I will use this page for teaching Environmental Modelling (EVSC90020), a masters subject that runs in first semester at The University of Melbourne.
The subject first introduces students to elementary modelling issues (the purpose of modelling, model development, parameter estimation, model analysis, sensitivity analysis, evaluation, and application), and then covers a range of different environmental models. These include:
- population dynamics and population viability analysis;
- modelling of forest fire dynamics;
- climate modelling;
- noise propagation;
- models for environmental decision making;
- spatial prioritization; and
- species distribution modelling.
The last of these is one of the most highly-cited areas of research in environmental science. Over the previous 10 years, the three most highly-cited authors in the ISI research area of Environment/Ecology were species distribution modellers. And in total, seven of the top 20 authors are species distribution modellers. One of those highly-cited authors is Jane Elith from The University of Melbourne, who teaches in our subject. It is a honour to have her, along with all the other outstanding lecturers who contribute.
Environmental modelling inevitably uses mathematics. Some people find mathematics intimidating. In fact, the anticipation of mathematics might even be painful.
However, the mathematics used in this subject is not particularly difficult. For example, we discuss simple calculus and we expect you to understand it, but we don’t expect you to solve any differential equations. I suggest you simply relax, realise that some of the mathematical aspects will be hard, and work at understanding them. And if you are stuck, make sure you ask for assistance – helping you learn is the job of your lecturers and demonstrators, after all.
Importantly, however, some people are more susceptible to a sense of being “bad at mathematics” than others. Indeed, you might have heard people mention that men are better at mathematics than women. The evidence for that is unconvincing because it is often conflated with social conditioning and stereotyping. For example, the paucity of Fields Medalists is often cited, yet women and men have not had equal opportunity to excel in mathematics. Note, however, Maryam Mirzakhani won a Fields Medal in 2014, the first time it was awarded to a woman.
In fact, while the evidence that men outperform women at mathematics is flimsy, there is compelling evidence that stereotyping harms the performance of women in mathematics. The typical experiment on this works like this. A set of subjects (men and women) are allocated to a control group, and they sit a mathematics test. For these people, the difference in their performance is largely similar for both genders. The experimental group of subjects, however, are exposed to a stereotype threat; they are told that women are worse at mathematics than men. In these subjects, the performance of the women on the same maths test is substantially worse on average than for the men.
This response of degraded performance in response to a stereotype threat is mirrored in other stereotyping (e.g., racial) and in other areas of endeavour. The take home messages: 1) don’t let your own performance be degraded by falling for the trap of stereotypes; and 2) don’t expose others to similar stereotyping because you will unfairly harm their performance.
If you chose to do the subject, I hope you find it challenging and rewarding. And perhaps by the end of doing the subject, you will find environmental modelling less intimidating. After all, environmental models are simply things that people have made up.