Courses & Units

Modelling Biological Data KSM304


Quantitative skills are fundamental to many areas of biology and ecology. With massive increases in the availability of ecological and other kinds of
biological data, skill in developing models to describe and understand relationships in data and extrapolate beyond existing data, and an understanding
of the nuances of particular kinds of data, is fundamental in modern research. This unit builds on the foundation provided in Quantitative Methods in
Biology (KSM309) to familiarise students with more advanced model building techniques such as generalised linear models (GLMs, focus on binomial
and Poisson), generalised additive models (GAMs), and generalised linear mixed models (GLMMs). The unit will also introduce bootstrapping
methods, non-linear regression, model selection, regression trees, and species distribution models. It will equip students wanting to understand and/or
pursue modern and relevant research approaches in biology and ecology.


Unit name Modelling Biological Data
Unit code KSM304
Credit points 12.5
College/School College of Sciences and Engineering
Institute for Marine & Antarctic Studies
Discipline College Office - CSE|Ecology and Biodiversity
Coordinator Doctor Sophie Bestley
Available as an elective? Yes
Delivered By University of Tasmania
Level Advanced


Location Study period Attendance options Available to
Hobart Semester 2 On-Campus International Domestic


International students
Domestic students

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Key Dates

Study Period Start date Census date WW date End date
Semester 2 10/7/2023 8/8/2023 28/8/2023 15/10/2023

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2023 are indicative and subject to change. Finalised census dates for 2023 will be available from the 1st October 2022. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

  • Aquire practical knowledge of approaches to statistical modelling of biological and ecological data, and key skills in the operation of statistical software packages.
  • Develop the ability to construct statistical models that are fit for purpose, and critically evaluate alternative models in the context of a physical problem.
  • Demonstrate the ability to verbally and graphically communicate the results of statistical analyses clearly and accurately to both technical and non-technical audiences using globally adopted notations and conventions.
  • Understand the role of statistical modelling of biological and ecological data in a range of contexts and applications, and that the application and interpretation of statistical modelling may have important social and ethical implications.
Field of Education Commencing Student Contribution 1,3 Grandfathered Student Contribution 1,3 Approved Pathway Course Student Contribution 2,3 Domestic Full Fee 4
010999 $1,037.00 $1,037.00 not applicable $2,472.00

1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.



KSM309 or KMA253


Teaching Pattern

Semester 2 block taught (5 weeks), online recorded lectures, 2 x 1hr lectorial and 2 x 3 hr practical weekly

AssessmentBayesian Scripting (10%)|Exam (30%)|Assignments 1-4 (60%)
TimetableView the lecture timetable | View the full unit timetable



There is no compulsory reading for the unit. For some topics supplementary reading material with be provided in addition to the lecture slides, for those that seek a deeper understanding of the material.


 Note that no single text covers all the material in this course, but the books listed below may be of use as additional references. The others will available for short-term loan from the lecturers.

  1. Maindonald, J. and Braun, W.J. (2010). Data analysis and graphics using R. Cambridge University Press
  2. Mattiopoulos, J. (2011). How to be a quantitative ecologist. Wiley,
  3. McElreath, R. (2015) Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press.
  4. Zuur, A.F, Ieno, E.N, Smith, G.M. (2007). Analysing ecological data. Springer.
LinksBooktopia textbook finder

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