Advanced fish population dynamics and stock assessment
Course name: Advanced fish population dynamics and stock assessment
Semester: 105-2
Department:
Instructor: Yi-Jay Chang
Course No.: FISH 101
Credit: 3
Year/Half year: Half year
Required/Selected: Selected
Class time: TBD
Notes: This course is lectured in English
Website: TBD
Description
This course is a complete review of advanced population dynamics and stock assessment, methods to be applied at different level of data, and a review of relevant computer programs and tools. Course covers introduction, decision analysis to evaluate alternative management actions, Bayesian state-space modelling, Meta-analysis, Integrated analysis, and Spatial modelling in stock assessment
Assessment models of biomass dynamics model, age-structured production model, and integrated stock assessment model (e.g., Stock Synthesis, SS) will be included. Student will be familiar with methods in fish population dynamics and stock assessment (e.g.., Bayesian posterior distribution, Markov Chain Monte Carlo, state-space modelling, etc.) and proficient in parameter estimation (e.g., unfished biomass, spawning biomass, MSY), as well as the uncertainty, of an exploited fish population, and evaluation of harvest restrictions for fisheries management problems by using various computer programs and tools (e.g., AD Model Builder [ADMB], WinBUGS/JAGS, SS).
The course draws examples from real fisheries in the world and provides student broad experiences of various fishery data and fish biology. The course is primarily for students of fisheries and marine ecology, but should also appeal to those interested in conservation ecology and ecological modelling.
Objective
The main objective of the course was to become proficient with background and tools to conduct advanced stock assessment modelling for fisheries. Student will develop professional skills of data analysis, quantitative fish population modelling, and theory and implication of fish harvest management. Student will carry out fisheries data analysis, modelling, and interpretation on a regular basis throughout the course. The course expects student will develop their own model and application. Course will provide basic programming training by following the examples using Excel, R, ADMB, WinBUGS/JAGS.
Prerequisites
Students are required of FISH808 fish population dynamics and stock assessment
Grade
Participation/attendance (10%)
Homework (70%): Total five homeworks.
Final exam (20%): This will be 50-minute closed-book exams on DATE [TBD] that test knowledge of materials from the previous lectures, readings and homework exercise.
Textbook
Haddon, M. 2001. Modelling and Quantitative Methods in Fisheries. Chapman and Hall, London, 406 pp.
F. Funk, T.J. Quinn II, J. Heifetz, J.N. Ianelli, J.E. Powers, J.F. Schweigert, P.J. Sullivan, and C.I. Zhang. 1998. Fishery Stock Assessment Models. Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks.
Millar, R.B. (2011) Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB. John Wiley & Sons, Hoboken, NJ, USA.
Office hours
TBD
Course outline
Lecture 1
Introduction and overview: what is stock assessment?
Lecture 2
Surplus production model
Lecture 3
Overview the decision analysis and projection
Lecture 4
AD Model Builder and Stock Assessment
Lecture 5
AD Model Builder and Stock Assessment
Lecture 6
State-space modeling
Example: One-way linear random-effects model
Lecture 7
Bayesian stock assessment- Bayes Rule
Lecture 8
Bayesian stock assessment- Bayesian Integration using sampling/importance resampling (SIR)
Lecture 9
Bayesian stock assessment- Bayesian Integration using Markov Chain Monte Carlo (MCMC)
Lecture 10
Bayesian state-space stock assessment using WinBUGS/JAGS
Lecture 11
Meta-analysis using WinBUGS/JAGS
Lecture 12
Age- structure production model
Lecture 13
Integrated stock assessment model
Lecture 14
Integrated stock assessment model: Stock synthesis example (1)
Lecture 15
Integrated stock assessment model: Stock synthesis example (2)
Lecture 16
Spatial modelling in stock assessment (1)
Lecture 17
Spatial modelling in stock assessment (2)