Fisheries and Population Modelling Methods Using R

Credit Value
3 credits

Since about 2000 the most commonly used programming language for fisheries and ecological modeling and data analysis has become R (R Core Team 2019) and undergraduate and graduate courses in ecology and statistics have become commonly taught with R as the statistical software of choice. R is commonly used among fisheries scientists and ecologists in government agencies, academia, non-governmental organizations and consultancies. R is used, among other things, for conventional statistical analyses, for plotting purposes, to code up computer simulation models representing fish, invertebrate and mammal population dynamics, and to manage implementations of Bayesian statistical packages such as WinBUGS, JAGS and STAN. Numerous books have been written on R programming (e.g., Bolker 2006; Crawley 2007; Wickham 2019), and interfacing R with Bayesian software such as WinBUGS (e.g., Kery 2010); these books offer support for self-guided and course-centered learning of R in these types of applications. However, it has been less common for graduate courses focused primarily on R programming to be offered to graduate students in fisheries. This course aims to do just that with the aid of a recently published book: Using R for Modeling and Quantitative Methods in Fisheries by Dr. Malcolm Haddon (2020). Topics covered include an introduction to programming and plotting in R, writing functions, modelling simple population models and model parameter estimation in fisheries and applied ecology. This course is intended for graduate students in the Oceans and Fisheries Program (OCF); interested students in other programs are encouraged to contact the instructor.

One first year undergraduate course in statistics and one undergraduate or graduate course in fisheries or population dynamics modeling (e.g., FISH 504, FISH 505, FISH 509 or FISH 510). Familiarity with R is not necessary but would be preferable.

Course Organization
Each week one three-hour class will be scheduled. In the first hour of class, the instructor will lead a 60-minute seminar and this will be followed by a 30-minute discussion of the assigned reading. In the following 1.5 hours, the instructor will lead in-class computer coding and modeling exercises mostly from the assigned reading for that week.

Literature References

  • Bolker, B.M., 2006. Ecological Models and Data in R. Princeton University Press. 408 pages.
  • Crawley, M.J. 2007. The R Book. John Wiley and Sons, Chichester, England. 942 pages.
  • Haddon, M. 2020. Using R for modelling and quantitative methods in fisheries. CRC Press. 337 pages. [Note: an electronic version of this book is available to be borrowed at the UBC library]
  • Kery, M. 2007. Introduction to WinBUGS for Ecologists: Bayesian Approach to Regression, ANOVA, Mixed Models and Related Analyses. Academic Press.
  • R Core Team 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL
  • Wickham, H. 2019. Advanced R. 2nd Edition. CRC Press, Chapman and Hall, Boca Raton, FL. 588 pages.


Dr. Murdoch McAllister

Note: Maximum class size: 15 students