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A history of whaling. 10 th Century – records of whaling 1400-1700 Atlantic Arctic fishery – targeting the right whale 1600-1900 the Pacific fishery – more right whales 1800-1970s Sperm whale fishery Quantity of oil in a sperm whale made it an attractive target

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A history of whaling

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    1. A history of whaling 10th Century – records of whaling 1400-1700 Atlantic Arctic fishery – targeting the right whale 1600-1900 the Pacific fishery – more right whales 1800-1970s Sperm whale fishery Quantity of oil in a sperm whale made it an attractive target Innovation: Possible to make margarine of almost 100 percent whale oil.

    2. Sperm Whale (Physeter macrocephalus)

    3. 1712 – Americans hunt sperm whale 1860 – Norwegians introduce steam-powered boats and explosive harpoons Factory ships and newer technologies more species, more oceans, more countries

    4. Blue whale Sei whale Minke whale Fin whale

    5. 1946 17 nations signed a license where the International Whaling Commission (IWC) set a maximum catch in the Antarctic. 1949-1960 – IWC sets annual “fixed” quotas for all whaling 1972 - the United Nations called for a cessation of whaling and the United States Congress passed an Endangered Species Act 1987 - whale sanctuaries were declared in the 1970s and ’80s, and a general moratorium on commercial whaling, adopted by the IWC in 1982, took effect in 1987

    6. Populations III: Harvest Models Clupea harengus Odocoileus virginianus Pinus sylvestris Oncorhynchus tshawytscha

    7. Review r – intrinsic or per capita growth rate dN/dt = r*N – exponential growth Nt=N0*ert (We’re keeping it discrete) Bye bye fuzzy duckling!!

    8. Rabbits in Australia – invasive species can grow exponentially at first

    9. Review Logistic growth – S-shaped or sigmoid curve K – carrying capacity Modify with “unused” component of K (K-N)/K = (1-N/K) – used interchangeably dN/dt = r*N*(1-N/K) Logistic growth r=0.25 K=100

    10. Review Ceratotherium simum Exponential K=100 Logistic

    11. Review Environmental resistance Exponential K=100 Logistic

    12. How do we use this information to create harvesting quotas? Two types of mortality: Additive – added mortality causes a reduction in survival any hunting is added mortality if we want to control a population of invasives Compensatory – added mortality does not affect survival, up to a threshold harvesting/ hunting is mortality “that would have happened anyway” e.g. starvation, predation, disease We assume that a “compensatory” decrease in non-harvest mortality occurs – perhaps due to extra food availability

    13. K Inflection point K/2 Logistic growth r=0.25 K=100

    14. K/2 K MSY MSY = Maximum Sustainable Yield Logistic growth r=0.25 K=100

    15. ? ? h K/2 K MSY Logistic growth r=0.25 K=100 OSY – Optimal Sustainable Yield

    16. MSY? Recruitment K K K N Problems with setting quotas Estimating numbers is not easy hard to obtain reliable MSY You can’t just stop people that easily noncompliance is a huge issue K varies with environment = MSY changes

    17. Factors that affect K • Density-independent factors • Weather (storms, cold, drought) • Density-independent diseases (DDT poisoning) • Density-dependent factors • Food • Space (territories, denning sites, nest cavities) • Density-dependent epizootics (rabies, SARS)

    18. Trophic effects on K – remove large fish, remove fish waste, removes fertilizer, removes smaller fish, up the food chain, less fish to catch

    19. E2 E1 EMSY H=q*E*N Yield = efficiency*Effort*Population Fixed Effort harvest E2 > E1 > EMSY

    20. Logistic Allee model dN/dt N Hindsight always helps – the Allee effect Low population density is prone to sudden extinction Fewer mating opportunities; simply too few to be fit enough

    21. Peruvian anchoveta (Engraulis ringens) • 1960-1972 – world’s largest fishery • MSY estimated at 10 million tonnes/year • Expanded fishing fleet plus El Niño events meant collapse • 20,000 people relied on it, so politically harmful to close • Repeated collapses – 1973, 1986 – still not recovered.

    22. Making a better model Fish, deer, trees are not all one size or age • We prefer adult or mature organisms • Life-history events – reproduction, growth occur at different times • Next Lecture: life-tables and age-structure