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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. , , , . , # . , I # --- a , , , w , , I , , , , , I , , , , , I I I I I I I I I Feee & 8 0 I 3 , , 7 , I " , " " , o w u - m * oeoC3Co 8 8 , ' ? a ! ' J ? r ? r - , 7 -m I , # . # . , , B m b s 22 9 9 , o b , 0 0 0 8 0 - E2 g g 7 0 0 u s , u , , O m o h 7 0 0 0 0 0 0 01- o o o C I o o 0 0 a I 0 . 0 ~ I S . . I I I I , , . . . . _ h r . o C CD D I - - ~ o o o o 99 Ej O ~ F-2 %~ o o o o o l o 1 o o C E " N l h ' d rcloE5cu(?o Q ) m ( D d 0 ~ o o O C I N O o o o o o o o o o o ~88~~-f~~LNdNd $8 I--- 0 I 8 - ? 3 E 8 - 3 0 ie 8 9 - e O 0 ~ -, ,g , , O * W C a 0 ? . 7 = T r n r n r - I 0 0 N W m 0 0 0 7 C 3 ' d I 0 V, C4 . I 0 0 ' . -o - ?s - os ~o r co l so E o - ~ o o s os L~ i 8~ ~ ~ ~ ~ I o 83?38%88F?88%~k5; , , , , , , ~ G~ G0 G - - o o - = T . . ' - o ~ 0 0 0 0 0 0 0 0 1 0 0 0 . . . . ,, 0 0 m z 0 a = ? 0 0 g o 0 0 3 0 03 m rd , N P ! 0 0 O C m m w 0 0 , O ~ r ,F, A 0 0 0 , , , a,?,@ - U 7 0 0 , , h 0 0 0 03 d , , -0 , I . Figure 3.10. The niche overlap index between every two groups of the lobster ecosystem 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. m m m 0 0 , + d N - m N p a p J m m P J * N m + m ~ O a ~ , N d d 0 0 p d 0 m 0 0 0 d ~ + 0 W ~ 9 * " 0 9 0 9 9 9 0 0 0 q 9 9 9 " 0 9 0 " ~ p ~ o ~ ~ ~ ~ o g~ 8 0 *4 y 4~ 0 " ~0 ~ 2o8 8 0~5 8 ~0 d0 o9o-9 0 -9 * ~ ~ 0 ' m m c h - 4 0 0* p c h s * ~ & m * N 9 0 0 0 000 gsg 6 09 060 ~0 ~ *~ or dt m - od O * mm s ~$ ~% d ~ P to- N mm c h * -9 ~ 0 0 po?? l m* ~ q 9 9 9 q q q 9 y q 9 0 8 S o o q y 0 0 0 ~ ~ ~ ~ 9 m N g m N c h m ' n * m m m m m ~ * d m - m * m m m ~ m * S m d 0 d 0 0 0 m 0 0 0 * ~ - 0 9 0 ~ 9 9 0 ~ " 9 0 9 0 0 9 0 0 q 9 q ~ 0 o Q o ~ ~ ~ 0 ~ y y o ~ 0 0 Q o o g "0 *m9o'9X ~0' ~ 0mm0+ C~9ONmW 9C~ b9 d-9Cw9h M0ww9b~° m w m m m q . 0 - - ~ 6 0 0 9 0 9 ? " ~ 0 ? 0 ? ? 6 6 6 ? 4 o y ~ y o ~ ? y & O y ~ 6 d d ? o 0 9 4 q w m b W * V + * m C Q N N W d d 3 ~ ~ w 0 b M ~ O m 0 ~ 0 - ° 9 0 0 0 q 9 0 C-3 ~ 0 30??6669969?q9909099 PI n 0 ~ 0 ~ ~ 0 $ d b . - ~ m N m m m m m * N d W m V w * d s ~ m 0 0 m * * 0 * * 0 ~ m 0 0 * * d 0 ~ 0 Q 0 0 - " ? 9 0 0 9 0 0 0 9 9 0 0 9 0 9 o o o y ~ y o o ~ o o o y y o?o ~ 3 0 N b m m t - m w m m c h m P W N 3 0 0 * 4 m 4 ~ 0 W d 0 ~ d Nszso *m o m 0 0 " ? * 9 0 9 4 9 9 9 0 9 0 0 9 0 " ~ d j60o???p6????? o o o q o p - * w ~ ~ ~ 2 ~d m m ~ T f w m W m ~ ~ N T dJ w- " ?29q"? = 0 y 0 ~ 0 9 ~ o 9 9 o 9 9o o o 9 ~ o ~ Q 9 6 0 ~ ~ 0 ~ ~ 0 o9 o ~p 0 Q rn b ch ~ g ~ ~ d 6 o d~ ~o 3~ ~o * d * o m o a o ~ o mmmm ~ m ~ N 9 Y ~ ~ ~ g o 0 0 d d ~ o o o o 0 0 0 0 9 5 p p ? o I o p 6 0 0 6 * Q O y Q Q ~ " rn4rnWWdwch ~ d * N ~ m 3 d ~ ~ w2 - o 4 ~ g- z ? z g 8 + N ? " 0 9 ~ ~ ~ q o q q o 9 0 0 9 0 9 0 9 ~ 3yoQQ000 o o o O CI 3 opzgsg g = ? 0 ? 0 ? 0 ~ 4 6009 060 * * ~ ~ \ l m - ~ \ l m ~ w ~ b b ~ o N ~ 0 0 0 o ~ " ~ ? N 9 0 ~ 0 0 0 9 9 9 0 0 0 0 0 9 0 o 9 n Q ~4 a 2 %%G% Q * @zag =ad0 h a Y w h m %" a% aC ~ ? ~ 0 0 0 ~ .-wn L b a o 8 g E a 'i Z N a a .m 2.22 aza o a e e e " . ~ - . a u . g as $ n r zg.2 h 2 @ n m d h s u o ~~Y~~ 3 1 % o u a '5: E %% su =%Q w sG .