeding on a variety of invertebrates and fish species. But
there is very few papers concerning how much lobster occupied in the cod's stomach
contents. Therefore, like what I did for the other groundfish groups, I assumed that the
lobster consisted of 0-4% of the cod's stomach contents (Okey, 2001), with a default
value of 2.1%. Besides that, cod stomach contents also included of 41.9% squid, 14.7%
Atlantic herring, 12.2% silver hake, 10% shrimp, 11.2% other fishes and 7.9% other
groups (Bowman et al., 2000).
3.4.12. Cusk (Brosme brosme) and Atlantic Wolfish (Anarhichas lupus)
The other two groundfish that may prey on lobster are cusk and Atlantic wolfish.
Because there was little information available on the cusk and Atlantic wolfish stock
structure and biomass, I built two production models to estimate their stock biomasses
using catch and survey abundance index data. To evaluate the quality of estimated stock
biomass, I compared the temporal variations in the estimated stock biomass with the
survey abundance index (Fig. 3.3 - Fig. 3.6) .The estimates of stock biomass were closely
related to the survey abundance indices. The stock biomass of both species experienced
substantial decreases after 1980. The cusk biomass was estimated as 0.193 t/km2 for the
mid-1980s and 0.075 t/krn2 for the mid-1990s. The wolfish was 0.058 tkm2 and 0.0125
t/krn2 in the mid-1980s and 1990s, respectively.
The PA3 values for cusk were about 0.341 yea8 and 0.428 year-' for the mid1980s and mid-1990s, respectively. For wolfish, the values were 0.418 year-' and 0.583
yearm'and the Q/B values for cusk were 2.2
and 1.8
for the mid-1980s and
mid- 1990s, respectively (Fishbase, www.fishbase.org).
Like for other groundfish species, I also assumed that lobster consisted of 0 ~ 4 %
of the cusk and wolfish stomach contents by weight. The diet compositions of cusk
included 3% macrozooplankton, 2.5% microbenthos, 23.4% crab, 17.3% echinoderm,
16.5% shrimp, 17.8% other fishes, 18.2% detritus and 1.3%juvenile lobster. The diet
compositions of wolfish consisted of 4.2% macrozooplankton, 0.2% microbenthos, 40%
shelled mollusk, 4.8% crab, 3 1.9% echinoderm, 7% squid, 9.4% shrimp, 2.1 % detritus
and 0.4% juvenile lobster.
Figure 3.3. Correlationship between production-model predicted cusk biomass and
observed CPUEs during 1963-1998, with a high correlation coefficient.
0
1
0
I
I
0.5
1
1
I
I
I
I
1.5
2
2.5
3
3.5
Survey Index (kgltow)
Figure 3.4. Predicted cusk biomass during 1963-1998 by using production model.
1960
1965
1970
1975
1980
Year
1985
1990
1995
2ooo
I
Figure 3.5. Correlationship between production-model predicted Atlantic wolfish
biomass and observed CPUEs during 1970-1999, with a high correlation coefficient.
0
0.5
1
2
1.5
2.5
3
3.5
Survery Index (kgltow)
Figure 3.6. Predicted Atlantic wolfish biomass during 1968-1999 by using production
model.
1965
1970
1975
1980
1985
Year
1990
1995
2000
2005
I
3.4.13. Other fishes
There are 118 families of 252 species of fishes in the GOM (Collette and KleinMacPhee, 2002). It was clear that incorporating them all in the model was not practical.
Because my model only focused on those fish species that were trophically related to the
lobster, I included a group in the model as all other fish species that were not included in
the fish groups discussed above, but potentially had trophic interactions with lobster and
other species included in the model. This fish group includes demersal species such as
haddock, sand lance, sculpin, goosefish, and flounder (but excluding those listed in other
groups) as well as pelagic species such as capelin, mackerel, redfish, and bluefish (but
not herring and squid).
There was little information on the stock biomass estimates for most fish species
in the GOM. The Ecopath models built for other ecosystems included an "other fishes"
groups, but their "other fishes" groups were likely to have a different species composition,
suggesting their data could not be used in this study. Thus, it was almost impossible to
estimate the B, P/B, Q/B and Dcijvalue for the "other fishes" group defined for the GOM
ecosystem in this study. With lack of other information, I arbitrarily assumed the biomass
of the "other fishes" group in the GOM being about 10 t/krn2with the P/B ratio of 2.66
and Q/B ratio of 3.6
With these starting values, the model could start the
iteration and find the balance while adjusting these assumed values.
Heyrnans (2001) described the diet of pelagic feeders and demersal feeders in the
GOM. All the diets were combined for estimating the average diet of other fishes (Table
3.2).
Table 3.2. Percentage diet composition of other fishes (Heymans, 2001).
Phytoplankton
Microzooplankton
Macrozooplankton
Microbenthods
Crab
Juvenile lobster
Squid
Shrimp
Atlantic herring
Skate
Atlantic cod
Other fishes
Detritus
Sum
small
pelagic
feeders
10
7
100
large
pelagic
feeders
large
demersal
. feeders
small
demersal
feeders
average
2.5
100
1
100
100
2
100
3.4.14. Macrozooplankton and Micorzooplankton
Ln the model, protozoa, rotifera, cladocera, copepoda, hyperiidea, mysidacea
euphausiacea, and penaeidea were all included in the zooplankton. Microzooplankton is
the zooplankton that is smaller than 200 microns, while the macrozooplankton is bigger
in size (Shen and Shen, 2004). Though these two groups are not the direct food for
lobsters, they perform as two important transporters that pass the energy of primary
production to higher trophic levels.
The biomass of macrozooplankton from 1977-1981 for the GOM was 61 t/krn2
and the biomass of microzooplankton was 43% of macrozooplankton biomass (Sherman
et al., 1987). Therefore, I set the macrozooplankton biomass as 61 t/krn2 and
microzooplankton biomass as 26 t/km2. The annual PIE3 of macrozooplankton was
estimated at 7
and 25 yeaf' for microzooplankton (Sherman et al., 1987). The Q/B
value was 21.87 year-' and 125 year-' for macrozooplankton and microzooplankton,
respectively (Okey, 2001). All these data that I described above were for both the mid1980s and the mid- 1990s. Microzooplankton feed on phytoplankton, while
macrozooplankton feed on microzooplankton and phytoplankton at a ratio of
approximately 28:72 (Heymans, 2001).
3.4.15. Microbenthos
The microbenthic group was mainly polychaetes and worms in this model. They
were included because of their importance as foods for many organisms included in the
model. Their biomass was estimated as 14.1 t k n 2 (Heymans, 2001), P/B ratio as 1.8
and Q/B ratio could be calculated because the conversion efficiency (PIQ) was
approximately 60% (Heyrnans, 2001). Following previous studies, microbenthos were
assumed to consume 50% detritus, 10% phytoplankton, 30% algae, 8% zooplankton and
2% benthos, on average (Bundy et al., 2000; Okey, 2001).
3.4.16. Phytoplankton
The GOM usually has two-phytoplankton bloom cycles every year. The strongest
pulse of phytoplankton usually occurs during late-winter and early-spring when cold
nutritious deep water mixes with warm shallow water. The second phytoplankton bloom
often occurs during late-summer and early-fall with the deepening of the upper mixed
layer. The bloom provided important foods for many organisms in their critical life
history stages (most in their larval stages).
The Satellite Oceanography Data Laboratory of the University of Maine has
monitored the phytoplankton variability since 1997. The average chlorophyll-a
concentration on the sea surface was approximately 1.96 mg/m3. February-March bloom
biomass was the greatest from surface to 25-35m below, and May-June biomass was
mainly in the upper 30m. The maximum chlorophyll layer occurred within 15-20m of the
surface from June through September (O'Reilly and Zetlin, 1998). I estimated the
phytoplankton biomass in the upper 30m of the water column. The general chlorophyll-a
concentration in the GOM was estimated as 60 mg/m2, which could be translated to 3
g ~ / m 2to, 9.68 g/m2dry weight, and to 97.8 t/krn2 wet weight, using a chlorophyll-a
/Carbon conversion rate of approximately 50, and a carboddry weight ratio and dry
weightjwet weight ratio of 0.31 and 10, respectively (Heyrnans, 2001). My estimate of
97.8 t/km2 from the satellite data in this study was similar to the reported 99 t/km2 for the
GOM by Heyrnans (2001) for the 1977-1986 time period.
Phytoplankton was classified as a group in almost all studies that involved
Ecopath modeling, but the P B values were different among the studies. In my model, I
used a annual P/B value of 88
(O'Reilly et al., 1987). h addition, 1.5% of the
phytoplankton was assumed to be exported from the GOM (Christensen et al., 2000).
3.4.17. Macroalgae
The macroalgae included all the kelps and sea grasses. As another important
primary production, and also an important food incoming for the microbenthos and
shelled mollusk, macroalgae group can't be ignored for its important indirect effects on
the lobster ecosystem.
Following previous study, biomass value was assumed to be 52.096 t h 2 and PIB
ratio was assumed to be 4 year' both for 1980s and 1990s (Okey and Pugliese, 2001).
3.4.18. Bait (herring)
Every year, thousands of metric tons of herring were used as bait to catch lobsters
along the coast of the northeast US. After each haul of a lobster trap, the used bait would
be discarded back into the water where the trap was set. In some studies, this was
considered as one of the reasons for large increases in lobster catch and biomass, and for
the thriving of the benthic communities (Grabowski et al., 2003).
