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## FISH STOCK ASSMNT

by: Percy Wintheiser

41

0

1

# FISH STOCK ASSMNT FISH 458

Percy Wintheiser
UW
GPA 3.77

Ray Hilborn

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COURSE
PROF.
Ray Hilborn
TYPE
Study Guide
PAGES
1
WORDS
KARMA
50 ?

## Popular in Aquatic And Fishery Sciences

This 1 page Study Guide was uploaded by Percy Wintheiser on Wednesday September 9, 2015. The Study Guide belongs to FISH 458 at University of Washington taught by Ray Hilborn in Fall. Since its upload, it has received 41 views. For similar materials see /class/192245/fish-458-university-of-washington in Aquatic And Fishery Sciences at University of Washington.

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Date Created: 09/09/15
458 Midterm study guide Midterm exam will be on May 9 The following are sample exam questions N E 4 V39 0 gt1 9 0 4 Using the terms Nay for numbers at age and year sa for the survival rate from natural mortality uy for the exploitation rate and va for the age speci c vulnerability to exploitation write the equations to calculate the numbers at year yl as a function of year y assuming a constant annual recruitment of age 1 individuals of R Use a plus group of age 20 Write the equilibrium numbers at age for the above model assuming no shing mortality Write the equilibrium numbers at age for the above model assuming a shing mortality of u De ne state variables parameters forcing functions functional relationships logical relationships De ne yield per recruit spawning biomass per recruit Write the equation for the logistic growth model Compare and contrast how the logistic growth model and fully age structured models treat shing morality somatic growth and recruitment Where does density dependence enter into each model Discuss the criteria used for choice of the kind of model to use for any speci c application Compare and contrast the uses of the likelihood ratio and the Akaike information criterion in model selection Discuss the relationship between likelihood and sum of squares as criteria for determining model tting Discuss reasons that a nonlinear function minimizer such as SOLVER may fail to nd the true minimum If you have three independent experiments to estimate some parameter and you have the likelihood pro le from each experiment how do you calculate the likelihood pro le of all the information combined What is the relationship between the probability of the data given the hypothesis and the likelihood of the hypothesis given the data Discuss why we might wish to use a robust estimation technique instead of likelihood or sum of squares

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