$ %.2?2 : = a z * a u 354 Z ? E E E ~ O ~ ~ I sW a m~ o u~ ~ Z 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 m + P 0 0 0 0 a m b a * b m a m m m m F + w o 0 0 ~ ~ a m m m o m b q m o m m o m . - o ba 8 o o" 9q 9o 9q 9o 9q *o9o0 9o9o9q9o9o9o0g9o9o 9 q m 0 0 m m 0 0 Q W b + W * b N + O m W + O m + + ~ 0 m m 0 m m 0 + ~ 0 0 0~ 000 0 0 0 ~ q 0 ~ 0 q 0 q ~ 0 0 qq0q q g qoo 9"999-?fl9999999 999 999 b m a m m m m m + m b P m m m m b b " m d b m o ~ m o + O ~ m M m m O m + O N o + b o o Y'f?q99099-9'f?9999o999" Z ? q o qq qqqoqoqqq qqqo 9 a a d + b m m N m m m m + + + ~ m a d o W m O ~ 0 0 ~ + + 0 0 0 0 m m 0 0 0 0 0 N 0 0 " 9 C!q99999999"999"99 3q?0 qqoqoqoqqoqqqqq 5 0 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 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 (A s+ Phytoplankton Macroalgae 28.3 5 Microzooplankton 6W macro zoo plankton^, Microbenthos Shelled mollusk 3 0- Crab 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 - 1 r - I . -- Fleettdfort dvnamics ~iomass/originalbiomass 1 .! I 0.0 j gy&hx& r-... ; , - 7 1 0.00 - 11985- -- - - - - - - - - - ~- - ' 1 - -- - ~~ 1990 Years - - ~ r Q/B r Weight r Feed.trn. 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 Plot 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 -- Plot 1 - Tracer -- ~ ] --- - Fleetleffat dynamics ...I + 1 r: 1. 1 ~i.!l3nttcI ~ r r r ~ r ICH ?,~ I :' ,,., ..I-r .. . - I I I I .1 1; 0'78 I -"7 c r -- - . - - - 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 Forcing functions -- r Qverlay r Save 1 Apply FF -- Run Ecogm Equilibrium 1 - -- ) Act 1 Tracer 1 r Ecopath r [j L~near r Fleet/effort dynamnx I,., ... , - L j 8 - / d < 2.0 : 0.0 j 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. REFERENCES Acheson, J.M. and Brewer, J.F., 2003. Changes in the Territorial System of the Maine Lobster Industry. In: N. Dolsak and E. Ostrom (Editors), The Commons in the New Millennium. MIT Press, Cambridge, MA, pp. 37-59. Arias-Gonzalez, J.E., Nunez-Lara, E., Gonzalez-Salas, C. and Galzin, R., 2004. Trophic Models for Investigation of Fishing Effect on Coral Reef Ecosystems. Ecological Modelling, 172: 197-212. Arreguin-Sanchez, F. and Manickchand-Heileman, S., 1998. The Trophic Role of Lutjanid Fish and Lmpacts of Their Fisheries in Two Ecosystems in the Gulf of Mexico. Journal of Fish Biology, 53(Supplement A): 143-153. Arreguin-Sanchez, F., Zetina-Rejon, M., Manickchand-Heileman, S., Ramirez-Rodriguez, M. and c, L.V., 2004. Simulated Response to Harvesting Strategies in an Exploited Ecosystem in the Southwestern Gulf of Mexico. Ecological Modelling, 172: 421?32. ASMFC, 2000. American Lobster Stock Assessment Report for Peer Review, Atlantic State Marine Fisheries commission, Providence, RI. Balch, W.M. et al., 2004. A Multi-year Record of Hydrographic and Bio-optical Properties in the Gulf of Maine: I. Spatial and Temporal Variability. Progress in Oceanography, 63: 57-98. Bowman, R.E., Stillwell, C.E., Michaels, W.L. and Grosslein, M.D., 2000. Food of Northwest Atlantic Fishes and Two Common Species of Squid. NMFS-NE-155. Brando, V.E., Ceccarelli, R., Libralato, S. and Ravagnan, G., 2004. Assessment of Environmental Management Effects in a Shallow Water Basin Using Massbalance Models. Ecological Modelling, 172: 213-232. Brodziak, J., 2001. Silver hake, Northeast Fisheries