According to Grabowski et al. (2003), about 60% (48,000
- 70,000 tons) of the
herring landed in New England was reintroduced into the water as lobster bait. With the
historical trap-numbers data, I estimated the weight of bait discarded back into the GOM
as 0.667 t/km2 and 0.6 tlkrn2 for the mid-1980s and mid-1990s, respectively. The herring
baits were not living organisms, and thus PIE3 values were set at 1
3.4.19. Detritus
Detritus is the particle organic materials (POM) from the waste of the living
groups. Townsend (1997) suggested that the re-suspension of benthic material was 39 mg
~ l m ~ l dwhich
a ~ , was approximately 142 tons wet weight per square kilometer per year
(t/km21year), when converted using 1gC=lO grams wet weight (Christensen and Pauly,
1992). This could be considered as the quantity of detritus that were imported into the
ecosystem. Heyrnans (2001) also calculated the detritus pool as 155.7 t/krn21year.
In conclusion, a substantial database was compiled as input data for the lobster
ecosystem model developed in this study. The summary of the input data is described in
Table 3.3 to Table 3.8.
Table 3.3. Input data for the Ecopath model of the mid-1980s.
Group name
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microbenthods
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring (CH)
Silver hake (MB)
Skate
Cunner (TA)
Cusk (BB)
Atlantic cod (GM)
Red hake (UC)
Tautog (TO)
Atlantic wolfish (AL)
Other fishes
Bait (Herring)
Detritus
.
Habitat
area
(fraction)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Biomass in
habitat area
(t/km2)
97.8
52.096
25
61
14.1
97.75
0.537
8.4
0.22
0.696
0.333
0.292
0.0106
0.193
0.266
0.117
0.053
0.058
10
0.667
155.7
P/B (/year)
88
4
40
7
1.8
1.22
1.38
2.4
1.2
1.2
1.5
4
0.7
0.735
0.296
0.4
0.341
1.05
0.323
0.752
0.418
2.66
1
-
Q/B (/year)
EE
-
P/Q
BA
Detritus import
(t/km2lyear)
-
-
-
125
21.87
-
-
0.6
23
8.5
12.3
8.2
3.7
7
15
10.1
4.26
1.4
4.1
2.2
2.58
2.6
2
1.8
3.6
0.95
0.95
0.95
-
-
-
-
-
-
-
-
0.65
0.3
0.4
0.4
0.7
0.7
0.7
0.2
0.4
0.7
0.4 1
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.41
-
-
-
-
-
-
-
-
142
Table 3.4. Input data for the Ecopath model of the mid-1990s.
Group name
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microbenthods
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring (CH)
Silver hake (MB)
Skate
Cunner (TA)
Cusk (BB)
Atlantic cod (GM)
Red hake (UC)
Tautog (TO)
Atlantic wolfish (AL)
Other fishes
Bait (Herring)
Detritus
Habitat
area
(fraction)
1
Biomass in
habitat area
(t/km2)
P/B (/year)
97.8
88
1
1
0.6
155.7
1
-
Q/B (/year)
EE
P/Q
BA
Detritus i m ~ o r t
(t/km2lYearj
-
-
-
-
-
-
-
-
142
Table 3.5. Diet composition for the lobster ecosystem in the Gulf of Maine (group 3-9). Values are proportions of the diet composition
by weight or by volume.
-
-
Analyzing methods
-
4
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microbenthos
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderm
Squid
Shrimp
Atlantic herring
Silver hake
Skate
Cunner
Cusk
Atlantic cod
Red hake
Tautog
-
-
3
4
5
6
7
8
9
by weight
by weight
by weight
by weight
by weight
by volume
by weight
(Heymans, (Heymans,
200 1)
2001)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
-
1
0.72
0.28
(Bundy et al.,
2000; Okey'
200 1)
0.1
0.3
0.065
0.015
0.02
(Okey9200
0.25
0.25
(Carter and
(Grabowski et
Steele, 1982b; al., 2003; Ojeda
(Ojeda and
Dearborn, 1991) Grabowski et and Dearborn,
al., 2003)
1991)
0.003
0.129
0.437
0.1
0.2 14
0.029
0.004
0.017
0.162
0.223
0.122
0.073
0.214
0.007
0.068
0.14
0.156
0.035
Table 3.5 continued
21
22
23
24
Atlantic wolf7sh
Other fishes
Bait (Herring)
Detritus
Sum
0.5
1
1
1
0.5
1
0.068
1
0.087
1
1
Table 3.6. Diet composition for the lobster ecosystem in the Gulf of Maine (group 10-16). Values are proportions of the diet
composition by weight or by volume.
Analyzing methods
10
by weight
(Okey,
2001)
P
\O
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microbenthos
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderm
Squid
Shrimp
Atlantic herring
Silver hake
Skate
Cunner
Cusk
Atlantic cod
Red hake
Tautog
Atlantic wolfish
11
by weight
(Bowman et
al., 2000;
Bundy et al.,
2000)
0.028
0.24
0.028
0.2
0.01
12
by weight
(Heymans,
2001)
13
by weight
14
by weight
15
by weight
16
by occurrence
(Bowman et al.,
@owman et al.,
(Bowman et al., 2000; Hanson
(Darbyson et
2000; Okey,
aI., 2003)
2000)
and Lanteigne,
200 1)
2000)
0.73
0.23
0.426
0.043
0.098
0.09
0.497
0.086
0.01 1
0.24
0.12
0.03
0.011
0.024
0.086
0.039
0.054
0.018
0.022
0.026
0.015
0.082
0.487
0.037
0.068
0.067
0.168
0.067
0.368
0.029
0.127
Table 3.6 continued
22 Other fishes
23 Bait (Herring)
24 Detritus
Sum
0.032
0.47
1
0.1 62
1
0.52
1
0.02
0.25
0.3 14
0.024
0.02
0.01 1
0.108
0.134
1
1
1
1
Table 3.7 continued
22 Other fishes
23 Bait (Herring)
24 Detritus
0.178
0.1 12
0.183
0.182
0.027
0.056
0.185
0.047
0.418
0.02 1
Table 3.8. Reference for the model.
P/B
Q B or P/Q
(O'Reilly et al.,
1987)
(Okey and Pugliese, 2001)
(Sherman et al.,
(Okey, 2001)
1987)
(Okey, 2001)
Macrozooplankton (Sherman et al., 1987) (Sherman et al.,
1987)
(Heymans, 2001) (Heymans, 2001)
Microbenthos
(Heymans, 200 1)
Shelled mollusk
(Okey, 2001)
(Okey, 2001)
(Okey and Pugliese, 2001)
(Okey, 2001)
Crab
(Overholtz, 2000) (Mackinson, 2001)
Juvenile lobster
Adult lobster
Univ. of Maine
(Okey, 2001)
(Okey, 2001)
unpublished data
(Okey, 2001)
(Okey, 2001)
Echinoderm
Univ. of Maine
unpublished paper
(Sissenwine, 1987) (Sissenwine, 1987)
Squid
34Ih SAW
(Okey, 2001)
(Bundy et al., 2000; Okey
Shrimp
and Pugliese, 2001)
(Overholtz, 2000; www.fishbase.org
Atlantic herring
(Overholtz, 2000)
Sissenwine, 1987)
linear regression
www.fishbase.org www.fishbase.org
Silver hake
1lthSaws
(Brodziak, 200 1)
www.fishbase.org www.fishbase.org
Skate
(Heymans, 2001)
3othSAW
3othSAW
www.fishbase.org www.fishbase.org
Cunner
Estimated
www.fishbase.org www.fishbase.org
Cusk
Production model
Biomass
Phytoplankton
University of Maine
satellite data
Macroalgae
(Okey and Pugliese,
2001)
Microzooplankton (Sherman et al., 1987)
u
l
w
EE
GS
-
-
(Park et al., 1974)
-
Default value
-
(Winberg, 1956)
(Winberg, 1956)
(Winberg, 1956)
(Winberg, 1956)
(Halform et al., 1996)
(Okey, 2001)
Estimated
Estimated
-
Default value
Estimated
(Scavia et al., 1974)
(Halform et al., 1996)
(Winberg, 1956)
-
(Winberg, 1956)
-
(Winberg, 1956)
-
(Winberg, 1956)
(Winberg, 1956)
-
Table 3.8 continued
Atlantic cod
33d SAW
Red hake
11th Saws
3oth
Saws
Tautog
Production model
Atlantic wolfish
Estimated
Other fishes
Bait (Herring)
Detritus
(Grabowski et al.,
2003)
Dept. of Marine
Resource (ME) data
(Heymans, 2001)
-
(Winberg, 1956)
(Winberg, 1956)
(Winberg, 1956)
(Winberg, 1956)
(Winberg, 1956)
-
-
-
-
-
-
33rdSAW
www.fishbase.org
www.fishbase.org
www.fishbase.org
Summarized from
papers
Estimate as 1
www.fishbase.org
www.fishbase.org
www.fishbase.org
www.fishbase.org
Summarized from papers
-
-
-
3.5. Balancing the Model and Addressing Uncertainty
One of the most important steps in modeling is to verify whether a model yields
an output that is biologically realistic and conforms with reality. For the Ecopath model,
this could be done by examining if ecotrophic efficiency (EE) estimated in modeling for
each group was less than 1 since no more biomass could be used than was produced by
any group of organisms in the model. It was inevitable for the EEs of some groups to
have values greater than 1 in the first modeling iteration when the initial inputs were used.
This might result fiom the inaccuracy or errors in some of the input data. Those groups
with an EE larger than 1 were often referred to as "unbalanced groups".
