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Effects of Temporal Closures and Gear Modifications on the Population of Dusky Sharks in the Northwestern Atlantic Ocean

Permanent Link: http://ufdc.ufl.edu/UFE0022820/00001

Material Information

Title: Effects of Temporal Closures and Gear Modifications on the Population of Dusky Sharks in the Northwestern Atlantic Ocean
Physical Description: 1 online resource (131 p.)
Language: english
Creator: Morgan, Alexia
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: atlantic, bayesian, bycatch, fisheries, gear, longlline, management, modeling, overfishing, shark, spatial
Fisheries and Aquatic Sciences -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: My objectives in this study were two-fold. First, I built an age-structured model to assess the effects of fishing on population trends for dusky shark. This model included sensitivity analyses to assess the effects of time/area closures, reduced mortality as a result of reduced soak times for the bottom longline fishery, full selectivity of age-zero animals, removal of Catch Per Unit Effort (CPUE) series and changes to other model parameters on overall population sizes. My second objective was to build a spatial model that evaluated the effects time/area closures and changes to other model parameters had on the population size of three life-stages (juvenile, subadult and adult) of the dusky shark. Results showed that the impacts of fishing already imposed on the dusky shark would be difficult to overcome even with the implementation of time/area closures, gear modifications and/or catch and discards being reduced for another 20 years. Results of the base case, all scenarios and sensitivity analyses except for one (increasing virgin biomass) of the age-structured model indicated that the population of dusky sharks in the northwestern Atlantic Ocean is at less than 60% of virgin biomass. Recent work has shown that the Maximum Sustainable Yield (MSY) for dusky sharks may be well above 50% of the carrying capacity. Fisheries managers must determine whether the high depletion rates reported in these models suggest this species is overfished, and would therefore require long-term targets for population recovery to sustainable levels. The base case version of the spatial model illustrated that the majority of total density (numbers) for all three stages (juvenile, subadult and adult) occurred in the closed region of the model, but the effects of fishing were still seen in the boundaries between open and closed areas. Sensitivity analyses showed that parameter values affected the densities and the redistribution of fishing effort for all three stages. However, all of the models (except for the open and closed versions) indicated that the highest densities were found in the closed portion of the model and that the redistribution of fishing effort was concentrated into a very small area on either side of the closures. Neonate densities in all models generally followed the same trends as total density, and recruitment densities varied very little throughout individual models. Results of these models suggest that dusky shark populations have been heavily reduced and that research into the effects of time/area closures on dusky shark populations should continue. Fisheries managers should investigate new management options that will reduce the fishing mortality of dusky sharks caught as bycatch. Managers should also increase observer effort in fisheries that catch dusky sharks as bycatch and improve on recording and reporting by fishers and dealers. Future research should be aimed at determining ways to reduce fishing mortality rates for the dusky shark and improving our knowledge of parameters used in spatial modeling.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Alexia Morgan.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Allen, Micheal S.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022820:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022820/00001

Material Information

Title: Effects of Temporal Closures and Gear Modifications on the Population of Dusky Sharks in the Northwestern Atlantic Ocean
Physical Description: 1 online resource (131 p.)
Language: english
Creator: Morgan, Alexia
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: atlantic, bayesian, bycatch, fisheries, gear, longlline, management, modeling, overfishing, shark, spatial
Fisheries and Aquatic Sciences -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: My objectives in this study were two-fold. First, I built an age-structured model to assess the effects of fishing on population trends for dusky shark. This model included sensitivity analyses to assess the effects of time/area closures, reduced mortality as a result of reduced soak times for the bottom longline fishery, full selectivity of age-zero animals, removal of Catch Per Unit Effort (CPUE) series and changes to other model parameters on overall population sizes. My second objective was to build a spatial model that evaluated the effects time/area closures and changes to other model parameters had on the population size of three life-stages (juvenile, subadult and adult) of the dusky shark. Results showed that the impacts of fishing already imposed on the dusky shark would be difficult to overcome even with the implementation of time/area closures, gear modifications and/or catch and discards being reduced for another 20 years. Results of the base case, all scenarios and sensitivity analyses except for one (increasing virgin biomass) of the age-structured model indicated that the population of dusky sharks in the northwestern Atlantic Ocean is at less than 60% of virgin biomass. Recent work has shown that the Maximum Sustainable Yield (MSY) for dusky sharks may be well above 50% of the carrying capacity. Fisheries managers must determine whether the high depletion rates reported in these models suggest this species is overfished, and would therefore require long-term targets for population recovery to sustainable levels. The base case version of the spatial model illustrated that the majority of total density (numbers) for all three stages (juvenile, subadult and adult) occurred in the closed region of the model, but the effects of fishing were still seen in the boundaries between open and closed areas. Sensitivity analyses showed that parameter values affected the densities and the redistribution of fishing effort for all three stages. However, all of the models (except for the open and closed versions) indicated that the highest densities were found in the closed portion of the model and that the redistribution of fishing effort was concentrated into a very small area on either side of the closures. Neonate densities in all models generally followed the same trends as total density, and recruitment densities varied very little throughout individual models. Results of these models suggest that dusky shark populations have been heavily reduced and that research into the effects of time/area closures on dusky shark populations should continue. Fisheries managers should investigate new management options that will reduce the fishing mortality of dusky sharks caught as bycatch. Managers should also increase observer effort in fisheries that catch dusky sharks as bycatch and improve on recording and reporting by fishers and dealers. Future research should be aimed at determining ways to reduce fishing mortality rates for the dusky shark and improving our knowledge of parameters used in spatial modeling.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Alexia Morgan.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Allen, Micheal S.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022820:00001


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1 EFFECTS OF TEMPORAL CLOSURES AND GEAR MODIFICATIONS ON THE POPULATION OF DUSKY SHARKS IN THE NORTHWESTERN ATLANTIC OCEAN By ALEXIA C. MORGAN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 Alexia C. Morgan

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3 To my mom, dad, Chris and Xander

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4 ACKNOWLEDGMENTS I would like to thank m y committee members, Mike S. Allen, Enric Corts, Colin Simpfendorfer, George H. Burgess, Debra Murie, and Alan B. Bolten. I gratefully acknowledge funding provided by the National Shark Research Consortium, Florida Program for Shark Research.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ............................................................................................................... 4 LIST OF TABLES ...........................................................................................................................8 LIST OF FIGURES .......................................................................................................................10 ABSTRACT ...................................................................................................................... .............12 CHAP TER 1 DUSKY SHARK BA CKGROUND ....................................................................................... 14 Background .................................................................................................................... .........14 Statement of Problem and Objectives ..................................................................................... 16 Fisheries Management .......................................................................................................... ..18 Life History of the Dusky Shark .............................................................................................20 Distribution .................................................................................................................. ...........21 Conclusions .............................................................................................................................22 2 AGE-STRUCTURED MODEL ............................................................................................. 24 Introduction .................................................................................................................. ...........24 Data for Model ........................................................................................................................25 Commercial Catches ........................................................................................................25 Recreational Catches .......................................................................................................27 Catch Rates .............................................................................................................................28 Virginia Institute of Marine Sc ience (VIMS) Monitoring Data ......................................28 Bottom Longline Observer Program (BLLOP) ............................................................... 28 Pelagic Longline Observer Program (PLLOP) ................................................................ 29 Large Pelagic Survey (LPS) ............................................................................................ 29 Catch Rate Analyses ........................................................................................................... ....30 Age-structured Model .............................................................................................................31 Components of the Model ............................................................................................... 31 Parameter Estimation ....................................................................................................... 34 Sensitivity An alysis ......................................................................................................... 36 Scenario 1: Commercial and recreational catches closure in the future................... 36 Scenario 2: Simulated closure by re moval of North Carolina catches ..................... 36 Scenario 3: Simulating reduced soak times .............................................................. 37 Scenario 4: Increased commercial catch series ........................................................ 37 Scenario 5: reduced recreational catch series ...........................................................37 Sensitivity 1: R* prior distribu tion increased ........................................................... 37 Sensitivity 2: Z increased .........................................................................................38 Sensitivities 3-6: Parameters increased by 10% .......................................................38 Sensitivity 7: BLLOP CPUE series removed ........................................................... 38

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6 Sensitivity 8: Combined CPUE series ......................................................................38 Sensitivity 9: Selectivity ........................................................................................... 39 Results .....................................................................................................................................39 Catches ....................................................................................................................... .....39 Catch Rates ......................................................................................................................40 Selectivity Curves ............................................................................................................ 42 Base Case Model .............................................................................................................43 Scenario 1: Commercial and Recreational Catches Closure in the Future ...................... 44 Scenario 2: Simulated Closure by Re moval of North Carolina Catches ......................... 44 Scenario 3: Simulating Reduced Soak Times ................................................................. 45 Scenario 4: Increased Commercial Catch Series ............................................................. 45 Scenario 5: Reduced Recreational Catch Series .............................................................. 45 Sensitivity 1: R* Prior Distribution Increased .................................................................46 Sensitivity 2: Z Increased ................................................................................................46 Sensitivity 3: R* Increased by 10% .................................................................................46 Sensitivity 4: Z Increased by 10% ...................................................................................47 Sensitivity 5: M Increased by 10% ..................................................................................47 Sensitivity 6: Selectivities Increased by 10% .................................................................. 47 Sensitivity 7: BLLOP CPUE Series Removed ................................................................ 48 Sensitivity 8: Combined CPUE Series ............................................................................ 48 Sensitivity 9: Selectivity .................................................................................................. 48 Discussion .................................................................................................................... ...........49 3 SPATIAL MODEL ................................................................................................................. 81 Introduction .................................................................................................................. ...........81 Spatial Model ..........................................................................................................................82 Components of the Model ............................................................................................... 82 Parameters .................................................................................................................... ...85 Sensitivity An alysis ......................................................................................................... 87 Sensitivity 1: Closed area /no fishing mortality ........................................................ 87 Sensitivity 2: Open area model ................................................................................ 87 Sensitivity 3: Fishing mortality rate set to Fmsy ...................................................... 87 Sensitivity 4: High standard deviation of the gravity model (effpow) ..................... 87 Sensitivity 5: High compensation ratio (reck) .......................................................... 87 Sensitivity 6: High natural mortality rates (M) ........................................................ 88 Sensitivity 7: Low natural mortality rates (M) .........................................................88 Sensitivity 8: High standard deviation of the spatial distribution of neonate settlement (sdldist) ................................................................................................88 Sensitivity 9: Low standard deviation of the spatial distribution of neonate settlement (sdldist) ................................................................................................88 Sensitivity 10: High move ment rates (emig) ............................................................ 88 Sensitivity 11: Low move ment rates (emig) ............................................................ 89 Results .....................................................................................................................................89 Base Case .........................................................................................................................89 Sensitivity 1: Closed Area/no Fishing Mortality ............................................................. 92 Sensitivity 2: Open Area Model ...................................................................................... 92

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7 Sensitivity 3: Fishing Mortality Rate Set to Fmsy .......................................................... 93 Sensitivity 5: High Compensation Ratio (reck) ............................................................... 94 Sensitivity 6: High Natural Mortality Rates (M) ............................................................. 94 Sensitivity 7: Low Natural Mortality Rates (M) ............................................................. 95 Sensitivity 8: High Standard Deviation of the Spatial Distribution of Neonate Settlement (sdldist) ...................................................................................................... 95 Sensitivity 9: Low Standard Deviation of the Spatial Distribution of Neonate Settlement (sdldist) ...................................................................................................... 96 Sensitivity 10: High Movement Rates (emig) ................................................................. 96 Sensitivity 11: Low Movement Rates (emig) .................................................................. 97 Discussion .................................................................................................................... ...........97 4 CONCLUSIONS, MANAGEMENT AND RESEARCH RECOMENDATIONS .............. 118 Conclusions ...........................................................................................................................118 Management and Research Recommendations ....................................................................119 APPENDIX A TIME AREA CLOSURE MAP ............................................................................................120 B DUSKY SHARK BIOL OGICAL DATA ............................................................................ 121 C SELECTIVITY CURVES .................................................................................................... 123 D SELECTIVITY PARAMETERS .........................................................................................124 LIST OF REFERENCES .............................................................................................................125 BIOGRAPHICAL SKETCH .......................................................................................................131

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8 LIST OF TABLES Table page 2-1 Five alternative scenarios and nine sensit ivity analyses of the base case model for the age-structured m odel ..........................................................................................................57 2-2 Total amount (kg) of dusky sharks repor ted landed and discarded in comm ercial fisheries ..................................................................................................................... .........58 2-3 Constructed historical catc h series (k g) for commercial and recreational landings and commercial (Pelagic Long line Logbook) discards of the dusky shark. .............................59 2-4 Total amount (kg) of dusky sharks re ported discarded in the P elagic Longline Logbook (PLL). .................................................................................................................60 2-5 Total amount (kg) of dusky sharks repor ted caught in recreational fisheries ....................61 2-6 Catch rate series (CPUE) and coefficient of variations (CV) used in analyses .................62 2-7 Combined Catch Per Unit Effort (CPU E) series, upper confid ence intervals (UCI) and lower confidence intervals (LCI). ............................................................................... 63 2-8 Selectivity parameters for the double logi stic distribution fitted to age data allowing for full exploitation of age-zero anim als. ........................................................................... 64 2-9 Regional landings of dusky shark ......................................................................................65 2-10 Parameter outputs......................................................................................................... ......66 3-1 Parameter values ( F = f ishing mortality rate absent any closure, M = natural mortality, effort = total kilometers modeled, q = fishing mortality concentrated into only areas that are open to fishing, R = average natural recruitment rate per cell, emig = movement rate, reck = compensation ratio, sdldist = standard deviation of spatial distribution of neonate settlement, jper = scaling constant for total neonate settlement from each source cell, and = parameters from Beverton Holt stock recruitment function, effpow = standard deviation of gravity model, dpow = power parameter, sort = relaxation weight and Hi = habitat quality for individual cells) for the base case version of the spatial model. .................................................................................... 103 3-2 Parameter values for the sensitivity anal ysis of the spatial m odel (Fmsy = fishing mortality rate that would produce maximum sustainable yield (MSY), effpow = standard deviation of the gravity model and sdldist = standard deviation of the spatial distribution of neonate settlement). .................................................................................. 104 3-3 Total number of dusky sharks found in thr ee areas (northern open, closed, southern open) and three life-stages (juvenile, subadult and adult) for the base case and sensitivity analysis of the spatial m odel. .......................................................................... 105

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9 B-1 Life history parameters for the dusky sh ark (Enric Corts, personal communication). .. 122 D-1 Selectivity parameters for the double logi stic distribution fitted to age data for the f ollowing four data sets: Bottom Longlin e Observer Program (BLLOP), Virginia Institute of Marine Science (VIMS), La rge Pelagic Survey (LPS) and Pelagic Longline Observer Program (PLLOP) (Enr ic Corts personal communication). ............ 124

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10 LIST OF FIGURES Figure page 2-1 Combined Catch Per Unit Effort (CPUE) series................................................................68 2-2 Dusky shark commercial la ndings (kg) from the general canvass SE and NE (GNSE and GNNE), quota monitoring system (QMS), Coastal Fisheries Logbook (CFL) and Pelagic Longline Logbook (PLL). .....................................................................................69 2-3 Dusky shark commercial discards (kg) from the Pelagic Longline Logbook (PLL) and Bottom Longline Observer Program (BLLOP). .......................................................... 70 2-4 Dusky shark recreational catches and dead discards (kg) from the Marine Recreational Fishery Statistics Survey (MRFSS), Headboat (HBOAT) and Texas Parks and Wildlife Department (TXWPD). ....................................................................... 71 2-5 Total amount (kg) of dusky shark commercial catches and Bottom Longline Observer Program discards. ...............................................................................................72 2-6 Combined Catch Per Unit Effort (CPUE) i ndices for the Virginia Institute of Marine Science (VIMS) ................................................................................................................ .73 2-7 Standardized (with 95% c onfidence intervals) and nominal Catch Per Unit Effort (CPUE) indices. .................................................................................................................74 2-8 Prior vs. posterior distribu tions for unknown parameters .................................................. 76 2-9 Mature median biomass (kg) for the base case and five scenarios ....................................79 2-10 Mature median biomass (kg) for the ba se case and nine sens itivity analyses ................... 80 3-1 Densities (N) of juvenile dusky sharks from the base case and sensitivity analyses ....... 106 3-2 Neonate densities (N) found within the j uvenile dusky shark life-stage model for the base case and sensit ivity analyses .................................................................................... 107 3-3 Recruit densities (N) found within the juvenile dusky shark life-stage model for the base case and sensit ivity analyses .................................................................................... 108 3-4 Redistribution of fishing effort for juve nile dusky sharks from the base case and sensitivity analyses...........................................................................................................109 3-5 Densities (N) of subadult dusky sharks from the base case and sensitivity analyses ......110 3-6 Neonate densities (N) found within the subadult dusky shark life-stage model for the base case and sensit ivity analyses .................................................................................... 111

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11 3-7 Recruit densities (N) found within the subadult dusky shark life-stage m odel for the base case and sensit ivity analyses .................................................................................... 112 3-8 Redistribution of fishing effort for suba dult dusky sharks from the base case and sensitivity analyses...........................................................................................................113 3-9 Densities (N) of adult dusky sharks fro m the base case and sensitivity analyses ............ 114 3-10 Neonate densities (N) found within the a dult dusky shark life-stage model for the base case and sensit ivity analyses .................................................................................... 115 3-11 Recruit densities (N) found w ithin the adult dusky shark life-stage model for the base case and sensitivity analyses ............................................................................................ 116 3-12 Redistribution of fishing effort for a dult dusky sharks from the base case and sensitivity analyses...........................................................................................................117 A-1 Map of the time/area closure currently in effect off the coast of North Carolina ............ 120

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12 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy EFFECTS OF TEMPORAL CLOSURES AND GEAR MODIFICATIONS ON THE POPULATION OF DUSKY SHARKS IN THE NORTHWESTERN ATLANTIC OCEAN By Alexia C. Morgan December 2008 Chair: Mike S. Allen Major: Fisheries a nd Aquatic Sciences My objectives in this study were two-fold. First, I built an age-structured model to assess the effects of fishing on population trends for dusky shark. This model included sensitivity analyses to assess the effects of time/area closures reduced mortality as a result of reduced soak times for the bottom longline fishery, full selectivity of age-zero animals, removal of Catch Per Unit Effort (CPUE) series and changes to other model parameters on overall population sizes. My second objective was to build a spatial model that evaluated the effects time/area closures and changes to other model parameters had on the population size of three life-stages (juvenile, subadult and adult) of the dusky shark. Results showed that the impacts of fishing already imposed on the dusky shark would be difficult to overcome even with the implementa tion of time/area closures, gear modifications and/or catch and discards being reduced for another 20 years. Results of the base case, all scenarios and sensitivity analyses except for one (increasing virgin biomass) of the agestructured model indicated that the population of dusky sharks in the northwestern Atlantic Ocean is at less than 60% of virgin biomass. Recent work has shown that the Maximum Sustainable Yield (MSY) for dusky sharks may be well above 50% of the carrying capacity. Fisheries managers must determine whether the high depletion rates repo rted in these models

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13 suggest this species is overfish ed, and would therefore require long-term targets for population recovery to sustainable levels. The base case version of the sp atial model illustrated that th e majority of total density (numbers) for all three stages (juvenile, subadult and adult) occurred in th e closed region of the model, but the effects of fishing were still seen in the boundaries between open and closed areas. Sensitivity analyses showed that parameter values affected the densities and the redistribution of fishing effort for all three stages. However, al l of the models (except for the open and closed versions) indicated that the highe st densities were found in the closed portion of the model and that the redistribution of fishing effort was concentrated into a ve ry small area on either side of the closures. Neonate densities in all models ge nerally followed the same trends as total density, and recruitment densities varied very little throughout individual models. Results of these models suggest that dusky shark populations have been heavily reduced and that research into the effects of time/ar ea closures on dusky shark populations should continue. Fisheries managers should investig ate new management options that will reduce the fishing mortality of dusky sharks caught as bycat ch. Managers should also increase observer effort in fisheries that catch dusky sharks as bycatch and improve on recording and reporting by fishers and dealers. Future re search should be aimed at determining ways to reduce fishing mortality rates for the dusky shark and improving our knowledge of parameters used in spatial modeling.