Science Center. Bundy, A., Lilly, G.R. and Shelton, P.A., 2000. A Mass Balance Model of the New Foundland - Labrador Shelf. Canadian Technical Report of Fisheries and Aquatic Sciences, 23 10. Cadrin, S.Z. et al., 1999. Application of Catch-Survey Models to the Northern Shrimp Fishery in the Gulf of Maine. North American Journal of Fisheries Management, 19: 55 1-568. Carrer, S., Halling-Sorensen, B. and Bendoricchio, G., 2000. Modelling the Fate of Dioxins in a Trophic Network by Coupling an Ecotoxicological and an Ecopath Model. Ecological Modelling, 126: 201-223. Carter, J.A. and Steele, D.H., 1982a. Attraction to and Selection of Prey by Immature Lobster Homarus americans. Woods Hole Oceanographic Institution Technical Report. Carter, J.A. and Steele, D.H., 1982b. Stomach Contents of Immature Lobster (Homarus americanus) from Placentia Bay, New Foundland. Canadian Journal of Zoology, 60: 337-347. Chen, Y. and Hunter, M., 2003. Assessing the Green Sea Urchin (Strongylocentrotus droebachiensis) Stock in Maine, USA. Fisheries Research, 60: 527-537. Chen, Y., Kanaiwa, M. and Wilson, C., 2005. Developing and Evaluating a Sizestructured Stock Assessment Model for the American Lobster, Homarus americanus, Fishery. New Zealand Journal of Marine and Freshwater Research, 39: 645-660. Chenoweth, S. and McGown, J., 1997. Sea Cucumber in Maine Fishery and Biology, Maine Department of Marine Resources. Christensen, V. and Pauly, D., 1992. ECOPATH I1 - a Software for Balancing Steadystate Ecosystem Models and Calculating Network Characteristics. Ecological Modelling, 61: 169-185. Christensen, V., Walters, C.J. and Pauly, D., 2000. Ecopath with Ecosim: A User's Guide, Vancouver, Canada. Collette, B.B. and Klein-MacPhee, G., 2002. Bigelow and Schroeder's Fishes of the Gulf of Maine. Smithsonian Institution. Collie, S.J. and Sissenwine, M.P., 1983. Estimating Population Size from Relative Abundance Data Measured with Error. Canadian Journal of Fisheries and Aquatic Sciences, 40(11): 1871-1879. Cook, B., 2005. Lobster Boat Diplomacy: the Canada-US Grey Zone. Marine Policy, 29(5): 385-390. Cooper, R.A. and Uzmann, J.R., 1980. Ecology of Juvenile and Adult Homarus. The Biology and Management of Lobster, 11. Academic Press, New York, 97-142 pp. Darbyson, E., Swain, D.P., Chabot, D. and Castonguay, M., 2003. Diet Variation in Feeding Rate and Prey Composition of Herring and Mackerel in the Southern Gulf of St Lawrence. Journal of Fish Biology, 63: 1235-1257. Duarte, L.O. and Garcia, C.B., 2004. Trophic Role of Small Pelagic Fishes in a Tropical Upwelling Ecosystem. Ecological Modelling, 172: 323-338. Elner, R.W. and Campbell, A,, 1981. Force, Function and Mechanical Advantage in the Chelae of the American lobster, Homarus americanus, (Decapoda: Crustacea). Journal of Zoology, 193: 269-286. Ennis, G.P., 1973. Food, Feeding, and Condition of Lobsters, Homarus americanus, Throughout the Seasonal Cycle in Bonavista Bay, Newfoundland. Journal of the Fisheries Research Board of Canada, 230: 1905-1909. Fogarty, M.J. and Idoine, J.S., 1988. Application of a Yield and Egg Production Model Based on Size to an Offshore American Lobster Population. Transactions of the American Fisheries Society, 