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Lecture 1 topics

Lecture 1 topics. Why managers cannot avoid making predictions Approaches to prediction Components of population change What is a “population”? How natural populations behave. Predictions that fisheries managers are forced to make.

adrian-jensen
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Lecture 1 topics

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  1. Lecture 1 topics • Why managers cannot avoid making predictions • Approaches to prediction • Components of population change • What is a “population”? • How natural populations behave

  2. Predictions that fisheries managers are forced to make • Responses of stocks to changes in harvest policies (management strategies) • Effect of regulatory choices on harvest rates (management tactics) • Impacts of habitat change on production • Efficacy and impacts of stock “enhancement” programs

  3. Approaches to prediction • Religious (trust intuition, common sense) • Comparative (trend interpolation, extrapolation from past or similar cases) • Reductionist (problem components) • Experimental (Try it, carefully)

  4. Religion: logging is harmful to pacific salmon FACTORS IMPACTED BY LOGGING

  5. But logging also has potentially beneficial effects for some species like coho salmon FACTORS IMPACTED BY LOGGING

  6. So experiments were conducted, and showed immediate bad effects on spawning success as expected:

  7. But then the experiments showed large positive effects on survival of the eggs that did hatch; these results have never been acknowledged in regulatory policies for habitat protection

  8. Reductionist approach • Divide prediction problem into “experimental components” that are each relatively manageable • This involves two types of assertions • Tautologies (balance statements) that define the components of change, eg new n=old n+recruits-mortalities • Functional relationships (how the components vary)

  9. M W C What is a population? • An arbitrary collection of individuals living in a defined area (Births-Deaths+Immigrants-Emigrants) • A large enough collection of individuals to be closed to migration (stock: B-D) • Bonaparte plateau example (six lakes) N M Rainbow trout Pikeminnow (small streams) D

  10. A typically messy example of fisheries data (from Rob Ahrens, UBC)

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