Mathematical Models In Ecology Pdf

An Introduction to Mathematical Models in Ecology and Evolution

Time and Space

SECOND EDITION

MICHAEL GILLMAN

Department of Biological Sciences

The Open University

Walton Hall

Milton Keynes

MK7 6AA

A John Wiley & Sons, Ltd., Publication

ECOLOGICAL METHODS AND CONCEPTS SERIES

This series, successor to the Methods in Ecology series edited by John Lawton and Gene Likens, presents the latest ideas and techniques across the whole ﬁeld of ecology and their application, from genetic to the global, from pest management to policy development. Books may be singleor multi-authored and will address emerging new areas within the ﬁeld as well as updating well-established areas of endeavour. The new Series Editor is Professor Roger Kitching of Grifﬁth University, Brisbane, who will welcome suggestions for works within the series. Email: r.kitching@grifﬁth.edu.au

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An Introduction to Mathematical Models in Ecology and Evolution

Time and Space

SECOND EDITION

MICHAEL GILLMAN

Department of Biological Sciences

The Open University

Walton Hall

Milton Keynes

MK7 6AA

A John Wiley & Sons, Ltd., Publication

Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwell's publishing program has been merged with Wiley's global Scientiﬁc, Technical and Medical business to form Wiley-Blackwell.

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Library of Congress Cataloguing-in-Publication Data

Gillman, Michael.

An introduction to mathematical models in ecology and evolution : time and space / Michael Gillman. – 2nd ed.

p. cm. – (Ecological methods and concepts series)

Rev. ed. of: An introduction to ecological modelling / Michael Gillman, Rosemary Hails. 1997. Includes bibliographical references and index.

ISBN 978-1-4051-7515-9 (pbk. : alk. paper) – ISBN 978-1-4051-9489-1 (hbk. : alk. paper) 1. Ecology–Mathematical models. 2. Evolution (Biology)–Mathematical models.

I. Gillman, Michael. Introduction to ecological modelling. II. Title. QH541.15.M3G5 2009

577.01'5118–dc22

2008046481

A catalogue record for this book is available from the British Library.

Set in 9.5 on 12 pt Meridien by SNP Best-set Typesetter Ltd., Hong Kong

Printed and bound in Malaysia

1 2009

Preface, vi

1 Introduction, 1

2 Simple models of temporal change, 21

3 Stochastic models, 44

4 Modelling structured populations, 60

5 Regulation in temporal models, 72

6 Modelling interactions, 96

7 Community models, 122

8 Spatial models, 135 References, 149 Index, 157

Preface

This book is an introduction to the key methods and underlying concepts of mathematical models in ecology and evolution. It is intended to serve the needs of a broad range of undergraduate and postgraduate ecology and evolution students who need to access the mathematical and statistical modelling literature essential to their subjects. It assumes minimal mathematics and statistics knowledge (see below) while covering a wide variety of methods, many of which are at the forefront of ecological and evolutionary research. The book will also highlight the applications of modelling to practical problems such as sustainable harvesting and biological control.

There are many other ways in which this book could have been written and you will ﬁnd examples of quite different treatments of modelling in the literature. In particular the book could focus on (and be lead by) applications, for example by asking whether models are helpful in understanding climate change or saving cod populations or reducing the incidence of malaria. The answer is yes to all of these but it was felt that it is better to try to understand the general principles underlying the models and then examine the applications. Doubtless my ideas of synthesis and generality are not those of others but it is an attempt to detect and reveal order. I also wanted to write a book with a lighter touch and so have avoided writing lengthy descriptions of method. Hopefully this makes the book accessible to a wider readership.

Understanding of the text will be helped by a familiarity with the basics of the following mathematical and statistical methods and concepts:

• manipulation of algebraic equations,

• logarithms and powers,

• differentiation,

• variance and standard error,

• signiﬁcance and hypothesis testing.

Many thanks to Hils and Ed for encouragement and valuable comments. I have learnt much from interactions with colleagues at the Open University and previously at Imperial College. My interest in mathematical models was inspired by the lectures of Brian Goodwin and John Maynard Smith.

Michael Gillman

September 2008

CHAPTER 1

Introduction

1.1 What is a model?

A model is some representation of reality. In everyday life we are familiar with physical models of reality such as toy cars and ﬁlm sets. Physical models have also been used extensively in science, perhaps most famously as the ball-and-stick model of DNA by Crick and Watson. Such physical models have been largely replaced by computer images and/or mathematical representations. In ecology and evolution the models are almost entirely of a mathematical nature. Reference to models and modelling in this book can therefore be read as mathematical models or modelling. Fortunately we do not have to be highly skilled mathematicians to construct and use such models. This text will show how to develop ecological and evolutionary models which have a wide application across the life sciences and are relevant to many other branches of science. Indeed, the understanding of modelling beneﬁts from interplay between the sciences.

An important ﬁrst step is to understand the overlap between statistical analysis and mathematical modelling. One of the central aims of statistical analysis is the description of trends and distributions in sets of data. For example, we might wish to provide a description of the change over time in the average size of a population. We can do this by using the statistical method of regression which provides a mathematical description of a line or curve of best ﬁt through the data. The equation of the resulting line or curve is also a mathematical model of the population and could be used to predict change over time, with the possibility of extrapolating beyond the last time point. Of course, we need to be cautious over the extent to which extrapolations are performed, or, at the very least, make the user aware of the possible problems. Regression is introduced in the next section. A mathematical summary that allows prediction and extrapolation is therefore one valuable use of models. Expressing the model as a mathematical formula provides a brevity and formality of description. It also allows manipulation of the model and provides the opportunity for discovery of emergent properties not apparent from non-mathematical reasoning. Another related use of models is that they allow a simpliﬁcation of reality. An alternative to producing models from observed temporal or spatial data is that we can build them from