117: 350-362. Gasalla, M.A. and Rossi-Wongtschowski, C.L.D.B., 2004. Contribution of Ecosystem Analysis to Investigating the Effects of Changes in Fishing Strategies in the South Brazil Bight Coastal Ecosystem. Ecological Modelling, 172: 283-306. Grabowski, J. et al., 2003. Is Herring Bait Use Supplementing Lobster Populations in the Gulf of Maine? Integrating Empirical Results into Lobster Population Models. Halform, E., Schito, N. and Ulanowicz, R.E., 1996. Energy Flow through the Lake Ontario Food Web Conceptual Model and an Attempt at Mass Balance. Ecological Modelling, 86: 1-36. Hanson, J.M. and Lanteigne, M., 2000. Evaluation of Atlantic Cod Predation on American Lobster in the Southern Gulf of St. Lawrence, with Comments on Other Potential Fish Predators. Transaction of the American Fisheries Society, 129: 1329. Haynes, E.B. and Wigley, R.L., 1969. Biology of the Northern Shrimp, Pandalus borealis, in the Gulf of Maine. Transaction of the American Fisheries Society, 98: 60-76. Heymans, J.J., 2001. The Gulf of Maine, 1977-1986. Fisheries Center Research Reports, 9(4): 128-150. Heyrnans, J.J., Shannon, L.J. and Jarre, A,, 2004. Changes in the Northern Benguela Ecosystem over Three Decades: 1970s, 1980s, and 1990s. Ecological Modelling, 172: 175-195. Hilborn, R. and Walters, C.J., 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, London. Jarre-Teichmann, A., Shannon, L.J. and Moloney, C.L., 1998. Comparing Trophic Flows in the Southern Benguela to Those in Other Upwelling Ecosystem. South African Journal of Marine Science, 19: 39 1-414. Juinio, M.A.R. and Cobb, J.S., 1992. Natural Diet and Feeding Habits of the Postlarval Lobster, Homarus americanus. Marine Ecology Progress Series, 85: 83-91. Kanaiwa, M., Chen, Y. and Hunter, M., 2005. Assessing a Stock Assessment Framework for the Green Sea Urchin Strongylocentrotus drobachiensis Fishery in Maine, USA. Fisheries Research, 74: 96-1 15. Lavalli, K.L., 1988. Food Capture in Post-larval Lobsters. American Zoology, 28(4): 154A. Mackinson, S., 2001. Representing Trophic Interactions in the North Sea in the 1880s, Using the Ecopath Mass-balance Approach. Fisheries Center Research Reports, 9(4): 35-98. Mayo, R. and O'Brien, L., 2000. Atlantic Cod. Mendy, A.N. and Buchary, et. al., 2001. Constructing an Icelandic Marine Ecosystem Model for 1997 Using a Mass-balance Modelling Approach. Fisheries Center Research Reports, 2001 : 182-197. Moody, K. and Steneck, R.S., 1993. Mechanisms of Predation among Large Decapod Crustaceans of the Gulf of Maine Coast: Functional vs. Phylogenetic Patterns. Journal of Experimental Marine Biology and Ecology, 168: 111- 124. Morales-Zarate, M.V., Arreguin-Sanchez, F., Lopez-Martinez, J. and Lluch-Cota, S.E., 2004. Ecosystem Trophic Structure and Energy Flux in the Northern Gulf of California, Mexico. Ecological Modelling, 174: 33 1-345. NEFSC, 1990. 