When the unbalanced groups were encountered in modeling, I used the
"automatic mass balance" function that built-in in the Ecopath model to re-evaluate and
modify parameters to achieve the goal of having EE smaller than 1 for all groups. This
"automatic mass balance" function could adjust each parameter within a defined
confidence interval (normally, 20% higher or lower than the input parameter value). As
for the model that could not be balanced using the above approach, I had to tune the input
data manually by increasing the ranges for key parameters so that the model could search
for appropriate parameters from a wider range of values.
A good model relies on reliable input data. Because input data used in this study
were collected from different sources, large uncertainty might exist in data, which might
influence the quality of this study. Ecopath provided a systematic approach to evaluating
and describing the uncertainty involved in the model construction and parameterization.
The Pedigree submodel was used to document the data sources, estimate confidence
intervals of each parameter and provide an overall index (pedigree index) of the model's
quality, which are averaged over all parameters and functional groups of the model. The
Ecoranger submodel was used to set the probability distribution for the input parameters,
and sensitivity analysis submodel was used to evaluate the impact of changing each of the
input parameters on modeling outputs for each group in the ecosystem (Christensen et al.,
2000).
Taking the Ecopath model developed for the mid-1 980s as an example, the
pedigree index for the whole ecosystem was calculated as 0.63 (the pedigree index for the
mid-1990s model was 0.65 1) based on the confidence interval options selected for each
parameter of each group. Compared with some models in other places, this pedigree
index was higher (Mendy and Buchary, 2001), suggesting that the GOM lobster
ecosystem model had a higher confidence interval and is more trustworthy.
To evaluate the possible impact of inaccurate data on modeling, I conducted a
sensitivity analysis to evaluate how a change in an input parameter in group i could effect
on another parameter for group j. The change in the input parameter and consequent
changes in other parameters was measured in the percentage of the original values of the
parameters. Because the output of the sensitivity analysis was extensive, it was not
included in the results, but is presented in Appendix I. The analysis suggested the model
results were most sensitive to the input data of other fish species groups.
3.6. Results
3.6.1. Base Estimation
After extensive search and computer iteration, balanced lobster models were
constructed for the GOM lobster ecosystem in the two time periods, mid-1980s and mid1990s. The key outputs included the estimates of trophic levels, biomass, PIB, QIB, EE,
and P/Q values for each group included in the model (Table 3.9 and Table 3.10). The
biomass, P/B, QIB, and P/Q were the estimates tuned in the Ecopath model. For the
primary production in the mid-1980 model, the biomass of kelp was about half of the
P
phytoplankton biomass. Its production was 21 6 t ~ k r n ~ / ~(ePa =
r B * -), which was also
B
much less than those of the phytoplankton (8606.4 tlkm2/year) (Table 3.9), suggesting
that it was not the macroalgae, but the phytoplankton that was the beginning of the
grazing food chain. For the mid-1990s model, the kelp biomass was doubled to 124.4
P
t/km2tyear, but the production was still 622 t/km2iyear ( P = B * - ), much less than the
B
production of phytoplankton (Tables 3.10). Therefore, phytoplankton was the main
primary producer in the GOM.
The biomasses of shelled mollusk, crab, juvenile lobster, adult lobster, and
Atlantic herring were doubled, tripled or even more than tripled, but the biomasses of
Atlantic cod and cusk decreased by nearly a half from 1980s to 1990s (Tables 3.9,3.10).
These results were consistent with previous studies(ASMFC, 2000; NEFSC, 1999;
NEFSC, 2000; NEFSC, 2001a; NEFSC, 2001b; NEFSC, 2002). By comparing changes in
P/B ratio between the mid-1980s and mid-1990s, I found that the P/B values for silver
hake and Atlantic cod increased, implying they were subject to higher fishing mortality in
the mid-1 990s. The P/B value for Atlantic herring and other fishes decreased from the
mid-1980s to mid-1990s, which might result from a decrease of their predators during the
time period. The EE values also showed an increase for the groups of phytoplankton,
macroalgae, microbenthos, echinoderms, and shrimp, indicating that the groups were
preyed or grazed upon more heavily, or subject to higher fishing mortality from 1980s to
1990s. It should also be noted that the EE value for the detritus group was increased from
the mid-1980s to mid-1990s, suggesting that the detritus were better utilized in the 1990s
ecosystem with large increases in stock biomass of crustacean species.
Table 3.9. Basic output data for the mid-1980s Ecopath model.
ul
\O
Group name
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microbenthods
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring (CH)
Silver hake (MB)
Skate
Cunner (TA)
Cusk (BB)
Atlantic cod (GM)
Red hake (UC)
Tautog (TO)
Atlantic wolfish (AL)
Other fishes
Bait (Herring)
Detritus
Effective
Trophic
level
1
1
2
2.28
2.1 1
2.01
3.22
3.15
2.72
2.29
3.22
2.43
3.09
3.89
3.96
3.63
3.55
4.22
3.54
2.98
3.28
3.71
1
1
Biomass in
habitat area
(t/km2)
97.8
54
25
61
25.38
9.8
2.74
0.222
0.285
12.096
1.938
1.628
1.809
0.777
0.39
0.0624
0.391
0.752
0.0468
0.352
0.0297
1.945
1.86
155.7
P/B
(/year)
88
4
40
7
1.8
1.22
1.38
2.4
1.2
1.2
1.5
4
0.7
0.735
0.296
0.4
0.341
1.05
0.323
0.752
0.418
2.6
1
-
Q/B
(/year)
125
21.87
3
23
8
12.3
8.2
3.7
7
15
10.1
4.26
1.4
4.1
2.2
2.58
2.6
2
1.8
3.6
-
EE
0.483
0.407
0.402
0.041
0.359
0.989
0.99
0.99
0.462
0.557
0.988
0.357
0.99
0.99
0.988
0.99
0.99
0.99
0.807
0.98
0.982
0.99
0.99
0.04
p/Q
-
0.32
0.32
0.6
0.053
0.162
0.195
0.146
0.324
0.214
0.267
0.069
0.173
0.21 1
0.098
0.155
0.407
0.124
0.376
0.232
0.722
Detritus
import
(t/km21year)
-
-
-
-
-
-
-
-
-
142
Table 3.10. Basic output data for the mid-1990s Ecopath model.
Q\
O
Group name
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microben thods
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring (CH)
Silver hake (MB)
Skate
Cunner (TA)
Cusk (BB)
Atlantic cod (GM)
Red hake (UC)
Tautog (TO)
Atlantic wolfish (AL)
Other fishes
Bait (Herring)
Detritus
Effective
Trophic
level
1
1
2
2.28
2.1 1
2.01
3.2
3.29
3.19
2.29
3.27
2.43
3.08
3.87
3.92
3.63
3.57
4.26
3.5
3.21
3.28
3.64
1
1
Biomass in
habitat area
(t/km2)
97.8
124.4
25
61
38.349
92.298
10.282
0.483
0.469
22.638
0.998
0.812
4.901
1.116
0.272
0.1
0.243
0.415
0.2
0.446
0.1
3.735
1.333
155.7
P/B
(/year)
88
5
40
7
1.8
1.22
1.38
2.4
1.2
1.2
1.5
4
0.519
0.505
1.049
0.4
0.428
0.927
0.343
0.752
0.583
2.66
1
-
Q/B
(/year)
-
125
21.87
3
23
8.5
12.3
8.2
3.7
7
15
10.1
4.26
1.5
4.2
2.2
2.58
2.7
2
1.8
3.6
EE
0.538
0.908
0.445
0.054
0.486
0.369
0.99
0.99
0.428
0.85
0.99
0.99
0.99
0.99
0.99
0.345
0.901
0.99
0.071
0.901
0.084
0.914
0.99
0.226
p/Q
Detritus
import
(t/km21year)
-
-
0.32
0.32
0.6
0.053
0.162
0.195
0.146
0.324
0.214
0.267
0.05 1
0.119
0.699
0.095
0.195
0.359
0.127
0.376
0.324
0.739
-
-
-
142
3.6.2. The Trophic Structure
I used effective trophic level (ETL), trophic aggregation, and throughput to
describe how the energy flows in the ecosystem. The ETL is a fraction that defines the
relative trophic level of a group in the ecosystem. This parameter was derived from
analyzing the food web of the ecosystem. The trophic level of the a certain group is
divided by the trophic level of the primary production group so that we can get the ETL,
with the assumption that the ETL of the primary production group in this ecosystem is
1.O. Trophic aggregation was used to integrate the trophic flows of different groups in the
ecosystem into several aggregated trophic levels in order to simplify the complex food
web. The throughput was the sum of all the trophic flows passing through a trophic level
in a given time. Besides that, I also used the "trophic level decomposition" tables to
evaluate how mass and energy flowed through a trophic network and to explain how the
energy was transferred between the trophic levels.
From mid- 1980s to mid- 1990s, the ETL for most groups were pretty stable, but
for some key species the effective trophic level changed a lot, such as juvenile lobster,
increased from 3.15 to 3.29, adult lobster increase from 2.72 to 3.19, tautog from 2.98 to
3.21, while the ETL of other fishes group decrease from 3.71 to 3.64. This might suggest
that the fish species composition change from large and high trophic level species to
small and low trophic level fish species.
The GOM lobster ecosystem in the mid-1980s could be aggregated into twelve
trophic levels, but the values from Level VII to Level XI1 were extremely small (Table
3.11). Thus, effectively the GOM ecosystem in the mid-1980s consisted of six aggregated
trophic levels (from Level I to Level VI; Table 3.1 1). Trophic flows that transmitted
through for each aggregated trophic level are summarized in Table 3.1 1. For example, for
the aggregated trophic Level I in the mid-1 980s, the throughput was about 16009.47
t/km21year, in which 142 t/km21year (0.8%) were imported, 5140.848 t/krn21year(32.2%)
were consumed by the groups of higher trophic levels, 5439. t/km21year(34%) were
exported in forms such as harvesting (e.g., kelp) or fishing, and 5287.076 t/km21year
(33%) became detritus and were recycled. Most energy flows in the system occurred on
trophic Level I to Level 111.