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14 CHAPTER 1 DUSKY SHARK BACKGROUND Background Managem ent of sharks in the western Nort h Atlantic Ocean has been politically and socially contentious. Since 1993, resource mana gers, fishery biologist s and fishers have struggled to create a management plan that w ould allow sustainable fishing and be economically viable for fishers. The current National Marine Fisheries Service (NMFS) Fishery Management Plan (FMP) for Atlantic sharks manages 39 speci es, which are separated into four management categories: large coastal, small coastal, pelagi c, and prohibited sharks (NMFS, 2006). However, biologists and independent stock assessment referees have suggested that sharks be managed on a species level rather than as groups. Due to the complexity of the multi-species fishery that currently exists for large coastal sharks, this s uggestion has only recently been enacted for the sandbar shark (Carcharhinus plumbeus) (NMFS, 2008). These new species-specific regulations include a sandbar shark research fishery with a heavily reduced quota and a non-sandbar large coastal shark fishery with a 33-shark/day limit (N MFS, 2008). Scientific work should continue to explore whether species-level management is possible for additional species, including the dusky shark. Sharks are susceptible to overfishing because of their K-selected life history characteristics (Corts, 2002a; Heppell et al ., 1999; Corts, 1999). Many species associated with commercial and recreational fisheries grow slowly, have late ages at matura tion, and exhibit limited maximum reproductive capacity. It has been esti mated that several shark species, such as the sandbar (Carcharhinus plumbeus ) (Sminkey and Musick, 1994; Corts, 1999) and dusky ( Carcharhinus obscurus ) (Simpfendorfer, 1999) shark, have a capacity to increase their population size at well below 10% per year. Musick et al (2000) noted that species with

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15 intrinsic rates of increase below 10% were particularly vulnerable to fishing mortality. Simpfendorfer (1999) used a demographic model for dusky sharks and found that only 4% of each age class can be caught sustainably. Similar findings have been found for sea birds and sea turtles whose populations are sensitive to juvenile and adult mortality rates, respectively and are considered long-lived sp ecies (sea turtles) (Crouse, 1999; Ru ssell, 1999). Corts (2002a) found through a probability-based el asticity analysis that (i.e., the finite population growth rate) for shark populations is most sensitive to the survival of juvenile stages. Sharks considered at the Kselected end of the spectrum, had high juvenile survival and low age-0 survival (or fertility) elasticities (used in demographic modeling to provid e a measure of the effect of small alterations in single matrix vital rates or elements). The dusky shark was categorized as being in the slow end of the life history pattern sp ectrum in his study, and was show n to lack the biological traits necessary to allow a return to original population sizes after m oderate exploitation (< 10%) of adult and juvenile life-stages (C orts, 2002a). Heppell et al. (1999) also stated that long-lived marine animals, such as sharks, have low first year survival elasticities and that even a small decrease in survival of adult or juvenile age classes can substantially reduce population abundances. In addition, animals with low juvenile and adult survival are unlikely to increase fecundity or juvenile survival rate in order to compensate fo r low population size (Heppell et al ., 1999). The NMFS has implemented several management decisions aimed at protecting the dusky shark and several other species from becoming overfished. These include placing 19 species (including the dusky shark) on the pr ohibited species list, making it illegal to target and/or land these species either recreationally or comme rcially) (NMFS 1999), implementing a time/area closure off North Carolina from January throug h July to protect juvenile dusky and sandbar

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16 sharks from longline fishing mortality (NMFS, 2003) and reducing the Total Allowable Catch for the directed large coastal non-sandbar shark fishery (NMFS, 2008). However, these actions have not prevented the dusky shark from bei ng caught and discarded as bycatch in several fisheries (Alexia Morgan unpublished data; Beerkircher et al., 2002) or in state waters. A recent stock assessment of the dusky shark showed decl ines of 60-80% relative to virgin biomass (Corts et al., 2006). As we continue to see signs of population dec lines for this species, it has become apparent that additional changes to current management plan s must be made. An increase in the size of the current time/area closure, gear restrictions (i .e. reducing soak time), and/or individual species management for the dusky shark are potential changes that could be implemented. These measures have been and continue to be c ontentious issues among re searchers, resource managers, and fishers. Utilizing models to simulate the effect of these alternative management plans on the dusky shark, a prohib ited species, may show that sp ecies-specific management or alternative management options can and should be implemented (Pelletier et al ., 2008). Models are essential to fisheries manageme nt because they depict and project the population in a mathematical framework using existi ng data, and allow inputs to be modified to explore a range of policy options. The major pitf all of models is that they are a simplified depiction of the system and thus may not incl ude important interactio ns (process error), and parameter inputs to the model can have high uncertainty (observa tion error). Poorly designed models or well-designed models utilizing poor or limited data can lead to the implementation of incorrect management plans. Statement of Problem and Objectives In June 2000, the dusky shark (listed by NMFS as a Candidate Species under the Endangered Species Act (ESA), and listed as vulnerable in the nor thwest Atlantic and Gulf of

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17 Mexico by the IUCN Red List of Threatened Species for 2004) and 13 additional species were placed on the prohibited species list, which di sallows landing these sh arks (NMFS, 1999). However, many of these species, including the dusky shark, continue to be caught and discarded as bycatch in both the commercial and recreatio nal fisheries (Alexia Mo rgan, unpublished data). As a result, some populations continue to decline due to species-specific responses to capture on fishing gear (Morgan and Burgess, 2007), severa l age/size classes being caught (Alexia Morgan, unpublished data), discard mortality and the inab ility of the current quota-based management system to address these individual issues. The dusky shark has been exposed to high fish ing mortality aimed at multiple size classes over the past few decades (Corts et al ., 2006, Alexia Morgan, unpublis hed data). The main fishery for dusky sharks in the United States, pr ior to being classified as a prohibited species, was the directed shark bottom longline, which ope rates along the U.S. east coast and Gulf of Mexico. Dusky sharks are also caught in th e U.S. pelagic longline fishery and are taken incidentally in several other fisheries as well as recreati onally (Corts et al ., 2006). The overall goal of this study was to comple te a stock assessment of the dusky shark population in the northwest Atlantic Ocean and to utilize a spatial model to investigate whether time/area closures are an adequate management tool for the dusky shark. My objectives were two fold. First, I built an age-structured model for dusky shark, which included sensitivity analyses to assess effects of time/area closures, reduced mortality as a result of reduced soak times for the bottom longline fishery, a different selectivity curve, the removal of the Bottom Longline Observer Program (BLLOP) Catch Per Unit Effort (CPUE) series, combined CPUE series for all catch rates and changes to other model parameters on overall population sizes. My second objective was to build a spatial model that evaluated effects of time/area closures on the

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18 population sizes of three life-stag es (juvenile, subadult and adult) of the dusky shark. This second approach has yet to be used in a stock assessment for this species of shark but spatial modeling has been used to assess stock abundance of the school shark ( Galeorhinus galeus ) in Australia (Punt et al., 2000). Both modeling appr oaches were used to assess whether alternative management plans such as increased time/area cl osures, species specific management, or gear modifications could decrease fishing induced mo rtality and predict sustainable protection of dusky sharks in the northwestern Atlantic Ocean. These approaches were different than those used by Corts et al. (2006), who used surplus production, age-structured production and catchfree models, fewer years of data, no spatial analysis and different sensitivity analyses and target reference points. Fisheries Management The initial F ederal Fishery Management Plan (FMP) for sharks was enacted in 1993 in reaction to concern that increased commercial an d recreational fishing effort for sharks had caused over-fishing (NMFS, 1993). In 1999, sharks in the Northwest Atlantic Ocean and Gulf of Mexico became managed under the Fishery Manage ment Plan for Atlantic Tunas, Swordfish, and Sharks and subsequent amendments (Hi ghly Migratory Species [HMS] FMP) (NMFS, 1999). In July 2006 the Final Consolidated Fish ery Management Plan for Atlantic Tunas, Swordfish and Sharks was published (NMFS, 2006) This FMP is a result of the 2002 and 2006 stock assessments of large and small coastal sharks, respectively (NMFS, 2006). The current HMS FMP manages four groups of sharks: large coas tal sharks (11 species) small coastal sharks (four species), pelagic sharks (five species) a nd prohibited sharks (19 species, including the dusky shark), with each group managed separa tely (NMFS, 2006). The dusky shark has only been assessed as an individual sp ecies by Corts et al. (2006).

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19 Time and area closures and Marine Protec ted Areas (MPAs) are becoming management tools for state, federal and internationally mana ged fisheries (Pelletier et al., 2008). The MPAs and other closures are commonly used as a management tool to protec t biodiversity, repairing demographic structure of a population and spawning stocks. The effects of the closures can be analyzed through candidate indicato rs, such as enhancing fisheries yields outside of the closure and increasing the stability of th e population (Pelle tier et al., 2008). The re sults of most studies have shown that improving fishery yields within closed areas only works when the fishery is already overexploited (Apostolaki et al., 2002). Internationally, pelagic fishes (Goodyear, 1999), mollusks (Dredge, 1992), and reef fishes (Galal et al., 2002) are being managed with MPAs because they can provide protecti on of specific size classes, sexes, and/or species from excessive fishing mortality. The success of closures depends on their size, time peri od of closure, the lifestages that use the closed area (Horwood et al., 1998; Dinmore et al., 2003; Tang and Chen, 2003), movement rates of fish, and additional catch a nd/or effort controls th at will alleviate the effect of fishing effort displacemen t (Walters, 2000; Rijnsdorp et al., 2001; Walters et al., 2007). Determination of which size class or sex is in need of protection de pends upon the demographic attributes of the species, which size class or sex historically ha s been targeted, and the current status of the population in questi on. There has been great debate among fisheries researchers and fishers over the use and success of MPAs and time area closures as management tools (Pelletier et al., 2008). A time/area closure was implemented in 2005 from January to July off the coast of North Carolina in an effort to protect juvenile dus ky and sandbar sharks from longline fishing and discard mortality (NMFS, 2003). This closure is intended to protect dusky sharks for 7 months of the year. The closed area is small (Oregon Inlet, NC at 35o41N offshore to 74o51W, then

PAGE 20

20 following the 60 fathom contour to 35o30N and 74o46W and continuing along the 60 fathom contour south to 33o51N and 76o24W (see Appendix A)) for a highly mobile species and will not protect dusky sharks that move out of th e area during the specified closed times. No previous studies have evaluated the potential for success of this time/area closure in protecting highly migratory species, and thus there is no indication of whether th is closure will be beneficial to the dusky and/or any other spec ies of large coastal sh ark. This study was the first to assess the effects of time/area closures on the dusky shar k through the use of bot h age-structured and spatial modeling. Life History of the Dusky Shark The dusky shark has one of the longest repr oductive cycles (three years) of all shark species, can reach lengths of 360 cm fork lengths (FL) and lives between 40-50 years (Natanson et al., 1995). Age and growth estimates and length fr equency analyses from the North Atlantic Ocean (Natanson et al., 1995) and Western Australia (Sim pfendorfer, 2002) are reported in Appendix B. Growth parameters for males ranged from L=345.4-373 cm FL (theoretical average maximum) and K =0.038-0.043/year (g rowth coefficient) and for females L= 336.5349 cm FL and K=0.039-0.045/year. Females and male s are estimated to reach sexual maturity at 21 and 19 years of age, resp ectively, and litters range from 3-14 pups with a 1:1 sex ratio. The gestation period has been estimated to be as l ong as 24 months with a one -year resting cycle in between (Branstetter and Burgess, 1996). Dus ky sharks are viviparous, giving birth to live young, ranging in size between 70-100 cm TL (L ast and Stevens, 1994; Natanson et al, 1995) (Appendix A). Mating occurs between July and September a nd birth occurs between May and June in nursery grounds located from South Carolina to Virginia. Juvenile dusky sharks (> 150 cm FL) also inhabit other coastal beach nursery grounds from New Jersey to South Carolina (Castro,

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21 1996). The growth rate of juvenile dusky sharks ranges from 8 cm/year to 11 cm/year (Simpfendorfer, 2000). Females can store sperm in the oviducal glands. Time of mating and subsequent fertilization has not been defined. Thus, the dusky shark is a long-lived fish, but previous studies have hypothesized a range of natural mortality rates. Natural mortality (M) in the dusky shark has been estimated using Hoenigs (1983) method, resulti ng in a value for M of 0.083/year (Romine et al., 2002) and 0.08/year (Simpfendorfer, 1999). Estimates of natural mortality using Paulys (1980) method resulted in a value of 0.11/year (S impfendorfer, 1999). Corts et al. (2006) calculated M ranging from 0.02-0.1 using Rikhter and Efanovs (1976), Pauly (1980), Hoenig (1983), and Jensen (1996) methods. Size-specific natural mortality rates of 0.21 and 0.25 for age-0 sharks and 0.08 for age 40+ sharks were calculated through Pe terson and Wroblewskis (1984) and Lorenzens (1996) methods, respectively. Chen and Watana bes ( 1989) method resulte d in an M of 0.16 for age-0 sharks and 0.05 for age 30 sharks and an aver age of 0.05 for sharks age 31+ (Corts et al,. 2006) (Appendix B). Because of th e late age at maturity of the dusky shark, increasing the natural mortality of age-0 animals has been s hown to have little effect on population abundance (Simpfendorfer, 1999). Distribution The dusky shark is a coastal-pelagic species inhabiting warm-tem perate and tropical inshore and offshore waters. It is found from the continental shelf to the open ocean and in depths ranging from the surface to 400 m. In western North Atlantic waters this species is found from New England to Brazil, includ ing the Gulf of Mexico and Caribbean Sea. It also occupies the eastern Atlantic, western Mediterranean, western Indian, western Pacific, and the eastern Pacific Oceans (Compagno, 1984).

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22 Dusky sharks are highly migratory and in the western North Atlantic moves northwards along the US east coast as the water warms duri ng the spring and summer and southward as the waters cool during fall and wi nter (Musick and Colvocoresse s, 1986). Tagging studies have documented a maximum distance traveled of 2,052 nautical miles, a maximum speed of 41.3 km/day and a maximum time at liberty of 15.8 years (Kohler et al., 1998). The NMFS Cooperative Shark Tagging Program has tagged dus ky sharks in New England and recaptured these sharks in the southwestern Gulf of Mexico and Yucatan Peninsula. These tagging studies have also indicated that there is a single popul ation within the North Atlantic Ocean, with no mixing with dusky sharks outside of the North At lantic, although this has not been corroborated with any genetic work (Romine et al., 2002). Dusky sharks can be found in similar areas to the sandbar, bignose ( Carcharhinus altimus ), silky ( Carcharhinus falciformis ), whitetip ( Carcharhinus longimanus ) and Galapagos (Carcharhinus galapagenis) sharks (Compagno, 1984). Young sharks do not migrate as far north and south as adults do and some partial sexual segregation has been suggested (Compagno, 1984). Dusky sharks tend to avoid areas of low salinity (Compagno, 1984). Conclusions Managem ent efforts over the past several y ears have aimed at protecting the dusky shark from overfishing. Despite these efforts a recen t stock assessment (Corts et al., 2006) has indicated this species is overfis hed and that management has been unable to protect this species from being caught as bycatch in several fisheries. In the following two ch apters I describe the results of my analysis using an age-structured and spatial model to determine if alternative management efforts, such as increasing the time area closure and/or re ducing the soak time for longline gear, could improve the status of the dusky shark population. The age-structured model

PAGE 23

23 is different from ones previously used to assess this species (Corts et al., 2006) and this is the first time a spatial model has been used to assess the dusky shark population.

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24 CHAPTER 2 AGE-STRUCTURED MODEL Introduction Age-structured m odels divide a population into age classes and follow these cohorts separately throughout the simula tion. Simpler models such as production models combine growth, reproduction and mortality, making it impossible to look at the dynamic interactions occurring between these processe s (Hadden, 2001). Age-structured models can also incorporate multiple gear types and biological information (Simpfendorfer. and Burgess, 2002). The life history patterns of the dusky shark and its ex ploitation in several fisheries made an agestructured model appropriate for this species. Exploitation rates for dusky sharks vary between size classes; therefore the ability of an age-structured model to follow each size class separately is a very useful tool for stock assessment a nd decision analysis. Corts et al. (2006) have previously used a surplus pr oduction, catch free and age-stru ctured production model to determine the status of the dusky shark. I used a different age-structured model, three more years of data, a different sele ctivity curve, removed the Bottom Longline Observer Program (BLLOP) Catch Per Unit Effort Series (CPUE), combined CPUE series for all catch rates and included analyses on the effects of time/ area closures and reduced soak times. The age-structured model used to address obj ective 1 was coded in Excel and utilized a Bayesian framework. A Bayesian approach allows for the incorpor ation of uncertainty and prior knowledge of unknown parameters during the initial stages of model development (Punt and Hilborn, 1997). The model was similar to ones used by Punt and Walker (1998) and Simpfendorfer and Burgess (2002). For my analys is the model was modified to include: age specific natural mortality rates, lognormal prior distributions for unknown parameters, and the use of fork lengths instead of pre-caudal lengths to determine weights. Multiple fisheries, gear

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25 types and selectivities were included in the m odel. Catch rate data were assumed to be proportional to biomass and catch rates were comp ared to the exploitable biomass of specific survey gear (Simpfendorfer and Burgess, 2002). Th e model was used to provide estimates of the current (for 2006) and future (2025) status of the population comp ared to the virgin population (assumed to be around 1972 based on historical knowledge of the fishery [Simpfendorfer and Burgess, 2002]) and to analyze the impact va rying parameter values and commercial and recreational catches would have on population levels. One base case model, five scenarios and six sensitivity analyses were run. Parameter valu es and catches used in the sensitivity analysis and scenarios can be found in Table 2-1. Data for Model All biological data obtained from the literatur e are reported in Appendix B. Size at age was taken from Natanson et al. ( 1995). Natural mortality rates used in this model assumed a linear decrease for age 0 to mature animals (Corts et al., 2006). Commercial Catches Comm ercial landings for dusky sharks were collected from the following sources and provided to me by Enric Corts (personal comm unication): National Marine Fisheries Service (NMFS) South East Fisheries Science Center (SEFSC) quota monitoring system (now known as pelagic dealer compliance), the Northeast (NE) and Southeast (SE) regional general canvass landings (now known as Accumulated Landings System), the SEFSC Co astal Fisheries Logbook Program (CFL) and the Pelagic Long line Logbook (PLL) (Table 2-2). The quota monitoring system collects informa tion from seafood dealers who 1) hold a Federal dealer permit for sharks, 2) are selected by the SEFSC to report and 3) are located in the SE region. This dataset provide s species specific informati on. The general canvass landings data set collects data directly fr om all seafood dealers and is cons idered a more complete dataset

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26 because it includes state landings, but tends to have poorer species specific data and a large proportion of unclassified sharks than the quota monitoring system. The CFL collects data from commercial fishers holding either a 1) Gulf of Mexico Reef Fish permit, 2) South Atlantic Snapper-Grouper permit or 3) King and Spanish m ackerel or Shark permits. The PLL collects data from commercial fishers participating in the pelagic longline fishery targeting tuna and tuna-like species. Fishers must submit a l ogbook for any trip in which a highly migratory species was caught (Corts et al., 2006). The total amount of dusky sharks landed in commercial fisheries was calculated by taking the maximum amount (kg Dressed Weight [dw]) from the quota monitoring, coastal logbook and SE general canvass landings data (the maxi mum number is used because landings can be reported to all three database s) and adding it to the maximum amount (kg dw) from the NE general canvass landings data a nd pelagic longline logbook (Table 22) (Corts et al., 2006). I constructed historical catches for the time period of 1972-1989 using a linear increa se in catches from 0 kg in 1972 to 17,000 kg in 1989 (Table 2-3). Reported catches from the 1980s are known to be unrealistic (Enric Corts, NOAA, personal communication) so I incl uded those years in the historical data set in an effort to produce a more realistic catch series. Catches were increased from 0 to 17,000 kg so that catches in 1989-1991, prior to the drastic increases in catches due to the explosion of the fishery, would be similar (Tables 2-2 and 2-3). Dead discard (animals discarded back to sea dead) estimates were collected from the PLL (Table 2-4) and BLLOP (Table 2-2) and provided to me by Enri c Corts. Discards from the BLLOP were added to the commercial landings to obtain the total commercial catches for the time period. The PLL discards were counted separately and repr esented the total amount of discards because they have a different select ivity curve (described below) than the BLLOP

PAGE 27

27 discards and commercial catches. All weights (commercial and di scard) were converted from pounds (lb)to kg for inclusion in the age-structured model. I constructed historical discards for the time period 1972 to 1991 using a linear increa se in discards from 0 kg in 1972 to 30,000 kg in 1991 (Table 2-3). I ended th e linear increase at 30,000 kg so that it would be near the estimated discards reported in 1991. Discard esti mates varied through the years but it is unlikely that discards prior to 1991 were more than thos e seen during the expansio n of the fishery in the 1990s. Individual states were used to make up thr ee broad regions (Gulf of Mexico (GOM), Mid Atlantic (MA) and South Atlantic (SA)). The GOM was made up of the west coast of Florida to Texas, the MA is all states between Virginia and New York and the SA was the Florida east coast through North Carolina. Recreational Catches Data were collected from the Marine Recr eational Fisheries Sta tistic Survey (MRFSS), NMFS Headboat Survey (HBOAT) a nd Texas Pa rks and Wildlife Department Recreational Fishing Survey (TXPWD) for recreational fishing estimates (Table 2-5) and provided to me by Enric Corts (personal communication). The total amount of dusky shark catches in recreational fisheries was calculated by adding the amount re ported from the three recreational surveys (reported in estimated numbers of fish caught). Detailed information on these three datasets can be found in Shark Evaluation Annual Reports (Cor ts et al., 2006). All weights were converted to kg for inclusion in the age-structured model. I constructed historic al catches for the time period 1972 to 1980 using a linea r increase in catches from 0 kg in 1972 to 200,000 kg in 1980 (Table 2-3). Recreational catches were highest during the 1980s a nd I kept the historical catch series larger in the 1970s to co incide with the expansion of th e recreational fishery for sharks (George Burgess, FLMNH, personal communication).

