Science, Faculty of
Resources, Environment and Sustainability (IRES), Institute for
DSpace
UBCV
Christensen, Line Bang
Master of Science - MSc
University of British Columbia
The relationship between humans and marine mammals extends back for centuries and covers a multitude of facets; literature and spirituality, necessities (food) and luxuries (corsets). Their exploitation history changed from small-scale hunting for foodlfur, to large commercial ventures for oil, to limited hunting as marine mammals became the poster child for environmental movements. The International Whaling Commission, national and regional bodies assess the current status of marine mammals. Information on stock size relative to carrying capacities is, however, hard to come by. Assessments to date have been limited to a relatively small number of stocks/species because data quality, availability and politics. To address concerns over stock status, the analyses have to be expanded to all exploited populations. Employing a Bayesian approach to stochastic stock reduction analysis, I construct probability distributions over historical stock sizes. The method allows me to use historical catch time series to estimate a distribution over population parameters, the intrinsic rate of growth and carrying capacity, that give rise to extant populations. I tested this stock reconstruction approach on simulated data sets generated from a reference model, and then applied it to available catch and abundance data. Simulation tests indicate that parameter estimates were unbiased. Globally, I find that aggregated information on all marine mammal populations indicate a decline of 22% (0-62%) in numbers, and 76% (58-86%) in biomass. The decline has been greatest for the great whales, with a 64% (40-79%) decline in numbers, and an 81% (69-89%) decline in biomass. My estimates are consistent with published estimates of carrying capacity. These estimates are intended to provide a status overview, rather than direct management advice, affording us the ability to begin calculating how humans have impacted these large fauna in their marine ecosystems. The International Convention on the Regulation of Whaling (ICRW) predicates that stocks must be maintained at their most productive levels. Also, a moratorium on whaling exists, awaiting better science. This represents the fundamental conflict in interest between pro-whaling nations and those with morallethical opposition to whaling, intent on upholding the moratorium. Results in this thesis indicate that the Japanese hunt of minke whales could increase and abide by the ICRW, yet the majority of western IWC signatories want to see commercial hunting banned altogether.
RECONSTRUCTING HISTORICAL ABUNDANCES OF EXPLOITED MARINE M A M M A L S A T THE GLOBAL SCALE by LINE BANG CHRISTENSEN B.Sc, The University of British Columbia, 2004. A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE F A C U L T Y OF GRADUATE STUDIES (Resource Management and Environmental Studies) THE UNIVERSITY OF BRITISH COLUMBIA September 2006 © Line Bang Christensen, 2006 Abstract The relationship between humans and marine mammals extends back for centuries and covers a multitude of facets; literature and spirituality, necessities (food) and luxuries (corsets). Their exploitation history changed from small-scale hunting for food/fur, to large commercial ventures for oil, to limited hunting as marine mammals became the poster child for environmental movements. The International Whaling Commission, national and regional bodies assess the current status of marine mammals. Information on stock size relative to carrying capacities is, however, hard to come by. Assessments to date have been limited to a relatively small number of stocks/species because data quality, availability and politics. To address concerns over stock status, the analyses have to be expanded to all exploited populations. Employing a Bayesian approach to stochastic stock reduction analysis, I construct probability distributions over historical stock sizes. The method allows me to use historical catch time series to estimate a distribution over population parameters, the intrinsic rate of growth and carrying capacity, that give rise to extant populations. I tested this stock reconstruction approach on simulated data sets generated from a reference model, and then applied it to available catch and abundance data. Simulation tests indicate that parameter estimates were unbiased. Globally, I find that aggregated information on all marine mammal populations indicate a decline of 22% (0-62%) in numbers, and 76% (58-86%) in biomass. The decline has been greatest for the great whales, with a 64% (40-79%) decline in numbers, ii and an 81% (69-89%) decline in biomass. M y estimates are consistent with published estimates of carrying capacity. These estimates are intended to provide a status overview, rather than direct management advice, affording us the ability to begin calculating how humans have impacted these large fauna in their marine ecosystems. The International Convention on the Regulation of Whaling (ICRW) predicates that stocks must be maintained at their most productive levels. Also, a moratorium on whaling exists, awaiting better science. This represents the fundamental conflict in interest between pro-whaling nations and those with moral/ethical opposition to whaling, intent on upholding the moratorium. Results in this thesis indicate that the Japanese hunt of minke whales could increase and abide by the ICRW, yet the majority of western IWC signatories want to see commercial hunting banned altogether. iii Table of Contents Abstract i' Table of Contents iv List of Tables vi List of Figures vii List of Appendices x Acknowledgements xi Dedication xii CHAPTER 1 General introduction and thesis objectives 1 1.1 Thesis objectives 3 1.2 History of Marine Mammal Hunting 3 1.2.1 Early hunting 3 1.2.2 Modern Whaling 5 1.2.3 Reporting catches 8 1.3 History of Marine Mammal Management 9 1.3.1 International Governance 9 1.3.2 National Governance 13 CHAPTER 2 Marine Mammal Stochastic Stock Reduction Analysis 14 2.1 Methods 14 2.1.1 Data sources 14 2.1.2 Production model 15 2.1.3 Stochastic Stock Reduction Analysis 16 2.2 Application to simulated data • 20 2.2.1 Estimating parameters - single stock 25 2.2.2 Estimating parameters - aggregated stocks 27 2.2.3 Struck-but-loss ratios 30 CHAPTER 3 Population Traj ectories 33 3.1 Population trajectories of exploited cetaceans 35 3.1.1 Great whales 35 Sei whale, Balaenoptera borealis 35 Southern Right whale, Eubalaena australis 37 Sperm whale, Physeter catodon 38 Fin whale, Balaenoptera physalus 39. Gray whale, Eschrichtius robustus 42 Blue whale, Balaenoptera musculus 44 Bowhead whale, Balaena mysticetus '. 47 Eden/Bryde's and Bryde's whale, Balaenoptera edeni and brydei 48 Humpback whale, Megaptera novaengliae 50 Common minke whale, Balaenoptera acutorostrata 52 Antarctic minke whale, Balaenoptera bonaerensis 54 North Atlantic right whale, Eubalaena glacialis 55 North Pacific right whale, Eubalaena japonica 56 3.1.2 Smaller whales and large dolphins 58 Short-finned pilot whale, Globicephala macrorhynchus 58 Baird's beaked whale, Berardius bairdii 59 Beluga, Delphinapterus leucas 60 Killer whale, Orcimts orca 62 Long-finned pilot whale, Globicephala melas 64 Northern bottlenose whale, Hyperoodon ampullatus 66 False killer whale, Pseudorca crassidens 67 IV Narwhal, Monodon monoceros 68 3.1.3 Smaller Dolphins and Porpoises 71 Pantropical spotted dolphin, Stenella attenuate! 71 Spinner dolphin, Stenella longirostris 73 Short beaked common dolphin, Delphinus delphis 74 Dall's porpoise, Phocoenoides dalli 76 Bottlenose dolphin, Tursitops truncatus 77 Northern right whale dolphin, Lissodelphis borealis 79 Harbour Porpoise, Phocoena phocoena 80 Atlantic white-sided dolphin, Lagenorhynchus acutus 83 3.2 Population trajectories of exploited pinnipeds 85 3.2.1 True seals : 85 Ribbon seal, Histriophoca fasciata 85 Ringed seal, Pusa hispida 86 Southern elephant seal, Mirounga leonine 89 Gray seal, Halichoerus grypus 90 Harp seal, Pagophilus groenlandicus 91 Hooded seal, Cystophora cristata 94 Bearded seal, Erignathus barbatus 96 Harbour seal, Phoca vitulina 97 Largha or spotted seal, Phoca largha 98 3.2.2 Eared seals 100 Antarctic far seal, Arctocephalus gazelle 100 South African and Australian far seal, Arctocephalus pusillus 102 Northern far seal, Callorhinus ursinus 103 South American sea lion, Otaria flavenscens 104 New Zealand fur seal, Arctocephalus forsteri 105 California sea lion, Zalophus californianus 106 Steller sea lion, Eumetopias jubatus 108 3.2.3 Walrus 110 Walrus, Odobenus rosmarus 110 3.3 The big picture 114 3.3.1 Marine mammal catches 115 3.3.2 Marine mammal numbers 119 3.3.3 Marine mammal biomass •• 122 3.3.4 The great whales 125 CHAPTER 4 Discussion 128 4.1 Data 128 4.2 Methodology 131 4.3 Results 138 4.4 Conclusion 146 Literature Cited 147 Appendix 1 Species list 184 Appendix 2 Model input 188 Appendix 3 R code • 194 Appendix 4 Abundances of species with no documented exploitation 198 Appendix 5 Catch data 201 Appendix 6 Catch data sources 228 v List of Tables Table 1-1: Species for which the IWC has published abundance estimates (IWC, 2006) 10 Table 3-1. Definition of confidence ID's, their meanings and associated proportion of K 34 Table 3-2. Populations of sei whales 35 Table 3-3. Population of Southern right whales 37 Table 3-4. Population of sperm whales 38 Table 3-5. Populations of fin whales 40 Table 3-6. Populations of gray whales 43 Table 3-7. Populations of the blue whales 45 Table 3-8. Population of Arctic bowhead whales 47 Table 3-9. Populations in the Eden/Bryde whale complex 48 Table 3-10. Populations of humpback whales 50 Table 3-11. Populations of common minke whales 53 Table 3-12. Population of Antarctic minke whales 55 Table 3-13. Population of North Atlantic right whales 56 Table 3-14. Population of North Pacific right whales 57 Table 3-15. Populations of short-finned pilot whales 58 Table 3-16. Populations of Baird's beaked whales 59 Table 3-17. Population of beluga whales 61 Table 3-18. Populations of killer whales 62 Table 3-19. Populations of long-finned pilot whales 65 Table 3-20. Population of northern bottlenose whales 66 Table 3-21. Populations of false killer whales : 68 Table 3-22. Populations of narwhals 69 Table 3-23. Populations of pantropical spotted dolphins 72 Table 3-24. Populations of spinner dolphins 73 Table 3-25. Populations of short beaked common dolphins 75 Table 3-26. Populations of dalls porpoises 77 Table 3-27. Populations of bottlenose dolphins 78 Table 3-28. Population of northern right whale dolphins 79 Table 3-29. Populations of harbour porpoises 81 Table 3-30. Populations of Atlantic white-sided dolphins 84 Table 3-31. Populations of ribbon seals 85 Table 3-32. Populations of ringed seals 87 Table 3-33. Population of southern elephant seals 89 Table 3-34. Populations of gray seals 90 Table 3-35. Populations of harp seals 92 Table 3-36. Populations of hooded seals 94 Table 3-37. Populations of bearded seals 96 Table 3-38. Populations of harbour seals 97 Table 3-39. Populations of largha or spotted seals 98 Table 3-40. Population of Antarctic fur seals 101 Table 3-41. Population table for the South African and Australian fur seal 102 Table 3-42. Populations of Northern fur seals 103 Table 3-43. Populations of South American sea lions 105 Table 3-44. Populations of California sea lions 107 Table 3-45. Populations of Steller sea lions 108 Table 3-46. Populations of walruses 110 Table 3-47. Catch by species since 1800 126 Table 4-1: Available published and predicted carrying capacities / pre-exploitation abundances 143 VI List of Figures Figure 2-1. There is no bias in the sample model for the r m a x and K parameters 22 Figure 2-2. Bias in the sample model assuming both observation and process errors 23 Figure 2-3. Bias estimates for the r m a x and K parameters 24 Figure 2-4. Simulated and estimated population trajectories with a) some recovery and b) no/limited recovery 26 Figure 2-5. Boxplots for the bias ratios for r m a x and K estimated for a single stock 27 Figure 2-6. Simulated and estimated population trajectories for the aggregated populations 29 Figure 2-7. Boxplots for the bias ratios for r m a x and K for the aggregated population 30 Figure 2-8. The effect of struck-but-loss rates on estimating K 31 Figure 3-1. Sample population trajectory 34 Figure 3-2. Population trajectories for North Atlantic sei whales 36 Figure 3-3. Population trajectories for North Pacific sei whales 36 Figure 3-4. Population trajectories for Southern Hemisphere sei whales 37 Figure 3-5. Population trajectory for Southern right whales 38 Figure 3-6. Population trajectories for sperm whales 39 Figure 3-7. Population trajectories for North Atlantic fin whales 40 Figure 3-8. Population trajectories for North Pacific fin whales 41 Figure 3-9. Population trajectories for Southern Hemisphere fin whales 41 Figure 3-10. Population trajectories for Northeastern Pacific gray whales 43 Figure 3-11. Population trajectories for Northwestern Pacific gray whales 44 Figure 3-12. Population trajectory for North Atlantic blue whales 45 Figure 3-13. Population trajectory for North Pacific blue whales 46 Figure 3-14. Population trajectory for Southern Hemisphere blue whales 46 Figure 3-15. Population trajectory for Arctic bowhead whales 47 Figure 3-16. Population trajectory for the North Pacific Eden/Bryde whale complex 49 Figure 3-17. Population trajectory for the Southern Hemisphere Eden/Bryde whale complex 49 Figure 3-18. Population trajectory for North Atlantic humpback whales 51 Figure 3-19. Population trajectory for North Pacific humpback whales 51 Figure 3-20. Population trajectory for Southern Hemisphere humpback whales 52 Figure 3-21. Population trajectory for North Atlantic minke whales 53 Figure 3-22. Population trajectory for North Pacific minke whales 54 Figure 3-23. Population trajectory for Antarctic minke whales 55 Figure 3-24. Population trajectory for North Atlantic right whales 56 Figure 3-25. Population trajectory of North Pacific right whales 57 Figure 3-26. Population trajectory for Japanese short-finned pilot whales 59 Figure 3-27. Population trajectory for Japanese Baird's beaked whales 60 Figure 3-28. Population trajectory for beluga whales 61 Figure 3-29. Population trajectory for North Atlantic killer whales 63 Figure 3-30. Population trajectory for North Pacific killer whales 63 Figure 3-31. Population trajectory for Southern Hemisphere killer whales 64 Figure 3-32. Population trajectory for Faroe Island long-finned pilot whales 65 Figure 3-33. Population trajectory for Northwest Atlantic (Newfoundland) pilot whales 66 Figure 3-34. Population trajectory for northern bottlenose whales 67 Figure 3-35. Population trajectory for Japanese false killer whales 68 Figure 3-36. Population trajectory for Canadian Baffin Bay narwhals 70 Figure 3-37. Population, trajectory for Hudson Bay narwhals 70 Figure 3-38. Population trajectory for Greenlandic Baffin Bay narwhals 71 Figure 3-39. Population trajectory for Eastern Tropical Pacific pantropical spotted dolphins 72 Figure 3-40. Population trajectory for Japanese pantropical spotted dolphins 73 Figure 3-41. Population trajectory for Eastern Tropical Pacific spinner dolphins ; 74 vii Figure 3-42. Population trajectory for Eastern Tropical Pacific short beaked common dolphins 75 Figure 3-43. Population trajectory for Northwest Atlantic short beaked common dolphins 76 Figure 3-44. Population trajectory for Japanese dalls porpoises 77 Figure 3-45. Population trajectory for Northwest Atlantic bottlenose dolphins 78 Figure 3-46. Population trajectory for Japanese bottlenose dolphins 79 Figure 3-47. Population trajectory for northern right whale dolphins 80 Figure 3-48. Population trajectory for Greenlandic harbour porpoises 81 Figure 3-49. Population trajectory for North Sea harbour porpoises 82 Figure 3-50. Population trajectory for Baltic harbour porpoises 82 Figure 3-51. Population trajectory for Western North Atlantic harbour porpoises 83 Figure 3-52. Population trajectory for Northwest Atlantic white-sided dolphins 84 Figure 3-53. Population trajectory for Bering Sea ribbon seals 86 Figure 3-54. Population trajectory for North Atlantic / Arctic ringed seals 87 Figure 3-55. Population trajectory for Baltic ringed seals 88 Figure 3-56. Population trajectory for North Pacific / Arctic ringed seals 88 Figure 3-57. Population trajectory for southern elephant seals 89 Figure 3-58. Population trajectory for Icelandic gray seals 90 Figure 3-59. Population trajectory for Scottish gray seals 91 Figure 3-60. Population trajectory for West Ice (East Greenland) harp seals 92 Figure 3-61. Population trajectory for Northwest Atlantic harp seals 93 Figure 3-62. Population trajectory for White Sea harp seals 93 Figure 3-63. Population trajectory for Jan Mayen hooded seals 95 Figure 3-64. Population trajectory for Northwest Altantic hooded seals '. 95 Figure 3-65. Population trajectory for Bering/Chukchi bearded seals 96 Figure 3-66. Population trajectory for California harbour seals 97 Figure 3-67. Population trajectory for Bering largha/spotted seals 99 Figure 3-68. Population trajectory for Northeast Pacific largha/spotted seals 99 Figure 3-69. Population trajectory for Okhotsk Sea largha/spotted seals 100 Figure 3-70. Population trajectory for Antarctic fur seals 101 Figure 3-71. Population trajectory for the South African fur seal 102 Figure 3-72. Population trajectory for Northern fur seals : 104 Figure 3-73. Population trajectory for South American sea lions 105 Figure 3-74. Population trajectory for California sea lion 107 Figure 3-75. Population trajectory for Eastern Alaska Steller sea lions 109 Figure 3-76. Population trajectory for Western Alaska Steller sea lions 109 Figure 3-77. Population trajectory for Chukchi / Bering Sea walruses 111 Figure 3-78. Population tracjectory for East Greenlandic walruses I l l Figure 3-79. Population trajectory for Northwater walruses 112 Figure 3-80. Population trajectory for Spitsbergen walruses 112 Figure 3-81. Population trajectory for West Greenlandic walruses 113 Figure 3-82. Trends in marine mammal biomass and abundance from 1800 - 2001. T 115 Figure 3-83. World catch of marine mammals, in numbers (thin line) and biomass (thick line) from 1750 to 2001. The decline in catchy by weight clearly precedes the decline by numbers 116 Figure 3-84. Average weight of marine mammals caught during 1530 - 2001, calculated as a moving average over 31 years 117 Figure 3-85. Average weight of marine mammals caught during 1900 - 2001, estimated from total weight of mammals caught relative to numbers caught 118 Figure 3-86. Decline in numbers of marine mammals from 1800 to 2001 119 Figure 3-87. Marine mammal global abundance in numbers 120 Figure 3-88. Global aggregated abundance of cetaceans and pinnipeds, (in numbers) from 1800 to 2001.121 Figure 3-89. Decline in the biomass of marine mammals 122 Figure 3-90. Marine mammal global abundance in biomass aggregated by groups from 1800 to 2001 .... 123 Figure 3-91. Marine mammal global abundance by cetaceans and pinnipeds, from 1800-2001 124 Figure 3-92. Decline in great whales numbers and biomass from 1800 to 2001 125 viii Figure 3-93. Sequential depletion of the great whale and average body weight of landed animals Figure 4-1. Population trajectories for the Western Alaska population of Steller sea lions IX List of Appendices Appendix 1 Species list Appendix 2 Model input Appendix 3 R code Appendix 4 Abundance of marine mammals with no documented exploitation Appendix 5 Catch data x Acknowledgements Thank you to my supervisor Steve Martell, for the great advice, the challenges and all the fun in between. To Jordan Beblow, who briefly acted as my research assistant and made the never-ending data manageable. To Kristin Kaschner, for the initial work that spurred this project. Thank you to my committee: Carl Walters for the inspiration and the fishing trips, Andrew Trites, for showing me a thing or two about fieldwork, sea lions and a stinky minke, and Daniel Pauly for making sure the big picture never faded. Thank you to the people who make this place, the ladies and gents of the fisheries centre. In particular to Megan and Meaghan - the early morning workouts and late evening wine-outs. To Rob, Bob and Nathan - I think I called you my big brothers of fisheries at some point. To Tiphaine, Pamela and Michelle - for being marine mammal obsessed and much too fun to party with. To Jennifer, Sarah, Chad, Megan, Collette, Kerrie, Divya and Pramod for the chit-chats and all the fun. Thank you to my parents Ellen and Vil ly , and my sister Anne. Also thank you to my family in Denmark - all the Bang's and Christensen's who remind me who I am. And last, but not least, thank you to my best friends - Christine Meggy, Daina Pedwell and Brad Tindall because the world does not, as they remind me, revolve around fish and whales, but around dinner parties, traveling, stilettos, martini's, friends, dancing, interior decorating, partying, clothes, wine, kittens, discussions, boating, men, cars, cooking, bbq'ing, baking, play fighting, laughing and ... well, remembering to have fun. xi Dedication This work is dedicated to my father's family; the fishermen and their wives who have lived with the North Sea in their homes. My great-grandparents Jorgen Christian (Krsenfus) and Anne Marie Christensen, my grandparents Martin (Maggefus, Magge Frasnde) and Gudrun Christensen, my uncle Jorgen and father Vi l ly Christensen. xn CHAPTER 1 General introduction and thesis objectives The relationship between man and marine mammal extends back for centuries and covers a multitude of facets, from the literary adventures of Moby Dick (Melville, 1851) and First Nations' folklore/spirituality, to the necessities of life: blubber for light and food, fur for warmth, bladders for water carriers, and the luxuries: blubber for soap and perfume and baleen for corsets. The dynamics driving this relationship, as well as the relationship itself, has since changed drastically. Sven Foyn, a Norwegian, spurred much of the development that initiated the period referred to as modern whaling which saw changes in technology and targeting beginning in the 1860s (Tonnessen, 1982). The great whales were reduced to the sum of their products: oil and baleen winning out over food, clothing and culture. It was a change from a hunt dominated by cultural identity to one dominated by a lifestyle en masse for the crews of larger commercial operations. The question becomes how has this impacted marine mammal populations and the ecosystems they live in? Marine food webs have been fished down and with the exploitation, dynamics in the oceans have been radically altered (Christensen, 1996, Pauly et al, 1998). But how much and what are the relative differences, in numbers and biomasses, of these top fauna of our marine ecosystems. This has not been quantified on a global scale, and I attempt to fill this gap in our knowledge. In addition, shifting baselines (Pauly, 1995) make for a world where it becomes difficult to imagine oceans full of whales - which surely influences management goals. As a consequence of anthropogenic impacts, the current capacity of the oceans and its ability to support past marine mammal abundance levels is unknown. 1 In this thesis I have reconstructed population trajectories for documented exploited marine mammal species and stocks at the scale of ocean basins. This is done to help decipher what has happened to the stocks, defined as groups of animals of the same species occupying separate areas, over the last decades, and the scale at which exploitation has affected their abundances. By quantifying marine mammal population histories, it is possible to begin to calculate how humans have impacted their marine ecosystem food webs over time. I want to stress that the population trajectories developed are meant to be used in the context described above. Applying them within the context of management could be inappropriate because the scale at which they are assessed and reconstructed is too coarse. In addition, while single species assessment is the International Whaling Commission's (IWC) method of choice, the consequences of managing species that naturally co-exist and potentially affect each other (e.g. krill, minke whales and crabeater seals as described by Mori and Butterworth, 2005) in this manner should be considered. Walters et al. (2005) explain how managing all species at maximum sustainable yield is technically challenging because of complex ecosystem interactions that are not fully understood. Stakeholders need to think about what population status objectives for marine mammal populations ought to be. Should populations be maintained at their most productive levels (as predicated by the International Convention on the Regulation of Whaling) or should the goal be to conserve and/or rebuild marine mammal stocks? This work is intended to set the stage so that stakeholders can make informed decisions when facing those options. 2 1.1 Thesis objectives In this thesis, my objectives are to quantify the history of marine mammal populations, in terms of both numbers and biomass, subsequent to the commencement of hunting, on a global scale. I intend to do this for all exploited marine mammal populations with sufficient abundance and catch data. For each species, population trajectories are carried back to the year of the first documented hunting. In the remainder of chapter 1,1 attempt to briefly summarize the history of marine mammal hunting. In Chapter 2, I describe the method I have selected to analyze the species histories, and evaluate potential bias in the method. Chapter 3 presents the results for exploited marine mammal species, and aggregated results for all marine mammals, including those that have not been subject to documented hunting. Chapter 4 presents a discussion of the data obtained, methodology developed and results generated. 1.2 History of Marine Mammal Hunting 1.2.1 Early hunting The beginnings of aboriginal subsistence whale hunting are unknown. In the arctic, hunting of bowheads was documented in Greenland from 800-1000 years ago, in Alaska the hunt dates back even farther (Reeves, 2002). In Korea, evidence of hunting dates back to a sandstone carving from 6000 BC showing a whale being harpooned from a boat (Baker and Clapham, 2002). The Neolithic times likely marked the beginning of subsistence hunting for seals, sea lions, walruses and sea otters (Baker and Clapham, 3 2002). In Northern Scandinavia, the hunt for ringed seals dates back to some 6000 - 4000 years before present (Nygaard, 1989). In the 11 t h century whaling developed into an industry, as documented by tax records, led by the Basques who hunted the Northern Right Whale in the Bay of Biscay (Aguilar, 1986, Clapham et al, 1999, Baker and Clapham, 2002). By the end of the sixteenth century this whaling had expanded into the New World, with Basque boats hunting bowhead and right whales in Labrador (Baker and Clapham, 2002). Following in their footsteps and often with Basque harpooners, whaling was expanded into the Arctic by the Dutch and Germans, the British, and French (Baker and Clapham, 2002). In the early seventeenth century the Basque boats migrated to Spitsbergen, where they found Dutch, English, German, Danish and Norwegian whalers (Isachsen, 1929). Marine mammals were hunted using a variety of techniques, many of them local to specific areas. One of the most common methods for the capture of whales was the harpoon-line-float contraption; here, hunters in boats could follow the harpooned whale, which would both be slowed down and marked by the float (Mitchell et al, 1986). Other techniques included the harpoon-line-fast boat, the 'Nantucket sleighride', where the harpoon line remained attached to the boat, electrocution, netting, drugs and poisons, gas injection and rifles (Mitchell et al, 1986). An interesting hunting method was developed in the Philippines where mainly Bryde and Pygmy Bryde whales are hunted in Pamilacan by men who jump with big hooks from boats and onto the animals. This method likely evolved from the manta ray hunt and dates back at least to the early 20 t h century (Reeves, 4 2002). The last technique is the mechanically propelled harpoon, the device that brought with it modern whaling, with its extreme capability, i.e., range, power and precision. 