Table 3.11. Distributions of import, consumption by predators, export, and flow to
detritus, respiration, and throughput through aggregated trophic levels in the Gulf of
Maine ecosystem in the mid-1 980s. (Unit: t/km21year)
Consumption
Flow to
TL \ Flow Import by Predator Export Detritus Respiration Throughput
0.001
XI1
0
0
0
0
XI
0.001
0
0
0.001
0.002
X
0.003
0
0.002
0.003
0.008
IX
0.01
0.001
0.006
0.009
0.025
VIII
0.032
0.002
0.019
0.03
0.083
VII
0.106
0.006
0.064
0.098
0.274
VI
0.348
0.019
0.223
0.316
0.906
V
1.139
0.067
0.833
1.038
3.078
IV
3.752
0.279
5.718
5.044
14.793
I11
16.147
0.532
37.185
406.16
460.024
I1
463.213
0.376
214.962
4374.099
5052.65
I
142
5 140.848
5439.5 5287.076
0
16009.47
Sum
142
5625.599
5440.8 5546.087 4786.798
21 541.32
Table 3.12 shows the trophic decomposition of the groups in the GOM ecosystem
in the mid-1980s. The primary production and detritus were in trophic Level I, taking
74.3% of the throughput of the entire ecosystem. Level I1 was mainly zooplanktons and
benthos; and its throughput was about 23.5% of the throughput for the entire ecosystem.
Crab, juvenile lobster and pelagic species mainly belonged to Level 111, taking 2% of the
total throughput of the system. Level IV mainly consisted of demersal species taking
0.07% of the overall throughput of the ecosystem (Table 3.1 1 and Table 3.12).
Table 3.12. Trophic decomposition of the groups in the Gulf of Maine ecosystem in the
mid-1 980s.
Group\TL
Phvto~lankton
Macroalgae
Microzooplan kto
Macrozooplankt
Microbenthods
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring
Silver hake
Skate
Cunner
Cusk
Atlantic cod
Red hake
Tautog
Atlantic wolfish
Other fishes
Bait (Herring)
Detritus
I1
I11
IV
V
VI
VII VIII
IX
X ETL
The ecosystem for the mid-1 990s consisted of eight trophic levels (Table 3-13),
but the values in the upper three levels were so low that they could be ignored. Thus, the
ecosystem of the mid-1990s could be effectively simplified to five aggregated trophic
levels. The throughputs of different trophic levels measured as the percentage of the
entire system throughput were 67.5%, 29.8%, 2.6%, and 0.1% for trophic Levels I to IV,
respectively (Table 3.13). For organisms in trophic level I, most of throughput were taken
by the form of the predation, but for organisms in the upper trophic levels, respiration
took the most throughput (Table 3.13).
Table 3.13. Distributions of import, consumption by predators, export, and flow to
detritus, respiration, and throughput through aggregated trophic levels in the Gulf of
Maine ecosystem in the mid-1990s. (Unit: t/km21year)
Consumption
Flow to
TL \ Flow Import by Predator Export Detritus Respiration Throughput
VIII
0.001
0
0.002
0.002
0.005
0.001
VII
0.005
0.017
0.02
0.043
VI
0.042
0.1 18
0.13
0.295
0.005
V
0.291
0.056
1.019
0.958
2.324
IV
2.292
8.402
2 1.947
0.457
10.795
I11
21.874
1.497
97.453
429.017
549.841
I1
549.902
0.654
1014.312 4806.963
6371.83
I
142
6371.899
14440.37
0
4009.943 3916.528
21386.65
Sum
142
6946.306
4012.613 5040.244 5245.492
Comparing the two models for the mid-1980s and mid-1990s, the total
throughputs within the ecosystem during the two time periods were close. However, there
was a large difference in the throughput distribution among different trophic levels (Table
3.1 1 and Table 3.13). The throughput of trophic Level I in the mid-1 980s was 16009.47
tun2/year, but this value was decreased to 14440.37 t/krn2lyear.The utilization rate was
increased from 32% in the mid-1980s to 44% in the mid-1990s. The production of
trophic Level I1 was increased from 5052.65 t/km21yearin the mid-1980s to the 6371.83
t/km2/year in the mid-1990s with the utilization rate decreasing from 9.1% to 8.6%. The
reduction of the primary production over that time might result from loss of critical
habitats and pollutions associated with coastal development activities in the 1980s and
1990s. The increase of the secondary production might be the result of heavy exploitation
since the 1980s.
Table 3.14. Trophic decomposition of the groups in the Gulf of Maine ecosystem in the
mid- 1990s.
Group\TL
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microbenthods
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring
Silver hake
Skate
Cunner
Cusk
Atlantic cod
Red hake
Tautog
Atlantic wolfish
Other fishes
Bait (Herring)
Detritus
I
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
I1
0
0
1
0.72
0.918
0.99
0.082
0.128
0.302
0.756
0.178
0.61
0.02
0.012
0.158
0.131
0.167
0.05
0.058
0.34
0.021
0.132
0
0
I11
0
0
0
0.28
0.077
0.01
0.82
0.609
0.343
0.227
0.602
0.354
0.902
0.414
0.303
0.285
0.297
0.132
0.541
0.224
0.744
0.612
0
0
IV
0
0
0
0
0.004
0
0.088
0.238
0.308
0.016
0.212
0.036
0.074
0.485
0.408
0.515
0.462
0.566
0.343
0.388
0.206
0.212
0
0
V
0
0
0
0
0
0
0.01
0.022
0.041
0.001
0.007
0
0.003
0.076
0.126
0.062
0.065
0.221
0.05
0.044
0.028
0.037
0
0
VI
0
0
0
0
0
0
0.001
0.002
0.005
0
0.001
0
0,001
0,011
0.005
0.006
0.008
0.026
0.007
0.004
0.001
0.007
0
0
VII VIII ETL
0
0
1
0
0
1
0
0
2
0
0
2.28
0
0
2.1 1
0
0
2.01
0
0
3.2
0
0
3.29
0.001 0
3.19
0
0
2.29
0
0
3.27
0
0
2.43
0
0
3.08
0.002 0
3.87
0
0
3.92
0.001 0
3.63
0,001 0
3.57
0.004 0.001
4.26
0.001 0
3.5
0
0
3.21
0
0
3.28
0.001 0
3.64
0
0
1
0
0
1
3.6.3. Summary statistics
Ecopath yielded a number of statistics to assess the status of an ecosystem and to
describe the scale, stability, and maturity states of the ecosystem. The total throughput,
the sum of the four flow components (total consumption, total export, total respiration,
and total flows to detritus) was 18419 t/km21year in the mid-1980s (Table 3.15). The
overall production of all groups was 10347 t/km21year. The mean trophic level of the
catch in the fishery was 3.1 1.
The ratio between system primary production (Pp) and respiration (R) was
considered as an important parameter to describe the maturity of an ecosystem. For a
mature system, the ratio should approach to 1; however, for the early developmental
stages of a system, the production was expected to exceed respiration and the ratio was
likely to be greater than 1. For an ecosystem with the ratio less than 1, it might suffer
from organic pollution. From Table 3.15, the net primary production (NPP) was 8824.261
t/km21year,which was much bigger than the total respiration, leading to a net system
production (NSP) of 461 3.748 t/krn21year.Thus, the ecosystem in the mid- 1980s could be
classified as an immature ecosystem because the Pp/R ratio is 2.096, or greater than 1.
Connectance Index (CI), the ratio of the number of actual links to the number of
possible links in a given food web, and System Omnivory Index (SOI), the average of
omnivory index of all consumers weighted by the logarithm of each consumer's food
intake, are the two of the most important statistics both reflecting the complexity of the
inner linkages within the ecosystem (Here the omnivory index is the variance of the
trophic level of a consumer's prey groups, which ranges from 0 to 1. Higher omnivory
index value means predation on many trophic levels). The more mature the ecosystem is,
the more complex the inner linkage (food web) among groups would be (Christensen et
al., 2000). In the mid-1980s, the CI and SO1 were 0.265 and 0.315, respectively.
Throughput cycled indicated the total trophic flows that recycled in the ecosystem.
Finn's cycling index (FCI) is the fraction of an ecosystem's throughput that is recycled.
Finn's mean path length (FML) represents the average number of groups that a flow
passes through. A high FCI and FML are always two features in a mature ecosystem. In
the mid-1980s, the throughput cycled was 7.79 t/krn21year,the FCI and FML were 0.06%
and 1.595, respectively.
In addition to the statistics described above, there are other indices that describe
the maturity and complexity of an ecosystem, such as the ratio between a system's
primary production and its total biomass (PpIB), the ratio between fishery yields and net
primary production (Gross efficiency), and the biomasslthroughput. All of these
parameters can be used to describe the ecosystem.
Table 3.15 also summarizes the statistics for assessing the ecosystem status in the
mid-1990s. The total system throughput in the mid-1990s increased 2990 t/krn21yearto
21,409 t/km21year,and the total production of the system increased from 10347
t/km2lyear in the mid- 1980s to 10,899 t/km21year.The mean trophic level of the catch of
all commercial species in the lobster ecosystem in the Gulf of Maine was 3.05. The NSP
was reduced to 3984.242 t/krn21year,resulting in the Pp/R ratio decreasing to 1.76. The
CI and SO1 were 0.265 and 0.275, respectively, for the ecosystem in the mid-1990s. The
throughput cycled increased to 778.98 t/krn21year, and the FCI and FML also increased
from those in the 1980s to 3.64% and 2.3 13, respectively. The Pp/R ratio, CI, SOI, FCI
and FML parameters all suggest that the ecosystem in the mid-1990s was a more mature
and more complex ecosystem, compared with that in the 1980s.