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28 Individual states were used to make up thr ee broad regions (Gulf of Mexico (GOM), Mid Atlantic (MA) and South Atlantic (SA)). The GOM was made up of the west coast of Florida to Texas, the MA is all states between Virginia and New York and the SA was the Florida east coast through North Carolina. Catch Rates Virginia Institute of Marine Science (VIMS) Monitoring Data The VIMS data set is a fishery-independent scientific survey, which began in 1974 and continues today. Data for the present study were available f or 1974-2006. The survey uses longline gear to sample 4-5 stations during several cruises that operate predominantly in the summer in the coastal waters of Virginia. No dusky sharks were caught during 1986, 1988 and 1994 despite surveys being completed. The variable s year, station, season (spring, summer, fall, winter-based on months), average depth and the tim e the sets were started were used for catch rate analysis of this series. Effort was th e number of hooks per set multiplied by soak time (hrs. fished). I developed the catch rate series for this data set. Additional information on this data set and variables used in the catch rate anal ysis can be found in Corts et al. (2006). Bottom Longline Observer Program (BLLOP) The BLLOP is a fishery depende nt comm ercial data set that began in 1994 and continues today. Data for these analyses were availabl e from 1994-2006. Fisheries observers were placed aboard commercial bottom longline vessels from Ne w Jersey to Texas and recorded information pertaining to the number of sharks caught, size sex, location and disposition of sharks for individual sets. The program was voluntary in nature from 1994-2001 and became mandatory in 2002. Vessels that hold a current directed shar k permit are selected at random for observer coverage. The variables year, area (Gulf of Me xico, South Atlantic and Mid-Atlantic Bight), hook type (small, small J, small C, medium, medium J, medium C, large, large J, large C), set

PAGE 29

29 depth, bait type (little tunny, Atla ntic sharpnose shark, other shark, ot her teleost, skate or ray, eel, and other) season (spring, summer, fall and winte r) and time of day the sets were made were used for catch rate analysis of this series. For the Catch Per Unit Effort (CPUE) analysis the dataset was divided into the two time pe riods (1994-2001 and 2002-2006) and two separate analyses were run (John Carlson personal communi cation). Effort was the number of hooks per set multiplied by the length (miles) of longline per set and the soak time (hrs.). Additional information on this data set can and variables used in the catch rate analysis can be found in Corts et al. (2006). Pelagic Longline Observer Program (PLLOP) This is a fishery-dependent comm ercial data set, which started in 1992 and continues to this day. Data for these analyses were ava ilable for 1992-2006. Fishery observers were placed aboard commercial pelagic longline vessels targe ting tuna and tuna-like species from the Grand Banks to Brazil and recorded shark bycatch. The variables year, whether it was an experimental set, whether light sticks were used, fishing quarter of a partic ular year, area, gear (bottom longline or pelagic longline), qua rtile for nominal tuna catch rate (per 1000 hooks) and quartile for nominal swordfish catch rate (per 1000 hooks) were used for catch rate analyses (Enric Corts, NOAA, personnel communication). Effort was the number of dusky sharks caught per 1,000 hooks. Additional information on this data se t can and variables used in the catch rate analysis can be found in Corts et al. (2006). Large Pelagic Survey (LPS) This data set is a fisherydependent recreational data se t, which started in 1986 and continues to this day. Data for these analyses were availa ble from 1986-2006 and consisted of angler interviews. Data on rod and reel and handline fisheries from Massachusetts to Virginia was collected for this program. The variables ye ar, state, interview type (phone or dockside),

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30 tournament (yes or no), boat t ype (private, party, headboat) and month were used for catch rate analysis of this data set. Effort was th e number of dusky sharks caught per 100 trips. I developed the catch rate series fo r this data set. Additional info rmation on this data set can and variables used in the catch rate an alysis can be found in Brown (2002). Catch Rate Analyses Catch ra tes for the Bottom Longline Observer Program (BLLOP), Virginia Institute of Marine Science (VIMS), Pelagic Longline Ob server Program (PLLOP), and Large Pelagic Survey (LPS) data sets (Table 2-6) were incl uded in all model runs. A Generalized Linear Model (GLM) was used to standardize these catch rates. This methodology has previously been used in the analysis of other shark species (e.g. Corts et al., 2002; Corts et al., 2006). The model treats the proportion of sets with positive catch (binomial error with logit function) and the sets with positive catches (Poisson error dist ribution and log link function) separately due to the zero inflated nature of the catch distributions. Mode ls were fitted using a SAS GENMOD procedure (SAS Institute Inc., 1999) and a forward stepwise approach, testing each factor one at a time. A null model was run first and then indi vidual factors were entered one at a time. The results were ranked from greatest to smallest re duction in deviance per degree of freedom when compared to the null model. The factor with the largest reduction in deviance per degree of freedom was integrated into the model if 1) the e ffect of the factor was significant at least at the 5% level based on results of a Type III likelihood ratio test from a Chi-Square statistic and 2) the deviance per degree of freedom was reduced by at l east 1% compared to the less complex model. Year was always included as a factor because it is needed to develop a time series (Corts, 2002b; Corts et al, 2002; Corts et al., 2006). A deviance analysis table including the de viance for proportion of positive observations and the deviance for the positive catch rates was us ed to summarize the results. The final model

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31 was run using a computer program that uses the SAS GLIMMIX macro. Akaikes Information Criterion (AIC), Schwarzs Bayesian Criterion and the residual likelihood (-2Res L) were used for goodness-of-fit tests. A Type III test of fi xed effects was used to test the significance of each individual factor. Relative indices were calculated through the final mixed model as the product of the year effect least squares m eans (LSMeans) from the binomial and Poisson components, which used bias correction terms to calculate the confiden ce intervals (Corts, 2002b; Corts et al., 2002; Corts et al. 2006). The standardized cat ch rates were all used in the model. Age-structured Model Components of the Model The m odel is age (Natanson et al., 1995) and se x structured (catch di vided equally between males and females) and uses a Virtual Populati on Analysis (VPA) type of simulation approach (equation 2-1), 2/ ,,1 ,,1 2/ ,, ,, 2/ ,,1 2M/ ,, 1,,0 1,,1) e(M tga M tga M tga M tga M tga tga tg tgaeCeNeCeN eC N N N g gxa xa a 1 0 (2-1) where N a,g,t is the number of individuals of age a, sex g in year t M is instantaneous natural mortality, xg is the maximum age of sex g and C is total catch in numbers (Simpfendorfer and Burgess, 2002). Total catch was found using equation 2-2, j jtga tgaCC,,, ,, (2-2) where tgaC,, is the total catch of age a, sex g, in year t summed across all j gear types (Simpfendorfer and Burgess, 2002). The number of pups born each year was cal culated using equations 2-3 and 2-4,

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32 2/ '' ,,02 rteP Scb S Nfg t t tg (2-3) ''' ,, a x ma atfga tPPNS (2-4) where N is the number of pups of sex g, born in each year t, b and c are parameter values of the Beverton-Holt recruitm ent relationship, '' gPis the proportion of pups that are female (g=f indicates the sex of the pups is female, a=m indicates age is mature), St is the egg production in year t, z is the proportion of R* (virgin biomass) that recruits when St is at 20% of the virgin level (Corts, 2004), aP is the number of pups per pregnant female at age a,'' aP is the proportion of females of age a that are pregnant, et is an error term and 2 r is the standard deviation of et (Simpfendorfer and Burgess, 2002). The parameter values R* and z are estimated through the Sampling Importance Resampling (SIR) algor ithm discussed in more detail below (Simpfendorfer and Burgess, 2002). Parameter values for b and c are calculated from R*, z and S* (unexploited egg production) using equations 2-5 and 2-6, *8.0 2.0 1 R z z S b (2-5) *8.0 2.0 zR z c (2-6) where recruitment is assumed to be affect ed by process error and an error term ( 2,0 ~r tN) is included (Simpfendorfer and Burgess, 2002). The catch was found with usi ng equations 2-7 and 2-8, 1 1 ,,, 2/ ,,,,,,,, j i itga M tgajgajtjtgaC eNFC (2-7)

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33 jBCFe tjtjt,/,, (2-8) where jtgaC,,, is the catch for sex g, age a animals at time t with gear j, jtF, is the fishing mortality that occurs at time t and gear type j, and jgav,, is the selectivity of gear type j for animals of sex g and age a. The model assumes all catch is ta ken in the middle of the year after half of the natural mortality has occurred and in sequential order by fisheries (Commercial, recreational and discards)(Simpfe ndorfer and Burgess, 2002). Exploitable biomass was found with equation 2-9, a j i itga M tgajgaga g j e tCeNw B1 1 ,,, 2/ ,,,,., (2-9) where e jtB, is the exploitable biomass for year t, gear type j, and gaw, is the weight of sex g, age a animals (Simpfendorfer and Burgess, 2002). A power curve (equation 2-10) was used to calculate the weight of animals: lwb ga gaLlwaW, (2-10) where gaL,is the length (Natanson et al., 1995) for sex g and age a and lwa and lwb are constants of the length-weight relationship from a power curve (Simpfendorfer and Burgess, 2002). The total and mature biomasses were found using equations 2-11 and 2-12, a gatga g t twN B,,, (2-11) x ma gatfga m twNB,,, (2-12) where t tB is the total biomass for year t and m tB is the mature female biomass at time t (Simpfendorfer and Burgess, 2002). The pre-exploitation populat ion was found using equation 2-13,

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34 M M oga M ga g e gaeeN eN PeR Nra1/,,1 0,,1 ''' 0,,2g gxa xa a 1 0 (2-13) where0,, gaN is pre-exploitation (Simpfendorfer and Bu rgess, 2002). The exploitable biomass of survey gear was found using equation 2-14, gaj jtga M tgahgaga e htC eNvw B,,, 2/ ,,,,, (2-14) where e htB, is the exploitable biomass in year t for survey gear h (Simpfendorfer and Burgess, 2002). Parameter Estimation Selectivities were based on age frequenc y distributions from the Bottom Longline Observer Program (BLLOP), Virginia Institute of Marine Science (VIMS), Large Pelagic Survey (LPS) and the Pelagic Longline Observer Prog ram (PLLOP) data sets. An age-length key (Natanson et al., 1995) was used to create agefrequency distributions from length frequency distributions. The BLLOP selectivity was used for the BLLOP CPUE series, commercial catches and BLLOP discards, VIMS selectivity was used for the VIMS CPUE, LPS selectivity was used for the LPS CPUE and recreational catc hes and the PLLOP selectivity was used for the PLLOP CPUE and PLL discards. The distribution was scaled to the maximum selectivity value and a double logistic distribution was fitted to ag e for each data set (Corts et al., 2006) using equations 2-15 and 2-16, a aaCCVmax (2-15) dcx baxe e xf/50 /501 1 1 1 1 (2-16)

PAGE 35

35 where aV is the selectivity value at age a, aC is the catch at age a, xf is the double logistic function, a50 and c50 are median ages of the ascending a nd descending arm of the double logistic equation and the variables b and d are the slopes respectively. Se lectivity curves and parameter estimates for the all models except for one sensitivity analysis (sensitivity 9) were provided by Enric Corts (personal communication). In sensitiv ity 9, I adjusted the parameters of the double logistic distribution to allow for full selectivity of age 0 animals. Additional information on the methods used to construct the selectivity curv es can be found in Corts et al. (2006). Model fit compared observed catch rates to th e population size in th e model using equation 2-17, 2ln 2 1 lnln ln2 2 )( 2 h h ht e hth ht jn BqI L (2-17) where jL lnis the log likelihood of the lognormal distribution, It,h is the catch rate for year t of survey gear type h, qh is the catchability coefficient of gear type h and 2 h is the variance of the residuals from survey gear h. Populations were projected to year 2025 to show the effects of various harvesting strategies had on the simulated population into the future. Th e SIR algorithm was used to estimate posterior distributions for the priors ( R*, z, qh and 2 h) used in the model. The SIR algorithm worked by 1) randomly selecting a value from the prior distributions of each of the four parameters listed above, 2) the model simulated the pop ulation through time with the selected parameters, 3) the total likelihood of the model was calculated, 4) steps 1-3 were repeated 1000-7000 times, and 5) 2000 model runs with replacement (selection was based on their total likelihood) were made and used to co nstruct the posterior di stribution (Simpfendorfer and Burgess, 2002). A lognormal prior distribution with a mean of 20,000 and standard

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36 deviation of 10,000 was used for R* (Colin Simpfendorfer personal communication) and a lognormal prior distribution with a mean of 0.3 and standard deviation of 0.09 was used for z (Corts 2004). The pr ior distribution for 2 h was a uniform distribution with bounds [0;0.8] (Simpfendorfer and Burgess, 2002) The prior distribution for qh was independent and normally distributed with starting values of 0.00001 for the VIMS, LPS and PLLOP catch rates and 0.000001 for the BLLOP catch rates. Sensitivity Analysis Scenario 1: Commercial and recreational ca tches closure in the future It is unlikely that catches of dusky sharks between now and 2025 will remain at zero, therefore I added catches and disc ards from 2007 to 2025 to the model. I developed this model by taking the average catches (commercial, recreational and discards) from 2002-2006 and applying them to 2007-2025. I used the year s 2002-2006 because they occur after the dusky shark was placed on the prohibited species list and allow a year (2001) for the new management plan to take full effect in the fishery. The base case model was rerun including catches for 2007 to 2025, simulating continued catches of the dus ky shark for the duration of the model time line. Scenario 2: Simulated closure by removal of N orth Carolina catches I simulated an increase in the current time ar ea closure to include all of North Carolina from 2007-2025. The general canvass, CFL and recrea tional datasets were used to determine the average percent of total dusky shark catches caught in North Carolina between 2002 and 2006. This average percent was removed from the 20072025 catches used in th e previous model and this model was then run with the new 2007-2025 cat ch series, plus the original data from 19722006.

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37 Scenario 3: Simulating reduced soak times The BLLOP average discard rates (included in total commercial catches) for 2002 to 2006 were reduced by 43% and applied to 2007-20205, in an effort to simulate the effects of reducing the soak times used in bottom longline fishing. The reduction was based on the BLLOP database and results from Morgan and Burgess (2007), which showed that reducing the soak time to less than 10 hours resulted in a reducti on of initial mortality of 43%. Similar data were not available from any of the other data. This model was run with the new catch series from 2007 to 2025 (included the average commercia l and recreational catches from 2002 to 2006 used in scenario 1 and applied to 2007-2025, plus these reduced discards from 2002-2006 also applied to 20072025) and the original catch series from 1972-2006. Scenario 4: Increased commercial catch series There is some uncertainty surrounding the amount of both recreational and commercial catches reported due to changes in the design of sampling programs, species identification problems and general issues with logbook complian ce. Commercial catches in the early years seemed very low, scattered and unrealistic given the growth of the bottom longline fishery during those years. I therefore increased the commercial catch series from 1972-2006 by 25% for this scenario. Scenario 5: reduced recrea tiona l catch series The recreational catches that have been reported for the dusky shark seem very high, so I reduced the recreational catches from 1972-2006 by 25% in this scenario. Sensitivity 1: R* prior distribution increased During the development of the base case model it became clear that changes made to R* had a substantial effect on the out come of the model. I increase d the lognormal prior distribution for R* to a mean of 60,000 with a SD of 30,000 to determine how much influence this parameter

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38 had on the population size. The sa me catches used in the base case model were used in this model. Sensitivity 2: Z increas ed I increased the prior distribution of z to a lognormal distribution w ith an increased mean of 0.6 and a SD of 0.24. The higher values used in the prior distribution of this model are near the biological maximum estimated for this species and allow for more compensation (Simpfendorfer et al., 2000; Corts, 2004). The same catches used in the base case model were used in this model. Sensitivities 3-6: Parameters increa sed by 10% Parameter values ( R*, Z, M and selectivities) in these four sensitivity analyses were increased by 10% from their base case values This enabled the determination of which parameters had the largest overall effect on the models prediction of total and mature biomass in 2006 and 2025 when compared to virgin biomass. The same catches used in the base case model were used in this model. Sensitivity 7: BLLOP CP UE series removed The BLLOP catch rate series was the only series taken from the directed shark fishery. There is uncertainty as to whether the drop in CPUE seen after 1999 in this series was a factor of actual population declines for the du sky shark or an artifact of the species being placed on the prohibited species list in 2000. I removed the series from this sensitivity analysis to test whether this sharp decline from 1999 to 2000 was affecting the fit of the model. The same catches used in the base case model were used in this model. Sensitivity 8: Combined CPUE series I combined all four of the catch rate series into one catch rate and used only this series in this sensitivity analysis (Table 2-7 and Figure 2-1). The same catches used in the base case

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39 model were used in this model. The series we re combined using a GLM with the CPUEs as the dependent variable and year and individual series as the independe nt variables. The results of the GLM were divided by the mean CPUE before bei ng used in this analysis. It should be noted that this combined series contained the entire BLLOP series and therefore, the caveats dealing with the dusky shark being placed on the prohibited species list (see above) may still apply. Sensitivity 9: Selectivity The age frequency distributions for two (BLLOP and VIMS) of the data sets used for the selectivity analysis showed that age-zero an imals were the most commonly caught. The selectivities used in the base case and all ot her scenarios and sensit ivity analysis assumed exploitation began at age 1. In this analysis, I changed th e slope of the double logistic distribution for all four series to allow for the age-zero class to be fully exploited to fishing (Table 2-8). The same catches used in the base case model were used in this model. Results Catches The highest catches of dusky sharks occurred in the 1990s with both landings and discards peaking in the mid 1990s (Figure 2-2). Commercial la ndings of dusky sharks were highest in 1992-2000 and peaked in 1995 and 1996 (Figure 2-2) Catches dropped off drastically in 2001 after dusky sharks were placed on the prohibited species list (Figure 2-2). Commercial discards increased from 1993 to 1995 with a very large peak in 1994 (Figure 2-3.). Recreational catches were highest during the first year of reporti ng (1981) but remained high through 1992 (Figure 24). After a very large decrease in recreational catches in 1993, catch es rose in 1994-1997 and fell again in 1998 (Figure 2-4). Recreational catches have remained low since 1998. The majority of the catches come from the MRFSS survey (Figure 2-4). Total catches of dusky sharks peaked in 1995 (Figure 2-5). The amount of dusky sharks caught dropped dramatically