1.2.2 Modern Whaling "Optimism is a good characteristic, but if carried to an excess, it becomes foolishness. We are prone to speak of the resources of this country as inexhaustible; this is not so." - Theodore Roosevelt (1907) Modern whaling emerged rapidly, precipitated by an ingenious development by a Norwegian, Sven Foyn, in the late 1800s. Foyn's invention marked the beginning of the period of massive over-exploitation of many of the world's great whales. Foyn equipped a steam boat with a bow-mounted canon capable of firing an exploding grenade harpoon (Tonnessen, 1982, Mitchell et al., 1986). The harpoon would bury itself inside the mammal, the grenade would explode, and the dying whale, fastened to the boat by the harpoon line, was secured (T0nnessen, 1982). And in this manner modern whaling saw its beginning in Norway's northernmost county, Finnmark (Tonnessen, 1982). According to Tonnessen (1982), modern whaling can be split into three categories: the Finnmark whaling described above (1864-1904), global whaling (1883-1924), and pelagic whaling (1925-1986). I would add a fourth period (1986-present) of scientific whaling and continued commercial Norwegian whaling. In modern whaling, steam boats allowed boats to go further and faster, the harpoon made whaling safer and more efficient. This would eventually lead to expansion both in terms of species caught and also into new 5 areas. The aggressive sperm whales and the cold Antarctic waters were no longer feared by the whalers dry on their bigger, quicker boats. The major development that made large-scale pelagic whaling possible was the introduction of floating factory ships supplied by steam catcher boats. Slips and winches moved flensing, the removal of blubber in long strips from the whale carcass, from an outboard 'round' procedure, where the whale, attached to the side of the boat, rolled around in the water as it was flensed, to an on-board 'long' undertaking, where steam winches pulled up strips of blubber that men with flensing knives separated from the carcass much like it was done when shore-based, improving the processing efficiency (Isachsen, 1929). The on-board processing of blubber, in big boilers allowed even more freedom, as whales no longer had to be towed to shore within a limited amount of time. Lastly, the development of technology, from radio, planes to spot whales, GPS, and so forth helped find whales as they became increasingly sparse. Both nationally and internationally, Norwegians were the pioneers of modern whaling. They formed the crew on most early expeditions, and maintained their positions as the best captains and harpooners for the bulk of the whaling years. The three companies that opened up Antarctic whaling were either entirely owned, or started and managed by Norwegians. The pioneers in that region were three Norwegians all heralding from Sanderfjord: C A . Larsen (see below), Christen Christensen, who developed one of the first two floating factory steamships and worked with the refinement and transport of oil (Tonnessen, 1982), and Adolf Amandus Andersen, who began western Antarctic whaling, though on a much smaller scale and less influentially 6 than the two others (Tannessen, 1982). The Compania Argentina de Pesca Sociedad Anonima (Pesca), established at Grytviken in South Georgia can be named as the pioneer of modern Antarctic whaling and was initiated by Captain C. A . Larsen, who also acted as technical leader and whaling manager (Hart, 2004). But the scale of the whaling operations proved too much for the oceans, and Thomas Huxley's (1883) statement that "all the great sea-fisheries are inexhaustible" once again proved itself incorrect. The reason the scale and magnitude of the decline was possible was the ability of the operations to switch between species. This allowed the stocks to be almost completely depleted as the opearations remained profitable for much longer than they would otherwise have had (e.g., Southern blue whales). However, according to Schneider and Pearce (2004), the 1986 moratorium was the result mainly of market forces, in terms of cost increasing per unit effort as stocks decreased, and to a lesser extent the influence of environmental organizations. Presently, aboriginal subsistence hunting is allowed in a number of smaller communities. Seal 'culling' programs are in place in Canada, and seals are caught for subsistence purposes in a number of small communities. Whaling, as it existed before the moratorium, is gone. Norway, with its original abstention to the moratorium, still legally hunts minke whales. Japan has a legal scientific whaling program which has often found itself accused of being a disguise for a commercial whaling operation. Iceland was readmitted to the IWC in 2002, under a clause stating that commercial hunting could recommence in 2006 warranting adherence to IWC hunting-regulations (Anon., 2002). The political forces at work in the whaling debate are complex. It is worth noting that the 7 Wakayama school district in Japan has re-introduced whale meat in school lunches (Head, June 19, 2005) and Norway has been unable to fill their quotas in 2006 (Black, July 13, 2006). The 2006 IWC meeting saw the following declaration passed, which included the following: FURTHER NOTING that the moratorium, which was clearly intended as a temporary measure is no longer necessary, that the Commission adopted a robust and risk-averse procedure (RMP) for calculating quotas for abundant stocks of baleen whales in 1994 and that the IWC's own Scientific Committee has agreed that many species and stocks of whales are abundant and sustainable whaling is possible CONCERNED that after 14 years of discussion and negotiation, the IWC has failed to complete and implement a management regime to regulate commercial whaling DECLARE our commitment to normalize the functions of the IWC based on the terms of the ICRW and other relevant international law, respect for cultural diversity and traditions of coastal peoples and the fundamental principles of sustainable use of resources, and the need for science-based policy and rulemaking that are accepted as the world standard for the management of marine resources " - St Kitts and Nevis Declaration, IWC Meeting 58 1.2.3 Reporting catches Whaling operators have reported their landings since the late 1860s (Schneider and Pearce, 2004). In 1929, the Whaling Committee of the International Council for the Study of the Sea (later renamed the International Council for the Exploration of the Sea) recommended that the Norwegian government create an institution, Komiteen for 8 Internasjonal hvalfang-statistiskk, the Committee for Whaling Statistics or the Bureau of International Whaling Statistics (BIWS), appropriately located in Sanderfjord, to collect, collate and annually publish global statistics on the industry, including catches, effort and financial statistics (The Committee for Whaling Statistics, 1930, Grieves, 1972). When the IWC was set up, Article VII of the International Convention for the Regulation of Whaling (ICRW) included provisions to ensure that data was transmitted to BIWS (Grieves, 1972). In other marine mammal hunts, catches may be documented by hunters, and possibly submitted to and recorded by national governments and institutions. National programs aimed at interviewing members of whaling/sealing communities to extrapolate catch estimates, exist in some subsistence hunting nations. However, the accuracy of these estimates is variable, and their feasibility is usually dependent on annual funding. 1.3 History of Marine Mammal Management 1.3.1 International Governance According to the ICRW, signed in 1946, whale stocks are "great natural resources" and management should aim at "achieving the optimum level of whale stocks" for future generations, as is in "the interest of the nations of the world". Part of the ICRW's mandate was the creation of an international management body, the IWC. The ICRW was ratified and came into force in 1948, and 1949 marked the first meeting of the IWC. Since then, the commission has met, on average, on an annual basis. In keeping with the objectives of the ICRW, the IWC placed a moratorium on all commercial 9 whaling in 1986. The moratorium was instituted to allow recovery for severely depleted stocks and in order to enable scientists to complete a comprehensive evaluation of all stocks. This was done to ensure that the resources could be managed in accordance to ICRW regulations, aimed at keeping abundances at the levels that are postulated to produce the maximum sustained yield. This work was to be completed by 1990 at which time whaling would resume and follow well outlined catch limit rules (see section 4.2). Assessments have been started for all species, but the commission has only agreed to publish abundance information for 11 stocks from 7 species (6 great whales and pilot whales; Table 1-1) - the remaining stocks/species have either not been assessed in detail, and/or the estimates have been deemed to have too much statistical uncertainty. Table 1-1: Species for which the IWC has published abundance estimates (IWC, 2006). Species Population Year(s) to which the estimate applies Minke Whales Southern Hemisphere 1982/83- 1988/89* North Atlantic 1987-95 North West Pacific and Okhotsk Sea 1989-90 Blue Whales Southern Hemisphere 1980-2000 Fin Whales North Atlantic 1969-1989 Gray Whales Eastern North Pacific 1997/98 Western North Pacific Current Bowhead Whales Bering-Chukchi-Beaufort Seas stock 1993 Humpback Whales Western North Atlantic 1992/93 Southern Hemisphere south of 60S in summer (i.e., incomplete) 1988 Pilot Whales Central and Eastern North Atlantic 1989 * This is now considered unreliable because of newer surveys, indicating a significantly smaller population than was previously estimated The main reasons for this shortcoming are the complexity and difficulty of the task due mostly to data limitations. It also relates, however, to the political situation and corresponding changes in objectives of some of the IWC member countries. Public 10 attitudes in these countries have shifted dramatically with the development of environmental consciousness, partly due to the campaigns of non-governmental organizations (NGO's) (Raustiala, 1997). In particular, Greenpeace's "Save the Whale" campaign gathered considerable media attention. The plight of the whales came to symbolize the state of the environment, and the participation of NGO's in IWC meetings grew rapidly (Oberthur, 1998). This marks a shift in values, whales have again become more than a resource, their intelligence is admired and they are attributed existence value. In fact, this has put a number of countries directly at odds with the International Convention for the Regulation of Whaling to which they are signatories. I just want to briefly suggest some of the reasons why the comprehensive assessments of the great whale stocks have not yet been completed. When the. IWC finishes the assessments, a legal re-opening of whaling follows. This is something a shrinking proportion of IWC signatories, currently roughly half, are opposed to. Many new countries have joined the IWC, even though they have no historical whaling ties for this reason. Japan has been accused of 1) leveraging development aid, and 2) presenting whales in the light of easting fish that would otherwise be available for human consumption, to encourage (poorer) nations to join and vote with the pro-whaling delegation (Kaschner and Pauly, 2004). Japan and Norway would like to see whaling legally resume in line with ICRW. The fact remains, that the status quo is not bad, the anti-whalers have a moratorium and the pro-whalers are still whaling, but it can not last because the IWC becomes ineffectual as an institution. Japan has its scientific whaling program, which is opposed, but legal, and Norway did not sign the moratorium, so they 11 can legally hunt, and Iceland re-joined the IWC with a clause to allow hunting. The anti-whaling countries currently have a maintained ban on whaling as they would like, and are unwilling to accept any form of commercial scale hunting. As I see it, a change is needed in the underlying political framework. Tough decisions must be made before the IWC falls apart because all confidence in the institution is lost. The ethical and moral arguments against whaling must be brought forward for discussion; the reliance on deferring science that effectively will never be complete is not sustainable. There exists a conflict in interest between those who do and those who do not believe that whaling should continue, and i f there is to be a global perspective on the issue, it must be discussed. As it stands, the international convention sanctions whaling, while at the same time the international management board vetoes it. I do not intend this to be a comprehensive critique of the IWC, nor do I profess to know all the intricacies of the workings, or of all the Commission's work. In addition, this thesis deals with all exploited marine mammal populations, not just those that fall within the jurisdiction of the IWC. Along with the IWC, the North Atlantic Marine Mammal Commission (NAMMCO), which deals with the conservation, management and study of marine mammals in the North Atlantic Ocean, is an international management body. Otherwise, responsibility is delegated to individual countries to manage marine mammal populations within their exclusive economic zones. This, of course is problematic for management as marine mammals do not respect national borders, often cover more than one exclusive economic zone and well into the high seas, and often do not have clearly identified migration patterns. 12 1.3.2 National Governance National institutions also collect data on catch, incidental mortality and abundance of marine mammals as well as carrying out stock assessments to comply with national regulations. In the United States, this is done by the National Oceanic and Atmospheric Adimistations' Office of Protected Resources in accordance with the Marine Mammal Protection Act and the Magnus-Stevens Fishery Conservation and Management Act. In Canada, Fisheries and Oceans Canada (DFO) manages marine mammal populations. Similarly, the rest of the world has marine mammal regulations specific to each country and its perceived need for management actions. Data on most marine mammals are collected by such groups and by university research groups, e.g. the Marine Mammal Research Unit at the University of British Columbia, Canada and the Sea Mammal Research Unit at St. Andrews, Scotland. 13 CHAPTER 2 Marine Mammal Stochastic Stock Reduction Analysis This chapter describes the modeling approach used to estimate population trajectories for the exploited marine mammal populations. Trajectories begin at the onset of recorded harvest, which is assumed to be the actual onset of hunting for these purposes. Thus, the population size at this initial time is assumed to be the 'pre-exploitation' population size or the carrying capacity of the environment for this species. A l l models are run to year 2001 only (rather than to 2006) because of limitations in data availability, and for the sake of consistency. 2.1 Methods 2.1.1 Data sources The catch data used originates from the International Whaling Commission's Bureau of International Whaling Statistics. These data of catch records, by species and date, along with additional, but inconsistent information on, e.g., sex, length, weight and expedition dates. Other sources of catch data used in this thesis are mainly transcribed log-book entries from expeditions, and information collected or estimated nationally by individual governments and institutions. To find this information, Jordan Beblow and I conducted an extensive literature search. A l l catch and abundance data we could find and access were added to an existing database on recent abundance information for all marine mammal species that was set up by Kristin Kaschner (Kaschner, 2004). The database now contains information on catches and abundances through time, broken down by areas where they were reported. Model input abundance data is listed in Appendix 4, and 14 the catch data in Appendix 5. The great whales were either assessed over their entire range, or were split into applicable stocks by the North Atlantic, North Pacific and Southern Hemisphere oceans. Exceptions to this are the gray whales, which were assessed by the Northwestern and Northeastern Pacific. The smaller mammals, which tend to have smaller ranges, were assessed by ocean basins where possible and otherwise by regions with reported catch, e.g. Japanese waters, West Ice (off East Greenland's coast), Newfoundland, Eastern Tropical Pacific, Baltic Sea and so on. 2.1.2 Production model The production model is one of the simplest population models; it is a logistic growth model that assumes no errors in reported catch: where N is numbers, for r m a x is the maximum intrinsic rate of population growth, K is the carrying capacity, C is observed catch, and t is the subscript for time. The production model depends on K , the carrying capacity which makes it density-dependent. To evaluate the population size (N m s y ) that maximizes production, we take the derivative of the yearly population change, Nt+i - N t , with respect to N t , then set it to zero to find the inflection point and solve: (1) 2r N = 0 (2) dN. max K which gives us: 15 (3) The International Convention for the Regulation of Whaling (ICRW) specifies that marine mammal populations must be managed at their optimal population sizes, where production is maximized. This means that stocks should be maintained at the N m s y level that is half their carrying capacity (3). The biological reference point B m s y , is taken to be N m S y times the mean weight of the adults of the species. To estimate the r m a x and K parameters as set up in (1), it is necessary for stock to have exhibited historical variation in size (Hilborn and Walters, 1992), i.e., we need to observe recovery in order to resolve confounding between r m a x and K . There is a tradeoff between these two parameters, in that a catch history, especially i f that history is a one-way-trip, can be explained as either a highly productive small stock or a large stock with low productivity. Data to distinguish between these populations are often found in recovery and rebuilding information. This is a point we need to be very aware of, as many of the marine mammal stock histories follow from one-way-trip catch data, the result of increasing effort and declining catch per effort. 2.1.3 Stochastic Stock Reduction Analysis Population trajectories are modeled for all marine mammal species for which exploitation levels have been recorded. To do this we employ stock reduction analysis (SRA), a method first introduced by Kimura and Tagart (1982). SRA allows the use of historical catch time series to estimate a range of possible population parameters (rm a x , 16 K), that give rise to extant populations. Given the available data and our objective to determine stock size over time, we implement a production model based on the logistic model of population dynamics and driven by removal information: A ^ = A ^ + r m a x A ^ ( l - ^ K ' - C , (4) N,=K (5) where N t is the number of mammals in the population at time t (1,2, ... z, where z is the number of years for which we have data), r m a x is net production, i.e., growth + new production - mortality; K is pre-exploitation numbers or carrying capacity; N] is assumed to be K at the onset of harvest; C t is the catch, in numbers, at time t, and w t are independent process errors at time t. SRA generates a single population trajectory dependent on the selected parameter values of r m a x and K. A stochastic SRA generates a single population trajectory conditional on r m a x , K and a random anomaly sequence w t. A more interesting question is determining the probability of a stock being at the observed abundance level(s) at time(s) t, given the observed removal information and the assumption that the stock followed a stationary production relationship with mean r m a x and mean carrying capacity K , with realistic variation in these parameters. Bayesian stochastic SRA (SSRA) (Walters et al, 2006) proceeds, by (1) generating several thousand trajectories of N t ' s by randomly drawing from a prior distribution of r m a x , K and w t values, and (2) simulating the N t sequence conditional on the observed catches (C t). (3) For each simulated N t sequence calculating the likelihood 17 of having obtained the observed abundances (yt). (4) resampling each of the trajectories with sample probability proportional to its likelihood, giving us a posterior probability density for the parameters of interest. To implement the SSRA as outlined above, I used the following procedure. First, draw values for r m a x , K and w t from prior distributions. A normal prior distribution was assumed for r m a x , with mean 0.04 (standard deviation (SD) = 0.04) for cetaceans, 0.02 (SD = 0.02) for sperm whales and 0.12 (SD=0.06) for pinnipeds, (default mean values are from Wade, 1998). K was drawn from a uniform prior distribution between 'reasonable' population size bounds that gives rise to extant populations in the deterministic case (i.e., w t = 0). Lastly, w t 's were drawn from a random normal distribution with mean 0 and standard deviation = a w (these are the process error terms). A total error term, K = 0.1 was specified and distributed amongst process errors rw = yjl~-~p*4K and observation errors , where p determines the proportion of the error allocated to each error term (0.3 < p < 0.6) depending on the certainty associated with the observed abundances, y t 's. A population trajectory was then generated using equations (5) and (4) above. The likelihood of obtaining the observed abundances, y t, was calculated as: log(cTr) + ^ -log(2;r) 2 z +: *=l 2o-y where n is the number of abundance observations, a y is the standard deviation in the abundance estimate, the observation error. z t is the lognormal residual: 18 z, = log(/V,)-log(v,) (7) The r m a x and K combination and their associated likelihood were stored, and the above steps repeated 50,000 times. At this point, the procedure had produced a prior distribution of population trajectories. The next step was to resample these trajectories based on their associated importance weights. This was done using the importance sampling procedure recommended by Schnute (1994) and McAllister and Ianelli (1997). The posterior probably density function was calculated by resampling from the set of trajectories stored above, with sample probability proportional to the importance weights/likelihoods. Lastly, I calculated summary statistics including the marginal posterior for K. The marginal distributions are used to calculate the most likely estimate (the median), along with the 95% credible interval of the distribution values for K . This was done using the quantile function in R (R Development Core Team, 2005). I also calculated how much K-N the population has been depleted, i.e., depletion = — * 100%. K I have programmed all of the calculations discussed above in the statistical programming language R (R Development Core Team, 2005), and the code can be found in Appendix 3. The biggest advantage to this model, as compared to frequentist methods, is that it allows us to be explicit about the uncertainties in our estimate conditional on the assumed values of p and K . The main reason I have selected the logistic model is that it requires 19 only limited input data, a catch time series and one or more absolute abundance estimates and that it has a minimal number of parameters. Often for the marine mammal populations, this is all the data that are available. 2.2 Application to simulated data To evaluate the potential bias in the model, I generated a set of simulated data using the production model described above and a time-series following the expansion and collapse trend that many of the marine mammal hunts underwent. I then ran the SSRA with the catch data and between one and three absolute abundance estimates all between the years 1970 and 2000 (with simulated observation error) to see i f the model can reproduce the carrying capacity and intrinsic rate of growth parameters used to generate the simulated observed abundance estimates, within some reasonable bound. This is the first of two test conditions. The second test condition identifies whether the model can handle aggregated stocks, i.e., i f there are two or more distinct stocks occupying a single ocean that are aggregated and assessed as one stock. These two test conditions are run under two sets of realistic catch scenarios. The first represents a one-way trip, i.e., little or no allowance is made for recovery of the stock, whereas the second catch history allows some recovery in stock size, with catches stopping in 1986, when the whaling moratorium came into effect. To check for relative bias in the estimates of both for r m a x and K , I created boxplots of the log2 ratios between estimated and observed parameter values. Note that a bias-ratio of mean of zero indicates no bias, an upward bias-ratio of 1 indicates 20 overestimation of the parameter value by a factor of 2, and a downward bias-ratio of 1 indicates underestimation of the parameter value by a factor of 2. First, to check that the model was set up correctly without bias, I set up a population with K= 160,000 and for r m a x = 0.04 and assumed no observation error when generating the abundance observations. In the estimation model, I assumed observation errors accounted for only 10% of the total error (K), while process errors accounted for the remaining 90%. Total error K = 0.1, observation error has mean = 0 and standard deviation = a = -Jproportion *4K = Vo.01*0.1 , and process error, mean = 0 and standard deviation =r„. 0.1 . When I do this, there was no bias in the r m a x or K estimates, which indicated that my estimation model is capable of regenerating the simulated parameters (Figure 2-1). I then added observation error to the simulated data, with mean = 0 and standard deviation =a = Vo.3*0.1, with K = 0.1. The process errors, mean = 0 and standard deviation = TW = Vo.l*0.7 , made up the remaining 70% of K . I found that the model became just slightly biased, accepting a few more underestimates for the r m a x parameter and correspondingly a few more overestimates for the K parameter (Figure 2-2). However, i f I tightened the prior on the intrinsic rate of growth from mean = 0.04, standard deviation = 0.04 to standard deviation = 0.02, the estimates are unbiased (Figure 2-2). This is because we are telling the model to place more trust in the 0.04 estimate of r m ax , countering the effects of the observation errors that cause the estimated trajectories to deviate from the real trajectory. 21 i n o CO E m CO CD o d CM o i n o r max K Figure 2-1. There is no bias in the sample model for the r ^ and K parameters, set at 0.04 and 160,000 when assuming no observation error and a prior on the r m a x parameter with mean = 0.04 and standard deviation = 0.04. NOTE: A median of 0 indicates no bias; a value of +/- 1 indicates an over/underestimate by a factor of 2. 22 XJ "co £ In 0 CO CD CN o (b) 10 o o o o r max r max K Figure 2-2. (a) there is a slight bias the sample model for the r m a x (set at 0.04) and K (set at 160,000) parameters when assuming both observation and process errors. The r m a x parameter is slightly underestimated (with a prior on r m a x with mean 0.04 and standard deviation = 0.04) and the K parameter is slightly overestimated, (b) the bias disappears when the prior on r m a x is narrowed to mean = 0.04 and standard deviation 0.02. The solids line in the boxes represent the median bias. 23 The estimate of a parameter will always lie between its true value and the prior placed on it when using Bayesian statistics. Consequently, I found that when I use slightly higher or lower estimates for r m a x , I got negative or positive biases, respectively, in the r m a x parameter, and an ensuing bias in the K parameter (Figure 2-3). T3 0) CO E -•—» CD CM o (a) (b) m o o o i n o m o o o m o r max K r max K Figure 2-3. Bias estimates for the r m a x and K parameters with K = 160,000 and (a) r m a x = 0.036 and (b) r m a x = 0.044. The estimates are (a) positively and (b) negatively biased because the estimates for r m a x fall between the real r m a x and the prior on r m a x (mean = 0.04, standard deviation 0.04). 