Table 3.15. The overall system properties of the lobster ecosystem in the Gulf of Maine
for the mid- 1980s and mid- 1990s.
Parameter
Sum of all consumption
Sum of all exports
Sum of all respiratory flows
Sum of all flows into detritus
Total system throughput
Sum of all production
Mean trophic level of the catch
Gross efficiency (catchlnet p.p.)
Calculated total net primary production
Total primary production/total respiration
Net system production
Total primary production/total biomass
Total biomass/total throughput
Total biomass (excluding detritus)
Total catches
Connectance Index
System Omnivory Index
Throughput cycled (including detritus)
Finn's cycling index
Finn's mean path length
Finn's straight-through path length
Finn's straight-through path length
Throughput cycled (including detritus)
mid-1 980s
4904.764
4558.71 1
421 0.5 13
4745.432
18419
10347
3.1 1
0.000145
8824.261
2.096
461 3.748
29.3 84
0.016
300.303
1.283
0.265
0.315
7.79
0.06
mid-1990s
6968.827
4012.613
5245.491
5 182.244
2 1409
10899
3.05
0.000289
9229.733
1.76
3984.242
18.937
0.023
487.39
2.671
0.265
0.275
778.98
3.64
1.595
1.112
1.594
7.79
2.313
2.101
2.229
778.98
Units
t/km2/year
t/km2/year
t/km2/year
t/km2/year
t/km2/year
t/km2/year
t/krn2/year
t/km2/year
t/km2
t/km2/year
t/km2/year
% of total
throughput
without detritus
with detritus
t/km2/year
3.6.4. Food Web Analyses
The food web analysis included the estimates and analysis of two indices: total
number of pathways and mean length of pathways. The total number of pathways
describes the total possible pathways that the trophic flows pass from primary production
to a predator. The higher trophic level an organism was, the larger the number of food
web pathways would be and the longer the mean length of pathways would be (Table
3.16). Overall, the total number of food web pathways in the ecosystem was 4910 with
the mean length of pathways being 7.18. The unusually high number of food web
pathways included cannibalisms shown in many groups included in the model.
Table 3.16. The food chain analysis of groups in the lobster ecosystem in the Gulf of
Maine.
Group name
Phytoplankton
Algae
Microzooplankton
Macrozooplankton
Microbenthods
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring (CH)
Silver hake (MB)
Skate
Cunner (TA)
Cusk (BB)
Atlantic cod (GM)
Red hake (UC)
Tautog (TO)
Atlantic wolfish (AL)
Other fishes
Bait (Herring)
Detritus
Summary
Number of food web
pathways
Mean length of pathways
1
2
6
4
568
805
1475
14
1834
11
856
202 1
2633
2291
2287
2332
4111
1385
3245
880
1
1.5
1.67
1.25
6.02
6.05
6.65
2.14
6.63
2.27
6.53
6.64
6.7
6.78
6.78
6.24
7.17
7
733
5.54
-
-
4910
7.18
3.6.5. Niche Overlap and Mixed Trophic Impact Analyses
Ecopath yielded the prey overlap index and predator overlap index between every
two groups included in the model. These indices quantified trophic overlap of every two
groups in the ecosystem and described the niche overlap. The niche overlap for the mid1980s and mid-1990s was almost the same (Figure 3.7 and Figure 3.10)
The prey overlap index reflected the comparability of food origins between
groups and was used for analyzing the food competition between each pair of groups
included in the model. The values of prey overlap index ranged between 0 and 1. The
larger the value was, the more similar the two groups were. The prey overlap index of
microbenthos and shelled mollusk (group 5 and group 6) was close to 1 (Fig. 3.7),
suggesting they shared a similar role in the ecosystem as a predator.
The predator overlap index reflected if the two species had the common predator.
The values of predator overlap index and prey overlap index ranged between 0 and 1. The
larger the value was, the more similar the two groups were. The predator overlap index of
shelled mollusk and crab (group 6 and group 7) was close to 1 in the mid-1980s. This
suggested that these two groups were likely to be subject to mortality by common
predators (Fig. 3.7). For the shelled mollusk and echinoderms (group 6 and group lo),
because their predator overlap index and prey overlap index were high (Fig. 3.7), these
two groups could be aggregated into a single group.
Figure 3.7. The niche overlap index between every two groups of the lobster ecosystem
in the Gulf of Maine in the mid-1980s. The numbers in the plot are group numbers and
the values of the indices are shown in Figure 3.8. and Figure 3.9. For example, 5.6 means
the prey overlap index of microbenthos (group 5) and shelled mollusks (group 6) is 0.959,
while the predator overlap index of these two groups are 0.239.
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in the Gulf of Maine in the mid-1 990s.
6 15.2s
0 la."
The mixed trophic impact (MTI) reflected the mutual benefit or mutual harmful
impacts among groups, with the value ranging from -1 to 1. If the two groups mutually
benefited, the MTI had a value greater than 0, or the MTI had a negative value. Because
the MTI assess both the direct and indirect interactions among different groups, this
parameter was used to estimate the impact of changing the biomass of one group on the
biomass of the other groups. Mixed trophic impacts for the mid-1 980s model and the
mid-1990s model were summarized in Table 3.17 to Table 3.20. Most groups had a
negative direct impact on their preferred preys, but a direct positive impact on their direct
predators. Most groups had a negative impact on themselves because of intra-species
competitions for resources. For the cannibalism species, the MTIs within their groups
were positive. In addition, fishing activity also had direct negative impacts on most
commercial fish groups. Taking the juvenile lobster group as an example, it had a
negative MTI on the crab group due to food competition and negative MTIs on
echinoderm and squid due to predation. On the other hand, juvenile lobster had positive
MTIs on groundfish groups, but groundfish groups had negative MTIs on the juvenile
lobster. Herring bait had a positive MTI impact on the juvenile lobster and adult lobster.
All these results were consistent with our understanding of trophic interactions among
species.
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Table 3.18. Mixed trophic impacts data for the mid-1980s model (group14-24 and fleet 1). Groups in the left Column Two were the
groups that were changed and numbers in the right first row were the number of groups, which were influenced. This table show the
combined direct and indirect trophic impact resulting fiom a small increase in the abundance of a group might have on the other
groups in the ecosystem.
4
\O
Group Impact
1 Phytoplankton
2 Algae
3 Microzooplankton
4 Macrozooplankton
5 Microbenthods
6 Shelled mollusk
7 Crab
8 Juvenile lobster
9 Adult lobster
10 Echinoderms
11 Squid
12 Shrimp
13 Atlantic herring
14 Silver hake
15 Skate
16 Cunner
17 Cusk
18 Atlantic cod
19 Redhake
20 Tautog
21 Atlantic wolfish
22 Other fishes
Table 3.18 continued
23 Bait (Herring)
24 Detritus
25 Fleet1
0.005 -0.022 -0.041 -0.029 -0.005 -0.02 -0.033 -0.029
-0.028
0.08 0.1 14 0.141 0.019 -0.074 0.354 0.083
-0.055
-0.3 -0.615 -0.628 -0.042 -0.653 -0.618 -0.693
0.031 -0.185
0.022 -0.027
0.059 0.217
0.001 0.027
0 0.132
0.024 -0.312
Table 3.19 continued
23 Bait (Herring)
24 Detritus
25 Fleet1
0 -0.002 -0.001 0.002 -0.002 0.003 -0.009 0.058 0.146 0.001 -0.001 -0.004 -0.006
0.23 0.102 0.346 -0.036
-0.02 -0.214 0.006 -0.046 0.154 0.218 0.1 18 0.085 0.091
0.023 0.014 -0.078 0.153 0.01 -0.015 0.044 0.021 -0.617 -0.021 0.007 0.057 -0.338
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Microbenthos
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Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring
Silver hake
Skate
Cunner
Cusk
Atlantic cod
Red hake
Tautog
Atlantic wolfish
0ther fishes
Bait
Detritus
Fleet1
Phytoplankton
Macroalgae
Microzooplankton
Macrozooplankton
Microbenthos
Shelled mollusk
Crab
Juvenile lobster
Adult lobster
Echinoderms
Squid
Shrimp
Atlantic herring
Silver hake
Skate
Cunner
Cusk
Atlantic cod
Red hake
Tautog
Atlantic wolfish
Other fishes
Bait
Detritus
Fleet 1
5
!3
f:
0
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s+
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Macroalgae
28.3
5
Microzooplankton 6W
macro zoo plankton^,
Microbenthos
Shelled mollusk
3
0-
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K
Juvenile lobster
Adult lobster
W
"
<
Echinoderms
is+5
Squid
3
s
Shrimp
Atlantic herring
Silver hake
Skate
Cunner
Cusk
Atlantic cod
Red hake
Atlantic wolfish
Other fishes
Bait
Detritus
Fleet 1
%
P
I
c.
6
*
3.7. Discussion
The complexity of the Gulf of Maine ecosystem makes it difficult to evaluate
interactions among different species without using a quantitative modeling approach.