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40 after 2000 (Figure 2-5). The dusky shark became offi cially protected in 2000; however, landings and discards were still reported after this year. Some of thes e reports can be attributed to misidentification, but it is also likely that dus ky sharks were illegally caught and landed after 2000. This species has a high at-vessel mortality rate (Morgan and Burgess, 2007) when caught on bottom longline gear and it is pos sible that rather than discard the dead animals, fishers kept and landed them. Results for the general canvass data indicate th e majority of dusky sharks were landed in the MA (52.5%) followed by the SA (27.4%) and GOM (20.1%) (Table 2-9). The CFL showed the majority of dusky sharks represented in the CFL were landed in the GOM (64.4%) followed by the SA (26.5%) and MS (9.1%) and the QMS, which only covers the southeast US region, reported the majority of dusky sharks were caught in the GOM (53.8%) followed by the SA (46%) and no landings were reported for the MA re gion (Table 2-9). The recreational data sets showed that overall the majority of dusky sh arks were caught in the MA (Table 2-9). Catch Rates The four catch rate series analyzed all show ed decreasing trends over time (Figure 2-6). The VIMS fishery-independent da ta set began to show an incr ease in 2006; however, this was the last year of data so it is unclear whether this reflects an actual increa se in dusky shark relative abundance or is due to some other factors. The two observer programs (BLLOP and PLLOP) showed declines in catch rates after 1999, with the BLLOP show ing substantial declines. The BLLOP data set began to show an increase in catch rates by 2006, but these rates were still much lower than those seen in the earl y years of the program. More y ears of data will have to be collected to determine if this increasing trend continues. Catch rates (standardized) for the VIMS data set were high at the be ginning of the time series (1974) but dropped dramatically by 1981 (Figure 2-7A). Dusky shark catch rates

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41 remained very low until 2006, when they became even higher than those seen in the first three years (Figure 2-7A). Year (P<0.0001) was incl uded in the final model for the proportion of positive catches and area (P<0. 0001) was significant in the m odel for positive catches. The BLLOP program was a voluntary program from 1994 to2001 and mandatory from 2002 to2006. Catch rates were standardized separate ly for the two time periods in an effort to compensate for any differences between obser ver coverage during th e two time periods (John Carlson, personal communication). Catch rates were the highest during the voluntary years (1994-1999) and dropped to the lowest in 2000 (F igure 2-7B). The program only observed vessels in the SA during this year, which ma y account for the very low catch rate. The mandatory years all had relatively low catch rate values with a slight increase by 2006. The factors year, area, bait type, hook were included in the final model for the proportion of positive catches and the factors year, seas on, area and time of day were incl uded in the final model for the positive catches for the voluntary time period. The final models for the mandatory time period included the factors year, area and set depth for proportion of positive catches, and year for positive catches. The PLLOP catch rates were higher in 1992 and 1994 than any other year but over the whole time series remained very low (Figure 2-7C ). The lowest catch rate was in 2003 after which catch rates began to climb slowly thr ough 2006, which was the highest catch rate since 2000. The factors area, year and the interaction between area a nd tuna catch rate (TQR) were included in the final model for proportion of posi tive catches. Year, area, swordfish catch rate (SQR), TQR, and the interacti ons between year and area, year and quarter and SQR and area were included in the final model for positive catches.

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42 The LPS data set remained low for the entire time series but there was a decrease in the mid 1990s (Figure 2-7D). This decrease coincide s with the decrease in recreational catches reported by the MRFSS dataset. The catch rate series started high in 1986 and had several slight increases and decreases through 1993. The catch rates have remained low since 1994, with the lowest rate occurring in 2006. The nominal catch rates were higher than the standardized catch rates through the entire time series, but both catch rates did followed a similar pattern (Figure 27D). The factors state and year were included in the final model for the proportion of positive sets and the factors interview type and year we re included in the final model for the positive catches. Selectivity Curves The LPS dataset had the smallest age structur e and the BLLOP datase t had the largest age structure (Appendix C). These resu lts are most likely a factor of the sampling designs of the programs. The LPS program samples from recreational fishers and the BLLOP program observed the predominant fishery for dusky sharks and sampled from a much larger area. Age frequency distributions (Appendix C) showed that age-0 animals were most commonly caught in the BLLOP and VIMS data sets. The PLLOP and LPS data sets had the highest age frequency distributions for dusky sharks ages 0, 4 and 5 (Appendix C). Selectivit y curves for the VIMS and BLLOP datasets showed a steep increase from age 0 to approximately age 5 and 7 respectively, with the BLLOP selectivity curve staying relatively steady until age 22 after which it began a decline (Appendix C). The VIMS selec tivity curve remained fairly steady until age 16 followed by a decline. The LPS selectivity curve had a sharp increase from age 0 to at 5 and then began a shallow decrease after age 8 (Appendi x C). The PLLOP selectivity curve also had a steady increase from age 0 to 5, at which point it remained fairly steady until age 16 when the

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43 curve began a steady decline (Appendix C). Parame ter estimates for these selectivity curves are shown in Appendix D. Base Case Model The base case model showed that the current median total population of dusky sharks (B2006) has been reduced to 43% of the virgin biomass (B0) and similar results (47% of B0) were seen for the median mature biomass in 2006. Estimates of the parameters R* and z were 20,548 and 0.35 respectively (Table 2-10). The poste rior distribution was similar to the prior distribution for the parameter R* indicating that the data were uninformative for this parameter (Figure 2-8A). The majority of the distribution for R* occurred around 20,000 for both the prior and posterior distributions and both distributions flattened out around 50,000 (Figure 2-8A). The prior and posterior distributions for the re mainder of unknown parameters were different; suggesting the data were informative for thes e parameters (Figures 2-7B-F). The prior distribution for z descended from around 0.3 to 0.5, wh ile the posterior distribution peaked around 0.6, but both distributions included similar values (Figure 2-8B). The prior distribution for qvims peaked at the beginning (~0.1) and end (~1) of the distribution but remained relatively flat in between these peaks (Figure 2-8C). Th e posterior distribution peaked around 0.3 and then had a second smaller peak around 0.45 (Figure 2-8C ). The two distributions had a similar range of values (Figure 2-8C). The pr ior and posterior distributions for qbllop were very different and did not overlap or share a similar range of values (Figure 2-8D). The pr ior distribution peaked around 6, while the posterior dist ribution peaked around 0.3 (Figure 2-8D). The distributions for the parameter qpllop had different peaks, with the prior peaking around 0.2 and 1 and the distribution continuing past 1.5 (F igure 2-8E). The posterior di stribution peaked around 0.4 and did not go past 1 (Figure 2-8E). The posterior distribution for qlps had several peaks throughout the distribution while the prior had two peaks around 0.5 and 1.3 (Figure 2-8F). The

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44 distributions both had high proportions occurring around 0.7, but the prior distribution continued past 2, while the posterior distribu tion did not go past 1 (Figure 2-8F). The catch data used in this model ended in 2006 and the model continued to estimate the population through 2025 (B2025) (Figures 2-8 and 2-9). This simulated a fishing mortality rate of zero for the remainder of the years; however, total B2025/B0 (43% of B0) was still similar to those levels seen in 2006 and the mature B20205/B0 (41% of B0) was reduced even further (Table 2-10). These trends indicate long recovery times from overfishing, wh ere 20 years of no fishing caused only slight increases in population biomass in the simulations (Figure 2-9 and 2-9). Scenario 1: Commercial and Recreation al Catches Closure in the Future Continuing the catches through 2025 resulted in a slightly more pessim istic total (37% of B0) and mature (36% of B0) B20205/B0 when compared to the results of the base ca se model (41% and 43% of B0 respectively) (Table 2-10). Declines in mature biomass began in 1974 and continued through 2025, with large declines occurring in the early 1980s (Figure 2-9). Thus, the model predicted that mortality due to discards and illegal landings of dusky sharks in the future would prevent recovery of dusky shark populations from the current population levels found today. Scenario 2: Simulated Closure by Removal of North Carolina Catches The median mature (34% of B0) and total (35% of B0) B20205/B0 were very similar to the results from scenario 1 (reduced by 7% and 8% respectively from the base case model) (Table 210). Mature biomass declined from 1974 to 2025, w ith the largest declines occurring in the mid 1990s (Figure 2-9). These results suggest that increasi ng the time/area closure to include all of North Carolina will have little effect, positive or negative, on the population size of the dusky shark.

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45 Scenario 3: Simulating Reduced Soak Times Reducing the soak time to less than 10 hours cau sed a slight decrease in median total (8%) and mature (6%) B20205/B0 (Table 2-10). These results were very similar to those found in scenarios 1 and 2 with median total and mature B20205/B0 being 35% of B0 (Table 2-10). This would suggest that implementing a restriction on the amount of soak time for the bottom longline fishery would do little to further protect the dusky shark from p opulation declines estimated in the base case model (Figure 2-9). Scenario 4: Increased Co mmercial Catch Series The increase in commercial catches resulted in a slightly more pessimistic outcome when compared to the base case model. Total B2006/B0 had a median value of 36% of B0 and B2025/B0 had a median value of 35% of B0 (Table 2-10). This was a re duction in median population size of 7% and 8% respectively from the resu lts of the base case model. Mature B2006/B0 and B2025/B0 had median values of 43% and 34% of B0 respectively, which was a reduction of 5% and 7% respectively from the base case model (Table 2-10 ). Mature biomass began to decrease in 1974 and continued through the entire time series. The most significant declines in population biomass occurred in 1974-2001 (Figure 2-9). Scenario 5: Reduced Recreational Catch Series Decreasing recreational catches resulted in a slightly less pessimistic outcome when compared to the base case model, with a slight increase in biomass towards the end of the time series (Figure 2-9). The median total B2006/B0 and B2025/B0 were 49% and 48% of B0 respectively, which was an increase in median popul ation size of 6% and 5% respectively (Table 2-10). Median mature biomass B2006/B0 (52% of B0) was increased by 4% and by 6% in B2025/B0 (47% of B0) (Table 2-10). Mature biomass began d ecreasing in 1974 with large decreases in

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46 mature biomass starting in 1986. The biomass began to increase in 2016 and this increase continued through 2025 (Figure 2-9). Sensitivity 1: R* Prior Distribution Increased Increasing the prior distribution of R* resulted in an estimated median value of 62,247 (Table 2-10). This caused very significant ch anges to percent biomass (total and mature) reduction when compared to the base case model, with the median total and mature B2006/B0 and B2025/B0 being at more than 80% of B0, which is twice the level f ound in the base case model (Table 2-10). Median total B2006/B0 was 82% of B0 and B2025/B0 was 84% of B0 (Table 2-10). Mature median B2006/B0 and B2025/B0 was 82% of B0 (Table 2-10). Mature biomass declined fairly steadily in 1974-2013 at whic h point the biomass began increa sing slightly (Figure 2-10). Sensitivity 2: Z Increased Increasing the prior of Z resulted in a median estimate of Z = 0.54 (Table 2-10) and had the opposite effect of increasing R* and median total and mature biomasses were significantly reduced when compared to the base case model. The median mature and total B2006/B0 (37% and 28% of B0 respectively) and B2025/B0 (35% and 28% of B0 respectively) were reduced by over 10% when compared to the base case model (T able 2-10). Mature biomass decreased from 1974 to 2006, increased slightly in 2008 and 2009 and then continued to decrease through 2025. Larger decreases in mature biomass were s een in the early and mi d 1990s (Figure 2-10). Sensitivity 3: R* Increased by 10% This sensitivity analysis ( R* ) was the only one of the four th at resulted in higher total and mature B2006/B0 and B2025/B0 biomasses compared to the results of the base case model (Table 210). Total and mature B2006/B0 were 50% of B0 and B2025/B0 were 46% and 44% of B0 respectively, which were increases of 7%, 2%, and 3%, respectively, when compared to the base

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47 case model (Table 2-10). Mature biomass was larger but followed a similar trend to that seen in the base case model (Figure 2-10). Sensitivity 4: Z Increased by 10% Increasing Z caused total and mature B2006/B0 and B2025/B0 to decrease compared to the results of the base case model (Table 2-10). Biomasses were reduced from ~40% in the base case model to around 20% of B0 for total B2006/B0 and B2025/B0 and for the mature B2025/B0 (Table 2-10). The mature B2006/B0 was slightly higher (29% of B0) but was still reduced with respect to the base case model (48% of B0) (Table 2-10). Mature biomass estimates followed a trend similar to that of the base case model but appe ared to drop off more rapidly in the late 1990s (Figure 2-10). Sensitivity 5: M Increased by 10% Increasing M lead to the largest change in bioma ss estimates when compared to the base case model and resulted in the most pessimistic out come of all sensitivity and scenario analyses (Table 2-10). Total B2006/B0 and B2025/B0 and mature B2025/B0 were all reduced from ~40% of B0 (base case) to less then 10% of B0 (Table 2-10). Mature B2006/B0 was reduced from 48% of B0 in the base case model to 16% of B0 in this model (Table 2-10). Mature biomass had a much more dramatic drop off in the mid to late 1990s, when compared to the base case model (Figure 2-10). Sensitivity 6: Selectivities Increased by 10% Changing the selectivity values caused the sm allest changes to the overall population size when compared to the results of the base case model (Tab le 2-10). The results were very similar to those predicted by the base case model. Total B2006/B0 and B2025/B0 was reduced by 3% (39% of B0) and 5% (38% of B0) compared to the base case m odel respectively and mature B2006/B0 and B2025/B0 was reduced by 4% (44% and 37% of B0) when compared to the base case model

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48 (Table 2-10). Mature biomass followed a very sim ilar trend to that seen in the base case model (Figure 2-10). Sensitivity 7: BLLOP CP UE Series Removed Removal of the BLLOP CPUE series resulted in slightly higher total and mature B2006/B0 and B2025/B0 compared to the results of the base case model (Table 2-10). The mature B2006/B0 and B2025/B0 was reduced to 52% and 47% of B0 respectively. This was an increase of 5% of B2006/B0 and 6%of B2025/B0 compared to the base case model (Table 2-10). Total B2006/B0 and B2025/B0 was reduced to 48% of B0, which was an increase of 5% compared to the base case model (Table 2-10). Mature biomass through the tim e series was similar to those levels from the base case model (Figure 2-10). Sensitivity 8: Combined CPUE Series The use of a single catch rate series did not change the results from the base case model, except for mature B2006/B0, which was 1% higher than in the base case model (Table 2-10). However, the confidence intervals of the series (Table 2-7) were very poor and may suggest combining the catch rate series is not the most appropriate method. Mature biomass through the time series was also the same as in the base case model (Figure 2-10). Sensitivity 9: Selectivity Allowing for full exploitation of the age-zero age class caused the population to be more reduced compared to the base case mode l for all results except for mature B2006/B0. Total median B2006/B0 and B2025/B0 was reduced to 39% of B0 and mature B2025/B0 was reduced to 37% of B0, which were reductions of 5% and 4% respec tively of the base ca se model (Table 2-10). Mature B2006/B0 was increased to 49% of B0, which was an increase of 2% compared to the base case model (Table 2-10). Mature biomass declined through the time series until the last few years, when it began to incr ease slightly (Figure 2-10).

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49 Discussion My results show that the impacts of previous fishing on the dusky sh ark would be difficult to overcome, even with the implementation of time/area closures, gear modifications and/or catches being reduced for another 20 years. The base case model, all scenarios and all sensitivities except for one (sensitivity 1) re sulted in the dusky sharks in the northwestern Atlantic Ocean being at less than 60% of virg in biomass. Recent work has shown that the Maximum Sustainable Yield (MSY) for the dusky shark and other shark species may be well above 50% of the carrying capacity due to a low value of z and the high inflection point of the population growth curve (Cortes, 2008; Simpfendor fer et al., 2008; Cortes et al., 2006). This would suggest that the dusky shark could sustai n much less fishing pressure than previously thought, under the assumption that MSY occurs at 50% of the carrying capacity. Fisheries managers must consider whether MSY should be increased for the dusky shark and whether the results of these models indicate the population is overfished. The declines seen here are si milar to those seen in the sc hool shark population (13-45% of B1995/B1927) in southern Australia (Punt and Walker, 1998). Increasing the parameter values for M resulted in the most significant changes in percent biomass (tot al and mature) reduction when compared to the base case model. The scenar ios that looked at the effects of alternative management plans such as increasing the time/a rea closure or reducing the soak time did not provide very encouraging result s and indicated that biomass reductions would continue into 2025. The impacts of fishing already placed on th is species may be diff icult to overcome even with catches being reduced for another 20 years. The reductions in biomass seen in these models are due to both commercial and recreational fi shing that increased in the 1980s and 1990s and continues today, albeit at much reduced levels combined with the life history patterns of the dusky shark. This species has a long lag time be tween birth and age at sexual maturity, as well

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50 as a very long reproductive life cy cle (three years), with only 1/3 of all females reproducing in a single year. This long lag between life-stages and their low reproductive effort makes it very difficult for this species to quickly replace animals lost to fishing mortality. Generations may be needed for this species to fully recover despite ef forts to protect it by placing it on the prohibited species list, instituting a larger time/ar ea closure and/or gear restrictions. Corts et al.s (2006) dusky shark assessment used similar data but three different models (Bayesian surplus production, age-structured production model and catch-free age-structured production model). The results fr om that assessment were more pessimistic overall than those presented herein, with the surplus production mode l indicating a decline of greater than 80% in 2003 relative to virgin biomass, and the age-stru ctured model showing declines of 62-80% of virgin biomass. Three sensitivity analyses in the present assessment-increasing the prior distributions for Z and M-resulted in a reduction in biomass sim ilar to those found in Corts et al. (2006). Despite the differences, both assessment s indicated that the du sky shark biomass has been greatly reduced with respec t to virgin levels. The declin e in population size seen in both assessments is a consequence of declining catch rates, increased fishing mortality during the 1990s, a decrease in biomass over time and multiple age classes being represented in the catch. These factors when combined with the dusky shar ks life history characte ristics (slow growth, late age at maturity, long reproductiv e cycles), make this species very susceptible to overfishing. The differences between these two stock asse ssments are most likely due to the use of different models and parameter estimates and i ndicate the importance of model selection during the assessment process. Age-structured models differ from production models in that they consider cohorts and follow these cohorts sepa rately throughout the si mulation. Production models combine growth, reproduction and mort ality, making it impossible to look at the

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51 dynamic interactions occurring between thes e processes (Haddon, 2001). Age-structured models allow multiple gear types and biological information to be included, whereas production models do not (Simpfendorfer and Burgess, 2002). Bayesian modeling allows for the incorporation of uncertainty during the initial stag es of the development of the model and the use of historical fishing data (Punt and Hilborn, 1997). Ludwig a nd Walters (1985) suggested that the selection of a model used in a stock as sessment should be dependent on how much information is available and that in some inst ances simpler production models can provide better estimates. The life history patterns of the dusky sh ark, its exploitation in several fisheries and the availability of catch and catch rate data, fishery selectivities, growth, fecundity and maturity made an age-structured model appropriate for th is species. Exploitation and natural mortality rates for dusky sharks vary between size classes; therefore the ability of an age-structured model to follow each size class separately is a useful tool for stock a ssessment and decision analysis. However, because of the uncertainty in many mo del parameters for this species, the use of several different models and assessment techniqu es likely adds to the strength of the overall findings and provides more insi ght to fishery managers. Simpfendorfer and Burgess (2002) used a similar model to the one used in this assessment to assess the population of small coastal sharks in the northwest Atlantic Ocean. The results of their sensitivity analysis showed that the model was not very su sceptible to changes in input parameters, catches and/or catch rates. Howe ver, they suggested that the high level of uncertainty for all of the results made it difficult to identify individual factors affecting the model. The results of all models in this asse ssment were also surr ounded by a large level of uncertainty, except for sensitivity analysis 1 in which R* was significantly increased. The reduction in uncertainty found in this sensitivity analysis suggests that R* is a very important

