Review: Despite the importance of mathematics and statistics in ecology it still remains one of the more difficult areas to teach. It's hard to see why since most people who take advanced courses have the ability to handle abstract concepts by definition. Perhaps it's the way that we get into the subject. With this end in mind, two texts have been juxtaposed in the review system giving this reviewer a chance to see two very different approaches to the topic.

In this text (see here for the other), the emphasis is on the analysis of methods and their application. We start with a brief overview of maths and modelling in science. Chapter two focusses on data from basic description and classification through to chemical and physics equations. Chapter three looks at equations and their solutions. Like the previous chapter this is very much an introductory crash-course in mathematics, confirming the author's stated intention of working at an beginner's level to bring all up to speed. It's also a feature of the book that the mathematics covers a very wide range of potential applications from "pure" to "applied" sciences. Chapter four moves on to the basic linear relationship and how that can be analysed. Logarithmic and exponential functions are a key part of ecological theory and a basic outline of these techniques makes chapter five useful. This is allied to the next chapter which is an outline of calculus and differentiation. From this point, the work turns to statistics with an opening chapter looking at basic notation, means, deviation, frequency and probability. From this we move on to the various statistical techniques dealing with distributions and errors. In a change, chapter 9 looks at the scientific method and how maths and statistics can be used to better refine the work. Having gained the data, the next stage is analysis. Thus chapter 10 deals with those tests (e.g. f and t tests) where the parameters are reasonably well known whilst chapter 11 deals with comparisons of events such as the chi-squared test. Chapter 12 continues the analysis only with non-parametric tests ranging from Wilcoxon to Freidman. A final chapter starts out by looking at experimental design but finishes by looking at analysis of variance. Appendices have a range of test questions and answers.

This is an interesting text. It's almost the opposite of the other one reviewed here and it too will have both supporters and those preferring other methods. It's greatest difference is that it has numerous worked examples. The text is limited compared to the space given over to showing how a test can be achieved and giving similar examples to work on. The rise of computer software has meant that this text can plan its examples using spreadsheets (as well as Minitab and SPSS). To help, there is a website which has a range of information, examples, tutorials and updates making this a very useful addition. Against this range of material one must note that the text has a bias towards formulae and examples which although essential, might deter the complete novice. For those who have some basic idea of maths and who want an effective way to refresh the memory or add basic skills this is a very good text.