24 2.2.1 Estimating parameters - single stock I set up the simulation-estimation model and ran it 100 times. The model was run with true values of K = 185,000 and r m a x = 0.042 K = 0.1, of which 30% is attributed to observation error and 70% to process error, i.e., ay = Vo. 1*0.3 and rw = Vo.1*0.7 to simulate the catch and observed abundance estimates using a logistic model equivalent to the one provided above assuming both process and observation errors present. The result of each simulation-estimation process was an estimated r m a x and K value and an associated population trajectory. The result of the entire simulation estimation process is a distribution over the most likely estimates for the net growth (rm a x) and carrying capacity/pre-exploitation numbers (K) parameters. A sample simulation estimation result is seen in Figure 2-4 for each of the two scenarios, i.e., with some and no/limited recovery. The box-plots indicating relative bias in the for r m a x and K parameter estimates, for the 'some recovery' scenario, are shown in Figure 2-5. They indicate that there is negative bias (-0.1) in the r m a x parameter as expected because it is higher than the mean, and consequently we also see a positive bias (0.07) in the K parameter. 25 1850 1870 1890 1910 1930 1950 1970 1990 Year 1850 1870 1890 1910 1930 1950 1970 1990 Year Figure 2-4. Simulated and estimated population trajectories with a) some recovery and b) no/limited recovery. The solid line represents the most likely estimate for the species population trajectory, the dotted lines represents the 95% credible interval around that trajectory, and the dots are the abundance estimates used to estimate model parameters. The dot-dash line is the 'real' simulated population trajectory, and the vertical lines are the catch data the population was subjected to. 26 T3 0 co E in CD CM CD O d d r max K Figure 2-5. Boxplots for the bias ratios for r m a x (0.042) and K (185,000) estimated for a single stock. r m a x is negatively biased because of the effects of the prior pulling the estimated r m a x parameter towards the prior on r m a x that has mean = 0.04 and standard deviation = 0.04. As a result, the K parameter is positively biased. 2.2.2 Estimating parameters - aggregated stocks The second robustness issue we need to address is how the model functions when data from several stocks are aggregated (both in terms of catches and abundance numbers) into a single entity on which we perform our assessment. This is done in most of this thesis, because of the lack of geographic information, beyond ocean basins, for most of the catches. To evaluate this, I again use the production model and set up three separate stocks and give each of them catch histories and run them to produce 3 abundance estimates. The simulation-estimation model was run 100 times, with the r m a x parameter for the three stocks set at 0.038, 0.036, and 0.044 respectively, with carrying 27 capacities of 15,000, 130,000 and 40,000 respectively, K = 0.1, of which 30% is attributed to observation error and 70% to process error, i.e., a = Vo. 1*0.3 and rw = Vo.l*0.7 . A sample simulation-estimation result is seen in Figure 2-6 for each of the two scenarios, i.e., with some and no/limited recovery. The box-plots indicating relative bias in the for r m a x and K parameter estimates, for the 'some recovery' scenario, are shown in Figure 2-7. The bias plot indicates that when compared to a mean of 0.036 (the r m a x parameter for the largest stock), the estimated r m a x parameter positively biased (0.1) because the prior pushes the estimate toward 0.04. The K parameter estimate, however, is unbiased. 28 < 0> E 3.0 2 .5 2 .0 1.5 1.0 0 .5 0 .0 1 8 5 0 1 8 7 0 1 8 9 0 1 9 1 0 1 9 3 0 Y e a r 1 9 5 0 1 9 7 0 1 9 9 0 15 10 ^ CD 5 S d 0 3 .0 r ™ 2 .0 > -I 5 o 1.0 E 05 0.5 0 .0 n 15 1 8 5 0 1 8 7 0 1 8 9 0 1 9 1 0 1 9 3 0 Y e a r 1 9 5 0 1 9 7 0 1 9 9 0 10 ? CD 5 & d 0 Figure 2-6. Simulated and estimated population trajectories for the aggregated populations with a) some recovery and b) no/limited recovery. The solid line represents the most likely estimate for the species population trajectory, the dotted lines represents the 95% credible interval around that trajectory, and the red dots are the abundance estimates used to hone in the estimates. The dot-dash line is the 'real' simulated population trajectory, and the vertical lines are the catch data the population was subjected to. 29 •a £ E 00 CD CN o i n o If) o r max K Figure 2-7. Boxplots for the bias ratios for r m a l (0.038, 0.036, 0.044) and K (15,000, 130,000, 40,000) for the aggregated populations. The rmax parameter is biased slightly upward (0.1) when compared to the 'real' value 0.036 (the rmax value for the largest population), because the prior on rmax (mean = 0.04, standard deviation = 0.04) drives it upwards. The K parameter, however, is unbiased. 2.2.3 Struck-but-loss ratios During water-based hunting for marine mammals, an animal is often struck, e.g., with a harpoon or shot on an ice-floe, but may be lost, e.g., it may be wounded but manage to swim away, or the boat could sink. Of these animals, the ones that end up dying because the actions of hunters should be counted in our analyses. They are, however, often not accounted for. Especially in earlier catch records such rates are rarely, i f ever seen although they are now being collected at least for the ongoing regulated subsistence whaling hunts. The rate at which this happens is called the struck-but-loss 30 rate. It has been speculated to be as high as 35% for many of the great whaling enterprises. Whitehead (2002) used a correction factor of 1.5 on early sperm-whale catches, to account for amongst others oil/whale ratio, whales caught but not processed and wrecked ships (Whitehead, 2002). The IWC used a struck-but-loss rate of 35% in their comprehensive assessment of southern right whale (IWC, 2001, Baker and Clapham, 2004). Adding a struck-but-loss rate increases the estimates of K proportionally to that rate (Baker and Clapham, 2004). In Figure 2-8 I ran a simulation model to check this and we clearly see that the observed and predicted increase in K virtually mirror each other, indicating that an increase in catches of a certain percentage will produce an increase in K of the same percentage i f struck-but-loss rates are constant over time 150 r 100 K 50 0 obs pred 0 0.1 0.2 0.3 Struck-but-lost rate 0.4 0.5 Figure 2-8. The effect of struck-but-loss rates on estimating K. The solid line represents an increase in K by the struck-but-loss rate, and the dashed line shows the increase in K found by running a simulation with the struck-but-loss rate applied to catches. 31 •I decided not to include a default struck-but-loss rate for this thesis because I am looking to find the documented, and thus minimum, decline that has occurred. The true decline is likely much larger due to a confluence of factors, struck-but-loss rates, non-, under- and misreporting. This is especially true for the subsistence hunts for which struck-but-loss rates are likely high, but for which there rarely exists good data; in fact most often there are no data at all for these hunts. 32 CHAPTER 3 Population Trajectories The following chapter presents the results of the stochastic stock reduction analysis (SSRA) for marine mammal populations for which I have documented catch figures and where absolute abundance information was available. The results are presented in two ways, first in a table showing the year in which harvesting began, the initial and current population sizes and the percent decline (if any) in the population. The second display is a figure such as Figure 3-1, which has as its x-axis years from the onset of harvest up to 2001, and as the y-axis population numbers. The figure shows: (1) the most likely estimate of the population trajectory for the stock represented by the solid dark line; (2) the 95% credible intervals on this trajectory represented by the dotted light lines; (3) the available absolute abundance estimates represented by the dots, and (4) catches in vertical bars with their associated scale on the secondary y-axis. Information on sizes and sources and confidence in the abundance points can be found in Appendix 2. For each population, the stock's initial size, the year that estimate applies to (the year where harvesting began), the 2001 population size and the level of K-N depletion = — *100% are given in a table. Lastly, for all populations K , the total K error, was assumed to be 0.1, with the proportion associated with observation errors determined as shown in Table 3-1. Information on all abundance numbers used as input to the model and their associated CID's can be found in Appendix 2. If more than one abundance estimate exists for the species, I use the highest associated CID. 33 in < o CO _ CD E 0.8 r 0.6 h 0.4 h 0.2 h 0.0 60 1 50 H 4 0 CN < H 30 o O 20 10 0 1910 1920 1930 1940 1950 1960 1970 1980 1990 2 0 0 0 Y e a r Figure 3-1. Sample population trajectory. The solid line indicates the most likely population trajectory (the median of the posterior), the stippled lines the 95% credible interval,,the vertical lines the catches applied, and the dots the abundances estimates to which the analyses are tuned. Table 3-1. Definition of confidence ID's, their meanings and associated proportion of K attributed to observation error. Confidence ID Meaning Proportion of K 1 Dedicated marine mammal survey with known survey area (map or clearly defined area) and information about uncertainties (CV, SD) 0.3 2 Dedicated marine mammal survey, without definite area description or map and information about uncertainties (CV, SD) 0.4 3 Survey without area description or time period, but giving a range (i.e., min to max estimate) 0.5 4 Very general estimate, no specific time period or area, no uncertainties (mostly secondary references) 0.5 5 Outdated general estimates, guesstimates or inferred from other species and unknown. 0.5 34 3.1 Population trajectories of exploited cetaceans 3.1.1 Great whales Sei whale, Balaenoptera borealis The sei whale can be found in all the world's oceans, preferring subpolar-tropical water temperatures (Kawamura, 1974, Horwood, 1987, COSEWIC, 2003, Kaschner, 2004). Sei whale hunting is documented from 1885 in the North Atlantic, and 1904 in the North Pacific and Southern Hemispheres (Table 3-2). As the stocks of blue and fin whales were depleted, the sei whale became the main target of whalers in the Antarctic (Reeves et al, 2003). A l l three stocks of sei whales are depleted, the North Atlantic stock by 34% (Table 3-2, Figure 3-2), the North Pacific stock by 79% (Table 3-2, Figure 3-3), and the Southern Hemisphere stock by 84% (Table 3-2, Figure 3-4). Overall, this represents a global decline by 80% for the sei whale population (Table 3-2). Table 3-2. Populations of sei whales Ocean Basin Start of doc. huntes Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) North Atlantic 1885 10600( 7420- 18800) 6990 ( 5240- 9240) 34 North Pacific 1904 68400 ( 54600- 85600) 14700 ( 8040-25100) 79 Southern Hemisphere 1904 167000(157000- 190000) 27400(14500-41400) 84 Global 1885 246000 (227000 - 294000) 49090 (41300-75700) 80 35 CO < o w CO 3 T3 20 15 £ 10 0) E 3 0 n 8 1890 1910 1930 1950 Year Figure 3-2. Population trajectories for North Atlantic sei whales 1970 1990 H 4 0 CM < o CD O < o in ro 3 > C CD E 3 1910 1930 1950 Year Figure 3-3. Population trajectories for North Pacific sei whales 1970 1990 36 Southern Right whale, Eubalaena australis The Southern right whale inhabits the Southern Hemisphere, preferring polar-subtropical temperature ranges (Ohsumi and Kasamatsu, 1983, Hamner et al, 1988, Kaschner, 2004). The stock has been depleted by 92%, numbering over 86,000 whales in 1785 and falling to its current level of approximately 6,700 individuals (Table 3-3, Figure 3-5). There is some evidence of recovery in this right whale stock (Figure 3-5) (Bannister, 2001, Reeves et al, 2003). Table 3-3. Population of Southern right whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by (%) Southern Hemisphere 1785 86100 (73400-98300) 6740 (4580- 11100) 92 37 1780 1810 1840 1870 1900 1930 1960 1990 Year Figure 3-5. Population trajectory for Southern right whales Sperm whale, Physeter catodon The sperm whale ranges throughout the world's oceans, in polar to tropical water (Kasuya and Miyashita, 1988, Davis et al, 1998, Jaquet and Gendron, 2002). The population has declined by 61%, from an estimated 1 million to 376,000 individuals globally since exploitation began in 1800 (Table 3-4, Figure 3-6). Sperm whales remain a highly valued species for their meat in Japan (Reeves et al, 2003). Table 3-4. Population of sperm whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by(%) Global 1800 957000 (751000 1350000) 376000 (296000 476000) 61 38 < o ro 3 T3 20 r 15 I 10 cu E ° 3 0 tM!l!i!Miini|i;iiititfI.ti! iMlmiii! 1800 1830 1860 1890 1920 1950 1980 Y e a r Figure 3-6. Population trajectories for sperm whales - i 40 H 30 CO < 20 o "co O 10 Fin whale, Balaenoptera physalus Fin whales are endemic to all the world's oceans and range from polar to tropical waters (Zerbini et al, 1997, Rice, 1998, Kasamatsu et al, 2000, Aguilar, 2002, Kaschner, 2004). They have declined by approximately 24% in the North Atlantic (Table 1-1, Figure 3-7), 53% in the North Pacific (Table 3-5, Figure 3-8), and 96% in the Southern Hemisphere (Table 3-5, Figure 3-9), where they are a rare sight today (Reeves et al, 2003). Globally, they have declined by 86% since 1876 (Table 3-5). 39 Table 3-5. Populations of fin whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by (%) North Atlantic 1876 72900 ( 54900- 111000) 55700(42200- 68200) 24 North Pacific 1903 64500 ( 49600- 88000) 30600(15300- 43900) 53 Southern Hemisphere 1904 625000 (469000 - 737000) 23300(14700- 49100) 96 Global 1876 762000 (574000 - 936000) 109600 (72200- 161000) 86 1880 1900 1920 1940 1960 1980 2000 Y e a r Figure 3-7. Population trajectories for North Atlantic fin whales 40 9 8 7 6 5 4 3 2 ^ 1 0 1900 1920 1940 1960 Y e a r Figure 3-8. Population trajectories for North Pacific fin whales < o w CD 3 > C CU E 3 1980 5 4 H 3 i 1 2000 oo < 2 ~ ^ CD O l O < o CD 3 > u c CD E 3 8 r 6 h 4 h 40 0 1910 1930 1950 1970 Y e a r Figure 3-9. Population trajectories for Southern Hemisphere fin whales H 30 1990 CO < o H 20 o -*-» CD O 10 41 Gray whale, Eschrichtius robustus In the last 300 - 400 years, the North Atlantic gray whale went extinct (Reeves et al, 2003), however, I do not have catch information so I can not reconstruct the historical abundance of that population. In the north Pacific, two separate stocks of gray whales are recognized. Both stocks range from subpolar to subtropical waters (Gardner and Chavez-Rosales, 2000, Jones and Swartz, 2002, Weller et al, 2002, Deecke, 2004, Kaschner, 2004), but behaving quite differently in terms of abundance. The Northeastern Pacific stock is recovering from the exploitation that began back in 1600, with the current population only 25% below the estimate of carrying capacity (Table 3-6, Figure 3-10). The rapid growth in the last 1990s (Figure 3-10) cannot be replicated in the model, indicating that either immigration is occurring, or the carrying capacity of the species and/or the maximum intrinsic rate of growth have increased. The abundance estimates themselves do not appear to be suspect, having been estimated from a shore-based station near Monterey, California, in a consistent manner. The Northwestern Pacific stock is not faring so well; exploitation began in 1890 and at current the stock is depleted by 96%. It currently numbers only a few hundreds. (Table 3-6, Figure 3-11). Questions of genetic bottlenecks must be considered (Swartz et al, 2006). Globally, gray whales have declined by 35%. 42 Table 3-6. Populations of gray whales Ocean Start of Pre-exploitation numbers 2001 numbers Depleted Basin doc. hunt (Mean, 95% CI) (Mean, 95% CI) by(%) Northeast Pacific 1600 21200 (18700-25500) 15800(14600- 17800) 25 Northwest 1890 3400 ( 2880- 3580) 136 ( 97 - 187) 96 Pacific Global 1600 24600 (21600-29100) 15936(14700- 18000) 35 co < o CO _ > T3 E 3 30 25 20 15 10 5 0 i i i i i I I i I i I i i M i i i i i i i § 7 6 5 4 3 2 .1 0 1600 1650 1700 1750 1800 1850 1900 1950 2000 Y e a r Figure 3-10. Population trajectories for Northeastern Pacific gray whales CM < o CO O 43 Blue whale, Balaenoptera musculus Blue whales roam the world's oceans, ranging from polar to tropical waters (Zerbini et al, 1997, Perry et al, 1999, Kaschner, 2004). In the North Atlantic, hunting began in 1868, and it has reduced the population size by 95% (Table 3-7, Figure 3-12). In the North Pacific, hunting commenced in 1903, but stocks have recovered to 46% of their estimated carrying capacity (Table 3-7, Figure 3-13). In the Southern Hemispherea a bleak picture emerges: whaling began in 1904, and in 2001 the population estimated to be less than 1% of its original size (Table 3-7, Figure 3-14) and likely not recovering. Globally, this amounts to a depletion level of 99% for the blue whales. 44 Table 3-7. Populations of the blue whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Deplete d by (%) North Atlantic 1868 7430 ( 5920 - 8480) 367( 263 - 551) 95 North Pacific 1903 5850 ( 4590 - 8640) 3180 (2230-4140) 46 Southern Hemisphere 1904 327000 (298000 - 359000) 1180( 885 - 1490) 99.6 Global 1868 340000 (309000-376000) 4730(3378-6180) 99 co < o __ CO _ "> T3 C 0) E 3 1870 1890 1910 1930 1950 1970 1990 Y e a r Figure 3-12. Population trajectory for North Atlantic blue whales CM < o o CO O 45 1900 1920 1940 1960 Y e a r Figure 3-13. Population trajectory for North Pacific blue whales 1980 2000 < o __ CO _ > T3 C 40 r 30 h 20 h a) • | 10 3 o 40 30 co < 20 o to O 10 1910 1930 1950 1970 1990 Y e a r Figure 3-14. Population trajectory for Southern Hemisphere blue whales 46 Bowhead whale, Balaena mysticetus Bowhead whales occurs only in the Northern Hemisphere, where they are limited to polar waters (Klinowska, 1991, Jefferson et al, 1993, Kaschner, 2004). According to the IWC, five stocks, which were all hunted heavily, comprise this population (Reeves et al, 2003). I estimate that the aggregate population has declined by 89%, although it is displaying some recent recovery (Table 3-8, Figure 3-15). Table 3-8. Population of Arctic bowhead whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) Arctic 1650 89000 (67000- 114000) 9450(7500- 10800) 89 15 r < o I 10 > T3 " 5 (D E 0 m y 1650 1700 1750 1800 1850 1900 1950 2000 Year Figure 3-15. Population trajectory for Arctic bowhead whales 30 25 20 CN < o 15 — sz o ro 10 O 5 0 47 Eden/Bryde's and Bryde's whale, Balaenoptera edeni and brydei These whales are also globally distributed, and prefer subtropical - full tropical water (Nemoto, 1959, Ohsumi, 1977, Cummings, 1985, Klinowska, 1991, Kaschner, 2004). In the North Atlantic, catches of Eden/Bryde whales are documented starting in 1925, but a complete absence of abundance data makes assessment impossible. In fact the species is listed as data deficient on the IUCN red list. The resemblance of this species to the sei whale has also caused some confusion in reporting, in addition to misreporting by whalers from the former Soviet Union. The numbers I do have, however, indicate the following: in the North Pacific, hunting started in 1946 and it has depleted the population by 21% (Table 3-9, Figure 3-16). In the Southern Hemisphere, the estimated decline from the whaling initiated in 1909 is only 3% (Table 3-9, Figure 3-17). Globally this gives a decline of 10% (Table 3-9). Table 3-9. Populations in the Eden/Bryde whale complex Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by (%) North Atlantic 1925 No information No information No info North Pacific 1946 52200( 41800- 64800) 41100(30900- 53500) 21 Southern Hemisphere 1909 94100( 69800-126000) 91300 (66700- 123000) 3 Global 1909 146000(112000- 191000) 132000 (97600- 177000) 10 48 < o CD _ '> C 0 E n 20 i 15 1950 1960 1970 1980 1990 2000 Y e a r Figure 3-16. Population trajectory for the North Pacific Eden/Bryde whale complex 15 < o 1 10 -a > C " 5 .Q E Z3 I II CM < O 10 o O 1910 1930 1950 1970 1990 Year Figure 3-17. Population trajectory for the Southern Hemisphere Eden/Bryde whale complex 7 6 5 4 2 CM < 3 o CO O 2 1 0 49 Humpback whale, Megaptera novaengliae Humpback whales range from polar to tropical waters globally (Winn and Reichley, 1985, Clapham, 2002, Hamazaki, 2002, Kaschner, 2004). Although the stocks were intensively hunted, they are all showing signs of recovery (Figure 3-18, Figure 3-19, Figure 3-20). The North Atlantic population is depleted by 23%, the North Pacific by 57%> and in the Southern Hemisphere humpbacks are down 89% (Table 3-10). Globally, this amounts to a 82% depletion (Table 3-10). Table 3-10. Populations of humpback whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by(%) North Atlantic 1664 16200( 11300- 33300) 12400( 9950- 15300) 23 North Pacific 1900 16500 ( 10500- 24100) 7170( 5260- 9700) 57 Southern Hemisphere 1904 199000(144000-228000) 22500(16300-34000) 89 Global 1664 232000 (166000-285000) 42070 (31500-59000) 82 50 1910 1930 1950 1970 1990 Year Figure 3-20. Population trajectory for Southern Hemisphere humpback whales Common minke whale, Balaenoptera acutorostrata The common minke whale occurs in the Northern Hemisphere (North Atlantic and Pacific stocks), and in the Indian Ocean, part of the Southern Ocean (dwarf stock) (Reeves et al, 2003). I do not have numbers for the latter stock. The hunt for minke whales began relatively late, in 1926 in the North Atlantic and in 1940 in the North Pacific (Table 3-11). My estimates show that both populations have shown signs of recovery, with the North Atlantic stock depleted by 26% and the North Pacific stock by 32%) (Table 3-11, Figure 3-21, Figure 3-22). Minke whales are currently hunted from 52 Norway, Iceland and Greenland in the North Atlantic, and in the North Pacific by the Japanese. Globally, the common minke whales have been depleted by 27% (Table 3-11). Table 3-11. Populations of common minke whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) North Atlantic 1926 211000(159000-284000) 157000(118000-210000) 26 North Pacific 1940 47000( 36700- 60300) 31900( 23900- 41400) 32 Global 1926 258000(196000-344000) 189000(142000-251000) 27 1930 1940 1950 1960 1970 1980 1990 2000 Y e a r Figure 3-21. Population trajectory for North Atlantic minke whales 53 1940 1950 1960 1970 1980 1990 2000 Y e a r F i g u r e 3 - 2 2 . Population trajectory for North Pacific minke whales Antarctic minke whale, Balaenoptera bonaerensis It has only been within the last decade that the distinctness between the common and antarctic minke whale has been recognized (Reeves et al, 2003). The Antarctic minke whale ranges from polar to tropical waters in the Southern hemisphere (Ribic et al, 1991, Rice, 1998, Murase et al, 2002, Kaschner, 2004). Whaling for minkes was only initiated in 1921, after the larger species of baleen whales had begun to collapse. While they were hunted intensively, they still have a very large population and have begun recovering, although they are still hunted in the Antarctic by the Japanese, resulting in a depletion of 16% (Reeves et al, 2003), (Table 3-12, Figure 3-23). 54 Table 3-12. Population of Antarctic minke whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Southern Hemisphere 1921 379000 (300000 - 478000) 318000 (250000-404000) 16 M l l i l 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Figure 3-23. Population trajectory for Antarctic minke whales North Atlantic right whale, Eubalaena glacialis The North Atlantic right whale ranges from subpolar to tropical waters (Mitchell et al, 1983, Gaskin, 1991, Kenney, 2002, Kaschner, 2004). The species may be nearing extinction, having been hunted since 1530 with a current population size in the low hundreds, about 3% of pre-exploitation numbers (Table 3-13, Figure 3-24). 55 Table 3-13. Population of North Atlantic right whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) North Atlantic 1530 14100(10100-27800) 368 (257 - 469) 97 30 CO h 25 15 20 I 15 ^ 10 CD _ Q E = 5 0 i i i i i i rT rrrnnriTrrfi i r i T r n n i T T T r J o 30 25 20 15 - 10 - 5 o * o -*—» 05 o 1530 1590 1650 1710 1770 1830 1890 1950 2010 Year Figure 3-24. Population trajectory for North Atlantic right whales. NOTE: catches are plotted as a line instead of histograms for visual ease. North Pacific right whale, Eubalaena japonica The North Pacific right whale ranges from subpolar to subtropical waters (Jefferson et al, 1993, Tynan et al, 2001, Kenney, 2002, Kaschner, 2004). This species is faring slightly better than its North Atlantic cousins, with current population numbers of about 1300, and depleted by 86% since hunting began in 1835 (Table 3-14). 56 Table 3-14. Population of North Pacific right whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) North Pacific 1835 9720(8540- 12600) 1340 (679-2070) 86 co < o CO _ 20 r 15 h s 10 0 E 3 Q Lj- iini|lllililll|:iM[|lll|IIHilMI|ll[illlM|IM|[|[lljlllilMH|!lll 1830 1850 1870 1890 1910 1930 1950 1970 1990 Y e a r Figure 3-25. Population trajectory of North Pacific right whales 30 25 20 H 15 10 5 0 o CO O 57 3.1.2 Smaller whales and large dolphins Short-finned pilot whale, Globicephala macrorhynchus The short-finned pilot whale is found throughout the worlds' oceans, and ranges from polar to warm temperature waters (Smith et al, 1986, Payne and Heinemann, 1993, Wade and Gerrodette, 1993, Davis et al, 1998, Reeves et al, 2003, Kaschner, 2004). Catches are not well documented in Japan, and are much smaller than the estimated population size and so no significant decrease in abundance is estimated (Table 3-15, Figure 3-26). Table 3-15. Populations of short-finned pilot whales Ocean Start of Pre-exploitation numbers 2001 numbers Depleted Basin doc. (Mean, 95% CI) (Mean, 95% CI) by (%) hunt Japan 1948 56400( 46700- 67100) 54700( 45300- 65200) 3 Global 1948 226400(163000-507000) 225000(161000-505000) 1 58 7 6 5 4 3 2 _ 1 0 _ 1950 1960 1970 1980 1990 Y e a r Figure 3-26. Population trajectory for Japanese short-finned pilot whales < o ro > C CD E Z3 9 8 7 6 5 4 3 2 1 0 CM < O ro O 2000 Baird's beaked whale, Berardius bairdii Baird's beaked whale is found in the North Pacific, and it inhabits polar to subtropical waters (Reeves and Mitchell, 1993, Kasuya, 2002, D'Amico et al, 2003, Kaschner, 2004). The Japanese stock of Baird's beaked whale has been hunted since 1907 and is showing a decline of 28% in numbers (Table 3-16, Figure 3-27); the global stock is down 26%. Table 3-16. Populations of Baird's beaked whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Japan 1907 9010(7080- 11800) 6450 (5010- 8230) 28 Global 1907 9670(7410- 12800) 7110 (5340-9200) 26 59 C O < o CO _ > T3 C E 3 20 r 1 5 h s 1 0 5 h 0 1 9 1 0 1 9 3 0 1 9 5 0 1 9 7 0 Y e a r Figure 3-27. Population trajectory for Japanese Baird's beaked whales 1 9 9 0 -I 4 0 30 20 o —* CO O 1 0 Beluga, Delphinapterus leucas The beluga whale's distribution is circumpolar in the Northern Hemisphere (Watts et al, 1991, Rice, 1998, O'Corry-Crowe, 2002, Kaschner, 2004). The IWC currently recognizes 29 beluga stocks. However for this assessment, I have added up the catches and assessed the belugas as a single stock. This means that, while local depletion of isolated populations, for example in Cook Inlet and Ungava Bay, is evident, the stock as a whole, which experienced a significant decline, currently has a steady population level (Table 3-17, Figure 3-28). For the west Greenlandic stock, catch data exists stretching back to 1862, although with some holes. As recommended by Heide-Jorgensen and Rosing-Avid (2002), who complied and summarized the west Greenlandic catch 60 data, I have interpolated numbers to fill such gaps, because: "for population modeling in will be necessary to interpolate years without reported catches, to spread out the average figures over the years involved and to assume some level of harvesting before 1862" (Heide-J0rgensen and Rosing-Asvid, 2002). However, I decided to begin the catches in 1862 as no indication of previous substantial hunts exists. Table 3-17. Population of beluga whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Global 1862 170000(115000 289000) 96500 (71200 132000) 43 3 0 I 25 CO 15 20 I 1 5 c CD E 1 0 5 0 L. iihllii I 1 8 6 0 1 8 8 0 1 9 0 0 1 9 2 0 1 9 4 0 Y e a r Figure 3-28. Population trajectory for beluga whales i 5 H 4 cp H 3 1 9 6 0 1 9 8 0 2 0 0 0 o -*-» CO 2 O - 1 J 0 61 Killer whale, Orcinus orca Killer whales have a circumglobal distribution, and range from polar to tropical waters (Jefferson et al, 1993, Kasamatsu et al, 2000, IWC/BIWS, 2001, Ford, 2002, Kaschner, 2004). While some populations are hunted, the species appears to be doing fine. In the North Atlantic hunting started in 1954, and current numbers are only reduced by 8% remaining right around the 10,000 individuals (Table 3-18, Figure 3-29). In the North Pacific and Southern Hemisphere, where hunting started in 1935 and 1953, respectively, the populations are depleted at 16 and 2% respectively (Table 3-18, Figure 3-30, Figure 3-31). Globally, this represents a decline of 5% for killer whales (Table 3-18). Table 3-18. Populations of killer whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) North Atlantic 1954 9990 ( 7550- 13300) 9210( 6840- 12400) 8 North Pacific 1935 5140 ( 3720- 7090) 4340( 3230- 5850) 16 Southern Hemisphere 1953 26400 (19600 - 35800) 25700(18900- 35000) 3 Global 1935 50000 (35600-72100) 47800 (33700 - 69200) 5 62 co < o co •g > T3 CD E 20 15 s 10 h 0 i , , 1960 1970 1980 Year F i g u r e 3 - 2 9 . Population trajectory for North Atlantic killer whales 1990 2000 30 25 20 15 10 5 0 o co O 8 CO < o w CO Z} > C CD E Zi 2 h 0 n 20 i 15 1940 1950 1960 1970 1980 1990 2000 Year F i g u r e 3 - 3 0 . Population trajectory for North Pacific killer whales 1 0 -5 CO O 0 63 C O < o 40 30 £ 20 CD | 10 100 80 H 60 o 40™ i 20 0 1960 1970 1980 1990 2000 Year Figure 3-31. Population trajectory for Southern Hemisphere killer whales 0 Long-finned pilot whale, Globicephala melas The long-finned pilot whale has a global distribution, from polar to warm temperate waters (Findlay et al, 1992, Jefferson et al, 1993, Kasamatsu and Joyce, 1995, Kaschner, 2004). The Faroe Island population, which has a long traditional catch history beginning in 1709, is only slightly depleted at 5%, indicating that this hunt must be sustainable (Table 3-19, Figure 3-32). Off Newfoundland, however, the population seems to be significantly depleted, having dropped 61% in numbers (Table 3-19, Figure 3-33). Globally, this represents a decline of 7% for long-finned pilot whales (Table 3-19). 