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52 parameter and that the input values for this para meter greatly drive the outcome of the model. However, if one assumes the higher value of R* is more realistic, then the outcome of the model sharply contrasts with the results of the base ca se scenario and those of Corts et al. (2006). Therefore it is unlikely that this higher value of R* is applicable to the dusky shark. Simpfendorfer et al.s (2000) sens itivity analyses of an age and sex-structured model of the whiskery shark ( Furgaleus macki), which used a similar model, showed that the results were most influenced by changes in catch and effort data. This was largely driven by the availability and accuracy problems (mis-identification and no n-reporting) associated with these data and similar issues are thought to exis t in the U.S. dusky shark fishery. Scenarios where commercial catches were increased and recreational catches we re decreased to compensate for these issues resulted in changes to the reduc tion in percent biomass when compared to virgin levels. As would be expected, increasing the commercial ca tches resulted in a more pessimistic model outcome and decreasing recreational catches lead to a more optimistic model outcome. The results of the sensitivity analyses where parameters were increased by 10% showed that this model was most susceptible to changes made to the natural mortality ( M ) rates. Natural mortality rates for this species are already ve ry high and increasing them by 10% drove the population size down to approximately 10% of virg in biomass. The fact that changes to M had such a large impact on the model outcomes is very important since the original values used were estimated through indirect methods and therefor e there is a certain level of uncertainty surrounding them. Direct estimates of M could be determined through tagging and telemetry studies, but these can be expensive and time cons uming. Tagging studies can become biased if information on tag recapture, tag sh edding rates and tag re porting rates is missing or incomplete. Catch curve analysis can be used when tagging an d telemetry studies are not practical but this

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53 analysis comes with a number of assumptions th at may not be a realistic representation of the population. As technology conti nues to improve, the use of ta gging and telemetry studies for species such as the dusky shark will b ecome more realistic (Simpfendorfer, 2005). Two of the model scenarios (1 and 2) can be us ed to assess the utility of time/area closures in protecting the dusky shark. Scenario 1 can be used to simulate the current time/area closure into the future because it is based on the average amount of catches that have occurred since the closure was put into place. The results showed slightly lower biomass levels than in the base case model, which had no catches past 2006. This would suggest that th e reduction in catches likely to be caused by the current time/area cl osure combined with the prohibited species classification, would not be able to stop the redu ction in the population si ze over time. Scenario 2 where catches for 2007-2025 were reduced by rem oving North Carolina cat ches, gave results very similar to those from scenario 1. This suggests that the reduction in catches resulting from increasing the size of the time/area closure in the future (to include all of North Carolina) would do little to further protect the dus ky shark. The apparent inability of time/area closures to protect dusky sharks is related to historically high fishin g mortality rates, the time period of the closure (6 months), size of the closure, high movement rates and the biology of this species. Morgan and Burgess (2007) suggested that placing a restriction on the length of soak time in the bottom longline fishery may reduce fish ing mortality rates suffered by obligate ram ventilators such as the dusky shark. The length of soak time has also been found to be important in several other fisheries. Ward and Myers (2002) determined that the total mortality associated with increased soak time in pelagic longline fisheries might be fairly underestimated. They suggested this was due to animals being cons umed while caught on a long line during long soak times or the catch falling off of the gear. Th e number of harbor porpoises caught in gillnet

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54 fisheries in the Northwest Atlantic Ocean was f ound to be directly associated with soak time, with the longer soak-times catching more por poises (Hood, 2002). In New Zealand a limit on the amount of soak time for set-net fisheries was implemented in order to reduce the number of fish destroyed by sea lice, and to reduce the amount of fishing gear lost during fishing operations (Francis, 1999). The calculations I made using the BLLOP dataset indicated that reducing the soak time in the bottom longline fishery to less than 10 hours would re duce the mortality of dusky sharks by 43%. Scenario 3 that used these data resulted in similar biomass reductions to those found in scenario 1. This would suggest that implementing a reduction in soak time on bottom longline gear would add little protection to the dusky shark and that their population will continue to decline over time. The BLLOP catch rate series showed large declines after the dusky shark was placed on the prohibited species list (2000) This dataset reports observed catches and it is more realistic that fishers were able to change their fishing techniques to avoid catchin g dusky sharks once they became prohibited, then it is that the population dr opped so substantially within a year or two. Due to the uncertainty of the accuracy of dusky shar k catch rates in the last few years of data for this series, I conducted a sensitivity analysis where the BLLOP series was removed and a sensitivity analysis where the four catch rate se ries were combined into a single series. The removal of the BLLOP series caused only a slight increase in population size compared to the base model. The use of a single catch rate se ries provided the same results as the base case model. This would indicate that the three othe r catch rates series show a similar downward trend in catch rates over the time seri es and that including the BLLOP series in the base case model was appropriate and did not substant ially affect the model outcome.

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55 Age-zero and juvenile sharks were the most frequently caught according to the four datasets that included le ngth data that was assigned to ag es using an age length key (BLLOP, VIMS, PLLOP and LPS). Age length keys are usef ul when direct ageing of animals represented in the catch is not possible. However, errors in ageing can occur, leading to incorrect age length keys, which could in turn impact the results of an age-structured model. Errors in ageing could lead to incorrect growth rate estimates, age at sexual maturity estimates, selectivities and the subsequent partitioning of catch to incorrect ages. In these analyses, multiple ages were caught in all of the fisheries but very few adult an imals were ever represented in the catch. Representation of multiple ages in both the co mmercial and recreational fisheries targeting dusky sharks should be of concern to fisheries manage rs because previous research has shown that low level exploitation of age-zero dusky sharks can be sustained only if no other age classes were caught (Simpfendorfer, 1999). This was highlighted in the sensitivity analysis that allowed for full exploitation of the age-zero age class. Increasing the exploi tation of this age-class, while still allowing other age classes to be exploited, lead to a reduc tion in biomass compared to the base case model. The high exploitation of agezero and juveniles in both the recreational and commercial fisheries appears to have a substan tial impact on the ability of this population to increase in size (in terms of biomass). Future work should include additional research into at-vessel mortality rates by soak time for the pelagic longline fishery, mark/recapture programs, post-release survivability, spatial distribution of different age cl asses and movement. Manageme nt should continue improving on species identification wo rkshops for fishers and dealers a nd work on improving the quality of reported data. It may also be beneficial to investigate the effect s that changes in the

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56 methodologies of reporting to various datasets have had. Additional models that require fewer data could also be used to assess this species.

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57 Table 2-1. Five alternative scen arios and nine sensitivity analyses of the base case model for the age-structured model ( R* = virgin biomass, z = steepness parameter, M = natural mortality, sd = standard deviation, B LLOP = Bottom Longline Observer Program, VIMS = Virginia Institute of Marine Scie nce, LPS = Large Pelagic Survey, PLLOP = Pelagic Longline Observer Program and CPUE = Catch Per Unit Effort). Model Parameter Scenario 1 Catch continues through 2025 Average commercial catches, BLLOP discards and recreational discards for 2002-2006 applied to 2007-2025 Scenario 2 Time/area closure Average North Carolina catches (commercial and recreational) removed from 2007-2025 catch series used in scenario 1 Scenario 3 Reduced soak time/fishing mortality Reduce bottom longline dis cards used in 2007-2025 catch series from scenario 1 by 43% Scenario 4 Commercial catch series increased Commercial catches increased by 25% Scenario 5 Recreational catch series lowered Recreational catches reduced by 25% Sensitivity 1 R* increased Lognormal distribution: 60,000 (mean) /30,000 (sd) Sensitivity 2 Z increased Lognormal di stribution: 0.6 (mean) /0.24 (sd) Sensitivity 3 10% increase in R* Lognormal distribution: 22,000 (mean) /11,00 (sd) Sensitivity 4 10% increase in z Lognormal distribution: 0.33 (mean) / 0.01(sd) Sensitivity 5 10% increase in M Increased for each age Sensitivity 6 10% increase in Selectivities Increased for each data set (BLLOP, VIMS, LPS and PLLOP) Sensitivity 7 Removal of one CPUE Series Removed BLLOP CPUE series from model Sensitivity 8 Combined CPUE series Combined all four series into one single series Sensitivity 9 Selectivity Allowed age-0 animals to be fully exploited

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58 Table 2-2. Total amount (kg) of dusky sharks reported landed and discarded in commercial fisheries from the following sources: Ca nvass South East (SE), Canvass North East (NE), Quota Monitoring System (QMS), Commercial Fisheries Logbook (CFL), Bottom Longline Observer Program (BLLOP) (discards) and Pelagic Longline Logbook (PLL). Year Canvass SE Canvass NE QMS CFL PLL BLLOP discards Total 1982 18 18 1983 5 5 1984 0 0 1985 2,251 0 2,251 1986 0 0 0 1987 38 5 43 1988 767 61 828 1989 451 240 691 1990 18,122 418 18,540 1991 15,031 0321 15,353 1992 64,289 1,0517565054,057 70,658 1993 27,455 17,134 1,2488981,6061,732 50,073 1994 39,043 16,530 14,21910,23012,7822,464 95,268 1995 44,924 13,956 148,581106,89326,0619,375 349,791 1996 42,724 9,407 122,75688,31420,2367,746 291,183 1997 16,467 3,484 33,22623,90411,4482,097 90,626 1998 19,631 872 35,92825,8479,6232,267 94,168 1999 31,779 20,602 26,56619,1136,9512,005 107,017 2000 11,262 57,739 36,38226,17410,8902,296 144,743 2001 66 370 6647255 57 861 2002 1,893 2,089 51737292258 5,221 2003 3,677 6,886 1289266269 11,118 2004 447 18 000226 691 2005 289 107 000 18 415 2006 1814 83 000114 2,012

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59 Table 2-3. Constructed historic al catch series (kg) for commer cial and recreati onal landings and commercial (Pelagic Lo ngline Logbook) discards of the dusky shark. Year Commercial Recreational Discards 1972 0 00 1973 1,000 28,0002,000 1974 2,000 52,0003,500 1975 3,000 78,0005,000 1976 4,000 100,0006,500 1977 5,000 125,0008,000 1978 6,000 152,0009,500 1979 7,000 175,00011,000 1980 8,000 200,00012,500 1981 9,000 14,000 1982 10,000 15,500 1983 11,000 17,200 1984 12,000 19,000 1985 13,000 20,500 1986 14,000 22,000 1987 15,000 23,500 1988 16,000 25,000 1989 17,000 26,500 1990 28,000 1991 30,000

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60 Table 2-4. Total amount (kg) of dusky sharks reported discarded in the Pelagic Longline Logbook (PLL). Year PLL 1992 31,811 1993 16,663 1994 125,496 1995 14,577 1996 6,838 1997 12,835 1998 17,587 1999 5,469 2000 13,750 2001 1,347 2002 0 2003 0 2004 0 2005 0 2006 0

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61 Table 2-5. Total amount (kg) of dusky sharks re ported caught in recreati onal fisheries from the following sources: Marine Recreational Fisheries Statistical Survey (MRFSS), Headboat and Texas Parks and Wildlife Department (TXPWD). Year MRFSS Headboat TXPWD Total 1981 225,432 225,432 1982 53,829 53,829 1983 132,814 132,814 1984 225,492 225,492 1985 117,196 4,009117,196 1986 121,963 646807126,618 1987 152,322 534 0153,663 1988 98,983 45677099,438 1989 72,173 672 073,615 1990 69,029 165 069,194 1991 90,768 386 091,154 1992 168,298 1,701489169,999 1993 17,122 1,983 019,593 1994 55,427 829 056,256 1995 46,568 967 047,536 1996 88,746 1,54010490,286 1997 81,575 1,0858182,763 1998 25,867 707 026,656 1999 29,548 1,666 031,214 2000 16,227 86872517,094 2001 35,550 147 036,423 2002 5,739 369 06,108 2003 15,791 22140016,012 2004 0 156392556 2005 19,168 7413019,634 2006 346 954,009571

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62 Table 2-6. Catch rate series (CPU E) and coefficient of variations (CV) used in analyses from the following four data sets: Virginia Institute of Marine Science (VIMS) (product of hooks per set and soak time (hrs.) per set) Large Pelagic Survey (LPS) (number of dusky sharks caught per 100 trips), Botto m Longline Observer Program (BLLOP) (number of hooks per set multiplied by the leng th of the longline (miles) per set and soak time (hrs.) per set)(provided by J ohn Carlson, personal communication) and Pelagic Longline Observer Program (PLLOP) (number of dusky sharks caught per 1,000 hooks) (provided by Enric Corts, personal communication). VIMS LPS BLLOP PLLOP Year CPUE CV CPUE CV CPUE CV CPUE CV 1974 2.40 2.02 1975 2.93 0.1 1976 3.03 1.76 1977 0.39 6.03 1978 2.16 1.69 1979 1.78 1.90 1980 2.05 0.59 1981 0.93 1.04 1982 0.20 9.09 1983 0.29 6.94 1984 0.46 4.70 1985 0.18 11.09 1986 1.860.32 1987 0.67 3.20 2.550.22 1988 1.460.61 1989 0.11 14.01 2.320.28 1990 0.03 16.30 1.390.32 1991 0.13 4.13 1.710.29 1992 0.01 41.68 0.450.99 3.60 2.50 1993 0.11 7.38 1.350.49 1.71 1.19 1994 0.431.3511.960.29 2.71 1.88 1995 0.08 9.28 0.660.8818.760.24 1.11 0.77 1996 0.24 2.54 0.940.9615.510.24 0.96 0.67 1997 0.00 77.62 0.681.1923.790.25 0.46 0.32 1998 0.07 7.91 0.851.2318.050.29 1.29 0.90 1999 0.28 2.99 0.771.8019.210.36 0.35 0.24 2000 0.39 1.95 0.501.634.520.75 0.72 0.50 2001 0.19 3.68 0.412.407.820.44 0.28 0.20 2002 0.55 1.66 0.741.603.160.52 0.16 0.11 2003 0.27 3.21 0.521.135.110.36 0.08 0.06 2004 0.48 1.71 0.561.025.060.38 0.47 0.32 2005 0.52 1.48 0.581.096.300.49 0.37 0.26 2006 9.09 0.33 0.262.296.740.55 0.72 0.50

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63 Table 2-7. Combined Catch Per Unit Effort (CPUE) series, upper conf idence intervals (UCI) and lower confidence intervals (LCI). Combined Year CPUE LCI UCI 1974 2.88 0.0023921.45 1975 4.89 0.0046662.28 1976 5.41 0.0047362.95 1977 0.39 0.000525.43 1978 2.26 0.0023084.72 1979 1.55 0.0012109.52 1980 2.03 0.0012763.40 1981 0.66 0.000901.64 1982 0.32 0.000434.51 1983 0.35 0.000475.43 1984 0.41 0.000563.53 1985 0.31 0.000425.91 1986 2.14 0.0022856.09 1987 4.27 0.0035694.24 1988 1.44 0.0011914.50 1989 3.39 0.0034524.26 1990 0.02 0.0003.09 1991 0.02 0.0003.82 1992 0.01 0.0000.69 1993 0.01 0.0000.51 1994 0.01 0.0000.90 1995 0.10 0.0025.50 1996 0.01 0.0000.35 1997 0.06 0.0022.14 1998 0.02 0.0000.67 1999 0.02 0.0000.73 2000 <0.00 0.0000.02 2001 <0.00 0.0000.04 2002 <0.00 0.0000.01 2003 <0.00 0.0000.02 2004 <0.00 0.0000.02 2005 <0.00 0.0000.03 2006 <0.01 0.0000.30

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64 Table 2-8. Selectivity parameters for the double logistic distribution fitted to age data allowing for full exploitation of age-zero animals, for the following four data sets: Bottom Longline Observer Program (BLLOP), Virginia Institute of Marine Science (VIMS), Large Pelagic Survey (LPS) and Pelagic Longline Observer Program (PLLOP) (Enric Corts personal communication). Data set Parameter estimates a50 b c50 d BLLOP 4 5250 32 4 VIMS 2 440 28 5 LPS 2 600 24 5 PLLOP 1.5 440 28 5

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65 Table 2-9. Regional landings of dusky shark (GOM = Gulf of Mexico, SA = South Atlantic, MA = Mid-Atlantic region and UNK= unknown) from the general canvass (GC), quota monitoring (QMS), Coastal Fi sheries Logbook (CFL) and recrea tional data sets for all years combined. Region Data set Percent GOM General canvass 20.1 MA General canvass 52.5 SA General canvass 27.4 GOM QMS 53.8 SA QMS 46.0 UNK QMS 0.2 GOM CFL 64.4 MA CFL 9.1 SA CFL 26.5 GOM Recreational 24.9 MA Recreational 53.7 SA Recreational 21.4

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66Table 2-10. Parameter outputs ( R* = virgin biomass, z = steepness parameter, qvims = catchability of Virginia Institute of Marine Science (VIMS) survey gear, qbllop = catchability of Bottom Longline Observer Program (BLLOP) fishing gear, qlps = catchability of Large Pelagic Survey (LPS) fishing gear and qpll = catchability of Pelagi c Longline Logbook (PLL) fishing gear) from the Sampling Importance Resampling algorithm (median estimates with 95% confidence intervals (in parenthesis)) and population status for total and mature B2 006/B0 (biomass in 2006/virgin biomass (1972)) and B2025/B0 (biomass in 2025/virgin biomass (1972)) (med ian estimates with 95% confidence inte rvals (in parenthesis)) for the base case model, five scenarios a nd nine sensitivity analysis. Model R* (CI) z (CI) qvims (CI) qbllop (CI) qlps (CI) qpll (CI) Total B2006/B0 (CI) % Total B2025/B0 (CI) % Mature B2006/B0 (CI) % Mature B2025/B0 (CI) % Base case Base case 20,548 (8,81333,656) 0.35 (0.230.57) 0.22 (0.010.60) 0.25 (0.140.66) 0.31 (0.010.66) 0.39 (0.130.64) 43 (0-65) 43 (0-65) 47 (0-70) 41 (0-64) Scenario 1 Catch 2006-20025 19,426 (8,21934,937) 0.33 (0.210.42) 0.41 (0.030.76) 0.36 (0.090.73) 0.48 (0.110.65) 0.44 (0.030.77) 40 (0-65) 37 (0-63) 45 (6-68) 36 (0-63) Scenario 2 Removal of North Carolina catches 16,065 (6,68631,453) 0.28 (0.20.45) 0.39 (0.060.72) 0.41 (0.010.76) 0.43 (0.030.7) 0.34 (0.030.7) 38 (0.60) 35 (0-59) 44 (18-63) 34 (0-58) Scenario 3 Reduced discard mortality by 43% 17,618 (6,97025,894) 0.26 (0.200.35) 0.27 (0.010.58) 0.65 (0.270.74) 0.55 (0.190.77) 0.54 (0.10.78) 38 (0-68) 35 (0-68) 45 (0-69) 35 (0-66) Scenario 4 High commercial catch 19,814 (11,61631,493) 0.29 (0.20.45) 0.52 (0.070.75) 0.36 (0.110.76) 0.23 (0.080.67) 0.46 (0.070.75) 36 (0-61) 35 (0-62) 43 (1-66) 34 (0-61) Scenario 5 Low recreational catch 19,164 (10,86029,338) 0.36 (0.20.69) 0.17 (0.0010.65) 0.52 (0.060.70) 0.32 (0.080-.73) 0.51 (0.030.72) 49 (5-68) 48 (4-69) 52 (15-68) 47 (5-67) Sensitivity 1 R* increased 62,247 (46,97283,437) 0.40 (0.300.40) 0.55 (0.410.64) 0.47 (0.040. 69) 0.68 (0.440.76) 0.46 (0.030.59) 82 (75-88) 84 (76-90) 82 (74-88) 82 (74-88) Sensitivity 2 Z increased 16,350 (9,21637,019) 0.54 (0.310.97) 0.49 (0.010.69) 0.60 (0.020.77) 0.50 (0.010.75) 0.34 (0.010.79) 28 (0-71) 28 (0-72) 37 (0-71) 35 (0-70)

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67Table 2-10. Continued Model R* (CI) Z (CI) qvims (CI) qbllop (CI) qlps (CI) qpll (CI) Total B2006/B0 (CI) % Total B2025/B0 (CI) % Mature B2006/B0 (CI) % Mature B2025/B0 (CI) % Sensitivity 3 10% increase R 21,345 (13,34024,164) 0.28 (0.210.46) 0.49 (0.050.79) 0.46 (0.050.75) 0.38 (0.320.50) 0.1 (0.080.70) 50 (8-55) 46 (8-55) 50 (11-56) 44 (7-53) Sensitivity 4 10% increase Z 15,218 (13,26338,052) 0.230.210.55) 0.34 (0.130.67) 0.27 (0.130.60) 0.25 (0.20.54) 0.27 (0.150.71) 22 (8-69) 21 (7-68) 29 (12-70) 20 (7-68) Sensitivity 5 10% increase M 21,085 (11,18224,193) 0.28 (0.20.38) 0.67 (0.260.76) 0.54 (0.200.73) 0.55 (0.070.76) 0.23 (0.020.78) 9 (0-20) 8 (0-1) 16 (0-29) 9 (0-20) Sensitivity 6 10% increase selectivity 19,186 (18,80823,462) 0.34 (0.20.36) 0.36 (0.18 -0.5) 0.23 (0.010.43) 0.13 (0.10.45) 0.31 (0.160.36) 39 (35-49) 38 (33-49) 44 (43-53) 37 (33-48) Sensitivity 7 Bottom Longline Observer Program Catch Per Unit Effort removed 21,751 (10,98028,499) 0.31 (0.280.50) 0.54 (0.280.75) 0.37 (0.100.71) 0.36 (0.050.65) 0.26 (0.130.71) 48 (12-61) 48 (12-62) 52 (19-62) 47 (13-60) Sensitivity 8 Combined Catch Per Unit Effort series 20,372 (10,65426328) 0.30 (0.20.45) 0.34 (0.090.61) 0.08 (0.020.60) 0.68 (0.060.77) 0.44 (-.070.70) 43 (0-58) 43 (0-58) 48 (0-59) 41 (0-56) Sensitivity 9 Selectivity 19,653 (6,01333,008) 0.20 (0.20.41) 0.38 (0.180.69) 0.50 (0.080.77) 0.47 (0.090.78) 0.31 (0.020.66) 39 (0-65) 39 (0-65) 49 (0-69) 37 (0-64)

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68 Year 197019751980198519901995200020052010 CPUE 0 1 2 3 4 5 6 Combined CPUE Figure 2-1. Combined Catch Pe r Unit Effort (CPUE) series. The single series includes the following four series; Virginia Institute of Marine Science (VIMS), Bottom Longline Observer Program (BLLOP), Pelagic Longline Observer Program (PLLOP) and Large Pelagic Survey (LPS).

