Simulistics

Here's a pdf document which I'm going to use as the basis of a practical class to introduce Simile and how it can be used to develop and analyse a model. It is aimed at undergraduate biology/ecology students whoc will have little if any programming experience and even less mathematics.

This is the first time I've written anything like this. If people see potential pitfalls in the approach or ways to improve the description to make it clearer then I'd like to hear about it.

Jon

Attached are the models (and output interfaces) from some of the chapters in the book by Hannon and Ruth.

Hannon, B & Ruth, M 1997 "Modelling dynamic biological systems" (Springer, New York)

Chapter 2 has some simple population growth models

Chapter 3 has some stochastic population growth models where death rate is a random variable

Chapter 4 has some population models with time lags that show chaotic behaviour

Chapter 7 has a model for a catalyzed reaction, which could describe enzyme dynamics.

Here are a couple of Simile models which try and show that statistical inference is largely about comparing data against the output of a null model. In statistical text books the null model is not usually mentioned explicitly. Instead the null hypothesis, and the the assmptions of a test are presented, but the null hypothesis and the assumptions are really specifying an underlying null model.

These two Simile models look at a t-test (actually a paired t-test) and a one-way ANOVA. They use the same data set, which comprises two samples, each with 20 observations.

Attached are the three toy models used in the book by Grant and Swannack.

The "hunter-gatherer" model (Example 1),

the "population extinction" model (Example 2)

and the "managing the commons" model (Example 3).

Each model has a .sml file with the model in it and a .shf file with the output interface.

This material will form part of an undergraduate course on ecological modelling, to be run at University College Dublin, so any feedback would be very much welcomed.