Spring 2023 - REM 668 G100

Special Topics (3)

Sim. Modelling Natural Sys.

Class Number: 7814

Delivery Method: In Person


  • Course Times + Location:

    Jan 4 – Apr 11, 2023: Mon, 2:30–4:20 p.m.

    Jan 4 – Apr 11, 2023: Wed, 2:30–4:20 p.m.



Special Topics in areas not currently offered within the offerings of the resource and environmental management program.


Contemporary environmental challenges involve complex interactions and feedback effects linking our social and economic choices to natural system processes. Simulation modelling helps us to understand, assess, and optimize these choices to better reflect resource management goals in the presence of uncertainties.


The intention is to introduce simulation modelling methods and techniques used in contemporary natural resource management systems. The course provides hands-on experience where students develop research and problem-solving skills in scientific computing, model building, and analysis applied to a variety of resource management issues. After completing this course, students will be able to:

  1. Apply modelling (MS Excel and R) programming skills to resource management problems. Skills include conceptual models, scientific programming, R functions and packages, flow diagramming, pseudocode, data IO, graphics, and parallel processing;
  2. Develop quantitative deterministic and stochastic simulation models ranging from population dynamics to simple management systems;
  3. Implement Monte Carlo simulation procedures to evaluate performance of monitoring and research designs, estimate parameters for non-linear Bayesian models, and to simulate performance of decision-making procedures used in resource management;
  4. Develop fast and efficient computer programs using parallel processing and interpolation methods;
  5. Design and implement single and multi-variable numerical optimization procedures involving simulation models;
  6. Critically evaluate study designs and environmental management policies by conducting sensitivity analyses to modelling assumptions;
  7. Communicate the rationale, approach, results, and implications of simulation analyses to resource managers and stakeholders.


  • Simulation assignments (Short modelling and programming assignments approximately weekly) 40%
  • Peer feedback 10%
  • Term Project 50%


Term Project:
Each student will identify and implement a narrowly defined simulation research project in four parts:
a) Project concept and scoping (10%) – a 15 minute presentation and discussion of project concept
b) Preliminary presentation (10%) – present concept and leading in-class discussion of direction and alternatives
c) Final presentation (30%) – 20-minute presentation of final project



This is an applied, hands-on modelling course. Students are required to bring a laptop computer to every class

Microsoft Excel is a spreadsheet tool that can be used to create powerful models or help in developing models that can then be refined in more complex programming platforms (like R). Microsoft Office is available to download for all SFU students (www.sfu.ca/microsoft365/).

R statistical computing software. Download the most recent Windows or Mac version at the R-project website. (http://www.r-project.org/).


Papers will be provided in class through Canvas,


Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:


SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html