11th Northeast Regional Stock Assessment Workshop, Northeast Fisheries Science Center Reference Document. NEFSC, 1999.30th Northeast Regional Stock Assessment Workshop, Northeast Fisheries Science Center Reference Document. NEFSC, 2000.32nd Northeast Regional Stock Assessment Workshop, Northeast Fisheries Science Center Reference Document. NEFSC, 2001a. 33rd Northeast Regional Stock Assessment Workshop, Northeast Fisheries Science Center Reference Document. NEFSC, 2001b. 34th Northeast Regional Stock Assessment Workshop, Northeast Fisheries Science Center Reference Document. NEFSC, 2002.36th Northeast Regional Stock Assessment Workshop, Northeast Fisheries Science Center Reference Document. NRC, 1997. Improving Fish Stock Assessments. National Academy Press, Washington, D. C. NRC, 1999. Sustaining Marine Fisheries. National Academy Press, Washington, D. C. O'Brien, L., 1999. Factors Lnfluencing the Rate of Sexual Maturity and the Effect on Spawning Stock for the Georges Bank and Gulf of Maine Atlantic cod Gadus morhua stocks. Journal of Northwestern Atlantic Fisheries Science, 25: 179-203. Odum, E.P., 1969. The Strategy of Ecosystem Development. Science, 104: 262-270. Ojeda, F.P. and Dearborn, J.H., 1991. Feeding Ecology of Benthic Mobile Predators: Experimental Analyses of Their Influence in Rocky Subtidal Communities of the Gulf of Maine. Journal of Experimental Marine Biology and Ecology, 149: 13-44. Okey, T.A., 2001. A "Straw-man" Ecopath Model of the Middle Atlantic Bight Continental Shelf, United States. Fisheries Center Research Reports, 9(4): 151166. Okey, T.A. and Pauly, D., 1998. Trophic Mass-balance Model of Alaska's Prince William Sound Ecosystem, for the Post-spill Period 1994-1996. Fisheries Center Research Reports, 6: 144. Okey, T.A. and Pugliese, R., 2001. A Preliminary Ecopath Model of the Atlantic Continental Shelf Adjacent to the Southeastern United States. Fisheries Center Research Reports, 9(4): 167-18 1. O'Reilly, J.E., Evans-Zetlin, C. and Busch, D.A., 1987. 21. Primary Production. In: R.H. Backus and D.W. Bourne (Editors), Georges Bank. MIT Press, Cambridge, MA, pp. 220-233. O'Reilly, J.E. and Zetlin, C., 1998. Seasonal, Horizontal, and Vertical Distribution of Phytoplankton Chlorophyll a in the Northeast U.S. Continental Shelf Ecosystem. NOAA Technical Report NMFS 139, NOAA, Seattle, Washington. Overholtz, W., 2000. Atlantic Herring. Northeast Fisheries Science Center. Palma, A.T., Steneck, R.S. and Wilson, C.J., 1999. Settlement-driven, Multiscale Demographic Patterns of Large Benthic Decapods in the Gulf of Maine. Journal of Experimental Marine Biology and Ecology, 241 : 107-136. Park, R.A., O'Neill, R.V. and Bloomfield, J.A., 1974. A Generalized Model for Simulating Lake Ecosystems. Simulation, 23: 33-50. Pinnegar, J.K. and Polunin, N.V.C., 2004. Predicting Indirect Effects of Fishing in Mediterranean Rocky Littoral Communities Using a Dynamic Simulation Model. Ecological Modelling, 172: 249-267. Polis, G.A., 1994. Food Webs, Trophic Cascades, and Community Structure. Australian Journal of Ecology, 19: 121-136. Quinn, T.J. and Deriso, R.B., 1999. Quantitative Fish Dynamics. Oxford University Press, New York. Ricker, W.E., 1975. Computation and Interpretation of Biological Statistics of Fish Populations. Bulletin of the Fisheries Research Board of Canada, 191. Canadian Government Publishing Center, Ottawa, Canada, 382 pp. Sainte-Marie, B. and Chabot, D., 2002. Ontogenetic Shifts in Natural Diet During Benthic Stages of American Lobster (Homarus americanus), off the Magdalen Island. Fish Bulletin, 1OO(1): 106-1 16. Scavia, D., Blooomfield, J.A. and Fisher, J.S., 1974. Documentation of CLEANX: A Generalized Model for Simulating the Open-water Ecosystem of Lakes. Simulation, 23. Schick, D.F., 1991. Pandalid Shrimp Distribution Relative to Bottom type and Availability to Research and Commercial Trawls in the Gulf of Maine. ICES C.M. K:8, Copenhagen. Sherman, K. et al., 1987. 