372 pages, Figs
Mathematical modelling is an essential tool in present-day ecological research. Yet for many ecologists it is still problematic to apply modelling in their research. In our experience, the major problem is at the conceptual level: proper understanding of what a model is, how ecological relations can be translated consistently into mathematical equations, how models are solved, steady states calculated and interpreted. Many textbooks jump over these conceptual hurdles to dive into detailed formulations or the mathematics of solution. This book attempts to fill that gap.
It introduces essential concepts for mathematical modelling, explains the mathematics behind the methods, and helps readers to implement models and obtain hands-on experience. Throughout the book, emphasis is laid on how to translate ecological questions into interpretable models in a practical way. The book aims to be an introductory textbook at the undergraduate-graduate level, but will also be useful to seduce experienced ecologists into the world of modelling. The range of ecological models treated is wide, from Lotka-Volterra type of principle-seeking models to environmental or ecosystem models, and including matrix models, lattice models and sequential decision models. All chapters contain a concise introduction into the theory, worked-out examples and exercises. All examples are implemented in the open-source package R, thus taking away problems of software availability for use of the book. All code used in the book is availbable on a dedicated website.
Review: "This is the book many of us were waiting for, maybe for longer than the time since R entered our computational toolbox." ... "To return to my first sentence, yes, this is the book I was waiting for. It exceeds my expectations and it is very practical with an optimal mix of theoretical and numerical topics. It is of particular use for aquatic ecologists, and definitely worth to be considered by ecologists from other fields." ... "One point I very much enjoyed is the link between ecological, chemical and hydrophysical topics. I am not aware of any other book that brings these together in such a concise and understandable way. I completely agree with the authors that "this book is written for young researchers who want to get more out of their data than just description" and I warmly recommend it for all, who are young in mind. It helps to democratise modelling knowledge and provides a highly efficient method to supply ecologists with this urgently needed competence." A(c) 2009 Thomas Petzoldt Endorsements: "This outstanding book provides a comprehensive and extremely clear treatment on the development, implementation, use and testing of ecological models. It embraces and covers the diverse approaches used by ecologists and biogeochemists; e.g. from simple food-web models to time-dependent transport-reaction models. Numerous, instructive examples are provided and implemented in R, a public domain programming language. I have successfully used a draft version for master courses on biogeochemical modeling and strongly recommend it to ecologists and biogeochemists interested to elucidate the functioning of natural ecosystems." Jack J. Middelburg, Professor in Biogeochemistry and Senior Scientist at the Netherlands Institute of Ecology. "Soetaert and Herman have provided an unexpected, back-door solution to producing cross-platform, ecological models. Robust, cross-platform statistical packages have been difficult to find. Moreover, many statisticians are not satisfied with black-box programs whose algorithms they have not checked. To solve both problems simultaneously, many have moved to doing their statistics on the R platform. R is a high-level, open-source programming language strong on both statistical computing and graphic output." "Commercial providers of modeling software have been far better in providing cross-platform support than have statistical package providers. Mathematica and Stella have focused on user friendliness of basic functions and graphing, making it easy to learn as you go. Matlab has a somewhat steeper learning curve, but is blazingly fast with matrix operations on large data sets and sets of simulated data. All are fairly expensive to license and upgrade. I find R less intuitive than any of these other programs." "Nevertheless, Soetaert and Herman make a good case for giving R a serious look as a modeling too Aus den Rezensionen: "Forscher mochten ihre muhsam gesammelten Daten nicht bloss beschreiben. Sie wunschen sich, mit Hilfe von Modellierung ein tieferes Verstandnis zu gewinnen. Dabei kann sie dieses handliche, nobel produzierte Buch unterstutzen. Als Begleitlekture zu A-kologievorlesungen der Autoren entstanden, holt es auch Nichtstudenten ab, die Abiturwissen in Mathematik und A-kologie mitbringen. ! Empfindlichkeitsanalysen und die Bewertung von Gleichgewichtszustanden komplettieren das Handwerkszeug ..." (in: c't magazin fur Computertechnik, 8/June/2009, Issue 13, S. 198)
Preface Chapter 1. Introduction Chapter 2. Model Formulation Chapter 3. Spatial Components and Transport Chapter 4. Parameterisation Chapter 5. Model Solution - Analytical Methods Chapter 6. Model Solution -Numerical Methods Chapter 7. Stability and Steady-State Chapter 8. Multiple Time Scales and Equilibrium Processes Chapter 9. Discrete Time Models Chapter 10. Dynamic Programming Chapter 11. Testing and Validating the Model Chapter 12. Further Reading and References
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