64 Table 3-19. Populations of long-finned pilot whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Faroe Islands -Central and Eastern North Atlantic 1709 813000(710000- 895000) 773000(668000- 864000) 5 Northwest Atlantic (Newfoundland) 1947 57800( 50800- 67100) 22400( 12900- 33900) 61 Rest of the world 1070000 (824000 - 1300000) 995000 (744000 - 1230000) 7 ID < O w CO _ '> T3 C CD E Z2 9 8 7 6 5 4 3 2 1 0 -1 5 III 1700 1740 1780 1820 1860 1900 1940 1980 Y e a r Figure 3-32. Population trajectory for Faroe Island long-finned pilot whales i o co < o 2 H O 65 Northern bottlenose whale, Hyperoodon ampullatus The northern bottlenose whale is only found in the North Atlantic, in warm temperate to polar waters (Benjaminsen and Christensen, 1979, Jefferson et al., 1993, D'Amico et al., 2003, Kaschner, 2004). The stock, which has recorded catches all they way back to 1584 is depleted by 16%, and currently numbering just under 50,000 individuals (Table 3-20, Figure 3-34). Table 3-20. Population of northern bottlenose whales Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) North Atlantic 1584 57800 (44200 - 84700) 48800 (37600 - 64300) 16 66 9 r 8 i i i i i i r r r n i H T T T T T T T T T i i i i t r 1580 1630 1680 1730 1780 1830 1880 1930 1980 Y e a r Figure 3-34. Population trajectory for northern bottlenose whales 30 25 20 O J 15 — JZ o •4—» ro 10 O 5 0 False killer whale, Pseudorca crdssidens The false killer whale is at home in all the world's oceans, in warm temperate to tropical waters (Miyazaki and Wada, 1978, Wade and Gerrodette, 1993, Stacey et al, 1994, de Boer and Simmonds, 2003, Kaschner, 2004). The Japanese sub-population is the only stock for which I have catch data. Documented hunting here commenced in 1965, and has had only a slight impact on the stock, with current numbers depleted by 6% (Table 3-21, Figure 3-35). However, this may be a vast underestimate as the stock is likely still hunted. Globally, the population is depleted by 1% (Table 3-21). 67 T a b l e 3 - 2 1 . Populations of false killer whales O c e a n S t a r t o f P r e - e x p l o i t a t i o n n u m b e r s 2001 n u m b e r s D e p l e t e d B a s i n d o c . ( M e a n , 9 5 % C I ) ( M e a n , 9 5 % C I ) b y (%) h u n t Japan 1965 17100(13600- 21600) 16600(13100- 21000) 3 Global 1965 57600 (25400 - 242000) 57100 (24900-241000) 1 ro 3 '> C CD E 3 30 co h 25 a 20 5 15 10 0 1970 1980 1990 Year F i g u r e 3 - 3 5 . Population trajectory for Japanese false killer whales 2000 C M < O 2 " CD O Narwhal, Monodon monoceros The narwhal is endemic to the Northern Hemisphere, and occurs in polar water (Jefferson et al, 1993, Heide-Jorgensen, 2002, Kaschner, 2004). Narwhals have been hunted extensively for hundreds, or thousands of years. The Northeast Atlantic 68 population of Norway (Svalbard) is largely extinct (Hrynyshyn, 2004), the species hunted by the Vikings for their long unicorn-like horns that made these creatures seem almost mythical (Pluskowski, 2004). The Canadian Baffin Bay stock, hunted since 1977, is depleted by 10% (Table 3-22, Figure 3-36). The Greenlandic Baffin Bay stock is faring much worse, at only 38% of its population size in 1977 (Table 3-22, Figure 3-38). The Hudson Bay stock does not look much better, with a depletion level of 66% since documented hunting began in 1977 (Table 3-22, Figure 3-37). Each of these stocks are still being hunted, and are facing continuous decline. Globally, the stocks have decreased by 24% since 1977 (Table 3-22). Table 3-22. Populations of narwhals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Baffin Bay Canada 1977 48000(38600- 59800) 43000 (33500 - 54800) 10 Hudson Bay 1977 10500 ( 8920- 12400) 3580 ( 2640- 4910) 66 Baffin Bay Greenland 1977 17800(15200- 21000) 6820 ( 5010- 9310) 62 Global 1977 94600 (77500- 115000) 71700 (56000-90800) 24 69 6 r - - ' - l 6 < o ra 4 __ CD 13 _ > T 3 C 0) - O E 3 3 h o 1 9 8 0 1 9 9 0 Y e a r Figure 3-36. Population trajectory for Canadian Baffin Bay narwhals 1 4 N o o ro i 2 O H 1 o 2 0 0 0 co < o _> CD _ > T 3 C CD E 3 2 0 1 5 h K 1 0 5 h 0 1 9 8 0 1 9 9 0 Y e a r Figure 3-37. Population trajectory for Hudson Bay narwhals 2 0 0 0 7 6 5 1 0 CM < H 4 2 3 o CD O 2 70 1980 1990 2000 Y e a r Figure 3-38. Population trajectory for Greenlandic Baffin Bay narwhals 3.1.3 Smaller Dolphins and Porpoises Pantropical spotted dolphin, Stenella attenuata The pantropical spotted dolphin, true to its name, ranges over the tropical waters of the world's oceans (Miyazaki et al, 1974, Fiedler and Reilly, 1994, Hamazaki, 2002, Kaschner, 2004). The eastern tropical stock, caught as by-catch in the tuna fisheries of the Eastern Tropical Pacific and documented since 1959, is depleted and at only 38% of its original population size (Table 3-23, Figure 3-39). In Japan, where the species is caught commercially, with catch data going back to 1970, the population seems steady 71 (Table 3-23, Figure 3-3). This could again be an issue of the Japanese limiting data availability as indications are this hunt is ongoing. Finding reliable documentation has, however, proved difficult. Globally, the stock is depleted by 57% (Table 3-23). Table 3-23. Populations of pantropical spotted dolphins Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Eastern Tropical Pacific 1959 4590000 (3740000 - 5740000) 1730000 (1350000 - 2190000) 62 Japan 1970 455000( 368000- 574000) 449000( 362000- 568000) 1 Global 1959 5060000 (4110000 - 6410000) 2200000 (1720000 - 2850000) 57 72 -I 40 H 30 0 1970 1980 1990 Y e a r Figure 3-40. Population trajectory for Japanese pantropical spotted dolphins 2000 CM < H 20 o ro O 10 0 Spinner dolphin, Stenella longirostris The spinner dolphin occurs in tropical waters of the oceans (Miyazaki and Wada, 1978, Davis et al, 1998, De Boer, 2000, Perrin, 2002, Kaschner, 2004). The spinner dolphin is caught as incidental by-catch in the tuna fishery in the Eastern Tropical Pacific. Since 1959, this has resulted in a 29% drop in population numbers (Table 3-24, Figure 3-41). Globally, the decline is 28% (Table 3-24). Table 3-24. Populations of spinner dolphins Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Eastern Tropical Pacific 1959 2630000 (2130000 - 3260000) 1880000 (1480000 - 2350000) 29 Global 1959 2660000 (2150000 - 3360000) 1910000 (1500000 - 2450000) 28 73 40 r 30 h £ 20 10 0 ~~l 20 1990 i 15 1960 1970 1980 Y e a r Figure 3-41. Population trajectory for Eastern Tropical Pacific spinner dolphins 2000 < 10 o -+—* CD O • 0 Short beaked common dolphin, Delphinus delphis The short beaked common dolphin is found all the world's oceans, from cold temperate to tropical waters (Selzer and Payne, 1988, Rice, 1998, Perrin, 2002, Kaschner, 2004). They are taken as by-catch in the Eastern Tropical Pacific, although at documented levels the population has only decreased by 4% since 1959 (Table 3-25, Figure 3-42). In Japanese waters, the dolphin is killed incidentally as by-catch of various fisheries, but a commercial hunt also exists. Since 1989, this has caused a 7% drop in population levels (Table 3-25, Figure 3-43). Again, finding reliable catch data from Japanese waters has been a struggle, and the decline is probably larger. Globally, the population has decreased by 3% (Table 3-25). 74 Table 3-25. Populations of short beaked common dolphins Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Eastern Tropical Pacific 1959 3290000 (2630000 - 3910000) 3160000(2510000 - 3780000) 4 Northwest Atlantic 1989 40800( 32600- 51100) 37900( 29800 - 48200) 7 Global 3940000 (3030000 - 5660000) 3800000 (2910000 - 5530000) 3 -I 3 0 2 5 2 0 co < o 1 5 — o • I — ' 1 0 O 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 Y e a r Figure 3-42. Population trajectory for Eastern Tropical Pacific short beaked common dolphins 75 is 4 h 1 h 1990 2000 Year Figure 3-43. Population trajectory for Northwest Atlantic short beaked common dolphins Dall's porpoise, Phocoenoides dalli Dall's porpoises are found in the North Pacific, from subpolar to warm temperate waters (Jones et al, 1987, Jefferson, 1988, Miyashita and Kasuya, 1988, Kaschner, 2004). This porpoise is mainly taken as a commercial catch, but by-catch is also a significant source of mortality. The Dall's porpoise in Japan is depleted by some 48% since hunting commenced in 1963 (Table 3-26, Figure 3-44). This is a stock for which good catch data are available from Japan. On a global scale, Dall's porpoise populations are depleted by 24% (Table 3-26). 76 Table 3-26. Populations of dalls porpoises Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Japan 1963 724000(593000- 899000) 378000(236000- 561000) 48 Global 1963 1440000 (941000 - 1660000) 1090000 (584000- 1320000) 24 Bottlenose dolphin, Tursitops truncatus The bottlenose dolphin ranges throughout the oceans in cold-temperate to full tropical waters (Jefferson et al, 1993, Wells and Scott, 1999, Wells and Scott, 2002, Kaschner, 2004). The bottlenose dolphin has been caught in small quantities off the 77 California coast for live-capture, but is otherwise mostly killed as incidental by-catch although a commercial hunt has gone on in Japan, and most likely continues although I again have no acess to reliable numbers. In the Northwest Atlantic, the decrease in abundance stands at only 5% since 1950 (Table 3-27, Figure 3-45). However the dip is recent, so the stock is declining. In Japan, the decline is rather insignificant, at only 2% since 1966 (Table 3-27, Figure 3-46). Table 3-27. Populations of bottlenose dolphins Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) Northwest Atlantic 1950 32000( 25500- 40500) 30400( 23800 - 38800) 5 Japan 1966 176000(139000-222000) 173000(136000-219000) 2 Global 1950 524000 (405000 - 779000) 519000 (400000-774000) 1 5 r 4 h 3 h £ 2 0 I 1950 1960 1970 1980 1990 2000 Y e a r Figure 3-45. Population trajectory for Northwest Atlantic bottlenose dolphins C M < 2 ~ J Z O CO O 78 < o CD •g '> T 3 c CD E 3 30 25 20 15 10 5 0 1970 1980 1990 Y e a r Figure 3-46. Population trajectory for Japanese bottlenose dolphins H 5 0 30 25 20 C M < o 15 — o -t—' CO 10 O 2000 Northern right whale dolphin, Lissodelphis borealis The northern right whale dolphin is found only in the North Pacific, where it lives in subpolar - subtropical waters (Bjorge et al., 1991, Kaschner, 2004). The population is depleted at some 68% of its 1978 abundance (Table 3-28, Figure 3-47). Table 3-28. Population of northern right whale dolphins Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) North Pacific 1978 408000(345000-491000) 277000 (203000 - 369000) 32 79 Harbour Porpoise, Phocoena phocoena The harbour porpoise is endemic to the Northern Hemisphere, and occur from subpolar to warm temperate waters (Gaskin et al, 1993, Read and Westgate, 1997, Raum-Suryan and Harvey, 1998, Kaschner, 2004). The porpoise is hunted commercially in Greenlandic and Baltic waters, and taken as by-catch in the North Sea and Western North Atlantic. In Greenlandic waters, the population, hunted since 1900, is severely depleted at 89% (Table 3-29, Figure 3-48). In the North Sea, the population is declining, having been depleted by 21% since 1950 (Table 3-29, Figure 3-49). In the Baltic, the species has been caught since 1716, is showing recovery and is now at 52% of its original population size (Table 3-29, Figure 3-50). In the western North Atlantic (Newfoundland 80 and New England), the population has, since 1989, been declining by 17% (Table 3-29, Figure 3-51). Globally, this means harbour porpoises are depleted by 24% (Table 3-29). Table 3-29. Populations of harbour porpoises Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Greenland 1900 47700( 35300- 71000) 5090( 0- 11800) 89 North Sea 1950 324000 (263000-401000) 257000(195000-335000) 21 Baltic 1716 78400 ( 40500 - 146000) 40500( 30800- 50200) 48 Western North Atlantic 1989 127000 ( 97800- 166000) 106000 ( 76800 - 145000) 17 Global 704000 (479000 - 997000) 535000(345000 - 755000) 24 Atlantic white-sided dolphin, Lagenorhynchus acutus The Atlantic white-sided dolphin, is as its name indicates, found in the North Atlantic, and it inhabits subpolar to warm temperate waters (Sergeant et al, 1980, Selzer and Payne, 1988, Leopold and Couperus, 1995, Hamazaki, 2002, Kaschner, 2004). In Northwest Atlantic waters it is depleted by a minimal 4% since 1950 (Table 3-30, Figure 3-52). Catch data for the Faroe Islands exists (Appendix 5) but I have yet to come across an abundance estimate for this area. Globally, the decline of Atlantic white-sided dolphins is limited at 1% (Table 3-30). 83 Table 3-30. Populations of Atlantic white-sided dolphins Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Northwest Atlantic - USA 1950 21000(15300- 28600) 20100(14400- 27700) 4 Global 1950 103000 (52300-217000) 102000 (51400-216000) 1 1950 1960 1970 1980 Year 1990 30 25 20 15 10 5 0 o ro O 2000 Figure 3-52. Population trajectory for Northwest Atlantic white-sided dolphins 84 3.2 Population trajectories of exploited pinnipeds 3.2.1 True seals Ribbon seal, Histriophoca fasciata The ribbon seal is found in the North Pacific, in polar-subpolar waters (Jefferson et al, 1993, Fedoseev, 2002, Mizuno et al, 2002, Kaschner, 2004). Their hunting history includes both commercial and subsistence catch, beginning in 1950. The population declined in numbers in the 1960s, but have since recovered and are now at 97% of their 1950 population size (Table 3-31, Figure 3-53). Globally, the ribbon seal population is at 99% of carrying capacity (Table 3-31). Table 3-31. Populations of ribbon seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) Bering Sea 1950 135000(113000- 164000) 131000(111000- 154000) 3 Global 1950 505000 (363000 - 664000) 501000 (361000-654000) 1 85 Ringed seal, Pusa hispida The ringed seal is found in the Northern Hemisphere, in polar-subpolar waters (Miyazaki, 2002, Kaschner, 2004). The population was hunted on a subsistence basis in the North Pacific, and commercially elsewhere. In the North Atlantic/Arctic the population has declined in abundance by 55% since 1954, and the process of recovery seems to have started (Table 3-32, Figure 3-54). In the Baltic, the population is depleted, with current numbers at 5% of the 1909 abundance level (Table 3-32, Figure 3-55). I had to run 150,000 simulations, instead of 50,000, because the model was having a hard time reconciling the data. I suspect there are gaps in hunting records, but have no other sources of catch data. In the North Pacific/Arctic, the decline in abundance seems to have been 86 very slight, and a depletion level of only 1% registers (Table 3-32, Figure 3-56). Globally, the decline in ringed seal abundance is 27% (Table 3-32). Table 3-32. Populations of ringed seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) North Atlantic / Arctic 1954 3810000(2790000 - 5570000) 1710000 ( 72700-2600000) 55 Baltic 1909 150000( 130000- 161000) 8150 ( 4670- 11300) 95 North Pacific / Arctic 1903 4610000 (3430000 - 5820000) 4570000 (3400000 - 5780000) 1 Global 1903 8570000(6350000 - 11600000) 6290000 (3480000 - 8390000) 27 87 < o CD 3 •g > 20 r 15 h ^ 10 h 0) E 3 0 1910 1930 1950 1970 Year Figure 3-55. Population trajectory for Baltic ringed seals 6 5 4 3 2 1 0 1990 CD < O w CD 3 > T 3 C CU E 3 1900 1920 1940 1960 Year Figure 3-56. Population trajectory for North Pacific / Arctic ringed seals 1980 2000 10 8 < A O 4 TO 2 - 1 0 40 30 co < o 20 o CD O 10 88 Southern elephant seal, Mirounga leonine The southern elephant seal roams the southern oceans, and ranges from polar to tropical waters (Boyd and Arnbom, 1991, Ling and Bryden, 1992, Hindell et al, 1999, Bradshaw et al, 2002, Kaschner, 2004). Population numbers have been relatively steady since 1820, with only a 1% reported decline, although a cumulative total of over 1.2 million animals have been killed (Table 3-33, Figure 3-57). Table 3-33. Population of southern elephant seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by(%) Southern Hemisphere 1820 739000 (550000- 1070000) 733000 (549000 - 997000) 1 20 r - | 20 Co ^ 15 - i 15 1820 1850 1880 1910 1940 1970 2000 Y e a r Figure 3-57. Population trajectory for southern elephant seals 89 Gray seal, Halichoerus grypus Gray seals occur in the North Atlantic, in subpolar to cold temperate waters (Jefferson et al, 1993, Hall, 2002, Kaschner, 2004). In Icelandic waters the population has declined 65% form its 1950 levels (Table 3-34, Figure 3-58). In Scottish waters, on the other hand, the population was only minimally affected and is at 98% of its 1950 abundance (Table 3-34, Figure 3-59). This gives a global decline of a limited 4% (Table 3-34). Table 3-34. Populations of gray seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Iceland 1950 16500( 12600- 22000) 5800( 0- 12000) 65 Scotland 1950 115000( 84900- 156000) 113000 ( 83000- 154000) 2 Global 1950 299000(226000 - 393000) 286000 (211000-381000) 4 -I 20 1950 1960 1970 1980 Y e a r Figure 3-58. Population trajectory for Icelandic gray seals 1990 2000 15 CM < H 10 o CO O 90 < o co T3 20 r 15 h I 10 .o E Zi 0 _1_ 40 1950 1960 1970 1980 1990 2000 Y e a r Figure 3-59. Population trajectory for Scottish gray seals Harp seal, Pagophilus groenlandicus 30 CM < 20 o 03 O 10 0 The harp seal lives in the polar to cold temperate waters of the Northern Hemisphere (Reijnders et al, 1993, Kaschner, 2004). The West Ice stock (East Greenland), while depleted, is showing signs of recovery and is at 62% of its 1946 abundance (Table 3-35, Figure 3-60). In the Northwest Atlantic, the population appears to be declining, with current numbers indicating a 43% decline since 1895 (Table 3-35, Figure 3-61). The White Sea stock has been depleted by 60% since 1897, but is showing signs of recovery (Table 3-35, Figure 3-62). Globally, this corresponds to a decline of 36% (Table 3-35). 91 Table 3-35. Populations of harp seals Ocean Basin Start of Pre-exploitation numbers 2001 numbers Depleted doc. (Mean, 95% CI) (Mean, 95% CI) by (%) hunt West Ice (East 1946 627000( 406000 - 388000( 223000- 38 Greenland) 1110000) 495000) Northwest 1895 6840000 ( 5060000- 3930000 (2990000 - 43 Atlantic (the front and gulf) 11000000) 5160000) White Sea 1897 5570000( 4740000-6430000) 2230000(1600000-2960000) 60 Global 1895 17800000(14300000-23500000) 11300000 (8910000-13600000) 36 < o CO •g > T3 C CD - O £ 20 r 15 h 10 h 5 h 0 1950 1990 1960 1970 1980 Year Figure 3-60. Population trajectory for West Ice (East Greenland) harp seals 2000 5 4 3 o o "I 2 ~ CO O - i 1 92 V C O < O w CD 3 > C CD X I £ 3 20 15 5 10 h 5 h 0 _ 1900 1920 1940 1960 1980 Year Figure 3-61. Population trajectory for Northwest Atlantic harp seals 2000 CD < O CD 3 ;> T3 CD X i E 3 1900 1920 1940 1960 Year Figure 3-62. Population trajectory for White Sea harp seals 1980 2000 93 Hooded seal, Cystophora cristata Hooded seals live in the North Atlantic Ocean, in polar to cold temperate water (Kovacs and Lavigne, 1986, Reijnders et al, 1993, Kaschner, 2004). The Jan Mayen stock has been depleted by 68% since 1940, but has begun to show signs of recovery (Table 3-36, Figure 3-63). The Northwest Atlantic stock is depleted by 20%, and recovering from a hunt that began in 1946 (Table 3-36, Figure 3-64). Globally, the hooded seal is depleted by 50% (Table 3-36). Table 3-36. Populations of hooded seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Jan Mayen 1940 1170000 ( 788000- 1740000) 403000(147000- 689000) 68 Northwest Atlantic 1946 607000( 461000- 844000) 488000(364000- 627000) 20 Global 1940 1780000 (1250000 - 2580000) 891000 (511000- 1320000) 50 94 < o CD 3 > T3 0 E 3 20 15 s 10 0 1940 1950 1960 1970 1980 Y e a r Figure 3-63. Population trajectory for Jan Mayen hooded seals 1990 2000 to < o w CD 3 •o > T3 C 0 E 3 40 1950 1960 1970 1980 Y e a r Figure 3-64. Population trajectory for Northwest Altantic hooded seals 1990 2000 30 co < H 20 3 CD O H 10 95 Bearded seal, Erignathus barbatus The bearded seal is found in the Northern hemisphere, in polar-subpolar waters (Reijnders et al, 1993, Kovacs, 2002, Kaschner, 2004). In the Bering/Chukchi Sea, where it is caught in a subsistence hunt, the bearded seal population has been declining since 1966 and is currently depleted by 24% (Table 3-37, Figure 3-65). Globally, this means that the bearded seal is depleted by 11% (Table 3-37). Table 3-37. Populations of bearded seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) Bering / Chukchi 1966 298000 (227000 - 382000) 226000(121000-321000) 24 Global 1966 668000 (357000 - 642000) 596000(251000-581000) 11 40 9 < - 8 o in 30 — 7 individual; 20 - - ... 6 5 4 Number of 10 0 -3 2 1 0 o 1970 1980 1990 2000 Year Figure 3-65. Population trajectory for Bering/Chukchi bearded seals 96 Harbour seal, Phoca vitulina The harbour seals ranges from subpolar to warm temperate waters in the Northern Hemisphere (Burns, 2002, Kaschner, 2004). The California stock has been depleted by 8% (Table 3-38, Figure 3-66), however, the global population is at 99% of carrying capacity (Table 3-38). Table 3-38. Populations of harbour seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) California 1991 33500 ( 26800- 41900) 30700 ( 24100- 39000) 8 Global 1991 384000 (377000-422000) 381000 (374000-419000) 1 < o CO '> T3 C 0 E 3 5 r 4 3 2 1 20 15 CM < 10 o -i—» CO O 0 1991 1995 2000 Y e a r - 1 0 Figure 3-66. Population trajectory for California harbour seals 97 Largha or spotted seal, Phoca largha The largha or spotted seal ranges from polar to warm temperate waters in the North Pacific (Burns, 2002, Kaschner, 2004). The Bering Sea population showed a decline due to hunting, but has recovered to 94% of its 1965 abundance (Table 3-39, Figure 3-67). In the Northeast Pacific waters off Alaska, the population, which is subjected to a subsistence hunt with documented catches since 1966, is depleted by 60% (Table 3-39, Figure 3-68). The Sea of Okhotsk population shows a similar trend to the Bering Sea population, with documented hunting also commencing in 1965, including a period of decline followed by recovery leaving the population at only a 6% decline (Table 3-39, Figure 3-69). Table 3-39. Populations of largha or spotted seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by (%) Bering Sea 1965 215000(166000-281000) 202000 (147000 - 268000) 6 Northeast Pacific (Alaska) 1966 80600( 64600- 106000) 32800( 11500- 54600) 59 Sea of Okhotsk 1965 232000(178000-303000) 205000 (144000 - 267000) 12 Global 1965 532000 (413000-695000) 444000(307000 - 594000) 17 98 < o •g 30 25 75 20 S 15 h " 10 E I 50 1970 1980 1990 Year Figure 3-67. Population trajectory for Bering largha/spotted seals 2000 6 5 4 co < o o CO 2 O 1 0 < o in co > C CD E 3 20 r 15 5 10 h 0 1970 1980 1990 Year Figure 3-68. Population trajectory for Northeast Pacific largha/spotted seals 2000 99 1970 1980 1990 Year Figure 3-69. Population trajectory for Okhotsk Sea largha/spotted seals 2000 3.2.2 Eared seals Antarctic fur seal, Arctocephalus gazelle The Antarctic fur seal lives in polar and subpolar waters in the Southern Hemisphere (Ribic et al, 1991, Reijnders et al, 1993, Kaschner, 2004). The population has recovered remarkably from its most depleted levels in the 1830s. However, this population is a bit of a problem case: documented catches start in 1790 and end by 1830, but almost 100 years elapsed before the population began recovering at a significant rate. 100 However, when this recovery began, the population exploded. The model compensates for the 100 year low recovery by setting the for r m a x parameter low which means I may be overestimating K . Moreover, I cannot reproduce the strong increase seen in recent decades. Table 3-40. Population of Antarctic fur seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Southern Hemisphere 1790 1580000(1010000- 1970000) 1270000 (901000 - 1680000) 20 101 South African and Australian fur seal, Arctocephaluspusillus The South African and Australian fur seal occurs in warm temperate to subtropical waters in the Southern Hemisphere (Reijnders et al., 1993, Kaschner, 2004). The species showed some decline, but seems to be well on its way to full recovery, with a current population size of 91% of the 1900 abundance level (Table 3-41, Figure 3-71). Globally, this species has been depleted by 9% (Table 3-41). Table 3-41. Population table for the South African and Australian fur seal Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) South Africa 1900 1740000 (1380000 - 2270000) 1580000 (1270000 - 1970000) 9 Global 1780000 (1410000 - 2320000) 1620000 (1300000 - 2020000) 9 30 r 25 to _ _ "co 20 % 15 c 2 10 . Q E 3 5 0 1900 1920 1940 1960 Year Figure 3-71. Population trajectory for the South African fur seal 1980 2000 9 8 7 6 5 4 3 2 1 0 < o -I—» CO O 102 Northern fur seal, Callorhinus ursinus The Northern fur seal lives in the subpolar to cold temperate waters of the North Pacific (Gentry, 1981, Baba et al, 2000, Gentry, 2002, Kaschner, 2004). The Pribilof population, which counts for the majority of the population, saw a precipitous decline in the late 1800s, but has since had time to recover. The current population is depleted by 1% from its 1786 pre-exploitation level (Table 3-42, Figure 3-72). Globally, the species is also depleted by 1% (Table 3-42). The Pribilof population accounts for the majority of the total population, with separate population found on the Commander, Kuril , Robben and San Miguel Islands. Table 3-42. Populations of Northern fur seals Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) Pribilof 1786 1390000 (1290000 - 2300000) 1290000 (1530000 - 1680000) 1 Global 1786 1730000 (1620000 - 2650000) 1630000 (1860000 - 2030000) 1 103 1780 1810 1840 1870 1900 1930 1960 1990 Year Figure 3-72. Population trajectory for Northern fur seals South American sea lion, Otaria flavenscens The South American sea lion occurs in polar to subtropical waters in the Southern Hemisphere (Jefferson et al., 1993, Reijnders et al., 1993, Kaschner, 2004). The North Patagonian / Falklands population faced a huge decline in numbers after hunting began in 1930, but have seen limited recovery since them and are now at 49% of their original population size (Table 3-43, Figure 3-73). 