25. Zooplankton Production and the Fisheries of the Northeastern Shelf. In: R.H. Backus and D.W. Bourne (Editors), Georges Bank. MIT Press, Cambridge, MA. Sherr, B. and Sherr, E., 2004. Globed: Microzooplankton in the Northern California Current System. Shumway, S.E., Perkins, H.C., Schick, D.F. and Stickney, A.P., 1985. Synopsis of Biological Data on the Pink Shrimp, Pandalus borealis Kroyer 1838. NOAA Technical Report NMFS 30. Sissenwine, M.P., 1987. 3 1. Fish and Squid Production. In: R.H. Backus and D.W. Bourne (Editors), Georges Bank. MIT Press, Cambridge, MA, pp. 347-350. Sparre, P., 1991. Introduction to Multispecies Virtual Population Analysis. ICES Marine Science Symposium, 193: 12-21. Spees, J.L., Chang, S.A., Snyder, M.J. and Chang, E.S., 2002. Thermal Acclimation and Stress in the American Lobster, Homarus americanus: Equivalent Temperature Shifts Elicit Unique Gene Expression Patterns for Molecular Chaperones and Polyubiquitin. Cell Stress & Chaperones, 7(1): 97-106. Stauble, D.K., 2004. Development of a National-Scale Inventory of Shoreline Change Data for Identification of Erosion and Accretion, U.S. Army Corps of Engineers, Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS. Steimle, F.W., 1987. 29. Production by the Benthic Fauna. In: R.H. Backus and D.W. Bourne (Editors), Georges Bank. MIT Press, Cambridge, MA, pp. 3 10-314. Steneck, R.S. and Wilson, C.J., 1998. Why Are There So Many Lobsters in Penobscot Bay? Gulf of Maine News, Summer 1998: 1-5. Steneck, R.S. and Wilson, C.J., 2001. Large-scale and Long-term Spatial and Temporal Patterns in Demography and Landings of the American Lobster, Homarus arnericanus, in Maine. Marine Freshwater Research, 52: 1303-1320. Taylor, P.H., 2004. Green Gold: Scientific Findings for Management of Maine's Sea Urchin Fishery., Maine Department of Marine Resources, Boothbay Harbor, ME. Townsend, D.W., 1997. Cycling of Carbon and Nitrogen in the Gulf of Maine. In: G.T. Wallace and E.F. Bbraasch (Editors), Proceedings of Gulf of Maine Ecosystem Dynamic Scientific Symposium and Workshop. RARGOM Report, Hanover, NH, pp. 117-134. Ulanowicz, R.E., 1986. Growth and Development: Ecosystem Phenomenology. Springer Verlag, New York, 203 pp. Vega-Cendejas, M.E. and Arreguin-Sanchez, F., 2001. Energy Fluxes in a Mangrove Ecosystem from a Coastal Lagoon in Yucatan Peninsula, Mexico. Ecological Modelling, 137(2-3): 1 19-133. Wahle, R.A. and Steneck, R.S., 1991. Recruitment habitats and nursery grounds of the American lobster, Homarus americanus: A demographic bottleneck? Marine Ecology Progress Series, 69: 23 1-243. Winberg, G.G., 1956. Rate of Metabolism and Food Requirements of Fishes. Translation Series of Fisheries Research Board of Canada, 253. 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.