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69 Year 1980198519901995200020052010 Landings (kg) 0 100000 200000 300000 400000 GNSE GNNE QMS CFL PLL Figure 2-2. Dusky shark commerc ial landings (kg) from the gene ral canvass SE and NE (GNSE and GNNE), quota monitoring system (QMS), Coastal Fisheries Logbook (CFL) and Pelagic Longline Logbook (PLL).

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70 Year 19901992199419961998200020022004200620082010 Discards (kg) 0 20000 40000 60000 80000 100000 120000 140000 PLL BLLOP Figure 2-3. Dusky shark comme rcial discards (kg) from the Pelagic Longline Logbook (PLL) and Bottom Longline Observ er Program (BLLOP).

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71 Year 19751980198519901995200020052010 Catches (kg) 0 50000 100000 150000 200000 250000 MRFSS HBOAT TXPWD Figure 2-4. Dusky shark recreational catches and dead discards (kg) from the Marine Recreational Fishery Statistics Survey (MRFSS), Headboat (HBOAT) and Texas Parks and Wildlife Department (TXWPD).

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72 Year 1980198519901995200020052010 Total Catches (kg) 0 100000 200000 300000 400000 Figure 2-5. Total amount (kg) of dusky shark commercial catches and Bottom Longline Observer Program discards.

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73 Year 197019751980198519901995200020052010 CPUE 0 5 10 15 20 25 VIMS LPS BLLOP PLLOP Figure 2-6. Combined Catch Per Un it Effort (CPUE) indices for th e Virginia Institute of Marine Science (VIMS) (product of hooks per set a nd soak time (hrs.) per set), Large Pelagic Survey (LPS) (number of dusky sharks caught per 100 trips), Bottom Longline Observer Program (BLLOP) (number of hooks per set multiplied by the length of the longline (miles) per set a nd soak time (hrs.) per set)(John Carlson, personal communication) and Pelagic Longline Obse rver Program (PLLOP) (number of dusky sharks caught per 1,000 hooks) (provided by Enric Corts, personal communication).

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74 Year 1975198019851990199520002005 CPUE -10 0 10 20 30 40 50 Standardized Nominal Year 19921994199619982000200220042006 CPUE 0 5 10 15 20 25 30 35 Standardized Nominal Figure 2-7. Standardized (with 95% confidence intervals) and nominal Catch Per Unit Effort (CPUE) indices. A) Virginia Institute of marine Science (VIMS) (product of hooks per set and soak time (hrs.) per set). B) Bottom Longline Observer Program (BLLOP) (number of hooks pe r set multiplied by the leng th of the longline (miles) per set and soak time (hrs.) per set) (J ohn Carlson, personal co mmunication). C) Pelagic Longline Observer Program (PLLOP) (number of dusky sharks caught per 1,000 hooks) (Enric Corts personal communi cation). D) Large Pelagic Survey (LPS) (number of dusky sharks caught per 100 trips). A B

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75 Year 19921994199619982000200220042006 CPUE -4 -2 0 2 4 6 8 10 12 Standardized Nominal Year 1985 1990 1995 2000 2005 CPUE -4 -2 0 2 4 6 8 Standardized Nominal Figure 2-7. Continued. C D

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76 Distribution 0 20000400006000080000100000120000 Proportion 0 20 40 60 80 Prior distribution Posterior distribution Distribution 0.20.30.40.50.60.70.80.91.0 Proportion 0 20 40 60 80 100 Prior distribution Posterior distribution Figure 2-8. Prior vs. posterior dist ributions for unknown parameters A) R* (virgin biomass), B) z (steepness parameter), C) qvims (catchability of Virginia Institute of Marine Science (VIMS) survey gear), D) qbllop (catchability of Bottom Longline Observer Program (BLLOP) fishing gear), E) qpllop (catchabiliy of Pelagic Longline Observer Program (PLLOP) fishing gear), and F) qlps (catchability of Large Pelagic Survey (LPS) fishing gear). B A

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77 Distribution 0.0 0.2 0.4 0.6 0.8 1.0 Proportion 0 20 40 60 80 100 Prior distribution Posterior distribution Distribution 02 04 06 0 Proportion 0 20 40 60 80 100 Prior distribution Posterior distribution Figure 2-8. Continued. D C

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78 Distribution 0.0 0.5 1.0 1.5 2.0 Proportion 0 20 40 60 80 100 Prior distribution Posterior distribution Distribution 012345 Proportion 0 20 40 60 80 100 Prior distribution Posterior dsitribution Figure 2-8. Continued. F E

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79 Year 1970198019902000201020202030 Biomass (kg) 0 500000 1000000 1500000 2000000 2500000 3000000 Base case Catches 2007-2025 Increased comm. catches Decreased rec. catches Reduced discard mortality NC catches removed Figure 2-9. Mature median biomass (kg) for the base case and five scenarios (catches continued from 2007-2025, commercial catches incr eased by 25%, recreational catches decreased by 25%, reduced discard mortalit y and North Carolina catches removed) of the base case model.

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80 Year 1970198019902000201020202030 Biomass (kg) 0 2000000 4000000 6000000 8000000 10000000 Base case R* (10% increase) Z (10% increase) M (10% increase) Selectivity (10% increase) Z R* Selectivity Combined catch rate series BLLOP series removed Figure 2-10. Mature median biomass (kg) for the base case and nine sensitivity analyses ( R* (virgin biomass) increased by 10%, z (steepness parameter) increased by 10%, M (natural mortality) increased by 10%, selectivities increased by 10%, z mean and standard deviation increased, R* mean and standard deviati on increased, selectivities allowed for full exploitation of age-zero cl ass, four Catch Per Unit Effort (CPUE) series combined into one and Bottom Longline Observer Program (BLLOP) CPUE series removed) of the base case model.

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81 CHAPTER 3 SPATIAL MODEL Introduction Spatially explicit models can be used to inve stigate and evaluate the usefulness and success of time/area closures or marine protected areas in protecting mari ne species. The complexity of spatial models can range from simple one-dimensional models to complex models that combine movement, fishing and population dynamics (Pel letier and Mahevas, 2005). A spatially explicit model has been used for a stock assessment of the school shark in Aust ralia (Punt et al., 2000) but has never been used for the dusky shark. A one-dimensional spatial model was adapted from Walters and Hil born (2007) and used to evaluate the effects of time/area closures on the population sizes of three life-stages (juvenile, subadult and adult) of the dusky shark. This appr oach has yet to be used in a stock assessment for any species of shark. The new model was modified to include these three life-stages (juvenile, subadult and adult) with different natu ral mortality rates and movement rates and was used to model the time/area closure off the coast of North Carolina. This model allows the user to identify marine protected areas using spatial cells that are connected through the dispersal of neonates and adult animal s determine changes in the compensation of juvenile post-settlement survival, identify how displaced fishing effort w ill affect the abundance of fish outside the model area, identify long-term changes in abundance and harvest and change s in mean fecundity (Walters et al ., 2007). The size of the protected area and parameters within the model can be changed, giving the user the ability to test various hypotheses. Since this type of model has not previously been used on elasmobranchs, many pa rameter values were unknown and I used this opportunity to investigate what effect different parameter va lues would have on the model outputs as well as the effects of increasing/d ecreasing the size of the time/area closure.

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82 Ages were used to develop the stages (bot h sexes combined) were juvenile (ages 2-6), subadult (ages 7-19) and adult (age s 20+) based on Natanson et al. (1995). The effort or number of cells (1 nautical mile) modeled was 200 (50 op en cells (northern area), 130 closed cells and 20 open cells (southern area) in that order), based on assuming the closed area off the coast of North Carolina is approximately 370 km long. The follo wing coordinates outline the time/area closure: 350 41N to 300 51N and west of 740 46 W, roughly following the 60 fathom contour line, diagonally south to 760 24W and north to 740 51W (Appendix A). Spatial Model Components of the Model The spatial aspect of the model was created by identifying n spatial cells (i) that each extend for 1 nautical mile along the shore and o ffshore far enough to protect the entire life cycle of the animal, in theory. Cells that were closed to fishing were given the value 0 and those open to fishing were given the value 1 (Walters et al., 2007). The models used in these analyses represent the entire coast of North Carolina and the closed area within th at area (Appendix A). Movement only occured within the modeled area a nd the movement of animals outside of this area was not accounted for. The long-term impact of protection to animals in each of these cells was calculated through a procedure that converged within 20 iterations. Each cell was re lated to other cells so that numerical equations were solved using a proced ure termed successive over-relaxation, which also helps eliminate chatter within the iteration process. The initial estimate of the number of animals in each cell is given in equation 3-1, MareaF R Nj o i 1 (3-1)

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83 where Ni is the number of older animals in an individual cell summed over all ages, Ro is the average natural recruitment rate per cell (scaling parameter that indicates unit of measurement for Ni), F is fishing mortality rate absent any closures, areaj is 1 or 0 depending on whether the individual cell is open or closed, respectively, and M is natural mortality (Walters et al., 2007). Equation 3-1 was held constant and estimates we re used to solve equations 3-3, 3-4 and 35 and update the mixing effects (see below). It erations updated the values for these three equations. The results were considered fixed constants and were combined with a relaxation weight to determine a new iterative estimate using equation 3-2, 1 1 1 1 1 1 21i dpow j i i dpow i j j i iNsorwt N N emigmFq N N NemigLr sortwt N (3-2) where sortwt is the relaxation weight, emig is the movement rate into areaj per year, q is all fishing mortality that would occur if the whole area was open, which has been concentrated into only open areas, and dpow is an empircal power parameter used to predict mean fecundity, Fi is the fishing effort redistribution and r(Li) is local recruitment rate, which is a function of the neonate settlement rate Li (assumed to be normally distributed) (Walters et al., 2007). The equilibrium age structure for each cell was calcul ated by determining a rate at which cohorts could die off in each cell. Dispersal to a nd from other cells wa s calculated using the emig parameter (Walters et al., 2007). These steps were repeated until Ni converged on a fixed value (Walters et al., 2007): The Beverton-Holt (BH) recruitment func tion was calculated using equation 3-3, i ii iL Lh Lr 1 (3-3)

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84 where hi is the habitat quality for the individual cell, is the maximum survival rate of neonates from settlement to recruitment ([compensation ratio*avg. natural recruitment)/neonates per recruit]), and / is the maximum recruitment rate, ( =[compensation ratio 1]/neonates per recruit). The compensation ratio is the ratio of maximum neonate survival at low densities to survival at unfished natural abund ances (Walters et al., 2007). Th is parameter is typically used for teleost fish but is similar to the steepness parameter ( z ) used in Chapter 2. I used a different equation than that used in Chapter 2 in an effort to provide more estimates of the unknown parameters from this function. Neonate cont ributions to the model were calculated using equation 3-4, 215.0 1sdldist j i ieNjperL (3-4) where jper is a scaling constant for total neonate settlement from each individual cell, and sdldist is the standard deviation of the spatial distribution of neonate settlement (Walters et al 2007). The recruitment function and neonate contri bution equations affect the dispersal of neonates by determining the sum of neonate cont ributions from other cells, and identify postsettlement density dependence. The exponentia l term in the neonate contribution equation allows for neonate settlement to follow a normal distribution, while the parameter jper is a scaling constant for ne onate settlement and sdldist identifies an area beyond an individual cell where settlement rates drop off. For example, if sdldist equals 5 then neonate settlement would drop off five miles from the ce ll. The habitat quality for neonates of each spatial cell is incorporated into the Beverton-Holt recruitment function equation. Cells with unsuitable habitat were given a value of hi = 0 and cells with suitable habitat, hi = 1. The inclusion of this term also lead to the assumption that emigra tion rates (per year) increased to emig/hi and immigration rates decreased to hi/emig (Walters et al., 2007).

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85 Fishing effort redistribution ( Fi) within the model was calcu lated using equation 3-5, effpow N i effpow Nc ii iieC e effortF1 1 (3-5) where effort is the total number of spatial cells, Ci is the spatial cell and effpow is the standard deviation of the gravity mode l (Walters et al., 2007). A multinomial logit model was used to predict the redistribution of fishing effort when individual cells were closed to fishing. This allowed the model to identify areas near the boundaries of an MPA where concentrations of fishing effort may occur, by assuming the likeliness of a fisher to fish in a particular cell (i.e. effpow : the higher the value the more evenly spread out fishing effort is) is proportional to the logarithm of abundance in that particular area. The number of animals at these locations could also be affected by spillover (when animals from protected areas move into non-protected areas) but this would be dependent upon their movement rates (Walters et al., 2007). Parameters The fishing mortality rate absent any closures ( F ) was taken from Corts et al., (2006) and annual juvenile, subadult and adult natural mortality rates were taken from the values provided in Chapter 2. The average natural mortality for each stage was calculated from the assumed linear decrease for age 0 to mature animals used in Chap ter 2. The parameter effort used to calculate fishing effort within the model was the total number of cells modeled, which was 200. The parameter for all mortality that would occur if the entire area was open, which has been concentrated into only areas that are open ( q) was calculated by multiplying F by the effort and dividing by the sum or all cells (0 if closed a nd 1 if open) (Walters et al., 2007). Movement ( emig ) rates for juveniles were based on dusky shar ks tagged in Wester n Australia (Colin

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86 Simpfendorfer personal communication) and rates for subadult and adult animals were estimated based on these rates and knowledge of the moveme nt patterns of larger dusky sharks (Colin Simpfendorfer, personal communica tion). The compensation ratio ( reck ) was set slightly higher than the maximum value (1) of the steepness parameter ( z ) to allow for the lower levels of compensation, compared to teleosts, expected fo r this species. Information on the standard deviation of the spatial distri bution of neonate settlement (sdldist), average recruitment rate ( Ro ), power parameter ( dpow ) and standard deviation of the gravity model ( effpow ) does not exist for sharks. I therefore used a starti ng value of the base case model a nd used the sensitivity analysis to investigate alternative values. The relaxation weight ( sorwt ) was estimated based on information provided in Walters et al., 2007. The scaling constant for total neonate settlement from each source cell ( jper ) was calculated using equati on 3-6 (Walters et al., 2007). 14.32 1 sdldist (3-6) The two Beverton Holt parameters and were calculated as mentioned above. Habitat ( hi ) areas for recruits (in terms of individual cells) were given the value 1 indicating they were of good quality (Walters et al., 2007). This type of model has yet to be applied to highly migratory species and many of the parameters were unknown for this species. Base case parameter values we re generally estimated based on knowledge of the species or calculated through the use of other parameters within the model. All parameters and parameter values used in the base case model are listed in Table 3-1. In order to fully understand the im pact these estimates had on the outcome of the model, several sensitivity analysis were run. I investigated the impact of changes ma de to the fishing and natural mortality rates, movement rates, the co mpensation ratio and the st andard deviations of the gravity model and spatial distribution of neonate settlement. Additionally, I investigated the

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87 effects of opening and closing the entire model to fishing. The parameter values used in each of the sensitivity analyses are presented in Table 3-2. The ability to easily run many sensitivity analyses investigating the effects of various parameter estimates was a key benefit of this approach. Sensitivity Analysis I ran one base case scenario and 11 sensitivity analyses models (described below) (Table 32). Sensitivity 1: Closed a rea/no fishing mortality The entire area was closed to fishing in this model by setting all of the cells to 0. This sensitivity analysis simulated a time/area closure fo r the entire coast of North Carolina instead of the small closure currently in effect, which was represented in the base case model. Sensitivity 2: Open area model The entire area was opened to fishing in this model by setting all of the cells to 1. This allowed the model to simulate that there was no time/area closure in effect. Sensitivity 3: Fishing mort ality rate set to F msy The fishing mortality rate was reduced to the value of Fmsy presented in Corts et al. (2006). Sensitivity 4: High standard deviation of the gravity model (effpow) The standard deviation of the gravity model ( effpow ) was increased to investigate what effect this would have on the densities and/or re distribution of fishing effort within the model. There are no published values of this parameter for shark species. Sensitivity 5: High comp ensation ratio (reck) I used a higher value, which allowed for additional compensation due to increased neonate survival.

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88Sensitivity 6: High natural mortality rates (M) The highest natural mortality rates for each stage were taken from the values used in Chapter 2 (Appendix B) and applied to this model. Sensitivity 7: Low natural mortality rates (M) The lowest natural mortality rates for each stage were taken from the values used in Chapter 2 (Appendix B) and applied to this model. Sensitivity 8: High standard deviation of the spatia l distribution of neonate settlement (sdldist) Similar to the effpow parameter, there is no published estimate for the standard deviation of the spatial distribution of neonate settlement for sharks. I used an estimated higher value than the one used in the base case model for this sensiti vity analysis. This allowed neonates to spread out more throughout the modeled area. Sensitivity 9: Low standard deviation of the spatial distribution of neonate settlement (sdldist) Similar to the effpow parameter, there is no published estimate for the standard deviation of the spatial distribution of neonate settlement for sharks. I used an estimated lower value than the one used in the base case model for this sensitivity analysis. Th is caused neonates to spread out less throughout the modeled area. Sensitivity 10: High m ovement rates (emig) The data presented by Colin Simpfendorfer (personal communication) was based primarily on juvenile animals from a differe nt region and the movement rates observed varied substantially for juveniles. I increased the movement rate s in this model using data provided by Colin Simpfendorfer (personal communication) a nd by making educated assumptions.