104 Table 3-43. Populations of South American sea lions Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by (%) North Patagonia (Falklands) 1930 110000 ( 86200- 141000) 52200( 32600- 95400) 53 Global 1930 290000 (226000-381000) 232000(173000- 335000) 20 20 r 15 £ 10 0 1930 1940 1950 1960 1970 1980 1990 2000 Year Figure 3-73. Population trajectory for South American sea lions New Zealand fur seal, Arctocephalus forsteri The New Zealand fur seals occupies subpolar to warm temperate waters, around New Zealand and South Australia (Jefferson et al., 1993, Kaschner, 2004). As documented, the exploitation history of this species is remarkable. In New Zealand, 105 340,000 fur seals were thought to have been killed between 1804 and 1813 (Matlin, 1998). After this there are no documented catches until 1991-1996 where 6,416 fur seals were reported killed as bycatch in the hoki fishery (Manly et al, 2002). This has to be reconciled with an abundance estimate of 40,000 fur seals in New Zealand in the early 1980s (Reijnders et al, 1993), although this estimate is considered out of date and the population may be somewhat higher (60,000+) (Matlin, 1998). I cannot reconstruct this population trajectory. I find that the population should have recovered to much higher levels i f they were at a population level where they could have sustained a hunt of 340,000 individuals. Historical population size for the New Zealand fur seal population has been estimated at 1,250,000 (Richards, 1994, Matlin, 1998). However, I can not reproduce this number and end up with a population of 40,000 individuals given their documented exploitation history. There are two possible explanations for this: (1) either the catches are not well documented or (2) there has been a massive change in carrying capacity for the species. The catch history seems plausible, though given the history of fur seal hunting in the Antarctic (Hart, 2004), I believe it more likely that there was a change in carrying capacity, likely due to interspecific competition and biological changes in the ecosystem. California sea lion, Zalophus californianus The California sea lion is endemic to North Pacific waters, and occurs in warm temperate and tropical waters (Heath, 2002, Kaschner, 2004). The California stock has 106 seen a 6% decline, a result of the species being taken as by-catch in a number of fisheries (Table 3-44, Figure 3-74). Globally, this accounts for 5% depletion in the species (Table 3-44). Table 3-44. Populations of California sea lions Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by (%) California 1980 176000(123000- 194000) 165000(113000- 183000) 6 Global 1980 225000(156000-247000) 214000(146000-236000) 5 0 1980 ± 1990 Year Figure 3-74. Population trajectory for California sea lion 2000 5 4 2 ~ 1 0 107 Steller sea lion, Eumetopias jubatus The Stellar sea lion inhabits the subpolar and cold temperate waters of the North Pacific (Rice, 1998, Baba et al, 2000, Kaschner, 2004). The Eastern Alaska stock is doing quite well, with limited depletion of only 3% since 1912. This is very different for the Western Alaska stock, which has been depleted by 64% since 1959 (Table 3-45, Figure 3-75, Figure 3-76 ). Globally, Steller sea lions have declined by 43% from 1800 to 2001. This estimate also includes the Russian and the California/Oregon/Washington populations. See section 4.2 for a discussion on the Western Alaska population trends. Table 3-45. Populations of Steller sea lions Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 95% CI) 2001 numbers (Mean, 95% CI) Depleted by(%) Eastern Alaska 1912 33000( 26700 - 63500) 31900 (26300-38600) 3 Western Alaska 1959 95300 ( 87700- 101000) 34000 (28900 - 40300) 64 Global 1912 143000(129000- 180000) 81400 (70700-94400) 43 108 < o w CD 3 •g '> T3 c CD E 3 1910 1930 1950 1970 1990 Y e a r CO < o - I — • CD O Figure 3-75. Population trajectory for Eastern Alaska Steller sea lions 20 < o ~ 15 CO 3 > ^ 10 h T3 0 E 3 5 h 0 1960 - i 40 1970 1980 Year Figure 3-76. Population trajectory for Western Alaska Steller sea lions 1990 2000 30 CM < O 20 o -*—' CO O H 10 0 109 3.2.3 Walrus Walrus, Odobenus rosmarus The walrus is endemic to the polar waters of the Northern Hemisphere (Rice, 1998, Kastelein, 2002, Kaschner, 2004). The Spitsbergen and Franz Josef Land population is depleted by 98%, while the West Greenlandic stock is depleted by 91% of its 1900 abundance, and the Northwater population is at 60% of its 1950 population (Table 3-46, Figure 3-80, Figure 3-81, Figure 3-79). The Chukchi/Bering Sea population is at 68% of its 1869 population, and the East Greenlandic population is at 94% of its 1950 population (Table 3-46, Figure 3-77, Figure 3-78). Globally, the species is depleted by 47% (Table 3-46), this estimate includes the Northwest Atlantic and Laptev Sea population. Table 3-46. Populations of walruses Ocean Basin Start of doc. hunt Pre-exploitation numbers (Mean, 9 5 % CI) 2001 numbers (Mean, 9 5 % CI) Depleted by (%) Chukchi-Bering 1869 265000 (204000 - 408000) 181000(112000-247000) 32 East Greenland 1950 1090 ( 813 - 1470) 1030 ( 753 - 1440) 6 Northwater 1950 4380 ( 2980 - 7680) 1760 ( 1360- 2290) 60 Spitsbergen & Franz Josef Land 1660 82100 ( 53900- 90300) 2080 ( 1760- 3070) 98 West Greenland 1900 11100 ( 7040- 16400) 1040 ( 758- 1410) 91 Global 1660 378000 (268000 - 527000) • 201000(131000-271000) 47 110 l O < o CO > CD E 3 1870 1890 1910 1930 1950 1970 1990 Year Figure 3-77. Population trajectory for Chukchi / Bering Sea walruses CM < O co •g > 20 r 15 s 10 CD E 0 l 1950 1960 1970 1980 Year Figure 3-78. Population tracjectory for East Greenlandic walruses 111 10 8 6 4 2 0 o CO O 1990 2000 CO < o to CO •g > c CD JD E 6 h 0 1950 1960 1970 1980 Year Figure 3-79. Population trajectory for Northwater walruses -I 20 H 15 H 10 i 5 0 1990 2000 100 r C O o 80 CO •I 60 T3 40 JD § 20 40 30 H 20 o * o CO O 10 o i i i i i i i i i i i i i i i i i i i i i i i i i n i T V I i i i i f 0 1660 1710 1760 1810 1860 1910 1960 2010 Year Figure 3-80. Population trajectory for Spitsbergen walruses 112 113 3.3 The big picture Population trajectories were generated for all marine mammals with documented exploitation histories. This represents 45 of the 115 marine mammal species globally. Figure 3-82 shows a summary of the results of the analysis. Marine mammal abundance has declined 22% (0-62%) in numbers, and 76% (58-86%) in biomass from 1800 to 2001. Cumulative catches over the same time period have totaled 74.5 million in numbers, corresponding to 135.6 million tonnes (t) in biomass. The world catch offish in the 1970s was between 60 and 64 million t/year, increasing to around 80 million t/year between 1985 and 1995 (Christensen, 2006). In 1930 and 1937, marine mammal landings totaled over 4 and 3.7 million t respectively. This illustrates how huge these hunts were, especially when compared to the fisheries catches, which, during that early period, would have been much smaller than now. Marine mammal abundance in 1800 was 125 million t, which is a small fraction of the overall estimate of fish biomass, estimated at 800 billion t (S. Jennings, CEFAS, pers. comm to V . Christensen; 2006) and even up to 2 trillion t (V. Christensen, Fisheries Centre, U B C , pers. comm; 2006). 114 140 4.5 1800 1850 1900 1950 2000 Years Figure 3-82. Trends in marine mammal biomass and abundance from 1800 - 2001. The thick line shows abundance in biomass, the stippled line shows abundance in numbers over time. The thin line shows catch in biomass and the medium line shows catch in numbers over time. 3.3.1 Marine mammal catches The earliest documented catch I have acquired is for the year 1530, when 300 North Atlantic right whales were estimated to have been caught (Aguilar, 1986 and Reeves, 1999). However, it is not until the late 1700s that the size of marine mammal hunts becomes significant (Figure 3-83). The total number documented marine mammals caught, since 1530 is 74,537,339 (135,641,639 t) of which 73,042,166 (131,239,231 t) were caught after 1800. The average weight of individuals caught (biomass/numbers) is very high up until the late 1700s, when a drastic decline occured, due to the onset of fur seal catches which strongly increased numbers caught, but only slightly increased the catch in weight (Figure 3-84). From the late 1700s to the early 1900s, catch in both numbers and biomass are relatively low, albeit with some fluctuations. This likely just 115 identifies the start of reporting for a number of hunted stocks. The spike beginning in 1821 is important, however, as it represents a drastic increase in the catch of Antarctic fur seals coupled with the beginning of the Southern elephant seal hunt. The next interesting feature of this graph is the development that happened from the early 1900s until the Second World War, where biomass increases disproportionably to numbers caught. This, or course, is because great whales increasingly figure in catches. In particular, it is due to catch of the biggest and heaviest of the species, the blue whale. 116 25 -, 20 J 15 4 10 A 5 1530 1580 1630 1680 1730 1780 1830 1880 1930 1980 Figure 3-84. Average weight of marine mammals caught during 1530 - 2001, calculated as a moving average over 31 years. The average weight is estimated from total weight of mammals caught relative to numbers caught. A drop is noted in catch in weight and by numbers under the First World War, and again in 1931, although only by weight (Figure 3-83). In 1930, the second largest annual catch of blue whales (the 1937 season holds the record) virtually flooded the market, leading to a crisis that saw vastly reduced catches (T0nnessen, 1982). During the Second World War, catches dropped again as a large portion of the whaling fleet was converted to tankers and transport ships (Tonnessen, 1982). The only great whale species that did not experience a large decline in catches was the sperm whale; in fact in 1943, it made up 38% of the world's marine mammal catch by biomass. The decline in catches was not so precipitous for the smaller marine mammals, many of which were caught for subsistence. After WWII, catches increased both by numbers and weight, although more so by numbers, indicating that the greatest whales were already depleted, and the whaling fleets were forced to target the smaller species. 117 The total biomass caught declines rapidly in the 1960s, followed by a collapse in catch by numbers. This collapse is much bigger in terms of biomass than numbers, indicating depletion of the great whales. In 1986, when the IWC moratorium on whaling came into force, catch by weight is severely reduced, while numbers caught begins increasing again, indicating increased catches of smaller mammals, and/or perhaps better documentation of smaller mammal catches. Figure 3-85 clearly shows the buildup in average weight of the catch as the great whales were hunted in great numbers especially in the Southern Hemisphere. This was followed by a declining trend, indicating that these large mammals made up a declining proportion of the total catch. 8 1900 1920 1940 1960 1980 2000 Y e a r s Figure 3-85. Average weight of marine mammals caught during 1900 - 2001, estimated from total weight of mammals caught relative to numbers caught. 118 3.3.2 Marine mammal numbers Figure 3-86 shows the abundance of marine mammals in numbers from 1800 -2001. Abundance levels have been declining steadily, with a total decline of 22% (0-62%) between 1800 and 2001. In terms of numbers, the total population went from 76 (58-115) to 60 (44-93) million. The 2001 abundance marks a bit of an increase from the 1983 numbers of 56.4 million, which equaled a of 28% depletion from 1800. 140 r 120 100 -<? 80 -20 -0 I ' ' ' L 1800 1850 1900 1950 2000 Y e a r s Figure 3-86. Decline in numbers of marine mammals from 1800 to 2001. The solid line is the median, and the dotted lines represent the 95% credible intervals. The decline in the groups that make up the marine mammal populations is shown in Figure 3-87. True seals constitute the greatest proportion of marine mammal numbers, 119 decreasing from 1900 to the 1980, but increasing in abundance since. The smaller dolphins and porpoises have declined since the 1960s. The eared seals and walrus declined up till about 1900, since then they have shown signs of recovery. The abundance trend for the smaller whales and larger dolphins seems steady, while the great whales have decreased since the early 1900s. 4 5 4 0 35 30 CD < o 25 K CD E 2 0 =J z 15 10 5 •(1) True seals -(2) Smal ler dolphins and porpoises •(3) Eared seals and walrus • (4) Smal ler whales and larger dolphins •(5) Great whales (3) 1 8 0 0 1850 (2) 1900 Yea rs 1950 2 0 0 0 Figure 3-87. Marine mammal global abundance in numbers. Figure 3-88 shows abundance for cetaceans and pinnipeds. Pinnipeds, clearly decline earlier than the cetaceans, leveling off in the 1970s, and increasing again from the 1980s on. The cetaceans show a remarkable trend consistent with rapid over-exploitation; their abundance was reduced from 24.5 million animals in 1958 to 18.8 million in 1977, 120 which is a 22% decline in 20 years. The cetacean population seems to have leveled off since the late 1970s, and has even increased slightly in recent years. 55 r 50 45 40 g, 35 0 JD E c —30 .E ? CO ° • D .9- 2 5 'c c 20 15 10 0 • P i n n i p e d s C e t a c e a n s 30 27 24 21 18 0 15 JD E 3 C CD — < co o c •«-CD 0 O CO 12 0 O + 3 0 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 Y e a r s F i g u r e 3-88. Global aggregated abundance of cetaceans and pinnipeds, (in numbers) from 1800 to 2001. 121 3.3.3 Marine mammal biomass This section examines what has happened to marine mammal abundance in terms of biomass. The graphs are like the above except for the change in units. This lets us appreciate the true dominance of the great whales in the ecosystems. Figure 3-89 shows the decline in biomass of 76% (58-86%) for aggregated marine mammal population from 1800 to 2001. It is evident that the great whales, although accounting for only 5% of the total numbers of marine mammals in 1800, account for the vast majority, 92%, of the total marine mammal biomass The decline is staggering, with current great whale biomass making up 72% of the total biomass and 2% of the total numbers. 180 r 160 :. 140 h 20 -0 ' ' ' ' L 1800 1850 1900 1950 2000 Years Figure 3-89. Decline in the biomass of marine mammals. The solid line is the median, and the dotted lines represent the 95% credible interval. 122 Figure 3-90 shows the decline by mammal groups from 1800 to 2001. The great whales constitute the greatest to marine mammal abundance by weight. Although the decline by numbers for this group did not look remarkable (Figure 3-87), the decline in biomass is very pronounced (Figure 3-90, see next section). True seal and smaller dolphin and porpoise biomass has declined proportionally to their respective abundance in numbers. The smaller whales and larger dolphin biomass and the eared seals and walrus are slowly declining over time (Figure 3-90). 123 Figure 3-91 shows the decline in biomass for pinnipeds, and cetaceans. It is apparent that the bulk of the decline is occurring in the cetacean population. The pinnipeds, while also declining, have done so at a much lesser rate. \ \ •P inn ipeds C e t a c e a n s \ 120 100 80 60 ^ 40 20 0 0 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 Years Figure 3-91. Marine mammal global abundance by cetaceans and pinnipeds, from 1800-2001. 124 3.3.4 The great whales In the sections above it has become clear that the great whales make up the majority of marine mammal biomass and have experienced the greatest rate and magnitude of depletion. Figure 3-92 shows the scale of decline: great whale biomass declined at a greater rate and to a greater extent than the corresponding numbers from 1800 to 2001, indicating that the largest whales were hunted first. 5 4 CD 0 ° 12 a> n 1 2 -z 1 -0 -160 r 140 -120 -2 100 -< o C 80 -in u> n E 60 -o 35 40 -20 -0 -1800 1850 1900 1950 2000 Years Figure 3-92. Decline in great whales numbers and biomass from 1800 to 2001. The solid lines are the medians and the dotted lines the 95% credible intervals. 125 The total number of whales caught since 1800 by species are listed in Table 3-47. In Figure 3-93 I have plotted the catches of the five species that were caught in the greatest amounts since 1900. It is evident that sequential depletion of the great whales has occurred. First to go were the blue whales, followed by the fin, sperm and sei whale. The latter peaking species is the minke whale, which only started to be caught in large quantities in the 1970s, and collapsed when the moratorium cames into effect, although the catch can be seen to be increasing again. The solid line in Figure 3-93 is the average weight of the whales caught, and it saw an increase up till the 1930's as blue whales comprised larger and larger portions of the catch. Since then, the average weight of whales caught has declined, with a precipitous trough during the Second World War. There is a slight increase in average weight in 1987-89, when fin whales accounted for a relatively larger portion of the limited reported catch. Table 3-47. Catch by species since 1800 Species Catches since 1800 Species Catches since 1800 Sperm whale 1,014,449 Minke whale 280,341 Fin whale 872,524 Humpback whale 253,080 Blue whale 373,870 Right whale 107,052 Sei whale 295,885 Bowhead whale 55,293 126 90 75 63 a E 45 o O 30 15 Average weight m fin • sperm • blue • sei • minke 50 40 30 D) <D 5 0) 20 2 e > < 10 1900 1920 1940 1960 1980 Years 2000 Figure 3-93. Sequential depletion of the great whale and average body weight of landed animals 127 CHAPTER 4 Discussion This section is divided into three sections to discuss the data, methodology and results, respectively. The overriding message in this section is that the results are only as good as the data, and the data are often incomplete. Howevver, they are the best available documentation of the history of marine mammal populations. Paraticularly, the data are too sparse to investigate finer details of the historical changes in abundance associated with natural and anthropogenic affect. 4.1 Data The primary limitations I have encountered in conducting the analyses in this thesis had to do with data availability and reliability. The early hunts were not documented, and much of the history of sealing remains a mystery. In this section, I will explore the causes and explanations for some of the obstacles that have presented themselves. Regarding availability of abundance data, one of the species for which there were major problems was the Antarctic minke whales. There have been significant problems with the estimation of their stock size due to conflicting trends in the data collected. The International Decade of Cetacean Research (IDRC), now the Southern Ocean Whale and Ecosystem Research (SOWER) circumpolar cruises, 1982/83-1988/89 led to the IWC Science Committee agreeing that the best estimate of minke whale abundance in the Antarctic was 761,000 (95% CL: 510,000 - 1,140,000) in 1991 (IWC, 1991, Tamura and 128 Ohsumi, 2000). Branch (2001) estimated that from the 1878/79-1983/83 and 1985/86-1990/91 IDRC/SOWER surveys the best estimates of abundance were 608,000 (c.v. 0.130) and 766,000 (c.v. 0.091) respectively (Branch and Butterworth, 2001). The 1991/92-1997/98, SOWER third circumpolar cruises resulted in an estimate of abundance of 268,000 (c.v. 0.093), although the survey is considered incomplete (Branch and Butterworth, 2001, IWC, 2002). The updated estimate for the 1991//92-2000/01 was reported at 312,000 (IWC, 2003). Erring on the side of caution, I chose to use the 312,000 estimate in my analysis. The Scientific Committee, however, while acknowledging a decline in minke abundance estimates, is not concluding that minke whale abundance in the Southern Hemisphere is actually declining (IWC, 2003). A number of hypotheses for the differences have been suggested, the two most important of which were: 1) open areas sighting rates decreasing because an increased proportion of minke whales within pack-ice, which is affected by climatological connections, and 2) distributional changes in minke whale stocks due to shifts in sea ice extent (Branch, 2006, IWC, 2006). For my SSRA, the implications of having uncertain estimates will affect the estimate of total biomass of this species, but given the limited catches (Figure 3-23, Appendix 5) the population trend, while variable in magnitude dependent on the absolute size of the population, would follow the same trend (i.e., my results will not be significantly altered). The problem I faced when attempting to assess the North Atlantic Bryde's whales, for which catch data exists beginning in 1925 (Table 3-9, Appendix 5), was that no abundance estimate is available for this stock. This meant that the probability of a 129 population trajectory could not be calculated for the species in this region. This means that any changes in numbers over time for this stock is not reflected in the estimated global trend, and also the estimated absolute size of all marine mammal abundances does not include the North Atlantic bryde's whale. The California sea lion has been hunted for subsistence for almost 5,000 years. However, there are only a very limited amount of historical catch records (NMFS, 2003). In fact, catch data are only available from 1980 on (Figure 3-74, Appendix 5). This means that the trend generated by the U.S. pup count index which has been increasing since the late 1970s (NMFS, 2003) cannot be reproduced by my SSRA. As well, it seems that E l Nino drastically reduced pup production in three years since 1980 (DeLong et al, 1991, NMFS, 2003). However, there is not enough trend information on abundances for the SSRA, which would otherwise be able to capture this trend, to pick it up. Again, the 'real' trend for this species will not be reflected in my SSRA. However, the relative size of this population to the aggregate mammal group means that the importance of missing this information is limited. Given the nature of water-based mammal hunts, where animals are readily lost from sight, occurrences of struck-but-lost animals are unavoidable. It is most likely that the magnitude of struck-but-loss rates has declined over time. The rates likely decreased as the efficiency of hunting methods and technology improved, and reporting systems became increasingly organized. In terms of our model predictions, the carrying capacity (K) would be underestimated and there will be downward bias on recovery goals, because a larger number of mammals have been removed from the stock than documented. 130 Conversely, the intrinsic rate of growth (rm a x), would tend to be overestimated. This is structural problem, because of the way the logistic equation is set up. The addition to a N year's population depends on the term r m a x ( l -) for population size N t in year t. If K is K underestimated the ratio of N t to K will downwardly bias the density dependence term, and the r m a x parameter will be inflated artificially. Thus struck-but-loss rates could have profound effects on the results in this model. To add these effects, I would have had to either estimate the rates based on very limited information (if any) on these rates over time, i.e., employ guesswork, or I would have had to increase the number of parameters estimated. However, the already limited information that this data set contains led me to steer away from this path. M y estimates can, however, in their current form be seen as approximating a minimum rate of decline for marine mammal populations, which is likely to be larger because of undocumented exploitation and issues like the struck-but-loss rate. This is my main motivation for not including struck-but-loss ratios in the SSRA 4.2 Methodology To assemble the population reconstructions I employed a production model that assumed logistic growth. The method was chosen because of its limited data requirements (catch information and indices of absolute abundance, both in numbers). Although effort data exists for many hunts, problems with the influence of technological development and the lull in catches during the Second World War make these very unreliable indices (i.e., capture probabilities have presumably increased over time, 131 biasing catch rate indices) and so I have chosen not to use them. In addition, for many of the species no indication of age or sex are given for catch numbers, which means that I can not use age- or sex-structured models. The major problems I ran into are the effects of working with data describing trajectories that are essentially 'one-way trips', i.e., the population trends are determined by decline, with no subsequent recovery. Thus, several explanations may exist for the observed data, all consistent with the maximum net growth and carrying capacity tradeoff (i.e., the catches could be taken from a large slow-growing population or from a small fast-growing population). Fortunately, I had some auxiliary data for likely net recruitment rates in both cetaceans and pinnipeds, and thus could somewhat constrain the estimates in the form of a prior on rmax. There are some species where recovery has occurred (e.g. North Pacific and Southern Hemisphere sei whales and Being Sea ribbon seal), and in these cases the time series do provide information about r m a x and K . However, some species (such as the blue whale) have shown no recovery over the last 20 years under the moratorium, and yet there have been no documented catches. If illegal catching is going on (Baker and Palumbi, 1994) then it could be that the hunt is impeding the recovery, and estimated model parameters will be biased downwards in net recruitment and upward carrying capacity. The IWC's catch limit algorithm (CLA) is based on a surplus production model, p much like the SSRA, set up as Pt = Pt_x + r(l —) 2 - C,_, , where P t is the population K in year t, Q is the catch in year t, r is the productivity parameter, and K = P 0 (Cooke, 132 1999). According to Cooke (1999), adding age-structure or a time-lag (either to age at fishing or reproduction), as well as the exponent of 2 in the equation, does not significantly impact the performance or behavior of the model. The C L A performs well for management purposes, for which it uses a control law which sets the total allowable P catch at TAC = brPt (— - a), where 'a ' and 'b' are intercept and slope parameters of the K plot of P, vs. T A C (Cooke, 1999). The IWC has set the V parameter at 54% of K , as a safety measure to ensure that the population does not drop below its most productive level due to scientific error in estimating relative stock size. Thus, i f the size of the stock reaches 54% or less of its K , hunting is banned to allow recovery. Stock reduction analysis (SRA) and its extension, stochastic stock reduction analysis (SSRA), as methods, were described by Walters et al. (2006), who applied the models to Fraser River white sturgeon and Georgia Strait lingcod. Simiarly, M . K . McAllister assessed the Gulf of Mexico Snapper for the Southeast Fisheries Science Centre's Southeast Data, Assessment and Review (SEDAR) workshop on the Gulf of Mexico Red Snapper (SEDAR 07) and found high density-dependence, a surprising insight, in juvenile mortality rates, a critical step to assessing the effects of the shrimp fishery bycatch (Walters et al, 2006). In fact, Walters et al. (2006) state that "SRA should be a required assessment component rather than a methodology for use only in data-poor situations", adding to confidence in my SSRA framework. However, there were some situations, related to non-stationary effects, where the structural assumptions of the SSRA, led to an inability to replicate documented observations of and trends in abundance. 133 A first example of the consequences of the omission of non-stationary effects, (i.e., systematic changes in r m a x and K) is provided by the assessment of the Western Alaska Steller sea lion population. These mammals have been studied in depth, and their population numbers have been estimated since 1956 (Trites and Larkin, 1996, Trites, Fisheries Centre, U B C , pers. comm., 2006). These sea lions are known to have increased from the late 1950s up until the early 1970s, after which they went into a precipitous decline (Figure 4-1). Given the documented catch history (Figure 3-76, Appendix 5), I have been unable to reproduce the population trajectory reported by Trites and Larkin (1996) (Figure 4-1). There are a number of hypotheses as to the explanation for the declining abundance of the stock, including the nutritional stress hypothesis that pin the decline on changes in prey availability and consequent changes in diet to include less 'healthy' food (Trites and Larkin, 1996, Rosen and Trites, 2000, Trites and Donnelly, 2003). Other hypotheses include incidental take, legal and illegal shooting, changes in carrying capacity due to environmental variation (e.g. climate change) and other changes in productivity, as well as disease and predation. Guenette et al. (in press) suggest that the decline is best explained by a combination of effects including fishing, predation, competition, and ocean productivity. While there is controversy as to the relative merits of the hypotheses, the decline is agreed on. This represents a case that my SSRA is unable to reproduce, and more complex modeling is required to model the non-stationary effects. 