This study, although preliminary in nature, provides an effective tool for understanding
the lobster ecosystem in the Gulf of Maine and testing hypotheses with respect to trophic
interactions of different species and impacts of the dynamics of one species on the
population dynamics of another species in the same ecosystem. For example, this study
suggests that the reduction of groundfish biomass tend to result in an increase in the
lobster stock biomass and herring stock biomass as a result of reduced predations (or
natural mortality). This study also confirms that herring baits tend to have positive
impacts on the lobster stock biomass. Aside from these expected results, I also got some
unexpected conclusions. For example, the ecosystem in the Gulf of Maine in the middle
1980s tended to be less mature than the ecosystem in the middle 1990s. In general, the
modeling conclusions are biologically reasonable and attest some theories that formed in
other studies.
From the analyses of MTIs, I found that the negative impacts that groundfish
species had on the large invertebrate species were not as obvious as predicted in other
studies (e.g., ASMFC 2000). On contrast, some impacts, such as the impacts of silver
hake on juvenile lobster, skate on juvenile lobster and shrimp, Atlantic cod on adult
lobster, crab and shrimp, were all positive. This is likely to result from the predation of
groundfish on some invertebrates, Atlantic herring, or squid, which reduces the
competition of preys among those groups. Because the positive impacts tend to exceed
the negative impacts in the relationships of these species, the comprehensive MTI is
shown to be a net positive value.
Although the lobster ecosystem in the Gulf of Maine was still a developing
ecosystem, it might be inappropriate to classify it as an immature ecosystem. The Pp/R
ratio was about 1.76 in the middle 1990s (Table 3.21), much lower than that in the Gulf
of Mexico (Arreguin-Sanchez et al., 2004)and the CI, SO1 is lower than those in the
Northern Gulf of California (Morales-Zarate et al., 2004). Besides that, the mean trophic
level of catch statistics were rather high (Table 3.21), compared to the other two
ecosystems. In addition, the higher total throughput and higher NPP index suggested that
the Gulf of Maine was a productive area.
Table 3.2 1. The comparison of some system index between the Gulf of Maine in the mid1990s, the Northern Gulf of California, and the Gulf of Mexico.
Mean
Total
trophic level
throughput NPP
PpIR CI
SO1 of catch
GOM in mid-1 990s
2 1409
9229.7 1.76 0.265 0.275 3.05
2133.5 1.61 0.32 0.55 2.93
Northern Gulf of California 6633
1 1293
4668.7 3.86 0.28 0.17 2.82
Gulf of Mexico
There might be some factors that might affect the performance of the model in
describing the structure of the ecosystem in the mid-1980s and mid-1990s. For example,
for the groups without biomass data, their biomasses were estimated by the Ecopath
submodel. These estimates represented the minimal biomass required to make the
Ecopath model balance. Other information such as diet compositions for each species
was estimated fi-om different studies or from studies conducted in an area other than Gulf
of Maine. A food web is complex (Polis, 1994). Thus, the diet composition was likely to
be different even in two near ecosystems or even in the same ecosystem. The data were
likely to be different among seasons. All these uncertainties and variations would
introduce errors in modeling. Future studies should be focused on the better estimation of
input data.
The energy flows in the lobster ecosystems in mid-1980s and mid-1990s in the
Gulf of Maine are reflected in Figure 3.13 to Figure 3.16.
Figure 3.13. Flow diagram for the mid-1 980s Ecopath model. (Unit: t/km21year)
-
-
Connector
4Other export
4 FI(UVtodetm16
7
Resplmlon
Impart
Fl
it:;!,
CorourE
Tl7Tt P'l
Chapter 4
THE SIMULATION OF LOBSTER ECOSYSTEM DYNAMICS IN THE GULF
OF MAINE
In Chapter 3, I used Ecopath to build two models for the GOM lobster ecosystem
for the mid-1980s and 1990s. These two models were considered as two "snapshots" in
the evolution of the GOM lobster ecosystem. They described the structure of the
ecosystem in the defined time (i.e., mid-1980s and mid-1990s), but were unable to
describe the dynamic nature of the ecosystem.
In this chapter, I used Ecosim to simulate the dynamics of the lobster ecosystem
in the GOM from 1985 to 1997, and evaluated the long-term impact on the ecosystem
under different scenarios of exploitation rates for the Atlantic cod. Such a study would
provide critical information on the responses of the lobster ecosystems to the exploitation.
4.1. Basic Equations of the Ecosim Model
The Ecosim model simulates a dynamic structured ecosystem model by using the
output of the Ecopath model. It is based on the following equation:
dB.
dt
Pi
Bi
2= Bi*--
CQ,+ I i -(MOi + <
+ei)*Bi,
dBi
where -is the growth rate of stock biomass Bi for group i ,Pi is the production of
dt
group i, Qi is the consumption rate of predator j on the group i calculated
as Qji = B j * ( Q 1 B )
* Dci, MOi is the non-predation natural mortality rate, Fi is the
fishing mortality rate, and Ii and ei are the emigration and immigration rates, respectively
(Chstensen et al., 2000). This equation describes how the biomass of each organism
group in the ecosystem changes with time, which provides the information on the
dynamics of ecosystem.
4.2. Methods and Materials
In order to build an Ecosim model, I loaded a historical data file in the form of
CSV (comma separated values) format (Table 4.1). Each column in the data file
described how a measurement varied over time. The input data included relative biomass
or absolute biomass, fishing effort, fishing mortality, total mortality andfor total
catches(Chstensen et al., 2000). Sometimes time forcing data could also be inputted to
fix the corresponding time series data exactly as input. However, the data in my model
are fishing mortality, which performed as driving data and restricted the development of
the Ecosim model, and relative biomass that worked as reference data to evaluate the
accuracy of results.
The Ecopath model established for the mid-1980s were used as the initial status
of the Ecosim model. Thus, I started to model the ecosystem dynamics from 1985 (i.e.,
starting time). Fish growth over the time period was considered in this study (i.e., from
1985 to 1997) by linking the juvenile group of each organisms to their adult group, such
as the juvenile and adult lobsters. The Ecosim ecosystem dynamics model was driven by
the historical data (Table 4.1). All the parameters used to define the ecosystem in 1985
were the same as those defined for the mid-1980s in Chapter 3. The lobster ecosystem
dynamics were evaluated under different levels of fishing mortality for the Atlantic cod.
Such a study evaluated the impacts of different fishing mortality of cod on the ecosystem
dynamics, which described how the changes in the cod population biomass might
influence the biomass of other species in the ecosystem including American lobster.
Table 4.1. CSV file for Ecosim. The unit for the observed survey indices (CPUEs) is kgltow.
Title
pool code
type
1985
Atlantic
cod F
18
4
0.74
Adult
Atlantic
lobster F cod CPUE
9
4
0.62
18
0
8.1
Atlantic
Silver hake
herring
CPUE
CPUE
13
0
0.175
14
0
9.7
Cusk
Atlantic
CPUE
CPUE
17
0
2.1
21
0
2.2
American lobster
Predict biomass from
cod CPUE
growth model
Atlantic
18
0
8.1
19
1
4.9
4.3. Results
4.3.1. Simulation of fish stock biomass using the proposed ecosystem
simulation model
Ecosim yielded a time series data of biomass for the 24 groups included in the
ecosystem model from 1985 to 1997 (Figure 4.1). Some groups, such as cunner, crab and
silver hake, had experienced substantial increases from 1985 to 1997. Organisms, such as
cusk, shelled mollusk, red hake, echinoderms, Atlantic herring, macroalgae, lobsters and
other fishes had experienced some growth. Organisms did not show large changes from
1985 to 1997, such as microbenthos, microzooplankton, phytoplankton,
macrozooplankton, skate, squid, detritus, tautog and Atlantic wolfish. Groups such as
Atlantic cod and shrimp experienced substantial decrease from 1985 to 1997. These
results were consistent with the outputs yielded from the Ecopath model for the mid1990s.
Figure 4.1a. Ecosim run form for the lobster ecosystem in the Gulf of Maine fi-om 1985 to 1997. The color lines of the upper panel
indicate the predicted biomasses for the microalgae (group 2), shelled mollusk (group 6), Atlantic cod (group 18), other fishes (group
22), and total catch, while the plot of the lower panel is the observed fishing mortality for Atlantic cod.
Run info ) Group info] Stage
-
-
1
1
Flow control Mediat~on] Forcing functions] Apply FF
- -
- -
F value
I~ndividual
grouldl~tlanticcod [GM]
r Save end state r
-
1
Run Ecosim Equilibrium
-
--
-
-.-
I F = 0 1 Reset Fs 1 Values 1 Time series I Hide 1 Plot I Tracer 1
rOvetlavr
- Save
- r Econath r R -Liear
-
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r
-
I
.
--
Fleettdfort dvnamics
~iomass/originalbiomass
1
.!
I
0.0 j
gy&hx&
r-... ;
,
-
7
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0.00
-
11985-
--
-
-
-
-
-
- -
- -
~- -
'
1
-
--
-
~~
1990
Years
-
-
~
r Q/B
r Weight
r
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Indices
r TL's
__Il ""1
,.9Y5
--
- .
Monte Carla I
1
- - --
-
!
-
Resulk
--
. -
I
-
Figure 4. lb. Ecosim run form for the lobster ecosystem in the Gulf of Maine fiom 1985 to 1997. The color lines of the upper panel
indicate the predicted biomasses for the crab (group 7),juvenile lobster (group 8), adult lobster (group 9), red hake (group 19) and
Atlantic wolfish (group 21), while the plot of the lower panel is the observed fishing mortality for Atlantic cod.
Run info 1
-
Group info] St3ge
1
Fbw controll Mediation
Ilndividual grouldf~tlantic
cod [GM)
r
Save end state
I- .