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89Sensitivity 11: Low mo vement rates (emig) The data presented by Colin Simpfendorfer (personal communication) was based primarily on juvenile animals from a differe nt region and the movement rate s observed varied substantially for juveniles. I decreased the movement rate s in this model using data provided by Colin Simpfendorfer (personal communication) a nd by making educated assumptions. Results The model outputs include the total densitie s (number) for each stage (juvenile, subadult and adult), as well as the amount of neonates and r ecruits found with that stage in the model and the amount of effort that is re distributed throughout the modele d area. These results give a visual representation of how and where dusky shar k densities build up and decrease within the modeled area. Additionally, I calculated the tota l number of animals found in each of the three areas (northern closed, open and southern closed) for the base case and sensitivity analysis, allowing for a comparison of how closing and ope ning the area to fishing and changing other parameters effected the total number of dusky sharks found in each area of the model. Base Case The total number of juvenile dusky sharks f ound in the closed area was over 6 times that found in the northern closed area and over 10 times that found in the southern open area (Table 3-3). The density of juvenile dusky sharks remained low throughout the northern area of open fishing and began to increase slight ly during the first sec tion of the closed area. A large increase in density was not seen until approximately 18 nm in to the closed area. This increase in density continued for 65 nm at which point it leveled. The density remained at this level throughout the remainder of the closed area and th en drastically declined to leve ls somewhat above those of the northern open fishing area, once the southern ar ea became open to fishing again (Figure 3-1). Neonate density within this stages model graduall y increased at the beginning of the closed area

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90 when compared to juvenile density. The decr ease in density as the closed area became open again in the southern area was also more gradual than what was seen in the juvenile density curve. Additionally, neonate density began to increase quicker approximately 19 nm before the closed area began and started to decrease approximately 56 nm before the closed area ended (Figure 3-2). Recruitment density within this stages model remained very flat throughout the open and closed areas (Figure 3-3). Redistributio n of fishing effort was concentrated for 4 nm on the outside edge of the southern sect ion of the closed area (Figure 3-4). Subadult density followed a similar trend to that seen with juvenile dusky sharks but with overall density being higher (F igure 3-5). The total number of subadult animals found in the northern open area was close to 10 times less than the amount found in the closed area and just slightly less than in the southern open area (Table 3-3). This was the opposite of the results from the juvenile and adult models, where the total number of animals was higher in the northern open area than in the southern (Table 3-3). Density began to increase approximately 28 nm into the closed area, remained steady after 128 nm into the closed area and then dropped off significantly once the southern open area began. Density in the southern open area remained higher than 3333in northern open area (Figure 3-5). Neonate de nsity within this stages model had a slow steady increase in density from the beginning of the northern open area and continuing to a peak about 139 nm into the closed area. The curve th en began a steady decrease until 19 km into the southern open area, at which point density declin es drastically (Figure 3-6). Recruitment density within this model was flat through the entire area (Figure 3-7), similar to what was seen in the juvenile stage and fishing effort was concentrated into the first 4 nm of the southern open area (Figure 3-8).

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91 Adult density was higher then juvenile and s ubadult densities. There were over 11 and 17 times more adults found in the closed area than in the northern and southern open areas respectively (Table 3-3). Density increased slow ly for the first 56 nm of the northern open area and then began a large increase that plateaued around 50 nm into the closed area and continued for 143 nm. After the plateau, th ere was a large declin e in density throughout the remainder of the southern open area, resulting in densities si milar to those in the nor thern open area (Figure 39). Neonate density within this stages model, increased from the start of the northern open area into the closed area and reached a plateau 107 nm into the closed area, continued for 26 nm, and then began to decrease again (Figure 3-10). R ecruitment density within this stages model, remained fairly steady throughout the whole area (Figure 3-11). The redistribution of fishing effort was maximized in the last nm of the nor thern open area directly before the closed area began. This was is in sharp contrast to what was seen in the previous two stages (Figure 3-12). This model converged quickly for the redistribu tion of fishing effort for both the juvenile (Figure 3-4) and subadult (Figure 3-8) stages but not for the adult (Figure 3-12) stage. The majority of this effort switched for each of the iterations and alternated between the last open cell in the northern area and the first cell of the southern open area. There was also instability in the convergence of total adult density in the cells surr ounding the start and finish of the closed area. These convergence issues are likely related to the high fishing mortalit y, high movement rates and natural mortality rates of th e adult stage combined with the effpow parameter. The convergence issues did not occur in sensitivity an alyses where fishing mortality (sensitivity 3) and/or the movement rate was reduced (sen sitivity 11), when natural mortality was increased/decreased (sensitivities 6 and 7) or when the parameter effpow was increased (sensitivity 4). These issues were not seen in any of the model runs for the juvenile and/or

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92 subadult stages. Despite this, it did appear that the redistribution of fishing effort for the adult stage became concentrated around the northern and southern end of the closed area. Sensitivity 1: Closed Area/no Fishing Mortality The closed area model resulted in a very flat total density curve for all three life-stages, when compared to the base case model. The tota l number of animals foun d in the northern area increased from those found in the base case model but the numbers found in the original closed area remained similar but slightly higher, than those found in the base cas e model (Table 3-3). The lower numbers found in the northern area, co mpared to the closed area, was due to the high movement rates that allowed the animals to move out of the modeled area but the model did not allow for them to move back in. This wa s a result of their lower natural mortality rates compared to the other two stages. Densities seen in these three life-stages were very similar to those seen during the plateaus/peaks of the base case model, with subadu lt density (Figure 3-5) being higher than juvenile densit y (Figure 3-1), and adult density (Figure 3-9) being the highest overall. Neonate density within all three model stages showed a large increase for the first 35 nm, compared to the long steep increase seen in the base case model. This was followed by a plateau that lasted for ~110 nm, at which point density began to drop to levels seen at the beginning of the model (Figures 3-2, 3-6, and 3-10 ). Recruitment densities in all three modeled stages remained very similar throughout the mode l and were similar to th e base case models. Fishing effort was redistributes to the outskirts of the modeled ar ea for all three stages (Figures 3-4, 3-8, and 3-12). This was in sharp contra st to what was seen in the base case model. Sensitivity 2: Open Area Model In the open area model, densities for all thee stag es were much lower than those seen in the closed area/no fishing mortality model but followed a very similar flat trend (Figures 3-1, 3-5 and 3-9). Total numbers of animals in the northern area were very similar to those found in the

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93 base case model, while those in the closed and southern area were very reduced compared to the base case model (Table 3-3). Total numbers were lower in the northern and southern areas compared to the closed area because of movement out of the modeled area without replacement. Juvenile, subadult and adult densitie s were approximately three, four and ten times lower (respectively) than the values seen in th e closed area model. Neonate and recruitment densities within each stages model also followed similar trends to those seen in the previous model. The redistribution of fishing effort wa s similar for juvenile (Figure 3-4) and subadult (Figure 3-8) stages. The major ity of effort was evenly distributed throughout the model, which was in direct contrast to the results of the base case model (Figure 3-4, 3-8 and 3-12). Sensitivity 3: Fishing Mort ality Rate Set to Fmsy The reduction in the fishing mortality rate caused the total density curves for all three stages to become very flat throughout the model in stead of curved as seen in the base case model (Figures 3-1, 3-5 and 3-9). The total number of animals found in all three areas and stages was very similar to those found in sensitivity 1 (Table 3-3). The densities were very similar to the peak densities seen in the base case model. A dult density was the highest of all three stages and increased much more in the closed area compared to the previous two stages but was still very similar to the values seen in the base case m odel. Neonate densities found within each stage started higher and increased more quickly when compared to the ba se case (Figures 3-2, 3-6 and 3-10). The peak densities were similar to those seen in the base case model. Recruitment densities followed similar trends to the base ca se model but were slightly higher in value. Fishing efforts for juvenile (Fi gure 3-4) and subadults (Figure 3-8) were redistributed (when compared to the base case model) to the southern open area with the majority of effort falling within the first open cell of that area. Adult fishing effort was redistributed (when compared to

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94 the base case model) to the two cells directly be fore and after the closed area, with no fishing occurring in the closed area (figure 3-13). Sensitivity 4: High Standard Deviat ion of the Gravity Model (effpow) Density curves for juvenile, subadults and adu lts followed very similar trends to what was seen in the base case model. The total number of animals found in all stages and areas was similar to, but slightly less than those found in th e base case model (Table 3-3). Densities were slightly lower for juveniles and subadults compar ed to the base case model but were nearly the same for adults. Neonate and recruitment curves also followed similar patterns to the base case model, with slightly lower densities found in the juvenile and subadult stage models. The redistribution of fishing effort wa s different for all three stages compared to the base case model and overall effort was much less. Effort for j uveniles (Figure 3-4) and subadults (Figure 3-8) was redistributed primarily into the southern open area but also to a lesser extent into the northern open area. Adult effort was primarily redistributed to the cells directly before and after the closure and was slightly more distributed than in the base case model (Figure 3-12). Sensitivity 5: High Compensation Ratio (reck) The change in this parameter value only sligh tly effected juvenile, subadult and adult total numbers (Table 3-3). Neonate density and the re distribution of fishing effort were not changed when compared to the base case model. The num ber of recruits was increased for the juvenile and subadult stages and decreased slightly for ad ults, when compared to the base case model (Figures 3-3, 3-7 and 3-11). Sensitivity 6: High Natural Mortality Rates (M) The curves for juvenile, subadult and adult density, neonate density, and recruitment density were all similar to those seen in the base case model. The total number of animals for all three stages and areas were lower than those found in the base case model (Table 3-3). The

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95 densities were lower for all curves, with adult density being affected the most. The redistribution of fishing effort for juvenile and subadult stages was the same as seen in the base case model. However, in the adult stage effort was redistri buted to the first cell in the southern open area (Figure 3-12) compared to the last cell in the northern closure in the base case model. Sensitivity 7: Low Natural Mortality Rates (M) Low natural mortality rates had the opposite effect as seen in the model were high natural mortality rates were used, with regard to total de nsity and the redistributio n of fishing effort in the adult stage. The total numbe r of animals in all three region s was higher than those found in the base case model, except for subadult animal s in the southern open region, which only decreased slightly in numbers (Table 3-3). The curves for juvenile, suba dult and adult densities, neonate density and recruitment density all rema ined similar to the curves in the base case model. Densities increased for all stages but the subadult density increased the most. The redistribution of fishing effort for the adult stag e did change from the base case model (Figure 312). Effort was redistributed to the cells immediately prior to the closed area in the northern section and immediately after the cl osed area in the southern secti on. Fishing effort in the adult stage was also much lower in this model when compared to the base case scenario. Effort redistribution did not change for the juvenile or subadult stages. Sensitivity 8: High Standard Deviation of th e Spatial Distribution of Neonate Settlement (sdldist) The increase in this parameter lead to a decrease in neonate densities found within each of three stages models (Figures 3-2, 3-6 and 3-10) and an increase in recruitment densities within the models for the juvenile (Figure 3-3) and subadu lt stages (Figure 3-7) with respect to the base case model. Recruitment densities found in the adult stage model became lower (Figure 3-11) and the redistribution of fishing effort for juveni les decreased (Figures 3-4) when compared to

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96 the base case model. The curves for all four gr aphs remained very similar to the base case model, with neonate density bei ng decreased the most within th e adult stages model (Figure 310) and recruitment densities being increased the most within the juvenile stages model (Figure 3-3). Sensitivity 9: Low Standard Deviation of the Spatial Distribution of Neonate Settlement (sdldist) Lowering this parameter resulted in changes to the curves for neonate (Figure 3-2, 3-6 and 3-10) and recruitment (Figures 3-3, 3-7 and 3-11) densities found within all three stages with respect to the base case model. The highest neonate densities in the closed area remained the same as densities seen in the base case model. Recruitment densities were higher in the juvenile (Figure 3-3) and subadult (Figure 3-7) stages modes, compared to the base case model but lower in the adult stages model (Figure 3-11). The redistribution of fishing effort changed in the juvenile (Figure 3-4) and adult (Figure 3-12) stages when compar ed to the base case model. Effort was reduced in the juvenile stage and be came concentrated into the first cell of the southern closure in the adult stage (Figure 3-8). Sensitivity 10: High M ovement Rates (emig) The change to this parameter did not affect the curves for total, neonate or recruitment densities for the three stages when compared to the base case model. The total number of juvenile animals was reduced slightly in all th ree regions, and the total number of subadult and adult animals was increased slightly in the nor thern open area and the closed area of the model compared to the base case model (Table 3-3). Total numbers of subadults was reduced in the southern open area and increased slightly for ad ult animals compared to the base case model (Table 3-3). The redistribution of fishing effort for the adult stag e was moved to the first cell of the southern open area (Figure 3-12) but did not change for the juvenile or sub adult stages.

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97Sensitivity 11: Low Movement Rates (emig) Juvenile total (Figure 3-1) density and the ne onate (Figure 3-2) and recruitment (Figure 33) densities found within this m odel, were all lower and the re distribution of fishing effort (Figure 3-4) was higher in this model compared to the previous and base case models. The total number of animals of all three life-stages found in the three regi ons was reduced compared to the base case model (Table 3-3). To tal and neonate density (Figures 3-5 and 3-6) were slightly lower in the subadult and adult stage models but recruitment densities did not change. The redistribution of fishing effort di d not change in the subadult stag e but was slightly higher in the juvenile stage (Figure 3-4) and was much lowe r in the adult stage (Figure 3-12) and became concentrated in the last cell of the northern op en area and first cell of the southern open area. Discussion The base case model showed that the majority of total and neonate density for all three lifestages of the dusky shark occurred in the closed re gion of the model but that the effects of fishing were still seen in the boundari es between open and closed areas. This fishing effect was predicted by Walters (2000) and Walters et al ., (2007) due to dispersa l imbalance within the model. Fish that are lost via dispersal near the closure boundary are not replaced through immigration. Juvenile and subadult densities to ok longer to build up in the closed area, which was most likely a factor of their slower moveme nt rates when compared to the adult stage. Sensitivity analysis showed that parameter values affected the densities and the redistribution of fishing effort for all three stages. However, all of the models (except for sensitivities 1 and 2) indicated that the highest densi ties were found in the closed portion of the model and that the redistribution of fishing effort was concentrated into very few cells on either side of the closures. Highly concentrated redistribution of fishing effort has also been reported in the plaice box closure for beam trawlers (Pastoors et al., 2000) and cod box closures (Rijnsdorp et al., 2001).

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98 Neonate densities within all models generally foll owed the same trends as the total densities in those models and recruitment densities varied very little. The sensitivity analysis (3) with a reduction in fishing mortality (set to Fmsy ) resulted in the least changes to total densities between the cl osed and open areas, except for the sensitivity analyses that were entirely open (2) and/or closed (1) to fishin g. In these models, densities remained flat throughout the entire model. These results coincide with ot her studies that have suggested time/area closures are most effective when overall mortality is also reduced (Horwood et al., 1998; Rijnsdorp et al., 2001; Chapman et al., 2005) and studies that showed larger reserves provide more protection to shark species (Cha pman et al., 2005; Heupel and Simpfendorfer, 2005). In the reduced fishing mortality model, neonate densities built up more quickly and earlier prior to the closed area and decreased more slowly and later after the closed areas, when compared to the base case model. This occurred because of the moderate, if any, changes to total densities seen in the model. Neonate densities were very low in the open area model because of the low total densities in the model. Additionally in the reduced fishing mortality model, fishing effort was much lower and was redi stributed to more nms for juve niles and subadults than in the base case model. The number of animals was able to build up in the closed area and was reduced only slightly once the area became open to fishing. This allowed the redistribution of fishing effort to be more evenly spread out into the sout hern open area. In the a dult stage of this model, total densities were similar into the northern and southern closed regions and therefore the redistribution of fishing effort built up on either side of the closure. As would be suspected, the redistribution of fishing effort in the open model stayed around one for the entire model and zero for the model that was completely closed to fishing. With the entire area open to fishing, effort

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99 could be evenly spread out between fishers and when the entire area was closed effort could not be redistributed within the model. Guenette and Pitcher (1999) sugge sted that marine reserves provide protection to managed fish stocks but that these advantages are reduced for highly mobile species. Rapid movement of fish into and out of closed areas is thought to hinder the protection offered by a closed area (Horwood et al., 1998) because the fish become vulnerable to fishing once outside of the protected area. Changes made to movement rates in the sensitivity analyses (10 and 11) of this model only had slight effects on the outcome of the model when compared to the base case model. This was because movement rates were so high in the original model that increasing them did not affect the total outcome of the model. Horwood et al. (1998) suggested that a combination of closed area management and a reduction in fishing mortality outside of the closed area should be implemented to provide th e most adequate protection. Chapman et al. (2005) suggested that the best wa y to protect highly mobile shark species was through the use of an ecosystem-based management approach that includes a closed area surrounded by an area with regulated fishing activities. These two management techniques have been implemented for the dusky shark but do not appear (Corts et al ., 2006) to be enough to allow the population to recover from exploitation. Lowering the natural mortality rates (sensitivity analyses 6 and 7) allowed for the populations of each stage to build up in density a nd increasing the natural mortality rates lead to a reduction in total density. Natural mortality rates were decreased the most for the juvenile stage and the least for the adult stage. Attempts to reduce the natural mortality rates more in the adult stage resulted in the model not working and resulting in a number error. Therefore, I only reduced the rate as much as the model would al low (Table 3-2) and did use the lowest rates

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100 presented in Chapter 2. This caused the larges t change in density being seen in the subadult stage because of the larger original densities and larger change to the natural mortality rates. Fishing effort was lower in the adult stages because it was spread out between two nms instead of being concentrated into one nm. Neonate densities were changed when the spa tial distribution of neona te settlement rates were altered (sensitivity analyses 8 and 9). Sp reading out neonate settlement (increasing the parameter value) resulted in a decrease in total neonate numbers in the closed area because neonates were spread out more throughout the entire area of the m odel. Lowering this parameter caused the neonates to become more concentrated into the closed area. The redistribution of fishing effort was lower for juveniles in these two models because total density in the southern open area was much lower than in the base case m odel. Increasing the standard deviation of the gravity model reduced total fishing effort because it spread the effort out between more cells in the open areas. Increasing the compensation ra tio allowed for increased compensation in the form of increased survival of neonates, therefor e leading to increases in recruitment densities within the models. The value of 1.5 used in the base model indicates a slight change in neonate mortality when the spawning biomass changes. In te leosts this ratio is highest for species with a high maximum number of spawners per recruit (G oodwin et al., 2006). This ratio is thought to be lower for dusky sharks and similar species, because increased compensation is more likely going to be a factor of increased growth rates and subsequent earlier ages at maturity. The already low natural mortality rates for neona tes of this species would make increased compensation in the form of increas ed survival for neonates unlikely. Spatial modeling of the dusky shark is hamp ered by our lack of knowledge of their movement patterns, movement rates and spatia l structure (Conroy et al., 1995). The data

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101 presented by Colin Simpfendorfer (personal comm unication) were helpful in creating possible movement rates but were based primarily on juve nile animals from a different region. Colin Simpfendorfer (personal communication) also indi cated that the movement rates they observed varied substantially for juveniles. While the mode ls show a build up of density within the closed area, the high movement rates of this species make s it very likely that dusky sharks move in and out of the area during the 7 month closure. Moveme nt out of the area into open fishing areas still leaves them susceptible to fishing pressure as do es the 5 months the time/area closure is not in effect. An increase in numbers within closed areas has been reported in many Marine Protected Area (MPA) studies (Halpern and Warmer, 2002). Walters et al. (2007) suggested that this phenomenon does not provide very useful informa tion because it is only a snapshot of a small area and because there is no indication of how long it will take for these effects to be seen. This model was also unable to incorporat e the temporal closure aspect of this time/area closure. In the future, more work (e.g., tag/recapture and satellite tagging studies) on the movement of dusky sharks in the northwest Atlantic Ocean ne eds to be completed. This information may allow for the use of a more advanced spatial mode l that can also incorporate the seasonal aspect of the time/area closure (Pelletier et al., 2008). Without all of this information the full effect of time/area closures on the population of this species can not be completely analyzed without great uncertainty (Conroy et al., 1995). Additional research should also be completed on the redistribution of fishing effort to the areas surrounding a closed area, because succ essful management of species protected by time/area closures is dependent on this redistribution (Apostolaki et al., 2002). Apostolaki et al. (2002) showed that short-term losses associated with the implementation of a reserve can be reduced if the redistribution of fi shing effort is spread out over the remainder of the fishing area.

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102 The results of this study show that the re distribution of fishing effort becomes highly concentrated in the outskirts of the closure, which would suggest that short-term losses may be high in this closure. Redistribution of fishing effort is likely influenced by the location of the closure (distance from dock), season, market pr ices of targeted species and fuel costs. Information on the long term effects of this redi stribution and whether th is change in fishing practice negatively impacts other species or relocate s fishing to areas previously not fished needs to be investigated (Dinmore et al., 2003).