134 300 0 -I 1 1 1 - i 1 1 1950 1960 1970 1980 1990 2000 2010 Years F i g u r e 4-1. Population trajectories for the Western Alaska population of Steller sea lions with the solid line representing estimates from Trites and Larkin (1996) and A.W. Trites (Fisheries Centre, UBC, pers. Comm.., 2006), and the stippled line representing this study. A less understood situation in which non-stationary effects perplexes historical assessments is for the Northeast Pacific gray whale, whose population trends are very well documented. The history of exploitation and recovery for this species is bewildering. The whale has a very long catch history, that begins in 1660 and is still ongoing, with a cumulative total of over 73,000 whales caught (Table 3-6, Appendix 5). The problem for the eastern North Pacific gray whale is that recent abundance information suggests a high intrinsic rate of growth. However, given the decline in catches 100 years ago, there is a lag, with recovery happening slowly, that is not consistent with that hypothesis (Punt et al, 2004). 135 The problem with this stock is that it is violating the assumptions of the production model I employ, i.e., there are likely systematic changes in K and/or r m a X -Similar conclusions have been drawn by other authors, and it is generally accepted that a density-dependent population trajectory models cannot explain the abundance trends (Lankester and Beddington, 1986, Butterworth et al, 2002, Punt et al, 2004). To resolve these disparities, it is necessary for either the 1988 carrying capacity to be twice the 1846 size, or 1846 to 1900 commercial catches must be overestimated by a factor of 2.5, or estimated subsistence catches prior to 1846 must be underestimated by a factor of 3 (Butterworth et al, 2002). Perhaps, more plausible are some of explanations for why these models have been unable to reconcile catch histories and abundance information, including changes in carrying capacity over time and the presence cyclic population dynamics (Butterworth et al, 2002, Witting, 2003, Punt et al, 2004). Gray whale assessments have been conducted in two ways. The first method involves shortening the modeled time period, Wade (2002) constrained analysis to the 1967-1996 time period, Punt et al (2004) began their models in either 1930 or 1968, and using Bayesian statistics to assess the likelihood of different trajectories (Punt and Butterworth, 2002, Wade, 2002, Punt et al, 2004). This hinges on the age-structure of the populations being stable at the starting year. Given the low catches in this time period this is a reasonable assumption, and the robustness of the models under varying starting years further backs this up (Punt and Butterworth, 2002, Punt et al, 2004). The methodology of the second for of assessments involves inertial dynamics as described by Witting (2003). Here, the dynamic equations of the assessment model are 136 altered, such that life history traits become density-dependent and specific to whales at time of birth. That is, each whale has its own set of intrinsic values (determined by the state of the population at its time of birth relative to a reference level), which remains constant over time, but differs between animals allowing for cyclical patterns in abundance (Witting, 2003, Punt et al, 2004). The resulting population estimations were able to reconcile the catch histories and recent abundance information (Witting, 2003). The main differences in the models pertain the assessment time frame and to future predictions, where either a decline (Witting, 2003) or a steady-state is predicted (Punt et al, 2004). Again, this is a case for which my SSRA becomes too simplistic, and the necessity of more complex models or differently structured models is evident. The implications of the discrepancies for the global model are, however, limited given overall consistency with a trend that indicates the population is well on its way to full recovery. The problem with non-stationary effects in model parameters can also been seen when looking at carrying capacity estimates generated by D N A analyses. Roman and Palumbi (2003) use this method to generate a pre-exploitation estimate of 360,000 fin whales in the North Atlantic. To generate such numbers in SSRA, the reported catches would have to have been consistently underestimated by a factor of 10. There could be a number of reasons for the apparent discrepancies. Traditional knowledge offers some indications that marine mammal abundances may have varied by orders of magnitude over relatively short periods in time (Maschner et al, in review). The concept of the existence of a single carrying capacity may thus be overly simplistic as we have seen in 137 this section. If this is indeed the case, the framework on which most management objectives are based will have to be reconsidered. However, there is, in most cases, not enough information in available data to conclude this. This does raise an interesting question of whether all the estimates of carrying capacity I am generating could occur simultaneously given the varying onsets of exploitation. 4.3 Results To validate my results, I compared the estimates of carrying capacity from this study with published estimates (Table 4-1). Of the 24 specie/stock/area combinations with published carrying capacity estimates, 16 have estimates that fall within the 95% confidence limits I generated for the equivalent specie/stock combination. These are Southern Hemisphere sei whales, North Pacific sei whales, Southern right whales, sperm whales, Southern Hemisphere fin whales, North Atlantic fin whales, Northern Hemisphere humpback whales, North Pacific humpback whales, North Pacific right whales, Newfoundland's long finned pilot whales, Northern bottlenose whales, Pantropical spotted dolphins in the Eastern Tropical Pacific, Bering sea ribbon seals, the Falkland stock of South American sea lions in North Patagonia, Californian California sea lions, and Spitsbergen stock of walrus. Of the remaining 8, 3 come very close. These are the North Pacific fin whales, the Eastern Tropical Pacific spinner dolphins and the Okhotsk sea largha seals. I estimate the North Pacific fin whale carrying capacity to be 64,500 (49,600 -88,000). This can be compared to the historical abundance estimated by Ohsumi and 138 Wada (1974), set at 42,000 to 45,000 whales based on a population model incorporating catch and abundance data (Ohsumi and Wada, 1974, Carretta et al, 2004). That estimate falls just below the lower confidence bound of my estimate, 49,600. For the Eastern Tropical Pacific spinner dolphin, my pre-exploitation estimate is 2,630,000 (2,130,000 - 3,260,000), which differs only marginally from a published estimate of 2,008,000 (Smith, 1983). The differences may be due to problems with estimating the size of by-catch and allocating dolphin by-catch to spinner or spotted dolphins. Given the level of uncertainty, this estimate is close. For the largha seal in the Sea of Okhotsk, I estimated a carrying capacity of 232,000 (178,000 - 303,000). Fedoseev's (1970) estimate of 170,000 is just below my lower 95% credible interval, 178,000. The estimate of 170,000 is based on late 1960s aerial surveys of the area, which I consider an estimate of carrying capacity because hunting in the Okhotsk Sea is documented as beginning only in 1965. Perhaps the slightly smaller estimate of Fedoseev (1970) is due to it pertaining to a period after the onset of the hunt. The remaining 5 species warrant some explanation. We begin with the gray whale, for which my total estimate for the Northern Hemisphere is 24,600 (15,900 -29,000), while the published estimate is 45,000 (Nowak and Walker, 1991). The discrepancy here is due to the data violating the structural model assumptions of the SSRA, i.e., there are likely systematic changes in K and/or r m a x . , (see section 4.2), and I expect to underestimate the size of this population. 139 For blue whales, my Northern Hemisphere estimate was 14,500 (10,510 - 17,120), which is to be compared with a published estimate of 20,100 (Nowak and Walker, 1991). However, Nowak's (1991) estimates are from a general reference book "Walker's mammals of the world", citing another mammal handbook (Yochem and Leatherwood, 1985). However, I can not make the numbers cited there add to 20,100. I find that, given the number of estimates listed, the total would be 9,100-10,600, which falls within our 95% credible interval. In the Southern Hemisphere population, my estimate is 327,000 (298,000 - 359,000) and Nowak's (1991) is 200,000, which is again cited from Yochem (1985). Yochem (1985) also reference an estimate of "more than 200,000" by (Rice, 1978), and also mention that (Gambell, 1976) examined 5 sources to give an estimate of 150,000-210,000. The discrepancy may be due to the non-recovery of the stock, which in the absence of hunting, is suggesting a very low intrinsic rate of growth for these animals. If this rate is negatively biased, the result would be a positive bias in the carrying capacity parameter. In the Southern Hemisphere, my SSRA predicts that at least some recovery should be occurring given the ban on hunting blue whales. However, continued hunting is likely still threatening the species (Baker and Palumbi, 1994). According to my SSRA, the annual human induced mortality required to circumvent recovery of the JV Antarctic blue whales would be equal to Ntrmax(l - ) , which for the current K population would be (N, = 1180, r m a x = 0.01, K = 327,000) between 11 and 12 whales per year. Anything higher than this level would result in stock declines. So the question becomes: is the annual human induced mortality, including ship strikes, entanglement, 140 hunting, etc., this high or is the intrinsic rate of growth just very low, or are we seeing a combination of effects? The next species, for which a divergence between this model and published estimates exists, is the Southern Hemisphere humpback whale. I estimate the carrying capacity of this species to be 199,000 (144,000 - 228,000), while the published estimate is 100,000 (Nowak and Walker, 1991). This was taken from Johnson and Wolman (Johnson and Wolman, 1982), who state that the original Southern Hemisphere humpback whale size was "about 100,000". However, no reference for how this was calculated is given. The last stock for which my estimates of historical stock size differ is for the Spitsbergen stock of Walrus. M y carrying capacity estimate was 82,100 (53,900 -90,300), while the published estimate by Weslawski et al. (2000) is 25,000. However, the authors of that estimate mention that walrus hunting was i l l documented, and their estimate is considered a guesstimate based on the population size of Franz Joseph Land relative to the size of that area. M y estimate covers both Franz Joseph Land and Spitsbergen, but is based on the assumption that the total catch of 90,740 animals, which does not include subsistence harvest (Ross, 1993), was split evenly in the time period 1660-1911. There is a lot of uncertainty in these estimates, which likely explains the quite significant differences. In conclusion, Table 4-1 serves as a good validation of the model, and given the uncertainties (section 4.1 and 4.2) inherent in this kind of analysis on a global scale, I am 141 confident the method is contextually appropriate and may be used with due consideration of uncertainty in catch and abundance estimates. Recall that these estimates are not generated for management purposes, but for the purposes of looking at the overall trends in marine mammal abundance. 142 T a b l e 4-1: Available published and predicted carrying capacities / pre-exploitation abundances Species - O c e a n bas in ( S H = S o u t h e r n hemisphere , N H = N o r t h e r n hemisphere , E T P = E a s t e r n T r o p i c a l Pacif ic) Onse t of r e c o r d e d harvest (year) Pred ic t ed pre-explo i tat ion n u m b e r s (95% C o n f i d e n c e level) P u b l i s h e d C a r r y i n g capacit ies (P = Pacif ic) Sei whale - S H 1904 167 ,000(157 ,000-190 ,000) 200,000' Sei w h a l e - N P 1904 68,400 ( 5 4 , 6 0 0 - 85,600) 70,000" (P), 42 ,000" ' (NP) , 58-62 ,000 i i l b (NP) Southern right whale 1785 86,100 (73 ,400-98 ,300) 80,000"' c Sperm whale 1800 957,000 (751 ,000- 1,350,000) 1,100,000'" F in w h a l e - S H 1904 625,000 (469,000 - 737,000) 600,000' F i n whale - N A 1876 72,900 (54 ,900- 111,000) 30,000 - 50,000 X V 1 ", 50,000 -100 ,000 x v i v F i n whale - N P 1903 64 ,500 (49 ,600 - 88,000) 4 2 , 0 0 0 - 4 5 , 0 0 0 v Gray whale 1600 24 ,600(15 ,900-29 ,000) 45,000' Blue whale - S H 1904 327,000 (298,000 - 359,000) 200 ,000 ' , 200 ,000 x v c , 150,000-2 1 0 , 0 0 0 x v d - x v c Blue whale - N H 1868 14 ,500(10 ,510-17 ,120) 20 ,100 ' , 9 ,100 -10 ,600 x v c Bowhead whale 1650 89,000 (67,000 - 114,000) 50,000 v ", 74,000™', 43,000' Humpback whale - S H 1904 199 ,000(144 ,000-228 ,000) 100,000' Humpback whale - N H 1664 32,700 (21 ,800-57 ,400) 50,000' Humpback whale - N P 1664 16,500 ( 10 ,500- 24,100) N P 15,000 V l (North P) Nor th Pacific right whale 1835 9,720 ( 8 , 5 4 0 - 1 2 , 6 0 0 ) 11 ,000+ , v (Northeast P) Long-finned pilot whale -Newfoundland 1947 57,800 ( 5 0 , 8 0 0 - 6 7 , 1 0 0 ) 60,000" (Newfoundland) Northern Bottlenose whale - N A 1584 57800 (44200 - 84700) 4 0 , 0 0 0 - 100,000 (Eastern N A ) x v b Pantropical spotted dolphin -E T P 1959 4,590,000 (3 ,740 ,000 -5,740,000) 5,590,000"' (Eastern Tropical Pacif ic) Spinner dolphin - E T P 1959 2,630,000 (2 ,130 ,000 -3,260,000) 2,008,000"' (Eastern Tropical Pacif ic) R ibbon seal - Ber ing sea 1950 1 3 5 , 0 0 0 ( 1 1 3 , 0 0 0 - 164,000) 120,000"" (Ber ing sea) Largha - Okhotsk 1965 2 3 2 , 0 0 0 ( 1 7 8 , 0 0 0 - 3 0 3 , 0 0 0 ) 170,000"'" (Okhotsk) South American sea l ion -Falklands 1930 110,000 (86 ,200 -141 ,000 ) 137,000"' v (Falklands) Cal i fornia sea l ion - Cal i fornia 1980 155 ,000(123 ,000-194 ,000) 145,000 x v ( U S A and M e x i c o Pacif ic coast), 67 ,000 x v i ( U S Stock) Walrus - Spitsbergen 1660 82,100 ( 5 3 , 9 0 0 - 9 0 , 3 0 0 ) 25 ,000 x v " (Spitsbergen) i = (Nowak and Walker , 1991), i i = (Horwood, 2002), i i i = (T i l lman , 1977) and (Carretta et al, 2003), i i ib = (Ohsumi and Wada, 1974), i i i c = (Baker and Clapham, 2004), iv = ( N M F S , 1991) and (Angl iss and Lodge, 2003), v = (Ohsumi and Wada, 1974) and (Carretta et al, 2004), v i = (Rice , 1978) and (Calambokidis et al, 1997), v i i = (Woodby and Bo tk in , 1993) and (Finley, 2001), v i i i = (Hacquebord and Leinenga, 1994), (Weslawski et al, 2000), (Woodby and Bo tk in , 1993), (Angl iss et al, 2001), (Mi tche l l and Reeves, 1981), (Finley, 2001) and (Rugh et al, 2003), ix = (Whitehead, 2002), x = (Mercer, 1975), x i = (Smith, 1983), x i i = (Burns, 1994), x i i i = ( M i z u n o et al, 2002) and (Fedoseev, 1970), x iv = (Dans et al, 2004) (Godoy, 1963), xv = (Le Boeuf et al, 1983, Reijnders et al, 1993), xv i = (Carretta et al, 2003), x v i i = (Weslawski et al, 2000), x v i i i = (Sergeant, 1977), xv iv = (Sigurjonsson, 1995). xvb =(Nowak and Walker , 1991), (Benjaminsen and Christensen, 1979), xvc = ( Y o c h e m and Leatherwood, 1985), x v d = (Gambel l , 1976). 143 The only other aggregated estimate of global scale abundance and biomass estimates I have come across was for 'cetaceans', and it was divided into the North Atlantic, North Pacific and Southern Hemisphere (Tamura, 2003). Tamura and Ohsumi's (2003) global estimate of cetacean biomass was just over 34 million t, whereas my estimate is 25 million t (19 - 35 million t). Their estimates fall within my confidence limits. However, there are some obvious reasons for the differences. The main difference lies with the estimate of minke whale abundance. Despite the evidence of ambiguity in mine whale abundance estimates, and recent evidence suggested a significantly smaller stock size that was estimated in 1991 (IWC, 1991, Tamura and Ohsumi, 2000) (as discussed in 4.1), Tamura and Ohsumi (2003) are using the estimate of 761,000 minke whales in the Southern Hemisphere. Additionally, they are using an estimate of global sperm whale abundance of 523,778, which is significantly higher than my estimate of 376,000. This estimate for sperm whale ignores the recent work of Whitehead (2002), who analyzed available abundance estimates extensively and came up with a 'best' current estimate of 361,400, which depends on scaling localized abundance estimates by primary productivity to generate a global estimate. I find that, i f I use Tamura and Ohsumi's (2003) estimates for Sperm and Antarctic minke abundance my estimate of global cetacean biomass increases to 30.7 million t, which is close to, although still below their number. Tamura and Ohsumi (2003) work attempted to estimate prey consumption for cetaceans, to quantify competition with commercial fisheries. Their report has been heavily critizised by Holt (2006), who identifies a number of unjustified assumptions and 144 leaps of faith and alleges that they are over inflating their estimates to scale up the problem. This takes us back to the context of management. At this years IWC meeting, the St. Kitts and Nevis declaration was passed, including this segment: "...ACCEPTING that scientific research has shown that whales consume huge quantities offish making the issue a matter offood security for coastal nations and requiring that the issue of management of whale stocks must be considered in a broader context of ecosystem management since ecosystem management has now become an international standard ..." - St. Kitts and Nevis Declaration, IWC Meeting 58 This is exactly the argument that Tamura and Ohsumi (2003) make. This argument has led to a number of papers by Kaschner and colleagues on overlap between mammals and fisheries (Kaschner and Pauly, 2004, Kaschner et al., in press), and most recently to a paper looking at this issue historically with the abundance trends presented in this paper (Kaschner et al., 2006). Kaschner and Pauly (2004) conclude that spatial overlap of marine mammals and commercial fishery operations are low, and finds no evidence of competition on a global scale. In Kaschner et al. (2006) we conclude that marine mammals are being replaced by fisheries as top consumers in almost all of the areas where they intersect. This stands in stark contrast to the report by Tamura (2003) and the St. Kitts and Nevis declaration. It is my hope that this thesis will lead to more of this kind of work, where marine mammals are seen in context of their histories and biology. 145 4.4 Conclusion In this thesis I have found that globally aggregated information on all marine mammal populations indicate a decline of 22% (0-62%) in numbers, and 76% (58-86%) in biomass. The decline has been greatest for the great whales, which were exposed to sequential declines dependent on animal size and speed. 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(1997) A review of the occurrence and distribution of whales of the genus Balaenoptera along the Brazilian Coast. Reports of the International Whaling Commission, 407-417. 183 Appendix 1 Species list This is a list of the marine mammal species including their common and scientific names, information on whether exploitation data exists, a Male:Female ratio (expressed as % of the population that is female) and the average weights for females and males in t. Data for the three latter columns are taken from Trites and Pauly (1998). Common name Scientific name Exploited Male : Female ratio (% 2) Avg. weight female (t) Avg. weight male (t) Sei whale Balaenoptera borealis Y 0.5 17.3869 16.2347 Southern right whale Eubalaena australis Y 0.5 19.576 16.5743 Sperm whale Physeter catodon Y 0.5 10.0976 26.9387 Fin whale Balaenoptera physalus Y 0.5 59.8189 51.3614 Gray whale Eschrichtius robustus Y 0.5 15.6528 15.0904 Blue whale Balaenoptera musculus Y 0.5 110.1258 95.3471 Bowhead whale Balaena mysticetus Y 0.5 30.7448 31.4056 Eden/Bryde's whale Balaenoptera edeni Y 0.5 16.9047 15.3808 Humpback whale Megaptera novaeangliae Y 0.5 32.493 28.3234 Dwarf minke whale Balaenoptera acutorostrata Y 0.5 7.0111 6.1213 North Atlantic right whale Eubalaena glacialis Y 0.5 24.9599 21.8054 Antarctic minke whale Balaenoptera bonaerensis Y 0.5 7.0111 6.1213 Bryde's whale Balaenoptera brydei Assessed with North Pacific right whale Eubalaena japonica Y 0.5 24.9599 21.8054 Ribbon seal Histriophoca fasciata Y 0.5 0.0714 0.0715 Ringed seal Pusa hispida Y 0.5 0.0407 0.0443 Southern elephant seal Mirounga leonina Y 0.5 0.3267 0.5428 Subantarctic fur seal Arctocephalus tropicalis N 0.6 0.0246 0.0301 Gray seal Halichoerus grypus Y 0.5 0.1519 0.1683 Harp seal Pagophilus groenlandicus Y 0.5 0.0922 0.0922 Crabeater seal Lobodon carcinophagus 0.5 0.2162 0.196 Hooded seal Cystophora cristata Y 0.5 0.1402 0.1086 Juan Fernandez fur seal Arctocephalus philippii N 0.6 0.031 0.0402 Leopard seal Hydrurga leptonyx N 0.5 0.5462 0.3823 Northern elephant seal Mirounga angustirostris N 0.5 0.3303 0.4117 184 Walrus Odobenus rosmarus Y 0.5 0.5302 0.6425 Weddell seal Leptonychotes weddellii N 0.5 0.1625 0.1541 Bearded seal Erignathus barbatus Y 0.5 0.1998 0.1998 Antarctic fur seal Arctocephalus gazella Y 0.6 0.0227 0.0307 South African and Y Australian fur seal Arctocephalus pusillus 0.6 0.059 0.054 Northern fur seal Callorhinus ursinus Y 0.6 0.0253 0.0302 Pacific white-sided Lagenorhynchus N dolphin obliquidens 0.5 0.0727 0.0836 Pantropical spotted dolphin Stenella attenuata Y 0.5 0.0591 0.0717 Risso's dolphin Grampus griseus N 0.5 0.2113 0.2359 Short-finned pilot Globicephala Y whale macrorhynchus 0.5 0.467 0.8189 Southern bottlenose N whale Hyperoodon planifrons 0.5 1.3306 0.8268 Spinner dolphin Stenella longirostris Y 0.5 0.0395 0.0431 Striped dolphin Stenella coeruleoalba N 0.5 0.1145 0.1167 Baird's beaked whale Berardius bairdii Y 0.5 3.7936 2.4787 Beluga or white Y whale (sea marshmallow) Delphinapterus leucas 0.5 0.2885 0.3375 short beaked common Y dolphin Delphinus delphis 0.5 0.0683 0.092 Dall's porpoise Phocoenoides dalli Y 0.5 0.0613 0.0613 Killer whale Orcinus orca Y 0.5 1.9738 2.5871 Long-finned pilot Y whale Globicephala melas 0.5 0.6721 1.0285 Northern bottlenose Y whale Hyperoodon ampullatus 0.5 1.6399 1.7382 Bottlenose dolphin Tursiops truncatus Y 0.5 0.1719 0.2032 Northern right whale Y dolphin Lissodelphis borealis 0.5 0.0683 0.1411 Ross seal Ommatophoca rossii N 0.5 0.163 0.1285 Rough-toothed N dolphin Steno bredanensis 0.5 0.0877 0.0963 South American sea Y lion Otaria flavescens 0.6 0.1198 0.1026 Southern right whale N dolphin Lissodelphis peronii 0.5 0.0683 0.0547 False killer whale Pseudorca crassidens Y 0.5 0.4645 0.6916 Fraser's dolphin Lagenodelphis hosei N 0.5 0.0954 0.0954 Harbour porpoise Phocoena phocoena Y 0.5 0.0326 0.0295 Harbour seal Phoca vitulina Y 0.5 0.0584 0.0688 Atlantic spotted dolphin Stenella frontalis N 0.5 0.0675 0.0654 Atlantic white-sided Y dolphin Lagenorhynchus acutus 0.5 0.078 0.1054 California sea lion Zalophus californianus Y 0.6 0.0856 0.0621 Hooker's or New Phocarctos hookeri N 0.6 0.0856 0.1752 185 Zealand sea lion Hourglass dolphin Lagenorhynchus cruciger ' N 0.5 0.0391 0.0295 Largha or spotted seal Phoca largha Y 0.5 0.0389 0.05 Melon-headed whale Peponocephala electra N 0.5 0.1054 0.1036 Narwhal Monodon monoceros Y 0.5 0.2622 0.3881 White-beaked Lagenorhynchus N dolphin albirostris 0.5 0.1356 0.1467 Guadalupe fur seal Arctocephalus townsendi N 0.6 0.0243 0.029 Pygmy killer whale Feresa attenuata N 0.5 0.078 0.1169 Pygmy right whale Caperea marginata N 0.5 2.1602 1.9694 Pygmy sperm whale Kogia breviceps N 0.5 0.1766 0.1766 Steller sea lion Eumetopias jubatus Y 0.6 0.1864 0.2136 Dwarf sperm whale Kogia simus N 0.5 0.1008 0.1008 Neophocaena N Finless porpoise phocaenoides 0.5 0.0381 0.0429 Hawaiian monk seal Monachus schauinslandi N 0.5 0.163 0.0986 Clymene dolphin Stenella clymene N 0.5 0.0468 0.0468 Mediterranean monk N seal Monachus monachus 0.5 0.2809 0.2488 long-beaked common N dolphin Delphinus capensis 0.5 0.0683 0.092 South American fur N seal Arctocephalus australis 0.6 0.031 0.029 New Zealand fur seal Arctocephalus forsteri N 0.6 0.031 0.0343 Galapagos fur seal Arctocephalus galapagoensis N 0.6 0.0187 0.0166 Pygmy beaked whale Mesoplodon peruvianus N 0.5 0.1921 0.1768 Sowerby's beaked whale Mesoplodon bidens N 0.5 0.4622 0.4482 Stejneger's beaked whale Mesoplodon stejnegeri N 0.5 0.508 0.4018 Strap-toothed whale Mesoplodon layardii N 0.5 0.7464 0.5159 Gervais' beaked N whale Mesoplodon europaeus 0.5 0.4963 0.2887 Ginkgo-toothed N beaked whale Mesoplodon ginkgodens 0.5 0.4295 0.3209 Gray's beaked whale Mesoplodon grayi N 0.5 0.527 0.4754 Hector's beaked N whale Mesoplodon hectori 0.5 0.3361 0.2515 Andrews' beaked N whale Mesoplodon bowdoini 0.5 0.3625 0.3053 Arnoux's beaked N whale Berardius arnuxii 0.5 1.809 1.6561 Blainville's beaked N whale Mesoplodon densirostris 0.5 0.3901 0.5076 Cuvier's beaked N whale Ziphius cavirostris 0.5 0.8863 0.7708 Hubb's beaked whale Mesoplodon carlhubbsi N 0.5 0.5246 0.4145 Tasman or Shepherd's beaked whale Tasmacetus shepherdi N 0.5 0.8863 0.7892 186 True's beaked whale Mesoplodon mirus N 0.5 0.4734 0.4163 Longman's beaked whale lndopacetus pacificus ""N 0.5 1.2095 0.9279 Spade-toothed beaked whale Mesoplodon traversii N 0.5 0.454017 0.376908 Perrin's beaked whale Mesoplodon perrini N 0.5 0.454017 0.376908 Peale's dolphin Lagenorhynchus australis N 0.5 0.0586 0.0586 Dusky dolphin Lagenorhynchus obscurus N 0.5 0.0446 0.0553 Franciscana Pontoporia blainvillei N 0.5 0.0313 0.0223 Heaviside's dolphin Cephalorhynchus heavisidii N 0.5 0.0327 0.0327 Hector's dolphin Cephalorhynchus hectori N 0.5 0.0367 0.0298 Atlantic hump-backed dolphin Sousa teuszii N 0.5 0.0719 0.082 Australian sea lion Neophoca cinerea N 0.6 0.059 0.0709 Black dolphin Cephalorhynchus eutropia N 0.5 0.0304 0.0313 Burmeister's porpoise Phocoena spinipinnis N 0.5 0.0423 0.0423 Commerson's dolphin Cephalorhynchus commersonii N 0.5 0.0295 0.0274 Pacific hump-backed dolphin Sousa chinensis N 0.5 0.0788 0.1524 Irrawaddy dolphin Orcaella brevirostris N 0.5 0.0697 0.1054 Tucuxi Sotalia fluviatdis N 0.5 0.0386 0.0386 Vaquita Phocoena sinus •N - 0.5 0.0241 0.0204 Galapagos sea lion Zalophus wollebaeki N 0.5 0.0856 0.0621 Indian hump-backed dolphin Sousa plumbea . N . 