~iornasshriginalbiom
1_]
Foranp functions
F value
- .
- -
1
.
(
F=0
f Qverlay
1
1 Apply FF
Run Ecosim Equilibrium
I Reset F'o 1 Values I Time series I Hide I
r Save
Ecopath
-I
Linear
r
I
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1 Tracer I
FleeWeffort dynamics
..-
r
c
r
r
r
I
&B
Weight
Feed.trn.
Indices
I
i
TL'S
--
--
-- -
-
-
-
--
Monte Carlo I
-
- -
.. .
1990
--
-
1995
Years
-
--
-
Results
---
--
I
Figure 4 . 1 ~Ecosim
.
run form for the lobster ecosystem in the Gulf of Maine fiom 1985 to 1997. The color lines of the upper panel
indicate the predicted biomasses for the Atlantic herring (group 13), silver hake (group 14) and bait (group 23), while the plot of the
lower panel is the observed fishing mortality for Atlantic cod.
1
1
Flow control Mediation
~
--
-
1
Fotc!ng functions ] Apply FF
-- -
F value
]
-
-
[
F = 0 Reset F's
Sgve
r
I
1
Run Ecosim Equilibrium
Values
I
-
Time series
I Hi& I
R Linear
r
Ecopath
1
--
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1
-
Tracer
--
~
]
--- -
Fleetleffat dynamics
...I
+ 1 r: 1. 1
~i.!l3nttcI ~ r r r ~ r ICH
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I
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-"7
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r
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-
.
-
-
-
1995
1990
Years
i1 ,
Yield
Biomass
Weight
QIB
r
Feed.tm.
r Indices
r TL's
C SIR plot
Y
1
"
I
I
0.00 _I
-- .
1
1 - 1..-.
-
.-
-
Monte Carlo Runs
Results
Figure 4.le. Ecosim run form for the lobster ecosystem in the Gulf of Maine from 1985 to 1997. The color lines of the upper panel
indicate the predicted biomasses for the shrimp (group 12) and cusk (group 17), while the plot of the lower panel is the observed
fishing mortality for Atlantic cod.
Run tnfo 1
--
Grogp Info] Sbge
-
-
]
W A I ~ t l a n t i cod
c [GM]
r
Save end state
r
3
130r~~ornass/or~gmal
b~omass
1
F value I F = 0 ) Reset F's ) Values 1 Time series 1 Hi&
Flpw control Mediation
-.- -
3
1
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--
r Qverlay r Save
1 Apply FF
--
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1
-
--
)
Act
1
Tracer
1
r Ecopath r [j L~near r Fleet/effort dynamnx
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, - L j
8 -
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fi Biomass
r Yield
r Q~B
Weight
c Feed.tm.
r Indices
TL's
r S/R plot
r
r
.-...-..
1990
-
Years
--
--
-
-
-
......,
Monte Carlo Runs
--
Results
1995
--
The predicted biomass and observed survey data (CPUEs) for some commercial
fish species were plotted in Figure 4.2. The temporal trend of the predicted biomass of
Atlantic cod was similar to that for the observed survey data. General temporal patterns
of stock abundance index for other species were also well represented by the stock
biomass estimated from the Ecosim model, although there were discrepancies in some
years (Fig. 4.2). The temporal trend of the American lobster population biomass
predicted by the Ecosim model was almost identical to that of the predicted biomass by
catch equation (Fig. 4.2). This suggested that the simulated ecosystem model could
describe the dynamics of fish populations and ecosystems well.
Figure 4.2a. Stock biomass predicted from the Ecosim model and survey data observed
from the NMFS bottom trawl survey programs for Atlantic cod in the Gulf of Maine from
1
Atlantic cod
I -Observed
1
Year
survey index
il
Figure 4.2b. Stock biomass predicted from the Ecosim model and survey data observed
from the NMFS bottom trawl survey programs for Atlantic herring in the Gulf of Maine
from 1985 to 1997.
Atlantic herring
Year
Figure 4 . 2 ~ Stock
.
biomass predicted from the Ecosim model and survey data observed
from the NMFS bottom trawl survey programs for silver hake in the Gulf of Maine from
Silver hake
I -Observed survey index I
-Ecosim predicted biomass I
Year
Figure 4.2d. Stock biomass predicted from the Ecosim model and survey data observed
from the NMFS bottom trawl survey programs for cusk in the Gulf of Maine from 1985
Cusk
Feb-82
Nov-84
Aug-87
May-90
Year
Jan-93
Oct-95
Jul-98
Apr-01
I
I
Figure 4.2e. Stock biomass predicted from the Ecosim model and survey data observed
from the NMFS bottom trawl survey programs for Atlantic wolfish in the Gulf of Maine
from 1985 to 1997.
Atlantic wolfish
-
2.50E-02
- 2.00E-02
h
- 5.00E-03
Feb-82
Nov-84
Aug-87
May-90
Year
Jan-93
Oct-95
Jul-98
Apr-01
Figure 4.2f. Stock biomass predicted from the Ecosim model and survey data observed
from the NMFS bottom trawl survey programs for red hake in the Gulf of Maine from
Red hake
Year
Figure 4.2g. Stock biomass predicted from the Ecosim model and biomass predicted from
catch equation for adult lobster in the Gulf of Maine from 1985 to 1997.
Adult lobster
1 0.35
Observed survey index
Ecosirn predicted biomass
I
0.05
Year
4.3.2. Evaluating the Impact of Alternative Fishing Mortality Rates of
Atlantic Cod on American Lobster
The biomass dynamics of adult lobster and Atlantic cod from 1985 to 1997 were
estimated under different levels of fishing mortality rates using the developed Ecosim
model (Fig. 4.3). This analysis showed how the lobster and cod might have changed had
the fishing mortality rate of cod been in the different levels. Six hypothetical fishing
mortality levels were assumed for the Atlantic cod. Each fishing mortality was applied to
the cod fishery from 1985 to 1997. When the fishing mortality for Atlantic cod was low
over the time period of 1985 to 1997, cod stock biomass Atlantic cod biomass increased
continuously and slowly over the time and the lobster stock biomass decreased in the first
few years following by small growth. Overall the lobster stock biomass seemed not
changed much during the time period of 1985 to 1997 (Fig. 4.3). When cod fishing
mortality was high (e.g., F = 0.8 and 1.6), the cod stock biomass decreased substantially
from 1985 to 1997 and the lobster stock biomass decreased in the first few years but
increased quickly afterwards. The lobster stock biomass increased almost 50% from 1985
to 1997 (Fig. 4.3). Thus, the large increase in lobster biomass was likely to respond to the
depletion of the cod stock and the cod was likely to act as a controlling factor on the
growth of lobster stock when the cod biomass was high. A negative relationship was
likely to exist between lobster biomass and cod biomass.
The analysis showed both cod and lobster stock biomass decreased in the initial
years regardless of fishing mortality rates for the cod (Fig. 4.3). The differences among
different fishing mortality scenarios were that the biomass of cod would recover quickly
to the previous abundance or even higher levels when the fishing mortality was low, but
continued decreasing when the fishing mortality was high. Therefore, high fishing
mortality was likely to be an important factor in leading to the decrease in the Atlantic
cod fishery.
4.4 Discussion
I used the Ecosim to describe the dynamics of the GOM lobster ecosystem for the
time period of 1985 to 1997. The model performed well in describing the GOM lobster
dynamics. The developed model could be used for exploring potential impacts of changes
in any species biomass included in the model on the dynamics of other species and for
evaluating the impacts of changes in fishing mortality on a particular species on the
dynamics of other species. Such a study could help us better understand the interactions
of different species in the ecosystem and identify key species in the ecosystem that might
dominate the dynamics of the ecosystem. Thus, this study provides an invaluable tool for
future analyses of ecological interactions of species in the GOM ecosystem.
Figure 4.3. Ecosim plots simulate the biomass changes for adult lobster and Atlantic cod
in the GOM from 1985 to 1997, under different cod fishing mortality. The green lines
represent the predicted lobster biomass and the purple lines show the predicted cod
biomass.
;-7
"10
- -
-
- - ---
.
. Biomasslorginal
--
-
--
F=0.05 i
biomass
d
r\.---
20 7
rl
lo
..
--
1.
-ao
-
-
- ---
./ -
-
--
-
A
-
-
-
--
--
,,
-
r ssb
1985
-
-- --------
.-
>.-.
-
-
1%
Yea:
i
--
-
- .-
F=0.1
Biomass/orginal biomass
20
-
-
7
10
,
,-- _-
-a o
1385
-
*-
---
-
--
-
?
-
-
1995
1390
Yea.
A - -
,--
-
-
.. . .
.- ..-
-
--
--
F=0.2
Biomass/orginal biomass
20
I
--
---
-
..
.-
-----.
..- - . - --
/-
.-
-
1990
30
1905
*
. Biomass/orginal biomass
20
F=0.4
'
1 0 --.
'.*
----
------
-\
-
--
-
----
--
-
__----
-
ao
-
1985
1995
1990
(C.Y.
It
Biomass/orginal biomass
-50
i
20
10
.
.
-
..
...- - .
.
..
Biomass/orginal biomass
...
F=1.6
1'
I-
-----
-
. '.. .
-
.
-
L-
Y-<
I
U0 -
-3
1985
--_
-
--
-
-
-_
-
-.
-
-- - ... -
.-
1'35
1390
I'm1
- -
- -
The decrease in the biomass of both cod and lobster in the first few years was
surprising. I expected the lobster stock would increase quickly after reduced cod stock
biomass. This might result from complicated trophic interactions among different species
in the ecosystem. Lobster only consisted of a small proportion of the cod diet. The impact
of cod on the lobster was likely to result fiom direct predation as well as indirect factors
such as via changes in the biomass of lobster competitors (for foods) and preys.
