FISH 6003 Syllabus

Statistics and Study Design for Fisheries

  • Instructor: TBA
  • Times: TBA
  • Office Hours: TBA

Official Course Description

Deriving trends from data is a key aspect of fisheries science. In this course, students learn the fundamentals of data exploration, study design, and statistical modelling.

The course will be taught from an applied perspective - while theory will be covered as needed, emphasis will be placed on when, where, and how to use these models.

Learning Outcomes

The fundmental competencies that you will develop in this course are:

  • Able to design a statistically powerful study
  • Able to define, build, and run an appropriate model for a dataset, using R Statistical Software
  • Understand regression-type analysis ranging from simple linear regression to more complex generalized linear mixed effects models, and how to apply them
  • Understand and be able to use model selection approaches
  • Use power analysis to determine the size of study needed
  • Be able to apply the technique of meta-analysis to measure weight of evidence on a given topic

We will focus extensively on regression-type models due to their ubiquity in the life sciences, and their ability to be applied to many relevant situations within fisheries. Our goal is to develop a statistical mindset, and understand how to make smart decisions during design and analysis.

Expectations and Aspirations

Statistics can be intimidating, but that doesn’t need to be the case. This class will dymystify common statistical techniques that are relevant to pretty much anyone in fisheries science, and will help you learn how to do them yourself in R. We will start with the fundamentals and conclude with some fairly advanced analysis. We will focus on statistical principles, such that you are empowered to self-study and learn new techniques after the class is over.

My expectations are that you engage meaningfully with the course material. Come in and do your best, and don’t be afraid to make mistakes. This will be a supportive environment.

It is expected that you are an intermediate-level R user. Normally, students enrolled in FISH 6003 will have completed FISH 6002 or an equivalent training course, or demonstrated their abilities in R through some other way. I do not intend to spend much time teaching R specifically, so if you have not taken 6002 then please ensure you are familiar with:

  • Basic R syntax, including mathematical operators
  • Basic R coding style
  • R Projects and R Studio
  • Importing and Exporting data
  • Tidy data, including using tidyverse packages to clean and shape data for analysis
  • Plotting (in both ggplot and base plot)
  • Basic familiarity with R Markdown

Course Structure

The course will meet twice weekly - one 1-hr block and one 2-hr block. Lectures will balance theory and practice, and there will be in-class activities.

Generally, a topic will be introduced within a lecture on Tuesday. On Wednesday, we will discuss how to implement the theory into practice, using R.

Reference Books

We will mostly rely on primary literature in this course. I do not require that you buy any books for this course, although I have compiled a list of some key useful references below.

Important papers

Data exploration: Zuur, A.F., Ieno, E.N., and Elphick, C.S. (2009). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution 1:1, 3-14.

LMs and GLMs: Introudction to Generalized Linear Models. STAT 504: Analysis of Discrete Data, Penn State Eberly College of Science

Mixed models:

Power analysis: Kain, M.P., Bolker, B.M., and McCoy, M.W. (2015). A practical guide and power analysis for GLMMs: detecting among treatment variation in random effects. PeerJ e1226. doi: 10.7717/peerj.1226

Conducting and reporting regressions: Zuur, A.F., and Ieno, E.N. (2016). A protocol for conducting and presenting results of regression-type analyses. Methods in Ecology and Evolution 7:5, 636-645.

Meta-analysis: Harrison, F. (2011). Getting started with meta-analysis. Methods in Ecology and Evolution 2:1, 1-10.

Useful online courses

Useful textbooks

Course Policies

Social Media

I will post relevant information using the Twitter hashtag #MIStats. You may use this hashtag as well. Please do not post things that occur in class (e.g. quotes, pictures) without permission.

Code of Conduct

You have the right to expect a supportive, safe environment in this course. This course will be governed by my Fisheries Science Code of Conduct, which all participants are expected to respect.

Digital Competency

Students are expected to have computer competency. You should be able to operate Microsoft Word, Powerpoint, and Excel, or equivalent (e.g. OpenOffice or Google Docs). You should be able to download and install software onto your computer. Please install R Statistical Software and RStudio prior to begining the course.

If you lack these skills, please consult training materials on your own time. Please bring a laptop to every class.

Students should be proficient with R Statistical Software. Normally, students will have completed FISH 6002 or equivalent training prior to starting FISH 6003. FISH 6002 is not a formal pre-requisite - you may demonstrate R competency in other ways.

E-mail Policy

E-mail is not a primary tool for communication in this class. If you have questions about course content, your order of operation should be:

  1. Check the syllabus
  2. Ask in class, or discuss with colleagues
  3. Ask on Microsoft Teams (this way, everyone can benefit from an answer)
  4. Request a meeting with me (normally, to be held during office hours)

If emailing me a meeting request, use the subject line “FISH 6003: Meeting request.” Please indicate three potential meeting times (Start with my office hours. Only if those don’t work, propose alternatives) and explain in 1-3 lines what you want to meet about.

E-mail is impersonal, burdeonsome, and adds to confusion, so let’s minimize it.

Microsoft Teams

Microsoft Teams is a collaboration suite that we will use in this class. We will use it for submission of all assignments, and I will give feedback through this platform.

All Marine Institute students have access to this by default. Students not enrolled at MI will coordinate with the instructor to get temporary Teams access.

Class Participation

There will be a LOT going on in this class. Most assignments are designed to be completed mostly in-class time. The class is highly collaborative, meaning you need to be present to do it.

Accomodations will be made for serious illness or other extenuating circumstances. However, it is the student’s responsibility to stay caught up with course materials - and missing in-class activities will result in a decreased participation grade.

So please, don’t make it your plan to miss class!

Academic Honesty

This course is governed by MUN’s regulations on academic misconduct.

Course Schedule

Chapter Dates Theme
1 January 7 and 8 Introduction, and the Philosophy of Statistics
2 January 14, 15, 28, 29 Data Exploration
3 February 4 and 5 Simple Linear Regression
4 February 11 and 12 Multiple Linear Regression
X February 17 - 21 [Winter semester break]
5 February 25 and 26 Model Selection
6 March 3 and 4 Generalized Linear Models: Part 1
7 March 10 and 11 Generalized Linear Models: Part 2
8 March 17 and 18 Mixed Models
9 March 24 and 25 Generalized Linear Mixed Models
10 March 31 and April 1 Generalized Linear Mixed Models: Part 2
11 April 7 and 8 Meta-Analysis
12 April 7 and 8 Brief Intro to Power Analysis, and Bayesian Stats

Assignments and Grading

  • Major assignment (60%)
  • Minor assignments (30%)
  • Participation in in-class activities and assignments (10%)