0.5 0.0719 0.082 Indian Ocean bottlenose dolphin Tursiops aduncus N 0.5 0.1719 0.2032 Arabian common dolphin Delphinus tropicalis N 0.5 0.0683 0.092 Spectacled porpoise Phocoena dioptrica N 0.5 0.051 0.064 187 Appendix 2 Model input Species - Area (NA = North Atlantic, NP = North Pacific, SH = Southern Hemisphere, G = Global, A = Arctic, NEP = Northeast Pacific, NWP = Northwest Pacific, NWA = Northwest Atlantic) Carrying Capacity uniform prior (lower bound -upper bound) c I D Abundance points the models are fit to for each species (year, number) Sources Sei whale - NA 5,000-20,000 5 1991,4,000 1995, 9,250 (Braham, 1991), (Perry et al, 1999), (NAAMCO, 1997) Sei whale - NP 5,000-150,000 3 1974, 9,110 1989, 10,300 (Tillman, 1977),(Carretta et al, 2001), http://luna.pos.to/whale/iwc chair92 11.ht ml Sei whale - SH 150,000-300,000 1 1965, 40,000 (Borchers etal, 1990), (Klinowska, 1991), (IWC, 1996), (Perry et al, 1999) Right whale - SH 80,000-150,000 4 1972, 4300 (Masaki, 1972), (Cummings, 1985), (IWC, 1998), (Perry et al, 1999) Sperm whale - G 500,000-1,500,000 5 1999,361,400 (Whitehead, 2002) Fin whale - NA 40,000-100,000 5 1969-1989, 47,300 (IWC, 1992), (Tamura and Ohsumi, 2000), (IWC, 2004) Fin whale - NP 50,000- 3 1973, 16,150 i 100,000 1975, 16,625 (Braham, 1991), (Perry et al, 1999) Fin whale - SH 500,000-1,000,000 1 1978-1988, 15,178 (IWC, 1996, Perry et al, 1999) Gray whale - NEP 20,000-30 ,000 3 1900, 1967-2001, several 1.900, 160 (Buckland et al, 1993), (Butterworth et al, 2002), (Hobbs and Rugh, 1999), (Tamura and Ohsumi, 2000) 1967, 13,012 1968, 12,244 1969, 12,777 1970, 11,170 1971,9,841 1972, 16,962 1973, 14,817 1974, 13,134 1975, 14,811 1976, 15,950 1977, 17,127 1978, 13,300 1979, 16,581 (Townsend, 1886), (Buckland et al, 1993), (Butterworth et al, 2002), (IWC, 1989), (Laake et al, 1994),, (Hobbs et al, 1996), (Buckland and Breiwick, 2002),, (Tamura and Ohsumi, 2000), (Hobbs and Rugh, 1999),(Angliss and Lodge, 2003), (IWC, 2003), (Deecke, 2004) 1 h t t p : / /www.nmfs .noaa .gov /^ r /PR2 /S tock_Asses smen t_Program/Ce taceans /F in_Wha le_ (CA-OR-WA) /po03f inwha lecao rwa .pd f 188 1984,21,942 1985, 20,450 1987,21,113 1992, 17,647 1993,23,109 1995, 22,263 1997, 26,300 1997, 26,635 2001, 18,761 Gray whale - NWP 3,000-5,000 4 1982, 150 (Yablokov and Bogoslovskaya, 1984), (Klinowska, 1991), (Jones and Swartz, 2002) Blue whale - NA 6,000-12,000 3 1995, 330 (NWA only) (Perry et al, 1999) Blue whale - NP 4,000-8,000 5 1975, 1600 1995, 3300 (Perry et al, 1999), (Gambell, 1976) Blue whale - SH 340,000-480,000 3 1980-2000, 900 1995, 1260 (IWC, 2004), (IWC, 1996), (Perry et al, 1999) Bowhead whale - A 70,000-100,000 4 1978,5189 1980,4998 1981,5756 1982, 7874 1983,7547 1985,6839 1986,11100 1988,7379 1993,9000 2001, 11270 2001, 10660 (Woodby and Botkin, 1993), (Angliss et al, 2001) , (Finley, 2001), (Mitchell and Reeves, 1981), (Vladimirov, 1994, Shelden and Rugh, 1995), (Mitchell and Reeves, 1982), (Finley et al, 1990), (Cosens et al, 1997), (Clapham et al, 1999), (Rugh and Shelden, 2002) , (Zeh et al, 1993), (Tamura and Ohsumi, 2000), (Rugh et al, 2003), (Tillman, 1984), (Reeves and Leatherwood, 1985), (Trites et al, 1997), (Angliss and Lodge, 2003), (Raftery et al, 1995), (da Silva et al, 2000), (Angliss et al, 2001), (Tamura and Ohsumi, 2000), (Weslawski et al, 2000), (Raftery and Zeh, 1991), (Zeh et al, 1995), (Zeh et al, 1994), (George et al, 2002), (George et al, 2004) Bryde's whale - NP 20,000-80,000 2 1986, 35,639 (Miyashita, 1986, Grass et al, 1993, Kato, 2002) Bryde's whale - SH 40,000-140,000 3 1975, 89,000 (Ohsumi, 1981, Tamura and Ohsumi, 2000) Humpback whale -NA 13,000-19,000 1 1992, 11,570 1992, 10,600 (Stevick et al, 2003), (Waring et al, 2002), (Smith et al, 1999), (Perry et al, 1999) Humpback whale -NP 10,000-25,000 5 1995, 7000 1999, 6000 (Calambokidis et al, 2001), (Perry et al, 1999), (Calambokidis et al, 1997) Humpback whale -SH 100,000-300,000 5 1980, 19,851 1985, 20,000 1995, 17,000 (Butterworth et al, 1995), (IWC, 1996), (Laws and Hofman, 1977), (Tamura and Ohsumi, 1999), (Perry et al, 1999) Commmon minke whale - NA 100,000-250,000 3 1995, 149,000 (IWC, 2004), (Tamura and Ohsumi, 2000) Common minke whale - NP 40,000-60,000 2 1990, 27570 (Moore etal, 2000), (Barlow, 1997), (Carretta et al, 2001), (Buckland et al, 1992), (IWC, 2004), (Tamura and Ohsumi, 2000) 189 Antarctic minke whale - SH 250,000-450,000 1 1995,312,000 (IWC, 2003) Right whale - NA 30,000 -40,000 3 1992, 295 1995,291 (Knowlton et al, 1994), (Perry et al, 1999), (Kraus et al, 2001), (Waring et al, 2002) Right whale - NP 8,000 -15,000 4 1970, 225 1990, 1200 1995, 7000 1978- 1987, 9,718 (Wada, 1971, Klinowska, 1991), (IWC, 1998), (Perry et al, 1999), (Tamura and Ohsumi, 2000) Short-finned pilot whale - Japan 40,000-70,000 1 1985,53„608 1985,53,347 1990, 53,347 (IWC, 1987, Miyashita, 1993, Stacey and Baird, 1993) Baird's beaked whale - Japan 5,000-15,000 1 1990, 6289 (Kasuya and Miyashita, 1997, Kasuya, 2002) Beluga whale -Arctic 50,000-400,000 5 1999, 92,500 (Frost era/., 1993, R. Hobbs Beluga abundance in Bering Sea. pers. comm. to Angliss, R. P. National Marine Mammal Laboratory, Seattle, 2000, Hobbs etal, 2000, IWC, 2000, Angliss and Lodge, 2002) Killer whale - NA 2,000-20,000 5 1987, 8600 (Christensen, 1988, Gunnlaugsson and Sigurjonsson, 1990, Sigurjonsson and Vikingsson, 1997, Dahlheim and Heyning, 1999) Killer whale - NP 1,000-10,000 5 1995,4,000 (Kaschner, 2004) Killer whale - SH 10,000-60,000 5 1990, 24,800 (Branch and Butterworth, 2001) Long-finned pilot whale - Faroe Islands 700,000-900,000 1 1989,780000 (Buckland et al, 1993) Long-finned pilot whale -Newfoundland 10,000-100,000 5 1980, 13000 (Hay, 1982, Buckland etal, 1993) Northern bottlenose whale - NA 20,000-90,000 5 1994, 44,500 (Kaschner, 2004) False killer whale -Japan 10,000-30,000 1 1985,16000 1990,16000 (Miyashita, 1993) Narwhal - Baffin Bay Canada 20,000-80,000 1 1996, 43358 (Innes et al, 2002, COSEWIC, 2004) Narwhal - Hudson Bay 5,000-20,000 3 2001,3500 (COSEWIC, 2004) Narwhal - Baffin Bay Greenland 10,000-25,000 5 2001,6650 (WWF, 2005) Pantropical spotted dolphin - ETP 3,000,000-7,000,000 1 1998, 1862559 1999,1377082 (Gerrodette, 1999, Gerrodette, 2000, Culik, 2002) Pantropical spotted dolphin - Japan 3,000,000-7,500,000 1 1985,438064 1990, 438064 (Miyashita, 1993) Spinner dolphin -ETP 1,500,000-3,500,000 1 1990, 1651000 (Wade and Gerrodette, 1993) Short beaked common dolphin -ETP 2,000,000-4,000,000 1 1988,3112300 1990,3093300 (Holt and Sexton, 1990, Wade and Gerrodette, 1993, Evans, 1994) Short beaked 20,000- 1 1998,37509 (Waring et al, 2002, Palka et al, in review) 190 common dolphin -NWA 60,000 Dall's porpoise -Japan 400,000-1,200,000 5 1991,443000 (Bass, 2005) Bottlenose dolphin - N W A 10,000-50,000 1 1998, 30633 (Waring etal, 2002) Bottlenose dolphin - Japan 100,000-250,000 2 1985, 168792 1990, 168792 (Miyashita, 1993) Northern right whale dolphin - NP 200,000-600,000 3 1985,307784 1990,307784 1991,247000 (Miyashita, 1991, Hobbs and Jones, 1993, Mangel, 1993, Miyashita, 1993) Harbour porpoise -Greenland 20,000-80,000 4 1988, 15000 (Klinowska, 1991) Harbour porpoise -North Sea 150,000-500,000 1 1994, 279367 (IWC, 1996, Read, 1999) Harbour porpoise -Baltic 5,000-100,000 1 1994, 36046 (IWC, 1996, Read, 1999) Harbour porpoise -NWA 60,000-190,000 3 1995, 101700 1996, 111400 (Kingsley and Reeves, 1998, Palka, 2000, Waring et al, 2002) Atlantic white-sided dolphin - NWA/US 500-40,000 5 1991, 20400 1992, 20400 (Palka, 1995, Palka et al, 1997) Ribbon seal -Bering sea 40,000-200,000 5 1955, 120000 1969, 65000 1975, 95000 1987, 130,000 (Burns, 1981, Burns, 1994, Angliss and Lodge, 2002, Fedoseev, 2002) Ringed seal - NA & Arctic 2,000,000-6,000,000 3 1985,1289000 (Lunn et al, 1997, Born et al, 1998, Kingsley, 1998, Reeves, 1998) Ringed seal - Baltic 120,000-180,000 4 1945,25000 1955, 6000 1975, 10000 2001,5500 (Harkonen et al, 1998, Anon., 2001) Ringed seal - NP & Arctic 3,000,000-6,000,000 5 1986, 4470560 (Popov, 1982, Frost et al, 1988, Reijnders etal, 1993, Reeves, 1998, Angliss and Lodge, 2002) Southern elephant seal - SH 500,000-1,500,000 5 1982, 750,000 1990, 664,000 (Laws, 1984, Knox, 1994, Laws, 1994, Le Boeuf and Laws, 1994) Gray seal - Iceland 3,000-30,000 5 1985, 11600 (Hauksson, 1987, Reijnders et al, 1993) Gray seal - Scotland 50,000-200,000 4 2001, 110500 (Duck, 2005) Harp seal - West Ice 100,000-1,200,000 5 1990,286000 (ICES, 1994, Lavigne, 2002) Harp seal - NWA 300,000-12,000,000 5 1990,3100000 1994, 4751000 2000,5200000 (Shelton et al, 1996, Warren et al, 1997, Healey and Stenson, 2000, Waring et al, 2002, Hammill and Stenson, 2003) Harp seal - White Sea 3,000,000-8,000,000 5 1928,3250000 1952,1350000 1959, 1200000 1998, 1750000 1999,2180000 (Dorofeev, 1956, Surkov, 1957, Surkov, 1957, ICES, 1999, Nilssen et al, 2000, Lavigne, 2002) Hooded seal - Jan Mayen 400,000-1,800,000 4 1985, 2000000 (ICES, 1991, Reijnders etal, 1993) Hooded seal - 400,000- 4 1984,325000 (Bowen et al, 1987, Reijnders et al, 1993) 191 NWA 900,000 1990,425000 (Stenson, 1994, Waring et al, 2002) Bearded seal - 200,000- 5 1975, 250000 (Popov, 1976, Reijnders etal, 1993, Bering / Chukchi 400,000 1980,275000 Angliss and Lodge, 2002) Sea Harbour seal - 20,000- 1 2000, 30293 (Carretta et al, 2002) California 50,000 Largha or spotted 100,000- 5 1975,225000 (Popov, 1982, Reijnders etal, 1993, Burns, seal, Bering Sea 350,000 1980,135000 2002) Largha or spotted 30,000- 1 1992,59214 (Rugh et al., 1995, Angliss and Lodge, seal, Northeast 150,000 2002) Pacific (Alaska) Largha or spotted 100,000- 5 1977, 200000 (Reijnders et al, 1993, Mizuno et al, 2002) seal, Sea of 350,000 1985,130000 Okhotsk Antarctic Fur seal 2,500,000- 5 1930, 100 (Bonner, 1981), (Laws, 1984). (Knox, 3,500,000 1978, 554,000 1982, 930,000 1990, 1,600,000 1999, 100 1994), (Arnould, 2002) South African and 800,000- 5 1971,850000 (Shaughnessy, 1982,. Arnould, 2002) Australian fur seal 3,000,000 1993,1700000 Northern fur seal 1,000,000- 4 1912,216000 (Kenyon et al, 1954, Briggs and Fowler, 2,500,000 1945,1500000 1983,877000 1990,900000 1997,1002516 1984, Trites, 1992, Reijnders et al, 1993, Antonelis et al, 1996, Hill et al, 1998, Angliss and Lodge, 2003, Anon., 2004) South American sea 80,000- 5 1938, 137500 (Carrara, 1952, Godoy, 1963, Dans et al, lion, North 180,000 1946,18000 2004) Patagonia 2001,44842 (Falklands) New Zealand fur 400,000- 5 1991,40000 (Reijnders etal, 1993) seal, New Zealand 500,000 California sea lion, 100,000- 5 1990,113000 (Carretta et al, 2002, Heath, 2002) California 500,000 1999,182000 1999,209000 (Stewart et al, 1990, Reijnders et al, 1993) Steller sea lion - 10,000- 1 1994,30403 (Hill et al, 1998, Sease et al, 2001, Angliss Northeast Alaska 100,000 1996,31208 1998,31208 and Lodge, 2002) Steller sea lion - 50,000- 3 1960, 140000 (Merrick et al, 1987, Sease etal, 2001, Northwest Alaska 300,000 1976, 109800 1978,109800 1990,30525 1991,29405 1992,27299 1994,24136 1996,22210 1999, 34595 2000, 34595 2001,34779 Angliss and Lodge, 2002, Sease and Gudmundson, 2002, Angliss and Lodge, 2003) Walrus - 100,000- 3 1972,142200 (Fay etal, 1997) Chukchi/Bering Sea 600,000 1980,256900 1985,242600 192 Walrus - East Greenland 500-2,000 5 2001, 1000 (Born, 2005) Walrus -Northwater 500-10,000 5 1999, 1500 2001, 1850 (Born et al, 1995, Born, 2005) Walrus -Spitsbergen & Franz Josef Land 10,000-80,000 2 1990, 2000 1993,2000 (Gjertz and Wiig, 1995) Walrus - West Greenland 5,000-20,000 5 2001, 1000 (Born, 2005) 193 Appendix 3 R code # Programmer: Line Bang Christensen # Project Name: Reconstructing historical marine mammal biomass at the global scale # mmsra.dat (Marine Mammal Stock Reduction Analysis) # Date: 2005/2006 # Version: many # Comments: RMES 499 - masters thesis graphics.off() memory.size(4095) seed = round(runif( 1,1,1000)) set.seed(seed) spec = scan("mmsra.dat'',skip=2,what='character',sep-\n',nlines=l) area = scan(''rnmsra.dat'',skip=4,what='character',nlines=l) meanr = scan("mmsra.dat",skip=6,nmax=l) sdr = scan("mmsra.dat",skip=8,nmax=l) kap = scan("mmsra.dat",skip=10,nmax=l) cid = scan("mmsra.dat",skip=12,nmax=l) minK = scan("mmsra.dat",skip=14,nmax=l) maxK = scan("mmsra.dat",skip=16,nmax=l) byr = scan("mmsra.dat",skip=18,nmax=l) nyr = scan("mmsra.dat",skip=20,nmax=l) syr = scan("mmsra.dat",skip=22,nlines=l) yt = scan("mmsra.dat",skip=24,nlines=l) ct = scan("mmsra.dat",skip=26) # specie # area # mean r #stdr # total error term # confidence id, used to set prop # minimum carrying capacity # maximum carrying capacity # begin year # end year # survey year # survey - abundance estimates # catch data prop = 0.5 if (cid = 1) {prop = 0.3} if(cid = 2) {prop = 0.4} if (cid == 3) {prop = 0.5} if(cid = 4) {prop = 0.5} sig = sqrt(prop)*sqrt(kap) tau = sqrt(l-prop)*sqrt(kap) yr=byr:nyr n=length(yr) iyr = syr-min(yr)+l ci=matrix(nrow=n,ncol=3) meanEst = vector(length=n) ## "popdy"=function(theta, niter=l, tau=0) #tau here represent process errors. { • r=theta[l,]; k=theta[2,] Nt= matrix(0,nrow=n,ncol=niter); Nt[ 1 ,]=k wt=matrix(rnorm(n*niter)*tau,nrow=n,ncol=niter) like=vector(mode="numeric",length=niter) for(iin l:(n-l)) { #proportion of error attributed to observation errors # std in the residuals for yt (observation error) # process errors 194 aa=r*(l-Nt[i,]/k)*exp(wt[i,]) Nt[i+l,]=Nt[i,]+Nt[i,]*aa-ct[i] Nt[i+l,Nt[i+l,]<0]=0 } #now calculate liklelihood zt = 0 zt=log(Nt[iyr,])-log(yt) #residuals zbar=0 # i.e., absolute abundance estimates if(niter ==1) like=sum(dnorm(zt,zbar,sig,log=T)) if(niter > 1) like = rowSums(apply(zt,l,dnorm,mean=zbar,sd=sig,log=T)) like[Nt[n,]<=0]=0; like=like-min(like); like[Nt[n,]<=0]=-le70 prior=dnorm(r,mean=meanr,sd=sdr,log=T) pop=list(); pop$Nt=Nt[l:n,]; pop$like=like+prior; pop$wt=exp(wt);return(pop) "fitmodel"=function() #This gets the maximum likelihood estimates of k and r. { fun=function(theta) popdy(matrix(theta))$like fit=optim(theta,fun,control=list(fnscale=-1, maxit=2000, method="BFGS")) return(fit) "calc Y"=function(Nmax) { b=integer a=l; exponent=0 while(Nmax/a>99){ a = a*10;exponent=exponent+l} b=Nmax/a; b = ceiling(b); while(b%% 10! =0) {b=b+1} if(b==10){b=b/10;exponent = exponent+1} if(b==50|| b==60 || b==70 ||b== 80 ||b== 90){b=b/10;exponent = exponent+1} byval = c(20,l0,5,2,1); i = 3 if (b%%byval[5]==0 && b/byval[5]>=4) {i=5} if (b%%byval[4]==0 && b/byval[4]>=4) {i=4} if (b%%byval[3]==0 && b/byval[3]>=4) {i=3} if (b%%byval[2]==0 && b/byval[2]>=4) {i=2} if (b%%byval[l]=0 && b/byval[l]>=4) {i=l} vals = seq(0,b,by=byval[i]) yaxis=list(); yaxis$vals=vals; yaxis$exponent=exponent; return(yaxis); "sir"=function(niter=l 000) { rtry=runif(niter,0,(meanr*2)) ktry=runif(niter,minK,maxK) theta=rbind(rtry,ktry) sir=popdy(theta,niter,tau)#generate samples 195 #importance weights p=sir$like; maxp=max(na.omit(p)) #importance weight p=exp(p-maxp); #Must divide by the probability density function for each Ro. p[p=="NA"]=0 ix=sample( 1 :niter,niter,replace=T,prob=p) a = seq(1500,2010,by=10);b=a[a>=(byr-9)] dd = calcY(max(ct)) if(dd$exponent==0)ylabel2="Catch" else if(dd$exponent==l) ylabel2 = "Catch (*10)" else ylabel2 = paste("Catch ( 10 A",dd$exponent,")",sep="") for(i in 1 :n) ci[i,] = signif(quantile(sir$Nt[i,ix],c(0.025,0.5,0.975)),3) ninit=ci[l,2] nend=ci[n,2] d=calcY(ci[l,3]) if(d$exponent==0) ylabel="Number of individuals" else if(d$exponent=l) ylabel = "Number of individuals (*10)" else ylabel = paste("Number of individuals (10A",d$exponent,")",sep="") write (ci[l,l],file=paste(spec,area,"popCImin.txt")) for(i in 2:n) write (ci[i,l],file=paste(spec,area,"popCImin.txt"),append=T) write ("\n#Depleted by",file=paste(spec,area,"popCImin.txt"),append=T) depmin = (ci[ 1,1 ]-ci[n, 1 ])/ci[ 1,1 ]* 100 if(depmin>90)depmin=signif(depmin,3) else depmin=signif(depmin,2) write (depmin,file=paste(spec,area,"popCImin.txt"),append=T) write (ci[l,3],file=paste(spec,area,"popCImax.txt")) for(i in 2:n) write (ci[i,3],file=paste(spec,area,"popCImax.txt"),append=T) write ("\n#Depleted by",file=paste(spec,area,"popCImax.txt"),append=T) depmax = (ci[l,3]-ci[n,3])/ci[l,3]*100 if(depmax>90)depmax=signif(depmax,3) else depmax=signif(depmax,2) write (depmax,file=paste(spec,area,"popCImax.txt"),append=T) #plot confidence intervals xl l(height=4,width=6) par(mar=c(5,4,2,4)) plot(yr,ci[,3]/(10Ad$exponent),ylim=c(0,max(d$vals)),xlab="Year", ylab="",type='n',yaxt="n",xaxt="n",frame=F,axes=F) axis(side=l ,at=b,las=l ,tcl=0.5) axis(side=2,tcl=0.5,las=l, at=d$vals);mtext(ylabel,2,line=3) lines(byr:nyr, ci[,3]/(10Ad$exponent), col="steelblue",lty=3,lwd=2) lines(byr:nyr, ci[,l]/(10Ad$exponent),col="steelblue",lty=3,lwd=2) lines(byr:nyr, ci[,2]/(10Ad$exponent),col="darkblue",lwd=2) 196 points(syr,yt/(10Ad$exponent),pch=20,cex=1.8,col="red") par(new=TRUE) plot(yr,ct/(10Add$exponent),xaxt=Mn^yaxt="n^xlab=''^ ylab^ e=F,axes=F,ylim=c(0,max(dd$vals))) axis(side=4,tcl=0.5,las=l,at=dd$vals); mtext(ylabel2,4,line=2); savePlot(filename=paste(spec,area,"popSimplePlot"), type="wmf',device=dev.cur()) write(ci[l,2],fde=paste(spec,area,"pop.txt")) for(i in 2:n) { write(ci[i,2],file=paste(spec,area,"pop.txt"),append=T) } dep = (ninit-nend)/ninit *100 if(dep>90)dep=signif(dep,3) else dep=signif(dep,2) write("\n#Depleted by",file=paste(spec,area,"pop.txt"),append=T) write(dep,fde=paste(spec,area,"pop.txt"),append=T rm(ci) xll() plot(ktry [ ix], rtry [ ix] ,pch=2 0) savePlot(filename=paste(spec,area,"Posterior"), type="jpg",device=dev.cur()) windows() split.screen(c(2,l)) split.screen(c(l,2),2) screen(l); plot(rtry[ix],type="l",ylab="Intrinsic rate of growth (r)",las=l,main="(a)") screen(3); hist(rtry[ix],xlab="Intrinsic rate of growth (r)",main="(b)",breaks=50) yy=density(rtry[ix].adjust=2,from =0, to =meanr*2) screen(4);plot(yy,xlab="Intrinsic rate of growth (r)",main="(c)") lines(c(0,0,0.1,0.1),c(0,l,l,0),lty=2) #uniform prior distribution xx=seq(0,0.1,by=0.001) yy=dnorm(xx,mean=meanr,sd=sdr) lines(xx,yy,lty=2,col="red") close.screen(all = TRUE) # exit split-screen mode savePlot(filename=paste(spec,area,"R"), type="jpg",device=dev.cur()) #Now plot statistics for carrying capacity K windows() split.screen(c(2,l)) split. screen(c( 1,2),2) screen(l); plot(ktry[ix],type="l",ylab="Carrying capacity",las=l,main="(a)") screen(3); hist(ktry[ix],xlab="Carrying capacity(xl000)",main="(b)",breaks=50) yy=density(ktry[ix],adjust=2,from = minK, to = maxK) screen(4);plot(yy,xlab="Carrying capacity(x 1000)",main="(c)") lines(c(minK,minK,maxK,maxK),c(0,.01 ,.01,0),lty=2) #prior distribution close.screen(all = TRUE) savePlot(filename=paste(spec,area,"K"), type="jpg",device=dev.cur()) # return(sir) sir( 100000) 197 Appendix 4 Abundances of species with no documented exploitation The columns in the table below list the species, the range (i.e., entire i f no assessments have been done for any stocks from the species. Otherwise it is listed as rest, and includes estimates of the stocks of the species for which no assessments were conducted). Lastly, the minimum, mean and maximum abundances are given (Kaschner, 2004). Note that I have used the minimum and maximum estimates from (Kaschner, 2004) as proxies for 95% confidence intervals, and included them in my calculations of the aggregated estimates of marine mammal populations. Specie range Min mean max Subantarctic fur seal entire 310,000 350,000 400,000 Gray seal rest 128,000 167,000 215,000 Harp seal rest 4,100,000 4,750,000 5,000,000 Crabeater seal entire 10,000,000 12,500,000 20,000,000 Juan Fernandez fur seal entire 15,000 18,000 30,000 Leopard seal entire 220,000 296,454 440,000 Northern elephant seal entire 61,000 101,000 150,000 Walrus rest 14,000 14,500 15,000 Weddell seal entire 200,000 400,000 1,000,000 Bearded sela rest 130,000 195,000 260,000 South African/Australian fur seal rest 30,000 40,000 50,000 Northern fur seal* rest 334,000 344,000 354,000 Pacific white-sided dolphin entire 200,000 990,000 4,200,000 Pantropical spotted dolphin rest 6,050 19,500 95,600 Risso's dolphin entire 170,000 308,000 1,000,000 Short-finned pilot whale rest 116,000 170,000 440,000 Southern bottlenose whale entire 450,000 560,000 700,000 Spinner dolphin rest 16,000 33,200 101,000 Striped dolphin entire 1,960,000 2,700,000 7,000,000 Baird's beaked whale rest 330 660 990 Short beaked common dolphin rest 370,000 607,000 1,700,000 Dall's porpoise rest 348,000 713,000 762,000 Killer whale rest 4,700 8,500 15,900 Long finned pilot whale rest 63,000 200,000 337,000 Bottlenose dolphin rest 240,000 316,000 516,000 Ross seal entire 100,000 130,000 400,000 198 Rough-toothed dolphin entire 90,000 150,000 500,000 South American sea lion rest 140,000 180,000 240,000 Southern right whale dolphin entire 50,000 270,000 1,000,000 False killer whale rest 11,800 40,520 220,000 Fraser's dolphin entire 150,000 300,000 1,000,000 Harbour porpoise rest 42,000 126,800 212,500 Harbour seal rest 350,000 350,000 380,000 Atlantic spotted dolphin entire 40,000 80,000 400,000 Atlantic white-sided dolphin rest 37,000 82,300 188,500 California sea lion rest 33,000 49,000 53,000 Hooker's or New Zealand sea lion entire 11,100 12,500 14,000 Hourglass dolphin entire 100,000 145,000 200,000 Largha or spotted seal rest 4,500 4,500 4,500 Melon headed whale entire 39,000 51,000 200,000 Narwhal rest 14,800 18,300 21,800 White-beaked dolphin entire 16,000 26,000 60,000 Guadalupe fur seal entire 3,000 7,400 10,000 Pygmy killer whale entire 20,000 40,000 100,000 Pygmy right whale entire 1,000 3,000 10,000 Pygmy sperm whale entire 3,200 5,300 15,000 Dwarf sperm whale entire 8,000 12,500 36,000 Finless porpoise entire 10,000 20,000 40,000 Hawaiian monk seal entire 1,437 1,463 1,500 Clymene dolphin entire 12,000 18,000 56,000 Mediterranean monk seal entire 300 380 470 Long-beaked common dolphin entire 20,000 32,000 87,000 South American fur lion entire 235,000 285,000 320,000 New Zealand fur seal entire 135,000 150,000 200,000 Galapagos fur seal entire 30,000 40,000 50,000 Pygmy beaked whale entire 1,000 2,500 5,000 Sowerby's beaked whale entire 1,000 1,500 3,000 Stejneger's beaked whale entire 1,000 1,500 3,000 Strap-toothed whale entire 1,000 1,500 3,000 Gervais' beaked whale entire 1,000 1,500 3,000 Ginkgo-toothed beaked whale entire 1,000 1,500 3,000 Gray's beaked whale entire 1,000 1,500 3,000 Hector's beaked whale entire 1,000 1,500 3,000 Andrews' beaked whale entire 1,000 1,500 3,000 Arnoux's beaked whale entire 1,000 1,500 3,000 Blainville's beaked whale entire 10,000 15,000 30,000 Cuvier's beaked whale entire 21,700 28,000 70,000 Hubb's beaked whale entire 1,000 1,500 3,000 Tasman or Shepherd's beaked whale entire 1,000 1,500 3,000 True's beaked whale entire 1,000 1,500 3,000 Longman's beaked whale entire 1,000 5,000 10,000 Spade-toothed beaked whale entire 1,000 1,500 3,000 Perrin's beaked whale entire 1,000 1,500 3,000 199 Peak's dolphin entire 1,000 3,000 10,000 Dusky dolphin entire 4,039 10,000 20,000 Franciscana entire 4,000 20,000 60,000 Heaviside's dolphin entire 1,000 3,000 5,000 Hector's dolphin entire 5,300 7,300 10,000 Atlantic hump-backed dolphin entire 120 500 1,000 Australian sea lion entire 9,300 10,500 11,700 Black dolphin entire 1,000 1,500 3,000 Burmeister's porpoise entire 5,000 10,000 50,000 Commerson's dolphin entire 800 1,300 5,000 Pacific hump-backed dolphin entire 2,600 1,300 1,100 Irrawaddy dolphin entire 2,600 1,300 1,000 Tucuxi entire 1,000 3,000 10,000 Vaquita entire 77 567 1,073 Galapagos sea lion entire 10,000 14,000 25,000 Indian hump-backed dolphin entire 600 1,200 2,400 Indian Ocean bottlenose dolphin entire 1,500 5,000 7,500 Arabian common dolphin entire 5,000 10,000 15,000 Spectacled porpoise entire 1,000 3,000 10,000 Steller sea lion** rest 15,464 15,464 15,464 *Reijnders, 1993. * Angliss, 2002. 200 Appendix 5 Catch data See appendix 6 for sources. Species Right whale NA Northern bottlenose whale NA Gray whale NEP Bow-head Arctic Walrus Spits-bergen Hump -back NA Long finned pilot faroe Harbour porpoise baltic Right whale SH Onset of hunt 1530 1584 1600 1650 1660 1664 1709 1716 1785 1530 300 1531 300 1532 300 1533 300 1534 300 1535 300 1536 300 1537 300 1538 300 1539 300 1540 300 1541 300 1542 300 1543 300 1544 300 1545 300 1546 300 1547 300 1548 300 1549 300 1550 300 1551 300 1552 300 1553 300 1554 300 1555 300 1556 300 1557 300 1558 300 1559 300 1560 300 1561 300 1562 300 1563 300 1564 300 201 1565 300 1566 300 1567 300 1568 300 1569 300 1570 300 1571 300 1572 300 1573 300 1574 300 1575 300 1576 300 1577 300 1578 300 1579 300 1580 300 1581 300 1582 300 1583 300 1584 300 3 1585 300 0 1586 300 0 1587 300 0 1588 300 0 1589 300 0 1590 300 0 1591 300 0 1592 300 0 1593 300 0 1594 300 0 1595 300 0 1596 300 0 1597 300 0 1598 300 0 1599 300 0 1600 300 0 160 1601 300 0 160 1602 300 0 160 1603 300 0 160 1604 300 0 160 1605 300 0 160 1606 300 0 160 1607 300 0 160 1608 300 0 160 1609 300 0 160 1610 300 0 160 1611 0 0 160 202 1612 0 0 160 1613 0 0 160 1614 0 0 160 1615 0 0 160 1616 0 0 160 1617 0 0 160 1618 0 2 160 1619 0 0 160 1620 0 0 160 1621 0 0 160 1622 0 6 160 1623 0 0 160 1624 0 0 160 1625 0 0 160 1626 0 1 160 1627 0 0 160 1628 0 0 160 1629 0 0 160 1630 0 0 160 1631 0 3 160 1632 0 0 160 1633 0 1 160 1634 0 1 160 1635 0 0 160 1636 0 0 160 1637 0 2 160 1638 0 0 160 1639 0 0 160 1640 0 0 160 1641 0 0 160 1642 0 0 160 1643 0 0 160 1644 0 0 160 1645 0 0 160 1646 0 0 160 1647 0 0 160 1648 0 0 160 1649 0 0 160 1650 0 0 160 25 1651 0 0 160 25 1652 0 0 160 25 1653 0 0 160 25 1654 0 0 160 25 1655 0 0 160 25 1656 0 0 160 25 1657 0 0 160 25 1658 0 0 160 25 203 1659 0 0 160 25 1660 0 0 160 532 360 1661 0 0 160 532 360 1662 0 0 160 532 360 1663 0 0 160 532 360 1664 0 0 160 532 360 7 1665 0 0 160 533 360 30 1666 0 0 160 533 360 17 1667 0 0 160 533 360 26 1668 0 0 160 533 360 7 1669 0 0 160 533 360 17 1670 0 0 160 1032 360 17 1671 0 0 160 1032 360 17 1672 0 0 160 1032 360 17 1673 0 0 160 1033 360 17 1674 0 0 160 1033 360 17 1675 0 0 160 1033 360 17 1676 0 0 160 1033 360 17 1677 0 0 160 1033 360 17 1678 0 0 160 1033 360 17 1679 0 0 160 1034 360 0 1680 0 0 160 1216 360 0 1681 0 0 160 1216 360 0 1682 0 0 160 1216 360 0 1683 0 0 160 1216 360 0 1684 0 0 160 1216 360 0 1685 0 0 160 1216 360 26 1686 0 0 160 1216 360 26 1687 0 0 160 1216 360 26 1688 0 0 160 1215 360 26 1689 0 0 160 1215 360 26 1690 0 0 160 748 360 26 1691 0 0 160 748 360 16 1692 0 0 160 748 360 26 1693 0 0 160 748 360 26 1694 0 0 160 748 360 26 1695 0 0 160 748 360 26 1696 118 0 160 747 360 26 1697 118 0 160 747 360 26 1698 118 0 160 747 360 26 1699 118 0 160 747 360 26 1700 118 0 160 959 360 26 1701 118 0 160 959 360 26 1702 118 0 160 959 360 26 1703 118 0 160 959 360 26 1704 118 0 160 959 360 26 1705 118 0 160 959 360 26 204 1706 118 0 160 958 360 26 1707 118 0 160 958 •360 26 1708 118 0 160 958 360 26 1709 118 5 160 958 360 26 655 1710 118 0 160 585 360 26 655 1711 118 4 160 585 360 26 655 1712 118 0 160 585 360 26 655 1713 118 1 160 585 360 26 655 1714 118 0 160 585 360 26 655 1715 118 3 160 585 360 26 655 1716 118 3 160 584 360 26 655 1000 1717 118 2 160 584 360 26 655 1000 1718 118 0 160 584 360 26 655 1000 1719 118 7 160 628 360 26 655 1000 1720 118 5 160 566 360 26 655 1000 1721 118 3 160 566 360 26 655 1000 1722 118 2 160 566 360 26 655 1000 -1723 118 6 160 566 360 26 -655 1000 1724 118 1 160 566 360 26 655 1000 1725 118 2 160 566 360 26 655 1000 1726 118 4 160 565 360 26 655 1000 1727 118 0 160 564 360 26 655 1000 1728 118 4 160 564 360 26 655 1000 1729 118 2 160 564 360 26 655 1000 1730 118 0 160 524 360 34 655 1000 1731 118 0 160 524 360 34 655 1000 1732 118 3 160 524 360 34 655 1000 1733 118 0 160 524 360 34 655 1000 1734 118 3 160 524 360 34 655 1000 1735 0 1 160 524 360 34 655 1000 1736 0 0 160 523 360 34 655 1000 1737 0 3 160 522 360 34 655 1000 1738 0 3 160 522 360 34 655 1000 1739 0 0 160 522 360 34 655 1000 1740 0 0 160 812 360 34 655 1000 1741 0 1 160 812 360 34 655 605 1742 0 2 160 812 360 34 655 605 1743 0 0 160 812 360 34 655 605 1744 0 0 160 812 360 34 655 605 1745 0 0 160 812 360 34 655 605 1746 0 6 160 812 360 34 655 605 1747 0 0 160 811 360 34 655 605 1748 0 3 160 812 360 34 655 605 1749 0 0 160 812 360 34 655 605 1750 0 1 160 633 360 42 655 605 1751 0 0 260 633 360 21 655 605 1752 0 2 260 633 360 21 655 605 205 1753 0 0 260 633 360 21 655 605 1754 0 3 260 633 360 21 655 605 1755 0 1 .260 634 360 21 655 605 1756 0 0 260 634 360 21 655 306 1757 0 0 260 634 360 21 655 306 1758 0 . 0 260 634 360 21 655 306 1759 0 0 260 635 360 21 655 306 1760 0 0 260 550 360 21 655 306 1761 0 0 260 550 360 21 655 306 1762 0 0 260 550 360 21 655 306 1763 0 1 260 550 360 21 655 306 1764 0 0 260 550 360 21 655 306 1765 0 0 260 550 360 21 655 306 1766 0 0 260 552 360 21 655 306 1767 0 3 260 552 360 21 655 306 1768 0 0 260 552 360 21 655 306 1769 0 0 260 552 360 21 655 306 1770 0 0 260 513 360 21 655 306 1771 0 1 260 513 360 21 655 306 1772 0 0 260 513 360 21 655 306 1773 0 0 260 513 360 21 655 306 1774 0 0 260 513 360 21 655 306 1775 0 0 260 513 360 21 655 306 1776 0 0 260 514 360 21 655 306 1777 0 1 260 514 360 29 655 135 1778 0 3 260 513 360 29 655 135 1779 0 0 260 513 360 29 655 135 1780 0 0 260 454 360 26 655 135 1781 0 0 260 454 360 26 655 135 1782 0 2 260 454 360 26 655 135 1783 0 0 260 454 360 26 655 135 1784 0 0 260 454 360 26 655 135 1785 0 0 260 454 360 26 655 135 74 1786 0 0 260 453 360 29 655 135 74 1787 0 0 260 454 360 29 655 135 74 1788 0 0 260 454 360 29 655 135 74 1789 0 0 260 454 360 29 655 135 74 1790 0 0 260 219 360 38 655 135 236 1791 0 0 260 219 360 38 655 135 236 1792 0 0 260 219 360 38 655 0 236 1793 0 0 260 219 360 38 655 0 236 1794 0 0 260 219 360 38 655 0 235 1795 0 0 260 219 360 38 655 0 19 1796 0 3 260 219 360 38 655 0 19 1797 0 0 260 219 360 33 655 0 19 1798 0 0 260 220 360 40 655 0 18 1799 0 0 260 219 360 38 655 0 18 206 1800 0 0 260 51 360 38 655 0 30 1801 0 0 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0 0 171 45 0 11 1086 0 0 1976 0 0 165 92 0 15 531 0 0 1977 0 0 187 111 0 19 898 0 0 1978 0 2 184 17 0 36 1192 0 0 1979 0 0 183 27 0 38 1673 0 0 1980 0 0 181 30 0 28 2775 0 0 1981 0 3 136 26 0 16 2909 0 0 1982 0 3 168 19 0 21 2652 0 0 1983 0 0 171 18 0 24 1689 0 0 1984 0 0 169 25 0 24 1921 0 0 1985 0 0 170 17 0 12 2595 0 0 1986 0 0 171 28 0 6 1676 0 0 1987 0 0 159 29 0 14 1450 0 0 210 1988 0 3 151 23 0 7 1738 0 0 1989 0 2 180 18 0 12 1260 0 0 1990 0 0 162 30 0 9 917 0 0 1991 0 0 169 27 0 9 722 0 0 1992 0 3 0 50 0 3 1572 0 0 1993 0 8 0 52 0 2 808 0 0 1994 0 0 44 46 0 1 1201 0 0 1995 0 0 92 57 0 0 228 0 0 1996 0 0 43 45 0 2 1524 0 0 1997 0 0 79 66 0 0 1162 0 0 1998 0 0 125 55 0 3 815 0 0 1999 0 0 124 48 0 2 608 0 0 2000 0 0 115 48 0 2 588 0 0 2001 0 0 112 76 0 2 0 0 0 Species Northern fur seal Antarctic fur seal Sperm Elephant seal SH Right whale NP Beluga Blue whale NA Walrus Chukchi -Bering Fin whale NA Onset of hunt 1786 1790 1800 1820 1835 1862 1868 1869 1876 1786 48781 1787 48781 1788 48781 1789 48781 1790 48781 0 1791 48781 11000 1792 48781 22000 1793 48781 33000 1794 48781 44000 1795 48781 55000 1796 48781 66000 1797 48781 77000 1798 48781 88000 1799 48781 99000 1800 48781 110000 827 1801 48781 104500 827 1802 48781 99000 827 1803 48781 93500 827 1804 48781 88000 827 1805 48781 82500 827 1806 48781 77000 827 1807 48781 71500 827 1808 48781 66000 827 1809 48781 60500 827 2 1 1 1810 48781 55000 944 1811 48781 49500 944 1812 48781 44000 944 1813 48781 38500 944 1814 48781 33000 944 1815 48781 27500 944 1816 48781 22000 944 1817 48781 16500 944 1818 48781 11000 944 1819 48781 5500 944 1820 48781 0 3866 11495 1821 48781 320000 3866 11495 1822 48781 284444 3866 11495 1823 48781 248888 3866 11495 1824 48781 213332 3866 11495 1825 48781 177776 3866 11495 1826 48780 142220 3866 11495 1827 48780 106664 3866 11495 1828 48780 71108 3866 11495 1829 48780 35552 3866 11495 1830 48780 0 4467 11495 1831 48780 0 4467 11495 •1832 48780 0 4467 11495 1833 48780 0 4467 11495 1834 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150 0 4957 5500 0 4196 5 8000 120 1943 117184 0 5580 5500 0 3774 1 8000 341 1944 47652 0 2614 5500 0 4201 10 8000 612 1945 76964 0 1899 5500 0 3838 14 8000 541 1946 64523 0 4550 5500 0 3914 36 8000 1144 1947 61447 0 8742 5500. 