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103 Table 3-1. Parameter values ( F = fishing mortality rate absent any closure, M = natural mortality, effort = total kilometers modeled, q = fishing mortality concentrated into only areas that are open to fishing, R = average natural recruitment rate per cell, emig = movement rate, reck = compensation ratio, sdldist = standard deviation of spatial distribution of neonate settlement, jper = scaling constant for total neonate settlement from each source cell, and = parameters from Beverton Holt stock recruitment function, effpow = standard deviation of gravity model, dpow = power parameter, sort = relaxation weight and Hi = habitat quality for indivi dual cells) for the base case version of the spatial model. Parameter Juvenile Subadult AdultReference F 0.4 0.4 0.4Corts et al., 2006 M 0.21 0.13 0.05Corts et al., 2006 Effort 200 200 200 q 1.14 1.14 1.14Walters et al., 2007 Ro 50 50 50 Emig 240 400 1000 Colin Simpfendorfer (personal communication); Simpfendorfer personal communication Reck 1.5 1.5 1.5 Enric Corts pers onal communication; Simpfendorfer et al., 2000; Corts, 2004 sdldist 20 20 20 Jper 0.02 0.02 0.02Walters et al. 2007 13.8 1.8 2.2 Walters et al. 2007 0.85 1.07 1.06 Walters et al., 2007 Effpow 2 2 2 Dpow 0.4 0.4 0.4 Walters et al., 2007; Walters personal communication Sort 0.5 0.5 0.5 Walters et al., 2007; Walters personal communication Hi 1 1 1Walters et al., 2007

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104 Table 3-2. Parameter values for the sensitivit y analysis of the spatial model (Fmsy = fishing mortality rate that would produce maximum sustainable yield (MSY), effpow = standard deviation of the gravity model and sdldist = standard deviation of the spatial distribution of neonate settlement). Sensitivity Parameter Reference 1. Entire habitat closed to fishing cells=0 2. Entire habitat open to fishing cells=1 3. Fishing mortality reduced to Fmsy 0.006 Corts et al., 2006 4. effpow increased 5 5. Compensation ratio ( reck) increased 2.5 6. Natural mortality rates increased juvenile=0.226, subadult=0.176, adult=0.083 Corts et al., 2006 7. Natural mortality rates decreased juvenile=0.185, subadult=0.087, adult=0.046 Corts et al., 2006 8. sdldist increased 50 9. sdldist decreased 1 10. Increased movement rates juvenile=400, subadult=600, adult=1200 Colin Simpfendorfer (personal communication); Simpfendorfer personal communication 11. Decreased movement rates juvenile=120, subadult=200, adult=500 Colin Simpfendorfer (personal communication); Simpfendorfer personal communication

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105 Table 3-3. Total number of dusky sharks found in three areas (northern open, closed, southern open) and three life-stages (juvenile, subadult and adult) for the base case and sensitivity analysis of the spatial model. Total (Numbers) Model Area Life-stage Juvenile Subadult Adult Base case Northern open area 4,0914,67810,604 Closed area 27,08843,245125,192 Southern open area 2,5577,6887,295 Sensitivity 1 Northern open area 11,72319,08549,942 Closed area 30,48649,626129,851 Southern open area 4,6907,63519,976 Sensitivity 2 Northern open area 3,9214,5745,498 Closed area 10,19711,89414,296 Southern open area 1,5691,8302,199 Sensitivity 3 Northern open area 11,37718,24144,968 Closed area 30,32449,222129,026 Southern open area 4,6487,57018,216 Sensitivity 4 Northern open area 3,1874,24810,312 Closed area 23,88441,504125,102 Southern open area 3,2565,6067,039 Sensitivity 5 Northern open area 4,1504,68310,605 Closed area 27,24543,258125,191 Southern open area 2,5745,0177,293 Sensitivity 6 Northern open area 3,9584,2917,039 Closed area 25,15732,17374,490 Southern open area 2,3623,6803,968 Sensitivity 7 Northern open area 4,2485,10112,452 Closed area 30,58264,123137,227 Southern open area 2,9107,5449,171 Sensitivity 8 Northern open area 3,6564,68110,604 Closed area 26,30943,134125,192 Southern open area 1,4845,0087,296 Sensitivity 9 Northern open area 3,6554,68010,567 Closed area 26,31143,134125,179 Southern open area 1,4835237,318 Sensitivity 10 Northern open area 3,9434,68910,654 Closed area 26,46443,349125,339 Southern open area 1,6555,6167,402 Sensitivity 11 Northern open area 2,6804,63510,207 Closed area 24,09342,916124,688 Southern open area 1,1353,8656,930

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106 Kilometers 0255075100125150175200 Density (N) 40 60 80 100 120 140 160 180 200 220 240 260 Base case Low movement rates Open area Closed area Fmsy Figure 3-1. Densities (N) of juvenile dusky shar ks from the base case and sensitivity analyses (low movement rates, entire area open to fish ing, entire area closed to fishing, fishing mortality reduced to Fmsy (fishing mortality rate that would produce Maximum Sustainable Yield (MSY)) of the base case mode l. Vertical lines represent the closed area of the model.

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107 Kilometers 0255075100125150175200 Neonates (N) 0 50 100 150 200 250 Base case Closed area Fmsy High sdldist Low sdldist Low movement rates Figure 3-2. Neonate densities (N) found within the juvenile dusky shark life-stage model for the base case and sensitivity analyses (entire area closed to fishing, fishing mortality reduced to Fmsy, (fishing mortality rate that would produce Maximum Sustainable Yield (MSY), high standard deviation of the spatial distribution of neonate settlement ( sdlidst), low sdldist and low movement rates) of the ba se case model. Solid vertical lines represent the clos ed area of the model.

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108 Cells 0255075100125150175200 Recruits (N) 16 18 20 22 24 26 28 30 32 Base case High sdldist Low sdldist High Reck Figure 3-3. Recruit densities (N ) found within the juvenile dusky shark life-stage model for the base case and sensitivity analyses (high sta ndard deviation of th e spatial distribution of neonate settlement ( sdldist ), low sdldist high compensation ration (reck ) increased) of the base case model. Solid ve rtical lines represent the closed area of the model.

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109 Kilometers 0255075100125150175200 Fishing Effort 0 25 50 75 100 125 150 175 200 225 Base case Closed area Open area Fmsy High sdldist Low sdldist Low movement rates Figure 3-4. Redistribution of fi shing effort for juvenile dusky sharks from the base case and sensitivity analyses (entire area closed to fi shing, entire area open to fishing, fishing effort reduced to Fmsy (fishing mortality rate that would produce Maximum Sustainable Yield (MSY), high standard devi ation of spatial distribution of neonate settlement ( sdldist ), low sdldist and low movement rates) of the base case model. Solid vertical lines represent the closed area of the model.

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110 Kilometers 0255075100125150175200 Density (N) 50 100 150 200 250 300 350 400 Base case Low movement rates Open area Closed area Fmsy Figure 3-5. Densities (N) of subadult dusky sharks from the base case and sensitivity analyses (low movement rates, entire area open to fi shing, entire area closed to fishing and fishing effort reduced to Fmsy (fishing mortality rate that would produce Maximum Sustainable Yield (MSY)) of the base case m odel. Solid vertical lines represent the closed area of the model.

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111 Kilometers 0255075100125150175200 Neonates (N) 0 50 100 150 200 250 300 350 400 Base case Closed area Fmsy High sdldist Low sdldist Low movement rates Figure 3-6. Neonate densities (N) found within the subadult dusky shark life-stage model for the base case and sensitivity analyses (entire area closed to fishing, fishing effort reduced to Fmsy (fishing mortality rate that woul d produce Maximum Sustainable Yield (MSY), high standard deviation of spatial distribution of ne onate settlement (sdldist ), low sdldist and low movement rates) of the base case model. Solid vertical lines represent the closed area of the model.

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112 Kilometers 0255075100125150175200 Recruits (N) 1.5 2.0 2.5 3.0 3.5 4.0 4.5 Base case High sdldist Low sdldist High Reck Figure 3-7. Recruit densities (N) found within the subadult dus ky shark life-stage model for the base case and sensitivity analyses (high sta ndard deviation of spatial distribution of neonate settlement (sdldist ), low sdldist and high compensation ratio ( reck )) of the base case model. Solid vertical lines represent the closed area of the model.

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113 Kilometers 0255075100125150175200 Fishing Effort 0 25 50 75 100 125 150 175 200 Base case Closed area Open area Fmsy Effpow Figure 3-8. Redistribution of fi shing effort for subadult dusky sharks from the base case and sensitivity analyses (entire area closed to fi shing, entire area open to fishing, fishing effort reduced to Fmsy (fishing mortality rate that would produce Maximum Sustainable Yield (MSY) and high standa rd deviation of the gravity model ( effpow )) of the base case model. Solid vertical lines represent the closed area of the model.

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114 Kilometers 0255075100125150175200 Denstity (N) 0 200 400 600 800 1000 1200 Base case Low movement rates Open area Closed area Fmsy Figure 3-9. Densities (N) of adult dusky sharks from the base case and sensitivity analyses (low movement rates, entire area open to fishing, entire area closed to fishing and fishing effort reduced to Fmsy (fishing mortality rate that would produce Maximum Sustainable Yield (MSY)) of the base case m odel. Solid vertical lines represent the closed area of the model.

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115 Kilometers 0255075100125150175200 Neonates (N) 0 200 400 600 800 1000 1200 Base case Closed area Fmsy High sdldist Low sdldist Low movement rates Figure 3-10. Neonate densities (N) found within the adult dusky shark life-stage model for the base case and sensitivity analyses (entire area closed to fishing, fishing effort reduced to Fmsy (fishing mortality rate that woul d produce Maximum Sustainable Yield (MSY), high standard deviation of spatial distribution of ne onate settlement (sdldist ), low sdldist and low movement rates) of the base case model. Solid vertical lines represent the closed area of the model.

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116 Kilometers 0255075100125150175200 Recruits (N) 2.00 2.01 2.02 2.03 2.04 2.05 2.06 Base case High sdldist Low sdldist High Reck Figure 3-11. Recruit densities (N) found within the adult dusky shark life-stage model for the base case and sensitivity analyses (high sta ndard deviation of spatial distribution of neonate settlement (sdldist ), low sdldist and high compensation ratio ( reck )) of the base case model. Solid vertical lines represent the closed area of the model.

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117 Kilometers 0255075100125150175200 Fishing Effort 0 25 50 75 100 125 150 175 200 225 Base case Closed area Open area Fmsy High effpow High movement rates Low movement rates Low sdldist High natural mortality Low natural mortality Figure 3-12. Redistribution of fishing effort for adult dusky sharks from the base case and sensitivity analyses (entire area closed to fi shing, entire area open to fishing, fishing effort reduced to Fmsy (fishing mortality rate that would produce Maximum Sustainable Yield (MSY), standard deviation of gravity model ( effpow ) increased, high movement rates, low movement rates, low standard deviation of spatial distribution of neonate settlement ( sdldist ), high natural mortality rates and low natural mortality rates) of the base case model. Solid vertical lines represent the closed area of the model.

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118 CHAPTER 4 CONCLUSIONS, MANAGEMENT AND RESEARCH RECOMENDATIONS Conclusions Results from the age-structured model showed that fishing mortality, even at reduced levels, will lead to the dusky shar k continuing to be reduced to levels of less than 60% of virgin biomass. The results also indicated that if catches have been underestimated and/or underreported through the years that the stock coul d already be considered more heavily reduced with respect to virgin biomass. Management efforts already in place, including placing the species on the NMFS prohibited species list and tim e/area closures, are not likely to result in zero fishing mortality over the next 20 years. This inability of management measures to fully protect this species is largely due to the dusky shark being caught as bycatch in several fisheries (Beerkircher et al., 2002; Alexia Morgan, unpublished data). It is likely that the population of dusky sharks in the northwestern Atlantic Ocean will remain heavily reduced during the next 20 years. Fisheries managers must be extremely proactive in determining additional management options and research needs that will help reduce and preferably eliminate fishing mortality for this species in the future. The results of the spatial model offer a gene ral understanding of the theoretical effects time/area closures would have on the dusky shark population but a more complex model incorporating a temporal component and better da ta are needed for a full analysis. Simulated area closures caused the density of dusky sharks to become concentrated within the closed area. However, Walters et al. (2007) suggest caution when drawing conclusions based on insideoutside density comparisons, because the model does not include baselines with which to compare the abundances to. A more complex mo del that can combine population dynamics with the seasonal aspect of the time area closure, sp atial movement, historical fishing effort, and

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119 length at catch data and can simulate the cl osure through time would provide more reliable results. Such a complex model was not used in th is assessment because this information is either lacking for this species or the historical time frame is too short at this point in time for use in such complex models. Management and Research Recommendations Managers should focus on trying to reduce the fishing mortality dusky sharks suffer as a result of being caught as bycatch. To help redu ce or eliminate this type of fishing mortality managers should attempt to reduce the number of vessels involved in these fisheries, therefore reducing the number of interactions between fishing gear and this species. Fishing effort could be reduced by decreasing the quotas for targeted species in these fisherie s, instituting by-back vessel programs or directing fishers towards fish eries that do not encount er this species. Managers should also increase obs erver effort in these fisheries and improve on recording and reporting by fishers and dealers, which will impr ove the data used in future assessments. Future research aimed at reducing fishing mortality and/or improvi ng our knowledge of the effects of time/area closures should include; cr oss checking logbook data with observer data and dealer landings to determine any discrepancies that may affect the total catches used in this and other models, develop direct estimates for natural mortality rates, investigate at-vessel mortality rates by soak time for the pelagic longline fishery, initiate mark/recapture tagging programs, investigate post release survivability in gillne t, trawl, pelagic longline, bottom longline and recreational fisheries, develop direct selectivity estimates for these same fisheries, collect length at capture data for these fisheries and determin e the spatial distribution and movement rates of different age classes. Information gleaned through such additional research could be used to improve upon future assessments.

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120 APPENDIX A TIME AREA CLOSURE MAP Figure A-1. Map of the time/area closure currently in effect off the coast of North Carolina. (NMFS. 2003. Final Amendment 1 to the fishery management plan for Atlantic tunas, swordfish and sharks. National Ma rine Fisheries Service, Highly Migratory Species Division, Silver Spring, MD. Figure 4-1, p. 4-110.).

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121 APPENDIX B DUSKY SHARK BIOLOGICAL DATA

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122 Table B-1. Life history parame ters for the dusky shark (Enric Corts, personal communication). Parameter Definition Value Units Reference males females K Brody growth coefficient 0.038-0.0430.039-0.045 yr-1 Natanson et al., 1995; Simpfendorfer, 2002a Linf Theoretical maximum length 345-373336-349 cm fork length Natanson et al., 1995; t0 Age at zero length -6.28-7. 04 yr Natanson et al., 1995 tmat Age at maturity 19 21 yr Corts et al., 2006 Lmat Length at maturity 231 235 cm fork lengthCorts et al., 2006 tmax Lifespan >25 >33 yr Natanson et al., 1995 Lmax Maximum observed length 299 308 cm fork lengthNatanson et al ., 1995; L0 Size at birth 68-81 cm fork lengthNatanson et al ., 1995 Reproductive frequency 2 or 3 yr Branstetter and Burgess, 1996 Sex ratio at birth 1 to 1 dimensionless mx Mean number of pups 7.1 pups Branstetter and Burgess, 1996 a Scalar coefficient of weight on length 3.2415x10-5sexes combined dimensionlessKohler et al 1995 b Power coefficient of weight on length 2.7862 sexes combined dimensionlessKohler et al 1995 M0 range Age-0 instantaneous natural mortality rate 0.248 yr-1 Corts et al ., 2006 S0 range Age-0 annual survivorship 0.78-0.98 yr-1 Corts et al ., 2006 M1-mat range Age-1 to maturity M 0.087-0.238 yr-1 Corts et al., 2006 S1-mat range Age-1 to maturity S 0.80-0.98 yr-1 Corts et al., 2006 Mad range Adult instantaneous natural mortality rate 0.026-0083 yr-1 Simpfendorfer, 1999; Romine et al.Sad range Adult annual survivorship 0.90-0.98 yr-1 Corts et al ., 2006

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123 APPENDIX C SELECTIVITY CURVES Age 01 02 03 04 0 Age Frequency (%) 0 20 40 60 80 100 Selectivit y 0.0 0.2 0.4 0.6 0.8 1.0 Age 0510152025303540 Age Frequency (%) 0 20 40 60 80 100 Selectivit y 0.0 0.2 0.4 0.6 0.8 1.0 Year 0510152025303540 Age Frequency (%) 0 20 40 60 80 100 Selectivit y 0.0 0.2 0.4 0.6 0.8 1.0 Year 0510152025303540 Age Frequency (%) 0 20 40 60 80 100 Selectivit y 0.0 0.2 0.4 0.6 0.8 1.0 Figure C-1. Selectivity curves fit to age distri butions for the following four data sets: A) Bottom Longline Observer Program (BLLOP), B) Virginia Institute of Marine Science (VIMS), C) Large Pelagic Survey (LPS) and D) Pelagic Longline Observer Program (PLLOP) (Enric Corts personal communication). A B C D

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124 APPENDIX D SELECTIVITY PARAMETERS Table D-1. Selectivity param eters for the double logistic distribution fitte d to age data for the following four data sets: Bottom Longline Observer Program (BLLOP), Virginia Institute of Marine Science (VIMS), Large Pelagic Survey (LPS) and Pelagic Longline Observer Program (PLLOP) (E nric Corts personal communication). Data set Parameter estimates a50 b c50 d BLLOP 4 1 32 4 VIMS 2 1 28 5 LPS 2 0.75 24 5 PLLOP 1.5 1 28 5

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125 LIST OF REFERENCES Apostolaki, P., E.J. Milner-Gulland, M.K. Mc Allister, and G.P. Kirkwood. 2002. Modeling the effects of establish ing a marine reserve for m obile fish species. Can. J. Fish. Aqu. Sci. 59:405-415 Beerkircher, L, E. Corts, and M. Shivji. 2002. Characteristics of sh ark bycatch observed on pelagic longlines off the southeastern United States, 1992-2000. Mar. Fish. Rev. 64:4049. Branstetter, S. and G.H. Burgess. 1996. Co mmercial Shark Fishery Observer Program. Characterization and comparisons of the direct ed commercial shark fishery in the eastern Gulf of Mexico and off North Carolina through an observer program. Fin. Rep., MARFIN Award NA57FF008, 64 p. Castro, J.I. 1996. The sharks of North American waters. Texas A&M University Press, College Station, TX., 180 p. Chapman, D.D., E.K. Pikitch, E. Babcock, a nd M.S. Shivji. 2005. Marine reserve design and evaluation using automated acoustic tele metry: a case-study i nvolving coral reefassociated sharks in the Mesoamerican Caribbean. Mar. Tech. Soc. J. 39:42-55. Chen, S.B. and S. Watanabe. 1989. Age dependen ce of natural mortality coefficient in fish population dynamics. Nipp. Sui. Gak. 55:205-208. Compagno, L.J.V. 1984. FAO speci es catalogue Sharks of the world. An annotated and illustrated catalogue of shark species known to date. FAO Fish. Syn., no. 125, vol. 4, part 2 (Carcharhiniformes), 489 p. Conroy, M.J., Cohen, Y., James, F.C., Matsin os, Y.G. and B.A., Mauer. 1995. Parameter estimation, reliability, and model improvement for spatially explicitly models of animal populations. Eco. Appl. 5:17-19. Corts, E. 1999. A stochastic stage-based population model of th e sandbar shark in the western North Atlantic. In J.A., Musick (Editors), Life in the Slow Lane; Ecology and Conservation of Long-Lived Marine Anim als, p. 115-136. Amer. Fish. Symp. 23. Corts, E. 2002a. Incorporating uncertainty into demographic m odeling: Application to shark populations and their conserva tion. Con. Bio. 16:1048-1062. Corts, E. 2002b. Stock assessment of small coasta l sharks in the U.S. Atlantic and Gulf of Mexico. Sustainable Fisheries Division Contribution SFD-01/02-152, 133 p.

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131 BIOGRAPHICAL SKETCH I graduated from Nova Southeastern Univer sitys Oceanographic Center in 2001 with a Master of Science degree. After completing my masters degree I began working for the Florida Program for Shark Research located at the Univers ity of Floridas Museum of Natural History. In 2003 I began working toward my Ph.D. with the Department of Fisheries and Aquatic Sciences at the University of Florida.