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Ecological research is becoming increasingly quantitative, yet students often opt out of courses in mathematics and statistics, unwittingly limiting their ability to carry out research in the future. How to be a Quantitative Ecologist provides a practical introduction to quantitative ecology for students and practitioners who have realised that they need this opportunity. The text is addressed to readers who haven't used mathematics since school, who were perhaps more confused than enlightened by their undergraduate lectures in statistics and who have never used a computer for much more than word processing and data entry. From this starting point, it slowly but surely instils an understanding of mathematics, statistics and programming, sufficient for initiating research in ecology.
How to be a Quantitative Ecologist's practical value is enhanced by extensive use of biological examples and the computer language R for graphics, programming and data analysis.
- Provides a complete introduction to mathematics statistics and computing for ecologists.
- Presents a wealth of ecological examples demonstrating the applied relevance of abstract mathematical concepts, showing how a little technique can go a long way in answering interesting ecological questions.
- Covers elementary topics, including the rules of algebra, logarithms, geometry, calculus, descriptive statistics, probability, hypothesis testing and linear regression.
- Explores more advanced topics including fractals, non-linear dynamical systems, likelihood and Bayesian estimation, generalised linear, mixed and additive models, and multivariate statistics.
- R boxes provide step-by-step recipes for implementing the graphical and numerical techniques outlined in each section.
How I chose to write this book, and why you might choose to read it
0. How to start a meaningful relationship with your computer
1. How to make mathematical statements
2. How to describe regular shapes and patterns
3. How to change things, one step at a time
4. How to change things, continuously
5. How to work with accumulated change
6. How to keep stuff organised in tables
7. How to visualise and summarise data
8. How to put a value on uncertainty
9. How to identify different kinds of randomness
10. How to see the forest from the trees
11. How to separate the signal from the noise
12. How to measure similarity
"With a book like this, there is no excuse for people to be afraid of maths, and to be ignorant of what it can do."
- Prof Tim Benton, Faculty of Biological Sciences, University of Leeds
"After a course of one or two semesters using this textbook, he says, students should have the absolute minimum of knowledge about quantitative research that ecologists need, but can provide a foundation for students who want to move further in that direction."
- Book News, 1 August 2011