0 3797 39 8000 1205 1948 70142 0 11217 5500 0 3623 110 8000 1742 1949 70990 0 9016 5500 0 3615 79 8000 1328 1950 60204 0 10409 5500 0 3525 55 8000 1502 214 1951 50771 0 18710 5500 1 18 53 8000 1372 1952 63922 0 11558 5500 0 431 15 8000 727 1953 66669 0 10058 5500 0 431 15 8000 614 1954 63882 0 18010 5500 0 1984 16 8000 530 1955 65453 0 16702 5500 0 424 13 8000 586 1956 122826 0 18590 5500 0 619 9 8000 481 1957 93618 0 20911 5500 0 722 11 8000 722 1958 78919 0 21846 5500 0 432 7 8000 528 1959 57810 0 21298 5500 0 568 7 8000 426 1960 40616 0 20344 5500 0 407 0 5166 413 1961 95974 0 22793 5500 0 382 0 4433 463 1962 77915 0 24695 5500 0 408 1 3508 504 1963 85254 0 33321 5500 0 316 0 2974 347 1964 64206 0 31994 5500 0 386 3 2475 385 1965 51020 0 26177 5500 0 538 1 2630 700 1966 52472 0 28752 0 0 792 1 3717 902 1967 50229 0 26424 0 0 707 2 2287 1117 1968 46893 0 26308 0 0 1369 0 2375 1093 1969 32817 0 27934 0 0 1088 1 1847 916 1970 36307 0 25521 0 0 1640 1 2410 1094 1971 27338 0 23115 0 0 855 1 2812 761 1972 33173 0 20370 0 0 1064 0 2843 696 1973 28482 0 22305 ,. 0 0 1425 1 2872 381 1974 33027 0 22041 0 0 1066 0 2615 410 1975 29148 0 21045 0 0 964 0 3643 383 1976 23096 0 17134 0 0 1623 1 4260 518 1977 28444 0 12722 0 0 1197 2 3838 308 1978 24885 0 11065 0 0 1031 6 4344 711 1979 25762 0 8536 0 0 1059 0 4271 730 1980 25000 0 2092 0 0 1163 0 5553 468 1981 25000 0 1456 0 0 1754 0 6670 410 1982 25000 0 621 0 0 1580 0 6378 356 1983 25000 0 414 0 0 1205 0 5835 277 1984 25000 0 463 0 0 1468 0 10272 281 1985 25000 0 400 0 0 1178 0 8917 218 1986 1605 0 200 0 0 1267 0 12500 85 1987 1605 0 191 0 0 1284 0 12500 89 1988 1605 0 0 0 0 842 0 12500 77 1989 1605 0 0 0 0 1297 0 12500 82 1990 1605 0 0 0 0 1517 0 9000 19 1991 1605 0 0 0 0 1172 0 • 8600 18 1992 1605 0 0 0 0 1039 0 6000 22 1993 1605 0 0 0 0 1346 0 4000 14 1994 1605 0 0 0 0 1118 0 4600 22 1995 1605 0 0 0 0 1235 0 5000 12 1996 1605 0 0 0 0 1063 0 6003 19 1997 1380 0 0 0 0 1165 0 4259 13 215 1998 1558 0 0 0 0 1241 0 4810 11 1999 1193 0 0 0 0 948 0 7757 9 2000 750 0 5 0 0 1237 0 6114 7 2001 781 0 8 0 0 643 0 5000 13 Spe-cies Sei whale NA Gray whale NWP Harp seal NWA Harp seal White sea Hump -back whale NP Wal-rus West Green -land SA/ SAus fur seal Harbour porpoise Greenland Fin whale NP Blue whale NP Onset of hunt 1885 1890 1895 1897 1900 1900 1900 1900 1903 1903 1885 771 1886 61 1887 206 1888 144 1889 5 1890 213 27 1891 0 27 1892 0 24 1893 0 4 1894 0 4 1895 0 4 249000 1896 106 4 249000 1897 513 5 249000 60089 1898 547 5 249000 75355 1899 117 5 249000 55347 1900 39 5 249000 37246 11 14 5291 285 1901 22 2 249000 38211 105 11 5303 285 1902 33 2 249000 65873 0 59 5052 285 1903 59 2 249000 20124 287 5 6389 285 345 225 1904 31 2 249000 28715 281 65 3890 285 690 264 1905 36 2 249000 20000 0 202 7870 285 0 0 1906 326 2 249000 19209 58 69 9733 285 272 96 1907 151 2 249000 24941 0 110 6575 285 0 0 1908 232 2 249000 20185 24 37 5925 285 345 26 1909 223 2 249000 26872 103 34 5936 550 352 57 1910 299 2 249000 24978 29 11 4498 550 217 97 1911 134 2 249000 36432 60 52 6891 550 974 243 1912 108 2 159000 51699 383 68 7829 550 978 348 1913 203 3 159000 67919 166 27 9032 550 879 116 1914 288 21 159000 26937 725 102 4815 550 1041 208 1915 0 2 159000 51736 141 61 1391 550 870 108 216 1916 6 2 159000 42218 132 102 2726 550 798 100 1917 0 2 159000 39370 53 15 8691 550 783 82 1918 154 10 159000 128044 20 16 9630 550 720 27 1919 305 4 159000 150940 59 217 12419 430 563 57 1920 481 3 159000 203387 395 32 14545 430 703 145 1921 91 3 159000 129992 101 52 12842 430 470 53 1922 160 6 159000 163375 82 64 15104 430 394 36 1923 255 17 159000 231073 225 195 16306 430 385 64 1924 216 17 159000 314026 227 52 19488 620 490 74 1925 317 150 159000 469447 822 70 19901 620 801 228 1926 399 58 159000 371262 996 168 15417 620 823 286 1927 185 49 159000 360788 1116 138 15960 620 579 198 1928 200 28 159000 368427 278 282 17825 620 418 223 1929 209 19 159000 185390 429 168 12683 620 1128 528 1930 72 16 159000 310295 622 249 928 800 707 148 1931 60 17 159000 254058 73 182 812 800 496 23 1932 82 16 159000 258837 99 328 823 800 413 17 1933 30 11 159000 256942 121 85 9826 820 568 15 1934 187 55 159000 225493 276 71 3624 600 775 67 1935 132 35 159000 245529 505 97 2842 600 941 163 1936 158 103 159000 213130 262 470 16210 580 788 54 1937 173 15 159000 185491 247 545 17150 1090 879 69 1938 106 55 159000 84444 121 629 13063 680 682 41 1939 59 31 159000 138799 215 526 19427 981 703 15 1940 0 110 27500 12965 161 625 11374 660 663 49 1941 51 62 27500 134059 97 547 13900 980 1183 76 1942 52 106 27500 5362 64 319 20126 580 819 15 1943 45 104 27500 34931 116 303 22784 770 594 15 1944 33 0 27500 25409 65 272 20020 390 540 4 1945 33 30 27500 85484 11 448 17522 440 169 10 1946 12 22 332500 79778 29 498 19252 330 350 10 1947 20 9 332500 162735 22 334 22043 470 274 34 1948 69 19 332500 159886 155 270 14387 130 491 53 1949 80 26 332500 210427 98 95 15592 260 441 19 1950 39 11 332500 216164 122 39 29487 265 489 18 1951 267 13 332500 230477 68 280 37833 380 681 72 1952 57 44 325292 154222 128 283 37449 550 1159 122 1953 166 38 291070 106562 115 284 33760 715 1221 142 1954 123 39 285351 166637 279 288 35094 915 2167 207 1955 158 59 350687 118982 194 200 39869 880 2132 142 1956 100 122 402167 97068 253 413 43186 915 2238 151 1957 87 98 260148 134261 328 206 46111 1040 2225 143 1958 102 148 316455 135229 487 195 33164 565 2696 125 1959 93 196 330846 110452 470 353 35823 775 2499 149 1960 55 173 295288 107605 297 114 42842 1145 2140 86 1961 79 212 201646 107617 501 124 49601 795 1954 92 1962 57 147 330273 117163 1312 117 46176 872 2080 112 217 1963 30 180 353937 95250 2339 54 55842 910 2559 443 1964 104 210 352650 82870 280 73 55059 1065 4084 140 1965 95 176 245326 25678 310 48 64860 930 3183 134 1966 62 220 331980 33317 5 43 49270 940 2907 0 1967 121 0 340382 35237 0 20 66339 1130 2293 0 1968 485 0 201596 35251 0 35 72302 1531 1909 0 1969 573 0 297034 30496 2 99 76413 1340 1320 0 1970 150 0 265548 34379 0 37 83701 1360 625 0 1971 490 0 238322 36292 0 46 78955 1390 637 0 1972 328 0 137661 43573 4 62 80553 1260 761 0 1973 156 0 134828 43081 0 62 82551 860 467 0 1974 14 0 156564 36883 0 50 67753 740 466 0 1975 366 0 182899 40447 0 82 75306 655 176 0 1976 13 0 178742 42665 0 100 62623 527 43 0 1977 138 0 169793 41707 0 48 77712 1000 26 0 1978 29 0 180490 36344 0 59 73097 795 34 0 1979 98 0 181706 48531 0 74 75624 1075 18 0 1980 103 0 191131 51702 0 55 76400 1220 4 0 1981 100 0 229815 61031 0 76 76400 995 1 0 1982 71 0 198547 75960 0 63 76400 850 0 0 1983 100 0 87555 82089 0 54 34000 910 0 0 1984 95 0 62254 73971 0 60 34000 855 0 0 1985 38 0 44701 80050 0 51 34000 860 1 0 1986 40 0 56914 80149 0 35 34000 690 0 0 1987 20 0 89536 64797 0 63 34000 605 0 0 1988 10 0 137280 70999 0 59 34000 550 0 0 1989 2 0 112105 42877 0 59 34000 395 0 0 1990 0 0 110755 52285 0 58 0 495 0 0 1991 0 0 107196 53308 0 59 0 550 0 0 1992 0 0 126516 44115 0 58 0 595 0 0 1993 0 0 85078 49016 0 242 0 680 0 0 1994 0 0 125944 51572 0 243 0 1716 0 0 1995 0 0 136946 43328 0 268 0 1135 0 0 1996 0 0 326715 50570 0 178 0 1682 0 0 1997 0 0 415931 41418 0 245 0 1550 0 0 1998 0 0 452328 15034 0 171 0 2051 0 0 1999 0 0 427401 37150 0 185 0 1830 0 0 2000 0 0 284252 51127 0 195 60000 1607 0 0 2001 0 0 379713 49516 0 118 0 2216 0 0 218 Speci es Ringed seal NP&A Sei whale NP Sei whale SH Fin whale SH Blue whale SH Hump -back whale SH Bairds beaked whale Japan Bry-de's whale SH Ring-ed seal Baltic Steller sea lion E Alaska Onset of hunt 1903 1904 1904 1904 1904 1904 1907 1909 1909 1912 1903 4512 1904 0 39 0 4 11 180 1905 0 0 97 104 51 311 1906 0 2 0 93 68 441 1907 0 0 0 122 106 1391 15 1908 0 0 215 295 245 3407 15 1909 0 2 346 437 212 5810 15 3 10000 1910 5527 156 225 694 387 8077 15 0 10000 1911 5500 375 49 1916 1235 10371 15 73 10000 1912 8912 236 201 4724 2505 11101 15 188 10000 0 1913 6521 369 351 5211 2774 10103 15 85 10000 2627 1914 6557 243 393 4779 5127 7589 15 0 10000 750 1915 4306 723 218 6076 5636 3760 15 66 10000 4089 1916 0 440 181 2916 4387 577 15 26 10000 0 1917 0 607 199 2213 3173 153 15 0 10000 0 1918 0 743 195 3148 2046 191 15 32 10000 0 1919 0 534 367 3658 2009 393 15 16 0 0 1920 0 569 107 5677 3002 562 15 17 0 0 1921 0 474 150 2563 4521 370 15 0 0 0 1922 0 390 139 4289 6774 1690 15 2 0 220 1923 0 493 390 3909 4918 1556 15 11 0 1885 1924 0 642 672 5654 6966 1362 15 84 6000 2706 1925 0 522 440 10488 6422 2448 15 29 6000 2827 1926 0 587 1255 7017 8665 1846 15 60 6000 1956 1927 0 599 553 5841 10106 1290 15 31 6000 1663 1928 4800 312 2123 7679 13900 1369 15 59 6000 1142 1929 6500 733 465 13234 18726 1026 15 27 6000 1359 1930 6800 842 359 11159 30457 1434 15 14 6000 1068 1931 7450 418 44 3337 6659 420 15 0 6000 1357 1932 2117 372 35 5509 18983 506 15 0 6000 1128 1933 25800 391 26 7823 17432 1079 14 0 6000 923 1934 0 301 324 13205 16612 3277 14 0 6000 786 1935 0 386 186 10289 17870 4901 14 0 6000 623 1936 0 348 804 15759 14598 8806 14 7 6000 3867 1937 0 449 285 29312 15119 7192 14 36 6000 2585 1938 0 553 133 21376 14127 3810 14 0 6000 3249 1939 0 677 138 19303 11518 293 14 0 6000 1245 219 1940 0 432 128 4387 1754 562 14 0 0 134 1941 0 641 51 1226 51 178 14 0 0 111 1942 0 256 86 980 127 • 227 14 0 0 208 1943 0 354 231 933 38 174 14 0 0 45 1944 0 736 102 2479 1361 263 14 0 0 97 1945 0 74 119 9430 3646 461 14 0 0 293 1946 0 573 490 14746 9083 248 14 0 0 304 1947 0 532 810 22006 7122 254 14 55 0 275 1948 0 706 788 19965 7731 461 76 238 0 113 1949 0 959 1504 20636 6240 5869 95 157 0 359 1950 0 639 1157 20068 7035 5355 197 100 0 2110 1951 0 771 1638 24050 5147 4582 242 23 0 231 1952 0 1314 1849 23971 4002 3492 322 0 0 252 1953 0 809 1576 28568 2888 2952 270 7 0 311 1954 2318 1057 825 27505 2270 3898 230 0 0 180 1955 3103 804 908 30932 2023 6338 258 0 0 275 1956 2148 1054 1972 28373 1715 3149 297 0 2950 339 1957 5388 882 3617 27817 1769 4774 186 34 2950 521 1958 2743 1549 3076 27477 1250 8065 229 29 2950 1103 1959 8814 1820 4698 25578 936 15774 186 41 2950 3288 1960 12433 1239 6409 27296 1743 14902 147 9 2950 2050 1961 17278 943 7285 27074 1143 7179 133 10 2950 812 1962 38693 2067 6968 17910 1748 3745 145 70 2950 1386 1963 20950 2581 10906 14197 1508 843 160 136 2950 1015 1964 21376 3661 21963 7958 3347 269 189 681 2950 952 1965 23695 3185 21298 3919 1477 2203 172 428 2950 548 1966 21029 4478 17611 3882 665 1093 171 151 2950 227 1967 23752 6113 16367 3079 462 929 107 89 2950 70 1968 15936 5749 11311 3762 674 5 117 8 2950 15 1969 15707 5158 10802 3120 920 1 138 33 2950 2 1970 17848 3723 9371 8498 834 1700 113 19 2950 2 1971 15954 2698 7376 2337 538 3 118 488 . 2950 2 1972 15713 2326 4536 1823 7 0 86 3 2950 2 1973 7606 1856 4885 1340 1 9 40 322 2950 2 1974 6951 1280 4311 1010 0 4 40 467 2950 2 1975 8781 508 1997 232 0 8 40 418 2950 2 1976 6628 0 1875 8 0 4 40 639 0 2 1977 5599 0 590 2 0 4 40 501 0 2 1978 5836 0 101 0 0 11 40 302 0 2 1979 2500 0 65 0 0 0 40 420 0 2 1980 2500 0 0 0 0 0 40 211 0 2 1981 2500 0 0 0 0 0 40 162 0 2 1982 2500 0 0 0 0 0 40 320 0 2 1983 2500 0 0 1 0 0 40 333 0 2 1984 2500 0 0 0 0 0 40 0 0 2 1985 3750 0 0 0 0 0 40 0 0 2 1986 4140 0 0 0 0 0 40 0 0 2 220 1987 4707 0 0 0 0 0 40 0 0 2 1988 5398 0 0 0 0 0 58 0 0 2 1989 3777 0 0 0 0 0 60 0 0 2 1990 3797 0 0 0 0 0 60 0 0 46 1991 723 0 0 0 0 0 60 0 0 46 1992 781 0 0 0 0 0 60 0 0 46 1993 530 0 0 0 0 0 60 0 0 46 1994 628 0 0 0 0 0 60 0 0 46 1995 0 0 0 0 0 0 60 0 0 46 1996 0 0 0 0 0 0 60 0 0 46 1997 0 0 0 0 0 0 60 0 0 46 1998 0 0 0 0 0 0 60 0 0 46 1999 0 0 0 0 0 0 60 0 0 46 2000 9567 0 0 0 0 0 62 0 0 46 2001 9567 1 0 0 0 0 62 0 0 46 Species Minke whale SH Bryde whale NA Minke whale NA SA sea lionN Patagonia Killer whale NP Minke whale NP Hooded seal Jan Mayen Bryde whale NP Harp seal West Ice Hooded seal NWA . Onset of hunt 1921 1925 1926 1930 1935 1940 1940 1946 1946 1946 1921 1 1922 0 1923 0 1924 0 1925 0 1926 8 1927 0 4 1928 0 0 1929 0 6 1930 0 28 6492 1931 0 0 6492 1932 0 0 6492 1933 0 0 6492 1934 0 0 6492 1935 0 3 6492 3 1936 0 3 6492 3 1937 0 6 6492 3 1938 0 1353 6492 26 1939 0 918 6492 26 1940 0 552 6493 26 95 15000 1941 0 2124 6493 26 184 15000 1942 0 2148 6493 26 243 15000 1943 0 1627 6493 26 183 15000 221 1944 0 1363 6493 26 168 15000 1945 0 1797 6493 26 10 15000 1946 0 1.92E+03 6493 44 0 56409 71 36070 7500 1947 0 2601 6492 51 0 56409 159 36070 7500 1948 0 3628 6492 78 266 56409 107 36070 7500 1949 1 3995 6492 95 193 56409 143 36070 7500 1950 0 2035 6492 65 244 56409 243 36070 10500 1951 4 2861 6492 107 343 69429 280 39590 10500 1952 6 3414 6492 99 489 69429 412 39590 10500 1953 12 2525 6492 108 414 69429 47 39590 10500 1954 0 3591 6492 147 371 69630 2 40097 12694 1955 36 4402 6492 101 446 69773 95 39813 12444 1956 45 3782 6492 111 567 53491 27 27608 11686 1957 11 3741 6492 108 696 53639 43 27596 12094 1958 11 4457 6492 98 812 53592 301 27813 12192 1959 4 3210 6492 72 629 53961 305 27598 12060 1960 3 3409 6492 101 586 54331 407 27788 12430 1961 3 3345 0 59 443 47955 172 23502 11846 1962 21 3473 0 55 409 48118 504 23491 11590 1963 119 3469 0 64 605 47933 210 23505 12284 1964 60 3007 0 103 737 47722 74 23402 14870 1965 81 2759 0 172 608 47114 8 23348 14144 1966 389 2532 0 139 666 32538 63 18567 19142 1967 1115 2586 0 111 621 32215 63 18570 18716 1968 610 3186 0 36 555 32147 541 18690 18284 1969 767 2822 0 25 631 31897 459 18625 19144 1970 915 2706 0 29 1045 32199 509 18703 18324 1971 4161 2648 0 15 1017 30994 1289 11217 18768 1972 6584 3041 0 5 1108 22043 571 15272 20266 1973 8543 2447 0 3 1423 27126 1099 11996 20808 1974 7885 2141 0 2 938 27613 1733 14873 21102 1975 7185 2178 0 5 931 28280 1803 5254 22858 1976 8676 2553 0 1 855 8129 1829 12842 23960 1977 6000 2254 0 2 1281 21091 1316 17191 23002 1978 6156 1970 0 1 1497 21805 966 16919 22770 1979 7897 2430 0 5 1315 25849 1397 15449 22724 1980 7142 2463 0 2 1305 13870 1124 13875 31058 1981 7903 2282 0 6 1135 14526 863 15951 30990 1982 7301 2419 0 5 1226 17888 802 12213 32296 1983 6680 2341 0 0 775 1942 697 7995 19310 1984 5568 1228 0 0 745 1927 709 2564 17728 1985 5567 1152 0 0 449 5814 357 1156 14188 1986 4969 526 0 0 380 7820 317 5331 13796 1987 273 463 0 0 304 14112 317 15819 13333 1988 241 148 0 0 0 9187 0 17163 11000 1989 330 90 0 0 2 181 0 4429 11000 1990 327 100 0 0 0 1236 0 6292 636 1991 288 107 0 0 0 2542 0 6695 6321 222 1992 330 209 0 0 0 8793 0 9633 119 1993 330 342 0 0 0 384 0 3520 19 1994 330 389 0 0 21 4744 0 8193 149 1995 440 380 0 0 100 933 0 8206 857 1996 440 564 0 0 77 811 0 6427 25754 1997 438 665 0 0 100 934 0 2161 7058 1998 389 801 0 0 100 6332 1 1884 10148 1999 439 776 0 0 100 0 0 803 201 2000 440 642 0 0 42 0 43 12343 10 2001 440 708 0 0 101 0 50 2992 140 Species Long Short Ribbon Gray Gray seal Walrus Walrus Bottle- Har- Atlantic finned finned seal seal Scotland E Green- North- nose bour white-pilot pilot Bering Iceland land water dolphin por- sided whale whale NWA poise dolphin NWA Japan North Sea NWA (US) Onset of 1947 1948 1950 1950 1950 1950 1950 1950 1950 1950 hunt 1947 0 1948 215 725 1949 0 890 1950 172 715 75 0 0 0 0 0 0 0 1951 3102 618 75 0 0 0 0 0 0 0 1952 3155 335 75 0 0 0 0 0 0 0 1953 3584 460 75 0 0 0 0 0 0 0 1954 2298 75 75 0 0 0 0 0 0 0 1955 6612 61 75 0 0 0 0 0 0 0 1956 9794 297 75 0 0 4 0 0 0 0 1957 7831 174 75 0 0 4 0 0 0 0 1958 789 197 75 0 0 4 0 0 0 0 1959 1725 144 75 0 0 4 0 0 0 0 1960 1957 168 75 0 0 4 180 0 0 0 1961 6262 133 13075 0 0 4 180 0 0 0 1962 150 80 13075 293 0 4 180 0 0 0 1963 221 228 13075 568 0 4 180 0 0 0 1964 2849 217 13075 593 0 4 180 0 0 0 1965 1520 288 13075 767 0 4 180 0 0 0 1966 887 199 13075 404 0 4 180 0 0 0 1967 739 237 13075 449 0 4 180 0 0 0 1968 204 166 75 524 0 4 180 0 0 0 1969 123 130 75 579 0 4 180 0 0 0 1970 155 152 75 404 0 4 180 0 0 0 1971 4 181 75 557 0 4 180 0 0 0 1972 0 91 75 415 0 4 180 0 0 0 1973 0 0 75 483 0 4 180 0 0 0 223 1974 0 0 75 406 0 4 180 0 0 0 1975 0 0 75 122 0 4 180 0 0 0 1976 0 0 75 274 0 4 180 0 0 0 1977 0 0 75 96 3154 4 180 0 0 0 1978 0 0 75 146 2785 4 180 0 0 0 1979 0 0 75 344 2648 4 180 0 0 0 1980 0 0 75 85 3028 4 180 0 0 0 1981 0 0 75 28 2833 4 180 0 0 0 1982 0 0 75 488 0 4 180 0 0 0 1983 0 0 75 1366 0 4 180 0 0 0 1984 0 0 75 782 0 4 180 0 0 0 1985 0 0 75 0 0 4 180 0 0 0 1986 0 0 75 0 0 4 180 0 0 0 1987 0 0 75 982 0 4 180 0 6630 0 1988 0 0 75 1645 0 4 180 0 6727 0 1989 0 0 75 0 0 4 180 72 5230 0 1990 0 0 75 586 0 4 180 115 5257 0 1991 0 0 75 393 0 4 180 130 6573 41 1992 0 0 75 828 0 4 180 101 7099 154 1993 0 0 75 1760 0 8 145 107 7421 205 1994 0 0 75 1615 0 8 145 18 7566 240 1995 0 0 75 1327 0 8 145 22 7308 80 1996 0 0 75 935 0 8 145 73 6762 114 1997 0 0 75 1274 0 8 145 435 5731 140 1998 0 0 75 567 0 8 145 390 4974 34 1999 0 0 75 662 0 8 145 394 3840 69 2000 0 0 75 500 0 8 .. 145 74 3226 26 2001 0 0 75 500 0 8 145 0 2867 26 Species Killer whale SH Ringed seal NA&A Killer whale NA Pan-tropical spotted ETP Spinner ETP Short beaked common dolphin ETP Steller sea lion W Alaska Dall's porpoise Japan False killer whale Japan Largha or spotted seal Bering Onset of hunt 1953 1954 1954 1959 1959 1959 1959 1963 1965 1965 1953 21 1954 11 30758 13 1955 33 37986 27 1956 54 46527 40 1957 75 44888 48 1958 110 48010 39 1959 55 51625 69 16305 6452 728 3271 1960 64 149822 82 349835 138426 15619 3271 1961 0 154094 111 387807 153451 17314 3271 1962 1 142931 124 157183 62196 7018 3271 224 1963 10 163459 90 175380 69396 7830 3271 9040 1964 1 172164 80 284791 112689 12715 3271 9440 1965 9 160673 105 334874 132506 14951 3271 9180 2 5000 1966 5 168229 163 272465 107812 12165 3271 7980 0 5000 1967 0 144587 37 182559 72237 8151 3271 5150 0 5000 1968 6 153456 86 165969 65672 7410 3271 6020 5 5000 1969 23 160356 232 317914 125795 14194 3271 7020 0 5000 1970 23 169882 247 300764 119009 13428 3271 8060 1 5000 1971 9 164302 59 173135 68508 7730 3271 5210 5 5000 1972 22 167753 30 254642 100760 11369 3271 5190 2 5000 1973 55 177444 1 141410 18598 5000 417 7230 0 5000 1974 47 173834 9 90783 18561 5000 418 6470 0 5000 1975 24 172851 2 99696 18526 5000 418 7350 0 5000 1976 29 171869 3 68250 16888 5000 418 9899 0 5000 1977 77 181232 15 21631 10440 5000 418 9358 35 5000 1978 49 190757 102 19096 7193 5000 1560 8426 445 5000 1979 916 282493 227 11981 7160 6597 1058 6843 395 5000 1980 0 229671 57 22614 9240 2211 1149 6920 245 5000 1981 0 231809 20 20608 8673 3349 863 12629 0 5000 1982 0 226393 5 19123 7056 1504 1963 18736 6 5000 1983 0 221962 3 7017 5416 1036 1711 17056 0 0 1984 0 139099 0 17854 13165 7409 1590 13119 0 0 1985 0 129086 0 34064 15832 7143 1074 13617 0 0 1986 0 70000 0 72109 30568 24307 655 18234 0 0 1987 0 70000 0 54664 16384 24634 244 26611 0 0 1988 0 70000 0 40541 22338 16176 44 40367 0 0 1989 0 70000 0 57428 23547 14353 31 32113 0 0 1990 0 70000 0 35194 12330 5029 31 24895 0 0 1991 0 70000 0 13826 8853 3458 31 14332 0 0 1992 0 70000 0 6531 4838 3652 580 11403 0 0 1993 0 69945 0 1896 1233 311 518 14318 0 0 1994 0 132216 0 2161 1362 252 447 15947 0 0 1995 0 72560 0 1811 1099 201 370 12396 0 0 1996 0 90309 0 1363 897 158 210 16100 0 0 1997 0 80387 0 1765 889 181 195 18540 0 0 1998 0 82108 0 639 671 466 209 11385 0 0 1999 0 83453 0 611 555 120 209 14807 0 0 2000 0 80425 0 730 537 287 200 16171 0 0 2001 0 78615 0 903 841 343 230 16650 0 0 225 Species Larga or spotted seal Okhotsk Bearded seal Chukchi Bottle-nose dolphin Japan Largha or spotted seal NEP Gray seal Sable Island Pan-tropical spotted Japan Narwhal Baffin Canada Narwhal Hudson Onset of hunt 1965 1966 1966 1966 1967 1970 1977 1977 1965 8000 1966 8000 7472 460 2400 1967 8000 8309 13 2400 1000 1968 8000 5627 14 2400 1000 1969 8000 3758 2 2400 1000 1970 8000 4292 1 2400 1000 1645 1971 8000 3244 0 2400 1000 0 1972 8000 2781 0 2400 1000 448 1973 8000 2793 84 2400 1000 206 1974 8000 2856 35 2400 1000 0 1975 8000 2420 38 2400 1000 102 1976 8000 3769 0 2400 1720 468 1977 8000 5954 899 1000 1720 344 245 245 1978 8000 6788 1012 1000 1720 756 261 273 1979 8000 6788 565 1000 1720 0 309 371 1980 8000 6788 2756 1000 1720 1058 324 376 1981 8000 6788 18 1000 1720 0 366 434 1982 8000 6788 131 1000 1720 3799 382 426 1983 0 6788 0 1000 1720 2945 333 355 1984 0 6788 0 1000 0 0 258 312 1985 0 6788 0 986 0 0 298 330 1986 0 6788 0 986 0 0 247 261 1987 0 6788 0 1000 0 0 145 215 1988 0 6788 0 1000 0 0 234 286 1989 0 6788 0 1000 0 0 326 358 1990 0 6788 0 5265 0 0 258 292 1991 0 6788 0 5265 0 0 355 393 1992 0 6788 0 5265 0 629 305 345 1993 0 6788 0 5265 0 0 318 346 1994 0 6788 0 5265 40 0 344 356 1995 0 6788 0 5265 364 0 237 277 1996 0 6788 0 5265 132 0 267 321 1997 0 6788 0 5265 72 0 236 326 1998 0 6788 0 5265 275 0 357 407 1999 0 6788 0 5265 98 0 378 700 2000 0 6788 0 5265 342 0 547 665 2001 0 6788 0 5265 76 0 415 631 226 Species Narwal Baffin Greenland Northern right whale dolphin NP California sea lion Califonia Short beaked common dolphin NWA Harbour porpoise WNA Harbour seal California Onset of harvest 1977 1978 1980 1989 1989 1991 1977 387 1978 612 355 1979 377 355 1980 462 1007 1571 1981 609 982 45 1982 461 8599 0 1983 439 10044 4327 1984 666 13547 2469 1985 256 18581 2359 1986 237 18892 4288 1987 505 19443 2722 1988 500 22128 3207 1989 312 31820 0 540 2304 1990 1057 28746 0 893 7543 1991 587 12449 0 229 2015 601 1992 587 0 0 259 3488 1204 1993 614 0 0 273 1826 475 1994 995 0 948 163 2207 227 1995 485 0 773 96 1705 228 1996 691 0 1093 212 1861 296 1997 745 0 1468 525 1979 349 1998 775 0 1443 17 1251 392 1999 863 0 1527 195 399 662 2000 600 0 1613 273 21 415 2001 673 0 1291 126 0 329 227 Appendix 6 Catch data sources Specie Area Sources Antarctic fur seal Southern Hemisphere (Richards, 2003, Mori and Butterworth, 2005) Atlantic white-sided dolphin Northwest Atlantic (Waring et al, 2003) Baird's beaked whale Japan (Ohsumi, 1975, Ohsumi, 1983, Klinowska, 1991, Reeves and Mitchell, 1993, IWC, 2004) Bearded seal Chukchi sea (Burns, 1981, Ridgway and Harrison, 1981, Angliss and Lodge, 2002) Beluga Arctic (Mitchell, 1975, Doidge and Finley, 1994, Richard, 1994, Frost and Suydam, 1995, Frost, 1998, Mahoney and Shelden, 2000, Angliss and Lodge, 2002, Heide-Jorgensenand Rosing-Asvid, 2002) Blue whale North Atlantic (Sigurjonsson and Gunnlaugsson, 1990, Sigurjonsson, 1995, Sigurjonsson, 1997, Reeves et al., 1998, IWC/BIWS, 2001) Blue whale North Pacific (Ohsumi and Wada, 1972, IWC/BIWS, 2001, Nichol et al, 2002, Carretta et al, 2004) Blue whale Southern Hemisphere (IWC/BIWS, 2001, Anon., 2005) Bottlenose dolphin Northwest. Atlantic (Waring et al, 2003) Bottlenose dolphin Japan (Kasuya, 1985) Bowhead whale Arctic (Ross, 1993, Stoker and Krupnik, 1993, Woodby and Botkin, 1993, Hacquebord, 1999, IWC/BIWS, 2001) Bryde's whale Southern Hemisphere (IWC/BIWS, 2001) Bryde's whale North Atlantic (IWC/BIWS, 2001) Bryde's whale North Pacific (Tillman and Breiwick, 1983, Holt, 1986) California sea lion California (DeMaster et al, 1985, Reijnders et al, 1993, Forney et al, 2000, Anon., 2001, Carretta et al, 2003) Dall's porpoise Japan (Jones, 1990, Northridge, 1991, Yatsu et al, 1994, IWC, 2002, Bass, 2005) Elephant seal Southern Hemisphere (Reeves et al, 1992, Laws, 1994, Le Boeuf and Laws, 1994, Anon., 1999) False killer whale Japan (Kasuya, 1985) Fin whale North Atlantic (Mitchell, 1974, IWC/BIWS, 2001, Waring et al, 2003) Fin whale North Pacific (Doroshenko, 2000, IWC/BIWS, 2001, Nichol et al, 2002, Angliss and Lodge, 2003) Fin whale Southern Hemisphere (IWC/BIWS, 2001) 228 Gray seal Iceland (Anon., 2001) Gray seal Scotland (Bonner, 1990) Gray seal Sable Island (Anon., 2001, Waring et al, 2003) Gray whale Northeast Pacific (IWC, 1993, Urban-Ramirez etal, 2003) Gray whale Northwest Pacific (Kato and Kasuya, 2002) Harbour Porpoise Baltic sea (MacKenzie et al, 2002) Harbour Porpoise Greenland (Anon., 1969, Kapel, 1971, Kapel, 1975, Mitchell, 1975, Teilmann and Dietz, 1998, Stenson, 2003, Anon., 2006) Harbour Porpoise North Sea (Stenson, 2003) Harbour Porpoise Northwest Atlantic (NEFSC, 2001, Read et al, 2003, Stenson, 2003) Harbour seal California (Forney et al, 2000, Carretta et al, 2003) Harp seal Northwest Atlantic (Kapel, 1986, Stenson et al, 1999, Anon., 2001, Walsh et al, 2001, Read et al, 2003, DFO, 2004, Fink and Lavigne, 2005) Harp seal White Sea (Sergeant, 1991, ICES, 2003) Harp seal West Ice (Kapel, 1986, Anon., 2001, Anon., 2001, Fink and Lavigne, 2005) Hooded seal Jan Mayen (ICES, 1990) Hooded seal Northwest Atlantic (Kapel, 1986, Reijnders etal, 1993, Anon., 2001) Humpback whale North Atlantic (IWC/BIWS, 2001, Reeves et al, 2001, Smith et al, 2002, Smith and Reeves, 2003) Humpback whale North Pacific (Clapham et al, 1997, IWC/BIWS, 2001, Nichol et al, 2002, Calambokidis and Barlow, 2004) Humpback whale Southern Hemisphere (IWC/BIWS, 2001, Clapham and Baker, 2002, Garrigue et al, 2004, Anon., 2005) Killer whale North Pacific (Bigg and Wolman, 1975, Mitchell, 1975, Ohsumi, 1975, Hoyt, 1990) Killer whale Southern Hemisphere (Mitchell, 1975, Hoyt, 1990) Killer whale North Atlantic (Mitchell, 1975, Hoyt, 1990) Largha or spotted seal Bering sea (Popov, 1982) Largha or spotted seal Okhotsk sea (Popov, 1982) Largha or spotted seal Northeast Pacific (Angliss and Lodge, 2002) Long finned pilot whale Faroe Islands (Anon., 2004) Long finned pilot whale Northwest Atlantic (Mercer, 1975) Minke whale Southern Hemisphere (IWC/BIWS, 2001) Minke whale North Atlantic (IWC/BIWS, 2001) Minke whale North Pacific (IWC/BIWS, 2001, Allison (IWC, pers. comm. to Caretta, J.V. et al, 2003.), Angliss and Lodge, 2003, Carretta et al, 2003) Narwhal Baffin Bay -Canada (COSEWIC, 2004) Narwhal Hudson Bay (COSEWIC, 2004) Narwhal Baffin Bay - (COSEWIC, 2004) 229 Greenland (Anon., 2006) Northern bottlenose whale North Atlantic (Lubbock, 1937, Mitchell, 1975, Christensen et al., 1977, Reeves et al, 1993, Bloch et al, 1996) Northern fur seal North Pacific (Busch, 1985, Anon., 1993, Angliss and Lodge, 2003) Northern right whale dolphin North Pacific (Mangel, 1993, Yatsu et al, 1994) Pantropical spotted dolphin Eastern Tropical Pacific (Smith, 1979, Allen, 1985, Wade, 1995, IATTC, 2006) Pantropical spotted dolphin Japan (IWC, 1984, Kasuya, 1985) Ribbon seal Bering sea 0 Right whale North Atlantic (Aguilar, 1986, Reeves and Mitchell, 1986, Reeves et al, 1999, IWC/BIWS, 2001, Commission, 2003) Right whale Southern Hemisphere (Du Pasquier, 1986, IWC/BIWS, 2001) Right whale North Pacific (Reeves et al, 1985, Best, 1987, IWC/BIWS, 2001, Nichol et al, 2002, Angliss and Lodge, 2003) Ringed seal North Pacific and Arctic (Frost, 1985, Kelly, 1988, Reeves etal, 1998, Angliss and Lodge, 2002) Ringed seal Baltic sea (Harkonenera/., 1998) Ringed seal North Atlantic and Arctic (Reijnders et al, 1993, Reeves et al, 1998, Teilmann and Kapel, 1998, NAMMCO, 2003) Sei whale North Atlantic (Horwood, 1987, IWC/BIWS, 2001) Sei whale North Pacific (IWC/BIWS, 2001, Nichol et al, 2002, Carretta et al, 2003) Sei whale Southern Hemisphere (IWC/BIWS, 2001) Short beaked common dolphin Eastern Tropical Pacific (Wade, 1995, IATTC, 2006) Short beaked common dolphin Northwest Atlantic (Waring et al, 1999, Waring et al, 2003) Short finned pilot whale Japan (Ohsumi, 1975) South African / Australian fur seal South Africa (Warneke and Shaughnessy, 1985, Wickens et al, 1991, Reijnders et al, 1993) South American sea lion North Patagonia (Strange, 1979, Anon., 1999, Dans et al, 2004) Sperm whale Global (IWC, 1969, Best, 1976, Ohsumi, 1980, Gosho etal, 1984, Mitchell and Kozicki, 1984, Christensen et al, 1992, Barnes, 1996, Sigurjonsson, 1997, Brownell etal, 1998 (unpublished), Kasuya, 1998 (unpublished), Perry et al, 1999, Carretta et al, 2001, IWC/BIWS, 2001, Nichol et al, 2002, Reeves, 2002, Waring et al, 2002, Anon., 2005) Spinner dolphin Eastern Tropical Pacific (Wade, 1995, Anon., 2005, IATTC, 2006) Steller sea lion East Alaska (Bigg, 1984, Merrick et al, 1987) Steller sea lion West Alaska (Woodley and Lavigne, 1991, Angliss and Lodge, 2003) Walrus Spitsbergen (Ross, 1993) Walrus Chukchi-Bering sea (Fay et al, 1989, Anon., 2002) Walrus West Greenland (Born, 2005) 230 Walrus East Greenland (Born, 2005) Walrus Northwater (Born, 2005) 231
Thesis/Dissertation
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Resource Management and Environmental Studies
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Graduate
Reconstructing historical abundances of exploited marine mammals at the global scale
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