This study suggests a negative relationship between the dynamics of Atlantic cod
and American lobster. Thus, I could not reject the hypothesis that the recent increases in
lobster stock biomass resulted from a decrease in cod stock biomass. This also suggests
that a future recovery of the cod is likely to lead to a decrease in the American lobster
stock, although such an impact depends on the magnitude of cod recovery. The currently
used single species based stock assessment model could not identify such an interaction
between the cod and lobster. Lack of consideration of trophic interactions in current
assessment and management of cod fishery may result in an unexpected result, a reduced
lobster stock. Such a consequence should be considered when we develop a management
strategy for the recovery of the Atlantic cod stock in GOM. This study suggests the
importance of using an ecosystem model in assessing fisheries stock dynamics and
developing an ecosystem-based fisheries management.
It should be noted that for some species their observed population dynamics were
not well described by the model for some years (Fig. 4.3). This might result fiom the fact
that Ecosim was only a food web based model describing the trophic relationships of
organisms in the ecosystem, but lacked a mechanism to incorporate temporal variations
in physical environments, such as water temperature and salinity. The impacts of these
external variables that determined the physical ecosystem on the ecosystem dynamics
were incorporated into modeling via a single parameter called "other natural mortality"
(death due to reasons other than predations). This parameter was assumed to be constant
over the modeling time period. Thus, implicitly the physical environment of the
ecosystem was assumed to be time invariant. Thus, changes in these variables over time
might introduce uncertainties in modeling Potential large uncertainty associated with the
input data might also contribute to the errors in modeling. Nevertheless, such an approach
provides us with an analytical tool to explore the interactions of different species in the
ecosystem, which cannot be accomplished with a single species-based population model
that is currently used in the GOM.
The ecosystem model developed in this study, although preliminary in nature and
data input, provides a new approach to evaluating the trophic interactions of lobsters and
other organisms in the GOM, helps us better understand the ecosystem dynamics in the
GOM, and yields the information critical to the development of an ecosystem-based
management for the lobster fishery in the GOM. More studies are needed, however, to
reduce possible uncertainty in input data and to evaluate the performance of the model.
The interactions of lobsters with species other than Atlantic cod should also be explored.
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APPENDIX: Results of sensitivity analysis
Group
C
t3
w
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
1
1
2
2
3
3
3
3
4
4
4
4
4
4
5
5
5
5
5
5
5
6
6
Input
parameter
Biom
Prodhiom
Biom
Prodhiom
Biom
Biom
Prodhiom
Conshiom
Biom
1
1
2
2
1
3
3
1
1
Estimated
parameter
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
EE
EE
Conshiom
Conshiom
Biom
Biom
Prodlbiom
Conshiom
Conshiom
Biom
Biom
Biom
Prodhiom
Conshiom
Conshiom
Conshiom
Biom
Biom
3
4
4
1
3
2
4
5
5
2
4
5
2
6
EE
EE
EE
Conshiom
EE
Conshiom
EE
EE
EE
Conshiom
EE
EE
Conshiom
EE
Group
-50%
1
1
1
1
-0.366
1
1
-0.366
-0.099
-0.393
1
1
-0.099
-0.393
-0.059
-0.035
0.904
1
-0.059
-0.035
-0.048
-0.159
1
-40%
0.667
0.667
0.667
0.667
-0.292
0.667
0.667
-0.292
-0.079
-0.315
0.667
0.667
-0.079
-0.315
-0.047
-0.028
0.603
0.667
-0.047
-0.028
-0.038
-0.127
0.667
-30%
0.429
0.429
0.429
0.429
-0.219
0.429
0.429
-0.219
-0.059
-0.236
0.429
0.429
-0.059
-0.236
-0.035
-0.021
0.388
0.429
-0.035
-0.021
-0.029
-0.095
0.429
% change in input parameter
-20% -10% 0%
10% 20%
0.25
0.25
0.25
0.25
-0.146
0.25
0.25
-0.146
-0.04
-0.157
0.25
0.25
-0.04
-0.157
-0.023
-0.014
0.226
0.25
-0.023
-0.014
-0.019
-0.064
0.25
0.1 11
0.1 11
0.1 11
0.1 11
-0.073
0.1 11
0.1 11
-0.073
-0.02
-0.079
0.1 11
0.1 11
-0.02
-0.079
-0.012
-0.007
0.1
0.1 11
-0.012
-0.007
-0.01
-0.032
0.1 11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-0.091
-0.091
-0.091
-0.091
0.073
-0.091
-0.091
0.073
0.02
0.079
-0.09 1
-0.091
0.02
0.079
0.012
0.007
-0.082
-0.091
0.012
0.007
0.01
0.032
-0.091
-0.167
-0.167
-0.167
-0.167
0.146
-0.167
-0.167
0.146
0.04
0.157
-0.167
-0.167
0.04
0.157
0.023
0.014
-0.15 1
-0.167
0.023
0.014
0.019
0.064
-0.167
30%
-0.23 1
-0.23 1
-0.23 1
-0.23 1
0.219
-0.23 1
-0.231
0.219
0.059
0.236
-0.23 1
-0.23 1
0.059
0.236
0.035
0.021
-0.209
-0.23 1
0.035
0.021
0.029
0.095
-0.23 1
40%
-0.286
-0.286
-0.286
-0.286
0.292
-0.286
-0.286
0.292
0.079
0.315
-0.286
-0.286
0.079
0.315
0.047
0.028
-0.258
-0.286
0.047
0.028
0.038
0.127
-0.286
50%
-0.333
-0.333
-0.333
-0.333
0.366
-0.333
-0.333
0.366
0.099
0.393
-0.333
-0.333
0.099
0.393
0.059
0.035
-0.301
-0.333
0.059
0.035
0.048
0.159
-0.333
Prodhiom
Conshiom
Biom
Biom
Biom
Prodhiom
Conshiom
Conshiom
Conshiom
Biom
Biom
Biom
Biom
Prodhiom
Conshiom
Conshiom
Conshiom
Conslbiom
Biom
Biom
Biom
Prodhiom
Conshiom
Conshiom
Biom
Biom
Prod/biom
Conshiom
6
2
6
7
10
7
6
7
10
7
8
10
23
8
7
8
10
23
7
9
23
9
7
23
5
10
10
5
EE
Conshiom
EE
EE
EE
EE
EE
EE
EE
EE
EE
EE
Conshiom
EE
EE
EE
EE
Conshiom
EE
EE
Conshiom
EE
EE
Conshiom
EE
EE
EE
EE
1
-0.159
-0.1 12
0.482
-0.111
1
-0.1 12
-0.259
-0.1 11
-0.14
-0.05
-0.147
1
-0.14
0.667
-0.127
-0.09
0.321
-0.089
0.667
-0.09
-0.207
-0.089
-0.1 12
-0.04
-0.1 18
0.667
-0.1 12
-0.05
-0.147
-0.076
1
-0.201
1
-0.076
-0.201
-0.243
0.877
1
-0.243
-0.04
-0.1 18
-0.061
0.667
-0.161
0.667
-0.061
-0.161
-0.194
0.585
0.667
-0.194
0.429
-0.095
-0.067
0.206
-0.067
0.429
-0.067
-0.156
-0.067
-0.084
-0.861
-0.03
-0.088
0.429
-0.084
-0.903
-0.03
-0.088
-0.046
0.429
-0.121
0.429
-0.046
-0.121
-0.146
0.376
0.429
-0.146
0.25
-0.064
-0.045
0.12
-0.044
0.25
-0.045
-0.104
-0.044
-0.056
-0.502
-0.02
-0.059
0.25
-0.056
-0.602
-0.02
-0.059
-0.03
0.25
-0.08
0.25
-0.03
-0.08
-0.097
0.219
0.25
-0.097
0.1 11
-0.032
-0.022
0.054
-0.022
0.1 11
-0.022
-0.052
-0.022
-0.028
-0.223
-0.01
-0.029
0.1 11
-0.028
-0.301
-0.01
-0.029
-0.015
0.1 11
-0.04
0.1 11
-0.015
-0.04
-0.049
0.097
0.1 11
-0.049
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Conshiom
Biom
Biom
Biom
Biom
Biom
Prodhiom
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
Biom
Biom
Prodhiom
Conshiom
Biom
Biom
Prodhiom
Conshiom
Biom
Biom
Biom
Biom
Biom
Biom
Prod/biom
Cons/biom
Biom
Biom
Biom
Prodhiom
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
Biom
Prod/biom
Biom
Biom
Biom
Prodhiom
Conshiom
Conshiom
Biom
Prodhiom
Biom
Biom
Biom
Biom
Biom
Biom
Biom
Biom
Biom
Biom
Prodhiom
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
Conshiom
Biom
Prod~biom
23
22
4
7
8
11
12
13
15
18
22
23
23
23
Conshiom
EE
EE
EE
EE
EE
EE
EE
EE
EE
EE
Conshiom
Conshiom
Conshiom
BIOGRAPHY OF THE AUTHOR
Yuying Zhang was born in Wenzhou, China on May 7th,1981. She graduated
from East China University of Science and Technology, China in 2003 with a Bachelor's
degree in Computer Science and Technology. She attended the University of Maine and
entered the School of Marine Science in 2003.
After receiving her degree, Yuying will continue her Ph. D. study in the
University of Maine; lucubrate in the Fisheries Population Dynamics field. Yuying is a
candidate for the Master of Science degree in Oceanography from The University of
Maine in August, 2005.